From 836693200747be20fefb13131fe35ef2649d79f4 Mon Sep 17 00:00:00 2001 From: Kulhalli Date: Sat, 22 Jul 2023 12:56:15 -0400 Subject: [PATCH 1/9] Initial commit --- classification_analysis.ipynb | 2754 +++++++++++++++++++++++++++++++-- 1 file changed, 2664 insertions(+), 90 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index d5b9b88..789d1ad 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "09429718", "metadata": {}, "source": [ "# Classification Analysis\n", @@ -10,6 +11,7 @@ }, { "cell_type": "markdown", + "id": "e0cb31cf", "metadata": {}, "source": [ "## Dependencies" @@ -17,7 +19,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 341, + "id": "2e136f3f", "metadata": {}, "outputs": [], "source": [ @@ -29,7 +32,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 342, + "id": "a1505557", "metadata": {}, "outputs": [], "source": [ @@ -40,17 +44,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 343, + "id": "ac4092a3", "metadata": {}, "outputs": [], "source": [ "# for analysized view\n", - "import emeval.analysed.phone_view as eapv" + "import emeval.analysed.phone_view as eapv\n", + "from emeval.analysed.location_smoothing import add_dist, calDistance" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 344, + "id": "dff4c0cc", "metadata": {}, "outputs": [], "source": [ @@ -59,7 +66,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 345, + "id": "5395929e", "metadata": {}, "outputs": [], "source": [ @@ -70,7 +78,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 346, + "id": "5b3eafd4", "metadata": {}, "outputs": [], "source": [ @@ -81,7 +90,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 347, + "id": "03d894bd", "metadata": {}, "outputs": [], "source": [ @@ -93,7 +103,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 348, + "id": "217ec510", "metadata": {}, "outputs": [], "source": [ @@ -105,7 +116,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 349, + "id": "6c27fdd8", "metadata": {}, "outputs": [], "source": [ @@ -118,7 +130,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 350, + "id": "f3e62931", "metadata": {}, "outputs": [], "source": [ @@ -128,7 +141,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 351, + "id": "5cc856cf", "metadata": {}, "outputs": [], "source": [ @@ -137,6 +151,7 @@ }, { "cell_type": "markdown", + "id": "845eb857", "metadata": {}, "source": [ "## Load in Phone Views from the file spec" @@ -144,9 +159,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 352, + "id": "34d152eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After iterating over 1 entries, entry found\n", + "Found spec = Round trip car and bike trip in the South Bay\n", + "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", + "After iterating over 1 entries, entry found\n", + "Found spec = Multi-modal car scooter BREX trip to San Jose\n", + "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", + "After iterating over 1 entries, entry found\n", + "Found spec = Multimodal multi-train, multi-bus, ebike trip to UC Berkeley\n", + "Evaluation ran from 2019-07-16T00:00:00-07:00 -> 2020-04-30T00:00:00-07:00\n" + ] + } + ], "source": [ "DATASTORE_LOC = \"bin/data\"\n", "AUTHOR_EMAIL = \"shankari@eecs.berkeley.edu\"\n", @@ -157,60 +189,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 353, + "id": "191a8e98", "metadata": {}, "outputs": [], "source": [ - "pv_la = eipv.PhoneView(sd_la)" + "%%capture\n", + "pv_la = eipv.PhoneView(sd_la)\n", + "# pv_sj = eipv.PhoneView(sd_sj)\n", + "# pv_ucb = eipv.PhoneView(sd_ucb)" + ] + }, + { + "cell_type": "markdown", + "id": "0d61207a", + "metadata": {}, + "source": [ + "### Get sensed data for each trip" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 354, + "id": "a29ec8a0", "metadata": {}, "outputs": [], "source": [ - "pv_sj = eipv.PhoneView(sd_sj)" + "FILE_MAPPING = {\n", + " pv_la: \"unimodal_trip_car_bike_mtv_la\",\n", + " # pv_sj: \"car_scooter_brex_san_jose\",\n", + " # pv_ucb: \"train_bus_ebike_mtv_ucb\"\n", + "}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 355, + "id": "27237fc1", "metadata": {}, "outputs": [], "source": [ - "pv_ucb = eipv.PhoneView(sd_ucb)" + "%%capture\n", + "ems.fill_sensed_section_ranges(pv_la)\n", + "# ems.fill_sensed_section_ranges(pv_sj)\n", + "# ems.fill_sensed_section_ranges(pv_ucb)" ] }, { "cell_type": "markdown", + "id": "a0caef04", "metadata": {}, "source": [ - "### Get sensed data for each trip" + "## Get sensed and ground truth temporal histories (timelines)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 356, + "id": "eb5df932", "metadata": {}, "outputs": [], "source": [ - "%%capture\n", - "ems.fill_sensed_section_ranges(pv_la)\n", - "ems.fill_sensed_section_ranges(pv_sj)\n", - "ems.fill_sensed_section_ranges(pv_ucb)" + "from pathlib import Path\n", + "\n", + "def get_gt_location_data(trip_id: str, section_id: str):\n", + "\n", + " root = Path(\"./bin/data\")\n", + " return_file = None\n", + "\n", + " workdir = root\n", + " if (workdir / trip_id).exists():\n", + " workdir = workdir / trip_id\n", + " assert workdir.is_dir(), f\"{trip_id} is a file, not a dir.\"\n", + " if (workdir / section_id).exists():\n", + " workdir = workdir / section_id\n", + " assert workdir.is_dir(), f\"{trip_id}.{section_id} is a file, not a dir.\"\n", + " files = [f for f in workdir.iterdir()]\n", + " assert files is not None and len(files) > 0, f\"No files found for {trip_id=} and {section_id=}\"\n", + "\n", + " # sort the files by run number (just in case).\n", + " files = list(sorted(files, key=lambda x: int(x.name.split(\"_\")[-1])))\n", + "\n", + " # Concatenate all the trips into a single consolidated df but add an extra column that allows you\n", + " # to filter on the run number (if required).\n", + " df = pd.read_csv(files[0])\n", + " df['run'] = '0'\n", + "\n", + " for ix, run_file in enumerate(files[1:]):\n", + " tdf = pd.read_csv(run_file)\n", + " tdf['run'] = str(ix+1)\n", + " df = pd.concat([df, tdf], axis=0)\n", + " \n", + " return_file = df.reset_index(drop=True, inplace=False)\n", + " \n", + " return return_file" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 357, + "id": "effccbd6", "metadata": {}, + "outputs": [], "source": [ - "## Get sensed and ground truth temporal histories (timelines)" + "# x = get_gt_location_data('unimodal_trip_car_bike_mtv_la', 'suburb_bicycling')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 358, + "id": "b1dd54d7", + "metadata": {}, + "outputs": [], + "source": [ + "# display(x.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "id": "c92e50d1", "metadata": {}, "outputs": [], "source": [ @@ -229,9 +327,10 @@ " continue\n", " tr_ss = []\n", " tr_gts = []\n", + " gt_dfs = []\n", + " \n", " for i, tr in enumerate(r[\"evaluation_trip_ranges\"]):\n", " for ss in tr[\"sensed_section_ranges\"]:\n", - " ## append the sensed section data\n", " tr_ss.append(ss)\n", " for section in tr[\"evaluation_section_ranges\"]:\n", " ## get the ground truth section data\n", @@ -240,22 +339,74 @@ " tr['start_ts'],\n", " tr['end_ts'])\n", " \n", - " if section_gt_leg[\"type\"] == \"WAITING\": \n", + " if section_gt_leg[\"type\"] == \"WAITING\":\n", " continue\n", + " \n", " gts = {'start_ts': section['start_ts'], \n", " 'end_ts': section['end_ts'], \n", - " 'mode': section_gt_leg['mode']}\n", + " 'mode': section_gt_leg['mode']\n", + " }\n", + "\n", " tr_gts.append(gts)\n", + "\n", + " gt_location_data = get_gt_location_data(FILE_MAPPING[pv], tr['trip_id_base'])\n", + " run_id = r['trip_run']\n", + " print(run_id, tr['trip_id'], tr['trip_id'])\n", + " gt_location_data = gt_location_data.query(\"source == @os\")\n", + " gt_location_data = gt_location_data.loc[gt_location_data.run == str(run_id), :].reset_index(drop=True, inplace=False)\n", + " gt_dfs.append(gt_location_data)\n", + "\n", + " print(5*'-')\n", + "\n", " # now, we build a timeline for each trip\n", " trip = tr.copy()\n", " trip['ss_timeline'] = tr_ss\n", " trip['gts_timeline'] = tr_gts\n", + " trip['gt_location_df'] = gt_dfs\n", + " \n", " trips.append(trip)\n", + " \n", " return trips" ] }, + { + "cell_type": "code", + "execution_count": 374, + "id": "b0ec3c7e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", + "0 suburb_bicycling_0 suburb_bicycling_0\n", + "-----\n", + "1 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", + "1 suburb_bicycling_0 suburb_bicycling_0\n", + "-----\n", + "2 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", + "2 suburb_bicycling_0 suburb_bicycling_0\n", + "-----\n", + "0 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", + "0 suburb_bicycling_0 suburb_bicycling_0\n", + "-----\n", + "1 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", + "1 suburb_bicycling_0 suburb_bicycling_0\n", + "-----\n", + "2 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", + "2 suburb_bicycling_0 suburb_bicycling_0\n", + "-----\n" + ] + } + ], + "source": [ + "trips = get_trip_ss_and_gts_timeline(pv_la, 'android', 'HAHFDC')" + ] + }, { "cell_type": "markdown", + "id": "66601561", "metadata": {}, "source": [ "## Define the Base Mode Maps" @@ -263,6 +414,7 @@ }, { "cell_type": "markdown", + "id": "33aa0b6b", "metadata": {}, "source": [ "#### raw base mode map" @@ -270,7 +422,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 332, + "id": "e26ef8f3", "metadata": {}, "outputs": [], "source": [ @@ -304,6 +457,7 @@ }, { "cell_type": "markdown", + "id": "eeef9eae", "metadata": {}, "source": [ "#### cleaned base mode map\n", @@ -313,7 +467,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 333, + "id": "3eb538fe", "metadata": {}, "outputs": [], "source": [ @@ -357,6 +512,7 @@ }, { "cell_type": "markdown", + "id": "e2dce5ef", "metadata": {}, "source": [ "### inferred base mode maps\n", @@ -366,6 +522,7 @@ }, { "cell_type": "markdown", + "id": "c8703301", "metadata": {}, "source": [ "#### random forest base mode map" @@ -373,7 +530,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 334, + "id": "c6a21a16", "metadata": {}, "outputs": [], "source": [ @@ -413,6 +571,7 @@ }, { "cell_type": "markdown", + "id": "2ec81457", "metadata": {}, "source": [ "#### rule+GIS base mode map" @@ -420,7 +579,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 335, + "id": "d9dd0c1f", "metadata": {}, "outputs": [], "source": [ @@ -460,6 +620,7 @@ }, { "cell_type": "markdown", + "id": "0994d892", "metadata": {}, "source": [ "#### Pad the start at end of the timelines for a given trip, while also filling in gaps in the middle" @@ -467,7 +628,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 336, + "id": "1defdb2f", "metadata": {}, "outputs": [], "source": [ @@ -489,6 +651,7 @@ " 'end_ts' : gt_timeline[-1]['end_ts']\n", " }\n", " )\n", + "\n", " if len(gt_timeline) == 0:\n", " gt_timeline.append(\n", " {\n", @@ -497,6 +660,7 @@ " 'end_ts' : ss_timeline[-1]['end_ts']\n", " }\n", " )\n", + "\n", " if 'data' in ss_timeline[0]:\n", " start_misalignment = ss_timeline[0]['data']['start_ts'] - gt_timeline[0]['start_ts']\n", " end_misalignment = ss_timeline[-1]['data']['end_ts'] - gt_timeline[-1]['end_ts']\n", @@ -532,7 +696,7 @@ " 'end_ts' : ss['start_ts']\n", " }\n", " )\n", - " \n", + " \n", " ## the timeline is continuous, and we can fill our section ##\n", " ss_aligned_timeline.append(ss)\n", " ### fill in end ###\n", @@ -547,6 +711,7 @@ " 'end_ts' : ss['end_ts'] - end_misalignment\n", " }\n", " )\n", + "\n", " ####### FILL IN GT TIMELINE #######\n", " ### fill in start ###\n", " if start_misalignment < 0:\n", @@ -554,7 +719,7 @@ " {\n", " 'mode' : 'NO_GT_START',\n", " 'start_ts' : gt_timeline[0]['start_ts'] + start_misalignment,\n", - " 'end_ts' : gt_timeline[0]['start_ts']\n", + " 'end_ts' : gt_timeline[0]['start_ts'],\n", " }\n", " )\n", " ### fill in meat ###\n", @@ -584,6 +749,7 @@ }, { "cell_type": "markdown", + "id": "8a647018", "metadata": {}, "source": [ "#### Get the classification metrics (true/false positive, true/false negative) for each Base Mode for a given trip/set-of-trips" @@ -591,7 +757,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 253, + "id": "b9ed7ad6", "metadata": {}, "outputs": [], "source": [ @@ -605,26 +772,65 @@ " trips = test_trip if type(test_trip) is list else [test_trip]\n", " TP, FN, FP, TN = {}, {}, {}, {}\n", " for trip in trips:\n", + " gt_location_df = trip['gt_location_df']\n", + " print(gt_location_df.shape)\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for mode in set(BASE_MODE.values()):\n", " for ss in ss_timeline:\n", " for gts in gt_timeline:\n", " if ss['end_ts'] >= gts['start_ts'] and ss['start_ts'] <= gts['end_ts']:\n", - " dur = min(ss['end_ts'], gts['end_ts']) - max(ss['start_ts'], gts['start_ts'])\n", + " range_start = max(ss['start_ts'], gts['start_ts'])\n", + " range_end = min(ss['end_ts'], gts['end_ts'])\n", + "\n", + " filtered_gt_distance = gt_location_df.loc[\n", + " (gt_location_df.ts >= range_start) & (gt_location_df.ts <= range_end), :\n", + " ]\n", + "\n", + " if filtered_gt_distance.shape[0] == 0:\n", + " dist = 0\n", + " else:\n", + " dist = add_dist(filtered_gt_distance).distance.sum()\n", + " \n", + " if dist > 0:\n", + " print(f\"{range_start=}, {range_end=}\")\n", + " print(f\"{gt_location_df.ts.min()=}, {gt_location_df.ts.max()=}\")\n", + " print(\"Computed distance: \", dist)\n", + " print(50*'-')\n", + "\n", + " # if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", + " # TP[mode] = TP.setdefault(mode, 0) + dur\n", + " # elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", + " # FP[mode] = FP.setdefault(mode, 0) + dur\n", + " # elif BASE_MODE[mode] != BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", + " # FN[mode] = FN.setdefault(mode, 0) + dur\n", + " # else:\n", + " # TN[mode] = TN.setdefault(mode, 0) + dur\n", + "\n", " if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", - " TP[mode] = TP.setdefault(mode, 0) + dur\n", + " TP[mode] = TP.setdefault(mode, 0) + dist\n", " elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", - " FP[mode] = FP.setdefault(mode, 0) + dur\n", + " FP[mode] = FP.setdefault(mode, 0) + dist\n", " elif BASE_MODE[mode] != BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", - " FN[mode] = FN.setdefault(mode, 0) + dur\n", + " FN[mode] = FN.setdefault(mode, 0) + dist\n", " else:\n", - " TN[mode] = TN.setdefault(mode, 0) + dur\n", + " TN[mode] = TN.setdefault(mode, 0) + dist\n", " \n", " return TP, FP, FN, TN" ] }, + { + "cell_type": "code", + "execution_count": 254, + "id": "d5ddf739", + "metadata": {}, + "outputs": [], + "source": [ + "# get_binary_class_in_sec('android', 'HAHFDC', pv_la, RBMM)" + ] + }, { "cell_type": "markdown", + "id": "e1798e4a", "metadata": {}, "source": [ "# $F_\\beta$ score\n", @@ -635,7 +841,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 255, + "id": "7cc628e5", "metadata": {}, "outputs": [], "source": [ @@ -665,6 +872,7 @@ }, { "cell_type": "markdown", + "id": "90cf63ee", "metadata": {}, "source": [ "#### Get the support for each base mode in a set of trips, which is the sum of confusion matrix row sums for each mode that maps to a base mode, $M_{bm} = \\{ m : b(m) = bm, m \\in M \\}$ " @@ -672,7 +880,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 256, + "id": "7553fa01", "metadata": {}, "outputs": [], "source": [ @@ -683,8 +892,12 @@ " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", " support = {}\n", " for trip in trips:\n", + "\n", + " gt_loc_df = trip['gt_location_df']\n", + "\n", " ## get gts dur\n", " gt_dur = 0\n", + " # gt_dist = 0\n", " for gts in trip['gts_timeline']:\n", " mode = BASE_MODE[gts['mode']]\n", " support[mode] = support.setdefault(mode, 0) + gts['end_ts'] - gts['start_ts']\n", @@ -702,18 +915,20 @@ }, { "cell_type": "markdown", + "id": "ced133d3", "metadata": {}, "source": [ "### Weighted $F_1$ Score\n", "\n", - "\\begin{equation}\\label{eq:w_f_score}\n", + "\\begin{equation}\n", " F_1^{avg} = \\sum_{i=1}^{|BM|} \\left[ \\sum_{j=1} ^ {|M_{bm_i}|} \\sum_{k = 1}^{|M^{inf}|} cm_{j, k} \\right] \\cdot F_1^{bm}\n", "\\end{equation}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 257, + "id": "22e85696", "metadata": {}, "outputs": [], "source": [ @@ -731,7 +946,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 258, + "id": "dbe77394", "metadata": {}, "outputs": [], "source": [ @@ -754,11 +970,12 @@ " ax[i].set_xticklabels(df.columns, rotation = 80)\n", " title = f\"$F_1$ Scores by Base Mode for Phones Running {os} at Various Configuration Settings\"\n", " plt.suptitle(title, weight='bold', size='x-large')\n", - " fig.savefig(f\"images/f_scores_for_{os}\", bbox_inches=\"tight\")" + " fig.savefig(f\"images/distance_f_scores_for_{os}\", bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", + "id": "d2d47aff", "metadata": {}, "source": [ "#### Plot $F$ scores for android/ios on select configuration settings" @@ -766,7 +983,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 259, + "id": "dc35d951", "metadata": {}, "outputs": [], "source": [ @@ -789,11 +1007,12 @@ " ax[i].set_xticklabels(df.columns, rotation = 80)\n", "# title = f\"$F_1$ Scores by Base Mode for Selected OS Setting Configurations\"\n", "# plt.suptitle(title, weight='bold', size='x-large')\n", - " fig.savefig(f\"images/f_scores_selected\", bbox_inches=\"tight\")" + " fig.savefig(f\"images/distance_f_scores_selected\", bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", + "id": "7c28321e", "metadata": {}, "source": [ "## Confusion Matrix\n", @@ -802,6 +1021,7 @@ }, { "cell_type": "markdown", + "id": "4c276e1e", "metadata": {}, "source": [ "#### cleaned index map" @@ -809,7 +1029,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 260, + "id": "c43d8bfe", "metadata": {}, "outputs": [], "source": [ @@ -828,6 +1049,7 @@ }, { "cell_type": "markdown", + "id": "dac4c013", "metadata": {}, "source": [ "#### inferred index map" @@ -835,7 +1057,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 261, + "id": "16c43040", "metadata": {}, "outputs": [], "source": [ @@ -855,7 +1078,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 262, + "id": "00d1e031", "metadata": {}, "outputs": [], "source": [ @@ -872,13 +1096,28 @@ " else:\n", " trips = test_trip if type(test_trip) is list else [test_trip]\n", " for trip in trips:\n", + " gt_location_df = trip['gt_location_df']\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for ss in ss_timeline:\n", " cm = {}\n", " for gts in gt_timeline:\n", " if ss['end_ts'] >= gts['start_ts'] and ss['start_ts'] <= gts['end_ts']:\n", - " dur = min(ss['end_ts'], gts['end_ts']) - max(ss['start_ts'], gts['start_ts'])\n", - " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", + " # dur = min(ss['end_ts'], gts['end_ts']) - max(ss['start_ts'], gts['start_ts'])\n", + " range_start = max(ss['start_ts'], gts['start_ts'])\n", + " range_end = min(ss['end_ts'], gts['end_ts'])\n", + "\n", + " filtered_gt_distance = gt_location_df.loc[\n", + " (gt_location_df.ts >= range_start) & (gt_location_df.ts <= range_end), :\n", + " ]\n", + "\n", + " if filtered_gt_distance.shape[0] == 0:\n", + " dist = 0\n", + " else:\n", + " dist = add_dist(filtered_gt_distance).distance.sum()\n", + " \n", + " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", + " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dist\n", + " \n", " cm['sensed_mode'] = ss['mode']\n", " \n", " cm_l.append(cm)\n", @@ -887,7 +1126,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 263, + "id": "f5a13d1c", "metadata": {}, "outputs": [], "source": [ @@ -917,7 +1157,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 264, + "id": "4ecc8ccb", "metadata": {}, "outputs": [], "source": [ @@ -931,16 +1172,19 @@ " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum()\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " fname = f\"images/raw_cm_{os}\"\n", + " # fname = f\"images/raw_cm_{os}\"\n", + " fname = f\"images/raw_distance_cm_{os}\"\n", " elif d_type == 'clean':\n", " title = f\"Confusion Matrices for Clean Output Data on Phones Running {os} \\n by Calibration Settings\"\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " fname = f\"images/clean_cm_{os}\"\n", + " # fname = f\"images/clean_cm_{os}\"\n", + " fname = f\"images/clean_distance_cm_{os}\"\n", " elif d_type == 'random_forest' or 'gis':\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " fname = f\"images/{d_type}_cm_{os}\"\n", + " # fname = f\"images/{d_type}_cm_{os}\"\n", + " fname = f\"images/{d_type}_distance_cm_{os}\"\n", " if d_type == 'random_forest':\n", " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os} \\n by Calibration Settings\"\n", " else:\n", @@ -976,6 +1220,7 @@ }, { "cell_type": "markdown", + "id": "85facc4b", "metadata": {}, "source": [ "#### plot the confusion matrices at each pipeline output stage on android/ios for select configuration settings" @@ -983,7 +1228,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 265, + "id": "b1fe08b0", "metadata": {}, "outputs": [], "source": [ @@ -996,7 +1242,8 @@ " fig.text(0.5, -0.1, 'Predicted Label', ha='center', fontsize='xx-large')\n", " fig.text(0.08, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", " title = f\"Confusion Matrices for Phones Running {os}:{role}\"\n", - " fname = f\"images/selected_cm_{os}\"\n", + " # fname = f\"images/selected_cm_{os}\"\n", + " fname = f\"images/selected_distance_cm_{os}\"\n", " for k, pv in enumerate(\n", " [[pv_la, pv_sj, pv_ucb], \n", " [mcv_la, mcv_sj, mcv_ucb],\n", @@ -1017,11 +1264,18 @@ " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", " ).fillna(0)\n", " cm = ax[k].imshow(df.transpose(), interpolation='nearest', cmap=plt.cm.coolwarm, aspect='auto')\n", - " title_map = {0 : f'raw output confusion matrix \\n{os}:{role}', \n", - " 1 : f'master clean output confusion matrix \\n{os}:{role}',\n", - " 2 : f'GIST clean output confusion matrix \\n{os}:{role}', \n", - " 3 : f'random forest output confusion matrix \\n{os}:{role}', \n", - " 4 : f'GIS output confusion matrix \\n{os}:{role}'}\n", + " # title_map = {0 : f'raw output confusion matrix \\n{os}:{role}', \n", + " # 1 : f'master clean output confusion matrix \\n{os}:{role}',\n", + " # 2 : f'GIST clean output confusion matrix \\n{os}:{role}', \n", + " # 3 : f'random forest output confusion matrix \\n{os}:{role}', \n", + " # 4 : f'GIS output confusion matrix \\n{os}:{role}'}\n", + "\n", + " title_map = {0 : f'raw output distance confusion matrix \\n{os}:{role}', \n", + " 1 : f'master clean output distance confusion matrix \\n{os}:{role}',\n", + " 2 : f'GIST clean output distance confusion matrix \\n{os}:{role}', \n", + " 3 : f'random forest output distance confusion matrix \\n{os}:{role}', \n", + " 4 : f'GIS output distance confusion matrix \\n{os}:{role}'}\n", + " \n", " ax[k].set_title(title_map[k])\n", " tick_marks = np.arange(len(df))\n", " ax[k].set_yticks(np.arange(len(df.columns)))\n", @@ -1044,6 +1298,7 @@ }, { "cell_type": "markdown", + "id": "59be3e33", "metadata": {}, "source": [ "## Analyzed Data" @@ -1051,6 +1306,7 @@ }, { "cell_type": "markdown", + "id": "19f773be", "metadata": {}, "source": [ "#### cleaned view" @@ -1058,7 +1314,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, + "id": "15ca1d1d", "metadata": {}, "outputs": [], "source": [ @@ -1067,7 +1324,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 207, + "id": "bdc483e2", "metadata": {}, "outputs": [], "source": [ @@ -1077,7 +1335,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 208, + "id": "af22eb20", "metadata": {}, "outputs": [], "source": [ @@ -1087,11 +1346,2176 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 209, + "id": "cd8c8f8f", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finished copying unimodal_trip_car_bike_mtv_la, starting overwrite\n", + "Found spec = Round trip car and bike trip in the South Bay\n", + "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", + "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", + "android dict_keys(['ucb-sdb-android-1', 'ucb-sdb-android-2', 'ucb-sdb-android-3', 'ucb-sdb-android-4'])\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_0 HAHFDC v/s HAMFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_1 HAHFDC v/s HAMFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_2 HAHFDC v/s HAMFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_3 HAHFDC v/s MAHFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_4 HAHFDC v/s MAHFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_5 HAHFDC v/s MAHFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = []\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-07-27T17:42:38.727000-07:00', '2019-07-27T17:51:10-07:00'), ('2019-07-27T17:51:11-07:00', '2019-07-27T17:51:30-07:00'), ('2019-07-27T19:03:05.796040-07:00', '2019-07-27T19:08:26-07:00'), ('2019-07-27T19:08:27-07:00', '2019-07-27T19:08:35-07:00'), ('2019-07-27T19:08:36-07:00', '2019-07-27T19:12:34-07:00'), ('2019-07-27T19:12:35-07:00', '2019-07-27T19:12:48-07:00'), ('2019-07-27T19:12:49-07:00', '2019-07-27T19:17:09-07:00'), ('2019-07-27T19:17:10-07:00', '2019-07-27T19:17:47-07:00'), ('2019-07-27T19:17:49-07:00', '2019-07-27T19:21:19-07:00')]\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = ['2019-07-27T17:42:38.727000-07:00', '2019-07-27T17:51:11-07:00']\n", + "Before filtering, trips = [('2019-07-27T17:42:38.727000-07:00', '2019-07-27T17:51:10-07:00'), ('2019-07-27T17:51:11-07:00', '2019-07-27T17:51:30-07:00'), ('2019-07-27T19:03:05.796040-07:00', '2019-07-27T19:08:26-07:00'), ('2019-07-27T19:08:27-07:00', '2019-07-27T19:08:35-07:00'), ('2019-07-27T19:08:36-07:00', '2019-07-27T19:12:34-07:00'), ('2019-07-27T19:12:35-07:00', '2019-07-27T19:12:48-07:00'), ('2019-07-27T19:12:49-07:00', '2019-07-27T19:17:09-07:00'), ('2019-07-27T19:17:10-07:00', '2019-07-27T19:17:47-07:00'), ('2019-07-27T19:17:49-07:00', '2019-07-27T19:21:19-07:00')]\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = ['2019-07-27T19:03:05.796040-07:00', '2019-07-27T19:08:27-07:00', '2019-07-27T19:08:36-07:00', '2019-07-27T19:12:35-07:00', '2019-07-27T19:12:49-07:00', '2019-07-27T19:17:10-07:00', '2019-07-27T19:17:49-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-07-28T10:22:19-07:00', '2019-07-28T10:31:39-07:00'), ('2019-07-28T10:31:40.691000-07:00', '2019-07-28T10:32:44-07:00'), ('2019-07-28T11:56:08.727000-07:00', '2019-07-28T12:07:54-07:00')]\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = ['2019-07-28T10:22:19-07:00', '2019-07-28T10:31:40.691000-07:00']\n", + "Before filtering, trips = [('2019-07-28T10:22:19-07:00', '2019-07-28T10:31:39-07:00'), ('2019-07-28T10:31:40.691000-07:00', '2019-07-28T10:32:44-07:00'), ('2019-07-28T11:56:08.727000-07:00', '2019-07-28T12:07:54-07:00')]\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = ['2019-07-28T11:56:08.727000-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-07-28T15:07:06-07:00', '2019-07-28T15:15:40-07:00'), ('2019-07-28T15:15:42-07:00', '2019-07-28T15:18:02-07:00'), ('2019-07-28T16:35:21.561569-07:00', '2019-07-28T16:53:49-07:00')]\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = ['2019-07-28T15:07:06-07:00', '2019-07-28T15:15:42-07:00']\n", + "Before filtering, trips = [('2019-07-28T15:07:06-07:00', '2019-07-28T15:15:40-07:00'), ('2019-07-28T15:15:42-07:00', '2019-07-28T15:18:02-07:00'), ('2019-07-28T16:35:21.561569-07:00', '2019-07-28T16:53:49-07:00')]\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = ['2019-07-28T16:35:21.561569-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-08-11T17:53:27-07:00', '2019-08-11T17:53:43-07:00'), ('2019-08-11T17:53:45-07:00', '2019-08-11T18:06:30-07:00'), ('2019-08-11T19:12:16.193794-07:00', '2019-08-11T19:25:34-07:00'), ('2019-08-11T19:25:35-07:00', '2019-08-11T19:25:47-07:00'), ('2019-08-11T19:25:48-07:00', '2019-08-11T19:30:30-07:00'), ('2019-08-11T19:30:31-07:00', '2019-08-11T19:31:09-07:00')]\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = ['2019-08-11T17:53:27-07:00', '2019-08-11T17:53:45-07:00']\n", + "Before filtering, trips = [('2019-08-11T17:53:27-07:00', '2019-08-11T17:53:43-07:00'), ('2019-08-11T17:53:45-07:00', '2019-08-11T18:06:30-07:00'), ('2019-08-11T19:12:16.193794-07:00', '2019-08-11T19:25:34-07:00'), ('2019-08-11T19:25:35-07:00', '2019-08-11T19:25:47-07:00'), ('2019-08-11T19:25:48-07:00', '2019-08-11T19:30:30-07:00'), ('2019-08-11T19:30:31-07:00', '2019-08-11T19:31:09-07:00')]\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = ['2019-08-11T19:12:16.193794-07:00', '2019-08-11T19:25:35-07:00', '2019-08-11T19:25:48-07:00', '2019-08-11T19:30:31-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-08-31T10:12:58.699000-07:00', '2019-08-31T10:21:57-07:00'), ('2019-08-31T10:21:58-07:00', '2019-08-31T10:22:59-07:00'), ('2019-08-31T11:33:11.236317-07:00', '2019-08-31T11:46:28-07:00'), ('2019-08-31T11:46:29-07:00', '2019-08-31T11:46:41-07:00'), ('2019-08-31T11:46:42-07:00', '2019-08-31T11:51:54-07:00')]\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = ['2019-08-31T10:12:58.699000-07:00', '2019-08-31T10:21:58-07:00']\n", + "Before filtering, trips = [('2019-08-31T10:12:58.699000-07:00', '2019-08-31T10:21:57-07:00'), ('2019-08-31T10:21:58-07:00', '2019-08-31T10:22:59-07:00'), ('2019-08-31T11:33:11.236317-07:00', '2019-08-31T11:46:28-07:00'), ('2019-08-31T11:46:29-07:00', '2019-08-31T11:46:41-07:00'), ('2019-08-31T11:46:42-07:00', '2019-08-31T11:51:54-07:00')]\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = ['2019-08-31T11:33:11.236317-07:00', '2019-08-31T11:46:29-07:00', '2019-08-31T11:46:42-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-08-31T15:01:52-07:00', '2019-08-31T15:13:36-07:00'), ('2019-08-31T16:32:38.765812-07:00', '2019-08-31T16:50:36-07:00')]\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = ['2019-08-31T15:01:52-07:00']\n", + "Before filtering, trips = [('2019-08-31T15:01:52-07:00', '2019-08-31T15:13:36-07:00'), ('2019-08-31T16:32:38.765812-07:00', '2019-08-31T16:50:36-07:00')]\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = ['2019-08-31T16:32:38.765812-07:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", + "Before filtering, trips = [('2019-07-27T17:46:18.389000-07:00', '2019-07-27T17:46:59-07:00'), ('2019-07-27T17:47:24.433000-07:00', '2019-07-27T17:53:24-07:00'), ('2019-07-27T17:53:53-07:00', '2019-07-27T17:56:56.168000-07:00'), ('2019-07-27T19:01:31.072749-07:00', '2019-07-27T19:21:12-07:00')]\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = ['2019-07-27T17:46:18.389000-07:00', '2019-07-27T17:47:24.433000-07:00', '2019-07-27T17:53:53-07:00']\n", + "Before filtering, trips = [('2019-07-27T17:46:18.389000-07:00', '2019-07-27T17:46:59-07:00'), ('2019-07-27T17:47:24.433000-07:00', '2019-07-27T17:53:24-07:00'), ('2019-07-27T17:53:53-07:00', '2019-07-27T17:56:56.168000-07:00'), ('2019-07-27T19:01:31.072749-07:00', '2019-07-27T19:21:12-07:00')]\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = ['2019-07-27T19:01:31.072749-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", + "Before filtering, trips = [('2019-07-28T10:21:47.338000-07:00', '2019-07-28T10:32:22-07:00'), ('2019-07-28T11:50:20.124734-07:00', '2019-07-28T12:01:27-07:00'), ('2019-07-28T12:01:57-07:00', '2019-07-28T12:01:57-07:00'), ('2019-07-28T12:02:28-07:00', '2019-07-28T12:03:58-07:00'), ('2019-07-28T12:05:28-07:00', '2019-07-28T12:10:00-07:00')]\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = ['2019-07-28T10:21:47.338000-07:00']\n", + "Before filtering, trips = [('2019-07-28T10:21:47.338000-07:00', '2019-07-28T10:32:22-07:00'), ('2019-07-28T11:50:20.124734-07:00', '2019-07-28T12:01:27-07:00'), ('2019-07-28T12:01:57-07:00', '2019-07-28T12:01:57-07:00'), ('2019-07-28T12:02:28-07:00', '2019-07-28T12:03:58-07:00'), ('2019-07-28T12:05:28-07:00', '2019-07-28T12:10:00-07:00')]\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = ['2019-07-28T11:50:20.124734-07:00', '2019-07-28T12:01:57-07:00', '2019-07-28T12:02:28-07:00', '2019-07-28T12:05:28-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", + "Before filtering, trips = [('2019-07-28T15:07:04.625000-07:00', '2019-07-28T15:15:09-07:00'), ('2019-07-28T15:15:36-07:00', '2019-07-28T15:19:11-07:00'), ('2019-07-28T16:34:59.625707-07:00', '2019-07-28T16:54:30.729000-07:00')]\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = ['2019-07-28T15:07:04.625000-07:00', '2019-07-28T15:15:36-07:00']\n", + "Before filtering, trips = [('2019-07-28T15:07:04.625000-07:00', '2019-07-28T15:15:09-07:00'), ('2019-07-28T15:15:36-07:00', '2019-07-28T15:19:11-07:00'), ('2019-07-28T16:34:59.625707-07:00', '2019-07-28T16:54:30.729000-07:00')]\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = ['2019-07-28T16:34:59.625707-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", + "Before filtering, trips = [('2019-08-11T17:42:34.280993-07:00', '2019-08-11T17:49:37.090000-07:00'), ('2019-08-11T17:56:40-07:00', '2019-08-11T18:06:14-07:00'), ('2019-08-11T18:06:15-07:00', '2019-08-11T18:09:59-07:00'), ('2019-08-11T19:12:27.932309-07:00', '2019-08-11T19:23:25-07:00'), ('2019-08-11T19:23:26-07:00', '2019-08-11T19:23:38-07:00'), ('2019-08-11T19:23:39-07:00', '2019-08-11T19:25:21-07:00'), ('2019-08-11T19:25:22-07:00', '2019-08-11T19:25:52-07:00'), ('2019-08-11T19:26:02-07:00', '2019-08-11T19:30:16-07:00')]\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = ['2019-08-11T17:42:34.280993-07:00', '2019-08-11T17:56:40-07:00', '2019-08-11T18:06:15-07:00']\n", + "Before filtering, trips = [('2019-08-11T17:42:34.280993-07:00', '2019-08-11T17:49:37.090000-07:00'), ('2019-08-11T17:56:40-07:00', '2019-08-11T18:06:14-07:00'), ('2019-08-11T18:06:15-07:00', '2019-08-11T18:09:59-07:00'), ('2019-08-11T19:12:27.932309-07:00', '2019-08-11T19:23:25-07:00'), ('2019-08-11T19:23:26-07:00', '2019-08-11T19:23:38-07:00'), ('2019-08-11T19:23:39-07:00', '2019-08-11T19:25:21-07:00'), ('2019-08-11T19:25:22-07:00', '2019-08-11T19:25:52-07:00'), ('2019-08-11T19:26:02-07:00', '2019-08-11T19:30:16-07:00')]\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = ['2019-08-11T19:12:27.932309-07:00', '2019-08-11T19:23:26-07:00', '2019-08-11T19:23:39-07:00', '2019-08-11T19:25:22-07:00', '2019-08-11T19:26:02-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", + "Before filtering, trips = [('2019-08-31T10:12:03.331000-07:00', '2019-08-31T10:22:00-07:00'), ('2019-08-31T10:22:05.540000-07:00', '2019-08-31T10:23:27-07:00'), ('2019-08-31T11:33:47.875566-07:00', '2019-08-31T11:51:52-07:00')]\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = ['2019-08-31T10:12:03.331000-07:00', '2019-08-31T10:22:05.540000-07:00']\n", + "Before filtering, trips = [('2019-08-31T10:12:03.331000-07:00', '2019-08-31T10:22:00-07:00'), ('2019-08-31T10:22:05.540000-07:00', '2019-08-31T10:23:27-07:00'), ('2019-08-31T11:33:47.875566-07:00', '2019-08-31T11:51:52-07:00')]\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = ['2019-08-31T11:33:47.875566-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", + "Before filtering, trips = [('2019-08-31T15:01:48.399000-07:00', '2019-08-31T15:12:46-07:00'), ('2019-08-31T16:32:54-07:00', '2019-08-31T16:50:30-07:00')]\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = ['2019-08-31T15:01:48.399000-07:00']\n", + "Before filtering, trips = [('2019-08-31T15:01:48.399000-07:00', '2019-08-31T15:12:46-07:00'), ('2019-08-31T16:32:54-07:00', '2019-08-31T16:50:30-07:00')]\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = ['2019-08-31T16:32:54-07:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_0 HAHFDC v/s HAMFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_1 HAHFDC v/s HAMFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_2 HAHFDC v/s HAMFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_3 HAHFDC v/s MAHFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_4 HAHFDC v/s MAHFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_5 HAHFDC v/s MAHFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = []\n", + "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", + "ios dict_keys(['ucb-sdb-ios-1', 'ucb-sdb-ios-2', 'ucb-sdb-ios-3', 'ucb-sdb-ios-4'])\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_0 HAHFDC v/s MAHFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_1 HAHFDC v/s MAHFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_2 HAHFDC v/s MAHFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_3 HAHFDC v/s HAMFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_4 HAHFDC v/s HAMFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_5 HAHFDC v/s HAMFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = []\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-07-27T17:48:26.003052-07:00', '2019-07-27T17:55:14.984543-07:00'), ('2019-07-27T19:02:21.000540-07:00', '2019-07-27T19:04:49.996204-07:00'), ('2019-07-27T19:04:50.996161-07:00', '2019-07-27T19:05:06.995687-07:00'), ('2019-07-27T19:05:07.995770-07:00', '2019-07-27T19:12:40.996040-07:00'), ('2019-07-27T19:12:41.996006-07:00', '2019-07-27T19:13:02.995287-07:00'), ('2019-07-27T19:13:03.995252-07:00', '2019-07-27T19:14:55.991418-07:00'), ('2019-07-27T19:14:56.991384-07:00', '2019-07-27T19:18:07.999237-07:00'), ('2019-07-27T19:18:08.999212-07:00', '2019-07-27T19:18:24.998764-07:00'), ('2019-07-27T19:18:25.998734-07:00', '2019-07-27T19:18:54.997776-07:00'), ('2019-07-27T19:18:55.997741-07:00', '2019-07-27T19:19:48.995914-07:00'), ('2019-07-27T19:19:53.995742-07:00', '2019-07-27T19:21:41.992005-07:00')]\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = ['2019-07-27T17:48:26.003052-07:00']\n", + "Before filtering, trips = [('2019-07-27T17:48:26.003052-07:00', '2019-07-27T17:55:14.984543-07:00'), ('2019-07-27T19:02:21.000540-07:00', '2019-07-27T19:04:49.996204-07:00'), ('2019-07-27T19:04:50.996161-07:00', '2019-07-27T19:05:06.995687-07:00'), ('2019-07-27T19:05:07.995770-07:00', '2019-07-27T19:12:40.996040-07:00'), ('2019-07-27T19:12:41.996006-07:00', '2019-07-27T19:13:02.995287-07:00'), ('2019-07-27T19:13:03.995252-07:00', '2019-07-27T19:14:55.991418-07:00'), ('2019-07-27T19:14:56.991384-07:00', '2019-07-27T19:18:07.999237-07:00'), ('2019-07-27T19:18:08.999212-07:00', '2019-07-27T19:18:24.998764-07:00'), ('2019-07-27T19:18:25.998734-07:00', '2019-07-27T19:18:54.997776-07:00'), ('2019-07-27T19:18:55.997741-07:00', '2019-07-27T19:19:48.995914-07:00'), ('2019-07-27T19:19:53.995742-07:00', '2019-07-27T19:21:41.992005-07:00')]\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = ['2019-07-27T19:02:21.000540-07:00', '2019-07-27T19:04:50.996161-07:00', '2019-07-27T19:05:07.995770-07:00', '2019-07-27T19:12:41.996006-07:00', '2019-07-27T19:13:03.995252-07:00', '2019-07-27T19:14:56.991384-07:00', '2019-07-27T19:18:08.999212-07:00', '2019-07-27T19:18:25.998734-07:00', '2019-07-27T19:18:55.997741-07:00', '2019-07-27T19:19:53.995742-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-07-28T10:23:25.343677-07:00', '2019-07-28T10:31:44.997968-07:00'), ('2019-07-28T10:31:45.997937-07:00', '2019-07-28T10:34:14.992393-07:00'), ('2019-07-28T11:51:07.259256-07:00', '2019-07-28T12:02:23.994942-07:00'), ('2019-07-28T12:02:24.994908-07:00', '2019-07-28T12:05:25.988719-07:00'), ('2019-07-28T12:05:26.988686-07:00', '2019-07-28T12:06:38.986224-07:00'), ('2019-07-28T12:06:39.986190-07:00', '2019-07-28T12:06:58.996818-07:00'), ('2019-07-28T12:06:59.997020-07:00', '2019-07-28T12:07:27.998720-07:00'), ('2019-07-28T12:07:28.998719-07:00', '2019-07-28T12:07:37.998624-07:00'), ('2019-07-28T12:07:38.998607-07:00', '2019-07-28T12:09:25.995176-07:00'), ('2019-07-28T12:09:30.995004-07:00', '2019-07-28T12:10:03.993867-07:00')]\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = ['2019-07-28T10:23:25.343677-07:00', '2019-07-28T10:31:45.997937-07:00']\n", + "Before filtering, trips = [('2019-07-28T10:23:25.343677-07:00', '2019-07-28T10:31:44.997968-07:00'), ('2019-07-28T10:31:45.997937-07:00', '2019-07-28T10:34:14.992393-07:00'), ('2019-07-28T11:51:07.259256-07:00', '2019-07-28T12:02:23.994942-07:00'), ('2019-07-28T12:02:24.994908-07:00', '2019-07-28T12:05:25.988719-07:00'), ('2019-07-28T12:05:26.988686-07:00', '2019-07-28T12:06:38.986224-07:00'), ('2019-07-28T12:06:39.986190-07:00', '2019-07-28T12:06:58.996818-07:00'), ('2019-07-28T12:06:59.997020-07:00', '2019-07-28T12:07:27.998720-07:00'), ('2019-07-28T12:07:28.998719-07:00', '2019-07-28T12:07:37.998624-07:00'), ('2019-07-28T12:07:38.998607-07:00', '2019-07-28T12:09:25.995176-07:00'), ('2019-07-28T12:09:30.995004-07:00', '2019-07-28T12:10:03.993867-07:00')]\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = ['2019-07-28T11:51:07.259256-07:00', '2019-07-28T12:02:24.994908-07:00', '2019-07-28T12:05:26.988686-07:00', '2019-07-28T12:06:39.986190-07:00', '2019-07-28T12:06:59.997020-07:00', '2019-07-28T12:07:28.998719-07:00', '2019-07-28T12:07:38.998607-07:00', '2019-07-28T12:09:30.995004-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-07-28T15:07:32.811940-07:00', '2019-07-28T15:15:24.998590-07:00'), ('2019-07-28T15:15:26.998613-07:00', '2019-07-28T15:18:05.993966-07:00'), ('2019-07-28T16:35:57.908857-07:00', '2019-07-28T16:45:56.997239-07:00'), ('2019-07-28T16:45:57.997204-07:00', '2019-07-28T16:46:03.996983-07:00'), ('2019-07-28T16:46:04.996947-07:00', '2019-07-28T16:53:34.996686-07:00'), ('2019-07-28T16:53:36.996612-07:00', '2019-07-28T16:55:43.830019-07:00')]\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = ['2019-07-28T15:07:32.811940-07:00', '2019-07-28T15:15:26.998613-07:00']\n", + "Before filtering, trips = [('2019-07-28T15:07:32.811940-07:00', '2019-07-28T15:15:24.998590-07:00'), ('2019-07-28T15:15:26.998613-07:00', '2019-07-28T15:18:05.993966-07:00'), ('2019-07-28T16:35:57.908857-07:00', '2019-07-28T16:45:56.997239-07:00'), ('2019-07-28T16:45:57.997204-07:00', '2019-07-28T16:46:03.996983-07:00'), ('2019-07-28T16:46:04.996947-07:00', '2019-07-28T16:53:34.996686-07:00'), ('2019-07-28T16:53:36.996612-07:00', '2019-07-28T16:55:43.830019-07:00')]\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = ['2019-07-28T16:35:57.908857-07:00', '2019-07-28T16:45:57.997204-07:00', '2019-07-28T16:46:04.996947-07:00', '2019-07-28T16:53:36.996612-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-08-11T17:53:59.032591-07:00', '2019-08-11T18:06:13.990799-07:00'), ('2019-08-11T18:06:14.990764-07:00', '2019-08-11T18:07:53.987217-07:00'), ('2019-08-11T19:12:38.044344-07:00', '2019-08-11T19:15:15.997580-07:00'), ('2019-08-11T19:15:16.997539-07:00', '2019-08-11T19:15:20.997372-07:00'), ('2019-08-11T19:15:21.997330-07:00', '2019-08-11T19:21:48.998054-07:00'), ('2019-08-11T19:21:49.998110-07:00', '2019-08-11T19:22:08.998388-07:00'), ('2019-08-11T19:22:09.998376-07:00', '2019-08-11T19:24:17.993807-07:00'), ('2019-08-11T19:24:18.993765-07:00', '2019-08-11T19:27:35.986123-07:00'), ('2019-08-11T19:27:36.986084-07:00', '2019-08-11T19:30:19.996613-07:00'), ('2019-08-11T19:30:22.996500-07:00', '2019-08-11T19:32:37.991355-07:00')]\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = ['2019-08-11T17:53:59.032591-07:00', '2019-08-11T18:06:14.990764-07:00']\n", + "Before filtering, trips = [('2019-08-11T17:53:59.032591-07:00', '2019-08-11T18:06:13.990799-07:00'), ('2019-08-11T18:06:14.990764-07:00', '2019-08-11T18:07:53.987217-07:00'), ('2019-08-11T19:12:38.044344-07:00', '2019-08-11T19:15:15.997580-07:00'), ('2019-08-11T19:15:16.997539-07:00', '2019-08-11T19:15:20.997372-07:00'), ('2019-08-11T19:15:21.997330-07:00', '2019-08-11T19:21:48.998054-07:00'), ('2019-08-11T19:21:49.998110-07:00', '2019-08-11T19:22:08.998388-07:00'), ('2019-08-11T19:22:09.998376-07:00', '2019-08-11T19:24:17.993807-07:00'), ('2019-08-11T19:24:18.993765-07:00', '2019-08-11T19:27:35.986123-07:00'), ('2019-08-11T19:27:36.986084-07:00', '2019-08-11T19:30:19.996613-07:00'), ('2019-08-11T19:30:22.996500-07:00', '2019-08-11T19:32:37.991355-07:00')]\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = ['2019-08-11T19:12:38.044344-07:00', '2019-08-11T19:15:16.997539-07:00', '2019-08-11T19:15:21.997330-07:00', '2019-08-11T19:21:49.998110-07:00', '2019-08-11T19:22:09.998376-07:00', '2019-08-11T19:24:18.993765-07:00', '2019-08-11T19:27:36.986084-07:00', '2019-08-11T19:30:22.996500-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-08-31T10:25:07.058955-07:00', '2019-08-31T11:48:40.995877-07:00'), ('2019-08-31T11:48:41.995913-07:00', '2019-08-31T11:52:13.989582-07:00'), ('2019-08-31T11:52:15.989512-07:00', '2019-08-31T11:54:17.429140-07:00')]\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = [('2019-08-31T10:25:07.058955-07:00', '2019-08-31T11:48:40.995877-07:00'), ('2019-08-31T11:48:41.995913-07:00', '2019-08-31T11:52:13.989582-07:00'), ('2019-08-31T11:52:15.989512-07:00', '2019-08-31T11:54:17.429140-07:00')]\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = ['2019-08-31T11:48:41.995913-07:00', '2019-08-31T11:52:15.989512-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-08-31T15:00:59.157359-07:00', '2019-08-31T15:13:17.989944-07:00'), ('2019-08-31T15:13:18.989909-07:00', '2019-08-31T15:15:01.986328-07:00'), ('2019-08-31T16:32:26.722881-07:00', '2019-08-31T16:35:00.997696-07:00'), ('2019-08-31T16:35:01.997660-07:00', '2019-08-31T16:35:10.997329-07:00'), ('2019-08-31T16:35:11.997292-07:00', '2019-08-31T16:43:29.995417-07:00'), ('2019-08-31T16:43:30.995383-07:00', '2019-08-31T16:47:20.987332-07:00'), ('2019-08-31T16:47:21.987296-07:00', '2019-08-31T16:50:10.996794-07:00'), ('2019-08-31T16:50:14.996661-07:00', '2019-08-31T16:52:52.080658-07:00')]\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = ['2019-08-31T15:00:59.157359-07:00', '2019-08-31T15:13:18.989909-07:00']\n", + "Before filtering, trips = [('2019-08-31T15:00:59.157359-07:00', '2019-08-31T15:13:17.989944-07:00'), ('2019-08-31T15:13:18.989909-07:00', '2019-08-31T15:15:01.986328-07:00'), ('2019-08-31T16:32:26.722881-07:00', '2019-08-31T16:35:00.997696-07:00'), ('2019-08-31T16:35:01.997660-07:00', '2019-08-31T16:35:10.997329-07:00'), ('2019-08-31T16:35:11.997292-07:00', '2019-08-31T16:43:29.995417-07:00'), ('2019-08-31T16:43:30.995383-07:00', '2019-08-31T16:47:20.987332-07:00'), ('2019-08-31T16:47:21.987296-07:00', '2019-08-31T16:50:10.996794-07:00'), ('2019-08-31T16:50:14.996661-07:00', '2019-08-31T16:52:52.080658-07:00')]\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = ['2019-08-31T16:32:26.722881-07:00', '2019-08-31T16:35:01.997660-07:00', '2019-08-31T16:35:11.997292-07:00', '2019-08-31T16:43:30.995383-07:00', '2019-08-31T16:47:21.987296-07:00', '2019-08-31T16:50:14.996661-07:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", + "Before filtering, trips = [('2019-07-27T19:01:45.481526-07:00', '2019-07-27T19:21:37.427862-07:00')]\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = [('2019-07-27T19:01:45.481526-07:00', '2019-07-27T19:21:37.427862-07:00')]\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = ['2019-07-27T19:01:45.481526-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", + "Before filtering, trips = [('2019-07-28T10:23:13.947510-07:00', '2019-07-28T10:31:48.066216-07:00'), ('2019-07-28T10:31:54.494439-07:00', '2019-07-28T10:34:30.450632-07:00'), ('2019-07-28T11:50:42.000985-07:00', '2019-07-28T12:10:40.324661-07:00')]\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = ['2019-07-28T10:23:13.947510-07:00', '2019-07-28T10:31:54.494439-07:00']\n", + "Before filtering, trips = [('2019-07-28T10:23:13.947510-07:00', '2019-07-28T10:31:48.066216-07:00'), ('2019-07-28T10:31:54.494439-07:00', '2019-07-28T10:34:30.450632-07:00'), ('2019-07-28T11:50:42.000985-07:00', '2019-07-28T12:10:40.324661-07:00')]\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = ['2019-07-28T11:50:42.000985-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", + "Before filtering, trips = [('2019-07-28T15:05:58.387160-07:00', '2019-07-28T15:15:28.774266-07:00'), ('2019-07-28T15:15:35.207299-07:00', '2019-07-28T15:18:09.393904-07:00'), ('2019-07-28T16:35:11.805413-07:00', '2019-07-28T16:55:39.003900-07:00')]\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = ['2019-07-28T15:05:58.387160-07:00', '2019-07-28T15:15:35.207299-07:00']\n", + "Before filtering, trips = [('2019-07-28T15:05:58.387160-07:00', '2019-07-28T15:15:28.774266-07:00'), ('2019-07-28T15:15:35.207299-07:00', '2019-07-28T15:18:09.393904-07:00'), ('2019-07-28T16:35:11.805413-07:00', '2019-07-28T16:55:39.003900-07:00')]\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = ['2019-07-28T16:35:11.805413-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", + "Before filtering, trips = [('2019-08-11T17:56:11.652814-07:00', '2019-08-11T17:56:52.001655-07:00'), ('2019-08-11T17:56:58.999691-07:00', '2019-08-11T17:57:31.995701-07:00'), ('2019-08-11T17:57:35.995450-07:00', '2019-08-11T18:05:22.996270-07:00'), ('2019-08-11T18:06:40.993286-07:00', '2019-08-11T18:07:46.990754-07:00'), ('2019-08-11T19:12:12.022208-07:00', '2019-08-11T19:21:45.998302-07:00'), ('2019-08-11T19:21:58.997940-07:00', '2019-08-11T19:21:58.997940-07:00'), ('2019-08-11T19:22:05.997700-07:00', '2019-08-11T19:23:36.994141-07:00'), ('2019-08-11T19:24:15.992582-07:00', '2019-08-11T19:24:24.992223-07:00'), ('2019-08-11T19:24:35.991783-07:00', '2019-08-11T19:28:32.996612-07:00'), ('2019-08-11T19:28:41.997831-07:00', '2019-08-11T19:28:41.997831-07:00'), ('2019-08-11T19:28:48.998278-07:00', '2019-08-11T19:31:06.993943-07:00'), ('2019-08-11T19:32:41.990081-07:00', '2019-08-11T19:32:41.990081-07:00')]\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = ['2019-08-11T17:56:11.652814-07:00', '2019-08-11T17:56:58.999691-07:00', '2019-08-11T17:57:35.995450-07:00', '2019-08-11T18:06:40.993286-07:00']\n", + "Before filtering, trips = [('2019-08-11T17:56:11.652814-07:00', '2019-08-11T17:56:52.001655-07:00'), ('2019-08-11T17:56:58.999691-07:00', '2019-08-11T17:57:31.995701-07:00'), ('2019-08-11T17:57:35.995450-07:00', '2019-08-11T18:05:22.996270-07:00'), ('2019-08-11T18:06:40.993286-07:00', '2019-08-11T18:07:46.990754-07:00'), ('2019-08-11T19:12:12.022208-07:00', '2019-08-11T19:21:45.998302-07:00'), ('2019-08-11T19:21:58.997940-07:00', '2019-08-11T19:21:58.997940-07:00'), ('2019-08-11T19:22:05.997700-07:00', '2019-08-11T19:23:36.994141-07:00'), ('2019-08-11T19:24:15.992582-07:00', '2019-08-11T19:24:24.992223-07:00'), ('2019-08-11T19:24:35.991783-07:00', '2019-08-11T19:28:32.996612-07:00'), ('2019-08-11T19:28:41.997831-07:00', '2019-08-11T19:28:41.997831-07:00'), ('2019-08-11T19:28:48.998278-07:00', '2019-08-11T19:31:06.993943-07:00'), ('2019-08-11T19:32:41.990081-07:00', '2019-08-11T19:32:41.990081-07:00')]\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = ['2019-08-11T19:12:12.022208-07:00', '2019-08-11T19:21:58.997940-07:00', '2019-08-11T19:22:05.997700-07:00', '2019-08-11T19:24:15.992582-07:00', '2019-08-11T19:24:35.991783-07:00', '2019-08-11T19:28:41.997831-07:00', '2019-08-11T19:28:48.998278-07:00', '2019-08-11T19:32:41.990081-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", + "Before filtering, trips = [('2019-08-31T10:20:46.525764-07:00', '2019-08-31T10:21:18.003078-07:00'), ('2019-08-31T10:22:33.994846-07:00', '2019-08-31T10:24:27.990058-07:00'), ('2019-08-31T11:34:10.217073-07:00', '2019-08-31T11:43:11.998456-07:00'), ('2019-08-31T11:43:20.998398-07:00', '2019-08-31T11:43:28.998243-07:00'), ('2019-08-31T11:43:35.998062-07:00', '2019-08-31T11:46:45.991218-07:00'), ('2019-08-31T11:48:27.987473-07:00', '2019-08-31T11:48:34.987216-07:00'), ('2019-08-31T11:48:41.986959-07:00', '2019-08-31T11:51:00.997739-07:00'), ('2019-08-31T11:52:41.994148-07:00', '2019-08-31T11:53:59.924421-07:00')]\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = ['2019-08-31T10:20:46.525764-07:00', '2019-08-31T10:22:33.994846-07:00']\n", + "Before filtering, trips = [('2019-08-31T10:20:46.525764-07:00', '2019-08-31T10:21:18.003078-07:00'), ('2019-08-31T10:22:33.994846-07:00', '2019-08-31T10:24:27.990058-07:00'), ('2019-08-31T11:34:10.217073-07:00', '2019-08-31T11:43:11.998456-07:00'), ('2019-08-31T11:43:20.998398-07:00', '2019-08-31T11:43:28.998243-07:00'), ('2019-08-31T11:43:35.998062-07:00', '2019-08-31T11:46:45.991218-07:00'), ('2019-08-31T11:48:27.987473-07:00', '2019-08-31T11:48:34.987216-07:00'), ('2019-08-31T11:48:41.986959-07:00', '2019-08-31T11:51:00.997739-07:00'), ('2019-08-31T11:52:41.994148-07:00', '2019-08-31T11:53:59.924421-07:00')]\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = ['2019-08-31T11:34:10.217073-07:00', '2019-08-31T11:43:20.998398-07:00', '2019-08-31T11:43:35.998062-07:00', '2019-08-31T11:48:27.987473-07:00', '2019-08-31T11:48:41.986959-07:00', '2019-08-31T11:52:41.994148-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", + "Before filtering, trips = [('2019-08-31T15:02:50.999717-07:00', '2019-08-31T15:03:26.998827-07:00'), ('2019-08-31T15:03:30.998668-07:00', '2019-08-31T15:03:30.998668-07:00'), ('2019-08-31T15:03:38.998345-07:00', '2019-08-31T15:12:28.995477-07:00'), ('2019-08-31T15:13:25.995546-07:00', '2019-08-31T15:14:30.993356-07:00'), ('2019-08-31T16:32:33.961561-07:00', '2019-08-31T16:39:48.985268-07:00'), ('2019-08-31T16:39:55.984972-07:00', '2019-08-31T16:39:55.984972-07:00'), ('2019-08-31T16:40:02.984676-07:00', '2019-08-31T16:43:46.995780-07:00'), ('2019-08-31T16:45:30.991822-07:00', '2019-08-31T16:47:16.987788-07:00'), ('2019-08-31T16:47:23.987522-07:00', '2019-08-31T16:49:46.998160-07:00'), ('2019-08-31T16:51:40.994053-07:00', '2019-08-31T16:52:58.057469-07:00')]\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = ['2019-08-31T15:02:50.999717-07:00', '2019-08-31T15:03:30.998668-07:00', '2019-08-31T15:03:38.998345-07:00', '2019-08-31T15:13:25.995546-07:00']\n", + "Before filtering, trips = [('2019-08-31T15:02:50.999717-07:00', '2019-08-31T15:03:26.998827-07:00'), ('2019-08-31T15:03:30.998668-07:00', '2019-08-31T15:03:30.998668-07:00'), ('2019-08-31T15:03:38.998345-07:00', '2019-08-31T15:12:28.995477-07:00'), ('2019-08-31T15:13:25.995546-07:00', '2019-08-31T15:14:30.993356-07:00'), ('2019-08-31T16:32:33.961561-07:00', '2019-08-31T16:39:48.985268-07:00'), ('2019-08-31T16:39:55.984972-07:00', '2019-08-31T16:39:55.984972-07:00'), ('2019-08-31T16:40:02.984676-07:00', '2019-08-31T16:43:46.995780-07:00'), ('2019-08-31T16:45:30.991822-07:00', '2019-08-31T16:47:16.987788-07:00'), ('2019-08-31T16:47:23.987522-07:00', '2019-08-31T16:49:46.998160-07:00'), ('2019-08-31T16:51:40.994053-07:00', '2019-08-31T16:52:58.057469-07:00')]\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = ['2019-08-31T16:32:33.961561-07:00', '2019-08-31T16:39:55.984972-07:00', '2019-08-31T16:40:02.984676-07:00', '2019-08-31T16:45:30.991822-07:00', '2019-08-31T16:47:23.987522-07:00', '2019-08-31T16:51:40.994053-07:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_0 HAHFDC v/s MAHFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_1 HAHFDC v/s MAHFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_2 HAHFDC v/s MAHFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_3 HAHFDC v/s HAMFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_4 HAHFDC v/s HAMFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_5 HAHFDC v/s HAMFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", + "After filtering, trips = []\n", + "Finished copying car_scooter_brex_san_jose, starting overwrite\n", + "Found spec = Multi-modal car scooter BREX trip to San Jose\n", + "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", + "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", + "android dict_keys(['ucb-sdb-android-1', 'ucb-sdb-android-2', 'ucb-sdb-android-3', 'ucb-sdb-android-4'])\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_0 HAHFDC v/s HAMFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_1 HAHFDC v/s HAMFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_2 HAHFDC v/s HAMFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_3 HAHFDC v/s MAHFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_4 HAHFDC v/s MAHFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_5 HAHFDC v/s MAHFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_6 MAMFDC v/s HAMFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = []\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-07-22T11:54:28.840000-07:00', '2019-07-22T11:57:57-07:00'), ('2019-07-22T11:57:58-07:00', '2019-07-22T12:22:10-07:00'), ('2019-07-22T16:09:04.460140-07:00', '2019-07-22T16:24:35-07:00'), ('2019-07-22T16:24:36-07:00', '2019-07-22T16:24:48-07:00'), ('2019-07-22T16:24:49-07:00', '2019-07-22T16:27:10-07:00'), ('2019-07-22T16:27:11-07:00', '2019-07-22T16:27:45-07:00'), ('2019-07-22T16:27:47-07:00', '2019-07-22T16:29:04-07:00'), ('2019-07-22T16:29:05-07:00', '2019-07-22T16:29:30-07:00'), ('2019-07-22T16:29:30-07:00', '2019-07-22T16:29:56-07:00'), ('2019-07-22T16:29:57-07:00', '2019-07-22T16:30:09-07:00'), ('2019-07-22T16:30:10-07:00', '2019-07-22T16:36:06-07:00'), ('2019-07-22T16:36:07-07:00', '2019-07-22T16:36:19-07:00'), ('2019-07-22T16:36:20-07:00', '2019-07-22T16:37:37-07:00'), ('2019-07-22T16:37:38-07:00', '2019-07-22T16:37:59-07:00'), ('2019-07-22T16:38:01-07:00', '2019-07-22T16:38:27-07:00'), ('2019-07-22T16:54:55.404858-07:00', '2019-07-22T17:33:43-07:00'), ('2019-07-22T17:33:44-07:00', '2019-07-22T17:34:19-07:00'), ('2019-07-22T17:34:20-07:00', '2019-07-22T17:34:44-07:00'), ('2019-07-22T17:34:45.700000-07:00', '2019-07-22T17:39:30-07:00'), ('2019-07-22T17:39:31-07:00', '2019-07-22T17:39:44-07:00'), ('2019-07-22T17:39:45-07:00', '2019-07-22T17:45:09-07:00')]\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = ['2019-07-22T11:54:28.840000-07:00', '2019-07-22T11:57:58-07:00']\n", + "Before filtering, trips = [('2019-07-22T11:54:28.840000-07:00', '2019-07-22T11:57:57-07:00'), ('2019-07-22T11:57:58-07:00', '2019-07-22T12:22:10-07:00'), ('2019-07-22T16:09:04.460140-07:00', '2019-07-22T16:24:35-07:00'), ('2019-07-22T16:24:36-07:00', '2019-07-22T16:24:48-07:00'), ('2019-07-22T16:24:49-07:00', '2019-07-22T16:27:10-07:00'), ('2019-07-22T16:27:11-07:00', '2019-07-22T16:27:45-07:00'), ('2019-07-22T16:27:47-07:00', '2019-07-22T16:29:04-07:00'), ('2019-07-22T16:29:05-07:00', '2019-07-22T16:29:30-07:00'), ('2019-07-22T16:29:30-07:00', '2019-07-22T16:29:56-07:00'), ('2019-07-22T16:29:57-07:00', '2019-07-22T16:30:09-07:00'), ('2019-07-22T16:30:10-07:00', '2019-07-22T16:36:06-07:00'), ('2019-07-22T16:36:07-07:00', '2019-07-22T16:36:19-07:00'), ('2019-07-22T16:36:20-07:00', '2019-07-22T16:37:37-07:00'), ('2019-07-22T16:37:38-07:00', '2019-07-22T16:37:59-07:00'), ('2019-07-22T16:38:01-07:00', '2019-07-22T16:38:27-07:00'), ('2019-07-22T16:54:55.404858-07:00', '2019-07-22T17:33:43-07:00'), ('2019-07-22T17:33:44-07:00', '2019-07-22T17:34:19-07:00'), ('2019-07-22T17:34:20-07:00', '2019-07-22T17:34:44-07:00'), ('2019-07-22T17:34:45.700000-07:00', '2019-07-22T17:39:30-07:00'), ('2019-07-22T17:39:31-07:00', '2019-07-22T17:39:44-07:00'), ('2019-07-22T17:39:45-07:00', '2019-07-22T17:45:09-07:00')]\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = ['2019-07-22T16:09:04.460140-07:00', '2019-07-22T16:24:36-07:00', '2019-07-22T16:24:49-07:00', '2019-07-22T16:27:11-07:00', '2019-07-22T16:27:47-07:00', '2019-07-22T16:29:05-07:00', '2019-07-22T16:29:30-07:00', '2019-07-22T16:29:57-07:00', '2019-07-22T16:30:10-07:00', '2019-07-22T16:36:07-07:00', '2019-07-22T16:36:20-07:00', '2019-07-22T16:37:38-07:00', '2019-07-22T16:38:01-07:00', '2019-07-22T16:54:55.404858-07:00', '2019-07-22T17:33:44-07:00', '2019-07-22T17:34:20-07:00', '2019-07-22T17:34:45.700000-07:00', '2019-07-22T17:39:31-07:00', '2019-07-22T17:39:45-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-07-23T08:50:28-07:00', '2019-07-23T08:58:18-07:00'), ('2019-07-23T08:58:19-07:00', '2019-07-23T08:58:32-07:00'), ('2019-07-23T08:58:33-07:00', '2019-07-23T09:17:27-07:00'), ('2019-07-23T09:17:28-07:00', '2019-07-23T09:18:56-07:00'), ('2019-07-23T12:40:03.240301-07:00', '2019-07-23T12:41:31-07:00'), ('2019-07-23T12:42:20-07:00', '2019-07-23T12:42:22-07:00'), ('2019-07-23T12:44:18-07:00', '2019-07-23T12:45:32-07:00'), ('2019-07-23T12:45:34-07:00', '2019-07-23T12:45:34-07:00'), ('2019-07-23T12:45:59-07:00', '2019-07-23T12:56:31-07:00'), ('2019-07-23T12:56:32-07:00', '2019-07-23T12:57:04-07:00'), ('2019-07-23T12:57:07-07:00', '2019-07-23T13:03:26-07:00'), ('2019-07-23T13:03:27-07:00', '2019-07-23T13:04:26-07:00'), ('2019-07-23T13:04:29-07:00', '2019-07-23T13:05:15-07:00'), ('2019-07-23T13:07:01.517002-07:00', '2019-07-23T13:51:12-07:00'), ('2019-07-23T13:51:14-07:00', '2019-07-23T14:00:24-07:00'), ('2019-07-23T14:03:24-07:00', '2019-07-23T16:07:16-07:00')]\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = ['2019-07-23T08:50:28-07:00', '2019-07-23T08:58:19-07:00', '2019-07-23T08:58:33-07:00', '2019-07-23T09:17:28-07:00']\n", + "Before filtering, trips = [('2019-07-23T08:50:28-07:00', '2019-07-23T08:58:18-07:00'), ('2019-07-23T08:58:19-07:00', '2019-07-23T08:58:32-07:00'), ('2019-07-23T08:58:33-07:00', '2019-07-23T09:17:27-07:00'), ('2019-07-23T09:17:28-07:00', '2019-07-23T09:18:56-07:00'), ('2019-07-23T12:40:03.240301-07:00', '2019-07-23T12:41:31-07:00'), ('2019-07-23T12:42:20-07:00', '2019-07-23T12:42:22-07:00'), ('2019-07-23T12:44:18-07:00', '2019-07-23T12:45:32-07:00'), ('2019-07-23T12:45:34-07:00', '2019-07-23T12:45:34-07:00'), ('2019-07-23T12:45:59-07:00', '2019-07-23T12:56:31-07:00'), ('2019-07-23T12:56:32-07:00', '2019-07-23T12:57:04-07:00'), ('2019-07-23T12:57:07-07:00', '2019-07-23T13:03:26-07:00'), ('2019-07-23T13:03:27-07:00', '2019-07-23T13:04:26-07:00'), ('2019-07-23T13:04:29-07:00', '2019-07-23T13:05:15-07:00'), ('2019-07-23T13:07:01.517002-07:00', '2019-07-23T13:51:12-07:00'), ('2019-07-23T13:51:14-07:00', '2019-07-23T14:00:24-07:00'), ('2019-07-23T14:03:24-07:00', '2019-07-23T16:07:16-07:00')]\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = ['2019-07-23T12:40:03.240301-07:00', '2019-07-23T12:42:20-07:00', '2019-07-23T12:44:18-07:00', '2019-07-23T12:45:34-07:00', '2019-07-23T12:45:59-07:00', '2019-07-23T12:56:32-07:00', '2019-07-23T12:57:07-07:00', '2019-07-23T13:03:27-07:00', '2019-07-23T13:04:29-07:00', '2019-07-23T13:07:01.517002-07:00', '2019-07-23T13:51:14-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-07-27T09:17:55-07:00', '2019-07-27T09:23:12-07:00'), ('2019-07-27T09:23:13-07:00', '2019-07-27T09:34:32-07:00'), ('2019-07-27T09:34:33-07:00', '2019-07-27T09:36:31-07:00'), ('2019-07-27T09:36:32-07:00', '2019-07-27T09:36:44-07:00'), ('2019-07-27T09:36:45-07:00', '2019-07-27T09:41:08-07:00'), ('2019-07-27T12:56:09.498616-07:00', '2019-07-27T13:04:07-07:00'), ('2019-07-27T13:04:08-07:00', '2019-07-27T13:04:20-07:00'), ('2019-07-27T13:04:22-07:00', '2019-07-27T13:05:00-07:00'), ('2019-07-27T13:05:01-07:00', '2019-07-27T13:05:27-07:00'), ('2019-07-27T13:05:40-07:00', '2019-07-27T13:07:35-07:00'), ('2019-07-27T13:07:36-07:00', '2019-07-27T13:08:27-07:00'), ('2019-07-27T13:08:28-07:00', '2019-07-27T13:08:53-07:00'), ('2019-07-27T13:08:54-07:00', '2019-07-27T13:10:37-07:00'), ('2019-07-27T13:10:38-07:00', '2019-07-27T13:17:07-07:00'), ('2019-07-27T13:17:08-07:00', '2019-07-27T13:17:49-07:00'), ('2019-07-27T13:23:36.196758-07:00', '2019-07-27T14:01:59-07:00'), ('2019-07-27T14:02:00-07:00', '2019-07-27T14:10:27-07:00')]\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = ['2019-07-27T09:17:55-07:00', '2019-07-27T09:23:13-07:00', '2019-07-27T09:34:33-07:00', '2019-07-27T09:36:32-07:00', '2019-07-27T09:36:45-07:00']\n", + "Before filtering, trips = [('2019-07-27T09:17:55-07:00', '2019-07-27T09:23:12-07:00'), ('2019-07-27T09:23:13-07:00', '2019-07-27T09:34:32-07:00'), ('2019-07-27T09:34:33-07:00', '2019-07-27T09:36:31-07:00'), ('2019-07-27T09:36:32-07:00', '2019-07-27T09:36:44-07:00'), ('2019-07-27T09:36:45-07:00', '2019-07-27T09:41:08-07:00'), ('2019-07-27T12:56:09.498616-07:00', '2019-07-27T13:04:07-07:00'), ('2019-07-27T13:04:08-07:00', '2019-07-27T13:04:20-07:00'), ('2019-07-27T13:04:22-07:00', '2019-07-27T13:05:00-07:00'), ('2019-07-27T13:05:01-07:00', '2019-07-27T13:05:27-07:00'), ('2019-07-27T13:05:40-07:00', '2019-07-27T13:07:35-07:00'), ('2019-07-27T13:07:36-07:00', '2019-07-27T13:08:27-07:00'), ('2019-07-27T13:08:28-07:00', '2019-07-27T13:08:53-07:00'), ('2019-07-27T13:08:54-07:00', '2019-07-27T13:10:37-07:00'), ('2019-07-27T13:10:38-07:00', '2019-07-27T13:17:07-07:00'), ('2019-07-27T13:17:08-07:00', '2019-07-27T13:17:49-07:00'), ('2019-07-27T13:23:36.196758-07:00', '2019-07-27T14:01:59-07:00'), ('2019-07-27T14:02:00-07:00', '2019-07-27T14:10:27-07:00')]\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = ['2019-07-27T12:56:09.498616-07:00', '2019-07-27T13:04:08-07:00', '2019-07-27T13:04:22-07:00', '2019-07-27T13:05:01-07:00', '2019-07-27T13:05:40-07:00', '2019-07-27T13:07:36-07:00', '2019-07-27T13:08:28-07:00', '2019-07-27T13:08:54-07:00', '2019-07-27T13:10:38-07:00', '2019-07-27T13:17:08-07:00', '2019-07-27T13:23:36.196758-07:00', '2019-07-27T14:02:00-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-08-05T10:30:03.679000-07:00', '2019-08-05T10:35:53-07:00'), ('2019-08-05T10:35:54-07:00', '2019-08-05T10:48:57-07:00'), ('2019-08-05T10:48:58-07:00', '2019-08-05T10:53:42-07:00'), ('2019-08-05T10:53:43-07:00', '2019-08-05T10:54:08-07:00'), ('2019-08-05T10:54:10-07:00', '2019-08-05T10:56:28-07:00'), ('2019-08-05T10:56:29-07:00', '2019-08-05T10:56:54-07:00'), ('2019-08-05T15:04:44.545249-07:00', '2019-08-05T15:18:28-07:00'), ('2019-08-05T15:18:29-07:00', '2019-08-05T15:22:21-07:00'), ('2019-08-05T15:22:23-07:00', '2019-08-05T15:28:04-07:00'), ('2019-08-05T15:34:47.975703-07:00', '2019-08-05T16:09:37-07:00'), ('2019-08-05T16:09:38-07:00', '2019-08-05T16:18:54-07:00')]\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = ['2019-08-05T10:30:03.679000-07:00', '2019-08-05T10:35:54-07:00', '2019-08-05T10:48:58-07:00', '2019-08-05T10:53:43-07:00', '2019-08-05T10:54:10-07:00', '2019-08-05T10:56:29-07:00']\n", + "Before filtering, trips = [('2019-08-05T10:30:03.679000-07:00', '2019-08-05T10:35:53-07:00'), ('2019-08-05T10:35:54-07:00', '2019-08-05T10:48:57-07:00'), ('2019-08-05T10:48:58-07:00', '2019-08-05T10:53:42-07:00'), ('2019-08-05T10:53:43-07:00', '2019-08-05T10:54:08-07:00'), ('2019-08-05T10:54:10-07:00', '2019-08-05T10:56:28-07:00'), ('2019-08-05T10:56:29-07:00', '2019-08-05T10:56:54-07:00'), ('2019-08-05T15:04:44.545249-07:00', '2019-08-05T15:18:28-07:00'), ('2019-08-05T15:18:29-07:00', '2019-08-05T15:22:21-07:00'), ('2019-08-05T15:22:23-07:00', '2019-08-05T15:28:04-07:00'), ('2019-08-05T15:34:47.975703-07:00', '2019-08-05T16:09:37-07:00'), ('2019-08-05T16:09:38-07:00', '2019-08-05T16:18:54-07:00')]\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = ['2019-08-05T15:04:44.545249-07:00', '2019-08-05T15:18:29-07:00', '2019-08-05T15:22:23-07:00', '2019-08-05T15:34:47.975703-07:00', '2019-08-05T16:09:38-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-08-06T11:32:03.781000-07:00', '2019-08-06T11:37:31-07:00'), ('2019-08-06T11:37:32-07:00', '2019-08-06T11:40:38-07:00'), ('2019-08-06T11:41:07-07:00', '2019-08-06T11:41:19-07:00'), ('2019-08-06T11:41:20-07:00', '2019-08-06T11:42:02-07:00'), ('2019-08-06T11:42:05-07:00', '2019-08-06T11:42:15-07:00'), ('2019-08-06T11:42:16-07:00', '2019-08-06T11:46:25-07:00'), ('2019-08-06T11:46:38-07:00', '2019-08-06T11:46:51-07:00'), ('2019-08-06T11:47:27-07:00', '2019-08-06T11:49:54-07:00'), ('2019-08-06T11:50:08-07:00', '2019-08-06T11:58:23-07:00'), ('2019-08-06T15:49:51.919479-07:00', '2019-08-06T15:56:05-07:00'), ('2019-08-06T15:56:06-07:00', '2019-08-06T15:56:18-07:00'), ('2019-08-06T15:56:19-07:00', '2019-08-06T16:04:21-07:00'), ('2019-08-06T16:04:22-07:00', '2019-08-06T16:05:44-07:00'), ('2019-08-06T16:05:45-07:00', '2019-08-06T16:11:24-07:00'), ('2019-08-06T16:18:38.031755-07:00', '2019-08-06T17:00:36-07:00'), ('2019-08-06T17:00:37-07:00', '2019-08-06T17:10:03-07:00')]\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = ['2019-08-06T11:32:03.781000-07:00', '2019-08-06T11:37:32-07:00', '2019-08-06T11:41:07-07:00', '2019-08-06T11:41:20-07:00', '2019-08-06T11:42:05-07:00', '2019-08-06T11:42:16-07:00', '2019-08-06T11:46:38-07:00', '2019-08-06T11:47:27-07:00', '2019-08-06T11:50:08-07:00']\n", + "Before filtering, trips = [('2019-08-06T11:32:03.781000-07:00', '2019-08-06T11:37:31-07:00'), ('2019-08-06T11:37:32-07:00', '2019-08-06T11:40:38-07:00'), ('2019-08-06T11:41:07-07:00', '2019-08-06T11:41:19-07:00'), ('2019-08-06T11:41:20-07:00', '2019-08-06T11:42:02-07:00'), ('2019-08-06T11:42:05-07:00', '2019-08-06T11:42:15-07:00'), ('2019-08-06T11:42:16-07:00', '2019-08-06T11:46:25-07:00'), ('2019-08-06T11:46:38-07:00', '2019-08-06T11:46:51-07:00'), ('2019-08-06T11:47:27-07:00', '2019-08-06T11:49:54-07:00'), ('2019-08-06T11:50:08-07:00', '2019-08-06T11:58:23-07:00'), ('2019-08-06T15:49:51.919479-07:00', '2019-08-06T15:56:05-07:00'), ('2019-08-06T15:56:06-07:00', '2019-08-06T15:56:18-07:00'), ('2019-08-06T15:56:19-07:00', '2019-08-06T16:04:21-07:00'), ('2019-08-06T16:04:22-07:00', '2019-08-06T16:05:44-07:00'), ('2019-08-06T16:05:45-07:00', '2019-08-06T16:11:24-07:00'), ('2019-08-06T16:18:38.031755-07:00', '2019-08-06T17:00:36-07:00'), ('2019-08-06T17:00:37-07:00', '2019-08-06T17:10:03-07:00')]\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = ['2019-08-06T15:49:51.919479-07:00', '2019-08-06T15:56:06-07:00', '2019-08-06T15:56:19-07:00', '2019-08-06T16:04:22-07:00', '2019-08-06T16:05:45-07:00', '2019-08-06T16:18:38.031755-07:00', '2019-08-06T17:00:37-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-08-07T09:21:47.112000-07:00', '2019-08-07T09:24:05-07:00'), ('2019-08-07T09:24:06-07:00', '2019-08-07T09:25:36-07:00'), ('2019-08-07T09:25:37-07:00', '2019-08-07T09:26:05-07:00'), ('2019-08-07T09:26:06-07:00', '2019-08-07T09:28:28-07:00'), ('2019-08-07T09:28:29-07:00', '2019-08-07T09:29:18-07:00'), ('2019-08-07T09:29:19-07:00', '2019-08-07T09:29:43-07:00'), ('2019-08-07T09:29:44-07:00', '2019-08-07T09:47:18-07:00'), ('2019-08-07T09:47:19-07:00', '2019-08-07T09:53:19.901000-07:00'), ('2019-08-07T11:19:17.788957-07:00', '2019-08-07T11:24:27-07:00'), ('2019-08-07T13:28:32.366548-07:00', '2019-08-07T13:40:21-07:00'), ('2019-08-07T13:40:52-07:00', '2019-08-07T13:41:30-07:00'), ('2019-08-07T13:41:31-07:00', '2019-08-07T13:46:12-07:00'), ('2019-08-07T13:46:13-07:00', '2019-08-07T13:46:24-07:00'), ('2019-08-07T13:46:25-07:00', '2019-08-07T13:48:21-07:00'), ('2019-08-07T13:49:47-07:00', '2019-08-07T13:50:12-07:00'), ('2019-08-07T13:50:14-07:00', '2019-08-07T13:55:15-07:00'), ('2019-08-07T13:55:47-07:00', '2019-08-07T13:55:51-07:00'), ('2019-08-07T13:55:52-07:00', '2019-08-07T14:03:08-07:00'), ('2019-08-07T14:03:09-07:00', '2019-08-07T14:07:48-07:00'), ('2019-08-07T14:07:49-07:00', '2019-08-07T14:08:45-07:00'), ('2019-08-07T14:08:46-07:00', '2019-08-07T14:41:51-07:00'), ('2019-08-07T14:42:09-07:00', '2019-08-07T14:51:41-07:00')]\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = ['2019-08-07T09:21:47.112000-07:00', '2019-08-07T09:24:06-07:00', '2019-08-07T09:25:37-07:00', '2019-08-07T09:26:06-07:00', '2019-08-07T09:28:29-07:00', '2019-08-07T09:29:19-07:00', '2019-08-07T09:29:44-07:00', '2019-08-07T09:47:19-07:00']\n", + "Before filtering, trips = [('2019-08-07T09:21:47.112000-07:00', '2019-08-07T09:24:05-07:00'), ('2019-08-07T09:24:06-07:00', '2019-08-07T09:25:36-07:00'), ('2019-08-07T09:25:37-07:00', '2019-08-07T09:26:05-07:00'), ('2019-08-07T09:26:06-07:00', '2019-08-07T09:28:28-07:00'), ('2019-08-07T09:28:29-07:00', '2019-08-07T09:29:18-07:00'), ('2019-08-07T09:29:19-07:00', '2019-08-07T09:29:43-07:00'), ('2019-08-07T09:29:44-07:00', '2019-08-07T09:47:18-07:00'), ('2019-08-07T09:47:19-07:00', '2019-08-07T09:53:19.901000-07:00'), ('2019-08-07T11:19:17.788957-07:00', '2019-08-07T11:24:27-07:00'), ('2019-08-07T13:28:32.366548-07:00', '2019-08-07T13:40:21-07:00'), ('2019-08-07T13:40:52-07:00', '2019-08-07T13:41:30-07:00'), ('2019-08-07T13:41:31-07:00', '2019-08-07T13:46:12-07:00'), ('2019-08-07T13:46:13-07:00', '2019-08-07T13:46:24-07:00'), ('2019-08-07T13:46:25-07:00', '2019-08-07T13:48:21-07:00'), ('2019-08-07T13:49:47-07:00', '2019-08-07T13:50:12-07:00'), ('2019-08-07T13:50:14-07:00', '2019-08-07T13:55:15-07:00'), ('2019-08-07T13:55:47-07:00', '2019-08-07T13:55:51-07:00'), ('2019-08-07T13:55:52-07:00', '2019-08-07T14:03:08-07:00'), ('2019-08-07T14:03:09-07:00', '2019-08-07T14:07:48-07:00'), ('2019-08-07T14:07:49-07:00', '2019-08-07T14:08:45-07:00'), ('2019-08-07T14:08:46-07:00', '2019-08-07T14:41:51-07:00'), ('2019-08-07T14:42:09-07:00', '2019-08-07T14:51:41-07:00')]\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = ['2019-08-07T13:28:32.366548-07:00', '2019-08-07T13:40:52-07:00', '2019-08-07T13:41:31-07:00', '2019-08-07T13:46:13-07:00', '2019-08-07T13:46:25-07:00', '2019-08-07T13:49:47-07:00', '2019-08-07T13:50:14-07:00', '2019-08-07T13:55:47-07:00', '2019-08-07T13:55:52-07:00', '2019-08-07T14:03:09-07:00', '2019-08-07T14:07:49-07:00', '2019-08-07T14:08:46-07:00', '2019-08-07T14:42:09-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_0 MAMFDC v/s HAMFDC MAMFDC_0 2\n", + "Before filtering, trips = [('2020-03-04T11:18:57.820000-08:00', '2020-03-04T11:49:31.869000-08:00'), ('2020-03-04T11:50:02.373000-08:00', '2020-03-04T11:51:37.476000-08:00'), ('2020-03-04T15:39:25.508701-08:00', '2020-03-04T16:05:07.506000-08:00'), ('2020-03-04T16:17:44.739394-08:00', '2020-03-04T16:41:53-08:00'), ('2020-03-04T16:41:58-08:00', '2020-03-04T16:42:34.693000-08:00'), ('2020-03-04T16:43:06.014000-08:00', '2020-03-04T17:00:16.451000-08:00'), ('2020-03-04T17:00:48.515000-08:00', '2020-03-04T17:10:31.521000-08:00')]\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = ['2020-03-04T11:18:57.820000-08:00', '2020-03-04T11:50:02.373000-08:00']\n", + "Before filtering, trips = [('2020-03-04T11:18:57.820000-08:00', '2020-03-04T11:49:31.869000-08:00'), ('2020-03-04T11:50:02.373000-08:00', '2020-03-04T11:51:37.476000-08:00'), ('2020-03-04T15:39:25.508701-08:00', '2020-03-04T16:05:07.506000-08:00'), ('2020-03-04T16:17:44.739394-08:00', '2020-03-04T16:41:53-08:00'), ('2020-03-04T16:41:58-08:00', '2020-03-04T16:42:34.693000-08:00'), ('2020-03-04T16:43:06.014000-08:00', '2020-03-04T17:00:16.451000-08:00'), ('2020-03-04T17:00:48.515000-08:00', '2020-03-04T17:10:31.521000-08:00')]\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = ['2020-03-04T15:39:25.508701-08:00', '2020-03-04T16:17:44.739394-08:00', '2020-03-04T16:41:58-08:00', '2020-03-04T16:43:06.014000-08:00', '2020-03-04T17:00:48.515000-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", + "Before filtering, trips = [('2019-07-22T11:55:16.121000-07:00', '2019-07-22T12:20:50-07:00'), ('2019-07-22T12:21:19.740000-07:00', '2019-07-22T12:22:51-07:00'), ('2019-07-22T16:18:09.925358-07:00', '2019-07-22T16:34:23-07:00'), ('2019-07-22T16:34:55.539000-07:00', '2019-07-22T16:36:27-07:00'), ('2019-07-22T16:36:57-07:00', '2019-07-22T16:39:58-07:00'), ('2019-07-22T16:55:02.396931-07:00', '2019-07-22T17:34:44.171000-07:00'), ('2019-07-22T17:35:01-07:00', '2019-07-22T17:45:02-07:00')]\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = ['2019-07-22T11:55:16.121000-07:00', '2019-07-22T12:21:19.740000-07:00']\n", + "Before filtering, trips = [('2019-07-22T11:55:16.121000-07:00', '2019-07-22T12:20:50-07:00'), ('2019-07-22T12:21:19.740000-07:00', '2019-07-22T12:22:51-07:00'), ('2019-07-22T16:18:09.925358-07:00', '2019-07-22T16:34:23-07:00'), ('2019-07-22T16:34:55.539000-07:00', '2019-07-22T16:36:27-07:00'), ('2019-07-22T16:36:57-07:00', '2019-07-22T16:39:58-07:00'), ('2019-07-22T16:55:02.396931-07:00', '2019-07-22T17:34:44.171000-07:00'), ('2019-07-22T17:35:01-07:00', '2019-07-22T17:45:02-07:00')]\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = ['2019-07-22T16:18:09.925358-07:00', '2019-07-22T16:34:55.539000-07:00', '2019-07-22T16:36:57-07:00', '2019-07-22T16:55:02.396931-07:00', '2019-07-22T17:35:01-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", + "Before filtering, trips = [('2019-07-23T08:51:18.945000-07:00', '2019-07-23T09:18:57.215000-07:00'), ('2019-07-23T09:19:29-07:00', '2019-07-23T09:21:33.601000-07:00'), ('2019-07-23T12:42:42.736306-07:00', '2019-07-23T12:46:24-07:00'), ('2019-07-23T12:46:54-07:00', '2019-07-23T13:05:46-07:00'), ('2019-07-23T13:08:20.846226-07:00', '2019-07-23T13:51:11.665000-07:00'), ('2019-07-23T13:51:24-07:00', '2019-07-23T14:01:54.639000-07:00')]\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = ['2019-07-23T08:51:18.945000-07:00', '2019-07-23T09:19:29-07:00']\n", + "Before filtering, trips = [('2019-07-23T08:51:18.945000-07:00', '2019-07-23T09:18:57.215000-07:00'), ('2019-07-23T09:19:29-07:00', '2019-07-23T09:21:33.601000-07:00'), ('2019-07-23T12:42:42.736306-07:00', '2019-07-23T12:46:24-07:00'), ('2019-07-23T12:46:54-07:00', '2019-07-23T13:05:46-07:00'), ('2019-07-23T13:08:20.846226-07:00', '2019-07-23T13:51:11.665000-07:00'), ('2019-07-23T13:51:24-07:00', '2019-07-23T14:01:54.639000-07:00')]\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = ['2019-07-23T12:42:42.736306-07:00', '2019-07-23T12:46:54-07:00', '2019-07-23T13:08:20.846226-07:00', '2019-07-23T13:51:24-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", + "Before filtering, trips = [('2019-07-27T09:14:44-07:00', '2019-07-27T09:40:10-07:00'), ('2019-07-27T09:40:37.915000-07:00', '2019-07-27T09:42:13.223000-07:00'), ('2019-07-27T12:56:27.402749-07:00', '2019-07-27T13:11:49-07:00'), ('2019-07-27T13:12:19-07:00', '2019-07-27T13:13:35.147000-07:00'), ('2019-07-27T13:13:49-07:00', '2019-07-27T13:18:35-07:00'), ('2019-07-27T13:22:30.733146-07:00', '2019-07-27T14:01:21-07:00'), ('2019-07-27T14:01:48-07:00', '2019-07-27T14:10:51-07:00')]\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = ['2019-07-27T09:14:44-07:00', '2019-07-27T09:40:37.915000-07:00']\n", + "Before filtering, trips = [('2019-07-27T09:14:44-07:00', '2019-07-27T09:40:10-07:00'), ('2019-07-27T09:40:37.915000-07:00', '2019-07-27T09:42:13.223000-07:00'), ('2019-07-27T12:56:27.402749-07:00', '2019-07-27T13:11:49-07:00'), ('2019-07-27T13:12:19-07:00', '2019-07-27T13:13:35.147000-07:00'), ('2019-07-27T13:13:49-07:00', '2019-07-27T13:18:35-07:00'), ('2019-07-27T13:22:30.733146-07:00', '2019-07-27T14:01:21-07:00'), ('2019-07-27T14:01:48-07:00', '2019-07-27T14:10:51-07:00')]\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = ['2019-07-27T12:56:27.402749-07:00', '2019-07-27T13:12:19-07:00', '2019-07-27T13:13:49-07:00', '2019-07-27T13:22:30.733146-07:00', '2019-07-27T14:01:48-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", + "Before filtering, trips = [('2019-08-05T10:31:21.571000-07:00', '2019-08-05T10:56:01-07:00'), ('2019-08-05T10:56:02-07:00', '2019-08-05T10:57:53-07:00'), ('2019-08-05T14:56:18.114727-07:00', '2019-08-05T15:03:45.046000-07:00'), ('2019-08-05T15:03:50-07:00', '2019-08-05T15:11:29-07:00'), ('2019-08-05T15:11:30-07:00', '2019-08-05T15:12:07.339000-07:00'), ('2019-08-05T15:12:23.349000-07:00', '2019-08-05T15:19:46-07:00'), ('2019-08-05T15:19:47-07:00', '2019-08-05T15:22:43-07:00'), ('2019-08-05T15:22:45-07:00', '2019-08-05T15:24:02-07:00'), ('2019-08-05T15:24:08.190000-07:00', '2019-08-05T15:24:13.222000-07:00'), ('2019-08-05T15:24:19.009000-07:00', '2019-08-05T15:26:26-07:00'), ('2019-08-05T15:34:24.884068-07:00', '2019-08-05T16:09:02-07:00'), ('2019-08-05T16:09:03-07:00', '2019-08-05T16:20:08.090000-07:00')]\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = ['2019-08-05T10:31:21.571000-07:00', '2019-08-05T10:56:02-07:00']\n", + "Before filtering, trips = [('2019-08-05T10:31:21.571000-07:00', '2019-08-05T10:56:01-07:00'), ('2019-08-05T10:56:02-07:00', '2019-08-05T10:57:53-07:00'), ('2019-08-05T14:56:18.114727-07:00', '2019-08-05T15:03:45.046000-07:00'), ('2019-08-05T15:03:50-07:00', '2019-08-05T15:11:29-07:00'), ('2019-08-05T15:11:30-07:00', '2019-08-05T15:12:07.339000-07:00'), ('2019-08-05T15:12:23.349000-07:00', '2019-08-05T15:19:46-07:00'), ('2019-08-05T15:19:47-07:00', '2019-08-05T15:22:43-07:00'), ('2019-08-05T15:22:45-07:00', '2019-08-05T15:24:02-07:00'), ('2019-08-05T15:24:08.190000-07:00', '2019-08-05T15:24:13.222000-07:00'), ('2019-08-05T15:24:19.009000-07:00', '2019-08-05T15:26:26-07:00'), ('2019-08-05T15:34:24.884068-07:00', '2019-08-05T16:09:02-07:00'), ('2019-08-05T16:09:03-07:00', '2019-08-05T16:20:08.090000-07:00')]\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = ['2019-08-05T14:56:18.114727-07:00', '2019-08-05T15:03:50-07:00', '2019-08-05T15:11:30-07:00', '2019-08-05T15:12:23.349000-07:00', '2019-08-05T15:19:47-07:00', '2019-08-05T15:22:45-07:00', '2019-08-05T15:24:08.190000-07:00', '2019-08-05T15:24:19.009000-07:00', '2019-08-05T15:34:24.884068-07:00', '2019-08-05T16:09:03-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", + "Before filtering, trips = [('2019-08-06T11:27:08.369000-07:00', '2019-08-06T11:29:25.527000-07:00'), ('2019-08-06T11:30:39-07:00', '2019-08-06T11:58:09-07:00'), ('2019-08-06T15:49:12.199884-07:00', '2019-08-06T15:54:21-07:00'), ('2019-08-06T15:54:22-07:00', '2019-08-06T15:54:41.664000-07:00'), ('2019-08-06T15:54:46.712000-07:00', '2019-08-06T16:08:21-07:00'), ('2019-08-06T16:08:22-07:00', '2019-08-06T16:08:34-07:00'), ('2019-08-06T16:08:35-07:00', '2019-08-06T16:10:48-07:00'), ('2019-08-06T16:16:24.124618-07:00', '2019-08-06T16:46:02-07:00'), ('2019-08-06T16:46:03-07:00', '2019-08-06T16:46:15-07:00'), ('2019-08-06T16:46:16-07:00', '2019-08-06T17:00:07-07:00'), ('2019-08-06T17:00:08-07:00', '2019-08-06T17:11:15.095000-07:00')]\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = ['2019-08-06T11:27:08.369000-07:00', '2019-08-06T11:30:39-07:00']\n", + "Before filtering, trips = [('2019-08-06T11:27:08.369000-07:00', '2019-08-06T11:29:25.527000-07:00'), ('2019-08-06T11:30:39-07:00', '2019-08-06T11:58:09-07:00'), ('2019-08-06T15:49:12.199884-07:00', '2019-08-06T15:54:21-07:00'), ('2019-08-06T15:54:22-07:00', '2019-08-06T15:54:41.664000-07:00'), ('2019-08-06T15:54:46.712000-07:00', '2019-08-06T16:08:21-07:00'), ('2019-08-06T16:08:22-07:00', '2019-08-06T16:08:34-07:00'), ('2019-08-06T16:08:35-07:00', '2019-08-06T16:10:48-07:00'), ('2019-08-06T16:16:24.124618-07:00', '2019-08-06T16:46:02-07:00'), ('2019-08-06T16:46:03-07:00', '2019-08-06T16:46:15-07:00'), ('2019-08-06T16:46:16-07:00', '2019-08-06T17:00:07-07:00'), ('2019-08-06T17:00:08-07:00', '2019-08-06T17:11:15.095000-07:00')]\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = ['2019-08-06T15:49:12.199884-07:00', '2019-08-06T15:54:22-07:00', '2019-08-06T15:54:46.712000-07:00', '2019-08-06T16:08:22-07:00', '2019-08-06T16:08:35-07:00', '2019-08-06T16:16:24.124618-07:00', '2019-08-06T16:46:03-07:00', '2019-08-06T16:46:16-07:00', '2019-08-06T17:00:08-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", + "Before filtering, trips = [('2019-08-07T09:21:38.051000-07:00', '2019-08-07T09:26:01-07:00'), ('2019-08-07T09:26:02-07:00', '2019-08-07T09:28:11-07:00'), ('2019-08-07T09:28:12-07:00', '2019-08-07T09:47:19-07:00'), ('2019-08-07T09:47:20-07:00', '2019-08-07T09:48:20.642000-07:00'), ('2019-08-07T13:40:28.027609-07:00', '2019-08-07T13:43:30-07:00'), ('2019-08-07T13:43:31-07:00', '2019-08-07T13:44:15.469000-07:00'), ('2019-08-07T13:45:13-07:00', '2019-08-07T13:47:39-07:00'), ('2019-08-07T13:47:40-07:00', '2019-08-07T13:48:17-07:00'), ('2019-08-07T13:48:18-07:00', '2019-08-07T13:55:09-07:00'), ('2019-08-07T13:55:11-07:00', '2019-08-07T13:55:44-07:00'), ('2019-08-07T13:55:45-07:00', '2019-08-07T14:02:01-07:00'), ('2019-08-07T14:02:02-07:00', '2019-08-07T14:06:47.031000-07:00'), ('2019-08-07T14:07:04.400000-07:00', '2019-08-07T14:08:34-07:00'), ('2019-08-07T14:08:35-07:00', '2019-08-07T14:41:21-07:00'), ('2019-08-07T14:41:23-07:00', '2019-08-07T14:41:24-07:00'), ('2019-08-07T14:41:45-07:00', '2019-08-07T14:52:18.053000-07:00')]\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = ['2019-08-07T09:21:38.051000-07:00', '2019-08-07T09:26:02-07:00', '2019-08-07T09:28:12-07:00', '2019-08-07T09:47:20-07:00']\n", + "Before filtering, trips = [('2019-08-07T09:21:38.051000-07:00', '2019-08-07T09:26:01-07:00'), ('2019-08-07T09:26:02-07:00', '2019-08-07T09:28:11-07:00'), ('2019-08-07T09:28:12-07:00', '2019-08-07T09:47:19-07:00'), ('2019-08-07T09:47:20-07:00', '2019-08-07T09:48:20.642000-07:00'), ('2019-08-07T13:40:28.027609-07:00', '2019-08-07T13:43:30-07:00'), ('2019-08-07T13:43:31-07:00', '2019-08-07T13:44:15.469000-07:00'), ('2019-08-07T13:45:13-07:00', '2019-08-07T13:47:39-07:00'), ('2019-08-07T13:47:40-07:00', '2019-08-07T13:48:17-07:00'), ('2019-08-07T13:48:18-07:00', '2019-08-07T13:55:09-07:00'), ('2019-08-07T13:55:11-07:00', '2019-08-07T13:55:44-07:00'), ('2019-08-07T13:55:45-07:00', '2019-08-07T14:02:01-07:00'), ('2019-08-07T14:02:02-07:00', '2019-08-07T14:06:47.031000-07:00'), ('2019-08-07T14:07:04.400000-07:00', '2019-08-07T14:08:34-07:00'), ('2019-08-07T14:08:35-07:00', '2019-08-07T14:41:21-07:00'), ('2019-08-07T14:41:23-07:00', '2019-08-07T14:41:24-07:00'), ('2019-08-07T14:41:45-07:00', '2019-08-07T14:52:18.053000-07:00')]\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = ['2019-08-07T13:40:28.027609-07:00', '2019-08-07T13:43:31-07:00', '2019-08-07T13:45:13-07:00', '2019-08-07T13:47:40-07:00', '2019-08-07T13:48:18-07:00', '2019-08-07T13:55:11-07:00', '2019-08-07T13:55:45-07:00', '2019-08-07T14:02:02-07:00', '2019-08-07T14:07:04.400000-07:00', '2019-08-07T14:08:35-07:00', '2019-08-07T14:41:23-07:00', '2019-08-07T14:41:45-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_0 MAMFDC v/s HAMFDC HAMFDC_0 2\n", + "Before filtering, trips = [('2020-03-04T11:19:07.002000-08:00', '2020-03-04T11:21:43.471000-08:00'), ('2020-03-04T11:22:14-08:00', '2020-03-04T11:27:44-08:00'), ('2020-03-04T11:28:15-08:00', '2020-03-04T11:29:16-08:00'), ('2020-03-04T11:29:43.805000-08:00', '2020-03-04T11:51:52.474000-08:00'), ('2020-03-04T15:39:11.043641-08:00', '2020-03-04T15:50:04.474000-08:00'), ('2020-03-04T15:51:07.500000-08:00', '2020-03-04T15:51:07.500000-08:00'), ('2020-03-04T15:51:39-08:00', '2020-03-04T16:04:52.414000-08:00'), ('2020-03-04T16:18:16.630496-08:00', '2020-03-04T17:00:03.398000-08:00'), ('2020-03-04T17:00:09-08:00', '2020-03-04T17:10:27.930000-08:00')]\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = ['2020-03-04T11:19:07.002000-08:00', '2020-03-04T11:22:14-08:00', '2020-03-04T11:28:15-08:00', '2020-03-04T11:29:43.805000-08:00']\n", + "Before filtering, trips = [('2020-03-04T11:19:07.002000-08:00', '2020-03-04T11:21:43.471000-08:00'), ('2020-03-04T11:22:14-08:00', '2020-03-04T11:27:44-08:00'), ('2020-03-04T11:28:15-08:00', '2020-03-04T11:29:16-08:00'), ('2020-03-04T11:29:43.805000-08:00', '2020-03-04T11:51:52.474000-08:00'), ('2020-03-04T15:39:11.043641-08:00', '2020-03-04T15:50:04.474000-08:00'), ('2020-03-04T15:51:07.500000-08:00', '2020-03-04T15:51:07.500000-08:00'), ('2020-03-04T15:51:39-08:00', '2020-03-04T16:04:52.414000-08:00'), ('2020-03-04T16:18:16.630496-08:00', '2020-03-04T17:00:03.398000-08:00'), ('2020-03-04T17:00:09-08:00', '2020-03-04T17:10:27.930000-08:00')]\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = ['2020-03-04T15:39:11.043641-08:00', '2020-03-04T15:51:07.500000-08:00', '2020-03-04T15:51:39-08:00', '2020-03-04T16:18:16.630496-08:00', '2020-03-04T17:00:09-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_0 HAHFDC v/s HAMFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_1 HAHFDC v/s HAMFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_2 HAHFDC v/s HAMFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_3 HAHFDC v/s MAHFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_4 HAHFDC v/s MAHFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_5 HAHFDC v/s MAHFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_6 MAMFDC v/s HAMFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = []\n", + "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", + "ios dict_keys(['ucb-sdb-ios-1', 'ucb-sdb-ios-2', 'ucb-sdb-ios-3', 'ucb-sdb-ios-4'])\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_0 HAHFDC v/s MAHFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_1 HAHFDC v/s MAHFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_2 HAHFDC v/s MAHFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_3 HAHFDC v/s HAMFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_4 HAHFDC v/s HAMFDC accuracy_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_5 HAHFDC v/s HAMFDC accuracy_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_6 MAMFDC v/s MAHFDC accuracy_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = []\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-07-22T11:56:08.032915-07:00', '2019-07-22T12:21:02.993575-07:00'), ('2019-07-22T12:21:04.993506-07:00', '2019-07-22T12:26:43.996640-07:00'), ('2019-07-22T16:17:18.612421-07:00', '2019-07-22T16:20:22.000669-07:00'), ('2019-07-22T16:20:23.000472-07:00', '2019-07-22T16:22:13.994219-07:00'), ('2019-07-22T16:22:14.994340-07:00', '2019-07-22T16:22:23.995039-07:00'), ('2019-07-22T16:22:24.995082-07:00', '2019-07-22T16:23:21.994065-07:00'), ('2019-07-22T16:23:22.994028-07:00', '2019-07-22T16:23:26.993878-07:00'), ('2019-07-22T16:23:27.993840-07:00', '2019-07-22T16:53:14.991262-07:00'), ('2019-07-22T16:53:15.991227-07:00', '2019-07-22T16:55:26.986709-07:00'), ('2019-07-22T16:55:27.986675-07:00', '2019-07-22T17:34:29.994910-07:00'), ('2019-07-22T17:34:30.994874-07:00', '2019-07-22T17:47:20.998055-07:00')]\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = ['2019-07-22T11:56:08.032915-07:00', '2019-07-22T12:21:04.993506-07:00']\n", + "Before filtering, trips = [('2019-07-22T11:56:08.032915-07:00', '2019-07-22T12:21:02.993575-07:00'), ('2019-07-22T12:21:04.993506-07:00', '2019-07-22T12:26:43.996640-07:00'), ('2019-07-22T16:17:18.612421-07:00', '2019-07-22T16:20:22.000669-07:00'), ('2019-07-22T16:20:23.000472-07:00', '2019-07-22T16:22:13.994219-07:00'), ('2019-07-22T16:22:14.994340-07:00', '2019-07-22T16:22:23.995039-07:00'), ('2019-07-22T16:22:24.995082-07:00', '2019-07-22T16:23:21.994065-07:00'), ('2019-07-22T16:23:22.994028-07:00', '2019-07-22T16:23:26.993878-07:00'), ('2019-07-22T16:23:27.993840-07:00', '2019-07-22T16:53:14.991262-07:00'), ('2019-07-22T16:53:15.991227-07:00', '2019-07-22T16:55:26.986709-07:00'), ('2019-07-22T16:55:27.986675-07:00', '2019-07-22T17:34:29.994910-07:00'), ('2019-07-22T17:34:30.994874-07:00', '2019-07-22T17:47:20.998055-07:00')]\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = ['2019-07-22T16:17:18.612421-07:00', '2019-07-22T16:20:23.000472-07:00', '2019-07-22T16:22:14.994340-07:00', '2019-07-22T16:22:24.995082-07:00', '2019-07-22T16:23:22.994028-07:00', '2019-07-22T16:23:27.993840-07:00', '2019-07-22T16:53:15.991227-07:00', '2019-07-22T16:55:27.986675-07:00', '2019-07-22T17:34:30.994874-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-07-23T08:51:11.537258-07:00', '2019-07-23T09:17:00.993716-07:00'), ('2019-07-23T09:17:01.993705-07:00', '2019-07-23T09:20:04.997658-07:00'), ('2019-07-23T12:54:46.097426-07:00', '2019-07-23T13:04:21.997633-07:00'), ('2019-07-23T13:04:22.997599-07:00', '2019-07-23T13:08:46.987311-07:00'), ('2019-07-23T13:08:47.987273-07:00', '2019-07-23T13:51:02.990666-07:00'), ('2019-07-23T13:51:03.990633-07:00', '2019-07-23T14:03:45.429897-07:00')]\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = ['2019-07-23T08:51:11.537258-07:00', '2019-07-23T09:17:01.993705-07:00']\n", + "Before filtering, trips = [('2019-07-23T08:51:11.537258-07:00', '2019-07-23T09:17:00.993716-07:00'), ('2019-07-23T09:17:01.993705-07:00', '2019-07-23T09:20:04.997658-07:00'), ('2019-07-23T12:54:46.097426-07:00', '2019-07-23T13:04:21.997633-07:00'), ('2019-07-23T13:04:22.997599-07:00', '2019-07-23T13:08:46.987311-07:00'), ('2019-07-23T13:08:47.987273-07:00', '2019-07-23T13:51:02.990666-07:00'), ('2019-07-23T13:51:03.990633-07:00', '2019-07-23T14:03:45.429897-07:00')]\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = ['2019-07-23T12:54:46.097426-07:00', '2019-07-23T13:04:22.997599-07:00', '2019-07-23T13:08:47.987273-07:00', '2019-07-23T13:51:03.990633-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-07-27T09:19:10.999951-07:00', '2019-07-27T09:40:03.997236-07:00'), ('2019-07-27T09:40:04.997202-07:00', '2019-07-27T09:40:16.996794-07:00'), ('2019-07-27T09:40:20.996656-07:00', '2019-07-27T09:44:24.514260-07:00'), ('2019-07-27T12:56:41.702540-07:00', '2019-07-27T12:58:58.997901-07:00'), ('2019-07-27T12:58:59.997857-07:00', '2019-07-27T12:59:53.995479-07:00'), ('2019-07-27T12:59:54.995434-07:00', '2019-07-27T12:59:58.995258-07:00'), ('2019-07-27T12:59:59.995213-07:00', '2019-07-27T13:07:10.996253-07:00'), ('2019-07-27T13:07:11.996214-07:00', '2019-07-27T13:07:20.995871-07:00'), ('2019-07-27T13:07:21.995832-07:00', '2019-07-27T13:16:57.993784-07:00'), ('2019-07-27T13:16:59.993730-07:00', '2019-07-27T13:24:14.990963-07:00'), ('2019-07-27T13:24:15.990926-07:00', '2019-07-27T13:25:08.989007-07:00'), ('2019-07-27T13:25:09.988971-07:00', '2019-07-27T13:25:28.988283-07:00'), ('2019-07-27T13:25:29.988246-07:00', '2019-07-27T14:01:22.990213-07:00'), ('2019-07-27T14:01:23.990182-07:00', '2019-07-27T14:13:36.438750-07:00')]\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = ['2019-07-27T09:19:10.999951-07:00', '2019-07-27T09:40:04.997202-07:00', '2019-07-27T09:40:20.996656-07:00']\n", + "Before filtering, trips = [('2019-07-27T09:19:10.999951-07:00', '2019-07-27T09:40:03.997236-07:00'), ('2019-07-27T09:40:04.997202-07:00', '2019-07-27T09:40:16.996794-07:00'), ('2019-07-27T09:40:20.996656-07:00', '2019-07-27T09:44:24.514260-07:00'), ('2019-07-27T12:56:41.702540-07:00', '2019-07-27T12:58:58.997901-07:00'), ('2019-07-27T12:58:59.997857-07:00', '2019-07-27T12:59:53.995479-07:00'), ('2019-07-27T12:59:54.995434-07:00', '2019-07-27T12:59:58.995258-07:00'), ('2019-07-27T12:59:59.995213-07:00', '2019-07-27T13:07:10.996253-07:00'), ('2019-07-27T13:07:11.996214-07:00', '2019-07-27T13:07:20.995871-07:00'), ('2019-07-27T13:07:21.995832-07:00', '2019-07-27T13:16:57.993784-07:00'), ('2019-07-27T13:16:59.993730-07:00', '2019-07-27T13:24:14.990963-07:00'), ('2019-07-27T13:24:15.990926-07:00', '2019-07-27T13:25:08.989007-07:00'), ('2019-07-27T13:25:09.988971-07:00', '2019-07-27T13:25:28.988283-07:00'), ('2019-07-27T13:25:29.988246-07:00', '2019-07-27T14:01:22.990213-07:00'), ('2019-07-27T14:01:23.990182-07:00', '2019-07-27T14:13:36.438750-07:00')]\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = ['2019-07-27T12:56:41.702540-07:00', '2019-07-27T12:58:59.997857-07:00', '2019-07-27T12:59:54.995434-07:00', '2019-07-27T12:59:59.995213-07:00', '2019-07-27T13:07:11.996214-07:00', '2019-07-27T13:07:21.995832-07:00', '2019-07-27T13:16:59.993730-07:00', '2019-07-27T13:24:15.990926-07:00', '2019-07-27T13:25:09.988971-07:00', '2019-07-27T13:25:29.988246-07:00', '2019-07-27T14:01:23.990182-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", + "Before filtering, trips = [('2019-08-05T10:30:22.521594-07:00', '2019-08-05T10:32:50.994496-07:00'), ('2019-08-05T10:33:08.466827-07:00', '2019-08-05T10:34:27.990568-07:00'), ('2019-08-05T10:34:30.990447-07:00', '2019-08-05T10:34:48.989717-07:00'), ('2019-08-05T10:34:49.989676-07:00', '2019-08-05T10:55:58.992105-07:00'), ('2019-08-05T10:55:59.992069-07:00', '2019-08-05T10:59:18.455394-07:00'), ('2019-08-05T15:04:33.485448-07:00', '2019-08-05T15:13:55.998247-07:00'), ('2019-08-05T15:13:56.998283-07:00', '2019-08-05T15:14:05.998424-07:00'), ('2019-08-05T15:14:06.998425-07:00', '2019-08-05T15:22:49.995738-07:00'), ('2019-08-05T15:22:50.995700-07:00', '2019-08-05T15:22:54.995555-07:00'), ('2019-08-05T15:22:55.995518-07:00', '2019-08-05T15:26:44.987143-07:00'), ('2019-08-05T15:26:46.987070-07:00', '2019-08-05T15:34:52.985577-07:00'), ('2019-08-05T15:34:53.985543-07:00', '2019-08-05T16:09:15.989569-07:00'), ('2019-08-05T16:09:17.989504-07:00', '2019-08-05T16:21:31.436115-07:00')]\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = ['2019-08-05T10:30:22.521594-07:00', '2019-08-05T10:33:08.466827-07:00', '2019-08-05T10:34:30.990447-07:00', '2019-08-05T10:34:49.989676-07:00', '2019-08-05T10:55:59.992069-07:00']\n", + "Before filtering, trips = [('2019-08-05T10:30:22.521594-07:00', '2019-08-05T10:32:50.994496-07:00'), ('2019-08-05T10:33:08.466827-07:00', '2019-08-05T10:34:27.990568-07:00'), ('2019-08-05T10:34:30.990447-07:00', '2019-08-05T10:34:48.989717-07:00'), ('2019-08-05T10:34:49.989676-07:00', '2019-08-05T10:55:58.992105-07:00'), ('2019-08-05T10:55:59.992069-07:00', '2019-08-05T10:59:18.455394-07:00'), ('2019-08-05T15:04:33.485448-07:00', '2019-08-05T15:13:55.998247-07:00'), ('2019-08-05T15:13:56.998283-07:00', '2019-08-05T15:14:05.998424-07:00'), ('2019-08-05T15:14:06.998425-07:00', '2019-08-05T15:22:49.995738-07:00'), ('2019-08-05T15:22:50.995700-07:00', '2019-08-05T15:22:54.995555-07:00'), ('2019-08-05T15:22:55.995518-07:00', '2019-08-05T15:26:44.987143-07:00'), ('2019-08-05T15:26:46.987070-07:00', '2019-08-05T15:34:52.985577-07:00'), ('2019-08-05T15:34:53.985543-07:00', '2019-08-05T16:09:15.989569-07:00'), ('2019-08-05T16:09:17.989504-07:00', '2019-08-05T16:21:31.436115-07:00')]\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = ['2019-08-05T15:04:33.485448-07:00', '2019-08-05T15:13:56.998283-07:00', '2019-08-05T15:14:06.998425-07:00', '2019-08-05T15:22:50.995700-07:00', '2019-08-05T15:22:55.995518-07:00', '2019-08-05T15:26:46.987070-07:00', '2019-08-05T15:34:53.985543-07:00', '2019-08-05T16:09:17.989504-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", + "Before filtering, trips = [('2019-08-06T11:35:37.402004-07:00', '2019-08-06T11:58:20.998245-07:00'), ('2019-08-06T11:58:22.998188-07:00', '2019-08-06T12:00:05.994768-07:00'), ('2019-08-06T15:53:14.898153-07:00', '2019-08-06T16:06:01.982309-07:00'), ('2019-08-06T16:06:02.982265-07:00', '2019-08-06T16:09:57.992944-07:00'), ('2019-08-06T16:09:59.992870-07:00', '2019-08-06T16:19:14.988584-07:00'), ('2019-08-06T16:19:15.988549-07:00', '2019-08-06T17:00:13.993821-07:00'), ('2019-08-06T17:00:14.993789-07:00', '2019-08-06T17:12:38.245867-07:00')]\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = ['2019-08-06T11:35:37.402004-07:00', '2019-08-06T11:58:22.998188-07:00']\n", + "Before filtering, trips = [('2019-08-06T11:35:37.402004-07:00', '2019-08-06T11:58:20.998245-07:00'), ('2019-08-06T11:58:22.998188-07:00', '2019-08-06T12:00:05.994768-07:00'), ('2019-08-06T15:53:14.898153-07:00', '2019-08-06T16:06:01.982309-07:00'), ('2019-08-06T16:06:02.982265-07:00', '2019-08-06T16:09:57.992944-07:00'), ('2019-08-06T16:09:59.992870-07:00', '2019-08-06T16:19:14.988584-07:00'), ('2019-08-06T16:19:15.988549-07:00', '2019-08-06T17:00:13.993821-07:00'), ('2019-08-06T17:00:14.993789-07:00', '2019-08-06T17:12:38.245867-07:00')]\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = ['2019-08-06T15:53:14.898153-07:00', '2019-08-06T16:06:02.982265-07:00', '2019-08-06T16:09:59.992870-07:00', '2019-08-06T16:19:15.988549-07:00', '2019-08-06T17:00:14.993789-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", + "Before filtering, trips = [('2019-08-07T09:23:02.547250-07:00', '2019-08-07T09:44:11.985745-07:00'), ('2019-08-07T09:44:12.985707-07:00', '2019-08-07T09:45:16.998617-07:00'), ('2019-08-07T09:45:17.998619-07:00', '2019-08-07T09:47:13.995059-07:00'), ('2019-08-07T09:47:14.995023-07:00', '2019-08-07T09:49:59.531748-07:00'), ('2019-08-07T13:41:09.983535-07:00', '2019-08-07T13:43:59.998353-07:00'), ('2019-08-07T13:44:00.998310-07:00', '2019-08-07T13:45:03.996617-07:00'), ('2019-08-07T13:45:05.996582-07:00', '2019-08-07T13:45:36.995543-07:00'), ('2019-08-07T13:45:37.995502-07:00', '2019-08-07T14:05:12.984882-07:00'), ('2019-08-07T14:05:16.984712-07:00', '2019-08-07T14:05:46.994138-07:00'), ('2019-08-07T14:05:47.994515-07:00', '2019-08-07T14:07:23.996792-07:00'), ('2019-08-07T14:07:24.996754-07:00', '2019-08-07T14:07:33.996404-07:00'), ('2019-08-07T14:07:34.996366-07:00', '2019-08-07T14:32:19.991592-07:00'), ('2019-08-07T14:32:20.991552-07:00', '2019-08-07T14:36:09.998307-07:00'), ('2019-08-07T14:36:10.998355-07:00', '2019-08-07T14:41:24.987819-07:00'), ('2019-08-07T14:41:27.987708-07:00', '2019-08-07T14:54:10.991285-07:00')]\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = ['2019-08-07T09:23:02.547250-07:00', '2019-08-07T09:44:12.985707-07:00', '2019-08-07T09:45:17.998619-07:00', '2019-08-07T09:47:14.995023-07:00']\n", + "Before filtering, trips = [('2019-08-07T09:23:02.547250-07:00', '2019-08-07T09:44:11.985745-07:00'), ('2019-08-07T09:44:12.985707-07:00', '2019-08-07T09:45:16.998617-07:00'), ('2019-08-07T09:45:17.998619-07:00', '2019-08-07T09:47:13.995059-07:00'), ('2019-08-07T09:47:14.995023-07:00', '2019-08-07T09:49:59.531748-07:00'), ('2019-08-07T13:41:09.983535-07:00', '2019-08-07T13:43:59.998353-07:00'), ('2019-08-07T13:44:00.998310-07:00', '2019-08-07T13:45:03.996617-07:00'), ('2019-08-07T13:45:05.996582-07:00', '2019-08-07T13:45:36.995543-07:00'), ('2019-08-07T13:45:37.995502-07:00', '2019-08-07T14:05:12.984882-07:00'), ('2019-08-07T14:05:16.984712-07:00', '2019-08-07T14:05:46.994138-07:00'), ('2019-08-07T14:05:47.994515-07:00', '2019-08-07T14:07:23.996792-07:00'), ('2019-08-07T14:07:24.996754-07:00', '2019-08-07T14:07:33.996404-07:00'), ('2019-08-07T14:07:34.996366-07:00', '2019-08-07T14:32:19.991592-07:00'), ('2019-08-07T14:32:20.991552-07:00', '2019-08-07T14:36:09.998307-07:00'), ('2019-08-07T14:36:10.998355-07:00', '2019-08-07T14:41:24.987819-07:00'), ('2019-08-07T14:41:27.987708-07:00', '2019-08-07T14:54:10.991285-07:00')]\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = ['2019-08-07T13:41:09.983535-07:00', '2019-08-07T13:44:00.998310-07:00', '2019-08-07T13:45:05.996582-07:00', '2019-08-07T13:45:37.995502-07:00', '2019-08-07T14:05:16.984712-07:00', '2019-08-07T14:05:47.994515-07:00', '2019-08-07T14:07:24.996754-07:00', '2019-08-07T14:07:34.996366-07:00', '2019-08-07T14:32:20.991552-07:00', '2019-08-07T14:36:10.998355-07:00', '2019-08-07T14:41:27.987708-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_0 MAMFDC v/s MAHFDC MAMFDC_0 2\n", + "Before filtering, trips = [('2020-03-04T11:23:32.652451-08:00', '2020-03-04T11:38:54.651307-08:00'), ('2020-03-04T15:54:04.384972-08:00', '2020-03-04T16:04:15.285221-08:00'), ('2020-03-04T16:18:38.735253-08:00', '2020-03-04T16:59:50.758160-08:00'), ('2020-03-04T17:00:29.649558-08:00', '2020-03-04T17:10:53.029543-08:00')]\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = ['2020-03-04T11:23:32.652451-08:00']\n", + "Before filtering, trips = [('2020-03-04T11:23:32.652451-08:00', '2020-03-04T11:38:54.651307-08:00'), ('2020-03-04T15:54:04.384972-08:00', '2020-03-04T16:04:15.285221-08:00'), ('2020-03-04T16:18:38.735253-08:00', '2020-03-04T16:59:50.758160-08:00'), ('2020-03-04T17:00:29.649558-08:00', '2020-03-04T17:10:53.029543-08:00')]\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = ['2020-03-04T15:54:04.384972-08:00', '2020-03-04T16:18:38.735253-08:00', '2020-03-04T17:00:29.649558-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", + "Before filtering, trips = [('2019-07-22T11:54:54.640858-07:00', '2019-07-22T12:12:05.875441-07:00'), ('2019-07-22T16:22:32.772816-07:00', '2019-07-22T16:59:29.946628-07:00'), ('2019-07-22T16:59:34.244279-07:00', '2019-07-22T17:01:26.617760-07:00'), ('2019-07-22T17:01:33.056928-07:00', '2019-07-22T17:02:52.728498-07:00'), ('2019-07-22T17:02:57.023672-07:00', '2019-07-22T17:34:27.164005-07:00'), ('2019-07-22T17:34:33.674745-07:00', '2019-07-22T17:44:21.778416-07:00')]\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = ['2019-07-22T11:54:54.640858-07:00']\n", + "Before filtering, trips = [('2019-07-22T11:54:54.640858-07:00', '2019-07-22T12:12:05.875441-07:00'), ('2019-07-22T16:22:32.772816-07:00', '2019-07-22T16:59:29.946628-07:00'), ('2019-07-22T16:59:34.244279-07:00', '2019-07-22T17:01:26.617760-07:00'), ('2019-07-22T17:01:33.056928-07:00', '2019-07-22T17:02:52.728498-07:00'), ('2019-07-22T17:02:57.023672-07:00', '2019-07-22T17:34:27.164005-07:00'), ('2019-07-22T17:34:33.674745-07:00', '2019-07-22T17:44:21.778416-07:00')]\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = ['2019-07-22T16:22:32.772816-07:00', '2019-07-22T16:59:34.244279-07:00', '2019-07-22T17:01:33.056928-07:00', '2019-07-22T17:02:57.023672-07:00', '2019-07-22T17:34:33.674745-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", + "Before filtering, trips = [('2019-07-23T08:49:54.660657-07:00', '2019-07-23T08:58:21.855733-07:00'), ('2019-07-23T08:58:51.517955-07:00', '2019-07-23T09:16:55.170476-07:00'), ('2019-07-23T09:17:01.651527-07:00', '2019-07-23T09:20:02.468521-07:00'), ('2019-07-23T12:44:01.787071-07:00', '2019-07-23T13:09:48.107456-07:00'), ('2019-07-23T13:09:54.556277-07:00', '2019-07-23T13:51:03.793421-07:00'), ('2019-07-23T13:51:08.814324-07:00', '2019-07-23T14:01:31.227720-07:00')]\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = ['2019-07-23T08:49:54.660657-07:00', '2019-07-23T08:58:51.517955-07:00', '2019-07-23T09:17:01.651527-07:00']\n", + "Before filtering, trips = [('2019-07-23T08:49:54.660657-07:00', '2019-07-23T08:58:21.855733-07:00'), ('2019-07-23T08:58:51.517955-07:00', '2019-07-23T09:16:55.170476-07:00'), ('2019-07-23T09:17:01.651527-07:00', '2019-07-23T09:20:02.468521-07:00'), ('2019-07-23T12:44:01.787071-07:00', '2019-07-23T13:09:48.107456-07:00'), ('2019-07-23T13:09:54.556277-07:00', '2019-07-23T13:51:03.793421-07:00'), ('2019-07-23T13:51:08.814324-07:00', '2019-07-23T14:01:31.227720-07:00')]\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = ['2019-07-23T12:44:01.787071-07:00', '2019-07-23T13:09:54.556277-07:00', '2019-07-23T13:51:08.814324-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", + "Before filtering, trips = [('2019-07-27T09:14:49.266143-07:00', '2019-07-27T09:20:59.728975-07:00'), ('2019-07-27T09:21:06.143658-07:00', '2019-07-27T09:40:24.548289-07:00'), ('2019-07-27T09:40:31.009991-07:00', '2019-07-27T09:44:19.851731-07:00'), ('2019-07-27T12:57:04.715789-07:00', '2019-07-27T13:26:03.481910-07:00'), ('2019-07-27T13:26:09.990369-07:00', '2019-07-27T14:01:16.042674-07:00'), ('2019-07-27T14:01:22.545097-07:00', '2019-07-27T14:11:46.145185-07:00')]\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = ['2019-07-27T09:14:49.266143-07:00', '2019-07-27T09:21:06.143658-07:00', '2019-07-27T09:40:31.009991-07:00']\n", + "Before filtering, trips = [('2019-07-27T09:14:49.266143-07:00', '2019-07-27T09:20:59.728975-07:00'), ('2019-07-27T09:21:06.143658-07:00', '2019-07-27T09:40:24.548289-07:00'), ('2019-07-27T09:40:31.009991-07:00', '2019-07-27T09:44:19.851731-07:00'), ('2019-07-27T12:57:04.715789-07:00', '2019-07-27T13:26:03.481910-07:00'), ('2019-07-27T13:26:09.990369-07:00', '2019-07-27T14:01:16.042674-07:00'), ('2019-07-27T14:01:22.545097-07:00', '2019-07-27T14:11:46.145185-07:00')]\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = ['2019-07-27T12:57:04.715789-07:00', '2019-07-27T13:26:09.990369-07:00', '2019-07-27T14:01:22.545097-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", + "Before filtering, trips = [('2019-08-05T10:32:22.335140-07:00', '2019-08-05T10:55:34.997035-07:00'), ('2019-08-05T10:56:02.996000-07:00', '2019-08-05T10:57:49.991994-07:00'), ('2019-08-05T15:04:31.337241-07:00', '2019-08-05T15:09:56.988751-07:00'), ('2019-08-05T15:10:21.987585-07:00', '2019-08-05T15:10:21.987585-07:00'), ('2019-08-05T15:10:52.986137-07:00', '2019-08-05T15:10:52.986137-07:00'), ('2019-08-05T15:11:00.985764-07:00', '2019-08-05T15:13:08.979793-07:00'), ('2019-08-05T15:13:59.977416-07:00', '2019-08-05T15:13:59.977416-07:00'), ('2019-08-05T15:14:07.989907-07:00', '2019-08-05T15:22:42.979539-07:00'), ('2019-08-05T15:22:50.979212-07:00', '2019-08-05T15:22:50.979212-07:00'), ('2019-08-05T15:22:56.978963-07:00', '2019-08-05T15:26:47.992232-07:00'), ('2019-08-05T15:34:35.991219-07:00', '2019-08-05T15:34:42.990969-07:00'), ('2019-08-05T15:34:45.990864-07:00', '2019-08-05T16:08:43.994686-07:00'), ('2019-08-05T16:09:33.992770-07:00', '2019-08-05T16:18:29.994664-07:00')]\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = ['2019-08-05T10:32:22.335140-07:00', '2019-08-05T10:56:02.996000-07:00']\n", + "Before filtering, trips = [('2019-08-05T10:32:22.335140-07:00', '2019-08-05T10:55:34.997035-07:00'), ('2019-08-05T10:56:02.996000-07:00', '2019-08-05T10:57:49.991994-07:00'), ('2019-08-05T15:04:31.337241-07:00', '2019-08-05T15:09:56.988751-07:00'), ('2019-08-05T15:10:21.987585-07:00', '2019-08-05T15:10:21.987585-07:00'), ('2019-08-05T15:10:52.986137-07:00', '2019-08-05T15:10:52.986137-07:00'), ('2019-08-05T15:11:00.985764-07:00', '2019-08-05T15:13:08.979793-07:00'), ('2019-08-05T15:13:59.977416-07:00', '2019-08-05T15:13:59.977416-07:00'), ('2019-08-05T15:14:07.989907-07:00', '2019-08-05T15:22:42.979539-07:00'), ('2019-08-05T15:22:50.979212-07:00', '2019-08-05T15:22:50.979212-07:00'), ('2019-08-05T15:22:56.978963-07:00', '2019-08-05T15:26:47.992232-07:00'), ('2019-08-05T15:34:35.991219-07:00', '2019-08-05T15:34:42.990969-07:00'), ('2019-08-05T15:34:45.990864-07:00', '2019-08-05T16:08:43.994686-07:00'), ('2019-08-05T16:09:33.992770-07:00', '2019-08-05T16:18:29.994664-07:00')]\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = ['2019-08-05T15:04:31.337241-07:00', '2019-08-05T15:10:21.987585-07:00', '2019-08-05T15:10:52.986137-07:00', '2019-08-05T15:11:00.985764-07:00', '2019-08-05T15:13:59.977416-07:00', '2019-08-05T15:14:07.989907-07:00', '2019-08-05T15:22:50.979212-07:00', '2019-08-05T15:22:56.978963-07:00', '2019-08-05T15:34:35.991219-07:00', '2019-08-05T15:34:45.990864-07:00', '2019-08-05T16:09:33.992770-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", + "Before filtering, trips = [('2019-08-06T11:34:43.409583-07:00', '2019-08-06T11:57:28.991871-07:00'), ('2019-08-06T11:59:12.987802-07:00', '2019-08-06T11:59:44.986549-07:00'), ('2019-08-06T15:49:55.973336-07:00', '2019-08-06T15:53:34.992962-07:00'), ('2019-08-06T15:53:44.992496-07:00', '2019-08-06T15:53:55.991987-07:00'), ('2019-08-06T15:54:01.991707-07:00', '2019-08-06T15:54:55.989197-07:00'), ('2019-08-06T15:55:05.988733-07:00', '2019-08-06T15:55:12.988407-07:00'), ('2019-08-06T15:55:20.988035-07:00', '2019-08-06T15:55:39.987151-07:00'), ('2019-08-06T15:56:11.985663-07:00', '2019-08-06T15:58:26.979387-07:00'), ('2019-08-06T15:58:35.990800-07:00', '2019-08-06T15:59:10.998218-07:00'), ('2019-08-06T15:59:35.997998-07:00', '2019-08-06T15:59:54.997354-07:00'), ('2019-08-06T16:00:00.997122-07:00', '2019-08-06T16:09:08.992409-07:00'), ('2019-08-06T16:10:12.989837-07:00', '2019-08-06T16:19:13.985583-07:00'), ('2019-08-06T16:19:16.985463-07:00', '2019-08-06T16:59:55.992178-07:00'), ('2019-08-06T17:00:32.990865-07:00', '2019-08-06T17:03:49.998598-07:00'), ('2019-08-06T17:15:17.928442-07:00', '2019-08-06T18:09:35.999116-07:00')]\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = ['2019-08-06T11:34:43.409583-07:00', '2019-08-06T11:59:12.987802-07:00']\n", + "Before filtering, trips = [('2019-08-06T11:34:43.409583-07:00', '2019-08-06T11:57:28.991871-07:00'), ('2019-08-06T11:59:12.987802-07:00', '2019-08-06T11:59:44.986549-07:00'), ('2019-08-06T15:49:55.973336-07:00', '2019-08-06T15:53:34.992962-07:00'), ('2019-08-06T15:53:44.992496-07:00', '2019-08-06T15:53:55.991987-07:00'), ('2019-08-06T15:54:01.991707-07:00', '2019-08-06T15:54:55.989197-07:00'), ('2019-08-06T15:55:05.988733-07:00', '2019-08-06T15:55:12.988407-07:00'), ('2019-08-06T15:55:20.988035-07:00', '2019-08-06T15:55:39.987151-07:00'), ('2019-08-06T15:56:11.985663-07:00', '2019-08-06T15:58:26.979387-07:00'), ('2019-08-06T15:58:35.990800-07:00', '2019-08-06T15:59:10.998218-07:00'), ('2019-08-06T15:59:35.997998-07:00', '2019-08-06T15:59:54.997354-07:00'), ('2019-08-06T16:00:00.997122-07:00', '2019-08-06T16:09:08.992409-07:00'), ('2019-08-06T16:10:12.989837-07:00', '2019-08-06T16:19:13.985583-07:00'), ('2019-08-06T16:19:16.985463-07:00', '2019-08-06T16:59:55.992178-07:00'), ('2019-08-06T17:00:32.990865-07:00', '2019-08-06T17:03:49.998598-07:00'), ('2019-08-06T17:15:17.928442-07:00', '2019-08-06T18:09:35.999116-07:00')]\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = ['2019-08-06T15:49:55.973336-07:00', '2019-08-06T15:53:44.992496-07:00', '2019-08-06T15:54:01.991707-07:00', '2019-08-06T15:55:05.988733-07:00', '2019-08-06T15:55:20.988035-07:00', '2019-08-06T15:56:11.985663-07:00', '2019-08-06T15:58:35.990800-07:00', '2019-08-06T15:59:35.997998-07:00', '2019-08-06T16:00:00.997122-07:00', '2019-08-06T16:10:12.989837-07:00', '2019-08-06T16:19:16.985463-07:00', '2019-08-06T17:00:32.990865-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", + "Before filtering, trips = [('2019-08-07T09:29:53.010319-07:00', '2019-08-07T09:46:37.994607-07:00'), ('2019-08-07T09:48:10.990654-07:00', '2019-08-07T09:49:52.986320-07:00'), ('2019-08-07T13:41:28.814627-07:00', '2019-08-07T13:43:55.998161-07:00'), ('2019-08-07T13:44:00.997942-07:00', '2019-08-07T13:44:36.996425-07:00'), ('2019-08-07T13:45:04.995069-07:00', '2019-08-07T13:45:32.993803-07:00'), ('2019-08-07T13:45:38.993531-07:00', '2019-08-07T13:48:32.985658-07:00'), ('2019-08-07T13:48:51.984799-07:00', '2019-08-07T13:48:57.984525-07:00'), ('2019-08-07T13:49:06.984119-07:00', '2019-08-07T13:49:49.982172-07:00'), ('2019-08-07T13:50:07.981358-07:00', '2019-08-07T13:50:07.981358-07:00'), ('2019-08-07T13:50:13.981086-07:00', '2019-08-07T13:53:57.991503-07:00'), ('2019-08-07T13:54:04.991202-07:00', '2019-08-07T13:54:04.991202-07:00'), ('2019-08-07T13:54:11.990904-07:00', '2019-08-07T14:01:19.993742-07:00'), ('2019-08-07T14:02:11.991568-07:00', '2019-08-07T14:05:32.983162-07:00'), ('2019-08-07T14:05:35.983035-07:00', '2019-08-07T14:05:44.991592-07:00'), ('2019-08-07T14:05:47.993097-07:00', '2019-08-07T14:41:20.991832-07:00'), ('2019-08-07T14:42:12.989707-07:00', '2019-08-07T14:51:51.997587-07:00')]\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = ['2019-08-07T09:29:53.010319-07:00', '2019-08-07T09:48:10.990654-07:00']\n", + "Before filtering, trips = [('2019-08-07T09:29:53.010319-07:00', '2019-08-07T09:46:37.994607-07:00'), ('2019-08-07T09:48:10.990654-07:00', '2019-08-07T09:49:52.986320-07:00'), ('2019-08-07T13:41:28.814627-07:00', '2019-08-07T13:43:55.998161-07:00'), ('2019-08-07T13:44:00.997942-07:00', '2019-08-07T13:44:36.996425-07:00'), ('2019-08-07T13:45:04.995069-07:00', '2019-08-07T13:45:32.993803-07:00'), ('2019-08-07T13:45:38.993531-07:00', '2019-08-07T13:48:32.985658-07:00'), ('2019-08-07T13:48:51.984799-07:00', '2019-08-07T13:48:57.984525-07:00'), ('2019-08-07T13:49:06.984119-07:00', '2019-08-07T13:49:49.982172-07:00'), ('2019-08-07T13:50:07.981358-07:00', '2019-08-07T13:50:07.981358-07:00'), ('2019-08-07T13:50:13.981086-07:00', '2019-08-07T13:53:57.991503-07:00'), ('2019-08-07T13:54:04.991202-07:00', '2019-08-07T13:54:04.991202-07:00'), ('2019-08-07T13:54:11.990904-07:00', '2019-08-07T14:01:19.993742-07:00'), ('2019-08-07T14:02:11.991568-07:00', '2019-08-07T14:05:32.983162-07:00'), ('2019-08-07T14:05:35.983035-07:00', '2019-08-07T14:05:44.991592-07:00'), ('2019-08-07T14:05:47.993097-07:00', '2019-08-07T14:41:20.991832-07:00'), ('2019-08-07T14:42:12.989707-07:00', '2019-08-07T14:51:51.997587-07:00')]\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = ['2019-08-07T13:41:28.814627-07:00', '2019-08-07T13:44:00.997942-07:00', '2019-08-07T13:45:04.995069-07:00', '2019-08-07T13:45:38.993531-07:00', '2019-08-07T13:48:51.984799-07:00', '2019-08-07T13:49:06.984119-07:00', '2019-08-07T13:50:07.981358-07:00', '2019-08-07T13:50:13.981086-07:00', '2019-08-07T13:54:04.991202-07:00', '2019-08-07T13:54:11.990904-07:00', '2019-08-07T14:02:11.991568-07:00', '2019-08-07T14:05:35.983035-07:00', '2019-08-07T14:05:47.993097-07:00', '2019-08-07T14:42:12.989707-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_0 MAMFDC v/s MAHFDC MAHFDC_0 2\n", + "Before filtering, trips = [('2020-03-04T11:25:40.046017-08:00', '2020-03-04T11:49:32.557874-08:00'), ('2020-03-04T11:49:38.998093-08:00', '2020-03-04T11:59:42.420861-08:00'), ('2020-03-04T15:39:13.617109-08:00', '2020-03-04T16:20:39.147647-08:00'), ('2020-03-04T16:20:49.320324-08:00', '2020-03-04T16:59:56.247860-08:00'), ('2020-03-04T17:00:02.727822-08:00', '2020-03-04T17:11:06.714911-08:00')]\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = ['2020-03-04T11:25:40.046017-08:00', '2020-03-04T11:49:38.998093-08:00']\n", + "Before filtering, trips = [('2020-03-04T11:25:40.046017-08:00', '2020-03-04T11:49:32.557874-08:00'), ('2020-03-04T11:49:38.998093-08:00', '2020-03-04T11:59:42.420861-08:00'), ('2020-03-04T15:39:13.617109-08:00', '2020-03-04T16:20:39.147647-08:00'), ('2020-03-04T16:20:49.320324-08:00', '2020-03-04T16:59:56.247860-08:00'), ('2020-03-04T17:00:02.727822-08:00', '2020-03-04T17:11:06.714911-08:00')]\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = ['2020-03-04T15:39:13.617109-08:00', '2020-03-04T16:20:49.320324-08:00', '2020-03-04T17:00:02.727822-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_0 HAHFDC v/s MAHFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_1 HAHFDC v/s MAHFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_2 HAHFDC v/s MAHFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_3 HAHFDC v/s HAMFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_4 HAHFDC v/s HAMFDC power_control_1 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_5 HAHFDC v/s HAMFDC power_control_2 2\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_6 MAMFDC v/s MAHFDC power_control_0 2\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", + "After filtering, trips = []\n", + "Finished copying train_bus_ebike_mtv_ucb, starting overwrite\n", + "Found spec = Multimodal multi-train, multi-bus, ebike trip to UC Berkeley\n", + "Evaluation ran from 2019-07-16T00:00:00-07:00 -> 2020-04-30T00:00:00-07:00\n", + "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", + "android dict_keys(['ucb-sdb-android-1', 'ucb-sdb-android-2', 'ucb-sdb-android-3', 'ucb-sdb-android-4'])\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_0 HAHFDC v/s HAMFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_1 HAHFDC v/s HAMFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_2 HAHFDC v/s HAMFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_3 HAHFDC v/s MAHFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_4 HAHFDC v/s MAHFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_5 HAHFDC v/s MAHFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_6 MAMFDC v/s HAMFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_7 MAMFDC v/s HAMFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_8 MAMFDC v/s HAMFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_9 MAMFDC v/s MAHFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_10 MAMFDC v/s MAHFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_11 MAMFDC v/s MAHFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = []\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 3\n", + "Before filtering, trips = [('2019-07-24T07:53:44-07:00', '2019-07-24T08:07:50-07:00'), ('2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:19-07:00'), ('2019-07-24T09:08:20-07:00', '2019-07-24T09:08:55-07:00'), ('2019-07-24T09:08:57-07:00', '2019-07-24T09:10:44-07:00'), ('2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:34-07:00'), ('2019-07-24T10:15:35-07:00', '2019-07-24T10:18:24-07:00'), ('2019-07-24T10:19:09-07:00', '2019-07-24T10:25:24-07:00'), ('2019-07-24T10:25:25-07:00', '2019-07-24T10:25:57-07:00'), ('2019-07-24T14:12:02.984072-07:00', '2019-07-24T14:26:15-07:00'), ('2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:16-07:00'), ('2019-07-24T16:42:18-07:00', '2019-07-24T17:01:23-07:00'), ('2019-07-24T17:01:24-07:00', '2019-07-24T17:01:45-07:00'), ('2019-07-24T17:01:46-07:00', '2019-07-24T17:02:24-07:00'), ('2019-07-24T17:02:25-07:00', '2019-07-24T17:06:29-07:00'), ('2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:05-07:00'), ('2019-07-24T18:00:06-07:00', '2019-07-24T18:08:27.933000-07:00'), ('2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:23-07:00'), ('2019-07-24T18:25:24-07:00', '2019-07-24T18:29:22-07:00'), ('2019-07-24T18:32:22-07:00', '2019-07-24T18:44:07-07:00'), ('2019-07-24T18:46:20.995584-07:00', '2019-07-24T19:29:49-07:00')]\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = ['2019-07-24T07:53:44-07:00', '2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:20-07:00', '2019-07-24T09:08:57-07:00', '2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:35-07:00', '2019-07-24T10:19:09-07:00', '2019-07-24T10:25:25-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:53:44-07:00', '2019-07-24T08:07:50-07:00'), ('2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:19-07:00'), ('2019-07-24T09:08:20-07:00', '2019-07-24T09:08:55-07:00'), ('2019-07-24T09:08:57-07:00', '2019-07-24T09:10:44-07:00'), ('2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:34-07:00'), ('2019-07-24T10:15:35-07:00', '2019-07-24T10:18:24-07:00'), ('2019-07-24T10:19:09-07:00', '2019-07-24T10:25:24-07:00'), ('2019-07-24T10:25:25-07:00', '2019-07-24T10:25:57-07:00'), ('2019-07-24T14:12:02.984072-07:00', '2019-07-24T14:26:15-07:00'), ('2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:16-07:00'), ('2019-07-24T16:42:18-07:00', '2019-07-24T17:01:23-07:00'), ('2019-07-24T17:01:24-07:00', '2019-07-24T17:01:45-07:00'), ('2019-07-24T17:01:46-07:00', '2019-07-24T17:02:24-07:00'), ('2019-07-24T17:02:25-07:00', '2019-07-24T17:06:29-07:00'), ('2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:05-07:00'), ('2019-07-24T18:00:06-07:00', '2019-07-24T18:08:27.933000-07:00'), ('2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:23-07:00'), ('2019-07-24T18:25:24-07:00', '2019-07-24T18:29:22-07:00'), ('2019-07-24T18:32:22-07:00', '2019-07-24T18:44:07-07:00'), ('2019-07-24T18:46:20.995584-07:00', '2019-07-24T19:29:49-07:00')]\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = ['2019-07-24T14:12:02.984072-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:53:44-07:00', '2019-07-24T08:07:50-07:00'), ('2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:19-07:00'), ('2019-07-24T09:08:20-07:00', '2019-07-24T09:08:55-07:00'), ('2019-07-24T09:08:57-07:00', '2019-07-24T09:10:44-07:00'), ('2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:34-07:00'), ('2019-07-24T10:15:35-07:00', '2019-07-24T10:18:24-07:00'), ('2019-07-24T10:19:09-07:00', '2019-07-24T10:25:24-07:00'), ('2019-07-24T10:25:25-07:00', '2019-07-24T10:25:57-07:00'), ('2019-07-24T14:12:02.984072-07:00', '2019-07-24T14:26:15-07:00'), ('2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:16-07:00'), ('2019-07-24T16:42:18-07:00', '2019-07-24T17:01:23-07:00'), ('2019-07-24T17:01:24-07:00', '2019-07-24T17:01:45-07:00'), ('2019-07-24T17:01:46-07:00', '2019-07-24T17:02:24-07:00'), ('2019-07-24T17:02:25-07:00', '2019-07-24T17:06:29-07:00'), ('2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:05-07:00'), ('2019-07-24T18:00:06-07:00', '2019-07-24T18:08:27.933000-07:00'), ('2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:23-07:00'), ('2019-07-24T18:25:24-07:00', '2019-07-24T18:29:22-07:00'), ('2019-07-24T18:32:22-07:00', '2019-07-24T18:44:07-07:00'), ('2019-07-24T18:46:20.995584-07:00', '2019-07-24T19:29:49-07:00')]\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = ['2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:18-07:00', '2019-07-24T17:01:24-07:00', '2019-07-24T17:01:46-07:00', '2019-07-24T17:02:25-07:00', '2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:06-07:00', '2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:24-07:00', '2019-07-24T18:32:22-07:00', '2019-07-24T18:46:20.995584-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 3\n", + "Before filtering, trips = [('2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:21:25-07:00'), ('2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:10:20-07:00'), ('2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:52:34.591000-07:00'), ('2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:17-07:00'), ('2019-07-25T10:15:24-07:00', '2019-07-25T10:18:07-07:00'), ('2019-07-25T10:19:00-07:00', '2019-07-25T10:20:29-07:00'), ('2019-07-25T10:20:30-07:00', '2019-07-25T10:27:40-07:00'), ('2019-07-25T14:10:29-07:00', '2019-07-25T14:21:25-07:00'), ('2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:50-07:00'), ('2019-07-25T16:36:52-07:00', '2019-07-25T16:38:24-07:00'), ('2019-07-25T16:38:25-07:00', '2019-07-25T16:38:36-07:00'), ('2019-07-25T16:38:38-07:00', '2019-07-25T16:40:49-07:00'), ('2019-07-25T16:40:51-07:00', '2019-07-25T16:41:49-07:00'), ('2019-07-25T16:41:51-07:00', '2019-07-25T16:43:10-07:00'), ('2019-07-25T16:43:11-07:00', '2019-07-25T16:43:35-07:00'), ('2019-07-25T16:43:37-07:00', '2019-07-25T16:48:28-07:00'), ('2019-07-25T16:48:29-07:00', '2019-07-25T16:49:33-07:00'), ('2019-07-25T16:49:34-07:00', '2019-07-25T16:56:37-07:00'), ('2019-07-25T16:56:38-07:00', '2019-07-25T16:58:26-07:00'), ('2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:02-07:00'), ('2019-07-25T18:03:03-07:00', '2019-07-25T18:12:52.716000-07:00'), ('2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:40-07:00'), ('2019-07-25T18:25:41-07:00', '2019-07-25T18:28:18-07:00'), ('2019-07-25T18:34:29.732609-07:00', '2019-07-25T19:10:58-07:00')]\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = ['2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:24-07:00', '2019-07-25T10:19:00-07:00', '2019-07-25T10:20:30-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:21:25-07:00'), ('2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:10:20-07:00'), ('2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:52:34.591000-07:00'), ('2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:17-07:00'), ('2019-07-25T10:15:24-07:00', '2019-07-25T10:18:07-07:00'), ('2019-07-25T10:19:00-07:00', '2019-07-25T10:20:29-07:00'), ('2019-07-25T10:20:30-07:00', '2019-07-25T10:27:40-07:00'), ('2019-07-25T14:10:29-07:00', '2019-07-25T14:21:25-07:00'), ('2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:50-07:00'), ('2019-07-25T16:36:52-07:00', '2019-07-25T16:38:24-07:00'), ('2019-07-25T16:38:25-07:00', '2019-07-25T16:38:36-07:00'), ('2019-07-25T16:38:38-07:00', '2019-07-25T16:40:49-07:00'), ('2019-07-25T16:40:51-07:00', '2019-07-25T16:41:49-07:00'), ('2019-07-25T16:41:51-07:00', '2019-07-25T16:43:10-07:00'), ('2019-07-25T16:43:11-07:00', '2019-07-25T16:43:35-07:00'), ('2019-07-25T16:43:37-07:00', '2019-07-25T16:48:28-07:00'), ('2019-07-25T16:48:29-07:00', '2019-07-25T16:49:33-07:00'), ('2019-07-25T16:49:34-07:00', '2019-07-25T16:56:37-07:00'), ('2019-07-25T16:56:38-07:00', '2019-07-25T16:58:26-07:00'), ('2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:02-07:00'), ('2019-07-25T18:03:03-07:00', '2019-07-25T18:12:52.716000-07:00'), ('2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:40-07:00'), ('2019-07-25T18:25:41-07:00', '2019-07-25T18:28:18-07:00'), ('2019-07-25T18:34:29.732609-07:00', '2019-07-25T19:10:58-07:00')]\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = ['2019-07-25T14:10:29-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:21:25-07:00'), ('2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:10:20-07:00'), ('2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:52:34.591000-07:00'), ('2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:17-07:00'), ('2019-07-25T10:15:24-07:00', '2019-07-25T10:18:07-07:00'), ('2019-07-25T10:19:00-07:00', '2019-07-25T10:20:29-07:00'), ('2019-07-25T10:20:30-07:00', '2019-07-25T10:27:40-07:00'), ('2019-07-25T14:10:29-07:00', '2019-07-25T14:21:25-07:00'), ('2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:50-07:00'), ('2019-07-25T16:36:52-07:00', '2019-07-25T16:38:24-07:00'), ('2019-07-25T16:38:25-07:00', '2019-07-25T16:38:36-07:00'), ('2019-07-25T16:38:38-07:00', '2019-07-25T16:40:49-07:00'), ('2019-07-25T16:40:51-07:00', '2019-07-25T16:41:49-07:00'), ('2019-07-25T16:41:51-07:00', '2019-07-25T16:43:10-07:00'), ('2019-07-25T16:43:11-07:00', '2019-07-25T16:43:35-07:00'), ('2019-07-25T16:43:37-07:00', '2019-07-25T16:48:28-07:00'), ('2019-07-25T16:48:29-07:00', '2019-07-25T16:49:33-07:00'), ('2019-07-25T16:49:34-07:00', '2019-07-25T16:56:37-07:00'), ('2019-07-25T16:56:38-07:00', '2019-07-25T16:58:26-07:00'), ('2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:02-07:00'), ('2019-07-25T18:03:03-07:00', '2019-07-25T18:12:52.716000-07:00'), ('2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:40-07:00'), ('2019-07-25T18:25:41-07:00', '2019-07-25T18:28:18-07:00'), ('2019-07-25T18:34:29.732609-07:00', '2019-07-25T19:10:58-07:00')]\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = ['2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:52-07:00', '2019-07-25T16:38:25-07:00', '2019-07-25T16:38:38-07:00', '2019-07-25T16:40:51-07:00', '2019-07-25T16:41:51-07:00', '2019-07-25T16:43:11-07:00', '2019-07-25T16:43:37-07:00', '2019-07-25T16:48:29-07:00', '2019-07-25T16:49:34-07:00', '2019-07-25T16:56:38-07:00', '2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:03-07:00', '2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:41-07:00', '2019-07-25T18:34:29.732609-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 3\n", + "Before filtering, trips = [('2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:24:22-07:00'), ('2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:17-07:00'), ('2019-07-26T09:09:18-07:00', '2019-07-26T09:09:29-07:00'), ('2019-07-26T09:09:30-07:00', '2019-07-26T09:09:55-07:00'), ('2019-07-26T09:09:56-07:00', '2019-07-26T09:10:23.602000-07:00'), ('2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:13:57-07:00'), ('2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:17.764000-07:00'), ('2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:23:59-07:00'), ('2019-07-26T10:24:00-07:00', '2019-07-26T10:27:28-07:00'), ('2019-07-26T14:16:52.077016-07:00', '2019-07-26T14:29:03-07:00'), ('2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:20:42-07:00'), ('2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:29:57-07:00'), ('2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:46-07:00'), ('2019-07-26T16:55:47-07:00', '2019-07-26T16:55:55-07:00'), ('2019-07-26T16:55:56-07:00', '2019-07-26T16:59:06-07:00'), ('2019-07-26T16:59:07-07:00', '2019-07-26T16:59:31-07:00'), ('2019-07-26T16:59:33-07:00', '2019-07-26T17:00:20-07:00'), ('2019-07-26T17:00:21-07:00', '2019-07-26T17:14:19-07:00'), ('2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:49-07:00'), ('2019-07-26T17:33:52-07:00', '2019-07-26T17:34:02-07:00'), ('2019-07-26T17:34:03-07:00', '2019-07-26T17:51:47-07:00'), ('2019-07-26T17:53:29-07:00', '2019-07-26T18:01:31.067000-07:00'), ('2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:21:11-07:00'), ('2019-07-26T18:34:21.552356-07:00', '2019-07-26T19:03:48-07:00')]\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = ['2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:18-07:00', '2019-07-26T09:09:30-07:00', '2019-07-26T09:09:56-07:00', '2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:24:00-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:24:22-07:00'), ('2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:17-07:00'), ('2019-07-26T09:09:18-07:00', '2019-07-26T09:09:29-07:00'), ('2019-07-26T09:09:30-07:00', '2019-07-26T09:09:55-07:00'), ('2019-07-26T09:09:56-07:00', '2019-07-26T09:10:23.602000-07:00'), ('2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:13:57-07:00'), ('2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:17.764000-07:00'), ('2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:23:59-07:00'), ('2019-07-26T10:24:00-07:00', '2019-07-26T10:27:28-07:00'), ('2019-07-26T14:16:52.077016-07:00', '2019-07-26T14:29:03-07:00'), ('2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:20:42-07:00'), ('2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:29:57-07:00'), ('2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:46-07:00'), ('2019-07-26T16:55:47-07:00', '2019-07-26T16:55:55-07:00'), ('2019-07-26T16:55:56-07:00', '2019-07-26T16:59:06-07:00'), ('2019-07-26T16:59:07-07:00', '2019-07-26T16:59:31-07:00'), ('2019-07-26T16:59:33-07:00', '2019-07-26T17:00:20-07:00'), ('2019-07-26T17:00:21-07:00', '2019-07-26T17:14:19-07:00'), ('2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:49-07:00'), ('2019-07-26T17:33:52-07:00', '2019-07-26T17:34:02-07:00'), ('2019-07-26T17:34:03-07:00', '2019-07-26T17:51:47-07:00'), ('2019-07-26T17:53:29-07:00', '2019-07-26T18:01:31.067000-07:00'), ('2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:21:11-07:00'), ('2019-07-26T18:34:21.552356-07:00', '2019-07-26T19:03:48-07:00')]\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = ['2019-07-26T14:16:52.077016-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:24:22-07:00'), ('2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:17-07:00'), ('2019-07-26T09:09:18-07:00', '2019-07-26T09:09:29-07:00'), ('2019-07-26T09:09:30-07:00', '2019-07-26T09:09:55-07:00'), ('2019-07-26T09:09:56-07:00', '2019-07-26T09:10:23.602000-07:00'), ('2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:13:57-07:00'), ('2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:17.764000-07:00'), ('2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:23:59-07:00'), ('2019-07-26T10:24:00-07:00', '2019-07-26T10:27:28-07:00'), ('2019-07-26T14:16:52.077016-07:00', '2019-07-26T14:29:03-07:00'), ('2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:20:42-07:00'), ('2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:29:57-07:00'), ('2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:46-07:00'), ('2019-07-26T16:55:47-07:00', '2019-07-26T16:55:55-07:00'), ('2019-07-26T16:55:56-07:00', '2019-07-26T16:59:06-07:00'), ('2019-07-26T16:59:07-07:00', '2019-07-26T16:59:31-07:00'), ('2019-07-26T16:59:33-07:00', '2019-07-26T17:00:20-07:00'), ('2019-07-26T17:00:21-07:00', '2019-07-26T17:14:19-07:00'), ('2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:49-07:00'), ('2019-07-26T17:33:52-07:00', '2019-07-26T17:34:02-07:00'), ('2019-07-26T17:34:03-07:00', '2019-07-26T17:51:47-07:00'), ('2019-07-26T17:53:29-07:00', '2019-07-26T18:01:31.067000-07:00'), ('2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:21:11-07:00'), ('2019-07-26T18:34:21.552356-07:00', '2019-07-26T19:03:48-07:00')]\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = ['2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:47-07:00', '2019-07-26T16:55:56-07:00', '2019-07-26T16:59:07-07:00', '2019-07-26T16:59:33-07:00', '2019-07-26T17:00:21-07:00', '2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:52-07:00', '2019-07-26T17:34:03-07:00', '2019-07-26T17:53:29-07:00', '2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:34:21.552356-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 3\n", + "Before filtering, trips = [('2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:28:06-07:00'), ('2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:24-07:00'), ('2019-09-10T09:07:26-07:00', '2019-09-10T09:08:04-07:00'), ('2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:03.425000-07:00'), ('2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:19:45-07:00'), ('2019-09-10T10:28:08.205705-07:00', '2019-09-10T10:36:42-07:00'), ('2019-09-10T13:39:33.901504-07:00', '2019-09-10T13:52:51-07:00'), ('2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:46-07:00'), ('2019-09-10T16:17:47-07:00', '2019-09-10T16:30:23-07:00'), ('2019-09-10T16:30:25-07:00', '2019-09-10T16:31:33-07:00'), ('2019-09-10T16:31:35-07:00', '2019-09-10T16:41:32-07:00'), ('2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:52-07:00'), ('2019-09-10T17:32:53-07:00', '2019-09-10T17:33:26-07:00'), ('2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:52-07:00'), ('2019-09-10T18:01:55-07:00', '2019-09-10T18:04:20-07:00'), ('2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:18-07:00'), ('2019-09-10T18:32:30-07:00', '2019-09-10T18:36:12.959000-07:00'), ('2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:04:41-07:00'), ('2019-09-10T19:06:01-07:00', '2019-09-10T19:21:41-07:00')]\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = ['2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:26-07:00', '2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:28:08.205705-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:28:06-07:00'), ('2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:24-07:00'), ('2019-09-10T09:07:26-07:00', '2019-09-10T09:08:04-07:00'), ('2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:03.425000-07:00'), ('2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:19:45-07:00'), ('2019-09-10T10:28:08.205705-07:00', '2019-09-10T10:36:42-07:00'), ('2019-09-10T13:39:33.901504-07:00', '2019-09-10T13:52:51-07:00'), ('2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:46-07:00'), ('2019-09-10T16:17:47-07:00', '2019-09-10T16:30:23-07:00'), ('2019-09-10T16:30:25-07:00', '2019-09-10T16:31:33-07:00'), ('2019-09-10T16:31:35-07:00', '2019-09-10T16:41:32-07:00'), ('2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:52-07:00'), ('2019-09-10T17:32:53-07:00', '2019-09-10T17:33:26-07:00'), ('2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:52-07:00'), ('2019-09-10T18:01:55-07:00', '2019-09-10T18:04:20-07:00'), ('2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:18-07:00'), ('2019-09-10T18:32:30-07:00', '2019-09-10T18:36:12.959000-07:00'), ('2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:04:41-07:00'), ('2019-09-10T19:06:01-07:00', '2019-09-10T19:21:41-07:00')]\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = ['2019-09-10T13:39:33.901504-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:28:06-07:00'), ('2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:24-07:00'), ('2019-09-10T09:07:26-07:00', '2019-09-10T09:08:04-07:00'), ('2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:03.425000-07:00'), ('2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:19:45-07:00'), ('2019-09-10T10:28:08.205705-07:00', '2019-09-10T10:36:42-07:00'), ('2019-09-10T13:39:33.901504-07:00', '2019-09-10T13:52:51-07:00'), ('2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:46-07:00'), ('2019-09-10T16:17:47-07:00', '2019-09-10T16:30:23-07:00'), ('2019-09-10T16:30:25-07:00', '2019-09-10T16:31:33-07:00'), ('2019-09-10T16:31:35-07:00', '2019-09-10T16:41:32-07:00'), ('2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:52-07:00'), ('2019-09-10T17:32:53-07:00', '2019-09-10T17:33:26-07:00'), ('2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:52-07:00'), ('2019-09-10T18:01:55-07:00', '2019-09-10T18:04:20-07:00'), ('2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:18-07:00'), ('2019-09-10T18:32:30-07:00', '2019-09-10T18:36:12.959000-07:00'), ('2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:04:41-07:00'), ('2019-09-10T19:06:01-07:00', '2019-09-10T19:21:41-07:00')]\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = ['2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:47-07:00', '2019-09-10T16:30:25-07:00', '2019-09-10T16:31:35-07:00', '2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:53-07:00', '2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:55-07:00', '2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:30-07:00', '2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:06:01-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 3\n", + "Before filtering, trips = [('2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:27:59-07:00'), ('2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:58-07:00'), ('2019-09-11T09:04:59-07:00', '2019-09-11T09:06:08.980000-07:00'), ('2019-09-11T09:06:35-07:00', '2019-09-11T09:08:08-07:00'), ('2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:54-07:00'), ('2019-09-11T10:17:55-07:00', '2019-09-11T10:19:20-07:00'), ('2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:11-07:00'), ('2019-09-11T10:36:14-07:00', '2019-09-11T10:37:20-07:00'), ('2019-09-11T13:46:38.982742-07:00', '2019-09-11T14:00:33-07:00'), ('2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:46-07:00'), ('2019-09-11T16:32:47-07:00', '2019-09-11T16:43:11-07:00'), ('2019-09-11T16:43:12-07:00', '2019-09-11T16:43:55-07:00'), ('2019-09-11T16:43:56-07:00', '2019-09-11T16:50:21-07:00'), ('2019-09-11T16:50:22-07:00', '2019-09-11T16:50:34-07:00'), ('2019-09-11T16:50:35-07:00', '2019-09-11T16:52:00-07:00'), ('2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:41.983000-07:00'), ('2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:07:02.071000-07:00'), ('2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:53-07:00'), ('2019-09-11T18:15:54-07:00', '2019-09-11T18:16:44-07:00'), ('2019-09-11T18:17:18-07:00', '2019-09-11T18:18:51-07:00'), ('2019-09-11T18:18:52-07:00', '2019-09-11T18:20:23-07:00'), ('2019-09-11T18:23:23-07:00', '2019-09-11T18:43:23-07:00')]\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = ['2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:59-07:00', '2019-09-11T09:06:35-07:00', '2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:55-07:00', '2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:14-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:27:59-07:00'), ('2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:58-07:00'), ('2019-09-11T09:04:59-07:00', '2019-09-11T09:06:08.980000-07:00'), ('2019-09-11T09:06:35-07:00', '2019-09-11T09:08:08-07:00'), ('2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:54-07:00'), ('2019-09-11T10:17:55-07:00', '2019-09-11T10:19:20-07:00'), ('2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:11-07:00'), ('2019-09-11T10:36:14-07:00', '2019-09-11T10:37:20-07:00'), ('2019-09-11T13:46:38.982742-07:00', '2019-09-11T14:00:33-07:00'), ('2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:46-07:00'), ('2019-09-11T16:32:47-07:00', '2019-09-11T16:43:11-07:00'), ('2019-09-11T16:43:12-07:00', '2019-09-11T16:43:55-07:00'), ('2019-09-11T16:43:56-07:00', '2019-09-11T16:50:21-07:00'), ('2019-09-11T16:50:22-07:00', '2019-09-11T16:50:34-07:00'), ('2019-09-11T16:50:35-07:00', '2019-09-11T16:52:00-07:00'), ('2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:41.983000-07:00'), ('2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:07:02.071000-07:00'), ('2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:53-07:00'), ('2019-09-11T18:15:54-07:00', '2019-09-11T18:16:44-07:00'), ('2019-09-11T18:17:18-07:00', '2019-09-11T18:18:51-07:00'), ('2019-09-11T18:18:52-07:00', '2019-09-11T18:20:23-07:00'), ('2019-09-11T18:23:23-07:00', '2019-09-11T18:43:23-07:00')]\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = ['2019-09-11T13:46:38.982742-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:27:59-07:00'), ('2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:58-07:00'), ('2019-09-11T09:04:59-07:00', '2019-09-11T09:06:08.980000-07:00'), ('2019-09-11T09:06:35-07:00', '2019-09-11T09:08:08-07:00'), ('2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:54-07:00'), ('2019-09-11T10:17:55-07:00', '2019-09-11T10:19:20-07:00'), ('2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:11-07:00'), ('2019-09-11T10:36:14-07:00', '2019-09-11T10:37:20-07:00'), ('2019-09-11T13:46:38.982742-07:00', '2019-09-11T14:00:33-07:00'), ('2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:46-07:00'), ('2019-09-11T16:32:47-07:00', '2019-09-11T16:43:11-07:00'), ('2019-09-11T16:43:12-07:00', '2019-09-11T16:43:55-07:00'), ('2019-09-11T16:43:56-07:00', '2019-09-11T16:50:21-07:00'), ('2019-09-11T16:50:22-07:00', '2019-09-11T16:50:34-07:00'), ('2019-09-11T16:50:35-07:00', '2019-09-11T16:52:00-07:00'), ('2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:41.983000-07:00'), ('2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:07:02.071000-07:00'), ('2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:53-07:00'), ('2019-09-11T18:15:54-07:00', '2019-09-11T18:16:44-07:00'), ('2019-09-11T18:17:18-07:00', '2019-09-11T18:18:51-07:00'), ('2019-09-11T18:18:52-07:00', '2019-09-11T18:20:23-07:00'), ('2019-09-11T18:23:23-07:00', '2019-09-11T18:43:23-07:00')]\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = ['2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:47-07:00', '2019-09-11T16:43:12-07:00', '2019-09-11T16:43:56-07:00', '2019-09-11T16:50:22-07:00', '2019-09-11T16:50:35-07:00', '2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:54-07:00', '2019-09-11T18:17:18-07:00', '2019-09-11T18:18:52-07:00', '2019-09-11T18:23:23-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 3\n", + "Before filtering, trips = [('2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:10-07:00'), ('2019-09-17T08:23:12-07:00', '2019-09-17T08:23:16-07:00'), ('2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:05:11-07:00'), ('2019-09-17T09:14:10-07:00', '2019-09-17T09:14:24-07:00'), ('2019-09-17T09:14:25-07:00', '2019-09-17T09:18:49-07:00'), ('2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:10:33-07:00'), ('2019-09-17T10:16:01-07:00', '2019-09-17T10:18:53-07:00'), ('2019-09-17T10:33:16.758010-07:00', '2019-09-17T10:39:26-07:00'), ('2019-09-17T13:46:24.897379-07:00', '2019-09-17T13:57:36.104000-07:00'), ('2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:36.868000-07:00'), ('2019-09-17T16:16:57-07:00', '2019-09-17T16:34:20-07:00'), ('2019-09-17T16:34:22-07:00', '2019-09-17T16:35:48-07:00'), ('2019-09-17T16:35:50-07:00', '2019-09-17T16:36:16-07:00'), ('2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:52.671000-07:00'), ('2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:34:54.982000-07:00'), ('2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:58:59-07:00'), ('2019-09-17T17:59:00-07:00', '2019-09-17T18:13:52-07:00'), ('2019-09-17T18:13:53-07:00', '2019-09-17T18:15:09-07:00'), ('2019-09-17T18:15:10-07:00', '2019-09-17T18:58:00-07:00'), ('2019-09-17T18:58:14.010000-07:00', '2019-09-17T19:14:13-07:00')]\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = ['2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:12-07:00', '2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:14:10-07:00', '2019-09-17T09:14:25-07:00', '2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:16:01-07:00', '2019-09-17T10:33:16.758010-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:10-07:00'), ('2019-09-17T08:23:12-07:00', '2019-09-17T08:23:16-07:00'), ('2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:05:11-07:00'), ('2019-09-17T09:14:10-07:00', '2019-09-17T09:14:24-07:00'), ('2019-09-17T09:14:25-07:00', '2019-09-17T09:18:49-07:00'), ('2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:10:33-07:00'), ('2019-09-17T10:16:01-07:00', '2019-09-17T10:18:53-07:00'), ('2019-09-17T10:33:16.758010-07:00', '2019-09-17T10:39:26-07:00'), ('2019-09-17T13:46:24.897379-07:00', '2019-09-17T13:57:36.104000-07:00'), ('2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:36.868000-07:00'), ('2019-09-17T16:16:57-07:00', '2019-09-17T16:34:20-07:00'), ('2019-09-17T16:34:22-07:00', '2019-09-17T16:35:48-07:00'), ('2019-09-17T16:35:50-07:00', '2019-09-17T16:36:16-07:00'), ('2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:52.671000-07:00'), ('2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:34:54.982000-07:00'), ('2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:58:59-07:00'), ('2019-09-17T17:59:00-07:00', '2019-09-17T18:13:52-07:00'), ('2019-09-17T18:13:53-07:00', '2019-09-17T18:15:09-07:00'), ('2019-09-17T18:15:10-07:00', '2019-09-17T18:58:00-07:00'), ('2019-09-17T18:58:14.010000-07:00', '2019-09-17T19:14:13-07:00')]\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = ['2019-09-17T13:46:24.897379-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:10-07:00'), ('2019-09-17T08:23:12-07:00', '2019-09-17T08:23:16-07:00'), ('2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:05:11-07:00'), ('2019-09-17T09:14:10-07:00', '2019-09-17T09:14:24-07:00'), ('2019-09-17T09:14:25-07:00', '2019-09-17T09:18:49-07:00'), ('2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:10:33-07:00'), ('2019-09-17T10:16:01-07:00', '2019-09-17T10:18:53-07:00'), ('2019-09-17T10:33:16.758010-07:00', '2019-09-17T10:39:26-07:00'), ('2019-09-17T13:46:24.897379-07:00', '2019-09-17T13:57:36.104000-07:00'), ('2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:36.868000-07:00'), ('2019-09-17T16:16:57-07:00', '2019-09-17T16:34:20-07:00'), ('2019-09-17T16:34:22-07:00', '2019-09-17T16:35:48-07:00'), ('2019-09-17T16:35:50-07:00', '2019-09-17T16:36:16-07:00'), ('2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:52.671000-07:00'), ('2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:34:54.982000-07:00'), ('2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:58:59-07:00'), ('2019-09-17T17:59:00-07:00', '2019-09-17T18:13:52-07:00'), ('2019-09-17T18:13:53-07:00', '2019-09-17T18:15:09-07:00'), ('2019-09-17T18:15:10-07:00', '2019-09-17T18:58:00-07:00'), ('2019-09-17T18:58:14.010000-07:00', '2019-09-17T19:14:13-07:00')]\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = ['2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:57-07:00', '2019-09-17T16:34:22-07:00', '2019-09-17T16:35:50-07:00', '2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:59:00-07:00', '2019-09-17T18:13:53-07:00', '2019-09-17T18:15:10-07:00', '2019-09-17T18:58:14.010000-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_0 MAMFDC v/s HAMFDC MAMFDC_0 3\n", + "Before filtering, trips = [('2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:24:06.675000-08:00'), ('2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:12:44.716000-08:00'), ('2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:10:07.733000-08:00'), ('2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:18:05.678000-08:00'), ('2019-11-19T10:25:55.207920-08:00', '2019-11-19T10:33:11.647000-08:00'), ('2019-11-19T13:29:35.029448-08:00', '2019-11-19T13:44:08.725000-08:00'), ('2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:19:06.800000-08:00'), ('2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:30:59.809000-08:00'), ('2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:03.795000-08:00'), ('2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:41:47.921000-08:00'), ('2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:19.888000-08:00'), ('2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:35:56.814000-08:00'), ('2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:06.679000-08:00'), ('2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:57:39.650000-08:00'), ('2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:57:55.616000-08:00'), ('2019-11-19T18:58:27.623000-08:00', '2019-11-19T19:16:08.638000-08:00')]\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = ['2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:25:55.207920-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:24:06.675000-08:00'), ('2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:12:44.716000-08:00'), ('2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:10:07.733000-08:00'), ('2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:18:05.678000-08:00'), ('2019-11-19T10:25:55.207920-08:00', '2019-11-19T10:33:11.647000-08:00'), ('2019-11-19T13:29:35.029448-08:00', '2019-11-19T13:44:08.725000-08:00'), ('2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:19:06.800000-08:00'), ('2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:30:59.809000-08:00'), ('2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:03.795000-08:00'), ('2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:41:47.921000-08:00'), ('2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:19.888000-08:00'), ('2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:35:56.814000-08:00'), ('2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:06.679000-08:00'), ('2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:57:39.650000-08:00'), ('2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:57:55.616000-08:00'), ('2019-11-19T18:58:27.623000-08:00', '2019-11-19T19:16:08.638000-08:00')]\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = ['2019-11-19T13:29:35.029448-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:24:06.675000-08:00'), ('2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:12:44.716000-08:00'), ('2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:10:07.733000-08:00'), ('2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:18:05.678000-08:00'), ('2019-11-19T10:25:55.207920-08:00', '2019-11-19T10:33:11.647000-08:00'), ('2019-11-19T13:29:35.029448-08:00', '2019-11-19T13:44:08.725000-08:00'), ('2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:19:06.800000-08:00'), ('2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:30:59.809000-08:00'), ('2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:03.795000-08:00'), ('2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:41:47.921000-08:00'), ('2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:19.888000-08:00'), ('2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:35:56.814000-08:00'), ('2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:06.679000-08:00'), ('2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:57:39.650000-08:00'), ('2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:57:55.616000-08:00'), ('2019-11-19T18:58:27.623000-08:00', '2019-11-19T19:16:08.638000-08:00')]\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = ['2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:58:27.623000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_1 MAMFDC v/s HAMFDC MAMFDC_1 3\n", + "Before filtering, trips = [('2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:29:36.043000-08:00'), ('2019-11-20T08:31:06-08:00', '2019-11-20T09:09:48-08:00'), ('2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:27.080000-08:00'), ('2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:19:13.196000-08:00'), ('2019-11-20T10:25:14.928144-08:00', '2019-11-20T10:31:59.682000-08:00'), ('2019-11-20T13:45:47.834701-08:00', '2019-11-20T13:59:41.806000-08:00'), ('2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:15.760000-08:00'), ('2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:35:34.822000-08:00'), ('2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:06.836000-08:00'), ('2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:11.819000-08:00'), ('2019-11-20T16:44:43.788000-08:00', '2019-11-20T16:47:30-08:00'), ('2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:50:41.894000-08:00'), ('2019-11-20T17:51:13.823000-08:00', '2019-11-20T17:58:51.934000-08:00'), ('2019-11-20T18:00:47-08:00', '2019-11-20T18:12:42.631000-08:00'), ('2019-11-20T18:13:24-08:00', '2019-11-20T18:17:02-08:00'), ('2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:04:45.847000-08:00'), ('2019-11-20T19:05:17.765000-08:00', '2019-11-20T19:22:25.813000-08:00')]\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = ['2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:31:06-08:00', '2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:25:14.928144-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:29:36.043000-08:00'), ('2019-11-20T08:31:06-08:00', '2019-11-20T09:09:48-08:00'), ('2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:27.080000-08:00'), ('2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:19:13.196000-08:00'), ('2019-11-20T10:25:14.928144-08:00', '2019-11-20T10:31:59.682000-08:00'), ('2019-11-20T13:45:47.834701-08:00', '2019-11-20T13:59:41.806000-08:00'), ('2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:15.760000-08:00'), ('2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:35:34.822000-08:00'), ('2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:06.836000-08:00'), ('2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:11.819000-08:00'), ('2019-11-20T16:44:43.788000-08:00', '2019-11-20T16:47:30-08:00'), ('2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:50:41.894000-08:00'), ('2019-11-20T17:51:13.823000-08:00', '2019-11-20T17:58:51.934000-08:00'), ('2019-11-20T18:00:47-08:00', '2019-11-20T18:12:42.631000-08:00'), ('2019-11-20T18:13:24-08:00', '2019-11-20T18:17:02-08:00'), ('2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:04:45.847000-08:00'), ('2019-11-20T19:05:17.765000-08:00', '2019-11-20T19:22:25.813000-08:00')]\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = ['2019-11-20T13:45:47.834701-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:29:36.043000-08:00'), ('2019-11-20T08:31:06-08:00', '2019-11-20T09:09:48-08:00'), ('2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:27.080000-08:00'), ('2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:19:13.196000-08:00'), ('2019-11-20T10:25:14.928144-08:00', '2019-11-20T10:31:59.682000-08:00'), ('2019-11-20T13:45:47.834701-08:00', '2019-11-20T13:59:41.806000-08:00'), ('2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:15.760000-08:00'), ('2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:35:34.822000-08:00'), ('2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:06.836000-08:00'), ('2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:11.819000-08:00'), ('2019-11-20T16:44:43.788000-08:00', '2019-11-20T16:47:30-08:00'), ('2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:50:41.894000-08:00'), ('2019-11-20T17:51:13.823000-08:00', '2019-11-20T17:58:51.934000-08:00'), ('2019-11-20T18:00:47-08:00', '2019-11-20T18:12:42.631000-08:00'), ('2019-11-20T18:13:24-08:00', '2019-11-20T18:17:02-08:00'), ('2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:04:45.847000-08:00'), ('2019-11-20T19:05:17.765000-08:00', '2019-11-20T19:22:25.813000-08:00')]\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = ['2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:43.788000-08:00', '2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:51:13.823000-08:00', '2019-11-20T18:00:47-08:00', '2019-11-20T18:13:24-08:00', '2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:05:17.765000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_2 MAMFDC v/s HAMFDC MAMFDC_2 3\n", + "Before filtering, trips = [('2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:30:25.528000-08:00'), ('2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:13:39.520000-08:00'), ('2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:19:07.899000-08:00'), ('2019-12-03T10:27:42.457942-08:00', '2019-12-03T10:34:05.923000-08:00'), ('2019-12-03T14:14:57.823089-08:00', '2019-12-03T14:28:10.982000-08:00'), ('2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:21:06.134000-08:00'), ('2019-12-03T16:22:10.099000-08:00', '2019-12-03T16:43:35.074000-08:00'), ('2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:53:47.065000-08:00'), ('2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:02:53.212000-08:00'), ('2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:13.086000-08:00'), ('2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:21:05.021000-08:00'), ('2019-12-03T18:23:24-08:00', '2019-12-03T19:00:40-08:00'), ('2019-12-03T19:00:46-08:00', '2019-12-03T19:04:52.217000-08:00'), ('2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:12:57.287000-08:00'), ('2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:15:37.265000-08:00'), ('2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:18.232000-08:00'), ('2019-12-03T19:18:50.254000-08:00', '2019-12-03T19:34:52.341000-08:00')]\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = ['2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:27:42.457942-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:30:25.528000-08:00'), ('2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:13:39.520000-08:00'), ('2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:19:07.899000-08:00'), ('2019-12-03T10:27:42.457942-08:00', '2019-12-03T10:34:05.923000-08:00'), ('2019-12-03T14:14:57.823089-08:00', '2019-12-03T14:28:10.982000-08:00'), ('2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:21:06.134000-08:00'), ('2019-12-03T16:22:10.099000-08:00', '2019-12-03T16:43:35.074000-08:00'), ('2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:53:47.065000-08:00'), ('2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:02:53.212000-08:00'), ('2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:13.086000-08:00'), ('2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:21:05.021000-08:00'), ('2019-12-03T18:23:24-08:00', '2019-12-03T19:00:40-08:00'), ('2019-12-03T19:00:46-08:00', '2019-12-03T19:04:52.217000-08:00'), ('2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:12:57.287000-08:00'), ('2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:15:37.265000-08:00'), ('2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:18.232000-08:00'), ('2019-12-03T19:18:50.254000-08:00', '2019-12-03T19:34:52.341000-08:00')]\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = ['2019-12-03T14:14:57.823089-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:30:25.528000-08:00'), ('2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:13:39.520000-08:00'), ('2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:19:07.899000-08:00'), ('2019-12-03T10:27:42.457942-08:00', '2019-12-03T10:34:05.923000-08:00'), ('2019-12-03T14:14:57.823089-08:00', '2019-12-03T14:28:10.982000-08:00'), ('2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:21:06.134000-08:00'), ('2019-12-03T16:22:10.099000-08:00', '2019-12-03T16:43:35.074000-08:00'), ('2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:53:47.065000-08:00'), ('2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:02:53.212000-08:00'), ('2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:13.086000-08:00'), ('2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:21:05.021000-08:00'), ('2019-12-03T18:23:24-08:00', '2019-12-03T19:00:40-08:00'), ('2019-12-03T19:00:46-08:00', '2019-12-03T19:04:52.217000-08:00'), ('2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:12:57.287000-08:00'), ('2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:15:37.265000-08:00'), ('2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:18.232000-08:00'), ('2019-12-03T19:18:50.254000-08:00', '2019-12-03T19:34:52.341000-08:00')]\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = ['2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:22:10.099000-08:00', '2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:23:24-08:00', '2019-12-03T19:00:46-08:00', '2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:50.254000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_0 MAMFDC v/s MAHFDC MAMFDC_0 3\n", + "Before filtering, trips = [('2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:03.655000-08:00'), ('2019-12-09T08:32:22-08:00', '2019-12-09T08:49:38.668000-08:00'), ('2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:09:44.698000-08:00'), ('2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:16:53.719000-08:00'), ('2019-12-09T10:23:10.725965-08:00', '2019-12-09T10:33:25.686000-08:00'), ('2019-12-09T13:58:31.663882-08:00', '2019-12-09T14:12:18.784000-08:00'), ('2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:20:09.881000-08:00'), ('2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:20.964000-08:00'), ('2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:38:29.928000-08:00'), ('2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:44:56.867000-08:00'), ('2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:24:18.860000-08:00'), ('2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:32:58.400000-08:00'), ('2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:08:19-08:00'), ('2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:19:22-08:00'), ('2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:07:41.745000-08:00'), ('2019-12-09T19:08:13.704000-08:00', '2019-12-09T19:24:18.739000-08:00')]\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = ['2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:22-08:00', '2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:23:10.725965-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:03.655000-08:00'), ('2019-12-09T08:32:22-08:00', '2019-12-09T08:49:38.668000-08:00'), ('2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:09:44.698000-08:00'), ('2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:16:53.719000-08:00'), ('2019-12-09T10:23:10.725965-08:00', '2019-12-09T10:33:25.686000-08:00'), ('2019-12-09T13:58:31.663882-08:00', '2019-12-09T14:12:18.784000-08:00'), ('2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:20:09.881000-08:00'), ('2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:20.964000-08:00'), ('2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:38:29.928000-08:00'), ('2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:44:56.867000-08:00'), ('2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:24:18.860000-08:00'), ('2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:32:58.400000-08:00'), ('2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:08:19-08:00'), ('2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:19:22-08:00'), ('2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:07:41.745000-08:00'), ('2019-12-09T19:08:13.704000-08:00', '2019-12-09T19:24:18.739000-08:00')]\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = ['2019-12-09T13:58:31.663882-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:03.655000-08:00'), ('2019-12-09T08:32:22-08:00', '2019-12-09T08:49:38.668000-08:00'), ('2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:09:44.698000-08:00'), ('2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:16:53.719000-08:00'), ('2019-12-09T10:23:10.725965-08:00', '2019-12-09T10:33:25.686000-08:00'), ('2019-12-09T13:58:31.663882-08:00', '2019-12-09T14:12:18.784000-08:00'), ('2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:20:09.881000-08:00'), ('2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:20.964000-08:00'), ('2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:38:29.928000-08:00'), ('2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:44:56.867000-08:00'), ('2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:24:18.860000-08:00'), ('2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:32:58.400000-08:00'), ('2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:08:19-08:00'), ('2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:19:22-08:00'), ('2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:07:41.745000-08:00'), ('2019-12-09T19:08:13.704000-08:00', '2019-12-09T19:24:18.739000-08:00')]\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = ['2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:08:13.704000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_1 MAMFDC v/s MAHFDC MAMFDC_1 3\n", + "Before filtering, trips = [('2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:29:41.502000-08:00'), ('2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:36:36.408000-08:00'), ('2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:48:49.622000-08:00'), ('2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:50:24.676000-08:00'), ('2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:09:30.505000-08:00'), ('2019-12-11T09:12:27.062000-08:00', '2019-12-11T09:33:55.675000-08:00'), ('2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:43:34.307000-08:00'), ('2019-12-11T10:45:58.221500-08:00', '2019-12-11T10:53:40.259000-08:00'), ('2019-12-11T14:08:18.199610-08:00', '2019-12-11T14:21:15.254000-08:00'), ('2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:24:41.276000-08:00'), ('2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:26.377000-08:00'), ('2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:35:58.518000-08:00'), ('2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:37:36.280000-08:00'), ('2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:38:40.302000-08:00'), ('2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:43:30.369000-08:00'), ('2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:45:03.276000-08:00'), ('2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:33:36.324000-08:00'), ('2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:43:50.423000-08:00'), ('2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:03:35.864000-08:00'), ('2019-12-11T18:14:27.208076-08:00', '2019-12-11T18:59:09.722000-08:00'), ('2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:01:12.130000-08:00'), ('2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:06:28.563000-08:00'), ('2019-12-11T19:07:04.196000-08:00', '2019-12-11T19:21:47.554000-08:00')]\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = ['2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:12:27.062000-08:00', '2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:45:58.221500-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:29:41.502000-08:00'), ('2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:36:36.408000-08:00'), ('2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:48:49.622000-08:00'), ('2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:50:24.676000-08:00'), ('2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:09:30.505000-08:00'), ('2019-12-11T09:12:27.062000-08:00', '2019-12-11T09:33:55.675000-08:00'), ('2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:43:34.307000-08:00'), ('2019-12-11T10:45:58.221500-08:00', '2019-12-11T10:53:40.259000-08:00'), ('2019-12-11T14:08:18.199610-08:00', '2019-12-11T14:21:15.254000-08:00'), ('2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:24:41.276000-08:00'), ('2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:26.377000-08:00'), ('2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:35:58.518000-08:00'), ('2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:37:36.280000-08:00'), ('2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:38:40.302000-08:00'), ('2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:43:30.369000-08:00'), ('2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:45:03.276000-08:00'), ('2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:33:36.324000-08:00'), ('2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:43:50.423000-08:00'), ('2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:03:35.864000-08:00'), ('2019-12-11T18:14:27.208076-08:00', '2019-12-11T18:59:09.722000-08:00'), ('2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:01:12.130000-08:00'), ('2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:06:28.563000-08:00'), ('2019-12-11T19:07:04.196000-08:00', '2019-12-11T19:21:47.554000-08:00')]\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = ['2019-12-11T14:08:18.199610-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:29:41.502000-08:00'), ('2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:36:36.408000-08:00'), ('2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:48:49.622000-08:00'), ('2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:50:24.676000-08:00'), ('2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:09:30.505000-08:00'), ('2019-12-11T09:12:27.062000-08:00', '2019-12-11T09:33:55.675000-08:00'), ('2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:43:34.307000-08:00'), ('2019-12-11T10:45:58.221500-08:00', '2019-12-11T10:53:40.259000-08:00'), ('2019-12-11T14:08:18.199610-08:00', '2019-12-11T14:21:15.254000-08:00'), ('2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:24:41.276000-08:00'), ('2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:26.377000-08:00'), ('2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:35:58.518000-08:00'), ('2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:37:36.280000-08:00'), ('2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:38:40.302000-08:00'), ('2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:43:30.369000-08:00'), ('2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:45:03.276000-08:00'), ('2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:33:36.324000-08:00'), ('2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:43:50.423000-08:00'), ('2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:03:35.864000-08:00'), ('2019-12-11T18:14:27.208076-08:00', '2019-12-11T18:59:09.722000-08:00'), ('2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:01:12.130000-08:00'), ('2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:06:28.563000-08:00'), ('2019-12-11T19:07:04.196000-08:00', '2019-12-11T19:21:47.554000-08:00')]\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = ['2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:14:27.208076-08:00', '2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:07:04.196000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_2 MAMFDC v/s MAHFDC MAMFDC_2 3\n", + "Before filtering, trips = [('2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:27:01.474000-08:00'), ('2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:10:45.548000-08:00'), ('2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:14:30.481000-08:00'), ('2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:17:06.028000-08:00'), ('2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:21.951000-08:00'), ('2020-02-06T10:29:52.939000-08:00', '2020-02-06T10:30:57.059000-08:00'), ('2020-02-06T13:08:18.314395-08:00', '2020-02-06T13:21:52.184000-08:00'), ('2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:16.147000-08:00'), ('2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:11.210000-08:00'), ('2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:37:43.092000-08:00'), ('2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:47:23.156000-08:00'), ('2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:28:53.992000-08:00'), ('2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:37:58.996000-08:00'), ('2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:50:42.901000-08:00'), ('2020-02-06T17:51:14.950000-08:00', '2020-02-06T17:59:16.948000-08:00'), ('2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:51-08:00'), ('2020-02-06T18:15:57-08:00', '2020-02-06T18:16:09-08:00'), ('2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:57:24.095000-08:00'), ('2020-02-06T18:58:29.052000-08:00', '2020-02-06T19:17:04.015000-08:00')]\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = ['2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:52.939000-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:27:01.474000-08:00'), ('2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:10:45.548000-08:00'), ('2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:14:30.481000-08:00'), ('2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:17:06.028000-08:00'), ('2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:21.951000-08:00'), ('2020-02-06T10:29:52.939000-08:00', '2020-02-06T10:30:57.059000-08:00'), ('2020-02-06T13:08:18.314395-08:00', '2020-02-06T13:21:52.184000-08:00'), ('2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:16.147000-08:00'), ('2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:11.210000-08:00'), ('2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:37:43.092000-08:00'), ('2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:47:23.156000-08:00'), ('2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:28:53.992000-08:00'), ('2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:37:58.996000-08:00'), ('2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:50:42.901000-08:00'), ('2020-02-06T17:51:14.950000-08:00', '2020-02-06T17:59:16.948000-08:00'), ('2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:51-08:00'), ('2020-02-06T18:15:57-08:00', '2020-02-06T18:16:09-08:00'), ('2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:57:24.095000-08:00'), ('2020-02-06T18:58:29.052000-08:00', '2020-02-06T19:17:04.015000-08:00')]\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = ['2020-02-06T13:08:18.314395-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:27:01.474000-08:00'), ('2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:10:45.548000-08:00'), ('2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:14:30.481000-08:00'), ('2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:17:06.028000-08:00'), ('2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:21.951000-08:00'), ('2020-02-06T10:29:52.939000-08:00', '2020-02-06T10:30:57.059000-08:00'), ('2020-02-06T13:08:18.314395-08:00', '2020-02-06T13:21:52.184000-08:00'), ('2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:16.147000-08:00'), ('2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:11.210000-08:00'), ('2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:37:43.092000-08:00'), ('2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:47:23.156000-08:00'), ('2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:28:53.992000-08:00'), ('2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:37:58.996000-08:00'), ('2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:50:42.901000-08:00'), ('2020-02-06T17:51:14.950000-08:00', '2020-02-06T17:59:16.948000-08:00'), ('2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:51-08:00'), ('2020-02-06T18:15:57-08:00', '2020-02-06T18:16:09-08:00'), ('2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:57:24.095000-08:00'), ('2020-02-06T18:58:29.052000-08:00', '2020-02-06T19:17:04.015000-08:00')]\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = ['2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:51:14.950000-08:00', '2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:57-08:00', '2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:58:29.052000-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 3\n", + "Before filtering, trips = [('2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:07:20-07:00'), ('2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:11:36-07:00'), ('2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:13:45.097000-07:00'), ('2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:17:21-07:00'), ('2019-07-24T10:19:52-07:00', '2019-07-24T10:25:47-07:00'), ('2019-07-24T10:26:18-07:00', '2019-07-24T10:26:59-07:00'), ('2019-07-24T14:12:34.873161-07:00', '2019-07-24T14:25:52-07:00'), ('2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:26-07:00'), ('2019-07-24T16:42:32-07:00', '2019-07-24T16:50:48-07:00'), ('2019-07-24T16:51:18-07:00', '2019-07-24T16:51:18-07:00'), ('2019-07-24T16:51:48-07:00', '2019-07-24T16:52:48-07:00'), ('2019-07-24T16:53:18-07:00', '2019-07-24T16:53:48-07:00'), ('2019-07-24T16:54:18-07:00', '2019-07-24T17:02:19.337000-07:00'), ('2019-07-24T17:02:49-07:00', '2019-07-24T17:08:12.860000-07:00'), ('2019-07-24T17:20:28.892637-07:00', '2019-07-24T17:59:58-07:00'), ('2019-07-24T18:00:27-07:00', '2019-07-24T18:12:34.361000-07:00'), ('2019-07-24T18:14:19-07:00', '2019-07-24T18:27:52-07:00'), ('2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:29:52-07:00'), ('2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:41:32-07:00'), ('2019-07-24T19:42:02-07:00', '2019-07-24T19:59:34-07:00')]\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = ['2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:19:52-07:00', '2019-07-24T10:26:18-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:07:20-07:00'), ('2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:11:36-07:00'), ('2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:13:45.097000-07:00'), ('2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:17:21-07:00'), ('2019-07-24T10:19:52-07:00', '2019-07-24T10:25:47-07:00'), ('2019-07-24T10:26:18-07:00', '2019-07-24T10:26:59-07:00'), ('2019-07-24T14:12:34.873161-07:00', '2019-07-24T14:25:52-07:00'), ('2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:26-07:00'), ('2019-07-24T16:42:32-07:00', '2019-07-24T16:50:48-07:00'), ('2019-07-24T16:51:18-07:00', '2019-07-24T16:51:18-07:00'), ('2019-07-24T16:51:48-07:00', '2019-07-24T16:52:48-07:00'), ('2019-07-24T16:53:18-07:00', '2019-07-24T16:53:48-07:00'), ('2019-07-24T16:54:18-07:00', '2019-07-24T17:02:19.337000-07:00'), ('2019-07-24T17:02:49-07:00', '2019-07-24T17:08:12.860000-07:00'), ('2019-07-24T17:20:28.892637-07:00', '2019-07-24T17:59:58-07:00'), ('2019-07-24T18:00:27-07:00', '2019-07-24T18:12:34.361000-07:00'), ('2019-07-24T18:14:19-07:00', '2019-07-24T18:27:52-07:00'), ('2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:29:52-07:00'), ('2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:41:32-07:00'), ('2019-07-24T19:42:02-07:00', '2019-07-24T19:59:34-07:00')]\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = ['2019-07-24T14:12:34.873161-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:07:20-07:00'), ('2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:11:36-07:00'), ('2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:13:45.097000-07:00'), ('2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:17:21-07:00'), ('2019-07-24T10:19:52-07:00', '2019-07-24T10:25:47-07:00'), ('2019-07-24T10:26:18-07:00', '2019-07-24T10:26:59-07:00'), ('2019-07-24T14:12:34.873161-07:00', '2019-07-24T14:25:52-07:00'), ('2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:26-07:00'), ('2019-07-24T16:42:32-07:00', '2019-07-24T16:50:48-07:00'), ('2019-07-24T16:51:18-07:00', '2019-07-24T16:51:18-07:00'), ('2019-07-24T16:51:48-07:00', '2019-07-24T16:52:48-07:00'), ('2019-07-24T16:53:18-07:00', '2019-07-24T16:53:48-07:00'), ('2019-07-24T16:54:18-07:00', '2019-07-24T17:02:19.337000-07:00'), ('2019-07-24T17:02:49-07:00', '2019-07-24T17:08:12.860000-07:00'), ('2019-07-24T17:20:28.892637-07:00', '2019-07-24T17:59:58-07:00'), ('2019-07-24T18:00:27-07:00', '2019-07-24T18:12:34.361000-07:00'), ('2019-07-24T18:14:19-07:00', '2019-07-24T18:27:52-07:00'), ('2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:29:52-07:00'), ('2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:41:32-07:00'), ('2019-07-24T19:42:02-07:00', '2019-07-24T19:59:34-07:00')]\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = ['2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:32-07:00', '2019-07-24T16:51:18-07:00', '2019-07-24T16:51:48-07:00', '2019-07-24T16:53:18-07:00', '2019-07-24T16:54:18-07:00', '2019-07-24T17:02:49-07:00', '2019-07-24T17:20:28.892637-07:00', '2019-07-24T18:00:27-07:00', '2019-07-24T18:14:19-07:00', '2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:42:02-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 3\n", + "Before filtering, trips = [('2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:23:55-07:00'), ('2019-07-25T08:31:30-07:00', '2019-07-25T09:08:09-07:00'), ('2019-07-25T09:08:42-07:00', '2019-07-25T09:09:39.760000-07:00'), ('2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:13:57.477000-07:00'), ('2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:21.763000-07:00'), ('2019-07-25T10:21:46-07:00', '2019-07-25T10:26:16-07:00'), ('2019-07-25T10:26:46-07:00', '2019-07-25T10:28:16-07:00'), ('2019-07-25T14:08:49.955683-07:00', '2019-07-25T14:21:40.801000-07:00'), ('2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:18.863000-07:00'), ('2019-07-25T16:37:37-07:00', '2019-07-25T16:47:36-07:00'), ('2019-07-25T16:48:06-07:00', '2019-07-25T16:48:16.914000-07:00'), ('2019-07-25T16:48:36-07:00', '2019-07-25T16:48:48.839000-07:00'), ('2019-07-25T16:49:07-07:00', '2019-07-25T16:49:53.901000-07:00'), ('2019-07-25T16:50:00-07:00', '2019-07-25T16:53:30-07:00'), ('2019-07-25T16:54:00-07:00', '2019-07-25T16:55:00-07:00'), ('2019-07-25T16:56:01-07:00', '2019-07-25T16:58:01-07:00'), ('2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:01:40-07:00'), ('2019-07-25T18:02:15-07:00', '2019-07-25T18:12:28.966000-07:00'), ('2019-07-25T18:13:48-07:00', '2019-07-25T18:25:10-07:00'), ('2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:30:11-07:00'), ('2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:41:47-07:00'), ('2019-07-25T19:42:15.525000-07:00', '2019-07-25T19:58:17-07:00')]\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = ['2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:31:30-07:00', '2019-07-25T09:08:42-07:00', '2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:46-07:00', '2019-07-25T10:26:46-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:23:55-07:00'), ('2019-07-25T08:31:30-07:00', '2019-07-25T09:08:09-07:00'), ('2019-07-25T09:08:42-07:00', '2019-07-25T09:09:39.760000-07:00'), ('2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:13:57.477000-07:00'), ('2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:21.763000-07:00'), ('2019-07-25T10:21:46-07:00', '2019-07-25T10:26:16-07:00'), ('2019-07-25T10:26:46-07:00', '2019-07-25T10:28:16-07:00'), ('2019-07-25T14:08:49.955683-07:00', '2019-07-25T14:21:40.801000-07:00'), ('2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:18.863000-07:00'), ('2019-07-25T16:37:37-07:00', '2019-07-25T16:47:36-07:00'), ('2019-07-25T16:48:06-07:00', '2019-07-25T16:48:16.914000-07:00'), ('2019-07-25T16:48:36-07:00', '2019-07-25T16:48:48.839000-07:00'), ('2019-07-25T16:49:07-07:00', '2019-07-25T16:49:53.901000-07:00'), ('2019-07-25T16:50:00-07:00', '2019-07-25T16:53:30-07:00'), ('2019-07-25T16:54:00-07:00', '2019-07-25T16:55:00-07:00'), ('2019-07-25T16:56:01-07:00', '2019-07-25T16:58:01-07:00'), ('2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:01:40-07:00'), ('2019-07-25T18:02:15-07:00', '2019-07-25T18:12:28.966000-07:00'), ('2019-07-25T18:13:48-07:00', '2019-07-25T18:25:10-07:00'), ('2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:30:11-07:00'), ('2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:41:47-07:00'), ('2019-07-25T19:42:15.525000-07:00', '2019-07-25T19:58:17-07:00')]\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = ['2019-07-25T14:08:49.955683-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:23:55-07:00'), ('2019-07-25T08:31:30-07:00', '2019-07-25T09:08:09-07:00'), ('2019-07-25T09:08:42-07:00', '2019-07-25T09:09:39.760000-07:00'), ('2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:13:57.477000-07:00'), ('2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:21.763000-07:00'), ('2019-07-25T10:21:46-07:00', '2019-07-25T10:26:16-07:00'), ('2019-07-25T10:26:46-07:00', '2019-07-25T10:28:16-07:00'), ('2019-07-25T14:08:49.955683-07:00', '2019-07-25T14:21:40.801000-07:00'), ('2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:18.863000-07:00'), ('2019-07-25T16:37:37-07:00', '2019-07-25T16:47:36-07:00'), ('2019-07-25T16:48:06-07:00', '2019-07-25T16:48:16.914000-07:00'), ('2019-07-25T16:48:36-07:00', '2019-07-25T16:48:48.839000-07:00'), ('2019-07-25T16:49:07-07:00', '2019-07-25T16:49:53.901000-07:00'), ('2019-07-25T16:50:00-07:00', '2019-07-25T16:53:30-07:00'), ('2019-07-25T16:54:00-07:00', '2019-07-25T16:55:00-07:00'), ('2019-07-25T16:56:01-07:00', '2019-07-25T16:58:01-07:00'), ('2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:01:40-07:00'), ('2019-07-25T18:02:15-07:00', '2019-07-25T18:12:28.966000-07:00'), ('2019-07-25T18:13:48-07:00', '2019-07-25T18:25:10-07:00'), ('2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:30:11-07:00'), ('2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:41:47-07:00'), ('2019-07-25T19:42:15.525000-07:00', '2019-07-25T19:58:17-07:00')]\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = ['2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:37-07:00', '2019-07-25T16:48:06-07:00', '2019-07-25T16:48:36-07:00', '2019-07-25T16:49:07-07:00', '2019-07-25T16:50:00-07:00', '2019-07-25T16:54:00-07:00', '2019-07-25T16:56:01-07:00', '2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:02:15-07:00', '2019-07-25T18:13:48-07:00', '2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:42:15.525000-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 3\n", + "Before filtering, trips = [('2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:23:51-07:00'), ('2019-07-26T08:31:55.399292-07:00', '2019-07-26T08:58:43-07:00'), ('2019-07-26T09:01:43-07:00', '2019-07-26T09:02:43-07:00'), ('2019-07-26T09:03:42-07:00', '2019-07-26T09:06:13-07:00'), ('2019-07-26T09:10:12-07:00', '2019-07-26T09:12:11-07:00'), ('2019-07-26T09:12:35-07:00', '2019-07-26T09:13:33.039000-07:00'), ('2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:10:23-07:00'), ('2019-07-26T10:14:25-07:00', '2019-07-26T10:18:26-07:00'), ('2019-07-26T10:18:56-07:00', '2019-07-26T10:28:02-07:00'), ('2019-07-26T10:28:07-07:00', '2019-07-26T10:29:38.725000-07:00'), ('2019-07-26T14:16:49.550055-07:00', '2019-07-26T14:29:26.786000-07:00'), ('2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:21:02-07:00'), ('2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:30:34.748000-07:00'), ('2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:48:47-07:00'), ('2019-07-26T16:49:18-07:00', '2019-07-26T16:49:18-07:00'), ('2019-07-26T16:49:47-07:00', '2019-07-26T16:53:47-07:00'), ('2019-07-26T16:54:18-07:00', '2019-07-26T16:55:18-07:00'), ('2019-07-26T16:55:48-07:00', '2019-07-26T17:01:47-07:00'), ('2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:22-07:00'), ('2019-07-26T17:51:52-07:00', '2019-07-26T18:01:34.987000-07:00'), ('2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:22:37.770000-07:00'), ('2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:28:43.694000-07:00'), ('2019-07-26T18:33:15-07:00', '2019-07-26T18:33:24.739000-07:00'), ('2019-07-26T18:34:16-07:00', '2019-07-26T19:41:47-07:00'), ('2019-07-26T19:42:00-07:00', '2019-07-26T20:00:26.738000-07:00')]\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = ['2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:31:55.399292-07:00', '2019-07-26T09:01:43-07:00', '2019-07-26T09:03:42-07:00', '2019-07-26T09:10:12-07:00', '2019-07-26T09:12:35-07:00', '2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:14:25-07:00', '2019-07-26T10:18:56-07:00', '2019-07-26T10:28:07-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:23:51-07:00'), ('2019-07-26T08:31:55.399292-07:00', '2019-07-26T08:58:43-07:00'), ('2019-07-26T09:01:43-07:00', '2019-07-26T09:02:43-07:00'), ('2019-07-26T09:03:42-07:00', '2019-07-26T09:06:13-07:00'), ('2019-07-26T09:10:12-07:00', '2019-07-26T09:12:11-07:00'), ('2019-07-26T09:12:35-07:00', '2019-07-26T09:13:33.039000-07:00'), ('2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:10:23-07:00'), ('2019-07-26T10:14:25-07:00', '2019-07-26T10:18:26-07:00'), ('2019-07-26T10:18:56-07:00', '2019-07-26T10:28:02-07:00'), ('2019-07-26T10:28:07-07:00', '2019-07-26T10:29:38.725000-07:00'), ('2019-07-26T14:16:49.550055-07:00', '2019-07-26T14:29:26.786000-07:00'), ('2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:21:02-07:00'), ('2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:30:34.748000-07:00'), ('2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:48:47-07:00'), ('2019-07-26T16:49:18-07:00', '2019-07-26T16:49:18-07:00'), ('2019-07-26T16:49:47-07:00', '2019-07-26T16:53:47-07:00'), ('2019-07-26T16:54:18-07:00', '2019-07-26T16:55:18-07:00'), ('2019-07-26T16:55:48-07:00', '2019-07-26T17:01:47-07:00'), ('2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:22-07:00'), ('2019-07-26T17:51:52-07:00', '2019-07-26T18:01:34.987000-07:00'), ('2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:22:37.770000-07:00'), ('2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:28:43.694000-07:00'), ('2019-07-26T18:33:15-07:00', '2019-07-26T18:33:24.739000-07:00'), ('2019-07-26T18:34:16-07:00', '2019-07-26T19:41:47-07:00'), ('2019-07-26T19:42:00-07:00', '2019-07-26T20:00:26.738000-07:00')]\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = ['2019-07-26T14:16:49.550055-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:23:51-07:00'), ('2019-07-26T08:31:55.399292-07:00', '2019-07-26T08:58:43-07:00'), ('2019-07-26T09:01:43-07:00', '2019-07-26T09:02:43-07:00'), ('2019-07-26T09:03:42-07:00', '2019-07-26T09:06:13-07:00'), ('2019-07-26T09:10:12-07:00', '2019-07-26T09:12:11-07:00'), ('2019-07-26T09:12:35-07:00', '2019-07-26T09:13:33.039000-07:00'), ('2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:10:23-07:00'), ('2019-07-26T10:14:25-07:00', '2019-07-26T10:18:26-07:00'), ('2019-07-26T10:18:56-07:00', '2019-07-26T10:28:02-07:00'), ('2019-07-26T10:28:07-07:00', '2019-07-26T10:29:38.725000-07:00'), ('2019-07-26T14:16:49.550055-07:00', '2019-07-26T14:29:26.786000-07:00'), ('2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:21:02-07:00'), ('2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:30:34.748000-07:00'), ('2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:48:47-07:00'), ('2019-07-26T16:49:18-07:00', '2019-07-26T16:49:18-07:00'), ('2019-07-26T16:49:47-07:00', '2019-07-26T16:53:47-07:00'), ('2019-07-26T16:54:18-07:00', '2019-07-26T16:55:18-07:00'), ('2019-07-26T16:55:48-07:00', '2019-07-26T17:01:47-07:00'), ('2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:22-07:00'), ('2019-07-26T17:51:52-07:00', '2019-07-26T18:01:34.987000-07:00'), ('2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:22:37.770000-07:00'), ('2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:28:43.694000-07:00'), ('2019-07-26T18:33:15-07:00', '2019-07-26T18:33:24.739000-07:00'), ('2019-07-26T18:34:16-07:00', '2019-07-26T19:41:47-07:00'), ('2019-07-26T19:42:00-07:00', '2019-07-26T20:00:26.738000-07:00')]\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = ['2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:49:18-07:00', '2019-07-26T16:49:47-07:00', '2019-07-26T16:54:18-07:00', '2019-07-26T16:55:48-07:00', '2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:52-07:00', '2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:33:15-07:00', '2019-07-26T18:34:16-07:00', '2019-07-26T19:42:00-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 3\n", + "Before filtering, trips = [('2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:28:20.210000-07:00'), ('2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:38.118000-07:00'), ('2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:11:42.707000-07:00'), ('2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:13:36.272000-07:00'), ('2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:16:02.221000-07:00'), ('2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:31-07:00'), ('2019-09-10T10:35:47.152000-07:00', '2019-09-10T10:37:43.239000-07:00'), ('2019-09-10T13:38:52.057667-07:00', '2019-09-10T13:52:21.432000-07:00'), ('2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:17:55.660000-07:00'), ('2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:36.221000-07:00'), ('2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:43.502000-07:00'), ('2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:42.270000-07:00'), ('2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:31:55.671000-07:00'), ('2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:39:51-07:00'), ('2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:31:26-07:00'), ('2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:31-07:00'), ('2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:04:07.088000-07:00'), ('2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:28.207000-07:00'), ('2019-09-10T19:04:34.934000-07:00', '2019-09-10T19:23:56.153000-07:00'), ('2019-09-10T19:52:15.471889-07:00', '2019-09-10T20:11:03-07:00')]\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = ['2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:47.152000-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:28:20.210000-07:00'), ('2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:38.118000-07:00'), ('2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:11:42.707000-07:00'), ('2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:13:36.272000-07:00'), ('2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:16:02.221000-07:00'), ('2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:31-07:00'), ('2019-09-10T10:35:47.152000-07:00', '2019-09-10T10:37:43.239000-07:00'), ('2019-09-10T13:38:52.057667-07:00', '2019-09-10T13:52:21.432000-07:00'), ('2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:17:55.660000-07:00'), ('2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:36.221000-07:00'), ('2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:43.502000-07:00'), ('2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:42.270000-07:00'), ('2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:31:55.671000-07:00'), ('2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:39:51-07:00'), ('2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:31:26-07:00'), ('2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:31-07:00'), ('2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:04:07.088000-07:00'), ('2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:28.207000-07:00'), ('2019-09-10T19:04:34.934000-07:00', '2019-09-10T19:23:56.153000-07:00'), ('2019-09-10T19:52:15.471889-07:00', '2019-09-10T20:11:03-07:00')]\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = ['2019-09-10T13:38:52.057667-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:28:20.210000-07:00'), ('2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:38.118000-07:00'), ('2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:11:42.707000-07:00'), ('2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:13:36.272000-07:00'), ('2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:16:02.221000-07:00'), ('2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:31-07:00'), ('2019-09-10T10:35:47.152000-07:00', '2019-09-10T10:37:43.239000-07:00'), ('2019-09-10T13:38:52.057667-07:00', '2019-09-10T13:52:21.432000-07:00'), ('2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:17:55.660000-07:00'), ('2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:36.221000-07:00'), ('2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:43.502000-07:00'), ('2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:42.270000-07:00'), ('2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:31:55.671000-07:00'), ('2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:39:51-07:00'), ('2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:31:26-07:00'), ('2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:31-07:00'), ('2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:04:07.088000-07:00'), ('2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:28.207000-07:00'), ('2019-09-10T19:04:34.934000-07:00', '2019-09-10T19:23:56.153000-07:00'), ('2019-09-10T19:52:15.471889-07:00', '2019-09-10T20:11:03-07:00')]\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = ['2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:34.934000-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 3\n", + "Before filtering, trips = [('2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:00.453000-07:00'), ('2019-09-11T08:27:11-07:00', '2019-09-11T08:27:30-07:00'), ('2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:31.994000-07:00'), ('2019-09-11T08:29:45-07:00', '2019-09-11T09:07:24-07:00'), ('2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:07:45.456000-07:00'), ('2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:47.200000-07:00'), ('2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:16:32.070000-07:00'), ('2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:23-07:00'), ('2019-09-11T10:35:29.162000-07:00', '2019-09-11T10:36:17.153000-07:00'), ('2019-09-11T13:45:54.217396-07:00', '2019-09-11T13:59:10.181000-07:00'), ('2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:32:05.241000-07:00'), ('2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:41.380000-07:00'), ('2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:20.397000-07:00'), ('2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:32.653000-07:00'), ('2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:10.265000-07:00'), ('2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:11.518000-07:00'), ('2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:36.180000-07:00'), ('2019-09-11T16:51:50.762000-07:00', '2019-09-11T16:55:00.270000-07:00'), ('2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:55:57-07:00'), ('2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:07:15.156000-07:00'), ('2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:11-07:00'), ('2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:42.414000-07:00'), ('2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:19:57-07:00'), ('2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:43:25-07:00'), ('2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:21-07:00'), ('2019-09-11T19:38:22-07:00', '2019-09-11T19:39:11-07:00'), ('2019-09-11T19:39:12-07:00', '2019-09-11T19:41:26-07:00'), ('2019-09-11T19:41:31.703000-07:00', '2019-09-11T19:59:01.862000-07:00'), ('2019-09-11T20:20:30.259241-07:00', '2019-09-11T20:34:02.137000-07:00'), ('2019-09-11T20:34:10-07:00', '2019-09-11T20:36:18-07:00')]\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = ['2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:11-07:00', '2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:45-07:00', '2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:29.162000-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:00.453000-07:00'), ('2019-09-11T08:27:11-07:00', '2019-09-11T08:27:30-07:00'), ('2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:31.994000-07:00'), ('2019-09-11T08:29:45-07:00', '2019-09-11T09:07:24-07:00'), ('2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:07:45.456000-07:00'), ('2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:47.200000-07:00'), ('2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:16:32.070000-07:00'), ('2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:23-07:00'), ('2019-09-11T10:35:29.162000-07:00', '2019-09-11T10:36:17.153000-07:00'), ('2019-09-11T13:45:54.217396-07:00', '2019-09-11T13:59:10.181000-07:00'), ('2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:32:05.241000-07:00'), ('2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:41.380000-07:00'), ('2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:20.397000-07:00'), ('2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:32.653000-07:00'), ('2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:10.265000-07:00'), ('2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:11.518000-07:00'), ('2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:36.180000-07:00'), ('2019-09-11T16:51:50.762000-07:00', '2019-09-11T16:55:00.270000-07:00'), ('2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:55:57-07:00'), ('2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:07:15.156000-07:00'), ('2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:11-07:00'), ('2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:42.414000-07:00'), ('2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:19:57-07:00'), ('2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:43:25-07:00'), ('2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:21-07:00'), ('2019-09-11T19:38:22-07:00', '2019-09-11T19:39:11-07:00'), ('2019-09-11T19:39:12-07:00', '2019-09-11T19:41:26-07:00'), ('2019-09-11T19:41:31.703000-07:00', '2019-09-11T19:59:01.862000-07:00'), ('2019-09-11T20:20:30.259241-07:00', '2019-09-11T20:34:02.137000-07:00'), ('2019-09-11T20:34:10-07:00', '2019-09-11T20:36:18-07:00')]\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = ['2019-09-11T13:45:54.217396-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:00.453000-07:00'), ('2019-09-11T08:27:11-07:00', '2019-09-11T08:27:30-07:00'), ('2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:31.994000-07:00'), ('2019-09-11T08:29:45-07:00', '2019-09-11T09:07:24-07:00'), ('2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:07:45.456000-07:00'), ('2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:47.200000-07:00'), ('2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:16:32.070000-07:00'), ('2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:23-07:00'), ('2019-09-11T10:35:29.162000-07:00', '2019-09-11T10:36:17.153000-07:00'), ('2019-09-11T13:45:54.217396-07:00', '2019-09-11T13:59:10.181000-07:00'), ('2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:32:05.241000-07:00'), ('2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:41.380000-07:00'), ('2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:20.397000-07:00'), ('2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:32.653000-07:00'), ('2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:10.265000-07:00'), ('2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:11.518000-07:00'), ('2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:36.180000-07:00'), ('2019-09-11T16:51:50.762000-07:00', '2019-09-11T16:55:00.270000-07:00'), ('2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:55:57-07:00'), ('2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:07:15.156000-07:00'), ('2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:11-07:00'), ('2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:42.414000-07:00'), ('2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:19:57-07:00'), ('2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:43:25-07:00'), ('2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:21-07:00'), ('2019-09-11T19:38:22-07:00', '2019-09-11T19:39:11-07:00'), ('2019-09-11T19:39:12-07:00', '2019-09-11T19:41:26-07:00'), ('2019-09-11T19:41:31.703000-07:00', '2019-09-11T19:59:01.862000-07:00'), ('2019-09-11T20:20:30.259241-07:00', '2019-09-11T20:34:02.137000-07:00'), ('2019-09-11T20:34:10-07:00', '2019-09-11T20:36:18-07:00')]\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = ['2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:50.762000-07:00', '2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:22-07:00', '2019-09-11T19:39:12-07:00', '2019-09-11T19:41:31.703000-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 3\n", + "Before filtering, trips = [('2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:25:26.027000-07:00'), ('2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:13.064000-07:00'), ('2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:06-07:00'), ('2019-09-17T09:14:07-07:00', '2019-09-17T09:16:17.829000-07:00'), ('2019-09-17T09:16:27-07:00', '2019-09-17T09:16:27-07:00'), ('2019-09-17T09:17:07-07:00', '2019-09-17T09:19:15.624000-07:00'), ('2019-09-17T09:20:54-07:00', '2019-09-17T09:21:09-07:00'), ('2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:15.879000-07:00'), ('2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:16:02.134000-07:00'), ('2019-09-17T10:34:24.364919-07:00', '2019-09-17T10:38:35.238000-07:00'), ('2019-09-17T13:45:10.086634-07:00', '2019-09-17T13:58:24.606000-07:00'), ('2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:06.682000-07:00'), ('2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:48.009000-07:00'), ('2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:21:55.113000-07:00'), ('2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:26.909000-07:00'), ('2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:37.005000-07:00'), ('2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:23.944000-07:00'), ('2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:34.719000-07:00'), ('2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:53.855000-07:00'), ('2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:09.048000-07:00'), ('2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:36:27.607000-07:00'), ('2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:29.722000-07:00'), ('2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:36:07.656000-07:00'), ('2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:07.998000-07:00'), ('2019-09-17T17:54:13.014000-07:00', '2019-09-17T17:57:46.274000-07:00'), ('2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:51.915000-07:00'), ('2019-09-17T18:13:57-07:00', '2019-09-17T18:14:12.058000-07:00'), ('2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:57:54.933000-07:00'), ('2019-09-17T18:58:15.167000-07:00', '2019-09-17T19:15:26.928000-07:00')]\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = ['2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:07-07:00', '2019-09-17T09:16:27-07:00', '2019-09-17T09:17:07-07:00', '2019-09-17T09:20:54-07:00', '2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:34:24.364919-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:25:26.027000-07:00'), ('2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:13.064000-07:00'), ('2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:06-07:00'), ('2019-09-17T09:14:07-07:00', '2019-09-17T09:16:17.829000-07:00'), ('2019-09-17T09:16:27-07:00', '2019-09-17T09:16:27-07:00'), ('2019-09-17T09:17:07-07:00', '2019-09-17T09:19:15.624000-07:00'), ('2019-09-17T09:20:54-07:00', '2019-09-17T09:21:09-07:00'), ('2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:15.879000-07:00'), ('2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:16:02.134000-07:00'), ('2019-09-17T10:34:24.364919-07:00', '2019-09-17T10:38:35.238000-07:00'), ('2019-09-17T13:45:10.086634-07:00', '2019-09-17T13:58:24.606000-07:00'), ('2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:06.682000-07:00'), ('2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:48.009000-07:00'), ('2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:21:55.113000-07:00'), ('2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:26.909000-07:00'), ('2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:37.005000-07:00'), ('2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:23.944000-07:00'), ('2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:34.719000-07:00'), ('2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:53.855000-07:00'), ('2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:09.048000-07:00'), ('2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:36:27.607000-07:00'), ('2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:29.722000-07:00'), ('2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:36:07.656000-07:00'), ('2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:07.998000-07:00'), ('2019-09-17T17:54:13.014000-07:00', '2019-09-17T17:57:46.274000-07:00'), ('2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:51.915000-07:00'), ('2019-09-17T18:13:57-07:00', '2019-09-17T18:14:12.058000-07:00'), ('2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:57:54.933000-07:00'), ('2019-09-17T18:58:15.167000-07:00', '2019-09-17T19:15:26.928000-07:00')]\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = ['2019-09-17T13:45:10.086634-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:25:26.027000-07:00'), ('2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:13.064000-07:00'), ('2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:06-07:00'), ('2019-09-17T09:14:07-07:00', '2019-09-17T09:16:17.829000-07:00'), ('2019-09-17T09:16:27-07:00', '2019-09-17T09:16:27-07:00'), ('2019-09-17T09:17:07-07:00', '2019-09-17T09:19:15.624000-07:00'), ('2019-09-17T09:20:54-07:00', '2019-09-17T09:21:09-07:00'), ('2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:15.879000-07:00'), ('2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:16:02.134000-07:00'), ('2019-09-17T10:34:24.364919-07:00', '2019-09-17T10:38:35.238000-07:00'), ('2019-09-17T13:45:10.086634-07:00', '2019-09-17T13:58:24.606000-07:00'), ('2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:06.682000-07:00'), ('2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:48.009000-07:00'), ('2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:21:55.113000-07:00'), ('2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:26.909000-07:00'), ('2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:37.005000-07:00'), ('2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:23.944000-07:00'), ('2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:34.719000-07:00'), ('2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:53.855000-07:00'), ('2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:09.048000-07:00'), ('2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:36:27.607000-07:00'), ('2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:29.722000-07:00'), ('2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:36:07.656000-07:00'), ('2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:07.998000-07:00'), ('2019-09-17T17:54:13.014000-07:00', '2019-09-17T17:57:46.274000-07:00'), ('2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:51.915000-07:00'), ('2019-09-17T18:13:57-07:00', '2019-09-17T18:14:12.058000-07:00'), ('2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:57:54.933000-07:00'), ('2019-09-17T18:58:15.167000-07:00', '2019-09-17T19:15:26.928000-07:00')]\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = ['2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:13.014000-07:00', '2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:57-07:00', '2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:58:15.167000-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_0 MAMFDC v/s HAMFDC HAMFDC_0 3\n", + "Before filtering, trips = [('2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:24:26-08:00'), ('2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:09:14-08:00'), ('2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:10:31.187000-08:00'), ('2019-11-19T09:11:01-08:00', '2019-11-19T09:11:01-08:00'), ('2019-11-19T09:12:01-08:00', '2019-11-19T09:12:01-08:00'), ('2019-11-19T09:28:42-08:00', '2019-11-19T10:13:34.996000-08:00'), ('2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:15:40.011000-08:00'), ('2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:31:47-08:00'), ('2019-11-19T10:32:17-08:00', '2019-11-19T10:33:20-08:00'), ('2019-11-19T13:33:00.893654-08:00', '2019-11-19T13:43:06.461000-08:00'), ('2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:19:15-08:00'), ('2019-11-19T16:20:13-08:00', '2019-11-19T16:41:46-08:00'), ('2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:27.199000-08:00'), ('2019-11-19T17:27:56-08:00', '2019-11-19T17:38:25.580000-08:00'), ('2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:21.207000-08:00'), ('2019-11-19T17:56:42-08:00', '2019-11-19T17:57:56.994000-08:00'), ('2019-11-19T17:58:42-08:00', '2019-11-19T18:57:46-08:00'), ('2019-11-19T18:57:51-08:00', '2019-11-19T19:13:02.049000-08:00')]\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = ['2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:11:01-08:00', '2019-11-19T09:12:01-08:00', '2019-11-19T09:28:42-08:00', '2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:32:17-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:24:26-08:00'), ('2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:09:14-08:00'), ('2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:10:31.187000-08:00'), ('2019-11-19T09:11:01-08:00', '2019-11-19T09:11:01-08:00'), ('2019-11-19T09:12:01-08:00', '2019-11-19T09:12:01-08:00'), ('2019-11-19T09:28:42-08:00', '2019-11-19T10:13:34.996000-08:00'), ('2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:15:40.011000-08:00'), ('2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:31:47-08:00'), ('2019-11-19T10:32:17-08:00', '2019-11-19T10:33:20-08:00'), ('2019-11-19T13:33:00.893654-08:00', '2019-11-19T13:43:06.461000-08:00'), ('2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:19:15-08:00'), ('2019-11-19T16:20:13-08:00', '2019-11-19T16:41:46-08:00'), ('2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:27.199000-08:00'), ('2019-11-19T17:27:56-08:00', '2019-11-19T17:38:25.580000-08:00'), ('2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:21.207000-08:00'), ('2019-11-19T17:56:42-08:00', '2019-11-19T17:57:56.994000-08:00'), ('2019-11-19T17:58:42-08:00', '2019-11-19T18:57:46-08:00'), ('2019-11-19T18:57:51-08:00', '2019-11-19T19:13:02.049000-08:00')]\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = ['2019-11-19T13:33:00.893654-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:24:26-08:00'), ('2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:09:14-08:00'), ('2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:10:31.187000-08:00'), ('2019-11-19T09:11:01-08:00', '2019-11-19T09:11:01-08:00'), ('2019-11-19T09:12:01-08:00', '2019-11-19T09:12:01-08:00'), ('2019-11-19T09:28:42-08:00', '2019-11-19T10:13:34.996000-08:00'), ('2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:15:40.011000-08:00'), ('2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:31:47-08:00'), ('2019-11-19T10:32:17-08:00', '2019-11-19T10:33:20-08:00'), ('2019-11-19T13:33:00.893654-08:00', '2019-11-19T13:43:06.461000-08:00'), ('2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:19:15-08:00'), ('2019-11-19T16:20:13-08:00', '2019-11-19T16:41:46-08:00'), ('2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:27.199000-08:00'), ('2019-11-19T17:27:56-08:00', '2019-11-19T17:38:25.580000-08:00'), ('2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:21.207000-08:00'), ('2019-11-19T17:56:42-08:00', '2019-11-19T17:57:56.994000-08:00'), ('2019-11-19T17:58:42-08:00', '2019-11-19T18:57:46-08:00'), ('2019-11-19T18:57:51-08:00', '2019-11-19T19:13:02.049000-08:00')]\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = ['2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:20:13-08:00', '2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:56-08:00', '2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:42-08:00', '2019-11-19T17:58:42-08:00', '2019-11-19T18:57:51-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_1 MAMFDC v/s HAMFDC HAMFDC_1 3\n", + "Before filtering, trips = [('2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:29:31-08:00'), ('2019-11-20T08:30:32-08:00', '2019-11-20T09:08:44.076000-08:00'), ('2019-11-20T09:09:09-08:00', '2019-11-20T09:10:07-08:00'), ('2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:09:48-08:00'), ('2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:16:31.704000-08:00'), ('2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:13-08:00'), ('2019-11-20T10:30:42-08:00', '2019-11-20T10:31:53-08:00'), ('2019-11-20T13:48:22.696511-08:00', '2019-11-20T14:00:18-08:00'), ('2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:40-08:00'), ('2019-11-20T16:24:45-08:00', '2019-11-20T16:43:41.689000-08:00'), ('2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:01-08:00'), ('2019-11-20T17:48:30.803000-08:00', '2019-11-20T17:59:02.509000-08:00'), ('2019-11-20T18:00:24-08:00', '2019-11-20T18:12:23.046000-08:00'), ('2019-11-20T18:12:28-08:00', '2019-11-20T18:16:59-08:00'), ('2019-11-20T18:17:28-08:00', '2019-11-20T19:03:21-08:00'), ('2019-11-20T19:03:51-08:00', '2019-11-20T19:22:55.727000-08:00')]\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = ['2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:30:32-08:00', '2019-11-20T09:09:09-08:00', '2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:42-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:29:31-08:00'), ('2019-11-20T08:30:32-08:00', '2019-11-20T09:08:44.076000-08:00'), ('2019-11-20T09:09:09-08:00', '2019-11-20T09:10:07-08:00'), ('2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:09:48-08:00'), ('2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:16:31.704000-08:00'), ('2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:13-08:00'), ('2019-11-20T10:30:42-08:00', '2019-11-20T10:31:53-08:00'), ('2019-11-20T13:48:22.696511-08:00', '2019-11-20T14:00:18-08:00'), ('2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:40-08:00'), ('2019-11-20T16:24:45-08:00', '2019-11-20T16:43:41.689000-08:00'), ('2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:01-08:00'), ('2019-11-20T17:48:30.803000-08:00', '2019-11-20T17:59:02.509000-08:00'), ('2019-11-20T18:00:24-08:00', '2019-11-20T18:12:23.046000-08:00'), ('2019-11-20T18:12:28-08:00', '2019-11-20T18:16:59-08:00'), ('2019-11-20T18:17:28-08:00', '2019-11-20T19:03:21-08:00'), ('2019-11-20T19:03:51-08:00', '2019-11-20T19:22:55.727000-08:00')]\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = ['2019-11-20T13:48:22.696511-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:29:31-08:00'), ('2019-11-20T08:30:32-08:00', '2019-11-20T09:08:44.076000-08:00'), ('2019-11-20T09:09:09-08:00', '2019-11-20T09:10:07-08:00'), ('2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:09:48-08:00'), ('2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:16:31.704000-08:00'), ('2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:13-08:00'), ('2019-11-20T10:30:42-08:00', '2019-11-20T10:31:53-08:00'), ('2019-11-20T13:48:22.696511-08:00', '2019-11-20T14:00:18-08:00'), ('2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:40-08:00'), ('2019-11-20T16:24:45-08:00', '2019-11-20T16:43:41.689000-08:00'), ('2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:01-08:00'), ('2019-11-20T17:48:30.803000-08:00', '2019-11-20T17:59:02.509000-08:00'), ('2019-11-20T18:00:24-08:00', '2019-11-20T18:12:23.046000-08:00'), ('2019-11-20T18:12:28-08:00', '2019-11-20T18:16:59-08:00'), ('2019-11-20T18:17:28-08:00', '2019-11-20T19:03:21-08:00'), ('2019-11-20T19:03:51-08:00', '2019-11-20T19:22:55.727000-08:00')]\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = ['2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:45-08:00', '2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:30.803000-08:00', '2019-11-20T18:00:24-08:00', '2019-11-20T18:12:28-08:00', '2019-11-20T18:17:28-08:00', '2019-11-20T19:03:51-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_2 MAMFDC v/s HAMFDC HAMFDC_2 3\n", + "Before filtering, trips = [('2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:27:39-08:00'), ('2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:25-08:00'), ('2019-12-03T09:12:56-08:00', '2019-12-03T09:14:36.293000-08:00'), ('2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:11:29-08:00'), ('2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:19:25.295000-08:00'), ('2019-12-03T10:27:55.101354-08:00', '2019-12-03T10:33:26-08:00'), ('2019-12-03T14:14:50.838179-08:00', '2019-12-03T14:27:28-08:00'), ('2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:21:04-08:00'), ('2019-12-03T16:22:03-08:00', '2019-12-03T16:25:22.376000-08:00'), ('2019-12-03T16:25:35-08:00', '2019-12-03T16:25:54.381000-08:00'), ('2019-12-03T16:26:03-08:00', '2019-12-03T16:43:40.687000-08:00'), ('2019-12-03T16:44:05-08:00', '2019-12-03T16:45:05-08:00'), ('2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:53:58-08:00'), ('2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:03:50.551000-08:00'), ('2019-12-03T18:05:41-08:00', '2019-12-03T18:16:26.261000-08:00'), ('2019-12-03T18:16:52-08:00', '2019-12-03T18:20:24-08:00'), ('2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:32.250000-08:00'), ('2019-12-03T19:16:38-08:00', '2019-12-03T19:34:25.275000-08:00')]\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = ['2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:56-08:00', '2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:27:55.101354-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:27:39-08:00'), ('2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:25-08:00'), ('2019-12-03T09:12:56-08:00', '2019-12-03T09:14:36.293000-08:00'), ('2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:11:29-08:00'), ('2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:19:25.295000-08:00'), ('2019-12-03T10:27:55.101354-08:00', '2019-12-03T10:33:26-08:00'), ('2019-12-03T14:14:50.838179-08:00', '2019-12-03T14:27:28-08:00'), ('2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:21:04-08:00'), ('2019-12-03T16:22:03-08:00', '2019-12-03T16:25:22.376000-08:00'), ('2019-12-03T16:25:35-08:00', '2019-12-03T16:25:54.381000-08:00'), ('2019-12-03T16:26:03-08:00', '2019-12-03T16:43:40.687000-08:00'), ('2019-12-03T16:44:05-08:00', '2019-12-03T16:45:05-08:00'), ('2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:53:58-08:00'), ('2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:03:50.551000-08:00'), ('2019-12-03T18:05:41-08:00', '2019-12-03T18:16:26.261000-08:00'), ('2019-12-03T18:16:52-08:00', '2019-12-03T18:20:24-08:00'), ('2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:32.250000-08:00'), ('2019-12-03T19:16:38-08:00', '2019-12-03T19:34:25.275000-08:00')]\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = ['2019-12-03T14:14:50.838179-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:27:39-08:00'), ('2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:25-08:00'), ('2019-12-03T09:12:56-08:00', '2019-12-03T09:14:36.293000-08:00'), ('2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:11:29-08:00'), ('2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:19:25.295000-08:00'), ('2019-12-03T10:27:55.101354-08:00', '2019-12-03T10:33:26-08:00'), ('2019-12-03T14:14:50.838179-08:00', '2019-12-03T14:27:28-08:00'), ('2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:21:04-08:00'), ('2019-12-03T16:22:03-08:00', '2019-12-03T16:25:22.376000-08:00'), ('2019-12-03T16:25:35-08:00', '2019-12-03T16:25:54.381000-08:00'), ('2019-12-03T16:26:03-08:00', '2019-12-03T16:43:40.687000-08:00'), ('2019-12-03T16:44:05-08:00', '2019-12-03T16:45:05-08:00'), ('2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:53:58-08:00'), ('2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:03:50.551000-08:00'), ('2019-12-03T18:05:41-08:00', '2019-12-03T18:16:26.261000-08:00'), ('2019-12-03T18:16:52-08:00', '2019-12-03T18:20:24-08:00'), ('2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:32.250000-08:00'), ('2019-12-03T19:16:38-08:00', '2019-12-03T19:34:25.275000-08:00')]\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = ['2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:22:03-08:00', '2019-12-03T16:25:35-08:00', '2019-12-03T16:26:03-08:00', '2019-12-03T16:44:05-08:00', '2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:05:41-08:00', '2019-12-03T18:16:52-08:00', '2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:38-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_0 MAMFDC v/s MAHFDC MAHFDC_0 3\n", + "Before filtering, trips = [('2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:07.875000-08:00'), ('2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:49.681000-08:00'), ('2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:09:16.277000-08:00'), ('2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:10:32.251000-08:00'), ('2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:16:28.664000-08:00'), ('2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:31.435000-08:00'), ('2019-12-09T10:31:36.485000-08:00', '2019-12-09T10:33:28.166000-08:00'), ('2019-12-09T13:59:35.045878-08:00', '2019-12-09T14:11:38.602000-08:00'), ('2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:19.619000-08:00'), ('2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:29.963000-08:00'), ('2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:00.364000-08:00'), ('2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:14.142000-08:00'), ('2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:04.768000-08:00'), ('2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:16.441000-08:00'), ('2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:33.650000-08:00'), ('2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:35:51.577000-08:00'), ('2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:09.319000-08:00'), ('2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:20.163000-08:00'), ('2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:12.327000-08:00'), ('2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:44:58.651000-08:00'), ('2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:13.755000-08:00'), ('2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:34:12.901000-08:00'), ('2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:06:55.029000-08:00'), ('2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:23:47.868000-08:00'), ('2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:35.284000-08:00'), ('2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:07.149000-08:00'), ('2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:52.661000-08:00'), ('2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:49:42.385000-08:00'), ('2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:53:19.879000-08:00'), ('2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:14.686000-08:00'), ('2019-12-09T19:04:40.160000-08:00', '2019-12-09T19:22:24.170000-08:00')]\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = ['2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:36.485000-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:07.875000-08:00'), ('2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:49.681000-08:00'), ('2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:09:16.277000-08:00'), ('2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:10:32.251000-08:00'), ('2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:16:28.664000-08:00'), ('2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:31.435000-08:00'), ('2019-12-09T10:31:36.485000-08:00', '2019-12-09T10:33:28.166000-08:00'), ('2019-12-09T13:59:35.045878-08:00', '2019-12-09T14:11:38.602000-08:00'), ('2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:19.619000-08:00'), ('2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:29.963000-08:00'), ('2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:00.364000-08:00'), ('2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:14.142000-08:00'), ('2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:04.768000-08:00'), ('2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:16.441000-08:00'), ('2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:33.650000-08:00'), ('2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:35:51.577000-08:00'), ('2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:09.319000-08:00'), ('2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:20.163000-08:00'), ('2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:12.327000-08:00'), ('2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:44:58.651000-08:00'), ('2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:13.755000-08:00'), ('2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:34:12.901000-08:00'), ('2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:06:55.029000-08:00'), ('2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:23:47.868000-08:00'), ('2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:35.284000-08:00'), ('2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:07.149000-08:00'), ('2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:52.661000-08:00'), ('2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:49:42.385000-08:00'), ('2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:53:19.879000-08:00'), ('2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:14.686000-08:00'), ('2019-12-09T19:04:40.160000-08:00', '2019-12-09T19:22:24.170000-08:00')]\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = ['2019-12-09T13:59:35.045878-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:07.875000-08:00'), ('2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:49.681000-08:00'), ('2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:09:16.277000-08:00'), ('2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:10:32.251000-08:00'), ('2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:16:28.664000-08:00'), ('2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:31.435000-08:00'), ('2019-12-09T10:31:36.485000-08:00', '2019-12-09T10:33:28.166000-08:00'), ('2019-12-09T13:59:35.045878-08:00', '2019-12-09T14:11:38.602000-08:00'), ('2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:19.619000-08:00'), ('2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:29.963000-08:00'), ('2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:00.364000-08:00'), ('2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:14.142000-08:00'), ('2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:04.768000-08:00'), ('2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:16.441000-08:00'), ('2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:33.650000-08:00'), ('2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:35:51.577000-08:00'), ('2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:09.319000-08:00'), ('2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:20.163000-08:00'), ('2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:12.327000-08:00'), ('2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:44:58.651000-08:00'), ('2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:13.755000-08:00'), ('2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:34:12.901000-08:00'), ('2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:06:55.029000-08:00'), ('2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:23:47.868000-08:00'), ('2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:35.284000-08:00'), ('2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:07.149000-08:00'), ('2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:52.661000-08:00'), ('2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:49:42.385000-08:00'), ('2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:53:19.879000-08:00'), ('2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:14.686000-08:00'), ('2019-12-09T19:04:40.160000-08:00', '2019-12-09T19:22:24.170000-08:00')]\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = ['2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:40.160000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_1 MAMFDC v/s MAHFDC MAHFDC_1 3\n", + "Before filtering, trips = [('2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:28:40.265000-08:00'), ('2019-12-11T08:29:12-08:00', '2019-12-11T08:49:48.429000-08:00'), ('2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:50:44.071000-08:00'), ('2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:33.931000-08:00'), ('2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:49.122000-08:00'), ('2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:05.825000-08:00'), ('2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:09:27.870000-08:00'), ('2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:28:57.247000-08:00'), ('2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:34:31.422000-08:00'), ('2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:06.408000-08:00'), ('2019-12-11T10:52:11.490000-08:00', '2019-12-11T10:53:46.589000-08:00'), ('2019-12-11T14:08:06.879449-08:00', '2019-12-11T14:21:17.004000-08:00'), ('2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:04.611000-08:00'), ('2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:47.408000-08:00'), ('2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:12.765000-08:00'), ('2019-12-11T16:28:17-08:00', '2019-12-11T16:40:05.580000-08:00'), ('2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:20.804000-08:00'), ('2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:22.923000-08:00'), ('2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:23.723000-08:00'), ('2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:45:09.282000-08:00'), ('2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:32:45.073000-08:00'), ('2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:38:29.227000-08:00'), ('2019-12-11T17:45:33.125000-08:00', '2019-12-11T17:59:56.877000-08:00'), ('2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:19.042000-08:00'), ('2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:27.952000-08:00'), ('2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:04:18.099000-08:00'), ('2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:45.669000-08:00'), ('2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:10.864000-08:00'), ('2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:37.016000-08:00'), ('2019-12-11T19:02:42.076000-08:00', '2019-12-11T19:21:08.356000-08:00')]\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = ['2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:29:12-08:00', '2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:11.490000-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:28:40.265000-08:00'), ('2019-12-11T08:29:12-08:00', '2019-12-11T08:49:48.429000-08:00'), ('2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:50:44.071000-08:00'), ('2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:33.931000-08:00'), ('2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:49.122000-08:00'), ('2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:05.825000-08:00'), ('2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:09:27.870000-08:00'), ('2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:28:57.247000-08:00'), ('2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:34:31.422000-08:00'), ('2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:06.408000-08:00'), ('2019-12-11T10:52:11.490000-08:00', '2019-12-11T10:53:46.589000-08:00'), ('2019-12-11T14:08:06.879449-08:00', '2019-12-11T14:21:17.004000-08:00'), ('2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:04.611000-08:00'), ('2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:47.408000-08:00'), ('2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:12.765000-08:00'), ('2019-12-11T16:28:17-08:00', '2019-12-11T16:40:05.580000-08:00'), ('2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:20.804000-08:00'), ('2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:22.923000-08:00'), ('2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:23.723000-08:00'), ('2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:45:09.282000-08:00'), ('2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:32:45.073000-08:00'), ('2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:38:29.227000-08:00'), ('2019-12-11T17:45:33.125000-08:00', '2019-12-11T17:59:56.877000-08:00'), ('2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:19.042000-08:00'), ('2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:27.952000-08:00'), ('2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:04:18.099000-08:00'), ('2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:45.669000-08:00'), ('2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:10.864000-08:00'), ('2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:37.016000-08:00'), ('2019-12-11T19:02:42.076000-08:00', '2019-12-11T19:21:08.356000-08:00')]\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = ['2019-12-11T14:08:06.879449-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:28:40.265000-08:00'), ('2019-12-11T08:29:12-08:00', '2019-12-11T08:49:48.429000-08:00'), ('2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:50:44.071000-08:00'), ('2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:33.931000-08:00'), ('2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:49.122000-08:00'), ('2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:05.825000-08:00'), ('2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:09:27.870000-08:00'), ('2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:28:57.247000-08:00'), ('2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:34:31.422000-08:00'), ('2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:06.408000-08:00'), ('2019-12-11T10:52:11.490000-08:00', '2019-12-11T10:53:46.589000-08:00'), ('2019-12-11T14:08:06.879449-08:00', '2019-12-11T14:21:17.004000-08:00'), ('2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:04.611000-08:00'), ('2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:47.408000-08:00'), ('2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:12.765000-08:00'), ('2019-12-11T16:28:17-08:00', '2019-12-11T16:40:05.580000-08:00'), ('2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:20.804000-08:00'), ('2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:22.923000-08:00'), ('2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:23.723000-08:00'), ('2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:45:09.282000-08:00'), ('2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:32:45.073000-08:00'), ('2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:38:29.227000-08:00'), ('2019-12-11T17:45:33.125000-08:00', '2019-12-11T17:59:56.877000-08:00'), ('2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:19.042000-08:00'), ('2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:27.952000-08:00'), ('2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:04:18.099000-08:00'), ('2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:45.669000-08:00'), ('2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:10.864000-08:00'), ('2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:37.016000-08:00'), ('2019-12-11T19:02:42.076000-08:00', '2019-12-11T19:21:08.356000-08:00')]\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = ['2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:17-08:00', '2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:45:33.125000-08:00', '2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:42.076000-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_2 MAMFDC v/s MAHFDC MAHFDC_2 3\n", + "Before filtering, trips = [('2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:29:48.443000-08:00'), ('2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:31:09.327000-08:00'), ('2020-02-06T08:32:19-08:00', '2020-02-06T08:33:28.510000-08:00'), ('2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:32.847000-08:00'), ('2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:11:53.823000-08:00'), ('2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:10:56.852000-08:00'), ('2020-02-06T10:14:29.192000-08:00', '2020-02-06T10:16:39.807000-08:00'), ('2020-02-06T13:06:11.492273-08:00', '2020-02-06T13:21:49.811000-08:00'), ('2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:23:57.824000-08:00'), ('2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:39.115000-08:00'), ('2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:09.506000-08:00'), ('2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:35:52.757000-08:00'), ('2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:12.965000-08:00'), ('2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:28.130000-08:00'), ('2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:33.983000-08:00'), ('2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:10.023000-08:00'), ('2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:43.645000-08:00'), ('2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:48:24.953000-08:00'), ('2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:16.908000-08:00'), ('2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:36:34.457000-08:00'), ('2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:49:46.900000-08:00'), ('2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:56:24.499000-08:00'), ('2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:20.622000-08:00'), ('2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:06:07.700000-08:00'), ('2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:40.684000-08:00'), ('2020-02-06T18:56:45.755000-08:00', '2020-02-06T19:16:46.406000-08:00')]\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = ['2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:32:19-08:00', '2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:14:29.192000-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:29:48.443000-08:00'), ('2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:31:09.327000-08:00'), ('2020-02-06T08:32:19-08:00', '2020-02-06T08:33:28.510000-08:00'), ('2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:32.847000-08:00'), ('2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:11:53.823000-08:00'), ('2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:10:56.852000-08:00'), ('2020-02-06T10:14:29.192000-08:00', '2020-02-06T10:16:39.807000-08:00'), ('2020-02-06T13:06:11.492273-08:00', '2020-02-06T13:21:49.811000-08:00'), ('2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:23:57.824000-08:00'), ('2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:39.115000-08:00'), ('2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:09.506000-08:00'), ('2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:35:52.757000-08:00'), ('2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:12.965000-08:00'), ('2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:28.130000-08:00'), ('2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:33.983000-08:00'), ('2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:10.023000-08:00'), ('2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:43.645000-08:00'), ('2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:48:24.953000-08:00'), ('2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:16.908000-08:00'), ('2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:36:34.457000-08:00'), ('2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:49:46.900000-08:00'), ('2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:56:24.499000-08:00'), ('2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:20.622000-08:00'), ('2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:06:07.700000-08:00'), ('2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:40.684000-08:00'), ('2020-02-06T18:56:45.755000-08:00', '2020-02-06T19:16:46.406000-08:00')]\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = ['2020-02-06T13:06:11.492273-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:29:48.443000-08:00'), ('2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:31:09.327000-08:00'), ('2020-02-06T08:32:19-08:00', '2020-02-06T08:33:28.510000-08:00'), ('2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:32.847000-08:00'), ('2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:11:53.823000-08:00'), ('2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:10:56.852000-08:00'), ('2020-02-06T10:14:29.192000-08:00', '2020-02-06T10:16:39.807000-08:00'), ('2020-02-06T13:06:11.492273-08:00', '2020-02-06T13:21:49.811000-08:00'), ('2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:23:57.824000-08:00'), ('2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:39.115000-08:00'), ('2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:09.506000-08:00'), ('2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:35:52.757000-08:00'), ('2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:12.965000-08:00'), ('2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:28.130000-08:00'), ('2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:33.983000-08:00'), ('2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:10.023000-08:00'), ('2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:43.645000-08:00'), ('2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:48:24.953000-08:00'), ('2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:16.908000-08:00'), ('2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:36:34.457000-08:00'), ('2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:49:46.900000-08:00'), ('2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:56:24.499000-08:00'), ('2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:20.622000-08:00'), ('2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:06:07.700000-08:00'), ('2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:40.684000-08:00'), ('2020-02-06T18:56:45.755000-08:00', '2020-02-06T19:16:46.406000-08:00')]\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = ['2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:45.755000-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-android-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_0 HAHFDC v/s HAMFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_1 HAHFDC v/s HAMFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_2 HAHFDC v/s HAMFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_3 HAHFDC v/s MAHFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_4 HAHFDC v/s MAHFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_5 HAHFDC v/s MAHFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_6 MAMFDC v/s HAMFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_7 MAMFDC v/s HAMFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_8 MAMFDC v/s HAMFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_9 MAMFDC v/s MAHFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_10 MAMFDC v/s MAHFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_11 MAMFDC v/s MAHFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = []\n", + "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", + "ios dict_keys(['ucb-sdb-ios-1', 'ucb-sdb-ios-2', 'ucb-sdb-ios-3', 'ucb-sdb-ios-4'])\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_0 HAHFDC v/s MAHFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_1 HAHFDC v/s MAHFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_2 HAHFDC v/s MAHFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_3 HAHFDC v/s HAMFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_4 HAHFDC v/s HAMFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_5 HAHFDC v/s HAMFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_6 MAMFDC v/s MAHFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_7 MAMFDC v/s MAHFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_8 MAMFDC v/s MAHFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_9 MAMFDC v/s HAMFDC accuracy_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_10 MAMFDC v/s HAMFDC accuracy_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:ACCURACY_CONTROL_11 MAMFDC v/s HAMFDC accuracy_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = []\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 3\n", + "Before filtering, trips = [('2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:14:29.985872-07:00'), ('2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:57.992460-07:00'), ('2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:37:09.228022-07:00'), ('2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:10:43.989785-07:00'), ('2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:25.991375-07:00'), ('2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:16.987673-07:00'), ('2019-07-24T10:25:25.987373-07:00', '2019-07-24T10:35:08.192261-07:00'), ('2019-07-24T14:13:46.520613-07:00', '2019-07-24T14:27:08.994135-07:00'), ('2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:00.991895-07:00'), ('2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:08.986795-07:00'), ('2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:13.992662-07:00'), ('2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:18.992464-07:00'), ('2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:22.998500-07:00'), ('2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:32.998418-07:00'), ('2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:03.989389-07:00'), ('2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:20:30.993219-07:00'), ('2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:53.996238-07:00'), ('2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:46.998221-07:00'), ('2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:48.998363-07:00'), ('2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:01.998787-07:00'), ('2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:21.998584-07:00'), ('2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:37.985919-07:00'), ('2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:42.995913-07:00'), ('2019-07-24T19:40:46.995787-07:00', '2019-07-24T19:59:34.986324-07:00')]\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = ['2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:25.987373-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:14:29.985872-07:00'), ('2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:57.992460-07:00'), ('2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:37:09.228022-07:00'), ('2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:10:43.989785-07:00'), ('2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:25.991375-07:00'), ('2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:16.987673-07:00'), ('2019-07-24T10:25:25.987373-07:00', '2019-07-24T10:35:08.192261-07:00'), ('2019-07-24T14:13:46.520613-07:00', '2019-07-24T14:27:08.994135-07:00'), ('2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:00.991895-07:00'), ('2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:08.986795-07:00'), ('2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:13.992662-07:00'), ('2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:18.992464-07:00'), ('2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:22.998500-07:00'), ('2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:32.998418-07:00'), ('2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:03.989389-07:00'), ('2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:20:30.993219-07:00'), ('2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:53.996238-07:00'), ('2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:46.998221-07:00'), ('2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:48.998363-07:00'), ('2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:01.998787-07:00'), ('2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:21.998584-07:00'), ('2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:37.985919-07:00'), ('2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:42.995913-07:00'), ('2019-07-24T19:40:46.995787-07:00', '2019-07-24T19:59:34.986324-07:00')]\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = ['2019-07-24T14:13:46.520613-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:14:29.985872-07:00'), ('2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:57.992460-07:00'), ('2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:37:09.228022-07:00'), ('2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:10:43.989785-07:00'), ('2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:25.991375-07:00'), ('2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:16.987673-07:00'), ('2019-07-24T10:25:25.987373-07:00', '2019-07-24T10:35:08.192261-07:00'), ('2019-07-24T14:13:46.520613-07:00', '2019-07-24T14:27:08.994135-07:00'), ('2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:00.991895-07:00'), ('2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:08.986795-07:00'), ('2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:13.992662-07:00'), ('2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:18.992464-07:00'), ('2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:22.998500-07:00'), ('2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:32.998418-07:00'), ('2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:03.989389-07:00'), ('2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:20:30.993219-07:00'), ('2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:53.996238-07:00'), ('2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:46.998221-07:00'), ('2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:48.998363-07:00'), ('2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:01.998787-07:00'), ('2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:21.998584-07:00'), ('2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:37.985919-07:00'), ('2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:42.995913-07:00'), ('2019-07-24T19:40:46.995787-07:00', '2019-07-24T19:59:34.986324-07:00')]\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = ['2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:46.995787-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 3\n", + "Before filtering, trips = [('2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:17.989881-07:00'), ('2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:54.987120-07:00'), ('2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:16.987692-07:00'), ('2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:44.987044-07:00'), ('2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:07:45.987011-07:00'), ('2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:08:35.985266-07:00'), ('2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:19:40.995220-07:00'), ('2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:13:37.228350-07:00'), ('2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:37.998554-07:00'), ('2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:38.991190-07:00'), ('2019-07-25T10:26:40.991124-07:00', '2019-07-25T10:29:22.998803-07:00'), ('2019-07-25T14:12:13.247218-07:00', '2019-07-25T14:23:37.001393-07:00'), ('2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:29.995108-07:00'), ('2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:46.995361-07:00'), ('2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:51.994124-07:00'), ('2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:21.992900-07:00'), ('2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:29.992571-07:00'), ('2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:14.988224-07:00'), ('2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:21.987933-07:00'), ('2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:04.996904-07:00'), ('2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:09.996823-07:00'), ('2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:35.987966-07:00'), ('2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:00:38.993944-07:00'), ('2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:06.990192-07:00'), ('2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:22.998205-07:00'), ('2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:31.997085-07:00'), ('2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:30.994596-07:00'), ('2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:29.995121-07:00'), ('2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:43.988375-07:00'), ('2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:13.998284-07:00'), ('2019-07-25T19:41:14.998255-07:00', '2019-07-25T19:52:24.990528-07:00')]\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = ['2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:40.991124-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:17.989881-07:00'), ('2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:54.987120-07:00'), ('2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:16.987692-07:00'), ('2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:44.987044-07:00'), ('2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:07:45.987011-07:00'), ('2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:08:35.985266-07:00'), ('2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:19:40.995220-07:00'), ('2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:13:37.228350-07:00'), ('2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:37.998554-07:00'), ('2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:38.991190-07:00'), ('2019-07-25T10:26:40.991124-07:00', '2019-07-25T10:29:22.998803-07:00'), ('2019-07-25T14:12:13.247218-07:00', '2019-07-25T14:23:37.001393-07:00'), ('2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:29.995108-07:00'), ('2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:46.995361-07:00'), ('2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:51.994124-07:00'), ('2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:21.992900-07:00'), ('2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:29.992571-07:00'), ('2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:14.988224-07:00'), ('2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:21.987933-07:00'), ('2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:04.996904-07:00'), ('2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:09.996823-07:00'), ('2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:35.987966-07:00'), ('2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:00:38.993944-07:00'), ('2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:06.990192-07:00'), ('2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:22.998205-07:00'), ('2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:31.997085-07:00'), ('2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:30.994596-07:00'), ('2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:29.995121-07:00'), ('2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:43.988375-07:00'), ('2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:13.998284-07:00'), ('2019-07-25T19:41:14.998255-07:00', '2019-07-25T19:52:24.990528-07:00')]\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = ['2019-07-25T14:12:13.247218-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:17.989881-07:00'), ('2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:54.987120-07:00'), ('2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:16.987692-07:00'), ('2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:44.987044-07:00'), ('2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:07:45.987011-07:00'), ('2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:08:35.985266-07:00'), ('2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:19:40.995220-07:00'), ('2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:13:37.228350-07:00'), ('2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:37.998554-07:00'), ('2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:38.991190-07:00'), ('2019-07-25T10:26:40.991124-07:00', '2019-07-25T10:29:22.998803-07:00'), ('2019-07-25T14:12:13.247218-07:00', '2019-07-25T14:23:37.001393-07:00'), ('2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:29.995108-07:00'), ('2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:46.995361-07:00'), ('2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:51.994124-07:00'), ('2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:21.992900-07:00'), ('2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:29.992571-07:00'), ('2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:14.988224-07:00'), ('2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:21.987933-07:00'), ('2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:04.996904-07:00'), ('2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:09.996823-07:00'), ('2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:35.987966-07:00'), ('2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:00:38.993944-07:00'), ('2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:06.990192-07:00'), ('2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:22.998205-07:00'), ('2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:31.997085-07:00'), ('2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:30.994596-07:00'), ('2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:29.995121-07:00'), ('2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:43.988375-07:00'), ('2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:13.998284-07:00'), ('2019-07-25T19:41:14.998255-07:00', '2019-07-25T19:52:24.990528-07:00')]\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = ['2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:14.998255-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 3\n", + "Before filtering, trips = [('2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:14.989143-07:00'), ('2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:06.995584-07:00'), ('2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:42.998029-07:00'), ('2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:55.997011-07:00'), ('2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:07.991729-07:00'), ('2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:23.990077-07:00'), ('2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:10:56.112940-07:00'), ('2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:28.998600-07:00'), ('2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:43.985705-07:00'), ('2019-07-26T10:26:46.985601-07:00', '2019-07-26T10:29:26.995188-07:00'), ('2019-07-26T14:17:28.484846-07:00', '2019-07-26T14:28:57.998624-07:00'), ('2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:39:12.984392-07:00'), ('2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:30.998380-07:00'), ('2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:43.998158-07:00'), ('2019-07-26T16:49:44.998131-07:00', '2019-07-26T16:59:48.993276-07:00'), ('2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:03:20.986680-07:00'), ('2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:42.990365-07:00'), ('2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:28.987602-07:00'), ('2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:34.990709-07:00'), ('2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:22.992207-07:00'), ('2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:07.993219-07:00'), ('2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:17.993264-07:00'), ('2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:29.991621-07:00'), ('2019-07-26T19:41:30.991588-07:00', '2019-07-26T19:59:19.997824-07:00')]\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = ['2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:46.985601-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:14.989143-07:00'), ('2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:06.995584-07:00'), ('2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:42.998029-07:00'), ('2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:55.997011-07:00'), ('2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:07.991729-07:00'), ('2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:23.990077-07:00'), ('2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:10:56.112940-07:00'), ('2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:28.998600-07:00'), ('2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:43.985705-07:00'), ('2019-07-26T10:26:46.985601-07:00', '2019-07-26T10:29:26.995188-07:00'), ('2019-07-26T14:17:28.484846-07:00', '2019-07-26T14:28:57.998624-07:00'), ('2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:39:12.984392-07:00'), ('2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:30.998380-07:00'), ('2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:43.998158-07:00'), ('2019-07-26T16:49:44.998131-07:00', '2019-07-26T16:59:48.993276-07:00'), ('2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:03:20.986680-07:00'), ('2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:42.990365-07:00'), ('2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:28.987602-07:00'), ('2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:34.990709-07:00'), ('2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:22.992207-07:00'), ('2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:07.993219-07:00'), ('2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:17.993264-07:00'), ('2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:29.991621-07:00'), ('2019-07-26T19:41:30.991588-07:00', '2019-07-26T19:59:19.997824-07:00')]\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = ['2019-07-26T14:17:28.484846-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:14.989143-07:00'), ('2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:06.995584-07:00'), ('2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:42.998029-07:00'), ('2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:55.997011-07:00'), ('2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:07.991729-07:00'), ('2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:23.990077-07:00'), ('2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:10:56.112940-07:00'), ('2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:28.998600-07:00'), ('2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:43.985705-07:00'), ('2019-07-26T10:26:46.985601-07:00', '2019-07-26T10:29:26.995188-07:00'), ('2019-07-26T14:17:28.484846-07:00', '2019-07-26T14:28:57.998624-07:00'), ('2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:39:12.984392-07:00'), ('2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:30.998380-07:00'), ('2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:43.998158-07:00'), ('2019-07-26T16:49:44.998131-07:00', '2019-07-26T16:59:48.993276-07:00'), ('2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:03:20.986680-07:00'), ('2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:42.990365-07:00'), ('2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:28.987602-07:00'), ('2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:34.990709-07:00'), ('2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:22.992207-07:00'), ('2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:07.993219-07:00'), ('2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:17.993264-07:00'), ('2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:29.991621-07:00'), ('2019-07-26T19:41:30.991588-07:00', '2019-07-26T19:59:19.997824-07:00')]\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = ['2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:44.998131-07:00', '2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:30.991588-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 3\n", + "Before filtering, trips = [('2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:04.989569-07:00'), ('2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:06:48.988289-07:00'), ('2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:19:51.644861-07:00'), ('2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:10:25.512905-07:00'), ('2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:58.998805-07:00'), ('2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:33.996101-07:00'), ('2019-09-10T10:35:34.996066-07:00', '2019-09-10T10:38:55.077118-07:00'), ('2019-09-10T13:40:35.920080-07:00', '2019-09-10T13:51:17.997642-07:00'), ('2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:03.987696-07:00'), ('2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:23.986862-07:00'), ('2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:26.997538-07:00'), ('2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:31.997356-07:00'), ('2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:31.995397-07:00'), ('2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:38.995117-07:00'), ('2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:11.986578-07:00'), ('2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:18.986295-07:00'), ('2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:11.991803-07:00'), ('2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:43:27.983883-07:00'), ('2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:37.987616-07:00'), ('2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:20.985929-07:00'), ('2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:32.985458-07:00'), ('2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:45.984949-07:00'), ('2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:20.985497-07:00'), ('2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:55.990572-07:00'), ('2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:17.998957-07:00'), ('2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:21:27.804565-07:00'), ('2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:29.990256-07:00'), ('2019-09-10T19:04:30.990230-07:00', '2019-09-10T19:16:28.650569-07:00')]\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = ['2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:34.996066-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:04.989569-07:00'), ('2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:06:48.988289-07:00'), ('2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:19:51.644861-07:00'), ('2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:10:25.512905-07:00'), ('2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:58.998805-07:00'), ('2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:33.996101-07:00'), ('2019-09-10T10:35:34.996066-07:00', '2019-09-10T10:38:55.077118-07:00'), ('2019-09-10T13:40:35.920080-07:00', '2019-09-10T13:51:17.997642-07:00'), ('2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:03.987696-07:00'), ('2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:23.986862-07:00'), ('2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:26.997538-07:00'), ('2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:31.997356-07:00'), ('2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:31.995397-07:00'), ('2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:38.995117-07:00'), ('2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:11.986578-07:00'), ('2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:18.986295-07:00'), ('2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:11.991803-07:00'), ('2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:43:27.983883-07:00'), ('2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:37.987616-07:00'), ('2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:20.985929-07:00'), ('2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:32.985458-07:00'), ('2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:45.984949-07:00'), ('2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:20.985497-07:00'), ('2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:55.990572-07:00'), ('2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:17.998957-07:00'), ('2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:21:27.804565-07:00'), ('2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:29.990256-07:00'), ('2019-09-10T19:04:30.990230-07:00', '2019-09-10T19:16:28.650569-07:00')]\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = ['2019-09-10T13:40:35.920080-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:04.989569-07:00'), ('2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:06:48.988289-07:00'), ('2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:19:51.644861-07:00'), ('2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:10:25.512905-07:00'), ('2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:58.998805-07:00'), ('2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:33.996101-07:00'), ('2019-09-10T10:35:34.996066-07:00', '2019-09-10T10:38:55.077118-07:00'), ('2019-09-10T13:40:35.920080-07:00', '2019-09-10T13:51:17.997642-07:00'), ('2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:03.987696-07:00'), ('2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:23.986862-07:00'), ('2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:26.997538-07:00'), ('2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:31.997356-07:00'), ('2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:31.995397-07:00'), ('2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:38.995117-07:00'), ('2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:11.986578-07:00'), ('2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:18.986295-07:00'), ('2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:11.991803-07:00'), ('2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:43:27.983883-07:00'), ('2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:37.987616-07:00'), ('2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:20.985929-07:00'), ('2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:32.985458-07:00'), ('2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:45.984949-07:00'), ('2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:20.985497-07:00'), ('2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:55.990572-07:00'), ('2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:17.998957-07:00'), ('2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:21:27.804565-07:00'), ('2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:29.990256-07:00'), ('2019-09-10T19:04:30.990230-07:00', '2019-09-10T19:16:28.650569-07:00')]\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = ['2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:30.990230-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 3\n", + "Before filtering, trips = [('2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:52.294066-07:00'), ('2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:52.988634-07:00'), ('2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:12.987914-07:00'), ('2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:26.258317-07:00'), ('2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:19:29.430865-07:00'), ('2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:10:36.991290-07:00'), ('2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:47.537008-07:00'), ('2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:13:55.123096-07:00'), ('2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:49.988092-07:00'), ('2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:26.995452-07:00'), ('2019-09-11T10:35:27.995418-07:00', '2019-09-11T10:38:02.990150-07:00'), ('2019-09-11T13:47:36.966267-07:00', '2019-09-11T13:56:26.988645-07:00'), ('2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:44.985888-07:00'), ('2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:54.996579-07:00'), ('2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:46.997128-07:00'), ('2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:49.994833-07:00'), ('2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:24.985809-07:00'), ('2019-09-11T19:41:25.985785-07:00', '2019-09-11T19:52:09.994150-07:00')]\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = ['2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:27.995418-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:52.294066-07:00'), ('2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:52.988634-07:00'), ('2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:12.987914-07:00'), ('2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:26.258317-07:00'), ('2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:19:29.430865-07:00'), ('2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:10:36.991290-07:00'), ('2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:47.537008-07:00'), ('2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:13:55.123096-07:00'), ('2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:49.988092-07:00'), ('2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:26.995452-07:00'), ('2019-09-11T10:35:27.995418-07:00', '2019-09-11T10:38:02.990150-07:00'), ('2019-09-11T13:47:36.966267-07:00', '2019-09-11T13:56:26.988645-07:00'), ('2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:44.985888-07:00'), ('2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:54.996579-07:00'), ('2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:46.997128-07:00'), ('2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:49.994833-07:00'), ('2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:24.985809-07:00'), ('2019-09-11T19:41:25.985785-07:00', '2019-09-11T19:52:09.994150-07:00')]\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = ['2019-09-11T13:47:36.966267-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:52.294066-07:00'), ('2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:52.988634-07:00'), ('2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:12.987914-07:00'), ('2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:26.258317-07:00'), ('2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:19:29.430865-07:00'), ('2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:10:36.991290-07:00'), ('2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:47.537008-07:00'), ('2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:13:55.123096-07:00'), ('2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:49.988092-07:00'), ('2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:26.995452-07:00'), ('2019-09-11T10:35:27.995418-07:00', '2019-09-11T10:38:02.990150-07:00'), ('2019-09-11T13:47:36.966267-07:00', '2019-09-11T13:56:26.988645-07:00'), ('2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:44.985888-07:00'), ('2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:54.996579-07:00'), ('2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:46.997128-07:00'), ('2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:49.994833-07:00'), ('2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:24.985809-07:00'), ('2019-09-11T19:41:25.985785-07:00', '2019-09-11T19:52:09.994150-07:00')]\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = ['2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:25.985785-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 3\n", + "Before filtering, trips = [('2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:47.988508-07:00'), ('2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:54.988226-07:00'), ('2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:37.994272-07:00'), ('2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:13:42.990275-07:00'), ('2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:17.989026-07:00'), ('2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:22.988847-07:00'), ('2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:09.993130-07:00'), ('2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:10:33.994700-07:00'), ('2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:32.986399-07:00'), ('2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:14.993892-07:00'), ('2019-09-17T10:38:15.993857-07:00', '2019-09-17T10:41:31.987041-07:00'), ('2019-09-17T13:47:15.251863-07:00', '2019-09-17T13:58:44.994771-07:00'), ('2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:05.998218-07:00'), ('2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:28.994621-07:00'), ('2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:40.994124-07:00'), ('2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:00.990812-07:00'), ('2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:25.989777-07:00'), ('2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:00.998392-07:00'), ('2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:05.998422-07:00'), ('2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:21.986533-07:00'), ('2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:01.996997-07:00'), ('2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:37.995494-07:00'), ('2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:05.995717-07:00'), ('2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:08.996167-07:00'), ('2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:48.996109-07:00'), ('2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:53.995967-07:00'), ('2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:45.997537-07:00'), ('2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:58.998622-07:00'), ('2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:13.998873-07:00'), ('2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:44.998498-07:00'), ('2019-09-17T18:57:48.998388-07:00', '2019-09-17T19:15:11.436184-07:00')]\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = ['2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:15.993857-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:47.988508-07:00'), ('2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:54.988226-07:00'), ('2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:37.994272-07:00'), ('2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:13:42.990275-07:00'), ('2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:17.989026-07:00'), ('2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:22.988847-07:00'), ('2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:09.993130-07:00'), ('2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:10:33.994700-07:00'), ('2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:32.986399-07:00'), ('2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:14.993892-07:00'), ('2019-09-17T10:38:15.993857-07:00', '2019-09-17T10:41:31.987041-07:00'), ('2019-09-17T13:47:15.251863-07:00', '2019-09-17T13:58:44.994771-07:00'), ('2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:05.998218-07:00'), ('2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:28.994621-07:00'), ('2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:40.994124-07:00'), ('2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:00.990812-07:00'), ('2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:25.989777-07:00'), ('2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:00.998392-07:00'), ('2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:05.998422-07:00'), ('2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:21.986533-07:00'), ('2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:01.996997-07:00'), ('2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:37.995494-07:00'), ('2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:05.995717-07:00'), ('2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:08.996167-07:00'), ('2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:48.996109-07:00'), ('2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:53.995967-07:00'), ('2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:45.997537-07:00'), ('2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:58.998622-07:00'), ('2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:13.998873-07:00'), ('2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:44.998498-07:00'), ('2019-09-17T18:57:48.998388-07:00', '2019-09-17T19:15:11.436184-07:00')]\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = ['2019-09-17T13:47:15.251863-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:47.988508-07:00'), ('2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:54.988226-07:00'), ('2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:37.994272-07:00'), ('2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:13:42.990275-07:00'), ('2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:17.989026-07:00'), ('2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:22.988847-07:00'), ('2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:09.993130-07:00'), ('2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:10:33.994700-07:00'), ('2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:32.986399-07:00'), ('2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:14.993892-07:00'), ('2019-09-17T10:38:15.993857-07:00', '2019-09-17T10:41:31.987041-07:00'), ('2019-09-17T13:47:15.251863-07:00', '2019-09-17T13:58:44.994771-07:00'), ('2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:05.998218-07:00'), ('2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:28.994621-07:00'), ('2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:40.994124-07:00'), ('2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:00.990812-07:00'), ('2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:25.989777-07:00'), ('2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:00.998392-07:00'), ('2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:05.998422-07:00'), ('2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:21.986533-07:00'), ('2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:01.996997-07:00'), ('2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:37.995494-07:00'), ('2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:05.995717-07:00'), ('2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:08.996167-07:00'), ('2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:48.996109-07:00'), ('2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:53.995967-07:00'), ('2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:45.997537-07:00'), ('2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:58.998622-07:00'), ('2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:13.998873-07:00'), ('2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:44.998498-07:00'), ('2019-09-17T18:57:48.998388-07:00', '2019-09-17T19:15:11.436184-07:00')]\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = ['2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:48.998388-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_0 MAMFDC v/s MAHFDC MAMFDC_0 3\n", + "Before filtering, trips = [('2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:22:18.514685-08:00'), ('2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:31:55.506960-08:00'), ('2019-11-19T08:32:32.350269-08:00', '2019-11-19T08:57:43.612899-08:00'), ('2019-11-19T18:36:25.759361-08:00', '2019-11-19T18:56:57.802682-08:00'), ('2019-11-19T19:00:06.562795-08:00', '2019-11-19T19:15:02.292352-08:00')]\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = ['2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:32:32.350269-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:22:18.514685-08:00'), ('2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:31:55.506960-08:00'), ('2019-11-19T08:32:32.350269-08:00', '2019-11-19T08:57:43.612899-08:00'), ('2019-11-19T18:36:25.759361-08:00', '2019-11-19T18:56:57.802682-08:00'), ('2019-11-19T19:00:06.562795-08:00', '2019-11-19T19:15:02.292352-08:00')]\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = [('2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:22:18.514685-08:00'), ('2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:31:55.506960-08:00'), ('2019-11-19T08:32:32.350269-08:00', '2019-11-19T08:57:43.612899-08:00'), ('2019-11-19T18:36:25.759361-08:00', '2019-11-19T18:56:57.802682-08:00'), ('2019-11-19T19:00:06.562795-08:00', '2019-11-19T19:15:02.292352-08:00')]\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = ['2019-11-19T18:36:25.759361-08:00', '2019-11-19T19:00:06.562795-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_1 MAMFDC v/s MAHFDC MAMFDC_1 3\n", + "Before filtering, trips = [('2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:16.465450-08:00'), ('2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:09.850701-08:00'), ('2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:22.279812-08:00'), ('2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:19:22.344031-08:00'), ('2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:10:43.312654-08:00'), ('2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:22.016576-08:00'), ('2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:00.492542-08:00'), ('2019-11-20T10:30:13.433821-08:00', '2019-11-20T10:31:05.215166-08:00'), ('2019-11-20T16:21:48.584273-08:00', '2019-11-20T16:47:55.026384-08:00'), ('2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:29:25.179988-08:00'), ('2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:17:26.588916-08:00'), ('2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:14.170458-08:00'), ('2019-11-20T19:03:34.685705-08:00', '2019-11-20T19:21:36.441834-08:00')]\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = ['2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:13.433821-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:16.465450-08:00'), ('2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:09.850701-08:00'), ('2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:22.279812-08:00'), ('2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:19:22.344031-08:00'), ('2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:10:43.312654-08:00'), ('2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:22.016576-08:00'), ('2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:00.492542-08:00'), ('2019-11-20T10:30:13.433821-08:00', '2019-11-20T10:31:05.215166-08:00'), ('2019-11-20T16:21:48.584273-08:00', '2019-11-20T16:47:55.026384-08:00'), ('2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:29:25.179988-08:00'), ('2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:17:26.588916-08:00'), ('2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:14.170458-08:00'), ('2019-11-20T19:03:34.685705-08:00', '2019-11-20T19:21:36.441834-08:00')]\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = [('2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:16.465450-08:00'), ('2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:09.850701-08:00'), ('2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:22.279812-08:00'), ('2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:19:22.344031-08:00'), ('2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:10:43.312654-08:00'), ('2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:22.016576-08:00'), ('2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:00.492542-08:00'), ('2019-11-20T10:30:13.433821-08:00', '2019-11-20T10:31:05.215166-08:00'), ('2019-11-20T16:21:48.584273-08:00', '2019-11-20T16:47:55.026384-08:00'), ('2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:29:25.179988-08:00'), ('2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:17:26.588916-08:00'), ('2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:14.170458-08:00'), ('2019-11-20T19:03:34.685705-08:00', '2019-11-20T19:21:36.441834-08:00')]\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = ['2019-11-20T16:21:48.584273-08:00', '2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:34.685705-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAMFDC_2 MAMFDC v/s MAHFDC MAMFDC_2 3\n", + "Before filtering, trips = [('2019-12-03T08:34:31.514499-08:00', '2019-12-03T08:59:11.351828-08:00'), ('2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:08.523274-08:00'), ('2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:02:50.132149-08:00'), ('2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:06.060127-08:00'), ('2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:39.202682-08:00'), ('2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:12:58.559972-08:00'), ('2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:17.285688-08:00'), ('2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:15:49.020898-08:00'), ('2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:16:14.887171-08:00'), ('2019-12-03T19:17:26.007153-08:00', '2019-12-03T19:34:21.386348-08:00')]\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = ['2019-12-03T08:34:31.514499-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:34:31.514499-08:00', '2019-12-03T08:59:11.351828-08:00'), ('2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:08.523274-08:00'), ('2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:02:50.132149-08:00'), ('2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:06.060127-08:00'), ('2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:39.202682-08:00'), ('2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:12:58.559972-08:00'), ('2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:17.285688-08:00'), ('2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:15:49.020898-08:00'), ('2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:16:14.887171-08:00'), ('2019-12-03T19:17:26.007153-08:00', '2019-12-03T19:34:21.386348-08:00')]\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = [('2019-12-03T08:34:31.514499-08:00', '2019-12-03T08:59:11.351828-08:00'), ('2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:08.523274-08:00'), ('2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:02:50.132149-08:00'), ('2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:06.060127-08:00'), ('2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:39.202682-08:00'), ('2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:12:58.559972-08:00'), ('2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:17.285688-08:00'), ('2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:15:49.020898-08:00'), ('2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:16:14.887171-08:00'), ('2019-12-03T19:17:26.007153-08:00', '2019-12-03T19:34:21.386348-08:00')]\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = ['2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:17:26.007153-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_0 MAMFDC v/s HAMFDC MAMFDC_0 3\n", + "Before filtering, trips = [('2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:06:37.944693-08:00'), ('2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:20:59.696820-08:00'), ('2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:10:31.162319-08:00'), ('2019-12-09T10:14:45.676057-08:00', '2019-12-09T10:34:49.015549-08:00'), ('2019-12-09T13:59:47.353818-08:00', '2019-12-09T14:11:51.806524-08:00'), ('2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:45:50.005862-08:00'), ('2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:23:55.047962-08:00'), ('2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:13:46.651298-08:00'), ('2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:03:53.735313-08:00'), ('2019-12-09T19:04:27.016722-08:00', '2019-12-09T19:22:41.288239-08:00')]\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = ['2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:14:45.676057-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:06:37.944693-08:00'), ('2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:20:59.696820-08:00'), ('2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:10:31.162319-08:00'), ('2019-12-09T10:14:45.676057-08:00', '2019-12-09T10:34:49.015549-08:00'), ('2019-12-09T13:59:47.353818-08:00', '2019-12-09T14:11:51.806524-08:00'), ('2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:45:50.005862-08:00'), ('2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:23:55.047962-08:00'), ('2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:13:46.651298-08:00'), ('2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:03:53.735313-08:00'), ('2019-12-09T19:04:27.016722-08:00', '2019-12-09T19:22:41.288239-08:00')]\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = ['2019-12-09T13:59:47.353818-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:06:37.944693-08:00'), ('2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:20:59.696820-08:00'), ('2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:10:31.162319-08:00'), ('2019-12-09T10:14:45.676057-08:00', '2019-12-09T10:34:49.015549-08:00'), ('2019-12-09T13:59:47.353818-08:00', '2019-12-09T14:11:51.806524-08:00'), ('2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:45:50.005862-08:00'), ('2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:23:55.047962-08:00'), ('2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:13:46.651298-08:00'), ('2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:03:53.735313-08:00'), ('2019-12-09T19:04:27.016722-08:00', '2019-12-09T19:22:41.288239-08:00')]\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = ['2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:04:27.016722-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_1 MAMFDC v/s HAMFDC MAMFDC_1 3\n", + "Before filtering, trips = [('2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:23.631599-08:00'), ('2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:26.364479-08:00'), ('2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:08:02.187124-08:00'), ('2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:19:22.475962-08:00'), ('2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:28:50.194294-08:00'), ('2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:17.242258-08:00'), ('2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:51:54.782130-08:00'), ('2019-12-11T10:53:39.657251-08:00', '2019-12-11T10:54:12.254016-08:00'), ('2019-12-11T14:09:56.022566-08:00', '2019-12-11T14:21:18.750383-08:00'), ('2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:47:28.114405-08:00'), ('2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:05:47.762147-08:00'), ('2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:32:30.836701-08:00'), ('2019-12-11T17:33:32.242876-08:00', '2019-12-11T17:58:10.742261-08:00'), ('2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:37.561033-08:00'), ('2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:38.852364-08:00'), ('2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:36:54.180465-08:00'), ('2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:37:46.237784-08:00'), ('2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:07.182655-08:00'), ('2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:18.261479-08:00'), ('2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:17.982331-08:00'), ('2019-12-11T19:02:37.295973-08:00', '2019-12-11T19:20:39.726243-08:00')]\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = ['2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:53:39.657251-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:23.631599-08:00'), ('2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:26.364479-08:00'), ('2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:08:02.187124-08:00'), ('2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:19:22.475962-08:00'), ('2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:28:50.194294-08:00'), ('2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:17.242258-08:00'), ('2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:51:54.782130-08:00'), ('2019-12-11T10:53:39.657251-08:00', '2019-12-11T10:54:12.254016-08:00'), ('2019-12-11T14:09:56.022566-08:00', '2019-12-11T14:21:18.750383-08:00'), ('2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:47:28.114405-08:00'), ('2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:05:47.762147-08:00'), ('2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:32:30.836701-08:00'), ('2019-12-11T17:33:32.242876-08:00', '2019-12-11T17:58:10.742261-08:00'), ('2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:37.561033-08:00'), ('2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:38.852364-08:00'), ('2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:36:54.180465-08:00'), ('2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:37:46.237784-08:00'), ('2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:07.182655-08:00'), ('2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:18.261479-08:00'), ('2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:17.982331-08:00'), ('2019-12-11T19:02:37.295973-08:00', '2019-12-11T19:20:39.726243-08:00')]\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = ['2019-12-11T14:09:56.022566-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:23.631599-08:00'), ('2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:26.364479-08:00'), ('2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:08:02.187124-08:00'), ('2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:19:22.475962-08:00'), ('2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:28:50.194294-08:00'), ('2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:17.242258-08:00'), ('2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:51:54.782130-08:00'), ('2019-12-11T10:53:39.657251-08:00', '2019-12-11T10:54:12.254016-08:00'), ('2019-12-11T14:09:56.022566-08:00', '2019-12-11T14:21:18.750383-08:00'), ('2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:47:28.114405-08:00'), ('2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:05:47.762147-08:00'), ('2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:32:30.836701-08:00'), ('2019-12-11T17:33:32.242876-08:00', '2019-12-11T17:58:10.742261-08:00'), ('2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:37.561033-08:00'), ('2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:38.852364-08:00'), ('2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:36:54.180465-08:00'), ('2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:37:46.237784-08:00'), ('2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:07.182655-08:00'), ('2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:18.261479-08:00'), ('2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:17.982331-08:00'), ('2019-12-11T19:02:37.295973-08:00', '2019-12-11T19:20:39.726243-08:00')]\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = ['2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:33:32.242876-08:00', '2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:37.295973-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:MAMFDC_2 MAMFDC v/s HAMFDC MAMFDC_2 3\n", + "Before filtering, trips = [('2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:25:02.736740-08:00'), ('2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:34:58.642714-08:00'), ('2020-02-06T08:35:36.623463-08:00', '2020-02-06T08:57:36.177141-08:00'), ('2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:56:51.030249-08:00'), ('2020-02-06T18:57:10.358475-08:00', '2020-02-06T19:19:47.315187-08:00')]\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = ['2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:35:36.623463-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:25:02.736740-08:00'), ('2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:34:58.642714-08:00'), ('2020-02-06T08:35:36.623463-08:00', '2020-02-06T08:57:36.177141-08:00'), ('2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:56:51.030249-08:00'), ('2020-02-06T18:57:10.358475-08:00', '2020-02-06T19:19:47.315187-08:00')]\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = [('2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:25:02.736740-08:00'), ('2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:34:58.642714-08:00'), ('2020-02-06T08:35:36.623463-08:00', '2020-02-06T08:57:36.177141-08:00'), ('2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:56:51.030249-08:00'), ('2020-02-06T18:57:10.358475-08:00', '2020-02-06T19:19:47.315187-08:00')]\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = ['2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:57:10.358475-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 3\n", + "Before filtering, trips = [('2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:31:57.580078-07:00'), ('2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:08:38.203095-07:00'), ('2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:18:30.399375-07:00'), ('2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:13:48.365399-07:00'), ('2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:20:58.020761-07:00'), ('2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:22.112203-07:00'), ('2019-07-24T10:25:28.593253-07:00', '2019-07-24T10:28:36.923432-07:00'), ('2019-07-24T14:14:50.357894-07:00', '2019-07-24T14:28:31.561832-07:00'), ('2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:20:53.411158-07:00'), ('2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:50.559638-07:00'), ('2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:49.171143-07:00'), ('2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:04.015177-07:00'), ('2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:07.621832-07:00'), ('2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:20.487449-07:00'), ('2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:26.033059-07:00'), ('2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:37.517945-07:00'), ('2019-07-24T19:40:43.961104-07:00', '2019-07-24T20:03:02.696398-07:00')]\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = ['2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:28.593253-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:31:57.580078-07:00'), ('2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:08:38.203095-07:00'), ('2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:18:30.399375-07:00'), ('2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:13:48.365399-07:00'), ('2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:20:58.020761-07:00'), ('2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:22.112203-07:00'), ('2019-07-24T10:25:28.593253-07:00', '2019-07-24T10:28:36.923432-07:00'), ('2019-07-24T14:14:50.357894-07:00', '2019-07-24T14:28:31.561832-07:00'), ('2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:20:53.411158-07:00'), ('2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:50.559638-07:00'), ('2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:49.171143-07:00'), ('2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:04.015177-07:00'), ('2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:07.621832-07:00'), ('2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:20.487449-07:00'), ('2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:26.033059-07:00'), ('2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:37.517945-07:00'), ('2019-07-24T19:40:43.961104-07:00', '2019-07-24T20:03:02.696398-07:00')]\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = ['2019-07-24T14:14:50.357894-07:00']\n", + "Before filtering, trips = [('2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:31:57.580078-07:00'), ('2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:08:38.203095-07:00'), ('2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:18:30.399375-07:00'), ('2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:13:48.365399-07:00'), ('2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:20:58.020761-07:00'), ('2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:22.112203-07:00'), ('2019-07-24T10:25:28.593253-07:00', '2019-07-24T10:28:36.923432-07:00'), ('2019-07-24T14:14:50.357894-07:00', '2019-07-24T14:28:31.561832-07:00'), ('2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:20:53.411158-07:00'), ('2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:50.559638-07:00'), ('2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:49.171143-07:00'), ('2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:04.015177-07:00'), ('2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:07.621832-07:00'), ('2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:20.487449-07:00'), ('2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:26.033059-07:00'), ('2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:37.517945-07:00'), ('2019-07-24T19:40:43.961104-07:00', '2019-07-24T20:03:02.696398-07:00')]\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = ['2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:43.961104-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 3\n", + "Before filtering, trips = [('2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:31:12.051866-07:00'), ('2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:07:58.642741-07:00'), ('2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:19:23.151481-07:00'), ('2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:10:34.997833-07:00'), ('2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:19.866236-07:00'), ('2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:14.799433-07:00'), ('2019-07-25T10:26:21.253659-07:00', '2019-07-25T10:29:43.114747-07:00'), ('2019-07-25T14:10:21.006354-07:00', '2019-07-25T14:24:14.246334-07:00'), ('2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:00:55.257620-07:00'), ('2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:36:58.767438-07:00'), ('2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:21.243044-07:00'), ('2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:41.622076-07:00'), ('2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:07.090042-07:00'), ('2019-07-25T19:41:13.521212-07:00', '2019-07-25T19:59:06.124861-07:00')]\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = ['2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:21.253659-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:31:12.051866-07:00'), ('2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:07:58.642741-07:00'), ('2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:19:23.151481-07:00'), ('2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:10:34.997833-07:00'), ('2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:19.866236-07:00'), ('2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:14.799433-07:00'), ('2019-07-25T10:26:21.253659-07:00', '2019-07-25T10:29:43.114747-07:00'), ('2019-07-25T14:10:21.006354-07:00', '2019-07-25T14:24:14.246334-07:00'), ('2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:00:55.257620-07:00'), ('2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:36:58.767438-07:00'), ('2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:21.243044-07:00'), ('2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:41.622076-07:00'), ('2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:07.090042-07:00'), ('2019-07-25T19:41:13.521212-07:00', '2019-07-25T19:59:06.124861-07:00')]\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = ['2019-07-25T14:10:21.006354-07:00']\n", + "Before filtering, trips = [('2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:31:12.051866-07:00'), ('2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:07:58.642741-07:00'), ('2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:19:23.151481-07:00'), ('2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:10:34.997833-07:00'), ('2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:19.866236-07:00'), ('2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:14.799433-07:00'), ('2019-07-25T10:26:21.253659-07:00', '2019-07-25T10:29:43.114747-07:00'), ('2019-07-25T14:10:21.006354-07:00', '2019-07-25T14:24:14.246334-07:00'), ('2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:00:55.257620-07:00'), ('2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:36:58.767438-07:00'), ('2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:21.243044-07:00'), ('2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:41.622076-07:00'), ('2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:07.090042-07:00'), ('2019-07-25T19:41:13.521212-07:00', '2019-07-25T19:59:06.124861-07:00')]\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = ['2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:13.521212-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 3\n", + "Before filtering, trips = [('2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:22:52.964088-07:00'), ('2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:20.898491-07:00'), ('2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:06.468568-07:00'), ('2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:20.000168-07:00'), ('2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:11:37.049495-07:00'), ('2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:19:59.901535-07:00'), ('2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:11:03.621074-07:00'), ('2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:34.247812-07:00'), ('2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:42.000133-07:00'), ('2019-07-26T10:26:48.446216-07:00', '2019-07-26T10:29:48.992491-07:00'), ('2019-07-26T14:17:32.931680-07:00', '2019-07-26T14:30:09.172231-07:00'), ('2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:03:28.030293-07:00'), ('2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:35:00.449989-07:00'), ('2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:41.551743-07:00'), ('2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:12:55.142156-07:00'), ('2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:46.679733-07:00'), ('2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:01.080074-07:00'), ('2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:50.406806-07:00'), ('2019-07-26T19:40:56.849526-07:00', '2019-07-26T19:55:37.720556-07:00'), ('2019-07-26T20:31:55.991693-07:00', '2019-07-26T20:36:35.168455-07:00')]\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = ['2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:48.446216-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:22:52.964088-07:00'), ('2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:20.898491-07:00'), ('2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:06.468568-07:00'), ('2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:20.000168-07:00'), ('2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:11:37.049495-07:00'), ('2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:19:59.901535-07:00'), ('2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:11:03.621074-07:00'), ('2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:34.247812-07:00'), ('2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:42.000133-07:00'), ('2019-07-26T10:26:48.446216-07:00', '2019-07-26T10:29:48.992491-07:00'), ('2019-07-26T14:17:32.931680-07:00', '2019-07-26T14:30:09.172231-07:00'), ('2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:03:28.030293-07:00'), ('2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:35:00.449989-07:00'), ('2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:41.551743-07:00'), ('2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:12:55.142156-07:00'), ('2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:46.679733-07:00'), ('2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:01.080074-07:00'), ('2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:50.406806-07:00'), ('2019-07-26T19:40:56.849526-07:00', '2019-07-26T19:55:37.720556-07:00'), ('2019-07-26T20:31:55.991693-07:00', '2019-07-26T20:36:35.168455-07:00')]\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = ['2019-07-26T14:17:32.931680-07:00']\n", + "Before filtering, trips = [('2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:22:52.964088-07:00'), ('2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:20.898491-07:00'), ('2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:06.468568-07:00'), ('2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:20.000168-07:00'), ('2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:11:37.049495-07:00'), ('2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:19:59.901535-07:00'), ('2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:11:03.621074-07:00'), ('2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:34.247812-07:00'), ('2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:42.000133-07:00'), ('2019-07-26T10:26:48.446216-07:00', '2019-07-26T10:29:48.992491-07:00'), ('2019-07-26T14:17:32.931680-07:00', '2019-07-26T14:30:09.172231-07:00'), ('2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:03:28.030293-07:00'), ('2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:35:00.449989-07:00'), ('2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:41.551743-07:00'), ('2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:12:55.142156-07:00'), ('2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:46.679733-07:00'), ('2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:01.080074-07:00'), ('2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:50.406806-07:00'), ('2019-07-26T19:40:56.849526-07:00', '2019-07-26T19:55:37.720556-07:00'), ('2019-07-26T20:31:55.991693-07:00', '2019-07-26T20:36:35.168455-07:00')]\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = ['2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:56.849526-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 3\n", + "Before filtering, trips = [('2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:28.998484-07:00'), ('2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:23:47.994102-07:00'), ('2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:04.993347-07:00'), ('2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:52.995613-07:00'), ('2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:47.995985-07:00'), ('2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:06:56.995628-07:00'), ('2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:07:16.994833-07:00'), ('2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:08:59.990727-07:00'), ('2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:13:38.214558-07:00'), ('2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:50.993486-07:00'), ('2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:35:06.992333-07:00'), ('2019-09-10T10:37:10.987396-07:00', '2019-09-10T10:37:10.987396-07:00'), ('2019-09-10T13:39:58.465782-07:00', '2019-09-10T14:00:13.442113-07:00'), ('2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:23.987310-07:00'), ('2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:22:17.996807-07:00'), ('2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:26.993856-07:00'), ('2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:39:30.989524-07:00'), ('2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:43:06.998211-07:00'), ('2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:02.982773-07:00'), ('2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:39.988618-07:00'), ('2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:01:07.997481-07:00'), ('2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:04:22.989608-07:00'), ('2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:03:57.987675-07:00'), ('2019-09-10T19:05:21.984291-07:00', '2019-09-10T19:22:08.993003-07:00')]\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = ['2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:37:10.987396-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:28.998484-07:00'), ('2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:23:47.994102-07:00'), ('2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:04.993347-07:00'), ('2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:52.995613-07:00'), ('2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:47.995985-07:00'), ('2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:06:56.995628-07:00'), ('2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:07:16.994833-07:00'), ('2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:08:59.990727-07:00'), ('2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:13:38.214558-07:00'), ('2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:50.993486-07:00'), ('2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:35:06.992333-07:00'), ('2019-09-10T10:37:10.987396-07:00', '2019-09-10T10:37:10.987396-07:00'), ('2019-09-10T13:39:58.465782-07:00', '2019-09-10T14:00:13.442113-07:00'), ('2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:23.987310-07:00'), ('2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:22:17.996807-07:00'), ('2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:26.993856-07:00'), ('2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:39:30.989524-07:00'), ('2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:43:06.998211-07:00'), ('2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:02.982773-07:00'), ('2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:39.988618-07:00'), ('2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:01:07.997481-07:00'), ('2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:04:22.989608-07:00'), ('2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:03:57.987675-07:00'), ('2019-09-10T19:05:21.984291-07:00', '2019-09-10T19:22:08.993003-07:00')]\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = ['2019-09-10T13:39:58.465782-07:00']\n", + "Before filtering, trips = [('2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:28.998484-07:00'), ('2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:23:47.994102-07:00'), ('2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:04.993347-07:00'), ('2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:52.995613-07:00'), ('2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:47.995985-07:00'), ('2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:06:56.995628-07:00'), ('2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:07:16.994833-07:00'), ('2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:08:59.990727-07:00'), ('2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:13:38.214558-07:00'), ('2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:50.993486-07:00'), ('2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:35:06.992333-07:00'), ('2019-09-10T10:37:10.987396-07:00', '2019-09-10T10:37:10.987396-07:00'), ('2019-09-10T13:39:58.465782-07:00', '2019-09-10T14:00:13.442113-07:00'), ('2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:23.987310-07:00'), ('2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:22:17.996807-07:00'), ('2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:26.993856-07:00'), ('2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:39:30.989524-07:00'), ('2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:43:06.998211-07:00'), ('2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:02.982773-07:00'), ('2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:39.988618-07:00'), ('2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:01:07.997481-07:00'), ('2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:04:22.989608-07:00'), ('2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:03:57.987675-07:00'), ('2019-09-10T19:05:21.984291-07:00', '2019-09-10T19:22:08.993003-07:00')]\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = ['2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:05:21.984291-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 3\n", + "Before filtering, trips = [('2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:40.201473-07:00'), ('2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:04.993430-07:00'), ('2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:13.996239-07:00'), ('2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:07:02.997920-07:00'), ('2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:13:49.008844-07:00'), ('2019-09-11T10:14:31.986705-07:00', '2019-09-11T10:18:27.993159-07:00'), ('2019-09-11T13:38:48.905707-07:00', '2019-09-11T14:04:38.027181-07:00'), ('2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:25.987323-07:00'), ('2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:04.982910-07:00'), ('2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:16.982375-07:00'), ('2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:52.980768-07:00'), ('2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:02.980323-07:00'), ('2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:46.998078-07:00'), ('2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:51.997941-07:00'), ('2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:49:35.992012-07:00'), ('2019-09-11T16:52:35.984353-07:00', '2019-09-11T16:53:31.995191-07:00'), ('2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:55:08.998033-07:00'), ('2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:07.993171-07:00'), ('2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:15:22.989788-07:00'), ('2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:24.995586-07:00'), ('2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:20:12.991176-07:00'), ('2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:40.995901-07:00'), ('2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:50.992955-07:00'), ('2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:05.993969-07:00'), ('2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:15.993548-07:00'), ('2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:40:56.986748-07:00'), ('2019-09-11T19:41:45.984677-07:00', '2019-09-11T19:51:52.991758-07:00')]\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = ['2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:14:31.986705-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:40.201473-07:00'), ('2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:04.993430-07:00'), ('2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:13.996239-07:00'), ('2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:07:02.997920-07:00'), ('2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:13:49.008844-07:00'), ('2019-09-11T10:14:31.986705-07:00', '2019-09-11T10:18:27.993159-07:00'), ('2019-09-11T13:38:48.905707-07:00', '2019-09-11T14:04:38.027181-07:00'), ('2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:25.987323-07:00'), ('2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:04.982910-07:00'), ('2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:16.982375-07:00'), ('2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:52.980768-07:00'), ('2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:02.980323-07:00'), ('2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:46.998078-07:00'), ('2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:51.997941-07:00'), ('2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:49:35.992012-07:00'), ('2019-09-11T16:52:35.984353-07:00', '2019-09-11T16:53:31.995191-07:00'), ('2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:55:08.998033-07:00'), ('2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:07.993171-07:00'), ('2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:15:22.989788-07:00'), ('2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:24.995586-07:00'), ('2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:20:12.991176-07:00'), ('2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:40.995901-07:00'), ('2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:50.992955-07:00'), ('2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:05.993969-07:00'), ('2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:15.993548-07:00'), ('2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:40:56.986748-07:00'), ('2019-09-11T19:41:45.984677-07:00', '2019-09-11T19:51:52.991758-07:00')]\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = ['2019-09-11T13:38:48.905707-07:00']\n", + "Before filtering, trips = [('2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:40.201473-07:00'), ('2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:04.993430-07:00'), ('2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:13.996239-07:00'), ('2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:07:02.997920-07:00'), ('2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:13:49.008844-07:00'), ('2019-09-11T10:14:31.986705-07:00', '2019-09-11T10:18:27.993159-07:00'), ('2019-09-11T13:38:48.905707-07:00', '2019-09-11T14:04:38.027181-07:00'), ('2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:25.987323-07:00'), ('2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:04.982910-07:00'), ('2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:16.982375-07:00'), ('2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:52.980768-07:00'), ('2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:02.980323-07:00'), ('2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:46.998078-07:00'), ('2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:51.997941-07:00'), ('2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:49:35.992012-07:00'), ('2019-09-11T16:52:35.984353-07:00', '2019-09-11T16:53:31.995191-07:00'), ('2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:55:08.998033-07:00'), ('2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:07.993171-07:00'), ('2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:15:22.989788-07:00'), ('2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:24.995586-07:00'), ('2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:20:12.991176-07:00'), ('2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:40.995901-07:00'), ('2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:50.992955-07:00'), ('2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:05.993969-07:00'), ('2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:15.993548-07:00'), ('2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:40:56.986748-07:00'), ('2019-09-11T19:41:45.984677-07:00', '2019-09-11T19:51:52.991758-07:00')]\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = ['2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:52:35.984353-07:00', '2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:41:45.984677-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 3\n", + "Before filtering, trips = [('2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:22:36.999940-07:00'), ('2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:06.996105-07:00'), ('2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:19:33.994082-07:00'), ('2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:11:10.109991-07:00'), ('2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:18:46.997222-07:00'), ('2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:37:45.999127-07:00'), ('2019-09-17T10:39:30.994917-07:00', '2019-09-17T10:39:30.994917-07:00'), ('2019-09-17T13:49:09.097899-07:00', '2019-09-17T13:59:56.013239-07:00'), ('2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:35:45.991191-07:00'), ('2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:38:20.984250-07:00'), ('2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:25:30.996922-07:00'), ('2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:45:54.989123-07:00'), ('2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:53:53.992627-07:00'), ('2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:24.998304-07:00'), ('2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:54:51.998071-07:00'), ('2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:07.985333-07:00'), ('2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:52.991919-07:00'), ('2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:12.991138-07:00'), ('2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:57:24.986239-07:00'), ('2019-09-17T18:58:27.983784-07:00', '2019-09-17T19:14:24.990711-07:00')]\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = ['2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:39:30.994917-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:22:36.999940-07:00'), ('2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:06.996105-07:00'), ('2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:19:33.994082-07:00'), ('2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:11:10.109991-07:00'), ('2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:18:46.997222-07:00'), ('2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:37:45.999127-07:00'), ('2019-09-17T10:39:30.994917-07:00', '2019-09-17T10:39:30.994917-07:00'), ('2019-09-17T13:49:09.097899-07:00', '2019-09-17T13:59:56.013239-07:00'), ('2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:35:45.991191-07:00'), ('2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:38:20.984250-07:00'), ('2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:25:30.996922-07:00'), ('2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:45:54.989123-07:00'), ('2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:53:53.992627-07:00'), ('2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:24.998304-07:00'), ('2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:54:51.998071-07:00'), ('2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:07.985333-07:00'), ('2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:52.991919-07:00'), ('2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:12.991138-07:00'), ('2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:57:24.986239-07:00'), ('2019-09-17T18:58:27.983784-07:00', '2019-09-17T19:14:24.990711-07:00')]\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = ['2019-09-17T13:49:09.097899-07:00']\n", + "Before filtering, trips = [('2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:22:36.999940-07:00'), ('2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:06.996105-07:00'), ('2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:19:33.994082-07:00'), ('2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:11:10.109991-07:00'), ('2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:18:46.997222-07:00'), ('2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:37:45.999127-07:00'), ('2019-09-17T10:39:30.994917-07:00', '2019-09-17T10:39:30.994917-07:00'), ('2019-09-17T13:49:09.097899-07:00', '2019-09-17T13:59:56.013239-07:00'), ('2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:35:45.991191-07:00'), ('2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:38:20.984250-07:00'), ('2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:25:30.996922-07:00'), ('2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:45:54.989123-07:00'), ('2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:53:53.992627-07:00'), ('2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:24.998304-07:00'), ('2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:54:51.998071-07:00'), ('2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:07.985333-07:00'), ('2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:52.991919-07:00'), ('2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:12.991138-07:00'), ('2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:57:24.986239-07:00'), ('2019-09-17T18:58:27.983784-07:00', '2019-09-17T19:14:24.990711-07:00')]\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = ['2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:58:27.983784-07:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_0 MAMFDC v/s MAHFDC MAHFDC_0 3\n", + "Before filtering, trips = [('2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:47.004690-08:00'), ('2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:03.645276-08:00'), ('2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:31.357361-08:00'), ('2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:20.292947-08:00'), ('2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:19:09.341645-08:00'), ('2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:10:32.077868-08:00'), ('2019-11-19T10:13:36.219994-08:00', '2019-11-19T10:50:08.700526-08:00'), ('2019-11-19T13:30:32.297753-08:00', '2019-11-19T13:57:46.989274-08:00'), ('2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:23.954445-08:00'), ('2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:26:54.042529-08:00'), ('2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:39.573812-08:00'), ('2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:45.986560-08:00'), ('2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:13.984202-08:00'), ('2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:08:55.631840-08:00'), ('2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:23:42.335100-08:00'), ('2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:26:03.429241-08:00'), ('2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:34.312814-08:00'), ('2019-11-19T18:57:40.735147-08:00', '2019-11-19T19:11:10.511688-08:00')]\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = ['2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:13:36.219994-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:47.004690-08:00'), ('2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:03.645276-08:00'), ('2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:31.357361-08:00'), ('2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:20.292947-08:00'), ('2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:19:09.341645-08:00'), ('2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:10:32.077868-08:00'), ('2019-11-19T10:13:36.219994-08:00', '2019-11-19T10:50:08.700526-08:00'), ('2019-11-19T13:30:32.297753-08:00', '2019-11-19T13:57:46.989274-08:00'), ('2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:23.954445-08:00'), ('2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:26:54.042529-08:00'), ('2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:39.573812-08:00'), ('2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:45.986560-08:00'), ('2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:13.984202-08:00'), ('2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:08:55.631840-08:00'), ('2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:23:42.335100-08:00'), ('2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:26:03.429241-08:00'), ('2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:34.312814-08:00'), ('2019-11-19T18:57:40.735147-08:00', '2019-11-19T19:11:10.511688-08:00')]\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = ['2019-11-19T13:30:32.297753-08:00']\n", + "Before filtering, trips = [('2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:47.004690-08:00'), ('2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:03.645276-08:00'), ('2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:31.357361-08:00'), ('2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:20.292947-08:00'), ('2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:19:09.341645-08:00'), ('2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:10:32.077868-08:00'), ('2019-11-19T10:13:36.219994-08:00', '2019-11-19T10:50:08.700526-08:00'), ('2019-11-19T13:30:32.297753-08:00', '2019-11-19T13:57:46.989274-08:00'), ('2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:23.954445-08:00'), ('2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:26:54.042529-08:00'), ('2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:39.573812-08:00'), ('2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:45.986560-08:00'), ('2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:13.984202-08:00'), ('2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:08:55.631840-08:00'), ('2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:23:42.335100-08:00'), ('2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:26:03.429241-08:00'), ('2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:34.312814-08:00'), ('2019-11-19T18:57:40.735147-08:00', '2019-11-19T19:11:10.511688-08:00')]\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = ['2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:40.735147-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_1 MAMFDC v/s MAHFDC MAHFDC_1 3\n", + "Before filtering, trips = [('2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:30:28.838335-08:00'), ('2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:23.913983-08:00'), ('2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:19:12.547719-08:00'), ('2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:13:41.632416-08:00'), ('2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:39.400633-08:00'), ('2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:06.085103-08:00'), ('2019-11-20T10:30:12.525417-08:00', '2019-11-20T10:32:39.311538-08:00'), ('2019-11-20T13:46:56.194173-08:00', '2019-11-20T14:02:03.579157-08:00'), ('2019-11-20T16:20:36.265804-08:00', '2019-11-20T16:59:54.562054-08:00'), ('2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:08.878808-08:00'), ('2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:17.941704-08:00'), ('2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:01.511979-08:00'), ('2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:29.877977-08:00'), ('2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:14.193466-08:00'), ('2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:26.985673-08:00'), ('2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:33.402700-08:00'), ('2019-11-20T19:03:39.825179-08:00', '2019-11-20T19:22:12.873443-08:00')]\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = ['2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:12.525417-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:30:28.838335-08:00'), ('2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:23.913983-08:00'), ('2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:19:12.547719-08:00'), ('2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:13:41.632416-08:00'), ('2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:39.400633-08:00'), ('2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:06.085103-08:00'), ('2019-11-20T10:30:12.525417-08:00', '2019-11-20T10:32:39.311538-08:00'), ('2019-11-20T13:46:56.194173-08:00', '2019-11-20T14:02:03.579157-08:00'), ('2019-11-20T16:20:36.265804-08:00', '2019-11-20T16:59:54.562054-08:00'), ('2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:08.878808-08:00'), ('2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:17.941704-08:00'), ('2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:01.511979-08:00'), ('2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:29.877977-08:00'), ('2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:14.193466-08:00'), ('2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:26.985673-08:00'), ('2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:33.402700-08:00'), ('2019-11-20T19:03:39.825179-08:00', '2019-11-20T19:22:12.873443-08:00')]\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = ['2019-11-20T13:46:56.194173-08:00']\n", + "Before filtering, trips = [('2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:30:28.838335-08:00'), ('2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:23.913983-08:00'), ('2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:19:12.547719-08:00'), ('2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:13:41.632416-08:00'), ('2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:39.400633-08:00'), ('2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:06.085103-08:00'), ('2019-11-20T10:30:12.525417-08:00', '2019-11-20T10:32:39.311538-08:00'), ('2019-11-20T13:46:56.194173-08:00', '2019-11-20T14:02:03.579157-08:00'), ('2019-11-20T16:20:36.265804-08:00', '2019-11-20T16:59:54.562054-08:00'), ('2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:08.878808-08:00'), ('2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:17.941704-08:00'), ('2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:01.511979-08:00'), ('2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:29.877977-08:00'), ('2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:14.193466-08:00'), ('2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:26.985673-08:00'), ('2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:33.402700-08:00'), ('2019-11-20T19:03:39.825179-08:00', '2019-11-20T19:22:12.873443-08:00')]\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = ['2019-11-20T16:20:36.265804-08:00', '2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:39.825179-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s MAHFDC:MAHFDC_2 MAMFDC v/s MAHFDC MAHFDC_2 3\n", + "Before filtering, trips = [('2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:35.995366-08:00'), ('2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:09:59.149219-08:00'), ('2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:18:53.639832-08:00'), ('2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:12:10.203423-08:00'), ('2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:03.418188-08:00'), ('2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:29.410172-08:00'), ('2019-12-03T10:32:35.895938-08:00', '2019-12-03T10:35:25.508647-08:00'), ('2019-12-03T14:15:19.779091-08:00', '2019-12-03T14:28:27.915522-08:00'), ('2019-12-03T16:18:29.018600-08:00', '2019-12-03T16:45:38.443121-08:00'), ('2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:07.610096-08:00'), ('2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:19:26.156419-08:00'), ('2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:32.592792-08:00'), ('2019-12-03T19:16:39.033753-08:00', '2019-12-03T19:33:59.407157-08:00')]\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = ['2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:35.895938-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:35.995366-08:00'), ('2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:09:59.149219-08:00'), ('2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:18:53.639832-08:00'), ('2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:12:10.203423-08:00'), ('2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:03.418188-08:00'), ('2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:29.410172-08:00'), ('2019-12-03T10:32:35.895938-08:00', '2019-12-03T10:35:25.508647-08:00'), ('2019-12-03T14:15:19.779091-08:00', '2019-12-03T14:28:27.915522-08:00'), ('2019-12-03T16:18:29.018600-08:00', '2019-12-03T16:45:38.443121-08:00'), ('2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:07.610096-08:00'), ('2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:19:26.156419-08:00'), ('2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:32.592792-08:00'), ('2019-12-03T19:16:39.033753-08:00', '2019-12-03T19:33:59.407157-08:00')]\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = ['2019-12-03T14:15:19.779091-08:00']\n", + "Before filtering, trips = [('2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:35.995366-08:00'), ('2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:09:59.149219-08:00'), ('2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:18:53.639832-08:00'), ('2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:12:10.203423-08:00'), ('2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:03.418188-08:00'), ('2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:29.410172-08:00'), ('2019-12-03T10:32:35.895938-08:00', '2019-12-03T10:35:25.508647-08:00'), ('2019-12-03T14:15:19.779091-08:00', '2019-12-03T14:28:27.915522-08:00'), ('2019-12-03T16:18:29.018600-08:00', '2019-12-03T16:45:38.443121-08:00'), ('2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:07.610096-08:00'), ('2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:19:26.156419-08:00'), ('2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:32.592792-08:00'), ('2019-12-03T19:16:39.033753-08:00', '2019-12-03T19:33:59.407157-08:00')]\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = ['2019-12-03T16:18:29.018600-08:00', '2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:39.033753-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_0 MAMFDC v/s HAMFDC HAMFDC_0 3\n", + "Before filtering, trips = [('2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:02.997904-08:00'), ('2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:52.990794-08:00'), ('2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:06:52.987840-08:00'), ('2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:09:01.982248-08:00'), ('2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:13:31.918377-08:00'), ('2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:42.984735-08:00'), ('2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:31:16.998112-08:00'), ('2019-12-09T10:32:00.997463-08:00', '2019-12-09T10:33:12.994380-08:00'), ('2019-12-09T14:00:35.477504-08:00', '2019-12-09T14:14:10.014684-08:00'), ('2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:36.991534-08:00'), ('2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:06.990175-08:00'), ('2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:23:44.985734-08:00'), ('2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:51.982696-08:00'), ('2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:23.978527-08:00'), ('2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:28.978298-08:00'), ('2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:00.996115-08:00'), ('2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:06.995858-08:00'), ('2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:28.989666-08:00'), ('2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:35.989359-08:00'), ('2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:48.975690-08:00'), ('2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:53.975472-08:00'), ('2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:42:38.997047-08:00'), ('2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:45:29.992397-08:00'), ('2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:24:12.984065-08:00'), ('2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:54:39.998231-08:00'), ('2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:06:48.986297-08:00'), ('2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:13:35.996933-08:00'), ('2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:03:36.987332-08:00'), ('2019-12-09T19:04:49.984501-08:00', '2019-12-09T19:22:07.998542-08:00')]\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = ['2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:32:00.997463-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:02.997904-08:00'), ('2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:52.990794-08:00'), ('2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:06:52.987840-08:00'), ('2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:09:01.982248-08:00'), ('2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:13:31.918377-08:00'), ('2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:42.984735-08:00'), ('2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:31:16.998112-08:00'), ('2019-12-09T10:32:00.997463-08:00', '2019-12-09T10:33:12.994380-08:00'), ('2019-12-09T14:00:35.477504-08:00', '2019-12-09T14:14:10.014684-08:00'), ('2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:36.991534-08:00'), ('2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:06.990175-08:00'), ('2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:23:44.985734-08:00'), ('2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:51.982696-08:00'), ('2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:23.978527-08:00'), ('2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:28.978298-08:00'), ('2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:00.996115-08:00'), ('2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:06.995858-08:00'), ('2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:28.989666-08:00'), ('2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:35.989359-08:00'), ('2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:48.975690-08:00'), ('2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:53.975472-08:00'), ('2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:42:38.997047-08:00'), ('2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:45:29.992397-08:00'), ('2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:24:12.984065-08:00'), ('2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:54:39.998231-08:00'), ('2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:06:48.986297-08:00'), ('2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:13:35.996933-08:00'), ('2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:03:36.987332-08:00'), ('2019-12-09T19:04:49.984501-08:00', '2019-12-09T19:22:07.998542-08:00')]\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = ['2019-12-09T14:00:35.477504-08:00']\n", + "Before filtering, trips = [('2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:02.997904-08:00'), ('2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:52.990794-08:00'), ('2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:06:52.987840-08:00'), ('2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:09:01.982248-08:00'), ('2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:13:31.918377-08:00'), ('2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:42.984735-08:00'), ('2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:31:16.998112-08:00'), ('2019-12-09T10:32:00.997463-08:00', '2019-12-09T10:33:12.994380-08:00'), ('2019-12-09T14:00:35.477504-08:00', '2019-12-09T14:14:10.014684-08:00'), ('2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:36.991534-08:00'), ('2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:06.990175-08:00'), ('2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:23:44.985734-08:00'), ('2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:51.982696-08:00'), ('2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:23.978527-08:00'), ('2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:28.978298-08:00'), ('2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:00.996115-08:00'), ('2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:06.995858-08:00'), ('2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:28.989666-08:00'), ('2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:35.989359-08:00'), ('2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:48.975690-08:00'), ('2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:53.975472-08:00'), ('2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:42:38.997047-08:00'), ('2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:45:29.992397-08:00'), ('2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:24:12.984065-08:00'), ('2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:54:39.998231-08:00'), ('2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:06:48.986297-08:00'), ('2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:13:35.996933-08:00'), ('2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:03:36.987332-08:00'), ('2019-12-09T19:04:49.984501-08:00', '2019-12-09T19:22:07.998542-08:00')]\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = ['2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:04:49.984501-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_1 MAMFDC v/s HAMFDC HAMFDC_1 3\n", + "Before filtering, trips = [('2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:48.997300-08:00'), ('2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:07:32.988685-08:00'), ('2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:09:17.984138-08:00'), ('2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:31:42.463791-08:00'), ('2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:16.993563-08:00'), ('2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:51:34.987578-08:00'), ('2019-12-11T10:53:29.982589-08:00', '2019-12-11T10:53:29.982589-08:00'), ('2019-12-11T14:09:27.128966-08:00', '2019-12-11T14:20:17.998176-08:00'), ('2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:23.999911-08:00'), ('2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:36.995171-08:00'), ('2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:42.994896-08:00'), ('2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:34:35.981686-08:00'), ('2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:54.998462-08:00'), ('2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:09.993086-08:00'), ('2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:17.992738-08:00'), ('2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:11.982599-08:00'), ('2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:22.992821-08:00'), ('2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:42:50.998011-08:00'), ('2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:44:23.995125-08:00'), ('2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:47:13.987558-08:00'), ('2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:08.997055-08:00'), ('2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:56.992243-08:00'), ('2019-12-11T17:49:59.992114-08:00', '2019-12-11T17:57:36.986911-08:00'), ('2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:04:35.999708-08:00'), ('2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:02:04.988549-08:00'), ('2019-12-11T19:03:03.997993-08:00', '2019-12-11T19:19:55.998326-08:00')]\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = ['2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:53:29.982589-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:48.997300-08:00'), ('2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:07:32.988685-08:00'), ('2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:09:17.984138-08:00'), ('2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:31:42.463791-08:00'), ('2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:16.993563-08:00'), ('2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:51:34.987578-08:00'), ('2019-12-11T10:53:29.982589-08:00', '2019-12-11T10:53:29.982589-08:00'), ('2019-12-11T14:09:27.128966-08:00', '2019-12-11T14:20:17.998176-08:00'), ('2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:23.999911-08:00'), ('2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:36.995171-08:00'), ('2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:42.994896-08:00'), ('2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:34:35.981686-08:00'), ('2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:54.998462-08:00'), ('2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:09.993086-08:00'), ('2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:17.992738-08:00'), ('2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:11.982599-08:00'), ('2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:22.992821-08:00'), ('2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:42:50.998011-08:00'), ('2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:44:23.995125-08:00'), ('2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:47:13.987558-08:00'), ('2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:08.997055-08:00'), ('2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:56.992243-08:00'), ('2019-12-11T17:49:59.992114-08:00', '2019-12-11T17:57:36.986911-08:00'), ('2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:04:35.999708-08:00'), ('2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:02:04.988549-08:00'), ('2019-12-11T19:03:03.997993-08:00', '2019-12-11T19:19:55.998326-08:00')]\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = ['2019-12-11T14:09:27.128966-08:00']\n", + "Before filtering, trips = [('2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:48.997300-08:00'), ('2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:07:32.988685-08:00'), ('2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:09:17.984138-08:00'), ('2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:31:42.463791-08:00'), ('2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:16.993563-08:00'), ('2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:51:34.987578-08:00'), ('2019-12-11T10:53:29.982589-08:00', '2019-12-11T10:53:29.982589-08:00'), ('2019-12-11T14:09:27.128966-08:00', '2019-12-11T14:20:17.998176-08:00'), ('2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:23.999911-08:00'), ('2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:36.995171-08:00'), ('2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:42.994896-08:00'), ('2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:34:35.981686-08:00'), ('2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:54.998462-08:00'), ('2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:09.993086-08:00'), ('2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:17.992738-08:00'), ('2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:11.982599-08:00'), ('2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:22.992821-08:00'), ('2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:42:50.998011-08:00'), ('2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:44:23.995125-08:00'), ('2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:47:13.987558-08:00'), ('2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:08.997055-08:00'), ('2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:56.992243-08:00'), ('2019-12-11T17:49:59.992114-08:00', '2019-12-11T17:57:36.986911-08:00'), ('2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:04:35.999708-08:00'), ('2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:02:04.988549-08:00'), ('2019-12-11T19:03:03.997993-08:00', '2019-12-11T19:19:55.998326-08:00')]\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = ['2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:59.992114-08:00', '2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:03:03.997993-08:00']\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " MAMFDC v/s HAMFDC:HAMFDC_2 MAMFDC v/s HAMFDC HAMFDC_2 3\n", + "Before filtering, trips = [('2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:36.990703-08:00'), ('2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:02.989606-08:00'), ('2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:20.993964-08:00'), ('2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:09:44.993512-08:00'), ('2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:39.991604-08:00'), ('2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:13:45.669769-08:00'), ('2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:07.997757-08:00'), ('2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:28:56.988343-08:00'), ('2020-02-06T10:29:17.987473-08:00', '2020-02-06T10:29:54.985938-08:00'), ('2020-02-06T13:09:00.519690-08:00', '2020-02-06T13:20:40.004119-08:00'), ('2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:16.992790-08:00'), ('2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:26:44.986028-08:00'), ('2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:40.983469-08:00'), ('2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:28.981276-08:00'), ('2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:28:56.996158-08:00'), ('2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:32.988036-08:00'), ('2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:50.987243-08:00'), ('2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:05.983943-08:00'), ('2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:11.983681-08:00'), ('2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:39:41.990607-08:00'), ('2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:20.986232-08:00'), ('2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:31.983098-08:00'), ('2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:37.982832-08:00'), ('2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:45:35.993998-08:00'), ('2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:48:47.985500-08:00'), ('2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:28:15.991941-08:00'), ('2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:30.997907-08:00'), ('2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:49:48.992221-08:00'), ('2020-02-06T17:50:44.989805-08:00', '2020-02-06T17:52:36.984973-08:00'), ('2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:56:25.994272-08:00'), ('2020-02-06T18:57:46.990999-08:00', '2020-02-06T19:15:28.996294-08:00')]\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = ['2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:29:17.987473-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:36.990703-08:00'), ('2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:02.989606-08:00'), ('2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:20.993964-08:00'), ('2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:09:44.993512-08:00'), ('2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:39.991604-08:00'), ('2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:13:45.669769-08:00'), ('2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:07.997757-08:00'), ('2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:28:56.988343-08:00'), ('2020-02-06T10:29:17.987473-08:00', '2020-02-06T10:29:54.985938-08:00'), ('2020-02-06T13:09:00.519690-08:00', '2020-02-06T13:20:40.004119-08:00'), ('2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:16.992790-08:00'), ('2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:26:44.986028-08:00'), ('2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:40.983469-08:00'), ('2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:28.981276-08:00'), ('2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:28:56.996158-08:00'), ('2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:32.988036-08:00'), ('2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:50.987243-08:00'), ('2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:05.983943-08:00'), ('2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:11.983681-08:00'), ('2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:39:41.990607-08:00'), ('2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:20.986232-08:00'), ('2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:31.983098-08:00'), ('2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:37.982832-08:00'), ('2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:45:35.993998-08:00'), ('2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:48:47.985500-08:00'), ('2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:28:15.991941-08:00'), ('2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:30.997907-08:00'), ('2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:49:48.992221-08:00'), ('2020-02-06T17:50:44.989805-08:00', '2020-02-06T17:52:36.984973-08:00'), ('2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:56:25.994272-08:00'), ('2020-02-06T18:57:46.990999-08:00', '2020-02-06T19:15:28.996294-08:00')]\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = ['2020-02-06T13:09:00.519690-08:00']\n", + "Before filtering, trips = [('2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:36.990703-08:00'), ('2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:02.989606-08:00'), ('2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:20.993964-08:00'), ('2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:09:44.993512-08:00'), ('2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:39.991604-08:00'), ('2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:13:45.669769-08:00'), ('2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:07.997757-08:00'), ('2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:28:56.988343-08:00'), ('2020-02-06T10:29:17.987473-08:00', '2020-02-06T10:29:54.985938-08:00'), ('2020-02-06T13:09:00.519690-08:00', '2020-02-06T13:20:40.004119-08:00'), ('2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:16.992790-08:00'), ('2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:26:44.986028-08:00'), ('2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:40.983469-08:00'), ('2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:28.981276-08:00'), ('2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:28:56.996158-08:00'), ('2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:32.988036-08:00'), ('2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:50.987243-08:00'), ('2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:05.983943-08:00'), ('2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:11.983681-08:00'), ('2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:39:41.990607-08:00'), ('2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:20.986232-08:00'), ('2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:31.983098-08:00'), ('2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:37.982832-08:00'), ('2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:45:35.993998-08:00'), ('2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:48:47.985500-08:00'), ('2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:28:15.991941-08:00'), ('2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:30.997907-08:00'), ('2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:49:48.992221-08:00'), ('2020-02-06T17:50:44.989805-08:00', '2020-02-06T17:52:36.984973-08:00'), ('2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:56:25.994272-08:00'), ('2020-02-06T18:57:46.990999-08:00', '2020-02-06T19:15:28.996294-08:00')]\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = ['2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:50:44.989805-08:00', '2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:57:46.990999-08:00']\n", + " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", + " ucb-sdb-ios-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_0 HAHFDC v/s MAHFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_1 HAHFDC v/s MAHFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_2 HAHFDC v/s MAHFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_3 HAHFDC v/s HAMFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_4 HAHFDC v/s HAMFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_5 HAHFDC v/s HAMFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_6 MAMFDC v/s MAHFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_7 MAMFDC v/s MAHFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_8 MAMFDC v/s MAHFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_9 MAMFDC v/s HAMFDC power_control_0 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_10 MAMFDC v/s HAMFDC power_control_1 3\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", + "After filtering, trips = []\n", + " ==============================\n", + " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", + " fixed:POWER_CONTROL_11 MAMFDC v/s HAMFDC power_control_2 3\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", + "After filtering, trips = []\n", + "Before filtering, trips = []\n", + "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", + "After filtering, trips = []\n" + ] + } + ], "source": [ "mcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "mcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", @@ -1100,10 +3524,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 210, + "id": "65556b87", "metadata": {}, "outputs": [], "source": [ + "%%capture\n", "gcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "gcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "gcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" @@ -1111,6 +3537,7 @@ }, { "cell_type": "markdown", + "id": "e368b41a", "metadata": {}, "source": [ "#### inferred view random forest\n", @@ -1120,10 +3547,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 211, + "id": "3f9ff053", "metadata": {}, "outputs": [], "source": [ + "%%capture\n", "rfv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "rfv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "rfv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" @@ -1131,6 +3560,7 @@ }, { "cell_type": "markdown", + "id": "80c810a7", "metadata": {}, "source": [ "#### inferred view GIS\n", @@ -1139,10 +3569,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 212, + "id": "f550a507", "metadata": {}, "outputs": [], "source": [ + "%%capture\n", "gisv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "gisv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "gisv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" @@ -1150,6 +3582,7 @@ }, { "cell_type": "markdown", + "id": "37441e82", "metadata": {}, "source": [ "# Results " @@ -1157,6 +3590,7 @@ }, { "cell_type": "markdown", + "id": "c3bd30c1", "metadata": {}, "source": [ "#### Raw data" @@ -1164,26 +3598,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 266, + "id": "77ccbbc6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", + "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n" + ] + }, + { + "data": { + "image/png": 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twPDERwAAADj8HV63sQUA4LA1UN27WGfzIXI2+8RTOmrad97fPcLIvd31QHf6B8o0N+0u4nn8UW2ZOa2SbTuqw45/zlOn17S/9cPOce4W2B9PPac2KfGnt20bYeTebr1jW/r7q2lu3n0zgeUnTM/sWS3ZsnVsNxOAI1XTHgWuH9M/huLd+2N9at+Pc+qKhtdbXfbm9gwl0CxIc85Mxz5m7J+NqS3qfUzRNsLIvbUUlSxOS1Zk941Wqkl+Uu7M84qZB3OLwB6cLwD1xAUAYDRb1/+wpj1j7hPHPHfGnDNTFE0pH72Afef2e9PbszmtbS6whsczcQGoJy4A9cQFoJ64AACMpKmSvOyZLalUdudj/uSegdy/evjrFA4ns6bX5p927pqY3NP6dbZP0DrAgWlkTvqhbHaas3qPnPm+cbwGvWXtZ8f0feTaNyrPHji8yb8FGJ74CACHp7JIymLvv2EBHKlKIZEjXGWyNwAAAGOxaF5LTbt/oEznzkMjQXXZotaa9j0Pjb1IeE9vmUfW9Nb0HVV3vD094cT2mvbP7xnbHX6BA3PcMVNr2rfftX3Mc7t7qrn/4Z21xzt66gijgcesSe3nY1OSGaMU795fN5S17+kziyn7HH9buaumfVYxJcUEfPm2IwM17anj/FPetLrxD6XngPcEjMz5AlBPXAAARrOr88Ga9vTZTxjz3KbmjkyZfnxNX1fnQwdjW8AkEheAeuICUE9cAOqJCwDASJ57TnMWzNqdR7ijq8yXbj78C3/Nm1Fk2fyhfM5qWeaBNRNTDPicE2tzWh84Agqww+NRI3PSD2WnFbWFuu8vx37t2/11OegnFe0jjGxcnj1weJN/CzA88REAAAAOf82TvQEAABiLp55V+0XTAyt6Uk5Mrua4LV1YW8B87cb+cc1ft7E/xx/VNtg+alFL7rhv72Sr2TObMmfm0Cn8xi392bxtd/HQmdMqefo503LeGVOzcG5zZkxrSld3NVs7B3LPQ9356S+68uM7dh0yrxk83hx7VG3B4JVrxlegf9Wa7iw/Yfpg+7hlU3LLz7cejK3BYet75Y6a9olpT2UCEoS/WN2ab5Wdg+2mJL9czN7nnHtS+zl9SnYnTZdlmVuzK98qO3NP2Z1N6c9AysxIU5akNWcVU/LMYnoWFi3DHXYvzal9vn0Z3wd5/fgVZe8II4GDwfkCUE9cAABG07XjkZp2+5Sl45rfPnVJdm6/d7C9q/OhzJx3zkHZGzA5xAWgnrgA1BMXgHriAgAwnCVzizzjzKEiuNf/oC+7evYx4TAwvSN59XNa0lQZyr28/cFqtu44+BdRPPXUppx90tDrO1At8707Bg76OsCBa1RO+sHylXJbPl3dnBXpSWeqaU4yPU2Zn5acVnTkScWUPKGYMupx6j2/mJnPlVvS/2h++dfL7XlZOSdtRWWf875fdmZDhq6TOzqtOXEfRcIblWcPHN7k3wIMT3wEAACAw58i4QAAHPLaWotc9JRpNX0/vG3XJO2m1tQplUyf2lTTt3Hr+IqE149fPG/4hKYT9igkniSr1vclSZ53/vT8xovmpKO9NjGrZVpTZkxrytGLW/Pc82dk5dreXPO5Tbnt3r0LkAMjmz6tOTNn1L4v120Y3/uofvxRSzoOeF9wOOsqq/l6ua2m7/xi2gijx6e7rGZj+nN32ZWvl9tzR2qTYV5bzMtxRdsIs3e7r6y9UmZZ0Zp1ZV/+rro2P8/eyTUb0p8N6c+t5a58styY5xcz87piftpHSaqentpzjM3lQDKOnPTNqT3HWBVFwmGiOF8A6okLAMBo+nq3p79ve01fW8eCcR2jtWNhTbt756oD3hcwecQFoJ64ANQTF4B64gIAMJxKkbzsmUPFsu9eMZBb769O8q4OvkqRdLQl82cVOeXopjzllKa0tw4lWW7aXs3nb+o7KGu1NCczpxY5ekGRJ53cnOMX1+Z/fvVH/Vm7+eAXIwcOzETmpE+U75SdNe2+JF3pz/r0546yK9eVm3Ni2nJpZV6eWEwd83EXFS25tJiXq8oNSZKN6c9fV9fkjyqLR8xpv6fszv+trhtsV5L8TmXfv3M2Ks8eOHzJvwUYnvgIAAAARwZFwgEAOOS9+pdmZ/aMoVPXHbsG8j83d+5jRuNMrSvM3d1TTU/v+JI7t+8YqGlPaR8+kWn2jLpCodv6c+mL5+SXnjlzTOsctag173jDovzrf23KV793aLx+8HgwfWrtr85d3QPp7hlfovyWbbUJ5tOm+nUc9uVj5cZsydDn49RU8vxibJ93e9pRDuRV1fvHNLYjRV5fzM8LKrNGHVtffLsn1by1+ki2Z2CEGUP6k3yp3Ja7yu5cUVmaOcXI8WBZ0Zrby6Fk6LvTlRdkbK/D+rIvm+v2szOH30U+cKhwvgDUExcAgNEM9O2oaVea2tPUPL6LblpaZ9W0+/t3DD8QeFwQF4B64gJQT1wA6okLAMBwnnVWU5bM3X1NQk9fmf/6Xv8oMx4fLnlqcy44fWy5E/evHsh/fKsvO8dXLy1J0t6aXPHa9jGN7ekt88Wb+/Oju0fPHwUa72DlpB9q7ktP/qy6Kr9azMlrirkpimL0SUleXJmdarXMv5Ub05/k5uzMm6oP5eJiVk4rOjI7TelLmdXpy83ljny73D6YNd+c5PeKhTmzmLLPNRqVZw8cvuTfAgxPfAQAAIAjg9/WAQA4pJ13xpT8f8+oTcD69y9tyc5dh0aRy/a22kSq3r7xFQgfbk7HCEXCp3TU9p9xUkfmzR46pb/rwe588+bOPLSqN929ZebMbMoTT+nI8582Y/CYTU1FXveSudm4pT8/ubMrwOg6OmoL9Pf0jj/+1M+ZUndMYMj3y85cX26t6XtNMS/Ti4l538xKU15UzMoLilmZOcY16ottf7i6bjBxuT1FLi5m5dxiauamOd2p5qGyJ18vt+fODH32PpCe/GV1df6ysizNIyRmn56OfDnbBts3lTvyhrKa9mL4c4U9fbPcvldflyLhMGGcLwD1xAUAYDQDA7V/o69UWsd9jKamttpj9vu7PzyeiQtAPXEBqCcuAPXEBQCg3oJZRZ79xKFrDL7+k/5s3TH+axwer+58eCA/uHMg966a2HzJzl1lbrqjPz+8ayC7eiZ0KWA/NTon/UDNTXPOLabmpLRnWdGa6amkSJHODOT+sjs/KnfmluwaHF8mua7cnDJlLi3mj3mdl1bm5Lxyaj5Xbsn3yx1Zn/58rNy4+4AjeGKm5NLKvJxYjH4DhUbl2QOHL/m3AMMTHwEAAODIoEg4AByi7r///txxxx1ZsWJFOjs7U61WM2vWrMyaNSvLly/PGWeckdbW8Sfzw+PJMUta83uvqk1U+tldu/K1mzonaUd7a2+rLdLZ13/gRcLbWodPYJpaVyT8sQLh1WqZj39hc67/dm0x0DUb+nLHfd35yo3b8443LMqyRbtjRqVS5E2vnp83vXdFunqOnIRf2F8d7bVfdPfuz5fnPQP7PCaw24NlT/62uram7+xMyQuLmSPMOHBbM5Avl9tSTfLLmZUpoyR+95XV9NVlQW9Mf5JkWVrznsrSzC9aan5+YtGe52ZmPlfdnKvLjYP9d6U7/1luziuLucOudW4xLVPLymCy9M5U86lyU143SiL3hrIvnyu37NVfTdJTVtM2hiLjwPg4XwDqiQsAwGjqC25Vmsb/vV+lUlvEqzqgiBc8nokLQD1xAagnLgD1xAUAYE9Fkpc9oyUtzbuvR1i5oZqb7hjY96TDzMlHVVIpkr6BMg+tnbhrJaZPKfKUU5tTKZLv3TGQnr4JWwrYD5ORk76/Ti7a855iac7OlBQjFMQ+tejIJZmde8vu/E11TVZnKOh8ptyS5WVHnlpMG/OaA9n9mdGc0QtwX1TMyEuL2TmmaBt1bCPz7IHDl/xbgOGJjwAAAHBkUCQcAA4hN998cz760Y/m85//fNavX7/Psa2trXnyk5+cV73qVXnlK1+ZuXP3L+HhnnvuyfLly2v6li1bloceeiiVyv4VD/zWt76VZz/72aOOa21tzcyZMzN37tycccYZOe+88/LKV74yRx999H6ty+Fl7qymvP23Fqajfej/w/Wb+/J/P7FhEnc1unI/8kjLMU4aIdcrX/rO9r0KhO9p09aBvP8ja/M3f7w0Ux+9q++0KU15wdNn5L/+Z9u49wtHurG+Z4HxWV/25T3VVenaIzF4QZrzvyqLRkx4Hs2UVPLRynGD7TJldqaa9enLHWVXvlV2ZlsGsjH9+US5KV8rt+XtlSU5uWgf8ZgjXS4zNZVhE5f39JLKnGyq9ue/y62Dff9dbskvl7PTMUzh7ilFJb9czMqnys2DfZ8rt2RutTm/XJk97Boby75cUV01WFi83v69ksB4OV8A6okLAMDo9uO3dr/ow2FOXADqiQtAPXEBqCcuAMCR7GmnN+XohbtzEQeqZT773b79ur7hUPU/P+3PjbcPZXG2NCdT2pIlcys57ZhKTlzalOamIqcc3ZRTjm7K9+/ozxd+0D/u16CnN/mrf+8ZbBdF0t6azJ5W5LhFlTzxxKZM6ygya1qR553bknOXN+eT/9OblRsPoxcbHscmIid9Ip07juLeJxXt+ZvK0fnj6iNZtUeh8GurG3NeZWqaRnl+fWU1V5cb88Vya8YasW4ot+eGcnuenKl5Y2VB5u0jV76RefbAkUP+LcDwxEcAAAA4PCkSDgCHgDvvvDN/8Ad/kG984xtjntPb25sbb7wxN954Y9761rfmjW98Y975zndm3rx541r76quv3qtvxYoV+frXv54XvOAF4zrWePX29mbDhg3ZsGFD7rrrrlx33XV5+9vfnhe/+MX5u7/7uyxdunRC1+fQNWNaJe+6fFHmzho6Xd2yvT/v++e16dw5/jvbTqTuntr9tLaMP2GstaU2Wamnd/gv5rqH6d/VVc2nv7Jl1DU2bR3IF765Pb/2wqGCos940jRFwmEMurprUxXb2sZ/d+y21tr3ef0x4Ui3tezPn1VXZlP6B/tmpynvrRyVmcX+//mqUhRZmL2TiU9Ie84vpuc3ynm5ttyY6x9NJt6Q/ryrujIfqCzLMUXbsMdsLyqpJHuV4P6VYvY+E5cf8xvFvHyj3D5YxLsz1fwkO/P0TB92/MuLuflJuSv3pDtJUib5l3JDvjewI88rZuT4oi2tqWRz+vOTcme+XG4dTGqfl+Zs3OM1bU2RVknSMCGcLwD1xAUAYDRNzR017epAzwgjR1Yd6K1pV5o6RhgJPB6IC0A9cQGoJy4A9cQFAOAxs6cXef6ThvItb7xtIGs2H14Fw7p6kq6evZ/Tw+sG8v07B3LMwv688sLWzJ6++5qO85/QnObm5LPf7d9rzr6USbbu2HudNZvK3PlwNV/7cX8ufnJznvaE3a/37OlFXv/C1vzzF3qzbsvh9ZrD481E5aQfSqYXTfmjyuK8tfrIYKHvlenNbdmVJ2bqiPMGyjLvq67OLdk12FckeWqm5TmVGTkx7ZmRpvSlzIb05eflrlxfbs3qR4uR/zA7c1f1kby3sjTHF+3DrtHoPHvg8CT/FmB44iMAHMaKyu4HALuJiRzhvAMAYJJ99KMfzZOe9KRhC4RPnz49T37yk/NLv/RLefWrX53nP//5OfPMM9PRUZuA39PTkw996EN5+tOfPq61+/v7c+211w77s6uuumpcxzpYqtVqPvvZz+aMM87Iz372s0nZA5Nr6pRK3nX54ixZ0DrYt33HQN77T2uzduP4kjMbobsuyXT/ioTXzqkvPL6v/h/etnPEouL1vv3jzpr2skWtmTHNrwQwmq6uui/PW8f/vmltrf3Cvf6YcCTrLAfyrurKrHo0gThJZjyajL2kaN3HzAPXXlTyO5UF+ZVi1mDfrlTzweralOXIn69tw/xJ7aJixpjXPL+YVtN3W7lrhNFJS1HkHZXFOTa1r8Wd6cqHy3X5g+ojeWP1obyzujKfLbcMFgifnkp+v7KwZs5UfwqECeN8AagnLgAAo2mqK7hVrfaOMHJk9YW/6guDAY8v4gJQT1wA6okLQD1xAQB4zEuf3jx4XcKm7dV845ZD79qLifbwujL/8sXe7Oweyv88b3lzTj364OZO9g0kX/h+f268beg1bm8t8vJnjV4AF5g4k5mT3mgnFu05O1Nq+n6yj3z0JPn3clNNgfDWFPmzypK8o2lJnlJMy9yiOS1FkSlFJccUbXlRZXb+vnJMnrdHjvz2DOTPq6uzvRw5j62RefbA4Un+LcDwxEcAAAA4MqgMBACT6Morr8xv//Zvp7u7e7CvUqnkNa95Tb75zW9m06ZNufnmm3P99dfnE5/4RL761a/m1ltvzaZNm/KFL3whl156aZqbh+5iv+dxxuJLX/pS1q5dO+zP/vu//zsbN27cvydWZ+nSpXnwwQf3etx111359re/nQ984AM55ZRTauZs2bIlF198cbZs2XJQ9sDjQ0d7kT/9nUU5ZslQ8tWOXQN57z+vzcp1ffuYOXl2ddcW7m5vq6StdXyFwmdOq/1SbWfX8EXCdw3Tf+/DPcOMHN6mrQPZvK022XfpgsMr0Q0mwo5dte+bjvamtLeN79fp2bNqk747dx55ifcwnJ3lQP6sujIPZegi1Wmp5L2VpTmmaGvYPl5TzMucDH0eP5Ce/CwjJxTXF9uelaYsLMZ+ccfytNe0V5b7vkh3btGSv64cnYuLmWne58jdzkxH/rZyTNrr9jl7TLOB/eF8AagnLgAAo2lqmVrTrg50Z6C/a1zH6Out/R6tuXnaCCOBxwNxAagnLgD1xAWgnrgAACTJecubcuLSoRzIz93Yn/4jtM7Xlh1lbvhpbX7FM8+amNzJr/64P9t2DhUkXzqvkhOXumQbJsOhkpPeSOcUtb8PPlSOfH1ZZzmQ/yprf/d7Y7Eg5xb7/v2vpajk94qFOT1DN5PalP5cV24ecU6j8+yBw4/8W4DhiY8AAABwZPCNMwBMks9//vN5xzveUdN3yimn5Kc//Wk+9rGP5cILL0xLy/AJEB0dHbnkkkvyr//6r7nzzjvzile8Yr/2cNVVV9W0L7nkksF/9/b25uMf//h+Hbdec3Nzjj322L0ey5cvzzOf+cz88R//cW677ba8+c1vrpm3bt26XHnllQdlDxz62tuKvPMNi3LCsqHkq11d1fzFR9bm4dWHbkLPjl3V7NhVm0E7b9b4kkjnza4dv2bj8AXRV2/Yu3/L9vFl79aPnzbVrwQwmu2d/dneWfv+Wzi/fYTRw1tUN37l6vFdjAeHo11lNVdUV+W+DCUkT0kl76kszfHF+N5jB6qtqOSpdUnOt5QjFwlfmtqbbMwZZ/HtOUXt+O0Z/fO8vajkTZWF+afKcXlNMTdnpiPz0pzWFOlIJcvSmouKGXlvZWneVzkqC4uWrKhLij7xME1yh0OB8wWgnrgAAIympXVmmlum1/T1dK0f1zF6utbVtNunLj3gfQGTR1wA6okLQD1xAagnLgAASfLcJw3lJN71yEA2bS8za1qxz8f0jtpjVCrZa0zT4/RSg1vvr83JPHp+kfbWEQYfgP6B5M6Ha9c6+ajH6YsGj2OHUk56Iy2oK7y9r3z0H5c7052hmxosTEueU8wY0zqVosivVebW9P1PuS1lWQ47fjLy7IHDi/xbgOGJjwAAAHBkmJhbYAMA+/TAAw/k0ksvrUmGOO+88/LlL385c+fO3cfMvZ100kn59Kc/nYsvvjgf+MAHxjxv7dq1+dKXvjTYPvnkk/OP//iP+dKXvpRqtZokufrqq/OWt7xlXPvZX83NzfnQhz6U2267Ld/85jcH+z/2sY/lyiuvTFEUDdkHk6Ottcif/PainHzs0JdLXd3VvP9f1ub+Rw7dAuGPWbmuL6cc1zTYXjSvOavWD1/oezgL59aelq9aN/zclWv37u8bGD6paiR9/bXjW5u9t2AsHlqxK2eeNnOwfdTijjy8cuQCwvWWLKr98vyhFWOfC4ej7rKa91RX5e50D/Z1pMi7K0tzctGxj5kTpz4heU058jnI0UVrbt2jiHhLxvd5Wj++L2P/PF9UtOQVxdy8IqP/3nB3ahN1Ts7kvLZwpHC+ANQTFwCA0XRMOyadW24fbHfvWpUp048Z8/zuXWtqjzeOucChSVwA6okLQD1xAagnLgAALUOXMuSUo5tyytFNIw8ewcypRd72a201fX/32Z6s2Ty+6xUOBTu7k13dZaa0787VrFSKzJ5eZM2mg/9cNm6rPebcGa7PgEY6FHPSG6WtLh+9J9URxz64RwH1JDmz6BjXtZqnpyPNKdL/aM57Z6pZk74syd53YJjMPHvg8CH/FmB44iMAAAAc/tyWGgAmwdve9rZs3bp1sD1r1qx89rOfHXeB8D297nWvy/XXXz/m8ddee236+/sH25deemmWLVuWiy66aLDvtttuy49+9KP93tN4FUWxV1HytWvX5s4772zYHmi8lpYib/uthTn1+KEvlrp7qrnyo+tyz0M9+5h56FixtraI6J7FzkfT1lrk6CW1SVEr1gxflHRnVzUbt/TX9E1tH98p/dSO2vGdO0dOAgOGPPDIzpr26afMGPPc9rZKTjx26j6PB0eSnrKaP6+uyp17FLBuS5E/qyzNqZOYjN08joTiY1N7IcyODIxrrZ11SdjTM/4LckZTlmVNgnWSnHGYJ7vDZHO+ANQTFwCA0UyZflxNu3PLHWOeO9DflV3b79/n8YDHH3EBqCcuAPXEBaCeuAAAsLeBuhTQ5gm6knqg7nKMiVoH2NuhmpPeKNvL2vz1GfvIR99Zl+s+K83jWqupKDKjriTF9hHy5x8PefbAoU/+LcDwxEcAAAA4/PnKGQAa7N57781nP/vZmr4PfvCDOeqoow742CeccMKYx1599dWD/65UKnnta1+bZHex8T1dddVVB7yv8XjqU5+6V9+KFSsaugcap6W5yNt+c2FOP3Eo+aq3r5oPXL0uv3igexJ3Nj4/u6urpn3aCWMvEn7K8e1pbhoqSvrAyp5s2zFy4e6f3lVb7HPZopYxr9XclCyaVzt+07b+EUYDe7r5J5tr2mefMXOEkXs76wkz07xHxvfd93dmy9a+g7Y3eDzpLat5X3V1btsjGbs1Rd5VWZrTiymTuLNkY2o/E2cVIyc/P6mYWlNSfF360luO/cYbD5e1N0KZu4+19tfP05X1ezyn09ORJUXrPmYAB8r5AlBPXAAARjNr/pNr2ts3/WzMc7dv/nnKPS78njrjpLS2zTlYWwMmibgA1BMXgHriAlBPXAAAqNXclEytrVGbHV3Djz1QM6cWNe2JWgeodSjnpDfK3am97m7OPvLRp9YV2u7J2PPeH9OV2rsvtI9QouLxkGcPHPrk3wIMT3wEAACAw59vRgCgwT70oQ+lWh1KbFi4cGF+4zd+o6F7+O53v5t77rlnsP2c5zxnsEj5S17yksycOTPbtm1LknzqU5/KBz/4wUyZ0pgEmdmzZ+/Vt3nz5mFG8njX1JT8r9ctyJnLawuE//XV63P7vY+fAuFJcutdXenpraatdfeXY8uPa8+SBS1ZvX70L8cuPG9aTftHt+0aYeRuP7h1Z553/tCdfc86ZUo+/ZWtY9rn6Sd1pKV5KNVq+46BrFrnCzwYi5t/uiXdPQNpb9udHHnGqTNz9FEdeWTl6JncL3zOopr2d76/cUL2CIe6vrLM+6ur87MMfda1pMg7K0ty1iGQjP3TcmdNe0lGvhHH3KI5p6Q9v3g0ubo/ya3ZlfMybcQ5e7qlrP28f0I6Rhi5/z5TrT2HvrgYe9IPsH+cLwD1xAUAYDSzFpyXSqUt1eruC507t9yRXTsezpRpx4w6d/3Kr9S05yx6xoTsEWgscQGoJy4A9cQFoJ64AAD8+b/1jD6oznGLK3nDL7UOtrd0lvnAp8d/nEPRCUsqqVSGrpvo7SuzfVe5jxn776SltUVyN24ff+FdYHwO9Zz0Rugtq/l+uaOm74yM/Nzn1JWTuL8cX7xfXfamq66w+Oy6wuOPeTzk2QOHPvm3AMMTHwEAAODwN/xtWgGACfOVr9Qm1L/uda9LS8vIBQgnwlVXXVXTvuyyywb/3dHRkVe84hWD7e3bt+czn/lMo7aW7du379XX3t7esPVpjEoleetrF+ScU4cSkPr7y3zw2vW59e7Rv4g61PT2lfnBrbWFRX/lotELcS6e35wnnzF1sN0/UObGW3bsY0Zyx33dWb9pqLD3iUe35dTjx/YeedGFtXv66S/2XZAcGNLTU803v7ehpu83Xnb0qPOWLenIM8+fN9ju76/m699ef9D3B4e6gbLMX1VX5yd7JGM3J3l7ZXHOKaaOPLFBflTuyH2pTXZ+SrHvROTn1BXd/q/qljGtdUe5K/dk6IYolSTnHuTX4H+q22oS349PW55eTD+oawB7c74A1BMXAIDRNDW1Z+7iZ9X0rbrvU6PO69qxIpvX3jjYLoqmzF/6nIO+P6DxxAWgnrgA1BMXgHriAgDAkCLJRWfXFsO9Z2U1AxNQu3v5skqOml97ifadDysSDhPpUM9Jb5T/LLdkU/oH25Uk5+3j+Z9R1Bba/kW68sg4CoV/udxW016alswsmkcYfWjn2QOPD/JvAYYnPgIAAMDhT5FwAGiglStX5oEHHqjpu+iiixq6h87Ozlx33XWD7RkzZuQlL3lJzZg9i4YnydVXX92IrSVJfvKTn+zVd/zxxzdsfSZeUSRv/vX5Oa+uOPbf/tv63HLn469A+GOu++rW9PeXg+1nP3l6nvSEKSOOb2ku8sZfm5+W5mKw75s3d2bdpv4R5yRJtZp86su1yVGXv3JeZkzb96n9Jc+akTNOHkrqqlbL/NcN2/YxA6h39ScfTl/fUOL2Lz13UZ7+5Lkjjm9tKfKOP1ie1pah9+f1X1+bVWu7R5wDh6OBsszflGtyc4ZuqNGU5H9XFufJoxTiHo97y+58v+wc97x7yu58sLq2pu8J6cixRds+5z23mJFlaR1s/zxdoyYwby378+Hqupq+pxfTs7hoHWHGbgNluc+f7+mmsjN/Xw6t0ZTkzZWFaSqKkScBB43zBaCeuAAAjGbZyZel2OMC6g0rv5LNa7834vjqQE/uu/WvUlaHbii6YNkL0z516YTuE2gccQGoJy4A9cQFoJ64AAAcbs4/rSnTO0Yft6dKkbz0mc05ekHttRXfv3NgxDlL5xU57ZjxX2Z91Lwir3hWS03fA2uqWbdl7PmewPg0Kie9kW6obs+Wct/XkdX7anVrPlVuqul7TjEjC4qWEWYkx6ctSzP082qSD1bXZlc5+o0NflLuzPVlbY78BcX0fc5pZJ49cPiSfwswPPERAA5DReHh4eHhUf+AI5gi4QDQQN/7Xm3CfVEUOffccxu6h0996lPZtWvXYPsVr3hFOjpqM+ee9rSn5eSTTx5sf/vb3859993XkP398z//c0177ty5Of300xuyNo3xu782L087uzb56lNf2pKHVvZm/uzmcT32LLA9krbWYuT5LbXzZ0ytjDi2MsqZ8/rN/fnSd7fV9P2vSxfkBU+fnqam2rFLF7TkXW9clFOOax/s275jINd9deuozydJvnfLztx5/1BB9cXzW/K+Ny/JGSe37zV2Snsll/7KnLz2V2q/4Pvyd7dn1bq+vcYDI1u9rjvXfWFVTd/73n5aXvZLS9JcF4+OOWpKPvy+s3LmaTMH+7Zu78vVn3q4IXuFQ8nfletyY7mjpu+1xbwcn/asK/vG9ejdRyLyxvTn/dU1+b2Bh3JddXNWlL0p91Fc+5GyJx+prs//rj6SHRk6bmuKvLGyYNTn1VQU+e3K/Jo/rl1VbshHquuzo9z7gpKflTvzv6srsiZDn7/TUslrinmjrvV71YdzVXVD7iq7Uh3hOT1c9uRvqmvyl9U12TNV/LXFvJxQ7H2OMJKNI7z29QnoA8mI/522DfP84UjhfAGoJy4AAKNpn7oki497WU3f3T+5Imse/Gyq1dq/4+/qfDh3/OB/pXPL7YN9zS0zsuzkyxqxVaBBxAWgnrgA1BMXgHriAgDQSK3NyaxpxbCP5rprJ6a2jzy2so/LQc5d3pQ/emVbXnFhS045upLWkWvfprkpOev4Sn7/Ja059+Tmmp/dcu9AHlgzcu7pzKlFXvO81vzBS1vzrDObMn/mvq9RWTCryCVPbc7lv9yaKe1DY/v6y/z391yfAROpUTnpj+kqqyPO70ttPvf2DIw4dmAf+exfL7flt6oP5m+ra/Ojcke697Gve8vuvH9gdf6+XF+z+tw0j5qPXhRFXlupHXN/evKH1Yfzw3LHsDn328uBfLy6Me+trqrJTZ+eSl5SzN7neo3MswcOX/JvAYYnPgIAAMDhrXn0IQDAwbJqVe0f3BcuXJjZs/edFHGwXXXVVTXtyy67bNhxl156ad75zncOtq+++uq8//3vn8it5YMf/GA+97nP1fT99m//dpqbnbIcTp513vS9+l7zojl5zYvmjPtY7/6HNbnz/n3fqfapZ03Nm141f0zHe80vz81rfnn4u+W+6b0rsmFL/7A/e8wnrt+Soxa15pxTpyRJmpuLvP6l8/Ky583Ogyt70t1TzYK5LTluaWsqldqE0L+5Zl22do69oObfXLM+73vz4ixZ0JokWTSvJe+6fHE2bO7PQ6t70tNbZs7Mppx0TPtexdR/fk9X/u0Lm8e8FjDkn659IMcdPSXnn7s7VrS0VPLWy0/KZa88Jnc/sCO7uvqzdGFHTj5hWs37vLevmnf8xR3ZtKV3srYOk+aGcvtefdeUG3NNuXHcx3p/5aickSn7HPNwevOxcmM+Vm5MRyo5Jq2ZkaZMKSrpS5kdZTUPpydbs/fnbmuKvKuyJMcUbWPaz9nF1Px2MT//r9ww2PeFcmu+XG7N8nRkbtGcnrKaB9OT9ak9j2hOkT+uLM6iYh9XrjxqW/rzX+WW/Fe5JR0pckzaMjvNaS2KbCsHsjq9ex0/SV5ZzMlLK+M7x3pbdcWwx6q3Kf35reqDw/7somJG/rBYNK514XDifAGoJy4AAKM55tQ3ZFfnQ9m64eYkSVn258E7/i4r7/23TJ15Upqap6R71+rs3HZvsscl30WlJaec+760tg//3Qbw+CUuAPXEBaCeuADUExcAgEY5/bimvPxZo+c+JskLn9KSFz5l+J/91b/3ZOuOkYvntjYXOfvEppx9YlOqZZnN28ts6SzT1ZsMVJO2lt0FyBfMKtLctHdx7188MpDPfndshbsXzank4idXcvGTk+7eMuu2lNnZXaanL2mqJFPakoWzK5k+Ze91evvLfOxrfVm/deTnAhy4Ruekf6/szIfLdWM63r728dHKcVmYkWNmb8rcUG7PDeX2VJIsTksWpiVTikoqKdJZDuTBEXLfp6eS91SWZnYx+rWXTyum51eKrvx3uXWwb3X68t7q6kxPJSemPTOKpvSXZdalLw+mZ68VW1LkbZUlmVbU3RFiGI3KswcOb/JvAYYnPgIAAMDhS8VNAGigzZtri/LOmjWroevffvvt+eEPfzjYPumkk3LBBRcMO/a1r31t3vWud6Va3X0H+muvvTbvfe9709Q0ehLHWPX09GTdunW5+eab85GPfCTf+MY3an5+8skn50/+5E8O2nqPWb9+fTZs2DD6wD3cd999B30fHH7KMvnba9fn8lfOywVnTxvsnzW9KWefOnzy2NbOgfzDpzbkrgd7xrXWjl3VvPef1+b3f31+TjuhY7B//pzmzJ8z8mn+DTd35l8+szGPvrWBcapWk3f91Z15++8vz3OfuWCwf87s1pz/pOEL8W7e0pv3feiu3HrntkZtE3hUV6q5K4/eUGSUay+Wpz1vqizMcWMsEP6YSyqzU6kWubrckJ5HF+lPcke6RlxzVpryjsqSnFp0DD9gH7pSjvqcpqWSNxYL8szKjHEfHzhwzheAeuICwJHL9xGMVVE0ZfmTrsh9P//rbFr9zcH+vt4t2brhh8POaWmdnROf+PbMmHtmo7YJNJC4ANQTF4B64gJQT1wAOLL5ToLDXaUoMm9mkXkzRx/b21/mmz/tz3d+PpDqftTtbm8tcszCvYuBD+eRddV87nt9WbtZgXDgwFWTrEpfVqVv1Nz3szIlb6kszLxxFNJ+fTE/M9OcT5ab0r/HAp2p5qfZtc81F6Q5b6ksyhnFvour76nRefbA4Uf+LcDwxEcAAAA4fCkSDgANtGnTppp2o4uEX3XVVTXtSy+9dMSxRx11VJ773Ofma1/7WpJk9erV+fKXv5xLLrlk3Os+/PDDKYqxJcg95swzz8znP//5zJhx8Asb/uM//mPe8573HPTjQpL09Jb58L9tyA9u3ZkXXTgzJx/bPuy4zp0DuelnO/MfX9mSzp37V7F709aBvPsf1ua550/P8582PccuHb6o6cBAmTvu685/fn1rfvFA936tBQzp6q7mir/+Rb75vQ151UuW5fRThv+s2ra9L/9z4/pc9YmHs3V7X4N3CUees9KRtxQLc0t25c6yKxvTP+qcthQ5N1NzUWVGzsvUcZ+zPuaFlVk5u5yST5abcnO5M10Z/rN9dpry/xWz8svFrEwtxn7znV8pZuemckceSs8IR95tXprz3GJGfqWYnWnjOD5w8DlfAOqJCwBHJt9HMB5NzVOy/JwrsnHRs7L6gf/Ijq13DjuuuWVG5i55do4++XVpaZvV2E0CDSUuAPXEBaCeuADUExcAjly+k+Bw87nv9uXUY5pywpJKlswt0tI8en7n+q3V/Oy+gfzknoFs3zW2de5fXc113+7NSUubcuyiSmZNG32d3r4yd6+o5pZ7B3LXiv27FgQgSX65Mitzy+b8ouzK+jHkvrenyNmZml+qzMpZ4yjW/ZiiKPLyYk6eVk7L9eXWfKvcnh37zE5PlqU1zy9m5gXFzHQUlXGvOdF59sDhT/4twPDERwAAADg8KRIOAEeI3t7efPzjHx9sVyqVvPa1r93nnMsuu2ywSHiSXH311ftVJHw8nvCEJ+Tyyy/PG97whrS2tk7oWkyOV7z1wYau9+0f7ci3f7S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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw')" + "# plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw')\n", + "plot_cm('ios', [pv_la, pv_sj], 'raw')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 219, + "id": "99c90603", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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zJKtd5sqvTdDcGZkaNyZlj+ofOHNquq6/YopKirwDbutwGDpk/xwdsn+OXnm7Rr+5a8OQtP8AyeBWbO07H1ut+n24Qg0D9NlfpQ6tCu/Q0UaLvhNH+9Fw2BPLBMTj2m+P17GH5sa8vcNh6MB9snTgPln64JNG3f73LWoYoYG7Dof0nYtLddpxBQNuO2lcqq74ylgdc0iOfn3XZlXXxT5u8fJLSzV7WjrjJIBhQLsBAAAAAACJQUg4AAAAMAQO2Cdbt94wRx63vZPJjsoObdjcovaOsArzPZo5NaMrHCDF69A1354q0zD02HPlw3HaQEKUFjk0a2LsA7J3VX62qe99MUuFuQ7b+kDQUlllUI0tYfmDllI9pkblO5Sf7YjySX1Ldnl2lc9vafmGgTvDGIb0jbMyNXeyvWxNrWGVVQbV7rNUkO1QabFDptHZSSYr3dR3Pp+p2//dqPXbRmYnIiTeRsunxWpL2Oc3KtRnQLhThsbLrWw5lWaYarZCWhcRVN6qsG63KtQYDulMM/aQ4JBl6dbwDn2sVtv6LDk0UR6lGqYqrIA2ytcVmNKgkG4Kl+tGc4xmDSK825CUK6dGyaUMOeQxDPksS5UKaKt86nmFbVdA14e36QZztOYYqXEfK5E+tVr1i3C5AhFRMoVyarw88hqmaqyg1qq9q0w+WbrLqpIVlk4lKBxDKC3VocsvGbPLnzNjcqpuvmaSUlPs3xla20Nau7FNDU1BuZyGSoo8GjfGK0ePgWzHHJKj7Eynrr9to4Kh2IPCLzyzSBefParX+soav7ZX+NTYHJTLZSg326UJY7xK8cb3feaYg3OUnhbfPgCGjmlKv/jBTB16YJ5tfX2DX2s3tqilLajRxSmaOjG9a3BsXo5bt1w/W1f+ZImWrmwajtMGAAB94LmO3d3k8amav2/2cJ+Gzju9RNMnpw/3aXR564P4Jvfry0grE4aOZVn6TXiH3lVLr9+NkVulcsttGGq0QlqvDlv99s565OvNkgGDtVdb7bohvE3tEXWtKTI1RR5lG04FLEs75NfWiNDtt6xmNVoh/dQcLZfR/8DnZJVn53m1JiUefHjq+TGydPhCKq/0q7rOr7b2sAxDykhzaHxpivKyuwOYHA5DZy8oVFG+Wzf9cbPCMVbjFhe49asfTFJJkX1iSX8grA1b2lXXEJA/YCkt1aGxJR4VF8Q2ufVw2hPLhL0T72lA4nGdAQCQWFMmpuug/WIPBhwK4bClmjq/tu1oV1NzQB2+sFI8pkYVeTVhbJptMsSxY1L1+5v20TW/WKZPlzfG9Pk/u2amjj28dxjg5rJWbSlrk88fVnaWS9MnZyizR3Dyzu8Q1960nKBw7BGS1S5z/BH5Sk/bs+ICZk5N120/mdm7H2dbUKs3tKq+ISCXy9DoYq8mlKbaQkGPPyJfOVlO/fDm1QoGY+/HCQyXQ42B2xiXWW36ZbhcwR7tSIakSfKoyHCp1Qpro3xq6hG2/YbVrDYrrOvMkq4xQSPJnlgmIF5jivtue6mu6xxD0NAYkMNhqLjQo4ljU2zjFg7eL0u//clUXX3TWtU3jqwxfqYp/eL7k3Tg3Czb+kAwrNUb2lRT55fXY2ri2BQV5Xf/G8ydkaFbrp2iK3+xRs0t/U8esNMxh+Tscd+DgN0B7QYAAAAAACQOtV0AAADYq9xw60qtWBN741F7x8CNycWFHv3iBzNtAeHbytt1213rtPBTe+fUjDSnvnbheJ17+uiudVd+Y7LKytt7bQvsaQJBS/VN4V5h3rsiPdXQ987PUmFO92fWN4f0xJttWrTaL5+/d6fOjDRDcye5dchcr3aly+dQlqe+Kaxr/zy4EJKvfi5DU0q7O8gvWu1TRx/ljnTOMam2gPBgyNL/XmnVW4s7FOqR1zAq36GLT0nX5DGdx3A5DX373Ez9/G/1amyl0yy6uWQoX07t0MAh9bFKkaHDjQwdZWRqurzyGPbJOCzL0gdq0d/C1aruEat9j1WtcZZb82IIKZGk+6waW3CIU9JXjQKdZGTbAlW2Wj79MVyp1eqQJAVk6Zfh7fqTOV65Rv/VbIak0XJpvpGuOUaKZihF6Ubf948WK6RXrCY9ZNWq7bMAlQ5Zui28Q3ea45UWZb9orjGKNS2OgBOvzIE3klRpBXRreIctIHyUXLrMLOz1b99ihfSgVatnrIaudX+1qlRiuWL+/wQM5Bvnlyg/t/PZ1toeUlpK/M9ol8vQD745zjawpMMX1r3/K9dzb9QqELA/+0YVuvXNC0brkP26O7DOm5Whc04u0H+fqYrpmOcsKOgVEP76+/X6z9OV2ryto9f2hiHNmJymww/M0gGzM+IpXi/VdX6leBwEiAMJdtklE20dYAOBsP54zwY9+eIO20C08aWpuvbyqZozo/Oe4nGb+tV1s3Xxdz9Wbb0/6ecNAAB647mOPZXfH1Z1rV+jR3kTfqzRxV59+bzSruXWtqDSUuPvwnbX/Vv0z/9ti3u/aZPS9POrp3Uth8OWnnsttnf4aIaqTBiZXrIaewVqz1KKLjMLNc6wD9oOWZZet5r0d6u6Kxw7KEt3hCt0dz91u34rrNvDFbaAcLcMXWLka4GRJXdE3fgOy6+/h6v1UY967SVq0xNWvT5v9B8olYzyDCRPTnUoPKQB4smo58fI0uEL6/1PGrVwSZNWrm/V5m0dsqI0XU6flKpLzx2lebO661MPOyBbZ59coEeeqx7wWFkZjl5h2jV1ft33aIXeXtig9o7ef8vZmU7N3zdTJxyeO6g26Zv/vFmrN8Q+aW8sfVt6Go4yAYnCexqQeFxnAAAMD58/rOoan8aU7PrkZpYlbdnWpnc/qtUnyxq0fFWTmlv7DhJMT3Po1ONH6Svnj+sK20tNcehnV8/Qhd9eqNa2/t9BTz+xuFdA+OLlDfrtXeu0aav9XddhSicdU6T/+9pkZaR3HsvtMnX9ldP1xW99NOCxgN1Vstplqmt9SvE6Ehac+YvfrdXKtb0npIwm1jost8vQdZdPiejHGdJfH9yqZ16ulD+iH2dJkUffuXS8Djuwu358/7nZOu/0Ufr34+Uxnx8Qj68YBbrAyBt4wwjr1aFbwju6lg1JJxiZ/e5TYwV0c0SY9gx5dblZpNIebTsBK6znrUb9w+oeWfGRWvWAVauLjfy4zzVRYxCkPbNMwK5at7lNL75Zq4VLGrWjqnddWl6OS186q1inHtv9Xbt0lFfX/99Eff/GtXEfb9X6Vt38p01x7VNdF1sd39e+OLpXQPjjL1bpX4/uUEvEd/z9Z2fo8i+P7WqzKi3x6qdXTNTVv1wX17n1da4pHpMAcSBBaDcAAAAAACBxqNECAADAXqWu3q+KKt+Qfua3Lp6orMzugN6y8jZ96wefqqGxd0hqc2tQv/vrelXW+PSdL0+UJDkchq74+iRdfPnHCg/dOGhgWAVDlsqrQ9q8I6jNOwLasiOo7dUhTRrj0jVfyhr4A2J04UnptoDwNVsD+uP/mvoMB9+pudXSu0t9enepT6YRdTObRJcnbEm1jfHfALLTTU0cbX+1f2dJ7zDRSPnZpo470N6x7e7HmrVkXe+G9R01Id3+70ZddUFWV1B4Rqqp049I1QMvtPbaHnsHp6Sx8miy4dEUeTXZ8Gq8PFqldv04HH8wT6RsOXS2kaOTjWx5jegdKw3D0CHK0CwzVdeGy1Sm7r/hv4ardaeZKsPo/0KvsPx62rJP1PFDs0QHG+m9th1reHSTOUbXh7d1BYg0K6yHrFp9xyjq9zjHGpk60YztfpFuOHSmkaO5Vop+GC5Tx2edX+sU0vNWo84dIOQlUo7hVJHhGnjDON1v1ai5R4jLKLl0q1mq7D6CVNINh75pFCo/7NQ/rRpJUljS38LV+qOZKscA/5+Agcybla4FR3V2MAsGLf3rsQp968LRA+zV29EH5dhCUcJhS7/4wyYtWtbc5/Y7qvz62R2bdN13x+vI+dld679wWpEefb5awVD/USkTS736ynklXcuBYFi33LVF7yxsjLqPZUkr17Vq5bpW/T2Ovud1DQGt29ymdZvatXZTm9ZsbFNDU1C3/miy9pnR+54HYGiUFHn1+dPt96Prf71S73xY22vbzWVt+r/rl+oPN83t6gibnenSV84fp9/cuWud3QEAwK7juY49RSAQ1uZt7VqzoUWr17dozYZWbdzapjnTM3THz2cl/Pg/+PYkeTydL7RvvF+r7Eyn9p0Vfz17Y3NQjc19h9j054KzSmzLi1c0accuth0OVZkwMv3Psk/yOkspuskcI2cfdZoOw9DxRpbGWR79MFzWNcFiwwB1u29ZzbYJOA1J15kl2i/KBIujDLeuN0v06/AOW+D3I1adzrRybKHYw1GenrLl0GR5O9sTDK+myKscw6kfhcq0XO0D7h+LZNXzY2T55o9XKxRjTtjqDW360a0bdPU3xur4w7r/bs8/vVhPvlSjQLD/etzvXlJqqzdeurpFN9y+sc8g7Z0amoJ66a06vfRWncxBZIjUNwZVWZO4QdHDUSYgEXhPAxKP6wwAgOQIBMLatLVVq9e3aNW6Zq1e36wNm1s1d0am/vSrfXf5859/rVLPvFwR07YtrSH998lt+mRpve789byukN6CPI/OPLlEDz5a1u/+F5831ra8eHmDrrh+qUJ99KMKhaXnXq3Uxi2tuvPX8+Rxd75w5ua4YzoWsDtIVrtMXb1faza2as2Gls+O1ar6xoDu+PnMhLVZ1DUEVFE9tOOzJOnYw/JtAerhsKWf3LpGC5f03a+yvNKn6369Rj/7/lQdfUh3UOEFZ47W/562hxQCQyXLcChL8U+m+kjY3k4zRykqNtz97vNvq1YtPfrsz5BXN5ljek0y6zJMfc7IUYHl0s3h7oD8J6x6LbCyVBjneIJEjUGQ9swyAYNhSfpgcaP+9dgOrdvU/+SxtfUB/f7eMm3c2q7LL+3+zj1nWrqOPjhHb3xQ3797F40AAQAASURBVM/evfkD4YS0RZWWeHXWgkLbur88uE2PPt/35O2Lljfre79Yo9/dMK2r7WrujIy4yrRznMTajW1au7G1a5zEb66bon1mZAz8AQDiQrsBAAAAAACJRUg4AAAAsAtKirw69vAC27qbf7+mz4Dwnv79WJkO3i9H+++TI0maMDZNJx9brGdfia3zLTCSvb/Mpzc/6VAwxkHZgzVvmlsHzOgeuFxeE9Qf/tsof/+Xn004hv6eySrPYBw61yNHj6TzytqQ1pUNHIpy+uGpcjq693t3SUefAeE7BYLSP59p1k+/liOXs3O/w/bx6oUP2lXTwOwGe5vjjEydbGT16oApSRqCPtTT5NXfzAn9hoNHyjQcusYcpSvDW7q6im6TX+vk01R5+933IatOPa+a44zMPoNDdvIYpq40i/Xd8BYFPyvwy1ajzrFy+u2g21fAykAmGl6dYeTovz1CWz6wWnSu4gsJT4QKy693LHto8hVmcZ8B4T2dY+bqk1Crln4W/lImv16zmnSCQWgSBs/jNnXlV0q7lh97oUobtgwuYOjgeZm25fc/aYwaEN7Tnf/apkP2y5TL2XnvSkt1aM70NC1e0RJ1H9OUrvraWDmd3feHP/xjW78B4ZFinWTo69euUl1j/MFpAHbdV84fJ5er+3vNs69U9NkBdie/P6xf3rFG9//pALk/2++0E4r14KNlKq8ceEIgAACQODzXsSd44Y1qPfVSpfyB4QlDOHNBkfaZ2fnu3dIa1B/u2aQbvjclacd3u00de1i+bd1zr/Y9EDVWw10mJNZmy6cq2etUvmkWDljfO8Xw6iQjS89YDV3rPuqnbvcjyz4p60FKixoQvpNhGPqmWagPwy1dZ9imsFaoTfuq732TVZ6d7jTHK3eAOuOhkKx6fowssQaE72RZ0p/v26bD9s9SirczuCU9zaF9ZqTr437qgA87IMs2QeSW7R26/raN8vljbyMdaZPF74llwt6L9zQg8bjOAABIvOdfrdQTz5cntN62r4Dugazb1Kr/PrlNX/7iuK51Rx2c329w98RxaSopSrGtu+Mv6wc8/ur1LXr6xR06t0fI2GHz8wgJx24vWe0yF1/xqeoa4hhEMcIdckCObfndhfVRA8J7+sM9m3TYATld7zDpaU7tMyNTi5bF3icTSCSfFdbbEf3vB+pDX2759arV1LXslKErzeK+x3J85hAjXccamXrts/0CsvSQVasrjOJdOPuhsyeWCRism/6wKe6g7qdfqdG+MzN0xPzu5+Vxh+fGHRKeKF84rcg21vGTZU1RA8J3qm8M6rd/26LfXj+1a92XzyvRWx/WDzj282s/XKm6BsZJAMlEuwEAAAAAAIkVe9oSAAAAgF4OPTBXjh5Bu8tXN2npyqZ+9uj20OPbbMunHk/HFOwZ2jqspARqn3V0qm35gedb4goIj1WyyjMYh821hx+/s2TgRnGXU9p/use27oUPBg5SrawL69O13R2PnA5DB83y9LMH9lTphqPfDpi7KstwxhUQvtMEw6MZsg8uWWa19buPzwrr3YiOtucYAwdwjzbcOrhHQEtI0pvWwCHCg3FARBBMueLrAJgoC61W9cx/mCavZhkpUbfv6SzT3nn/FSu2705ANF85b5SKCzqfSeWVPv3r8cFPvFNcaA8B+uDT2P4+6xuDWrPRfs8ZXdT/c/LI+dmaMqH7+8ziFc166e26fvYYPALCgeHhdps6+jD7xGIPPLp1wP3Kytv19gc1XctOp6kTjioc8vMDAACx47mOPUVLa2jYAsIL8936xoXdYTJ3/2tL0gMrjjo4V+lp3YHFzS1Bvd3PILWBjIQyIbEqZP//mS+nJhixtY0cFFG3u0PR/zYqI343v5+A655yDKemREyUWW5FP06yyrNTMgLCd4d6fowcbR1hrVhrD+UvGaAe98vnjrIt/+EfZXGFaY9Ee2KZsHfiPQ1IPK4zAACSo7k1OGz1tgN5/2N7/enokv77CJYU2+uqKqo7tH5za5St7d7+sMa2XDoqtv6IwEiWrHaZPa1toqTIfi95b1Fs/SrrGgJavb7Ftm70KG+UrYHke9dqUc8e+GkydegAbUJvWs22PvuHGukqiWHC03MNe3/9d6xm+a2RUQ+8J5YJGKx4A8J3eurlatvyPjMyhuJ0hsT8fTNtyw8/VxnTfstWt2j1hu53h1GFHs2dOXC5CAgHkot2AwAAAAAAEo+QcAAAAGAX7DMr27b84Sexh/p9vKRe/kB3Z5TZ0zOVlzNwpxYA0tSxLo3K6w4WWLs1oHVle1enjqljXSrMdXQtB0OW3ls2cEj4rIluedzdkxus3xZQRW1sKejvLrV//rxphIRjZJkUEWZSp/7vC5+oTT51d76fLq9KY+hgKknHG1m25feslihb7poMOWzL7RoZg4FWWPbJBfYzUqNs2ds+SpVT3feh1WpXnbV33cMxdGZOTtXpx+d3Lf/hn2W7NKjG67FXmdfUxT54prrWvm16miPKlp1OOSbftvzQU7F1gAWw+zhovxyleLvvBctWNWrrtoEn6JGkZ1+xT3hw1KH5UbYEAADJwHMd2HVXf3OSUlM6r6MlK5v0zCtVST+HU4+zDy575e2aXapHGAllQmJ1RAQL5Cv20Ot8uWzLLYreFtMh+3Hy4gjXLjDiOE6SypNMu0M9P0aW5lb7326KN3o36rnT01Va0h0itGx1i5avjS3YbKTaE8uEvRfvaUDicZ0BAICmZnu/vp31odH0/O4gSdU1vpiPVRWxbUZ64iegAzAyRfbjrK6NPUC1KmJb7iUYSV62Gm3LRxsZchv9R328H9F2cZyRGWVLu1LDo2k9JprtkKXFaovxTBNrTywTkGzrt9jr6LweU2mp/X9XT4Zxo73KzuxuV/YHwlq6KvY22I+XNtmWjzgwe6hODcAQod0AAAAAAIDEIyQcAAAA2AWF+fYw0o1bYh88GQha2r6ju/HL4TB08P65Q3ZuwJ7siH3t115kePXe4PB97P8Gy9b71dQ6cJjJ7In2EIe1W2IPP11XFlAw1H2MccVOZaQZ/ewBJJcp+99jYIBA7U8s+3N7ThxB17OUYovv3iif6hMQdF0l+zWaq+HvuCdJNREB7OOM2CcNcBmmRvUIlAlLWmQRQIH4uZyGrvraWDnMzmv/pbfrtHjFrgX51DfY/7bdrtifc5HbNrdED0oqKXRrnxnpXcsV1T4tiaMDLIDdw8H72d/xFy9rjLJlb0tWNCoY7A5vmzYpQznZrn72AAAAicRzHdg1C44u0Px52ZIkvz+s3969IennUFLk0dwZ9gHmz746+Am7RkKZkHg5EWHd/jgmcYysn07vp243JyKse6C6bds5RQR/R048aTtOksqTTLtDPT9GlsI8+/ew2obobaULjrJ/B3zp7dgnjR+p9sQyYe/FexqQeFxnAACguNBrW66p6z/0u7beHs7rdsc+fNntsm/b1EI9DbC3qouos4u8P/Qncttm7iUYIXZYfq2QPUDzxIjJTCPVW0FtUvez1yFpplJiPuZsw77tSOivvyeWCRgOoVDvdl6Xc/jH9+Xn2uv/yit8CgRjb5PeVGa/Tx60b//3SQDJR7sBAACJYRmSZRj88MMPP/x0/Qz3nRkYXoSEAwAAALsgM90+kLqlNb4OZJEdziaNT9vlcwL2BtPG2Rt/V22KPeh6T5DiMbTfdHsg7ztL+u94v1NJgf2+tWF77Pctf0DaXm0PPB2d74yyNZB8Oyz7AJNc9f/3ucWyXzfTDW+ULXvzGqbGyX4dbpU/ytaD95rVZFueG0fASSK1yH4vSIuzmjE9YvvNiu0eBvT0pbOKVVrSed02NAX014e27/JnLl9rD+qePD72ay5y2zWb2qJuu8/MdNvyroabAxiZJoyzv+MvX90UZcveOnxhbYiYiGzCWOoMAAAYLjzXgcHLzXbp25eM71r+12PbtLU8+RN/nnxsoUyzu7fomg0tWr85+rt7f0ZKmZB4U+SVq8fklNvkly8ilDua9Zb9b2KKotc/z4wINNhgxf73tCGibnVKP/XcySpPMu0O9fwYOUYXezR9Uvf3sHDY0rLV0QNEek70KEmfLG9O2Lkly55YJuy9eE8DEo/rDAAALDi2yLb8ydKGfrdfva5ZPn93fdP4MakxB4VPm5xhW161jndWYG+1dJX93WPqxNjfJaZEbLt6PX0zMTK8bDXZpmOdJI8mDtCmsSWiDWi8PPIasffZnxHR/rTVGv42kD2xTMBwKCmyt3EGg5Yam4d/YoyMyLHWbaEoW/atNWL7wny3UlOIRAJGEtoNAAAAAABIPJK8AAAAsFc5Y0GJLvnCOI0vTVVmhkuhYFiNzUFVVHdo6cpGfbioXktWxj5zbSBoHzTtcsXX6OyO2H586cgI/gRGsux0UzkZjq7luqaQ6ps7r8WMNEMHzfRo3jSPCrJNpaea6vBbamoNa8O2oJZt8GvJWr9in4N+ZJo/0yOPqzvEoaE5pOUbYuvcNirPYVuuqo+vw011fUjjirurE0blO7R6y94V0o6Rqc0K6VPZQ32mDtBxdltE2McoxTf7/Ci5tLFHR9Uyy6d9hjDE+9lwg96wuge6OCR9zsiJ+3NesBr133CdyuRTs8JySsqQQwVyaaaRov2NVM2K87ydsk+/GYjzzhq5fRkddBGnyeNSdO7JhV3Ldz+4Xc0t8T3T+vL8G3U65+TCru/pJx2Zq/89Uyl/oP+/8UP3z1JRvrtrefO2dq3f3B51+2kRA1FWre/u6DZvVrqOPSRH0yalKT/HJYdDamwOqbzSp09WNOvNDxpUWcM1A+wOxo+xP1+37Yh+X+jL9h0dmjapexDshNLUAQfcAgCAxOC5DgzeFV+boMyMzjrlTVvb9NAT5Uk/B9OUFhxdYFv33GtVg/68kVAmJEeqYepYI1MvWp3tx35Zetlq1GkD1NOGLEvPWA22dceZmVG3P9HI0uNWvYKf1Zu+bDXpHCtXngFCEd63mlWt7sHeY+XW5H7qxZNVnmQa6fX8GDlys5y6/rvj5XB0t2+8szB6XWtejkv5ud11vtW1ftXUd7aJZmc6dcwhOTps/ywVF7qVleFUW3tYDU0BrVzXqo+WNOuDxY2ydqFR+pRj8nTBGUUqHeVRZrpTwZClppaQqmr9WrG2RR8vbdbytdEDzkdimYChxnsakHhcZwAA7N3OPqVEC47pDgkPBsP631Pb+92nrT2kF16r0BkLSiRJHo9Dp59QrEef7b8O1TSlc04rsa17/tWKQZ45gGQ5/YQiXXTOaI0dnaqsjM/qsJoDqqj2a9nqJn30aYOWrYo/8P/ZV6v0hc+VdPXjPPmYQv37iXL5/f1Penn4/FwVF3SHpm7a2qa1G+OrQwMSIWRZes2yB2ieYGQNuF9kAPYoI742kOKINpOtEQHdA0nEGIQ9sUzAcDhyfrZtee2m1rjbcArz3Pr+N8Zp2sRU5eW45PWYamkNqbE5qA1b2rVsdbPe/qhBza2xj9EIBu0n4XIZUbbsm8vZe/txo1NsYy0ADC/aDQAAAAAASDxCwgEAALBXOeGoQvsKt6nUVKdGFXk1b3a2LjlvnFata9Ld923Sx0saBvy8yBm2ew6ojEVexPZjR9ORBBjIuFH2V9kdNZ2dTY6a59W5x6bK67EHJrichjJSTY0ucOrIeV6V1wT1n5datWrz7htsffg+Htvye8t8CsfQmSfVayg91f7vU9cUX6BqXZO9c21hriPKlkByvWA1ytcjeDpNpuYo+nO12QqpWfa/54I4w0MKDJd6Zl2Xa9fuKx1WWDUKao3VrpetJq2QvaPMxUa+JhieKHtH95Zl72AfkNSuoKoU1AqrXQ9bdZosjy4x87Wvkdb3h0TIkP3ar7NCUhz99+pk/w61XQQeI3amKV31tbFyftYJdOGSJr3+fsOQfHZljV//eHiHvnnBaEmdnV9/9O3xuuWuzfL5+37YTp2Qou99tbRrORS2dOe/+h8QN2VCim25rLxDRflufe+rpZo3K6PX9l6PQ0X5bs2blaGLzy7W82/U6u//KY96TgCGX0a6U1mZ9u8WldUdcX1G5PZjSlKibAkAABKJ5zoweEcdkqujDs6TJIXDlm77y4Zeg0KTYf6+2SrI665X6/CF9OrbNYP6rJFSJiTPJUa+FlutqvqsTvMfVo3GWO6odalBy9KdVqUteHquUnSo0qMeo9hw6RIjX/dY1ZKkGgX1m/AOXW2OkjdKUPhaq0N/DFd2LZuSvmkW9rltssuTLCOxnh8jh2lKGWkOlZZ4ddC+mTr1mHylpXa3bZRX+vSn+6PX406NqMPdWt75fe7UY/L0tS+WKDXF3k7idpnKznRq/JgUnXJMvrZs79BdD2zT4hUtgzr/Yw6xh/e7JaWmOFRc4Nbc6ek6/3PFWrOxTf94uDzmYwx3mYChxHsakHhcZwAA7H28HlOF+R7NmpapU08o1rzZ2bbf333/Jm3YPHA43133bdKB83JUUtT57P/2lydqy7a2qGMzHA5D13x7ii0k7OMl9XrjvcHV4QJInuMOz7ctd9VhFXq176xMXXTOGK1e36K/PbhVi5Y1xvy5FVU+/e3BrfrOpeMlSUUFHt1w5RTdeMc6+aIEhU+blKZrLpvYtRwKWfr9PZviLhOQCJ+oVbU9+s67Zegoo3df4Ug7Itos4m0DKYzYvllhtVghpRuxjQFKxBiEPbFMQLJ5PaZOOtr+DH7349ifszuNKvRoVKF9fFJOlqmcLJfGj0nRcYfl6psXjtHzr9fon4/sUIev/8k6JKmpxT5OKDc7vmu8r+3HjPIQEg6MELQbAAAAAACQHISEAwAAABFmTMnU734xV/96ZKv++q/N/W67paxN+83J7lqeNS1TT79UEdNxigo8tkACSUpLI2wXGEh2uj0MoaE5rC8cn6bj58fWIFyS79QVX8zUf15u1RuL4muEHglGFzg0vsTemP7OktjKkeq1J/j6/Jb8cWYdNLfaO/WkeuJIBQYSpNIK6D9WnW3d6Ua2XEb0v89W2QPyPTKihq1EkxURlN2qgTu97dRihXR+eENM26bI0FeNAp1kZsdzenFZL59uCG/XuUauLjLyZPTzbydJpYZby63uEPM1atdJyorpWFVWQHUR//7x/NsBXzitSJPGdT732ztC+uN924b08x97oVqmIV36+VFyOU0dun+W/vqrGXr29RqtWNuq+saAXE5TJUUeHbpfpo45NEcuZ+f9IxAM6457y7RkVf8hKblZ9me5x2PqDz+bquzMgavsXU5Tnzu+QDMmp+mG325UXWNwwH0AJF9Gmv16bu8IxdRBvqf6RvuX9fQ0mvUAABgOPNeBwclId+qKr07oWn7yxUqtWDM8oaInH2sPTn7rgzq1tMU3gaY0ssqE5MkwHLrZLNXN4XJtlE9+WfppeLsONdJ1mJGhMXLLLUNNCmm11aEXrAZt7xFwMFVe/cgsGbDO9UwzR+GwpX9ZNQpK+lCt+k54sxYY2ZpppChHDgVkqVwBfWi16E2rqStOwinpu0aR5hoDT0idrPIkw3DX82Nk+daFo3XWSQUxbfvpymbd+petvSaF7ymyDre2PhDXMcaN9uqXV0/SXQ9s19OvJibUbNrEVN18zST995lK/fORgfup7A5lAmLFexqQeFxnAADs2dLTHHrxP4fHtG1bW1B/uGdDzGMkmluCuvzHS3Tzj2dp2qQMeT0O3f7zuXrjvWq9/m61tmxrl88fUnamS7OnZ+qMBSUaN6a7XmvFmiZd/6uVgyoXgJFn+uR0/eYnM/Tg49t1z0NlMe/38DM7ZJrS184fK5fL1OHzc3XfHfvoqZcqtWx1s+oaAnI5DY0Z5dVhB+bq+CPy5XJ91o8zENZtd2/UpyuaElUsIC4vh+1/i4ca6TGFWke2g2QrvjGHKYYptwz5e8yW2qqw0uP8nP7EOwZhTywTkGxf+UKJ8nqEaTe3BvXCG4lpt0nxOnT2yUU6cN8s/eKOjdqyvf/xi2Xl9t8X5LqVn+tSTV1sgxdnTOkdzt9zEmAAw4t2AwAAAAAAkoO3ZQAAAOwVqmp8ev/jWq1a16zNZW1qag7KsixlZrg0bVK6Dj0wTwfvn9u1vWkauuS8cTINQ3ffvynq5y5e3qCzTinpWj7q0Hz97q/r5YuhYWvBMUW91qWm8BUdGEhKRND1jAku5WZ2d/hYXxbQO0s6VFYZlC8g5WSYmjXRpaP388rr6ez46TANnX9imuoaw1q63p/U899Vh+/jtS2v2RJQdX1sjekel/3fzh+0omwZXeQ+Hjcd3jC8ApalW8M71N4juKNQTp1j5Pazl9Qu+9+yW/H/LUfu024NbXhIthw63cjWSUa2smLoCBwpT04dYKRpirwqNdzKkClDhpoV0garQwutVn2itq7tLUkPW3WyZOkSo/9AiNlK0fNq7Fp+z2rRN6xwTAEsr1u9O923E7yCGI0t8eiCz3V/j77v0QpV1gz9s/yR56v14ZImnXtyoQ7bP0vFBW599bySfvf5ZHmz7vlvudZvae93O0lKj+is+v2vje0KCG/vCOnZ12q1cGmTauoC8npMTRybopOOzNXsaeld+0wZn6qf/N8EXX3zOoXizzUDkGApKfbr3OeP/1kXuU9qCh3dAQAYDjzXgcG5/MvjlZvtliRV1/r0t39vHZbzyMp06tD9c2zrnnutalCfNVLKhOQrMlz6rTlWr1qNesFq1Ab59I7Vones6CHxGTJ1ppGjs41cOWMMDzjbzNWBVpoet+r1vtWiKgV1v1Uj9dOcs69SdYmZr8mGN/pGw1SeRNsd6vkxsrz/SaOeeqVGnyxvHnDbyAne583KUEGeu2t5xdoWvfBWnTZuaVeHL6y8HJcOmJuh047N7/qu53AY+vZFo1VV69eHn8YWSFRd59fCJU1as7FNW8t9am4NygpLmekOTR6fqoP2zdQBczO7tjdNQ+d/rliGYegfD+8YkWUCEoH3NCDxuM4AAEBtvV+PPL1dT75Yrsam6BNt9aWiyqevf3+xTjmuSGcsGKVpkzJ03BGFOu6Iwqj7NDQF9N8ntunBx8oUCsXfvxlA8lTX+vTBJw1avb5FW7a1q6ll5/gsp6ZOSNch++do/rzsru1N09BF54yRaRhxta3896kden9Rg77wuRIdMT9XxYVefeNL4/rd5+MlDfrLA1u1blPrYIsHDKlGK6iFsre/nGhkxbRvhzU07SA9A7Vj6bOfyDEIe2KZgGQ67IAsnXmi/Tv1Px8uV3Nr7IMJgkFLy9e2aPHyZm0qa1d1nV/tHWF5PaYK892aMy1dxx+eq5wek8+WjvLqlh9N0RU/XaOq2uhjN+obgyor71BpSXfb9fGH5eo/T1cOeF5ej6nDD8jutT7VS50iMFLQbgAAAAAAQHKQQAgAwAi1YcMGrVixQmVlZWpublY4HFZ2drays7M1bdo0zZkzR263e+APAvZyq9Y263s3LNVHi+ujbNGu5aub9Oiz5Zo+OV0/vXqGxo5O7frtRZ8fq+VrmvTOh7V97v3+x3VqagkoM72z0Tsz3aWvnj9ed/5zY7/nVZjv0flnlfZa73QYcrtN+QfROAbsLVI99k5gOwPCw5alR15t08sf2YM5K+tCWr0loNc+7tCVX8xUSUHnq7BpGPrK6em69s/16vDvHp3JHaZ00GyPbd07Szpi3t8bEegdHERIeCBin8jPBJLtj1aF1qr7OjAlfc8sHjCsuiOiM6hbA4dbR/JEdEqN/Mxd1aCQnrcaFZb0OWUrNcag8KmGVz83RmueUmVECWuZYaToNOVondWh28I7VK5A1+8eseo1zUrRwUZ6n/tK0gFGutIsU62flblVYT1k1erLA3SCrbYCetzq/b0sLMlnheWJIWQcey/DkL73tbFyuzv/TtZuatOTL1Un7HgO05BlScEYBp299HadHnmuSlu2D/xcdjmNrjLstDOIZcu2dl1320ZV1wVsv1+/pV0vvV2ncxYU6BsXjO5aP3NKms47tUgPPTVwx1kAyZUS0Sl9MO/5Pp+9037kZwIAgOTguQ7E76B52TrxqO56ojv+vklt7cMzw9VJRxXI5ep+D9++o0Ofrog/1HQklQnDIyxLYUkuGTLUb2638uXUhUaejjQy4g7UDkkyJDljCEU41sjU2UaOxhmeAbeNlKzyJNLuUM+PkeWAuRkyTckfCGv5mv5DgiInetxZhxsOW/r7f8r16Av2uultFT4tWdWip16u0S+vnqhxY1IkdQYgXfONsbr4qpVq64j+97VmQ6t+dOuGfgPMV65v01Ov1GjKhBRde9k4jSnuDlj44ulFWr2hVe9/Ev0Zl+wyAYnEexqQeFxnAAAgL8etM04eJdOU/vfU9rjrQx1mZ/8rf8CSNUD3q4rqDt3z4Ga98lYVAeHACLZqXYuuvnGlPl7SGHWbFWta9PgLFZo2KU3XXzFFpSUpXb+78OzRWrmuWe8ujDa+qzeHw5BlWQqGBn4neeH1Kv33qXJtKmsfcFsgWV6zmtRzqo1Rcmm2UqJu31Nk+LVrEO0gkSHc/bWDJGMMwp5YJiBZJo5N0TXfHG9b9/HSJj39Sk3Mn/GP/5Xr+Tdq1BBlEqCNW9v1wSeN+ucj5brorFE677QimWbntZOX7dJPr5yo7/xkdb/HePXdOl36+ZKu5c+fVqSX36lTbX2gn72kS84dpfS03vFHKV7GGAEjBe0GAAAAAAAkByHhAACMIB9++KH+/ve/66mnnlJVVVW/27rdbs2fP1/nn3++vvCFLygvL29Qx1y7dq2mTZtmW1daWqrNmzfLNAfXePbGG2/omGOOGXA7t9utrKws5eXlac6cOTrwwAP1hS98QWPHjh3UcYG+vL+oLuZtV69v0TeuXqy//maexo7pDgq/7JIJem9hrcJ9tFe1tYf08FPb9dULxnetO/+sMaqu9enhp7f3eZyCPLdu//kcZaRH+To+UC9YYC8XLXvg1Y86egWE91TfHNYd/2nSz76erdTPOoikpZg6Zn+vnn9/9+gIuu9UtzJSu5/PbR1hLVrtG/TnDeZuwy0KI8kD4Rq9btnDEi4x8jXbSI2yR3SDizUZfBhKqkz93ZzQtWzJUqvCqlJAK6x2vWE1q1Eh1SioB61avWQ16lqzRFMNbz+f2umAODqhTjG8us0cq2vCW7W9R4fW+8I1OtBMkyPKTTfVMPU5I1sPWd3ftR636pUXdupzZk6f+9RYAf00vL0rWDzSyImWwUh15okFmjk5TVLnRBe/u6dM4QQ8l1xOQ18/v0SnH5ff1al1ICcekasTj8jV+5806k/3bVNNP51Yo71qt7SG+gwI7+nRF6qVl+vSOQsKu9advaBAj79YrQ4fYSzASGbxRRoAgD0Gz3Wgf6kpDl31jYldy2++XxtX2MNQO/mYQtvyc6/33w7fl5FWJiTfSqtdvw3vUJX6HiwdqUZB/d6q1D+tGl1k5OskM2vAfQJWWPdaNXrWaoi5/eY1q0mvWU2arzRdZhYq33DFtF8yyjMckl3Pj5HlwScq9PiL3SHXbrehzHSnJo1N0aH7Z2nerAy5nKYO2jdLB+2bpadertZdD2yPWsccrU368Zeqe4Vp91RdF9B1t23U3b+crvS0zgHNGelOnX58vv77TPRn0MKl0cPBI63b1K4rf75Ov7thikpHdbcbfeW8En24uGnElAlIJt7TgMTjOgMAYM/S2hbSOV/9oGvZNAylpTk1qtCjfWZl66SjC5WT7VZxgVdf/9IEnX7iKF13y0qtXhfb++ucGZm64fvTVVIUWwhqcYFX1105Xd++dKL+8q9NevqlikGVC0Bifbi4IeZt12xo1bd/vFx//uVsjR3dfS/4xoVj9f6i+j7HZ/Xkchq67OJxOnNBccz9OBccU6gFxxTq3YV1uuNvm1Rd54/5fIFEecWyT+x4vJEZNax6IIPZy4hjr2SMQeh9fvEb6WUCEqEgz6Ubr56k1JTuMN2Kap9+fdfmuD7noadi+54dCFi693/lqq7z6/JLu8f7T5mQqmMOydHr70fvL/Hky9U695TCrsDvjDSnfnnNZF33m/VRg8LPOblQZ51U2OfvqJYERi7aDQAAAAAASAxCwgEAGAFWrlypK664Qq+88krM+/j9fr3zzjt65513dNVVV+myyy7Tddddp/z8/LiOfe+99/ZaV1ZWppdfflknnXRSXJ8VL7/fr+rqalVXV2v16tV6+OGHde211+rMM8/UH/7wB40ePTqhxwf60twS1E9vW6V7bt+vqyPZ+NI07TcnWx8vaehzn/sf3qqD98/VrGmZkiTTNHTlNybrmMMK9MzLO7RuY4t8/rDycz06eP9cnXXyKKWmdn4Vr6zuUFFB96BNny8kf4CGMaA/vj6ukbaOsJ58q3XAfeubw3rpw3adeVRa17qDZ3t2m5Dww/exhwN/tNKnQGz5DZKkDr/9387ljL+Tmttl3yfyM4FkeTJcr/9a9slAzjRydLaZG9P+XtlTen1Rgqv744/YJ/Iz+2MahorUO7Blkrw6xMjQl6x83WfV6BmrQZJUraB+Et6mW81SjTM8cZ9rfzIMh642R+mq8Nau8Jlt8muZ2rSv0qLu93kjT4usNq1Vh6TOiQf+ZlXr3VCLTjAyNdHwyC1TdQpqkdWq560GtX92hHw5VdMjgMYtQ25jcJMUYe9QXODWpecWdy0/9kKVNm4d+ue3aUo/vWKCDtwns2tdOGzpvUWNevmdOq3d1Kam5pBcLkOFeW7tMzNdZ5yQrzHFnc/oQ/bL0ozJafrRrRuinp/PbykUtuSIGLjy2AtV/QaE73TfIxU66Yi8rjCWzHSnDpybobcXNg622AASoL0jZFv2eBxRtozO47Y/GyM/EwAAJAfPdSA+37porIoKOuuvWlqD+v09m4btXGZOSdeEsd0TCoZCll4YREj4SCoTkm+J1aZfhLfL3yO6O09OnWZkaz8jVUVyySNTzQppk3x602rWm1aTQpIaFdKfrEqtC3foO0Zh1NCHkGXppnC5PlFb1zpD0sFK13FmpibLq0w5FJClagW01GrTM1aDyj8LKPhIrVod3qobzdGaOMBEk8koT7IMdz0/Rpbm1pCaWyO/Y/m0Ym2rnnqlRrOmpukH3xyn4gK3JOlzJxTI7Tb1u3vK+vy8jo7ef0+tbSHd/+jA4QnVdQE98nyVLj13VNe64w7NGdJA7ebWkG65c4v++POpXX1bxpZ4tc/MdC1e0dLnPiO9TEA8eE8DEo/rDACAPZtlSRVVvl7r121s0Vsf1OqvD2zSty+ZqHNP7xzTU1zo1e9vnKtv/WCxNm1t67VfT/vPzdZvbpht+/5QVePTI89s10ef1Km8skMdvrAyM5yaMiFdJxxVqBOPKpTTaSon261rL5+mGVMydOuf1w1toQEkXXNLUDfesU5/+fWcrjqscWNSNW9WlhYti97f0WFKN/1wmg6al9O1Lhy29PZHdXrxjWqt2dCixuagXE5DRfkezZuTpbNPLlZpSWcY+WEH5mrW1AxdfeNKrd/c/z0LSKTVVru2qjus3pR0nBH7RKwpEW0WkW0asUhkO8hgxiDsiWUCEi0706lbrp2iglx317rahoCuvWW9GpvjGFQ4CE+/UqP952Tq0P2zu9adfnxBvyHhrW0h/fZvW/XTK7snYp84NkV/v3Wmnn21RguXNKq2ISC3y9SkcSlacHS+5kzrDvSvrvWrIK+7rC1t1CkCIwXtBgAAJJBhdv4AADpxT8RejisAAIBh9ve//137779/nwHhGRkZmj9/vk499VRdcMEFOvHEEzV37lylpKTYtvP5fLrjjjt0+OGHx3XsYDCo++67r8/f3XPPPXF91lAJh8N67LHHNGfOHH366afDcg7A2g0t+mixvaH6oP2jB44Gg5Z+fPMKrdtkH2i5z6wsXXfldP3zDwfoobvn648376MLzyntCghvaArolj+ute3T3JrYhnlgT+DrI5T607V++QbO1JQkvb/M3qm9pMCpjNThDTGIRU6GqZkT7IHCb3/aEddnRAasuwcREh4ZLN7X/w8g0V4MN+geq9q27mQjS181C2L+jBTZ/5Z7BqLEKnKflCGsbPYapr5pFuoMI7trXZvCuj1cIcsa+utusuHVPKXa1i2y+u8Y7zIM/dgcpfFy29avVLt+b1XqivBWXRberOvC2/SYVd8VEJ4hU5ebRbZ90qimxACu/EqpvJ91ICuv9Olfjw8cWjIYF55RbAsI7/CFdcPtG3XjHzfrg8VNqmsIKhiy1N4R1pbtHXrq5Rp968dr9MKbtV37ZGc69YurJigjPXqHtw5f707tr7wbvbNsTz5/WO8uarCtmzsjve+NAQyb9vaITrDu+J91brf9PhL5mQAAIDl4rgOx23dWpk47vrve5+5/bVFdQ4yV9wlwynGFtuUPF9ertj6+8xlpZUJyNVpB/Sa8w1YXPF9putMcp3PNXE00vEozHHIahnIMp/Yz0vQ9s1i3mKXK6FHn+aLVqEet6HU//7FqbQHhbhm6wSzRjx0lOshIV57hlMswlGqYGmd4dLqZoz+Z43SC0V2P1aSQfhEuV5MV/RmTrPIky0iv58fIsmJtq37wK3tYwoKj8nTIfpl9bt/eRx3uu4sa+6zb7csr79onuh03JkVZGc44znhg67e065PlzbZ1B8zpuzzS7lEmIFa8pwGJx3UGAMDezecL63d/Xa//PLGta116mlM/uWp6v/tlZ7r082tm2ILC3vmwRhd+e6EefLRM6za1qrUtpFDIUn1DQB8trtcv71ijb1/7qRqauutdz1hQogvPKR36ggFIunWbWvXxEnsg+Px52f3uc9G5Y2wB4R2+kH70q9X66W1r9d7HnW09wWBnP87N29r1+PMV+spVS/Tsq90T2mVnufSrH01XZjr1Vxg+L1v2v/39laY8I/a/Sa+x6+0gvoh9hnqy1HjHIOyJZQISKSPNoVuunaLSUd0TRTc0BXTtr9apvLL3pD+J8J+n7OM2ZkxOU1pq/8HA737coDvvL1Mo3H29pqU4dN5pRfrNdVN1729m6e6bZ+iab463BYQ//mKVFq+wt321EhIOjBi0GwAAAAAAkByMaAAAYBjdcsst+vrXv66Oju6AT9M0ddFFF+n1119XbW2tPvzwQz3zzDN68MEH9eKLL2rJkiWqra3V008/rUsuuUROZ3fHgJ6fE4vnnntOFRV9B6s9+eSTqqmpGVzBIowePVqbNm3q9bN69Wq9+eabuvXWWzV9ur2zYH19vRYsWKD6+uEfWIq904ef2Ac3Th7f/2zvNXV+feuaxXri+XIFAgMPoFy0pF5f/d6iXrPc1tX74z9ZYC/T1tG7E9jG7bEH7Nc1hdXQbL/2ivPin7U62Q6d65FpdneIK6sMamtFfI3g7T77v53HbcjtirJxFJGB6m0+QsKRXK+Fm3SnVWXr2nm8kanLjMKo+/QlVfbr3idLHVZsIQg7Nch+DSYi6PoiI1+5Pc51o3z6VInpZLqfYf++s9kauNNgnuHSb8yxWmBkKZYuy3OVot+Z43p1xs2JaW/srU4+Ok/zZmV0Lf/hn2XyB4b++ZOR5tA5J9snG/jTfdu0cGlzlD06BYKW7ri3TEtWdU8aVJDr1vmnF0XdJ7Kzal1DQJU1sb8LrFpvvw+MLfFG2RLAcGlps7+jpHgd8nri+66Qk23/ss7EYgAADA+e60Bs3G5T13xrUlc99pKVTXrmlaoB9kocr8fUMYfm2dY992p85zPSyoTke8KqV2OPeuAxcuuH5iilGv23K003UvRDs8S27iGrVg1W7/t/sxXSExGB25cZhTrA6H9SOJdh6rtGkWare5L5WgX1sFUXdZ9klCeZdpd6fowclTV+PfiEvZ/U50/pu32ppY/AgdXrW2M+VnVtoNfEFKUlnpj3j9XHy+z11xNKo9cV7y5lAmLBexqQeFxnAABAkv7yr02qru3uRzhtUoYO3Dcn6vZfPHOMcrLdXcuby1r1k1+vVNsAoV8r1jTrhl+vtK37yvnjlJ0VZydnACPSR5/a68AnjkuNsqWUke7UF06310ff8bdN+nBxQ7/HCAQt3Xb3Bn26ojuUuSDPowvPHh3/CQNDoMMK622rxbbuBDMrrs9Ii2gHaVSc44escK8Q7vQEtIPEMwZhTywTkCipKaZ+9cPJmji2uz24qSWoa29Zry3b48sS2BVrNrapqaW7Xs/hMDRu9MBjF554qVrX37peZeUDn2tbe0h//OdW3fWvbcrPsb8DMIk7MHLQbgAAAAAAQHIwogEAgGHy1FNP6cc//rFt3fTp07V48WLdf//9Ovroo+Vy9d2hLSUlRaeddpr++c9/auXKlTrvvPMGdQ733HOPbfm0007r+m+/368HHnhgUJ8byel0avz48b1+pk2bpiOPPFLXXHONli1bpv/7v/+z7VdZWalbbrllSM4BiNeOKnvjc3bmwB1MO3xh/ebOdTr/soX6y/2btGhJvSqrO9ThC6m1LahNW1v13KsVuuL6Jfq/65eqosqn8aX2zm2r17dE+XQAO1XW9e4E1tAS54D/iO3TU0b+6/Fhc+0daN5ZEn+HntZ2S63t9rLnZsYXkJ6XZd++qo//H0CivBVu0u+tCvX8Kz7ayNDlRpEMw4i6X18yDUevDqHViq/zWLVl375EQz8gxWOYOjgiDOYTKzEh4YWG/fybYux06zVMfccs0l3mBF1k5GmuUpQvp9wylCJTpXLrWCNTN5qjdZM5RkWGS2WWPQx5skGIBKK76Ozirv/+8NNGlVf6VZTv7vcnJ8sePO9wqNc2Tof9vnHgPplK8XY/53ZU+fTyO9FDlXqyLPUKlzn+8Nyo22+vsHcWr2uM7/4T2dk1M52gfWCkaWoOqqnZfq0WFcQX6F8csf228vZdPi8AABA/nutAbE46Kl+jR3X+rYdClh58bLuKCzwD/rhd9jq6rEyn7ffZmYN75z36kDylpXbvW1fv1/uL4pugeqSVCcn3bkR4wzlGjtxGbG1K+xipmtUjwNsvS29ZvSej+9hqVUePMIMiuXSckRnTMUzD0BdNexj+q1ajLKvvCfaSUZ5k2l3q+TGyvPFBg215+uQ0paX2bi+NrMOV4q/HrU1CPW5Ftb29JaufZ8zuUiYgFrynAYnHdQYAACTJ7w/rrQ9qbOsO2i96SPgxhxXYlh98tEz+QN91VZEWLW3Qp8sbupa9HoeOP7Lvyb0A7F4qquz1Uv2Nzzp4XrZSUrrr68orO/TCG9UxHceypPse3mZbt+DogihbA4n1jtWs9h6jHrLl0Hyl9bNHb6Mi2iyq4mwDidw+Q6bSB5g4djDiGYOwJ5YJSIQUr6mbfzBZUyd23zda20K67tb12rg1uXVsliVV10a0R2XE1j60aHmzvv7Dlfr5HRv1/Bs12rK9XU3NQQWCYVXX+bVsTYv+8uA2ffnqFXr6lc73jtISe53i2k2JGT8FIH60GwAAAAAAkBz0zgYAYBhs3LhRl1xyiW1g5oEHHqjnn39eeXl5/ezZ25QpU/Tf//5XCxYs0K233hrzfhUVFXruuee6lqdOnao777xTzz33nMLhzg4I9957r6688sq4zmewnE6n7rjjDi1btkyvv/561/r7779ft9xyS9yhj8Cu8vnsIboeT+wdRnZUduj+h7fq/oe3DrjtrGn2Ad4r1zbFfBxgb1Ve07tzVTAUWwfyru0jJph2jfC34+njXSrI6b4PBYKWPljeexB3LHbUhDS5tDswoTDHoYra2DusFWTb74c74tgX2BXvWs26PSIg/DAjXVcaxTIH+V2xVG6tUnfg/g4FVKrYw6orIjqZliYo6Hq03LblHREB20PFI/u/o0/xTcBQbLh0npGn8zTwO80a2TvxTO0RLgNE8vQI1jpo3ywdtG9W3J9RkOvW/bfPtK277Po1tk6yE8fa/w6XrIpvAp9la1rkD4S7gsCyMpwqKXKrvLL3Nbt5W4fmzcroWg7EOBiue3v79ely8c4MjESby9o0d2b3PWvMqBRt2RZ7Z/WSYnsn2M1ldHQHAGC48FwHBuZxd7+/OxyGbr1+xqA+57KLx+uyi8d3Lb/zUZ2uv3VN3J9zynH28JgX36xWKL7qrhFXJiRXhxXWjog64H2M1Chb920fI1UrrO76p7XqPQHsJtnbe+YaKXH1j5itFDllKPhZ0HizOs+7JKJeOVnlSbbdpZ4fI0djc1DNLUFlfBZu7TANFRe4tWGLvd1iy7bef9+7Wo/rTkA9rj/iGB5X9OD/3aVMQKx4TwMSj+sMAABI0tbt9nfmMaP67uvn9ZgaU2L/3cdLGuI61sdLGrTv7Oyu5VlTM/RIXJ8AYCTy+SPqsNzR67AmjbeHKC9eHt8Yq6Urm+z9ODNdGl3s1faK4a/Pxt7lZcv+t3uskSlHnGMeSg23eswzqx1WfIHalZFtIHG0n8QjnjEIe2KZgKHm9Zi66ZrJmjklvWtdW3tI1/1mvdZsHJ76tXie5ZHClvTuxw169+OGAbctyHWpIK+7nbu6zq/a+vjuEwASi3YDAAAAAAASL/baNwAAMGR++MMfqqGhoWs5Oztbjz32WNwB4T19+ctf1jPPPBPz9vfdd5+CPRJSL7nkEpWWlurYY4/tWrds2TItXLhw0OcUL8MweoWSV1RUaOXKlUk7B2Cn7Ez7jO8NTYlpTN5/n2zb8uJljQk5DrAnaeuwVNdkD6ZO9cbXWS5y+5b2+AY/J9vhc+0d1z5Z41Nbx+DOeXuNPSF90ujYE9LdLml0oT0kfHt1MMrWwND50GrRbeEd6nnlH6w0XWOMiruzbE9jI8I+VluxdwDvsMLaHBHeMjYidGWoOCM6mQaUmHtWk2W/t2Yq9klS4mFZlpZY9k48cwxCwjH80lPtf/P1jfG9A4TDUnOL/TrKyuj7ObupzD54LvLYA0mL2D7yuABGho1bW23Ls6dnRtmyN6/H1OSIQW+RnwcAAJKH5zqwexkzyqu5M+zX6XOvVQ3T2WB31are9S05im/W2ZyIOtbIOti+jpMd5zEchqHMiG6gTX2ce7LKk2y7Sz0/RpbICahdzt5tTS1tIVXX2ieAjKyXHUhkvW9TAupxM9Pt13FjS/S2292lTECseE8DEo/rDAAASFIwaA8EdEWZoCo9rXddU229v48to4vcPitiTAeA3VPktdzYz/is9DR7/VNdQ3z3kVBYamq215FlZcZXFw7squ2WXytl7yd8gpEVZevoxkUEYG+WTx1W7GHVKy37OYwzEtMGEs8YhD2xTMBQcrsM/eL7kzRnWndAeEdHSD+5bYNWrhu+urXIMRGNzYkZS7jvrAzb8tJVLQk5DoDBo90AAAAAAIDEIyQcAIAkW7dunR577DHbuttvv11jxozZ5c+eNGlSzNvee++9Xf9tmqYuvvhiSZ1h4z3dc889u3xe8Tj44IN7rSsrK0vqOQCSNHOavUG5ps4XZcvB239utkqKugMxP1nWoG072vvZA8BOyzbYO3uW5MfecdPpkApyIkJAm2PvVJZsKR5D86bZO8K9s2Tw96QVG+ydaqeOi70D/ZRSl5yO7kHyWyqCam4d2QHr2P19bLXolvAO9exCdoDS9AOzZJcCwiVpfyPVtrzMin32+RVqt0WqTJRHOUZiOpHXyN6BLjtBx1kje3hKboKOs1TtqupRptlKUUmCOugC8Whps3fo9nri79Dt9dqr3Ns7+v6OsXBpk8Lh7mdocaFbLlfs97TxY+zB+jV18Q2EAZAcHy6qsy3PmxP7QJ99ZmXJ6ey+p6zZ0Kz6hsRMYAYAAAbGcx3YvZxybKFteemqJpWVxx4cDEhSWh+D/TsUX3tSR8SEj94+umtGHscX5zEkqX0Qx+k8v6EvT7LtLvX8GDlcLkOZEUEG9Y19Bxl8tKTJtjxujDf24zgNjSqyt/Emoh53+iT7NVBX3//3zN2hTECseE8DEo/rDAAASFJhvv1dsD5KYG9La+/36xRvfPVHKV57HVZ7B5NTAXuCGVPSbcs1/Uwg0NJqv+5TBtGPM/LeE60fJ5AoL1uNtuWZStGYQfSVzzWcGt9jctOQ1Ct8vD/LIwK19zfSomy5a+IZg7AnlgkYKi6XoV9cNUn7zuwe1+zzh3XD7zZq2ZrhC8vOTHeouND+TlA7QHvUYC04Ot+2/PwbNQk5DoDBo90AAAAAAIDEo0YaAIAku+OOOxQOd3cuKSoq0pe+9KWknsPbb7+ttWvXdi0fd9xxXSHlZ511lrKystTY2NkZ4aGHHtLtt9+u1NTUPj9rqOXk5PRaV1dX18eWQOK4XYaOOqTAtm7xssYoWw/el84ttS0/+UL5kB8D2FMtWuXXUfO6wzFnT3Tpybdi23f6OJdczu4Qzua2sHbUjNxO5AfN9sjdIzS0piGk1ZsH3/i9YqNfvoAlz2efOXmMS8V5DlXUDvxvcOhc+yDxxWuGfgIFoKfFVqt+Fd6hYI/gkXlK1Y/NUXLtYkB452elyS1D/s8+f7U6VGb5VRpDJ9xXLXuIwiFGepQtd91iq9W2XKLYw/1j5bfCet+ydxyco8S8AzwStn+/X2DE3iEIe6dzLlsW9z5zp6frNz+e3LVcUe3XJd9f2e8+kZ1VJ41LibJl30qK3EpLiZiIpLHvZ3ZdQ1Cr1rdq1tTOe4fLaWrezIxeAS3RHDDXPqnRsrWtUbYEMJw+XFyvDl+oa9KBOTOyNHZMirZuG3hQyynHFduW33qfju4AAAwnnuvAwB55tkKPPFsR9353/Hym9p3VXT90y5/W64U3qgd9HqYpnXiUvZ3vuVerBvVZI6VMGB5ew1SqTLX1CNLeKJ/mxlFvuj4iQCDH6B1mkhvRhXODFV/bS7nlV3tE2HdOH4HgySpPsu0u9fwYOebNzJDD7G5j6vCFogYZvLOwQace2x1GcMCcTN3/aGzPhX1mpsvt6h7g3NAU1NbyoW1bdbkMHXaAvY1lyer+QyJGepmAePCeBiQe1xkAAJCk+fPsY3zKyvv+LtDhC6ulNaj0tO76rqkTM/TJsoaYjzV9sr1+prafIGEAuwe3y9CRB+Xa1n26Ino/ycgA8ckT4gsAHl3sVVqqvd69LsrkBkAihCxLr0e0P5xgZA768w4x0rXZ6u57/6rVpP1iCMYus/y2oGuvDM1LwNiAwYxB2BPLBOwqp8PQT6+YqP3mdN8v/P6wfva7Dfp0RfMwnpl09CG5tra1uoaAtiZgkvZZU9M0Z1r3+0BZeYeWrhq+cHQAfaPdAAAAAACAxItvKm4AALDLXnjhBdvyl7/8ZblcQx/y15977rnHtnzppZd2/XdKSorOO++8ruWmpiY98sgjyTo1NTX17ujj9Xr72BJInAvPGavC/O7ZrYMhS+8trB3SY5x8bJHmz+vu6LZ2Q7Nef4dwACBWa7YEVNPQHWo9vsSlKaWxzYN14sH2wM9l60d2p8/DI4K531myax1p/EHpk9X2wdoLDh44BLUo19S8qd2BCsGQpY9WMOgbibPMatNN4fKuYA9JmqsUXWeWyGUMTZWW1zB1WETox6PWwBPUbLf8to6fDklHGRnRd9gFC60WrZf9WjsoAUElj1r1qlWwa9mUdGAMnW3j9Wq4UZ+qrWt5ojw6PEH/dkC8lkaEp8yakqaxJZ4oW/d26jH5tuWy8g41NkefhOOlt+33m7MXFETZ0m721DRNn9R9fYbClhbGGC4OILl8vrBef9f+rv+lc8YOuF9pSYqOPKT7nhIMhvXym4MLNQQAAEOD5zqw+zhoXo7yc7vrslvbgnrj/aFt58PeY47s7ScvWrFPLF1vBfVRRIDALPVuj5lj2NetUru2xhEU/nzEOY2WS1lG321myShPsu0O9fwYOQxDuuCMItu6j5c2Kxiy+tz+01Utqqjuvh6nTUzV7GmxtZ2ce3KhbXnh0qGvwz3v1EIV9HjmhUKWPvq0/+OM9DIB8eA9DUg8rjMAAHDIAbmaMcUebPr2h9HrWxdHBIJ/7qRRMR8rN9ulw+fn2dYtWRF7/RWAken8M0erIK+7H2YoZOmDRfVRt/804rqfMz1D48bEXhf9uRPt9X9bt7ersSkYZWtg6C1Sq+rU3Xc4ReYu9ZU/2si0hYG8Z7Wo3Bp4DFRkW8nhRobcQzQGw36c+Mcg7IllAnaFaUrXXz5B8/ftnhg2EAzrxj9s1KJlwxsQnp3p1AVn2EN9P1g89N/RPW5DV3zZXu/4j4fLh/w4AHYd7QYAAAAAACQeIeEAACTRtm3btHHjRtu6Y489Nqnn0NzcrIcffrhrOTMzU2eddZZtm56h4ZJ07733JuPUJEmLFi3qtW7ixIlJOz72LCcdU6ic7PhC+E8/sVhfOX+cbd3zr1aosrr/gdiOOL5ZH3VIvn743aldy8FgWDf/Ya1C4bhOFdirhS3p8TfabOsuOTVDGalGlD06nTA/RTPGdw+UDoctvfDBwLNUD5fSQofGjeoOcgiHLb23dNeDuZ96u8022P2wfbzaZ4o76vZOh3TpqRlyObv/fd9d0qHqBm5cSIzVVrtuDG+3BYTPUop+Yo6WZ4g7cl5g5KlnXMqrVpM+jAg66clvhfX7cIWCPc7tBCNLo4zo19A6q0PvW/F3zltrdej2cIVt3SylaLwRPbT4tXCT6q34OrO/GG7QQ5Z94M5xRqYKjYG/R4WsvoMz+vKe1aw/WZVdyw5J/2cWyWH0f+8GkmXDlnaV7eiejMPhMPSDb45Tinfg+84BczJ0xon2kPC3Fzb0u89Lb9Vpy/bu482blTFgUHhWhlPf/7q9A91bHzZoR9XInvQE2Jvd++8tCgS6vzefenxxr0GtPbldhn58xTS5Xd33nmdertD2il2bLAgAAOw6nuvA7uHU4+wBpq+9W6sOH3XZGJzI0Ia3rWa9Hh44FDdghXV7uELtPeqRU2Rovz4CBCbKo9HqrosNS7o9XKE2a+C/20VWq56x7GEqh/UTNJGM8gyHZNTzY2T53An5ys2KbQLpnRwO6aqvlmrGZPvf7VOv1ETdJxyW/vnIDtu6q746VlkZ/R/7nAUFmjer+3oLhS3975nKqNsfd2iOsjPjK8/JR+fqS2faQxleertOVbWBfvdLVpmAZOE9DUg8rjMAAHZ/0yen68iDoz+/o+43JUM3XDXdtm7x8gZt3NIadZ9X37EHhR13RIFOPLowytbdXE5DN1w1Q6mp3e+nbW1BffRJ9CBhAMl1wpH5ysmKb3zWqccX6pLPj7Gte+H1KlXWRO/vuH5Tm7Zu7x7f4XAY+vHlk2Pqxzl/32ydfbK9zuxNJpJFkr0UtofnHmlkyLsL4x9KDLeOM7on7AjK0h3hCvn7aUf6wGrRq1Z3+49Ths43+v8ukMwxCHtimYDBMg3p2m9P0KEHZHetCwYt3fynTfpwgIlh4zFmlEcHz8saeMMecrKcuvHqScrtMT7bHwjrP09V9LNXJzOO257XY+rGqydrfGn3pCBvf1SvdwYYiwFg+NBuAABAAhgGP/zwww8/kT/AXiy+XuUAAGCXvPvuu7ZlwzB0wAEHJPUcHnroIbW1dYeqnnfeeUpJSbFtc+ihh2rq1Klau3atJOnNN9/U+vXrNXny5ISf3913321bzsvL0+zZsxN+XOyZTjthlH7wnal6/Z1qvfpOtRYva4gaAjB9crou/vxYHXWoPZSvqsanv/5r04DHuv9PB+i9j+v0xrs1Wrm2SX3lZU4Ym6qLPz9WJx5dZFv/l/s3ad3G6IOUgd1RTobZZ4eOrHR7RYzDlPKy+u754fNbammPHj770UqfjtzPq2ljOzubFOU6dO3F2XrghRat2mwf/JziMXT6Eak6Yb79mffqxx3aURMaEeXpy+H7em3LKzYGVN+862EmNQ1hvbqwXScdnNq17ltnZ+h/r7TqrcUdtkkLivMcuuSUdE0u7e7U09wW1tNv20PasXepsQLq68qJ7EQZklRp9R1G4JWpLMPRa/0Gq0M/C2+3hY2MlkuXmYVqVEiN1sDX7E5uGcox+q/+KjbcOt3I0eM9glRuCZfrq0aBTjKy5epRgVxm+fTHcKVWqbsTSobMATuY1iiom8M7NE61OsrI1MFGusbIJSNK5fRWy6cXrEY9ZzXY/p3dMnSZ2f+AmZetRv3ZqtThRoYON9I1R6lROxavszr0cLhO78v+PSRPTl1k5Pe5T6TvhrfoACNNhxnpmiqvzD7KtMXy6WGrTm9GBKVfbORrkuHttT0wnP7x8A7d8H8TupanTEjVn34xVX/9d3mfHW0z0h0668QCfeG0Ijl7TKbR2BzUI89X99q+p7Al3f3ANt10zSQ5zM59v3nBaBXmufXA4xVqabPf7+bNStfll5SqpKh7ooDmlqD+8bA90KUvXo8ZNeTF7bJft1kZDhXl9x2IVF3nV5hcNSAu5ZUdevjp7brg7NKudTddO1N/vGeDnnxxh4LB7u8848ak6trLp2ruzO6O+A1NAd370JaknjMAAOgbz3XsKQpy3XI4etfh5EZMvOtwGCou6HuyuvaOkBqb4xtQnQw5WS4dvF+2bd2zr1YNz8lgj3CkkaHHrHptUucErpak31kVWhfu0LlGrnL7qH9earXpnnC1Nso+6es5Rq7S+6gTNwxDF5v5+lW4u45ng3z6XniLvmoW6ECl9apLbrJCesqq1yNWna0OOUOmzjJyhrU8PbVbYTX12ZogBWRvM2tSKGp7Qr6c/U42mYx6fowsC47M01fPK9G7HzfozQ8btHR1i9o7+q64dLsMHbp/ls7/XJHGj7G3Fb/yTp2WrOq/r8br7zfolGNaNHd6uiRpdLFHd9wwRX/8Z5k+WWHfNy3VoS+dWaSzF9jbcp58qVpby6NPBH3SUXn6vy+X6u2FDXrrwwYtWdUin7/v8kyZkKIvnl6kw3uERUiddbf3PTpwXXGyygQkC+9pQOJxnQEAkBwFeVHqbXPsfXgcDkPFhf3U2zb1rrctyPfoV9fN1obNLXrpzSq99UGNtm5r7+MTOo0vTdUZC0bp7FNK5HR29z30+UL67V3r+i3HK29V6cKzSzVlYuc7p2ka+sn3pmvGlAw98EiZaut7BwPvNydbl391oqZOsk9w98CjZWpuHXn10EC8ktEuk+I1lZXRd4BtzyA+ScrKdEY9TnWtzzaOoKdTjyvU9785UW+8X6s33qvVpyuaoo7PmjYpTReePVpHHmSvd62u9eme/5RFLcdOf/v3Vt14zbQen5euv946V3fet1nvL2rotX1mulPnnDpKF55lv281NgX036fKBzweMFTqraA+ln0yjRN7hGEP1gVGnt63WtSizmtulTp0fXibLjeLVdpjAtSAFdYLVqPutez9l88ycgYMuU72GIQ9sUzAYHz/G+N09MH29t1/PLxd6ze3R+3PH01dY0CBQN/jFnOzXfrF9ydp49Z2vfpund79uEHllX2386R4TZ1wRJ4uOKO41/eVh56sUEV19Mk+djr12HwdcWCOXn6nVh992tTn9xivx9QR87N16edLVJDbXdaKKp/++M+Bvy/0/Jzo4yQivgdlOBknAQwB2g0AAAAAAEgsQsIBAEii7du325aLioqUkxN9gGYi3HPPPbblSy+9tM/tLrnkEl133XVdy/fee69uvvnmRJ6abr/9dj3++OO2dV//+tfldPKVBYPn9Th08nHFOvm4YoVClrbtaNeOyg61tgUVClvKynBp8oR05eX0btxtbAroqp8uVV1D34ORe8rOcuuCs0p1wVmlamsLasOWVtXW++X3h5Wd5VJpSapGFfUOwfznf7bo349vG5KyAiPJDy7KUn52/8EAkpST6dAt38nt83fvLe3QP57pf1D2XY826dqLs1Sc1/msKMx16KoLslTbGFJZZVC+gKXsDIcmljjlcto7167c5Ncjr7b29bHDVp6enA5p/kx7B9x3lgzd7NiPvt6mknyn5kx2f3Y8QxeclK7TDk/VloqgfH5L+dmmxhY7baG/gaClux5tUmNrfIHn2LP8MFymKg08+KJWQX0t3PdkG8camfqeUdxr/YdWi1pl71m1XQF9Nxx/54/ZStGvHKUDbneJka+tlk+L1Bl+H5T0F6ta/7XqNFEepRimKq2ANshniy1xytB1ZkmfwSl92SK/7rdqdL9VoxSZGie3MuVQqmEqIEstVlhb5FNDH6Epbhn6iVmicUbfHfN78svSa1aTXrOaZEoaJZeK5FKqYcqUoWYrpE1RjpMhUz83Rw8Yrr5To4J6wqrXE1a9UmRonDzKkVNuw1CjFVK5/H3+rXzByNXZZt/3S2A4vftxox59oUrn9Ag8GVPs1S+umqjG5qDWbW5TU3NQTqepony3Jo1NsYWDS5LfH9Yv/7RZrW0DT2rwyYoW3f3Adn3n4jFd6846qUCnHZen1evbVFMfkMdtauLYFBUX2N9Z/IGwfnXXFlXWDNzR9ogDs3X1N8YOuJ0kff380fr6+aP7/N3FV62M6XgA7O66b6MmjE3VIQd0DnxzuUxd9a0puvQL47RmY4va2oMaXZSiqZPSZZrd9xR/IKwf/3JFn4NkAQDA8OC5jj3BH2+apeLCgSduK8jz6D937dfn7154vUq3/HnDUJ/aLjvxqAJb+MPGLW1avZ6JejF4pmHoWnOUfhgu66pPtSQ9bTXoWatB4+VRkVzyGJ31rhvlU30f9a4HKE3nGNHrQw81MnSG0a4nrYaudeUK6MZwuTJkarK8yjQcClqWKhXQJvl6HcUlQz80S/oN7k5WeXZ612rW763KAbeTpH9YNfqHVdPn7/5uTlCR+g96SFY9P0YOr8fUcYfl6rjDchUOWyqv8qmy2q/WtpACQUupKQ4V5rk1drRHLmfv0I8PFjfqjntjCxj4xR826Xc/maLSUZ3Pz5Iij371w8mqrPFr49Z2dfjCystxafqk1F5hA4tXNOtv/xk4iMjrMXXC4bk64fBchcKWyit8qqzxq7U9pHC4c8LKiaUpvcIYJKmpJajrfrNR9Y2xB6clo0xAsvCeBiQe1xkAAIl316/n9TnWIFJhvkeP3nNwn7977tUK/fKONVH3nTQ+XZeNT9dll0xUa1tQG7e0qrEpoNa2kJwuQ5npLk0cl9bnuIoOX0g/vHG5Nm1t6/f8LEu67pYVuvvX87oCzk3T0HmfG6NzTh2tDZtbVF7ZIZ8vrMwMp6ZMTFd+bu8+ke8trNUDj8YeDAiMZMlolznq4Dxd+93JMZ3PZReP12UXj+/zd1+87BNVVEefGM7rcWjB0YVacHShQiFL2ys6VFHlU0tbUOGwlJnh1ORxqb0mOJCkxuaAfnDTqpjGZ739YZ3+93S5zju9pGtdaUmKfvWjGWpsCmjNxlY1NgfkcpoqLvBo8vhUW/uQ1NmP82e3r1VLDP04gaHyumWfOnWc3JpqpETdPlb5hks/Nkt0Q3i7gp+1cqxSh74T3qxJ8qjYcKnNCmuDfGqMaNc5UGm6MMaJUpM5BmFPLBMwGCcc0ftv+evnj9HXzx/Tx9b9u/qXa7V0gMlxJ45N0cSxnWMFWlqD2rytQ03NQbV1hJTidagg16WJY1N7jY+QpGdfq9aDT1TEdC6GYWjfWRnad1aGwmFLFdV+bdvRoZbWkDweUzlZTk0e37sNakeVTz/69Xo19DH5UTRHzM/WNd8cH9O237hgjL5xQd//thdduZxxEkAcaDcAAAAAACBxqJUGACCJ6urqbMvZ2dlJPf7y5cv10UcfdS1PmTJFhx12WJ/bXnzxxfrJT36i8GdT395333268cYb5XAMHJAaK5/Pp8rKSn344Yf661//qldeecX2+6lTp+pHP/rRkB1vp6qqKlVXVw+8YQ/r168f8vNA8jkchsaNSdW4MakDbrvw03r98o7Vqq6Nv6EpNdWpOTOy+t2mqTmg2+5ap1ffju9vEYBda7ul2//dpK+ekaFpY7sHROdlOZSXFf2Z9c6nHXrghRaFR3DO9bxpbqWndnd2aWoNa8m6oWv8tizpL4836eJTM2xh5JlppuZM6t0xd+c53Pt0s9aVxd7ZBtgdOIzO8JQ/WpV622ruWt+gkD5Rm9THvSJbDl1pFmuWMfD3ir60K6zV+iz4f4B70TR59R2zSBNiCAiPFFZnyPp2BQY8zj5K1ZVmkfKN/sNWommXNWCZ0mXqMqNQR5qZgzoGkAx//Xe5GpqCuuisYlvH06wMpw6Y0//fbkW1X7/921YtXR17ANlTr9QoHLb09fNL5PV0fn9xOU3NmZ4edZ+6hoBu/MMmrVzf/8A7ACNDOCz95Ncrde3l03T8kd2TEOTmuHXI/n2HqtXV+3XTHau1ZGVjsk4TAJAAtEfseXiuAyPbKccW2Jafey22cGKgPyWGWzebpbo9vEPr1R2OEpa0UT5tlC9qfagh6UQjS183CuQ0eg+k7umrRoGy5NS/rdquMARJalZYi6PUU+9UKKeuNIs1J4b66mSVJ9mGo54fI4dpGhpT7NWY4oHDljp8YT30VIUefq5KoRjzgZpbQvrRrzfoB98ap7k96m2L8t0qyu+7XVWSXnizVn/85zaFw1E36ZPDNFRa4lVpycDlWbyiWbf9datq6gcOV+op2WUCEon3NCDxuM4AYPdAmwTikRbDeIedlq9u0q1/XqsNm1tj2n77jg5958ef6oarpmvGlO6+Vg6HoamTMjR1UkbUfcNhS0+9tEO//9sGhUIjuJM3ADkchsaOTtHY0QMHIC9a2qBb/rRB1XWxj4W4874tamgM6NIvlNr7cWa6NH/f7H73rajq0C1/3qBPVzTFfDxgKLxs2f/mTjBie9bGYo6RquvMUbojXNkVmm1JWi+f1lt9h/sfaWTocqNIjkG06SRjDMKeWCZgd5Ke5tTsadHHK+zU3hHS3Q9s0/Nv1A7qOKZpqKTIo5Ki/sdDvbeoQb/7+1Y1NjNmEdgd0G4AAAAAAEDiEBIOAEAS1dbaG8GSHRJ+zz332JYvueSSqNuOGTNGxx9/vF566SVJUnl5uZ5//nmddtppcR93y5YtMuJseJ87d66eeuopZWYOfXjgnXfeqZ///OdD/rkYeR5+aptqan2aMyNLo4oGHjzZ1h7SwsV1evTZci1a2hDXsf735DYddWi+Jo9Pl8MR/e+9orpDz71Sof89uV3NrTRYA0Ohvjms2x5o1JHzvDp6P69Ki/p+1Q2FLa3ZEtAz77TtFiHXh+9jv299sNyn0BAPtvYFpL890axFq306cX6KJo3puwNbS1tYC1f59NTbbWppo9M99kwphqkfGKN0mJWux8P1WrMz7DpChkwdbmToQiNPWUZsVWv7KEVXGkX6RG1aabWrRgPfgzwydIDSdKyZqQOVFvP36c+Z2cqznFpltasqhuN4ZWie0nSqma19BhGEcoaRo/esFm2WT/3dovLl1PFGps4wcpRuDN3EQ0Ci/O+ZKr37caPOOCFfxx6So4z0/q/3Ldva9cJbdXru9Vp1+OJ/YD/zWq0WLW/Wl84q1iH7ZSktpe/rpLYhoGdfq9HjL1arrZ0UFmB30t4R1k9/s0qvv1ut888q1ezpfdd5NTYF9Oo7VbrnwS1qaIov3AkAMPLQHrFn4rkOjEyzp2XYJgr2B8J6+a2aYTwj7ElKDbduM8fqTatZz1sNWqOOfrMD3DJ0qJGuU41sTTcGDkiRJMMw9HkjV4da6XrGatAbVpNa+q11lUrl1olGlk4yspRimP1um+zyDIdE1vNjZLnj3q06eL8szZuZocnjUuR2D/z3v7W8Q6+9V6+X366LO1BbkqrrArrm5vU65Zg8nXZsviaN6/taCIUsLVnVogefrNDyNbGFpz3xUrVq6/2aOSVdxQXRA7p3au8IadHyZj39So0+XRn7hJWRElkmINl4TwMSj+sMAEY+2iQQzaIlDbrpd6s1f78c7TMzS0UFA4+raO8I6f2P6/T8axV6b2Fd3Mfcuq1d37x6sU44qkhnnjxKs6ZlyjSj94Hs8IX05ns1evTZ7VqxpjnqdgCGzyPPVqi6zq850zJUXBjDfaQ9pIVLG/TECxX6ZNngwrr//US53vqwTmefXKzjjyhQZkb/9bmbytr03KtVeuaVSrV30L8SybXSatc2dQfhO2XoaGNox8UeYKTrT6ZX/7Zq9bbVHLUdaZq8OsvM0WFG9Ik5IiV7DMJOe2KZgJFo6/YO/fvJCs2dnq7J41Pl9Qzctla2o0Mvv1W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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw')" + "# plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw')\n", + "plot_cm('android', [pv_la, pv_sj], 'raw')" ] }, { "cell_type": "markdown", + "id": "a90599d9", "metadata": {}, "source": [ "#### Cleaned data" @@ -1192,6 +3684,7 @@ { "cell_type": "code", "execution_count": null, + "id": "37b50ace", "metadata": { "scrolled": false }, @@ -1203,6 +3696,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e2ca2f18", "metadata": {}, "outputs": [], "source": [ @@ -1211,6 +3705,7 @@ }, { "cell_type": "markdown", + "id": "c0f79ef9", "metadata": {}, "source": [ "#### Random Forrest" @@ -1219,6 +3714,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c047ac17", "metadata": {}, "outputs": [], "source": [ @@ -1228,6 +3724,7 @@ { "cell_type": "code", "execution_count": null, + "id": "490b3f29", "metadata": {}, "outputs": [], "source": [ @@ -1236,6 +3733,7 @@ }, { "cell_type": "markdown", + "id": "2b1757a2", "metadata": {}, "source": [ "#### GIS" @@ -1243,16 +3741,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 220, + "id": "d7e67855", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[220], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplot_cm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mios\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mgisv_la\u001b[49m\u001b[43m,\u001b[49m\u001b[43mgisv_sj\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgis\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mINDEX_MAP\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mIIM\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[216], line 20\u001b[0m, in \u001b[0;36mplot_cm\u001b[0;34m(os, pv, d_type, INDEX_MAP)\u001b[0m\n\u001b[1;32m 18\u001b[0m fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimages/clean_distance_cm_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m d_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom_forest\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgis\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 20\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43mget_confusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrole\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpv\u001b[49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msensed_mode\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39mrename(index\u001b[38;5;241m=\u001b[39mINDEX_MAP)\n\u001b[1;32m 21\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(df, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28msorted\u001b[39m(df\u001b[38;5;241m.\u001b[39mindex, key\u001b[38;5;241m=\u001b[39msort_key))\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# fname = f\"images/{d_type}_cm_{os}\"\u001b[39;00m\n", + "Cell \u001b[0;32mIn[214], line 10\u001b[0m, in \u001b[0;36mget_confusion_matrix\u001b[0;34m(os, role, pv, test, test_trip)\u001b[0m\n\u001b[1;32m 8\u001b[0m trips \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pv :\n\u001b[0;32m---> 10\u001b[0m trips\u001b[38;5;241m.\u001b[39mextend(\u001b[43mget_trip_ss_and_gts_timeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrole\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m trips \u001b[38;5;241m=\u001b[39m test_trip \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(test_trip) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlist\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m [test_trip]\n", + "Cell \u001b[0;32mIn[170], line 39\u001b[0m, in \u001b[0;36mget_trip_ss_and_gts_timeline\u001b[0;34m(pv, os, role)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;66;03m# Do this only once.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gt_location_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m gt_location_data \u001b[38;5;241m=\u001b[39m get_gt_location_data(\u001b[43mFILE_MAPPING\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpv\u001b[49m\u001b[43m]\u001b[49m, tr[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrip_id_base\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 40\u001b[0m gt_location_data \u001b[38;5;241m=\u001b[39m gt_location_data\u001b[38;5;241m.\u001b[39mquery(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource == @os\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# now, we build a timeline for each trip\u001b[39;00m\n", + "\u001b[0;31mKeyError\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)" + "# plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)\n", + "plot_cm('ios', [gisv_la,gisv_sj], 'gis', INDEX_MAP=IIM)" ] }, { "cell_type": "code", "execution_count": null, + "id": "d6dc3b96", "metadata": {}, "outputs": [], "source": [ @@ -1261,6 +3787,7 @@ }, { "cell_type": "markdown", + "id": "a51ad7d6", "metadata": {}, "source": [ "## Combined views" @@ -1269,6 +3796,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0b97a649", "metadata": {}, "outputs": [], "source": [ @@ -1278,6 +3806,7 @@ { "cell_type": "code", "execution_count": null, + "id": "fe0dcbd0", "metadata": {}, "outputs": [], "source": [ @@ -1286,6 +3815,7 @@ }, { "cell_type": "markdown", + "id": "9a9934bf", "metadata": {}, "source": [ "## Selected Setting" @@ -1294,6 +3824,7 @@ { "cell_type": "code", "execution_count": null, + "id": "48730a7f", "metadata": {}, "outputs": [], "source": [ @@ -1303,6 +3834,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ed63da40", "metadata": {}, "outputs": [], "source": [ @@ -1312,6 +3844,7 @@ { "cell_type": "code", "execution_count": null, + "id": "b16aa8ed", "metadata": {}, "outputs": [], "source": [ @@ -1321,6 +3854,7 @@ { "cell_type": "code", "execution_count": null, + "id": "8bdb32e2", "metadata": {}, "outputs": [], "source": [ @@ -1336,6 +3870,7 @@ }, { "cell_type": "markdown", + "id": "e054d623", "metadata": {}, "source": [ "#### get percentage of no sensed predicted mode for a given ground truth mode (in this case ground truth mode = walking)" @@ -1344,6 +3879,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0134478c", "metadata": {}, "outputs": [], "source": [ @@ -1361,6 +3897,7 @@ }, { "cell_type": "markdown", + "id": "64ba1bb1", "metadata": {}, "source": [ "# Unit Testing\n", @@ -1372,6 +3909,7 @@ }, { "cell_type": "markdown", + "id": "98e34edb", "metadata": {}, "source": [ "## Example timelines" @@ -1379,6 +3917,7 @@ }, { "cell_type": "markdown", + "id": "97203364", "metadata": {}, "source": [ "### No sensed at the beggining, No GT at the end, Multimodal\n", @@ -1429,6 +3968,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2bb776ec", "metadata": {}, "outputs": [], "source": [ @@ -1447,6 +3987,7 @@ }, { "cell_type": "markdown", + "id": "94bab14e", "metadata": {}, "source": [ "#### get_binary_class_in_sec" @@ -1455,6 +3996,7 @@ { "cell_type": "code", "execution_count": null, + "id": "42cc04ba", "metadata": {}, "outputs": [], "source": [ @@ -1464,6 +4006,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f9ba7e54", "metadata": {}, "outputs": [], "source": [ @@ -1475,6 +4018,7 @@ }, { "cell_type": "markdown", + "id": "dca00cf1", "metadata": {}, "source": [ "#### get_F_score" @@ -1483,6 +4027,7 @@ { "cell_type": "code", "execution_count": null, + "id": "41db01bf", "metadata": {}, "outputs": [], "source": [ @@ -1492,6 +4037,7 @@ }, { "cell_type": "markdown", + "id": "9326fa32", "metadata": {}, "source": [ "#### get_confusion_matrix" @@ -1500,6 +4046,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e2e1305c", "metadata": {}, "outputs": [], "source": [ @@ -1513,6 +4060,7 @@ }, { "cell_type": "markdown", + "id": "32ebcb06", "metadata": {}, "source": [ "### No sensed at beggining and end, multimodal\n", @@ -1549,6 +4097,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0e1f1a32", "metadata": {}, "outputs": [], "source": [ @@ -1564,6 +4113,7 @@ { "cell_type": "code", "execution_count": null, + "id": "dc44411b", "metadata": {}, "outputs": [], "source": [ @@ -1573,6 +4123,7 @@ { "cell_type": "code", "execution_count": null, + "id": "69ae1ab3", "metadata": {}, "outputs": [], "source": [ @@ -1585,6 +4136,7 @@ { "cell_type": "code", "execution_count": null, + "id": "aa2b6bf1", "metadata": {}, "outputs": [], "source": [ @@ -1595,6 +4147,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a2b93c8e", "metadata": {}, "outputs": [], "source": [ @@ -1606,6 +4159,7 @@ }, { "cell_type": "markdown", + "id": "8a4f8a69", "metadata": {}, "source": [ "### No ground truth at beggining and end, unimodal\n", @@ -1639,6 +4193,7 @@ { "cell_type": "code", "execution_count": null, + "id": "8de04469", "metadata": {}, "outputs": [], "source": [ @@ -1654,6 +4209,7 @@ { "cell_type": "code", "execution_count": null, + "id": "315e9116", "metadata": {}, "outputs": [], "source": [ @@ -1663,6 +4219,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e4ecc213", "metadata": {}, "outputs": [], "source": [ @@ -1675,6 +4232,7 @@ { "cell_type": "code", "execution_count": null, + "id": "46add409", "metadata": {}, "outputs": [], "source": [ @@ -1685,6 +4243,7 @@ { "cell_type": "code", "execution_count": null, + "id": "02253a79", "metadata": {}, "outputs": [], "source": [ @@ -1695,6 +4254,7 @@ }, { "cell_type": "markdown", + "id": "5a04d76f", "metadata": {}, "source": [ "## Unimodal Sensed Timeline With Gap\n", @@ -1721,6 +4281,7 @@ { "cell_type": "code", "execution_count": null, + "id": "bdbbcf02", "metadata": {}, "outputs": [], "source": [ @@ -1741,6 +4302,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1c3d5018", "metadata": {}, "outputs": [], "source": [ @@ -1750,6 +4312,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4cc339e6", "metadata": {}, "outputs": [], "source": [ @@ -1765,6 +4328,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1b3eebb6", "metadata": {}, "outputs": [], "source": [ @@ -1774,6 +4338,7 @@ }, { "cell_type": "markdown", + "id": "917d25dc", "metadata": {}, "source": [ "## Unimodal Sensed Timeline With Gap\n", @@ -1787,6 +4352,7 @@ { "cell_type": "code", "execution_count": null, + "id": "9e4e4342", "metadata": {}, "outputs": [], "source": [ @@ -1796,6 +4362,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4616e877", "metadata": {}, "outputs": [], "source": [ @@ -1805,6 +4372,7 @@ { "cell_type": "code", "execution_count": null, + "id": "3ee7c0bd", "metadata": {}, "outputs": [], "source": [ @@ -1819,6 +4387,7 @@ }, { "cell_type": "markdown", + "id": "7df9e139", "metadata": {}, "source": [ "## No ss, gts" @@ -1827,6 +4396,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6d9c34c0", "metadata": {}, "outputs": [], "source": [ @@ -1836,6 +4406,7 @@ }, { "cell_type": "markdown", + "id": "5f2e1557", "metadata": {}, "source": [ "## No ss" @@ -1844,6 +4415,7 @@ { "cell_type": "code", "execution_count": null, + "id": "5541d107", "metadata": {}, "outputs": [], "source": [ @@ -1853,6 +4425,7 @@ }, { "cell_type": "markdown", + "id": "2c63092b", "metadata": {}, "source": [ "## No gts" @@ -1861,6 +4434,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2e5eff99", "metadata": {}, "outputs": [], "source": [ @@ -1871,9 +4445,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "emissioneval", "language": "python", - "name": "python3" + "name": "emissioneval" }, "language_info": { "codemirror_mode": { @@ -1885,7 +4459,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.4" } }, "nbformat": 4, From cae37b850aa39be74f56c4e5bf8afeb60d5c7242 Mon Sep 17 00:00:00 2001 From: Kulhalli Date: Sat, 22 Jul 2023 13:38:47 -0400 Subject: [PATCH 2/9] Cleared outputs --- classification_analysis.ipynb | 2398 +-------------------------------- 1 file changed, 59 insertions(+), 2339 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index 789d1ad..a97fec3 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 341, + "execution_count": null, "id": "2e136f3f", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 342, + "execution_count": null, "id": "a1505557", "metadata": {}, "outputs": [], @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 343, + "execution_count": null, "id": "ac4092a3", "metadata": {}, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 344, + "execution_count": null, "id": "dff4c0cc", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 345, + "execution_count": null, "id": "5395929e", "metadata": {}, "outputs": [], @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 346, + "execution_count": null, "id": "5b3eafd4", "metadata": {}, "outputs": [], @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 347, + "execution_count": null, "id": "03d894bd", "metadata": {}, "outputs": [], @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 348, + "execution_count": null, "id": "217ec510", "metadata": {}, "outputs": [], @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 349, + "execution_count": null, "id": "6c27fdd8", "metadata": {}, "outputs": [], @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": null, "id": "f3e62931", "metadata": {}, "outputs": [], @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 351, + "execution_count": null, "id": "5cc856cf", "metadata": {}, "outputs": [], @@ -159,26 +159,10 @@ }, { "cell_type": "code", - "execution_count": 352, + "execution_count": null, "id": "34d152eb", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "After iterating over 1 entries, entry found\n", - "Found spec = Round trip car and bike trip in the South Bay\n", - "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", - "After iterating over 1 entries, entry found\n", - "Found spec = Multi-modal car scooter BREX trip to San Jose\n", - "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", - "After iterating over 1 entries, entry found\n", - "Found spec = Multimodal multi-train, multi-bus, ebike trip to UC Berkeley\n", - "Evaluation ran from 2019-07-16T00:00:00-07:00 -> 2020-04-30T00:00:00-07:00\n" - ] - } - ], + "outputs": [], "source": [ "DATASTORE_LOC = \"bin/data\"\n", "AUTHOR_EMAIL = \"shankari@eecs.berkeley.edu\"\n", @@ -189,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 353, + "execution_count": null, "id": "191a8e98", "metadata": {}, "outputs": [], @@ -210,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 354, + "execution_count": null, "id": "a29ec8a0", "metadata": {}, "outputs": [], @@ -224,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 355, + "execution_count": null, "id": "27237fc1", "metadata": {}, "outputs": [], @@ -245,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 356, + "execution_count": null, "id": "eb5df932", "metadata": {}, "outputs": [], @@ -287,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 357, + "execution_count": null, "id": "effccbd6", "metadata": {}, "outputs": [], @@ -297,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 358, + "execution_count": null, "id": "b1dd54d7", "metadata": {}, "outputs": [], @@ -307,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 369, + "execution_count": null, "id": "c92e50d1", "metadata": {}, "outputs": [], @@ -371,35 +355,10 @@ }, { "cell_type": "code", - "execution_count": 374, + "execution_count": null, "id": "b0ec3c7e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", - "0 suburb_bicycling_0 suburb_bicycling_0\n", - "-----\n", - "1 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", - "1 suburb_bicycling_0 suburb_bicycling_0\n", - "-----\n", - "2 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", - "2 suburb_bicycling_0 suburb_bicycling_0\n", - "-----\n", - "0 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", - "0 suburb_bicycling_0 suburb_bicycling_0\n", - "-----\n", - "1 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", - "1 suburb_bicycling_0 suburb_bicycling_0\n", - "-----\n", - "2 suburb_city_driving_weekend_0 suburb_city_driving_weekend_0\n", - "2 suburb_bicycling_0 suburb_bicycling_0\n", - "-----\n" - ] - } - ], + "outputs": [], "source": [ "trips = get_trip_ss_and_gts_timeline(pv_la, 'android', 'HAHFDC')" ] @@ -422,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": null, "id": "e26ef8f3", "metadata": {}, "outputs": [], @@ -467,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 333, + "execution_count": null, "id": "3eb538fe", "metadata": {}, "outputs": [], @@ -530,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 334, + "execution_count": null, "id": "c6a21a16", "metadata": {}, "outputs": [], @@ -579,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": null, "id": "d9dd0c1f", "metadata": {}, "outputs": [], @@ -628,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 336, + "execution_count": null, "id": "1defdb2f", "metadata": {}, "outputs": [], @@ -757,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": null, "id": "b9ed7ad6", "metadata": {}, "outputs": [], @@ -820,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": null, "id": "d5ddf739", "metadata": {}, "outputs": [], @@ -841,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": null, "id": "7cc628e5", "metadata": {}, "outputs": [], @@ -880,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": null, "id": "7553fa01", "metadata": {}, "outputs": [], @@ -927,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": null, "id": "22e85696", "metadata": {}, "outputs": [], @@ -946,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": null, "id": "dbe77394", "metadata": {}, "outputs": [], @@ -983,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": null, "id": "dc35d951", "metadata": {}, "outputs": [], @@ -1029,7 +988,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": null, "id": "c43d8bfe", "metadata": {}, "outputs": [], @@ -1057,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": null, "id": "16c43040", "metadata": {}, "outputs": [], @@ -1078,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": null, "id": "00d1e031", "metadata": {}, "outputs": [], @@ -1126,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": null, "id": "f5a13d1c", "metadata": {}, "outputs": [], @@ -1157,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": null, "id": "4ecc8ccb", "metadata": {}, "outputs": [], @@ -1228,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": null, "id": "b1fe08b0", "metadata": {}, "outputs": [], @@ -1314,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "id": "15ca1d1d", "metadata": {}, "outputs": [], @@ -1324,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": null, "id": "bdc483e2", "metadata": {}, "outputs": [], @@ -1335,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": null, "id": "af22eb20", "metadata": {}, "outputs": [], @@ -1346,2176 +1305,12 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": null, "id": "cd8c8f8f", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finished copying unimodal_trip_car_bike_mtv_la, starting overwrite\n", - "Found spec = Round trip car and bike trip in the South Bay\n", - "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", - "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", - "android dict_keys(['ucb-sdb-android-1', 'ucb-sdb-android-2', 'ucb-sdb-android-3', 'ucb-sdb-android-4'])\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_0 HAHFDC v/s HAMFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_1 HAHFDC v/s HAMFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_2 HAHFDC v/s HAMFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_3 HAHFDC v/s MAHFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_4 HAHFDC v/s MAHFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_5 HAHFDC v/s MAHFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = []\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-07-27T17:42:38.727000-07:00', '2019-07-27T17:51:10-07:00'), ('2019-07-27T17:51:11-07:00', '2019-07-27T17:51:30-07:00'), ('2019-07-27T19:03:05.796040-07:00', '2019-07-27T19:08:26-07:00'), ('2019-07-27T19:08:27-07:00', '2019-07-27T19:08:35-07:00'), ('2019-07-27T19:08:36-07:00', '2019-07-27T19:12:34-07:00'), ('2019-07-27T19:12:35-07:00', '2019-07-27T19:12:48-07:00'), ('2019-07-27T19:12:49-07:00', '2019-07-27T19:17:09-07:00'), ('2019-07-27T19:17:10-07:00', '2019-07-27T19:17:47-07:00'), ('2019-07-27T19:17:49-07:00', '2019-07-27T19:21:19-07:00')]\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = ['2019-07-27T17:42:38.727000-07:00', '2019-07-27T17:51:11-07:00']\n", - "Before filtering, trips = [('2019-07-27T17:42:38.727000-07:00', '2019-07-27T17:51:10-07:00'), ('2019-07-27T17:51:11-07:00', '2019-07-27T17:51:30-07:00'), ('2019-07-27T19:03:05.796040-07:00', '2019-07-27T19:08:26-07:00'), ('2019-07-27T19:08:27-07:00', '2019-07-27T19:08:35-07:00'), ('2019-07-27T19:08:36-07:00', '2019-07-27T19:12:34-07:00'), ('2019-07-27T19:12:35-07:00', '2019-07-27T19:12:48-07:00'), ('2019-07-27T19:12:49-07:00', '2019-07-27T19:17:09-07:00'), ('2019-07-27T19:17:10-07:00', '2019-07-27T19:17:47-07:00'), ('2019-07-27T19:17:49-07:00', '2019-07-27T19:21:19-07:00')]\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = ['2019-07-27T19:03:05.796040-07:00', '2019-07-27T19:08:27-07:00', '2019-07-27T19:08:36-07:00', '2019-07-27T19:12:35-07:00', '2019-07-27T19:12:49-07:00', '2019-07-27T19:17:10-07:00', '2019-07-27T19:17:49-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-07-28T10:22:19-07:00', '2019-07-28T10:31:39-07:00'), ('2019-07-28T10:31:40.691000-07:00', '2019-07-28T10:32:44-07:00'), ('2019-07-28T11:56:08.727000-07:00', '2019-07-28T12:07:54-07:00')]\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = ['2019-07-28T10:22:19-07:00', '2019-07-28T10:31:40.691000-07:00']\n", - "Before filtering, trips = [('2019-07-28T10:22:19-07:00', '2019-07-28T10:31:39-07:00'), ('2019-07-28T10:31:40.691000-07:00', '2019-07-28T10:32:44-07:00'), ('2019-07-28T11:56:08.727000-07:00', '2019-07-28T12:07:54-07:00')]\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = ['2019-07-28T11:56:08.727000-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-07-28T15:07:06-07:00', '2019-07-28T15:15:40-07:00'), ('2019-07-28T15:15:42-07:00', '2019-07-28T15:18:02-07:00'), ('2019-07-28T16:35:21.561569-07:00', '2019-07-28T16:53:49-07:00')]\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = ['2019-07-28T15:07:06-07:00', '2019-07-28T15:15:42-07:00']\n", - "Before filtering, trips = [('2019-07-28T15:07:06-07:00', '2019-07-28T15:15:40-07:00'), ('2019-07-28T15:15:42-07:00', '2019-07-28T15:18:02-07:00'), ('2019-07-28T16:35:21.561569-07:00', '2019-07-28T16:53:49-07:00')]\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = ['2019-07-28T16:35:21.561569-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-08-11T17:53:27-07:00', '2019-08-11T17:53:43-07:00'), ('2019-08-11T17:53:45-07:00', '2019-08-11T18:06:30-07:00'), ('2019-08-11T19:12:16.193794-07:00', '2019-08-11T19:25:34-07:00'), ('2019-08-11T19:25:35-07:00', '2019-08-11T19:25:47-07:00'), ('2019-08-11T19:25:48-07:00', '2019-08-11T19:30:30-07:00'), ('2019-08-11T19:30:31-07:00', '2019-08-11T19:31:09-07:00')]\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = ['2019-08-11T17:53:27-07:00', '2019-08-11T17:53:45-07:00']\n", - "Before filtering, trips = [('2019-08-11T17:53:27-07:00', '2019-08-11T17:53:43-07:00'), ('2019-08-11T17:53:45-07:00', '2019-08-11T18:06:30-07:00'), ('2019-08-11T19:12:16.193794-07:00', '2019-08-11T19:25:34-07:00'), ('2019-08-11T19:25:35-07:00', '2019-08-11T19:25:47-07:00'), ('2019-08-11T19:25:48-07:00', '2019-08-11T19:30:30-07:00'), ('2019-08-11T19:30:31-07:00', '2019-08-11T19:31:09-07:00')]\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = ['2019-08-11T19:12:16.193794-07:00', '2019-08-11T19:25:35-07:00', '2019-08-11T19:25:48-07:00', '2019-08-11T19:30:31-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-08-31T10:12:58.699000-07:00', '2019-08-31T10:21:57-07:00'), ('2019-08-31T10:21:58-07:00', '2019-08-31T10:22:59-07:00'), ('2019-08-31T11:33:11.236317-07:00', '2019-08-31T11:46:28-07:00'), ('2019-08-31T11:46:29-07:00', '2019-08-31T11:46:41-07:00'), ('2019-08-31T11:46:42-07:00', '2019-08-31T11:51:54-07:00')]\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = ['2019-08-31T10:12:58.699000-07:00', '2019-08-31T10:21:58-07:00']\n", - "Before filtering, trips = [('2019-08-31T10:12:58.699000-07:00', '2019-08-31T10:21:57-07:00'), ('2019-08-31T10:21:58-07:00', '2019-08-31T10:22:59-07:00'), ('2019-08-31T11:33:11.236317-07:00', '2019-08-31T11:46:28-07:00'), ('2019-08-31T11:46:29-07:00', '2019-08-31T11:46:41-07:00'), ('2019-08-31T11:46:42-07:00', '2019-08-31T11:51:54-07:00')]\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = ['2019-08-31T11:33:11.236317-07:00', '2019-08-31T11:46:29-07:00', '2019-08-31T11:46:42-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-08-31T15:01:52-07:00', '2019-08-31T15:13:36-07:00'), ('2019-08-31T16:32:38.765812-07:00', '2019-08-31T16:50:36-07:00')]\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = ['2019-08-31T15:01:52-07:00']\n", - "Before filtering, trips = [('2019-08-31T15:01:52-07:00', '2019-08-31T15:13:36-07:00'), ('2019-08-31T16:32:38.765812-07:00', '2019-08-31T16:50:36-07:00')]\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = ['2019-08-31T16:32:38.765812-07:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", - "Before filtering, trips = [('2019-07-27T17:46:18.389000-07:00', '2019-07-27T17:46:59-07:00'), ('2019-07-27T17:47:24.433000-07:00', '2019-07-27T17:53:24-07:00'), ('2019-07-27T17:53:53-07:00', '2019-07-27T17:56:56.168000-07:00'), ('2019-07-27T19:01:31.072749-07:00', '2019-07-27T19:21:12-07:00')]\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = ['2019-07-27T17:46:18.389000-07:00', '2019-07-27T17:47:24.433000-07:00', '2019-07-27T17:53:53-07:00']\n", - "Before filtering, trips = [('2019-07-27T17:46:18.389000-07:00', '2019-07-27T17:46:59-07:00'), ('2019-07-27T17:47:24.433000-07:00', '2019-07-27T17:53:24-07:00'), ('2019-07-27T17:53:53-07:00', '2019-07-27T17:56:56.168000-07:00'), ('2019-07-27T19:01:31.072749-07:00', '2019-07-27T19:21:12-07:00')]\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = ['2019-07-27T19:01:31.072749-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", - "Before filtering, trips = [('2019-07-28T10:21:47.338000-07:00', '2019-07-28T10:32:22-07:00'), ('2019-07-28T11:50:20.124734-07:00', '2019-07-28T12:01:27-07:00'), ('2019-07-28T12:01:57-07:00', '2019-07-28T12:01:57-07:00'), ('2019-07-28T12:02:28-07:00', '2019-07-28T12:03:58-07:00'), ('2019-07-28T12:05:28-07:00', '2019-07-28T12:10:00-07:00')]\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = ['2019-07-28T10:21:47.338000-07:00']\n", - "Before filtering, trips = [('2019-07-28T10:21:47.338000-07:00', '2019-07-28T10:32:22-07:00'), ('2019-07-28T11:50:20.124734-07:00', '2019-07-28T12:01:27-07:00'), ('2019-07-28T12:01:57-07:00', '2019-07-28T12:01:57-07:00'), ('2019-07-28T12:02:28-07:00', '2019-07-28T12:03:58-07:00'), ('2019-07-28T12:05:28-07:00', '2019-07-28T12:10:00-07:00')]\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = ['2019-07-28T11:50:20.124734-07:00', '2019-07-28T12:01:57-07:00', '2019-07-28T12:02:28-07:00', '2019-07-28T12:05:28-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", - "Before filtering, trips = [('2019-07-28T15:07:04.625000-07:00', '2019-07-28T15:15:09-07:00'), ('2019-07-28T15:15:36-07:00', '2019-07-28T15:19:11-07:00'), ('2019-07-28T16:34:59.625707-07:00', '2019-07-28T16:54:30.729000-07:00')]\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = ['2019-07-28T15:07:04.625000-07:00', '2019-07-28T15:15:36-07:00']\n", - "Before filtering, trips = [('2019-07-28T15:07:04.625000-07:00', '2019-07-28T15:15:09-07:00'), ('2019-07-28T15:15:36-07:00', '2019-07-28T15:19:11-07:00'), ('2019-07-28T16:34:59.625707-07:00', '2019-07-28T16:54:30.729000-07:00')]\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = ['2019-07-28T16:34:59.625707-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", - "Before filtering, trips = [('2019-08-11T17:42:34.280993-07:00', '2019-08-11T17:49:37.090000-07:00'), ('2019-08-11T17:56:40-07:00', '2019-08-11T18:06:14-07:00'), ('2019-08-11T18:06:15-07:00', '2019-08-11T18:09:59-07:00'), ('2019-08-11T19:12:27.932309-07:00', '2019-08-11T19:23:25-07:00'), ('2019-08-11T19:23:26-07:00', '2019-08-11T19:23:38-07:00'), ('2019-08-11T19:23:39-07:00', '2019-08-11T19:25:21-07:00'), ('2019-08-11T19:25:22-07:00', '2019-08-11T19:25:52-07:00'), ('2019-08-11T19:26:02-07:00', '2019-08-11T19:30:16-07:00')]\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = ['2019-08-11T17:42:34.280993-07:00', '2019-08-11T17:56:40-07:00', '2019-08-11T18:06:15-07:00']\n", - "Before filtering, trips = [('2019-08-11T17:42:34.280993-07:00', '2019-08-11T17:49:37.090000-07:00'), ('2019-08-11T17:56:40-07:00', '2019-08-11T18:06:14-07:00'), ('2019-08-11T18:06:15-07:00', '2019-08-11T18:09:59-07:00'), ('2019-08-11T19:12:27.932309-07:00', '2019-08-11T19:23:25-07:00'), ('2019-08-11T19:23:26-07:00', '2019-08-11T19:23:38-07:00'), ('2019-08-11T19:23:39-07:00', '2019-08-11T19:25:21-07:00'), ('2019-08-11T19:25:22-07:00', '2019-08-11T19:25:52-07:00'), ('2019-08-11T19:26:02-07:00', '2019-08-11T19:30:16-07:00')]\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = ['2019-08-11T19:12:27.932309-07:00', '2019-08-11T19:23:26-07:00', '2019-08-11T19:23:39-07:00', '2019-08-11T19:25:22-07:00', '2019-08-11T19:26:02-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", - "Before filtering, trips = [('2019-08-31T10:12:03.331000-07:00', '2019-08-31T10:22:00-07:00'), ('2019-08-31T10:22:05.540000-07:00', '2019-08-31T10:23:27-07:00'), ('2019-08-31T11:33:47.875566-07:00', '2019-08-31T11:51:52-07:00')]\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = ['2019-08-31T10:12:03.331000-07:00', '2019-08-31T10:22:05.540000-07:00']\n", - "Before filtering, trips = [('2019-08-31T10:12:03.331000-07:00', '2019-08-31T10:22:00-07:00'), ('2019-08-31T10:22:05.540000-07:00', '2019-08-31T10:23:27-07:00'), ('2019-08-31T11:33:47.875566-07:00', '2019-08-31T11:51:52-07:00')]\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = ['2019-08-31T11:33:47.875566-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", - "Before filtering, trips = [('2019-08-31T15:01:48.399000-07:00', '2019-08-31T15:12:46-07:00'), ('2019-08-31T16:32:54-07:00', '2019-08-31T16:50:30-07:00')]\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = ['2019-08-31T15:01:48.399000-07:00']\n", - "Before filtering, trips = [('2019-08-31T15:01:48.399000-07:00', '2019-08-31T15:12:46-07:00'), ('2019-08-31T16:32:54-07:00', '2019-08-31T16:50:30-07:00')]\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = ['2019-08-31T16:32:54-07:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_0 HAHFDC v/s HAMFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_1 HAHFDC v/s HAMFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_2 HAHFDC v/s HAMFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_3 HAHFDC v/s MAHFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_4 HAHFDC v/s MAHFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_5 HAHFDC v/s MAHFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = []\n", - "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", - "ios dict_keys(['ucb-sdb-ios-1', 'ucb-sdb-ios-2', 'ucb-sdb-ios-3', 'ucb-sdb-ios-4'])\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_0 HAHFDC v/s MAHFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_1 HAHFDC v/s MAHFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_2 HAHFDC v/s MAHFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_3 HAHFDC v/s HAMFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_4 HAHFDC v/s HAMFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_5 HAHFDC v/s HAMFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = []\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-07-27T17:48:26.003052-07:00', '2019-07-27T17:55:14.984543-07:00'), ('2019-07-27T19:02:21.000540-07:00', '2019-07-27T19:04:49.996204-07:00'), ('2019-07-27T19:04:50.996161-07:00', '2019-07-27T19:05:06.995687-07:00'), ('2019-07-27T19:05:07.995770-07:00', '2019-07-27T19:12:40.996040-07:00'), ('2019-07-27T19:12:41.996006-07:00', '2019-07-27T19:13:02.995287-07:00'), ('2019-07-27T19:13:03.995252-07:00', '2019-07-27T19:14:55.991418-07:00'), ('2019-07-27T19:14:56.991384-07:00', '2019-07-27T19:18:07.999237-07:00'), ('2019-07-27T19:18:08.999212-07:00', '2019-07-27T19:18:24.998764-07:00'), ('2019-07-27T19:18:25.998734-07:00', '2019-07-27T19:18:54.997776-07:00'), ('2019-07-27T19:18:55.997741-07:00', '2019-07-27T19:19:48.995914-07:00'), ('2019-07-27T19:19:53.995742-07:00', '2019-07-27T19:21:41.992005-07:00')]\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = ['2019-07-27T17:48:26.003052-07:00']\n", - "Before filtering, trips = [('2019-07-27T17:48:26.003052-07:00', '2019-07-27T17:55:14.984543-07:00'), ('2019-07-27T19:02:21.000540-07:00', '2019-07-27T19:04:49.996204-07:00'), ('2019-07-27T19:04:50.996161-07:00', '2019-07-27T19:05:06.995687-07:00'), ('2019-07-27T19:05:07.995770-07:00', '2019-07-27T19:12:40.996040-07:00'), ('2019-07-27T19:12:41.996006-07:00', '2019-07-27T19:13:02.995287-07:00'), ('2019-07-27T19:13:03.995252-07:00', '2019-07-27T19:14:55.991418-07:00'), ('2019-07-27T19:14:56.991384-07:00', '2019-07-27T19:18:07.999237-07:00'), ('2019-07-27T19:18:08.999212-07:00', '2019-07-27T19:18:24.998764-07:00'), ('2019-07-27T19:18:25.998734-07:00', '2019-07-27T19:18:54.997776-07:00'), ('2019-07-27T19:18:55.997741-07:00', '2019-07-27T19:19:48.995914-07:00'), ('2019-07-27T19:19:53.995742-07:00', '2019-07-27T19:21:41.992005-07:00')]\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = ['2019-07-27T19:02:21.000540-07:00', '2019-07-27T19:04:50.996161-07:00', '2019-07-27T19:05:07.995770-07:00', '2019-07-27T19:12:41.996006-07:00', '2019-07-27T19:13:03.995252-07:00', '2019-07-27T19:14:56.991384-07:00', '2019-07-27T19:18:08.999212-07:00', '2019-07-27T19:18:25.998734-07:00', '2019-07-27T19:18:55.997741-07:00', '2019-07-27T19:19:53.995742-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-07-28T10:23:25.343677-07:00', '2019-07-28T10:31:44.997968-07:00'), ('2019-07-28T10:31:45.997937-07:00', '2019-07-28T10:34:14.992393-07:00'), ('2019-07-28T11:51:07.259256-07:00', '2019-07-28T12:02:23.994942-07:00'), ('2019-07-28T12:02:24.994908-07:00', '2019-07-28T12:05:25.988719-07:00'), ('2019-07-28T12:05:26.988686-07:00', '2019-07-28T12:06:38.986224-07:00'), ('2019-07-28T12:06:39.986190-07:00', '2019-07-28T12:06:58.996818-07:00'), ('2019-07-28T12:06:59.997020-07:00', '2019-07-28T12:07:27.998720-07:00'), ('2019-07-28T12:07:28.998719-07:00', '2019-07-28T12:07:37.998624-07:00'), ('2019-07-28T12:07:38.998607-07:00', '2019-07-28T12:09:25.995176-07:00'), ('2019-07-28T12:09:30.995004-07:00', '2019-07-28T12:10:03.993867-07:00')]\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = ['2019-07-28T10:23:25.343677-07:00', '2019-07-28T10:31:45.997937-07:00']\n", - "Before filtering, trips = [('2019-07-28T10:23:25.343677-07:00', '2019-07-28T10:31:44.997968-07:00'), ('2019-07-28T10:31:45.997937-07:00', '2019-07-28T10:34:14.992393-07:00'), ('2019-07-28T11:51:07.259256-07:00', '2019-07-28T12:02:23.994942-07:00'), ('2019-07-28T12:02:24.994908-07:00', '2019-07-28T12:05:25.988719-07:00'), ('2019-07-28T12:05:26.988686-07:00', '2019-07-28T12:06:38.986224-07:00'), ('2019-07-28T12:06:39.986190-07:00', '2019-07-28T12:06:58.996818-07:00'), ('2019-07-28T12:06:59.997020-07:00', '2019-07-28T12:07:27.998720-07:00'), ('2019-07-28T12:07:28.998719-07:00', '2019-07-28T12:07:37.998624-07:00'), ('2019-07-28T12:07:38.998607-07:00', '2019-07-28T12:09:25.995176-07:00'), ('2019-07-28T12:09:30.995004-07:00', '2019-07-28T12:10:03.993867-07:00')]\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = ['2019-07-28T11:51:07.259256-07:00', '2019-07-28T12:02:24.994908-07:00', '2019-07-28T12:05:26.988686-07:00', '2019-07-28T12:06:39.986190-07:00', '2019-07-28T12:06:59.997020-07:00', '2019-07-28T12:07:28.998719-07:00', '2019-07-28T12:07:38.998607-07:00', '2019-07-28T12:09:30.995004-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-07-28T15:07:32.811940-07:00', '2019-07-28T15:15:24.998590-07:00'), ('2019-07-28T15:15:26.998613-07:00', '2019-07-28T15:18:05.993966-07:00'), ('2019-07-28T16:35:57.908857-07:00', '2019-07-28T16:45:56.997239-07:00'), ('2019-07-28T16:45:57.997204-07:00', '2019-07-28T16:46:03.996983-07:00'), ('2019-07-28T16:46:04.996947-07:00', '2019-07-28T16:53:34.996686-07:00'), ('2019-07-28T16:53:36.996612-07:00', '2019-07-28T16:55:43.830019-07:00')]\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = ['2019-07-28T15:07:32.811940-07:00', '2019-07-28T15:15:26.998613-07:00']\n", - "Before filtering, trips = [('2019-07-28T15:07:32.811940-07:00', '2019-07-28T15:15:24.998590-07:00'), ('2019-07-28T15:15:26.998613-07:00', '2019-07-28T15:18:05.993966-07:00'), ('2019-07-28T16:35:57.908857-07:00', '2019-07-28T16:45:56.997239-07:00'), ('2019-07-28T16:45:57.997204-07:00', '2019-07-28T16:46:03.996983-07:00'), ('2019-07-28T16:46:04.996947-07:00', '2019-07-28T16:53:34.996686-07:00'), ('2019-07-28T16:53:36.996612-07:00', '2019-07-28T16:55:43.830019-07:00')]\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = ['2019-07-28T16:35:57.908857-07:00', '2019-07-28T16:45:57.997204-07:00', '2019-07-28T16:46:04.996947-07:00', '2019-07-28T16:53:36.996612-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-08-11T17:53:59.032591-07:00', '2019-08-11T18:06:13.990799-07:00'), ('2019-08-11T18:06:14.990764-07:00', '2019-08-11T18:07:53.987217-07:00'), ('2019-08-11T19:12:38.044344-07:00', '2019-08-11T19:15:15.997580-07:00'), ('2019-08-11T19:15:16.997539-07:00', '2019-08-11T19:15:20.997372-07:00'), ('2019-08-11T19:15:21.997330-07:00', '2019-08-11T19:21:48.998054-07:00'), ('2019-08-11T19:21:49.998110-07:00', '2019-08-11T19:22:08.998388-07:00'), ('2019-08-11T19:22:09.998376-07:00', '2019-08-11T19:24:17.993807-07:00'), ('2019-08-11T19:24:18.993765-07:00', '2019-08-11T19:27:35.986123-07:00'), ('2019-08-11T19:27:36.986084-07:00', '2019-08-11T19:30:19.996613-07:00'), ('2019-08-11T19:30:22.996500-07:00', '2019-08-11T19:32:37.991355-07:00')]\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = ['2019-08-11T17:53:59.032591-07:00', '2019-08-11T18:06:14.990764-07:00']\n", - "Before filtering, trips = [('2019-08-11T17:53:59.032591-07:00', '2019-08-11T18:06:13.990799-07:00'), ('2019-08-11T18:06:14.990764-07:00', '2019-08-11T18:07:53.987217-07:00'), ('2019-08-11T19:12:38.044344-07:00', '2019-08-11T19:15:15.997580-07:00'), ('2019-08-11T19:15:16.997539-07:00', '2019-08-11T19:15:20.997372-07:00'), ('2019-08-11T19:15:21.997330-07:00', '2019-08-11T19:21:48.998054-07:00'), ('2019-08-11T19:21:49.998110-07:00', '2019-08-11T19:22:08.998388-07:00'), ('2019-08-11T19:22:09.998376-07:00', '2019-08-11T19:24:17.993807-07:00'), ('2019-08-11T19:24:18.993765-07:00', '2019-08-11T19:27:35.986123-07:00'), ('2019-08-11T19:27:36.986084-07:00', '2019-08-11T19:30:19.996613-07:00'), ('2019-08-11T19:30:22.996500-07:00', '2019-08-11T19:32:37.991355-07:00')]\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = ['2019-08-11T19:12:38.044344-07:00', '2019-08-11T19:15:16.997539-07:00', '2019-08-11T19:15:21.997330-07:00', '2019-08-11T19:21:49.998110-07:00', '2019-08-11T19:22:09.998376-07:00', '2019-08-11T19:24:18.993765-07:00', '2019-08-11T19:27:36.986084-07:00', '2019-08-11T19:30:22.996500-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-08-31T10:25:07.058955-07:00', '2019-08-31T11:48:40.995877-07:00'), ('2019-08-31T11:48:41.995913-07:00', '2019-08-31T11:52:13.989582-07:00'), ('2019-08-31T11:52:15.989512-07:00', '2019-08-31T11:54:17.429140-07:00')]\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = [('2019-08-31T10:25:07.058955-07:00', '2019-08-31T11:48:40.995877-07:00'), ('2019-08-31T11:48:41.995913-07:00', '2019-08-31T11:52:13.989582-07:00'), ('2019-08-31T11:52:15.989512-07:00', '2019-08-31T11:54:17.429140-07:00')]\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = ['2019-08-31T11:48:41.995913-07:00', '2019-08-31T11:52:15.989512-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-08-31T15:00:59.157359-07:00', '2019-08-31T15:13:17.989944-07:00'), ('2019-08-31T15:13:18.989909-07:00', '2019-08-31T15:15:01.986328-07:00'), ('2019-08-31T16:32:26.722881-07:00', '2019-08-31T16:35:00.997696-07:00'), ('2019-08-31T16:35:01.997660-07:00', '2019-08-31T16:35:10.997329-07:00'), ('2019-08-31T16:35:11.997292-07:00', '2019-08-31T16:43:29.995417-07:00'), ('2019-08-31T16:43:30.995383-07:00', '2019-08-31T16:47:20.987332-07:00'), ('2019-08-31T16:47:21.987296-07:00', '2019-08-31T16:50:10.996794-07:00'), ('2019-08-31T16:50:14.996661-07:00', '2019-08-31T16:52:52.080658-07:00')]\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = ['2019-08-31T15:00:59.157359-07:00', '2019-08-31T15:13:18.989909-07:00']\n", - "Before filtering, trips = [('2019-08-31T15:00:59.157359-07:00', '2019-08-31T15:13:17.989944-07:00'), ('2019-08-31T15:13:18.989909-07:00', '2019-08-31T15:15:01.986328-07:00'), ('2019-08-31T16:32:26.722881-07:00', '2019-08-31T16:35:00.997696-07:00'), ('2019-08-31T16:35:01.997660-07:00', '2019-08-31T16:35:10.997329-07:00'), ('2019-08-31T16:35:11.997292-07:00', '2019-08-31T16:43:29.995417-07:00'), ('2019-08-31T16:43:30.995383-07:00', '2019-08-31T16:47:20.987332-07:00'), ('2019-08-31T16:47:21.987296-07:00', '2019-08-31T16:50:10.996794-07:00'), ('2019-08-31T16:50:14.996661-07:00', '2019-08-31T16:52:52.080658-07:00')]\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = ['2019-08-31T16:32:26.722881-07:00', '2019-08-31T16:35:01.997660-07:00', '2019-08-31T16:35:11.997292-07:00', '2019-08-31T16:43:30.995383-07:00', '2019-08-31T16:47:21.987296-07:00', '2019-08-31T16:50:14.996661-07:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", - "Before filtering, trips = [('2019-07-27T19:01:45.481526-07:00', '2019-07-27T19:21:37.427862-07:00')]\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = [('2019-07-27T19:01:45.481526-07:00', '2019-07-27T19:21:37.427862-07:00')]\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = ['2019-07-27T19:01:45.481526-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", - "Before filtering, trips = [('2019-07-28T10:23:13.947510-07:00', '2019-07-28T10:31:48.066216-07:00'), ('2019-07-28T10:31:54.494439-07:00', '2019-07-28T10:34:30.450632-07:00'), ('2019-07-28T11:50:42.000985-07:00', '2019-07-28T12:10:40.324661-07:00')]\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = ['2019-07-28T10:23:13.947510-07:00', '2019-07-28T10:31:54.494439-07:00']\n", - "Before filtering, trips = [('2019-07-28T10:23:13.947510-07:00', '2019-07-28T10:31:48.066216-07:00'), ('2019-07-28T10:31:54.494439-07:00', '2019-07-28T10:34:30.450632-07:00'), ('2019-07-28T11:50:42.000985-07:00', '2019-07-28T12:10:40.324661-07:00')]\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = ['2019-07-28T11:50:42.000985-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", - "Before filtering, trips = [('2019-07-28T15:05:58.387160-07:00', '2019-07-28T15:15:28.774266-07:00'), ('2019-07-28T15:15:35.207299-07:00', '2019-07-28T15:18:09.393904-07:00'), ('2019-07-28T16:35:11.805413-07:00', '2019-07-28T16:55:39.003900-07:00')]\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = ['2019-07-28T15:05:58.387160-07:00', '2019-07-28T15:15:35.207299-07:00']\n", - "Before filtering, trips = [('2019-07-28T15:05:58.387160-07:00', '2019-07-28T15:15:28.774266-07:00'), ('2019-07-28T15:15:35.207299-07:00', '2019-07-28T15:18:09.393904-07:00'), ('2019-07-28T16:35:11.805413-07:00', '2019-07-28T16:55:39.003900-07:00')]\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = ['2019-07-28T16:35:11.805413-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", - "Before filtering, trips = [('2019-08-11T17:56:11.652814-07:00', '2019-08-11T17:56:52.001655-07:00'), ('2019-08-11T17:56:58.999691-07:00', '2019-08-11T17:57:31.995701-07:00'), ('2019-08-11T17:57:35.995450-07:00', '2019-08-11T18:05:22.996270-07:00'), ('2019-08-11T18:06:40.993286-07:00', '2019-08-11T18:07:46.990754-07:00'), ('2019-08-11T19:12:12.022208-07:00', '2019-08-11T19:21:45.998302-07:00'), ('2019-08-11T19:21:58.997940-07:00', '2019-08-11T19:21:58.997940-07:00'), ('2019-08-11T19:22:05.997700-07:00', '2019-08-11T19:23:36.994141-07:00'), ('2019-08-11T19:24:15.992582-07:00', '2019-08-11T19:24:24.992223-07:00'), ('2019-08-11T19:24:35.991783-07:00', '2019-08-11T19:28:32.996612-07:00'), ('2019-08-11T19:28:41.997831-07:00', '2019-08-11T19:28:41.997831-07:00'), ('2019-08-11T19:28:48.998278-07:00', '2019-08-11T19:31:06.993943-07:00'), ('2019-08-11T19:32:41.990081-07:00', '2019-08-11T19:32:41.990081-07:00')]\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = ['2019-08-11T17:56:11.652814-07:00', '2019-08-11T17:56:58.999691-07:00', '2019-08-11T17:57:35.995450-07:00', '2019-08-11T18:06:40.993286-07:00']\n", - "Before filtering, trips = [('2019-08-11T17:56:11.652814-07:00', '2019-08-11T17:56:52.001655-07:00'), ('2019-08-11T17:56:58.999691-07:00', '2019-08-11T17:57:31.995701-07:00'), ('2019-08-11T17:57:35.995450-07:00', '2019-08-11T18:05:22.996270-07:00'), ('2019-08-11T18:06:40.993286-07:00', '2019-08-11T18:07:46.990754-07:00'), ('2019-08-11T19:12:12.022208-07:00', '2019-08-11T19:21:45.998302-07:00'), ('2019-08-11T19:21:58.997940-07:00', '2019-08-11T19:21:58.997940-07:00'), ('2019-08-11T19:22:05.997700-07:00', '2019-08-11T19:23:36.994141-07:00'), ('2019-08-11T19:24:15.992582-07:00', '2019-08-11T19:24:24.992223-07:00'), ('2019-08-11T19:24:35.991783-07:00', '2019-08-11T19:28:32.996612-07:00'), ('2019-08-11T19:28:41.997831-07:00', '2019-08-11T19:28:41.997831-07:00'), ('2019-08-11T19:28:48.998278-07:00', '2019-08-11T19:31:06.993943-07:00'), ('2019-08-11T19:32:41.990081-07:00', '2019-08-11T19:32:41.990081-07:00')]\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = ['2019-08-11T19:12:12.022208-07:00', '2019-08-11T19:21:58.997940-07:00', '2019-08-11T19:22:05.997700-07:00', '2019-08-11T19:24:15.992582-07:00', '2019-08-11T19:24:35.991783-07:00', '2019-08-11T19:28:41.997831-07:00', '2019-08-11T19:28:48.998278-07:00', '2019-08-11T19:32:41.990081-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", - "Before filtering, trips = [('2019-08-31T10:20:46.525764-07:00', '2019-08-31T10:21:18.003078-07:00'), ('2019-08-31T10:22:33.994846-07:00', '2019-08-31T10:24:27.990058-07:00'), ('2019-08-31T11:34:10.217073-07:00', '2019-08-31T11:43:11.998456-07:00'), ('2019-08-31T11:43:20.998398-07:00', '2019-08-31T11:43:28.998243-07:00'), ('2019-08-31T11:43:35.998062-07:00', '2019-08-31T11:46:45.991218-07:00'), ('2019-08-31T11:48:27.987473-07:00', '2019-08-31T11:48:34.987216-07:00'), ('2019-08-31T11:48:41.986959-07:00', '2019-08-31T11:51:00.997739-07:00'), ('2019-08-31T11:52:41.994148-07:00', '2019-08-31T11:53:59.924421-07:00')]\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = ['2019-08-31T10:20:46.525764-07:00', '2019-08-31T10:22:33.994846-07:00']\n", - "Before filtering, trips = [('2019-08-31T10:20:46.525764-07:00', '2019-08-31T10:21:18.003078-07:00'), ('2019-08-31T10:22:33.994846-07:00', '2019-08-31T10:24:27.990058-07:00'), ('2019-08-31T11:34:10.217073-07:00', '2019-08-31T11:43:11.998456-07:00'), ('2019-08-31T11:43:20.998398-07:00', '2019-08-31T11:43:28.998243-07:00'), ('2019-08-31T11:43:35.998062-07:00', '2019-08-31T11:46:45.991218-07:00'), ('2019-08-31T11:48:27.987473-07:00', '2019-08-31T11:48:34.987216-07:00'), ('2019-08-31T11:48:41.986959-07:00', '2019-08-31T11:51:00.997739-07:00'), ('2019-08-31T11:52:41.994148-07:00', '2019-08-31T11:53:59.924421-07:00')]\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = ['2019-08-31T11:34:10.217073-07:00', '2019-08-31T11:43:20.998398-07:00', '2019-08-31T11:43:35.998062-07:00', '2019-08-31T11:48:27.987473-07:00', '2019-08-31T11:48:41.986959-07:00', '2019-08-31T11:52:41.994148-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", - "Before filtering, trips = [('2019-08-31T15:02:50.999717-07:00', '2019-08-31T15:03:26.998827-07:00'), ('2019-08-31T15:03:30.998668-07:00', '2019-08-31T15:03:30.998668-07:00'), ('2019-08-31T15:03:38.998345-07:00', '2019-08-31T15:12:28.995477-07:00'), ('2019-08-31T15:13:25.995546-07:00', '2019-08-31T15:14:30.993356-07:00'), ('2019-08-31T16:32:33.961561-07:00', '2019-08-31T16:39:48.985268-07:00'), ('2019-08-31T16:39:55.984972-07:00', '2019-08-31T16:39:55.984972-07:00'), ('2019-08-31T16:40:02.984676-07:00', '2019-08-31T16:43:46.995780-07:00'), ('2019-08-31T16:45:30.991822-07:00', '2019-08-31T16:47:16.987788-07:00'), ('2019-08-31T16:47:23.987522-07:00', '2019-08-31T16:49:46.998160-07:00'), ('2019-08-31T16:51:40.994053-07:00', '2019-08-31T16:52:58.057469-07:00')]\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = ['2019-08-31T15:02:50.999717-07:00', '2019-08-31T15:03:30.998668-07:00', '2019-08-31T15:03:38.998345-07:00', '2019-08-31T15:13:25.995546-07:00']\n", - "Before filtering, trips = [('2019-08-31T15:02:50.999717-07:00', '2019-08-31T15:03:26.998827-07:00'), ('2019-08-31T15:03:30.998668-07:00', '2019-08-31T15:03:30.998668-07:00'), ('2019-08-31T15:03:38.998345-07:00', '2019-08-31T15:12:28.995477-07:00'), ('2019-08-31T15:13:25.995546-07:00', '2019-08-31T15:14:30.993356-07:00'), ('2019-08-31T16:32:33.961561-07:00', '2019-08-31T16:39:48.985268-07:00'), ('2019-08-31T16:39:55.984972-07:00', '2019-08-31T16:39:55.984972-07:00'), ('2019-08-31T16:40:02.984676-07:00', '2019-08-31T16:43:46.995780-07:00'), ('2019-08-31T16:45:30.991822-07:00', '2019-08-31T16:47:16.987788-07:00'), ('2019-08-31T16:47:23.987522-07:00', '2019-08-31T16:49:46.998160-07:00'), ('2019-08-31T16:51:40.994053-07:00', '2019-08-31T16:52:58.057469-07:00')]\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = ['2019-08-31T16:32:33.961561-07:00', '2019-08-31T16:39:55.984972-07:00', '2019-08-31T16:40:02.984676-07:00', '2019-08-31T16:45:30.991822-07:00', '2019-08-31T16:47:23.987522-07:00', '2019-08-31T16:51:40.994053-07:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_0 HAHFDC v/s MAHFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T17:38:54.143985-07:00 -> 2019-07-27T17:54:56.504297-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T18:59:17.435039-07:00 -> 2019-07-27T19:20:57.464819-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_1 HAHFDC v/s MAHFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T10:19:03.776588-07:00 -> 2019-07-28T10:32:24.080722-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T11:48:06.675345-07:00 -> 2019-07-28T12:09:44.829831-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_2 HAHFDC v/s MAHFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T15:02:04.965219-07:00 -> 2019-07-28T15:16:50.532115-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-28T16:33:42.064345-07:00 -> 2019-07-28T16:54:40.320724-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_3 HAHFDC v/s HAMFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T17:51:19.220633-07:00 -> 2019-08-11T18:07:09.679044-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-11T19:10:44.864440-07:00 -> 2019-08-11T19:31:44.679491-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_4 HAHFDC v/s HAMFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T10:07:27.557744-07:00 -> 2019-08-31T10:23:08.473621-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T11:30:50.166396-07:00 -> 2019-08-31T11:52:38.771930-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_5 HAHFDC v/s HAMFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T14:57:42.798072-07:00 -> 2019-08-31T15:14:48.798746-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-31T16:30:05.301877-07:00 -> 2019-08-31T16:51:33.719355-07:00\n", - "After filtering, trips = []\n", - "Finished copying car_scooter_brex_san_jose, starting overwrite\n", - "Found spec = Multi-modal car scooter BREX trip to San Jose\n", - "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", - "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", - "android dict_keys(['ucb-sdb-android-1', 'ucb-sdb-android-2', 'ucb-sdb-android-3', 'ucb-sdb-android-4'])\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_0 HAHFDC v/s HAMFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_1 HAHFDC v/s HAMFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_2 HAHFDC v/s HAMFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_3 HAHFDC v/s MAHFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_4 HAHFDC v/s MAHFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_5 HAHFDC v/s MAHFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_6 MAMFDC v/s HAMFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = []\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-07-22T11:54:28.840000-07:00', '2019-07-22T11:57:57-07:00'), ('2019-07-22T11:57:58-07:00', '2019-07-22T12:22:10-07:00'), ('2019-07-22T16:09:04.460140-07:00', '2019-07-22T16:24:35-07:00'), ('2019-07-22T16:24:36-07:00', '2019-07-22T16:24:48-07:00'), ('2019-07-22T16:24:49-07:00', '2019-07-22T16:27:10-07:00'), ('2019-07-22T16:27:11-07:00', '2019-07-22T16:27:45-07:00'), ('2019-07-22T16:27:47-07:00', '2019-07-22T16:29:04-07:00'), ('2019-07-22T16:29:05-07:00', '2019-07-22T16:29:30-07:00'), ('2019-07-22T16:29:30-07:00', '2019-07-22T16:29:56-07:00'), ('2019-07-22T16:29:57-07:00', '2019-07-22T16:30:09-07:00'), ('2019-07-22T16:30:10-07:00', '2019-07-22T16:36:06-07:00'), ('2019-07-22T16:36:07-07:00', '2019-07-22T16:36:19-07:00'), ('2019-07-22T16:36:20-07:00', '2019-07-22T16:37:37-07:00'), ('2019-07-22T16:37:38-07:00', '2019-07-22T16:37:59-07:00'), ('2019-07-22T16:38:01-07:00', '2019-07-22T16:38:27-07:00'), ('2019-07-22T16:54:55.404858-07:00', '2019-07-22T17:33:43-07:00'), ('2019-07-22T17:33:44-07:00', '2019-07-22T17:34:19-07:00'), ('2019-07-22T17:34:20-07:00', '2019-07-22T17:34:44-07:00'), ('2019-07-22T17:34:45.700000-07:00', '2019-07-22T17:39:30-07:00'), ('2019-07-22T17:39:31-07:00', '2019-07-22T17:39:44-07:00'), ('2019-07-22T17:39:45-07:00', '2019-07-22T17:45:09-07:00')]\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = ['2019-07-22T11:54:28.840000-07:00', '2019-07-22T11:57:58-07:00']\n", - "Before filtering, trips = [('2019-07-22T11:54:28.840000-07:00', '2019-07-22T11:57:57-07:00'), ('2019-07-22T11:57:58-07:00', '2019-07-22T12:22:10-07:00'), ('2019-07-22T16:09:04.460140-07:00', '2019-07-22T16:24:35-07:00'), ('2019-07-22T16:24:36-07:00', '2019-07-22T16:24:48-07:00'), ('2019-07-22T16:24:49-07:00', '2019-07-22T16:27:10-07:00'), ('2019-07-22T16:27:11-07:00', '2019-07-22T16:27:45-07:00'), ('2019-07-22T16:27:47-07:00', '2019-07-22T16:29:04-07:00'), ('2019-07-22T16:29:05-07:00', '2019-07-22T16:29:30-07:00'), ('2019-07-22T16:29:30-07:00', '2019-07-22T16:29:56-07:00'), ('2019-07-22T16:29:57-07:00', '2019-07-22T16:30:09-07:00'), ('2019-07-22T16:30:10-07:00', '2019-07-22T16:36:06-07:00'), ('2019-07-22T16:36:07-07:00', '2019-07-22T16:36:19-07:00'), ('2019-07-22T16:36:20-07:00', '2019-07-22T16:37:37-07:00'), ('2019-07-22T16:37:38-07:00', '2019-07-22T16:37:59-07:00'), ('2019-07-22T16:38:01-07:00', '2019-07-22T16:38:27-07:00'), ('2019-07-22T16:54:55.404858-07:00', '2019-07-22T17:33:43-07:00'), ('2019-07-22T17:33:44-07:00', '2019-07-22T17:34:19-07:00'), ('2019-07-22T17:34:20-07:00', '2019-07-22T17:34:44-07:00'), ('2019-07-22T17:34:45.700000-07:00', '2019-07-22T17:39:30-07:00'), ('2019-07-22T17:39:31-07:00', '2019-07-22T17:39:44-07:00'), ('2019-07-22T17:39:45-07:00', '2019-07-22T17:45:09-07:00')]\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = ['2019-07-22T16:09:04.460140-07:00', '2019-07-22T16:24:36-07:00', '2019-07-22T16:24:49-07:00', '2019-07-22T16:27:11-07:00', '2019-07-22T16:27:47-07:00', '2019-07-22T16:29:05-07:00', '2019-07-22T16:29:30-07:00', '2019-07-22T16:29:57-07:00', '2019-07-22T16:30:10-07:00', '2019-07-22T16:36:07-07:00', '2019-07-22T16:36:20-07:00', '2019-07-22T16:37:38-07:00', '2019-07-22T16:38:01-07:00', '2019-07-22T16:54:55.404858-07:00', '2019-07-22T17:33:44-07:00', '2019-07-22T17:34:20-07:00', '2019-07-22T17:34:45.700000-07:00', '2019-07-22T17:39:31-07:00', '2019-07-22T17:39:45-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-07-23T08:50:28-07:00', '2019-07-23T08:58:18-07:00'), ('2019-07-23T08:58:19-07:00', '2019-07-23T08:58:32-07:00'), ('2019-07-23T08:58:33-07:00', '2019-07-23T09:17:27-07:00'), ('2019-07-23T09:17:28-07:00', '2019-07-23T09:18:56-07:00'), ('2019-07-23T12:40:03.240301-07:00', '2019-07-23T12:41:31-07:00'), ('2019-07-23T12:42:20-07:00', '2019-07-23T12:42:22-07:00'), ('2019-07-23T12:44:18-07:00', '2019-07-23T12:45:32-07:00'), ('2019-07-23T12:45:34-07:00', '2019-07-23T12:45:34-07:00'), ('2019-07-23T12:45:59-07:00', '2019-07-23T12:56:31-07:00'), ('2019-07-23T12:56:32-07:00', '2019-07-23T12:57:04-07:00'), ('2019-07-23T12:57:07-07:00', '2019-07-23T13:03:26-07:00'), ('2019-07-23T13:03:27-07:00', '2019-07-23T13:04:26-07:00'), ('2019-07-23T13:04:29-07:00', '2019-07-23T13:05:15-07:00'), ('2019-07-23T13:07:01.517002-07:00', '2019-07-23T13:51:12-07:00'), ('2019-07-23T13:51:14-07:00', '2019-07-23T14:00:24-07:00'), ('2019-07-23T14:03:24-07:00', '2019-07-23T16:07:16-07:00')]\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = ['2019-07-23T08:50:28-07:00', '2019-07-23T08:58:19-07:00', '2019-07-23T08:58:33-07:00', '2019-07-23T09:17:28-07:00']\n", - "Before filtering, trips = [('2019-07-23T08:50:28-07:00', '2019-07-23T08:58:18-07:00'), ('2019-07-23T08:58:19-07:00', '2019-07-23T08:58:32-07:00'), ('2019-07-23T08:58:33-07:00', '2019-07-23T09:17:27-07:00'), ('2019-07-23T09:17:28-07:00', '2019-07-23T09:18:56-07:00'), ('2019-07-23T12:40:03.240301-07:00', '2019-07-23T12:41:31-07:00'), ('2019-07-23T12:42:20-07:00', '2019-07-23T12:42:22-07:00'), ('2019-07-23T12:44:18-07:00', '2019-07-23T12:45:32-07:00'), ('2019-07-23T12:45:34-07:00', '2019-07-23T12:45:34-07:00'), ('2019-07-23T12:45:59-07:00', '2019-07-23T12:56:31-07:00'), ('2019-07-23T12:56:32-07:00', '2019-07-23T12:57:04-07:00'), ('2019-07-23T12:57:07-07:00', '2019-07-23T13:03:26-07:00'), ('2019-07-23T13:03:27-07:00', '2019-07-23T13:04:26-07:00'), ('2019-07-23T13:04:29-07:00', '2019-07-23T13:05:15-07:00'), ('2019-07-23T13:07:01.517002-07:00', '2019-07-23T13:51:12-07:00'), ('2019-07-23T13:51:14-07:00', '2019-07-23T14:00:24-07:00'), ('2019-07-23T14:03:24-07:00', '2019-07-23T16:07:16-07:00')]\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = ['2019-07-23T12:40:03.240301-07:00', '2019-07-23T12:42:20-07:00', '2019-07-23T12:44:18-07:00', '2019-07-23T12:45:34-07:00', '2019-07-23T12:45:59-07:00', '2019-07-23T12:56:32-07:00', '2019-07-23T12:57:07-07:00', '2019-07-23T13:03:27-07:00', '2019-07-23T13:04:29-07:00', '2019-07-23T13:07:01.517002-07:00', '2019-07-23T13:51:14-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-07-27T09:17:55-07:00', '2019-07-27T09:23:12-07:00'), ('2019-07-27T09:23:13-07:00', '2019-07-27T09:34:32-07:00'), ('2019-07-27T09:34:33-07:00', '2019-07-27T09:36:31-07:00'), ('2019-07-27T09:36:32-07:00', '2019-07-27T09:36:44-07:00'), ('2019-07-27T09:36:45-07:00', '2019-07-27T09:41:08-07:00'), ('2019-07-27T12:56:09.498616-07:00', '2019-07-27T13:04:07-07:00'), ('2019-07-27T13:04:08-07:00', '2019-07-27T13:04:20-07:00'), ('2019-07-27T13:04:22-07:00', '2019-07-27T13:05:00-07:00'), ('2019-07-27T13:05:01-07:00', '2019-07-27T13:05:27-07:00'), ('2019-07-27T13:05:40-07:00', '2019-07-27T13:07:35-07:00'), ('2019-07-27T13:07:36-07:00', '2019-07-27T13:08:27-07:00'), ('2019-07-27T13:08:28-07:00', '2019-07-27T13:08:53-07:00'), ('2019-07-27T13:08:54-07:00', '2019-07-27T13:10:37-07:00'), ('2019-07-27T13:10:38-07:00', '2019-07-27T13:17:07-07:00'), ('2019-07-27T13:17:08-07:00', '2019-07-27T13:17:49-07:00'), ('2019-07-27T13:23:36.196758-07:00', '2019-07-27T14:01:59-07:00'), ('2019-07-27T14:02:00-07:00', '2019-07-27T14:10:27-07:00')]\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = ['2019-07-27T09:17:55-07:00', '2019-07-27T09:23:13-07:00', '2019-07-27T09:34:33-07:00', '2019-07-27T09:36:32-07:00', '2019-07-27T09:36:45-07:00']\n", - "Before filtering, trips = [('2019-07-27T09:17:55-07:00', '2019-07-27T09:23:12-07:00'), ('2019-07-27T09:23:13-07:00', '2019-07-27T09:34:32-07:00'), ('2019-07-27T09:34:33-07:00', '2019-07-27T09:36:31-07:00'), ('2019-07-27T09:36:32-07:00', '2019-07-27T09:36:44-07:00'), ('2019-07-27T09:36:45-07:00', '2019-07-27T09:41:08-07:00'), ('2019-07-27T12:56:09.498616-07:00', '2019-07-27T13:04:07-07:00'), ('2019-07-27T13:04:08-07:00', '2019-07-27T13:04:20-07:00'), ('2019-07-27T13:04:22-07:00', '2019-07-27T13:05:00-07:00'), ('2019-07-27T13:05:01-07:00', '2019-07-27T13:05:27-07:00'), ('2019-07-27T13:05:40-07:00', '2019-07-27T13:07:35-07:00'), ('2019-07-27T13:07:36-07:00', '2019-07-27T13:08:27-07:00'), ('2019-07-27T13:08:28-07:00', '2019-07-27T13:08:53-07:00'), ('2019-07-27T13:08:54-07:00', '2019-07-27T13:10:37-07:00'), ('2019-07-27T13:10:38-07:00', '2019-07-27T13:17:07-07:00'), ('2019-07-27T13:17:08-07:00', '2019-07-27T13:17:49-07:00'), ('2019-07-27T13:23:36.196758-07:00', '2019-07-27T14:01:59-07:00'), ('2019-07-27T14:02:00-07:00', '2019-07-27T14:10:27-07:00')]\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = ['2019-07-27T12:56:09.498616-07:00', '2019-07-27T13:04:08-07:00', '2019-07-27T13:04:22-07:00', '2019-07-27T13:05:01-07:00', '2019-07-27T13:05:40-07:00', '2019-07-27T13:07:36-07:00', '2019-07-27T13:08:28-07:00', '2019-07-27T13:08:54-07:00', '2019-07-27T13:10:38-07:00', '2019-07-27T13:17:08-07:00', '2019-07-27T13:23:36.196758-07:00', '2019-07-27T14:02:00-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-08-05T10:30:03.679000-07:00', '2019-08-05T10:35:53-07:00'), ('2019-08-05T10:35:54-07:00', '2019-08-05T10:48:57-07:00'), ('2019-08-05T10:48:58-07:00', '2019-08-05T10:53:42-07:00'), ('2019-08-05T10:53:43-07:00', '2019-08-05T10:54:08-07:00'), ('2019-08-05T10:54:10-07:00', '2019-08-05T10:56:28-07:00'), ('2019-08-05T10:56:29-07:00', '2019-08-05T10:56:54-07:00'), ('2019-08-05T15:04:44.545249-07:00', '2019-08-05T15:18:28-07:00'), ('2019-08-05T15:18:29-07:00', '2019-08-05T15:22:21-07:00'), ('2019-08-05T15:22:23-07:00', '2019-08-05T15:28:04-07:00'), ('2019-08-05T15:34:47.975703-07:00', '2019-08-05T16:09:37-07:00'), ('2019-08-05T16:09:38-07:00', '2019-08-05T16:18:54-07:00')]\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = ['2019-08-05T10:30:03.679000-07:00', '2019-08-05T10:35:54-07:00', '2019-08-05T10:48:58-07:00', '2019-08-05T10:53:43-07:00', '2019-08-05T10:54:10-07:00', '2019-08-05T10:56:29-07:00']\n", - "Before filtering, trips = [('2019-08-05T10:30:03.679000-07:00', '2019-08-05T10:35:53-07:00'), ('2019-08-05T10:35:54-07:00', '2019-08-05T10:48:57-07:00'), ('2019-08-05T10:48:58-07:00', '2019-08-05T10:53:42-07:00'), ('2019-08-05T10:53:43-07:00', '2019-08-05T10:54:08-07:00'), ('2019-08-05T10:54:10-07:00', '2019-08-05T10:56:28-07:00'), ('2019-08-05T10:56:29-07:00', '2019-08-05T10:56:54-07:00'), ('2019-08-05T15:04:44.545249-07:00', '2019-08-05T15:18:28-07:00'), ('2019-08-05T15:18:29-07:00', '2019-08-05T15:22:21-07:00'), ('2019-08-05T15:22:23-07:00', '2019-08-05T15:28:04-07:00'), ('2019-08-05T15:34:47.975703-07:00', '2019-08-05T16:09:37-07:00'), ('2019-08-05T16:09:38-07:00', '2019-08-05T16:18:54-07:00')]\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = ['2019-08-05T15:04:44.545249-07:00', '2019-08-05T15:18:29-07:00', '2019-08-05T15:22:23-07:00', '2019-08-05T15:34:47.975703-07:00', '2019-08-05T16:09:38-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-08-06T11:32:03.781000-07:00', '2019-08-06T11:37:31-07:00'), ('2019-08-06T11:37:32-07:00', '2019-08-06T11:40:38-07:00'), ('2019-08-06T11:41:07-07:00', '2019-08-06T11:41:19-07:00'), ('2019-08-06T11:41:20-07:00', '2019-08-06T11:42:02-07:00'), ('2019-08-06T11:42:05-07:00', '2019-08-06T11:42:15-07:00'), ('2019-08-06T11:42:16-07:00', '2019-08-06T11:46:25-07:00'), ('2019-08-06T11:46:38-07:00', '2019-08-06T11:46:51-07:00'), ('2019-08-06T11:47:27-07:00', '2019-08-06T11:49:54-07:00'), ('2019-08-06T11:50:08-07:00', '2019-08-06T11:58:23-07:00'), ('2019-08-06T15:49:51.919479-07:00', '2019-08-06T15:56:05-07:00'), ('2019-08-06T15:56:06-07:00', '2019-08-06T15:56:18-07:00'), ('2019-08-06T15:56:19-07:00', '2019-08-06T16:04:21-07:00'), ('2019-08-06T16:04:22-07:00', '2019-08-06T16:05:44-07:00'), ('2019-08-06T16:05:45-07:00', '2019-08-06T16:11:24-07:00'), ('2019-08-06T16:18:38.031755-07:00', '2019-08-06T17:00:36-07:00'), ('2019-08-06T17:00:37-07:00', '2019-08-06T17:10:03-07:00')]\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = ['2019-08-06T11:32:03.781000-07:00', '2019-08-06T11:37:32-07:00', '2019-08-06T11:41:07-07:00', '2019-08-06T11:41:20-07:00', '2019-08-06T11:42:05-07:00', '2019-08-06T11:42:16-07:00', '2019-08-06T11:46:38-07:00', '2019-08-06T11:47:27-07:00', '2019-08-06T11:50:08-07:00']\n", - "Before filtering, trips = [('2019-08-06T11:32:03.781000-07:00', '2019-08-06T11:37:31-07:00'), ('2019-08-06T11:37:32-07:00', '2019-08-06T11:40:38-07:00'), ('2019-08-06T11:41:07-07:00', '2019-08-06T11:41:19-07:00'), ('2019-08-06T11:41:20-07:00', '2019-08-06T11:42:02-07:00'), ('2019-08-06T11:42:05-07:00', '2019-08-06T11:42:15-07:00'), ('2019-08-06T11:42:16-07:00', '2019-08-06T11:46:25-07:00'), ('2019-08-06T11:46:38-07:00', '2019-08-06T11:46:51-07:00'), ('2019-08-06T11:47:27-07:00', '2019-08-06T11:49:54-07:00'), ('2019-08-06T11:50:08-07:00', '2019-08-06T11:58:23-07:00'), ('2019-08-06T15:49:51.919479-07:00', '2019-08-06T15:56:05-07:00'), ('2019-08-06T15:56:06-07:00', '2019-08-06T15:56:18-07:00'), ('2019-08-06T15:56:19-07:00', '2019-08-06T16:04:21-07:00'), ('2019-08-06T16:04:22-07:00', '2019-08-06T16:05:44-07:00'), ('2019-08-06T16:05:45-07:00', '2019-08-06T16:11:24-07:00'), ('2019-08-06T16:18:38.031755-07:00', '2019-08-06T17:00:36-07:00'), ('2019-08-06T17:00:37-07:00', '2019-08-06T17:10:03-07:00')]\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = ['2019-08-06T15:49:51.919479-07:00', '2019-08-06T15:56:06-07:00', '2019-08-06T15:56:19-07:00', '2019-08-06T16:04:22-07:00', '2019-08-06T16:05:45-07:00', '2019-08-06T16:18:38.031755-07:00', '2019-08-06T17:00:37-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-08-07T09:21:47.112000-07:00', '2019-08-07T09:24:05-07:00'), ('2019-08-07T09:24:06-07:00', '2019-08-07T09:25:36-07:00'), ('2019-08-07T09:25:37-07:00', '2019-08-07T09:26:05-07:00'), ('2019-08-07T09:26:06-07:00', '2019-08-07T09:28:28-07:00'), ('2019-08-07T09:28:29-07:00', '2019-08-07T09:29:18-07:00'), ('2019-08-07T09:29:19-07:00', '2019-08-07T09:29:43-07:00'), ('2019-08-07T09:29:44-07:00', '2019-08-07T09:47:18-07:00'), ('2019-08-07T09:47:19-07:00', '2019-08-07T09:53:19.901000-07:00'), ('2019-08-07T11:19:17.788957-07:00', '2019-08-07T11:24:27-07:00'), ('2019-08-07T13:28:32.366548-07:00', '2019-08-07T13:40:21-07:00'), ('2019-08-07T13:40:52-07:00', '2019-08-07T13:41:30-07:00'), ('2019-08-07T13:41:31-07:00', '2019-08-07T13:46:12-07:00'), ('2019-08-07T13:46:13-07:00', '2019-08-07T13:46:24-07:00'), ('2019-08-07T13:46:25-07:00', '2019-08-07T13:48:21-07:00'), ('2019-08-07T13:49:47-07:00', '2019-08-07T13:50:12-07:00'), ('2019-08-07T13:50:14-07:00', '2019-08-07T13:55:15-07:00'), ('2019-08-07T13:55:47-07:00', '2019-08-07T13:55:51-07:00'), ('2019-08-07T13:55:52-07:00', '2019-08-07T14:03:08-07:00'), ('2019-08-07T14:03:09-07:00', '2019-08-07T14:07:48-07:00'), ('2019-08-07T14:07:49-07:00', '2019-08-07T14:08:45-07:00'), ('2019-08-07T14:08:46-07:00', '2019-08-07T14:41:51-07:00'), ('2019-08-07T14:42:09-07:00', '2019-08-07T14:51:41-07:00')]\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = ['2019-08-07T09:21:47.112000-07:00', '2019-08-07T09:24:06-07:00', '2019-08-07T09:25:37-07:00', '2019-08-07T09:26:06-07:00', '2019-08-07T09:28:29-07:00', '2019-08-07T09:29:19-07:00', '2019-08-07T09:29:44-07:00', '2019-08-07T09:47:19-07:00']\n", - "Before filtering, trips = [('2019-08-07T09:21:47.112000-07:00', '2019-08-07T09:24:05-07:00'), ('2019-08-07T09:24:06-07:00', '2019-08-07T09:25:36-07:00'), ('2019-08-07T09:25:37-07:00', '2019-08-07T09:26:05-07:00'), ('2019-08-07T09:26:06-07:00', '2019-08-07T09:28:28-07:00'), ('2019-08-07T09:28:29-07:00', '2019-08-07T09:29:18-07:00'), ('2019-08-07T09:29:19-07:00', '2019-08-07T09:29:43-07:00'), ('2019-08-07T09:29:44-07:00', '2019-08-07T09:47:18-07:00'), ('2019-08-07T09:47:19-07:00', '2019-08-07T09:53:19.901000-07:00'), ('2019-08-07T11:19:17.788957-07:00', '2019-08-07T11:24:27-07:00'), ('2019-08-07T13:28:32.366548-07:00', '2019-08-07T13:40:21-07:00'), ('2019-08-07T13:40:52-07:00', '2019-08-07T13:41:30-07:00'), ('2019-08-07T13:41:31-07:00', '2019-08-07T13:46:12-07:00'), ('2019-08-07T13:46:13-07:00', '2019-08-07T13:46:24-07:00'), ('2019-08-07T13:46:25-07:00', '2019-08-07T13:48:21-07:00'), ('2019-08-07T13:49:47-07:00', '2019-08-07T13:50:12-07:00'), ('2019-08-07T13:50:14-07:00', '2019-08-07T13:55:15-07:00'), ('2019-08-07T13:55:47-07:00', '2019-08-07T13:55:51-07:00'), ('2019-08-07T13:55:52-07:00', '2019-08-07T14:03:08-07:00'), ('2019-08-07T14:03:09-07:00', '2019-08-07T14:07:48-07:00'), ('2019-08-07T14:07:49-07:00', '2019-08-07T14:08:45-07:00'), ('2019-08-07T14:08:46-07:00', '2019-08-07T14:41:51-07:00'), ('2019-08-07T14:42:09-07:00', '2019-08-07T14:51:41-07:00')]\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = ['2019-08-07T13:28:32.366548-07:00', '2019-08-07T13:40:52-07:00', '2019-08-07T13:41:31-07:00', '2019-08-07T13:46:13-07:00', '2019-08-07T13:46:25-07:00', '2019-08-07T13:49:47-07:00', '2019-08-07T13:50:14-07:00', '2019-08-07T13:55:47-07:00', '2019-08-07T13:55:52-07:00', '2019-08-07T14:03:09-07:00', '2019-08-07T14:07:49-07:00', '2019-08-07T14:08:46-07:00', '2019-08-07T14:42:09-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_0 MAMFDC v/s HAMFDC MAMFDC_0 2\n", - "Before filtering, trips = [('2020-03-04T11:18:57.820000-08:00', '2020-03-04T11:49:31.869000-08:00'), ('2020-03-04T11:50:02.373000-08:00', '2020-03-04T11:51:37.476000-08:00'), ('2020-03-04T15:39:25.508701-08:00', '2020-03-04T16:05:07.506000-08:00'), ('2020-03-04T16:17:44.739394-08:00', '2020-03-04T16:41:53-08:00'), ('2020-03-04T16:41:58-08:00', '2020-03-04T16:42:34.693000-08:00'), ('2020-03-04T16:43:06.014000-08:00', '2020-03-04T17:00:16.451000-08:00'), ('2020-03-04T17:00:48.515000-08:00', '2020-03-04T17:10:31.521000-08:00')]\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = ['2020-03-04T11:18:57.820000-08:00', '2020-03-04T11:50:02.373000-08:00']\n", - "Before filtering, trips = [('2020-03-04T11:18:57.820000-08:00', '2020-03-04T11:49:31.869000-08:00'), ('2020-03-04T11:50:02.373000-08:00', '2020-03-04T11:51:37.476000-08:00'), ('2020-03-04T15:39:25.508701-08:00', '2020-03-04T16:05:07.506000-08:00'), ('2020-03-04T16:17:44.739394-08:00', '2020-03-04T16:41:53-08:00'), ('2020-03-04T16:41:58-08:00', '2020-03-04T16:42:34.693000-08:00'), ('2020-03-04T16:43:06.014000-08:00', '2020-03-04T17:00:16.451000-08:00'), ('2020-03-04T17:00:48.515000-08:00', '2020-03-04T17:10:31.521000-08:00')]\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = ['2020-03-04T15:39:25.508701-08:00', '2020-03-04T16:17:44.739394-08:00', '2020-03-04T16:41:58-08:00', '2020-03-04T16:43:06.014000-08:00', '2020-03-04T17:00:48.515000-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", - "Before filtering, trips = [('2019-07-22T11:55:16.121000-07:00', '2019-07-22T12:20:50-07:00'), ('2019-07-22T12:21:19.740000-07:00', '2019-07-22T12:22:51-07:00'), ('2019-07-22T16:18:09.925358-07:00', '2019-07-22T16:34:23-07:00'), ('2019-07-22T16:34:55.539000-07:00', '2019-07-22T16:36:27-07:00'), ('2019-07-22T16:36:57-07:00', '2019-07-22T16:39:58-07:00'), ('2019-07-22T16:55:02.396931-07:00', '2019-07-22T17:34:44.171000-07:00'), ('2019-07-22T17:35:01-07:00', '2019-07-22T17:45:02-07:00')]\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = ['2019-07-22T11:55:16.121000-07:00', '2019-07-22T12:21:19.740000-07:00']\n", - "Before filtering, trips = [('2019-07-22T11:55:16.121000-07:00', '2019-07-22T12:20:50-07:00'), ('2019-07-22T12:21:19.740000-07:00', '2019-07-22T12:22:51-07:00'), ('2019-07-22T16:18:09.925358-07:00', '2019-07-22T16:34:23-07:00'), ('2019-07-22T16:34:55.539000-07:00', '2019-07-22T16:36:27-07:00'), ('2019-07-22T16:36:57-07:00', '2019-07-22T16:39:58-07:00'), ('2019-07-22T16:55:02.396931-07:00', '2019-07-22T17:34:44.171000-07:00'), ('2019-07-22T17:35:01-07:00', '2019-07-22T17:45:02-07:00')]\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = ['2019-07-22T16:18:09.925358-07:00', '2019-07-22T16:34:55.539000-07:00', '2019-07-22T16:36:57-07:00', '2019-07-22T16:55:02.396931-07:00', '2019-07-22T17:35:01-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", - "Before filtering, trips = [('2019-07-23T08:51:18.945000-07:00', '2019-07-23T09:18:57.215000-07:00'), ('2019-07-23T09:19:29-07:00', '2019-07-23T09:21:33.601000-07:00'), ('2019-07-23T12:42:42.736306-07:00', '2019-07-23T12:46:24-07:00'), ('2019-07-23T12:46:54-07:00', '2019-07-23T13:05:46-07:00'), ('2019-07-23T13:08:20.846226-07:00', '2019-07-23T13:51:11.665000-07:00'), ('2019-07-23T13:51:24-07:00', '2019-07-23T14:01:54.639000-07:00')]\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = ['2019-07-23T08:51:18.945000-07:00', '2019-07-23T09:19:29-07:00']\n", - "Before filtering, trips = [('2019-07-23T08:51:18.945000-07:00', '2019-07-23T09:18:57.215000-07:00'), ('2019-07-23T09:19:29-07:00', '2019-07-23T09:21:33.601000-07:00'), ('2019-07-23T12:42:42.736306-07:00', '2019-07-23T12:46:24-07:00'), ('2019-07-23T12:46:54-07:00', '2019-07-23T13:05:46-07:00'), ('2019-07-23T13:08:20.846226-07:00', '2019-07-23T13:51:11.665000-07:00'), ('2019-07-23T13:51:24-07:00', '2019-07-23T14:01:54.639000-07:00')]\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = ['2019-07-23T12:42:42.736306-07:00', '2019-07-23T12:46:54-07:00', '2019-07-23T13:08:20.846226-07:00', '2019-07-23T13:51:24-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", - "Before filtering, trips = [('2019-07-27T09:14:44-07:00', '2019-07-27T09:40:10-07:00'), ('2019-07-27T09:40:37.915000-07:00', '2019-07-27T09:42:13.223000-07:00'), ('2019-07-27T12:56:27.402749-07:00', '2019-07-27T13:11:49-07:00'), ('2019-07-27T13:12:19-07:00', '2019-07-27T13:13:35.147000-07:00'), ('2019-07-27T13:13:49-07:00', '2019-07-27T13:18:35-07:00'), ('2019-07-27T13:22:30.733146-07:00', '2019-07-27T14:01:21-07:00'), ('2019-07-27T14:01:48-07:00', '2019-07-27T14:10:51-07:00')]\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = ['2019-07-27T09:14:44-07:00', '2019-07-27T09:40:37.915000-07:00']\n", - "Before filtering, trips = [('2019-07-27T09:14:44-07:00', '2019-07-27T09:40:10-07:00'), ('2019-07-27T09:40:37.915000-07:00', '2019-07-27T09:42:13.223000-07:00'), ('2019-07-27T12:56:27.402749-07:00', '2019-07-27T13:11:49-07:00'), ('2019-07-27T13:12:19-07:00', '2019-07-27T13:13:35.147000-07:00'), ('2019-07-27T13:13:49-07:00', '2019-07-27T13:18:35-07:00'), ('2019-07-27T13:22:30.733146-07:00', '2019-07-27T14:01:21-07:00'), ('2019-07-27T14:01:48-07:00', '2019-07-27T14:10:51-07:00')]\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = ['2019-07-27T12:56:27.402749-07:00', '2019-07-27T13:12:19-07:00', '2019-07-27T13:13:49-07:00', '2019-07-27T13:22:30.733146-07:00', '2019-07-27T14:01:48-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", - "Before filtering, trips = [('2019-08-05T10:31:21.571000-07:00', '2019-08-05T10:56:01-07:00'), ('2019-08-05T10:56:02-07:00', '2019-08-05T10:57:53-07:00'), ('2019-08-05T14:56:18.114727-07:00', '2019-08-05T15:03:45.046000-07:00'), ('2019-08-05T15:03:50-07:00', '2019-08-05T15:11:29-07:00'), ('2019-08-05T15:11:30-07:00', '2019-08-05T15:12:07.339000-07:00'), ('2019-08-05T15:12:23.349000-07:00', '2019-08-05T15:19:46-07:00'), ('2019-08-05T15:19:47-07:00', '2019-08-05T15:22:43-07:00'), ('2019-08-05T15:22:45-07:00', '2019-08-05T15:24:02-07:00'), ('2019-08-05T15:24:08.190000-07:00', '2019-08-05T15:24:13.222000-07:00'), ('2019-08-05T15:24:19.009000-07:00', '2019-08-05T15:26:26-07:00'), ('2019-08-05T15:34:24.884068-07:00', '2019-08-05T16:09:02-07:00'), ('2019-08-05T16:09:03-07:00', '2019-08-05T16:20:08.090000-07:00')]\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = ['2019-08-05T10:31:21.571000-07:00', '2019-08-05T10:56:02-07:00']\n", - "Before filtering, trips = [('2019-08-05T10:31:21.571000-07:00', '2019-08-05T10:56:01-07:00'), ('2019-08-05T10:56:02-07:00', '2019-08-05T10:57:53-07:00'), ('2019-08-05T14:56:18.114727-07:00', '2019-08-05T15:03:45.046000-07:00'), ('2019-08-05T15:03:50-07:00', '2019-08-05T15:11:29-07:00'), ('2019-08-05T15:11:30-07:00', '2019-08-05T15:12:07.339000-07:00'), ('2019-08-05T15:12:23.349000-07:00', '2019-08-05T15:19:46-07:00'), ('2019-08-05T15:19:47-07:00', '2019-08-05T15:22:43-07:00'), ('2019-08-05T15:22:45-07:00', '2019-08-05T15:24:02-07:00'), ('2019-08-05T15:24:08.190000-07:00', '2019-08-05T15:24:13.222000-07:00'), ('2019-08-05T15:24:19.009000-07:00', '2019-08-05T15:26:26-07:00'), ('2019-08-05T15:34:24.884068-07:00', '2019-08-05T16:09:02-07:00'), ('2019-08-05T16:09:03-07:00', '2019-08-05T16:20:08.090000-07:00')]\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = ['2019-08-05T14:56:18.114727-07:00', '2019-08-05T15:03:50-07:00', '2019-08-05T15:11:30-07:00', '2019-08-05T15:12:23.349000-07:00', '2019-08-05T15:19:47-07:00', '2019-08-05T15:22:45-07:00', '2019-08-05T15:24:08.190000-07:00', '2019-08-05T15:24:19.009000-07:00', '2019-08-05T15:34:24.884068-07:00', '2019-08-05T16:09:03-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", - "Before filtering, trips = [('2019-08-06T11:27:08.369000-07:00', '2019-08-06T11:29:25.527000-07:00'), ('2019-08-06T11:30:39-07:00', '2019-08-06T11:58:09-07:00'), ('2019-08-06T15:49:12.199884-07:00', '2019-08-06T15:54:21-07:00'), ('2019-08-06T15:54:22-07:00', '2019-08-06T15:54:41.664000-07:00'), ('2019-08-06T15:54:46.712000-07:00', '2019-08-06T16:08:21-07:00'), ('2019-08-06T16:08:22-07:00', '2019-08-06T16:08:34-07:00'), ('2019-08-06T16:08:35-07:00', '2019-08-06T16:10:48-07:00'), ('2019-08-06T16:16:24.124618-07:00', '2019-08-06T16:46:02-07:00'), ('2019-08-06T16:46:03-07:00', '2019-08-06T16:46:15-07:00'), ('2019-08-06T16:46:16-07:00', '2019-08-06T17:00:07-07:00'), ('2019-08-06T17:00:08-07:00', '2019-08-06T17:11:15.095000-07:00')]\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = ['2019-08-06T11:27:08.369000-07:00', '2019-08-06T11:30:39-07:00']\n", - "Before filtering, trips = [('2019-08-06T11:27:08.369000-07:00', '2019-08-06T11:29:25.527000-07:00'), ('2019-08-06T11:30:39-07:00', '2019-08-06T11:58:09-07:00'), ('2019-08-06T15:49:12.199884-07:00', '2019-08-06T15:54:21-07:00'), ('2019-08-06T15:54:22-07:00', '2019-08-06T15:54:41.664000-07:00'), ('2019-08-06T15:54:46.712000-07:00', '2019-08-06T16:08:21-07:00'), ('2019-08-06T16:08:22-07:00', '2019-08-06T16:08:34-07:00'), ('2019-08-06T16:08:35-07:00', '2019-08-06T16:10:48-07:00'), ('2019-08-06T16:16:24.124618-07:00', '2019-08-06T16:46:02-07:00'), ('2019-08-06T16:46:03-07:00', '2019-08-06T16:46:15-07:00'), ('2019-08-06T16:46:16-07:00', '2019-08-06T17:00:07-07:00'), ('2019-08-06T17:00:08-07:00', '2019-08-06T17:11:15.095000-07:00')]\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = ['2019-08-06T15:49:12.199884-07:00', '2019-08-06T15:54:22-07:00', '2019-08-06T15:54:46.712000-07:00', '2019-08-06T16:08:22-07:00', '2019-08-06T16:08:35-07:00', '2019-08-06T16:16:24.124618-07:00', '2019-08-06T16:46:03-07:00', '2019-08-06T16:46:16-07:00', '2019-08-06T17:00:08-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", - "Before filtering, trips = [('2019-08-07T09:21:38.051000-07:00', '2019-08-07T09:26:01-07:00'), ('2019-08-07T09:26:02-07:00', '2019-08-07T09:28:11-07:00'), ('2019-08-07T09:28:12-07:00', '2019-08-07T09:47:19-07:00'), ('2019-08-07T09:47:20-07:00', '2019-08-07T09:48:20.642000-07:00'), ('2019-08-07T13:40:28.027609-07:00', '2019-08-07T13:43:30-07:00'), ('2019-08-07T13:43:31-07:00', '2019-08-07T13:44:15.469000-07:00'), ('2019-08-07T13:45:13-07:00', '2019-08-07T13:47:39-07:00'), ('2019-08-07T13:47:40-07:00', '2019-08-07T13:48:17-07:00'), ('2019-08-07T13:48:18-07:00', '2019-08-07T13:55:09-07:00'), ('2019-08-07T13:55:11-07:00', '2019-08-07T13:55:44-07:00'), ('2019-08-07T13:55:45-07:00', '2019-08-07T14:02:01-07:00'), ('2019-08-07T14:02:02-07:00', '2019-08-07T14:06:47.031000-07:00'), ('2019-08-07T14:07:04.400000-07:00', '2019-08-07T14:08:34-07:00'), ('2019-08-07T14:08:35-07:00', '2019-08-07T14:41:21-07:00'), ('2019-08-07T14:41:23-07:00', '2019-08-07T14:41:24-07:00'), ('2019-08-07T14:41:45-07:00', '2019-08-07T14:52:18.053000-07:00')]\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = ['2019-08-07T09:21:38.051000-07:00', '2019-08-07T09:26:02-07:00', '2019-08-07T09:28:12-07:00', '2019-08-07T09:47:20-07:00']\n", - "Before filtering, trips = [('2019-08-07T09:21:38.051000-07:00', '2019-08-07T09:26:01-07:00'), ('2019-08-07T09:26:02-07:00', '2019-08-07T09:28:11-07:00'), ('2019-08-07T09:28:12-07:00', '2019-08-07T09:47:19-07:00'), ('2019-08-07T09:47:20-07:00', '2019-08-07T09:48:20.642000-07:00'), ('2019-08-07T13:40:28.027609-07:00', '2019-08-07T13:43:30-07:00'), ('2019-08-07T13:43:31-07:00', '2019-08-07T13:44:15.469000-07:00'), ('2019-08-07T13:45:13-07:00', '2019-08-07T13:47:39-07:00'), ('2019-08-07T13:47:40-07:00', '2019-08-07T13:48:17-07:00'), ('2019-08-07T13:48:18-07:00', '2019-08-07T13:55:09-07:00'), ('2019-08-07T13:55:11-07:00', '2019-08-07T13:55:44-07:00'), ('2019-08-07T13:55:45-07:00', '2019-08-07T14:02:01-07:00'), ('2019-08-07T14:02:02-07:00', '2019-08-07T14:06:47.031000-07:00'), ('2019-08-07T14:07:04.400000-07:00', '2019-08-07T14:08:34-07:00'), ('2019-08-07T14:08:35-07:00', '2019-08-07T14:41:21-07:00'), ('2019-08-07T14:41:23-07:00', '2019-08-07T14:41:24-07:00'), ('2019-08-07T14:41:45-07:00', '2019-08-07T14:52:18.053000-07:00')]\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = ['2019-08-07T13:40:28.027609-07:00', '2019-08-07T13:43:31-07:00', '2019-08-07T13:45:13-07:00', '2019-08-07T13:47:40-07:00', '2019-08-07T13:48:18-07:00', '2019-08-07T13:55:11-07:00', '2019-08-07T13:55:45-07:00', '2019-08-07T14:02:02-07:00', '2019-08-07T14:07:04.400000-07:00', '2019-08-07T14:08:35-07:00', '2019-08-07T14:41:23-07:00', '2019-08-07T14:41:45-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_0 MAMFDC v/s HAMFDC HAMFDC_0 2\n", - "Before filtering, trips = [('2020-03-04T11:19:07.002000-08:00', '2020-03-04T11:21:43.471000-08:00'), ('2020-03-04T11:22:14-08:00', '2020-03-04T11:27:44-08:00'), ('2020-03-04T11:28:15-08:00', '2020-03-04T11:29:16-08:00'), ('2020-03-04T11:29:43.805000-08:00', '2020-03-04T11:51:52.474000-08:00'), ('2020-03-04T15:39:11.043641-08:00', '2020-03-04T15:50:04.474000-08:00'), ('2020-03-04T15:51:07.500000-08:00', '2020-03-04T15:51:07.500000-08:00'), ('2020-03-04T15:51:39-08:00', '2020-03-04T16:04:52.414000-08:00'), ('2020-03-04T16:18:16.630496-08:00', '2020-03-04T17:00:03.398000-08:00'), ('2020-03-04T17:00:09-08:00', '2020-03-04T17:10:27.930000-08:00')]\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = ['2020-03-04T11:19:07.002000-08:00', '2020-03-04T11:22:14-08:00', '2020-03-04T11:28:15-08:00', '2020-03-04T11:29:43.805000-08:00']\n", - "Before filtering, trips = [('2020-03-04T11:19:07.002000-08:00', '2020-03-04T11:21:43.471000-08:00'), ('2020-03-04T11:22:14-08:00', '2020-03-04T11:27:44-08:00'), ('2020-03-04T11:28:15-08:00', '2020-03-04T11:29:16-08:00'), ('2020-03-04T11:29:43.805000-08:00', '2020-03-04T11:51:52.474000-08:00'), ('2020-03-04T15:39:11.043641-08:00', '2020-03-04T15:50:04.474000-08:00'), ('2020-03-04T15:51:07.500000-08:00', '2020-03-04T15:51:07.500000-08:00'), ('2020-03-04T15:51:39-08:00', '2020-03-04T16:04:52.414000-08:00'), ('2020-03-04T16:18:16.630496-08:00', '2020-03-04T17:00:03.398000-08:00'), ('2020-03-04T17:00:09-08:00', '2020-03-04T17:10:27.930000-08:00')]\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = ['2020-03-04T15:39:11.043641-08:00', '2020-03-04T15:51:07.500000-08:00', '2020-03-04T15:51:39-08:00', '2020-03-04T16:18:16.630496-08:00', '2020-03-04T17:00:09-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_0 HAHFDC v/s HAMFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_1 HAHFDC v/s HAMFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_2 HAHFDC v/s HAMFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_3 HAHFDC v/s MAHFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_4 HAHFDC v/s MAHFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_5 HAHFDC v/s MAHFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_6 MAMFDC v/s HAMFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = []\n", - "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", - "ios dict_keys(['ucb-sdb-ios-1', 'ucb-sdb-ios-2', 'ucb-sdb-ios-3', 'ucb-sdb-ios-4'])\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_0 HAHFDC v/s MAHFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_1 HAHFDC v/s MAHFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_2 HAHFDC v/s MAHFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_3 HAHFDC v/s HAMFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_4 HAHFDC v/s HAMFDC accuracy_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_5 HAHFDC v/s HAMFDC accuracy_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_6 MAMFDC v/s MAHFDC accuracy_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = []\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-07-22T11:56:08.032915-07:00', '2019-07-22T12:21:02.993575-07:00'), ('2019-07-22T12:21:04.993506-07:00', '2019-07-22T12:26:43.996640-07:00'), ('2019-07-22T16:17:18.612421-07:00', '2019-07-22T16:20:22.000669-07:00'), ('2019-07-22T16:20:23.000472-07:00', '2019-07-22T16:22:13.994219-07:00'), ('2019-07-22T16:22:14.994340-07:00', '2019-07-22T16:22:23.995039-07:00'), ('2019-07-22T16:22:24.995082-07:00', '2019-07-22T16:23:21.994065-07:00'), ('2019-07-22T16:23:22.994028-07:00', '2019-07-22T16:23:26.993878-07:00'), ('2019-07-22T16:23:27.993840-07:00', '2019-07-22T16:53:14.991262-07:00'), ('2019-07-22T16:53:15.991227-07:00', '2019-07-22T16:55:26.986709-07:00'), ('2019-07-22T16:55:27.986675-07:00', '2019-07-22T17:34:29.994910-07:00'), ('2019-07-22T17:34:30.994874-07:00', '2019-07-22T17:47:20.998055-07:00')]\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = ['2019-07-22T11:56:08.032915-07:00', '2019-07-22T12:21:04.993506-07:00']\n", - "Before filtering, trips = [('2019-07-22T11:56:08.032915-07:00', '2019-07-22T12:21:02.993575-07:00'), ('2019-07-22T12:21:04.993506-07:00', '2019-07-22T12:26:43.996640-07:00'), ('2019-07-22T16:17:18.612421-07:00', '2019-07-22T16:20:22.000669-07:00'), ('2019-07-22T16:20:23.000472-07:00', '2019-07-22T16:22:13.994219-07:00'), ('2019-07-22T16:22:14.994340-07:00', '2019-07-22T16:22:23.995039-07:00'), ('2019-07-22T16:22:24.995082-07:00', '2019-07-22T16:23:21.994065-07:00'), ('2019-07-22T16:23:22.994028-07:00', '2019-07-22T16:23:26.993878-07:00'), ('2019-07-22T16:23:27.993840-07:00', '2019-07-22T16:53:14.991262-07:00'), ('2019-07-22T16:53:15.991227-07:00', '2019-07-22T16:55:26.986709-07:00'), ('2019-07-22T16:55:27.986675-07:00', '2019-07-22T17:34:29.994910-07:00'), ('2019-07-22T17:34:30.994874-07:00', '2019-07-22T17:47:20.998055-07:00')]\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = ['2019-07-22T16:17:18.612421-07:00', '2019-07-22T16:20:23.000472-07:00', '2019-07-22T16:22:14.994340-07:00', '2019-07-22T16:22:24.995082-07:00', '2019-07-22T16:23:22.994028-07:00', '2019-07-22T16:23:27.993840-07:00', '2019-07-22T16:53:15.991227-07:00', '2019-07-22T16:55:27.986675-07:00', '2019-07-22T17:34:30.994874-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-07-23T08:51:11.537258-07:00', '2019-07-23T09:17:00.993716-07:00'), ('2019-07-23T09:17:01.993705-07:00', '2019-07-23T09:20:04.997658-07:00'), ('2019-07-23T12:54:46.097426-07:00', '2019-07-23T13:04:21.997633-07:00'), ('2019-07-23T13:04:22.997599-07:00', '2019-07-23T13:08:46.987311-07:00'), ('2019-07-23T13:08:47.987273-07:00', '2019-07-23T13:51:02.990666-07:00'), ('2019-07-23T13:51:03.990633-07:00', '2019-07-23T14:03:45.429897-07:00')]\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = ['2019-07-23T08:51:11.537258-07:00', '2019-07-23T09:17:01.993705-07:00']\n", - "Before filtering, trips = [('2019-07-23T08:51:11.537258-07:00', '2019-07-23T09:17:00.993716-07:00'), ('2019-07-23T09:17:01.993705-07:00', '2019-07-23T09:20:04.997658-07:00'), ('2019-07-23T12:54:46.097426-07:00', '2019-07-23T13:04:21.997633-07:00'), ('2019-07-23T13:04:22.997599-07:00', '2019-07-23T13:08:46.987311-07:00'), ('2019-07-23T13:08:47.987273-07:00', '2019-07-23T13:51:02.990666-07:00'), ('2019-07-23T13:51:03.990633-07:00', '2019-07-23T14:03:45.429897-07:00')]\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = ['2019-07-23T12:54:46.097426-07:00', '2019-07-23T13:04:22.997599-07:00', '2019-07-23T13:08:47.987273-07:00', '2019-07-23T13:51:03.990633-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-07-27T09:19:10.999951-07:00', '2019-07-27T09:40:03.997236-07:00'), ('2019-07-27T09:40:04.997202-07:00', '2019-07-27T09:40:16.996794-07:00'), ('2019-07-27T09:40:20.996656-07:00', '2019-07-27T09:44:24.514260-07:00'), ('2019-07-27T12:56:41.702540-07:00', '2019-07-27T12:58:58.997901-07:00'), ('2019-07-27T12:58:59.997857-07:00', '2019-07-27T12:59:53.995479-07:00'), ('2019-07-27T12:59:54.995434-07:00', '2019-07-27T12:59:58.995258-07:00'), ('2019-07-27T12:59:59.995213-07:00', '2019-07-27T13:07:10.996253-07:00'), ('2019-07-27T13:07:11.996214-07:00', '2019-07-27T13:07:20.995871-07:00'), ('2019-07-27T13:07:21.995832-07:00', '2019-07-27T13:16:57.993784-07:00'), ('2019-07-27T13:16:59.993730-07:00', '2019-07-27T13:24:14.990963-07:00'), ('2019-07-27T13:24:15.990926-07:00', '2019-07-27T13:25:08.989007-07:00'), ('2019-07-27T13:25:09.988971-07:00', '2019-07-27T13:25:28.988283-07:00'), ('2019-07-27T13:25:29.988246-07:00', '2019-07-27T14:01:22.990213-07:00'), ('2019-07-27T14:01:23.990182-07:00', '2019-07-27T14:13:36.438750-07:00')]\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = ['2019-07-27T09:19:10.999951-07:00', '2019-07-27T09:40:04.997202-07:00', '2019-07-27T09:40:20.996656-07:00']\n", - "Before filtering, trips = [('2019-07-27T09:19:10.999951-07:00', '2019-07-27T09:40:03.997236-07:00'), ('2019-07-27T09:40:04.997202-07:00', '2019-07-27T09:40:16.996794-07:00'), ('2019-07-27T09:40:20.996656-07:00', '2019-07-27T09:44:24.514260-07:00'), ('2019-07-27T12:56:41.702540-07:00', '2019-07-27T12:58:58.997901-07:00'), ('2019-07-27T12:58:59.997857-07:00', '2019-07-27T12:59:53.995479-07:00'), ('2019-07-27T12:59:54.995434-07:00', '2019-07-27T12:59:58.995258-07:00'), ('2019-07-27T12:59:59.995213-07:00', '2019-07-27T13:07:10.996253-07:00'), ('2019-07-27T13:07:11.996214-07:00', '2019-07-27T13:07:20.995871-07:00'), ('2019-07-27T13:07:21.995832-07:00', '2019-07-27T13:16:57.993784-07:00'), ('2019-07-27T13:16:59.993730-07:00', '2019-07-27T13:24:14.990963-07:00'), ('2019-07-27T13:24:15.990926-07:00', '2019-07-27T13:25:08.989007-07:00'), ('2019-07-27T13:25:09.988971-07:00', '2019-07-27T13:25:28.988283-07:00'), ('2019-07-27T13:25:29.988246-07:00', '2019-07-27T14:01:22.990213-07:00'), ('2019-07-27T14:01:23.990182-07:00', '2019-07-27T14:13:36.438750-07:00')]\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = ['2019-07-27T12:56:41.702540-07:00', '2019-07-27T12:58:59.997857-07:00', '2019-07-27T12:59:54.995434-07:00', '2019-07-27T12:59:59.995213-07:00', '2019-07-27T13:07:11.996214-07:00', '2019-07-27T13:07:21.995832-07:00', '2019-07-27T13:16:59.993730-07:00', '2019-07-27T13:24:15.990926-07:00', '2019-07-27T13:25:09.988971-07:00', '2019-07-27T13:25:29.988246-07:00', '2019-07-27T14:01:23.990182-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 2\n", - "Before filtering, trips = [('2019-08-05T10:30:22.521594-07:00', '2019-08-05T10:32:50.994496-07:00'), ('2019-08-05T10:33:08.466827-07:00', '2019-08-05T10:34:27.990568-07:00'), ('2019-08-05T10:34:30.990447-07:00', '2019-08-05T10:34:48.989717-07:00'), ('2019-08-05T10:34:49.989676-07:00', '2019-08-05T10:55:58.992105-07:00'), ('2019-08-05T10:55:59.992069-07:00', '2019-08-05T10:59:18.455394-07:00'), ('2019-08-05T15:04:33.485448-07:00', '2019-08-05T15:13:55.998247-07:00'), ('2019-08-05T15:13:56.998283-07:00', '2019-08-05T15:14:05.998424-07:00'), ('2019-08-05T15:14:06.998425-07:00', '2019-08-05T15:22:49.995738-07:00'), ('2019-08-05T15:22:50.995700-07:00', '2019-08-05T15:22:54.995555-07:00'), ('2019-08-05T15:22:55.995518-07:00', '2019-08-05T15:26:44.987143-07:00'), ('2019-08-05T15:26:46.987070-07:00', '2019-08-05T15:34:52.985577-07:00'), ('2019-08-05T15:34:53.985543-07:00', '2019-08-05T16:09:15.989569-07:00'), ('2019-08-05T16:09:17.989504-07:00', '2019-08-05T16:21:31.436115-07:00')]\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = ['2019-08-05T10:30:22.521594-07:00', '2019-08-05T10:33:08.466827-07:00', '2019-08-05T10:34:30.990447-07:00', '2019-08-05T10:34:49.989676-07:00', '2019-08-05T10:55:59.992069-07:00']\n", - "Before filtering, trips = [('2019-08-05T10:30:22.521594-07:00', '2019-08-05T10:32:50.994496-07:00'), ('2019-08-05T10:33:08.466827-07:00', '2019-08-05T10:34:27.990568-07:00'), ('2019-08-05T10:34:30.990447-07:00', '2019-08-05T10:34:48.989717-07:00'), ('2019-08-05T10:34:49.989676-07:00', '2019-08-05T10:55:58.992105-07:00'), ('2019-08-05T10:55:59.992069-07:00', '2019-08-05T10:59:18.455394-07:00'), ('2019-08-05T15:04:33.485448-07:00', '2019-08-05T15:13:55.998247-07:00'), ('2019-08-05T15:13:56.998283-07:00', '2019-08-05T15:14:05.998424-07:00'), ('2019-08-05T15:14:06.998425-07:00', '2019-08-05T15:22:49.995738-07:00'), ('2019-08-05T15:22:50.995700-07:00', '2019-08-05T15:22:54.995555-07:00'), ('2019-08-05T15:22:55.995518-07:00', '2019-08-05T15:26:44.987143-07:00'), ('2019-08-05T15:26:46.987070-07:00', '2019-08-05T15:34:52.985577-07:00'), ('2019-08-05T15:34:53.985543-07:00', '2019-08-05T16:09:15.989569-07:00'), ('2019-08-05T16:09:17.989504-07:00', '2019-08-05T16:21:31.436115-07:00')]\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = ['2019-08-05T15:04:33.485448-07:00', '2019-08-05T15:13:56.998283-07:00', '2019-08-05T15:14:06.998425-07:00', '2019-08-05T15:22:50.995700-07:00', '2019-08-05T15:22:55.995518-07:00', '2019-08-05T15:26:46.987070-07:00', '2019-08-05T15:34:53.985543-07:00', '2019-08-05T16:09:17.989504-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 2\n", - "Before filtering, trips = [('2019-08-06T11:35:37.402004-07:00', '2019-08-06T11:58:20.998245-07:00'), ('2019-08-06T11:58:22.998188-07:00', '2019-08-06T12:00:05.994768-07:00'), ('2019-08-06T15:53:14.898153-07:00', '2019-08-06T16:06:01.982309-07:00'), ('2019-08-06T16:06:02.982265-07:00', '2019-08-06T16:09:57.992944-07:00'), ('2019-08-06T16:09:59.992870-07:00', '2019-08-06T16:19:14.988584-07:00'), ('2019-08-06T16:19:15.988549-07:00', '2019-08-06T17:00:13.993821-07:00'), ('2019-08-06T17:00:14.993789-07:00', '2019-08-06T17:12:38.245867-07:00')]\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = ['2019-08-06T11:35:37.402004-07:00', '2019-08-06T11:58:22.998188-07:00']\n", - "Before filtering, trips = [('2019-08-06T11:35:37.402004-07:00', '2019-08-06T11:58:20.998245-07:00'), ('2019-08-06T11:58:22.998188-07:00', '2019-08-06T12:00:05.994768-07:00'), ('2019-08-06T15:53:14.898153-07:00', '2019-08-06T16:06:01.982309-07:00'), ('2019-08-06T16:06:02.982265-07:00', '2019-08-06T16:09:57.992944-07:00'), ('2019-08-06T16:09:59.992870-07:00', '2019-08-06T16:19:14.988584-07:00'), ('2019-08-06T16:19:15.988549-07:00', '2019-08-06T17:00:13.993821-07:00'), ('2019-08-06T17:00:14.993789-07:00', '2019-08-06T17:12:38.245867-07:00')]\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = ['2019-08-06T15:53:14.898153-07:00', '2019-08-06T16:06:02.982265-07:00', '2019-08-06T16:09:59.992870-07:00', '2019-08-06T16:19:15.988549-07:00', '2019-08-06T17:00:14.993789-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 2\n", - "Before filtering, trips = [('2019-08-07T09:23:02.547250-07:00', '2019-08-07T09:44:11.985745-07:00'), ('2019-08-07T09:44:12.985707-07:00', '2019-08-07T09:45:16.998617-07:00'), ('2019-08-07T09:45:17.998619-07:00', '2019-08-07T09:47:13.995059-07:00'), ('2019-08-07T09:47:14.995023-07:00', '2019-08-07T09:49:59.531748-07:00'), ('2019-08-07T13:41:09.983535-07:00', '2019-08-07T13:43:59.998353-07:00'), ('2019-08-07T13:44:00.998310-07:00', '2019-08-07T13:45:03.996617-07:00'), ('2019-08-07T13:45:05.996582-07:00', '2019-08-07T13:45:36.995543-07:00'), ('2019-08-07T13:45:37.995502-07:00', '2019-08-07T14:05:12.984882-07:00'), ('2019-08-07T14:05:16.984712-07:00', '2019-08-07T14:05:46.994138-07:00'), ('2019-08-07T14:05:47.994515-07:00', '2019-08-07T14:07:23.996792-07:00'), ('2019-08-07T14:07:24.996754-07:00', '2019-08-07T14:07:33.996404-07:00'), ('2019-08-07T14:07:34.996366-07:00', '2019-08-07T14:32:19.991592-07:00'), ('2019-08-07T14:32:20.991552-07:00', '2019-08-07T14:36:09.998307-07:00'), ('2019-08-07T14:36:10.998355-07:00', '2019-08-07T14:41:24.987819-07:00'), ('2019-08-07T14:41:27.987708-07:00', '2019-08-07T14:54:10.991285-07:00')]\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = ['2019-08-07T09:23:02.547250-07:00', '2019-08-07T09:44:12.985707-07:00', '2019-08-07T09:45:17.998619-07:00', '2019-08-07T09:47:14.995023-07:00']\n", - "Before filtering, trips = [('2019-08-07T09:23:02.547250-07:00', '2019-08-07T09:44:11.985745-07:00'), ('2019-08-07T09:44:12.985707-07:00', '2019-08-07T09:45:16.998617-07:00'), ('2019-08-07T09:45:17.998619-07:00', '2019-08-07T09:47:13.995059-07:00'), ('2019-08-07T09:47:14.995023-07:00', '2019-08-07T09:49:59.531748-07:00'), ('2019-08-07T13:41:09.983535-07:00', '2019-08-07T13:43:59.998353-07:00'), ('2019-08-07T13:44:00.998310-07:00', '2019-08-07T13:45:03.996617-07:00'), ('2019-08-07T13:45:05.996582-07:00', '2019-08-07T13:45:36.995543-07:00'), ('2019-08-07T13:45:37.995502-07:00', '2019-08-07T14:05:12.984882-07:00'), ('2019-08-07T14:05:16.984712-07:00', '2019-08-07T14:05:46.994138-07:00'), ('2019-08-07T14:05:47.994515-07:00', '2019-08-07T14:07:23.996792-07:00'), ('2019-08-07T14:07:24.996754-07:00', '2019-08-07T14:07:33.996404-07:00'), ('2019-08-07T14:07:34.996366-07:00', '2019-08-07T14:32:19.991592-07:00'), ('2019-08-07T14:32:20.991552-07:00', '2019-08-07T14:36:09.998307-07:00'), ('2019-08-07T14:36:10.998355-07:00', '2019-08-07T14:41:24.987819-07:00'), ('2019-08-07T14:41:27.987708-07:00', '2019-08-07T14:54:10.991285-07:00')]\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = ['2019-08-07T13:41:09.983535-07:00', '2019-08-07T13:44:00.998310-07:00', '2019-08-07T13:45:05.996582-07:00', '2019-08-07T13:45:37.995502-07:00', '2019-08-07T14:05:16.984712-07:00', '2019-08-07T14:05:47.994515-07:00', '2019-08-07T14:07:24.996754-07:00', '2019-08-07T14:07:34.996366-07:00', '2019-08-07T14:32:20.991552-07:00', '2019-08-07T14:36:10.998355-07:00', '2019-08-07T14:41:27.987708-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_0 MAMFDC v/s MAHFDC MAMFDC_0 2\n", - "Before filtering, trips = [('2020-03-04T11:23:32.652451-08:00', '2020-03-04T11:38:54.651307-08:00'), ('2020-03-04T15:54:04.384972-08:00', '2020-03-04T16:04:15.285221-08:00'), ('2020-03-04T16:18:38.735253-08:00', '2020-03-04T16:59:50.758160-08:00'), ('2020-03-04T17:00:29.649558-08:00', '2020-03-04T17:10:53.029543-08:00')]\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = ['2020-03-04T11:23:32.652451-08:00']\n", - "Before filtering, trips = [('2020-03-04T11:23:32.652451-08:00', '2020-03-04T11:38:54.651307-08:00'), ('2020-03-04T15:54:04.384972-08:00', '2020-03-04T16:04:15.285221-08:00'), ('2020-03-04T16:18:38.735253-08:00', '2020-03-04T16:59:50.758160-08:00'), ('2020-03-04T17:00:29.649558-08:00', '2020-03-04T17:10:53.029543-08:00')]\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = ['2020-03-04T15:54:04.384972-08:00', '2020-03-04T16:18:38.735253-08:00', '2020-03-04T17:00:29.649558-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 2\n", - "Before filtering, trips = [('2019-07-22T11:54:54.640858-07:00', '2019-07-22T12:12:05.875441-07:00'), ('2019-07-22T16:22:32.772816-07:00', '2019-07-22T16:59:29.946628-07:00'), ('2019-07-22T16:59:34.244279-07:00', '2019-07-22T17:01:26.617760-07:00'), ('2019-07-22T17:01:33.056928-07:00', '2019-07-22T17:02:52.728498-07:00'), ('2019-07-22T17:02:57.023672-07:00', '2019-07-22T17:34:27.164005-07:00'), ('2019-07-22T17:34:33.674745-07:00', '2019-07-22T17:44:21.778416-07:00')]\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = ['2019-07-22T11:54:54.640858-07:00']\n", - "Before filtering, trips = [('2019-07-22T11:54:54.640858-07:00', '2019-07-22T12:12:05.875441-07:00'), ('2019-07-22T16:22:32.772816-07:00', '2019-07-22T16:59:29.946628-07:00'), ('2019-07-22T16:59:34.244279-07:00', '2019-07-22T17:01:26.617760-07:00'), ('2019-07-22T17:01:33.056928-07:00', '2019-07-22T17:02:52.728498-07:00'), ('2019-07-22T17:02:57.023672-07:00', '2019-07-22T17:34:27.164005-07:00'), ('2019-07-22T17:34:33.674745-07:00', '2019-07-22T17:44:21.778416-07:00')]\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = ['2019-07-22T16:22:32.772816-07:00', '2019-07-22T16:59:34.244279-07:00', '2019-07-22T17:01:33.056928-07:00', '2019-07-22T17:02:57.023672-07:00', '2019-07-22T17:34:33.674745-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 2\n", - "Before filtering, trips = [('2019-07-23T08:49:54.660657-07:00', '2019-07-23T08:58:21.855733-07:00'), ('2019-07-23T08:58:51.517955-07:00', '2019-07-23T09:16:55.170476-07:00'), ('2019-07-23T09:17:01.651527-07:00', '2019-07-23T09:20:02.468521-07:00'), ('2019-07-23T12:44:01.787071-07:00', '2019-07-23T13:09:48.107456-07:00'), ('2019-07-23T13:09:54.556277-07:00', '2019-07-23T13:51:03.793421-07:00'), ('2019-07-23T13:51:08.814324-07:00', '2019-07-23T14:01:31.227720-07:00')]\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = ['2019-07-23T08:49:54.660657-07:00', '2019-07-23T08:58:51.517955-07:00', '2019-07-23T09:17:01.651527-07:00']\n", - "Before filtering, trips = [('2019-07-23T08:49:54.660657-07:00', '2019-07-23T08:58:21.855733-07:00'), ('2019-07-23T08:58:51.517955-07:00', '2019-07-23T09:16:55.170476-07:00'), ('2019-07-23T09:17:01.651527-07:00', '2019-07-23T09:20:02.468521-07:00'), ('2019-07-23T12:44:01.787071-07:00', '2019-07-23T13:09:48.107456-07:00'), ('2019-07-23T13:09:54.556277-07:00', '2019-07-23T13:51:03.793421-07:00'), ('2019-07-23T13:51:08.814324-07:00', '2019-07-23T14:01:31.227720-07:00')]\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = ['2019-07-23T12:44:01.787071-07:00', '2019-07-23T13:09:54.556277-07:00', '2019-07-23T13:51:08.814324-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 2\n", - "Before filtering, trips = [('2019-07-27T09:14:49.266143-07:00', '2019-07-27T09:20:59.728975-07:00'), ('2019-07-27T09:21:06.143658-07:00', '2019-07-27T09:40:24.548289-07:00'), ('2019-07-27T09:40:31.009991-07:00', '2019-07-27T09:44:19.851731-07:00'), ('2019-07-27T12:57:04.715789-07:00', '2019-07-27T13:26:03.481910-07:00'), ('2019-07-27T13:26:09.990369-07:00', '2019-07-27T14:01:16.042674-07:00'), ('2019-07-27T14:01:22.545097-07:00', '2019-07-27T14:11:46.145185-07:00')]\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = ['2019-07-27T09:14:49.266143-07:00', '2019-07-27T09:21:06.143658-07:00', '2019-07-27T09:40:31.009991-07:00']\n", - "Before filtering, trips = [('2019-07-27T09:14:49.266143-07:00', '2019-07-27T09:20:59.728975-07:00'), ('2019-07-27T09:21:06.143658-07:00', '2019-07-27T09:40:24.548289-07:00'), ('2019-07-27T09:40:31.009991-07:00', '2019-07-27T09:44:19.851731-07:00'), ('2019-07-27T12:57:04.715789-07:00', '2019-07-27T13:26:03.481910-07:00'), ('2019-07-27T13:26:09.990369-07:00', '2019-07-27T14:01:16.042674-07:00'), ('2019-07-27T14:01:22.545097-07:00', '2019-07-27T14:11:46.145185-07:00')]\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = ['2019-07-27T12:57:04.715789-07:00', '2019-07-27T13:26:09.990369-07:00', '2019-07-27T14:01:22.545097-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 2\n", - "Before filtering, trips = [('2019-08-05T10:32:22.335140-07:00', '2019-08-05T10:55:34.997035-07:00'), ('2019-08-05T10:56:02.996000-07:00', '2019-08-05T10:57:49.991994-07:00'), ('2019-08-05T15:04:31.337241-07:00', '2019-08-05T15:09:56.988751-07:00'), ('2019-08-05T15:10:21.987585-07:00', '2019-08-05T15:10:21.987585-07:00'), ('2019-08-05T15:10:52.986137-07:00', '2019-08-05T15:10:52.986137-07:00'), ('2019-08-05T15:11:00.985764-07:00', '2019-08-05T15:13:08.979793-07:00'), ('2019-08-05T15:13:59.977416-07:00', '2019-08-05T15:13:59.977416-07:00'), ('2019-08-05T15:14:07.989907-07:00', '2019-08-05T15:22:42.979539-07:00'), ('2019-08-05T15:22:50.979212-07:00', '2019-08-05T15:22:50.979212-07:00'), ('2019-08-05T15:22:56.978963-07:00', '2019-08-05T15:26:47.992232-07:00'), ('2019-08-05T15:34:35.991219-07:00', '2019-08-05T15:34:42.990969-07:00'), ('2019-08-05T15:34:45.990864-07:00', '2019-08-05T16:08:43.994686-07:00'), ('2019-08-05T16:09:33.992770-07:00', '2019-08-05T16:18:29.994664-07:00')]\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = ['2019-08-05T10:32:22.335140-07:00', '2019-08-05T10:56:02.996000-07:00']\n", - "Before filtering, trips = [('2019-08-05T10:32:22.335140-07:00', '2019-08-05T10:55:34.997035-07:00'), ('2019-08-05T10:56:02.996000-07:00', '2019-08-05T10:57:49.991994-07:00'), ('2019-08-05T15:04:31.337241-07:00', '2019-08-05T15:09:56.988751-07:00'), ('2019-08-05T15:10:21.987585-07:00', '2019-08-05T15:10:21.987585-07:00'), ('2019-08-05T15:10:52.986137-07:00', '2019-08-05T15:10:52.986137-07:00'), ('2019-08-05T15:11:00.985764-07:00', '2019-08-05T15:13:08.979793-07:00'), ('2019-08-05T15:13:59.977416-07:00', '2019-08-05T15:13:59.977416-07:00'), ('2019-08-05T15:14:07.989907-07:00', '2019-08-05T15:22:42.979539-07:00'), ('2019-08-05T15:22:50.979212-07:00', '2019-08-05T15:22:50.979212-07:00'), ('2019-08-05T15:22:56.978963-07:00', '2019-08-05T15:26:47.992232-07:00'), ('2019-08-05T15:34:35.991219-07:00', '2019-08-05T15:34:42.990969-07:00'), ('2019-08-05T15:34:45.990864-07:00', '2019-08-05T16:08:43.994686-07:00'), ('2019-08-05T16:09:33.992770-07:00', '2019-08-05T16:18:29.994664-07:00')]\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = ['2019-08-05T15:04:31.337241-07:00', '2019-08-05T15:10:21.987585-07:00', '2019-08-05T15:10:52.986137-07:00', '2019-08-05T15:11:00.985764-07:00', '2019-08-05T15:13:59.977416-07:00', '2019-08-05T15:14:07.989907-07:00', '2019-08-05T15:22:50.979212-07:00', '2019-08-05T15:22:56.978963-07:00', '2019-08-05T15:34:35.991219-07:00', '2019-08-05T15:34:45.990864-07:00', '2019-08-05T16:09:33.992770-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 2\n", - "Before filtering, trips = [('2019-08-06T11:34:43.409583-07:00', '2019-08-06T11:57:28.991871-07:00'), ('2019-08-06T11:59:12.987802-07:00', '2019-08-06T11:59:44.986549-07:00'), ('2019-08-06T15:49:55.973336-07:00', '2019-08-06T15:53:34.992962-07:00'), ('2019-08-06T15:53:44.992496-07:00', '2019-08-06T15:53:55.991987-07:00'), ('2019-08-06T15:54:01.991707-07:00', '2019-08-06T15:54:55.989197-07:00'), ('2019-08-06T15:55:05.988733-07:00', '2019-08-06T15:55:12.988407-07:00'), ('2019-08-06T15:55:20.988035-07:00', '2019-08-06T15:55:39.987151-07:00'), ('2019-08-06T15:56:11.985663-07:00', '2019-08-06T15:58:26.979387-07:00'), ('2019-08-06T15:58:35.990800-07:00', '2019-08-06T15:59:10.998218-07:00'), ('2019-08-06T15:59:35.997998-07:00', '2019-08-06T15:59:54.997354-07:00'), ('2019-08-06T16:00:00.997122-07:00', '2019-08-06T16:09:08.992409-07:00'), ('2019-08-06T16:10:12.989837-07:00', '2019-08-06T16:19:13.985583-07:00'), ('2019-08-06T16:19:16.985463-07:00', '2019-08-06T16:59:55.992178-07:00'), ('2019-08-06T17:00:32.990865-07:00', '2019-08-06T17:03:49.998598-07:00'), ('2019-08-06T17:15:17.928442-07:00', '2019-08-06T18:09:35.999116-07:00')]\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = ['2019-08-06T11:34:43.409583-07:00', '2019-08-06T11:59:12.987802-07:00']\n", - "Before filtering, trips = [('2019-08-06T11:34:43.409583-07:00', '2019-08-06T11:57:28.991871-07:00'), ('2019-08-06T11:59:12.987802-07:00', '2019-08-06T11:59:44.986549-07:00'), ('2019-08-06T15:49:55.973336-07:00', '2019-08-06T15:53:34.992962-07:00'), ('2019-08-06T15:53:44.992496-07:00', '2019-08-06T15:53:55.991987-07:00'), ('2019-08-06T15:54:01.991707-07:00', '2019-08-06T15:54:55.989197-07:00'), ('2019-08-06T15:55:05.988733-07:00', '2019-08-06T15:55:12.988407-07:00'), ('2019-08-06T15:55:20.988035-07:00', '2019-08-06T15:55:39.987151-07:00'), ('2019-08-06T15:56:11.985663-07:00', '2019-08-06T15:58:26.979387-07:00'), ('2019-08-06T15:58:35.990800-07:00', '2019-08-06T15:59:10.998218-07:00'), ('2019-08-06T15:59:35.997998-07:00', '2019-08-06T15:59:54.997354-07:00'), ('2019-08-06T16:00:00.997122-07:00', '2019-08-06T16:09:08.992409-07:00'), ('2019-08-06T16:10:12.989837-07:00', '2019-08-06T16:19:13.985583-07:00'), ('2019-08-06T16:19:16.985463-07:00', '2019-08-06T16:59:55.992178-07:00'), ('2019-08-06T17:00:32.990865-07:00', '2019-08-06T17:03:49.998598-07:00'), ('2019-08-06T17:15:17.928442-07:00', '2019-08-06T18:09:35.999116-07:00')]\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = ['2019-08-06T15:49:55.973336-07:00', '2019-08-06T15:53:44.992496-07:00', '2019-08-06T15:54:01.991707-07:00', '2019-08-06T15:55:05.988733-07:00', '2019-08-06T15:55:20.988035-07:00', '2019-08-06T15:56:11.985663-07:00', '2019-08-06T15:58:35.990800-07:00', '2019-08-06T15:59:35.997998-07:00', '2019-08-06T16:00:00.997122-07:00', '2019-08-06T16:10:12.989837-07:00', '2019-08-06T16:19:16.985463-07:00', '2019-08-06T17:00:32.990865-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 2\n", - "Before filtering, trips = [('2019-08-07T09:29:53.010319-07:00', '2019-08-07T09:46:37.994607-07:00'), ('2019-08-07T09:48:10.990654-07:00', '2019-08-07T09:49:52.986320-07:00'), ('2019-08-07T13:41:28.814627-07:00', '2019-08-07T13:43:55.998161-07:00'), ('2019-08-07T13:44:00.997942-07:00', '2019-08-07T13:44:36.996425-07:00'), ('2019-08-07T13:45:04.995069-07:00', '2019-08-07T13:45:32.993803-07:00'), ('2019-08-07T13:45:38.993531-07:00', '2019-08-07T13:48:32.985658-07:00'), ('2019-08-07T13:48:51.984799-07:00', '2019-08-07T13:48:57.984525-07:00'), ('2019-08-07T13:49:06.984119-07:00', '2019-08-07T13:49:49.982172-07:00'), ('2019-08-07T13:50:07.981358-07:00', '2019-08-07T13:50:07.981358-07:00'), ('2019-08-07T13:50:13.981086-07:00', '2019-08-07T13:53:57.991503-07:00'), ('2019-08-07T13:54:04.991202-07:00', '2019-08-07T13:54:04.991202-07:00'), ('2019-08-07T13:54:11.990904-07:00', '2019-08-07T14:01:19.993742-07:00'), ('2019-08-07T14:02:11.991568-07:00', '2019-08-07T14:05:32.983162-07:00'), ('2019-08-07T14:05:35.983035-07:00', '2019-08-07T14:05:44.991592-07:00'), ('2019-08-07T14:05:47.993097-07:00', '2019-08-07T14:41:20.991832-07:00'), ('2019-08-07T14:42:12.989707-07:00', '2019-08-07T14:51:51.997587-07:00')]\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = ['2019-08-07T09:29:53.010319-07:00', '2019-08-07T09:48:10.990654-07:00']\n", - "Before filtering, trips = [('2019-08-07T09:29:53.010319-07:00', '2019-08-07T09:46:37.994607-07:00'), ('2019-08-07T09:48:10.990654-07:00', '2019-08-07T09:49:52.986320-07:00'), ('2019-08-07T13:41:28.814627-07:00', '2019-08-07T13:43:55.998161-07:00'), ('2019-08-07T13:44:00.997942-07:00', '2019-08-07T13:44:36.996425-07:00'), ('2019-08-07T13:45:04.995069-07:00', '2019-08-07T13:45:32.993803-07:00'), ('2019-08-07T13:45:38.993531-07:00', '2019-08-07T13:48:32.985658-07:00'), ('2019-08-07T13:48:51.984799-07:00', '2019-08-07T13:48:57.984525-07:00'), ('2019-08-07T13:49:06.984119-07:00', '2019-08-07T13:49:49.982172-07:00'), ('2019-08-07T13:50:07.981358-07:00', '2019-08-07T13:50:07.981358-07:00'), ('2019-08-07T13:50:13.981086-07:00', '2019-08-07T13:53:57.991503-07:00'), ('2019-08-07T13:54:04.991202-07:00', '2019-08-07T13:54:04.991202-07:00'), ('2019-08-07T13:54:11.990904-07:00', '2019-08-07T14:01:19.993742-07:00'), ('2019-08-07T14:02:11.991568-07:00', '2019-08-07T14:05:32.983162-07:00'), ('2019-08-07T14:05:35.983035-07:00', '2019-08-07T14:05:44.991592-07:00'), ('2019-08-07T14:05:47.993097-07:00', '2019-08-07T14:41:20.991832-07:00'), ('2019-08-07T14:42:12.989707-07:00', '2019-08-07T14:51:51.997587-07:00')]\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = ['2019-08-07T13:41:28.814627-07:00', '2019-08-07T13:44:00.997942-07:00', '2019-08-07T13:45:04.995069-07:00', '2019-08-07T13:45:38.993531-07:00', '2019-08-07T13:48:51.984799-07:00', '2019-08-07T13:49:06.984119-07:00', '2019-08-07T13:50:07.981358-07:00', '2019-08-07T13:50:13.981086-07:00', '2019-08-07T13:54:04.991202-07:00', '2019-08-07T13:54:11.990904-07:00', '2019-08-07T14:02:11.991568-07:00', '2019-08-07T14:05:35.983035-07:00', '2019-08-07T14:05:47.993097-07:00', '2019-08-07T14:42:12.989707-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_0 MAMFDC v/s MAHFDC MAHFDC_0 2\n", - "Before filtering, trips = [('2020-03-04T11:25:40.046017-08:00', '2020-03-04T11:49:32.557874-08:00'), ('2020-03-04T11:49:38.998093-08:00', '2020-03-04T11:59:42.420861-08:00'), ('2020-03-04T15:39:13.617109-08:00', '2020-03-04T16:20:39.147647-08:00'), ('2020-03-04T16:20:49.320324-08:00', '2020-03-04T16:59:56.247860-08:00'), ('2020-03-04T17:00:02.727822-08:00', '2020-03-04T17:11:06.714911-08:00')]\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = ['2020-03-04T11:25:40.046017-08:00', '2020-03-04T11:49:38.998093-08:00']\n", - "Before filtering, trips = [('2020-03-04T11:25:40.046017-08:00', '2020-03-04T11:49:32.557874-08:00'), ('2020-03-04T11:49:38.998093-08:00', '2020-03-04T11:59:42.420861-08:00'), ('2020-03-04T15:39:13.617109-08:00', '2020-03-04T16:20:39.147647-08:00'), ('2020-03-04T16:20:49.320324-08:00', '2020-03-04T16:59:56.247860-08:00'), ('2020-03-04T17:00:02.727822-08:00', '2020-03-04T17:11:06.714911-08:00')]\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = ['2020-03-04T15:39:13.617109-08:00', '2020-03-04T16:20:49.320324-08:00', '2020-03-04T17:00:02.727822-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_0 HAHFDC v/s MAHFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T11:51:30.509112-07:00 -> 2019-07-22T12:22:52.411165-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-22T16:11:03.391155-07:00 -> 2019-07-22T17:45:12.805215-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_1 HAHFDC v/s MAHFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T08:46:49.339100-07:00 -> 2019-07-23T09:19:38.321992-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-23T12:41:39.755117-07:00 -> 2019-07-23T14:01:03.379727-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_2 HAHFDC v/s MAHFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T09:15:29.809285-07:00 -> 2019-07-27T09:40:44.135222-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-27T12:53:38.202683-07:00 -> 2019-07-27T14:11:01.009420-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_3 HAHFDC v/s HAMFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T10:28:00.249002-07:00 -> 2019-08-05T10:56:19.148538-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-05T15:02:33.864901-07:00 -> 2019-08-05T16:19:14.399231-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_4 HAHFDC v/s HAMFDC power_control_1 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T11:28:13.260763-07:00 -> 2019-08-06T11:59:45.816486-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-06T15:46:11.694115-07:00 -> 2019-08-06T17:10:26.460179-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_5 HAHFDC v/s HAMFDC power_control_2 2\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T09:19:20.832793-07:00 -> 2019-08-07T09:49:22.819000-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-08-07T13:40:16.767767-07:00 -> 2019-08-07T14:51:48.819000-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_6 MAMFDC v/s MAHFDC power_control_0 2\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T11:20:16.665268-08:00 -> 2020-03-04T11:51:48.554702-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-03-04T15:34:28.447122-08:00 -> 2020-03-04T17:09:43.546934-08:00\n", - "After filtering, trips = []\n", - "Finished copying train_bus_ebike_mtv_ucb, starting overwrite\n", - "Found spec = Multimodal multi-train, multi-bus, ebike trip to UC Berkeley\n", - "Evaluation ran from 2019-07-16T00:00:00-07:00 -> 2020-04-30T00:00:00-07:00\n", - "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", - "android dict_keys(['ucb-sdb-android-1', 'ucb-sdb-android-2', 'ucb-sdb-android-3', 'ucb-sdb-android-4'])\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_0 HAHFDC v/s HAMFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_1 HAHFDC v/s HAMFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_2 HAHFDC v/s HAMFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_3 HAHFDC v/s MAHFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_4 HAHFDC v/s MAHFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_5 HAHFDC v/s MAHFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_6 MAMFDC v/s HAMFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_7 MAMFDC v/s HAMFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_8 MAMFDC v/s HAMFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_9 MAMFDC v/s MAHFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_10 MAMFDC v/s MAHFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_11 MAMFDC v/s MAHFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = []\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 3\n", - "Before filtering, trips = [('2019-07-24T07:53:44-07:00', '2019-07-24T08:07:50-07:00'), ('2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:19-07:00'), ('2019-07-24T09:08:20-07:00', '2019-07-24T09:08:55-07:00'), ('2019-07-24T09:08:57-07:00', '2019-07-24T09:10:44-07:00'), ('2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:34-07:00'), ('2019-07-24T10:15:35-07:00', '2019-07-24T10:18:24-07:00'), ('2019-07-24T10:19:09-07:00', '2019-07-24T10:25:24-07:00'), ('2019-07-24T10:25:25-07:00', '2019-07-24T10:25:57-07:00'), ('2019-07-24T14:12:02.984072-07:00', '2019-07-24T14:26:15-07:00'), ('2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:16-07:00'), ('2019-07-24T16:42:18-07:00', '2019-07-24T17:01:23-07:00'), ('2019-07-24T17:01:24-07:00', '2019-07-24T17:01:45-07:00'), ('2019-07-24T17:01:46-07:00', '2019-07-24T17:02:24-07:00'), ('2019-07-24T17:02:25-07:00', '2019-07-24T17:06:29-07:00'), ('2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:05-07:00'), ('2019-07-24T18:00:06-07:00', '2019-07-24T18:08:27.933000-07:00'), ('2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:23-07:00'), ('2019-07-24T18:25:24-07:00', '2019-07-24T18:29:22-07:00'), ('2019-07-24T18:32:22-07:00', '2019-07-24T18:44:07-07:00'), ('2019-07-24T18:46:20.995584-07:00', '2019-07-24T19:29:49-07:00')]\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = ['2019-07-24T07:53:44-07:00', '2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:20-07:00', '2019-07-24T09:08:57-07:00', '2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:35-07:00', '2019-07-24T10:19:09-07:00', '2019-07-24T10:25:25-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:53:44-07:00', '2019-07-24T08:07:50-07:00'), ('2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:19-07:00'), ('2019-07-24T09:08:20-07:00', '2019-07-24T09:08:55-07:00'), ('2019-07-24T09:08:57-07:00', '2019-07-24T09:10:44-07:00'), ('2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:34-07:00'), ('2019-07-24T10:15:35-07:00', '2019-07-24T10:18:24-07:00'), ('2019-07-24T10:19:09-07:00', '2019-07-24T10:25:24-07:00'), ('2019-07-24T10:25:25-07:00', '2019-07-24T10:25:57-07:00'), ('2019-07-24T14:12:02.984072-07:00', '2019-07-24T14:26:15-07:00'), ('2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:16-07:00'), ('2019-07-24T16:42:18-07:00', '2019-07-24T17:01:23-07:00'), ('2019-07-24T17:01:24-07:00', '2019-07-24T17:01:45-07:00'), ('2019-07-24T17:01:46-07:00', '2019-07-24T17:02:24-07:00'), ('2019-07-24T17:02:25-07:00', '2019-07-24T17:06:29-07:00'), ('2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:05-07:00'), ('2019-07-24T18:00:06-07:00', '2019-07-24T18:08:27.933000-07:00'), ('2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:23-07:00'), ('2019-07-24T18:25:24-07:00', '2019-07-24T18:29:22-07:00'), ('2019-07-24T18:32:22-07:00', '2019-07-24T18:44:07-07:00'), ('2019-07-24T18:46:20.995584-07:00', '2019-07-24T19:29:49-07:00')]\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = ['2019-07-24T14:12:02.984072-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:53:44-07:00', '2019-07-24T08:07:50-07:00'), ('2019-07-24T08:30:17.302493-07:00', '2019-07-24T09:08:19-07:00'), ('2019-07-24T09:08:20-07:00', '2019-07-24T09:08:55-07:00'), ('2019-07-24T09:08:57-07:00', '2019-07-24T09:10:44-07:00'), ('2019-07-24T09:19:09.561793-07:00', '2019-07-24T10:15:34-07:00'), ('2019-07-24T10:15:35-07:00', '2019-07-24T10:18:24-07:00'), ('2019-07-24T10:19:09-07:00', '2019-07-24T10:25:24-07:00'), ('2019-07-24T10:25:25-07:00', '2019-07-24T10:25:57-07:00'), ('2019-07-24T14:12:02.984072-07:00', '2019-07-24T14:26:15-07:00'), ('2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:16-07:00'), ('2019-07-24T16:42:18-07:00', '2019-07-24T17:01:23-07:00'), ('2019-07-24T17:01:24-07:00', '2019-07-24T17:01:45-07:00'), ('2019-07-24T17:01:46-07:00', '2019-07-24T17:02:24-07:00'), ('2019-07-24T17:02:25-07:00', '2019-07-24T17:06:29-07:00'), ('2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:05-07:00'), ('2019-07-24T18:00:06-07:00', '2019-07-24T18:08:27.933000-07:00'), ('2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:23-07:00'), ('2019-07-24T18:25:24-07:00', '2019-07-24T18:29:22-07:00'), ('2019-07-24T18:32:22-07:00', '2019-07-24T18:44:07-07:00'), ('2019-07-24T18:46:20.995584-07:00', '2019-07-24T19:29:49-07:00')]\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = ['2019-07-24T16:36:40.823566-07:00', '2019-07-24T16:42:18-07:00', '2019-07-24T17:01:24-07:00', '2019-07-24T17:01:46-07:00', '2019-07-24T17:02:25-07:00', '2019-07-24T17:17:46.456333-07:00', '2019-07-24T18:00:06-07:00', '2019-07-24T18:10:38.307479-07:00', '2019-07-24T18:25:24-07:00', '2019-07-24T18:32:22-07:00', '2019-07-24T18:46:20.995584-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 3\n", - "Before filtering, trips = [('2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:21:25-07:00'), ('2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:10:20-07:00'), ('2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:52:34.591000-07:00'), ('2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:17-07:00'), ('2019-07-25T10:15:24-07:00', '2019-07-25T10:18:07-07:00'), ('2019-07-25T10:19:00-07:00', '2019-07-25T10:20:29-07:00'), ('2019-07-25T10:20:30-07:00', '2019-07-25T10:27:40-07:00'), ('2019-07-25T14:10:29-07:00', '2019-07-25T14:21:25-07:00'), ('2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:50-07:00'), ('2019-07-25T16:36:52-07:00', '2019-07-25T16:38:24-07:00'), ('2019-07-25T16:38:25-07:00', '2019-07-25T16:38:36-07:00'), ('2019-07-25T16:38:38-07:00', '2019-07-25T16:40:49-07:00'), ('2019-07-25T16:40:51-07:00', '2019-07-25T16:41:49-07:00'), ('2019-07-25T16:41:51-07:00', '2019-07-25T16:43:10-07:00'), ('2019-07-25T16:43:11-07:00', '2019-07-25T16:43:35-07:00'), ('2019-07-25T16:43:37-07:00', '2019-07-25T16:48:28-07:00'), ('2019-07-25T16:48:29-07:00', '2019-07-25T16:49:33-07:00'), ('2019-07-25T16:49:34-07:00', '2019-07-25T16:56:37-07:00'), ('2019-07-25T16:56:38-07:00', '2019-07-25T16:58:26-07:00'), ('2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:02-07:00'), ('2019-07-25T18:03:03-07:00', '2019-07-25T18:12:52.716000-07:00'), ('2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:40-07:00'), ('2019-07-25T18:25:41-07:00', '2019-07-25T18:28:18-07:00'), ('2019-07-25T18:34:29.732609-07:00', '2019-07-25T19:10:58-07:00')]\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = ['2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:24-07:00', '2019-07-25T10:19:00-07:00', '2019-07-25T10:20:30-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:21:25-07:00'), ('2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:10:20-07:00'), ('2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:52:34.591000-07:00'), ('2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:17-07:00'), ('2019-07-25T10:15:24-07:00', '2019-07-25T10:18:07-07:00'), ('2019-07-25T10:19:00-07:00', '2019-07-25T10:20:29-07:00'), ('2019-07-25T10:20:30-07:00', '2019-07-25T10:27:40-07:00'), ('2019-07-25T14:10:29-07:00', '2019-07-25T14:21:25-07:00'), ('2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:50-07:00'), ('2019-07-25T16:36:52-07:00', '2019-07-25T16:38:24-07:00'), ('2019-07-25T16:38:25-07:00', '2019-07-25T16:38:36-07:00'), ('2019-07-25T16:38:38-07:00', '2019-07-25T16:40:49-07:00'), ('2019-07-25T16:40:51-07:00', '2019-07-25T16:41:49-07:00'), ('2019-07-25T16:41:51-07:00', '2019-07-25T16:43:10-07:00'), ('2019-07-25T16:43:11-07:00', '2019-07-25T16:43:35-07:00'), ('2019-07-25T16:43:37-07:00', '2019-07-25T16:48:28-07:00'), ('2019-07-25T16:48:29-07:00', '2019-07-25T16:49:33-07:00'), ('2019-07-25T16:49:34-07:00', '2019-07-25T16:56:37-07:00'), ('2019-07-25T16:56:38-07:00', '2019-07-25T16:58:26-07:00'), ('2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:02-07:00'), ('2019-07-25T18:03:03-07:00', '2019-07-25T18:12:52.716000-07:00'), ('2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:40-07:00'), ('2019-07-25T18:25:41-07:00', '2019-07-25T18:28:18-07:00'), ('2019-07-25T18:34:29.732609-07:00', '2019-07-25T19:10:58-07:00')]\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = ['2019-07-25T14:10:29-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:08:41.151000-07:00', '2019-07-25T08:21:25-07:00'), ('2019-07-25T08:30:10.155039-07:00', '2019-07-25T09:10:20-07:00'), ('2019-07-25T09:16:25.425119-07:00', '2019-07-25T09:52:34.591000-07:00'), ('2019-07-25T09:54:31.795500-07:00', '2019-07-25T10:15:17-07:00'), ('2019-07-25T10:15:24-07:00', '2019-07-25T10:18:07-07:00'), ('2019-07-25T10:19:00-07:00', '2019-07-25T10:20:29-07:00'), ('2019-07-25T10:20:30-07:00', '2019-07-25T10:27:40-07:00'), ('2019-07-25T14:10:29-07:00', '2019-07-25T14:21:25-07:00'), ('2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:50-07:00'), ('2019-07-25T16:36:52-07:00', '2019-07-25T16:38:24-07:00'), ('2019-07-25T16:38:25-07:00', '2019-07-25T16:38:36-07:00'), ('2019-07-25T16:38:38-07:00', '2019-07-25T16:40:49-07:00'), ('2019-07-25T16:40:51-07:00', '2019-07-25T16:41:49-07:00'), ('2019-07-25T16:41:51-07:00', '2019-07-25T16:43:10-07:00'), ('2019-07-25T16:43:11-07:00', '2019-07-25T16:43:35-07:00'), ('2019-07-25T16:43:37-07:00', '2019-07-25T16:48:28-07:00'), ('2019-07-25T16:48:29-07:00', '2019-07-25T16:49:33-07:00'), ('2019-07-25T16:49:34-07:00', '2019-07-25T16:56:37-07:00'), ('2019-07-25T16:56:38-07:00', '2019-07-25T16:58:26-07:00'), ('2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:02-07:00'), ('2019-07-25T18:03:03-07:00', '2019-07-25T18:12:52.716000-07:00'), ('2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:40-07:00'), ('2019-07-25T18:25:41-07:00', '2019-07-25T18:28:18-07:00'), ('2019-07-25T18:34:29.732609-07:00', '2019-07-25T19:10:58-07:00')]\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = ['2019-07-25T16:33:15.168485-07:00', '2019-07-25T16:36:52-07:00', '2019-07-25T16:38:25-07:00', '2019-07-25T16:38:38-07:00', '2019-07-25T16:40:51-07:00', '2019-07-25T16:41:51-07:00', '2019-07-25T16:43:11-07:00', '2019-07-25T16:43:37-07:00', '2019-07-25T16:48:29-07:00', '2019-07-25T16:49:34-07:00', '2019-07-25T16:56:38-07:00', '2019-07-25T17:22:06.924278-07:00', '2019-07-25T18:03:03-07:00', '2019-07-25T18:13:58.926000-07:00', '2019-07-25T18:25:41-07:00', '2019-07-25T18:34:29.732609-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 3\n", - "Before filtering, trips = [('2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:24:22-07:00'), ('2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:17-07:00'), ('2019-07-26T09:09:18-07:00', '2019-07-26T09:09:29-07:00'), ('2019-07-26T09:09:30-07:00', '2019-07-26T09:09:55-07:00'), ('2019-07-26T09:09:56-07:00', '2019-07-26T09:10:23.602000-07:00'), ('2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:13:57-07:00'), ('2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:17.764000-07:00'), ('2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:23:59-07:00'), ('2019-07-26T10:24:00-07:00', '2019-07-26T10:27:28-07:00'), ('2019-07-26T14:16:52.077016-07:00', '2019-07-26T14:29:03-07:00'), ('2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:20:42-07:00'), ('2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:29:57-07:00'), ('2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:46-07:00'), ('2019-07-26T16:55:47-07:00', '2019-07-26T16:55:55-07:00'), ('2019-07-26T16:55:56-07:00', '2019-07-26T16:59:06-07:00'), ('2019-07-26T16:59:07-07:00', '2019-07-26T16:59:31-07:00'), ('2019-07-26T16:59:33-07:00', '2019-07-26T17:00:20-07:00'), ('2019-07-26T17:00:21-07:00', '2019-07-26T17:14:19-07:00'), ('2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:49-07:00'), ('2019-07-26T17:33:52-07:00', '2019-07-26T17:34:02-07:00'), ('2019-07-26T17:34:03-07:00', '2019-07-26T17:51:47-07:00'), ('2019-07-26T17:53:29-07:00', '2019-07-26T18:01:31.067000-07:00'), ('2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:21:11-07:00'), ('2019-07-26T18:34:21.552356-07:00', '2019-07-26T19:03:48-07:00')]\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = ['2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:18-07:00', '2019-07-26T09:09:30-07:00', '2019-07-26T09:09:56-07:00', '2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:24:00-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:24:22-07:00'), ('2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:17-07:00'), ('2019-07-26T09:09:18-07:00', '2019-07-26T09:09:29-07:00'), ('2019-07-26T09:09:30-07:00', '2019-07-26T09:09:55-07:00'), ('2019-07-26T09:09:56-07:00', '2019-07-26T09:10:23.602000-07:00'), ('2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:13:57-07:00'), ('2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:17.764000-07:00'), ('2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:23:59-07:00'), ('2019-07-26T10:24:00-07:00', '2019-07-26T10:27:28-07:00'), ('2019-07-26T14:16:52.077016-07:00', '2019-07-26T14:29:03-07:00'), ('2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:20:42-07:00'), ('2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:29:57-07:00'), ('2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:46-07:00'), ('2019-07-26T16:55:47-07:00', '2019-07-26T16:55:55-07:00'), ('2019-07-26T16:55:56-07:00', '2019-07-26T16:59:06-07:00'), ('2019-07-26T16:59:07-07:00', '2019-07-26T16:59:31-07:00'), ('2019-07-26T16:59:33-07:00', '2019-07-26T17:00:20-07:00'), ('2019-07-26T17:00:21-07:00', '2019-07-26T17:14:19-07:00'), ('2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:49-07:00'), ('2019-07-26T17:33:52-07:00', '2019-07-26T17:34:02-07:00'), ('2019-07-26T17:34:03-07:00', '2019-07-26T17:51:47-07:00'), ('2019-07-26T17:53:29-07:00', '2019-07-26T18:01:31.067000-07:00'), ('2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:21:11-07:00'), ('2019-07-26T18:34:21.552356-07:00', '2019-07-26T19:03:48-07:00')]\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = ['2019-07-26T14:16:52.077016-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:13:56.672000-07:00', '2019-07-26T08:24:22-07:00'), ('2019-07-26T08:31:58.409877-07:00', '2019-07-26T09:09:17-07:00'), ('2019-07-26T09:09:18-07:00', '2019-07-26T09:09:29-07:00'), ('2019-07-26T09:09:30-07:00', '2019-07-26T09:09:55-07:00'), ('2019-07-26T09:09:56-07:00', '2019-07-26T09:10:23.602000-07:00'), ('2019-07-26T09:10:32.228000-07:00', '2019-07-26T09:13:57-07:00'), ('2019-07-26T09:19:19.797482-07:00', '2019-07-26T10:14:17.764000-07:00'), ('2019-07-26T10:14:22.830000-07:00', '2019-07-26T10:23:59-07:00'), ('2019-07-26T10:24:00-07:00', '2019-07-26T10:27:28-07:00'), ('2019-07-26T14:16:52.077016-07:00', '2019-07-26T14:29:03-07:00'), ('2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:20:42-07:00'), ('2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:29:57-07:00'), ('2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:46-07:00'), ('2019-07-26T16:55:47-07:00', '2019-07-26T16:55:55-07:00'), ('2019-07-26T16:55:56-07:00', '2019-07-26T16:59:06-07:00'), ('2019-07-26T16:59:07-07:00', '2019-07-26T16:59:31-07:00'), ('2019-07-26T16:59:33-07:00', '2019-07-26T17:00:20-07:00'), ('2019-07-26T17:00:21-07:00', '2019-07-26T17:14:19-07:00'), ('2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:49-07:00'), ('2019-07-26T17:33:52-07:00', '2019-07-26T17:34:02-07:00'), ('2019-07-26T17:34:03-07:00', '2019-07-26T17:51:47-07:00'), ('2019-07-26T17:53:29-07:00', '2019-07-26T18:01:31.067000-07:00'), ('2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:21:11-07:00'), ('2019-07-26T18:34:21.552356-07:00', '2019-07-26T19:03:48-07:00')]\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = ['2019-07-26T16:12:53.007562-07:00', '2019-07-26T16:25:21.935801-07:00', '2019-07-26T16:39:46.628494-07:00', '2019-07-26T16:55:47-07:00', '2019-07-26T16:55:56-07:00', '2019-07-26T16:59:07-07:00', '2019-07-26T16:59:33-07:00', '2019-07-26T17:00:21-07:00', '2019-07-26T17:18:04.033054-07:00', '2019-07-26T17:33:52-07:00', '2019-07-26T17:34:03-07:00', '2019-07-26T17:53:29-07:00', '2019-07-26T18:09:34.791777-07:00', '2019-07-26T18:34:21.552356-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 3\n", - "Before filtering, trips = [('2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:28:06-07:00'), ('2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:24-07:00'), ('2019-09-10T09:07:26-07:00', '2019-09-10T09:08:04-07:00'), ('2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:03.425000-07:00'), ('2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:19:45-07:00'), ('2019-09-10T10:28:08.205705-07:00', '2019-09-10T10:36:42-07:00'), ('2019-09-10T13:39:33.901504-07:00', '2019-09-10T13:52:51-07:00'), ('2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:46-07:00'), ('2019-09-10T16:17:47-07:00', '2019-09-10T16:30:23-07:00'), ('2019-09-10T16:30:25-07:00', '2019-09-10T16:31:33-07:00'), ('2019-09-10T16:31:35-07:00', '2019-09-10T16:41:32-07:00'), ('2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:52-07:00'), ('2019-09-10T17:32:53-07:00', '2019-09-10T17:33:26-07:00'), ('2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:52-07:00'), ('2019-09-10T18:01:55-07:00', '2019-09-10T18:04:20-07:00'), ('2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:18-07:00'), ('2019-09-10T18:32:30-07:00', '2019-09-10T18:36:12.959000-07:00'), ('2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:04:41-07:00'), ('2019-09-10T19:06:01-07:00', '2019-09-10T19:21:41-07:00')]\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = ['2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:26-07:00', '2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:28:08.205705-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:28:06-07:00'), ('2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:24-07:00'), ('2019-09-10T09:07:26-07:00', '2019-09-10T09:08:04-07:00'), ('2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:03.425000-07:00'), ('2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:19:45-07:00'), ('2019-09-10T10:28:08.205705-07:00', '2019-09-10T10:36:42-07:00'), ('2019-09-10T13:39:33.901504-07:00', '2019-09-10T13:52:51-07:00'), ('2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:46-07:00'), ('2019-09-10T16:17:47-07:00', '2019-09-10T16:30:23-07:00'), ('2019-09-10T16:30:25-07:00', '2019-09-10T16:31:33-07:00'), ('2019-09-10T16:31:35-07:00', '2019-09-10T16:41:32-07:00'), ('2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:52-07:00'), ('2019-09-10T17:32:53-07:00', '2019-09-10T17:33:26-07:00'), ('2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:52-07:00'), ('2019-09-10T18:01:55-07:00', '2019-09-10T18:04:20-07:00'), ('2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:18-07:00'), ('2019-09-10T18:32:30-07:00', '2019-09-10T18:36:12.959000-07:00'), ('2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:04:41-07:00'), ('2019-09-10T19:06:01-07:00', '2019-09-10T19:21:41-07:00')]\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = ['2019-09-10T13:39:33.901504-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:11:45.580000-07:00', '2019-09-10T08:28:06-07:00'), ('2019-09-10T08:30:57.622500-07:00', '2019-09-10T09:07:24-07:00'), ('2019-09-10T09:07:26-07:00', '2019-09-10T09:08:04-07:00'), ('2019-09-10T09:19:09.871847-07:00', '2019-09-10T10:14:03.425000-07:00'), ('2019-09-10T10:14:13.451000-07:00', '2019-09-10T10:19:45-07:00'), ('2019-09-10T10:28:08.205705-07:00', '2019-09-10T10:36:42-07:00'), ('2019-09-10T13:39:33.901504-07:00', '2019-09-10T13:52:51-07:00'), ('2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:46-07:00'), ('2019-09-10T16:17:47-07:00', '2019-09-10T16:30:23-07:00'), ('2019-09-10T16:30:25-07:00', '2019-09-10T16:31:33-07:00'), ('2019-09-10T16:31:35-07:00', '2019-09-10T16:41:32-07:00'), ('2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:52-07:00'), ('2019-09-10T17:32:53-07:00', '2019-09-10T17:33:26-07:00'), ('2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:52-07:00'), ('2019-09-10T18:01:55-07:00', '2019-09-10T18:04:20-07:00'), ('2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:18-07:00'), ('2019-09-10T18:32:30-07:00', '2019-09-10T18:36:12.959000-07:00'), ('2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:04:41-07:00'), ('2019-09-10T19:06:01-07:00', '2019-09-10T19:21:41-07:00')]\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = ['2019-09-10T16:11:43.889654-07:00', '2019-09-10T16:17:47-07:00', '2019-09-10T16:30:25-07:00', '2019-09-10T16:31:35-07:00', '2019-09-10T16:52:22.829326-07:00', '2019-09-10T17:32:53-07:00', '2019-09-10T17:48:06.319446-07:00', '2019-09-10T18:01:55-07:00', '2019-09-10T18:17:33.412259-07:00', '2019-09-10T18:32:30-07:00', '2019-09-10T18:36:18.030000-07:00', '2019-09-10T19:06:01-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 3\n", - "Before filtering, trips = [('2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:27:59-07:00'), ('2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:58-07:00'), ('2019-09-11T09:04:59-07:00', '2019-09-11T09:06:08.980000-07:00'), ('2019-09-11T09:06:35-07:00', '2019-09-11T09:08:08-07:00'), ('2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:54-07:00'), ('2019-09-11T10:17:55-07:00', '2019-09-11T10:19:20-07:00'), ('2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:11-07:00'), ('2019-09-11T10:36:14-07:00', '2019-09-11T10:37:20-07:00'), ('2019-09-11T13:46:38.982742-07:00', '2019-09-11T14:00:33-07:00'), ('2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:46-07:00'), ('2019-09-11T16:32:47-07:00', '2019-09-11T16:43:11-07:00'), ('2019-09-11T16:43:12-07:00', '2019-09-11T16:43:55-07:00'), ('2019-09-11T16:43:56-07:00', '2019-09-11T16:50:21-07:00'), ('2019-09-11T16:50:22-07:00', '2019-09-11T16:50:34-07:00'), ('2019-09-11T16:50:35-07:00', '2019-09-11T16:52:00-07:00'), ('2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:41.983000-07:00'), ('2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:07:02.071000-07:00'), ('2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:53-07:00'), ('2019-09-11T18:15:54-07:00', '2019-09-11T18:16:44-07:00'), ('2019-09-11T18:17:18-07:00', '2019-09-11T18:18:51-07:00'), ('2019-09-11T18:18:52-07:00', '2019-09-11T18:20:23-07:00'), ('2019-09-11T18:23:23-07:00', '2019-09-11T18:43:23-07:00')]\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = ['2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:59-07:00', '2019-09-11T09:06:35-07:00', '2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:55-07:00', '2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:14-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:27:59-07:00'), ('2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:58-07:00'), ('2019-09-11T09:04:59-07:00', '2019-09-11T09:06:08.980000-07:00'), ('2019-09-11T09:06:35-07:00', '2019-09-11T09:08:08-07:00'), ('2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:54-07:00'), ('2019-09-11T10:17:55-07:00', '2019-09-11T10:19:20-07:00'), ('2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:11-07:00'), ('2019-09-11T10:36:14-07:00', '2019-09-11T10:37:20-07:00'), ('2019-09-11T13:46:38.982742-07:00', '2019-09-11T14:00:33-07:00'), ('2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:46-07:00'), ('2019-09-11T16:32:47-07:00', '2019-09-11T16:43:11-07:00'), ('2019-09-11T16:43:12-07:00', '2019-09-11T16:43:55-07:00'), ('2019-09-11T16:43:56-07:00', '2019-09-11T16:50:21-07:00'), ('2019-09-11T16:50:22-07:00', '2019-09-11T16:50:34-07:00'), ('2019-09-11T16:50:35-07:00', '2019-09-11T16:52:00-07:00'), ('2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:41.983000-07:00'), ('2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:07:02.071000-07:00'), ('2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:53-07:00'), ('2019-09-11T18:15:54-07:00', '2019-09-11T18:16:44-07:00'), ('2019-09-11T18:17:18-07:00', '2019-09-11T18:18:51-07:00'), ('2019-09-11T18:18:52-07:00', '2019-09-11T18:20:23-07:00'), ('2019-09-11T18:23:23-07:00', '2019-09-11T18:43:23-07:00')]\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = ['2019-09-11T13:46:38.982742-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:14:53.130000-07:00', '2019-09-11T08:27:59-07:00'), ('2019-09-11T08:28:40.551754-07:00', '2019-09-11T09:04:58-07:00'), ('2019-09-11T09:04:59-07:00', '2019-09-11T09:06:08.980000-07:00'), ('2019-09-11T09:06:35-07:00', '2019-09-11T09:08:08-07:00'), ('2019-09-11T09:18:26.551065-07:00', '2019-09-11T10:17:54-07:00'), ('2019-09-11T10:17:55-07:00', '2019-09-11T10:19:20-07:00'), ('2019-09-11T10:30:34.072766-07:00', '2019-09-11T10:36:11-07:00'), ('2019-09-11T10:36:14-07:00', '2019-09-11T10:37:20-07:00'), ('2019-09-11T13:46:38.982742-07:00', '2019-09-11T14:00:33-07:00'), ('2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:46-07:00'), ('2019-09-11T16:32:47-07:00', '2019-09-11T16:43:11-07:00'), ('2019-09-11T16:43:12-07:00', '2019-09-11T16:43:55-07:00'), ('2019-09-11T16:43:56-07:00', '2019-09-11T16:50:21-07:00'), ('2019-09-11T16:50:22-07:00', '2019-09-11T16:50:34-07:00'), ('2019-09-11T16:50:35-07:00', '2019-09-11T16:52:00-07:00'), ('2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:41.983000-07:00'), ('2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:07:02.071000-07:00'), ('2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:53-07:00'), ('2019-09-11T18:15:54-07:00', '2019-09-11T18:16:44-07:00'), ('2019-09-11T18:17:18-07:00', '2019-09-11T18:18:51-07:00'), ('2019-09-11T18:18:52-07:00', '2019-09-11T18:20:23-07:00'), ('2019-09-11T18:23:23-07:00', '2019-09-11T18:43:23-07:00')]\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = ['2019-09-11T16:27:27.810889-07:00', '2019-09-11T16:32:47-07:00', '2019-09-11T16:43:12-07:00', '2019-09-11T16:43:56-07:00', '2019-09-11T16:50:22-07:00', '2019-09-11T16:50:35-07:00', '2019-09-11T17:16:37.819500-07:00', '2019-09-11T17:56:47.641000-07:00', '2019-09-11T18:08:13.811000-07:00', '2019-09-11T18:15:54-07:00', '2019-09-11T18:17:18-07:00', '2019-09-11T18:18:52-07:00', '2019-09-11T18:23:23-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 3\n", - "Before filtering, trips = [('2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:10-07:00'), ('2019-09-17T08:23:12-07:00', '2019-09-17T08:23:16-07:00'), ('2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:05:11-07:00'), ('2019-09-17T09:14:10-07:00', '2019-09-17T09:14:24-07:00'), ('2019-09-17T09:14:25-07:00', '2019-09-17T09:18:49-07:00'), ('2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:10:33-07:00'), ('2019-09-17T10:16:01-07:00', '2019-09-17T10:18:53-07:00'), ('2019-09-17T10:33:16.758010-07:00', '2019-09-17T10:39:26-07:00'), ('2019-09-17T13:46:24.897379-07:00', '2019-09-17T13:57:36.104000-07:00'), ('2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:36.868000-07:00'), ('2019-09-17T16:16:57-07:00', '2019-09-17T16:34:20-07:00'), ('2019-09-17T16:34:22-07:00', '2019-09-17T16:35:48-07:00'), ('2019-09-17T16:35:50-07:00', '2019-09-17T16:36:16-07:00'), ('2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:52.671000-07:00'), ('2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:34:54.982000-07:00'), ('2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:58:59-07:00'), ('2019-09-17T17:59:00-07:00', '2019-09-17T18:13:52-07:00'), ('2019-09-17T18:13:53-07:00', '2019-09-17T18:15:09-07:00'), ('2019-09-17T18:15:10-07:00', '2019-09-17T18:58:00-07:00'), ('2019-09-17T18:58:14.010000-07:00', '2019-09-17T19:14:13-07:00')]\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = ['2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:12-07:00', '2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:14:10-07:00', '2019-09-17T09:14:25-07:00', '2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:16:01-07:00', '2019-09-17T10:33:16.758010-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:10-07:00'), ('2019-09-17T08:23:12-07:00', '2019-09-17T08:23:16-07:00'), ('2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:05:11-07:00'), ('2019-09-17T09:14:10-07:00', '2019-09-17T09:14:24-07:00'), ('2019-09-17T09:14:25-07:00', '2019-09-17T09:18:49-07:00'), ('2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:10:33-07:00'), ('2019-09-17T10:16:01-07:00', '2019-09-17T10:18:53-07:00'), ('2019-09-17T10:33:16.758010-07:00', '2019-09-17T10:39:26-07:00'), ('2019-09-17T13:46:24.897379-07:00', '2019-09-17T13:57:36.104000-07:00'), ('2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:36.868000-07:00'), ('2019-09-17T16:16:57-07:00', '2019-09-17T16:34:20-07:00'), ('2019-09-17T16:34:22-07:00', '2019-09-17T16:35:48-07:00'), ('2019-09-17T16:35:50-07:00', '2019-09-17T16:36:16-07:00'), ('2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:52.671000-07:00'), ('2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:34:54.982000-07:00'), ('2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:58:59-07:00'), ('2019-09-17T17:59:00-07:00', '2019-09-17T18:13:52-07:00'), ('2019-09-17T18:13:53-07:00', '2019-09-17T18:15:09-07:00'), ('2019-09-17T18:15:10-07:00', '2019-09-17T18:58:00-07:00'), ('2019-09-17T18:58:14.010000-07:00', '2019-09-17T19:14:13-07:00')]\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = ['2019-09-17T13:46:24.897379-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:10:33.181000-07:00', '2019-09-17T08:23:10-07:00'), ('2019-09-17T08:23:12-07:00', '2019-09-17T08:23:16-07:00'), ('2019-09-17T08:32:46.823959-07:00', '2019-09-17T09:05:11-07:00'), ('2019-09-17T09:14:10-07:00', '2019-09-17T09:14:24-07:00'), ('2019-09-17T09:14:25-07:00', '2019-09-17T09:18:49-07:00'), ('2019-09-17T09:24:02.966953-07:00', '2019-09-17T10:10:33-07:00'), ('2019-09-17T10:16:01-07:00', '2019-09-17T10:18:53-07:00'), ('2019-09-17T10:33:16.758010-07:00', '2019-09-17T10:39:26-07:00'), ('2019-09-17T13:46:24.897379-07:00', '2019-09-17T13:57:36.104000-07:00'), ('2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:36.868000-07:00'), ('2019-09-17T16:16:57-07:00', '2019-09-17T16:34:20-07:00'), ('2019-09-17T16:34:22-07:00', '2019-09-17T16:35:48-07:00'), ('2019-09-17T16:35:50-07:00', '2019-09-17T16:36:16-07:00'), ('2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:52.671000-07:00'), ('2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:34:54.982000-07:00'), ('2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:58:59-07:00'), ('2019-09-17T17:59:00-07:00', '2019-09-17T18:13:52-07:00'), ('2019-09-17T18:13:53-07:00', '2019-09-17T18:15:09-07:00'), ('2019-09-17T18:15:10-07:00', '2019-09-17T18:58:00-07:00'), ('2019-09-17T18:58:14.010000-07:00', '2019-09-17T19:14:13-07:00')]\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = ['2019-09-17T16:12:00.780607-07:00', '2019-09-17T16:16:57-07:00', '2019-09-17T16:34:22-07:00', '2019-09-17T16:35:50-07:00', '2019-09-17T16:52:12.093506-07:00', '2019-09-17T17:27:55.081000-07:00', '2019-09-17T17:37:54.982000-07:00', '2019-09-17T17:59:00-07:00', '2019-09-17T18:13:53-07:00', '2019-09-17T18:15:10-07:00', '2019-09-17T18:58:14.010000-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_0 MAMFDC v/s HAMFDC MAMFDC_0 3\n", - "Before filtering, trips = [('2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:24:06.675000-08:00'), ('2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:12:44.716000-08:00'), ('2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:10:07.733000-08:00'), ('2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:18:05.678000-08:00'), ('2019-11-19T10:25:55.207920-08:00', '2019-11-19T10:33:11.647000-08:00'), ('2019-11-19T13:29:35.029448-08:00', '2019-11-19T13:44:08.725000-08:00'), ('2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:19:06.800000-08:00'), ('2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:30:59.809000-08:00'), ('2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:03.795000-08:00'), ('2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:41:47.921000-08:00'), ('2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:19.888000-08:00'), ('2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:35:56.814000-08:00'), ('2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:06.679000-08:00'), ('2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:57:39.650000-08:00'), ('2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:57:55.616000-08:00'), ('2019-11-19T18:58:27.623000-08:00', '2019-11-19T19:16:08.638000-08:00')]\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = ['2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:25:55.207920-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:24:06.675000-08:00'), ('2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:12:44.716000-08:00'), ('2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:10:07.733000-08:00'), ('2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:18:05.678000-08:00'), ('2019-11-19T10:25:55.207920-08:00', '2019-11-19T10:33:11.647000-08:00'), ('2019-11-19T13:29:35.029448-08:00', '2019-11-19T13:44:08.725000-08:00'), ('2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:19:06.800000-08:00'), ('2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:30:59.809000-08:00'), ('2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:03.795000-08:00'), ('2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:41:47.921000-08:00'), ('2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:19.888000-08:00'), ('2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:35:56.814000-08:00'), ('2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:06.679000-08:00'), ('2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:57:39.650000-08:00'), ('2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:57:55.616000-08:00'), ('2019-11-19T18:58:27.623000-08:00', '2019-11-19T19:16:08.638000-08:00')]\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = ['2019-11-19T13:29:35.029448-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:11:13.593000-08:00', '2019-11-19T08:24:06.675000-08:00'), ('2019-11-19T08:31:09.631221-08:00', '2019-11-19T09:12:44.716000-08:00'), ('2019-11-19T09:19:41.842213-08:00', '2019-11-19T10:10:07.733000-08:00'), ('2019-11-19T10:14:22.204000-08:00', '2019-11-19T10:18:05.678000-08:00'), ('2019-11-19T10:25:55.207920-08:00', '2019-11-19T10:33:11.647000-08:00'), ('2019-11-19T13:29:35.029448-08:00', '2019-11-19T13:44:08.725000-08:00'), ('2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:19:06.800000-08:00'), ('2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:30:59.809000-08:00'), ('2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:03.795000-08:00'), ('2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:41:47.921000-08:00'), ('2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:19.888000-08:00'), ('2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:35:56.814000-08:00'), ('2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:06.679000-08:00'), ('2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:57:39.650000-08:00'), ('2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:57:55.616000-08:00'), ('2019-11-19T18:58:27.623000-08:00', '2019-11-19T19:16:08.638000-08:00')]\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = ['2019-11-19T16:11:23.488895-08:00', '2019-11-19T16:20:11.762000-08:00', '2019-11-19T16:31:31.746000-08:00', '2019-11-19T16:32:36.949000-08:00', '2019-11-19T16:56:31.273228-08:00', '2019-11-19T17:27:51.775000-08:00', '2019-11-19T17:40:44.972334-08:00', '2019-11-19T17:57:39.650000-08:00', '2019-11-19T17:59:16.630000-08:00', '2019-11-19T18:58:27.623000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_1 MAMFDC v/s HAMFDC MAMFDC_1 3\n", - "Before filtering, trips = [('2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:29:36.043000-08:00'), ('2019-11-20T08:31:06-08:00', '2019-11-20T09:09:48-08:00'), ('2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:27.080000-08:00'), ('2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:19:13.196000-08:00'), ('2019-11-20T10:25:14.928144-08:00', '2019-11-20T10:31:59.682000-08:00'), ('2019-11-20T13:45:47.834701-08:00', '2019-11-20T13:59:41.806000-08:00'), ('2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:15.760000-08:00'), ('2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:35:34.822000-08:00'), ('2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:06.836000-08:00'), ('2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:11.819000-08:00'), ('2019-11-20T16:44:43.788000-08:00', '2019-11-20T16:47:30-08:00'), ('2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:50:41.894000-08:00'), ('2019-11-20T17:51:13.823000-08:00', '2019-11-20T17:58:51.934000-08:00'), ('2019-11-20T18:00:47-08:00', '2019-11-20T18:12:42.631000-08:00'), ('2019-11-20T18:13:24-08:00', '2019-11-20T18:17:02-08:00'), ('2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:04:45.847000-08:00'), ('2019-11-20T19:05:17.765000-08:00', '2019-11-20T19:22:25.813000-08:00')]\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = ['2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:31:06-08:00', '2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:25:14.928144-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:29:36.043000-08:00'), ('2019-11-20T08:31:06-08:00', '2019-11-20T09:09:48-08:00'), ('2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:27.080000-08:00'), ('2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:19:13.196000-08:00'), ('2019-11-20T10:25:14.928144-08:00', '2019-11-20T10:31:59.682000-08:00'), ('2019-11-20T13:45:47.834701-08:00', '2019-11-20T13:59:41.806000-08:00'), ('2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:15.760000-08:00'), ('2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:35:34.822000-08:00'), ('2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:06.836000-08:00'), ('2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:11.819000-08:00'), ('2019-11-20T16:44:43.788000-08:00', '2019-11-20T16:47:30-08:00'), ('2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:50:41.894000-08:00'), ('2019-11-20T17:51:13.823000-08:00', '2019-11-20T17:58:51.934000-08:00'), ('2019-11-20T18:00:47-08:00', '2019-11-20T18:12:42.631000-08:00'), ('2019-11-20T18:13:24-08:00', '2019-11-20T18:17:02-08:00'), ('2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:04:45.847000-08:00'), ('2019-11-20T19:05:17.765000-08:00', '2019-11-20T19:22:25.813000-08:00')]\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = ['2019-11-20T13:45:47.834701-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:12:26.947000-08:00', '2019-11-20T08:29:36.043000-08:00'), ('2019-11-20T08:31:06-08:00', '2019-11-20T09:09:48-08:00'), ('2019-11-20T09:16:12.757693-08:00', '2019-11-20T10:14:27.080000-08:00'), ('2019-11-20T10:14:58.354000-08:00', '2019-11-20T10:19:13.196000-08:00'), ('2019-11-20T10:25:14.928144-08:00', '2019-11-20T10:31:59.682000-08:00'), ('2019-11-20T13:45:47.834701-08:00', '2019-11-20T13:59:41.806000-08:00'), ('2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:15.760000-08:00'), ('2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:35:34.822000-08:00'), ('2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:06.836000-08:00'), ('2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:11.819000-08:00'), ('2019-11-20T16:44:43.788000-08:00', '2019-11-20T16:47:30-08:00'), ('2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:50:41.894000-08:00'), ('2019-11-20T17:51:13.823000-08:00', '2019-11-20T17:58:51.934000-08:00'), ('2019-11-20T18:00:47-08:00', '2019-11-20T18:12:42.631000-08:00'), ('2019-11-20T18:13:24-08:00', '2019-11-20T18:17:02-08:00'), ('2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:04:45.847000-08:00'), ('2019-11-20T19:05:17.765000-08:00', '2019-11-20T19:22:25.813000-08:00')]\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = ['2019-11-20T16:16:31.601751-08:00', '2019-11-20T16:24:47.806000-08:00', '2019-11-20T16:36:06.836000-08:00', '2019-11-20T16:36:39.755000-08:00', '2019-11-20T16:44:43.788000-08:00', '2019-11-20T17:15:53.232473-08:00', '2019-11-20T17:51:13.823000-08:00', '2019-11-20T18:00:47-08:00', '2019-11-20T18:13:24-08:00', '2019-11-20T18:18:39.196000-08:00', '2019-11-20T19:05:17.765000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_2 MAMFDC v/s HAMFDC MAMFDC_2 3\n", - "Before filtering, trips = [('2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:30:25.528000-08:00'), ('2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:13:39.520000-08:00'), ('2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:19:07.899000-08:00'), ('2019-12-03T10:27:42.457942-08:00', '2019-12-03T10:34:05.923000-08:00'), ('2019-12-03T14:14:57.823089-08:00', '2019-12-03T14:28:10.982000-08:00'), ('2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:21:06.134000-08:00'), ('2019-12-03T16:22:10.099000-08:00', '2019-12-03T16:43:35.074000-08:00'), ('2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:53:47.065000-08:00'), ('2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:02:53.212000-08:00'), ('2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:13.086000-08:00'), ('2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:21:05.021000-08:00'), ('2019-12-03T18:23:24-08:00', '2019-12-03T19:00:40-08:00'), ('2019-12-03T19:00:46-08:00', '2019-12-03T19:04:52.217000-08:00'), ('2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:12:57.287000-08:00'), ('2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:15:37.265000-08:00'), ('2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:18.232000-08:00'), ('2019-12-03T19:18:50.254000-08:00', '2019-12-03T19:34:52.341000-08:00')]\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = ['2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:27:42.457942-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:30:25.528000-08:00'), ('2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:13:39.520000-08:00'), ('2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:19:07.899000-08:00'), ('2019-12-03T10:27:42.457942-08:00', '2019-12-03T10:34:05.923000-08:00'), ('2019-12-03T14:14:57.823089-08:00', '2019-12-03T14:28:10.982000-08:00'), ('2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:21:06.134000-08:00'), ('2019-12-03T16:22:10.099000-08:00', '2019-12-03T16:43:35.074000-08:00'), ('2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:53:47.065000-08:00'), ('2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:02:53.212000-08:00'), ('2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:13.086000-08:00'), ('2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:21:05.021000-08:00'), ('2019-12-03T18:23:24-08:00', '2019-12-03T19:00:40-08:00'), ('2019-12-03T19:00:46-08:00', '2019-12-03T19:04:52.217000-08:00'), ('2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:12:57.287000-08:00'), ('2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:15:37.265000-08:00'), ('2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:18.232000-08:00'), ('2019-12-03T19:18:50.254000-08:00', '2019-12-03T19:34:52.341000-08:00')]\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = ['2019-12-03T14:14:57.823089-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:16:57.027000-08:00', '2019-12-03T08:30:25.528000-08:00'), ('2019-12-03T08:41:38.478000-08:00', '2019-12-03T09:13:39.520000-08:00'), ('2019-12-03T09:17:52.781573-08:00', '2019-12-03T10:19:07.899000-08:00'), ('2019-12-03T10:27:42.457942-08:00', '2019-12-03T10:34:05.923000-08:00'), ('2019-12-03T14:14:57.823089-08:00', '2019-12-03T14:28:10.982000-08:00'), ('2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:21:06.134000-08:00'), ('2019-12-03T16:22:10.099000-08:00', '2019-12-03T16:43:35.074000-08:00'), ('2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:53:47.065000-08:00'), ('2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:02:53.212000-08:00'), ('2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:13.086000-08:00'), ('2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:21:05.021000-08:00'), ('2019-12-03T18:23:24-08:00', '2019-12-03T19:00:40-08:00'), ('2019-12-03T19:00:46-08:00', '2019-12-03T19:04:52.217000-08:00'), ('2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:12:57.287000-08:00'), ('2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:15:37.265000-08:00'), ('2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:18.232000-08:00'), ('2019-12-03T19:18:50.254000-08:00', '2019-12-03T19:34:52.341000-08:00')]\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = ['2019-12-03T16:16:05.528018-08:00', '2019-12-03T16:22:10.099000-08:00', '2019-12-03T17:18:43.082299-08:00', '2019-12-03T17:55:55.109000-08:00', '2019-12-03T18:05:35.378000-08:00', '2019-12-03T18:16:45.011000-08:00', '2019-12-03T18:23:24-08:00', '2019-12-03T19:00:46-08:00', '2019-12-03T19:05:25.275000-08:00', '2019-12-03T19:13:29.239000-08:00', '2019-12-03T19:16:09.207000-08:00', '2019-12-03T19:18:50.254000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_0 MAMFDC v/s MAHFDC MAMFDC_0 3\n", - "Before filtering, trips = [('2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:03.655000-08:00'), ('2019-12-09T08:32:22-08:00', '2019-12-09T08:49:38.668000-08:00'), ('2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:09:44.698000-08:00'), ('2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:16:53.719000-08:00'), ('2019-12-09T10:23:10.725965-08:00', '2019-12-09T10:33:25.686000-08:00'), ('2019-12-09T13:58:31.663882-08:00', '2019-12-09T14:12:18.784000-08:00'), ('2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:20:09.881000-08:00'), ('2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:20.964000-08:00'), ('2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:38:29.928000-08:00'), ('2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:44:56.867000-08:00'), ('2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:24:18.860000-08:00'), ('2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:32:58.400000-08:00'), ('2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:08:19-08:00'), ('2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:19:22-08:00'), ('2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:07:41.745000-08:00'), ('2019-12-09T19:08:13.704000-08:00', '2019-12-09T19:24:18.739000-08:00')]\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = ['2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:22-08:00', '2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:23:10.725965-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:03.655000-08:00'), ('2019-12-09T08:32:22-08:00', '2019-12-09T08:49:38.668000-08:00'), ('2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:09:44.698000-08:00'), ('2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:16:53.719000-08:00'), ('2019-12-09T10:23:10.725965-08:00', '2019-12-09T10:33:25.686000-08:00'), ('2019-12-09T13:58:31.663882-08:00', '2019-12-09T14:12:18.784000-08:00'), ('2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:20:09.881000-08:00'), ('2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:20.964000-08:00'), ('2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:38:29.928000-08:00'), ('2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:44:56.867000-08:00'), ('2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:24:18.860000-08:00'), ('2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:32:58.400000-08:00'), ('2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:08:19-08:00'), ('2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:19:22-08:00'), ('2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:07:41.745000-08:00'), ('2019-12-09T19:08:13.704000-08:00', '2019-12-09T19:24:18.739000-08:00')]\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = ['2019-12-09T13:58:31.663882-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:16:31.716000-08:00', '2019-12-09T08:32:03.655000-08:00'), ('2019-12-09T08:32:22-08:00', '2019-12-09T08:49:38.668000-08:00'), ('2019-12-09T08:57:02.697000-08:00', '2019-12-09T09:09:44.698000-08:00'), ('2019-12-09T09:17:03.227965-08:00', '2019-12-09T10:16:53.719000-08:00'), ('2019-12-09T10:23:10.725965-08:00', '2019-12-09T10:33:25.686000-08:00'), ('2019-12-09T13:58:31.663882-08:00', '2019-12-09T14:12:18.784000-08:00'), ('2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:20:09.881000-08:00'), ('2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:20.964000-08:00'), ('2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:38:29.928000-08:00'), ('2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:44:56.867000-08:00'), ('2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:24:18.860000-08:00'), ('2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:32:58.400000-08:00'), ('2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:08:19-08:00'), ('2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:19:22-08:00'), ('2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:07:41.745000-08:00'), ('2019-12-09T19:08:13.704000-08:00', '2019-12-09T19:24:18.739000-08:00')]\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = ['2019-12-09T16:12:39.376341-08:00', '2019-12-09T16:21:13.846000-08:00', '2019-12-09T16:36:52.962000-08:00', '2019-12-09T16:39:33.916000-08:00', '2019-12-09T16:52:19.574923-08:00', '2019-12-09T17:25:25.073000-08:00', '2019-12-09T17:46:20.933410-08:00', '2019-12-09T18:18:06.461582-08:00', '2019-12-09T18:27:46.810000-08:00', '2019-12-09T19:08:13.704000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_1 MAMFDC v/s MAHFDC MAMFDC_1 3\n", - "Before filtering, trips = [('2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:29:41.502000-08:00'), ('2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:36:36.408000-08:00'), ('2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:48:49.622000-08:00'), ('2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:50:24.676000-08:00'), ('2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:09:30.505000-08:00'), ('2019-12-11T09:12:27.062000-08:00', '2019-12-11T09:33:55.675000-08:00'), ('2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:43:34.307000-08:00'), ('2019-12-11T10:45:58.221500-08:00', '2019-12-11T10:53:40.259000-08:00'), ('2019-12-11T14:08:18.199610-08:00', '2019-12-11T14:21:15.254000-08:00'), ('2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:24:41.276000-08:00'), ('2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:26.377000-08:00'), ('2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:35:58.518000-08:00'), ('2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:37:36.280000-08:00'), ('2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:38:40.302000-08:00'), ('2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:43:30.369000-08:00'), ('2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:45:03.276000-08:00'), ('2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:33:36.324000-08:00'), ('2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:43:50.423000-08:00'), ('2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:03:35.864000-08:00'), ('2019-12-11T18:14:27.208076-08:00', '2019-12-11T18:59:09.722000-08:00'), ('2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:01:12.130000-08:00'), ('2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:06:28.563000-08:00'), ('2019-12-11T19:07:04.196000-08:00', '2019-12-11T19:21:47.554000-08:00')]\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = ['2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:12:27.062000-08:00', '2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:45:58.221500-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:29:41.502000-08:00'), ('2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:36:36.408000-08:00'), ('2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:48:49.622000-08:00'), ('2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:50:24.676000-08:00'), ('2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:09:30.505000-08:00'), ('2019-12-11T09:12:27.062000-08:00', '2019-12-11T09:33:55.675000-08:00'), ('2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:43:34.307000-08:00'), ('2019-12-11T10:45:58.221500-08:00', '2019-12-11T10:53:40.259000-08:00'), ('2019-12-11T14:08:18.199610-08:00', '2019-12-11T14:21:15.254000-08:00'), ('2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:24:41.276000-08:00'), ('2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:26.377000-08:00'), ('2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:35:58.518000-08:00'), ('2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:37:36.280000-08:00'), ('2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:38:40.302000-08:00'), ('2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:43:30.369000-08:00'), ('2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:45:03.276000-08:00'), ('2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:33:36.324000-08:00'), ('2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:43:50.423000-08:00'), ('2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:03:35.864000-08:00'), ('2019-12-11T18:14:27.208076-08:00', '2019-12-11T18:59:09.722000-08:00'), ('2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:01:12.130000-08:00'), ('2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:06:28.563000-08:00'), ('2019-12-11T19:07:04.196000-08:00', '2019-12-11T19:21:47.554000-08:00')]\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = ['2019-12-11T14:08:18.199610-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:11:26.979000-08:00', '2019-12-11T08:29:41.502000-08:00'), ('2019-12-11T08:30:43.871000-08:00', '2019-12-11T08:36:36.408000-08:00'), ('2019-12-11T08:38:11.645000-08:00', '2019-12-11T08:48:49.622000-08:00'), ('2019-12-11T08:50:24.676000-08:00', '2019-12-11T08:50:24.676000-08:00'), ('2019-12-11T08:52:59.675000-08:00', '2019-12-11T09:09:30.505000-08:00'), ('2019-12-11T09:12:27.062000-08:00', '2019-12-11T09:33:55.675000-08:00'), ('2019-12-11T10:38:10.887967-08:00', '2019-12-11T10:43:34.307000-08:00'), ('2019-12-11T10:45:58.221500-08:00', '2019-12-11T10:53:40.259000-08:00'), ('2019-12-11T14:08:18.199610-08:00', '2019-12-11T14:21:15.254000-08:00'), ('2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:24:41.276000-08:00'), ('2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:26.377000-08:00'), ('2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:35:58.518000-08:00'), ('2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:37:36.280000-08:00'), ('2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:38:40.302000-08:00'), ('2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:43:30.369000-08:00'), ('2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:45:03.276000-08:00'), ('2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:33:36.324000-08:00'), ('2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:43:50.423000-08:00'), ('2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:03:35.864000-08:00'), ('2019-12-11T18:14:27.208076-08:00', '2019-12-11T18:59:09.722000-08:00'), ('2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:01:12.130000-08:00'), ('2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:06:28.563000-08:00'), ('2019-12-11T19:07:04.196000-08:00', '2019-12-11T19:21:47.554000-08:00')]\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = ['2019-12-11T16:17:52.197284-08:00', '2019-12-11T16:25:46.298000-08:00', '2019-12-11T16:35:58.518000-08:00', '2019-12-11T16:36:31.344000-08:00', '2019-12-11T16:38:08.403000-08:00', '2019-12-11T16:39:11.338000-08:00', '2019-12-11T16:44:02.307000-08:00', '2019-12-11T16:50:28.203860-08:00', '2019-12-11T17:34:09.403000-08:00', '2019-12-11T17:46:31.496000-08:00', '2019-12-11T18:14:27.208076-08:00', '2019-12-11T19:01:12.130000-08:00', '2019-12-11T19:02:16.567000-08:00', '2019-12-11T19:07:04.196000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_2 MAMFDC v/s MAHFDC MAMFDC_2 3\n", - "Before filtering, trips = [('2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:27:01.474000-08:00'), ('2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:10:45.548000-08:00'), ('2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:14:30.481000-08:00'), ('2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:17:06.028000-08:00'), ('2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:21.951000-08:00'), ('2020-02-06T10:29:52.939000-08:00', '2020-02-06T10:30:57.059000-08:00'), ('2020-02-06T13:08:18.314395-08:00', '2020-02-06T13:21:52.184000-08:00'), ('2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:16.147000-08:00'), ('2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:11.210000-08:00'), ('2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:37:43.092000-08:00'), ('2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:47:23.156000-08:00'), ('2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:28:53.992000-08:00'), ('2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:37:58.996000-08:00'), ('2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:50:42.901000-08:00'), ('2020-02-06T17:51:14.950000-08:00', '2020-02-06T17:59:16.948000-08:00'), ('2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:51-08:00'), ('2020-02-06T18:15:57-08:00', '2020-02-06T18:16:09-08:00'), ('2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:57:24.095000-08:00'), ('2020-02-06T18:58:29.052000-08:00', '2020-02-06T19:17:04.015000-08:00')]\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = ['2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:52.939000-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:27:01.474000-08:00'), ('2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:10:45.548000-08:00'), ('2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:14:30.481000-08:00'), ('2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:17:06.028000-08:00'), ('2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:21.951000-08:00'), ('2020-02-06T10:29:52.939000-08:00', '2020-02-06T10:30:57.059000-08:00'), ('2020-02-06T13:08:18.314395-08:00', '2020-02-06T13:21:52.184000-08:00'), ('2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:16.147000-08:00'), ('2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:11.210000-08:00'), ('2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:37:43.092000-08:00'), ('2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:47:23.156000-08:00'), ('2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:28:53.992000-08:00'), ('2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:37:58.996000-08:00'), ('2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:50:42.901000-08:00'), ('2020-02-06T17:51:14.950000-08:00', '2020-02-06T17:59:16.948000-08:00'), ('2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:51-08:00'), ('2020-02-06T18:15:57-08:00', '2020-02-06T18:16:09-08:00'), ('2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:57:24.095000-08:00'), ('2020-02-06T18:58:29.052000-08:00', '2020-02-06T19:17:04.015000-08:00')]\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = ['2020-02-06T13:08:18.314395-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:17:54.066000-08:00', '2020-02-06T08:27:01.474000-08:00'), ('2020-02-06T08:33:17.674182-08:00', '2020-02-06T09:10:45.548000-08:00'), ('2020-02-06T09:12:22.496000-08:00', '2020-02-06T09:14:30.481000-08:00'), ('2020-02-06T09:17:30.481000-08:00', '2020-02-06T10:17:06.028000-08:00'), ('2020-02-06T10:22:57.080719-08:00', '2020-02-06T10:29:21.951000-08:00'), ('2020-02-06T10:29:52.939000-08:00', '2020-02-06T10:30:57.059000-08:00'), ('2020-02-06T13:08:18.314395-08:00', '2020-02-06T13:21:52.184000-08:00'), ('2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:16.147000-08:00'), ('2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:11.210000-08:00'), ('2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:37:43.092000-08:00'), ('2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:47:23.156000-08:00'), ('2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:28:53.992000-08:00'), ('2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:37:58.996000-08:00'), ('2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:50:42.901000-08:00'), ('2020-02-06T17:51:14.950000-08:00', '2020-02-06T17:59:16.948000-08:00'), ('2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:51-08:00'), ('2020-02-06T18:15:57-08:00', '2020-02-06T18:16:09-08:00'), ('2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:57:24.095000-08:00'), ('2020-02-06T18:58:29.052000-08:00', '2020-02-06T19:17:04.015000-08:00')]\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = ['2020-02-06T16:17:24.294441-08:00', '2020-02-06T16:24:48.186000-08:00', '2020-02-06T16:37:43.092000-08:00', '2020-02-06T16:38:16.128000-08:00', '2020-02-06T16:51:03.883336-08:00', '2020-02-06T17:29:59.035000-08:00', '2020-02-06T17:40:08.130000-08:00', '2020-02-06T17:51:14.950000-08:00', '2020-02-06T18:05:06.937000-08:00', '2020-02-06T18:15:57-08:00', '2020-02-06T18:16:45.087000-08:00', '2020-02-06T18:58:29.052000-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 3\n", - "Before filtering, trips = [('2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:07:20-07:00'), ('2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:11:36-07:00'), ('2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:13:45.097000-07:00'), ('2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:17:21-07:00'), ('2019-07-24T10:19:52-07:00', '2019-07-24T10:25:47-07:00'), ('2019-07-24T10:26:18-07:00', '2019-07-24T10:26:59-07:00'), ('2019-07-24T14:12:34.873161-07:00', '2019-07-24T14:25:52-07:00'), ('2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:26-07:00'), ('2019-07-24T16:42:32-07:00', '2019-07-24T16:50:48-07:00'), ('2019-07-24T16:51:18-07:00', '2019-07-24T16:51:18-07:00'), ('2019-07-24T16:51:48-07:00', '2019-07-24T16:52:48-07:00'), ('2019-07-24T16:53:18-07:00', '2019-07-24T16:53:48-07:00'), ('2019-07-24T16:54:18-07:00', '2019-07-24T17:02:19.337000-07:00'), ('2019-07-24T17:02:49-07:00', '2019-07-24T17:08:12.860000-07:00'), ('2019-07-24T17:20:28.892637-07:00', '2019-07-24T17:59:58-07:00'), ('2019-07-24T18:00:27-07:00', '2019-07-24T18:12:34.361000-07:00'), ('2019-07-24T18:14:19-07:00', '2019-07-24T18:27:52-07:00'), ('2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:29:52-07:00'), ('2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:41:32-07:00'), ('2019-07-24T19:42:02-07:00', '2019-07-24T19:59:34-07:00')]\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = ['2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:19:52-07:00', '2019-07-24T10:26:18-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:07:20-07:00'), ('2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:11:36-07:00'), ('2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:13:45.097000-07:00'), ('2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:17:21-07:00'), ('2019-07-24T10:19:52-07:00', '2019-07-24T10:25:47-07:00'), ('2019-07-24T10:26:18-07:00', '2019-07-24T10:26:59-07:00'), ('2019-07-24T14:12:34.873161-07:00', '2019-07-24T14:25:52-07:00'), ('2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:26-07:00'), ('2019-07-24T16:42:32-07:00', '2019-07-24T16:50:48-07:00'), ('2019-07-24T16:51:18-07:00', '2019-07-24T16:51:18-07:00'), ('2019-07-24T16:51:48-07:00', '2019-07-24T16:52:48-07:00'), ('2019-07-24T16:53:18-07:00', '2019-07-24T16:53:48-07:00'), ('2019-07-24T16:54:18-07:00', '2019-07-24T17:02:19.337000-07:00'), ('2019-07-24T17:02:49-07:00', '2019-07-24T17:08:12.860000-07:00'), ('2019-07-24T17:20:28.892637-07:00', '2019-07-24T17:59:58-07:00'), ('2019-07-24T18:00:27-07:00', '2019-07-24T18:12:34.361000-07:00'), ('2019-07-24T18:14:19-07:00', '2019-07-24T18:27:52-07:00'), ('2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:29:52-07:00'), ('2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:41:32-07:00'), ('2019-07-24T19:42:02-07:00', '2019-07-24T19:59:34-07:00')]\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = ['2019-07-24T14:12:34.873161-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:53:03.939000-07:00', '2019-07-24T08:07:20-07:00'), ('2019-07-24T08:28:23.349792-07:00', '2019-07-24T09:11:36-07:00'), ('2019-07-24T09:19:18.807406-07:00', '2019-07-24T10:13:45.097000-07:00'), ('2019-07-24T10:14:15.120000-07:00', '2019-07-24T10:17:21-07:00'), ('2019-07-24T10:19:52-07:00', '2019-07-24T10:25:47-07:00'), ('2019-07-24T10:26:18-07:00', '2019-07-24T10:26:59-07:00'), ('2019-07-24T14:12:34.873161-07:00', '2019-07-24T14:25:52-07:00'), ('2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:26-07:00'), ('2019-07-24T16:42:32-07:00', '2019-07-24T16:50:48-07:00'), ('2019-07-24T16:51:18-07:00', '2019-07-24T16:51:18-07:00'), ('2019-07-24T16:51:48-07:00', '2019-07-24T16:52:48-07:00'), ('2019-07-24T16:53:18-07:00', '2019-07-24T16:53:48-07:00'), ('2019-07-24T16:54:18-07:00', '2019-07-24T17:02:19.337000-07:00'), ('2019-07-24T17:02:49-07:00', '2019-07-24T17:08:12.860000-07:00'), ('2019-07-24T17:20:28.892637-07:00', '2019-07-24T17:59:58-07:00'), ('2019-07-24T18:00:27-07:00', '2019-07-24T18:12:34.361000-07:00'), ('2019-07-24T18:14:19-07:00', '2019-07-24T18:27:52-07:00'), ('2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:29:52-07:00'), ('2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:41:32-07:00'), ('2019-07-24T19:42:02-07:00', '2019-07-24T19:59:34-07:00')]\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = ['2019-07-24T16:37:59.037744-07:00', '2019-07-24T16:42:32-07:00', '2019-07-24T16:51:18-07:00', '2019-07-24T16:51:48-07:00', '2019-07-24T16:53:18-07:00', '2019-07-24T16:54:18-07:00', '2019-07-24T17:02:49-07:00', '2019-07-24T17:20:28.892637-07:00', '2019-07-24T18:00:27-07:00', '2019-07-24T18:14:19-07:00', '2019-07-24T18:29:27.757000-07:00', '2019-07-24T18:33:50.497498-07:00', '2019-07-24T19:42:02-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 3\n", - "Before filtering, trips = [('2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:23:55-07:00'), ('2019-07-25T08:31:30-07:00', '2019-07-25T09:08:09-07:00'), ('2019-07-25T09:08:42-07:00', '2019-07-25T09:09:39.760000-07:00'), ('2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:13:57.477000-07:00'), ('2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:21.763000-07:00'), ('2019-07-25T10:21:46-07:00', '2019-07-25T10:26:16-07:00'), ('2019-07-25T10:26:46-07:00', '2019-07-25T10:28:16-07:00'), ('2019-07-25T14:08:49.955683-07:00', '2019-07-25T14:21:40.801000-07:00'), ('2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:18.863000-07:00'), ('2019-07-25T16:37:37-07:00', '2019-07-25T16:47:36-07:00'), ('2019-07-25T16:48:06-07:00', '2019-07-25T16:48:16.914000-07:00'), ('2019-07-25T16:48:36-07:00', '2019-07-25T16:48:48.839000-07:00'), ('2019-07-25T16:49:07-07:00', '2019-07-25T16:49:53.901000-07:00'), ('2019-07-25T16:50:00-07:00', '2019-07-25T16:53:30-07:00'), ('2019-07-25T16:54:00-07:00', '2019-07-25T16:55:00-07:00'), ('2019-07-25T16:56:01-07:00', '2019-07-25T16:58:01-07:00'), ('2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:01:40-07:00'), ('2019-07-25T18:02:15-07:00', '2019-07-25T18:12:28.966000-07:00'), ('2019-07-25T18:13:48-07:00', '2019-07-25T18:25:10-07:00'), ('2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:30:11-07:00'), ('2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:41:47-07:00'), ('2019-07-25T19:42:15.525000-07:00', '2019-07-25T19:58:17-07:00')]\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = ['2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:31:30-07:00', '2019-07-25T09:08:42-07:00', '2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:46-07:00', '2019-07-25T10:26:46-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:23:55-07:00'), ('2019-07-25T08:31:30-07:00', '2019-07-25T09:08:09-07:00'), ('2019-07-25T09:08:42-07:00', '2019-07-25T09:09:39.760000-07:00'), ('2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:13:57.477000-07:00'), ('2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:21.763000-07:00'), ('2019-07-25T10:21:46-07:00', '2019-07-25T10:26:16-07:00'), ('2019-07-25T10:26:46-07:00', '2019-07-25T10:28:16-07:00'), ('2019-07-25T14:08:49.955683-07:00', '2019-07-25T14:21:40.801000-07:00'), ('2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:18.863000-07:00'), ('2019-07-25T16:37:37-07:00', '2019-07-25T16:47:36-07:00'), ('2019-07-25T16:48:06-07:00', '2019-07-25T16:48:16.914000-07:00'), ('2019-07-25T16:48:36-07:00', '2019-07-25T16:48:48.839000-07:00'), ('2019-07-25T16:49:07-07:00', '2019-07-25T16:49:53.901000-07:00'), ('2019-07-25T16:50:00-07:00', '2019-07-25T16:53:30-07:00'), ('2019-07-25T16:54:00-07:00', '2019-07-25T16:55:00-07:00'), ('2019-07-25T16:56:01-07:00', '2019-07-25T16:58:01-07:00'), ('2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:01:40-07:00'), ('2019-07-25T18:02:15-07:00', '2019-07-25T18:12:28.966000-07:00'), ('2019-07-25T18:13:48-07:00', '2019-07-25T18:25:10-07:00'), ('2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:30:11-07:00'), ('2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:41:47-07:00'), ('2019-07-25T19:42:15.525000-07:00', '2019-07-25T19:58:17-07:00')]\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = ['2019-07-25T14:08:49.955683-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:08:51.835000-07:00', '2019-07-25T08:23:55-07:00'), ('2019-07-25T08:31:30-07:00', '2019-07-25T09:08:09-07:00'), ('2019-07-25T09:08:42-07:00', '2019-07-25T09:09:39.760000-07:00'), ('2019-07-25T09:18:52.161210-07:00', '2019-07-25T10:13:57.477000-07:00'), ('2019-07-25T10:15:00.755000-07:00', '2019-07-25T10:21:21.763000-07:00'), ('2019-07-25T10:21:46-07:00', '2019-07-25T10:26:16-07:00'), ('2019-07-25T10:26:46-07:00', '2019-07-25T10:28:16-07:00'), ('2019-07-25T14:08:49.955683-07:00', '2019-07-25T14:21:40.801000-07:00'), ('2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:18.863000-07:00'), ('2019-07-25T16:37:37-07:00', '2019-07-25T16:47:36-07:00'), ('2019-07-25T16:48:06-07:00', '2019-07-25T16:48:16.914000-07:00'), ('2019-07-25T16:48:36-07:00', '2019-07-25T16:48:48.839000-07:00'), ('2019-07-25T16:49:07-07:00', '2019-07-25T16:49:53.901000-07:00'), ('2019-07-25T16:50:00-07:00', '2019-07-25T16:53:30-07:00'), ('2019-07-25T16:54:00-07:00', '2019-07-25T16:55:00-07:00'), ('2019-07-25T16:56:01-07:00', '2019-07-25T16:58:01-07:00'), ('2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:01:40-07:00'), ('2019-07-25T18:02:15-07:00', '2019-07-25T18:12:28.966000-07:00'), ('2019-07-25T18:13:48-07:00', '2019-07-25T18:25:10-07:00'), ('2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:30:11-07:00'), ('2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:41:47-07:00'), ('2019-07-25T19:42:15.525000-07:00', '2019-07-25T19:58:17-07:00')]\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = ['2019-07-25T16:30:05.272338-07:00', '2019-07-25T16:37:37-07:00', '2019-07-25T16:48:06-07:00', '2019-07-25T16:48:36-07:00', '2019-07-25T16:49:07-07:00', '2019-07-25T16:50:00-07:00', '2019-07-25T16:54:00-07:00', '2019-07-25T16:56:01-07:00', '2019-07-25T17:22:03.659388-07:00', '2019-07-25T18:02:15-07:00', '2019-07-25T18:13:48-07:00', '2019-07-25T18:25:35.011000-07:00', '2019-07-25T18:34:04.680217-07:00', '2019-07-25T19:42:15.525000-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 3\n", - "Before filtering, trips = [('2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:23:51-07:00'), ('2019-07-26T08:31:55.399292-07:00', '2019-07-26T08:58:43-07:00'), ('2019-07-26T09:01:43-07:00', '2019-07-26T09:02:43-07:00'), ('2019-07-26T09:03:42-07:00', '2019-07-26T09:06:13-07:00'), ('2019-07-26T09:10:12-07:00', '2019-07-26T09:12:11-07:00'), ('2019-07-26T09:12:35-07:00', '2019-07-26T09:13:33.039000-07:00'), ('2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:10:23-07:00'), ('2019-07-26T10:14:25-07:00', '2019-07-26T10:18:26-07:00'), ('2019-07-26T10:18:56-07:00', '2019-07-26T10:28:02-07:00'), ('2019-07-26T10:28:07-07:00', '2019-07-26T10:29:38.725000-07:00'), ('2019-07-26T14:16:49.550055-07:00', '2019-07-26T14:29:26.786000-07:00'), ('2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:21:02-07:00'), ('2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:30:34.748000-07:00'), ('2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:48:47-07:00'), ('2019-07-26T16:49:18-07:00', '2019-07-26T16:49:18-07:00'), ('2019-07-26T16:49:47-07:00', '2019-07-26T16:53:47-07:00'), ('2019-07-26T16:54:18-07:00', '2019-07-26T16:55:18-07:00'), ('2019-07-26T16:55:48-07:00', '2019-07-26T17:01:47-07:00'), ('2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:22-07:00'), ('2019-07-26T17:51:52-07:00', '2019-07-26T18:01:34.987000-07:00'), ('2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:22:37.770000-07:00'), ('2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:28:43.694000-07:00'), ('2019-07-26T18:33:15-07:00', '2019-07-26T18:33:24.739000-07:00'), ('2019-07-26T18:34:16-07:00', '2019-07-26T19:41:47-07:00'), ('2019-07-26T19:42:00-07:00', '2019-07-26T20:00:26.738000-07:00')]\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = ['2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:31:55.399292-07:00', '2019-07-26T09:01:43-07:00', '2019-07-26T09:03:42-07:00', '2019-07-26T09:10:12-07:00', '2019-07-26T09:12:35-07:00', '2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:14:25-07:00', '2019-07-26T10:18:56-07:00', '2019-07-26T10:28:07-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:23:51-07:00'), ('2019-07-26T08:31:55.399292-07:00', '2019-07-26T08:58:43-07:00'), ('2019-07-26T09:01:43-07:00', '2019-07-26T09:02:43-07:00'), ('2019-07-26T09:03:42-07:00', '2019-07-26T09:06:13-07:00'), ('2019-07-26T09:10:12-07:00', '2019-07-26T09:12:11-07:00'), ('2019-07-26T09:12:35-07:00', '2019-07-26T09:13:33.039000-07:00'), ('2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:10:23-07:00'), ('2019-07-26T10:14:25-07:00', '2019-07-26T10:18:26-07:00'), ('2019-07-26T10:18:56-07:00', '2019-07-26T10:28:02-07:00'), ('2019-07-26T10:28:07-07:00', '2019-07-26T10:29:38.725000-07:00'), ('2019-07-26T14:16:49.550055-07:00', '2019-07-26T14:29:26.786000-07:00'), ('2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:21:02-07:00'), ('2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:30:34.748000-07:00'), ('2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:48:47-07:00'), ('2019-07-26T16:49:18-07:00', '2019-07-26T16:49:18-07:00'), ('2019-07-26T16:49:47-07:00', '2019-07-26T16:53:47-07:00'), ('2019-07-26T16:54:18-07:00', '2019-07-26T16:55:18-07:00'), ('2019-07-26T16:55:48-07:00', '2019-07-26T17:01:47-07:00'), ('2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:22-07:00'), ('2019-07-26T17:51:52-07:00', '2019-07-26T18:01:34.987000-07:00'), ('2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:22:37.770000-07:00'), ('2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:28:43.694000-07:00'), ('2019-07-26T18:33:15-07:00', '2019-07-26T18:33:24.739000-07:00'), ('2019-07-26T18:34:16-07:00', '2019-07-26T19:41:47-07:00'), ('2019-07-26T19:42:00-07:00', '2019-07-26T20:00:26.738000-07:00')]\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = ['2019-07-26T14:16:49.550055-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:13:43.987000-07:00', '2019-07-26T08:23:51-07:00'), ('2019-07-26T08:31:55.399292-07:00', '2019-07-26T08:58:43-07:00'), ('2019-07-26T09:01:43-07:00', '2019-07-26T09:02:43-07:00'), ('2019-07-26T09:03:42-07:00', '2019-07-26T09:06:13-07:00'), ('2019-07-26T09:10:12-07:00', '2019-07-26T09:12:11-07:00'), ('2019-07-26T09:12:35-07:00', '2019-07-26T09:13:33.039000-07:00'), ('2019-07-26T09:19:08.810500-07:00', '2019-07-26T10:10:23-07:00'), ('2019-07-26T10:14:25-07:00', '2019-07-26T10:18:26-07:00'), ('2019-07-26T10:18:56-07:00', '2019-07-26T10:28:02-07:00'), ('2019-07-26T10:28:07-07:00', '2019-07-26T10:29:38.725000-07:00'), ('2019-07-26T14:16:49.550055-07:00', '2019-07-26T14:29:26.786000-07:00'), ('2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:21:02-07:00'), ('2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:30:34.748000-07:00'), ('2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:48:47-07:00'), ('2019-07-26T16:49:18-07:00', '2019-07-26T16:49:18-07:00'), ('2019-07-26T16:49:47-07:00', '2019-07-26T16:53:47-07:00'), ('2019-07-26T16:54:18-07:00', '2019-07-26T16:55:18-07:00'), ('2019-07-26T16:55:48-07:00', '2019-07-26T17:01:47-07:00'), ('2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:22-07:00'), ('2019-07-26T17:51:52-07:00', '2019-07-26T18:01:34.987000-07:00'), ('2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:22:37.770000-07:00'), ('2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:28:43.694000-07:00'), ('2019-07-26T18:33:15-07:00', '2019-07-26T18:33:24.739000-07:00'), ('2019-07-26T18:34:16-07:00', '2019-07-26T19:41:47-07:00'), ('2019-07-26T19:42:00-07:00', '2019-07-26T20:00:26.738000-07:00')]\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = ['2019-07-26T16:05:28.733507-07:00', '2019-07-26T16:24:59.701133-07:00', '2019-07-26T16:39:59.603211-07:00', '2019-07-26T16:49:18-07:00', '2019-07-26T16:49:47-07:00', '2019-07-26T16:54:18-07:00', '2019-07-26T16:55:48-07:00', '2019-07-26T17:18:30.099273-07:00', '2019-07-26T17:51:52-07:00', '2019-07-26T18:04:34.987000-07:00', '2019-07-26T18:27:09.605464-07:00', '2019-07-26T18:33:15-07:00', '2019-07-26T18:34:16-07:00', '2019-07-26T19:42:00-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 3\n", - "Before filtering, trips = [('2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:28:20.210000-07:00'), ('2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:38.118000-07:00'), ('2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:11:42.707000-07:00'), ('2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:13:36.272000-07:00'), ('2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:16:02.221000-07:00'), ('2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:31-07:00'), ('2019-09-10T10:35:47.152000-07:00', '2019-09-10T10:37:43.239000-07:00'), ('2019-09-10T13:38:52.057667-07:00', '2019-09-10T13:52:21.432000-07:00'), ('2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:17:55.660000-07:00'), ('2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:36.221000-07:00'), ('2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:43.502000-07:00'), ('2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:42.270000-07:00'), ('2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:31:55.671000-07:00'), ('2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:39:51-07:00'), ('2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:31:26-07:00'), ('2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:31-07:00'), ('2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:04:07.088000-07:00'), ('2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:28.207000-07:00'), ('2019-09-10T19:04:34.934000-07:00', '2019-09-10T19:23:56.153000-07:00'), ('2019-09-10T19:52:15.471889-07:00', '2019-09-10T20:11:03-07:00')]\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = ['2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:47.152000-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:28:20.210000-07:00'), ('2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:38.118000-07:00'), ('2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:11:42.707000-07:00'), ('2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:13:36.272000-07:00'), ('2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:16:02.221000-07:00'), ('2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:31-07:00'), ('2019-09-10T10:35:47.152000-07:00', '2019-09-10T10:37:43.239000-07:00'), ('2019-09-10T13:38:52.057667-07:00', '2019-09-10T13:52:21.432000-07:00'), ('2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:17:55.660000-07:00'), ('2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:36.221000-07:00'), ('2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:43.502000-07:00'), ('2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:42.270000-07:00'), ('2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:31:55.671000-07:00'), ('2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:39:51-07:00'), ('2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:31:26-07:00'), ('2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:31-07:00'), ('2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:04:07.088000-07:00'), ('2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:28.207000-07:00'), ('2019-09-10T19:04:34.934000-07:00', '2019-09-10T19:23:56.153000-07:00'), ('2019-09-10T19:52:15.471889-07:00', '2019-09-10T20:11:03-07:00')]\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = ['2019-09-10T13:38:52.057667-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:16:30.741000-07:00', '2019-09-10T08:28:20.210000-07:00'), ('2019-09-10T08:31:12.185000-07:00', '2019-09-10T09:07:38.118000-07:00'), ('2019-09-10T09:07:43.212000-07:00', '2019-09-10T09:11:42.707000-07:00'), ('2019-09-10T09:17:06.068000-07:00', '2019-09-10T10:13:36.272000-07:00'), ('2019-09-10T10:14:17.344000-07:00', '2019-09-10T10:16:02.221000-07:00'), ('2019-09-10T10:26:11.325255-07:00', '2019-09-10T10:35:31-07:00'), ('2019-09-10T10:35:47.152000-07:00', '2019-09-10T10:37:43.239000-07:00'), ('2019-09-10T13:38:52.057667-07:00', '2019-09-10T13:52:21.432000-07:00'), ('2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:17:55.660000-07:00'), ('2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:36.221000-07:00'), ('2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:43.502000-07:00'), ('2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:42.270000-07:00'), ('2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:31:55.671000-07:00'), ('2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:39:51-07:00'), ('2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:31:26-07:00'), ('2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:31-07:00'), ('2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:04:07.088000-07:00'), ('2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:28.207000-07:00'), ('2019-09-10T19:04:34.934000-07:00', '2019-09-10T19:23:56.153000-07:00'), ('2019-09-10T19:52:15.471889-07:00', '2019-09-10T20:11:03-07:00')]\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = ['2019-09-10T16:12:15.599000-07:00', '2019-09-10T16:18:11.583000-07:00', '2019-09-10T16:28:51.019000-07:00', '2019-09-10T16:29:53.535000-07:00', '2019-09-10T16:30:47.358000-07:00', '2019-09-10T16:32:00.720000-07:00', '2019-09-10T16:52:20.279171-07:00', '2019-09-10T17:46:13.746483-07:00', '2019-09-10T18:01:37.671000-07:00', '2019-09-10T18:17:29.067119-07:00', '2019-09-10T19:04:34.934000-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 3\n", - "Before filtering, trips = [('2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:00.453000-07:00'), ('2019-09-11T08:27:11-07:00', '2019-09-11T08:27:30-07:00'), ('2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:31.994000-07:00'), ('2019-09-11T08:29:45-07:00', '2019-09-11T09:07:24-07:00'), ('2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:07:45.456000-07:00'), ('2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:47.200000-07:00'), ('2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:16:32.070000-07:00'), ('2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:23-07:00'), ('2019-09-11T10:35:29.162000-07:00', '2019-09-11T10:36:17.153000-07:00'), ('2019-09-11T13:45:54.217396-07:00', '2019-09-11T13:59:10.181000-07:00'), ('2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:32:05.241000-07:00'), ('2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:41.380000-07:00'), ('2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:20.397000-07:00'), ('2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:32.653000-07:00'), ('2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:10.265000-07:00'), ('2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:11.518000-07:00'), ('2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:36.180000-07:00'), ('2019-09-11T16:51:50.762000-07:00', '2019-09-11T16:55:00.270000-07:00'), ('2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:55:57-07:00'), ('2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:07:15.156000-07:00'), ('2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:11-07:00'), ('2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:42.414000-07:00'), ('2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:19:57-07:00'), ('2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:43:25-07:00'), ('2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:21-07:00'), ('2019-09-11T19:38:22-07:00', '2019-09-11T19:39:11-07:00'), ('2019-09-11T19:39:12-07:00', '2019-09-11T19:41:26-07:00'), ('2019-09-11T19:41:31.703000-07:00', '2019-09-11T19:59:01.862000-07:00'), ('2019-09-11T20:20:30.259241-07:00', '2019-09-11T20:34:02.137000-07:00'), ('2019-09-11T20:34:10-07:00', '2019-09-11T20:36:18-07:00')]\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = ['2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:11-07:00', '2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:45-07:00', '2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:29.162000-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:00.453000-07:00'), ('2019-09-11T08:27:11-07:00', '2019-09-11T08:27:30-07:00'), ('2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:31.994000-07:00'), ('2019-09-11T08:29:45-07:00', '2019-09-11T09:07:24-07:00'), ('2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:07:45.456000-07:00'), ('2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:47.200000-07:00'), ('2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:16:32.070000-07:00'), ('2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:23-07:00'), ('2019-09-11T10:35:29.162000-07:00', '2019-09-11T10:36:17.153000-07:00'), ('2019-09-11T13:45:54.217396-07:00', '2019-09-11T13:59:10.181000-07:00'), ('2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:32:05.241000-07:00'), ('2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:41.380000-07:00'), ('2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:20.397000-07:00'), ('2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:32.653000-07:00'), ('2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:10.265000-07:00'), ('2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:11.518000-07:00'), ('2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:36.180000-07:00'), ('2019-09-11T16:51:50.762000-07:00', '2019-09-11T16:55:00.270000-07:00'), ('2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:55:57-07:00'), ('2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:07:15.156000-07:00'), ('2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:11-07:00'), ('2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:42.414000-07:00'), ('2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:19:57-07:00'), ('2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:43:25-07:00'), ('2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:21-07:00'), ('2019-09-11T19:38:22-07:00', '2019-09-11T19:39:11-07:00'), ('2019-09-11T19:39:12-07:00', '2019-09-11T19:41:26-07:00'), ('2019-09-11T19:41:31.703000-07:00', '2019-09-11T19:59:01.862000-07:00'), ('2019-09-11T20:20:30.259241-07:00', '2019-09-11T20:34:02.137000-07:00'), ('2019-09-11T20:34:10-07:00', '2019-09-11T20:36:18-07:00')]\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = ['2019-09-11T13:45:54.217396-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:15:22.333000-07:00', '2019-09-11T08:27:00.453000-07:00'), ('2019-09-11T08:27:11-07:00', '2019-09-11T08:27:30-07:00'), ('2019-09-11T08:29:31.994000-07:00', '2019-09-11T08:29:31.994000-07:00'), ('2019-09-11T08:29:45-07:00', '2019-09-11T09:07:24-07:00'), ('2019-09-11T09:07:30.361000-07:00', '2019-09-11T09:07:45.456000-07:00'), ('2019-09-11T09:18:33.286849-07:00', '2019-09-11T10:13:47.200000-07:00'), ('2019-09-11T10:13:56.993000-07:00', '2019-09-11T10:16:32.070000-07:00'), ('2019-09-11T10:31:04.729998-07:00', '2019-09-11T10:35:23-07:00'), ('2019-09-11T10:35:29.162000-07:00', '2019-09-11T10:36:17.153000-07:00'), ('2019-09-11T13:45:54.217396-07:00', '2019-09-11T13:59:10.181000-07:00'), ('2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:32:05.241000-07:00'), ('2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:41.380000-07:00'), ('2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:20.397000-07:00'), ('2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:32.653000-07:00'), ('2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:10.265000-07:00'), ('2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:11.518000-07:00'), ('2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:36.180000-07:00'), ('2019-09-11T16:51:50.762000-07:00', '2019-09-11T16:55:00.270000-07:00'), ('2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:55:57-07:00'), ('2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:07:15.156000-07:00'), ('2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:11-07:00'), ('2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:42.414000-07:00'), ('2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:19:57-07:00'), ('2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:43:25-07:00'), ('2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:21-07:00'), ('2019-09-11T19:38:22-07:00', '2019-09-11T19:39:11-07:00'), ('2019-09-11T19:39:12-07:00', '2019-09-11T19:41:26-07:00'), ('2019-09-11T19:41:31.703000-07:00', '2019-09-11T19:59:01.862000-07:00'), ('2019-09-11T20:20:30.259241-07:00', '2019-09-11T20:34:02.137000-07:00'), ('2019-09-11T20:34:10-07:00', '2019-09-11T20:36:18-07:00')]\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = ['2019-09-11T16:25:28.930305-07:00', '2019-09-11T16:33:03.817000-07:00', '2019-09-11T16:44:49.163000-07:00', '2019-09-11T16:45:25.759000-07:00', '2019-09-11T16:48:40.182000-07:00', '2019-09-11T16:49:15.335000-07:00', '2019-09-11T16:50:16.591000-07:00', '2019-09-11T16:51:50.762000-07:00', '2019-09-11T17:17:09.295019-07:00', '2019-09-11T17:56:02.589000-07:00', '2019-09-11T18:08:12.635000-07:00', '2019-09-11T18:16:16.694000-07:00', '2019-09-11T18:16:47.398000-07:00', '2019-09-11T18:31:49.909269-07:00', '2019-09-11T18:45:45.880194-07:00', '2019-09-11T19:38:22-07:00', '2019-09-11T19:39:12-07:00', '2019-09-11T19:41:31.703000-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 3\n", - "Before filtering, trips = [('2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:25:26.027000-07:00'), ('2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:13.064000-07:00'), ('2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:06-07:00'), ('2019-09-17T09:14:07-07:00', '2019-09-17T09:16:17.829000-07:00'), ('2019-09-17T09:16:27-07:00', '2019-09-17T09:16:27-07:00'), ('2019-09-17T09:17:07-07:00', '2019-09-17T09:19:15.624000-07:00'), ('2019-09-17T09:20:54-07:00', '2019-09-17T09:21:09-07:00'), ('2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:15.879000-07:00'), ('2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:16:02.134000-07:00'), ('2019-09-17T10:34:24.364919-07:00', '2019-09-17T10:38:35.238000-07:00'), ('2019-09-17T13:45:10.086634-07:00', '2019-09-17T13:58:24.606000-07:00'), ('2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:06.682000-07:00'), ('2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:48.009000-07:00'), ('2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:21:55.113000-07:00'), ('2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:26.909000-07:00'), ('2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:37.005000-07:00'), ('2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:23.944000-07:00'), ('2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:34.719000-07:00'), ('2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:53.855000-07:00'), ('2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:09.048000-07:00'), ('2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:36:27.607000-07:00'), ('2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:29.722000-07:00'), ('2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:36:07.656000-07:00'), ('2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:07.998000-07:00'), ('2019-09-17T17:54:13.014000-07:00', '2019-09-17T17:57:46.274000-07:00'), ('2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:51.915000-07:00'), ('2019-09-17T18:13:57-07:00', '2019-09-17T18:14:12.058000-07:00'), ('2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:57:54.933000-07:00'), ('2019-09-17T18:58:15.167000-07:00', '2019-09-17T19:15:26.928000-07:00')]\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = ['2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:07-07:00', '2019-09-17T09:16:27-07:00', '2019-09-17T09:17:07-07:00', '2019-09-17T09:20:54-07:00', '2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:34:24.364919-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:25:26.027000-07:00'), ('2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:13.064000-07:00'), ('2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:06-07:00'), ('2019-09-17T09:14:07-07:00', '2019-09-17T09:16:17.829000-07:00'), ('2019-09-17T09:16:27-07:00', '2019-09-17T09:16:27-07:00'), ('2019-09-17T09:17:07-07:00', '2019-09-17T09:19:15.624000-07:00'), ('2019-09-17T09:20:54-07:00', '2019-09-17T09:21:09-07:00'), ('2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:15.879000-07:00'), ('2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:16:02.134000-07:00'), ('2019-09-17T10:34:24.364919-07:00', '2019-09-17T10:38:35.238000-07:00'), ('2019-09-17T13:45:10.086634-07:00', '2019-09-17T13:58:24.606000-07:00'), ('2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:06.682000-07:00'), ('2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:48.009000-07:00'), ('2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:21:55.113000-07:00'), ('2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:26.909000-07:00'), ('2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:37.005000-07:00'), ('2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:23.944000-07:00'), ('2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:34.719000-07:00'), ('2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:53.855000-07:00'), ('2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:09.048000-07:00'), ('2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:36:27.607000-07:00'), ('2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:29.722000-07:00'), ('2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:36:07.656000-07:00'), ('2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:07.998000-07:00'), ('2019-09-17T17:54:13.014000-07:00', '2019-09-17T17:57:46.274000-07:00'), ('2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:51.915000-07:00'), ('2019-09-17T18:13:57-07:00', '2019-09-17T18:14:12.058000-07:00'), ('2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:57:54.933000-07:00'), ('2019-09-17T18:58:15.167000-07:00', '2019-09-17T19:15:26.928000-07:00')]\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = ['2019-09-17T13:45:10.086634-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:06:57.721000-07:00', '2019-09-17T08:25:26.027000-07:00'), ('2019-09-17T08:26:12.531477-07:00', '2019-09-17T08:35:13.064000-07:00'), ('2019-09-17T08:35:17.403000-07:00', '2019-09-17T09:14:06-07:00'), ('2019-09-17T09:14:07-07:00', '2019-09-17T09:16:17.829000-07:00'), ('2019-09-17T09:16:27-07:00', '2019-09-17T09:16:27-07:00'), ('2019-09-17T09:17:07-07:00', '2019-09-17T09:19:15.624000-07:00'), ('2019-09-17T09:20:54-07:00', '2019-09-17T09:21:09-07:00'), ('2019-09-17T09:26:30.043000-07:00', '2019-09-17T10:14:15.879000-07:00'), ('2019-09-17T10:14:20.853000-07:00', '2019-09-17T10:16:02.134000-07:00'), ('2019-09-17T10:34:24.364919-07:00', '2019-09-17T10:38:35.238000-07:00'), ('2019-09-17T13:45:10.086634-07:00', '2019-09-17T13:58:24.606000-07:00'), ('2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:06.682000-07:00'), ('2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:48.009000-07:00'), ('2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:21:55.113000-07:00'), ('2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:26.909000-07:00'), ('2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:37.005000-07:00'), ('2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:23.944000-07:00'), ('2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:34.719000-07:00'), ('2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:53.855000-07:00'), ('2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:09.048000-07:00'), ('2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:36:27.607000-07:00'), ('2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:29.722000-07:00'), ('2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:36:07.656000-07:00'), ('2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:07.998000-07:00'), ('2019-09-17T17:54:13.014000-07:00', '2019-09-17T17:57:46.274000-07:00'), ('2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:51.915000-07:00'), ('2019-09-17T18:13:57-07:00', '2019-09-17T18:14:12.058000-07:00'), ('2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:57:54.933000-07:00'), ('2019-09-17T18:58:15.167000-07:00', '2019-09-17T19:15:26.928000-07:00')]\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = ['2019-09-17T16:13:08.698000-07:00', '2019-09-17T16:17:37.759000-07:00', '2019-09-17T16:20:53.056000-07:00', '2019-09-17T16:22:16.762000-07:00', '2019-09-17T16:22:31.927000-07:00', '2019-09-17T16:22:42.044000-07:00', '2019-09-17T16:25:29.103000-07:00', '2019-09-17T16:26:55.745000-07:00', '2019-09-17T16:29:58.913000-07:00', '2019-09-17T16:30:23.770000-07:00', '2019-09-17T16:52:01.984999-07:00', '2019-09-17T17:25:34.770000-07:00', '2019-09-17T17:42:23.199373-07:00', '2019-09-17T17:54:13.014000-07:00', '2019-09-17T18:00:08.634000-07:00', '2019-09-17T18:13:57-07:00', '2019-09-17T18:14:52.514000-07:00', '2019-09-17T18:58:15.167000-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_0 MAMFDC v/s HAMFDC HAMFDC_0 3\n", - "Before filtering, trips = [('2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:24:26-08:00'), ('2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:09:14-08:00'), ('2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:10:31.187000-08:00'), ('2019-11-19T09:11:01-08:00', '2019-11-19T09:11:01-08:00'), ('2019-11-19T09:12:01-08:00', '2019-11-19T09:12:01-08:00'), ('2019-11-19T09:28:42-08:00', '2019-11-19T10:13:34.996000-08:00'), ('2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:15:40.011000-08:00'), ('2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:31:47-08:00'), ('2019-11-19T10:32:17-08:00', '2019-11-19T10:33:20-08:00'), ('2019-11-19T13:33:00.893654-08:00', '2019-11-19T13:43:06.461000-08:00'), ('2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:19:15-08:00'), ('2019-11-19T16:20:13-08:00', '2019-11-19T16:41:46-08:00'), ('2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:27.199000-08:00'), ('2019-11-19T17:27:56-08:00', '2019-11-19T17:38:25.580000-08:00'), ('2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:21.207000-08:00'), ('2019-11-19T17:56:42-08:00', '2019-11-19T17:57:56.994000-08:00'), ('2019-11-19T17:58:42-08:00', '2019-11-19T18:57:46-08:00'), ('2019-11-19T18:57:51-08:00', '2019-11-19T19:13:02.049000-08:00')]\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = ['2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:11:01-08:00', '2019-11-19T09:12:01-08:00', '2019-11-19T09:28:42-08:00', '2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:32:17-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:24:26-08:00'), ('2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:09:14-08:00'), ('2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:10:31.187000-08:00'), ('2019-11-19T09:11:01-08:00', '2019-11-19T09:11:01-08:00'), ('2019-11-19T09:12:01-08:00', '2019-11-19T09:12:01-08:00'), ('2019-11-19T09:28:42-08:00', '2019-11-19T10:13:34.996000-08:00'), ('2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:15:40.011000-08:00'), ('2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:31:47-08:00'), ('2019-11-19T10:32:17-08:00', '2019-11-19T10:33:20-08:00'), ('2019-11-19T13:33:00.893654-08:00', '2019-11-19T13:43:06.461000-08:00'), ('2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:19:15-08:00'), ('2019-11-19T16:20:13-08:00', '2019-11-19T16:41:46-08:00'), ('2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:27.199000-08:00'), ('2019-11-19T17:27:56-08:00', '2019-11-19T17:38:25.580000-08:00'), ('2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:21.207000-08:00'), ('2019-11-19T17:56:42-08:00', '2019-11-19T17:57:56.994000-08:00'), ('2019-11-19T17:58:42-08:00', '2019-11-19T18:57:46-08:00'), ('2019-11-19T18:57:51-08:00', '2019-11-19T19:13:02.049000-08:00')]\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = ['2019-11-19T13:33:00.893654-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:08:21.198000-08:00', '2019-11-19T08:24:26-08:00'), ('2019-11-19T08:30:14.122120-08:00', '2019-11-19T09:09:14-08:00'), ('2019-11-19T09:10:31.187000-08:00', '2019-11-19T09:10:31.187000-08:00'), ('2019-11-19T09:11:01-08:00', '2019-11-19T09:11:01-08:00'), ('2019-11-19T09:12:01-08:00', '2019-11-19T09:12:01-08:00'), ('2019-11-19T09:28:42-08:00', '2019-11-19T10:13:34.996000-08:00'), ('2019-11-19T10:14:36.875000-08:00', '2019-11-19T10:15:40.011000-08:00'), ('2019-11-19T10:17:14.102935-08:00', '2019-11-19T10:31:47-08:00'), ('2019-11-19T10:32:17-08:00', '2019-11-19T10:33:20-08:00'), ('2019-11-19T13:33:00.893654-08:00', '2019-11-19T13:43:06.461000-08:00'), ('2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:19:15-08:00'), ('2019-11-19T16:20:13-08:00', '2019-11-19T16:41:46-08:00'), ('2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:27.199000-08:00'), ('2019-11-19T17:27:56-08:00', '2019-11-19T17:38:25.580000-08:00'), ('2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:21.207000-08:00'), ('2019-11-19T17:56:42-08:00', '2019-11-19T17:57:56.994000-08:00'), ('2019-11-19T17:58:42-08:00', '2019-11-19T18:57:46-08:00'), ('2019-11-19T18:57:51-08:00', '2019-11-19T19:13:02.049000-08:00')]\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = ['2019-11-19T16:12:34.381969-08:00', '2019-11-19T16:20:13-08:00', '2019-11-19T16:56:24.829161-08:00', '2019-11-19T17:27:56-08:00', '2019-11-19T17:38:36.690597-08:00', '2019-11-19T17:56:42-08:00', '2019-11-19T17:58:42-08:00', '2019-11-19T18:57:51-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_1 MAMFDC v/s HAMFDC HAMFDC_1 3\n", - "Before filtering, trips = [('2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:29:31-08:00'), ('2019-11-20T08:30:32-08:00', '2019-11-20T09:08:44.076000-08:00'), ('2019-11-20T09:09:09-08:00', '2019-11-20T09:10:07-08:00'), ('2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:09:48-08:00'), ('2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:16:31.704000-08:00'), ('2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:13-08:00'), ('2019-11-20T10:30:42-08:00', '2019-11-20T10:31:53-08:00'), ('2019-11-20T13:48:22.696511-08:00', '2019-11-20T14:00:18-08:00'), ('2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:40-08:00'), ('2019-11-20T16:24:45-08:00', '2019-11-20T16:43:41.689000-08:00'), ('2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:01-08:00'), ('2019-11-20T17:48:30.803000-08:00', '2019-11-20T17:59:02.509000-08:00'), ('2019-11-20T18:00:24-08:00', '2019-11-20T18:12:23.046000-08:00'), ('2019-11-20T18:12:28-08:00', '2019-11-20T18:16:59-08:00'), ('2019-11-20T18:17:28-08:00', '2019-11-20T19:03:21-08:00'), ('2019-11-20T19:03:51-08:00', '2019-11-20T19:22:55.727000-08:00')]\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = ['2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:30:32-08:00', '2019-11-20T09:09:09-08:00', '2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:42-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:29:31-08:00'), ('2019-11-20T08:30:32-08:00', '2019-11-20T09:08:44.076000-08:00'), ('2019-11-20T09:09:09-08:00', '2019-11-20T09:10:07-08:00'), ('2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:09:48-08:00'), ('2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:16:31.704000-08:00'), ('2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:13-08:00'), ('2019-11-20T10:30:42-08:00', '2019-11-20T10:31:53-08:00'), ('2019-11-20T13:48:22.696511-08:00', '2019-11-20T14:00:18-08:00'), ('2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:40-08:00'), ('2019-11-20T16:24:45-08:00', '2019-11-20T16:43:41.689000-08:00'), ('2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:01-08:00'), ('2019-11-20T17:48:30.803000-08:00', '2019-11-20T17:59:02.509000-08:00'), ('2019-11-20T18:00:24-08:00', '2019-11-20T18:12:23.046000-08:00'), ('2019-11-20T18:12:28-08:00', '2019-11-20T18:16:59-08:00'), ('2019-11-20T18:17:28-08:00', '2019-11-20T19:03:21-08:00'), ('2019-11-20T19:03:51-08:00', '2019-11-20T19:22:55.727000-08:00')]\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = ['2019-11-20T13:48:22.696511-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:13:52.381000-08:00', '2019-11-20T08:29:31-08:00'), ('2019-11-20T08:30:32-08:00', '2019-11-20T09:08:44.076000-08:00'), ('2019-11-20T09:09:09-08:00', '2019-11-20T09:10:07-08:00'), ('2019-11-20T09:18:03.920187-08:00', '2019-11-20T10:09:48-08:00'), ('2019-11-20T10:14:28.047000-08:00', '2019-11-20T10:16:31.704000-08:00'), ('2019-11-20T10:25:50.776695-08:00', '2019-11-20T10:30:13-08:00'), ('2019-11-20T10:30:42-08:00', '2019-11-20T10:31:53-08:00'), ('2019-11-20T13:48:22.696511-08:00', '2019-11-20T14:00:18-08:00'), ('2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:40-08:00'), ('2019-11-20T16:24:45-08:00', '2019-11-20T16:43:41.689000-08:00'), ('2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:01-08:00'), ('2019-11-20T17:48:30.803000-08:00', '2019-11-20T17:59:02.509000-08:00'), ('2019-11-20T18:00:24-08:00', '2019-11-20T18:12:23.046000-08:00'), ('2019-11-20T18:12:28-08:00', '2019-11-20T18:16:59-08:00'), ('2019-11-20T18:17:28-08:00', '2019-11-20T19:03:21-08:00'), ('2019-11-20T19:03:51-08:00', '2019-11-20T19:22:55.727000-08:00')]\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = ['2019-11-20T16:17:31.189564-08:00', '2019-11-20T16:24:45-08:00', '2019-11-20T17:18:48.078000-08:00', '2019-11-20T17:48:30.803000-08:00', '2019-11-20T18:00:24-08:00', '2019-11-20T18:12:28-08:00', '2019-11-20T18:17:28-08:00', '2019-11-20T19:03:51-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_2 MAMFDC v/s HAMFDC HAMFDC_2 3\n", - "Before filtering, trips = [('2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:27:39-08:00'), ('2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:25-08:00'), ('2019-12-03T09:12:56-08:00', '2019-12-03T09:14:36.293000-08:00'), ('2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:11:29-08:00'), ('2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:19:25.295000-08:00'), ('2019-12-03T10:27:55.101354-08:00', '2019-12-03T10:33:26-08:00'), ('2019-12-03T14:14:50.838179-08:00', '2019-12-03T14:27:28-08:00'), ('2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:21:04-08:00'), ('2019-12-03T16:22:03-08:00', '2019-12-03T16:25:22.376000-08:00'), ('2019-12-03T16:25:35-08:00', '2019-12-03T16:25:54.381000-08:00'), ('2019-12-03T16:26:03-08:00', '2019-12-03T16:43:40.687000-08:00'), ('2019-12-03T16:44:05-08:00', '2019-12-03T16:45:05-08:00'), ('2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:53:58-08:00'), ('2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:03:50.551000-08:00'), ('2019-12-03T18:05:41-08:00', '2019-12-03T18:16:26.261000-08:00'), ('2019-12-03T18:16:52-08:00', '2019-12-03T18:20:24-08:00'), ('2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:32.250000-08:00'), ('2019-12-03T19:16:38-08:00', '2019-12-03T19:34:25.275000-08:00')]\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = ['2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:56-08:00', '2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:27:55.101354-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:27:39-08:00'), ('2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:25-08:00'), ('2019-12-03T09:12:56-08:00', '2019-12-03T09:14:36.293000-08:00'), ('2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:11:29-08:00'), ('2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:19:25.295000-08:00'), ('2019-12-03T10:27:55.101354-08:00', '2019-12-03T10:33:26-08:00'), ('2019-12-03T14:14:50.838179-08:00', '2019-12-03T14:27:28-08:00'), ('2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:21:04-08:00'), ('2019-12-03T16:22:03-08:00', '2019-12-03T16:25:22.376000-08:00'), ('2019-12-03T16:25:35-08:00', '2019-12-03T16:25:54.381000-08:00'), ('2019-12-03T16:26:03-08:00', '2019-12-03T16:43:40.687000-08:00'), ('2019-12-03T16:44:05-08:00', '2019-12-03T16:45:05-08:00'), ('2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:53:58-08:00'), ('2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:03:50.551000-08:00'), ('2019-12-03T18:05:41-08:00', '2019-12-03T18:16:26.261000-08:00'), ('2019-12-03T18:16:52-08:00', '2019-12-03T18:20:24-08:00'), ('2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:32.250000-08:00'), ('2019-12-03T19:16:38-08:00', '2019-12-03T19:34:25.275000-08:00')]\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = ['2019-12-03T14:14:50.838179-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:16:50.463000-08:00', '2019-12-03T08:27:39-08:00'), ('2019-12-03T08:34:29.781524-08:00', '2019-12-03T09:12:25-08:00'), ('2019-12-03T09:12:56-08:00', '2019-12-03T09:14:36.293000-08:00'), ('2019-12-03T09:18:14.597246-08:00', '2019-12-03T10:11:29-08:00'), ('2019-12-03T10:16:15.890000-08:00', '2019-12-03T10:19:25.295000-08:00'), ('2019-12-03T10:27:55.101354-08:00', '2019-12-03T10:33:26-08:00'), ('2019-12-03T14:14:50.838179-08:00', '2019-12-03T14:27:28-08:00'), ('2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:21:04-08:00'), ('2019-12-03T16:22:03-08:00', '2019-12-03T16:25:22.376000-08:00'), ('2019-12-03T16:25:35-08:00', '2019-12-03T16:25:54.381000-08:00'), ('2019-12-03T16:26:03-08:00', '2019-12-03T16:43:40.687000-08:00'), ('2019-12-03T16:44:05-08:00', '2019-12-03T16:45:05-08:00'), ('2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:53:58-08:00'), ('2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:03:50.551000-08:00'), ('2019-12-03T18:05:41-08:00', '2019-12-03T18:16:26.261000-08:00'), ('2019-12-03T18:16:52-08:00', '2019-12-03T18:20:24-08:00'), ('2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:32.250000-08:00'), ('2019-12-03T19:16:38-08:00', '2019-12-03T19:34:25.275000-08:00')]\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = ['2019-12-03T16:15:29.460108-08:00', '2019-12-03T16:22:03-08:00', '2019-12-03T16:25:35-08:00', '2019-12-03T16:26:03-08:00', '2019-12-03T16:44:05-08:00', '2019-12-03T17:18:29.666618-08:00', '2019-12-03T17:54:21.966000-08:00', '2019-12-03T18:05:41-08:00', '2019-12-03T18:16:52-08:00', '2019-12-03T18:24:17.317101-08:00', '2019-12-03T19:16:38-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_0 MAMFDC v/s MAHFDC MAHFDC_0 3\n", - "Before filtering, trips = [('2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:07.875000-08:00'), ('2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:49.681000-08:00'), ('2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:09:16.277000-08:00'), ('2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:10:32.251000-08:00'), ('2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:16:28.664000-08:00'), ('2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:31.435000-08:00'), ('2019-12-09T10:31:36.485000-08:00', '2019-12-09T10:33:28.166000-08:00'), ('2019-12-09T13:59:35.045878-08:00', '2019-12-09T14:11:38.602000-08:00'), ('2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:19.619000-08:00'), ('2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:29.963000-08:00'), ('2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:00.364000-08:00'), ('2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:14.142000-08:00'), ('2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:04.768000-08:00'), ('2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:16.441000-08:00'), ('2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:33.650000-08:00'), ('2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:35:51.577000-08:00'), ('2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:09.319000-08:00'), ('2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:20.163000-08:00'), ('2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:12.327000-08:00'), ('2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:44:58.651000-08:00'), ('2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:13.755000-08:00'), ('2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:34:12.901000-08:00'), ('2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:06:55.029000-08:00'), ('2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:23:47.868000-08:00'), ('2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:35.284000-08:00'), ('2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:07.149000-08:00'), ('2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:52.661000-08:00'), ('2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:49:42.385000-08:00'), ('2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:53:19.879000-08:00'), ('2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:14.686000-08:00'), ('2019-12-09T19:04:40.160000-08:00', '2019-12-09T19:22:24.170000-08:00')]\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = ['2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:36.485000-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:07.875000-08:00'), ('2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:49.681000-08:00'), ('2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:09:16.277000-08:00'), ('2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:10:32.251000-08:00'), ('2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:16:28.664000-08:00'), ('2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:31.435000-08:00'), ('2019-12-09T10:31:36.485000-08:00', '2019-12-09T10:33:28.166000-08:00'), ('2019-12-09T13:59:35.045878-08:00', '2019-12-09T14:11:38.602000-08:00'), ('2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:19.619000-08:00'), ('2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:29.963000-08:00'), ('2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:00.364000-08:00'), ('2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:14.142000-08:00'), ('2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:04.768000-08:00'), ('2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:16.441000-08:00'), ('2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:33.650000-08:00'), ('2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:35:51.577000-08:00'), ('2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:09.319000-08:00'), ('2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:20.163000-08:00'), ('2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:12.327000-08:00'), ('2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:44:58.651000-08:00'), ('2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:13.755000-08:00'), ('2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:34:12.901000-08:00'), ('2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:06:55.029000-08:00'), ('2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:23:47.868000-08:00'), ('2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:35.284000-08:00'), ('2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:07.149000-08:00'), ('2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:52.661000-08:00'), ('2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:49:42.385000-08:00'), ('2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:53:19.879000-08:00'), ('2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:14.686000-08:00'), ('2019-12-09T19:04:40.160000-08:00', '2019-12-09T19:22:24.170000-08:00')]\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = ['2019-12-09T13:59:35.045878-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:18:54.653000-08:00', '2019-12-09T08:32:07.875000-08:00'), ('2019-12-09T08:32:18.082000-08:00', '2019-12-09T09:06:49.681000-08:00'), ('2019-12-09T09:06:59.646000-08:00', '2019-12-09T09:09:16.277000-08:00'), ('2019-12-09T09:18:04.893068-08:00', '2019-12-09T10:10:32.251000-08:00'), ('2019-12-09T10:14:17.333000-08:00', '2019-12-09T10:16:28.664000-08:00'), ('2019-12-09T10:27:14.054911-08:00', '2019-12-09T10:31:31.435000-08:00'), ('2019-12-09T10:31:36.485000-08:00', '2019-12-09T10:33:28.166000-08:00'), ('2019-12-09T13:59:35.045878-08:00', '2019-12-09T14:11:38.602000-08:00'), ('2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:19.619000-08:00'), ('2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:29.963000-08:00'), ('2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:00.364000-08:00'), ('2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:14.142000-08:00'), ('2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:04.768000-08:00'), ('2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:16.441000-08:00'), ('2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:33.650000-08:00'), ('2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:35:51.577000-08:00'), ('2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:09.319000-08:00'), ('2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:20.163000-08:00'), ('2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:12.327000-08:00'), ('2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:44:58.651000-08:00'), ('2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:13.755000-08:00'), ('2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:34:12.901000-08:00'), ('2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:06:55.029000-08:00'), ('2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:23:47.868000-08:00'), ('2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:35.284000-08:00'), ('2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:07.149000-08:00'), ('2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:52.661000-08:00'), ('2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:49:42.385000-08:00'), ('2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:53:19.879000-08:00'), ('2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:14.686000-08:00'), ('2019-12-09T19:04:40.160000-08:00', '2019-12-09T19:22:24.170000-08:00')]\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = ['2019-12-09T16:15:06.017000-08:00', '2019-12-09T16:20:24.667000-08:00', '2019-12-09T16:24:35.001000-08:00', '2019-12-09T16:25:05.380000-08:00', '2019-12-09T16:30:19.196000-08:00', '2019-12-09T16:31:09.823000-08:00', '2019-12-09T16:33:21.495000-08:00', '2019-12-09T16:33:38.687000-08:00', '2019-12-09T16:38:58.834000-08:00', '2019-12-09T16:39:14.367000-08:00', '2019-12-09T16:40:45.482000-08:00', '2019-12-09T16:43:17.355000-08:00', '2019-12-09T16:50:04.621778-08:00', '2019-12-09T17:24:18.801000-08:00', '2019-12-09T17:48:50.257081-08:00', '2019-12-09T18:16:33.774349-08:00', '2019-12-09T18:28:20.339000-08:00', '2019-12-09T18:28:40.337000-08:00', '2019-12-09T18:31:42.606000-08:00', '2019-12-09T18:31:57.787000-08:00', '2019-12-09T18:50:17.868000-08:00', '2019-12-09T18:56:07.181000-08:00', '2019-12-09T19:04:40.160000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_1 MAMFDC v/s MAHFDC MAHFDC_1 3\n", - "Before filtering, trips = [('2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:28:40.265000-08:00'), ('2019-12-11T08:29:12-08:00', '2019-12-11T08:49:48.429000-08:00'), ('2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:50:44.071000-08:00'), ('2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:33.931000-08:00'), ('2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:49.122000-08:00'), ('2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:05.825000-08:00'), ('2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:09:27.870000-08:00'), ('2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:28:57.247000-08:00'), ('2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:34:31.422000-08:00'), ('2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:06.408000-08:00'), ('2019-12-11T10:52:11.490000-08:00', '2019-12-11T10:53:46.589000-08:00'), ('2019-12-11T14:08:06.879449-08:00', '2019-12-11T14:21:17.004000-08:00'), ('2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:04.611000-08:00'), ('2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:47.408000-08:00'), ('2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:12.765000-08:00'), ('2019-12-11T16:28:17-08:00', '2019-12-11T16:40:05.580000-08:00'), ('2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:20.804000-08:00'), ('2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:22.923000-08:00'), ('2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:23.723000-08:00'), ('2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:45:09.282000-08:00'), ('2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:32:45.073000-08:00'), ('2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:38:29.227000-08:00'), ('2019-12-11T17:45:33.125000-08:00', '2019-12-11T17:59:56.877000-08:00'), ('2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:19.042000-08:00'), ('2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:27.952000-08:00'), ('2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:04:18.099000-08:00'), ('2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:45.669000-08:00'), ('2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:10.864000-08:00'), ('2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:37.016000-08:00'), ('2019-12-11T19:02:42.076000-08:00', '2019-12-11T19:21:08.356000-08:00')]\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = ['2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:29:12-08:00', '2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:11.490000-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:28:40.265000-08:00'), ('2019-12-11T08:29:12-08:00', '2019-12-11T08:49:48.429000-08:00'), ('2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:50:44.071000-08:00'), ('2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:33.931000-08:00'), ('2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:49.122000-08:00'), ('2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:05.825000-08:00'), ('2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:09:27.870000-08:00'), ('2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:28:57.247000-08:00'), ('2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:34:31.422000-08:00'), ('2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:06.408000-08:00'), ('2019-12-11T10:52:11.490000-08:00', '2019-12-11T10:53:46.589000-08:00'), ('2019-12-11T14:08:06.879449-08:00', '2019-12-11T14:21:17.004000-08:00'), ('2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:04.611000-08:00'), ('2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:47.408000-08:00'), ('2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:12.765000-08:00'), ('2019-12-11T16:28:17-08:00', '2019-12-11T16:40:05.580000-08:00'), ('2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:20.804000-08:00'), ('2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:22.923000-08:00'), ('2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:23.723000-08:00'), ('2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:45:09.282000-08:00'), ('2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:32:45.073000-08:00'), ('2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:38:29.227000-08:00'), ('2019-12-11T17:45:33.125000-08:00', '2019-12-11T17:59:56.877000-08:00'), ('2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:19.042000-08:00'), ('2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:27.952000-08:00'), ('2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:04:18.099000-08:00'), ('2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:45.669000-08:00'), ('2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:10.864000-08:00'), ('2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:37.016000-08:00'), ('2019-12-11T19:02:42.076000-08:00', '2019-12-11T19:21:08.356000-08:00')]\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = ['2019-12-11T14:08:06.879449-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:16:18.811000-08:00', '2019-12-11T08:28:40.265000-08:00'), ('2019-12-11T08:29:12-08:00', '2019-12-11T08:49:48.429000-08:00'), ('2019-12-11T08:50:23.906000-08:00', '2019-12-11T08:50:44.071000-08:00'), ('2019-12-11T08:51:04.377000-08:00', '2019-12-11T08:56:33.931000-08:00'), ('2019-12-11T08:56:38.866000-08:00', '2019-12-11T08:56:49.122000-08:00'), ('2019-12-11T08:56:54.010000-08:00', '2019-12-11T09:08:05.825000-08:00'), ('2019-12-11T09:08:10.904000-08:00', '2019-12-11T09:09:27.870000-08:00'), ('2019-12-11T09:12:27.870000-08:00', '2019-12-11T10:28:57.247000-08:00'), ('2019-12-11T10:32:20.165000-08:00', '2019-12-11T10:34:31.422000-08:00'), ('2019-12-11T10:45:32.985683-08:00', '2019-12-11T10:52:06.408000-08:00'), ('2019-12-11T10:52:11.490000-08:00', '2019-12-11T10:53:46.589000-08:00'), ('2019-12-11T14:08:06.879449-08:00', '2019-12-11T14:21:17.004000-08:00'), ('2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:04.611000-08:00'), ('2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:47.408000-08:00'), ('2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:12.765000-08:00'), ('2019-12-11T16:28:17-08:00', '2019-12-11T16:40:05.580000-08:00'), ('2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:20.804000-08:00'), ('2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:22.923000-08:00'), ('2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:23.723000-08:00'), ('2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:45:09.282000-08:00'), ('2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:32:45.073000-08:00'), ('2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:38:29.227000-08:00'), ('2019-12-11T17:45:33.125000-08:00', '2019-12-11T17:59:56.877000-08:00'), ('2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:19.042000-08:00'), ('2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:27.952000-08:00'), ('2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:04:18.099000-08:00'), ('2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:45.669000-08:00'), ('2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:10.864000-08:00'), ('2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:37.016000-08:00'), ('2019-12-11T19:02:42.076000-08:00', '2019-12-11T19:21:08.356000-08:00')]\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = ['2019-12-11T16:10:35.523000-08:00', '2019-12-11T16:24:55.199000-08:00', '2019-12-11T16:27:52.458000-08:00', '2019-12-11T16:28:17-08:00', '2019-12-11T16:40:10.638000-08:00', '2019-12-11T16:40:25.793000-08:00', '2019-12-11T16:43:27.980000-08:00', '2019-12-11T16:44:28.763000-08:00', '2019-12-11T16:50:12.689512-08:00', '2019-12-11T17:33:05.328000-08:00', '2019-12-11T17:45:33.125000-08:00', '2019-12-11T18:00:02.081000-08:00', '2019-12-11T18:01:27.952000-08:00', '2019-12-11T18:01:32.840000-08:00', '2019-12-11T18:16:15.956496-08:00', '2019-12-11T19:00:55.789000-08:00', '2019-12-11T19:01:31.207000-08:00', '2019-12-11T19:02:42.076000-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_2 MAMFDC v/s MAHFDC MAHFDC_2 3\n", - "Before filtering, trips = [('2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:29:48.443000-08:00'), ('2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:31:09.327000-08:00'), ('2020-02-06T08:32:19-08:00', '2020-02-06T08:33:28.510000-08:00'), ('2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:32.847000-08:00'), ('2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:11:53.823000-08:00'), ('2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:10:56.852000-08:00'), ('2020-02-06T10:14:29.192000-08:00', '2020-02-06T10:16:39.807000-08:00'), ('2020-02-06T13:06:11.492273-08:00', '2020-02-06T13:21:49.811000-08:00'), ('2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:23:57.824000-08:00'), ('2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:39.115000-08:00'), ('2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:09.506000-08:00'), ('2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:35:52.757000-08:00'), ('2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:12.965000-08:00'), ('2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:28.130000-08:00'), ('2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:33.983000-08:00'), ('2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:10.023000-08:00'), ('2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:43.645000-08:00'), ('2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:48:24.953000-08:00'), ('2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:16.908000-08:00'), ('2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:36:34.457000-08:00'), ('2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:49:46.900000-08:00'), ('2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:56:24.499000-08:00'), ('2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:20.622000-08:00'), ('2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:06:07.700000-08:00'), ('2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:40.684000-08:00'), ('2020-02-06T18:56:45.755000-08:00', '2020-02-06T19:16:46.406000-08:00')]\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = ['2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:32:19-08:00', '2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:14:29.192000-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:29:48.443000-08:00'), ('2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:31:09.327000-08:00'), ('2020-02-06T08:32:19-08:00', '2020-02-06T08:33:28.510000-08:00'), ('2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:32.847000-08:00'), ('2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:11:53.823000-08:00'), ('2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:10:56.852000-08:00'), ('2020-02-06T10:14:29.192000-08:00', '2020-02-06T10:16:39.807000-08:00'), ('2020-02-06T13:06:11.492273-08:00', '2020-02-06T13:21:49.811000-08:00'), ('2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:23:57.824000-08:00'), ('2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:39.115000-08:00'), ('2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:09.506000-08:00'), ('2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:35:52.757000-08:00'), ('2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:12.965000-08:00'), ('2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:28.130000-08:00'), ('2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:33.983000-08:00'), ('2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:10.023000-08:00'), ('2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:43.645000-08:00'), ('2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:48:24.953000-08:00'), ('2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:16.908000-08:00'), ('2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:36:34.457000-08:00'), ('2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:49:46.900000-08:00'), ('2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:56:24.499000-08:00'), ('2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:20.622000-08:00'), ('2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:06:07.700000-08:00'), ('2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:40.684000-08:00'), ('2020-02-06T18:56:45.755000-08:00', '2020-02-06T19:16:46.406000-08:00')]\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = ['2020-02-06T13:06:11.492273-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:16:27.886000-08:00', '2020-02-06T08:29:48.443000-08:00'), ('2020-02-06T08:30:03.600000-08:00', '2020-02-06T08:31:09.327000-08:00'), ('2020-02-06T08:32:19-08:00', '2020-02-06T08:33:28.510000-08:00'), ('2020-02-06T08:34:55.903000-08:00', '2020-02-06T09:10:32.847000-08:00'), ('2020-02-06T09:10:42.976000-08:00', '2020-02-06T09:11:53.823000-08:00'), ('2020-02-06T09:12:33.814994-08:00', '2020-02-06T10:10:56.852000-08:00'), ('2020-02-06T10:14:29.192000-08:00', '2020-02-06T10:16:39.807000-08:00'), ('2020-02-06T13:06:11.492273-08:00', '2020-02-06T13:21:49.811000-08:00'), ('2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:23:57.824000-08:00'), ('2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:39.115000-08:00'), ('2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:09.506000-08:00'), ('2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:35:52.757000-08:00'), ('2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:12.965000-08:00'), ('2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:28.130000-08:00'), ('2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:33.983000-08:00'), ('2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:10.023000-08:00'), ('2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:43.645000-08:00'), ('2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:48:24.953000-08:00'), ('2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:16.908000-08:00'), ('2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:36:34.457000-08:00'), ('2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:49:46.900000-08:00'), ('2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:56:24.499000-08:00'), ('2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:20.622000-08:00'), ('2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:06:07.700000-08:00'), ('2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:40.684000-08:00'), ('2020-02-06T18:56:45.755000-08:00', '2020-02-06T19:16:46.406000-08:00')]\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = ['2020-02-06T16:16:40.145979-08:00', '2020-02-06T16:24:18.084000-08:00', '2020-02-06T16:33:44.189000-08:00', '2020-02-06T16:34:14.586000-08:00', '2020-02-06T16:36:02.863000-08:00', '2020-02-06T16:36:18.032000-08:00', '2020-02-06T16:36:33.179000-08:00', '2020-02-06T16:37:46.728000-08:00', '2020-02-06T16:43:15.080000-08:00', '2020-02-06T16:43:48.686000-08:00', '2020-02-06T16:53:05.363404-08:00', '2020-02-06T17:28:21.895000-08:00', '2020-02-06T17:39:50.059000-08:00', '2020-02-06T17:53:55.855000-08:00', '2020-02-06T17:58:24.642500-08:00', '2020-02-06T18:01:25.698000-08:00', '2020-02-06T18:08:01.526000-08:00', '2020-02-06T18:56:45.755000-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-android-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_0 HAHFDC v/s HAMFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_1 HAHFDC v/s HAMFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_2 HAHFDC v/s HAMFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_3 HAHFDC v/s MAHFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_4 HAHFDC v/s MAHFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_5 HAHFDC v/s MAHFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_6 MAMFDC v/s HAMFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_7 MAMFDC v/s HAMFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_8 MAMFDC v/s HAMFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_9 MAMFDC v/s MAHFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_10 MAMFDC v/s MAHFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_11 MAMFDC v/s MAHFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = []\n", - "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", - "ios dict_keys(['ucb-sdb-ios-1', 'ucb-sdb-ios-2', 'ucb-sdb-ios-3', 'ucb-sdb-ios-4'])\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-1 accuracy_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_0 HAHFDC v/s MAHFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_1 HAHFDC v/s MAHFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_2 HAHFDC v/s MAHFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_3 HAHFDC v/s HAMFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_4 HAHFDC v/s HAMFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_5 HAHFDC v/s HAMFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_6 MAMFDC v/s MAHFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_7 MAMFDC v/s MAHFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_8 MAMFDC v/s MAHFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_9 MAMFDC v/s HAMFDC accuracy_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_10 MAMFDC v/s HAMFDC accuracy_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:ACCURACY_CONTROL_11 MAMFDC v/s HAMFDC accuracy_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = []\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-2 evaluation_0 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_0 HAHFDC v/s MAHFDC HAHFDC_0 3\n", - "Before filtering, trips = [('2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:14:29.985872-07:00'), ('2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:57.992460-07:00'), ('2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:37:09.228022-07:00'), ('2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:10:43.989785-07:00'), ('2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:25.991375-07:00'), ('2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:16.987673-07:00'), ('2019-07-24T10:25:25.987373-07:00', '2019-07-24T10:35:08.192261-07:00'), ('2019-07-24T14:13:46.520613-07:00', '2019-07-24T14:27:08.994135-07:00'), ('2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:00.991895-07:00'), ('2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:08.986795-07:00'), ('2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:13.992662-07:00'), ('2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:18.992464-07:00'), ('2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:22.998500-07:00'), ('2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:32.998418-07:00'), ('2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:03.989389-07:00'), ('2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:20:30.993219-07:00'), ('2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:53.996238-07:00'), ('2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:46.998221-07:00'), ('2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:48.998363-07:00'), ('2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:01.998787-07:00'), ('2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:21.998584-07:00'), ('2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:37.985919-07:00'), ('2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:42.995913-07:00'), ('2019-07-24T19:40:46.995787-07:00', '2019-07-24T19:59:34.986324-07:00')]\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = ['2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:25.987373-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:14:29.985872-07:00'), ('2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:57.992460-07:00'), ('2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:37:09.228022-07:00'), ('2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:10:43.989785-07:00'), ('2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:25.991375-07:00'), ('2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:16.987673-07:00'), ('2019-07-24T10:25:25.987373-07:00', '2019-07-24T10:35:08.192261-07:00'), ('2019-07-24T14:13:46.520613-07:00', '2019-07-24T14:27:08.994135-07:00'), ('2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:00.991895-07:00'), ('2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:08.986795-07:00'), ('2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:13.992662-07:00'), ('2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:18.992464-07:00'), ('2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:22.998500-07:00'), ('2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:32.998418-07:00'), ('2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:03.989389-07:00'), ('2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:20:30.993219-07:00'), ('2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:53.996238-07:00'), ('2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:46.998221-07:00'), ('2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:48.998363-07:00'), ('2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:01.998787-07:00'), ('2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:21.998584-07:00'), ('2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:37.985919-07:00'), ('2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:42.995913-07:00'), ('2019-07-24T19:40:46.995787-07:00', '2019-07-24T19:59:34.986324-07:00')]\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = ['2019-07-24T14:13:46.520613-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:54:04.385489-07:00', '2019-07-24T08:14:29.985872-07:00'), ('2019-07-24T08:29:45.498660-07:00', '2019-07-24T09:10:57.992460-07:00'), ('2019-07-24T09:10:58.992426-07:00', '2019-07-24T09:37:09.228022-07:00'), ('2019-07-24T09:57:13.987198-07:00', '2019-07-24T10:10:43.989785-07:00'), ('2019-07-24T10:14:25.853500-07:00', '2019-07-24T10:23:25.991375-07:00'), ('2019-07-24T10:23:26.991341-07:00', '2019-07-24T10:25:16.987673-07:00'), ('2019-07-24T10:25:25.987373-07:00', '2019-07-24T10:35:08.192261-07:00'), ('2019-07-24T14:13:46.520613-07:00', '2019-07-24T14:27:08.994135-07:00'), ('2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:00.991895-07:00'), ('2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:08.986795-07:00'), ('2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:13.992662-07:00'), ('2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:18.992464-07:00'), ('2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:22.998500-07:00'), ('2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:32.998418-07:00'), ('2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:03.989389-07:00'), ('2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:20:30.993219-07:00'), ('2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:53.996238-07:00'), ('2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:46.998221-07:00'), ('2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:48.998363-07:00'), ('2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:01.998787-07:00'), ('2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:21.998584-07:00'), ('2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:37.985919-07:00'), ('2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:42.995913-07:00'), ('2019-07-24T19:40:46.995787-07:00', '2019-07-24T19:59:34.986324-07:00')]\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = ['2019-07-24T16:39:48.110149-07:00', '2019-07-24T16:44:01.991856-07:00', '2019-07-24T16:46:09.986757-07:00', '2019-07-24T16:51:14.992623-07:00', '2019-07-24T16:51:19.992425-07:00', '2019-07-24T16:56:23.998502-07:00', '2019-07-24T16:56:33.998400-07:00', '2019-07-24T17:02:11.989073-07:00', '2019-07-24T17:21:50.689725-07:00', '2019-07-24T17:59:54.996205-07:00', '2019-07-24T18:26:47.998295-07:00', '2019-07-24T18:26:49.998424-07:00', '2019-07-24T18:27:02.998795-07:00', '2019-07-24T18:27:22.998560-07:00', '2019-07-24T18:33:38.985886-07:00', '2019-07-24T19:40:46.995787-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_1 HAHFDC v/s MAHFDC HAHFDC_1 3\n", - "Before filtering, trips = [('2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:17.989881-07:00'), ('2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:54.987120-07:00'), ('2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:16.987692-07:00'), ('2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:44.987044-07:00'), ('2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:07:45.987011-07:00'), ('2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:08:35.985266-07:00'), ('2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:19:40.995220-07:00'), ('2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:13:37.228350-07:00'), ('2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:37.998554-07:00'), ('2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:38.991190-07:00'), ('2019-07-25T10:26:40.991124-07:00', '2019-07-25T10:29:22.998803-07:00'), ('2019-07-25T14:12:13.247218-07:00', '2019-07-25T14:23:37.001393-07:00'), ('2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:29.995108-07:00'), ('2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:46.995361-07:00'), ('2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:51.994124-07:00'), ('2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:21.992900-07:00'), ('2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:29.992571-07:00'), ('2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:14.988224-07:00'), ('2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:21.987933-07:00'), ('2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:04.996904-07:00'), ('2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:09.996823-07:00'), ('2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:35.987966-07:00'), ('2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:00:38.993944-07:00'), ('2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:06.990192-07:00'), ('2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:22.998205-07:00'), ('2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:31.997085-07:00'), ('2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:30.994596-07:00'), ('2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:29.995121-07:00'), ('2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:43.988375-07:00'), ('2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:13.998284-07:00'), ('2019-07-25T19:41:14.998255-07:00', '2019-07-25T19:52:24.990528-07:00')]\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = ['2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:40.991124-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:17.989881-07:00'), ('2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:54.987120-07:00'), ('2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:16.987692-07:00'), ('2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:44.987044-07:00'), ('2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:07:45.987011-07:00'), ('2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:08:35.985266-07:00'), ('2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:19:40.995220-07:00'), ('2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:13:37.228350-07:00'), ('2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:37.998554-07:00'), ('2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:38.991190-07:00'), ('2019-07-25T10:26:40.991124-07:00', '2019-07-25T10:29:22.998803-07:00'), ('2019-07-25T14:12:13.247218-07:00', '2019-07-25T14:23:37.001393-07:00'), ('2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:29.995108-07:00'), ('2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:46.995361-07:00'), ('2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:51.994124-07:00'), ('2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:21.992900-07:00'), ('2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:29.992571-07:00'), ('2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:14.988224-07:00'), ('2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:21.987933-07:00'), ('2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:04.996904-07:00'), ('2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:09.996823-07:00'), ('2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:35.987966-07:00'), ('2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:00:38.993944-07:00'), ('2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:06.990192-07:00'), ('2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:22.998205-07:00'), ('2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:31.997085-07:00'), ('2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:30.994596-07:00'), ('2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:29.995121-07:00'), ('2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:43.988375-07:00'), ('2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:13.998284-07:00'), ('2019-07-25T19:41:14.998255-07:00', '2019-07-25T19:52:24.990528-07:00')]\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = ['2019-07-25T14:12:13.247218-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:11:36.166866-07:00', '2019-07-25T08:31:17.989881-07:00'), ('2019-07-25T08:31:43.509226-07:00', '2019-07-25T09:06:54.987120-07:00'), ('2019-07-25T09:06:55.987197-07:00', '2019-07-25T09:07:16.987692-07:00'), ('2019-07-25T09:07:17.987682-07:00', '2019-07-25T09:07:44.987044-07:00'), ('2019-07-25T09:07:45.987011-07:00', '2019-07-25T09:07:45.987011-07:00'), ('2019-07-25T09:08:09.421264-07:00', '2019-07-25T09:08:35.985266-07:00'), ('2019-07-25T09:18:16.997968-07:00', '2019-07-25T09:19:40.995220-07:00'), ('2019-07-25T09:20:37.993334-07:00', '2019-07-25T10:13:37.228350-07:00'), ('2019-07-25T10:14:01.207154-07:00', '2019-07-25T10:22:37.998554-07:00'), ('2019-07-25T10:22:38.998526-07:00', '2019-07-25T10:26:38.991190-07:00'), ('2019-07-25T10:26:40.991124-07:00', '2019-07-25T10:29:22.998803-07:00'), ('2019-07-25T14:12:13.247218-07:00', '2019-07-25T14:23:37.001393-07:00'), ('2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:29.995108-07:00'), ('2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:46.995361-07:00'), ('2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:51.994124-07:00'), ('2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:21.992900-07:00'), ('2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:29.992571-07:00'), ('2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:14.988224-07:00'), ('2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:21.987933-07:00'), ('2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:04.996904-07:00'), ('2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:09.996823-07:00'), ('2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:35.987966-07:00'), ('2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:00:38.993944-07:00'), ('2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:06.990192-07:00'), ('2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:22.998205-07:00'), ('2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:31.997085-07:00'), ('2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:30.994596-07:00'), ('2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:29.995121-07:00'), ('2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:43.988375-07:00'), ('2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:13.998284-07:00'), ('2019-07-25T19:41:14.998255-07:00', '2019-07-25T19:52:24.990528-07:00')]\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = ['2019-07-25T16:35:31.905761-07:00', '2019-07-25T16:38:32.994984-07:00', '2019-07-25T16:38:47.995412-07:00', '2019-07-25T16:39:52.994084-07:00', '2019-07-25T16:40:22.992859-07:00', '2019-07-25T16:40:30.992528-07:00', '2019-07-25T16:42:15.988180-07:00', '2019-07-25T16:42:22.987889-07:00', '2019-07-25T16:45:05.996866-07:00', '2019-07-25T16:45:10.996669-07:00', '2019-07-25T16:57:36.987925-07:00', '2019-07-25T17:22:56.965372-07:00', '2019-07-25T17:57:08.990120-07:00', '2019-07-25T18:00:28.998085-07:00', '2019-07-25T18:01:32.997063-07:00', '2019-07-25T18:17:31.994578-07:00', '2019-07-25T18:23:35.994898-07:00', '2019-07-25T18:33:44.988339-07:00', '2019-07-25T19:41:14.998255-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:HAHFDC_2 HAHFDC v/s MAHFDC HAHFDC_2 3\n", - "Before filtering, trips = [('2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:14.989143-07:00'), ('2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:06.995584-07:00'), ('2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:42.998029-07:00'), ('2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:55.997011-07:00'), ('2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:07.991729-07:00'), ('2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:23.990077-07:00'), ('2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:10:56.112940-07:00'), ('2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:28.998600-07:00'), ('2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:43.985705-07:00'), ('2019-07-26T10:26:46.985601-07:00', '2019-07-26T10:29:26.995188-07:00'), ('2019-07-26T14:17:28.484846-07:00', '2019-07-26T14:28:57.998624-07:00'), ('2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:39:12.984392-07:00'), ('2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:30.998380-07:00'), ('2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:43.998158-07:00'), ('2019-07-26T16:49:44.998131-07:00', '2019-07-26T16:59:48.993276-07:00'), ('2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:03:20.986680-07:00'), ('2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:42.990365-07:00'), ('2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:28.987602-07:00'), ('2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:34.990709-07:00'), ('2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:22.992207-07:00'), ('2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:07.993219-07:00'), ('2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:17.993264-07:00'), ('2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:29.991621-07:00'), ('2019-07-26T19:41:30.991588-07:00', '2019-07-26T19:59:19.997824-07:00')]\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = ['2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:46.985601-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:14.989143-07:00'), ('2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:06.995584-07:00'), ('2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:42.998029-07:00'), ('2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:55.997011-07:00'), ('2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:07.991729-07:00'), ('2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:23.990077-07:00'), ('2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:10:56.112940-07:00'), ('2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:28.998600-07:00'), ('2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:43.985705-07:00'), ('2019-07-26T10:26:46.985601-07:00', '2019-07-26T10:29:26.995188-07:00'), ('2019-07-26T14:17:28.484846-07:00', '2019-07-26T14:28:57.998624-07:00'), ('2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:39:12.984392-07:00'), ('2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:30.998380-07:00'), ('2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:43.998158-07:00'), ('2019-07-26T16:49:44.998131-07:00', '2019-07-26T16:59:48.993276-07:00'), ('2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:03:20.986680-07:00'), ('2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:42.990365-07:00'), ('2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:28.987602-07:00'), ('2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:34.990709-07:00'), ('2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:22.992207-07:00'), ('2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:07.993219-07:00'), ('2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:17.993264-07:00'), ('2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:29.991621-07:00'), ('2019-07-26T19:41:30.991588-07:00', '2019-07-26T19:59:19.997824-07:00')]\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = ['2019-07-26T14:17:28.484846-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:14:25.696261-07:00', '2019-07-26T08:23:14.989143-07:00'), ('2019-07-26T08:23:15.989102-07:00', '2019-07-26T08:34:06.995584-07:00'), ('2019-07-26T08:34:07.995542-07:00', '2019-07-26T08:46:42.998029-07:00'), ('2019-07-26T08:46:44.998163-07:00', '2019-07-26T08:47:55.997011-07:00'), ('2019-07-26T08:47:56.996974-07:00', '2019-07-26T09:12:07.991729-07:00'), ('2019-07-26T09:12:08.991693-07:00', '2019-07-26T09:21:23.990077-07:00'), ('2019-07-26T09:21:24.990181-07:00', '2019-07-26T10:10:56.112940-07:00'), ('2019-07-26T10:14:03.359590-07:00', '2019-07-26T10:20:28.998600-07:00'), ('2019-07-26T10:20:29.998579-07:00', '2019-07-26T10:26:43.985705-07:00'), ('2019-07-26T10:26:46.985601-07:00', '2019-07-26T10:29:26.995188-07:00'), ('2019-07-26T14:17:28.484846-07:00', '2019-07-26T14:28:57.998624-07:00'), ('2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:39:12.984392-07:00'), ('2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:30.998380-07:00'), ('2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:43.998158-07:00'), ('2019-07-26T16:49:44.998131-07:00', '2019-07-26T16:59:48.993276-07:00'), ('2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:03:20.986680-07:00'), ('2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:42.990365-07:00'), ('2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:28.987602-07:00'), ('2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:34.990709-07:00'), ('2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:22.992207-07:00'), ('2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:07.993219-07:00'), ('2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:17.993264-07:00'), ('2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:29.991621-07:00'), ('2019-07-26T19:41:30.991588-07:00', '2019-07-26T19:59:19.997824-07:00')]\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = ['2019-07-26T16:15:36.854135-07:00', '2019-07-26T16:41:33.261270-07:00', '2019-07-26T16:49:31.998374-07:00', '2019-07-26T16:49:44.998131-07:00', '2019-07-26T17:00:07.992488-07:00', '2019-07-26T17:20:16.288004-07:00', '2019-07-26T17:51:43.990324-07:00', '2019-07-26T18:15:29.987562-07:00', '2019-07-26T18:28:44.990342-07:00', '2019-07-26T18:34:27.992021-07:00', '2019-07-26T19:40:08.993235-07:00', '2019-07-26T19:40:18.993277-07:00', '2019-07-26T19:41:30.991588-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_0 HAHFDC v/s HAMFDC HAHFDC_0 3\n", - "Before filtering, trips = [('2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:04.989569-07:00'), ('2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:06:48.988289-07:00'), ('2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:19:51.644861-07:00'), ('2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:10:25.512905-07:00'), ('2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:58.998805-07:00'), ('2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:33.996101-07:00'), ('2019-09-10T10:35:34.996066-07:00', '2019-09-10T10:38:55.077118-07:00'), ('2019-09-10T13:40:35.920080-07:00', '2019-09-10T13:51:17.997642-07:00'), ('2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:03.987696-07:00'), ('2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:23.986862-07:00'), ('2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:26.997538-07:00'), ('2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:31.997356-07:00'), ('2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:31.995397-07:00'), ('2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:38.995117-07:00'), ('2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:11.986578-07:00'), ('2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:18.986295-07:00'), ('2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:11.991803-07:00'), ('2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:43:27.983883-07:00'), ('2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:37.987616-07:00'), ('2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:20.985929-07:00'), ('2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:32.985458-07:00'), ('2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:45.984949-07:00'), ('2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:20.985497-07:00'), ('2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:55.990572-07:00'), ('2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:17.998957-07:00'), ('2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:21:27.804565-07:00'), ('2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:29.990256-07:00'), ('2019-09-10T19:04:30.990230-07:00', '2019-09-10T19:16:28.650569-07:00')]\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = ['2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:34.996066-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:04.989569-07:00'), ('2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:06:48.988289-07:00'), ('2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:19:51.644861-07:00'), ('2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:10:25.512905-07:00'), ('2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:58.998805-07:00'), ('2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:33.996101-07:00'), ('2019-09-10T10:35:34.996066-07:00', '2019-09-10T10:38:55.077118-07:00'), ('2019-09-10T13:40:35.920080-07:00', '2019-09-10T13:51:17.997642-07:00'), ('2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:03.987696-07:00'), ('2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:23.986862-07:00'), ('2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:26.997538-07:00'), ('2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:31.997356-07:00'), ('2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:31.995397-07:00'), ('2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:38.995117-07:00'), ('2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:11.986578-07:00'), ('2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:18.986295-07:00'), ('2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:11.991803-07:00'), ('2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:43:27.983883-07:00'), ('2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:37.987616-07:00'), ('2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:20.985929-07:00'), ('2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:32.985458-07:00'), ('2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:45.984949-07:00'), ('2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:20.985497-07:00'), ('2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:55.990572-07:00'), ('2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:17.998957-07:00'), ('2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:21:27.804565-07:00'), ('2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:29.990256-07:00'), ('2019-09-10T19:04:30.990230-07:00', '2019-09-10T19:16:28.650569-07:00')]\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = ['2019-09-10T13:40:35.920080-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:16:48.704241-07:00', '2019-09-10T08:31:04.989569-07:00'), ('2019-09-10T08:31:05.989530-07:00', '2019-09-10T09:06:48.988289-07:00'), ('2019-09-10T09:07:23.998441-07:00', '2019-09-10T09:19:51.644861-07:00'), ('2019-09-10T09:32:55.783426-07:00', '2019-09-10T10:10:25.512905-07:00'), ('2019-09-10T10:14:26.201447-07:00', '2019-09-10T10:33:58.998805-07:00'), ('2019-09-10T10:33:59.998803-07:00', '2019-09-10T10:35:33.996101-07:00'), ('2019-09-10T10:35:34.996066-07:00', '2019-09-10T10:38:55.077118-07:00'), ('2019-09-10T13:40:35.920080-07:00', '2019-09-10T13:51:17.997642-07:00'), ('2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:03.987696-07:00'), ('2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:23.986862-07:00'), ('2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:26.997538-07:00'), ('2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:31.997356-07:00'), ('2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:31.995397-07:00'), ('2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:38.995117-07:00'), ('2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:11.986578-07:00'), ('2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:18.986295-07:00'), ('2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:11.991803-07:00'), ('2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:43:27.983883-07:00'), ('2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:37.987616-07:00'), ('2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:20.985929-07:00'), ('2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:32.985458-07:00'), ('2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:45.984949-07:00'), ('2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:20.985497-07:00'), ('2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:55.990572-07:00'), ('2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:17.998957-07:00'), ('2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:21:27.804565-07:00'), ('2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:29.990256-07:00'), ('2019-09-10T19:04:30.990230-07:00', '2019-09-10T19:16:28.650569-07:00')]\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = ['2019-09-10T16:13:29.797007-07:00', '2019-09-10T16:20:04.987654-07:00', '2019-09-10T16:20:24.986820-07:00', '2019-09-10T16:23:27.997501-07:00', '2019-09-10T16:23:32.997319-07:00', '2019-09-10T16:31:32.995357-07:00', '2019-09-10T16:31:39.995079-07:00', '2019-09-10T16:35:12.986537-07:00', '2019-09-10T16:35:19.986255-07:00', '2019-09-10T16:40:13.991722-07:00', '2019-09-10T16:55:50.932984-07:00', '2019-09-10T17:08:38.987576-07:00', '2019-09-10T17:09:21.985890-07:00', '2019-09-10T17:09:33.985419-07:00', '2019-09-10T17:09:46.984909-07:00', '2019-09-10T17:31:24.985349-07:00', '2019-09-10T17:51:56.990534-07:00', '2019-09-10T18:01:21.998837-07:00', '2019-09-10T18:22:55.765021-07:00', '2019-09-10T19:04:30.990230-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_1 HAHFDC v/s HAMFDC HAHFDC_1 3\n", - "Before filtering, trips = [('2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:52.294066-07:00'), ('2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:52.988634-07:00'), ('2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:12.987914-07:00'), ('2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:26.258317-07:00'), ('2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:19:29.430865-07:00'), ('2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:10:36.991290-07:00'), ('2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:47.537008-07:00'), ('2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:13:55.123096-07:00'), ('2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:49.988092-07:00'), ('2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:26.995452-07:00'), ('2019-09-11T10:35:27.995418-07:00', '2019-09-11T10:38:02.990150-07:00'), ('2019-09-11T13:47:36.966267-07:00', '2019-09-11T13:56:26.988645-07:00'), ('2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:44.985888-07:00'), ('2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:54.996579-07:00'), ('2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:46.997128-07:00'), ('2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:49.994833-07:00'), ('2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:24.985809-07:00'), ('2019-09-11T19:41:25.985785-07:00', '2019-09-11T19:52:09.994150-07:00')]\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = ['2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:27.995418-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:52.294066-07:00'), ('2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:52.988634-07:00'), ('2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:12.987914-07:00'), ('2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:26.258317-07:00'), ('2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:19:29.430865-07:00'), ('2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:10:36.991290-07:00'), ('2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:47.537008-07:00'), ('2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:13:55.123096-07:00'), ('2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:49.988092-07:00'), ('2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:26.995452-07:00'), ('2019-09-11T10:35:27.995418-07:00', '2019-09-11T10:38:02.990150-07:00'), ('2019-09-11T13:47:36.966267-07:00', '2019-09-11T13:56:26.988645-07:00'), ('2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:44.985888-07:00'), ('2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:54.996579-07:00'), ('2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:46.997128-07:00'), ('2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:49.994833-07:00'), ('2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:24.985809-07:00'), ('2019-09-11T19:41:25.985785-07:00', '2019-09-11T19:52:09.994150-07:00')]\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = ['2019-09-11T13:47:36.966267-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:26:23.872133-07:00', '2019-09-11T08:32:52.294066-07:00'), ('2019-09-11T08:32:59.812851-07:00', '2019-09-11T09:04:52.988634-07:00'), ('2019-09-11T09:04:53.988597-07:00', '2019-09-11T09:05:12.987914-07:00'), ('2019-09-11T09:05:13.987877-07:00', '2019-09-11T09:07:26.258317-07:00'), ('2019-09-11T09:07:32.660621-07:00', '2019-09-11T09:19:29.430865-07:00'), ('2019-09-11T09:29:08.072674-07:00', '2019-09-11T10:10:36.991290-07:00'), ('2019-09-11T10:13:47.537008-07:00', '2019-09-11T10:13:47.537008-07:00'), ('2019-09-11T10:13:55.123096-07:00', '2019-09-11T10:13:55.123096-07:00'), ('2019-09-11T10:14:03.094249-07:00', '2019-09-11T10:31:49.988092-07:00'), ('2019-09-11T10:31:50.988059-07:00', '2019-09-11T10:35:26.995452-07:00'), ('2019-09-11T10:35:27.995418-07:00', '2019-09-11T10:38:02.990150-07:00'), ('2019-09-11T13:47:36.966267-07:00', '2019-09-11T13:56:26.988645-07:00'), ('2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:44.985888-07:00'), ('2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:54.996579-07:00'), ('2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:46.997128-07:00'), ('2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:49.994833-07:00'), ('2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:24.985809-07:00'), ('2019-09-11T19:41:25.985785-07:00', '2019-09-11T19:52:09.994150-07:00')]\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = ['2019-09-11T16:38:20.999709-07:00', '2019-09-11T16:51:45.985847-07:00', '2019-09-11T17:19:55.996537-07:00', '2019-09-11T17:55:47.997093-07:00', '2019-09-11T18:32:50.994796-07:00', '2019-09-11T19:41:25.985785-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAHFDC_2 HAHFDC v/s HAMFDC HAHFDC_2 3\n", - "Before filtering, trips = [('2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:47.988508-07:00'), ('2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:54.988226-07:00'), ('2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:37.994272-07:00'), ('2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:13:42.990275-07:00'), ('2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:17.989026-07:00'), ('2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:22.988847-07:00'), ('2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:09.993130-07:00'), ('2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:10:33.994700-07:00'), ('2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:32.986399-07:00'), ('2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:14.993892-07:00'), ('2019-09-17T10:38:15.993857-07:00', '2019-09-17T10:41:31.987041-07:00'), ('2019-09-17T13:47:15.251863-07:00', '2019-09-17T13:58:44.994771-07:00'), ('2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:05.998218-07:00'), ('2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:28.994621-07:00'), ('2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:40.994124-07:00'), ('2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:00.990812-07:00'), ('2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:25.989777-07:00'), ('2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:00.998392-07:00'), ('2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:05.998422-07:00'), ('2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:21.986533-07:00'), ('2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:01.996997-07:00'), ('2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:37.995494-07:00'), ('2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:05.995717-07:00'), ('2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:08.996167-07:00'), ('2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:48.996109-07:00'), ('2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:53.995967-07:00'), ('2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:45.997537-07:00'), ('2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:58.998622-07:00'), ('2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:13.998873-07:00'), ('2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:44.998498-07:00'), ('2019-09-17T18:57:48.998388-07:00', '2019-09-17T19:15:11.436184-07:00')]\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = ['2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:15.993857-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:47.988508-07:00'), ('2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:54.988226-07:00'), ('2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:37.994272-07:00'), ('2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:13:42.990275-07:00'), ('2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:17.989026-07:00'), ('2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:22.988847-07:00'), ('2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:09.993130-07:00'), ('2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:10:33.994700-07:00'), ('2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:32.986399-07:00'), ('2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:14.993892-07:00'), ('2019-09-17T10:38:15.993857-07:00', '2019-09-17T10:41:31.987041-07:00'), ('2019-09-17T13:47:15.251863-07:00', '2019-09-17T13:58:44.994771-07:00'), ('2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:05.998218-07:00'), ('2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:28.994621-07:00'), ('2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:40.994124-07:00'), ('2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:00.990812-07:00'), ('2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:25.989777-07:00'), ('2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:00.998392-07:00'), ('2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:05.998422-07:00'), ('2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:21.986533-07:00'), ('2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:01.996997-07:00'), ('2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:37.995494-07:00'), ('2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:05.995717-07:00'), ('2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:08.996167-07:00'), ('2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:48.996109-07:00'), ('2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:53.995967-07:00'), ('2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:45.997537-07:00'), ('2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:58.998622-07:00'), ('2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:13.998873-07:00'), ('2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:44.998498-07:00'), ('2019-09-17T18:57:48.998388-07:00', '2019-09-17T19:15:11.436184-07:00')]\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = ['2019-09-17T13:47:15.251863-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:05:56.784463-07:00', '2019-09-17T08:30:47.988508-07:00'), ('2019-09-17T08:30:48.988467-07:00', '2019-09-17T08:30:54.988226-07:00'), ('2019-09-17T08:30:56.988147-07:00', '2019-09-17T08:35:37.994272-07:00'), ('2019-09-17T08:35:38.994234-07:00', '2019-09-17T09:13:42.990275-07:00'), ('2019-09-17T09:14:11.989239-07:00', '2019-09-17T09:14:17.989026-07:00'), ('2019-09-17T09:14:18.988988-07:00', '2019-09-17T09:14:22.988847-07:00'), ('2019-09-17T09:14:23.988811-07:00', '2019-09-17T09:21:09.993130-07:00'), ('2019-09-17T09:21:18.678313-07:00', '2019-09-17T10:10:33.994700-07:00'), ('2019-09-17T10:14:52.737307-07:00', '2019-09-17T10:34:32.986399-07:00'), ('2019-09-17T10:34:33.986362-07:00', '2019-09-17T10:38:14.993892-07:00'), ('2019-09-17T10:38:15.993857-07:00', '2019-09-17T10:41:31.987041-07:00'), ('2019-09-17T13:47:15.251863-07:00', '2019-09-17T13:58:44.994771-07:00'), ('2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:05.998218-07:00'), ('2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:28.994621-07:00'), ('2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:40.994124-07:00'), ('2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:00.990812-07:00'), ('2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:25.989777-07:00'), ('2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:00.998392-07:00'), ('2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:05.998422-07:00'), ('2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:21.986533-07:00'), ('2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:01.996997-07:00'), ('2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:37.995494-07:00'), ('2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:05.995717-07:00'), ('2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:08.996167-07:00'), ('2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:48.996109-07:00'), ('2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:53.995967-07:00'), ('2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:45.997537-07:00'), ('2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:58.998622-07:00'), ('2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:13.998873-07:00'), ('2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:44.998498-07:00'), ('2019-09-17T18:57:48.998388-07:00', '2019-09-17T19:15:11.436184-07:00')]\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = ['2019-09-17T16:14:38.666914-07:00', '2019-09-17T16:17:06.998167-07:00', '2019-09-17T16:18:29.994579-07:00', '2019-09-17T16:18:41.994083-07:00', '2019-09-17T16:20:01.990771-07:00', '2019-09-17T16:20:26.989735-07:00', '2019-09-17T16:24:01.998405-07:00', '2019-09-17T16:24:06.998420-07:00', '2019-09-17T16:36:22.986492-07:00', '2019-09-17T16:56:02.996955-07:00', '2019-09-17T17:25:38.995456-07:00', '2019-09-17T17:47:06.995680-07:00', '2019-09-17T17:54:09.996132-07:00', '2019-09-17T17:54:49.996080-07:00', '2019-09-17T17:54:54.995937-07:00', '2019-09-17T18:00:46.997502-07:00', '2019-09-17T18:13:59.998665-07:00', '2019-09-17T18:14:14.998868-07:00', '2019-09-17T18:57:48.998388-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_0 MAMFDC v/s MAHFDC MAMFDC_0 3\n", - "Before filtering, trips = [('2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:22:18.514685-08:00'), ('2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:31:55.506960-08:00'), ('2019-11-19T08:32:32.350269-08:00', '2019-11-19T08:57:43.612899-08:00'), ('2019-11-19T18:36:25.759361-08:00', '2019-11-19T18:56:57.802682-08:00'), ('2019-11-19T19:00:06.562795-08:00', '2019-11-19T19:15:02.292352-08:00')]\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = ['2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:32:32.350269-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:22:18.514685-08:00'), ('2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:31:55.506960-08:00'), ('2019-11-19T08:32:32.350269-08:00', '2019-11-19T08:57:43.612899-08:00'), ('2019-11-19T18:36:25.759361-08:00', '2019-11-19T18:56:57.802682-08:00'), ('2019-11-19T19:00:06.562795-08:00', '2019-11-19T19:15:02.292352-08:00')]\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = [('2019-11-19T08:13:41.058276-08:00', '2019-11-19T08:22:18.514685-08:00'), ('2019-11-19T08:23:18.941068-08:00', '2019-11-19T08:31:55.506960-08:00'), ('2019-11-19T08:32:32.350269-08:00', '2019-11-19T08:57:43.612899-08:00'), ('2019-11-19T18:36:25.759361-08:00', '2019-11-19T18:56:57.802682-08:00'), ('2019-11-19T19:00:06.562795-08:00', '2019-11-19T19:15:02.292352-08:00')]\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = ['2019-11-19T18:36:25.759361-08:00', '2019-11-19T19:00:06.562795-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_1 MAMFDC v/s MAHFDC MAMFDC_1 3\n", - "Before filtering, trips = [('2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:16.465450-08:00'), ('2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:09.850701-08:00'), ('2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:22.279812-08:00'), ('2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:19:22.344031-08:00'), ('2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:10:43.312654-08:00'), ('2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:22.016576-08:00'), ('2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:00.492542-08:00'), ('2019-11-20T10:30:13.433821-08:00', '2019-11-20T10:31:05.215166-08:00'), ('2019-11-20T16:21:48.584273-08:00', '2019-11-20T16:47:55.026384-08:00'), ('2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:29:25.179988-08:00'), ('2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:17:26.588916-08:00'), ('2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:14.170458-08:00'), ('2019-11-20T19:03:34.685705-08:00', '2019-11-20T19:21:36.441834-08:00')]\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = ['2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:13.433821-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:16.465450-08:00'), ('2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:09.850701-08:00'), ('2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:22.279812-08:00'), ('2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:19:22.344031-08:00'), ('2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:10:43.312654-08:00'), ('2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:22.016576-08:00'), ('2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:00.492542-08:00'), ('2019-11-20T10:30:13.433821-08:00', '2019-11-20T10:31:05.215166-08:00'), ('2019-11-20T16:21:48.584273-08:00', '2019-11-20T16:47:55.026384-08:00'), ('2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:29:25.179988-08:00'), ('2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:17:26.588916-08:00'), ('2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:14.170458-08:00'), ('2019-11-20T19:03:34.685705-08:00', '2019-11-20T19:21:36.441834-08:00')]\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = [('2019-11-20T08:24:51.894915-08:00', '2019-11-20T09:04:16.465450-08:00'), ('2019-11-20T09:04:30.497197-08:00', '2019-11-20T09:06:09.850701-08:00'), ('2019-11-20T09:06:16.271218-08:00', '2019-11-20T09:07:22.279812-08:00'), ('2019-11-20T09:07:43.894733-08:00', '2019-11-20T09:19:22.344031-08:00'), ('2019-11-20T09:21:08.556138-08:00', '2019-11-20T10:10:43.312654-08:00'), ('2019-11-20T10:14:44.860515-08:00', '2019-11-20T10:28:22.016576-08:00'), ('2019-11-20T10:28:28.550196-08:00', '2019-11-20T10:30:00.492542-08:00'), ('2019-11-20T10:30:13.433821-08:00', '2019-11-20T10:31:05.215166-08:00'), ('2019-11-20T16:21:48.584273-08:00', '2019-11-20T16:47:55.026384-08:00'), ('2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:29:25.179988-08:00'), ('2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:17:26.588916-08:00'), ('2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:14.170458-08:00'), ('2019-11-20T19:03:34.685705-08:00', '2019-11-20T19:21:36.441834-08:00')]\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = ['2019-11-20T16:21:48.584273-08:00', '2019-11-20T17:19:36.024166-08:00', '2019-11-20T17:32:25.179988-08:00', '2019-11-20T18:19:51.194147-08:00', '2019-11-20T19:03:34.685705-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAMFDC_2 MAMFDC v/s MAHFDC MAMFDC_2 3\n", - "Before filtering, trips = [('2019-12-03T08:34:31.514499-08:00', '2019-12-03T08:59:11.351828-08:00'), ('2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:08.523274-08:00'), ('2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:02:50.132149-08:00'), ('2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:06.060127-08:00'), ('2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:39.202682-08:00'), ('2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:12:58.559972-08:00'), ('2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:17.285688-08:00'), ('2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:15:49.020898-08:00'), ('2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:16:14.887171-08:00'), ('2019-12-03T19:17:26.007153-08:00', '2019-12-03T19:34:21.386348-08:00')]\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = ['2019-12-03T08:34:31.514499-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:34:31.514499-08:00', '2019-12-03T08:59:11.351828-08:00'), ('2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:08.523274-08:00'), ('2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:02:50.132149-08:00'), ('2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:06.060127-08:00'), ('2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:39.202682-08:00'), ('2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:12:58.559972-08:00'), ('2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:17.285688-08:00'), ('2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:15:49.020898-08:00'), ('2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:16:14.887171-08:00'), ('2019-12-03T19:17:26.007153-08:00', '2019-12-03T19:34:21.386348-08:00')]\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = [('2019-12-03T08:34:31.514499-08:00', '2019-12-03T08:59:11.351828-08:00'), ('2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:08.523274-08:00'), ('2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:02:50.132149-08:00'), ('2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:06.060127-08:00'), ('2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:39.202682-08:00'), ('2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:12:58.559972-08:00'), ('2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:17.285688-08:00'), ('2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:15:49.020898-08:00'), ('2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:16:14.887171-08:00'), ('2019-12-03T19:17:26.007153-08:00', '2019-12-03T19:34:21.386348-08:00')]\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = ['2019-12-03T18:59:48.633175-08:00', '2019-12-03T19:01:14.982441-08:00', '2019-12-03T19:03:00.753158-08:00', '2019-12-03T19:04:12.499968-08:00', '2019-12-03T19:12:45.647899-08:00', '2019-12-03T19:13:05.017667-08:00', '2019-12-03T19:14:23.739032-08:00', '2019-12-03T19:16:08.428836-08:00', '2019-12-03T19:17:26.007153-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_0 MAMFDC v/s HAMFDC MAMFDC_0 3\n", - "Before filtering, trips = [('2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:06:37.944693-08:00'), ('2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:20:59.696820-08:00'), ('2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:10:31.162319-08:00'), ('2019-12-09T10:14:45.676057-08:00', '2019-12-09T10:34:49.015549-08:00'), ('2019-12-09T13:59:47.353818-08:00', '2019-12-09T14:11:51.806524-08:00'), ('2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:45:50.005862-08:00'), ('2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:23:55.047962-08:00'), ('2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:13:46.651298-08:00'), ('2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:03:53.735313-08:00'), ('2019-12-09T19:04:27.016722-08:00', '2019-12-09T19:22:41.288239-08:00')]\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = ['2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:14:45.676057-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:06:37.944693-08:00'), ('2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:20:59.696820-08:00'), ('2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:10:31.162319-08:00'), ('2019-12-09T10:14:45.676057-08:00', '2019-12-09T10:34:49.015549-08:00'), ('2019-12-09T13:59:47.353818-08:00', '2019-12-09T14:11:51.806524-08:00'), ('2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:45:50.005862-08:00'), ('2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:23:55.047962-08:00'), ('2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:13:46.651298-08:00'), ('2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:03:53.735313-08:00'), ('2019-12-09T19:04:27.016722-08:00', '2019-12-09T19:22:41.288239-08:00')]\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = ['2019-12-09T13:59:47.353818-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:33:17.660239-08:00', '2019-12-09T09:06:37.944693-08:00'), ('2019-12-09T09:07:29.257941-08:00', '2019-12-09T09:20:59.696820-08:00'), ('2019-12-09T09:26:09.145559-08:00', '2019-12-09T10:10:31.162319-08:00'), ('2019-12-09T10:14:45.676057-08:00', '2019-12-09T10:34:49.015549-08:00'), ('2019-12-09T13:59:47.353818-08:00', '2019-12-09T14:11:51.806524-08:00'), ('2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:45:50.005862-08:00'), ('2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:23:55.047962-08:00'), ('2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:13:46.651298-08:00'), ('2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:03:53.735313-08:00'), ('2019-12-09T19:04:27.016722-08:00', '2019-12-09T19:22:41.288239-08:00')]\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = ['2019-12-09T16:17:43.391544-08:00', '2019-12-09T16:55:45.996946-08:00', '2019-12-09T17:25:07.441017-08:00', '2019-12-09T18:24:28.999269-08:00', '2019-12-09T19:04:27.016722-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_1 MAMFDC v/s HAMFDC MAMFDC_1 3\n", - "Before filtering, trips = [('2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:23.631599-08:00'), ('2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:26.364479-08:00'), ('2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:08:02.187124-08:00'), ('2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:19:22.475962-08:00'), ('2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:28:50.194294-08:00'), ('2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:17.242258-08:00'), ('2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:51:54.782130-08:00'), ('2019-12-11T10:53:39.657251-08:00', '2019-12-11T10:54:12.254016-08:00'), ('2019-12-11T14:09:56.022566-08:00', '2019-12-11T14:21:18.750383-08:00'), ('2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:47:28.114405-08:00'), ('2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:05:47.762147-08:00'), ('2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:32:30.836701-08:00'), ('2019-12-11T17:33:32.242876-08:00', '2019-12-11T17:58:10.742261-08:00'), ('2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:37.561033-08:00'), ('2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:38.852364-08:00'), ('2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:36:54.180465-08:00'), ('2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:37:46.237784-08:00'), ('2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:07.182655-08:00'), ('2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:18.261479-08:00'), ('2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:17.982331-08:00'), ('2019-12-11T19:02:37.295973-08:00', '2019-12-11T19:20:39.726243-08:00')]\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = ['2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:53:39.657251-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:23.631599-08:00'), ('2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:26.364479-08:00'), ('2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:08:02.187124-08:00'), ('2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:19:22.475962-08:00'), ('2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:28:50.194294-08:00'), ('2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:17.242258-08:00'), ('2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:51:54.782130-08:00'), ('2019-12-11T10:53:39.657251-08:00', '2019-12-11T10:54:12.254016-08:00'), ('2019-12-11T14:09:56.022566-08:00', '2019-12-11T14:21:18.750383-08:00'), ('2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:47:28.114405-08:00'), ('2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:05:47.762147-08:00'), ('2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:32:30.836701-08:00'), ('2019-12-11T17:33:32.242876-08:00', '2019-12-11T17:58:10.742261-08:00'), ('2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:37.561033-08:00'), ('2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:38.852364-08:00'), ('2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:36:54.180465-08:00'), ('2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:37:46.237784-08:00'), ('2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:07.182655-08:00'), ('2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:18.261479-08:00'), ('2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:17.982331-08:00'), ('2019-12-11T19:02:37.295973-08:00', '2019-12-11T19:20:39.726243-08:00')]\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = ['2019-12-11T14:09:56.022566-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:17:17.219686-08:00', '2019-12-11T09:03:23.631599-08:00'), ('2019-12-11T09:03:37.797703-08:00', '2019-12-11T09:04:26.364479-08:00'), ('2019-12-11T09:04:32.771799-08:00', '2019-12-11T09:08:02.187124-08:00'), ('2019-12-11T09:09:02.429938-08:00', '2019-12-11T09:19:22.475962-08:00'), ('2019-12-11T09:22:33.733995-08:00', '2019-12-11T10:28:50.194294-08:00'), ('2019-12-11T10:31:58.392448-08:00', '2019-12-11T10:49:17.242258-08:00'), ('2019-12-11T10:49:23.757065-08:00', '2019-12-11T10:51:54.782130-08:00'), ('2019-12-11T10:53:39.657251-08:00', '2019-12-11T10:54:12.254016-08:00'), ('2019-12-11T14:09:56.022566-08:00', '2019-12-11T14:21:18.750383-08:00'), ('2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:47:28.114405-08:00'), ('2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:05:47.762147-08:00'), ('2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:32:30.836701-08:00'), ('2019-12-11T17:33:32.242876-08:00', '2019-12-11T17:58:10.742261-08:00'), ('2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:37.561033-08:00'), ('2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:38.852364-08:00'), ('2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:36:54.180465-08:00'), ('2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:37:46.237784-08:00'), ('2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:07.182655-08:00'), ('2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:18.261479-08:00'), ('2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:17.982331-08:00'), ('2019-12-11T19:02:37.295973-08:00', '2019-12-11T19:20:39.726243-08:00')]\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = ['2019-12-11T16:22:42.090307-08:00', '2019-12-11T16:52:05.652969-08:00', '2019-12-11T17:08:47.762147-08:00', '2019-12-11T17:33:32.242876-08:00', '2019-12-11T18:08:06.594009-08:00', '2019-12-11T18:32:43.979378-08:00', '2019-12-11T18:35:52.873622-08:00', '2019-12-11T18:37:46.237784-08:00', '2019-12-11T18:38:07.830446-08:00', '2019-12-11T18:40:28.932263-08:00', '2019-12-11T18:42:46.382399-08:00', '2019-12-11T19:02:37.295973-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:MAMFDC_2 MAMFDC v/s HAMFDC MAMFDC_2 3\n", - "Before filtering, trips = [('2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:25:02.736740-08:00'), ('2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:34:58.642714-08:00'), ('2020-02-06T08:35:36.623463-08:00', '2020-02-06T08:57:36.177141-08:00'), ('2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:56:51.030249-08:00'), ('2020-02-06T18:57:10.358475-08:00', '2020-02-06T19:19:47.315187-08:00')]\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = ['2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:35:36.623463-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:25:02.736740-08:00'), ('2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:34:58.642714-08:00'), ('2020-02-06T08:35:36.623463-08:00', '2020-02-06T08:57:36.177141-08:00'), ('2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:56:51.030249-08:00'), ('2020-02-06T18:57:10.358475-08:00', '2020-02-06T19:19:47.315187-08:00')]\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = [('2020-02-06T08:23:15.894629-08:00', '2020-02-06T08:25:02.736740-08:00'), ('2020-02-06T08:26:00.821645-08:00', '2020-02-06T08:34:58.642714-08:00'), ('2020-02-06T08:35:36.623463-08:00', '2020-02-06T08:57:36.177141-08:00'), ('2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:56:51.030249-08:00'), ('2020-02-06T18:57:10.358475-08:00', '2020-02-06T19:19:47.315187-08:00')]\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = ['2020-02-06T18:42:26.341399-08:00', '2020-02-06T18:57:10.358475-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-3 evaluation_1 dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_0 HAHFDC v/s MAHFDC MAHFDC_0 3\n", - "Before filtering, trips = [('2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:31:57.580078-07:00'), ('2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:08:38.203095-07:00'), ('2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:18:30.399375-07:00'), ('2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:13:48.365399-07:00'), ('2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:20:58.020761-07:00'), ('2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:22.112203-07:00'), ('2019-07-24T10:25:28.593253-07:00', '2019-07-24T10:28:36.923432-07:00'), ('2019-07-24T14:14:50.357894-07:00', '2019-07-24T14:28:31.561832-07:00'), ('2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:20:53.411158-07:00'), ('2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:50.559638-07:00'), ('2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:49.171143-07:00'), ('2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:04.015177-07:00'), ('2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:07.621832-07:00'), ('2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:20.487449-07:00'), ('2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:26.033059-07:00'), ('2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:37.517945-07:00'), ('2019-07-24T19:40:43.961104-07:00', '2019-07-24T20:03:02.696398-07:00')]\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = ['2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:28.593253-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:31:57.580078-07:00'), ('2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:08:38.203095-07:00'), ('2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:18:30.399375-07:00'), ('2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:13:48.365399-07:00'), ('2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:20:58.020761-07:00'), ('2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:22.112203-07:00'), ('2019-07-24T10:25:28.593253-07:00', '2019-07-24T10:28:36.923432-07:00'), ('2019-07-24T14:14:50.357894-07:00', '2019-07-24T14:28:31.561832-07:00'), ('2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:20:53.411158-07:00'), ('2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:50.559638-07:00'), ('2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:49.171143-07:00'), ('2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:04.015177-07:00'), ('2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:07.621832-07:00'), ('2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:20.487449-07:00'), ('2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:26.033059-07:00'), ('2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:37.517945-07:00'), ('2019-07-24T19:40:43.961104-07:00', '2019-07-24T20:03:02.696398-07:00')]\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = ['2019-07-24T14:14:50.357894-07:00']\n", - "Before filtering, trips = [('2019-07-24T07:53:46.303272-07:00', '2019-07-24T08:31:57.580078-07:00'), ('2019-07-24T08:32:19.169481-07:00', '2019-07-24T09:08:38.203095-07:00'), ('2019-07-24T09:10:38.912765-07:00', '2019-07-24T09:18:30.399375-07:00'), ('2019-07-24T09:21:26.975952-07:00', '2019-07-24T10:13:48.365399-07:00'), ('2019-07-24T10:14:11.087447-07:00', '2019-07-24T10:20:58.020761-07:00'), ('2019-07-24T10:21:04.440507-07:00', '2019-07-24T10:25:22.112203-07:00'), ('2019-07-24T10:25:28.593253-07:00', '2019-07-24T10:28:36.923432-07:00'), ('2019-07-24T14:14:50.357894-07:00', '2019-07-24T14:28:31.561832-07:00'), ('2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:20:53.411158-07:00'), ('2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:50.559638-07:00'), ('2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:49.171143-07:00'), ('2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:04.015177-07:00'), ('2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:07.621832-07:00'), ('2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:20.487449-07:00'), ('2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:26.033059-07:00'), ('2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:37.517945-07:00'), ('2019-07-24T19:40:43.961104-07:00', '2019-07-24T20:03:02.696398-07:00')]\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = ['2019-07-24T16:39:20.902216-07:00', '2019-07-24T17:21:21.346664-07:00', '2019-07-24T17:59:56.990865-07:00', '2019-07-24T18:16:55.588682-07:00', '2019-07-24T18:25:10.460159-07:00', '2019-07-24T18:27:14.058288-07:00', '2019-07-24T18:27:26.916014-07:00', '2019-07-24T18:36:32.459952-07:00', '2019-07-24T19:40:43.961104-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_1 HAHFDC v/s MAHFDC MAHFDC_1 3\n", - "Before filtering, trips = [('2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:31:12.051866-07:00'), ('2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:07:58.642741-07:00'), ('2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:19:23.151481-07:00'), ('2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:10:34.997833-07:00'), ('2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:19.866236-07:00'), ('2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:14.799433-07:00'), ('2019-07-25T10:26:21.253659-07:00', '2019-07-25T10:29:43.114747-07:00'), ('2019-07-25T14:10:21.006354-07:00', '2019-07-25T14:24:14.246334-07:00'), ('2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:00:55.257620-07:00'), ('2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:36:58.767438-07:00'), ('2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:21.243044-07:00'), ('2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:41.622076-07:00'), ('2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:07.090042-07:00'), ('2019-07-25T19:41:13.521212-07:00', '2019-07-25T19:59:06.124861-07:00')]\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = ['2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:21.253659-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:31:12.051866-07:00'), ('2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:07:58.642741-07:00'), ('2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:19:23.151481-07:00'), ('2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:10:34.997833-07:00'), ('2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:19.866236-07:00'), ('2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:14.799433-07:00'), ('2019-07-25T10:26:21.253659-07:00', '2019-07-25T10:29:43.114747-07:00'), ('2019-07-25T14:10:21.006354-07:00', '2019-07-25T14:24:14.246334-07:00'), ('2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:00:55.257620-07:00'), ('2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:36:58.767438-07:00'), ('2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:21.243044-07:00'), ('2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:41.622076-07:00'), ('2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:07.090042-07:00'), ('2019-07-25T19:41:13.521212-07:00', '2019-07-25T19:59:06.124861-07:00')]\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = ['2019-07-25T14:10:21.006354-07:00']\n", - "Before filtering, trips = [('2019-07-25T08:22:27.146326-07:00', '2019-07-25T08:31:12.051866-07:00'), ('2019-07-25T08:32:32.249643-07:00', '2019-07-25T09:07:58.642741-07:00'), ('2019-07-25T09:08:51.620024-07:00', '2019-07-25T09:19:23.151481-07:00'), ('2019-07-25T09:21:04.616961-07:00', '2019-07-25T10:10:34.997833-07:00'), ('2019-07-25T10:14:14.418024-07:00', '2019-07-25T10:22:19.866236-07:00'), ('2019-07-25T10:22:26.309698-07:00', '2019-07-25T10:26:14.799433-07:00'), ('2019-07-25T10:26:21.253659-07:00', '2019-07-25T10:29:43.114747-07:00'), ('2019-07-25T14:10:21.006354-07:00', '2019-07-25T14:24:14.246334-07:00'), ('2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:00:55.257620-07:00'), ('2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:36:58.767438-07:00'), ('2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:21.243044-07:00'), ('2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:41.622076-07:00'), ('2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:07.090042-07:00'), ('2019-07-25T19:41:13.521212-07:00', '2019-07-25T19:59:06.124861-07:00')]\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = ['2019-07-25T16:33:42.715895-07:00', '2019-07-25T17:23:01.133597-07:00', '2019-07-25T17:39:58.767438-07:00', '2019-07-25T18:01:29.798915-07:00', '2019-07-25T18:33:48.071711-07:00', '2019-07-25T19:41:13.521212-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s MAHFDC:MAHFDC_2 HAHFDC v/s MAHFDC MAHFDC_2 3\n", - "Before filtering, trips = [('2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:22:52.964088-07:00'), ('2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:20.898491-07:00'), ('2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:06.468568-07:00'), ('2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:20.000168-07:00'), ('2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:11:37.049495-07:00'), ('2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:19:59.901535-07:00'), ('2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:11:03.621074-07:00'), ('2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:34.247812-07:00'), ('2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:42.000133-07:00'), ('2019-07-26T10:26:48.446216-07:00', '2019-07-26T10:29:48.992491-07:00'), ('2019-07-26T14:17:32.931680-07:00', '2019-07-26T14:30:09.172231-07:00'), ('2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:03:28.030293-07:00'), ('2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:35:00.449989-07:00'), ('2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:41.551743-07:00'), ('2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:12:55.142156-07:00'), ('2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:46.679733-07:00'), ('2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:01.080074-07:00'), ('2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:50.406806-07:00'), ('2019-07-26T19:40:56.849526-07:00', '2019-07-26T19:55:37.720556-07:00'), ('2019-07-26T20:31:55.991693-07:00', '2019-07-26T20:36:35.168455-07:00')]\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = ['2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:48.446216-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:22:52.964088-07:00'), ('2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:20.898491-07:00'), ('2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:06.468568-07:00'), ('2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:20.000168-07:00'), ('2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:11:37.049495-07:00'), ('2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:19:59.901535-07:00'), ('2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:11:03.621074-07:00'), ('2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:34.247812-07:00'), ('2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:42.000133-07:00'), ('2019-07-26T10:26:48.446216-07:00', '2019-07-26T10:29:48.992491-07:00'), ('2019-07-26T14:17:32.931680-07:00', '2019-07-26T14:30:09.172231-07:00'), ('2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:03:28.030293-07:00'), ('2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:35:00.449989-07:00'), ('2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:41.551743-07:00'), ('2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:12:55.142156-07:00'), ('2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:46.679733-07:00'), ('2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:01.080074-07:00'), ('2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:50.406806-07:00'), ('2019-07-26T19:40:56.849526-07:00', '2019-07-26T19:55:37.720556-07:00'), ('2019-07-26T20:31:55.991693-07:00', '2019-07-26T20:36:35.168455-07:00')]\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = ['2019-07-26T14:17:32.931680-07:00']\n", - "Before filtering, trips = [('2019-07-26T08:22:52.964088-07:00', '2019-07-26T08:22:52.964088-07:00'), ('2019-07-26T08:27:29.341601-07:00', '2019-07-26T08:39:20.898491-07:00'), ('2019-07-26T08:39:57.669632-07:00', '2019-07-26T08:46:06.468568-07:00'), ('2019-07-26T08:46:28.099438-07:00', '2019-07-26T08:51:20.000168-07:00'), ('2019-07-26T08:51:26.413524-07:00', '2019-07-26T09:11:37.049495-07:00'), ('2019-07-26T09:12:29.399212-07:00', '2019-07-26T09:19:59.901535-07:00'), ('2019-07-26T09:29:08.803343-07:00', '2019-07-26T10:11:03.621074-07:00'), ('2019-07-26T10:14:05.005783-07:00', '2019-07-26T10:20:34.247812-07:00'), ('2019-07-26T10:20:40.658027-07:00', '2019-07-26T10:26:42.000133-07:00'), ('2019-07-26T10:26:48.446216-07:00', '2019-07-26T10:29:48.992491-07:00'), ('2019-07-26T14:17:32.931680-07:00', '2019-07-26T14:30:09.172231-07:00'), ('2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:03:28.030293-07:00'), ('2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:35:00.449989-07:00'), ('2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:41.551743-07:00'), ('2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:12:55.142156-07:00'), ('2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:46.679733-07:00'), ('2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:01.080074-07:00'), ('2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:50.406806-07:00'), ('2019-07-26T19:40:56.849526-07:00', '2019-07-26T19:55:37.720556-07:00'), ('2019-07-26T20:31:55.991693-07:00', '2019-07-26T20:36:35.168455-07:00')]\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = ['2019-07-26T16:16:33.707536-07:00', '2019-07-26T17:18:38.656346-07:00', '2019-07-26T17:38:00.449989-07:00', '2019-07-26T17:51:48.001065-07:00', '2019-07-26T18:13:01.588731-07:00', '2019-07-26T18:28:53.106799-07:00', '2019-07-26T18:34:15.111178-07:00', '2019-07-26T19:40:56.849526-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_0 HAHFDC v/s HAMFDC HAMFDC_0 3\n", - "Before filtering, trips = [('2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:28.998484-07:00'), ('2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:23:47.994102-07:00'), ('2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:04.993347-07:00'), ('2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:52.995613-07:00'), ('2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:47.995985-07:00'), ('2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:06:56.995628-07:00'), ('2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:07:16.994833-07:00'), ('2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:08:59.990727-07:00'), ('2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:13:38.214558-07:00'), ('2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:50.993486-07:00'), ('2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:35:06.992333-07:00'), ('2019-09-10T10:37:10.987396-07:00', '2019-09-10T10:37:10.987396-07:00'), ('2019-09-10T13:39:58.465782-07:00', '2019-09-10T14:00:13.442113-07:00'), ('2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:23.987310-07:00'), ('2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:22:17.996807-07:00'), ('2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:26.993856-07:00'), ('2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:39:30.989524-07:00'), ('2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:43:06.998211-07:00'), ('2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:02.982773-07:00'), ('2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:39.988618-07:00'), ('2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:01:07.997481-07:00'), ('2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:04:22.989608-07:00'), ('2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:03:57.987675-07:00'), ('2019-09-10T19:05:21.984291-07:00', '2019-09-10T19:22:08.993003-07:00')]\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = ['2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:37:10.987396-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:28.998484-07:00'), ('2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:23:47.994102-07:00'), ('2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:04.993347-07:00'), ('2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:52.995613-07:00'), ('2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:47.995985-07:00'), ('2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:06:56.995628-07:00'), ('2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:07:16.994833-07:00'), ('2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:08:59.990727-07:00'), ('2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:13:38.214558-07:00'), ('2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:50.993486-07:00'), ('2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:35:06.992333-07:00'), ('2019-09-10T10:37:10.987396-07:00', '2019-09-10T10:37:10.987396-07:00'), ('2019-09-10T13:39:58.465782-07:00', '2019-09-10T14:00:13.442113-07:00'), ('2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:23.987310-07:00'), ('2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:22:17.996807-07:00'), ('2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:26.993856-07:00'), ('2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:39:30.989524-07:00'), ('2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:43:06.998211-07:00'), ('2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:02.982773-07:00'), ('2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:39.988618-07:00'), ('2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:01:07.997481-07:00'), ('2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:04:22.989608-07:00'), ('2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:03:57.987675-07:00'), ('2019-09-10T19:05:21.984291-07:00', '2019-09-10T19:22:08.993003-07:00')]\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = ['2019-09-10T13:39:58.465782-07:00']\n", - "Before filtering, trips = [('2019-09-10T08:21:37.696816-07:00', '2019-09-10T08:22:28.998484-07:00'), ('2019-09-10T08:22:48.996950-07:00', '2019-09-10T08:23:47.994102-07:00'), ('2019-09-10T08:24:04.993347-07:00', '2019-09-10T08:24:04.993347-07:00'), ('2019-09-10T08:24:26.992375-07:00', '2019-09-10T08:30:52.995613-07:00'), ('2019-09-10T08:30:55.995482-07:00', '2019-09-10T09:06:47.995985-07:00'), ('2019-09-10T09:06:53.995748-07:00', '2019-09-10T09:06:56.995628-07:00'), ('2019-09-10T09:07:01.995430-07:00', '2019-09-10T09:07:16.994833-07:00'), ('2019-09-10T09:08:37.991605-07:00', '2019-09-10T09:08:59.990727-07:00'), ('2019-09-10T09:19:37.386754-07:00', '2019-09-10T10:13:38.214558-07:00'), ('2019-09-10T10:14:24.669218-07:00', '2019-09-10T10:30:50.993486-07:00'), ('2019-09-10T10:30:55.993290-07:00', '2019-09-10T10:35:06.992333-07:00'), ('2019-09-10T10:37:10.987396-07:00', '2019-09-10T10:37:10.987396-07:00'), ('2019-09-10T13:39:58.465782-07:00', '2019-09-10T14:00:13.442113-07:00'), ('2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:23.987310-07:00'), ('2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:22:17.996807-07:00'), ('2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:26.993856-07:00'), ('2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:39:30.989524-07:00'), ('2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:43:06.998211-07:00'), ('2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:02.982773-07:00'), ('2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:39.988618-07:00'), ('2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:01:07.997481-07:00'), ('2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:04:22.989608-07:00'), ('2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:03:57.987675-07:00'), ('2019-09-10T19:05:21.984291-07:00', '2019-09-10T19:22:08.993003-07:00')]\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = ['2019-09-10T16:11:16.253061-07:00', '2019-09-10T16:18:30.986993-07:00', '2019-09-10T16:23:16.994287-07:00', '2019-09-10T16:23:33.993555-07:00', '2019-09-10T16:42:09.982697-07:00', '2019-09-10T16:55:06.280174-07:00', '2019-09-10T17:31:38.998297-07:00', '2019-09-10T17:51:42.988493-07:00', '2019-09-10T18:03:31.991683-07:00', '2019-09-10T18:17:26.881283-07:00', '2019-09-10T19:05:21.984291-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_1 HAHFDC v/s HAMFDC HAMFDC_1 3\n", - "Before filtering, trips = [('2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:40.201473-07:00'), ('2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:04.993430-07:00'), ('2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:13.996239-07:00'), ('2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:07:02.997920-07:00'), ('2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:13:49.008844-07:00'), ('2019-09-11T10:14:31.986705-07:00', '2019-09-11T10:18:27.993159-07:00'), ('2019-09-11T13:38:48.905707-07:00', '2019-09-11T14:04:38.027181-07:00'), ('2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:25.987323-07:00'), ('2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:04.982910-07:00'), ('2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:16.982375-07:00'), ('2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:52.980768-07:00'), ('2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:02.980323-07:00'), ('2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:46.998078-07:00'), ('2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:51.997941-07:00'), ('2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:49:35.992012-07:00'), ('2019-09-11T16:52:35.984353-07:00', '2019-09-11T16:53:31.995191-07:00'), ('2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:55:08.998033-07:00'), ('2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:07.993171-07:00'), ('2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:15:22.989788-07:00'), ('2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:24.995586-07:00'), ('2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:20:12.991176-07:00'), ('2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:40.995901-07:00'), ('2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:50.992955-07:00'), ('2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:05.993969-07:00'), ('2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:15.993548-07:00'), ('2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:40:56.986748-07:00'), ('2019-09-11T19:41:45.984677-07:00', '2019-09-11T19:51:52.991758-07:00')]\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = ['2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:14:31.986705-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:40.201473-07:00'), ('2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:04.993430-07:00'), ('2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:13.996239-07:00'), ('2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:07:02.997920-07:00'), ('2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:13:49.008844-07:00'), ('2019-09-11T10:14:31.986705-07:00', '2019-09-11T10:18:27.993159-07:00'), ('2019-09-11T13:38:48.905707-07:00', '2019-09-11T14:04:38.027181-07:00'), ('2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:25.987323-07:00'), ('2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:04.982910-07:00'), ('2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:16.982375-07:00'), ('2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:52.980768-07:00'), ('2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:02.980323-07:00'), ('2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:46.998078-07:00'), ('2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:51.997941-07:00'), ('2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:49:35.992012-07:00'), ('2019-09-11T16:52:35.984353-07:00', '2019-09-11T16:53:31.995191-07:00'), ('2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:55:08.998033-07:00'), ('2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:07.993171-07:00'), ('2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:15:22.989788-07:00'), ('2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:24.995586-07:00'), ('2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:20:12.991176-07:00'), ('2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:40.995901-07:00'), ('2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:50.992955-07:00'), ('2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:05.993969-07:00'), ('2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:15.993548-07:00'), ('2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:40:56.986748-07:00'), ('2019-09-11T19:41:45.984677-07:00', '2019-09-11T19:51:52.991758-07:00')]\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = ['2019-09-11T13:38:48.905707-07:00']\n", - "Before filtering, trips = [('2019-09-11T08:27:35.094448-07:00', '2019-09-11T08:30:40.201473-07:00'), ('2019-09-11T08:30:46.808805-07:00', '2019-09-11T09:06:04.993430-07:00'), ('2019-09-11T09:06:05.993844-07:00', '2019-09-11T09:06:13.996239-07:00'), ('2019-09-11T09:06:15.996624-07:00', '2019-09-11T09:07:02.997920-07:00'), ('2019-09-11T09:19:19.210680-07:00', '2019-09-11T10:13:49.008844-07:00'), ('2019-09-11T10:14:31.986705-07:00', '2019-09-11T10:18:27.993159-07:00'), ('2019-09-11T13:38:48.905707-07:00', '2019-09-11T14:04:38.027181-07:00'), ('2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:25.987323-07:00'), ('2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:04.982910-07:00'), ('2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:16.982375-07:00'), ('2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:52.980768-07:00'), ('2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:02.980323-07:00'), ('2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:46.998078-07:00'), ('2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:51.997941-07:00'), ('2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:49:35.992012-07:00'), ('2019-09-11T16:52:35.984353-07:00', '2019-09-11T16:53:31.995191-07:00'), ('2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:55:08.998033-07:00'), ('2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:07.993171-07:00'), ('2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:15:22.989788-07:00'), ('2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:24.995586-07:00'), ('2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:20:12.991176-07:00'), ('2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:40.995901-07:00'), ('2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:50.992955-07:00'), ('2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:05.993969-07:00'), ('2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:15.993548-07:00'), ('2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:40:56.986748-07:00'), ('2019-09-11T19:41:45.984677-07:00', '2019-09-11T19:51:52.991758-07:00')]\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = ['2019-09-11T16:30:07.269784-07:00', '2019-09-11T16:35:29.987146-07:00', '2019-09-11T16:37:08.982732-07:00', '2019-09-11T16:37:20.982196-07:00', '2019-09-11T16:37:57.980545-07:00', '2019-09-11T16:38:07.980101-07:00', '2019-09-11T16:39:51.997941-07:00', '2019-09-11T16:39:56.997782-07:00', '2019-09-11T16:52:35.984353-07:00', '2019-09-11T17:18:37.000814-07:00', '2019-09-11T17:57:15.994592-07:00', '2019-09-11T18:12:11.993006-07:00', '2019-09-11T18:16:07.987924-07:00', '2019-09-11T18:16:32.997312-07:00', '2019-09-11T18:21:02.989165-07:00', '2019-09-11T18:32:46.995649-07:00', '2019-09-11T18:33:54.992787-07:00', '2019-09-11T19:38:06.993926-07:00', '2019-09-11T19:38:16.993505-07:00', '2019-09-11T19:41:45.984677-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " HAHFDC v/s HAMFDC:HAMFDC_2 HAHFDC v/s HAMFDC HAMFDC_2 3\n", - "Before filtering, trips = [('2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:22:36.999940-07:00'), ('2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:06.996105-07:00'), ('2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:19:33.994082-07:00'), ('2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:11:10.109991-07:00'), ('2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:18:46.997222-07:00'), ('2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:37:45.999127-07:00'), ('2019-09-17T10:39:30.994917-07:00', '2019-09-17T10:39:30.994917-07:00'), ('2019-09-17T13:49:09.097899-07:00', '2019-09-17T13:59:56.013239-07:00'), ('2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:35:45.991191-07:00'), ('2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:38:20.984250-07:00'), ('2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:25:30.996922-07:00'), ('2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:45:54.989123-07:00'), ('2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:53:53.992627-07:00'), ('2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:24.998304-07:00'), ('2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:54:51.998071-07:00'), ('2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:07.985333-07:00'), ('2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:52.991919-07:00'), ('2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:12.991138-07:00'), ('2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:57:24.986239-07:00'), ('2019-09-17T18:58:27.983784-07:00', '2019-09-17T19:14:24.990711-07:00')]\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = ['2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:39:30.994917-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:22:36.999940-07:00'), ('2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:06.996105-07:00'), ('2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:19:33.994082-07:00'), ('2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:11:10.109991-07:00'), ('2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:18:46.997222-07:00'), ('2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:37:45.999127-07:00'), ('2019-09-17T10:39:30.994917-07:00', '2019-09-17T10:39:30.994917-07:00'), ('2019-09-17T13:49:09.097899-07:00', '2019-09-17T13:59:56.013239-07:00'), ('2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:35:45.991191-07:00'), ('2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:38:20.984250-07:00'), ('2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:25:30.996922-07:00'), ('2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:45:54.989123-07:00'), ('2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:53:53.992627-07:00'), ('2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:24.998304-07:00'), ('2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:54:51.998071-07:00'), ('2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:07.985333-07:00'), ('2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:52.991919-07:00'), ('2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:12.991138-07:00'), ('2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:57:24.986239-07:00'), ('2019-09-17T18:58:27.983784-07:00', '2019-09-17T19:14:24.990711-07:00')]\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = ['2019-09-17T13:49:09.097899-07:00']\n", - "Before filtering, trips = [('2019-09-17T08:13:51.789537-07:00', '2019-09-17T08:22:36.999940-07:00'), ('2019-09-17T08:37:11.363045-07:00', '2019-09-17T09:14:06.996105-07:00'), ('2019-09-17T09:14:39.994755-07:00', '2019-09-17T09:19:33.994082-07:00'), ('2019-09-17T09:20:26.998075-07:00', '2019-09-17T10:11:10.109991-07:00'), ('2019-09-17T10:14:47.395200-07:00', '2019-09-17T10:18:46.997222-07:00'), ('2019-09-17T10:35:34.181786-07:00', '2019-09-17T10:37:45.999127-07:00'), ('2019-09-17T10:39:30.994917-07:00', '2019-09-17T10:39:30.994917-07:00'), ('2019-09-17T13:49:09.097899-07:00', '2019-09-17T13:59:56.013239-07:00'), ('2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:35:45.991191-07:00'), ('2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:38:20.984250-07:00'), ('2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:25:30.996922-07:00'), ('2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:45:54.989123-07:00'), ('2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:53:53.992627-07:00'), ('2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:24.998304-07:00'), ('2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:54:51.998071-07:00'), ('2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:07.985333-07:00'), ('2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:52.991919-07:00'), ('2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:12.991138-07:00'), ('2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:57:24.986239-07:00'), ('2019-09-17T18:58:27.983784-07:00', '2019-09-17T19:14:24.990711-07:00')]\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = ['2019-09-17T16:32:23.005124-07:00', '2019-09-17T16:37:47.985729-07:00', '2019-09-17T16:55:09.005501-07:00', '2019-09-17T17:26:48.993547-07:00', '2019-09-17T17:46:12.988362-07:00', '2019-09-17T17:54:24.998304-07:00', '2019-09-17T17:54:51.998071-07:00', '2019-09-17T17:55:23.996925-07:00', '2019-09-17T18:00:11.985170-07:00', '2019-09-17T18:13:54.991843-07:00', '2019-09-17T18:14:13.991098-07:00', '2019-09-17T18:58:27.983784-07:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_0 MAMFDC v/s MAHFDC MAHFDC_0 3\n", - "Before filtering, trips = [('2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:47.004690-08:00'), ('2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:03.645276-08:00'), ('2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:31.357361-08:00'), ('2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:20.292947-08:00'), ('2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:19:09.341645-08:00'), ('2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:10:32.077868-08:00'), ('2019-11-19T10:13:36.219994-08:00', '2019-11-19T10:50:08.700526-08:00'), ('2019-11-19T13:30:32.297753-08:00', '2019-11-19T13:57:46.989274-08:00'), ('2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:23.954445-08:00'), ('2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:26:54.042529-08:00'), ('2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:39.573812-08:00'), ('2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:45.986560-08:00'), ('2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:13.984202-08:00'), ('2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:08:55.631840-08:00'), ('2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:23:42.335100-08:00'), ('2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:26:03.429241-08:00'), ('2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:34.312814-08:00'), ('2019-11-19T18:57:40.735147-08:00', '2019-11-19T19:11:10.511688-08:00')]\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = ['2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:13:36.219994-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:47.004690-08:00'), ('2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:03.645276-08:00'), ('2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:31.357361-08:00'), ('2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:20.292947-08:00'), ('2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:19:09.341645-08:00'), ('2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:10:32.077868-08:00'), ('2019-11-19T10:13:36.219994-08:00', '2019-11-19T10:50:08.700526-08:00'), ('2019-11-19T13:30:32.297753-08:00', '2019-11-19T13:57:46.989274-08:00'), ('2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:23.954445-08:00'), ('2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:26:54.042529-08:00'), ('2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:39.573812-08:00'), ('2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:45.986560-08:00'), ('2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:13.984202-08:00'), ('2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:08:55.631840-08:00'), ('2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:23:42.335100-08:00'), ('2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:26:03.429241-08:00'), ('2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:34.312814-08:00'), ('2019-11-19T18:57:40.735147-08:00', '2019-11-19T19:11:10.511688-08:00')]\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = ['2019-11-19T13:30:32.297753-08:00']\n", - "Before filtering, trips = [('2019-11-19T08:15:58.881633-08:00', '2019-11-19T08:22:47.004690-08:00'), ('2019-11-19T08:22:53.436487-08:00', '2019-11-19T08:29:03.645276-08:00'), ('2019-11-19T08:29:47.944148-08:00', '2019-11-19T08:31:31.357361-08:00'), ('2019-11-19T08:31:38.984947-08:00', '2019-11-19T09:10:20.292947-08:00'), ('2019-11-19T09:10:57.519670-08:00', '2019-11-19T09:19:09.341645-08:00'), ('2019-11-19T09:28:51.173254-08:00', '2019-11-19T10:10:32.077868-08:00'), ('2019-11-19T10:13:36.219994-08:00', '2019-11-19T10:50:08.700526-08:00'), ('2019-11-19T13:30:32.297753-08:00', '2019-11-19T13:57:46.989274-08:00'), ('2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:23.954445-08:00'), ('2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:26:54.042529-08:00'), ('2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:39.573812-08:00'), ('2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:45.986560-08:00'), ('2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:13.984202-08:00'), ('2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:08:55.631840-08:00'), ('2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:23:42.335100-08:00'), ('2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:26:03.429241-08:00'), ('2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:34.312814-08:00'), ('2019-11-19T18:57:40.735147-08:00', '2019-11-19T19:11:10.511688-08:00')]\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = ['2019-11-19T16:16:05.403615-08:00', '2019-11-19T16:57:29.397525-08:00', '2019-11-19T17:27:00.453669-08:00', '2019-11-19T17:56:45.986560-08:00', '2019-11-19T17:56:52.396005-08:00', '2019-11-19T17:57:35.575232-08:00', '2019-11-19T18:09:18.158197-08:00', '2019-11-19T18:24:30.622756-08:00', '2019-11-19T18:27:15.638714-08:00', '2019-11-19T18:57:40.735147-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_1 MAMFDC v/s MAHFDC MAHFDC_1 3\n", - "Before filtering, trips = [('2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:30:28.838335-08:00'), ('2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:23.913983-08:00'), ('2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:19:12.547719-08:00'), ('2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:13:41.632416-08:00'), ('2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:39.400633-08:00'), ('2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:06.085103-08:00'), ('2019-11-20T10:30:12.525417-08:00', '2019-11-20T10:32:39.311538-08:00'), ('2019-11-20T13:46:56.194173-08:00', '2019-11-20T14:02:03.579157-08:00'), ('2019-11-20T16:20:36.265804-08:00', '2019-11-20T16:59:54.562054-08:00'), ('2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:08.878808-08:00'), ('2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:17.941704-08:00'), ('2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:01.511979-08:00'), ('2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:29.877977-08:00'), ('2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:14.193466-08:00'), ('2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:26.985673-08:00'), ('2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:33.402700-08:00'), ('2019-11-20T19:03:39.825179-08:00', '2019-11-20T19:22:12.873443-08:00')]\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = ['2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:12.525417-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:30:28.838335-08:00'), ('2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:23.913983-08:00'), ('2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:19:12.547719-08:00'), ('2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:13:41.632416-08:00'), ('2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:39.400633-08:00'), ('2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:06.085103-08:00'), ('2019-11-20T10:30:12.525417-08:00', '2019-11-20T10:32:39.311538-08:00'), ('2019-11-20T13:46:56.194173-08:00', '2019-11-20T14:02:03.579157-08:00'), ('2019-11-20T16:20:36.265804-08:00', '2019-11-20T16:59:54.562054-08:00'), ('2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:08.878808-08:00'), ('2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:17.941704-08:00'), ('2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:01.511979-08:00'), ('2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:29.877977-08:00'), ('2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:14.193466-08:00'), ('2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:26.985673-08:00'), ('2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:33.402700-08:00'), ('2019-11-20T19:03:39.825179-08:00', '2019-11-20T19:22:12.873443-08:00')]\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = ['2019-11-20T13:46:56.194173-08:00']\n", - "Before filtering, trips = [('2019-11-20T08:28:08.214664-08:00', '2019-11-20T08:30:28.838335-08:00'), ('2019-11-20T08:31:03.530633-08:00', '2019-11-20T09:07:23.913983-08:00'), ('2019-11-20T09:07:45.515214-08:00', '2019-11-20T09:19:12.547719-08:00'), ('2019-11-20T09:20:46.752963-08:00', '2019-11-20T10:13:41.632416-08:00'), ('2019-11-20T10:14:48.596566-08:00', '2019-11-20T10:26:39.400633-08:00'), ('2019-11-20T10:26:45.845530-08:00', '2019-11-20T10:30:06.085103-08:00'), ('2019-11-20T10:30:12.525417-08:00', '2019-11-20T10:32:39.311538-08:00'), ('2019-11-20T13:46:56.194173-08:00', '2019-11-20T14:02:03.579157-08:00'), ('2019-11-20T16:20:36.265804-08:00', '2019-11-20T16:59:54.562054-08:00'), ('2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:08.878808-08:00'), ('2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:17.941704-08:00'), ('2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:01.511979-08:00'), ('2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:29.877977-08:00'), ('2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:14.193466-08:00'), ('2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:26.985673-08:00'), ('2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:33.402700-08:00'), ('2019-11-20T19:03:39.825179-08:00', '2019-11-20T19:22:12.873443-08:00')]\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = ['2019-11-20T16:20:36.265804-08:00', '2019-11-20T17:19:27.327588-08:00', '2019-11-20T17:48:15.301970-08:00', '2019-11-20T18:05:24.357088-08:00', '2019-11-20T18:12:14.101324-08:00', '2019-11-20T18:17:44.111136-08:00', '2019-11-20T19:03:20.604009-08:00', '2019-11-20T19:03:33.402700-08:00', '2019-11-20T19:03:39.825179-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s MAHFDC:MAHFDC_2 MAMFDC v/s MAHFDC MAHFDC_2 3\n", - "Before filtering, trips = [('2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:35.995366-08:00'), ('2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:09:59.149219-08:00'), ('2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:18:53.639832-08:00'), ('2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:12:10.203423-08:00'), ('2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:03.418188-08:00'), ('2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:29.410172-08:00'), ('2019-12-03T10:32:35.895938-08:00', '2019-12-03T10:35:25.508647-08:00'), ('2019-12-03T14:15:19.779091-08:00', '2019-12-03T14:28:27.915522-08:00'), ('2019-12-03T16:18:29.018600-08:00', '2019-12-03T16:45:38.443121-08:00'), ('2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:07.610096-08:00'), ('2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:19:26.156419-08:00'), ('2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:32.592792-08:00'), ('2019-12-03T19:16:39.033753-08:00', '2019-12-03T19:33:59.407157-08:00')]\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = ['2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:35.895938-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:35.995366-08:00'), ('2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:09:59.149219-08:00'), ('2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:18:53.639832-08:00'), ('2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:12:10.203423-08:00'), ('2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:03.418188-08:00'), ('2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:29.410172-08:00'), ('2019-12-03T10:32:35.895938-08:00', '2019-12-03T10:35:25.508647-08:00'), ('2019-12-03T14:15:19.779091-08:00', '2019-12-03T14:28:27.915522-08:00'), ('2019-12-03T16:18:29.018600-08:00', '2019-12-03T16:45:38.443121-08:00'), ('2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:07.610096-08:00'), ('2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:19:26.156419-08:00'), ('2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:32.592792-08:00'), ('2019-12-03T19:16:39.033753-08:00', '2019-12-03T19:33:59.407157-08:00')]\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = ['2019-12-03T14:15:19.779091-08:00']\n", - "Before filtering, trips = [('2019-12-03T08:19:06.616476-08:00', '2019-12-03T08:35:35.995366-08:00'), ('2019-12-03T08:35:43.731022-08:00', '2019-12-03T09:09:59.149219-08:00'), ('2019-12-03T09:12:04.560350-08:00', '2019-12-03T09:18:53.639832-08:00'), ('2019-12-03T09:25:08.570770-08:00', '2019-12-03T10:12:10.203423-08:00'), ('2019-12-03T10:15:11.470732-08:00', '2019-12-03T10:32:03.418188-08:00'), ('2019-12-03T10:32:09.901798-08:00', '2019-12-03T10:32:29.410172-08:00'), ('2019-12-03T10:32:35.895938-08:00', '2019-12-03T10:35:25.508647-08:00'), ('2019-12-03T14:15:19.779091-08:00', '2019-12-03T14:28:27.915522-08:00'), ('2019-12-03T16:18:29.018600-08:00', '2019-12-03T16:45:38.443121-08:00'), ('2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:07.610096-08:00'), ('2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:19:26.156419-08:00'), ('2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:32.592792-08:00'), ('2019-12-03T19:16:39.033753-08:00', '2019-12-03T19:33:59.407157-08:00')]\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = ['2019-12-03T16:18:29.018600-08:00', '2019-12-03T17:18:39.599550-08:00', '2019-12-03T17:54:15.528126-08:00', '2019-12-03T18:26:43.493859-08:00', '2019-12-03T19:16:39.033753-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_0 MAMFDC v/s HAMFDC HAMFDC_0 3\n", - "Before filtering, trips = [('2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:02.997904-08:00'), ('2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:52.990794-08:00'), ('2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:06:52.987840-08:00'), ('2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:09:01.982248-08:00'), ('2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:13:31.918377-08:00'), ('2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:42.984735-08:00'), ('2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:31:16.998112-08:00'), ('2019-12-09T10:32:00.997463-08:00', '2019-12-09T10:33:12.994380-08:00'), ('2019-12-09T14:00:35.477504-08:00', '2019-12-09T14:14:10.014684-08:00'), ('2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:36.991534-08:00'), ('2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:06.990175-08:00'), ('2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:23:44.985734-08:00'), ('2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:51.982696-08:00'), ('2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:23.978527-08:00'), ('2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:28.978298-08:00'), ('2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:00.996115-08:00'), ('2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:06.995858-08:00'), ('2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:28.989666-08:00'), ('2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:35.989359-08:00'), ('2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:48.975690-08:00'), ('2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:53.975472-08:00'), ('2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:42:38.997047-08:00'), ('2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:45:29.992397-08:00'), ('2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:24:12.984065-08:00'), ('2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:54:39.998231-08:00'), ('2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:06:48.986297-08:00'), ('2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:13:35.996933-08:00'), ('2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:03:36.987332-08:00'), ('2019-12-09T19:04:49.984501-08:00', '2019-12-09T19:22:07.998542-08:00')]\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = ['2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:32:00.997463-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:02.997904-08:00'), ('2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:52.990794-08:00'), ('2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:06:52.987840-08:00'), ('2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:09:01.982248-08:00'), ('2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:13:31.918377-08:00'), ('2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:42.984735-08:00'), ('2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:31:16.998112-08:00'), ('2019-12-09T10:32:00.997463-08:00', '2019-12-09T10:33:12.994380-08:00'), ('2019-12-09T14:00:35.477504-08:00', '2019-12-09T14:14:10.014684-08:00'), ('2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:36.991534-08:00'), ('2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:06.990175-08:00'), ('2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:23:44.985734-08:00'), ('2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:51.982696-08:00'), ('2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:23.978527-08:00'), ('2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:28.978298-08:00'), ('2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:00.996115-08:00'), ('2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:06.995858-08:00'), ('2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:28.989666-08:00'), ('2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:35.989359-08:00'), ('2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:48.975690-08:00'), ('2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:53.975472-08:00'), ('2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:42:38.997047-08:00'), ('2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:45:29.992397-08:00'), ('2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:24:12.984065-08:00'), ('2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:54:39.998231-08:00'), ('2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:06:48.986297-08:00'), ('2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:13:35.996933-08:00'), ('2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:03:36.987332-08:00'), ('2019-12-09T19:04:49.984501-08:00', '2019-12-09T19:22:07.998542-08:00')]\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = ['2019-12-09T14:00:35.477504-08:00']\n", - "Before filtering, trips = [('2019-12-09T08:18:49.412364-08:00', '2019-12-09T08:27:02.997904-08:00'), ('2019-12-09T08:27:37.996577-08:00', '2019-12-09T08:29:52.990794-08:00'), ('2019-12-09T08:29:54.990708-08:00', '2019-12-09T09:06:52.987840-08:00'), ('2019-12-09T09:07:15.986844-08:00', '2019-12-09T09:09:01.982248-08:00'), ('2019-12-09T09:18:57.786788-08:00', '2019-12-09T10:13:31.918377-08:00'), ('2019-12-09T10:14:10.003256-08:00', '2019-12-09T10:29:42.984735-08:00'), ('2019-12-09T10:29:46.984562-08:00', '2019-12-09T10:31:16.998112-08:00'), ('2019-12-09T10:32:00.997463-08:00', '2019-12-09T10:33:12.994380-08:00'), ('2019-12-09T14:00:35.477504-08:00', '2019-12-09T14:14:10.014684-08:00'), ('2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:36.991534-08:00'), ('2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:06.990175-08:00'), ('2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:23:44.985734-08:00'), ('2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:51.982696-08:00'), ('2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:23.978527-08:00'), ('2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:28.978298-08:00'), ('2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:00.996115-08:00'), ('2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:06.995858-08:00'), ('2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:28.989666-08:00'), ('2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:35.989359-08:00'), ('2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:48.975690-08:00'), ('2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:53.975472-08:00'), ('2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:42:38.997047-08:00'), ('2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:45:29.992397-08:00'), ('2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:24:12.984065-08:00'), ('2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:54:39.998231-08:00'), ('2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:06:48.986297-08:00'), ('2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:13:35.996933-08:00'), ('2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:03:36.987332-08:00'), ('2019-12-09T19:04:49.984501-08:00', '2019-12-09T19:22:07.998542-08:00')]\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = ['2019-12-09T16:17:45.322825-08:00', '2019-12-09T16:21:58.990539-08:00', '2019-12-09T16:22:12.989905-08:00', '2019-12-09T16:24:51.982696-08:00', '2019-12-09T16:24:58.982381-08:00', '2019-12-09T16:26:28.978298-08:00', '2019-12-09T16:26:33.978176-08:00', '2019-12-09T16:31:06.995858-08:00', '2019-12-09T16:31:11.995642-08:00', '2019-12-09T16:33:35.989359-08:00', '2019-12-09T16:33:40.989141-08:00', '2019-12-09T16:38:53.975472-08:00', '2019-12-09T16:38:58.975252-08:00', '2019-12-09T16:44:36.994701-08:00', '2019-12-09T16:54:23.048679-08:00', '2019-12-09T17:25:02.981913-08:00', '2019-12-09T17:56:40.993220-08:00', '2019-12-09T18:12:10.210029-08:00', '2019-12-09T18:24:03.002822-08:00', '2019-12-09T19:04:49.984501-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_1 MAMFDC v/s HAMFDC HAMFDC_1 3\n", - "Before filtering, trips = [('2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:48.997300-08:00'), ('2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:07:32.988685-08:00'), ('2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:09:17.984138-08:00'), ('2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:31:42.463791-08:00'), ('2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:16.993563-08:00'), ('2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:51:34.987578-08:00'), ('2019-12-11T10:53:29.982589-08:00', '2019-12-11T10:53:29.982589-08:00'), ('2019-12-11T14:09:27.128966-08:00', '2019-12-11T14:20:17.998176-08:00'), ('2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:23.999911-08:00'), ('2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:36.995171-08:00'), ('2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:42.994896-08:00'), ('2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:34:35.981686-08:00'), ('2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:54.998462-08:00'), ('2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:09.993086-08:00'), ('2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:17.992738-08:00'), ('2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:11.982599-08:00'), ('2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:22.992821-08:00'), ('2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:42:50.998011-08:00'), ('2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:44:23.995125-08:00'), ('2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:47:13.987558-08:00'), ('2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:08.997055-08:00'), ('2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:56.992243-08:00'), ('2019-12-11T17:49:59.992114-08:00', '2019-12-11T17:57:36.986911-08:00'), ('2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:04:35.999708-08:00'), ('2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:02:04.988549-08:00'), ('2019-12-11T19:03:03.997993-08:00', '2019-12-11T19:19:55.998326-08:00')]\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = ['2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:53:29.982589-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:48.997300-08:00'), ('2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:07:32.988685-08:00'), ('2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:09:17.984138-08:00'), ('2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:31:42.463791-08:00'), ('2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:16.993563-08:00'), ('2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:51:34.987578-08:00'), ('2019-12-11T10:53:29.982589-08:00', '2019-12-11T10:53:29.982589-08:00'), ('2019-12-11T14:09:27.128966-08:00', '2019-12-11T14:20:17.998176-08:00'), ('2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:23.999911-08:00'), ('2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:36.995171-08:00'), ('2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:42.994896-08:00'), ('2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:34:35.981686-08:00'), ('2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:54.998462-08:00'), ('2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:09.993086-08:00'), ('2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:17.992738-08:00'), ('2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:11.982599-08:00'), ('2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:22.992821-08:00'), ('2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:42:50.998011-08:00'), ('2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:44:23.995125-08:00'), ('2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:47:13.987558-08:00'), ('2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:08.997055-08:00'), ('2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:56.992243-08:00'), ('2019-12-11T17:49:59.992114-08:00', '2019-12-11T17:57:36.986911-08:00'), ('2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:04:35.999708-08:00'), ('2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:02:04.988549-08:00'), ('2019-12-11T19:03:03.997993-08:00', '2019-12-11T19:19:55.998326-08:00')]\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = ['2019-12-11T14:09:27.128966-08:00']\n", - "Before filtering, trips = [('2019-12-11T08:25:55.075238-08:00', '2019-12-11T08:29:48.997300-08:00'), ('2019-12-11T08:29:53.997095-08:00', '2019-12-11T09:07:32.988685-08:00'), ('2019-12-11T09:08:07.987169-08:00', '2019-12-11T09:09:17.984138-08:00'), ('2019-12-11T09:20:34.992229-08:00', '2019-12-11T10:31:42.463791-08:00'), ('2019-12-11T10:32:43.690178-08:00', '2019-12-11T10:49:16.993563-08:00'), ('2019-12-11T10:49:19.993433-08:00', '2019-12-11T10:51:34.987578-08:00'), ('2019-12-11T10:53:29.982589-08:00', '2019-12-11T10:53:29.982589-08:00'), ('2019-12-11T14:09:27.128966-08:00', '2019-12-11T14:20:17.998176-08:00'), ('2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:23.999911-08:00'), ('2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:36.995171-08:00'), ('2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:42.994896-08:00'), ('2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:34:35.981686-08:00'), ('2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:54.998462-08:00'), ('2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:09.993086-08:00'), ('2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:17.992738-08:00'), ('2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:11.982599-08:00'), ('2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:22.992821-08:00'), ('2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:42:50.998011-08:00'), ('2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:44:23.995125-08:00'), ('2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:47:13.987558-08:00'), ('2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:08.997055-08:00'), ('2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:56.992243-08:00'), ('2019-12-11T17:49:59.992114-08:00', '2019-12-11T17:57:36.986911-08:00'), ('2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:04:35.999708-08:00'), ('2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:02:04.988549-08:00'), ('2019-12-11T19:03:03.997993-08:00', '2019-12-11T19:19:55.998326-08:00')]\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = ['2019-12-11T16:27:46.282007-08:00', '2019-12-11T16:28:29.999166-08:00', '2019-12-11T16:29:42.994896-08:00', '2019-12-11T16:29:46.994714-08:00', '2019-12-11T16:35:44.998467-08:00', '2019-12-11T16:35:59.998376-08:00', '2019-12-11T16:38:17.992738-08:00', '2019-12-11T16:38:22.992518-08:00', '2019-12-11T16:42:16.982383-08:00', '2019-12-11T16:42:27.994840-08:00', '2019-12-11T16:44:23.995125-08:00', '2019-12-11T16:46:49.988626-08:00', '2019-12-11T16:53:41.431802-08:00', '2019-12-11T17:32:53.995111-08:00', '2019-12-11T17:49:59.992114-08:00', '2019-12-11T18:04:31.579515-08:00', '2019-12-11T18:26:13.001532-08:00', '2019-12-11T19:03:03.997993-08:00']\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " MAMFDC v/s HAMFDC:HAMFDC_2 MAMFDC v/s HAMFDC HAMFDC_2 3\n", - "Before filtering, trips = [('2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:36.990703-08:00'), ('2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:02.989606-08:00'), ('2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:20.993964-08:00'), ('2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:09:44.993512-08:00'), ('2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:39.991604-08:00'), ('2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:13:45.669769-08:00'), ('2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:07.997757-08:00'), ('2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:28:56.988343-08:00'), ('2020-02-06T10:29:17.987473-08:00', '2020-02-06T10:29:54.985938-08:00'), ('2020-02-06T13:09:00.519690-08:00', '2020-02-06T13:20:40.004119-08:00'), ('2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:16.992790-08:00'), ('2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:26:44.986028-08:00'), ('2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:40.983469-08:00'), ('2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:28.981276-08:00'), ('2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:28:56.996158-08:00'), ('2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:32.988036-08:00'), ('2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:50.987243-08:00'), ('2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:05.983943-08:00'), ('2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:11.983681-08:00'), ('2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:39:41.990607-08:00'), ('2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:20.986232-08:00'), ('2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:31.983098-08:00'), ('2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:37.982832-08:00'), ('2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:45:35.993998-08:00'), ('2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:48:47.985500-08:00'), ('2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:28:15.991941-08:00'), ('2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:30.997907-08:00'), ('2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:49:48.992221-08:00'), ('2020-02-06T17:50:44.989805-08:00', '2020-02-06T17:52:36.984973-08:00'), ('2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:56:25.994272-08:00'), ('2020-02-06T18:57:46.990999-08:00', '2020-02-06T19:15:28.996294-08:00')]\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = ['2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:29:17.987473-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:36.990703-08:00'), ('2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:02.989606-08:00'), ('2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:20.993964-08:00'), ('2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:09:44.993512-08:00'), ('2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:39.991604-08:00'), ('2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:13:45.669769-08:00'), ('2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:07.997757-08:00'), ('2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:28:56.988343-08:00'), ('2020-02-06T10:29:17.987473-08:00', '2020-02-06T10:29:54.985938-08:00'), ('2020-02-06T13:09:00.519690-08:00', '2020-02-06T13:20:40.004119-08:00'), ('2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:16.992790-08:00'), ('2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:26:44.986028-08:00'), ('2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:40.983469-08:00'), ('2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:28.981276-08:00'), ('2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:28:56.996158-08:00'), ('2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:32.988036-08:00'), ('2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:50.987243-08:00'), ('2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:05.983943-08:00'), ('2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:11.983681-08:00'), ('2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:39:41.990607-08:00'), ('2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:20.986232-08:00'), ('2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:31.983098-08:00'), ('2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:37.982832-08:00'), ('2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:45:35.993998-08:00'), ('2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:48:47.985500-08:00'), ('2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:28:15.991941-08:00'), ('2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:30.997907-08:00'), ('2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:49:48.992221-08:00'), ('2020-02-06T17:50:44.989805-08:00', '2020-02-06T17:52:36.984973-08:00'), ('2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:56:25.994272-08:00'), ('2020-02-06T18:57:46.990999-08:00', '2020-02-06T19:15:28.996294-08:00')]\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = ['2020-02-06T13:09:00.519690-08:00']\n", - "Before filtering, trips = [('2020-02-06T08:17:02.594690-08:00', '2020-02-06T08:24:36.990703-08:00'), ('2020-02-06T08:24:51.990069-08:00', '2020-02-06T08:25:02.989606-08:00'), ('2020-02-06T08:25:21.988805-08:00', '2020-02-06T08:33:20.993964-08:00'), ('2020-02-06T08:33:23.993835-08:00', '2020-02-06T09:09:44.993512-08:00'), ('2020-02-06T09:10:23.991873-08:00', '2020-02-06T09:19:39.991604-08:00'), ('2020-02-06T09:19:47.991263-08:00', '2020-02-06T10:13:45.669769-08:00'), ('2020-02-06T10:15:17.171800-08:00', '2020-02-06T10:25:07.997757-08:00'), ('2020-02-06T10:25:11.997611-08:00', '2020-02-06T10:28:56.988343-08:00'), ('2020-02-06T10:29:17.987473-08:00', '2020-02-06T10:29:54.985938-08:00'), ('2020-02-06T13:09:00.519690-08:00', '2020-02-06T13:20:40.004119-08:00'), ('2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:16.992790-08:00'), ('2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:26:44.986028-08:00'), ('2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:40.983469-08:00'), ('2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:28.981276-08:00'), ('2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:28:56.996158-08:00'), ('2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:32.988036-08:00'), ('2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:50.987243-08:00'), ('2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:05.983943-08:00'), ('2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:11.983681-08:00'), ('2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:39:41.990607-08:00'), ('2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:20.986232-08:00'), ('2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:31.983098-08:00'), ('2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:37.982832-08:00'), ('2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:45:35.993998-08:00'), ('2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:48:47.985500-08:00'), ('2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:28:15.991941-08:00'), ('2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:30.997907-08:00'), ('2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:49:48.992221-08:00'), ('2020-02-06T17:50:44.989805-08:00', '2020-02-06T17:52:36.984973-08:00'), ('2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:56:25.994272-08:00'), ('2020-02-06T18:57:46.990999-08:00', '2020-02-06T19:15:28.996294-08:00')]\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = ['2020-02-06T16:18:31.988628-08:00', '2020-02-06T16:24:21.992562-08:00', '2020-02-06T16:27:10.984839-08:00', '2020-02-06T16:27:46.983195-08:00', '2020-02-06T16:28:49.994170-08:00', '2020-02-06T16:29:02.997183-08:00', '2020-02-06T16:33:38.987772-08:00', '2020-02-06T16:33:55.987022-08:00', '2020-02-06T16:35:11.983681-08:00', '2020-02-06T16:35:17.983417-08:00', '2020-02-06T16:41:01.987073-08:00', '2020-02-06T16:41:25.986012-08:00', '2020-02-06T16:42:37.982832-08:00', '2020-02-06T16:42:40.982700-08:00', '2020-02-06T16:48:08.987225-08:00', '2020-02-06T16:53:52.883482-08:00', '2020-02-06T17:29:03.989849-08:00', '2020-02-06T17:40:42.997446-08:00', '2020-02-06T17:50:44.989805-08:00', '2020-02-06T18:10:40.008324-08:00', '2020-02-06T18:57:46.990999-08:00']\n", - " -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*\n", - " ucb-sdb-ios-4 power_control dict_keys(['role', 'manual/evaluation_transition', 'calibration_transitions', 'calibration_ranges', 'evaluation_transitions', 'evaluation_ranges'])\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_0 HAHFDC v/s MAHFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T07:51:42.185629-07:00 -> 2019-07-24T10:26:37.702858-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T14:12:16.706653-07:00 -> 2019-07-24T14:25:34.793104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-24T16:37:07.662981-07:00 -> 2019-07-24T19:59:19.661789-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_1 HAHFDC v/s MAHFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T08:06:28.267119-07:00 -> 2019-07-25T10:28:44.244487-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T14:08:15.215784-07:00 -> 2019-07-25T14:21:54.693573-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-25T16:33:11.145783-07:00 -> 2019-07-25T19:59:28.351553-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_2 HAHFDC v/s MAHFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T08:11:14.820516-07:00 -> 2019-07-26T10:28:25.407298-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T14:16:33.511475-07:00 -> 2019-07-26T14:28:24.678987-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-07-26T16:15:27.436353-07:00 -> 2019-07-26T19:59:48.141316-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_3 HAHFDC v/s HAMFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T08:12:39.439087-07:00 -> 2019-09-10T10:37:19.789012-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T13:39:45.196231-07:00 -> 2019-09-10T13:51:53.609973-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-10T16:09:30.117800-07:00 -> 2019-09-10T19:22:38.863709-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_4 HAHFDC v/s HAMFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T08:12:17.541266-07:00 -> 2019-09-11T10:37:45.750265-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T13:46:33.762365-07:00 -> 2019-09-11T13:58:42.086465-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-11T16:25:59.496105-07:00 -> 2019-09-11T19:57:46.810545-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_5 HAHFDC v/s HAMFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T08:08:01.759346-07:00 -> 2019-09-17T10:39:57.392104-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T13:45:59.444274-07:00 -> 2019-09-17T13:58:36.404812-07:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-09-17T16:11:19.759616-07:00 -> 2019-09-17T19:14:00.649343-07:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_6 MAMFDC v/s MAHFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T08:09:29.176817-08:00 -> 2019-11-19T10:32:50.450871-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T13:30:58.939198-08:00 -> 2019-11-19T13:43:34.331953-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-19T16:12:27.999223-08:00 -> 2019-11-19T19:15:38.499878-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_7 MAMFDC v/s MAHFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T08:10:53.826874-08:00 -> 2019-11-20T10:31:18.152474-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T13:46:23.419753-08:00 -> 2019-11-20T13:59:25.243121-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-11-20T16:17:15.618203-08:00 -> 2019-11-20T19:21:32.010795-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_8 MAMFDC v/s MAHFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T08:15:09.999146-08:00 -> 2019-12-03T10:34:51.291555-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T14:13:40.984645-08:00 -> 2019-12-03T14:27:07.489758-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-03T16:14:39.999867-08:00 -> 2019-12-03T19:34:45.349746-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_9 MAMFDC v/s HAMFDC power_control_0 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T08:14:31.756040-08:00 -> 2019-12-09T10:33:24.083811-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T13:59:01.664712-08:00 -> 2019-12-09T14:11:49.919419-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-09T16:13:29.483522-08:00 -> 2019-12-09T19:23:19.123903-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_10 MAMFDC v/s HAMFDC power_control_1 3\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T08:12:44.752437-08:00 -> 2019-12-11T10:53:44.995113-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T14:08:58.567197-08:00 -> 2019-12-11T14:20:58.765902-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2019-12-11T16:18:33.636605-08:00 -> 2019-12-11T19:20:33.149999-08:00\n", - "After filtering, trips = []\n", - " ==============================\n", - " dict_keys(['trip_id', 'trip_id_base', 'trip_run', 'start_ts', 'end_ts', 'duration', 'eval_common_trip_id', 'eval_role', 'eval_role_base', 'eval_role_run', 'evaluation_trip_ranges', 'background/battery', 'battery_df', 'background/location', 'background/filtered_location', 'location_df', 'filtered_location_df', 'background/motion_activity', 'motion_activity_df', 'statemachine/transition', 'transition_df'])\n", - " fixed:POWER_CONTROL_11 MAMFDC v/s HAMFDC power_control_2 3\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T08:14:46.468857-08:00 -> 2020-02-06T10:30:08.723332-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T13:06:59.787174-08:00 -> 2020-02-06T13:20:49.285179-08:00\n", - "After filtering, trips = []\n", - "Before filtering, trips = []\n", - "Filter range = 2020-02-06T16:17:24.789623-08:00 -> 2020-02-06T19:16:10.669478-08:00\n", - "After filtering, trips = []\n" - ] - } - ], + "outputs": [], "source": [ "mcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "mcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", @@ -3524,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": null, "id": "65556b87", "metadata": {}, "outputs": [], @@ -3547,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": null, "id": "3f9ff053", "metadata": {}, "outputs": [], @@ -3569,7 +1364,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": null, "id": "f550a507", "metadata": {}, "outputs": [], @@ -3598,52 +1393,10 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": null, "id": "77ccbbc6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='suburb_city_driving_weekend_0', section section['trip_id']='walk_end_0', os='ios', found (524, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n", - "For trip tr['trip_id']='freeway_driving_weekday_0', section section['trip_id']='walk_end_0', os='ios', found (917, 8) entries\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw')\n", "plot_cm('ios', [pv_la, pv_sj], 'raw')" @@ -3651,23 +1404,12 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": null, "id": "99c90603", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw')\n", "plot_cm('android', [pv_la, pv_sj], 'raw')" @@ -3741,35 +1483,10 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": null, "id": "d7e67855", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[220], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplot_cm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mios\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mgisv_la\u001b[49m\u001b[43m,\u001b[49m\u001b[43mgisv_sj\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgis\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mINDEX_MAP\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mIIM\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[216], line 20\u001b[0m, in \u001b[0;36mplot_cm\u001b[0;34m(os, pv, d_type, INDEX_MAP)\u001b[0m\n\u001b[1;32m 18\u001b[0m fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimages/clean_distance_cm_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m d_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom_forest\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgis\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 20\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43mget_confusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrole\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpv\u001b[49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msensed_mode\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39mrename(index\u001b[38;5;241m=\u001b[39mINDEX_MAP)\n\u001b[1;32m 21\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(df, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28msorted\u001b[39m(df\u001b[38;5;241m.\u001b[39mindex, key\u001b[38;5;241m=\u001b[39msort_key))\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# fname = f\"images/{d_type}_cm_{os}\"\u001b[39;00m\n", - "Cell \u001b[0;32mIn[214], line 10\u001b[0m, in \u001b[0;36mget_confusion_matrix\u001b[0;34m(os, role, pv, test, test_trip)\u001b[0m\n\u001b[1;32m 8\u001b[0m trips \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pv :\n\u001b[0;32m---> 10\u001b[0m trips\u001b[38;5;241m.\u001b[39mextend(\u001b[43mget_trip_ss_and_gts_timeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrole\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m trips \u001b[38;5;241m=\u001b[39m test_trip \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(test_trip) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlist\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m [test_trip]\n", - "Cell \u001b[0;32mIn[170], line 39\u001b[0m, in \u001b[0;36mget_trip_ss_and_gts_timeline\u001b[0;34m(pv, os, role)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;66;03m# Do this only once.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gt_location_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m gt_location_data \u001b[38;5;241m=\u001b[39m get_gt_location_data(\u001b[43mFILE_MAPPING\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpv\u001b[49m\u001b[43m]\u001b[49m, tr[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrip_id_base\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 40\u001b[0m gt_location_data \u001b[38;5;241m=\u001b[39m gt_location_data\u001b[38;5;241m.\u001b[39mquery(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource == @os\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# now, we build a timeline for each trip\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: " - ] - }, 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)\n", "plot_cm('ios', [gisv_la,gisv_sj], 'gis', INDEX_MAP=IIM)" @@ -4444,10 +2161,13 @@ } ], "metadata": { + "interpreter": { + "hash": "e9f66c3b7bd3dc9f1ccabd654979094500fde00f3127413feb35bf80f0ad2368" + }, "kernelspec": { - "display_name": "emissioneval", + "display_name": "Python 3.11.4 ('emissioneval')", "language": "python", - "name": "emissioneval" + "name": "python3" }, "language_info": { "codemirror_mode": { From d6d2064c2dc5faa3729e6e5ffcfa74f395b1f744 Mon Sep 17 00:00:00 2001 From: Kulhalli Date: Sat, 22 Jul 2023 16:59:55 -0400 Subject: [PATCH 3/9] Made suggested ammends --- classification_analysis.ipynb | 91 +++++++++++++---------------------- 1 file changed, 33 insertions(+), 58 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index a97fec3..50af5e1 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -180,7 +180,7 @@ "source": [ "%%capture\n", "pv_la = eipv.PhoneView(sd_la)\n", - "# pv_sj = eipv.PhoneView(sd_sj)\n", + "pv_sj = eipv.PhoneView(sd_sj)\n", "# pv_ucb = eipv.PhoneView(sd_ucb)" ] }, @@ -201,7 +201,7 @@ "source": [ "FILE_MAPPING = {\n", " pv_la: \"unimodal_trip_car_bike_mtv_la\",\n", - " # pv_sj: \"car_scooter_brex_san_jose\",\n", + " pv_sj: \"car_scooter_brex_san_jose\",\n", " # pv_ucb: \"train_bus_ebike_mtv_ucb\"\n", "}" ] @@ -215,7 +215,7 @@ "source": [ "%%capture\n", "ems.fill_sensed_section_ranges(pv_la)\n", - "# ems.fill_sensed_section_ranges(pv_sj)\n", + "ems.fill_sensed_section_ranges(pv_sj)\n", "# ems.fill_sensed_section_ranges(pv_ucb)" ] }, @@ -236,7 +236,7 @@ "source": [ "from pathlib import Path\n", "\n", - "def get_gt_location_data(trip_id: str, section_id: str):\n", + "def get_reference_trajectory(trip_id: str, section_id: str):\n", "\n", " root = Path(\"./bin/data\")\n", " return_file = None\n", @@ -269,26 +269,6 @@ " return return_file" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "effccbd6", - "metadata": {}, - "outputs": [], - "source": [ - "# x = get_gt_location_data('unimodal_trip_car_bike_mtv_la', 'suburb_bicycling')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1dd54d7", - "metadata": {}, - "outputs": [], - "source": [ - "# display(x.head())" - ] - }, { "cell_type": "code", "execution_count": null, @@ -311,7 +291,7 @@ " continue\n", " tr_ss = []\n", " tr_gts = []\n", - " gt_dfs = []\n", + " gt_traj = []\n", " \n", " for i, tr in enumerate(r[\"evaluation_trip_ranges\"]):\n", " for ss in tr[\"sensed_section_ranges\"]:\n", @@ -333,20 +313,17 @@ "\n", " tr_gts.append(gts)\n", "\n", - " gt_location_data = get_gt_location_data(FILE_MAPPING[pv], tr['trip_id_base'])\n", + " gt_trajectory_data = get_reference_trajectory(FILE_MAPPING[pv], tr['trip_id_base'])\n", " run_id = r['trip_run']\n", - " print(run_id, tr['trip_id'], tr['trip_id'])\n", - " gt_location_data = gt_location_data.query(\"source == @os\")\n", - " gt_location_data = gt_location_data.loc[gt_location_data.run == str(run_id), :].reset_index(drop=True, inplace=False)\n", - " gt_dfs.append(gt_location_data)\n", - "\n", - " print(5*'-')\n", + " gt_trajectory_data = gt_trajectory_data.query(\"source == @os\")\n", + " gt_trajectory_data = gt_trajectory_data.loc[gt_trajectory_data.run == str(run_id), :].reset_index(drop=True, inplace=False)\n", + " gt_traj.append(gt_trajectory_data)\n", "\n", " # now, we build a timeline for each trip\n", " trip = tr.copy()\n", " trip['ss_timeline'] = tr_ss\n", " trip['gts_timeline'] = tr_gts\n", - " trip['gt_location_df'] = gt_dfs\n", + " trip['gt_trajectory'] = pd.concat(gt_traj, axis=0).reset_index(drop=True, inplace=False)\n", " \n", " trips.append(trip)\n", " \n", @@ -731,8 +708,7 @@ " trips = test_trip if type(test_trip) is list else [test_trip]\n", " TP, FN, FP, TN = {}, {}, {}, {}\n", " for trip in trips:\n", - " gt_location_df = trip['gt_location_df']\n", - " print(gt_location_df.shape)\n", + " gt_trajectory = trip['gt_trajectory']\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for mode in set(BASE_MODE.values()):\n", " for ss in ss_timeline:\n", @@ -741,8 +717,8 @@ " range_start = max(ss['start_ts'], gts['start_ts'])\n", " range_end = min(ss['end_ts'], gts['end_ts'])\n", "\n", - " filtered_gt_distance = gt_location_df.loc[\n", - " (gt_location_df.ts >= range_start) & (gt_location_df.ts <= range_end), :\n", + " filtered_gt_distance = gt_trajectory.loc[\n", + " (gt_trajectory.ts >= range_start) & (gt_trajectory.ts <= range_end), :\n", " ]\n", "\n", " if filtered_gt_distance.shape[0] == 0:\n", @@ -752,10 +728,12 @@ " \n", " if dist > 0:\n", " print(f\"{range_start=}, {range_end=}\")\n", - " print(f\"{gt_location_df.ts.min()=}, {gt_location_df.ts.max()=}\")\n", + " print(f\"{filtered_gt_distance.ts.min()=}, {filtered_gt_distance.ts.max()=}\")\n", " print(\"Computed distance: \", dist)\n", " print(50*'-')\n", "\n", + " # dur = range_end - range_start\n", + "\n", " # if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", " # TP[mode] = TP.setdefault(mode, 0) + dur\n", " # elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", @@ -777,16 +755,6 @@ " return TP, FP, FN, TN" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5ddf739", - "metadata": {}, - "outputs": [], - "source": [ - "# get_binary_class_in_sec('android', 'HAHFDC', pv_la, RBMM)" - ] - }, { "cell_type": "markdown", "id": "e1798e4a", @@ -1055,7 +1023,7 @@ " else:\n", " trips = test_trip if type(test_trip) is list else [test_trip]\n", " for trip in trips:\n", - " gt_location_df = trip['gt_location_df']\n", + " gt_trajectory = trip['gt_trajectory']\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for ss in ss_timeline:\n", " cm = {}\n", @@ -1065,15 +1033,15 @@ " range_start = max(ss['start_ts'], gts['start_ts'])\n", " range_end = min(ss['end_ts'], gts['end_ts'])\n", "\n", - " filtered_gt_distance = gt_location_df.loc[\n", - " (gt_location_df.ts >= range_start) & (gt_location_df.ts <= range_end), :\n", + " filtered_gt_trajectory = gt_trajectory.loc[\n", + " (gt_trajectory.ts >= range_start) & (gt_trajectory.ts <= range_end), :\n", " ]\n", "\n", - " if filtered_gt_distance.shape[0] == 0:\n", + " if filtered_gt_trajectory.shape[0] == 0:\n", " dist = 0\n", " else:\n", - " dist = add_dist(filtered_gt_distance).distance.sum()\n", - " \n", + " dist = add_dist(filtered_gt_trajectory).distance.sum()\n", + " \n", " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dist\n", " \n", @@ -1126,6 +1094,12 @@ " y=.95\n", " fig.text(0.5, 0.0, 'Predicted Label', ha='center', fontsize='xx-large')\n", " fig.text(0.04, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", + "\n", + " # Small snippet to create an op folder.\n", + " op_dir = Path(\"./images\")\n", + " if not op_dir.exists():\n", + " op_dir.mkdir()\n", + "\n", " for k, role in enumerate([\"HAHFDC\", \"HAMFDC\", \"MAHFDC\"]):\n", " if d_type =='raw':\n", " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", @@ -1312,9 +1286,10 @@ }, "outputs": [], "source": [ + "%%capture\n", "mcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "mcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", - "mcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" + "# mcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" ] }, { @@ -1327,7 +1302,7 @@ "%%capture\n", "gcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "gcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", - "gcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" + "# gcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" ] }, { @@ -1350,7 +1325,7 @@ "%%capture\n", "rfv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "rfv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", - "rfv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" + "# rfv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" ] }, { @@ -1372,7 +1347,7 @@ "%%capture\n", "gisv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "gisv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", - "gisv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" + "# gisv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" ] }, { From e7f8fddb261116ecb12e571e084592d77ffbad5e Mon Sep 17 00:00:00 2001 From: Kulhalli Date: Mon, 24 Jul 2023 13:22:44 -0400 Subject: [PATCH 4/9] Added output folder to .gitignore; made additional suggested amends --- .gitignore | 3 + classification_analysis.ipynb | 233 +++++++++++++++------------------- 2 files changed, 108 insertions(+), 128 deletions(-) diff --git a/.gitignore b/.gitignore index ad8685f..58a1520 100644 --- a/.gitignore +++ b/.gitignore @@ -112,3 +112,6 @@ ENV/ # data dumps **/*.zip + +# outputs +images/ \ No newline at end of file diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index 50af5e1..d970c92 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -139,16 +139,6 @@ "import importlib" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "5cc856cf", - "metadata": {}, - "outputs": [], - "source": [ - "import arrow" - ] - }, { "cell_type": "markdown", "id": "845eb857", @@ -181,7 +171,7 @@ "%%capture\n", "pv_la = eipv.PhoneView(sd_la)\n", "pv_sj = eipv.PhoneView(sd_sj)\n", - "# pv_ucb = eipv.PhoneView(sd_ucb)" + "pv_ucb = eipv.PhoneView(sd_ucb)" ] }, { @@ -192,20 +182,6 @@ "### Get sensed data for each trip" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "a29ec8a0", - "metadata": {}, - "outputs": [], - "source": [ - "FILE_MAPPING = {\n", - " pv_la: \"unimodal_trip_car_bike_mtv_la\",\n", - " pv_sj: \"car_scooter_brex_san_jose\",\n", - " # pv_ucb: \"train_bus_ebike_mtv_ucb\"\n", - "}" - ] - }, { "cell_type": "code", "execution_count": null, @@ -216,7 +192,7 @@ "%%capture\n", "ems.fill_sensed_section_ranges(pv_la)\n", "ems.fill_sensed_section_ranges(pv_sj)\n", - "# ems.fill_sensed_section_ranges(pv_ucb)" + "ems.fill_sensed_section_ranges(pv_ucb)" ] }, { @@ -235,37 +211,39 @@ "outputs": [], "source": [ "from pathlib import Path\n", + "from typing import Union\n", "\n", - "def get_reference_trajectory(trip_id: str, section_id: str):\n", + "def get_reference_trajectory(spec_file: str, trip_id: str) -> Union[pd.DataFrame, None]:\n", "\n", " root = Path(\"./bin/data\")\n", " return_file = None\n", "\n", " workdir = root\n", - " if (workdir / trip_id).exists():\n", - " workdir = workdir / trip_id\n", - " assert workdir.is_dir(), f\"{trip_id} is a file, not a dir.\"\n", - " if (workdir / section_id).exists():\n", - " workdir = workdir / section_id\n", - " assert workdir.is_dir(), f\"{trip_id}.{section_id} is a file, not a dir.\"\n", + " if (workdir / spec_file).exists():\n", + " workdir = workdir / spec_file\n", + " assert workdir.is_dir(), f\"{spec_file} is a file, not a dir.\"\n", + " if (workdir / trip_id).exists():\n", + " workdir = workdir / trip_id\n", + " assert workdir.is_dir(), f\"{spec_file}.{trip_id} is a file, not a dir.\"\n", " files = [f for f in workdir.iterdir()]\n", - " assert files is not None and len(files) > 0, f\"No files found for {trip_id=} and {section_id=}\"\n", + " assert files is not None and len(files) > 0, f\"No files found for {spec_file=} and {trip_id=}\"\n", "\n", " # sort the files by run number (just in case).\n", " files = list(sorted(files, key=lambda x: int(x.name.split(\"_\")[-1])))\n", "\n", " # Concatenate all the trips into a single consolidated df but add an extra column that allows you\n", " # to filter on the run number (if required).\n", - " df = pd.read_csv(files[0])\n", - " df['run'] = '0'\n", - "\n", - " for ix, run_file in enumerate(files[1:]):\n", - " tdf = pd.read_csv(run_file)\n", - " tdf['run'] = str(ix+1)\n", - " df = pd.concat([df, tdf], axis=0)\n", + " for ix, run_file in enumerate(files):\n", + " if return_file is None:\n", + " return_file = pd.read_csv(run_file)\n", + " return_file['run'] = ix\n", + " else:\n", + " tdf = pd.read_csv(run_file)\n", + " tdf['run'] = ix\n", + " return_file = pd.concat([return_file, tdf], axis=0)\n", + " \n", + " return_file.reset_index(drop=True, inplace=True)\n", " \n", - " return_file = df.reset_index(drop=True, inplace=False)\n", - " \n", " return return_file" ] }, @@ -286,9 +264,10 @@ " for phone_label, phone_detail_map in phone_map.items():\n", " if \"control\" in phone_detail_map[\"role\"]:\n", " continue\n", - " for r in phone_detail_map[\"evaluation_ranges\"]:\n", + " for run_ix, r in enumerate(phone_detail_map[\"evaluation_ranges\"]):\n", " if r['eval_role_base'] != role:\n", " continue\n", + "\n", " tr_ss = []\n", " tr_gts = []\n", " gt_traj = []\n", @@ -297,6 +276,7 @@ " for ss in tr[\"sensed_section_ranges\"]:\n", " tr_ss.append(ss)\n", " for section in tr[\"evaluation_section_ranges\"]:\n", + "\n", " ## get the ground truth section data\n", " section_gt_leg = pv.spec_details.get_ground_truth_for_leg(tr['trip_id_base'],\n", " section['trip_id_base'],\n", @@ -313,33 +293,25 @@ "\n", " tr_gts.append(gts)\n", "\n", - " gt_trajectory_data = get_reference_trajectory(FILE_MAPPING[pv], tr['trip_id_base'])\n", - " run_id = r['trip_run']\n", - " gt_trajectory_data = gt_trajectory_data.query(\"source == @os\")\n", - " gt_trajectory_data = gt_trajectory_data.loc[gt_trajectory_data.run == str(run_id), :].reset_index(drop=True, inplace=False)\n", - " gt_traj.append(gt_trajectory_data)\n", + " trajectory_data = get_reference_trajectory(pv.spec_details.CURR_SPEC_ID, tr['trip_id_base'])\n", + " run_id = run_ix\n", + " trajectory_data = trajectory_data.loc[\n", + " (trajectory_data.source == os) &\n", + " (trajectory_data.run == run_id), :\n", + " ].reset_index(drop=True, inplace=False)\n", + "\n", + " gt_traj.append(trajectory_data)\n", "\n", " # now, we build a timeline for each trip\n", " trip = tr.copy()\n", " trip['ss_timeline'] = tr_ss\n", " trip['gts_timeline'] = tr_gts\n", - " trip['gt_trajectory'] = pd.concat(gt_traj, axis=0).reset_index(drop=True, inplace=False)\n", - " \n", + " trip['trajectory_data'] = pd.concat(gt_traj, axis=0).reset_index(drop=True, inplace=False)\n", " trips.append(trip)\n", " \n", " return trips" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0ec3c7e", - "metadata": {}, - "outputs": [], - "source": [ - "trips = get_trip_ss_and_gts_timeline(pv_la, 'android', 'HAHFDC')" - ] - }, { "cell_type": "markdown", "id": "66601561", @@ -698,7 +670,10 @@ "metadata": {}, "outputs": [], "source": [ - "def get_binary_class_in_sec(os, role, pv, BASE_MODE, test=False, test_trip=None):\n", + "def get_binary_class_in_sec(os, role, pv, BASE_MODE, test=False, test_trip=None, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion} is not valid criteria\"\n", + "\n", " if not test:\n", " if type(pv) is not list: pv = [pv]\n", " trips = []\n", @@ -708,7 +683,7 @@ " trips = test_trip if type(test_trip) is list else [test_trip]\n", " TP, FN, FP, TN = {}, {}, {}, {}\n", " for trip in trips:\n", - " gt_trajectory = trip['gt_trajectory']\n", + " trajectory_data = trip['trajectory_data']\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for mode in set(BASE_MODE.values()):\n", " for ss in ss_timeline:\n", @@ -717,40 +692,36 @@ " range_start = max(ss['start_ts'], gts['start_ts'])\n", " range_end = min(ss['end_ts'], gts['end_ts'])\n", "\n", - " filtered_gt_distance = gt_trajectory.loc[\n", - " (gt_trajectory.ts >= range_start) & (gt_trajectory.ts <= range_end), :\n", - " ]\n", + " if criterion == 'duration':\n", + " dur = range_end - range_start\n", "\n", - " if filtered_gt_distance.shape[0] == 0:\n", - " dist = 0\n", - " else:\n", - " dist = add_dist(filtered_gt_distance).distance.sum()\n", + " if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", + " TP[mode] = TP.setdefault(mode, 0) + dur\n", + " elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", + " FP[mode] = FP.setdefault(mode, 0) + dur\n", + " elif BASE_MODE[mode] != BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", + " FN[mode] = FN.setdefault(mode, 0) + dur\n", + " else:\n", + " TN[mode] = TN.setdefault(mode, 0) + dur\n", " \n", - " if dist > 0:\n", - " print(f\"{range_start=}, {range_end=}\")\n", - " print(f\"{filtered_gt_distance.ts.min()=}, {filtered_gt_distance.ts.max()=}\")\n", - " print(\"Computed distance: \", dist)\n", - " print(50*'-')\n", - "\n", - " # dur = range_end - range_start\n", - "\n", - " # if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", - " # TP[mode] = TP.setdefault(mode, 0) + dur\n", - " # elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", - " # FP[mode] = FP.setdefault(mode, 0) + dur\n", - " # elif BASE_MODE[mode] != BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", - " # FN[mode] = FN.setdefault(mode, 0) + dur\n", - " # else:\n", - " # TN[mode] = TN.setdefault(mode, 0) + dur\n", - "\n", - " if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", - " TP[mode] = TP.setdefault(mode, 0) + dist\n", - " elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", - " FP[mode] = FP.setdefault(mode, 0) + dist\n", - " elif BASE_MODE[mode] != BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", - " FN[mode] = FN.setdefault(mode, 0) + dist\n", " else:\n", - " TN[mode] = TN.setdefault(mode, 0) + dist\n", + " filtered_trajectory = trajectory_data.loc[\n", + " (filtered_trajectory.ts >= range_start) & (filtered_trajectory.ts <= range_end), :\n", + " ]\n", + "\n", + " if filtered_trajetory.shape[0] > 0:\n", + " dist = add_dist(filtered_trajectory).distance.sum()\n", + " else:\n", + " dist = 0\n", + "\n", + " if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", + " TP[mode] = TP.setdefault(mode, 0) + dist\n", + " elif BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] != BASE_MODE[gts['mode']]:\n", + " FP[mode] = FP.setdefault(mode, 0) + dist\n", + " elif BASE_MODE[mode] != BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", + " FN[mode] = FN.setdefault(mode, 0) + dist\n", + " else:\n", + " TN[mode] = TN.setdefault(mode, 0) + dist\n", " \n", " return TP, FP, FN, TN" ] @@ -819,12 +790,9 @@ " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", " support = {}\n", " for trip in trips:\n", - "\n", - " gt_loc_df = trip['gt_location_df']\n", - "\n", " ## get gts dur\n", " gt_dur = 0\n", - " # gt_dist = 0\n", + "\n", " for gts in trip['gts_timeline']:\n", " mode = BASE_MODE[gts['mode']]\n", " support[mode] = support.setdefault(mode, 0) + gts['end_ts'] - gts['start_ts']\n", @@ -1010,7 +978,8 @@ "metadata": {}, "outputs": [], "source": [ - "def get_confusion_matrix(os, role, pv, test=False, test_trip=None):\n", + "def get_confusion_matrix(os, role, pv, test=False, test_trip=None, criterion='duration'):\n", + "\n", " cm_l = []\n", " if not test:\n", " assert os in ['android', 'ios'], 'UNKNOWN OS'\n", @@ -1023,7 +992,7 @@ " else:\n", " trips = test_trip if type(test_trip) is list else [test_trip]\n", " for trip in trips:\n", - " gt_trajectory = trip['gt_trajectory']\n", + " trajectory_data = trip['trajectory_data']\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for ss in ss_timeline:\n", " cm = {}\n", @@ -1033,17 +1002,25 @@ " range_start = max(ss['start_ts'], gts['start_ts'])\n", " range_end = min(ss['end_ts'], gts['end_ts'])\n", "\n", - " filtered_gt_trajectory = gt_trajectory.loc[\n", - " (gt_trajectory.ts >= range_start) & (gt_trajectory.ts <= range_end), :\n", - " ]\n", + " if criterion == 'distance':\n", + "\n", + " filtered_trajectory_data = trajectory_data.loc[\n", + " (trajectory_data.ts >= range_start) & (trajectory_data.ts <= range_end), :\n", + " ]\n", "\n", - " if filtered_gt_trajectory.shape[0] == 0:\n", - " dist = 0\n", + " if filtered_trajectory_data.shape[0] > 0:\n", + " dist = add_dist(filtered_trajectory_data).distance.sum()\n", + " else:\n", + " dist = 0\n", + " \n", + " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", + " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dist\n", " else:\n", - " dist = add_dist(filtered_gt_trajectory).distance.sum()\n", - " \n", - " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", - " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dist\n", + " \n", + " dur = range_end - range_start\n", + "\n", + " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", + " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", " \n", " cm['sensed_mode'] = ss['mode']\n", " \n", @@ -1089,7 +1066,10 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_cm(os, pv, d_type, INDEX_MAP=None):\n", + "def plot_cm(os, pv, d_type, INDEX_MAP=None, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion=} is not defined.\"\n", + "\n", " fig, ax = plt.subplots(1,3, figsize=(22,10), dpi=300, sharey=True)\n", " y=.95\n", " fig.text(0.5, 0.0, 'Predicted Label', ha='center', fontsize='xx-large')\n", @@ -1103,18 +1083,18 @@ " for k, role in enumerate([\"HAHFDC\", \"HAMFDC\", \"MAHFDC\"]):\n", " if d_type =='raw':\n", " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", - " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum()\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum()\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", " # fname = f\"images/raw_cm_{os}\"\n", " fname = f\"images/raw_distance_cm_{os}\"\n", " elif d_type == 'clean':\n", " title = f\"Confusion Matrices for Clean Output Data on Phones Running {os} \\n by Calibration Settings\"\n", - " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", " # fname = f\"images/clean_cm_{os}\"\n", " fname = f\"images/clean_distance_cm_{os}\"\n", " elif d_type == 'random_forest' or 'gis':\n", - " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", " # fname = f\"images/{d_type}_cm_{os}\"\n", " fname = f\"images/{d_type}_distance_cm_{os}\"\n", @@ -1289,7 +1269,7 @@ "%%capture\n", "mcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "mcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", - "# mcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" + "mcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" ] }, { @@ -1302,7 +1282,7 @@ "%%capture\n", "gcv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", "gcv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))\n", - "# gcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" + "gcv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/cleaned_section\"))" ] }, { @@ -1325,7 +1305,7 @@ "%%capture\n", "rfv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "rfv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", - "# rfv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" + "rfv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, master_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" ] }, { @@ -1347,7 +1327,7 @@ "%%capture\n", "gisv_la = copy.deepcopy(eapv.create_analysed_view(pv_la, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", "gisv_sj = copy.deepcopy(eapv.create_analysed_view(pv_sj, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))\n", - "# gisv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" + "gisv_ucb = copy.deepcopy(eapv.create_analysed_view(pv_ucb, gis_spec, \"analysis/recreated_location\", \"analysis/cleaned_trip\", \"analysis/inferred_section\"))" ] }, { @@ -1373,8 +1353,7 @@ "metadata": {}, "outputs": [], "source": [ - "# plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw')\n", - "plot_cm('ios', [pv_la, pv_sj], 'raw')" + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" ] }, { @@ -1386,8 +1365,7 @@ }, "outputs": [], "source": [ - "# plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw')\n", - "plot_cm('android', [pv_la, pv_sj], 'raw')" + "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" ] }, { @@ -1407,7 +1385,7 @@ }, "outputs": [], "source": [ - "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM)" + "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance')" ] }, { @@ -1417,7 +1395,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM)" + "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance')" ] }, { @@ -1435,7 +1413,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM)" + "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance')" ] }, { @@ -1445,7 +1423,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM)" + "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance')" ] }, { @@ -1463,8 +1441,7 @@ "metadata": {}, "outputs": [], "source": [ - "# plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)\n", - "plot_cm('ios', [gisv_la,gisv_sj], 'gis', INDEX_MAP=IIM)" + "plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance')" ] }, { @@ -1474,7 +1451,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM)" + "plot_cm('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance')" ] }, { From 462613e40996da9535f156150c6d52521fc927ff Mon Sep 17 00:00:00 2001 From: Kulhalli Date: Tue, 25 Jul 2023 11:36:48 -0400 Subject: [PATCH 5/9] Removed OS filter for trajectories and optimized code --- classification_analysis.ipynb | 355 +++++++++++++++++++--------------- 1 file changed, 199 insertions(+), 156 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index d970c92..f378699 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -26,20 +26,7 @@ "source": [ "# for reading and validating data\n", "import emeval.input.spec_details as eisd\n", - "import emeval.input.phone_view as eipv\n", - "import emeval.input.eval_view as eiev" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1505557", - "metadata": {}, - "outputs": [], - "source": [ - "import emeval.viz.phone_view as ezpv\n", - "import emeval.viz.eval_view as ezev\n", - "import emeval.viz.geojson as ezgj" + "import emeval.input.phone_view as eipv" ] }, { @@ -64,18 +51,6 @@ "import emeval.metrics.segmentation as ems" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "5395929e", - "metadata": {}, - "outputs": [], - "source": [ - "# Metrics helpers\n", - "import emeval.metrics.dist_calculations as emd\n", - "import emeval.metrics.reference_trajectory as emr" - ] - }, { "cell_type": "code", "execution_count": null, @@ -84,8 +59,7 @@ "outputs": [], "source": [ "import pandas as pd\n", - "pd.options.display.float_format = '{:.6f}'.format\n", - "import arrow" + "pd.options.display.float_format = '{:.6f}'.format" ] }, { @@ -96,9 +70,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "import scipy as sp\n", - "import scipy.interpolate\n", - "import scipy.integrate" + "from pathlib import Path" ] }, { @@ -122,21 +94,19 @@ "outputs": [], "source": [ "# For maps\n", - "import geopandas as gpd\n", - "import shapely as shp\n", - "import folium\n", - "import branca.element as bre" + "import geopandas as gpd" ] }, { "cell_type": "code", "execution_count": null, - "id": "f3e62931", + "id": "2b9b6813", "metadata": {}, "outputs": [], "source": [ - "# For easier debugging while working on modules\n", - "import importlib" + "output_dir = Path(\"./images\")\n", + "if not output_dir.exists():\n", + " output_dir.mkdir()" ] }, { @@ -154,6 +124,8 @@ "metadata": {}, "outputs": [], "source": [ + "## Retrieve the specs for the given experiment.\n", + "\n", "DATASTORE_LOC = \"bin/data\"\n", "AUTHOR_EMAIL = \"shankari@eecs.berkeley.edu\"\n", "sd_la = eisd.FileSpecDetails(DATASTORE_LOC, AUTHOR_EMAIL, \"unimodal_trip_car_bike_mtv_la\")\n", @@ -171,7 +143,9 @@ "%%capture\n", "pv_la = eipv.PhoneView(sd_la)\n", "pv_sj = eipv.PhoneView(sd_sj)\n", - "pv_ucb = eipv.PhoneView(sd_ucb)" + "pv_ucb = eipv.PhoneView(sd_ucb)\n", + "\n", + "## %%capture is a cell-level Jupyter magic functioon that redirects the stdout to null. " ] }, { @@ -192,7 +166,9 @@ "%%capture\n", "ems.fill_sensed_section_ranges(pv_la)\n", "ems.fill_sensed_section_ranges(pv_sj)\n", - "ems.fill_sensed_section_ranges(pv_ucb)" + "ems.fill_sensed_section_ranges(pv_ucb)\n", + "\n", + "## Fill in the sensed sections." ] }, { @@ -210,38 +186,38 @@ "metadata": {}, "outputs": [], "source": [ - "from pathlib import Path\n", - "from typing import Union\n", + "def get_reference_trajectory(spec_id: str, trip_id: str) -> pd.DataFrame:\n", "\n", - "def get_reference_trajectory(spec_file: str, trip_id: str) -> Union[pd.DataFrame, None]:\n", + " '''\n", + " Return a reference trajectory datafrane given a spec ID and a trip ID.\n", + " '''\n", "\n", " root = Path(\"./bin/data\")\n", - " return_file = None\n", + " return_file = pd.DataFrame()\n", "\n", " workdir = root\n", - " if (workdir / spec_file).exists():\n", - " workdir = workdir / spec_file\n", - " assert workdir.is_dir(), f\"{spec_file} is a file, not a dir.\"\n", + " if (workdir / spec_id).exists():\n", + " workdir = workdir / spec_id\n", + " assert workdir.is_dir(), f\"{spec_id} is a file, not a dir.\"\n", " if (workdir / trip_id).exists():\n", " workdir = workdir / trip_id\n", - " assert workdir.is_dir(), f\"{spec_file}.{trip_id} is a file, not a dir.\"\n", + " assert workdir.is_dir(), f\"{spec_id}.{trip_id} is a file, not a dir.\"\n", " files = [f for f in workdir.iterdir()]\n", - " assert files is not None and len(files) > 0, f\"No files found for {spec_file=} and {trip_id=}\"\n", + " assert files is not None and len(files) > 0, f\"No files found for {spec_id=} and {trip_id=}\"\n", "\n", " # sort the files by run number (just in case).\n", " files = list(sorted(files, key=lambda x: int(x.name.split(\"_\")[-1])))\n", "\n", - " # Concatenate all the trips into a single consolidated df but add an extra column that allows you\n", - " # to filter on the run number (if required).\n", " for ix, run_file in enumerate(files):\n", - " if return_file is None:\n", - " return_file = pd.read_csv(run_file)\n", - " return_file['run'] = ix\n", - " else:\n", - " tdf = pd.read_csv(run_file)\n", - " tdf['run'] = ix\n", - " return_file = pd.concat([return_file, tdf], axis=0)\n", + " # Concatenate all the trips into a single consolidated df but add an extra column that allows you\n", + " # to filter on the run number (if required).\n", + "\n", + " # This drops the first (Unnamed: 0) column from the dataframe.\n", + " temp_df = pd.read_csv(run_file).iloc[:, 1:]\n", + " temp_df['run'] = ix\n", + " return_file = pd.concat([return_file, temp_df], axis=0)\n", " \n", + " # Reset the concatenated df's indices for safe slicing and subsetting.\n", " return_file.reset_index(drop=True, inplace=True)\n", " \n", " return return_file" @@ -264,24 +240,33 @@ " for phone_label, phone_detail_map in phone_map.items():\n", " if \"control\" in phone_detail_map[\"role\"]:\n", " continue\n", - " for run_ix, r in enumerate(phone_detail_map[\"evaluation_ranges\"]):\n", - " if r['eval_role_base'] != role:\n", + " for run_ix, run in enumerate(phone_detail_map[\"evaluation_ranges\"]):\n", + " if run['eval_role_base'] != role:\n", " continue\n", "\n", " tr_ss = []\n", " tr_gts = []\n", - " gt_traj = []\n", + " trajectories = []\n", + " ss_location = []\n", " \n", - " for i, tr in enumerate(r[\"evaluation_trip_ranges\"]):\n", - " for ss in tr[\"sensed_section_ranges\"]:\n", + " # Start iterating over trips.\n", + " for i, trip in enumerate(run[\"evaluation_trip_ranges\"]):\n", + "\n", + " # We also need the sensed trip location for computing the support.\n", + " ss_location.append(trip['location_df'])\n", + "\n", + " # Start iterating over every sensed section of the trip.\n", + " for ss in trip[\"sensed_section_ranges\"]:\n", " tr_ss.append(ss)\n", - " for section in tr[\"evaluation_section_ranges\"]:\n", + " \n", + " # Start iterating over every evaluation section of the trip.\n", + " for section in trip[\"evaluation_section_ranges\"]:\n", "\n", " ## get the ground truth section data\n", - " section_gt_leg = pv.spec_details.get_ground_truth_for_leg(tr['trip_id_base'],\n", + " section_gt_leg = pv.spec_details.get_ground_truth_for_leg(trip['trip_id_base'],\n", " section['trip_id_base'],\n", - " tr['start_ts'],\n", - " tr['end_ts'])\n", + " trip['start_ts'],\n", + " trip['end_ts'])\n", " \n", " if section_gt_leg[\"type\"] == \"WAITING\":\n", " continue\n", @@ -293,20 +278,26 @@ "\n", " tr_gts.append(gts)\n", "\n", - " trajectory_data = get_reference_trajectory(pv.spec_details.CURR_SPEC_ID, tr['trip_id_base'])\n", + " trajectory_data = get_reference_trajectory(pv.spec_details.CURR_SPEC_ID, trip['trip_id_base'])\n", " run_id = run_ix\n", + "\n", + " # Again, we reset indices to ensure safe dataframe slicing.\n", + " # NOTE: We do not filter the reference trajectories by OS.\n", " trajectory_data = trajectory_data.loc[\n", - " (trajectory_data.source == os) &\n", " (trajectory_data.run == run_id), :\n", " ].reset_index(drop=True, inplace=False)\n", "\n", - " gt_traj.append(trajectory_data)\n", + " trajectories.append(trajectory_data)\n", "\n", " # now, we build a timeline for each trip\n", - " trip = tr.copy()\n", + " trip = trip.copy()\n", " trip['ss_timeline'] = tr_ss\n", " trip['gts_timeline'] = tr_gts\n", - " trip['trajectory_data'] = pd.concat(gt_traj, axis=0).reset_index(drop=True, inplace=False)\n", + "\n", + " # We concatenate all of the trajectories for a trip into one dataframe.\n", + " trip['trajectory_data'] = pd.concat(trajectories, axis=0).reset_index(drop=True, inplace=False)\n", + " trip['location_data'] = ss_location\n", + " \n", " trips.append(trip)\n", " \n", " return trips" @@ -672,7 +663,7 @@ "source": [ "def get_binary_class_in_sec(os, role, pv, BASE_MODE, test=False, test_trip=None, criterion='duration'):\n", "\n", - " assert criterion in ['duration', 'distance'], f\"{criterion} is not valid criteria\"\n", + " assert criterion in ['duration', 'distance'], f\"{criterion=} is not implemented or recognized.\"\n", "\n", " if not test:\n", " if type(pv) is not list: pv = [pv]\n", @@ -706,10 +697,10 @@ " \n", " else:\n", " filtered_trajectory = trajectory_data.loc[\n", - " (filtered_trajectory.ts >= range_start) & (filtered_trajectory.ts <= range_end), :\n", + " (trajectory_data.ts >= range_start) & (trajectory_data.ts <= range_end), :\n", " ]\n", "\n", - " if filtered_trajetory.shape[0] > 0:\n", + " if filtered_trajectory.shape[0] > 0:\n", " dist = add_dist(filtered_trajectory).distance.sum()\n", " else:\n", " dist = 0\n", @@ -744,13 +735,16 @@ "metadata": {}, "outputs": [], "source": [ - "def get_F_score(os, role, pv, BASE_MODE, beta=1, test=False, test_trip=None):\n", + "def get_F_score(os, role, pv, BASE_MODE, beta=1, test=False, test_trip=None, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion}= not recognized or implemented.\"\n", + "\n", " if not test:\n", " assert os in ['android', 'ios'], 'UNKNOWN OS'\n", " assert role in ['accuracy_control', 'HAHFDC', 'HAMFDC', 'MAHFDC', 'power_control'], \"UNKNOWN ROLE\"\n", - " (TP, FP, FN, TN) = get_binary_class_in_sec(os, role, pv, BASE_MODE)\n", + " (TP, FP, FN, TN) = get_binary_class_in_sec(os, role, pv, BASE_MODE, criterion=criterion)\n", " else:\n", - " (TP, FP, FN, TN) = get_binary_class_in_sec(os, role, pv, BASE_MODE, test=True, test_trip=test_trip)\n", + " (TP, FP, FN, TN) = get_binary_class_in_sec(os, role, pv, BASE_MODE, test=True, test_trip=test_trip, criterion=criterion)\n", " F_score = {}\n", " for mode in set(BASE_MODE.values()):\n", " numerator = (1 + beta**2) * TP.setdefault(mode, 0)\n", @@ -783,28 +777,73 @@ "metadata": {}, "outputs": [], "source": [ - "def get_support(os, role, pv, BASE_MODE):\n", + "def get_support(os, role, pv, BASE_MODE, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion=} not recognized or implemented.\"\n", + "\n", " if type(pv) is not list: pv = [pv]\n", " trips = []\n", " for v in pv:\n", " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", " support = {}\n", " for trip in trips:\n", - " ## get gts dur\n", - " gt_dur = 0\n", + "\n", + " # get the trajectory.\n", + " trip_trajectory = trip['trajectory_data']\n", + "\n", + " # get the location info.\n", + " location_df = trip['location_data']\n", + "\n", + " gt_dur, ss_dur = 0, 0\n", + " gt_dist, ss_dist = 0, 0\n", "\n", " for gts in trip['gts_timeline']:\n", " mode = BASE_MODE[gts['mode']]\n", - " support[mode] = support.setdefault(mode, 0) + gts['end_ts'] - gts['start_ts']\n", - " gt_dur += (gts['end_ts'] - gts['start_ts'])\n", + " if criterion == 'distance':\n", + "\n", + " # Retrieve the relevant data.\n", + " filtered_trajectory = trip_trajectory.loc[\n", + " (trip_trajectory.ts >= gts['start_ts']) & (trip_trajectory.ts <= gts['end_ts']), :\n", + " ]\n", + "\n", + " if filtered_trajectory.shape[0] > 0:\n", + " dist = add_dist(filtered_trajectory).distance.sum()\n", + " else:\n", + " dist = 0\n", + "\n", + " support[mode] = support.setdefault(mode, 0) + dist\n", + " gt_dist += dist\n", + " else:\n", + " duration = gts['end_ts'] - gts['start_ts']\n", + " support[mode] = support.setdefault(mode, 0) + duration\n", + " gt_dur += duration\n", + "\n", " ## check if there is a NO_GT mode\n", - " ss_dur = 0\n", " for ss in trip['ss_timeline']:\n", - " try:\n", - " ss_dur += (ss['end_ts'] - ss['start_ts'])\n", - " except:\n", - " ss_dur += (ss['data']['end_ts'] - ss['data']['start_ts'])\n", - " support['NO_GT'] = support.setdefault('NO_GT', 0) + max(0, ss_dur - gt_dur)\n", + " if criterion == 'duration':\n", + " try:\n", + " ss_dur += (ss['end_ts'] - ss['start_ts'])\n", + " except:\n", + " ss_dur += (ss['data']['end_ts'] - ss['data']['start_ts'])\n", + " else:\n", + "\n", + " # Filter the location data.\n", + " filtered_location = location_df.loc[\n", + " (location_df.ts >= ss['start_ts']) & (location_df.ts <= ss['end_ts']), :\n", + " ]\n", + "\n", + " if filtered_location.shape[0] > 0:\n", + " dist = add_dist(filtered_location).distance.sum()\n", + " else:\n", + " dist = 0\n", + " \n", + " ss_dist += dist\n", + "\n", + " if criterion == 'duration':\n", + " support['NO_GT'] = support.setdefault('NO_GT', 0) + max(0, ss_dur - gt_dur)\n", + " else:\n", + " support['NO_GT'] = support.setdefault('NO_GT', 0) + max(0, ss_dist - gt_dist)\n", + " \n", " return support" ] }, @@ -827,10 +866,10 @@ "metadata": {}, "outputs": [], "source": [ - "def weighted_f_score(os, role, pv, BASE_MODE):\n", - " support = get_support(os, role, pv, BASE_MODE)\n", + "def weighted_f_score(os, role, pv, BASE_MODE, criterion='duration'):\n", + " support = get_support(os, role, pv, BASE_MODE, criterion=criteriono)\n", " total_support = sum(support.values())\n", - " F_scores = get_F_score(os, role, pv, BASE_MODE)\n", + " F_scores = get_F_score(os, role, pv, BASE_MODE, criterion=criterion)\n", " weighted_f_score = sum(\n", " support[mode]/total_support * F_scores.setdefault(mode, 0) \n", " for mode in support.keys()\n", @@ -846,14 +885,17 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_f_scores(os):\n", + "def plot_f_scores(os, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion=} is not implemented or recognized.\"\n", + "\n", " fig, ax = plt.subplots(1,3, figsize = (15,5), dpi=300, sharey=True, sharex=True)\n", " for i, role in enumerate(['HAHFDC', 'HAMFDC', 'MAHFDC']):\n", - " raw = get_F_score(os, role, [pv_la, pv_sj, pv_ucb], RBMM)\n", - " m_clean = get_F_score(os, role, [mcv_la, mcv_sj, mcv_ucb], CBMM)\n", - " g_clean = get_F_score(os, role, [gcv_la, gcv_sj, gcv_ucb], CBMM)\n", - " rf = get_F_score(os, role, [rfv_la, rfv_sj, rfv_ucb], RFBMM)\n", - " gis = get_F_score(os, role, [gisv_la, gisv_sj, gisv_ucb], GISBMM)\n", + " raw = get_F_score(os, role, [pv_la, pv_sj, pv_ucb], RBMM, criterion=criterion)\n", + " m_clean = get_F_score(os, role, [mcv_la, mcv_sj, mcv_ucb], CBMM, criterion=criterion)\n", + " g_clean = get_F_score(os, role, [gcv_la, gcv_sj, gcv_ucb], CBMM, criterion=criterion)\n", + " rf = get_F_score(os, role, [rfv_la, rfv_sj, rfv_ucb], RFBMM, criterion=criterion)\n", + " gis = get_F_score(os, role, [gisv_la, gisv_sj, gisv_ucb], GISBMM, criterion=criterion)\n", " df = pd.DataFrame(\n", " [raw, m_clean, g_clean, rf, gis], \n", " index = ['raw', 'master clean', 'GIS clean', 'random forest', 'GIS'], \n", @@ -865,7 +907,7 @@ " ax[i].set_xticklabels(df.columns, rotation = 80)\n", " title = f\"$F_1$ Scores by Base Mode for Phones Running {os} at Various Configuration Settings\"\n", " plt.suptitle(title, weight='bold', size='x-large')\n", - " fig.savefig(f\"images/distance_f_scores_for_{os}\", bbox_inches=\"tight\")" + " fig.savefig(f\"images/{criterion}_f_scores_for_{os}\", bbox_inches=\"tight\")" ] }, { @@ -883,14 +925,17 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_f_scores_selected():\n", + "def plot_f_scores_selected(criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion=} is not implemented or recognized.\"\n", + "\n", " fig, ax = plt.subplots(1,2, figsize = (10,3), dpi=300, sharey=True, sharex=True)\n", " for i, (os, role) in enumerate([['android', 'HAMFDC'], ['ios', 'HAHFDC']]):\n", - " raw = get_F_score(os, role, [pv_la, pv_sj, pv_ucb], RBMM)\n", - " m_clean = get_F_score(os, role, [mcv_la, mcv_sj, mcv_ucb], CBMM)\n", - " g_clean = get_F_score(os, role, [gcv_la, gcv_sj, gcv_ucb], CBMM)\n", - " rf = get_F_score(os, role, [rfv_la, rfv_sj, rfv_ucb], RFBMM)\n", - " gis = get_F_score(os, role, [gisv_la, gisv_sj, gisv_ucb], GISBMM)\n", + " raw = get_F_score(os, role, [pv_la, pv_sj, pv_ucb], RBMM, criterion=criterion)\n", + " m_clean = get_F_score(os, role, [mcv_la, mcv_sj, mcv_ucb], CBMM, criterion=criterion)\n", + " g_clean = get_F_score(os, role, [gcv_la, gcv_sj, gcv_ucb], CBMM, criterion=criterion)\n", + " rf = get_F_score(os, role, [rfv_la, rfv_sj, rfv_ucb], RFBMM, criterion=criterion)\n", + " gis = get_F_score(os, role, [gisv_la, gisv_sj, gisv_ucb], GISBMM, criterion=criterion)\n", " df = pd.DataFrame(\n", " [raw, m_clean, g_clean, rf, gis], \n", " index = ['raw', 'master clean', 'GIS clean', 'random forest', 'GIS'], \n", @@ -900,8 +945,6 @@ " df.T.plot(style='o', ax=ax[i], title=f'$F_1$ Scores by Base Mode \\n{os}:{role} ').legend(loc='lower left')\n", " ax[i].set_xticks(range(len(df.T)))\n", " ax[i].set_xticklabels(df.columns, rotation = 80)\n", - "# title = f\"$F_1$ Scores by Base Mode for Selected OS Setting Configurations\"\n", - "# plt.suptitle(title, weight='bold', size='x-large')\n", " fig.savefig(f\"images/distance_f_scores_selected\", bbox_inches=\"tight\")" ] }, @@ -998,7 +1041,6 @@ " cm = {}\n", " for gts in gt_timeline:\n", " if ss['end_ts'] >= gts['start_ts'] and ss['start_ts'] <= gts['end_ts']:\n", - " # dur = min(ss['end_ts'], gts['end_ts']) - max(ss['start_ts'], gts['start_ts'])\n", " range_start = max(ss['start_ts'], gts['start_ts'])\n", " range_end = min(ss['end_ts'], gts['end_ts'])\n", "\n", @@ -1011,15 +1053,14 @@ " if filtered_trajectory_data.shape[0] > 0:\n", " dist = add_dist(filtered_trajectory_data).distance.sum()\n", " else:\n", + " # print('Sensed section: ', ss, ' | GT section: ', gts, ' | Trip ID: ', trip['trip_id'], ' | OS: ', os, ' | Role: ', role)\n", + " # print(50*'-')\n", " dist = 0\n", " \n", - " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dist\n", " else:\n", " \n", " dur = range_end - range_start\n", - "\n", - " # cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dur\n", " \n", " cm['sensed_mode'] = ss['mode']\n", @@ -1066,38 +1107,31 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_cm(os, pv, d_type, INDEX_MAP=None, criterion='duration'):\n", + "def plot_cm(os, pv, d_type, INDEX_MAP=None, criterion='duration', normalization=None):\n", "\n", " assert criterion in ['duration', 'distance'], f\"{criterion=} is not defined.\"\n", + " assert normalization in ['rows', 'columns', 'all', None], f\"{normalization=} is not reecognized or supported\"\n", "\n", " fig, ax = plt.subplots(1,3, figsize=(22,10), dpi=300, sharey=True)\n", " y=.95\n", " fig.text(0.5, 0.0, 'Predicted Label', ha='center', fontsize='xx-large')\n", " fig.text(0.04, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", "\n", - " # Small snippet to create an op folder.\n", - " op_dir = Path(\"./images\")\n", - " if not op_dir.exists():\n", - " op_dir.mkdir()\n", - "\n", " for k, role in enumerate([\"HAHFDC\", \"HAMFDC\", \"MAHFDC\"]):\n", " if d_type =='raw':\n", " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum()\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " # fname = f\"images/raw_cm_{os}\"\n", - " fname = f\"images/raw_distance_cm_{os}\"\n", + " fname = f\"images/raw_{criterion}_cm_{os}\"\n", " elif d_type == 'clean':\n", " title = f\"Confusion Matrices for Clean Output Data on Phones Running {os} \\n by Calibration Settings\"\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " # fname = f\"images/clean_cm_{os}\"\n", - " fname = f\"images/clean_distance_cm_{os}\"\n", + " fname = f\"images/clean_{criterion}_cm_{os}\"\n", " elif d_type == 'random_forest' or 'gis':\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " # fname = f\"images/{d_type}_cm_{os}\"\n", - " fname = f\"images/{d_type}_distance_cm_{os}\"\n", + " fname = f\"images/{d_type}_{criterion}_cm_{os}\"\n", " if d_type == 'random_forest':\n", " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os} \\n by Calibration Settings\"\n", " else:\n", @@ -1107,10 +1141,16 @@ " df = df.reindex(\n", " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", " ).fillna(0)\n", - "# df = df.div(df.sum(axis=1), axis=0)\n", + "\n", + " if normalization == 'rows':\n", + " df = df/np.sum(df, axis=1, keepdims=True)\n", + " elif normalization == 'columns':\n", + " df = df/np.sum(df, axis=0, keepdims=True)\n", + " elif normalization == 'all':\n", + " df = df/df.sum()\n", + "\n", " cm = ax[k].imshow(df.transpose(), interpolation='nearest', cmap=plt.cm.coolwarm, aspect='auto')\n", " ax[k].set_title(role)\n", - " # plt.colorbar(cm, ax=ax[0])\n", " tick_marks = np.arange(len(df))\n", " ax[k].set_yticks(np.arange(len(df.columns)))\n", " ax[k].set_xticks(np.arange(len(df)))\n", @@ -1121,7 +1161,6 @@ " ax[k].text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", " color='white' \n", " if df.transpose().iat[i,j] < color_thresh \n", - "# or df.transpose().iat[i,j] in df.max()\n", " else 'black')\n", " fig.subplots_adjust(right=0.8)\n", " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n", @@ -1146,7 +1185,10 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_select_cm(os, role):\n", + "def plot_select_cm(os, role, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], f\"{criterion} not in supported criteria.\"\n", + "\n", " IIM = {0 : 'UNKNOWN', 1 : 'WALKING', 2 : 'BICYCLING', 3 : 'BUS', 4 : 'TRAIN', 5 : 'CAR', 6 : 'AIR_OR_HSR', 7 : 'SUBWAY', 8 : 'TRAM', 9 : 'LIGHT_RAIL'\n", " }\n", " CIM = {0 : 'IN_VEHICLE', 1 : 'BICYCLING', 2 : 'ON_FOOT', 3 : 'STILL', 4 : 'UNKNOWN', 5 : 'TILTING', 7 : 'WALKING', 8 : 'RUNNING', 9 : 'NONE', 10 : 'STOPPED_WHILE_IN_VEHICLE', 11 : 'AIR_OR_HSR'}\n", @@ -1155,8 +1197,7 @@ " fig.text(0.5, -0.1, 'Predicted Label', ha='center', fontsize='xx-large')\n", " fig.text(0.08, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", " title = f\"Confusion Matrices for Phones Running {os}:{role}\"\n", - " # fname = f\"images/selected_cm_{os}\"\n", - " fname = f\"images/selected_distance_cm_{os}\"\n", + " fname = f\"images/selected_{criterion}_cm_{os}\"\n", " for k, pv in enumerate(\n", " [[pv_la, pv_sj, pv_ucb], \n", " [mcv_la, mcv_sj, mcv_ucb],\n", @@ -1164,30 +1205,25 @@ " [rfv_la, rfv_sj, rfv_ucb], \n", " [gisv_la, gisv_sj, gisv_ucb]]):\n", " if k == 0:\n", - " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum()\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum()\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", " elif k == 1 or k == 2:\n", - " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum().rename(index=CIM)\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=CIM)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", "\n", " else:\n", - " df = pd.DataFrame(get_confusion_matrix(os, role, pv)).groupby('sensed_mode').sum().rename(index=IIM)\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=IIM)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", " df = df.reindex(\n", " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", " ).fillna(0)\n", " cm = ax[k].imshow(df.transpose(), interpolation='nearest', cmap=plt.cm.coolwarm, aspect='auto')\n", - " # title_map = {0 : f'raw output confusion matrix \\n{os}:{role}', \n", - " # 1 : f'master clean output confusion matrix \\n{os}:{role}',\n", - " # 2 : f'GIST clean output confusion matrix \\n{os}:{role}', \n", - " # 3 : f'random forest output confusion matrix \\n{os}:{role}', \n", - " # 4 : f'GIS output confusion matrix \\n{os}:{role}'}\n", - "\n", - " title_map = {0 : f'raw output distance confusion matrix \\n{os}:{role}', \n", - " 1 : f'master clean output distance confusion matrix \\n{os}:{role}',\n", - " 2 : f'GIST clean output distance confusion matrix \\n{os}:{role}', \n", - " 3 : f'random forest output distance confusion matrix \\n{os}:{role}', \n", - " 4 : f'GIS output distance confusion matrix \\n{os}:{role}'}\n", + "\n", + " title_map = {0 : f'raw output ({criterion}) confusion matrix \\n{os}:{role}', \n", + " 1 : f'master clean output ({criterion}) confusion matrix \\n{os}:{role}',\n", + " 2 : f'GIST clean output ({criterion}) confusion matrix \\n{os}:{role}', \n", + " 3 : f'random forest output ({criterion}) confusion matrix \\n{os}:{role}', \n", + " 4 : f'GIS output ({criterion}) confusion matrix \\n{os}:{role}'}\n", " \n", " ax[k].set_title(title_map[k])\n", " tick_marks = np.arange(len(df))\n", @@ -1203,10 +1239,7 @@ " else 'black')\n", " fig.subplots_adjust(right=0.8)\n", " cbar_ax = fig.add_axes([0.825, 0.15, 0.025, 0.7])\n", - " fig.colorbar(cm, cax=cbar_ax)\n", - "# plt.suptitle(title, weight='bold', size='x-large', y=y)\n", - "\n", - "# plt.savefig(fname=fname, bbox_inches=\"tight\")" + " fig.colorbar(cm, cax=cbar_ax)" ] }, { @@ -1356,6 +1389,16 @@ "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc21852e", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance', normalization='columns')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1469,7 +1512,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_f_scores('ios')" + "plot_f_scores('ios', criterion='distance')" ] }, { @@ -1479,7 +1522,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_f_scores('android')" + "plot_f_scores('android', criterion='distance')" ] }, { @@ -1517,7 +1560,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_f_scores_selected()" + "plot_f_scores_selected(criterion='distance')" ] }, { @@ -1528,12 +1571,12 @@ "outputs": [], "source": [ "print(\n", - " \"GIS android \\t\",weighted_f_score('android', 'HAMFDC', [gisv_la,gisv_sj,gisv_ucb], GISBMM), '\\n',\n", - " \"GIS ios \\t\", weighted_f_score('ios', 'HAHFDC', [gisv_la,gisv_sj,gisv_ucb], GISBMM)\n", + " \"GIS android \\t\",weighted_f_score('android', 'HAMFDC', [gisv_la,gisv_sj,gisv_ucb], GISBMM, criterion='distance'), '\\n',\n", + " \"GIS ios \\t\", weighted_f_score('ios', 'HAHFDC', [gisv_la,gisv_sj,gisv_ucb], GISBMM, criterion='distance')\n", ")\n", "print(\n", - " \"Random Forest android \\t\",weighted_f_score('android', 'HAMFDC', [rfv_la,rfv_sj,rfv_ucb], GISBMM), '\\n',\n", - " \"Random Forest ios \\t\",weighted_f_score('ios', 'HAHFDC', [rfv_la,rfv_sj,rfv_ucb], GISBMM)\n", + " \"Random Forest android \\t\",weighted_f_score('android', 'HAMFDC', [rfv_la,rfv_sj,rfv_ucb], GISBMM, criterion='distance'), '\\n',\n", + " \"Random Forest ios \\t\",weighted_f_score('ios', 'HAHFDC', [rfv_la,rfv_sj,rfv_ucb], GISBMM, criterion='distance')\n", ")" ] }, @@ -1553,10 +1596,10 @@ "outputs": [], "source": [ "for pv_l in [[pv_la, pv_sj, pv_ucb],[mcv_la, mcv_sj, mcv_ucb],[rfv_la,rfv_sj,rfv_ucb],[gisv_la,gisv_sj,gisv_ucb]]:\n", - " df = pd.DataFrame(get_confusion_matrix('android', 'HAMFDC', pv_l)).groupby('sensed_mode').sum().rename(index=IIM)\n", + " df = pd.DataFrame(get_confusion_matrix('android', 'HAMFDC', pv_l, criterion='distance')).groupby('sensed_mode').sum().rename(index=IIM)\n", " w_n = (df['WALKING']['NO_SENSED_START'] + df['WALKING']['NO_SENSED_MIDDLE'] + df['WALKING']['NO_SENSED_END'])\n", " print(\"NO_SENSED trip perdiction for WALKING android:HAMFDC: \\t\", w_n / df['WALKING'].sum())\n", - " df = pd.DataFrame(get_confusion_matrix('ios', 'HAHFDC', pv_l)).groupby('sensed_mode').sum().rename(index=IIM)\n", + " df = pd.DataFrame(get_confusion_matrix('ios', 'HAHFDC', pv_l, criterion='distance')).groupby('sensed_mode').sum().rename(index=IIM)\n", " try:\n", " w_n = (df['WALKING']['NO_SENSED_START'] + df['WALKING']['NO_SENSED_MIDDLE'] + df['WALKING']['NO_SENSED_END'])\n", " except:\n", From b1eb149e2302c524ebb7075b6bdfbf7a99761c68 Mon Sep 17 00:00:00 2001 From: Kulhalli Date: Tue, 25 Jul 2023 17:43:12 -0400 Subject: [PATCH 6/9] Renamed some references; optimized imports --- classification_analysis.ipynb | 63 ++++++++++++++++++++++++++--------- 1 file changed, 48 insertions(+), 15 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index f378699..978c2e2 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -145,7 +145,7 @@ "pv_sj = eipv.PhoneView(sd_sj)\n", "pv_ucb = eipv.PhoneView(sd_ucb)\n", "\n", - "## %%capture is a cell-level Jupyter magic functioon that redirects the stdout to null. " + "## %%capture is a cell-level Jupyter magic function that redirects the stdout to null. " ] }, { @@ -945,7 +945,7 @@ " df.T.plot(style='o', ax=ax[i], title=f'$F_1$ Scores by Base Mode \\n{os}:{role} ').legend(loc='lower left')\n", " ax[i].set_xticks(range(len(df.T)))\n", " ax[i].set_xticklabels(df.columns, rotation = 80)\n", - " fig.savefig(f\"images/distance_f_scores_selected\", bbox_inches=\"tight\")" + " fig.savefig(f\"images/{criterion}_f_scores_selected\", bbox_inches=\"tight\")" ] }, { @@ -1110,7 +1110,7 @@ "def plot_cm(os, pv, d_type, INDEX_MAP=None, criterion='duration', normalization=None):\n", "\n", " assert criterion in ['duration', 'distance'], f\"{criterion=} is not defined.\"\n", - " assert normalization in ['rows', 'columns', 'all', None], f\"{normalization=} is not reecognized or supported\"\n", + " assert normalization in ['pred', 'gt', 'all', None], f\"{normalization=} is not reecognized or supported\"\n", "\n", " fig, ax = plt.subplots(1,3, figsize=(22,10), dpi=300, sharey=True)\n", " y=.95\n", @@ -1142,12 +1142,18 @@ " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", " ).fillna(0)\n", "\n", - " if normalization == 'rows':\n", - " df = df/np.sum(df, axis=1, keepdims=True)\n", - " elif normalization == 'columns':\n", - " df = df/np.sum(df, axis=0, keepdims=True)\n", + " if normalization == 'pred':\n", + " # After transposing, predictions are axis=0\n", + " df = df/df.sum(axis=1, skipna=True)\n", + " elif normalization == 'gt':\n", + " # After transposing, GTs aree axis=1\n", + " df = df/df.sum(axis=0, skipna=True)\n", " elif normalization == 'all':\n", - " df = df/df.sum()\n", + " # Axis-agnostic.\n", + " df = df/df.sum(skipna=True)\n", + " \n", + " # div-by-zero causes NaN.\n", + " df = df.fillna(0)\n", "\n", " cm = ax[k].imshow(df.transpose(), interpolation='nearest', cmap=plt.cm.coolwarm, aspect='auto')\n", " ax[k].set_title(role)\n", @@ -1158,10 +1164,18 @@ " ax[k].set_xticklabels(df.index, rotation=80)\n", " color_thresh = df.max().max() / 4\n", " for i, j in itertools.product(range(df.shape[1]), range(df.shape[0]) ):\n", - " ax[k].text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", - " color='white' \n", - " if df.transpose().iat[i,j] < color_thresh \n", - " else 'black')\n", + "\n", + " if normalization is None:\n", + " ax[k].text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", + " color='white' \n", + " if df.transpose().iat[i,j] < color_thresh \n", + " else 'black')\n", + " else:\n", + " ax[k].text(j, i, np.round(df.transpose().iat[i,j], 3), horizontalalignment='center', \n", + " color='white' \n", + " if df.transpose().iat[i,j] < color_thresh \n", + " else 'black')\n", + " \n", " fig.subplots_adjust(right=0.8)\n", " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n", " fig.colorbar(cm, cax=cbar_ax)\n", @@ -1386,7 +1400,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw')" ] }, { @@ -1396,7 +1410,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance', normalization='columns')" + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" ] }, { @@ -1484,7 +1498,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance')" + "plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance', normalization='gt')" ] }, { @@ -1525,6 +1539,17 @@ "plot_f_scores('android', criterion='distance')" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb912632", + "metadata": {}, + "outputs": [], + "source": [ + "def export_confusion_matrix(matrix: pd.DataFrame, output_dir: Path):\n", + " pass" + ] + }, { "cell_type": "markdown", "id": "9a9934bf", @@ -2153,6 +2178,14 @@ "test_trip = [{'ss_timeline' : [{'mode' : 'WALKING', 'start_ts' : 0, 'end_ts' : 1}], 'gts_timeline' : []}]\n", "get_binary_class_in_sec(..., ..., ..., test_BMM, test=True, test_trip=test_trip)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8448910", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 84a51b1b713208ea72ed047e22cd3a179abc40b1 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Fri, 28 Jul 2023 11:02:29 -0400 Subject: [PATCH 7/9] Cleaning logs and incorporating proper count normalization --- classification_analysis.ipynb | 735 ++++++++++++++++++++++++++-------- 1 file changed, 564 insertions(+), 171 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index 978c2e2..13b2e6a 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "09429718", + "id": "f789b3ed", "metadata": {}, "source": [ "# Classification Analysis\n", @@ -11,7 +11,7 @@ }, { "cell_type": "markdown", - "id": "e0cb31cf", + "id": "e1b41b19", "metadata": {}, "source": [ "## Dependencies" @@ -38,7 +38,7 @@ "source": [ "# for analysized view\n", "import emeval.analysed.phone_view as eapv\n", - "from emeval.analysed.location_smoothing import add_dist, calDistance" + "import emeval.analysed.location_smoothing as location_smoothing" ] }, { @@ -70,7 +70,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "from pathlib import Path" + "import pathlib" ] }, { @@ -104,14 +104,14 @@ "metadata": {}, "outputs": [], "source": [ - "output_dir = Path(\"./images\")\n", + "output_dir = pathlib.Path(\"./images\")\n", "if not output_dir.exists():\n", " output_dir.mkdir()" ] }, { "cell_type": "markdown", - "id": "845eb857", + "id": "0f0e2934", "metadata": {}, "source": [ "## Load in Phone Views from the file spec" @@ -150,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "0d61207a", + "id": "c092aa71", "metadata": {}, "source": [ "### Get sensed data for each trip" @@ -173,7 +173,7 @@ }, { "cell_type": "markdown", - "id": "a0caef04", + "id": "f546c09e", "metadata": {}, "source": [ "## Get sensed and ground truth temporal histories (timelines)" @@ -182,45 +182,66 @@ { "cell_type": "code", "execution_count": null, - "id": "eb5df932", + "id": "0757b7c3", "metadata": {}, "outputs": [], "source": [ - "def get_reference_trajectory(spec_id: str, trip_id: str) -> pd.DataFrame:\n", + "def fetch_trajectories(spec_id: str, run_ix: int) -> pd.DataFrame:\n", "\n", " '''\n", " Return a reference trajectory datafrane given a spec ID and a trip ID.\n", " '''\n", "\n", - " root = Path(\"./bin/data\")\n", + " root = pathlib.Path(\"./bin/data\")\n", " return_file = pd.DataFrame()\n", "\n", " workdir = root\n", - " if (workdir / spec_id).exists():\n", - " workdir = workdir / spec_id\n", - " assert workdir.is_dir(), f\"{spec_id} is a file, not a dir.\"\n", - " if (workdir / trip_id).exists():\n", - " workdir = workdir / trip_id\n", - " assert workdir.is_dir(), f\"{spec_id}.{trip_id} is a file, not a dir.\"\n", - " files = [f for f in workdir.iterdir()]\n", - " assert files is not None and len(files) > 0, f\"No files found for {spec_id=} and {trip_id=}\"\n", - "\n", - " # sort the files by run number (just in case).\n", - " files = list(sorted(files, key=lambda x: int(x.name.split(\"_\")[-1])))\n", - "\n", - " for ix, run_file in enumerate(files):\n", - " # Concatenate all the trips into a single consolidated df but add an extra column that allows you\n", - " # to filter on the run number (if required).\n", - "\n", - " # This drops the first (Unnamed: 0) column from the dataframe.\n", - " temp_df = pd.read_csv(run_file).iloc[:, 1:]\n", - " temp_df['run'] = ix\n", - " return_file = pd.concat([return_file, temp_df], axis=0)\n", - " \n", - " # Reset the concatenated df's indices for safe slicing and subsetting.\n", - " return_file.reset_index(drop=True, inplace=True)\n", - " \n", - " return return_file" + " assert (workdir / spec_id).exists(), \"F1\"\n", + "\n", + " workdir = workdir / spec_id\n", + " assert workdir.exists() and workdir.is_dir(), \"F2\"\n", + "\n", + " found_glob = list(workdir.glob(f'*/*_{run_ix}'))\n", + "\n", + " assert len(found_glob) > 0, f\"No files found for {spec_id=}, {run_ix=}\"\n", + "\n", + " df = pd.DataFrame()\n", + "\n", + " for file in found_glob:\n", + " tdf = pd.read_csv(file)\n", + " tdf = tdf.iloc[:, 1:]\n", + " df = pd.concat([df, tdf], axis=0)\n", + " \n", + " return df.reset_index(drop=True, inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d0c0f79", + "metadata": {}, + "outputs": [], + "source": [ + "def get_reference_trajectory(pv, os, role):\n", + "\n", + " dfs = []\n", + "\n", + " for phone_os, phone_map in pv.map().items():\n", + " if os != phone_os:\n", + " continue\n", + " for phone_label, phone_detail_map in phone_map.items():\n", + " if \"control\" in phone_detail_map[\"role\"]:\n", + " continue\n", + " for run_ix, run in enumerate(phone_detail_map[\"evaluation_ranges\"]):\n", + " if run['eval_role_base'] != role:\n", + " continue\n", + " \n", + " df = fetch_trajectories(pv.spec_details.CURR_SPEC_ID, run_ix)\n", + " df['run_ix'] = run_ix\n", + "\n", + " dfs.append(df.copy())\n", + " \n", + " return dfs" ] }, { @@ -246,7 +267,7 @@ "\n", " tr_ss = []\n", " tr_gts = []\n", - " trajectories = []\n", + " ref_trajectories = []\n", " ss_location = []\n", " \n", " # Start iterating over trips.\n", @@ -257,8 +278,9 @@ "\n", " # Start iterating over every sensed section of the trip.\n", " for ss in trip[\"sensed_section_ranges\"]:\n", + "\n", " tr_ss.append(ss)\n", - " \n", + "\n", " # Start iterating over every evaluation section of the trip.\n", " for section in trip[\"evaluation_section_ranges\"]:\n", "\n", @@ -270,7 +292,7 @@ " \n", " if section_gt_leg[\"type\"] == \"WAITING\":\n", " continue\n", - " \n", + "\n", " gts = {'start_ts': section['start_ts'], \n", " 'end_ts': section['end_ts'], \n", " 'mode': section_gt_leg['mode']\n", @@ -278,24 +300,10 @@ "\n", " tr_gts.append(gts)\n", "\n", - " trajectory_data = get_reference_trajectory(pv.spec_details.CURR_SPEC_ID, trip['trip_id_base'])\n", - " run_id = run_ix\n", - "\n", - " # Again, we reset indices to ensure safe dataframe slicing.\n", - " # NOTE: We do not filter the reference trajectories by OS.\n", - " trajectory_data = trajectory_data.loc[\n", - " (trajectory_data.run == run_id), :\n", - " ].reset_index(drop=True, inplace=False)\n", - "\n", - " trajectories.append(trajectory_data)\n", - "\n", " # now, we build a timeline for each trip\n", " trip = trip.copy()\n", " trip['ss_timeline'] = tr_ss\n", " trip['gts_timeline'] = tr_gts\n", - "\n", - " # We concatenate all of the trajectories for a trip into one dataframe.\n", - " trip['trajectory_data'] = pd.concat(trajectories, axis=0).reset_index(drop=True, inplace=False)\n", " trip['location_data'] = ss_location\n", " \n", " trips.append(trip)\n", @@ -305,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "66601561", + "id": "34822553", "metadata": {}, "source": [ "## Define the Base Mode Maps" @@ -313,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "33aa0b6b", + "id": "92bfb059", "metadata": {}, "source": [ "#### raw base mode map" @@ -356,7 +364,7 @@ }, { "cell_type": "markdown", - "id": "eeef9eae", + "id": "d17bfcee", "metadata": {}, "source": [ "#### cleaned base mode map\n", @@ -411,7 +419,7 @@ }, { "cell_type": "markdown", - "id": "e2dce5ef", + "id": "b50bf002", "metadata": {}, "source": [ "### inferred base mode maps\n", @@ -421,7 +429,7 @@ }, { "cell_type": "markdown", - "id": "c8703301", + "id": "d535b22c", "metadata": {}, "source": [ "#### random forest base mode map" @@ -470,7 +478,7 @@ }, { "cell_type": "markdown", - "id": "2ec81457", + "id": "05963457", "metadata": {}, "source": [ "#### rule+GIS base mode map" @@ -519,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "0994d892", + "id": "389af2d9", "metadata": {}, "source": [ "#### Pad the start at end of the timelines for a given trip, while also filling in gaps in the middle" @@ -648,7 +656,7 @@ }, { "cell_type": "markdown", - "id": "8a647018", + "id": "b271e8c8", "metadata": {}, "source": [ "#### Get the classification metrics (true/false positive, true/false negative) for each Base Mode for a given trip/set-of-trips" @@ -668,13 +676,19 @@ " if not test:\n", " if type(pv) is not list: pv = [pv]\n", " trips = []\n", + " trajectories = []\n", " for v in pv:\n", " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", + " trajectories.extend(get_reference_trajectory(v, os, role))\n", " else:\n", " trips = test_trip if type(test_trip) is list else [test_trip]\n", + "\n", + " assert len(trips) == len(trajectories)\n", + "\n", " TP, FN, FP, TN = {}, {}, {}, {}\n", - " for trip in trips:\n", - " trajectory_data = trip['trajectory_data']\n", + " missed = 0\n", + " total = 0\n", + " for ref_trajectory, trip in zip(trajectories, trips):\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for mode in set(BASE_MODE.values()):\n", " for ss in ss_timeline:\n", @@ -696,14 +710,15 @@ " TN[mode] = TN.setdefault(mode, 0) + dur\n", " \n", " else:\n", - " filtered_trajectory = trajectory_data.loc[\n", - " (trajectory_data.ts >= range_start) & (trajectory_data.ts <= range_end), :\n", + "\n", + " filtered_trajectory = ref_trajectory.loc[\n", + " (ref_trajectory.ts >= range_start) & (ref_trajectory.ts <= range_end), :\n", " ]\n", "\n", " if filtered_trajectory.shape[0] > 0:\n", - " dist = add_dist(filtered_trajectory).distance.sum()\n", + " dist = location_smoothing.add_dist(filtered_trajectory).distance.sum()\n", " else:\n", - " dist = 0\n", + " dist - 0\n", "\n", " if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", " TP[mode] = TP.setdefault(mode, 0) + dist\n", @@ -713,13 +728,13 @@ " FN[mode] = FN.setdefault(mode, 0) + dist\n", " else:\n", " TN[mode] = TN.setdefault(mode, 0) + dist\n", - " \n", + " \n", " return TP, FP, FN, TN" ] }, { "cell_type": "markdown", - "id": "e1798e4a", + "id": "0ac6517f", "metadata": {}, "source": [ "# $F_\\beta$ score\n", @@ -764,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "90cf63ee", + "id": "25bb0c0e", "metadata": {}, "source": [ "#### Get the support for each base mode in a set of trips, which is the sum of confusion matrix row sums for each mode that maps to a base mode, $M_{bm} = \\{ m : b(m) = bm, m \\in M \\}$ " @@ -807,7 +822,7 @@ " ]\n", "\n", " if filtered_trajectory.shape[0] > 0:\n", - " dist = add_dist(filtered_trajectory).distance.sum()\n", + " dist = location_smooothing.add_dist(filtered_trajectory).distance.sum()\n", " else:\n", " dist = 0\n", "\n", @@ -849,7 +864,7 @@ }, { "cell_type": "markdown", - "id": "ced133d3", + "id": "b7d4cb0a", "metadata": {}, "source": [ "### Weighted $F_1$ Score\n", @@ -912,7 +927,7 @@ }, { "cell_type": "markdown", - "id": "d2d47aff", + "id": "a13ea136", "metadata": {}, "source": [ "#### Plot $F$ scores for android/ios on select configuration settings" @@ -950,7 +965,7 @@ }, { "cell_type": "markdown", - "id": "7c28321e", + "id": "fcfe9878", "metadata": {}, "source": [ "## Confusion Matrix\n", @@ -959,7 +974,7 @@ }, { "cell_type": "markdown", - "id": "4c276e1e", + "id": "b5504b5b", "metadata": {}, "source": [ "#### cleaned index map" @@ -987,7 +1002,7 @@ }, { "cell_type": "markdown", - "id": "dac4c013", + "id": "0461be70", "metadata": {}, "source": [ "#### inferred index map" @@ -1021,6 +1036,8 @@ "metadata": {}, "outputs": [], "source": [ + "import IPython.display as IDisplay\n", + "\n", "def get_confusion_matrix(os, role, pv, test=False, test_trip=None, criterion='duration'):\n", "\n", " cm_l = []\n", @@ -1030,32 +1047,42 @@ " if type(pv) is not list:\n", " pv = [pv]\n", " trips = []\n", + " trajectories = []\n", " for v in pv :\n", " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", + " trajectories.extend(get_reference_trajectory(v, os, role))\n", + " \n", + " assert len(trips) == len(trajectories)\n", + "\n", " else:\n", " trips = test_trip if type(test_trip) is list else [test_trip]\n", - " for trip in trips:\n", - " trajectory_data = trip['trajectory_data']\n", + " for ref_trajectory, trip in zip(trajectories, trips):\n", " ss_timeline, gt_timeline = align_timelines(trip)\n", " for ss in ss_timeline:\n", " cm = {}\n", " for gts in gt_timeline:\n", + "\n", + " ## This checks to see is a sensed section begins after a gt section starts.\n", " if ss['end_ts'] >= gts['start_ts'] and ss['start_ts'] <= gts['end_ts']:\n", " range_start = max(ss['start_ts'], gts['start_ts'])\n", " range_end = min(ss['end_ts'], gts['end_ts'])\n", "\n", " if criterion == 'distance':\n", "\n", - " filtered_trajectory_data = trajectory_data.loc[\n", - " (trajectory_data.ts >= range_start) & (trajectory_data.ts <= range_end), :\n", + " filtered_trajectory_data = ref_trajectory.loc[\n", + " (ref_trajectory.ts >= range_start) & (ref_trajectory.ts <= range_end), :\n", " ]\n", "\n", " if filtered_trajectory_data.shape[0] > 0:\n", - " dist = add_dist(filtered_trajectory_data).distance.sum()\n", + " dist = location_smoothing.add_dist(filtered_trajectory_data).distance.sum()\n", " else:\n", - " # print('Sensed section: ', ss, ' | GT section: ', gts, ' | Trip ID: ', trip['trip_id'], ' | OS: ', os, ' | Role: ', role)\n", - " # print(50*'-')\n", " dist = 0\n", + "\n", + " if gts['mode'] != 'NO_GT_MIDDLE':\n", + " if ss['mode'] == 'NO_SENSED_MIDDLE':\n", + " dur = range_end - range_start\n", + " TEN_MINUTES = 10 * 60\n", + " assert dur < TEN_MINUTES, f\"{dur=} > {TEN_MINUTES}\"\n", " \n", " cm[gts['mode']] = cm.setdefault(gts['mode'], 0) + dist\n", " else:\n", @@ -1103,7 +1130,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4ecc8ccb", + "id": "e31f82d0", "metadata": {}, "outputs": [], "source": [ @@ -1117,6 +1144,9 @@ " fig.text(0.5, 0.0, 'Predicted Label', ha='center', fontsize='xx-large')\n", " fig.text(0.04, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", "\n", + " ## TODO: Use thee GIS Confusion matrix.\n", + " save_df = None\n", + "\n", " for k, role in enumerate([\"HAHFDC\", \"HAMFDC\", \"MAHFDC\"]):\n", " if d_type =='raw':\n", " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", @@ -1131,6 +1161,8 @@ " elif d_type == 'random_forest' or 'gis':\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " if (os == 'android' and role == 'HAMFDC') or (os == 'ios' and role == 'HAHFDC'):\n", + " save_df = df\n", " fname = f\"images/{d_type}_{criterion}_cm_{os}\"\n", " if d_type == 'random_forest':\n", " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os} \\n by Calibration Settings\"\n", @@ -1151,6 +1183,9 @@ " elif normalization == 'all':\n", " # Axis-agnostic.\n", " df = df/df.sum(skipna=True)\n", + "\n", + " if normalization is not None:\n", + " fname = fname + \"_normalized\"\n", " \n", " # div-by-zero causes NaN.\n", " df = df.fillna(0)\n", @@ -1181,12 +1216,14 @@ " fig.colorbar(cm, cax=cbar_ax)\n", " plt.suptitle(title, weight='bold', size='x-large', y=y)\n", "\n", - " plt.savefig(fname=fname, bbox_inches=\"tight\")" + " plt.savefig(fname=fname, bbox_inches=\"tight\")\n", + "\n", + " return save_df" ] }, { "cell_type": "markdown", - "id": "85facc4b", + "id": "54616c57", "metadata": {}, "source": [ "#### plot the confusion matrices at each pipeline output stage on android/ios for select configuration settings" @@ -1195,7 +1232,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b1fe08b0", + "id": "7e65ffd6", "metadata": {}, "outputs": [], "source": [ @@ -1256,9 +1293,222 @@ " fig.colorbar(cm, cax=cbar_ax)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d8d54ad", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare confusion matrices for various metrics\n", + "def compare_cm(oses, roles, criteria, normalization=None):\n", + "\n", + " pv = [gisv_sj, gisv_ucb, gisv_la]\n", + " \n", + " fig, ax = plt.subplots(1,len(roles) * len(criteria), figsize=(30,8), dpi=300, sharey=True)\n", + " y=.95\n", + " fig.text(0.5, -0.1, 'Predicted Label', ha='center', fontsize='xx-large')\n", + " fig.text(0.08, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", + " title = f\"Confusion Matrices for Phones Running {','.join(str(zip(oses, roles)))}\"\n", + " fname = f\"images/compare_{','.join(criteria)}_cm_{', '.join(oses)}\"\n", + "\n", + " for ci, criterion in enumerate(criteria):\n", + " print(f\"Generating values for {criterion}\")\n", + " for cj, (os, role) in enumerate(zip(oses, roles)):\n", + " print(f\"Focusing on {os}, {role}\")\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=IIM)\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " df = df.reindex(\n", + " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", + " ).fillna(0)\n", + " \n", + " if normalization == 'pred':\n", + " # After transposing, predictions are axis=0\n", + " df = df.div(df.sum(axis=1), axis=0)\n", + " elif normalization == 'gt':\n", + " # After transposing, GTs aree axis=1\n", + " df = df.div(df.sum(axis=0), axis=1)\n", + " elif normalization == 'all':\n", + " # Axis-agnostic.\n", + " df = df/df.sum(axis=None, skipna=True)\n", + " \n", + " df = df.fillna(0)\n", + " \n", + " # Iterate through all the matrices: i=0,j=0 -> k=0\n", + " k = len(roles)*ci+cj\n", + " cm = ax[k].imshow(df.transpose(), interpolation='nearest', cmap=plt.cm.coolwarm, aspect='auto')\n", + " \n", + " ax[k].set_title(f'GIS output ({criterion}) confusion matrix \\n{os}:{role}')\n", + " tick_marks = np.arange(len(df))\n", + " ax[k].set_yticks(np.arange(len(df.columns)))\n", + " ax[k].set_xticks(np.arange(len(df)))\n", + " ax[k].set_yticklabels(df)\n", + " ax[k].set_xticklabels(df.index, rotation=80)\n", + " color_thresh = df.max().max() / 4\n", + " for i, j in itertools.product(range(df.shape[1]), range(df.shape[0]) ):\n", + " if normalization is None:\n", + " ax[k].text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", + " color='white' \n", + " if df.transpose().iat[i,j] < color_thresh \n", + " else 'black')\n", + " else:\n", + " ax[k].text(j, i, (np.round(df.transpose().iat[i,j], 3)), horizontalalignment='center', \n", + " color='white' \n", + " if df.transpose().iat[i,j] < color_thresh \n", + " else 'black')\n", + " fig.subplots_adjust(right=0.8)\n", + " cbar_ax = fig.add_axes([0.825, 0.15, 0.025, 0.7])\n", + " fig.colorbar(cm, cax=cbar_ax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ce3d2ac", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cm_for_one_role(os, pv, d_type, role, INDEX_MAP=None, criterion='duration'):\n", + " '''\n", + " role: one of [\"HAHFDC\", \"HAMFDC\", \"MAHFDC\"]\n", + " '''\n", + "\n", + " assert criterion in ['duration', 'distance'], \"Criterion unknown.\"\n", + "\n", + " fig, ax = plt.subplots(1,1, figsize=(12,10), dpi=300, sharey=True)\n", + " y=.95\n", + " fig.text(0.5, 0.0, 'Predicted Label', ha='center', fontsize='xx-large')\n", + " fig.text(0.04, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", + " if d_type =='raw':\n", + " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum()\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " fname = f\"images/raw_cm_{os}_{criterion}\"\n", + " elif d_type == 'clean':\n", + " title = f\"Confusion Matrices for Clean Output Data on Phones Running {os} \\n by Calibration Settings\"\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " fname = f\"images/clean_cm_{os}_{criterion}\"\n", + " elif d_type == 'random_forest' or 'gis':\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " fname = f\"images/{d_type}_cm_{os}_{criterion}\"\n", + " if d_type == 'random_forest':\n", + " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os}\"\n", + " else:\n", + " title = f\"Confusion Matrices for Inferred Output Data (GIS) on Phones Running {os}\"\n", + " else:\n", + " assert 0, f'INVALID d_type {d_type}'\n", + " df = df.reindex(\n", + " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", + " ).fillna(0)\n", + "\n", + " cm = ax.imshow(df.transpose(), interpolation='nearest', cmap=plt.cm.coolwarm, aspect='auto')\n", + " ax.set_title(role)\n", + "\n", + " tick_marks = np.arange(len(df))\n", + " ax.set_yticks(np.arange(len(df.columns)))\n", + " ax.set_xticks(np.arange(len(df)))\n", + " ax.set_yticklabels(df, fontsize=12)\n", + " ax.set_xticklabels(df.index, rotation=80, fontsize=12)\n", + " color_thresh = df.max().max() / 4\n", + " for i, j in itertools.product(range(df.shape[1]), range(df.shape[0]) ):\n", + " ax.text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", + " color='white' \n", + " if df.transpose().iat[i,j] < color_thresh \n", + "# or df.transpose().iat[i,j] in df.max()\n", + " else 'black')\n", + " fig.subplots_adjust(right=0.8)\n", + " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n", + " fig.colorbar(cm, cax=cbar_ax)\n", + " plt.suptitle(title, weight='bold', size='x-large', y=y)\n", + "\n", + " plt.savefig(fname=fname, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b1fb03b", + "metadata": {}, + "outputs": [], + "source": [ + "def get_confusion_matrix_df(os, pv, d_type, phone_configuration, INDEX_MAP=None, criterion='duration'):\n", + "\n", + " assert criterion in ['duration', 'distance'], \"Criterion unknown.\"\n", + "\n", + " role = phone_configuration\n", + " if d_type =='raw':\n", + " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum()\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " fname = f\"images/raw_cm_{os}_{criterion}\"\n", + " elif d_type == 'clean':\n", + " title = f\"Confusion Matrices for Clean Output Data on Phones Running {os} \\n by Calibration Settings\"\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " fname = f\"images/clean_cm_{os}_{criterion}\"\n", + " elif d_type == 'random_forest' or 'gis':\n", + " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", + " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", + " fname = f\"images/{d_type}_cm_{os}_{criterion}\"\n", + " if d_type == 'random_forest':\n", + " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os} \\n by Calibration Settings\"\n", + " else:\n", + " title = f\"Confusion Matrices for Inferred Output Data (GIS) on Phones Running {os} \\n by Calibration Settings\"\n", + " else:\n", + " assert 0, f'INVALID d_type {d_type}'\n", + " print(title)\n", + " df = df.reindex(\n", + " columns=['WALKING', 'BICYCLING', 'E_BIKE', 'ESCOOTER', 'CAR', 'BUS', 'SUBWAY', 'LIGHT_RAIL', 'TRAIN', 'NO_GT_START', 'NO_GT_MIDDLE', 'NO_GT_END']\n", + " ).fillna(0)\n", + " df = df.rename(mapper= {x: str.lower(x) for x in df.columns}, axis=1)\n", + " df = df.rename(mapper= {x: str.lower(x) for x in df.index}, axis=0)\n", + " return df.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f10e66d", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm_for_one_role('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', role='HAMFDC', INDEX_MAP=IIM, criterion='distance')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d93631e", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm_for_one_role('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', role='HAHFDC', INDEX_MAP=IIM, criterion='distance')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc7157d4", + "metadata": {}, + "outputs": [], + "source": [ + "android_confusion_GIS_HAMFDC = get_confusion_matrix_df(\n", + " 'android', [gisv_la,gisv_sj,gisv_ucb], 'gis', phone_configuration = 'HAMFDC', INDEX_MAP=IIM, criterion='distance'\n", + " )\n", + "\n", + "ios_confusion_GIS_HAHFDC = get_confusion_matrix_df(\n", + " 'ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', phone_configuration = 'HAHFDC', INDEX_MAP=IIM, criterion='distance'\n", + " )\n", + " \n", + "%store android_confusion_GIS_HAMFDC\n", + "%store ios_confusion_GIS_HAHFDC" + ] + }, { "cell_type": "markdown", - "id": "59be3e33", + "id": "e88cec3f", "metadata": {}, "source": [ "## Analyzed Data" @@ -1266,7 +1516,7 @@ }, { "cell_type": "markdown", - "id": "19f773be", + "id": "fa84d034", "metadata": {}, "source": [ "#### cleaned view" @@ -1307,7 +1557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cd8c8f8f", + "id": "8e558dce", "metadata": { "scrolled": true }, @@ -1322,7 +1572,7 @@ { "cell_type": "code", "execution_count": null, - "id": "65556b87", + "id": "8f8c8b12", "metadata": {}, "outputs": [], "source": [ @@ -1334,7 +1584,7 @@ }, { "cell_type": "markdown", - "id": "e368b41a", + "id": "5bb9bfbf", "metadata": {}, "source": [ "#### inferred view random forest\n", @@ -1345,7 +1595,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3f9ff053", + "id": "dd2008a7", "metadata": {}, "outputs": [], "source": [ @@ -1357,7 +1607,7 @@ }, { "cell_type": "markdown", - "id": "80c810a7", + "id": "af2ffee6", "metadata": {}, "source": [ "#### inferred view GIS\n", @@ -1379,7 +1629,7 @@ }, { "cell_type": "markdown", - "id": "37441e82", + "id": "10940205", "metadata": {}, "source": [ "# Results " @@ -1387,7 +1637,7 @@ }, { "cell_type": "markdown", - "id": "c3bd30c1", + "id": "65d31694", "metadata": {}, "source": [ "#### Raw data" @@ -1396,38 +1646,92 @@ { "cell_type": "code", "execution_count": null, - "id": "77ccbbc6", + "id": "911338be", "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw')" + "compare_cm(oses=['ios'], roles=['HAHFDC'], criteria=['duration', 'distance'], normalization='pred')" ] }, { "cell_type": "code", "execution_count": null, - "id": "dc21852e", + "id": "26d36308", "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" + "compare_cm(oses=['ios'], roles=['HAHFDC'], criteria=['duration', 'distance'])" ] }, { "cell_type": "code", "execution_count": null, - "id": "99c90603", + "id": "18616683", + "metadata": {}, + "outputs": [], + "source": [ + "compare_cm(oses=['android'], roles=['HAMFDC'], criteria=['duration', 'distance'], normalization='pred')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcb29103", + "metadata": {}, + "outputs": [], + "source": [ + "compare_cm(oses=['android'], roles=['HAMFDC'], criteria=['duration', 'distance'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb8f520e", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')\n", + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c50388bd", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='duration')\n", + "plot_cm('ios', [pv_la, pv_sj, pv_ucb], 'raw', criterion='duration', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69c5854d", "metadata": { "scrolled": false }, "outputs": [], "source": [ - "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')" + "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance')\n", + "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw', criterion='distance', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c9df7ad", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw', criterion='duration')\n", + "plot_cm('android', [pv_la, pv_sj, pv_ucb], 'raw', criterion='duration', normalization='gt')" ] }, { "cell_type": "markdown", - "id": "a90599d9", + "id": "623f062e", "metadata": {}, "source": [ "#### Cleaned data" @@ -1436,23 +1740,47 @@ { "cell_type": "code", "execution_count": null, - "id": "37b50ace", + "id": "e2bac11c", "metadata": { "scrolled": false }, "outputs": [], "source": [ - "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance')" + "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance')\n", + "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance', normalization='gt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "e2ca2f18", + "id": "b60d586e", "metadata": {}, "outputs": [], "source": [ - "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance')" + "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='duration')\n", + "plot_cm('ios', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='duration', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1aef318", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance')\n", + "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='distance', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13cd61bb", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='duration')\n", + "plot_cm('android', [mcv_la, mcv_sj, mcv_ucb], 'clean', CIM, criterion='duration', normalization='gt')" ] }, { @@ -1460,32 +1788,56 @@ "id": "c0f79ef9", "metadata": {}, "source": [ - "#### Random Forrest" + "#### Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "466563da", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance')\n", + "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance', normalization='gt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "c047ac17", + "id": "55cefd8a", "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance')" + "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='duration')\n", + "plot_cm('ios', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='duration', normalization='gt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "490b3f29", + "id": "68a2e472", "metadata": {}, "outputs": [], "source": [ - "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance')" + "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance')\n", + "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='distance', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0325ee26", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='duration')\n", + "plot_cm('android', [rfv_la,rfv_sj,rfv_ucb], 'random_forest', INDEX_MAP=IIM, criterion='duration', normalization='gt')" ] }, { "cell_type": "markdown", - "id": "2b1757a2", + "id": "e13a1fc5", "metadata": {}, "source": [ "#### GIS" @@ -1494,17 +1846,39 @@ { "cell_type": "code", "execution_count": null, - "id": "d7e67855", + "id": "59859139", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('ios', [gisv_sj, gisv_ucb, gisv_la], 'gis', INDEX_MAP=IIM, criterion='duration')\n", + "plot_cm('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance', normalization='gt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef174908", "metadata": {}, "outputs": [], "source": [ - "plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance', normalization='gt')" + "plot_cm('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='distance')\n", + "plot_cm('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='duration', normalization='gt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "d6dc3b96", + "id": "deb8ae69", + "metadata": {}, + "outputs": [], + "source": [ + "plot_cm('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', INDEX_MAP=IIM, criterion='duration')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94c5cf62", "metadata": {}, "outputs": [], "source": [ @@ -1513,7 +1887,7 @@ }, { "cell_type": "markdown", - "id": "a51ad7d6", + "id": "af7cf575", "metadata": {}, "source": [ "## Combined views" @@ -1522,7 +1896,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0b97a649", + "id": "5e07213d", "metadata": {}, "outputs": [], "source": [ @@ -1532,7 +1906,17 @@ { "cell_type": "code", "execution_count": null, - "id": "fe0dcbd0", + "id": "f59cf80d", + "metadata": {}, + "outputs": [], + "source": [ + "plot_f_scores('ios', criterion='duration')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "103559d5", "metadata": {}, "outputs": [], "source": [ @@ -1542,17 +1926,16 @@ { "cell_type": "code", "execution_count": null, - "id": "cb912632", + "id": "80ea86eb", "metadata": {}, "outputs": [], "source": [ - "def export_confusion_matrix(matrix: pd.DataFrame, output_dir: Path):\n", - " pass" + "plot_f_scores('android', criterion='duration')" ] }, { "cell_type": "markdown", - "id": "9a9934bf", + "id": "9068e7c7", "metadata": {}, "source": [ "## Selected Setting" @@ -1561,7 +1944,7 @@ { "cell_type": "code", "execution_count": null, - "id": "48730a7f", + "id": "8d55c863", "metadata": {}, "outputs": [], "source": [ @@ -1571,7 +1954,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ed63da40", + "id": "b0c4f431", "metadata": {}, "outputs": [], "source": [ @@ -1581,7 +1964,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b16aa8ed", + "id": "e458aa97", "metadata": {}, "outputs": [], "source": [ @@ -1591,7 +1974,17 @@ { "cell_type": "code", "execution_count": null, - "id": "8bdb32e2", + "id": "3bc20b81", + "metadata": {}, + "outputs": [], + "source": [ + "plot_f_scores_selected(criterion='duration')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d68ed7a", "metadata": {}, "outputs": [], "source": [ @@ -1607,7 +2000,7 @@ }, { "cell_type": "markdown", - "id": "e054d623", + "id": "e8d121fc", "metadata": {}, "source": [ "#### get percentage of no sensed predicted mode for a given ground truth mode (in this case ground truth mode = walking)" @@ -1616,7 +2009,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0134478c", + "id": "3fd0d9a9", "metadata": {}, "outputs": [], "source": [ @@ -1634,7 +2027,7 @@ }, { "cell_type": "markdown", - "id": "64ba1bb1", + "id": "68cbb972", "metadata": {}, "source": [ "# Unit Testing\n", @@ -1646,7 +2039,7 @@ }, { "cell_type": "markdown", - "id": "98e34edb", + "id": "dfb09777", "metadata": {}, "source": [ "## Example timelines" @@ -1654,7 +2047,7 @@ }, { "cell_type": "markdown", - "id": "97203364", + "id": "9110de02", "metadata": {}, "source": [ "### No sensed at the beggining, No GT at the end, Multimodal\n", @@ -1705,7 +2098,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2bb776ec", + "id": "4ea8e689", "metadata": {}, "outputs": [], "source": [ @@ -1724,7 +2117,7 @@ }, { "cell_type": "markdown", - "id": "94bab14e", + "id": "bcf659f6", "metadata": {}, "source": [ "#### get_binary_class_in_sec" @@ -1733,7 +2126,7 @@ { "cell_type": "code", "execution_count": null, - "id": "42cc04ba", + "id": "147f27c8", "metadata": {}, "outputs": [], "source": [ @@ -1743,7 +2136,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f9ba7e54", + "id": "e293e6f4", "metadata": {}, "outputs": [], "source": [ @@ -1755,7 +2148,7 @@ }, { "cell_type": "markdown", - "id": "dca00cf1", + "id": "ccb0879f", "metadata": {}, "source": [ "#### get_F_score" @@ -1764,7 +2157,7 @@ { "cell_type": "code", "execution_count": null, - "id": "41db01bf", + "id": "66bf7aa2", "metadata": {}, "outputs": [], "source": [ @@ -1774,7 +2167,7 @@ }, { "cell_type": "markdown", - "id": "9326fa32", + "id": "0cfa9930", "metadata": {}, "source": [ "#### get_confusion_matrix" @@ -1783,7 +2176,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e2e1305c", + "id": "6901ca3c", "metadata": {}, "outputs": [], "source": [ @@ -1797,7 +2190,7 @@ }, { "cell_type": "markdown", - "id": "32ebcb06", + "id": "aae00365", "metadata": {}, "source": [ "### No sensed at beggining and end, multimodal\n", @@ -1834,7 +2227,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0e1f1a32", + "id": "71a21371", "metadata": {}, "outputs": [], "source": [ @@ -1850,7 +2243,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dc44411b", + "id": "8164cca3", "metadata": {}, "outputs": [], "source": [ @@ -1860,7 +2253,7 @@ { "cell_type": "code", "execution_count": null, - "id": "69ae1ab3", + "id": "e7468b31", "metadata": {}, "outputs": [], "source": [ @@ -1873,7 +2266,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aa2b6bf1", + "id": "106d42f6", "metadata": {}, "outputs": [], "source": [ @@ -1884,7 +2277,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a2b93c8e", + "id": "c6050b92", "metadata": {}, "outputs": [], "source": [ @@ -1896,7 +2289,7 @@ }, { "cell_type": "markdown", - "id": "8a4f8a69", + "id": "9355e413", "metadata": {}, "source": [ "### No ground truth at beggining and end, unimodal\n", @@ -1930,7 +2323,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8de04469", + "id": "0cfd32f7", "metadata": {}, "outputs": [], "source": [ @@ -1946,7 +2339,7 @@ { "cell_type": "code", "execution_count": null, - "id": "315e9116", + "id": "0d0e9c95", "metadata": {}, "outputs": [], "source": [ @@ -1956,7 +2349,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e4ecc213", + "id": "1fd37903", "metadata": {}, "outputs": [], "source": [ @@ -1969,7 +2362,7 @@ { "cell_type": "code", "execution_count": null, - "id": "46add409", + "id": "e9010a42", "metadata": {}, "outputs": [], "source": [ @@ -1980,7 +2373,7 @@ { "cell_type": "code", "execution_count": null, - "id": "02253a79", + "id": "fec35d85", "metadata": {}, "outputs": [], "source": [ @@ -1991,7 +2384,7 @@ }, { "cell_type": "markdown", - "id": "5a04d76f", + "id": "06257bb5", "metadata": {}, "source": [ "## Unimodal Sensed Timeline With Gap\n", @@ -2018,7 +2411,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bdbbcf02", + "id": "c4e24c09", "metadata": {}, "outputs": [], "source": [ @@ -2039,7 +2432,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1c3d5018", + "id": "ce1aad79", "metadata": {}, "outputs": [], "source": [ @@ -2049,7 +2442,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4cc339e6", + "id": "8ed30f1a", "metadata": {}, "outputs": [], "source": [ @@ -2065,7 +2458,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1b3eebb6", + "id": "0165ceb8", "metadata": {}, "outputs": [], "source": [ @@ -2075,7 +2468,7 @@ }, { "cell_type": "markdown", - "id": "917d25dc", + "id": "5930ba04", "metadata": {}, "source": [ "## Unimodal Sensed Timeline With Gap\n", @@ -2089,7 +2482,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9e4e4342", + "id": "ec351b4a", "metadata": {}, "outputs": [], "source": [ @@ -2099,7 +2492,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4616e877", + "id": "f5005c82", "metadata": {}, "outputs": [], "source": [ @@ -2109,7 +2502,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3ee7c0bd", + "id": "5582eee6", "metadata": {}, "outputs": [], "source": [ @@ -2124,7 +2517,7 @@ }, { "cell_type": "markdown", - "id": "7df9e139", + "id": "50bb709a", "metadata": {}, "source": [ "## No ss, gts" @@ -2133,7 +2526,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6d9c34c0", + "id": "d646560a", "metadata": {}, "outputs": [], "source": [ @@ -2143,7 +2536,7 @@ }, { "cell_type": "markdown", - "id": "5f2e1557", + "id": "21267e22", "metadata": {}, "source": [ "## No ss" @@ -2152,7 +2545,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5541d107", + "id": "a3b2ae6f", "metadata": {}, "outputs": [], "source": [ @@ -2162,7 +2555,7 @@ }, { "cell_type": "markdown", - "id": "2c63092b", + "id": "e08a8a14", "metadata": {}, "source": [ "## No gts" @@ -2171,7 +2564,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2e5eff99", + "id": "89f4ef64", "metadata": {}, "outputs": [], "source": [ @@ -2182,7 +2575,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a8448910", + "id": "8e92a835", "metadata": {}, "outputs": [], "source": [] @@ -2193,9 +2586,9 @@ "hash": "e9f66c3b7bd3dc9f1ccabd654979094500fde00f3127413feb35bf80f0ad2368" }, "kernelspec": { - "display_name": "Python 3.11.4 ('emissioneval')", + "display_name": "emissioneval", "language": "python", - "name": "python3" + "name": "emissioneval" }, "language_info": { "codemirror_mode": { @@ -2207,7 +2600,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.8.2" } }, "nbformat": 4, From 70598f4d5044d9d714cdf6e1def70486f371b48b Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Sun, 30 Jul 2023 13:04:09 -0400 Subject: [PATCH 8/9] Clean up code; incorporate CSV export for downstream tasks --- classification_analysis.ipynb | 85 ++++++++++++++++++++++++++--------- 1 file changed, 64 insertions(+), 21 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index 13b2e6a..85d72b5 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -186,22 +186,37 @@ "metadata": {}, "outputs": [], "source": [ - "def fetch_trajectories(spec_id: str, run_ix: int) -> pd.DataFrame:\n", + "def fetch_trajectories(spec_id: str, run_ix: int, with_ends: bool = False) -> pd.DataFrame:\n", "\n", " '''\n", " Return a reference trajectory datafrane given a spec ID and a trip ID.\n", + " e.g.: for spec_id=car_scooter_brex_san_jose, run_ix=0, and with_ends=True,\n", + " the function fetches the following files and concatenates them in a dataframe along axis=0:\n", + "\n", + " car_scooter_brex_san_jose\n", + " | - no_ends\n", + " | - with_ends\n", + " | - bus trip with e-scooter access\n", + " | - city_bus_rapid_transit_0\n", + " | - city_escooter_0\n", + " | - walk_back_from_bus_0\n", + " | - freeway_driving_weekday\n", + " | - freeway_driving_weekday_0\n", " '''\n", "\n", " root = pathlib.Path(\"./bin/data\")\n", " return_file = pd.DataFrame()\n", "\n", " workdir = root\n", - " assert (workdir / spec_id).exists(), \"F1\"\n", + " assert (workdir / spec_id).exists(), f\"{spec_id} not found.\"\n", "\n", " workdir = workdir / spec_id\n", - " assert workdir.exists() and workdir.is_dir(), \"F2\"\n", + " assert workdir.is_dir(), f\"{spec_id} found, but is not a directory.\"\n", "\n", - " found_glob = list(workdir.glob(f'*/*_{run_ix}'))\n", + " if with_ends:\n", + " found_glob = list(workdir.glob(f'./with_ends/*/*_{run_ix}'))\n", + " else:\n", + " found_glob = list(workdir.glob(f'./no_ends/*/*_{run_ix}'))\n", "\n", " assert len(found_glob) > 0, f\"No files found for {spec_id=}, {run_ix=}\"\n", "\n", @@ -209,6 +224,7 @@ "\n", " for file in found_glob:\n", " tdf = pd.read_csv(file)\n", + " # Ignore the first (Unnamed: 0) column.\n", " tdf = tdf.iloc[:, 1:]\n", " df = pd.concat([df, tdf], axis=0)\n", " \n", @@ -222,7 +238,11 @@ "metadata": {}, "outputs": [], "source": [ - "def get_reference_trajectory(pv, os, role):\n", + "def get_reference_trajectory(pv, os, role, with_ends=False):\n", + "\n", + " '''\n", + " Return a list of dataframes for every run in a given `PhoneView`. Filters by `os` and `role`.\n", + " '''\n", "\n", " dfs = []\n", "\n", @@ -236,7 +256,7 @@ " if run['eval_role_base'] != role:\n", " continue\n", " \n", - " df = fetch_trajectories(pv.spec_details.CURR_SPEC_ID, run_ix)\n", + " df = fetch_trajectories(pv.spec_details.CURR_SPEC_ID, run_ix, with_ends=with_ends)\n", " df['run_ix'] = run_ix\n", "\n", " dfs.append(df.copy())\n", @@ -679,7 +699,7 @@ " trajectories = []\n", " for v in pv:\n", " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", - " trajectories.extend(get_reference_trajectory(v, os, role))\n", + " trajectories.extend(get_reference_trajectory(v, os, role, with_ends=True))\n", " else:\n", " trips = test_trip if type(test_trip) is list else [test_trip]\n", "\n", @@ -718,7 +738,7 @@ " if filtered_trajectory.shape[0] > 0:\n", " dist = location_smoothing.add_dist(filtered_trajectory).distance.sum()\n", " else:\n", - " dist - 0\n", + " dist = 0\n", "\n", " if BASE_MODE[mode] == BASE_MODE[ss['mode']] and BASE_MODE[mode] == BASE_MODE[gts['mode']]:\n", " TP[mode] = TP.setdefault(mode, 0) + dist\n", @@ -920,7 +940,7 @@ " df.T.plot(style='o', ax=ax[i], title=f' {role} ').legend(loc='lower left')\n", " ax[i].set_xticks(range(len(df.T)))\n", " ax[i].set_xticklabels(df.columns, rotation = 80)\n", - " title = f\"$F_1$ Scores by Base Mode for Phones Running {os} at Various Configuration Settings\"\n", + " title = f\"$F_1$ Scores by Base Mode and {criterion=} for Phones Running {os} at Various Configuration Settings\"\n", " plt.suptitle(title, weight='bold', size='x-large')\n", " fig.savefig(f\"images/{criterion}_f_scores_for_{os}\", bbox_inches=\"tight\")" ] @@ -957,7 +977,7 @@ " columns=['WALKING', 'CYCLING', 'AUTOMOTIVE', 'CAR', 'BUS', 'TRAIN', 'SUBWAY']\n", " )\n", " \n", - " df.T.plot(style='o', ax=ax[i], title=f'$F_1$ Scores by Base Mode \\n{os}:{role} ').legend(loc='lower left')\n", + " df.T.plot(style='o', ax=ax[i], title=f'$F_1$ Scores for {criterion=} by Base Mode \\n{os}:{role} ').legend(loc='lower left')\n", " ax[i].set_xticks(range(len(df.T)))\n", " ax[i].set_xticklabels(df.columns, rotation = 80)\n", " fig.savefig(f\"images/{criterion}_f_scores_selected\", bbox_inches=\"tight\")" @@ -1036,8 +1056,6 @@ "metadata": {}, "outputs": [], "source": [ - "import IPython.display as IDisplay\n", - "\n", "def get_confusion_matrix(os, role, pv, test=False, test_trip=None, criterion='duration'):\n", "\n", " cm_l = []\n", @@ -1050,7 +1068,7 @@ " trajectories = []\n", " for v in pv :\n", " trips.extend(get_trip_ss_and_gts_timeline(v, os, role))\n", - " trajectories.extend(get_reference_trajectory(v, os, role))\n", + " trajectories.extend(get_reference_trajectory(v, os, role, with_ends=True))\n", " \n", " assert len(trips) == len(trajectories)\n", "\n", @@ -1073,6 +1091,12 @@ " (ref_trajectory.ts >= range_start) & (ref_trajectory.ts <= range_end), :\n", " ]\n", "\n", + " if gts['mode'] == 'E_BIKE':\n", + " print(f\"GT: E_BIKE, Predicted: {GISBMM[ss['mode']]}\")\n", + " # print(f\"GT time: {arrow.get(gts['start_ts'])} -> {arrow.get(gts['end_ts'])}\")\n", + " # print(f\"SS time: {arrow.get(ss['start_ts'])} -> {arrow.get(ss['end_ts'])}\")\n", + " # print(10*'~')\n", + "\n", " if filtered_trajectory_data.shape[0] > 0:\n", " dist = location_smoothing.add_dist(filtered_trajectory_data).distance.sum()\n", " else:\n", @@ -1141,12 +1165,10 @@ "\n", " fig, ax = plt.subplots(1,3, figsize=(22,10), dpi=300, sharey=True)\n", " y=.95\n", + " ROUND_PRECISION = 3\n", " fig.text(0.5, 0.0, 'Predicted Label', ha='center', fontsize='xx-large')\n", " fig.text(0.04, 0.5, 'True Label', va='center', rotation='vertical', fontsize='xx-large')\n", "\n", - " ## TODO: Use thee GIS Confusion matrix.\n", - " save_df = None\n", - "\n", " for k, role in enumerate([\"HAHFDC\", \"HAMFDC\", \"MAHFDC\"]):\n", " if d_type =='raw':\n", " title = f\"Confusion Matrices for Raw Output Data on Phones Running {os} \\n by Calibration Settings\"\n", @@ -1161,8 +1183,6 @@ " elif d_type == 'random_forest' or 'gis':\n", " df = pd.DataFrame(get_confusion_matrix(os, role, pv, criterion=criterion)).groupby('sensed_mode').sum().rename(index=INDEX_MAP)\n", " df = pd.DataFrame(df, index=sorted(df.index, key=sort_key))\n", - " if (os == 'android' and role == 'HAMFDC') or (os == 'ios' and role == 'HAHFDC'):\n", - " save_df = df\n", " fname = f\"images/{d_type}_{criterion}_cm_{os}\"\n", " if d_type == 'random_forest':\n", " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os} \\n by Calibration Settings\"\n", @@ -1199,14 +1219,16 @@ " ax[k].set_xticklabels(df.index, rotation=80)\n", " color_thresh = df.max().max() / 4\n", " for i, j in itertools.product(range(df.shape[1]), range(df.shape[0]) ):\n", - "\n", " if normalization is None:\n", + " # If no normalization is used, the result is a large integer.\n", " ax[k].text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", " color='white' \n", " if df.transpose().iat[i,j] < color_thresh \n", " else 'black')\n", " else:\n", - " ax[k].text(j, i, np.round(df.transpose().iat[i,j], 3), horizontalalignment='center', \n", + " # However, if normalization is used, we get FP numbers between 0-1 that visually overlap over each other in the CM.\n", + " # Therefore, we round the numbers off to a predefined precision to maintain visual coherence.\n", + " ax[k].text(j, i, np.round(df.transpose().iat[i,j], ROUND_PRECISION), horizontalalignment='center', \n", " color='white' \n", " if df.transpose().iat[i,j] < color_thresh \n", " else 'black')\n", @@ -1346,6 +1368,7 @@ " ax[k].set_xticklabels(df.index, rotation=80)\n", " color_thresh = df.max().max() / 4\n", " for i, j in itertools.product(range(df.shape[1]), range(df.shape[0]) ):\n", + " ## Explanation for this conditional is provided in plot_cm().\n", " if normalization is None:\n", " ax[k].text(j, i, (int(df.transpose().iat[i,j])), horizontalalignment='center', \n", " color='white' \n", @@ -1467,6 +1490,26 @@ " return df.transpose()" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "250a294b", + "metadata": {}, + "outputs": [], + "source": [ + "compare_cm(oses=['ios'], roles=['HAHFDC'], criteria=['duration', 'distance'], normalization='pred')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd342807", + "metadata": {}, + "outputs": [], + "source": [ + "compare_cm(oses=['android'], roles=['HAMFDC'], criteria=['duration', 'distance'], normalization='pred')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -2600,7 +2643,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.11.4" } }, "nbformat": 4, From f12ef08d22e65e5bb02b4753daf6c212dabda7a8 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Tue, 22 Aug 2023 10:10:19 -0400 Subject: [PATCH 9/9] Cleaned-up code and outputs --- classification_analysis.ipynb | 165 +++++++++++++++++++++++++--------- 1 file changed, 121 insertions(+), 44 deletions(-) diff --git a/classification_analysis.ipynb b/classification_analysis.ipynb index 85d72b5..43028f7 100644 --- a/classification_analysis.ipynb +++ b/classification_analysis.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "2e136f3f", "metadata": {}, "outputs": [], @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ac4092a3", "metadata": {}, "outputs": [], @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "dff4c0cc", "metadata": {}, "outputs": [], @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "5b3eafd4", "metadata": {}, "outputs": [], @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "03d894bd", "metadata": {}, "outputs": [], @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "217ec510", "metadata": {}, "outputs": [], @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "6c27fdd8", "metadata": {}, "outputs": [], @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "2b9b6813", "metadata": {}, "outputs": [], @@ -119,10 +119,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "34d152eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After iterating over 1 entries, entry found\n", + "Found spec = Round trip car and bike trip in the South Bay\n", + "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", + "After iterating over 1 entries, entry found\n", + "Found spec = Multi-modal car scooter BREX trip to San Jose\n", + "Evaluation ran from 2019-07-20T00:00:00-07:00 -> 2020-04-29T17:00:00-07:00\n", + "After iterating over 1 entries, entry found\n", + "Found spec = Multimodal multi-train, multi-bus, ebike trip to UC Berkeley\n", + "Evaluation ran from 2019-07-16T00:00:00-07:00 -> 2020-04-30T00:00:00-07:00\n" + ] + } + ], "source": [ "## Retrieve the specs for the given experiment.\n", "\n", @@ -135,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "191a8e98", "metadata": {}, "outputs": [], @@ -158,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "27237fc1", "metadata": {}, "outputs": [], @@ -181,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "0757b7c3", "metadata": {}, "outputs": [], @@ -233,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "8d0c0f79", "metadata": {}, "outputs": [], @@ -266,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "c92e50d1", "metadata": {}, "outputs": [], @@ -349,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "e26ef8f3", "metadata": {}, "outputs": [], @@ -394,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "3eb538fe", "metadata": {}, "outputs": [], @@ -457,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "c6a21a16", "metadata": {}, "outputs": [], @@ -506,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "d9dd0c1f", "metadata": {}, "outputs": [], @@ -555,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "1defdb2f", "metadata": {}, "outputs": [], @@ -684,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "b9ed7ad6", "metadata": {}, "outputs": [], @@ -765,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "7cc628e5", "metadata": {}, "outputs": [], @@ -807,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "7553fa01", "metadata": {}, "outputs": [], @@ -896,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "22e85696", "metadata": {}, "outputs": [], @@ -915,7 +931,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "dbe77394", "metadata": {}, "outputs": [], @@ -955,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "dc35d951", "metadata": {}, "outputs": [], @@ -1002,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "c43d8bfe", "metadata": {}, "outputs": [], @@ -1030,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "16c43040", "metadata": {}, "outputs": [], @@ -1051,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "00d1e031", "metadata": {}, "outputs": [], @@ -1122,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "f5a13d1c", "metadata": {}, "outputs": [], @@ -1153,7 +1169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "e31f82d0", "metadata": {}, "outputs": [], @@ -1253,7 +1269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "7e65ffd6", "metadata": {}, "outputs": [], @@ -1317,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "2d8d54ad", "metadata": {}, "outputs": [], @@ -1386,7 +1402,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "9ce3d2ac", "metadata": {}, "outputs": [], @@ -1419,7 +1435,7 @@ " if d_type == 'random_forest':\n", " title = f\"Confusion Matrices for Inferred Output Data (Random Forest) on Phones Running {os}\"\n", " else:\n", - " title = f\"Confusion Matrices for Inferred Output Data (GIS) on Phones Running {os}\"\n", + " title = f\"Confusion Matrices for Inferred Output Data (GIS) on Phones Running {os} \\n by Calibration Settings\"\n", " else:\n", " assert 0, f'INVALID d_type {d_type}'\n", " df = df.reindex(\n", @@ -1451,7 +1467,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "6b1fb03b", "metadata": {}, "outputs": [], @@ -1512,20 +1528,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "2f10e66d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GT: E_BIKE, Predicted: WALKING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n" + ] + }, + { + "data": { + "image/png": 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PgWTNX3zKbHeuju2CCy3+ql7Vo8DOYv/9ZG+hhV9Xmj77mNq3DtQ1LS7swBNR21s9bgzTgj+KP66H3R9+aS1Vkhb8cbrQwq+LMjMvvP/57rM2l3ZCvf2WMP0w6xjFo6jQXnvPirWKhafUOt9axa3Xhxa/VrEvSfc8tMHitYo33t+jiR+1vmKtwkuNIn1KLB5xdpJefrKB2eeOcZ/vd4jPHT1vqmr2nujYiVRt/zfRPpMBDqKs1kUv6tohxOx9x+GjKXru9R0WnYOMnAMc9bMXAAAAYCmD9z4AAACVhaWFXxfN/9P8y7jmjXyKiLysWhU3s8frtxlbMI9LzNa+Q+a7ktWo6lZEtHU6tfGXj5fzpcdJKdlabaCoqix/ps5tA1S/jtelx1t2JWnpKgo/gPxu6BgiX5/L971ITM7SijXFf+FQvaqH2ePVG4q+4OBKsfFZ+jffhddX7iBmK6lpOdqe78KrmtVsPw9Q2SWlZNv1y8D4xCyLvsS86EB0irbvNj8HtG4WYKOsAFhzN2hJmr3gpNnj1s0Dio0v63OAvc9pQGVXFsfYLV1DzS4+XLHmnI6fKn7XdkmaNtP8wu4bO4XKzdVURPQFN3SqokaRvpce/7M1Tr8vi7EwYwDFcdS/zbddUWQq2a8gy5r1GqCyqx5mvmZ5+my6DkSnGOr793rzQopaRawlXtc26FLxhiTt3JNouNjhxznHzR7fdnNYEZEXuLk56fqO5oVm02YdLXGeYyfT9Pe6yz+Pi4uTbukaWkwP4Opn9VrF7+W4VlHCXNKFzx0Nr/jcsdGBPnf0vLls3hMBjqSs1kUveuSKXf8kadyE/Vadg0riqJ+9AAAAAEtR/AUAAGzi4BHzi4083J3k7Vn8Ww13d/Pnz8UaLzw7my/2ykItW7i1S5DZ4+Xr4pWVXfKCYFn+TD2vN8/x5wVnDM8FVCa331LN7PHSv86UuMDv4WF+/J09b/zLjjNnzWP9fFwN97VEYrL5OePiLmAAKof9B83vmlslyLaF8AAst++QeQG4h7uzfLzt8/eZcwBQOXXtEGL2+Pdlxi4+PHI8Vbv2XL5o28vTWe1aBRXTQ+rdvbrZ4+9+KflCbABXv1ZR/mY3scnOztVCg+caS1mzXgNUdp751yzPWbBmmS/2yuLLK7VoGmD2eP3mWMNzbNwWp8ysyxdsRzXyU3Bg0Z9Vrm0daPYz7didoKPHSy5sl1RgR7Gu14UUEQlUbvsPma8f2HWtIt9cIQbWKnrfav5+4LuZBXckLA+tovzNbriXnZ2rhX86RlEa4IisWRdtFeWv8JqXb3S7dWe8XXYcBgAAACoSir8AAIBN5BRyAyYXl+LvIh2XYF644Opq/K1J/jtUJ6XkGO5bkmpV3BTVwNusbfEKY19wltXPVC3UzWx3tdNnM7Vtt7E7fAKVSY0wD7Vs6m/WNn/JqRL7xcZlmj12s+RYdjOPTUyybkfFkoRVMb/T77nYzCIiAVRE2bnmF0W6lLB7BwD7y8kpeLGyi4t9ll85BwCVT1CAq+rXvbwOkJ2dW2A34OJs3hlv9rj9NUUXf9Wo5mG2o+DJmDRt3h5fZDyAiuP2buYXYK/ZGKvYeNuva1i7XgNUdufzr1m6WfL9Q741y+TsQuNCQ9zNHh86Yvx7h6zsPJ24YldSZ2dTse852rc2f27LDuPvbbbtSlB29uUvphrW81VggH1uwgVczcpyrSL/XK4lzFOjmodamX3uSHeYzx233WK+65e93hMBFYU155pe+T575C/sBgAAAFAQxV8AAMAmqoea370tOztPicnFF2Tt2mf+pWFkuGcRkQXVyxe777Cxu0Ea0a1zoJycLl88uT86VYeOpRvqW1Y/U4srCr8kaevu5CIigcrttlvCzI7nPQeStP9wyRcs5L+zXIN6PkVEFtQwX+zu/UlFRFqvVnVPNWnod+lxbm6etuS7mBNAxXblnWcl6TwFoEC5y39cZmfnKiHRPhcGcQ4AKp+64eY3qTkQnaL0jELuxFOEnbsTzR5H1PYqIlJmhV+StGlbvOF5AFy9vL2cdX2+nXN+W2KfCzCtXa8BKrs9+5OUkXn573+dml6GC8AaRvqaPS5qzdIv345gySmFF4kVJSlfUVm9Ot5FREoR+d7f7NyTWERkQekZuTqYrzAtonbRcwGVVY0yXKvIP9e5uOLXKgp+7oizdUpWKew90YKlFKUAxbFmXbR18wCzx/9sdYxzAAAAAODIXEoOAQAAKFmntuZ3at0fnaq8gjd4MrNoZaz6dq9yaXesbp0D9cvvZ5SZVXzH61r7qWrI5WKz6OPpOnDENsVfTibp5o6BZm2LVxpfaCyrn6lBXfMF1N0HLn/J2bKJj25oH6BGdb0UHOgqZ2eTEpKydTImQ1v+TdaK9fE6c56706Hic3KSetxofnfG3wx+QTd/yWndf2etS3fEve2WME2bdUyZmcVfXNmlfbDCQi/vyHXoSIr2HrRtcWZwoJvefKmJXJwvXyS1fM1ZnT6TYdN5ADguL09ntW1p/n7l3322LzQFYJnrO1Yxe7znQHKJn4mswTkAqJzq/MN//wABAABJREFU5LuY+cpdNYw4cdo8Pv94V2rSwM/s8ZUXYrdpEaBu11dVkwa+qhLsfmHNITFLx06maeO2OP2x8gyfTYCr1C1dQuXh7nzp8bnzGVq36bzN5ynNeg1Q2aWm5WjRn6fVu3t1SZK7u7N63RKmWQtOFtvPyUnqe3t1s7aFywo/7rKyzdc/XV0tu59w/h3G6tQquuC8Tk3z545b+v7mVLoa1rtc1BZRy8thdg0CHMUN15XfWsXuEtYqGtc3L0rdufdyfJsWAbqla2iBzx3HT6Zp47Z4/fG3/T533Fzoe6JYu8wFVBSWrouGBLmpSvDl3UZjzqbr7PkLBaOBAa7q1jVUXdqHqHqYpwL8XZWSmq24+Czt2J2gtZtitWr9ebucywAAAABHR/EXAAAoNQ93J93aOcisbc3mku/QGHMuS5NnntbD91/40jE02E0vPlpb//vqqDIyC1+taxDhqaeG1rz0OCc3T1/8cKIU2Zu7ppmvQoIuF2GlZ+Tqr3XGi7/K6mdqUMf8S9FjpzIUGuyqZ4bVVMsmvgXiPdzdVDXETa2a+uqBO6tq0YpYTZxxqsicgIrg2tZBCg25/MVBekaOlq6IMdT3VEy6vpx6WE8+WE+SFFbFQ28831ivv79bGUXcXb9RfV+99J+Glx7n5OTpo68OlOInuMDZSfL1cVV4LS91bBus3t2rycf78ke5E6fS9OGXpZ8HwNWjT/dq8vS4fAFCUnK2Nu+IL7+EAMjTw0m332J+EfOKtefsMhfnAKByqlnNw+xxzFnLLnQ8fcZ8R/MAP1f5ersoqZDdPBpFmu9mHH0sVWGh7nr5yYZq0yKwQLynh7PCQj3UtmWgHuxfR78uOaUJkw8V+dkJgGO6vZv5e5mFf8Yoxw6HcWnWawBIX0w9rLatAlW96oUbxD0+tK6OHE/VxiJ26nR2Nun5x+ubFUlt3Banv9YU/nklIcn8vcGV35cYEZwvvnaNwou/fH1c5O/natYWcza90Nii5I+vWd2ziEigcvL0cNJtt1Q1a1u5zj5rFb2tWKtolK/468j/f+546T8Niv3c0aZloIb3D9f8JacvfO4o4aZ9lsq/vrNwuX3eEwEVhTXrogWO/+Opki6sez4xtK68vMwvaXXzd1Ogv5vqhnurd/fqOnw0RR9/faDI9z8AAABARUXxFwCgRDk5Odq9e7cOHz6sEydOKCkpSRkZGfL29pa/v7/8/f1Vq1YtNWvWTJ6efLFSGQ3tF6aggMtf0iWlZGvRSmN3QJuz5JycnKTBfcPk6uKk61r766u3G2rB8vP6d3+K4hKz5eJsUo2q7mrfyk83dAiQq8uFO0dmZefq0ykntH1PSgmzGNctXxHb6k0JSkmzbEW/LH6mwADzt3Hubk76dEx9+fuW/PbO1cVJvW4KUeNIb43+6LDiEgpe6AVUBPm/aPhrzTklp+QY7v/zvONycpIeeSBCrq5O6tI+RD9MaKu5i05q+78Jio3LkqurSTWre6rztSHq1jX00p1ws7Jy9d5n+6y6EPupB+vpnt41Sw6UtGlbnMZ+uEfxCezmB1QWYaHuGnJfuFnbL/OPKzubgm6gPD06qK5Cgi5fxJyYnKX5S07ZfB7OAUDldeUNICQpLj7Tov5p6bnKyMiR+xV3sPf2di60+Cv4ivOZdOGmP99+2FqB/iVf/O3q6qS+t9VQVCM/Pf/GTp2PsyxPAOWjbri3Gtc33/Vv/lLbv5eRSr9eA1R2ScnZ+s8r2/TfV5qqYT1febg768M3muuvNWe1fPVZHTmepozMHAX4uSqqkZ96d6+u8Ct22Nq1N1Gj3vm3yPGPHEtV62YBlx43bein+UuM7c5XtYq72S4e0oX3G4XxzffeJi09R+kWFo7H5VsTzf9+CajsHhkUYbZWkZScZfh4tkRYqLuG3FvbrO2X+SdKXKsIDjT/fOHh7qRvPmhl+HPHXbdVV9NGvnph7C6bfe6oG+5VYEcydigFimfNumhIvuP/7PlMi74fjajtrQ/eaK5Pvj6g2b8XvwMqAAAAUJGw+oVKZd26derQoYNZW6dOnfT3339bNd6OHTvUvHnzAu0PPvigvvnmG6vG/OWXX3TPPfeYtd19992aMWOG4TEOHjyo+vXrKy/fHtcNGzbUnj17rMorv+joaEVERJi1de3aVX/99ZdNxs/vr7/+0g033GDWNnjwYE2ZMsXqMZ977jl98MEHBdpvuukmzZ07Vz4+5ne4ff311/XGG2+YtU2ePFlDhgwpca46deroyJEjBdoDAgJ06NAhBQYWvHOVUS4uLsrJufzFqK1+D6dOndLPP/+sX3/9VevXr1dqamqJfZydndWgQQO1b99ed955p7p16yZ3d/cS++Hqdl1rP91xc4hZ29RZMRZ9YT9r0Tlt2Jakvt2r6LrWfqoa4qZhd1crts/mXUma9MtpHTySZlXehfH3dda1Lc0X9BcbLGLLz94/k4+X+RemI4fXvFT4lZaeo9//itXG7Uk6G5clD3cn1a3loW6dgxTVwPtSn8hwT40aEa4X3j2oHK6vQAUT4Oeqjm2Dzdp+s+IC7B/nHNeaf2J1/5011bVDiKpV9dBjg+sW2+efLXH6fOoh7TuYbPF8Rv297pxm/35SG7YY35kQwNXPxcWksS80kfcVd708GZOmH2YdK8esAHRpH6x+vWqYtX39fbSSkm17kwXOAUDl5uVhvg5gzd3tMzJzzYq/vDxdJBXcQSz/hdOvPNXw0gWYqWk5mrvopNZtjNXZ8xny8HBWZB1v3X5LNbVo6n+pT8N6vnr75SZ64uVtysmhQBVwdL3yFWRt3hGvE6cs24HHCFut1wCV3ekzGXro2S3qeVNV9e5eTQ3r+eqmzqG6qXNokX3iE7P089zj+mH2sWL/Nm/ZGa87e1a/9LjrdSH66OsDhnb07H5D1QJtF95vFOTpaZv3NuZzFV5oBlRGXdoHq9/tZbRW8XzjfGsV6Zo+u+S1ivyfO15+0vxzx7xFJ7VuU5zOnM+Qp7uzIiO8ddvNYQU+d7z1UmONeGW7TT535C9S32Kn90RARWHtuqiPj/nx36ZFgKpWubzj+fZ/E/Tb0tPafzhZ6ek5Cgl2V/vWQbqzR7VLu4K5OJv0zCORijmbodX/nLfRTwQAAAA4Noq/UKm0adNGvr6+SkpKutS2YcMGpaamysvLq5iehSuqwGb58uXWpljomPmLnkoyadKkAoVfkrR3716tXr1aHTt2tDa9CiEnJ0cPP/ywJk2aVOC5O++8Uz/++GOZFSzFx8frv//9r8aNG1cm8xlx6NAhjR49WjNmzFBWlmW7iFzcIWz37t2aPHmy/Pz81LdvX7300ktq0KCBnTJGeYqo5aFnH6xl1rZpZ5IWLLd8cc3Z2aTcvDxlG1iYX7oqVrMWndWREwUvUCqNm64LvLQDlySdjMnQjr3W7ypmr5/J1cUkN1cns7YqQRe+DDlyIl2jPjysc7Hmx+/BI2lauipOd90aoofuu/zFbZNIb93dM1Q/zT9j5EcCrhrdb6x6aRcuSTp+Mk1bdiZYNZazs0l5eTK0o8bvy05r+uxjOny05KLp0mh/TZCcnE3KyMzVtl3W/VwArj4v/aeBmja8fDf+7Jw8vfXRXovvjA3AdiLreOu1ZxqZta3fHKs5drjjLOcAoHLLf4F0ZlbpL5D29Ch4gbSri0nubuZrDhcvwDp8NEUjx+zQmXPmaxf7Dibr92Uxuq9PTf1neL1L7c0a+2tg31qaOuOoxbkCKDsuLiZ1y1ew8Zuddv2y5XoNUNk5O0nOTiZlZuWpkK+FzZw+m66JP0Trj5VnSiyOWLsxVonJWfLzcZUk+fm4avj9dfT5lEPF9gsNcdf9d9Yq0O7ibJKbm5MyS3gfkv95IzIyzO9qV9h7G6AyiqzjrVFPNzRrW785VnMW2v7v+0sjGqhJvrWKtz8uea2i8M8dF67ROHw0Rc++vrPg545DFz931NCIYbb/3OHiYlK36/O/J2LXL6AopVkXzV/8eXHdITc3TxMmH9JPc4+bPX/0RJo2b4/XzN9O6MM3mimi9oUb3jo5mTTqmYbqO3y9UtO42y0AAAAqPqeSQ4CKw8XFRZ06dTJry8zM1OrVq60ar6jir4MHD+rYMevuulza4q+cnBxNnTq1yOcnTpxoTVoVRmZmpu65555CC7+GDh2qX375pcx3qvrss8+sfr3YUk5OjsaOHavGjRvrhx9+sLjwqzCJiYmaPHmymjRpokGDBikmJsYGmcJRVAly1din65jdSTHmXKbGfW3Zwrqri0mPDaiuCW/UV4+uwQr0dy2xzy2dgvTlWw015sk6CgksOd6oWzoFmT1e8rd1u37Z+2dyKuIdXHJqTqGFX1eavficZi8+a9Z2Z7cQebjzthAVy203m9+dccEfln9B5+pi0jMPR2rqp9fojlurKSjQrcQ+PW8K07QJbfXeqKaqElxyfGEm/XREfYevu/Sv/2P/6LEXt+jDL/dr47YLO325ujqpY9tgff5uS418JLLI8wKAiuOhAXXU40bzc9tXUw9RAAqUo6pV3DVuTLNLd5uVpFMx6Rr7gW12Xb8S5wAA+ZV0kbe1fZycTYW2JyVnF1r4daWf5h4vcIHWvb1rytODDyyAI+t8bbAC/C6vRyYlZ2v56nN2mcsW6zUApGaN/fTDF231/BMN1KKJv5yL+Pt9UVgVD736dCPNntRevbqFFRubmpajX349YdZ2/501dXe+XT2uVCXYTR++0Uy+PkXce9jAm5DCbmwKwHJVQ9z1v9FRBdYq3vxwr83nenBAuLrfaF4s9dV3hw2tVTg5Ff25o7DCryv9NPdEgc8d9/wfe/cdHVW19nH8N5NJ7yFACC0Qei8WLDS7YkHF7hXL9bVibyBFigqWa8Gu2LFdsVcUKaIISO8lJNT03tvk/YNrwsnMJDPJJJkk389arOXZ2WfvfSSzObPPefZzYcd6f++we0/0Z8PcEwHNXX3XRc0m+3PAZ9/Yfr6PlZJWrPtmbDFkFgsJ9tal46IdngMAAAC0JGT+QqszduxY/fjjj4ayZcuW6cwzz3SpnYqKCq1YscLhz5ctW6Z//etfLrWZkpKi7du3G8qioqLUp08fB2fY+umnn3T48GGHP//ss8/0wgsvKDg42KWxtQT5+fkaP368fv31V5uf3X///Xr66adlcrDA0JCKioo0bdo0vfvuu43e9z+ys7N18cUX15i1Ljo6WmPGjFHPnj3Vpk0btWnTRt7e3srNzdXhw4e1a9cu/fXXX4qLi7M5t7y8XB988IEuv/xynX/++Q15KWgkocFeeuKB7oqMqApsyMgq1ZRn9ik71/kdlcxmadqkrjp+UNWOcFZrhf5cn6Nf/8jUnoQC5eSWy9vbpLYR3hrSN0gXnBGpTlFHgzRHDA1R3x4BmvL0Pu07WFSva+rTPUAxnfwqj8vLK/TLH5kut9MY11RcUqFya4W8qj0Y+fLn1BoDv/7x/hdJOmtkhIICjgbuhQRZdNzAYK38mxdH0TL07x2s7l0DK4/Lyiv0wxLXXibyMktPTh2gk4ZXBYVarRVa8VeaflySrJ17c5WVUyofb7Pat/XV8EFhuvT8jurS8Wg22VNPjFT/PiG6d9pm7Yl3LYNgbl6Z4aHFPzZvz9Gi749oUL8QTb+vrzq0PzpnXXp+R/n4mDV3/m6X+gHQfFx+YUddf2VXQ9nHXx7UR186fggKoGGFhXrr+VmD1C6yagOZtIxi3TNts7Jy6r+ZyrGYAwBIUmG1Hayr75LvjOrnFBbZruEUF1tVXl5h8xL5p18fqvEFzH+8+WG8xp0RVfnyd2iIt0YMj2iwQBIA9Xf+mR0Mx7+uSKlTBp7auGO9BoA0fFCYnp4+QL6+VRvzpaQV6/PvDmvN+gwdSS5SUbFVIcEW9ewWpDNHt9NZo9vJYjErPMxHj0zqrb49g/XUy3sc9vH+fw9oxPCIyszDZrNJ9/xfD409pa2++yVRe/blqbjEqsgIX40YHqGLz+1Q+fJ3cmpRZfYO6Wh2rpJS28Cu6vchx16Ps5y5twFak7BQbz03a6DNWsW9092/VnHZhR11/RXGtYpPvjqkj51cqyguqd/3jrcWJrj9e8f5ZxqDY3/9vWHuiYDmzh3rovb+zc7LL9NbC+NrPTclrVgff3lQ//evbpVlZ49trw8+b/pNtwEAAICGRvAXWh17WbRqCnhxZMuWLUpLq1o48vLyUnl51ZfTpUuXuhz8Vd+sX5JtZi+TyWTYKS0/P1+ffvqp/v3vf7vUbnOXmZmp8847T3/99ZfNz+bMmaNHH320CUZV5YMPPtD999+vgQMHNnrfWVlZGjNmjDZt2mTzMz8/P/373//WHXfc4XQQ4t69e/X555/r9ddfV0JCgptHC08QFOilJx/srk4dqhbzsnPLNPnpfTqSXOJSW1df2N4QJFVUbNUTr+zX2s25hnpl5RU6cKRYB44U68flGbrjXx119qijARmhwRbNvCdGt0/fo9z8uj/YO2tUuOH47y25ysiyDb6oTWNdU3Gx1ZB1TZJ+dTJYrbikQn+uy9ZZI6uCWgb1CST4Cy1G9ZeW/lqXobQM1+an66/sagj8Kiou19Qnt2vVOmNGwLKycsUfKFD8gQJ9/VOi7r+tpy4462j/4aE+emr6QE2862/l5Lo+nziyeXuOJj26SW/9Z1jlLpQXnNVBK9eka+XqdLf1A8AzXHBWlCbdFGso++KHw3rp7X1NNCIAwUEWvTB7kLp0Cqgsy8wu0T3TNutQYqFb+2IOAPCPguovSNcl+MvXuReki4rLFRhgfHz042/OBWgUFVu1fFWq4XvZ0IFhBH8BHqpdpK+OH2JcF/12cWKD9OWO9RqgtQsL8dbMB/saAqVWrk7TzGd3qqBaoHhmVqnWbMjUmg2Z+urHI3pq+sDKtcSLzonW4aQiLVxk/yXpsrIKTXlim555bKB6dguqLB/cP1SD+4c6HF9WTqnmzt+t52YNqizLzbe/LuqOwHYfH+MzkuptAq1JcJBFz88eaLNWce+0LTqUWL8NNKu74KwoTbqxu6Hsyx+OuLxWYe97x09Lk50816oVf6UZsooOHVD37x3tIn113GDjPdF3iwlSB6pz17po9fsWSVrxV5oKi5wLuPxpabIh+Ktbl0CFhXorK9u9ga4AAACAp6lfzmugGRo2bJhCQ42L0n///bfy813LyFA9UOvqq682ZI2yF8jlapuSa8FfKSkp+u677wxl119/vaKijDsUvf322y6PrTlLTEzUqFGjbAK/zGazXn311SYP/JIkq9WqRx55pNH7LSsr08UXX2w38OuCCy5QXFyc5s+f71L2uR49euiRRx5RXFycPvjgA3Xr1q32k9BsBPib9fj93dSts39lWW5emaY8vU8HjtS+C9uxggK9dMnZkYaylz84bBMkVV1pWYVeePeQNu/MqyyLjPDRFee3c6n/Y/n6mDT6hDBD2c8rMuxXrkFjXlNegXFBNCO7VCnpzi9m7owrMBx3jvZzUBNoXvx8zTp9ZFtD2Xe/uPbSUnCQRVeO72woe/bVPTaBX9WVllVo3ku7tX5LVmVZu0hfXXdZF5f6d0ZicpHe+WS/oeyaSzo7qA2guTp7bDs9eHsvmY/J9vndL4l69tW9TTgqoHULDPDS87MGqccxL0Dm5Jbq3mmbFX+goIYzXcccAOBY+dU2hwkL9XFQ0z5/P7P8qmXUyLOTcViyfUk7PbNESSnOr/ts22VcB4k55qUwAJ7lvNPbGzJu7NmXp11xeTWcUTfuWK8BIF05vpPCw6ruARIO5mvavO12X6A+1rZduZo+b7uh7Maruios1NvhOWkZJbr1wQ366scjKi2t/UXsdZsyddO962yCyzMy7Qd55hUY7zf8/bzk5+va6yvhYcbxOwo0A1q6wAAvPTdzoHrEVFurmL5F8QfdvFYxpp0euK2nYa3i+1+T9Oxrrq9V5NX7e0eO4bhr57p/7zi3ke6JgObMneui1T//krRtZ46dmvYlpxYrLd04X3Rl7QEAAACtAMFfaHXMZrNGjRplKCstLdXKlStdaqd6oNYll1yiAQMGVB7Hx8dr//79ckV9g7/ef/99lZYaX/y/8cYbdfXVVxvKVq1apR07drg0tuZq3759OuWUU7R161ZDube3tz766CPdeuutTTKu7t27a+jQoYayH374QcuXL2/UccycOdPu790999yjr776StHR0XVu22w269prr9W2bdv00EMPyWzmn5zmzt/PrDn3dVOvblWLZvkF5Zr6n3jtO+j6jnHHDwqWv1/VS0eJKcVOZ66qqJA++sa489sZp4Q7qF27kceHGbJoZWSXavUm5xcX/9GY13Q4ybiYmelilrL0LOO/FyGBXg5qAs3Laae2NewUmZ5Zoj/XuJYN66TjIgxzwuGkQv2wxLndJisqZBOUde7pUQ5q18+vK1IMx/17hyiIzzLQYpw+sq2m3N3H8NLBz0uTNXf+7iYcFdC6Bfh76T8zB6lPz+DKsrz8Mt03Y4v2xLu2qVBtmAMAVHew2g7aUe18HdS0L6qdcdOX7JxShy9IHzxs7Cs9w7UNf9Kq1Q8JcfxiOYCmdd4ZxjWLhgrIcsd6DQBp7CnGIMqFiw6qpLTCqXPXbc7Sxq1Zlcd+vl46Y1TNm+oVFVv19Ct7dNVta/X6+/FatylTyalFKiouV35BmeIP5OuHJUm6e+om3TV1s5JSihVTLfhi5177wRM5uWXKyTU+p2jf1rVN6qKq1T90xL2ZmIHmwN/fS88+NtBmreL+x7ZqbwOsVUy+u7dxrWJZ3dcqbL53OAgWdSQt3Vg/NNjioGbtxp3e3nD83S9k/QKO5e510YNHbIPF0lydAzLcNwcAAAAAzQVv4qNVshdQ5UqmroqKCq1YsaLy2GQyaeTIkRo9enSd20xOTrYJyOrUqZN69OjhdBvVM3p1795dp556qiZOnGhTd8GCBU6321xt2bJFp556quLj4w3lAQEB+uabb3TFFVc00ciO/s7MnTvXpvyhhx5qtDHs3LlTTz75pE35xIkT9dxzz7ktWMvf31/z5s3T4sWLFRkZWfsJ8Ei+PibNureb+vYIrCwrKCzXtOfitTu+bg/Tuh+TPUySNu90bVFwy658w26TocEWRbd3bdfrf5w9KsJwvOSPTFlr38jSRmNe0/5qmdZKy1wbcGm1B8I+3twWomU4/8wOhuOffktWuYuf557H7FgnSes3Z7l0/qatWSo55rMcFuKtTh38azijbrKySw0vSHh5mdShvfv7AdD4xpwcqen39ZHlmBcplqxM0Zznd6rCuXe6ALiZn69Zz8wYqAF9QirLCgrKdP9jW7RjT82Zfl3FHADAnv3Vduzv6OJ3jOhq3xUSasgAEH/AuJ5RfQ2hNtXr+7LmAHik4YPC1DGqam4oLrHq52UpNZxRd+5YrwFaOz9fszpFG/89/3tTlkttVK/fv1ew/YrVJCYX6f3/HtBdUzfrkhtX6/QJK3XWFX/o2jv+1uPP7zK02793iOHc7bsdb7RX/X7E1TXU6Chj8FdN9zdAS+Tna9Yz0wfYrFU8MHOr29cqRp8UqWn39jasVfy2MlWPP7+rzmsV1TMFOZNl8Fgl1Z6N1vVZ5/BBYYqudk+0eHnD3BMBzVFDrIvG77f9N9vlOaBafR8f1h4AAADQ8rHlAVole8FfS5cudfr8zZs3Kz29akfCgQMHqk2bNhozZoxeeuklQ5v2Aq/sqW/Wrz///NMmeOxf//qXJGnQoEEaMmSINm7cWPmzDz74QE8++aS8vVvmrqt//fWXzjvvPGVmGjPuhIWF6fvvv9fJJ5/cRCOrctZZZ+mMM87Qr7/+Wlm2Zs0aff7555owYUKD9z9lyhSVl5cbyrp376758+c3SH+nn356g7SLhufjbdLMe7ppQK+qwK+iYqtmPJ+gHXvr/iAtMMC4+JaZXeqgpn1Wq5STV6424VXthARZdCTZtR2hOrb3MVybJP38u3PZuqprzGuKP2gMugsMcC3bT/X6OQ52+waak87R/hrcP9RQVpcdq4MCjV+TMrJcm1fKrVJOTqki21TtxB8a4q1Die7febasrHogp8lBTQDNxakntNFjD/SVxVJ1P7B8VZpmPr2jTsHpAOrPx8esp6cPNNxnFBaV64FZW7V1p+sZg2vCHADAkX37jQFZPWIC5etrVnGxc5PDwH7GF7Grt3esuATjz4KCXHuUVD0jcXaua+sjABrH+Wcas34tX5Wq3Dz3rxG6a70GaO2qr1lKrmfJqV4/tAGycw4fHGY43rAl22HdfQfyNahf1fwwoE+I/ljrXFZAP1+zesQYn+3sO+DeLEeAJ/PxMeupaQNs1ioenL3N7WsVp5wQocce6GNYq1ixKk0zn6nfWsXeat9J7M1zNQmuVr+u3zvGVcuEumJVWoPcEwHNUUOti+bmlyk5tciQ9dPlOaDaWkVOLp9bAM1PUVGR4uLimnoYQIOIjY2Vn59rGb4B1I7gL7RKgwcPVkREhDIyMirL1q1bp9zcXAUH177DWfVArX8yfo0aNarGeq60KbkW/GUvk9d1111X+d8TJ040BH+lpKTou+++08UXX+x0H83FL7/8oosvvlj5+cbFwqioKP38888aNGhQE43M1rx583Tcccep4pjtsB599FGNHz9eFkvDTdEJCQn6+uuvbcrnzp3r1GcArYe3xaQZd8docN+qLDjFJVbNfCFBW3fX7yFafoHxaYCfr+s7Mfn7Gc8pcvKFp2OdNdKY9Wvr7nwdTip2ULtmjXlNf2/JldVaIbP5aKBHVFsfeVtMKi1zbnu9mE7GL1dpGbyIheZvXLWXljZty9aBw64HXOVVC4b093UtuFKS/P2N5xQWlTuoWXc+3iabFzQysvgsA83ZScMjNPuRfvI+ZpfaP9ama/pT29kVH2giPt4mPTV1gIYNCqssKy4u18Ozt2rTNscvMdYFcwCAmqRnlmhPfF5lpmKLxazB/UK1ZoNzG9gMGxBmOP5rXYb9iv/72bFrDtHt/eTjbVKJkxnAunc1voidkl63dRYADSco0EujT4o0lH23OKlB+nLXeg3Q2lVfs5SOPk/Iy3d+3dHfr2HXLIcPCjNkG12/JavGDbFWr8vQ+HOiK4+HDgx1WLe6wf1DDYEou+JylcnaKFoJH2+T5k3tb7NW8cicbW5fqxgxPFyzH7azVvH0jnqvVdT3e0e3LsbvHanprgXESv/cE7UxlH33S8PcEwHNTUOvi66qdh/QvUuglijVqXO9LSabjOgpaaw9AGh+4uLiNGDAgKYeBtAgtm7dqv79+zf1MIAWh3y3aJVMJlNlwNY/ysrKtHLlSqfOrx6oNWbMGElS27Zt1a9fv8ry/fv3Kz4+vk5tSs4Hf+Xl5emzzz4zlJ166qnq3r175fHVV19tE0xkL2Csufv88891/vnn2wR+devWTStXrvSowC9JGjZsmK688kpD2e7du/Xmm282aL/vv/++rNW24erQoUOLDAZE3Vm8TJo2qauG9a8KCCwptWrW/ARt3JFX7/bTqz2Ei+3i76CmfdHtfRRQLbjC1UxbZpN0+snhhrKfVzh++ak2jXlNGVll2hFXlXnN22LWkH5BduvaM3yAMdBzWz2D+YCmZjZL545tbyir6y7SaRnGhwM9Y53/bElSpw7+Cgyolj3MxV14nTF8cLi8vKoyfRUWlSuVlyqBZuv4IeF6fHI/+RzzIsXq9Rl69IltNln+ADQOi8WkJ6b01/FDq74zFJdY9cjj27Ruc5Zb+2IOAOCMFavSDMfnnR7loKZRl07+6t+nKvNXQWG51mxwvP6RllFi2MHb29us4YPDHdav7sRhxo12Nrv5BVQA9XfW6PbyPWazmyPJhW6/v5Hcu14DtHZFxVabALBe3V3b0LFPD+M6p6uZw2pz7YTOhuOvfzpSY/3VGzJVVFwVgDawb6i6dHLuuUr1+6Dq90lAS2WxmPT45P46fohxrWLyE9vd/m/5cUPC9PgjtmsVU5/c7pa1ivSMEm3dVffvHSOGGevWJRjlrNHtqt0TFTXIPRHQ3DTGuuiyP4z/dp843PnP//DB4Ya5KTO7RAkHC2o4AwAAAGgZCP5Cq2UvsMqZTF0VFRVasWJF5bHJZDJk/KoeVOZMm8nJydq5c6ehrGvXroqJian1XEn69NNPlZdnDMQ4NuuXJLVr107nnnuuoeynn37S4cOHneqjOViwYIGuvPJKlZQYH1QMGDBAK1euVGxsbBONrGaPP/64fHx8DGWzZs2yCWBzp2+++cam7IYbbmjQbGNoXsxmafLtXXT8oKoXg0rLrHr85f1av7X+gV+StGWnsZ1+PQPVJdrX6fPPG2Pche1gYpGyc13bpfL4QcFqE16VNaegsFy/r81yqY1jNfY1/bLSuLv3JWdHOqhp1L9ngPrEBlQel1srtHZzrtPjBDzRScPbKLJN1ectv6BMv610bne46tZvMT4gHNg3VDGdAxzUtnXRuR0Mx/sPFSgrx727zppM0g1XdDWUrV6fwcvhQDM1ZECo5k7tb3jR4O9NmXrk8W1OZ/UE4F5eZmn2w/100nFV9+ilpVZNnbvN6Sw7zmIOAOCsxctSVFZeNS+MPjlSnTrU/oL0tZd2MRz/tjKl1t30f1hi3O3+yvGdnBrj4H6h6t+7aj2pvLxCq/6u+0Y7ABpG9Wxc3zdQhgt3rtcAkDZsyTIcX3h2B/sV7YgI89apJxifQbgzQ9C5p7XXCUOrAsB3x+VqaS2f9+Jiq5b+YaxT/b7Fns7R/hp1TPbCsjKrflme4uKIgebHyyzNeqivTjqu6rNWWmrVtLnb3b9W0T9Ucx+1XauY/MR2t65V/PBrsuH4ios6OnXeoH4h6lf9e0cN2Y0dGXdGtXuiX8n6BTTWuuj6zZk6klyVIbRvzxAN7u9cFtCrLjauUfzFugMAAABaCd7yR6tlL/hr6dKltZ63adMmZWRUfWns37+/IiOrFpdHjx6tV1991dDmDTfcUGOb9vo97bTTah3LP6pn8PLz89Pll19uU++6667Tt99+W3lcXl6u9957T1OmTHG6L0/1zDPP6MEHH7QpHzFihH744QeFhzu/Q0xj69atm2699Va9+OKLlWVJSUl69tlnNX36dLf3l5OTo/Xr19uU/5PBDjCbpIdu6aKTh1UtrJWVVWjuqwe0ZpP7AoTiDhTpUGKxOnU4+vDfy8ukB27urIfn7VNhkbXGc4cPCNKFZxgfUq78uw67uY0y7kS9fHWWikvq/sCisa/pl98zdMlZkerS0U+SNKRfsC4+K1JfLna8w2VosJfuu8m4++bva7KVmOr+rERAYzr/LOMDul9XpKiouObPnSN79uXpwKECdel0NODL4mXStPv6aNKUTSoorDnI9MRh4brsfOPDyeovMBxrwvnRWvpHmku77Hp5mfTwnb0MO/dL0qLva95JF4Bn6t87RE9PGyC/Y16k2LA1Sw/N3qqSkrrNYwDqx2yWZjzQV6NGGF8mnP7Udv251r0vEjAHAHDFocRC/bgkSRecdfRFbx9vsx69p7funrrJYTDXqSe2MbzQWFJq1dsf76+1r+9/TdIVF3VSty6BkqTjBofrios66dOvDzk8JyzUW1Pu7m0o+21lqg4nFdXaH4DG07NboPr0qMoWVF5eoR+WJNdwRt25c70GgLRkZapGHvM95fSRbfXn3+lavKzmwCdvi0nT7+urgICq10MKCsq0Zr3jF7i9zFK5kx/X0SdF6uE7e1Uel5VZ9cSLu506/+2P9uuMke3k/b/sHePOiNKKVWlauSbdbn0fb5Om3N3bkO3ju1+SuN9Ai2c2S9Pv72OzVjHj6R36081BD/17B+upaf1t1ioenr3N7WsVPyxJ0hUXdaz2vaOjPv3a8SbG9r53LP0jVUdcnAd6dAtU72r3RD820D0R0Fw05rpouVV644MEPfZA38qyyXf10q0PbVRWtuNNNa8c30nHHZMlsLy8Qh8uOujWsQFAU5lqjlYHk3ftFQEPlFhRqjlW3lsCGhrBX2i1+vfvr7Zt2yo1tepl3PXr1ysnJ0chISEOz6ueyat6pq+6ZP6yV8decJo9O3bs0KpVqwxlF110kUJDbXdDueCCCxQeHq7MzKqF/LfffluTJ0+WyWRyqj9PNGXKFD355JM25WeddZa++OILBQYGNsGoXDNt2jS98847ys2tCqx55plndNttt6lt27Zu7WvDhg2qqDC+DGIymXT88ce7tR80X/fe1EmjTwgzlL27KEl79xeqXRvXvmBmZpfVuPvbO4sSNe3OmMrjnjEBenFGT735yRG7gWbBgV4af2akLh/XThZL1byVnVumRT+6tmNsWIhFJwwyzvc/raj/gmVjXpO1QnrtoyOafX83eZmPnvt/V0WrXaSPFn6VrLwCY5DKkH5BuvO6jopuX7Xbbm5emd5dlFiXSwU8RniYt04+zhjM+e3i+u3O+Nr78XpiSv/K4z49grXguWGavyDO7sONkGCLLr+wk/41obMslqoXD7JySvXxl44fOJx/Zgfddn13Lf8zTUt+T9WGrVkOA8x8fMwaNaKNJl7eVd27Gu9vfvwtSes3Z7l4lQCc0baNj7y8bL8vRYQbs/d6eZkU1c5+xs/ConJl55TZlPfsHqRnHxtoePlq/6ECPfvqHoWHunbfVVJiVUaWe7MMAq3VlLt66/SR7Qxlr78fr9378hx+zh3JyCxxGJDRFHNAQ85pABrnM7bgowSNPilSIcFH54lB/UL1/JzBmjt/lw4cqtox29ti0kXndNCdN8Yazv/ky0NKTi2u9VqsVumFN+P07GMDK6/prn/HKqqdr97+aL9y841jPG5wmB64vac6RVdlIsvJLdXrH8TX2hfQmjXFv83nn2nMFLRmQ4ZS0mqfF1zVEOs1QGv364oUXXNJZ/XsHiRJMptNmnZvH/XtGawPPz9od4OpYQPDNOmm7uoVG2wo/3DRQZt/z4/1/kvH6c+/M7TsjzRt352jCjtfa7p1CdB1l3XRWWPaG8pffz9ee/blOXVNR5KL9N9vD+vqS6o2rZvzSD/NXxCnr39OVNkxz5i6dgrQI5N6aVC/qufgWTmlTgW2A83dZHtrFR8kNMBaRaCemWG7VvGf1/YqPMz9axVWq/TCW3F6dkbV945JN8Uqqq2f3v7Y/veO+2/raciAfPR7R4JLY5Ok86tlQl2zIbNB7omA5qSx1kX/8cvyFF10TgcNHRAmSeocHaDXnxqqZ17do7UbjUHqQYFeuvGqGF1xkTHr1+ffHVbCwYJax8O6KIDmoIPJW11Nrs23AIDWheAvtFomk0ljxozRf//738qy8vJy/f777xo3bpzD86oHalXPlhQVFaVevXpp9+7dkqSDBw8qLi5OsbHGh+w1tWmvXUeqZ/2SpIkTJ9qt6+vrqyuvvNKQmSwuLk4rVqywCVprDqxWq2677Ta99tprNj+bMGGCFi5cKB8fHztnep7IyEg9+OCDhkxfubm5mjVrlubPn+/Wvv753TxW9+7dFRYW5tZ+GkNKSoohgNMZe/fubaDRtBxnnBJhU/bvKzro31d0sFO7Zg/NjdOWXfkOf/7nuhx98XOqLjm7KsixU5SvZt7TTdm5Zdq7v1A5uWWyWExqH+mj7p39DQFS0tEdq598db/yC13bae70k8MNbcUfKtTu+MIaznBOY1/Thu15ev2jI7r92qpMQ+PPjNS4sRHaGVeg9Mwy+fiYFNvFX+0jjXNiaalVc18/oOQ0XhRH83bu2PaGgKu4hDzt2FO/TIXLV6Xpk68O6crxVQ8QunQM0NPTByorp1S79uYqO6dUFotZHdr7qme3IMMYJKm4xKpp87YrL7/mbGF+vl46e2x7nT22vazWCh1KLFRSSpFy88tUVlqhAH8vRbXzU0zngModcI+1ck265s23/fcdgHu8Om+oOrT3q7Veu0hfLVowwu7PfliSpMef32VTPvLENgoOMi7NdO0UoA9fdn1jhvVbsjRpyiaXzwNg69zTo2zK7rgxVnfc6Hhtx5E7J2/Uhq32M/o2xRzQkHMagMb5jKWml2jKE9v0n1mDKjNeDO4XqoUvH69dcbk6klSkwACLescGKTzMuA6wck263lzofDDW2o2ZeuHNvbrv1p6VZZdf2EkXnxutbbtylJpeIl9fs3p2C7K57pLSo1kIEpPJwgHUpLH/bfa2mHTmaOPLnN/90jABWQ2xXgO0dhUV0qNzt+m1eUMrX1Q2m026/MJOunRcR8Ul5OlIcpGKi60KCbaoZ/cgRUbYvjj459r0WjNkhIX66OqLO+vqizuroKBMcfvzlZ5ZopISq8JCvdU5OsDu/PXuJ/v10ZeOM4Xa8+p7+9StS4BOOq6NJMnb26z7bu2p66/oql378lRQWKaO7f3VKzZIZnPV85SSUqumPL7NbtAb0NKce1p7m7I7buiuO27o7nJbk6ZsqmGtItLuWsUHLx3ncj8btmRp0qOba63398YsvfBWnO67pUdl2WUXdtT4czto2+5cpaUXy8fH8feOx57Z6fL3Dm+LSWeOapx7IqA5aax10WNNeWKbXn9qqLp0CpAkdYr21/OzBykppUh79uWpsLhcbdv4qn/vEEPmT+nousXLb8c5NR7WRQEAANASEPyFVm3s2LGG4C/paCCWo+Avq9WqFStWGMpGjRplU2/06NGGAJtly5Y5DP5KSkrSrl3GL4Y9evRQ586d7dY/VmlpqT744ANDWVRUlM466yyH50ycONEQ/CUdDSBrjsFfn376qUpKbBfzb775Zr322msym21fjvZk9913n1555RUlJVUtKr7++uu699571b2764u2jhw6ZPvApV27dnZqer5XXnlFM2fObOphoJ7e/CRRWTll+tf49oaghtBgi4YPCK7hTCk5rUTPvnWwxgAzR84aGW44Xrwi00FN1zX2NX27JF1Wa4X+fUW0/HyP9udtMWtg7yCH52Rkl2rOS/u1Y2/tu2ABnm5ctd0Z3fWAbv6COGVml+imq2MMDxPCQrx14jDbQNljJSYX6fHndzr1UONYZrNJXToGqEvHgFrrFhWX671PD2jhFwdVXl7zznkAAAAAWo4NW7M15fFtevSe3pUBXmazSX17hqhvzxC75yxenqx583fL6treOVr0/RGVWyt0542x8vfzknT0hewh/9uV2570zKMBalt35rjWGYAGN+qkSIWGVGXtyMwq0e+r0xukr4ZarwFau8OJRbpjykZNv6+P4d99Ly+TesUG22T4OpbVWqFvFifqhTfjXFpPDAiwaGDf0Brr5OSW6plX92jJ765t2nh0XNK0edv1yKTeOuOYYIyIcB+dNNz+OmxGZonmPL9Tm7a7tv4KwDN98f0RWa0VuuOG7sbvHf0dzz3pmSV69MntdfreYe+eaOWahrknAlCznNwy3T1ts6bf36cyA5gkRbXzU1Q7x8Fa3y5O1DOv7lG5i+scAAAAQHNG8BdatbFjx9qULV261GH9TZs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7BrrDokWLcs899yTZMAnFLrvskqOOOipJ11Yfrqury5NPPpkvfelLaWlpKReN19XVpVAopF+/fhk3blwOPPDA9OvXL/Pnz88jjzySJ598snx8fX19Zs2alS984Qu54oortvp7Bqqn9L0//fTTM2bMmLzzne8sF643Njbm1ltvzZve9KZce+21GTdunAIwoOzF+aC+vj5z587N3XffnbvvvjuXXXZZVq9enXXr1iVJGhsbUywWy/c2Sysh19fXZ9CgQdlrr72q9VaA7UBpkr1kw++i5ubmPPfcc2ltbc2gQYPSq1evlz3epHpAqRA92ZBH9thjjyTpMKHei5Vyx5w5c/Lcc8+Vn7tMnDixfD3Tld9HpX1mzJiRJOX7uqXrH7+xoDpK3/F77rknP/rRj5JseMbS0tKSJPnYxz6WT3/60xk1alSSlCfmKhV+lZ7Blu6nDh8+PMOHD8/rXve6fPKTn8xDDz2Ua665Jpdddlnmzp2bJOXJuVpbW/Ptb387hxxySHnSYAAA6A6KvwCAHU5bW1t5BrhisZihQ4fmkEMOqaiP0o36J554IsmGARFDhgwpzyS38WxQG8++Xzr2yCOPzPe///0sXLgwyYbZ99esWZPevXtv/hsDqmL16tW56aabkmx4ENDc3Jx3v/vd6dmzZ5cGHpQeID7wwANJNuSMhoaGfO5zn8sHP/jBzJo1K0899VSmTp2aqVOn5vHHH8/TTz+dJUuWZP369UleyEktLS25/fbbK1rJENg8f/rTn/LYY4+VV+E68MADc+GFF2a//fYrPyQsXQOUrgP69euXCy+8MM3NzbngggtSX1+flpaW/OIXv8idd96ZxYsXp2fPnmlpaemwcs/OO++cgw8+OPvss09OPvnkjBgxIj169CjHYpATbD8ef/zxPPXUU+XtQw89NFOmTEnyyoOBSt/1K6+8srxCYLFYLM9Ye9BBB+Uzn/lMjjvuuLS2tmbNmjVZsWJF/va3v+VHP/pRfvOb35QHICTJrbfemkceecTqX/AqVro+KBQK+cd//Mf86le/ykc+8pEsXLgwDQ0NaWhoyMMPP5yTTjop3/72tzsUrZeOK/XjWgN2DKV7FKWJaZ5//vncddddufzyy/PEE0+UJ8JqaGgo/+4pTVhRyhnt7e0ZNGhQ3vrWt2bKlCn553/+5/Ts2bOK7wqodRvfF02SK6+8MlOnTs3o0aMzceLE7LTTThk9enRGjBiRIUOGZMCAAenTp0+am5uTvPREGu6XwI6jVERaLBbT3NxcLrx6uRxQyhFPPPFEVq1alfr6+hQKhfJ9mkomx1iyZEnmz5+furq6tLe3p3///hk6dOgWvitgS5S+4z/4wQ/Kv2NKE+2dd955+fjHP55+/fqlUCikrq6u02Sem5rcs1AolCe82H///bPffvvlXe96V771rW/lpz/9adauXVsuFluwYEG++c1v5sgjj3RfBQCAbqP4CwDY4axatSrTp08vb0+YMCG77rprxf3MmTMn69evT2NjY9ra2jJu3Lj07du3036bupn3mte8JmvXri1vP/HEE+WbjcD25e677y7P6Nje3p63ve1tmTx5cpJXXsFjYxsPcmhubs64ceOSJBMnTszEiRPz5je/ubxvS0tLnnjiicybNy9f+cpXcsstt6ShoSELFizI/fffn8MPP7w8CAvYOv785z+npaUlTU1NaW1tzemnn54DDzwwyQsPBTd1DdDQ0JD//M//zC233JJ77rknDQ0NmT9/fnkQdmnm/D59+uSII47IBz7wgUyaNGmTxRmlAQgeGsL24+GHHy6v+psk+++/fwYPHtylY+vr67N48eJceeWV5X/nSwWohx56aP7nf/4nkydPTqFQSFNTUwYMGJABAwZk/PjxOeaYY/KJT3wi3/jGN9LQ0JCmpqY899xz+fWvf529997bdQO8ypUGLB533HFZv359PvWpT2XOnDnl1+++++58+tOfzllnnZVdd901w4cPT2Nj4yYHOm5c2G7wEmz/NrXC15w5c3L33Xfn3nvvzc9+9rMsXry4POt9U1NTeSXyxsbGFAqFrF+/PvX19RkxYkR23333fOhDH8ree++dvfbaq8MEWQAv5W9/+1uSDTmpWCxm4cKF5YnzSgYNGpSRI0dm3LhxmTBhQiZMmJBx48Zl1KhRGTZsWAYPHpz+/fund+/eHe7LADuGUmFHsVhMnz59svvuuyd55eKvJJk+fXpaWlrKueOggw7q8nlL11IzZszIsmXLyhP0lIrP3G+B6qmvr8+KFSvyy1/+MuvXry9PYPGhD30o//Ef/1GelLeS1flKE2WU1NXVZfLkyfniF7+YQYMG5aKLLkpbW1s5v9x000257rrrcvzxxytKBwCgW7jjDgDscBYvXpxnnnmmvD1+/PgMHDiwy8eXbuSXHkiWBjuVCsi6MhNcW1tbBg4cmJUrV6ZYLGbBggXp379/5W8GqKpisZiHH344LS0tSZIePXrk9a9/ffr169flPkoP/jYe5NC/f//sueee5e3SuZINDxZ69OiRKVOmZMqUKXnyySfzpz/9qfxQcdWqVeX9gK2nNKCgvb09PXr0yDHHHNOlB/nt7e0ZPHhwDjnkkNxzzz3l73axWEyhUEifPn1y1FFH5TOf+Uz23Xff8nGl2Sc3fjjoew7bl0KhkMcff7z8b3v//v0zefLkigYBXXPNNeWi840LML7+9a+Xi89fnBtKA42+9KUv5a677ioXnibJokWL0tramqampi1+f8D24R3veEfa2tryuc99LrNmzUqyIT/98Y9/zLRp0zJ58uQMGTIk48ePz0477ZQJEyZk/PjxGT16dAYNGvSSBV+lnKQgDLYPG6/w1dLSkpaWlvzpT3/KZZddltmzZ+fBBx9MkvIKGC9e4au0MtiwYcPy5je/ubzCV2kim+SF3zh+twAvp7W1NXPmzEldXV35WqKhoaG8anGp4HTZsmVZtmxZh4n9GhsbM3To0IwePTo77bRT+X/jxo3LiBEjMmzYsAwcODD9+vVLz549u7QCELB9euKJJ5JsuMYZMmRIJk6cmOTli79Kr02fPr18vZO8UPzVlWuY0n2Zxx57LGvXri0/p9l///07vA5se21tbbniiiuyatWqcuHXmDFjcs4555QLv7rLkCFDcsEFF2T58uX5/ve/X76WWbt2be64444cf/zxrjMAAOgWir8AgB3OggULOsy2P2TIkPTo0aPLx5du1E+bNi1Jyg8lS7PIdeVG/pIlSzJ69OgOM20nXSscA2rH+vXry7kgSUaOHFle+aer6urqsn79+sydO7ecT4YPH56xY8cmeekHjKWB4294wxsyfPjwLFiwIEkya9asPP/88wpKYSuaPXt2nnvuufLqn5Ws3FMaRHDIIYfk+9//fnlQQGnVv6985Sv5yEc+Ut5/48JPYPvW0tKS5557rrw9cuTIDB06tMvHr1q1Kr/61a+yZs2a8kDs+vr6nHrqqfm7v/u7lzyuoaEhxWIxTU1NOeGEE/Lwww9n/fr1STYUny9btizDhw/f/DcGbDdKxZ7vfve78+ijj+aiiy4q55QkmTdvXubNm5dkw8QWvXv3zoABAzJ48OAMHz48o0ePzvjx4zNhwoTywOqRI0emd+/eir6gxr3UCl933nln7rvvvlx22WVZvXp1eXKb0opdpSLyYrFYXuFr+PDhmTx5cj784Q9nt912y3777dfpfIpBga56/PHHk2zIO62trUk2DNbe2MYFYaX7p4VCIW1tbVmwYEEWLFhQLlpNkt69e2fEiBEZM2ZMh2L2MWPGZMSIERkyZEgGDBiQPn36pLm5uXwOYPv09NNPp7W1tXyvduzYsenTp88rHle6NnryySeTvPDMZcqUKR1e74pHH320w+Q6pQIyuQWqZ968ebnhhhuSbPhd069fv3zwgx/MyJEju31MRun3z/e+973ceeedmTZtWorFYurr6/OrX/0q//7v/15+7gsAAFtC8RcAsMNZsmRJli1bVt7u27dvevXq1eXjS4O299tvv3z84x/P4sWLs3jx4vz93/99kpe/kV+68dfS0tKh4Gz48OFZvXp1lx5GALVj+fLl5QEKSbLzzjt3mOX6lZRywosHOUycOLE8uOqlckrpocSkSZPKq30lG4q/SoMWgK3jr3/9a5IX/s3fZ599klRWxD1+/Phy4VfpuE9+8pMdCr82Pgew/VuzZk2HSSiGDh2anj17Jnn5WeZLrz3yyCP54x//2GEg9ejRo/O+973vFc9d2v+kk07KmWeeWW6bNm1aRasgA9u3pqam/OEPf8gNN9yQX/ziF1mzZk2SlAckNTU1pVAopFAolFcCWrZsWWbPnp1kw2+Qnj17pm/fvhkwYECGDh2aESNGZOzYsZk4cWKGDRuWt73tbV0uige2vo1X+Gpvb8/SpUtz77335tJLL82MGTPKv202XuGrtG9pAHR7e3sGDRqUo48+OpMnT87JJ5/caeDii69l/I4Buqq0snopb0yYMCHDhw/PwoULs3LlynJhant7e9rb2zscW8pvpWNLRWFr1qzJrFmzMmvWrNxxxx3l/QcOHJhRo0Zl3Lhx5WL28ePHZ9SoURk2bFgGDx6cfv36mVQLtjN/+9vfkryQR3bdddckXbtX+/zzz+fZZ59NXV1d2tvb07Nnz4wZM6bL5y71v/FzoiTllb9M6AXVM3Xq1Dz00EPl7cmTJ+eYY45J0v2/Vzbu7wtf+EJOOOGE8sR+Tz/9dFauXNmt5wMAYMel+AsA2OG0tbVl/fr1qaurKw9sqGTlr5K3vvWteetb35pkQwFIqXCrKzfy165dm+eff768PXLkyKxfv17xF2xnFi1alEWLFpW3x44dm379+nX5+NLAqtIgh4aGhhQKhfJKgqXXX87zzz+fsWPH5vHHH0+xWMyiRYvKA8mBreP+++8v/3dDQ0MmT56cpGurf5YGRa5duzbJhkHYLS0tmThxYj796U+/bAEIsH1rbW3N4sWLO7S90r/zyQuDB377298m2VAsXso3hxxySA477LAux9DQ0JBhw4Zl6dKlaW9vT3Nzc5qbm+UeeBXaeKDj2rVrc+edd+arX/1qHn744SxatKhDIWlDQ0Pa2trKA6rr6urS2NjYYSB1sVhMe3t71qxZkzVr1mTRokXlGfIbGxvTr1+/rFmzJlOnTlX8BVW0qRW+5s6dm9tvvz33339/fvGLX2TRokXla4lNrfBVWnlnzJgxmTRpUj70oQ9lypQp2WOPPcr7l1jhC9hSpXssxWIxjY2N+fd///eceOKJefDBB/PMM89k9uzZmTt3bubPn5/Fixdn2bJlWblyZdasWZO2trZyoerGNlUU1t7enuXLl2f58uWZPn16ed/GxsYMHTo0Y8aMydixY7PLLrvkq1/96rZ580C3eOCBB8r/XV9fnz333DPJy9+rLV3DzJo1K0uWLCkXv5eezZSujV5JaTXCWbNmJXlh5cI99tij/DpQHXfddVfmzZuXhoaGtLe3581vfnN5Ir+t+d085JBDss8++2Tq1KkpFovp3bt3brrppnJuAgCALaH4CwDY4ZQGMzU1NWX9+vVZsGBBkspW6yjtXxrc0NXZ8ks3EksDpUqGDRvWafAEUPvmz5+fpUuXlrf79+9f0apbpZxQmml73bp1SV6YmbIrli9fnsGDB5cfZJaKzyrNaUDXPfjgg0k2DBLo0aNH+aFdVx4Yloo6H3vssQ7tb3rTm9KnT58uDywAtj+DBg0q/waor6/P7Nmz07dv3yQvnT9Kg5GmT5+em2++udxeGkxUmq32lXJHqZ+VK1dmwoQJee6555JsWDls0aJFGT58+Ja/QaCm1NfXZ8GCBbntttty8cUX5+677y4Pjm5ubk5bW1u5qKutrS1TpkxJoVDIs88+m+XLl5fzzIv73NTqGm1tbeUV1nfaaadt9yaBDkr3AVpaWrJmzZrccccd+fGPf5yZM2eW7zuUCrwaGhrKhZ+le5zt7e0ZMmRI/uEf/iH77LNP3vnOd3b6Tr/4XoMBzcCWKt1jKRQK6d27dw455JCMHj06o0ePLu/T1taW5557Ls8++2zmzJmTWbNmZc6cOXnmmWeyYMGCLFmyJMuXL8+qVauybt268kqmG6urqyuvblgq1ihdxyxYsCALFizIAw88kDFjxuSrX/2qe6uwHdm4iLSr92o3vke7Zs2a8r6lFbu6MslXydy5c7No0aLyhKNjxoxJz549TbQDVfaXv/wlra2t5TEYxx577DYZjzF8+PC84Q1vyN/+9rc0NzdnzZo1eeKJJ5J4dgsAwJYzwhgA2OGUVtwq3Vgr3eSr5Eb+xsdvjoULF3Yo/howYEB69eq12f0B1bFy5cosX768vN2nT5/07t27y8eX8sj555+fk08+OdOnT8/06dPLK3i83IPB0oPD1tbWDvlo+PDhWbFiRQYMGFDhuwG6aurUqUk2PKjr169fJk2alKRrAx9L1xsbD26qr6/Pa1/72q0ULVAr6urqsnr16tTV1aVQKGThwoVd/k1xww03lItGS3nkoIMOyqGHHpqk679NXjzAYMiQIeXic2D79eLv9pw5c/Lzn/88v/71r3PfffeV20vFHuvXry+3vfa1r81HP/rR7LXXXikWi3n22Wczf/78zJkzJ7Nnz87TTz+dZ555JgsXLsyaNWs2ubpGU1NTWltbs8suu6SpqclgJqiS+vr6/OIXv8i9996byy67LCtXriwXcm68wlfp+7l+/frU19dn5MiR2WuvvfLhD384e+yxRyZPntzpO1y6B+G7DXS3Rx99NMmG65kBAwZk/PjxSTbkq1LeaWxszKhRozJq1KgcdNBB5WPXrFmThQsXZu7cuXn66acze/bszJkzJ/PmzcvChQuzdOnSPP/881m9enVaWlrS3t5enhywpFTc3tTUlLVr1+Z1r3tdOR45D7YPG9+r7d+/f3nVrSSvWIA1ffr0DsUhpRzT1Um+6uvr8+STT+b5558vF3/tvffeSTbkMRN/QnW0t7fnoYceKk9y0atXr+y7777b5LwNDQ0ZM2ZMkhfu2ZrwDwCA7uJXJgCww2lqasqgQYPKs1IvXrw4CxYsyMiRI7fJ+VtaWjJ16tS0trYmSXr27JlBgwZ5AADbofb29vKM2UmyatWqzbqB36NHj+y///7lWSVLXm6AwcYrCS5ZsqTcPmLEiIqLWYGua2lpyTPPPFMu3hg+fHhGjRpVXjnjlQYGlF5/6KGHkrwwKGHy5MlJtqy4HKhty5Ytyz777JO//OUvSTY89L/yyivz8Y9//CUHFpZmpP/hD39YnsSiVHhx1FFHlVfj6GruaWlp6bBq6fDhw9OzZ88tf3NA1Wy80s+iRYty4YUX5pZbbsnjjz+eZEPBR6nYo1gsZv369Rk6dGj222+//Nd//Vde85rXpE+fPuX+9ttvvyTJ2rVry5NdlO6bPPvss+WCsKeffjrz5s3Lc889V76/UcpJfo9AdSxbtiwnn3xykpQHH5euLzZeza9YLGbw4ME55phjMnny5PzzP/9zeXBiiRW+gG1h2bJlWbp0aTlnjRo1qvycZlP3WDe+91JXV5fevXtn4sSJmThxYqd+X1zMPmfOnCxYsCDPPfdcli1blpUrV2bNmjXlFRBLeXJbDAwHus/69eszd+7c8r3awYMHZ8iQIUle/j5r6TfLY4891uH3S+kZTSWTfE2fPj0tLS3l8x144IGb92aAbvPEE09kxYoVaWxsTFtbW/bbb780NTVt9RX5Snlg6NChHcajJBvus5gMGACALWWEMQCwwznwwAPT1NSUZEMh2A033JDFixdn5MiRW/2GX7Kh2Kw0C12yYbb9Fz+cBLYPpUGOTU1NWbduXbkIqzSzWyWKxWIKhULFM2mvWrUqCxcuLG8PHTo0zc3NFZ0b6LqNB1K3trZmt912S48ePZJ0HBRQmkm6NCCp9FopN5RW8CkNSth555079QG8ugwfPjwHH3xw/vKXv6SpqSnr16/PFVdckVNPPTX9+/fvsO/GA66vvvrqTJs2LQ0NDeXrhYEDB+ad73xn+XdNVy1btqzDdcPgwYM7FH0A25/6+vrcdNNNue666/K9732vwwQVdXV1aWtrK890PWTIkJx44ol55zvfmde//vXlPkqDFje+DunVq1d69eqV4cOHZ7fddkuy4fpmzZo1WblyZZYuXZrnnnsu8+bNy7x58/Lggw/mmGOO6dQPsO1MmzYtyYaJplpaWspF5MViMf369cuoUaMyZsyYfPCDH8zkyZPLv2U2VbBpUgpgWyit+tXQ0JBCoVC+N/Jyk2O8+DqjlOc2fn3QoEEZNGhQ9tprr/J+7e3tWbRoUebNm9ehmP2ZZ57JggULsmLFijz66KP5+7//+yTyIGwvXnyvdvXq1fne976XYcOGZcyYMRk+fHgGDRqUAQMGpHfv3uV7s6UJOWfNmpXkhXu5e+65Z5Ku5YBSPpo+fXqH1VVLxV9+F0H1PPzww0le+B6WVvUrFApbdRWu0vlGjRrVYQKOtWvXbnIldQAAqJTiLwBgh7PbbruVb661tbWlWCzm97//ffbee++teiO+9MBy+vTpeeSRR8rt48ePz4QJE7baeYGt58UrcJSKrjZncEBdXd1mPXCYO3duli5dWh7U1bdvXzPHwVb04IMPJnnhId7DDz+c8847LxMnTsyUKVMyadKk9OnTZ5Pf51Jh6Ny5c7N27do0NDSkvb09Y8aMyYABA7bp+wC2rdIkE6Xr/tLgxAceeCCf+9zncs4556R///7la4jS/992220577zzkqQ8i3WSHH744Zk8efJLDorclEKhkOnTp5evX+rr6zNy5EjFX7CdWrduXX7729/m5z//ef7whz9k3bp1HQYyFovFtLW1paGhIQMGDMh//Md/5Oijjy6v7LXx5Dcvdy9k4/0aGhrSr1+/9OvXL6NHjy7vs379+qxcuTK9e/dOYrA0VMvGqwuXlAq/jjjiiHzqU5/K4Ycf3um40nd84+M2VWAB0N0eeOCB8n/X1dWViy4qWUX0pfLVxkVh9fX1aWhoyKhRozJq1KgOq/KsWbMmCxcuzDPPPJMnn3wyBx98cPkYoPa9uMBjzpz/j737Do+qzhc//p6e3nvvAZJQpIgiYEFFLIvoquuK3auuu6vs7t3i3ateXXVt6+quXURc7IW1LYoVBaWFkhASkhDSJm2STHqZ+vsjv3OYCQFRU0j4vJ7HZ83MmXPm7PPM1+/5fj+lmv/+7/9Gp9MRERFBfHy8ug+bnJxMfHw8MTExxMfH09XVhdlsVrsPRkREEBISctTXVsaJ0tJS4ODYpXQQlHFEiLGzdetW9d/1ej1Tp04d1esnJCTQ2dmpjk3d3d2yBiuEEEIIIYaFJH8JIYQQ4rjjdrs5//zzWbVqlZos8fTTT3PZZZeRmJg4otcFeOeddygpKVEDvmfOnElycvKIXVcIMXIiIyOJjIzEYrEAA518Dhw4MGrd/Do7O9XgLiWgKzo6WgK0hBhBSmCS8t/1/fv3c+eddwIDm4hhYWEkJiaSlZVFXl4eeXl5TJ48mZSUFDUgu76+HhgIAHA6nWpla5vNhsFgkN+wEBOQ8ru+6KKLePbZZykuLlarUj/++OPU1tZy0003MX36dAwGA2azmR07dvCPf/yDPXv2qOOH2+0mPT2dq6++Wv37aLW1tbF7927177CwMLKzs4fvJoUQo+qNN95QxwKdToder1cTzZXq0nPmzOHcc8/l1ltvJSAgQJ17KF3BjsbhjvNMCjMajYSHh//4mxJC/Cjbtm0DDiY8eCZuVlRUcPPNNxMdHU1ycjKpqalMmjSJjIwMkpOTCQsLO2yAsmdSmAQxCyGGk+cai8FgUJO/hmNd5LuSwpT3/fz8SE1NJTU1dcgEWSHEsU2Z/yhJnsq+r9PppLGxkcbGRrWYF6B2N05OTsZkMtHc3Ky+l5iYSFdXFz09PYSEhKjF/oaijCPd3d3U1NSoXZd1Op261itrvEKMHc/CGL6+vuoa6Ej/LpWxoaenB0CNB1Gu6/mcJoQQQgghxA8hyV9CCCGEOK643W5MJhPnnXceq1atQq/XY7PZOHDgAHfffTfPPffciC266XQ6mpubef/999W/nU4nixYtIigoaNivJ4QYeSeeeCJ2ux0Ag8HAtm3baGlpITU1dUQX8JVz19fXU1RUpL4eFRU1aolnQhyvlMSJobpluFwuLBYLTU1N5Ofn8+qrrwIDQQWRkZGkpKQwa9YsysvL1fNptVq1qvTggAKn06meXyrvCzExJCQkcNZZZ1FcXIzL5UKn0+FyuXjnnXd45513iIqKIikpCYfD4VW92uVyodcPLOVefvnlnH322QBH1TVU6Q5WXl6uBj7AQFBTenr68N+kEGJUbNy4EQA/Pz9sNpua8OVwOFi8eDEXX3wxy5Yt86pcr4w7w0HmJUIcezzXBzyfVdra2mhrawNg79696HQ6DAYDvr6+BAYGEh4eTmxsLKmpqWRmZpKZmUl6ejoJCQn4+fkNmfDl2VFHnlWEED+UZ1Erf39/Jk2aBIzsPGOoMWtwQpgQYvxQErv6+vq8Xtdqteo/gJoQ1tvbS1VVFVVVVeqxyu++qqqKyy+/nKioKNLS0khMTCQuLo6oqChCQ0MJCgrC39/fq5hGXV0dLS0tatJZZmYmwPfq1C6EGH7Ks5HL5SIkJET9bY7071JZdykpKVGv53A4iIiI8HpfCCGEEEKIH0qSv4QQQghxXFEW42fMmMHcuXPZvHkzBoMBu93O6tWryczM5Pe//z2AWg17uDidTu68807MZjMajQa73U5ISAiLFy8etmsIIUZXdHQ0ERERdHZ2qklgq1atYtasWSMaKKBsDmzbto09e/aor2dlZalVJYUQI0MJtFYquXp243G5XEMmhfX391NdXU11dTVfffWV+p4SpL169WqamppIT08nLy+PjIwMAgIChpyHKNdRziFBBEKMPw888ACbNm1i+/bt6PV69bfudDppamqiqakJQE0Mg4FAAbvdzowZM/iv//ov/Pz8jvp6SmD2J598wp49e9BqtbhcLmbOnCnzBiHGsYKCAgC1mnRAQACnn346t956K7NmzSIwMBDwTliXeYMQE5uSRKGsT+j1erRarZpI7nK5cDqd6j99fX1YrVaqq6vZtWsXer0eo9GIr68vwcHBREZGkpCQQHp6upoUlpqaSmxs7GE7CHomhcmYI4T4LmVlZcDAWmd4eDgpKSnA6CeZS9KXEOPX4sWLSU1Npampifr6eiwWC21tbfT393t1L1XodDqv+ZFn8a3W1lY++OADr2MjIiKIi4sjOTmZ5ORkUlJSiI+PJzIykuzsbDZu3EhXV5e61jJt2jRAkr+EGEvt7e20tLSov8vo6GiioqJG9XdZWlrq9beswQohhBBCiOEiyV9CCCGEOO44nU5SUlI477zz2Lx5M06nE71ej8Ph4I9//CNNTU08+OCDw5L45bmI+Pbbb/PUU0+p1/L19WX58uUYjcYR7RAkhBg5Op2Oyy+/nLvvvltNJF2zZg1XXXUVc+bMGfYkUoUyXrz66qvU1dWp154zZw6xsbHDfj0hxACbzcZVV11FcXExe/fupaurSw0Q8KQEWcLBZC3PhDAlGFJ5f9u2bWzbtk39bGhoKImJiWRnZ5Obm8vUqVOZNGkSqampaLVaqQwpxDjmdrsxGo289tpr3HjjjXz22Wfqe0pCpzJ+KGOHUp06KiqK5557jvj4+O91TWXMWLt2LTabDZPJRH9/P2eeeaaaHCKEGH+UKtLp6enMmzePFStWMHXqVK+OpEpQoxBi4uvv7+eee+6hqKiInTt3UlZWphab8KTRaLyeV5SgZ5fLhd1ux263093dTXNzM/v370er1aLX6zGZTAQEBBASEkJ0dDTJyclkZGSQmZlJRkYGycnJhIeHSwKFEOKo1dTU0N/fj8FgwOFwEB8fL88nQojv7fbbb6ejo4Pm5mYaGhqoq6vDbDZTW1tLbW0t9fX1NDY20tLSQnt7u5oE70mZv+j1enUdRpkjNTY20tjY6NVJ3dfXl8jISNLS0mhtbcVms2EwGACYNWvWqN6/EOJQe/fuBQ4W1po8efIhzykj1clYOU9lZaV6HYC8vDyv94UQQgghhPihNG7PiCMhhBBCiOOEEgQ1d+5ctm7dik6n86pKO2vWLG699VYWLVpEeHg48P0XAR0OB3r9QK79k08+yZ133klLS4sabHnCCSfw4osvkpubK8lfQoxDyu923bp1XHDBBcDBTj7z5s1j7dq1REREjFgluW3btnHqqafS29urJpVu376dE044YdivJYQYWmNjI0VFRWzfvp38/HwKCgooKysbsqrsUAEEnp3CDrfxqPDx8SEyMpKUlBSmTJnCtGnTyMjIYOHChWpwgRBifCkpKeGJJ57go48+orq6GrvdfkiCqCInJ4ennnqKU0455Qc9O7zyyitcc8012O12tFotBoMBs9lMWFjYcN2OEGIUNTU1kZaWxt13382CBQskwFAIMaT9+/eze/du8vPz2b59O0VFRdTV1Q15rNLVeHDA8+G2kXU6HQaDAR8fHwIDAwkPDycuLo6UlBQmTZpEQkIC55xzDiaTaSRvUQgxjr333nssXbpU/fv666/n2WeflW45QohhYbPZsFqtWCwWGhoaMJvNalKY2Wymvr6epqYmWltb6erqGvIcSoEezyJfStK8J2Xd12638/nnn3PqqafKWCbEGHriiSf41a9+hdFoxGazMW/ePG699VaioqLIyMggNjb2sGurg/dsfmj8xvz589m0aRM6nQ6n00l9fT3R0dE/5raEEEIIIYQAJPlLCCGEEMe5wsJCFi9eTH19vVr9SZkepaamsmjRIi6++GJOOeUUfH19hzyH8pmhunDs3buXxx57jNdee43Ozk41QUOn0/Hwww9z6623SuKXEOOcw+Hgggsu4KOPPlLHAafTyfnnn89jjz1GSkrKsF+zs7OTs88+m82bN6uBWTk5ORQWFg77tYQQBykb/BqN5ojdt8rLyw8Jsqyvrx/yWCXIUjm/khSm8JwjDJVU1tzcLMkbQoxjTqeT999/nw8//JAtW7ZQU1MDQEBAAAaDAV9fX6699lqWL19OVFTUD+oq2tTUxOLFi9m1axcw0GHwkksuYc2aNcN9O0KIUeJ0Ojlw4AAZGRmAd/EZIcTxS+n05RmkPFhPTw/79u1j586dbNu2jV27drF37146OzuHPF6v13s9rwzVLUOhBD4bjUZ0Oh0NDQ34+PgMw50JISaiN954gz//+c9oNBosFgv/+7//y4oVK37QM48QQniGvh1pz7W7u5vW1lYaGxupr6+nrq6OmpoaNTmssbERi8VCW1sbfX19Q55Dp9OpXZaVZHnl3xsbG4mMjBz2+xNCHL1rrrmG1atXq91FtVotfn5++Pr6EhoaSkxMDMnJyWRmZpKVlUVGRgYpKSmEhoYe9pyeSWHfldhptVqZOXMmVVVVuN1uAgIC6OjoGO7bFEIIIYQQxylJ/hJCCCHEce/NN9/kd7/7nRpoqWwKeE6TEhMTmTVrFtOnT2fatGmkpKQQGxvrtYDf29tLe3s7er2e8vJynn32WTZu3Eh5eTkwEHih0+mw2+2sWLGC22+/nfDwcEn+EmIC+OCDD7jkkkvo6+tTk7E0Gg3nn38+v/3tb5k9ezY+Pj5q4sb3rRbnOU5YrVbuv/9+Hn74YbVqXWhoKP/7v//LbbfdJmOKEKPMM1nru4IsS0pK2LFjB9u3b2fnzp0UFxcftrKsXq/3qio7+DpOp5OQkBBaW1tH4K6EEKNh8H+znU4nVVVVVFVV0d3dTWZmJtnZ2Yc9/miv8cQTT/DrX/9anTckJCTw+OOPs3TpUpk3CDEBjMXv2LMruhDi2KU8Ryi/WaWz11AsFgtFRUVqAYtdu3ZRXl4+ZKKXkuSlPK8oXcJcLpe6JhIVFUVDQ8PI3ZwQYtxrbW3lwIEDalee+fPnk56eLs8oQohhdTTPLi6Xi46ODpqbm2lsbFQTwZROYfX19TQ0NNDS0kJ7e/thE+FlrVaIY8PMmTPZuXOn2nVrMCWBS6/Xq52Mw8LCiI+PJzU1VV2TTU9PJzEx8bAFLdxut9cY43K50Ol05Ofns2jRIrq6unA6ncycOZNt27ZJgrsQQgghhBgWkvwlhBBCCAGsWrWKO++8k7q6OnUDQEnO8Ozy4ePjQ1hYGMHBwRiNRnx9fQkICMDHx4egoCDa2tr4/PPPcTgc2Gw2ALWjGAwsAp511lmsWbOGiIiIMbtfIcTwu/HGG3nuuefUsUP53cfExPDb3/6WG264gaCgoCE/e7iqlMr4o1Txr6mp4a677mLVqlUAahD3eeedx+rVq49YlU4IMXq+T5BlU1MTRUVFbN++nfz8fHbv3k1ZWdmQXb6UIEu9Xk9vby8nn3wyGzduxOVyfWe1SSHEsUsJlB7OAEdlXHjttde4/fbbqaysxNfXl97eXi677DKef/55/Pz8hu16QoiJyzMAW+YcQkwMns8r39XVuKKiQu1qnJ+fz549ezCbzUMeq9PpMJlM9PT0sGTJEj744IORugUhxAR2tN17RuK6knQmxPHjaJLCbDYbbW1tNDU10dDQ4JUQZjabqa+vp6mpifb2dtrb25k+fTo7duyQBA8hxpiybqE86wwusnekTsY6nQ6DwYCvry/BwcFERESQlJREeno6WVlZZGZmkpaWRlxc3GHHj3/9619cf/31ANjtdm644QaeeeYZ6douhBBCCCGGhSR/CSGEEEL8f1999RW33HILRUVFwMFAbSUY4minTVqt1iuAU6PR4HQ60Wq1zJs3j7Vr1xIWFjaStyKEGANWq5UzzzyTnTt3otVqcbvdarc/AF9fX84991yWLl1KTk4O8fHxhIaGHtUmYENDA19//TX33nsvBQUFwMHEr7i4OJ5++mnOO+88CVIQ4hj3fYIsy8vLKSgoUJPC9uzZQ319vdcx1157Lc8//7wEYgsxgSjPHcORDGY2m7nsssvYtGmTWunWaDTy5ptvcv7558vYIYQ4rCMlpTY1NVFeXk5/fz/bt2/nnXfeYerUqTzzzDNj8E2FEMPJcw30SF2N+/r6KCkpYefOnWpX471799LR0aEe86tf/YrHHntstL66EGKcGpzoNdTaprKWAgN7L8O99inPRUIIxdEmn3Z3d9Pa2kpTUxP19fXU19ezd+9eTjjhBJYvXy7JX0KMoY6ODpYsWUJ9fT2VlZVDxndotVqvgn0ul0v9ZyjK8SaTCT8/P0JDQ4mOjiYlJYWMjAyys7PJyMggISGByMhI7rnnHu666y70ej12u51nnnmGG264QcYGIYQQQggxLCT5SwghhBCCgxt8ygLck08+SUlJifq+RqM5ZGNRWQT0DIbw3IhUAizdbjdBQUH87Gc/47HHHsNoNMqGohAT1IEDB1iyZAn79u3DYDB4JY4qHQRjY2NJSUkhKyuLnJwcEhIS8Pf3JygoiODgYLWSXE1NDRs2bKCqqoovv/ySLVu2ABySWPrCCy9wxRVXSLU4IcYxz43FIwVZ9vb2UlJSwu7du/n444/5+c9/LomfQowTY/E73b17N++++y579+6lsbGRyspKfvKTn3DHHXdIMQohhOpIwUf79++nt7eXhoYG1q1bR3FxMT09PezZs4fW1lZ13UOpYi1rHUJMPN+nq3FzczNFRUVs2LCBc845h9mzZ4/mVxVCTABOp5P8/Hy6urqIjIwkNTWVgICAYTu/Z4Krp02bNjFp0iTCw8OH7VpCiInjaLqEKd2EDAbDaH0tIcQR9PT00NjYyP79+9m7dy+FhYUUFRVRVlZGS0vLkJ/R6XTq+ogyZ1BiPQZTYkf0ej0+Pj4EBAQQHh5OUlISe/fupaKiAoPBgN1uZ8uWLcyePVvWTIQQQgghxLCQ5C8hhBBCiP9PCci02WwUFRXxn//8h3Xr1rFv377DLgIqXTuUDmFOpxM4mPhlMplYtGgRN998M0uWLAGkkqQQE5Xy266pqeGmm25i3bp1gPeG4FCPX0qVuKCgIPR6PWazmdbWVsLDw+nr66O7uxsYGFc8k0OcTid/+tOf+Mtf/iJJH0JMQN8nyFIIMf4cbTXp4dLT04PZbCYwMJCYmJgRv54Q4tjj+SwxWE9PD01NTWg0GrZs2cLnn3+O1WrFbDbzzTffAKDX63E4HMDBtRCtVovNZuNf//oXP//5zyUhXYjjyPfpaiyEEENR1lLdbjc7d+7kscceIz8/H7fbTXNzM21tbQBERkZy8sknc9ZZZ3H66aeTlpb2g67nOU9paWmhu7ubt99+m08//ZTt27ezdetWkpOTh+v2hBDHgSM9Ywkhjj1ut5v29nbMZjOlpaUUFRWxZ88eioqKOHDgAD09PUN+Tq/XH1IIWIkJGcxzTUSJH+ns7MTf33/4b0gIIYQQQhyXJPlLCCGEEOIImpub2bp1K5s3b2bPnj1UVVXR3NxMV1cXPT099Pf3ex0fHBxMd3c3cXFxXH755Zx00kksWbJEAiCEOE4oQQQNDQ08//zzrF69mv379wOo3QOVhf/BiR0Kz/cBDAaD1yaCy+XCaDTyv//7v/zP//yPBFgKcZzxHDuk458Q48OuXbvU7p8hISEYjcbDHns01aSP1uGq2gshJrbv+u23trYCA0HPn3zyCfv27cNqtZKfn09xcTEmk8lrrUOv16uVrvV6PRqNBrfbjcPhUKtYFxYWkpOTMyr3J4Q4tinPKrIWKoQ4EiXxq7m5mb/97W/8/e9/p6+vz+sYZS6jJFeEhIRw2mmnsWLFCk455ZQjFtkbar3U6XTy/vvvU1payieffMJnn33mNe9RriOEEEKI44vdbqe1tZXKykr27dtHYWEhe/bsoaSkhOrq6iELe2q1Wq+CfS6XS/1Hed/lchEdHU19fb3s5QohhBBCiGEjyV9CCCGEEEMYXK3N5XLR2tpKTU0NNTU1NDQ00NnZqQZUOZ1Oenp6iI+P54wzziAkJISQkBBZxBPiOGaz2Xj//fd57rnn+Prrr+nt7fV6XxkflIQwzw0CZRNAo9Gg1Wqx2+3q56ZPn869997LOeecI5sFQgghxDiwcOFC+vv7SU1NJS4ujoSEBOLj44mPjyc6OpqIiAiCgoKOmKQ1nElhQoiJw+VyeT1LDNbd3Y1Op2PHjh18++23WK1W9u3bx9dff43VasVoNKqdhuFgcJJWq1U7cXgmnSudv+Bgx3OAvr6+Iya2CiFGVlNTEwaDAT8/PwwGgyR+CyHGhd7eXq677jpee+01AHUdVKEktCuJ58ozkdFo5Nlnn+XKK68cMgHMc720srKS6upqVq5cya5duzCbzWoivOfez+TJkykqKjpiQpkQQnhSuvn4+PhgNBplvUaICUjpkl5RUaF2CduzZw9lZWU0NzcP+RmlS7per6e3t5czzzyTjz/+GKfTKQUyhBBCCCHEsJDkLyGEEEKIH8HpdKrdfIYiiRlCHJ8G//a3bt3K2rVr+eqrr9TK+p5B3EN1/9JoNGoiqr+/PzExMdxyyy38/Oc/JzIycvRuRghxTJFNQiHGl/7+fnx9fb1e8/HxISQkhMjISKKjo9VEMCUpLDY2lujoaMLCwvD39z/suSUhTIjj1+Dnjf7+fkwmE21tbWzcuJGqqipaWlr4/PPPKSwsRK/X09LSckgFahjoNKzRaHA6nV4dL4baOoqOjiY3NxeTyURycjILFy4kJydHun4JMcbmzZtHSEgIOTk5ZGRkkJaWRmxsLGFhYQQEBODj46N27hNCiGPF73//ex599FF1j0XpGuiZbA6oXTU8C/FptVrWrVvHGWeccchzUVdXF++88w7btm3j3Xffpba29pC5j9KZw2g00t/fz4UXXsjbb7+Nw+GQLutCiCNSkkR37NjBc889R1JSErGxscycOZO8vLyx/npCiBHmdrtpb2/HbDZTVlamJoTt3buXiooKenp6vI5fsWIFjzzyiMwxhBBCCCHEsJHkLyGEEEIcF5Rq1UdK1Dra83h25FEolbAlGFsI4ckzQbS/v5/q6moKCwvZvn07u3btory8nPb2djXYAAaq3ra1tREREcGJJ55IYmIiJ598MpdccolU0xfiOHKkTh719fX4+PgQGho6Bt9MCPF9FBQUMH36dHx9fbHb7WqQ4VACAwMJDQ0lKiqK2NhYr4Sw+Ph4YmJiiIqKIiQkROYEQhzHlDWJ8vJyCgsL6ezspKioiHXr1tHR0YHNZqOhoeGQzymVp91uNw6HA41Gg06n8+oyDAcDp91uNzNmzCAvL4+FCxeSl5eHTqcjLi6O6OjoIb+TEGL0Wa1WwsPDD3k9KiqK5ORksrKyyMzMJCMjg+TkZKKjowkJCcHf3x+j0ShrmUKIMVFUVMSMGTPUfRuF2+0mODiYyMhIOjo6sFgsXgnpnt26TjzxRD799FO1YEZvby+PP/44b7zxBgUFBTidTnVdRUkcU5LHlL0i5dpPPfUUN954oxTcEUJ8J2WcePTRR/ntb3+rvn7XXXdxxx13SAdBIY5Tdrud1tZWqqqqKCkpobi4mM8++4y77rqLJUuWyBxDCCGEEEIMGykpIIQQQojjghLU5ElJ2FI2AI8mUOlwx8lCvhBiKJ7jjslkIjMzk8zMTJYtW6a+XltbS1VVFa2trTidTqKiokhNTaWvrw9fX19iYmLG4qsLIUbRUBt/ytzCbDbjdDqpq6vj008/pbS0lKKiIh5//HHmzZs3Fl9XCPE9bN++HRgIRFTodDqv5wclIayzs5POzk6qq6u9jg0ODiY8PJzo6Gji4uLUpLDk5GQyMzOJi4sjJCQEnU6nFquQ5xMhJia73c4NN9ygji01NTV0dnYecpySIKp0ztDpdDidTmw2G4DaZdjlchEZGUl8fDyRkZFs3rxZPZ+vry/PPvssM2fOPCSAcfDfkvglxNgpLi4GBjqGK4nmTqeTpqYmmpqa2LZtm3qswWAgLi6OtLQ0srKyyMrKIi0tjcTERCIiIggKCsLX1xeDwSBzCSHEiGlra2PlypU4HA4MBgNOpxOAxMRE/vSnPxEfH09YWBgGg4G2tjZ27drFxx9/zBdffKEWygHYunUrO3bsYP78+WzcuJHrrruOiooKNelLSXpX1lyU8VGr1RIaGkpiYiJXXXUVubm5zJo1C5B9HiHE0auoqECv16PX6+nr6yM3NxcYuoOyEGLiMxgMREdHEx0dzZw5cw55XxK/hBBCCCHEcJHkLyGEEEIcF+6++24yMzPJysoiOTmZiIiIw27keSaFyWafEGI4KQHZnmNMQkICCQkJh/2MVIMTYuJQKkoPnl8oXTdaWlowmUx88803bNq0iebmZqqqqtiwYQM2mw2j0agGbUtiqBDjg9vtZtKkSbjdbhobG+no6MDpdKoBjgolONGzMr0SnNja2kpraytlZWXq8SaTifDwcJKTk5kyZQonnngiy5YtIywszKtrjyRkCDGxFBQU8NJLL3m9pgRNu91udRxR5gsKJVlrypQpaDQakpOTOfXUU5kyZQomk4msrCwSExNJTEyks7MTjUZDSEgIYWFhwMG5i6yVCHHsUZK7uru71deUIlhKJ3IYWFuw2+1UVVVRVVXFF198oR4fGBhIYmIi6enpaqewtLQ0YmNjCQsLIzAwEJPJpI4xQgjxYxQVFfHZZ58BB9dJlixZwpNPPklSUtIhxy9YsIALLriAV199laeffhqLxQIMzE/WrFmDw+HgzDPPVNdcdTodGo1GTYLXaDQ4nU7i4uI47bTTyMrK4tJLLyUjI+OQOY2McUKI76Ls1ezbt08dZwCmT58OSBKpEEIIIYQQQoiRpXFL2REhhBBCTHD79+8nMzOT0NBQfH19CQsLIzY2lrS0NDUhLCMjg8TERPz9/Yc8h7JxCIfv/iWEOP54Pk79mHHBMyFMxhchJobBHTE8dXV1odVqaW9vZ/369VRUVNDa2srmzZvZsWMHAQEBXl08lG4dMBBAYDQa6enpGZX7EEL8OK2trVRXV2OxWKitraW2thaz2YzZbKa+vp6mpiasVqtXwLYnrVbrFbitJIUpQZKeTCYTF1xwATfeeCOnn376iN6XEGJsvPDCC1x//fX4+PioCV5DjQfK+oaPjw+nn346M2bMICAggJSUFKZNm6Z+znOu0tXVRVBQkJqAOm3aNHbs2CHPJ0Ic4x5++GGeeuoptFotra2t9PT00N/fP+SxyrzCM6HT6XQOOY4AREZGkpKSonYxz8zMJDk5maioKEJDQ/Hz88NkMknBGiHE93Lvvfdyzz334HA4cLlcnHLKKTz00EOceOKJhy2ao7jvvvv485//jF6vx+FwEB0dTWJiItu3b1fHImX9xM/Pj9jYWGbPns3y5ctJTEwkJyfnkLmNFM0QQnxfvb29zJgxg7KyMjXp1G63j/XXEkIIIYQQQghxHJDOX0IIIYSY8Hbt2gVAR0cHVquVuro6ioqK0Gq1GAwGfH19CQoKIjIykoSEBNLS0sjOzlar3MbHx6vVIgfz7BImSRtCHB+sVitBQUGHHRcAr2TRoyHjhxDj1+F+70qgktKxq7CwkM2bN9Pe3k5xcTFffPEFLS0t6HQ62tra1M9pNBq144bBYFADMjUaDUajkf7+fuLj44EjJ5gJIY4NYWFhauccRVdXFy0tLTQ1NVFXV6cmg9XW1lJXV0dDQwMWi4W2tjZsNtuQAdme3TyUJHKbzcbatWspLCzkmmuu4brrrjvk2kKI8W379u3AwaBml8tFYGAgUVFR5ObmcuqppxIZGUlUVBRz5swhKCgIu92OwWBQz+HZvUtJJtXr9ZSXlwOg1+ux2+2kpKSoY4w8qwhx7Pqv//ovFi9eTG1tLZWVlVRWVlJTU0N9fT3Nzc1YrVY6Ozvp6elREy0GG9wlTOk+arFYsFgsancxGOg2GBcX51VUKz09ncTERMLCwggPDycwMHDU7l8IMf5s2rQJm82GwWDA5XJxww03cOKJJwKHT/pyOp3odDp+85vfsGnTJtatW4dOp6OxsZHGxkZ0Op1aKCMqKoply5Yxb948li1bhq+vr3oe5RjP68g8RwhxtJRno8rKSlpaWtTnpYyMDEDWaoUYa7fddhtJSUnk5eWRnp5OdHT0YQv/CiGEEEIIMV5J8pcQQgghJrydO3cCqJWxlYq2TqcTp9NJX18fVquVqqoqduzYgU6nw2g04u/vT3BwMNHR0SQnJ5ORkUF2djYZGRmkpKQQERFx2EV8z6QwWegXYuLo6+vj5ptvJjU1lfj4eOLj44mLiyM6OtorwGmooIHh6hImhBg7nsHPyma+8rfD4UCv19PX18fmzZsxm81YLBa++OILtWtGY2OjWgVWCQ6AgSBrGAhmcrvd6t9KRw+FUsH/nHPOUb+PEOLYNzhJNCAggICAAJKTk9VjnE4n7e3tNDc309jYSF1dnVensLq6OhobG2lpaaGjo0N9llF4jkUlJSX84Q9/oKmpibvuukuCHISYQLZu3Qp4dyefNWsWjz32GLm5uV7HKu8riV+D5y4wEGCtHLdjxw6vz0+aNEn9nHT1EeLYFRQURG5urtcY4HA4sFgsmM1mqqqqqKyspKqqCrPZTENDAy0tLbS1tdHV1UVfX98h8woYmFsMTgpzOp3Y7Xaqqqqoqqriiy++UI8PDQ3FYDBwxx138Itf/GJ0bl4IMS4pcw6n04ler+fcc8/9zs/odDocDgc+Pj5Mnz6djz76yGtu43Q60Wq1rFixgiuvvNKr06nCs4ifEEL8EMo4UlpaSldXl1pQ43DdlYUQo6e9vZ3HH3/c67XExEQmTZqkPi9NnjyZ5ORkIiIivIrkCCGEEEIIMZ5I8pcQQgghJrxPP/0UgO7ubvU1nU6ndu1Rqj0qSWEulwu73U53dzdNTU2UlZXxzTffoNfr8fHxISAggLCwMGJjY0lNTSUrK4usrCwyMjJITEzE399/yMV95TqSECbE+FVQUMAbb7yBTqfDZDIRFhZGZGQksbGxxMXFkZCQQEJCAvHx8cTExBAVFUVoaCg+Pj7f2SUMJClMiGPV4YKlzWYzxcXF9Pb2smvXLtavX09LSwt9fX1UVlYech6le5cy71AqUzscDq/OGg6HA4C0tDQCAgLw9/fnxBNP5OSTT8bHx4cpU6YASCC2EOPE0SSF63Q6tUtYVlaW+l5/fz9WqxWLxUJ9fb3aJay8vJz9+/dTU1OD2WxWOwQqzzgOh4NHHnkEo9HI3XffLeOFEBNEXV0dgLqu4Ha7+eKLL5g6dSrXXXcdt912Gzk5Oeoxng63DqGMR/n5+eprBoOByZMnj8QtCCFGgGdCqFarRa/XExsbS2xsLLNmzVKP6+npoaGhwatLWHV1NXV1dTQ1NdHa2kpHRwfd3d309/cPmRSm1WrVf5RrK0nsQ3UVE0IITzU1NTQ1NaHT6XA6ncyaNeuouxUrhXJOOukk/P396erqQqPRqF1M33rrLc4991x1rcXtdkuHLyHEsFLmW3v37sVms6nj0uzZs8fyawkhgOLiYgD8/f2x2WzY7XZqamqoqanhk08+UY/z8fEhNTWVyZMnk5eXR05ODtnZ2cTHxxMaGirzBSGEEEIIccyT5C8hhBBCTHgrV65ky5Yt7Nq1ix07dlBcXIzVaj0keAEGNhA9gxc8k8JsNhs2m42Ojg7q6uooKipCq9ViMBjw9fUlKCiIiIgIEhISSE9PV5PC0tLSiI+PVxPOhBDjV2FhITCQcNHT00NPTw+1tbVqh0GNRkNQUBDh4eFER0cTGxtLfHy8mhAWHx9PdHQ0kZGRBAcHo9frvzMpTDYahBh7Wq2W4uJi2trasFqtrFu3jl27dmGz2aioqKClpeWQzyiVI5XELiX4yLOblzIXUX7nvr6+XHbZZSxcuJDQ0FBCQkKYM2cOJpNJKscKMcEcbVK4yWQiJiaGmJgY8vLy1Pc6Ojqor6+ntLSUTz/9lA8//JCKigqcTicGg0FNArv//vuZO3cu559//ojfkxBiZLlcLh588EHuu+8+SkpKgIHnEoPBgNPp5KWXXmLr1q1cfvnlXHzxxaSnpx/VeZXxSOnC4XK58Pf3V5O/ZP4hxLHvcJ1slOQH5X0/Pz/S0tJIS0tjwYIF6nFWq5X6+nq1o1dlZSU1NTXU19fT3NyM1Wqls7OTnp4eHA7HkEleJpOJ/v5+TjrppBG9VyHE+KasrWq1WpxOJ1OnTgW+X7ecuLg4r447APfeey8XXHCBeox0+BJCjARlXCkpKfFavznhhBMAeXYSYixt27YNGCh4oTwDeXYyVopW9PX1UVxcTHFxMe+88476+dDQUDIzM8nJySEvL48pU6aQkZFBdHQ0/v7+Y3VbQgghhBBCHELj9nwiFUIIIYQ4TjQ3N1NUVMSOHTvIz89n9+7dlJeX09/ff8ixGo1GTQpTgiaUhLChaLVadDodRqMRPz8/QkJCiI6OJi0tjYiICK6++mpyc3NH+haFECPgqquu4l//+hcGg0GtHuu5aTBUUikMJIGEhIQQERFBTEwMcXFxXklhcXFxREdHEx4eTmBg4GGvL13ChBh9r7/+Ok888QR9fX3s2LEDl8ulVqhWGI1GHA4HbrdbTer0TPLyFB8fD0BkZCQzZsxg1apV6jgyd+5cvvnmmyN+H0kCE+L4dDRJ4atXr+ahhx5i79696jjhcrmYMmUKO3fuPGLSuRBi/LBarfzrX//in//8J+Xl5QDqXEJx4YUXcuutt6rJHcr6xZHmECEhIXR0dKDRaEhKSmL79u2Eh4eP4J0IIcaCZ5ewIyVHOBwOLBYLZrNZTQirqqrCbDbT0NBAS0sLbW1tdHV10dfXp44zXV1d+Pn5jdr9CCHGl7vvvpu77rpLTV5/6KGHWLFihdod/UiUQO4NGzZw2mmnYTQasdlsZGRksGvXLhl7hBCjZt68eXz77bfqGnFjYyORkZFj/bWEOK793//9H48++ig+Pj60t7cPGfMBB+M4PNdOjxT3kZiYSHZ2Nrm5ueTm5jJ58mSSk5OJiIjAaDSO2P0IIYQQQghxONL5SwghhBATntLBy7PKU0REBAsXLmThwoVex1ZWVlJQUEB+fj75+fns2bOH6upq7Hb7IedVOnkpQVaei4Mulwu73U53dzcWi4WysjK2bdtGf38/Z555Jrm5uer3EUKMH3q9noCAAHp7e4dM9DrcpoHdbsdisWCxWCguLlaP9/PzIzQ0lKioKGJiYtTuYImJicTFxREbG0tUVBShoaH4+PjImCHEGFi7di0bN24EDv633+l0otfr0el02O12NdFLo9Goc4agoCD8/PzQarXMnTuX2bNnExISQkpKCrNnzyYsLIyPP/6YVatWodPpcDgcpKSkAAMdwZRAzMG/e0n8EuL4dKRuHkpC+lVXXcWsWbP4/e9/z7p169Tx4sCBA6xZs4ZrrrlmtL+2EGIEhIaGcuONN5Kdnc3KlSv5/PPPsVqtaoKn3W5n7dq1rF27losuuog//elPaiX6w7FYLHR0dKjznLi4OEn8EmKC+q4uYTDwzKHX64mNjSU2NpZZs2apx/X09NDY2EhNTY1Xl7DKykq0Wq0kXwghjig/Px8YGHNMJpPaafRo1jyVYjxlZWVenzn11FPx8/M7qgQyIcTEMhbF8lpbW6mrq0Oj0eB0OgkMDJTELyGOAVdccQUzZ85Ui1conYwtFgtWq5WOjo4jdjL2jPsA1FiPmpoaampq+PTTT9VjTSYTaWlpTJ48WU0Ky8rKIiEhgdDQUNnLFUIIIYQQI0qSv4QQQggx4SkJX4MpCWFKsKRWqyUlJYWUlBQuuOAC9bi+vj727dvHrl27yM/PZ8eOHZSUlNDa2jpk8odyLWXTQbmGw+EAYMqUKer3EkKMLw8++CB/+MMfaGxs5MCBA5SWlrJv3z7Kysqorq6mra3tOzcNPJNFe3p66OnpwWw2q8dqNBqCgoIIDw8nKirqkC5h8fHxxMTEEBYWRnh4uCSCCDHC9u7dCwx08FO6e8FAJXyHw4FOpyMqKor+/n6ys7NZuHAhUVFRREREsGDBApKTk+nt7cXf3189p5IgVlpaCqB2EVSCnpTXhBDiSIYK3s7JyeG+++7jwIEDlJSUYDAY6O3tZcuWLVxzzTXSPVCICcJkMnH22WezaNEinnvuOR544AGqqqqAgTmLVqvF4XCwdu1atmzZwiWXXMKVV17J1KlTvc6jjAlFRUXAwPzD6XSSnp7u9b4QYuL7rqQw5X0/Pz9SU1NJTU31Oq61tZWurq7R+rpCiHGqoKAAGJhjBAYGkpmZCRzdXomyHrN9+3b1HDqdjnnz5o3QtxVCHIsaGxsJCAjA39//sGPHSCSFKfOh/fv3Y7Va1WcnZc9XElCFGFvp6enqWgagFuU0m81qsYrq6mq1k3FzczNWq5Wuri614OfguA8lxkTZv3G73TidTvr7+ykuLqa4uJh33nlHPT40NJRJkyYRFxfHjTfeyKJFi0bt/oUQQgghxPFDkr+EEEIIcdwaKoDJs0sYDCRs+Pj4MG3aNKZNm8ZVV12lHtvS0kJRURE7duxg69at7Nmzh/Lycvr6+gDURUDl351OJz4+PiQmJo7C3QkhRkJ4eDjh4eFkZWVxyimn4HQ66evro7Ozk+bmZsxmM/v376e0tJTS0lLKy8upra2lv79/yGRRvV5/yKaB0+mkvb2d9vZ2Kioq1GMNBgMhISFERESQkJCAwWDgf/7nfzjppJNG8/8CIY47+/btA1CTuAEmTZrEySefTHx8PEFBQcycOZOTTz4Zm82G0WjEaDR6nUNJ/FICqJWgg23btqmvGwyG71XxWgghhuJyuZg2bRqXXXYZd911l/o8cuDAAerq6oiLixvjbyiEGC5KcOFNN93E8uXLeeKJJ/jb3/5GU1MTcHBNora2lkcffZSCggJ+8YtfsGTJEoxGI263G7vdjslkUrtwKJ9T5iSeQZNCiOPTUElhnl3ClPfDwsIICwsbi68ohBgn7HY7VVVV6hwlKiqKtLQ04OjWQZRjdu7cCQw8+wQEBJCbmwtIER0hjhe33HILUVFRJCYmkpCQQFxcHDExMURGRhIaGorBYPjOpLAfsvaqJJyWlJTQ29urJn9Nnz7d69xCiLExuJOxwWAgLi6OuLg4Zs+erR7X3d1NY2MjtbW1VFdXq52MzWYzTU1NtLS00N7eTnd3N/39/V77QgqtVuvVJUzZ37VarXz77bcAzJs3j0WLFkliqBBCCCGEGHaS/CWEEEII4eFwXcI8k8KUY8LDw1mwYAELFixQj3O5XLz99tv8+c9/pqysTN3IVDYBlEqWUj1biPFPo9Gg1+sJCAggICCA2NhY8vLycLvd2Gw2ent7aW9vx2KxUF1dTVlZGfv27aO0tJQDBw5QX19/xE0DZYxwOp24XC61Sp3FYqG4uBiA22+/fVTvWYjjTVVVlRoUbbPZcLvd6PV6srOzWb58OQsXLvQ6Xkn6cjqdXkleCuV3rfyvErDkdrsJCAhg0qRJgCR/CSF+OGV8ufLKK3nqqadobGxEq9Wya9cu+vv7x/jbCSGGk2fXcX9/f375y18ye/ZsVq5cyccff0xLS4va5dzhcPDpp5/y6aefctZZZ3HHHXdw8sknYzKZgIMdNNxuN0ajURLShRBHdLguYUIIcSRlZWXAwBzG4XAQExODRqP5zr0SZU9GOUZZF3W5XISEhKidCGVcEmLiq6qqUrvs6PV6goODCQ8PJyYmhtjYWOLj40lMTCQ+Pp64uDiio6OJiIggKCjoiF1OFUczjhQXF2O32zEYDADMmjXrqD8rhBg5R9vJ2N/fn7S0NDUBXdHa2kp9fT01NTVUVVVRVVVFbW0tdXV1WCwWrFYrHR0d9PT04HA4cLlcXtdW4kGMRiN9fX3Mnz9ffU8IIYQQQojhJMlfQgghhBBHQUn4Gmoj0mKxsGfPHrZv386LL76I1Wqlp6dH/RygJn/NnDkTkApwQkxkGo0Gk8mEyWQiJCSE5ORkZs2ahdPpxGaz0d3djdVqpb6+nsrKSkpLS9m3bx/l5eVUVVXR1tbmtWmgnNPzH61Wi91uJycnZ4zuUojjQ0FBAQD9/f3qXMDhcLB+/XqqqqpYtmwZF198sZq05ZkkfiTKXEIJfHK5XISFhZGSkgLIhqAQ4sdLSUlRkzrcbjctLS2EhISM7ZcSQowIZd7g5+fHaaedxgknnMCqVat45JFHMJvNuFwuNUHd5XKxfv16tmzZwtKlS7nmmmtYsGABhYWF6vvBwcFkZWV5nVsIIYaiPP+AFLoSQnw3pQCOMlbU1dXx4IMPEhYWRkJCAtHR0YSFhREcHIy/v7+aWKGMMxqNBovFQldXFzqdDqfTSXx8vHQdFOI4snfvXgB8fHzo6+ujpaWFlpYWSktL1WN8fHwIDQ0lMjKSmJgY4uLiSEhIICEhgfj4eGJjY4mKiiIsLAw/P7/v7BIG3kklJSUlXsedcMIJgHQfFOJY9X07GXvuuypFOevq6qisrKSqqgqz2UxVVRU9PT3U19ere0hKApjNZgMgOzsbkLFBCCGEEEIMP0n+EkIIIYQ4CkoAg5LEpdPp2LJlC6+//jolJSWsX78el8ulLuzBQNU5z25hcHChTwKohDj+6HQ6fH198fX1JSIigszMTBYsWIDD4aCvr4+Ojg5aWlqora2lrKyMpqYm9u7dy5dffklbWxuAWjnObrcTHh5OcHDw2N6UEBPcWWedxRNPPMHTTz9NYWEhTqcTgN7eXnbt2sWuXbvYvn07t956K6eddpo6D1B+q0dSVVWFzWZDr9fjcDhITEwkICBgNG5LCHEcaGlpYcqUKVRXV+N2uwkNDaWtrY3Q0NCx/mpCiBGkJG/ddttt3HDDDdx333384x//oKurC0DtTNre3s7q1avZt28f1113Hfv27QMGnjfCw8MlIV0IcVhutxuXy4VOp/MaIySoUQjxXfLz84GDCRV79+7lj3/8IwDh4eHExsaSmJhISkoKycnJJCYmEhsbS2RkJEFBQSQmJrJr1y7gYLG9zMxM9ZwybxFi4vv666+BgYQMrVbrNR9xuVy4XC76+vqor6/3SsoACAwMJCwsjKioKLVLmJIUFhcXR0xMDJGRkYSEhGA0Gg8ZU7RaLS6XiwMHDgDgcDgA2fcVYjz6ri5hMPCbNxgMxMXFERcXp3b5A6ipqeHhhx9Wi/t5djJ1uVzExMQQEBAg8xMhhBBCCDEiJPlLCCGEEGIIgxfjtFotVquVPXv2sGXLFp5//nmam5tpbW0FBpI6lM5ger0et9utLvwbDAYyMzO54ooruPLKK9XzCSEEDCSKBgQEEBAQQFxcHHl5eSxevJgPP/wQt9vNvn37aGtrQ6vVqgklLpdLrT4n1bWFGDkmk4kbbriB7OxsVq5cyccff4zVakWn06HVarHZbLz33nu89957nHfeedxxxx3MmjXriBt6ym9W6bChHJuRkeH1vhBC/Bh2u53AwED17+joaDX5QwgxcSnPDG63G39/f+666y6WLFnCc889x7vvvkt7e7t6nMvlYvPmzWzevFmde7jdbpKSkvDz81ML3wghjl9DjQNKp+Oamhra2tro6+vjzTffpKenh3/+859j9E2FEOPB9u3bgYPjiDJvcTgcaveePXv2qMcbDAYiIyOJj48nMTGR3Nxcdu/eDRzcv0lISAAGxiu9/vChLxJ8LcTE4HA4CAoKwuVy0dXVhcvlOuQYZXzxLNLlcDjo7Oyks7OTqqoqr2ODg4OJiIggOjqauLg4r6Sw2NhYYmJiCA0NJSwsDIvFQlNTk/o8FRcXh5+fn4wxQkwQnt1GwXuvpqCggPLyclavXk1+fj69vb1YrVZ1PqPRaNDr9dhsNiZNmqR+XtZVhBBCCCHEcJPkLyGEEEIID8oinrKoZ7fbKSgoYM2aNezdu5fPPvvMq8OXsongdDrV1x0OBwaDgYULFzJ58mSuu+46MjMz8ff3H+O7E0IcazwDqVpbW2lqauKll15i48aNlJaW0tTUBKCOORqNBoPBgMPhYNq0aWP51YU4buj1ek4//XROP/10XnnlFf7v//5PrehoMBjQaDQ4nU7WrVvHrl27+OlPf8ry5cuZPn36kOdT5hpKxWsYCMCePHkycLACthBC/Bh9fX1qoQqAyMhITCbTGH4jIcRo8axgbTAYmDdvHhkZGUyaNImVK1dSUVEBgNFoBAbmJk6nU33mUIKoJUBJiOPH4boX63Q63G431dXVAJSUlLBu3ToaGhpobm5m69atanL5hRdeOOrfWwgxvpSUlABgs9kA1O7qnslgcLDDoN1up66ujrq6OrZt28Y777yjnkuZu2zatIkHHniAsLAwEhMTiYqKIjw8nODgYPz9/TEYDOo1hBDj34033sh5551HU1MTNTU1VFVVUVlZSXV1NXV1dTQ3N+N0OtXxRXG4ccbpdNLa2kprayulpaXq8T4+PoSEhBAZGUlMTAyxsbFkZmaqRUGHKtAnz09CjG9DPRO1trby6quvkp+fzxdffEFNTY3XZ5TfvbLno8xxFixYoJ5TCCGEEEKI4SbJX0IIIYQ4rg3urqHVamlvb2fnzp3s2LGDF154gerqajWQQa/Xo9Fo1A5fyiYkQEBAAPHx8dxwww1MnTqVE0880avavhBCDB5zdDodn3zyCbt37+b9999n48aNwMENAWWcUcYch8NBb28vGo2G+fPnAxK8IMRoUH67l19+OZdeeilPPPEE//jHP9i/fz9wMEGztraWRx99lF27dvGrX/2Ks88+G19f3yE3DpWK1263G5PJxJQpU9RzCSHEj9XR0UFdXZ36d2hoKH5+fmP4jYQQYyk6Opo//vGPXH311dx33308+eSTalCSZ5CiRqPhm2++4ZlnnmHx4sUkJSV5FaIQQox/Q3UaVpJGOzo6cDqddHZ2smHDBnbt2kV7ezu7d+8mPz9fTQZTumxotVpMJhP9/f3MnTt3LG5HCDFO2O12brzxRpqamjCbzdTX12OxWGhtbaWnpweHw3HIZ7RaLTqdTp2DKN17lH93u918+eWXfPnllwCEhYURGxtLUlISKSkpJCcnk5iYSGxsLJGRkYSGhhIUFISfn590XBdinEpPTyc9PR0Y6ALW09NDR0eHWlivrq7OKymspqaGhoYG2tvbj2qcUfZi+vr6aGhooKGhgcLCQvV4k8mEzWZTOw3OmjULkAQPISYC5ZmooKCAAwcOsGrVKrZs2YLValXXT/R6PU6nE61Wi1arVWNETCYTJpOJiy++mNNOO01N/pKkUCGEEEIIMRI0bnkKFUIIIcRxyDPQQenYtW3bNl555RWKi4v5/PPP1cU7pWKbTqfD4XCon3W5XJhMJubNm8eUKVO4+uqrycvLUxPEBl9HCHH88hwLLBYL7e3trFq1is8//5zKykoaGxsB1LFFGUeUjQMl4HLBggWcdNJJLFiwgNNPP106eAgxypTA5/7+frZs2cKLL77IunXraGxsVDf8PAMJzjzzTP7whz9w+umnq68pHf9SUlKorq5Gq9USGRnJN998Q2pqqgRXCzFBKMGI4N2FZ7S8+eabXH/99XR2dgJw6623cs899xAQEDCq30MIcWxQkjWUwKP8/Hwee+wxXn/9da9nDuVYGJjH/OlPf+LUU09VzyNrHEKMH4MLUAx+zujo6MDf35/CwkI2bdpEY2Mj1dXVfPHFFzQ2NmIymdR5BAwELiqddpSAZ2VMsNvtfPbZZ5x22mmje5NCiHGns7OTlpYWGhsbqa+vp7a2FrPZTG1tLXV1dWpnwfb2djXQejCtVqs+YylrqUMldcBAF9TIyEji4+PVpLDU1FTCwsJYsmQJQUFBI3m7QohhdjTrpv39/XR1ddHe3q6ON2azmaqqKvWf2tpampqa6OvrG/IcSpcwz71eZZzRaDQYDAZsNhtvvfUWy5YtU9d7hRDHvsHPSQ6Hg+7ublavXs327dvZtGkTBw4c8PqMwWBQk9CV/VqAvLw80tLSuPDCC7ngggvw8fHBx8dn1O9JCCGEEEIcXyT5SwghhBDHhaE2BKxWKwUFBWzbto3nnnuOpqYm2tvbgYOVmJTgKLfbjdPpBAaqN2VkZHDNNdcwdepUTjrpJPz9/b3OLQFRQhy/hurwA7B+/Xp2797Nf/7zHzZs2KB28gIwGo04nU6cTqfX60pV2gsvvJCf/vSnREVFkZCQMOr3JIQYmsPh4LnnnuPRRx+lvLwcGPg9w8GggNDQUJYsWcJNN93EvHnzALDZbPj4+Khzk5ycHHbu3ClBAkIcB5R5wkgmhNlsNv7nf/6HRx55BBgIjnzuuee45pprRuR6QoiRZbPZ6O/vJzAw0Cup9Meqrq7m9ddfZ82aNRQXF6vrGBqNBofDoV5r8eLF3HbbbZx11lk/+ppCiJFxpHVIh8OhJm5t2LCBsrIy2tvb+eabb9i0aRM6nY6uri56e3sBvIIZPTthOBwOtTuGkjQKqGsYZrOZ2NjYEb5TIcRE5HQ6aWtro7m5mYaGBurq6jCbzdTU1KhdwhobG2lpaaGzs1PtPuhJo9GoyRpwMPFd2dMZSlNTExERESN2X0KIY4fb7aa3t5fOzk7a2tqwWCw0NDQc0iWsrq6O5ubmIccOJeFUGW/6+/spKysjPT1dinkJcQzzXEfx/K0WFBRQUVHB6tWr+frrr+nt7VWfiQ7X4cvHxwe9Xs+ll17KokWLyM3NJScnx+t6EiMihBBCCCFGmiR/CSGEEGJCG7zAZrPZKCgo4KWXXqKkpITPP/9c7fzldrvVDUKn0+n1uslk4pRTTmHKlClce+21ZGVl4evre9jrCCGOT55jgdVqpaGhgZdeeokvv/ySiooKLBYLcLDDl8FgAPAKnAKYP38+8+bNY/78+Zxzzjle70kVSSGODZ6/976+Ph5//HEeeeQRr9+5Z+efOXPmcNVVV3HttddSWlrKtGnTMBgM2O12zjvvPN577z0JFBBignjwwQfp7Oxk8uTJJCUlkZiYSFRUlNfzw2BKAOOPSQpTxqXS0lIWL15MZWUlALGxsbz11lucdNJJP+i8Qoix9fHHH7Nu3Tp++9vfkpiYOOzn37dvHw8++CCrVq1SX1OqWitjU3p6OhdeeCGXXnopU6dOlecRIY4Rg9cjlb8tFgubN2+mvb2diooK1q1bR21tLQBms3nIcxmNRrWavSfleUZZxwBISEggISEBjUbD9OnTWbBgAZdeeulI3KIQYgLyDE850rNPX18fVquVpqYmGhoa1A5hZrNZTQqzWCy0trbS09Mz5DmUpFXPDqd2ux1fX1+6u7uH98aEEOOOw+Ggp6eHjo4OWltbsVgs1NXVUVNTQ2VlpZoU1tDQoBYP9TRUMqoQ4tgwVKHO5uZmXn75ZbZv387GjRupqqry+ozRaFSL+nk+/+Tk5JCdnc3SpUs5//zzMRqN6jqv7NkKIYQQQojRJslfQgghhJjwOjs72bJlC7t27eKFF16gurpa3QzU6/VqQNPgDl9BQUHExsZyww03MGPGDGbPnk1AQIDXuSVIW4jj21CJn+vXr2fnzp18+OGHbNq0CTgY1HC4Dl/h4eFER0dz3nnncfHFF5OQkEBMTIzXNWS8EeLYpPxGbTYbu3btYuXKlaxdu5bm5mYAtcq+4uyzzyYuLo5Vq1ap48BvfvMbHn74YdkoFGKCiIiIoKuri7CwMIKDg4mIiCA6OprExESSkpJITU0lKSmJuLg4oqKiDltEwjOB9LuSwpR5gt1u55577uEvf/kLPj4+9PX1cfbZZ/Puu++qnQmFEOPLtddey4svvojBYODss89m+fLlnHnmmYSEhPyo8w4OhNqxYwf33nsva9euVY/x7AAEMGvWLH71q1+xfPnyH3VtIcSP47k+UFxcTG1tLVarlW+++YYNGzbgcDhoaGigpaUF8P4t6/V6tFqt2uFPSYrwLErjmSSRmppKTEwMKSkpLFq0iKSkJAwGA1OmTJGOOUKIYXW0SWGdnZ20tLTQ1NSkdgnzTAprbGzEYrHQ3t6OzWYb8hxz5sxh8+bNsg4jxHHoaPZZ+vv76e7upr29nZaWFrUrYVVVFdXV1RQXFxMUFKQWGJXioEIcG4b6fRcUFFBeXs7q1avZvHkzbW1t6rOPwWBQE708O3yZTCb8/PxYtmwZixYtYvr06WRnZ3udV377QgghhBBirEjylxBCCCEmJKfTyc6dO1m9ejXFxcVs3LgRm82mVmnS6XTodDocDodXhy+DwcCpp57K5MmTueKKKzjhhBO8Ai1lIU8IAd5jQWNjI+3t7axevZr169dTU1NDU1MTcLAythJcpQQcKGPO/PnzWbBgASeddBJLlizxuoYEHwgxPlmtVl566SWeeeYZSkpK0Gg0ape/wVUjNRoNer2e559/nuXLl8vvXogJoLW19bCB0Hq9Hj8/P4KCgggNDSUyMpLY2FgSExNJTk4mJSWFpKQkYmNjj5jU4XK51GCGwc8m999/P4888gitra1q8ulTTz3FjTfeKInkQoxTp5xyCt988w16vR6Xy0VCQgInn3wyF198MfPnzycyMhIYeH4Yalz4Pmpqali7di0vvfQShYWF2O12NTFESQ5JTU1l//79sj4ixBh66aWXWL9+PQcOHKC3t5fdu3cz1HavUrleec7Q6XT09/cPeU5lXlJSUqKul+p0OlauXMmVV1455GdkHBBCjAbPghiH43Q6aWtro7m5WU3SUJLCamtrqa+vp6mpifb2dpqbm7nmmmtYuXIlDocDvV4/WrcihBjH3G43vb29dHV1YbFY0Ov1ZGdny1qLEMcAz+cSm81GT08Pq1evZsuWLWzevJnKykqv45X9Gs8CGAB5eXlkZWVx/vnnc8EFF+Dv768eK3s3QgghhBDiWCHJX0IIIYSYkJYuXcqHH36odtpQFuOUbjuA2nHH39+f1NRUrr32WqZPn84JJ5xAUFCQ1/kkmEGI49tQY8BHH33Erl27+Pjjj9mwYYNXJy/PDl+eXX+ioqIIDw/nggsu4LLLLiMyMpK4uDhAOgkKMRF4/o7b29t5+OGHeeihh9TET8/5iHKcyWTitdde44ILLvCqMimEGJ+++uorTj31VHx9fXE6nWpnHeXfh2IymfD39yc4OJiwsDCio6OJi4sjOTlZTQpLSEggOjoaX19fr8/29vZiMpkoLS3l/vvv5+2336anpweDwYDdbmfu3Lm8+OKLZGVljcbtCyFGgK+vL/39/WryuKe5c+dy+eWXc9lllw1rB54dO3bwwAMP8OabbwIDRS18fHzo6elh+fLlrF69WgKfhBgjVVVVpKameq1BKEmaTqcTrVaLTqdTC0+Ad+cvf39/oqOjsdvtTJs2jQULFhATE0NQUBAXXHAB06dPp6CgAK1WS3BwMF9++SV5eXleXcmVcwohxFg52i5hfX19WK1WLBYL9fX11NfXU1RUxE9+8hNOOeUU2fcRQgghJoidO3dy4MAB1qxZwxdffIHNZqO3txc42OFLo9Gg0+nUpC9/f3+0Wi2XXnopZ599NpMnT2bKlCle55W5ghBCCCGEONZI8pcQQgghJqT4+Hjq6+vR6/W43W61Or4SLKXValm4cCF5eXlcc801TJ48GaPRqH5eFvKEEIO1tLTQ0NDAyy+/zPr16zGbzTQ2NgKoCV56vV6tiO9p/vz5LFy4kLlz50qHLyEmOGXeodPpcLvd7Nmzh8cff5w333yTjo4O4NDApEWLFvHHP/6R0047TX1N5iJCjE9PPPEEv/rVr4Z8T6fTodVq1QBsl8ulPqcMptVq8fX1JSAggJCQEMLDwwkODiYkJISwsDACAwMJDw+nsbGRr776itLSUtra2tTxRaPR4HK5+OijjzjzzDMlQFuIcaqtrY3ExES6u7sB1OcNjUbj1ZknNTWVc889l6VLlzJjxgy1oI3SafRox4DBBSk2bdrE3XffzSeffKK+9ve//51f//rXUrxCiDGybt06zj33XLXoDKD+r2eSFwysjzqdTqKjoznjjDPIyMggMDCQadOmkZubi81mw2AweP2W/fz86OvrQ6PRkJqayo4dOw4pkiWEEMeqo00Kk3mMEBOXss7iueeiFOYBvtfz0eHIGCLEsaGhoYE33niDb775hq1btx7S4UvphKwkfinjQF5eHpMmTeK8885j2bJl6josyJ6tEEIIIYQ49knylxBCCCEmnJ6eHgICAryq0cLAYnxubi6/+93vmDVr1iGVmzy79ChBmUII8dVXX/HVV1+xfv16Nm7c6LVB4Nnhy7Pqdnh4OPHx8ZxzzjlceumlxMTEEBMTA0hChxDHo/r6et58801WrlzJvn371A5fSuKH4uyzz2bFihWcddZZY/hthRA/xtatW/nss89obm6moqKC6upq6urqaG5uVgOzPSkVZ5VnD8+ksKEozzhDLekqSaculwu9Xs/y5ctZuXLl8N6gEGJUdXV1sXr1at58802++uor9XWlU6jSWVCRm5vL+eefzxVXXMHkyZO9zvV9AhQ9AyMbGhr497//zZo1a/jmm2/YsGED8+fPl4BHIcbI3XffzV133YXRaFSTQENCQvD19UWj0TB//nymT59OYGAgkyZNYu7cufj4+GCz2fDx8RnynA6HA71eT01NDcnJyWqBm4ULF/LFF1+M8h0KIcTwk66FQkxch3susdlsOBwO/Pz8jvhZkLFBiPHqpptu4tlnn1X/1uv1OJ1ONBoNWq1W3bM1mUwEBASwdOlSFi9eTG5uLtnZ2V7nkr1bIYQQQggxXkjylxBCCCEmnJ07dzJz5kwMBgN2u11N1NDr9aSnp5OZmcnJJ59MUlIS2dnZZGVlERgYOOS5lOBJQF0oFEIcXxYuXMjXX38NHOzwpSSIDq6ufcopp3D66aczd+5cFi9e7HUeqRYnhKivr+fee+/l6aefVucXBoNBDdzWaDRkZGSwdOlSfvazn5GTk6N2MZUgBCHGD4fDQU9PDx0dHbS2tmKxWKirq6OmpobKykoqKyupra2lvr6e9vb2Ic+hJHZ4Jns5nU41MEnp/qMkimk0GvR6PTabDYCrr76alStXytghxARSUFDAiy++yHvvvUdFRYX6urJWMTjB9IwzzmD58uWcffbZREdH/+jrb9q0ifvuu4+XX36ZkJCQH30+IcQPc/755/Phhx+qgY1ut5sZM2bw2GOPccopp9DR0XHYTl3KM8jg9U0l0FHpKqasqV5//fU8++yzEggphBBCiGOO535LS0sLzc3NfPnll+zZswetVovRaMRut+NyuTCZTMTGxpKSkkJWVhZJSUmHzJdk/VWI8Wf69OkUFBRgMpnUNVE4mNg5depUpkyZwpIlSzj//PPx8/PDYDAABwtgCCGEEEIIMd5I8pcQQgghJpyVK1dyww03qAv7CmXh3nP6YzKZCA0NJTExkaysLHJzc8nNzWXSpEmkpKQcNlHD5XKp55EuYUJMbAEBAfT09KiJXxqNRg22TktLIzg4mLPOOovLLruMiIgI4uPjAUn2EkIcpCRuKAGThYWFPPzww7zyyiuHJJEqZs6cya233soVV1wxJt9ZCDH8+vv76e7upr29nZaWFhoaGqirq6OqqoqqqioqKysxm800NjbS19c35DmULsUwEKTgGZzkdrtJTEzk1ltv5de//rUkjwoxQXgWn+jo6KCwsJD33nuPdevWsX//fnp7e9Hr9V7dz5WEkJiYGObOncuyZcs4/fTTiY2NBQ6fADIUGUeEOLZMmjSJ0tJSDAaD+izhcrlISEjgd7/7Hb/+9a/VY482aUsJfLznnnu488471XM/9NBDrFixQtY3hBDj2veZ9wghxgdljrNt2zY+++wzXn75ZYqKivD19aW3t9frWI1Gg8FgwGAwYDKZCAsLIzk5mWnTpnHSSScxZ84cEhMTx+hOhBA/hr+/P729vereivK/p556KpdeeikzZ85k1qxZY/01hRBCCCGEGFaS/CWEEEKICeemm27i2WefxWg04nA4gIMVnpSFP8/AJSUg21NAQAARERGkpKQwefJkNSksMzOTmJiYw17bM4B78HWEEONPY2MjsbGx6HQ6NVBASeCYNWsWS5cu5Re/+MVhq2orxw819gghjm9ms5l3332Xl156id27d2Oz2dDpdGpXQWXMycrK4oknnuCMM84Y428shPghvitpwu1209vbS2dnJ21tbVgsFhoaGqitrVUTwqqrq6mrq6O5udmrs49Wq8XX15fu7m4mTZrEZZddxkUXXUROTs5o3JoQYoxVV1fz5ptvsmbNGnbv3u313uCkchioiP2zn/2Myy+/XC1Y8X1I4LQQY8/tdvOnP/2Jxx9/XE0UV6rVOxwODAYD06ZN45ZbbmHp0qUEBwcf1XmV5K+lS5fy3nvvodfrMRgMvP322yxevFg6fwkhxo0jJat2d3fT29tLRETEKH8rIcRI+O///m/efPNNqqurgYPPQFqtFp1O59VFfSh6vZ7IyEhyc3NZsmQJ5557LmlpaWi1Wpn7CDEO1NXVkZCQoBbuBNQ12KCgIE466SQSExNJSkoiOzub5ORkoqKiCA4OxtfXF4PBIL9zIYQQQggxLknylxBCCCEmnOuuu44333wTp9M5ZIU35R/PhDDlPcVQCWE6nY6goCBiY2NJT08nJyeH3NxcJk+eTEZGBoGBgUN+H6mSLcT49fnnn7No0SKMRiM2m0193WAwMHnyZIKDg/Hz8yMxMZGEhARSUlJISUkhISGB6Oho/P39D3tuJXhSksKEOL6VlJTwwAMP8Morr2C3270CFAwGA729vaxevZrly5fLnEKI44jD4aCnp4eOjg6sVistLS309vbS0dFBY2Mj7e3tOBwOYmJiOPvss/H39yc6Onqsv7YQYhQMTsLq6uriq6++4o033mDDhg1UV1fjdrvR6/Xqs4bT6VQ7iMXHx7NkyRKWLVvGzJkzCQsLU88rzyZCjA+1tbXcc889rFq1Si18pQQqA8THx3PxxRdzww03kJ2drQZEKl0EB1MCnNPS0qisrESr1RIREcGmTZtIT0+X5xAhxDFncIf1wXp6evDz82Pnzp18++23WK1Wqquree6553jnnXdYunSpjG1CjFP19fVcdNFFbN68GRhI4vJM/FDGBSXxS3nNs2u62+1W502K9PR0brvtNm655Rbg6DuoCiHGxkcffcSSJUvw8fHBbrerv+3DhcGaTCYSEhJIT08nOzubrKws0tPTiY+PJyIigsDAQEwmEwaDYZTvRAghhBBCiO9Hkr+EEEIIMSH19/ezf/9+iouLKSgooKioiNLSUsxmM+3t7Ycs6isL+EpSmOcU6buSwkwmE6GhoSQkJJCdnU1OTg55eXlMmjSJ5ORk9Hq9bCQKMU49/PDD/P73v8fX15f+/v5DkkYVGo0GX19fAgICCA0NJTw8nJiYGOLj40lOTiY5OZmkpCTi4+OJjIw87OaB5xgjgZdCjA3lv9kNDQ08//zzNDQ0MHv2bLKyskhJSSEoKOiIiZ3f5zpwcJ6xY8cO7r//ft5++231GCXxND8/nxkzZvzoawohjm3yzCCE+DGampr44osvePXVV/nPf/6jJoQA6rqEZ9X7zMxMlixZwlVXXcX06dO9ziXjkRDHLuX3abVaWb9+PS+88AIbN26kt7dX7Xaj/NZDQkL4/e9/z69//Wv8/Py8Pj+Yw+HAaDSq702ZMoWdO3eqncWEEGKseI5bg8cwpXNhf38/X3/9NdXV1TQ3N7Np0ya+/fZbjEYjFosFu92ufmbfvn1kZmbKfEeIcWjPnj3ceOONfPvtt2rxLDhYyGLwvo1ncvxgSkKYMg4oz09z5szhiSeeYObMmSN4J0KIH+u+++7jz3/+8yGvazQadDqd+htX1kIO1wUwODiY5ORkMjMzyc7OJjMzk9TUVGJjYwkNDcXf3x+j0XjYzqJCCCGEEEKMNkn+EkIIIcSEc6RNu9bWVsrLy9mzZw+FhYXs3buX/fv309jYSHd3t9exR9slTHl98LQqNDSUvr4+/vWvf7Fs2bLhuj0hxChavHgx69ev93pNr9cf9aaBXq/Hz8+PwMBAwsLCiIyMJDY2loSEBK+ksJiYGMLDww/7PZQxRhLChBg9Q20ezps3j+eff57s7Oxhu47n3KKxsZH//Oc/rFy5ki1btqhjS3d3N76+vsN2TSHE+OdZvVoIIQarra1l9erVvPPOO+zcuVN9XRk3Bj+/zJs3jyuuuIJzzjmHpKSk0f66QogfoaKigr///e+89NJLdHR0oNVqMRgMuN1utYN5ZmYmN998M8uXLz9k7UFZayguLiYnJwe9Xo/D4WDJkiV88MEHkhwhhBg1g5O8DtfZq66ujt27d9PT08Pu3bv5/PPPqaurw+l0UlNTc8jxGo0Gg8GA0+nE7XZ7JcgLIY59Sgeu6upq/vu//5s333wTvV6vJnUp77vdbrKzs5k/fz4Oh4Pu7m4MBgPd3d309vbS2dlJfX09FouFnp4eXC6XWqTP5XJ5dQ2aNWsWf/nLXzjrrLPG8taFEEfw+uuv8+6779LV1YXZbKapqQmr1UpPT8+Qx2u1WjUpDAZ+906n87AJorGxsaSkpKhdwjIzM0lKSiIqKorg4GB8fX0xGAyyNiuEEEIIIUadJH8JIYQQYsJyu93qgt13BUbW1tZSUlJCYWEhhYWFlJSUUFVVRUtLixoooTjapDC9Xo/NZmPDhg3Mnz9fgiWEGIfefvttvvzyS8rKyiguLh4ygAC+/6aByWTC39+f4OBgwsLCiI6OJjY2lqSkJDUpLCEhgejoaAICAkbs/oQQ3pRggRdeeIE777wTs9lMQEAAXV1dnHXWWdx1113MnTt3xL/H1q1beeqpp1i9ejXR0dHU19fLPEIIIYQQR+RZlEar1dLT00NJSQnr1q3j3XffpaSkhK6uLrRardrJRylm4Xa7iYiIYPbs2SxbtoxFixaRmJiIRqNRn2kkoEmIY8vgtciSkhLuvvtuXnvtNfUYnU6nBjNHRESwZMkSrr32WmbPno2vr6+6dmEwGHjllVe44oorMBgM2O12VqxYwSOPPILT6ZQq90KIEXWkcaaoqIjm5masVisff/wxO3fuxGazUVVVhdVqPeR4vV6PRqNRE92VAl7KOm16ejplZWXq+o8QYvz4xS9+wTPPPIPb7Uan06m/84yMDC655BKuu+46/P391X8UfX19tLa20traSmVlJYWFhRQUFLBx40bMZrN6nJJApsyxpk+fzssvv8zkyZNH90aFEEeto6OD1tZWGhsbqaurw2w2U1tbi9lspq6ujoaGBpqbm2lra/PqAupJp9Oh0+nUuA+Xy3XYRHGTyUR8fDwZGRlkZWWRnZ1Neno6MTExJCYmHrHIpxBCCCGEEMNFkr+EEEIIcdxRgh7gyElh/f39VFRUsHfvXgoLC9mzZw9lZWWYzWba2toOSepQzqMsDiqbii0tLYSGho7sTQkhRlx3dzeNjY3s37+foqIi9uzZw549eygvL6e1tXXIzwy1aaAEVw6m0Wjw9fUlICCAkJAQIiIiiImJIT4+npSUFKKiopg/f75U4hdihLlcLvLy8iguLlar3k+bNo033niDzMzMEb+2Z4e/jz76iB07dnD77bdLYJIQQgghfrCOjg7efvtt1qxZwxdffOH1nmdhG8WkSZO49NJLufzyy0d8/iOE+HE8u+N0dXXx6aef8vTTT/Ppp5+qzxfKcQDBwcHcfPPN/O53vyMsLEw9z29+8xv+/ve/q13DnnzySa6//npJ/hJCDBtlbXSoMaW7u5uKigrsdjt79uxh3bp1WCwWrFar2sVUq9V67cl4dvPSaDRqMohnQUBl7PPz88PpdLJ06VJeffVVHA6HmgwvhDj2ffbZZ1x++eVYLBZ0Op06ntxyyy3cfvvtxMbGeh2v7AMfaQ5jsVj49NNPWbt2LZ988gnt7e1e67Iul4vFixfz/vvvq4mkQohjn9PppK2tDYvFoiaF1dbWqklh9fX1NDY20tLSQkdHx2H3a5WCn8p8wul0HtJF3dMjjzzCihUrpIifEEIIIYQYcZL8JYQQQgjx/3kmhR1pId9qtVJWVkZRURGFhYUUFRVRUVFBQ0MD3d3dXseGhobS0tIy4t9dCDE23G43bW1tmM1mSktL1YSwvXv3cuDAAXp7e4f8nF6vVzcev2vTQK/XExwcTFtbG//+978599xzR+x+hDjeuVwu/vGPf7BixQpgYJPP39+fjRs3MnXq1BG5ZmNjI83NzcTFxREcHHxIgpfNZsNoNI7ItYUQI89sNhMcHKxWjfX398dkMh315wcnfnoGMkoggRDiuwzu2tXb28v27dt54403+OyzzygvL1eDn5VxRRmvNBoNsbGxnHnmmSxbtox58+Z5JYoIIY5dhYWFPPvss7z11ls0Njai1+vVccBmswEQERHBLbfcwrXXXktiYiJnnHEGX3zxBTqdDl9fX9atW8e8efOkCIUQYtg1NDTQ29tLe3s7n376Kfn5+fT09FBcXEx5efkhxytJWg6HQy205Xa7cTgcXgHWSiJsZGQkPT09pKWlsWDBAmJjYwkKCmLevHlkZ2fj4+MjgdlCjDNnnXUWn3/+uZrY7na7+dvf/sZtt9121OdQEuYHr6eYzWZWrVrF008/TV1dnTrGaLVaHA4H//znP/nFL34hCfFCHKM8w16P9N/2vr4+rFYrTU1NNDQ0qF3Camtrqauro76+HovFQmtrKz09PUOeQ6vVqklhcHDdtr+/nzVr1nD55ZfLWCGEEEIIIUacJH8JIYQQQhyBUj1OcaTFutraWkpKSigsLGTv3r1s2bKF9PR01q5dK4ESQhxn7HY7zc3NVFVVqePCnj17KCkpoaamZsjPDLVpoFSrVTY0d+/eTV5e3mjeihDHlaqqKpYsWUJxcTEA/v7+/Pa3v+Wuu+4ascCgPXv2sGTJElpaWpgxYwbJyclcd911nHrqqTJ3EGKccjqdfPzxx3zyySd8+umnlJWVERwcTHJyMpMmTWL+/PmcffbZ36ubp1SmF0IMp66uLr766iveeOMN1q5dS2dnp/qeZxKYIigoiGeffZZLLrlEgqWFGEe2bt3KX/7yFz744AP1Nb1erxahUZIhbrnlFpYvX47VakWr1RIbG8v27duJjo4ew28vhBjv7HY7VqsVnU5HQUEBX375JQ0NDdTV1fHNN99gtVoxmUz09/ern1E6sGs0GgwGg3oeJRHDc34CB4O8AwIC+NWvfkVUVBR5eXnMnTtXfYby9fUdvZsWQgy7bdu2ceKJJ6rjg6+vLzfddBOPPPLIsCZZFBcX89Of/pS9e/eqBUKdTicnnHAC//73v0lISBiW6wghRsfRJoV1dnbS0tJCY2Mj9fX1aocw5Z/GxkYsFgvt7e1qIQ1PythUUlJCVlaWrJkIIYQQQogRJ8lfQgghhBA/gGeXMI1Gc9jgbKnuJITw1N3dTWNjI/v372fv3r1q98CysjJaW1uH/IxS0dbpdNLT06MGPgghht+aNWu48sorMRqN2Gw2TjvtNN59910CAgJG9LoPPvggf/zjH9W/TzjhBD7//HOCgoJG9LpCiOGjbOw3NDTw4IMP8swzz9Db2+tVhd5TSEgId9xxxxGrVDc0NPDll1/S3NxMdXU1XV1dhIeHM2nSJPLy8khPT8ff31+CCoQQhzW485fSJcPzmaK9vZ2XX36Z9957j23btmG1WoGDax16vZ7+/n7efvttLrzwQhlzhBgHPH/7DoeDjz76iEceeYQNGzaoxyhFZsA72cLtdjNr1iy2bNkiv3UhxI/y0Ucfcfvtt1NSUqJ2xXA6ncDA+KR04TEYDGi1WpxOpzoWeSaiazQafHx86O3tJTc3lzlz5qDT6XjuuefUcWvmzJls27ZtyO/hdDqPuIcjhDh2dXZ2cvfdd/PII4+oyaLz58/nww8/HLb1WqUIqE6no6amhkWLFlFWVoZGo0Gn0+FwOPjkk08444wzhuV6Qoix5RnfcThOp5O2tjaam5vVxHWz2UxNTQ1ms5n6+noaGxtpbW2lvb1dXWuRuYYQQgghhBgNUi5WCCGEEBPWSAYkHW7xzjMpTOniI4QQCn9/f9LS0khLS+PMM88EBsaqtrY2zGYzpaWlFBUVUVhYSHFxMRUVFfT29uJ0OgkICMBgMEgnQSFGiN1uVyviO51ODAYDl1xyCQEBASOezH399dfz0EMP0dnZidPpZPfu3TQ0NEjylxDjiEajwW63c/PNN/Puu+8CYDQacTgcapV65R+3201HRwe/+c1vOHDgAH/961/x9fVVn19aW1t55plneO211ygsLFSvodfr0ev1BAYGkpGRweLFi7nxxhuJioqSZAwhJqChApIGJ3Mprx3u+WDw60oXDbfbTWNjI1qtlqioKC666CL8/f3R6/V8+umnajVrpRsxDCSnD/4+Qohjk+dvX6/Xc95555GYmMhLL73EW2+9hdlsVhMhlEBFz992dna2/NaFED/a6tWr2bVrF0ajkb6+PjQaDUajEbfbjd1uBwaKXin/DuDr60tvby+hoaGccsopREVFERYWxsKFC5k2bRo2m42UlBRKS0t57rnn0Ov12O120tPTgYG1ncGFs2SPRojxq6ioiC+//BKA/v5+4uLiuPnmm4d1vVZJ8nK73SQmJvLMM8+waNEitfOXRqPh+eefl+QvISaIoZ5zBncJ0+l0hIeHEx4eTnZ2tvpeX18fVqsVi8VCfX09dXV1VFRUqGu/sj4rhBBCCCFGgyR/CSGEEGLCsNvtuFwuTCYTcPiApLFIChNCiMPRaDSEhoYSGhpKbm4uy5YtAwbGtObmZmpqati5c6d0/BJihB04cIAdO3YAA8lfJ598Mqeeeiow8v99Dw4O5vrrr+eBBx5Ap9Oh1Wp58cUXue+++0b0ukKI4XXLLbfw4YcfAqiV610ul1qN3uFwAAeDD7VaLf/4xz9YsGABF110ERqNhn379rFixQo++ugjADVJQ6mC73A46Ovrw2Kx8O233/Loo4/y97//nSuvvHJsbloIMWKGWrfw7OBlt9sxGo1otVq6urpoaGhAr9cTHByMxWKhtLQUq9WKyWTC4XBQVVXF/v376erqwtfXl56eHg4cOEBlZSWBgYGEh4fT39+vdttQOBwOjEYjycnJo3bvQojhN23aNB555BHOPfdc/u///o+vv/5aDZrW6/VqoifAjh07eOedd5g/fz6RkZFj+K2FEONZaWkpcLDzlkajURPMDQaDmvQ1Z84ctavxCSecwMKFC/Hz88PHx4fo6Oghz/3xxx+r/67RaNTAbNmfEWJi2bRpE+Xl5erf8+fP55RTTgGG//euPAMtWLCA5cuXs3r1arU7akFBAU6nE61WK4kdQhwjlK598OMTvY8UU+J5jI+PD7GxscTGxjJ16lT1vb6+viOeRwghhBBCiOEkyV9CCCGEmDD+85//sG3bNnJycoiOjiYqKorQ0FCCgoLw8/NTF/5k4U0IMR4YDAZ1E2HOnDnq6xLEIMTIKCoqwmw2q3+fcsopapDzSM4dXC4XOp2O6dOnAwOV+fv7+6msrAQY8a5jQojhUVhYyPPPP+8VCOR0OgkKCqK7u5vg4GDcbjdWq1UNrlaO+9vf/saSJUtwOp1cdtll7N69GxjoHOZyubySxpTPKAUtOjs7ufnmm9FoNCxfvny0b1sIMUKqq6tpaGhAp9Ph7+9PfX095eXltLS0YDAYcDgc1NTUUFZWRk9PDwEBAfT29lJRUUFdXR2BgYH4+/vT2trqFYSk0WjU4ChPzc3NHDhwAPAOmjIYDNhsNjWgWroQCzF+Kb/f008/ndNPP5133nmHv/71r2zfvv2QY4uLi7n++uu55ppruPnmm8nIyFDPMThBVAghDmffvn3AwbHD5XIxf/58MjMzSUhI4LTTTiMuLg6tVqt27hpscNdTm82G0Whk586dwMBzkcFgYMqUKaNwR0KI0fb111/T2dmJVqvF5XKxdOlS4uLigJFZr1XmS0uXLuWVV15REz8cDgf5+fle+zRCiLHhdrvp7+/Hx8dnxPdNvispTHnfx8dnRL+HEEIIIYQQniT5SwghhBATxsMPP8ymTZvw8fEhJCSE+Ph4kpKSSElJITk5mYSEBGJiYoiIiCAkJITAwEB8fX3H+msLIYQQYoy53W7y8/Pp7e0FBhIuZsyYMSqbdkoAU1ZWFpMmTaKkpASAzs5OGhsbD1vlWghx7LBYLPzjH/8ABpIm7HY7Op2Oc845h1/+8peccsopWCwWDhw4wNatW3nnnXfYtWsXTqcTt9vN9u3baW9v589//jOFhYXAQCKoUhVfp9Phcrm8OnLodDr1797eXm6//XamTZvmVXVWCDF+XXHFFVgsFqqqqujr68NoNOLj40NHR8cRP6fMK9rb22lvb8dgMGAymdRO6TqdTu204RlM7Xa71eAlz7FGGYeU5C/PqtdCiPHFs3OgRqPhJz/5CVlZWbz88su89dZb7N+/X03s0mg0tLW18eijj/L0009z44038vvf/56YmJgxvgshxHhRU1NDT0+P2llQSaiIjIzk9ttvJy0tzet4ZS4yOMl88N9KkPeuXbvUz/n5+TFp0qQhjxdCjF+dnZ3s2LFDHR+MRiNLly4d0d+5cu65c+cyffp0tm3bhl6vp6KiQi0aJgUxhBhbW7Zs4eSTTyYvL4/s7Gx+9rOfceGFF47qd5BiGEIIIYQQYixJ8pcQQgghJoyamhoA+vv7aWhooKGhgfz8fPV9Pz8/oqOjiY+PVxPCkpOTiYuLIzo6mvDwcIKDg/H398doNB7xWkqghBBiYrLZbBgMBvmdC3GcsNvtVFRUqH9HR0eTlJQ0atd3uVzMmDEDg8EADGwelpSU0NbWJslfQowDhYWFfPXVV8DAeKLX6/mv//ov/vnPf6rH+Pv7k5KSwmmnncbNN9/ML37xC1555RUAwsLC+Mc//sELL7ygBhA5HA7mzZtHcnIyoaGh6PV62traKC0tpaioSE0A0Wg06HQ6zGYzf//731m5cqXMX4QY5ywWCxs3bgQGgg91Oh02m03tdOF2u7Hb7cDBjoBKh8DBXb2U4wwGg9pJ0OFwEBoaSkBAAO3t7fT19ZGcnExSUhJGo5HOzk4CAwNJT08nPj4em83GwoUL1e8jhBjflHmCTqcjNzeX+++/n7lz5/LAAw+wefNmtYuO8nvv6+vjySef5JNPPuGqq67ioosuIjU1dSxvQQgxDtTX1wMDzzUajUbt2vPOO+/wySef8Lvf/Y7bbruNwMBAgCETv4aiJH95dhULCwtTxyV5FhJi4iguLqa2tha9Xo/D4WDatGmYTKYRT75yuVxER0er45NOp6O/vx+TyQTIOCPEWFMSwAsLCyksLCQlJYULL7wQp9M54p3AhBBCCCGEOBZI8pcQQgghJoTe3l6qq6uBgwGQnhVtnU4nPT09HDhwgAMHDqiBVAChoaHExMSQlJSkJoQlJSURGxtLZGQkYWFhBAUF4efnpy4ayuK+EBPb7bffzgcffMBpp53G5MmTycnJIS0tjejoaPz8/Mb66wkhhll3dzfNzc3q35GRkaPaHVSZs3gGEdTU1BAVFTVq30EI8cNt2LCB8vJytRvXueeey4oVKwC8Ag+U7l1BQUE8/PDDmM1mNmzYQGdnJytXrlSPiYuL49e//jVXXnmlV4eNjo4Oampq+Pbbb1m9ejVbt25Vq+gDvP3229x7773ExsaO8v8DQojhVFRUBAzMC2w2Gy6XC41Gg9vtxmazodFoCA4OpqenB7vdTlBQkJq41dbWhslkIiMjg7i4OPR6PX19fURERJCSkkJ8fDwOhwO73U5ERASZmZkEBQXR2tqK2+0mLi5OnY/09/djNBq91j9kLUSIicWzC9hPfvIT/vWvf/HXv/6V4uJiALULmM1mo6ioiLvuuov8/Hx++ctfMm/evDH+9kKIY1laWhr3338/zz77LAcOHABQk9g7Ozu58847ee655/jlL3/J9ddfT1hY2FGfu66ujt7eXvX5KyEhgaCgoJG6FSHEGNm5cydwcN305JNPBka+G7FyvSlTpvDll1+q3ZC7uroAeSYSYqxt374dGJhXaLVaZs6cOcbfSAghhBBCiNElyV9CCCGEmBAqKyvRarX4+vrS3d19SLVrQE0IUxbmleBLq9WK1WpVAxsA9Ho9ERERxMXFkZycTEpKCklJSSQmJhIdHU1kZCQhISEEBgbi4+Mji/1CTDBffPEFpaWllJaWer2enJxMdnY2eXl55ObmMmnSJJKTkwkPD1c79gghxp/e3l5aW1vVv4OCgtTA59Hq9tnd3U1SUhLbt2/H5XLJmCLEOLJx40ZcLhdGoxGn08lVV11Feno6gFfFWa1Wi1arxWazERMTw6mnnsqGDRvo6emht7cXgICAAP70pz9xyy23AAc7iWk0GoKCgsjJySEnJ4ef/vSnXHvttaxduxaDwYDdbqe/v5/Vq1fzxz/+UToVCzGOKUGOgJr0FRcXx9VXX01OTg7d3d1qN8GIiAg6OjpwuVzExMSQkJAAQFtbGwaDAX9//6O6pmfAtJK0qsyFpHq2EBOX5xqpVqvliiuu4MQTT2TNmjW8/PLLHDhwQO3Go9Fo6Onp4fXXX+fDDz9Uu5AKIcRQIiIi+MMf/sC1117LQw89xGOPPaYmUOj1etxuN7W1tTz44INs3bqVq6++mvnz5xMcHHzYZxllrCooKAAGnq+cTicZGRle7wshJoYtW7YAA79tnU7HnDlzRu3aDoeDhIQEda/ZaDTS1NQ0atcXQhzejh07gIG1Cj8/P7Kzs4EjdypX1jU++ugjtm7dyqRJk0hISCA3N1cSyIUQQgghxLgjyV9CCCGEmBDCwsJ4/vnnaWtro7q6mtraWurr62lsbKSlpYWOjg6cTidOp9PrcxqNBr1e79UlzOVy4XA4aGhooKGhQV1EBPD19SU6OpqEhASvLmEJCQlERUURHh5OSEgIfn5+aqCUEGL8qaioQKPRqBVplXGhqqqKqqoq1q9frx7r6+tLWloaU6ZMITc3l9zcXDIzM0lISCA4OFgCr4UYJzw7fzkcDrXz12j9hnt7e706C0ZHR9PY2EhoaOioXF8I8cMpiRoOhwN/f39OO+20Ix5vNBoByM3NJSwsjNbWVgwGAzabjauuuorLLrsMGAhMGJwI6na7cTgcBAcH8+ijj1JbW8u2bdswGo3YbDZqa2uBg8FRQojx56uvvgJQAw01Gg0Wi4WPPvqImJgYzjnnHJKSktDrD93eUYKlQ0JC1HO43W6v8UDpJHa4Oc7gsUPGEiEmPmVdVKPRkJWVxR133MGUKVN48skn+fbbb9U5icFgoK+vT+28IYQQh6Osp0ZGRnL//fdz8cUX88QTT/D222/T09MDDIw5ra2tvPPOO3z22WfccMMNrFixQu1kPHjOoiR3ee7XaLVaJk+erF5TCDFx5OfnAwO/baUYDhw5wWO46PV6NSFEo9GocyDl+8iejxBjZ9++fcDAvCAsLIzU1FTg6PZxXn31Vf71r3+pf69bt46zzz77e/2uOzs76enpITAwEF9fXxkPhBBCCCHEqJPkLyGEEEJMCNHR0Vx99dUA9Pf3Y7VasVgs1NfXU1dXR21tLWazGbPZTH19PU1NTbS2ttLd3Y3D4TjkfEpVfs+kMKfTSW9vL5WVlVRWVrJx40b1+JCQEGJjY0lMTCQpKYn09HT+8Ic/jMq9CyGGV1NTE+3t7cDAeKLQaDTquKAs5ivjQlFREUVFRbz55pvq8eHh4WRmZpKTk0NeXh5TpkwhPT2d6OhorwQPIcTYCwgIoKGhARiYAxQXFxMWFjaq38Fut9PZ2an+HRUVJRuHQowD1dXVtLW1odfrcTgcTJ06leDg4KP6bFpamjrXUJI8rrzySsLDw4GhEy6UoCO73U5SUhLz589n27ZtaqBjd3c3HR0dUrVWiHFs5syZFBQUcODAAfU1m81Gfn4++fn5TJo0iQsvvJCrr76azMxMr88OnjsMFRgpHTGEEEfidrvR6/VcdtllXHrppTzzzDPcc8891NfXY7fbAZgxY8YYf0shxLFOo9GozzNKx57Y2FhycnJ48cUXKSsrw+12q8Uu2tvbefjhh3nmmWf45S9/ya233kpUVNSQ5962bRswMF4ZjUY1+UvWUISYWEpLS4EfluDxYygdgqxWKzAwhjmdTrVQmCR/CTF26urq6O3tVX+X8fHxR7UOq8xJGhoa0Ov16HQ6+vv7ycrKOuprK2PDa6+9xhdffEFaWhphYWFcdNFFJCcn/+B7EkIIIYQQ4vuS5C8hhBBCTAhKsKNGo8FkMhETE0NMTAx5eXnqMV1dXbS2ttLY2Eh9fT1ms5na2lpqa2upq6ujoaGB5uZm2tra6O/vVwMwPel0Oq/ED5fLhdPppK2tjba2NoqLiwHIyMjgD3/4g1qNUggxftTW1hIWFoavry/t7e309/fjdDrV3/vgDoKHSxZtaWmhpaWFzZs3ex2flJREdnY2eXl55ObmMnnyZJKSkoiIiDiku4cQYnT09/ej1+vV37jD4cDpdI7qZn5HR4fasQcgIiJCDSoQQhy7CgoKgIPBR7m5uQBH9Rzg5+dHd3c3Wq0Wh8NBYmIis2fPPqrrKnOGBQsWsHLlSjVx3Wq1YrPZftC9CCGODb/85S85+eSTWbt2LR999BGVlZU4nU61K3FJSQn3338/f/3rXznzzDP52c9+xumnn05iYuJYf3UhxCgZ6jnF5XKpXXI81y6/L89iNzqdjptuuonzzz+fp556ipdeeona2lpOOOGEH30PQojjT2JiIr///e+55JJLuP/++3nuuefUZxej0YjL5aKzs5PHH3+cb775hquvvprFixcTHR3tdZ7CwkJgYNwLDAwkOzsbkOQvISaSmpoa+vv71QSPhIQEAgMDR/U7KN2FNBoNfX19xMXFjer1hRCHUuYAWq0Wp9NJRkYGcHTrsD09Per6isPhwGAwfK+kUuWY9957jw8//FB9feHChSQnJ0tiqBBCCCGEGDWS/CWEEEKICWGoxTTPhDAY6OoREBBAUlKSeozL5aK9vZ3m5mYaGxvVLmFKp7C6ujoaGxtpaWmho6NjyMQPjUaDXq9Hq9Wi1+vp6enhpJNO8voOQojx44QTTmDnzp1UV1dTWlrKvn372L9/PzU1NVgsFtrb2+nt7cVut6tJYUebLOpwOKiurqa6uppPPvlEPdbX15e0tDQmT55MXl4eOTk5ZGVlER8fT0hIiGwYCDHCOjo6yMvLY8uWLcDAf9vXrVvHpZdeOmrfYf/+/VRXV6t/h4aGEhAQMGrXF0L8MPn5+eq/a7Vater8kZ4DlGCAuro6YGDO4HK5mDlzJnB0AQuKqVOnqolfABaLRbp+CTHOhYSEcPrpp3P66adTV1fH66+/zmuvvaZ2uYCB8cblcrF+/XqKioqYN28el1xyCYsWLfIaA6QgjRATk7JGUF1djY+PD1FRUV5FaYaDZwfS+Ph47r77bubPn8/Pf/5zpk2bNmzXEUIcP9xuN263m5SUFJ555hluuukm/vrXv/L222+rSWAajYbu7m42bNjA9u3bueKKK1ixYgVZWVno9Xr6+/upqKhQzxcZGanu98j6qRATx+AEj/T0dGB0nm+UsaSqqgo4uL6jJJnIWCPE2Pkx67AHDhygpaUFjUaD2+3+XoljyvUAGhsb0ev1aDQa7Ha72j1MxgYhhBBCCDFaJPlLCCGEEBPWkRLClPe1Wi2hoaGEhoaSmZmpvtff309bWxtNTU00NDQckhRWX19PU1MTVquVrq4uHA4HgLpJOWPGDAAJshJinEpMTCQxMZF58+apr/X19VFTU8P+/fspLS2lrKyMiooK6urqaGlpobOzk76+PhwOx2G7hCnjjmdSmNPppLe3l6KiIoqKinjrrbfU44OCgjjxxBP5+OOPR+fGhThOJSYmkpqaypYtWzCZTLS1tfHtt99y6aWXqhXvR9qePXtoaWlR/05LSyM4OHjEryuE+HG2b98ODDxnmEwmpkyZAhx5w9/lcqHT6dSuYcrxSheNoykgoQQu2O12APR6PQ6HA7fbrXYHkqADIcYvpbhEXFwcK1as4Oc//zlff/01b731Ft988w1ms1l9pjCbzbzxxhu88cYbZGRkcPnll3PRRReRl5cnaxJCTBCeAYnl5eW8+OKLVFVVUV9fT01NDXa7nbCwMKZMmcKCBQs4+eSTyczMxGAwfO85wVDBj729vXzwwQdUVVUxadIkNcBRCCG+D41G4zUezZgxgwcffJATTzyRl156iZKSEux2O3r9QAhLd3c3zzzzDM8//zw333wzd955pzpHUp5/kpOTf9BYJ4Q4tilrLTCwz6oUy3E6nWoi6Y/tdno4yjyoublZvaZWq1UT0GSsEWLseK7DGo1GNfnrSL9LZbwoLS2lu7tbLaajxHJ8n6TS+vp66urq1KKgUVFRBAYGyjxECCGEEEKMKkn+EkIIIcS4V1VVhcViYdasWd957OEW3gYnhZlMJqKjo4mOjiYvL099r7u7m9bWVhobG6mvr8dsNqsJYWazmebmZnbv3s2cOXN+/I0JIcaUy+Xy2kT08fEhMzOTzMxMFi9erB7X1tZGZWUl5eXl7Nu3j/LyciorK2loaKCtrY3u7m76+/vVLmFDJYUpCWHKBoMyJnV0dHhtMo5GAooQxxtlozAsLAxA/X1+8sknlJeXqxUgR1JzczPffPONGsQUGBjIpEmT5DcvxDigVKN2uVwEBQWpBSW+K+gADlardbvdGAyGowpY8DyHRqNRq94rn0lMTPR6XwgxPg0OPIqKiuKiiy7itNNO45NPPuHll1/mgw8+UN/X6XTodDrKy8t58MEHeeONNzjzzDP5yU9+wowZMwgNDQUOBi7K+CDE+OF2u9Fqtdjtdu655x5Wr15NTU3NIcdVVlayY8cO1qxZw5QpU7j22mv5xS9+gY+PzxEDGgfPGZQOG3v37qW6upoXX3yR7du309bWRnt7OykpKSN1q0KI41BycjK/+c1vOPvss/nLX/7C66+/jsPhQKvVqkUtnE4nTz31FNu3bycmJsbr80oyqlJgQwgxMXiul+h0Ovr7+wEwGAxDHq8kYgDD0g21trYWq9WqJonExsbi5+cnay1CjDHPddjAwMCj6rqlrMMWFxdjs9nUJHMlqfRoftPK81RZWRnt7e3qZ3JyctT3ZR4ihBBCCCFGiyR/CSGEEGLce+WVV/j222957733vld1Jk/flRSmvO/v74+/v78aVAkDC3rt7e00NzfT1NREVVXV91owFEIcm4YaS5Sqkp5JYSEhIUyfPp3p06d7HVdfX8+BAwcoLS2ltLSU8vJyamtraWpqoqOjg56eHux2u5oUpmxOKpSNzFNPPRWQ8USIkaL8ts477zxefvll2tvb0ev1FBcX88ILL3DfffeN2LWVecvWrVv58MMP1YCClJQUrzFFCHFsstvtVFdXo9FocLvdREZGqs8JR/rvtjLH2LlzJzAwbwgICCA7O/s7P6tQnlPKysq8jlfOIYSYmMLCwrj00ku59NJLqays5OWXX+bdd98lPz9fTWDv6+ujpKSEkpISXn/9dc4991yuuOIKTjvtNAlGEmIcUjp9XnbZZXzwwQfY7XY1qHmoNQolcet3v/sdb7zxBq+88gppaWmHnFd5FlHmMRqNhj179vDmm29SVlbG559/TlNTk/qMogRJzp49e7T/LxBCTGDKc01OTg6vvvoqv/71r7n//vv54IMPsNlswMA46HK52Lx5MzCQ9O5wONDpdGoBDQm6FmJiUTqlK/smv/nNb/jNb35DZGQkOTk5zJw5k5kzZzJ9+nSysrIOm/CldApTOg9+1/7x4AQPZR6kdHl3Op3qnEgIMbpcLhf79+8HUNdhk5OTgSOvpSrvFRcXexUD/j6xHJ4JZH19fepz1wknnOD1vhBCCCGEEKNBnkqFEEIIMe7t3r2bDz74gA8//JBzzz13WLvjDLXgN7hLmFarJTQ0lNDQUDIzM5k3b96wXFsIcexRNgkHU4KtPI+Ji4sjLi7Oa0zo6+ujtraW/fv3q0lhFRUV1NXV0dLSQmdnJ319fTgcDvU6SgKIJH8JMbIWL15MaGgoHR0daleMhx56iDPOOIMzzjgDl8t12DHgh1Aq+Hd3d/PGG2/gdDrx9fWlt7eXk046SRI4hBgH9u3bBxwMPkxOTkav12O329Hr9YcdL5Rgo7KyMmAgeCE8PJzU1FTg+/03v6SkxOv5ROYNQkx8ym8+JSWFFStWcMYZZ/Dhhx/y73//m3379uFwONDr9ej1epqamli1ahWrVq1i+vTp/OxnP2Px4sXk5uaq44RUrxfi2OZyubjzzjtZu3at+jyijANK0qdnwLNOp1PXKLZv385VV13F+++/T0hIiHpOZe20p6eH3bt38+2337Jy5UqamppoaWlRz6nT6dSECpPJhMPhYMaMGaP7f4AQYlxxuVwcOHCA0NBQ/P39MZlMRzzecz4CcNJJJ/Hkk09y1lln8dJLL7Fjxw6vxC4lEUTZl5k0aRJw+G5AQojxx263U1VVpc55lDmJ0+nEYrHw5Zdf8uWXX3p9Ji0tjalTpzJr1ixmzpxJXl4ecXFxQ+4Vu91uXC6XV/L84LGopKRETfCAg0kiQoixo6yj6vV6HA4HSUlJapfQoynCVV5eDqAW4lS6dn2fosJ79+5V947gYGEMWVMRQgghhBCjSZK/hBBCCDHu7dmzB4C77rqL2bNnExUVNaLBS0fqEqZsRAghji+HSwgZvIno4+NDRkYGGRkZnH322epxbW1tVFVVUVZWRmlpKWVlZdTW1lJQUMCcOXPUawghRtadd97JNddcg9FoxG6343K5+MlPfsLbb7+t/maHKwlMGRsefvhh3nvvPQC1qvUFF1zgdYwQ4ti0a9cu4GCQQF5eHnBo4KHnfMDtdqPT6aiqqsJms6kBCwkJCfj7+x/1tZUAptLSUuBggNK0adMAmTcIMZF5/r79/PyYO3cuc+fO5U9/+hNvv/02r7zyCh9//DEOh0M93u12s2vXLioqKli/fj0XXXQRS5cuJSYmRj3fcCe6CyGGx1dffcUDDzygdvpSOuA4HA58fX0B6O3t9eom7hnAvGnTJh5//HHuuOMO9X2dTsezzz7LJ598wtq1a70+69lVTAlsdLlcdHd3AzB37tzRuG0hxDhVUFDA9ddfz6xZs0hISGDhwoXMnz//Oz/nOf9ISEjgl7/8JSeccAKPPvoo77//vvrspCSAADgcDq666ipuuukmfvazn5GUlDRi9yWEGD3KOofBYFCfaZT/VRLBPOc6drudiooKKioq+Pe//62ex9fXl+zsbKZPn87s2bOZMWMGU6ZMISgoaMikMJfLhd1uR6fTsWfPHq8Ej1mzZgGy1iLEWNq5cydw8HeYlZUFHF33z+bmZurr69FoNDidToKDgwkPDz/qaw8u5DV4HVZiQ4QQ4v+xd99hctV1//+fU3dne7b3TbaSTiqhSxORIqARG4qoKO1GRBRQ1JsiIvLFghoUAcUCKkRpBryRKiSkZ3ezSXazvfc+uzNzZn5/7O+c7CYbSCDbktfjuvZid+bMKVzXfHLO5/MuIiIymZT8JSIiIjNeeXk5NpuNzZs3c99993HfffdNyQS8gqREZH/jTfibiaKjk8Li4uKIi4uzFgpMjY2NpKenT9bpihzzzjnnHE466STeeustXC4XwWCQwcFBPvWpT/GNb3yD66+/fkzF/PfLTBZ/6aWX+OlPf0pPT48VwPTRj35USZ8iM8TGjRuBfYmab775Jr/4xS9IT0+nsLCQnJwcYmJixr0f2L59O7Dve15QUACMBCwcasCA1+ulrq4Om81GIBDAbreTl5d3JC5NRGYQM8E0IiKCyy+/nPPPP5+NGzfy1FNP8corr1BbW2t1A+vt7eXll1/m5Zdf5lvf+haf/OQnueyyyzjnnHMUrCQyDbW0tPDYY48RCoWsAOhgMEhGRgYXXnghc+bMITs7m2AwSG1tLZs3b+btt9+moaEBwErceuSRR7j55pvxeDyUlJTwla98he3btzM0NITTObJUbD6j2O12/H7/mM/Pnj2b8847j4KCAutZRURkPK+//jpbtmxhy5YtAHzve9/j1FNPPaznHPP56qSTTuKkk07i5Zdf5q677uK1116ztjHHp5qaGn70ox+xbds2rrrqKlatWoXH47GSWnV/IzLzVFVVWb+PTlA3mQmgZvKFucYyuoOXYRh4vV62bdvGtm3beOyxx6zPJyUlMW/ePJYtW8ayZctYsmQJBQUFOBwOwsPDAaipqRlzjEWLFgEaU0Sm0ubNm63fHQ4Hc+fOfc/PmPcflZWVdHd3Y7fbMQyDefPmAfs6Ir8Xm82Gz+ezuhKaCanmfK7WcURERERkMin5S0RERGa0iooK/H4/YWFhDA8P8/DDD3Puuedy9tlnT8n5PPHEEyxfvpz8/PwpOb6ITH8HSxQdnRRmLlQq8Utk8gSDQdLT0/n2t7/NZz/7Wfr7+3E6ndjtdnp6evjJT37C9u3bufzyy1mxYgWpqanWgv/BgopGByGY25nf78cee4yvfe1r+Hw+q+Kkw+HgxhtvJDExcRKvXETeL7PirPldf+utt9i0aRPh4eFER0eTmJhIRkYGc+bMoaioiIKCAnJycigsLLS6F8PI2GF2DQsEAlYV63e7Z7DZbNTU1NDe3m519TGfQQ4nsFJEZr79v+/x8fGce+65fOhDH+KVV17hr3/9K0888QRDQ0PWNmFhYQwODvKHP/yB//znP5x88slceumlnHjiiaSmpgKHHgQlIhPnrbfe4o033rD+DgaDXHzxxdx2221WBwrY1/WipaWFV199lV/+8pe88847VnJEb28vTz/9NMnJyXz84x+nv78fwCp4Yd47GIaBYRgkJCSQmZnJsmXLuOKKK0hPTyc3N3fSr19EZh6zyIWZQGEGZpvPTIdi/66kZ511FosXL+bpp5/moYceYuvWrWM6lvb09PDkk0/y5JNP8pWvfIVvfvObVjC2iMwsoVCICy64gJaWFvbu3UtJSQk7duygrKyMvXv30trayuDg4JjPmHMi+yeFmQlh5vOSuU1bWxuvvfbamIRSgNzcXBYtWsTixYvZsWPHmEI7SvAQmXpm8lcwGMTj8VgJXO/2vTTHg7KyMgYHB61nnqVLl455/90cbB42Ly/Pet7SPKyIiIiITCZb6HBm2kRERESmmbfeeotTTjnFquxmGAannXYa//jHP45IZ47D8cADD3DTTTfxzjvvjAnAEBERkZnl/vvv5+abbwb2LR6a0yfR0dGcddZZfOxjH+NDH/oQOTk5h7Xvl156iT/+8Y/88Y9/tBLBDMPA5XLxiU98gj/96U/WgqKITG8pKSm0tbVZnfvGY7PZcDgcuN1uPB4PsbGxZGZmUl5eTlNTEw6HA6fTyaOPPsqnPvWpgx7L7Oxjs9kIBoM4nU7++c9/8qlPfQrDMAgEAnzyk5/kiSeeUMKGiBxwL9HZ2clf/vIXnn76adavX4/X6z3gMw6Hgw9/+MNcdtllfPKTn7SCtkVk6nz1q1/lscceIxAIEAqFOPXUU3nooYc47rjjMAzDmg/dX2dnJ6tXr+aVV16xOoadcMIJOBwO/vvf/+J0OrHZbGM6fDkcDs466yzOPPNM5s2bx7nnnnvA/YQCG0XkvSxZsoTt27fjcDiIjo7mlVdeYfHixUdsnuOVV17hoYce4oUXXmBgYMAqnAFYY2V8fDyf+tSnuOaaa6zAcBE5OtTU1LBr1y6Ki4spKSlh165d1NbW0tHRYd3XmPYvqDO6SNfoscNMot+fOddTWFjIrl27dB8kMsXMeVi73U5aWhobNmx4zwKa5hzpLbfcwo9//GNcLhd+v59HHnmEK6644pDmUM3v/vPPP8/q1asJBAIEAgEuueQSnnrqKavTuoiIiIjIZFHyl4iIiMxow8PDXH/99Tz88MNjum/cdddd3HbbbZNyDvX19Xz1q1/lX//6FwBDQ0O43e5JObaIiIgcef39/dxxxx388pe/xOv1WvcYZqKWKSEhgblz57JixQpWrlxJfn4+8fHxhIWF0dXVRW9vL+Hh4Xi9Xl588UVef/11duzYQVdXF06n06pUbRgG5557Lr/5zW/IyspS8pfIDNDZ2TmmS58ZND26Sr35Mx6zSqwZjDRnzhzmzJlDZmYmRUVFFBYWkpeXR05ODrGxsePu45577vVPxYcAAQAASURBVOG73/0uTqcTv9/Pvffey80336yAJBEBxiaN2u12hoeH2blzJ+vWrePpp5+mtLTU6gbmdDoJBALWZzMyMrj66qsnbV5FRMY3b948du3aZVWUf+655/joRz960O1DoRCBQACXy8WmTZu4+OKLaWxsHHPfEQqFrKDH+Ph4FixYwMc+9jEuvfRS4uLirPsO3U+IyPsRERHB0NAQNpuN3NxcNm/eTExMzBE/zjPPPMPtt99OcXExsK/Dj/kMFhERwcKFC/nhD3/IGWecccSPLyITz3yeAQ6a8A7g9XqprKxk586d7Nixg9LSUvbs2UNTUxPd3d0HdPYZnRQ2+r3RXcLMZA6/38/q1at58sknleAhMoX6+vqIjY21/q1funQp77zzDnBgoudo5jPQxRdfzDPPPGM9B23bto1FixYd0jqMmSB23333ceutt+JwOPD7/dx9993ceuutGhtEREREZNLp7lNERERmtLCwMG688UbefPNNdu3aZQVSP/jgg5x55pmsWrVqwo4dCAR47rnnuPrqq2lpaQEgKysLt9utAAkROSzmmGEmkc6dO5fMzEwtGIhMkaioKO69914Mw+Chhx5icHAQl8tFKBTC6XQSCoUIBoN0dHTw5ptv8uabb1qftdvtpKenM2vWLPx+P9XV1QwNDVkLk4C1DzPx66yzzuLee+8lKytL9xAiM8TQ0BAXX3wxzc3N7N69m66urnETvRwOh1VB1hw7zO3MAOxQKMTevXuprKzEbrfjcrkIDw8nOjqahIQEMjIyyM3NpbCwkMLCQmbPnk1BQQHl5eVjApXM7sNKHhU5Oo0OfDSZ48noewfzXmL/+4mwsDCWLFnCkiVLuPXWW3nmmWd49tlnefnll6mtrbW2dzgcNDQ0UFJSAqBugiJTpLGxkT179lhdJ9LT09818QtGxgeXy0UwGGT58uUUFhZayV/mfYfdbsfv93PFFVfwiU984oB9mgUq9EwiIoeroaGBoaEha9zKyMg44olf5n3JRRddxDnnnMMf/vAHHnzwQUpLS8c8Zw0PD7Nhwwa2b9/OGWecofsZkRlovHsRcw7FfDay2+14PB7mz5/P/PnzWb16tbVtR0cH5eXllJSUUFxcTGlpKVVVVbS2tjIwMDBmv+Z90uiiX+ZzlznXIiJTZ+fOncDIfEUwGKSoqGjcMWL03Kz5DBQMBqmurgawit4UFRVZ27wXcxuzA6B5P7F06VJg/LFKRERERGQiKZJQREREZjTDMJg7dy7XXnstN998M0NDQ7hcLpqbm/nJT37Co48+SnR09BE7nhmo3dDQwI9//GN+8YtfABAeHs7Q0BCLFy+2thMROVTm4sCtt97Kjh07gJEFhcbGRlJSUqby1ESOSea/9/fffz+nn346t9xyC7t27bLeNxf8zMDq0QuJgUCA+vp66uvrx2wzOqnLZrNZC40XX3wxP/7xj8nPz7e2F5HpLz09naeffppQKER3dze1tbWUl5dTXFxMSUkJO3fupLq6muHh4THBQzAyBozuEmYGLhmGYf0MDQ3R3d1NXV0d27dvx+Fw4Ha78Xg8xMbGkpKSwp49e4B9gQ3z58+39i8iM5NhGAckXYzuEri/0ZXpDcMgLCwMu91OV1cXra2tOJ1O4uLirCSSzs5OK6G9trbWCnw0kz1g3xiycuXKMccQkcm1Y8cOgsEgLpcLwzA45ZRTgENLyLTb7YRCIc444wxeffXVA7paPP7443zyk5+0gidHf8/1nReR92t0Fy7DMKx5jiNZ5Gb0+OfxePjiF79IYWEhjzzyCC+++CIdHR3Y7Xbcbjder5dTTz0V0DOSyNHiYM9FZrEdcxu73U5CQgIJCQkHFAmtra1l165dFBcXU1xczO7du6mpqaGzsxOfz2dtZ+5PCR4iU2/r1q3Avn/Py8vLeeyxx0hJSaGwsJDMzExrPmR/tbW1tLS0WGs0GRkZhIWFHVLXL9j33S8vLwf2jQ2LFi0a876IiIiIyGRR8peIiIjMaA6Hg1AoxLXXXssLL7zAv/71LwKBAE6nk6effpoLLriAK6644ogdz2azsW7dOr773e+yZcuWMUHcAAsWLBjzt4jIoerv78fn8+FyufD7/QBK/BKZIma1V4CLLrqIVatW8eSTT/LEE0+wfft2BgcHrUACc9tgMDgmOHt0df3R1fMDgQDBYJCUlBSuuuoqvvWtbxEZGTnFVywi75fNZmPWrFnMmjWLxYsX84lPfAIAv99Pe3s7VVVVVlBRSUkJu3fvpr6+3koAHc1ut49JCjO7hJmJpYFAgMHBQTo6OqisrLTGGcMwiI6OJjk5eVKvXUSOPDOgua+vD8MwiIuLw2azUVFRYSVzRUVFUVNTQ2VlJZ2dnYSFhTE0NERdXR2VlZUMDAwQExPDwMAAlZWVtLS0EBsbS3h4OF1dXWMCGt1ut/XsYTLvgcwgRxGZGps2bbJ+t9vtrFix4rA+b7PZyMnJAUY6D/v9fmJiYrjtttv49Kc/DWB1AhMRORL2H7fmzp0LTGyhPLfbzRlnnMEZZ5zB73//e+6++24qKirwer3Avs4eGutEjm5mkZ39mfO15jZ2u53s7Gyys7P58Ic/bG3n9XqprKxk586d1vzN3r172blzp7Xuq3FEZOqY9xjm93nr1q1cd911VpGspKQksrKyyM/Pp7CwkIKCArKzs8nKyqKxsZH+/n4r+ctM2vL5fDidzjFrOQfT09NjdVQ2DAOPx0NaWtrEX7iIiIiIyDiU/CUiIiJHjQcffJAVK1bQ1dVlVV268cYbOeuss8jKyjoix/jOd77DL37xC2uSMBQK4XK58Hq9xMfHW4sFSv4SkUNlJotUVVXR0tJijV/mouKRrI4rIodu9L/lycnJXHPNNZxwwgmsW7eOf/7zn2zduvWgyRtmktf+AoEAWVlZnHvuuXzta19TULXIUczlcpGWlkZaWhonnXSS9frg4CBNTU1UVFRYQUWlpaVUVFSMeY4ZzeFwWEFMZkKpGcDkcDgwDIPjjjsO0H2DyEy1YcMG/vrXv9LX12clZNXV1VFRUUEgECAuLo6BgQGqq6vx+/243W7cbjf9/f3vul8zwLGnp4eenh5sNhtut9tKKvX5fOMmtIOK24hMtdEBjmFhYcybNw84tO+keT/Q1dUF7Ev+WrlyJddee621X32/ReRI2rx5MzAyvrjdbiv5a6LHGnPM+8IXvsBnPvMZ1qxZw49+9CPa2tqIiorSeCdyDDvY/MjopDC73Y7H42H+/PnMnz+f1atXW9t1dXUxa9asSTlXETk4s7uo+b01DIPBwcExRbI2btyIw+EgLCyMyMhI4uLiyM7Opqenh8HBQWtu1XyuCgsLG/dYo4v8mXOvZvEds7upOQ97KF2ZRURERESONCV/iYiIyIxns9kIBoPMmTOHH/7wh3zta1/DbrfjdDrp6enh2muv5ZlnnvlAgZCVlZV8/vOf56233hpT/clut+Pz+Tj77LP5wQ9+wIknnmidk4jIoTAXEXbv3s3AwIA1fixevNh6X0SmnsPhYOXKlaxcuZLvfe97NDc389Zbb7F161ZKS0uprKyktraW7u5ugsEgkZGRJCYmEgqFCIVCnH766ZxzzjksWrTI+n6LyLEnIiKCvLw88vLyOPfcc4GRf+u7u7upq6tjz549lJSUUFJSws6dO6mqqmJ4eBjDMA7Yl9PpxO12Mzg4yLJlyyb7UkTkCPrZz37GE0888Z7bmcmgPp8Pn89nzX3snxRqFqsJBoNWMJLZOd3s/GVWxw8EAgc8c3g8HuLj4yfkWkXk0JgBjsFgkKioKAoLC4HDS/7atWsXsG9e4aMf/SiRkZEKUhSRCbFjxw5gZAyKjo62um5N9FqJueZjFur72te+RlFREXv27LHOR2OeiIw23lrx6EIYZoEMJX6JTA8bN24ERorrHaxIlvnj9/vp7++npaXFuhcIhULW3OpTTz1FRUUFGRkZFBUVUVRURG5uLllZWYSFhY07PpSVlY1Zv12yZIm1XxERERGRyabkLxERETkqmBNxX/ziF3nhhRd45plncDqdOBwOnnvuOR566CG++tWvHnJwg5mM4ff7+etf/8rVV19Nf3+/FSxlTiY6HA5uu+02brjhBpKSkib6MkXkKGQuDuzcuRO/34/TOfKYtnz5cuDg1SlFZGqlpqZy6aWXcskll+Dz+fB6vQwNDREIBGhpaaGlpQWn08ncuXPJysrC5/PhdrsBVYQUkbFsNhuzZs1i1qxZLFq0iE984hMA+P1+2tvbqaqqYteuXRQXF1NSUsLu3bupr68nEAhYXQYXLVpk7UtEZp7t27cDWPcKZmBSKBSyuorabDYMw8AwDOx2u3UvYSZzAVYy12ijP2duEx4ezsDAgNVVLD09nWAwSG9vLzExMVxwwQWAugmKTJVAIEBVVZVVbT45OZns7Gzg0P6tN7fZtm0bMHJPER4ezqpVqw55HyIih8MwDKqqqoCR+5ikpKTDGreOBPM4LpeLD3/4w3z4wx8G0PyLyFFm/3nV0UUwPgizOIaITC/9/f185CMfoa6ujr179zI0NHTQIlmjE8LNYjiA9Vxls9morq6mpqYGh8OBy+UiIiKC2NhYkpKSyMrKIi8vj6KiIgoKCsjOziYrK4vKykoCgcAB67d6rhIRERGRqaDkLxERETmquFwubr/9djZs2EBLS4s1yffjH/+YM888k4KCAmty72DM96uqqrj77rt55JFHrCpvZtAUwNy5c/nhD3/Ixz72MUBBUSLy/pjj0a5du8ZUiVu6dOlUnZKIHAabzUZYWBhhYWHWa5mZmQds53a7rXsMBRKIyKFwuVykpaWRlpbGSSedZL3u9XppamqioqKCXbt28cYbb3DGGWcACjoQmanKy8utYjN+v9963W63j0ncMl8zK1rDSEdBj8dDd3c3hmGQnZ1t3Yv09/eTnJxMQUEBKSkp+P1+HA4HmZmZFBYWEhUVRV9fH2FhYeTl5ZGUlITX67XOQXMcIlOjvLwc2JfQmZOTg8vlIhgMYrPZ3vPfe/N5o6ysDBiZs0xNTaWgoADQd1tEjjxz3HI6ne9r3DrS3msNSERmnv3nVQcGBoiMjHzP+5qpGodE5MiIiorihRdewOfz0dHRQXV19QFFsurq6g4ohAOMKZxjFvc1E8PMolper5eOjg4qKyvZuHEjDoeDsLAwIiMjiYuLIzMzk4qKijHrt2bnL40rIiIiIjIVlPwlIiIiR51ly5Zx/fXX893vfteqkl1VVcU999xjJXK9G5vNxt///nf+93//l9LSUhwOB8FgEKfTic/nw+l08tnPfpbbb7+d3NxcAOs4IiKHyxw79u7dC2AFds6fPx/Q4oHI0UTfZxE5EjweD7m5ueTm5vLhD3+Y//mf/5nqUxKRD6CmpsYKUjIMg8TERCIiIujp6cHn81FUVERGRgYOh4PBwUFSU1PJy8sjLS3NSgBLS0uzkrm6u7txuVzMnj2b8PBwYF9g5KHweDx4PJ6JuVgROSRbt24F9s0X5Ofnj/l7PGZAtPnftrY2ent7cTgcGIZBWloaCQkJE3/yInJMMsctc94jLy8POLRxayJo/kXk6GOz2fj3v//Nzp076e/vp7q6mvr6evr7+0lJSWH27NnMmTOHrKwscnJyyMzMJCEhQWu3IkcJt9ttFck68cQTrdcHBgZoaWlh79697Ny5k5KSEkpKSqioqKCjo8OaNxnN4XAckBRmGIZVaMfv99Pf309LSwt79uwBOKBAMKiohoiIiIhMDSV/iYiIyFHpm9/8Js899xzr168HRibkHn/8cS644AIuvfTSA7Y3FxoDgQA33HADv//97xkcHLQqb5uJXzk5Odx6661cccUVuN1u6/NaTBSRD6K1tZXm5mZsNhvBYJBZs2Yxa9asqT4tERERERGZYIZhcOedd7J06VKGh4cxDMPq0BMZGUl7ezsul4v09HTrM4FAAKdz/OWdrKws63dzrsNM/DIMw+psbr5v0ryGyPSxefNmYF+xqcbGRtatW0d4eDipqanExcURHR1NRESE9d3d/78lJSXAvg6CZiJGMBhUkKKIHHHmuAUj405DQwPPPvssERERpKamMmvWLGJiYoiIiLDGIN17iMjBjE4Oraur48knn+TRRx+lq6uL5uZmazsz8X1/ERERzJ49m0WLFnHmmWdy8sknk5+fj8vlAkaei8zEDxGZ2SIjI60iWeeccw4wMoZ0d3fT0NDAnj17rISwnTt3UlVVhdfrHdNh3eR0OsfMlwSDQWs7swt7UlISMTEx6jIqIiIiIlPGFhrvSVhERETkKLB582ZWrVplVWIKhUIUFRXx5ptvjlvptri4mMsvv5wdO3YAWAFR5qTeBRdcwO23386KFSsm9TpE5OhlBl299dZbnHfeeQwODmIYBqeccgqvv/66grJERERERI4BhxI0ZAYejQ5S3D+Z61D3JSLT24c+9CFef/11nE6n1RkwPDycpKQkMjMzyc7OZs6cOWRnZ5ORkUFKSgqJiYnExsYSFhZGVFQUDzzwADfddBMul4tAIMAPf/hDvv3tb+P3+3E6nRonROSIOuOMM3jttdfGjFsul4ukpCTS09PJyclh9uzZZGdnk5mZSWpqKomJicTFxREVFfWeXUd1fyNy7DC/716vlyeeeIK7776byspK632zCIbZocdut4/p4GO+PlpsbCznnnsun/vc57jgggsm72JEZFrx+/20t7dTW1tLWVkZJSUlFBcXs3v3bmpra8f9jDnGOJ1OvF4vZ555Jv/3f/+nJFIRERERmTLq/CUiIiJHrWXLlnHnnXdy2223ERYWxvDwMLt37+a2227joYcewu/343K58Pl8PPzww3zjG9/A5/ONCZoyDIPIyEi+/e1vc/311xMbG6uFRhE5YsxaHLt27cLr9VoJp0uWLJniMxMRERERkclizjEEg0FsNtu4cw42m+2AwKLxAo00XyEy8+3atcv63ZynHBoaoq6ujrq6Ot5++23r/ZiYGFJSUsjKyiInJ4fs7GyWLFnCM888A4zMO7jdbvLz8wGsjhfj0ZyniLxfO3fuBMbey/j9fhobG2lsbGTTpk3WtpGRkaSkpJCZmTkmKSwjI4Pk5GTi4+OJjY0lMjISt9sNHPz+RuOWyNHHZrMxODjIN77xDX7zm98AHFDsAvaNN6O79Jg/5uvmez09Pfz1r3/lxRdf5PTTT+faa6+1OgSJyLHD5XKRlpZGWloaJ5xwgvX64OAgzc3NVFZWUlpaSklJCaWlpZSXl9PR0UEwGMTv9wMwf/58gHG7DoqIiIiITAYlf4mIiMhR7Utf+hIvvvgir732mpUA9uSTT3Luuedy6aWXUlpayh133MHf/vY37Ha7tTBpdvtatmwZd9xxB+eddx6AuvCIHKWmOlCgrKyMQCBgBWEtX74cUOCmiIiIiMixRPMNItLT00NrayuA1T0HRpI9zblLGJmjNAyD3t5eent7KS8vt7YdHexsGAZOp5N33nkHp9NJXFwcycnJzJo1i+joaDwej7XteHMQUz1fIiLTX29vL21tbdjt9jHJF+ZYZI4hZkeegYEBKisrx3TyAYiPjyctLY2srCxmz55NTk4OWVlZpKWlkZSURHx8PNHR0URERFgJ8Bq3RI5Ol19+OWvXrh0zrtjt9jFdvcy/R98vmcz7Jti3rhsKhejp6eGZZ55h7969fPvb3+Yzn/mMnsFEhIiICHJzc8nNzeXss88GRu4nuru7aWhoYM+ePezatYv//Oc/fOQjHwG0fisiIiIiU8cWUikCERERmYFGV297r6qPL774Ipdddhm9vb243W58Ph8f+chHWLFiBU899RQ7d+7E4XBYiweBQICwsDCuuOIKbrnlFnJycsbsT0SOLmVlZbz++uvMnz/fCoCKjIwkLCxs3Er6R5I5rlx00UU899xzOJ1OAoEAO3bsYMGCBRN6bBERERERmf7MJRzNR4gcG2pqarj66qsZHh6msbGR9vZ2enp6xg1shrFJYWZihVnUynzNFBYWRnJyMhkZGWO67WRmZpKSkkJCQgJxcXFERUURFhY2KdcrIjPf+vXrOemkk3C5XPj9fpzOkfrD7zVumUKh0EG3dblcJCYmkpGRQXZ2tpUUlpmZSWpqKomJida45fF4jvzFicik6u/v58c//jF33XUXbrfb6rQDI2NFREQEy5cvJyYmxrqnmTVrFi0tLezdu5fdu3dTXl7O0NDQAfsevZ5sJpD99Kc/5X/+538m5+JEREREREREjgAlf4mIiMhRw0wIAw5ICrvpppt44IEHAMZ0+AoEAthsNqvqWzAYpLCwkFtvvZXPfe5zVlKYgqxEjl433ngjP/vZzwgPDycpKYk5c+ZQUFBAUVERubm5ZGdnk5iYSGxsLB6PB7fbfUSrQRqGwZIlSygtLQVGxrKhoSHcbvcRO4aIiIiIiEx/6jYuIqFQiNbWVjo7O2lqaqKhoYGGhgbq6+tpaGigqanJer+3t3fcfZhznfvv1wx03l9MTAwpKSlkZWWNSQrLyMggPj6e448/XmOTiBzUr371K6677jorUePMM8/k+OOPp7y8nNbWViuJtb+/n6GhIcYLTzHHrYMls+4vMjKSlJQUMjMzDxi3EhISWLZsmcYtkRlo7dq1fPzjHwewCuUBzJo1i+uvv56zzz6bwsJCkpOTgZEkU4fDMWYNNxAIsHXrVjZs2MDbb7/N+vXrqa+vJxAIWGvBdrsdwzAIhUKsWbOGq666avIvVkREREREROR9UPKXiIiIzDidnZ08+uijzJ07l6ysLNLT00lISHjXz9TV1XH++edTUlIypuqtzWbD4XBYCwiXXnop3//+91m4cOGEX4eITA/nn38+//rXv3A4HOMGFERGRpKZmUleXh6FhYUUFBSQm5trjT3R0dGEh4fjdDoPK1HUTCytqqripJNOoq2tjWAwSHZ2NtXV1Qr8FBERERE5RowuOlNfX8/g4CAdHR10d3fj8XhIS0sjIyODqKioQ9oX7OsUFgqFMAzDKoKjZwyRmWlwcJDOzk7a2tpobGwckxDW0NBAc3MzbW1tdHV1jdvtAkYKYo0OkDYTK8ZbKrbb7cTGxlJYWMjbb789odcmIjPblVdeyWOPPYbT6cRut/Pkk0/ysY99jJ6eHtrb26mrq6Ompoaqqirq6upobGwck8Q6MDCAz+cbd9+jE8JgXyLruyWzFhYW8s4770zY9YrIkWU+CxUXF/ONb3yDl19+mbCwMIaHhwH4xCc+wQ9/+EPy8/OtzxiGgcPhGLMfc1wY/bzT0dHBm2++ydNPP83f//53vF7vmPeDwSCzZs3i+eefZ9WqVRN5mSIiIiIiIiJHhJK/REREZMZ55plnuPjii8nLyyM6Oprk5GTS0tLIycmxqjxmZWWRmpo6JjDq3//+N6tXr6avr896zbwVio+P5+abb+aGG24gPDx80q9JRKZOamoqra2tVgCUufhnBkkeLJggISGBnJwc8vPzKSoqIj8/n5ycHFJTU5k1axaRkZG43W6cTue4nzeTu1588UUuvfRS/H4/gUCA888/n2effXbcBUwRERERETm6mMGOFRUVPPHEEzzzzDNs2rQJm81GYmIis2bNIisri1WrVrF69WoWLVoEjN8lbHQSWU9PD9HR0Ur2EjnK9fT00NHRQUtLy5iksPr6ehobG2lpabG67pjFr/bncDjGJFeYhXEMw2DevHmUlJRM2vWIyMyzdOlStm3bhsPhIDo6mn//+98sW7bsoNt3dnbS2NhIbW0t1dXV1NTUUFdXR1NTE+3t7XR1ddHX14fX633XcctMbIeReyBzLtcctzS3KjIzmM81t9xyCw8++CCDg4NW169rr72W2267jbS0NGD8pK9DVVFRwS233MLTTz8NYN37GIbBeeedx/PPPz/meUpE5P0YHYar8UREREREJsL4UYgiIiIi09iGDRsA2Lt3r/WazWYjPDycqKgoYmNjSUhIICUlhYyMDLKzsykoKCA5OZlTTz2V559/3lo4cLlcnHXWWdx6662ceuqpAAQCAWvxQJNyIke3oaEh+vr6cLvd71phdrzK2B0dHXR0dLBlyxZrW4fDQVpaGnPmzKGgoICioiLy8vLIysoiKSmJ2NhYPB4PbrfbWtTctWsXw8PD1rizfPlyQOOPiIiIiMjRznwmeOGFF7jrrrtYv349AC6XC7/fT1tbG21tbezZs4eXX36Zu+++m+uuu44f/ehHREREHBCcaBgGL7zwAiUlJezYsYPq6moMwyAvL48TTjiBE088kXnz5hETE6PARpFp6gtf+AJZWVkcd9xxLFy4kMWLF495f//ufrGxscTGxpKbm2tt4/f76e7upq2tjZaWlgMSwpqammhpaaGzs5O+vj4MwzigE7rb7cYwDGu+VETkYHbv3g2M3NfEx8db3XnM8Wr0uGWz2YiPjyc+Pp4FCxZY+zDvexoaGqyEsOrqahoaGmhpabE6og4MDOD1eg8Ys2Dk/skwDE4++eQxxxWR6c1ut+P3+/nDH/4wJvHrYx/7GHfffTcxMTHWtu8n8cscC/Ly8vjtb3/L3Llzue+++/D5fNb91H/+8x/Wrl3LJZdcouckEflANH6IiIiIyERT8peIiIjMONu3bwcgIiICv99vJWJ4vV68Xi9tbW1UVFQAIwsBEREReDweMjMzreQOs5PP4sWLufbaaznhhBOsinHjdekxK0eOriYpIjPf8PAw3/nOd+jo6KC2tpbm5mba29vp7u6mv78fr9dLMBgct/vX/pWxg8EggUDACqh64403rG0jIiLIzMwkPz+fgoICCgsLyc7OZtGiRWzcuJFgMGgtXJqVcTXWiIiIiIgc3ex2O8XFxXzta1+jvr7eeiYIBALYbLYxXS3MeYkHH3wQr9fLz3/+czwej7Wv//u//+MXv/gFL730EsPDwwDW5zZv3sxf//pX4uPjOe+887jppps4/vjjARTcKDKNNDQ08Pjjj+PxeHA6nZx99tk89dRTY7YZ7/u6f3V5l8tFUlISSUlJzJs3z3pvcHCQrq4uWltbaWpqspLCGhoaaGhooLm5mdbWVrq7u6051NHJGSIi+2tsbMTr9eJwODAMg4yMDGJjY4F949XBxi1z7LLb7bhcLtLT00lPT2fFihXWdgMDA7S0tFBbW2slhNXW1tLY2EhrayudnZ309vYyMDBg7c+8x9H9jcjMEAqFeOqpp2hubsZutxMIBIiJieHee+8lOjr6A+9/9Fgwa9Ysvv3tb9PR0cGaNWusZ67h4WFefvllLrnkkg98PBE59pjzKlu2bKGiooKMjAzi4+OZM2cO4eHhU316IiIiInKUsYVU8khERERmmLi4OHp7e8e8Nl5nnoMlbIxedMzMzCQtLY3IyEiysrLIzs5m9uzZ5OTkkJ2dTWpqKlFRUQc9l2AwqIQwkaPE0NAQLS0t1NXVjQkmaGhooLW1lY6ODiuYYGhoaNx9mIuFdrsdGJnwNwxj3LEIICEhAZ/PR19fn1XRsrq6muzs7Am7ThERERERmR56eno466yz2LJlCw6Hw3puONiyjZnMZbfbWbt2LRdeeCEAjz32GN///vepq6sD9nXsMecs7Hb7mDkSj8fDHXfcwU033TQJVykih2rdunV89KMfteYHrrjiCh555BGrS+Dh2j8p7GB6enro6OigpaWFxsZGGhoaaGxspLi4mP/3//4fRUVF7+t6ROToZ45bZtfSK6+8kocffvgDjVuHUoSvs7OTxsZGamtrrU5hjY2N7Nixgz/96U8sWLBACe4iM0Rrays33XQTf/rTnwAIDw/nqquu4qc//en7HksOxhwXBgcHOe2006znMMMwyMrK4tVXX2XOnDlH7Hgicmwwx6qrr76atWvXkp6eDsCjjz56QCdnEREREZEPSp2/REREZEYJhULcd999VFZWsnv3bvbu3UttbS09PT2H1JknEAhYC4ihUIja2lpqa2uBkSCI8PBwIiMjiYuLIyEhgZSUFDIyMsjJySEnJ4fZs2eTkZFBcnIybrf7iC46iMjkM8cCu91OeHi49V0/5ZRTrG16enpoamoaU2G2rq6OxsZG2tra6Orqoq+vj8HBQfx+P4FA4IDj7J+gGgqFCAQCdHR0ACPjTyAQwO12K/FLREREROQYEAqF+Pvf/86WLVtwuVwYhkEoFMLhcJCQkEB2djbJycl0dXVRWVlJS0uL9ewSDAZ54IEHuPDCC/nvf//LV7/6Vfx+vzUHYnbsMRO/AGtuJBQK4fV6ufPOO4mNjeXLX/7yVP5vEJFRNm3aBOybQzC7br3fOp4HS3ow92e+HxsbS2xsLLm5udY2gUCArq4uEhMT39exReTYsGXLFut3u93OcccdB3ywcWv/sWt0lzDz/fj4eOLj48d0JwwEArS0tFgB10r8EpkZysrKrHsggMLCQj75yU8CR/57bD4PRUREcNNNN/H5z38ewzBwOBzU1dXR09NzRI8nIscGc95l586dtLa20traCkB8fPxUnpaIiIiIHKWU/CUiIiIzis1m40tf+hKBQACv10tvby8dHR3U19dTWVnJnj17KC8vZ+/evTQ0NDA8PIxhGAfsZ3RSWCgUIhgMYhgGXq8Xr9dLe3s7FRUV1rYej4fo6GhmzZpFYmIiqamp5OTkEB8fzxVXXEFKSspk/68QkSNg/4CC8YIJzCAoM3gBRqq4tbW10dDQMKbCbH19Pc3NzbS3t9Pd3U1/fz9er/egnQgdDgcwsjDg9/spLCy09q/kUhERERGRo9eOHTv44x//COx7HsjMzOSaa67h61//OjDS1aKtrY2tW7fyxz/+kf/85z9Wh7DGxkbKysr4+te/jt/vtzoFGYaB2+3G5XIxMDBwQHEK8zmnt7eXO++8k5NPPpm5c+dO9uWLyDg2b94MjMwJuFwuax5iIgKf97d/lzCn00lSUtIRPa6IHH02btwIjIwhbrebefPmAUd23DpYF7DR87h2ux2n00lGRsYRO66ITI533nmH6upqqwPXaaedxpIlS4CJSeI093nRRRdRVFREWVmZ9Qz16quvcvzxxx/xY4rI0a+vr4+mpiZrzsbj8ZCVlTXVpyUiIiIiRyElf4mIiMiMY7fbcbvduN1uYmNjycrK4vjjj8cwDHw+H4ODg/T09NDa2kptbS0VFRVWUlh1dTVNTU0YhnFAUtj+nXnMZA3DMOjv76e/v5+mpiZre4/Hg9fr5bOf/eykXr+ITJxDDSaw2+2kpKSQkpLC0qVLre2GhoZoaWmhvr7e6hJWU1NDY2MjLS0tdHZ20tPTw8DAAENDQ9Y4ZP538eLF1vFEREREROTo9d///pfi4mIAhoeHSUpK4ic/+Qmf/OQnCYVC2Gw20tPTSU9PZ/HixVxxxRVcc801rFmzBhh5Lrn99tvZvHmzlfiVmJjIpZdeSm5uLtnZ2dhsNhobG9myZQsbNmygoqLC2rfdbqeuro777ruPRx55ZCr/V4jI/2/Hjh3AyJxkdHQ0RUVFwOR0r1GHHBF5P8x7GXPcMgtbTda4pbFLZOZ76623GB4etrohX3TRRXg8ngk/rtvt5oILLmDnzp24XC58Ph8lJSWAivOJyKEz51iqqqro6OiwXsvLy5viMxMRERGRo5WSv0REROSoYXbo8ng8JCQkkJuby6pVqwgEAgwPD9Pf309nZydNTU1UV1dTUVHB7t272bt3LzU1NfT09By0M4/ZJQz2JYV5vV5sNhuZmZmTfakiMsneKynMfD88PJycnBxycnI4+eSTre16enpoamqirq6O6upqqqurqauro6mpidbWVvr7+6mqqmLlypUAWlgUERERETnKrVu3js7OTitx67rrruOjH/0owAHdiQ3DwOl0cscdd7B582Y2btxIVVUV1dXV2Gw2AoEAZ599Nt/97nc57bTTxhzH7/fT3t7Oxo0bWbNmDevWrbMqUQO88cYbtLS0qKO5yBQzDIOqqipg5HuflJREdnY2oMQsEZmegsEglZWVwL5xKycnB9C4JSKHzuwgaBgGNpuN5cuXT/gxDcPA5XJZY5bD4cDv91vPSCrOJyKHKhgM4nA42LVrFwMDA9Y9kNnBUERERETkSFPyl4iIiBz1nE4nTqeTyMhIUlJSmDt3LqFQCL/fj9frpa+vj/b2dhoaGti7dy/l5eXs2bOHyspKGhoaxnTnMZmJGYsWLQJUBU7kaDA6+fNQK8eOt93oLmHm+7GxscTGxnLccceNOZ459tTX11NcXMwFF1xgfU5ERERERI5eW7ZsAfY9h3z+858nKirqgO1sNhtOpxPDMEhMTGTp0qVs3LgRn8+H3W4nFAqxYMEC7rrrLlauXGntz5yjcLlcpKWlcdFFF3HhhRfy+c9/nj/96U+4XC4Aamtref7557nyyis1tyEyhfbs2QNgJYTm5OTgcrmsgjMiItPN7t27gZHuOYFAgNmzZ2vcEpHD0tjYSGNjIw6HA8MwmDdvHnFxcRN+XPOZJyEhgcTERNrb24GR8WxwcJCIiIgJPwcRObqUlZXh9/txOkdCcScjkVVEREREjk1K/hIREZFjks1mw+1243a7iY2NJTMzk+OPPx7DMPD5fAwODtLT00Nrayu1tbXs3buX3bt3U15eTnV1Ne3t7QSDQWbPng2oCpzI0eBIBTm+V5cw81h2u53k5GSSk5NZsmQJF1544RE5voiIiIiITG/V1dVjghwXLFhgVZ0/GIfDAcDKlSt59NFH8fl81ntf//rXWbZsGXDw55pAIIDT6eQnP/kJmzdvZteuXYSFhTE8PMzg4OARujIReb+2bdsG7CsGU1hYCOyrJC8iMt2UlJQAWPck5r2Mxi0ROVQ7duwARp5hDMNgxYoVwMQX3DTvt5KSksa87vV68fl8Sv4SkUNmjie7du0aEy+ydOnSqTolERERETnKKflLREREZBSHw4HH48Hj8ZCQkEBubi6rVq0iEAgwPDxMf38/nZ2dtLS0UFJSwpIlS4AjlzQiIpPHMAwcDgebN2/md7/7HV6vl8WLF5Obm0tRURH5+flHNFDhvZLCDrXbmIiIiIiIzGzbt28HsJK/Vq5cCex7Rnk3WVlZ+Hw+67ORkZF86lOfes/POZ1OgsEgKSkprFq1it27dxMIBACoq6tjaGiI8PDwI3B1IvJ+bN682frd4XAwd+7cKTwbEZH3lpqayle/+lX6+/tpbGzk9NNPn+pTEpEZZtOmTdbvdrudBQsWAJNXcDMrK4vOzk7r78HBQSIjIyfl2CJydDBjRCorK4GReR2AefPmTdk5iYiIiMjRTclfIiIiIofA6XTidDqJjIwkJSWFuXPnctppp1kTekrYEJl5HA4HPp+Pb3zjG7zxxhs4HA6eeuopDMPguuuu46abbiI5OXnCz0NJXyIiIiIixxYzyDEUCuFwOFi8ePF7fsYsGDEwMADsSxw79dRTiYiIOKTq+Ob755xzDo899pgVlNTR0YHL5foglyQiH5CZ/BUMBvF4PFawoOYLRGS6OvXUU1m1ahXd3d10dHSQlpYGoK5fInLIRj8XhYWFTdr9j/lsFQqFCAaDOJ1ODMPAMAw9F4nIYWtpaaGpqQmbzUYwGCQhIYG4uLipPi0REREROUop+UtERETkMJgLAqBuXyJHg29+85u89dZbwMj3u7+/ny9/+cvccccdhIWFHfHjmRUrFbwlIiIiInLs2rJlC7AvyNHs8PNuzwnBYBCHw0F5efmY182uYYdTHT8rKwsYKXQTCATo6urC4XCMmfMQkclVWloKjHyXY2NjmTNnDqD5RxGZ3lwuF0lJSSQlJU31qYjIDFRcXAyMPOtERUVRWFgITF7y165du4CR+61AIEBCQgJwaB2ZRUTMIjwVFRV0d3djt9sxDENdv0RERERkQin5S0REROQwjF5wOJSq2iIyfW3dupUHH3zQWsRzOBysXr2a3/zmNxN2zNFjiBLBRERERESOTaODHKOjo8nPzwfe/dnAfH4wE8eCwSBhYWFW17BDea4w5zHa2tqAfUklZkVqJX+JTI3e3l7a29ux2+0Eg0EyMzPJyMiw3g8Gg9b3U93DRWQ6CYVCVidRh8Oh8UlEDlkgEKCqqsrqwJWUlERmZuakHNt8ttqzZ8+Y17Ozsyfl+CJydDDHkrKyMoaGhqzkr2XLlk3xmYmIiIjI0UzJXyIiIiKHaHBwkJ6eHuLj4wkLC1Pil8gM1tjYyC9+8QsAazJ+1apV/PrXv56wY7722msYhkFubi6ZmZk4nXocExERERE51gwPD1NfX4/NZiMYDJKSkmIFGb5bwLQ5B7F9+3ZgJBkkJiaGoqKi9/ysyQxM2rt375i/zeQzEZkaO3fuBEYSJ4LBIDabjfLycuLi4khKSjroHOTopDDNU4rIVLDZbJrjFJH3xexo7HA4CAQCzJkzh7CwMODAZ5uJKqRXWVk5Zv8LFy6ckOOIyNGtrKyMQCCAy+UCUPKXiIiIiEwozcSJiIiIvAezMvbLL7/Mb3/7W9LS0oiNjeWSSy7hxBNPnOrTE5H34eWXX+bZZ58FwO/3M3fuXG6++Wbi4uIIBAITErTg9Xr5whe+wKxZs0hMTGTOnDncfvvtFBYWHvFjiYiIiIjI9LR7925gX5BjTk4Odrv9PbtumYkdZpBkKBQiMTGR2bNnA4cWoGhuU1FRMebvBQsWHPI+ROTIMzv6md/BrVu3cvbZZxMbG0tGRgZz5syhsLCQwsJC8vLyyM7OxuPxjJvwFQqFxgRI63stIkeaec+yfv16nnzySbKyssjPzycnJ4eCggIiIiLe935h31gYCoUIBoPAyH2QxjORo8vWrVuBfc85/f39/Pvf/8bj8ZCenk5iYiIxMTHA+M8p5phxsPffjXnM/YtizJ8//33tT0SOTeZYYnYRNMeS448/fqpOSURERESOAUr+EhEREXkP5kTdf/7zH5577jnsdjvBYJBly5Zx4oknvmeAlohMP2vXrqWjowO3243P5+MLX/gCH/3oRwEmrFrt6aefzpIlS3jppZcoLy9nw4YNJCUl8f/+3//TOCIiIiIicowwkzzMICGzGITP58Ptdh80sNFms1FVVWUVqwgEAmRlZR1WgPX+QY5mQLWSv0Sm1qZNm4B9c5DDw8PU1dVRV1dHaWkpDocDl8uFx+MhJiaGxMREsrKyyMvLo6ioiIKCAnJzc0lPTz9ogsToLmFKChORD8IcP9asWcMf/vAH4uPjsdlsFBUVcccdd3DmmWe+r7lOc3uv14vH48Fms+FwOI74+YvI9LB582Zg3/3P22+/zZe+9CXi4+NJS0sjIyODzMxMMjMzSU9PJy0tjeTkZOLj460xYjyHkhRms9kYHh6mtrYWm81GIBDAZrNRUFDwrp8TERnNHD+qq6ux2WwYhgFgdWgXEREREZkISv4SEREReQ9mcFR1dTV2ux2Hw0EwGOS4444DtAggMtPU1dXxxhtvACNdv5KTk/nEJz4xbsXsIyUYDOLxePjyl7/MSy+9hMvlwufzsXnzZoaHh3G73RN2bBERERERmT5GBzk6nU5OOukkAMLCwqxtQqGQFTRks9kIBoO4XC6Ki4ut1wArONHsWH4oBgcHqaurs4KUnE4nc+bMOTIXJyLvi9n5IhgMjkl2CAaDBINBAoEAgUAAr9dLZ2cn1dXVbNmyBYfDgdvtJjIykri4OFJSUpg9ezb5+fkUFhZSUFDA7NmziY+PP+gYMTopbCLnRUTk6PLyyy/z6quvAtDb20sgEODkk09myZIlwOGvmXi9Xl588UVqa2spLy+nqakJu91OTk4OS5cuZfHixeTm5hIeHn5Y9z0iMn1t27bN+t1ms+Hz+aivr6e+vp4dO3ZYr0dHRxMfH09KSsqYpLCMjAwyMjJITU0lMTGRWbNm4XQ63zMpLBgM4nA4qK6upr29HZvNRigUIj8/33pfY4yIvBfzGaq2tpa2tjZr7iYnJ2fCioyKiIiIiICSv0RERETekzlZV1VVZQVEAFbyl4jMLK+//jodHR3WIuAll1xCbm7uhB7TXCw85ZRTOPHEE3n77bex2WyUlpbyxhtvcPbZZ6v7l4iIiIjIMcDs/GUmeH3/+9/n9ddfZ9GiRSxdupTjjjuOsLCwMcFCZiLIhg0bxrw2d+5ca1/vZXT3sNFBju8ngUxEjqw9e/YA+xKxAoGA9Z7D4bC6eYVCISshzPzx+/0MDAzQ2trKnj17eOutt3A6nYSFhVnB0unp6cyZM4eCggIKCwvJz8+3Ogfqey8i78ff//53GhsbATAMg8zMTG666SZmzZp12Pv661//ysMPP8ybb77J0NAQgDXmAURGRpKbm8v555/PddddR3p6OoDmUkVmuP/+97/ASIE+GLnnMZ97zHsewzDo7e2lt7eX6upq67Mul4vY2FgSExNJSUkhPT19TFJYeno6KSkpJCQkEBsbC+xLSjWPsWfPHvr7+7Hb7QSDQRYtWgTouUhEDo15H7Jnzx76+voOGEtERERERCaKkr9ERERE3sXoqk0tLS3WxF1mZiZhYWFaYBSZYQzDYPv27cDI9zs+Pp4PfehDk3bs1NRUcnJyePvtt3G73XR2dtLT02Odj8YTEREREZGjW1lZGbAvyaOkpISSkpIx22RkZLBw4UKWLl3K8uXLmTdvHoWFhZSWlgIjzw5hYWFWUFEgEBjTuWe854rRgUkDAwPW/MbixYut90Vk8jU2NuL1enE4HBiGQXp6Oscffzzl5eXU19fj9XqtToCjmZ0tzKJVZkKp+TM8PExvby8NDQ2UlJTgcDhwuVx4PB5iYmJITEwkKyuLvLw8ioqKKCgoIDc3l7S0NCsoWkRkPK2trTzzzDPW2OR2u7nhhhtITk4+7H195zvf4aGHHqKzsxMYSegwDINgMAiMJGsMDAxQXFxMcXExv/rVr/jRj37E1772Nc2jisxgQ0NDnHbaabS1tdHQ0EBHR4d1DzOa2RHVTMYy73f8fj/t7e20t7eza9cua3uPx8OsWbNISkoiNTX1gC5haWlpxMfHk5OTQ1lZGT6fz7rvWb58+eT9DxCRGc+cQykrK2N4eNgaS5YtWzaVpyUiIiIixwAlf4mIiIi8CzM4qqKiYkzVpoULFwIjwVoKiBCZOfr7+8cEVubk5DBv3jxg4pOvzLHi3HPPZe3atQwPDwNQXl6OYRgaS0REREREjnKdnZ10d3fjcDgIBoPjBjMGAgEaGhpoaGhg3bp11mfNRA8YKSwRHh5OdHQ0AGFhYQccywyMhH0dze12Ozt37sTn81mdxVasWAGg6vYiU2THjh3AyHfQMAzOPvtsHnvsMSoqKujs7KS2tpa9e/eyZ88eysvLqaqqoqmpaUx3MNPoMSUUCo3pFBYIBAgEAni9Xjo7O6murmbLli04HA7cbjeRkZEkJiZis9n45z//OeEd0kVk5nrzzTdpamoiLCyM4eFhjjvuOL70pS8d9n5+9KMfcc899wBY90bm2GaOZ+a9j5nk2tvby/e//338fj/XX3/9kbsoEZlUbreb3/zmN/T09NDa2kpDQwN1dXVUV1dTU1NDXV0dzc3N9PX1jXvPY7fbDxgjDMPA6/Xi9XppbGy0igACxMTEEB8fT0pKCikpKeTn5/Pqq6+OWeNdunSptW8Rkfdijj9lZWVjxhIlkoqIiIjIRFPyl4iIiMi7MKs27dy5k6GhoQOqNqm6pMjM0t7eTn19vfV3bm4uKSkpwOR9n1etWsXw8LC1iPjOO+8o8UtERERE5BhQXFwM7AtgNpOyzO4W5hyEmbwxOpgxEAiMeWbp7Oxk+fLlxMTEMG/ePJYsWcKyZctYunQpRUVFeDweK8HL3CeMzG+M7vJlBjmKyNTYsmWL9bvdbrc6+uXl5ZGfn8/KlSsJBAIMDw8zMDBAV1cXzc3NVFdXU15ebv3U1tbS2dn5ngHSoxPCzB+/38/AwACtra0A76t7j4gcGwzD4KWXXrJ+B7jkkkuIi4s7rOJW//rXv7jtttvGdDCEsZ1I9x/PzG3b2tq46667OOGEE1i5cuWRuCwRmWR2u53Zs2ePeW14eJi+vj56enpob2+npaWFhoYGampqrJ+GhgZaW1sZHh62xo3RHA6HNQ6Z9zyGYdDb20tvby/V1dXWtqOftQCr6KeSv0TkUJhjRXl5OXDgWCIiIiIiMlGU/CUiIiLyLt6rapOSv0RmlubmZtrb262/4+Pjcblck3JsM7gzEAgQGRnJ8PAwgUCArq4uAKsSv4iIiIiIHJ2Sk5O56qqraGpqYteuXVY1+9HM5AwzoNoMgjbnH8z/mslhvb29rF+/nvXr14/ZT0ZGBgsWLGDZsmUsW7aMRYsWkZeXR11dHbAvMGnBggVj9isik2vjxo3AyHfd7XZTVFQEMGYe0ul04nQ6iYyMJDk5maKiIk477TT8fj9DQ0P09fXR0dFBQ0MDlZWVVpewvXv3Ul9fj9frPWiA9OhEU5/PR3JyMlFRUZN09SIy0zQ3N7Np0yZgJDlr3rx5nH322cB730uYc5/bt2/nvvvuA0bGIcMwrPud5ORkjjvuOE4//XTCwsKsLoUlJSX4fD6rI1hbWxs/+MEPeOGFFybwakVkIpnrJaawsDDCwsJITEwkLy8PGBk3vF4vfX19dHV10dbWRlNTE/X19VaXsNraWpqammhvb8cwDOs5yjRet2XDMKxOzIZhEBkZSWpq6uRdvIgcFfr6+mhoaLDGkrCwMLKysqb6tERERETkKKfkLxEREZF3sX/VJnMR0qzCq+AokZmls7OT7u5u6++oqCgiIiIm9Ry8Xi/Z2dmUlZUBEB0dDWg8ERERERE52s2dO5c1a9ZYfzc1NbF7925KSkooLi6mrKyM6upq2tvbGRoaGvNZs9uFKRQKEQqFsNvt1o/5eiAQoKGhgYaGBl588UXrM6MTPQzDIDY2loSEhAm+ahF5N6M7AkZHR1vJX+9VHMZms+F2u3G73cTExJCRkcGiRYsIBoP4fD4GBwfp6emhra2Nuro6KioqrKSwqqoqmpqaxg2QNhNCRUTGs3PnTqqqqqy/TzrpJObOnQscerecP//5z7z66qsAVvJFKBTi7LPP5q677mLlypX4/X5CoRBer5eysjJ+97vf8dhjj43pDLZlyxbefvttTjzxxCN3gSIyaQ62HjI6KcxutxMZGWklZ5njTSAQYGBggN7eXjo7O2ltbaWxsZHa2lorKayurs4qtnGwzqhOpxOfz2ft93A6GIrI9DC6yMVkFdg0x6mqqio6Ojqw2+0YhkFhYeGkHF9EREREjm1K/hIRERF5DwMDA9TV1Vkde5xOJ7Nnz57q0xKR98Gsim2z2bDb7QQCAcLCwib1HEZX74aR7mN9fX1WEpiIiIiIiBydgsGgFSRkt9tJS0sjLS2ND33oQ9Y2gUCAqqoqdu3axY4dOygtLWXXrl3U19fT1dV1QKKGuV8z4MkMit6/o08wGCQQCGAYBk6nk0AgYAU5qguxyNQIBoNUVlYCI9/dpKQkcnJygPdfIMZutxMeHk54eDjx8fHMmTOHlStXEggEGB4eZmBggK6uLpqbm6murqa8vNxKCNu0aRPHH3/8kbo8ETkKbd26la6uLuvvJUuWEBcXd0iftdvt1NXV8Y9//AMYuVcx11w++tGP8utf/5qsrCwMw8DlcgHgdrtZtWoVq1atIisrix/84AfY7Xbcbjetra289NJLnHjiiUrYEDmKHEpSmNPpJDY2ltjY2DFddoaHh+nv76e7u5uOjg6am5tpaGigpqbG+mloaKC1tZXh4WF8Ph+A9Vw0OsFURKaXg81bjPea2VXUnHs50sU3zX3v3r2bgYEBa/96lhIRERGRyaDkLxEREZkxQqGQVQly/4rXE3U8m81GdXU1HR0dVgXKgoICQMFRIjORGQTgdrsZHh6mvr4emJzvszlmBQKBMd3HEhIS1PVLREREROQYMN4zh5kQZr7vdDopKCigoKCACy+80Nqur6+P8vJydu7cSXFxMaWlpVRUVNDU1ERfX9+YfZrzF2aimLn/0RX0AZYuXXrkL1JEDtmePXsArITMnJwcXC7XmODmI8XpdOJ0OomMjCQ5OZmioiJOO+00q0hOX18fzc3NZGRkHNHjisjRwzAM9u7da/0dHx/Pcccdd1jj1d/+9jdqa2uBfes9Ho+H++67z0rg2D+Jy7xX+t73vsf//d//8eabb1r3Nq2trXi9Xjwezwe9PBGZ5g5lrAkLCyMsLIyEhATy8vKAkTHE6/XS19dHV1cXbW1tNDc3U1dXR319Pe+884713KV1GpHpy2634/f72bNnDy6Xi8bGRurq6hgYGLASQQsLC0lOTp7whHDzPqSsrAy/34/TORJ+u2LFigk9roiIiIgIKPlLREREZoD29nYSExOx2WzjTtaNrpx9JJPC9q/aZLfbCQaDLFmyxHpfRGaWnp6eqT4FOjs7aW1ttf6OiYmZ9O5jIiIiIiIyPYyXEBYKhawfGAmCjo6OZunSpQckbDU3N7N7925KS0vZsWMHZWVlVFVV0d7eztDQ0Jht9583Wb58ufW6iEy+bdu2Afu+g6MLTk1GBxubzYbb7cbtdhMTE6PELxF5V8PDw7S3t1t/p6amkpCQcMif7+zs5Omnn2Z4eNhaa3E6nVx11VVW153xjL5XWr16NRs3bmR4eBiALVu2MDAwoOQvERljdCK93W4nMjKSyMhIUlNTrfEmEAhYHVFTUlKAA5NPRWR6qKio4JFHHmHPnj1s3bqVqqoq7HY70dHRhEIhvF4vfr8fGFlznT9/PitXruTEE09k6dKlVpENGElm/6DdwMzPlpSUjBlvVGBHRERERCaDkr9ERERkWuvq6mLlypUUFBSQk5NDQUEBhYWF5Ofnk52dTXR09LsGSpneT0ef0VWbfD6fVbVp2bJl73ufIjK1PB4PycnJVvJVQ0MD5eXlVoDVRPP5fJSUlODz+YCRSpRxcXHWooOIiIiIiMjBCtuYHTLMbex2O6mpqaSmpnL66adb2wUCAaqrqykrK6O4uJiSkhJ2795NfX09nZ2dVsD08ccfb+1LRCbfpk2brN8dDgfz5s2bwrMREXl3AwMDdHR0WH8nJCTgdrsB3rVjYTAYxG63s337dt566y3sdruV/JWdnc3ll19+yOdw8cUXc8MNN1hdTsvLyw8rAU1Epp7ZodhMvpiIZ5GD7XP0WOV0OomNjSU2NvaIH19Ejoyqqip+/OMf8/e//33MPYjD4cAwDKvg5+gCwr29vbz99tu8/fbb/OxnP6OgoICTTz6Zj33sY5x//vlWvMcHYcaIdHd343Q6rTXfd0tmFxERERE5UpT8JSIiItPa9u3bqa6uprGxEcMwcLlchIeHExMTQ2JiIpmZmeTm5lJYWEhhYSG5ublkZGTgcrneNVDqULqEme+VlZWNSSQzk79EZOY54YQTrAUAt9vNO++8Q0NDAwUFBVYgwkRqb2+3KnsDxMfHk52dPaHHFBERERGRo8N7dUSHkSAkp9NJfn4++fn5XHjhhdZ2fX19VFRUUFpayrZt25g/f/6knbuIHGjLli3AyHfY4/FYwYJKyBSR6cgwDNra2qy/zc5d8O7jljnf+swzzwAjCRdmMvtpp512WF0yDMMgNTWVtrY2DMMgKirKSgTT2CkyM9hstgOSL8YrcjFRxxaR6c0wDBwOB2vXruXrX/86dXV1AGOSx0fHepjjxegYEPO1YDBIeXk55eXlrF27lqVLl3L55Zdz8cUXExcX94HO0+/3c/XVV3PWWWdRW1tLf38/s2bN+kD7FBERERE5FEr+EhERkWlt+/bt1u+GYWAYBkNDQ3R3d1NbW8vWrVtxOBy43W4iIiKIi4sjOTmZ7Oxs8vPzraSw2bNnk5yc/J6BUqOTwsyJwb1791rbAFZwlBYJRGaerKwsIiMjsdls+P1+AB566CE+9KEPTWjil5lYVlZWRklJifV6Tk4OeXl5E3ZcERERERGZHsx5B7PC/ZH0bh3RzaQwh8NBdHQ0S5YsYcmSJXzuc587oucgIoevrKwMGPm+xsbGkp+fD4z/nRYRmWrx8fG0tLQAI/cVe/bsec+OOeaay9atW3n11Vet1wKBAC6Xi/PPPx/YF+j9XvsZGBggOzub5uZmAFJSUmhqaiItLe0IXKGITIYbb7yR+Ph4li5dynHHHcecOXOw2+2HVORC67IiRz+Hw8FDDz3EtddeOybJy2TGa4xOBNv/8+b2DofDSgrr6enhlVdeYfv27ezatYvrr7+ejIyM951A7nK5uOSSSwDwer0MDAy830sWERERETksSv4SERGRae31118HRhKtRnfzCgaDGIZhLRQGAgEGBwdpb2+noqKC9evX43Q6CQsLIyoqilmzZpGWlsacOXPIz8+nqKiI/Px8srOziY6OPmhQRXNzM42NjdhsNgzDIC4ujoSEhEm7fhE58j772c/yv//7vzgcDgzD4LnnnuPpp5/m0ksvnbAqseYC5VNPPcXOnTutYx9//PHq/CUiIiIicgzYf95h/+r279Wd/HAdbH/jVcMWkcnX09NjJVEAZGRkKHlBRKY1wzDw+/3Y7XYMw6C7u5vh4eFD+uwLL7xARUUFsC9oe9WqVaxYsQI49KRXwzDGBHknJSURCAQO5zJEZAoZhsHPfvYz6+/w8HCSk5OZPXs28+bNY+HChSxYsIDCwkJSUlIOOjYYhgFMzHOUiEwdwzD417/+xTXXXDMm4dOM0wgGg1biuc/nY86cOSQmJtLX10dbWxttbW3j3puY8x82m43Ozk5+/OMf89prr/HHP/6RvLy89702bH7O4/Hg8Xg+2MWLiIiIiBwiJX+JiIjItGa323E6nQddRHQ6ndbkvxnAZP74fD58Ph99fX00NTVRVlaG3W7H5XIRHh5OTEwMiYmJZGRkkJeXR0FBAYWFheTl5ZGamkp4eDhdXV309vZaC5pz584F9nXxEZGZw5yEP/PMM7nzzjuBkTFmYGCAO+64g+OOO4558+ZNyLEdDgc1NTX84x//sP42DIPzzjuPuLi4CTmmiIiIiIhMD4FAgO9973ssXryY+fPnk5ubS0RExHtWt5+IBK2DdUQXkcnV0dFBXl4evb29tLW1MWvWLEBzjiIyffX09LBkyRJef/11bDYboVCIP/7xj9xyyy0HHbtsNht+v5+HH36YwcFBbDablbx1/vnnk5GRYW33bsz3fT4fnZ2d1utJSUkKthaZQcrLywFwu90EAgF8Ph+1tbXU1tZaxUABYmJiSEtLIz8/n/nz57Nw4ULmz59PXl4e0dHR4z7P7F9cQ/dTIjOHuX67efNmbrjhhjHJWKFQCMMwWLRoEeeccw6LFy/mIx/5COHh4bS2tuL3+xkaGrJ+qqqq2LZtG6WlpVRUVNDQ0IBhGNjtdkKhEA6HA7vdzoYNGzj77LN5+eWXyc3NfV/nrcRTEREREZkKSv4SERGRae2JJ56gq6uLuro69uzZQ3FxMSUlJZSVlVFdXY3f7z/gM/t3CTMnBc1uYYZhMDQ0RHd3N7W1tWzduhWHw4Hb7SYiIoK4uDiSkpIoLCyko6OD/v5+K/lr6dKlk/2/QESOEHNMWL58OR//+Mf529/+Zi0S7tixg5tuuomf/exnFBYWHvFjB4NBbrrpJpqbm62gh6SkJM4555wjfiwREREREZleSktL+dGPfgSAy+UiPj6e7OxsjjvuOBYsWMDChQs57rjjyMnJOWiQ4uiksNEVsEVkZsrNzWXPnj3W/KTTObJkq++2iExXqampLF26lNdffx2n04nf7+dvf/sbX/ziF0lJSRmzrWEYVvGrxx57jJqaGpxOp1W4Lzk5mdWrVx92Qnp7ezvNzc3W37NmzSIyMvKIXJ+ITLzt27cDI89EozsSw9h7oL6+Pnp7e9m9ezfPP/88MFIMdNasWWRlZVFYWMjChQtZuHAhc+fOZfbs2TgcjvcsrqHnKJHpx0z06uzs5Fe/+hVVVVW4XC4MwyAUCpGTk8P111/PZZddZiWNm6Kiog7Y36mnnsoll1xCc3MzpaWlPPXUU/zjH//A6/UCI2NJIBDAbrdTU1PDlVdeybp16wgPD5+U6xURERER+aCU/CUiIiLTmsPhIDExkcTERJYsWcJll10GwPDwMG1tbVRXV7Nz505KSkooKSlh9+7dNDU1jZsUZrfbcTgc1sS+mQwWCoUIBAIEAgEGBwdpb2+noqKCDRs2YLPZrIVKgGXLlgEKxBCZqUKhEB6Ph8svv5yXXnqJnp4e7HY7wWCQF198kS9+8Yv8/Oc/t77rgLUAebjfe3PBwjAMfvSjH/H000/jcrnw+/1ERETw+c9/nvDw8DEV7ERERERE5OhjBjmGhYXh9/tpbW2lpaWFjRs3WttERESQlJREXl4ec+fOZdGiRcyfP5+CggKSkpLGTQpTdXuRmc1mszFr1iyr65f5mojIdGPOX+bl5QFY9x/btm3jtttu4+677yYpKclaRzH/+9xzz3HXXXeN2Q/AWWedxZw5cw6r22EgEKC0tJTBwUFgJHg7IyNDnb9EZpBXX30VgIGBAes1m82G0+kcs3ZrjjHm++br7e3ttLW1sWXLFp544gkAwsPDSUpKYs6cOcydO5eFCxeyYMECCgsLSU1NPegYYxgGgLoii0wTf/3rX3nxxRcBrE6hixcv5v777+fMM88EwO/343K5xnxudOd0GIkHiY2NJTY2lqKiIi699FJ6enr49a9/zY9//GO6u7utDqY2m43XX3+d+++/n+985ztarxURERGRGcEWMu+CRURERI4CfX19NDc3U1FRQWlpKcXFxZSWlrJ37156enrG/YzT6bQm/83AqdGV4MyFB7/fz9atW1m8ePGkXY+IHHlmQudVV13Fww8/jMPhIBQKYbfbCQQCAHzlK1/hy1/+MitWrBjz2VAoNGZs2H8RYHSyKEBLSwv/+7//y+9+9ztrUcLv97Ny5UqefPJJcnJytJggIiIiInKU+/rXv87Pf/5zPB4PPp9v3GBGYMzr5nuxsbGkp6eTn5/P/PnzWbRoEfPmzSMvL4+IiIhJuwYRERGRzs5OTj/9dEpLS63OGQAnn3wyV155Jccffzzh4eG0t7ezceNG1qxZQ0VFhVWULxAIsHDhQn7yk59wzjnnHFbyV2trK9/85jf54x//CIx0Ivvtb3/L+eefP2HXKyJHVmNjI2+88QbFxcVs3LiRkpISmpqaxt12dCev0Wu3pnd7jgKIiYkhLS2N/Px85s2bZz1H5efnEx0dfYSvTEQ+qIULF1JaWmoV7MzOzuaFF15g3rx5AIe9lmqu55pruQMDA7zyyivccccdbNq0CcA6Vn5+PuvWrSM3N3dCrk1ERERE5EhS8peIiIgc9YLBIF1dXdTV1bFnzx6Ki4spKSlh586d1NTU4PP5DviMmfBlMruDeb1ewsLCJvP0RWQCmJP+H/rQh3jzzTdxOp1WUlcoFMLhcJCbm8spp5zChRdeyJIlS8jIyDjkYASv18vTTz/NAw88wJYtWwBwu934fD7i4uK49957+cpXvqLELxERERGRY8D8+fMpKysb85pZiMZ8Ntm/CI1pdAEKk8vlIj4+nuzsbBYvXszKlStZtGgR+fn5xMfHAwcWphARERE5Eu666y6+973vYbfbrXsWs4NOYmIiKSkpdHd309DQAOy7rzEL7N13331cf/31uN3uQzqemSD22muvcd1111FaWgrACSecwO9+9zvmzZunOVaRGWxwcJBdu3axdetWNm7cyLZt2ygrK6Ovr2/c7Q9W0BPe+znK4XAQHx9PTk4OqampXHHFFVx66aUTdGUicqjeeOMNTj/9dKsjF8DatWv52Mc+dsSP9d///pevfOUr7Nq1C7vdbhUGvf/++7nxxhsPKzFdRERERGQqKPlLREREjlnDw8O0t7dTVVVFWVkZJSUlFBcXs2fPHhobGw/YPiUlhaamJi0kihxFWlpauOKKK3jxxReBkcW/0UGXppycHIqKili4cCF5eXmkpKSQlZVFdnY2MTExNDQ0UFNTQ0NDA42NjTz88MPU1NRY1W+dTqe1CHnvvfdy/fXXEx4ervFEREREROQY8Oc//5mNGzdSVlbGrl27qK2tHXc7u91udcY4nKSwyMhIUlJSWL58OR//+Me5+OKLcblc1nZ65hAREZEjxTAMLrzwQtatWzfmvmX/OdXR86wOhwPDMDjzzDP529/+xqxZsw7reA6Hg1tuuYWf/OQn2Gw2DMPg+uuv55577lEnVJEZZP+xwhxDxtPW1kZJSQlbtmxh48aNbN++nfLy8nG7fJkFPc19mWsxB3uOcjgcBAIB7rzzTr7zne+ocIbIFOru7ub73/8+v/jFL6ykq49+9KM888wzR3w+w0zsWrduHZ/97Gfp6uqy7lHOO+88Hn/8caugjoiIiIjIdKXkLxEREZH99Pf309TUREVFBaWlpezcuZM333yTxYsX87e//U2LACIz2HgLBRUVFdx999385S9/sToBmpVrzYpv5ufcbjcej8dalPT5fAwPD5OamorNZqO6uhrACnoYXYEyFApxzTXXcM899xAdHT2p1y0iIiIiItPDwMAALS0t7N27l507d1JSUkJJSQkVFRV0dHSM+xmHw2E9o+xf3X7/JR673U5+fj7XXHMNX/3qV9W9XERERI4Yc460sbGRm266iSeffNJ6z5xLNedezXsW8/eioiKeffZZ8vPzD/u4wWCQBQsWsGvXLtxuNz6fj2effZbzzz//yFyYiEyp0UlhNpvtXddg9+7dy/bt29m8eTObNm2itLR03IKeMPIcZe5r9HOUy+XC5/Px0ksvcfbZZ6vTj8gU2rx5M1dffTWbNm0CIDc3l/vvv5+PfexjE/LdNPd55ZVX8vvf/95KCAWoqakhPT39iB5PRERERORIU/KXiIiIyCEwDAOfz4fH45nqUxGR96GxsZGUlBSr4uz+iwXNzc3cf//9/PnPf6a1tdWa7DeTtkYHK4xmt9vHvO50Osdsa7fbMQyDsLAwvvSlL/Hggw9O7IWKiIiIiMiMEwqF6O7upqGhgT179lgJYWVlZVRWVuL1esf9nFnd3mazWcGShmEAI93LP/WpT/GNb3yDrKysybwcEREROQbU1dWxZs0ann32WSorKxkcHDzotmeddRYPPPAACxYseF9dPNasWcN1111HMBjEZrMRExNDXV0dUVFRH/QyRGQaG130wm63HzQJZHBwkN27d7N161Y2btzI1q1bKSsro6+vb9ztw8LCGB4eprGxkdTU1Ak7fxF5b7/61a+47bbb6OvrIxQK8ZnPfIb777+flJSUCe1kXlVVxfz58/H7/RiGgdvt5u9//zsXXHDBhBxPRERERORIUfKXiIiIiIgc9U499VROPfVUfvjDH77rdhs2bOD+++/n73//+wHvvVv1WjPg0gy6HJ0QtnDhQm688UauuOKKCV2oEBERERGRo4vf76ejo4Oamhp27dpFcXExJSUl7Nq1i7q6ugO6fgFjnksALr/8cn75y18qOFpEREQmxPr161m3bh2bNm2ipqYGv9+P2+0mEAiQnJzMtddey0c+8hGio6MxDONdO/qMp7a2lrPOOou9e/cC4Ha7ueKKK1izZs1EXI6ITHOju4TBSHevg625tLW1UVJSwpYtW9i4cSPbt2+noqLCKpgxXsE/EZlcl19+OX/6059wuVz4/X6efPJJVq9ePeHH7ezs5Ctf+Qpr167FbrfjcDj41re+xZ133qm1XBERERGZ1pxTfQIiIiIiIiITbfv27RQXFxMdHc0tt9xywKS9uVB4wgkn8Ne//pXdu3ezdu1aXnnlFcrKymhvb2doaMhaFBzNDFjY/705c+Zw7bXXcumllzJ79uz3FdwgIiIiIiLHLpfLRWpqKqmpqZxwwgnW64ODgzQ1NVFcXMy2bdvYtGkTO3bsoLm5GcMwrKr4wWCQxx9/nFAoxB/+8IdxuyCLiIiIvB/mfcWqVatYtWoVgUCApqYmmpubCYVCzJ07l+joaGv7UCh0yHOjZtD18PAwv//979m7d6/VqSczM5OPf/zjY7YTkWOHzWYbdywZnRRmbpOUlMQZZ5zBGWecMWbbiooK6urqAPSMJDLF3n77bWBfMuaHPvShCT+mYRjEx8ezcOFC1q5dS1hYGF6vl66uLutctJ4rIiIiItOVkr9ERERE3kUwGLSqZovIzNTe3k5/fz8Ad999NzExMXzxi18kIiLC2mb/73hRURHf+ta3WL16NcXFxRQXF1NeXk59fT09PT309/czMDBAV1cXXq8Xp9PJggULSExMJCkpiSuvvJKlS5eSlJQEoMQvERERERE5YiIiIsjLyyMvL48LL7yQpqYmNm7cyNNPP81f/vIXAoEADofDSgD75z//yV/+8hc+/elPK0haREREjggzWcK8t3A6nWRlZZGVlTXu9odz/2Hu85FHHuHBBx8cc7wzzzzTSuTQPY2ImA6WFBYMBq2kErNIRn5+Pvn5+YRCISV+iUyh1tZWKisrcTgcGIbBnDlzrHXViWTePyQlJREeHo7f7wfA4/EAaD1XRERERKY1JX+JiIiIMLKYOHqS31xcNP/2+Xy43e6pPEUReZ9KSkoACA8PZ3BwkOuvv55AIMANN9zwrlUd7Xa7FVB58cUX4/P5aG1tpbW1le7uboLBIFFRUSQkJNDb24vNZuP4448fd39aKBARERERkYngcDjIzMwkMzOTSy65hNtvv50bbriBF198ERjpHtbX18df/vIXzj//fGJiYqb4jEVERORosn8CVigUGvf1w2G326mpqeHxxx+nra0Nu92O1+slKiqKT3/60zidTnXrEZFDYiZ8jWauASuBVGRqFRcXAyPfU8MwWLFiBTDxHfnMfaempuLxeKyOX0NDQ/T39xMVFTVhxxYRERER+aCU/CUiIiLHnP2rXI83yW+z2SgtLaW4uJjKykoKCgpYvXr1VJyuiHxAZvIXgNvtxufzceONN9LV1cUPfvAD4OALCaODFdxutxVU+W5USV9ERERERCbT6OeWwsJCHnnkEb72ta/x7LPP4vf7cTgcvPjii+zYsYNTTjlFzywiIiIyYY7UPUZ/fz9f+MIXOOWUU2hqaqKqqoqPfOQjLF++HECJXyIzSGdnJ3V1daSkpBAVFUVYWBgul2vKzkfPQiLTw6ZNm6zf7XY7S5YsAfbNcUy09PR0ent7rb8HBgYICwublGOLiIiIiLxfSv4SERGRo9rogCbDMHA4HNbfZrKHz+dj06ZNtLe309jYyIsvvkhZWRmhUIiGhga8Xi/33nvvAfsTkZnhK1/5Cm+//TZ/+ctfgJHK98FgkDvuuIPBwUHuvfde7Hb7uN/v8b7v41WvHf1ZjREiIiIiIjKZ9n8GSUtL45577mHr1q3U19djs9nw+/3s2bOHU045Rc8sIiIiMu3Nnz+f+fPnAyOdOFpbW4mJiSE6OnqKz0xEDtevf/1rbr/9dk466STmzJlDYWEh+fn55OTkkJycTFxcHJGRkbjdbhwOx1SfrohMEjP5KxQK4Xa7rX/3J2vOIiYmxoofCYVCDA8PT2liqoiIiIjIoVDyl4iIiBx1QqEQoVAIu90+ZnLQ4XDQ0NBAVVUVQ0NDbNy4kX//+9/09vbS3d1NZWUlMDKhaCZ3mNWdTjjhhMm/EBE5IsLCwlizZg1xcXH8+te/xu/3W+PDL3/5S4LBIHfddRfh4eGHtL/xFh0UPCkiIiIiItPJvHnzuPTSS/n5z3+OYRgAvPrqq3zhC19QQKWIiIhMe6MLcIWHh5OdnT3FZyQi75eZ4PHWW2/x1ltvWa+73W7S09PJzc2loKCAoqIicnNzyczMJDExkZiYGDweDy6XS93+RI5CO3bsAEYK9kZFRVFQUABM/JqrWSC4trYWGIkh8fl8REVFjXlfRERERGQ6UvKXiIiIzGhmNabRbDYbNpuNPXv24PV6aWlpYd26dZSUlOD1etm9ezft7e0H7MvlchEKhTAMA7vdPqZL2GRXmhKRIys6OprbbruNYDDIb3/7W4LBIDabDa/Xyy9/+UsiIyO56aabiImJmepTFRERERER+UDMzsSf/OQn+fnPf47T6cTv97N7926rorXmN0RERGQ6+yD3Kmbi2Afdj4gcGcXFxcC+dVgY+Z76fD6qq6uprq7mP//5j7V9dHQ02dnZ5OXlUVhYSEFBAXPmzCEtLY34+Hiio6MJCwtThx6RGa6iogIYGQ8SExOtRO+J/rfbHId27tw55vWsrKwx74uIiIiITEdK/hIREZEZIRgMAhxQZcnhcOD1emlubsYwDDZt2sQrr7xCR0cH9fX1bNq0iWAwiNPpJBAIWJ9zOp0YhoHNZrOSvAKBgJUQEgwGCQaD+P1+4uPjSUhImNTrFZEjLyMjg5/85Cf09fXxt7/9zUr0HB4e5s4778ThcHDrrbdqwVBERERERI4KdrudWbNm0dvbC3DI3Y5FREREZjIlfIlMH8FgkMrKSgBrndZMrDALcZprv8FgEMMw6Ovro7S0lNLS0jH7Sk5OJicnx0oIKygoIDs7m5SUFOLi4oiMjMTtdqvTscgMUF5eTigUsmI4cnJy8Hg8k3oOVVVVwL4xqaioaFKPLyIiIiLyfij5S0RERKad8bp5mRP/HR0d2O12mpqaePnll9m1axc9PT2sX7+eyspKXC4Xfr/f+py5aBAIBHA6ndb+DcMgFAoRCoWsxDKAyMhIvF4vs2fP5sQTTyQmJsbq+hUMBg9IPhORmSMYDBIZGcnjjz+Ox+Phd7/7HU6n0xo3fvCDH9Da2sqDDz5oba/vvIiIiIiIzFShUIj09HS6uroASEhIoLu7m7i4uKk9MREREZEjzOxsumXLFioqKsjIyCA+Pp45c+YoAV5kCu3ZswfYV5QzFAoRHh6OzWbD6/WOWaM1ORwOq3CnuY5rGAatra20trayceNGa1uXy0V6ejq5ubkUFhZSWFhIbm4uWVlZJCYmEhMTg8fjweVyab1HZBrZvn07sC8GJDU1FYDBwUE8Hs+EJnKbx9w/+WvhwoVj3hcRERERmY6U/CUiIiLTjsPhIBgM0tfXR3h4OOvXr2fDhg10dnayd+9eXn75ZbxeLw6Hg8HBQetzdrsdv9+P3W63Er38fv+YqlEwtupjZmYmy5YtIz09ndjYWFatWsWyZcuw2+1EREQQGxs7Zv8iMnPZ7XYrufS3v/0tiYmJ3HvvvQC43W4Mw+BXv/oV/f39/OY3v8HtdisBTEREREREZhxz3iMYDOL1eq3XExMTraAmERERkaOJmfz129/+lrVr15Keng7Ao48+yuLFi6f47ESOXVu3bgVG1n4Nw8Bms5GZmcknPvEJHA4HlZWVVFVV0dbWRmdnJ729vVYRz9FsNhtOpxO73W4lhRmGgd/vp6amhpqaGl555RVr++joaLKyssjPz6egoMBKCktKSmL+/PnqDiYyxcwkTvPf73//+998+tOfJjs7m6KiImbPnk1aWhqzZs0iKiqK8PBwK/7jg7LZbPj9fmpra7HZbNZ4k5+fb70vIiIiIjJdKflLREREppXu7m4eeeQRuru7eeONN9i0aRMej4euri5r4s1ut1uV4NxuN7Cvm5fD4SAUCuHz+YCRyTmHw0EgEGDRokXs2bOHoaEhAGbNmsXvf/97li1bRkxMzLjnM14XMhGZuczvcygU4u677yYsLIw777wTn89nVZN8/PHHcTgc3HfffcTHx0/xGYuIiIiIiLw/g4ODtLe3W3/HxsYSFhY2hWckIiIiMjHMAl47d+60ugMBmt8VmWKlpaUAVqHOYDBIRUUFjz76KFdffTV33XUXUVFRlJWVUV1dTWNjI/X19TQ0NNDY2EhLSwudnZ309/dbRT5Hs9vtOBwOawwwu4T19fWxc+dOdu7cOWb7pKQkmpubJ/7CReRdmYmhoVCIUChEY2MjTz755JhtUlNTmT17tpXAmZ+fT3Z2NsnJycTFxREREYHb7T6sWA4z2ay2tpbW1lZsNhvBYJA5c+ZYY5SKgoqIiIjIdKbkLxEREZlWXnvtNb75zW+OeW1gYAC73U54eDiBQIBAIGBN5ptJXrCvalxUVJSV0JWens7pp5/O4sWLsdlsLFq0CBhJCktMTOSUU07B5XJZ1eb2n8xT4pfI0clms2Gz2bjxxhsBuPfeexkeHrYqRj7++ONWYlhCQsIUn62IiIiIiMjhCYVC1NTU0Nvbaz3/xMfHExERMdWnJiIiIjIh+vr6aGpqwuFwEAwG8Xg8ZGVlTfVpiRzTvva1rzEwMMCDDz5oFfl0Op20trZy//33s379er761a9ywQUXcNpppwHQ399PV1cXra2tNDc309DQQENDg5UU1tTURFtbG11dXQwPD1sFQ0czi/2Zaz42m43h4WE8Ho+V7KEED5Gps2XLFgDru2h+X83XAoEAzc3NNDc3s379eutzYWFhpKenk5ubS2FhodXVLzMzk8TERGJiYvB4PLhcrnft4LV79276+/utosMLFy4ccz4iIiIiItOVkr9ERERkWtm8eTMAHo/H6tBlTsKbf5tJXsFgkEWLFpGcnExERASnnnoqq1atIjIyklmzZpGTkwPsq+DU2tpKIBDA6XQSCATIzMzE5XIRCoWU5CVyjIqNjeW73/0uAwMDrFmzhsHBQatb4Jo1a3A6ndxxxx3ExcVZSaLmj4iIiIiIyLsxDMOak7Db7ZP6HNHd3T2mknZ8fDyFhYWTdnwRERGRyWLeb1VVVdHR0WG9lpeXN8VnJiKZmZncc889XHzxxaxZs4bnnnuOgYEBYCRhc926dWzbto133nmHL3/5y8yZM4eoqCiioqLGJG8Gg0F6e3tpb2+ntbWVxsbGMQlhDQ0NNDc3097eTm9vL4ZhWMlmgPUstnLlSmt/SvAQmRper5dAIEBERARer5dQKHRAEqfNZhvT1c/cZnh4mKqqKqqqqnj55Zet7WNiYsjOziY/P5/CwkIKCgqYM2cOqampxMfHExUVRXh4OKFQCJfLxe7duxkeHrZiRJYtWzZ5/wNERERERD4AJX+JiIjItGJWefL7/cDIRB5ATk4O6enphIWFMX/+fD784Q+TnJyMx+NhwYIF407QB4PBMRXd3n77bWDfBH9BQYF1DCVyiBybQqEQTqeT++67D7fbzT333GO9FgwGefDBB+nq6uKxxx47aJKouSChpDARERERERltvGcIM2BpopLCzCDGPXv2jKmOnZOTo+QvEREROSoFg0EcDge7du1iYGDAurdasmTJFJ+ZiACEh4dz+umnM3v2bObPn8/vfvc7amtrrW4/zc3N3HPPPTz22GN861vf4oYbbrA+axiGlQASFxdHXFwc+fn51vs+n4/u7m7a2tpoaWmhsbGR+vp6KymssbGRlpYW+vr66OrqYtGiRVPxv0BERjEMg3vuuYe2tjaqq6tpbGyktbWVzs5Oent7GRwcxOfzEQgEDvis3W63fmBkjsUwDHp7eykpKaGkpGTM9ikpKcyePZuCggKrS9jixYt5++23rfsHgOXLl1v7FxERERGZzpT8JSIiItPKzp07gX0JWaFQiDPPPJMrr7yS1atX43QeePtiBk6NnuQzA6hgZALRbrdbFa9hZOJu7ty51vYicmwanSB69913k56ezvXXX29N+DscDv70pz9RV1fHJZdcQnZ2Njk5OWRkZJCUlDRmgWG0UChkjS1KChMRERERObZ0dHSQlJTEihUrWLx4MStWrGDJkiXMnTuXyMjIcZPCgsGgVVjiYM8Zh8J8Dlm7di1bt261up8vWrRoTOV8ERERkaNNWVkZfr/fWkcyA7lFZHrIycnhu9/9Ll/4whe48847efjhh63uXOHh4TQ1NXHzzTfz5z//mRtuuIFLLrkEj8czZh+j13RtNhtut5vk5GSSk5OZP3++9d7AwACdnZ20tbXR1NREc3MzJSUlXHLJJYASPESmUlRUFFdffbX1d0dHB42NjdTW1lJdXU1NTQ11dXU0NzfT1tZGd3c3fX19DA4OYhjGAV3CACtJ1FyPDQaDBAIBWlpaaGlpYcOGDda2brfbGgPMfS1cuBDQ2CAiIiIi058tpGhnERERmSaGh4etSXxzYi4UCpGens7pp5/Opz71KU488UQSExOtKtmHUiE7EAjgdDq54IILeOGFF3A6nbhcLv7xj39wzjnnjEkcE5Fjk5kkarPZ+P3vf8/3v/99q/Kk+cgUGxtLZGQk8fHxJCUlkZaWRlZWFrNnz2b27NlkZWWRlpZGXFzc1F6MiIiIiIhMqTfffJPTTjtt3PdSUlJYsGABS5cutZLD8vPzDzq3YRiGVbBidKGbg23rcDj497//zZe+9CXq6+sJCwtjeHiYp59+mosvvvhIXJ6IiIjItGKu8Xz605/mySefxOVy4ff7efPNNznppJOm+vRE5P9nFs0zn2nWr1/PT3/6U9auXYvf7x+zbU5ODpdddhlXXnkleXl5OByOd13PHV2M72C0HiwyfQSDwXctnun3+2ltbaW+vp7a2lqqqqqora2lvr6elpYWOjo66Onpob+/n6GhoXGL/dpsNhwOh3Ucs6CwmXRqvhYeHs7g4OCEXq+IiIiIyJGi5C8RERGZNgYGBrjlllt45plnqKurA/ZNugE4nU6uuuoqvvWtb5GdnQ2898Qg7OsElpOTQ11dHXa7naSkJN5++21mz55tvS8ix579FwSHhoZ44IEHuPfee+nt7bWSvw722BQWFkZkZCSxsbHEx8eTkpJCeno6OTk55OTkMHv2bDIzM0lJSTmgQqWIiIiIiBydfv7zn/P1r38dj8dDIBCw5jYOVqEaoKCggEWLFrF8+XKWLl3KwoULSU1NPexj/+tf/+K6666juroaGHnmyc/PZ+PGjcTGxn6QyxIRERGZ1k444QQ2btyI3W4nGAzS2dmpQl0i01xVVRVPPvkkjz/+OBUVFVZBCzMZLDs7m9tvv50vfelL1mcOJ4nrUJLCRGTqjV6Lfbfv98DAAM3NzdTV1VFTU0NVVRV1dXU0NjbS2tpKZ2cnvb29DAwM4PP5xt2HuX9zrFm8eDFbt261xh8RERERkelMyV8iIiIyrQwNDfHnP/+ZX//612zbtg3DMAgLCyMUClkTdB6Ph6uuuorrr7+e3NzcQ9qvz+cjPDzcmtyfP38+W7du1QSeyDFs9CR+dXU1f/vb31izZg1NTU0MDQ1ZiaXBYBCHwzFmsWF0Zbj92e12PB4PUVFRxMXFkZKSgtvt5vLLL+fyyy+flGsTEREREZGp89nPfpa//OUvREREEAgErO7lZgEbu91uPV8Eg0ECgcC4+4mMjKSoqIilS5eyfPlyjj/+eHJycoiNjcUwDDo7OxkaGsLhcNDY2MgDDzzA66+/TkdHBw6HA8MwCA8P56677uIb3/jGZP4vEBEREZlULS0trFixgoaGBkKhEAkJCbS1tU31aYnIIdq1axd33nknf/nLX4CRgqBOp5OhoSEAli5dyk033cSnPvUpJXKJHEPMpLD3Kgbc0dFBU1MTNTU11NTUUF1dTX19PU1NTbS1tdHd3U1fXx+Dg4MHrO9+/vOf57HHHiMQCOB0Oif6kkREREREPhAlf4mIiMi0NDw8zE9/+lPuu+8+Ojs7AazJtkAggMfjYdmyZVx++eWcf/75pKenj7sfczKwuLiYxYsX43Q6CQQCXHjhhfzzn/9U1y+RY8z+3/k33niDl156iTVr1tDR0WG9brfbsdlsGIYxpgPhaDabDYfDYe3PDOY8WCX/Bx54gBtuuEHjjoiIiIjIUW7FihVs3rx53PecTueYDsNmYpiZEGY+KxiGcdCCE4mJiWRnZzNr1iz6+/vZvn27VcAiFArhcrmsSvkf/vCHWbdu3cRcqIiIiMgUMzsA/fe//+W8887D6/ViGAannnoqr7322lSfnoi8B/O5yCyOsX79en7yk5/w9NNPW9uYzzk5OTlcfPHFXH755SxatAin06n1FpFj0OguYe+WFOb3+2lra6O+vt5KCKupqaGhoYGWlhZ6enooKyvj17/+NV/96lfV+UtEREREZgSVKxAREZFpx+z29e1vf5vVq1fzq1/9iscee8xKArPZbHi9Xt58803Wr1/Pm2++yU033cSiRYuAsQsFZseebdu2WZ8FKCwsBLDeF5GjmxkEYLPZGBoaYseOHdxzzz1s2bKFuro6K4nLrMJvVt5PS0vj3HPPJS8vz1oYqKuro7m5mZ6ennEr9Nvtdmt/oVAIp9OJ1+tlyZIlk33ZIiIiIiIyBTZu3EhjYyPFxcVs2bKFjRs3UlxczN69ew/6DGE+r4wuKjG6S5j5nmEYtLe3097ePubz5nyH0+m0ksZOP/10fvCDHwAoiElERESOSmbwd1lZGUNDQ9jtdgzDYNmyZVN8ZiJyKMx1GTOJa9WqVdx///2ceuqpPP744+zYsYNAIIDdbqempoaf/exn/N///R8333wzl19+ufUcZK4Bicj0ZxjGmHmMw3WwhK/RSWF2ux2Xy0V6ejrp6emsXLnS2m5gYIDm5mYaGhooLS3l/PPPtz4jIiIiIjLdqfOXiIiITHsdHR08++yz/O53v2Pz5s0MDw/jdDqx2Wz4fD5ruy984QvccsstFBUVWa8FAgGcTif/8z//w4MPPojL5SIUCvHQQw/xxS9+UcFPIseQzs5O/vvf//LLX/6Sl19+2QqIdLvdBAKBMYsCCxcu5Nxzz+Waa65h9uzZDA8P09/fT29vL+3t7bS0tNDY2EhNTY2VFGZWihsaGhr3+G1tbSQkJEza9YqIiIiIyPQSDAYpLy9n+/btbNy4kS1btlBaWkpra+u42zscDmvOYryksNHJYmbhCbPj19lnn83999/PwoULJ+36RERERCabucZz00038cADD1gdUB9//HE++9nPTvXpicgHsHHjRh544AGeeOIJAFwuF3a7neHhYQCWLFnCLbfcwurVq6fyNEVkmjPXf9+tS5iIiIiIyEyh5C8RERGZMdrb2/npT3/K/fffb03su91uq/K1w+GgsLCQz3zmM6xevZr8/Hyr0tspp5zCW2+9hcPhICIignXr1nHiiSeqEpzIMaC9vZ0//elPrF27ltdff9163QykHJ1EunjxYq655hrOOusscnNz33W/oVAIr9dLX18f3d3dtLW10dzcTH19/ZguYdXV1dZ5iIiIiIjIscNM1oJ93b3G09fXR1lZGVu2bGHz5s1s2bKF3bt3Mzg4OO72ZtLXeK/l5uZy2WWX8a1vfYvY2Ngje0EiIiIi04wZzH3hhRfy/PPP43Q6CQQCFBcXM3/+/Kk+PRF5H8xnHTNJY8OGDXz/+9/npZdesl53OBwYhkFqairnn38+V1xxBStXrsTpdE7ZeYvIe3vmmWcwDIOFCxeSnZ2N2+2ekvPYv0uYiIiIiMhMoeQvERERmfbMKtZmteva2lruvfdeHnvsMbxeLzA28Mlut3PRRRdx4403cvLJJ2O320lISKCrqwubzUZmZiabNm0iKSlpyq5JRCaWz+ejq6uLBx54gOeff57S0lIAnE4nhmFYE/mGYRAXF8eCBQu48cYbOeuss4iJibHeez+dAQ3DYGBggN7eXrq6umhsbCQUCvGRj3zECkYQEREREZFj0+hOXaM7eI2nqamJ4uJitmzZwqZNm9i5cyednZ34fD6cTicJCQnY7XZ6e3tZtWoVF110EUuWLGHBggWTfFUiIiIiUycQCLBkyRJ27twJjNxvmfdLIjJzmV2PbTYbLS0t/OMf/+Dhhx9m8+bNB2y7aNEibr31Vi677LIpOFMROVRz585l9+7dJCcnk5+fzxNPPEFmZuZUn5aIiIiIyIyh5C8RERGZkdrb21m3bh2PPvooGzZsYHBwEIfDgc1mIxAIWNt99rOf5XOf+xznnXcedrudYDDIypUrWb9+/RSevYhMtO9+97v89Kc/ZWhoyOrwZ7fbMQzDShSNiIhg9erVXHLJJVx00UXWZ99PgpaSukRERERE5IMwE8LMZ4uDFaIIhULU1tbS0NBAKBQiLS2NOXPmEAqFxhS5eD+FLERERERmEvO+qbKykpNOOon29naCwSA5OTlUVVVN9emJyAc03rpLSUkJDzzwAH/6058IBALWs5PP5+O8887j+eef13qNyDTmcDisddrjjjuOLVu2EB4efsifV8cuERERETnWqdSRiIiIzEiJiYl87nOf47TTTuP+++/nd7/7HYODg9hsNtxuNzAS7PTnP/+ZdevWjflsXl4egJUQIiJHl7179/LDH/4Ql8tFMBjE6XQSCoWshcDo6GiuvvpqLr30UlauXAmMXUR8P4uCWkgUEREREZEP4mDzE8FgkGAwaG1jt9vJyckhJydnzHajn0mU+CUiIiLHAnNOd8+ePfT19VkFABctWjTVpyYi75OZ2DFeh+T29nYArrvuOrq6uvjHP/4xpnDGKaecMunnKyKHrqKiglAohMvlIhAIkJWVdViJX2ahG63JioiIiMixTMlfIiIiMiOZFZ2ys7P52c9+xnXXXcfdd9/NH/7wB3w+HzASFBUKhejo6LAW/QDy8/MB8Pv9uFwuJYCJHGV27twJjAQ/Op1Oqxvg8ccfzznnnMPNN99MXFwcTqfTSgLVQoGIiIiIiBxJ5hwEfLBq1GbC1/7MeRE9y4iIiMixyrwfKisrY3h42EoAWbZs2VSelogcovE6FttsNmw2GxUVFXi9Xpqbm/nPf/7D1q1bGRoaoqysjLa2Nmvb0f9dsGDBmL9FZHrZtm2b9XsoFKKoqAg4tO7lHR0dnHHGGURGRjJ//nxWrVrFl7/85Yk8XRERERGRaUnJXyIiIjItBINBa0L/UJjbmRXgCgoK+OlPf8oFF1zAb3/7W1599VX8fr8VIGXuPxQKsX37dmpqag6oki0iR4fNmzcDWN2+Tj31VC677DIuueQS0tLSrO3U/U9ERERERCbKRD9rKKBRREREjnXm/VBZWRnBYNAKHF++fPlUnpaI7Gd0J+PRHA4HAwMDNDU1YbPZeOutt3jzzTdpa2ujubmZd955h2AwOKbIn81mw+124/P5rP0NDw8DUFhYOIlXJSKHy1y/hZHv/9y5c9/zM2aXz+rqaiorKxkcHGTDhg28+uqrfPnLXz6kxLHRiouLiY6OJiYmhoiIiMPqPCYiIiIiMh0o+UtERESmhdET/uYk3qEYnTAWFxfHJz7xCebNm8evfvUrnnjiCTo7O3E4HFaHH4BnnnmG119/nc997nP8f+zdeXwV1d3H8c9dk5CNLCQhK/u+i6IoCi4VV9ytW931oTy22sX2sdW6Vmtr1Vq12lbrjoqi1n1DVIgICAiBhGxkgySQkP0m996Z+/yR1wyENUBCAn7fr1dezb1z5swMzR3nnnN+v991113HuHHjuv6CRKTHfPXVVwBMnz6dm2++mSlTphAfHw90vL8o8EtERERERLqK9V1j6dKl/POf/yQ1NZWhQ4eSlZXF6NGjiYuL2+9+oWOwl2EY9nv6XiMiIiI/VNZzUH5+PrAtwGTs2LE9dk4isjPrs1pdXU0oFKK6upoPP/yQgoICGhoaWLZsGYWFhXZQl8Xj8QDt33/CwsIIBAKYponf78fhcNjfiyZOnEhqaioDBgw46NcmIp23bNkyoP2/1+Hh4Xbw157WhVjB3evWrSMYDBIeHk5rayszZ84Eto2Z7Ik1XlNQUMBZZ51FREQEaWlpHH/88dxxxx1dcGUiIiIiIgePgr9ERESkx3333Xd88MEHXHXVVaSlpR1w9upRo0bx97//nWuvvZZ7772X+fPn29tcLhehUIi6ujr++c9/kpOTwzXXXMOpp55KYmKiXUlMi6dEDl1jx47ltttu48QTT7TvJ1aVL2XHFxERERGR7mB91/jnP//Jv/71L2JjYwmFQgwfPpw//OEPnH766fuU7AY6Jq9oaGggPDwcr9e7T1mtRURERA5njY2NVFRU2IEgYWFhZGRk9PRpifygtbS02NW4li5dyuLFi6mrq6OwsJCFCxfi8/nwer34fD6gPTjM5XLh9/vxeDw4nU7a2toIBAI4HA5CoRBtbW0kJiaSlZWFx+PhqKOOYvr06SQkJNCvXz9GjBjRk5csIp2Qk5MDtI91xMTEMGTIEKBzyTrXrl1LIBDA7W5f6jpx4kSgc1XRrbGVwsJCKisr8fv95OXlYZomd9xxB8Fg0O5XRERERKS305OriIiI9Lj//ve/3HXXXSxdupQzzjiDmTNnkpaWhtPpxDRNQqHQPi1ssrI7Tpw4kRdffJFPPvmERx99lAULFthZ4KyJgwULFrBgwQLOOussfvWrXzFt2jQFh4gc4h555BH792AwaAd0WgFgIiIiIiIi3eHzzz9nwYIFQPuCx0AgwPHHH88xxxzT6T62D/jy+Xy8/fbbFBYWUlJSwsaNGwmFQqSlpTFp0iQmTZrEiBEj7EAzjWeIiIhIT7DmZKBzC7i7gvXsU1xcTE1NDU6nE8MwGDZs2EE5vojs3l133cWnn37K2rVriYiIoK6uzt7mcrkwDIPW1lbCw8Px+/0d7iGBQACAgQMH4vF4SE1N5dRTT2X48OFERUUxYcIEEhMTD/YlicgBamlpobKyEofDgWmaJCcnk56evtf9rOeKvLy8DlW+rOCvzjx3WPvl5OTYgeJtbW2cdNJJ+3MpIiIiIiI9SsFfIiIi0uO+/fZbAN555x3eeecdhg4dyiWXXMKll166XxN12w/yRUREcPbZZ5OZmcmzzz7LvHnzqKqqwuFw4PF4ADAMg//+97988MEHnH766dxyyy2ccMIJXXNxInLQbb/oUZnaRERERETkYHnzzTcpLS0F2hNRZGRkcPPNNxMXFwfsPSP19t9lXnnlFZ544gmWLVtmZ83f3osvvkhmZiannHIKN910E8OHD9+pDxEREZGutLvkWrt6zzAM+7nE6XR2+fOJ1XdeXh7Nzc12/xMmTOjS44jIvmloaODPf/5zh4pdTqcTp9NJMBjEMAx7W2trK9HR0fTt2xeHw8ExxxzDtGnTiImJYcCAARx33HFA+/1k+ySh23/nUdI/kUPDunXrgPZ520AgwMCBA4G9j2FY24qLiwHsRL9Wtb/OPF9YbXJzczEMw75n7EsAmYiIiIhIb6GVkCIiItLjrMG+sLAwDMMgPz+fe+65h3/961+cdNJJXHDBBUyZMoWkpCSgfSDf4XDs02ThhAkTePTRR7nooou4/fbb+eKLL+zBPWviMRQK8c4775CYmMgJJ5ygBVMihyBroi8vL4/bbruNnJwcjj/+eMaOHcuUKVOYNGmSAsJERERERKTL1dfX8/bbbxMMBoH2jPY33XQTaWlpne7DGoP47W9/y9NPP21nyPd4PJimaVdHdzgctLW1kZ+fT35+Pk899RR33303//d//2ePb2g8Q0RERLqa0+kkEAiwfv16PB4PGzdupKysjObmZmJjY8nIyGDYsGEkJSV1CNToDlYVj3Xr1hEIBOwx3yOPPLJbjysie5abmwu0f4exqnhZ87ppaWn4fD6Sk5OZMWMGAwYMoF+/fkyZMoURI0bg8/mIiIiw+7K+17hcrg5zw9t/11HQhsihYcWKFfbvDofDTmBjmuZenxnKy8upqqrC6XRimib9+/enT58+nR77sO4T+fn59jEBxo0b12G7iIiIiMihQKseRUREpEf5/X42bNhg/+5yuXC5XBiGwcaNG3nhhRdYuHAhM2fO5IorruDYY4/tMADX2UE9a1Lg2GOP5fPPP+ett97iz3/+M9nZ2fYAn9frxTAMEhIS9qlvEek9nE4nfr+fhx9+mPnz5wOwfv16AI466ij+8Y9/MGHCBH2+RURERESkSy1atIiKigrCwsJoa2tjyJAhXH/99fvcz3333ceDDz4ItH+/CYVCBINBO6jL5XLZ4yKhUIhQKIRpmtx///20tbVx55136ruOiIiIdLmCggKeeeYZ1q9fz4oVKyguLsbpdBIdHU0oFMLn89mBHjExMYwePZqjjjqKY445hkmTJpGVlYXH4wGwE/MdyDOLte+aNWs6jPVOmjTpAK9URA7Ed999Z/9ufTYzMzMZP348M2fO5JxzziElJWWXn/+IiAgMw7CDQbafD1ZwhsihbenSpfbvbreb0aNH73UfK+Fnfn4+DQ0N9n3D2tcwjE4n/GxsbKS8vByHw4FhGISFhZGenr4fVyIiIiIi0rMU/CUiIiI9KhAIcMcdd/Duu+/y3Xff2RmyHQ6Hnb2ptLSUp59+mqeffpqxY8dy1VVXceaZZzJ06NBOTw7uOClw9tlnM3DgQF544QXefPNNSkpK7L6OOeYY+xxE5NBhTQL88Y9/5LXXXgPaJwt9Ph833HAD9957L4mJiUDXfr6tLLO6Z4iIiIiI/DCZpsnHH38MtC8+AjjnnHOIjY3FMIxOV7745JNPuP322zsEd1n/ay2cNAzDPgZs+x7S1NTEww8/zNSpU/nRj37UZdcmIiIiP2zFxcU8+OCDzJs3j5qaGvt9K4lffX09gF2dB6ChoYHs7Gyys7N59NFHGTp0KMceeyyzZs3ijDPO6PRC7T2xnpfq6upwu934/X4ARo4cecB9i8j+W7ZsGbDtu4z1e0tLC++88w6LFi1i4MCBZGRk0L9/fzIzM0lPT7cTc1pJQre3/fchzcOIHJqsyl+maRIVFWVX/tpTYKd1H8nNzaW1tRWn04lhGEyePLnTx7XuHUVFRdTW1tp9WMfflzEbEREREZHeQMFfIiIi0qMiIyP57W9/yznnnMNHH33E/Pnzyc3NpaGhgVAohMfjsQf2TNNk9erV3Hfffbz77rucc845nH766QwYMACXy2Vnu+5Mxkin08n48eMZP348M2bM4N5772XJkiUAjBs3DlAgh8ihxul0Ul1dzT/+8Q/q6upwuVz4fD7OOecc/vSnPxEbG9stx93+XmGapp2NX0REREREfhiqq6v59ttvAQgGg4wYMYJTTz0V2PvYgjWOkZOTwwMPPABgL0ayxkNSU1MZN24c06ZNIzIykrKyMr799luWL19OS0uLnUCnsbGRu+66S8FfIiIickCshdDz58/n5ptvpqysDGh/RrGS9pmmaQdibB+4br1vvWeaJvn5+eTn5zN//nwmTZrEFVdcwTnnnEPfvn0P6DwDgQCzZ8/mpJNOorS0lKamJuLi4g6oTxE5MKtXrwa2BVyEQiHKysrs+wiA1+slMjKSvn37kpCQQFJSEqmpqWRkZJCVlUVWVhbp6emkpKQQGRm526Av0zQBFBQmcgjIzc0F2j+3cXFxDB48GOjceoy1a9faVUMBO/irM/ta96K8vDyam5vtfSZMmGBvFxERERE5lCj4S0RERHpceHg4EyZMYMKECdx44428+eabvPTSSyxYsMDO1gjbBvBqa2tZsGABCxYs4C9/+QsXX3wxl112GePHj9+ngAtrgdUZZ5zBGWecwUsvvcTDDz/MoEGDuvwaRaT7tba28tBDD1FdXW1nxM/MzOQf//hHlwd+WZMFOTk55ObmMmLECEaMGKGgLxERERGRH6D8/HyKiors11OmTGHUqFHAnrNYw7axjldffZUFCxYAHRdKnnrqqdx3331MmjTJXmjd1tZGYWEhL774Ik8++SQ+n8/uLycnh48//lgBYCIiIrLfXC4XTz31FHPmzOkQ5GWxAi62DwTbcX+r/faJ++rr61mwYAGrVq0iNzeXm266ibS0NPvZZ195PB7OPfdcAHw+H83Nzft7ySLSRZYuXQq0J8VwuVx20Kh1HzAMA7/fj9/vZ+vWrRQXFwPt34siIiKIiooiLi6OhIQEUlJSSEtLswPCMjMzSUtLo1+/fng8nl1+17KqhFl9KihMpOdVVVXR1NRkVw1NTU3tVLC2Nee6fv16YFuglpXId2/jLdvvs27dOgKBgF19dF8CyEREREREehMFf4mIiEivYE0O9u3bl2uuuYbLLruM5cuXM3fuXD777DPy8/M7TBQ4HA5M06SsrIy//OUvPPfcc5xwwglcdNFFTJ06ldTUVLvf3Q3ub5950ul08uMf/5hjjz324F20iHSp9evX88ILLwDtg/lJSUnccccdJCUl2Z/zrmLdU5xOJ9dffz11dXU4nU4SExO5//77ufzyy/F4PF12PBERERER6b1WrFhBTU2N/XrChAnEx8d3al+Hw0F5eTlvvfUWgL0QyTAMzjzzTP72t78xYMAAgsEgbrcbp9OJ2+1m3LhxPPjgg2RlZfHzn/8caF8A3dDQwIcffsiPfvQju2qHiIiISGcZhsEHH3zAT3/6U3suBrCTbZmmaSfa8vv9DBw4kMTERBobG9m8eTObN2+mra1tp36tamAOh4Pa2loefPBBFi5cyIsvvsjgwYP3OwDM2i8iIoKIiIgDu3gROSBNTU3MnDmTsrIyCgsLaW1txTCMndptH7hlBZAahkFLSwstLS1UV1fbbd1uN3369CE6Opq4uDiSkpJISUnpUCUsIyOD/v37k5CQoIAvkV4oJycH2Fbl3Kr61Zm529bWVkpLS3E4HASDQZxOJ0OGDAE6F7hltcnNze1Q5WvSpEmd7kNEREREpDdR8JeIiIj0CjsO7IWFhTF16lTGjh3L6aefzuuvv868efNobGy0JwrcbjculwvTNNm8eTPz5s3j66+/5pRTTuHyyy/nlFNO6dDv7iYPrTYul4sBAwZ030WKSLf69NNPqaysxOv14vf7mT59Otdccw3Quexv+2PkyJE8+OCDzJkzh0AgQHV1NX/605+47LLLuuV4IiIiIiLSu5imSUFBgT1WER0dzciRI/cp6GrevHl25TDTNAmFQoSHh3PffffZ4xRWUNj2xzVNkzlz5vDBBx/w/vvv221qampobGwkOjq6C65QREREfgis+ZPly5fz85//vMN8SigUwjAMxo0bxymnnML48eOZOXMm4eHhVFdXEwgEaG1ttX+Ki4tZuXIlOTk5FBQUUFFRgWEYdvUfK8nfkiVLOPnkk/nss88YNGjQfp23Fm2L9B5RUVG8//77+P1+ampq2LBhA7m5uaxevZo1a9aQl5dHWVkZgUBgp32dTqd9bwDsgLBgMEhDQwMNDQ1UVFTY7b1eL1FRUcTGxhIfH09ycjL9+/cnMzOTrKwsBgwYQEpKCkOHDtV9QqSHLVu2zP7d4XAwcuRIgA7BWDuynkNKSkqoqamxq6NbgV+dTfpptbHGXKyxm85WaxcRERER6W0U/CUiIiK9WnR0NDNnzmTmzJk88sgjvPjii7z++ut89dVXBINBoH2Q0OVyEQqFqKys5IUXXuCFF15g+PDhXH755cyaNYsxY8ZocF/kMNbU1MT7778PQCAQIDY2losvvhig27Pdn3feedx1111s2rQJ0zQpLy9nzZo1dtY4ERERERE5fPn9frZs2WK/Tk5O7nTVL2j/LvPmm2/S0tJiVzl3Op3ccMMNjB07drf7OZ1Oe5HSRRddxMKFC2lubgZg1apVNDQ0KPhLREREOsVaYF1bW8sTTzxBcXExHo8HwzAIhUJkZWVx0003cfHFF5OWltZh36ioqJ36mzZtGueeey6VlZXk5OTwxhtv8NZbb+Hz+YD2oHarekdJSQnXXHMNH374IeHh4QflekWke3m9Xvr370///v055phj7Pebm5upqqqisLCQtWvXsmbNGtasWUNBQQE1NTWYprlTXy6XC5fLZQd+WEFhfr+f2tpaamtrKS4uBrCrAEZFRREXF0dUVBRffvmlqgKK9LDly5cD7c8bYWFh9liHaZp2tb4d13FYzybr16+nsbERp9OJaZqMHz/e3rezgVtVVVVs2rTJHnNJSEigb9++XXeBIiIiIiIHkYK/REREpNezBvujo6O57rrrOPHEE/nss8944403WLlyJVu3bsU0TTweD4A9+J+Xl8ef//xnPvroI8466yzOOusshgwZgtvttts4nU4FhYkcBvLz81m3bh3Qfg+YNm0aRx11FND9Wdvi4+O59tprufvuu3G73bS0tDB37lwmTZq024qDIiIiIiJyePD5fNTU1Niv4+PjCQsLA3ZfgXz7batXr+brr7+2xycMwyAtLY0rrrii0+dw5plncu211wLtCx7z8/NJSEg4gKsSERGRH6LXXnuNjz76CMBeID1+/HgeeughTjzxRKA98ZY1F2OxKndYzz1Op5PY2FhiY2MZPnw45513HvX19Tz55JM8+OCD1NXV2YEcDoeDL7/8koceeojf/e53Gk8VOYxFRkYyaNAgBg0axCmnnAK03z/q6uqoqKhg/fr1dkDY2rVrKS4uxufz2ZV6tud2u+2kf1Z1QsMwaGlpoaWlherqamJjY4mIiNB9RaSHrVq1Cmhf8xEREUFKSgrATs8TFtM0CQaDeL1ecnJyCAQC9ud98uTJnT6utRakoKCA+vp6nE4nhmHYVb+6O3moiIiIiEh3UPCXiIiI9HrbB254PB6GDx/O8OHDmT17Nm+//TYvv/wyb7/9Nn6/325nDeI3NDTw9ddf8/XXX/PQQw9x4YUXcumll3LMMcdoME/kMLJixQqqq6vt19OmTbMnD7pzUs+aGBg2bBgOh8O+X1mLPzWpKCIiIiJyeAuFQh0qf5mm2anxBut7wjvvvAO0L160kt9MnTqVI444otPn4Pf7ycjIoLS0FNM0iYqK0vcQERER6TTrueHxxx+nqqoKp9OJ3+8nMzOTl156yV4kHQqFdrlQe0/B7tb4aGxsLDfddBNjxozh7rvvZtmyZUD7/E8oFOK5557jkksuYdCgQd10lSLSGzkcDuLi4oiLi2PMmDGcd955QHug6ZYtWygtLWXdunWsWbOG1atXk5eXR2lpKcFgkGAw2KEvp9OJy+XC4/HQ0tJiB4l09juaiHSPgoICoP25wOfzceedd5KVlUVmZiYDBgwgKyuL9PR0+vXrh9frxel04vV6AVi/fn2Hz/CECROAzs39WsHp69atw+fz2cFf1niLtV1ERERE5FCi4C8RERE55FhVu1wuF+eccw5nnnkmOTk5vPHGG3zwwQesXbsWn8+Hy+WyM2eHQiGqq6t5/PHHeeWVV5g6dSoXXXQRxx9/PBkZGXYWy+6uECQiXc80TVauXGlnf4yOjmbMmDEHZTJv+8mG4cOHk5ubC0BVVRUbN24kNTW1289BRERERER6Tnx8PFVVVUD794O8vDyioqL2uI+1CHrVqlV8/vnn9ntWZuszzzwT2HsWaqufpqYmMjIy2LBhAwApKSlUVVWRmZnZBVcoIiIiPwRfffUVOTk59lwJwKOPPmoHfsG+J9lyOBwd9omMjOTMM88kLi6O66+/3h5LdbvdFBQU8Pbbb3PLLbdorkZE8Hg89O/fn/79+zNlyhT7/ZaWFiorKykqKiInJ4c1a9aQk5NDfn4+NTU1mKZJIBAA6BC4KiI9o6CgANM0cbvdGIZBa2sr7733Hi6Xi4iICKKjo4mLiyMxMZGUlBQyMjLIysoiKyuLIUOGkJOTA2A/mxx33HEAHSr/WWMju3tOWbduHcFg0A5gt4K/lDRHRERERA5FCv4SERGRQ47D4eiw+MntdjN+/HjGjBnDzJkzefPNN5k7dy4bN260g0FcLhculwvTNKmtreXdd98lOzubE088kUsuuYRzzjlHk4kih6jW1lbKysrs16mpqaSlpR2044dCIUaNGmVPKDqdTtauXbtT1kkRERERETn8GIZBMBi0F0o3NjbS0tIC7H0h0fvvv28verYWJB555JEcffTRAJ0epwgGg/ZCKIB+/frZ4yEiIiIie1NXV8e8efOAbQFbp59+OrNmzbIXVHcV0zQ59thj+etf/8pll13G1q1b7f4//fRTrrzySuLj47vseCJyeOnTpw+DBg1i0KBBnHzyyUD7d6m6ujoqKipYv349ubm5fP7558ycORNQgIdIT1q1ahUAXq8Xv99vB2sZhkFTUxNNTU1s2rTJbu/1eunTpw+xsbFER0dTUFCAw+HAMAzCw8NZvnw5ycnJ9O/f3656vqvPuGma9jhLXl4esG3cZeLEiUDnx1xERERERHoTBX+JiIjIYcPlcjF16lSmTp3KAw88wJtvvskrr7zC559/TmNjI7AtcCwUClFTU8Prr7/O66+/TlxcHI8//jg//vGPe/gqRGRfNTU1sWXLFvt1fHw8bnf7V52uXpywK1Z1wbi4OPuYlZWVJCYmdutxRURERESk59XX1zN58mTef/99HA4HTqeTZ599lj/96U+7rVphfYf497//bY9XWMFbZ5xxhl2xa2/fZaztbW1t1NTU2O8nJSURERHRJdcnIiIih7/CwkKys7OB9meSQYMGce211wJdP77qdDoxTZOZM2cya9YsnnvuObva6ccff0xra2uXHUtEfhgcDgdxcXHExcUxZswYAG677TZ7+56qKYtI91qyZAmAnSTH4nK5cDqd9jOGaZoYhoHf78fv91NXV2e3tdoYhsH1119PbGwsKSkppKWl2VXCBgwYQFpaGklJSXi9XpxOpz0eU1paageQAQwdOrRDvyIiIiIihxIFf4mIiMhhIRQKYZqmvbDK7XZz3nnnMXHiRBYvXsw777xDdnY2lZWVGIZhL8hyOp14PB62bt1qTyoejGAREek6Pp+PrVu32q+jo6MP+kLHuro60tPTWbZsGaFQiKioKLsSmIiIiIiIHL7i4+Pt4C+3200gEODtt9/muuuusxcUWayFzaFQiGeeeYaioiL7tWmaxMbGctFFF+HxePbpHLZs2dIhU3ZcXBxRUVFdcn0iIiJy+FuyZAnr16+3A9SPPvpouxJpd8yVWIuxb7/9dubOnUsgEMAwDLxeL9999x2pqaldfkwRERE5+M4//3z69etHWVkZeXl5FBYWUlFRQVtb2y4rlrvdbvs5wVr7Ya3dCAQCdhUvaA8gi4iIIDo6mri4OBITE0lJSSEjI4OMjAxGjhyJ3++nsrLSrtaelZWFx+PZbbIeEREREZHeTsFfIiIi0iuEQiGg40SilfV6+4E3a6HUjqyKXttvc7vdDB06lKFDh3LRRRfxySef8Omnn/LKK6/YGbGtQDCA8ePH73QOInJoqK6utn9vaWmxFzoerM+z3+/H6/Xar1NSUqiqqiI2NvagHF9ERERERA4+awHSkCFD7CzSDoeD9evX85vf/IYHH3yQzMxM+7uCNWbx3//+l7vuugugQ/bpU045hUGDBu3TIiTTNMnJyaG+vt7uLyMjQ8FfIiIi0mnZ2dk0NDTg8XgIBALMmjWL5ORkoHvHV2NjYznttNOYP38+TqeTUCjEkiVLOPPMM5WkT0RE5DAwZcoUJk+ejN/vp6Wlhbq6OjZv3kxJSQmFhYXk5eVRUFDAhg0b2LRpE8FgcKc+rIS+DofDDuKyKoU1NTXR1NTUISGO1+ulT58+9O3bF6fTSW1trV15dNy4cQAK/hIRERGRQ5aCv0RERKRHWQNru5rE237ALRgM4na7cblcdoYmt9tNXFwcmzdvJj8/n61btxIeHo5hGGzYsIGioiIaGhrwer34fD42btxITk4OdXV1HY5nDSKOGDGi+y9YRLpcTEwMW7ZssT/Xa9euJSEh4aCeQ1tbW4fqY0lJSbjd+rolIiIiInI4s6pjnHfeeTz++OMsWbIEt9tNMBjkrbfeYsOGDVx11VVMnjyZyMhIqqqqWLp0Kc899xzl5eUdEtiMHj2aa6+9dp/Poa6uju+//95+HR8fz/Dhw7vk+kREROSHITs7G9iWkG/69OndfkzDMIiPj2fs2LHMnz+fsLAwfD6fPcZqmuYuEwGKiHQVKzEpKDGoSHeyKnRFRESQkJDA4MGDOfroowkGg7S2ttLc3ExtbS2bNm1iw4YN5Ofns379egoKCigtLaW+vt5+Rtmx3+3XmVgBYX6/H7/fT11dHdD++bbWnRxxxBEH7bpFRERERLqDViOKiIhIj/D5fEREROB0OqmqqrKDuaKioqiqqiI/P5+amhq8Xi/BYJDS0lIKCgpobm4mMjISn89HYWEh5eXlREVFERkZSW1tLY2NjQD2QJ+VPXt72w/wORwOgsEgqampREREKJukyCFo69atxMTE0NzcjGEYeL1e6uvr6du370H7PNfV1VFeXm6/jouLIzw8/KAcW0REREREeo7D4aBPnz5cfvnlfPfddwSDQXuh8sqVK7n55pvp06cPycnJ+Hw+KisrAeys01bSiMsvv5yTTjrJ3rY3VjKdvLw8li5dar+fmZnJ4MGDu/oyRURE5DBVXV1NUVERLpcLwzAYOHAg/fr16/bjWuO2/fr1Izw8nEAgAEBERASAAr9EpNtpPlikZ1lrQ6KiokhOTmbkyJGEQiECgQA+n4/Gxka2bNlCRUUFhYWFdlBYUVER5eXltLW17XItiNvttsdVrCphlsmTJwOdG3cREREREemNFPwlIiIiPeKXv/wl33zzDYZhEB0djc/no6ioiPr6ejvz09atWztkXduRNSjf2trKli1bcLlchIWFEQwGMQwDl8tlTxpaA3/W4qodBwJHjRoFKJukyKHIMAxGjx5tZ6htaWnhv//9Lz/5yU8OWkBnbm5uh+CvxMREoqOju/24IiIiIiLSO8yZM4dFixYxd+5cQqEQLpcLh8OBaZq0tLRQXFwMtC9kNk2TUCiE0+kkEAhw8sknc8011+xT9WBrvOSjjz5izZo19oLtyZMnK/hLREREOm316tVA+9yJYRgceeSRwLZA8+5i9Z2SkmLPB0H7fE9TUxNRUVHddmwR+eGy5oy+++47CgoKSEtLIz4+noEDByqhn0gv4HA48Hq9eL1eYmNjSU9PZ8KECXZFr5aWFurr66murqa0tJTCwkLy8vLIz89nw4YNVFZWEgwGd+rXGkMZN24coOAvERERETl0KfhLREREDjq/388//vGPnd53Op04nU58Ph8+nw+v1wtAIBAgFArhdrtxOBx2BsgdgzoMw7CDvrZ/3adPH6Kjo2lsbKSlpYX+/fuTlZWF1+ulqakJv9/P5ZdfDijLm8ihJhQKMXToUFJTUwEIDw+noaGBFStW8JOf/OSgBHQGg0FWrlzZofLgkCFDFPwlIiIiIvID8+STTxIXF8eTTz7ZIbO0Nd4B28YyrMQ0w4cP58knn9znChtWENnbb79tj5kYhsGPfvQjIiMju/S6RERE5PC1bNky+3en08nEiRMB9piYryulpqbS0NBgv25ubiYsLOygHFtEfnis72P//Oc/mT9/vj239OyzzzJ+/PgePjsR2R2Xy2UnEE5ISGDQoEEcffTRBINB2traaGpqora2lsrKSoqLiykoKCAvL4/CwkJKS0upq6sDID09vWcvRERERETkACn4S0RERA669evXA+DxeOzFUKZpYppmh+Arv98PQFRUFC0tLQSDQdxuN4MHDyYsLIy6ujo7yCI1NRW3201LS4s94JeamkooFCIQCJCYmMiQIUOIj4+3K4qlpKQQFxcHYGeAUpYnkUOLNVGXlJQEtN83HA4Hc+fO5cYbb2TEiBHdlqXWOnZFRQWLFy+2309MTGTkyJFdfjwREREREendYmNjueuuuxgyZAivv/46a9eupampyR7z2J7L5WL69Ok8/vjjDB48eL+qFj/11FOsW7cOaE+cEx0dzfTp07vqckREROQHwAr+CoVCeL1eRo8eDRy8RHkxMTF2Ur9QKERbWxsej+egHFtEfnisuaK1a9dSXV1NdXU1APHx8T15WiKyn9xuN263m8jISJKTkxk5ciTTp08nEAjg8/lobGykpqaGDRs20NzcDOycYFhERERE5FCi4C8RERE56FauXAlgZ7oOhUL06dOHtLQ0Bg4cyMCBA+nfvz/QHhSWlJTEoEGDSElJobGxEdM0SU5OZujQoQDU1tbicrk6DMzvKdgjOTl5p3Zutx6LRA5F1uf8iiuu4Nlnn6W1tRWn00lVVRV//OMfef7557stqNOaHPj888/58ssvcbvdBINBhg0bxrhx47rlmCIiIiIi0rslJiZyyy23cMEFF7Bw4UI+/PBD8vLyaGtrw+v12uMXN954IzNnziQ8PLxDFfPO2rRpEw899JCdOMfpdHLRRReRkJDQHZclIiIih6nvv/8eaJ8riYqKsudduntRtDU3U1paCrQHxvv9fqKiojpsFxHpao2NjWzatMmuphwREUFGRkZPn5aIdBGHw4HX68Xr9RIbG0t6ejrjx4+3q5oq8EtEREREDmVa5SwiIiIH3aJFi+zfHQ4HoVCIfv36cdFFF3H++eczYcKEfeqvX79+QHsghmmauFwue1LQMAwcDkeHScLtszlp8lDk8DBp0iSGDx/OqlWr7MH71157jaOPPpr/+Z//6fLPeigUwul0Ul5ezquvvgq0VzMMBoOccMIJpKend+nxRERERETk0JKRkcHll1/O5ZdfjmEYNDc309LSQt++fQkPD7fbhUKhTgd+WeMZra2t/Oc//6GoqIiwsDDa2tpITU3lggsu6NBOREREZG8KCgqA9ueHxMREMjMzge5fGG2N4a5du7bD+1YAhrVdRKSrWN+TiouLqampsd8bPHhwD5+ZyOHLqoRurdfoybEKjZOIiIiIyOFAq51FRETkoGtsbATA7/cTCoXwer2UlJRw3333MWnSJI477jgee+wxcnJyOuxnmiaGYex20s/hcOy0YGr7QLDt24nI4cXr9fKb3/wGaP/cOxwO/H4/t956K6+88gqBQADArjZ4oEzTBODOO+9k4cKFALS2thIeHs7ZZ58NaIGCiIiIiIi0c7lcxMTEkJKS0iHwC/ZvjOL555/noYce6rD/8ccfz8knn7zffYqIiMgPT35+PqFQCLfbTSgUIisri4iIiIN6DsXFxcC2sdThw4cf1OOLyA+HNa+Tm5tLc3Oz/b1p4sSJPXlaIoc1p9OJ2+22525FREREROTAKPhLREREDronn3yS//znP5x66qmEQiH8fj/QvjjJ4XCwePFi7r77bm699VaeffZZqqqqgPbBQZfLpYAKEdmlk08+mVNOOYVgMIjL5cLlctHS0sIVV1zBH/7wB+rr67skq5yVmf/hhx/mmWeeoa2tDafTSSgU4uKLL2b06NGAFlyKiIiIiEjXcjgclJSU8Nprr1FbW4vH46G1tZXo6GguvfRSXC6XvaBRREREZG9WrVoFYCfQS0lJAaClpaXb52GsY+4Y/DV27NgO20VEutq6desIBAL2HM7kyZN7+IxEDi/Wf9PfeOMNTjzxRG6++Waee+45vvvuOztZ5/7YcbzDShps/YiIiIiI/BA4Qnr6FRERkR5SV1fHsmXLeP311/niiy/YsGGDHbQRDAbtdsnJyVx00UVcfPHFTJ06tQfPWER6K8MwcLlcfPrpp1x66aVs2bIFj8eDaZp2ta8hQ4Zw7bXXMnPmTEaPHo3b7QY6VgNzOBwdFhZYEwnbv+fz+bj11lt5/PHH7YBU0zSJiYkhOzubkSNHHsQrFxERERGRH5La2lrWrFnDqlWrWLt2Lfn5+Zx00kn89Kc/JTY2tqdPT0RERA4hv/nNb/jzn/+Mx+MhGAzSv39/pk2bRmZmJsOHD2fAgAH079+fuLg4oqKiCA8Pt8dUu0IgEGDSpEmsXbsWwE4W2JXHEBGxmKaJ0+nkkksu4dVXX8Xj8RAIBPj66681/yzSxYLBIOeddx7vvvuu/V6/fv14/vnn7QTBnU2iuX3b1tZWDMMgMjKyW85bRERERKS3U/CXiIiI9LjW1lYWLlzIq6++yquvvorP5wPagzC8Xi9+vx+Xy0VGRgbTpk3jvPPOY8qUKSQnJwPtQR87BmyIyA9PMBjE7Xbz73//m+uvvx4At9ttB3CZpklsbCzjxo3jRz/6EaeddhoTJkzo1L2jtbWVpqYm3nzzTR577DFycnJwOBx2sGpMTAw//elP+eMf/2hPIIqIiIiIiHS3YDBIKBTC4/H09KmIiIjIIeZHP/oRn376KW63u0NCvu2lpKQwYMAAhg4dyrBhwxgyZAiZmZkkJSXRt29f+vTpg9frxeVydfq41iLuwsJCpk6dSk1NDaZpMnDgQAoLCzW+KiLdasqUKSxduhSn04lpmtTW1tK3b9+ePi2Rw8obb7zBnDlzqK6uJiIiAp/Px2OPPca1115LeHh4p/uxnhny8/P5+OOPycvLY8mSJVRXV9OvXz9Gjx7N0UcfzbRp0xg6dKg9NrIvwWUiIiIiIocSBX+JiIhIj9px4K22tpa5c+fyxhtvkJ2dTWtr6077REVFceqpp3LppZdy7rnnHszTFZFeLhQKEQqFePbZZ7npppvse4h1n9nx64/X62XSpElMmDCB4cOHM3LkSNLT02lubqaiooLa2loaGhrIz8/nrbfeYtOmTXZ/21cpvO6663jssccICwvThIKIiIiIiIiIiIj0eomJidTW1tqBVi6Xyx7XNE1ztwFhYWFhpKamMmjQIIYNG8awYcMYNGgQ6enpJCYmEhMTQ0REBB6PZ5fjpNb46fvvv8+FF15IIBAgGAxy9tln89Zbb9lJvkREulpVVRVHHnkkFRUVhEIhEhIS2Lx5c0+flshh55JLLuH111+3523HjBnDkiVLiIiI6NT+28+13nfffbz00kvk5uYC7XO0u1ruOnPmTG655RZOOeWUnfoQERERETlcKPhLREREegXrkcThcBAIBMjLy+Ojjz5i3rx5rFq1itbWVpxOJ16vl7a2Nrv94MGDueiiizj77LOZMGECYWFhdn8azBP54QoEAvznP//h0UcfZe3atQA4nU4cDof9EwqFdruAITw8nOjoaFpaWmhubrb3t7LOOp1Oe98bb7yR22+/ndTUVGWlFRERERERERERkV7P5/PRv39/gsEgPp9vl4uorQRY1nhnKBTCNE0Mw9hlnzExMWRmZjJkyBCGDRvG0KFDGThwICkpKcTHxxMVFUV4eLhdtfThhx/m17/+NS6Xi0AgwF133cXtt9+u4C8R6XLW3M2iRYs47bTT8Pl8GIbBtGnTWLhwYU+fnshhZcOGDYwfP57m5mZM0yQqKorHHnuMK6+8cp/6MQyDSy65hPnz52MYBh6PB9M0MU0TwJ6vdTgc9pxtZGQks2fP5u6778br9Xb5tYmIiIiI9DSNmImIiEivsH2glsfjYcyYMYwZM4Zf/vKXvPvuu7zyyivMnz+/QyUwh8NBYWEhDz/8MAsWLODss8/mwgsvZPDgwR2yU1qBHiLyw+HxeLj++uvJyMjgj3/8I19//bU9GWBNDmwfJGotZID2+0Zra6t9v9l+MsFqFwgECAsL43/+53/4wx/+QN++fQEU+CUiIiIiIiIiIiK9nmEY3H///WzevJkNGzawceNGqqurqa2tpaGhgZaWFvx+/y6TZ1mLrbcPCjMMg4aGBtasWcOaNWs6tE9OTmbAgAEMHTrUrhI2fvx4srOzMU3THpedPHmy3b+ISFeyAlzXrVtnJxw1DIMjjjiih89M5PDz+eef09jYSHh4OK2trUyaNInLLrtsn/rw+XzccsstzJs3r0NSTiu5pxWc7nA47PnfUChEY2MjTzzxBGFhYdx1113dcXkiIiIiIj1Klb9ERESkV7ImC63sjo2NjaxevZo333yTTz75hIKCAlpbW3G73QSDQXvQ3ul0MmvWLC699FJOO+00+vTp05OXISI9ZMfqf6+99hr/+te/+Oyzzzpksd1+kYK13/YZ46w+dlzkMGXKFG644Qauvvrq7rwMERERERERERERkW5XU1PDxo0bKS0tZcOGDZSUlFBWVkZlZSWbN2+mrq6OxsZGWlpadlv5a/uF2NCeZGtXwWMAXq8Xp9NJa2srLpcLwzAoLS0lPT29265RRH64DMPA5XLxy1/+kocffhiPx0MgEOCFF17Y56AUEdk9v9/PNddcw8svv2wHdz/00EP87Gc/63RlT9M0efHFF7nqqqtwuVwdgrsAOwBsR1ZCYNM08Xg8fPDBB5x44oldeHUiIiIiIj1PwV8iIiJyyPnmm2+YN28eL774ItXV1fb7Xq8XwzAIhUIkJydz5JFHct5553HCCSeQnp5uTyA6HA5ljhT5gbAm9ACqqqpYunQpH374IdnZ2RQWFtLQ0LDL/ZxOpz2ZYL12OByMGjWKn//855x44okMGDCgQ/8iIiIiIiIiIiIihwrTNO2F0rsSCASorq6mvLyc0tJSiouLKS0tpby8nKqqKmpqaqivr6epqYnW1tbdLsR2uVz2cazkW1YAmfVeeHg4LS0t3Xq9IvLDZSUMPOuss3jvvffs5KKrV69m9OjRPX16IoeNwsJCzj77bNatWwfAUUcdxVNPPcX48eN3Sty5I2vOdeHChdx8882sWrUKt9uNaZqYponT6WTYsGGceuqpzJgxA5/Px+rVq/nyyy9ZuXIlzc3N9vOGVdlv6dKlez2uiIiIiMihRMFfIiIicsiwBvUsfr+fN954g9dee42vvvqK2tpaYOdsT1OnTuXiiy/mkksuITEx8aCft4j0PtXV1eTl5ZGXl0dBQQFlZWVs3LiRjRs3Ul5ejsPhYODAgaSnpxMREcHAgQO59NJLGT58OFFRUQAK/BIREREREREREZHDRigUsudW9pRAr7m5mcrKSsrKyigpKaG4uNgeX62urqa2tpaGhgaam5vx+/277MPq3+VyEQgEGD9+PCtWrNCYq4h0m2AwyMSJE1m7di3Qfs/z+/2dqkQkIp3z1ltv8ZOf/ISmpiYAfv7zn3PPPffYc6t7YgVpXXfddTzzzDMdAsdDoRDXXnst9957L8nJyR32q6+v5/HHH+eBBx6gqanJTgjcr18/nn32WU4//fRuuVYRERERkZ6gb7AiIiJyyLAmA63JR6/Xy49//GOOOuooFixYwNy5c/n222/twcTw8HCCwSCLFy9m8eLF3HfffZx11lmcd955TJkyhfj4+B67FhHpGVYQaVJSEklJSRx77LH4fD58Ph/BYBCv10tUVBT19fXU1tYSExND//79O/RhTT5oEYKIiIiIiIiIiIgcLnZXBcxadG1tj4yMZPDgwQwePLhDu5qaGjZt2kRJSQklJSVs2LCB8vJyNm3axObNm6mrq6OxsZGWlha78pdpmgCMHz/ePpaISFey7l+lpaVs3rwZh8OBaZpkZWUp8Euki61atcpeq+F0Ohk/fnynAr+g/TlkzZo1fPTRR/b+TqeTQCDADTfcwJ/+9CdiY2Ptz7T1DBEbG8ttt91GYmIiv/jFL2hpaSEsLIzNmzfz7bffcvrpp++UZFhERERE5FClb7EiIiJyyNl+8tHhcNiTjNdddx2LFy/m5ZdfZt68eVRXV3doV11dzb///W8WLVrEySefzJVXXskRRxzRE5cgIj1kx4F9p9NJZGQkkZGRHd7v168f/fr1A9oXIGy/8GFXCyBEREREREREREREDke7CgrbvkqYtT0hIYGEhATGjBljtwsEAmzevJny8nI7IKykpISKigqqqqqor69n3bp1HHPMMXZfIiJdyQoUWb9+PY2NjTidTkzTZNy4cT19aiKHlWAwSHFxsf06KSmJoUOH7lMfr776KpWVlUD7/KxhGKSkpHDXXXcRGxsLbHtW2H7ONxgMcsMNNzBv3jw+/fRTO8h848aNNDU1dToATURERESkt1Pwl4iIiPRK208aWqzsTdsP5BmGgdPptNtNnTqVCRMmMGfOHD799FPee+89lixZQkNDgz2Yn5ubS25uLt999x1ff/21Mj2JHKIMw+iQcba7Pse6P4iIiIiIiIiIiMjhZMe5lX21typh0D6u6vF4SE1NJTU1laOOOspu19zcTGVlJRUVFeTk5HDGGWfY+4iIdCXrnrRu3Tra2tpwuVwAShAq0sV8Ph+1tbX265SUFPr27dvp/Tdt2sRbb71lP6OYpkl4eDg//elPSU5O3uO+VhW/K664goULFxIIBABYvny5/buIiIiIyOFAwV8iIiJy0OwYZGUYxk4BG1abXU0aWu1CoRCGYeB2u3G5XDQ3N1NdXY3b7SYuLo6amhq2bt1KZmYmxx9/PNXV1axcudLuw+v10trayvTp07v1ekWke1kTdDvaPijsQBYwiIiIiIiIiIiIiByOdje2eqD2FhRmbY+MjGTw4MEMHjyY448/vsP+IiJdybqvrFu3DtM07fvf5MmTe/K0RA47zc3NbNmyxX4dGxtrB2VZzwC7Ym1bsmQJOTk5uFwuHA4HpmkyZMgQLrnkkk6fw0knnUQgELCDxzZs2EBcXNyBXZiIiIiISC+i4C8RERE5aHbM2GgNrvt8PkzTJDIyEqfTSVlZGZs3b8btdhMdHU1ZWRmFhYVs3bqVsLAw2traKC0tpbCwkJaWFqKiomhqaqK4uJiysjJiY2MJCwujvr6elpYWYNvAfigUsiuITZo0qcM2ETl0zJgxgw0bNnDCCScwceJEjjjiCEaNGkV8fPwuFy5YQaOAHRCmz76IiIiIiIiIiIj80LzzzjsYhsHYsWPJzMzE6/V2+zF3NR67Y5UwEZHuYN1f8vPzAex54rFjx/bYOYkcjhwOB5s3b7ZfBwIB+xljT3OyDocDv9/P22+/DbSvIbE+pyeffDKDBw/eY/AYbAsga2lpIS0tjU2bNgGQmJh4wNVORURERER6EwV/iYiIyEFRUlJCbm4uUVFRlJSUUFRURE1NjR3MVV5eTn5+PoFAgJiYGJqbmykuLqa5uZk+ffrg9Xqpq6vb63EcDge1tbX26+0HB3c0YcIEex8RObSsWLGChoYGnn/+eZ5//nn7/bi4OEaNGsWkSZOYPHkyEyZMYPjw4Xi9Xju73PZM08Q0TXvxgRYZiIiIiIiIiIiIyOHsN7/5DXl5eSQlJTFkyBDmzp1Lenr6QT8PJegSkYOlsbGRiooKHA4HhmEQFhZGRkZGT5+WyGElLi6O6upqoH2Nxpo1a/ZadcsK2vrmm2/Izs4G2udug8EgMTExnHbaafZ7nala2traSnp6OhUVFQD069ePqqoqUlNTD+TSRERERER6DQV/iYiIyEHx5JNP8uCDDwLtGda8Xi+tra173MfpdOJyuWhpaaGlpQWn04nb7cY0TbuCj9vtxuFw2BW9rIE/l8tFKBQiGAzag4Zut5tgMIjf7wdg0KBB3XvRItItamtraWhowOv1dsj0ZhgGW7duZdGiRSxatKjDPhkZGYwdO5bJkydzxBFHMG7cOLKysnA6nbsM+DIMw+5b2eBERERERERERETkcLF+/XoAqquriY+PJzExcZ/2V8UuEdkf2yfrPFj3Dmuep7i4mJqaGpxOJ4ZhMGzYsINyfJEfEp/Ph9vttj9nfr+fzZs307dv393uY82/vv3225SUlADYzxjHHXcc48ePB+hU4BdAMBikra3Nfp2UlLQ/lyIiIiIi0msp+EtEREQOiq+//hrArvTV2traIUjLCrRwuVz2IJ8V5GUFgQF24Ba0D/IFAoGdjmUYhh0c5nA4CA8Pp7W1lUAgQFRUFHFxcQwdOtQ+hiYnRQ4tOTk5QPvnOxgMAnRYbLB9QJcVBFpWVkZZWRnvv/++3Y/H42HYsGFMmDCByZMnM2nSJEaPHk18fPwuJxGse9X2xxERERERERERERE5VBQUFBAKhfB4PASDQTIyMggPD+/0/oZhdJjHERHZ0e7mXnsiEZ/Vd15eHs3NzXb/EyZM6NLjiEh71a0pU6bw4Ycf4nQ68fv9PPPMM9x///0dknnuqK6ujmeeeQa/329X53M4HMyaNavTwVtW321tbWzZssV+PzExkYiIiAO/OBERERGRXkLBXyIiInJQFBQUANiDdm63235tcTqddiCHxeFw2BW9oD14LDw8nPr6egzDICUlhbS0NEKhEI2NjfTr149hw4aRlJREIBDA5XKRkZHBsGHDiIyMpLm5mYiICAYOHGj3LyKHltLSUmDb/WFH1j3DCgiD9mDR7ScOTdMkEAiQk5NDTk4OL730kt22b9++jBo1ikmTJjF58mQmTpzIsGHDCAsLs+9d27MWPIiIiIiIiIiIiIj0ZitXrrR/D4VCDB8+HOjcGGdNTQ0zZswgMjKS0aNHc/TRR3Pdddd15+mKyCHI6XQSCARYv349Ho+HjRs3UlZWRnNzM7Gxsfa8bVJSUrfPrVjzROvWrSMQCNhzPEceeWS3Hlfkhyg5OZlRo0bx4Ycf4na78fv9vPPOO1x99dV2tT3rMxkMBvF4PDQ3N/PYY49RX1+P2+2253ezsrK44IIL9vkcNm3aRHV1tf06Li6OyMjIrrlAEREREZFeQMFfIiIi0u1qa2vtQTan04lhGHbFrr59+xIeHk5DQwNtbW2MHDmStLQ0nE4nTU1NJCcnM3ToUJKTkzFNE4fDQWpqKsOHDycqKor6+nq8Xi8DBw4kOjoawzBobm4mJiamU+em4C+RQ0soFOKyyy7j9NNPp7CwkJycHL7//nvWrl1LQUEBVVVVNDU1ddjH4XB0qNplTSw4HA47S611LzAMg7q6OhYvXszixYs79JOens64ceOYNGkSRx55JGPHjmXAgAEK/BIREREREREREZFDwvLly+3fXS4XI0eO3Os+VrWODRs2UFRUREtLC0uWLOGLL77guuuu2+fkWKtXryY6OpqYmBj69OmzT5XHRKR3Kygo4JlnnmH9+vWsWLGC4uJinE4n0dHRhEIhfD6fPUccExPD6NGjOeqoozjmmGOYNGkSWVlZeDweoH2+5kCrgVn7rlmzpkPloUmTJh3glYrI9qzP16hRo/B6vQSDQZxOJ+vWrePXv/41jz76KAMGDLA/g9bn/OWXX+aBBx6w+7GSfp511lnExcXt0zNGW1sb33//vZ18ODw8nKysLLxeb1deqoiIiIhIj1Lwl4iIiHS7hoYGTjvtNGJiYoiPj2fw4MGkpqbamZuSk5MZNmwY0dHR1NTU4PF4yMjIwOl0Au3VwTozKBcKhXC5XHbgl2EYOBwOu59QKGQPPCroS+TQZH124+LimDx5MpMnT+6wvaKigtzcXNasWcOaNWtYt24dJSUlbNmyhba2tp362r5ymBUU5nQ67R/r/WAwSHl5OeXl5bz//vsd+vnxj3/Myy+/3GHiUERERERERERERKS3WbZsGdC+uDo8PNwO/trTuKZpmrhcLtatW0cwGCQ8PJzW1lZmzpwJbBtX3RNr7LSgoICzzjqLiIgI0tLSOP7447njjju64MpEpCcVFxfz4IMPMm/ePGpqauz3XS4XhmFQX18PbEvKB+3zx9nZ2WRnZ/Poo48ydOhQjj32WGbNmsUZZ5xhV+k6ENY8T11dnV2JCOhU4KuIdJ4153rllVfy17/+lbVr1+J2u3E4HPz3v/9lyZIlnH/++UyePJnY2FgAPvnkE1555RV8Pl+HQM/jjjuOyy+/3O53b6xnjM2bN7N69Wr7/aSkpA4VxzSHKyIiIiKHAwV/iYiISLfLysrivffes1+bpmkPtu8oLi5up3ZW4Neugrlg26DfjgN2O2aBUtCXyOElFArZwVvWvSEtLY20tDROOukku10gEKCoqIh169bx/fffk5OTQ15eHhUVFdTV1dkVwbZnmqbdt3WvcblcHSYfXC4XPp+PPn362O10jxEREREREREREZHeKicnB2gfy4yJiWHIkCEAu52z2d7atWsJBAJ2QMbEiROBfVuYXVhYSGVlJX6/n7y8PEzT5I477iAYDHZJoIeIHDxWRZ758+dz8803U1ZWBmxLsGfNs1jzs9vP71rvW++Zpkl+fj75+fnMnz+fSZMmccUVV3DOOefQt2/fAzrPQCDA7NmzOemkkygtLaWpqanDfLSIdA3r8/z73/+eSy+91F7r4fF4qK6u5umnn+bVV18lPDycjRs3AtvWb1hJfAGuv/56jjzyyA597on1jLFq1SpWrFhhvz9o0CAGDBjQxVcpIiIiItKzNHomIiIi3W7Hib/dBW/taMfBvF0Fc4nID9f2GSK3Z1UVBOxJheHDhzN8+HDOOeccu119fT35+fnk5OSwevVqcnJyKCwspKqqisbGxp2OFQqF7ECx7bPZHnvssXYbERERERERERERkd6opaWFyspKuzpHcnIy6enpe93PmqvJy8vrMC5qBX91dmE2tAefGYZBWFgYbW1tHZJ4icihxeVy8dRTTzFnzpwOQV4WK8He9oFgO+6/fbI9Kyisvr6eBQsWsGrVKnJzc7nppptIS0vb7wR8Ho+Hc889FwCfz0dzc/P+XrKIdML555/Pz3/+cx599FG7eqjT6cQwDGpra4Ftn3nrx+12EwwGueqqq7jwwgv36XjWM8bbb79NUVERHo+HQCDA1KlTycrKAjSHKyIiIiKHDwV/iYiISI/RIJuIdIddLTbYfgIB2icVYmNjmTx5MpMnT+7QduPGjeTm5pKTk8P3339Pbm4uGzZsYMuWLbS1tdntrECwfclwKyIiIiIiIiIiItIT1q1bB4Db7SYQCDBw4ECAvQZUWNuKi4uBbeOiI0aM6LB9T6w2ubm5GIZhj+HuSwCZiPQehmHwwQcf8NOf/hSn02l/xh0OB4ZhYJomsbGxAPj9fgYOHEhiYiKNjY1s3ryZzZs3d5hvsVjVwBwOB7W1tTz44IMsXLiQF198kcGDB+93AJi1X0REBBEREQd28SKyRx6Ph9tvvx2Px8OTTz5pB1xaVQGthJuwLXArGAzyox/9iL/+9a+Eh4fv0/FcLhdbt27lo48+ArY9c8ycOROPx9NVlyUiIiIi0iso+EtERERERA57O2actFiZJK02TqeT1NRUUlNTOfHEE+12gUCA4uJi1q1bx/fff09OTg7r16+nsLCQ4cOHH7TrEBEREREREREREdkfK1assH93OBz2uKZVlWNPysvLqaqqsiv49O/fnz59+nQ6EMMK7srPz7ePCTBu3LgO20Wkd7M+88uXL+fnP/95h3tAKBTCMAzGjRvHKaecwvjx45k5cybh4eFUV1cTCARobW21f4qLi1m5ciU5OTkUFBRQUVFhB4eGQiG7WtCSJUs4+eST+eyzzxg0aNB+nbeS94kcXPHx8fzud7+jX79+vPLKK+Tl5eHz+XaqAAjtwWJz5szh1ltvpW/fvvsc5GmaJo899hhlZWU4HA4CgQCZmZkcffTRXXlJIiIiIiK9goK/RERERETkB8vhcOxyYYNpmna2OafTicfjYdiwYQwbNoxZs2bZ7RobG5UlUkRERERERERERHq9pUuX2r+73W5Gjx69131M08TpdJKfn09DQ4O9GNva1zAM3O7OLTtpbGykvLzcrgwUFhZGenr6flyJiPQEKyCjtraWJ554guLiYjweD4ZhEAqFyMrK4qabbuLiiy8mLS2tw75RUVE79Tdt2jTOPfdcKisrycnJ4Y033uCtt97C5/MB7fepYDCI0+mkpKSEa665hg8//HCfqwKJSM+IjY3l17/+NbNnz+azzz5j2bJlFBUVsXXrVpqbm2lpaWH69OlccskljBkzBq/Xaz937It169bx8MMP2xXFIiIiuOSSS3C73ftdLVBEREREpLdS8JeIiIiIiMgOdjWxEAqF7B8Al8tFdHT0wT41ERERERERERERkX1mVf4yTZOoqCi78teeFllbY6G5ubm0trbidDoxDIPJkyd3+rjWwuuioiJqa2vtPqzjG4ax18pjItJ7vPbaa3z00UdAe4I90zQZP348Dz30ECeeeCIAgUAAj8fTYT/rfmIFYjidTmJjY4mNjWX48OGcd9551NfX8+STT/Lggw9SV1dnB3M4HA6+/PJLHnroIX73u98poEPkEBAKhexnjlmzZjFz5kzq6+vx+XwkJSXtlFwzFAp1OvDLChKrra3lH//4B/X19YSHh9Pa2sqQIUM455xzuuGKRERERER63r6lShAREREREfmBcjgcOJ1OXC6XFiOIiIiIiIiIiIjIISU3NxdoXzAdFxfH4MGDAToVQLF27VoMw7BfW8FfndnXCvjIy8ujubnZ3mfChAkdtotI72Z9dh9//HGqqqpwOp34/X4yMzN56aWX7MCvUCi0U+CXtf+u7hlWgEgoFCI2NpabbrqJ559/nsmTJ9sJ+az9nnvuOYqKihT4JXIIcDgcHeZTw8LCSEpKIisri4iICEzT3Kl9Z1lBYg8//DD//ve/O+x/2mmnMWXKlH3uU0RERETkUKDgLxEREREROaRVVFTQ1NREQ0MDtbW1tLW17dP+O04umKZpTzSKiIiIiIiIiIiIHOqqqqpoamrC5XIRCoVITU0lLi5ur/tZi7bXr18PbAvUGjduHLDnqmEWa59169YRCATshdj7EkAmIr3DV199RU5Ojl3xC+DRRx9l1KhRdpt9/Uxbifes/SIjIznzzDN5+OGHGTFihN3O7XZTUFDA22+/Dew8tyMih5bOVvnanXXr1vHhhx/S2toKgM/nIzk5mYsuugjQPUJEREREDk/unj4BERERERGRfWUYBh999BGffPIJn376Kfn5+cTGxpKVlcWIESOYNm0ap556KpmZmXvty5pcCAaDuN3uA55sEBEREREREREREelNcnJygPaxUMMw7KpfpmnudTy0tbWV0tJSHA4HwWAQp9PJkCFDgM4FeVhtcnNzOyTcmjRpUqf7EJGeV1dXx7x584BtVbxOP/10Zs2a1aE6V1cwTZNjjz2Wv/71r1x22WVs3brV7v/TTz/lyiuvJD4+vsuOJyKHnujoaB555BEqKiooKytj/fr1TJkyxQ5G1XyviIiIiByOFPwlIiIiIiKHBGvysLKykgcffJCnnnoKn89nT/ht3ryZzZs3s2zZMl588UX69u3LHXfcwc0337zbPisrK/niiy/YsmULpaWlNDU1kZCQwIgRIxg7diyDBw8mMjKyyycuRURERERERERERA6WZcuW2b87HA5GjhwJ0CEYa0fWmGhJSQk1NTU4HA5CoZAd+NWZwDHYtvi6qKgIaE/sBWhxtsghprCwkOzsbKD98z9o0CCuvfZagC6fQ3E6nZimycyZM5k1axbPPfcchmHgcrn4+OOP7Uo/IvLDlZ6eTnp6OgCBQICWlhYiIyNxu7UcVkREREQOX3raFRERERGRQ4LD4SAQCDB79mzefvttALxeL8FgkFAohNPptH9CoRANDQ384he/oLi4mAceeICIiAh7ArK2tpannnqKuXPnsnr1avsYbrcbt9tNdHQ0Q4YMYebMmdx4440kJSUpAExEREREREREREQOScuXLwfaAzTCwsIYO3Ys0B7AYVXw2XHs0xoPXb9+PY2NjXYwxvjx4+19Oxu4VVVVxaZNm3A4HJimSUJCAn379u26CxSRbrdkyRLWr19vB4IeffTRHH300UD3VPCz7i+33347c+fOJRAIYBgGXq+X7777jtTU1C4/pojsu40bN+L1eomOjiYsLGyPbbtrrtXj8RAbG9vl/YqIiIiI9DYK/hIRERERkUPGnDlzeO+994D2iT/DMOwFCqFQiGAwCIDL5bLbPPbYYxx//PGcf/75OBwO8vLyuOWWW/jwww+B9gmBUCiEYRgEg0GCwSCtra1s3ryZ7OxsHn74YR555BF+8pOf9MxFi4iIiIiIiIiIiByAVatWAe0BWxEREaSkpADtY6O7YpomwWAQr9dLTk4OgUDAHnOdPHlyp49rBYgVFBRQX19vj+laVb+sSj4i0vtlZ2fT0NCAx+MhEAgwa9YskpOTge4J/rLExsZy2mmnMX/+fDv535IlSzjzzDOVtE+kF5g9ezYJCQkMHjyYjIwMUlNTSUpKIj4+npiYGPr06WNX49LnVURERETkwCj4S0REREREDgmrV6/mX//6F06n054cMAyDmJgYmpubiY2NJRQKsXXrVgzDALZNIvz1r3/l9NNPxzAMfvzjH9uLHbxer72QAdqDxqx9rEnDxsZGZs+ejcPh4IorrjjYly0iIiIiIiIiIiJyQAoKCoD2MU+fz8edd95JVlYWmZmZDBgwgKysLNLT0+nXrx9erxen04nX6wVg/fr1mKZpB2lNmDAB6NwC7lAoBMC6devw+Xx28NcRRxzRYbuI9H7Z2dlAe1AnwPTp07v9mIZhEB8fz9ixY5k/fz5hYWH4fD62bt1qn4sCSEV6TnV1Nf/973/t1y6Xi4SEBNLS0sjIyOjwjNG/f38SExOJi4sjOjqaiIgIBYOJiIiIiOwjBX+JiIiIiEivt3nzZh577DGgfeLAyjR72mmn8b//+78cd9xxbN68meLiYr799lvefPNNVq5ciWEYhEIhli1bRn19Pb///e9ZvXo1AG63G7/fb/dpmqYdNGa9Z732+XzcdtttjB8/nnHjxh3kqxcRERERERERERHZPwUFBZimidvtxjAMWltbee+993C5XERERBAdHU1cXByJiYmkpKSQkZFBVlYWWVlZDBkyhJycHGBbwMdxxx0HYAdchEIhO5HW7hZxr1u3jmAwaFcas4K/tOhb5NBQXV1NUVGRPW8ycOBA+vXr1+3Hte4R/fr1Izw8nEAgAEBERASAAr9EetjatWsBiIyMpK2tjWAwSHV1NdXV1axYscJuFx4eTlJSEunp6fYzRlZWFmlpaSQnJ5OQkEBsbCxRUVGEhYXt8Ziq+CciIiIiP2QK/hIRERERkV5v9erVfPnllwAEAgHcbjc33HADf//73+02kZGRDBgwgBkzZjB79mx++tOf8vLLLwMQHx/PY489xjPPPIPT6QQgGAxy7LHHkpWVRVxcHG63m7q6OtavX09OTg4NDQ1A++Siy+WioqKCRx55hH//+9+aVBAREREREREREZFDwqpVqwDwer34/X47WMswDJqammhqamLTpk12e6/XS58+fYiNjSU6OpqCggIcDgeGYRAeHs7y5ctJTk6mf//+REVF7TboyzRNu7JXXl4esK3S18SJEwHssVoR6d2spHpW9b4jjzwSaP+cd+fn2Oo7JSWFiIgIu+JXa2srTU1NREVFdduxRWTvFi9eDEBzczPQ/pl1uVz2Z9dKvNna2kppaSmlpaX2PgCxsbEdAs8HDBhARkYGqampJCUlERcXR2xsLH369MHtbl/mqjlaEREREfkhU/CXiIiIiIj0egsXLqSgoMDOKnnGGWdwyy23AGAYhp3d0ZpEiImJ4S9/+QsVFRUsXLiQxsZG/v3vf9ttUlNT+dnPfsZPfvITUlJS7OM0NDRQVlZGdnY2zz33HN9++y2GYdhZbd944w3uu+8++vfvf5D/BURERERERERERET23ZIlSwBoaWnp8L61ONtaRG2Nrfr9fvx+P3V1dXZbq41hGFx//fX2Yu20tDS7eseAAQNIS0sjKSkJr9eL0+m0F3+XlpbaAWQAQ4cO7dCviPRuy5Yts393Op12AKcV0NndUlNT7YR90B5osrfqQCLS/RISEpg0aRLNzc1UVFTQ1NRkz6luz+Vy4XK5cDgcdhB6MBikvr6e+vp6O0gc2u8xiYmJpKamkpGRwYABA8jKyiIjI4OUlBT69etHXFwcUVFR9OnTR88SIiIiIvKDouAvERERERHp9b7++mtM08Tr9WIYBldeeSWDBw8GsAO/AHtBgd/vJyUlhenTp7Nw4UJaWlrw+XwAREVF8X//93/MmTMH2FZJzOFwEBMTw+jRoxk9ejQXXngh11xzDfPnz8fj8RAIBGhra+O5557jt7/9LaFQSBMKIiIiIiIiIiIi0qudf/759OvXj7KyMvLy8igsLKSiooK2tjY7GGt7bre7Q8UOq4KXw+EgEAh0WKDtcrmIiIggOjqauLg4EhMT7QoeGRkZjBw5Er/fT2VlJQ6HA9M0ycrKwuPxdHvFIBHpOlbwVygUwuv1Mnr0aODgBXDGxMTYiQBDoRBtbW14PJ6DcmwR2b2LL76Yk08+mdraWqqqqigvL6ekpITi4mJKSkooLy+nurqaQCCwx2cOKyjMNE2CwSDV1dVUV1ezcuVKu214eDhJSUmkp6eTmZlpB4WlpqaSkpJCXFwcSUlJxMTEHMR/ARERERGRg0vBXyIiIiIi0uutWLECgGAwSGRkJDNmzNhje6/XC8CYMWOIj4+ntrYWj8eD3+/nyiuv5Mc//jHQnql2xwlCK9tcbGwsDz/8MOXl5SxduhSv14vf76e8vBxoX/iwfeCZiIiIiIhIb7W7RZl/+MMfuPPOO/epry+++GK338kWLFjA9OnT9/HsRPbuP//5D1dfffUutxUXFzNgwICDe0Jy0AwYMICSkpKd3r/yyiv5z3/+c/BP6DBlJVDa0QknnMAXX3xx8E9IutSUKVOYPHkyfr+flpYW6urq2Lx5MyUlJRQWFpKXl0dBQQEbNmxg06ZNBIPBnfqwkm45HA47iMuqFNbU1ERTUxObNm2y23u9Xvr06UPfvn1xOp3U1tbidDoxTZNx48YBKPhL5BDy/fffA+2f26ioqINWvc+6T5SWlgLtAad+v5+oqKgO20WkZ/Tt25e+ffvaCTtN08Tn89HY2Gg/b1RWVlJWVsaGDRvYsGEDJSUlbNy4kZqamt0+c1jVSa0+DcOgtbWV0tJSSktLWbx4sd0+JiaG9PR0wsPD+eUvf8kll1yiBJ4iIiIicthS8JeIiIiIiPRqpaWl1NXV4Xa7CQaDjBs3jtjY2E7tO2jQINra2oD2yQGAn/zkJyQkJADsMnjL4XDYlb4yMzOZNm0aS5cuJRQKAdDc3ExDQ4Myx4mIiIiIiIiIiMghwarQFRERQUJCAoMHD+boo48mGAzS2tpKc3MztbW1bNq0iQ0bNpCfn8/69espKCigtLSU+vp6e3x1x36toDDYtkDb7/fj9/upq6sD2sdcrUXcRxxxxEG7bhHpGgUFBUB78rzExEQyMzOB7g/+suZl1q5d2+H9jIyMDttFpHdwOp1ERkYSGRlJSkoKI0aMACAQCNDS0kJDQwNbt26lurqajRs3UlpaSnFxMRs2bKCsrIzKykqampp2+8xhzetaVUlN06ShocG+R7S0tNjblcBTRERERA5HCv4SEREREZFezcooaU0ijhkzBuhcRsc+ffrQ3NyM0+kkGAySkZHBkUce2anjWhXBjj/+eP79739TX18PwNatW/H7/ft1LSIiIiIiXeVAF9m5XC7Cw8OJjo4mJSWFzMxMRo0axeTJkznhhBNITEzsojMVEflhufPOO7nrrrt2uU3VqkSkt3G73URFRREVFUVycjIjR44kFAoRCATsyh1btmyhoqKCwsJCOyisqKiI8vJy2traMAxjl/1uX7Fj+0XckydPBlC1HpFDRH5+PqFQyE7Ql5WVRURExEE9h+LiYmBbsNfw4cMP6vFF5MB4PB5iY2OJjY21gzcBOwC9vr6empoaqqqqKC8vp6SkxK4UVl5eTnV1NYFAoMMzhzUu5nQ68Xg8tLW1MWXKFPs9EREREZHDkYK/RERERESkV1u+fLn9u9PpZOTIkcCeMzqGQiEcDgcbN24E2he2mqZpZ5XtTOCYZdy4cXbgF8DmzZtV9UtEREREDnmGYdDc3ExzczOVlZWsXLmSd955B2hfQDNt2jSuuuoqLrvsMrxebw+frYiIiIgcLA6HA6/Xi9frJTY2lvT0dCZMmGBX9GppaaG+vp7q6mpKS0spLCwkLy+P/Px8NmzYQGVlJcFgcKd+rfHccePGAVqYLXKoWLVqFbDtM5uSkgK0V9iJiIjo1upf1jF3DP4aO3Zsh+0icmgKDw8nPDychIQEBg0aBLTP4VoB6LW1tWzdupXKykrKy8vZunUr77zzDitXrgS23ROspJ3Dhg0Dur8qoYiIiIhIT1Hwl4iIiIiI9GrLli0D2gfww8LCGDVqFLDngXvTNHG5XHbVMKv9pEmT7L72xgogCwQCAHZWy1AohNfrtbeLiIiIiBxuQqEQX375JV9++SW///3vuf/++/nJT37S06cl0iOmT5/OwoULd3r/hBNO4Isvvjj4JyQiItJDXC4XERERRERE2Iu0jz76aILBIG1tbTQ1NVFbW0tlZSXFxcUUFBSQl5dHYWEhpaWl1NXVAZCent6zFyIi+2Tp0qXAtjmTTz75hEsuuYTMzEyGDx/OgAED6N+/P3FxcURFRREeHo7b3TXL0aw5mtLSUhwOh131Z8iQIfZ2ETk8WPcYp9NJZGQkkZGRdrBpa2srn3/+Oc8++ywNDQ3Ats+/w+HANE2ysrI0fysiIiIihz0Ff4mIiIiISK+2evVqoD2gKyYmhqFDhwJ7ntSzgrusqmGhUAiPx2NXDevMoL81OVBUVNRhn4yMjA7bRUREREQOZxs3buTKK6/ktdde44UXXiAuLq6nT0lEREREehG3243b7SYyMpLk5GRGjhzJ9OnTCQQCduWOmpoaNmzYQHNzM6CxVZFDyYoVK4D2z20oFGLjxo28+uqrHdqkpKQwYMAAhg4dyrBhwxgyZAiZmZkkJSXRt29f+vTpg9frxeVydfq41n2itLSU6upqO8Bj4MCBuN1uTNNU5S+Rw8SuPs8bN25kxYoVZGdn8+KLL1JTU2M/R7hcLkzTBMDr9dLa2moHhRqG0WUBqCIiIiIivY2edEVEREREpNfaPqNjKBSiX79+dvDVnhYHWBME209KRkVFMXz48L3ua7ECyPLz8zu0t/oQEREREfkhee+99zj++OP5/PPP6devX0+fjoiIiIj0Yg6HA6/Xi9frJTY2lvT0dMaPH2+PuSrwS+TQ8d133wHbgjNcLpf9GTZNk2AwSGVlJZWVlXzzzTf2fmFhYaSmpjJo0CCGDRvGsGHDGDRoEOnp6SQmJhITE0NERAQej2eP94S8vDyamppwOp2YpsnYsWM7nI+IHLoMw8DlcuF0OgkEAgAsWLCA1157jby8PBYtWgRgf/6te1AwGCQUCuF0OmltbcXr9XLhhRf25KWIiIiIiBwUCv4SEREREZFeKy8vD8AeyM/KysLtdhMIBHC73budELQm/PLz84H2ScCEhAQGDhwI7NvigtzcXHtRAsCECRP2uQ8RERERkYPp9ttvJz4+fo9tWltbqampYePGjSxatIiSkpK99rtmzRrOOussFi5cSFhYWFedroiIiIh0I9M0MU0Th8OB0+ns0XFNjamKHFp8Ph/BYJA+ffrg8/kIhUJ2tR2Lw+GwgzcAu01bWxvFxcUUFxfz2Wef2e1jYmLIzMxkyJAhDBs2jKFDhzJw4EBSUlKIj48nKiqK8PBwQqEQHo+HvLw82tra7KphRxxxxMH7BxCRLrVj5U+Xy0VFRQXff/89ixcv5plnnqGpqYnGxkagvbqodU+xKv4FAgFcLhdhYWFMmDCBa665hhEjRtiBoar6JSIiIiKHMz3tioiIiIhIr7Vy5UpgWzCXNXDv8Xg6tDNN054wCIVCuFwuSkpK8Pv9uN1ugsEg6enpREZGdvrY1kTi+vXrgW2VwMaPHw9ooYKIiIiI9F7XXHMNAwYM2Kd98vLyeOihh3j++edpa2vbbbslS5Zw++238+CDDx7gWcqhavr06R0SZIiIiEjv5nQ6VR1HRPaLYRjcf//9bN68mQ0bNrBx40aqq6upra2loaGBlpYW/H4/wWBwp32te8/2QWGGYdDQ0MCaNWtYs2ZNh/bJyckMGDCAoUOH2lXCxo8fT3Z2NqZp2nM2kydPtvsXkUODVbXL4XBgmiatra0sWrSIV155hfz8fLvClzXPayUAtSp8Wb/37duXadOmMW7cOK699lpSUlIIDw/v4asTERERETl4FPwlIiIiIiK91tKlS4FtmeC+/vprHnvsMVJTUxk2bBhZWVnExMTscpJv1apVwLYgraFDhwLbJhg6w+fzUVZWZk8qOJ1OBg8e3BWXJiIiIiLSqwwfPpynn36a2bNnc8EFF1BUVLTbtg8//DDXX3+9/YwtIiIiIr2LNZ76xhtv8PjjjzNu3DgmTpzI2LFjGTt27E7JtTprx7FVwzA6vFbCLJHDS1RUFLNnz7ZfW9WjS0tL2bBhAyUlJZSVlVFZWcnmzZupq6ujsbGRlpYWDMPYqUoYYFcJs+4XpmkSDAapqqqiqqqKJUuW2G29Xq99j7H6spIEKvhLpPfa8XnB6XRSVVXF0qVL+eabb3j++eepqanB5/MB7Uk/rUqlbrcbwzDspDNxcXEMHTqU66+/npEjR3L00Ufv9PnfsaKYiIiIiMjhSsFfIiIiIiLSa61YsQLYVnVr8eLFLFu2jPDwcKKjo0lMTCQtLY2BAwcyfPhwhg4dSlZWFsOGDeuQNdLpdNoTgsFgEJfLhcPhsH92ZE0SlJSUsGXLFjvT3JAhQ4B9CyATERERETmUTJw4kezsbCZPnkxZWdku2wSDQf7yl7/w1FNPHeSzExEREZHOsJJZPffcc3zxxRd88cUXAPTr14/nn3+eU089dZ8WSlttnU4nra2tGIZBZGSkXYlHRA5fpmnacykJCQkkJCTY8y0AgUCA6upqysvLKS0tpbi4mNLSUsrLy6mqqqKmpob6+nqamprs+4dhGB2O4XA4OszbhEIhTNPE7/fb2w3DIDw8nPT09IN6/SLSeYZh2AGewWCQQCDA119/zcsvv0xeXh7ffPMN0D5va1X0c7lcBINB+14TDAaJjY1lxowZjBkzhiuuuKJD8qFQKLTTM4wCv0RERETkh0LBXyIiIiIi0mvl5eUB2wbyQ6EQra2ttLa2UldXR1lZGStXrsTlcuH1eomIiCA2Npb09HTy8/OB9olJj8dDQkIC0J4pcldM07QnC6zMcnl5eTQ1NdmTEBMmTLDPR0RERETkcJWUlMSbb77JlClTdpmpHWDu3Ln87W9/Iyws7CCfnYiIiIh0xttvv823334LQEREBD6fjzvuuIMTTjgB6PxCaWvMND8/n48//pi8vDyWLFlCdXU1/fr1Y/To0Rx99NFMmzaNoUOH2lXFVIVD5PCwqwo71hyJ0+nE4/GQlpZGWloaU6ZMsds1NzdTWVlJWVkZJSUlFBcXU1ZWxsaNG6murqa2tpaGhgaam5vx+/0Eg8HdHtvlchEIBBg+fDiwLcBERHrWjskyXS4X5eXlrFq1iuzsbJ599lnq6+tpaWkBwO1uX6pqGAZut9sO8nS5XERHRzNmzBiuu+46RowYwZFHHmm3t+jZQkRERER+6BT8JSIiIiIivVJtbS2bN28G2icBnE6nnfkR2icUrJ9gMEgwGKSlpYWamhqKioo6tAsEAtx+++0888wzpKenM3z4cIYNG8bgwYPJysoiNja2w+SE9fvatWvx+/325MIRRxwBKIOciIiIiBz+Jk+ezAUXXMBrr722y+0NDQ18+eWXnHLKKQf5zERERESkM+bNm8eWLVtwOBz4fD7GjBnDNddcQ3h4eKf2txZYOxwO7rvvPl566SVyc3MB7Mo8JSUlLFu2jOeeew6AmTNncsstt3DKKafYbTSWKnJ4se4LO9q+Go/D4SAyMpLBgwczePDgDu1qamrYtGkTJSUllJSUsGHDBsrLy9m0aRObN2+mrq6OxsZGWlpa7AphVlKS8ePH28cSkZ5jBX05nU4Mw8Dv9/PVV1/x0ksvUVhYyOLFi4FtzwtWNTDDMOx9g8EgcXFxHH/88YwbN46rr76atLQ0O4h8++NY9EwhIiIiIj90Cv4SEREREZFeqbW1lXPOOYfKykry8vLYunXrLqsOuFwuO8NjKBSyA8Jg26RCKBSisLCQoqIiOxNleHg40dHRJCQkkJaWxqBBgxg2bBjDhg1jwIABDB06lPz8/A6TiJMnT7b7FRERERE53N100027Df4CWLx4sYK/RERERHqhDRs28P777wPtY6ZRUVH88pe/JCIiotN9OBwODMPgkksuYf78+RiGgcfjscdfHQ6HvfDb4XAQDAb5+OOPWbRoEbNnz+buu+/G6/V21yWKSC+zq6Cw7auEWdsTEhJISEhgzJgxdrtAIMDmzZspLy+3A8JKSkqoqKigqqqK+vp61q1bxzHHHGP3JSI9x+l0UlVVRXZ2NkuXLuW5555j69at+Hw+oL3ClzVn63K5ME3TDuZMSEhgyJAh3HjjjQwfPpwpU6bsssKg9ZwhIiIiIiLbKPhLRERERER6pdTUVN58801CoRB1dXWUlpaSn5/P6tWrWbNmDWvXrmXDhg20tbXZEwYWh8PRoUqYNcFoGIb909raSl1dHWVlZaxatQqXy4XX6yUiIoLY2FiSk5NZv349sC2r5OjRo+3+RUREREQOd1OmTKFPnz60tLTscvu6deu65bjr1q3jo48+YsmSJaxfv57y8nKampoIBALExcWRlZXFlVdeyZw5c/arf7/fz5dffsmiRYtYsWIFRUVFbNq0iebmZvx+P3369CEuLo6BAwcyduxYpk2bxqmnnkpsbGwXX+nefffdd7z77rtkZ2eTm5vLli1b8Pl8REZGEhcXx7Bhw5gwYQKnn346xx13nF21+HBkGAbffPMN2dnZLFu2jOLiYsrKymhsbMTn8+H1eomJiSEmJoaBAwcycuRIRo8ezfTp0xk6dGinjvHNN9/wzTffdHivvLx8l23Ly8t55JFH9vk6br755n3eZ1c2bdrE559/TnZ2NuvWraO4uJja2lpaWlrsSgspKSkMGTKEI488khkzZjB16tSDvniutbWVDz74gI8//piVK1dSWFhIQ0MDoVCI6OhoUlJSGDVqFFOnTuWss87aqTKEHDzNzc1kZ2ezevVqcnNzyc3NZePGjTQ0NNDY2Ijf7yciIoKYmBjS0tIYPHgwEydO5IQTTuDII4/sVQsza2tr+eCDD/jss8/IycmhpKSEhoYGAoEA0dHRpKWlMXr0aGbMmMFZZ51Fampqj5ynYRhkZ2fz9ddfs3z5cgoLC6moqKCpqYnW1lZ7jGrAgAGMHj2aY489ltNOO42kpKQeOV85tHz++ec0NjYSHh5Oa2srkyZN4rLLLtunPnw+H7fccgvz5s2zg7yCweBOVTwcDoc9fhoKhWhsbOSJJ54gLCyMu+66qzsuT0QOEsMw7M/5/thblTDATtiXmppKamoqRx11lN2uubmZyspKKioqyMnJ4YwzzrD3EZGesWTJEp544gny8vL49ttvgfZEnYZh2Ak7g8Gg/WxgGAbR0dGccsopjB49mssvv7zDd/TtqwZaNA8rIiIiIrJrjpBqYYuIiIiIyCEoEAiwZcsWiouLyc3NtYPC8vLydrs4z+l0dggKs7LU7qqiGNAheCw6Opr6+vruuRgRERERkX20p4UwxcXFDBgwoEuOc8wxx+wUDLP9tsWLF+923//85z9cffXVu9y24zmGQiFeeuklHnnkEZYvX77X87ryyiv5z3/+s9d221u9ejWPPPIIb7zxxj4/24eFhTFr1ix++9vfMnHixH3ad3+89dZb3HPPPXz33Xed3icjI4NbbrmFOXPm7FRlY3d/L3/4wx+488479+ncvvjiC2bMmLHLbQsWLGD69On71N/erF27lscee4w33niDzZs371cfAwYM4PTTT+eaa67hiCOO2G27O++8s9sXqR/ItJxhGLz22ms89dRTfPXVV7v9Lrs7aWlp3HDDDfzsZz+jb9+++30endHY2Mif/vQnnnzySWprazu930knncTvf//7nf6O9uV+0tP29He0P/eu7rR06VLeeustFixYwLJlywgEAvvVT1JSEldffTX/+7//S3p6epee44ABAygpKdnp/V39WxYVFfHHP/6RF154Ab/f36n+3W43s2bN4u6772bUqFFdccp7VVxczKOPPsrcuXOpqqrap31dLhc/+tGPuPXWW7v0fjt9+nQWLly40/snnHACX3zxRZcdRw4Ov9/PNddcw8svv4zL5QLgoYce4mc/+xnBYLBTweKmafLiiy9y1VVX2RU7gA4VfHb13zQr0MM0TTweDx988AEnnnhiF16diBzOtg8CUfCHSO90xRVX8NJLLwEdK3xtHwwO0LdvX0aMGMENN9zA6NGjGT9+/E5jFTsGfYmIiIiIyJ4dvikgRURERETksObxeOjfvz/9+/dn6tSp9vstLS1s2rSJgoIC1q5dy+rVq8nJyaGgoICtW7fucnGclYkOsCcpTNO0M9gahsGIESOA9oUPyiopIiIiIj8UiYmJu93WVckRcnNzufrqq3cbZHagysrK+MUvfsEbb7yx34E3bW1tvPbaa7z22mtceeWVPPTQQyQkJHTxmUJlZSVXXnklH3/88T7va13nP//5T55//nkmT57c5ed3MBUXF/OrX/2KN99884D72rBhA0888QRPPPEEEydO5NNPPyU+Pr4LzvLgeffdd7n55pspLCzc7z4qKir4wx/+wCOPPMKDDz7Idddd14VnuM0HH3zA1Vdfvc9BLQCfffYZn332GVdffTWPPfYYkZGR3XCGP2w1NTX85S9/4dVXX6W4uLhL+qyuruZPf/oTjz76KL/61a+444478Hg8XdJ3Zz3yyCPcdttt+Hy+fdovGAzyxhtv8NZbb/HrX/+ae+65p9uqKNbW1vJ///d//Pvf/96pin1nGYbBBx98wAcffMAZZ5zB448/TlZWVhefqRzqysrKWLFiBdD+N3PUUUdxwgknANhjoLtjVe346quvePjhh4GOAV1Op5Nhw4Zx6qmnMmPGDHw+H6tXr+bLL79k5cqVNDc328cJBAL85je/YenSpVrcLXIIeueddzAMg7Fjx5KZmblT0EZ32FXQ145VwkSkZy1btgxon6cNBoMA9nNCWloa06dPZ9SoUVx66aVkZmZ2SMhpPWdY9GwgIiIiIrJvFPwlIiIiIiKHlT59+jB48GAGDx7MqaeeCrRPDtbV1VFWVsb69etZs2YNa9asYe3atRQXF9PW1rbLRTdutxuv10tLS8ses8OLiIiIiByuYmJidruttbX1gPv/+OOPufDCC2loaDjgvnblpZde4n/+539oamrqsj6fe+45PvvsM+bPn9+lAVYrV67ktNNOo7Ky8oD6WbduHccffzxz587l7LPP7qKzO7ieeOIJfvnLX3bJ39iOVqxYQUNDwyET/NXc3MyNN95oZ1bvClu3buX666/no48+4j//+U+XBljdf//9/O53vzugCmcAzz77LN9//z3vvfceycnJXXR2Au33iAceeKBb+m5tbeXee+/lgw8+4N133yUlJaVbjrO9YDDIDTfcwLPPPntA/RiGwQMPPEB2djZvv/02sbGxXXSG7T755BMuu+yy/a5guCvvvfceixYt4pVXXmHmzJld1q8c+lavXk1ZWZn9+phjjmHw4MHA3hdZW4EVL7zwAqtWrbIXc1v39auvvpp77723w7354osvpr6+nscff5wHHniApqYme2F3aWkp77//PqeffnqXXqOIdL/f/OY35OXlkZSUxJAhQ5g7d26XV/jsDFUBE+ldrIQkwWDQDu42TZOTTz6ZadOmcfbZZzNs2DCcTmeHz+6OwZvbf2fUZ1xEREREpHMU/CUiIiIiIoc9h8NBXFwccXFxjBs3jgsuuACAQCDAli1bKC4uJjc3l9WrV7NmzRry8vIoLy8nGAzaWevGjRtn9yUiIiIi8kOxp+pe4eHhB9T3l19+yaxZs7olwAfg17/+NX/5y1+6pe/y8nKmT5/Oe++9Z1fSOBArVqxgxowZXVZNzefzccEFF/DRRx8xY8aMLunzYAgGg1xzzTW88MILPX0qvcKmTZs49dRTWb16dbf0P2/ePCoqKvjoo4+Ijo4+4P7uuOMO7rnnni44s3bLly9n5syZfPXVV13Wpxwcy5cv59hjj2XRokXdHgA2Z86cAw782t7ChQuZMWMGX3zxxR4DoPfF3/72N2655ZZdVqM/UHV1dZx55pm89NJLXHzxxV3evxyaVq1aZQe+O51Oxo8fT1RUVKf2dTgcrFmzho8++sje3+l0EggEuOGGG/jTn/5EbGxsh8XeALGxsdx2220kJibyi1/8gpaWFsLCwti8eTPffvstp59+ul05TEQODevXrwfaK3zGx8fvsSr0rqhil8jhp6ioiGAwiNvttudPof354bvvvmPLli189NFHpKamkpmZSVpaGunp6fTv35/k5GQSEhKIjY3F5XLtdr7Vum9oPlZEREREZGcK/hIRERERkR8sj8dD//796d+/P1OnTrXf9/l8bNq0iYKCAnJzc/nqq6/sBZOabBARERGRH5ItW7bsdtuBLIrfuHEj55577i4Dv5xOJ1OmTGHUqFEkJycTFxdHS0sLpaWlLFu2jFWrVu21/5tuuom///3ve20XHR3Nsccey6BBg0hISCA6Opqamho2bdrEV199RXFx8W73bW5u5owzzmDx4sV2soj9UV5ezhlnnLHXwC+3280JJ5zAkCFDSEtLIxQKUVFRQX5+Pl9++eVO1YwDgQDnn39+twUOdbVgMMj555/PO++806n2Q4YMYfLkyfTr14+kpCTcbjdbt25l69at5ObmsmLFii6t+Hawbdq0iRNOOIH8/Py9th04cCDHHHMMKSkpJCQkAFBVVUVRURFffPHFHv8dsrOzmTVrFh9//DFu9/5PGz7zzDOdCvzq168fM2bMIC0tjeTkZLZu3UpFRQVLlizZ5bWuXLmSa665RhVjDiIrcc7gwYOJjY0lNjaWyMhImpqaqK+vp6ioiOXLl1NRUbHHfoqKijj77LNZtGgRHo+nW871iSee4Omnn97ltsTERGbMmEF6err9t7Zx40aWLFliL2bfnRUrVnDBBRfwwQcf2NWL9tdDDz3Er371q722Cw8P5+ijj2bEiBH2otitW7dSXV3N4sWLWbdu3W73NQyDyy67jPj4eE455ZQDOl859AUCgQ7PL0lJSQwdOnSf+nj11VftSqSmaWIYBikpKdx11112VTxrjHT7gA6rEt+8efP49NNP7WeTjRs30tTU1OkANBHpeQUFBYRCITweD8FgkIyMjH1K/mEYxh6DO0Tk0GSNx3i9XqA9UMuqEFpbW0ttbW2H9l6vl7i4OPr160f//v1JTU0lPT2d9PR00tLSSElJISkpifj4ePs5YVf3DVUJExERERFpp+AvERERERGRHURERDBo0CAGDRrEj370I372s5/19CmJiIiIiBx0gUBgj4FDmZmZ+9337Nmzd1oU1K9fP/7whz9w8cUX7zGrfEFBwR4DwB566KE9Bn45HA5mzZrFL37xC6ZOnbrHhf1r167lvvvu45VXXumw2MjS3NzMueeey8qVK/ercpJpmlx++eVs2rRpt21iY2O56667uOyyy3b771JdXc0LL7zAXXfdRWNjo/3+1q1bueGGG/b5vHrC//7v/+418Cs5OZlbb72Vc845h0GDBu2xrWmarF27lrfeeotXX32VNWvW7PUc7rzzTu68884O702fPp2FCxfu1PaEE07giy++2Guf+6O1tZVZs2btMfArPj6eOXPmcP3115ORkbHbdn6/n/fee4/f/e53uw0eWbBgAf/3f//Hn//85/0635ycHObMmbPHNlOmTOGuu+7ipJNO2m2Q2bJly/jLX/7Cq6++2uH9119/nebm5v06N9m7vn37MnPmTGbNmsWxxx67x7+n7eXl5fHMM8/w9NNPU1dXt8s2S5cu5f777+eOO+7owjNuV1xczLx583Z6f/Lkydxzzz2cfPLJu/1b++6773jooYd4+eWXd9v/J598wn333XdA5z5v3jx+/etf77HNjBkz+PWvf81JJ51kL6LdlZKSEv785z/z9NNPEwgEdtpuGAY//vGPWblyZaf/P5TDU2tra4dnrJSUFPr27dvp/Tdt2sRbb72FYRg4nU5M0yQ8PJyf/vSnJCcn73Ff6zN3xRVXsHDhQvtvdfny5bv8uxWR3mvlypX276FQiOHDhwPbgrr2pKamhhkzZhAZGcno0aM5+uijue6667rzdEXkIFm2bBkALS0tHd53Op24XC47KNwKHvf7/VRVVVFVVdXhO3lkZCTx8fEkJyfTv39/0tLSyMjIIC0tjdTUVFJSUkhMTCQuLo6wsLA9VglTMJiIiIiI/JAo+EtERERERERERERERHbyzTff7LSgZ3sjR47c776///77Dq+vuOIKHnvsMbuaxJ4MGTKEIUOG7HJbdnY2t9566273HTZsGHPnzmXixImdOs9Ro0bx0ksvMXv2bM4999xdVkIrKirit7/9LY8//nin+tzeE088scvAIsuJJ57I888/T1pa2h77SUpK4pe//CUXXHABV1xxBV999ZW97f3339/n8zrYXnzxRZ566qndbne5XNx2223ceuutna4a4nQ6GTNmDGPGjOH3v/89K1as4IEHHthlsEhv84tf/IKlS5fudvtVV13FY4891ql/C6/Xy7nnnsvZZ5/NnXfeyb333rvLdn/961+54IILmDJlyj6daygU4uqrr95lFT9o///hvvvu49e//vVeFwpPnjyZuXPnctFFF3Httdd2CCg6FP6ODzXHH388v/jFLzj99NP3qzLX8OHD+dOf/sQtt9zCnDlzePPNN3fZ7oEHHuDGG2/ca9DIvvryyy87vHY4HNxzzz389re/3evf2qRJk3jppZe46KKLuPrqq9m6desu291zzz2cf/75jB49ep/Pr6ioiKuuumqXgcPQHpDzwgsvcPLJJ3eqv6ysLP7+97/z05/+lLPOOouioqKd2tTW1jJ79mzefffdfT5fOXw0Nzd3eF6JjY21g7L2tEDa2rZkyRJycnLsij2maTJkyBAuueSSTp/DSSedRCAQsIPHNmzYQFxc3IFdmIgcVMuXL7d/d7lcnfruZ91HNmzYQFFRES0tLSxZsoQvvviC6667rlOBY9tbvXo10dHRxMTE0KdPn32qPCYi3ePCCy8kLS2NsrIy8vLyKCgooLS0lIaGBkzT3Km9y+WynymsKmGGYdDc3ExzczNlZWV2W6fTSUxMDImJiaSkpHSoEpaamkpqairJycl2lVxVFxQRERGRHyIFf4mIiIiIiIiIiIiIyE72Fsx0zDHHdMlx5syZw2OPPXbAi3b8fj9XX331LhccAZx66qm8/vrr+1Wh67jjjiM7O5vp06dTUVGx0/Z//OMf/O///u8+BcTV1dXxhz/8Ybfbp02bxnvvvbdPixyzsrL44IMPOOmkk1iyZEmn9+tJVVVV3HTTTbvdHhERwdy5czn77LMP6DgTJ07k1VdfZf369SQkJBxQX93piy+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_cm_for_one_role('android', [gisv_la,gisv_sj,gisv_ucb], 'gis', role='HAMFDC', INDEX_MAP=IIM, criterion='distance')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "1d93631e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: WALKING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: NO_SENSED\n", + "GT: E_BIKE, Predicted: CYCLING\n", + "GT: E_BIKE, Predicted: CYCLING\n" + ] + }, + { + "data": { + "image/png": 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uWBlNO7jnVlyUjI+cOaxl+9HZG+Ofz1V3a93JdjJN1TWNbQa/tnXLA+ti8IBUnPLmgS37TnnzwLjtwXWxua79gaVSiTjntCFxwuGVkWyvgO28eWZFvHlmRfzzuar4f9eu3GFd3aE588XCoF9r9qEBMmTeADJl3gAyZd4AMmXegP4nP5WIT5w3Id5x0qhO36848ZgRceIxI+LhJ1bFD698JVau7vihdT2pL44J+hLXGwBbJBKJSCQ8hJfeyXsXeobwFxAREQceeGA89NBD2S5jl7n00kvjnHPO6bDd0qVL44477ojLLrss5syZk/ba66+/Hh/72Mfi1ltv3UVVdp977703TjvttKiuTv9j+GHDhsXdd98dM2bM6NJ5n3766Z0vDnrAgMr8uOzS6TFsSGHLvlVrauMzX3s21m2o7/R5Pn7u+BgxtCgiIjZWNcSPf/lqt9cKfVlVTVNU1Wz/pLb6eHH+5rjz7+tjyvii+MwHRsSwwVtW1TrxyAFRkJ+IK65Z0e45P3DqkBg6aEv76prG+PX1K7u97rr65mhsao687W4A3vLAuk4FrK65bXUcM7MiSku2PGGuvDQv9t+nNB6dXdVm+2Qy4sIPjYz99ylt2dfU1BxPPFsdDz6xIV59bXNsrGqMVCoRQwfmx7TJxXHSkQNi1LCCiIh407SymLxHUVz8szdi4Rtt33zcXJv+/2H7lc06o6Ag/fuxudaNGNjWps3pT5ksLMz8KZOFBemfze3PCfQt5g0gU+YNIFPmDSBT5g3o3/KSEd/56tSYdcDWB+s2NTXH3x9fFXfevzzmvrox1m2oj4L8ZAwfWhgHTB8Qp588OnYfXRIREYcdPCT22asiPvu1Z+OVBd374L6u6otjgt7O9QYAAHRN5n/xB9CHjRw5Mj74wQ/G7Nmz4/zzz2/1+m233RaPP/54FirrvBtuuCFOPvnkVsGvsWPHxsMPP9zl4Bf0FuVlqfjJN6bH7mNKWvatXV8Xn/nas7F46aZOn2e/qZVxyvEjW7Z/ftX8WL3WE92gO704f3N87aeLY0PV1l/GH3tIZbxpWmmb7feZVBzHHVLRsn31X1fF2g275hf5tbXbh9YiHvznhs4dW9ccjz+THvSaOqn9VS/f9dZBacGv2rqm+NYvlsT3f700/u+56li7vjEaGreErV5fVhd3/G19fPrbr8V9j65vOaayPBX//ZFRUV7a9o94rcNfmT9xZ/vA2PbnhP5u06btblYWdCVkmX6Dc/tzAn2LeQPIlHkDyJR5A8iUeQP6t3POHJsWktpc2xhfvHRO/Pd3XoiH/7k6Vq2pi4aG5qjZ1BgLXquJG25bEu//xJNx6z1LW44ZWFkQ3//6tKgoz43nkffFMUFv53oDAAC6RvgLoA2pVCp+8YtfxPTp01u9duONN2ahos75zW9+E2eeeWbU1aUHVPbee+945JFHYtKkSVmqDHpGaUleXHbp9Jg4rqxl34aN9fHZrz0bC16r6fR5CgqS8eVPTo7kv1f9eXrOurjl7qUdHAV0xYrVDXHdnavT9p167MBW7QryE/Hx9wxr+Vw+/+qmuPeRzoWxuqJ6U3qwae2Ghli5puNVv/7jpYWb07bHDC9os11ZSTJOOSZ9vL+8dkU89cKO56yGhub4+Z9WxJxXtrYbMjA/Tn/LoDbb12xOH09RYTIKCzILgFWWp99Eqa5xEwW2VVWTPkcUF+VFUWFmv3YZOCA/bXtjdefnHaD3MW8AmTJvAJkybwCZMm9A/1VeloozT90tbd//XPlKPPavNTs8rr6hOb73s5fjqefWtewbNqQw3v/O3XdFmRnpi2OCvsD1BgAAdI3wF0A78vLy4iMf+Uir/bNnz85CNR374Q9/GOeff340Nqb/IfZBBx0Uf//732P06NFZqgx6RklxXvzokumx16Tyln1V1Q1xwUXPxSsLqndwZGsnvHl4jBm1ZZWexsbm+N31r8WIYYUdfhVutypOZUV+2usDKvPb6g76vYf/lb5K1p7jiqKkOP3zdNSbymPk0C0Bqsam5rjx7jUxdFCqw6/8VHrAqaIsL+31yrL0QNN/LFlRn7a9dn1mNwzWrk//97istO1+DtinNIq3uZmxbFV9PPjExk710dwccd2d6Tcojz64os22G6ubYmN1ek1DB2b2hMph27VfsrK+nZbQP23Y2BAbNqZ/LoYPLcroHCO2a794SedXLQV6H/MGkCnzBpAp8waQKfMG9F+zDhwUJcVb72W8sWxT3HH/8k4d29wc8ds/L0rbd8IxI7q1vq7oi2OCvsD1BgAAdI31qAF24KCDDmq1b9myZVmoZMe+8pWvxHe+851W+4855pi4+eabo6ysrI2joO8oKkzGDy+aFlP32hp6qKlpiM9d/Fy8+ErnQhTbKizYGsTIy0vEjy5pvQpgZ3zivAnxifMmtGz//fFVceG3nu/SuaAvW1/VGBurG6P83wGpvGQihg/OjwWLa1vaFGwTrsxLJuLrH+9aqPmc04bGOacNbdl+4pmq+O6vWq/s9/rS2th3r5KW7fqG5oz62b59QX7bq2ztMbowbXvOy51fpTBiywpo9fVNkf/v709FWV6MHJofS9sIZi1eVhdTJhS3bI8YWhCLl3c+wDV8SHqAdfGyunZaQv+18PWamL53Zcv2mJHFsWhx5z/Xo0ak36xc+HpmcwLQ+5g3gEyZN4BMmTeATJk3oH+aNC79bwqeenZdRsc/M2dd1NU3tdzPGVCRH2NGFsfipdkLZPTFMUFf4XoDAAAyZ+UvgB2oqGi9ekZTU1MWKmlbU1NTfPSjH20z+HXaaafF7bffLvhFn1dQkIwffH1a7LvP1l8MbtrcGJ+/dE7Mmbshi5UBmWhsTA9Lbb9iV09buCQ92FRa3PbKXe0p3W7lsu1X3Wqv3bqNbbdrT1NTxMaa9GuTinZWM3ttafqYJo/r/BP0CgsSMXa7oNr25wMi5r+WvtrotsH0jhQVJmPiHqU7PB/Q95g3gEyZN4BMmTeATJk3oH8qK01/fviadZndA2hsitiwIf2Bc5UV+e207hl9cUzQV7jeAACAzAl/AezAqlWrWu0bOnRoGy17Xn19fZx99tnxi1/8otVr5557blx//fVRWFjYxpHQdxTkJ+L7X50a+08f0LKvtrYxvvSNOfHM8+uzVxiQkfxUIsq3Cyyt29iQpWq2eOqF6mhq2hpIGz4kP6NA2u4jC9K2V69rezzVm9KDW4UFmYfeirY7ZlNt20H12S9sdxNlUnGb7dqy94TiSOVt7Wfe65tjfYZBNegPnvjXmrTt/aZVttOytX33qYxUauuvaV6atzHWruv86nxA72TeADJl3gAyZd4AMmXegP6pqjr9PkZxYWYPxYuIKN7uQXqbNmf3PkJfHBP0Fa43AAAgc6mOmwD0X//4xz9a7ZsxY0bPF7KdmpqaOOOMM+LOO+9s9drnPve5+MEPfhCJRHZXTIFdLZVKxLe/sk8ctN/Aln21dU3x5W89H/96dt1Onfu6W96I6255I+PjLv/2vrH/tAEt29+6bG7ccf/ynaoF+oPpk4sjL7n1363NtU2xZruw1G0PrYvbHlqX8bm/8enRMXVSScv2T3+/LB58YmOHx61d3xgvLdgcUyZsCUjlpxIxfXJx/Ov5mk71u9/e6U+be2HepjbbrVmfPs7xYzILbo8cmh8l2914XLeh7RuPs1+sidq6pigs2HIzZK/xxTF6eH68sbzjmyFHz0x/2t4Tz3h6HrTlidlrY3NtYxT9+48Ipk2pjN3HFMdri9ueA7Z14jEj0rb//ljrB1EAfY95A8iUeQPIlHkDyJR5A/qnVWtq07YnTSjL6PgxI4ujtGS7lbbWZrbSVnfri2OCvsL1BgAAZM7KXwDtqKqqissvv7zV/tNOOy0L1Wy1fv36OP7449sMfn3rW9+KH/7wh4Jf9Hl5yYhvfGnvmHXg4JZ99fVN8dXvPh//nL02i5UBmUokIt751kFp+2a/WBMNOfDgxAce35C2fcqbB7bTMt2UCUWx5x5FLduNTc3xVDuhsTmvpN/A2Gt8cYwZUdBm27a85bD0p+AtXlYXG6ra/ubV1TfHo7Or0vaddtygNttua9Sw/Jg5fWuYraGxOf7xZMcBOuiPamub4sFHVqbte+/pu3d43G6jiuOIWUNathsamuLev63o9vqA3GPeADJl3gAyZd4AMmXegP7pqefWp21Pm1IZe+xW0k7r1t5+wsi07UWLa2LdhuyuxNMXxwR9hesNgNYSiWQkkr589dKvhEgK9ASfNIA2rFy5Mk455ZR44430lX8OO+ywOOqoo7JTVESsWLEijjrqqHj44YfT9ieTybjyyivjK1/5SpYqg56TTEZc9PkpccTM9F/off37L8Sj/7cmi5VB/3bikZUxsCKv44bbyEtGfPzs4TF5XHHa/jv/vq4bK+u6Bx7fEK8v3fpUyOmTS+KUNw/Y4TGVZXnxyfcOT9v36FNVsWxV2zcDFyyujTeWb31KZF5eIj7z/uFRVNhxkHu/KSVx0pHp9Tz2dFXbjf/t2jtWR31Dc8v2MTMr4qBppe22z08l4hNnD4/8/K0/Ot7/2IZ2xwNE/O81i6K+vqll+6RjR8RhbxrcbvuC/ER85dOTo2Cbz9lt9y6LN5Zt3qV1ArnDvAFkyrwBZMq8AWTKvAH9zyvzq+K1xVsfZJfKS8TXLtgrSoo7vvdz8P4D450nj07bt32oIxv64pigL3G9AQAAmRH+AoiITZs2xeLFi+Puu++Oz3zmM7HnnnvGgw8+mNZm9OjR8bvf/S5LFUYsWrQoDjvssHj66afT9ufn58c111wTH/nIR7JTGPSwr3xqchxz+LC0fb/83YJ4eX5VjBhWmNFXQb5V8qC7HDurMq68eI/4zPuHx4FTS3cYXirIT8RhB5TFj768exwzsyLttQef2BDPvbypnSN7VlNzxG9uXBWNTVvDUue+Y2h88PQhUVrc+kep6ZOL49sXjImRQ7eu3LWxujH+cOuqHfbzh1vSX5+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_cm_for_one_role('ios', [gisv_la,gisv_sj,gisv_ucb], 'gis', role='HAHFDC', INDEX_MAP=IIM, criterion='distance')" ] @@ -1567,7 +1644,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "15ca1d1d", "metadata": {}, "outputs": [], @@ -1577,7 +1654,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "bdc483e2", "metadata": {}, "outputs": [], @@ -1588,7 +1665,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "af22eb20", "metadata": {}, "outputs": [], @@ -1659,7 +1736,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "f550a507", "metadata": {}, "outputs": [],