diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 5f0cdac5..4a932b25 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -23,8 +23,7 @@ jobs: pip install codecov pip install pytest pip install pytest-cov - pip install -r requirements.txt - pip install -e . + pip install -e .[web,pdf] - name: Test with pytest run: | pytest --cov=./ diff --git a/dev/gui/dev_gui_secB.py b/dev/gui/dev_gui_secB.py index 9f7eddfc..14197e12 100644 --- a/dev/gui/dev_gui_secB.py +++ b/dev/gui/dev_gui_secB.py @@ -9,6 +9,7 @@ import pickle from pyhdx.web.apps import main_app from pyhdx.web.base import DEFAULT_COLORS, STATIC_DIR +from pyhdx.web.utils import load_state from pyhdx.web.sources import DataSource from pyhdx.batch_processing import yaml_to_hdxm from pyhdx.fileIO import csv_to_protein @@ -16,47 +17,50 @@ import numpy as np from pathlib import Path import pandas as pd +import yaml ctrl = main_app() directory = Path(__file__).parent root_dir = directory.parent.parent -data_dir = root_dir / 'tests' / 'test_data' +data_dir = root_dir / 'tests' / 'test_data' / 'input' test_dir = directory / 'test_data' fpath_1 = root_dir / 'tests' / 'test_data' / 'ecSecB_apo.csv' fpath_2 = root_dir / 'tests' / 'test_data' / 'ecSecB_dimer.csv' -fpaths = [fpath_1, fpath_2] -files = [p.read_bytes() for p in fpaths] +yaml_dict = yaml.safe_load(Path(data_dir / 'data_states.yaml').read_text()) +# fpaths = [fpath_1, fpath_2] +# files = [p.read_bytes() for p in fpaths] +# +# +# d1 = { +# 'filenames': ['ecSecB_apo.csv', 'ecSecB_dimer.csv'], +# 'd_percentage': 95, +# 'control': ('Full deuteration control', 0.167), +# 'series_name': 'SecB WT apo', +# 'temperature': 30, +# 'temperature_unit': 'celsius', +# 'pH': 8., +# 'c_term': 165 +# } +# +# d2 = { +# 'filenames': ['ecSecB_apo.csv', 'ecSecB_dimer.csv'], +# 'd_percentage': 95, +# 'control': ('Full deuteration control', 0.167), +# 'series_name': 'SecB his dimer apo', +# 'temperature': 30, +# 'temperature_unit': 'celsius', +# 'pH': 8., +# 'c_term': 165 +# } -d1 = { - 'filenames': ['ecSecB_apo.csv', 'ecSecB_dimer.csv'], - 'd_percentage': 95, - 'control': ('Full deuteration control', 0.167), - 'series_name': 'SecB WT apo', - 'temperature': 30, - 'temperature_unit': 'celsius', - 'pH': 8., - 'c_term': 165 -} - -d2 = { - 'filenames': ['ecSecB_apo.csv', 'ecSecB_dimer.csv'], - 'd_percentage': 95, - 'control': ('Full deuteration control', 0.167), - 'series_name': 'SecB his dimer apo', - 'temperature': 30, - 'temperature_unit': 'celsius', - 'pH': 8., - 'c_term': 165 -} - -yaml_dicts = {'testname_123': d1, 'SecB his dimer apo': d2} +#yaml_dicts = {'testname_123': d1, 'SecB his dimer apo': d2} def reload_dashboard(): - data_objs = {k: yaml_to_hdxm(v, data_dir=data_dir) for k, v in yaml_dicts.items()} + data_objs = {k: yaml_to_hdxm(v, data_dir=data_dir) for k, v in yaml_dict.items()} for k, v in data_objs.items(): v.metadata['name'] = k ctrl.data_objects = data_objs @@ -96,21 +100,24 @@ def reload_dashboard(): def init_dashboard(): - file_input = ctrl.control_panels['PeptideFileInputControl'] - file_input.input_files = files - file_input.fd_state = 'Full deuteration control' - file_input.fd_exposure = 0.167*60 - file_input.pH = 8 - file_input.temperature = 273.15 + 30 - file_input.d_percentage = 90. - - file_input.exp_state = 'SecB WT apo' - file_input.dataset_name = 'SecB_tetramer' - file_input._action_add_dataset() - - file_input.exp_state = 'SecB his dimer apo' - file_input.dataset_name = 'SecB_dimer' # todo catch error duplicate name - file_input._action_add_dataset() + for k, v in yaml_dict.items(): + load_state(ctrl, v, data_dir=data_dir, name=k) + + # file_input = ctrl.control_panels['PeptideFileInputControl'] + # file_input.input_files = files + # file_input.fd_state = 'Full deuteration control' + # file_input.fd_exposure = 0.167*60 + # file_input.pH = 8 + # file_input.temperature = 273.15 + 30 + # file_input.d_percentage = 90. + # + # file_input.exp_state = 'SecB WT apo' + # file_input.dataset_name = 'SecB_tetramer' + # file_input._action_add_dataset() + # + # file_input.exp_state = 'SecB his dimer apo' + # file_input.dataset_name = 'SecB_dimer' # todo catch error duplicate name + # file_input._action_add_dataset() # initial_guess = ctrl.control_panels['InitialGuessControl'] # initial_guess._action_fit() diff --git a/docs/examples/01_basic_usage.ipynb b/docs/examples/01_basic_usage.ipynb index 99f56ed8..052b639d 100644 --- a/docs/examples/01_basic_usage.ipynb +++ b/docs/examples/01_basic_usage.ipynb @@ -14,12 +14,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "outputs": [], "source": [ "from pyhdx import PeptideMasterTable, read_dynamx, HDXMeasurement\n", - "from pyhdx.plot import plot_peptides\n", + "from pyhdx.plot import peptide_coverage\n", "import matplotlib.pyplot as plt\n", + "import proplot as pplt\n", "from pathlib import Path" ], "metadata": { @@ -48,7 +49,7 @@ { "cell_type": "code", "source": [ - "fpath = Path() / '..' / '..' / 'tests' / 'test_data' / 'ecSecB_apo.csv'\n", + "fpath = Path() / '..' / '..' / 'tests' / 'test_data' / 'input' / 'ecSecB_apo.csv'\n", "data = read_dynamx(fpath, time_unit='min')\n", "data.size" ], @@ -58,13 +59,13 @@ "name": "#%%\n" } }, - "execution_count": 2, + "execution_count": 3, "outputs": [ { "data": { - "text/plain": "567" + "text/plain": "9072" }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -99,13 +100,13 @@ "name": "#%%\n" } }, - "execution_count": 3, + "execution_count": 4, "outputs": [ { "data": { - "text/plain": "array([ 8., 8., 8., 8., 8., 8., 8., 8., 8., 6., 6., 6., 6.,\n 6., 6., 6., 6., 6., 12., 12., 12., 12., 12., 12., 12., 12.,\n 12., 13., 13., 13., 13., 13., 13., 13., 13., 13., 14., 14., 14.,\n 14., 14., 14., 14., 14., 14., 20., 20., 20., 20., 20.])" + "text/plain": "0 8.0\n2 8.0\n1 8.0\n3 8.0\n4 8.0\n5 8.0\n6 8.0\n7 8.0\n8 8.0\n9 6.0\n11 6.0\n10 6.0\n12 6.0\n13 6.0\n14 6.0\n15 6.0\n16 6.0\n17 6.0\n18 12.0\n20 12.0\n19 12.0\n21 12.0\n22 12.0\n23 12.0\n24 12.0\n25 12.0\n26 12.0\n27 13.0\n29 13.0\n28 13.0\n30 13.0\n31 13.0\n32 13.0\n33 13.0\n34 13.0\n35 13.0\n36 14.0\n38 14.0\n37 14.0\n39 14.0\n40 14.0\n41 14.0\n42 14.0\n43 14.0\n44 14.0\n45 20.0\n47 20.0\n46 20.0\n48 20.0\n49 20.0\nName: ex_residues, dtype: float64" }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -137,13 +138,13 @@ "name": "#%%\n" } }, - "execution_count": 6, + "execution_count": 5, "outputs": [ { "data": { - "text/plain": "array([ 0. , 0. , 5.0734 , 2.486444, 2.857141, 3.145738,\n 3.785886, 4.08295 , 4.790625, 0. , 0. , 3.642506,\n 1.651437, 1.860919, 2.107151, 2.698036, 2.874801, 3.449561,\n 0. , 0. , 5.264543, 1.839924, 2.508343, 2.969332,\n 3.399092, 3.485568, 4.318144, 0. , 0. , 6.3179 ,\n 2.532099, 3.306167, 3.996718, 4.38941 , 4.379495, 5.283969,\n 0. , 0. , 6.812215, 3.11985 , 3.874881, 4.342807,\n 4.854057, 4.835639, 5.780219, 0. , 0. , 10.8151 ,\n 5.432395, 6.1318 ])" + "text/plain": "0 0.000000\n1 0.000000\n2 5.073400\n3 2.486444\n4 2.857141\n5 3.145738\n6 3.785886\n7 4.082950\n8 4.790625\n9 0.000000\n10 0.000000\n11 3.642506\n12 1.651437\n13 1.860919\n14 2.107151\n15 2.698036\n16 2.874801\n17 3.449561\n18 0.000000\n19 0.000000\n20 5.264543\n21 1.839924\n22 2.508343\n23 2.969332\n24 3.399092\n25 3.485568\n26 4.318144\n27 0.000000\n28 0.000000\n29 6.317900\n30 2.532099\n31 3.306167\n32 3.996718\n33 4.389410\n34 4.379495\n35 5.283969\n36 0.000000\n37 0.000000\n38 6.812215\n39 3.119850\n40 3.874881\n41 4.342807\n42 4.854057\n43 4.835639\n44 5.780219\n45 0.000000\n46 0.000000\n47 10.815100\n48 5.432395\n49 6.131800\nName: uptake, dtype: float64" }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -175,13 +176,13 @@ "name": "#%%\n" } }, - "execution_count": 7, + "execution_count": 6, "outputs": [ { "data": { - "text/plain": "441" + "text/plain": "10584" }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -201,13 +202,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "outputs": [ { "data": { "text/plain": "(pyhdx.models.HDXMeasurement,\n 7,\n array([ 0. , 10.02 , 30. , 60. , 300. ,\n 600. , 6000.00048]),\n 'My HDX measurement',\n 'SecB WT apo')" }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -226,9 +227,9 @@ { "cell_type": "markdown", "source": [ - "Iterating over a ``HDXMeasurement`` object returns a set of ``PeptideMeasurements`` each with their own attributes describing\n", + "Iterating over a ``HDXMeasurement`` object returns a set of ``HDXTimepoint`` each with their own attributes describing\n", "the topology of the coverage. When creating the object, peptides which are not present in all timepoints are removed, such\n", - "that all timepoints and ``PeptideMeasurements`` have identical coverage.\n", + "that all timepoints and ``HDXTimepoint`` have identical coverage.\n", "\n", "Note that the internal time units in PyHDX are seconds." ], @@ -242,9 +243,9 @@ { "cell_type": "code", "source": [ - "fig, ax = plt.subplots(figsize=(14, 5))\n", + "fig, ax = pplt.subplots(figsize=(10, 5))\n", "i = 0\n", - "plot_peptides(hdxm[i], ax, 20, cbar=True)\n", + "peptide_coverage(ax, hdxm[i].data, 20, cbar=True)\n", "t = ax.set_title(f'Peptides t = {hdxm.timepoints[i]}')\n", "l = ax.set_xlabel('Residue number')" ], @@ -254,15 +255,18 @@ "name": "#%%\n" } }, - "execution_count": 9, + "execution_count": 17, "outputs": [ { "data": { - "text/plain": "
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\n" 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}, "metadata": { - "needs_background": "light" + "image/png": { + "width": 1000, + "height": 500 + } }, "output_type": "display_data" } @@ -270,23 +274,26 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" 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}, "metadata": { - "needs_background": "light" + "image/png": { + "width": 1000, + "height": 500 + } }, "output_type": "display_data" } ], "source": [ - "fig, ax = plt.subplots(figsize=(14, 5))\n", + "fig, ax = pplt.subplots(figsize=(10, 5))\n", "i = 3\n", - "plot_peptides(hdxm[i], ax, 20, cbar=True)\n", + "peptide_coverage(ax, hdxm[i].data, 20, cbar=True)\n", "t = ax.set_title(f'Peptides t = {hdxm.timepoints[i]}')\n", "l = ax.set_xlabel('Residue number')" ], @@ -312,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "outputs": [], "source": [ "from pyhdx.fileIO import csv_to_hdxm\n", diff --git a/docs/examples/04_exporting_output.ipynb b/docs/examples/04_exporting_output.ipynb deleted file mode 100644 index 96a49113..00000000 --- a/docs/examples/04_exporting_output.ipynb +++ /dev/null @@ -1,67 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Under construction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# Topics:\n", - "# Exporting data\n", - "# Exporting fit result\n", - "# plotting functions\n", - "# Creating output pdf\n", - "# creating output pml\n", - "\n", - "\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/docs/examples/04_plot_output.ipynb b/docs/examples/04_plot_output.ipynb new file mode 100644 index 00000000..b968619c --- /dev/null +++ b/docs/examples/04_plot_output.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Plot output" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "from pyhdx.fileIO import load_fitresult\n", + "from pyhdx.batch_processing import yaml_to_hdxm\n", + "import proplot as pplt\n", + "from pyhdx.plot import *\n", + "import yaml\n", + "from pathlib import Path" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "", + "text/markdown": "HDX Measurement: SecB_tetramer

Number of peptides: 63
Number of residues: 146 (10 - 156)
Number of timepoints: 7
Timepoints: 0.00, 10.02, 30.00, 60.00, 300.00, 600.00, 6000.00 seconds
Coverage Percentage: 88.39
Average redundancy: 5.49
Temperature: 303.15 K
pH: 8.0
" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dir = Path() / '..' / '..' / 'tests' / 'test_data' / 'input'\n", + "output_dir = Path() / '..' / '..' / 'tests' / 'test_data' / 'output'\n", + "yaml_dict = yaml.safe_load(Path(data_dir / 'data_states.yaml').read_text())\n", + "\n", + "state = 'SecB_tetramer'\n", + "hdxm = yaml_to_hdxm(yaml_dict[state], data_dir=data_dir, name=state)\n", + "hdxm\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "A figure of peptide coverage graphs showing RFU per peptide per exposure timepoint:\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=4, ncols=2, refaspect=3.0, figwidth=6.3)", + "image/png": 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sSdJxxx2nCRMmSJJ+8pOfxL3F5JlnntGzzz4rqfdCxqFQSB9++KFeeeWVWLDq67///a+kSAW3nuvBRG8x+dvf/tbvPTt37tQll1wy4M84HNF+3nfffXr33Xfjvmbp0qV65ZVX9P7772uPPfYY0/aijjvuOEmRM/TxfjYp8gdONPCddNJJvZ4788wzJUWuDnjsscf6vfcXv/iF2tvblZ2d3W9R6dNOO01ZWVnq7OzUtdde2++9TzzxROwPleh2huuMM86QFAnRv/3tb/s9/7vf/S4WnEfaNgBg/CDn9JZuOWe33XbToYceKkm64oor4k6a3X///bGTln2vNiTnAEByMFmHtDdjxgz9+Mc/lhQ5I/v1r39d//znP+Xz+fTuu+/quuuu0ze/+U11dXWptLRUp59+eq/3X3rppdq2bZtmzpypK6+8Mvb4L3/5S3k8HtXW1urSSy+Nu2273a41a9bo0EMP1SOPPKJVq1bp/fff15VXXqlTTjlF4XBYEydO7BWu3W63li5dKkn64IMPdPjhh+uJJ56IvfeKK66IBZhZs2bphz/8Yey9Rx99tDIzI3e/L168WHfccYc++OAD+Xw+vf7667rwwgt1yy23SJJOPPHEXrdKHHTQQZKkv//971qyZIneeOMNrVq1So8//rgOO+wwlZeXx86+jsZ5552nOXPmKBgM6vjjj9fSpUv17rvvatWqVXr++ef17W9/W8uWLZMkXXzxxfJ4PL3eX1ZWpvnz52v+/PnaunXrsLf79a9/XXvttZckacmSJbr88sv11ltvye/36+2339aVV16pk046SaFQSDNmzOi33s9JJ52kRYsWSZJ+8IMf6Oabb9ann36qd999V9/73vdiff7+978fWzcmyuv16txzz5UUCe/nnnuu3n//ff33v//VjTfeGHtuv/326zdJONTPfMABB+j444+XJF133XW64oor9O9//1sfffSRfv7zn+uaa66J/fzRqxgAAKmHnJPeOUeKXDWZlZWlTZs26dBDD9UDDzwQ+zwvvvji2GTswoUL+91SSs4BUktYUqctw5D/hZP9w6Q4W1NTE58x0l5nZ6cuvPBCPfDAAwO+prS0VH/72980Y8aM2GMvvfSSvvnNb0qK3B4Qvdw/6pFHHtE555wjKXKmNnrJ/vXXX68bbrhBBx98sEpKSvSXv/wl7jYLCgr0xBNPxAJkT1dffXUscMYzc+ZMPfXUU5o/f36vx3/3u98NeGY96stf/rKeeOKJXtW2PvvsMx155JG9Kq5FZWZm6ve//73WrFmjW2+9VZdddlmvbZx77rl66KGHdNppp+nuu+8ecLvr1q3TiSeeqPXr1w/4mu9///u66aab+i2SfNRRR8XOon/++eeDLnjd1/r163XiiSdq3bp1A75m3rx5evTRR7VgwYJ+z23ZskXHHnvsgO8/5phj9PDDD8f+gOipo6NDp5xyyoC3psydO1fPPvts3PV4hvqZGxsbdfzxx8fWkulr//331zPPPDOi204AAOMPOae3dMs5kvToo4/qvPPOi3vLqCTtu++++utf/6opU6b0e46cA6SGwsJCFc7N0uWff8WQ9n6151uqXduh2tpaQ9pDb1xZBygSwv74xz/q6aef1te//nXttttuysrKUn5+vg444ADdeOONeuutt3oF2J63hZx88sn9AqwknXLKKTrssMMkST/60Y/U1NTU63mbzabf/e53evzxx/XVr35VEydOlMPh0Jw5c3Teeefp448/jhtgpchtB6+88opOPvlkTZ8+XdnZ2SooKNB+++2nX/ziF/rggw/6BdhoP1566SWdfPLJmjlzphwOhzIzMzV58mQdeeSRuvvuu/XSSy/1CrCStNdee+mtt97Saaedpt122y32nuOOO04vvfRS7HaYsZgzZ47ee+89XX/99frSl76kCRMmKCsrS7vttptOPPFEPffcc7r55psHrWY2GrNnz9bbb7+t66+/XgceeKAmTpyozMxMFRYW6itf+Yp+85vf6J133ok7USdF1rF76623dNlll6mkpERut1sTJkzQgQceqD/+8Y967LHH4gZYKXIbzhNPPKE77rhDBxxwgCZMmCC3263S0lJddtlleuuttwZcOHso+fn5euWVV3TDDTdo3333VV5envLy8rTvvvvq17/+tV5++WUCLACkAXJOeuccKXI79Ntvv60zzjgj9rkUFBTowAMP1K233qrXX3897kSdRM4BgGTgyjogCaJnnL/yla8MutgvAADAeEPOAQDrKSws1MS52fr55/9jSHu/3vNN1a1t58o6k3BlHQAAAAAAAGARTNYBAAAAAAAAFsFkHQAAAAAAAGAR8VcCBQAAAAAAQErp5JqtcYHfEgAAAAAAAGARVIMFAAAAAABIYdFqsBd/fogh7d285xtUgzURt8ECAAAAAACkuLBs6lKGYW3BPNwGCwAAAAAAAFgEk3UAAAAAAACARTBZBwAAAAAAAFgEa9YBAAAAAACkgU6D1qyDuUydrJs9e7ZaWlo0ffp0MzcDAABS3JYtW+R2u7V+/fpkd0USGQcAABjHajkHyWfqZF1LS4s6Ojpktxt7t21HR4fWrl2rcDhsaLsDsdlsmjt3rrKyskxpv7OzU5KUmcmFjpI5v1+zf4epiP3SOOn4WabacToVjbf9sqOjQy0tLcnuRoxZGUdKrfEz3vazRCDnWAP7pnHS8bNMpeN0qhpv+6XVcg6Sz9Q9d/r06bLb7fr8888NbfeTTz7Rfvvtp/u/NUklnmxD2+6rPNCuM57coUcffVSLFi0yZRtVVVWSpOLiYlPaH2+iv9/vPVCi3UrdI3pvU02HJCm3aNcX2XZfi/50ermpv8NUxH5pnHT8LKPj+Po/ztLs+U7D2q2viwSvCRN3fX2tX92mK87bwBgfofG2X+65554KhULJ7kaMWRlH2jV+7vqSW/PzzV1eeHVjSN//oMW08TPe9rNEiP5+f7ZstrwlIzs+NtZGjoH5hbuOgRXlbfrN2es5Bo4Q+6Zx0vGzjI7jZVfN04KZLsParW2I/C1TWLDrbxn/pladfd0axvgIjbf90mo5B8k3PqaZB1DiydaiqY5kdwMm2a3Urd0X5Y3oPQ1V7ZKkgmJzJ3EBDM/s+U6V7pNjWHs11ZEQWzSZM8tIffPz7frCRLOjWqfJ7WMg3hKn5i4c2fGxripyDJxYzDEQsIIFM11auCDXsPaqaiN/yxQX8rcMzBGW1GlQndHEXFeavqgGCwAAAAAAAFgEk3UAAAAAAACARYzr22ABAAAAAAAwtLBs6lKGYW3BPFxZBwAAAAAAAFjEuL6yrjzQHvfxyp2dqm8zppLKxvoOQ9rByG33jbx0dbQabF2farBWUFFRoUAgYErbHo9HXq/XlLaBsXjzlQZtWNNqWHs7G7skSXn5u84IbtkU+S7w+Xxx38P4wHi1ujGkeAUgqtpCamg3ZlnnimaWh06WivK2Eb8nWg22ZlvvarBWQM5BOnrpvTr5Nxn3t0ZDU2SMF+TuGuObtgclxc85jA0gdY3LyTqPxyO3y6kzntwR93m7TQoZmD3tNikYDBrXIAbl8Xjkcjv1p9PLDWvT5XbK4/EY1t5IVVRUqKR0gVpbzAnULrdT5T4/X9awjGAwKLtd+uOvtyVsm2VlZXEfd7ud8jE+MI5Ec873P4j/ByA5Z3zzeDxyu536zdnrDWvTTc4BEioYDMpuk37x580J22a8nON2OeUrZ2wAqWhcTtZ5vV75yv1xz975fD6VlZXpgZOLVTJ57CWvy6vbdfrjVXI4HGNuC8Pj9XpV7ov/+x1KTU2NJKmoqKjX48k+6xQIBNTa0qZTlu+ryaXGlXeXpGpfkx4p+48CgQBf1LAMh8OhUEh64KLZKpnhMqzdmp2RM85FecP7+irf3KrTb1vP+MC4Mpycc8//5GrBhLHHOH99p855s4mck0Ber1e+FM05Zz+4h3YrzTG07e2+Zi1bvIrjOCzF4XAoFJYeOHU3lRaP/W/OqJrmyB0ERTlDrynmq2rX6Q9vZ2xgxDrDxqxZB3ONy8k6KRJ0BjsolUzO1qJpzgT2CEYa6vc7kKqqKklScXGx0V0yxOTSXE1fVJDsbgAJUzLDpUVzjPvDrap7aYLiCVlDvBIY34b6HlwwIVP7Fo3bGJf2UjXn7FaaI++ivGR3A0iY0uJsLZpu3N+cVd0nJYuHeVISQOqiwAQAAAAAAABgEUzWAQAAAAAAABbB9bUAAAAAAAApLiybOmXMmnVh2QxpB/Gl7GRdeXW7Ke2YUZZ+oMWCjZbsxYchlT9XrWpfk6Ft1m6IVAuMV869L/YBJNoLH9WrfEurYe01NEfWcinIGd7X18bKSIXL4YyPgTBuYEX++k5T2jE655Bx0svK5wPaXt5saJuBDZHvEHIOrOh5X7PKq4yrpt3QGikwUeAaejJlQ23k+D2WjCMxbgCrSrnJOo/HI7fLqdMfrzKsTbfLKY/Ho4qKCpWWLlCLSWXpzeZ2O+Wj7H1SBINB2e3Si1etNm0b8cq598U+gEQJBoOy26SlD29NdlckDW98DMTtcspXzriBNURzzjlvGnfiJxVyDt9vyRXNOc9ctcG0bZBzYCWxnPOCsRdxjMZYMo5EzgGsKuUm67xer3zlfkPPCkfPNnzyySdqaWnTX5bO04LdXYa1X9sQqW5YWGBedUP/xlYtuXYNpb2TxOFwKBSSfnvrHM2dY9y+MxJr17Xqxz9Zxz6AhHA4HAqFpQdOLlbJ5GzD2q1piZxxLnInpuR8eXW7Tn+8inEDy0hEzrnvsrkq8RrzXVXTGMk4RfnmZZzyiladecNaxmkSRXPO738zV/OSlHPWrGvVBT9jP0BiRHPOsiMmaMFE4/6krm0LSZIKnYlZWt5f16mzX65n3KSRsGTgbbAwU8pN1kmRIGvmwWbB7i4tXJBrWHtVtZFbbYsLjfuDFtY0d45Le++Vk+xuAAlTMjlbi6Y5DWuvamfklo/ivJT8+gKGxeycU+J1aeE8Y3J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+ }, + "metadata": { + "image/png": { + "width": 629, + "height": 551 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes, colorbar = peptide_coverage_figure(hdxm.data)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "We can also make only a single plot of the peptide data, specifing which data field to use for the colors and\n", + "specifing a custom colormap and data range (norm):" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=1, ncols=1, refaspect=3, figwidth=6.3)", + "image/png": 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+ }, + "metadata": { + "image/png": { + "width": 629, + "height": 255 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pplt.subplots(width='160mm', aspect=3)\n", + "cbar = peptide_coverage(ax, hdxm[1].data, color_field='uptake',\n", + " cmap='viridis', norm=pplt.Norm('linear', 0, 10))\n", + "ax.format(xlabel='Residue Number', title=f'Uncorrected D-uptake, t={hdxm.timepoints[1]:.2f} seconds')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Scatterplots of RFUs per exposure time:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=4, ncols=2, refaspect=3.0, figwidth=6.3)", + "image/png": 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+ }, + "metadata": { + "image/png": { + "width": 629, + "height": 518 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes, cbars = residue_time_scatter_figure(hdxm)\n", + "axes.format(ylabel='RFU')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Plot all exposure timepoints one one axis, with log scale colormap:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=1, ncols=1, refaspect=3, figwidth=6.3)", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 629, + "height": 229 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pplt.subplots(width='160mm', aspect=3)\n", + "residue_scatter(ax, hdxm)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next we load a previous fit result to plot ΔG and ΔΔGs:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "(0.7513929473733795, 1.094485656305879)" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitresult = load_fitresult(output_dir / 'ecsecb_tetramer_dimer')\n", + "\n", + "fitresult.mse_loss, fitresult.total_loss" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=1, ncols=2, refaspect=2.5, figwidth=6.3)", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 629, + "height": 154 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes, cbars = dG_scatter_figure(fitresult.output.df)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=1, ncols=1, refaspect=2.5, figwidth=6.3)", + "image/png": 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+ }, + "metadata": { + "image/png": { + "width": 629, + "height": 292 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes, cbars = ddG_scatter_figure(fitresult.output.df, reference=1)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Using Panda's built-in plotting of dataframes:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=1, ncols=1, refaspect=3, figwidth=6.3)", + "image/png": 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+ }, + "metadata": { + "image/png": { + "width": 629, + "height": 244 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pplt.subplots(width='160mm', aspect=3)\n", + "ax = fitresult.losses.plot(ax=ax)\n", + "\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Plotting of the mean squared error of the fit per peptide for each fitted protein state:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "Figure(nrows=2, ncols=1, refaspect=2.5, figwidth=6.3)", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 629, + "height": 550 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "peptide_mse_figure(fitresult, aspect=2.5, ncols=1)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "name": "conda-env-py38_torch_cuda-py", + "language": "python", + "display_name": "Python [conda env:py38_torch_cuda]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/pyhdx/__init__.py b/pyhdx/__init__.py index 501de776..da0c0d76 100644 --- a/pyhdx/__init__.py +++ b/pyhdx/__init__.py @@ -1,10 +1,10 @@ -from .models import PeptideMasterTable, PeptideMeasurements, HDXMeasurement, Coverage, HDXMeasurementSet +from .models import PeptideMasterTable, HDXTimepoint, HDXMeasurement, Coverage, HDXMeasurementSet from .fileIO import read_dynamx -from .fitting_torch import TorchSingleFitResult, TorchBatchFitResult +from .fitting_torch import TorchFitResult from ._version import get_versions try: - from .output import Output, Report + from .output import FitReport except ModuleNotFoundError: pass diff --git a/pyhdx/batch_processing.py b/pyhdx/batch_processing.py index d51873c4..29f8bf7d 100644 --- a/pyhdx/batch_processing.py +++ b/pyhdx/batch_processing.py @@ -4,7 +4,8 @@ time_factors = {"s": 1, "m": 60., "min": 60., "h": 3600, "d": 86400} -temperature_offsets = {'C': 273.15, 'celsius': 273.15, 'K': 0, 'kelvin': 0} +temperature_offsets = {'c': 273.15, 'celsius': 273.15, 'k': 0, 'kelvin': 0} + def yaml_to_hdxmset(yaml_dict, data_dir=None, **kwargs): """reads files according to `yaml_dict` spec from `data_dir into HDXMEasurementSet""" diff --git a/pyhdx/config.ini b/pyhdx/config.ini index e9632af9..b4f83ae9 100644 --- a/pyhdx/config.ini +++ b/pyhdx/config.ini @@ -5,3 +5,16 @@ n_workers = 10 [fitting] dtype = float64 device = cpu + +[plotting] +# Sizes are in mm +ncols = 2 +page_width = 160 +cbar_width = 2.5 +peptide_coverage_aspect = 3 +peptide_mse_aspect = 3 +residue_scatter_aspect = 3 +deltaG_aspect = 2.5 +linear_bars_aspect=30 +loss_aspect = 2.5 +rainbow_aspect = 4 \ No newline at end of file diff --git a/pyhdx/config.py b/pyhdx/config.py index 0e3cef79..fc8da136 100644 --- a/pyhdx/config.py +++ b/pyhdx/config.py @@ -74,6 +74,15 @@ def get(self, *args, **kwargs): """configparser get""" return self._config.get(*args, **kwargs) + def getint(self, *args, **kwargs): + return self._config.getint(*args, **kwargs) + + def getfloat(self, *args, **kwargs): + return self._config.getfloat(*args, **kwargs) + + def getboolean(self, *args, **kwargs): + return self._config.getboolean(*args, **kwargs) + def set(self, *args, **kwargs): """configparser set""" self._config.set(*args, **kwargs) diff --git a/pyhdx/fileIO.py b/pyhdx/fileIO.py index e8279709..e8ee7ac8 100644 --- a/pyhdx/fileIO.py +++ b/pyhdx/fileIO.py @@ -18,6 +18,8 @@ PEPTIDE_DTYPES = { 'start': int, 'end': int, + '_start': int, + '_end': int } @@ -196,7 +198,7 @@ def csv_to_hdxm(filepath_or_buffer, comment='#', **kwargs): if df.columns.nlevels == 2: hdxm_list = [] for state in df.columns.unique(level=0): - subdf = df[state].dropna(how='all') + subdf = df[state].dropna(how='all').astype(PEPTIDE_DTYPES) m = metadata.get(state, {}) hdxm = pyhdx.models.HDXMeasurement(subdf, **m) hdxm_list.append(hdxm) @@ -343,10 +345,10 @@ def save_fitresult(output_dir, fit_result, log_lines=None): dataframe_to_file(output_dir / 'losses.csv', fit_result.losses) dataframe_to_file(output_dir / 'losses.txt', fit_result.losses, fmt='pprint') - if isinstance(fit_result.data_obj, pyhdx.HDXMeasurement): - fit_result.data_obj.to_file(output_dir / 'HDXMeasurement.csv') - if isinstance(fit_result.data_obj, pyhdx.HDXMeasurementSet): - fit_result.data_obj.to_file(output_dir / 'HDXMeasurements.csv') + if isinstance(fit_result.hdxm_set, pyhdx.HDXMeasurement): + fit_result.hdxm_set.to_file(output_dir / 'HDXMeasurement.csv') + if isinstance(fit_result.hdxm_set, pyhdx.HDXMeasurementSet): + fit_result.hdxm_set.to_file(output_dir / 'HDXMeasurements.csv') loss = f'Total_loss {fit_result.total_loss:.2f}, mse_loss {fit_result.mse_loss:.2f}, reg_loss {fit_result.reg_loss:.2f}' \ f'({fit_result.regularization_percentage:.2f}%)' @@ -379,12 +381,9 @@ def load_fitresult(fit_dir): if pth.is_dir(): fit_result = csv_to_dataframe(fit_dir / 'fit_result.csv') losses = csv_to_dataframe(fit_dir / 'losses.csv') - try: - data_obj = csv_to_hdxm(fit_dir / 'HDXMeasurement.csv') - result_klass = pyhdx.fitting_torch.TorchSingleFitResult - except FileNotFoundError: - data_obj = csv_to_hdxm(fit_dir / 'HDXMeasurements.csv') - result_klass = pyhdx.fitting_torch.TorchBatchFitResult + + data_obj = csv_to_hdxm(fit_dir / 'HDXMeasurements.csv') + result_klass = pyhdx.fitting_torch.TorchFitResult elif pth.is_file(): raise DeprecationWarning('`load_fitresult` only loads from fit result directories') fit_result = csv_to_dataframe(fit_dir) diff --git a/pyhdx/fitting.py b/pyhdx/fitting.py index 74cde61d..9a9417f3 100644 --- a/pyhdx/fitting.py +++ b/pyhdx/fitting.py @@ -1,4 +1,5 @@ from collections import namedtuple +from dataclasses import dataclass, field from functools import partial import numpy as np @@ -10,9 +11,9 @@ from tqdm import trange from pyhdx.fit_models import SingleKineticModel, TwoComponentAssociationModel, TwoComponentDissociationModel -from pyhdx.fitting_torch import DeltaGFit, TorchSingleFitResult, TorchBatchFitResult +from pyhdx.fitting_torch import DeltaGFit, TorchFitResult from pyhdx.support import temporary_seed -from pyhdx.models import Protein +from pyhdx.models import Protein, HDXMeasurementSet from pyhdx.config import cfg EmptyResult = namedtuple('EmptyResult', ['chi_squared', 'params']) @@ -133,17 +134,13 @@ def fit_rates_half_time_interpolate(hdxm): dataclass with fit result """ + # find t_50 interpolated = np.array( - [np.interp(0.5, d_uptake, hdxm.timepoints) for d_uptake in hdxm.rfu_residues]) + [np.interp(0.5, d_uptake, hdxm.timepoints) for d_uptake in hdxm.rfu_residues.to_numpy()]) #iterate over residues + rate = np.log(2) / interpolated # convert to rate - output = np.empty_like(interpolated, dtype=[('r_number', int), ('rate', float)]) - output['r_number'] = hdxm.coverage.r_number - output['rate'] = np.log(2) / interpolated - - protein = Protein(output, index='r_number') - t50FitResult = namedtuple('t50FitResult', ['output']) # todo dataclass? - - result = t50FitResult(output=protein) + output = pd.DataFrame({'rate': rate}, index=hdxm.coverage.r_number) + result = GenericFitResult(output=output, fit_function='fit_rates_half_time_interpolate') return result @@ -469,7 +466,8 @@ def fit_gibbs_global(hdxm, initial_guess, r1=R1, epochs=EPOCHS, patience=PATIENC patience=patience, stop_loss=stop_loss, callbacks=callbacks) losses = _loss_df(losses_array) fit_kwargs.update(optimizer_kwargs) - result = TorchSingleFitResult(hdxm, model, losses=losses, **fit_kwargs) + hdxm_set = HDXMeasurementSet([hdxm]) + result = TorchFitResult(hdxm_set, model, losses=losses, **fit_kwargs) return result @@ -596,7 +594,7 @@ def _batch_fit(hdx_set, initial_guess, reg_func, fit_kwargs, optimizer_kwargs): model, criterion, reg_func, **loop_kwargs) losses = _loss_df(losses_array) fit_kwargs.update(optimizer_kwargs) - result = TorchBatchFitResult(hdx_set, model, losses=losses, **fit_kwargs) + result = TorchFitResult(hdx_set, model, losses=losses, **fit_kwargs) return result @@ -649,7 +647,7 @@ def __init__(self, hdxm, intervals, results, models): assert len(results) == len(models) # assert len(models) == len(block_length) self.hdxm = hdxm - self.r_number = hdxm.coverage.r_number + self.r_number = hdxm.coverage.r_number #pandas RangeIndex self.intervals = intervals #inclusive, excluive self.results = results self.models = models @@ -756,20 +754,28 @@ def tau(self): return 1 / self.rate def get_output(self, names): - # change to property which gives all parameters as output - dtype = [('r_number', int)] + [(name, float) for name in names] - array = np.full_like(self.r_number, np.nan, dtype=dtype) - array['r_number'] = self.r_number + + # this does not seem to work: + #df_dict = {name: getattr(self, name, self.get_param(name)) for name in names} + df_dict = {} for name in names: try: - array[name] = getattr(self, name) + df_dict[name] = getattr(self, name) except AttributeError: - array[name] = self.get_param(name) - return array + df_dict[name] = self.get_param(name) + + df = pd.DataFrame(df_dict, index=self.r_number) + + return df @property def output(self): - """:class:`~pyhdx.Protein`: Protein object with fitted rates per residue""" - array = self.get_output(['rate', 'k1', 'k2', 'r']) - return Protein(array, index='r_number') + """:class:`~pandas.Dataframe`: Dataframe with fitted rates per residue""" + df = self.get_output(['rate', 'k1', 'k2', 'r']) + return df + +@dataclass +class GenericFitResult: + output: pd.DataFrame + fit_function: str # name of the function used to generate the fit result diff --git a/pyhdx/fitting_torch.py b/pyhdx/fitting_torch.py index 23cfdd73..3ab6a716 100644 --- a/pyhdx/fitting_torch.py +++ b/pyhdx/fitting_torch.py @@ -13,6 +13,7 @@ # TORCH_DTYPE = t.double # TORCH_DEVICE = t.device('cpu') + class DeltaGFit(nn.Module): def __init__(self, deltaG): super(DeltaGFit, self).__init__() @@ -101,13 +102,13 @@ class TorchFitResult(object): Parameters ---------- - data_obj : :class:`~pyhdx.models.HDXMeasurement` or :class:`~pyhdx.models.HDXMeasurementSet` + hdxm_set : :class:`~pyhdx.models.HDXMeasurementSet` model **metdata """ - def __init__(self, data_obj, model, losses=None, **metadata): - self.data_obj = data_obj + def __init__(self, hdxm_set, model, losses=None, **metadata): + self.hdxm_set = hdxm_set self.model = model self.losses = losses self.metadata = metadata @@ -118,7 +119,13 @@ def __init__(self, data_obj, model, losses=None, **metadata): self.metadata['reg_loss'] = self.reg_loss self.metadata['regularization_percentage'] = self.regularization_percentage self.metadata['epochs_run'] = len(self.losses) - self.output = None # implemented by subclasses + + names = [hdxm.name for hdxm in self.hdxm_set.hdxm_list] + + dfs = [self.generate_output(hdxm, self.deltaG[g_column]) for hdxm, g_column in zip(self.hdxm_set, self.deltaG)] + df = pd.concat(dfs, keys=names, axis=1) + + self.output = df @property def mse_loss(self): @@ -150,10 +157,10 @@ def deltaG(self): """ g_values = self.model.deltaG.cpu().detach().numpy().squeeze() - if g_values.ndim == 1: - deltaG = pd.Series(g_values, index=self.data_obj.coverage.index) - else: - deltaG = pd.DataFrame(g_values.T, index=self.data_obj.coverage.index, columns=self.data_obj.names) + # if g_values.ndim == 1: + # deltaG = pd.Series(g_values, index=self.hdxm_set.coverage.index) + # else: + deltaG = pd.DataFrame(g_values.T, index=self.hdxm_set.coverage.index, columns=self.hdxm_set.names) return deltaG @@ -195,66 +202,82 @@ def generate_output(hdxm, deltaG): def to_file(self, file_path, include_version=True, include_metadata=True, fmt='csv', **kwargs): metadata = self.metadata if include_metadata else include_metadata - dataframe_to_file(file_path, self.output.df, include_version=include_version, include_metadata=metadata, + dataframe_to_file(file_path, self.output, include_version=include_version, include_metadata=metadata, fmt=fmt, **kwargs) def get_mse(self): """np.ndarray: Returns the mean squared error per peptide per timepoint. Output shape is Np x Nt""" - d_calc = self(self.data_obj.timepoints) - mse = (d_calc - self.data_obj.d_exp) ** 2 + d_calc = self(self.hdxm_set.timepoints) + mse = (d_calc - self.hdxm_set.d_exp) ** 2 return mse - -class TorchSingleFitResult(TorchFitResult): - def __init__(self, *args, **kwargs): - super(TorchSingleFitResult, self).__init__(*args, **kwargs) - - df = self.generate_output(self.data_obj, self.deltaG) - self.output = Protein(df) - def __call__(self, timepoints): - """ timepoints: Nt array (will be unsqueezed to 1 x Nt) - output: Np x Nt array""" + """timepoints: shape must be Ns x Nt, or Nt and will be reshaped to Ns x 1 x Nt + output: Ns x Np x Nt array""" #todo fix and tests - dtype = t.float64 - - with t.no_grad(): - tensors = self.data_obj.get_tensors() - inputs = [tensors[key] for key in ['temperature', 'X', 'k_int']] - inputs.append(t.tensor(timepoints, dtype=dtype).unsqueeze(0)) - output = self.model(*inputs) - return output.detach().numpy() - - -class TorchBatchFitResult(TorchFitResult): - def __init__(self, *args, **kwargs): - super(TorchBatchFitResult, self).__init__(*args, **kwargs) - names = [hdxm.name for hdxm in self.data_obj.hdxm_list] - - dfs = [self.generate_output(hdxm, self.deltaG[g_column]) for hdxm, g_column in zip(self.data_obj, self.deltaG)] - df = pd.concat(dfs, keys=names, axis=1) - - self.output = Protein(df) + timepoints = np.array(timepoints) + if timepoints.ndim == 1: + time_reshaped = np.tile(timepoints, (self.hdxm_set.Ns, 1, 1)) + elif timepoints.ndim == 2: + Ns, Nt = timepoints.shape + assert Ns == self.hdxm_set.Ns, "First dimension of 'timepoints' must match the number of samples" + time_reshaped = timepoints.reshape(Ns, 1, Nt) + elif timepoints.ndim == 3: + assert timepoints.shape[0] == self.hdxm_set.Ns, "First dimension of 'timepoints' must match the number of samples" + time_reshaped = timepoints + else: + raise ValueError("Invalid timepoints number of dimensions, must be <=3") - def __call__(self, timepoints): - """timepoints: must be Ns x Nt, will be reshaped to Ns x 1 x Nt - output: Ns x Np x Nt array""" - #todo fix and tests dtype = t.float64 - assert timepoints.shape[0] == self.data_obj.Ns, 'Invalid shape of timepoints' with t.no_grad(): - tensors = self.data_obj.get_tensors() + tensors = self.hdxm_set.get_tensors() inputs = [tensors[key] for key in ['temperature', 'X', 'k_int']] - time_tensor = t.tensor(timepoints.reshape(self.data_obj.Ns, 1, timepoints.shape[1]), dtype=dtype) + time_tensor = t.tensor(time_reshaped, dtype=dtype) inputs.append(time_tensor) output = self.model(*inputs) + + # todo return as dataframe? return output.detach().numpy() + def __len__(self): + return self.hdxm_set.Ns + + + +# class TorchSingleFitResult(TorchFitResult): +# def __init__(self, *args, **kwargs): +# super(TorchSingleFitResult, self).__init__(*args, **kwargs) +# +# df = self.generate_output(self.hdxm_set, self.deltaG) +# self.output = Protein(df) +# +# def __call__(self, timepoints): +# """ timepoints: Nt array (will be unsqueezed to 1 x Nt) +# output: Np x Nt array""" +# #todo fix and tests +# dtype = t.float64 +# +# with t.no_grad(): +# tensors = self.hdxm_set.get_tensors() +# inputs = [tensors[key] for key in ['temperature', 'X', 'k_int']] +# inputs.append(t.tensor(timepoints, dtype=dtype).unsqueeze(0)) +# +# output = self.model(*inputs) +# return output.detach().numpy() +# +# def __len__(self): +# return 1 + + +# class TorchBatchFitResult(TorchFitResult): +# def __init__(self, *args, **kwargs): +# super(TorchBatchFitResult, self).__init__(*args, **kwargs) + class Callback(object): diff --git a/pyhdx/models.py b/pyhdx/models.py index d108033c..0d8cf876 100644 --- a/pyhdx/models.py +++ b/pyhdx/models.py @@ -1,4 +1,5 @@ import textwrap +import warnings from functools import reduce, partial import numpy as np @@ -582,7 +583,10 @@ def X_norm(self): @property def Z_norm(self): """:class:`~numpy.ndarray`: `Z` coefficient matrix normalized column wise.""" - return self.Z / np.sum(self.Z, axis=0)[np.newaxis, :] + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning) + z_norm = self.Z / np.sum(self.Z, axis=0)[np.newaxis, :] + return z_norm def get_sections(self, gap_size=-1): """Get the intervals of independent sections of coverage. @@ -651,7 +655,7 @@ def __init__(self, data, **metadata): cov_kwargs = {kwarg: metadata.get(kwarg, default) for kwarg, default in zip(['c_term', 'n_term', 'sequence'], [0, 1, ''])} - self.peptides = [PeptideMeasurements(df, **cov_kwargs) for df in intersected_data] + self.peptides = [HDXTimepoint(df, **cov_kwargs) for df in intersected_data] # Create coverage object from the first time point (as all are now equal) self.coverage = Coverage(intersected_data[0], **cov_kwargs) @@ -688,7 +692,12 @@ def __str__(self): pH: {self.pH} """ - return textwrap.dedent(s) + return textwrap.dedent(s.lstrip('\n')) + + def _repr_markdown_(self): + s = str(self) + s = s.replace('\n', '
') + return s @property def name(self): @@ -721,6 +730,8 @@ def Nt(self): return len(self.timepoints) def __len__(self): + import warnings + warnings.warn('Use hdxm.Nt instead', DeprecationWarning) return len(self.timepoints) def __iter__(self): @@ -866,7 +877,7 @@ def to_file(self, file_path, include_version=True, include_metadata=True, fmt='c dataframe_to_file(file_path, df, include_version=include_version, include_metadata=metadata, fmt=fmt, **kwargs) -class PeptideMeasurements(Coverage): +class HDXTimepoint(Coverage): """ Class with subset of peptides corresponding to only one state and exposure @@ -881,7 +892,7 @@ def __init__(self, data, **kwargs): assert len(np.unique(data['exposure'])) == 1, 'Exposure entries are not unique' assert len(np.unique(data['state'])) == 1, 'State entries are not unique' - super(PeptideMeasurements, self).__init__(data, **kwargs) + super(HDXTimepoint, self).__init__(data, **kwargs) self.state = self.data['state'][0] self.exposure = self.data['exposure'][0] @@ -904,9 +915,7 @@ def name(self): @property def rfu_residues(self): """:class:`~pandas.Series`: Relative fractional uptake (RFU) per residue. Obtained by weighted averaging""" - array = self.Z_norm.T.dot(self.rfu_peptides) - series = pd.Series(array, index=self.index) - return series + return self.weighted_average('rfu') def calc_rfu(self, residue_rfu): """ diff --git a/pyhdx/output.py b/pyhdx/output.py index 25898dd9..f8419ffe 100644 --- a/pyhdx/output.py +++ b/pyhdx/output.py @@ -1,50 +1,53 @@ -""" -This module allows users to generate a .pdf output report from their HDX measurement - -(Currently partially out of date) -""" - - -import matplotlib.pyplot as plt -import matplotlib as mpl -import numpy as np import os +import tempfile import uuid -import shutil -from functools import lru_cache, partial -from pyhdx.support import grouper, autowrap -from pyhdx.plot import plot_peptides -from pyhdx.fitting_torch import TorchSingleFitResult -from tqdm.auto import tqdm +from concurrent import futures +from functools import partial +from importlib import import_module +from pathlib import Path -import pylatex as pyl +import matplotlib.pyplot as plt +import numpy as np import proplot as pplt -import tempfile +import pylatex as pyl +from tqdm.auto import tqdm +from pyhdx.plot import FitResultPlotBase geometry_options = { "lmargin": "1in", - "rmargin": "1.5in" + "rmargin": "1in" } +#assuming A4 210 mm width +PAGE_WIDTH = 210 - pplt.units(geometry_options['lmargin'], dest='mm') - pplt.units(geometry_options['rmargin'], dest='mm') -# plot_defaults = { -# ''} +class BaseReport(object): + pass -class Report(object): - """ +class Report(BaseReport): + def __init__(self, hdxm_set, **kwargs): + raise NotImplementedError() - .pdf output document - """ - def __init__(self, output, title=None, doc=None, add_date=True): - self.title = title or f'Fit report for {output.fit_result.data_obj.name}' - self.output = output +class FitReport(FitResultPlotBase): + """ + Create .pdf output of a fit result + """ + def __init__(self, fit_result, title=None, doc=None, add_date=True, temp_dir=None, **kwargs): + super().__init__(fit_result) + self.title = title or f'Fit report' self.doc = doc or self._init_doc(add_date=add_date) - self._temp_dir = self.make_temp_dir() + self._temp_dir = temp_dir or self.make_temp_dir() + self._temp_dir = Path(self._temp_dir) + + self.figure_queue = [] + self.tex_dict = {} # dictionary gathering lists of partial functions which when executed generate the tex output + self._figure_number = 0 #todo automate def make_temp_dir(self): + #todo pathlib _tmp_path = os.path.abspath(os.path.join(tempfile.gettempdir(), str(id(self)))) if not os.path.exists(_tmp_path): @@ -53,7 +56,9 @@ def make_temp_dir(self): def _init_doc(self, add_date=True): doc = pyl.Document(geometry_options=geometry_options) + doc.packages.append(pyl.Package('float')) doc.packages.append(pyl.Package('hyperref')) + doc.preamble.append(pyl.Command('title', self.title)) if add_date: doc.preamble.append(pyl.Command('date', pyl.NoEscape(r'\today'))) @@ -61,212 +66,176 @@ def _init_doc(self, add_date=True): doc.preamble.append(pyl.Command('date', pyl.NoEscape(r''))) doc.append(pyl.NoEscape(r'\maketitle')) doc.append(pyl.NewPage()) + doc.append(pyl.Command('tableofcontents')) + doc.append(pyl.NewPage()) return doc - def _save_fig(self, fig, *args, extension='pdf', **kwargs): - filename = '{}.{}'.format(str(uuid.uuid4()), extension.strip('.')) - filepath = os.path.join(self._temp_dir, filename) - fig.savefig(filepath, *args, **kwargs) - return filepath + # def _save_fig(self, fig, *args, extension='pdf', **kwargs): + # filename = '{}.{}'.format(str(uuid.uuid4()), extension.strip('.')) + # filepath = os.path.join(self._temp_dir, filename) + # fig.savefig(filepath, *args, **kwargs) + # return filepath + + def reset_doc(self, add_date=True): + self.doc = self._init_doc(add_date=add_date) + + + + def add_standard_figure(self, name, **kwargs): + extension = '.pdf' + self.tex_dict[name] = {} + + module = import_module('pyhdx.plot') + f = getattr(module, name) + arg_dict = self._get_arg(name) + width = kwargs.pop('width', PAGE_WIDTH) + + + for args_name, arg in arg_dict.items(): + fig_func = partial(f, arg, width=width, **kwargs) #todo perhaps something like fig = lazy(func(args, **kwargs))? + file_name = '{}.{}'.format(str(uuid.uuid4()), extension.strip('.')) + file_path = self._temp_dir / file_name + + self.figure_queue.append((file_path, fig_func)) + + tex_func = partial(_place_figure, file_path) + self.tex_dict[name][args_name] = [tex_func] + + # def _get_args(self, plot_func_name): + # #Add _figure suffix if not present + # if not plot_func_name.endswith('_figure'): + # plot_func_name += '_figure' + # + # if plot_func_name == 'peptide_coverage_figure': + # return {hdxm.name: [hdxm.data] for hdxm in self.fit_result.hdxm_set.hdxm_list} + # elif plot_func_name == 'residue_time_scatter_figure': + # return {hdxm.name: [hdxm] for hdxm in self.fit_result.hdxm_set.hdxm_list} + # elif plot_func_name == 'residue_scatter_figure': + # return {'All states': [self.fit_result.hdxm_set]} + # elif plot_func_name == 'dG_scatter_figure': + # return {'All states': [self.fit_result.output]} + # elif plot_func_name == 'ddG_scatter_figure': + # return {'All states': [self.fit_result.output.df]} # Todo change protein object to dataframe! + # elif plot_func_name == 'linear_bars_figure': + # return {'All states': [self.fit_result.output.df]} + # elif plot_func_name == 'rainbowclouds_figure': + # return {'All states': [self.fit_result.output.df]} + # else: + # raise ValueError(f"Unknown plot function {plot_func_name!r}") + + def add_peptide_uptake_curves(self, layout=(5, 4), time_axis=None): + extension = '.pdf' + self.tex_dict['peptide_uptake'] = {} + + nrows, ncols = layout + n = nrows*ncols + time = time_axis or self.get_fit_timepoints() + if time.ndim == 1: + time = np.tile(time, (len(self.fit_result), 1)) # todo move shape change to FitResult object + + d_calc = self.fit_result(time) # Ns x Np x Nt + + fig_factory = partial(pplt.subplots, ncols=ncols, nrows=nrows, sharex=1, sharey=1, width=f'{PAGE_WIDTH}mm') + + # iterate over samples + for hdxm, d_calc_s in zip(self.fit_result.hdxm_set, d_calc): + name = hdxm.name + indices = range(hdxm.Np) + chunks = [indices[i:i + n] for i in range(0, len(indices), n)] + + tex = [] + for chunk in chunks: + file_name = '{}.{}'.format(str(uuid.uuid4()), extension.strip('.')) + file_path = self._temp_dir / file_name + + fig_func = partial(_peptide_uptake_figure, fig_factory, chunk, time[0], d_calc_s, hdxm) + self.figure_queue.append((file_path, fig_func)) + + tex_func = partial(_place_figure, file_path) + tex.append(tex_func) + + self.tex_dict['peptide_uptake'][name] = tex + + def generate_latex(self, sort_by='graphs'): # graphs = [] #todo allow for setting which graphs to output + if sort_by == 'graphs': + for graph_type, state_dict in self.tex_dict.items(): + #todo map graph type to human readable section name + with self.doc.create(pyl.Section(graph_type)): + for state, tex_list in state_dict.items(): + with self.doc.create(pyl.Subsection(state)): + [tex_func(doc=self.doc) for tex_func in tex_list] + else: + raise NotImplementedError('Sorting by protein state not implemented') + + def generate_figures(self, executor='local'): + if isinstance(executor, futures.Executor): + exec_klass = executor + elif executor == 'process': + exec_klass = futures.ProcessPoolExecutor() + elif executor == 'local': + exec_klass = LocalThreadExecutor() + else: + raise ValueError("Invalid value for 'executor'") - def test_mpl(self): - fig = plt.figure() - plt.plot([2,3,42,1]) + total = len(self.figure_queue) + ft = [exec_klass.submit(run, item) for item in self.figure_queue] + with tqdm(total=total, desc='Generating figures') as pbar: + for future in futures.as_completed(ft): + pbar.update(1) - file_path = self._save_fig(fig) + def generate_pdf(self, file_path, cleanup=True, **kwargs): + defaults = {'compiler_args': ['--xelatex']} + defaults.update(kwargs) - with self.doc.create(pyl.Figure(position='htbp')) as plot: - plot.add_image(pyl.NoEscape(file_path), width=pyl.NoEscape(r'1\textwidth')) - plot.add_caption('I am a caption.') + self.doc.generate_pdf(file_path, **defaults) + # + # if cleanup: + # #try: + # self._temp_dir.clean() + # #except: - def add_coverage_figures(self, layout=(6, 2), close=True, **kwargs): - raise NotImplementedError() - funcs = [partial(self.output._make_coverage_graph, i, **kwargs) for i in range(len(self.output.series))] - self.make_subfigure(funcs, layout=layout, close=close) - - def add_peptide_figures(self, ncols=4, nrows=5, **kwargs): - - Np = self.output.fit_result.data_obj.Np - indices = range(Np) - n = ncols*nrows - chunks = [indices[i:i + n] for i in range(0, len(indices), n)] - for chunk in tqdm(chunks): - with self.doc.create(pyl.Figure(position='ht')) as tex_fig: - fig = self.output._make_peptide_subplots(chunk, ncols=ncols, nrows=nrows, **kwargs) - file_path = self._save_fig(fig) - plt.close(fig) - - tex_fig.add_image(file_path, width=pyl.NoEscape(r'\textwidth')) - - #self.make_subfigure(funcs, layout=layout, close=close) - - def make_subfigure(self, fig_funcs, layout=(5, 4), close=True): - #todo figure out how to iterate properly - n = np.product(layout) - chunks = grouper(n, fig_funcs) - w = str(1/layout[1]) - pbar = tqdm(total=len(fig_funcs)) - for chunk in chunks: - with self.doc.create(pyl.Figure(position='ht')) as tex_fig: - for i, fig_func in enumerate(chunk): - if fig_func is None: - continue - with self.doc.create(pyl.SubFigure(position='b', width=pyl.NoEscape(w + r'\linewidth'))) as subfig: - fig = fig_func() - file_path = self._save_fig(fig, bbox_inches='tight') # todo access these kwargs - if close: - plt.close(fig) - subfig.add_image(file_path, width=pyl.NoEscape(r'\linewidth')) - if i % layout[1] == layout[1] - 1: - self.doc.append('\n') - pbar.update(1) - - self.doc.append(pyl.NewPage()) - - def test_subfigure(self): - fig = plt.figure() - plt.plot([2,3,42,1]) - - file_path = self._save_fig(fig) - - with self.doc.create(pyl.Figure(position='h!')) as kittens: - w = str(0.25) - for i in range(8): - with self.doc.create(pyl.SubFigure( - position='b', - width=pyl.NoEscape(w + r'\linewidth'))) as left_kitten: - - left_kitten.add_image(file_path, - width=pyl.NoEscape(r'\linewidth')) - left_kitten.add_caption(f'Kitten on the {i}') - if i % 4 == 3: - self.doc.append('\n') - kittens.add_caption("Two kittens") - - def rm_temp_dir(self): - """Remove the temporary directory specified in ``_tmp_path``.""" - - if os.path.exists(self._temp_dir): - shutil.rmtree(self._temp_dir) - - def generate_pdf(self, file_path): - self.doc.generate_pdf(file_path, compiler='pdflatex') - - -class Output(object): - # Currently only TorchSingleFitResult support - def __init__(self, fit_result, time_axis=None, **settings): - assert isinstance(fit_result, TorchSingleFitResult), "Invalid type of `fit_result`" - self.settings = {'fit_time_axis': 'Log'} - self.settings.update(settings) - - #todo restore multiple fit results functionality - self.fit_result = fit_result - self.fit_timepoints = time_axis or self.get_fit_timepoints() - self.d_calc = self.fit_result(self.fit_timepoints) - - def get_fit_timepoints(self): - timepoints = self.fit_result.data_obj.timepoints - x_axis_type = self.settings.get('fit_time_axis', 'Log') - num = 100 - if x_axis_type == 'Linear': - time = np.linspace(0, timepoints.max(), num=num) - elif x_axis_type == 'Log': - elem = timepoints[np.nonzero(timepoints)] - time = np.logspace(np.log10(elem.min()) - 1, np.log10(elem.max()), num=num, endpoint=True) - - return time - - def add_peptide_fits(self, ax_scale='log', fit_names=None): - pass - - def peptide_graph_generator(self, **kwargs): - for i in range(len(self.series.coverage)): - yield from self._make_peptide_graph(i, **kwargs) - - def _make_peptide_subplots(self, indices, **fig_kwargs): - """yield single peptide grpahs""" - nrows = fig_kwargs.pop('nrows', int(np.floor(np.sqrt(len(indices))))) - ncols = fig_kwargs.pop('ncols', int(np.ceil(len(indices) / nrows))) - - default_kwargs = {'sharex': 1, 'sharey': 1, 'ncols': ncols, 'nrows': nrows} - default_kwargs.update(fig_kwargs) - - fig, axes = pplt.subplots(**default_kwargs) - axes_iter = iter(axes) - for i, ax in zip(indices, axes_iter): - ax.plot(self.fit_timepoints, self.d_calc[i], color='r') - ax.scatter(self.fit_result.data_obj.timepoints, self.fit_result.data_obj.d_exp.to_numpy()[i], color='k') - - start = self.fit_result.data_obj.coverage.data['_start'][i] - end = self.fit_result.data_obj.coverage.data['_end'][i] - ax.set_title(f'Peptide_{i}: {start} - {end}') - - t_unit = fig_kwargs.get('time_unit', 'min') - t_unit = f'({t_unit})' if t_unit else t_unit - - # turn off remaining axes - #todo proplot issue - axes.format(xscale='log', xlabel=f'Time' + t_unit, ylabel='Corrected D-uptake', xformatter='log') - xlim = axes[0].get_xlim() - for ax in axes_iter: - #ax.axis('off') - ax.set_axis_off() - axes.format(xlim=xlim) - - return fig - - def _make_peptide_graph(self, index, figsize=(4,4), ax_scale='log', **fig_kwargs): - """yield single peptide grpahs""" - - fig, ax = plt.subplots(figsize=figsize) - if ax_scale == 'log': - ax.set_xscale('log') - ax.get_xaxis().get_major_formatter().set_scientific(True) - - ax.plot(self.fit_timepoints, self.d_calc[index], color='r') - ax.scatter(self.fit_result.data_obj.timepoints, self.fit_result.data_obj.d_exp[index], color='k') - - t_unit = fig_kwargs.get('time_unit', 'min') - t_unit = f'({t_unit})' if t_unit else t_unit - ax.set_xlabel(f'Time' + t_unit) - ax.set_ylabel('Corrected D-uptake') - start = self.fit_result.data_obj.coverage.data['_start'][index] - end = self.fit_result.data_obj.coverage.data['_end'][index] - ax.set_title(f'peptide_{start}_{end}') - - - #ax.legend() - plt.tight_layout() - - return fig - - def _make_coverage_graph(self, index, figsize=(14, 4), cbar=True, **fig_kwargs): - raise NotImplementedError("coverage not implemented") - peptides = self.series[index] - - cmap = fig_kwargs.get('cmap', 'jet') - if cbar: - fig, (ax_main, ax_cbar) = plt.subplots(1, 2, figsize=figsize, gridspec_kw={'width_ratios': [40, 1], 'wspace': 0.025}) - - norm = mpl.colors.Normalize(vmin=0, vmax=100) - cmap = mpl.cm.get_cmap(cmap) - cb1 = mpl.colorbar.ColorbarBase(ax_cbar, cmap=mpl.cm.get_cmap(cmap), - norm=norm, - orientation='vertical', ticks=[0, 100]) - cb1.set_label('Uptake %', x=-1, rotation=270) - # cbar_ax.xaxis.set_ticks_position('top') - cb1.set_ticks([0, 100]) - else: - fig, ax_main = plt.subplots(figsize=figsize) - wrap = autowrap(peptides) - plot_peptides(peptides, wrap, ax_main, **fig_kwargs) - ax_main.set_xlabel('Residue number') - t_unit = fig_kwargs.get('time_unit', '') - fig.suptitle(f'Deuterium uptake at t={peptides.exposure} ' + t_unit) - plt.tight_layout() +def _place_figure(file_path, width=r'\textwidth', doc=None): + with doc.create(pyl.Figure(position='H')) as tex_fig: + tex_fig.add_image(str(file_path), width=pyl.NoEscape(width)) + + +def _peptide_uptake_figure(fig_factory, indices, _t, _d, hdxm): + fig, axes = fig_factory() + axes_iter = iter(axes) + for i in indices: + ax = next(axes_iter) + ax.plot(_t, _d[i], color='r') + ax.scatter(hdxm.timepoints, hdxm.d_exp.iloc[i], color='k') + + start, end = hdxm.coverage.data.iloc[i][['_start', '_end']] + ax.format(title=f'Peptide_{i}: {start} - {end}') + + for ax in axes_iter: + ax.axis('off') + # todo second y axis with RFU + axes.format(xscale='log', xlabel='Time (s)', ylabel='Corrected D-uptake', xformatter='log', ylim=(0, None)) + + return fig + + +def run(item): + file_path, fig_func = item + fig = fig_func() + if not isinstance(fig, plt.Figure): + fig = fig[0] + fig.savefig(file_path) + plt.close(fig) + + +class LocalThreadExecutor(futures.Executor): + + def submit(self, f, *args, **kwargs): + future = futures.Future() + future.set_result(f(*args, **kwargs)) + return future - return fig + def shutdown(self, wait=True): + pass \ No newline at end of file diff --git a/pyhdx/plot.py b/pyhdx/plot.py index dcfe9d3b..22397c3e 100644 --- a/pyhdx/plot.py +++ b/pyhdx/plot.py @@ -1,310 +1,1279 @@ -""" -Outdated module -""" +from contextlib import contextmanager +from copy import copy +from pathlib import Path import matplotlib as mpl import matplotlib.pyplot as plt -from matplotlib.patches import Rectangle import numpy as np +import pandas as pd import proplot as pplt -import pyhdx -from pyhdx.support import autowrap, rgb_to_hex -from pyhdx.fileIO import load_fitresult -import warnings - - -no_coverage = '#8c8c8c' -node_pos = [10, 25, 40] # in kJ/mol -linear_colors = ['#ff0000', '#00ff00', '#0000ff'] # red, green, blue -rgb_norm = plt.Normalize(node_pos[0], node_pos[-1], clip=True) -rgb_cmap = mpl.colors.LinearSegmentedColormap.from_list("rgb_cmap", list(zip(rgb_norm(node_pos), linear_colors))) -rgb_cmap.set_bad(color=no_coverage) +from matplotlib.axes import Axes +from matplotlib.patches import Rectangle +from scipy.stats import kde +from tqdm import tqdm -diff_colors = ['#54278e', '#ffffff', '#006d2c'][::-1] -diff_node_pos = [-10, 0, 10] -diff_norm = plt.Normalize(diff_node_pos[0], diff_node_pos[-1], clip=True) -diff_cmap = mpl.colors.LinearSegmentedColormap.from_list("diff_cmap", list(zip(diff_norm(diff_node_pos), diff_colors))) -diff_cmap.set_bad(color=no_coverage) +from pyhdx.config import cfg +from pyhdx.fileIO import load_fitresult +from pyhdx.support import autowrap, color_pymol, apply_cmap -cbar_width = 0.075 +try: + from pymol import cmd +except ModuleNotFoundError: + cmd = None dG_ylabel = 'ΔG (kJ/mol)' ddG_ylabel = 'ΔΔG (kJ/mol)' - r_xlabel = 'Residue Number' -errorbar_kwargs = { +ERRORBAR_KWARGS = { 'fmt': 'o', 'ecolor': 'k', 'elinewidth': 0.3, 'markersize': 0, - 'alpha': 0.75 + 'alpha': 0.75, + 'capthick': 0.3, + 'capsize': 0. + } -scatter_kwargs = { +SCATTER_KWARGS = { 's': 7 } -def plot_residue_map(pm, scores=None, ax=None, cmap='jet', bad='k', cbar=True, **kwargs): # pragma: no cover - """ - FUNCTION IS MOST LIKELY OUT OF DATE - - Parameters - ---------- - pm - scores - ax - cmap - bad - cbar - kwargs - - Returns - ------- - - """ - - warnings.warn("This function will be removed", DeprecationWarning) +RECT_KWARGS = { + 'linewidth': 0.5, + 'linestyle': '-', + 'edgecolor': 'k'} - img = (pm.X > 0).astype(float) - if scores is not None: - img *= scores[:, np.newaxis] - elif pm.rfu is not None: - img *= pm.rfu[:, np.newaxis] - - ma = np.ma.masked_where(img == 0, img) - cmap = mpl.cm.get_cmap(cmap) - cmap.set_bad(color=bad) +CBAR_KWARGS = { + 'space': 0, + 'width': cfg.getfloat('plotting', 'cbar_width') / 25.4, + 'tickminor': True +} - ax = plt.gca() if ax is None else ax - ax.set_facecolor(bad) - im = ax.imshow(ma, cmap=cmap, **kwargs) - if cbar: - cbar = plt.colorbar(im, ax=ax) - cbar.set_label('Uptake (%)') +def peptide_coverage_figure(data, wrap=None, cmap='turbo', norm=None, color_field='rfu', subplot_field='exposure', + rect_fields=('start', 'end'), rect_kwargs=None, **figure_kwargs): - ax.set_xlabel('Residue number') - ax.set_ylabel('Peptide index') + subplot_values = data[subplot_field].unique() + sub_dfs = {value: data.query(f'`{subplot_field}` == {value}') for value in subplot_values} + n_subplots = len(subplot_values) + ncols = figure_kwargs.pop('ncols', min(cfg.getint('plotting', 'ncols'), n_subplots)) + nrows = figure_kwargs.pop('nrows', int(np.ceil(n_subplots / ncols))) + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + cbar_width = figure_kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'peptide_coverage_aspect')) + cmap = pplt.Colormap(cmap) + norm = norm or pplt.Norm('linear', vmin=0, vmax=1) + start_field, end_field = rect_fields + if wrap is None: + wrap = max([autowrap(sub_df[start_field], sub_df[end_field]) for sub_df in sub_dfs.values()]) + fig, axes = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, **figure_kwargs) + rect_kwargs = rect_kwargs or {} + axes_iter = iter(axes) + for value, sub_df in sub_dfs.items(): + ax = next(axes_iter) + peptide_coverage(ax, sub_df, cmap=cmap, norm=norm, color_field=color_field, wrap=wrap, cbar=False, **rect_kwargs) + ax.format(title=f'{subplot_field}: {value}') + for ax in axes_iter: + ax.axis('off') -def plot_peptides(pm, ax, wrap=None, - color=True, labels=False, cbar=False, - intervals='corrected', cmap='jet', **kwargs): - """ + start, end = data[start_field].min(), data[end_field].max() + pad = 0.05*(end-start) + axes.format(xlim=(start-pad, end+pad), xlabel=r_xlabel) - TODO: needs to be checked if intervals (start, end) are still accurately taking inclusive, exclusive into account - Plots peptides as rectangles in the provided axes + if not cmap.monochrome: + cbar_ax = fig.colorbar(cmap, norm, width=cbar_width) + cbar_ax.set_label(color_field, labelpad=-0) + else: + cbar_ax = None - Parameters - ---------- - pm - wrap - ax - color - labels - cmap - kwargs + return fig, axes, cbar_ax - Returns - ------- - """ +def peptide_coverage(ax, data, wrap=None, cmap='turbo', norm=None, color_field='rfu', rect_fields=('start', 'end'), + labels=False, cbar=True, **kwargs): + start_field, end_field = rect_fields + data = data.sort_values(by=[start_field, end_field]) - wrap = wrap or autowrap(pm.data['start'], pm.data['end']) - rect_kwargs = {'linewidth': 1, 'linestyle': '-', 'edgecolor': 'k'} - rect_kwargs.update(kwargs) + wrap = wrap or autowrap(data[start_field], data[end_field]) + cbar_width = kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 + rect_kwargs = {**RECT_KWARGS, **kwargs} - cmap = mpl.cm.get_cmap(cmap) - norm = mpl.colors.Normalize(vmin=0, vmax=1) + cmap = pplt.Colormap(cmap) + norm = norm or pplt.Norm('linear', vmin=0, vmax=1) i = -1 - - for p_num, idx in enumerate(pm.data.index): - e = pm.data.loc[idx] + for p_num, idx in enumerate(data.index): + elem = data.loc[idx] if i < -wrap: i = -1 - if color: - c = cmap(norm(e['rfu'])) - else: - c = '#707070' - - if intervals == 'corrected': - start, end = 'start', 'end' - elif intervals == 'original': - start, end = '_start', '_end' + if color_field is None: + color = cmap(0.5) else: - raise ValueError(f"Invalid value '{intervals}' for keyword 'intervals', options are 'corrected' or 'original'") + color = cmap(norm(elem[color_field])) - width = e[end] - e[start] - rect = Rectangle((e[start] - 0.5, i), width, 1, facecolor=c, **rect_kwargs) + width = elem[end_field] - elem[start_field] + rect = Rectangle((elem[start_field] - 0.5, i), width, 1, facecolor=color, **rect_kwargs) ax.add_patch(rect) if labels: rx, ry = rect.get_xy() cy = ry cx = rx ax.annotate(str(p_num), (cx, cy), color='k', fontsize=6, va='bottom', ha='right') - i -= 1 - if cbar: - scalar_mappable = mpl.cm.ScalarMappable(norm=norm, cmap=cmap) - plt.colorbar(scalar_mappable, label='Percentage D') - ax.set_ylim(-wrap, 0) - end = pm.interval[1] - ax.set_xlim(0, end) + start, end = data[start_field].min(), data[end_field].max() + pad = 0.05*(end-start) + ax.set_xlim(start-pad, end+pad) ax.set_yticks([]) + if cbar and color_field: + cbar_ax = ax.colorbar(cmap, norm=norm, width=cbar_width) + cbar_ax.set_label(color_field, labelpad=-0) + else: + cbar_ax = None -def plot_fitresults(fitresult_path, plots='all', renew=False): - #fit_result = csv_to_dataframe(fitresult_path / 'fit_result.csv') + return cbar_ax - history_path = fitresult_path / 'model_history.csv' - check_exists = lambda x: False if renew else x.exists() - try: # temp hack as batch results do not store hdxms - fit_result = load_fitresult(fitresult_path) - df = fit_result.output - - dfs = [df] - names = [''] - hdxm_s = [fit_result.data_obj] - loss_list = [fit_result.losses] - if history_path.exists(): - history_list = [csv_to_dataframe(history_path)] - else: - history_list = [] - except FileNotFoundError: - df = csv_to_dataframe(fitresult_path / 'fit_result.csv') - dfs = [df[c] for c in df.columns.levels[0]] - names = [c + '_' for c in df.columns.levels[0]] - loss_list = [csv_to_dataframe(fitresult_path / 'losses.csv')] +def residue_time_scatter_figure(hdxm, field='rfu', cmap='turbo', norm=None, scatter_kwargs=None, cbar_kwargs=None, + **figure_kwargs): + """per-residue per-exposure values for field `field` by weighted averaging """ - hdxm_s = [] + n_subplots = hdxm.Nt + ncols = figure_kwargs.pop('ncols', min(cfg.getint('plotting', 'ncols'), n_subplots)) + nrows = figure_kwargs.pop('nrows', int(np.ceil(n_subplots / ncols))) + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'residue_scatter_aspect')) + cbar_width = figure_kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 - if history_path.exists(): - history_df = csv_to_dataframe(history_path) - history_list = [history_df[c] for c in history_df.columns.levels[0]] - else: - history_list = [] + cmap = pplt.Colormap(cmap) # todo allow None as cmap + norm = norm or pplt.Norm('linear', vmin=0, vmax=1) - full_width = 170 / 25.4 - width = 120 / 25.4 - aspect = 4 - cmap = rgb_cmap - norm = rgb_norm + fig, axes = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, sharey=4, **figure_kwargs) + scatter_kwargs = scatter_kwargs or {} + axes_iter = iter(axes) + for hdx_tp in hdxm: + ax = next(axes_iter) + residue_time_scatter(ax, hdx_tp, field=field, cmap=cmap, norm=norm, cbar=False, **scatter_kwargs) #todo cbar kwargs? (check with other methods) + ax.format(title=f'exposure: {hdx_tp.exposure:.1f}') - COV_SCALE = 1. + for ax in axes_iter: + ax.axis('off') - if plots == 'all': - plots = ['losses', 'deltaG', 'pdf', 'coverage', 'history'] - - if 'losses' in plots: - for loss_df in loss_list: # Mock loop to use break - output_path = fitresult_path / 'losses.png' - if check_exists(output_path): - break - -# losses = loss_df.drop('reg_percentage', axis=1) - loss_df.plot() - - mse_loss = loss_df['mse_loss'] - reg_loss = loss_df.iloc[:, 1:].sum(axis=1) - reg_percentage = 100*reg_loss / (mse_loss + reg_loss) - fig = plt.gcf() - ax = plt.gca() - ax1 = ax.twinx() - reg_percentage.plot(ax=ax1, color='k') - ax1.set_xlim(0, None) - plt.savefig(output_path) - plt.close(fig) - if 'deltaG' in plots: - for result, name in zip(dfs, names): - output_path = fitresult_path / f'{name}deltaG.png' - if check_exists(output_path): - break + axes.format(xlabel=r_xlabel, ylabel=field) - fig, axes = pplt.subplots(nrows=1, width=width, aspect=aspect) - ax = axes[0] + cbar_kwargs = cbar_kwargs or {} + cbars = [] + for ax in axes: + if not ax.axison: + continue - yvals = result['deltaG'] * 1e-3 - rgba_colors = cmap(norm(yvals), bytes=True) - hex_colors = rgb_to_hex(rgba_colors) - ax.scatter(result.index, yvals, c=hex_colors, **scatter_kwargs) - ylim = ax.get_ylim() - ax.errorbar(result.index, yvals, yerr=result['covariance'] * 1e-3 * COV_SCALE, **errorbar_kwargs, zorder=-1) + cbar = add_cbar(ax, cmap, norm, **cbar_kwargs) + cbars.append(cbar) - ax.format(ylim=ylim, ylabel=dG_ylabel, xlabel=r_xlabel) + return fig, axes, cbars - plt.savefig(output_path, transparent=False) - plt.close(fig) - if 'pdf' in plots: - for i in range(1): - output_path = fitresult_path / 'fit_report' - if check_exists(fitresult_path / 'fit_report.pdf'): - break +def residue_time_scatter(ax, hdx_tp, field='rfu', cmap='turbo', norm=None, cbar=True, **kwargs): + cmap = pplt.Colormap(cmap) # todo allow None as cmap + norm = norm or pplt.Norm('linear', vmin=0, vmax=1) + cbar_width = kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 + + scatter_kwargs = {**SCATTER_KWARGS, **kwargs} + values = hdx_tp.weighted_average(field) + colors = cmap(norm(values)) + ax.scatter(values.index, values, c=colors, **scatter_kwargs) + + if not cmap.monochrome and cbar: + add_cbar(ax, cmap, norm, width=cbar_width) + - output = pyhdx.Output(fit_result) +def residue_scatter_figure(hdxm_set, field='rfu', cmap='viridis', norm=None, scatter_kwargs=None, + **figure_kwargs): + n_subplots = hdxm_set.Ns + ncols = figure_kwargs.pop('ncols', min(cfg.getint('plotting', 'ncols'), n_subplots)) + nrows = figure_kwargs.pop('nrows', int(np.ceil(n_subplots / ncols))) #todo disallow setting rows + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + cbar_width = figure_kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'residue_scatter_aspect')) - report = pyhdx.Report(output, title=f'Fit report {fit_result.data_obj.name}') - report.add_peptide_figures() - report.generate_pdf(output_path) + cmap = pplt.Colormap(cmap) + if norm is None: + tps = np.unique(np.concatenate([hdxm.timepoints for hdxm in hdxm_set])) + tps = tps[np.nonzero(tps)] + norm = pplt.Norm('log', vmin=tps.min(), vmax=tps.max()) + else: + tps = np.unique(np.concatenate([hdxm.timepoints for hdxm in hdxm_set])) - if 'coverage' in plots: - for hdxm in hdxm_s: - output_path = fitresult_path / f'{hdxm.name}_coverage.png' - if check_exists(output_path): - break + fig, axes = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, **figure_kwargs) + axes_iter = iter(axes) + scatter_kwargs = scatter_kwargs or {} + for hdxm in hdxm_set: + ax = next(axes_iter) + residue_scatter(ax, hdxm, cmap=cmap, norm=norm, field=field, cbar=False, **scatter_kwargs) + ax.format(title=f'{hdxm.name}') - n_rows = int(np.ceil(len(hdxm.timepoints) / 2)) + for ax in axes_iter: + ax.axis('off') - fig, axes = pplt.subplots(ncols=2, nrows=n_rows, sharex=True, width=full_width, aspect=4) - axes_list = list(axes[:, 0]) + list(axes[:, 1]) + #todo function for this? + locator = pplt.Locator(norm(tps)) + cbar_ax = fig.colorbar(cmap, width=cbar_width, ticks=locator) + formatter = pplt.Formatter('simple', precision=1) + cbar_ax.ax.set_yticklabels([formatter(t) for t in tps]) + cbar_ax.set_label('Exposure time (s)', labelpad=-0) - for label, ax, pm in zip(hdxm.timepoints, axes_list, hdxm): - plot_peptides(pm, ax, linewidth=0.5) - ax.format(title=label, xlabel=r_xlabel) + axes.format(xlabel=r_xlabel) - plt.savefig(output_path, transparent=False) + return fig, axes, cbar_ax + + +def residue_scatter(ax, hdxm, field='rfu', cmap='viridis', norm=None, cbar=True, **kwargs): + cmap = pplt.Colormap(cmap) + tps = hdxm.timepoints[np.nonzero(hdxm.timepoints)] + norm = norm or pplt.Norm('log', tps.min(), tps.max()) + + cbar_width = kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 + scatter_kwargs = {**SCATTER_KWARGS, **kwargs} + for hdx_tp in hdxm: + if isinstance(norm, mpl.colors.LogNorm) and hdx_tp.exposure == 0.: + continue + values = hdx_tp.weighted_average(field) + color = cmap(norm(hdx_tp.exposure)) + scatter_kwargs['color'] = color + ax.scatter(values.index, values, **scatter_kwargs) + + if cbar: + locator = pplt.Locator(norm(tps)) + cbar_ax = ax.colorbar(cmap, width=cbar_width, ticks=locator) + formatter = pplt.Formatter('simple', precision=1) + cbar_ax.ax.set_yticklabels([formatter(t) for t in tps]) + cbar_ax.set_label('Exposure time (s)', labelpad=-0) + + +def dG_scatter_figure(data, cmap=None, norm=None, scatter_kwargs=None, cbar_kwargs=None, **figure_kwargs): + protein_states = data.columns.get_level_values(0).unique() + + n_subplots = len(protein_states) + ncols = figure_kwargs.pop('ncols', min(cfg.getint('plotting', 'ncols'), n_subplots)) + nrows = figure_kwargs.pop('nrows', int(np.ceil(n_subplots / ncols))) + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'deltaG_aspect')) + sharey = figure_kwargs.pop('sharey', 1) + + cmap_default, norm_default = default_cmap_norm('dG') + cmap = cmap or cmap_default + cmap = pplt.Colormap(cmap) + norm = norm or norm_default + + fig, axes = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, sharey=sharey, **figure_kwargs) + axes_iter = iter(axes) + scatter_kwargs = scatter_kwargs or {} + for state in protein_states: + sub_df = data[state] + ax = next(axes_iter) + colorbar_scatter(ax, sub_df, cmap=cmap, norm=norm, cbar=False, **scatter_kwargs) + ax.format(title=f'{state}') + + for ax in axes_iter: + ax.set_axis_off() + + # Set global ylims + ylims = [lim for ax in axes if ax.axison for lim in ax.get_ylim()] + axes.format(ylim=(np.max(ylims), np.min(ylims)), yticklabelloc='none', ytickloc='none') + + cbar_kwargs = cbar_kwargs or {} + cbars = [] + cbar_norm = pplt.Norm('linear', norm.vmin*1e-3, norm.vmax*1e-3) + for ax in axes: + if not ax.axison: + continue + + cbar = add_cbar(ax, cmap, cbar_norm, **cbar_kwargs) + cbars.append(cbar) + + return fig, axes, cbars + +#alias +deltaG_scatter_figure = dG_scatter_figure + + +def ddG_scatter_figure(data, reference=None, cmap=None, norm=None, scatter_kwargs=None, cbar_kwargs=None, + **figure_kwargs): + protein_states = data.columns.get_level_values(0).unique() + if reference is None: + reference_state = protein_states[0] + elif isinstance(reference, int): + reference_state = protein_states[reference] + elif reference in protein_states: + reference_state = reference + else: + raise ValueError(f"Invalid value {reference!r} for 'reference'") + + dG_test = data.xs('deltaG', axis=1, level=1).drop(reference_state, axis=1) + dG_ref = data[reference_state, 'deltaG'] + ddG = dG_test.subtract(dG_ref, axis=0) + ddG.columns = pd.MultiIndex.from_product([ddG.columns, ['deltadeltaG']], names=['State', 'quantity']) + + cov_test = data.xs('covariance', axis=1, level=1).drop(reference_state, axis=1)**2 + cov_ref = data[reference_state, 'covariance']**2 + cov = cov_test.add(cov_ref, axis=0).pow(0.5) + cov.columns = pd.MultiIndex.from_product([cov.columns, ['covariance']], names=['State', 'quantity']) + + combined = pd.concat([ddG, cov], axis=1) + + n_subplots = len(protein_states) - 1 + ncols = figure_kwargs.pop('ncols', min(cfg.getint('plotting', 'ncols'), n_subplots)) + nrows = figure_kwargs.pop('nrows', int(np.ceil(n_subplots / ncols))) + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'deltaG_aspect')) + sharey = figure_kwargs.pop('sharey', 1) + + cmap_default, norm_default = default_cmap_norm('ddG') + cmap = cmap or cmap_default + cmap = pplt.Colormap(cmap) + norm = norm or norm_default + + fig, axes = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, sharey=sharey, **figure_kwargs) + axes_iter = iter(axes) + scatter_kwargs = scatter_kwargs or {} + for state in protein_states: + if state == reference_state: + continue + sub_df = combined[state] + ax = next(axes_iter) + colorbar_scatter(ax, sub_df, y='deltadeltaG', cmap=cmap, norm=norm, cbar=False, **scatter_kwargs) + title = f'{state} - {reference_state}' + ax.format(title=title) + + for ax in axes_iter: + ax.set_axis_off() + + # Set global ylims + ylim = np.abs([lim for ax in axes if ax.axison for lim in ax.get_ylim()]).max() + axes.format(ylim=(ylim, -ylim), yticklabelloc='none', ytickloc='none') + + cbar_kwargs = cbar_kwargs or {} + cbars = [] + cbar_norm = pplt.Norm('linear', norm.vmin*1e-3, norm.vmax*1e-3) + for ax in axes: + if not ax.axison: + continue + + cbar = add_cbar(ax, cmap, cbar_norm, **cbar_kwargs) + cbars.append(cbar) + + return fig, axes, cbars + + +deltadeltaG_scatter_figure = ddG_scatter_figure + + +def peptide_mse_figure(fit_result, cmap='Haline', norm=None, rect_kwargs=None, **figure_kwargs): + n_subplots = len(fit_result) + + ncols = figure_kwargs.pop('ncols', min(cfg.getint('plotting', 'ncols'), n_subplots)) + nrows = figure_kwargs.pop('nrows', int(np.ceil(n_subplots / ncols))) + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'peptide_mse_aspect')) + + cmap = pplt.Colormap(cmap) + + fig, axes = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, **figure_kwargs) + axes_iter = iter(axes) + mse = fit_result.get_mse() #shape: Ns, Np, Nt + cbars = [] + rect_kwargs = rect_kwargs or {} + for i, mse_sample in enumerate(mse): + mse_peptide = np.mean(mse_sample, axis=1) + + hdxm = fit_result.hdxm_set.hdxm_list[i] + peptide_data = hdxm.coverage.data + + data_dict = {'start': peptide_data['start'], 'end': peptide_data['end'], 'mse': mse_peptide[:hdxm.Np]} + mse_df = pd.DataFrame(data_dict) + + ax = next(axes_iter) + vmax = mse_df['mse'].max() + norm = norm or pplt.Norm('linear', vmin=0, vmax=vmax) + #color bar per subplot as norm differs + #todo perhaps unify color scale? -> when global norm, global cbar + cbar_ax = peptide_coverage(ax, mse_df, color_field='mse', norm=norm, cmap=cmap, **rect_kwargs) + cbar_ax.set_label('MSE') + cbars.append(cbar_ax) + ax.format(xlabel=r_xlabel, title=f'{hdxm.name}') + + return fig, axes, cbars + + +def loss_figure(fit_result, **figure_kwargs): + ncols = 1 + nrows = 1 + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'loss_aspect')) # todo loss aspect also in config? + + fig, ax = pplt.subplots(ncols=ncols, nrows=nrows, width=figure_width, aspect=aspect, **figure_kwargs) + fit_result.losses.plot(ax=ax) + # ax.plot(fit_result.losses, legend='t') # altnernative proplot plotting + + # ox = ax.alty() + # reg_loss = fit_result.losses.drop('mse_loss', axis=1) + # total = fit_result.losses.sum(axis=1) + # perc = reg_loss.divide(total, axis=0) * 100 + # perc.plot(ax=ox) #todo formatting (perc as --, matching colors, legend) + # + + ax.format(xlabel="Number of epochs", ylabel='Loss') + + return fig, ax + + +def linear_bars_figure(data, reference=None, field='deltaG', norm=None, cmap=None, labels=None, **figure_kwargs): + #todo add sorting + protein_states = data.columns.get_level_values(0).unique() + + if isinstance(reference, int): + reference_state = protein_states[reference] + elif reference in protein_states: + reference_state = reference + elif reference is None: + reference_state = None + else: + raise ValueError(f"Invalid value {reference!r} for 'reference'") + + if reference_state: + test = data.xs(field, axis=1, level=1).drop(reference_state, axis=1) + ref = data[reference_state, field] + plot_data = test.subtract(ref, axis=0) + plot_data.columns = pd.MultiIndex.from_product([plot_data.columns, [field]], names=['State', 'quantity']) + + cmap_default, norm_default = default_cmap_norm('ddG') + n_subplots = len(protein_states) - 1 + else: + plot_data = data + cmap_default, norm_default = default_cmap_norm('dG') + n_subplots = len(protein_states) + + cmap = cmap or cmap_default + norm = norm or norm_default + + ncols = 1 + nrows = n_subplots + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'linear_bars_aspect')) + cbar_width = figure_kwargs.pop('cbar_width', cfg.getfloat('plotting', 'cbar_width')) / 25.4 + + fig, axes = pplt.subplots(nrows=nrows, ncols=ncols, aspect=aspect, width=figure_width, hspace=0) + axes_iter = iter(axes) + labels = labels or protein_states + if len(labels) != len(protein_states): + raise ValueError('Number of labels provided must be equal to the number of protein states') + for label, state in zip(labels, protein_states): + if state == reference_state: + continue + + values = plot_data[state, field] + rmin, rmax = values.index.min(), values.index.max() + extent = [rmin - 0.5, rmax + 0.5, 0, 1] + + img = np.expand_dims(values, 0) + + ax = next(axes_iter) + from matplotlib.axes import Axes + Axes.imshow(ax, norm(img), aspect='auto', cmap=cmap, vmin=0, vmax=1, interpolation='None', + extent=extent) + + # ax.imshow(img, aspect='auto', cmap=cmap, norm=norm, interpolation='None', discrete=False, + # extent=extent) + ax.format(yticks=[]) + ax.text(1.02, 0.5, label, horizontalalignment='left', + verticalalignment='center', transform=ax.transAxes) + + axes.format(xlabel=r_xlabel) + + sclf = 1e-3 # todo kwargs / check value of filed + cmap_norm = copy(norm) + cmap_norm.vmin *= sclf + cmap_norm.vmax *= sclf + + if field == 'deltaG': + label = dG_ylabel + elif field == 'deltaG' and reference_state: + label = ddG_ylabel + else: + label = '' + + fig.colorbar(cmap, norm=cmap_norm, loc='b', label=label, width=cbar_width) + + return fig, axes + + +def rainbowclouds_figure(data, reference=None, field='deltaG', norm=None, cmap=None, update_rc=True, **figure_kwargs): + # todo add sorting + if update_rc: + plt.rcParams["image.composite_image"] = False + + protein_states = data.columns.get_level_values(0).unique() + + if isinstance(reference, int): + reference_state = protein_states[reference] + elif reference in protein_states: + reference_state = reference + elif reference is None: + reference_state = None + else: + raise ValueError(f"Invalid value {reference!r} for 'reference'") + + if reference_state: + test = data.xs(field, axis=1, level=1).drop(reference_state, axis=1) + ref = data[reference_state, field] + plot_data = test.subtract(ref, axis=0) + plot_data.columns = pd.MultiIndex.from_product([plot_data.columns, [field]], names=['State', 'quantity']) + + cmap_default, norm_default = default_cmap_norm('ddG') + else: + plot_data = data + cmap_default, norm_default = default_cmap_norm('dG') + + cmap = cmap or cmap_default + norm = norm or norm_default + plot_data = plot_data.xs(field, axis=1, level=1) + + #scaling + plot_data *= 1e-3 + norm.vmin = norm.vmin * 1e-3 + norm.vmax = norm.vmax * 1e-3 + + f_data = [plot_data[column].dropna().to_numpy() for column in plot_data.columns] # todo make funcs accept dataframes + f_labels = plot_data.columns + + ncols = 1 + nrows = 1 + figure_width = figure_kwargs.pop('width', cfg.getfloat('plotting', 'page_width')) / 25.4 + aspect = figure_kwargs.pop('aspect', cfg.getfloat('plotting', 'rainbow_aspect')) + + boxplot_width = 0.1 + orientation = 'vertical' + + strip_kwargs = dict(offset=0.0, orientation=orientation, s=2, colors='k', jitter=0.2, alpha=0.25) + kde_kwargs = dict(linecolor='k', offset=0.15, orientation=orientation, fillcolor=False, fill_cmap=cmap, + fill_norm=norm, y_scale=None, y_norm=0.4, linewidth=1) + boxplot_kwargs = dict(offset=0.2, sym='', linewidth=1., linecolor='k', orientation=orientation, + widths=boxplot_width) + + fig, axes = pplt.subplots(nrows=nrows, ncols=ncols, width=figure_width, aspect=aspect, hspace=0) + ax = axes[0] + stripplot(f_data, ax=ax, **strip_kwargs) + kdeplot(f_data, ax=ax, **kde_kwargs) + boxplot(f_data, ax=ax, **boxplot_kwargs) + label_axes(f_labels, ax=ax, rotation=45) + if field == 'deltaG': + label = dG_ylabel + elif field == 'deltaG' and reference_state: + label = ddG_ylabel + else: + label = '' + ax.format(xlim=(-0.75, len(f_data) - 0.5), ylabel=label, yticklabelloc='left', ytickloc='left', + ylim=ax.get_ylim()[::-1]) + + add_cbar(ax, cmap, norm) + + return fig, ax + + +def colorbar_scatter(ax, data, y='deltaG', yerr='covariance', cmap=None, norm=None, cbar=True, **kwargs): + #todo make error bars optional + #todo custom ylims? scaling? + cmap_default, norm_default = default_cmap_norm(y) + + if y in ['deltaG', 'deltadeltaG']: + sclf = 1e-3 # deltaG are given in J/mol but plotted in kJ/mol + else: + if cmap is None or norm is None: + raise ValueError("No valid `cmap` or `norm` is given.") + sclf = 1e-3 + + + cmap = cmap or cmap_default + cmap = pplt.Colormap(cmap) + norm = norm or norm_default + + colors = cmap(norm(data[y])) + + #todo errorbars using proplot kwargs? + errorbar_kwargs = {**ERRORBAR_KWARGS, **kwargs.pop('errorbar_kwargs', {})} + scatter_kwargs = {**SCATTER_KWARGS, **kwargs} + ax.scatter(data.index, data[y]*sclf, color=colors, **scatter_kwargs) + with autoscale_turned_off(ax): + ax.errorbar(data.index, data[y]*sclf, yerr=data[yerr]*sclf, zorder=-1, + **errorbar_kwargs) + ax.set_xlabel(r_xlabel) + # Default y labels + labels = {'deltaG': dG_ylabel, 'deltadeltaG': ddG_ylabel} + label = labels.get(y, '') + ax.set_ylabel(label) + + ylim = ax.get_ylim() + if (ylim[0] < ylim[1]) and y == 'deltaG': + ax.set_ylim(*ylim[::-1]) + elif y == 'deltadeltaG': + ylim = np.max(np.abs(ylim)) + ax.set_ylim(ylim, -ylim) + + + if cbar: + cbar_norm = copy(norm) + cbar_norm.vmin *= sclf + cbar_norm.vmax *= sclf + cbar = add_cbar(ax, cmap, cbar_norm) + else: + cbar = None + + return cbar + + +def cmap_norm_from_nodes(colors, nodes, bad=None): + nodes = np.array(nodes) + if not np.all(np.diff(nodes) > 0): + raise ValueError("Node values must be monotonically increasing") + + norm = pplt.Norm('linear', vmin=nodes.min(), vmax=nodes.max(), clip=True) + color_spec = list(zip(norm(nodes), colors)) + cmap = pplt.Colormap(color_spec) + bad = bad or cfg.get('plotting', 'no_coverage') + cmap.set_bad(bad) + + return cmap, norm + + +def default_cmap_norm(datatype): + if datatype in ['deltaG', 'dG']: + return get_cmap_norm_preset('vibrant', 10e3, 40e3) + elif datatype in ['deltadeltaG', 'ddG']: + return get_cmap_norm_preset('PRGn', -10e3, 10e3) + elif datatype == 'rfu': + norm = pplt.Norm('linear', 0, 1) + cmap = pplt.Colormap('turbo') + return cmap, norm + elif datatype == 'mse': + cmap = pplt.Colormap('Haline') + return cmap, None + else: + raise ValueError(f"Invalid datatype {datatype!r}") + + +def get_cmap_norm_preset(name, vmin, vmax): + # Paul Tol colour schemes: https://personal.sron.nl/~pault/#sec:qualitative + + #todo warn if users use diverging colors with non diverging vmin/vmax? + colors, bad = get_color_scheme(name) + nodes = np.linspace(vmin, vmax, num=len(colors), endpoint=True) + + cmap, norm = cmap_norm_from_nodes(colors, nodes, bad) + + return cmap, norm + + +def get_color_scheme(name): + # Paul Tol colour schemes: https://personal.sron.nl/~pault/#sec:qualitative + if name == 'rgb': + colors = ['#0000ff', '#00ff00', '#ff0000'] # red, green, blue + bad = '#8c8c8c' + elif name == 'bright': + colors = ['#ee6677', '#288833', '#4477aa'] + bad = '#bbbbbb' + elif name == 'vibrant': + colors = ['#CC3311', '#009988', '#0077BB'] + bad = '#bbbbbb' + elif name == 'muted': + colors = ['#882255', '#117733', '#332288'] + bad = '#dddddd' + elif name == 'pale': + colors = ['#ffcccc', '#ccddaa', '#bbccee'] + bad = '#dddddd' + elif name == 'dark': + colors = ['#663333', '#225522', '#222255'] + bad = '#555555' + elif name == 'delta': # Original ddG colors + colors = ['#006d2c', '#ffffff', '#54278e'] # Green, white, purple (flexible, no change, rigid) + bad = '#ffee99' + elif name == 'sunset': + colors = ['#a50026', '#dd3d2d', '#f67e4b', '#fdb366', '#feda8b', '#eaeccc', '#c2e4ef', '#98cae1', '#6ea6cd', + '#4a7bb7', '#364b9a'] + bad = '#ffffff' + elif name == 'BuRd': + colors = ['#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#f7f7f7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac'] + bad = '#ffee99' + elif name == 'PRGn': + colors = ['#1b7837', '#5aae61', '#acd39e', '#d9f0d3', '#f7f7f7', '#e7d4e8', '#c2a5cf', '#9970ab', '#762a83'] + bad = '#ffee99' + else: + raise ValueError(f"Color scheme '{name}' not found") + + return colors, bad + + +def pymol_figures(data, output_path, pdb_file, reference=None, field='deltaG', cmap=None, norm=None, extent=None, + orient=True, views=None, name_suffix='', + additional_views=None, img_size=(640, 640)): + + protein_states = data.columns.get_level_values(0).unique() + + if isinstance(reference, int): + reference_state = protein_states[reference] + elif reference in protein_states: + reference_state = reference + elif reference is None: + reference_state = None + else: + raise ValueError(f"Invalid value {reference!r} for 'reference'") + + if reference_state: + test = data.xs(field, axis=1, level=1).drop(reference_state, axis=1) + ref = data[reference_state, field] + plot_data = test.subtract(ref, axis=0) + plot_data.columns = pd.MultiIndex.from_product([plot_data.columns, [field]], names=['State', 'quantity']) + + cmap_default, norm_default = default_cmap_norm('ddG') + else: + plot_data = data + cmap_default, norm_default = default_cmap_norm('dG') + + cmap = cmap or cmap_default + norm = norm or norm_default + #plot_data = plot_data.xs(field, axis=1, level=1) + + for state in protein_states: + if state == reference_state: + continue + + values = plot_data[state, field] + rmin, rmax = extent or [None, None] + rmin = rmin or values.index.min() + rmax = rmax or values.index.max() + + values = values.reindex(pd.RangeIndex(rmin, rmax+1, name='r_number')) + colors = apply_cmap(values, cmap, norm) + name = f'pymol_ddG_{state}_ref_{reference_state}' if reference_state else f'pymol_dG_{state}' + name += name_suffix + pymol_render(output_path, pdb_file, colors, name=name, orient=orient, views=views, additional_views=additional_views, + img_size=img_size) + + +def pymol_render(output_path, pdb_file, colors, name='Pymol render', orient=True, views=None, additional_views=None, img_size=(640, 640)): + if cmd is None: + raise ModuleNotFoundError("Pymol module is not installed") + + px, py = img_size + + cmd.reinitialize() + cmd.load(pdb_file) + if orient: + cmd.orient() + cmd.set('antialias', 2) + cmd.set('fog', 0) + + color_pymol(colors, cmd) + + if views: + for i, view in enumerate(views): + cmd.set_view(view) + cmd.ray(px, py, renderer=0, antialias=2) + output_file = output_path / f'{name}_view_{i}.png' + cmd.png(str(output_file)) + + else: + cmd.ray(px, py, renderer=0, antialias=2) + output_file = output_path / f'{name}_xy.png' + cmd.png(str(output_file)) + + cmd.rotate('x', 90) + + cmd.ray(px, py, renderer=0, antialias=2) + output_file = output_path / f'{name}_xz.png' + cmd.png(str(output_file)) + + cmd.rotate('z', -90) + + cmd.ray(px, py, renderer=0, antialias=2) + output_file = output_path / f'{name}_yz.png' + cmd.png(str(output_file)) + + additional_views = additional_views or [] + + for i, view in enumerate(additional_views): + cmd.set_view(view) + cmd.ray(px, py, renderer=0, antialias=2) + output_file = output_path / f'{name}_view_{i}.png' + cmd.png(str(output_file)) + + +def add_cbar(ax, cmap, norm, **kwargs): + """Truncate or expand cmap such that it covers axes limit and and colorbar to axes""" + + N = cmap.N + ymin, ymax = np.min(ax.get_ylim()), np.max(ax.get_ylim()) + values = np.linspace(ymin, ymax, num=N) + + norm_clip = copy(norm) + norm_clip.clip = True + colors = cmap(norm_clip(values)) + + cb_cmap = pplt.Colormap(colors) + + cb_norm = pplt.Norm('linear', vmin=ymin, vmax=ymax) #todo allow log norms? + cbar_kwargs = {**CBAR_KWARGS, **kwargs} + reverse = np.diff(ax.get_ylim()) < 0 + + cbar = ax.colorbar(cb_cmap, norm=cb_norm, reverse=reverse, **cbar_kwargs) + + return cbar + + +#https://stackoverflow.com/questions/38629830/how-to-turn-off-autoscaling-in-matplotlib-pyplot +@contextmanager +def autoscale_turned_off(ax=None): + ax = ax or plt.gca() + lims = [ax.get_xlim(), ax.get_ylim()] + yield + ax.set_xlim(*lims[0]) + ax.set_ylim(*lims[1]) + + +def stripplot(data, ax=None, jitter=0.25, colors=None, offset=0., orientation='vertical', **scatter_kwargs): + ax = ax or plt.gca() + color_list = _prepare_colors(colors, len(data)) + + for i, (d, color) in enumerate(zip(data, color_list)): + jitter_offsets = (np.random.rand(d.size) - 0.5) * jitter + cat_var = i * np.ones_like(d) + jitter_offsets + offset # categorical axis variable + if orientation == 'vertical': + ax.scatter(cat_var, d, color=color, **scatter_kwargs) + elif orientation == 'horizontal': + ax.scatter(d, len(data) - cat_var, color=color, **scatter_kwargs) + + +def _prepare_colors(colors, N): + if not isinstance(colors, list): + return [colors]*N + else: + return colors + + +# From joyplot +def _x_range(data, extra=0.2): + """ Compute the x_range, i.e., the values for which the + density will be computed. It should be slightly larger than + the max and min so that the plot actually reaches 0, and + also has a bit of a tail on both sides. + """ + try: + sample_range = np.nanmax(data) - np.nanmin(data) + except ValueError: + return [] + if sample_range < 1e-6: + return [np.nanmin(data), np.nanmax(data)] + return np.linspace(np.nanmin(data) - extra*sample_range, + np.nanmax(data) + extra*sample_range, 1000) + + +def kdeplot(data, ax=None, offset=0., orientation='vertical', + linecolor=None, linewidth=None, zero_line=True, x_extend=1e-3, y_scale=None, y_norm=None, fillcolor=False, fill_cmap=None, + fill_norm=None): + assert not (y_scale and y_norm), "Cannot set both 'y_scale' and 'y_norm'" + y_scale = 1. if y_scale is None else y_scale + + color_list = _prepare_colors(linecolor, len(data)) + + for i, (d, color) in enumerate(zip(data, color_list)): + #todo remove NaNs? + + # Perhaps also borrow this part from joyplot + kde_func = kde.gaussian_kde(d) + kde_x = _x_range(d, extra=0.4) + kde_y = kde_func(kde_x)*y_scale + if y_norm: + kde_y = y_norm*kde_y / kde_y.max() + bools = kde_y > x_extend * kde_y.max() + kde_x = kde_x[bools] + kde_y = kde_y[bools] + + cat_var = len(data) - i + kde_y + offset # x in horizontal + cat_var_zero = (len(data) - i)*np.ones_like(kde_y) + offset + + # x = i * np.ones_like(d) + jitter_offsets + offset # 'x' like, could be y axis + if orientation == 'horizontal': + plot_x = kde_x + plot_y = cat_var + img_data = kde_x.reshape(1, -1) + elif orientation == 'vertical': + plot_x = len(data) - cat_var + plot_y = kde_x + img_data = kde_x[::-1].reshape(-1, 1) + else: + raise ValueError(f"Invalid value '{orientation}' for 'orientation'") + + line, = ax.plot(plot_x, plot_y, color=color, linewidth=linewidth) + if zero_line: + ax.plot([plot_x[0], plot_x[-1]], [plot_y[0], plot_y[-1]], color=line.get_color(), linewidth=linewidth) + + if fillcolor: + #todo refactor to one if/else orientation + color = line.get_color() if fillcolor is True else fillcolor + if orientation == 'horizontal': + ax.fill_between(kde_x, plot_y, np.linspace(plot_y[0], plot_y[-1], num=plot_y.size, endpoint=True), + color=color) + elif orientation == 'vertical': + ax.fill_betweenx(kde_x, len(data) - cat_var, len(data) - cat_var_zero, color=color) + + if fill_cmap: + fill_norm = fill_norm or (lambda x: x) + color_img = fill_norm(img_data) + + xmin, xmax = np.min(plot_x), np.max(plot_x) + ymin, ymax = np.min(plot_y), np.max(plot_y) + extent = [xmin-offset, xmax-offset, ymin, ymax] if orientation == 'horizontal' else [xmin, xmax, ymin-offset, ymax-offset] + im = Axes.imshow(ax, color_img, aspect='auto', cmap=fill_cmap, extent=extent) # left, right, bottom, top + fill_line, = ax.fill(plot_x, plot_y, facecolor='none') + im.set_clip_path(fill_line) + + +def boxplot(data, ax, offset=0., orientation='vertical', widths=0.25, linewidth=None, linecolor=None, **kwargs): + if orientation == 'vertical': + vert = True + positions = np.arange(len(data)) + offset + elif orientation == 'horizontal': + vert = False + positions = len(data) - np.arange(len(data)) - offset + else: + raise ValueError(f"Invalid value '{orientation}' for 'orientation', options are 'horizontal' or 'vertical'") + + #todo for loop + boxprops = kwargs.pop('boxprops', {}) + whiskerprops = kwargs.pop('whiskerprops', {}) + medianprops = kwargs.pop('whiskerprops', {}) + + boxprops['linewidth'] = linewidth + whiskerprops['linewidth'] = linewidth + medianprops['linewidth'] = linewidth + + boxprops['color'] = linecolor + whiskerprops['color'] = linecolor + medianprops['color'] = linecolor + + Axes.boxplot(ax, data, vert=vert, positions=positions, widths=widths, boxprops=boxprops, whiskerprops=whiskerprops, + medianprops=medianprops, **kwargs) + + +def label_axes(labels, ax, offset=0., orientation='vertical', **kwargs): + #todo check offset sign + if orientation == 'vertical': + ax.set_xticks(np.arange(len(labels)) + offset) + ax.set_xticklabels(labels, **kwargs) + elif orientation == 'horizontal': + ax.set_yticks(len(labels) - np.arange(len(labels)) + offset) + ax.set_yticklabels(labels, **kwargs) + + +class FitResultPlotBase(object): + def __init__(self, fit_result): + self.fit_result = fit_result + + #todo equivalent this for axes? + def _make_figure(self, figure_name, **kwargs): + if not figure_name.endswith('_figure'): + figure_name += '_figure' + + function = globals()[figure_name] + args_dict = self._get_arg(figure_name) + + # return dictionary + # keys: either protein state name (hdxm.name) or 'All states' + figures_dict = {name: function(arg, **kwargs) for name, arg in args_dict.items()} + return figures_dict + + def make_figure(self, figure_name, **kwargs): + figures_dict = self._make_figure(figure_name, **kwargs) + if len(figures_dict) == 1: + return next(iter(figures_dict.values())) + else: + return figures_dict + + def get_fit_timepoints(self): + all_timepoints = np.concatenate([hdxm.timepoints for hdxm in self.fit_result.hdxm_set]) + + #x_axis_type = self.settings.get('fit_time_axis', 'Log') + x_axis_type = 'Log' # todo configureable + num = 100 + if x_axis_type == 'Linear': + time = np.linspace(0, all_timepoints.max(), num=num) + elif x_axis_type == 'Log': + elem = all_timepoints[np.nonzero(all_timepoints)] + start = np.log10(elem.min()) + end = np.log10(elem.max()) + pad = (end - start)*0.1 + time = np.logspace(start-pad, end+pad, num=num, endpoint=True) + else: + raise ValueError("Invalid value for 'x_axis_type'") + + return time + + # repeated code with fitreport (pdf) -> base class for fitreport + def _get_arg(self, plot_func_name): + #Add _figure suffix if not present + if not plot_func_name.endswith('_figure'): + plot_func_name += '_figure' + + if plot_func_name == 'peptide_coverage_figure': + return {hdxm.name: hdxm.data for hdxm in self.fit_result.hdxm_set.hdxm_list} + elif plot_func_name == 'residue_time_scatter_figure': + return {hdxm.name: hdxm for hdxm in self.fit_result.hdxm_set.hdxm_list} + elif plot_func_name == 'residue_scatter_figure': + return {'All states': self.fit_result.hdxm_set} + elif plot_func_name == 'dG_scatter_figure': + return {'All states': self.fit_result.output} + elif plot_func_name == 'ddG_scatter_figure': + return {'All states': self.fit_result.output} + elif plot_func_name == 'linear_bars_figure': + return {'All states': self.fit_result.output} + elif plot_func_name == 'rainbowclouds_figure': + return {'All states': self.fit_result.output} + elif plot_func_name == 'peptide_mse_figure': + return {'All states': self.fit_result} + elif plot_func_name == 'loss_figure': + return {'All states': self.fit_result} + else: + raise ValueError(f"Unknown plot function {plot_func_name!r}") + + +ALL_PLOT_TYPES = ['peptide_coverage', 'residue_scatter', 'dG_scatter', 'ddG_scatter', 'linear_bars', 'rainbowclouds', + 'peptide_mse', 'loss'] + + +class FitResultPlot(FitResultPlotBase): + def __init__(self, fit_result, output_path=None, **kwargs): + super().__init__(fit_result) + self.output_path = Path(output_path) if output_path else None + if output_path and not output_path.is_dir(): + raise ValueError(f"Output path {output_path!r} is not a valid directory") + + #todo save kwargs / rc params? / style context (https://matplotlib.org/devdocs/tutorials/introductory/customizing.html) + + def save_figure(self, fig_name, ext='.png', **kwargs): + figures_dict = self._make_figure(fig_name, **kwargs) + + if self.output_path is None: + raise ValueError(f"No output path given when `FitResultPlot` object as initialized") + for name, fig_tup in figures_dict.items(): + fig = fig_tup if isinstance(fig_tup, plt.Figure) else fig_tup[0] + + if name == 'All states': # todo variable for 'All states' + file_name = f"{fig_name.replace('_figure', '')}{ext}" + else: + file_name = f"{fig_name.replace('_figure', '')}_{name}{ext}" + file_path = self.output_path / file_name + fig.savefig(file_path) plt.close(fig) - if 'history' in plots: - for h_df, name in zip(history_list, names): - output_path = fitresult_path / f'{name}history.png' - if check_exists(output_path): - break + def plot_all(self, **kwargs): + for plot_type in tqdm(ALL_PLOT_TYPES): + fig_kwargs = kwargs.get(plot_type, {}) + self.save_figure(plot_type, **fig_kwargs) + - num = len(h_df.columns) - max_epochs = max([int(c) for c in h_df.columns]) +def plot_fitresults(fitresult_path, reference=None, plots='all', renew=False, cmap_and_norm=None, output_path=None, + output_type='.png', **save_kwargs): + """ - cmap = mpl.cm.get_cmap('winter') - norm = mpl.colors.Normalize(vmin=1, vmax=max_epochs) - colors = iter(cmap(np.linspace(0, 1, num=num))) + Parameters + ---------- + fitresult_path + plots + renew + cmap_and_norm: :obj:`dict`, optional + Dictionary with cmap and norms to use. If `None`, reverts to defaults. + Dict format: {'dG': (cmap, norm), 'ddG': (cmap, norm)} - fig, axes = pplt.subplots(nrows=1, width=width, aspect=aspect) - ax = axes[0] - for key in h_df: - c = next(colors) - to_hex(c) + output_type: list or str - ax.scatter(h_df.index, h_df[key] * 1e-3, color=to_hex(c), **scatter_kwargs) - ax.format(xlabel=r_xlabel, ylabel=dG_ylabel) + Returns + ------- + + """ + # batch results only + history_path = fitresult_path / 'model_history.csv' + output_path = output_path or fitresult_path + output_type = list([output_type]) if isinstance(output_type, str) else output_type + fitresult = load_fitresult(fitresult_path) + + protein_states = fitresult.output.df.columns.get_level_values(0).unique() + + if isinstance(reference, int): + reference_state = protein_states[reference] + elif reference in protein_states: + reference_state = reference + elif reference is None: + reference_state = None + else: + raise ValueError(f"Invalid value {reference!r} for 'reference'") + + # todo needs tidying up + cmap_and_norm = cmap_and_norm or {} + dG_cmap, dG_norm = cmap_and_norm.get('dG', (None, None)) + dG_cmap_default, dG_norm_default = default_cmap_norm('dG') + ddG_cmap, ddG_norm = cmap_and_norm.get('ddG', (None, None)) + ddG_cmap_default, ddG_norm_default = default_cmap_norm('ddG') + dG_cmap = ddG_cmap or dG_cmap_default + dG_norm = dG_norm or dG_norm_default + ddG_cmap = ddG_cmap or ddG_cmap_default + ddG_norm = ddG_norm or ddG_norm_default + + #check_exists = lambda x: False if renew else x.exists() + #todo add logic for checking renew or not + + if plots == 'all': + plots = ['loss', 'rfu_coverage', 'rfu_scatter', 'dG_scatter', 'ddG_scatter', 'linear_bars', 'rainbowclouds', + 'peptide_mse'] + + + # def check_update(pth, fname, extensions, renew): + # # Returns True if the target graph should be renewed or not + # if renew: + # return True + # else: + # pths = [pth / (fname + ext) for ext in extensions] + # return any([not pth.exists() for pth in pths]) + + # plots = [p for p in plots if check_update(output_path, p, output_type, renew)] + + if 'loss' in plots: + loss_df = fitresult.losses + loss_df.plot() + + mse_loss = loss_df['mse_loss'] + reg_loss = loss_df.iloc[:, 1:].sum(axis=1) + reg_percentage = 100*reg_loss / (mse_loss + reg_loss) + fig = plt.gcf() + ax = plt.gca() + ax1 = ax.twinx() + reg_percentage.plot(ax=ax1, color='k') + ax1.set_xlim(0, None) + for ext in output_type: + f_out = output_path / ('loss' + ext) + plt.savefig(f_out) + plt.close(fig) + + if 'rfu_coverage' in plots: + for hdxm in fitresult.hdxm_set: + fig, axes, cbar_ax = peptide_coverage_figure(hdxm.data) + for ext in output_type: + f_out = output_path / (f'rfu_coverage_{hdxm.name}' + ext) + plt.savefig(f_out) + plt.close(fig) + + #todo rfu_scatter_timepoint + + if 'rfu_scatter' in plots: + fig, axes, cbar = residue_scatter_figure(fitresult.hdxm_set) + for ext in output_type: + f_out = output_path / (f'rfu_scatter' + ext) + plt.savefig(f_out) + plt.close(fig) + + if 'dG_scatter' in plots: + fig, axes, cbars = dG_scatter_figure(fitresult.output.df, cmap=dG_cmap, norm=dG_norm) + for ext in output_type: + f_out = output_path / (f'dG_scatter' + ext) + plt.savefig(f_out) + plt.close(fig) + + if 'ddG_scatter' in plots: + fig, axes, cbars = ddG_scatter_figure(fitresult.output.df, reference=reference, cmap=ddG_cmap, norm=ddG_norm) + for ext in output_type: + f_out = output_path / (f'ddG_scatter' + ext) + plt.savefig(f_out) + plt.close(fig) + + if 'linear_bars' in plots: + fig, axes = linear_bars_figure(fitresult.output.df) + for ext in output_type: + f_out = output_path / (f'dG_linear_bars' + ext) + plt.savefig(f_out) + plt.close(fig) + + if reference_state: + fig, axes = linear_bars_figure(fitresult.output.df, reference=reference) + for ext in output_type: + f_out = output_path / (f'ddG_linear_bars' + ext) + plt.savefig(f_out) + plt.close(fig) + + if 'rainbowclouds' in plots: + fig, ax = rainbowclouds_figure(fitresult.output.df) + for ext in output_type: + f_out = output_path / (f'dG_rainbowclouds' + ext) + plt.savefig(f_out) + plt.close(fig) + + if reference_state: + fig, axes = rainbowclouds_figure(fitresult.output.df, reference=reference) + for ext in output_type: + f_out = output_path / (f'ddG_rainbowclouds' + ext) + plt.savefig(f_out) + plt.close(fig) - values = np.linspace(0, max_epochs, endpoint=True, num=num) - colors = cmap(norm(values)) - tick_labels = np.linspace(0, max_epochs, num=5) + if 'peptide_mse' in plots: + fig, axes, cbars = peptide_mse_figure(fitresult) + for ext in output_type: + f_out = output_path / (f'peptide_mse' + ext) + plt.savefig(f_out) + plt.close(fig) + + + + + # + # if 'history' in plots: + # for h_df, name in zip(history_list, names): + # output_path = fitresult_path / f'{name}history.png' + # if check_exists(output_path): + # break + # + # num = len(h_df.columns) + # max_epochs = max([int(c) for c in h_df.columns]) + # + # cmap = mpl.cm.get_cmap('winter') + # norm = mpl.colors.Normalize(vmin=1, vmax=max_epochs) + # colors = iter(cmap(np.linspace(0, 1, num=num))) + # + # fig, axes = pplt.subplots(nrows=1, width=width, aspect=aspect) + # ax = axes[0] + # for key in h_df: + # c = next(colors) + # to_hex(c) + # + # ax.scatter(h_df.index, h_df[key] * 1e-3, color=to_hex(c), **scatter_kwargs) + # ax.format(xlabel=r_xlabel, ylabel=dG_ylabel) + # + # values = np.linspace(0, max_epochs, endpoint=True, num=num) + # colors = cmap(norm(values)) + # tick_labels = np.linspace(0, max_epochs, num=5) + # + # cbar = fig.colorbar(colors, values=values, ticks=tick_labels, space=0, width=cbar_width, label='Epochs') + # ax.format(yticklabelloc='None', ytickloc='None') + # + # plt.savefig(output_path) + # plt.close(fig) - cbar = fig.colorbar(colors, values=values, ticks=tick_labels, space=0, width=cbar_width, label='Epochs') - ax.format(yticklabelloc='None', ytickloc='None') - plt.savefig(output_path) - plt.close(fig) \ No newline at end of file diff --git a/pyhdx/web/controllers.py b/pyhdx/web/controllers.py index 8b925728..1c360de1 100644 --- a/pyhdx/web/controllers.py +++ b/pyhdx/web/controllers.py @@ -543,7 +543,7 @@ def add_fit_result(self, future): name = self._guess_names.pop(future.key) results = future.result() - dfs = [result.output.df for result in results] + dfs = [result.output for result in results] combined_results = pd.concat(dfs, axis=1, keys=list(self.parent.data_objects.keys()), names=['state_name', 'quantity']) @@ -577,7 +577,7 @@ def _action_fit(self): elif self.fitting_model == 'Half-life (λ)': # this is practically instantaneous and does not require dask futures = self.parent.client.map(fit_rates_half_time_interpolate, self.parent.data_objects.values()) - dask_future = self.parent.client.submit(lambda args: args, futures) + dask_future = self.parent.client.submit(lambda args: args, futures) #combine multiple futures into one future self._guess_names[dask_future.key] = self.guess_name self.parent.future_queue.append((dask_future, self.add_fit_result)) @@ -661,7 +661,7 @@ def add_fit_result(self, future): # List of single fit results if isinstance(result, list): self.parent.fit_results[name] = list(result) - output_dfs = {fit_result.data_obj.name: fit_result.output.df for fit_result in result} + output_dfs = {fit_result.hdxm_set.name: fit_result.output for fit_result in result} df = pd.concat(output_dfs.values(), keys=output_dfs.keys(), axis=1) # create mse losses dataframe @@ -670,9 +670,9 @@ def add_fit_result(self, future): # Determine mean squared errors per peptide, summed over timepoints mse = single_result.get_mse() mse_sum = np.sum(mse, axis=1) - peptide_data = single_result.data_obj[0].data + peptide_data = single_result.hdxm_set[0].data data_dict = {'start': peptide_data['start'], 'end': peptide_data['end'], 'total_mse': mse_sum} - dfs[single_result.data_obj.name] = pd.DataFrame(data_dict) + dfs[single_result.hdxm_set.name] = pd.DataFrame(data_dict) mse_df = pd.concat(dfs.values(), keys=dfs.keys(), axis=1) #todo d calc for single fits @@ -683,12 +683,12 @@ def add_fit_result(self, future): # todo needs cleaning up state_dfs = {} for single_result in result: - tp_flat = single_result.data_obj.timepoints + tp_flat = single_result.hdxm_set.timepoints elem = tp_flat[np.nonzero(tp_flat)] time_vec = np.logspace(np.log10(elem.min()) - 1, np.log10(elem.max()), num=100, endpoint=True) d_calc_state = single_result(time_vec) #shape Np x Nt - hdxm = single_result.data_obj + hdxm = single_result.hdxm_set peptide_dfs = [] pm_data = hdxm[0].data @@ -703,20 +703,20 @@ def add_fit_result(self, future): # Create losses/epoch dataframe # ----------------------------- - losses_dfs = {fit_result.data_obj.name: fit_result.losses for fit_result in result} + losses_dfs = {fit_result.hdxm_set.name: fit_result.losses for fit_result in result} losses_df = pd.concat(losses_dfs.values(), keys=losses_dfs.keys(), axis=1) else: # one batchfit result self.parent.fit_results[name] = result # todo this name can be changed by the time this is executed - df = result.output.df + df = result.output # df.index.name = 'peptide index' # Create MSE losses df (per peptide, summed over timepoints) # ----------------------- mse = result.get_mse() dfs = {} - for mse_sample, hdxm in zip(mse, result.data_obj): + for mse_sample, hdxm in zip(mse, result.hdxm_set): peptide_data = hdxm[0].data mse_sum = np.sum(mse_sample, axis=1) # Indexing of mse_sum with Np to account for zero-padding @@ -729,15 +729,15 @@ def add_fit_result(self, future): # Create d_calc dataframe # ----------------------- - tp_flat = result.data_obj.timepoints.flatten() + tp_flat = result.hdxm_set.timepoints.flatten() elem = tp_flat[np.nonzero(tp_flat)] time_vec = np.logspace(np.log10(elem.min()) - 1, np.log10(elem.max()), num=100, endpoint=True) - stacked = np.stack([time_vec for i in range(result.data_obj.Ns)]) + stacked = np.stack([time_vec for i in range(result.hdxm_set.Ns)]) d_calc = result(stacked) state_dfs = {} - for hdxm, d_calc_state in zip(result.data_obj, d_calc): + for hdxm, d_calc_state in zip(result.hdxm_set, d_calc): peptide_dfs = [] pm_data = hdxm[0].data for d_peptide, idx in zip(d_calc_state, pm_data.index): diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index dd820bab..00000000 --- a/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -symfit -numpy -tqdm -scikit-image -scipy -panel>=0.11.0 -matplotlib -bokeh -dask[distributed] -torch -param -pandas -hdxrate>=0.2.0 -lumen -holoviews -colorcet diff --git a/setup.cfg b/setup.cfg index 0a099eb0..c5c59120 100644 --- a/setup.cfg +++ b/setup.cfg @@ -53,7 +53,7 @@ web = hvplot pdf = pylatex - proplot==0.6.4 + proplot>=0.9.2 docs = sphinx>=3.2.1 ipykernel @@ -63,6 +63,8 @@ docs = sphinx_rtd_theme docutils==0.16 sphinx_copybutton +pymol = + pymol diff --git a/templates/04_SecB_batch_fit_and_checkpoint.py b/templates/04_SecB_batch_fit_and_checkpoint.py index baf281c1..a3b78aa0 100644 --- a/templates/04_SecB_batch_fit_and_checkpoint.py +++ b/templates/04_SecB_batch_fit_and_checkpoint.py @@ -12,7 +12,7 @@ import numpy as np from matplotlib import cm -from pyhdx.fileIO import csv_to_protein, read_dynamx, dataframe_to_file +from pyhdx.fileIO import csv_to_protein, read_dynamx, dataframe_to_file, save_fitresult from pyhdx.fitting import fit_gibbs_global_batch from pyhdx.fitting_torch import CheckPoint from pyhdx.models import PeptideMasterTable, HDXMeasurement, HDXMeasurementSet @@ -71,3 +71,6 @@ #Machine readable output result.to_file(output_dir / 'Batch_fit_result.csv', fmt='csv') + +#Save full fitresult +save_fitresult(output_dir / 'SecB_tetramer_dimer_batch', result) diff --git a/templates/07_load_fitresult.py b/templates/07_load_fitresult.py index f76bc337..a9ca17b8 100644 --- a/templates/07_load_fitresult.py +++ b/templates/07_load_fitresult.py @@ -12,14 +12,14 @@ time = np.logspace(-3, 2, num=100) d_calc = fit_result(time) -d_exp = fit_result.data_obj.d_exp +d_exp = fit_result.hdxm_set.d_exp i = 20 # index of the protein to view fit_result.losses[['total_loss', 'mse_loss', 'reg_loss']].plot() fig, ax = plt.subplots() -ax.scatter(fit_result.data_obj.timepoints, d_exp[i], color='k') +ax.scatter(fit_result.hdxm_set.timepoints, d_exp[i], color='k') ax.plot(time, d_calc[i], color='r') ax.set_xscale('log') ax.set_xlabel('Time (min)') diff --git a/templates/08_fit_report_pdf.py b/templates/08_fit_report_pdf.py index e13f43ce..24195cf7 100644 --- a/templates/08_fit_report_pdf.py +++ b/templates/08_fit_report_pdf.py @@ -1,13 +1,30 @@ """Generate a pdf output with all peptide fits. Requires pdflatex""" -from pyhdx.output import Output, Report +from pyhdx.output import FitReport from pyhdx.fileIO import load_fitresult from pathlib import Path +from concurrent import futures +import proplot as pplt + current_dir = Path().cwd() -fit_result = load_fitresult(current_dir / 'output' / 'SecB_fit') +fit_result = load_fitresult(current_dir / 'output' / 'SecB_tetramer_dimer_batch') + +tmp_dir = Path(__file__).parent / 'temp' +tmp_dir.mkdir(exist_ok=True) + +if __name__ == '__main__': + + report = FitReport(fit_result, temp_dir=tmp_dir) + report.add_standard_figure('peptide_coverage_figure') + report.add_standard_figure('residue_time_scatter_figure') + report.add_standard_figure('residue_scatter_figure') + report.add_standard_figure('dG_scatter_figure', ncols=1, aspect=3) + report.add_standard_figure('ddG_scatter_figure', ncols=1, reference=0) + report.add_standard_figure('linear_bars', cmap='viridis', norm=pplt.Norm('linear', 15e3, 35e3)) #todo name from kwargs + report.add_standard_figure('rainbowclouds') -output = Output(fit_result) + executor = futures.ProcessPoolExecutor(max_workers=10) + report.generate_figures(executor=executor) -report = Report(output) -report.add_peptide_figures() -report.generate_pdf(current_dir / 'output' / 'SecB_fit_report') \ No newline at end of file + report.generate_latex() + report.generate_pdf(current_dir / 'output' / 'fit_report') \ No newline at end of file diff --git a/templates/09_plot_output.py b/templates/09_plot_output.py new file mode 100644 index 00000000..db3e511a --- /dev/null +++ b/templates/09_plot_output.py @@ -0,0 +1,30 @@ +""" +Automagically plot all available figures from a fit result +""" + +from pyhdx.fileIO import load_fitresult +from pyhdx.plot import FitResultPlot +from pathlib import Path + + +from pyhdx.config import reset_config + +#%% + +# __file__ = Path().cwd() / 'templates'/ 'script.py' # Uncomment for PyCharm scientific mode + + +cwd = Path(__file__).parent +output_dir = cwd / 'output' / 'figures' +output_dir.mkdir(exist_ok=True) +fit_result = load_fitresult(cwd / 'output' / 'SecB_tetramer_dimer_batch') + +fr_plot = FitResultPlot(fit_result, output_path=output_dir) + +kwargs = { + 'residue_scatter': {'cmap': 'BuGn'}, # change default colormap + 'ddG_scatter': {'reference': 1} # Set reference for ΔΔG to the second (index 1 state) (+ APO state (tetramer)) +} + +fr_plot.plot_all(**kwargs) + diff --git a/tests/gen_docs_example_result.py b/tests/gen_docs_example_result.py new file mode 100644 index 00000000..6ab6c2cd --- /dev/null +++ b/tests/gen_docs_example_result.py @@ -0,0 +1,23 @@ +"""Obtain ΔG for ecSecB tetramer and dimer""" +from pathlib import Path +from pyhdx.batch_processing import yaml_to_hdxmset +from pyhdx.fileIO import csv_to_dataframe, save_fitresult +from pyhdx.fitting import fit_gibbs_global_batch +import yaml + +cwd = Path(__file__).parent + +data_dir = cwd / 'test_data' / 'input' +output_dir = cwd / 'test_data' / 'output' + +yaml_dict = yaml.safe_load(Path(data_dir / 'data_states.yaml').read_text()) + +hdx_set = yaml_to_hdxmset(yaml_dict, data_dir=data_dir) + +initial_guess_rates = csv_to_dataframe(output_dir / 'ecSecB_guess.csv') + +guesses = hdx_set.guess_deltaG([initial_guess_rates['rate']]*2) +fit_kwargs = yaml.safe_load(Path(data_dir / 'fit_settings.yaml').read_text()) + +fr = fit_gibbs_global_batch(hdx_set, guesses, **fit_kwargs) +save_fitresult(output_dir / 'ecsecb_tetramer_dimer', fr) diff --git a/tests/test_data/input/fit_settings.yaml b/tests/test_data/input/fit_settings.yaml new file mode 100644 index 00000000..8c21562f --- /dev/null +++ b/tests/test_data/input/fit_settings.yaml @@ -0,0 +1,9 @@ +r1: 1 +r2: 1 +epochs: 200000 +stop_loss: 1.e-6 +patience: 100 +optimizer: SGD +lr: 1.e+4 +momentum: 0.5 +nesterov: True diff --git a/tests/test_fileIO.py b/tests/test_fileIO.py index a0b81409..46a916f4 100644 --- a/tests/test_fileIO.py +++ b/tests/test_fileIO.py @@ -1,7 +1,7 @@ import pyhdx from pyhdx.fileIO import read_dynamx, csv_to_dataframe, csv_to_protein, dataframe_to_stringio, dataframe_to_file, \ save_fitresult, load_fitresult -from pyhdx.models import Protein, PeptideMasterTable, HDXMeasurement +from pyhdx.models import Protein, PeptideMasterTable, HDXMeasurement, HDXMeasurementSet from pyhdx.fitting import fit_gibbs_global from pathlib import Path from io import StringIO @@ -116,10 +116,12 @@ def test_read_write_tables(self, tmp_path): # .. add tests def test_load_save_fitresult(self, tmp_path): + #todo missing read batch result test + fpath = Path(tmp_path) / 'fit_result_single.csv' self.fit_result.to_file(fpath) df = csv_to_dataframe(fpath) - assert df.attrs['metadata'] == self.fit_result.metadata + assert df.attrs['metadata'] == self.fit_result.metadata fit_result_dir = Path(tmp_path) / 'fit_result' save_fitresult(fit_result_dir, self.fit_result, log_lines=['test123']) @@ -129,15 +131,11 @@ def test_load_save_fitresult(self, tmp_path): fit_result_loaded = load_fitresult(fit_result_dir) assert isinstance(fit_result_loaded.losses, pd.DataFrame) - assert isinstance(fit_result_loaded.data_obj, HDXMeasurement) - - timepoints = np.linspace(0, 30*60, num=100) - d_calc = fit_result_loaded(timepoints) - assert d_calc.shape == (self.hdxm.Np, len(timepoints)) + assert isinstance(fit_result_loaded.hdxm_set, HDXMeasurementSet) timepoints = np.linspace(0, 30*60, num=100) d_calc = fit_result_loaded(timepoints) - assert d_calc.shape == (self.hdxm.Np, len(timepoints)) + assert d_calc.shape == (1, self.hdxm.Np, len(timepoints)) losses = csv_to_dataframe(fit_result_dir / 'losses.csv') fr_load_with_hdxm_and_losses = load_fitresult(fit_result_dir) diff --git a/tests/test_fitting.py b/tests/test_fitting.py index b6b9d2e9..c0645ad1 100644 --- a/tests/test_fitting.py +++ b/tests/test_fitting.py @@ -1,7 +1,8 @@ import pytest from pyhdx import PeptideMasterTable, HDXMeasurement from pyhdx.fileIO import read_dynamx, csv_to_protein, csv_to_dataframe, save_fitresult, load_fitresult -from pyhdx.fitting import fit_rates_weighted_average, fit_gibbs_global, fit_gibbs_global_batch, fit_gibbs_global_batch_aligned +from pyhdx.fitting import fit_rates_weighted_average, fit_gibbs_global, fit_gibbs_global_batch, \ + fit_gibbs_global_batch_aligned, fit_rates_half_time_interpolate, GenericFitResult from pyhdx.models import HDXMeasurementSet from pyhdx.config import cfg import numpy as np @@ -43,7 +44,7 @@ def setup_class(cls): cluster = LocalCluster() cls.address = cluster.scheduler_address - def test_initial_guess(self): + def test_initial_guess_wt_average(self): result = fit_rates_weighted_average(self.reduced_hdxm) output = result.output @@ -51,6 +52,12 @@ def test_initial_guess(self): check_rates = csv_to_protein(output_dir / 'ecSecB_reduced_guess.csv') pd.testing.assert_series_equal(check_rates['rate'], output['rate']) + def test_initial_guess_half_time_interpolate(self): + result = fit_rates_half_time_interpolate(self.reduced_hdxm) + assert isinstance(result, GenericFitResult) + assert result.output.index.name == 'r_number' + assert result.output['rate'].mean() == pytest.approx(0.04343354509254464) + # todo additional tests: # result = fit_rates_half_time_interpolate() @@ -65,7 +72,7 @@ def test_dtype_cuda(self): fr_global = fit_gibbs_global(self.hdxm_apo, gibbs_guess, epochs=1000, r1=2) out_deltaG = fr_global.output for field in ['deltaG', 'k_obs', 'covariance']: - assert_series_equal(check_deltaG[field], out_deltaG[field], rtol=0.01, check_dtype=False) + assert_series_equal(check_deltaG[field], out_deltaG[self.hdxm_apo.name, field], rtol=0.01, check_dtype=False) else: with pytest.raises(AssertionError, match=r".* CUDA .*"): fr_global = fit_gibbs_global(self.hdxm_apo, gibbs_guess, epochs=1000, r1=2) @@ -79,7 +86,8 @@ def test_dtype_cuda(self): out_deltaG = fr_global.output for field in ['deltaG', 'k_obs']: - assert_series_equal(check_deltaG[field], out_deltaG[field], rtol=0.01, check_dtype=False) + assert_series_equal(check_deltaG[field], out_deltaG[self.hdxm_apo.name, field], rtol=0.01, + check_dtype=False, check_names=False) cfg.set('fitting', 'dtype', 'float64') @@ -96,10 +104,11 @@ def test_global_fit(self): check_deltaG = csv_to_protein(output_dir / 'ecSecB_torch_fit.csv') for field in ['deltaG', 'covariance', 'k_obs']: - assert_series_equal(check_deltaG[field], out_deltaG[field], rtol=0.01) + assert_series_equal(check_deltaG[field], out_deltaG[self.hdxm_apo.name, field], rtol=0.01, + check_names=False) mse = fr_global.get_mse() - assert mse.shape == (self.hdxm_apo.Np, self.hdxm_apo.Nt) + assert mse.shape == (1, self.hdxm_apo.Np, self.hdxm_apo.Nt) @pytest.mark.skip(reason="Longer fit is not checked by default due to long computation times") def test_global_fit_extended(self): diff --git a/tests/test_models.py b/tests/test_models.py index 3dba3491..6db7eac7 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -1,6 +1,6 @@ import pytest import os -from pyhdx import PeptideMeasurements, PeptideMasterTable, HDXMeasurement +from pyhdx import HDXTimepoint, PeptideMasterTable, HDXMeasurement from pyhdx.models import Protein, Coverage from pyhdx.fileIO import read_dynamx, csv_to_protein, csv_to_hdxm, csv_to_dataframe import numpy as np