WIP: ANdrea COmmit - temporary working on boxplots

eval/script/compute_overview.py

@@ -6,6 +6,13 @@
 from tqdm import tqdm
 from datetime import datetime
 
+import numpy as np
+
+import metrics
+import io_stream
+import visuals
+
+
 __SEQUENCES_MAP__ = {
     # name sequence : [density (0=low, 1=high), layout(0=stripe, 1=ring)]
     "sequence_1" :  [0,0],
@@ -37,7 +44,7 @@
 
 def main(out_subdir : str,
          csv_sequ_paths : list[str],
-         csv_subsequ_paths : list[str]) -> None:
+         csv_subsequ_paths : list[list[str]]) -> None:
     """
         We need to regroup the results of boxplots in the categories of tag params (density/layout):
         - a) low/high density
@@ -46,20 +53,20 @@ def main(out_subdir : str,
         Second we will average all the tools and provide an overview based uniquely on the mean for à`and `b`.
 
         Here's the final graphs/results to output:
-        ----------------------- part 1 - per tool summary -----------------------
-        - 1.A) position boxplot - high/low density for stripe layout
-        - 1.B) position boxplot - high/low density for ring layout
+        # ----------------------- part 1 - per tool summary(SUSPENDED) -----------------------
+        # - 1.A) position boxplot - high/low density for stripe layout
+        # - 1.B) position boxplot - high/low density for ring layout
 
-        - 2.A) rotation boxplot - high/low density for stripe layout
-        - 2.B) rotation boxplot - high/low density for ring layout
+        # - 2.A) rotation boxplot - high/low density for stripe layout
+        # - 2.B) rotation boxplot - high/low density for ring layout
 
-        - 3.A) tags detection boxplot - high/low density for stripe layout
-        - 3.B) tags detection boxplot - high/low density for ring layout
+        # - 3.A) tags detection boxplot - high/low density for stripe layout
+        # - 3.B) tags detection boxplot - high/low density for ring layout
 
-        - 4.A) coverage distribution - high density for stripe layout
-        - 4.B) coverage distribution - high density for ring layout
-        - 4.C) coverage distribution - low density for stripe layout
-        - 4.D) coverage distribution - low density for ring layout
+        # - 4.A) coverage distribution - high density for stripe layout
+        # - 4.B) coverage distribution - high density for ring layout
+        # - 4.C) coverage distribution - low density for stripe layout
+        # - 4.D) coverage distribution - low density for ring layout
 
         ----------------------- part 2 - fabrication summary --------------------
         - 5) preparation time columns - mean high/ mean low density for stripe and ring layout
@@ -69,10 +76,55 @@ def main(out_subdir : str,
         - 9) coverage distribution - mean high/ mean low density for stripe and ring layout (4 lines)
 
         :param out_subdir: path to the output directory to dump results/graphs
-        :param csv_sequ_paths: list of paths to the csv files containing the results of the analysis per sequence
+        :param csv_sequ_paths: list of list of paths to the csv files containing the results of the analysis per sequence
     """
+    #================================================
+    ## Part 1
+    #================================================
+
+    # # parse the csv by the 4 groups and merge them by category
+    # sequences_map_group : list[list[str]] = [[],[],[],[]]
+
+    # csv_subsequ_paths_lowD_stripe : list[str] = []
+    # for i in tqdm(__SEQUENCES_MAP_LOWD_STRIPE__):
+    #     csv_subsequ_paths_lowD_stripe.extend(csv_subsequ_paths[i])
+    # csv_subsequ_paths_lowD_ring : list[str] = []
+    # for i in tqdm(__SEQUENCES_MAP_LOWD_RING__):
+    #     csv_subsequ_paths_lowD_ring.extend(csv_subsequ_paths[i])
+    # csv_subsequ_paths_highD_stripe : list[str] = []
+    # for i in tqdm(__SEQUENCES_MAP_HIGHD_STRIPE__):
+    #     csv_subsequ_paths_highD_stripe.extend(csv_subsequ_paths[i])
+    # csv_subsequ_paths_highD_ring : list[str] = []
+    # for i in tqdm(__SEQUENCES_MAP_HIGHD_RING__):
+    #     csv_subsequ_paths_highD_ring.extend(csv_subsequ_paths[i])
+
+    # sequences_map_group[0] = csv_subsequ_paths_lowD_stripe
+    # sequences_map_group[1] = csv_subsequ_paths_lowD_ring
+    # sequences_map_group[2] = csv_subsequ_paths_highD_stripe
+    # sequences_map_group[3] = csv_subsequ_paths_highD_ring
+
+    # #================================================
+    # # for each group:
+    # # 1. check if one typology exists
+    # # 2. if other exists, merge all the same csv into one
+    # sequence_map_data : list[list[list[str]]] = [[],[],[],[]]
+
+    # for idx, path_lst in enumerate(tqdm(sequences_map_group)):
+    #     sequence_map_data[idx] = io_stream.merge_csv_by_categ(csv_paths=path_lst)
+
+    # #================================================
+    # # prepare the data and labels for the boxplots
+
+
+
+
+
+
+
+
 
     #================================================
+    ## Part 2
     #================================================
 
     # mean and merge all csv based on the density/layout/stripe/ring matrix (20 csv -> 4 csv)
@@ -81,33 +133,66 @@ def main(out_subdir : str,
     csv_sequ_paths_highD_stripe : list[str] = [x for x in csv_sequ_paths if int(x.split("/")[-6].split("_")[0]) in __SEQUENCES_MAP_HIGHD_STRIPE__]
     csv_sequ_paths_highD_ring : list[str] = [x for x in csv_sequ_paths if int(x.split("/")[-6].split("_")[0]) in __SEQUENCES_MAP_HIGHD_RING__]
 
+    # #================================================
+    # ## 6.A) LOWDENSITY STRIPE
+    # # merge all values by line in csv by averaging them
+    # tool_id : int = 0  x
+    # nbr_operations : int = 0
+    # average_time_per_operation : float = 0.0
+    # mean_drift_position_m : float = 0.0
+    # mean_drift_position_q1 : float = 0.0
+    # mean_drift_position_q3 : float = 0.0
+    # mean_drift_position_min : float = 0.0
+    # mean_drift_position_max : float = 0.0
+    # mean_drift_rotation_m : float = 0.0
+    # mean_drift_rotation_q1 : float = 0.0
+    # mean_drift_rotation_q3 : float = 0.0
+    # mean_drift_rotation_min : float = 0.0
+    # mean_drift_rotation_max : float = 0.0
+    # tags_m : float = 0.0
+    # tags_q1 : float = 0.0
+    # tags_q3 : float = 0.0
+    # tags_min : float = 0.0
+    # tags_max : float = 0.0
+    # coverage_m : float = 0.0
+    # mean_coverage_perc_quintile1 : float = 0.0
+    # mean_coverage_perc_quintile2 : float = 0.0
+    # mean_coverage_perc_quintile3 : float = 0.0
+    # mean_coverage_perc_quintile4 : float = 0.0
+    # mean_coverage_perc_quintile5 : float = 0.0
+
     #================================================
-    ## 6.A) LOWDENSITY STRIPE
-    # merge all values by line in csv by averaging them
-    tool_id : int = 0
-    nbr_operations : int = 0
-    average_time_per_operation : float = 0.0
-    mean_drift_position_m : float = 0.0
-    mean_drift_position_q1 : float = 0.0
-    mean_drift_position_q3 : float = 0.0
-    mean_drift_position_min : float = 0.0
-    mean_drift_position_max : float = 0.0
-    mean_drift_rotation_m : float = 0.0
-    mean_drift_rotation_q1 : float = 0.0
-    mean_drift_rotation_q3 : float = 0.0
-    mean_drift_rotation_min : float = 0.0
-    mean_drift_rotation_max : float = 0.0
-    tags_m : float = 0.0
-    tags_q1 : float = 0.0
-    tags_q3 : float = 0.0
-    tags_min : float = 0.0
-    tags_max : float = 0.0
-    coverage_m : float = 0.0
-    mean_coverage_perc_quintile1 : float = 0.0
-    mean_coverage_perc_quintile2 : float = 0.0
-    mean_coverage_perc_quintile3 : float = 0.0
-    mean_coverage_perc_quintile4 : float = 0.0
-    mean_coverage_perc_quintile5 : float = 0.0
+    # convert csv to data
+    # NB.:! we are skipping the saber_sawblade if it exists because its performance is too bad and we know
+    data_lowD_stripe = io_stream.cvt_csv_summary_to_data(csv_paths=csv_sequ_paths_lowD_stripe)
+    data_lowD_ring = io_stream.cvt_csv_summary_to_data(csv_paths=csv_sequ_paths_lowD_ring)
+    data_highD_stripe = io_stream.cvt_csv_summary_to_data(csv_paths=csv_sequ_paths_highD_stripe)
+    data_highD_ring = io_stream.cvt_csv_summary_to_data(csv_paths=csv_sequ_paths_highD_ring)
+
+    # get the avrages of each values in the summary groups
+    avr_data_lowD_stripe = metrics.compute_average_summary_values(data=data_lowD_stripe)
+    avr_data_lowD_ring = metrics.compute_average_summary_values(data=data_lowD_ring)
+    avr_data_highD_stripe = metrics.compute_average_summary_values(data=data_highD_stripe)
+    avr_data_highD_ring = metrics.compute_average_summary_values(data=data_highD_ring)
+
+    #================================================
+    # visualize and dump
+
+    # position drift
+    pair_pos_stripe = np.array([data_lowD_stripe[2], data_highD_stripe[2]])
+    pair_pos_ring = np.array([data_lowD_ring[2], data_highD_ring[2]])
+
+    print(f"pair ring: {pair_pos_ring}")
+
+    visuals.draw_double_boxplot(data_a=pair_pos_stripe,
+                                data_b=pair_pos_ring,
+                                labels=["low density stripe", "low density ring", "high density stripe", "high density ring"])
+
+
+
+
+    #================================================
+    # print csv + latex table
 
 
 
@@ -145,6 +230,30 @@ def main(out_subdir : str,
     for csv in _csv_sequ_paths:
         _csv_sequ_paths_nbr.append(int(csv.split("/")[-6].split("_")[0]))
     _csv_sequ_paths = [x for _,x in sorted(zip(_csv_sequ_paths_nbr,_csv_sequ_paths))]
+    # check if there are all 0 summary values
+    for path in _csv_sequ_paths:
+        with open(path, 'r') as f:
+            lns = f.readlines()
+            lns.pop(0)
+            all_zero : bool = True
+            for ln in lns:
+                ln = ln.split(",")
+                for i in range(1,ln.__len__()):
+                    if float(ln[i]) != 0.0:
+                        all_zero = False
+                        break
+                if not all_zero:
+                    break
+            if all_zero:
+                _csv_sequ_paths.remove(path)
+                print(f"\033[93m[WARNING]: all values are 0 in {path}, removing it from the list\n\033[0m")
+
+
+
+
+
+
+
 
     _csv_subsequ_paths : list[list[str]] = []
     _in_subdirs = os.listdir(args.inDir)
@@ -169,8 +278,6 @@ def main(out_subdir : str,
             continue
         _csv_subsequ_paths.append(_csv_lst_temp)
 
-
-
     main(out_subdir=_out_subdir,
          csv_sequ_paths=_csv_sequ_paths,
          csv_subsequ_paths=_csv_subsequ_paths)

eval/script/io_stream.py

@@ -737,4 +737,108 @@ def dump_summary_as_tex_table(out_dir : str,
     # save as a latex table
     table_path = os.path.join(table_dir, "table.tex")
     with open(table_path, 'w') as tf:
-        tf.write(df.to_latex(index=False))
+        tf.write(df.to_latex(index=False))
+
+#===============================================================================
+# summary
+#===============================================================================
+
+def merge_csv_by_categ(csv_paths : list[str]) -> list[str]:
+    """
+        To merge the csv paths by categories of tool labels.
+
+        :param list_paths: list of csv paths
+        :return: list of merged csv paths by category as numpy array
+    """
+    csv_data__circular_sawblade_140 = []
+    csv_data__saber_sawblade_t1 = []
+    csv_data__drill_hinge_cutter_bit_50 = []
+    csv_data__drill_auger_bit_20_200 = []
+    csv_data__drill_auger_bit_25_500 = []
+    csv_data__drill_oblique_hole_bit_40 = []
+    csv_data__st_screw_120 = []
+    csv_data__st_screw_100 = []
+    csv_data__st_screw_80 = []
+    csv_data__st_screw_45 = []
+
+    lst_TOOLS_keys = list(metrics.TOOLS.keys())
+
+    for path in csv_paths:
+        name_csv_file = os.path.basename(path).split(".")[0]
+        if name_csv_file in lst_TOOLS_keys:
+            id = lst_TOOLS_keys.index(name_csv_file)
+            with open(path, 'r') as f:
+                csv_data = f.read()
+            for idx, row in enumerate(csv_data.split("\n")):
+                if idx == 0:
+                    continue
+                if row == "":
+                    continue
+                if "nan" in row:
+                    continue
+                np_temp_data = np.array(row.split(";"))
+
+                if lst_TOOLS_keys[id] == "circular_sawblade_140":
+                    csv_data__circular_sawblade_140.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "saber_sawblade_t1":
+                    csv_data__saber_sawblade_t1.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "drill_hinge_cutter_bit_50":
+                    csv_data__drill_hinge_cutter_bit_50.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "drill_auger_bit_20_200":
+                    csv_data__drill_auger_bit_20_200.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "drill_auger_bit_25_500":
+                    csv_data__drill_auger_bit_25_500.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "drill_oblique_hole_bit_40":
+                    csv_data__drill_oblique_hole_bit_40.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "st_screw_120":
+                    csv_data__st_screw_120.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "st_screw_100":
+                    csv_data__st_screw_100.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "st_screw_80":
+                    csv_data__st_screw_80.append(np_temp_data)
+                elif lst_TOOLS_keys[id] == "st_screw_45":
+                    csv_data__st_screw_45.append(np_temp_data)
+
+    return [np.array(csv_data__circular_sawblade_140),
+            np.array(csv_data__saber_sawblade_t1),
+            np.array(csv_data__drill_hinge_cutter_bit_50),
+            np.array(csv_data__drill_auger_bit_20_200),
+            np.array(csv_data__drill_auger_bit_25_500),
+            np.array(csv_data__drill_oblique_hole_bit_40),
+            np.array(csv_data__st_screw_120),
+            np.array(csv_data__st_screw_100),
+            np.array(csv_data__st_screw_80),
+            np.array(csv_data__st_screw_45)]
+
+def cvt_csv_summary_to_data(csv_paths : list[str]) -> list[str]:
+    """
+        To convert the csv summary of sequences to one averaged data (no tools subdivision).
+        NB.:! we are skipping the saber_sawblade if it exists because are too bad!
+
+        :param csv_paths: list of csv paths
+        :return: list of averaged data
+    """
+    csv_data_out = []
+    for path in csv_paths:
+        with open(path, 'r') as f:
+            csv_data = f.read()
+        for idx, row in enumerate(csv_data.split("\n")):
+            if idx == 0:
+                continue
+            if row == "":
+                continue
+            if "nan" in row:
+                continue
+            if "saber_sawblade_t1" in row:  ## <--! we are not including the saber_sawblade_t1 because it is too bad!
+                continue
+
+            temp_data = row.split(";")[0].split(',')
+            temp_data.pop(0)
+            temp_data = [float(x) for x in temp_data]
+
+            csv_data_out.append(temp_data)
+
+    np_csv_data = np.array(csv_data_out)
+    np_csv_data = np_csv_data.transpose()
+
+    return np_csv_data

eval/script/metrics.py

@@ -467,4 +467,17 @@ def compute_fab_results(out_dir : str) -> None:
         print(f"mean_coverage_perc_quintiles2 [percentage]: {TOOLS[id][1].mean_coverage_perc_quintiles[1]}")
         print(f"mean_coverage_perc_quintiles3 [percentage]: {TOOLS[id][1].mean_coverage_perc_quintiles[2]}")
         print(f"mean_coverage_perc_quintiles4 [percentage]: {TOOLS[id][1].mean_coverage_perc_quintiles[3]}")
-        print(f"mean_coverage_perc_quintiles5 [percentage]: {TOOLS[id][1].mean_coverage_perc_quintiles[4]}")
+        print(f"mean_coverage_perc_quintiles5 [percentage]: {TOOLS[id][1].mean_coverage_perc_quintiles[4]}")
+
+# ================================================================================================
+# ================================== summary metrics ============================================
+# ================================================================================================
+
+def compute_average_summary_values(data : np.array(float)) -> np.array(float):
+    """
+        This function computes the average values of the summary metrics.
+    """
+    avr_data = []
+    for idx, param in enumerate(data):
+        avr_data.append(np.mean(param))
+    return np.array(avr_data)