Update of whole scripts data

generateAndTrain_maxwell.sh

@@ -54,7 +54,7 @@ for counter in {0..4}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    python generate_data_model_random_maxwell.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --renderer "maxwell" --step 40 --random 1 --percent 1
+                    python generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --renderer "maxwell" --step 40 --random 1 --percent 1
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
                     #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'

generateAndTrain_maxwell_custom.sh

@@ -55,7 +55,7 @@ for counter in {0..4}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    python generate_data_model_random_maxwell.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    python generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
                     #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}

generate_all_data.py

@@ -176,7 +176,7 @@ def generate_data_svd(data_type, mode):
             f.write(str(min_val_found) + '\n')
             f.write(str(max_val_found) + '\n')
 
-    print("%s : end of data generation\n" % mode)
+    print("%s_%s : end of data generation\n" % (data_type, mode))
 
 
 def main():
@@ -191,7 +191,7 @@ def main():
         print('python generate_all_data.py --metric lab')
         sys.exit(2)
     try:
-        opts, args = getopt.getopt(sys.argv[1:], "hms", ["help=", "metric=", "step="])
+        opts, args = getopt.getopt(sys.argv[1:], "hms", ["help=", "metric="])
     except getopt.GetoptError:
         # print help information and exit:
         print('python generate_all_data.py --metric all')

generate_data_model_random.py

@@ -17,6 +17,7 @@ from PIL import Image
 from ipfml import processing, metrics
 
 from modules.utils import config as cfg
+from modules.utils import data as dt
 
 # getting configuration information
 config_filename         = cfg.config_filename
@@ -24,8 +25,9 @@ zone_folder             = cfg.zone_folder
 min_max_filename        = cfg.min_max_filename_extension
 
 # define all scenes values
-scenes_list             = cfg.scenes_names
-scenes_indexes          = cfg.scenes_indices
+all_scenes_list         = cfg.scenes_names
+all_scenes_indices      = cfg.scenes_indices
+
 choices                 = cfg.normalization_choices
 path                    = cfg.dataset_path
 zones                   = cfg.zones_indices
@@ -38,10 +40,11 @@ min_max_ext             = cfg.min_max_filename_extension
 
 generic_output_file_svd = '_random.csv'
 
-min_value_interval = sys.maxsize
-max_value_interval = 0
+min_value_interval      = sys.maxsize
+max_value_interval      = 0
+
 
-def construct_new_line(path_seuil, interval, line, norm, sep, index):
+def construct_new_line(path_seuil, interval, line, choice, norm):
     begin, end = interval
 
     line_data = line.split(';')
@@ -52,7 +55,11 @@ def construct_new_line(path_seuil, interval, line, norm, sep, index):
 
     # TODO : check if it's always necessary to do that (loss of information for svd)
     if norm:
-        metrics = processing.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
+
+        if choice == 'svdne':
+            metrics = processing.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
+        if choice == 'svdn':
+            metrics = processing.normalize_arr(metrics)
 
     with open(path_seuil, "r") as seuil_file:
         seuil_learned = int(seuil_file.readline().strip())
@@ -63,15 +70,13 @@ def construct_new_line(path_seuil, interval, line, norm, sep, index):
         line = '0'
 
     for idx, val in enumerate(metrics):
-        if index:
-            line += " " + str(idx + 1)
-        line += sep
+        line += ';'
         line += str(val)
     line += '\n'
 
     return line
 
-def get_min_max_value_interval(_filename, _interval, _choice, _metric):
+def get_min_max_value_interval(_scenes_list, _filename, _interval, _choice, _metric):
 
     global min_value_interval, max_value_interval
 
@@ -83,7 +88,7 @@ def get_min_max_value_interval(_filename, _interval, _choice, _metric):
     for id_scene, folder_scene in enumerate(scenes):
 
         # only take care of maxwell scenes
-        if folder_scene in scenes_list:
+        if folder_scene in _scenes_list:
 
             scene_path = os.path.join(path, folder_scene)
 
@@ -99,6 +104,7 @@ def get_min_max_value_interval(_filename, _interval, _choice, _metric):
             random.shuffle(zones_folder)
 
             for id_zone, zone_folder in enumerate(zones_folder):
+
                 zone_path = os.path.join(scene_path, zone_folder)
                 data_filename = _metric + "_" + _choice + generic_output_file_svd
                 data_file_path = os.path.join(zone_path, data_filename)
@@ -111,10 +117,10 @@ def get_min_max_value_interval(_filename, _interval, _choice, _metric):
                 # check if user select current scene and zone to be part of training data set
                 for line in lines:
 
-
                     begin, end = _interval
 
                     line_data = line.split(';')
+
                     metrics = line_data[begin+1:end+1]
                     metrics = [float(m) for m in metrics]
 
@@ -130,7 +136,7 @@ def get_min_max_value_interval(_filename, _interval, _choice, _metric):
                     counter += 1
 
 
-def generate_data_model(_filename, _interval, _choice, _metric, _scenes = scenes_list, _nb_zones = 4, _percent = 1, _norm = False, _sep=':', _index=True):
+def generate_data_model(_scenes_list, _filename, _interval, _choice, _metric, _scenes, _nb_zones = 4, _percent = 1, _random=0, _step=1, _norm = False):
 
     output_train_filename = _filename + ".train"
     output_test_filename = _filename + ".test"
@@ -142,18 +148,18 @@ def generate_data_model(_filename, _interval, _choice, _metric, _scenes = scenes
     if not os.path.exists(output_data_folder):
         os.makedirs(output_data_folder)
 
-    train_file = open(output_train_filename, 'w')
-    test_file = open(output_test_filename, 'w')
-
     scenes = os.listdir(path)
 
     # remove min max file from scenes folder
     scenes = [s for s in scenes if min_max_filename not in s]
 
+    train_file_data = []
+    test_file_data  = []
+
     for id_scene, folder_scene in enumerate(scenes):
 
         # only take care of maxwell scenes
-        if folder_scene in scenes_list:
+        if folder_scene in _scenes_list:
 
             scene_path = os.path.join(path, folder_scene)
 
@@ -166,7 +172,9 @@ def generate_data_model(_filename, _interval, _choice, _metric, _scenes = scenes
                 zones_folder.append("zone"+index_str)
 
             # shuffle list of zones (=> randomly choose zones)
-            random.shuffle(zones_folder)
+            # only in random mode
+            if _random:
+                random.shuffle(zones_folder)
 
             for id_zone, zone_folder in enumerate(zones_folder):
                 zone_path = os.path.join(scene_path, zone_folder)
@@ -179,48 +187,63 @@ def generate_data_model(_filename, _interval, _choice, _metric, _scenes = scenes
 
                 num_lines = len(lines)
 
-                lines_indexes = np.arange(num_lines)
-                random.shuffle(lines_indexes)
+                # randomly shuffle image
+                if _random:
+                    random.shuffle(lines)
 
                 path_seuil = os.path.join(zone_path, seuil_expe_filename)
 
                 counter = 0
                 # check if user select current scene and zone to be part of training data set
-                for index in lines_indexes:
-                    line = construct_new_line(path_seuil, _interval, lines[index], _norm, _sep, _index)
+                for data in lines:
 
                     percent = counter / num_lines
+                    image_index = int(data.split(';')[0])
+
+                    if image_index % _step == 0:
+                        line = construct_new_line(path_seuil, _interval, data, _choice, _norm, _sep, _index)
 
-                    if id_zone < _nb_zones and folder_scene in _scenes and percent <= _percent:
-                        train_file.write(line)
-                    else:
-                        test_file.write(line)
+                        if id_zone < _nb_zones and folder_scene in _scenes and percent <= _percent:
+                            train_file_data.append(line)
+                        else:
+                            test_file_data.append(line)
 
                     counter += 1
 
                 f.close()
 
+    train_file = open(output_train_filename, 'w')
+    test_file = open(output_test_filename, 'w')
+
+    for line in train_file_data:
+        train_file.write(line)
+
+    for line in test_file_data:
+        test_file_data.write(line)
+
     train_file.close()
     test_file.close()
 
 
 def main():
 
-    p_custom = False
+    p_custom    = False
+    p_step      = 1
+    p_renderer  = 'all'
 
     if len(sys.argv) <= 1:
         print('Run with default parameters...')
-        print('python generate_data_model_random.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --percent 0.7 --sep : --rowindex 1 --custom min_max_filename')
+        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 renderer all --custom min_max_filename')
         sys.exit(2)
     try:
-        opts, args = getopt.getopt(sys.argv[1:], "ho:i:k:s:n:p:r:c", ["help=", "output=", "interval=", "kind=", "metric=","scenes=", "nb_zones=", "percent=", "sep=", "rowindex=", "custom="])
+        opts, args = getopt.getopt(sys.argv[1:], "ho:i:k:s:n:r:p:s:r:c", ["help=", "output=", "interval=", "kind=", "metric=","scenes=", "nb_zones=", "random=", "percent=", "step=", "renderer=", "custom="])
     except getopt.GetoptError:
         # print help information and exit:
-        print('python generate_data_model_random.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --percent 0.7 --sep : --rowindex 1 --custom min_max_filename')
+        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 --renderer all --custom min_max_filename')
         sys.exit(2)
     for o, a in opts:
         if o == "-h":
-            print('python generate_data_model_random.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --percent 0.7 --sep : --rowindex 1 --custom min_max_filename')
+            print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 --renderer all --custom min_max_filename')
             sys.exit()
         elif o in ("-o", "--output"):
             p_filename = a
@@ -234,30 +257,38 @@ def main():
             p_scenes = a.split(',')
         elif o in ("-n", "--nb_zones"):
             p_nb_zones = int(a)
+        elif o in ("-r", "--random"):
+            p_randrom = int(a)
         elif o in ("-p", "--percent"):
             p_percent = float(a)
         elif o in ("-s", "--sep"):
             p_sep = a
-        elif o in ("-r", "--rowindex"):
-            if int(a) == 1:
-                p_rowindex = True
-            else:
-                p_rowindex = False
+        elif o in ("-s", "--step"):
+            p_step = int(a)
+        elif o in ("-r", "--renderer"):
+            p_renderer = a
+
+            if p_renderer not in cfg.renderer_choices:
+                assert False, "Unknown renderer choice, %s" % cfg.renderer_choices
         elif o in ("-c", "--custom"):
             p_custom = a
         else:
             assert False, "unhandled option"
 
+    # list all possibles choices of renderer
+    scenes_list = dt.get_renderer_scenes_names(p_renderer)
+    scenes_indices = dt.get_renderer_scenes_indices(p_renderer)
+
     # getting scenes from indexes user selection
     scenes_selected = []
 
     for scene_id in p_scenes:
-        index = scenes_indexes.index(scene_id.strip())
+        index = scenes_indices.index(scene_id.strip())
         scenes_selected.append(scenes_list[index])
 
     # find min max value if necessary to renormalize data
     if p_custom:
-        get_min_max_value_interval(p_filename, p_interval, p_kind, p_metric)
+        get_min_max_value_interval(scenes_list, p_filename, p_interval, p_kind, p_metric)
 
         # write new file to save
         if not os.path.exists(custom_min_max_folder):
@@ -271,7 +302,7 @@ def main():
             f.write(str(max_value_interval) + '\n')
 
     # create database using img folder (generate first time only)
-    generate_data_model(p_filename, p_interval, p_kind, p_metric, scenes_selected, p_nb_zones, p_percent, p_custom, p_sep, p_rowindex)
+    generate_data_model(scenes_list, p_filename, p_interval, p_kind, p_metric, scenes_selected, p_nb_zones, p_percent, p_random, p_step, p_custom)
 
 if __name__== "__main__":
     main()

generate_data_model_random_maxwell.py

@@ -1,308 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Sep 14 21:02:42 2018
-
-@author: jbuisine
-"""
-
-from __future__ import print_function
-import sys, os, getopt
-import numpy as np
-import random
-import time
-import json
-
-from PIL import Image
-from ipfml import processing, metrics
-
-from modules.utils import config as cfg
-from modules.utils import data as dt
-
-# getting configuration information
-config_filename         = cfg.config_filename
-zone_folder             = cfg.zone_folder
-min_max_filename        = cfg.min_max_filename_extension
-
-# define all scenes values
-all_scenes_list         = cfg.scenes_names
-all_scenes_indices      = cfg.scenes_indices
-
-choices                 = cfg.normalization_choices
-path                    = cfg.dataset_path
-zones                   = cfg.zones_indices
-seuil_expe_filename     = cfg.seuil_expe_filename
-
-metric_choices          = cfg.metric_choices_labels
-output_data_folder      = cfg.output_data_folder
-custom_min_max_folder   = cfg.min_max_custom_folder
-min_max_ext             = cfg.min_max_filename_extension
-
-generic_output_file_svd = '_random.csv'
-
-min_value_interval      = sys.maxsize
-max_value_interval      = 0
-
-
-def construct_new_line(path_seuil, interval, line, choice, norm):
-    begin, end = interval
-
-    line_data = line.split(';')
-    seuil = line_data[0]
-    metrics = line_data[begin+1:end+1]
-
-    metrics = [float(m) for m in metrics]
-
-    # TODO : check if it's always necessary to do that (loss of information for svd)
-    if norm:
-
-        if choice == 'svdne':
-            metrics = processing.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
-        if choice == 'svdn':
-            metrics = processing.normalize_arr(metrics)
-
-    with open(path_seuil, "r") as seuil_file:
-        seuil_learned = int(seuil_file.readline().strip())
-
-    if seuil_learned > int(seuil):
-        line = '1'
-    else:
-        line = '0'
-
-    for idx, val in enumerate(metrics):
-        line += ';'
-        line += str(val)
-    line += '\n'
-
-    return line
-
-def get_min_max_value_interval(_scenes_list, _filename, _interval, _choice, _metric):
-
-    global min_value_interval, max_value_interval
-
-    scenes = os.listdir(path)
-
-    # remove min max file from scenes folder
-    scenes = [s for s in scenes if min_max_filename not in s]
-
-    for id_scene, folder_scene in enumerate(scenes):
-
-        # only take care of maxwell scenes
-        if folder_scene in _scenes_list:
-
-            scene_path = os.path.join(path, folder_scene)
-
-            zones_folder = []
-            # create zones list
-            for index in zones:
-                index_str = str(index)
-                if len(index_str) < 2:
-                    index_str = "0" + index_str
-                zones_folder.append("zone"+index_str)
-
-            # shuffle list of zones (=> randomly choose zones)
-            random.shuffle(zones_folder)
-
-            for id_zone, zone_folder in enumerate(zones_folder):
-
-                zone_path = os.path.join(scene_path, zone_folder)
-                data_filename = _metric + "_" + _choice + generic_output_file_svd
-                data_file_path = os.path.join(zone_path, data_filename)
-
-                # getting number of line and read randomly lines
-                f = open(data_file_path)
-                lines = f.readlines()
-
-                counter = 0
-                # check if user select current scene and zone to be part of training data set
-                for line in lines:
-
-                    begin, end = _interval
-
-                    line_data = line.split(';')
-                    print(line_data)
-
-                    metrics = line_data[begin+1:end+1]
-                    metrics = [float(m) for m in metrics]
-
-                    min_value = min(metrics)
-                    max_value = max(metrics)
-
-                    if min_value < min_value_interval:
-                        min_value_interval = min_value
-
-                    if max_value > max_value_interval:
-                        max_value_interval = max_value
-
-                    counter += 1
-
-
-def generate_data_model(_scenes_list, _filename, _interval, _choice, _metric, _scenes, _nb_zones = 4, _percent = 1, _random=0, _step=1, _norm = False):
-
-    output_train_filename = _filename + ".train"
-    output_test_filename = _filename + ".test"
-
-    if not '/' in output_train_filename:
-        raise Exception("Please select filename with directory path to save data. Example : data/dataset")
-
-    # create path if not exists
-    if not os.path.exists(output_data_folder):
-        os.makedirs(output_data_folder)
-
-    scenes = os.listdir(path)
-
-    # remove min max file from scenes folder
-    scenes = [s for s in scenes if min_max_filename not in s]
-
-    train_file_data = []
-    test_file_data  = []
-
-    for id_scene, folder_scene in enumerate(scenes):
-
-        # only take care of maxwell scenes
-        if folder_scene in _scenes_list:
-
-            scene_path = os.path.join(path, folder_scene)
-
-            zones_folder = []
-            # create zones list
-            for index in zones:
-                index_str = str(index)
-                if len(index_str) < 2:
-                    index_str = "0" + index_str
-                zones_folder.append("zone"+index_str)
-
-            # shuffle list of zones (=> randomly choose zones)
-            # only in random mode
-            if _random:
-                random.shuffle(zones_folder)
-
-            for id_zone, zone_folder in enumerate(zones_folder):
-                zone_path = os.path.join(scene_path, zone_folder)
-                data_filename = _metric + "_" + _choice + generic_output_file_svd
-                data_file_path = os.path.join(zone_path, data_filename)
-
-                # getting number of line and read randomly lines
-                f = open(data_file_path)
-                lines = f.readlines()
-
-                num_lines = len(lines)
-
-                # randomly shuffle image
-                if _random:
-                    random.shuffle(lines)
-
-                path_seuil = os.path.join(zone_path, seuil_expe_filename)
-
-                counter = 0
-                # check if user select current scene and zone to be part of training data set
-                for data in lines:
-
-                    percent = counter / num_lines
-                    image_index = int(data.split(';')[0])
-
-                    if image_index % _step == 0:
-                        line = construct_new_line(path_seuil, _interval, data, _choice, _norm, _sep, _index)
-
-                        if id_zone < _nb_zones and folder_scene in _scenes and percent <= _percent:
-                            train_file_data.append(line)
-                        else:
-                            test_file_data.append(line)
-
-                    counter += 1
-
-
-    train_file = open(output_train_filename, 'w')
-    test_file = open(output_test_filename, 'w')
-
-    for line in train_file_data:
-        train_file.write(line)
-
-    for line in test_file_data:
-        test_file_data.write(line)
-
-    train_file.close()
-    test_file.close()
-
-
-def main():
-
-    p_custom    = False
-    p_step      = 1
-    p_renderer  = 'all'
-
-    if len(sys.argv) <= 1:
-        print('Run with default parameters...')
-        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 renderer all --custom min_max_filename')
-        sys.exit(2)
-    try:
-        opts, args = getopt.getopt(sys.argv[1:], "ho:i:k:s:n:r:p:s:r:c", ["help=", "output=", "interval=", "kind=", "metric=","scenes=", "nb_zones=", "random=", "percent=", "step=", "renderer=", "custom="])
-    except getopt.GetoptError:
-        # print help information and exit:
-        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 --renderer all --custom min_max_filename')
-        sys.exit(2)
-    for o, a in opts:
-        if o == "-h":
-            print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 --renderer all --custom min_max_filename')
-            sys.exit()
-        elif o in ("-o", "--output"):
-            p_filename = a
-        elif o in ("-i", "--interval"):
-            p_interval = list(map(int, a.split(',')))
-        elif o in ("-k", "--kind"):
-            p_kind = a
-        elif o in ("-m", "--metric"):
-            p_metric = a
-        elif o in ("-s", "--scenes"):
-            p_scenes = a.split(',')
-        elif o in ("-n", "--nb_zones"):
-            p_nb_zones = int(a)
-        elif o in ("-r", "--random"):
-            p_randrom = int(a)
-        elif o in ("-p", "--percent"):
-            p_percent = float(a)
-        elif o in ("-s", "--sep"):
-            p_sep = a
-        elif o in ("-s", "--step"):
-            p_step = int(a)
-        elif o in ("-r", "--renderer"):
-            p_renderer = a
-
-            if p_renderer not in cfg.renderer_choices:
-                assert False, "Unknown renderer choice, %s" % cfg.renderer_choices
-        elif o in ("-c", "--custom"):
-            p_custom = a
-        else:
-            assert False, "unhandled option"
-
-    # list all possibles choices of renderer
-    scenes_list = dt.get_renderer_scenes_names(p_renderer)
-    scenes_indices = dt.get_renderer_scenes_indices(p_renderer)
-
-    # getting scenes from indexes user selection
-    scenes_selected = []
-
-    for scene_id in p_scenes:
-        index = scenes_indices.index(scene_id.strip())
-        scenes_selected.append(scenes_list[index])
-
-    # find min max value if necessary to renormalize data
-    if p_custom:
-        get_min_max_value_interval(scenes_list, p_filename, p_interval, p_kind, p_metric)
-
-        # write new file to save
-        if not os.path.exists(custom_min_max_folder):
-            os.makedirs(custom_min_max_folder)
-
-        min_max_folder_path = os.path.join(os.path.dirname(__file__), custom_min_max_folder)
-        min_max_filename_path = os.path.join(min_max_folder_path, p_custom)
-
-        with open(min_max_filename_path, 'w') as f:
-            f.write(str(min_value_interval) + '\n')
-            f.write(str(max_value_interval) + '\n')
-
-    # create database using img folder (generate first time only)
-    generate_data_model(scenes_list, p_filename, p_interval, p_kind, p_metric, scenes_selected, p_nb_zones, p_percent, p_random, p_step, p_custom)
-
-if __name__== "__main__":
-    main()

run_maxwell_simulation.sh

@@ -38,7 +38,7 @@ for size in {"4","8","16","26","32","40"}; do
                             echo "Run simulation for model ${MODEL_NAME}"
 
                             # by default regenerate model
-                            python generate_data_model_random_maxwell.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1
+                            python generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 

run_maxwell_simulation_custom.sh

@@ -39,7 +39,7 @@ for size in {"4","8","16","26","32","40"}; do
                             echo "Run simulation for model ${MODEL_NAME}"
 
                             # by default regenerate model
-                            python generate_data_model_random_maxwell.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                            python generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}