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@@ -32,17 +32,17 @@ def main():
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if len(sys.argv) <= 1:
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print('Run with default parameters...')
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- print('python predict_seuil_expe_maxwell.py --interval "0,20" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
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+ print('python predict_seuil_expe_maxwell_curve.py --interval "0,20" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
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sys.exit(2)
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try:
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opts, args = getopt.getopt(sys.argv[1:], "ht:m:o:l", ["help=", "interval=", "model=", "mode=", "metric=", "limit_detection="])
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except getopt.GetoptError:
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# print help information and exit:
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- print('python predict_seuil_expe_maxwell.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
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+ print('python predict_seuil_expe_maxwell_curve.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
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sys.exit(2)
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for o, a in opts:
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if o == "-h":
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- print('python predict_seuil_expe_maxwell.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
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+ print('python predict_seuil_expe_maxwell_curve.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
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sys.exit()
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elif o in ("-t", "--interval"):
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p_interval = a
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@@ -160,13 +160,13 @@ def main():
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# end of scene => display of results
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# construct path using model name for saving threshold map folder
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- model_treshold_path = os.path.join(threshold_map_folder, p_model_file.split('/')[-1].replace('.joblib', ''))
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+ model_threshold_path = os.path.join(threshold_map_folder, p_model_file.split('/')[-1].replace('.joblib', ''))
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# create threshold model path if necessary
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- if not os.path.exists(model_treshold_path):
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- os.makedirs(model_treshold_path)
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+ if not os.path.exists(model_threshold_path):
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+ os.makedirs(model_threshold_path)
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- map_filename = os.path.join(model_treshold_path, simulation_curves_zones + folder_scene)
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+ map_filename = os.path.join(model_threshold_path, simulation_curves_zones + folder_scene)
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f_map = open(map_filename, 'w')
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for line in block_predictions_str:
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