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import argparse |
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import os |
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from pathlib import Path |
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import numpy as np |
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import pandas as pd |
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def main(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-i', type = str, help ="Input directory") |
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parser.add_argument('-o', type = str, help="Output directory") |
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parser.add_argument('-file_format', default = "*.npy", type = str, help = "File to open") |
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args = parser.parse_args() |
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if not args.o: |
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args.o = args.i |
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if not os.path.exists(args.o): |
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os.makedirs(args.o) |
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input_list = _get_file_paths(args.i, args.file_format) |
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offset_x, offset_y = _get_x_y_offsets(input_list) |
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_save_csv(args.o, offset_x, "x_offsets") |
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_save_csv(args.o, offset_y, "y_offsets") |
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def _get_file_paths(input_dir, img_format): |
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input_dir = Path(input_dir) |
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path_list = [str(path) for path in list(sorted(input_dir.rglob(img_format)))] |
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return path_list |
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def _save_csv(output_dir, df, name): |
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df.to_csv(output_dir+"/"+name+".csv") |
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def _get_x_y_offsets(input_list): |
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nFOV = len(input_list) |
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num_datachannels = 16 |
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offset_array_x = np.zeros(shape = (nFOV, num_datachannels+1)) |
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offset_array_y = np.zeros(shape = (nFOV, num_datachannels+1)) |
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for fov in range(nFOV): |
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tnx = np.load(input_list[fov], allow_pickle = True) |
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offset_array_x[fov,0] = int(fov+1) |
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offset_array_y[fov,0] = int(fov+1) |
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for j in range(num_datachannels): |
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offset_array_x[fov,j+1] = tnx[j].params[0][-1] |
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offset_array_y[fov,j+1] = tnx[j].params[1][-1] |
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pd_columns = ['FOV', |
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'Offset b/w IR1 & IR 1', |
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'Offset b/w IR1 & IR 2', |
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'Offset b/w IR1 & IR 3', |
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'Offset b/w IR1 & IR 4', |
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'Offset b/w IR1 & IR 5', |
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'Offset b/w IR1 & IR 6', |
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'Offset b/w IR1 & IR 7', |
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'Offset b/w IR1 & IR 8'] |
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offset_pd_x = pd.DataFrame(offset_array_x[:, ::2], columns=pd_columns) |
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offset_pd_y = pd.DataFrame(offset_array_y[:, ::2], columns=pd_columns) |
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return offset_pd_x, offset_pd_y |
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if __name__ == '__main__': |
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main() |
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