67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import numpy as np
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import os
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import json
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from jsmin import jsmin # type:ignore
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import argh
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from functions.get_trials import get_trials
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from functions.get_experiments import get_experiments
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def loader(
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filename: str = "config_M_Sert_Cre_49.json", fpath: str = "/data_1/hendrik/gevi"
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) -> None:
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if os.path.isfile(filename) is False:
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print(f"{filename} is missing")
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exit()
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with open(filename, "r") as file:
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config = json.loads(jsmin(file.read()))
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raw_data_path: str = os.path.join(
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config["basic_path"],
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config["recoding_data"],
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config["mouse_identifier"],
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config["raw_path"],
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)
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experiments = get_experiments(raw_data_path).numpy()
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n_exp = experiments.shape[0]
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for i_exp in range(0, n_exp):
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trials = get_trials(raw_data_path, experiments[i_exp]).numpy()
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n_tri = trials.shape[0]
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for i_tri in range(0, n_tri):
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experiment_name: str = (
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f"Exp{experiments[i_exp]:03d}_Trial{trials[i_tri]:03d}"
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)
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tmp_fname = os.path.join(
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fpath,
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"output_" + config["mouse_identifier"],
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experiment_name + "_acceptor_donor.npz",
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)
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print(f'Processing file "{tmp_fname}"...')
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tmp = np.load(tmp_fname)
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tmp_data_sequence = tmp["data_donor"]
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tmp_light_signal = tmp["data_acceptor"]
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if (i_exp == 0) and (i_tri == 0):
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mask = tmp["mask"]
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new_shape = [n_exp, *tmp_data_sequence.shape]
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data_sequence = np.zeros(new_shape)
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light_signal = np.zeros(new_shape)
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# Here you might want to use the exp fit and removal...
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data_sequence[i_exp] += tmp_data_sequence / n_tri
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light_signal[i_exp] += tmp_light_signal / n_tri
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np.save("dsq_" + config["mouse_identifier"], data_sequence)
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np.save("lsq_" + config["mouse_identifier"], light_signal)
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np.save("msq_" + config["mouse_identifier"], mask)
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if __name__ == "__main__":
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argh.dispatch_command(loader)
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