72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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import os
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from functions.create_logger import create_logger
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from functions.load_config import load_config
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from functions.get_trials import get_trials
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import h5py # type: ignore
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import torch
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control_file_handle = h5py.File("ROI_control_49.mat", "r")
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control_roi = (np.array(control_file_handle["roi"]).T) > 0
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control_file_handle.close()
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control_roi = control_roi.reshape((control_roi.shape[0] * control_roi.shape[1]))
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s_darken_file_handle = h5py.File("ROI_sDarken_49.mat", "r")
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s_darken_roi = (np.array(s_darken_file_handle["roi"]).T) > 0
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s_darken_file_handle.close()
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s_darken_roi = s_darken_roi.reshape((s_darken_roi.shape[0] * s_darken_roi.shape[1]))
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mylogger = create_logger(
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save_logging_messages=True, display_logging_messages=True, log_stage_name="test_xxx"
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)
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config = load_config(mylogger=mylogger)
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experiment_id: int = 1
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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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data_path: str = "output"
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trails = get_trials(path=raw_data_path, experiment_id=experiment_id)
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for i in range(0, trails.shape[0]):
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trial_id = int(trails[i])
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experiment_name: str = f"Exp{experiment_id:03d}_Trial{trial_id:03d}"
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mylogger.info(f"Loading files for {experiment_name}")
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data = np.load(os.path.join(data_path, f"{experiment_name}_ratio_sequence.npz"))
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rs = data["ratio_sequence"]
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rs = rs.reshape((rs.shape[0] * rs.shape[1], rs.shape[2]))
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rs_c = rs[control_roi, :]
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rs_c_core, _, _ = torch.linalg.svd(torch.tensor(rs_c.T), full_matrices=False)
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rs_c_core = rs_c_core[:, 0].numpy()
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rs_s = rs[s_darken_roi, :]
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rs_s_core, _, _ = torch.linalg.svd(torch.tensor(rs_s.T), full_matrices=False)
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rs_s_core = rs_s_core[:, 0].numpy()
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rs_s_core -= rs_s_core.mean(keepdims=True)
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rs_c_core -= rs_c_core.mean(keepdims=True)
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rs_c_core *= (rs_s_core * rs_c_core).sum() / (rs_c_core**2).sum()
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if i == 0:
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ratio_sequence = rs_s_core - rs_c_core
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else:
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ratio_sequence += rs_s_core - rs_c_core
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ratio_sequence /= float(trails.shape[0])
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t = np.arange(0, ratio_sequence.shape[0]) / 100.0
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plt.plot(t, ratio_sequence, label="sDarken-control")
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plt.legend()
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plt.xlabel("Time [sec]")
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plt.show()
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