183 lines
6.1 KiB
Python
183 lines
6.1 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import argh
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import scipy # type: ignore
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import json
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import os
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from jsmin import jsmin # type:ignore
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def func_pow(x, a, b, c):
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return -a * x**b + c
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def func_exp(x, a, b, c):
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return a * np.exp(-x / b) + c
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# mouse: int = 0, 1, 2, 3, 4
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def plot(
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filename: str = "config_M_Sert_Cre_49.json",
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experiment: int = 4,
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skip_timesteps: int = 100,
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# If there is no special ROI... Get one! This is just a backup
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roi_control_path_default: str = "roi_controlM_Sert_Cre_49.npy",
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roi_sdarken_path_default: str = "roi_sdarkenM_Sert_Cre_49.npy",
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remove_fit: bool = False,
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fit_power: bool = False, # True => -ax^b ; False => exp(-b)
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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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if os.path.isdir(raw_data_path) is False:
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print(f"ERROR: could not find raw directory {raw_data_path}!!!!")
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exit()
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with open(f"meta_{config["mouse_identifier"]}_exp{experiment:03d}.json", "r") as file:
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metadata = json.loads(jsmin(file.read()))
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experiment_names = metadata['sessionMetaData']['experimentNames'][str(experiment)]
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roi_control_path: str = f"roi_control{config["mouse_identifier"]}.npy"
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roi_sdarken_path: str = f"roi_sdarken{config["mouse_identifier"]}.npy"
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if os.path.isfile(roi_control_path) is False:
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print(f"Using replacement RIO: {roi_control_path_default}")
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roi_control_path = roi_control_path_default
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if os.path.isfile(roi_sdarken_path) is False:
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print(f"Using replacement RIO: {roi_sdarken_path_default}")
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roi_sdarken_path = roi_sdarken_path_default
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print("Load data...")
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data = np.load("dsq_" + config["mouse_identifier"] + ".npy", mmap_mode="r")
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print("Load light signal...")
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light = np.load("lsq_" + config["mouse_identifier"] + ".npy", mmap_mode="r")
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print("Load mask...")
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mask = np.load("msq_" + config["mouse_identifier"] + ".npy")
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roi_control = np.load(roi_control_path)
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roi_control *= mask
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assert roi_control.sum() > 0, "ROI control empty"
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roi_darken = np.load(roi_sdarken_path)
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roi_darken *= mask
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assert roi_darken.sum() > 0, "ROI sDarken empty"
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plt.figure(1)
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a_show = data[experiment - 1, :, :, 1000].copy()
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a_show[(roi_darken + roi_control) < 0.5] = np.nan
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plt.imshow(a_show)
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plt.title(f"{config["mouse_identifier"]} -- Experiment: {experiment}")
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plt.show(block=False)
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plt.figure(2)
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a_dontshow = data[experiment - 1, :, :, 1000].copy()
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a_dontshow[(roi_darken + roi_control) > 0.5] = np.nan
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plt.imshow(a_dontshow)
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plt.title(f"{config["mouse_identifier"]} -- Experiment: {experiment}")
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plt.show(block=False)
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plt.figure(3)
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if remove_fit:
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light_exp = light[experiment - 1, :, :, skip_timesteps:].copy()
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else:
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light_exp = light[experiment - 1, :, :, :].copy()
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light_exp[(roi_darken + roi_control) < 0.5, :] = 0.0
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light_signal = light_exp.mean(axis=(0, 1))
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light_signal -= light_signal.min()
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light_signal /= light_signal.max()
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if remove_fit:
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a_exp = data[experiment - 1, :, :, skip_timesteps:].copy()
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else:
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a_exp = data[experiment - 1, :, :, :].copy()
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if remove_fit:
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combined_matrix = (roi_darken + roi_control) > 0
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idx = np.where(combined_matrix)
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for idx_pos in range(0, idx[0].shape[0]):
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temp = a_exp[idx[0][idx_pos], idx[1][idx_pos], :]
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temp -= temp.mean()
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data_time = np.arange(0, temp.shape[0], dtype=np.float32) + skip_timesteps
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data_time /= 100.0
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data_min = temp.min()
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data_max = temp.max()
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data_delta = data_max - data_min
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a_min = data_min - data_delta
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b_min = 0.01
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a_max = data_max + data_delta
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if fit_power:
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b_max = 10.0
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else:
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b_max = 100.0
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c_min = data_min - data_delta
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c_max = data_max + data_delta
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try:
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if fit_power:
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popt, _ = scipy.optimize.curve_fit(
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f=func_pow,
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xdata=data_time,
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ydata=np.nan_to_num(temp),
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bounds=([a_min, b_min, c_min], [a_max, b_max, c_max]),
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)
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pattern: np.ndarray | None = func_pow(data_time, *popt)
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else:
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popt, _ = scipy.optimize.curve_fit(
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f=func_exp,
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xdata=data_time,
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ydata=np.nan_to_num(temp),
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bounds=([a_min, b_min, c_min], [a_max, b_max, c_max]),
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)
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pattern = func_exp(data_time, *popt)
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assert pattern is not None
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pattern -= pattern.mean()
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scale = (temp * pattern).sum() / (pattern**2).sum()
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pattern *= scale
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except ValueError:
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print(f"Fit failed: Position ({idx[0][idx_pos]}, {idx[1][idx_pos]}")
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pattern = None
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if pattern is not None:
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temp -= pattern
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a_exp[idx[0][idx_pos], idx[1][idx_pos], :] = temp
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darken = a_exp[roi_darken > 0.5, :].sum(axis=0) / (roi_darken > 0.5).sum()
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lighten = a_exp[roi_control > 0.5, :].sum(axis=0) / (roi_control > 0.5).sum()
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light_signal *= darken.max() - darken.min()
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light_signal += darken.min()
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time_axis = np.arange(0, lighten.shape[-1], dtype=np.float32) + skip_timesteps
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time_axis /= 100.0
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plt.plot(time_axis, light_signal, c="k", label="light")
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plt.plot(time_axis, darken, label="sDarken")
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plt.plot(time_axis, lighten, label="control")
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plt.title(f"{config["mouse_identifier"]} -- Experiment: {experiment} ({experiment_names})")
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plt.legend()
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plt.show()
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if __name__ == "__main__":
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argh.dispatch_command(plot)
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