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geci_loader.py
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geci_loader.py
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import numpy as np
import os
import json
from jsmin import jsmin # type:ignore
import argh
from functions.get_trials import get_trials
from functions.get_experiments import get_experiments
import scipy # type: ignore
def func_pow(x, a, b, c):
return -a * x**b + c
def func_exp(x, a, b, c):
return a * np.exp(-x / b) + c
def loader(
filename: str = "config_M_Sert_Cre_49.json",
fpath: str = "/data_1/hendrik/gevi",
skip_timesteps: int = 100,
# If there is no special ROI... Get one! This is just a backup
roi_control_path_default: str = "roi_controlM_Sert_Cre_49.npy",
roi_sdarken_path_default: str = "roi_sdarkenM_Sert_Cre_49.npy",
remove_fit: bool = True,
fit_power: bool = False, # True => -ax^b ; False => exp(-b)
) -> None:
if os.path.isfile(filename) is False:
print(f"{filename} is missing")
exit()
with open(filename, "r") as file:
config = json.loads(jsmin(file.read()))
raw_data_path: str = os.path.join(
config["basic_path"],
config["recoding_data"],
config["mouse_identifier"],
config["raw_path"],
)
if remove_fit:
roi_control_path: str = f"roi_control{config["mouse_identifier"]}.npy"
roi_sdarken_path: str = f"roi_sdarken{config["mouse_identifier"]}.npy"
if os.path.isfile(roi_control_path) is False:
print(f"Using replacement RIO: {roi_control_path_default}")
roi_control_path = roi_control_path_default
if os.path.isfile(roi_sdarken_path) is False:
print(f"Using replacement RIO: {roi_sdarken_path_default}")
roi_sdarken_path = roi_sdarken_path_default
roi_control: np.ndarray = np.load(roi_control_path)
roi_darken: np.ndarray = np.load(roi_sdarken_path)
experiments = get_experiments(raw_data_path).numpy()
n_exp = experiments.shape[0]
first_run: bool = True
for i_exp in range(0, n_exp):
trials = get_trials(raw_data_path, experiments[i_exp]).numpy()
n_tri = trials.shape[0]
for i_tri in range(0, n_tri):
experiment_name: str = (
f"Exp{experiments[i_exp]:03d}_Trial{trials[i_tri]:03d}"
)
tmp_fname = os.path.join(
fpath,
"output_" + config["mouse_identifier"],
experiment_name + "_acceptor_donor.npz",
)
print(f'Processing file "{tmp_fname}"...')
tmp = np.load(tmp_fname)
tmp_data_sequence = tmp["data_donor"]
tmp_data_sequence = tmp_data_sequence[:, :, skip_timesteps:]
tmp_light_signal = tmp["data_acceptor"]
tmp_light_signal = tmp_light_signal[:, :, skip_timesteps:]
if first_run:
mask = tmp["mask"]
new_shape = [n_exp, *tmp_data_sequence.shape]
data_sequence = np.zeros(new_shape)
light_signal = np.zeros(new_shape)
first_run = False
if remove_fit:
roi_control *= mask
assert roi_control.sum() > 0, "ROI control empty"
roi_darken *= mask
assert roi_darken.sum() > 0, "ROI sDarken empty"
if remove_fit:
combined_matrix = (roi_darken + roi_control) > 0
idx = np.where(combined_matrix)
for idx_pos in range(0, idx[0].shape[0]):
temp = tmp_data_sequence[idx[0][idx_pos], idx[1][idx_pos], :]
temp -= temp.mean()
data_time = np.arange(0, temp.shape[0], dtype=np.float32) + skip_timesteps
data_time /= 100.0
data_min = temp.min()
data_max = temp.max()
data_delta = data_max - data_min
a_min = data_min - data_delta
b_min = 0.01
a_max = data_max + data_delta
if fit_power:
b_max = 10.0
else:
b_max = 100.0
c_min = data_min - data_delta
c_max = data_max + data_delta
try:
if fit_power:
popt, _ = scipy.optimize.curve_fit(
f=func_pow,
xdata=data_time,
ydata=np.nan_to_num(temp),
bounds=([a_min, b_min, c_min], [a_max, b_max, c_max]),
)
pattern: np.ndarray | None = func_pow(data_time, *popt)
else:
popt, _ = scipy.optimize.curve_fit(
f=func_exp,
xdata=data_time,
ydata=np.nan_to_num(temp),
bounds=([a_min, b_min, c_min], [a_max, b_max, c_max]),
)
pattern = func_exp(data_time, *popt)
assert pattern is not None
pattern -= pattern.mean()
scale = (temp * pattern).sum() / (pattern**2).sum()
pattern *= scale
except ValueError:
print(f"Fit failed: Position ({idx[0][idx_pos]}, {idx[1][idx_pos]}")
pattern = None
if pattern is not None:
temp -= pattern
tmp_data_sequence[idx[0][idx_pos], idx[1][idx_pos], :] = temp
data_sequence[i_exp] += tmp_data_sequence
light_signal[i_exp] += tmp_light_signal
data_sequence[i_exp] /= n_tri
light_signal[i_exp] /= n_tri
np.save("dsq_" + config["mouse_identifier"], data_sequence)
np.save("lsq_" + config["mouse_identifier"], light_signal)
np.save("msq_" + config["mouse_identifier"], mask)
if __name__ == "__main__":
argh.dispatch_command(loader)