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David Rotermund 2024-08-12 12:03:43 +02:00 committed by GitHub
parent a053ef3935
commit cf191be8e7
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2 changed files with 106 additions and 7 deletions
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108
geci_loader.py
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@ -5,10 +5,26 @@ from jsmin import jsmin # type:ignore
import argh import argh
from functions.get_trials import get_trials from functions.get_trials import get_trials
from functions.get_experiments import get_experiments 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( def loader(
filename: str = "config_M_Sert_Cre_49.json", fpath: str = "/data_1/hendrik/gevi" 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: ) -> None:
if os.path.isfile(filename) is False: if os.path.isfile(filename) is False:
@ -25,9 +41,26 @@ def loader(
config["raw_path"], 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() experiments = get_experiments(raw_data_path).numpy()
n_exp = experiments.shape[0] n_exp = experiments.shape[0]
first_run: bool = True
for i_exp in range(0, n_exp): for i_exp in range(0, n_exp):
trials = get_trials(raw_data_path, experiments[i_exp]).numpy() trials = get_trials(raw_data_path, experiments[i_exp]).numpy()
n_tri = trials.shape[0] n_tri = trials.shape[0]
@ -46,18 +79,83 @@ def loader(
tmp = np.load(tmp_fname) tmp = np.load(tmp_fname)
tmp_data_sequence = tmp["data_donor"] tmp_data_sequence = tmp["data_donor"]
tmp_data_sequence = tmp_data_sequence[:, :, skip_timesteps:]
tmp_light_signal = tmp["data_acceptor"] tmp_light_signal = tmp["data_acceptor"]
tmp_light_signal = tmp_light_signal[:, :, skip_timesteps:]
if (i_exp == 0) and (i_tri == 0): if first_run:
mask = tmp["mask"] mask = tmp["mask"]
new_shape = [n_exp, *tmp_data_sequence.shape] new_shape = [n_exp, *tmp_data_sequence.shape]
data_sequence = np.zeros(new_shape) data_sequence = np.zeros(new_shape)
light_signal = np.zeros(new_shape) light_signal = np.zeros(new_shape)
first_run = False
# Here you might want to use the exp fit and removal... if remove_fit:
data_sequence[i_exp] += tmp_data_sequence / n_tri roi_control *= mask
light_signal[i_exp] += tmp_light_signal / n_tri 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("dsq_" + config["mouse_identifier"], data_sequence)
np.save("lsq_" + config["mouse_identifier"], light_signal) np.save("lsq_" + config["mouse_identifier"], light_signal)
np.save("msq_" + config["mouse_identifier"], mask) np.save("msq_" + config["mouse_identifier"], mask)

5
geci_plot.py
View file

@ -19,11 +19,11 @@ def func_exp(x, a, b, c):
def plot( def plot(
filename: str = "config_M_Sert_Cre_49.json", filename: str = "config_M_Sert_Cre_49.json",
experiment: int = 4, experiment: int = 4,
skip_timesteps: int = 100, skip_timesteps: int = 0,
# If there is no special ROI... Get one! This is just a backup # 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_control_path_default: str = "roi_controlM_Sert_Cre_49.npy",
roi_sdarken_path_default: str = "roi_sdarkenM_Sert_Cre_49.npy", roi_sdarken_path_default: str = "roi_sdarkenM_Sert_Cre_49.npy",
remove_fit: bool = True, remove_fit: bool = False,
fit_power: bool = False, # True => -ax^b ; False => exp(-b) fit_power: bool = False, # True => -ax^b ; False => exp(-b)
) -> None: ) -> None:
@ -154,6 +154,7 @@ def plot(
if pattern is not None: if pattern is not None:
temp -= pattern temp -= pattern
a_exp[idx[0][idx_pos], idx[1][idx_pos], :] = temp
darken = a_exp[roi_darken > 0.5, :].sum(axis=0) / (roi_darken > 0.5).sum() darken = a_exp[roi_darken > 0.5, :].sum(axis=0) / (roi_darken > 0.5).sum()
lighten = a_exp[roi_control > 0.5, :].sum(axis=0) / (roi_control > 0.5).sum() lighten = a_exp[roi_control > 0.5, :].sum(axis=0) / (roi_control > 0.5).sum()