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David Rotermund 2024-03-01 15:54:57 +01:00 committed by GitHub
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3 changed files with 347 additions and 13 deletions
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34
doit.sh Normal file
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@ -0,0 +1,34 @@
config="config_M_Sert_Cre_49.json"
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 1 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 1 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 2 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 2 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 3 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 3 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 4 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 4 -c $config
config="config_M_Sert_Cre_41.json"
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 1 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 1 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 2 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 2 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 3 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 3 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 4 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 4 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 5 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 5 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter.py -p -e 6 -c $config
/data_1/davrot/P3.11/bin/python3 olivia_data_plotter_svd.py -p -e 6 -c $config

150
olivia_data_plotter.py
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@ -6,16 +6,27 @@ from functions.load_config import load_config
from functions.get_trials import get_trials
import h5py # type: ignore
import torch
import scipy # type: ignore
import argh
from functions.data_raw_loader import data_raw_loader
def main(*, experiment_id: int = 1, config_filename: str = "config.json") -> None:
def main(
*,
experiment_id: int = 4,
config_filename: str = "config.json",
highpass_freqency: float = 0.5,
lowpass_freqency: float = 10.0,
butter_worth_order: int = 4,
log_stage_name: str = "olivia",
plot_show: bool = True,
) -> None:
mylogger = create_logger(
save_logging_messages=True,
display_logging_messages=True,
log_stage_name="test_xxx",
log_stage_name=log_stage_name,
)
config = load_config(mylogger=mylogger, filename=config_filename)
@ -61,12 +72,141 @@ def main(*, experiment_id: int = 1, config_filename: str = "config.json") -> Non
control = ratio_sequence[control_roi, :].mean(axis=0)
s_darken = ratio_sequence[s_darken_roi, :].mean(axis=0)
max_value = max(
[
control[config["skip_frames_in_the_beginning"] :].max(),
s_darken[config["skip_frames_in_the_beginning"] :].max(),
]
)
min_value = min(
[
control[config["skip_frames_in_the_beginning"] :].min(),
s_darken[config["skip_frames_in_the_beginning"] :].min(),
]
)
first_trial_id: int = int(get_trials(raw_data_path, experiment_id).min())
(
meta_channels,
meta_mouse_markings,
meta_recording_date,
meta_stimulation_times,
meta_experiment_names,
meta_trial_recording_duration,
meta_frame_time,
meta_mouse,
data,
) = data_raw_loader(
raw_data_path=raw_data_path,
mylogger=mylogger,
experiment_id=experiment_id,
trial_id=first_trial_id,
device=torch.device("cpu"),
force_to_cpu_memory=True,
config=config,
)
idx = config["required_order"].index("acceptor")
acceptor = data[..., idx].mean(axis=0).mean(axis=0)
acceptor -= acceptor[config["skip_frames_in_the_beginning"] :].min()
acceptor /= acceptor[config["skip_frames_in_the_beginning"] :].max()
acceptor_f0 = acceptor.clone()
acceptor_f0 *= max_value - min_value
acceptor_f0 += min_value
b, a = scipy.signal.butter(
butter_worth_order,
lowpass_freqency,
btype="low",
output="ba",
fs=1.0 / meta_frame_time,
)
control_f1 = scipy.signal.filtfilt(b, a, control)
s_darken_f1 = scipy.signal.filtfilt(b, a, s_darken)
b, a = scipy.signal.butter(
butter_worth_order,
highpass_freqency,
btype="high",
output="ba",
fs=1.0 / meta_frame_time,
)
control_f1 = scipy.signal.filtfilt(b, a, control_f1)
s_darken_f1 = scipy.signal.filtfilt(b, a, s_darken_f1)
max_value = max(
[
control_f1[config["skip_frames_in_the_beginning"] :].max(),
s_darken_f1[config["skip_frames_in_the_beginning"] :].max(),
]
)
min_value = min(
[
control_f1[config["skip_frames_in_the_beginning"] :].min(),
s_darken_f1[config["skip_frames_in_the_beginning"] :].min(),
]
)
acceptor_f1 = acceptor.clone()
acceptor_f1 *= max_value - min_value
acceptor_f1 += min_value
t = np.arange(0, control.shape[0]) / 100.0
plt.plot(t, control, label="control")
plt.plot(t, s_darken, label="sDarken")
plt.figure(figsize=(10, 10))
plt.subplot(2, 1, 1)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
acceptor_f0[config["skip_frames_in_the_beginning"] :],
color=(0.5, 0.5, 0.5),
label="light (acceptor)",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
control[config["skip_frames_in_the_beginning"] :],
label="control",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
s_darken[config["skip_frames_in_the_beginning"] :],
label="sDarken",
)
plt.title(
f"Experiment {experiment_id} {config['recoding_data']} {config['mouse_identifier']}"
)
plt.legend()
plt.xlabel("Time [sec]")
plt.subplot(2, 1, 2)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
acceptor_f1[config["skip_frames_in_the_beginning"] :],
color=(0.5, 0.5, 0.5),
label="light (acceptor)",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
control_f1[config["skip_frames_in_the_beginning"] :],
label=f"control ({highpass_freqency}Hz - {lowpass_freqency}Hz)",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
s_darken_f1[config["skip_frames_in_the_beginning"] :],
label=f"sDarken ({highpass_freqency}Hz - {lowpass_freqency}Hz)",
)
plt.legend()
plt.xlabel("Time [sec]")
plt.savefig(
f"olivia_both_Exp{experiment_id}_{config['recoding_data']}_{config['mouse_identifier']}.png",
dpi=300,
)
if plot_show:
plt.show()

172
olivia_data_plotter_svd.py
View file

@ -7,16 +7,73 @@ from functions.load_config import load_config
from functions.get_trials import get_trials
import h5py # type: ignore
import torch
import scipy # type: ignore
import argh
from functions.data_raw_loader import data_raw_loader
# def func(x, a, b, c, dt):
# return a * (x - dt) ** b + c
def main(*, experiment_id: int = 1, config_filename: str = "config.json") -> None:
# def fit(
# ratio_sequence: np.ndarray,
# t: np.ndarray,
# config: dict,
# ) -> tuple[np.ndarray, np.ndarray]:
# data_min = ratio_sequence[config["skip_frames_in_the_beginning"] :].min()
# data_max = ratio_sequence[config["skip_frames_in_the_beginning"] :].max()
# b_min = 1.0
# b_max = 3.0
# temp_1 = max([abs(data_min), abs(data_max)])
# a_min = -temp_1 - 2 * abs(data_max - data_min)
# a_max = +temp_1 + 2 * abs(data_max - data_min)
# try:
# popt, _ = scipy.optimize.curve_fit(
# f=func,
# xdata=t[config["skip_frames_in_the_beginning"] :],
# ydata=np.nan_to_num(
# ratio_sequence[config["skip_frames_in_the_beginning"] :]
# ),
# bounds=([a_min, b_min, a_min, -t[-1]], [a_max, b_max, a_max, t[-1]]),
# )
# a: float | None = float(popt[0])
# b: float | None = float(popt[1])
# c: float | None = float(popt[2])
# dt: float | None = float(popt[3])
# except ValueError:
# a = None
# b = None
# c = None
# dt = None
# print(a, b, c, dt)
# f1 = func(t, a, b, c, dt)
# ratio_sequence_f1 = ratio_sequence - f1
# return ratio_sequence_f1, f1
def main(
*,
experiment_id: int = 4,
config_filename: str = "config.json",
highpass_freqency: float = 0.5,
lowpass_freqency: float = 10.0,
butter_worth_order: int = 4,
log_stage_name: str = "olivia_svd",
scale_before_substraction: bool = True,
plot_show: bool = True,
) -> None:
mylogger = create_logger(
save_logging_messages=True,
display_logging_messages=True,
log_stage_name="test_xxx",
log_stage_name=log_stage_name,
)
config = load_config(mylogger=mylogger, filename=config_filename)
@ -58,10 +115,18 @@ def main(*, experiment_id: int = 1, config_filename: str = "config.json") -> Non
rs_s_core, _, _ = torch.linalg.svd(torch.tensor(rs_s.T), full_matrices=False)
rs_s_core = rs_s_core[:, 0].numpy()
rs_s_core -= rs_s_core.mean(keepdims=True)
rs_c_core -= rs_c_core.mean(keepdims=True)
rs_s_core -= rs_s_core[config["skip_frames_in_the_beginning"] :].mean(
keepdims=True
)
rs_c_core -= rs_c_core[config["skip_frames_in_the_beginning"] :].mean(
keepdims=True
)
rs_c_core *= (rs_s_core * rs_c_core).sum() / (rs_c_core**2).sum()
if scale_before_substraction:
rs_c_core *= (
rs_s_core[config["skip_frames_in_the_beginning"] :]
* rs_c_core[config["skip_frames_in_the_beginning"] :]
).sum() / (rs_c_core[config["skip_frames_in_the_beginning"] :] ** 2).sum()
if i == 0:
ratio_sequence = rs_s_core - rs_c_core
@ -72,9 +137,104 @@ def main(*, experiment_id: int = 1, config_filename: str = "config.json") -> Non
t = np.arange(0, ratio_sequence.shape[0]) / 100.0
plt.plot(t, ratio_sequence, label="sDarken - control")
# ratio_sequence_f1, f1 = fit(
# ratio_sequence=ratio_sequence,
# t=t,
# config=config,
# )
first_trial_id: int = int(get_trials(raw_data_path, experiment_id).min())
(
meta_channels,
meta_mouse_markings,
meta_recording_date,
meta_stimulation_times,
meta_experiment_names,
meta_trial_recording_duration,
meta_frame_time,
meta_mouse,
data,
) = data_raw_loader(
raw_data_path=raw_data_path,
mylogger=mylogger,
experiment_id=experiment_id,
trial_id=first_trial_id,
device=torch.device("cpu"),
force_to_cpu_memory=True,
config=config,
)
b, a = scipy.signal.butter(
butter_worth_order,
lowpass_freqency,
btype="low",
output="ba",
fs=1.0 / meta_frame_time,
)
ratio_sequence_f1 = scipy.signal.filtfilt(b, a, ratio_sequence)
b, a = scipy.signal.butter(
butter_worth_order,
highpass_freqency,
btype="high",
output="ba",
fs=1.0 / meta_frame_time,
)
ratio_sequence_f2 = scipy.signal.filtfilt(b, a, ratio_sequence_f1)
idx = config["required_order"].index("acceptor")
acceptor = data[..., idx].mean(axis=0).mean(axis=0)
acceptor -= acceptor[config["skip_frames_in_the_beginning"] :].min()
acceptor /= acceptor[config["skip_frames_in_the_beginning"] :].max()
acceptor *= (
ratio_sequence_f2[config["skip_frames_in_the_beginning"] :].max()
- ratio_sequence_f2[config["skip_frames_in_the_beginning"] :].min()
)
acceptor += ratio_sequence_f2[config["skip_frames_in_the_beginning"] :].min()
plt.figure(figsize=(10, 10))
plt.subplot(2, 1, 1)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
ratio_sequence[config["skip_frames_in_the_beginning"] :],
label=f"sDarken - control (scaled:{scale_before_substraction})",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
ratio_sequence_f1[config["skip_frames_in_the_beginning"] :],
label=f"low pass {lowpass_freqency} Hz",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
ratio_sequence_f2[config["skip_frames_in_the_beginning"] :],
label=f"high pass {highpass_freqency} Hz",
)
plt.xlabel("Time [sec]")
plt.title(
f"Experiment {experiment_id} {config['recoding_data']} {config['mouse_identifier']}"
)
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
acceptor[config["skip_frames_in_the_beginning"] :],
color=(0.5, 0.5, 0.5),
label="light (acceptor)",
)
plt.plot(
t[config["skip_frames_in_the_beginning"] :],
ratio_sequence_f2[config["skip_frames_in_the_beginning"] :],
label=f"high pass {highpass_freqency} Hz",
)
plt.legend()
plt.xlabel("Time [sec]")
plt.savefig(
f"olivia_Exp{experiment_id}_{config['recoding_data']}_{config['mouse_identifier']}.png",
dpi=300,
)
if plot_show:
plt.show()