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David Rotermund 2024-02-29 01:54:55 +01:00 committed by GitHub
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commit 37d72264e5
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2 changed files with 150 additions and 62 deletions
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63
olivia_data_plotter.py Normal file
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@ -0,0 +1,63 @@
import numpy as np
import matplotlib.pyplot as plt
import os
from functions.create_logger import create_logger
from functions.load_config import load_config
from functions.get_trials import get_trials
import h5py # type: ignore
control_file_handle = h5py.File("ROI_control_49.mat", "r")
control_roi = (np.array(control_file_handle["roi"]).T) > 0
control_file_handle.close()
control_roi = control_roi.reshape((control_roi.shape[0] * control_roi.shape[1]))
s_darken_file_handle = h5py.File("ROI_sDarken_49.mat", "r")
s_darken_roi = (np.array(s_darken_file_handle["roi"]).T) > 0
s_darken_file_handle.close()
s_darken_roi = s_darken_roi.reshape((s_darken_roi.shape[0] * s_darken_roi.shape[1]))
mylogger = create_logger(
save_logging_messages=True, display_logging_messages=True, log_stage_name="test_xxx"
)
config = load_config(mylogger=mylogger)
experiment_id: int = 1
raw_data_path: str = os.path.join(
config["basic_path"],
config["recoding_data"],
config["mouse_identifier"],
config["raw_path"],
)
data_path: str = "output"
trails = get_trials(path=raw_data_path, experiment_id=experiment_id)
for i in range(0, trails.shape[0]):
trial_id = int(trails[i])
experiment_name: str = f"Exp{experiment_id:03d}_Trial{trial_id:03d}"
mylogger.info(f"Loading files for {experiment_name}")
data = np.load(os.path.join(data_path, f"{experiment_name}_ratio_sequence.npz"))
if i == 0:
ratio_sequence = data["ratio_sequence"]
else:
ratio_sequence += data["ratio_sequence"]
ratio_sequence /= float(trails.shape[0])
ratio_sequence = ratio_sequence.reshape(
(ratio_sequence.shape[0] * ratio_sequence.shape[1], ratio_sequence.shape[2])
)
control = ratio_sequence[control_roi, :].mean(axis=0)
s_darken = ratio_sequence[s_darken_roi, :].mean(axis=0)
t = np.arange(0, control.shape[0]) / 100.0
plt.plot(t, control, label="control")
plt.plot(t, s_darken, label="sDarken")
plt.legend()
plt.xlabel("Time [sec]")
plt.show()

149
stage_4_process.py
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@ -708,68 +708,77 @@ def process_trial(
mylogger.info("Move time dimensions of data to the last dimension") mylogger.info("Move time dimensions of data to the last dimension")
data = data.movedim(1, -1) data = data.movedim(1, -1)
mylogger.info("Regression Acceptor") dual_signal_mode: bool = True
mylogger.info(f"Target: {config['target_camera_acceptor']}") if len(config["target_camera_acceptor"]) > 0:
mylogger.info( mylogger.info("Regression Acceptor")
f"Regressors: constant, linear and {config['regressor_cameras_acceptor']}" mylogger.info(f"Target: {config['target_camera_acceptor']}")
) mylogger.info(
target_id: int = config["required_order"].index(config["target_camera_acceptor"]) f"Regressors: constant, linear and {config['regressor_cameras_acceptor']}"
regressor_id: list[int] = [] )
for i in range(0, len(config["regressor_cameras_acceptor"])): target_id: int = config["required_order"].index(
regressor_id.append( config["target_camera_acceptor"]
config["required_order"].index(config["regressor_cameras_acceptor"][i]) )
regressor_id: list[int] = []
for i in range(0, len(config["regressor_cameras_acceptor"])):
regressor_id.append(
config["required_order"].index(config["regressor_cameras_acceptor"][i])
)
data_acceptor, coefficients_acceptor = regression(
mylogger=mylogger,
target_camera_id=target_id,
regressor_camera_ids=regressor_id,
mask=mask_negative,
data=data,
data_filtered=data_filtered,
first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
) )
data_acceptor, coefficients_acceptor = regression( if config["save_regression_coefficients"]:
mylogger=mylogger, temp_path = os.path.join(
target_camera_id=target_id, config["export_path"], experiment_name + "_coefficients_acceptor.npy"
regressor_camera_ids=regressor_id, )
mask=mask_negative, mylogger.info(f"Save acceptor coefficients to {temp_path}")
data=data, np.save(temp_path, coefficients_acceptor.cpu())
data_filtered=data_filtered, del coefficients_acceptor
first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
)
if config["save_regression_coefficients"]: mylogger.info("-==- Done -==-")
temp_path = os.path.join( else:
config["export_path"], experiment_name + "_coefficients_acceptor.npy" dual_signal_mode = False
if len(config["target_camera_donor"]) > 0:
mylogger.info("Regression Donor")
mylogger.info(f"Target: {config['target_camera_donor']}")
mylogger.info(
f"Regressors: constant, linear and {config['regressor_cameras_donor']}"
) )
mylogger.info(f"Save acceptor coefficients to {temp_path}") target_id = config["required_order"].index(config["target_camera_donor"])
np.save(temp_path, coefficients_acceptor.cpu()) regressor_id = []
del coefficients_acceptor for i in range(0, len(config["regressor_cameras_donor"])):
regressor_id.append(
config["required_order"].index(config["regressor_cameras_donor"][i])
)
mylogger.info("-==- Done -==-") data_donor, coefficients_donor = regression(
mylogger=mylogger,
mylogger.info("Regression Donor") target_camera_id=target_id,
mylogger.info(f"Target: {config['target_camera_donor']}") regressor_camera_ids=regressor_id,
mylogger.info( mask=mask_negative,
f"Regressors: constant, linear and {config['regressor_cameras_donor']}" data=data,
) data_filtered=data_filtered,
target_id = config["required_order"].index(config["target_camera_donor"]) first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
regressor_id = []
for i in range(0, len(config["regressor_cameras_donor"])):
regressor_id.append(
config["required_order"].index(config["regressor_cameras_donor"][i])
) )
data_donor, coefficients_donor = regression( if config["save_regression_coefficients"]:
mylogger=mylogger, temp_path = os.path.join(
target_camera_id=target_id, config["export_path"], experiment_name + "_coefficients_donor.npy"
regressor_camera_ids=regressor_id, )
mask=mask_negative, mylogger.info(f"Save acceptor donor to {temp_path}")
data=data, np.save(temp_path, coefficients_donor.cpu())
data_filtered=data_filtered, del coefficients_donor
first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]), mylogger.info("-==- Done -==-")
) else:
dual_signal_mode = False
if config["save_regression_coefficients"]:
temp_path = os.path.join(
config["export_path"], experiment_name + "_coefficients_donor.npy"
)
mylogger.info(f"Save acceptor donor to {temp_path}")
np.save(temp_path, coefficients_donor.cpu())
del coefficients_donor
mylogger.info("-==- Done -==-")
del data del data
del data_filtered del data_filtered
@ -783,14 +792,21 @@ def process_trial(
mylogger.info(f"CUDA memory: {free_mem//1024} MByte") mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
mylogger.info("Calculate ratio sequence") mylogger.info("Calculate ratio sequence")
if config["classical_ratio_mode"]: if dual_signal_mode:
mylogger.info("via acceptor / donor") if config["classical_ratio_mode"]:
ratio_sequence: torch.Tensor = data_acceptor / data_donor mylogger.info("via acceptor / donor")
mylogger.info("via / mean over time") ratio_sequence: torch.Tensor = data_acceptor / data_donor
ratio_sequence /= ratio_sequence.mean(dim=-1, keepdim=True) mylogger.info("via / mean over time")
ratio_sequence /= ratio_sequence.mean(dim=-1, keepdim=True)
else:
mylogger.info("via 1.0 + acceptor - donor")
ratio_sequence = 1.0 + data_acceptor - data_donor
else: else:
mylogger.info("via 1.0 + acceptor - donor") mylogger.info("mono signal model")
ratio_sequence = 1.0 + data_acceptor - data_donor if len(config["target_camera_donor"]) > 0:
ratio_sequence = data_donor.clone()
else:
ratio_sequence = data_acceptor.clone()
mylogger.info("Remove nan") mylogger.info("Remove nan")
ratio_sequence = torch.nan_to_num(ratio_sequence, nan=0.0) ratio_sequence = torch.nan_to_num(ratio_sequence, nan=0.0)
@ -884,6 +900,15 @@ if (config["save_as_python"] is False) and (config["save_as_matlab"] is False):
mylogger.info("ERROR: STOP!!!") mylogger.info("ERROR: STOP!!!")
exit() exit()
if (len(config["target_camera_donor"]) == 0) and (
len(config["target_camera_acceptor"]) == 0
):
mylogger.info(
"Configure at least target_camera_donor or target_camera_acceptor correctly."
)
mylogger.info("ERROR: STOP!!!")
exit()
device = get_torch_device(mylogger, config["force_to_cpu"]) device = get_torch_device(mylogger, config["force_to_cpu"])
mylogger.info(f"Create directory {config['export_path']} in the case it does not exist") mylogger.info(f"Create directory {config['export_path']} in the case it does not exist")