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bloergl
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3 changed files with 100 additions and 93 deletions
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@ -18,7 +18,7 @@ def func_exp(x, a, b, c):
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def loader(
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filename: str = "config_M_Sert_Cre_49.json",
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fpath: str = "/data_1/hendrik/gevi",
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fpath: str|None = None,
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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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@ -27,6 +27,9 @@ def loader(
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fit_power: bool = False, # True => -ax^b ; False => exp(-b)
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) -> None:
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if fpath is None:
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fpath = os.getcwd()
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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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@ -42,8 +45,8 @@ def loader(
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)
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if remove_fit:
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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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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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@ -72,7 +75,7 @@ def loader(
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)
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tmp_fname = os.path.join(
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fpath,
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"output_" + config["mouse_identifier"],
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config["export_path"],
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experiment_name + "_acceptor_donor.npz",
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)
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print(f'Processing file "{tmp_fname}"...')
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@ -156,9 +159,9 @@ def loader(
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light_signal[i_exp] += tmp_light_signal
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data_sequence[i_exp] /= n_tri
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light_signal[i_exp] /= n_tri
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np.save("dsq_" + config["mouse_identifier"], data_sequence)
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np.save("lsq_" + config["mouse_identifier"], light_signal)
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np.save("msq_" + config["mouse_identifier"], mask)
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np.save(os.path.join(fpath, config["export_path"], "dsq_" + config["mouse_identifier"]), data_sequence)
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np.save(os.path.join(fpath, config["export_path"], "lsq_" + config["mouse_identifier"]), light_signal)
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np.save(os.path.join(fpath, config["export_path"], "msq_" + config["mouse_identifier"]), mask)
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if __name__ == "__main__":
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36
geci_plot.py
36
geci_plot.py
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@ -1,3 +1,5 @@
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# %%
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import numpy as np
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import matplotlib.pyplot as plt
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import argh
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@ -18,15 +20,16 @@ def func_exp(x, a, 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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fpath: str | None = None,
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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 fpath is None:
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fpath = os.getcwd()
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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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@ -45,30 +48,25 @@ def plot(
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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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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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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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assert os.path.isfile(roi_control_path)
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assert os.path.isfile(roi_sdarken_path)
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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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data = np.load(os.path.join(fpath, config["export_path"], "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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light = np.load(os.path.join(fpath, config["export_path"], "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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mask = np.load(os.path.join(fpath, config["export_path"], "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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@ -82,14 +80,14 @@ def plot(
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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.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.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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@ -174,7 +172,7 @@ def plot(
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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.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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@ -1,3 +1,5 @@
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#%%
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import numpy as np
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import torch
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import torchvision as tv # type: ignore
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@ -568,84 +570,88 @@ def process_trial(
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data -= heartbeat_coefficients.unsqueeze(1) * volume_heartbeat.unsqueeze(
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0
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).movedim(-1, 1)
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# data_herzlos = data.clone()
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mylogger.info("-==- Done -==-")
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donor_heartbeat_factor = heartbeat_coefficients[donor_index, ...].clone()
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acceptor_heartbeat_factor = heartbeat_coefficients[acceptor_index, ...].clone()
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del heartbeat_coefficients
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if config["gevi"]: # UDO scaling performed!
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donor_heartbeat_factor = heartbeat_coefficients[donor_index, ...].clone()
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acceptor_heartbeat_factor = heartbeat_coefficients[acceptor_index, ...].clone()
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del heartbeat_coefficients
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if device != torch.device("cpu"):
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torch.cuda.empty_cache()
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mylogger.info("Empty CUDA cache")
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free_mem = cuda_total_memory - max(
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[
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torch.cuda.memory_reserved(device),
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torch.cuda.memory_allocated(device),
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]
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)
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mylogger.info(f"CUDA memory: {free_mem // 1024} MByte")
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if device != torch.device("cpu"):
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torch.cuda.empty_cache()
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mylogger.info("Empty CUDA cache")
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free_mem = cuda_total_memory - max(
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[
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torch.cuda.memory_reserved(device),
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torch.cuda.memory_allocated(device),
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]
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)
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mylogger.info(f"CUDA memory: {free_mem // 1024} MByte")
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mylogger.info("Calculate scaling factor for donor and acceptor")
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donor_factor: torch.Tensor = (
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donor_heartbeat_factor + acceptor_heartbeat_factor
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) / (2 * donor_heartbeat_factor)
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acceptor_factor: torch.Tensor = (
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donor_heartbeat_factor + acceptor_heartbeat_factor
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) / (2 * acceptor_heartbeat_factor)
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mylogger.info("Calculate scaling factor for donor and acceptor")
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donor_factor: torch.Tensor = (
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donor_heartbeat_factor + acceptor_heartbeat_factor
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) / (2 * donor_heartbeat_factor)
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acceptor_factor: torch.Tensor = (
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donor_heartbeat_factor + acceptor_heartbeat_factor
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) / (2 * acceptor_heartbeat_factor)
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del donor_heartbeat_factor
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del acceptor_heartbeat_factor
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del donor_heartbeat_factor
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del acceptor_heartbeat_factor
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if config["save_factors"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_donor_factor.npy"
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)
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mylogger.info(f"Save donor factor to {temp_path}")
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np.save(temp_path, donor_factor.cpu())
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if config["save_factors"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_donor_factor.npy"
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)
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mylogger.info(f"Save donor factor to {temp_path}")
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np.save(temp_path, donor_factor.cpu())
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_acceptor_factor.npy"
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)
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mylogger.info(f"Save acceptor factor to {temp_path}")
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np.save(temp_path, acceptor_factor.cpu())
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mylogger.info("-==- Done -==-")
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_acceptor_factor.npy"
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)
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mylogger.info(f"Save acceptor factor to {temp_path}")
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np.save(temp_path, acceptor_factor.cpu())
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mylogger.info("-==- Done -==-")
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mylogger.info("Scale acceptor to heart beat amplitude")
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mylogger.info("Calculate mean")
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mean_values_acceptor = data[
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acceptor_index, config["skip_frames_in_the_beginning"] :, ...
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].nanmean(dim=0, keepdim=True)
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mylogger.info("Scale acceptor to heart beat amplitude")
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mylogger.info("Calculate mean")
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mean_values_acceptor = data[
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acceptor_index, config["skip_frames_in_the_beginning"] :, ...
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].nanmean(dim=0, keepdim=True)
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mylogger.info("Remove mean")
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data[acceptor_index, ...] -= mean_values_acceptor
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mylogger.info("Apply acceptor_factor and mask")
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data[acceptor_index, ...] *= acceptor_factor.unsqueeze(
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0
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) * mask_positve.unsqueeze(0)
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mylogger.info("Add mean")
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data[acceptor_index, ...] += mean_values_acceptor
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mylogger.info("-==- Done -==-")
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mylogger.info("Remove mean")
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data[acceptor_index, ...] -= mean_values_acceptor
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mylogger.info("Apply acceptor_factor and mask")
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data[acceptor_index, ...] *= acceptor_factor.unsqueeze(
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0
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) * mask_positve.unsqueeze(0)
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mylogger.info("Add mean")
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data[acceptor_index, ...] += mean_values_acceptor
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mylogger.info("-==- Done -==-")
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mylogger.info("Scale donor to heart beat amplitude")
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mylogger.info("Calculate mean")
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mean_values_donor = data[
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donor_index, config["skip_frames_in_the_beginning"] :, ...
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].nanmean(dim=0, keepdim=True)
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mylogger.info("Remove mean")
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data[donor_index, ...] -= mean_values_donor
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mylogger.info("Apply donor_factor and mask")
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data[donor_index, ...] *= donor_factor.unsqueeze(0) * mask_positve.unsqueeze(0)
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mylogger.info("Add mean")
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data[donor_index, ...] += mean_values_donor
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mylogger.info("-==- Done -==-")
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mylogger.info("Scale donor to heart beat amplitude")
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mylogger.info("Calculate mean")
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mean_values_donor = data[
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donor_index, config["skip_frames_in_the_beginning"] :, ...
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].nanmean(dim=0, keepdim=True)
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mylogger.info("Remove mean")
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data[donor_index, ...] -= mean_values_donor
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mylogger.info("Apply donor_factor and mask")
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data[donor_index, ...] *= donor_factor.unsqueeze(0) * mask_positve.unsqueeze(0)
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mylogger.info("Add mean")
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data[donor_index, ...] += mean_values_donor
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mylogger.info("-==- Done -==-")
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else:
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mylogger.info("GECI does not require acceptor/donor scaling, skipping!")
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mylogger.info("-==- Done -==-")
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mylogger.info("Divide by mean over time")
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data /= data[:, config["skip_frames_in_the_beginning"] :, ...].nanmean(
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dim=1,
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keepdim=True,
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)
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mylogger.info("-==- Done -==-")
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mylogger.info("Divide by mean over time")
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data /= data[:, config["skip_frames_in_the_beginning"] :, ...].nanmean(
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dim=1,
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keepdim=True,
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)
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mylogger.info("-==- Done -==-")
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data = data.nan_to_num(nan=0.0)
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mylogger.info("Preparation for regression -- Gauss smear")
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