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66
callum_config_M0134M.json
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66
callum_config_M0134M.json
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{
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"basic_path": "/data_1/fatma/GEVI/",
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"recoding_data": "2024-11-18",
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"mouse_identifier": "M0134M_SessionA",
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"raw_path": "raw",
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"export_path": "output_M0134M_SessionA",
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"ref_image_path": "ref_images_M0134M_SessionA",
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"raw_path": "raw",
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"heartbeat_remove": true,
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"gevi": true, // true => gevi, false => geci
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// Ratio Sequence
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"classical_ratio_mode": true, // true: a/d false: 1+a-d
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// Regression
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"target_camera_acceptor": "acceptor",
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"regressor_cameras_acceptor": [
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"oxygenation",
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"volume"
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],
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"target_camera_donor": "donor",
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"regressor_cameras_donor": [
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"oxygenation",
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"volume"
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],
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// binning
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"binning_enable": true,
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"binning_at_the_end": false,
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"binning_kernel_size": 4,
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"binning_stride": 4,
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"binning_divisor_override": 1,
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// alignment
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"alignment_batch_size": 200,
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"rotation_stabilization_threshold_factor": 3.0, // >= 1.0
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"rotation_stabilization_threshold_border": 0.9, // <= 1.0
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// Heart beat detection
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"lower_freqency_bandpass": 5.0, // Hz
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"upper_freqency_bandpass": 14.0, // Hz
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"heartbeat_filtfilt_chuck_size": 10,
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// Gauss smear
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"gauss_smear_spatial_width": 8,
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"gauss_smear_temporal_width": 0.1,
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"gauss_smear_use_matlab_mask": false,
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// LED Ramp on
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"skip_frames_in_the_beginning": 100, // Frames
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// PyTorch
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"dtype": "float32",
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"force_to_cpu": false,
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// Save
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"save_as_python": true, // produces .npz files (compressed)
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"save_as_matlab": false, // produces .hd5 file (compressed)
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// Save extra information
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"save_alignment": false,
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"save_heartbeat": false,
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"save_factors": false,
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"save_regression_coefficients": false,
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"save_aligned_as_python": false,
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"save_aligned_as_matlab": false,
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"save_oxyvol_as_python": false,
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"save_oxyvol_as_matlab": false,
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// Not important parameter
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"required_order": [
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"acceptor",
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"donor",
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"oxygenation",
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"volume"
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]
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}
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66
callum_config_M3905F.json
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66
callum_config_M3905F.json
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@ -0,0 +1,66 @@
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{
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"basic_path": "/data_1/fatma/GEVI_GECI_ES",
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"recoding_data": "session_B",
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"mouse_identifier": "M3905F",
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"raw_path": "raw",
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"export_path": "output_M3905F_session_B",
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"ref_image_path": "ref_images_M3905F_session_B",
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"raw_path": "raw",
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"heartbeat_remove": true,
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"gevi": true, // true => gevi, false => geci
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// Ratio Sequence
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"classical_ratio_mode": true, // true: a/d false: 1+a-d
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// Regression
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"target_camera_acceptor": "acceptor",
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"regressor_cameras_acceptor": [
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"oxygenation",
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"volume"
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],
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"target_camera_donor": "donor",
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"regressor_cameras_donor": [
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"oxygenation",
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"volume"
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],
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// binning
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"binning_enable": true,
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"binning_at_the_end": false,
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"binning_kernel_size": 4,
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"binning_stride": 4,
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"binning_divisor_override": 1,
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// alignment
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"alignment_batch_size": 200,
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"rotation_stabilization_threshold_factor": 3.0, // >= 1.0
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"rotation_stabilization_threshold_border": 0.9, // <= 1.0
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// Heart beat detection
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"lower_freqency_bandpass": 5.0, // Hz
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"upper_freqency_bandpass": 14.0, // Hz
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"heartbeat_filtfilt_chuck_size": 10,
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// Gauss smear
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"gauss_smear_spatial_width": 8,
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"gauss_smear_temporal_width": 0.1,
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"gauss_smear_use_matlab_mask": false,
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// LED Ramp on
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"skip_frames_in_the_beginning": 100, // Frames
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// PyTorch
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"dtype": "float32",
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"force_to_cpu": false,
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// Save
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"save_as_python": true, // produces .npz files (compressed)
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"save_as_matlab": false, // produces .hd5 file (compressed)
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// Save extra information
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"save_alignment": false,
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"save_heartbeat": false,
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"save_factors": false,
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"save_regression_coefficients": false,
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"save_aligned_as_python": false,
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"save_aligned_as_matlab": false,
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"save_oxyvol_as_python": true,
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"save_oxyvol_as_matlab": true,
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// Not important parameter
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"required_order": [
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"acceptor",
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"donor",
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"oxygenation",
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"volume"
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]
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}
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@ -137,7 +137,7 @@ def main(*, config_filename: str = "config.json") -> None:
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valmin=0,
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valmax=value_sort_max,
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valinit=threshold,
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valstep=value_sort_max / 100.0,
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valstep=value_sort_max / 1000.0,
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)
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axbutton_accept = fig.add_axes(rect=(0.3, 0.85, 0.2, 0.04))
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button_accept = Button(
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@ -1,4 +1,4 @@
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#%%
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# %%
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import numpy as np
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import torch
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@ -49,6 +49,11 @@ def process_trial(
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else:
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cuda_total_memory = 0
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mylogger.info("")
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mylogger.info("(A) LOADING DATA, REFERENCE, AND MASK")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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raw_data_path: str = os.path.join(
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config["basic_path"],
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config["recoding_data"],
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@ -183,6 +188,12 @@ def process_trial(
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mylogger.info("-==- Done -==-")
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if config["binning_enable"] and (config["binning_at_the_end"] is False):
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mylogger.info("")
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mylogger.info("(B-OPTIONAL) BINNING")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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mylogger.info("Binning of data")
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mylogger.info(
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(
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@ -251,6 +262,11 @@ def process_trial(
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mylogger.info(f"Data shape: {data.shape}")
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mylogger.info("-==- Done -==-")
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mylogger.info("")
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mylogger.info("(C) ALIGNMENT OF SECOND TO FIRST CAMERA")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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mylogger.info("Preparing alignment")
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mylogger.info("Re-order Raw data")
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data = data.moveaxis(-2, 0).moveaxis(-1, 0)
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@ -462,6 +478,81 @@ def process_trial(
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data *= mask_positve.unsqueeze(0).unsqueeze(0).type(dtype=dtype)
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mylogger.info("-==- Done -==-")
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if config["save_aligned_as_python"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_aligned.npz"
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)
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mylogger.info(f"Save aligned data and mask to {temp_path}")
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np.savez_compressed(
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temp_path,
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data=data.cpu(),
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mask=mask_positve.cpu(),
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acceptor_index=acceptor_index,
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donor_index=donor_index,
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oxygenation_index=oxygenation_index,
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volume_index=volume_index,
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)
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if config["save_aligned_as_matlab"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_aligned.hd5"
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)
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mylogger.info(f"Save aligned data and mask to {temp_path}")
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file_handle = h5py.File(temp_path, "w")
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_ = file_handle.create_dataset(
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"mask",
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data=mask_positve.movedim(0, -1).type(torch.uint8).cpu(),
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compression="gzip",
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compression_opts=9,
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)
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_ = file_handle.create_dataset(
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"data",
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data=data.movedim(1, -1).movedim(0, -1).cpu(),
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compression="gzip",
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compression_opts=9,
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)
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_ = file_handle.create_dataset(
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"acceptor_index",
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data=torch.tensor((acceptor_index,)),
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compression="gzip",
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compression_opts=9,
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)
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_ = file_handle.create_dataset(
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"donor_index",
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data=torch.tensor((donor_index,)),
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compression="gzip",
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compression_opts=9,
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)
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_ = file_handle.create_dataset(
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"oxygenation_index",
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data=torch.tensor((oxygenation_index,)),
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compression="gzip",
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compression_opts=9,
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)
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_ = file_handle.create_dataset(
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"volume_index",
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data=torch.tensor((volume_index,)),
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compression="gzip",
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compression_opts=9,
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)
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mylogger.info("Reminder: How to read with matlab:")
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mylogger.info(f"mask = h5read('{temp_path}','/mask');")
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mylogger.info(f"data_acceptor = h5read('{temp_path}','/data');")
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file_handle.close()
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mylogger.info("")
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mylogger.info("(D) INTER-FRAME INTERPOLATION")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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mylogger.info("Interpolate the 'in-between' frames for oxygenation and volume")
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data[oxygenation_index, 1:, ...] = (
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data[oxygenation_index, 1:, ...] + data[oxygenation_index, :-1, ...]
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@ -477,6 +568,12 @@ def process_trial(
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assert config["heartbeat_remove"]
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if config["heartbeat_remove"]:
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mylogger.info("")
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mylogger.info("(E-OPTIONAL) HEARTBEAT REMOVAL")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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mylogger.info("Extract heartbeat from volume signal")
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heartbeat_ts: torch.Tensor = bandpass(
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data=data[volume_index, ...].movedim(0, -1).clone(),
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@ -574,8 +671,16 @@ def process_trial(
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mylogger.info("-==- Done -==-")
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if config["gevi"]: # UDO scaling performed!
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mylogger.info("")
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mylogger.info("(F-OPTIONAL) DONOR/ACCEPTOR SCALING")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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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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acceptor_heartbeat_factor = heartbeat_coefficients[
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acceptor_index, ...
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].clone()
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del heartbeat_coefficients
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if device != torch.device("cpu"):
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@ -590,16 +695,38 @@ def process_trial(
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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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# 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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donor_factor = torch.sqrt(
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acceptor_heartbeat_factor / donor_heartbeat_factor
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)
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acceptor_factor = 1 / donor_factor
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# import matplotlib.pyplot as plt
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# plt.pcolor(donor_factor, vmin=0.5, vmax=2.0)
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# plt.colorbar()
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# plt.show()
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# plt.pcolor(acceptor_factor, vmin=0.5, vmax=2.0)
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# plt.colorbar()
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# plt.show()
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# TODO remove
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del donor_heartbeat_factor
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del acceptor_heartbeat_factor
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# import matplotlib.pyplot as plt
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# plt.pcolor(torch.std(data[acceptor_index, config["skip_frames_in_the_beginning"] :, ...], axis=0), vmin=0, vmax=500)
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# plt.colorbar()
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# plt.show()
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# plt.pcolor(torch.std(data[donor_index, config["skip_frames_in_the_beginning"] :, ...], axis=0), vmin=0, vmax=500)
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# plt.colorbar()
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# plt.show()
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# TODO remove
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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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@ -614,38 +741,66 @@ def process_trial(
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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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# TODO we have to calculate means first!
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mylogger.info("Extract means for acceptor and donor first")
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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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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("Scale acceptor to heart beat amplitude")
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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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acceptor_factor_correction = np.sqrt(
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mean_values_acceptor / mean_values_donor
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)
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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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) * acceptor_factor_correction * 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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# data[donor_index, ...] *= donor_factor.unsqueeze(
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# 0
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# ) * mask_positve.unsqueeze(0)
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donor_factor_correction = 1 / acceptor_factor_correction
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data[donor_index, ...] *= donor_factor.unsqueeze(
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0
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) * donor_factor_correction * 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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# import matplotlib.pyplot as plt
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# plt.pcolor(mean_values_acceptor[0])
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# plt.colorbar()
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# plt.show()
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# plt.pcolor(mean_values_donor[0])
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# plt.colorbar()
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# plt.show()
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# TODO remove
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# TODO SCHNUGGEL
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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("")
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mylogger.info("(G) CONVERSION TO RELATIVE SIGNAL CHANGES (DIV/MEAN)")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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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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@ -653,6 +808,11 @@ def process_trial(
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)
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mylogger.info("-==- Done -==-")
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mylogger.info("")
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mylogger.info("(H) CLEANING BY REGRESSION")
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mylogger.info("-----------------------------------------------")
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mylogger.info("")
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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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spatial_width = float(config["gauss_smear_spatial_width"])
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|
|
@ -805,6 +965,107 @@ def process_trial(
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|||
data_donor = data[target_id, ...].clone()
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data_donor[mask_negative, :] = 0.0
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||||
|
||||
# TODO clean up --->
|
||||
if config["save_oxyvol_as_python"] or config["save_oxyvol_as_matlab"]:
|
||||
|
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mylogger.info("")
|
||||
mylogger.info("(I-OPTIONAL) SAVE OXY/VOL/MASK")
|
||||
mylogger.info("-----------------------------------------------")
|
||||
mylogger.info("")
|
||||
|
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# extract oxy and vol
|
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mylogger.info("Save Oxygenation/Volume/Mask")
|
||||
data_oxygenation = data[oxygenation_index, ...].clone()
|
||||
data_volume = data[volume_index, ...].clone()
|
||||
data_mask = mask_positve.clone()
|
||||
|
||||
# bin, if required...
|
||||
if config["binning_enable"] and config["binning_at_the_end"]:
|
||||
mylogger.info("Binning of data")
|
||||
mylogger.info(
|
||||
(
|
||||
f"kernel_size={int(config['binning_kernel_size'])}, "
|
||||
f"stride={int(config['binning_stride'])}, "
|
||||
"divisor_override=None"
|
||||
)
|
||||
)
|
||||
|
||||
data_oxygenation = binning(
|
||||
data_oxygenation.unsqueeze(-1),
|
||||
kernel_size=int(config["binning_kernel_size"]),
|
||||
stride=int(config["binning_stride"]),
|
||||
divisor_override=None,
|
||||
).squeeze(-1)
|
||||
|
||||
data_volume = binning(
|
||||
data_volume.unsqueeze(-1),
|
||||
kernel_size=int(config["binning_kernel_size"]),
|
||||
stride=int(config["binning_stride"]),
|
||||
divisor_override=None,
|
||||
).squeeze(-1)
|
||||
|
||||
data_mask = (
|
||||
binning(
|
||||
data_mask.unsqueeze(-1).unsqueeze(-1).type(dtype=dtype),
|
||||
kernel_size=int(config["binning_kernel_size"]),
|
||||
stride=int(config["binning_stride"]),
|
||||
divisor_override=None,
|
||||
)
|
||||
.squeeze(-1)
|
||||
.squeeze(-1)
|
||||
)
|
||||
data_mask = (data_mask > 0).type(torch.bool)
|
||||
|
||||
if config["save_oxyvol_as_python"]:
|
||||
|
||||
# export it!
|
||||
temp_path = os.path.join(
|
||||
config["export_path"], experiment_name + "_oxygenation_volume.npz"
|
||||
)
|
||||
mylogger.info(f"Save data oxygenation and volume to {temp_path}")
|
||||
np.savez_compressed(
|
||||
temp_path,
|
||||
data_oxygenation=data_oxygenation.cpu(),
|
||||
data_volume=data_volume.cpu(),
|
||||
data_mask=data_mask.cpu(),
|
||||
)
|
||||
|
||||
if config["save_oxyvol_as_matlab"]:
|
||||
|
||||
temp_path = os.path.join(
|
||||
config["export_path"], experiment_name + "_oxygenation_volume.hd5"
|
||||
)
|
||||
mylogger.info(f"Save data oxygenation and volume to {temp_path}")
|
||||
file_handle = h5py.File(temp_path, "w")
|
||||
|
||||
data_mask = data_mask.movedim(0, -1)
|
||||
data_oxygenation = data_oxygenation.movedim(1, -1).movedim(0, -1)
|
||||
data_volume = data_volume.movedim(1, -1).movedim(0, -1)
|
||||
_ = file_handle.create_dataset(
|
||||
"data_mask",
|
||||
data=data_mask.type(torch.uint8).cpu(),
|
||||
compression="gzip",
|
||||
compression_opts=9,
|
||||
)
|
||||
_ = file_handle.create_dataset(
|
||||
"data_oxygenation",
|
||||
data=data_oxygenation.cpu(),
|
||||
compression="gzip",
|
||||
compression_opts=9,
|
||||
)
|
||||
_ = file_handle.create_dataset(
|
||||
"data_volume",
|
||||
data=data_volume.cpu(),
|
||||
compression="gzip",
|
||||
compression_opts=9,
|
||||
)
|
||||
mylogger.info("Reminder: How to read with matlab:")
|
||||
mylogger.info(f"data_mask = h5read('{temp_path}','/data_mask');")
|
||||
mylogger.info(f"data_oxygenation = h5read('{temp_path}','/data_oxygenation');")
|
||||
mylogger.info(f"data_volume = h5read('{temp_path}','/data_volume');")
|
||||
file_handle.close()
|
||||
# TODO <------ clean up
|
||||
|
||||
del data
|
||||
del data_filtered
|
||||
|
||||
|
|
@ -825,6 +1086,11 @@ def process_trial(
|
|||
|
||||
if dual_signal_mode is False:
|
||||
|
||||
mylogger.info("")
|
||||
mylogger.info("(J1-OPTIONAL) SAVE ACC/DON/MASK (NO RATIO!+OPT BIN@END)")
|
||||
mylogger.info("-----------------------------------------------")
|
||||
mylogger.info("")
|
||||
|
||||
mylogger.info("mono signal model")
|
||||
|
||||
mylogger.info("Remove nan")
|
||||
|
|
@ -917,6 +1183,11 @@ def process_trial(
|
|||
return
|
||||
# #####################
|
||||
|
||||
mylogger.info("")
|
||||
mylogger.info("(J2-OPTIONAL) BUILD AND SAVE RATIO (+OPT BIN@END)")
|
||||
mylogger.info("-----------------------------------------------")
|
||||
mylogger.info("")
|
||||
|
||||
mylogger.info("Calculate ratio sequence")
|
||||
|
||||
if config["classical_ratio_mode"]:
|
||||
|
|
@ -1102,3 +1373,5 @@ def main(
|
|||
|
||||
if __name__ == "__main__":
|
||||
argh.dispatch_command(main)
|
||||
|
||||
# %%
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue