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David Rotermund
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8 changed files with 648 additions and 420 deletions
60
config_M3879M_2021-10-05.json
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60
config_M3879M_2021-10-05.json
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@ -0,0 +1,60 @@
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{
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"basic_path": "/data_1/robert",
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"recoding_data": "2021-10-05",
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"mouse_identifier": "M3879M",
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"raw_path": "raw",
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"export_path": "output_M3879M_2021-10-05",
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"ref_image_path": "ref_images_M3879M_2021-10-05",
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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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"target_camera_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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// 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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60
config_M_Sert_Cre_41.json
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60
config_M_Sert_Cre_41.json
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@ -0,0 +1,60 @@
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{
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"basic_path": "/data_1/hendrik",
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"recoding_data": "2023-07-17",
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"mouse_identifier": "M_Sert_Cre_41",
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"raw_path": "raw",
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"export_path": "output_M_Sert_Cre_41",
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"ref_image_path": "ref_images_M_Sert_Cre_41",
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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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"target_camera_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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// 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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60
config_M_Sert_Cre_49.json
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60
config_M_Sert_Cre_49.json
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@ -0,0 +1,60 @@
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{
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"basic_path": "/data_1/hendrik",
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"recoding_data": "2023-03-15",
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"mouse_identifier": "M_Sert_Cre_49",
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"raw_path": "raw",
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"export_path": "output_M_Sert_Cre_49",
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"ref_image_path": "ref_images_M_Sert_Cre_49",
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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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"target_camera_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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// 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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@ -23,7 +23,7 @@ mylogger = create_logger(
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)
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config = load_config(mylogger=mylogger)
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experiment_id: int = 1
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experiment_id: int = 2
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raw_data_path: str = os.path.join(
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config["basic_path"],
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@ -1,7 +1,7 @@
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import os
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import torch
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import numpy as np
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import argh
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from functions.get_experiments import get_experiments
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from functions.get_trials import get_trials
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@ -11,21 +11,21 @@ from functions.get_torch_device import get_torch_device
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from functions.load_config import load_config
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from functions.data_raw_loader import data_raw_loader
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def main(*, config_filename: str = "config.json") -> None:
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mylogger = create_logger(
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save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_1"
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save_logging_messages=True,
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display_logging_messages=True,
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log_stage_name="stage_1",
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)
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config = load_config(mylogger=mylogger)
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config = load_config(mylogger=mylogger, filename=config_filename)
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if config["binning_enable"] and (config["binning_at_the_end"] is False):
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device: torch.device = torch.device("cpu")
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else:
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device = get_torch_device(mylogger, config["force_to_cpu"])
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dtype_str: str = config["dtype"]
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dtype: torch.dtype = getattr(torch, dtype_str)
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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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@ -117,9 +117,13 @@ for i in range(0, len(meta_channels)):
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filtfilt_chuck_size=10,
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)
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heartbeat_power = heartbeat_ts[..., config["skip_frames_in_the_beginning"] :].var(
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dim=-1
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)
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heartbeat_power = heartbeat_ts[
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..., config["skip_frames_in_the_beginning"] :
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].var(dim=-1)
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np.save(temp_path, heartbeat_power)
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mylogger.info("-==- Done -==-")
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if __name__ == "__main__":
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argh.dispatch_command(main)
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@ -3,6 +3,7 @@ import matplotlib
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import numpy as np
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import torch
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import os
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import argh
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from matplotlib.widgets import Slider, Button # type:ignore
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from functools import partial
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@ -11,11 +12,15 @@ from functions.create_logger import create_logger
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from functions.get_torch_device import get_torch_device
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from functions.load_config import load_config
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def main(*, config_filename: str = "config.json") -> None:
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mylogger = create_logger(
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save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_2"
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save_logging_messages=True,
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display_logging_messages=True,
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log_stage_name="stage_2",
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)
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config = load_config(mylogger=mylogger)
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config = load_config(mylogger=mylogger, filename=config_filename)
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path: str = config["ref_image_path"]
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use_channel: str = "donor"
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@ -24,9 +29,13 @@ temporal_width: float = 0.1
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threshold: float = 0.05
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heartbeat_mask_threshold_file: str = os.path.join(path, "heartbeat_mask_threshold.npy")
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heartbeat_mask_threshold_file: str = os.path.join(
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path, "heartbeat_mask_threshold.npy"
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)
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if os.path.isfile(heartbeat_mask_threshold_file):
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mylogger.info(f"loading previous threshold file: {heartbeat_mask_threshold_file}")
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mylogger.info(
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f"loading previous threshold file: {heartbeat_mask_threshold_file}"
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)
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threshold = float(np.load(heartbeat_mask_threshold_file)[0])
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mylogger.info(f"initial threshold is {threshold}")
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@ -37,45 +46,6 @@ heartbeat_mask_file: str = os.path.join(path, "heartbeat_mask.npy")
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device = get_torch_device(mylogger, config["force_to_cpu"])
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def next_frame(
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i: float, images: np.ndarray, image_handle: matplotlib.image.AxesImage
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) -> None:
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global threshold
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threshold = i
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display_image: np.ndarray = images.copy()
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display_image[..., 2] = display_image[..., 0]
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mask: np.ndarray = np.where(images[..., 2] >= i, 1.0, np.nan)[..., np.newaxis]
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display_image *= mask
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display_image = np.nan_to_num(display_image, nan=1.0)
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image_handle.set_data(display_image)
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return
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def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
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global threshold
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global image_3color
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global path
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global mylogger
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global heartbeat_mask_file
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global heartbeat_mask_threshold_file
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mylogger.info(f"Threshold: {threshold}")
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mask: np.ndarray = image_3color[..., 2] >= threshold
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mylogger.info(f"Save mask to: {heartbeat_mask_file}")
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np.save(heartbeat_mask_file, mask)
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mylogger.info(f"Save threshold to: {heartbeat_mask_threshold_file}")
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np.save(heartbeat_mask_threshold_file, np.array([threshold]))
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exit()
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def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
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exit()
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mylogger.info(f"loading image reference file: {image_ref_file}")
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image_ref: np.ndarray = np.load(image_ref_file)
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image_ref /= image_ref.max()
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@ -124,6 +94,41 @@ image_handle = plt.imshow(display_image, vmin=0, vmax=1, cmap="hot")
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mylogger.info("Add controls")
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def next_frame(
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i: float, images: np.ndarray, image_handle: matplotlib.image.AxesImage
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) -> None:
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nonlocal threshold
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threshold = i
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display_image: np.ndarray = images.copy()
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display_image[..., 2] = display_image[..., 0]
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mask: np.ndarray = np.where(images[..., 2] >= i, 1.0, np.nan)[..., np.newaxis]
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display_image *= mask
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display_image = np.nan_to_num(display_image, nan=1.0)
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image_handle.set_data(display_image)
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return
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def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
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nonlocal threshold
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nonlocal image_3color
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nonlocal path
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nonlocal mylogger
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nonlocal heartbeat_mask_file
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nonlocal heartbeat_mask_threshold_file
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mylogger.info(f"Threshold: {threshold}")
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mask: np.ndarray = image_3color[..., 2] >= threshold
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mylogger.info(f"Save mask to: {heartbeat_mask_file}")
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np.save(heartbeat_mask_file, mask)
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mylogger.info(f"Save threshold to: {heartbeat_mask_threshold_file}")
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np.save(heartbeat_mask_threshold_file, np.array([threshold]))
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exit()
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def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
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exit()
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axfreq = fig.add_axes(rect=(0.4, 0.9, 0.3, 0.03))
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slice_slider = Slider(
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ax=axfreq,
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@ -151,3 +156,7 @@ slice_slider.on_changed(
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mylogger.info("Display")
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plt.show()
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if __name__ == "__main__":
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argh.dispatch_command(main)
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@ -9,9 +9,10 @@ from matplotlib.widgets import Button # type:ignore
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from roipoly import RoiPoly # type:ignore
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from functions.create_logger import create_logger
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from functions.get_torch_device import get_torch_device
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from functions.load_config import load_config
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import argh
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def compose_image(image_3color: np.ndarray, mask: np.ndarray) -> np.ndarray:
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display_image = image_3color.copy()
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@ -20,74 +21,14 @@ def compose_image(image_3color: np.ndarray, mask: np.ndarray) -> np.ndarray:
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return display_image
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def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
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global mylogger
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global refined_mask_file
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global mask
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mylogger.info(f"Save mask to: {refined_mask_file}")
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np.save(refined_mask_file, mask)
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exit()
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def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
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global mylogger
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mylogger.info("Ended without saving the mask")
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exit()
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def on_clicked_add(event: matplotlib.backend_bases.MouseEvent) -> None:
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global new_roi
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global mask
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global image_3color
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global display_image
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global mylogger
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if len(new_roi.x) > 0:
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mylogger.info("A ROI with the following coordiantes has been added to the mask")
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for i in range(0, len(new_roi.x)):
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mylogger.info(f"{round(new_roi.x[i],1)} x {round(new_roi.y[i],1)}")
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mylogger.info("")
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new_mask = new_roi.get_mask(display_image[:, :, 0])
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mask[new_mask] = 0.0
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display_image = compose_image(image_3color=image_3color, mask=mask)
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image_handle.set_data(display_image)
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for line in ax_main.lines:
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line.remove()
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plt.draw()
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new_roi = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
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|
||||
def on_clicked_remove(event: matplotlib.backend_bases.MouseEvent) -> None:
|
||||
global new_roi
|
||||
global mask
|
||||
global image_3color
|
||||
global display_image
|
||||
if len(new_roi.x) > 0:
|
||||
mylogger.info(
|
||||
"A ROI with the following coordiantes has been removed from the mask"
|
||||
)
|
||||
for i in range(0, len(new_roi.x)):
|
||||
mylogger.info(f"{round(new_roi.x[i],1)} x {round(new_roi.y[i],1)}")
|
||||
mylogger.info("")
|
||||
new_mask = new_roi.get_mask(display_image[:, :, 0])
|
||||
mask[new_mask] = 1.0
|
||||
display_image = compose_image(image_3color=image_3color, mask=mask)
|
||||
image_handle.set_data(display_image)
|
||||
for line in ax_main.lines:
|
||||
line.remove()
|
||||
plt.draw()
|
||||
new_roi = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
|
||||
|
||||
|
||||
def main(*, config_filename: str = "config.json") -> None:
|
||||
mylogger = create_logger(
|
||||
save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_3"
|
||||
save_logging_messages=True,
|
||||
display_logging_messages=True,
|
||||
log_stage_name="stage_3",
|
||||
)
|
||||
|
||||
config = load_config(mylogger=mylogger)
|
||||
|
||||
device = get_torch_device(mylogger, config["force_to_cpu"])
|
||||
config = load_config(mylogger=mylogger, filename=config_filename)
|
||||
|
||||
path: str = config["ref_image_path"]
|
||||
use_channel: str = "donor"
|
||||
|
|
@ -121,6 +62,65 @@ image_handle = ax_main.imshow(display_image, vmin=0, vmax=1, cmap="hot")
|
|||
|
||||
mylogger.info("Add controls")
|
||||
|
||||
def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
|
||||
nonlocal mylogger
|
||||
nonlocal refined_mask_file
|
||||
nonlocal mask
|
||||
|
||||
mylogger.info(f"Save mask to: {refined_mask_file}")
|
||||
np.save(refined_mask_file, mask)
|
||||
|
||||
exit()
|
||||
|
||||
def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
|
||||
nonlocal mylogger
|
||||
mylogger.info("Ended without saving the mask")
|
||||
exit()
|
||||
|
||||
def on_clicked_add(event: matplotlib.backend_bases.MouseEvent) -> None:
|
||||
nonlocal new_roi # type: ignore
|
||||
nonlocal mask
|
||||
nonlocal image_3color
|
||||
nonlocal display_image
|
||||
nonlocal mylogger
|
||||
if len(new_roi.x) > 0:
|
||||
mylogger.info(
|
||||
"A ROI with the following coordiantes has been added to the mask"
|
||||
)
|
||||
for i in range(0, len(new_roi.x)):
|
||||
mylogger.info(f"{round(new_roi.x[i],1)} x {round(new_roi.y[i],1)}")
|
||||
mylogger.info("")
|
||||
new_mask = new_roi.get_mask(display_image[:, :, 0])
|
||||
mask[new_mask] = 0.0
|
||||
display_image = compose_image(image_3color=image_3color, mask=mask)
|
||||
image_handle.set_data(display_image)
|
||||
for line in ax_main.lines:
|
||||
line.remove()
|
||||
plt.draw()
|
||||
|
||||
new_roi = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
|
||||
|
||||
def on_clicked_remove(event: matplotlib.backend_bases.MouseEvent) -> None:
|
||||
nonlocal new_roi # type: ignore
|
||||
nonlocal mask
|
||||
nonlocal image_3color
|
||||
nonlocal display_image
|
||||
if len(new_roi.x) > 0:
|
||||
mylogger.info(
|
||||
"A ROI with the following coordiantes has been removed from the mask"
|
||||
)
|
||||
for i in range(0, len(new_roi.x)):
|
||||
mylogger.info(f"{round(new_roi.x[i],1)} x {round(new_roi.y[i],1)}")
|
||||
mylogger.info("")
|
||||
new_mask = new_roi.get_mask(display_image[:, :, 0])
|
||||
mask[new_mask] = 1.0
|
||||
display_image = compose_image(image_3color=image_3color, mask=mask)
|
||||
image_handle.set_data(display_image)
|
||||
for line in ax_main.lines:
|
||||
line.remove()
|
||||
plt.draw()
|
||||
new_roi = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
|
||||
|
||||
axbutton_accept = fig.add_axes(rect=(0.3, 0.85, 0.2, 0.04))
|
||||
button_accept = Button(
|
||||
ax=axbutton_accept, label="Accept", image=None, color="0.85", hovercolor="0.95"
|
||||
|
|
@ -135,7 +135,11 @@ button_cancel.on_clicked(on_clicked_cancel) # type: ignore
|
|||
|
||||
axbutton_addmask = fig.add_axes(rect=(0.3, 0.9, 0.2, 0.04))
|
||||
button_addmask = Button(
|
||||
ax=axbutton_addmask, label="Add mask", image=None, color="0.85", hovercolor="0.95"
|
||||
ax=axbutton_addmask,
|
||||
label="Add mask",
|
||||
image=None,
|
||||
color="0.85",
|
||||
hovercolor="0.95",
|
||||
)
|
||||
button_addmask.on_clicked(on_clicked_add) # type: ignore
|
||||
|
||||
|
|
@ -155,3 +159,7 @@ mylogger.info("Display")
|
|||
new_roi: RoiPoly = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
argh.dispatch_command(main)
|
||||
|
|
|
|||
|
|
@ -20,6 +20,8 @@ from functions.gauss_smear_individual import gauss_smear_individual
|
|||
from functions.regression import regression
|
||||
from functions.data_raw_loader import data_raw_loader
|
||||
|
||||
import argh
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def process_trial(
|
||||
|
|
@ -889,10 +891,19 @@ def process_trial(
|
|||
return
|
||||
|
||||
|
||||
def main(
|
||||
*,
|
||||
config_filename: str = "config.json",
|
||||
experiment_id_overwrite: int = -1,
|
||||
trial_id_overwrite: int = -1,
|
||||
) -> None:
|
||||
mylogger = create_logger(
|
||||
save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_4"
|
||||
save_logging_messages=True,
|
||||
display_logging_messages=True,
|
||||
log_stage_name="stage_4",
|
||||
)
|
||||
config = load_config(mylogger=mylogger)
|
||||
|
||||
config = load_config(mylogger=mylogger, filename=config_filename)
|
||||
|
||||
if (config["save_as_python"] is False) and (config["save_as_matlab"] is False):
|
||||
mylogger.info("No output will be created. ")
|
||||
|
|
@ -911,7 +922,9 @@ if (len(config["target_camera_donor"]) == 0) and (
|
|||
|
||||
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"
|
||||
)
|
||||
os.makedirs(config["export_path"], exist_ok=True)
|
||||
|
||||
raw_data_path: str = os.path.join(
|
||||
|
|
@ -925,11 +938,21 @@ if os.path.isdir(raw_data_path) is False:
|
|||
mylogger.info(f"ERROR: could not find raw directory {raw_data_path}!!!!")
|
||||
exit()
|
||||
|
||||
if experiment_id_overwrite == -1:
|
||||
experiments = get_experiments(raw_data_path)
|
||||
else:
|
||||
assert experiment_id_overwrite >= 0
|
||||
experiments = torch.tensor([experiment_id_overwrite])
|
||||
|
||||
for experiment_counter in range(0, experiments.shape[0]):
|
||||
experiment_id = int(experiments[experiment_counter])
|
||||
|
||||
if trial_id_overwrite == -1:
|
||||
trials = get_trials(raw_data_path, experiment_id)
|
||||
else:
|
||||
assert trial_id_overwrite >= 0
|
||||
trials = torch.tensor([trial_id_overwrite])
|
||||
|
||||
for trial_counter in range(0, trials.shape[0]):
|
||||
trial_id = int(trials[trial_counter])
|
||||
|
||||
|
|
@ -957,3 +980,7 @@ for experiment_counter in range(0, experiments.shape[0]):
|
|||
trial_id=trial_id,
|
||||
device=torch.device("cpu"),
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
argh.dispatch_command(main)
|
||||
|
|
|
|||
Loading…
Reference in a new issue