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config_M3879M_2021-10-05.json

@@ -0,0 +1,60 @@
+{
+    "basic_path": "/data_1/robert",
+    "recoding_data": "2021-10-05",
+    "mouse_identifier": "M3879M",
+    "raw_path": "raw",
+    "export_path": "output_M3879M_2021-10-05",
+    "ref_image_path": "ref_images_M3879M_2021-10-05",
+    // Ratio Sequence
+    "classical_ratio_mode": true, // true: a/d false: 1+a-d
+    // Regression
+    //"target_camera_acceptor": "acceptor",
+    "target_camera_acceptor": "",
+    "regressor_cameras_acceptor": [
+        "oxygenation",
+        "volume"
+    ],
+    "target_camera_donor": "donor",
+    "regressor_cameras_donor": [
+        //    "oxygenation",
+        "volume"
+    ],
+    // binning
+    "binning_enable": true,
+    "binning_at_the_end": false,
+    "binning_kernel_size": 4,
+    "binning_stride": 4,
+    "binning_divisor_override": 1,
+    // alignment
+    "alignment_batch_size": 200,
+    "rotation_stabilization_threshold_factor": 3.0, // >= 1.0
+    "rotation_stabilization_threshold_border": 0.9, // <= 1.0
+    // Heart beat detection
+    "lower_freqency_bandpass": 5.0, // Hz
+    "upper_freqency_bandpass": 14.0, // Hz
+    "heartbeat_filtfilt_chuck_size": 10,
+    // Gauss smear 
+    "gauss_smear_spatial_width": 8,
+    "gauss_smear_temporal_width": 0.1,
+    "gauss_smear_use_matlab_mask": false,
+    // LED Ramp on
+    "skip_frames_in_the_beginning": 100, // Frames
+    // PyTorch
+    "dtype": "float32",
+    "force_to_cpu": false,
+    // Save
+    "save_as_python": true, // produces .npz files (compressed)
+    "save_as_matlab": false, // produces .hd5 file (compressed)
+    // Save extra information
+    "save_alignment": false,
+    "save_heartbeat": false,
+    "save_factors": false,
+    "save_regression_coefficients": false,
+    // Not important parameter
+    "required_order": [
+        "acceptor",
+        "donor",
+        "oxygenation",
+        "volume"
+    ]
+}

config_M_Sert_Cre_41.json

@@ -0,0 +1,60 @@
+{
+    "basic_path": "/data_1/hendrik",
+    "recoding_data": "2023-07-17",
+    "mouse_identifier": "M_Sert_Cre_41",
+    "raw_path": "raw",
+    "export_path": "output_M_Sert_Cre_41",
+    "ref_image_path": "ref_images_M_Sert_Cre_41",
+    // Ratio Sequence
+    "classical_ratio_mode": true, // true: a/d false: 1+a-d
+    // Regression
+    //"target_camera_acceptor": "acceptor",
+    "target_camera_acceptor": "",
+    "regressor_cameras_acceptor": [
+        "oxygenation",
+        "volume"
+    ],
+    "target_camera_donor": "donor",
+    "regressor_cameras_donor": [
+        //    "oxygenation",
+        "volume"
+    ],
+    // binning
+    "binning_enable": true,
+    "binning_at_the_end": false,
+    "binning_kernel_size": 4,
+    "binning_stride": 4,
+    "binning_divisor_override": 1,
+    // alignment
+    "alignment_batch_size": 200,
+    "rotation_stabilization_threshold_factor": 3.0, // >= 1.0
+    "rotation_stabilization_threshold_border": 0.9, // <= 1.0
+    // Heart beat detection
+    "lower_freqency_bandpass": 5.0, // Hz
+    "upper_freqency_bandpass": 14.0, // Hz
+    "heartbeat_filtfilt_chuck_size": 10,
+    // Gauss smear 
+    "gauss_smear_spatial_width": 8,
+    "gauss_smear_temporal_width": 0.1,
+    "gauss_smear_use_matlab_mask": false,
+    // LED Ramp on
+    "skip_frames_in_the_beginning": 100, // Frames
+    // PyTorch
+    "dtype": "float32",
+    "force_to_cpu": false,
+    // Save
+    "save_as_python": true, // produces .npz files (compressed)
+    "save_as_matlab": false, // produces .hd5 file (compressed)
+    // Save extra information
+    "save_alignment": false,
+    "save_heartbeat": false,
+    "save_factors": false,
+    "save_regression_coefficients": false,
+    // Not important parameter
+    "required_order": [
+        "acceptor",
+        "donor",
+        "oxygenation",
+        "volume"
+    ]
+}

config_M_Sert_Cre_49.json

@@ -0,0 +1,60 @@
+{
+    "basic_path": "/data_1/hendrik",
+    "recoding_data": "2023-03-15",
+    "mouse_identifier": "M_Sert_Cre_49",
+    "raw_path": "raw",
+    "export_path": "output_M_Sert_Cre_49",
+    "ref_image_path": "ref_images_M_Sert_Cre_49",
+    // Ratio Sequence
+    "classical_ratio_mode": true, // true: a/d false: 1+a-d
+    // Regression
+    //"target_camera_acceptor": "acceptor",
+    "target_camera_acceptor": "",
+    "regressor_cameras_acceptor": [
+        "oxygenation",
+        "volume"
+    ],
+    "target_camera_donor": "donor",
+    "regressor_cameras_donor": [
+        //    "oxygenation",
+        "volume"
+    ],
+    // binning
+    "binning_enable": true,
+    "binning_at_the_end": false,
+    "binning_kernel_size": 4,
+    "binning_stride": 4,
+    "binning_divisor_override": 1,
+    // alignment
+    "alignment_batch_size": 200,
+    "rotation_stabilization_threshold_factor": 3.0, // >= 1.0
+    "rotation_stabilization_threshold_border": 0.9, // <= 1.0
+    // Heart beat detection
+    "lower_freqency_bandpass": 5.0, // Hz
+    "upper_freqency_bandpass": 14.0, // Hz
+    "heartbeat_filtfilt_chuck_size": 10,
+    // Gauss smear 
+    "gauss_smear_spatial_width": 8,
+    "gauss_smear_temporal_width": 0.1,
+    "gauss_smear_use_matlab_mask": false,
+    // LED Ramp on
+    "skip_frames_in_the_beginning": 100, // Frames
+    // PyTorch
+    "dtype": "float32",
+    "force_to_cpu": false,
+    // Save
+    "save_as_python": true, // produces .npz files (compressed)
+    "save_as_matlab": false, // produces .hd5 file (compressed)
+    // Save extra information
+    "save_alignment": false,
+    "save_heartbeat": false,
+    "save_factors": false,
+    "save_regression_coefficients": false,
+    // Not important parameter
+    "required_order": [
+        "acceptor",
+        "donor",
+        "oxygenation",
+        "volume"
+    ]
+}

olivia_data_plotter_svd.py

@@ -23,7 +23,7 @@ mylogger = create_logger(
 )
 config = load_config(mylogger=mylogger)
 
-experiment_id: int = 1
+experiment_id: int = 2
 
 raw_data_path: str = os.path.join(
     config["basic_path"],

stage_1_get_ref_image.py

@@ -1,7 +1,7 @@
 import os
 import torch
 import numpy as np
-
+import argh
 
 from functions.get_experiments import get_experiments
 from functions.get_trials import get_trials
@@ -11,115 +11,119 @@ from functions.get_torch_device import get_torch_device
 from functions.load_config import load_config
 from functions.data_raw_loader import data_raw_loader
 
-mylogger = create_logger(
-    save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_1"
-)
 
-config = load_config(mylogger=mylogger)
+def main(*, config_filename: str = "config.json") -> None:
-
+    mylogger = create_logger(
-if config["binning_enable"] and (config["binning_at_the_end"] is False):
+        save_logging_messages=True,
-    device: torch.device = torch.device("cpu")
+        display_logging_messages=True,
-else:
+        log_stage_name="stage_1",
-    device = get_torch_device(mylogger, config["force_to_cpu"])
-
-
-dtype_str: str = config["dtype"]
-dtype: torch.dtype = getattr(torch, dtype_str)
-
-raw_data_path: str = os.path.join(
-    config["basic_path"],
-    config["recoding_data"],
-    config["mouse_identifier"],
-    config["raw_path"],
-)
-
-mylogger.info(f"Using data path: {raw_data_path}")
-
-first_experiment_id: int = int(get_experiments(raw_data_path).min())
-first_trial_id: int = int(get_trials(raw_data_path, first_experiment_id).min())
-
-meta_channels: list[str]
-meta_mouse_markings: str
-meta_recording_date: str
-meta_stimulation_times: dict
-meta_experiment_names: dict
-meta_trial_recording_duration: float
-meta_frame_time: float
-meta_mouse: str
-data: torch.Tensor
-
-if config["binning_enable"] and (config["binning_at_the_end"] is False):
-    force_to_cpu_memory: bool = True
-else:
-    force_to_cpu_memory = False
-
-mylogger.info("Loading data")
-
-(
-    meta_channels,
-    meta_mouse_markings,
-    meta_recording_date,
-    meta_stimulation_times,
-    meta_experiment_names,
-    meta_trial_recording_duration,
-    meta_frame_time,
-    meta_mouse,
-    data,
-) = data_raw_loader(
-    raw_data_path=raw_data_path,
-    mylogger=mylogger,
-    experiment_id=first_experiment_id,
-    trial_id=first_trial_id,
-    device=device,
-    force_to_cpu_memory=force_to_cpu_memory,
-    config=config,
-)
-mylogger.info("-==- Done -==-")
-
-output_path = config["ref_image_path"]
-mylogger.info(f"Create directory {output_path} in the case it does not exist")
-os.makedirs(output_path, exist_ok=True)
-
-mylogger.info("Reference images")
-for i in range(0, len(meta_channels)):
-    temp_path: str = os.path.join(output_path, meta_channels[i] + ".npy")
-    mylogger.info(f"Extract and save: {temp_path}")
-    frame_id: int = data.shape[-2] // 2
-    mylogger.info(f"Will use frame id: {frame_id}")
-    ref_image: np.ndarray = (
-        data[:, :, frame_id, meta_channels.index(meta_channels[i])]
-        .clone()
-        .cpu()
-        .numpy()
     )
-    np.save(temp_path, ref_image)
-mylogger.info("-==- Done -==-")
 
-sample_frequency: float = 1.0 / meta_frame_time
+    config = load_config(mylogger=mylogger, filename=config_filename)
-mylogger.info(
+
+    if config["binning_enable"] and (config["binning_at_the_end"] is False):
+        device: torch.device = torch.device("cpu")
+    else:
+        device = get_torch_device(mylogger, config["force_to_cpu"])
+
+    raw_data_path: str = os.path.join(
+        config["basic_path"],
+        config["recoding_data"],
+        config["mouse_identifier"],
+        config["raw_path"],
+    )
+
+    mylogger.info(f"Using data path: {raw_data_path}")
+
+    first_experiment_id: int = int(get_experiments(raw_data_path).min())
+    first_trial_id: int = int(get_trials(raw_data_path, first_experiment_id).min())
+
+    meta_channels: list[str]
+    meta_mouse_markings: str
+    meta_recording_date: str
+    meta_stimulation_times: dict
+    meta_experiment_names: dict
+    meta_trial_recording_duration: float
+    meta_frame_time: float
+    meta_mouse: str
+    data: torch.Tensor
+
+    if config["binning_enable"] and (config["binning_at_the_end"] is False):
+        force_to_cpu_memory: bool = True
+    else:
+        force_to_cpu_memory = False
+
+    mylogger.info("Loading data")
+
     (
-        f"Heartbeat power {config['lower_freqency_bandpass']} Hz"
+        meta_channels,
-        f" - {config['upper_freqency_bandpass']} Hz,"
+        meta_mouse_markings,
-        f" sample-rate: {sample_frequency},"
+        meta_recording_date,
-        f" skipping the first {config['skip_frames_in_the_beginning']} frames"
+        meta_stimulation_times,
+        meta_experiment_names,
+        meta_trial_recording_duration,
+        meta_frame_time,
+        meta_mouse,
+        data,
+    ) = data_raw_loader(
+        raw_data_path=raw_data_path,
+        mylogger=mylogger,
+        experiment_id=first_experiment_id,
+        trial_id=first_trial_id,
+        device=device,
+        force_to_cpu_memory=force_to_cpu_memory,
+        config=config,
     )
-)
+    mylogger.info("-==- Done -==-")
 
-for i in range(0, len(meta_channels)):
+    output_path = config["ref_image_path"]
-    temp_path = os.path.join(output_path, meta_channels[i] + "_var.npy")
+    mylogger.info(f"Create directory {output_path} in the case it does not exist")
-    mylogger.info(f"Extract and save: {temp_path}")
+    os.makedirs(output_path, exist_ok=True)
 
-    heartbeat_ts: torch.Tensor = bandpass(
+    mylogger.info("Reference images")
-        data=data[..., i],
+    for i in range(0, len(meta_channels)):
-        low_frequency=config["lower_freqency_bandpass"],
+        temp_path: str = os.path.join(output_path, meta_channels[i] + ".npy")
-        high_frequency=config["upper_freqency_bandpass"],
+        mylogger.info(f"Extract and save: {temp_path}")
-        fs=sample_frequency,
+        frame_id: int = data.shape[-2] // 2
-        filtfilt_chuck_size=10,
+        mylogger.info(f"Will use frame id: {frame_id}")
+        ref_image: np.ndarray = (
+            data[:, :, frame_id, meta_channels.index(meta_channels[i])]
+            .clone()
+            .cpu()
+            .numpy()
+        )
+        np.save(temp_path, ref_image)
+    mylogger.info("-==- Done -==-")
+
+    sample_frequency: float = 1.0 / meta_frame_time
+    mylogger.info(
+        (
+            f"Heartbeat power {config['lower_freqency_bandpass']} Hz"
+            f" - {config['upper_freqency_bandpass']} Hz,"
+            f" sample-rate: {sample_frequency},"
+            f" skipping the first {config['skip_frames_in_the_beginning']} frames"
+        )
     )
 
-    heartbeat_power = heartbeat_ts[..., config["skip_frames_in_the_beginning"] :].var(
+    for i in range(0, len(meta_channels)):
-        dim=-1
+        temp_path = os.path.join(output_path, meta_channels[i] + "_var.npy")
-    )
+        mylogger.info(f"Extract and save: {temp_path}")
-    np.save(temp_path, heartbeat_power)
 
-mylogger.info("-==- Done -==-")
+        heartbeat_ts: torch.Tensor = bandpass(
+            data=data[..., i],
+            low_frequency=config["lower_freqency_bandpass"],
+            high_frequency=config["upper_freqency_bandpass"],
+            fs=sample_frequency,
+            filtfilt_chuck_size=10,
+        )
+
+        heartbeat_power = heartbeat_ts[
+            ..., config["skip_frames_in_the_beginning"] :
+        ].var(dim=-1)
+        np.save(temp_path, heartbeat_power)
+
+    mylogger.info("-==- Done -==-")
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)

stage_2_make_heartbeat_mask.py

@@ -3,6 +3,7 @@ import matplotlib
 import numpy as np
 import torch
 import os
+import argh
 
 from matplotlib.widgets import Slider, Button  # type:ignore
 from functools import partial
@@ -11,143 +12,151 @@ from functions.create_logger import create_logger
 from functions.get_torch_device import get_torch_device
 from functions.load_config import load_config
 
-mylogger = create_logger(
-    save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_2"
-)
 
-config = load_config(mylogger=mylogger)
+def main(*, config_filename: str = "config.json") -> None:
+    mylogger = create_logger(
+        save_logging_messages=True,
+        display_logging_messages=True,
+        log_stage_name="stage_2",
+    )
 
-path: str = config["ref_image_path"]
+    config = load_config(mylogger=mylogger, filename=config_filename)
-use_channel: str = "donor"
-spatial_width: float = 4.0
-temporal_width: float = 0.1
 
-threshold: float = 0.05
+    path: str = config["ref_image_path"]
+    use_channel: str = "donor"
+    spatial_width: float = 4.0
+    temporal_width: float = 0.1
 
-heartbeat_mask_threshold_file: str = os.path.join(path, "heartbeat_mask_threshold.npy")
+    threshold: float = 0.05
-if os.path.isfile(heartbeat_mask_threshold_file):
-    mylogger.info(f"loading previous threshold file: {heartbeat_mask_threshold_file}")
-    threshold = float(np.load(heartbeat_mask_threshold_file)[0])
 
-mylogger.info(f"initial threshold is {threshold}")
+    heartbeat_mask_threshold_file: str = os.path.join(
+        path, "heartbeat_mask_threshold.npy"
+    )
+    if os.path.isfile(heartbeat_mask_threshold_file):
+        mylogger.info(
+            f"loading previous threshold file: {heartbeat_mask_threshold_file}"
+        )
+        threshold = float(np.load(heartbeat_mask_threshold_file)[0])
 
-image_ref_file: str = os.path.join(path, use_channel + ".npy")
+    mylogger.info(f"initial threshold is {threshold}")
-image_var_file: str = os.path.join(path, use_channel + "_var.npy")
-heartbeat_mask_file: str = os.path.join(path, "heartbeat_mask.npy")
 
-device = get_torch_device(mylogger, config["force_to_cpu"])
+    image_ref_file: str = os.path.join(path, use_channel + ".npy")
+    image_var_file: str = os.path.join(path, use_channel + "_var.npy")
+    heartbeat_mask_file: str = os.path.join(path, "heartbeat_mask.npy")
 
+    device = get_torch_device(mylogger, config["force_to_cpu"])
 
-def next_frame(
+    mylogger.info(f"loading image reference file: {image_ref_file}")
-    i: float, images: np.ndarray, image_handle: matplotlib.image.AxesImage
+    image_ref: np.ndarray = np.load(image_ref_file)
-) -> None:
+    image_ref /= image_ref.max()
-    global threshold
-    threshold = i
 
-    display_image: np.ndarray = images.copy()
+    mylogger.info(f"loading image heartbeat power: {image_var_file}")
+    image_var: np.ndarray = np.load(image_var_file)
+    image_var /= image_var.max()
+
+    mylogger.info("Smear the image heartbeat power patially")
+    temp, _ = gauss_smear_individual(
+        input=torch.tensor(image_var[..., np.newaxis], device=device),
+        spatial_width=spatial_width,
+        temporal_width=temporal_width,
+        use_matlab_mask=False,
+    )
+    temp /= temp.max()
+
+    mylogger.info("-==- DONE -==-")
+
+    image_3color = np.concatenate(
+        (
+            np.zeros_like(image_ref[..., np.newaxis]),
+            image_ref[..., np.newaxis],
+            temp.cpu().numpy(),
+        ),
+        axis=-1,
+    )
+
+    mylogger.info("Prepare image")
+
+    display_image = image_3color.copy()
     display_image[..., 2] = display_image[..., 0]
-    mask: np.ndarray = np.where(images[..., 2] >= i, 1.0, np.nan)[..., np.newaxis]
+    mask = np.where(image_3color[..., 2] >= threshold, 1.0, np.nan)[..., np.newaxis]
     display_image *= mask
     display_image = np.nan_to_num(display_image, nan=1.0)
 
-    image_handle.set_data(display_image)
+    value_sort = np.sort(image_var.flatten())
-    return
+    value_sort_max = value_sort[int(value_sort.shape[0] * 0.95)]
+    mylogger.info("-==- DONE -==-")
+
+    mylogger.info("Create figure")
+
+    fig: matplotlib.figure.Figure = plt.figure()
+
+    image_handle = plt.imshow(display_image, vmin=0, vmax=1, cmap="hot")
+
+    mylogger.info("Add controls")
+
+    def next_frame(
+        i: float, images: np.ndarray, image_handle: matplotlib.image.AxesImage
+    ) -> None:
+        nonlocal threshold
+        threshold = i
+
+        display_image: np.ndarray = images.copy()
+        display_image[..., 2] = display_image[..., 0]
+        mask: np.ndarray = np.where(images[..., 2] >= i, 1.0, np.nan)[..., np.newaxis]
+        display_image *= mask
+        display_image = np.nan_to_num(display_image, nan=1.0)
+
+        image_handle.set_data(display_image)
+        return
+
+    def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
+        nonlocal threshold
+        nonlocal image_3color
+        nonlocal path
+        nonlocal mylogger
+        nonlocal heartbeat_mask_file
+        nonlocal heartbeat_mask_threshold_file
+
+        mylogger.info(f"Threshold: {threshold}")
+
+        mask: np.ndarray = image_3color[..., 2] >= threshold
+        mylogger.info(f"Save mask to: {heartbeat_mask_file}")
+        np.save(heartbeat_mask_file, mask)
+        mylogger.info(f"Save threshold to: {heartbeat_mask_threshold_file}")
+        np.save(heartbeat_mask_threshold_file, np.array([threshold]))
+        exit()
+
+    def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
+        exit()
+
+    axfreq = fig.add_axes(rect=(0.4, 0.9, 0.3, 0.03))
+    slice_slider = Slider(
+        ax=axfreq,
+        label="Threshold",
+        valmin=0,
+        valmax=value_sort_max,
+        valinit=threshold,
+        valstep=value_sort_max / 100.0,
+    )
+    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"
+    )
+    button_accept.on_clicked(on_clicked_accept)  # type: ignore
+
+    axbutton_cancel = fig.add_axes(rect=(0.55, 0.85, 0.2, 0.04))
+    button_cancel = Button(
+        ax=axbutton_cancel, label="Cancel", image=None, color="0.85", hovercolor="0.95"
+    )
+    button_cancel.on_clicked(on_clicked_cancel)  # type: ignore
+
+    slice_slider.on_changed(
+        partial(next_frame, images=image_3color, image_handle=image_handle)
+    )
+
+    mylogger.info("Display")
+    plt.show()
 
 
-def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
+if __name__ == "__main__":
-    global threshold
+    argh.dispatch_command(main)
-    global image_3color
-    global path
-    global mylogger
-    global heartbeat_mask_file
-    global heartbeat_mask_threshold_file
-
-    mylogger.info(f"Threshold: {threshold}")
-
-    mask: np.ndarray = image_3color[..., 2] >= threshold
-    mylogger.info(f"Save mask to: {heartbeat_mask_file}")
-    np.save(heartbeat_mask_file, mask)
-    mylogger.info(f"Save threshold to: {heartbeat_mask_threshold_file}")
-    np.save(heartbeat_mask_threshold_file, np.array([threshold]))
-    exit()
-
-
-def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
-    exit()
-
-
-mylogger.info(f"loading image reference file: {image_ref_file}")
-image_ref: np.ndarray = np.load(image_ref_file)
-image_ref /= image_ref.max()
-
-mylogger.info(f"loading image heartbeat power: {image_var_file}")
-image_var: np.ndarray = np.load(image_var_file)
-image_var /= image_var.max()
-
-mylogger.info("Smear the image heartbeat power patially")
-temp, _ = gauss_smear_individual(
-    input=torch.tensor(image_var[..., np.newaxis], device=device),
-    spatial_width=spatial_width,
-    temporal_width=temporal_width,
-    use_matlab_mask=False,
-)
-temp /= temp.max()
-
-mylogger.info("-==- DONE -==-")
-
-image_3color = np.concatenate(
-    (
-        np.zeros_like(image_ref[..., np.newaxis]),
-        image_ref[..., np.newaxis],
-        temp.cpu().numpy(),
-    ),
-    axis=-1,
-)
-
-mylogger.info("Prepare image")
-
-display_image = image_3color.copy()
-display_image[..., 2] = display_image[..., 0]
-mask = np.where(image_3color[..., 2] >= threshold, 1.0, np.nan)[..., np.newaxis]
-display_image *= mask
-display_image = np.nan_to_num(display_image, nan=1.0)
-
-value_sort = np.sort(image_var.flatten())
-value_sort_max = value_sort[int(value_sort.shape[0] * 0.95)]
-mylogger.info("-==- DONE -==-")
-
-mylogger.info("Create figure")
-
-fig: matplotlib.figure.Figure = plt.figure()
-
-image_handle = plt.imshow(display_image, vmin=0, vmax=1, cmap="hot")
-
-mylogger.info("Add controls")
-
-axfreq = fig.add_axes(rect=(0.4, 0.9, 0.3, 0.03))
-slice_slider = Slider(
-    ax=axfreq,
-    label="Threshold",
-    valmin=0,
-    valmax=value_sort_max,
-    valinit=threshold,
-    valstep=value_sort_max / 100.0,
-)
-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"
-)
-button_accept.on_clicked(on_clicked_accept)  # type: ignore
-
-axbutton_cancel = fig.add_axes(rect=(0.55, 0.85, 0.2, 0.04))
-button_cancel = Button(
-    ax=axbutton_cancel, label="Cancel", image=None, color="0.85", hovercolor="0.95"
-)
-button_cancel.on_clicked(on_clicked_cancel)  # type: ignore
-
-slice_slider.on_changed(
-    partial(next_frame, images=image_3color, image_handle=image_handle)
-)
-
-mylogger.info("Display")
-plt.show()

stage_3_refine_mask.py

@@ -9,9 +9,10 @@ from matplotlib.widgets import Button  # type:ignore
 from roipoly import RoiPoly  # type:ignore
 
 from functions.create_logger import create_logger
-from functions.get_torch_device import get_torch_device
 from functions.load_config import load_config
 
+import argh
+
 
 def compose_image(image_3color: np.ndarray, mask: np.ndarray) -> np.ndarray:
     display_image = image_3color.copy()
@@ -20,138 +21,145 @@ def compose_image(image_3color: np.ndarray, mask: np.ndarray) -> np.ndarray:
     return display_image
 
 
-def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
+def main(*, config_filename: str = "config.json") -> None:
-    global mylogger
+    mylogger = create_logger(
-    global refined_mask_file
+        save_logging_messages=True,
-    global mask
+        display_logging_messages=True,
+        log_stage_name="stage_3",
+    )
 
-    mylogger.info(f"Save mask to: {refined_mask_file}")
+    config = load_config(mylogger=mylogger, filename=config_filename)
-    np.save(refined_mask_file, mask)
 
-    exit()
+    path: str = config["ref_image_path"]
+    use_channel: str = "donor"
+
+    image_ref_file: str = os.path.join(path, use_channel + ".npy")
+    heartbeat_mask_file: str = os.path.join(path, "heartbeat_mask.npy")
+    refined_mask_file: str = os.path.join(path, "mask_not_rotated.npy")
+
+    mylogger.info(f"loading image reference file: {image_ref_file}")
+    image_ref: np.ndarray = np.load(image_ref_file)
+    image_ref /= image_ref.max()
+
+    mylogger.info(f"loading heartbeat mask: {heartbeat_mask_file}")
+    mask: np.ndarray = np.load(heartbeat_mask_file)
+
+    image_3color = np.concatenate(
+        (
+            np.zeros_like(image_ref[..., np.newaxis]),
+            image_ref[..., np.newaxis],
+            np.zeros_like(image_ref[..., np.newaxis]),
+        ),
+        axis=-1,
+    )
+
+    mylogger.info("-==- DONE -==-")
+
+    fig, ax_main = plt.subplots()
+
+    display_image = compose_image(image_3color=image_3color, mask=mask)
+    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"
+    )
+    button_accept.on_clicked(on_clicked_accept)  # type: ignore
+
+    axbutton_cancel = fig.add_axes(rect=(0.5, 0.85, 0.2, 0.04))
+    button_cancel = Button(
+        ax=axbutton_cancel, label="Cancel", image=None, color="0.85", hovercolor="0.95"
+    )
+    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",
+    )
+    button_addmask.on_clicked(on_clicked_add)  # type: ignore
+
+    axbutton_removemask = fig.add_axes(rect=(0.5, 0.9, 0.2, 0.04))
+    button_removemask = Button(
+        ax=axbutton_removemask,
+        label="Remove mask",
+        image=None,
+        color="0.85",
+        hovercolor="0.95",
+    )
+    button_removemask.on_clicked(on_clicked_remove)  # type: ignore
+
+    # ax_main.cla()
+
+    mylogger.info("Display")
+    new_roi: RoiPoly = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
+
+    plt.show()
 
 
-def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
+if __name__ == "__main__":
-    global mylogger
+    argh.dispatch_command(main)
-    mylogger.info("Ended without saving the mask")
-    exit()
-
-
-def on_clicked_add(event: matplotlib.backend_bases.MouseEvent) -> None:
-    global new_roi
-    global mask
-    global image_3color
-    global display_image
-    global 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:
-    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)
-
-
-mylogger = create_logger(
-    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"])
-
-path: str = config["ref_image_path"]
-use_channel: str = "donor"
-
-image_ref_file: str = os.path.join(path, use_channel + ".npy")
-heartbeat_mask_file: str = os.path.join(path, "heartbeat_mask.npy")
-refined_mask_file: str = os.path.join(path, "mask_not_rotated.npy")
-
-mylogger.info(f"loading image reference file: {image_ref_file}")
-image_ref: np.ndarray = np.load(image_ref_file)
-image_ref /= image_ref.max()
-
-mylogger.info(f"loading heartbeat mask: {heartbeat_mask_file}")
-mask: np.ndarray = np.load(heartbeat_mask_file)
-
-image_3color = np.concatenate(
-    (
-        np.zeros_like(image_ref[..., np.newaxis]),
-        image_ref[..., np.newaxis],
-        np.zeros_like(image_ref[..., np.newaxis]),
-    ),
-    axis=-1,
-)
-
-mylogger.info("-==- DONE -==-")
-
-fig, ax_main = plt.subplots()
-
-display_image = compose_image(image_3color=image_3color, mask=mask)
-image_handle = ax_main.imshow(display_image, vmin=0, vmax=1, cmap="hot")
-
-mylogger.info("Add controls")
-
-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"
-)
-button_accept.on_clicked(on_clicked_accept)  # type: ignore
-
-axbutton_cancel = fig.add_axes(rect=(0.5, 0.85, 0.2, 0.04))
-button_cancel = Button(
-    ax=axbutton_cancel, label="Cancel", image=None, color="0.85", hovercolor="0.95"
-)
-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"
-)
-button_addmask.on_clicked(on_clicked_add)  # type: ignore
-
-axbutton_removemask = fig.add_axes(rect=(0.5, 0.9, 0.2, 0.04))
-button_removemask = Button(
-    ax=axbutton_removemask,
-    label="Remove mask",
-    image=None,
-    color="0.85",
-    hovercolor="0.95",
-)
-button_removemask.on_clicked(on_clicked_remove)  # type: ignore
-
-# ax_main.cla()
-
-mylogger.info("Display")
-new_roi: RoiPoly = RoiPoly(ax=ax_main, color="r", close_fig=False, show_fig=False)
-
-plt.show()

stage_4_process.py

@@ -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(
@@ -889,71 +891,96 @@ def process_trial(
     return
 
 
-mylogger = create_logger(
+def main(
-    save_logging_messages=True, display_logging_messages=True, log_stage_name="stage_4"
+    *,
-)
+    config_filename: str = "config.json",
-config = load_config(mylogger=mylogger)
+    experiment_id_overwrite: int = -1,
-
+    trial_id_overwrite: int = -1,
-if (config["save_as_python"] is False) and (config["save_as_matlab"] is False):
+) -> None:
-    mylogger.info("No output will be created. ")
+    mylogger = create_logger(
-    mylogger.info("Change save_as_python and/or save_as_matlab in the config file")
+        save_logging_messages=True,
-    mylogger.info("ERROR: STOP!!!")
+        display_logging_messages=True,
-    exit()
+        log_stage_name="stage_4",
-
-if (len(config["target_camera_donor"]) == 0) and (
-    len(config["target_camera_acceptor"]) == 0
-):
-    mylogger.info(
-        "Configure at least target_camera_donor or target_camera_acceptor correctly."
     )
-    mylogger.info("ERROR: STOP!!!")
-    exit()
 
-device = get_torch_device(mylogger, config["force_to_cpu"])
+    config = load_config(mylogger=mylogger, filename=config_filename)
 
-mylogger.info(f"Create directory {config['export_path']} in the case it does not exist")
+    if (config["save_as_python"] is False) and (config["save_as_matlab"] is False):
-os.makedirs(config["export_path"], exist_ok=True)
+        mylogger.info("No output will be created. ")
+        mylogger.info("Change save_as_python and/or save_as_matlab in the config file")
+        mylogger.info("ERROR: STOP!!!")
+        exit()
 
-raw_data_path: str = os.path.join(
+    if (len(config["target_camera_donor"]) == 0) and (
-    config["basic_path"],
+        len(config["target_camera_acceptor"]) == 0
-    config["recoding_data"],
+    ):
-    config["mouse_identifier"],
-    config["raw_path"],
-)
-
-if os.path.isdir(raw_data_path) is False:
-    mylogger.info(f"ERROR: could not find raw directory {raw_data_path}!!!!")
-    exit()
-
-experiments = get_experiments(raw_data_path)
-
-for experiment_counter in range(0, experiments.shape[0]):
-    experiment_id = int(experiments[experiment_counter])
-    trials = get_trials(raw_data_path, experiment_id)
-    for trial_counter in range(0, trials.shape[0]):
-        trial_id = int(trials[trial_counter])
-
-        mylogger.info("")
         mylogger.info(
-            f"======= EXPERIMENT ID: {experiment_id} ==== TRIAL ID: {trial_id} ======="
+            "Configure at least target_camera_donor or target_camera_acceptor correctly."
         )
-        mylogger.info("")
+        mylogger.info("ERROR: STOP!!!")
+        exit()
 
-        try:
+    device = get_torch_device(mylogger, config["force_to_cpu"])
-            process_trial(
+
-                config=config,
+    mylogger.info(
-                mylogger=mylogger,
+        f"Create directory {config['export_path']} in the case it does not exist"
-                experiment_id=experiment_id,
+    )
-                trial_id=trial_id,
+    os.makedirs(config["export_path"], exist_ok=True)
-                device=device,
+
-            )
+    raw_data_path: str = os.path.join(
-        except torch.cuda.OutOfMemoryError:
+        config["basic_path"],
-            mylogger.info("WARNING: RUNNING IN FAILBACK MODE!!!!")
+        config["recoding_data"],
-            mylogger.info("Not enough GPU memory. Retry on CPU")
+        config["mouse_identifier"],
-            process_trial(
+        config["raw_path"],
-                config=config,
+    )
-                mylogger=mylogger,
+
-                experiment_id=experiment_id,
+    if os.path.isdir(raw_data_path) is False:
-                trial_id=trial_id,
+        mylogger.info(f"ERROR: could not find raw directory {raw_data_path}!!!!")
-                device=torch.device("cpu"),
+        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])
+
+            mylogger.info("")
+            mylogger.info(
+                f"======= EXPERIMENT ID: {experiment_id} ==== TRIAL ID: {trial_id} ======="
             )
+            mylogger.info("")
+
+            try:
+                process_trial(
+                    config=config,
+                    mylogger=mylogger,
+                    experiment_id=experiment_id,
+                    trial_id=trial_id,
+                    device=device,
+                )
+            except torch.cuda.OutOfMemoryError:
+                mylogger.info("WARNING: RUNNING IN FAILBACK MODE!!!!")
+                mylogger.info("Not enough GPU memory. Retry on CPU")
+                process_trial(
+                    config=config,
+                    mylogger=mylogger,
+                    experiment_id=experiment_id,
+                    trial_id=trial_id,
+                    device=torch.device("cpu"),
+                )
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)