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-# gevi
-
-## Main files:
-
-Anime.py : Is a class used for displaying 2d Movies from a 3D Matrix.
-
-ImageAlignment.py: Is a class used for aligning two images (move and rotate as well as an unused scale option). This source code is based on https://github.com/matejak/imreg_dft . Is was ported to PyTorch such it can run in GPUs.
-
-DataContailer.py: Main class for data pre-processing of the gevi raw data.  
-
-## Installation 
-
-The code was tested on a Python 3.11.2 (Linux) with the following pip packages installed:
-
-numpy scipy pandas flake8 pep8-naming black matplotlib seaborn ipython jupyterlab mypy dataclasses-json dataconf mat73 ipympl torch torchtext pywavelets scikit-image opencv-python scikit-learn tensorflow_datasets tensorboard tqdm argh sympy jsmin pybind11 pybind11-stubgen pigar asciichartpy torchvision torchaudio tensorflow natsort roipoly 
-
-Not all packages are necessary (probably these are enougth: torch torchaudio torchvision roipoly natsort numpy matplotlib) but this is our default in-house installation plus roipoly. 
-
-We used a RTX 3090 as test GPU. 
-
-For installing torch under Windows see here: https://pytorch.org/get-started/locally/ 
-
-
-## Data processing chain
-
-### SVD (requires donor and acceptor time series) remove_heartbeat: bool = True
-
-- start automatic_load
-  - try to load previous mask
-  - start: cleaned_load_data
-    - start: load_data
-    - work on XXXX.npy
-      - np.load
-      - organize acceptor (move to GPU memory)
-      - organize donor (move to GPU memory)
-      - move axis (move the time axis of the tensor)
-      - move intra timeseries
-        - donor time series and donor reference image
-        - acceptor time series and acceptor reference image
-      - rotate inter timeseries
-        - acceptor time series and donor reference image
-      - move inter timeseries
-        - acceptor time series and donor reference image
-    - spatial pooling (i.e. 2d average pooling layer; optional)
-        - acceptor(x,y,t) = acceptor(x,y,t) / acceptor(x,y,t).mean(t) + 1
-        - donor(x,y,t) = donor(x,y,t) / donor(x,y,t).mean(t) + 1
-    - remove the heart beat via SVD from donor and acceptor
-      - copy donor and acceptor and work on the copy with the SVD 
-      - remove the mean (over time)
-      - use Cholesky whitening on data with SVD
-      - scale the time series accoring the spatial whitening
-      - average time series over the spatial dimension (which is the global heart beat)
-      - use a normalized scalar product for getting spatial scaling factors
-      - scale the heartbeat with the spatial scaling factors into donor_residuum and acceptor_residuum
-      - store the heartbeat as well as substract it from the original donor and acceptor timeseries
-    - remove mean from donor and acceptor timeseries (- mean over time)
-    - remove linear trends from donor and acceptor timeseries (create a linear function and use a normalized scalar product for getting spatial scaling factors)
-  - use the SVD heart beat for determining the scaling factors for donor and acceptor (heartbeat_scale)
-    - apply bandpass donor_residuum (filtfilt)
-    - apply bandpass acceptor_residuum (filtfilt)
-    - a normalized scalar product is used to determine the scale factor scale(x,y) between donor_residuum(x,y,t) and acceptor_residuum(x,y,t)
-    - calculate mask (optional) ; based on the heart beat power at the spatial positions
-  - scale acceptor signal (heartbeat_scale_a(x,y) * result_a(x,y,t)) and donor signal (heartbeat_scale_d(x,y) * result_d(x,y,t))
-    - heartbeat_scale_a = torch.sqrt(scale)
-    - heartbeat_scale_d = 1.0 / (heartbeat_scale_a + 1e-20)
-  - result(x,y,t) = 1.0 + result_a(x,y,t) - result_d(x,y,t)
-  - gauss blur (optional)
-  - spatial binning (optional)
-  - update inital mask (optional)
-- end automatic_load
-
-### Classic (requires donor, acceptor, volume, and oxygenation time series) remove_heartbeat: bool = False
-
-- start automatic_load
-    - try to load previous mask
-    - start cleaned_load_data
-        - start load_data
-          - work on XXXX.npy
-            - np.load (load one trial)
-            - organize acceptor (move to GPU memory)
-            - organize donor (move to GPU memory)
-            - organize oxygenation (move to GPU memory)
-            - organize volume (move to GPU memory)
-            - move axis (move the time axis of the tensor)
-            - move intra timeseries
-              - donor time series and donor reference image; transformation also used on volume
-              - acceptor time series and acceptor reference image; transformation also used on oxygenation
-            - rotate inter timeseries
-              - acceptor time series and donor reference image; transformation also used on volume
-            - move inter timeseries
-              - acceptor time series and donor reference image; transformation also used on volume
-        - spatial pooling (i.e. 2d average pooling layer; optional)
-        - acceptor(x,y,t) = acceptor(x,y,t) / acceptor(x,y,t).mean(t) + 1
-        - donor(x,y,t) = donor(x,y,t) / donor(x,y,t).mean(t) + 1
-        - oxygenation(x,y,t) = oxygenation(x,y,t) / oxygenation(x,y,t).mean(t) + 1
-        - volume(x,y,t) = volume(x,y,t) / volume(x,y,t).mean(t) + 1
-        - frame shift
-          - the first frame of donor and acceptor time series is dropped
-          - the oxygenation and volume time series are interpolated between two frames (to compensate for the 5ms delay)
-    - measure heart rate (measure_heartbeat_frequency) i.e. find the frequency f_HB(x,y) with the highest power in the frequency band in the volume signal
-    - use "regression" (i.e. iterative non-orthogonal basis decomposition); remove offset, linear trend, oxygenation and volume timeseries
-    - donor: measure heart beat spectral power (measure_heartbeat_power) f_HB(x,y) +/- 3Hz; results in power_d(x,y)
-    - acceptor: measure heart beat spectral power (measure_heartbeat_power) f_HB(x,y) +/- 3Hz ; results in power_a(x,y)
-    - scale acceptor and donor signals via the powers
-      - scale(x,y) = power_d(x,y) / (power_a(x,y) + 1e-20)
-      - heartbeat_scale_a = torch.sqrt(scale)
-      - heartbeat_scale_d = 1.0 / (heartbeat_scale_a + 1e-20)
-    - result(x,y,t) = 1.0 + result_a(x,y,t) - result_d(x,y,t)
-    - gauss blur (optional)
-    - spatial binning (optional)
-- end automatic_load
-
-## DataContainer.py
-
-### Constructor
-
-    def __init__(
-        self,
-        path: str, # Path to the raw data
-        device: torch.device, # e.g. torch.device("cpu") or torch.device("cuda:0")
-        display_logging_messages: bool = False, # displays log messages on screen
-        save_logging_messages: bool = False, # writes log messages into a log file
-    ) -> None:
-
-### automatic_load
-    def automatic_load(  
-        self,
-        experiment_id: int = 1, # number of experiment
-        trial_id: int = 1, # number of the trial
-        start_position: int = 0, # number of frames cut away from the beginning of the time series
-        start_position_coefficients: int = 100, # number of frames ignored for calculating scaling factors and such
-        fs: float = 100.0, # sampling rate in Hz
-        use_regression: bool | None = None, # controls the "regression"; None is automatic mode. Needs all four time series!
-        remove_heartbeat: bool = True,  # i.e. use SVD=True or Classic=False
-        low_frequency: float = 5,  # Hz Butter Bandpass Heartbeat
-        high_frequency: float = 15,  # Hz Butter Bandpass Heartbeat
-        threshold: float | None = 0.5,  # threshold for the mask; None is no mask. valid values are between 0.0 and 1.0.
-        align: bool = True, # align the time series after loading them 
-        iterations: int = 1,  # SVD iterations: Do not touch! Keep at 1
-        lowrank_method: bool = True, # saves computational time if we know that we don't need all SVD values
-        lowrank_q: int = 6, # parameter for the low rank method. need to be at least 2-3x larger than the number of SVD values we want
-        remove_heartbeat_mean: bool = False, # allows us to remove a offset from the SVD heart signals (don't need that because of a bandpass filter)
-        remove_heartbeat_linear: bool = False, # allows us to remove a linear treand from the SVD heart signals (don't need that because of a bandpass filter)
-        bin_size: int = 4, # size of the kernel of the first 2d average pooling layer 
-        do_frame_shift: bool | None = None, # Do the frame shift or not. None = automatic mode. 
-        half_width_frequency_window: float = 3.0,  # Hz (on side ) measure_heartbeat_frequency
-        mmap_mode: bool = True, # controls the np.load 
-        initital_mask_name: str | None = None, # allows to store the map into a file (give filename here or None if you don't want to save it)
-        initital_mask_update: bool = True, # the mask is updated with new information from this trial
-        initital_mask_roi: bool = False, # enables a tool to refine the automatic map
-        gaussian_blur_kernel_size: int | None = 3, # parameter of a gauss blur layer: kernel_size
-        gaussian_blur_sigma: float = 1.0, # parameter of a gauss blur layer: sigma
-        bin_size_post: int | None = None, # size of the kernel of the second 2d average pooling layer 
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
-
-    This functions outputs the result(x,y,t) and mask(x,y), while the latter can be None.  
-
-### get_experiments
-        def get_experiments(
-        self,
-    ) -> torch.Tensor:
-
-Gives a list of available experiments in the path given to the constructor  
-
-### get_trials
-
-    def get_trials(self, experiment_id: int) -> torch.Tensor:
-
-Gives a list of available trials for this experiment in the path given to the constructor