# 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