maxfuse.model.Fusor.plot_canonical_correlations

Fusor.plot_canonical_correlations(batch=None, svd_components1=None, svd_components2=None, cca_components=None, filter_prop=0., randomized_svd=False, svd_runs=1, cca_max_iter=2000)[source]

Perform CCA on active arrays aligned by initial matching, and plot the canonical correlations.

Parameters:

  • batch (None or int or tuple of two integers) – The arrays to perform CCA on. If None, randomly select a batch index, if an integer, then the batches are self._batch1_to_batch2[batch], if a tuple of two integers, and batch[0] and batch[1] are selected.

  • svd_components1 (None or int, default=None) – If not None, perform SVD to reduce the dimension of self.active_arr1 before feeding it to CCA.

  • svd_components2 (None or int, default=None) – If not None, perform SVD to reduce the dimension of self.active_arr2 before feeding it to CCA.

  • cca_components (None or int, default=None) – Number of CCA components. If None, it is set to 100 or self.active_arr1.shape[1] or self.active_arr2.shape[1], whichever is smaller.

  • filter_prop (float, default=0.) – CCA is performed on top 1-filter_prop slice of the data on which the matched distances are smallest.

  • randomized_svd (bool, default=False) – Whether to perform randomized SVD.

  • svd_runs (int, default=1) – Perform multiple runs of SVD and the one with lowest Frobenious reconstruction error is selected.

  • cca_max_iter (int, default=2000) – Maximum iteration number for CCA.

Returns:

fig, ax