Additional functions¶
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limmbo.utils.utils.
boolanize
(string)[source]¶ Convert command line parameter “True”/”False” into boolean
Parameters: - string (string) –
- or "True" ("False") –
Returns: False/True
Return type: (bool)
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limmbo.utils.utils.
generate_permutation
(P, S, n, seed=12321, exclude_zero=False)[source]¶ Generate permutation.
Parameters: Returns: Returns list of length n containing [np.arrays] of length [S] with subsets/permutations of numbers range(P)
Return type: (list)
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limmbo.utils.utils.
getEigen
(covMatrix, reverse=True)[source]¶ Get eigenvectors and values of hermitian matrix:
Parameters: - covMatrix (array-like) – hermitian matrix
- reverse (bool) – if True (default): order eigenvalues (and vectors) in decreasing order
Returns: tuple containing:
- eigenvectors
- eigenvalues
Return type: (tuple)
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limmbo.utils.utils.
getVariance
(eigenvalue)[source]¶ Based on input eigenvalue computes cumulative sum and normalizes to overall sum to obtain variance explained
Parameters: eigenvalue (array-like) – eigenvalues Returns: variance explained Return type: (float)
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limmbo.utils.utils.
inflate_matrix
(bootstrap_traits, bootstrap, P, zeros=True)[source]¶ Project small matrix into large matrix using indeces provided:
Parameters: - bootstrap_traits (array-like) – [S x S] covariance matrix estimates
- bootstrap (array-like) – [S x 1] array with indices to project [S x S] matrix values into [P x P] matrix
- P (int) – total number of dimensions
- zeros (bool) – fill void spaces in large matrix with zeros (True, default) or nans (False)
Returns: Returns [P x P] matrix containing [S x S] matrix values at bootstrap indeces and zeros/nans elswhere
Return type: (numpy array)
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limmbo.utils.utils.
match
(samples_ref, data_compare, samples_compare, squarematrix=False)[source]¶ Match the order of data and ID matrices to a reference sample order,
Parameters: - samples_ref (array-like) – [M] sammple Ids used as reference
- data_compare (array-like) – [N x L] data matrix with [N] samples and [L] columns
- samples_compare (array-like) – [N] sample IDs to be matched to samples_ref
- squarematrix (bool) – is data_compare a square matrix i.e. samples in cols and rows
Returns: tuple containing:
- data_compare (numpy array): [M x L] data matrix of input data_compare
- samples_compare (numpy array): [M] sample IDs of input samples_compare
- samples_before (int): number of samples in data_compare/samples_compare before matching to samples_ref
- samples_after (int): number of samples in data_compare/samples_compare after matching to samples_ref
Return type: (tuple)
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limmbo.utils.utils.
nans
(shape)[source]¶ Create numpy array of NaNs
Parameters: shape (tuple) – shape of the empty array Returns: numpy array of NaNs Return type: (numpy array)
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limmbo.utils.utils.
regularize
(m, verbose=True)[source]¶ Make matrix positive-semi definite by ensuring minimum eigenvalue >= 0: add absolute value of minimum eigenvalue and 1e-4 (for numerical stability of abs(min(eigenvalue) < 1e-4 to diagonal of matrix
Parameters: m (array-like) – symmetric matrix Returns: Returns tuple containing: - positive, semi-definite matrix from input m (numpy array)
- minimum eigenvalue of input m
Return type: (tuple)