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Parameters ---------- clr : cooler.Cooler Cooler object cis_only : bool, optional Do iterative correction on intra-chromosomal data only. Inter-chromosomal data is ignored. trans_only : bool, optional Do iterative correction on inter-chromosomal data only. Intra-chromosomal data is ignored. ignore_diags : int or False, optional Drop elements occurring on the first ``ignore_diags`` diagonals of the matrix (including the main diagonal). chunksize : int or None, optional Split the contact matrix pixel records into equally sized chunks to save memory and/or parallelize. Set to ``None`` to use all the pixels at once. mad_max : int, optional Pre-processing bin-level filter. Drop bins whose log marginal sum is less than ``mad_max`` median absolute deviations below the median log marginal sum. min_nnz : int, optional Pre-processing bin-level filter. Drop bins with fewer nonzero elements than this value. min_count : int, optional Pre-processing bin-level filter. Drop bins with lower marginal sum than this value. blacklist : list or 1D array, optional An explicit list of IDs of bad bins to filter out when performing balancing. rescale_marginals : bool, optional Normalize the balancing weights such that the balanced matrix has rows / columns that sum to 1.0. The scale factor is stored in the ``stats`` output dictionary. map : callable, optional Map function to dispatch the matrix chunks to workers. Default is the builtin ``map``, but alternatives include parallel map implementations from a multiprocessing pool. x0 : 1D array, optional Initial weight vector to use. Default is to start with ones(n_bins). tol : float, optional Convergence criterion is the variance of the marginal (row/col) sum vector. max_iters : int, optional Iteration limit. store : bool, optional Whether to store the results in the file when finished. Default is False. store_name : str, optional Name of the column of the bin table to save to. Default name is 'weight'. Returns ------- bias : 1D array, whose shape is the number of bins in ``h5``. Vector of bin bias weights to normalize the observed contact map. Dropped bins will be assigned the value NaN. N[i, j] = O[i, j] * bias[i] * bias[j] stats : dict Summary of parameters used to perform balancing and the average magnitude of the corrected matrix's marginal sum at convergence. nnzNrrRrZnbins)Zdtyper0rQF) rHrnrormrjrlrKZ convergedr>Zdivisive_weightszr+r"gzip) compressionZcompression_optsr)"rvr@rrUrYrXappendr&rr isnanrWryrr r8rr9r.r:rr;rVZmedianlogr expr`rdrOopenZcreate_datasetattrsupdate)'rErjrkrlrmrnrorprJrqrHrIrGr2r3rrrsr}r1edgesZ base_filtersrLrDrFZmarg_nnzr-offsetsr\r]Zc_margZ logNzMargZ med_logNzMargZ dev_logNzMargcutoffrKr>statsgrpZh5optsrrrr sX      " $           )$rEr rjrtrkrtrlrurmrvrnrvrorvrprwrJrtrqrxrHryrIrvrGrvr2rr3rtrrrtrsrzr{r|)( __future__rrA functoolsroperatorrtypingrnumpyrZ_loggingrZ_typingrapir Zparallelr r utilr __all__rr? 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