from matplotlib import use as mplt_use mplt_use('Agg') import matplotlib.pyplot as plt import numpy as np import scipy.cluster.hierarchy as sch from matplotlib import rcParams import matplotlib.colors as colors def plot_correlation(corr_matrix, labels, plotFileName, vmax=None, vmin=None, colormap='jet', image_format=None, plot_numbers=False): num_rows = corr_matrix.shape[0] # set a font size according to figure length if num_rows < 6: font_size = 14 elif num_rows > 40: font_size = 5 else: font_size = int(14 - 0.25*num_rows) rcParams.update({'font.size': font_size}) # set the minimum and maximum values if vmax is None: vmax = 1 if vmin is None: vmin = 0 if corr_matrix.min() >= 0 else -1 # Compute and plot dendrogram. fig = plt.figure(figsize=(11, 9.5))
import argparse import os import numpy as np from past.builtins import map from scipy.sparse import triu from scipy.stats import pearsonr, spearmanr from hicmatrix import HiCMatrix as hm from hicexplorer._version import __version__ from hicexplorer.utilities import check_cooler # for plotting from matplotlib import use as mplt_use import matplotlib as mpl mplt_use('Agg') import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.ticker import FixedLocator import matplotlib.colors as pltcolors import logging log = logging.getLogger(__name__) def parse_arguments(args=None): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, add_help=False, description='Computes pairwise correlations between Hi-C matrices data. '
#!/usr/bin/env python # -*- coding: utf-8 -*- import os import sys import argparse import numpy as np from matplotlib import use as mplt_use mplt_use("Agg") import matplotlib.pyplot as plt import deeptools.countReadsPerBin as countR from deeptools import parserCommon from deeptools._version import __version__ def parse_arguments(args=None): parent_parser = parserCommon.getParentArgParse(binSize=False) read_options_parser = parserCommon.read_options() parser = argparse.ArgumentParser( parents=[required_args(), parent_parser, read_options_parser], formatter_class=argparse.RawDescriptionHelpFormatter, add_help=False, description=""" plotCoverage samples 1 million positions of the genome to build a coverage histogram. Multiple BAM files are accepted but all should correspond to the same genome assembly.