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.
