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#!/usr/bin/python3
# -*- coding: utf-8 -*-
from __future__ import division
import argparse
from collections import OrderedDict
import numpy as np
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['svg.fonttype'] = 'none'
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import matplotlib.gridspec as gridspec
from matplotlib import ticker
import copy
import sys
import plotly.offline as py
import plotly.graph_objs as go
# own modules
from deeptools import cm # noqa: F401
from deeptools import parserCommon
from deeptools import heatmapper
from deeptools.heatmapper_utilities import plot_single, plotly_single
from deeptools.utilities import convertCmap
from deeptools.computeMatrixOperations import filterHeatmapValues
debug = 0
old_settings = np.seterr(all='ignore')
plt.ioff()
def parse_arguments(args=None):
parser = argparse.ArgumentParser(
parents=[parserCommon.heatmapperMatrixArgs(),
parserCommon.heatmapperOutputArgs(mode='heatmap'),
parserCommon.heatmapperOptionalArgs(mode='heatmap')],
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='This tool creates a heatmap for '
'scores associated with genomic regions. '
'The program requires a matrix file '
'generated by the tool ``computeMatrix``.',
epilog='An example usage is: plotHeatmap -m matrix.gz',
usage='plotHeatmap -m matrix.gz\n'
'help: plotHeatmap -h / plotHeatmap --help',
add_help=False)
return parser
def process_args(args=None):
args = parse_arguments().parse_args(args)
args.heatmapHeight = args.heatmapHeight if args.heatmapHeight > 3 and args.heatmapHeight <= 100 else 10
if not matplotlib.colors.is_color_like(args.missingDataColor):
exit("The value {0} for --missingDataColor is not valid".format(args.missingDataColor))
args.boxAroundHeatmaps = True if args.boxAroundHeatmaps == 'yes' else False
return args
def prepare_layout(hm_matrix, heatmapsize, showSummaryPlot, showColorbar, perGroup, colorbar_position, fig):
"""
prepare the plot layout
as a grid having as many rows
as samples (+1 for colobar)
and as many rows as groups (or clusters) (+1 for profile plot)
"""
heatmapwidth, heatmapheight = heatmapsize
numcols = hm_matrix.get_num_samples()
numrows = hm_matrix.get_num_groups()
if perGroup:
numcols, numrows = numrows, numcols
# the rows have different size depending
# on the number of regions contained in the
if perGroup:
# heatmap
height_ratio = np.array([np.amax(np.diff(hm_matrix.group_boundaries))] * numrows)
# scale ratio to sum = heatmapheight
height_ratio = heatmapheight * (height_ratio.astype(float) / height_ratio.sum())
else:
# heatmap
height_ratio = np.diff(hm_matrix.group_boundaries)
# scale ratio to sum = heatmapheight
height_ratio = heatmapheight * (height_ratio.astype(float) / height_ratio.sum())
# convert the height_ratio from numpy array back to list
height_ratio = height_ratio.tolist()
# the width ratio is equal for all heatmaps
width_ratio = [heatmapwidth] * numcols
if showColorbar:
if colorbar_position == 'below':
numrows += 2 # a spacer needs to be added to avoid overlaps
height_ratio += [4 / 2.54] # spacer
height_ratio += [1 / 2.54]
else:
numcols += 1
width_ratio += [1 / 2.54]
if showSummaryPlot:
numrows += 2 # plus 2 because a spacer is added
# make height of summary plot
# proportional to the width of heatmap
sumplot_height = heatmapwidth
spacer_height = heatmapwidth / 8
# scale height_ratios to convert from row
# numbers to heatmapheigt fractions
height_ratio = np.concatenate([[sumplot_height, spacer_height], height_ratio])
grids = gridspec.GridSpec(numrows, numcols, height_ratios=height_ratio, width_ratios=width_ratio, figure=fig)
return grids
def addProfilePlot(hm, plt, fig, grids, iterNum, iterNum2, perGroup, averageType, plot_type, yAxisLabel, color_list, yMin, yMax, wspace, hspace, colorbar_position, label_rotation=0.0):
"""
A function to add profile plots to the given figure, possibly in a custom grid subplot which mimics a tight layout (if wspace and hspace are not None)
"""
if wspace is not None and hspace is not None:
if colorbar_position == 'side':
gridsSub = gridspec.GridSpecFromSubplotSpec(1, iterNum, subplot_spec=grids[0, :-1], wspace=wspace, hspace=hspace)
else:
gridsSub = gridspec.GridSpecFromSubplotSpec(1, iterNum, subplot_spec=grids[0, :], wspace=wspace, hspace=hspace)
ax_list = []
globalYmin = np.inf
globalYmax = -np.inf
for sample_id in range(iterNum):
if perGroup:
title = hm.matrix.group_labels[sample_id]
tickIdx = sample_id % hm.matrix.get_num_samples()
else:
title = hm.matrix.sample_labels[sample_id]
tickIdx = sample_id
if sample_id > 0 and len(yMin) == 1 and len(yMax) == 1:
ax_profile = fig.add_subplot(grids[0, sample_id])
else:
if wspace is not None and hspace is not None:
ax_profile = fig.add_subplot(gridsSub[0, sample_id])
else:
ax_profile = fig.add_subplot(grids[0, sample_id])
ax_profile.set_title(title)
for group in range(iterNum2):
if perGroup:
sub_matrix = hm.matrix.get_matrix(sample_id, group)
line_label = sub_matrix['sample']
else:
sub_matrix = hm.matrix.get_matrix(group, sample_id)
line_label = sub_matrix['group']
plot_single(ax_profile, sub_matrix['matrix'],
averageType,
color_list[group],
line_label,
plot_type=plot_type)
if sample_id > 0 and len(yMin) == 1 and len(yMax) == 1:
plt.setp(ax_profile.get_yticklabels(), visible=False)
if sample_id == 0 and yAxisLabel != '':
ax_profile.set_ylabel(yAxisLabel)
xticks, xtickslabel = hm.getTicks(tickIdx)
if np.ceil(max(xticks)) != float(sub_matrix['matrix'].shape[1] - 1):
tickscale = float(sub_matrix['matrix'].shape[1] - 1) / max(xticks)
xticks_use = [x * tickscale for x in xticks]
ax_profile.axes.set_xticks(xticks_use)
else:
ax_profile.axes.set_xticks(xticks)
ax_profile.axes.set_xticklabels(xtickslabel, rotation=label_rotation)
ax_list.append(ax_profile)
# align the first and last label
# such that they don't fall off
# the heatmap sides
ticks = ax_profile.xaxis.get_major_ticks()
ticks[0].label1.set_horizontalalignment('left')
ticks[-1].label1.set_horizontalalignment('right')
globalYmin = min(float(globalYmin), ax_profile.get_ylim()[0])
globalYmax = max(globalYmax, ax_profile.get_ylim()[1])
# It turns out that set_ylim only takes float64s
for sample_id, subplot in enumerate(ax_list):
localYMin = yMin[sample_id % len(yMin)]
localYMax = yMax[sample_id % len(yMax)]
lims = [globalYmin, globalYmax]
if localYMin:
if localYMax:
lims = (float(localYMin), float(localYMax))
else:
lims = (float(localYMin), lims[1])
elif localYMax:
lims = (lims[0], float(localYMax))
if lims[0] >= lims[1]:
lims = (lims[0], lims[0] + 1)
ax_list[sample_id].set_ylim(lims)
return ax_list
def plotlyMatrix(hm,
outFilename,
yMin=[None], yMax=[None],
zMin=[None], zMax=[None],
showSummaryPlot=False,
cmap=None, colorList=None, colorBarPosition='side',
perGroup=False,
averageType='median', yAxisLabel='', xAxisLabel='',
plotTitle='',
showColorbar=False,
label_rotation=0.0):
label_rotation *= -1.0
if colorBarPosition != 'side':
sys.error.write("Warning: It is not currently possible to have multiple colorbars with plotly!\n")
nRows = hm.matrix.get_num_groups()
nCols = hm.matrix.get_num_samples()
if perGroup:
nRows, nCols = nCols, nRows
profileHeight = 0.0
profileBottomBuffer = 0.0
if showSummaryPlot:
profileHeight = 0.2
profileBottomBuffer = 0.05
profileSideBuffer = 0.
profileWidth = 1. / nCols
if nCols > 1:
profileSideBuffer = 0.1 / (nCols - 1)
profileWidth = 0.9 / nCols
dataSummary = []
annos = []
fig = go.Figure()
fig['layout'].update(title=plotTitle)
xAxisN = 1
yAxisN = 1
# Summary plots at the top (if appropriate)
if showSummaryPlot:
yMinLocal = np.inf
yMaxLocal = -np.inf
for i in range(nCols):
xanchor = 'x{}'.format(xAxisN)
yanchor = 'y{}'.format(yAxisN)
xBase = i * (profileSideBuffer + profileWidth)
yBase = 1 - profileHeight
xDomain = [xBase, xBase + profileWidth]
yDomain = [yBase, 1.0]
for j in range(nRows):
if perGroup:
mat = hm.matrix.get_matrix(i, j)
xTicks, xTicksLabels = hm.getTicks(i)
label = mat['sample']
else:
mat = hm.matrix.get_matrix(j, i)
xTicks, xTicksLabels = hm.getTicks(j)
label = mat['group']
if j == 0:
fig['layout']['xaxis{}'.format(xAxisN)] = dict(domain=xDomain, anchor=yanchor, range=[0, mat['matrix'].shape[1]], tickmode='array', tickvals=xTicks, ticktext=xTicksLabels, tickangle=label_rotation)
fig['layout']['yaxis{}'.format(yAxisN)] = dict(anchor=xanchor, domain=yDomain)
trace = plotly_single(mat['matrix'], averageType, colorList[j], label)[0]
trace.update(xaxis=xanchor, yaxis=yanchor, legendgroup=label)
if min(trace['y']) < yMinLocal:
yMinLocal = min(trace['y'])
if max(trace['y']) > yMaxLocal:
yMaxLocal = max(trace['y'])
if i == 0:
trace.update(showlegend=True)
dataSummary.append(trace)
# Add the column label
if perGroup:
title = hm.matrix.group_labels[i]
else:
title = hm.matrix.sample_labels[i]
titleX = xBase + 0.5 * profileWidth
annos.append({'yanchor': 'bottom', 'xref': 'paper', 'xanchor': 'center', 'yref': 'paper', 'text': title, 'y': 1.0, 'x': titleX, 'font': {'size': 16}, 'showarrow': False})
xAxisN += 1
yAxisN += 1
# Adjust y-bounds as appropriate:
for i in range(1, yAxisN):
yMinUse = yMinLocal
if yMin[(i - 1) % len(yMin)] is not None:
yMinUse = yMin[(i - 1) % len(yMin)]
yMaxUse = yMaxLocal
if yMax[(i - 1) % len(yMax)] is not None:
yMaxUse = yMax[(i - 1) % len(yMax)]
fig['layout']['yaxis{}'.format(i)].update(range=[yMinUse, yMaxUse])
fig['layout']['yaxis1'].update(title=yAxisLabel)
# Add the heatmap
dataHeatmap = []
zMinLocal = np.inf
zMaxLocal = -np.inf
heatmapWidth = 1. / nCols
heatmapSideBuffer = 0.0
if nCols > 1:
heatmapWidth = .9 / nCols
heatmapSideBuffer = 0.1 / (nCols - 1)
heatmapHeight = 1.0 - profileHeight - profileBottomBuffer
for i in range(nCols):
xanchor = 'x{}'.format(xAxisN)
xBase = i * (heatmapSideBuffer + heatmapWidth)
# Determine the height of each heatmap, they have no buffer
lengths = [0.0]
for j in range(nRows):
if perGroup:
mat = hm.matrix.get_matrix(i, j)
else:
mat = hm.matrix.get_matrix(j, i)
lengths.append(mat['matrix'].shape[0])
fractionalHeights = heatmapHeight * np.cumsum(lengths).astype(float) / np.sum(lengths).astype(float)
xDomain = [xBase, xBase + heatmapWidth]
fig['layout']['xaxis{}'.format(xAxisN)] = dict(domain=xDomain, anchor='free', position=0.0, range=[0, mat['matrix'].shape[1]], tickmode='array', tickvals=xTicks, ticktext=xTicksLabels, title=xAxisLabel)
# Start adding the heatmaps
for j in range(nRows):
if perGroup:
mat = hm.matrix.get_matrix(i, j)
label = mat['sample']
start = hm.matrix.group_boundaries[i]
end = hm.matrix.group_boundaries[i + 1]
else:
mat = hm.matrix.get_matrix(j, i)
label = mat['group']
start = hm.matrix.group_boundaries[j]
end = hm.matrix.group_boundaries[j + 1]
regs = hm.matrix.regions[start:end]
regs = [x[2] for x in regs]
yanchor = 'y{}'.format(yAxisN)
yDomain = [heatmapHeight - fractionalHeights[j + 1], heatmapHeight - fractionalHeights[j]]
visible = False
if i == 0:
visible = True
fig['layout']['yaxis{}'.format(yAxisN)] = dict(domain=yDomain, anchor=xanchor, visible=visible, title=label, tickmode='array', tickvals=[], ticktext=[])
if np.min(mat['matrix']) < zMinLocal:
zMinLocal = np.min(mat['matrix'])
if np.max(mat['matrix']) < zMaxLocal:
zMaxLocal = np.max(mat['matrix'])
trace = go.Heatmap(z=np.flipud(mat['matrix']),
y=regs[::-1],
xaxis=xanchor,
yaxis=yanchor,
showlegend=False,
name=label,
showscale=False)
dataHeatmap.append(trace)
yAxisN += 1
xAxisN += 1
if showColorbar:
dataHeatmap[-1].update(showscale=True)
dataHeatmap[-1]['colorbar'].update(len=heatmapHeight, y=0, yanchor='bottom', ypad=0.0)
# Adjust z bounds and colorscale
for trace in dataHeatmap:
zMinUse = zMinLocal
zMaxUse = zMaxLocal
if zMin[0] is not None:
zMinUse = zMin[0]
if zMax[0] is not None:
zMaxUse = zMax[0]
trace.update(zmin=zMinUse, zmax=zMaxUse, colorscale=convertCmap(cmap[0], vmin=zMinUse, vmax=zMaxUse))
dataSummary.extend(dataHeatmap)
fig.add_traces(dataSummary)
fig['layout']['annotations'] = annos
py.plot(fig, filename=outFilename, auto_open=False)
def plotMatrix(hm, outFileName,
colorMapDict={'colorMap': ['binary'], 'missingDataColor': 'black', 'alpha': 1.0},
plotTitle='',
xAxisLabel='', yAxisLabel='', regionsLabel='',
zMin=None, zMax=None,
yMin=None, yMax=None,
averageType='median',
reference_point_label=None,
startLabel='TSS', endLabel="TES",
heatmapHeight=25,
heatmapWidth=7.5,
perGroup=False, whatToShow='plot, heatmap and colorbar',
plot_type='lines',
linesAtTickMarks=False,
image_format=None,
legend_location='upper-left',
box_around_heatmaps=True,
label_rotation=0.0,
dpi=200,
interpolation_method='auto'):
hm.reference_point_label = hm.parameters['ref point']
if reference_point_label is not None:
hm.reference_point_label = [reference_point_label] * hm.matrix.get_num_samples()
hm.startLabel = startLabel
hm.endLabel = endLabel
matrix_flatten = None
if zMin is None:
matrix_flatten = hm.matrix.flatten()
# try to avoid outliers by using np.percentile
zMin = np.percentile(matrix_flatten, 1.0)
if np.isnan(zMin):
zMin = [None]
else:
zMin = [zMin] # convert to list to support multiple entries
elif 'auto' in zMin:
matrix_flatten = hm.matrix.flatten()
auto_min = np.percentile(matrix_flatten, 1.0)
if np.isnan(auto_min):
auto_min = None
new_mins = [float(x) if x != 'auto' else auto_min for x in zMin]
zMin = new_mins
else:
new_mins = [float(x) for x in zMin]
zMin = new_mins
if zMax is None:
if matrix_flatten is None:
matrix_flatten = hm.matrix.flatten()
# try to avoid outliers by using np.percentile
zMax = np.percentile(matrix_flatten, 98.0)
if np.isnan(zMax) or zMax <= zMin[0]:
zMax = [None]
else:
zMax = [zMax]
elif 'auto' in zMax:
matrix_flatten = hm.matrix.flatten()
auto_max = np.percentile(matrix_flatten, 98.0)
if np.isnan(auto_max):
auto_max = None
new_maxs = [float(x) if x != 'auto' else auto_max for x in zMax]
zMax = new_maxs
else:
new_maxs = [float(x) for x in zMax]
zMax = new_maxs
if (len(zMin) > 1) & (len(zMax) > 1):
for index, value in enumerate(zMax):
if value <= zMin[index]:
sys.stderr.write("Warnirng: In bigwig {}, the given zmin ({}) is larger than "
"or equal to the given zmax ({}). Thus, it has been set "
"to None. \n".format(index + 1, zMin[index], value))
zMin[index] = None
if yMin is None:
yMin = [None]
if yMax is None:
yMax = [None]
if not isinstance(yMin, list):
yMin = [yMin]
if not isinstance(yMax, list):
yMax = [yMax]
plt.rcParams['font.size'] = 8.0
fontP = FontProperties()
showSummaryPlot = False
showColorbar = False
if whatToShow == 'plot and heatmap':
showSummaryPlot = True
elif whatToShow == 'heatmap and colorbar':
showColorbar = True
elif whatToShow == 'plot, heatmap and colorbar':
showSummaryPlot = True
showColorbar = True
# colormap for the heatmap
if colorMapDict['colorMap']:
cmap = []
for color_map in colorMapDict['colorMap']:
copy_cmp = copy.copy(plt.get_cmap(color_map))
cmap.append(copy_cmp)
cmap[-1].set_bad(colorMapDict['missingDataColor']) # nans are printed using this color
if colorMapDict['colorList'] and len(colorMapDict['colorList']) > 0:
# make a cmap for each color list given
cmap = []
for color_list in colorMapDict['colorList']:
cmap.append(matplotlib.colors.LinearSegmentedColormap.from_list(
'my_cmap', color_list.replace(' ', '').split(","), N=colorMapDict['colorNumber']))
cmap[-1].set_bad(colorMapDict['missingDataColor']) # nans are printed using this color
if len(cmap) > 1 or len(zMin) > 1 or len(zMax) > 1:
# position color bar below heatmap when more than one
# heatmap color is given
colorbar_position = 'below'
else:
colorbar_position = 'side'
# figsize: w,h tuple in inches
figwidth = heatmapWidth / 2.54
figheight = heatmapHeight / 2.54
if showSummaryPlot:
# the summary plot ocupies a height
# equal to the fig width
figheight += figwidth
numsamples = hm.matrix.get_num_samples()
if perGroup:
num_cols = hm.matrix.get_num_groups()
else:
num_cols = numsamples
total_figwidth = figwidth * num_cols
if showColorbar:
if colorbar_position == 'below':
figheight += 1 / 2.54
else:
total_figwidth += 1 / 2.54
fig = plt.figure(figsize=(total_figwidth, figheight), constrained_layout=True)
fig.suptitle(plotTitle, y=1 - (0.06 / figheight))
grids = prepare_layout(
hm.matrix,
(heatmapWidth, heatmapHeight),
showSummaryPlot,
showColorbar,
perGroup,
colorbar_position,
fig
)
# color map for the summary plot (profile) on top of the heatmap
cmap_plot = plt.get_cmap('jet')
numgroups = hm.matrix.get_num_groups()
if perGroup:
color_list = cmap_plot(np.arange(hm.matrix.get_num_samples()) / hm.matrix.get_num_samples())
else:
color_list = cmap_plot(np.arange(numgroups) / numgroups)
alpha = colorMapDict['alpha']
if image_format == 'plotly':
return plotlyMatrix(hm,
outFileName,
yMin=yMin, yMax=yMax,
zMin=zMin, zMax=zMax,
showSummaryPlot=showSummaryPlot, showColorbar=showColorbar,
cmap=cmap, colorList=color_list, colorBarPosition=colorbar_position,
perGroup=perGroup,
averageType=averageType, plotTitle=plotTitle,
xAxisLabel=xAxisLabel, yAxisLabel=yAxisLabel,
label_rotation=label_rotation)
# check if matrix is reference-point based using the upstream >0 value
# and is sorted by region length. If this is
# the case, prepare the data to plot a border at the regions end
regions_length_in_bins = [None] * len(hm.parameters['upstream'])
if hm.matrix.sort_using == 'region_length' and hm.matrix.sort_method != 'no':
for idx in range(len(hm.parameters['upstream'])):
if hm.parameters['ref point'][idx] is None:
regions_length_in_bins[idx] = None
continue
_regions = hm.matrix.get_regions()
foo = []
for _group in _regions:
_reg_len = []
for ind_reg in _group:
if isinstance(ind_reg, dict):
_len = ind_reg['end'] - ind_reg['start']
else:
_len = sum([x[1] - x[0] for x in ind_reg[1]])
if hm.parameters['ref point'][idx] == 'TSS':
_reg_len.append((hm.parameters['upstream'][idx] + _len) / hm.parameters['bin size'][idx])
elif hm.parameters['ref point'][idx] == 'center':
_len *= 0.5
_reg_len.append((hm.parameters['upstream'][idx] + _len) / hm.parameters['bin size'][idx])
elif hm.parameters['ref point'][idx] == 'TES':
_reg_len.append((hm.parameters['upstream'][idx] - _len) / hm.parameters['bin size'][idx])
foo.append(_reg_len)
regions_length_in_bins[idx] = foo
# plot the profiles on top of the heatmaps
if showSummaryPlot:
if perGroup:
iterNum = numgroups
iterNum2 = hm.matrix.get_num_samples()
else:
iterNum = hm.matrix.get_num_samples()
iterNum2 = numgroups
ax_list = addProfilePlot(hm, plt, fig, grids, iterNum, iterNum2, perGroup, averageType, plot_type, yAxisLabel, color_list, yMin, yMax, None, None, colorbar_position, label_rotation)
if legend_location != 'none':
ax_list[-1].legend(loc=legend_location.replace('-', ' '), ncol=1, prop=fontP,
frameon=False, markerscale=0.5)
first_group = 0 # helper variable to place the title per sample/group
for sample in range(hm.matrix.get_num_samples()):
sample_idx = sample
for group in range(numgroups):
group_idx = group
# add the respective profile to the
# summary plot
sub_matrix = hm.matrix.get_matrix(group, sample)
if showSummaryPlot:
if perGroup:
sample_idx = sample + 2 # plot + spacer
else:
group += 2 # plot + spacer
first_group = 1
if perGroup:
ax = fig.add_subplot(grids[sample_idx, group])
# the remainder (%) is used to iterate
# over the available color maps (cmap).
# if the user only provided, lets say two
# and there are 10 groups, colormaps they are reused every
# two groups.
cmap_idx = group_idx % len(cmap)
zmin_idx = group_idx % len(zMin)
zmax_idx = group_idx % len(zMax)
else:
ax = fig.add_subplot(grids[group, sample])
# see above for the use of '%'
cmap_idx = sample % len(cmap)
zmin_idx = sample % len(zMin)
zmax_idx = sample % len(zMax)
if group == first_group and not showSummaryPlot and not perGroup:
title = hm.matrix.sample_labels[sample]
ax.set_title(title)
if box_around_heatmaps is False:
# Turn off the boxes around the individual heatmaps
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
rows, cols = sub_matrix['matrix'].shape
# if the number of rows is too large, then the 'nearest' method simply
# drops rows. A better solution is to relate the threshold to the DPI of the image
if interpolation_method == 'auto':
if rows >= 1000:
interpolation_method = 'bilinear'
else:
interpolation_method = 'nearest'
# if np.clip is not used, then values of the matrix that exceed the zmax limit are
# highlighted. Usually, a significant amount of pixels are equal or above the zmax and
# the default behaviour produces images full of large highlighted dots.
# If interpolation='nearest' is used, this has no effect
sub_matrix['matrix'] = np.clip(sub_matrix['matrix'], zMin[zmin_idx], zMax[zmax_idx])
img = ax.imshow(sub_matrix['matrix'],
aspect='auto',
interpolation=interpolation_method,
origin='upper',
vmin=zMin[zmin_idx],
vmax=zMax[zmax_idx],
cmap=cmap[cmap_idx],
alpha=alpha,
extent=[0, cols, rows, 0])
img.set_rasterized(True)
# plot border at the end of the regions
# if ordered by length
if regions_length_in_bins[sample] is not None:
x_lim = ax.get_xlim()
y_lim = ax.get_ylim()
ax.plot(regions_length_in_bins[sample][group_idx],
np.arange(len(regions_length_in_bins[sample][group_idx])),
'--', color='black', linewidth=0.5, dashes=(3, 2))
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
if perGroup:
ax.axes.set_xlabel(sub_matrix['group'])
if sample < hm.matrix.get_num_samples() - 1:
ax.axes.get_xaxis().set_visible(False)
else:
ax.axes.get_xaxis().set_visible(False)
ax.axes.set_xlabel(xAxisLabel)
ax.axes.set_yticks([])
if perGroup and group == 0:
ax.axes.set_ylabel(sub_matrix['sample'])
elif not perGroup and sample == 0:
ax.axes.set_ylabel(sub_matrix['group'])
# Plot vertical lines at tick marks if desired
if linesAtTickMarks:
xticks_heat, xtickslabel_heat = hm.getTicks(sample)
xticks_heat = [x + 0.5 for x in xticks_heat] # There's an offset of 0.5 compared to the profile plot
if np.ceil(max(xticks_heat)) != float(sub_matrix['matrix'].shape[1]):
tickscale = float(sub_matrix['matrix'].shape[1]) / max(xticks_heat)
xticks_heat_use = [x * tickscale for x in xticks_heat]
else:
xticks_heat_use = xticks_heat
for x in xticks_heat_use:
ax.axvline(x=x, color='black', linewidth=0.5, dashes=(3, 2))
# add labels to last block in a column
if (perGroup and sample == numsamples - 1) or \
(not perGroup and group_idx == numgroups - 1):
# add xticks to the bottom heatmap (last group)
ax.axes.get_xaxis().set_visible(True)
xticks_heat, xtickslabel_heat = hm.getTicks(sample)
xticks_heat = [x + 0.5 for x in xticks_heat] # There's an offset of 0.5 compared to the profile plot
if np.ceil(max(xticks_heat)) != float(sub_matrix['matrix'].shape[1]):
tickscale = float(sub_matrix['matrix'].shape[1]) / max(xticks_heat)
xticks_heat_use = [x * tickscale for x in xticks_heat]
ax.axes.set_xticks(xticks_heat_use)
else:
ax.axes.set_xticks(xticks_heat)
ax.axes.set_xticklabels(xtickslabel_heat, size=8)
# align the first and last label
# such that they don't fall off
# the heatmap sides
ticks = ax.xaxis.get_major_ticks()
ticks[0].label1.set_horizontalalignment('left')
ticks[-1].label1.set_horizontalalignment('right')
ax.get_xaxis().set_tick_params(
which='both',
top=False,
direction='out')
if showColorbar and colorbar_position == 'below':
# draw a colormap per each heatmap below the last block
if perGroup:
col = group_idx
else:
col = sample
ax = fig.add_subplot(grids[-1, col])
tick_locator = ticker.MaxNLocator(nbins=3)
cbar = fig.colorbar(img, cax=ax, orientation='horizontal', ticks=tick_locator)
labels = cbar.ax.get_xticklabels()
ticks = cbar.ax.get_xticks()
if ticks[0] == 0:
# if the label is at the start of the colobar
# move it a bit inside to avoid overlapping
# with other labels
labels[0].set_horizontalalignment('left')
if ticks[-1] == 1:
# if the label is at the end of the colobar
# move it a bit inside to avoid overlapping
# with other labels
labels[-1].set_horizontalalignment('right')
# cbar.ax.set_xticklabels(labels, rotation=90)
if showColorbar and colorbar_position != 'below':
if showSummaryPlot:
# we don't want to colorbar to extend
# over the profiles and spacer top rows
grid_start = 2
else:
grid_start = 0
ax = fig.add_subplot(grids[grid_start:, -1])
fig.colorbar(img, cax=ax)
if box_around_heatmaps:
fig.get_layout_engine().set(wspace=0.10, hspace=0.025, rect=(0.04, 0, 0.96, 0.85))
else:
# When no box is plotted the space between heatmaps is reduced
fig.get_layout_engine().set(wspace=0.05, hspace=0.01, rect=(0.04, 0, 0.96, 0.85))
plt.savefig(outFileName, bbox_inches='tight', pad_inches=0.1, dpi=dpi, format=image_format)
plt.close()
def mergeSmallGroups(matrixDict):
group_lengths = [len(x) for x in matrixDict.values()]
min_group_length = sum(group_lengths) * 0.01
to_merge = []
i = 0
_mergedHeatMapDict = OrderedDict()
for label, ma in matrixDict.items():
# merge small groups together
# otherwise visualization is impaired
if group_lengths[i] > min_group_length:
if len(to_merge):
to_merge.append(label)
new_label = " ".join(to_merge)
new_ma = np.concatenate([matrixDict[item]
for item in to_merge], axis=0)
else:
new_label = label
new_ma = matrixDict[label]
_mergedHeatMapDict[new_label] = new_ma
to_merge = []
else:
to_merge.append(label)
i += 1
if len(to_merge) > 1:
new_label = " ".join(to_merge)
new_ma = np.array()
for item in to_merge:
new_ma = np.concatenate([new_ma, matrixDict[item]])
_mergedHeatMapDict[new_label] = new_ma
return _mergedHeatMapDict
def main(args=None):
args = process_args(args)
hm = heatmapper.heatmapper()
matrix_file = args.matrixFile.name
args.matrixFile.close()
hm.read_matrix_file(matrix_file)
if hm.parameters['min threshold'] is not None or hm.parameters['max threshold'] is not None:
filterHeatmapValues(hm, hm.parameters['min threshold'], hm.parameters['max threshold'])
if args.sortRegions == 'keep':
args.sortRegions = 'no' # These are the same thing
if args.kmeans is not None:
hm.matrix.hmcluster(args.kmeans, method='kmeans', clustering_samples=args.clusterUsingSamples)
elif args.hclust is not None:
print("Performing hierarchical clustering."
"Please note that it might be very slow for large datasets.\n")
hm.matrix.hmcluster(args.hclust, method='hierarchical', clustering_samples=args.clusterUsingSamples)
group_len_ratio = np.diff(hm.matrix.group_boundaries) / len(hm.matrix.regions)
if np.any(group_len_ratio < 5.0 / 1000):
problem = np.flatnonzero(group_len_ratio < 5.0 / 1000)
sys.stderr.write("WARNING: Group '{}' is too small for plotting, you might want to remove it. "
"There will likely be an error message from matplotlib regarding this "
"below.\n".format(hm.matrix.group_labels[problem[0]]))
if args.regionsLabel:
hm.matrix.set_group_labels(args.regionsLabel)
if args.samplesLabel and len(args.samplesLabel):
hm.matrix.set_sample_labels(args.samplesLabel)
if args.sortRegions != 'no':
sortUsingSamples = []
if args.sortUsingSamples is not None:
for i in args.sortUsingSamples:
if (i > 0 and i <= hm.matrix.get_num_samples()):
sortUsingSamples.append(i - 1)
else:
exit("The value {0} for --sortSamples is not valid. Only values from 1 to {1} are allowed.".format(args.sortUsingSamples, hm.matrix.get_num_samples()))
print('Samples used for ordering within each group: ', sortUsingSamples)
hm.matrix.sort_groups(sort_using=args.sortUsing,
sort_method=args.sortRegions,
sample_list=sortUsingSamples)
if args.silhouette:
if args.kmeans is not None:
hm.matrix.computeSilhouette(args.kmeans)
elif args.hclust is not None:
hm.matrix.computeSilhouette(args.args.hclust)
if args.outFileNameMatrix:
hm.save_matrix(args.outFileNameMatrix)
if args.outFileSortedRegions:
hm.save_BED(args.outFileSortedRegions)
colormap_dict = {'colorMap': args.colorMap,
'colorList': args.colorList,
'colorNumber': args.colorNumber,
'missingDataColor': args.missingDataColor,
'alpha': args.alpha}
plotMatrix(hm,
args.outFileName,
colormap_dict, args.plotTitle,
args.xAxisLabel, args.yAxisLabel, args.regionsLabel,
args.zMin, args.zMax,
args.yMin, args.yMax,
args.averageTypeSummaryPlot,
args.refPointLabel,
args.startLabel,
args.endLabel,
args.heatmapHeight,
args.heatmapWidth,
args.perGroup,
args.whatToShow,
linesAtTickMarks=args.linesAtTickMarks,
plot_type=args.plotType,
image_format=args.plotFileFormat,
legend_location=args.legendLocation,
box_around_heatmaps=args.boxAroundHeatmaps,
label_rotation=args.label_rotation,
dpi=args.dpi,
interpolation_method=args.interpolationMethod)
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