import numpy as np
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['svg.fonttype'] = 'none'
import matplotlib.colors as pltcolors
import plotly.graph_objs as go
old_settings = np.seterr(all='ignore')
[docs]def plot_single(ax, ma, average_type, color, label, plot_type='lines'):
"""
Adds a line to the plot in the given ax using the specified method
Parameters
----------
ax : matplotlib axis
matplotlib axis
ma : numpy array
numpy array The data on this matrix is summarized according
to the `average_type` argument.
average_type : str
string values are sum mean median min max std
color : str
a valid color: either a html color name, hex
(e.g #002233), RGB + alpha tuple or list or RGB tuple or list
label : str
label
plot_type: str
type of plot. Either 'se' for standard error, 'std' for
standard deviation, 'overlapped_lines' to plot each line of the matrix,
fill to plot the area between the x axis and the value or any other string to
just plot the average line.
Returns
-------
ax
matplotlib axis
Examples
--------
>>> import matplotlib.pyplot as plt
>>> import os
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> matrix = np.array([[1,2,3],
... [4,5,6],
... [7,8,9]])
>>> ax = plot_single(ax, matrix -2, 'mean', color=[0.6, 0.8, 0.9], label='fill light blue', plot_type='fill')
>>> ax = plot_single(ax, matrix, 'mean', color='blue', label='red')
>>> ax = plot_single(ax, matrix + 5, 'mean', color='red', label='red', plot_type='std')
>>> ax = plot_single(ax, matrix + 10, 'mean', color='#cccccc', label='gray se', plot_type='se')
>>> ax = plot_single(ax, matrix + 20, 'mean', color=(0.9, 0.5, 0.9), label='violet', plot_type='std')
>>> ax = plot_single(ax, matrix + 30, 'mean', color=(0.9, 0.5, 0.9, 0.5), label='violet with alpha', plot_type='std')
>>> leg = ax.legend()
>>> plt.savefig("/tmp/test.pdf")
>>> plt.close()
>>> fig = plt.figure()
>>> os.remove("/tmp/test.pdf")
"""
summary = np.ma.__getattribute__(average_type)(ma, axis=0)
# only plot the average profiles without error regions
x = np.arange(len(summary))
if isinstance(color, np.ndarray):
color = pltcolors.to_hex(color, keep_alpha=True)
ax.plot(x, summary, color=color, label=label, alpha=0.9)
if plot_type == 'fill':
ax.fill_between(x, summary, facecolor=color, alpha=0.6, edgecolor='none')
if plot_type in ['se', 'std']:
if plot_type == 'se': # standard error
std = np.std(ma, axis=0) / np.sqrt(ma.shape[0])
else:
std = np.std(ma, axis=0)
alpha = 0.2
# an alpha channel has to be added to the color to fill the area
# between the mean (or median etc.) and the std or se
f_color = pltcolors.colorConverter.to_rgba(color, alpha)
ax.fill_between(x, summary, summary + std, facecolor=f_color, edgecolor='none')
ax.fill_between(x, summary, summary - std, facecolor=f_color, edgecolor='none')
ax.set_xlim(0, max(x))
return ax
[docs]def plotly_single(ma, average_type, color, label, plot_type='line'):
"""A plotly version of plot_single. Returns a list of traces"""
summary = list(np.ma.__getattribute__(average_type)(ma, axis=0))
x = list(np.arange(len(summary)))
if isinstance(color, str):
color = list(matplotlib.colors.to_rgb(color))
traces = [go.Scatter(x=x, y=summary, name=label, line={'color': "rgba({},{},{},0.9)".format(color[0], color[1], color[2])}, showlegend=False)]
if plot_type == 'fill':
traces[0].update(fill='tozeroy', fillcolor=color)
if plot_type in ['se', 'std']:
if plot_type == 'se': # standard error
std = np.std(ma, axis=0) / np.sqrt(ma.shape[0])
else:
std = np.std(ma, axis=0)
x_rev = x[::-1]
lower = summary - std
trace = go.Scatter(x=x + x_rev,
y=np.concatenate([summary + std, lower[::-1]]),
fill='tozerox',
fillcolor="rgba({},{},{},0.2)".format(color[0], color[1], color[2]),
line=go.Line(color='transparent'),
showlegend=False,
name=label)
traces.append(trace)
return traces
[docs]def getProfileTicks(hm, referencePointLabel, startLabel, endLabel, idx):
"""
returns the position and labelling of the xticks that
correspond to the heatmap
As of deepTools 3, the various parameters can be lists, in which case we then need to index things (the idx parameter)
"""
w = hm.parameters['bin size']
b = hm.parameters['upstream']
a = hm.parameters['downstream']
if idx is not None:
w = w[idx]
b = b[idx]
a = a[idx]
try:
c = hm.parameters['unscaled 5 prime']
if idx is not None:
c = c[idx]
except:
c = 0
try:
d = hm.parameters['unscaled 3 prime']
if idx is not None:
d = d[idx]
except:
d = 0
m = hm.parameters['body']
if idx is not None:
m = m[idx]
tickPlotAdj = 0.5
if b < 1e5:
quotient = 1000
symbol = 'Kb'
else:
quotient = 1e6
symbol = 'Mb'
if m == 0:
xticks = [(k / w) - tickPlotAdj for k in [0, b, b + a]]
xtickslabel = ['{0:.1f}'.format(-(float(b) / quotient)),
referencePointLabel,
'{0:.1f}{1}'.format(float(a) / quotient, symbol)]
else:
xticks_values = [0]
xtickslabel = []
# only if upstream region is set, add a x tick
if b > 0:
xticks_values.append(b)
xtickslabel.append('{0:.1f}'.format(-(float(b) / quotient)))
xtickslabel.append(startLabel)
# set the x tick for the body parameter, regardless if
# upstream is 0 (not set)
if c > 0:
xticks_values.append(b + c)
xtickslabel.append("")
if d > 0:
xticks_values.append(b + c + m)
xtickslabel.append("")
xticks_values.append(b + c + m + d)
xtickslabel.append(endLabel)
if a > 0:
xticks_values.append(b + c + m + d + a)
xtickslabel.append('{0:.1f}{1}'.format(float(a) / quotient, symbol))
xticks = [(k / w) - tickPlotAdj for k in xticks_values]
xticks = [max(x, 0.5) for x in xticks]
return xticks, xtickslabel