def plotly_correlation(self, corr_matrix, plot_filename, labels, plot_title='',
vmax=None, vmin=None, plot_numbers=True,
colormap='jet'):
"""plot_correlation, but using plotly"""
textElement = []
for row in range(corr_matrix.shape[0]):
trow = []
for col in range(corr_matrix.shape[0]):
if plot_numbers:
trow.append("{:0.2f}".format(corr_matrix[row, col]))
else:
trow.append('')
textElement.append(trow)
zauto = True
if vmax is not None or vmin is not None:
zauto = False
convertedCmap = convertCmap(colormap)
fig = ff.create_annotated_heatmap(corr_matrix, x=labels, y=labels, colorscale=convertedCmap, showscale=True, zauto=zauto, zmin=vmin, zmax=vmax, annotation_text=textElement)
fig.layout['title'] = plot_title
offline.plot(fig, filename=plot_filename, auto_open=False)
def plotly_correlation(self, corr_matrix, plot_filename, labels, plot_title='',
vmax=None, vmin=None, plot_numbers=True,
colormap='jet'):
"""plot_correlation, but using plotly"""
textElement = []
for row in range(corr_matrix.shape[0]):
trow = []
for col in range(corr_matrix.shape[0]):
if plot_numbers:
trow.append("{:0.2f}".format(corr_matrix[row, col]))
else:
trow.append('')
textElement.append(trow)
zauto = True
if vmax is not None or vmin is not None:
zauto = False
convertedCmap = convertCmap(colormap)
fig = ff.create_annotated_heatmap(corr_matrix, x=labels, y=labels, colorscale=convertedCmap, showscale=True, zauto=zauto, zmin=vmin, zmax=vmax, annotation_text=textElement)
fig.layout['title'] = plot_title
offline.plot(fig, filename=plot_filename, auto_open=False)
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['data'] = dataSummary
fig['layout']['annotations'] = annos
py.plot(fig, filename=outFilename, auto_open=False)
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['data'] = dataSummary
fig['layout']['annotations'] = annos
py.plot(fig, filename=outFilename, auto_open=False)
