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)