def plot_label_heatmap(samples, bars_y = 30): angles = np.linspace(0., np.pi, len(samples[0]['y'])) min_x = np.amin(angles) max_x = np.amax(angles) radii = np.zeros((len(samples), len(angles))) for i, s in enumerate(samples): radii[i] = s['y'] min_y = np.amin(radii) max_y = np.amax(radii) heatmap_array = np.zeros((bars_y, len(angles))) for i in xrange(np.shape(radii)[0]): for j in xrange(np.shape(radii)[1]): dest_j = j dest_i = round((radii[i,j] - min_y) / (max_y - min_y) * bars_y)-1 heatmap_array[dest_i, dest_j] += 1 indices = np.linspace(min_y, max_y, bars_y) indices = ["%.1f" % i for i in indices] columns = np.linspace(0., 180., len(angles)) columns = ["%.1f" % i for i in columns] heatmap_array = np.flipud(heatmap_array) heatmap_frame = pandas.DataFrame(data=heatmap_array, index=reversed(indices), columns=columns) f = sns.heatmap(heatmap_frame) plt.subplots_adjust(top=0.9) plt.title("Labels Heatmap") sns.axlabel("Angle", "Radius") sns.plt.show(f)
def plot_kmeans_components(self, fig1, gs, kmeans_clusters, clrs, plot_title='Hb', num_subplots=1, flag_separate=1, gridspecs=[0, 0]): with sns.axes_style('darkgrid'): if flag_separate: ax1 = fig1.add_subplot(2, 1, num_subplots) else: ax1 = eval('fig1.add_subplot(gs' + gridspecs + ')') plt.gca().set_color_cycle(clrs) for ii in xrange(0, size(kmeans_clusters, 1)): plt.plot(kmeans_clusters[:, ii], lw=4, label=str(ii)) plt.locator_params(axis='y', nbins=4) sns.axlabel("Time (seconds)", "a.u") ax1.legend(prop={'size': 14}, loc='center left', bbox_to_anchor=(1, 0.5), ncol=1, fancybox=True, shadow=True) plt.title(plot_title, fontsize=14) plt.ylim((min(kmeans_clusters) - 0.0001, max(kmeans_clusters) + 0.0001)) plt.xlim((0, size(kmeans_clusters, 0))) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) self.plot_vertical_lines_onset() self.plot_vertical_lines_offset()
def plot_joint(self, cmap="BuGn"): """Plot the current joint distribution P(p, I).""" pal = sns.color_palette(cmap, 256) lc = pal[int(.7 * 256)] bg = pal[0] fig = plt.figure(figsize=(7, 7)) gs = plt.GridSpec(6, 6) p_lim = self.p_grid.min(), self.p_grid.max() I_lim = self.I_grid.min(), self.I_grid.max() ax1 = fig.add_subplot(gs[1:, :-1]) ax1.set(xlim=p_lim, ylim=I_lim) ax1.contourf(self.p_grid, self.I_grid, self.pI.T, 30, cmap=cmap) sns.axlabel("$p$", "$I$", size=16) ax2 = fig.add_subplot(gs[1:, -1], axis_bgcolor=bg) ax2.set(ylim=I_lim) ax2.plot(self.pI.sum(axis=0), self.I_grid, c=lc, lw=3) ax2.set_xticks([]) ax2.set_yticks([]) ax3 = fig.add_subplot(gs[0, :-1], axis_bgcolor=bg) ax3.set(xlim=p_lim) ax3.plot(self.p_grid, self.pI.sum(axis=1), c=lc, lw=3) ax3.set_xticks([]) ax3.set_yticks([])
def plot_scores(matched_signals, unique_clrs, ind, stimulus_on_time, stimulus_off_time): ######Plot mean signals according to color and boxplot of number of pixels in each plane sns.tsplot(np.array(matched_signals[ind].clr_grped_signal), linewidth=3, ci=95, err_style="ci_band", color=unique_clrs[ind]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") plot_vertical_lines_onset(stimulus_on_time) plot_vertical_lines_offset(stimulus_off_time) plt.axhline(y=0, linestyle='-', color='k', linewidth=1)
def plot_prediction_error(name, clf, X, y): plt.figure() cv = KFold(X.shape[0], 5, shuffle=True) predicted = cross_val_predict(clf, X, y, cv=cv) print("%.3f = mean squared error" % mean_squared_error(y, predicted)) sns.regplot(x=y[:1000], y=predicted[:1000]) sns.axlabel("actual", "predicted") plt.savefig("plot_validation_" + name + ".png")
def tshist(ax,X,Y): B=25 assert(X.size==Y.shape[1]) img=np.zeros((B,X.size)) for t in range(X.size): hc,be=np.histogram(Y[:,t],bins=B,range=(-1,1)) img[::-1,t]=hc.astype(np.float)/Y.shape[0] sns.heatmap(img,ax=ax,cmap='gray') sns.axlabel('X','A')
def grafico_l2(conjunto, xl=None, yl=None, titulox="", tituloy="", titulo="", filename="", tamanho=5): a = np.array(conjunto[0].map(_dic_cruzes)) b = np.array(conjunto[1].map(_dic_cruzes)) c = DataFrame([a, b]).transpose() c.columns = ["A", "B"] sns.lmplot("A", "B", c, x_jitter=0.2, y_jitter=0.3, size=tamanho) plt.title(titulo, fontsize=16) sns.axlabel(titulox, tituloy, fontsize=fontetamanho) plt.savefig(filename)
def grafico_j(conjunto, xl=None, yl=None, titulox="", tituloy="", filename="", tamanho=5, titulo=""): a = np.array(conjunto[0].map(float)) b = np.array(conjunto[1].map(float)) c = DataFrame([a, b]).transpose() c.columns = ["A", "B"] g = sns.jointplot( "A", "B", c, xlim=xl, ylim=yl, kind="reg", stat_func=stats.spearmanr, marginal_kws={"bins": 25}, size=tamanho ) # marginal_kws={"bins": 297} sns.axlabel(titulox, tituloy, fontsize=fontetamanho) plt.title(titulo, y=1.22, fontsize=20) plt.savefig(filename, bbox_inches="tight", format="png")
def plot_normed_intensities(normed_intensities, path=""): """ Plot the normalized intensities to show that the distributions overlap post-normalization """ for col in normed_intensities.columns: sns.kdeplot(np.log2(normed_intensities[col]).dropna(), label=col, shade=True) plt.legend(bbox_to_anchor=(1.3, 0.5), loc="center right") sns.despine() sns.axlabel("Log$_2$ Normalized Intensities", "Density") plt.tight_layout() plt.savefig(os.path.join(path, "normed_intensities_wcl.png"), dpi=300)
def plot_scores(self, fig1, gs, matched_signals, unique_clrs, plot_title='Habenula', gridspecs='[0,0]'): with sns.axes_style('dark'): ax1 = eval('fig1.add_subplot(gs' + gridspecs + ')') for ind in range(0, size(unique_clrs, 0)): sns.tsplot(array(matched_signals[ind].clr_grped_signal), linewidth=5, ci=95, err_style="ci_band", color=unique_clrs[ind]) ax1.locator_params(axis='y', nbins=4) sns.axlabel("Time (seconds)", "a.u") plt.title(plot_title, fontsize=14) self.plot_vertical_lines_onset() self.plot_vertical_lines_offset() plt.axhline(y=0, linestyle='-', color='k', linewidth=1)
def plot_ica_components(ica_components_plot, colors_ica, ax1,stimulus_on_time, stimulus_off_time): ########### Plot components ################## for ii in xrange(0, np.size(ica_components_plot, 1)): plt.plot(ica_components_plot[:,ii], color=colors_ica[ii]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") A = [] for ii in xrange(0,np.size(ica_components_plot, 1)): A = np.append(A, [str(ii+1)]) ax1.legend(A, loc=4) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) plot_vertical_lines_onset(stimulus_on_time) plot_vertical_lines_offset(stimulus_off_time)
def plot_matchedpixels(fig1, gs, matched_pixels, unique_clrs, gridspecs=[0, 0]): ax1 = eval('fig1.add_subplot(gs' + gridspecs + ')') with sns.axes_style("darkgrid"): for ii in xrange(0, size(matched_pixels, 0)): plt.plot(ii + 1, transpose(matched_pixels[ii, :]), 'o', color=unique_clrs[ii], markersize=10) plt.xlim([0, size(matched_pixels, 0) + 1]) for ii in range(0, size(unique_clrs, 0)): plt.plot(repeat(ii + 1, size(matched_pixels, 1)), transpose(matched_pixels[ii, :]), 's', color=unique_clrs[ii], markersize=10, markeredgecolor='k', markeredgewidth=2) x = range(0, size(unique_clrs, 0)+1) labels = [str(e) for e in x] plt.xticks(x, labels, rotation='vertical') sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True)
def graph_func2(perrault): sns.set( font_scale=.8) sns.axlabel('Color', 'Frequency' ) #colors from http://www.color-hex.com and wikipedia flatui = ["#4b0082","#ffd700", "#e6e6fa", "#ffff00", "#FF2400", "#ff6eb4", "#d2b48c", "#ff00ff", "#0000ff", "#C8A2C8", "#800080", "#FF007F", "#FD3F92", "#000000", "#dc143c", "#CCCCFF", "#ffffff", "#ff0000", "#631919", "#fffff0", "#ffa500","#730000", "#808000","#00ffff","#c0c0c0","#808080", "#7fffd4", "#808080", "#008000", "#f5f5dc", "#329999", "#f0ffff", "#FFEF00"] custom_palette = sns.color_palette(flatui) colors = perrault[0] occurences = perrault[1] ax2 = sns.barplot(colors, occurences, palette = custom_palette) fig2 = ax2.get_figure() for item in ax2.get_xticklabels(): item.set_rotation(45) sns.plt.title('Color Word Frequencies in Charles Perrault Stories') fig2.savefig('perrault_chart.png') fig2.clf()
def plot_pca_components(pca_components,ax1,stimulus_on_time, stimulus_off_time,required_pcs): ########### Plot components ################## for ii in range(np.size(pca_components,1)): if ii in required_pcs: plt.plot(pca_components[:,ii],'--', linewidth=4) else: plt.plot(pca_components[:,ii]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") A = [] for ii in xrange(0,np.size(pca_components, 1)): A = np.append(A, [str(ii)]) ax1.legend(A, loc=4) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) plot_vertical_lines_onset(stimulus_on_time) plot_vertical_lines_offset(stimulus_off_time)
def plot_boxplot(fig2, matched_pixels, unique_clrs): #### Plot Boxplot of number of pixels ## Dont plot boxplot if there is only one Z if np.size(matched_pixels,1) == 1: with sns.axes_style("darkgrid"): for ii in xrange(0,np.size(matched_pixels,0)): fig2 = plt.plot(ii+1,np.transpose(matched_pixels[ii,:]),'o', color=unique_clrs[ii]) plt.xlim([0,np.size(matched_pixels,0)+1]) else: fig2 = sns.boxplot(np.transpose(matched_pixels),linewidth=3, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(matched_pixels,1)), np.transpose(matched_pixels[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 2) sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True) return fig2
def plot_scatter_intensities(intensity_cols, experiment1, experiment2="Control_WCL", path=""): """ Create a scatter plot of the per-protein raw intensities between two experiments. Experiment names should be the names of the columns minus the "Intensity " part. """ if "Intensity %s"%experiment1 not in intensity_cols.columns: raise ValueError("Supplied column name not in dataframe") f = plt.figure(figsize=(3, 3)) plt.loglog() tmp_prot_groups = intensity_cols.loc[:, ["Intensity %s"%experiment1, "Intensity %s"%experiment2]] sns.regplot(tmp_prot_groups.ix[:, 1], tmp_prot_groups.ix[:, 0], fit_reg=False) xs = np.linspace(tmp_prot_groups.min()[0], tmp_prot_groups.max()[0]) plt.plot(xs, tmp_prot_groups.sum()[0]/tmp_prot_groups.sum()[1]*xs, "r", alpha=0.4) sns.axlabel("%s Raw Intensities"%experiment1.replace("_", " "), "%s Raw Intensities"%experiment2.replace("_", " ")) sns.despine() plt.tight_layout() plt.savefig(os.path.join(path, "%sv%s_intensities.png"%(experiment1, experiment2)), dpi=300)
def plot_pca_components(self, fig1, gs, pca_components, required_pcs, plot_title='Habneula', gridspecs='[0,0]'): ax1 = eval('fig1.add_subplot(gs' + gridspecs + ')') with sns.axes_style('darkgrid'): for ii in range(size(pca_components, 1)): if ii in required_pcs: plt.plot(pca_components[:, ii], '-', linewidth=5, label=str(ii)) else: plt.plot(pca_components[:, ii], '--', label=str(ii)) plt.title(plot_title, fontsize=14) sns.axlabel("Time (seconds)", "a.u") plt.locator_params(axis='y', nbins=4) sns.axlabel("Time (seconds)", "a.u") ax1.legend(prop={'size': 14}, loc='center left', bbox_to_anchor=(1, 0.5), ncol=1, fancybox=True, shadow=True) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) ax1.locator_params(axis='y', pad=50, nbins=2) plt.ylim((min(pca_components) - 0.0001, max(pca_components) + 0.0001)) self.plot_vertical_lines_onset() self.plot_vertical_lines_offset()
def universal_graph_func(text_variable,title_string,save_file_name_string): sns.set(font_scale=.8) sns.axlabel('Color', 'Frequency') # colors from http://www.color-hex.com and wikipedia flatui = ["#4b0082", "#ffd700", "#e6e6fa", "#ffff00", "#FF2400", "#ff6eb4", "#d2b48c", "#ff00ff", "#0000ff", "#C8A2C8", "#800080", "#FF007F", "#FD3F92", "#000000", "#dc143c", "#CCCCFF", "#ffffff", "#ff0000", "#631919", "#fffff0", "#ffa500", "#730000", "#808000", "#00ffff", "#c0c0c0", "#808080", "#7fffd4", "#808080", "#008000", "#f5f5dc", "#329999", "#f0ffff", "#FFEF00"] custom_palette = sns.color_palette(flatui) colors = text_variable[0] occurences = text_variable[1] ax = sns.barplot(colors, occurences, palette = custom_palette) fig = ax.get_figure() for item in ax.get_xticklabels(): item.set_rotation(45) sns.plt.title(title_string) fig.savefig(save_file_name_string) fig.clf()
def plot_heatmap(df, title, file_name, vmax=1.5, vmin=-1.5, cmap=sns.diverging_palette(220, 20, n=7, as_cmap=True), figsize=(16, 8), dpi=300): sns.set_style("white") fig = plt.figure(figsize=figsize) df = df.reindex(['G', 'A', 'V', 'I', 'L', 'M', 'F', 'Y', 'W', 'C', 'S', 'T', 'P', 'D', 'E', 'N', 'Q', 'H', 'K', 'R', '*']) df.index.values[-1:] = ['STOP'] if 77 in df.columns: df = df.loc[:, :76] if 0 in df.columns: df.drop(0, axis=1, inplace=True) sns.heatmap(df, square=True, linewidths=0.25, xticklabels=2, cmap=cmap, vmin=vmin, vmax=vmax) yeast_ubq = np.array(utils.canonical_yeast_ubq) mask = df.apply(lambda x: x.index.isin(np.where(yeast_ubq == x.name)[0]+1), axis=1) df.loc[:, :] = 1 sns.heatmap(df, mask=~mask, cmap=sns.dark_palette("grey", n_colors=1, as_cmap=True, reverse=True), xticklabels=2, cbar=False, square=True, alpha=0.6) plt.yticks(rotation=0) plt.title(title, fontsize=16) pos = fig.axes[1].get_position() fig.axes[1].set_position([pos.x0, 0.4, 0.1, 0.2]) sns.axlabel("Ub position", "Amino acid") if file_name is not None: plt.savefig(file_name, dpi=dpi, bbox_inches='tight', pad_inches=0) return fig
def graph_func3(grimm): sns.set( font_scale=.8) sns.axlabel('Color', 'Frequency' ) #colors from http://www.color-hex.com and wikipedia #flatui = ["#521515", "#fffff0", "#4b0082","#ffd700", "#7fffd4", "#e6e6fa", "#ffff00", # "#dc143c", "#ffc0cb", "#660000", "#808000", "#00ffff", "#d2b48c", "#c0c0c0", # "#ff00ff", "#0000ff", "#808080", "#ffec8b","#ab82ff", "#ee4000", "#993299", # "#ffaeb9", "#FF00FF", "#808080", '#f0ffff', "#008000", "#f5f5dc", "#008080", # "#CCCCFF","#ffa500", "#000000", "#ffffff", "#ff0000"] flatui = ["#4b0082","#ffd700", "#e6e6fa", "#ffff00", "#FF2400", "#ff6eb4", "#d2b48c", "#ff00ff", "#0000ff", "#C8A2C8", "#800080", "#FF007F", "#FD3F92", "#000000", "#dc143c", "#CCCCFF", "#ffffff", "#ff0000", "#631919", "#fffff0", "#ffa500","#730000", "#808000","#00ffff","#c0c0c0","#808080", "#7fffd4", "#808080", "#008000", "#f5f5dc", "#329999", "#f0ffff", "#FFEF00"] custom_palette = sns.color_palette(flatui) colors = grimm[0] occurences = grimm[1] ax3 = sns.barplot(colors, occurences, palette = custom_palette) fig3 = ax3.get_figure() for item in ax3.get_xticklabels(): item.set_rotation(45) sns.plt.title('Color Word Frequencies in Brothers Grimm Stories') fig3.savefig('grimm_chart.png') fig3.clf()
import numpy as np import seaborn as sns import matplotlib.pyplot as plt data = np.array(list(map(lambda x: list(map(float, x.split("\t"))), """10 100 250 750 2250 6200 10000 12000 0.244 10.666 20.033 45.213 195.22 949.797 1413.038 1836.463 0.001 0.008 0.059 1.201 29.594 595.976 2522.441 8134.59""".split("\n")))) sns.set(style="white", palette="muted") b, g, r, p = sns.color_palette("muted", 4) ax = sns.tsplot(data[1], time=data[0], condition="Learning",color=g) plt.tight_layout() ax.fill_between(data[0], 1e-12, data[1], color=g, alpha=0.25) ax2 = sns.tsplot(data[1] + data[2],condition="Learning + Inversion", time=data[0], color=b) ax2.fill_between(data[0], data[1] + data[2], data[1], color=b, alpha=0.25) plt.legend(loc='upper left') plt.title("System Computation time vs. Recommendable Documents") sns.axlabel("# Of Documents", "Time (s)") plt.savefig("images/computation_time.pdf", bbox_inches="tight")
# # - Plotting function parameters, e.g. sns.distplot(kde=False) # - Seaborn functions, called on the sns Seaborn object synonym # # Similar to how you use plt, matplotlib's import synonym, to customize matplotlib graphics, you use sns, seaborn's import synonym, to refer to a plot and call functions from <a href = "https://stanford.edu/~mwaskom/software/seaborn/api.html#style-frontend">Seaborn's API</a> to customize it. For example, to set the x-axis and y-axis labels, use the <a href = "https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.axlabel.html#seaborn.axlabel">.axlabel() function</a>: # # sns.distplot(births['prglngth'], kde=False) # sns.axlabel('Pregnancy Length, weeks', 'Frequency') # In[5]: import seaborn as sns get_ipython().magic('matplotlib inline') sns.distplot(births['prglngth'], kde=False) sns.axlabel('Pregnancy Length, weeks', 'Frequency') # ###6: Practice: customizing distplot() # Now it's your turn to practice customizing histograms using Seaborn! # ####Instructions # Plot a histogram of the birthord column with the following tweaks: # # x-axis label: Birth number # y-axis label: Frequency # style: "dark" # In[6]:
def plot_pca_figures(pca, maps, pts, clrs, recon,tt,unique_clrs, matched_pixels,matched_signals, Exp_Folder, filename_suffix, Exp_Name): #Plotting as pdf Figure_PDFDirectory = Exp_Folder+'Figures'+filesep if not os.path.exists(Figure_PDFDirectory): os.makedirs(Figure_PDFDirectory) pp = PdfPages(Figure_PDFDirectory+filename_suffix+'_PCA.pdf') sns.set_context("poster") dIPN = [Exp_Name.index(s) for s in Exp_Name if "dIPN" in s] vIPN = [Exp_Name.index(s) for s in Exp_Name if "vIPN" in s] ############ Plot Colormaps of scores ############ #If there is only one stack, else plot each stack if len(maps.shape)==3: #Plot colored maps for each stack with sns.axes_style("white"): fig2 = plt.imshow(maps[:,:,:].transpose((1,0,2))) plt.title(Exp_Name[0]) fig2 = plt.gcf() pp.savefig(fig2) plt.close() else: #Plot dIPN fig2 = plt.figure() for ii in range(0,np.size(dIPN,0)): with sns.axes_style("white"): plt.subplot(2,3,ii+1) plt.imshow(maps[:,:,dIPN[ii],:]) plt.axis('off') plt.title(Exp_Name[dIPN[ii]][15:18],fontsize=10) # plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() #Plot vIPN fig2 = plt.figure() for ii in range(0,np.size(vIPN,0)): with sns.axes_style("white"): plt.subplot(2,3,ii+1) plt.imshow(maps[:,:,vIPN[ii],:]) plt.axis('off') plt.title(Exp_Name[vIPN[ii]][15:18], fontsize=10) # plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() ########### Plot components ################## fig2 = plt.figure() sns.set_context("talk", font_scale=1.25) with sns.axes_style("dark"): ax1 = plt.subplot(231) plt.plot(pca.comps.T); plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") A = [] for ii in xrange(0,np.size(pca.comps.T, 0)): A = np.append(A, ['comp' + str(ii+1)]) ax1.legend(A, loc=4) plot_vertical_lines() plt.axhline(y=0, linestyle='-', color='k', linewidth=1) #Plot mean signals according to color and boxplot of number of pixels in each plane with sns.axes_style("dark"): for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.subplot(234) sns.tsplot(np.array(matched_signals[ii].clr_grped_signal), ci=95, err_style="ci_band", color=unique_clrs[ii]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") plot_vertical_lines() plt.axhline(y=0, linestyle='-', color='k', linewidth=1) matching = [Exp_Name.index(s) for s in Exp_Name if "vIPN" in s] temp_matched_pixels = matched_pixels[:,matching] with sns.axes_style("white"): fig2 = plt.subplot(232) fig2 = sns.boxplot(np.transpose(temp_matched_pixels),linewidth=2, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(temp_matched_pixels,1)), np.transpose(temp_matched_pixels[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 4) plt.title('vIPN') # plt.ylim((0, 1000)) sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True); matching = [Exp_Name.index(s) for s in Exp_Name if "dIPN" in s] temp_matched_pixels = matched_pixels[:,matching] with sns.axes_style("white"): fig2 = plt.subplot(233) fig2 = sns.boxplot(np.transpose(temp_matched_pixels),linewidth=2, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(temp_matched_pixels,1)), np.transpose(temp_matched_pixels[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 4) # plt.ylim((0, 1000)) sns.axlabel("Colors", "Number of Pixels") plt.title('dIPN') sns.despine(offset=10, trim=True); #Create an lm plot seperating Before and After number of pixels #Make Panda data frame A = np.zeros((3,np.size(matched_pixels,0)*np.size(matched_pixels,1)), dtype=np.int) count = 0 for ii in range(0,np.size(matched_pixels,0)): for jj in range(0,np.size(matched_pixels,1)): A[0,count] = matched_pixels[ii,jj] A[1,count] = ii if jj in [0,1,3,5]: A[2,count] = 1 #vIPN else: A[2,count] = 0 #dIPN count = count+1 A = np.transpose(A) B = pd.DataFrame({'Pixel':A[:,0], 'response':A[:,1], 'IPN':A[:,2]}) B["Region"] = B.IPN.map({1: "vIPN", 0: "dIPN"}) #Plot mean projection with sns.axes_style("white"): fig2 = plt.subplot(235) matching = [Exp_Name.index(s) for s in Exp_Name if "vIPN" in s] temp = np.max(maps[:,:,matching,:], axis=2) plt.imshow(temp.astype(np.float16).transpose((1,0,2))) plt.axis('off') plt.title('Max vIPN') fig2 = plt.subplot(236) matching = [Exp_Name.index(s) for s in Exp_Name if "dIPN" in s] temp = np.max(maps[:,:,matching,:], axis=2) plt.imshow(temp.astype(np.float16).transpose((1,0,2))) plt.axis('off') plt.title('Max dIPN') plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() with sns.axes_style("dark"): fig3 = plt.figure() g = sns.lmplot("Pixel", "IPN", B, y_jitter=.20, hue="response", fit_reg=False, palette=unique_clrs, markers="s", scatter_kws={"s": 50}) g.set(ylim = (-0.2, 1.2), yticks=[0, 1], yticklabels=["dIPN", "vIPN"]) plt.axhline(y=0.5, linestyle='-', color='w', linewidth=0.5) fig3 = plt.gcf() pp.savefig(fig3) plt.close() with sns.axes_style("dark"): fig3 = plt.figure() g = sns.lmplot("Pixel", "IPN", B, y_jitter=.20, fit_reg=True, palette=unique_clrs, markers="s", scatter_kws={"s": 50}) g.set(ylim = (-0.2, 1.2), yticks=[0, 1], yticklabels=["dIPN", "vIPN"]) plt.axhline(y=0.5, linestyle='-', color='w', linewidth=0.5) fig3 = plt.gcf() pp.savefig(fig3) plt.close() pp.close()
def plot_pca_maps_for_stacks(pca, maps, pts, clrs, recon, unique_clrs, matched_pixels, matched_signals, num_z_planes,\ Exp_Folder, filename_save_prefix, Stimulus_Name, stim_start, stim_end): filesep = os.path.sep # To save as pdf create file Figure_PDFDirectory = Exp_Folder+filesep+'Figures'+filesep if not os.path.exists(Figure_PDFDirectory): os.makedirs(Figure_PDFDirectory) pp = PdfPages(Figure_PDFDirectory+filename_save_prefix+'_PCA_Stacks.pdf') sns.set_context("poster") if num_z_planes!=0: Filenames_stim = [ii + ' Z='+ str(jj) for jj in num_z_planes for ii in Stimulus_Name] else: Filenames_stim = [ii + ' Z='+ str(jj+1) for jj in range(0,np.size(maps,2)/np.size(Stimulus_Name)) for ii in Stimulus_Name] ############ Plot Colormaps of scores ############ for ii in range(0,np.size(Filenames_stim)): with sns.axes_style("white"): fig2 = plt.imshow(maps[:,:,ii,:]) plt.title((Filenames_stim[ii])) plt.axis('off') fig2 = plt.gcf() pp.savefig(fig2) plt.close() ########### Plot components ################## fig2 = plt.figure() sns.set_context("talk", font_scale=1.25) with sns.axes_style("darkgrid"): ax1 = plt.subplot(221) plt.plot(pca.comps.T); plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") A = [] for ii in xrange(0,np.size(pca.comps.T, 0)): A = np.append(A, [str(ii+1)]) ax1.legend(A, loc=4) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) plot_vertical_lines(stim_start,stim_end) #Plot mean signals according to color and boxplot of number of pixels in each plane with sns.axes_style("darkgrid"): for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.subplot(223) sns.tsplot(np.array(matched_signals[ii].clr_grped_signal), linewidth=3, ci=95, err_style="ci_band", color=unique_clrs[ii]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") plot_vertical_lines(stim_start,stim_end) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) with sns.axes_style("white"): fig2 = plt.subplot(222) fig2 = sns.boxplot(np.transpose(matched_pixels),linewidth=3, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(matched_pixels,1)), np.transpose(matched_pixels[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True); plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() for stim in xrange(0,np.size(Stimulus_Name)): with sns.axes_style("white"): same_stim_folders = [Filenames_stim.index(ii) for ii in Filenames_stim if ii.find(Stimulus_Name[stim])==0] fig2 = plt.subplot(221) matched_pixels_stim = matched_pixels[:,same_stim_folders] if np.size(matched_pixels_stim,1) == 1: for ii in xrange(0,np.size(matched_pixels_stim,0)): fig2 = plt.plot(ii+1,np.transpose(matched_pixels_stim[ii,:]),'o', color=unique_clrs[ii]) plt.xlim([0,np.size(matched_pixels_stim,0)+1]) else: fig2 = sns.boxplot(np.transpose(matched_pixels_stim),linewidth=3, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(matched_pixels_stim,1)), np.transpose(matched_pixels_stim[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.subplot(223) sns.tsplot(np.array(matched_signals[ii].clr_grped_signal), linewidth=3, ci=95, err_style="ci_band", color=unique_clrs[ii]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") plot_vertical_lines(stim_start,stim_end) plt.axhline(y=0, linestyle='-', color='k', linewidth=1) fig2 = plt.subplot(222) temp = (np.mean(maps[:,:,same_stim_folders,:], axis=2)) plt.imshow(temp.astype(np.float16)) plt.axis('off') plt.title('Mean projection' + Stimulus_Name[stim]) plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() pp.close()
from matplotlib import pyplot as plt import seaborn as sns import pandas as pd import numpy as np # Script used to generate timing plot for different error correction methods timings = np.array([12, 15, 3, 3, 10, 67, 62]) IDs = ["Quake", "Bless", "Musket", "BFC", "Seecer", "BayesHammer", "MultiRes"] sns.barplot(x=IDs, y=timings) sns.axlabel("Algorithms", "Timings(in minutes)") sns.plt.title("Comparison of timings for error correction algorithms")
plt.figure(figsize=(16, 10)) sns.set_context("poster", font_scale=0.6) #rc={"lines.linewidth": 2.5} """ # the view of everthing sns.heatmap(refpubyear_count, linewidths=.5, yticklabels=3) ax.xaxis.tick_top() ax.invert_yaxis() sns.axlabel(conf + ' paper-published-year', 'reference-published-year') """ # the subset of reference years ax = sns.heatmap(refpubyear_count.loc[1975:2015], linewidths=.5, annot=True, fmt=".0f", cmap="GnBu") ax.xaxis.tick_top() ax.invert_yaxis() sns.axlabel(conf + ' paper-published-year', 'reference-published-year') plt.savefig(os.path.join(plot_dir, conf, conf+'_year_ref.png'), transparent=True) """ # box plot of the age of paper being cited dataframe: df_citing y: RefPubYear y: ref age (paperpubyear - refpubyear) x (group by): PaperPubYear """ plt.figure(figsize=(16, 4)) df_citing['RefAge'] = - df_citing['PaperPubYear'] + df_citing['RefPubYear'] axb = sns.boxplot(x="PaperPubYear", y="RefAge", data=df_citing, linewidth=1., palette=bar_colrs3) ylim = axb.get_ylim() axb.set_ylim([max(-60, ylim[0]), min(5, ylim[1])])
header=None, names=["inst", "time"], usecols=["time"], na_values=["t"]) except IOError: next color = '#000000' format = 'pdf' if sys.argv[1] == "eps": color = '#796045' format = 'eps' sns.set(font_scale=1.41) sns.set_style("whitegrid", rc={ "grid.color": color, "xtick.color": color, "ytick.color": color, "axes.edgecolor": color, "axes.labelcolor": color, "text.facecolor": 'white', }) sns.set_palette("Set1", desat=0.22) sns.axlabel("time needed", "") #"heuristic") ax = sns.boxplot(frame, orient="h", width=0.79) ax.get_figure().subplots_adjust(left=0.24) ax.set(xscale="log") ax.get_figure().savefig('build/tsplib_med_time.' + format, format=format, transparent=True)
# Simple plot of linear, quadratic, and cubic curves x = np.linspace(0, 2, 100) plt.plot(x, x, label='linear') plt.plot(x, x**2, label='quadratic') plt.plot(x, x**3, label='cubic') plt.xlabel('x label') plt.ylabel('y label') plt.title("Simple Plot") plt.legend(loc="best") plt.show() # Histogram x = np.random.normal(size=1000) sns.distplot(x, bins=20, kde=True, rug=False, label="Histogram w/o Density") sns.axlabel("Value", "Frequency") plt.title("Histogram of a Random Sample from a Normal Distribution") plt.legend() plt.show() # Scatter plot mean, cov = [5, 10], [(1, .5), (.5, 1)] data = np.random.multivariate_normal(mean, cov, 200) data_frame = pd.DataFrame(data, columns=["x", "y"]) sns.jointplot(x="x", y="y", data=data_frame, kind="reg").set_axis_labels("x", "y") plt.suptitle("Joint Plot of Two Variables with Bivariate and Univariate Graphs") plt.show() # Pairwise bivariate
for s in range(100): U[s, :] = e.policy_batch.eval(e.sess, X).flat tshist(axarr[i, 1], X, U) # column 2 log-loss surfaces after learning x0v, u0v = np.meshgrid(np.linspace(0, 1, 30), np.linspace(-1, 1, 30)) x0v, u0v = x0v.reshape((-1, 1)), u0v.reshape((-1, 1)) x1v = np.array([sim.fstep(x, u0v[j]) for j, x in enumerate(x0v)]).reshape( (-1, 1)) Lr = e.eval_1d(e.loss_log, x0v, u0v, x1v) sns.heatmap(Lr.reshape(30, 30), ax=axarr[i, 2], cmap='gray', vmin=-13, vmax=-.5) sns.axlabel('X', 'A') # column 3 - prediction loss under a variety of #Lp = e.eval_1d(e.loss_abs_p,x0v,u0v,x1v) #sns.heatmap(Lp.reshape(30,30),ax=axarr[i,3],cmap='gray',vmin=0.,vmax=1.) #Xr = e.eval_1d(e.loss_predict,X,X,X) # the second 2 args are hacks #axarr[i,3].plot(X,Xr) print(c) # # test # x0v = X # u0v = np.ones((e.batch_size,1)) # x1v = np.ones((e.batch_size,1)) # irrelevant # xp = e.eval_1d(e.x_predict,x0v,u0v,x1v) # pdb.set_trace()
print ('chisq test statistic = {}; p-value = {}'.format(*chisq)) # graph plotting with seaborn sns.set_style('white') sns.set_style('ticks') plt.figure(figsize=(8,5)) sns.distplot(obs_distances, bins=np.linspace(0, WINDOW, 41), norm_hist=True, color='#d8b365', hist_kws={'alpha': 0.3}, kde_kws={'bw': increment}, label='Observed') sns.distplot(exp_distances, bins=np.linspace(0, WINDOW, 41), norm_hist=True, color='#5ab4ac', hist_kws={'alpha': 0.3}, kde_kws={'bw': increment}, label='Expected') if args.dist == 'upstream_to_start': sns.plt.xlim(int(WINDOW * 1.01), int(WINDOW * -0.01)) sns.axlabel("Distance to gene 5' end (bp)", 'Density') elif args.dist == 'downstream_from_start': sns.plt.xlim(int(WINDOW * -0.01), int(WINDOW * 1.01)) sns.axlabel("Distance from gene 5' end (bp)", 'Density') elif args.dist == 'upstream_to_end': sns.plt.xlim(int(WINDOW * 1.01), int(WINDOW * -0.01)) sns.axlabel("Distance to gene 3' end (bp)", 'Density') elif args.dist == 'downstream_from_end': sns.plt.xlim(int(WINDOW * -0.01), int(WINDOW * 1.01)) sns.axlabel("Distance from gene 3' end (bp)", 'Density') plt.legend(loc=9, ncol=2) sns.despine(offset=10, trim=True) # save plot fig = plt.gcf()
plt.show() # Look at the distribution of quality by wine type red_wine = wine.ix[wine['type']=='red', 'quality'] white_wine = wine.ix[wine['type']=='white', 'quality'] sns.set_style("dark") print("""\nsns.distplot(red_wine, norm_hist=True, kde=False, color=red, label=Red wine)""") print(sns.distplot(red_wine, \ norm_hist=True, kde=False, color="red", label="Red wine")) print("""\nsns.distplot(white_wine, norm_hist=True, kde=False, color=white, label=White wine)""") print(sns.distplot(white_wine, \ norm_hist=True, kde=False, color="white", label="White wine")) sns.axlabel("Quality Score", "Density") plt.title("Distribution of Quality by Wine Type") plt.legend() #plt.show() # Test whether mean quality is different between red and white wines print("\nwine.groupby(['type'])[['quality']].agg(['std', 'mean'])") print(wine.groupby(['type'])[['quality']].agg(['std', 'mean'])) tstat, pvalue, df = sm.stats.ttest_ind(red_wine, white_wine) print("\n'tstat: %.3f pvalue: %.4f' % (tstat, pvalue)") print('tstat: %.3f pvalue: %.4f' % (tstat, pvalue)) # Fit a multivariate linear regression model #wine_standardized = (wine - wine.mean()) / wine.std() #formula_all = 'quality ~ alcohol + chlorides + citric_acid + density + fixed_acidity + free_sulfur_dioxide + pH + residual_sugar + sulphates + total_sulfur_dioxide + volatile_acidity' my_formula = 'quality ~ alcohol + chlorides + citric_acid + density + fixed_acidity + free_sulfur_dioxide + pH + residual_sugar + sulphates + total_sulfur_dioxide + volatile_acidity'
if args.end: endTime = re.sub('\.{1}','T',args.end) endTime+=":00" firstIndex = dateRange.index(endTime) else: firstIndex = 0 palette = it.cycle(sea.color_palette()) fig = plt.figure() sea.set_style('darkgrid') fig.suptitle('MS Band Hourly Summary',fontsize=16) ax1 = fig.add_subplot(311) ax1.plot_date(x[firstIndex:lastIndex],caloriesBurned[firstIndex:lastIndex],color=next(palette),linestyle='-',fillstyle='none') sea.axlabel('','Calories Burned') ax2 = fig.add_subplot(312) # 3 rows, 1 column, plot #2 ax2.plot_date(x[firstIndex:lastIndex],stepsTaken[firstIndex:lastIndex],color=next(palette),linestyle='-',fillstyle='none') sea.axlabel('','Steps Taken') ax3 = fig.add_subplot(313) # 3 rows, 1 column, plot #3 ax3.plot_date(x[firstIndex:lastIndex],avgHeartRate[firstIndex:lastIndex],color=next(palette),linestyle='-',fillstyle='none') ax3.plot_date(x[firstIndex:lastIndex],lowHeartRate[firstIndex:lastIndex],color=next(palette),linestyle='-',fillstyle='none') ax3.plot_date(x[firstIndex:lastIndex],peakHeartRate[firstIndex:lastIndex],color=next(palette),linestyle='-',fillstyle='none') ax3.xaxis.set_major_formatter(dates.DateFormatter('%m/%d/%Y %H:%M')) fig.autofmt_xdate() # angle the dates for easier reading sea.axlabel('Date','Heart Rate')
import seaborn as sns import matplotlib.pyplot as plt from datos import data sns.set(style="whitegrid") d = data('mtcars') sns.countplot(x="gear", hue="cyl", data=d) sns.axlabel("Number of Gears", "Frequency") plt.title('Car Distribution by Gear and Cylindres', family='Serif', size=16) plt.show()
roi_names, roi_coords = load_msdl_names_and_coords() stat_av = read_test('pc', 'av', 'avg') stat_v = read_test('pc', 'v', 'avg') stat2 = read_test2('pc', ['av', 'v'], 'avg') i, j = np.unravel_index(stat2.argmax(), stat2.shape) print 'av 1sample pval :', stat_av[i, j] print 'v 1sample pval :', stat_v[i, j] print roi_names[i], roi_names[j] print i, j m = np.eye(2) m[1,0] = stat2[i, j] m[0,1] = m[1,0] plot_connectome(m, [roi_coords[i], roi_coords[j]]) conn = [] behav = [] for i in range(len(dataset.subjects)): c = load_dynacomp_fc(dataset.subjects[i], session='func1', metric='pc', msdl=True) conn.append(c[i, j]) b = behav_data[i]['postRT'] - behav_data[i]['preRT'] behav.append(b) sns.jointplot(np.array(conn), np.array(behav), kind='kde') sns.axlabel('Connectivity', 'Behavior')
def plot_regression_error(clf, X_train, y_train, name): predicted = cross_val_predict(clf, X_train, y_train, cv=5) sns.regplot(x=y_train, y=predicted) sns.axlabel("actual", "predicted") plt.savefig("plot_validation_" + name + ".png")
def plot_pca_figures(pca, maps, pts, clrs, recon,tt,unique_clrs, matched_pixels,matched_signals, Exp_Folder, filename_suffix, Exp_Name): #Plotting as pdf Figure_PDFDirectory = Exp_Folder+'Figures'+filesep if not os.path.exists(Figure_PDFDirectory): os.makedirs(Figure_PDFDirectory) pp = PdfPages(Figure_PDFDirectory+filename_suffix+'_PCA.pdf') sns.set_context("poster") #Pick experiemnt names and seperate to indeces for plotting Fishnum = np.unique([np.int(ii[15:18]) for ii in Exp_Name]) ############ Plot Colormaps of scores ############ #If there is only one stack, else plot each stack if len(maps.shape)==3: #Plot colored maps for each stack with sns.axes_style("white"): fig2 = plt.imshow(maps[:,:,:].transpose((1,0,2))) plt.title(Exp_Name[0]) fig2 = plt.gcf() pp.savefig(fig2) plt.close() else: count = 0 for ii in range(0,np.size(Fishnum,0)): fig2 = plt.figure() count2=3 with sns.axes_style("white"): for jj in range(3): print str(ii) + ' ' + str(jj) if ii == 0 and jj == 2: continue if "Before" in Exp_Name[count]: plt.subplot(2,2,1) plt.imshow(maps[:,:,count,:].transpose((1,0,2))) plt.axis('off') plt.title(Exp_Name[count][15:-4]) count = count+1 else: plt.subplot(2,2,count2) plt.imshow(maps[:,:,count,:].transpose((1,0,2))) plt.axis('off') plt.title(Exp_Name[count][15:-4]) count = count+1 count2=count2+1 plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() ########### Plot components ################## fig2 = plt.figure() sns.set_context("talk", font_scale=1.25) with sns.axes_style("dark"): ax1 = plt.subplot(231) plt.plot(pca.comps.T); plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") A = [] for ii in xrange(0,np.size(pca.comps.T, 0)): A = np.append(A, ['comp' + str(ii+1)]) ax1.legend(A, loc=4) plot_vertical_lines() plt.axhline(y=0, linestyle='-', color='k', linewidth=1) #Plot mean signals according to color and boxplot of number of pixels in each plane with sns.axes_style("dark"): for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.subplot(234) sns.tsplot(np.array(matched_signals[ii].clr_grped_signal), ci=95, err_style="ci_band", color=unique_clrs[ii]) plt.locator_params(axis = 'y', nbins = 4) sns.axlabel("Time (seconds)","a.u") plot_vertical_lines() plt.axhline(y=0, linestyle='-', color='k', linewidth=1) matching = [Exp_Name.index(s) for s in Exp_Name if "Before" in s] temp_matched_pixels = matched_pixels[:,matching] with sns.axes_style("white"): fig2 = plt.subplot(232) fig2 = sns.boxplot(np.transpose(temp_matched_pixels),linewidth=2, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(temp_matched_pixels,1)), np.transpose(temp_matched_pixels[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 4) # plt.ylim((0, 600)) sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True); matching = [Exp_Name.index(s) for s in Exp_Name if "After" in s] temp_matched_pixels = matched_pixels[:,matching] with sns.axes_style("white"): fig2 = plt.subplot(233) fig2 = sns.boxplot(np.transpose(temp_matched_pixels),linewidth=2, widths=.5, color=unique_clrs) for ii in range(0,np.size(unique_clrs,0)): fig2 = plt.plot(np.repeat(ii+1,np.size(temp_matched_pixels,1)), np.transpose(temp_matched_pixels[ii,:]),'s', \ color=unique_clrs[ii], markersize=5, markeredgecolor='k', markeredgewidth=2) plt.locator_params(axis = 'y', nbins = 4) # plt.ylim((0, 600)) sns.axlabel("Colors", "Number of Pixels") sns.despine(offset=10, trim=True); #Create an lm plot seperating Before and After number of pixels #Make Panda data frame A = np.zeros((3,np.size(matched_pixels,0)*np.size(matched_pixels,1)), dtype=np.int) count = 0 for ii in range(0,np.size(matched_pixels,0)): for jj in range(0,np.size(matched_pixels,1)): A[0,count] = matched_pixels[ii,jj] A[1,count] = ii if jj in [0,2,5]: A[2,count] = 1 #Before Lesion else: A[2,count] = 0 #After Lesion count = count+1 A = np.transpose(A) B = pd.DataFrame({'Pixel':A[:,0], 'response':A[:,1], 'Lesion':A[:,2]}) B["BeforeAfter"] = B.Lesion.map({1: "Before", 0: "After"}) #Plot mean projection with sns.axes_style("white"): fig2 = plt.subplot(438) matching = [Exp_Name.index(s) for s in Exp_Name if "Before" in s] temp = np.max(maps[:,:,matching,:], axis=2) plt.imshow(temp.astype(np.float16).transpose((1,0,2))) plt.axis('off') plt.title('Max DRBefore') fig2 = plt.subplot(4,3,11) matching = [Exp_Name.index(s) for s in Exp_Name if "After" in s] temp = np.max(maps[:,:,matching[1:],:], axis=2)+0.2 plt.imshow(temp.astype(np.float16).transpose((1,0,2))) plt.axis('off') plt.title('Max DRAfter') plt.tight_layout() fig2 = plt.gcf() pp.savefig(fig2) plt.close() with sns.axes_style("dark"): fig3 = plt.figure() g = sns.lmplot("Pixel", "Lesion", B, y_jitter=.20, hue="response", fit_reg=False, palette=unique_clrs, markers="s", scatter_kws={"s": 50}) g.set(ylim = (-0.2, 1.2), yticks=[0, 1], yticklabels=["After", "Before"]) plt.axhline(y=0.5, linestyle='-', color='w', linewidth=0.5) fig3 = plt.gcf() pp.savefig(fig3) plt.close() with sns.axes_style("dark"): fig3 = plt.figure() g = sns.lmplot("Pixel", "Lesion", B, y_jitter=.20, fit_reg=True, palette=unique_clrs, markers="s", scatter_kws={"s": 50}) g.set(ylim = (-0.2, 1.2), yticks=[0, 1], yticklabels=["After", "Before"]) plt.axhline(y=0.5, linestyle='-', color='w', linewidth=0.5) fig3 = plt.gcf() pp.savefig(fig3) plt.close() matched_pixels1 = np.delete(matched_pixels, [0],0) A = np.zeros((3,np.size(matched_pixels1,0)*np.size(matched_pixels1,1)), dtype=np.float) count = 0 for ii in range(0,np.size(matched_pixels1,0)): for jj in range(0,np.size(matched_pixels1,1)): A[0,count] = matched_pixels1[ii,jj]/220 if ii == 2 or ii == 3 or ii == 4: A[1,count] = 1 else: A[1,count] = 0 if jj in [0,2,5]: A[2,count] = 0 #Before Lesion else: A[2,count] = 1 #After Lesion count = count+1 A = np.transpose(A) B = pd.DataFrame({'Cells':A[:,0], 'response':A[:,1], 'Lesion':A[:,2]}) B["Bef_aft"] = B.Lesion.map({0: "Before", 1: "CAfter"}) B["Response"] = B.response.map({0: "Excitatory On", 1: "Inhibitory On"}) fig2 = plt.figure() fig2 = sns.factorplot("Response", "Cells", "Bef_aft", B, kind="point", dodge=0.05) fig2 = plt.gcf() pp.savefig(fig2) plt.close() pp.close()
# - Plotting function parameters, e.g. sns.distplot(kde=False) # - Seaborn functions, called on the sns Seaborn object synonym # # Similar to how you use plt, matplotlib's import synonym, to customize matplotlib graphics, you use sns, seaborn's import synonym, to refer to a plot and call functions from <a href = "https://stanford.edu/~mwaskom/software/seaborn/api.html#style-frontend">Seaborn's API</a> to customize it. For example, to set the x-axis and y-axis labels, use the <a href = "https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.axlabel.html#seaborn.axlabel">.axlabel() function</a>: # # sns.distplot(births['prglngth'], kde=False) # sns.axlabel('Pregnancy Length, weeks', 'Frequency') # In[5]: import seaborn as sns get_ipython().magic('matplotlib inline') sns.distplot(births['prglngth'], kde=False) sns.axlabel('Pregnancy Length, weeks', 'Frequency') # ###6: Practice: customizing distplot() # Now it's your turn to practice customizing histograms using Seaborn! # ####Instructions # Plot a histogram of the birthord column with the following tweaks: # # x-axis label: Birth number # y-axis label: Frequency # style: "dark" # In[6]: