我们从Python开源项目中,提取了以下9个代码示例,用于说明如何使用seaborn.set_palette()。
def on_train_begin(self, logs={}): sns.set_style("whitegrid") sns.set_style("whitegrid", {"grid.linewidth": 0.5, "lines.linewidth": 0.5, "axes.linewidth": 0.5}) flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"] sns.set_palette(sns.color_palette(flatui)) # flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"] # sns.set_palette(sns.color_palette("Set2", 10)) plt.ion() # set plot to animated self.fig = plt.figure( figsize=(self.width * (1 + len(self.get_metrics(logs))), self.height)) # width, height in inches # move it to the upper left corner move_figure(self.fig, 25, 25)
def scoped_mpl_import(): import matplotlib matplotlib.rcParams['backend'] = MPL_BACKEND import matplotlib.pyplot as plt plt.rcParams['toolbar'] = 'None' # mute matplotlib toolbar import seaborn as sns sns.set(style="whitegrid", color_codes=True, font_scale=1.0, rc={'lines.linewidth': 1.0, 'backend': matplotlib.rcParams['backend']}) palette = sns.color_palette("Blues_d") palette.reverse() sns.set_palette(palette) return (matplotlib, plt, sns)
def set_styling(): sb.set_style("white") red = colors.hex2color("#bb3f3f") blue = colors.hex2color("#5a86ad") deep_colors = sb.color_palette("deep") green = deep_colors[1] custom_palette = [red, blue, green] custom_palette.extend(deep_colors[3:]) sb.set_palette(custom_palette) mpl.rcParams.update({"figure.figsize": np.array([6, 6]), "legend.fontsize": 12, "font.size": 16, "axes.labelsize": 16, "axes.labelweight": "bold", "xtick.labelsize": 16, "ytick.labelsize": 16})
def makeDishwasherFig(ax=None, zNorm=True, save=True): # ts = getGoodDishwasherTs() # ts.data = ar.zNormalizeCols(ts.data) ts = getFig1Ts(zNorm=True, whichTs=WHICH_DISHWASHER_TS) # ax = ts.plot(useWhichLabels=['ZC'], showLabels=False, capYLim=900) colors = DISHWASHER_COLOR_PALETTE * 3 # cycles thru almost three times colors[DISHWASHER_DIM_TO_HIGHLIGHT] = DISHWASHER_HIGHLIGHT_COLOR colors = colors[:ts.data.shape[1]] ts.data[:, 2] /= 2 # scale the ugliest dim to make pic prettier ax = ts.plot(showLabels=False, showBounds=False, capYLim=900, ax=ax, colors=colors) # resets palette... # ax = ts.plot(showLabels=False, showBounds=False, capYLim=900, ax=None) # works # ax.plot(ts.data[:, DISHWASHER_DIM_TO_HIGHLIGHT], color=DISHWASHER_HIGHLIGHT_COLOR) # sb.set_palette(DEFAULT_SB_PALETTE) sb.despine(left=True) ax.set_title("Dishwasher", y=TITLE_Y_POS) # ax.set_xlabel("Minute") plt.tight_layout() if save: saveFigWithName('dishwasher') # ------------------------------------------------ MSRC
def reset_plt(self): """ Reset the current matplotlib plot style. """ import matplotlib.pyplot as plt plt.gcf().subplots_adjust(bottom=0.15) if Settings()["report/xkcd_like_plots"]: import seaborn as sns sns.reset_defaults() mpl.use("agg") plt.xkcd() else: import seaborn as sns sns.reset_defaults() sns.set_style("darkgrid") sns.set_palette(sns.color_palette("muted")) mpl.use("agg")
def setupPalette(count, pal=None): # See http://xkcd.com/color/rgb/. These were chosen to be different "enough". colors = ['grass green', 'canary yellow', 'dirty pink', 'azure', 'tangerine', 'strawberry', 'yellowish green', 'gold', 'sea blue', 'lavender', 'orange brown', 'turquoise', 'royal blue', 'cranberry', 'pea green', 'vermillion', 'sandy yellow', 'greyish brown', 'magenta', 'silver', 'ivory', 'carolina blue', 'very light brown'] palette = sns.color_palette(palette=pal, n_colors=count) if pal else sns.xkcd_palette(colors) sns.set_palette(palette, n_colors=count) # For publications, call setupPlot("paper", font_scale=1.5)
def plot_example(missed, acknowledged): sensor_miss = import_sensorfile(missed) sensor_ack = import_sensorfile(acknowledged) # Window data mag_miss = window_data(process_input(sensor_miss)) mag_ack = window_data(process_input(sensor_ack)) # Window data mag_miss = window_data(process_input(sensor_miss)) mag_ack = window_data(process_input(sensor_ack)) # Filter setup kernel = 15 # apply filter mag_miss_filter = sci.medfilt(mag_miss, kernel) mag_ack_filter = sci.medfilt(mag_ack, kernel) # calibrate data mag_miss_cal = mf.calibrate_median(mag_miss) mag_miss_cal_filter = mf.calibrate_median(mag_miss_filter) mag_ack_cal = mf.calibrate_median(mag_ack) mag_ack_cal_filter = mf.calibrate_median(mag_ack_filter) # PLOT sns.set_style("white") current_palette = sns.color_palette('muted') sns.set_palette(current_palette) plt.figure(0) # Plot RAW missed and acknowledged reminders ax1 = plt.subplot2grid((2, 1), (0, 0)) plt.ylim([-1.5, 1.5]) plt.ylabel('Acceleration (g)') plt.plot(mag_miss_cal, label='Recording 1') plt.legend(loc='lower left') ax2 = plt.subplot2grid((2, 1), (1, 0)) # Plot Missed Reminder RAW plt.ylim([-1.5, 1.5]) plt.ylabel('Acceleration (g)') plt.xlabel('t (ms)') plt.plot(mag_ack_cal, linestyle='-', label='Recording 2') plt.legend(loc='lower left') # CALC AND SAVE STATS stats_one = sp.calc_stats_for_data_stream_as_dictionary(mag_miss_cal) stats_two = sp.calc_stats_for_data_stream_as_dictionary(mag_ack_cal) data = [stats_one, stats_two] write_to_csv(data, 'example_waves') plt.show()
def test_reconstruction(X, gt, n_clusters, filename, from_file=False): Ds = sdp_kmeans(X, n_clusters, method='cvx') if from_file: data = scipy.io.loadmat('{}{}.mat'.format(dir_name, filename)) rec_errors = data['rec_errors'] k_values = data['k_values'] else: k_values = np.arange(200 + len(X)) + 1 rec_errors = [] for k in k_values: print('{} / {}'.format(k, k_values[-1])) rec_errors_k = [] for trials in range(50): Y = symnmf_admm(Ds[-1], k=k) rec_errors_k.append(check_completely_positivity(Ds[-1], Y)) rec_errors.append(rec_errors_k) rec_errors = np.array(rec_errors) scipy.io.savemat('{}{}.mat'.format(dir_name, filename), dict(rec_errors=rec_errors, k_values=k_values)) sns.set_style('white') plt.figure(tight_layout=True) gs = gridspec.GridSpec(1, 3) ax = plt.subplot(gs[0]) plot_data_clustered(X, gt, ax=ax) for i, D_input in enumerate(Ds): ax = plt.subplot(gs[i + 1]) plot_matrix(D_input, ax=ax) if i == 0: ax.set_title('Original Gramian') else: ax.set_title('Layer {} (k={})'.format(i, n_clusters)) plt.savefig('{}{}_solution.pdf'.format(dir_name, filename)) plt.figure(tight_layout=True) mean = np.mean(rec_errors, axis=1) std = np.std(rec_errors, axis=1) sns.set_palette('muted') plt.fill_between(np.squeeze(k_values), mean - 2 * std, mean + 2 * std, alpha=0.3) plt.semilogy(np.squeeze(k_values), mean, linewidth=2) plt.semilogy([n_clusters, n_clusters], [mean.min(), mean.max()], linestyle='--', linewidth=2) plt.xlabel('$r$', size='xx-large') plt.ylabel('Relative reconstruction error', size='xx-large') plt.ylim(np.floor(rec_errors.min() * 1e3) / 1e3, 1) plt.savefig('{}{}_curve.pdf'.format(dir_name, filename))
def plot_2D_arrays(arrs, title='', xlabel='', xinterval=None, ylabel='', yinterval=None, line_names=[], simplified=False): """ Plots multiple arrays in the same plot based on the specifications. """ import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import seaborn as sns plt.clf() sns.set_style('darkgrid') sns.set(font_scale=1.5) sns.set_palette('husl', 8) for i, arr in enumerate(arrs): if arr.ndim != 2 or arr.shape[1] != 2: raise ValueError( 'The array should be 2D and the second dimension should be 2!' ' Shape: %s' % str(arr.shape) ) # Plot last one with black if i == len(arrs) - 1: plt.plot(arr[:, 0], arr[:, 1], color='black') else: plt.plot(arr[:, 0], arr[:, 1]) # If simplified, we don't show text anywhere if not simplified: plt.title(title[:30]) plt.xlabel(xlabel) plt.ylabel(ylabel) if line_names: plt.legend(line_names, loc=6, bbox_to_anchor=(1, 0.5)) if xinterval: plt.xlim(xinterval) if yinterval: plt.ylim(yinterval) plt.tight_layout() ############### # String handling ###############