Python matplotlib.pyplot.subplots_adjust() Examples
The following are 30 code examples for showing how to use matplotlib.pyplot.subplots_adjust(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: tensortrade Author: tensortrade-org File: matplotlib_trading_chart.py License: Apache License 2.0 | 6 votes |
def __init__(self, df): self.df = df # Create a figure on screen and set the title self.fig = plt.figure() # Create top subplot for net worth axis self.net_worth_ax = plt.subplot2grid((6, 1), (0, 0), rowspan=2, colspan=1) # Create bottom subplot for shared price/volume axis self.price_ax = plt.subplot2grid((6, 1), (2, 0), rowspan=8, colspan=1, sharex=self.net_worth_ax) # Create a new axis for volume which shares its x-axis with price self.volume_ax = self.price_ax.twinx() # Add padding to make graph easier to view plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0) # Show the graph without blocking the rest of the program plt.show(block=False)
Example 2
Project: Attention-Gated-Networks Author: ozan-oktay File: visualise_att_maps_epoch.py License: MIT License | 6 votes |
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None): plt.ion() filters = units.shape[2] n_columns = round(math.sqrt(filters)) n_rows = math.ceil(filters / n_columns) + 1 fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3)) fig.clf() for i in range(filters): ax1 = plt.subplot(n_rows, n_columns, i+1) plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap) plt.axis('on') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.colorbar() if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() # Epochs
Example 3
Project: Attention-Gated-Networks Author: ozan-oktay File: visualise_fmaps.py License: MIT License | 6 votes |
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None): plt.ion() filters = units.shape[2] n_columns = round(math.sqrt(filters)) n_rows = math.ceil(filters / n_columns) + 1 fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3)) fig.clf() for i in range(filters): ax1 = plt.subplot(n_rows, n_columns, i+1) plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap) plt.axis('on') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.colorbar() if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() # Load options
Example 4
Project: Attention-Gated-Networks Author: ozan-oktay File: visualise_attention.py License: MIT License | 6 votes |
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title=''): plt.ion() filters = units.shape[2] n_columns = round(math.sqrt(filters)) n_rows = math.ceil(filters / n_columns) + 1 fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3)) fig.clf() for i in range(filters): ax1 = plt.subplot(n_rows, n_columns, i+1) plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap) plt.axis('on') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.colorbar() if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() plt.suptitle(title)
Example 5
Project: Attention-Gated-Networks Author: ozan-oktay File: visualise_attention.py License: MIT License | 6 votes |
def plotNNFilterOverlay(input_im, units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title='', alpha=0.8): plt.ion() filters = units.shape[2] fig = plt.figure(figure_id, figsize=(5,5)) fig.clf() for i in range(filters): plt.imshow(input_im[:,:,0], interpolation=interp, cmap='gray') plt.imshow(units[:,:,i], interpolation=interp, cmap=colormap, alpha=alpha) plt.axis('off') plt.colorbar() plt.title(title, fontsize='small') if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() # plt.savefig('{}/{}.png'.format(dir_name,time.time())) ## Load options
Example 6
Project: RLTrader Author: notadamking File: TradingChart.py License: GNU General Public License v3.0 | 6 votes |
def __init__(self, df): self.df = df # Create a figure on screen and set the title self.fig = plt.figure() # Create top subplot for net worth axis self.net_worth_ax = plt.subplot2grid((6, 1), (0, 0), rowspan=2, colspan=1) # Create bottom subplot for shared price/volume axis self.price_ax = plt.subplot2grid((6, 1), (2, 0), rowspan=8, colspan=1, sharex=self.net_worth_ax) # Create a new axis for volume which shares its x-axis with price self.volume_ax = self.price_ax.twinx() # Add padding to make graph easier to view plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0) # Show the graph without blocking the rest of the program plt.show(block=False)
Example 7
Project: AMLSim Author: IBM File: plot_alert_pattern_subgraphs.py License: Apache License 2.0 | 6 votes |
def plot_alerts(_g, _bank_accts, _output_png): bank_ids = _bank_accts.keys() cmap = plt.get_cmap("tab10") pos = nx.nx_agraph.graphviz_layout(_g) plt.figure(figsize=(12.0, 8.0)) plt.axis('off') for i, bank_id in enumerate(bank_ids): color = cmap(i) members = _bank_accts[bank_id] nx.draw_networkx_nodes(_g, pos, members, node_size=300, node_color=color, label=bank_id) nx.draw_networkx_labels(_g, pos, {n: n for n in members}, font_size=10) edge_labels = nx.get_edge_attributes(_g, "label") nx.draw_networkx_edges(_g, pos) nx.draw_networkx_edge_labels(_g, pos, edge_labels, font_size=6) plt.legend(numpoints=1) plt.subplots_adjust(left=0, right=1, bottom=0, top=1) plt.savefig(_output_png, dpi=120)
Example 8
Project: supair Author: stelzner File: visualize.py License: MIT License | 6 votes |
def show_images(images, nrow=10, text=None, overlay=None, matplot=False): images = np.squeeze(images) if not matplot: images = np.stack([draw_overlay(images[j], None if text is None else text[j]) for j in range(len(images))], axis=0) vis.images(images, padding=4, nrow=nrow) else: fig, axes = plt.subplots(2, 4, figsize=(8, 4)) plt.subplots_adjust(top=1.0, bottom=0.0, left=0.1, right=0.9, wspace=0.1, hspace=-0.15) for i, image in enumerate(images): cur_axes = axes[i // 4, i % 4] setup_axis(cur_axes) cur_axes.imshow(image, cmap='gray', interpolation='none') if overlay is not None: cur_overlay = scipy.misc.imresize(overlay[i], image.shape) cur_axes.imshow(cur_overlay, cmap='RdYlGn', alpha=0.5) vis.matplot(plt) plt.close(fig)
Example 9
Project: bert-as-service Author: hanxiao File: example7.py License: MIT License | 6 votes |
def vis(embed, vis_alg='PCA', pool_alg='REDUCE_MEAN'): plt.close() fig = plt.figure() plt.rcParams['figure.figsize'] = [21, 7] for idx, ebd in enumerate(embed): ax = plt.subplot(2, 6, idx + 1) vis_x = ebd[:, 0] vis_y = ebd[:, 1] plt.scatter(vis_x, vis_y, c=subset_label, cmap=ListedColormap(["blue", "green", "yellow", "red"]), marker='.', alpha=0.7, s=2) ax.set_title('pool_layer=-%d' % (idx + 1)) plt.tight_layout() plt.subplots_adjust(bottom=0.1, right=0.95, top=0.9) cax = plt.axes([0.96, 0.1, 0.01, 0.3]) cbar = plt.colorbar(cax=cax, ticks=range(num_label)) cbar.ax.get_yaxis().set_ticks([]) for j, lab in enumerate(['ent.', 'bus.', 'sci.', 'heal.']): cbar.ax.text(.5, (2 * j + 1) / 8.0, lab, ha='center', va='center', rotation=270) fig.suptitle('%s visualization of BERT layers using "bert-as-service" (-pool_strategy=%s)' % (vis_alg, pool_alg), fontsize=14) plt.show()
Example 10
Project: WannaPark Author: dalmia File: mnist.py License: GNU General Public License v3.0 | 6 votes |
def plot_bad_images(images): """This takes a list of images misclassified by a pretty good neural network --- one achieving over 93 percent accuracy --- and turns them into a figure.""" bad_image_indices = [8, 18, 33, 92, 119, 124, 149, 151, 193, 233, 241, 247, 259, 300, 313, 321, 324, 341, 349, 352, 359, 362, 381, 412, 435, 445, 449, 478, 479, 495, 502, 511, 528, 531, 547, 571, 578, 582, 597, 610, 619, 628, 629, 659, 667, 691, 707, 717, 726, 740, 791, 810, 844, 846, 898, 938, 939, 947, 956, 959, 965, 982, 1014, 1033, 1039, 1044, 1050, 1055, 1107, 1112, 1124, 1147, 1181, 1191, 1192, 1198, 1202, 1204, 1206, 1224, 1226, 1232, 1242, 1243, 1247, 1256, 1260, 1263, 1283, 1289, 1299, 1310, 1319, 1326, 1328, 1357, 1378, 1393, 1413, 1422, 1435, 1467, 1469, 1494, 1500, 1522, 1523, 1525, 1527, 1530, 1549, 1553, 1609, 1611, 1634, 1641, 1676, 1678, 1681, 1709, 1717, 1722, 1730, 1732, 1737, 1741, 1754, 1759, 1772, 1773, 1790, 1808, 1813, 1823, 1843, 1850, 1857, 1868, 1878, 1880, 1883, 1901, 1913, 1930, 1938, 1940, 1952, 1969, 1970, 1984, 2001, 2009, 2016, 2018, 2035, 2040, 2043, 2044, 2053, 2063, 2098, 2105, 2109, 2118, 2129, 2130, 2135, 2148, 2161, 2168, 2174, 2182, 2185, 2186, 2189, 2224, 2229, 2237, 2266, 2272, 2293, 2299, 2319, 2325, 2326, 2334, 2369, 2371, 2380, 2381, 2387, 2393, 2395, 2406, 2408, 2414, 2422, 2433, 2450, 2488, 2514, 2526, 2548, 2574, 2589, 2598, 2607, 2610, 2631, 2648, 2654, 2695, 2713, 2720, 2721, 2730, 2770, 2771, 2780, 2863, 2866, 2896, 2907, 2925, 2927, 2939, 2995, 3005, 3023, 3030, 3060, 3073, 3102, 3108, 3110, 3114, 3115, 3117, 3130, 3132, 3157, 3160, 3167, 3183, 3189, 3206, 3240, 3254, 3260, 3280, 3329, 3330, 3333, 3383, 3384, 3475, 3490, 3503, 3520, 3525, 3559, 3567, 3573, 3597, 3598, 3604, 3629, 3664, 3702, 3716, 3718, 3725, 3726, 3727, 3751, 3752, 3757, 3763, 3766, 3767, 3769, 3776, 3780, 3798, 3806, 3808, 3811, 3817, 3821, 3838, 3848, 3853, 3855, 3869, 3876, 3902, 3906, 3926, 3941, 3943, 3951, 3954, 3962, 3976, 3985, 3995, 4000, 4002, 4007, 4017, 4018, 4065, 4075, 4078, 4093, 4102, 4139, 4140, 4152, 4154, 4163, 4165, 4176, 4199, 4201, 4205, 4207, 4212, 4224, 4238, 4248, 4256, 4284, 4289, 4297, 4300, 4306, 4344, 4355, 4356, 4359, 4360, 4369, 4405, 4425, 4433, 4435, 4449, 4487, 4497, 4498, 4500, 4521, 4536, 4548, 4563, 4571, 4575, 4601, 4615, 4620, 4633, 4639, 4662, 4690, 4722, 4731, 4735, 4737, 4739, 4740, 4761, 4798, 4807, 4814, 4823, 4833, 4837, 4874, 4876, 4879, 4880, 4886, 4890, 4910, 4950, 4951, 4952, 4956, 4963, 4966, 4968, 4978, 4990, 5001, 5020, 5054, 5067, 5068, 5078, 5135, 5140, 5143, 5176, 5183, 5201, 5210, 5331, 5409, 5457, 5495, 5600, 5601, 5617, 5623, 5634, 5642, 5677, 5678, 5718, 5734, 5735, 5749, 5752, 5771, 5787, 5835, 5842, 5845, 5858, 5887, 5888, 5891, 5906, 5913, 5936, 5937, 5945, 5955, 5957, 5972, 5973, 5985, 5987, 5997, 6035, 6042, 6043, 6045, 6053, 6059, 6065, 6071, 6081, 6091, 6112, 6124, 6157, 6166, 6168, 6172, 6173, 6347, 6370, 6386, 6390, 6391, 6392, 6421, 6426, 6428, 6505, 6542, 6555, 6556, 6560, 6564, 6568, 6571, 6572, 6597, 6598, 6603, 6608, 6625, 6651, 6694, 6706, 6721, 6725, 6740, 6746, 6768, 6783, 6785, 6796, 6817, 6827, 6847, 6870, 6872, 6926, 6945, 7002, 7035, 7043, 7089, 7121, 7130, 7198, 7216, 7233, 7248, 7265, 7426, 7432, 7434, 7494, 7498, 7691, 7777, 7779, 7797, 7800, 7809, 7812, 7821, 7849, 7876, 7886, 7897, 7902, 7905, 7917, 7921, 7945, 7999, 8020, 8059, 8081, 8094, 8095, 8115, 8246, 8256, 8262, 8272, 8273, 8278, 8279, 8293, 8322, 8339, 8353, 8408, 8453, 8456, 8502, 8520, 8522, 8607, 9009, 9010, 9013, 9015, 9019, 9022, 9024, 9026, 9036, 9045, 9046, 9128, 9214, 9280, 9316, 9342, 9382, 9433, 9446, 9506, 9540, 9544, 9587, 9614, 9634, 9642, 9645, 9700, 9716, 9719, 9729, 9732, 9738, 9740, 9741, 9742, 9744, 9745, 9749, 9752, 9768, 9770, 9777, 9779, 9792, 9808, 9831, 9839, 9856, 9858, 9867, 9879, 9883, 9888, 9890, 9893, 9905, 9944, 9970, 9982] n = len(bad_image_indices) bad_images = [images[j] for j in bad_image_indices] fig = plt.figure(figsize=(10, 15)) for j in xrange(1, n+1): ax = fig.add_subplot(25, 125, j) ax.matshow(bad_images[j-1], cmap = matplotlib.cm.binary) ax.set_title(str(bad_image_indices[j-1])) plt.xticks(np.array([])) plt.yticks(np.array([])) plt.subplots_adjust(hspace = 1.2) plt.show()
Example 11
Project: neural-network-animation Author: miloharper File: test_skew.py License: MIT License | 6 votes |
def test_skew_rectange(): fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(16, 12)) axes = axes.flat rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2)) axes[0].set_xlim([-4, 4]) axes[0].set_ylim([-4, 4]) axes[0].set_aspect('equal') for ax, (xrots, yrots) in zip(axes, rotations): xdeg, ydeg = 45 * xrots, 45 * yrots t = transforms.Affine2D().skew_deg(xdeg, ydeg) ax.set_title('Skew of {0} in X and {1} in Y'.format(xdeg, ydeg)) ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2, transform=t + ax.transData, alpha=0.5, facecolor='coral')) plt.subplots_adjust(wspace=0, left=0, right=1, bottom=0)
Example 12
Project: dal Author: montrealrobotics File: dal.py License: MIT License | 6 votes |
def init_figure(self): self.init_fig = True if self.args.figure == True:# and self.obj_fig==None: self.obj_fig = plt.figure(figsize=(16,12)) plt.set_cmap('viridis') self.gridspec = gridspec.GridSpec(3,5) self.ax_map = plt.subplot(self.gridspec[0,0]) self.ax_scan = plt.subplot(self.gridspec[1,0]) self.ax_pose = plt.subplot(self.gridspec[2,0]) self.ax_bel = plt.subplot(self.gridspec[0,1]) self.ax_lik = plt.subplot(self.gridspec[1,1]) self.ax_gtl = plt.subplot(self.gridspec[2,1]) self.ax_pbel = plt.subplot(self.gridspec[0,2:4]) self.ax_plik = plt.subplot(self.gridspec[1,2:4]) self.ax_pgtl = plt.subplot(self.gridspec[2,2:4]) self.ax_act = plt.subplot(self.gridspec[0,4]) self.ax_rew = plt.subplot(self.gridspec[1,4]) self.ax_err = plt.subplot(self.gridspec[2,4]) plt.subplots_adjust(hspace = 0.4, wspace=0.4, top=0.95, bottom=0.05)
Example 13
Project: dal Author: montrealrobotics File: dal_ros_aml.py License: MIT License | 6 votes |
def init_figure(self): self.init_fig = True if self.args.figure == True:# and self.obj_fig==None: self.obj_fig = plt.figure(figsize=(16,12)) plt.set_cmap('viridis') self.gridspec = gridspec.GridSpec(3,5) self.ax_map = plt.subplot(self.gridspec[0,0]) self.ax_scan = plt.subplot(self.gridspec[1,0]) self.ax_pose = plt.subplot(self.gridspec[2,0]) self.ax_bel = plt.subplot(self.gridspec[0,1]) self.ax_lik = plt.subplot(self.gridspec[1,1]) self.ax_gtl = plt.subplot(self.gridspec[2,1]) self.ax_pbel = plt.subplot(self.gridspec[0,2:4]) self.ax_plik = plt.subplot(self.gridspec[1,2:4]) self.ax_pgtl = plt.subplot(self.gridspec[2,2:4]) self.ax_act = plt.subplot(self.gridspec[0,4]) self.ax_rew = plt.subplot(self.gridspec[1,4]) self.ax_err = plt.subplot(self.gridspec[2,4]) plt.subplots_adjust(hspace = 0.4, wspace=0.4, top=0.95, bottom=0.05)
Example 14
Project: python3_ios Author: holzschu File: test_skew.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_skew_rectangle(): fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(8, 8)) axes = axes.flat rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2)) axes[0].set_xlim([-3, 3]) axes[0].set_ylim([-3, 3]) axes[0].set_aspect('equal', share=True) for ax, (xrots, yrots) in zip(axes, rotations): xdeg, ydeg = 45 * xrots, 45 * yrots t = transforms.Affine2D().skew_deg(xdeg, ydeg) ax.set_title('Skew of {0} in X and {1} in Y'.format(xdeg, ydeg)) ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2, transform=t + ax.transData, alpha=0.5, facecolor='coral')) plt.subplots_adjust(wspace=0, left=0.01, right=0.99, bottom=0.01, top=0.99)
Example 15
Project: mac-network Author: stanfordnlp File: visualization.py License: Apache License 2.0 | 5 votes |
def showImgAtts(instance): img = imread(inImgName(instance["imageId"])) length = len(instance["attentions"]["kb"]) # show images for j in range(length): fig, ax = plt.subplots() fig.set_figheight(figureImageDims[0]) fig.set_figwidth(figureImageDims[1]) showImgAtt(img, instance, j, ax) plt.subplots_adjust(bottom = 0, top = 1, left = 0, right = 1) savePlot(fig, outImgAttName(instance, j))
Example 16
Project: recaptcha-cracker Author: nocturnaltortoise File: main.py License: GNU General Public License v3.0 | 5 votes |
def show_checkbox_predictions(checkboxes, rows, cols, captcha_query, correct): fig, axes = plt.subplots(rows, cols) for checkbox in checkboxes: position = checkbox['position'] path = checkbox['path'] predictions = checkbox['predictions'] matching = checkbox['matching'] if path: path = os.path.join('../', path) x = position[0]-1 y = position[1]-1 axes[x, y].imshow(skimage.io.imread(path)) if matching: axes[x, y].set_title("Picked \n {0}".format(predictions)) else: axes[x, y].set_title(predictions) axes[x, y].set_xticks([]) axes[x, y].set_yticks([]) fig.suptitle("{0}, Correct".format(captcha_query)) if correct else fig.suptitle("{0}, Incorrect".format(captcha_query)) plt.subplots_adjust(hspace=0.5) plt.show()
Example 17
Project: CapsLayer Author: naturomics File: figure.py License: Apache License 2.0 | 5 votes |
def plot_activation(matrix, step, save_to=None): save_to = os.path.join(".", "activations") if save_to is None else save_to os.makedirs(save_to, exist_ok=True) if len(matrix.shape) != 2: raise ValueError('Input "matrix" should have 2 rank, but it is',str(len(matrix.shape))) num_label = matrix.shape[1] - 1 matrix = matrix[matrix[:, num_label].argsort()] fig, axes = plt.subplots(ncols=1, nrows=num_label, figsize=(15,12)) fig.suptitle("The probability of entity presence (step %s)"%str(step), fontsize=20) fig.tight_layout() for i, ax in enumerate(axes.flatten()): idx = num_label - (i + 1) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.set_ylim(0, 1.05) ax.set_ylabel("Capsule " + str(idx)) ax.yaxis.set_major_locator(ticker.NullLocator()) if idx > 0: ax.xaxis.set_major_locator(ticker.NullLocator()) else: ax.xaxis.set_major_locator(ticker.IndexLocator(base=500,offset=0)) ax.set_xlabel("Sample index ") ax.plot(matrix[:,idx]) ax_prime = ax.twinx() ax_prime.spines['top'].set_color('none') ax_prime.spines['bottom'].set_color('none') plt.subplots_adjust(hspace=0.2, left=0.05, right=0.95, bottom=0.05, top=.95) plt.savefig(os.path.join(save_to, "activation_%s.png" % str(step))) plt.close()
Example 18
Project: qb Author: Pinafore File: performance.py License: MIT License | 5 votes |
def plot_summary(summary_only, stats_dir, output): import seaborn as sns rows = parse_data(stats_dir) g = sns.factorplot(y='result', x='score', col='experiment', data=rows, kind='bar', ci=None, order=ANSWER_PLOT_ORDER, size=4, col_wrap=4, sharex=False) for ax in g.axes.flat: for label in ax.get_xticklabels(): label.set_rotation(30) plt.subplots_adjust(top=0.93) g.fig.suptitle('Feature Ablation Study') g.savefig(output, format='png', dpi=200)
Example 19
Project: reinforcement-learning-an-introduction Author: ShangtongZhang File: blackjack.py License: MIT License | 5 votes |
def figure_5_1(): states_usable_ace_1, states_no_usable_ace_1 = monte_carlo_on_policy(10000) states_usable_ace_2, states_no_usable_ace_2 = monte_carlo_on_policy(500000) states = [states_usable_ace_1, states_usable_ace_2, states_no_usable_ace_1, states_no_usable_ace_2] titles = ['Usable Ace, 10000 Episodes', 'Usable Ace, 500000 Episodes', 'No Usable Ace, 10000 Episodes', 'No Usable Ace, 500000 Episodes'] _, axes = plt.subplots(2, 2, figsize=(40, 30)) plt.subplots_adjust(wspace=0.1, hspace=0.2) axes = axes.flatten() for state, title, axis in zip(states, titles, axes): fig = sns.heatmap(np.flipud(state), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), yticklabels=list(reversed(range(12, 22)))) fig.set_ylabel('player sum', fontsize=30) fig.set_xlabel('dealer showing', fontsize=30) fig.set_title(title, fontsize=30) plt.savefig('../images/figure_5_1.png') plt.close()
Example 20
Project: reinforcement-learning-an-introduction Author: ShangtongZhang File: blackjack.py License: MIT License | 5 votes |
def figure_5_2(): state_action_values = monte_carlo_es(500000) state_value_no_usable_ace = np.max(state_action_values[:, :, 0, :], axis=-1) state_value_usable_ace = np.max(state_action_values[:, :, 1, :], axis=-1) # get the optimal policy action_no_usable_ace = np.argmax(state_action_values[:, :, 0, :], axis=-1) action_usable_ace = np.argmax(state_action_values[:, :, 1, :], axis=-1) images = [action_usable_ace, state_value_usable_ace, action_no_usable_ace, state_value_no_usable_ace] titles = ['Optimal policy with usable Ace', 'Optimal value with usable Ace', 'Optimal policy without usable Ace', 'Optimal value without usable Ace'] _, axes = plt.subplots(2, 2, figsize=(40, 30)) plt.subplots_adjust(wspace=0.1, hspace=0.2) axes = axes.flatten() for image, title, axis in zip(images, titles, axes): fig = sns.heatmap(np.flipud(image), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), yticklabels=list(reversed(range(12, 22)))) fig.set_ylabel('player sum', fontsize=30) fig.set_xlabel('dealer showing', fontsize=30) fig.set_title(title, fontsize=30) plt.savefig('../images/figure_5_2.png') plt.close()
Example 21
Project: AIX360 Author: IBM File: dipvae_utils.py License: Apache License 2.0 | 5 votes |
def plot_latent_traversal(explainer, input_images, args, dataset_obj, image_id_to_plot=0, num_sweeps=15, max_abs_edit_value=10.0, epoch=0, batch_id = 0, save_dir="results"): edit_dim_values = np.linspace(-1.0 *max_abs_edit_value, max_abs_edit_value, num_sweeps) f, axarr = plt.subplots(args.latent_dim, len(edit_dim_values), sharex=True, sharey=True) f.set_size_inches(10, 10* args.latent_dim / len(edit_dim_values)) for i in range(args.latent_dim): for j in range(len(edit_dim_values)): edited_images = convert_and_reshape(explainer.explain(input_images=input_images, edit_dim_id = i, edit_dim_value = edit_dim_values[j],edit_z_sample=False), dataset_obj) if edited_images.shape[2] == 1: axarr[i][j].imshow(edited_images[image_id_to_plot,:,:,0], cmap="gray", aspect='auto') else: axarr[i][j].imshow(edited_images[image_id_to_plot]*0.5 + 0.5, aspect='auto') #axarr[j][i].axis('off') if i == len(axarr) - 1: axarr[i][j].set_xlabel("z:" + str(np.round(edit_dim_values[j], 1))) if j == 0: axarr[i][j].set_ylabel("l:" + str(i)) axarr[i][j].set_yticks([]) axarr[i][j].set_xticks([]) plt.subplots_adjust(hspace=0, wspace=0) if not os.path.isdir(save_dir): os.makedirs(save_dir) f.savefig(os.path.join(save_dir, 'traversal_epoch_{}_batch_id_{}.png'.format(epoch, batch_id))) plt.close(fig=f)
Example 22
Project: supair Author: stelzner File: make_plots.py License: MIT License | 5 votes |
def make_perf(spair_file, air_file, output_file, max_t, add_annotation=None, directory='./plots', no_bg_file=None): spair_file = os.path.join(directory, spair_file) air_file = os.path.join(directory, air_file) output_file = os.path.join(directory, output_file) spn_data = rows_to_matrix(read_csv(spair_file)) air_data = rows_to_matrix(read_csv(air_file)) air_bins, air_means, air_stdev = process_perf(air_data, max_t + 20) spn_bins, spn_means, spn_stdev = process_perf(spn_data, max_t + 20) print(spair_file, 'AIR:', air_means[-2:], 'SuPAIR', spn_means[-2:]) fig, plot = plt.subplots(figsize=[3.8, 2.44]) plt.subplots_adjust(top=0.97, bottom=0.21, left=0.19, right=0.95, wspace=0.1, hspace=0.1) plot.fill_between(spn_bins, spn_means + spn_stdev, spn_means - spn_stdev, alpha=0.5) plot.plot(spn_bins, spn_means, linewidth=2.0, label='SuPAIR') if no_bg_file is not None: no_bg_file = os.path.join(directory, no_bg_file) nobg_data = rows_to_matrix(read_csv(no_bg_file)) nobg_bins, nobg_means, nobg_stdev = process_perf(nobg_data, max_t + 20) plot.fill_between(nobg_bins, nobg_means + nobg_stdev, nobg_means - nobg_stdev, alpha=0.5) plot.plot(nobg_bins, nobg_means, linewidth=2.0, label='SuPAIR w/o bg') plot.fill_between(air_bins, air_means + air_stdev, air_means - air_stdev, alpha=0.5) plot.plot(air_bins, air_means, linewidth=2.0, label='AIR') plot.set_xlim(min(air_bins), max_t) plot.set_ylim(-0.002, 1.0) plot.set_xlabel('time (s)', fontsize=14) plot.set_ylabel('count accuracy', fontsize=14) plot.tick_params(labelsize=12) plot.legend(loc='lower right') # if add_annotation is not None: # add_annotation(plot) vis.matplot(plt) plt.savefig(output_file)
Example 23
Project: verb-attributes Author: uwnlp File: fig_4.py License: MIT License | 5 votes |
def att_plot(top_labels, gt_ind, probs, fn): # plt.figure(figsize=(5, 5)) # # color_dict = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS) # colors = [color_dict[c] for c in # ['lightcoral', 'steelblue', 'forestgreen', 'darkviolet', 'sienna', 'dimgrey', # 'darkorange', 'gold']] # colors[gt_ind] = color_dict['crimson'] # w = 0.9 # plt.bar(np.arange(len(top_labels)), probs, w, color=colors, alpha=.9, label='data') # plt.axhline(0, color='black') # plt.ylim([0, 1]) # plt.xticks(np.arange(len(top_labels)), top_labels, fontsize=6) # plt.subplots_adjust(bottom=.15) # plt.tight_layout() # plt.savefig(fn) lab = deepcopy(top_labels) lab[gt_ind] += ' (gt)' d = pd.DataFrame(data={'probs': probs, 'labels':lab}) fig, ax = plt.subplots(figsize=(4,5)) ax.tick_params(labelsize=15) sns.barplot(y='labels', x='probs', ax=ax, data=d, orient='h', ci=None) ax.set(xlim=(0,1)) for rect, label in zip(ax.patches,lab): w = rect.get_width() ax.text(w+.02, rect.get_y() + rect.get_height()*4/5, label, ha='left', va='bottom', fontsize=25) # ax.yaxis.set_label_coords(0.5, 0.5) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.get_yaxis().set_visible(False) ax.get_xaxis().label.set_visible(False) fig.savefig(fn, bbox_inches='tight', transparent=True) plt.close('all')
Example 24
Project: gpkit Author: convexengineering File: plot_sweep.py License: MIT License | 5 votes |
def format_and_label_axes(var, posys, axes, ylabel=True): "Formats and labels axes" for posy, ax in zip(posys, axes): if ylabel: if hasattr(posy, "key"): ylabel = (posy.key.descr.get("label", posy.key.name) + " [%s]" % posy.key.unitstr(dimless="-")) else: ylabel = str(posy) ax.set_ylabel(ylabel) ax.grid(color="0.6") # ax.set_frame_on(False) for item in [ax.xaxis.label, ax.yaxis.label]: item.set_fontsize(12) for item in ax.get_xticklabels() + ax.get_yticklabels(): item.set_fontsize(9) ax.tick_params(length=0) ax.spines['left'].set_visible(False) ax.spines['top'].set_visible(False) for i in ax.spines.values(): i.set_linewidth(0.6) i.set_color("0.6") i.set_linestyle("dotted") xlabel = (var.key.descr.get("label", var.key.name) + " [%s]" % var.key.unitstr(dimless="-")) ax.set_xlabel(xlabel) # pylint: disable=undefined-loop-variable plt.locator_params(nbins=4) plt.subplots_adjust(wspace=0.15) # pylint: disable=too-many-locals,too-many-branches,too-many-statements
Example 25
Project: vnpy_crypto Author: birforce File: plotting.py License: MIT License | 5 votes |
def adjust_subplots(**kwds): import matplotlib.pyplot as plt passed_kwds = dict(bottom=0.05, top=0.925, left=0.05, right=0.95, hspace=0.2) passed_kwds.update(kwds) plt.subplots_adjust(**passed_kwds) #------------------------------------------------------------------------------- # Multiple impulse response (cum_effects, etc.) cplots
Example 26
Project: scikit-multiflow Author: scikit-multiflow File: evaluation_visualizer.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def on_new_train_step(self, sample_id, data_buffer): """ This is the listener main function, which gives it the ability to 'listen' for the caller. Whenever the EvaluationVisualiser should be aware of some new data, the caller will invoke this function, passing the new data buffer. Parameters ---------- sample_id: int The current sample id. data_buffer: EvaluationDataBuffer A buffer containing evaluation data for a single training / visualization step. Raises ------ ValueError: If an exception is raised during the draw operation. """ try: current_time = time.time() self._clear_annotations() self._update_plots(sample_id, data_buffer) # To mitigate re-drawing overhead for fast models use frame counter (default = 5 frames). # To avoid slow refresh rate in slow models use a time limit (default = 1 sec). if (self._frame_cnt == 5) or (current_time - self._last_draw_timestamp > 1): plt.subplots_adjust(right=0.72, bottom=0.22) # Adjust subplots to include metrics annotations if get_backend() == 'nbAgg': self.fig.canvas.draw() # Force draw in'notebook' backend plt.pause(1e-9) self._frame_cnt = 0 self._last_draw_timestamp = current_time else: self._frame_cnt += 1 except BaseException as exception: raise ValueError('Failed when trying to draw plot. Exception: {} | Type: {}'. format(exception, type(exception).__name__))
Example 27
Project: sdwan-harvester Author: sdnewhop File: core.py License: GNU General Public License v2.0 | 5 votes |
def create_pie_chart(elements, suptitle, png, figure_id): """ Create pie chart :param elements: dict with elements (dict) :param suptitle: name of chart (str) :param png: name of output file (str) :param figure_id: id of current plot (started with 1) (int) :return: None """ values = [value for value in elements.values()] keys = [key for key in elements.keys()] plt.figure(figure_id) plt.subplots_adjust(bottom=.05, left=.01, right=.99, top=.90, hspace=.35) explode = [0 for x in range(len(keys))] max_value = max(values) explode[list(values).index(max_value)] = 0.1 plt.pie(values, labels=keys, autopct=make_autopct(values), explode=explode, textprops={'fontsize': PIE_LABEL_FONT_SIZE}) plt.axis("equal") plt.suptitle(suptitle, fontsize=PIE_SUPTITLE_FONT_SIZE) plt.gcf().set_dpi(PIE_DPI) plt.savefig("{dest}/{png}/{result_file}".format(dest=RESULTS_DIR, png=PNG_DIR, result_file=png))
Example 28
Project: pbt Author: MattKleinsmith File: utils.py License: MIT License | 5 votes |
def plots(imgs, figsize=(12, 12), rows=None, cols=None, interp=None, titles=None, cmap='gray', fig=None): if not isinstance(imgs, list): imgs = [imgs] imgs = [np.array(img) for img in imgs] if not isinstance(cmap, list): if imgs[0].ndim == 2: cmap = 'gray' cmap = [cmap] * len(imgs) if not isinstance(interp, list): interp = [interp] * len(imgs) n = len(imgs) if not rows and not cols: cols = n rows = 1 elif not rows: rows = cols elif not cols: cols = rows if not fig: rows = int(np.ceil(len(imgs) / cols)) w = 12 h = rows * (w / cols + 1) figsize = (w, h) fig = plt.figure(figsize=figsize) fontsize = 13 if cols == 5 else 16 fig.set_figheight(figsize[1], forward=True) fig.clear() for i in range(len(imgs)): sp = fig.add_subplot(rows, cols, i+1) if titles: sp.set_title(titles[i], fontsize=fontsize) plt.imshow(imgs[i], interpolation=interp[i], cmap=cmap[i]) plt.axis('off') plt.subplots_adjust(0, 0, 1, 1, .1, 0) # plt.tight_layout() if fig: fig.canvas.draw()
Example 29
Project: lightnet Author: jing-vision File: tsne.py License: MIT License | 5 votes |
def tsne_plot(labels, tokens): "Creates and TSNE model and plots it" tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=23) X_2d = tsne_model.fit_transform(tokens) X_2d -= X_2d.min(axis=0) X_2d /= X_2d.max(axis=0) width = 1200 grid, to_plot = tsne_to_grid(X_2d) out_dim = int(width / np.sqrt(to_plot)) fig, ax = plt.subplots(figsize=(width/100, width/100)) plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None) for pos, label in zip(grid, labels[0:to_plot]): ax.scatter(pos[0], pos[1]) if False: ax.annotate(label, xy=(pos[0], pos[1]), xytext=(5, 2), fontsize=9, textcoords='offset points', ha='right', va='bottom') ab = AnnotationBbox(getImage(label, new_size = out_dim / 2), (pos[0], pos[1]), frameon=False) ax.add_artist(ab) plt.show()
Example 30
Project: pyhawkes Author: slinderman File: make_figure.py License: MIT License | 5 votes |
def make_figure_a(S, F, C): """ Plot fluorescence traces, filtered fluorescence, and spike times for three neurons """ col = harvard_colors() dt = 0.02 T_start = 0 T_stop = 1 * 50 * 60 t = dt * np.arange(T_start, T_stop) ks = [0,1] nk = len(ks) fig = create_figure((3,3)) for ind,k in enumerate(ks): ax = fig.add_subplot(nk,1,ind+1) ax.plot(t, F[T_start:T_stop, k], color=col[1], label="$F$") # Plot the raw flourescence in blue ax.plot(t, C[T_start:T_stop, k], color=col[0], lw=1.5, label="$\widehat{F}$") # Plot the filtered flourescence in red spks = np.where(S[T_start:T_stop, k])[0] ax.plot(t[spks], C[spks,k], 'ko', label="S") # Plot the spike times in black # Make a legend if ind == 0: # Put a legend above plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=0., prop={'size':9}) # Add labels ax.set_ylabel("$F_%d(t)$" % (k+1)) if ind == nk-1: ax.set_xlabel("Time $t$ [sec]") # Format the ticks ax.set_ylim([-0.1,1.0]) plt.locator_params(nbins=5, axis="y") plt.subplots_adjust(left=0.2, bottom=0.2) fig.savefig("figure3a.pdf") plt.show()