Python matplotlib.pylab.close() Examples
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code examples of matplotlib.pylab.close().
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Example #1
Source File: utils.py From hands-detection with MIT License | 7 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #2
Source File: utils.py From models with Apache License 2.0 | 6 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #3
Source File: utils.py From Gun-Detector with Apache License 2.0 | 6 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #4
Source File: utils.py From yolo_v2 with Apache License 2.0 | 6 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #5
Source File: utils.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #6
Source File: demo_ui.py From spriteworld with Apache License 2.0 | 6 votes |
def _setup_callbacks(self): """Default callbacks for the UI.""" # Pressing escape should stop the UI def _onkeypress(event): if event.key == 'escape': # Stop UI logging.info('Pressed escape, stopping UI.') plt.close(self._fig) sys.exit() self._fig.canvas.mpl_connect('key_release_event', _onkeypress) # Disable default keyboard shortcuts for key in ('keymap.fullscreen', 'keymap.home', 'keymap.back', 'keymap.forward', 'keymap.pan', 'keymap.zoom', 'keymap.save', 'keymap.quit', 'keymap.grid', 'keymap.yscale', 'keymap.xscale', 'keymap.all_axes'): plt.rcParams[key] = '' # Disable logging of some matplotlib events log.getLogger('matplotlib').setLevel('WARNING')
Example #7
Source File: drawing.py From BIRL with BSD 3-Clause "New" or "Revised" License | 6 votes |
def export_figure(path_fig, fig): """ export the figure and close it afterwords :param str path_fig: path to the new figure image :param fig: object >>> path_fig = './sample_figure.jpg' >>> export_figure(path_fig, plt.figure()) >>> os.remove(path_fig) """ assert os.path.isdir(os.path.dirname(path_fig)), \ 'missing folder "%s"' % os.path.dirname(path_fig) fig.subplots_adjust(left=0., right=1., top=1., bottom=0.) logging.debug('exporting Figure: %s', path_fig) fig.savefig(path_fig) plt.close(fig)
Example #8
Source File: prod_basis.py From pyscf with Apache License 2.0 | 6 votes |
def generate_png_chess_dp_vertex(self): """Produces pictures of the dominant product vertex a chessboard convention""" import matplotlib.pylab as plt plt.ioff() dab2v = self.get_dp_vertex_doubly_sparse() for i, ab in enumerate(dab2v): fname = "chess-v-{:06d}.png".format(i) print('Matrix No.#{}, Size: {}, Type: {}'.format(i+1, ab.shape, type(ab)), fname) if type(ab) != 'numpy.ndarray': ab = ab.toarray() fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(ab, interpolation='nearest', cmap=plt.cm.ocean) plt.colorbar() plt.savefig(fname) plt.close(fig)
Example #9
Source File: plotting_utils.py From fac-via-ppg with Apache License 2.0 | 6 votes |
def plot_alignment_to_numpy(alignment, info=None): fig, ax = plt.subplots(figsize=(6, 4)) im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none') fig.colorbar(im, ax=ax) xlabel = 'Decoder timestep' if info is not None: xlabel += '\n\n' + info plt.xlabel(xlabel) plt.ylabel('Encoder timestep') plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #10
Source File: utils.py From object_detection_with_tensorflow with MIT License | 6 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #11
Source File: camera_test.py From camera.py with MIT License | 6 votes |
def calibrate_division_model_test(): img = rgb2gray(plt.imread('test/kamera2.png')) y0 = np.array(img.shape)[::-1][np.newaxis].T / 2. z_n = np.linalg.norm(np.array(img.shape) / 2.) points = pilab_annotate_load('test/kamera2_lines.xml') points_per_line = 5 num_lines = points.shape[0] / points_per_line lines_coords = np.array([points[i * points_per_line:i * points_per_line + points_per_line] for i in xrange(num_lines)]) c = camera.calibrate_division_model(lines_coords, y0, z_n) import matplotlib.cm as cm plt.figure() plt.imshow(img, cmap=cm.gray) for line in xrange(num_lines): x = lines_coords[line, :, 0] plt.plot(x, lines_coords[line, :, 1], 'g') mc = camera.fit_line(lines_coords[line].T) plt.plot(x, mc[0] * x + mc[1], 'y') xy = c.undistort(lines_coords[line].T) plt.plot(xy[0, :], xy[1, :], 'r') plt.show() plt.close()
Example #12
Source File: plotting_utils.py From nonparaSeq2seqVC_code with MIT License | 6 votes |
def plot_alignment_to_numpy(alignment, info=None): fig, ax = plt.subplots(figsize=(6, 4)) im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none') fig.colorbar(im, ax=ax) xlabel = 'Decoder timestep' if info is not None: xlabel += '\n\n' + info plt.xlabel(xlabel) plt.ylabel('Encoder timestep') plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #13
Source File: resnet_wgan_gp_cifar10_train.py From Hands-On-Generative-Adversarial-Networks-with-Keras with MIT License | 6 votes |
def plot_losses(losses_d, losses_g, filename): losses_d = np.array(losses_d) fig, axes = plt.subplots(3, 2, figsize=(8, 8)) axes = axes.flatten() axes[0].plot(losses_d[:, 0]) axes[1].plot(losses_d[:, 1]) axes[2].plot(losses_d[:, 2]) axes[3].plot(losses_d[:, 3]) axes[4].plot(losses_g) axes[0].set_title("losses_d") axes[1].set_title("losses_d_real") axes[2].set_title("losses_d_fake") axes[3].set_title("losses_d_gp") axes[4].set_title("losses_g") plt.tight_layout() plt.savefig(filename) plt.close()
Example #14
Source File: metrics.py From tacotron2 with BSD 3-Clause "New" or "Revised" License | 6 votes |
def plot_alignment(alignments, text, _id, global_step, path): num_alignment = len(alignments) fig = plt.figure(figsize=(12, 16)) for i, alignment in enumerate(alignments): ax = fig.add_subplot(num_alignment, 1, i + 1) im = ax.imshow( alignment, aspect='auto', origin='lower', interpolation='none') fig.colorbar(im, ax=ax) xlabel = 'Decoder timestep' ax.set_xlabel(xlabel) ax.set_ylabel('Encoder timestep') ax.set_title("layer {}".format(i + 1)) fig.subplots_adjust(wspace=0.4, hspace=0.6) fig.suptitle(f"record ID: {_id}\nglobal step: {global_step}\ninput text: {str(text)}") fig.savefig(path, format='png') plt.close()
Example #15
Source File: plot.py From Tacotron2-PyTorch with MIT License | 6 votes |
def plot_alignment_to_numpy(alignment, info=None): fig, ax = plt.subplots(figsize=(6, 4)) im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none') fig.colorbar(im, ax=ax) xlabel = 'Decoder timestep' if info is not None: xlabel += '\n\n' + info plt.xlabel(xlabel) plt.ylabel('Encoder timestep') plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #16
Source File: utils.py From object_detection_kitti with Apache License 2.0 | 6 votes |
def visualize_voxel_spectral(points, vis_size=128): """Function to visualize voxel (spectral).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1)) ax = fig.add_subplot(111, projection='3d') ax.plot_trisurf( verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #17
Source File: variable_describe.py From convis with GNU General Public License v3.0 | 6 votes |
def _plot_to_string(): try: from StringIO import StringIO make_bytes = lambda x: x.buf except ImportError: from io import BytesIO as StringIO make_bytes = lambda x: x.getbuffer() try: from urllib import quote except: from urllib.parse import quote import base64 import matplotlib.pylab as plt imgdata = StringIO() plt.savefig(imgdata) plt.close() imgdata.seek(0) image = base64.encodestring(make_bytes(imgdata)) return str(quote(image))
Example #18
Source File: data_augmentation.py From ConvNetQuake with MIT License | 6 votes |
def plot_true_and_augmented_data(sample,noised_sample,label,n_examples): output_dir = os.path.split(FLAGS.output)[0] # Save augmented data plt.clf() fig, ax = plt.subplots(3,1) for t in range(noised_sample.shape[1]): ax[t].plot(noised_sample[:,t]) ax[t].set_xlabel('time (samples)') ax[t].set_ylabel('amplitude') ax[0].set_title('window {:03d}, cluster_id: {}'.format(n_examples,label)) plt.savefig(os.path.join(output_dir, "augmented_data", 'augmented_{:03d}.pdf'.format(n_examples))) plt.close() # Save true data plt.clf() fig, ax = plt.subplots(3,1) for t in range(sample.shape[1]): ax[t].plot(sample[:,t]) ax[t].set_xlabel('time (samples)') ax[t].set_ylabel('amplitude') ax[0].set_title('window {:03d}, cluster_id: {}'.format(n_examples,label)) plt.savefig(os.path.join(output_dir, "true_data", 'true__{:03d}.pdf'.format(n_examples))) plt.close()
Example #19
Source File: PlotComps.py From refinery with MIT License | 6 votes |
def plotModelInNewFigure(jobpath, hmodel, args): figHandle = pylab.figure() if args.doPlotData: Data = loadData(jobpath) plotData(Data) if hmodel.getObsModelName().count('ZMGauss') and hmodel.obsModel.D > 2: bnpy.viz.GaussViz.plotCovMatFromHModel(hmodel) elif hmodel.getObsModelName().count('Gauss'): bnpy.viz.GaussViz.plotGauss2DFromHModel(hmodel) elif args.dataName.lower().count('bars') > 0: pylab.close(figHandle) if args.doPlotTruth: Data = loadData(jobpath) else: Data = None bnpy.viz.BarsViz.plotBarsFromHModel(hmodel, Data=Data, sortBySize=args.doSort, doShowNow=False) else: raise NotImplementedError('Unrecognized data/obsmodel combo')
Example #20
Source File: diagnostics.py From photometrypipeline with GNU General Public License v3.0 | 6 votes |
def append_website(self, filename, content, replace_from='X?!do not replace anything!?X', keep_at='</BODY>',): """append content to an existing website: replace content starting at line containing `replace_from` until line containin `keep_at`; by default, all content following `replace_from` is replaced """ # read existing code existing_html = open(filename, 'r').readlines() # insert content into existing html outf = open(filename, 'w') delete = False for line in existing_html: if replace_from in line: delete = True continue if keep_at in line: outf.writelines(content) delete = False if delete: continue outf.writelines(line) outf.close()
Example #21
Source File: helpers.py From NeMo with Apache License 2.0 | 6 votes |
def plot_gate_outputs_to_numpy(gate_targets, gate_outputs): fig, ax = plt.subplots(figsize=(12, 3)) ax.scatter( range(len(gate_targets)), gate_targets, alpha=0.5, color='green', marker='+', s=1, label='target', ) ax.scatter( range(len(gate_outputs)), gate_outputs, alpha=0.5, color='red', marker='.', s=1, label='predicted', ) plt.xlabel("Frames (Green target, Red predicted)") plt.ylabel("Gate State") plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #22
Source File: helpers.py From NeMo with Apache License 2.0 | 5 votes |
def plot_spectrogram_to_numpy(spectrogram): fig, ax = plt.subplots(figsize=(12, 3)) im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation='none') plt.colorbar(im, ax=ax) plt.xlabel("Frames") plt.ylabel("Channels") plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #23
Source File: utils.py From models with Apache License 2.0 | 5 votes |
def visualize_voxel_scatter(points, vis_size=128): """Function to visualize voxel (scatter).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) ax = fig.add_subplot(111, projection='3d') x = [] y = [] z = [] (x_dimension, y_dimension, z_dimension) = points.shape for i in range(x_dimension): for j in range(y_dimension): for k in range(z_dimension): if points[i, j, k]: x.append(i) y.append(j) z.append(k) ax.scatter3D(x, y, z) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #24
Source File: prod_basis.py From pyscf with Apache License 2.0 | 5 votes |
def generate_png_spy_dp_vertex(self): """Produces pictures of the dominant product vertex in a common black-and-white way""" import matplotlib.pyplot as plt plt.ioff() dab2v = self.get_dp_vertex_doubly_sparse() for i,ab2v in enumerate(dab2v): plt.spy(ab2v.toarray()) fname = "spy-v-{:06d}.png".format(i) print(fname) plt.savefig(fname, bbox_inches='tight') plt.close() return 0
Example #25
Source File: utils.py From Hands-On-Generative-Adversarial-Networks-with-Keras with MIT License | 5 votes |
def plot_losses(losses_d, losses_g, filename): losses_d = np.array(losses_d) fig, axes = plt.subplots(2, 2, figsize=(8, 8)) axes = axes.flatten() axes[0].plot(losses_d[:, 0]) axes[1].plot(losses_d[:, 1]) axes[2].plot(losses_d[:, 2]) axes[3].plot(losses_g) axes[0].set_title("losses_d") axes[1].set_title("losses_d_real") axes[2].set_title("losses_d_fake") axes[3].set_title("losses_g") plt.tight_layout() plt.savefig(filename) plt.close()
Example #26
Source File: utils.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def visualize_voxel_scatter(points, vis_size=128): """Function to visualize voxel (scatter).""" points = np.rint(points) points = np.swapaxes(points, 0, 2) fig = p.figure(figsize=(1, 1), dpi=vis_size) ax = fig.add_subplot(111, projection='3d') x = [] y = [] z = [] (x_dimension, y_dimension, z_dimension) = points.shape for i in range(x_dimension): for j in range(y_dimension): for k in range(z_dimension): if points[i, j, k]: x.append(i) y.append(j) z.append(k) ax.scatter3D(x, y, z) ax.set_axis_off() fig.tight_layout(pad=0) fig.canvas.draw() data = np.fromstring( fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape( vis_size, vis_size, 3) p.close('all') return data
Example #27
Source File: speech_utils.py From python-dlpy with Apache License 2.0 | 5 votes |
def convert_one_audio_file_to_specgram(local_audio_file, converted_local_png_file): ''' Convert a local audio file into a png format with spectrogram. Parameters ---------- local_audio_file : string Local location to the audio file to be converted. converted_local_png_file : string Local location to store the converted audio file Returns ------- None Raises ------ DLPyError If anything goes wrong, it complains and prints the appropriate message. ''' try: import soundfile as sf import matplotlib.pylab as plt except (ModuleNotFoundError, ImportError): raise DLPyError('cannot import soundfile') data, sampling_rate = sf.read(local_audio_file) fig, ax = plt.subplots(1) fig.subplots_adjust(left=0, right=1, bottom=0, top=1) ax.axis('off') ax.specgram(x=data, Fs=sampling_rate) ax.axis('off') fig.savefig(converted_local_png_file, dpi=300, frameon='false') # this is the key to avoid mem leaking in notebook plt.ioff() plt.close(fig)
Example #28
Source File: plot.py From Tacotron2-PyTorch with MIT License | 5 votes |
def plot_spectrogram_to_numpy(spectrogram): fig, ax = plt.subplots(figsize=(12, 3)) im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation='none') plt.colorbar(im, ax=ax) plt.xlabel("Frames") plt.ylabel("Channels") plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #29
Source File: helpers.py From NeMo with Apache License 2.0 | 5 votes |
def plot_alignment_to_numpy(alignment, info=None): fig, ax = plt.subplots(figsize=(6, 4)) im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none') fig.colorbar(im, ax=ax) xlabel = 'Decoder timestep' if info is not None: xlabel += '\n\n' + info plt.xlabel(xlabel) plt.ylabel('Encoder timestep') plt.tight_layout() fig.canvas.draw() data = save_figure_to_numpy(fig) plt.close() return data
Example #30
Source File: show_leaned_filters.py From nnabla-examples with Apache License 2.0 | 5 votes |
def show(): args = get_args() # Load model nn.load_parameters(args.model_load_path) params = nn.get_parameters() # Show heatmap for name, param in params.items(): # SSL only on convolution weights if "conv/W" not in name: continue print(name) n, m, k0, k1 = param.d.shape w_matrix = param.d.reshape((n, m * k0 * k1)) # Filter x Channel heatmap fig, ax = plt.subplots() ax.set_title("{} with shape {} \n Filter x (Channel x Heigh x Width)".format( name, (n, m, k0, k1))) heatmap = ax.pcolor(w_matrix) fig.colorbar(heatmap) plt.pause(0.5) raw_input("Press Key") plt.close()