Python matplotlib.pyplot.close() Examples

def plot(PDF, figName, imgpath, show=False, save=True):
    # plot
    output = PDF.get_constraint_value()
    plt.plot(PDF.experimentalDistances,PDF.experimentalPDF, 'ro', label="experimental", markersize=7.5, markevery=1 )
    plt.plot(PDF.shellsCenter, output["pdf"], 'k', linewidth=3.0,  markevery=25, label="total" )

    styleIndex = 0
    for key in output:
        val = output[key]
        if key in ("pdf_total", "pdf"):
            continue
        elif "inter" in key:
            plt.plot(PDF.shellsCenter, val, STYLE[styleIndex], markevery=5, label=key.split('rdf_inter_')[1] )
            styleIndex+=1
    plt.legend(frameon=False, ncol=1)
    # set labels
    plt.title("$\\chi^{2}=%.6f$"%PDF.squaredDeviations, size=20)
    plt.xlabel("$r (\AA)$", size=20)
    plt.ylabel("$g(r)$", size=20)
    # show plot
    if save: plt.savefig(figName)
    if show: plt.show()
    plt.close() 

def sequencing_speed_over_time(dfs, path, figformat, title, plot_settings={}):
    time_duration = Plot(path=path + "TimeSequencingSpeed_ViolinPlot." + figformat,
                         title="Violin plot of sequencing speed over time")
    sns.set(style="white", **plot_settings)
    if "timebin" not in dfs:
        dfs['timebin'] = add_time_bins(dfs)
    mask = dfs['duration'] != 0
    ax = sns.violinplot(x=dfs.loc[mask, "timebin"],
                        y=dfs.loc[mask, "lengths"] / dfs.loc[mask, "duration"],
                        inner=None,
                        cut=0,
                        linewidth=0)
    ax.set(xlabel='Interval (hours)',
           ylabel="Sequencing speed (nucleotides/second)",
           title=title or time_duration.title)
    plt.xticks(rotation=45, ha='center', fontsize=8)
    time_duration.fig = ax.get_figure()
    time_duration.save(format=figformat)
    plt.close("all")
    return time_duration 

def quality_over_time(dfs, path, figformat, title, plot_settings={}):
    time_qual = Plot(path=path + "TimeQualityViolinPlot." + figformat,
                     title="Violin plot of quality over time")
    sns.set(style="white", **plot_settings)
    ax = sns.violinplot(x="timebin",
                        y="quals",
                        data=dfs,
                        inner=None,
                        cut=0,
                        linewidth=0)
    ax.set(xlabel='Interval (hours)',
           ylabel="Basecall quality",
           title=title or time_qual.title)
    plt.xticks(rotation=45, ha='center', fontsize=8)
    time_qual.fig = ax.get_figure()
    time_qual.save(format=figformat)
    plt.close("all")
    return time_qual 

def test_emg_plot():

    sampling_rate = 1000

    emg = nk.emg_simulate(duration=10, sampling_rate=1000, burst_number=3)
    emg_summary, _ = nk.emg_process(emg, sampling_rate=sampling_rate)

    # Plot data over samples.
    nk.emg_plot(emg_summary)
    # This will identify the latest figure.
    fig = plt.gcf()
    assert len(fig.axes) == 2
    titles = ["Raw and Cleaned Signal", "Muscle Activation"]
    for (ax, title) in zip(fig.get_axes(), titles):
        assert ax.get_title() == title
    assert fig.get_axes()[1].get_xlabel() == "Samples"
    np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks())
    plt.close(fig)

    # Plot data over time.
    nk.emg_plot(emg_summary, sampling_rate=sampling_rate)
    # This will identify the latest figure.
    fig = plt.gcf()
    assert fig.get_axes()[1].get_xlabel() == "Time (seconds)" 

def sample_images(self, epoch):
        r, c = 2, 5
        noise = np.random.normal(0, 1, (r * c, 100))
        sampled_labels = np.arange(0, 10).reshape(-1, 1)

        gen_imgs = self.generator.predict([noise, sampled_labels])

        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
                axs[i,j].set_title("Digit: %d" % sampled_labels[cnt])
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("images/%d.png" % epoch)
        plt.close() 

def plot_alignment(alignment, gs, dir=hp.logdir):
    """Plots the alignment.

    Args:
      alignment: A numpy array with shape of (encoder_steps, decoder_steps)
      gs: (int) global step.
      dir: Output path.
    """
    if not os.path.exists(dir): os.mkdir(dir)

    fig, ax = plt.subplots()
    im = ax.imshow(alignment)

    fig.colorbar(im)
    plt.title('{} Steps'.format(gs))
    plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png')
    plt.close(fig) 

def plot_images(imgs, targets, paths=None, fname='images.jpg'):
    # Plots training images overlaid with targets
    imgs = imgs.cpu().numpy()
    targets = targets.cpu().numpy()
    # targets = targets[targets[:, 1] == 21]  # plot only one class

    fig = plt.figure(figsize=(10, 10))
    bs, _, h, w = imgs.shape  # batch size, _, height, width
    bs = min(bs, 16)  # limit plot to 16 images
    ns = np.ceil(bs ** 0.5)  # number of subplots

    for i in range(bs):
        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
        boxes[[0, 2]] *= w
        boxes[[1, 3]] *= h
        plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
        plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
        plt.axis('off')
        if paths is not None:
            s = Path(paths[i]).name
            plt.title(s[:min(len(s), 40)], fontdict={'size': 8})  # limit to 40 characters
    fig.tight_layout()
    fig.savefig(fname, dpi=200)
    plt.close() 

def plot_some_results(pred_fn, test_generator, n_images=10):
    fig_ctr = 0
    for data, seg in test_generator:
        res = pred_fn(data)
        for d, s, r in zip(data, seg, res):
            plt.figure(figsize=(12, 6))
            plt.subplot(1, 3, 1)
            plt.imshow(d.transpose(1,2,0))
            plt.title("input patch")
            plt.subplot(1, 3, 2)
            plt.imshow(s[0])
            plt.title("ground truth")
            plt.subplot(1, 3, 3)
            plt.imshow(r)
            plt.title("segmentation")
            plt.savefig("road_segmentation_result_%03.0f.png"%fig_ctr)
            plt.close()
            fig_ctr += 1
            if fig_ctr > n_images:
                break 

def save_d_at_t(outputs, global_step, output_dir, metric_summary, N):
  """Save distance to goal at all time steps.
  
  Args:
    outputs        : [gt_dist_to_goal].
    global_step : number of iterations.
    output_dir     : output directory.
    metric_summary : to append scalars to summary.
    N              : number of outputs to process.

  """
  d_at_t = np.concatenate(map(lambda x: x[0][:,:,0]*1, outputs), axis=0)
  fig, axes = utils.subplot(plt, (1,1), (5,5))
  axes.plot(np.arange(d_at_t.shape[1]), np.mean(d_at_t, axis=0), 'r.')
  axes.set_xlabel('time step')
  axes.set_ylabel('dist to next goal')
  axes.grid('on')
  file_name = os.path.join(output_dir, 'dist_at_t_{:d}.png'.format(global_step))
  with fu.fopen(file_name, 'w') as f:
    fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
  file_name = os.path.join(output_dir, 'dist_at_t_{:d}.pkl'.format(global_step))
  utils.save_variables(file_name, [d_at_t], ['d_at_t'], overwrite=True)
  plt.close(fig)
  return None 

def classify(self, features, show=False):
        recs, _ = features.shape
        result_shape = (features.shape[0], len(self.root))
        scores = np.zeros(result_shape)
        print scores.shape
        R = Record(np.arange(recs, dtype=int), features)

        for i, T in enumerate(self.root):
            for idxs, result in classify(T, R):
                for idx in idxs.indexes():
                    scores[idx, i] = float(result[0]) / sum(result.values())


        if show:
            plt.cla()
            plt.clf()
            plt.close()

            plt.imshow(scores, cmap=plt.cm.gray)
            plt.title('Scores matrix')
            plt.savefig(r"../scratch/tree_scores.png", bbox_inches='tight')
        
        return scores 

def generator(vis=False):
    image_list = np.array(get_training_data())
    print('{} training images in {}'.format(image_list.shape[0], DATA_FOLDER))
    index = np.arange(0, image_list.shape[0])
    while True:
        np.random.shuffle(index)
        for i in index:
            try:
                im_fn = image_list[i]
                im = cv2.imread(im_fn)
                h, w, c = im.shape
                im_info = np.array([h, w, c]).reshape([1, 3])

                _, fn = os.path.split(im_fn)
                fn, _ = os.path.splitext(fn)
                txt_fn = os.path.join(DATA_FOLDER, "label", fn + '.txt')
                if not os.path.exists(txt_fn):
                    print("Ground truth for image {} not exist!".format(im_fn))
                    continue
                bbox = load_annoataion(txt_fn)
                if len(bbox) == 0:
                    print("Ground truth for image {} empty!".format(im_fn))
                    continue

                if vis:
                    for p in bbox:
                        cv2.rectangle(im, (p[0], p[1]), (p[2], p[3]), color=(0, 0, 255), thickness=1)
                    fig, axs = plt.subplots(1, 1, figsize=(30, 30))
                    axs.imshow(im[:, :, ::-1])
                    axs.set_xticks([])
                    axs.set_yticks([])
                    plt.tight_layout()
                    plt.show()
                    plt.close()
                yield [im], bbox, im_info

            except Exception as e:
                print(e)
                continue 

def __init__(self, title, varieties, data_points, attrs,
                 anim=False, data_func=None, is_headless=False):
        global anim_func

        plt.close()
        self.legend = ["Type"]
        self.title = title
        # self.anim = anim
        # self.data_func = data_func
        for i in varieties:
            data_points = len(varieties[i]["data"])
            break
        self.headless = is_headless
        self.draw_graph(data_points, varieties, attrs)

        # if anim and not self.headless:
        #     anim_func = animation.FuncAnimation(self.fig,
        #                                         self.update_plot,
        #                                         frames=1000,
        #                                         interval=500,
        #                                         blit=False) 

def _render_price(self, step_range, times, current_step):
        self.price_ax.clear()

        # Plot price using candlestick graph from mpl_finance
        self.price_ax.plot(times, self.df['close'].values[step_range], color="black")

        last_time = self.df.index.values[current_step]
        last_close = self.df['close'].values[current_step]
        last_high = self.df['high'].values[current_step]

        # Print the current price to the price axis
        self.price_ax.annotate('{0:.2f}'.format(last_close), (last_time, last_close),
                               xytext=(last_time, last_high),
                               bbox=dict(boxstyle='round',
                                         fc='w', ec='k', lw=1),
                               color="black",
                               fontsize="small")

        # Shift price axis up to give volume chart space
        ylim = self.price_ax.get_ylim()
        self.price_ax.set_ylim(ylim[0] - (ylim[1] - ylim[0]) * VOLUME_CHART_HEIGHT, ylim[1]) 

def _render_trades(self, step_range, trades):
        trades = [trade for sublist in trades.values() for trade in sublist]

        for trade in trades:
            if trade.step in range(sys.maxsize)[step_range]:
                date = self.df.index.values[trade.step]
                close = self.df['close'].values[trade.step]
                color = 'green'

                if trade.side is TradeSide.SELL:
                    color = 'red'

                self.price_ax.annotate(' ', (date, close),
                                       xytext=(date, close),
                                       size="large",
                                       arrowprops=dict(arrowstyle='simple', facecolor=color)) 

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) 

def plot_species(statistics, view=False, filename='speciation.svg'):
    """ Visualizes speciation throughout evolution. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    species_sizes = statistics.get_species_sizes()
    num_generations = len(species_sizes)
    curves = np.array(species_sizes).T

    fig, ax = plt.subplots()
    ax.stackplot(range(num_generations), *curves)

    plt.title("Speciation")
    plt.ylabel("Size per Species")
    plt.xlabel("Generations")

    plt.savefig(filename)

    if view:
        plt.show()

    plt.close() 

def plot_species(statistics, view=False, filename='speciation.svg'):
    """ Visualizes speciation throughout evolution. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    species_sizes = statistics.get_species_sizes()
    num_generations = len(species_sizes)
    curves = np.array(species_sizes).T

    fig, ax = plt.subplots()
    ax.stackplot(range(num_generations), *curves)

    plt.title("Speciation")
    plt.ylabel("Size per Species")
    plt.xlabel("Generations")

    plt.savefig(filename)

    if view:
        plt.show()

    plt.close() 

def plot_species(statistics, view=False, filename='speciation.svg'):
    """ Visualizes speciation throughout evolution. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    species_sizes = statistics.get_species_sizes()
    num_generations = len(species_sizes)
    curves = np.array(species_sizes).T

    fig, ax = plt.subplots()
    ax.stackplot(range(num_generations), *curves)

    plt.title("Speciation")
    plt.ylabel("Size per Species")
    plt.xlabel("Generations")

    plt.savefig(filename)

    if view:
        plt.show()

    plt.close() 

def plot_species(statistics, view=False, filename='speciation.svg'):
    """ Visualizes speciation throughout evolution. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    species_sizes = statistics.get_species_sizes()
    num_generations = len(species_sizes)
    curves = np.array(species_sizes).T

    fig, ax = plt.subplots()
    ax.stackplot(range(num_generations), *curves)

    plt.title("Speciation")
    plt.ylabel("Size per Species")
    plt.xlabel("Generations")

    plt.savefig(filename)

    if view:
        plt.show()

    plt.close() 

def plot_species(statistics, view=False, filename='speciation.svg'):
    """ Visualizes speciation throughout evolution. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    species_sizes = statistics.get_species_sizes()
    num_generations = len(species_sizes)
    curves = np.array(species_sizes).T

    fig, ax = plt.subplots()
    ax.stackplot(range(num_generations), *curves)

    plt.title("Speciation")
    plt.ylabel("Size per Species")
    plt.xlabel("Generations")

    plt.savefig(filename)

    if view:
        plt.show()

    plt.close() 

def test_eog_plot():

    eog_signal = nk.data("eog_200hz")["vEOG"]
    signals, info = nk.eog_process(eog_signal, sampling_rate=200)

    # Plot
    nk.eog_plot(signals)
    fig = plt.gcf()
    assert len(fig.axes) == 2

    titles = ["Raw and Cleaned Signal", "Blink Rate"]
    legends = [["Raw", "Cleaned", "Blinks"], ["Rate", "Mean"]]
    ylabels = ["Amplitude (mV)", "Blinks per minute"]

    for (ax, title, legend, ylabel) in zip(fig.get_axes(), titles, legends, ylabels):
        assert ax.get_title() == title
        subplot = ax.get_legend_handles_labels()
        assert subplot[1] == legend
        assert ax.get_ylabel() == ylabel

    assert fig.get_axes()[1].get_xlabel() == "Samples"
    np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks())
    plt.close(fig) 

def test_eda_plot():

    sampling_rate = 1000
    eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate, scr_number=6, noise=0, drift=0.01, random_state=42)
    eda_summary, _ = nk.eda_process(eda, sampling_rate=sampling_rate)

    # Plot data over samples.
    nk.eda_plot(eda_summary)
    # This will identify the latest figure.
    fig = plt.gcf()
    assert len(fig.axes) == 3
    titles = ["Raw and Cleaned Signal", "Skin Conductance Response (SCR)", "Skin Conductance Level (SCL)"]
    for (ax, title) in zip(fig.get_axes(), titles):
        assert ax.get_title() == title
    assert fig.get_axes()[2].get_xlabel() == "Samples"
    np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks(), fig.axes[2].get_xticks())
    plt.close(fig)

    # Plot data over seconds.
    nk.eda_plot(eda_summary, sampling_rate=sampling_rate)
    # This will identify the latest figure.
    fig = plt.gcf()
    assert fig.get_axes()[2].get_xlabel() == "Seconds" 

def spatial_heatmap(array, path, title=None, color="Greens", figformat="png"):
    """Taking channel information and creating post run channel activity plots."""
    logging.info("Nanoplotter: Creating heatmap of reads per channel using {} reads."
                 .format(array.size))
    activity_map = Plot(
        path=path + "." + figformat,
        title="Number of reads generated per channel")
    layout = make_layout(maxval=np.amax(array))
    valueCounts = pd.value_counts(pd.Series(array))
    for entry in valueCounts.keys():
        layout.template[np.where(layout.structure == entry)] = valueCounts[entry]
    plt.figure()
    ax = sns.heatmap(
        data=pd.DataFrame(layout.template, index=layout.yticks, columns=layout.xticks),
        xticklabels="auto",
        yticklabels="auto",
        square=True,
        cbar_kws={"orientation": "horizontal"},
        cmap=color,
        linewidths=0.20)
    ax.set_title(title or activity_map.title)
    activity_map.fig = ax.get_figure()
    activity_map.save(format=figformat)
    plt.close("all")
    return [activity_map] 

def sample_images(self, epoch):
        r, c = 5, 5
        noise = np.random.normal(0, 1, (r * c, self.latent_dim))
        gen_imgs = self.generator.predict(noise)

        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("images/mnist_%d.png" % epoch)
        plt.close() 

def sample_images(self, epoch, imgs):
        r, c = 3, 6

        masked_imgs, missing_parts, (y1, y2, x1, x2) = self.mask_randomly(imgs)
        gen_missing = self.generator.predict(masked_imgs)

        imgs = 0.5 * imgs + 0.5
        masked_imgs = 0.5 * masked_imgs + 0.5
        gen_missing = 0.5 * gen_missing + 0.5

        fig, axs = plt.subplots(r, c)
        for i in range(c):
            axs[0,i].imshow(imgs[i, :,:])
            axs[0,i].axis('off')
            axs[1,i].imshow(masked_imgs[i, :,:])
            axs[1,i].axis('off')
            filled_in = imgs[i].copy()
            filled_in[y1[i]:y2[i], x1[i]:x2[i], :] = gen_missing[i]
            axs[2,i].imshow(filled_in)
            axs[2,i].axis('off')
        fig.savefig("images/%d.png" % epoch)
        plt.close() 

def sample_images(self, epoch, imgs):
        r, c = 3, 6

        masked_imgs = self.mask_randomly(imgs)
        gen_imgs = self.generator.predict(masked_imgs)

        imgs = (imgs + 1.0) * 0.5
        masked_imgs = (masked_imgs + 1.0) * 0.5
        gen_imgs = (gen_imgs + 1.0) * 0.5

        gen_imgs = np.where(gen_imgs < 0, 0, gen_imgs)

        fig, axs = plt.subplots(r, c)
        for i in range(c):
            axs[0,i].imshow(imgs[i, :, :, 0], cmap='gray')
            axs[0,i].axis('off')
            axs[1,i].imshow(masked_imgs[i, :, :, 0], cmap='gray')
            axs[1,i].axis('off')
            axs[2,i].imshow(gen_imgs[i, :, :, 0], cmap='gray')
            axs[2,i].axis('off')
        fig.savefig("images/%d.png" % epoch)
        plt.close() 

def sample_interval(self, epoch):
        r, c = 5, 5
        z = np.random.normal(size=(25, self.latent_dim))
        gen_imgs = self.generator.predict(z)

        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("images/mnist_%d.png" % epoch)
        plt.close() 

def notify(self, algorithm, **kwargs):
        if algorithm.n_gen == 1 or algorithm.n_gen % self.nth_gen == 0:
            try:

                ret = self.do(algorithm.problem, algorithm, **kwargs)

                if self.do_show:
                    plt.show()

                if self.video is not None:
                    self.video.record()

                if self.do_close:
                    plt.close()

                return ret

            except Exception as ex:
                if self.exception_if_not_applicable:
                    raise ex 

def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'):
    """ Plots the population's average and best fitness. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    generation = range(len(statistics.most_fit_genomes))
    best_fitness = [c.fitness for c in statistics.most_fit_genomes]
    avg_fitness = np.array(statistics.get_fitness_mean())
    stdev_fitness = np.array(statistics.get_fitness_stdev())

    plt.plot(generation, avg_fitness, 'b-', label="average")
    #plt.plot(generation, avg_fitness - stdev_fitness, 'g-.', label="-1 sd")
    plt.plot(generation, avg_fitness + stdev_fitness, 'g-.', label="+1 sd")
    plt.plot(generation, best_fitness, 'r-', label="best")

    plt.title("Population's average and best fitness")
    plt.xlabel("Generations")
    plt.ylabel("Fitness")
    plt.grid()
    plt.legend(loc="best")
    if ylog:
        plt.gca().set_yscale('symlog')

    plt.savefig(filename)
    if view:
        plt.show()

    plt.close() 

def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'):
    """ Plots the population's average and best fitness. """
    if plt is None:
        warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
        return

    generation = range(len(statistics.most_fit_genomes))
    best_fitness = [c.fitness for c in statistics.most_fit_genomes]
    avg_fitness = np.array(statistics.get_fitness_mean())
    #stdev_fitness = np.array(statistics.get_fitness_stdev())

    plt.plot(generation, avg_fitness, 'b-', label="average")
    #plt.plot(generation, avg_fitness - stdev_fitness, 'g-.', label="-1 sd")
    #plt.plot(generation, avg_fitness + stdev_fitness, 'g-.', label="+1 sd")
    plt.plot(generation, best_fitness, 'r-', label="best")

    plt.title("Population's average and best fitness")
    plt.xlabel("Generations")
    plt.ylabel("Fitness")
    plt.grid()
    plt.legend(loc="best")
    if ylog:
        plt.gca().set_yscale('symlog')

    plt.savefig(filename)
    if view:
        plt.show()

    plt.close()