Python matplotlib.pyplot.show() Examples

The following are code examples for showing how to use matplotlib.pyplot.show(). They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. You can also save this page to your account.

Example 1
Project: lyricswordcloud   Author: qwertyyb   File: analyse.py    (license) View Source Project 15 votes vote down vote up
def showData(self):
    print('???,????ยทยทยท')
    mask = imread(self.picfile)
    imgcolor = ImageColorGenerator(mask)
    wcc = WordCloud(font_path='./msyhl.ttc', 
    mask=mask, background_color='white', 
    max_font_size=200, 
    max_words=300,
    color_func=imgcolor
    )
    wc = wcc.generate_from_frequencies(self.data)
    plt.figure()
    plt.imshow(wc)
    plt.axis('off')
    print('?????')
    plt.show() 
Example 2
Project: facebook-message-analysis   Author: szheng17   File: plot.py    (MIT License) View Source Project 8 votes vote down vote up
def plot_bar_chart(label_to_value, title, x_label, y_label):
    """
    Plots a bar chart from a dict.

    Args:
        label_to_value: A dict mapping ints or strings to numerical values (int
            or float).
        title: A string representing the title of the graph.
        x_label: A string representing the label for the x-axis.
        y_label: A string representing the label for the y-axis.
    """
    n = len(label_to_value)
    labels = sorted(label_to_value.keys())
    values = [label_to_value[label] for label in labels]
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.bar(range(n), values, align='center')
    plt.xticks(range(n), labels, rotation='vertical', fontsize='7')
    plt.gcf().subplots_adjust(bottom=0.2) # make room for x-axis labels
    plt.show() 
Example 3
Project: s2g   Author: caesar0301   File: test.py    (MIT License) View Source Project 8 votes vote down vote up
def test_point_projects_to_edge(self):
        # p = (114.83299055, 26.8892277)
        p = (121.428387, 31.027371)
        a = time.time()
        edges, segments = self.sg.point_projects_to_edges(p, 0.01)
        print(time.time() - a)

        if self.show_plots:
            plt.figure()
            s2g.plot_lines(MultiLineString(segments), color='orange')  # original roads
            for i in range(0, len(edges)):
                s, e = edges[i]
                sxy = self.sg.node_xy[s]
                exy = self.sg.node_xy[e]
                plt.plot([sxy[0], exy[0]], [sxy[1], exy[1]], color='green')  # graph edges
            plt.plot(p[0], p[1], color='red', markersize=12, marker='o')  # bridges
            plt.show() 
Example 4
Project: a-nice-mc   Author: ermongroup   File: __init__.py    (MIT License) View Source Project 7 votes vote down vote up
def visualize(self, zv, path):
        self.ax1.clear()
        self.ax2.clear()
        z, v = zv
        if path:
            np.save(path + '/trajectory.npy', z)

        z = np.reshape(z, [-1, 2])
        self.ax1.hist2d(z[:, 0], z[:, 1], bins=400)
        self.ax1.set(xlim=self.xlim(), ylim=self.ylim())

        v = np.reshape(v, [-1, 2])
        self.ax2.hist2d(v[:, 0], v[:, 1], bins=400)
        self.ax2.set(xlim=self.xlim(), ylim=self.ylim())

        if self.display:
            import matplotlib.pyplot as plt
            plt.show()
            plt.pause(0.1)
        elif path:
            self.fig.savefig(path + '/visualize.png') 
Example 5
Project: dpl   Author: ppengtang   File: test.py    (MIT License) View Source Project 7 votes vote down vote up
def vis_detections(im, class_name, dets, thresh=0.3):
    """Visual debugging of detections."""
    import matplotlib.pyplot as plt
    im = im[:, :, (2, 1, 0)]
    for i in xrange(np.minimum(10, dets.shape[0])):
        bbox = dets[i, :4]
        score = dets[i, -1]
        if score > thresh:
            plt.cla()
            plt.imshow(im)
            plt.gca().add_patch(
                plt.Rectangle((bbox[0], bbox[1]),
                              bbox[2] - bbox[0],
                              bbox[3] - bbox[1], fill=False,
                              edgecolor='g', linewidth=3)
                )
            plt.title('{}  {:.3f}'.format(class_name, score))
            plt.show() 
Example 6
Project: facebook-message-analysis   Author: szheng17   File: plot.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_line_graph_multiple_lines(x, label_to_values, title, x_label, y_label):
    if not all(len(x) == len(values) for values in label_to_values.values()):
        raise ValueError('values of label_to_values must have length len(x)')
    colors = ['b','g','r','c','m','y','k']
    line_styles = ['-','--',':']
    for (i, label) in enumerate(sorted(label_to_values.keys())):
        color = colors[i%len(colors)]
        line_style = line_styles[(i//len(colors))%len(line_styles)]
        plt.plot(x,
                 label_to_values[label],
                 label=label,
                 color=color,
                 linestyle=line_style)
    plt.legend(loc='center left', bbox_to_anchor=(1,0.5), prop={'size':9})
    plt.tight_layout(pad=9)
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.show()

# x_min, x_max for example proportion_initiated_by_user 
Example 7
Project: facebook-message-analysis   Author: szheng17   File: plot.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_histogram(x, n_bins, title, x_label, y_label):
    """
    Plots a histogram from a list of data.

    Args:
        x: A list of floats representing the data.
        n_bins: An int representing the number of bins to plot.
        title: A string representing the title of the graph.
        x_label: A string representing the label for the x-axis.
        y_label: A string representing the label for the y-axis.
    """
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.hist(x, bins=n_bins)
    plt.show()

    # probability 
Example 8
Project: pytorch-semseg   Author: meetshah1995   File: pascal_voc_loader.py    (MIT License) View Source Project 6 votes vote down vote up
def decode_segmap(self, temp, plot=False):
        label_colours = self.get_pascal_labels()
        r = temp.copy()
        g = temp.copy()
        b = temp.copy()
        for l in range(0, self.n_classes):
            r[temp == l] = label_colours[l, 0]
            g[temp == l] = label_colours[l, 1]
            b[temp == l] = label_colours[l, 2]

        rgb = np.zeros((temp.shape[0], temp.shape[1], 3))
        rgb[:, :, 0] = r / 255.0
        rgb[:, :, 1] = g / 255.0
        rgb[:, :, 2] = b / 255.0
        if plot:
            plt.imshow(rgb)
            plt.show()
        else:
            return rgb 
Example 9
Project: pytorch-semseg   Author: meetshah1995   File: ade20k_loader.py    (MIT License) View Source Project 6 votes vote down vote up
def decode_segmap(self, temp, plot=False):
        # TODO:(@meetshah1995)
        # Verify that the color mapping is 1-to-1
        r = temp.copy()
        g = temp.copy()
        b = temp.copy()
        for l in range(0, self.n_classes):
            r[temp == l] = 10 * (l%10)
            g[temp == l] = l
            b[temp == l] = 0

        rgb = np.zeros((temp.shape[0], temp.shape[1], 3))
        rgb[:, :, 0] = (r/255.0)
        rgb[:, :, 1] = (g/255.0)
        rgb[:, :, 2] = (b/255.0)
        if plot:
            plt.imshow(rgb)
            plt.show()
        else:
            return rgb 
Example 10
Project: kaggle_dsb2017   Author: astoc   File: unet_d8g_222f.py    (MIT License) View Source Project 6 votes vote down vote up
def get_masks(scans,masks_list):
    #%matplotlib inline
    scans1=scans.copy()
    maxv=255
    masks=np.zeros(shape=(scans.shape[0],1,img_rows,img_cols))
    for i_m in range(len(masks_list)):
        for i in range(-masks_list[i_m][3],masks_list[i_m][3]+1):
            for j in range(-masks_list[i_m][3],masks_list[i_m][3]+1):
                masks[masks_list[i_m][0],0,masks_list[i_m][2]+i,masks_list[i_m][1]+j]=1
        for i1 in range(-masks_list[i_m][3],masks_list[i_m][3]+1):
            scans1[masks_list[i_m][0],0,masks_list[i_m][2]+i1,masks_list[i_m][1]+masks_list[i_m][3]]=maxv=255
            scans1[masks_list[i_m][0],0,masks_list[i_m][2]+i1,masks_list[i_m][1]-masks_list[i_m][3]]=maxv=255
            scans1[masks_list[i_m][0],0,masks_list[i_m][2]+masks_list[i_m][3],masks_list[i_m][1]+i1]=maxv=255
            scans1[masks_list[i_m][0],0,masks_list[i_m][2]-masks_list[i_m][3],masks_list[i_m][1]+i1]=maxv=255
    for i in range(scans.shape[0]):
        print ('scan '+str(i))
        f, ax = plt.subplots(1, 2,figsize=(10,5))
        ax[0].imshow(scans1[i,0,:,:],cmap=plt.cm.gray)
        ax[1].imshow(masks[i,0,:,:],cmap=plt.cm.gray)
        plt.show()
    return(masks) 
Example 11
Project: human-rl   Author: gsastry   File: test_pipeline.py    (MIT License) View Source Project 6 votes vote down vote up
def test_penalty_env(env):
    import envs
    env = envs.create_env("Pong", location="bottom", catastrophe_type="1", 
                          classifier_file=save_classifier_path + '/0/final.ckpt')
    
    import matplotlib.pyplot as plt

    observation = env.reset()
    
    for _ in range(20):
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)
        plt.imshow(observation[:,:,0])
        plt.show()
        print('Cat: ', info['frame/is_catastrophe'])
        print('reward: ', reward)
        if done:
            break 
Example 12
Project: human-rl   Author: gsastry   File: test_pipeline.py    (MIT License) View Source Project 6 votes vote down vote up
def test_penalty_env(env):
    import envs
    env = envs.create_env("Pong", location="bottom", catastrophe_type="1", 
                          classifier_file=save_classifier_path + '/0/final.ckpt')
    
    import matplotlib.pyplot as plt

    observation = env.reset()
    
    for _ in range(20):
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)
        plt.imshow(observation[:,:,0])
        plt.show()
        print('Cat: ', info['frame/is_catastrophe'])
        print('reward: ', reward)
        if done:
            break 
Example 13
Project: zipline-chinese   Author: zhanghan1990   File: stock_select.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def analyze(context=None, results=None):
    import matplotlib.pyplot as plt

    # Plot the portfolio and asset data.
    ax1 = plt.subplot(211)
    results.algorithm_period_return.plot(ax=ax1,color='blue',legend=u'????')
    ax1.set_ylabel(u'??')
    results.benchmark_period_return.plot(ax=ax1,color='red',legend=u'????')

    # Show the plot.
    plt.gcf().set_size_inches(18, 8)
    plt.show()



# loading the data 
Example 14
Project: zipline-chinese   Author: zhanghan1990   File: doubleMA.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def analyze(context=None, results=None):
    import matplotlib.pyplot as plt
    import logbook
    logbook.StderrHandler().push_application()
    log = logbook.Logger('Algorithm')

    fig = plt.figure()
    ax1 = fig.add_subplot(211)

    results.algorithm_period_return.plot(ax=ax1,color='blue',legend=u'????')
    ax1.set_ylabel(u'??')
    results.benchmark_period_return.plot(ax=ax1,color='red',legend=u'????')

    plt.show()

# capital_base is the base value of capital
# 
Example 15
Project: ml   Author: hohoins   File: genBinFile.py    (license) View Source Project 6 votes vote down vote up
def writeBinaray(outputFile, imagePath, label):
    img = Image.open(imagePath)
    img = img.resize((imageSize, imageSize), PIL.Image.ANTIALIAS)
    img = (np.array(img))

    r = img[:,:,0].flatten()
    g = img[:,:,1].flatten()
    b = img[:,:,2].flatten()
    label = [label]

    out = np.array(list(label) + list(r) + list(g) + list(b), np.uint8)
    outputFile.write(out.tobytes())

    # if you want to show the encoded image. set up 'debugEncodedImage' flag
    if debugEncodedImage:
        showImage(r, g, b) 
Example 16
Project: ward-metrics   Author: phev8   File: visualisations.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_events_with_event_scores(gt_event_scores, detected_event_scores, ground_truth_events, detected_events, show=True):
    fig = plt.figure(figsize=(10, 3))
    for i in range(len(detected_events)):
        d = detected_events[i]
        plt.axvspan(d[0], d[1], 0, 0.5)
        plt.text((d[1] + d[0]) / 2, 0.2, detected_event_scores[i], horizontalalignment='center', verticalalignment='center')

    for i in range(len(ground_truth_events)):
        gt = ground_truth_events[i]
        plt.axvspan(gt[0], gt[1], 0.5, 1)
        plt.text((gt[1] + gt[0]) / 2, 0.8, gt_event_scores[i], horizontalalignment='center', verticalalignment='center')

    plt.tight_layout()

    if show:
        plt.show()
    else:
        plt.draw() 
Example 17
Project: pycpd   Author: siavashk   File: fishAffine3D.py    (MIT License) View Source Project 6 votes vote down vote up
def main():
    fish = loadmat('./data/fish.mat')

    X1 = np.zeros((fish['X'].shape[0], fish['X'].shape[1] + 1))
    X1[:,:-1] = fish['X']
    X2 = np.ones((fish['X'].shape[0], fish['X'].shape[1] + 1))
    X2[:,:-1] = fish['X']
    X = np.vstack((X1, X2))

    Y1 = np.zeros((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
    Y1[:,:-1] = fish['Y']
    Y2 = np.ones((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
    Y2[:,:-1] = fish['Y']
    Y = np.vstack((Y1, Y2))

    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    callback = partial(visualize, ax=ax)

    reg = affine_registration(X, Y)
    reg.register(callback)
    plt.show() 
Example 18
Project: speccer   Author: bensimner   File: profiler.py    (MIT License) View Source Project 6 votes vote down vote up
def make_new_pie_from_callers(callers, call_name=None):
    # plot the stats
    fig, ax = plt.subplots()

    if call_name:
        ax.set_title('Breakdown of {} callees'.format(call_name))

    labels, sizes, callbacks = make_pie_from_callers(callers)
    wedges, _ = ax.pie(sizes, labels=labels)

    for w in wedges:
        w.set_picker(True)

    def onclick(evt):
        l = evt.artist.get_label()
        cb = callbacks[l]
        if cb:
            if l == 'other':
                l = '{}/other'.format(call_name)
            make_new_pie_from_callers(cb, call_name=l)


    fig.canvas.mpl_connect('pick_event', onclick)
    ax.axis('equal')
    plt.show() 
Example 19
Project: genomedisco   Author: kundajelab   File: plot_quasar_scatter.py    (MIT License) View Source Project 6 votes vote down vote up
def main():
    parser = generate_parser()
    args = parser.parse_args()
    infile1 = h5py.File(args.input1, 'r')
    infile2 = h5py.File(args.input2, 'r')
    resolutions = numpy.intersect1d(infile1['resolutions'][...], infile2['resolutions'][...])
    chroms = numpy.intersect1d(infile2['chromosomes'][...], infile2['chromosomes'][...])
    results = {}
    data1 = load_data(infile1, chroms, resolutions)
    data2 = load_data(infile2, chroms, resolutions)
    infile1.close()
    infile2.close()
    results = {}
    results[(args.input1.split('/')[-1].strip('.quasar'), args.input2.split('/')[-1].strip('.quasar'))] = correlate_samples(data1, data2)
    for resolution in data1.keys():
        for chromo in chroms:
            plt.scatter(data1[resolution][chromo][1].flatten(),data2[resolution][chromo][1].flatten(),alpha=0.1,color='red')
            plt.show()
            plt.savefig(args.output+'.res'+str(resolution)+'.chr'+chromo+'.pdf') 
Example 20
Project: Google-QuickDraw   Author: ankonzoid   File: QuickDraw_noisy_classifier.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_labeled_images_random(image_list, label_list, categories, n, title_str, ypixels, xpixels, seed, filename):
    random.seed(seed)
    index_sample = random.sample(range(len(image_list)), n)
    plt.figure(figsize=(2*n, 2))
    #plt.suptitle(title_str)
    for i, ind in enumerate(index_sample):
        ax = plt.subplot(1, n, i + 1)
        plt.imshow(image_list[ind].reshape(ypixels, xpixels))
        plt.gray()
        ax.set_title(categories[label_list[ind]], fontsize=20)
        ax.get_xaxis().set_visible(False); ax.get_yaxis().set_visible(False)
    if 1:
        pylab.savefig(filename, bbox_inches='tight')
    else:
        plt.show()

# plot_unlabeled_images_random: plots unlabeled images at random 
Example 21
Project: Google-QuickDraw   Author: ankonzoid   File: QuickDraw_noisy_classifier.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_unlabeled_images_random(image_list, n, title_str, ypixels, xpixels, seed, filename):
    random.seed(seed)
    index_sample = random.sample(range(len(image_list)), n)
    plt.figure(figsize=(2*n, 2))
    plt.suptitle(title_str)
    for i, ind in enumerate(index_sample):
        ax = plt.subplot(1, n, i + 1)
        plt.imshow(image_list[ind].reshape(ypixels, xpixels))
        plt.gray()
        ax.get_xaxis().set_visible(False); ax.get_yaxis().set_visible(False)
    if 1:
        pylab.savefig(filename, bbox_inches='tight')
    else:
        plt.show()

# plot_compare: given test images and their reconstruction, we plot them for visual comparison 
Example 22
Project: Google-QuickDraw   Author: ankonzoid   File: QuickDraw_noisy_classifier.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_compare(x_test, decoded_imgs, filename):
    n = 10
    plt.figure(figsize=(2*n, 4))
    for i in range(n):
        # display original
        ax = plt.subplot(2, n, i + 1)
        plt.imshow(x_test[i].reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

        # display reconstruction
        ax = plt.subplot(2, n, i + 1 + n)
        plt.imshow(decoded_imgs[i].reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

    if 1:
        pylab.savefig(filename, bbox_inches='tight')
    else:
        plt.show()

# plot_img: plots greyscale image 
Example 23
Project: sampleRNN_ICLR2017   Author: soroushmehr   File: __init__.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_traing_info(x, ylist, path):
    """
    Loads log file and plot x and y values as provided by input.
    Saves as <path>/train_log.png
    """
    file_name = os.path.join(path, __train_log_file_name)
    try:
        with open(file_name, "rb") as f:
            log = pickle.load(f)
    except IOError:  # first time
        warnings.warn("There is no {} file here!!!".format(file_name))
        return
    plt.figure()
    x_vals = log[x]
    for y in ylist:
        y_vals = log[y]
        if len(y_vals) != len(x_vals):
            warning.warn("One of y's: {} does not have the same length as x:{}".format(y, x))
        plt.plot(x_vals, y_vals, label=y)
        # assert len(y_vals) == len(x_vals), "not the same len"
    plt.xlabel(x)
    plt.legend()
    #plt.show()
    plt.savefig(file_name[:-3]+'png', bbox_inches='tight')
    plt.close('all') 
Example 24
Project: pybot   Author: spillai   File: plot_utils.py    (license) View Source Project 6 votes vote down vote up
def plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=plt.cm.Greys, block=True):
    # Colormaps: jet, Greys
    cm_normalized = cm.astype(np.float32) / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm_normalized, interpolation='nearest', cmap=cmap)

    # Show confidences
    for i, cas in enumerate(cm): 
        for j, c in enumerate(cas): 
            if c > 0: 
                plt.text(j-0.1, i+0.2, c, fontsize=16, fontweight='bold', color='#b70000')

    f = plt.figure(1)
    f.clf()
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(target_names))
    plt.xticks(tick_marks, target_names, rotation=45)
    plt.yticks(tick_marks, target_names)
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show(block=block) 
Example 25
Project: pybot   Author: spillai   File: metrics.py    (license) View Source Project 6 votes vote down vote up
def plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=plt.cm.Greys):
    # Colormaps: jet, Greys
    cm_normalized = cm.astype(np.float32) / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm_normalized, interpolation='nearest', cmap=cmap)

    # Show confidences
    for i, cas in enumerate(cm): 
        for j, c in enumerate(cas): 
            if c > 0: 
                plt.text(j-0.1, i+0.2, c, fontsize=16, fontweight='bold', color='#b70000')

    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(target_names))
    plt.xticks(tick_marks, target_names, rotation=45)
    plt.yticks(tick_marks, target_names)
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show(block=True) 
Example 26
Project: didi_competition   Author: Heipiao   File: plot_traffic_data.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_single_day_traffic(df):
    y_tj_l1 = df["tj_level1_count"]
    y_tj_l2 = df["tj_level2_count"]
    y_tj_l3 = df["tj_level3_count"]
    y_tj_l4 = df["tj_level4_count"]

    x_time = df["time"]
    x_district = df["district"]

    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(x_time, x_district, y_tj_l1, )
    #ax.plot_surface(x_time, x_district, y_tj_l1)
    print(plt.get_backend())
    plt.show()
    plt.savefig("plot_traffic.png") 
Example 27
Project: lung-cancer-detector   Author: YichenGong   File: plot_utils.py    (MIT License) View Source Project 6 votes vote down vote up
def plot_3D(img, threshold=-400):
	verts, faces = measure.marching_cubes(img, threshold)

	fig = plt.figure(figsize=(10, 10))
	ax = fig.add_subplot(111, projection='3d')

	mesh = Poly3DCollection(verts[faces], alpha=0.1)
	face_color = [0.5, 0.5, 1]
	mesh.set_facecolor(face_color)
	ax.add_collection3d(mesh)

	ax.set_xlim(0, img.shape[0])
	ax.set_ylim(0, img.shape[1])
	ax.set_zlim(0, img.shape[2])

	plt.show() 
Example 28
Project: snake   Author: rhinech   File: bose_hubbard_mft.py    (license) View Source Project 6 votes vote down vote up
def load_data():
    """Draw the Mott lobes."""

    res = np.load(r'data_%d.npy' % GRID_SIZE)
    x = res[:, 0]
    y = res[:, 1]
    z = []
    for i, entry in enumerate(res):
        z.append(kinetic_energy(entry[2:], -1.))
    plt.pcolor(
        np.reshape(x, (GRID_SIZE, GRID_SIZE)),
        np.reshape(y, (GRID_SIZE, GRID_SIZE)),
        np.reshape(z, (GRID_SIZE, GRID_SIZE))
    )
    plt.xlabel('$dt/U$')
    plt.ylabel('$\mu/U$')
    plt.show() 
Example 29
Project: saapy   Author: ashapochka   File: plot_utils.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def plot_ecdf(x, y, xlabel='attribute', legend='x'):
    """
    Plot distribution ECDF
    x should be sorted, y typically from 1/len(x) to 1

    TODO: function should be improved to plot multiple overlayed ecdfs
    """
    plt.plot(x, y, marker='.', linestyle='none')

    # Make nice margins
    plt.margins(0.02)

    # Annotate the plot
    plt.legend((legend,), loc='lower right')
    _ = plt.xlabel(xlabel)
    _ = plt.ylabel('ECDF')

    # Display the plot
    plt.show() 
Example 30
Project: psola   Author: jcreinhold   File: estimation.py    (MIT License) View Source Project 6 votes vote down vote up
def test():
    """
    little test, NOT FOR REAL USE, put target filename as first and only argument
    will plot the pitch and strength v. time to screen
    safety is not guaranteed.
    """
    # imports specific to this test
    import sys
    import warnings
    from scipy.io import wavfile
    import matplotlib.pyplot as plt
    from psola.experiment_config import ExperimentConfig

    # get the data and do the estimation
    filename = sys.argv[1]    # filename is first command line arg
    cfg = ExperimentConfig()  # use default settings
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")  # ignore annoying WavFileWarning
        fs, data = wavfile.read(filename)
    pitch, t, strength = pitch_estimation(data, fs, cfg)

    # Plot estimated pitch and strength of pitch values
    f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(16,9))
    ax1.plot(t, pitch); ax1.set_title('Pitch v. Time'); ax1.set_ylabel('Freq (Hz)')
    ax2.plot(t, strength); ax2.set_title('Strength v. Time'); ax1.set_ylabel('Strength')
    plt.show() 
Example 31
Project: bayestsa   Author: thalesians   File: studysv.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def analyseparamsneighbourhood(svdata, params, includejumps, randomstate):
    parameterndarray = transformparameterndarray(np.array(params), includejumps)
    offsets = np.linspace(-.5, .5, 10)
    for dimension in range(params.dimensioncount):
        xs, ys = [], []
        parametername = params.getdimensionname(dimension)
        print('Perturbing %s...' % parametername)
        for offset in offsets:
            newparameterndarray = np.copy(parameterndarray)
            newparameterndarray[dimension] += offset
            xs.append(inversetransformparameterndarray(newparameterndarray, includejumps)[dimension])
            y = runsvljparticlefilter(svdata, sv.Params(*inversetransformparameterndarray(newparameterndarray, includejumps)), randomstate).stochfilter.loglikelihood
            ys.append(y)
        fig = plt.figure()
        plot = fig.add_subplot(111)
        plot.plot(xs, ys)
        plot.axvline(x=inversetransformparameterndarray(parameterndarray, includejumps)[dimension], color='red')
        plot.set_xlabel(parametername)
        plot.set_ylabel('loglikelihood')
        plt.show() 
Example 32
Project: kaggle-review   Author: daxiongshu   File: poke.py    (license) View Source Project 6 votes vote down vote up
def show_one_img_mask(data):
    w,h = 1918,1280
    a = randint(0,31)
    path = "../input/test"
    data = np.load(data).item()
    name,masks = data['name'][a],data['pred']
    img = Image.open("%s/%s"%(path,name))
    #img.show()
    plt.imshow(img)
    plt.show()
    mask = np.squeeze(masks[a])
    mask = imresize(mask,[h,w]).astype(np.float32)
    print(mask.shape,mask[0])
    img = Image.fromarray(mask*256)#.resize([w,h])
    plt.imshow(img)
    plt.show() 
Example 33
Project: mx-lsoftmax   Author: luoyetx   File: plot_beta.py    (BSD 3-Clause "New" or "Revised" License) View Source Project 6 votes vote down vote up
def plot_beta():
    '''plot beta over training
    '''
    beta = args.beta
    scale = args.scale
    beta_min = args.beta_min
    num_epoch = args.num_epoch
    epoch_size = int(float(args.num_examples) / args.batch_size)

    x = np.arange(num_epoch*epoch_size)
    y = beta * np.power(scale, x)
    y = np.maximum(y, beta_min)
    epoch_x = np.arange(num_epoch) * epoch_size
    epoch_y = beta * np.power(scale, epoch_x)
    epoch_y = np.maximum(epoch_y, beta_min)

    # plot beta descent curve
    plt.semilogy(x, y)
    plt.semilogy(epoch_x, epoch_y, 'ro')
    plt.title('beta descent')
    plt.ylabel('beta')
    plt.xlabel('epoch')
    plt.show() 
Example 34
Project: machine-learning   Author: zzw0929   File: logRegres.py    (license) View Source Project 6 votes vote down vote up
def plotBestFit(weights):
	import matplotlib.pyplot as plt
	dataMat, labelMat =  loadDataSet()
	dataArr =  array(dataMat)
	n = shape(dataArr)[0]
	xcord1 = []; ycord1 = []
	xcord2 = []; ycord2 = []
	for i in range(n):
		if int(labelMat[i]) == 1:
			xcord1.append(dataArr[i, 1]);ycord1.append(dataArr[i, 2])
		else:
			xcord2.append(dataArr[i, 1]);ycord2.append(dataArr[i, 2])
	fig = plt.figure()
	ax = fig.add_subplot(111)
	ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
	ax.scatter(xcord2, ycord2, s=30, c='green')
	x = arange(-3.0, 3.0, 0.1)
	y = (-weights[0]-weights[1]*x)/weights[2] # ??????
	ax.plot(x, y)
	plt.xlabel('X1');plt.ylabel('X2')
	plt.show()

# ??500??? 
Example 35
Project: machine-learning   Author: zzw0929   File: treePlotter.py    (license) View Source Project 6 votes vote down vote up
def createPlot(inTree):
    fig = plt.figure(1, facecolor='white')
    fig.clf()
    axprops = dict(xticks=[], yticks=[])
    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)    #no ticks
    #createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses 
    plotTree.totalW = float(getNumLeafs(inTree))
    plotTree.totalD = float(getTreeDepth(inTree))
    plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0;
    plotTree(inTree, (0.5,1.0), '')
    plt.show()

# def createPlot():
# 	fig = plt.figure(1, facecolor='white')
# 	fig.clf()
# 	createPlot.ax1 = plt.subplot(111, frameon=True)
# 	plotNode(U'a decision node',(0.5,0.1), (0.1,0.5), decisionNode)
# 	plotNode(U'a leaf node',(0.8,0.1), (0.3,0.8), leafNode)
# 	plt.show() 
Example 36
Project: PersonalizedMultitaskLearning   Author: mitmedialab   File: tensorFlowNetwork.py    (license) View Source Project 6 votes vote down vote up
def plotValResults(self, save_path=None, label=None):
		if label is not None:
			accs = self.training_val_results['acc'][label]
			aucs = self.training_val_results['auc'][label]
		else:
			accs = self.training_val_results['acc']
			aucs = self.training_val_results['auc']
		plt.figure()
		plt.plot([i * ACCURACY_LOGGED_EVERY_N_STEPS for i in range(len(accs))], accs)
		plt.plot([i * ACCURACY_LOGGED_EVERY_N_STEPS for i in range(len(aucs))], aucs)
		plt.xlabel('Training step')
		plt.ylabel('Validation accuracy')
		plt.legend(['Accuracy','AUC'])
		if save_path is None:
			plt.show()
		else:
			plt.savefig(save_path)
		plt.close() 
Example 37
Project: PersonalizedMultitaskLearning   Author: mitmedialab   File: tensorFlowNetworkMultiTask.py    (license) View Source Project 6 votes vote down vote up
def plotValResults(self, save_path=None, label=None):
		if label:
			accs = self.training_val_results_per_task['acc'][label]
			aucs = self.training_val_results_per_task['auc'][label]
		else:
			accs = self.training_val_results['acc']
			aucs = self.training_val_results['auc']
		plt.figure()
		plt.plot([i * self.accuracy_logged_every_n for i in range(len(accs))], accs)
		plt.plot([i * self.accuracy_logged_every_n for i in range(len(aucs))], aucs)
		plt.xlabel('Training step')
		plt.ylabel('Validation accuracy')
		plt.legend(['Accuracy','AUC'])
		if save_path is None:
			plt.show()
		else:
			plt.savefig(save_path) 
Example 38
Project: photinia   Author: XoriieInpottn   File: tsne.py    (license) View Source Project 6 votes vote down vote up
def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
    assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
    plt.figure(figsize=(18, 18))  # in inches
    x = low_dim_embs[:, 0]
    y = low_dim_embs[:, 1]
    plt.scatter(x, y)
    for i, label in enumerate(labels):
        x, y = low_dim_embs[i, :]
        plt.annotate(label,
                     xy=(x, y),
                     xytext=(5, 2),
                     textcoords='offset points',
                     ha='right',
                     va='bottom')
    plt.show()
    # plt.savefig(filename) 
Example 39
Project: klineyes   Author: tenstone   File: show_plot.py    (MIT License) View Source Project 6 votes vote down vote up
def mfi(df):
    df['date'] = pd.to_datetime(df.date)

    fig = plt.figure(figsize=(16, 9))
    gs = GridSpec(3, 1) # 2 rows, 3 columns
    fig.suptitle(df['date'][-1:].values[0])
    fig.set_label('MFI')
    price = fig.add_subplot(gs[:2, 0])
    price.plot(df['date'], df['close'], color='blue')

    indicator = fig.add_subplot(gs[2, 0], sharex=price)
    indicator.plot(df['date'], df['mfi'], c='pink')
    indicator.plot(df['date'], [20.]*len(df['date']), c='green')
    indicator.plot(df['date'], [80.]*len(df['date']), c='orange')

    price.grid(True)
    indicator.grid(True)
    plt.tight_layout()
    plt.show() 
Example 40
Project: klineyes   Author: tenstone   File: show_plot.py    (MIT License) View Source Project 6 votes vote down vote up
def atr(df):
    '''
    Average True Range
    :param df:
    :return:
    '''
    df['date'] = pd.to_datetime(df.date)

    fig = plt.figure(figsize=(16, 9))
    gs = GridSpec(3, 1) # 2 rows, 3 columns
    fig.suptitle(df['date'][-1:].values[0])
    fig.set_label('ATR')
    price = fig.add_subplot(gs[:2, 0])
    price.plot(df['date'], df['close'], color='blue')

    indicator = fig.add_subplot(gs[2, 0], sharex=price)
    indicator.plot(df['date'], df['atr'], c='pink')
    # indicator.plot(df['date'], [20.]*len(df['date']), c='green')
    # indicator.plot(df['date'], [80.]*len(df['date']), c='orange')

    price.grid(True)
    indicator.grid(True)
    plt.tight_layout()
    plt.show() 
Example 41
Project: klineyes   Author: tenstone   File: show_plot.py    (MIT License) View Source Project 6 votes vote down vote up
def rocr(df):
    '''
    Average True Range
    :param df:
    :return:
    '''
    df['date'] = pd.to_datetime(df.date)

    fig = plt.figure(figsize=(16, 9))
    gs = GridSpec(3, 1) # 2 rows, 3 columns
    fig.suptitle(df['date'][-1:].values[0])
    fig.set_label('ATR')
    price = fig.add_subplot(gs[:2, 0])
    price.plot(df['date'], df['close'], color='blue')

    indicator = fig.add_subplot(gs[2, 0], sharex=price)
    indicator.plot(df['date'], df['rocr'], c='pink')
    # indicator.plot(df['date'], [20.]*len(df['date']), c='green')
    # indicator.plot(df['date'], [80.]*len(df['date']), c='orange')

    price.grid(True)
    indicator.grid(True)
    plt.tight_layout()
    plt.show() 
Example 42
Project: CopyNet   Author: MultiPath   File: test_run.py    (MIT License) View Source Project 6 votes vote down vote up
def main():
    losses   = []
    accuracy = []
    for echo in xrange(4000):
        logger.info('Iteration = {}'.format(echo))
        train_data = simulator(M=20)

        print train_data['text'][-1]

        loss       = learner(train_data, fr=0.)
        losses.append(loss)
        accuracy  += train_data['acc']

        if echo % 100 == 99:
            plt.plot(accuracy)
            plt.show()

    # pkl.dump(losses, open('losses.temp.pkl')) 
Example 43
Project: CopyNet   Author: MultiPath   File: run.py    (MIT License) View Source Project 6 votes vote down vote up
def main():
    losses   = []
    accuracy = []
    for echo in xrange(4000):
        logger.info('Iteration = {}'.format(echo))
        train_data = simulator(M=20)

        print train_data['text'][-1]

        loss       = learner(train_data, fr=0.)
        losses.append(loss)
        accuracy  += train_data['acc']

        if echo % 100 == 99:
            plt.plot(accuracy)
            plt.show()

    # pkl.dump(losses, open('losses.temp.pkl')) 
Example 44
Project: linkedin_recommend   Author: duggalr2   File: linkedin_graph.py    (license) View Source Project 6 votes vote down vote up
def pieGraph(data_count):
    """
    Graph's a pie graph of the data with count values; Only includes data that appears more than once!
    Parameter: -data_count: dict
    """
    names, count = [], []
    for val, key in data_count.items():
        if key > 1:
            names.append(val)
            count.append(key)

    fig1, ax1 = plt.subplots()
    ax1.pie(count, labels=names, autopct='%1.1f%%', shadow=True, startangle=90)
    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
    # plt.tight_layout()
    plt.show() 
Example 45
Project: linkedin_recommend   Author: duggalr2   File: graph.py    (license) View Source Project 6 votes vote down vote up
def pie_graph(data_count):
    """
    Graph's a pie graph of the data with count values (only shows schools that appear more than once)
    Parameter: -data_count: dict
    """
    names, count = [], []
    for val, key in data_count.items():
        if key > 1:
            names.append(val)
            count.append(key)

    fig1, ax1 = plt.subplots()
    ax1.pie(count, labels=names, autopct='%1.1f%%', shadow=True, startangle=90)
    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
    # plt.tight_layout()
    plt.show() 
Example 46
Project: linkedin_recommend   Author: duggalr2   File: wrong.py    (license) View Source Project 6 votes vote down vote up
def barGraph(data_count):

    names, count_in = [], []
    data_count = sorted(data_count.items(), key=operator.itemgetter(1), reverse=True)
    for i in data_count:
        names.append(i[0])
        count_in.append(i[-1])

    plt.rcdefaults()
    fig, ax = plt.subplots()
    y_pos = np.arange(len(names))
    ax.barh(y_pos, count_in, align='center',
            color='green', ecolor='black')
    ax.set_yticks(y_pos)
    ax.set_yticklabels(names)
    ax.invert_yaxis()  # labels read top-to-bottom
    ax.set_xlabel('Categories')
    ax.set_title('# of job titles in each category')
    plt.show() 
Example 47
Project: AnomalyDetection   Author: JayZhuCoding   File: Monitor.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def plot_training_parameters(self):
        fr = open("training_param.csv", "r")
        fr.readline()
        lines = fr.readlines()
        fr.close()
        n = 100
        nu = np.empty(n, dtype=np.float64)
        gamma = np.empty(n, dtype=np.float64)
        diff = np.empty([n, n], dtype=np.float64)
        for row in range(len(lines)):
            m = lines[row].strip().split(",")
            i = row / n
            j = row % n
            nu[i] = Decimal(m[0])
            gamma[j] = Decimal(m[1])
            diff[i][j] = Decimal(m[2])
        plt.pcolor(gamma, nu, diff, cmap="coolwarm")
        plt.title("The Difference of Guassian Classifier with Different nu, gamma")
        plt.xlabel("gamma")
        plt.ylabel("nu")
        plt.xscale("log")
        plt.yscale("log")
        plt.colorbar()
        plt.show() 
Example 48
Project: mx-rfcn   Author: giorking   File: test_imdb.py    (license) View Source Project 6 votes vote down vote up
def visualize_gt_roidb(imdb, gt_roidb):
    """
    visualize gt roidb
    :param imdb: the imdb to be visualized
    :param gt_roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
    :return: None
    """
    import matplotlib.pyplot as plt
    import skimage.io
    for i in range(len(gt_roidb)):
        im_path = imdb.image_path_from_index(imdb.image_set_index[i])
        im = skimage.io.imread(im_path)
        roi_rec = gt_roidb[i]
        plt.imshow(im)
        for bbox, gt_class, overlap in zip(roi_rec['boxes'], roi_rec['gt_classes'], roi_rec['gt_overlaps']):
            box = plt.Rectangle((bbox[0], bbox[1]),
                                bbox[2] - bbox[0],
                                bbox[3] - bbox[1], fill=False,
                                edgecolor='g', linewidth=3)
            plt.gca().add_patch(box)
            plt.gca().text(bbox[0], bbox[1], imdb.classes[gt_class] + ' {}'.format(overlap[0, gt_class]), color='w')
        plt.show() 
Example 49
Project: adversarial-frcnn   Author: xiaolonw   File: test.py    (license) View Source Project 6 votes vote down vote up
def vis_detections(im, class_name, dets, thresh=0.3):
    """Visual debugging of detections."""
    import matplotlib.pyplot as plt
    im = im[:, :, (2, 1, 0)]
    for i in xrange(np.minimum(10, dets.shape[0])):
        bbox = dets[i, :4]
        score = dets[i, -1]
        if score > thresh:
            plt.cla()
            plt.imshow(im)
            plt.gca().add_patch(
                plt.Rectangle((bbox[0], bbox[1]),
                              bbox[2] - bbox[0],
                              bbox[3] - bbox[1], fill=False,
                              edgecolor='g', linewidth=3)
                )
            plt.title('{}  {:.3f}'.format(class_name, score))
            plt.show() 
Example 50
Project: adversarial-frcnn   Author: xiaolonw   File: minibatch.py    (license) View Source Project 6 votes vote down vote up
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
    """Visualize a mini-batch for debugging."""
    import matplotlib.pyplot as plt
    for i in xrange(rois_blob.shape[0]):
        rois = rois_blob[i, :]
        im_ind = rois[0]
        roi = rois[1:]
        im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
        im += cfg.PIXEL_MEANS
        im = im[:, :, (2, 1, 0)]
        im = im.astype(np.uint8)
        cls = labels_blob[i]
        plt.imshow(im)
        print 'class: ', cls, ' overlap: ', overlaps[i]
        plt.gca().add_patch(
            plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
                          roi[3] - roi[1], fill=False,
                          edgecolor='r', linewidth=3)
            )
        plt.show()