Python pyplot.xlim方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_roc_curve(y_true, y_score, size=None):
    """plot_roc_curve."""
    false_positive_rate, true_positive_rate, thresholds = roc_curve(
        y_true, y_score)
    if size is not None:
        plt.figure(figsize=(size, size))
        plt.axis('equal')
    plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy')
    plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
    plt.xlabel('False positive rate')
    plt.ylabel('True positive rate')
    plt.ylim([-0.05, 1.05])
    plt.xlim([-0.05, 1.05])
    plt.grid()
    plt.title('Receiver operating characteristic AUC={0:0.2f}'.format(
        roc_auc_score(y_true, y_score))) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def visualize_2D_trip(self,trip,tw_open,tw_close):
        plt.figure(figsize=(30,30))
        rcParams.update({'font.size': 22})
        # Plot cities
        colors = ['red'] # Depot is first city
        for i in range(len(tw_open)-1):
            colors.append('blue')
        plt.scatter(trip[:,0], trip[:,1], color=colors, s=200)
        # Plot tour
        tour=np.array(list(range(len(trip))) + [0])
        X = trip[tour, 0]
        Y = trip[tour, 1]
        plt.plot(X, Y,"--", markersize=100)
        # Annotate cities with TW
        tw_open = np.rint(tw_open)
        tw_close = np.rint(tw_close)
        time_window = np.concatenate((tw_open,tw_close),axis=1)
        for tw, (x, y) in zip(time_window,(zip(X,Y))):
            plt.annotate(tw,xy=(x, y))  
        plt.xlim(0,60)
        plt.ylim(0,60)
        plt.show()


    # Heatmap of permutations (x=cities; y=steps) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def visualize_2D_trip(self, trip):
        plt.figure(figsize=(30,30))
        rcParams.update({'font.size': 22})

        # Plot cities
        plt.scatter(trip[:,0], trip[:,1], s=200)

        # Plot tour
        tour=np.array(list(range(len(trip))) + [0])
        X = trip[tour, 0]
        Y = trip[tour, 1]
        plt.plot(X, Y,"--", markersize=100)

        # Annotate cities with order
        labels = range(len(trip))
        for i, (x, y) in zip(labels,(zip(X,Y))):
            plt.annotate(i,xy=(x, y))  

        plt.xlim(0,100)
        plt.ylim(0,100)
        plt.show()


    # Heatmap of permutations (x=cities; y=steps) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_wh_methods():  # from utils.utils import *; plot_wh_methods()
    # Compares the two methods for width-height anchor multiplication
    # https://github.com/ultralytics/yolov3/issues/168
    x = np.arange(-4.0, 4.0, .1)
    ya = np.exp(x)
    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2

    fig = plt.figure(figsize=(6, 3), dpi=150)
    plt.plot(x, ya, '.-', label='yolo method')
    plt.plot(x, yb ** 2, '.-', label='^2 power method')
    plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method')
    plt.xlim(left=-4, right=4)
    plt.ylim(bottom=0, top=6)
    plt.xlabel('input')
    plt.ylabel('output')
    plt.legend()
    fig.tight_layout()
    fig.savefig('comparison.png', dpi=200) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def make_plot(files, labels):
	plt.figure()
	for file_idx in range(len(files)):
		rot_err, trans_err = read_csv(files[file_idx])
		success_dict = count_success(trans_err)

		x_range = success_dict.keys()
		x_range.sort()
		success = []
		for i in x_range:
			success.append(success_dict[i])
		success = np.array(success)/total_cases

		plt.plot(x_range, success, linewidth=3, label=labels[file_idx])
		# plt.scatter(x_range, success, s=50)
	plt.ylabel('Success Ratio', fontsize=40)
	plt.xlabel('Threshold for Translation Error', fontsize=40)
	plt.tick_params(labelsize=40, width=3, length=10)
	plt.grid(True)
	plt.ylim(0,1.005)
	plt.yticks(np.arange(0,1.2,0.2))
	plt.xticks(np.arange(0,2.1,0.2))
	plt.xlim(0,2)
	plt.legend(fontsize=30, loc=4) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_pixels(file_name, candidate_data_single_band,
                reference_data_single_band, limits=None, fit_line=None):

    logging.info('Display: Creating pixel plot - {}'.format(file_name))
    fig = plt.figure()
    plt.hexbin(
        candidate_data_single_band, reference_data_single_band, mincnt=1)
    if not limits:
        min_value = 0
        _, ymax = plt.gca().get_ylim()
        _, xmax = plt.gca().get_xlim()
        max_value = max([ymax, xmax])
        limits = [min_value, max_value]
    plt.plot(limits, limits, 'k-')
    if fit_line:
        start = limits[0] * fit_line.gain + fit_line.offset
        end = limits[1] * fit_line.gain + fit_line.offset
        plt.plot(limits, [start, end], 'g-')
    plt.xlim(limits)
    plt.ylim(limits)
    plt.xlabel('Candidate DNs')
    plt.ylabel('Reference DNs')
    fig.savefig(file_name, bbox_inches='tight')
    plt.close(fig) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_histograms(file_name, candidate_data_multiple_bands,
                    reference_data_multiple_bands=None,
                    # Default is for Blue-Green-Red-NIR:
                    colour_order=['b', 'g', 'r', 'y'],
                    x_limits=None, y_limits=None):
    logging.info('Display: Creating histogram plot - {}'.format(file_name))
    fig = plt.figure()
    plt.hold(True)
    for colour, c_band in zip(colour_order, candidate_data_multiple_bands):
        c_bh, c_bins = numpy.histogram(c_band, bins=256)
        plt.plot(c_bins[:-1], c_bh, color=colour, linestyle='-', linewidth=2)
    if reference_data_multiple_bands:
        for colour, r_band in zip(colour_order, reference_data_multiple_bands):
            r_bh, r_bins = numpy.histogram(r_band, bins=256)
            plt.plot(
                r_bins[:-1], r_bh, color=colour, linestyle='--', linewidth=2)
    plt.xlabel('DN')
    plt.ylabel('Number of pixels')
    if x_limits:
        plt.xlim(x_limits)
    if y_limits:
        plt.ylim(y_limits)
    fig.savefig(file_name, bbox_inches='tight')
    plt.close(fig) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_mean_bootstrap_exponential_readme():
    X = np.random.exponential(7, 4)
    classical_samples = [np.mean(resample(X)) for _ in range(10000)]
    posterior_samples = mean(X, 10000)
    l, r = highest_density_interval(posterior_samples)
    classical_l, classical_r = highest_density_interval(classical_samples)
    plt.subplot(2, 1, 1)
    plt.title('Bayesian Bootstrap of mean')
    sns.distplot(posterior_samples, label='Bayesian Bootstrap Samples')
    plt.plot([l, r], [0, 0], linewidth=5.0, marker='o', label='95% HDI')
    plt.xlim(-1, 18)
    plt.legend()
    plt.subplot(2, 1, 2)
    plt.title('Classical Bootstrap of mean')
    sns.distplot(classical_samples, label='Classical Bootstrap Samples')
    plt.plot([classical_l, classical_r], [0, 0], linewidth=5.0, marker='o', label='95% HDI')
    plt.xlim(-1, 18)
    plt.legend()
    plt.savefig('readme_exponential.png', bbox_inches='tight') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def print_roc(self, y_true, y_scores, filename):
        '''
        Prints the ROC for this model.
        '''
        fpr, tpr, thresholds = metrics.roc_curve(y_true, y_scores)
        plt.figure()
        plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % self.roc_auc)
        plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.title('Receiver operating characteristic')
        plt.legend(loc="lower right")
        plt.savefig(filename)
        plt.close() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_progress_errk(self, claimed_acc=None, title='MobileNetV3', k=1):
        tr_str = 'train_error{}'.format(k)
        val_str = 'val_error{}'.format(k)
        plt.figure(figsize=(9, 8), dpi=300)
        plt.plot(self.data[tr_str], label='Training error')
        plt.plot(self.data[val_str], label='Validation error')
        if claimed_acc is not None:
            plt.plot((0, len(self.data[tr_str])), (1 - claimed_acc, 1 - claimed_acc), 'k--',
                     label='Claimed validation error ({:.2f}%)'.format(100. * (1 - claimed_acc)))
        plt.plot((0, len(self.data[tr_str])),
                 (np.min(self.data[val_str]), np.min(self.data[val_str])), 'r--',
                 label='Best validation error ({:.2f}%)'.format(100. * np.min(self.data[val_str])))
        plt.title('Top-{} error for {}'.format(k, title))
        plt.xlabel('Epoch')
        plt.ylabel('Error')
        plt.legend()
        plt.xlim(0, len(self.data[tr_str]) + 1)
        plt.savefig(os.path.join(self.log_path, 'top{}-{}.png'.format(k, self.local_rank))) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def plot_DOY(dates, y, mpl_cmap):
    """ Create a DOY plot

    Args:
        dates (iterable): sequence of datetime
        y (np.ndarray): variable to plot
        mpl_cmap (colormap): matplotlib colormap
    """
    doy = np.array([d.timetuple().tm_yday for d in dates])
    year = np.array([d.year for d in dates])

    sp = plt.scatter(doy, y, c=year, cmap=mpl_cmap,
                     marker='o', edgecolors='none', s=35)
    plt.colorbar(sp)

    months = mpl.dates.MonthLocator()  # every month
    months_fmrt = mpl.dates.DateFormatter('%b')

    plt.tick_params(axis='x', which='minor', direction='in', pad=-10)
    plt.axes().xaxis.set_minor_locator(months)
    plt.axes().xaxis.set_minor_formatter(months_fmrt)

    plt.xlim(1, 366)
    plt.xlabel('Day of Year') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def show_classification_areas(X, Y, lr):
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
    Z = lr.predict(np.c_[xx.ravel(), yy.ravel()])

    Z = Z.reshape(xx.shape)
    plt.figure(1, figsize=(30, 25))
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel1)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=np.abs(Y - 1), edgecolors='k', cmap=plt.cm.coolwarm)
    plt.xlabel('X')
    plt.ylabel('Y')

    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())

    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def _plot(self, results, x, y, x_label, y_label, curve, filename):
        r"""
        Contains the actual plot functionality.
        """
        plt.plot(x, y)
        plt.xlabel(x_label)
        plt.ylabel(y_label)
        plt.ylim([0.0, 1.0])
        plt.xlim([0.0, 1.0])
        if results == 'test':
            plt.title('{} test set {} curve'.format(self.method, curve))
        else:
            plt.title('{} train set {} curve'.format(self.method, curve))
        if filename is not None:
            plt.savefig(filename + '_' + curve + '.pdf')
            plt.close()
        else:
            plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def test_filter(self):
        fs = 8000
        f0 = 440
        sin = np.array([np.sin(2.0 * np.pi * f0 * n / fs)
                        for n in range(fs * 1)])
        noise = np.random.rand(len(sin)) - 0.5
        wav = sin + noise
        lpfed = low_pass_filter(wav, 500, n_taps=255, fs=fs)
        hpfed = high_pass_filter(wav, 1000, n_taps=255, fs=fs)

        lpfed_2d = low_pass_filter(np.vstack([wav, noise]).T, 500, fs=fs)
        hpfed_2d = high_pass_filter(np.vstack([wav, noise]).T, 1000, fs=fs)

        if saveflag:
            plt.figure()
            plt.plot(lpfed, label='lpf')
            plt.plot(hpfed, label='hpf')
            plt.legend()
            plt.xlim(0, 100)
            plt.savefig('filter.png') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import xlim [as 別名]
def save_precision_recall_curve(eval_labels, pred_labels, average_precision, smell, config, out_folder, dim, method):
    fig = plt.figure()
    precision, recall, _ = precision_recall_curve(eval_labels, pred_labels)

    step_kwargs = ({'step': 'post'}
                   if 'step' in signature(plt.fill_between).parameters
                   else {})
    plt.step(recall, precision, color='b', alpha=0.2,
             where='post')
    plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)

    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    if isinstance(config, cfg.CNN_config):
        title_str = smell + " (" + method + " - " + dim + ") - L=" + str(config.layers) + ", E=" + str(config.epochs) + ", F=" + str(config.filters) + \
                    ", K=" + str(config.kernel) + ", PW=" + str(config.pooling_window) + ", AP={0:0.2f}".format(average_precision)
    # plt.title(title_str)
    # plt.show()
    file_name = get_plot_file_name(smell, config, out_folder, dim, method, "_prc_")
    fig.savefig(file_name)