Python pylab.figure方法代码示例

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False):
    """plot_confusion_matrix."""
    cm = confusion_matrix(y_true, y_pred)
    fmt = "%d"
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        fmt = "%.2f"
    xticklabels = list(sorted(set(y_pred)))
    yticklabels = list(sorted(set(y_true)))
    if size is not None:
        plt.figure(figsize=(size, size))
    heatmap(cm, xlabel='Predicted label', ylabel='True label',
            xticklabels=xticklabels, yticklabels=yticklabels,
            cmap=plt.cm.Blues, fmt=fmt)
    if normalize:
        plt.title("Confusion matrix (norm.)")
    else:
        plt.title("Confusion matrix")
    plt.gca().invert_yaxis() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
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))) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def plot_learning_curve(train_sizes, train_scores, test_scores):
    """plot_learning_curve."""
    plt.figure(figsize=(15, 5))
    plt.title('Learning Curve')
    plt.xlabel("Training examples")
    plt.ylabel("AUC ROC")
    tr_ys = compute_stats(train_scores)
    te_ys = compute_stats(test_scores)
    plot_stats(train_sizes, tr_ys,
               label='Training score',
               color='navy')
    plot_stats(train_sizes, te_ys,
               label='Cross-validation score',
               color='orange')
    plt.grid(linestyle=":")
    plt.legend(loc="best")
    plt.show() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def __init__(self, add_inputs, title='', **kwargs):
        super(OffshorePlot, self).__init__(**kwargs)
        self.fig = plt.figure(num=None, facecolor='w', edgecolor='k') #figsize=(13, 8), dpi=1000
        self.shape_plot = self.fig.add_subplot(121)
        self.objf_plot = self.fig.add_subplot(122)

        self.targname = add_inputs
        self.title = title

        # Adding automatically the inputs
        for i in add_inputs:
            self.add(i, Float(0.0, iotype='in'))

        #sns.set(style="darkgrid")
        #self.pal = sns.dark_palette("skyblue", as_cmap=True)
        plt.rc('lines', linewidth=1)
        plt.ion()
        self.force_execute = True
        if not pa('fig').exists():
            pa('fig').mkdir() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def plot_wt_layout(wt_layout, borders=None, depth=None):
    fig = plt.figure(figsize=(6,6), dpi=2000)
    fs = 14
    ax = plt.subplot(111)

    if depth is not None:
        N = 100
        X, Y = plt.meshgrid(plt.linspace(depth[:,0].min(), depth[:,0].max(), N), 
                            plt.linspace(depth[:,1].min(), depth[:,1].max(), N))
        Z = plt.griddata(depth[:,0],depth[:,1],depth[:,2],X,Y, interp='linear')
        plt.contourf(X,Y,Z, label='depth [m]')
        plt.colorbar().set_label('water depth [m]')
    #ax.plot(wt_layout.wt_positions[:,0], wt_layout.wt_positions[:,1], 'or', label='baseline position')
    
    ax.scatter(wt_layout.wt_positions[:,0], wt_layout.wt_positions[:,1], wt_layout._wt_list('rotor_diameter'), label='baseline position')

    if borders is not None:
        ax.plot(borders[:,0], borders[:,1], 'r--', label='border')
        
    ax.set_xlabel('x [m]'); 
    ax.set_ylabel('y [m]')
    ax.axis('equal');
    ax.legend(loc='lower left') 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def plot_wind_rose(wind_rose):
    fig = plt.figure(figsize=(12,5), dpi=1000)

    # Plotting the wind statistics
    ax1 = plt.subplot(121, polar=True)
    w = 2.*np.pi/len(wind_rose.frequency)
    b = ax1.bar(np.pi/2.0-np.array(wind_rose.wind_directions)/180.*np.pi - w/2.0, 
                np.array(wind_rose.frequency)*100, width=w)

    # Trick to set the right axes (by default it's not oriented as we are used to in the WE community)
    mirror = lambda d: 90.0 - d if d < 90.0 else 360.0 + (90.0 - d)
    ax1.set_xticklabels([u'%d\xb0'%(mirror(d)) for d in linspace(0.0, 360.0,9)[:-1]]);
    ax1.set_title('Wind direction frequency');

    # Plotting the Weibull A parameter
    ax2 = plt.subplot(122, polar=True)
    b = ax2.bar(np.pi/2.0-np.array(wind_rose.wind_directions)/180.*np.pi - w/2.0, 
                np.array(wind_rose.A), width=w)
    ax2.set_xticklabels([u'%d\xb0'%(mirror(d)) for d in linspace(0.0, 360.0,9)[:-1]]);
    ax2.set_title('Weibull A parameter per wind direction sectors'); 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def test_hist_legacy(self):
        _check_plot_works(self.ts.hist)
        _check_plot_works(self.ts.hist, grid=False)
        _check_plot_works(self.ts.hist, figsize=(8, 10))
        # _check_plot_works adds an ax so catch warning. see GH #13188
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(self.ts.hist, by=self.ts.index.month)
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5)

        fig, ax = self.plt.subplots(1, 1)
        _check_plot_works(self.ts.hist, ax=ax)
        _check_plot_works(self.ts.hist, ax=ax, figure=fig)
        _check_plot_works(self.ts.hist, figure=fig)
        tm.close()

        fig, (ax1, ax2) = self.plt.subplots(1, 2)
        _check_plot_works(self.ts.hist, figure=fig, ax=ax1)
        _check_plot_works(self.ts.hist, figure=fig, ax=ax2)

        with pytest.raises(ValueError):
            self.ts.hist(by=self.ts.index, figure=fig) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def test_grouped_hist_multiple_axes(self):
        # GH 6970, GH 7069
        df = self.hist_df

        fig, axes = self.plt.subplots(2, 3)
        returned = df.hist(column=['height', 'weight', 'category'], ax=axes[0])
        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
        tm.assert_numpy_array_equal(returned, axes[0])
        assert returned[0].figure is fig
        returned = df.hist(by='classroom', ax=axes[1])
        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
        tm.assert_numpy_array_equal(returned, axes[1])
        assert returned[0].figure is fig

        with pytest.raises(ValueError):
            fig, axes = self.plt.subplots(2, 3)
            # pass different number of axes from required
            axes = df.hist(column='height', ax=axes) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def init_plot():
    ax = pl.figure().add_subplot(111, projection='3d')
    # hide axis, thank to
    # https://stackoverflow.com/questions/29041326/3d-plot-with-matplotlib-hide-axes-but-keep-axis-labels/
    ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
    ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
    ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
    # Get rid of the spines
    ax.w_xaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
    ax.w_yaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
    ax.w_zaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
    # Get rid of the ticks
    ax.set_xticks([])
    ax.set_yticks([])
    ax.set_zticks([])
    return (ax, [np.inf, -np.inf, np.inf, -np.inf, np.inf, -np.inf]) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def generate(self, filename, show=True):
        '''Generate a sample sequence, plot the resulting piano-roll and save
        it as a MIDI file.
        filename : string
            A MIDI file will be created at this location.
        show : boolean
            If True, a piano-roll of the generated sequence will be shown.'''

        piano_roll = self.generate_function()
        midiwrite(filename, piano_roll, self.r, self.dt)
        if show:
            extent = (0, self.dt * len(piano_roll)) + self.r
            pylab.figure()
            pylab.imshow(piano_roll.T, origin='lower', aspect='auto',
                         interpolation='nearest', cmap=pylab.cm.gray_r,
                         extent=extent)
            pylab.xlabel('time (s)')
            pylab.ylabel('MIDI note number')
            pylab.title('generated piano-roll') 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def on_press(self, event):
        if all((self.x0, self.y0)):
            self.x1 = event.xdata
            self.y1 = event.ydata

            # If both corners are defined
            if all((self.x0, self.y0, self.x1, self.y1)):
                self.rect.set_width(self.x1 - self.x0)
                self.rect.set_height(self.y1 - self.y0)
                self.rect.set_xy((self.x0, self.y0))
                self.ax.add_patch(self.rect)
                self.ax.figure.canvas.draw()
                self.on_draw()
                self.on_reset()

        else:
            self.x0 = event.xdata
            self.y0 = event.ydata 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def draw_adjacency_graph(adjacency_matrix,
                         node_color=None,
                         size=10,
                         layout='graphviz',
                         prog='neato',
                         node_size=80,
                         colormap='autumn'):
    """draw_adjacency_graph."""
    graph = nx.from_scipy_sparse_matrix(adjacency_matrix)

    plt.figure(figsize=(size, size))
    plt.grid(False)
    plt.axis('off')

    if layout == 'graphviz':
        pos = nx.graphviz_layout(graph, prog=prog)
    else:
        pos = nx.spring_layout(graph)

    if len(node_color) == 0:
        node_color = 'gray'
    nx.draw_networkx_nodes(graph, pos,
                           node_color=node_color,
                           alpha=0.6,
                           node_size=node_size,
                           cmap=plt.get_cmap(colormap))
    nx.draw_networkx_edges(graph, pos, alpha=0.5)
    plt.show()


# draw a whole set of graphs:: 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def draw_graph_row(graphs,
                   index=0,
                   contract=True,
                   n_graphs_per_line=5,
                   size=4,
                   xlim=None,
                   ylim=None,
                   **args):
    """draw_graph_row."""
    dim = len(graphs)
    size_y = size
    size_x = size * n_graphs_per_line * args.get('size_x_to_y_ratio', 1)
    plt.figure(figsize=(size_x, size_y))

    if xlim is not None:
        plt.xlim(xlim)
        plt.ylim(ylim)
    else:
        plt.xlim(xmax=3)

    for i in range(dim):
        plt.subplot(1, n_graphs_per_line, i + 1)
        graph = graphs[i]
        draw_graph(graph,
                   size=None,
                   pos=graph.graph.get('pos_dict', None),
                   **args)
    if args.get('file_name', None) is None:
        plt.show()
    else:
        row_file_name = '%d_' % (index) + args['file_name']
        plt.savefig(row_file_name,
                    bbox_inches='tight',
                    transparent=True,
                    pad_inches=0)
        plt.close() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def dendrogram(data,
               vectorizer,
               method="ward",
               color_threshold=1,
               size=10,
               filename=None):
    """dendrogram.

    "median","centroid","weighted","single","ward","complete","average"
    """
    data = list(data)
    # get labels
    labels = []
    for graph in data:
        label = graph.graph.get('id', None)
        if label:
            labels.append(label)
    # transform input into sparse vectors
    data_matrix = vectorizer.transform(data)

    # labels
    if not labels:
        labels = [str(i) for i in range(data_matrix.shape[0])]

    # embed high dimensional sparse vectors in 2D
    from sklearn import metrics
    from scipy.cluster.hierarchy import linkage, dendrogram
    distance_matrix = metrics.pairwise.pairwise_distances(data_matrix)
    linkage_matrix = linkage(distance_matrix, method=method)
    plt.figure(figsize=(size, size))
    dendrogram(linkage_matrix,
               color_threshold=color_threshold,
               labels=labels,
               orientation='right')
    if filename is not None:
        plt.savefig(filename)
    else:
        plt.show() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import figure [as 别名]
def plot_confusion_matrices(y_true, y_pred, size=12):
    """plot_confusion_matrices."""
    plt.figure(figsize=(size, size))
    plt.subplot(121)
    plot_confusion_matrix(y_true, y_pred, normalize=False)
    plt.subplot(122)
    plot_confusion_matrix(y_true, y_pred, normalize=True)
    plt.tight_layout(w_pad=5)
    plt.show()