Python pyplot.colorbar方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def visualize_sampling(self,permutations):
        max_length = len(permutations[0])
        grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0
        transposed_permutations = np.transpose(permutations)
        for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
            city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
            for u,v in zip(city_indices, counts):
                grid[t][u]+=v # update grid with counts from the batch of permutations
        # plot heatmap
        fig = plt.figure()
        rcParams.update({'font.size': 22})
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(grid, interpolation='nearest', cmap='gray')
        plt.colorbar()
        plt.title('Sampled permutations')
        plt.ylabel('Time t')
        plt.xlabel('City i')
        plt.show()

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

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def visualize_sampling(self, permutations):
        max_length = len(permutations[0])
        grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0

        transposed_permutations = np.transpose(permutations)
        for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
            city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
            for u,v in zip(city_indices, counts):
                grid[t][u]+=v # update grid with counts from the batch of permutations

        # plot heatmap
        fig = plt.figure()
        rcParams.update({'font.size': 22})
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(grid, interpolation='nearest', cmap='gray')
        plt.colorbar()
        plt.title('Sampled permutations')
        plt.ylabel('Time t')
        plt.xlabel('City i')
        plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def plot_attention(sentences, attentions, labels, **kwargs):
    fig, ax = plt.subplots(**kwargs)
    im = ax.imshow(attentions, interpolation='nearest',
                   vmin=attentions.min(), vmax=attentions.max())
    plt.colorbar(im, shrink=0.5, ticks=[0, 1])
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")
    ax.set_yticks(range(len(labels)))
    ax.set_yticklabels(labels, fontproperties=getChineseFont())
    # Loop over data dimensions and create text annotations.
    for i in range(attentions.shape[0]):
        for j in range(attentions.shape[1]):
            text = ax.text(j, i, sentences[i][j],
                           ha="center", va="center", color="b", size=10,
                           fontproperties=getChineseFont())

    ax.set_title("Attention Visual")
    fig.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
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) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def test_interpolate_grid_const_nn(self, sphere3_msh):
        data = sphere3_msh.elm.tag1
        f = mesh_io.ElementData(data, mesh=sphere3_msh)
        n = (200, 10, 1)
        affine = np.array([[1, 0, 0, -100.5],
                           [0, 1, 0, -5],
                           [0, 0, 1, 0],
                           [0, 0, 0, 1]], dtype=float)
        interp = f.interpolate_to_grid(n, affine, method='assign')
        '''
        import matplotlib.pyplot as plt
        plt.imshow(np.squeeze(interp))
        plt.colorbar()
        plt.show()
        assert False
        '''
        assert np.isclose(interp[100, 5, 0], 3)
        assert np.isclose(interp[187, 5, 0], 4)
        assert np.isclose(interp[193, 5, 0], 5)
        assert np.isclose(interp[198, 5, 0], 0) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def test_interpolate_grid_rotate_nn(self, sphere3_msh):
        data = np.zeros(sphere3_msh.elm.nr)
        b = sphere3_msh.elements_baricenters().value
        f = mesh_io.ElementData(data, mesh=sphere3_msh)
        # Assign quadrant numbers
        f.value[(b[:, 0] > 0) * (b[:, 1] > 0)] = 1.
        f.value[(b[:, 0] < 0) * (b[:, 1] > 0)] = 2.
        f.value[(b[:, 0] < 0) * (b[:, 1] < 0)] = 3.
        f.value[(b[:, 0] > 0) * (b[:, 1] < 0)] = 4.
        n = (200, 200, 1)
        affine = np.array([[np.cos(np.pi/4.), np.sin(np.pi/4.), 0, -141],
                           [-np.sin(np.pi/4.), np.cos(np.pi/4.), 0, 0],
                           [0, 0, 1, .5],
                           [0, 0, 0, 1]], dtype=float)
        interp = f.interpolate_to_grid(n, affine, method='assign')
        '''
        import matplotlib.pyplot as plt
        plt.imshow(np.squeeze(interp))
        plt.colorbar()
        plt.show()
        '''
        assert np.isclose(interp[190, 100, 0], 4)
        assert np.isclose(interp[100, 190, 0], 1)
        assert np.isclose(interp[10, 100, 0], 2)
        assert np.isclose(interp[100, 10, 0], 3) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def test_interpolate_grid_rotate_nodedata(self, sphere3_msh):
        data = np.zeros(sphere3_msh.nodes.nr)
        b = sphere3_msh.nodes.node_coord.copy()
        f = mesh_io.NodeData(data, mesh=sphere3_msh)
        # Assign quadrant numbers
        f.value[(b[:, 0] >= 0) * (b[:, 1] >= 0)] = 1.
        f.value[(b[:, 0] <= 0) * (b[:, 1] >= 0)] = 2.
        f.value[(b[:, 0] <= 0) * (b[:, 1] <= 0)] = 3.
        f.value[(b[:, 0] >= 0) * (b[:, 1] <= 0)] = 4.
        n = (200, 200, 1)
        affine = np.array([[np.cos(np.pi/4.), np.sin(np.pi/4.), 0, -141],
                           [-np.sin(np.pi/4.), np.cos(np.pi/4.), 0, 0],
                           [0, 0, 1, .5],
                           [0, 0, 0, 1]], dtype=float)
        interp = f.interpolate_to_grid(n, affine)
        '''
        import matplotlib.pyplot as plt
        plt.imshow(np.squeeze(interp), interpolation='nearest')
        plt.colorbar()
        plt.show()
        '''
        assert np.isclose(interp[190, 100, 0], 4)
        assert np.isclose(interp[100, 190, 0], 1)
        assert np.isclose(interp[10, 100, 0], 2)
        assert np.isclose(interp[100, 10, 0], 3) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def test_interpolate_grid_elmdata_linear(self, sphere3_msh):
        data = sphere3_msh.elements_baricenters().value[:, 0]
        f = mesh_io.ElementData(data, mesh=sphere3_msh)
        n = (130, 130, 1)
        affine = np.array([[1, 0, 0, -65],
                           [0, 1, 0, -65],
                           [0, 0, 1, 0],
                           [0, 0, 0, 1]], dtype=float)
        X, _ = np.meshgrid(np.arange(130), np.arange(130), indexing='ij')
        interp = f.interpolate_to_grid(n, affine, method='linear', continuous=True)
        '''
        import matplotlib.pyplot as plt
        plt.figure()
        plt.imshow(np.squeeze(interp))
        plt.colorbar()
        plt.show()
        '''
        assert np.allclose(interp[:, :, 0], X - 64.5, atol=1) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def test_interpolate_grid_elmdata_dicontinuous(self, sphere3_msh):
        data = sphere3_msh.elm.tag1
        f = mesh_io.ElementData(data, mesh=sphere3_msh)
        n = (200, 130, 1)
        affine = np.array([[1, 0, 0, -100.1],
                           [0,-1, 0, 65.1],
                           [0, 0, 1, 0],
                           [0, 0, 0, 1]], dtype=float)
        interp = f.interpolate_to_grid(n, affine, method='linear', continuous=False)
        '''
        import matplotlib.pyplot as plt
        plt.figure()
        plt.imshow(np.squeeze(interp))
        plt.colorbar()
        plt.show()
        '''
        assert np.allclose(interp[6:10, 65, 0], 5, atol=1e-1)
        assert np.allclose(interp[11:15, 65, 0], 4, atol=1e-1)
        assert np.allclose(interp[16:100, 65, 0], 3, atol=1e-1) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Epochs 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Load options 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title=''):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()
    plt.suptitle(title) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def plotNNFilterOverlay(input_im, units, figure_id, interp='bilinear',
                        colormap=cm.jet, colormap_lim=None, title='', alpha=0.8):
    plt.ion()
    filters = units.shape[2]
    fig = plt.figure(figure_id, figsize=(5,5))
    fig.clf()

    for i in range(filters):
        plt.imshow(input_im[:,:,0], interpolation=interp, cmap='gray')
        plt.imshow(units[:,:,i], interpolation=interp, cmap=colormap, alpha=alpha)
        plt.axis('off')
        plt.colorbar()
        plt.title(title, fontsize='small')
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

    # plt.savefig('{}/{}.png'.format(dir_name,time.time()))




## Load options 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
def imshow(data, which, levels):
    """
        Display order book data as an image, where order book data is either of
        `df_price` or `df_volume` returned by `load_hdf5` or `load_postgres`.
    """

    if which == 'prices':
        idx = ['askprc.' + str(i) for i in range(levels, 0, -1)]
        idx.extend(['bidprc.' + str(i) for i in range(1, levels + 1, 1)])
    elif which == 'volumes':
        idx = ['askvol.' + str(i) for i in range(levels, 0, -1)]
        idx.extend(['bidvol.' + str(i) for i in range(1, levels + 1, 1)])
    plt.imshow(data.loc[:, idx].T, interpolation='nearest', aspect='auto')
    plt.yticks(range(0, levels * 2, 1), idx)
    plt.colorbar()
    plt.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import colorbar [as 别名]
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')