Python matplotlib.pyplot.clim() Examples

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 

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 

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) 

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 

def _plot_connectivity_helper(self, ii, ji, mat_datai, data, lims=[1, 8]):
        """
        A debug function used to plot the adjacency/connectivity matrix.
        """
        from matplotlib.pyplot import quiver, colorbar, clim,  matshow
        I = ~np.isnan(mat_datai) & (ji != -1) & (mat_datai >= 0)
        mat_data = mat_datai[I]
        j = ji[I]
        i = ii[I]
        x = i.astype(float) % data.shape[1]
        y = i.astype(float) // data.shape[1]
        x1 = (j.astype(float) % data.shape[1]).ravel()
        y1 = (j.astype(float) // data.shape[1]).ravel()
        nx = (x1 - x)
        ny = (y1 - y)
        matshow(data, cmap='gist_rainbow'); colorbar(); clim(lims)
        quiver(x, y, nx, ny, mat_data.ravel(), angles='xy', scale_units='xy',
               scale=1, cmap='bone')
        colorbar(); clim([0, 1]) 

def plot_study(self, relative=True):
        if relative:
            z = self.rel_error
        else:
            z = self.error
        fig, ax = plt.subplots()
        cs = ax.contourf(self.P1, self.P2, z, np.linspace(0, 1, 101))

        fig.colorbar(cs, ticks=np.linspace(0, 1, 6))
        # plt.clim(0, 1)
        plt.xlabel(self.p1_name)
        plt.ylabel(self.p2_name)
        plt.show() 

def updateColorBar(self, val):
        """Update slider for scaling log colorbar in 2D hist."""
        histVMax = np.power(10, self.sHistC.val)
        plt.clim(vmax=histVMax) 

def create_plot(self, count, date_index):
        """
            Plots and saves a single world map image to the data folder.
            :param count: Current number of image processed. If it's the first image it's 0.
                          Needed for name of saved image (plot0, plot1 etc)
            :param date_index: Index for DATES array from which we will get data.
        """
        plot.figure(count)
        color_mesh = self.world_map.pcolormesh(Plotter.LONGITUDES, Plotter.LATITUDES,
                                               np.squeeze(Plotter.TEMPERATURES[date_index]),
                                               cmap=self.color_map)
        color_bar = self.world_map.colorbar(color_mesh, location="bottom", pad="10%")
        color_bar.set_label(Plotter.TEMPERATURE_UNIT)
        Plotter.draw_map_details(self.world_map)
        date = Plotter.get_display_date(Plotter.DATES[date_index])
        plot.title(f"Plot for {date}")
        # This scales the plot to -10,10 making those 2 mark "extremes"
        # but if we have a change bigger than 10
        # we won't be able to see it other than
        # it being extra red (aka we won't know if it's +11 or +15)
        plot.clim(-10, 10)
        file_path = f"{Plotter.PLOTS_DIR}plot{count + 1}.png"
        # bbox_inches="tight" remove whitespace around the image
        # facecolor=(0.94, 0.94, 0.94) , background color of image
        plot.savefig(file_path, dpi=142, bbox_inches="tight", facecolor=(0.94, 0.94, 0.94))
        plot.close() 

def _signal_recompose_get_wcorr(components, show=False):
    """Calculates the weighted correlation matrix for the time series.

    References
    ----------
    - https://www.kaggle.com/jdarcy/introducing-ssa-for-time-series-decomposition

    """
    # Reorient components
    components = components.T

    L = components.shape[1]
    K = components.shape[0] - L + 1

    # Calculate the weights
    w = np.array(list(np.arange(L) + 1) + [L] * (K - L - 1) + list(np.arange(L) + 1)[::-1])

    def w_inner(F_i, F_j):
        return w.dot(F_i * F_j)

    # Calculated weighted norms, ||F_i||_w, then invert.
    F_wnorms = np.array([w_inner(components[:, i], components[:, i]) for i in range(L)])
    F_wnorms = F_wnorms ** -0.5

    # Calculate Wcorr.
    Wcorr = np.identity(L)
    for i in range(L):
        for j in range(i + 1, L):
            Wcorr[i, j] = abs(w_inner(components[:, i], components[:, j]) * F_wnorms[i] * F_wnorms[j])
            Wcorr[j, i] = Wcorr[i, j]

    if show is True:
        ax = plt.imshow(Wcorr)
        plt.xlabel(r"$\tilde{F}_i$")
        plt.ylabel(r"$\tilde{F}_j$")
        plt.colorbar(ax.colorbar, fraction=0.045)
        ax.colorbar.set_label("$W_{i,j}$")
        plt.clim(0, 1)

        # For plotting purposes:
        min_range = 0
        max_range = len(Wcorr) - 1

        plt.xlim(min_range - 0.5, max_range + 0.5)
        plt.ylim(max_range + 0.5, min_range - 0.5)

    return Wcorr


# =============================================================================
# Filter method
# ============================================================================= 

def _plot_debug_slopes_directions(self):
        """
        A debug function to plot the direction calculated in various ways.
        """
        # %%
        from matplotlib.pyplot import matshow, colorbar, clim, title

        matshow(self.direction / np.pi * 180); colorbar(); clim(0, 360)
        title('Direction')

        mag2, direction2 = self._central_slopes_directions()
        matshow(direction2 / np.pi * 180.0); colorbar(); clim(0, 360)
        title('Direction (central difference)')

        matshow(self.mag); colorbar()
        title('Magnitude')
        matshow(mag2); colorbar(); title("Magnitude (Central difference)")

        # %%
        # Compare to Taudem
        filename = self.file_name
        os.chdir('testtiff')
        try:
            os.remove('test_ang.tif')
            os.remove('test_slp.tif')
        except:
            pass
        cmd = ('dinfflowdir -fel "%s" -ang "%s" -slp "%s"' %
               (os.path.split(filename)[-1], 'test_ang.tif', 'test_slp.tif'))
        taudem._run(cmd)

        td_file = GdalReader(file_name='test_ang.tif')
        td_ang, = td_file.raster_layers
        td_file2 = GdalReader(file_name='test_slp.tif')
        td_mag, = td_file2.raster_layers
        os.chdir('..')

        matshow(td_ang.raster_data / np.pi*180); clim(0, 360); colorbar()
        title('Taudem direction')
        matshow(td_mag.raster_data); colorbar()
        title('Taudem magnitude')

        matshow(self.data); colorbar()
        title('The test data (elevation)')

        diff = (td_ang.raster_data - self.direction) / np.pi * 180.0
        diff[np.abs(diff) > 300] = np.nan
        matshow(diff); colorbar(); clim([-1, 1])
        title('Taudem direction - calculated Direction')

        # normalize magnitudes
        mag2 = td_mag.raster_data
        mag2 /= np.nanmax(mag2)
        mag = self.mag.copy()
        mag /= np.nanmax(mag)
        matshow(mag - mag2); colorbar()
        title('Taudem magnitude - calculated magnitude')
        del td_file
        del td_file2
        del td_ang
        del td_mag 

def test_derivatives(self):
        import chumpy as ch
        from chumpy.utils import row
        import numpy as np
        from .renderer import DepthRenderer

        rn = DepthRenderer()

        # Assign attributes to renderer
        from .util_tests import get_earthmesh
        m = get_earthmesh(trans=ch.array([0,0,4]), rotation=ch.zeros(3))
        w, h = (320, 240)
        from .camera import ProjectPoints
        rn.camera = ProjectPoints(v=m.v, rt=ch.zeros(3), t=ch.zeros(3), f=ch.array([w,w])/2., c=ch.array([w,h])/2., k=ch.zeros(5))
        rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
        rn.set(v=m.v, f=m.f, bgcolor=ch.zeros(3))

        if visualize:
            import matplotlib.pyplot as plt
            plt.figure()
        for which in range(3):
            r1 = rn.r

            adder = np.zeros(3)
            adder[which] = .01
            change = rn.v.r * 0 + row(adder)
            dr_pred = rn.dr_wrt(rn.v).dot(change.ravel()).reshape(rn.shape)
            rn.v = rn.v.r + change

            r2 = rn.r
            dr_emp = r2 - r1

            # print np.mean(np.abs(dr_pred-dr_emp))

            self.assertLess(np.mean(np.abs(dr_pred-dr_emp)), .031)

            if visualize:
                plt.subplot(2,3,which+1)
                plt.imshow(dr_pred)
                plt.clim(-.01,.01)
                plt.title('emp')
                plt.subplot(2,3,which+4)
                plt.imshow(dr_emp)
                plt.clim(-.01,.01)
                plt.title('pred') 

def test_derivatives2(self):
        import chumpy as ch
        import numpy as np
        from .renderer import DepthRenderer

        rn = DepthRenderer()

        # Assign attributes to renderer
        from .util_tests import get_earthmesh
        m = get_earthmesh(trans=ch.array([0,0,4]), rotation=ch.zeros(3))
        w, h = (320, 240)
        from .camera import ProjectPoints
        rn.camera = ProjectPoints(v=m.v, rt=ch.zeros(3), t=ch.zeros(3), f=ch.array([w,w])/2., c=ch.array([w,h])/2., k=ch.zeros(5))
        rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
        rn.set(v=m.v, f=m.f, bgcolor=ch.zeros(3))

        if visualize:
            import matplotlib.pyplot as plt
            plt.ion()
            plt.figure()

        for which in range(3):
            r1 = rn.r

            adder = np.random.rand(rn.v.r.size).reshape(rn.v.r.shape)*.01
            change = rn.v.r * 0 + adder
            dr_pred = rn.dr_wrt(rn.v).dot(change.ravel()).reshape(rn.shape)
            rn.v = rn.v.r + change

            r2 = rn.r
            dr_emp = r2 - r1

            #print np.mean(np.abs(dr_pred-dr_emp))

            self.assertLess(np.mean(np.abs(dr_pred-dr_emp)), .024)

            if visualize:
                plt.subplot(2,3,which+1)
                plt.imshow(dr_pred)
                plt.clim(-.01,.01)
                plt.title('emp')
                plt.subplot(2,3,which+4)
                plt.imshow(dr_emp)
                plt.clim(-.01,.01)
                plt.title('pred')
                plt.draw()
                plt.show() 

def visualize_tree(estimator, X, y, boundaries=True,
                   xlim=None, ylim=None):
    estimator.fit(X, y)

    if xlim is None:
        xlim = (X[:, 0].min() - 0.1, X[:, 0].max() + 0.1)
    if ylim is None:
        ylim = (X[:, 1].min() - 0.1, X[:, 1].max() + 0.1)

    x_min, x_max = xlim
    y_min, y_max = ylim
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, alpha=0.2, cmap='rainbow')
    plt.clim(y.min(), y.max())

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow')
    plt.axis('off')

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)        
    plt.clim(y.min(), y.max())
    
    # Plot the decision boundaries
    def plot_boundaries(i, xlim, ylim):
        if i < 0:
            return

        tree = estimator.tree_
        
        if tree.feature[i] == 0:
            plt.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k')
            plot_boundaries(tree.children_left[i],
                            [xlim[0], tree.threshold[i]], ylim)
            plot_boundaries(tree.children_right[i],
                            [tree.threshold[i], xlim[1]], ylim)
        
        elif tree.feature[i] == 1:
            plt.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k')
            plot_boundaries(tree.children_left[i], xlim,
                            [ylim[0], tree.threshold[i]])
            plot_boundaries(tree.children_right[i], xlim,
                            [tree.threshold[i], ylim[1]])
            
    if boundaries:
        plot_boundaries(0, plt.xlim(), plt.ylim()) 

def visualize_tree(estimator, X, y, boundaries=True,
                   xlim=None, ylim=None):
    estimator.fit(X, y)

    if xlim is None:
        xlim = (X[:, 0].min() - 0.1, X[:, 0].max() + 0.1)
    if ylim is None:
        ylim = (X[:, 1].min() - 0.1, X[:, 1].max() + 0.1)

    x_min, x_max = xlim
    y_min, y_max = ylim
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, alpha=0.2, cmap='rainbow')
    plt.clim(y.min(), y.max())

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow')
    plt.axis('off')

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)        
    plt.clim(y.min(), y.max())
    
    # Plot the decision boundaries
    def plot_boundaries(i, xlim, ylim):
        if i < 0:
            return

        tree = estimator.tree_
        
        if tree.feature[i] == 0:
            plt.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k')
            plot_boundaries(tree.children_left[i],
                            [xlim[0], tree.threshold[i]], ylim)
            plot_boundaries(tree.children_right[i],
                            [tree.threshold[i], xlim[1]], ylim)
        
        elif tree.feature[i] == 1:
            plt.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k')
            plot_boundaries(tree.children_left[i], xlim,
                            [ylim[0], tree.threshold[i]])
            plot_boundaries(tree.children_right[i], xlim,
                            [tree.threshold[i], ylim[1]])
            
    if boundaries:
        plot_boundaries(0, plt.xlim(), plt.ylim()) 

def visualize_tree(estimator, X, y, boundaries=True,
                   xlim=None, ylim=None):
    estimator.fit(X, y)

    if xlim is None:
        xlim = (X[:, 0].min() - 0.1, X[:, 0].max() + 0.1)
    if ylim is None:
        ylim = (X[:, 1].min() - 0.1, X[:, 1].max() + 0.1)

    x_min, x_max = xlim
    y_min, y_max = ylim
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, alpha=0.2, cmap='rainbow')
    plt.clim(y.min(), y.max())

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow')
    plt.axis('off')

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)        
    plt.clim(y.min(), y.max())
    
    # Plot the decision boundaries
    def plot_boundaries(i, xlim, ylim):
        if i < 0:
            return

        tree = estimator.tree_
        
        if tree.feature[i] == 0:
            plt.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k')
            plot_boundaries(tree.children_left[i],
                            [xlim[0], tree.threshold[i]], ylim)
            plot_boundaries(tree.children_right[i],
                            [tree.threshold[i], xlim[1]], ylim)
        
        elif tree.feature[i] == 1:
            plt.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k')
            plot_boundaries(tree.children_left[i], xlim,
                            [ylim[0], tree.threshold[i]])
            plot_boundaries(tree.children_right[i], xlim,
                            [tree.threshold[i], ylim[1]])
            
    if boundaries:
        plot_boundaries(0, plt.xlim(), plt.ylim())