Python matplotlib.pyplot.clim() Examples

The following are 15 code examples for showing how to use matplotlib.pyplot.clim(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_att_maps_epoch.py    License: MIT License 6 votes vote down vote up
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 
Example 2
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_fmaps.py    License: MIT License 6 votes vote down vote up
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 
Example 3
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_attention.py    License: MIT License 6 votes vote down vote up
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) 
Example 4
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_attention.py    License: MIT License 6 votes vote down vote up
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 
Example 5
Project: pydem   Author: creare-com   File: dem_processing.py    License: Apache License 2.0 6 votes vote down vote up
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]) 
Example 6
Project: AeroPy   Author: leal26   File: fitting.py    License: MIT License 5 votes vote down vote up
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() 
Example 7
Project: segmentator   Author: ofgulban   File: gui_utils.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def updateColorBar(self, val):
        """Update slider for scaling log colorbar in 2D hist."""
        histVMax = np.power(10, self.sHistC.val)
        plt.clim(vmax=histVMax) 
Example 8
Project: code-jam-5   Author: python-discord   File: plot.py    License: MIT License 5 votes vote down vote up
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() 
Example 9
Project: NeuroKit   Author: neuropsychology   File: signal_recompose.py    License: MIT License 4 votes vote down vote up
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
# ============================================================================= 
Example 10
Project: pydem   Author: creare-com   File: dem_processing.py    License: Apache License 2.0 4 votes vote down vote up
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 
Example 11
Project: opendr   Author: mattloper   File: test_depth_renderer.py    License: MIT License 4 votes vote down vote up
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') 
Example 12
Project: opendr   Author: mattloper   File: test_depth_renderer.py    License: MIT License 4 votes vote down vote up
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() 
Example 13
Project: sklearn_pydata2015   Author: jakevdp   File: figures.py    License: BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up
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()) 
Example 14
Project: MachineLearning   Author: LocalGroupAstrostatistics2015   File: figures.py    License: BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up
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()) 
Example 15
Project: ESAC-stats-2014   Author: jakevdp   File: figures.py    License: BSD 2-Clause "Simplified" License 4 votes vote down vote up
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())