Python matplotlib.cm() Examples

The following are 30 code examples for showing how to use matplotlib.cm(). 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: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def apply_cmap(zs, cmap, vmin=None, vmax=None, unit=None, logrescale=False):
    '''
    apply_cmap(z, cmap) applies the given cmap to the values in z; if vmin and/or vmax are passed,
      they are used to scale z.

    Note that this function can automatically rescale data into log-space if the colormap is a
    neuropythy log-space colormap such as log_eccentricity. To enable this behaviour use the
    optional argument logrescale=True.
    '''
    zs = pimms.mag(zs) if unit is None else pimms.mag(zs, unit)
    zs = np.asarray(zs, dtype='float')
    if pimms.is_str(cmap): cmap = matplotlib.cm.get_cmap(cmap)
    if logrescale:
        if vmin is None: vmin = np.log(np.nanmin(zs))
        if vmax is None: vmax = np.log(np.nanmax(zs))
        mn = np.exp(vmin)
        u = zdivide(nanlog(zs + mn) - vmin, vmax - vmin, null=np.nan)
    else:        
        if vmin is None: vmin = np.nanmin(zs)
        if vmax is None: vmax = np.nanmax(zs)
        u = zdivide(zs - vmin, vmax - vmin, null=np.nan)
    u[np.isnan(u)] = -np.inf
    return cmap(u) 
Example 2
Project: defragTrees   Author: sato9hara   File: paper_synthetic2.py    License: MIT License 6 votes vote down vote up
def plotTZ(filename=None):
    t = np.linspace(0, 1, 101)
    z = 0.25 + 0.5 / (1 + np.exp(- 20 * (t - 0.5))) + 0.05 * np.cos(t * 2 * np.pi)
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    poly1 = [[0, 0]]
    poly1.extend([[t[i], z[i]] for i in range(t.size)])
    poly1.extend([[1, 0], [0, 0]])
    poly2 = [[0, 1]]
    poly2.extend([[t[i], z[i]] for i in range(t.size)])
    poly2.extend([[1, 1], [0, 1]])
    poly1 = plt.Polygon(poly1,fc=cmap(0.0))
    poly2 = plt.Polygon(poly2,fc=cmap(1.0))
    ax1.add_patch(poly1)
    ax1.add_patch(poly2)
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('True Data', size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Output y', size=22)
    plt.show()
    if not filename is None:
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 
Example 3
Project: defragTrees   Author: sato9hara   File: paper_synthetic1.py    License: MIT License 6 votes vote down vote up
def plotTZ(filename=None):
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    ax1.add_patch(pl.Rectangle(xy=[0, 0], width=0.5, height=0.5, facecolor=cmap(0.0), linewidth='2.0'))
    ax1.add_patch(pl.Rectangle(xy=[0.5, 0.5], width=0.5, height=0.5, facecolor=cmap(0.0), linewidth='2.0'))
    ax1.add_patch(pl.Rectangle(xy=[0, 0.5], width=0.5, height=0.5, facecolor=cmap(1.0), linewidth='2.0'))
    ax1.add_patch(pl.Rectangle(xy=[0.5, 0], width=0.5, height=0.5, facecolor=cmap(1.0), linewidth='2.0'))
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('True Data', size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Output y', size=22)
    plt.show()
    if not filename is None:
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 
Example 4
Project: Computable   Author: ktraunmueller   File: pyplot.py    License: MIT License 6 votes vote down vote up
def set_cmap(cmap):
    """
    Set the default colormap.  Applies to the current image if any.
    See help(colormaps) for more information.

    *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
    the name of a registered colormap.

    See :func:`matplotlib.cm.register_cmap` and
    :func:`matplotlib.cm.get_cmap`.
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)

    draw_if_interactive() 
Example 5
Project: Computable   Author: ktraunmueller   File: pyplot.py    License: MIT License 6 votes vote down vote up
def spy(Z, precision=0, marker=None, markersize=None, aspect='equal', hold=None, **kwargs):
    ax = gca()
    # allow callers to override the hold state by passing hold=True|False
    washold = ax.ishold()

    if hold is not None:
        ax.hold(hold)
    try:
        ret = ax.spy(Z, precision, marker, markersize, aspect, **kwargs)
        draw_if_interactive()
    finally:
        ax.hold(washold)
    if isinstance(ret, cm.ScalarMappable):
        sci(ret)
    return ret


################# REMAINING CONTENT GENERATED BY boilerplate.py ##############


# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost 
Example 6
Project: matplotlib-4-abaqus   Author: Solid-Mechanics   File: pyplot.py    License: MIT License 6 votes vote down vote up
def set_cmap(cmap):
    """
    Set the default colormap.  Applies to the current image if any.
    See help(colormaps) for more information.

    *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
    the name of a registered colormap.

    See :func:`matplotlib.cm.register_cmap` and
    :func:`matplotlib.cm.get_cmap`.
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)

    draw_if_interactive() 
Example 7
Project: matplotlib-4-abaqus   Author: Solid-Mechanics   File: pyplot.py    License: MIT License 6 votes vote down vote up
def spy(Z, precision=0, marker=None, markersize=None, aspect='equal', hold=None, **kwargs):
    ax = gca()
    # allow callers to override the hold state by passing hold=True|False
    washold = ax.ishold()

    if hold is not None:
        ax.hold(hold)
    try:
        ret = ax.spy(Z, precision, marker, markersize, aspect, **kwargs)
        draw_if_interactive()
    finally:
        ax.hold(washold)
    if isinstance(ret, cm.ScalarMappable):
        sci(ret)
    return ret


################# REMAINING CONTENT GENERATED BY boilerplate.py ##############


# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost 
Example 8
Project: neural-network-animation   Author: miloharper   File: pyplot.py    License: MIT License 6 votes vote down vote up
def set_cmap(cmap):
    """
    Set the default colormap.  Applies to the current image if any.
    See help(colormaps) for more information.

    *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
    the name of a registered colormap.

    See :func:`matplotlib.cm.register_cmap` and
    :func:`matplotlib.cm.get_cmap`.
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)

    draw_if_interactive() 
Example 9
Project: neural-network-animation   Author: miloharper   File: pyplot.py    License: MIT License 6 votes vote down vote up
def spy(Z, precision=0, marker=None, markersize=None, aspect='equal', hold=None, **kwargs):
    ax = gca()
    # allow callers to override the hold state by passing hold=True|False
    washold = ax.ishold()

    if hold is not None:
        ax.hold(hold)
    try:
        ret = ax.spy(Z, precision, marker, markersize, aspect, **kwargs)
        draw_if_interactive()
    finally:
        ax.hold(washold)
    if isinstance(ret, cm.ScalarMappable):
        sci(ret)
    return ret


################# REMAINING CONTENT GENERATED BY boilerplate.py ##############


# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost 
Example 10
Project: GraphicDesignPatternByPython   Author: Relph1119   File: pyplot.py    License: MIT License 6 votes vote down vote up
def gci():
    """
    Get the current colorable artist.  Specifically, returns the
    current :class:`~matplotlib.cm.ScalarMappable` instance (image or
    patch collection), or *None* if no images or patch collections
    have been defined.  The commands :func:`~matplotlib.pyplot.imshow`
    and :func:`~matplotlib.pyplot.figimage` create
    :class:`~matplotlib.image.Image` instances, and the commands
    :func:`~matplotlib.pyplot.pcolor` and
    :func:`~matplotlib.pyplot.scatter` create
    :class:`~matplotlib.collections.Collection` instances.  The
    current image is an attribute of the current axes, or the nearest
    earlier axes in the current figure that contains an image.
    """
    return gcf()._gci()


## Any Artist ##


# (getp is simply imported) 
Example 11
Project: GraphicDesignPatternByPython   Author: Relph1119   File: pyplot.py    License: MIT License 6 votes vote down vote up
def set_cmap(cmap):
    """
    Set the default colormap.  Applies to the current image if any.
    See help(colormaps) for more information.

    *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
    the name of a registered colormap.

    See :func:`matplotlib.cm.register_cmap` and
    :func:`matplotlib.cm.get_cmap`.
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap) 
Example 12
def init_plot_data(self):
        a = self.fig.add_subplot(111)

        x = np.arange(120.0) * 2 * np.pi / 60.0
        y = np.arange(100.0) * 2 * np.pi / 50.0
        self.x, self.y = np.meshgrid(x, y)
        z = np.sin(self.x) + np.cos(self.y)
        self.im = a.imshow(z, cmap=cm.RdBu)  # , interpolation='nearest')

        zmax = np.max(z) - ERR_TOL
        ymax_i, xmax_i = np.nonzero(z >= zmax)
        if self.im.origin == 'upper':
            ymax_i = z.shape[0] - ymax_i
        self.lines = a.plot(xmax_i, ymax_i, 'ko')

        self.toolbar.update()  # Not sure why this is needed - ADS 
Example 13
Project: python3_ios   Author: holzschu   File: test_colors.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def test_light_source_topo_surface():
    """Shades a DEM using different v.e.'s and blend modes."""
    fname = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)
    dem = np.load(fname)
    elev = dem['elevation']
    # Get the true cellsize in meters for accurate vertical exaggeration
    #   Convert from decimal degrees to meters
    dx, dy = dem['dx'], dem['dy']
    dx = 111320.0 * dx * np.cos(dem['ymin'])
    dy = 111320.0 * dy
    dem.close()

    ls = mcolors.LightSource(315, 45)
    cmap = cm.gist_earth

    fig, axes = plt.subplots(nrows=3, ncols=3)
    for row, mode in zip(axes, ['hsv', 'overlay', 'soft']):
        for ax, ve in zip(row, [0.1, 1, 10]):
            rgb = ls.shade(elev, cmap, vert_exag=ve, dx=dx, dy=dy,
                           blend_mode=mode)
            ax.imshow(rgb)
            ax.set(xticks=[], yticks=[]) 
Example 14
Project: python3_ios   Author: holzschu   File: pyplot.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def gci():
    """
    Get the current colorable artist.  Specifically, returns the
    current :class:`~matplotlib.cm.ScalarMappable` instance (image or
    patch collection), or *None* if no images or patch collections
    have been defined.  The commands :func:`~matplotlib.pyplot.imshow`
    and :func:`~matplotlib.pyplot.figimage` create
    :class:`~matplotlib.image.Image` instances, and the commands
    :func:`~matplotlib.pyplot.pcolor` and
    :func:`~matplotlib.pyplot.scatter` create
    :class:`~matplotlib.collections.Collection` instances.  The
    current image is an attribute of the current axes, or the nearest
    earlier axes in the current figure that contains an image.
    """
    return gcf()._gci()


## Any Artist ##


# (getp is simply imported) 
Example 15
Project: python3_ios   Author: holzschu   File: pyplot.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def set_cmap(cmap):
    """
    Set the default colormap.  Applies to the current image if any.
    See help(colormaps) for more information.

    *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
    the name of a registered colormap.

    See :func:`matplotlib.cm.register_cmap` and
    :func:`matplotlib.cm.get_cmap`.
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap) 
Example 16
Project: nmp_qc   Author: priba   File: Plotter.py    License: MIT License 5 votes vote down vote up
def plot_graph(self, am, position=None, cls=None, fig_name='graph.png'):

        with warnings.catch_warnings():
            warnings.filterwarnings("ignore")

            g = nx.from_numpy_matrix(am)

            if position is None:
                position=nx.drawing.circular_layout(g)

            fig = plt.figure()

            if cls is None:
                cls='r'
            else:
                # Make a user-defined colormap.
                cm1 = mcol.LinearSegmentedColormap.from_list("MyCmapName", ["r", "b"])

                # Make a normalizer that will map the time values from
                # [start_time,end_time+1] -> [0,1].
                cnorm = mcol.Normalize(vmin=0, vmax=1)

                # Turn these into an object that can be used to map time values to colors and
                # can be passed to plt.colorbar().
                cpick = cm.ScalarMappable(norm=cnorm, cmap=cm1)
                cpick.set_array([])
                cls = cpick.to_rgba(cls)
                plt.colorbar(cpick, ax=fig.add_subplot(111))


            nx.draw(g, pos=position, node_color=cls, ax=fig.add_subplot(111))

            fig.savefig(os.path.join(self.plotdir, fig_name)) 
Example 17
Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
def scale_for_cmap(cmap, x, vmin=Ellipsis, vmax=Ellipsis, unit=Ellipsis):
    '''
    scale_for_cmap(cmap, x) yields the values in x rescaled to be appropriate for the given
      colormap cmap. The cmap must be the name of a colormap or a colormap object.

    For a given cmap argument, if the object is a colormap itself, it is treated as cmap.name.
    If the cmap names a colormap known to neuropythy, neuropythy will rescale the values in x
    according to a heuristic.
    '''
    import matplotlib as mpl
    if isinstance(cmap, mpl.colors.Colormap): cmap = cmap.name
    (name, cm) = (None, None)
    if cmap not in colormaps:
        for (k,v) in six.iteritems(colormaps):
            if cmap in k:
                (name, cm) = (k, v)
                break
    else: (name, cm) = (cmap, colormaps[cmap])
    if cm is not None:
        cm = cm if len(cm) == 3 else (cm + (None,))
        (cm, (mn,mx), uu) = cm
        if vmin is Ellipsis: vmin = mn
        if vmax is Ellipsis: vmax = mx
        if unit is Ellipsis: unit = uu
    if vmin is Ellipsis: vmin = None
    if vmax is Ellipsis: vmax = None
    if unit is Ellipsis: unit = None
    x = pimms.mag(x) if unit is None else pimms.mag(x, unit)
    if name is not None and name.startswith('log_'):
        emn = np.exp(vmin)
        x = np.log(x + emn)
    vmin = np.nanmin(x) if vmin is None else vmin
    vmax = np.nanmax(x) if vmax is None else vmax
    return zdivide(x - vmin, vmax - vmin, null=np.nan) 
Example 18
Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
def guess_cortex_cmap(pname):
    '''
    guess_cortex_cmap(proptery_name) yields a tuple (cmap, (vmin, vmax)) of a cortical color map
      appropriate to the given property name and the suggested value scaling for the cmap. If the
      given property is not a string or is not recognized then the log_eccentricity axis is used
      and the suggested vmin and vmax are None.
    '''
    import matplotlib as mpl
    if isinstance(pname, mpl.colors.Colormap): pname = pname.name
    if not pimms.is_str(pname): return ('eccenflat', cmap_eccenflat, (None, None), None)
    if pname in colormaps: (cm,cmname) = (colormaps[pname],pname)
    else:
        # check each manually
        cm = None
        for (k,v) in six.iteritems(colormaps):
            if pname.endswith(k):
                (cmname,cm) = (k,v)
                break
        if cm is None:
            for (k,v) in six.iteritems(colormaps):
                if pname.startswith(k):
                    (cmname,cm) = (k,v)
                    break
    # we prefer log-eccentricity when possible
    if cm is None: return ('eccenflat', cmap_eccenflat, (None, None), None)
    if ('log_'+cmname) in colormaps:
        cmname = 'log_'+cmname
        cm = colormaps[cmname]
    return (cmname,) + (cm if len(cm) == 3 else cm + (None,)) 
Example 19
Project: NanoPlot   Author: wdecoster   File: utils.py    License: GNU General Public License v3.0 5 votes vote down vote up
def list_colormaps():
    print("{}".format(", ".join([c.strip() for c in cm.cmap_d.keys()])))
    sys.exit(0) 
Example 20
Project: yolo2-pytorch   Author: ruiminshen   File: visualize.py    License: GNU Lesser General Public License v3.0 5 votes vote down vote up
def __init__(self, alpha=0.5, cmap=None):
        self.alpha = alpha
        self.cm = matplotlib.cm.get_cmap(cmap) 
Example 21
Project: yolo2-pytorch   Author: ruiminshen   File: visualize.py    License: GNU Lesser General Public License v3.0 5 votes vote down vote up
def __call__(self, image, feature, debug=False):
        _feature = (feature * self.cm.N).astype(np.int)
        heatmap = self.cm(_feature)[:, :, :3] * 255
        heatmap = cv2.resize(heatmap, image.shape[1::-1], interpolation=cv2.INTER_NEAREST)
        canvas = (image * (1 - self.alpha) + heatmap * self.alpha).astype(np.uint8)
        if debug:
            cv2.imshow('max=%f, sum=%f' % (np.max(feature), np.sum(feature)), canvas)
            cv2.waitKey(0)
        return canvas 
Example 22
Project: yolo2-pytorch   Author: ruiminshen   File: visualize.py    License: GNU Lesser General Public License v3.0 5 votes vote down vote up
def __init__(self, config, state_dict, cmap=None):
        self.dot = graphviz.Digraph(node_attr=dict(config.items('digraph_node_attr')), graph_attr=dict(config.items('digraph_graph_attr')))
        self.dot.format = config.get('graph', 'format')
        self.state_dict = state_dict
        self.var_name = {t._cdata: k for k, t in state_dict.items()}
        self.seen = set()
        self.index = 0
        self.drawn = set()
        self.cm = matplotlib.cm.get_cmap(cmap)
        self.metric = eval(config.get('graph', 'metric'))
        metrics = [self.metric(t) for t in state_dict.values()]
        self.minmax = [min(metrics), max(metrics)] 
Example 23
Project: yolo2-pytorch   Author: ruiminshen   File: visualize.py    License: GNU Lesser General Public License v3.0 5 votes vote down vote up
def _tensor_color(self, tensor):
        level = self._norm(self.metric(tensor))
        fillcolor = self.cm(np.int(level * self.cm.N))
        fontcolor = self.cm(self.cm.N if level < 0.5 else 0)
        return matplotlib.colors.to_hex(fillcolor), matplotlib.colors.to_hex(fontcolor) 
Example 24
Project: cgpm   Author: probcomp   File: zero_corr.py    License: Apache License 2.0 5 votes vote down vote up
def plot_samples(samples, dist, noise, modelno, num_samples, timestamp):
    """Plot the observed samples and posterior samples side-by-side."""
    print 'Plotting samples %s %f' % (dist, noise)
    fig, ax = plt.subplots(nrows=1, ncols=2)
    fig.suptitle(
        '%s (noise %1.2f, sample %d)' % (dist, noise, modelno),
        size=16)
    # Plot the observed samples.
    T = simulate_dataset(dist, noise, num_samples)
    # ax[0].set_title('Observed Data')
    ax[0].text(
        .5, .95, 'Observed Data',
        horizontalalignment='center',
        transform=ax[0].transAxes)
    ax[0].set_xlabel('x1')
    ax[0].set_ylabel('x2')
    ax[0].scatter(T[:,0], T[:,1], color='k', alpha=.5)
    ax[0].set_xlim(simulator_limits[dist][0])
    ax[0].set_ylim(simulator_limits[dist][1])
    ax[0].grid()
    # Plot posterior distribution.
    # ax[1].set_title('CrossCat Posterior Samples')
    ax[1].text(
        .5, .95, 'CrossCat Posterior Samples',
        horizontalalignment='center',
        transform=ax[1].transAxes)
    ax[1].set_xlabel('x1')
    clusters = set(samples[:,2])
    colors = iter(matplotlib.cm.gist_rainbow(
        np.linspace(0, 1, len(clusters)+2)))
    for c in clusters:
        sc = samples[samples[:,2] == c][:,[0,1]]
        ax[1].scatter(sc[:,0], sc[:,1], alpha=.5, color=next(colors))
    ax[1].set_xlim(ax[0].get_xlim())
    ax[1].set_ylim(ax[0].get_ylim())
    ax[1].grid()
    # Save.
    # fig.set_tight_layout(True)
    fig.savefig(filename_samples_figure(dist, noise, modelno, timestamp))
    plt.close('all') 
Example 25
def _plot(self, a, key, title, gx, gy, num_x, num_y):
        pp.rcParams['figure.figsize'] = (
            self._image_width / 300, self._image_height / 300
        )
        pp.title(title)
        # Interpolate the data
        rbf = Rbf(
            a['x'], a['y'], a[key], function='linear'
        )
        z = rbf(gx, gy)
        z = z.reshape((num_y, num_x))
        # Render the interpolated data to the plot
        pp.axis('off')
        # begin color mapping
        norm = matplotlib.colors.Normalize(
            vmin=min(a[key]), vmax=max(a[key]), clip=True
        )
        mapper = cm.ScalarMappable(norm=norm, cmap='RdYlBu_r')
        # end color mapping
        image = pp.imshow(
            z,
            extent=(0, self._image_width, self._image_height, 0),
            cmap='RdYlBu_r', alpha=0.5, zorder=100
        )
        pp.colorbar(image)
        pp.imshow(self._layout, interpolation='bicubic', zorder=1, alpha=1)
        # begin plotting points
        for idx in range(0, len(a['x'])):
            pp.plot(
                a['x'][idx], a['y'][idx],
                marker='o', markeredgecolor='black', markeredgewidth=1,
                markerfacecolor=mapper.to_rgba(a[key][idx]), markersize=6
            )
        # end plotting points
        fname = '%s_%s.png' % (key, self._title)
        logger.info('Writing plot to: %s', fname)
        pp.savefig(fname, dpi=300)
        pp.close('all') 
Example 26
Project: defragTrees   Author: sato9hara   File: RulePlotter.py    License: MIT License 5 votes vote down vote up
def plotRule(mdl, X, d1, d2, alpha=0.8, filename='', rnum=-1, plot_line=[]):
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    if rnum <= 0:
        rnum = len(mdl.rule_)
    else:
        rnum = min(len(mdl.rule_), rnum)
    idx = np.argsort(mdl.weight_[:rnum])
    for i in range(rnum):
        r = mdl.rule_[idx[i]]
        box, vmin, vmax = __r2boxWithX(r, X)
        if mdl.modeltype_ == 'regression':
            c = cmap(mdl.pred_[idx[i]])
        elif mdl.modeltype_ == 'classification':
            r = mdl.pred_[idx[i]] / max(np.unique(mdl.pred_).size - 1, 1)
            c = cmap(r)
        ax1.add_patch(pl.Rectangle(xy=[box[0, d1], box[0, d2]], width=(box[1, d1] - box[0, d1]), height=(box[1, d2] - box[0, d2]), facecolor=c, linewidth='2.0', alpha=alpha))
    if len(plot_line) > 0:
        for l in plot_line:
            ax1.plot(l[0], l[1], 'k--')
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('Simplified Model (K = %d)' % (rnum,), size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Predictor y', size=22)
    plt.show()
    if not filename == '':
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 
Example 27
Project: defragTrees   Author: sato9hara   File: RulePlotter.py    License: MIT License 5 votes vote down vote up
def plotEachRule(mdl, X, d1, d2, alpha=0.8, filename='', rnum=-1, plot_line=[]):
    if rnum <= 0:
        rnum = len(mdl.rule_)
    else:
        rnum = min(len(mdl.rule_), rnum)
    m = rnum // 4
    if m * 4 < rnum:
        m += 1
    cmap = cm.get_cmap('cool')
    fig, ax = plt.subplots(m, 4 + 1, figsize=(4 * 4, 3 * m), gridspec_kw = {'width_ratios':[15, 15, 15, 15, 1]})
    idx = np.argsort(mdl.weight_[:rnum])
    for i in range(rnum):
        j = i // 4
        k = i - 4 * j
        r = mdl.rule_[idx[i]]
        box, vmin, vmax = __r2boxWithX(r, X)
        if mdl.modeltype_ == 'regression':
            c = cmap(mdl.pred_[idx[i]])
        elif mdl.modeltype_ == 'classification':
            r = mdl.pred_[idx[i]] / max(np.unique(mdl.pred_).size - 1, 1)
            c = cmap(r)
        ax[j, k].add_patch(pl.Rectangle(xy=[box[0, d1], box[0, d2]], width=(box[1, d1] - box[0, d1]), height=(box[1, d2] - box[0, d2]), facecolor=c, linewidth='2.0', alpha=alpha))
        if len(plot_line) > 0:
            for l in plot_line:
                ax[j, k].plot(l[0], l[1], 'k--')
        ax[j, k].set_xlim([0, 1])
        ax[j, k].set_ylim([0, 1])
        if k == 3:
            cbar = colorbar.ColorbarBase(ax[j, -1], cmap=cmap, format='%.1f', ticks=[0.0, 0.5, 1.0])
            cbar.ax.set_yticklabels([0.0, 0.5, 1.0])
            ax[j, -1].set_ylabel('Predictor y', size=12)
    plt.show()
    if not filename == '':
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 
Example 28
Project: Computable   Author: ktraunmueller   File: pyplot.py    License: MIT License 5 votes vote down vote up
def gci():
    """
    Get the current colorable artist.  Specifically, returns the
    current :class:`~matplotlib.cm.ScalarMappable` instance (image or
    patch collection), or *None* if no images or patch collections
    have been defined.  The commands :func:`~matplotlib.pyplot.imshow`
    and :func:`~matplotlib.pyplot.figimage` create
    :class:`~matplotlib.image.Image` instances, and the commands
    :func:`~matplotlib.pyplot.pcolor` and
    :func:`~matplotlib.pyplot.scatter` create
    :class:`~matplotlib.collections.Collection` instances.  The
    current image is an attribute of the current axes, or the nearest
    earlier axes in the current figure that contains an image.
    """
    return gcf()._gci() 
Example 29
Project: Computable   Author: ktraunmueller   File: pyplot.py    License: MIT License 5 votes vote down vote up
def autumn():
    '''
    set the default colormap to autumn and apply to current image if any.
    See help(colormaps) for more information
    '''
    rc('image', cmap='autumn')
    im = gci()

    if im is not None:
        im.set_cmap(cm.autumn)
    draw_if_interactive()


# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost 
Example 30
Project: Computable   Author: ktraunmueller   File: pyplot.py    License: MIT License 5 votes vote down vote up
def bone():
    '''
    set the default colormap to bone and apply to current image if any.
    See help(colormaps) for more information
    '''
    rc('image', cmap='bone')
    im = gci()

    if im is not None:
        im.set_cmap(cm.bone)
    draw_if_interactive()


# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost