Python pyplot.Normalize方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def test_Normalize():
    norm = mcolors.Normalize()
    vals = np.arange(-10, 10, 1, dtype=float)
    _inverse_tester(norm, vals)
    _scalar_tester(norm, vals)
    _mask_tester(norm, vals)

    # Handle integer input correctly (don't overflow when computing max-min,
    # i.e. 127-(-128) here).
    vals = np.array([-128, 127], dtype=np.int8)
    norm = mcolors.Normalize(vals.min(), vals.max())
    assert_array_equal(np.asarray(norm(vals)), [0, 1])

    # Don't lose precision on longdoubles (float128 on Linux):
    # for array inputs...
    vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
    norm = mcolors.Normalize(vals.min(), vals.max())
    assert_array_equal(np.asarray(norm(vals)), [0, 1])
    # and for scalar ones.
    eps = np.finfo(np.longdouble).resolution
    norm = plt.Normalize(1, 1 + 100 * eps)
    # This returns exactly 0.5 when longdouble is extended precision (80-bit),
    # but only a value close to it when it is quadruple precision (128-bit).
    assert 0 < norm(1 + 50 * eps) < 1 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def show(x, gray_scale=False, jet_cmap=False, filename=None):
    """ Show 'x' as an image on the screen.
    """
    if jet_cmap is False:
        img = data_to_image(x, gray_scale=gray_scale)
    else:
        if plt is None:
            printcn(WARNING, 'pyplot not defined!')
            return
        cmap = plt.cm.jet
        norm = plt.Normalize(vmin=x.min(), vmax=x.max())
        img = cmap(norm(x))
    if filename:
        plt.imsave(filename, img)
    else:
        plt.imshow(img)
        plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def draw_heatmaps(screen, surf, hm, thr=0.5, vmin=-15, vmax=10):
    hm_idx = [
            ( 8, 0*hmsurf_size[0], 0*hmsurf_size[1]),   # R. wrist
            ( 9, 1*hmsurf_size[0], 0*hmsurf_size[1]),   # L. wrist
            ( 6, 0*hmsurf_size[0], 1*hmsurf_size[1]),   # R. elbow
            ( 7, 1*hmsurf_size[0], 1*hmsurf_size[1]),   # L. elbow
            ( 3, 0*hmsurf_size[0], 2*hmsurf_size[1]),   # Head
            ( 0, 1*hmsurf_size[0], 2*hmsurf_size[1]),   # Pelvis
            (12, 0*hmsurf_size[0], 3*hmsurf_size[1]),   # R. knee
            (13, 1*hmsurf_size[0], 3*hmsurf_size[1])]   # L. knee

    for idx in hm_idx:
        h = np.transpose(hm[:,:,idx[0]].copy(), (1, 0))
        h[h < vmin] = vmin
        h[h > vmax] = vmax
        cmap = plt.cm.jet
        norm = plt.Normalize(vmin=vmin, vmax=vmax)
        cm = np.zeros((34, 34, 3))
        cm[1:33, 1:33, :] = cmap(norm(h))[:,:,0:3]
        cm = scipy.ndimage.zoom(cm, (5, 5, 1), order=1)
        pygame.surfarray.pixels3d(surf)[:,:,:] = np.array(255.*cm, dtype=int)
        screen.blit(surf, (idx[1] + img_size[0], idx[2])) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def get_norm(self, pad=None, i=None):
        """
        Compute the adopted normalization at a given value of `i`, given the
        value of <autofig.axes.AxDimension.pad> (or `pad`).

        See also:

        * <autofig.axes.AxDimension.norm>

        Arguments
        -----------
        * `pad` (float, optional, default=None): override the padding.  If not
            provided or None, will use <autofig.axes.AxDimension.pad>.
        * `i` (float, optional, default=None): the value to use for `i` when
            computing visible data and limits.

        Returns
        --------
        * (plt.Normalize object)
        """
        return plt.Normalize(*self.get_lim(pad=pad, i=i)) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def get_sizes(self, i=None):

        s = self.s.get_value(i=i, unit=self.axes_s.unit if self.axes_s is not None else None)

        if self.do_sizescale:
            if self.axes_s is not None:
                sizes = self.axes_s.normalize(s, i=i)
            else:
                # fallback on 0.01-0.05 mapping for just this call
                sall = self.s.get_value(unit=self.axes_s.unit if self.axes_s is not None else None)
                norm = plt.Normalize(np.nanmin(sall), np.nanmax(sall))
                sizes = norm(s) * 0.04+0.01

        else:
            if s is not None:
                sizes = s
            elif self.s.mode == 'pt':
                sizes = 1
            else:
                sizes = 0.02

        return sizes 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def _plot_3D_colored(x, y, z, color=None, rotate=False):
    if color is None:
        color = z

    # Create a set of line segments
    points = np.array([x, y, z]).T.reshape(-1, 1, 3)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)

    # Color
    norm = plt.Normalize(color.min(), color.max())
    cmap = plt.get_cmap("plasma")
    colors = cmap(norm(color))

    # Plot
    fig = plt.figure()
    ax = fig.gca(projection="3d")

    for i in range(len(x) - 1):
        seg = segments[i]
        (l,) = ax.plot(seg[:, 0], seg[:, 1], seg[:, 2], color=colors[i])
        l.set_solid_capstyle("round")

    if rotate is True:
        fig = _plot_3D_colored_rotate(fig, ax)

    return fig 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def imshow2d(data, ax=None, cmap2d='brightwheel', huenorm=None, huevmin=None,
             huevmax=None, lightnorm=None, lightvmin=None, lightvmax=None,
             **kwargs):
    """
    Plot 2 parameter 2D data array to current axis.

    :param data: numpy array with shape (2, nwidth, nheight). The first index
                 corresponds to the hue and the second to the lightness of the
                 colors.
    :param ax: a matplotlib axis instance.
    :param cmap: either:
                 numpy array with shape (nwidth, nheight, 4) that contains
                 the 4 rgba values in hue (width) and lightness (height).
                 Can be obtained by a call to get_cmap2d(name).
                 or:
                 name where name is one of the following strings:
                 'brightwheel', 'darkwheel', 'hardwheel', 'newwheel',
                 'smoothwheel', 'wheel'
    :param huenorm: a plt.Normalize() instance that normalizes the hue values.
    :param huevmin: the minimum of the huevalues. Only used if huenorm=None.
    :param huevmax: the maximum of the huevalues. Only used if huenorm=None.
    :param lightnorm: a plt.Normalize() instance that normalizes the lightness
                      values.
    :param lightvmin: the minimum of the lightness values.
                      Only used if lightnorm=None.
    :param lightvmax: the maximum of the lightness values.
                      Only used if lightnorm=None.
    :param **kwargs: remaining kwargs are passed to plt.imshow()
    """
    if ax is None:
        ax = plt.gca()
    rgb_data = data2d_to_rgb(data, cmap2d=cmap2d,
                             huenorm=huenorm, huevmin=huevmin,
                             huevmax=huevmax, lightnorm=lightnorm,
                             lightvmin=lightvmin, lightvmax=lightvmax)
    im = ax.imshow(rgb_data, **kwargs)
    return im 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def _get_custom_colormap(colortext):
    try:
        colors = _get_color(colortext)
        values = get_tick_val_col(colortext)
        if colors is None or values is None:
            return
        norm = plt.Normalize(min(values), max(values))
        tuples = list(zip(map(norm, values), colors))
        cmap = matplotlib.colors.LinearSegmentedColormap.from_list(colortext, tuples)
    except FileNotFoundError:
        LOG.warning('No such file or directory: "%s"' % colortext)
        return
    return cmap 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def test_LogNorm():
    """
    LogNorm ignored clip, now it has the same
    behavior as Normalize, e.g., values > vmax are bigger than 1
    without clip, with clip they are 1.
    """
    ln = mcolors.LogNorm(clip=True, vmax=5)
    assert_array_equal(ln([1, 6]), [0, 1.0]) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def test_PowerNorm():
    a = np.array([0, 0.5, 1, 1.5], dtype=float)
    pnorm = mcolors.PowerNorm(1)
    norm = mcolors.Normalize()
    assert_array_almost_equal(norm(a), pnorm(a))

    a = np.array([-0.5, 0, 2, 4, 8], dtype=float)
    expected = [0, 0, 1/16, 1/4, 1]
    pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
    assert_array_almost_equal(pnorm(a), expected)
    assert pnorm(a[0]) == expected[0]
    assert pnorm(a[2]) == expected[2]
    assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])

    # Clip = True
    a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
    expected = [0, 0, 0, 1, 1]
    pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
    assert_array_almost_equal(pnorm(a), expected)
    assert pnorm(a[0]) == expected[0]
    assert pnorm(a[-1]) == expected[-1]

    # Clip = True at call time
    a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
    expected = [0, 0, 0, 1, 1]
    pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
    assert_array_almost_equal(pnorm(a, clip=True), expected)
    assert pnorm(a[0], clip=True) == expected[0]
    assert pnorm(a[-1], clip=True) == expected[-1] 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def test_ndarray_subclass_norm(recwarn):
    # Emulate an ndarray subclass that handles units
    # which objects when adding or subtracting with other
    # arrays. See #6622 and #8696
    class MyArray(np.ndarray):
        def __isub__(self, other):
            raise RuntimeError

        def __add__(self, other):
            raise RuntimeError

    data = np.arange(-10, 10, 1, dtype=float)
    data.shape = (10, 2)
    mydata = data.view(MyArray)

    for norm in [mcolors.Normalize(), mcolors.LogNorm(),
                 mcolors.SymLogNorm(3, vmax=5, linscale=1),
                 mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()),
                 mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max()),
                 mcolors.PowerNorm(1)]:
        assert_array_equal(norm(mydata), norm(data))
        fig, ax = plt.subplots()
        ax.imshow(mydata, norm=norm)
        fig.canvas.draw()
        assert len(recwarn) == 0
        recwarn.clear() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def colorline(x, y, cmap=None, cm_range=(0, 0.7), **kwargs):
    """Colorline plots a trajectory of (x,y) points with a colormap"""

    # plt.plot(x, y, '-k', zorder=1)
    # plt.scatter(x, y, s=40, c=plt.cm.RdBu(np.linspace(0,1,40)), zorder=2, edgecolor='k')

    assert len(cm_range)==2, "cm_range must have (min, max)"
    assert len(x) == len(y), "x and y must have the same number of elements!"

    ax = kwargs.get('ax', plt.gca())
    lw = kwargs.get('lw', 2)
    if cmap is None:
        cmap=plt.cm.Blues_r

    t = np.linspace(cm_range[0], cm_range[1], len(x))

    points = np.array([x, y]).T.reshape(-1, 1, 2)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)

    lc = LineCollection(segments, cmap=cmap, norm=plt.Normalize(0, 1),
                        zorder=50)
    lc.set_array(t)
    lc.set_linewidth(lw)

    ax.add_collection(lc)

    return lc 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def create_cmap(values, colors):

    from matplotlib.pyplot import Normalize
    import matplotlib

    norm = Normalize(min(values), max(values))
    tuples = list(zip(map(norm, values), colors))
    cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
    return cmap, norm 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def compute_node_colors(self):
        """Compute the node colors. Also computes the colorbar."""
        data = [self.graph.nodes[n][self.node_color] for n in self.nodes]

        if self.group_order == "alphabetically":
            data_reduced = sorted(list(set(data)))
        elif self.group_order == "default":
            data_reduced = list(unique_everseen(data))

        dtype = infer_data_type(data)
        n_grps = num_discrete_groups(data)

        if dtype == "categorical" or dtype == "ordinal":
            if n_grps <= 8:
                cmap = get_cmap(
                    cmaps["Accent_{0}".format(n_grps)].mpl_colormap
                )
            else:
                cmap = n_group_colorpallet(n_grps)
        elif dtype == "continuous" and not is_data_diverging(data):
            cmap = get_cmap(cmaps["continuous"].mpl_colormap)
        elif dtype == "continuous" and is_data_diverging(data):
            cmap = get_cmap(cmaps["diverging"].mpl_colormap)

        for d in data:
            idx = data_reduced.index(d) / n_grps
            self.node_colors.append(cmap(idx))

        # Add colorbar if required.ListedColormap
        logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
        logging.debug("dtype: {0}".format(dtype))
        if len(data_reduced) > 1 and dtype == "continuous":
            self.sm = plt.cm.ScalarMappable(
                cmap=cmap,
                norm=plt.Normalize(
                    vmin=min(data_reduced),
                    vmax=max(data_reduced),  # noqa  # noqa
                ),
            )
            self.sm._A = [] 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Normalize [as 別名]
def compute_edge_colors(self):
        """Compute the edge colors."""
        data = [self.graph.edges[n][self.edge_color] for n in self.edges]
        data_reduced = sorted(list(set(data)))

        dtype = infer_data_type(data)
        n_grps = num_discrete_groups(data)
        if dtype == "categorical" or dtype == "ordinal":
            if n_grps <= 8:
                cmap = get_cmap(
                    cmaps["Accent_{0}".format(n_grps)].mpl_colormap
                )
            else:
                cmap = n_group_colorpallet(n_grps)
        elif dtype == "continuous" and not is_data_diverging(data):
            cmap = get_cmap(cmaps["weights"])

        for d in data:
            idx = data_reduced.index(d) / n_grps
            self.edge_colors.append(cmap(idx))
        # Add colorbar if required.
        logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
        logging.debug("dtype: {0}".format(dtype))
        if len(data_reduced) > 1 and dtype == "continuous":
            self.sm = plt.cm.ScalarMappable(
                cmap=cmap,
                norm=plt.Normalize(
                    vmin=min(data_reduced),
                    vmax=max(data_reduced),  # noqa  # noqa
                ),
            )
            self.sm._A = []