Python colors.NoNorm方法代码示例

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b) #, nbins=10)

        self.cbar_axis.set_major_locator(locator) 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b)

        self.cbar_axis.set_major_locator(locator) 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _locate(self, x):
        '''
        Given a set of color data values, return their
        corresponding colorbar data coordinates.
        '''
        if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
            b = self._boundaries
            xn = x
        else:
            # Do calculations using normalized coordinates so
            # as to make the interpolation more accurate.
            b = self.norm(self._boundaries, clip=False).filled()
            xn = self.norm(x, clip=False).filled()
        # The rest is linear interpolation with extrapolation at ends.
        y = self._y
        N = len(b)
        ii = np.searchsorted(b, xn)
        i0 = ii - 1
        itop = (ii == N)
        ibot = (ii == 0)
        i0[itop] -= 1
        ii[itop] -= 1
        i0[ibot] += 1
        ii[ibot] += 1

        #db = b[ii] - b[i0]
        db = np.take(b, ii) - np.take(b, i0)
        #dy = y[ii] - y[i0]
        dy = np.take(y, ii) - np.take(y, i0)
        z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db

        return z 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _ticker(self, locator, formatter):
        '''
        Return the sequence of ticks (colorbar data locations),
        ticklabels (strings), and the corresponding offset string.
        '''
        if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
            intv = self._values[0], self._values[-1]
        else:
            intv = self.vmin, self.vmax
        locator.create_dummy_axis(minpos=intv[0])
        formatter.create_dummy_axis(minpos=intv[0])
        locator.set_view_interval(*intv)
        locator.set_data_interval(*intv)
        formatter.set_view_interval(*intv)
        formatter.set_data_interval(*intv)

        b = np.array(locator())
        if isinstance(locator, ticker.LogLocator):
            eps = 1e-10
            b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
        else:
            eps = (intv[1] - intv[0]) * 1e-10
            b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
        self._manual_tick_data_values = b
        ticks = self._locate(b)
        ticklabels = formatter.format_ticks(b)
        offset_string = formatter.get_offset()
        return ticks, ticklabels, offset_string 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _locate(self, x):
        '''
        Given a set of color data values, return their
        corresponding colorbar data coordinates.
        '''
        if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
            b = self._boundaries
            xn = x
        else:
            # Do calculations using normalized coordinates so
            # as to make the interpolation more accurate.
            b = self.norm(self._boundaries, clip=False).filled()
            xn = self.norm(x, clip=False).filled()

        bunique = b
        yunique = self._y
        # trim extra b values at beginning and end if they are
        # not unique.  These are here for extended colorbars, and are not
        # wanted for the interpolation.
        if b[0] == b[1]:
            bunique = bunique[1:]
            yunique = yunique[1:]
        if b[-1] == b[-2]:
            bunique = bunique[:-1]
            yunique = yunique[:-1]

        z = np.interp(xn, bunique, yunique)
        return z 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _locate(self, x):
        '''
        Given a set of color data values, return their
        corresponding colorbar data coordinates.
        '''
        if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
            b = self._boundaries
            xn = x
        else:
            # Do calculations using normalized coordinates so
            # as to make the interpolation more accurate.
            b = self.norm(self._boundaries, clip=False).filled()
            xn = self.norm(x, clip=False).filled()

        # The rest is linear interpolation with extrapolation at ends.
        ii = np.searchsorted(b, xn)
        i0 = ii - 1
        itop = (ii == len(b))
        ibot = (ii == 0)
        i0[itop] -= 1
        ii[itop] -= 1
        i0[ibot] += 1
        ii[ibot] += 1

        db = np.take(b, ii) - np.take(b, i0)
        y = self._y
        dy = np.take(y, ii) - np.take(y, i0)
        z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
        return z 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _get_ticker_locator_formatter(self):
        """
        This code looks at the norm being used by the colorbar
        and decides what locator and formatter to use.  If ``locator`` has
        already been set by hand, it just returns
        ``self.locator, self.formatter``.
        """
        locator = self.locator
        formatter = self.formatter
        if locator is None:
            if self.boundaries is None:
                if isinstance(self.norm, colors.NoNorm):
                    nv = len(self._values)
                    base = 1 + int(nv / 10)
                    locator = ticker.IndexLocator(base=base, offset=0)
                elif isinstance(self.norm, colors.BoundaryNorm):
                    b = self.norm.boundaries
                    locator = ticker.FixedLocator(b, nbins=10)
                elif isinstance(self.norm, colors.LogNorm):
                    locator = _ColorbarLogLocator(self)
                elif isinstance(self.norm, colors.SymLogNorm):
                    # The subs setting here should be replaced
                    # by logic in the locator.
                    locator = ticker.SymmetricalLogLocator(
                                      subs=np.arange(1, 10),
                                      linthresh=self.norm.linthresh,
                                      base=10)
                else:
                    if mpl.rcParams['_internal.classic_mode']:
                        locator = ticker.MaxNLocator()
                    else:
                        locator = _ColorbarAutoLocator(self)
            else:
                b = self._boundaries[self._inside]
                locator = ticker.FixedLocator(b, nbins=10)
        _log.debug('locator: %r', locator)
        return locator, formatter 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _ticker(self, locator, formatter):
        '''
        Return the sequence of ticks (colorbar data locations),
        ticklabels (strings), and the corresponding offset string.
        '''
        if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
            intv = self._values[0], self._values[-1]
        else:
            intv = self.vmin, self.vmax
        locator.create_dummy_axis(minpos=intv[0])
        formatter.create_dummy_axis(minpos=intv[0])
        locator.set_view_interval(*intv)
        locator.set_data_interval(*intv)
        formatter.set_view_interval(*intv)
        formatter.set_data_interval(*intv)

        b = np.array(locator())
        if isinstance(locator, ticker.LogLocator):
            eps = 1e-10
            b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
        else:
            eps = (intv[1] - intv[0]) * 1e-10
            b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
        self._tick_data_values = b
        ticks = self._locate(b)
        formatter.set_locs(b)
        ticklabels = [formatter(t, i) for i, t in enumerate(b)]
        offset_string = formatter.get_offset()
        return ticks, ticklabels, offset_string 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def show_classes(self):
        '''Show the class values.'''
        import matplotlib.pyplot as plt
        from matplotlib.colors import ListedColormap, NoNorm
        from spectral import get_rgb

        if self.class_axes is not None:
            msg = 'ImageView.show_classes should only be called once.'
            warnings.warn(UserWarning(msg))
            return
        elif self.classes is None:
            raise Exception('Unable to display classes: class array not set.')

        cm = ListedColormap(np.array(self.class_colors) / 255.)
        self._update_class_rgb()
        kwargs = self.imshow_class_kwargs.copy()

        kwargs.update({'cmap': cm, 'vmin': 0, 'norm': NoNorm(),
                       'interpolation': self._interpolation})
        if self.axes is not None:
            # A figure has already been created for the view. Make it current.
            plt.figure(self.axes.figure.number)
        self.class_axes = plt.imshow(self.class_rgb, **kwargs)
        if self.axes is None:
            self.axes = self.class_axes.axes
        self.class_axes.set_zorder(1)
        if self.display_mode == 'overlay':
            self.class_axes.set_alpha(self._class_alpha)
        else:
            self.class_axes.set_alpha(1)
        #self.class_axes.axes.set_axis_bgcolor('black') 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _ticker(self, locator, formatter):
        '''
        Return the sequence of ticks (colorbar data locations),
        ticklabels (strings), and the corresponding offset string.
        '''
        if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
            intv = self._values[0], self._values[-1]
        else:
            intv = self.vmin, self.vmax
        locator.create_dummy_axis(minpos=intv[0])
        formatter.create_dummy_axis(minpos=intv[0])
        locator.set_view_interval(*intv)
        locator.set_data_interval(*intv)
        formatter.set_view_interval(*intv)
        formatter.set_data_interval(*intv)

        b = np.array(locator())
        if isinstance(locator, ticker.LogLocator):
            eps = 1e-10
            b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
        else:
            eps = (intv[1] - intv[0]) * 1e-10
            b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
        self._manual_tick_data_values = b
        ticks = self._locate(b)
        formatter.set_locs(b)
        ticklabels = [formatter(t, i) for i, t in enumerate(b)]
        offset_string = formatter.get_offset()
        return ticks, ticklabels, offset_string 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def plot_heatmap(ax, data, x_labels, y_labels, rotate=0):
    heatmap = ax.pcolor(data, cmap=plt.cm.Blues, norm=NoNorm())

    # put the major ticks at the middle of each cell
    ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
    ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)

    ax.xaxis.tick_top()
    ax.set_xticklabels(x_labels, minor=False, rotation=rotate)
    ax.set_yticklabels(y_labels, minor=False) 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _ticker(self):
        '''
        Return two sequences: ticks (colorbar data locations)
        and ticklabels (strings).
        '''
        locator = self.locator
        formatter = self.formatter
        if locator is None:
            if self.boundaries is None:
                if isinstance(self.norm, colors.NoNorm):
                    nv = len(self._values)
                    base = 1 + int(nv / 10)
                    locator = ticker.IndexLocator(base=base, offset=0)
                elif isinstance(self.norm, colors.BoundaryNorm):
                    b = self.norm.boundaries
                    locator = ticker.FixedLocator(b, nbins=10)
                elif isinstance(self.norm, colors.LogNorm):
                    locator = ticker.LogLocator()
                else:
                    locator = ticker.MaxNLocator()
            else:
                b = self._boundaries[self._inside]
                locator = ticker.FixedLocator(b, nbins=10)
        if isinstance(self.norm, colors.NoNorm):
            intv = self._values[0], self._values[-1]
        else:
            intv = self.vmin, self.vmax
        locator.create_dummy_axis(minpos=intv[0])
        formatter.create_dummy_axis(minpos=intv[0])
        locator.set_view_interval(*intv)
        locator.set_data_interval(*intv)
        formatter.set_view_interval(*intv)
        formatter.set_data_interval(*intv)

        b = np.array(locator())
        ticks = self._locate(b)
        inrange = (ticks > -0.001) & (ticks < 1.001)
        ticks = ticks[inrange]
        b = b[inrange]
        formatter.set_locs(b)
        ticklabels = [formatter(t, i) for i, t in enumerate(b)]
        offset_string = formatter.get_offset()
        return ticks, ticklabels, offset_string 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _process_colors(self):
        """
        Color argument processing for contouring.

        Note that we base the color mapping on the contour levels
        and layers, not on the actual range of the Z values.  This
        means we don't have to worry about bad values in Z, and we
        always have the full dynamic range available for the selected
        levels.

        The color is based on the midpoint of the layer, except for
        extended end layers.  By default, the norm vmin and vmax
        are the extreme values of the non-extended levels.  Hence,
        the layer color extremes are not the extreme values of
        the colormap itself, but approach those values as the number
        of levels increases.  An advantage of this scheme is that
        line contours, when added to filled contours, take on
        colors that are consistent with those of the filled regions;
        for example, a contour line on the boundary between two
        regions will have a color intermediate between those
        of the regions.

        """
        self.monochrome = self.cmap.monochrome
        if self.colors is not None:
            # Generate integers for direct indexing.
            i0, i1 = 0, len(self.levels)
            if self.filled:
                i1 -= 1
            # Out of range indices for over and under:
            if self.extend in ('both', 'min'):
                i0 = -1
            if self.extend in ('both', 'max'):
                i1 += 1
            self.cvalues = list(range(i0, i1))
            self.set_norm(colors.NoNorm())
        else:
            self.cvalues = self.layers
        self.set_array(self.levels)
        self.autoscale_None()
        if self.extend in ('both', 'max', 'min'):
            self.norm.clip = False

        # self.tcolors are set by the "changed" method 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _process_values(self, b=None):
        '''
        Set the :attr:`_boundaries` and :attr:`_values` attributes
        based on the input boundaries and values.  Input boundaries
        can be *self.boundaries* or the argument *b*.
        '''
        if b is None:
            b = self.boundaries
        if b is not None:
            self._boundaries = np.asarray(b, dtype=float)
            if self.values is None:
                self._values = 0.5*(self._boundaries[:-1]
                                        + self._boundaries[1:])
                if isinstance(self.norm, colors.NoNorm):
                    self._values = (self._values + 0.00001).astype(np.int16)
                return
            self._values = np.array(self.values)
            return
        if self.values is not None:
            self._values = np.array(self.values)
            if self.boundaries is None:
                b = np.zeros(len(self.values)+1, 'd')
                b[1:-1] = 0.5*(self._values[:-1] - self._values[1:])
                b[0] = 2.0*b[1] - b[2]
                b[-1] = 2.0*b[-2] - b[-3]
                self._boundaries = b
                return
            self._boundaries = np.array(self.boundaries)
            return
        # Neither boundaries nor values are specified;
        # make reasonable ones based on cmap and norm.
        if isinstance(self.norm, colors.NoNorm):
            b = self._uniform_y(self.cmap.N+1) * self.cmap.N - 0.5
            v = np.zeros((len(b)-1,), dtype=np.int16)
            v = np.arange(self.cmap.N, dtype=np.int16)
            self._boundaries = b
            self._values = v
            return
        elif isinstance(self.norm, colors.BoundaryNorm):
            b = np.array(self.norm.boundaries)
            v = np.zeros((len(b)-1,), dtype=float)
            bi = self.norm.boundaries
            v = 0.5*(bi[:-1] + bi[1:])
            self._boundaries = b
            self._values = v
            return
        else:
            b = self._uniform_y(self.cmap.N+1)

        self._process_values(b) 

# 需要导入模块: from matplotlib import colors [as 别名]
# 或者: from matplotlib.colors import NoNorm [as 别名]
def _get_ticker_locator_formatter(self):
        """
        This code looks at the norm being used by the colorbar
        and decides what locator and formatter to use.  If ``locator`` has
        already been set by hand, it just returns
        ``self.locator, self.formatter``.
        """
        locator = self.locator
        formatter = self.formatter
        if locator is None:
            if self.boundaries is None:
                if isinstance(self.norm, colors.NoNorm):
                    nv = len(self._values)
                    base = 1 + int(nv / 10)
                    locator = ticker.IndexLocator(base=base, offset=0)
                elif isinstance(self.norm, colors.BoundaryNorm):
                    b = self.norm.boundaries
                    locator = ticker.FixedLocator(b, nbins=10)
                elif isinstance(self.norm, colors.LogNorm):
                    locator = _ColorbarLogLocator(self)
                elif isinstance(self.norm, colors.SymLogNorm):
                    # The subs setting here should be replaced
                    # by logic in the locator.
                    locator = ticker.SymmetricalLogLocator(
                                      subs=np.arange(1, 10),
                                      linthresh=self.norm.linthresh,
                                      base=10)
                else:
                    if mpl.rcParams['_internal.classic_mode']:
                        locator = ticker.MaxNLocator()
                    else:
                        locator = _ColorbarAutoLocator(self)
            else:
                b = self._boundaries[self._inside]
                locator = ticker.FixedLocator(b, nbins=10)

        if formatter is None:
            if isinstance(self.norm, colors.LogNorm):
                formatter = ticker.LogFormatterSciNotation()
            elif isinstance(self.norm, colors.SymLogNorm):
                formatter = ticker.LogFormatterSciNotation(
                                        linthresh=self.norm.linthresh)
            else:
                formatter = ticker.ScalarFormatter()
        else:
            formatter = self.formatter

        self.locator = locator
        self.formatter = formatter
        _log.debug('locator: %r', locator)
        return locator, formatter