def _init(self):
"""Initialize from a tradition segment data or from an
existing colormap redefining the intrinsic alpha channel.
"""
# Inherit segment data from an existing colormap,
# possibly redefining alpha channel
if self._segmentdata.has_key('cmap'):
if self._segmentdata.has_key('alpha'):
alphad = self._segmentdata['alpha'] # save this
else:
alphad = None
cmap = self._segmentdata['cmap']
if not cmap._isinit: cmap._init()
self._segmentdata = cmap._segmentdata.copy()
if alphad != None:
self._segmentdata['alpha'] = alphad # revised alpha
# Scale the segment data if requested
# -----------------------------------
if self._scale != None:
scale = lambda x: min(max(self._scale(x), 0.0), 1.0)
data_s = {}
for key, val in self._segmentdata.iteritems():
valnew = [(scale(a), b, c) for a, b, c in val]
data_s[key] = valnew
self._segmentdata = data_s
# Reverse the segment data if requested
# -------------------------------------
if self._reverse:
data_r = {}
for key, val in self._segmentdata.iteritems():
valnew = [(1.0 - a, b, c) for a, b, c in reversed(val)]
data_r[key] = valnew
self._segmentdata = data_r
# Go for the LUT
# --------------
self._lut = npy.ones((self.N + 3, 4), npy.float)
self._lut[:-3, 0] = makeMappingArray(self.N, self._segmentdata['red'])
self._lut[:-3, 1] = makeMappingArray(self.N,
self._segmentdata['green'])
self._lut[:-3, 2] = makeMappingArray(self.N, self._segmentdata['blue'])
# RGB is always there by alpha may not, so we check for it
# --------------------------------------------------------
if self._segmentdata.has_key('alpha'):
self._lut[:-3, 3] = makeMappingArray(self.N,
self._segmentdata['alpha'])
# Save intrinsic alpha
# --------------------
self._alpha = self._lut[:-3, 3][:]
self._isinit = True
self._set_extremes()
def _custom_listed_color_map(self, name, N, firstBlack=False):
""" add the black color in front of 'name' color """
import matplotlib.cm as cm
from matplotlib import colors
mp = cm.datad[name]
new_mp1 = {'blue': colors.makeMappingArray(N-1, mp['blue']),
'green': colors.makeMappingArray(N-1, mp['green']),
'red': colors.makeMappingArray(N-1, mp['red'])}
new_mp2 = []
new_mp2.extend(zip(new_mp1['red'], new_mp1['green'], new_mp1['blue']))
if firstBlack == True:
new_mp2 = [(0,0,0)]+new_mp2 # the black color
return colors.ListedColormap(new_mp2, N=N-1), new_mp2
def _custom_listed_color_map(self, name, N, firstBlack=False):
""" add the black color in front of 'name' color """
import matplotlib.cm as cm
from matplotlib import colors
mp = cm.datad[name]
new_mp1 = {
'blue': colors.makeMappingArray(N - 1, mp['blue']),
'green': colors.makeMappingArray(N - 1, mp['green']),
'red': colors.makeMappingArray(N - 1, mp['red'])
}
new_mp2 = []
new_mp2.extend(zip(new_mp1['red'], new_mp1['green'], new_mp1['blue']))
if firstBlack == True:
new_mp2 = [(0, 0, 0)] + new_mp2 # the black color
return colors.ListedColormap(new_mp2, N=N - 1), new_mp2
def gen_gradient_mask(size,
palette,
icon_dir='.temp',
gradient_dir='horizontal'):
"""Generates a gradient color mask from a specified palette."""
mask_array = gen_mask_array(icon_dir, 'float32')
palette_func = gen_palette(palette)
gradient = np.array(makeMappingArray(size, palette_func.mpl_colormap))
# matplotlib color maps are from range of (0, 1). Convert to RGB.
gradient *= 255.
# Add new axis and repeat gradient across it.
gradient = np.tile(gradient, (size, 1, 1))
# if vertical, transpose the gradient.
if gradient_dir == 'vertical':
gradient = np.transpose(gradient, (1, 0, 2))
# Turn any nonwhite pixels on the icon into the gradient colors.
white = (255., 255., 255., 255.)
mask_array[mask_array != white] = gradient[mask_array != white]
image_colors = ImageColorGenerator(mask_array)
return image_colors, np.uint8(mask_array)
def plot_colourbar(clr_ax, cmap, data=None, mima=False, plusminus=False, zorder=1):
"""
Puts a colorbar under / behind histogram
"""
seisbar = cm.get_cmap(cmap)
ncolours = 32
seis_array = makeMappingArray(ncolours, seisbar)
color_arr = seis_array[np.newaxis, :]
color_arr = color_arr[:, :, :-1]
colour_roll = np.rollaxis(color_arr, 1)
colour_roll2 = np.rollaxis(colour_roll, 1)
clr_ax.imshow(colour_roll2, extent=[0, ncolours, 0, 1], aspect='auto', zorder=zorder)
clr_ax.set_yticks([])
clr_ax.set_xticks([])
ma, mi = np.amax(data), np.amin(data)
if mima:
clr_ax.text(0.95, 0.5, '{:3.0f}'.format(mi),
transform=clr_ax.transAxes,
horizontalalignment='center', verticalalignment='top',
fontsize=8)
clr_ax.text(0.05, 0.5, '{:3.0f}'.format(ma), transform=clr_ax.transAxes,
horizontalalignment='center',
fontsize=8)
if plusminus:
clr_ax.text(0.95, 0.5, "+",
transform=clr_ax.transAxes,
ha='right', color='w',
va='center', fontsize=16)
clr_ax.text(0.05, 0.5, "-",
transform=clr_ax.transAxes, color='k',
ha='left', va='center', fontsize=16)
return clr_ax
def plot_colourbar(clr_ax,
cmap,
data=None,
mima=False,
plusminus=False,
zorder=1):
"""
Puts a colorbar under / behind histogram
"""
seisbar = cm.get_cmap(cmap)
ncolours = 32
seis_array = makeMappingArray(ncolours, seisbar)
color_arr = seis_array[np.newaxis, :]
color_arr = color_arr[:, :, :-1]
colour_roll = np.rollaxis(color_arr, 1)
colour_roll2 = np.rollaxis(colour_roll, 1)
clr_ax.imshow(colour_roll2,
extent=[0, ncolours, 0, 1],
aspect='auto',
zorder=zorder)
clr_ax.set_yticks([])
clr_ax.set_xticks([])
ma, mi = np.amax(data), np.amin(data)
if mima:
clr_ax.text(0.95,
0.5,
'{:3.0f}'.format(mi),
transform=clr_ax.transAxes,
horizontalalignment='center',
verticalalignment='top',
fontsize=8)
clr_ax.text(0.05,
0.5,
'{:3.0f}'.format(ma),
transform=clr_ax.transAxes,
horizontalalignment='center',
fontsize=8)
if plusminus:
clr_ax.text(0.95,
0.5,
"+",
transform=clr_ax.transAxes,
ha='right',
color='w',
va='center',
fontsize=16)
clr_ax.text(0.05,
0.5,
"-",
transform=clr_ax.transAxes,
color='k',
ha='left',
va='center',
fontsize=16)
return clr_ax
def make_gradient(img_size, palette_name):
background = Image.new('RGBA', img_size, (0, 0, 0, 0))
palette = makeMappingArray(img_size[0], get_cmap(palette_name))
rgb_sequence = []
for x in range(img_size[0]):
color = palette[x]
# matplotlib color maps are from range of (0,1). Convert to RGB.
r = int(color[0] * 255)
g = int(color[1] * 255)
b = int(color[2] * 255)
rgb_sequence.append((r, g, b))
background.putdata(rgb_sequence * img_size[1])
return background
icon = "flag"
gradient_orientation = "h"
# http://stackoverflow.com/questions/7911451/pil-convert-png-or-gif-with-transparency-to-jpg-without
icon_path = fa_path + "%s.png" % icon
icon = Image.open(icon_path)
mask = Image.new("RGB", icon.size, (255,255,255))
mask.paste(icon,icon)
mask_wordcloud = np.array(mask)
# Create a linear gradient using the matplotlib color map
imgsize = icon.size
palette = makeMappingArray(imgsize[1], Spectral_9.mpl_colormap) # interpolates colors
for y in range(imgsize[1]):
for x in range(imgsize[0]):
if mask.getpixel((x,y)) != (255,255,255): # Only change nonwhite pixels of icon
color = palette[y] if gradient_orientation is "vertical" else palette[x]
# matplotlib color maps are from range of (0,1). Convert to RGB.
r = int(color[0] * 255)
g = int(color[1] * 255)
b = int(color[2] * 255)
mask.putpixel((x, y), (r, g, b))
# create coloring from image
[248, 223, 0],
[249, 226, 0],
[249, 228, 0],
[250, 230, 0],
[250, 232, 0],
[251, 235, 0],
[251, 237, 0],
[252, 239, 0],
[252, 242, 0],
[253, 244, 0],
[253, 246, 0],
[254, 249, 0],
[254, 251, 0],
[255, 253, 0],
[255, 255, 0]], dtype='uint8')
myCmaps ={'hot': _cetHot_dat,
'Blue-Black-Yellow': _cetBlueBlackYellow_dat,
'Blue-Black-Red': _cetBlueBlackRed_dat,
'rainbow': _cetRainbowDivering_dat,
'temperature': _temp_dat,
'Blue-Green-Yellow-Red': _bgylrd_dat}
keys = ['viridis', 'magma', 'inferno', 'plasma', 'gnuplot', 'gnuplot2', 'coolwarm', 'PuOr', 'RdYlGn', 'PiYG', 'PRGn', 'RdBu', 'winter']
for k in keys:
myCmaps[k] = np.array(makeMappingArray(256,get_cmap(k))[:,0:3]*255, dtype = 'uint8')
myCmaps['Spectral'] = np.array(makeMappingArray(256,get_cmap('Spectral_r'))[:,0:3]*255, dtype = 'uint8')
myCmaps['BuYlRd'] = np.array(makeMappingArray(256,get_cmap('RdYlBu_r'))[:,0:3]*255, dtype = 'uint8')
myCmaps_names = [key for key in myCmaps.keys()]
from matplotlib import cm
from matplotlib.colors import makeMappingArray, rgb_to_hsv, hsv_to_rgb
import numpy as np
import matplotlib.pyplot as plt
seisbar = cm.get_cmap('RdBu')
ncolours = 256
seis_array = makeMappingArray(ncolours, seisbar)
np.shape(seis_array)
color_arr = seis_array[np.newaxis, :]
color_arr = color_arr[:, :, :-1]
colour_roll = np.rollaxis(color_arr, 1)
seis_rgb_mtx = np.tile(colour_roll, (256, 1))
np.shape(seis_rgb_mtx)
seis_hsv = rgb_to_hsv(seis_rgb_mtx)
hues, lightness = np.mgrid[0:1:256j, 0:1.0:256j]
seis_hsv[:, :, 2] *= lightness
RGB = hsv_to_rgb(seis_hsv)
fig = plt.figure(figsize=(4, 5))
ax = fig.add_subplot(111, axisbg='k')
ax.imshow(RGB, origin="lower", extent=[0, 1, 0, 1])
plt.show()
def set_image(self,
image,
row,
col,
cmap=None,
vmin=None,
vmax=None,
vsym=False):
"""
Sets the data for a single window.
Parameters
----------
image : ndarray, ndim=2
The shape should be the same as the `shape` specified when
constructing the image grid.
row, col : int
The zero-index of the row and column to set.
cmap : cmap (from matplotlib.pylab.cm)
The color palette to use. Default is grayscale.
vmin, vmax : numerical or None
Defines the range of the color palette. None, which is default,
takes the range of the data.
vsym : bool
If True, this means that the color palette will always be centered
around 0. Even if you have specified both `vmin` and `vmax`, this
will override that and extend the shorter one. Good practice is to
specify neither `vmin` or `vmax` or only `vmax` together with this
option.
"""
from matplotlib import colors
from matplotlib.pylab import cm
from deepdish.plot.resample import resample_and_arrange_image
if cmap is None:
cmap = cm.gray
if vmin is None:
vmin = np.nanmin(image)
if vmax is None:
vmax = np.nanmax(image)
if vsym and -vmin != vmax:
mx = max(abs(vmin), abs(vmax))
vmin = -mx
vmax = mx
if vmin == vmax:
diff = 1
else:
diff = vmax - vmin
image_indices = np.clip((image - vmin) / diff, 0, 1) * 255
image_indices = image_indices.astype(np.uint8)
nan_mask = np.isnan(image).astype(np.uint8)
lut = colors.makeMappingArray(256, cmap)
rgb = resample_and_arrange_image(image_indices, nan_mask, self._shape,
lut)
x0 = row * (self._shape[0] + self._border)
x1 = (row + 1) * (self._shape[0] + self._border) + self._border
y0 = col * (self._shape[1] + self._border)
y1 = (col + 1) * (self._shape[1] + self._border) + self._border
self._data[x0:x1, y0:y1] = self._border_color
anchor = (self._border + row * (self._shape[0] + self._border),
self._border + col * (self._shape[1] + self._border))
selection = [
slice(anchor[0], anchor[0] + rgb.shape[0]),
slice(anchor[1], anchor[1] + rgb.shape[1])
]
nan_data = np.isnan(rgb)
rgb[nan_data] = 0.0
self._data[selection] = (rgb * ~nan_data +
self._border_color * nan_data)
def set_image(self, image, row, col, cmap=None, vmin=None, vmax=None,
vsym=False):
"""
Sets the data for a single window.
Parameters
----------
image : ndarray, ndim=2
The shape should be the same as the `shape` specified when
constructing the image grid.
row/col : int
The zero-index of the row and column to set.
cmap : cmap (from matplotlib.pylab.cm)
The color palette to use. Default is grayscale.
vmin/vmax : numerical or None
Defines the range of the color palette. None, which is default,
takes the range of the data.
vsym : bool
If True, this means that the color palette will always be centered
around 0. Even if you have specified both `vmin` and `vmax`, this
will override that and extend the shorter one. Good practice is to
specify neither `vmin` or `vmax` or only `vmax` together with this
option.
"""
from matplotlib import colors
from matplotlib.pylab import cm
from vzlog.image.resample import resample_and_arrange_image
if cmap is None:
if vsym:
# Pick a default that is white exactly at 0
cmap = cm.RdBu_r
else:
cmap = cm.gray
if vmin is None:
vmin = np.nanmin(image)
if vmax is None:
vmax = np.nanmax(image)
if vsym and -vmin != vmax:
mx = max(abs(vmin), abs(vmax))
vmin = -mx
vmax = mx
if vmin == vmax:
diff = 1
else:
diff = vmax - vmin
image_indices = np.clip((image - vmin) / diff, 0, 1) * 255
image_indices = image_indices.astype(np.uint8)
nan_mask = np.isnan(image).astype(np.uint8)
lut = colors.makeMappingArray(256, cmap)
rgb = resample_and_arrange_image(image_indices, nan_mask, self._shape,
lut)
x0 = row * (self._shape[0] + self._border)
x1 = (row + 1) * (self._shape[0] + self._border) + self._border
y0 = col * (self._shape[1] + self._border)
y1 = (col + 1) * (self._shape[1] + self._border) + self._border
self._data[x0:x1, y0:y1] = self._border_color
anchor = (self._border + row * (self._shape[0] + self._border),
self._border + col * (self._shape[1] + self._border))
selection = [slice(anchor[0], anchor[0] + rgb.shape[0]),
slice(anchor[1], anchor[1] + rgb.shape[1])]
nan_data = np.isnan(rgb)
rgb[nan_data] = 0.0
self._data[selection] = (rgb * ~nan_data +
self._border_color * nan_data)
def test_makeMappingArray(N, result):
data = [(0.0, 1.0, 1.0), (0.5, 0.2, 0.2), (1.0, 0.0, 0.0)]
assert_array_almost_equal(mcolors.makeMappingArray(N, data), result)