def add_cmap( cmapdict, name ):
#my_cmap = matplotlib.colors.LinearSegmentedColormap(name,cmapdict,256)
#pylab.register_cmap(name=name,cmap=my_cmap)
newjet = matplotlib.cm.get_cmap('jet')
newjet.set_under('b')
matplotlib.cm.register_cmap(name='newjet',cmap=newjet)
pylab.register_cmap(name='newjet',cmap=newjet)
def add_cmap(cmapdict, name):
#my_cmap = matplotlib.colors.LinearSegmentedColormap(name,cmapdict,256)
#pylab.register_cmap(name=name,cmap=my_cmap)
newjet = matplotlib.cm.get_cmap('jet')
newjet.set_under('b')
matplotlib.cm.register_cmap(name='newjet', cmap=newjet)
pylab.register_cmap(name='newjet', cmap=newjet)
def register_cmaps_from_cfg(cfg, logger):
"""Register cmap aliases into matplotlib"""
logger.debug('Registering colormaps')
for name in cfg['cmaps']:
cmap_name = get_cfg_cmap(cfg, name, check_aliases=False)
if (name != cmap_name
and cmap_name in matplotlib.cm.cmap_d.keys()): # add an alias
register_cmap(name, matplotlib.cm.cmap_d[cmap_name])
def RegisterCustomColormaps():
"""Adds the 'stoplight' and 'RdGn' colormaps to matplotlib's database
"""
import colorsys as cs
# stoplight colormap
Rd1 = [1.,0.,0.]; Rd2 = Rd1
Yl1 = [1.,1.,0.]; Yl2 = Yl1
Gn1 = [0.,1.,0.]; Gn2 = Gn1
val = 0.65
Rd1 = cs.rgb_to_hsv(Rd1[0],Rd1[1],Rd1[2])
Rd1 = cs.hsv_to_rgb(Rd1[0],Rd1[1],val )
Yl1 = cs.rgb_to_hsv(Yl1[0],Yl1[1],Yl1[2])
Yl1 = cs.hsv_to_rgb(Yl1[0],Yl1[1],val )
Gn1 = cs.rgb_to_hsv(Gn1[0],Gn1[1],Gn1[2])
Gn1 = cs.hsv_to_rgb(Gn1[0],Gn1[1],val )
p = 0
level1 = 0.5
level2 = 0.75
RdYlGn = {'red': ((0.0 , 0.0 ,Rd1[0]),
(level1-p, Rd2[0],Rd2[0]),
(level1+p, Yl1[0],Yl1[0]),
(level2-p, Yl2[0],Yl2[0]),
(level2+p, Gn1[0],Gn1[0]),
(1.00 , Gn2[0], 0.0)),
'green': ((0.0 , 0.0 ,Rd1[1]),
(level1-p, Rd2[1],Rd2[1]),
(level1+p, Yl1[1],Yl1[1]),
(level2-p, Yl2[1],Yl2[1]),
(level2+p, Gn1[1],Gn1[1]),
(1.00 , Gn2[1], 0.0)),
'blue': ((0.0 , 0.0 ,Rd1[2]),
(level1-p, Rd2[2],Rd2[2]),
(level1+p, Yl1[2],Yl1[2]),
(level2-p, Yl2[2],Yl2[2]),
(level2+p, Gn1[2],Gn1[2]),
(1.00 , Gn2[2], 0.0))}
plt.register_cmap(name='stoplight', data=RdYlGn)
# RdGn colormap
val = 0.8
Rd = cs.rgb_to_hsv(1,0,0)
Rd = cs.hsv_to_rgb(Rd[0],Rd[1],val)
Gn = cs.rgb_to_hsv(0,1,0)
Gn = cs.hsv_to_rgb(Gn[0],Gn[1],val)
RdGn = {'red': ((0.0, 0.0, Rd[0]),
(0.5, 1.0 , 1.0 ),
(1.0, Gn[0], 0.0 )),
'green': ((0.0, 0.0, Rd[1]),
(0.5, 1.0, 1.0 ),
(1.0, Gn[1], 0.0 )),
'blue': ((0.0, 0.0, Rd[2]),
(0.5, 1.0, 1.0 ),
(1.0, Gn[2], 0.0 ))}
plt.register_cmap(name='RdGn', data=RdGn)
def set_cmap(name='viridis'):
try:
import matplotlib.colormaps as cmaps
plt.register_cmap(name='viridis', cmap=cmaps.viridis)
except ImportError:
# this is the local version
from . import colormaps as cmaps
plt.register_cmap(name='viridis', cmap=cmaps.viridis)
finally:
plt.set_cmap(name)
# For some reason set_cmap creates a figure, so close it.
plt.close(plt.gcf())
def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):
'''
Function to offset the "center" of a colormap. Useful for
data with a negative min and positive max and you want the
middle of the colormap's dynamic range to be at zero
Input
-----
cmap : The matplotlib colormap to be altered
start : Offset from lowest point in the colormap's range.
Defaults to 0.0 (no lower ofset). Should be between
0.0 and `midpoint`.
midpoint : The new center of the colormap. Defaults to
0.5 (no shift). Should be between 0.0 and 1.0. In
general, this should be 1 - vmax/(vmax + abs(vmin))
For example if your data range from -15.0 to +5.0 and
you want the center of the colormap at 0.0, `midpoint`
should be set to 1 - 5/(5 + 15)) or 0.75
stop : Offset from highets point in the colormap's range.
Defaults to 1.0 (no upper ofset). Should be between
`midpoint` and 1.0.
'''
cdict = {'red': [], 'green': [], 'blue': [], 'alpha': []}
# regular index to compute the colors
reg_index = np.linspace(start, stop, 257)
# shifted index to match the data
shift_index = np.hstack([
np.linspace(0.0, midpoint, 128, endpoint=False),
np.linspace(midpoint, 1.0, 129, endpoint=True)
])
for ri, si in zip(reg_index, shift_index):
r, g, b, a = cmap(ri)
cdict['red'].append((si, r, r))
cdict['green'].append((si, g, g))
cdict['blue'].append((si, b, b))
cdict['alpha'].append((si, a, a))
newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)
plt.register_cmap(cmap=newcmap)
return newcmap
def input_parameters(parser):
hm_param = parser.add_argument_group('Heatmap options')
arg = hm_param.add_argument
arg('--dpi',
type=int,
default=150,
help="Image resolution in dpi [default 150]")
arg('-l', '--log_scale', action='store_true', help="Log scale")
arg('-s',
'--sqrt_scale',
action='store_true',
help="Square root scale")
arg('--no_slabels',
action='store_true',
help="Do not show sample labels")
arg('--minv',
type=float,
default=None,
help=
"Minimum value to display in the color map [default None meaning automatic]"
)
arg('--maxv',
type=float,
default=None,
help=
"Maximum value to display in the color map [default None meaning automatic]"
)
arg('--no_flabels',
action='store_true',
help="Do not show feature labels")
arg('--max_slabel_len',
type=int,
default=25,
help="Max number of chars to report for sample labels [default 15]"
)
arg('--max_flabel_len',
type=int,
default=25,
help="Max number of chars to report for feature labels [default 15]"
)
arg('--flabel_size',
type=int,
default=10,
help="Feature label font size [default 10]")
arg('--slabel_size',
type=int,
default=10,
help="Sample label font size [default 10]")
arg('--fdend_width',
type=float,
default=1.0,
help=
"Width of the feature dendrogram [default 1 meaning 100%% of default heatmap width]"
)
arg('--sdend_height',
type=float,
default=1.0,
help=
"Height of the sample dendrogram [default 1 meaning 100%% of default heatmap height]"
)
arg('--metadata_height',
type=float,
default=.05,
help=
"Height of the metadata panel [default 0.05 meaning 5%% of default heatmap height]"
)
arg('--metadata_separation',
type=float,
default=.01,
help=
"Distance between the metadata and data panels. [default 0.001 meaning 0.1%% of default heatmap height]"
)
arg('--image_size',
type=float,
default=8,
help=
"Size of the largest between width and eight size for the image in inches [default 8]"
)
arg('--cell_aspect_ratio',
type=float,
default=1.0,
help=
"Aspect ratio between width and height for the cells of the heatmap [default 1.0]"
)
col_maps = [
'Accent', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'Dark2', 'GnBu',
'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'Paired', 'Pastel1',
'Pastel2', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd', 'Purples',
'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Set1', 'Set2',
'Set3', 'Spectral', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot',
'autumn', 'binary', 'bone', 'brg', 'bwr', 'cool', 'copper', 'flag',
'gist_earth', 'gist_gray', 'gist_heat', 'gist_ncar',
'gist_rainbow', 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2',
'gray', 'hot', 'hsv', 'jet', 'ocean', 'pink', 'prism', 'rainbow',
'seismic', 'spectral', 'spring', 'summer', 'terrain', 'winter'
] + [n for n, c in Heatmap.my_colormaps]
for n, c in Heatmap.my_colormaps:
my_cmap = matplotlib.colors.LinearSegmentedColormap(n, c, 256)
pylab.register_cmap(name=n, cmap=my_cmap)
arg('-c', '--colormap', type=str, choices=col_maps, default='bbcry')
arg('--bottom_c',
type=str,
default=None,
help=
"Color to use for cells below the minimum value of the scale [default None meaning bottom color of the scale]"
)
arg('--top_c',
type=str,
default=None,
help=
"Color to use for cells below the maximum value of the scale [default None meaning bottom color of the scale]"
)
arg('--nan_c',
type=str,
default=None,
help="Color to use for nan cells [default None]")
"""
(1.0, 1.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(0.25,1.0, 1.0),
(0.5, 0.0, 0.0),
(0.75,0.0, 0.0),
(1.0, 1.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(0.25,0.6, 1.0),
(0.5, 1.0, 1.0),
(0.75,0.0, 0.0),
(1.0, 1.0, 0.0))
}
map = LinearSegmentedColormap('map', cdict)
pylab.register_cmap(cmap = map)
cmap = pylab.get_cmap('map')
levels = numpy.linspace(0, 4, 49)
g2 = [0]*numplots
g2[0] = read_walls(ff, z0, 'mc')
if able_to_read_file == False:
matplotlib.pyplot.plot(numpy.arange(0,10,1), [0]*10, 'k')
matplotlib.pyplot.suptitle('!!!!WARNING!!!!! There is data missing from this plot!', fontsize=25)
savedfilename = "figs/pair-correlation-" + str(int(ff*10)) + "-" + str(int(z0*100)) + "-" + str(int(10*theta/numpy.pi)) + ".pdf"
pylab.savefig(savedfilename)
exit(0)
rmax = len(g2[0][:,0])*dx
if os.path.exists(sockfile):
os.remove(sockfile)
server = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
server.bind(sockfile)
os.chmod(sockfile, 0775)
server.listen(5)
cmap = {
'red': ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'blue': ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'alpha': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0))
}
pl.register_cmap(name='custom_cmap', data=cmap)
pl.figure(frameon=False, figsize=(6,3))
try:
while True:
conn, addr = server.accept()
data = conn.recv(1024)
if data.startswith('/Library/WebServer/Documents/uploads') and data.endswith('.wav'):
try:
input, Fs = blurt.util.readwave(data)
upsample_factor = blurt.upsample_factor * Fs / float(blurt.Fs)
input = blurt.audioLoopback.processInput(input, Fs, blurt.Fc, upsample_factor)
Fs /= upsample_factor
pl.clf()
pl.axis('off')
ax = pl.axes((0,0,1,1), frameon=False, axisbg=(0,0,0,0))
def input_parameters( parser ):
hm_param = parser.add_argument_group('Heatmap options')
arg = hm_param.add_argument
arg( '--dpi', type=int, default=150,
help = "Image resolution in dpi [default 150]")
arg( '-l', '--log_scale', action='store_true',
help = "Log scale" )
arg( '-s', '--sqrt_scale', action='store_true',
help = "Square root scale" )
arg( '--no_slabels', action='store_true',
help = "Do not show sample labels" )
arg( '--minv', type=float, default=None,
help = "Minimum value to display in the color map [default None meaning automatic]" )
arg( '--maxv', type=float, default=None,
help = "Maximum value to display in the color map [default None meaning automatic]" )
arg( '--no_flabels', action='store_true',
help = "Do not show feature labels" )
arg( '--max_slabel_len', type=int, default=25,
help = "Max number of chars to report for sample labels [default 15]" )
arg( '--max_flabel_len', type=int, default=25,
help = "Max number of chars to report for feature labels [default 15]" )
arg( '--flabel_size', type=int, default=10,
help = "Feature label font size [default 10]" )
arg( '--slabel_size', type=int, default=10,
help = "Sample label font size [default 10]" )
arg( '--fdend_width', type=float, default=1.0,
help = "Width of the feature dendrogram [default 1 meaning 100%% of default heatmap width]")
arg( '--sdend_height', type=float, default=1.0,
help = "Height of the sample dendrogram [default 1 meaning 100%% of default heatmap height]")
arg( '--metadata_height', type=float, default=.05,
help = "Height of the metadata panel [default 0.05 meaning 5%% of default heatmap height]")
arg( '--metadata_separation', type=float, default=.01,
help = "Distance between the metadata and data panels. [default 0.001 meaning 0.1%% of default heatmap height]")
arg( '--image_size', type=float, default=8,
help = "Size of the largest between width and eight size for the image in inches [default 8]")
arg( '--cell_aspect_ratio', type=float, default=1.0,
help = "Aspect ratio between width and height for the cells of the heatmap [default 1.0]")
col_maps = ['Accent', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'Dark2', 'GnBu',
'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'Paired',
'Pastel1', 'Pastel2', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr',
'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn',
'Reds', 'Set1', 'Set2', 'Set3', 'Spectral', 'YlGn', 'YlGnBu',
'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone',
'brg', 'bwr', 'cool', 'copper', 'flag', 'gist_earth',
'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow',
'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray',
'hot', 'hsv', 'jet', 'ocean', 'pink', 'prism', 'rainbow',
'seismic', 'spectral', 'spring', 'summer', 'terrain', 'winter'] + [n for n,c in Heatmap.my_colormaps]
for n,c in Heatmap.my_colormaps:
my_cmap = matplotlib.colors.LinearSegmentedColormap(n,c,256)
pylab.register_cmap(name=n,cmap=my_cmap)
arg( '-c','--colormap', type=str, choices = col_maps, default = 'bbcry' )
arg( '--bottom_c', type=str, default = None,
help = "Color to use for cells below the minimum value of the scale [default None meaning bottom color of the scale]")
arg( '--top_c', type=str, default = None,
help = "Color to use for cells below the maximum value of the scale [default None meaning bottom color of the scale]")
arg( '--nan_c', type=str, default = None,
help = "Color to use for nan cells [default None]")
"""
[0.741388, 0.873449, 0.149561],
[0.751884, 0.874951, 0.143228],
[0.762373, 0.876424, 0.137064],
[0.772852, 0.877868, 0.131109],
[0.783315, 0.879285, 0.125405],
[0.793760, 0.880678, 0.120005],
[0.804182, 0.882046, 0.114965],
[0.814576, 0.883393, 0.110347],
[0.824940, 0.884720, 0.106217],
[0.835270, 0.886029, 0.102646],
[0.845561, 0.887322, 0.099702],
[0.855810, 0.888601, 0.097452],
[0.866013, 0.889868, 0.095953],
[0.876168, 0.891125, 0.095250],
[0.886271, 0.892374, 0.095374],
[0.896320, 0.893616, 0.096335],
[0.906311, 0.894855, 0.098125],
[0.916242, 0.896091, 0.100717],
[0.926106, 0.897330, 0.104071],
[0.935904, 0.898570, 0.108131],
[0.945636, 0.899815, 0.112838],
[0.955300, 0.901065, 0.118128],
[0.964894, 0.902323, 0.123941],
[0.974417, 0.903590, 0.130215],
[0.983868, 0.904867, 0.136897],
[0.993248, 0.906157, 0.143936]]
viridis = ListedColormap(_viridis_data, name='viridis')
pl.register_cmap(name='viridis', cmap=viridis)
#pl.set_cmap(viridis)
[0.496615, 0.826376, 0.306377], [0.506271, 0.828786, 0.300362],
[0.515992, 0.831158, 0.294279], [0.525776, 0.833491, 0.288127],
[0.535621, 0.835785, 0.281908], [0.545524, 0.838039, 0.275626],
[0.555484, 0.840254, 0.269281], [0.565498, 0.842430, 0.262877],
[0.575563, 0.844566, 0.256415], [0.585678, 0.846661, 0.249897],
[0.595839, 0.848717, 0.243329], [0.606045, 0.850733, 0.236712],
[0.616293, 0.852709, 0.230052], [0.626579, 0.854645, 0.223353],
[0.636902, 0.856542, 0.216620], [0.647257, 0.858400, 0.209861],
[0.657642, 0.860219, 0.203082], [0.668054, 0.861999, 0.196293],
[0.678489, 0.863742, 0.189503], [0.688944, 0.865448, 0.182725],
[0.699415, 0.867117, 0.175971], [0.709898, 0.868751, 0.169257],
[0.720391, 0.870350, 0.162603], [0.730889, 0.871916, 0.156029],
[0.741388, 0.873449, 0.149561], [0.751884, 0.874951, 0.143228],
[0.762373, 0.876424, 0.137064], [0.772852, 0.877868, 0.131109],
[0.783315, 0.879285, 0.125405], [0.793760, 0.880678, 0.120005],
[0.804182, 0.882046, 0.114965], [0.814576, 0.883393, 0.110347],
[0.824940, 0.884720, 0.106217], [0.835270, 0.886029, 0.102646],
[0.845561, 0.887322, 0.099702], [0.855810, 0.888601, 0.097452],
[0.866013, 0.889868, 0.095953], [0.876168, 0.891125, 0.095250],
[0.886271, 0.892374, 0.095374], [0.896320, 0.893616, 0.096335],
[0.906311, 0.894855, 0.098125], [0.916242, 0.896091, 0.100717],
[0.926106, 0.897330, 0.104071], [0.935904, 0.898570, 0.108131],
[0.945636, 0.899815, 0.112838], [0.955300, 0.901065, 0.118128],
[0.964894, 0.902323, 0.123941], [0.974417, 0.903590, 0.130215],
[0.983868, 0.904867, 0.136897], [0.993248, 0.906157, 0.143936]
]
viridis = ListedColormap(_viridis_data, name='viridis')
pl.register_cmap(name='viridis', cmap=viridis)
#pl.set_cmap(viridis)
"""
Determine the colors and mappings for the data and legend
"""
import pylab as p
import matplotlib.colors as mpcol
import numpy as np
# Compatability with 1.5 matplotlib colormaps
# Viridis_r is popular
from . import colormaps as cmaps
p.register_cmap(name='viridis_r', cmap=cmaps.viridis_r)
p.register_cmap(name='magma_r', cmap=cmaps.magma_r)
p.register_cmap(name='plasma_r', cmap=cmaps.plasma_r)
# Custom color mappings
diff_dict = {
'blue': [(0.0, 0.58203125, 0.58203125), (0.125, 0.703125, 0.703125), (0.25, 0.81640625, 0.81640625), (0.375, 0.91015625, 0.91015625), (0.4999, 0.96875, 0.96875), (0.5, 1.0, 1.0), (0.5001, 0.5625, 0.5625), (0.625, 0.37890625, 0.37890625), (0.75, 0.26171875, 0.26171875), (0.875, 0.15234375, 0.15234375), (1.0, 0.1484375, 0.1484375)],
'green': [(0.0, 0.2109375, 0.2109375), (0.125, 0.45703125, 0.45703125), (0.25, 0.67578125, 0.67578125), (0.375, 0.84765625, 0.84765625), (0.4999, 0.94921875, 0.94921875), (0.5, 1.0, 1.0), (0.5001, 0.875, 0.875), (0.625, 0.6796875, 0.6796875), (0.75, 0.42578125, 0.42578125), (0.875, 0.1875, 0.1875), (1.0, 0.0, 0.0)],
'red': [(0.0, 0.19140625, 0.19140625), (0.125, 0.26953125, 0.26953125), (0.25, 0.453125, 0.453125), (0.375, 0.66796875, 0.66796875), (0.4999, 0.875, 0.875), (0.5, 1.0, 1.0), (0.5001, 0.9921875, 0.9921875), (0.625, 0.98828125, 0.98828125), (0.75, 0.953125, 0.953125), (0.875, 0.83984375, 0.83984375), (1.0, 0.64453125, 0.64453125)]}
rb_diff_cmap = mpcol.LinearSegmentedColormap('mod_diff', diff_dict)
# Custom color mappings
cool_dict = {'blue': [(0.0, 0.0916, 0.0916), (0.365, 1.0, 1.0), (1.0, 1.0, 1.0)],
'green': [(0.0, 0.0, 0.0), (0.365, 0.0, 0.0), (1.0, 1.0, 1.0)],
'red': [(0.0, 0.0, 0.0), (0.746, 0.0, 0.0), (1.0, 0.0, 0.0)]}
cool_cmap = mpcol.LinearSegmentedColormap('mod_cool', cool_dict)
class VLimit(object):
"""
Calculate data plot limits
"""
return 0
print 'Using', filename
return data
# COLORS
cdict = {
'red': ((0.0, 0.0, 0.0), (0.25, 0.56, 0.2), (0.5, 1.0, 1.0),
(0.75, 1.0, 1.0), (1.0, 1.0, 0.0)),
'green': ((0.0, 0.0, 0.0), (0.25, 1.0, 1.0), (0.5, 0.0, 0.0),
(0.75, 0.0, 0.0), (1.0, 1.0, 0.0)),
'blue': ((0.0, 0.0, 0.0), (0.25, 0.6, 1.0), (0.5, 1.0, 1.0),
(0.75, 0.0, 0.0), (1.0, 1.0, 0.0))
}
map = LinearSegmentedColormap('map', cdict)
pylab.register_cmap(cmap=map)
cmap = pylab.get_cmap('map')
levels = numpy.linspace(0, 4, 49)
g2 = [0] * numplots
g2[0] = read_walls(ff, z0, 'mc')
if able_to_read_file == False:
matplotlib.pyplot.plot(numpy.arange(0, 10, 1), [0] * 10, 'k')
matplotlib.pyplot.suptitle(
'!!!!WARNING!!!!! There is data missing from this plot!', fontsize=25)
savedfilename = "figs/pair-correlation-" + str(int(ff * 10)) + "-" + str(
int(z0 * 100)) + "-" + str(int(10 * theta / numpy.pi)) + ".pdf"
pylab.savefig(savedfilename)
exit(0)
#-------------------------------------------------------------------------------
# custom cmap
#-------------------------------------------------------------------------------
bbcry = {
'red': ((0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.50, 0.0, 0.0),
(0.75, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.50, 1.0, 1.0),
(0.75, 0.0, 0.0), (1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0), (0.25, 1.0, 1.0), (0.50, 1.0, 1.0),
(0.75, 0.0, 0.0), (1.0, 0.0, 1.0)),
}
bbcry = mpl.colors.LinearSegmentedColormap("bbcry", bbcry, 256)
pylab.register_cmap(name="bbcry", cmap=bbcry)
#-------------------------------------------------------------------------------
# utility functions
#-------------------------------------------------------------------------------
def subseq(seq, index):
"""numpy-style slicing and indexing for lists"""
return [seq[i] for i in index]
def metacolors(values, cmap):
found_other = False
found_none = False
unique = set(values)
'blue': [(0.0, 0.0, 1.0), (1.0, 1.0, 0.0)]}],
["LightBlues",{'red':[(0.0, 1.0, 1.0), (1.0, 0.0, 1.0)],
'green': [(0.0, 1.0, 1.0), (1.0, 1.0, 1.0)],
'blue': [(0.0, 1.0, 1.0), (1.0, 1.0, 0.0)]}],
["NeonBlues",{'red':[(0.0, 1.0, 1.0), (1.0, 0.25, 1.0)],
'green': [(0.0, 1.0, 1.0), (1.0, .25, 1.0)],
'blue': [(0.0, 1.0, 1.0), (1.0, 1.0, 0.0)]}],
["NeonGreens", {'red':[(0.0, 0.0, 1.0), (1.0, .25, 1.0)],
'green': [(0.0, 0.0, 1.0),(1.0, 1.0, 1.0)],
'blue': [(0.0, 0.0, 1.0), (1.0, .25, 0.0)]}],
["Browns", {'red':[(0.0, 0.0, 1.0), (1.0, .7, 1.0)],
'green': [(0.0, 0.0, 1.0), (1.0, .45, 1.0)],
'blue': [(0.0, 0.0, 1.0), (1.0, .25, 0.0)]}]]
for spec in color_map_specs:
pylab.register_cmap(cmap=LinearSegmentedColormap(spec[0], spec[1]))
class MyNavToolbar(NavigationToolbar):
mytoolitems = (('Copy', 'Copy the figure', 'copy', 'do_copy'),
('Annotate', 'Annotate a segment', 'annotation', 'do_annotate'),)
def __init__(self, canvas, win):
NavigationToolbar.__init__(self, canvas, win)
self.win = win
path = os.path.abspath(__file__)
dir_path = os.path.dirname(path)
imagedir = os.path.join(dir_path,'images')
for text, tooltip_text, image_file, callback in self.mytoolitems:
if text is None:
self.addSeparator()
def add_cmap( cmapdict, name ):
my_cmap = matplotlib.colors.LinearSegmentedColormap(name,cmapdict,256)
pylab.register_cmap(name=name,cmap=my_cmap)
def RegisterCustomColormaps():
"""Adds the 'stoplight' and 'RdGn' colormaps to matplotlib's database
"""
import colorsys as cs
# stoplight colormap
Rd1 = [1.,0.,0.]; Rd2 = Rd1
Yl1 = [1.,1.,0.]; Yl2 = Yl1
Gn1 = [0.,1.,0.]; Gn2 = Gn1
val = 0.65
Rd1 = cs.rgb_to_hsv(Rd1[0],Rd1[1],Rd1[2])
Rd1 = cs.hsv_to_rgb(Rd1[0],Rd1[1],val )
Yl1 = cs.rgb_to_hsv(Yl1[0],Yl1[1],Yl1[2])
Yl1 = cs.hsv_to_rgb(Yl1[0],Yl1[1],val )
Gn1 = cs.rgb_to_hsv(Gn1[0],Gn1[1],Gn1[2])
Gn1 = cs.hsv_to_rgb(Gn1[0],Gn1[1],val )
p = 0
level1 = 0.5
level2 = 0.75
RdYlGn = {'red': ((0.0 , 0.0 ,Rd1[0]),
(level1-p, Rd2[0],Rd2[0]),
(level1+p, Yl1[0],Yl1[0]),
(level2-p, Yl2[0],Yl2[0]),
(level2+p, Gn1[0],Gn1[0]),
(1.00 , Gn2[0], 0.0)),
'green': ((0.0 , 0.0 ,Rd1[1]),
(level1-p, Rd2[1],Rd2[1]),
(level1+p, Yl1[1],Yl1[1]),
(level2-p, Yl2[1],Yl2[1]),
(level2+p, Gn1[1],Gn1[1]),
(1.00 , Gn2[1], 0.0)),
'blue': ((0.0 , 0.0 ,Rd1[2]),
(level1-p, Rd2[2],Rd2[2]),
(level1+p, Yl1[2],Yl1[2]),
(level2-p, Yl2[2],Yl2[2]),
(level2+p, Gn1[2],Gn1[2]),
(1.00 , Gn2[2], 0.0))}
plt.register_cmap(name='stoplight', data=RdYlGn)
# RdGn colormap
val = 0.8
Rd = cs.rgb_to_hsv(1,0,0)
Rd = cs.hsv_to_rgb(Rd[0],Rd[1],val)
Gn = cs.rgb_to_hsv(0,1,0)
Gn = cs.hsv_to_rgb(Gn[0],Gn[1],val)
RdGn = {'red': ((0.0, 0.0, Rd[0]),
(0.5, 1.0 , 1.0 ),
(1.0, Gn[0], 0.0 )),
'green': ((0.0, 0.0, Rd[1]),
(0.5, 1.0, 1.0 ),
(1.0, Gn[1], 0.0 )),
'blue': ((0.0, 0.0, Rd[2]),
(0.5, 1.0, 1.0 ),
(1.0, Gn[2], 0.0 ))}
plt.register_cmap(name='RdGn', data=RdGn)
# bias colormap
val = 0.8
per = 0.2 /2
Rd = cs.rgb_to_hsv(1,0,0)
Rd = cs.hsv_to_rgb(Rd[0],Rd[1],val)
Bl = cs.rgb_to_hsv(0,0,1)
Bl = cs.hsv_to_rgb(Bl[0],Bl[1],val)
RdBl = {'red': ((0.0 , 0.0, Bl[0]),
(0.5-per, 1.0 , 1.0 ),
(0.5+per, 1.0 , 1.0 ),
(1.0 , Rd[0], 0.0 )),
'green': ((0.0 , 0.0, Bl[1]),
(0.5-per, 1.0 , 1.0 ),
(0.5+per, 1.0 , 1.0 ),
(1.0 , Rd[1], 0.0 )),
'blue': ((0.0 , 0.0, Bl[2]),
(0.5-per, 1.0 , 1.0 ),
(0.5+per, 1.0 , 1.0 ),
(1.0 , Rd[2], 0.0 ))}
plt.register_cmap(name='bias', data=RdBl)
cm = LinearSegmentedColormap.from_list("wetdry",[[0.545882,0.400392,0.176078],
[0.586667,0.440392,0.198824],
[0.627451,0.480392,0.221569],
[0.668235,0.520392,0.244314],
[0.709020,0.560392,0.267059],
[0.749804,0.600392,0.289804],
[0.790588,0.640392,0.312549],
[0.831373,0.680392,0.335294],
[0.872157,0.720392,0.358039],
[0.912941,0.760392,0.380784],
[0.921961,0.788039,0.399020],
[0.899216,0.803333,0.412745],
[0.876471,0.818627,0.426471],
[0.853725,0.833922,0.440196],
[0.830980,0.849216,0.453922],
[0.808235,0.864510,0.467647],
[0.785490,0.879804,0.481373],
[0.762745,0.895098,0.495098],
[0.740000,0.910392,0.508824],
[0.717255,0.925686,0.522549],
[0.680392,0.933333,0.549020],
[0.629412,0.933333,0.588235],
[0.578431,0.933333,0.627451],
[0.527451,0.933333,0.666667],
[0.476471,0.933333,0.705882],
[0.425490,0.933333,0.745098],
[0.374510,0.933333,0.784314],
[0.323529,0.933333,0.823529],
[0.272549,0.933333,0.862745],
[0.221569,0.933333,0.901961],
[0.188627,0.910196,0.922157],
[0.173725,0.863922,0.923333],
[0.158824,0.817647,0.924510],
[0.143922,0.771373,0.925686],
[0.129020,0.725098,0.926863],
[0.114118,0.678824,0.928039],
[0.099216,0.632549,0.929216],
[0.084314,0.586275,0.930392],
[0.069412,0.540000,0.931569],
[0.054510,0.493725,0.932745],
[0.052157,0.447255,0.922549],
[0.062353,0.400588,0.900980],
[0.072549,0.353922,0.879412],
[0.082745,0.307255,0.857843],
[0.092941,0.260588,0.836275],
[0.103137,0.213922,0.814706],
[0.113333,0.167255,0.793137],
[0.123529,0.120588,0.771569],
[0.133725,0.073922,0.750000],
[0.143922,0.027255,0.728431],
[0.143137,0.013725,0.703922],
[0.131373,0.033333,0.676471],
[0.119608,0.052941,0.649020],
[0.107843,0.072549,0.621569],
[0.096078,0.092157,0.594118],
[0.084314,0.111765,0.566667],
[0.072549,0.131373,0.539216],
[0.060784,0.150980,0.511765],
[0.049020,0.170588,0.484314],
[0.037255,0.190196,0.456863]])
plt.register_cmap("wetdry",cm)
def plot_global_figure(control_dataset, data):
surf_temp_diff = data['data']['1pct']['surf_temp'] - data['data']['ctrl']['surf_temp']
precip_diff = data['data']['1pct']['precip'] - data['data']['ctrl']['precip']
f = plt.figure('global_temp_precip')
f.subplots_adjust(hspace=0.35, wspace=0.28)
vmin, vmax = -4., 20.
zero_point = 1 - (vmax / (vmax - vmin))
p2 = 0.3
cdict = {'red': ((0.0, 0.3, 0.3),
(zero_point, 0.9, 0.9),
(p2, 0.8, 0.8),
(1.0, 0.7, 0.7)),
'green': ((0.0, 0.5, 0.5),
(zero_point, 0.9, 0.9),
(p2, 0.8, 0.8),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.8, 0.8),
(zero_point, 0.9, 0.9),
(p2, 0.2, 0.2),
(1.0, 0.0, 0.0))}
cust_st_cmap = LinearSegmentedColormap('cust_st', cdict)
plt.register_cmap(cmap=cust_st_cmap)
plt.set_cmap('cust_st')
ax1 = plt.subplot(5, 2, 1)
plt.title('$\Delta$ Surface Temp.(K)\nAnnual')
general_plot(control_dataset, surf_temp_diff.mean(axis=0), vmin, vmax, cbar_ticks=np.arange(-4, 21, 4))
ax2 = plt.subplot(5, 2, 3)
plt.title('DJF')
general_plot(control_dataset, np.roll(surf_temp_diff, 1, axis=0)[:3, :, :].mean(axis=0), vmin, vmax, cbar_ticks=np.arange(-4, 21, 4))
ax3 = plt.subplot(5, 2, 5)
plt.title('JJA')
general_plot(control_dataset, np.roll(surf_temp_diff, 7, axis=0)[:3, :, :].mean(axis=0), vmin, vmax, cbar_ticks=np.arange(-4, 21, 4))
ax4 = plt.subplot(5, 2, 2)
vmin, vmax = -4., 4.
zero_point = 1 - (vmax / (vmax - vmin))
cdict = {'blue': ((0.0, 0.1, 0.1),
(zero_point, 0.9, 0.9),
(1.0, 0.8, 0.8)),
'green': ((0.0, 0.4, 0.4),
(zero_point, 0.9, 0.9),
(1.0, 0.0, 0.0)),
'red': ((0.0, 0.8, 0.8),
(zero_point, 0.9, 0.9),
(1.0, 0.0, 0.0))}
cust_pr_cmap = LinearSegmentedColormap('cust_pr', cdict)
plt.register_cmap(cmap=cust_pr_cmap)
plt.title('$\Delta$ Precipitation (mm/day)\nAnnual')
general_plot(control_dataset, precip_diff.mean(axis=0), vmin, vmax, cbar_loc='right', cbar_ticks=np.arange(-4, 4.1, 2))
plt.set_cmap('cust_pr')
ax5 = plt.subplot(5, 2, 4)
plt.title('DJF')
general_plot(control_dataset, np.roll(precip_diff, 1, axis=0)[:3, :, :].mean(axis=0), vmin, vmax, cbar_loc='right', cbar_ticks=np.arange(-4, 4.1, 2))
ax6 = plt.subplot(5, 2, 6)
plt.title('JJA')
general_plot(control_dataset, np.roll(precip_diff, 7, axis=0)[:3, :, :].mean(axis=0), vmin, vmax, cbar_loc='right', cbar_ticks=np.arange(-4, 4.1, 2))
ax7 = plt.subplot(5, 2, 7)
plt.title('Seasonal mean temperature')
plt.plot(np.arange(0.5, 11.6, 1), surf_temp_diff.mean(axis=(1, 2)), 'r-')
months = [calendar.month_name[i + 1][:1] for i in range(12)]
ax7.set_xticks(np.arange(12) + 1./2)
ax7.set_xticklabels(months)
plt.ylim((1, 4))
ax7.set_yticks(np.arange(1, 4.1, 1))
ax7.set_ylabel('$\Delta$ Temp. (K)')
ax8 = plt.subplot(5, 2, 9)
plt.title('Zonal mean temperature')
plt.plot(np.arange(-90, 90, 180./73.), surf_temp_diff.mean(axis=(0, 2)), 'r-')
ax8.set_xticks(np.arange(-90, 91, 45))
plt.xlim((-90, 90))
plt.ylim((0, 8))
ax8.set_yticks(np.arange(0, 8.1, 2))
ax8.set_ylabel('$\Delta$ Temp. (K)')
def north_south_fmt(x, pos):
if x > 0:
return u'%1.0f°S'%abs(x)
elif x == 0:
return u'0°'
else:
return u'%1.0f°N'%abs(x)
#return '%1.0fN'%abs(x)
formatter = FuncFormatter(north_south_fmt)
ax8.xaxis.set_major_formatter(formatter)
ax9 = plt.subplot(5, 2, 8)
plt.title('Seasonal mean precip.')
plt.plot(np.arange(0.5, 11.6, 1), precip_diff.mean(axis=(1, 2)))
months = [calendar.month_name[i + 1][:1] for i in range(12)]
ax9.set_xticks(np.arange(12) + 1./2)
ax9.set_xticklabels(months)
plt.ylim((0.05, 0.15))
ax9.set_yticks(np.arange(0.05, 0.16, 0.05))
ax9.set_ylabel('$\Delta$ Precip. (mm/day)')
ax10 = plt.subplot(5, 2, 10)
plt.title('Zonal mean precip.')
plt.plot(np.arange(-90, 90, 180./73.), precip_diff.mean(axis=(0, 2)))
ax10.set_xticks(np.arange(-90, 91, 45))
plt.xlim((-90, 90))
plt.ylim((-1, 1))
ax10.set_yticks(np.arange(-1, 1.1, 1))
ax10.set_ylabel('$\Delta$ Precip. (mm/day)')
ax10.xaxis.set_major_formatter(formatter)
"""
This module provides a set of throwback colormaps.
"""
__author__ = "Alex Drlica-Wagner"
import pylab as plt
from root import root_cmap
from ds9 import ds9_b
# Register with matplotlib
plt.register_cmap(name='root', cmap=root_cmap)
plt.cm.root = plt.cm.get_cmap('root')
plt.register_cmap(name='ds9_b', data=ds9_b)
plt.cm.ds9_b = plt.cm.get_cmap('ds9_b')
# Register here
cmap_d = dict()
cmap_d['root'] = plt.cm.root
cmap_d['ds9_b'] = plt.cm.ds9_b
locals().update(cmap_d)
def simulate_splitter(layout,
wavelength=1.5,
num_mod=10,
discretization_res=30,
north=2.0,
plot=True,
aspect='equal'):
"""
Simulates a symmetric splitter and returns the transmission and reflection
Parameters
----------
layout : LayoutView
layoutview of the splitter
wavelength: float
Wavelength of simulation in um
num_mod: int
number_of_modes
discretization_res : int
discratization resolution of the structure.
north : northbound limit of the simulation
plot : bool
plot the fiels if True
Retunrs
--------
transmission,reflection
absolute value of transmission and reflection
"""
# simulate again for the optimum wavelength
camfr.set_lambda(wavelength)
camfr.set_N(num_mod) # we can see convergence when increasing the number of modes to 20, 30, ... : the result doesn't change
camfr.set_polarisation(camfr.TM) # TE in 3D is equivalent to TM in 2D
camfr.set_upper_PML(-0.05)
camfr.set_lower_wall(camfr.slab_E_wall)
camfr.set_backward_modes(True)
if len(layout.ports):
sim_window = i3.SizeInfo(west=layout.ports["W0"].position.x,
east=layout.ports["E0"].position.x,
south=-0.0,
north=north)
else:
si_layout = layout.size_info()
sim_window = i3.SizeInfo(west=si_layout.west,
east=si_layout.east,
south=-0.0,
north=north)
stack_expr = camfr_stack_expr_for_structure(structure=layout,
discretisation_resolution=discretization_res,
window_size_info=sim_window,
process_flow=i3.TECH.VFABRICATION.PROCESS_FLOW)
camfr_stack = camfr.Stack(stack_expr)
# run the simulation
# set incident field
inc = np.zeros(camfr.N())
inc[0] = 1
camfr_stack.set_inc_field(inc)
camfr.set_lower_wall(camfr.slab_E_wall)
camfr_stack.calc()
transmission = abs(camfr_stack.T12(0, 0))**2
reflection = abs(camfr_stack.R12(0, 0))**2
if plot:
# Create a coordinate mesh
grid_step = 0.05
x_coords = np.arange(0.0, sim_window.width, grid_step)
y_coords = np.arange(0.0, sim_window.height, grid_step)
x_mesh, y_mesh = np.meshgrid(x_coords, y_coords)
n = np.ndarray(shape=np.shape(x_mesh), dtype=np.complex)
#################
# Extracting and plotting field profiles
#################
fields = np.ndarray(shape=np.shape(x_mesh), dtype=np.complex)
for ix, x in enumerate(x_coords):
for iy, y in enumerate(y_coords):
coord = camfr.Coord(y, 0.0, x) # different coordinate system
fields[iy, ix] = camfr_stack.field(coord).H2()
n[iy, ix] = camfr_stack.n(coord)
# Make a red-blue colormap
rb_map = {'red': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0)),
}
pylab.register_cmap(name='RedBlue', data=rb_map)
# Plot real part of fields with red-blue colormap
####################
# Overlay refractive index on field plot
####################
real_field = np.real(fields)
limits = [-np.max(np.abs(real_field)), np.max(np.abs(real_field))]
pylab.figure()
pylab.contourf(x_mesh, y_mesh, real_field, 100, cmap='RedBlue', clim=limits)
pylab.contourf(x_mesh, y_mesh, np.real(n), cmap='Greys', alpha=0.1)
pylab.axis('image')
ax = pylab.gca()
ax.set_aspect(aspect)
pylab.show()
return (transmission, reflection)
def load_ds9_cmap():
plt.register_cmap(name='ds9_b', data=ds9_b)
plt.cm.ds9_b = plt.cm.get_cmap('ds9_b')
return plt.cm.ds9_b
idx = [ 0 ]
#
for path in paths:
try:
master = sampleset.generate(os.path.join(config.master_dir, path))
except IOError: # not an image?
print >> sys.stderr, "Cannot load '%s' - skipping" % path
continue
master = reduce_image(master, RESIZE_FACT)
if not os.path.exists(master.path):
master.save()
master = numpy.array(master.image)
masters.append(master)
palette = numpy.random.rand(4000, 3)
discrete_cmap = matplotlib.colors.ListedColormap(palette, name = 'discrete')
pylab.register_cmap(name = 'discrete', cmap = discrete_cmap)
if require('CALC_FEATURES'):
with Timer("Compute the features ..."):
features = [ ]
for master in masters:
feat = local_binary_pattern(master, 8, 1)
feat = feat.astype(int)
features.append(feat)
if require('FEATURE_HIST'):
with Timer("Compute the local feature histograms ..."):
cdfs = [ ]
for (master, feat) in zip(masters, features):
cdf = cum_counts(feat)
cdfs.append(cdf)
def RegisterCustomColormaps():
"""Adds some new colormaps to matplotlib's database.
"""
import colorsys as cs
# score colormap
cm = LinearSegmentedColormap.from_list(
"score",
[[0.84765625, 0.37109375, 0.0078125], [0.45703125, 0.4375, 0.69921875],
[0.10546875, 0.6171875, 0.46484375]])
plt.register_cmap("score", cm)
# bias colormap
val = 0.8
per = 0.2 / 2
Rd = cs.rgb_to_hsv(1, 0, 0)
Rd = cs.hsv_to_rgb(Rd[0], Rd[1], val)
Bl = cs.rgb_to_hsv(0, 0, 1)
Bl = cs.hsv_to_rgb(Bl[0], Bl[1], val)
RdBl = {
'red': ((0.0, 0.0, Bl[0]), (0.5 - per, 1.0, 1.0),
(0.5 + per, 1.0, 1.0), (1.0, Rd[0], 0.0)),
'green': ((0.0, 0.0, Bl[1]), (0.5 - per, 1.0, 1.0),
(0.5 + per, 1.0, 1.0), (1.0, Rd[1], 0.0)),
'blue': ((0.0, 0.0, Bl[2]), (0.5 - per, 1.0, 1.0),
(0.5 + per, 1.0, 1.0), (1.0, Rd[2], 0.0))
}
plt.register_cmap(cmap=LinearSegmentedColormap('bias', RdBl))
cm = LinearSegmentedColormap.from_list(
"wetdry",
[[0.545882, 0.400392, 0.176078], [0.586667, 0.440392, 0.198824],
[0.627451, 0.480392, 0.221569], [0.668235, 0.520392, 0.244314],
[0.709020, 0.560392, 0.267059], [0.749804, 0.600392, 0.289804],
[0.790588, 0.640392, 0.312549], [0.831373, 0.680392, 0.335294],
[0.872157, 0.720392, 0.358039], [0.912941, 0.760392, 0.380784],
[0.921961, 0.788039, 0.399020], [0.899216, 0.803333, 0.412745],
[0.876471, 0.818627, 0.426471], [0.853725, 0.833922, 0.440196],
[0.830980, 0.849216, 0.453922], [0.808235, 0.864510, 0.467647],
[0.785490, 0.879804, 0.481373], [0.762745, 0.895098, 0.495098],
[0.740000, 0.910392, 0.508824], [0.717255, 0.925686, 0.522549],
[0.680392, 0.933333, 0.549020], [0.629412, 0.933333, 0.588235],
[0.578431, 0.933333, 0.627451], [0.527451, 0.933333, 0.666667],
[0.476471, 0.933333, 0.705882], [0.425490, 0.933333, 0.745098],
[0.374510, 0.933333, 0.784314], [0.323529, 0.933333, 0.823529],
[0.272549, 0.933333, 0.862745], [0.221569, 0.933333, 0.901961],
[0.188627, 0.910196, 0.922157], [0.173725, 0.863922, 0.923333],
[0.158824, 0.817647, 0.924510], [0.143922, 0.771373, 0.925686],
[0.129020, 0.725098, 0.926863], [0.114118, 0.678824, 0.928039],
[0.099216, 0.632549, 0.929216], [0.084314, 0.586275, 0.930392],
[0.069412, 0.540000, 0.931569], [0.054510, 0.493725, 0.932745],
[0.052157, 0.447255, 0.922549], [0.062353, 0.400588, 0.900980],
[0.072549, 0.353922, 0.879412], [0.082745, 0.307255, 0.857843],
[0.092941, 0.260588, 0.836275], [0.103137, 0.213922, 0.814706],
[0.113333, 0.167255, 0.793137], [0.123529, 0.120588, 0.771569],
[0.133725, 0.073922, 0.750000], [0.143922, 0.027255, 0.728431],
[0.143137, 0.013725, 0.703922], [0.131373, 0.033333, 0.676471],
[0.119608, 0.052941, 0.649020], [0.107843, 0.072549, 0.621569],
[0.096078, 0.092157, 0.594118], [0.084314, 0.111765, 0.566667],
[0.072549, 0.131373, 0.539216], [0.060784, 0.150980, 0.511765],
[0.049020, 0.170588, 0.484314], [0.037255, 0.190196, 0.456863]])
plt.register_cmap("wetdry", cm)
