def _generate_cmap(name, lutsize):
"""Generates the requested cmap from it's name *name*. The lut size is
*lutsize*."""
spec = datad[name]
# Generate the colormap object.
if 'red' in spec:
return colors.LinearSegmentedColormap(name, spec, lutsize)
else:
return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)
LUTSIZE = mpl.rcParams['image.lut']
_cmapnames = datad.keys() # need this list because datad is changed in loop
# Generate the reversed specifications ...
for cmapname in _cmapnames:
spec = datad[cmapname]
spec_reversed = _reverse_cmap_spec(spec)
datad[cmapname + '_r'] = spec_reversed
# Precache the cmaps with ``lutsize = LUTSIZE`` ...
# Use datad.keys() to also add the reversed ones added in the section above:
for cmapname in datad.iterkeys():
cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)
locals().update(cmap_d)
def _generate_cmap(name, lutsize):
"""Generates the requested cmap from it's name *name*. The lut size is
*lutsize*."""
spec = datad[name]
# Generate the colormap object.
if 'red' in spec:
return colors.LinearSegmentedColormap(name, spec, lutsize)
else:
return colors.LinearSegmentedColormap.from_list(spec, spec, lutsize)
LUTSIZE = mpl.rcParams['image.lut']
_cmapnames = datad.keys() # need this list because datad is changed in loop
# Generate the reversed specifications ...
for cmapname in _cmapnames:
spec = datad[cmapname]
spec_reversed = _reverse_cmap_spec(spec)
datad[cmapname + '_r'] = spec_reversed
# Precache the cmaps with ``lutsize = LUTSIZE`` ...
# Use datad.keys() to also add the reversed ones added in the section above:
for cmapname in datad.keys():
cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)
locals().update(cmap_d)
import netCDF4 as nc
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation
from matplotlib._cm_listed import cmaps as cmaps_listed # dict of colors with color objects
from matplotlib._cm import datad # dict of colors with actual maps
pdsi = nc.Dataset('f:/data/droughtindices/netcdfs/pdsi.nc')
array = pdsi.variables['value'][:]
dates = pdsi.variables['time'][:]
base = dt.datetime(1900, 1, 1)
dates2 = [base + dt.timedelta(d) for d in dates]
dates3 = ['PDSI: ' + dt.datetime.strftime(d, "%Y-%m") for d in dates2]
ckeys = list(cmaps_listed.keys())
[ckeys.insert(0, d) for d in list(datad.keys())]
ckeys.sort()
# Matplotlib:
def movie(array, titles=None, axis=0):
'''
if the time axis is not 0, specify which it is.
'''
if titles is None:
titles = ["" for t in range(len(array))]
if type(titles) is str:
titles = [titles + ': ' + str(t) for t in range(len(array))]
fig, ax = plt.subplots()
# 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
# 'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
def cmap_fun(cm):
def wrapper(x, alpha=None, bytes=False, lut=None, clim=None, norm=None):
cmap = plt.get_cmap(cm, lut=lut)
if norm is None:
clim = clim or (0., 1.)
norm = plt.Normalize(vmin=clim[0], vmax=clim[1])
x = norm(x)
return cmap(x, alpha=alpha, bytes=bytes)
return wrapper
# explicte declaration (for example)
def jet(x, alpha=None, bytes=False, lut=None, clim=None, norm=None):
return cmap_fun('jet')(x,
alpha=alpha,
bytes=bytes,
lut=lut,
clim=clim,
norm=norm)
# fill functions
locals().update({cm: cmap_fun(cm) for cm in datad.keys()})
locals().update({cm + '_r': cmap_fun(cm + '_r') for cm in datad.keys()})
"""Generates the requested cmap from it's name *name*. The lut size is
*lutsize*."""
spec = datad[name]
# Generate the colormap object.
if 'red' in spec:
return colors.LinearSegmentedColormap(name, spec, lutsize)
else:
return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)
LUTSIZE = mpl.rcParams['image.lut']
_cmapnames = list(
datad.keys()) # need this list because datad is changed in loop
# Generate the reversed specifications ...
for cmapname in _cmapnames:
spec = datad[cmapname]
spec_reversed = _reverse_cmap_spec(spec)
datad[cmapname + '_r'] = spec_reversed
# Precache the cmaps with ``lutsize = LUTSIZE`` ...
# Use datad.keys() to also add the reversed ones added in the section above:
for cmapname in datad.keys():
cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)
locals().update(cmap_d)
import re
import copy
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib._cm import datad
from mpl_toolkits.mplot3d import Axes3D
from .generic_utils import handleKeyError
from .metric_utils import silhouette_samples
from .metric_utils import silhouette_score
ALL_CMAP_NAMES = [
name for name in datad.keys()
if not re.search(r".*_r", name, re.IGNORECASE)
]
COLOR_TYPES = ["rgba", "rgb", "hex"]
def rgb2hex(rgb, max_val=1):
return "#" + "".join([format(int(255 / max_val * e), '02x')
for e in rgb]).upper()
def rgba2rgb(rgba, max_val=1):
alpha = rgba[-1]
rgb = rgba[:-1]
type_ = int if max_val == 255 else float
# compute the color as alpha against white