"""
This module provides a set of colormaps specific for solar data.
"""
from copy import deepcopy
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import astropy.units as u
from sunpy.visualization.colormaps import color_tables as ct
__all__ = ['show_colormaps', 'cmlist']
sdoaia94 = ct.aia_color_table(94*u.angstrom)
sdoaia131 = ct.aia_color_table(131*u.angstrom)
sdoaia171 = ct.aia_color_table(171*u.angstrom)
sdoaia193 = ct.aia_color_table(193*u.angstrom)
sdoaia211 = ct.aia_color_table(211*u.angstrom)
sdoaia304 = ct.aia_color_table(304*u.angstrom)
sdoaia335 = ct.aia_color_table(335*u.angstrom)
sdoaia1600 = ct.aia_color_table(1600*u.angstrom)
sdoaia1700 = ct.aia_color_table(1700*u.angstrom)
sdoaia4500 = ct.aia_color_table(4500*u.angstrom)
sohoeit171 = ct.eit_color_table(171*u.angstrom)
sohoeit195 = ct.eit_color_table(195*u.angstrom)
sohoeit284 = ct.eit_color_table(284*u.angstrom)
sohoeit304 = ct.eit_color_table(304*u.angstrom)
# Solar Orbiter EUI
# These are deliberately the same as AIA
solohri_euv174 = deepcopy(ct.aia_color_table(171*u.angstrom))
solohri_euv174.name = 'SolO EUI HRI 174 angstrom'
solofsi174 = deepcopy(ct.aia_color_table(171*u.angstrom))
solofsi174.name = 'SolO EUI FSI 174 angstrom'
solofsi304 = deepcopy(ct.aia_color_table(304*u.angstrom))
solofsi304.name = 'SolO EUI FSI 304 angstrom'
# Lyman alpha is a modified IDL red color table
solohri_lya1216 = ct.solohri_lya1216_color_table()
goesrsuvi94 = ct.suvi_color_table(94*u.angstrom)
goesrsuvi131 = ct.suvi_color_table(131*u.angstrom)
goesrsuvi171 = ct.suvi_color_table(171*u.angstrom)
goesrsuvi195 = ct.suvi_color_table(195*u.angstrom)
goesrsuvi284 = ct.suvi_color_table(284*u.angstrom)
goesrsuvi304 = ct.suvi_color_table(304*u.angstrom)
# The color tables below returns one of the fundamental color tables for SOHO
# LASCO images. These are not the same as those used in SSWIDL. This is
# because the SSWIDL color scaling for LASCO level 0.5 and 1.0 is highly
# compressed and does not display the data well.
soholasco2 = deepcopy(matplotlib.colormaps["gist_heat"])
soholasco2.name = 'SOHO LASCO C2'
soholasco3 = deepcopy(matplotlib.colormaps["bone"])
soholasco3.name = 'SOHO LASCO C3'
# These are the SSWIDL color tables.
sswidlsoholasco2 = ct.sswidl_lasco_color_table(2)
sswidlsoholasco3 = ct.sswidl_lasco_color_table(3)
stereocor1 = ct.cor_color_table(1)
stereocor2 = ct.cor_color_table(2)
stereohi1 = ct.stereo_hi_color_table(1)
stereohi2 = ct.stereo_hi_color_table(2)
yohkohsxtal = ct.sxt_color_table('al')
yohkohsxtwh = ct.sxt_color_table('wh')
hinodexrt = ct.xrt_color_table()
hinodesotintensity = ct.sot_color_table('intensity')
trace171 = ct.trace_color_table('171')
trace195 = ct.trace_color_table('195')
trace284 = ct.trace_color_table('284')
trace1216 = ct.trace_color_table('1216')
trace1550 = ct.trace_color_table('1550')
trace1600 = ct.trace_color_table('1600')
trace1700 = ct.trace_color_table('1700')
traceWL = ct.trace_color_table('WL')
hmimag = ct.hmi_mag_color_table()
kcor = deepcopy(matplotlib.colormaps["gist_gray"])
kcor.name = 'MLSO KCor'
rhessi = ct.rhessi_color_table()
std_gamma_2 = ct.std_gamma_2()
euvi195 = ct.euvi_color_table(195*u.angstrom)
euvi284 = ct.euvi_color_table(284*u.angstrom)
euvi304 = ct.euvi_color_table(304*u.angstrom)
euvi171 = ct.euvi_color_table(171*u.angstrom)
cmlist = {
'goes-rsuvi94': goesrsuvi94,
'goes-rsuvi131': goesrsuvi131,
'goes-rsuvi171': goesrsuvi171,
'goes-rsuvi195': goesrsuvi195,
'goes-rsuvi284': goesrsuvi284,
'goes-rsuvi304': goesrsuvi304,
'sdoaia94': sdoaia94,
'sdoaia131': sdoaia131,
'sdoaia171': sdoaia171,
'sdoaia193': sdoaia193,
'sdoaia211': sdoaia211,
'sdoaia304': sdoaia304,
'sdoaia335': sdoaia335,
'sdoaia1600': sdoaia1600,
'sdoaia1700': sdoaia1700,
'sdoaia4500': sdoaia4500,
'sohoeit171': sohoeit171,
'sohoeit195': sohoeit195,
'sohoeit284': sohoeit284,
'sohoeit304': sohoeit304,
'soholasco2': soholasco2,
'soholasco3': soholasco3,
'sswidlsoholasco2': sswidlsoholasco2,
'sswidlsoholasco3': sswidlsoholasco3,
'stereocor1': stereocor1,
'stereocor2': stereocor2,
'stereohi1': stereohi1,
'stereohi2': stereohi2,
'yohkohsxtal': yohkohsxtal,
'yohkohsxtwh': yohkohsxtwh,
'hinodexrt': hinodexrt,
'hinodesotintensity': hinodesotintensity,
'trace171': trace171,
'trace195': trace195,
'trace284': trace284,
'trace1216': trace1216,
'trace1550': trace1550,
'trace1600': trace1600,
'trace1700': trace1700,
'traceWL': traceWL,
'hmimag': hmimag,
'irissji1330': ct.iris_sji_color_table('1330'),
'irissji1400': ct.iris_sji_color_table('1400'),
'irissji1600': ct.iris_sji_color_table('1600'),
'irissji2796': ct.iris_sji_color_table('2796'),
'irissji2832': ct.iris_sji_color_table('2832'),
'irissji5000': ct.iris_sji_color_table('5000'),
'irissjiFUV': ct.iris_sji_color_table('FUV'),
'irissjiNUV': ct.iris_sji_color_table('NUV'),
'irissjiSJI_NUV': ct.iris_sji_color_table('SJI_NUV'),
'kcor': kcor,
'rhessi': rhessi,
'std_gamma_2': std_gamma_2,
'euvi171': euvi171,
'euvi195': euvi195,
'euvi284': euvi284,
'euvi304': euvi304,
'solar orbiterfsi174': solofsi174,
'solar orbiterfsi304': solofsi304,
'solar orbiterhri_euv174': solohri_euv174,
'solar orbiterhri_lya1216': solohri_lya1216,
}
# Register the colormaps with matplotlib so matplotlib.colormaps['sdoaia171'] works
for name, cmap in cmlist.items():
matplotlib.colormaps.register(cmap, name=name)
[docs]
def show_colormaps(search=None):
"""
Displays a plot of the custom color maps supported in SunPy.
Parameters
----------
search : str
A string to search for in the names of the color maps (e.g. aia, EIT,
171). Case insensitive.
Examples
--------
>>> import sunpy.visualization.colormaps as cm
>>> cm.show_colormaps() # doctest: +IGNORE_WARNINGS
>>> cm.show_colormaps(search='aia') # doctest: +IGNORE_WARNINGS
>>> cm.show_colormaps(search='171') # doctest: +IGNORE_WARNINGS
"""
if search is not None:
maps = sorted({k: v for (k, v) in cmlist.items() if k.lower().count(search.lower())})
if len(maps) == 0:
raise KeyError(f'No color maps found for search term "{search:s}"')
else:
maps = sorted(cmlist)
nmaps = len(maps) + 1
a = np.linspace(0, 1, 256).reshape(1, -1)
a = np.vstack((a, a))
fig = plt.figure(figsize=(7, 10), dpi=128)
fig.subplots_adjust(top=0.99, bottom=0.01, left=0.3, right=0.99)
for i, name in enumerate(maps):
ax = plt.subplot(nmaps, 1, i + 1)
ax.imshow(a, aspect='auto', cmap=name, origin='lower')
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
ax.set_ylabel(name, fontsize=10, horizontalalignment='right', verticalalignment='center', rotation=0)
plt.show()
