def helixmap():
# Construct our own cubehelix color map.
from matplotlib._cm import cubehelix
ch_data=cubehelix(1.0, 0.5, -1.5, 0.0)
ch_data=cm.revcmap(ch_data)
helix_map=colors.LinearSegmentedColormap('helix', ch_data, 256)
return helix_map
def calculate_em(self, wlen='171', dz=100, model=False):
"""
Calculate an approximation of the coronal EmissionMeasure using a given
TemperatureMap object and a particular AIA channel.
Parameters
----------
tmap : CoronaTemps.temperature.TemperatureMap
A TemperatureMap instance containing coronal temperature data
wlen : {'94' | '131' | '171' | '193' | '211' | '335'}
AIA wavelength used to approximate the emission measure. '171', '193'
and '211' are most likely to provide reliable results. Use of other
channels is not recommended.
"""
# Load the appropriate temperature response function
tresp = read('/imaps/holly/home/ajl7/CoronaTemps/aia_tresp')
resp = tresp['resp{}'.format(wlen)]
# Get some information from the TemperatureMap and set up filenames, etc
tempdata = self.data.copy()
tempdata[np.isnan(tempdata)] = 0.0
date = sunpy.time.parse_time(self.date)
if not model:
data_dir = self.data_dir
fits_dir = path.join(data_dir, '{:%Y/%m/%d}/{}'.format(date, wlen))
filename = path.join(fits_dir,
'*{0:%Y?%m?%d}?{0:%H?%M}*fits'.format(date))
if wlen == '94': filename = filename.replace('94', '094')
# Load and appropriately process AIA data
filelist = glob.glob(filename)
if filelist == []:
print 'AIA data not found :('
return
aiamap = Map(filename)
aiamap.data /= aiamap.exposure_time
aiamap = aiaprep(aiamap)
aiamap = aiamap.submap(self.xrange, self.yrange)
else:
fname = '/imaps/holly/home/ajl7/CoronaTemps/data/synthetic/{}/model.fits'.format(wlen)
if wlen == '94': fname = fname.replace('94', '094')
aiamap = Map(fname)
# Create new Map and put EM values in it
emmap = Map(self.data.copy(), self.meta.copy())
indices = np.round((tempdata - 4.0) / 0.05).astype(int)
indices[indices < 0] = 0
indices[indices > 100] = 100
#print emmap.shape, indices.shape, tempdata.shape, aiamap.shape, resp.shape
emmap.data = np.log10(aiamap.data / resp[indices])
#emmap.data = aiamap.data / resp[indices]
emmapcubehelix = _cm.cubehelix(s=2.8, r=-0.7, h=1.4, gamma=1.0)
cm.register_cmap(name='emhelix', data=emmapcubehelix)
emmap.cmap = cm.get_cmap('emhelix')
return emmap
def get_linecolors_from_cubehelix(N, gamma=0.8, hue=3.0, rot=1.8, start=-0.30):
"""Return N colors from the cubehelix for plots
"""
rgb_dict = cubehelix(h=hue, r=rot, gamma=gamma, s=start)
brightness = np.linspace(0.0, 1.0, num=N, endpoint=False)
rgb_list = [(rgb_dict['red'](b), rgb_dict['green'](b), rgb_dict['blue'](b))
for b in brightness]
ret = [(_norm(a), _norm(b), _norm(c)) for a, b, c in rgb_list]
return ret
def get_linecolors_from_cubehelix(N, gamma=0.8, hue=3.0, rot=1.8, start=-0.30):
"""Return N colors from the cubehelix for plots
"""
rgb_dict = cubehelix(h=hue, r=rot, gamma=gamma, s=start)
brightness = np.linspace(0.0, 1.0, num=N, endpoint=False)
rgb_list = [(rgb_dict['red'](b), rgb_dict['green'](b),
rgb_dict['blue'](b)) for b in brightness]
ret = [(_norm(a), _norm(b), _norm(c)) for a, b, c in rgb_list]
return ret
from matplotlib.colors import LinearSegmentedColormap
def generate_cmap_flame():
clrs = [(1, 1, 1), (0, 0.3, 1), (0, 1, 1), (0, 1, 0.3), (1, 1, 0),
(1, 0.5, 0), (1, 0, 0), (0.5, 0, 0)]
flame = LinearSegmentedColormap.from_list('flame', clrs)
#flame.set_bad(flame(0)) # set nan's and inf's to white
flame.set_bad('0.75') # set nan's and inf's to light gray
return flame
flame = generate_cmap_flame()
cubehelix = LinearSegmentedColormap('cubehelix',
cm.revcmap(_cm.cubehelix(1, 0, 1, 2.5)))
cubehelix.set_bad(cubehelix(0))
def cmap_powerlaw_adjust(cmap, a):
'''
returns a new colormap based on the one given
but adjusted via power-law:
newcmap = oldcmap**a
'''
if a < 0.:
return cmap
cdict = copy(cmap._segmentdata)
def fn(x):
import sunpy
from sunpy.map import Map
import numpy as np
from matplotlib import cm, _cm, rc, patches
rc('savefig', bbox='tight', pad_inches=0.5)
import matplotlib.pyplot as plt
import sys
from os.path import join, expanduser
sys.path.append(expanduser('~/CoronaTemps/'))
from temperature import TemperatureMap as TMap
from astropy import units as u
from scipy.interpolate import RegularGridInterpolator as rgi
from itertools import product
emmapcubehelix = _cm.cubehelix(s=2.8, r=-0.7, h=1.4, gamma=1.0)
cm.register_cmap(name='emhelix', data=emmapcubehelix)
emcm = cm.get_cmap('emhelix')
# Load a temperature map and get the EM data out of it
tmap = TMap('2011-02-15',
maps_dir=expanduser('~/coronal-backprojection/'),
n_params=3)
EMmap = Map(10.0**tmap.emission_measure, tmap.meta.copy())
EMlog = Map(tmap.emission_measure, tmap.meta.copy())
print np.nanmin(EMlog.data), np.nanmax(EMlog.data)
fig = plt.figure(figsize=(32, 24))
EMlog.plot(cmap=emcm)
plt.colorbar()
plt.savefig('input-em')
plt.close()
import sunpy
from sunpy.map import Map
import numpy as np
from matplotlib import cm, _cm, rc, patches
rc('savefig', bbox='tight', pad_inches=0.5)
import matplotlib.pyplot as plt
import sys
from os.path import join, expanduser
sys.path.append(expanduser('~/CoronaTemps/'))
from temperature import TemperatureMap as TMap
from astropy import units as u
from scipy.interpolate import RegularGridInterpolator as rgi
from itertools import product
emmapcubehelix = _cm.cubehelix(s=2.8, r=-0.7, h=1.4, gamma=1.0)
cm.register_cmap(name='emhelix', data=emmapcubehelix)
emcm = cm.get_cmap('emhelix')
# Load a temperature map and get the EM data out of it
tmap = TMap('2011-02-15', maps_dir=expanduser('~/coronal-backprojection/'),
n_params=3)
EMmap = Map(10.0**tmap.emission_measure, tmap.meta.copy())
EMlog = Map(tmap.emission_measure, tmap.meta.copy())
print np.nanmin(EMlog.data), np.nanmax(EMlog.data)
fig = plt.figure(figsize=(32, 24))
EMlog.plot(cmap=emcm)
plt.colorbar()
plt.savefig('input-em')
plt.close()
# Define ranges of reconstruction space
(0,0.3,1),
(0,1,1),
(0,1,0.3),
(1,1,0),
(1,0.5,0),
(1,0,0),
(0.5,0,0)
]
flame = LinearSegmentedColormap.from_list('flame', clrs)
#flame.set_bad(flame(0)) # set nan's and inf's to white
flame.set_bad('0.75') # set nan's and inf's to light gray
return flame
flame = generate_cmap_flame()
cubehelix = LinearSegmentedColormap('cubehelix',cm.revcmap(_cm.cubehelix(1,0,1,2.5)))
cubehelix.set_bad(cubehelix(0))
def cmap_powerlaw_adjust(cmap, a):
'''
returns a new colormap based on the one given
but adjusted via power-law:
newcmap = oldcmap**a
'''
if a < 0.:
return cmap
cdict = copy(cmap._segmentdata)
def fn(x):
return (x[0]**a, x[1], x[2])
for key in ('red','green','blue'):
def __init__(self, date=None, n_params=1, data_dir=None, maps_dir=None,
fname=None, infofile=None, submap=None, verbose=False,
force_temp_scan=False):
if (not fname and not date) or (fname and date):
print """You must specify either a date and time for which to create
temperatures or the name of a file containing a valid
TemperatureMap object."""
return
if date:
date = sunpy.time.parse_time(date)
if data_dir is None:
data_dir = '/media/huw/SDO_data/'
if maps_dir is None:
maps_dir='/media/huw/temperature_maps/{}pars/'.format(n_params)
fname = path.join(maps_dir, '{:%Y-%m-%dT%H_%M_%S}.fits'.format(date))
if infofile:
data_dir = None
maps_dir = open(infofile).readline()[:-1]
fname = path.join(maps_dir, '{:%Y-%m-%dT%H:%M:%S}.fits'.format(date))
fname.replace('/images/', '/data/')
if n_params != 1:
fname = fname.replace('.fits', '_full.fits')
if fname and not date:
data_dir = path.dirname(fname)
if verbose: print fname, data_dir
try:
newmap = Map(fname)
GenericMap.__init__(self, newmap.data[..., 0], newmap.meta)
self.goodness_of_fit = newmap.data[..., -1]
if newmap.data.shape[2] != 2:
self.dem_width = newmap.data[..., 1]
self.emission_measure = newmap.data[..., 2]
except ValueError:
cmdargs = ["python", path.join(cortemps, 'create_tempmap.py'),
date, n_params, data_dir, infofile, submap, verbose, force_temp_scan]
if n_params != 1:
cmdargs = ["mpiexec", "-n", 16] + cmdargs
cmdargs = [str(cmd) for cmd in cmdargs]
status = subp.call(cmdargs)
newmap = Map(path.join(cortemps, 'temporary.fits'))
subp.call(["rm", path.join(cortemps, 'temporary.fits')])
data, meta = newmap.data, newmap.meta
if verbose: print data.shape
GenericMap.__init__(self, data[..., 0], meta)
if data.shape[2] != 2:
data[data == 0] = np.nan
self.dem_width = data[..., 1]
self.emission_measure = data[..., 2]
self.goodness_of_fit = data[..., -1]
if verbose:
print self.shape
print self.goodness_of_fit.shape
if n_params != 1:
print self.dem_width.shape
print self.emission_measure.shape
lowx, highx = (self.xrange[0] / self.scale['x'],
self.xrange[1] / self.scale['x'])
lowy, highy = (self.yrange[0] / self.scale['y'],
self.yrange[1] / self.scale['y'])
x_grid, y_grid = np.mgrid[lowx:highx-1, lowy:highy-1]
r_grid = np.sqrt((x_grid ** 2.0) + (y_grid ** 2.0))
outer_rad = (self.rsun_arcseconds * 1.5) / self.scale['x']
self.data[r_grid > outer_rad] = None
self.meta['date-obs'] = str(date)
tmapcubehelix = _cm.cubehelix(s=2.8, r=0.7, h=2.0, gamma=1.0)
cm.register_cmap(name='temphelix', data=tmapcubehelix)
self.cmap = cm.get_cmap('temphelix')
self.data_dir = data_dir
self.maps_dir = maps_dir
self.temperature_scale = 'log'
self.region = None
self.region_coordinate = {'x': 0.0, 'y': 0.0}
if n_params == 3:
self.n_params = 3
else:
self.n_params = 1
return
After importing, these color maps can be used in exactly the
same way as matplotlib's internal color maps.
Each new color map is a variant on the cubhelix color map
with no rotation in color space, each one passing thorugh
a different color. The _l versions have more color variation
for smaller values than larger values.
Usage:
from densityplot import *
from pylab import *
new_colormap = cm.cubehelix_purple
new_colormap_reverse = cm.cubehelix_purple_r
"""
from matplotlib import _cm as c
c.datad['cubehelix_light'] = c.cubehelix(gamma=0.6)
c.datad['cubehelix_purple'] = c.cubehelix(gamma=0.6, s=0.5, r=0.0, h=1.0)
c.datad['cubehelix_red'] = c.cubehelix(gamma=0.6, s=1.0, r=0.0, h=1.0)
c.datad['cubehelix_yellow'] = c.cubehelix(gamma=0.6, s=1.5, r=0.0, h=1.0)
c.datad['cubehelix_green'] = c.cubehelix(gamma=0.6, s=2.0, r=0.0, h=1.0)
c.datad['cubehelix_teal'] = c.cubehelix(gamma=0.6, s=2.5, r=0.0, h=1.0)
c.datad['cubehelix_blue'] = c.cubehelix(gamma=0.6, s=3.0, r=0.0, h=1.0)
c.datad['cubehelix_purple_l'] = c.cubehelix(gamma=0.4, s=0.5, r=0.0, h=1.5)
c.datad['cubehelix_red_l'] = c.cubehelix(gamma=0.4, s=1.0, r=0.0, h=1.5)
c.datad['cubehelix_yellow_l'] = c.cubehelix(gamma=0.4, s=1.5, r=0.0, h=1.5)
c.datad['cubehelix_green_l'] = c.cubehelix(gamma=0.4, s=2.0, r=0.0, h=1.5)
c.datad['cubehelix_teal_l'] = c.cubehelix(gamma=0.4, s=2.5, r=0.0, h=1.5)
c.datad['cubehelix_blue_l'] = c.cubehelix(gamma=0.4, s=3.0, r=0.0, h=1.5)