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
# Load AIA response functions
resp = load_temp_responses()
# Load unnessecary map for its metadata
voidmap = Map(sunpy.AIA_171_IMAGE)
mapmeta = voidmap.meta
#rect = patches.Rectangle([25.0, 5.6], 1.0, 1.0, color='black', fill=True, clip_on=False)
# Run synthetic data through 1param tempmap method
for w, wid in enumerate(widths):#heights):
print '\nWidth:', wid
fig = plt.figure(figsize=(30, 12))
for wl, wlength in enumerate(['94', '131', '171', '193', '211', '335']):
#emiss = Map(emission[wl, :, :, w], mapmeta)
emiss = Map(emission[wl, :, w, :].copy(), mapmeta)
emiss.cmap = sunpy.cm.get_cmap('sdoaia{}'.format(wlength))
emiss.meta['naxis1'] = emiss.shape[1]
emiss.meta['naxis2'] = emiss.shape[0]
#emiss.meta['cdelt1'] = widths[1] - widths[0]
emiss.meta['cdelt1'] = heights[1] - heights[0] #np.log10(heights[1]) - np.log10(heights[0])
emiss.meta['cdelt2'] = temps[1] - temps[0]
#emiss.meta['crval1'] = widths[0]
emiss.meta['crval1'] = heights[0] #np.log10(heights[0])
emiss.meta['crval2'] = temps[0]
emiss.meta['crpix1'] = 0.5
emiss.meta['crpix2'] = 0.5
if wlength == '94': wlength = '094'
fits_dir = path.join(CThome, 'data', 'synthetic', wlength)
if not path.exists(fits_dir): makedirs(fits_dir)
emiss.save(path.join(fits_dir, 'model.fits'), clobber=True)
#print '----', emission[2, :, :, :].min(), emission[2, :, :, :].max()
