def find_temp(images,
t0=5.6,
force_temp_scan=False,
maps_dir=home + 'temperature_maps/'):
x, y = images[0].shape
n_wlens = len(images)
n_temps = int((7.0 - t0) / 0.01) + 1
temp = np.arange(t0, 7.01, 0.01)
try:
if force_temp_scan:
raise IOError
model = np.memmap(filename=home + 'synth_emiss_1pars',
dtype='float32',
mode='r',
shape=(n_temps, n_wlens))
except IOError:
print 'No synthetic emission data found. Re-scanning temperature range.'
resp = load_temp_responses()
logt = np.arange(0, 15.05, 0.05)
# Assume a width of the gaussian DEM distribution and normalise the height
width = 0.1
height = 1.0
delta_t = logt[1] - logt[0]
model = np.memmap(filename=home + 'synth_emiss_1pars',
dtype='float32',
mode='w+',
shape=(n_temps, n_wlens))
for t, meantemp in enumerate(temp):
dem = gaussian(logt, meantemp, width, height)
f = resp * dem
model[t, :] = np.sum(f, axis=1) * delta_t ### CHECK THIS AXIS!
normmod = model[t, 2]
model[t, :] = model[t, :] / normmod
model.flush()
ims_array = np.array([im.data for im in images])
print 'Calculating temperature values...',
temps, fits = calc_fits(ims_array, model, temp, n_temps, n_wlens, x, y, 1)
print 'Done.'
tempmap = temps[:, :, 0], images[2].meta.copy(), fits
# TODO: figure out how to change things in the header and save them.
return tempmap
def find_temp(images, t0=5.6, force_temp_scan=False, maps_dir=home+'temperature_maps/'):
x, y = images[0].shape
n_wlens = len(images)
n_temps = int((7.0 - t0) / 0.01) + 1
temp = np.arange(t0, 7.01, 0.01)
try:
if force_temp_scan:
raise IOError
model = np.memmap(filename=home+'synth_emiss_1pars', dtype='float32',
mode='r', shape=(n_temps, n_wlens))
except IOError:
print 'No synthetic emission data found. Re-scanning temperature range.'
resp = load_temp_responses()
logt = np.arange(0, 15.05, 0.05)
# Assume a width of the gaussian DEM distribution and normalise the height
width = 0.1
height = 1.0
delta_t = logt[1] - logt[0]
model = np.memmap(filename=home+'synth_emiss_1pars', dtype='float32',
mode='w+', shape=(n_temps, n_wlens))
for t, meantemp in enumerate(temp):
dem = gaussian(logt, meantemp, width, height)
f = resp * dem
model[t, :] = np.sum(f, axis=1) * delta_t ### CHECK THIS AXIS!
normmod = model[t, 2]
model[t, :] = model[t, :] / normmod
model.flush()
ims_array = np.array([im.data for im in images])
print 'Calculating temperature values...',
temps, fits = calc_fits(ims_array, model, temp, n_temps, n_wlens, x, y, 1)
print 'Done.'
tempmap = temps[:, :, 0], images[2].meta.copy(), fits
# TODO: figure out how to change things in the header and save them.
return tempmap
if verbose:
print model.max(axis=0)
else:
model = None
model = comm.bcast(model, root=0)
if verbose:
if rank == 0:
print "Calculating temperature values..."
print rank, images.shape, model.shape, parvals.shape, n_vals, n_wlens, x, y, n_params
print [im.max() for im in images]
print model.max(axis=0)
if n_params == 1:
parvals = parvals[:, 0]
temps = calc_fits(images, model, parvals, n_vals, n_wlens, x, y, n_params)
# Convert EM values to log scale if there are any
if temps.shape[2] > 2:
temps[..., 2] = np.log10(temps[..., 2])
if verbose:
print "Done."
# Get data all back in one place and save it
temps = comm.gather(temps, root=0)
if rank == 0:
if verbose:
print len(temps), temps[0].shape
temp = np.zeros(shape=(x, y * size, n_params + 1))
for p in range(size):
mini = (p / size) * temp.shape[1]
maxi = ((p + 1) / size) * temp.shape[1]
