def run(n,F,A,temp,exp,psf_r,name="map",save=False):
""" Brings together serveral programs to run point source Monte Carlo by
reading in template, source count distribution parameters, exposure
map, and the user defined PSF.
:param n: numpy array of index values
:param F: numpy array of flux break values
:param A: float of log10 norm for the SCD
:param temp: HEALPix numpy array of template
:param exp: HEALPix numpy array of exposure map
:param psf_r: user defined point spread function
:param name: string for the name of output .npy file
:param save: option to save map to .npy file
:returns: HEALPix format numpy array of simulated map
"""
# Int. SCD to find mean couts, Poisson draws for # of sources in template
num_src = iscd.run(n,F,A,temp)
# Draws fluxes for each source from the SCD
flux_arr = cf.run(num_src,n,F)
# Generate simulated counts map
map_arr = np.asarray(mm.run(num_src,flux_arr,temp,exp,psf_r))
# Save the file as an .npy file
if save:
np.save(str(name) + ".npy",map_arr.astype(np.int32))
print "Done simulation."
return map_arr.astype(np.int32)
def run(n,
F,
A,
temp,
exp,
psf_r,
name="map",
save=False,
flux_frac=np.array([1.]),
r_ROI=np.nan,
returnpsdat=False):
""" Brings together serveral programs to run point source Monte Carlo by
reading in template, source count distribution parameters, exposure
map, and the user defined PSF.
:param n: numpy array of index values, highest to lowest
:param F: numpy array of flux break values, highest to lowest
:param A: float of log10 norm for the SCD
:param temp: HEALPix numpy array of template
:param exp: HEALPix numpy array of exposure map
:param psf_r: user defined point spread function
:param name: string for the name of output .npy file
:param save: option to save map to .npy file
:param flux_frac: array of flux fractions to distribute between
different energy bins, default is 1 bin
:params r_ROI: maximum distance to draw sources from the galactic
center
:params returnpsdat: if true return data on the individual sources
format: [theta, phi, actual counts, expected counts, expected flux]
:returns: HEALPix format numpy array of simulated map
"""
# If exposure map is a 1D array, wrap it again so it has 1 energy bin
if len(np.shape(exp)) == 1:
exp = np.array([exp])
# Check exposure map and flux frac have same number of energy bins
assert(len(exp) == len(flux_frac)), \
"exposure and flux fraction must have the same number of energy bins"
# Check if flux breaks in correct order
if len(F) > 1:
assert(F[0] > F[-1]), \
"Flux array is in the wrong order, highest to lowest!"
# Int. SCD to find mean couts, Poisson draws for # of sources in template
num_src = iscd.run(n, F, A, temp)
# Draws fluxes for each source from the SCD
flux_arr = cf.run(num_src, n, F)
# Generate simulated counts map
map_arr, psdat = np.asarray(
mm.run(num_src, flux_arr, temp, exp, psf_r, flux_frac, r_ROI,
returnpsdat))
# Save the file as an .npy file
if save:
np.save(str(name) + ".npy", np.array(map_arr, dtype=np.int32))
print("Done simulation.")
if returnpsdat:
return np.array(map_arr, dtype=np.int32), np.array(psdat,
dtype=np.float)
else:
return np.array(map_arr, dtype=np.int32)
def heatmap():
import make_map
make_map.run()
return render_template('mymap.html')
def heatmap():
import make_map
make_map.run()
return render_template('mymap.html')