def process(sigma, field, name):
# output folder
folder = 'data/' + name
if not os.path.exists(folder):
os.makedirs(folder)
# tabulated energies, limit to energies where the interaction is possible
Emin = getEmin(sigma, field)
E = np.logspace(10, 23, 261) * eV
E = E[E > Emin]
# -------------------------------------------
# calculate interaction rates
# -------------------------------------------
# tabulated values of s_kin = s - mc^2
# Note: integration method (Romberg) requires 2^n + 1 log-spaced tabulation points
s_kin = np.logspace(6, 23, 2049) * eV**2
xs = getTabulatedXS(sigma, s_kin)
rate = interactionRate.calc_rate_s(s_kin, xs, E, field)
# save
fname = folder + '/rate_%s.txt' % field.name
data = np.c_[np.log10(E / eV), rate]
fmt = '%.2f\t%.6g'
header = '%s interaction rates\nphoton field: %s\nlog10(E/eV), 1/lambda [1/Mpc]' % (name, field.info)
np.savetxt(fname, data, fmt=fmt, header=header)
# -------------------------------------------
# calculate cumulative differential interaction rates for sampling s values
# -------------------------------------------
# find minimum value of s_kin
skin1 = getSmin(sigma) # s threshold for interaction
skin2 = 4 * field.getEmin() * E[0] # minimum achievable s in collision with background photon (at any tabulated E)
skin_min = max(skin1, skin2)
# tabulated values of s_kin = s - mc^2, limit to relevant range
# Note: use higher resolution and then downsample
skin = np.logspace(6.2, 23, 1680 + 1) * eV**2
skin = skin[skin > skin_min]
xs = getTabulatedXS(sigma, skin)
rate = interactionRate.calc_rate_s(skin, xs, E, field, cdf=True)
# downsample
skin_save = np.logspace(6.2, 23, 168 + 1) * eV**2
skin_save = skin_save[skin_save > skin_min]
rate_save = np.array([np.interp(skin_save, skin, r) for r in rate])
# save
data = np.c_[np.log10(E / eV), rate_save] # prepend log10(E/eV) as first column
row0 = np.r_[0, np.log10(skin_save / eV**2)][np.newaxis]
data = np.r_[row0, data] # prepend log10(s_kin/eV^2) as first row
fname = folder + '/cdf_%s.txt' % field.name
fmt = '%.2f' + '\t%.6g' * np.shape(rate_save)[1]
header = '%s cumulative differential rate\nphoton field: %s\nlog10(E/eV), d(1/lambda)/ds_kin [1/Mpc/eV^2] for log10(s_kin/eV^2) as given in first row' % (name, field.info)
np.savetxt(fname, data, fmt=fmt, header=header)
def process(sigma, field, name):
# output folder
folder = 'data/' + name
if not os.path.exists(folder):
os.makedirs(folder)
# tabulated energies, limit to energies where the interaction is possible
Emin = getEmin(sigma, field)
E = np.logspace(10, 23, 261) * eV
E = E[E > Emin]
# -------------------------------------------
# calculate interaction rates
# -------------------------------------------
# tabulated values of s_kin = s - mc^2
# Note: integration method (Romberg) requires 2^n + 1 log-spaced tabulation points
s_kin = np.logspace(6, 23, 2049) * eV**2
xs = getTabulatedXS(sigma, s_kin)
rate = interactionRate.calc_rate_s(s_kin, xs, E, field)
# save
fname = folder + '/rate_%s.txt' % field.name
data = np.c_[np.log10(E / eV), rate]
fmt = '%.2f\t%.6g'
header = '%s interaction rates\nphoton field: %s\nlog10(E/eV), 1/lambda [1/Mpc]' % (
name, field.info)
np.savetxt(fname, data, fmt=fmt, header=header)
# -------------------------------------------
# calculate cumulative differential interaction rates for sampling s values
# -------------------------------------------
# find minimum value of s_kin
skin1 = getSmin(sigma) # s threshold for interaction
skin2 = 4 * field.getEmin() * E[
0] # minimum achievable s in collision with background photon (at any tabulated E)
skin_min = max(skin1, skin2)
# tabulated values of s_kin = s - mc^2, limit to relevant range
# Note: use higher resolution and then downsample
skin = np.logspace(6.2, 23, 1680 + 1) * eV**2
skin = skin[skin > skin_min]
xs = getTabulatedXS(sigma, skin)
rate = interactionRate.calc_rate_s(skin, xs, E, field, cdf=True)
# downsample
skin_save = np.logspace(6.2, 23, 168 + 1) * eV**2
skin_save = skin_save[skin_save > skin_min]
rate_save = np.array([np.interp(skin_save, skin, r) for r in rate])
# save
data = np.c_[np.log10(E / eV),
rate_save] # prepend log10(E/eV) as first column
row0 = np.r_[0, np.log10(skin_save / eV**2)][np.newaxis]
data = np.r_[row0, data] # prepend log10(s_kin/eV^2) as first row
fname = folder + '/cdf_%s.txt' % field.name
fmt = '%.2f' + '\t%.6g' * np.shape(rate_save)[1]
header = '%s cumulative differential rate\nphoton field: %s\nlog10(E/eV), d(1/lambda)/ds_kin [1/Mpc/eV^2] for log10(s_kin/eV^2) as given in first row' % (
name, field.info)
np.savetxt(fname, data, fmt=fmt, header=header)