def read_beatlas_xdr():
from simulation import setup_simulation
simulation, stellardata = setup_simulation()
Nwalk, Nstep, Nproc, alist, starname, xdrfile, suffix,prior_vsini,prior_dist,data_source = simulation
imin, imax, ebvmin, ebvmax, rvmin, rvmax, plx0, dplx0, Nsig,vsini0, dvsini0 = stellardata
dims = ['M', 'ob', 'sig0', 'mr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
ctrlarr = [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]
tmp = 0
cont = 0
while tmp < len(ctrlarr):
if math.isnan(ctrlarr[tmp]) is True:
cont = cont + 1
tmp = tmp + 1
else:
tmp = tmp + 1
folder_models = 'bat/'
xdrPL = folder_models + 'beatlas_disk_flx.xdr' # 'PL.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrPL, quiet=False)
# F(lbd)] = 10^-4 erg/s/cm2/Ang
for i in range(np.shape(minfo)[0]):
for j in range(np.shape(minfo)[1]):
if minfo[i][j] < 0:
minfo[i][j] = 0.
for i in range(np.shape(models)[0]):
for j in range(np.shape(models)[1]):
if models[i][j] < 0. or models[i][j] == 0.:
models[i][j] = (models[i][j + 1] + models[i][j - 1]) / 2.
listpar[-1][0] = 0.
ranges = np.array([[listpar[0].min(), listpar[0].max()], \
[listpar[1].min(), listpar[1].max()], \
[listpar[2].min(), listpar[2].max()], \
[listpar[3].min(), listpar[3].max()], \
[listpar[4].min(), listpar[4].max()]])
# add ebv range
ranges = np.vstack([ranges, np.array([ebvmin, ebvmax])])
# add Rv range
ranges = np.vstack([ranges, np.array([rvmin, rvmax])])
# add dist range
ranges = np.vstack([ranges, np.array([plx0-Nsig*dplx0, plx0+Nsig*dplx0])])
return ctrlarr, minfo, models, lbdarr, listpar, dims, isig, ranges
def read_acol_xdr():
# print(params_tmp)
dims = ['M', 'ob', 'Hfrac', 'sig0', 'Rd', 'mr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
ctrlarr = [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]
tmp = 0
cont = 0
while tmp < len(ctrlarr):
if math.isnan(ctrlarr[tmp]) is True:
cont = cont + 1
tmp = tmp + 1
else:
tmp = tmp + 1
# Read the grid models, with the interval of parameters.
folder_models = 'models/'
xdrPL = folder_models + 'acol.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrPL, quiet=False)
# Filter (removing bad models)
for i in range(np.shape(minfo)[0]):
for j in range(np.shape(minfo)[1]):
if minfo[i][j] < 0:
minfo[i][j] = 0.
for i in range(np.shape(listpar)[0]):
for j in range(len(listpar[i])):
if listpar[i][j] < 0:
listpar[i][j] = 0.
mask = np.ones(len(minfo[0]), dtype=bool)
mask[[2, 6]] = False
result = []
for i in range(len(minfo)):
result.append(minfo[i][mask])
minfo = np.copy(result)
for i in range(np.shape(minfo)[0]):
minfo[i][3] = np.log10(minfo[i][3])
listpar[4] = np.log10(listpar[4])
listpar[4].sort()
listpar = list([
listpar[0], listpar[1], listpar[3], listpar[4], listpar[5], listpar[7],
listpar[8]
])
return ctrlarr, minfo, models, lbdarr, listpar, dims, isig
def read_befavor_xdr():
folder_models = 'models/'
dims = ['M', 'ob', 'Hfrac', 'sig0', 'Rd', 'mr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
ctrlarr = [np.NaN, np.NaN, 0.014, np.NaN, 0.0, 50.0, 60.0, 3.5, np.NaN]
tmp = 0
cont = 0
while tmp < len(ctrlarr):
if math.isnan(ctrlarr[tmp]) is True:
cont = cont + 1
tmp = tmp + 1
else:
tmp = tmp + 1
# Read the grid models, with the interval of parameters.
xdrPL = folder_models + 'BeFaVOr.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrPL, quiet=False)
# [models] = [F(lbd)]] = 10^-4 erg/s/cm2/Ang
for i in range(np.shape(minfo)[0]):
for j in range(np.shape(minfo)[1]):
if minfo[i][j] < 0:
minfo[i][j] = 0.
for i in range(np.shape(models)[0]):
for j in range(np.shape(models)[1]):
if models[i][j] < 0 and (j != 0 or j != len(models[i][j]) - 1):
models[i][j] = (models[i][j - 1] + models[i][j + 1]) / 2.
# delete columns of fixed par
cols2keep = [0, 1, 3, 8]
cols2delete = [2, 4, 5, 6, 7]
listpar = [listpar[i] for i in cols2keep]
minfo = np.delete(minfo, cols2delete, axis=1)
listpar[3].sort()
listpar[3][0] = 0.
return ctrlarr, minfo, models, lbdarr, listpar, dims, isig
def read_beatlas_xdr():
dims = ['M', 'ob', 'sig0', 'mr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
ctrlarr = [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]
tmp = 0
cont = 0
while tmp < len(ctrlarr):
if math.isnan(ctrlarr[tmp]) is True:
cont = cont + 1
tmp = tmp + 1
else:
tmp = tmp + 1
folder_models = 'models/'
xdrPL = folder_models + 'disk_flx.xdr' # 'PL.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrPL, quiet=False)
# F(lbd)] = 10^-4 erg/s/cm2/Ang
for i in range(np.shape(minfo)[0]):
for j in range(np.shape(minfo)[1]):
if minfo[i][j] < 0:
minfo[i][j] = 0.
for i in range(np.shape(models)[0]):
for j in range(np.shape(models)[1]):
if models[i][j] < 0. or models[i][j] == 0.:
models[i][j] = (models[i][j + 1] + models[i][j - 1]) / 2.
listpar[-1][0] = 0.
return ctrlarr, minfo, models, lbdarr, listpar, dims, isig
def read_befavor_xdr_complete():
folder_models = 'models/'
dims = ['M', 'ob', 'Hfrac', 'sig0', 'Rd', 'mr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
ctrlarr = [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]
tmp = 0
cont = 0
while tmp < len(ctrlarr):
if math.isnan(ctrlarr[tmp]) is True:
cont = cont + 1
tmp = tmp + 1
else:
tmp = tmp + 1
# Read the grid models, with the interval of parameters.
xdrPL = folder_models + 'aara_sed.xdr' # 'PL.xdr'
# xdrPL = folder_models + 'aara_final.xdr' # 'PL.xdr'
# xdrPL = folder_models + 'aara_acs.xdr' # 'PL.xdr'
# xdrPL = folder_models + 'disk_flx.xdr' # 'PL.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrPL, quiet=False)
# F(lbd)] = 10^-4 erg/s/cm2/Ang
for i in range(np.shape(minfo)[0]):
for j in range(np.shape(minfo)[1]):
if minfo[i][j] < 0:
minfo[i][j] = 0.
for i in range(np.shape(models)[0]):
for j in range(np.shape(models)[1]):
if models[i][j] < 0. or models[i][j] == 0.:
models[i][j] = (models[i][j + 1] + models[i][j - 1]) / 2.
# n0 to logn0
listpar[4] = np.log10(listpar[4])
listpar[4].sort()
minfo[:, 4] = np.log10(minfo[:, 4])
if True:
mask = []
tmp, idx = find_nearest(lbdarr, 1000)
for i in range(len(models)):
if models[i][idx] > 2.21834e-10:
mask.append(i)
# print(i)
# plt.plot(lbdarr, models[i], alpha=0.1)
tmp, idx = find_nearest(lbdarr, 80)
for i in range(len(models)):
if models[i][idx] > 2e-8:
mask.append(i)
# print(i)
# # plt.plot(lbdarr, models[i], alpha=0.1)
tmp, idx = find_nearest(lbdarr, 850)
for i in range(len(models)):
if models[i][idx] > 7e-11:
mask.append(i)
# print(i)
# plt.plot(lbdarr, models[i], alpha=0.1)
# plt.yscale('log')
# plt.xscale('log')
# plt.show()
new_models = np.delete(models, mask, axis=0)
new_minfo = np.delete(minfo, mask, axis=0)
models = np.copy(new_models)
minfo = np.copy(new_minfo)
# delete columns of fixed par
cols2keep = [0, 1, 3, 4, 5, 7, 8]
cols2delete = [2, 6]
listpar = [listpar[i] for i in cols2keep]
minfo = np.delete(minfo, cols2delete, axis=1)
listpar[3].sort()
# for i in range(len(models)):
# plt.plot(lbdarr, models[i], alpha=0.1)
# plt.yscale('log')
# plt.xscale('log')
# plt.show()
return ctrlarr, minfo, models, lbdarr, listpar, dims, isig