def griddataBA_new(minfo, models, params, isig, silent=True):
'''
Interpolates model grid
Usage:
model_interp = griddata(minfo, models, params, isig, silent=True)
where
minfo = grid of parameters
models = grid of models
params = parameters,
isig = (normalized) sigma0 index
Ex:
# read grid
xdrpath = 'beatlas/disk_flx.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrpath, quiet=True)
# find isig
dims = ['M', 'ob', 'sig0', 'nr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
# interpolation
params = [12.4, 1.44, 0.9, 4.4, 0.1]
model_interp = np.exp(griddataBA(minfo, np.log(models), params, isig))
If photospheric models are interpolated, let isig=None. For spectra,
it is recommended to enter the log of the grid of spectra as input,
as shown in the example above.
'''
# ranges
ranges = np.array([[parr.min(), parr.max()] for parr in minfo.T])
# find neighbours, delete coincidences
if phc.is_inside_ranges(isig, [0, len(params) - 1]):
# exclude sig0 dimension, to take all their entries for interpolation
keep, out, inside_ranges, params1, minfo1 = phc.find_neighbours(np.delete(params, isig), \
np.delete(minfo, isig, axis=1), \
np.delete(ranges.T, isig, axis=1).T, \
silent=silent)
params = np.hstack([params1, params[isig]])
minfo = np.vstack([minfo1.T, minfo[:, isig]]).T
else:
keep, out, inside_ranges, params, minfo = phc.find_neighbours(
params, minfo, ranges, silent=silent)
# interpolation
model_interp = griddata(minfo[keep], models[keep], params,
method='linear')[0]
if np.isnan(model_interp).any() or np.sum(model_interp) == 0.:
if not silent:
print(
'[griddataBA] Warning: linear interpolation didnt work, taking closest model'
)
model_interp = griddata(minfo[keep],
models[keep],
params,
method='nearest')[0]
return model_interp
def griddataBA(minfo, models, params, isig, silent=True):
'''
Interpolates model grid
Usage:
model_interp = griddata(minfo, models, params, isig, silent=True)
where
minfo = grid of parameters
models = grid of models
params = parameters,
isig = (normalized) sigma0 index
Ex:
# read grid
xdrpath = 'beatlas/disk_flx.xdr'
listpar, lbdarr, minfo, models = bat.readBAsed(xdrpath, quiet=True)
# find isig
dims = ['M', 'ob', 'sig0', 'nr', 'cosi']
dims = dict(zip(dims, range(len(dims))))
isig = dims["sig0"]
# interpolation
params = [12.4, 1.44, 0.9, 4.4, 0.1]
model_interp = np.exp(griddataBA(minfo, np.log(models), params, isig))
If photospheric models are interpolated, let isig=None. For spectra,
it is recommended to enter the log of the grid of spectra as input,
as shown in the example above.
'''
# ranges
ranges = _np.array([[parr.min(), parr.max()] for parr in minfo.T])
# find neighbours, delete coincidences
if _phc.is_inside_ranges(isig, [0, len(params)-1]):
# exclude sig0 dimension, to take all their entries for interpolation
keep, out, inside_ranges, params1, minfo1 = _phc.find_neighbours(_np.delete(params, isig), \
_np.delete(minfo, isig, axis=1), \
_np.delete(ranges.T, isig, axis=1).T, \
silent=silent)
params = _np.hstack([params1, params[isig]])
minfo = _np.vstack([minfo1.T, minfo[:, isig]]).T
else:
keep, out, inside_ranges, params, minfo = _phc.find_neighbours(params, minfo, ranges, silent=silent)
# interpolation
model_interp = _griddata(minfo[keep], models[keep], params, method='linear')[0]
if _np.isnan(model_interp).any() or _np.sum(model_interp) == 0.:
if not silent:
print('[griddataBA] Warning: linear interpolation didnt work, taking closest model')
model_interp = _griddata(minfo[keep], models[keep], params, method='nearest')[0]
return model_interp