# Copyright (c) 2020, Michael Boyle
# See LICENSE file for details: <https://github.com/moble/spherical_functions/blob/master/LICENSE>
### NOTE: The functions in this file are intended purely for inclusion in the Grid class. In
### particular, they assume that the first argument, `self` is an instance of Grid. They should
### probably not be used outside of that class.
def modes(self, ell_max=None, **kwargs):
"""Return mode weights of function decomposed into SWSHs
This method uses `spinsfast` to convert values on an equiangular grid to mode weights.
The output array has one less dimension than this object; rather than the last two axes giving
the values on the two-dimensional grid, the last axis gives the mode weights.
Parameters
==========
ell_max: None or int [defaults to None]
Maximum ell value in the output. If None, the result will have enough ell values to express
the data on the grid without aliasing: (max(n_phi, n_theta) - 1) // 2.
**kwargs: any types
Additional keyword arguments are passed through to the Modes constructor on output
"""
import copy
import numpy as np
import spinsfast
from .. import Modes
ell_max = ell_max or (max(n_phi, n_theta) - 1) // 2
metadata = copy.copy
return Modes(spinsfast.map2salm(self.view(np.ndarray), self.s, ell_max),
spin_weight=self.s, ell_min=0, ell_max=ell_max, **metadata)
def _check_broadcasting(self, array, reverse=False):
"""Test whether or not the given array can broadcast against this object"""
import numpy as np
if isinstance(array, type(self)):
try:
if reverse:
np.broadcast(array, self)
else:
np.broadcast(self, array)
except ValueError:
return False
else:
return True
else:
if np.ndim(array) > np.ndim(self)-2:
raise ValueError(f"Cannot broadcast array of {np.ndim(array)} dimensions against {type(self).__name__} "
f"object of fewer ({np.ndim(self)-2}) non-grid dimensions.\n"
"This is to ensure that scalars do not operate on individual "
"grid values; they must operate on all simultaneously.\n"
"If that is the case and you still want to broadcast, add more "
"dimensions before this object's first dimension.")
try:
if reverse:
np.broadcast(array, self[..., 0, 0])
else:
np.broadcast(self[..., 0, 0], array)
except ValueError:
return False
else:
return True
