Change behaviour of dtype on equivalent sources

Apply `dtype` argument only to the jacobian matrix and the predictions
for equivalent sources. Don't use the `dtype` for casting coordinates
and location of the sources.

harmonica/_equivalent_sources/cartesian.py

@@ -129,7 +129,7 @@ class EquivalentSources(vdb.BaseGridder):
  If True any predictions and Jacobian building is carried out in
  parallel through Numba's ``jit.prange``, reducing the computation time.
  If False, these tasks will be run on a single CPU. Default to True.
- dtype : data-type
+ dtype : dtype, optional
  The desired data-type for the predictions and the Jacobian matrix.
  Default to ``"float64"``.
 
@@ -215,21 +215,14 @@ def fit(self, coordinates, data, weights=None):
  Returns this estimator instance for chaining operations.
  """
  coordinates, data, weights = vdb.check_fit_input(coordinates, data, weights)
- coordinates, data, weights = cast_fit_input(
- coordinates, data, weights, self.dtype
- )
  # Capture the data region to use as a default when gridding.
  self.region_ = vd.get_region(coordinates[:2])
  coordinates = vdb.n_1d_arrays(coordinates, 3)
  if self.points is None:
- self.points_ = tuple(
- p.astype(self.dtype) for p in self._build_points(coordinates)
- )
+ self.points_ = self._build_points(coordinates)
  else:
  self.depth_ = None # set depth_ to None so we don't leave it unset
- self.points_ = tuple(
- p.astype(self.dtype) for p in vdb.n_1d_arrays(self.points, 3)
- )
+ self.points_ = vdb.n_1d_arrays(self.points, 3)
  jacobian = self.jacobian(coordinates, self.points_)
  self.coefs_ = vdb.least_squares(jacobian, data, weights, self.damping)
  return self
@@ -326,9 +319,7 @@ def predict(self, coordinates):
  check_is_fitted(self, ["coefs_"])
  shape = np.broadcast(*coordinates[:3]).shape
  size = np.broadcast(*coordinates[:3]).size
- coordinates = tuple(
- np.atleast_1d(i.astype(self.dtype)).ravel() for i in coordinates[:3]
- )
+ coordinates = tuple(np.atleast_1d(i).ravel() for i in coordinates[:3])
  data = np.zeros(size, dtype=self.dtype)
  self._predict_kernel[self.parallel](
  coordinates, self.points_, self.coefs_, data, self.greens_function

harmonica/_equivalent_sources/gradient_boosted.py

@@ -73,7 +73,7 @@ class EquivalentSourcesGB(EquivalentSources):
  If True any predictions and Jacobian building is carried out in
  parallel through Numba's ``jit.prange``, reducing the computation time.
  If False, these tasks will be run on a single CPU. Default to True.
- dtype : data-type
+ dtype : dtype, optional
  The desired data-type for the predictions and the Jacobian matrix.
  Default to ``"float64"``.
 
@@ -205,9 +205,6 @@ def fit(self, coordinates, data, weights=None):
  Returns this estimator instance for chaining operations.
  """
  coordinates, data, weights = vdb.check_fit_input(coordinates, data, weights)
- coordinates, data, weights = cast_fit_input(
- coordinates, data, weights, self.dtype
- )
  # Capture the data region to use as a default when gridding.
  self.region_ = get_region(coordinates[:2])
  # Ravel coordinates, data and weights to 1d-arrays
@@ -217,13 +214,9 @@ def fit(self, coordinates, data, weights=None):
  weights = weights.ravel()
  # Build point sources
  if self.points is None:
- self.points_ = tuple(
- p.astype(self.dtype) for p in self._build_points(coordinates)
- )
+ self.points_ = self._build_points(coordinates)
  else:
- self.points_ = tuple(
- p.astype(self.dtype) for p in vdb.n_1d_arrays(self.points, 3)
- )
+ self.points_ = vdb.n_1d_arrays(self.points, 3)
  # Initialize coefficients
  self.coefs_ = np.zeros_like(self.points_[0])
  # Fit coefficients through gradient boosting