Use python-native types in favor of numpy's

This resolves deprecation warnings in numpy>=1.2 .

src/domains/gl_space.py

@@ -59,11 +59,11 @@ class GLSpace(StructuredDomain):
 
     @property
     def shape(self):
-        return (np.int((self.nlat * self.nlon)),)
+        return (int((self.nlat * self.nlon)),)
 
     @property
     def size(self):
-        return np.int((self.nlat * self.nlon))
+        return int((self.nlat * self.nlon))
 
     @property
     def scalar_dvol(self):

src/domains/hp_space.py

@@ -53,7 +53,7 @@ class HPSpace(StructuredDomain):
 
     @property
     def size(self):
-        return np.int(12 * self.nside * self.nside)
+        return int(12 * self.nside * self.nside)
 
     @property
     def scalar_dvol(self):

src/domains/lm_space.py

@@ -131,7 +131,7 @@ class LMSpace(StructuredDomain):
         op = HarmonicTransformOperator(lm0, gl)
         kernel_lm = op.adjoint_times(kernel_sphere.weight(1)).val
         # evaluate the k lengths of the harmonic space
-        k_lengths = self.get_k_length_array().val.astype(np.int)
+        k_lengths = self.get_k_length_array().val.astype(int)
         return Field.from_raw(self, kernel_lm[k_lengths])
 
     @property

src/field.py

@@ -259,7 +259,7 @@ class Field(Operator):
             if np.isscalar(wgt):
                 fct *= wgt
             else:
-                new_shape = np.ones(len(self.shape), dtype=np.int)
+                new_shape = np.ones(len(self.shape), dtype=int)
                 new_shape[self._domain.axes[ind][0]:
                           self._domain.axes[ind][-1]+1] = wgt.shape
                 wgt = wgt.reshape(new_shape)

src/library/los_response.py

@@ -64,10 +64,10 @@ def _comp_traverse(start, end, shp, dist, lo, mid, hi, sig, erf):
         c_first = np.ceil(start[:, i]+direction*dmin)
         c_first = np.where(direction > 0., c_first, c_first-1.)
         c_first = (c_first-start[:, i])/dirx
-        pos1 = np.asarray((start[:, i]+dmin*direction), dtype=np.int)
+        pos1 = np.asarray((start[:, i]+dmin*direction), dtype=int)
         pos1 = np.sum(pos1*inc)
         cdist = np.empty(0, dtype=np.float64)
-        add = np.empty(0, dtype=np.int)
+        add = np.empty(0, dtype=int)
         for j in range(ndim):
             if direction[j] != 0:
                 step = inc[j] if direction[j] > 0 else -inc[j]

src/minimization/line_search.py

@@ -138,8 +138,8 @@ class LineSearch(metaclass=NiftyMeta):
                  max_zoom_iterations=100):
 
         self.preferred_initial_step_size = preferred_initial_step_size
-        self.c1 = np.float(c1)
-        self.c2 = np.float(c2)
+        self.c1 = float(c1)
+        self.c2 = float(c2)
         self.max_step_size = max_step_size
         self.max_iterations = int(max_iterations)
         self.max_zoom_iterations = int(max_zoom_iterations)

src/operators/distributors.py

@@ -97,8 +97,8 @@ class DOFDistributor(LinearOperator):
         firstaxis = self._target.axes[self._space][0]
         lastaxis = self._target.axes[self._space][-1]
         arrshape = self._target.shape
-        presize = np.prod(arrshape[0:firstaxis], dtype=np.int)
-        postsize = np.prod(arrshape[lastaxis+1:], dtype=np.int)
+        presize = np.prod(arrshape[0:firstaxis], dtype=int)
+        postsize = np.prod(arrshape[lastaxis+1:], dtype=int)
         self._hshape = (presize, self._domain[self._space].shape[0], postsize)
         self._pshape = (presize, self._dofdex.size, postsize)
 

src/operators/regridding_operator.py

@@ -66,7 +66,7 @@ class RegriddingOperator(LinearOperator):
         self._frac = [None] * ndim
         for d in range(ndim):
             tmp = np.arange(new_shape[d])*(newdist[d]/dom.distances[d])
-            self._bindex[d] = np.minimum(dom.shape[d]-2, tmp.astype(np.int))
+            self._bindex[d] = np.minimum(dom.shape[d]-2, tmp.astype(int))
             self._frac[d] = tmp-self._bindex[d]
 
     def apply(self, x, mode):

test/test_field.py

@@ -31,7 +31,7 @@ SPACE_COMBINATIONS = [(), SPACES[0], SPACES[1], SPACES]
 @pmp('domain', SPACE_COMBINATIONS)
 @pmp('attribute_desired_type',
      [['domain', ift.DomainTuple], ['val', np.ndarray],
-      ['shape', tuple], ['size', (np.int, np.int64)]])
+      ['shape', tuple], ['size', (int, np.int64)]])
 def test_return_types(domain, attribute_desired_type):
     attribute = attribute_desired_type[0]
     desired_type = attribute_desired_type[1]
@@ -286,7 +286,7 @@ def test_stdfunc():
     f = ift.Field.full(s, 27)
     assert_equal(f.val, 27)
     assert_equal(f.shape, (200,))
-    assert_equal(f.dtype, np.int)
+    assert_equal(f.dtype, int)
     fx = ift.full(f.domain, 0)
     assert_equal(f.dtype, fx.dtype)
     assert_equal(f.shape, fx.shape)