feat(_fast_tools): move _invert_no_zero to caput and add deprecation …

…warning

draco/util/_fast_tools.pyx

@@ -262,26 +262,3 @@ ctypedef fused real_or_complex:
     double complex
     float
     float complex
-
-@cython.wraparound(False)
-@cython.boundscheck(False)
-@cython.cdivision(True)
-cpdef _invert_no_zero(real_or_complex [:] array, real_or_complex [:] out):
-
-    cdef bint cond
-    cdef Py_ssize_t i = 0
-    cdef Py_ssize_t n = array.shape[0]
-    cdef double thresh, ar, ai
-    if (real_or_complex is cython.doublecomplex) or (real_or_complex is cython.double):
-        thresh = 1.0 / DBL_MAX
-    else:
-        thresh = 1.0 / FLT_MAX
-
-    if (real_or_complex is cython.doublecomplex) or (real_or_complex is cython.floatcomplex):
-        for i in prange(n, nogil=True):
-            cond = (fabs(array[i].real) < thresh) and (fabs(array[i].imag) < thresh)
-            out[i] = 0.0 if cond else 1.0 / array[i]
-    else:
-        for i in prange(n, nogil=True):
-            cond = fabs(array[i]) < thresh
-            out[i] = 0.0 if cond else 1.0 / array[i]

draco/util/tools.py

@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.lib.recfunctions import structured_to_unstructured
 
-from ._fast_tools import _calc_redundancy, _invert_no_zero
+from ._fast_tools import _calc_redundancy
 
 
 def cmap(i, j, n):
@@ -223,7 +223,7 @@ def apply_gain(vis, gain, axis=1, out=None, prod_map=None):
     return out
 
 
-def invert_no_zero(x, out=None):
+def invert_no_zero(*args, **kwargs):
     """Return the reciprocal, but ignoring zeros.
 
     Where `x != 0` return 1/x, or just return 0. Importantly this routine does
@@ -240,29 +240,14 @@ def invert_no_zero(x, out=None):
         Return the reciprocal of x. Where possible the output has the same memory layout
         as the input, if this cannot be preserved the output is C-contiguous.
     """
-    if not isinstance(x, (np.generic, np.ndarray)) or np.issubdtype(
-        x.dtype, np.integer
-    ):
-        with np.errstate(divide="ignore", invalid="ignore", over="ignore"):
-            return np.where(x == 0, 0.0, 1.0 / x)
-
-    if out is not None:
-        if x.shape != out.shape:
-            raise ValueError(
-                f"Input and output arrays don't have same shape: {x.shape} != {out.shape}."
-            )
-    else:
-        # This works even for MPIArrays, producing a correctly shaped MPIArray
-        out = np.empty_like(x, order="A")
-
-    # In order to be able to flatten the arrays to do element by element operations, we
-    # need to ensure the inputs are numpy arrays, and so we take a view which will work
-    # even if `x` (and thus `out`) are MPIArray's
-    _invert_no_zero(
-        x.view(np.ndarray).ravel(order="A"), out.view(np.ndarray).ravel(order="A")
-    )
+    from caput import tools
+    import warnings
 
-    return out
+    warnings.warn(
+        "Function invert_no_zero is deprecated - use 'caput.tools.invert_no_zero'",
+        category=DeprecationWarning,
+    )
+    return tools.invert_no_zero(*args, **kwargs)
 
 
 def extract_diagonal(utmat, axis=1):