Merge pull request statsmodels#7385 from bashtage/use-scipy-lazywhere

MAINT: Use scipy's lazywhere

statsmodels/compat/scipy.py

@@ -63,37 +63,3 @@ def _valarray(shape, value=np.nan, typecode=None):
     if not isinstance(out, np.ndarray):
         out = np.asarray(out)
     return out
-
-
-def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
-    """
-    np.where(cond, x, fillvalue) always evaluates x even where cond is False.
-    This one only evaluates f(arr1[cond], arr2[cond], ...).
-    For example,
-    >>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
-    >>> def f(a, b):
-        return a*b
-    >>> _lazywhere(a > 2, (a, b), f, np.nan)
-    array([ nan,  nan,  21.,  32.])
-    Notice it assumes that all `arrays` are of the same shape, or can be
-    broadcasted together.
-    """
-    if fillvalue is None:
-        if f2 is None:
-            raise ValueError("One of (fillvalue, f2) must be given.")
-        else:
-            fillvalue = np.nan
-    else:
-        if f2 is not None:
-            raise ValueError("Only one of (fillvalue, f2) can be given.")
-
-    arrays = np.broadcast_arrays(*arrays)
-    temp = tuple(np.extract(cond, arr) for arr in arrays)
-    tcode = np.mintypecode([a.dtype.char for a in arrays])
-    out = _valarray(np.shape(arrays[0]), value=fillvalue, typecode=tcode)
-    np.place(out, cond, f(*temp))
-    if f2 is not None:
-        temp = tuple(np.extract(~cond, arr) for arr in arrays)
-        np.place(out, ~cond, f2(*temp))
-
-    return out

statsmodels/distributions/discrete.py

@@ -1,7 +1,7 @@
 import numpy as np
 from scipy.stats import rv_discrete, nbinom, poisson
 from scipy.special import gammaln
-from statsmodels.compat.scipy import _lazywhere
+from scipy._lib._util import _lazywhere
 
 
 class genpoisson_p_gen(rv_discrete):

statsmodels/genmod/_tweedie_compound_poisson.py

@@ -17,7 +17,7 @@
     insurance claims data: dispersion modelling. ASTIN Bulletin 32: 143–157
 """
 import numpy as np
-from scipy._lib._util import _lazywhere as lazywhere
+from scipy._lib._util import _lazywhere
 from scipy.special import gammaln
 
 
@@ -76,10 +76,10 @@ def density_otherwise(y, mu, p, phi):
 
 
 def series_density(y, mu, p, phi):
-    density = lazywhere(np.array(y) > 0,
-                        (y, mu, p, phi),
-                        f=density_otherwise,
-                        f2=density_at_zero)
+    density = _lazywhere(np.array(y) > 0,
+                         (y, mu, p, phi),
+                         f=density_otherwise,
+                         f2=density_at_zero)
     return density