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MAINT: Replace find_common_types #16398

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merged 1 commit into from Jun 14, 2022
Merged

MAINT: Replace find_common_types #16398

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2 changes: 1 addition & 1 deletion scipy/linalg/_special_matrices.py
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Expand Up @@ -538,7 +538,7 @@ def block_diag(*arrs):
"greater than 2: %s" % bad_args)

shapes = np.array([a.shape for a in arrs])
out_dtype = np.find_common_type([arr.dtype for arr in arrs], [])
out_dtype = np.result_type(*[arr.dtype for arr in arrs])
out = np.zeros(np.sum(shapes, axis=0), dtype=out_dtype)

r, c = 0, 0
Expand Down
6 changes: 3 additions & 3 deletions scipy/signal/_ltisys.py
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Expand Up @@ -1414,7 +1414,7 @@ def __mul__(self, other):
c = self.C
d = np.dot(self.D, other)

common_dtype = np.find_common_type((a.dtype, b.dtype, c.dtype, d.dtype), ())
common_dtype = np.result_type(a.dtype, b.dtype, c.dtype, d.dtype)
return StateSpace(np.asarray(a, dtype=common_dtype),
np.asarray(b, dtype=common_dtype),
np.asarray(c, dtype=common_dtype),
Expand All @@ -1432,7 +1432,7 @@ def __rmul__(self, other):
c = np.dot(other, self.C)
d = np.dot(other, self.D)

common_dtype = np.find_common_type((a.dtype, b.dtype, c.dtype, d.dtype), ())
common_dtype = np.result_type(a.dtype, b.dtype, c.dtype, d.dtype)
return StateSpace(np.asarray(a, dtype=common_dtype),
np.asarray(b, dtype=common_dtype),
np.asarray(c, dtype=common_dtype),
Expand Down Expand Up @@ -1487,7 +1487,7 @@ def __add__(self, other):
"dimensions ({} and {})"
.format(self.D.shape, other.shape))

common_dtype = np.find_common_type((a.dtype, b.dtype, c.dtype, d.dtype), ())
common_dtype = np.result_type(a.dtype, b.dtype, c.dtype, d.dtype)
return StateSpace(np.asarray(a, dtype=common_dtype),
np.asarray(b, dtype=common_dtype),
np.asarray(c, dtype=common_dtype),
Expand Down
2 changes: 1 addition & 1 deletion scipy/sparse/_sputils.py
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Expand Up @@ -40,7 +40,7 @@ def upcast(*args):
if t is not None:
return t

upcast = np.find_common_type(args, [])
upcast = np.result_type(*args)

for t in supported_dtypes:
if np.can_cast(upcast, t):
Expand Down
2 changes: 1 addition & 1 deletion scipy/sparse/linalg/_interface.py
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Expand Up @@ -598,7 +598,7 @@ def _get_dtype(operators, dtypes=None):
for obj in operators:
if obj is not None and hasattr(obj, 'dtype'):
dtypes.append(obj.dtype)
return np.find_common_type(dtypes, [])
return np.result_type(*dtypes)


class _SumLinearOperator(LinearOperator):
Expand Down
2 changes: 1 addition & 1 deletion scipy/sparse/linalg/_matfuncs.py
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Expand Up @@ -225,7 +225,7 @@ def __init__(self, *args, **kwargs):
'must all have the same shape.')
self.shape = (n, n)
self.ndim = len(self.shape)
self.dtype = np.find_common_type([x.dtype for x in args], [])
self.dtype = np.result_type(*[x.dtype for x in args])
self._operator_sequence = args

def _matvec(self, x):
Expand Down
6 changes: 3 additions & 3 deletions scipy/spatial/distance.py
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Expand Up @@ -155,7 +155,7 @@ def _nbool_correspond_all(u, v, w=None):
ntf = (u & not_v).sum()
ntt = (u & v).sum()
else:
dtype = np.find_common_type([int], [u.dtype, v.dtype])
dtype = np.result_type(int, u.dtype, v.dtype)
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In theory, this can change the result in some cases, but in practice I can't think of a way to make it fail when the first part is [int]...
(in that case u.dtype == longdouble is honored. A change would happen there if the first part was a float/complex, where the longdouble would then be ignored.)

u = u.astype(dtype)
v = v.astype(dtype)
not_u = 1.0 - u
Expand All @@ -177,7 +177,7 @@ def _nbool_correspond_ft_tf(u, v, w=None):
nft = (not_u & v).sum()
ntf = (u & not_v).sum()
else:
dtype = np.find_common_type([int], [u.dtype, v.dtype])
dtype = np.result_type(int, u.dtype, v.dtype)
u = u.astype(dtype)
v = v.astype(dtype)
not_u = 1.0 - u
Expand Down Expand Up @@ -1395,7 +1395,7 @@ def dice(u, v, w=None):
if u.dtype == v.dtype == bool and w is None:
ntt = (u & v).sum()
else:
dtype = np.find_common_type([int], [u.dtype, v.dtype])
dtype = np.result_type(int, u.dtype, v.dtype)
u = u.astype(dtype)
v = v.astype(dtype)
if w is None:
Expand Down
12 changes: 6 additions & 6 deletions scipy/stats/_distn_infrastructure.py
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Expand Up @@ -2095,7 +2095,7 @@ def pdf(self, x, *args, **kwds):
args, loc, scale = self._parse_args(*args, **kwds)
x, loc, scale = map(asarray, (x, loc, scale))
args = tuple(map(asarray, args))
dtyp = np.find_common_type([x.dtype, np.float64], [])
dtyp = np.promote_types(x.dtype, np.float64)
x = np.asarray((x - loc)/scale, dtype=dtyp)
cond0 = self._argcheck(*args) & (scale > 0)
cond1 = self._support_mask(x, *args) & (scale > 0)
Expand Down Expand Up @@ -2136,7 +2136,7 @@ def logpdf(self, x, *args, **kwds):
args, loc, scale = self._parse_args(*args, **kwds)
x, loc, scale = map(asarray, (x, loc, scale))
args = tuple(map(asarray, args))
dtyp = np.find_common_type([x.dtype, np.float64], [])
dtyp = np.promote_types(x.dtype, np.float64)
x = np.asarray((x - loc)/scale, dtype=dtyp)
cond0 = self._argcheck(*args) & (scale > 0)
cond1 = self._support_mask(x, *args) & (scale > 0)
Expand Down Expand Up @@ -2178,7 +2178,7 @@ def cdf(self, x, *args, **kwds):
x, loc, scale = map(asarray, (x, loc, scale))
args = tuple(map(asarray, args))
_a, _b = self._get_support(*args)
dtyp = np.find_common_type([x.dtype, np.float64], [])
dtyp = np.promote_types(x.dtype, np.float64)
x = np.asarray((x - loc)/scale, dtype=dtyp)
cond0 = self._argcheck(*args) & (scale > 0)
cond1 = self._open_support_mask(x, *args) & (scale > 0)
Expand Down Expand Up @@ -2219,7 +2219,7 @@ def logcdf(self, x, *args, **kwds):
x, loc, scale = map(asarray, (x, loc, scale))
args = tuple(map(asarray, args))
_a, _b = self._get_support(*args)
dtyp = np.find_common_type([x.dtype, np.float64], [])
dtyp = np.promote_types(x.dtype, np.float64)
x = np.asarray((x - loc)/scale, dtype=dtyp)
cond0 = self._argcheck(*args) & (scale > 0)
cond1 = self._open_support_mask(x, *args) & (scale > 0)
Expand Down Expand Up @@ -2261,7 +2261,7 @@ def sf(self, x, *args, **kwds):
x, loc, scale = map(asarray, (x, loc, scale))
args = tuple(map(asarray, args))
_a, _b = self._get_support(*args)
dtyp = np.find_common_type([x.dtype, np.float64], [])
dtyp = np.promote_types(x.dtype, np.float64)
x = np.asarray((x - loc)/scale, dtype=dtyp)
cond0 = self._argcheck(*args) & (scale > 0)
cond1 = self._open_support_mask(x, *args) & (scale > 0)
Expand Down Expand Up @@ -2305,7 +2305,7 @@ def logsf(self, x, *args, **kwds):
x, loc, scale = map(asarray, (x, loc, scale))
args = tuple(map(asarray, args))
_a, _b = self._get_support(*args)
dtyp = np.find_common_type([x.dtype, np.float64], [])
dtyp = np.promote_types(x.dtype, np.float64)
x = np.asarray((x - loc)/scale, dtype=dtyp)
cond0 = self._argcheck(*args) & (scale > 0)
cond1 = self._open_support_mask(x, *args) & (scale > 0)
Expand Down