fix doctest

Author: kratsg

src/pyhf/infer/calculators.py

@@ -121,7 +121,7 @@ def cdf(self, value):
             >>> pyhf.set_backend("numpy")
             >>> bkg_dist = pyhf.infer.calculators.AsymptoticTestStatDistribution(0.0)
             >>> bkg_dist.cdf(0.0)
-            0.5
+            np.float64(0.5)
 
         Args:
             value (:obj:`float`): The test statistic value.
@@ -620,7 +620,7 @@ def expected_value(self, nsigma):
             >>> samples = normal.sample((100,))
             >>> dist = pyhf.infer.calculators.EmpiricalDistribution(samples)
             >>> dist.expected_value(nsigma=1)
-            6.15094381...
+            np.float64(6.15094381...)
 
             >>> import pyhf
             >>> import numpy.random as random

src/pyhf/probability.py

@@ -171,10 +171,10 @@ def log_prob(self, value):
             >>> independent = pyhf.probability.Independent(poissons)
             >>> values = pyhf.tensorlib.astensor([8.0, 9.0])
             >>> independent.log_prob(values)
-            -4.26248380...
+            np.float64(-4.26248380...)
             >>> broadcast_value = pyhf.tensorlib.astensor([11.0])
             >>> independent.log_prob(broadcast_value)
-            -4.34774364...
+            np.float64(-4.34774364...)
 
         Args:
             value (:obj:`tensor` or :obj:`float`): The value at which to evaluate the distribution

src/pyhf/tensor/numpy_backend.py

@@ -320,7 +320,7 @@ def percentile(
             >>> pyhf.set_backend("numpy")
             >>> a = pyhf.tensorlib.astensor([[10, 7, 4], [3, 2, 1]])
             >>> pyhf.tensorlib.percentile(a, 50)
-            3.5
+            np.float64(3.5)
             >>> pyhf.tensorlib.percentile(a, 50, axis=1)
             array([7., 2.])
 
@@ -384,7 +384,7 @@ def simple_broadcast(self, *args: Sequence[Tensor[T]]) -> Sequence[Tensor[T]]:
             ...   pyhf.tensorlib.astensor([1]),
             ...   pyhf.tensorlib.astensor([2, 3, 4]),
             ...   pyhf.tensorlib.astensor([5, 6, 7]))
-            [array([1., 1., 1.]), array([2., 3., 4.]), array([5., 6., 7.])]
+            (array([1., 1., 1.]), array([2., 3., 4.]), array([5., 6., 7.]))
 
         Args:
             args (Array of Tensors): Sequence of arrays
@@ -467,7 +467,7 @@ def poisson(self, n: Tensor[T], lam: Tensor[T]) -> ArrayLike:
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
             >>> pyhf.tensorlib.poisson(5., 6.)
-            0.16062314...
+            np.float64(0.16062314...)
             >>> values = pyhf.tensorlib.astensor([5., 9.])
             >>> rates = pyhf.tensorlib.astensor([6., 8.])
             >>> pyhf.tensorlib.poisson(values, rates)
@@ -510,7 +510,7 @@ def normal(self, x: Tensor[T], mu: Tensor[T], sigma: Tensor[T]) -> ArrayLike:
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
             >>> pyhf.tensorlib.normal(0.5, 0., 1.)
-            0.35206532...
+            np.float64(0.35206532...)
             >>> values = pyhf.tensorlib.astensor([0.5, 2.0])
             >>> means = pyhf.tensorlib.astensor([0., 2.3])
             >>> sigmas = pyhf.tensorlib.astensor([1., 0.8])
@@ -538,7 +538,7 @@ def normal_cdf(
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
             >>> pyhf.tensorlib.normal_cdf(0.8)
-            0.78814460...
+            np.float64(0.78814460...)
             >>> values = pyhf.tensorlib.astensor([0.8, 2.0])
             >>> pyhf.tensorlib.normal_cdf(values)
             array([0.7881446 , 0.97724987])