fix up again more with prints/list...

Author: kratsg

src/pyhf/infer/calculators.py

@@ -120,8 +120,8 @@ def cdf(self, value):
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
             >>> bkg_dist = pyhf.infer.calculators.AsymptoticTestStatDistribution(0.0)
-            >>> bkg_dist.cdf(0.0)
-            np.float64(0.5)
+            >>> print(bkg_dist.cdf(0.0))
+            0.5
 
         Args:
             value (:obj:`float`): The test statistic value.
@@ -619,8 +619,8 @@ def expected_value(self, nsigma):
             >>> normal = pyhf.probability.Normal(mean, std)
             >>> samples = normal.sample((100,))
             >>> dist = pyhf.infer.calculators.EmpiricalDistribution(samples)
-            >>> dist.expected_value(nsigma=1)
-            np.float64(6.15094381...)
+            >>> print(dist.expected_value(nsigma=1))
+            6.15094381...
 
             >>> import pyhf
             >>> import numpy.random as random

src/pyhf/probability.py

@@ -170,11 +170,11 @@ def log_prob(self, value):
             >>> poissons = pyhf.probability.Poisson(rates)
             >>> independent = pyhf.probability.Independent(poissons)
             >>> values = pyhf.tensorlib.astensor([8.0, 9.0])
-            >>> independent.log_prob(values)
-            np.float64(-4.26248380...)
+            >>> print(independent.log_prob(values))
+            -4.26248380...
             >>> broadcast_value = pyhf.tensorlib.astensor([11.0])
-            >>> independent.log_prob(broadcast_value)
-            np.float64(-4.34774364...)
+            >>> print(independent.log_prob(broadcast_value))
+            -4.34774364...
 
         Args:
             value (:obj:`tensor` or :obj:`float`): The value at which to evaluate the distribution

src/pyhf/tensor/numpy_backend.py

@@ -319,8 +319,8 @@ def percentile(
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
             >>> a = pyhf.tensorlib.astensor([[10, 7, 4], [3, 2, 1]])
-            >>> pyhf.tensorlib.percentile(a, 50)
-            np.float64(3.5)
+            >>> print(pyhf.tensorlib.percentile(a, 50))
+            3.5
             >>> pyhf.tensorlib.percentile(a, 50, axis=1)
             array([7., 2.])
 
@@ -380,11 +380,11 @@ def simple_broadcast(self, *args: Sequence[Tensor[T]]) -> Sequence[Tensor[T]]:
 
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
-            >>> pyhf.tensorlib.simple_broadcast(
+            >>> list(pyhf.tensorlib.simple_broadcast(
             ...   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.]))
+            ...   pyhf.tensorlib.astensor([5, 6, 7])))
+            [array([1., 1., 1.]), array([2., 3., 4.]), array([5., 6., 7.])]
 
         Args:
             args (Array of Tensors): Sequence of arrays
@@ -466,8 +466,8 @@ def poisson(self, n: Tensor[T], lam: Tensor[T]) -> ArrayLike:
 
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
-            >>> pyhf.tensorlib.poisson(5., 6.)
-            np.float64(0.16062314...)
+            >>> print(pyhf.tensorlib.poisson(5., 6.))
+            0.16062314...
             >>> values = pyhf.tensorlib.astensor([5., 9.])
             >>> rates = pyhf.tensorlib.astensor([6., 8.])
             >>> pyhf.tensorlib.poisson(values, rates)
@@ -509,8 +509,8 @@ 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.)
-            np.float64(0.35206532...)
+            >>> print(pyhf.tensorlib.normal(0.5, 0., 1.))
+            0.35206532...
             >>> values = pyhf.tensorlib.astensor([0.5, 2.0])
             >>> means = pyhf.tensorlib.astensor([0., 2.3])
             >>> sigmas = pyhf.tensorlib.astensor([1., 0.8])
@@ -537,8 +537,8 @@ def normal_cdf(
 
             >>> import pyhf
             >>> pyhf.set_backend("numpy")
-            >>> pyhf.tensorlib.normal_cdf(0.8)
-            np.float64(0.78814460...)
+            >>> print(pyhf.tensorlib.normal_cdf(0.8))
+            0.78814460...
             >>> values = pyhf.tensorlib.astensor([0.8, 2.0])
             >>> pyhf.tensorlib.normal_cdf(values)
             array([0.7881446 , 0.97724987])