more fixes

Author: kratsg

src/pyhf/infer/calculators.py

@@ -40,15 +40,16 @@ def generate_asimov_data(
     Example:
 
         >>> import pyhf
+        >>> import numpy as np
         >>> pyhf.set_backend("numpy")
         >>> model = pyhf.simplemodels.uncorrelated_background(
         ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
         ... )
         >>> observations = [51, 48]
         >>> data = observations + model.config.auxdata
         >>> mu_test = 1.0
-        >>> pyhf.infer.calculators.generate_asimov_data(mu_test, data, model, None, None, None)
-        array([ 60.61229858,  56.52802479, 270.06832542,  48.31545488])
+        >>> asimov_data = pyhf.infer.calculators.generate_asimov_data(mu_test, data, model, None, None, None)
+        >>> np.isclose(asimov_data, [ 60.61229858,  56.52802479, 270.06832542,  48.31545488])
         >>> pyhf.infer.calculators.generate_asimov_data(
         ...     mu_test, data, model, None, None, None, return_fitted_pars=True
         ... )
@@ -339,6 +340,7 @@ def teststatistic(self, poi_test):
         Example:
 
             >>> import pyhf
+            >>> import numpy as np
             >>> pyhf.set_backend("numpy")
             >>> model = pyhf.simplemodels.uncorrelated_background(
             ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
@@ -347,8 +349,9 @@ def teststatistic(self, poi_test):
             >>> data = observations + model.config.auxdata
             >>> mu_test = 1.0
             >>> asymptotic_calculator = pyhf.infer.calculators.AsymptoticCalculator(data, model, test_stat="qtilde")
-            >>> asymptotic_calculator.teststatistic(mu_test)
-            array(0.14043184)
+            >>> test_stat = asymptotic_calculator.teststatistic(mu_test)
+            >>> np.isclose(test_stat, 0.14043184)
+            True
             >>> asymptotic_calculator.fitted_pars
             HypoTestFitResults(asimov_pars=array([0.        , 1.0030482 , 0.96264534]), free_fit_to_data=array([0.        , 1.0030512 , 0.96266961]), free_fit_to_asimov=array([0.        , 1.00304893, 0.96263365]), fixed_poi_fit_to_data=array([1.        , 0.97224597, 0.87553894]), fixed_poi_fit_to_asimov=array([1.        , 0.97276864, 0.87142047]))
             >>> asymptotic_calculator.fitted_pars.free_fit_to_asimov  # best-fit parameters to Asimov dataset
@@ -478,6 +481,7 @@ def expected_pvalues(self, sig_plus_bkg_distribution, bkg_only_distribution):
         Example:
 
             >>> import pyhf
+            >>> import numpy as np
             >>> pyhf.set_backend("numpy")
             >>> model = pyhf.simplemodels.uncorrelated_background(
             ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
@@ -491,8 +495,8 @@ def expected_pvalues(self, sig_plus_bkg_distribution, bkg_only_distribution):
             >>> _ = asymptotic_calculator.teststatistic(mu_test)
             >>> sig_plus_bkg_dist, bkg_dist = asymptotic_calculator.distributions(mu_test)
             >>> CLsb_exp_band, CLb_exp_band, CLs_exp_band = asymptotic_calculator.expected_pvalues(sig_plus_bkg_dist, bkg_dist)
-            >>> CLs_exp_band
-            [array(0.00260626), array(0.01382005), array(0.06445321), array(0.23525644), array(0.57303621)]
+            >>> np.isclose(CLs_exp_band, [0.00260626, 0.01382005, 0.06445321, 0.23525644, 0.57303621])
+            True
 
         Args:
             sig_plus_bkg_distribution (~pyhf.infer.calculators.AsymptoticTestStatDistribution):
@@ -611,6 +615,7 @@ def expected_value(self, nsigma):
         Examples:
 
             >>> import pyhf
+            >>> import numpy as np
             >>> import numpy.random as random
             >>> random.seed(0)
             >>> pyhf.set_backend("numpy")
@@ -646,9 +651,9 @@ def expected_value(self, nsigma):
             ...     )
             ... )
             >>> n_sigma = pyhf.tensorlib.astensor([-2, -1, 0, 1, 2])
-            >>> dist.expected_value(n_sigma)
-            array([0.00000000e+00, 0.00000000e+00, 5.53671231e-04, 8.29987137e-01,
-                   2.99592664e+00])
+            >>> exp_values = dist.expected_value(n_sigma)
+            >>> np.isclose(exp_values, [0.00000000e+00, 0.00000000e+00, 5.53671231e-04, 8.29987137e-01, 2.99592664e+00])
+            True
 
         Args:
             nsigma (:obj:`int` or :obj:`tensor`): The number of standard deviations.

src/pyhf/infer/intervals/upper_limits.py

@@ -46,10 +46,10 @@ def toms748_scan(
         >>> obs_limit, exp_limits = pyhf.infer.intervals.upper_limits.toms748_scan(
         ...     data, model, 0., 5., rtol=0.01
         ... )
-        >>> obs_limit
-        array(1.0115693...)
-        >>> exp_limits
-        [array(0.5600747), array(0.75702605), array(1.06234693), array(1.50116923), array(2.05078912)]
+        >>> np.isclose(obs_limit, 1.01156939)
+        True
+        >>> np.isclose(exp_limits, [0.5600747, 0.75702605, 1.06234693, 1.50116923, 2.05078912])
+        array([ True,  True,  True,  True,  True])
 
     Args:
         data (:obj:`tensor`): The observed data.
@@ -161,10 +161,10 @@ def linear_grid_scan(
         >>> obs_limit, exp_limits, (scan, results) = pyhf.infer.intervals.upper_limits.upper_limit(
         ...     data, model, scan, return_results=True
         ... )
-        >>> obs_limit
-        array(1.0176408...)
-        >>> exp_limits
-        [array(0.59576921), array(0.76169166), array(1.08504773), array(1.50170482), array(2.06654952)]
+        >>> np.isclose(obs_limit, 1.01764089)
+        True
+        >>> np.isclose(exp_limits, [0.59576921, 0.76169166, 1.08504773, 1.50170482, 2.06654952])
+        array([ True,  True,  True,  True,  True])
 
     Args:
         data (:obj:`tensor`): The observed data.
@@ -225,10 +225,10 @@ def upper_limit(
         >>> obs_limit, exp_limits, (scan, results) = pyhf.infer.intervals.upper_limits.upper_limit(
         ...     data, model, scan, return_results=True
         ... )
-        >>> obs_limit
-        array(1.01764089)
-        >>> exp_limits
-        [array(0.59576921), array(0.76169166), array(1.08504773), array(1.50170482), array(2.06654952)]
+        >>> np.isclose(obs_limit, 1.01764089)
+        True
+        >>> np.isclose(exp_limits, [0.59576921, 0.76169166, 1.08504773, 1.50170482, 2.06654952])
+        array([ True,  True,  True,  True,  True])
 
     Args:
         data (:obj:`tensor`): The observed data.

src/pyhf/infer/mle.py

@@ -95,7 +95,7 @@ def fit(data, pdf, init_pars=None, par_bounds=None, fixed_params=None, **kwargs)
         >>> data = pyhf.tensorlib.astensor(observations + model.config.auxdata)
         >>> bestfit_pars, twice_nll = pyhf.infer.mle.fit(data, model, return_fitted_val=True)
         >>> np.isclose(bestfit_pars, [0.        , 1.0030512 , 0.96266961])
-        True
+        array([ True,  True,  True])
         >>> twice_nll
         array(24.98393521)
         >>> -2 * model.logpdf(bestfit_pars, data) == twice_nll
@@ -170,7 +170,7 @@ def fixed_poi_fit(
         ...     test_poi, data, model, return_fitted_val=True
         ... )
         >>> np.isclose(bestfit_pars, [1.        , 0.97224597, 0.87553894])
-        True
+        array([ True,  True,  True])
         >>> twice_nll
         array(28.92218013)
         >>> -2 * model.logpdf(bestfit_pars, data) == twice_nll

src/pyhf/infer/test_statistics.py

@@ -195,7 +195,7 @@ def qmu_tilde(
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params
         ... )
         array(3.93824492)
-        >>> test_stat, constrained, unconstrained = pyhf.infer.test_statistics.qmu_tilde(
+        >>> test_stat, (constrained, unconstrained) = pyhf.infer.test_statistics.qmu_tilde(
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params, return_fitted_pars=True
         ... )
         >>> test_stat
@@ -284,7 +284,7 @@ def tmu(mu, data, pdf, init_pars, par_bounds, fixed_params, return_fitted_pars=F
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params
         ... )
         array(3.9549891)
-        >>> test_stat, constrained, unconstrained = pyhf.infer.test_statistics.tmu(
+        >>> test_stat, (constrained, unconstrained) = pyhf.infer.test_statistics.tmu(
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params, return_fitted_pars=True
         ... )
         >>> test_stat
@@ -380,7 +380,7 @@ def tmu_tilde(
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params
         ... )
         array(3.93824492)
-        >>> test_stat, constrained, unconstrained = pyhf.infer.test_statistics.tmu_tilde(
+        >>> test_stat, (constrained, unconstrained) = pyhf.infer.test_statistics.tmu_tilde(
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params, return_fitted_pars=True
         ... )
         >>> test_stat
@@ -464,7 +464,7 @@ def q0(mu, data, pdf, init_pars, par_bounds, fixed_params, return_fitted_pars=Fa
         >>> fixed_params = model.config.suggested_fixed()
         >>> pyhf.infer.test_statistics.q0(test_mu, data, model, init_pars, par_bounds, fixed_params)
         array(2.98339447)
-        >>> test_stat, constrained, unconstrained = pyhf.infer.test_statistics.q0(
+        >>> test_stat, (constrained, unconstrained) = pyhf.infer.test_statistics.q0(
         ...     test_mu, data, model, init_pars, par_bounds, fixed_params, return_fitted_pars=True
         ... )
         >>> test_stat