MAINT: Remove problematic array structures

Author: bashtage

setup.cfg

@@ -77,7 +77,7 @@ filterwarnings =
     error:The probit link alias is deprecated:FutureWarning:
     error:Parsing dates in:UserWarning
     error:A value is trying to be set on a copy::
-    error:Conversion of an array with ndim:DeprecationWarning:statsmodels
+    error:Conversion of an array with ndim:DeprecationWarning:
 markers =
     example: mark a test that runs example code
     matplotlib: mark a test that requires matplotlib

statsmodels/distributions/copula/archimedean.py

@@ -21,9 +21,9 @@ def _debye(alpha):
     EPSILON = np.finfo(np.float64).eps * 100
 
     def integrand(t):
-        return t / (np.exp(t) - 1)
-
-    debye_value = integrate.quad(integrand, EPSILON, alpha)[0] / alpha
+        return np.squeeze(t / (np.exp(t) - 1))
+    _alpha = np.squeeze(alpha)
+    debye_value = integrate.quad(integrand, EPSILON, _alpha)[0] / _alpha
     return debye_value
 
 

statsmodels/emplike/originregress.py

@@ -250,6 +250,7 @@ def conf_int_el(self, param_num, upper_bound=None,
         f = lambda b0:  self.el_test(np.array([b0]), param_num,
                                      method=method,
                                  stochastic_exog=stochastic_exog)[0] - r0
-        lowerl = optimize.brentq(f, lower_bound, self.params[param_num])
-        upperl = optimize.brentq(f, self.params[param_num], upper_bound)
+        _param = np.squeeze(self.params[param_num])
+        lowerl = optimize.brentq(f, np.squeeze(lower_bound), _param)
+        upperl = optimize.brentq(f, _param, np.squeeze(upper_bound))
         return (lowerl, upperl)

statsmodels/genmod/families/tests/test_family.py

@@ -100,8 +100,10 @@ def test_tweedie_loglike_obs(power):
     scale = 2.9
 
     def pdf(y):
-        return np.exp(tweedie.loglike_obs(
-                    endog=y, mu=mu, scale=scale
-        ))
+        return np.squeeze(
+            np.exp(
+                tweedie.loglike_obs(endog=y, mu=mu, scale=scale)
+            )
+        )
 
     assert_allclose(pdf(0) + integrate.quad(pdf, 0, 1e2)[0], 1, atol=1e-4)

statsmodels/sandbox/distributions/extras.py

@@ -817,6 +817,7 @@ def _sf(self, x, *args, **kwargs):
             return 1.0 - self._cdf(x, *args, **kwargs)
 
     def _munp(self, n, *args, **kwargs):
+        args = [np.squeeze(arg) for arg in args]
         out = np.squeeze(self._mom0_sc(n, *args))
         if np.isscalar(out):
             return float(out)

statsmodels/sandbox/distributions/tests/test_transf.py

@@ -101,7 +101,7 @@ def setup_class(cls):
 
     def test_equivalent(self):
         xx, ppfq = self.xx, self.ppfq
-        for d1,d2 in self.dist_equivalents:
+        for j,(d1,d2) in enumerate(self.dist_equivalents):
 ##            print d1.name
             assert_almost_equal(d1.cdf(xx), d2.cdf(xx), err_msg='cdf'+d1.name)
             assert_almost_equal(d1.pdf(xx), d2.pdf(xx),
@@ -122,6 +122,8 @@ def test_equivalent(self):
                 d2mom = d2.dist.moment(3, *d2.args)
             else:
                 d2mom = d2.moment(3)
+            if j==3:
+                print("now")
             assert_almost_equal(d1.moment(3), d2mom,
                                 DECIMAL,
                                 err_msg='moment '+d1.name+d2.name)