Refactored to simplify

wafo/integrate.py

@@ -973,17 +973,15 @@ class _Gaussq(object):
         return jacob
 
     @staticmethod
-    def _warn(k, a_shape):
+    def _warn_msg(k, a_shape):
         n = len(k)
         if n > 1:
             if n == np.prod(a_shape):
-                tmptxt = 'All integrals did not converge'
+                msg = 'All integrals did not converge'
             else:
-                tmptxt = '%d integrals did not converge' % (n, )
-            tmptxt = tmptxt + '--singularities likely!'
-        else:
-            tmptxt = 'Integral did not converge--singularity likely!'
-        warnings.warn(tmptxt)
+                msg = '%d integrals did not converge' % (n, )
+            return msg + '--singularities likely!'
+        return 'Integral did not converge--singularity likely!'
 
     @staticmethod
     def _initialize(wfun, a, b, args):
@@ -996,6 +994,16 @@ class _Gaussq(object):
             a_out = zeros((a_out.size, 1))
         return a_out, b_out, args, a_shape
 
+
+    def _revert_nans_with_old(self, val, val_old):
+        if any(np.isnan(val)):
+            val[np.isnan(val)] = val_old[np.isnan(val)]
+
+
+    def _update_error(self, i, abserr, val, val_old, k):
+        if i > 1:
+            abserr[k] = abs(val_old[k] - val[k]) # absolute tolerance
+
     def __call__(self, fun, a, b, releps=1e-3, abseps=1e-3, alpha=0, beta=0,
                  wfun=1, trace=False, args=(), max_iter=11):
         self.trace = trace
@@ -1015,8 +1023,8 @@ class _Gaussq(object):
         dtype = np.result_type(fun((a_0+b_0)*0.5, *args))
         n_k = np.prod(a_shape)  # # of integrals we have to compute
         k = np.arange(n_k)
-        opts = (n_k, dtype)
-        val, val_old, abserr = zeros(*opts), ones(*opts), zeros(*opts)
+        opt = (n_k, dtype)
+        val, val_old, abserr = zeros(*opt), np.nan*ones(*opt), 1e100*ones(*opt)
         nodes_and_weights = self._nodes_and_weights
         for i in range(max_iter):
             x_n, weights = nodes_and_weights(num_nodes, wfun, alpha, beta)
@@ -1026,18 +1034,17 @@ class _Gaussq(object):
             y = fun(x, *params)
             self._plot_trace(x, y)
             val[k] = np.sum(weights * y, axis=1) * d_x[k]  # do the integration
-            if any(np.isnan(val)):
-                val[np.isnan(val)] = val_old[np.isnan(val)]
-            if 1 < i:
-                abserr[k] = abs(val_old[k] - val[k])  # absolute tolerance
+            self._revert_nans_with_old(val, val_old)
+            self._update_error(i, abserr, val, val_old, k)
+
             k, = np.where(abserr > np.maximum(abs(releps * val), abseps))
-                n_k = len(k)  # of integrals we have to compute again
-                if n_k == 0:
+            converged = len(k) == 0
+            if converged:
                 break
             val_old[k] = val[k]
             num_nodes *= 2  # double the # of basepoints and weights
-        else:
-            self._warn(k, a_shape)
+
+        _assert_warn(converged, self._warn_msg(k, a_shape))
 
         # make sure int is the same size as the integration  limits
         val.shape = a_shape