Merge branch 'check_negatives' into 'NIFTy_5'

Check for negative number of pixels or negative binbounds

See merge request !335

nifty5/domains/log_rg_space.py

@@ -50,6 +50,8 @@ class LogRGSpace(StructuredDomain):
 
         self._bindistances = tuple(bindistances)
         self._t_0 = tuple(t_0)
+        if min(self._bindistances) <= 0:
+            raise ValueError('Non-positive bindistances encountered')
 
         self._dim = int(reduce(lambda x, y: x*y, self._shape))
         self._dvol = float(reduce(lambda x, y: x*y, self._bindistances))

nifty5/domains/power_space.py

@@ -165,6 +165,8 @@ class PowerSpace(StructuredDomain):
 
         if binbounds is not None:
             binbounds = tuple(binbounds)
+            if min(binbounds) < 0:
+                raise ValueError('Negative binbounds encountered')
 
         key = (harmonic_partner, binbounds)
         if self._powerIndexCache.get(key) is None:

nifty5/domains/rg_space.py

@@ -54,6 +54,8 @@ class RGSpace(StructuredDomain):
         if np.isscalar(shape):
             shape = (shape,)
         self._shape = tuple(int(i) for i in shape)
+        if min(self._shape) < 0:
+            raise ValueError('Negative number of pixels encountered')
 
         if distances is None:
             if self.harmonic:
@@ -66,6 +68,8 @@ class RGSpace(StructuredDomain):
             temp = np.empty(len(self.shape), dtype=np.float64)
             temp[:] = distances
             self._distances = tuple(temp)
+        if min(self._distances) <= 0:
+            raise ValueError('Non-positive distances encountered')
 
         self._dvol = float(reduce(lambda x, y: x*y, self._distances))
         self._size = int(reduce(lambda x, y: x*y, self._shape))