fix list, add docs

Readme.md

@@ -54,6 +54,34 @@ print(result)
 # symbolic form. (Works best in Jupyter Notebooks)
 x.pretty_gauss_propagation('a', 'b', 'c')
 ```
+
+### Vectorized Input
+`SecondaryValue` supports vectorized input. As a rule-of-thump: Put
+the iterable (list, np.array) where you would put scalars.
+
+You can mix scalars and vectors as long as all errors and values are
+either scalar or have the same length.
+
+```python
+from SecondaryValue import SecondaryValue
+x = SecondaryValue('a**2+b')
+
+x(a=[[1,2,3]], b=1)
+# >> array([ 2.,  5., 10.])
+
+x(a=([1,2,3], 1), b=1)
+# >> (array([ 2.,  5., 10.]), array([2., 4., 6.]))
+
+x(a=([1,2,3], [1,2,3]), b=1)
+# >> (array([ 2.,  5., 10.]), array([ 2.,  8., 18.]))
+
+x(a=([1,2,3], [1,2,3]), b=([1,2,3], 1))
+# >> (array([ 2.,  6., 12.]), array([ 2.23606798,  8.06225775, 18.02775638]))
+
+# THAT DOES NOT WORK:
+x(a=([1,2,3], [1,2,3]), b=([1,2], 1))
+```
+
 ### Dependencies
 To make the calculation of complex values easier, one can define
 dependencies for a `SecondaryValue`:

SecondaryValue/SecondaryValue.py

@@ -67,7 +67,7 @@ class SecondaryValue:
                 continue
 
             tmp = sec_val(**kwargs)
-            kwargs[name] = tmp[0] if isinstance(tmp, Iterable) else tmp
+            kwargs[name] = tmp
             calc_deps[name] = tmp
 
         return kwargs, calc_deps
@@ -223,7 +223,8 @@ class SecondaryValue:
 
         # calulate the central value
         scalar_values, vector_values = filter_out_vecotrized(values)
-        central_value = self._calculate_central_value(scalar_values, vector_values)
+        central_value = self._calculate_central_value(scalar_values,
+                                                      vector_values)
 
         if not errors:
             return central_value