include helperfuns with mock import in conf.py

docs/source/conf.py

@@ -24,6 +24,7 @@
     'scipy',
     'statsmodels',
     'lmfit'
+    'helperfuns'
 ]
 
 

ewstools/ewstools.py

@@ -91,7 +91,7 @@ def ews_compute(raw_series,
     upto: int or 'Full'
         Time up to which EWS are computed. Enter 'Full' to use
         the entire time-series. Otherwise enter a time value.
-    ews: {'var','ac','sd','cv','skew','kurt','smax','cf','aic'}
+    ews: {'var', 'ac', 'sd', 'cv', 'skew', 'kurt', 'smax', 'cf', 'aic'}
 		 List of EWS to compute. Options include variance ('var'),
 		 autocorrelation ('ac'), standard deviation ('sd'), coefficient
 		 of variation ('cv'), skewness ('skew'), kurtosis ('kurt'), peak in
@@ -367,4 +367,3 @@ def ews_compute(raw_series,
 
 
 
-

ewstools/helperfuns.py

@@ -70,19 +70,17 @@ def pspec_welch(yVals,
         Time-series values
     dt: float
         Seperation between time-series points
-    ham_length: int (40)
+    ham_length: int
         Length of Hamming window (number of data points).
-    ham_offset: float (0.5)
+    ham_offset: float
         Hamming offest as a proportion of the Hamming window size.
-    w_cutoff: float (1)
+    w_cutoff: float
         Cutoff frequency used in power spectrum. Given as a proportion of the 
         maximum permissable frequency in the empirical
         power spectrum.
-    scaling: str ('spectrum')
-        Choose between
-            'spectrum' : computes the power spectrum
-            'density'  : computes the power spectral density, which is
-            normalised (area underneath =1).          
+    scaling: {'spectrum', 'density'}
+        Whether to compute the power spectrum ('spectrum') or
+        the power spectral density ('density'), which is normalised to have area 1.        
             
     Returns
     --------------------