update function documentation for detrending

Author: joshuabmoore

pyspi/calculator.py

@@ -37,9 +37,10 @@ class Calculator:
         configfile (str, optional):
             The location of the YAML configuration file for a user-defined subset. See :ref:`Using a reduced SPI set`, defaults to :code:`'</path/to/pyspi>/pyspi/config.yaml'`
         detrend (bool, optional):
-            Detrend the dataset along the time axis before computing SPIs, defaults to True.
+            If True, detrend the dataset along the time axis before normalising (if enabled), defaults to True.
         normalise (bool, optional):
-            Normalise the dataset along the time axis before computing SPIs, defaults to True.
+            If True, z-score normalise the dataset along the time axis before computing SPIs, defaults to True.
+            Detrending (if enabled) is always applied before normalisation.
     """
     _optional_dependencies = None
 

pyspi/data.py

@@ -42,9 +42,10 @@ class Data:
         dim_order (str, optional):
             Order of dimensions, accepts two combinations of the characters 'p', and 's' for processes and observations, defaults to 'ps'.
         detrend (bool, optional):
-            If True, data is detrended along the time dimension, defaults to True. 
+            If True, detrend the dataset along the time axis before normalising (if enabled), defaults to True.
         normalise (bool, optional):
-            If True, data is z-scored (normalised) along the time dimension, defaults to True.
+            If True, z-score normalise the dataset along the time axis before computing SPIs, defaults to True.
+            Detrending (if enabled) is always applied before normalisation.
         name (str, optional):
             Name of the dataset
         procnames (list, optional):

tests/test_SPIs.py

@@ -24,7 +24,7 @@ def compute_new_tables():
     benchmark_dataset = load_benchmark_dataset()
     # Compute new tables on the benchmark dataset
     np.random.seed(42)
-    calc = Calculator(dataset=benchmark_dataset)
+    calc = Calculator(dataset=benchmark_dataset, normalise=True, detrend=True)
     calc.compute()
     table_dict = dict()
     for spi in calc.spis: