change default setting for detrending

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pyspi"
-version = "2.0.0"
+version = "2.0.1"
 authors = [
     { name ="Oliver M. Cliff", email="oliver.m.cliff@gmail.com"},
 ]

pyspi/calculator.py

@@ -16,7 +16,7 @@
 class Calculator:
     """Compute all pairwise interactions.
 
-    The calculator takes in a multivariate time-series dataset, computes and stores all pairwise interactions for the dataset.
+    The calculator takes in a multivariate time-series dataset (MTS), computes and stores all pairwise interactions for the dataset.
     It uses a YAML configuration file that can be modified in order to compute a reduced set of pairwise methods.
 
     Example:
@@ -27,26 +27,25 @@ class Calculator:
 
     Args:
         dataset (:class:`~pyspi.data.Data`, array_like, optional):
-            The multivariate time series of M processes and T observations, defaults to None.
+            The multivariate time series of M processes and T observations, default=None.
         name (str, optional):
-            The name of the calculator. Mainly used for printing the results but can be useful if you have multiple instances, defaults to None.
+            The name of the calculator. Mainly used for printing the results but can be useful if you have multiple instances, default=None.
         labels (array_like, optional):
-            Any set of strings by which you want to label the calculator. This can be useful later for classification purposes, defaults to None.
+            Any set of strings by which you want to label the calculator. This can be useful later for classification purposes, default=None.
         subset (str, optional):
-            A pre-configured subset of SPIs to use. Options are "all", "fast", "sonnet", or "fabfour", defaults to "all".
+            A pre-configured subset of SPIs to use. Options are "all", "fast", "sonnet", or "fabfour", default="all".
         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):
-            If True, detrend the dataset along the time axis before normalising (if enabled), defaults to True.
+            If True, detrend each time series in the MTS dataset individually along the time axis, default=False.
         normalise (bool, optional):
-            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.
+            If True, z-score normalise each time series in the MTS dataset individually along the time axis, default=True.
     """
     _optional_dependencies = None
 
     def __init__(
         self, dataset=None, name=None, labels=None, subset="all", configfile=None,
-        detrend=True, normalise=True
+        detrend=False, normalise=True
     ):
         self._spis = {}
         self._excluded_spis = list()
@@ -511,7 +510,7 @@ def init_from_list(self, datasets, names, labels, **kwargs):
             self.add_calculator(calc)
 
     def init_from_yaml(
-        self, document, detrend=True, normalise=True, n_processes=None, n_observations=None, **kwargs
+        self, document, detrend=False, normalise=True, n_processes=None, n_observations=None, **kwargs
     ):
         datasets = []
         names = []

pyspi/data.py

@@ -38,30 +38,29 @@ class Data:
 
     Args:
         data (array_like, optional):
-            2-dimensional array with raw data, defaults to None.
+            2-dimensional array with raw data, default=None.
         dim_order (str, optional):
-            Order of dimensions, accepts two combinations of the characters 'p', and 's' for processes and observations, defaults to 'ps'.
+            Order of dimensions, accepts two combinations of the characters 'p', and 's' for processes and observations, default='ps'.
         detrend (bool, optional):
-            If True, detrend the dataset along the time axis before normalising (if enabled), defaults to True.
+            If True, detrend each time series in the MTS dataset individually along the time axis, default=False.
         normalise (bool, optional):
-            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.
+            If True, z-score normalise each time series in the MTS dataset individually along the time axis, default=True.
         name (str, optional):
             Name of the dataset
         procnames (list, optional):
-            List of process names with length the number of processes, defaults to None.
+            List of process names with length the number of processes, default=None.
         n_processes (int, optional):
-            Truncates data to this many processes, defaults to None.
+            Truncates data to this many processes, default=None.
         n_observations (int, optional):
-            Truncates data to this many observations, defaults to None.
+            Truncates data to this many observations, default=None.
 
     """
 
     def __init__(
         self,
         data=None,
         dim_order="ps",
-        detrend=True,
+        detrend=False,
         normalise=True,
         name=None,
         procnames=None,
@@ -183,19 +182,19 @@ def set_data(
             data = data[:, :n_observations]
 
         if self.detrend:
-            print(Fore.GREEN + "[1/2] De-trending the dataset...")
+            print(Fore.GREEN + "[1/2] Detrending time series in the dataset...")
             try:
                 data = detrend(data, axis=1)
             except ValueError as err:
                 print(f"Could not detrend data: {err}")
         else:
-            print(Fore.RED + "[1/2] Skipping detrending of the dataset...")
+            print(Fore.RED + "[1/2] Skipping detrending of time series in the dataset...")
 
         if self.normalise:
-            print(Fore.GREEN + "[2/2] Normalising (z-scoring) the dataset...\n")
+            print(Fore.GREEN + "[2/2] Normalising (z-scoring) each time series in the dataset...\n")
             data = zscore(data, axis=1, nan_policy="omit", ddof=1)
         else:
-            print(Fore.RED + "[2/2] Skipping normalisation of the dataset...\n")
+            print(Fore.RED + "[2/2] Skipping normalisation of time series in the dataset...\n")
 
         nans = np.isnan(data)
         if nans.any():

setup.py

@@ -64,7 +64,7 @@
                         'data/standard_normal.npy',
                         'data/cml7.npy']},
     include_package_data=True,
-    version='2.0.0',
+    version='2.0.1',
     description='Library for pairwise analysis of time series data.',
     author='Oliver M. Cliff',
     author_email='oliver.m.cliff@gmail.com',

tests/generate_benchmark_tables.py

@@ -29,7 +29,7 @@ def get_benchmark_tables(calc_list):
 
 for i in range(10):
     np.random.seed(42)
-    calc = Calculator(dataset=dataset, detrend=True, normalise=True)
+    calc = Calculator(dataset=dataset)
     calc.compute()
     store_calcs.append(calc)
 

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, normalise=True, detrend=True)
+    calc = Calculator(dataset=benchmark_dataset)
     calc.compute()
     table_dict = dict()
     for spi in calc.spis: