Change max count to 100
@@ -103,7 +103,7 @@ def get_decorrelation_time(timeseries_to_analyze):
return timeseries.statisticalInefficiency(timeseries_to_analyze)
-def get_equilibration_data_per_sample(timeseries_to_analyze, fast=True, max_subset=1000):
+def get_equilibration_data_per_sample(timeseries_to_analyze, fast=True, max_subset=100):
"""
Compute the correlation time and n_effective per sample with tuning to how you want your data formatted
@@ -119,7 +119,7 @@ def get_equilibration_data_per_sample(timeseries_to_analyze, fast=True, max_subs
----------
timeseries_to_analyze : np.ndarray
1-D timeseries to analyze for equilibration
- max_subset : int >= 1 or None, optional, default: 1000
+ max_subset : int >= 1 or None, optional, default: 100
Maximum number of points in the ``timeseries_to_analyze`` on which to analyze the equilibration on.
These are distributed uniformly over the timeseries so the final output will be size max_subset where indices
are placed approximately every ``(len(timeseries_to_analyze) - 1) / max_subset``.
