latest

README.md

@@ -104,7 +104,7 @@ These additional optional dependencies are needed to reproduce some portions of
 
 ## Implementation Notes
 
-+ Currently there are 131 continuous time models, including several delay differential equations. There is also a separate module with 10 discrete maps, which is currently being expanded. 
++ Currently there are 135 continuous time models, including several delay differential equations. There is also a separate module with 10 discrete maps, which is currently being expanded. 
 + The right hand side of each dynamical equation is compiled using `numba`, wherever possible. Ensembles of trajectories are vectorized where needed.
 + Attractor names, default parameter values, references, and other metadata are stored in parseable JSON database files. Parameter values are based on standard or published values, and default initial conditions were generated by running each model until the moments of the autocorrelation function all become stationary.
 + The default integration step is stored in each continuous-time model's `dt` field. This integration timestep was chosen based on the highest significant frequency observed in the power spectrum, with significance being determined relative to [random phase surrogates](https://en.wikipedia.org/wiki/Surrogate_data_testing). The `period` field contains the timescale associated with the dominant frequency in each system's power spectrum. When using the `model.make_trajectory()` method with the optional setting `resample=True`, integration is performed at the default `dt`. The integrated trajectory is then resampled based on the `period`. The resulting trajectories will have have consistant dominant timescales across models, despite having different integration timesteps.

dysts/base.py

@@ -689,9 +689,6 @@ def make_trajectory_ensemble(n, subset=None, use_multiprocessing=False, random_s
     if use_multiprocessing and not _has_multiprocessing:
         warnings.warn("Multiprocessing is not available on this system. Falling back to single-threaded mode.")
 
-    # We run this inside the function scope to avoid a circular import issue
-    # flows = importlib.import_module("dysts.flows", package=".flows")
-
     all_sols = dict()
     if use_multiprocessing and _has_multiprocessing:
         with Pool() as pool:
@@ -703,8 +700,6 @@ def make_trajectory_ensemble(n, subset=None, use_multiprocessing=False, random_s
 
     else:
         for equation_name in subset:
-            # eq = getattr(flows, equation_name)()
-            # eq.random_state = random_state
             sol = _compute_trajectory(equation_name, n, kwargs)
             all_sols[equation_name] = sol