fixed sklearn import req and some linting fixes

dysts/analysis.py

@@ -7,10 +7,10 @@
 import warnings
 
 import numpy as np
-from scipy.spatial.distance import cdist
-from scipy.stats import linregress
+from scipy.spatial.distance import cdist  # type: ignore
+from scipy.stats import linregress  # type: ignore
 
-from .flows import DynSys
+from .base import DynSys
 from .utils import (
     ComputationHolder,
     find_significant_frequencies,
@@ -21,7 +21,11 @@
 )
 
 if has_module("sklearn"):
-    from sklearn.linear_model import RidgeCV
+    from sklearn.linear_model import RidgeCV  # type: ignore
+else:
+    warnings.warn(
+        "Sklearn not installed. Will not be able to use ridge regression for gpdistance and corr_gpdim."
+    )
 
 
 def sample_initial_conditions(
@@ -226,6 +230,8 @@ def gpdistance(traj1, traj2, standardize=True, register=False, **kwargs):
     """
 
     if register:
+        if not has_module("sklearn"):
+            raise ImportError("Sklearn is required for registration")
         model = RidgeCV()
         model.fit(traj1, traj2)
         traj1 = model.predict(traj1)

pyproject.toml

@@ -20,7 +20,7 @@ dependencies = ["numpy", "scipy", "tqdm"]
 benchmarks = ["darts", "torch", "scikit-learn", "pandas", "tsfresh", "nolds"]
 numba = ["numba"]
 tests = ["matplotlib"]
-extra = ["sdeint", "sklearn"]
+extra = ["sdeint", "scikit-learn"]
 
 # Package data
 [tool.setuptools]

tests/test_analysis.py

@@ -1,6 +1,5 @@
-import matplotlib.pyplot as plt
-
 import dysts.flows as dfl
+from dysts.analysis import compute_timestep
 
 if __name__ == "__main__":
     num_points_per_period = 1024
@@ -10,26 +9,15 @@
     dyst_name = "Lorenz"
     system = getattr(dfl, dyst_name)()
 
-    traj = system.make_trajectory(
-        num_points,
-        resample=True,
+    dt = compute_timestep(
+        system,
+        total_length=num_points,
+        transient_fraction=0.2,
+        num_iters=5,
         pts_per_period=num_points_per_period,
-        postprocess=False,
+        return_period=True,
     )
-
-    print(traj.shape)
-    plt.plot(traj)
-    plt.show()
-
-    # dt = compute_timestep(
-    #     system,
-    #     total_length=num_points,
-    #     transient_fraction=0.2,
-    #     num_iters=5,
-    #     pts_per_period=num_points_per_period,
-    #     return_period=True,
-    # )
-    # print("result: ", dt)
+    print("result: ", dt)
 
 # TODO: Use scipy optimize for black box optimization of dt from initial guess
 # until it meets characteristic timescale criteria