CLN: Remove unused and unnecessary functions.

pysindy.GeneralizedLibrary.has_type - not used
pysindy.utils.ax_time_to_ax_spatial - limited use in previous refactor.

pysindy/feature_library/base.py

@@ -16,7 +16,6 @@
 from ..utils import comprehend_axes
 from ..utils import validate_no_reshape
 from ..utils import wrap_axes
-from ..utils.axes import ax_time_to_ax_sample
 
 
 class BaseFeatureLibrary(TransformerMixin):
@@ -188,16 +187,11 @@ def func(self, x, *args, **kwargs):
         else:
             if not sparse.issparse(x):
                 x = AxesArray(x, comprehend_axes(x))
-                x = ax_time_to_ax_sample(x)
                 reconstructor = np.array
             else:  # sparse arrays
                 reconstructor = type(x)
                 axes = comprehend_axes(x)
                 wrap_axes(axes, x)
-                # Can't use x = ax_time_to_ax_sample(x) b/c that creates
-                # an AxesArray
-                x.ax_sample = x.ax_time
-                x.ax_time = None
             result = wrapped_func(self, [x], *args, **kwargs)
             if isinstance(result, Sequence):  # e.g. transform() returns x
                 return reconstructor(result[0])
@@ -332,7 +326,7 @@ def transform(self, x_full):
                 xp[..., start_feature_index:end_feature_index] = lib.transform([x])[0]
 
                 current_feat += lib_n_output_features
-            xp = ax_time_to_ax_sample(AxesArray(xp, comprehend_axes(xp)))
+            xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
         if self.library_ensemble:
             xp_full = self._ensemble(xp_full)
@@ -565,7 +559,7 @@ def transform(self, x_full):
 
                     current_feat += lib_i_n_output_features * lib_j_n_output_features
 
-            xp = ax_time_to_ax_sample(AxesArray(xp, comprehend_axes(xp)))
+            xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
         if self.library_ensemble:
             xp_full = self._ensemble(xp_full)

pysindy/feature_library/custom_library.py

@@ -7,7 +7,6 @@
 from sklearn import __version__
 from sklearn.utils.validation import check_is_fitted
 
-from ..utils import ax_time_to_ax_sample
 from ..utils import AxesArray
 from ..utils import comprehend_axes
 from .base import BaseFeatureLibrary
@@ -231,7 +230,7 @@ def transform(self, x_full):
                     xp[..., library_idx] = f(*[x[..., j] for j in c])
                     library_idx += 1
 
-            xp = ax_time_to_ax_sample(AxesArray(xp, comprehend_axes(xp)))
+            xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
         if self.library_ensemble:
             xp_full = self._ensemble(xp_full)

pysindy/feature_library/fourier_library.py

@@ -6,7 +6,6 @@
 from ..utils import comprehend_axes
 from .base import BaseFeatureLibrary
 from .base import x_sequence_or_item
-from pysindy.utils.axes import ax_time_to_ax_sample
 
 
 class FourierLibrary(BaseFeatureLibrary):
@@ -175,7 +174,7 @@ def transform(self, x_full):
                     if self.include_cos:
                         xp[..., idx] = np.cos((i + 1) * x[..., j])
                         idx += 1
-            xp = ax_time_to_ax_sample(AxesArray(xp, comprehend_axes(xp)))
+            xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
         if self.library_ensemble:
             xp_full = self._ensemble(xp_full)

pysindy/feature_library/generalized_library.py

@@ -1,5 +1,3 @@
-from typing import Type
-
 import numpy as np
 from sklearn import __version__
 from sklearn.utils.validation import check_is_fitted
@@ -244,23 +242,6 @@ def fit(self, x_full, y=None):
 
         return self
 
-    def has_type(self, libtype: Type, exclusively=False) -> bool:
-        """Checks whether this library has a specific library type.
-
-        Parameters
-        ----------
-        libtype : A type of feature library
-        exclusively: whether to check all libraries
-
-        Returns
-        -------
-        Bool indicating whether specific library type is present
-        """
-        has_inst = map(lambda lib: isinstance(lib, libtype), self.libraries_)
-        if exclusively:
-            return all(has_inst)
-        return any(has_inst)
-
     @x_sequence_or_item
     def transform(self, x_full):
         """Transform data with libs provided below.

pysindy/feature_library/pde_library.py

@@ -6,7 +6,6 @@
 from sklearn import __version__
 from sklearn.utils.validation import check_is_fitted
 
-from ..utils import ax_time_to_ax_sample
 from ..utils import AxesArray
 from ..utils import comprehend_axes
 from .base import BaseFeatureLibrary
@@ -465,7 +464,7 @@ def transform(self, x_full):
                     shape,
                 )
                 library_idx += n_library_terms * self.num_derivatives * n_features
-            xp = ax_time_to_ax_sample(AxesArray(xp, comprehend_axes(xp)))
+            xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
         if self.library_ensemble:
             xp_full = self._ensemble(xp_full)

pysindy/feature_library/polynomial_library.py

@@ -233,12 +233,8 @@ def transform(self, x_full):
                 axes = comprehend_axes(x)
                 x = x.asformat("csr")
                 wrap_axes(axes, x)
-                # Can't use x = ax_time_to_ax_sample(x) b/c that creates
-                # an AxesArray
-                x.ax_sample = x.ax_time
-                x.ax_time = None
 
-            n_samples = x.shape[x.ax_sample]
+            n_samples = x.shape[x.ax_time]
             n_features = x.shape[x.ax_coord]
             if float(__version__[:3]) >= 1.0:
                 if n_features != self.n_features_in_:

pysindy/pysindy.py

@@ -21,7 +21,6 @@
 from .optimizers import SINDyOptimizer
 from .optimizers import SINDyPI
 from .optimizers import STLSQ
-from .utils import ax_time_to_ax_sample
 from .utils import AxesArray
 from .utils import comprehend_axes
 from .utils import concat_sample_axis
@@ -345,20 +344,17 @@ def fit(
         if u is not None:
             x = [np.concatenate((xi, ui), axis=xi.ax_coord) for xi, ui in zip(x, u)]
 
-        x = [ax_time_to_ax_sample(xi) for xi in x]
-        x_dot = [ax_time_to_ax_sample(xdoti) for xdoti in x_dot]
-
         if hasattr(self.optimizer, "unbias"):
             unbias = self.optimizer.unbias
 
         # backwards compatibility for ensemble options
         if ensemble and n_subset is None:
-            n_subset = x[0].shape[x[0].ax_sample]
+            n_subset = x[0].shape[x[0].ax_time]
         if library_ensemble:
             self.feature_library.library_ensemble = False
         if ensemble and not library_ensemble:
             if n_subset is None:
-                n_sample_tot = np.sum([xi.shape[xi.ax_sample] for xi in x])
+                n_sample_tot = np.sum([xi.shape[xi.ax_time] for xi in x])
                 n_subset = int(0.6 * n_sample_tot)
             optimizer = SINDyOptimizer(
                 EnsembleOptimizer(
@@ -382,7 +378,7 @@ def fit(
             self.coef_list = optimizer.optimizer.coef_list
         elif ensemble and library_ensemble:
             if n_subset is None:
-                n_sample_tot = np.sum([xi.shape[xi.ax_sample] for xi in x])
+                n_sample_tot = np.sum([xi.shape[xi.ax_time] for xi in x])
                 n_subset = int(0.6 * n_sample_tot)
             optimizer = SINDyOptimizer(
                 EnsembleOptimizer(
@@ -472,7 +468,6 @@ def predict(self, x, u=None, multiple_trajectories=False):
         if u is not None:
             u = validate_control_variables(x, u)
             x = [np.concatenate((xi, ui), axis=xi.ax_coord) for xi, ui in zip(x, u)]
-        x = [ax_time_to_ax_sample(xi) for xi in x]
         result = [self.model.predict([xi]) for xi in x]
         result = [
             self.feature_library.reshape_samples_to_spatial_grid(pred)
@@ -616,9 +611,6 @@ def score(
             x_dot_predict = [xd[:-1] for xd in x_dot_predict]
 
         x, x_dot = self._process_multiple_trajectories(x, t, x_dot)
-        x = [ax_time_to_ax_sample(xi) for xi in x]
-        x_dot = [ax_time_to_ax_sample(xdoti) for xdoti in x_dot]
-        x_dot_predict = [ax_time_to_ax_sample(xdip) for xdip in x_dot_predict]
 
         x_dot = concat_sample_axis(x_dot)
         x_dot_predict = concat_sample_axis(x_dot_predict)

pysindy/utils/__init__.py

@@ -1,4 +1,3 @@
-from .axes import ax_time_to_ax_sample
 from .axes import AxesArray
 from .axes import comprehend_axes
 from .axes import concat_sample_axis
@@ -58,7 +57,6 @@
 __all__ = [
     "AxesArray",
     "SampleConcatter",
-    "ax_time_to_ax_sample",
     "concat_sample_axis",
     "wrap_axes",
     "comprehend_axes",

pysindy/utils/axes.py

@@ -134,18 +134,6 @@ def comprehend_axes(x):
     return axes
 
 
-def ax_time_to_ax_sample(x: AxesArray) -> AxesArray:
-    """Relabel the time axis as a sample axis"""
-    if x.ax_sample is not None:
-        return x  # idempotence: f(x) = f(f(x))
-    if x.ax_time is None:
-        raise TypeError("Cannot reassign time axis if it doesn't exist.")
-    new_axes = x.__dict__
-    new_axes["ax_sample"] = new_axes["ax_time"]
-    new_axes["ax_time"] = None
-    return AxesArray(np.asarray(x), new_axes)
-
-
 class SampleConcatter(TransformerMixin):
     def __init__(self):
         pass