reordering in src style

.github/workflows/run-tests.yml

@@ -36,7 +36,7 @@ jobs:
 #        pip install -r requirements-dev.txt
     - name: Test with pytest
       run: |
-        py.test -v test
+        py.test test
     - name: Generate coverage report
       run: |
         pip install pytest

pykoopman/__init__.py → src/pykoopman/__init__.py

@@ -1,23 +1,23 @@
-from __future__ import annotations
-
-from pkg_resources import DistributionNotFound
-from pkg_resources import get_distribution
-
-try:
-    __version__ = get_distribution(__name__).version
-except DistributionNotFound:
-    pass
-
-from .koopman import Koopman
-from .koopman_continuous import KoopmanContinuous
-
-
-__all__ = [
-    "Koopman",
-    "KoopmanContinuous",
-    "common",
-    "differentiation",
-    "observables",
-    "regression",
-    "analytics",
-]
+from __future__ import annotations
+
+from pkg_resources import DistributionNotFound
+from pkg_resources import get_distribution
+
+try:
+    __version__ = get_distribution(__name__).version
+except DistributionNotFound:
+    pass
+
+from .koopman import Koopman
+from .koopman_continuous import KoopmanContinuous
+
+
+__all__ = [
+    "Koopman",
+    "KoopmanContinuous",
+    "common",
+    "differentiation",
+    "observables",
+    "regression",
+    "analytics",
+]

pykoopman/analytics/__init__.py → src/pykoopman/analytics/__init__.py

@@ -1,7 +1,7 @@
-from __future__ import annotations
-
-from ._base_analyzer import BaseAnalyzer
-from ._ms_pd21 import ModesSelectionPAD21
-from ._pruned_koopman import PrunedKoopman
-
-__all__ = ["BaseAnalyzer", "ModesSelectionPAD21", "PrunedKoopman"]
+from __future__ import annotations
+
+from ._base_analyzer import BaseAnalyzer
+from ._ms_pd21 import ModesSelectionPAD21
+from ._pruned_koopman import PrunedKoopman
+
+__all__ = ["BaseAnalyzer", "ModesSelectionPAD21", "PrunedKoopman"]

pykoopman/analytics/_base_analyzer.py → src/pykoopman/analytics/_base_analyzer.py

@@ -1,89 +1,89 @@
-"""module for implement modes analyzer for Koopman approximation"""
-from __future__ import annotations
-
-import abc
-
-import numpy as np
-
-
-class BaseAnalyzer(object):
-    """Base class for Koopman model analyzer.
-
-    Attributes:
-        model (Koopman): An instance of `pykoopman.koopman.Koopman`.
-        eigenfunction (Koopman.compute_eigenfunction): A function that evaluates Koopman
-            psi.
-        eigenvalues_cont (numpy.ndarray): Koopman lamda in continuous-time.
-        eigenvalues_discrete (numpy.ndarray): Koopman lamda in discrete-time.
-    """
-
-    def __init__(self, model):
-        """Initialize the BaseAnalyzer object.
-
-        Args:
-            model (Koopman): An instance of `pykoopman.koopman.Koopman`.
-        """
-        self.model = model
-        self.eigenfunction = self.model.psi
-        self.eigenvalues_cont = self.model.continuous_lamda_array
-        self.eigenvalues_discrete = self.model.lamda_array
-
-    def _compute_phi_minus_phi_evolved(self, t, validate_data_one_traj):
-        """Compute the difference between psi evolved and psi observed.
-
-        Args:
-            t (numpy.ndarray): Time stamp of this validation trajectory.
-            validate_data_one_traj (numpy.ndarray): Data matrix of this validation
-                trajectory.
-
-        Returns:
-            list: Linear residual for each mode.
-        """
-
-        # shape of phi = (num_samples, num_modes)
-        psi = self.eigenfunction(validate_data_one_traj.T).T
-
-        linear_residual_list = []
-        for i in range(len(self.eigenvalues_cont)):
-            linear_residual_list.append(
-                psi[:, i] - np.exp(self.eigenvalues_cont[i] * t) * psi[0:1, i]
-            )
-        return linear_residual_list
-
-    def validate(self, t, validate_data_one_traj):
-        """Validate Koopman psi.
-
-        Given a single trajectory, compute the norm of the difference
-        between observed psi and evolved psi for each mode.
-
-        Args:
-            t (numpy.ndarray): Time stamp of this validation trajectory.
-            validate_data_one_traj (numpy.ndarray): Data matrix of this validation
-                trajectory.
-
-        Returns:
-            list: Difference in norm for each mode.
-        """
-
-        linear_residual_list = self._compute_phi_minus_phi_evolved(
-            t, validate_data_one_traj
-        )
-        linear_residual_norm_list = [
-            np.linalg.norm(tmp) for tmp in linear_residual_list
-        ]
-        return linear_residual_norm_list
-
-    @abc.abstractmethod
-    def prune_model(self, *params, **kwargs):
-        """Prune the model.
-
-        This method should be implemented by the derived classes.
-
-        Args:
-            *params: Variable length argument list.
-            **kwargs: Arbitrary keyword arguments.
-
-        Raises:
-            NotImplementedError: If the method is not implemented by the derived class.
-        """
-        raise NotImplementedError
+"""module for implement modes analyzer for Koopman approximation"""
+from __future__ import annotations
+
+import abc
+
+import numpy as np
+
+
+class BaseAnalyzer(object):
+    """Base class for Koopman model analyzer.
+
+    Attributes:
+        model (Koopman): An instance of `pykoopman.koopman.Koopman`.
+        eigenfunction (Koopman.compute_eigenfunction): A function that evaluates Koopman
+            psi.
+        eigenvalues_cont (numpy.ndarray): Koopman lamda in continuous-time.
+        eigenvalues_discrete (numpy.ndarray): Koopman lamda in discrete-time.
+    """
+
+    def __init__(self, model):
+        """Initialize the BaseAnalyzer object.
+
+        Args:
+            model (Koopman): An instance of `pykoopman.koopman.Koopman`.
+        """
+        self.model = model
+        self.eigenfunction = self.model.psi
+        self.eigenvalues_cont = self.model.continuous_lamda_array
+        self.eigenvalues_discrete = self.model.lamda_array
+
+    def _compute_phi_minus_phi_evolved(self, t, validate_data_one_traj):
+        """Compute the difference between psi evolved and psi observed.
+
+        Args:
+            t (numpy.ndarray): Time stamp of this validation trajectory.
+            validate_data_one_traj (numpy.ndarray): Data matrix of this validation
+                trajectory.
+
+        Returns:
+            list: Linear residual for each mode.
+        """
+
+        # shape of phi = (num_samples, num_modes)
+        psi = self.eigenfunction(validate_data_one_traj.T).T
+
+        linear_residual_list = []
+        for i in range(len(self.eigenvalues_cont)):
+            linear_residual_list.append(
+                psi[:, i] - np.exp(self.eigenvalues_cont[i] * t) * psi[0:1, i]
+            )
+        return linear_residual_list
+
+    def validate(self, t, validate_data_one_traj):
+        """Validate Koopman psi.
+
+        Given a single trajectory, compute the norm of the difference
+        between observed psi and evolved psi for each mode.
+
+        Args:
+            t (numpy.ndarray): Time stamp of this validation trajectory.
+            validate_data_one_traj (numpy.ndarray): Data matrix of this validation
+                trajectory.
+
+        Returns:
+            list: Difference in norm for each mode.
+        """
+
+        linear_residual_list = self._compute_phi_minus_phi_evolved(
+            t, validate_data_one_traj
+        )
+        linear_residual_norm_list = [
+            np.linalg.norm(tmp) for tmp in linear_residual_list
+        ]
+        return linear_residual_norm_list
+
+    @abc.abstractmethod
+    def prune_model(self, *params, **kwargs):
+        """Prune the model.
+
+        This method should be implemented by the derived classes.
+
+        Args:
+            *params: Variable length argument list.
+            **kwargs: Arbitrary keyword arguments.
+
+        Raises:
+            NotImplementedError: If the method is not implemented by the derived class.
+        """
+        raise NotImplementedError