Minor docstring fixes

dfttoolkit/output.py

@@ -967,9 +967,9 @@ def get_n_initial_ks_states(self, include_spin_polarised=True) -> int:
 
         Parameters
         ----------
-        include_spin_polarised : bool, optional
+        include_spin_polarised : bool, default=True
             Whether to include the spin-down states in the count if the calculation is
-            spin polarised (the default is True).
+            spin polarised.
 
         Returns
         -------

dfttoolkit/utils/math_utils.py

@@ -240,10 +240,10 @@ def get_autocorrelation_function_manual_lag(
     ----------
     signal : 1D npt.NDArray
         Siganl for which the autocorrelation function should be calculated.
-    max_lag : Union[None, int], optional
+    max_lag : Union[None, int]
         Autocorrelation will be calculated for a range of 0 to max_lag,
         where max_lag is the largest lag for the calculation of the
-        autocorrelation function. The default is None.
+        autocorrelation function
 
     Returns
     -------

dfttoolkit/utils/vibrations_utils.py

@@ -56,6 +56,7 @@ def get_cross_correlation_function(
     return cross_correlation
 
 
+# TODO Fix docstrings and types
 def get_cross_spectrum(
     signal_0: npt.NDArray,
     signal_1: npt.NDArray,
@@ -83,16 +84,16 @@ def get_cross_spectrum(
         Second siganl for which the correlation function should be calculated.
     time_step : float
         DESCRIPTION.
-    bootstrapping_blocks : int, optional
-        DESCRIPTION. The default is 1.
-    bootstrapping_overlap : int, optional
-        DESCRIPTION. The default is 0.
-    zero_padding : int, optional
+    bootstrapping_blocks : int, default=1
+        DESCRIPTION
+    bootstrapping_overlap : int, default=0
+        DESCRIPTION
+    zero_padding : int, default=0
         Pad the cross correlation function with zeros to increase the frequency
         resolution of the FFT. This also avoids the effect of varying spectral
         leakage. However, it artificially broadens the resulting cross spectrum
         and introduces wiggles.
-    cutoff_at_last_maximum : bool, optional
+    cutoff_at_last_maximum : bool, default=False
         Cut off the cross correlation function at the last maximum to hide
         spectral leakage.
 
@@ -114,7 +115,8 @@ def get_cross_spectrum(
     signal_length = len(signal_0)
     block_size = int(
         np.floor(
-            signal_length * (1 + bootstrapping_overlap)
+            signal_length
+            * (1 + bootstrapping_overlap)
             / (bootstrapping_blocks + bootstrapping_overlap)
         )
     )
@@ -123,8 +125,7 @@ def get_cross_spectrum(
     cross_spectrum = []
 
     for block in range(bootstrapping_blocks):
-        block_start = int(np.ceil(block * block_size /
-                          (1 + bootstrapping_overlap)))
+        block_start = int(np.ceil(block * block_size / (1 + bootstrapping_overlap)))
         if block_start < 0:
             block_start = 0
 
@@ -204,8 +205,7 @@ def get_cross_spectrum_mem(
     """
     # Calculate the autocorrelation of the time series
     autocorr = np.correlate(signal_0, signal_1, mode="full") / len(signal_0)
-    autocorr = autocorr[len(autocorr) //
-                        2: len(autocorr) // 2 + model_order + 1]
+    autocorr = autocorr[len(autocorr) // 2 : len(autocorr) // 2 + model_order + 1]
 
     # Create a Toeplitz matrix from the autocorrelation function
     # R = toeplitz(autocorr[:-1])
@@ -309,8 +309,7 @@ def get_line_widths(
     res = [np.nan, np.nan, np.nan]
 
     if use_lorentzian:
-        res = lorentzian_fit(frequencies, power_spectrum,
-                             filter_maximum=filter_maximum)
+        res = lorentzian_fit(frequencies, power_spectrum, filter_maximum=filter_maximum)
 
     if np.isnan(res[0]):
         res = get_peak_parameters(frequencies, power_spectrum)
@@ -396,8 +395,7 @@ def _get_normal_mode_decomposition_numba(
             # Loop over atoms and components
             for i in range(number_of_cell_atoms):
                 for m in range(velocity_components):
-                    projection_sum += velocities[n,
-                                                 i, m] * eigenvectors[k, i, m]
+                    projection_sum += velocities[n, i, m] * eigenvectors[k, i, m]
 
             # Store the result in the projected velocities array
             velocities_projected[n, k] = projection_sum
@@ -408,9 +406,7 @@ def _get_normal_mode_decomposition_numpy(
 ) -> None:
 
     # Use einsum to perform the double summation over cell atoms and time steps
-    velocities_projected += np.einsum(
-        'tij,kij->tk', velocities, eigenvectors.conj()
-    )
+    velocities_projected += np.einsum("tij,kij->tk", velocities, eigenvectors.conj())
 
     # number_of_cell_atoms = velocities.shape[1]
     # number_of_frequencies = eigenvectors.shape[0]

dfttoolkit/vibrations.py

@@ -74,9 +74,9 @@ def get_displacements(self, displacement: float = 0.0025) -> list:
 
         Parameters
         ----------
-        displacement : float, optional
+        displacement : float, default=0.0025
             Displacement for finte difference calculation of vibrations in
-            Angstrom. The default is 0.0025 Angstrom.
+            Angstrom.
 
         Returns
         -------
@@ -133,9 +133,8 @@ def get_hessian(
 
         Parameters
         ----------
-        set_constrained_atoms_zero : bool, optional
+        set_constrained_atoms_zero : bool, default=False
             Set elements in Hessian that code for constrained atoms to zero.
-            The default is False.
 
         Returns
         -------
@@ -185,8 +184,8 @@ def get_symmetrized_hessian(self, hessian=None):
 
         Parameters
         ----------
-        hessian : TYPE, optional
-            DESCRIPTION. The default is None.
+        hessian : TYPE, default=None
+            DESCRIPTION
 
         Returns
         -------
@@ -227,13 +226,12 @@ def get_eigenvalues_and_eigenvectors(
         ----------
         hessian : npt.NDArray[np.float64], optional
             Hessian. The default is None.
-        only_real : bool, optional
+        only_real : bool, default=True
             Returns only real valued eigenfrequencies + eigenmodes
             (ATTENTION: if you want to also include instable modes, you have to
-            symmetrize the hessian as provided below). The default is True.
-        symmetrize_hessian : bool, optional
+            symmetrize the hessian as provided below).
+        symmetrize_hessian : bool, default=True
             Symmetrise the hessian only for this function (no global change).
-            The default is True.
 
         Returns
         -------
@@ -500,10 +498,10 @@ def get_cross_correlation_function(
             DESCRIPTION.
         time_step : float
             DESCRIPTION.
-        bootstrapping_blocks : int, optional
-            DESCRIPTION. The default is 1.
-        bootstrapping_overlap : int, optional
-            DESCRIOTION. The default is 0.
+        bootstrapping_blocks : int, default=1
+            DESCRIPTION
+        bootstrapping_overlap : int, default=0
+            DESCRIPTION
 
         Returns
         -------

tests/test_output.py

@@ -50,10 +50,10 @@ def test_get_geometry(self):
 
         if self._aims_fixture_no in [1, 2, 3, 5, 7, 9]:
             assert len(geom) == 3
-            assert geom.get_is_periodic() == False
+            assert geom.get_is_periodic() is False
         else:
             assert len(geom) == 2
-            assert geom.get_is_periodic() == True
+            assert geom.get_is_periodic() is True
 
     # TODO
     # def test_get_parameters(self):