fine touches to docstrings

Author: pavelkomarov

pynumdiff/kalman_smooth/_kalman_smooth.py

@@ -8,7 +8,7 @@ def kalman_filter(y, _t, xhat0, P0, A, Q, C, R, B=None, u=None, save_P=True):
 
     :param np.array y: series of measurements, stacked along axis 0.
     :param float or array[float] _t: This function supports variable step size. This parameter is either the constant
-        dt if given as a single float, or data locations if given as an array of same length as :code:`x`.
+        dt if given as a single float, or sample locations if given as an array of same length as :code:`x`.
     :param np.array xhat0: a priori guess of initial state at the time of the first measurement
     :param np.array P0: a priori guess of state covariance at the time of first measurement (often identity matrix)
     :param np.array A: state transition matrix, in discrete time if constant dt, in continuous time if variable dt
@@ -77,7 +77,7 @@ def rts_smooth(_t, A, xhat_pre, xhat_post, P_pre, P_post, compute_P_smooth=True)
     """Perform Rauch-Tung-Striebel smoothing, using information from forward Kalman filtering.
 
     :param float or array[float] _t: This function supports variable step size. This parameter is either the constant
-        dt if given as a single float, or data locations if given as an array of same length as :code:`x`.
+        dt if given as a single float, or sample locations if given as an array of same length as the state histories.
     :param np.array A: state transition matrix, in discrete time if constant dt, in continuous time if variable dt
     :param list[np.array] xhat_pre: a priori estimates of xhat from a kalman_filter forward pass
     :param list[np.array] xhat_post: a posteriori estimates of xhat from a kalman_filter forward pass

pynumdiff/linear_model/_linear_model.py

@@ -238,7 +238,7 @@ def rbfdiff(x, _t, sigma=1, lmbd=0.01):
 
     :param np.array[float] x: data to differentiate
     :param float or array[float] _t: This function supports variable step size. This parameter is either the constant
-        dt if given as a single float, or data locations if given as an array of same length as :code:`x`.
+        :math:`\\Delta t` if given as a single float, or data locations if given as an array of same length as :code:`x`.
     :param float sigma: controls width of radial basis function
     :param float lmbd: controls strength of bias toward data
 

pynumdiff/polynomial_fit/_polynomial_fit.py

@@ -11,7 +11,7 @@ def splinediff(x, _t, params=None, options={}, degree=3, s=None, num_iterations=
 
     :param np.array[float] x: data to differentiate
     :param float or array[float] _t: This function supports variable step size. This parameter is either the constant
-        dt if given as a single float, or data locations if given as an array of same length as :code:`x`.
+        :math:`\\Delta t` if given as a single float, or data locations if given as an array of same length as :code:`x`.
     :param list params: (**deprecated**, prefer :code:`degree`, :code:`cutoff_freq`, and :code:`num_iterations`)
     :param dict options: (**deprecated**, prefer :code:`num_iterations`) an empty dictionary or {'iterate': (bool)}
     :param int degree: polynomial degree of the spline. A kth degree spline can be differentiated k times.