diff --git a/docs/source/api.rst b/docs/source/api.rst
index 05d6f70..b1a26d0 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -18,7 +18,7 @@ and the classes implementing the ``StocProc`` interface.
 Function on mudule level
 ------------------------
 
-.. autofunction:: stocproc.loggin_setup
+.. autofunction:: stocproc.logging_setup
 .. autofunction:: stocproc.version
 .. autofunction:: stocproc.version_full
 
diff --git a/stocproc/stocproc.py b/stocproc/stocproc.py
index 9e774e1..b2fac75 100644
--- a/stocproc/stocproc.py
+++ b/stocproc/stocproc.py
@@ -1,5 +1,6 @@
 import abc
 from functools import partial
+from typing import Optional
 import numpy as np
 import time
 
@@ -80,6 +81,7 @@ class StocProc(abc.ABC):
     :param seed: if not ``None`` seed the random number generator with ``seed``
     :param t_axis: an explicit definition of times t_k (may be non equidistant)
     :param scale: passes ``scale`` to :py:func:`set_scale`
+    :param calc_deriv: whether to calculate the derivative of the stochastic process
 
     Note: :py:func:`new_process` is **not** called on init. If you want to evaluate a particular
     realization of the stocastic process, a new sample needs to be drawn by calling :py:func:`new_process`.
@@ -87,14 +89,18 @@ class StocProc(abc.ABC):
 
     """
 
-    def __init__(self, t_max=None, num_grid_points=None, seed=None, scale=1):
+    def __init__(
+        self, t_max=None, num_grid_points=None, seed=None, scale=1, calc_deriv=False
+    ):
         self.t_max = t_max
         self.num_grid_points = num_grid_points
         self.t = np.linspace(0, t_max, num_grid_points)
 
         self._z = None
         self._interpolator = None
+        self._interpolator_dot = None
         self._seed = seed
+        self.calc_deriv = calc_deriv
         if seed is not None:
             np.random.seed(seed)
         self._one_over_sqrt_2 = 1 / np.sqrt(2)
@@ -126,6 +132,21 @@ class StocProc(abc.ABC):
         else:
             return self._interpolator(t)
 
+    def dot(self, t: Optional[np.ndarray] = None) -> np.ndarray:
+        r"""Returns the derivative of the stochastic process.
+
+        Works the same as :meth:`__call__` in all other regards.
+        """
+        if self._z_dot is None:
+            raise RuntimeError(
+                "StocProc has NO random data, call 'new_process' to generate a new random process"
+            )
+
+        if t is None:
+            return self._z_dot
+        else:
+            return self._interpolator_dot(t)
+
     @abc.abstractmethod
     def calc_z(self, y):
         r"""*abstract method*
@@ -138,10 +159,34 @@ class StocProc(abc.ABC):
         """
         pass
 
+    @abc.abstractmethod
+    def calc_z_dot(self, y):
+        r"""*abstract method*
+
+        An implementation needs to map :math:`M` independent complex
+        valued and Gaussian distributed random variables :math:`Y_m`
+        (with :math:`\langle Y_m \rangle = 0 = \langle Y_m Y_{m'}\rangle` and :math:`\langle Y_m Y^\ast_{m'} = \delta_{mm'}\rangle`) to the discrete time derivative of stochastic
+        process :math:`z_n = z(t_n)`.
+
+        :return: the derivative of the discrete time stochastic process :math:`z_n`,
+                 array of complex numbers
+        """
+        pass
+
     def _calc_scaled_z(self, y):
-        r"""scaled the discrete process z with sqrt(scale), such that <z_i z^ast_j> = scale bcf(i,j)"""
+        r"""
+        scaled the discrete process z with sqrt(scale), such that <z_i
+        z^ast_j> = scale bcf(i,j)
+        """
         return self.sqrt_scale * self.calc_z(y)
 
+    def _calc_scaled_z_dot(self, y):
+        r"""
+        scaled derivative of the discrete process z with sqrt(scale),
+        such that <z_i z^ast_j> = scale bcf(i,j)
+        """
+        return self.sqrt_scale * self.calc_z_dot(y)
+
     @abc.abstractmethod
     def get_num_y(self):
         r"""*abstract method*
@@ -191,6 +236,10 @@ class StocProc(abc.ABC):
                 )
 
         self._z = self._calc_scaled_z(y)
+
+        if self.calc_deriv:
+            self._z_dot = self._calc_scaled_z_dot(y)
+
         log.debug(
             "proc_cnt:{} new process generated [{:.2e}s]".format(
                 self._proc_cnt, time.time() - t0
@@ -198,6 +247,12 @@ class StocProc(abc.ABC):
         )
         t0 = time.time()
         self._interpolator = fcSpline.FCS(x_low=0, x_high=self.t_max, y=self._z)
+
+        if self.calc_deriv:
+            self._interpolator_dot = fcSpline.FCS(
+                x_low=0, x_high=self.t_max, y=self._z_dot
+            )
+
         log.debug("created interpolator [{:.2e}s]".format(time.time() - t0))
 
     def set_scale(self, scale):
@@ -429,6 +484,7 @@ class StocProc_FFT(StocProc):
         seed=None,
         negative_frequencies=False,
         scale=1,
+        calc_deriv: bool = False,
     ):
         self.key = self.get_key(
             alpha=alpha, t_max=t_max, intgr_tol=intgr_tol, intpl_tol=intpl_tol
@@ -478,13 +534,20 @@ class StocProc_FFT(StocProc):
 
         t_max = (num_grid_points - 1) * dt
         super().__init__(
-            t_max=t_max, num_grid_points=num_grid_points, seed=seed, scale=scale
+            t_max=t_max,
+            num_grid_points=num_grid_points,
+            seed=seed,
+            scale=scale,
+            calc_deriv=calc_deriv,
         )
 
-        self.yl = spectral_density(dx * np.arange(N) + a + dx / 2) * dx / np.pi
+        self.omega_min = a + dx / 2
+        self.omega_k = dx * np.arange(N) + self.omega_min
+        self.yl = spectral_density(self.omega_k) * dx / np.pi
         self.yl = np.sqrt(self.yl)
+
         self.omega_min_correction = np.exp(
-            -1j * (a + dx / 2) * self.t
+            (-1j * self.omega_min * self.t)
         )  # self.t is from the parent class
 
     @staticmethod
@@ -499,25 +562,35 @@ class StocProc_FFT(StocProc):
         return (
             self.yl,
             self.num_grid_points,
+            self.omega_min,
             self.omega_min_correction,
+            self.omega_k,
             self.t_max,
             self._seed,
             self.scale,
             self.key,
+            self.calc_deriv,
         )
 
     def __setstate__(self, state):
         (
             self.yl,
             num_grid_points,
+            self.omega_min,
             self.omega_min_correction,
+            self.omega_k,
             t_max,
             seed,
             scale,
             self.key,
+            calc_deriv,
         ) = state
         super().__init__(
-            t_max=t_max, num_grid_points=num_grid_points, seed=seed, scale=scale
+            t_max=t_max,
+            num_grid_points=num_grid_points,
+            seed=seed,
+            scale=scale,
+            calc_deriv=calc_deriv,
         )
 
     def calc_z(self, y):
@@ -528,10 +601,21 @@ class StocProc_FFT(StocProc):
 
         and return values :math:`z_n` with :math:`t_n <= t_\mathrm{max}`.
         """
+
         z_fft = np.fft.fft(self.yl * y)
         z = z_fft[0 : self.num_grid_points] * self.omega_min_correction
+
         return z
 
+    def calc_z_dot(self, y: np.ndarray) -> np.ndarray:
+        r"""Calculate the discrete time stochastic process derivative using FFT algorithm
+        and return values :math:`\dot{z}_n` with :math:`t_n <= t_\mathrm{max}`.
+        """
+
+        z_dot_fft = np.fft.fft(-1j * self.omega_k * self.yl * y)
+        z_dot = z_dot_fft[0 : self.num_grid_points] * self.omega_min_correction
+        return z_dot
+
     def get_num_y(self):
         r"""The number of independent random variables :math:`Y_m` is given by the number of discrete nodes
         used by the Fast Fourier Transform algorithm.