added docs

stocproc/__init__.py

@@ -1,6 +1,32 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""
+Stochastic Process Module
+=========================
+
+This module contains two different implementation for generating stochastic processes for a
+given auto correlation function. Both methods are based on a time discrete process, however cubic
+spline interpolation is assured to be valid within a given tolerance.
+
+* simulate stochastic processes using Karhunen-Loève expansion :py:func:`stocproc.StocProc_KLE_tol`
+
+  Setting up the class involves solving an eigenvalue problem which grows with
+  the time interval the process is simulated on. Further generating a new process
+  involves a multiplication with that matrix, therefore it scales quadratically with the
+  time interval. Nonetheless it turns out that this method requires less random numbers
+  than the Fast-Fourier method.
+
+* simulate stochastic processes using Fast-Fourier method method :py:func:`stocproc.StocProc_FFT_tol`
+
+  Setting up this class is quite efficient as it only calculates values of the
+  associated spectral density. The number scales linear with the time interval of interest. However to achieve
+  sufficient accuracy many of these values are required. As the generation of a new process is based on
+  a Fast-Fouried-Transform over these values, this part is comparably lengthy.
+"""
+
+version = '0.2.0'
+
 from .stocproc import StocProc_FFT_tol
 from .stocproc import StocProc_KLE
 from .stocproc import StocProc_KLE_tol

stocproc/tools.py

@@ -1,44 +1,5 @@
 # -*- coding: utf8 -*-
-"""
-**Stochastic Process Module**
- 
-This module contains various methods to generate stochastic processes for a 
-given correlation function. There are two different kinds of generators. The one kind
-allows to generate the process for a given time grid, where as the other one generates a
-time continuous process in such a way that it allows to "correctly" interpolate between the
-solutions of the time discrete version.
- 
-    **time discrete methods:**
-        :py:func:`stochastic_process_kle` 
-        Simulate Stochastic Process using Karhunen-Loève expansion
-        
-            This method still needs explicit integrations weights for the 
-            numeric integrations. For convenience you can use
-             
-                :py:func:`stochastic_process_mid_point_weight` simplest approach, for
-                test reasons only, uses :py:func:`get_mid_point_weights` 
-                to calculate the weights
-                 
-                :py:func:`stochastic_process_trapezoidal_weight` little more sophisticated,
-                uses :py:func:`get_trapezoidal_weights_times` to calculate the weights
-                 
-                :py:func:`stochastic_process_simpson_weight`,
-                **so far for general use**, uses :py:func:`get_simpson_weights_times` 
-                to calculate the weights
-     
-         
-        :py:func:`stochastic_process_fft`
-        Simulate Stochastic Process using FFT method
-         
-    **time continuous methods:**
-        :py:class:`StocProc` 
-        Simulate Stochastic Process using Karhunen-Loève expansion and allows
-        for correct interpolation. This class still needs explicit integrations 
-        weights for the numeric integrations (use :py:func:`get_trapezoidal_weights_times`
-        for general purposes).
-         
-        .. todo:: implement convenient classes with fixed weights
-"""
+
 from __future__ import print_function, division
 
 from scipy.interpolate import InterpolatedUnivariateSpline