Update readme

.readthedocs.yaml

@@ -0,0 +1,29 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-20.04
+  tools:
+    python: "3.9"
+    # You can also specify other tool versions:
+    # nodejs: "16"
+    # rust: "1.55"
+    # golang: "1.17"
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: doc/conf.py
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+# formats:
+#    - pdf
+
+# Optionally declare the Python requirements required to build your docs
+python:
+  install:
+    - requirements: doc/requirements.txt

readme.md

@@ -13,60 +13,10 @@ Its development is supported by the [BMBF: FKZ 13GW0119](https://www.medizintech
 #### Weighted Comb Filter
 Artifacts can be _non-stationary_ and _non-sinusoidal_, but are required to be _periodic_. Comb filters natively support only frequencies which are integer divisibles of the sampling frequency. This can be circumvented by resampling the signal, and has been implemented.
 
-##### Creation
+#### Documentation
 
-The following example creates a kernel for a _classical_ causal comb filte for 
-an artifcat with a period of 10Hz and sampled at 1000Hz:
-```{python}        
-    kernel = create_kernel(
-        freq=10, fs=1000, width=1, left_mode="uniform", right_mode="none"
-    )
-```
-
-or the superposition of moving averages (SMA) filter as discussed e.g. by [1], 
-here for 5 periods at 10 Hz and at 1000Hz sampling rate:
+See
 
-```{python}    
-    kernel = create_kernel(freq=10, fs:1000, width:5, 
-                           left_mode:str='uniform', 
-                           right_mode:str='uniform')
-```    
-
-
-[1]: Kohli, S., Casson, A.J., 2015. Removal of Transcranial a.c. Current Stimulation 
-artifact from simultaneous EEG recordings by superposition of moving averages.
-Conf Proc IEEE Eng Med Biol Soc 2015, 3436–3439. 
-https://doi.org/10.1109/EMBC.2015.7319131
-
-##### Application
-
-The kernels, once created can be applied to the signal as follows:
-
-```{python}    
-    filtered_data = filter_2d(artifacted_data, freq=20, fs=1000, kernel:ndarray)
-```
-
-The kernel application is implemented for 2-dimensional data, which calls the 
-1-dimensional implementation in a for-loop. Consider that you have to specify
-the artifact frequency and sampling rate again. This is because the kernel only
-makes sense if the period is an integer divisible of the sampling rate. 
-If it is not, the signal is automaticall up-sampled, processed, and down-sampled.
-This allows to remove the artifact, but is far from perfect. Additionally, 
-the function estimates the period of the kernel, and throws an exception if 
-it does not match the specified frequency. This ensures that the correct kernel
-is used.
-
-### Periodic Component Removal
-
-An alternative is the creation and removal of periodic templates, until the 
-artifact power is sufficiently suppressed. This can be achieved using 
-
-```{python}
-    from artacs import StepwiseRemover
-    remover = StepwiseRemover(freq=20, s=1000)    
-    filtered_data =  remover.process(artifacted_data)
-```
+#### Matlab
 
 See also [![DOI](https://zenodo.org/badge/87182503.svg)](https://zenodo.org/badge/latestdoi/87182503) for a similar implementation in Matlab.
-
-