updated links in contributing_code.md to main branch

_doc/contributing_code.md

@@ -9,7 +9,7 @@ It is smart to make yourself aware of five resources and concepts that build the
 3) **Xarrays** are a package that makes work with labelled mult-dimensional arrays very simple. If you develop code for cedalion, you will youse Xarrays. To get started, make yourself acquainted with one of two key data types: *xarray.DataArray* and *xarray.DataSet*. Most functions that you write should expect an xarray DataArray as main input for the data that you want to process, alongside arguments that pass variables for additional info. You can find the official [Xarray documentation here](https://docs.xarray.dev/en/stable/).
 4) **Units**: One of the charms of Xarrays that Cedalion is taking advantage of is that functions can implicitly consider units and thus avoid typical errors from (missing) unit conversion. For example, if you work with coordinates for fNIRS optodes or landmarks, as long as they have a proper unit like "m", "cm", "mm" assigned, you do not have to explicitly take care of conversions anymore. To make use of this feature, Cedalion's functions should expect input arguments that are "Quantities" with "units" wherever possible. To assign a unit to your variable, simply import `from cedalion import Quantity, units` and multiply your variable with the right unit. For instance: `sd_distance = 3*units.cm`. Cedalion's units are based on the **pint** package, which is [documented here](https://pint.readthedocs.io/en/stable/index.html).
 5) **Data Containers and Data Structures**: The main objects to pass along your functions and processing pipelines. We are currently working on defining and documenting these. In the meantime, please work with Xarray DataArrays and variables with units as in and outputs for your functions, and be aware that the main format for fNIRS/DOT data that we read and write is the [SNIRF format](https://github.com/fNIRS/snirf). To easily get started, you can:
-    1. Check out the example notebook on [Using xarray-based data structures for calculating the Beer-Lambert transformation](https://github.com/ibs-lab/cedalion/blob/alex_working/examples/pruning_and_motion_artifacts.ipynb)
+    1. Check out the example notebook on [Using xarray-based data structures for calculating the Beer-Lambert transformation](https://github.com/ibs-lab/cedalion/blob/main/examples/pruning_and_motion_artifacts.ipynb)
     2. Check out the example notebook on [An example finger tapping analysis](https://github.com/ibs-lab/cedalion/blob/main/examples/new_conference_example2.ipynb)
     3. Use the following code snippet to load snirf data into Xarray DataArrays (amp, geo, od, conc, ml) and combine them into an Xarray DataSet. 
 ```# get example finger tapping dataset
@@ -87,7 +87,7 @@ Widely used general functionality is part of the files on the top level (e.g. ni
 ## Example for contributing new functionality to Cedalion
 In this example we will incorporate add a few simple functions to the toolbox: Some helper functions that use different quality thresholds to differentiate good from bad fNIRS/DOT channels. A function that combines these helper functions to  prune bad channels in a dataset. These are replicating approaches from the Homer3 "hmR_PruneChannels" function.
 
-The corresponding jupyter notebook example to showcase the resulging code is the following --> **[Pruning and Motion Artefacts Notebook Example](https://github.com/ibs-lab/cedalion/blob/alex_working/examples/pruning_and_motion_artifacts.ipynb)**. <--
+The corresponding jupyter notebook example to showcase the resulging code is the following --> **[Pruning and Motion Artefacts Notebook Example](https://github.com/ibs-lab/cedalion/blob/main/examples/pruning_and_motion_artifacts.ipynb)**. <--
 
 ### What functions and where do they functions belong?
 We will create three helper functions: `snr_range()`, `sd_range()` and `amp_range()` that use the SNR, the source-detector distance, and the signal amplitudes to assess whether they are within a desired (user-specified) range. We will then combine them in a `prune()` function. After browsing the */src/* directory we realize that all these functions should go to */siproc/quality.py* as they are dealing with assessing signal quality. To use these functions later on in our pipelines, we can
@@ -124,7 +124,7 @@ This generates a new function *function_name* with two input arguments *inputVar
 The input arguments should have expected data types assigned to them. Here, *inputVar1* is expected to be of the data type "NDTimeSeriesSchema", which is an xarray that contains at least two dimensions "channel" and "time". *inputVar2* is any input variable, that ideally (if applicable) should have a unit assigned to it.    
 The function is wrapped by putting `@cdc.validate_schemas`in front, which will check these data types and assert an error at runtime, if the inputs do not match the expected type.
 
-The following examples are implemented in the [quality.py module](https://github.com/ibs-lab/cedalion/blob/alex_working/src/cedalion/sigproc/quality.py)
+The following examples are implemented in the [quality.py module](https://github.com/ibs-lab/cedalion/blob/main/src/cedalion/sigproc/quality.py)
 
 ### The helper functions
 Now we can create the small helper functions that calculate and check the SNR, Source-Detector Distances and Amplitudes of fNIRS channels. Using the coordinates and units from Xarrays these are effectively implemented: