Update contributing_code.md

_doc/contributing_code.md

@@ -40,7 +40,7 @@ Widely used general functionality is part of the files on the top level (e.g. ni
 ## Data Classes and X-Arrays 
 Cedalion is using XArrays as main data structures for processing. There are currently two jupyter notebooks that explain these data structures:
 
-[Using xarray-based data structures for calculating the Beer-Lambert transformation](https://github.com/ibs-lab/cedalion/blob/main/examples/new_conference_example1.ipynb)
+[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)
 
 [An example finger tapping analysis](https://github.com/ibs-lab/cedalion/blob/main/examples/new_conference_example2.ipynb)
 
@@ -83,4 +83,4 @@ def function_name(inputVar1: cdt.NDTimeSeries, inputVar2: Quantity):
 
 This generates a new function *function_name* with two input arguments *inputVar1* and *inputVar2* that returns what it did as *something*.
 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 expected to be of the data type *Quantity* ==[TBD? @emiddell]==.   
-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 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.