Updated documentation path

CHANGELOG.rst

@@ -6,6 +6,7 @@ Version 1.0.2
 
 - Added new features and examples (cross-validation, synapse parameter correlation)
 - Changed afferent section types in accordance with MorphIO (1: soma, 2: axon, 3: basal dendrite, 4: apical dendrite)
+- Use of MorphIO collections
 - Improved readme and documentation
 
 

CONTRIBUTING.md

@@ -124,13 +124,13 @@ All model fitting functions are implemented as separate code modules (.py files)
   - `build()` for fitting model parameters against the data extracted during the previous step and initializing a model instance which will then be stored automatically as a .json file, optionally together with an associated HDF5 file
   - `plot()` for generating visualizations of the extracted data versus the model output, and storing them in the output folder
 
-Importantly, arbitrary parameters (optionally, including default values) can be added as keyword arguments to any of the three functions, values of which must be provided through a configuration file (see *Configuration file structure* in the [Documentation](https://connectome-manipulator.readthedocs.io/en/netneuro-24-0092-rev1/config_file_structure.html)) when launching model building.
+Importantly, arbitrary parameters (optionally, including default values) can be added as keyword arguments to any of the three functions, values of which must be provided through a configuration file (see *Configuration file structure* in the [Documentation](https://connectome-manipulator.readthedocs.io/en/stable/config_file_structure.html)) when launching model building.
 
 ## Manipulations
 
 All manipulations are derived from an abstract base class `Manipulation` which is implemented in [`/connectome_manipulation/manipulation/base.py`](connectome_manipulator/connectome_manipulation/manipulation/base.py). The base class provides access to the neurons of a network model (through `self.nodes`) as well as to the input (i.e., before a manipulation) and output (i.e., after a manipulation) synapse tables (through `self.writer`). An alternative (abstract) base class, `MorphologyCachingManipulation`, exists which additionally provides efficient access to morphologies (through `self._get_tgt_morphs`) including a caching mechanism, i.e., without reloading them from the file system in case of repeated invocations.
 
-A concrete manipulation must be implemented in a derived classes and stored in a separate code module (.py file) under [`/connectome_manipulation/manipulation`](connectome_manipulator/connectome_manipulation/manipulation). It must contain an implementation for the `apply()` method which must return a new synapse table (through `self.writer`) as a result of the manipulation. Importantly, arbitrary parameters (optionally, including default values) can be added as keyword arguments to the `apply()` method, values of which must be provided through a configuration file (see *Configuration file structure* in the [Documentation](https://connectome-manipulator.readthedocs.io/en/netneuro-24-0092-rev1/config_file_structure.html)) when launching a manipulation.
+A concrete manipulation must be implemented in a derived classes and stored in a separate code module (.py file) under [`/connectome_manipulation/manipulation`](connectome_manipulator/connectome_manipulation/manipulation). It must contain an implementation for the `apply()` method which must return a new synapse table (through `self.writer`) as a result of the manipulation. Importantly, arbitrary parameters (optionally, including default values) can be added as keyword arguments to the `apply()` method, values of which must be provided through a configuration file (see *Configuration file structure* in the [Documentation](https://connectome-manipulator.readthedocs.io/en/stable/config_file_structure.html)) when launching a manipulation.
 
 ## Structural comparison functions
 
@@ -139,4 +139,4 @@ All structural comparison functions are implemented as separate code modules (.p
   - `compute()` for computing specific structural features from a given connectome (e.g., connection probability by layer), which will be evaluated for both connectomes to compare and results of which will be automatically stored as .pickle files by the framework
   - `plot()` for plotting a graphical representation of individual feature instances (e.g., 2D matrix plot of connection probabilities by layer) or the difference between two such instances, which will be automatically stored in a compound output figure when comparing two connectomes
 
-Importantly, arbitrary parameters (optionally, including default values) can be added as keyword arguments to the two functions, values of which must be provided through a configuration file (see *Configuration file structure* in the [Documentation](https://connectome-manipulator.readthedocs.io/en/netneuro-24-0092-rev1/config_file_structure.html)) when launching a structural comparison.
+Importantly, arbitrary parameters (optionally, including default values) can be added as keyword arguments to the two functions, values of which must be provided through a configuration file (see *Configuration file structure* in the [Documentation](https://connectome-manipulator.readthedocs.io/en/stable/config_file_structure.html)) when launching a structural comparison.

README.rst

@@ -250,7 +250,7 @@ Examples can be found under `examples/ </examples>`_ in the repository.
 Documentation
 -------------
 
-The full documentation (API reference, CONFIG file structure, ...) can be found on `Read the Docs <https://connectome-manipulator.readthedocs.io/en/netneuro-24-0092-rev1>`_.
+The full documentation (API reference, CONFIG file structure, ...) can be found on `Read the Docs <https://connectome-manipulator.readthedocs.io>`_.
 
 How to contribute
 -----------------