diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 98e3b1f..ab4331c 100644 --- a/CHANGELOG.rst +++ b/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 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 3a2b875..bbf8bb8 100644 --- a/CONTRIBUTING.md +++ b/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. diff --git a/README.rst b/README.rst index ed9f8be..e594dae 100644 --- a/README.rst +++ b/README.rst @@ -250,7 +250,7 @@ Examples can be found under `examples/ `_ in the repository. Documentation ------------- -The full documentation (API reference, CONFIG file structure, ...) can be found on `Read the Docs `_. +The full documentation (API reference, CONFIG file structure, ...) can be found on `Read the Docs `_. How to contribute -----------------