Prepare release v0.5.0 (#532)

CHANGELOG.md

@@ -1,3 +1,91 @@
+# 0.5.0
+
+### API changes
+
+- Synapse views no longer exist (#447, #453, @jnsbck). Previous code such as
+```python
+net.IonotropicSynapse.set("IonotropicSynapse_s", 0.2)
+```
+must be updated to:
+```python
+net.set("IonotropicSynapse_s", 0.2)
+```
+For a more detailed tutorial on how to index synapses, see
+[this new tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html).  
+- Throughout the codebase, we renamed any occurance of `seg` (for `segment`) to `comp`
+(for `compartment`). The most notable user-facing changes are:
+  - `branch = jx.Branch(comp, ncomp=4)`
+  - `cell = jx.read_swc(fname, ncomp=4)`
+- New defaults for the SWC reader with `jx.read_swc()`. By default, we now have
+`assign_groups=True` (previously `False`) and `max_branch_len=None` (previously
+`300.0`).
+- We renamed `.view` to `.nodes`, e.g., `cell.branch(0).nodes` (#447, #453, @jnsbck).
+- We renamed `_update_nodes_with_xyz()` to `compute_compartment_centers()` (#520,
+@jnsbck)
+- We updated the way in which transformations are built (#455, @manuelgloeckler).
+Previous code such as
+```python
+tf = jx.ParamTransform(
+    lower={"radius": 0.1, "length": 2.0},
+    lower={"radius": 3.0, "length": 20.0},
+)
+```
+must be updated to:
+```python
+from jaxley.optimize.transforms import ParamTransform, SigmoidTransform
+transforms = [
+    {"radius": SigmoidTransform(lower=0.1, upper=3.0)},
+    {"length": SigmoidTransform(lower=2.0, upper=20.0)},
+]
+tf = jt.ParamTransform(transforms)
+```
+
+### New features
+
+- Added a new `delete_channel()` method (#521, @jnsbck)
+- Allow to write trainables to the module (#470, @michaeldeistler):
+```python
+net.make_trainable("radius")
+params = net.get_parameters()
+net.write_trainables(params)
+```
+- Expose the step function to allow for fine-grained simulation (#466, @manuelgloeckler)
+- More flexible and throrough viewing (#447, #453, @jnsbck)
+- Boolean indexing for cells, branches, and comps (@494, @jnsbck):
+```python
+r_greater_1 = net.nodes.groupby("global_cell_index")["radius"].mean() > 1
+net[r_greater_1].nodes.vis()
+```  
+- check if recordings are empty (#460, @deezer257)
+- enable `clamp` to be jitted and vmapped with `data_clamp()` (#374, @kyralianaka)
+
+### Bug fixes
+
+- allow for cells that were read from swc to be pickled (#525, @jnsbck)
+- fix units of `compute_current()` in channels (#461, @michaeldeistler)
+- fix issues with plotting when the morphology has a different number of compartments
+(#513, @jnsbck)
+
+### Documentation
+
+- new tutorial on synapse indexing (#464, @michaeldeistler, @zinaStef)
+- new tutorial on parameter sharing (#464, @michaeldeistler, @zinaStef)
+- new tutorial on modules and views (#493, @jnsbck)
+- improved tutorial on building channel models (#473, @simoneeb)
+- get rid of tensorflow dependency by defining our simple dataloader in the tutorial
+(#484, @jnsbck)
+- new FAQ about rate-based networks (#531, @michaeldeistler)
+
+### Code health
+
+- refactor tests with fixtures (@479, #499, @fabioseel, @jnsbck)
+- make several attributes private (#495, @ntolley)
+- move `read_swc.py` to new `io` folder (#524, @jnsbck)
+- faster testing for SWC and plotting (#479, @fabioseel)
+- automated tests to check if tutorials can be run (#480, @jnsbck)
+- add helpers to deprecate functions and kwargs (#516, @jnsbck)
+
+
 # 0.4.0
 
 ### New features

docs/faq/question_04.md

@@ -1,10 +1,12 @@
 # How can I implement rate-based neuron models in Jaxley?
 
 In this FAQ, we explain how one can implement rate-based neuron models of the form:
+
 $$
 \tau \frac{dV}{dt} = -V + \sum w_{\text{syn}} \phi(V_{\text{pre}})
 $$
-Here, $\phi$ is a nonlinearity such as a `TanH` or a `ReLU`.
+
+Here, $\phi$ is a nonlinearity such as a `tanh` or a `ReLU`.
 
 To implement this in `Jaxley`, we first have to set up a network consisting of 
 point-neurons:

jaxley/__version__.py

@@ -1,6 +1,6 @@
 # This file is part of Jaxley, a differentiable neuroscience simulator. Jaxley is
 # licensed under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
 
-VERSION = (0, 4, 0)
+VERSION = (0, 5, 0)
 
 __version__ = ".".join(map(str, VERSION))

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "Jaxley"
-version = "0.4.0"
+version = "0.5.0"
 description = "Differentiable neuron simulations."
 authors = [
     { name = "jaxleyverse", email = "[email protected]"},