Ratinabox

New plotting functions and demo focussing on head direction

Head direction selectivity can be plotted for _all_ cells.
python
Env = Environment()
Ag = Agent(Env)
HDCs = HeadDirectionCells(Ag, params={'n':3,'color':'C5'}) or any other
HDCs.plot_angular_rate_map()



Spatial rate maps can be plotted _averaged_ over all head directions (for use in instances where rate maps may be position and head direction selective).
This is supported by a new internal function `get_head_direction_averaged_state()` which calculates the state at all head direction 0 --> 2pi and then averages over these return the spatial receptive field.

python
Env = Environment()
Ag = Agent(Env)
GCs = GridCells(Ag, params={'n':3}) or any other.
GCs.plot_rate_map(method="groundtruth_headdirectionaveraged")

(in this case its actually redundant because grid cells have no head direction selectivity but in [this demo](https://github.com/RatInABox-Lab/RatInABox/blob/dev/demos/conjunctive_gridcells_example.ipynb) we show a more involved use case

New Conjunctive grid cells demo
In this demo we showcase `FeedForwardLayer` in probably its most simple use-case. Combining head direction cells and grid cells non-linearly to make head direction selective grid cells (conjunctive grid cells)

`AgentVectorCells` and `FieldOfViewAVCs`

`AgentVectorCells` are like `ObjectVectorCells` but respond to other `Agent`s in the `Environment`.
`FieldOfViewAVCs` are a subclass of AVCs arranged in a "field of view" as shown in the attached video. A demo can be found [here](https://github.com/RatInABox-Lab/RatInABox/blob/dev/demos/vector_cell_demo.ipynb) (scroll to the bottom). This relates to #89 and closes 91.

https://github.com/RatInABox-Lab/RatInABox/assets/41446693/300baddc-24ca-4495-aae6-916b3bf0a8a6


python
from ratinabox.Neurons import AgentVectorCells, FieldOfViewAVCs

Env = Environment()
Ag1 = Agent(Env)
Ag2 = Agent(Env)
AVCs_1to2 = FieldOfViewAVCs(Ag1, Other_Agent=Ag2)
AVCs_2to1 = FieldOfViewAVCs(Ag2, Other_Agent=Ag1)

remember to update both agents and neurons in the update loop:
for _ in range(int(20/Ag1.dt)):
Ag1.update()
Ag2.update()
AVCs_1to2.update()
AVCs_2to1.update()


Other minor changes
* Changes to `VectorCells` and `GridCell` API for how parameters are initialised/sampled (should be backward compatible, a warning might be thrown if you use old parameter names).
* Bug fixes
* New DNN demo

We made a website for RatInABox which will eventually host all the documentation, demos etc. It's hosted on Github pages and can be found inside `docs`. [https://ratinabox-lab.github.io/RatInABox](https://ratinabox-lab.github.io/RatInABox)



Then I uploaded v1.10.1 but for some reason the source distribution files included all the demos etc and blew up in size to 90MB (I have no idea what changed?!). I fixed that by adding a MANIFEST.in but in the long run I'd like to fix the bug another way.

Random spatially tuned `Neurons`
In this version, besides minor bug fixes, we are releasing a new `Neurons` subclass called `RandomSpatialNeurons` for when you require spatially tuned neurons but which aren't necesarily place cells or grid cells etc.

Users specify a lengthscale and these neurons sample a smooth random function from a Gaussian process with a squared exponential covariance function (roughly analogous to weighted sum of Gaussians). This is a much more "assumption free" way to model spatially tuned inputs and should be useful to a lot of users.

Note walls _still_ act correctly (covariance between points opposite side of a wall is high) and this works in 1D too.

Import like any neuron:

python
from ratinabox.Neurons import RandomSpatialNeurons


And use as follows:
python
Env = Environment()
Env.add_wall([[0.3,0.35],[0.3,0.85]])
Ag = Agent(Env)
RSNs = RandomSpatialNeurons(Ag,
params = {'n':3,'lengthscale':0.1,},)

RSNs.plot_rate_map()



python
RSNs = RandomSpatialNeurons(Ag,
params = {'n':3,'lengthscale':0.2,},)



python
Env = Environment(params={'dimensionality':'1D'})
Ag = Agent(Env)
RSNs = RandomSpatialNeurons(Ag,
params = {'n':10,'lengthscale':0.02,},)


Very minor change to resume compatibility with python >=3.7 (down from 3.10 in 1.9.2). See issue 79 and PR 81

Python requirement bumped from `>= 3.7` to `>= 3.10`. Comment on issue 79 if this causes problems to your workflow.