semvec/pypi-packages huggingface dataset:
graphesn 0.2.17
Python implementation of Deep Graph Echo State Networks
Requires Python: >=3.8

# Graph ESN library
Pytorch implementation of echo state networks for static graphs and discrete-time dynamic graphs.

## Installation
Easiest way to get our library is via python package:
```
pip install graphesn
```

## Usage
The library is quite straightforward to use:

```python
from graphesn import StaticGraphReservoir, Readout, initializer
from torch_geometric.data import Data

data = Data(...)

reservoir = StaticGraphReservoir(num_layers=3, in_features=8, hidden_features=16)
reservoir.initialize_parameters(recurrent=initializer('uniform', rho=.9), input=initializer('uniform', scale=1))
embeddings = reservoir(data.edge_index, data.x)

readout = Readout(num_features=reservoir.out_features, num_targets=3)
readout.fit(data=(embeddings, data.y), regularization=1e-3)
predictions = readout(embeddings)
```

## Code outlook
The library is contained in folder `src/graphesn`:
- `reservoir.py` implementation of reservoirs for static and discrete-time dynamic graphs;
- `matrix.py` random matrices generating functions;
- `readout.py` implementation of a linear readout for large-scale ridge regression;
- `data.py` classes to represent temporal and dynamic graphs;
- `dataset.py` some dynamic graph datasets;
- `util.py` general utilities.

The `examples` folder contains demos for our library on some common graph datasets.

## Contributing
***This research software is provided as-is***. We are working on this library in our spare time.

Code is released under the MIT license, see `LICENSE` for details.

If you find a bug, please open an issue to report it, and we will do our best to solve it. For general or technical questions, please email us rather than opening an issue.

## References
* C. Gallicchio, A. Micheli (2010). Graph Echo State Networks. The 2010 International Joint Conference on Neural Networks (IJCNN 2010), pp. 3967–3974.
* C. Gallicchio, A. Micheli (2020). Fast and Deep Graph Neural Networks. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20).
* C. Gallicchio, A. Micheli (2020). Ring Reservoir Neural Networks for Graphs. The 2020 International Joint Conference on Neural Networks (IJCNN 2020).
* D. Tortorella, A. Micheli (2021). Dynamic Graph Echo State Networks. Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), pp. 99–104.
python-netbox 0.0.24
Python NetBox Client
Requires Python: >=3

============================
Python Netbox Client
============================

python-netbox is a client for the Netbox (https://github.com/digitalocean/netbox) API.
It's based on the APIv2 which is released since version 2.0.0. It requires python 3.

The full documentation can be found here_.

.. _here: http://python-netbox.readthedocs.io/en/latest/#

-----------------
Installation
-----------------

To get the latest version from Github:

   $ pip3 install python-netbox

-----------------
Usage
-----------------
To start with the module:

    >>> from netbox import NetBox
    >>> netbox = NetBox(host='127.0.0.1', port=32768, use_ssl=False, auth_token='token')


-----------------
Examples
-----------------
Get all devices:

    >>> netbox.dcim.get_devices()

Get devices per rack:

    >>> netbox.dcim.get_devices(rack_id=1)

Get device by name

    >>> netbox.dcim.get_devices(name='device_name')

Create a site:

    >>> netbox.dcim.create_site('site1', 'site1')

Delete a site:

    >>> netbox.dcim.delete_site('site1')

Get IP address object:

    >>> netbox.ipam.get_ip_addresses(address='10.0.0.1/16')

-----------------
Support
-----------------
If you have questions or comments please send an email to thomas@tjrb.nl
pytorch-show 0.1.3
This is a tool to visualize models directly from py scripts
Requires Python: >=3.8,<4.0

# pytorch-show 

This is a tool to visualize models directly from py scripts  and it can automatically find all nn.Module in the file


**RUN**

`python main.py -f python_file -i 240 240 -d -n`
![img.png](https://s2.loli.net/2023/06/27/k4psbUREQHlXV2O.png)

![image-20230627194415687](https://s2.loli.net/2023/06/27/ahbLlf6yOZ57KYw.png)
todo-by 1.2.0
Runtime lifetimes for comments.

# todo_by

Runtime lifetimes for comments. Inspired by
[parker-codes/todo_by](https://github.com/parker-codes/todo_by).

To use the library, install it using `pip`:


```bash
$ pip install todo_by

```

Import and call the `todo_by` decorator and add a date like so:

```python
from todo_by import todo_by, todo_while

@todo_by("2050-06-01")
def my_function():
    # TODO: Implement this function by June 1st, 2050
```

If the current date is after June 1st, 2050, the `todo_by` decorator will
generate a `RuntimeError` with the message `"TODO by Jun 1, 2050 has passed"`.  
If the current date is on or before June 1st, 2030, the code will run normally.

You can also add specific TODO comments:

```python
@todo_by("2050-06-01", "Clean up implementation")
```

The `todo_while` decorator allows you to ensure todos are done based on a [SemVer
requirement](https://semver.org/). This also allows you to make blockers:

```python
@todo_while("1.0.0", "setup.py", "This has to be in the first major release")
def my_function():
   self.assertTrue(True)
```

If the current version in your `setup.py` (specify any file used for versioning
your code) is equal or greater than 1.0.0, the `todo_while` decorator will
generate a `RuntimeError` with the message 
`"TODO version requirement '1.0.0' not satisfied by current <version_number>"`.  
If the current version number is smaller than 1.0.0, the code will run normally.

## License:

```
Copyright 2023 Martin Simon

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```

## Buy me a coffee?

If you feel like buying me a coffee (or a beer?), donations are welcome:

```
BTC : bc1qq04jnuqqavpccfptmddqjkg7cuspy3new4sxq9
DOGE: DRBkryyau5CMxpBzVmrBAjK6dVdMZSBsuS
ETH : 0x2238A11856428b72E80D70Be8666729497059d95
LTC : MQwXsBrArLRHQzwQZAjJPNrxGS1uNDDKX6
```