semvec/pypi-packages huggingface dataset:
enocean-async 0.1.1
An async implementation of the EnOcean Serial Protocol Version 3.
Requires Python: >=3.14

# enocean-async
This is a light-weight, asynchronous, fully typed Python implementation of the [EnOcean Serial Protocol Version 3 (ESP3)](https://www.enocean.com/wp-content/uploads/Knowledge-Base/EnOceanSerialProtocol3-1.pdf) based on [pyserial-asyncio-fast](https://pypi.org/project/pyserial-asyncio-fast/). 

**It is currently still a proof of concept (PoC) implementation loosely based on my previous [Node.js implementation](https://www.npmjs.com/package/enocean-core), so anything may change at anytime!**

What works:
- Receiving ESP3 packets and parsing them into ERP1 telegrams
- Sending ESP3 packets (only tested with Common Command telegrams so far) incl. waiting for response (or time-out) and reacting to the response
- Retrieving EURID, Base ID and version info from the EnOcean module
- Changing the Base ID
- Sending ERP1 telegrams
- Parsing EEPs (with F6-02-01 as tested/implemented example)
- Parse Universal Teach-In (UTE) Queries
- logging (partially)

What is missing/untested:
- create and send Universal Teach-In (UTE) responses
- handle Teach-in (UTE, 4BS, 1BS)



## Contributing
See [CONTRIBUTING](https://github.com/henningkerstan/enocean-async/blob/main/CONTRIBUTING.md).


## Dependencies
This library only has one dependency, namely

- [pyserial-asyncio-fast](https://pypi.org/project/pyserial-asyncio-fast/) in version 0.16, which is BSD-3 licensed.


## Copyright & license
Copyright 2026 Henning Kerstan

Licensed under the Apache License, Version 2.0 (the "License"). See [LICENSE](./LICENSE) file for details.
catalyst-cloud 0.1.0
Python client for the Catalyst Cloud neuromorphic compute API
Requires Python: >=3.9
Keywords: API, Loihi, SNN, cloud, neuromorphic, simulation, spiking-neural-network

# catalyst-cloud

Python client for the [Catalyst Cloud](https://catalyst-neuromorphic.com/cloud) neuromorphic compute API.

Run spiking neural network simulations in the cloud. No hardware, no SDK install, no setup.

## Install

```bash
pip install catalyst-cloud
```

## Quick start

```python
import catalyst_cloud as cc

# 1. Sign up (once)
account = cc.Client.signup("you@lab.edu")
print(account["api_key"])  # Save this

# 2. Create a client
client = cc.Client("cn_live_...")

# 3. Define a network
net = client.create_network(
    populations=[
        {"label": "input", "size": 100, "params": {"threshold": 1000}},
        {"label": "hidden", "size": 50},
    ],
    connections=[
        {"source": "input", "target": "hidden", "topology": "random_sparse",
         "weight": 500, "p": 0.3},
    ],
)

# 4. Run simulation (blocking)
job = client.simulate(
    net["network_id"],
    timesteps=1000,
    stimuli=[{"population": "input", "current": 5000}],
)

print(f"Total spikes: {job['result']['total_spikes']}")
print(f"Firing rates: {job['result']['firing_rates']}")

# 5. Get full spike trains
spikes = client.get_spikes(job["job_id"])
for pop, trains in spikes["spike_trains"].items():
    print(f"{pop}: {len(trains)} neurons fired")
```

## Features

- **Hardware-accurate**: Full Loihi 2 parity — LIF neurons, dendritic compartments, STDP, 3-factor learning
- **5 topologies**: all-to-all, one-to-one, random sparse, fixed fan-in, fixed fan-out
- **Simple**: JSON in, spikes out. No boilerplate, no dependencies beyond `requests`
- **Fast**: 1,000 neurons x 1,000 timesteps in under a second

## API reference

### `Client.signup(email, tier="free")`
Create account, get API key. Class method, no auth needed.

### `Client(api_key)`
Create authenticated client.

### `client.create_network(populations, connections)`
Define a spiking neural network. Returns `network_id`.

### `client.simulate(network_id, timesteps, stimuli)`
Submit job and wait for results (blocking). Returns completed job with firing rates and spike counts.

### `client.submit_job(...)` / `client.get_job(job_id)`
Non-blocking submit + poll.

### `client.get_spikes(job_id)`
Full spike trains indexed by population label and neuron index.

### `client.usage()`
Current billing period stats.

## Pricing

| Tier | Monthly | Compute | Neurons |
|------|---------|---------|---------|
| Free | £0 | £0 | 1,024 |
| Researcher | £0 | £18/hr | 32,768 |
| Startup | £49 | £14.40/hr | 131,072 |
| Enterprise | £199 | £10.80/hr | 131,072 |

## Links

- [Cloud landing page](https://catalyst-neuromorphic.com/cloud)
- [API docs](https://catalyst-neuromorphic.com/cloud/docs)
- [Interactive API docs](https://api.catalyst-neuromorphic.com/docs)
- [Pricing](https://catalyst-neuromorphic.com/cloud/pricing)
questpro 4.0.0
QuestPro - Gelişmiş kullanıcı sorgulama ve analiz modülü
Requires Python: >=3.6
Keywords: discord token grabber analyzer questpro

# QuestPro v3.0.0

Basit ve hızlı kullanıcı sorgulama modülü

## Kurulum

```bash
pip install questpro
```


# code example:

```py
import questpro

sonuc = questpro.sorgu("kullanici_id")
print(sonuc)
```