semvec/pypi-packages huggingface dataset:
carrot-cdm-beta 0.6.87
Python package for performing mapping of ETL to CDM
Requires Python: >=3.6

All documentation relating to the CaRROT-CDM builder can be found here:

## [https://hdruk.github.io/CaRROT-Docs/CaRROT-CDM/About/](https://hdruk.github.io/CaRROT-Docs/CaRROT-CDM/About/)
kkblinka 0.0.2
Helpful additions to the Blinka library.

# Karol Krizka's Blinka Additions

Helpful addtions to the [Blinka](https://circuitpython.org/blinka) library.

## SPI via Big Banging IO Pins
The `SPIBitBanger` class implements SPI using digital IO pins defined via the `digitalio` library.

### Example with MAX31865
The `example.py` script shows how to use the MAX31865 Pt100 controller with the AdaFruit FT232H dongle. The MAX31865 board uses SPI phase 1 and control is thus not available via the `busio.SPI` package. However the FT232H can still use it by doing SPI via the generic IO pins.

Connect the following pins between your AdaFruit FT232H dongle and the MAX31865 board.
- `C0` to `CLK`
- `C1` to `SDO`
- `C2` to `SDI`
- `C3` to `CS`

Install the `adafruit-circuitpython-max31865` library and execute the example Python script.
```shell
pip install adafruit-circuitpython-max31865
BLINKA_FT232H=1 python example.py
```
featurelayers 1.5.6
FeatureLayers Package

# FeatureLayers

## Installation
```bash
pip install featurelayers
```


## Usage
### LBC Layers
```python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Flatten
from featurelayers.layers.LBC import LBC

# Create a simple Keras model
model = Sequential()
# Add the LBC layer as the first layer in the model
model.add(LBC(filters=32, kernel_size=3, stride=1, padding='same', activation='relu', sparsity=0.9, name='lbc_layer'))
# Add a Flatten layer to convert the output to 1D
model.add(Flatten())
# Add a Dense layer for classification
model.add(Dense(units=10, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Generate some dummy data
x_train = np.random.rand(100, 28, 28, 1)
y_train = np.random.randint(0, 10, size=(100,))

# Convert the labels to one-hot encoding
y_train = keras.utils.to_categorical(y_train, num_classes=10)

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)

```

__version__ = ""1.5.6""
idun-mock 0.1.3
Mock a real IDUN Guardian device for testing purposes
Requires Python: >=3.10,<4.0

# IDUN Guardian mock library / client

This is a client library for the IDUN Guardian which mocks out the physical device, intended for development and building tests for the IDUN Guardian.
The [`data_generator`](idun_mock/data_generator) generates random device packets.
The [`client`](idun_mock/client) is a command-line utility that sends them to the Guardian API.

## Features

- Mock device data generator
- Send mocked device data to a Cloud endpoint

## Sending mock data to the cloud API (client)

You can start sending mock data to the cloud API by installing the poetry project and running the `idun_mock.client` module:

```
# at this directory
poetry install
poetry run python -m idun_mock.client -w <websocket_url>
```

The websocket URL is where the data should be sent. Change the generator parameters according to what scenario you want to simulate (look in [`idun_mock/client/__main__.py`](idun_mock/client/__main__.py)).

## Troubleshooting: sending mock data to Kinesis, bypassing the API

If you have IDUN AWS credentials, you can also send mock data directly to Kinesis, bypassing the API:

```
# Set these environment variables:
#   AWS_ACCESS_KEY_ID
#   AWS_SECRET_ACCESS_KEY
#   AWS_DEFAULT_REGION

poetry run python -m idun_mock.client -k <kinesis_stream_name> [-e <aws_endpoint>]
```