semvec/pypi-packages huggingface dataset:
gcp-log 1.0.1
Format logs as GCP-compatible JSON.
Requires Python: >=3.8
Keywords: gcp,google,logging,logger

# gcp-log

Python library to format logs as [GCP](https://cloud.google.com/)-compatible JSON.

Features:

+ Works with standard [logging](https://docs.python.org/3/library/logging.html).
+ Supports [extra](https://docs.python.org/3/library/logging.html#logging.Logger.debug) for structured logging.
+ 100% compatible with [the official spec](https://cloud.google.com/logging/docs/structured-logging#special-payload-fields).
+ Non-invasive, doesn't start any threads or processes, doesn't make network or system calls.
+ Zero configuration.
+ Fast.
+ Pure Python.
+ Type-safe.
+ Zero dependency.
+ Optional integration with [orjson](https://pypi.org/project/orjson/) for better performance.

## Installation

```shell
python3 -m pip install gcp-log
```

**Optional:** If you also install [orjson](https://pypi.org/project/orjson/), gcp-log will automatically use it instead of stdlib [json](https://docs.python.org/3/library/json.html) for faster serialization:

```shell
python3 -m pip install orjson
```

## Usage

To use it, simply set `gcp_log.Formatter` as the [formatter](https://docs.python.org/3/library/logging.html#formatter-objects) for the logger:

```python
import logging
import gcp_log

logger = logging.getLogger()
handler = logging.StreamHandler()
formatter = gcp_log.Formatter()
handler.setFormatter(formatter)
logger.addHandler(handler)
```

And then you can use the logger:

```python
logger.info('aragorn', extra=dict(father='arathorn'))
```

The output:

```json
{"message":"aragorn","severity":"INFO","timestamp":"2022-03-17T10:09:58.393124+00:00Z","logging.googleapis.com/sourceLocation":{"file":"/full/path/to/example.py.py","line":24,"function":"test_gcp_formatter"},"father":"arathorn"}
```

A human-readable version:

```json
{
    "message": "aragorn",
    "severity": "INFO",
    "timestamp": "2022-03-17T10:09:58.393124+00:00Z",
    "logging.googleapis.com/sourceLocation": {
        "file": "/full/path/to/example.py.py",
        "line": 24,
        "function": "test_gcp_formatter",
    },
    "father": "arathorn",
}
```
mass-analytics 1.3.4
Streamline data preprocessing and enhance analysis with our Powerful Data Preparation Package.
Requires Python: >=3.7


# Mass-Analytics: Data Preparation Made Easy

[![PyPI Version](https://img.shields.io/pypi/v/mass-analytics.svg)](https://pypi.org/project/mass-analytics/)
[![Python Versions](https://img.shields.io/pypi/pyversions/mass-analytics.svg)](https://pypi.org/project/mass-analytics/)

## Overview

Mass-Analytics is a Python package designed to streamline the data preparation process. It provides a set of useful functions that simplify common data preparation tasks, making it easier for data scientists and analysts to get their data ready for analysis and modeling.

## Key Features

- Read data from various sources and formats.
- Perform data cleaning and preprocessing.
- Handle missing data effectively.
- Feature engineering and transformation.
- Simplify data transformation pipelines.

## Installation

You can install `mass-analytics` using `pip`.

```sh
pip install mass-analytics
```

## Usage

Import the `mass-analytics` package as `ma` and use its functions for data preparation tasks.

```python
import mass_analytics as ma

# Read data from a file
data_frame = ma.read_data("path/to/your/data.csv")

# Display data
print(data_frame)
```

## Authors

- Mejdi Trabelsi
- Mohamed Selim Alouini

## Project Status

The project is currently in development. We are actively working on adding more features and improving the existing ones. Your feedback and contributions are highly appreciated!
lsfb-dataset 2.0.1
A companion library for the LSFB-dataset
Requires Python: >=3.9

# LSFB Dataset

This library is a companion for the [French Belgian Sign Language dataset](https://lsfb.info.unamur.be/). You will find useful functions to load and manipulate the video from the LSFB dataset. Thuis library also provides scripts to download and sync the datasets.

This dataset is available under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license](https://creativecommons.org/licenses/by-nc-sa/4.0/).

The code of this library is available under the **MIT license**.

## Installation

Just type :
`python -m pip install lsfb-dataset`

## Useful links

- The code is available on [Github](https://github.com/Jefidev/lsfb-dataset)
- If you find any issue, please report them [here](https://github.com/Jefidev/lsfb-dataset/issues) 
- The full documentation of this library is available [here](https://jefidev.github.io/lsfb-dataset/)


 ## Cite

 If you use this library or the associated dataset for your research, please cite the following paper:

```
@inproceedings{Fink2021,
  doi = {10.1109/ijcnn52387.2021.9534336},
  url = {https://doi.org/10.1109/ijcnn52387.2021.9534336},
  year = {2021},
  month = jul,
  publisher = {{IEEE}},
  author = {Jerome Fink and Benoit Frenay and Laurence Meurant and Anthony Cleve},
  title = {{LSFB}-{CONT} and {LSFB}-{ISOL}: Two New Datasets for Vision-Based Sign Language Recognition},
  booktitle = {2021 International Joint Conference on Neural Networks ({IJCNN})}
}
```
odpswrap 0.0.1
This library provides convenient access to the Citybrain Platform API
Requires Python: >=3.7

# Citybrain Platform Python Library

The Citybrain Platform Python Library provides convenient access to the Citybrain Platform API from applications written in the Python language.

## installation

If you just want to use the package, just run:

```sh
pip install --upgrade citybrain-platform
```

## Usage

The library needs to be configured with your account's secret key which is available on [www.citybrain.org](https://www.citybrain.org/#/settings). Either set it as the `CITYBRAIN_APIKEY` environment variable before using the library:

```sh
export CITYBRAIN_APIKEY='...'
```

Or set `citybrain_platform.api_key` to its value:

```python
import citybrain_platform

citybrain_platform.api_key = "..."
```

## Example Code

Examples of how to use this Python library to accomplish various tasks.

### Table Operations

#### Create Table

```python
import citybrain_platform
from citybrain_platform.computing.data_types import Column, ColumnType

columns = [
    Column("col_str", ColumnType.STRING, "this is a comment"),
    Column("col_id", ColumnType.BIGINT),
]

partition_columns = [
    Column("col_pt", ColumnType.INT, "a column for partitioning")
]

ok = citybrain_platform.Computing.create_table(name="test_tbl", columns=columns, partition_columns=partition_columns)
print(ok)
```

#### Get Table Schema

```python
import citybrain_platform

schema = citybrain_platform.Computing.get_table_schema(name="test_tbl")
print(schema)
```

#### Upload Table Data

```python
import citybrain_platform

result = citybrain_platform.Computing.upload_table_data(name="test_tbl", append=True, csv_filepath="aa.csv", partition_key={"col_pt": "19"})
print(result)
```

#### Truncate Table

```python
import citybrain_platform

result = citybrain_platform.Computing.truncate_table(name="test_tbl", partition_key={"col_pt": "19"})
print(result)
```

#### Drop Table

```python
import citybrain_platform

result = citybrain_platform.Computing.drop_table(name="test_tbl")
print(result)
```

#### Make Table Public To Others

```python
import citybrain_platform

public_table_name = citybrain_platform.Computing.public_table(name="test_tbl")
print(public_table_name)
```

#### List Available Tables

```python
import citybrain_platform

tables = citybrain_platform.Computing.list_tables()
print(tables)
```

### Job Operations

#### Create Job

```python
import citybrain_platform

job_id = citybrain_platform.Computing.create_job(sql="select col_str from test_tbl limit 12;")
print(job_id)
```

#### Get Job Status

```python
import citybrain_platform

job_status = citybrain_platform.Computing.get_job_status(job_id="...")
print(job_status)
```

#### Stop Running Job

```python
import citybrain_platform

result = citybrain_platform.Computing.stop_job(job_id="...")
print(result)
```


#### Download Terminated Job Results

```python
import citybrain_platform

citybrain_platform.Computing.get_job_results(job_id="...", filepath="results.csv")
```
molann 1.1.7
"PyTorch Artificial Neural Networks (ANNs) for Molecular Systems",
Requires Python: >=3.7

MolANN
======

This package implements classes that define PyTorch Artificial Neural Network (ANN) functions taking into account alignment (translation and rotation), and functions that depend on molecular features (bonds, angles, etc.). 

Installation 
============

The package can be installed via `pip`:

.. code-block:: console

   pip install molann

The installation from source is described in the `Installation`_ page.

Simple example 
==============

The following code defines an ANN as a function of a bond distance and a dihedral angle.

.. code-block:: python

    import MDAnalysis as mda
    from molann.ann import FeatureLayer, PreprocessingANN, MolANN, create_sequential_nn
    from molann.feature import Feature

    # pdb file of the system
    pdb_filename = '/path/to/system.pdb'
    ref = mda.Universe(pdb_filename) 

    # define a feature that describes the bond between atoms 5 and 6.
    f1 = Feature('name', 'bond', ref.select_atoms('bynum 5 6'))
    # define a feature that describes the dihedral angle formed by the first 4 atoms.
    f2 = Feature('name', 'dihedral', ref.select_atoms('bynum 1 3 2 4'))

    # define feature layer using the above two features.
    f_layer = FeatureLayer([f1,f2], use_angle_value=False)

    # define the preprocessing layer. 
    # we do not need alignment, since both features f1 and f2 are translation- and rotation-invariant.
    pp_layer = PreprocessingANN(None, f_layer)

    output_dim = pp_layer.output_dimension()
    # define neural network layers that contain training parameters 
    nn = create_sequential_nn([output_dim, 5, 3])

    # define the final network
    model = MolANN(pp_layer, nn)

    torch.jit.script(model).save('model.pt')

More examples for each class can be found in the `MolANN docs`_ .

Documentataion
==============

Please refer to `MolANN docs`_.


.. _`Installation`:
  https://molann.readthedocs.io/en/latest/installation.html
.. _`MolANN docs`:
  https://molann.readthedocs.io/en/latest