semvec/pypi-packages huggingface dataset:
marlindata 0.0.201
Marlin | Data Framework
Requires Python: >=3.7

# ``marlin_data``
&copy; Rahul Tandon, RS Aqua 2024

## About
``marlin_data`` allows access to RSA's acoustic signature database. The module requests the RSA acoustic dataset required in order to run a machine learning (ML). Current version of ``marlin_data`` provides a predefined dataset for proof of concept and tutorial purposes. Future versions will allow for more specific datasets as well as definig training and validation datasets separately. 
> **NOTE**  | Numerical data in ``marlin_data`` is defined using Python's numpy and pandas library.


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> One-Line Box made with Blockquote -->

## Dependencies
`
import numpy as np
import pandas as pd
import requests, json
import logging
import dotenv
import os, sys
from dataclasses import dataclass
import random
`

## Installation

`from marlin_data import *`


## Quick Start

### Accessible Data 

#### Datafeed Instance

Each iteration over the Marlin data feed will provide once instance of the datafeed. Frequemcy time series in numpy and a pandas dataframe is available along with descrictive metadata.

`Feed Instance` -> `frequency_ts_np`

The frequency time series for this data snapshot ( NumPy Array )

        for feed_instance in data_feed: frequency_np_array = feed_instance['frequency_ts_np']
        
`Feed Instance` -> `frequency_ts_pd`

The frequency time series for this data snapshot (pandas `DataFrame`) 

        for feed_instance in data_feed: frequency_data_frame = feed_instance['frequency_ts_pd']

`Feed Instance` -> `meta_data`

Metadata for the associated snapshot. Metadata is a dictionary with the following defined fields:

* `snapshot_id` : unique id for the snapshot
* `data_frame_start` : timestamp for the start of the snapshot data frame
* `data_frame_end` : timestamp for the end of the snapshot data frame
* `listener_location` : geo location of the listening device for snapshot
* `location_name` : human friendly name of listening location
* `frame_delta_t` : delta t for the snapshot (s)

#### Signature Data

### Downloading data

Instantiate a `MarlinData` class.

        ` marlin_data = MarlinData()`
        
Download signature data from RSA signature database.

        `marlin_data.download_signatures`

Download simulation / ML run data required for datafeed.

        `marlin_data.download_simulation_snapshots()`

### Connecting to datafeed

Create the Marlin datafeed. This data feed can be iterated over in order to simulate a data feed into a model.

        `data_feed = MalrinDataStreamer()`

Connect the downloaded data to the datafeed instance.

        `data_feed.init_data(marlin_data.simulation_data, marlin_data.simulation_index) `

Iterate over the datafeed.

        for data_inst in data_feed:
                print (data_inst)
`
CodebaseLister 0.0.2
A package to list and document the contents of codebases, optionally using .gitignore rules.
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/CodebaseLister.svg)](https://badge.fury.io/py/CodebaseLister)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/codebaselister)](https://pepy.tech/project/codebaselister)

# CodebaseLister

`CodebaseLister` is a Python package designed to document the contents of codebases. It lists all files and their contents, taking into account `.gitignore` rules if required. This tool is useful for developers who need a comprehensive overview of their project's structure and files.

## Installation

To install `CodebaseLister`, you can use pip:

```bash
pip install CodebaseLister
```

## Usage

### As a Python Module

You can use `CodebaseLister` as a module in your Python scripts.

Example:

```python
from codebaselister import CodebaseLister

# Initialize CodebaseLister with optional parameters
lister = CodebaseLister(base_path='path_to_your_project', use_gitignore=True)

# Generate the listing file
listing_details = lister.generate_listing_file()
print(listing_details)
```

### Customizing Your Listing

You can customize the listing by adjusting the initialization parameters of the `CodebaseLister` class, such as `base_path`, `use_gitignore`, and `output_filename`, to fit the specific needs of your project.

## Output Example

When you run `CodebaseLister`, it creates a text file listing all the files in the specified path (excluding those ignored by `.gitignore` if enabled) along with their contents. Here's an example of the output details:

```
{
    "output_filename": "listing_20240113_150223.txt",
    "chars_count": 12345,
    "file_size": 0.5,  # Size in MB
    "files_count": 100
}
```

## Contributing

Contributions, issues, and feature requests are welcome! Feel free to check [issues page](https://github.com/chigwell/CodebaseLister/issues).

## License

[MIT](https://choosealicense.com/licenses/mit/)
profiley 2.0.0a3
Calculation of common astrophysical profiles

========
profiley
========

Profile calculations for galaxies and clusters

.. image:: https://readthedocs.org/projects/profiley/badge/?version=latest
    :target: https://profiley.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

To install, simply run ::

    pip install profiley

Some examples of the functionality available can be found in the `examples 
<examples/>`_. For more information see the `documentation 
<https://profiley.readthedocs.io/en/latest/index.html>`_.


References
==========

``profiley`` has been used in `Madhavacheril et al. (2020)`__.


Change Log
==========

Version 2.0.0 (ongoing)
-----------------------

* Renamed all relevant profiles from ``enclosed`` to ``cumulative``
* Fix critical bugs on critical surface density and background density
* Added API documentation

Version 1.5.0
-------------
* Added ``WebskyNFW`` profile
* Fixed bug in ``mdelta`` method, plus it is now a ``Profile`` method inherited by all profiles.

Version 1.4.4
-------------
* Fixed bug in the normalization of a 1-d surface density in ``xi2sigma``
* Fixed bug whereby only one beta was returned

Version 1.4.3
-------------
* Fixed bug calculating a single surface density in ``xi2sigma``

Version 1.4.0
-------------
* Added stand-alone offset calculation for arbitrary profiles

Version 1.3.2
-------------
* Fixed bug in installation (!)

Version 1.3.0
-------------
* Updated names of ``Profile`` methods
* Bug fixes

Version 1.2.2
-------------

* Moved ``background`` argument from ``BaseNFW`` to ``BaseCosmo`` so it is visible to all ``Profile`` objects
* Moved ``overdensity`` argument from ``BaseNFW`` to ``Profile``



.. _Madhavacheril: https://ui.adsabs.harvard.edu/abs/2020ApJ...903L..13M/abstract

__ Madhavacheril_
open-pydb 0.1.0
Open_PyDb is a unique way to change open source unstructure dataabases, it makes use of Jsonl files to store big amounts of data
Requires Python: >=3.8

# Open-PyDB

An unstructured database module for Python

Open-PyDB is a simple, lightweight, and flexible open-source database for that allows users to make use of unstruct data. It provides a simple, flexible and extensible way to data. The project contains the code for a simple database manager using a Client class to interact with a database The database manager supports creating and deleting databases and collections, as well as adding and deleting data from collections.

---

## Instalation

### For Windows

```Python
pip install open-pydb
```

### For Mac and Linux

```Python
pip3 install open-pydb
```

## Usage

Here's an example of how to use Open-DB:

```Python
from open_db import Client

# Create a new client
client = Client()

# Add a new database
db = client.add_db("Users")

# Get the database
db = client.get_db()

# Get the collections in the database
collections = db.get_collections()

# Add data to the first collection
collections[0].add_data(id="2", name="Get with me")

# Delete data from the first collection
collections[0].delete_data(collections[0].data)
```

## Data Model

Open-DB uses a simple data model based on the Data class. Here's an example of how to use the Data class:

```Python
from open_db_JOHN_PAUL.data_model import Data

# Create a new data object
data = Data(foo='bar', baz=42)

# Convert the data object to a JSON string
json_data = data.to_json()

# Convert a JSON string to a data object
new_data = Data.from_json(json_data)
```

## Contributing

We welcome contributions to Open-DB! If you'd like to contribute, please fork the repository and make your changes in a new branch. Once you're ready to submit your changes, open a pull request and we'll review your changes as soon as possible.

## License

Open-DB is released under the MIT License. See the LICENSE file for details.

## Contact

For any questions or issues, please feel free to contact at [johnpar2004@gaml.com](johnpar2004@gmail.com).