semvec/pypi-packages huggingface dataset:
stac-geoparquet 0.4.1
stac-geoparquet
Requires Python: >=3.8

# STAC-geoparquet

Convert STAC items to geoparquet.

## Purpose

This library helps convert [STAC Items](https://github.com/radiantearth/stac-spec/blob/master/overview.md#item-overview) to [geoparquet](https://github.com/opengeospatial/geoparquet). It might be useful for you if you host a large number of STAC items through a [STAC API](https://github.com/radiantearth/stac-api-spec), but want to provide a bulk API for users who need to access *many* items.

## Usage

`stac_geoparquet.to_dataframe` does it all. You give it a list of (STAC Item) dictionaries. It just converts them to a `geopandas.GeoDataFrame`, which can be written to parquet with `.to_parquet`.

```python
>>> import requests
>>> import stac_geoparquet
>>> item = requests.get("https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/items/ia_m_4209150_sw_15_060_20190828_20191105").json()
>>> df = stac_geoparquet.to_geodataframe([item])
>>> df.to_parquet("naip.parquet")
```

Note that `stac_geoparquet` lifts the keys in the item `properties` up to the top level of the DataFrame, similar to `geopandas.GeoDataFrame.from_features`.

```python
>>> list(df.columns)
['type',
 'stac_version',
 'stac_extensions',
 'id',
 'geometry',
 'bbox',
 'links',
 'assets',
 'collection',
 'gsd',
 'datetime',
 'naip:year',
 'proj:bbox',
 'proj:epsg',
 'naip:state',
 'proj:shape',
 'proj:transform']
```

We also provide `stac_geoparquet.to_dict` and `stac_geoparquet.to_item_collection` helpers that can be used to convert from DataFrames back to the original STAC items.

## pgstac integration

`stac_geoparquet.pgstac_reader` has some helpers for working with items coming from a `pgstac.items` table. It takes care of

- Rehydrating the dehydrated items
- Partitioning by time
- Injecting dynamic links and assets from a STAC API
jprm 1.1.0
Jellyfin Plugin Repository Manager
Requires Python: >=3.8.1
Keywords: Jellyfin plugin repository compile publish development

Installing
==========
From PyPI
---------
```bash
pip install --user jprm
```

From GitHub
-----------
```
pip install --user git+https://github.com/oddstr13/jellyfin-plugin-repository-manager
```

From local developement directory
---------------------------------
```
git clone https://github.com/oddstr13/jellyfin-plugin-repository-manager.git
pip install --user --editable jellyfin-plugin-metadata-manager
```

Stand-alone Python script
-------------------------
```
wget -O jprm.py https://raw.githubusercontent.com/oddstr13/jellyfin-plugin-repository-manager/master/jprm/__init__.py
```

Examples
========

Running jprm
------------

```
jprm --version
```

```
python3 -m jprm --version
```

```
python3 jprm.py --version
```

Initializing plugin repository
------------------------------

```
mkdir -p /path/to/repo
jprm repo init /path/to/repo
```

See [build_plugin.sh](https://github.com/oddstr13/jellyfin-plugin-repository-manager/blob/master/build_plugin.sh) for an example script.
bnltk-revamped 0.1.2
Fork of the original bnltk by ashwoolford
Keywords: python, nlp, bengali, bnltk

### This is a fork of [bnltk](https://github.com/ashwoolford/bnltk) by [ashwoolford](https://github.com/ashwoolford)

## Installation
```bash
pip install bnltk-revamped
```

### Why this fork?
- Last commit on the original repository was on 2019
- Meanwhile, the original repository has 4 open issues
- Some of the dependencies are outdated

### For more information, please visit the original repository [here](https://github.com/ashwoolford/bnltk)
Streamlit-Chart.js 0.0.1
A custom Streamlit component for Chart.js


# My Streamlit Chart Component

Welcome to My Streamlit Chart Component, a versatile charting library designed for the Streamlit framework. This component allows users to quickly and easily create a variety of interactive charts to visualize their data within a Streamlit application.

## Installation

To install My Streamlit Chart Component, run the following command:

```
pip install my-streamlit-chart-component
```

## Usage

After installation, you can use the component in your Streamlit app to render charts. Here's how to get started:

```python
import streamlit as st
from streamit_chartjs.st_chart_component import my_chart_component

# Define your data
data = {
    "labels": ["January", "February", "March", "April"],
    "datasets": [
        {
            "label": "My Data",
            "data": [10, 20, 30, 40],
            # Further dataset properties...
        }
    ],
}

# Render a chart
my_chart_component(data=data, chart_type="bar")
```

## Supported Chart Types

- Bar Chart
- Line Chart
- Doughnut Chart
- Pie Chart
- Radar Chart

## Parameters

- `data`: (dict) The data for the chart, structured as per Chart.js documentation.
- `chart_type`: (str) The type of chart to display (e.g., 'bar', 'line').
- `title`: (str) The title of the chart.
- `legend_position`: (str) The position of the chart's legend (e.g., 'top', 'bottom').
- `x_axis_title`: (str) The title for the x-axis.
- `y_axis_title`: (str) The title for the y-axis.
- Additional parameters will be added here...

## Future Enhancements (TODO)

- [ ] Add axis display toggles to control the visibility of the x-axis and y-axis.
- [ ] Implement grid line configuration options.
- [ ] Provide chart padding adjustments within the chart area.
- [ ] Enable detailed animation options for the chart renderings.
- [ ] Offer tooltip configuration for improved interactivity.
- [ ] Include aspect ratio control for better layout management.
- [ ] Introduce responsive behavior toggles to cater to different display sizes.
- [ ] Create predefined and custom color schemes for charts.
- [ ] Allow detailed dataset customization for properties like `pointStyle`, `borderDash`, etc.
- [ ] Enable users to pass custom JavaScript functions for callback hooks such as `onClick`, `onHover` events.

## Contributing

Contributions to My Streamlit Chart Component are welcome! Please read our contribution guidelines for details on how to submit changes and how to set up a development environment for the project.

Thank you for using My Streamlit Chart Component!