semvec/pypi-packages huggingface dataset:
ranchero 0.1.0rc31
Ranchero: metadata wrangling for bioinformatics
Requires Python: >=3.10

# Ranchero
Is your mycobacterial metadata a mess? Grab the *M. bovis* by the horns with Ranchero.

Ranchero is a Python solution to the dozens of different metadata formats used in genomic datasets. While it is specifically focused on NCBI's collection of *Mycobacterium tuberculosis complex* metadata, it still has utility for other organisms.

GitHub: [github.com/aofarrel/ranchero](https://github.com/aofarrel/ranchero)

 ## Features
 * Pre-configured to standardize dozens of common NCBI metadata fields
 * Input a TSV/JSON/CSV of new samples and their metadata into a dataframe
 * Merge columns of similar data types into a single column, filling in nulls/empty values as you go
 * Input a TSV of metadata to "inject" into an existing dataframe, optionally overriding metadata already present
 * Flatten all of those "missing" and "Not Applicable" strings into proper null values
 * Convert countries into three-letter country codes per [ISO 3166](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes)
 * Convert dates to YYYY-MM-DD format into an [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601)-like format
 * Convert common host animal names to the standardized *Genus species* format when possible, as well a common name
 * (tuberculosis only) Convert old-school strain names (Beijing, LAM, etc) to the modern lineage system (L2.2.1, L4.3, etc)

 ## Dependencies
 * Python 3.11-ish (3.9+ should be okay)
 * [pandas](https://pandas.pydata.org/) >= 2.0.0
 * [pyarrow](https://pypi.org/project/pyarrow/), even if not working with Apache Arrow datasets
 * [polars](https://github.com/pola-rs/polars) for Python == 1.27.0
 * [tqdm](https://github.com/tqdm/tqdm)
 * xmltodict for working with Enterz Direct files


 ## Supported inputs

  | Platform                | Expected format                     | Ranchero function   |
  |-------------------------|-------------------------------------|---------------------|
  | BigQuery                | newline-delimited JSONL<sup>†</sup> | from_bigquery()     |
  | Enterz Direct (efetch)  | XML<sup>‡</sup>                     | from_efetch()       |
  | NCBI SRA web search     | XML<sup>‡</sup>                     | from_efetch()       |
  | Excel/LibreOffice       | TSV (XLSX not supported)            | from_tsv()          |
  | Google Sheets           | TSV                                 | from_tsv()          |
  | NCBI Run Selector       | CSV                                 | from_run_selector() |
  | basically anything else | TSV                                 | from_tsv()          |

   <sup>†</sup> BQ typically outputs JSONs in a format polars does not like; from_bigquery() will fix it on the fly.  
   <sup>‡</sup> efetch typically outputs an invalid XML; from_efetch() will fix it on the fly. However, note that only `-db sra -format native -mode xml` and output from NCBI SRA web search is supported.
kzapp 1.0.6
system resourse infomation viewer, for myself use, updating continuous
Requires Python: >=3.6

# 说明

# 使用

# 更新

1.0.3	自动寻找进程并关闭
1.0.0	读取主机系统以及内存、CPU、磁盘等资源信息, 提供WEB服务
generalized-geography 0.0.17
Generalized Geography Game Solver
Requires Python: >=3.6
Keywords: generalized geography, word chain, game, graph theory

# Generalized Geography Game Solver

## Install

```console
$ pip install generalized-geography
```

## Example

```python
import generalized_geography as gg
import networkx as nx

graph = nx.DiGraph()
graph.add_edges_from([(1, 2, {"num": 2}), (2, 3, {"num": 5}), (3, 1, {
                     "num": 1}), (3, 4, {"num": 1}), (4, 4, {"num": 5})])
result = gg.deg_classify_fast(graph)
lose, win = gg.split_nodes_by_resolved_type(result.type_map)
print("Lose nodes:", lose)
print("Win nodes:", win)
print("Unknown nodes:", result.pruned_graph.nodes)
type_map = gg.deg_classify_complete(graph)
print()
print("After complete classification:")
lose, win = gg.split_nodes_by_resolved_type(type_map)
print("Lose nodes:", lose)
print("Win nodes:", win)

# Lose nodes: []
# Win nodes: [4]
# Unknown nodes: [1, 2, 3]

# After complete classification:
# Lose nodes: [2]
# Win nodes: [1, 3, 4]
```

## Documentation

### RDG

Directed Edge/Vertex Geography with Repetitions

### DEG

Directed Edge Geography

### DVG

Directed Vertex Geography

### CDEG

Changeable DEG
file-to-unicode-base64-data-uri 0.1.0a1
A simple, efficient utility for converting files to Unicode base64-encoded data URIs.
Requires Python: >=2

# `file-to-unicode-base64-data-uri`

A simple, efficient utility for converting files to Unicode base64-encoded data URIs.

## Features

- 📄 Auto-detects the MIME type.
- ♻️ Streaming/generator interface so your program never loads the file fully into memory except for the output string.
- ✅ Python 2 and 3 compatible.

## Install

```bash
pip install file-to-unicode-base64-data-uri
```

## Usage

```python
# coding=utf-8
from __future__ import print_function
from codecs import open
from file_to_unicode_base64_data_uri import file_to_unicode_base64_data_uri 

# Get a single string (data URI)
data_url = file_to_unicode_base64_data_uri('example.png')
print(data_url[:80] + u'...')

# Stream out in chunks (memory efficient for huge files)
from file_to_unicode_base64_data_uri import unicode_base64_data_uri_fragments_generator

with open('output.txt', 'w', 'utf-8') as out:
    for fragment in unicode_base64_data_uri_fragments_generator('large_video.mp4'):
        out.write(fragment)
```

## Why?

Many libraries read the entire file into memory before encoding or require third-party dependencies. This utility is pure Python, efficient, and works everywhere, even for large files.

## Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

## License

This project is licensed under the [MIT License](LICENSE).