semvec/pypi-packages huggingface dataset:
frameup 0.1.2
DataFrames all up in your web applications
Requires Python: >=3.7,<4.0
Keywords: Pandas,DataFrame,web applications

# DataFrameup

```
 $ pip install frameup
 $ frameup <path-to-csvfile>
```

Frameup is the easiest way to get your Pandas DataFrame up into a Python-based web application. Simply `import frameup` and your DataFrames will become URL query parameter, and pagination aware.

Zero dependencies, except Pandas of course.

## Quick look

Serve a csv as a frameup dataframe on localhost

```
 $ python -m frameup.serve <path-to-csv-file>
```

Then navigate to http://localhost:8000/. Use the [Pandas DataFrame query syntax](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html) in the query box.

... or, get a JSON payload:

```
 $ curl 'http://localhost:8000/?query=&limit=10&page=1' | python -m json.tool
```

## Use it in your web application

### Flask example

Given a template similar to example.js.html

```
from flask import Flask, jsonify, render_template, request, url_for
import pandas as pd
import frameup

app = Flask(__name__)

df = pd.read_csv(YOUR_CSV_FILE)

@app.route('/mydataframe')
def main():
    data = df.frameup.data(path=url_for('main'), **request.args)
    return render_template('example.j2.html', **data)
```

For something ajaxy, just replace the return with:

```
return jsonify(**data)
```

## On query parameter objects

Be sure the query parameter object you pass frameup does not return lists for values. `classes` is the only multi-valued parameter accepted, and should be passed as a comma-delimited list rather than multiple `classes` keys.

Python web frameworks all have their own way of dealing with the vagaries of GET parameter specification hell. Most implement some concept of a `MultiDict`, but the APIs for these vary from one framework to the next. Thus, the requirement of only single-valued GET params greatly simplifies things here.

## Other projects

Projects to review / learn from / use instead

 * [Datasette](https://github.com/simonw/datasette)
 * [Workbench](https://workbenchdata.com/)
vrt-generator 0.0.6
creating vrt corpora
Requires Python: >=3.5

# vrt_generator
Python class for creating vrt-annotated corpora.
Still in very early testing stage.

Install by typing:
```bash
pip install vrt_generator
```

Usage Example:
```python
from vrt import Corpus,  S, Text
with Corpus("~","meinkorpus",4,"text_name") as c:
    with Text(c, text_name="Text2") as t:
        with S(c) as s:
            s.writep("Test","TAG","TAG","Lemma")  
```

Features:
-
- Represent Corpus, Text, P and S Attributes
- Integration of spacy for automatic generation of a vrt-representation of texts
- Using Context Manager for xml-hierarchy representation
- Reduces to utf8mb3 and checks formatting compatibility
- If you want to add texts that are automatically POS-Tagged with Spacy, you might look at vrt_spacy
fillet 0.0.3
Assistance scripts for ONT MinKNOW
Requires Python: >=3.5

# fillet
---
## Table of Contents

<details>
<summary>Click to expand</summary>
<p>

- [Introduction](#introduction)
- [Installation](#installation)
  - [PyPI](#installing-fillet-with-pypi)
  - [GitHub](#installing-fillet-from-github)
- [Usage](#usage)
</p>
</details>

## Introduction

fillet is a simulation manager for Oxford Nanopore Technologies MinKNOW software, making adding simulation devices and
playback runs easier

## Installation

### Installing fillet with PyPI

```bash
pip install fillet
```

### Install fillet from GitHub

```bash
git clone https://github.com/alexomics/fillet
cd fillet
python setup.py install
```

## Usage

fillet installs with a terminal entry point as `fillet`

Add a bulk FAST5 file for simulation:
```bash
fillet sim -t <MinKNOW_config_filename> -b <bulk_file_to simulate>
```
Remove a bulk FAST5 file for simulation:
```bash
fillet sim -t <MinKNOW_config_filename>
```

Add a simulation device:
```bash
fillet add
```
Remove a simulation device:
```bash
fillet add -r
```