semvec/pypi-packages huggingface dataset:
janssqlitekernel 1.2.0
A jupyter SQLite3 kernel.
Requires Python: >=3.7

# janssqlitekernel

![Logo](https://raw.githubusercontent.com/jans-code/janssqlitekernel/main/janssqlitekernel/logo-svg.svg)

A simple SQLite3 kernel for jupyter.
Created using IPython's kernel and pexpect's REPLWrapper subclasses.
You cannot span commands over multiple inputs.
If you forget a semicolon at the end, the kernel will append it for you.
This feature is disabled for the SQLite dot-commands.

## Installation

- install SQLite3 from your distro's package manager
- get the kernel module via pip
- `pip install janssqlitekernel`
- then install kernelspec
- `janssqlitekernel`

## Dev Installation

- download/clone this project
- open shell in project folder
- `pip install -e ./`
- `jupyter kernelspec install --user janssqlitekernel`

## Uninstall

- `jupyter kernelspec uninstall janssqlitekernel`
- `pip uninstall janssqlitekernel`
wavchunk 1.0.2
Read or write INFO chunks in WAV files
Requires Python: >=3.6

# wav-chunk

Read or write INFO chunks from a [RIFF WAV](https://en.wikipedia.org/wiki/WAV) file.

## Installation

```sh
$ pip install wavchunk
```

## Dependencies

* Python 3.6 or higher

## Usage

By default, `wavchunk` adds or gets INFO chunk bytes after the WAV `data` chunk.

### Add INFO chunk

```python
import wavchunk

with open("infile.wav", "rb") as in_file:
    with open("outfile.wav", "wb") as out_file:
        wavchunk.add_chunk(in_file, "my data".encode(), out_file=out_file)
```

### Get INFO chunk

```python
import wavchunk

with open("infile.wav", "rb") as in_file:
    my_data = wavchunk.get_chunk(in_file).decode()
    print(my_data)
```

## Command-Line Interface

The `wavchunk` module can also be used from the command-line.
By default, WAV data is expected on stdin and is written to stdout so that commands can be chained together in a pipe.

### Add INFO chunk

```sh
$ python3 -m wavchunk add --data 'my data' < infile.wav > outfile.wav
```

### Get INFO chunk

```sh
$ python3 -m wavchunk get --data - < infile.wav
<data from chunk>
```

### Delete INFO chunk

```sh
$ python3 -m wavchunk get --delete --data mydata.bin < infile.wav > outfile.wav
```
dry-pipe 0.8.0
A Python DSL for bioinformatics pipelines
Requires Python: >=3.7.7

# A Python DSL for pipelines 

Don't Repeat Yourself (D.R.Y) while writing pipelines, and stay away from leaky abstractions, use DryPipe !

## What is a pipeline ?

A pipeline could be described as _"a bunch of programs feeding data to one another"._

Programs within a pipeline tend to:

1. run for a long time
2. need large amounts of resources (cpu, memory, disk space, etc), requiring clusters to run (Slurm, Torque,etc)
3. be written in different languaged, have different CLI interfaces, file formats, etc.
4. long CODE->RUN->DEBUG->CODE cycles

The length of the development cycle makes pipelines difficult to debug.


[https://drypipe.readthedocs.io/](https://drypipe.readthedocs.io/)
atmodata 0.2
A collection of high performant data loading tools for spatio-temporal ML based on torchdata
Requires Python: >=3.8

# atmodata
[![](https://img.shields.io/github/actions/workflow/status/sehoffmann/atmodata/run_tests.yml?logo=github)](https://github.com/sehoffmann/atmodata/actions/workflows/run_tests.yml)
[![](https://img.shields.io/github/actions/workflow/status/sehoffmann/atmodata/linting.yml?label=lint&logo=github)](https://github.com/sehoffmann/atmodata/actions/workflows/linting.yml)
[![](https://img.shields.io/pypi/v/atmodata)](https://pypi.org/project/atmodata/)

A collection of high performant data loading tools for spatio-temporal ML based on torchdata