semvec/pypi-packages huggingface dataset:
genlatex 0.25
A simple template based doc generator
License: MIT

# Templating with LaTex and Python

This year I'm trying to generate quizzes automatically to the degree possible.

I've cooked up a templating scheme using [jijna2](https://jinja.palletsprojects.com/en/2.11.x/) that works with LaTeX templates.

This [article](http://eosrei.net/articles/2015/11/latex-templates-python-and-jinja2-generate-pdfs) was very helpful.

Now that this has been published to pypi you can simply:

pip install genlatex

Once you have that, you can 'cd' to the directory with your template and data generator file and run getlatex using:

    genlatex [ options ] dataGeneratorFile.py

For example to create 10 versions of the example you could:

    cd example

    genlatex -n 10 tlo-EX-Data.py

Then check in the folder for your .tex files!

To convert to pdf en-masse, with bash:

    find *.tex |  xargs -n 1 pdflatex

If your dataGeneratorFile needs to import any modules you should add '.' to the PYTHONPATH, e.g.,

    PYTHONPATH=. genlatex -n 10 tlo-EX-Data.py

There is a shell script "buildZip.sh" that creates an importable .zip file that can be used to import quizzes into Sakai. It may also work for other LMS variations. Untested!

Update: Now you can run the full docker (Dockerfile-w-latex) version:

    docker run --rm -it -v `pwd`:/work/src genlatex-full doExport.sh -n 10 tlo-EX-Data.py

Or the mini-docker (Dockerfile)

    docker run --rm -it -v `pwd`:/work/src genlatex buildTeXs.sh -n 10 tlo-EX-Data.py

to build the mini-docker (without a full LaTeX install ~1.4GB)

    docker build -t genlatex .

to build the full docker build (with a full LaTeX install ~7GB)

    docker build -f Dockerfile-w-latex -t genlatex-full .

CHANGES:

    v 0.12: Added units to formatters (e.g., latex_float(2.31, units="m"))

    v 0.14: add units to latex_float and latex_vec, add some doc tests.

    v 0.15: Changed the path handling to make it more Windows friendly.

    v 0.16: Update README & other doc-strings

    v 0.17: Add file path to items.xml so export.zip would contain individual filenames for students/instructors.

    v 0.18: Possible breaking change. latex_float arguments are switching order, units first to save typing.
            Now you can type simply: latex_float(2.31,"m") and "m" will be units. If you want a different format
            then 'fmt' must be supplied as a *third* argument, or with a keyword, e.g. latex_float(2.234, fmt="{:0.2g}").

            Also, added a '-0' detector that should convert these to '0'.

    v 0.19: Modified the attachment file paths so that Canvas would accept them. Unfortunately canvas does not
      	    support file upload questions in their current quiz tool.

    v 0.20: Added eunit formatter to use "engineering" units.

    v 0.24: Various changes, includeing command line variables in templates.

    v 0.25: Force wheel in pypi
textra 0.6
Machine translation for Everyone
License: MIT
Keywords: TexTra translation python

[![Python](https://img.shields.io/pypi/pyversions/textra.svg)](https://badge.fury.io/py/textra)
[![PyPI](https://badge.fury.io/py/textra.svg)](https://badge.fury.io/py/textra)

# @TexTra
Machine translation for Everyone

# Install

```bash
pip install textra
```

# Usage

```bash
$ trans --help
usage: trans [-h] [--name NAME] [--key KEY] [--secret SECRET] [--engine ENGINE] [-s SOURCE] [-t TARGET] text

positional arguments:
  text

options:
  -h, --help            show this help message and exit
  --name NAME           User ID
  --key KEY
  --secret SECRET
  -e ENGINE, --engine ENGINE
  -s SOURCE, --source SOURCE
  -t TARGET, --target TARGET
```

# Examples

```bash
$ trans 'I am God. You are Dog.'
私は神だお前は犬だ
```

```bash
$ trans -s ja -t en 吾輩は猫である
I am the cat
```

You can get the key and secret from here.

![image](https://user-images.githubusercontent.com/12811398/186966682-5a563423-fbe2-4f3f-aa09-4a274ac0e778.png)
lnpanda 0.2.4
A tool which pairs Pandas to LND Lightning Network Data for Data Science
License: MIT
Requires Python: >=3.8

# lnpanda

lnpanda allows you to query Bitcoin lightning network data using Pandas dataframes. Pandas is a powerful data science tool, and the combination can be used to find insights about your node. In addition, pandas dataframes are a just convenient and powerful way to interact with your node, while staying on the command line!


## Install

```python
pip install lnpanda
```

## Environment Variables

Add information like node ip address, and directory containing:
- tls.cert 
- admin.macaroon

```bash
export CRED_PATH=/path/to/macaroon/and/tls/cert
export LND_NODE_IP=192.168.1.xx
```

## Basic Usage

```python
from lnpanda import lnpanda

# initialize lnpanda object
a = lnpanda()

# Get info about channel balances and fee rates in 1 view 
a.list_channels_and_fees()

# List routed transactions, shows eff_fee_rate of fwd
a.list_forwards()
```

## Using pandas queries

```python
# List channels with a fee rate > 100
a.list_channels_and_fees().query("fee_rate > 0.000100")

# Get sum of latest 25 routed transactions in sats
a.list_forwards().tail(25).fee_msat.sum()/1000

# Get a set of alias' of the last 10 outgoing forwards
outgoing_chan_ids = list(a.list_forwards().tail(10).chan_id_out)
set(map(lambda x: a.get_peer_alias(x), outgoing_chan_ids))
```