semvec/pypi-packages huggingface dataset:
giant-errorpages 0.1.0
A small package to trigger specific non-200 HTTP responses in a Django application.
License: MIT
Requires Python: >=3.6,<4.0
Keywords: error,404,403,500

# Giant Error Pages

A small package to trigger specific non-200 HTTP responses. This is useful for previewing your error pages while DEBUG=True. Once enabled, you will be able to access three new routes:

- /errors/404/, which will return a 404 response and error page.
- /errors/403/, which will return a 403 response and error page.
- /errors/500/, which will return a 500 response and error page.

## Installation

To install with Poetry, run:

    $ poetry add giant-errorpages

You should then add `"errorpages"` to the `INSTALLED_APPS` in your settings file. Finally, update your root `urlpatterns` to include:

    path("errors/", include("errorpages.urls"), name="errorpages"),

## Preparing for release
 
In order to prepare the package for a new release on TestPyPi and PyPi, you need to update the version number in the `pyproject.toml`. The version numbering must also follow the Semantic Version rules which can be found here https://semver.org/.
 
## Publishing
 
Once the version number has been updated, run:
 
   $ `poetry build` 

This will package the project ready for publication:

   $ `poetry publish`

...will then publish the package to PyPi. You will need to enter the username and password for the account which can be found in the company password manager.
ilabs.aclient 0.0.2
Asychronous client to connect to api.innodatalabs.com
License: MIT
Keywords: innodatalabs api rest async trio

# ilabs.client
[![Build Status](https://travis-ci.org/innodatalabs/ilabs.aclient.svg?branch=master)](https://travis-ci.org/innodatalabs/ilabs.aclient)
[![PyPI version](https://badge.fury.io/py/ilabs.aclient.svg)](https://badge.fury.io/py/ilabs.aclient)

Python client to access ilabs-api.innodata.com endpoints.

## Building

```
virtualenv .venv -p python3
. .venv/bin/activate
pip install -r requirements.txt
pip install pytest

PYTHONPATH=. pytest ilabs/aclient/test
```

## Usage

Quick start examples: https://github.com/innodatalabs/ilabs.aclient/tree/master/examples

User guide and API reference: https://innodatalabs.github.io/ilabs.aclient

## Command-Line Bulk Prediction Tool

This package also provides a CLI tool to run file-to-file prediction in bulk:
```
ilabs_bulk_predict -h

usage: ilabs_bulk_predict [-h] --domain DOMAIN [--user_key USER_KEY]

                          [--num_workers NUM_WORKERS]
                          input_dir output_dir

Sends all files from the input directory to prediction service and places
result in the output directory

positional arguments:
  input_dir             Directory where input files are located
  output_dir            Directory where output will be saved

optional arguments:
  -h, --help            show this help message and exit
  --domain DOMAIN, -d DOMAIN
                        Prediction domain
  --user_key USER_KEY, -u USER_KEY
                        Secret user key
  --num_workers NUM_WORKERS, -n NUM_WORKERS
                        Number of concurrent workers
```
mpu 0.23.1
Martins Python Utilities
License: MIT
Requires Python: >= 3.6
Keywords: utility

[![PyPI version](https://badge.fury.io/py/mpu.svg)](https://badge.fury.io/py/mpu)
[![Python Support](https://img.shields.io/pypi/pyversions/mpu.svg)](https://pypi.org/project/mpu/)
[![Documentation Status](https://readthedocs.org/projects/mpu/badge/?version=latest)](http://mpu.readthedocs.io/en/latest/?badge=latest)
[![Build Status](https://travis-ci.org/MartinThoma/mpu.svg?branch=master)](https://travis-ci.org/MartinThoma/mpu)
[![Coverage Status](https://coveralls.io/repos/github/MartinThoma/mpu/badge.svg?branch=master)](https://coveralls.io/github/MartinThoma/mpu?branch=master)

# mpu
Martins Python Utilities (mpu) is a collection of utility functions and classes
with no other dependencies.

The total size of the package will never be bigger than 10 MB and currently it
is 15.7 kB in zipped form. This makes it a candidate to include into AWS Lambda
projects.


## Installation

```bash
$ pip install git+https://github.com/MartinThoma/mpu.git
```

It can, of course, also be installed via PyPI.


## Usage

### Datastructures

```python-repl
>>> from mpu.datastructures import EList

>>> l = EList([2, 1, 0])
>>> l[2]
0

>>> l[[2, 0]]
[0, 2]

>>> l[l]
[0, 1, 2]
```

### Shell

To enhance your terminals output, you might want to do something like:

```python
from mpu.shell import Codes

print("{c.GREEN}{c.UNDERLINED}Works{c.RESET_ALL}".format(c=Codes))
```


### Quick Examples

Creating small example datastructures is a task I encounter once in a while
for StackExchange answers.

```python
from mpu.pd import example_df

df = example_df()
print(df)
```

gives

```
     country   population population_time    EUR
0    Germany   82521653.0      2016-12-01   True
1     France   66991000.0      2017-01-01   True
2  Indonesia  255461700.0      2017-01-01  False
3    Ireland    4761865.0             NaT   True
4      Spain   46549045.0      2017-06-01   True
5    Vatican          NaN             NaT   True
```


### Money

```python
import mpu
from fractions import Fraction

gross_income = mpu.units.Money("2345.10", "EUR")
net_income = gross_income * Fraction("0.80")
apartment = mpu.units.Money("501.23", "EUR")
savings = net_income - apartment
print(savings)
```

prints `1375.31 Euro`


### IO

* Download files with `mpu.io.download(source, sink)`
* Read CSV, JSON and pickle with `mpu.io.read(filepath)`
* Write CSV, JSON and pickle with `mpu.io.write(filepath, data)`
nanome-structure-prep 0.2.6
A Nanome plugin to clean up selected structures.
License: MIT
Keywords: virtual-reality,chemistry,python,api,plugin

# Nanome - Structure Prep

A plugin to add bonds and secondary structures to complexes. Useful for Nanome Quest, as those features are not available natively.

### Preparation

Install the latest version of [Python 3](https://www.python.org/downloads/)

| NOTE for Windows: replace `python3` in the following commands with `python` |
| - |

Install the latest `nanome` lib:

```sh
$ python3 -m pip install nanome --upgrade
```

### Installation

To install Structure Prep:

```sh
$ python3 -m pip install nanome-structure-prep
```

### Usage

To start Structure Prep:

```sh
$ nanome-structure-prep -a <plugin_server_address>
```

### Docker Usage

To run Structure Prep in a Docker container:

```sh
$ cd docker
$ ./build.sh
$ ./deploy.sh -a <plugin_server_address>
```

### Development

To run Structure Prep with autoreload:

```sh
$ python3 run.py -a <plugin_server_address>
```

### License

MIT