semvec/pypi-packages huggingface dataset:
x10-blackjack 1.1.4
A small blackjack game.
Requires Python: >=3.7

# Blackjack

## Usage
Perform the equivalent of the below line;
`pipenv install --python 3.7 x10-blackjack` (Tip: can also replace `pipenv install --python 3.7` with just `pip`).

And then, from within the same (hopefully virtual) environment you've now installed `x10-blackjack` into, execute with something equivalent to;
`pipenv run blackjack`

### If you instead want to install via Git
Ensure you've got `python3.7` and `pipenv` available on `$PATH`.
- PS: https://pipenv.pypa.io/en/latest/install/#installing-pipenv
Then perform `make install` as referenced in Developer/Installation steps.

## Developer Usage
### Automatic pipeline
This repository has a pipeline triggering on any and all commits pushed.
(Also merge-requests created).

So, if pipeline passes okay, then all is good.
Read on for further details on how to set-up dev-environment locally/manually.

### Requirements
1. `pipenv` available through PATH (e.g. installed through `pipsi` so as to contain it to USER PATH env).
2. `python3.7`

#### Docker
Execute the equivalent of the `Makefile`s `make docker` (and thus nothing besides `docker` is required on developer machine).

### Installation steps
1. `git clone <clone url>`
2. `cd <into cloned repo folder root>`
3. `make install`

### Development iteration steps
1. `<modify code>`
2. `make check_all` (or `make check_quick` if you don't want to run slower integration tests).
3. `make run`
4. `<feature/change specific test(s)>`
Repeat any steps (or sequence of them) as needed until `make check_all` passes before pushing to `master` branch (ideally...).

### Upload to PyPI manually
1. First create package; `make package`
2. Then upload `pipenv run twine upload dist/*`
Note: The command in list-item #2 relies on the variables `TWINE_USERNAME="__token__"` and `TWINE_PASSWORD=<token string>` having been set.

### Upload to PyPI automatically (read: with (GitLab) pipeline)
Push a `git tag` where the name of the tag is a semantic version (`<major>.<minor>.<patch>`).
camels 0.0.8
Interface to the CAMELS dataset.
Requires Python: >=3.6

![Build status](https://travis-ci.com/simonpf/camels.svg?branch=master) [![Build status](https://ci.appveyor.com/api/projects/status/e14q8e8x6449ne0g?svg=true)](https://ci.appveyor.com/project/simonpf/camels)

# The CAMELS dataset

This package provides an interface to the Catchment Attributes and Meteorology
for Large-sample Studies (CAMELS) dataset. This package only provides simplified
access to the data in Python, the actual work of generating the dataset has been
done by Andrew Newman and colleagues  at NCAR and is described in the following
article:

[A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A
large-sample watershed-scale hydrometeorological dataset for the contiguous USA.
Boulder, CO: UCAR/NCAR](https://dx.doi.org/10.5065/D6MW2F4D)

## Usage

### Pytorch

```
from camels.pytorch import Streamflow
gauge_id = 13331500

training_data = Streamflow(gauge_id, "training")
validation_data = Streamflow(gauge_id, "validation")
test_data = Streamflow(gauge_id, "testing")
```

![Overview of gauges in dataset](misc/overview.png)