semvec/pypi-packages huggingface dataset:
repertoire 0.0.2
Spaced repetition in plain text

# Repertoire
Spaced repetition is a technique that [some people](http://augmentingcognition.com/ltm.html) like to use to remember things.
In particular, things they would like to remember over many years, perhaps for the rest of their lives.
[Anki](https://apps.ankiweb.net/) is a popular program for doing this, but it seems unlikely that it will still be possible to run this program 10 years from now (its UI is already showing its age).

Repertoire is a minimalistic, extensible program for spaced repetition.
Memories are stored in plain text (which, in my view, will never become obsolete), and the testing interface can run in nothing more than a text-based console.

# Binders
There are three built-in *binders*, formats for flashcards (*sheets* in the musical analogy): quotes, people, and math.

## Quotes
Quotes ask you to identify your favorite quotations, their authors, and the dates when they were published.
The file is organized by work, decreasing overhead when you have multiple quotes from the same work:
```yaml
binder: quotes
sheets:
- title: Discourse on Method
  author: René Descartes
  date: 1637
  quotes:
  - quote: I think therefore I am
  - quote: Good sense is, of all things among men, the most equally distributed
- title: Crime and Punishment
  author: Fyodor Dostoevsky
  date: 1866
  quotes:
  - quote: Only to live, to live and live! Life, whatever it may be!
```

## People
The *people* binder tests you on topics of interest and important contributions of major figures in your field, as well as (optionally) their birth and/or death years.
It is organized by person:
```yaml
binder: people
sheets:
- name: Bill Thurston
  dates: 1946-2012
  topics: low-dimensional topology; foliation; geometrization conjecture
- name: Luitzen Egbertus Jan Brouwer
  dates: 1881-1966
  topics: fixed point theorem; intuitionism
```

## Math
The *math* binder is relatively open-ended, but can test you on definitions, theorem statements, proof key ideas, etc.
Its key feature is support for latex rendering in iTerm2:
```yaml
binder: math
sheets:
- name: Strong law of large numbers
  statement: |
    $\bar{X}_n \overset{a.s.}{\to} \mu$
- name: Weak law of large numbers
  statement: |
    $\lim_n \mathbb{P}[|\bar{X}_n - \mu| < \epsilon] = 0$
- name: Heine–Cantor theorem
  statement: |
    If $f : M \to N$ is a continuous function on a compact metric space $M$, then $f$ is uniformly continuous
  proof: |
    Find a finite subcover of the balls of half the radius needed to make $f$ change by no more than $\epsilon$, and take $\delta$ as their minimum radius.
```

# Installation
1. Install repertoire with `pip install repertoire`
1. If you want to use the math binder, make sure you have LaTeX installed (and use iTerm2), as well as ImageMagick (`brew install imagemagick` on MacOS)
dashjson 0.1.5
A tool for exporting (or importing) Datadog dashboards to (or from) json

[![Build Status](https://travis-ci.org/ouyi/dashjson.svg?branch=master)](https://travis-ci.org/ouyi/dashjson)

# dashjson.py -- a Datadog tool

A tool for exporting (or importing) Datadog dashboards to (or from) json. It allows the Datadog dashboard definitions to be

- version controlled as json files, and
- migrated across Datadog accounts

Tested with Python 3.6.7 and datadog 0.26.0.

## How to use

Create the credentials file containing the api\_key and the app\_key. Example content of the credentials file (your keys can be found at https://app.datadoghq.com/account/settings#api):

    # cat ~/.dashjson.json
    {
        "api_key": "abcdefg12345678",
        "app_key": "abcdefg987654321"
    }

Install via pip:

    python3 -m pip install --user dashjson

Show usage of the tool:

    python3 -m dashjson -h

## How to build (for contributors only)

These are the steps I used on Ubuntu 18.04.

Add the following lines to `~/.bashrc`:

    # Due to https://github.com/pypa/pipenv/issues/2122
    export PATH="${HOME}/.local/bin:$PATH"

    # Due to https://github.com/pypa/pipenv/issues/1382
    export PIPENV_VENV_IN_PROJECT=true

Install pip and pipenv:

    sudo apt install python3-pip
    python3 -m pip install --user pipenv

Clone and set up the project locally:

    git clone git@github.com:ouyi/dashjson.git
    cd dashjson
    pipenv install --dev
    pipenv run python -m unittest
    pipenv run python dashjson.py -h

Upload a release:

    rm -rf build/ dist/
    python setup.py sdist bdist_wheel
    pip install . -vvv
    python -m dashjson -h
    vim setup.py # bump version
    git add setup.py && git commit -m "Bump version"
    git tag -a my_tag -m "My message"
    git push origin master && git push origin --tags
    python -m twine upload dist/*

## TODOs

1. Automatically determine the dashboard type

For import, the information is available in the input file. For export, we can first [get a list of all screenboards](https://docs.datadoghq.com/api/?lang=python#get-all-screenboards) and then check existence by id.

2. Embed a code coverage badge

The coverage data can be generated with:

    coverage run --source=dashjson -m unittest && coverage report && coverage-badge -f -o coverage.svg

3. Automate the release process
alphavantage-wrapper 0.0.4
A simple wrapper for the Alpha Vantage API.

# Introduction

This is a simple wrapper for the [Alpha Vantage API](https://www.alphavantage.co). Since `quandl` disabled the free `WIKI` data source, I created this package that uses Alpha instead.

All results are converted to a `pandas`'s `DataFrame` to make data manipulation/computation/etc. faster.

## Disclaimer

1. I am not part of Alpha Vantage or have any relationship with the company.
1. Forgive me if I do not employ the best practices (code, comments, documentation), I have no prior experience in publishing a pip module or software development in a professional level.

# Install

You can install this package globally with:

```
pip install alphavantage_wrapper pandas
```

Or if you do not want to install globally, but on a per project basis for example, you can clone this repo (you will still need `pandas` package):

```
cd my-awesome-project
git clone http://github.com/regisin/alphavantage-wrapper
```

Tested using `Python 3`.

# Usage

```python
from alphavantage_wrapper.AlphaVantage import AlphaVantage

api_key = 'APIKeyGoesHere'

av = AlphaVantage(api_key)

# The company's stock symbol (ex.: Microsoft)
ticker = 'MSFT'
# Retrieval the 100 most recent financial data (daily time series, adjusted)
stock = av.get_time_series_daily_adjusted(ticker)
# do stuff with data
print(stock)
```

# Changelog

* v0.0.4
  * Added tests for time series API

* v0.0.3
  * Bug fixes

* v0.0.2
  * Bug fixes

* v0.0.1
  * First commit
  * Include LICENSE file
  * Include README
  * Implemented all time series functions as seen in https://www.alphavantage.co/documentation

# TO-DO

* For v0.1.0

- [ ] Improve source-code comments (function descriptions, add example)
- [ ] Time series example script
- [ ] Implement Forex functions
- [ ] Implement Cryptocurrencies functions
- [ ] Implement Technical Indicators functions
- [ ] Implement Sector Performances functions
- [x] Add unit tests
- [ ] Add CSV support, maybe?

* For v0.0.1

- [x] Create package structure
- [x] Write readme
- [x] Implement time series API functions
tmpk 1.1.1
Library and tool for Nintendo's TMPK archive format
Requires Python: >=3.6

## Library and tool for Nintendo's TMPK archive format

### Requirements
* Python 3.6+

### Usage

```
tmpk create  {files_to_include_in_archive}  {destination_archive}
tmpk update  {files_to_add_or_replace_in_archive}  {destination_archive}
tmpk extract {archive}
tmpk list {archive}
```

Pass -h to see a full list of commands or usage help for a specific command.