semvec/pypi-packages huggingface dataset:
sampleproject-wenxuanye 0.0.1
A sample Python project
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

# A sample Python project

![Python Logo](https://www.python.org/static/community_logos/python-logo.png "Sample inline image")

A sample project that exists as an aid to the [Python Packaging User
Guide][packaging guide]'s [Tutorial on Packaging and Distributing
Projects][distribution tutorial].

This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.

[The source for this project is available here][src].

Most of the configuration for a Python project is done in the `setup.py` file,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.

----

This is the README file for the project.

The file should use UTF-8 encoding and can be written using
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use]. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.

Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.

[packaging guide]: https://packaging.python.org
[distribution tutorial]: https://packaging.python.org/tutorials/packaging-projects/
[src]: https://github.com/pypa/sampleproject
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
StorageTopology 1.7
A scientific computation library for performance analysis of erasure coding and/or replication in a distributed data storage environment.
Requires Python: >=3.6

The StorageTopology library is a library supporting research within the work "Storage Latency Tradeoffs in Geographically Distributed Storage Settings".  This work is an undergraduate honors thesis written by Ethan Balcik of the Pennsylvania State University, who was advised by Viveck R. Cadambe, associate professor of electrical engineering.  The work explores the application of erasure coding in distributed data stores (such as Amazon DynamoDB, Apache Cassandra, and/or Google Spanner).  Particularly, it investigates the effects erasure coding has on latency in comparison to replication, and it explores the cases in which erasure coding counterintuitively outperforms replication.  A link to the work will soon be available to those interested in reading more about the nature of the research.

To install using pip, simply run the command,
```pip install StorageTopology --upgrade```

To contribute to the library, fork the below repository and feel free to send a pull request to the owner of the repository.
https://github.com/whatsacomputertho/StorageTopology
russianShellwhat 1.5.0
Submission correctness tests for shell languages

shellwhat
=========

[![Build Status](https://travis-ci.org/datacamp/shellwhat.svg?branch=master)](https://travis-ci.org/datacamp/shellwhat)
[![codecov](https://codecov.io/gh/datacamp/shellwhat/branch/master/graph/badge.svg)](https://codecov.io/gh/datacamp/shellwhat)
[![PyPI version](https://badge.fury.io/py/shellwhat.svg)](https://badge.fury.io/py/shellwhat)
[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat?ref=badge_shield)

`shellwhat` enables you to write Submission Correctness Tests (SCTs) for interactive Shell exercises on DataCamp.

- If you are new to teaching on DataCamp, check out https://instructor-support.datacamp.com.
- If you want to learn what SCTs are and how they work, visit [this article](https://instructor-support.datacamp.com/courses/course-development/submission-correctness-tests) specifically.
- For a complete overview of all functionality inside `shellwhat` and articles about what to use when, consult https://shellwhat.readthedocs.io.

Installing
----------

```bash
pip install shellwhat
```

Development
-----------

Install everything needed for developing:

```bash
pip install -r requirements.txt
pip install -e .
```

By default, the `DummyParser` is used, that does not parse the shell code.
Hence, you can not run tests that need this parser:

```bash
pytest -m "not osh"
```

If you also want to run these 'parser tests',
there is a Dockerfile to parse shell commands with
the [Oil parser](https://github.com/oilshell/oil):

```bash
# Look in Makefile for details
SHELLWHAT_PARSER='docker' make test
```


## License
[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat?ref=badge_large)
russianSqlwhat 3.8.1
Submission correctness tests for SQL

# sqlwhat

[![Build Status](https://travis-ci.org/datacamp/sqlwhat.svg?branch=master)](https://travis-ci.org/datacamp/sqlwhat)
[![codecov](https://codecov.io/gh/datacamp/sqlwhat/branch/master/graph/badge.svg)](https://codecov.io/gh/datacamp/sqlwhat)
[![PyPI version](https://badge.fury.io/py/sqlwhat.svg)](https://badge.fury.io/py/sqlwhat)

`sqlwhat` enables you to write Submission Correctness Tests (SCTs) for interactive SQL exercises on DataCamp.

- If you are new to teaching on DataCamp, check out https://authoring.datacamp.com.
- If you want to learn what SCTs are and how they work, visit [this article](https://authoring.datacamp.com/courses/exercises/technical-details/sct.html) specifically.
- For more information about writing SCTs for SQL exercises, consult https://sqlwhat.readthedocs.io.

## Installing

```
pip install sqlwhat     # install from pypi
make install            # install from source
```

## Reference

* API Docs: https://sqlwhat.readthedocs.io
* AST viewer: https://ast-viewer.datacamp.com
* Extensions: https://github.com/datacamp/sqlwhat-ext

## Raising issues with how SQL is parsed

Please raise an issue on the respsective parser repo:

* [antlr-tsql](https://github.com/datacamp/antlr-tsql)
* [antlr-psql](https://github.com/datacamp/antlr-plsql)

## Basic Use

```python
from sqlwhat.State import State    # State holds info needed for tests
from sqlwhat.Reporter import Reporter
from sqlwhat.checks import *       # imports all SCTs
from sqlalchemy import create_engine

code = "SELECT * FROM artists WHERE id < 100"

state = State(
    student_code = code,
    solution_code = code,
    pre_exercise_code = "",
    student_conn = create_engine('sqlite:///'),
    solution_conn = create_engine('sqlite:///'),
    student_result = {'id': [1,2,3], 'name': ['greg', 'jon', 'martha']},
    solution_result = {'id': [1,2,3], 'name': ['toby', 'keith', 'deb']},
    reporter = Reporter()
    )

# test below passes, since code is equal for student and solution
has_equal_ast(state)

# test below raises a TestFail error, since 'name' col of results
# doesn't match between student and solution results
check_result(state)
# shows error data
state.reporter.build_payload()

# can also be done using a chain
from sqlwhat.sct_syntax import Ex
Ex(state).check_result()
```

## Running unit tests

```bash
pytest -m "not backend"
```

If you also want to run the backend tests, you need to set a `GITHUB_TOKEN` environment variable with access to the (private) `sqlbackend` repository.
After this, you can:

```bash
make install
pytest
```

### Rules of testing

1. Running queries is the backend's job
2. If a test doesn't run queries, it doesn't need the backend
3. Very few tests should run queries

## Building Docs

Install sqlwhat and run ..

```
cd docs
make html
```
russhellwhat 1.5.0
Submission correctness tests for shell languages

shellwhat
=========

[![Build Status](https://travis-ci.org/datacamp/shellwhat.svg?branch=master)](https://travis-ci.org/datacamp/shellwhat)
[![codecov](https://codecov.io/gh/datacamp/shellwhat/branch/master/graph/badge.svg)](https://codecov.io/gh/datacamp/shellwhat)
[![PyPI version](https://badge.fury.io/py/shellwhat.svg)](https://badge.fury.io/py/shellwhat)
[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat?ref=badge_shield)

`shellwhat` enables you to write Submission Correctness Tests (SCTs) for interactive Shell exercises on DataCamp.

- If you are new to teaching on DataCamp, check out https://instructor-support.datacamp.com.
- If you want to learn what SCTs are and how they work, visit [this article](https://instructor-support.datacamp.com/courses/course-development/submission-correctness-tests) specifically.
- For a complete overview of all functionality inside `shellwhat` and articles about what to use when, consult https://shellwhat.readthedocs.io.

Installing
----------

```bash
pip install shellwhat
```

Development
-----------

Install everything needed for developing:

```bash
pip install -r requirements.txt
pip install -e .
```

By default, the `DummyParser` is used, that does not parse the shell code.
Hence, you can not run tests that need this parser:

```bash
pytest -m "not osh"
```

If you also want to run these 'parser tests',
there is a Dockerfile to parse shell commands with
the [Oil parser](https://github.com/oilshell/oil):

```bash
# Look in Makefile for details
SHELLWHAT_PARSER='docker' make test
```


## License
[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fdatacamp%2Fshellwhat?ref=badge_large)
russqlwhat 3.8.1
Submission correctness tests for SQL

# sqlwhat

[![Build Status](https://travis-ci.org/datacamp/sqlwhat.svg?branch=master)](https://travis-ci.org/datacamp/sqlwhat)
[![codecov](https://codecov.io/gh/datacamp/sqlwhat/branch/master/graph/badge.svg)](https://codecov.io/gh/datacamp/sqlwhat)
[![PyPI version](https://badge.fury.io/py/sqlwhat.svg)](https://badge.fury.io/py/sqlwhat)

`sqlwhat` enables you to write Submission Correctness Tests (SCTs) for interactive SQL exercises on DataCamp.

- If you are new to teaching on DataCamp, check out https://authoring.datacamp.com.
- If you want to learn what SCTs are and how they work, visit [this article](https://authoring.datacamp.com/courses/exercises/technical-details/sct.html) specifically.
- For more information about writing SCTs for SQL exercises, consult https://sqlwhat.readthedocs.io.

## Installing

```
pip install sqlwhat     # install from pypi
make install            # install from source
```

## Reference

* API Docs: https://sqlwhat.readthedocs.io
* AST viewer: https://ast-viewer.datacamp.com
* Extensions: https://github.com/datacamp/sqlwhat-ext

## Raising issues with how SQL is parsed

Please raise an issue on the respsective parser repo:

* [antlr-tsql](https://github.com/datacamp/antlr-tsql)
* [antlr-psql](https://github.com/datacamp/antlr-plsql)

## Basic Use

```python
from sqlwhat.State import State    # State holds info needed for tests
from sqlwhat.Reporter import Reporter
from sqlwhat.checks import *       # imports all SCTs
from sqlalchemy import create_engine

code = "SELECT * FROM artists WHERE id < 100"

state = State(
    student_code = code,
    solution_code = code,
    pre_exercise_code = "",
    student_conn = create_engine('sqlite:///'),
    solution_conn = create_engine('sqlite:///'),
    student_result = {'id': [1,2,3], 'name': ['greg', 'jon', 'martha']},
    solution_result = {'id': [1,2,3], 'name': ['toby', 'keith', 'deb']},
    reporter = Reporter()
    )

# test below passes, since code is equal for student and solution
has_equal_ast(state)

# test below raises a TestFail error, since 'name' col of results
# doesn't match between student and solution results
check_result(state)
# shows error data
state.reporter.build_payload()

# can also be done using a chain
from sqlwhat.sct_syntax import Ex
Ex(state).check_result()
```

## Running unit tests

```bash
pytest -m "not backend"
```

If you also want to run the backend tests, you need to set a `GITHUB_TOKEN` environment variable with access to the (private) `sqlbackend` repository.
After this, you can:

```bash
make install
pytest
```

### Rules of testing

1. Running queries is the backend's job
2. If a test doesn't run queries, it doesn't need the backend
3. Very few tests should run queries

## Building Docs

Install sqlwhat and run ..

```
cd docs
make html
```