semvec/pypi-packages huggingface dataset:
pytato 2021.2
Get Descriptions of Array Computations via Lazy Evaluation
License: MIT
Requires Python: ~=3.8

Pytato: Get Descriptions of Array Computations via Lazy Evaluation
==================================================================

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Imagine TensorFlow, but aimed at HPC. Produces a data flow graph, where the
edges carry arrays and the nodes are (give or take) static-control programs
that compute array outputs from inputs, possibly (but not necessarily)
expressed in `Loopy <https://github.com/inducer/loopy>`__. A core assumption is
that the graph represents a computation that's being repeated often enough that
it is worthwhile to do expensive processing on it (code generation, fusion,
OpenCL compilation, etc).

* `Documentation <https://documen.tician.de/pytato>`__ (read how things work, see an example)
* `Github <https://github.com/inducer/pytato>`__ (get latest source code, file bugs)

Pytato is licensed to you under the MIT/X Consortium license. See
the `documentation <https://documen.tician.de/pytato/misc.html>`__
for further details.

Numpy compatibility
-------------------

Pytato is written to pose no particular restrictions on the version of numpy
used for execution. To use mypy-based type checking on Pytato itself or
packages using Pytato, numpy 1.20 or newer is required, due to the
typing-based changes to numpy in that release.
Flask-Sharp 0.0.7
An automatic code API generator for Flask and JS.
License: MIT
Requires Python: >=3.6

# Sharp

Sharp is an automatic API generation library for Python Flask and JavaScript.

You write functions in the backend, and Sharp generates the necessary JavaScript to make
API calls to those functions. Sharp also validates argument types based on Python type
hints,

```python
@sharp.function()
def add(a: int, b: int):
    return a + b

sharp.generate("api.js")
```

Now `api.js` can be used from a JavaScript client,

```js
const result = await API.add(4, 3);
```

## Install

```
pip install Flask-Sharp
```

## Basic Example

Server-side code,

```python
app = Flask(__name__)
sharp = Sharp(app)

@sharp.function()
def add(a: int, b: int):
    return a + b
```

Sharp can be combined with a modern frontend framework and any web asset bundler, the code-gen is emmitted to a standalone JavaScript file,

```python
sharp.generate("src/js/sharp.gen.js")
```

And your front-end JavaScript is automatically generated and can be used as,

```js
const result = await API.add(4, 3);
```

On the backend Sharp will automatically validate that both `a` and `b` parameters are valid `ints`.

All Sharp messages are in plain JSON, producing human-readable messages and API routes.

For a more fully fledged example, please see the `example/` directory.

## Naming Routes

By default Sharp will try to name routes by combining the prefix with the function name.
However, Sharp can also name routes based on filenames and any arbitrary custom naming
function,

```python
from sharp import Sharp, naming

srp = Sharp(app, prefix="/api", naming=naming.file_based)
```

In this example, Sharp will name a function names `add` in a file called `math.py`
as `/api/math/add`.

## Type Checking

Sharp can verify basic Python type-hints and deal with default variables,

```python
@srp.function()
def repeat(name: str, times: int = 5):
    return name * times


@srp.function()
def reverse(names: List[str]):
    return names[::-1]
```

## Errors

If Sharp notices a missing parameter or a wrong type, it will generate a JSON response
body `{"error": "Message"}` with a response code 400. To throw errors within a sharp
function,

```python
from sharp.error import error

@sharp.function()
def add(a: int, b: int):
    if a < 0:
        return error("a cannot be negative.")

    return a + b
```

All API calls from the client return a promise. Errors can be caught with a try-catch or
a `catch` promise callback.

```js
API.add(-4, 3).then((r) => {
    // r is the result of the function.
}).catch((e) => {
    // e is the error message.
});
```

## License

Copyright (c) 2020 Shreyas Kapur. Released under MIT License.
pantry-cloud 1.2.0
Free JSON Storage API Implimentation In Python.
License: MIT
Requires Python: >=3.7.0
Keywords: pantry_cloud,JSON,API,CLOUD STORAGE

# pantry-cloud

![PyPI version](https://img.shields.io/pypi/v/pantry-cloud?color=orange&logo=pypi&logoColor=orange&style=flat-square)
![Language](https://img.shields.io/badge/python-3.7%2B-blue?logo=python&style=flat-square)
[![GitHub issues](https://img.shields.io/github/issues/sarvesh4396/pantry-cloud)](https://github.com/sarvesh4396/pantry-cloud/issues)
[![GitHub forks](https://img.shields.io/github/forks/sarvesh4396/pantry-cloud)](https://github.com/sarvesh4396/pantry-cloud/network)
[![GitHub stars](https://img.shields.io/github/stars/sarvesh4396/pantry-cloud)](https://github.com/sarvesh4396/pantry-cloud/stargazers)
[![GitHub license](https://img.shields.io/github/license/sarvesh4396/pantry-cloud)](https://github.com/sarvesh4396/pantry-cloud/blob/main/LICENSE)
![GitHub last commit (branch)](https://img.shields.io/github/last-commit/sarvesh4396/pantry-cloud/main?label=updated)
![Pull Requests](https://img.shields.io/badge/PRs-welcome-organge.svg?logo=git&logoColor=organge&style=flat-square)

This lightweight [package](https://github.com/sarvesh4396/pantry-cloud) is a python implementation of Free JSON Storage API [pantry](https://getpantry.cloud/).


## Features

- Creates a basket.
- Deletes a basket.
- Updates a basket.
- Shows account details.
- Writes basket data in json file.


> The aim of this package is to make `pantry` api usage simple.
## Installation

pantry-cloud requires [python3.7+](https://www.python.org/downloads/) to run.

#### Windows

```sh
pip install pantry-cloud
or
pip install --upgrade pantry-cloud
```

#### Linux

```sh
pip3 install pantry-cloud
or 
pip3 install --upgrade pantry-cloud
```

## Module Usage

#### Creating basket
```sh
from pantry_cloud import Pantry

pantry = Pantry(api_key='your api key')
res = pantry.create(basket='hello', inputfile='path to json file') # Use inputfile if you want to upload data
print(res)
```

#### Basket detail
```sh
from pantry_cloud import Pantry

pantry = Pantry(api_key='your api key')
res = pantry.basket(basket='hello', outputfile='path to json file') # Use outputfile if you want to write data data
print(res)
```
## CLI Usage
```sh
usage: pantry [-h] -a API [-o OUTPUT_FILE] [-i INPUT_FILE] [-s] [-b BASKET] [-u UPDATE] [-c CREATE] [-d DELETE]

optional arguments:
  -h, --help            show this help message and exit
  -a API, --api API     api key.
  -o OUTPUT_FILE, --output OUTPUT_FILE
                        output file.
  -i INPUT_FILE, --input INPUT_FILE
                        input file.
  -s, --show            Shows account details.
  -b BASKET, --basket BASKET
                        Shows data of a basket.
  -u UPDATE, --update UPDATE
                        Updates a basket.
  -c CREATE, --create CREATE
                        Creates a new basket , or replace an existing one.
  -d DELETE, --delete DELETE
                        Deletes the entire basket.
```