semvec/pypi-packages huggingface dataset:
readwise-api 0.3.0
An unofficial Python client for the Readwise Reader API.
License: MIT
Requires Python: >=3.11,<4.0

![logo](https://github.com/floscha/readwise-api/raw/main/logo.png)

# Readwise API

An unofficial Python client for the [Readwise Reader API](https://readwise.io/reader_api).

## Installation

`pip install readwise-api`

## Usage

### Prerequisites

First, you have to obtain a [Readwise access token](https://readwise.io/access_token).
Then, the token has to be stored, either into an *.env* file or an environment variable using `export READWISE_TOKEN=<your_token>`.

### Python API

```python
import readwise
```

**List all documents:**

```python
readwise.get_documents(location="new")
```

**Get a single document by its ID:**

```python
readwise.get_document_by_id("<document_id>")
```

### CLI

**List all documents:**

```shell
readwise list new
```

Naturally, the output can be saved to a JSON file:

```shell
readwise list new > new_documents.json
```

**Get a single document by its ID:**


```shell
readwise get <document_id>
```
disimpy 0.3.0
Massively parallel diffusion MR simulator
License: MIT

*******
Disimpy
*******

Disimpy is a Python package for generating simulated diffusion-weighted MR
signals that can be useful in the development and validation of data
acquisition and analysis methods. The data is generated by Monte Carlo random
walk simulations that run massively parallel on Nvidia CUDA-capable GPUs. If
you use Disimpy in work that leads to a scientific publication, please cite
[1]_, where the details about signal generation can also be found.

Requirements and installation
#############################

Follow the `installation instructions
<https://disimpy.readthedocs.io/en/latest/installation.html>`_.
    
Usage example
#############

Read the `tutorial <https://disimpy.readthedocs.io/en/latest/tutorial.html>`_
to learn how to use Disimpy.

Validation
##########

Disimpy's functionality has been validated by comparing its results to
analytical solutions and to results from other simulators (e.g., `Camino
<http://camino.cs.ucl.ac.uk/>`_ and `MISST
<http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.MISST>`_), and by automated
testing (:code:`disimpy.tests`). Examples of simulations used for validation
are provided `here
<https://disimpy.readthedocs.io/en/latest/validation.html>`_. However, Disimpy
is research software and some bugs undoubtedly remain. If you find any of them
or encounter unexpected behaviour, please open an `issue on GitHub
<https://github.com/kerkelae/disimpy/issues>`_.

Contribute
##########

If you want to contribute to the development of Disimpy, start by reading the
`contributing guidelines
<https://disimpy.readthedocs.io/en/latest/contributing.html>`_.

Support
#######

If you have questions or need help, open an `issue on Github
<https://github.com/kerkelae/disimpy/issues>`_.

References
##########

.. [1] Kerkelä et al., (2020). Disimpy: A massively parallel Monte Carlo
       simulator for generating diffusion-weighted MRI data in Python. Journal
       of Open Source Software, 5(52), 2527.
       https://doi.org/10.21105/joss.02527
pytojs 0.1
JavaScript to Python Translator & JavaScript interpreter written in 100% pure Python. (Fork from js2py)
License: MIT

Translates JavaScript to Python code. pytojs is able to translate and execute virtually any JavaScript code.

pytojs is written in pure python and does not have any dependencies. Basically an implementation of JavaScript core in pure python.


    import pytojs

    f = pytojs.eval_js( "function $(name) {return name.length}" )

    f("Hello world")

    # returns 11

Now also supports ECMA 6 through pytojs.eval_js6(js6_code)!

More examples at: https://github.com/PiotrDabkowski/pytojs
replicator-cli 0.0.7
Task runner for backups
License: MIT
Requires Python: >=3.7

Replicator is a task runner for backups.

You can describe your tasks declarative with YAML file and run task files with CRON.

Replicator supports notifications, it's useful for tasks that take a lot of time.

## Install

Install from pip `pip install replicator-cli`

Or build:
- Clone repository `git clone https://github.com/vitonsky/replicator.git`
- Build package with run `make build`
- Install package `pip install dist/replicator-0.0.1-py3-none-any.whl`

## Usage

```
usage: replicator [-h] config

Util to replicate backups from primary storage to a mirrors

positional arguments:
  config      Path to config file

optional arguments:
  -h, --help  show this help message and exit
```

To use util, first create task file.

Example task file for a local device
```yml
tasks:
    # Task may have name
    - name: 'Copy files to backup disk'
      run: rclone ./backups /path/to/local/mirror1

    - name: 'Upload to S3'
      run: rclone ./backups s3Replica:backups

    - name: 'Replicate on server'
      # Just run replicator on server with their own config and end locally
      run: ssh replicator@backup-server 'nohup replicator ./backups.yml > ./replicator.log 2>&1 </dev/null &'
```

Example task file for backup server
```yml
# Notifications config
notifications:
    telegram:
        # Define should notifications been sent
        enabled: true
        # Token for bot who will sent notifications
        # How to: https://core.telegram.org/bots/tutorial#getting-ready
        botToken: '123:token'
        # User IDs to receive notifications
        userIds:
            - 123456

# Replicate backup from main storage
tasks:
    - run: rclone s3Replica:backups s3Mirror1:backups
    - run: rclone s3Replica:backups s3Mirror2:backups
    - run: rclone s3Replica:backups s3Mirror3:backups
```

## TODO
- [x] Support few entries for replication
- [x] Provide commands to run, instead of paths
- [x] Provide instructions to install as binary
- [ ] Split the code
- [ ] Add docker image