semvec/pypi-packages huggingface dataset:
ignis 0.0.10
Intuitive library for training neural nets in PyTorch

# Intuitive library to help with training neural networks in PyTorch

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`ignis` is a high-level library that helps you write compact but full-featured training loops with metrics, early stops,
and model checkpoints for deep learning library [PyTorch](https://pytorch.org/).

With `ignis` you can solve Supervised Learning and Deep Reinforcement Learning problems.

You can extend `ignis` according to your own needs. You can implement custom functionalities by extending simple
abstract classes.

## Deep Reinforcement Learning algorithms

1.  Deep Q Network (DQN)
2.  Deep Deterministic Policy Gradients (DDPG)

## Installation

1.  Install PyTorch. You can find it here: [PyTorch](https://pytorch.org/)
2.  `pip install ignis`

## Examples

You can find examples in `examples/` directory

You can also run examples: `python examples/iris.py`

You might want to `export PYTHONPATH=/path/to/this/directory`

## Contribute

1.  Implement new functionalities
2.  Improve code design
3.  Improve comments and readme
4.  Tests
stockdatareader 1.0.0
Read online stock data

# StockDataReader
Read historic stock data

## Installation
Clone or download repository and
````
pip install stockdatatareader
````

## Example
Read the stock values from Alphabet (ISIN US02079K1079) from January 1, 2018 to September 15, 2018.
````python
from stockdatareader import StockDataReader

sdr = StockDataReader()
isin = 'US02079K1079'
start_date = '2018-01-01'
end_date = '2018-09-15'
stock_data = sdr.read_data(isin, start_date, end_date)
````
occ 0.0.2.dev20190912195130
Octavia Chicken Checker looks for abandoned Octavia load balancer artifacts, amphoras, and so forth. Optionally it will clean them up as well.

# Octavia Chicken Checker

Octavia Chicken Checker looks for abandoned Octavia load balancer artifacts, amphoras, and so forth. Optionally it will clean them up as well.

## Installation

```shell
$ pip install -r requirements.txt

$ pip install setup.py
```

## Running

```shell
$ occ list
```

## Development

This project includes a number of helpers in the `Makefile` to streamline common development tasks.

### Environment Setup

The following demonstrates setting up and working with a development environment:

```shell
### create a virtualenv for development

$ make virtualenv

$ source env/bin/activate


### run occ cli application

$ occ --help


### run pytest / coverage

$ make test
```

### Releasing to PyPi

Before releasing to PyPi, you must configure your login credentials:

**~/.pypirc**:

```ini
[pypi]
username = YOUR_USERNAME
password = YOUR_PASSWORD
```

Then use the included helper function via the `Makefile`:

```shell
$ make dist

$ make dist-upload
```

## Deployments

### Docker

Included is a basic `Dockerfile` for building and distributing `Octavia Chicken Checker`,
and can be built with the included `make` helper:

```
$ make docker

$ docker run -it occ --help
```
queueup 0.1
A distributed queue library layered over kombu

# QueueUp - A simple and easy queue interface for all of your needs


**QueueUp** -- Is an entirely integrated queue interface over kombu to completely include all settings inside of a single object.

The reason we use `kombu` and AMQP as a whole is to allow for complex objects and delivery guaruntees we normally wouldn't get with much newer platforms.




## Differences from QueueUp and Queue

You'd use the `QueueUp` library in the exact way you'd use the `queue.Queue` library. Let's look at the difference.

**An example for python's `queue`**
```py
import time
import random
import threading
from queue import Queue


# We're starting two threading daemons, 
# 1. one that pushes information into a queue, 
# 2. the other that reads information from the queue then publishes it



def queue_pusher(q):
    while True:
        q.put(random.randint(0, 1000))
        time.sleep(0.05)


def queue_reciever(q):
    while True:
        qitem = q.get(block=True)
        print(f"Printing {item}")
        time.sleep(0.05)

if __name__ == "__main__":
    common_queue = Queue()
    threading.Thread(target=queue_pusher, daemon=True, args=(common_queue,)).start()
    threading.Thread(target=queue_reciever, daemon=True, args=(common_queue,)).start()

    # Now the two queues will communicate with each other.

    while True:
        time.sleep(5)
```

**An example for `QueueUp`**


```py
import time
import random
import threading
from queueup import Queue


# We're starting two threading daemons, 
# 1. one that pushes information into a queue, 
# 2. the other that reads information from the queue then publishes it



def queue_pusher(q):
    while True:
        q.put(random.randint(0, 1000))
        time.sleep(0.05)


def queue_reciever(q):
    while True:
        qitem = q.get(block=True)
        print(f"Printing {item}")
        time.sleep(0.05)

if __name__ == "__main__":
    common_queue = Queue() # w/o parameters it returns a multiprocessing.Queue()
    threading.Thread(target=queue_pusher, daemon=True, args=(common_queue,)).start()
    threading.Thread(target=queue_reciever, daemon=True, args=(common_queue,)).start()

    # Now the two queues will communicate with each other.

    while True:
        time.sleep(5)
```
geostatsmodels 0.3.3
Kriging and geostatistics tools.

geostatsmodels
==============

This is an implementation of ideas from Clayton V. Deutsch and Michael Pyrcz's book _Geostatistical Reservoir Modeling_ in order to better understand geostatistics. I am working through things first in Python in order to prototype things quickly and make sure I understand them.

There is also an R package for geostatistics named gstat, but I have not used it much.

This software is licensed under the [MIT License](http://opensource.org/licenses/MIT).

Installation and Dependencies
-----------------------------

This package depends on:
 * `numpy`, the fundamental package for scientific computing with Python. 
 * `matplotlib`, a Python 2D plotting library which produces publication quality figures. 
 * scipy, a Python library which provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. 
 * `pandas`, a Python library which implements excellent tools for data analysis and modeling. 

The best/easiest way to install these packages is to use one of the Python distributions described [here](http://www.scipy.org/install.html). Anaconda has been successfully tested with geostatsmodels.

Most of those distributions should include `pip` or `conda`, which are command line tools for installing and 
managing Python packages.  You can use `pip` to install geostatsmodels itself.  

You may need to open a new terminal window to ensure that the newly installed versions of python and pip
are in your path.

To install geostatsmodels:

    pip install git+git://github.com/cjohnson318/geostatsmodels.git

Uninstalling and Updating
-------------------------

To uninstall:

    pip uninstall geostatsmodels

To update:

    pip install -U git+git://github.com/cjohnson318/geostatsmodels.git

Usage
------
Some notebooks exploring the functionality of `geostatsmodels` are included below.  

[Variogram Analysis](http://nbviewer.ipython.org/github/cjohnson318/geostatsmodels/blob/master/notebooks/VariogramAnalysis.ipynb)

[Kriging Example](http://nbviewer.ipython.org/github/cjohnson318/geostatsmodels/blob/master/notebooks/KrigingExample.ipynb)

More to come!