semvec/pypi-packages huggingface dataset:
PyChunkedGraph 0.1.113
Proofreading backend for Neuroglancer

# PyChunkedGraph


[![Build Status](https://travis-ci.org/seung-lab/PyChunkedGraph.svg?branch=master)](https://travis-ci.org/seung-lab/PyChunkedGraph)
[![codecov](https://codecov.io/gh/seung-lab/PyChunkedGraph/branch/master/graph/badge.svg)](https://codecov.io/gh/seung-lab/PyChunkedGraph)

The PyChunkedGraph is a proofreading and segmentation data management backend with (not limited to) the following features:
- Concurrent proofreading by multiple users without restrictions on the workflow
- Continuous versioning of proofreading edits
- Making changes visible to all users immediately
- Local mincut computations

## Scaling to large datasets


## Deployment 

While the PyChunkedGraph can be deployed as a stand-alone entity, we deploy it within our [annotation infrastructure](https://github.com/seung-lab/AnnotationPipelineOverview) which uses a CI Kubernetes deployment. We will make instructions and scripts for a mostly automated deployment to Google Cloud available soon.

## Building your own PyChunkedGraph



## System Design

As a backend the PyChunkedGraph can be combined with any frontend that adheres to its API. We use an adapted version of [neuroglancer](https://github.com/seung-lab/neuroglancer/tree/nkem-multicut) which is publicly available.


[system_design]: https://github.com/seung-lab/PyChunkedGraph/blob/master/ProofreadingDiagram.png "System Design"
![alt text][system_design]
Simplate 1.0.2
Simple object-based string templating in python

# Simplate
Python Simple Object-Based String Templating

## Usage
From formatted strings create re-usable template objects and implementation objects. The Simplate class (Simplate.Simplate)
allows you to dynamically create new Template classes with the string you initialize with, and (optionally) a title
for the template which becomes the name of your new class. 

```
import Simplate

mySimpleTemplate = Simplate.Simplate( "Hello {friend} how's your {pet}?", title="FriendlyLetter" )
print mySimpleTemplate.ImplementationClass
```
```
<class 'Simplate.Simplate.FriendlyLetter'>
```
```
newLetterToJohn = mySimpleTemplate.implement()
type(newLetterToJohn)
```
```
<class 'Simplate.Simplate.FriendlyLetter'>
```
```
newLetterToJohn.friend = "John"
newLetterToJohn.pet = "cute puppy"
str(newLetterToJohn)
```
```
"Hello John how's your cute puppy?"
```

Every call to Simplate.implement() will return a new instance of the dynamically-created template class, and the parameters
on the template string become the instance variables on the new instances of this new template class, while the template
itself is a shared class variable amongst all instances of this template. 

An optional third argument when initializing a Simplate is "defaults", which is what the default values are for the instance
variables of the template object. By default, defaults is an empty string.

## Examples
```
import Simplate
emailString = "Dear {name}, thank you for your {item}, unfortunately its sold, sincerely {author}."
emailTemplate = Simplate.Simplate(emailString, "EmailToCustomer")
emailToBob = emailTemplate.implement()
emailToBob.author = "Kyle Kimery"
emailToBob.name = "Bob"
emailToBob.item = "Baseball Merchandise"
print emailToBob
```
```
Dear Bob, thank you for your inquiry regarding Baseball Merchandise, I regret to inform you that it's gone, sincerely Kyle Kimery.
```
pixit 0.0.16
Label and annotate images using matplotlib widgets.

# pixit
Label and annotate images using matplotlib widgets.

# Installation
`pip install pixit`

# Usage
## ImageSet
The `ImageSet` data structure is derivative of the `Bunch` from scikit-learn. It is a dictionary-like object whose keys can be accessed as attributes. The `image` entry of the ImageSet should be a numpy array with images to be displayed using matplotlib. 

## label_images
The `label_images` method of `ImageSet` brings up a matplotlib GUI for labeling images as one of the `label_names`.

```python
from pixit import datasets

animals = datasets.load_animals()
print(animals.label_names) # ['elephant', 'giraffe', 'tiger', 'zebra']

animals.label_images()
```

## annotate_images
In development.