semvec/pypi-packages huggingface dataset:
bioimage 0.1.3
bioimage - library to make working with biomedical images more comfortable
Keywords: python,utils,files,bioimages,microscopy,imaging,numpy


# bioimage
Library for convenient handling of bioimages

Contains the following modules:

## io

Convenient file management, uses pims and pycomfort under the hood.
For example:
* load_bio_image(from_file: Path) that allows loading microscopy images
* load_frame(from_file: Path): that loads first frame of the microscopy image from the system path

## filters

Useful filters to process images.
For example:
* rolling ball filter to fix some issues on the background
* local_contrast filter that can be used together with thresholding to find boundaries of the objects
* gray2color that deals with fluorescent images
* glowing - for dealing with comparisons between normal and fluorescent images

## comparisons

When you need to compare multiple conditions
For example:
* show_images that displays images from the whole folder as a grid plot
* folder_to_conditions that allows splitting images in the folder to multiple conditions based on their namings
* get_glowing_overlays that allows combining fluorescent and phase-contrast images
hiutils 0.2.2
Utilities for health informatics
Requires Python: >=3.6

# HIUTILS
## A collection of utilities for health informatics

This is pre-alpha, anything might change, i.e. not ready for production use. 

## Application areas
### Text annotation / NLP
### Ontologies
### Knowledge graphs
### Statistics / summary data

# Installation
```
pip install hiutils
```


# Annotations
## Overview
We assume that annotations are in the format:

```
{
	document_id: {
		entities: {
			entitiy_id: {
				...properties...,
				cui : "concept_id",
				meta_anns: {
					'meta_ann_name': {'value': 'meta_ann_value',
					'confidence': confidence,
					'name': 'meta_ann_name'},
					...other meta...
				}
			}
		}

	}
}
```

## Basic process
The aim is to:
1. keep only some annotations based on context
2. convert from document->concepts to patient->concepts
3. limit to a subset of concepts relevant to our project
4. group some specific concepts into more general concepts e.g. specific subtypes of a disease -> any occurence of a that disease

To achieve these aims:
* 1 filter by meta_anns:
```
filtered = hi.annotations.filter_anns_meta(anns, {'Subject': ['Other']}, inplace=False)
```
* 2 aggregate to patient level
```
agg = hi.annotations.aggregate_docs(filtered, item2doc=pt2doc)
```
* 3+4 group relevant concepts and drop other concepts
```
groups = {'Group 1': set(['286933003', '70582006']), 'My other group': set(['60046008'])}
merged = hi.merge_concepts(agg, groups, keep_empty=False)
```