Metadata-Version: 2.1
Name: NeutronImaging
Version: 1.2
Summary: Neutron Imaging Reduction
Home-page: https://code.ornl.gov/sns-hfir-scse/imaging/neutronimaging
Author: C.Zhang
Author-email: zhangc@ornl.gov
Maintainer: C.Zhang
Maintainer-email: zhangc@ornl.gov
License: BSD
Keywords: neutron imaging mcp
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 (LGPLv2)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Requires-Dist: docopt
Requires-Dist: ipympl
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: NeuNorm
Requires-Dist: pandas
Requires-Dist: tqdm

# NeutronImagingScripts

This pakcage contains a suite of Python modules and scripts that are critical for the data reduction of Neutron Imaging 
at Oak Ridge National Laboratory.


## Overview

## Installation

### General users

Install the package (once published on pip) with

```bash
$ pip install NeutronImagingScripts
```

### Developers
For developers, it is __highly__ recommended to setup an isolated virtual environment for this repository.
After cloning this repository to your local machine, go to the root of this repo and use the follwing commands to 
install dependencies

```bash
$ pip install -r requirements.txt
$ pip install -r requirements_dev.txt
```
use the following command to install this package to your path
```bash
$ pip install -e .
```
> For unit test, run `pytest tests` at the root of this repo.

## Usage

### Use as a Package
Examples of using this package as a Python module are provided as Jupyter Notebooks insdie the `example` folder.

### Use as a commandline tool

#### _Generate Configuration File for Data Reduction_
To generate the `json` file that is needed for subsequent data reduction, use

```bash
$ generate_config.py IPTS-20267/raw/radiographs IPTS-20267/raw/ob IPTS-20267/raw/df IPTS-20267.json
```

where 

 - `IPTS-20267/raw/radiographs` contains the raw images
 - `IPTS-20267/raw/ob` contains open beam images (white field)
 - `IPTS-20267/raw/df` contains dark field images 

If you would like to have __multiple__ experiment configuration files __nested__ in one `json` file, simply use

```bash
$ generate_config.py IPTS-20267/raw/radiographs,IPTS-20267-2/raw/radiographs IPTS-20267/raw/ob IPTS-20267/raw/df IPTS-20267.json
```

notice that:
- You can have more than one folder for raw images, but they need to be within the same string separated by `,`.
- You can have only __one__ folder for open beam directory
- You cna have only __one__ folder for dark field directory

The command above will yield a `json` file with the following structure

```json
 {"IPTS-20267": {"CONFIG_DATA"},
  "IPTS-20268": {"CONFIG_DATA"}
 }
```

The default tolerance for the categorization with respect to aperture positions is 1mm.
However, you can change the default value by specify it as below

```bash
$ generate_config.py \
    IPTS-20267/raw/radiographs \
    IPTS-20267/raw/ob \
    IPTS-20267/raw/df \
    IPTS-20267.json --tolerance=2
```

#### _MCP Detector correction_
After installing this package, the scripts located in `scripts` should be visible in your Path.
Simpy type `mcp_detector_correction.py`, you should see the following

```bash
$ mcp_detector_correction.py
Usage:
    mcp_detector_correction [--skipimg] [--verbose] <input_dir> <output_dir>
    mcp_detector_correction (-h | --help)
    mcp_detector_correction --version
```

Therefore, you can process the example data with the following command at the root of this repo

```bash
$ mcp_detector_correction.py data tmp
```

and you will see the following in your terminal

```bash
$ mcp_detector_correction.py data tmp
Parsing input
Validating input arguments
Processing metadata
Loading images into memory
Perform correction
corrected image summary
	dimension:	(916, 512, 512)
	type:	float64
Writing data to tmp
```

> NOTE: make sure you create a `tmp` folder first.

## Developer Notes


