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Python wrapper for CONRAD (, a framework for cone beam radiography

Project description

# pyconrad

[![PyPI version](](
[![Build Status](](

A python wrapper for the CONRAD framework (

- [pyConrad](#pyconrad)
- [CONRAD](#conrad)
- [Installation](#installation)
- [Tests](#tests)
- [Changelog](#changelog)
- [Usage](#usage)
- [Basic example](#basic-example)
- [More Examples](#more-examples)
- [Autocomplete](#autocomplete)
- [Extension methods for java classes](#extension-methods-for-java-classes)
- [Frequently encountered problems](#frequently-encountered-problems)


CONRAD is a state-of-the-art software platform with extensive documentation. It is based on platform-independent technologies. Special libraries offer access to hardware acceleration such as CUDA and OpenCL. There is an easy interface for parallel processing. The software package includes different simulation tools that are able to generate 4-D projection and volume data and respective vector motion fields. Well known reconstruction algorithms such as FBP, DBP, and ART are included. All algorithms in the package are referenced to a scientific source. Please visit for more information.

# Installation

Install via pip :

pip install pyconrad

or if you downloaded this repository ( using:
pip install -e .

This will automatically install CONRAD and all python dependencies. Requirements for proper functioning are at Python of version 3.5 or newer and Java 8.

If you encounter a problem during the installation have a look at our wiki:

# Tests

If you want to test whether pyconrad is working correctly on your computer you may execute all tests included in this repo via:

python test
>>>>>>> develop
# Changelog
Can be found [here](

If you encounter any problems during installtion please have a look at our [wiki](!

# Usage

You can start CONRAD in Python like this:
import pyconrad

pyconrad.start_gui() # start ImageJ
pyconrad.start_reconstruction_pipeline_gui() # if you want to start CONRAD's reconstruction filter pipeline

Or you can run CONRAD Reconstrucion Pipeline from command line:
# or: conrad_imagej

## Basic example

You can access CONRAD's Java classes via or using the convinience class ClassGetter.

``` python
import pyconrad

# setup PyConrad
pyconrad.setup_pyconrad(min_ram='500M', max_ram='8G')
# Optional parameters for Java Virtual Machine RAM


# Create Phantom (edu.stanford.rsl.tutorial.phantoms.MickeyMouseGrid2D)
phantom =, 300)

# Access more easily using ClassGetter (# type: pyconrad.AutoCompleteConrad adds static auto-complete feature for
_ = pyconrad.ClassGetter(
) # type: pyconrad.AutoCompleteConrad

# You can add more namespaces also later

phantom2d = _.MickeyMouseGrid2D(200, 200)
phantom3d = _.NumericalSheppLogan3D(
200, 200, 200).getNumericalSheppLoganPhantom()

# Use Java method of class MickeyMouseGrid2D

Also memory transfers to numpy.ndarray are possible.
Data changes have to be synchronized:
``` python

# Create PyGrid from numpy array (must be of type pyconrad.java_float_dtype)
array = np.random.rand(4,2,3).astype(java_float_dtype)
pygrid2 = PyGrid.from_numpy(array)

# Manipulate data in using CONRAD at Position (x,y,z) = (1,2,4)
pygrid2.grid.setValue(5.0, [0,1,3])

# Print this pixel using Python indexes [z,y,x]
print("Before update: %f" % pygrid2[3,1,0])
# Python data must be synchronized with CONRAD
print("After update: %f" % pygrid2[3,1,0])

# Manipulate pixel using python
pygrid2[1,1,1] = 3.0
# Update CONRAD data

# Print
## More Examples

More examples can be found [here](examples)

## Autocomplete

As it might be difficult to remember the exact names of Java functions and classes, pyconrad provides basic autocomplete feature for CONRAD classes.
Just give your IDE a type hint that a object represents a certain Java namespace or class (`# type:`).

Works with pycharm:


## Extension methods for java classes
For easy transition between Java and Python we extented some important Java classes in Python to convert between the respective Java class and the respective numpy structure.
The following java classes are extended:
- PointND
- SimpleVector
- SimpleMatrix
- Numeric Grid(therefore all Grid1D - Grid4D)

with the methods:
- as_numpy (array or matrix depending on the class representation)
- from_numpy
- from_list
- from_tif
- save_tif
- save_vtk

## Frequently encountered problems
# Creating a PointND
_.PointND(3,3) # does not work
_.PointND([3,3]) # neither does this
_.PointND(JArray(JDouble)([3,2])) # works
_.PointND.from_numpy(np.array([2.1,3.1])) #works, uses extension method
_.PointND.from_list([2.1,3.1]) #works, uses extension method

# Getting PointND as numpy array
numpy_point = java_point.as_numpy()

# the same applies for SimpleVector
_.SimpleVector(JArray(JDouble)([3,2])) # works
_.SimpleVector.from_numpy(np.array([2.1,3.1])) #works, uses extension method
_.SimpleVector.from_list([2.1,3.1]) #works, uses extension method

#Getting SimpleVector as numpy array
numpy_vector = java_vector.as_numpy()

#the same applies for SimpleMatrix
SimpleMatrix(JArray(JDouble,2)([[1.1,2.2,3.3],[4.4,5.5,6.6]])) # works
SimpleMatrix.from_numpy(np.matrix([[1.1,2.2,3.3],[4.4,5.5,6.6]])) #works, uses extension method
SimpleMatrix.from_list([[1.1,2.2,3.3],[4.4,5.5,6.6]]) #works, uses extension method

#Getting SimpleMatrix as numpy matrix
numpy_matrix = java_matrix.as_numpy()

# Grid.setOrigin(...), setSpacing

# Creating nested enums
traj = _.HelicalTrajectory()
print(traj.getDetectorOffsetU()) # returns a float
enumval = _.['Projection$CameraAxisDirection'].values()[int(traj.getDetectorOffsetU())] # Convert back to enum
enumval = jvm.enumval_from_int('Projection$CameraAxisDirection', traj.getDetectorOffsetU()) # or like that

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