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pyprecag is a Python package containing processing functions used for analysis of precision agricultural data collected by hand or using on-the-go sensors like yield monitors. pyprecag functions underpin the tools found in PAT - Precision Agriculture Tools plugin for QGIS.
Data Types:
  • Software/Code
PAT - Precision Agriculture Tools plugin for QGIS is a suite of tools for Precision Agriculture (PA) and Precision Viticulture (PV) data analysis. The tools run within Quantum Geographic Information System (QGIS). PAT aims to provide an easy-to-use interface for processing PA/PV data through an established workflow developed for constructing maps using on-the-go data and for the analysis of PA/PV data analysis.
Data Types:
  • Software/Code
ASKAPsoft, the ASKAP Science Data Processor, provides data processing functionality, including: * Calibration * Spectral line imaging * Continuum imaging * Source detection and generation of source catalogs * Transient detection ASKAPsoft is developed as a part of the CSIRO Australian Square Kilometre Array Pathfinder (ASKAP) Science Data Processor component. ASKAPsoft is a key component in the ASKAP system. It is the primary software for storing and processing raw data, and initiating the archiving of resulting science data products into the data archive (CASDA). The processing pipelines within ASKAPsoft are largely written in C++ built on top of casacore and other third party libraries. The software is designed to be parallelised, where possible, for performance. ASKAPsoft is designed to be built and executed in a standard Unix/Linux environment and core dependencies must be fulfilled by the platform. These include, but are not limited to, a C/C++/Fortran compiler, Make, Python 2.7, Java 7 and MPI. More specific dependencies are downloaded by the ASKAPsoft build system and are installed within the ASKAPsoft development tree. Specific to the Debian platform, after a standard installation of Debian Wheezy (7.x) the following packages will need to be installed with apt-get: * g++ * gfortran * openjdk-7-jdk * python-dev * flex * bison * openmpi-bin * libopenmpi-dev * libfreetype6-dev * libpng12-dev More information regarding the building, installation and running of the software can be found in the README file in the root of the file structure that forms this collection. Source code can be accessed via the links in Related Materials section. ----- A further patch release, with a number of small pipeline fixes, along with several fixes to the processing software. Processing: * The imager would produce slightly different residual and restored images when different values of nchanpercore were used. This was due to the final gridding cycle not being synchronised correctly. This has been fixed and the images are now indepenent of nchanpercore. * The tree reduction used by imager has been improved to have a smaller memory footprint across the cores. * The selavy component fitting is improved in the way negative components are handled. Unless negative components are explicitly accepted, if a fit results in one or more components being negative then that fit will be rejected. * The primary beam used by linmos now has a FWHM scaling by 1.09 lambda/D, which should be more accurate. * The FITSImage interface (in Code/Base/accessors) will now report a human-readable error message (rather than a number code) when an error occurs. Pipelines: * CASDA uploads again include catalogues (which were left out due to fixes in 0.23.1). * There are new parameters CIMAGER_MAXUV and CCALIBRATOR_MAXUV that allow the imposition of an upper limit to the uv values in the continuum imaging/self-calibration. * Parsets for the imager were erroneously getting a "Cimager.Channels" selection that included the %w wildcard. This will no longer happen (unless cimager is used). * The default python module is now always loaded at the start of slurm scripts, to avoid python conflicts due to a user's particular environment. * There are stronger checks on the number of cores allocated to spectral-line imaging, ensuring that the number of channels must be an exact multiple of the nchanpercore. * The scaling on the beam-wise noise plots has been fixed, so that the scaled MADFM should be closer to the standard deviation in the absence of signal. * Cube stats are now also generated for continuum-cube residual images. * Several scripts have been tidied up with the aim of avoiding spurious errors (validationScience, for instance). * The ASKAPsoft version was being left off FITS headers. This now reflects the version string from the askapsoft module.
Data Types:
  • Software/Code
Command-line Python program for the automated analysis of Hydropathic, Order-promoting, Short Linear Motifs (HO-SLiMs) in proteins.
Data Types:
  • Software/Code
The DCM software is a Microsoft Windows™-based application for 3D microstructure characterization, modelling and visualization. It include the following functionalities: • Generate digital 3D representation of material compositional phases in a sample using the data-constrained modelling methodology and X-ray CT data, even for the cases that there are partial volumes of multiple phases in the same image voxel; • Visual presentation of 3D volumetric data. The visualization is compatible with various display devices, including stereo 3D monitors; • Exporting 3D data in various formats, including the web-friendly Web3D format for interactive 3D online view, animation, serial sectional slices, etc.; • Functionality of the software can be extended by adding plug-in modules to perform virtually unlimited 3D modelling. A set of C++ programming API is included for user convenience. The DcmLite can be downloaded for evaluation. Please feel free to contact us if you would be interested in acquiring a DCM software user licence, R&D collaboration, commercial exploitation, or knowing more about DCM. The DCM website is at http://research.csiro.au/dcm.
Data Types:
  • Software/Code
The energies obtained using first principles methods only accounts for the ground state (temperature T ~ 0 K, pressure P = 0 Pa) electronic energies, E, which are a part of the thermodynamic internal energies or free energies. QuickThermo is a software package that can be used to extend the ground state energy to finite temperatures and pressures. To do this QuickThermo uses ab initio thermodynamics, which combines the results calculated from first principles at the ground state and the extensive thermodynamic data measured at the standard state, to return the Gibb's free energy. QuickThermo is also capable of accounting for the effect of a mixture of reservoirs (e.g. including humidity effect) to capture the impact of complex surrounding environments. QuickThermo has a user friendly interface with a number of functionalities including a global database where users can expand their own library of elements and access them throughout different projects; a fitting tool based on Genetic Algorithm to extract Shomate coefficients from thermochemical data, and batch processing to facilitate calculations of a large range of environmental conditions. Some basic plotting tools are also embedded to provide insights on the data and outcomes. This version of QuickThermo runs on a Windows operating system. To install, download and unzip QuickThermo.zip, then run setup.exe
Data Types:
  • Software/Code
ASKAPsoft, the ASKAP Science Data Processor, provides data processing functionality, including: * Calibration * Spectral line imaging * Continuum imaging * Source detection and generation of source catalogs * Transient detection ASKAPsoft is developed as a part of the CSIRO Australian Square Kilometre Array Pathfinder (ASKAP) Science Data Processor component. ASKAPsoft is a key component in the ASKAP system. It is the primary software for storing and processing raw data, and initiating the archiving of resulting science data products into the data archive (CASDA). The processing pipelines within ASKAPsoft are largely written in C++ built on top of casacore and other third party libraries. The software is designed to be parallelised, where possible, for performance. ASKAPsoft is designed to be built and executed in a standard Unix/Linux environment and core dependencies must be fulfilled by the platform. These include, but are not limited to, a C/C++/Fortran compiler, Make, Python 2.7, Java 7 and MPI. More specific dependencies are downloaded by the ASKAPsoft build system and are installed within the ASKAPsoft development tree. Specific to the Debian platform, after a standard installation of Debian Wheezy (7.x) the following packages will need to be installed with apt-get: * g++ * gfortran * openjdk-7-jdk * python-dev * flex * bison * openmpi-bin * libopenmpi-dev * libfreetype6-dev * libpng12-dev More information regarding the building, installation and running of the software can be found in the README file in the root of the file structure that forms this collection. Source code can be accessed via the links in Related Materials section. ----- A patch release, addressing a few bugs in both processing software and pipeline scripts Pipelines: * Changes have been made to the scripts to make them robust in handling field names that contain spaces. This has also made them more robust to being run in a directory with a path that contains spaces. * An update has been made at Pawsey to the module used for the continuum validation task, and consequently a minor change has been made to the continuum sourcefinding script. Processing: * Enhancements have been made to the continuum-subtraction task ccontsubtract to speed it up - initial tests indicate a speed-up of 6-8x depending on platform. * The Selavy fitting algorithm now defaults to including a test on the size of the fitted Gaussians. This will prevent spuriously large fits from making it through to the catalogue, which has had detrimental effects in the calibration & continuum-subtraction. * A fix was made to the imager, solving a problem where the spectral-imaging option merged the first channel of its allocation without checking the frequency. Additionally, the user documentation has updated instructions about how best to set the modules on galaxy so that everything runs smoothly.
Data Types:
  • Software/Code
The DCM software is a Microsoft Windows™-based application for 3D microstructure characterization, modelling and visualization. It include the following functionalities: • Generate digital 3D representation of material compositional phases in a sample using the data-constrained modelling methodology and X-ray CT data, even for the cases that there are partial volumes of multiple phases in the same image voxel; • Visual presentation of 3D volumetric data. The visualization is compatible with various display devices, including stereo 3D monitors; • Exporting 3D data in various formats, including the web-friendly Web3D format for interactive 3D online view, animation, serial sectional slices, etc.; • Functionality of the software can be extended by adding plug-in modules to perform virtually unlimited 3D modelling. A set of C++ programming API is included for user convenience. The DcmLite can be downloaded for evaluation. Please feel free to contact us if you would be interested in acquiring a DCM software user licence, R&D collaboration, commercial exploitation, or knowing more about DCM. The DCM website is at http://research.csiro.au/dcm.
Data Types:
  • Software/Code
This is an application of the Spark wildfire prediction framework configured for interactive use through a graphical user interface on a personal computer.
Data Types:
  • Software/Code
Based on a general definition of a cluster and the quality of a clustering result, this code presents a new method for evaluating existing clustering algorithms, or undertaking clustering, capable of predicting the number and type of clusters and outliers present in a data set, regardless of the complexity of the distribution of points. This algorithm, referred to as iterative label spreading (ILS), can recognize the characteristics expected of a successful clustering result before any clustering algorithm has been applied, providing a type of hyper-parameter optimization for clustering. In this notebook the algorithm, is assessed using large benchmark two-dimensional synthetic data sets, with tutorial examples.
Data Types:
  • Software/Code