An automated online tool to forecast demand for new railway stations and analyse potential abstraction effects

A national trip end model to forecast demand for new local railway stations in Great Britain has been developed and implemented as an automated hosted tool on the Data and Analytics Facility for National Infrastructure (DAFNI). This paper presentation describes the novel characteristics of the underlying model, explains how it has been realised on DAFNI, and incorporates a live demonstration of the tool’s web interface.The underlying trip end model was calibrated on all existing local stations in mainland GB, and is unique in its scale, adoption of a high spatial resolution zoning system (trip generation is assessed for every postcode), and the incorporation of a station choice model. The latter allows probability-based station catchments to be defined, accounting for competition between stations and enabling the assessment of abstraction effects on existing stations. Testing of the model found that it produced more accurate demand forecasts than methods used during the scheme appraisal process for several recently opened stations and a new line. The model has already been used to produce demand forecasts for the Welsh Government and is capable of forecasting passenger numbers for new stations located anywhere in GB.Implementation of the model on DAFNI makes this powerful tool directly available to transport planning practitioners and other stakeholders for the first time. The data storage and transformation capabilities of DAFNI ensure that the model inputs are always available and up-to-date, freeing practitioners from onerous collection and processing tasks. The tool has the potential to transform the assessment of new station schemes, enabling the rapid review of options for individual stations or new lines. It can replace costly models developed on an ad hoc basis when a local need arises and can be a useful comparator tool to help assess the reliability of forecasts produced by locally developed models

TomoPhantom, a software package to generate 2D–4D analytical phantoms for CT image reconstruction algorithm benchmarks

In the field of computerized tomographic imaging, many novel reconstruction techniques are routinely tested using simplistic numerical phantoms, e.g. the well-known Shepp–Logan phantom. These phantoms cannot sufficiently cover the broad spectrum of applications in CT imaging where, for instance, smooth or piecewise-smooth 3D objects are common. TomoPhantom provides quick access to an external library of modular analytical 2D/3D phantoms with temporal extensions. In TomoPhantom, quite complex phantoms can be built using additive combinations of geometrical objects, such as, Gaussians, parabolas, cones, ellipses, rectangles and volumetric extensions of them. Newly designed phantoms are better suited for benchmarking and testing of different image processing techniques. Specifically, tomographic reconstruction algorithms which employ 2D and 3D scanning geometries, can be rigorously analyzed using the software. TomoPhantom also provides a capability of obtaining analytical tomographic projections which further extends the applicability of software towards more realistic, free from the “inverse crime” testing. All core modules of the package are written in the C-OpenMP language and wrappers for Python and MATLAB are provided to enable easy access. Due to C-based multi-threaded implementation, volumetric phantoms of high spatial resolution can be obtained with computational efficiency. Keywords: Phantoms, Tomography, Image reconstruction, Iterative methods, Open-sourc

dkazanc/TomoPhantom: TomoPhantom v.3.0

<ul> <li><pre><code>Totally refactored with Ctypes into two separate packages: C library + Python</code></pre> </li> <li><pre><code>Cython Wrappers removed</code></pre> </li> <li><pre><code>120 tests added!</code></pre> </li> <li><pre><code>All API exposed in TomoP2D and TomoP3D as Python functions (not Cython)</code></pre> </li> <li><pre><code>NEW [Documentation](https://dkazanc.github.io/TomoPhantom/) page with sphinx</code></pre> </li> <li><pre><code>Demos updated</code></pre> see some <a href="https://github.com/dkazanc/TomoPhantom/blob/master/CHANGES.md">changes</a> with respect to API.</li> </ul&gt

Crab and other Fossil Radiographs using neutron imaging Dataset

<p>Crab and other fossil radiographs </p

1200-talet: Ett århundrade för städer

Core Updates: Adds numpy backend for basic (same pattern) processing Framework restructuring to expose local and global slicing methods Allows multiple plugins to process 'raw' data Adds beamline logger for auto-processing Adds 'nprocs' option to previewing Fixes 'File write failed' error caused by ROMIO 2GB read/write limit and h5py create_dataset Replaces h5py create_dataset (with chunking) with direct hdf5 calls, to avoid initialisation of the created file. Updates configurator arg parser to allow negative values in a list Adds beginnings of a GUI for creating plugin templates Plugin Updates: Adds iterative plugin driver Adds iterative vo_centering plugin Adds edfsaver Improves vo_centering Fixes scikitimage to use values from parameters Fixes error in upper and lower bounds for darkandflatfieldcorrection Fixes distortion correction Fixes previewing and parameter tuning at the same time in reconstruction methods Improves all reconstruction wrappers: Improves astra toolbox wrappers Improves base recon and centre/outer padding Padding is now only allowed for suitable methods (e.g. yes for FBP but no for iterative methods

Crab Dataset

<p>Crab Dataset </p

TomoPhantom: software package to generate 2D–4D phantoms for CT image reconstruction algorithm benchmarks

<p>This poster describes the use and launch of TomoPhantom; a software based phantom generation toolkit. Within CT imaging many novel reconstruction techniques are routinely tested using simplistic numerical phantoms. This package has been needed for a long time across user groups; and it now allows users to have quick access to an external library to create advanced modular analytical 2D/3D phantoms with temporal extensions. Code has just been released and available at https://github.com/dkazanc/TomoPhantom as well as published in a use case paper in SoftwareX. </p

DiamondLightSource/Savu: Version 2.2.1

Core updates: Improves dockerfile for external use Improves HDF5 data chunking, with significant speed increases Plugin updates: Amends DistortionCorrection parameters as agreed and tested with DLS beamline staff Adds 'multiple' frames request with a maximum limit DezingFilter renamed as DezingerSimple Adds Dezinger plugin Adds updated version of Robert Atwood's C-implemented algorithm back in to Savu This is a faster more accurate implementation Updates to DezingerSimple Maximum 8 frames now requested due to memory issues Darks and flats now only processed 8 at a tim