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Repository containing several implementations for the calculation of Orthogonal Moments. This includes the most commonly used: discrete orthogonal moments, continuous orthogonal moments defined on a disk, and continuous orthogonal moments defined on a square cartesian grid. The majority of the moments are implemented recursively, enabling the computation of high orders.

About the Specification TranslatorThe Specification Translator is a tool that has been implemented as part of our research titled "Join Forces! Reusing Contracts for Deductive Verifiers through Automatic Translation". This tool will translate specifications in verified Java programs from one specification language into another. It supports the tools Krakatoa, OpenJML and VerCors.The tool takes an annotated Java program and a target tool as input. It will then generate an annotated Java program where the annotations have been translated.What's included in this artifact?This artifact contains the following things:- specification-translator.zip: A directory containing the Specification Translator tool as well as the examples used for the evaluation.- SpecTranslatorArtifact.ova: A Virtual Machine with the Specification Translator tool, as well as the OpenJML and VerCors verifiers. It also contains the examples used for the evaluation and a script to reproduce the evaluation.You can use the Virtual Machine to reproduce the evaluation including verification after translation.If you just want to use the Specification Translator or have a look at the input/output files of the evaluation, then the zip file is sufficient.License informationThe Specification Translator tool is shared under the CC-BY 4.0 license.The verifiers in the VM, as well as the examples and case studies used for the evaluation, are not licensed under the CC-BY 4.0 but under their original licenses which have been included.

This repository contains the code and secondary data used to initialize and calibrate OpenLISEM-pesticide v1. The related article presents a novel pesticide transport model extension for the OpenLISEM runoff and erosion model. The model was designed to simulate dissolved and particulate phase transport of pesticides during rainfall-runoff events from agricultural fields. We conceptualized pesticide transport during runoff with a mixing-layer, including soil-water partitioning of the pesticides in the soil, mass transfer from the mixing layer to the runoff water and runoff or infiltration transport. Entrainment and deposition of soil particles drive the transport of sorbed pesticides. The model was tested on two rainfall-runoff events with observed data in a small agricultural catchment in South-Limburg, the Netherlands. The model could, after calibration, simulate the transported loads and concentrations of metobromuron and glyphosate adequately. A sensitivity analysis showed that pesticide transport was mainly influenced by the initial field concentrations and the soil-water partitioning. To obtain accurate simulations of pesticide contents, correct simulation of runoff and erosion is a prerequisite.

This code is used for generating the results shown in the paper on aircraft path planning in continuous environments with deep reinforcement learning.When the paper is published it will be referenced here.The code is structured in 3 folders, all using the same population data, obtained from Eurostats. The discrete_environment folder contains all of the code related to the discretization, Dijkstra solutions and postprocessing of the Dijkstra output. The continuous_environment folder contains a fork of the BlueSky Open Air Traffic Simulator repository, with all of the plugins related to training the Deep Reinforcement Learning algorithm, and evaluating of the paths in the continuous environment. Finally the policy_plotter folder contains all of the tools for generating the visuals presented in the paper.Before running or going through the code make sure to read the ReadMe file.The software is also available on github: https://github.com/jangroter//PathplanningDRL

The TCRP model can be used to create a runoff pattern, taking into account the effects of tillage-induced roughness. The TCRP model has as main aim to adjustrunoff patterns based on topography for tillage induced roughness. The resulting output of TCRP are runoff pattern raster maps, which can be further used as inputfor hydrological modelling of surface runoff processes. The methodology to create the runoff pattern includes four steps. First, a topographically controlled runoff pattern is created using the standard single-flow, steepest descent algorithm. Next, a tillage-determined runoff pattern is created, i.e. a runoff pattern based on the assumption that water will always flow in the tillage direction. Then, decision rules are applied to determine for each cell whether the water will flow in tillage direction or in topographic direction. Finally, a flow direction map can be generated by combining the topographically determined runoff pattern with the tillage-controlled runoff pattern. This repository is an updated version of the original code, adjusted to run smoothly with the current version of PCRaster (v4.4.0)

This dataset is used to arrive to the results presented in the paper `Reducing committed emissions of heating towards 2050: Analysis of scenarios for the insulation of buildings and the decarbonisation of electricity generation' from Kaandorp et al. (2022). The dataset consists of a Python code together with the input data used to run the code. The code is used to compute which technology mix is to be applied in a neighbourhood to optimally minimise the carbon emissions associated with space heating between 2030 and 2050. The neighbourhoods used in this study are 'Felix Meritis', 'Prinses Irenebuurt', and 'Molenwijk'. The model is run for scenarios which represents different rates of the insulation of buildings and the decarbonisation of electricity production between 2020 and 2050.The python code requires the following data files (provided in this collection):- Address_Neigborhood_Heat_Demand.xlsx- Heat_technology.xlsx (or Heat_teachnology_highEFhydrogen.xlsx to run change the input of the emission factors related to hydrogen).- Scenario_Settings.xlsxThe data file 'Scenario_Setting.xlsx' is used for a first-order sensitivity analysis).The code in 'post_processing.py' is used to process the output data from 'Address_Gurobi_scenario_loop_5y_timestep.py' (in this dataset) to facilitate analysis.

https://lue.computationalgeography.org/doc/release/index.html

Contributors to this version: Éric Dupuis (@coxipi), Trevor James Smith (@Zeitsperre), Pascal Bourgault (@aulemahal), Ludwig Lierhammer (@ludwiglierhammer), David Huard (@huard). Announcements xclim: xarray-based climate data analytics has been published in the Journal of Open Source Software (DOI:10.21105/joss.05415). Users can now make use of the Cite this repository button in the sidebar for academic purposes. Many thanks to our core developers and user base for their fine contributions over the years! (GH/95, PR/250). xclim now officially supports Python3.11. (PR/1388). New indicators Several new indices and indicators: snowfall_{frequency | intensity} for calculating the {percentage of | mean snowfall intensity on} days with snowfall above a threshold. (GH/1352, PR/1358) {sfcWind | sfcWindmax}_{max | mean | min} for calculating the {max | mean | min} daily {mean | max} wind speed. (GH/1352, PR/1358) {precip | liquid_precip | solid_precip}_average} for calculating the mean daily {total precipitation | liquid precipitation | solid precipitation } amount. (GH/1352, PR/1358) {cold | dry}_spell_max_length for calculating maximum length of {cold | dry} spell events. (GH/1352, PR/1359). dry_spell_frequency for calculating total number of dry spells. (GH/1352, PR/1359). hardiness_zones with supported methods "usda" (USA) and "anbg" (Australia) for calculating hardiness classifications from climatologies. (GH/1290, PR/1396). New indicator late_frost_days for calculating the number of days where the daily minimum temperature is below a threshold over a given time period. (GH/1352, PR/1361). New features and enhancements xclim.sdba.processing.escore performance was improved with a faster runtime (PR/1360). New generic function (flux_and_rate_converter) converting flux to a rate (and vice-versa) using a density. snw_to_snd and snd_to_snw were refactored using this function. (GH/1352, PR/1358) New function (prsn_to_prsnd) to convert snowfall flux ([mass] / [area] / [time]) to snowfall rate ([length] / [time]) using snow density ([mass] / [volume]). (GH/1352, PR/1358) New variables: Snowfall rate prsnd and surface maximum wind speed sfcWindmax. (GH/1352, PR/1358). Docstring for freq links to pandas offset aliases documentation. (GH/1310, PR/1392). New function xclim.indces.run_length.extract_events for determining runs whose starting and stopping points are defined through run length conditions. (PR/1256). Stats functions frequency_analysis now takes method parameter to select other fitting methods such as PWM or MOM. (GH/1168, PR/1398). xclim.indices.frost_days now accepts an **indexer parameter for calculating frost days over a temporal subset of the given dataset. (GH/1352, PR/1361). New function xclim.indices.generic.get_zones attributing a histogram bin index (a zone) to each value in an input array. (GH/1290, PR/1396). Bug fixes Fixed a bug in xclim.core.calendar.time_bnds when using DataArrayResample objects, caused by an upstream change in xarray 2023.5.0. (GH/1368, PR/1377). ensembles.change_significance will returns NaNs when the input values are all NaNs, instead of failing. (GH/1379, PR/1380). Accelerated import of xclim by caching the compilation of guvectorize functions. (PR/1378). Fixed many issues with xclim.indices.helpers.cosine_of_solar_zenith_angle, the signature changed. (GH/1110, PR/1399). Internal changes In order to ensure documentation can be rebuilt at a later time, errors raised by sphinx linkcheck are now set to be ignored when building the documentation. (PR/1375). With the publication of xclim, the code repository now offers a CITATION.cff configuration for users to properly cite the software (APA formatted and raw BibTeX) for academic purposes. (GH/95, PR/250). Logging messages emitted when redefining units via pint (caused by logging interactions with dependencies) have been silenced. (GH/1373, PR/1384). Fixed some annotations and dev recipe dependencies issues to allow for the development of xclim inside a python3.11 environment. (GH/1376, PR/1381). The deprecated mamba-org/provision-with-micromamba GitHub Action has been replaced with mamba-org/setup-micromamba. (PR/1388). xclim GitHub CI workflows now run builds against Python3.11. (PR/1388). In indices, verify that all parameters of type Quantified that have a default value have their dimension declared. (GH/1293, PR/1393). Updated roy_extremeprecip_2021 to the newly published paper. (PR/1394). Two new GitHub CI Actions have been added to the existing Workflows (PR/1390): actions/add-to-project: Automatically adds issues to the xclim project. saadmk11/github-actions-version-updater: Updates GitHub Action versions in all Workflows (triggered monthly). Added method parameter to frequency_analysis and fa. (GH/1168, PR/1398). Breaking changes Signature of hot_spell_{frequency | max_length | total_length} : thresh_tasmax modified to thresh. (GH/1352, PR/1359).