The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform

Samapriya Roy - One of the best experts on this subject based on the ideXlab platform.

  • samapriya/Planet-GEE-Pipeline-CLI: Planet-GEE-Pipeline-CLI
    2017
    Co-Authors: Samapriya Roy
    Abstract:

    While moving between assets from Planet Inc and Google Earth Engine it was imperative to create a pipeline that allows for easy transitions between the two service end points and this tool is designed to act as a step by step process chain from Planet Assets to batch upload and modification within the Google Earth Engine environment. The ambition is apart from helping user with batch actions on assets along with interacting and extending capabilities of existing GEE CLI. It is developed case by case basis to include more features in the future as it becomes available or as need arises. This release also contains a Windows installer which bypasses the need for you to have admin permission, it does however require you to have python in the system path meaning when you open up Command Prompt you should be able to type python and start it within the Command Prompt Window. Post-installation using the installer you can just call ppipe using the Command Prompt similar to calling python. Give it a go post installation type ppipe -h The tool has been successfully tested on Windows 10 and Ubuntu 16 both running Python 2.7 Credits JetStream A portion of the work is supported by JetStream Grant TG-GEO160014. Also supported by Planet Labs Ambassador Program Original upload function adapted from Lukasz's asset manager tool Changelog v0.2.0 Metadata parser and Uploader Can now handle PlanetScope 4 Band Surface Reflectance Datasets General Improvements v0.1.9 Changes made to reflect updated GEE Addon tools general improvements v0.1.8 Minor fixes to parser and general improvements Planet Key is now stored in a configuration folder which is safer "C:\users.config\planet" Earth Engine now requires you to assign a field type for metadata meaning an alphanumeric column like satID cannot also have numeric values unless specified explicitly . Manifest option has been added to handle this (just use -mf "planetscope") Added capability to query download size and local disk capacity before downloading planet assets. Added the list function to generate list of collections or folders including reports Added the collection size tool which allows you to estimate total size or quota used from your allocated quota. ogr2ft feature is removed since Earth Engine now allows vector and table uploading

  • samapriya/Planet-GEE-Pipeline-CLI: Planet-GEE-Pipeline-CLI
    2017
    Co-Authors: Samapriya Roy
    Abstract:

    While moving between assets from Planet Inc and Google Earth Engine it was imperative to create a pipeline that allows for easy transitions between the two service end points and this tool is designed to act as a step by step process chain from Planet Assets to batch upload and modification within the Google Earth Engine environment. The ambition is apart from helping user with batch actions on assets along with interacting and extending capabilities of existing GEE CLI. It is developed case by case basis to include more features in the future as it becomes available or as need arises. This release also contains a Windows installer which bypasses the need for you to have admin permission, it does however require you to have python in the system path meaning when you open up Command Prompt you should be able to type python and start it within the Command Prompt Window. Post installation using the installer you can just call ppipe using the Command Prompt similar to calling python. Give it a go post installation type ppipe -h The tool has been successfully tested on Windows 10 and Ubuntu 16 both running Python 2.7 Changelog [0.1.7] - 2017-08-12 Compiled using Google Earth Engine API 1.1.9 Added & Removed Planet Key is now stored in a configuration folder which is safer "C:\users.config\planet" Added capability to query download size and local disk capacity before downloading planet assets. Added the list function to generate list of collections or folders including reports Added the collection size tool which allows you to estimate total size or quota used from your allocated quota. ogr2ft feature is removed since Earth Engine now allows vector and table uploading

  • Samapriya/Planet-Gee-Pipeline-Cli: Planet-Gee-Pipeline-Cli
    2017
    Co-Authors: Samapriya Roy
    Abstract:

    While moving between assets from Planet Inc and Google Earth Engine it was imperative to create a pipeline that allows for easy transitions between the two service end points and this tool is designed to act as a step by step process chain from Planet Assets to batch upload and modification within the Google Earth Engine environment. The ambition is apart from helping user with batch actions on assets along with interacting and extending capabilities of existing GEE CLI. It is developed case by case basis to include more features in the future as it becomes available or as need arises. This release also contains a Windows installer which bypasses the need for you to have admin permission, it does however require you to have python in the system path meaning when you open up Command Prompt you should be able to type python and start it within the Command Prompt Window. Post installation using the installer you can just call ppipe using the Command Prompt similar to calling python. Give it a go post installation type ppipe -h The tool has been successfully tested on Windows 10 and Ubuntu 16 both running Python 2.7 Changelog [0.1.8] - 2017-09-27 Compiled using Google Earth Engine API 1.1.9 Added & Removed Minor fixes to parser and general improvements Planet Key is now stored in a configuration folder which is safer "C:\users.config\planet" Earth Engine now requires you to assign a field type for metadata meaning an alphanumeric column like satID cannot also have numeric values unless specified explicitly . Manifest option has been added to handle this (just use -mf "planetscope") Added capability to query download size and local disk capacity before downloading planet assets. Added the list function to generate list of collections or folders including reports Added the collection size tool which allows you to estimate total size or quota used from your allocated quota. ogr2ft feature is removed since Earth Engine now allows vector and table uploading.

Daniel E. Paxson - One of the best experts on this subject based on the ideXlab platform.

  • NASA One-Dimensional Combustor Simulation--User Manual for S1D_ML
    2014
    Co-Authors: Thomas J. Stueber, Daniel E. Paxson
    Abstract:

    The work presented in this paper is to promote research leading to a closed-loop control system to actively suppress thermo-acoustic instabilities. To serve as a model for such a closed-loop control system, a one-dimensional combustor simulation composed using MATLAB software tools has been written. This MATLAB based process is similar to a precursor one-dimensional combustor simulation that was formatted as FORTRAN 77 source code. The previous simulation process requires modification to the FORTRAN 77 source code, compiling, and linking when creating a new combustor simulation executable file. The MATLAB based simulation does not require making changes to the source code, recompiling, or linking. Furthermore, the MATLAB based simulation can be run from script files within the MATLAB environment or with a compiled copy of the executable file running in the Command Prompt Window without requiring a licensed copy of MATLAB. This report presents a general simulation overview. Details regarding how to setup and initiate a simulation are also presented. Finally, the post-processing section describes the two types of files created while running the simulation and it also includes simulation results for a default simulation included with the source code.

Steven M. Geng - One of the best experts on this subject based on the ideXlab platform.

Thomas J. Stueber - One of the best experts on this subject based on the ideXlab platform.

  • NASA One-Dimensional Combustor Simulation--User Manual for S1D_ML
    2014
    Co-Authors: Thomas J. Stueber, Daniel E. Paxson
    Abstract:

    The work presented in this paper is to promote research leading to a closed-loop control system to actively suppress thermo-acoustic instabilities. To serve as a model for such a closed-loop control system, a one-dimensional combustor simulation composed using MATLAB software tools has been written. This MATLAB based process is similar to a precursor one-dimensional combustor simulation that was formatted as FORTRAN 77 source code. The previous simulation process requires modification to the FORTRAN 77 source code, compiling, and linking when creating a new combustor simulation executable file. The MATLAB based simulation does not require making changes to the source code, recompiling, or linking. Furthermore, the MATLAB based simulation can be run from script files within the MATLAB environment or with a compiled copy of the executable file running in the Command Prompt Window without requiring a licensed copy of MATLAB. This report presents a general simulation overview. Details regarding how to setup and initiate a simulation are also presented. Finally, the post-processing section describes the two types of files created while running the simulation and it also includes simulation results for a default simulation included with the source code.

Peter Kamerman - One of the best experts on this subject based on the ideXlab platform.

  • Analysis scripts and supplementary files: Barriers to implementing clinical trials on non-pharmacological treatments in developing countries – lessons learnt from addressing pain in HIV
    2019
    Co-Authors: Peter Kamerman, Victoria J Madden, Romy Parker, Dershnee Devan, Sarah Cameron, Kirsty Jackson, Cameron Reardon, Antonia Wadley
    Abstract:

    DESCRIPTIONThis repository contains analysis scripts (with outputs), figures from the manuscript, and supplementary files the HIV Pain (HIP) Intervention Study. All analysis scripts (and their outputs -- /outputs subdirectory) are found in HIP-study.zip, while PDF copies of the analysis outputs that are cited in the manuscript as supplementary material are found in the relevant supplement-*.pdf file.Note: Participant consent did not provide for the publication of their data, and hence neither the original nor cleaned data have been made available. However, we do not wish to bar access to the data unnecessarily and we will judge requests to access the data on a case-by-case basis. Examples of potential use cases include independent assessments of our analyses, and secondary data analyses. Please contact Peter Kamerman (peter.kamerman@gmail.com), Dr Tory Madden (torymadden@gmail.com, or open an issue on the GitHub repo (https://github.com/kamermanpr/HIP-study/issues).BIBLIOGRAPHIC INFORMATIONRepository citationKamerman PR, Madden VJ, Parker R, Devan D, Cameron S, Jackson K, Reardon C, Wadley A. Analysis scripts and supplementary files: Barriers to implementing clinical trials on non-pharmacological treatments in developing countries – lessons learnt from addressing pain in HIV. DOI: 10.6084/m9.figshare.7654637.Manuscript citationParker R, Madden VJ, Devan D, Cameron S, Jackson K, Kamerman P, Reardon C, Wadley A. Barriers to implementing clinical trials on non-pharmacological treatments in developing countries – lessons learnt from addressing pain in HIV. Pain Reports [submitted 2019-01-31]Manuscript abstractintroduction: Pain affects over half of people living with HIV/AIDS (LWHA) and pharmacological treatment has limited efficacy. Preliminary evidence supports non-pharmacological interventions. We previously piloted a multimodal intervention in amaXhosa women LWHA and chronic pain in South Africa with improvements seen in all outcomes, in both intervention and control groups. Methods: A multicentre, single-blind randomised controlled trial with 160 participants recruited was conducted to determine whether the multimodal peer-led intervention reduced pain in different populations of both male and female South Africans LWHA. Participants were followed up at Weeks 4, 8, 12, 24 and 48 to evaluate effects on the primary outcome of pain, and on depression, self-efficacy and health-related quality of life. Results: We were unable to assess the efficacy of the intervention due to a 58% loss to follow up (LTFU). Secondary analysis of the LTFU found that sociocultural factors were not predictive of LTFU. Depression, however, did associate with LTFU, with greater severity of depressive symptoms predicting LTFU at week 8 (p=0.01). Discussion: We were unable to evaluate the effectiveness of the intervention due to the high LTFU and the risk of retention bias. The different sociocultural context in South Africa may warrant a different approach to interventions for pain in HIV compared to resource-rich countries, including a concurrent strategy to address barriers to health care service delivery. We suggest that assessment of pain and depression need to occur simultaneously in those with pain in HIV. We suggest investigation of the effect of social inclusion on pain and depression. USING DOCKER TO RUN THE HIP-STUDY ANALYSIS SCRIPTSThese instructions are for running the analysis on your local machine.You need to have Docker installed on your computer. To do so, go to docker.com (https://www.docker.com/community-edition#/download) and follow the instructions for downloading and installing Docker for your operating system. Once Docker has been installed, follow the steps below, noting that Docker Commands are entered in a terminal Window (Linux and OSX/macOS) or Command Prompt Window (Windows). Windows users also may wish to install GNU Make (http://gnuwin32.sourceforge.net/downlinks/make.php) (required for the `make` method of running the scripts) and Git (https://gitforWindows.org/) version control software (not essential).Download the latest imageEnter: docker pull kamermanpr/docker-hip-study:v2.0.0Run the containerEnter: docker run -d -p 8787:8787 -v :/home/rstudio --name threshold -e USER=hip -e PASSWORD=study kamermanpr/docker-hip-study:v2.0.0Where refers to the path to the HIP-study directory on your computer, which you either cloned from GitHub (https://github.com/kamermanpr/HIP-study.git), `git clone https://github.com/kamermanpr/HIP-study`, or downloaded and extracted from figshare (https://doi.org/10.6084/m9.figshare.7654637).Login to RStudio Server- Open a web browser Window and navigate to: `localhost:8787`- Use the following login credentials: - Username: hip - Password: study Prepare the HIP-study directoryThe HIP-study directory comes with the outputs for all the analysis scripts in the _/outputs_ directory (html and md formats). However, should you wish to run the scripts yourself, there are several preparatory steps that are required:1. Acquire the data. The data required to run the scripts have not been included in the repo because participants in the studies did not consent to public release of their data. However, the data are available on request from Peter Kamerman (peter.kamerman@gmail.com). Once the data have been obtained, the files should be copied into a subdirectory named /data-original.2. Clean the /outputs directory by entering `make clean` in the Terminal tab in RStudio.Run the HIP-study analysis scriptsTo run all the scripts (including the data cleaning scripts), enter `make all` in the Terminal tab in RStudio.To run individual RMarkdown scripts (*.Rmd files)1. Generate the cleaned data using one of the following methods: - Enter `make data-cleaned/demographics.rds` in the Terminal tab in RStudio. - Enter `source('clean-data-script.R')` in the Console tab in RStudio. - Open the clean-data-script.R script through the File tab in RStudio, and then click the 'Source' button on the right of the Script console in RStudio for each script. 2. Run the individual script by: - Entering `make outputs/.html` in the Terminal tab in RStudio, OR - Opening the relevant \*.Rmd file through the File tab in RStudio, and then clicking the 'knit' button on the left of the Script console in RStudio. Shutting downOnce done, log out of RStudio Server and enter the following into a terminal to stop the Docker container: `docker stop hip`. If you then want to remove the container, enter: `docker rm threshold`. If you also want to remove the Docker image you downloaded, enter: `docker rmi kamermanpr/docker-hip-study:v2.0.0`

  • Analysis scripts and supplementary files: Was that painful or non-painful? The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context.
    2018
    Co-Authors: Peter Kamerman, Victoria Madden, Valeria Bellan, Mark Catley, Leslie Russek, Danny Camfferman, Lorimer Moseley
    Abstract:

    DESCRIPTIONThis repository contains analysis scripts (with outputs), figures from the manuscript, and supplementary files for two studies on the properties of the Sensation and Pain Rating Scale (SPARS). All analysis scripts (and their outputs -- /outputs subdirectory) are found in SPARS.zip, while PDF copies of the analysis outputs that are cited in the manuscript as supplementary material are found in the relevant supplement_*.pdf. Note: Participant consent did not provide for the publication of their data, and hence neither the original nor cleaned data have been made available. However, we do not wish to bar access to the data unnecessarily and we will judge requests to access the data on a case-by-case basis. Examples of potential use cases include independent assessments of our analyses, and secondary data analyses. Please contact Peter Kamerman (peter.kamerman@gmail.com), Dr Tory Madden (torymadden@gmail.com, or open an issue on the GitHub repo (https://github.com/kamermanpr/SPARS/issues).BIBLIOGRAPHIC INFORMATIONRepository citationKamerman P, Madden V, Bellan V, Catley M, Russek L, Camfferman D, Moseley L. Analysis scripts and supplementary files: Was that painful or non-painful? The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context. Figshare, 2018. DOI: 10.6084/m9.figshare.6561743.Manuscript citationMadden V, Kamerman P, Bellan V, Catley M, Russek L, Camfferman D, Moseley L. The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context. Journal of Pain [in press].Manuscript abstractIn experiments on pain perception, participants are frequently exposed to non-painful and painful stimuli, yet the conventional pain-rating scales lack a non-painful range and a clear point of transition from non-painful to painful events. The Sensation and Pain Rating Scale (SPARS) is a 0-100 scale that assesses the full stimulus intensity range, extending from no sensation (rating: -50) to worst pain imaginable (rating: +50), and it explicitly identifies pain threshold (rating: 0). Here, we tested the SPARS in two experiments using laser heat stimuli to establish its stimulus-response characteristics (Experiment 1, n = 19, 13 stimulus intensities applied 26 times each across a 1-4J range), and to compare it to 0-100 scales that access non-painful (0: no sensation, 100: painful) and painful (0: not painful, 100: worst pain imaginable) events (Experiment 2, n = 7, 9 stimulus intensities applied 36 times each across a 1.5-4.5J range). Despite high inter- and intra-individual variation, we found a reasonably consistent curvilinear stimulus-response relationship (the curve flattens around pain threshold), with stable response characteristics across the range of the scale. SPARS ratings tended to be lower than the 0-100 pain rating scale in the noxious stimulus intensity range, and greater than the 0-100 non-painful sensation scale in the non-noxious stimulus range; likely reflecting differences in scale dimensionality. The SPARS overcomes limitations of scale range inherent in conventional pain rating scales and, as such, is well suited to experimental studies in which distinguishing between painful and non-painful events is a priority.USING DOCKER TO RUN THE SPARS ANALYSIS SCRIPTSThese instructions are for running the analysis on your local machine.You need to have Docker installed on your computer. To do so, go to docker.com (https://www.docker.com/community-edition#/download) and follow the instructions for downloading and installing Docker for your operating system. Once Docker has been installed, follow the steps below, noting that Docker Commands are entered in a terminal Window (Linux and OSX/macOS) or Command Prompt Window (Windows). Windows users also may wish to install GNU Make (http://gnuwin32.sourceforge.net/downlinks/make.php) (required for the `make` method of running the scripts) and Git (https://gitforWindows.org/) version control software (not essential). Download the latest imageEnter: `docker pull kamermanpr/docker-spars:v1.1.2`Download the repositoryDownload the compressed zip file from GitHub (kamermanpr/SPARS (https://github.com/kamermanpr/SPARS), or from figshare [DOI: 10.6084/m9.figshare.6561743 (https://doi.org/10.6084/m9.figshare.6561743)]. Run the containerEnter: `docker run --name spars -d -p 8787:8787 -e USER=user -e PASSWORD=password kamermanpr/docker-spars:v1.1.2`Login to RStudio Server- Open a web browser Window and navigate to: `localhost:8787`- Use the following login credentials: - Username: _user_ - Password: _password_ Prepare the SPARS directoryOn the Files tab in the bottom right panel of RStudio, click on the 'Upload' button, navigate to the downloaded zip file, and upload the file (it will self extract).The SPARS directory comes with the outputs for all the analysis scripts in the /outputs directory (html and md formats). However, should you wish to run the scripts yourself, there are several preparatory steps that are required:1. Acquire the data. The data required to run the scripts have not been included in the repo because participants in the studies did not consent to public release of their data. However, the data are available on request from Tory Madden (torymadden@gmail.com) or Peter Kamerman (peter.kamerman@gmail.com). We will send you a zip file with the data. Using the directory tree in the Files tab of RStudio, open the SPARS directory. Repeat the upload procedure described above, but upload the zipped data file we supplied you with into the SPARS directory.2. In the SPARS directory, double-click on the SPARS.Rproj file, and follow the Prompts (RStudio will reload).3. Clean the /outputs and /figures directories by entering `make clean` in the Terminal tab in bottom right panel of RStudio.Run the SPARS analysis scriptsTo run all the scripts (including the data cleaning scripts), enter `make` in the Terminal tab.To run individual RMarkdown scripts (\*.Rmd files) 1. Generate the cleaned data using one of the following methods: - Enter `make data-cleaned/SPARS_A.rds` and then `make data-cleaned/SPARS_B.rds` in the Terminal tab; - Enter `source('0A-clean-data.R')` and then `source('0B-clean-data.R')` in the Console tab in bottom left panel of RStudio. - Open 0A-clean-data.R and 0B-clean-data.R scripts through the File tab, and then click the 'Source' button on top of the panel on the top left of RStudio for each script. 2. Run the individual script using one of the following methods: - Enter `make outputs/.html` in the Terminal tab; - Open the relevant \*.Rmd file through the File tab, and then click the 'knit' button on the top of the panel on the top left of RStudio. Shutting downOnce done, log out of RStudio and enter the following into a terminal to stop the Docker container: `docker stop spars`. If you then want to remove the container, enter: `docker rm spars`. If you also want to remove the Docker image you downloaded, enter: `docker rmi kamermanpr/docker-spars:v1.1.2`