DOCSLIB.ORG

Conda Build Meta.Yaml

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Conda Build Meta.Yaml Porting legacy software packages to the Conda Package Manager Joe Asercion, Fermi Science Support Center, NASA/GSFC Previous Process FSSC Previous Process FSSC Release Tag Previous Process FSSC Release Tag Code Ingestion Previous Process FSSC Release Tag Code Ingestion Builds on supported systems Previous Process FSSC Release Tag Code Ingestion Push back Builds on changes supported systems Testing Previous Process FSSC Release Tag Code Ingestion Push back Builds on changes supported systems Testing Packaging Previous Process FSSC Release Release Tag Code Ingestion All binaries and Push back Builds on source made changes supported systems available on the FSSC’s website Testing Packaging Previous Process Issues • Very long development cycle • Difficult dependency • Many bottlenecks management • Increase in build instability • Frequent library collision errors with user machines • Duplication of effort • Large number of individual • Large download size binaries to support Goals of Process Overhaul • Continuous Integration/Release Model • Faster report/patch release cycle • Increased stability in the long term • Increased Automation • Increase process efficiency • Increased Process Transparency • Improved user experience • Better dependency management Conda Package Manager • Languages: Python, Ruby, R, C/C++, Lua, Scala, Java, JavaScript • Combinable with industry standard CI systems • Developed and maintained by Anaconda (a.k.a. Continuum Analytics) • Variety of channels hosting downloadable packages Packaging with Conda Conda Build Meta.yaml Build.sh/bld.bat • Contains metadata of the package to • Executed during the build stage be build • Written like standard build script • Contains metadata needed for build • Dependencies, system requirements, etc • Ideally minimalist • Allows for staged environment • Any customization not handled by specificity meta.yaml can be implemented here. • Large amount of customization available • Jinja2 macros Meta.yaml Custom Variables Package versioning metadata Source code handling Run stage Build process parameters dependencies Build stage dependencies Host stage dependencies Test handling Defining Package Metadata Only mandatory sections: package/name & package version Package Source Build Descriptive metadata for the Conda can pull from multiple Specifies metadata which describes package. Name and version string sources natively. the build itself including build must be included. targets • url Build number is used to specify new • Local path builds of the same version • Git • Hg • Svn Conda can handle aggregate builds from multiple source locations Conda Build Environments Build Host Run • Meant for all low-level build • Host was added to represent • Packages required to run the package system libraries needed for the packages “that need to be compilation specific to the target platform • Installed automatically when the when the target…is not built package is installed • Anything that provides ‘sysroot’ necessarily the same as the • Stored as metadata in the binary files which is distributed via the native build platform.” Anaconda Cloud • These packages need to be able • Fermi lists the vast majority of • Good practice is to pin required to run on the build machine but its dependencies here versioning information to the be capable of outputting builds package. for a target platform • ’Host’ and ‘Build’ prefixes are • As of Conda-Build 3 you can almost always separate except augment packages in the build and in a few specific cases host sections with the ’run_exports’ header to negate the need for this section. (Weak vs. Strong) Conda Build Environments Build Host Run • Meant for all low-level build • Host was added to represent • Packages required to run the package system libraries needed for the packages “that need to be compilation specific to the target platform • Installed automatically when the when the target…is not built package is installed • Anything that provides ‘sysroot’ necessarily the same as the • Stored as metadata in the binary files which is distributed via the native build platform.” Anaconda Cloud • These packages need to be able • Fermi lists the vast majority of • Good practice is to pin required to run on the build machine but its dependencies here versioning information to the be capable of outputting builds package. for a target platform • ’Host’ and ‘Build’ prefixes are • As of Conda-Build 3 you can almost always separate except augment packages in the build and in a few specific cases host sections with the ’run_exports’ header to negate the need for this section. (Weak vs. Strong) Build Machine Conda Build Environments Build Host Run • Meant for all low-level build • Host was added to represent • Packages required to run the package system libraries needed for the packages “that need to be compilation specific to the target platform • Installed automatically when the when the target…is not built package is installed • Anything that provides ‘sysroot’ necessarily the same as the • Stored as metadata in the binary files which is distributed via the native build platform.” Anaconda Cloud • These packages need to be able • Fermi lists the vast majority of • Good practice is to pin required to run on the build machine but its dependencies here versioning information to the be capable of outputting builds package. for a target platform • ’Host’ and ‘Build’ prefixes are • As of Conda-Build 3 you can almost always separate except augment packages in the build and in a few specific cases host sections with the ’run_exports’ header to negate the need for this section. (Weak vs. Strong) Build Machine Target Machine Target Machine Conda Macros Conda build helpfully provides a number of Jinja2 functions which help automate and generalize the build process. Common Jinja2 use cases • Automatic compiler selection/setup • Pinning expressions • Templating Conda Macros Custom Variable Setting Conda build helpfully provides a number of Jinja2 functions which help Variable Calling automate and generalize the build process. Common Jinja2 use cases • Automatic compiler selection/setup • Pinning expressions • Templating Conda Macros Custom Variable Setting Conda build helpfully provides a number of Jinja2 functions which help Variable Calling automate and generalize the build process. Shell Common Jinja2 use cases Environment • Automatic compiler selection/setup reference • Pinning expressions • Templating Conda Macros Custom Variable Setting Conda build helpfully provides a number of Jinja2 functions which help Variable Calling automate and generalize the build process. Shell Common Jinja2 use cases Environment • Automatic compiler selection/setup reference • Pinning expressions • Templating Conda Compiler function Conda Compilers As of Anaconda 5 Conda provides a set of compilers which are Linux macOS recommended for use in build recipes. gcc_liunx_64 clang_liunx_64 g++_linux_64 clangxx_linux_64 Use of the Anaconda compilers gfortran_linux_64 gfortran_linux_64 helps make the recipe more agnostic with regards to the host machine. • Explicitly built for cross- compilation • Customizable Preprocessing Selectors Preprocessing selectors can be used to specify different meta-data cases for different build environments. This is most commonly used to specify different platforms/architectures as build/dependency targets. Almost all of the selector variables are Booleans and allow for logical statements to be passed in preprocessing. However, good practice is to employ comparison operators over specific selector variables. Selectors always follow a target in the format ‘TARGET # [ SELECTOR ]’ Build.sh • Default: Bash Script (.bat on Build Environment Variables Windows) • Specialized environmental • Executed in a special “Conda variables defined in the Build” environment Conda Build process. • Custom/explicit • Some are inherited and some environmental variables need are defined by Conda to be defined via meta.yaml PREFIX – Path to the build • Needed compiler flags are directory. Used by all systems specified here • MacOS/Windows have unique • Can use any installed variables scriptable build system (make, sCons, etc.) Distribution Anaconda Cloud is the primary distribution mechanism for the Conda Package manager. Organizations can have dedicated channels to distribute software built and packaged using Conda Build. Hosting for public projects are free. Private plans are available for a fee. Fermi has its own organization (the Fermi Channel) which distributes software which is developed and maintained directly by the Fermi Science Support Center Conda-Forge is another such organization. Anaconda Channels Channels organize packages by the user or group of users (Organization) that uploaded them. Label help differentiate different different packages hosted in a channel, effectively creating ‘sub-channels’. Label checking in Conda is strict. Including a label tag in a conda install or update command will search for matching packages with that label alone. Conda Forge Conda Forge Standardized Build Package Community Driven Process Centralization • Est 2016 • Conda Forge uses a Github • All feedstocks hosted on github integrated CI system in the conda-forge organization • Conda Forge seeks to expand upon the default packages • Linux -> Circle CI/Azure • Compiled packages hosted on uploaded by Anaconda Inc. the Conda-Forge Anaconda • MacOS -> Travis CI/Azure cloud channel • Packages are maintained by their uploaders in accordance • 6500+ packages currently with Conda Forge build availible standards
Load more

Recommended publications
  • Hsafew2019 S2b.Pdf
    How to work with SSM products From Download to Visualization Apostolos Giannakos Zentralanstalt für Meteorologie und Geodynamik (ZAMG) https://www.zamg.ac.at 1 Topics • Overview • ASCAT SSM NRT Products • ASCAT SSM CDR Products • Read and plot ASCAT SSM NRT Products • Read and plot ASCAT SSM CDR Products • Summary 2 H SAF ASCAT Surface Soil Moisture Products ASCAT SSM Near Real-Time (NRT) products – NRT products for ASCAT on-board Metop-A, Metop-B, Metop-C – Swath orbit geometry – Available 130 minutes after sensing – Various spatial resolutions • 25 km spatial sampling (50 km spatial resolution) • 12.5 km spatial sampling (25-34 km spatial resolution) • 0.5 km spatial sampling (1 km spatial resolution) ASCAT SSM Climate Data Record (CDR) products – ASCAT data merged for all Metop (A, B, C) satellites – Time series format located on an Earth fixed DGG (WARP5 Grid) – 12.5 km spatial sampling (25-34 km spatial resolution) – Re-processed every year (in January) – Extensions computed throughout the year until new release 3 Outlook: Near real-time surface soil moisture products CDOP3 H08 - SSM ASCAT NRT DIS Disaggregated Metop ASCAT NRT SSM at 1 km (pre-operational)* H101 - SSM ASCAT-A NRT O12.5 Metop-A ASCAT NRT SSM orbit 12.5 km sampling (operational) H102 - SSM ASCAT-A NRT O25 Metop-A ASCAT NRT SSM orbit 25 km sampling (operational) H16 - SSM ASCAT-B NT O12.5 Metop-B ASCAT NRT SSM orbit 12.5 km sampling (operational) H103 - SSM ASCAT-B NRT O25 Metop-B ASCAT NRT SSM orbit 25 km sampling (operational) 4 Architecture of ASCAT SSM Data Services
    [Show full text]
  • Installation Instructions
    Installation instructions The following instructions should be quite detailed and easy to follow. If you nevertheless encounter a problem which you cannot solve for yourself, please write an email to Thomas Foesel. Note: the monospaced text in this section are commands which have to be executed in a terminal. • for Linux/Mac: The terminal is simply the system shell. The "#" at the start of the line indicates that root privileges are required (so log in as root via su, or use sudo if this is configured suitably), whereas the commands starting with "$" can be executed as a normal user. • for Windows: Type the commands into the Conda terminal which is part of the Miniconda installation (see below). Installing Python, Theano, Keras, Matplotlib and Jupyter In the following, we show how to install these packages on the three common operating systems. There might be alternative ways to do so; if you prefer another one that works for you, this is also fine, of course. • Linux ◦ Debian/Mint/Ubuntu/... 1. # apt-get install python3 python3-dev python3- matplotlib python3-nose python3-numpy python3-pip 2. # pip3 install jupyter keras Theano ◦ openSUSE 1. # zypper in python3 python3-devel python3- jupyter_notebook python3-matplotlib python3-nose python3-numpy-devel 2. # pip3 install Theano keras • Mac 2. Download the installation script for the Miniconda collection (make sure to select Python 3.x, the upper row). In the terminal, go into the directory of this file ($ cd ...) and run # bash Miniconda3-latest-MacOSX-x86_64.sh. 3. Because there are more recent Conda versions than on the website, update it via conda update conda.
    [Show full text]
  • Introduction to Anaconda and Python: Installation and Setup
    ¦ 2020 Vol. 16 no. 5 Python FOR Research IN Psychology Introduction TO Anaconda AND Python: Installation AND SETUP Damien Rolon-M´ERETTE A B , Matt Ross A , Thadd´E Rolon-M´ERETTE A & Kinsey Church A A University OF Ottawa AbstrACT ACTING Editor Python HAS BECOME ONE OF THE MOST POPULAR PROGRAMMING LANGUAGES FOR RESEARCH IN THE PAST decade. Its free, open-source NATURE AND VAST ONLINE COMMUNITY ARE SOME OF THE REASONS be- Nareg Berberian (University OF Ot- HIND ITS success. Countless EXAMPLES OF INCREASED RESEARCH PRODUCTIVITY DUE TO Python CAN BE FOUND tawa) ACROSS A PLETHORA OF DOMAINS online, INCLUDING DATA science, ARTIfiCIAL INTELLIGENCE AND SCIENTIfiC re- Reviewers search. This TUTORIAL’S GOAL IS TO HELP USERS GET STARTED WITH Python THROUGH THE INSTALLATION AND SETUP TARIQUE SirAGY (Uni- OF THE Anaconda software. The GOAL IS TO SET USERS ON THE PATH TOWARD USING THE Python LANGUAGE BY VERSITY OF Ottawa) PREPARING THEM TO WRITE THEIR fiRST script. This TUTORIAL IS DIVIDED IN THE FOLLOWING fashion: A SMALL INTRODUCTION TO Python, HOW TO DOWNLOAD THE Anaconda software, THE DIFFERENT CONTENT THAT COMES WITH THE installation, AND A SIMPLE EXAMPLE RELATED TO IMPLEMENTING A Python script. KEYWORDS TOOLS Python, Psychology, Installation guide, Anaconda. Python, Anaconda. B [email protected] 10.20982/tqmp.16.5.S003 Introduction MON problems, VIDEO tutorials, AND MUCH more. These re- SOURCES ARE OFTEN FREE TO ACCESS AND COVER THE MOST com- WhY LEARN Python? Python IS AN object-oriented, inter- MON PROBLEMS DEVELOPERS RUN INTO AT ALL LEVELS OF DIffiCULTY. preted, mid-level PROGRAMMING LANGUAGE THAT IS EASY TO In ADDITION TO THE BENEfiTIAL FEATURES MENTIONED above, LEARN AND USE WHILE BEING VERSATILE ENOUGH TO TACKLE A vari- Python HAS MANY DIFFERENT QUALITIES SPECIfiCALLY TAILORED ETY OF TASKS (Helmus & Collis, 2016).
    [Show full text]
  • Tensorflow and Keras Installation Steps for Deep Learning Applications in Rstudio Ricardo Dalagnol1 and Fabien Wagner 1. Install
    Version 1.0 – 03 July 2020 Tensorflow and Keras installation steps for Deep Learning applications in Rstudio Ricardo Dalagnol1 and Fabien Wagner 1. Install OSGEO (https://trac.osgeo.org/osgeo4w/) to have GDAL utilities and QGIS. Do not modify the installation path. 2. Install ImageMagick (https://imagemagick.org/index.php) for image visualization. 3. Install the latest Miniconda (https://docs.conda.io/en/latest/miniconda.html) 4. Install Visual Studio a. Install the 2019 or 2017 free version. The free version is called ‘Community’. b. During installation, select the box to include C++ development 5. Install the latest Nvidia driver for your GPU card from the official Nvidia site 6. Install CUDA NVIDIA a. I installed CUDA Toolkit 10.1 update2, to check if the installed or available NVIDIA driver is compatible with the CUDA version (check this in the cuDNN page https://docs.nvidia.com/deeplearning/sdk/cudnn-support- matrix/index.html) b. Downloaded from NVIDIA website, searched for CUDA NVIDIA download in google: https://developer.nvidia.com/cuda-10.1-download-archive-update2 (list of all archives here https://developer.nvidia.com/cuda-toolkit-archive) 7. Install cuDNN (deep neural network library) – I have installed cudnn-10.1- windows10-x64-v7.6.5.32 for CUDA 10.1, the last version https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows a. To download you have to create/login an account with NVIDIA Developer b. Before installing, check the NVIDIA driver version if it is compatible c. To install on windows, follow the instructions at section 3.3 https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows d.
    [Show full text]
  • Conda-Build Documentation Release 3.21.5+15.G174ed200.Dirty
    conda-build Documentation Release 3.21.5+15.g174ed200.dirty Anaconda, Inc. Sep 27, 2021 CONTENTS 1 Installing and updating conda-build3 2 Concepts 5 3 User guide 17 4 Resources 49 5 Release notes 115 Index 127 i ii conda-build Documentation, Release 3.21.5+15.g174ed200.dirty Conda-build contains commands and tools to use conda to build your own packages. It also provides helpful tools to constrain or pin versions in recipes. Building a conda package requires installing conda-build and creating a conda recipe. You then use the conda build command to build the conda package from the conda recipe. You can build conda packages from a variety of source code projects, most notably Python. For help packing a Python project, see the Setuptools documentation. OPTIONAL: If you are planning to upload your packages to Anaconda Cloud, you will need an Anaconda Cloud account and client. CONTENTS 1 conda-build Documentation, Release 3.21.5+15.g174ed200.dirty 2 CONTENTS CHAPTER ONE INSTALLING AND UPDATING CONDA-BUILD To enable building conda packages: • install conda • install conda-build • update conda and conda-build 1.1 Installing conda-build To install conda-build, in your terminal window or an Anaconda Prompt, run: conda install conda-build 1.2 Updating conda and conda-build Keep your versions of conda and conda-build up to date to take advantage of bug fixes and new features. To update conda and conda-build, in your terminal window or an Anaconda Prompt, run: conda update conda conda update conda-build For release notes, see the conda-build GitHub page.
    [Show full text]
  • Sustainable Software Packaging for End Users with Conda
    Sustainable software packaging for end users with conda Chris Burr, Henry Schreiner, Enrico Guiraud, Javier Cervantes CHEP 2019 ○ 5th November 2019 Image: CERN-EX-66954B © 1998-2018 CERN LCG from EP-SFT LCG from LHCb [email protected] ○ CHEP 2019 ○ Sustainable software packaging for end users with conda https://xkcd.com/1987/2 LCG from EP-SFT LCG from LHCb Homebrew ROOT builtin GCC 7.3 GCC 8.2 GCC9.2 Homebrew + Python 3.7 Self compiled against Python 3.6 (but broken since updating to 3.7 homebrew) Clang 5 Clang 6 Clang 8 Clang 9 [email protected] ○ CHEP 2019 ○ Sustainable software packaging for end users with conda https://xkcd.com/1987/3 LCG from EP-SFT LCG from LHCb Homebrew ROOT builtin GCC 7.3 GCC 8.2 GCC9.2 Homebrew +Python 3.7 Self compiled against Python 3.6 (broken since updating to 3.7 homebrew) Clang 5 Clang 6 Clang 8 Clang 9 [email protected] ○ CHEP 2019 ○ Sustainable software packaging for end users with conda https://xkcd.com/1987/4 LCG from EP-SFT LCG from LHCb Homebrew ROOT builtin GCC 7.3 GCC 8.2 GCC9.2 Homebrew +Python 3.7 Self compiled against Python 3.6 (broken since updating to 3.7 homebrew) Clang 5 Clang 6 Clang 8 Clang 9 [email protected] ○ CHEP 2019 ○ Sustainable software packaging for end users with conda https://xkcd.com/1987/5 LCG from EP-SFT LCG from LHCb Homebrew ROOT builtin GCC 7.3 conda create !-name my-analysis \ GCC 8.2 python=3.7 ipython pandas matplotlib \ GCC9.2 root boost \ Homebrew +Python 3.7 tensorflow xgboost \ Self compiled against Python 3.6 (broken since updating
    [Show full text]
  • Conda Documentation Release
    conda Documentation Release Anaconda, Inc. Sep 14, 2021 Contents 1 Conda 3 2 Conda-build 5 3 Miniconda 7 4 Help and support 13 5 Contributing 15 6 Conda license 19 i ii conda Documentation, Release Package, dependency and environment management for any language—Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN, and more. Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux. Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer. It was created for Python programs, but it can package and distribute software for any language. Conda as a package manager helps you find and install packages. If you need a package that requires a different version of Python, you do not need to switch to a different environment manager, because conda is also an environment manager. With just a few commands, you can set up a totally separate environment to run that different version of Python, while continuing to run your usual version of Python in your normal environment. In its default configuration, conda can install and manage the thousand packages at repo.anaconda.com that are built, reviewed and maintained by Anaconda®. Conda can be combined with continuous integration systems such as Travis CI and AppVeyor to provide frequent, automated testing of your code. The conda package and environment manager is included in all versions of Anaconda and Miniconda. Conda is also included in Anaconda Enterprise, which provides on-site enterprise package and environment manage- ment for Python, R, Node.js, Java and other application stacks.
    [Show full text]
  • Local Platform Setup Anaconda Create a Virtual Environment
    Local Platform Setup This guide describes the steps to follow to install the virtual environment to run tensorflow locally on your machine. Anaconda Anaconda is the leading open data science platform powered by python or R. The open source version of Anaconda is a high performance distribution of Python and you’ll have access to over 720 packages that can easily be installed with conda. To install Anaconda, please click the link and choose the appropriate operating system installer and Python 3.7 version (recommended) to download: ​ ​ https://www.anaconda.com/distribution/ ● With the Graphical Installer, just follow the instruction to finish installation. ​ ​ ● If you download the MacOS or Linux Command Line Installer, open your terminal and go to ​ ​ directory that contains the Installer and run the sh file like the following ​ ​ ./Anaconda-v-0.x.y.sh ​ Permissions: If you couldn’t run this file, you can right click that file and choose the Properties, ​ in the Permission tab, please make sure the file is allowed to execute, OR you run the following command to change access permission via command line: ​ chmod +x Anaconda-v-0.x.y.sh Create a Virtual Environment Once the Anaconda is installed, the next step is to create a Python virtual environment. A virtual environment is a real good way to keep the dependencies required by different projects in separate places. Basically, it solves the “Project X depends on version 1.x but, Project Y needs ​ 4.x” dilemma, and keeps your global site-packages directory clean and manageable. For example, you can work on a project which requires matplotlib 1.5.3 while also maintaining a project which requires matplotlib 1.4.2.
    [Show full text]
  • Metview's Python Interface Opens New Possibilities
    from Newsletter Number 162 – Winter 2019/20 COMPUTING Metview’s Python interface opens new possibilities 11.4 11.5 10.4 11.7 11.6 12.2 12.6 12.9 12.2 12.0 13.5 13.3 12.7 14.1 13.1 12.8 13.0 13.5 12.0 12.7 11.8 12.7 12.5 12.6 13.4 12.6 14.5 14 13.9 13.3 14.0 13 13.1 12.5 13.2 12 13.5 14.0 13.1 14.1 14.1 11 13.8 14.1 14.6 13.6 14.1 13.6 13.1 13.7 10 13.8 12.8 12.7 www.ecmwf.int/en/about/media-centre/media-resources doi: 10.21957/hv3sp41ir5 Iain Russell, Linus Magnusson, Martin Janousek, Sándor Kertész Metview’s Python interface opens new possibilities This article appeared in the Computing section of ECMWF Newsletter No. 162 – Winter 2019/20, pp. 36–39 Metview’s Python interface opens new possibilities Iain Russell, Linus Magnusson, Martin Janousek, Sándor Kertész Metview is ECMWF’s interactive and batch processing software for accessing, manipulating and visualising meteorological data. Metview is used extensively both at ECMWF and in the Centre’s Member and Co-operating States. A national meteorological service may for example use it to plot fields produced by the ECMWF model and a regional model, calculate some additional fields such as temperature advection, and plot vertical cross sections. The recent addition of a Python interface to Metview has expanded its range of uses and has made it accessible to more potential users.
    [Show full text]
  • Python Packages – Cheat Sheet.Pdf
    Python Packages Cheat Sheet List of Popular Packages Overview If you are using Anaconda to manage your Python packages, for any of the following packages that need to be installed, copy the code to install into the Anaconda Prompt. When prompted, enter y to confirm yes to continue. Most of the packages should be pre-installed with a core installation of Anaconda; however, the ones highlighted in the summary table will most likely not be installed with a fresh installation of Anaconda. For a list of all packages that are pre-installed with a new installation of Anaconda please see: https://docs.anaconda.com/anaconda/packages/pkg-docs/ Popular Packages In the next two pages we have summarized 25 of the top Python packages used by business professionals that are also taught in the various Python courses at Marquee. The packages are listed in alphabetical order in the summary tables and also by usage category below: - Data manipulation: Numpy; Pandas - Web scraping: Beautiful Soup; Requests; Selenium; Urllib3 - Visualization: Bokeh; Matplotlib; Plotly; Seaborn - Dashboarding: Dash; Streamlit - Statistical analysis: SciPy; Statsmodels - File management: Camelot; OS; Pathlib; Pillow; Shutil; Tabula - Machine learning: NLTK; OpenCv; PyTesseract; Scikit-learn; SpaCy 1 © 2006 The Marquee Group Inc. Python Packages Cheat Sheet List of Popular Packages Package Conda Code to Install Category Additional Information Name beautifulsoup4 conda install -c anaconda beautifulsoup4 Web Scraping Links: https://www.crummy.com/software/BeautifulSoup/ Should
    [Show full text]
  • Machine Learning Algorithms Using R's Caret Package
    Using Interactive Jupyter Notebooks with R Earl F Glynn Kansas City R Users Group 2015-12-05 http://earlglynn.github.io/kc-r-users-jupyter/ Using Interactive Jupyter Notebooks with R • What is Jupyter? • R User Interface Evolution – Command Line – RStudio – RStudio with Markdown – Jupyter Notebook • Jupyter Markdown Cells • Jupyter Code Cells • Installation of Jupyter What is Jupyter? • http://jupyter.org/ • Language-agnostic parts of IPython (“Interactive Python”) http://ipython.org/ • Provides interactive data science and scientific computing across ~40 programming languages • Julia – Python – R R User Interface Evolution • R Command Line • RStudio • RStudio with Markdown • Jupyter Notebook Comparisons using ?lm help example R Command Line ?lm ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D9 <- lm(weight ~ group) lm.D90 <- lm(weight ~ group - 1) # omitting intercept anova(lm.D9) summary(lm.D90) opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(lm.D9, las = 1) # Residuals, Fitted, ... par(opar) Copy and paste to R console window R Command Line RStudio https://www.rstudio.com/products/RStudio/ RStudio RStudio RStudio RStudio with Markdown Markdown Basics: http://rmarkdown.rstudio.com/authoring_basics.html RStudio with Markdown Output to HTML, PDF, Word. Graphics output included.
    [Show full text]
  • A Software Package to Retrieve and Prepare ECMWF Data For
    Flex_extract v7.1.2 – A software package to retrieve and prepare ECMWF data for use in FLEXPART Anne Philipp1,2, Leopold Haimberger1, and Petra Seibert3 1Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria 2Aerosol Physics & Environmental Physics, University of Vienna, Vienna, Austria 3Institute of Meteorology, University of Natural Resources and Life Sciences, Vienna, Austria Correspondence: Anne Philipp ([email protected]) Abstract. Flex_extract is an open-source software package to efficiently retrieve and prepare meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) as input for the widely-used Lagrangian particle dispersion model FLEXPART and the related trajectory model FLEXTRA. ECMWF provides a variety of data sets which differ in a number of parameters (available fields, spatial and temporal resolution, forecast start times, level types etc.). Therefore, the 5 selection of the right data for a specific application and the settings needed to obtain them are not trivial. Consequently, the data sets which can be retrieved through flex_extract by both member-state users and public users as well as their proper- ties are explained. Flex_extract 7.1.2 is a substantially revised version with completely restructured code, mainly written in Python3, which is introduced with all its input and output files and an explanation of the four application modes. Software dependencies and the methods for calculating the native vertical velocity η_, the handling of flux data and the preparation of 10 the final FLEXPART input files are documented. Considerations for applications give guidance with respect to the selection of data sets, caveats related to the land-sea mask and orography, etc.
    [Show full text]