# SHARPlib
[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/cmake.yml)
[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/python.yml)
[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/wheels.yml)
[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/doxygen-gh-pages.yml)
[](https://anaconda.org/conda-forge/sharplib)
[](https://pypi.org/project/SHARPlib/)
[](https://anaconda.org/conda-forge/sharplib)
**Sounding and Hodograph Analysis and Research Program (SHARP)** C++ library for conducting analysis of atmospheric sounding profiles. Based on the NSHARP routines written by John Hart and Rich Thompson at the NWS Storm Prediction Center in Norman, Oklahoma.
This is a public mirror of the official version hosted on the NOAA Vlab Gitlab.
## Table of Contents
- [About SHARPlib](#About-SHARPlib)
- [Build/install C++](#building-sharplib-c++-library)
- [Build/install Python](#installing-sharplib-python-bindings)
- [Documentation](https://keltonhalbert.github.io/SHARPlib/)
## About SHARPlib
Since the 1990s, the National Weather Service (NWS) Storm Prediction Center (SPC) has actively researched, developed, and maintained various software packages and libraries in-house for the post-processing and visualization of atmospheric sounding data. Initially developed by John Hart, SHARP was developed to display and compute derived atmospheric indices from observed weather balloons, and vertical profiles from model forecast data. Eventually it was included in N-AWIPS/GEMPAK/AWIPS2 as NSHARP, used to process archive research data as SHARPTab, used to generate web graphics as SHARPGIF, used to post-process model and mesoanalysis gridded data, ported to Python as SHARPpy, and is used currently in SPC operations as BigSHARP.
SHARPpy sought to open source these computations and visualizations in order to facilitate reproducible open science, provide a cross-platform means of visualizing sounding data, and internally simplify the number of SHARP derivatives with unique code bases. While many of the goals were met by SHARPpy, it struggled in a few key areas:
- Pure python is not a performant solution when post-processing gridded data, particularly when dealing with parcel lifting routines.
- Maintaining an interactive data visualization application complete with live access data feeds along with a core computational library was challenging.
- The computational library component wasn't very generalizable, in large part because I wrote the code while taking an OOP class, and everything became a nail to the object hammer.
SHARPlib seeks to take the successes of SHARPpy, while having the benefit of more experience and hindsight. It is most analogous to the ```sharppy.sharptab``` import, but written in C++ for performance and wrapped for Python/Numpy using [nanobind](https://github.com/wjakob/nanobind). It is separate from any visualization software and dependencies, generalized to be more composable where appropriate, and optimised for performance.
## Building SHARPlib C++ Library
### C++ Version
Code is designed to target the **C++17** standard `-std=c++17`, and generally should not use **C++2X** features. This library is written to work on **RHEL8** systems, and with careful configuration, backwards to **RHEL7**, and should not attempt to use bleeding edge features at risk of breaking compatibility. The C++ 17 standard is not supported by the default RHEL7 compilers, but a version of GCC that supports the C++17 standard can be acquired through the **__devtoolset__** RHEL channel.
### BEFORE YOU BUILD
SHARPlib has some light-weight dependencies for testing, benchmarking, documentation building, string formatting, and creating python bindings. These can easily be downloaded for building by running the following command to download the dependencies from GitHub over SSH:
```bash
git submodule update --init --recursive
```
### Building SHARPlib (C++)
To build SHARPlib, execute the following commands in the project root directory:
```bash
cmake -B build .
cmake --build build -j N_BUILD_PROCESSES
```
This will build the static library in the ```{$POJECT_ROOT}/build``` directory in parallel with N_BUILD_PROCESSES. This isn't terribly useful by itself, so to install the static library, you can execute:
```bash
cmake -B build . --install-prefix=/path/to/where/you/want/SHARPlib
cmake --build build -j N_BUILD_PROCESSES
cmake --install build
```
If you wish to create a debug build, simply pass the following arguments to CMake:
```bash
cmake --build build -j N_BUILD_PROCESSES --config Debug
```
### Testing SHARPlib (C++)
For unit tests, we make use of the [doctest singe header source library](https://github.com/doctest/doctest) found in the `tests` directory. In order to build and run the tests, execute the following commands from the project root directory:
```bash
cmake -B build .
cmake --build build -j N_BUILD_PROCESSES --target SHARPlib_tests
ctest --test-dir build
```
## Installing SHARPlib Python Bindings
### Installing SHARPlib (Python)
SHARPlib is available via pip/PyPI, and can be installed for Linux, MacOS, and Windows using:
```bash
pip install SHARPlib
```
SHARPlib is also available via conda-forge, although it's current release candidate status means it is not yet a part of the main channels. Still, it can be installed using the following command:
```bash
conda install conda-forge/label/sharplib_rc::sharplib
```
### Building SHARPlib (Python)
SHARPlib C++ code can be called from Python using [nanobind](https://github.com/wjakob/nanobind) to handle the wrapping.
Building SHARPlib with its Python bindings is quite easy-- you can simply clone this repository and install it via pip from the current directory:
```bash
# Ensure the submodule dependencies are cloned
git submodule update --init --recursive
pip install .
```
If you desire to manually build the SHARPlib library + python bindings, you may execute:
```bash
cmake -B build . -DBUILD_PYBIND=ON
cmake --build build -j N_BUILD_PROCESSES
```
***NOTE: You will need a C++ compiler installed (i.e. cl, gcc, clang)***
### Testing SHARPlib (Python)
Python tests are facilitated using ```pytest```. Run:
```bash
pytest
```
in the root of this git repository to execute tests.
## Style Guide
Though there are likely to be instances where it will need to be deviated from, this code generally attempts to abide by the [ISO C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines). While line width requirements are generally archaic, where possible we attempt to __keep line lengths to a maximum of 80 characters__ in order to preserve split screen code editing.
Another note to the Style Guide is that, where possible/appropriate, full or verbose variable names are preferred to abbreviated ones when working with function parameters. For example, `temperature` or `pressure` is preferable to `temp` or `pres` when defining function arguments, so that it is abundantly clear to the code reader what is being passed through. This is especially the case with temperature, as `temp` is commonly used to refer to temporary variables, leading to confusion.
## Building the Documentation
To build the HTML documentation pages, the following can be executed in the terminal from the project root:
```bash
doxygen Doxyfile
```
This will generate the HTML pages using the docstring in the header files. Obviously, it requires that Doxygen be installed, [which can be found here.](https://doxygen.nl/)
Documentation is automatically built on push, [and can be found here](https://keltonhalbert.github.io/SHARPlib/)
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"description": "# SHARPlib\n[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/cmake.yml)\n[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/python.yml)\n[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/wheels.yml)\n[](https://github.com/keltonhalbert/SHARPlib/actions/workflows/doxygen-gh-pages.yml)\n[](https://anaconda.org/conda-forge/sharplib)\n[](https://pypi.org/project/SHARPlib/)\n\n[](https://anaconda.org/conda-forge/sharplib)\n\n\n**Sounding and Hodograph Analysis and Research Program (SHARP)** C++ library for conducting analysis of atmospheric sounding profiles. Based on the NSHARP routines written by John Hart and Rich Thompson at the NWS Storm Prediction Center in Norman, Oklahoma. \n\nThis is a public mirror of the official version hosted on the NOAA Vlab Gitlab.\n\n## Table of Contents\n- [About SHARPlib](#About-SHARPlib)\n- [Build/install C++](#building-sharplib-c++-library)\n- [Build/install Python](#installing-sharplib-python-bindings)\n- [Documentation](https://keltonhalbert.github.io/SHARPlib/)\n\n## About SHARPlib\nSince the 1990s, the National Weather Service (NWS) Storm Prediction Center (SPC) has actively researched, developed, and maintained various software packages and libraries in-house for the post-processing and visualization of atmospheric sounding data. Initially developed by John Hart, SHARP was developed to display and compute derived atmospheric indices from observed weather balloons, and vertical profiles from model forecast data. Eventually it was included in N-AWIPS/GEMPAK/AWIPS2 as NSHARP, used to process archive research data as SHARPTab, used to generate web graphics as SHARPGIF, used to post-process model and mesoanalysis gridded data, ported to Python as SHARPpy, and is used currently in SPC operations as BigSHARP.\n\nSHARPpy sought to open source these computations and visualizations in order to facilitate reproducible open science, provide a cross-platform means of visualizing sounding data, and internally simplify the number of SHARP derivatives with unique code bases. While many of the goals were met by SHARPpy, it struggled in a few key areas:\n\n - Pure python is not a performant solution when post-processing gridded data, particularly when dealing with parcel lifting routines.\n - Maintaining an interactive data visualization application complete with live access data feeds along with a core computational library was challenging.\n - The computational library component wasn't very generalizable, in large part because I wrote the code while taking an OOP class, and everything became a nail to the object hammer. \n\nSHARPlib seeks to take the successes of SHARPpy, while having the benefit of more experience and hindsight. It is most analogous to the ```sharppy.sharptab``` import, but written in C++ for performance and wrapped for Python/Numpy using [nanobind](https://github.com/wjakob/nanobind). It is separate from any visualization software and dependencies, generalized to be more composable where appropriate, and optimised for performance. \n\n## Building SHARPlib C++ Library\n### C++ Version\nCode is designed to target the **C++17** standard `-std=c++17`, and generally should not use **C++2X** features. This library is written to work on **RHEL8** systems, and with careful configuration, backwards to **RHEL7**, and should not attempt to use bleeding edge features at risk of breaking compatibility. The C++ 17 standard is not supported by the default RHEL7 compilers, but a version of GCC that supports the C++17 standard can be acquired through the **__devtoolset__** RHEL channel. \n\n### BEFORE YOU BUILD\nSHARPlib has some light-weight dependencies for testing, benchmarking, documentation building, string formatting, and creating python bindings. These can easily be downloaded for building by running the following command to download the dependencies from GitHub over SSH:\n```bash\ngit submodule update --init --recursive \n```\n\n### Building SHARPlib (C++)\nTo build SHARPlib, execute the following commands in the project root directory:\n```bash\ncmake -B build .\ncmake --build build -j N_BUILD_PROCESSES\n```\n\nThis will build the static library in the ```{$POJECT_ROOT}/build``` directory in parallel with N_BUILD_PROCESSES. This isn't terribly useful by itself, so to install the static library, you can execute: \n```bash\ncmake -B build . --install-prefix=/path/to/where/you/want/SHARPlib\ncmake --build build -j N_BUILD_PROCESSES\ncmake --install build\n```\n\nIf you wish to create a debug build, simply pass the following arguments to CMake:\n```bash\ncmake --build build -j N_BUILD_PROCESSES --config Debug\n```\n\n### Testing SHARPlib (C++)\nFor unit tests, we make use of the [doctest singe header source library](https://github.com/doctest/doctest) found in the `tests` directory. In order to build and run the tests, execute the following commands from the project root directory:\n\n```bash\ncmake -B build . \ncmake --build build -j N_BUILD_PROCESSES --target SHARPlib_tests\nctest --test-dir build\n```\n\n\n## Installing SHARPlib Python Bindings\n### Installing SHARPlib (Python)\nSHARPlib is available via pip/PyPI, and can be installed for Linux, MacOS, and Windows using:\n\n```bash\npip install SHARPlib\n```\n\nSHARPlib is also available via conda-forge, although it's current release candidate status means it is not yet a part of the main channels. Still, it can be installed using the following command: \n```bash\nconda install conda-forge/label/sharplib_rc::sharplib \n```\n\n### Building SHARPlib (Python)\nSHARPlib C++ code can be called from Python using [nanobind](https://github.com/wjakob/nanobind) to handle the wrapping.\nBuilding SHARPlib with its Python bindings is quite easy-- you can simply clone this repository and install it via pip from the current directory: \n```bash\n# Ensure the submodule dependencies are cloned\ngit submodule update --init --recursive \npip install .\n```\n\nIf you desire to manually build the SHARPlib library + python bindings, you may execute:\n```bash\ncmake -B build . -DBUILD_PYBIND=ON\ncmake --build build -j N_BUILD_PROCESSES\n```\n\n***NOTE: You will need a C++ compiler installed (i.e. cl, gcc, clang)***\n\n### Testing SHARPlib (Python)\nPython tests are facilitated using ```pytest```. 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