diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 000000000..40a247c06 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,63 @@ +version: 2.1 +jobs: + build: + docker: + - image: cimg/base:current + user: root + steps: + - checkout + - run: + name: Setup micromamba + command: | + apt update --yes && apt-get upgrade --yes + apt install -y --no-install-recommends wget ca-certificates git + cd $HOME + curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba + + - run: + name: Setup environment + command: | + eval "$($HOME/bin/micromamba shell hook -s posix)" + micromamba create -f environment.yml + micromamba activate RAiDER + pip install coveralls + echo url: https://cds.climate.copernicus.eu/api/v2 > $HOME/.cdsapirc + echo key: $cdsak >> $HOME/.cdsapirc + + echo { > $HOME/.ecmwfapirc + echo ' "url": "https://api.ecmwf.int/v1",' >> $HOME/.ecmwfapirc + echo ' "email": "'$ecmwfu'",' >> $HOME/.ecmwfapirc + echo ' "key": "'$ecmwfk'"' >> $HOME/.ecmwfapirc + echo } >> $HOME/.ecmwfapirc + + echo url: $NCUMloc > $HOME/.ncmrlogin + echo username: $NCUMu >> $HOME/.ncmrlogin + echo password: $NCUMp >> $HOME/.ncmrlogin + + python --version + python -c "import numpy; print(numpy.__version__)" + python -c "import pyproj; print(pyproj.__version__)" + - run: + name: Install RAiDER and test the install + command: | + eval "$($HOME/bin/micromamba shell hook -s posix)" + micromamba activate RAiDER + python -m pip install . + python -c "import RAiDER; from RAiDER.delay import main" + python -c "import RAiDER; from RAiDER.interpolator import interp_along_axis" + - run: + name: Run unit tests + shell: /bin/bash -l + command: | + eval "$($HOME/bin/micromamba shell hook -s posix)" + micromamba activate RAiDER + COV_OPTIONS=`python -c "import importlib;print(*(' --cov='+p for p in importlib.util.find_spec('RAiDER').submodule_search_locations))"` + pytest -m "not long" test/ $COV_OPTIONS --cov-report= + - run: + name: Report coverage + command: | + eval "$($HOME/bin/micromamba shell hook -s posix)" + micromamba activate RAiDER + python .circleci/fix_coverage_paths.py .coverage $(pwd)/tools/RAiDER/ + coverage report -mi + coveralls diff --git a/.circleci/fix_coverage_paths.py b/.circleci/fix_coverage_paths.py new file mode 100644 index 000000000..779d8bcad --- /dev/null +++ b/.circleci/fix_coverage_paths.py @@ -0,0 +1,23 @@ +import re +import sqlite3 +import sys + + +def main(file, replacement): + conn = sqlite3.connect(file) + cur = conn.cursor() + cur.execute("select path, id from file") + + patched = [ + (re.sub(r"/.*/RAiDER/", replacement, r[0]), r[1]) + for r in cur + ] + + cur.executemany("update file set path=? where id=?", patched) + + conn.commit() + conn.close() + + +if __name__ == '__main__': + main(*sys.argv[1:]) diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 000000000..b13072835 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,30 @@ +--- +name: Bug report +about: Create a report to help us improve +title: "[BUG]" +labels: bug +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Download +2. Command used +3. Error Output + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - RAiDER git tag: [e.g. git show --summary] + - OS + +**Additional context** +Add any other context about the problem her diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 000000000..283bd9f15 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,7 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + schedule: + # Check for updates to GitHub Actions every weekday + interval: "weekly" diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 000000000..abc8664fb --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,39 @@ + + +## Description + + +## Motivation and Context + + + +## How Has This Been Tested? + + + + + + + + + + + + + +## Screenshots (if appropriate): + +## Type of change + +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) + +## Checklist: + + +- [ ] I have added an explanation of what your changes do and why you'd like us to include them. +- [ ] I have written new tests for your core changes, as applicable. +- [ ] I have successfully ran tests with your changes locally. +- [ ] My change requires a change to the documentation. +- [ ] I have updated the documentation accordingly. diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 000000000..83f7a01e4 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,29 @@ +name: Docker Build + +on: + push: + branches: + - main + - dev + pull_request: + branches: + - main + - dev + +jobs: + call-version-info-workflow: + uses: ASFHyP3/actions/.github/workflows/reusable-version-info.yml@v0.6.0 + with: + conda_env_name: RAiDER + python_version: '3.10' + + call-docker-ghcr-workflow: + needs: call-version-info-workflow + uses: ASFHyP3/actions/.github/workflows/reusable-docker-ghcr.yml@v0.6.0 + with: + version_tag: ${{ needs.call-version-info-workflow.outputs.version_tag }} + release_branch: main + develop_branch: dev + user: ${{ github.actor }} + secrets: + USER_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml new file mode 100644 index 000000000..e6df68357 --- /dev/null +++ b/.github/workflows/changelog.yml @@ -0,0 +1,18 @@ +name: Changelog updated? + +on: + pull_request: + types: + - opened + - labeled + - unlabeled + - synchronize + branches: + - main + - dev + +jobs: + call-changelog-check-workflow: + uses: ASFHyP3/actions/.github/workflows/reusable-changelog-check.yml@v0.6.0 + secrets: + USER_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/labeled-pr.yml b/.github/workflows/labeled-pr.yml new file mode 100644 index 000000000..0471f4a6c --- /dev/null +++ b/.github/workflows/labeled-pr.yml @@ -0,0 +1,15 @@ +name: Is PR labeled? + +on: + pull_request: + types: + - opened + - labeled + - unlabeled + - synchronize + branches: + - main + +jobs: + call-labeled-pr-check-workflow: + uses: ASFHyP3/actions/.github/workflows/reusable-labeled-pr-check.yml@v0.6.0 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 000000000..f45f9cc36 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,16 @@ +name: Create Release + +on: + push: + tags: + - 'v*' + +jobs: + call-release-workflow: + uses: ASFHyP3/actions/.github/workflows/reusable-release.yml@v0.6.0 + with: + release_prefix: RAiDER + develop_branch: dev + sync_pr_label: bot + secrets: + USER_TOKEN: ${{ secrets.RELEASE_ACCESS_TOKEN }} diff --git a/.github/workflows/tag.yml b/.github/workflows/tag.yml new file mode 100644 index 000000000..9b097f8cf --- /dev/null +++ b/.github/workflows/tag.yml @@ -0,0 +1,15 @@ +name: Tag version + +on: + push: + branches: + - main + +jobs: + call-bump-version-workflow: + uses: ASFHyP3/actions/.github/workflows/reusable-bump-version.yml@v0.6.0 + with: + user: dbekaert + email: bekaertdavid@gmail.com + secrets: + USER_TOKEN: ${{ secrets.RELEASE_ACCESS_TOKEN }} diff --git a/.gitignore b/.gitignore index cfa3096ce..f904ce623 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,26 @@ -*.swp __pycache__ +.vscode +*.pyc +*~ +*.swp +*.DS_Store +.ftpignore +.ftpconfig +.ipynb_checkpoints +build +*.so +*.cmake +CMake* +Makefile +dummy* +weather_files +test/data *.log -test/ERA* -*.dat *.pdf -*.pyc +*.cpp +test/*/geom/*.dem +test/*/geom/*.nc +.eggs/ +dist/ +tools/RAiDER.egg-info/ +tools/bindings/utils/makePoints.c diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 000000000..185a25b9b --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,148 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [PEP 440](https://www.python.org/dev/peps/pep-0440/) +and uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.2.0] + +RAiDER package was refactored to use a configure file (yaml) to parse parameters. In addition, ocker container images +are provided with all the necessary dependencies pre-installed. Various models were tested for consistency +(others were disabled for the time being) with propagation delays computation support for zenith, and slant through +ray-tracing using orbit file. Modules were restructured for computational performance improvement. + +This release is the initial release to the NISAR ADT for supporting the generation of the stratospheric ancillary +correction using HRES model. + +### New/Updated Features ++ Supported for models (verified) + - ECWMF: HRES, ERA5, and ERA5T access verified and tested + - NASA GSFC: GMAO access fix for PYDAP changes + - NOAA: HRRR access through AWS S3 bucket (using [Herbie](https://github.com/blaylockbk/Herbie)) and correction on + incorrect loading of the pressure. + - NCMR: not tested + - Other models are currently disabled (MERRA-2, WRF, ERA-I) ++ Two flavors for computing propagation delays + - Zenith delays + - Slant delays through ray-tracing (supported through orbit file) ++ Refactoring and computational improvements for delay computation + - Better organized for individual function calls ++ When using a pre-existing weather model file, raise a warning instead of an error when the existing weather model + doesn't cover the entire AOI. ++ Support for delay outputs at + - 2D coordinate list (x,y) for a user-defined height or at topographic height + - 2D grid at user-specified output sampling (x,y) and coordinate projection for a user-defined height or by default + at topographic height + - 3D cube at weather model grid notes or at user-specified output sampling (x,y,z) and coordinate projection ++ Docker container images are provided with all the necessary dependencies pre-installed. See: ++ CLI has changed from `raiderDelay.py` to `raider.py` with options provided through configure file (yaml format). ++ Unit testing for slant ray-tracing ++ RAiDER-Docs documentation updated for changes in RAiDER package + +### Not implemented / supported in this release ++ Custom DEMs ++ Pypi (`pip install` capability) ++ Slant delays without an orbit file (e.g. own 2D or 3D LOS files) ++ Conventional slant delays with projection from zenith (e.g. orbit, 2D or 3D LOS files) ++ GUNW product input/output ++ Complete unit test coverage ++ Detailed API documentation ++ Accessing RAiDER as a Python Library. + +### Contributors +- Jeremy Maurer +- Brett Buzzanga +- Piyush Agram - _(Descartes Labs)_ +- Joseph Kennedy +- Matthew Licari +- David Bekaert + +## [0.1.0] + +RAiDER release for initial conda channel + +### Updates and highlights + ++ Zenith and Conventional slant delays on 3D cube ++ intersect with topography to get raster delays ++ initial conda release ++ Bug fixes ++ dem-stitcher for handling dem conversion to ellipsoidal heights + +### Contributors + +David Bekaert +Jeremy Maurer +Nate Kean + +## [0.0.2] + +### RAiDER pre-release in support of NISAR troposphere working group +This release includes features in support of the NISAR tropospheric working group recommendations in June 2021. Salient +features include full capabilities for statistical analysis of GNSS ZTD and comparison with weather model ZTD, addition +of the NCMR weather model from ISRO, and unit test suite update. + +### Pre-release +- GMAO, MERRA-2, ECMWF - ERA-5, ERA-5T, HRES, HRRR, and NCMR all working models +- Fixed height levels for weather model calculations +- Update tools for statistical analysis and plotting of weather model and GNSS zenith delays +- Unit test suite additions +- Bug fixes + +### Team contributions: +David Bekaert +Yang Lei +Simran Sangha +Jeremy Maurer +Charlie Marshak +Brett Buzzanga + +## [0.0.1] + +### RAiDER pre-release in support of AGU work +This release includes the features in support of the team's 2020 AGU presentation. Salient features include model +download, GNSS ZTD download, ZTD delay calculation at GNSS locations, statistical analysis of the GNSS and model ZTD. +Slant delay and large scale processing in ongoing development. + +### Pre-release +- Concurrent download option (tested for GMAO, HRES, ERA5) +- Include download and reader support for historic GMAO (2014-2-20 to 2017-12-01), not support in OpenDap (post 2017-12-01). +- Add support for historic HRES data with 91 model levels (pre-2013-6-26) +- Tools to merge RAiDER GNSS delay files, intersection with GNSS downloaded delays, residual computation +- Ability to re-plot grid analysis on statsPlot without need to re-run analysis. +- Clean up of stats class plotting +- Bug fixes + +### Team contributions +Jeremy Maurer +Yang Lei +Simran Sangha +David Bekaert +Brett Buzzanga + + +## [0.0.0] + +### First RAiDER pre-release +Predominant to be used for model download in support of upcoming statistical analysis for NISAR tropospheric noise working group. +Delay calculation and stats class in ongoing development. + +### Pre-release +- Download support for HRRR, GMAO, HRES, ERA5, ERAT, MERRA2 +- compatible readers for HRRR, GMAO, HRES, ERA5, ERAT, MERRA2, NCUM +- Zenith delay support and draft version for ray-tracing +- support for radar-coordinates, geo-coordinates, native model, and station nodes +- Stats class with GNSS download from UNR +- Stats class for performance evaluation (variogram, correlation, std, bias) with plotting +- Model documentation and first cut of sample Jupyter notebooks + +### Team contributions +Jeremy Maurer +Raymond Hogenson +Yang Lei +Rohan Weeden +Simran Sangha +Heresh Fattahi +David Bekaert diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f3e928289..b29736270 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,6 +1,6 @@ # Contributing Guidelines # -This document is inspired by similar instructions from PySAR, ISCE, gdal and jupyterhub. +This document is inspired by similar instructions from MintPY, ISCE, gdal and jupyterhub. These are several ways to contribute to the RAiDER framework: * Submitting bug reports and feature requests in RAiDER @@ -8,7 +8,7 @@ These are several ways to contribute to the RAiDER framework: * Fixing typos, code and improving documentation * Writing code for everyone to use -If you get stuck at any point you can create an [issue on GitHub](https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER/issues). +If you get stuck at any point you can create an [issue on GitHub](https://github.com/dbekaert/RAiDER/issues). For more information on contributing to open source projects, [GitHub's own guide](https://guides.github.com/activities/contributing-to-open-source/) is a great starting point if you are new to version control. @@ -18,20 +18,20 @@ is a great starting point if you are new to version control. ### Setting up the development environment ### -Fork InSAR-tools/RAiDER from GitHub UI, and then +Fork RAiDER from GitHub UI, and then ``` -git clone https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER.git +git clone https://github.com/dbekaert/RAiDER.git cd RAiDER git remote add my_user_name https://github.com/my_user_name/RAiDER.git ``` ### Setting up the documentation environment ### -Fork InSAR-tools/RAiDER-docs from GitHub UI, and then +Fork RAiDER-docs from GitHub UI, and then ``` -git clone https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER-docs.git +git clone https://github.com/dbekaert/RAiDER-docs.git cd RAiDER-docs git remote add my_user_name https://github.com/my_user_name/RAiDER-docs.git ``` @@ -99,4 +99,4 @@ git push -f origin my_new_feature_branch Once a pull request is issued it will be reviewed by multiple members before it will be approved and integrated into the main. ### Things you should NOT do -(For anyone with push rights to RAiDER or RAiDER-docs) Never modify a commit or the history of anything that has been committed to https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER and https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER-docs. +(For anyone with push rights to RAiDER or RAiDER-docs) Never modify a commit or the history of anything that has been committed to https://github.com/dbekaert/RAiDER and https://github.com/dbekaert/RAiDER-docs. diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..f87f40996 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,48 @@ +FROM condaforge/mambaforge:latest + +# For opencontainers label definitions, see: +# https://github.com/opencontainers/image-spec/blob/master/annotations.md +LABEL org.opencontainers.image.title="RAiDER" +LABEL org.opencontainers.image.description="Raytracing Atmospheric Delay Estimation for RADAR" +LABEL org.opencontainers.image.authors="David Bekaert, Jeremy Maurer , Simran Sangha , Piyush Agram " +LABEL org.opencontainers.image.licenses="Apache-2.0" +LABEL org.opencontainers.image.url="https://github.com/dbekaert/RAiDER" +LABEL org.opencontainers.image.source="https://github.com/dbekaert/RAiDER" +LABEL org.opencontainers.image.documentation="https://github.com/dbekaert/RAiDER" + +# Dynamic lables to define at build time via `docker build --label` +# LABEL org.opencontainers.image.created="" +# LABEL org.opencontainers.image.version="" +# LABEL org.opencontainers.image.revision="" + +ARG DEBIAN_FRONTEND=noninteractive +ENV PYTHONDONTWRITEBYTECODE=true + +RUN apt-get update && apt-get install -y --no-install-recommends git unzip vim && \ + apt-get clean && rm -rf /var/lib/apt/lists/* + +# Create non-root user/group with default inputs +ARG CONDA_UID=1000 +ARG CONDA_GID=1000 + +RUN groupadd -g "${CONDA_GID}" --system raider && \ + useradd -l -u "${CONDA_UID}" -g "${CONDA_GID}" --system -d /home/raider -m -s /bin/bash raider && \ + chown -R raider:raider /opt + +USER ${CONDA_UID} +WORKDIR /home/raider +SHELL ["/bin/bash", "-l", "-c"] + +COPY --chown=${CONDA_UID}:${CONDA_GID} . /RAiDER/ + +RUN mamba env create -f /RAiDER/environment.yml && \ + conda clean -afy + +# Ensure that environment is activated on startup +RUN echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.profile && \ + echo "conda activate RAiDER" >> ~/.profile + +RUN python -m pip install --no-cache-dir /RAiDER/ + +ENTRYPOINT ["/usr/bin/bash"] +CMD ["-l"] diff --git a/Installing_from_source.md b/Installing_from_source.md new file mode 100644 index 000000000..92affab93 --- /dev/null +++ b/Installing_from_source.md @@ -0,0 +1,34 @@ +## Common Installation Issues +1. This package uses GDAL and g++, both of which can be tricky to set up correctly. +GDAL in particular will often break after installing a new program +If you receive error messages such as the following: + +``` +ImportError: ~/anaconda3/envs/RAiDER/lib/python3.7/site-packages/matplotlib/../../../libstdc++.so.6: version `CXXABI_1.3.9' not found (required by ~/anaconda3/envs/RAiDER/lib/python3.7/site-packages/matplotlib/ft2font.cpython-37m-x86_64-linux-gnu.so) +ImportError: libtiledb.so.1.6.0: cannot open shared object file: No such file or directory +***cmake: ~/anaconda3/envs/RAiDER/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by cmake)*** +``` + +try running the following commands within your RAiDER conda environment: +``` +conda update --force-reinstall libstdcxx-ng +conda update --force-reinstall gdal libgdal +``` + +2. This package requires both C++ and C headers, and the system headers are used for some C libraries. If running on a Mac computer, and "python setup.py build" results in a message stating that some system library header file is missing, try the following steps, and accept the various licenses and step through the installation process. Try re-running the build step after each update: + + ``` + xcode-select --install + open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg + ``` + +## Testing your installation +To test the installation was successfull you can run the following tests: +``` +py.test test/ +raiderDelay.py -h +``` + +### To enable automatic CircleCI Tests from a pull requests + +You will need to make sure that CircleCI is an authorized OAuth application from Github. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + + Copyright (c) 2019-2020, California Institute of Technology ("Caltech"). + U.S. Government sponsorship acknowledged. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/LICENSE2.md b/LICENSE2.md new file mode 100644 index 000000000..f35cd5fef --- /dev/null +++ b/LICENSE2.md @@ -0,0 +1,10 @@ +Copyright (c) 2019-2020, California Institute of Technology ("Caltech"). All rights reserved. + +U.S. Government sponsorship acknowledged. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: +- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. +- Neither the name of Caltech nor its operating division, the Jet Propulsion Laboratory, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md old mode 100644 new mode 100755 index dbf914cf7..a401a581c --- a/README.md +++ b/README.md @@ -2,106 +2,101 @@ Raytracing Atmospheric Delay Estimation for RADAR [![Language](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://www.python.org/) +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/dbekaert/RAiDER/blob/dev/LICENSE) +[![CircleCI](https://circleci.com/gh/dbekaert/RAiDER.svg?style=svg)](https://circleci.com/gh/dbekaert/RAiDER) +[![Coverage Status](https://coveralls.io/repos/github/dbekaert/RAiDER/badge.svg?branch=dev)](https://coveralls.io/github/dbekaert/RAiDER?branch=dev) -RAiDER-tools is a package in Python which contains tools to calculate tropospheric corrections for Radar using a raytracing implementation. Its development was funded under the NASA Sea-level Change Team (NSLCT) program and the Earth Surface and Interior (ESI) program. +RAiDER-tools is a package in Python which contains tools to calculate tropospheric corrections for Radar using a raytracing implementation. Its development was funded under the NASA Sea-level Change Team (NSLCT) program, the Earth Surface and Interior (ESI) program, and the NISAR Science Team (NISAR-ST) (NTR-51433). U.S. Government sponsorship acknowledged. + +Copyright (c) 2019-2022, California Institute of Technology ("Caltech"). All rights reserved. THIS IS RESEARCH CODE PROVIDED TO YOU "AS IS" WITH NO WARRANTIES OF CORRECTNESS. USE AT YOUR OWN RISK. ## Contents +- [1. Getting Started](#1-getting-started) + - [Installing With Conda](#installing-with-conda) + - [Using the Docker Image](#using-the-docker-image) + - [Installing from Source](#installing-from-source) +- [2. Setup of third party weather model access](#2-setup-of-third-party-weather-model-access) +- [3. Running RAiDER and Documentation](#3-running-raider-and-documentation) +- [4. Citing](#4-citation) +- [5. Contributors](#5-contributors) +------ +## 1. Getting Started -1. [Software Dependencies](#software-dependencies) -2. [Installation](#installation) -3. [Running RAiDER](#running-raider) -- [dummy](#dummy) -4. [Documentation](#documentation) -5. [Citation](#citation) -6. [Contributors and community contributions](#contributors) - +RAiDER has been tested on the following systems: +- Ubuntu v.16 and up +- Mac OS v.10 and up ------- +RAiDER does **not** currently run on arm64 processors on Mac. We will update this note once the build becomes available. -## Software Dependencies -Below we list the dependencies for RAiDER +### Installing With Conda +RAiDER is available on [conda-forge](https://anaconda.org/conda-forge/raider). __[Conda](https://docs.conda.io/en/latest/index.html)__ is a cross-platform way to use Python that allows you to setup and use "virtual environments." These can help to keep dependencies for different sets of code separate. We recommend using [Miniforge](https://github.com/conda-forge/miniforge), a conda environment manager that uses conda-forge as its default code repo. Alternatively,see __[here](https://docs.anaconda.com/anaconda/install/)__ for help installing Anaconda and __[here](https://docs.conda.io/en/latest/miniconda.html)__ for installing Miniconda. -### Packages: +Installing RAiDER: ``` -* Python >= 3.5 (3.6 preferred) -* [PROJ 4](https://github.com/OSGeo/proj) github) >= 6.0 -* [GDAL](https://www.gdal.org/) and its Python bindings >= 3.0 +conda env create --name RAiDER -c conda-forge raider +conda activate RAiDER ``` -### Python dependencies +### Using the Docker image +RAiDER provides a [docker container image](https://docs.docker.com/get-started/) with all the necessary dependencies pre-installed. To get the latest released version: ``` -* [SciPy](https://www.scipy.org/) -* [netcdf4](http://unidata.github.io/netcdf4-python/netCDF4/index.html) -* [requests](https://2.python-requests.org/en/master/) +docker pull ghcr.io/dbekaert/raider:latest ``` - -### Python Jupyter dependencies +a specific release version (>=v0.2.0 only): ``` -* py3X-jupyter -* py3X-jupyter_client -* py3X-jupyter_contrib_nbextensions -* py3X-jupyter_nbextensions_configurator -* py3X-hide_code -* py3X-RISE +docker pull ghcr.io/dbekaert/raider:0.2.0 ``` - - ------- -## Installation -RAiDER package can be easily installed and used after the dependencies are installed and activated. Easiest way of installing RAiDER is to use the setup.py script as outlined below. For the required dependencies, we strongly recommend using [Anaconda](https://www.anaconda.com/distribution/) package manager for easy installation of dependencies in the python environment. - -Below we outline the different steps for setting up the RAiDER while leveraging Anaconda for installation of the requirements. Running the commands below will clone the RAiDER package to your local directory, create a conda environment with the name 'RAiDER', install dependencies to this environment and activate it. - +or the current development version: ``` -git clone https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER.git -conda env create -f ./RAiDER/environment.yml -conda activate RAiDER +docker pull ghcr.io/dbekaert/raider:test ``` -We have included a setup.py script which allows for easy compilation and installation of dependencies (c-code), as well as setting up the RAiDER package itself (python and command line tools). +To run the container and jump into a bash shell inside: ``` -python setup.py build -python setup.py install +docker run -it --rm ghcr.io/dbekaert/raider:latest ``` - -If not using the setup.py, users should ensure RAiDER and dependencies are included on their PATH and PYTHONPATH. For c-shell this can be done as follows (replace "RAiDERREPO" to the location where you have cloned the RAiDER repository): +To mount your current directory inside the container so that files will be written back to your local machine: ``` -setenv PYTHONPATH $PYTHONPATH:/RAiDERREPO/tools/RAiDER -set PATH $PATH:'/RAiDERREPO/tools/bin' +docker run -it -v ${PWD}:/home/raider/work --rm ghcr.io/dbekaert/raider:latest +cd work ``` +For more docker run options, see: . - -### Other installation options -The following pages might be of use to those trying to build third party packages from source. - +### Installing from source +You can also install RAiDER directly from source. Doing so is recommended for those who would like to [contribute to the source code](https://github.com/dbekaert/RAiDER/blob/dev/CONTRIBUTING.md), which we heartily encourage! For more details on installing from source see [here](https://github.com/dbekaert/RAiDER/blob/dev/Installing_from_source.md). +``` +git clone https://github.com/dbekaert/RAiDER.git +cd RAiDER +conda create -f environment.yml +conda activate RAiDER +python -m pip install -e . +``` ------ -## Running RAiDER - -The RAiDER scripts are highly modulized in Python and therefore allows for building your own processing workflow. Below, we show how to call some of the functionality. For detailed documentation, examples, and Jupyter notebooks see the [RAiDER-docs repository](https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER-docs). We welcome contributions of other examples on how to leverage the RAiDER (see [here](https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER/blob/master/CONTRIBUTING.md) for instructions). - -### dummy -EXAMPLE TODO - +## 2. Setup of third party weather model access +RAiDER has the ability to download weather models from third-parties; some of which require license agreements. See [here](WeatherModels.md) for details. ------ -## Documentation - -See the [RAiDER-docs repository](https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER-docs) for all documentation and Jupyter Notebook Tutorials. +## 3. Running RAiDER and Documentation +For detailed documentation, examples, and Jupyter notebooks see the [RAiDER-docs repository](https://github.com/dbekaert/RAiDER-docs). +We welcome contributions of other examples on how to leverage the RAiDER (see [here](https://github.com/dbekaert/RAiDER/blob/master/CONTRIBUTING.md) for instructions). +``` raiderDelay.py -h ``` provides a help menu and list of example commands to get started. +The RAiDER scripts are highly modulized in Python and allows for building your own processing workflow. ------ -## Citation +## 4. Citation TODO ------ -## Contributors +## 5. Contributors * David Bekaert * Jeremy Maurer * Raymond Hogenson -* [_other community members_](https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER/graphs/contributors) - -We welcome community contributions. For instructions see [here](https://github-fn.jpl.nasa.gov/InSAR-tools/RAiDER/blob/master/CONTRIBUTING.md). - - +* Piyush Agram _(Descartes Labs)_ +* Yang Lei +* Rohan Weeden +* Simran Sangha +* [_other community members_](https://github.com/dbekaert/RAiDER/graphs/contributors) +We welcome community contributions! For instructions see [here](https://github.com/dbekaert/RAiDER/blob/dev/CONTRIBUTING.md). diff --git a/WeatherModels.md b/WeatherModels.md new file mode 100644 index 000000000..eae7cbd5b --- /dev/null +++ b/WeatherModels.md @@ -0,0 +1,101 @@ +# Accessing weather model data +RAiDER has built-in support for a number of different weather models. RAiDER provides all of the interfacing to data servers required to access data for the different weather models, although some weather models require a license agreement and accounts to be set-up. Instructions for accessing data, including license-limited data, are provided below. It is the user's responsibility to accept license agreements for whatever model is desired. + +In addition, RAiDER provides functionality for adding additional weather models. See the [RAiDER-docs repository](https://github.com/dbekaert/RAiDER-docs) page on how to do this. We would love to expand the suite of supported models, and welcome any contributions. Please see the contributing guidelines or reach out through an issue ticket for help. + +------ + +## Contents + +1. [NOAA weather models (HRRR)](#noaa-weather-models-(hrrr)) +2. [ECMWF weather models (ERA5, ERA5T, ERAI, HRES)](#ecmwf-weather-models-(era5,-era5t,-erai,-hres)) +3. [NASA weather models (GMAO, MERRA2)](#nasa-weather-models-(gmao,-merra2)) + +#Potential download failure:# +ERA-5/ERA-I products require access to the ESA Copernicus servers. GMAO and MERRA-2 products require access to the NASA Earthdata servers. If you are unable to download products, ensure that you have registered and have downloaded the public API key, and accepted/added the license/application for type of product you wish to download as detailed below. + +------ + +## 1. NOAA weather models (HRRR) +High-resolution rapid refresh (HRRR) weather model data products are generated by __[NOAA](https://rapidrefresh.noaa.gov/hrrr/)__ for the coninental US (CONUS) but not archived beyond three days. However a public __[archive](home.chpc.utah.edu/~u0553130/Brian_Blaylock/hrrr_FAQ.html)__ is available at the University of Utah. This archive does not require a license agreement. This model has the highest spatial resolution available in RAiDER, with a horizontal grid spacing of about 3 km, and is provided in a Lambert conformal conic projection. + +[return to table of content](#contents) + + +------ + +## 2. ECMWF weather models (ERA5, ERA5T, ERAI, HRES) +The Copernicus Climate Data Store (CDS) provides access to the European Centre for Medium-Range Weather Forecasts (__[ECMWF](https://www.ecmwf.int/)__) provides a number of different weather models, including ERA5 and ERA5T reanalysis models. + +The ECMWF provides access to both reanalysis and real-time prediction models. You can read more information about their reanalysis models __[here](https://www.ecmwf.int/en/research/climate-reanalysis)__ and real-time model __[here](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products)__. ECMWF models are global, with horizontal resolution of about 30 km for ERA-I, ERA-5, and ERA-5T, and 6 km for Hi-RES. All of these models come in a global projection (EPSG 4326, WGS-84). + +### Accessing ERA5 and ERA5T weather reanalysis products from Copernicus CDS +1. Create an account on the Copernicus servers __[here](https://cds.climate.copernicus.eu/user)__ +2. Confirm your email, etc. +3. Install the public API key and client as instructed __[here](https://cds.climate.copernicus.eu/api-how-to)__: + + a. Copy the URL and API key from the webpage into a file in your home directory name ~/.cdsapirc + url: https://cds.climate.copernicus.eu/api/v2 + key: your_key_here + __**Note**: the key represents the API key obtained upon the registration of CDS API, and should be replaced with the user's own information.__ + + b. Install the CDS API using pip: + pip install cdsapi + ___**Note**: this step has been included in the conda install of RAiDER, thus can be omitted if one uses the recommended conda install of RAiDER___ +4. You must accept the [license](https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products) for each product you wish to download. + + +### Accessing ERAI, HRES + +ECMWF requires a license agreement to be able to access, download, and use their products. Instructions for completing this process is below. + +1. Create an account on the ECMWF servers __[here](https://accounts.ecmwf.int/auth/realms/ecmwf/protocol/openid-connect/auth?response_type=code&scope=openid%20email&client_id=apache-www&state=sBYlpcTRPhat8d6uuM9swLCxuP8&redirect_uri=https%3A%2F%2Fwww.ecmwf.int%2Foidc.cgi&nonce=RyEzBUy4m6oo_HxRQEmJxbc5jrKY4KFZd1Usgi8cpnM)__. The ERA-I model is open-access, while HRES requires a special liscence agreement. +2. Confirm your email, etc. +3. Install the public API key and client as instructed __[here](https://confluence.ecmwf.int/display/WEBAPI/Access+ECMWF+Public+Datasets#AccessECMWFPublicDatasets-key)__: + + a. Copy the URL and API key from the webpage into a file in your home directory name ~/.ecmwfapirc + { + "url" : "https://api.ecmwf.int/v1", + "key" : your key here, + "email" : your email here + } + + __**Note**: the email that is used to register the user account, and the key represents the API key obtained upon the registration of ECMWF API, and should be replaced with the user's own information.__ + + b. Install the ECMWF API using pip: + ```pip install ecmwf-api-client``` + ___**Note**: this step has been included in the conda install of RAiDER, thus can be omitted if one uses the recommended conda install of RAiDER___ + +[return to table of content](#contents) + +### 3. NASA weather models (GMAO, MERRA2) + +1. The Global Modeling and Assimilation Office (__[GMAO](https://www.nccs.nasa.gov/services/data-collections/coupled-products/geos5-forecast#:~:text=The%20Global%20Modeling%20and%20Assimilation,near%2Dreal%2Dtime%20production.)__) at NASA generates reanalysis weather models. GMAO products can also be accessed without a license agreement through the pyDAP interface implemented in RAiDER. GMAO has a horizontal grid spacing of approximately 33 km, and its projection is EPSG code 4326 (WGS-84). + +2. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (__[MERRA-2](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/#:~:text=MERRA%2D2%20is%20the%20first,(say)%20Greenland%20and%20Antarctica.)__) provides data beginning in 1980. MERRA-2 is also produced by NASA and has a spatial resolution of about 50 km and a global projection (EPSG 4326, WGS-84). + +Reference: __[The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)](https://journals.ametsoc.org/view/journals/clim/30/14/jcli-d-16-0758.1.xml)__, Ronald Gelaro, et al., 2017, J. Clim., doi: 10.1175/JCLI-D-16-0758.1 + +### Accessing NASA weather model data +1. Create an account on the NASA's Earthdata website [here](https://urs.earthdata.nasa.gov) +2. Confirm your email, etc. +3. Copy the login username and password to a file in your home directory name ~/.netrc + + machine urs.earthdata.nasa.gov + login + password + + __**Note**: the username and password represent the user's username and password.__ + +4. Add the application `NASA GESDISC DATA ARCHIVE` by clicking on the `Applications->Authorized Apps` on the menu after logging into your Earthdata profile, and then scrolling down to the application `NASA GESDISC DATA ARCHIVE` to approve it. _This seems not required for GMAO for now, but recommended to do so for all OpenDAP-based weather models._ +5. Install the OpenDAP using pip: + + pip install pydap==3.2.1 + + + ___**Note**: this step has been included in the conda install of RAiDER, thus can be omitted if one uses the recommended conda install of RAiDER___ + + ___**Note**: PyDAP v3.2.1 is required for now (thus specified in the above pip install command) because the latest v3.2.2 (as of now) has a known [bug](https://colab.research.google.com/drive/1f_ss1Oa3VzgAOd_p8sgekdnLVE5NW6s5) in accessing and slicing the GMAO data. This bug is expected to be fixed in newer versions of PyDAP.___ + +[return to table of content](#contents) + diff --git a/doc/Basic setup and testing.ipynb b/doc/Basic setup and testing.ipynb deleted file mode 100644 index ce7b2a93b..000000000 --- a/doc/Basic setup and testing.ipynb +++ /dev/null @@ -1,184 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Raytracing-based Atmospheric Delay Estimation - RADE\n", - "\n", - "## Getting set up to use the code\n", - "1. Create an account on the Copernicus servers here: https://cds.climate.copernicus.eu/user\n", - "2. Confirm your email, etc. \n", - "3. Install the public API key and client (https://cds.climate.copernicus.eu/api-how-to): \n", - " a. Copy the URL and API key from the webpage into a file in your home directory name ~/.cdsapirc \n", - " b. Install the CDS API using pip: pip install cdsapi. \n", - "4. You must accept the license for each product you wish to download: https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products\n", - "5. See the test_cdsapi.py script for details of the API. You can test that you can connect to the servers by running the test suite (described below)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup and source code installation" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/u/kriek1/maurer/Documents/test\n", - "build git_raytracing\n" - ] - } - ], - "source": [ - "baseDir = '~/Documents/test/'\n", - "%cd $baseDir\n", - "!mkdir -p build\n", - "!ls" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "build git_raytracing\r\n" - ] - } - ], - "source": [ - "# Uncomment the following line the first time you run this notebook\n", - "# !git clone ~/software/pythonmodules/raytracing/git_raytracing/\n", - "!ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Anaconda Installation\n", - "If you need anaconda installed, see [here](https://docs.anaconda.com/anaconda/install/)\n", - "\n", - "## Anaconda environment setup\n", - "Run the following from inside your base directory (baseDir):\n", - "\n", - "conda env create -f git_raytracing/doc/environment.yml\n", - "\n", - "source activate raytracing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Installing Geometry Module:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Environment Setup\n", - "### setup the following variables and paths, for example in a module file:\n", - "```\n", - "setenv SRC_DIR ~/software/pythonmodules/raytracing/git_raytracing\n", - "setenv GEOMETRY_DIR $SRC_DIR/Geometry\n", - "setenv GEOMETRY_LIB_DIR ~/software/pythonmodules/raytracing/build\n", - "\n", - "prepend-path PATH $GEOMETRY_LIB_DIR\n", - "prepend-path PATH $SRC_DIR\n", - "\n", - "append-path PYTHONPATH $GEOMETRY_LIB_DIR\n", - "append-path LD_LIBRARY_PATH $GEOMETRY_LIB_DIR\n", - "append-path PYTHONPATH $SRC_DIR\n", - "append-path PYTHONPATH $SRC_DIR/models\n", - "append-path PYTHONPATH $SRC_DIR/utils\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Run this if you use a module file for the environment setup\n", - "!module load raytracing" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Errno 2] No such file or directory: '../build'\n", - "/u/kriek1/maurer/Documents/test\n", - "CMake Error: The source directory \"/u/kriek1/maurer/Documents/git_raytracing/Geometry/cpp\" does not exist.\n", - "Specify --help for usage, or press the help button on the CMake GUI.\n", - "make: *** No targets specified and no makefile found. Stop.\n", - "(null): can't open file '../git_raytracing/Geometry/cython/setup.py': [Errno 2] No such file or directory\n" - ] - } - ], - "source": [ - "%cd ../build\n", - "!cmake . ../git_raytracing/Geometry/cpp/\n", - "!make \n", - "!python3 ../git_raytracing/Geometry/cython/setup.py build_ext -b ~/Documents/test/build" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test out the build\n", - "import Geo2rdr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/doc/README.txt b/doc/README.txt deleted file mode 100644 index 1ef35914c..000000000 --- a/doc/README.txt +++ /dev/null @@ -1,75 +0,0 @@ -# Raytracing-based Atmospheric Delay Estimation - RADE - -## Getting set up to use the code -1. Create an account on the Copernicus servers here: https://cds.climate.copernicus.eu/user -2. Confirm your email, etc. -3. Install the public API key and client (https://cds.climate.copernicus.eu/api-how-to): - a. Copy the URL and API key from the webpage into a file in your home directory name ~/.cdsapirc - b. Install the CDS API using pip: pip install cdsapi. -4. You must accept the license for each product you wish to download: https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products -5. See the test_cdsapi.py script for details of the API. You can test that you can connect to the servers by running the test suite (described below). - -## TODO: -Create a test script which makes the plot and does a sanity check - - -############################## -######## Installation ######## -############################## - -Installing Geometry Module: - -1- create a directory somewhre that you want to build the module -mkdir build -cd build - -2- setup the following variables: - -setenv GEOMETRY_DIR /home/fattahi/tools/ray_tracing/raytracing_current/Geometry -setenv GEOMETRY_LIB_DIR /home/fattahi/tools/ray_tracing/build - -append-path PYTHONPATH /home/fattahi/tools/ray_tracing/build -append-path LD_LIBRARY_PATH /home/fattahi/tools/ray_tracing/build - - -3- inside your build directory run the following command: - -cmake . /home/fattahi/tools/ray_tracing/raytracing_current/Geometry/cpp/ -make -python3 /home/fattahi/tools/ray_tracing/raytracing_current/Geometry/cython/setup.py build_ext -b /home/fattahi/tools/ray_tracing/build - -4- Now you should be able to call the geometry module from python e.g.: - -import Geo2rdr - -5- Look at an example in the test directory - - -############################## -######### Testing ########### -############################## - -To test out the code, cd into the main directory of the repository, which should contain the test/ folder and the Geometry/ folder. Run the following: - -*Step 1: Select the test -cd test -vi/nano/emacs test_scenario.py (whichever editor you like to use) - -Then choose one of the scenarios to test run. scenario_0 uses ERA-I and is a single point, scenario_5 uses WRF and is a small region, etc. -You can then run - -cd .. -python -m unittest - -and the code will run that scenario in debug mode, which should produce a -pdf plot of the weather model at 500 and 15000 meters, along with the -weather model in a pickled object called weatherObj.dat. If the test -completes successfully there will be a set of files in the test/ folder -containing the estimated delay in meters in ENVI-format files. You can run - -gdal2isce_xml.py -i -mdx.py - -to convert the files to ISCE-readable format and view them. - - diff --git a/environment.yml b/environment.yml index abf51f3d2..8f5e10837 100644 --- a/environment.yml +++ b/environment.yml @@ -5,21 +5,47 @@ # exit environment : conda deactivate name: RAiDER channels: - - defaults - conda-forge - - anaconda + - nodefaults dependencies: - - python=3 - - cartopy + - python>=3.8 + - pip + # For running - cdsapi + - cfgrib - cmake + - cxx-compiler + - cython - dask - - gcc + - dem_stitcher>=2.3.0 + - ecmwf-api-client - h5py - - krb5 + - herbie-data + - h5netcdf + - isce3==0.8.0=*_2 + - lxml - matplotlib + - netcdf4 - numpy - - gdal>=3.0 - - pyproj - - pygrib + - pandas + - progressbar + - pydap>3.2.2 + - pyproj>=2.2.0 + - rasterio>=1.3.0 + - s3fs - scipy + - shapely + - sysroot_linux-64 + - xarray + # For packaging and testing + - autopep8 + - pytest + - pytest-cov + - pytest-timeout + - pytest-console-scripts + - setuptools_scm >=6.2 + + # For RAiDER-docs + - jupyterlab + - jupyter_contrib_nbextensions + - wand diff --git a/issues.txt b/issues.txt deleted file mode 100644 index 4da75dd7f..000000000 --- a/issues.txt +++ /dev/null @@ -1,32 +0,0 @@ -# List of updates/tests needed (not in order of priority): - -1. Add support for HDF5 writing to complete height levels calculation -2. Check/finish implementation of HRRR, MERRA-2, and WRF -3. Finish developing tests for each of the supported models: - (ERA-I - still support?, ERA-5, MERRA-2, WRF, HRRR) - check PyAPS for reading grib files -4. Finish developing a test suite for each of the input option sets: - a. lat/lon/los files --> tested - b. lat/lon/statevector files --> not tested - c. bounding box/statevector file --> not tested - d. bounding box --> tested - e. WRF files --> not tested - f. generic weather model files --> not tested - g. list of stations w/lat,lons --> not tested - -> check netcdf for time series recording - -5. Clean up codes and implement function tests -6. Documentation: - a. Access and Installation (started) - b. Test suites (some started) - c. Examples (some started) - -7. Code scaling: - a. Currently using Dask for processor scaling, only moderately efficient - b. Implement memory-efficient interpolation for handling large regions - (e.g., only need a small number of grid nodes around each point) - -8. option to display geocoded result -9. CDS API temporary URLs are "suspicious" and blocked by JPL firewalls; currently no work-around -10.dummy-check for bounding box if dem, weather file exists -11. Do we want to be able to support non-regular lat/lon grids? Cubes can handle it but not 2D rasters right now diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..170f9830e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,60 @@ +[build-system] +requires =[ + "setuptools>=61.0", + "setuptools_scm[toml]>=6.2", + "wheel", + "numpy", + "cython", + "pybind11>=2.5.0" +] +build-backend = "setuptools.build_meta" + +[project] +name = "RAiDER" +authors = [ + {name="David Bekaert", email="david.bekaert@jpl.nasa.gov"}, + {name="Jeremy Maurer", email="maurer.jeremy@gmail.com"}, + {name="Piyush Agram", email="piyush@descarteslabs.com"}, + {name="Simran Sangha", email="simran.s.sangha@jpl.nasa.gov"} +] +description = "Raytracing Atmospheric Delay Estimation for RADAR" +readme = "README.md" +requires-python = ">=3.8" +license = {text = "Apache License 2.0"} +classifiers=[ + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Natural Language :: English", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", +] +# dependencies = [] +dynamic = ["version"] + +[project.urls] +homepage = "https://github.com/dbekaert/RAiDER" +documentation = "https://github.com/dbekaert/RAiDER-docs" +repository = "https://github.com/dbekaert/RAiDER" +"Bug Tracker" = "https://github.com/dbekaert/RAiDER/issues" + +[project.scripts] +"generateGACOSVRT.py" = "RAiDER.models.generateGACOSVRT:main" +"raiderDownloadGNSS.py" = "RAiDER.gnss.downloadGNSSDelays:main" +"raider.py" = "RAiDER.cli.raider:main" +"prepFromAria.py" = "RAiDER.cli.prepFromAria:main" +"raiderStats.py" = "RAiDER.cli.statsPlot:main" + +[tool.setuptools] +zip-safe = false + +[tool.setuptools.packages.find] +where = ["tools"] + +[tool.isort] +known_first_party = "RAiDER" +multi_line_output = 5 +default_section = "THIRDPARTY" + +[tool.setuptools_scm] diff --git a/setup.py b/setup.py old mode 100644 new mode 100755 index 653ad1c3a..a8a76bc1e --- a/setup.py +++ b/setup.py @@ -1,42 +1,47 @@ -#!/usr/bin/env python3 -#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# Author: David Bekaert & Piyush Agram +# Author: David Bekaert, Jeremy Maurer, and Piyush Agram # Copyright 2019, by the California Institute of Technology. ALL RIGHTS # RESERVED. United States Government Sponsorship acknowledged. # -#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import re +from pathlib import Path -from distutils.core import setup, Extension -import os +import numpy as np +from pybind11.setup_helpers import Pybind11Extension, build_ext +from setuptools import Extension, setup +# Cythonize should be imported after setuptools. See: +# https://cython.readthedocs.io/en/latest/src/userguide/source_files_and_compilation.html#configuring-the-c-build +from Cython.Build import cythonize # isort:skip -# Path where relax third party is download to -# Note Min-Cost-Flow-Class has its own liscence agreement, users to ensure proper use of third party license agreements. -relaxPath='tools/thirdParty/Min-Cost-Flow-Class' +# Parameter defs +UTIL_DIR = Path('tools') / 'bindings' / 'utils' -# If third party package is found compile as well -if os.path.isdir(relaxPath): - print('Installing ARIA-tools with support for RelaxIV') - module1 = Extension('ARIAtools.demo', sources = ['tools/bindings/relaxIVdriver.cpp', - 'tools/bindings/unwcompmodule.cpp', - os.path.join(relaxPath,'RelaxIV/RelaxIV.C')], include_dirs=['tools/include' ,os.path.join(relaxPath,'MCFClass'),os.path.join(relaxPath,'OPTUtils'),os.path.join(relaxPath,'RelaxIV')]) +pybind_extensions = [ + Pybind11Extension( + 'RAiDER.interpolate', + [ + 'tools/bindings/interpolate/src/module.cpp', + 'tools/bindings/interpolate/src/interpolate.cpp' + ], + ), +] - setup (name = 'ARIAtools', - version = '1.0', - description = 'This is the ARIA tools package with RelaxIV support', - ext_modules = [module1], - packages=['ARIAtools'], - package_dir={'ARIAtools': 'tools/ARIAtools'}, - scripts=['tools/bin/ariaPlot.py','tools/bin/ariaDownload.py','tools/bin/ariaExtract.py','tools/bin/ariaTSsetup.py']) -else: - # Third party package RelaxIV not found - print('Installing ARIA-tools without support for RelaxIV') - - setup (name = 'ARIAtools', - version = '1.0', - description = 'This is the ARIA tools package without RelaxIV support', - packages=['ARIAtools'], - package_dir={'ARIAtools': 'tools/ARIAtools'}, - scripts=['tools/bin/ariaPlot.py','tools/bin/ariaDownload.py','tools/bin/ariaExtract.py','tools/bin/ariaTSsetup.py']) +cython_extensions = cythonize( + [ + Extension( + name="RAiDER.makePoints", + sources=[str(f) for f in UTIL_DIR.glob("*.pyx")], + include_dirs=[np.get_include()] + ), + ], + quiet=True, + compiler_directives={'language_level': 3} +) +setup( + ext_modules=cython_extensions + pybind_extensions, + cmdclass={"build_ext": build_ext}, +) diff --git a/test.py b/test.py deleted file mode 100644 index 1f2749e17..000000000 --- a/test.py +++ /dev/null @@ -1,5 +0,0 @@ -from test.time_scenario import main - -main() - - diff --git a/test/__init__.py b/test/__init__.py index e69de29bb..1bc3f6e19 100644 --- a/test/__init__.py +++ b/test/__init__.py @@ -0,0 +1,22 @@ +import os + +from contextlib import contextmanager +from pathlib import Path + +test_dir = Path(__file__).parents[0] + + +@contextmanager +def pushd(dir): + """ + Change the current working directory within a context. + """ + prevdir = os.getcwd() + os.chdir(dir) + yield + os.chdir(prevdir) + + +TEST_DIR = test_dir.absolute() +DATA_DIR = os.path.join(TEST_DIR, "data") +GEOM_DIR = os.path.join(TEST_DIR, 'test_geom') diff --git a/test/_checkArgs.py b/test/_checkArgs.py new file mode 100644 index 000000000..8be5b17e0 --- /dev/null +++ b/test/_checkArgs.py @@ -0,0 +1,442 @@ +import datetime +import os +import pytest + +import multiprocessing as mp +import numpy as np +import pandas as pd + +from test import TEST_DIR, pushd + +# import RAiDER.runProgram +from RAiDER.cli.raider import ( + parseCMD, read_template_file, create_parser, read_template_file, +) +from RAiDER.checkArgs import checkArgs, makeDelayFileNames +from RAiDER.constants import _ZREF +from RAiDER.losreader import Zenith, Conventional, Raytracing + + +SCENARIO_1 = os.path.join(TEST_DIR, "scenario_1") +SCENARIO_2 = os.path.join(TEST_DIR, "scenario_2") + + +def isWriteable(dirpath): + '''Test whether a directory is writeable''' + try: + filehandle = open(os.path.join(dirpath, 'tmp.txt'), 'w') + filehandle.close() + return True + except IOError: + return False + + +@pytest.fixture +def parsed_args(tmp_path): + parser = create_parser() + args = parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + # '--latlon', 'latfile.dat', 'lonfile.dat', + '--bbox', '-1', '1', '-1', '1', + '--model', 'ERA5', + '--outformat', 'hdf5' + ]) + return args, parser + + +def test_checkArgs_outfmt_1(parsed_args): + '''Test that passing height levels with hdf5 outformat works''' + args, p = parsed_args + args.outformat = 'hdf5' + args.heightlvs = [10, 100, 1000] + checkArgs(args, p) + assert True + + +def test_checkArgs_outfmt_2(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.heightlvs = [10, 100, 1000] + args.outformat = 'envi' + with pytest.raises(ValueError): + checkArgs(args, p) + + +def test_checkArgs_outfmt_3(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.query_area = os.path.join(SCENARIO_2, 'stations.csv') + argDict = checkArgs(args, p) + assert argDict['flag'] == 'station_file' + + +def test_checkArgs_outfmt_4(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.query_area = [os.path.join(SCENARIO_1, 'geom', 'lat.dat'), os.path.join(SCENARIO_1, 'geom', 'lat.dat')] + argDict = checkArgs(args, p) + assert argDict['flag'] == 'files' + + +def test_checkArgs_outfmt_5(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.query_area = os.path.join(SCENARIO_2, 'stations.csv') + argDict = checkArgs(args, p) + assert pd.read_csv(argDict['wetFilenames'][0]).shape == (8, 4) + + +def test_checkArgs_outloc_1(parsed_args): + '''Test that the default output and weather model directories are correct''' + args, p = parsed_args + argDict = checkArgs(args, p) + out = argDict['out'] + wmLoc = argDict['wmLoc'] + assert os.path.abspath(out) == os.getcwd() + assert os.path.abspath(wmLoc) == os.path.join(os.getcwd(), 'weather_files') + + +def test_checkArgs_outloc_2(parsed_args, tmp_path): + '''Tests that the correct output location gets assigned when provided''' + with pushd(tmp_path): + args, p = parsed_args + args.out = tmp_path + argDict = checkArgs(args, p) + out = argDict['out'] + assert out == tmp_path + + +def test_checkArgs_outloc_2b(parsed_args, tmp_path): + ''' Tests that the weather model directory gets passed through by itself''' + with pushd(tmp_path): + args, p = parsed_args + args.out = tmp_path + args.wmLoc = 'weather_dir' + argDict = checkArgs(args, p) + assert argDict['wmLoc'] == 'weather_dir' + + +def test_checkArgs_outloc_3(parsed_args): + '''Tests that the weather model directory gets created when needed''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert os.path.isdir(argDict['wmLoc']) + + +def test_checkArgs_outloc_4(parsed_args): + '''Tests for creating writeable weather model directory''' + args, p = parsed_args + argDict = checkArgs(args, p) + + assert isWriteable(argDict['wmLoc']) + + +def test_ll_bounds_1(parsed_args): + '''Tests that lats out of bounds raises error''' + args, p = parsed_args + args.query_area[0] = -91 + with pytest.raises(ValueError): + checkArgs(args, p) + + +def test_ll_bounds_2(parsed_args): + '''Tests that lats out of bounds raises error''' + args, p = parsed_args + args.query_area[1] = 91 + with pytest.raises(ValueError): + checkArgs(args, p) + + +def test_los_1(parsed_args): + '''Tests that lats out of bounds raises error''' + args, p = parsed_args + args.lineofsight = 'los.rdr' + argDict = checkArgs(args, p) + assert isinstance(argDict['los'], Conventional) + assert argDict['los']._file == 'los.rdr' + + +def test_los_2(parsed_args): + '''Tests that lats out of bounds raises error''' + args, p = parsed_args + args.statevectors = 'sv.txt' + argDict = checkArgs(args, p) + assert isinstance(argDict['los'], Conventional) + assert argDict['los']._file == 'sv.txt' + + +def test_los_3(parsed_args): + '''Tests that lats out of bounds raises error''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert isinstance(argDict['los'], Zenith) + + +def test_models_1a(parsed_args): + '''Tests that the weather model gets passed through correctly''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert argDict['weather_model']['type'].Model() == 'ERA-5' + assert argDict['weather_model']['name'] == 'era5' + + +def test_models_1b(parsed_args): + '''Tests that the weather model gets passed through correctly''' + args, p = parsed_args + args.model = 'HRRR' + argDict = checkArgs(args, p) + assert argDict['weather_model']['type'].Model() == 'HRRR' + assert argDict['weather_model']['name'] == 'hrrr' + + +def test_models_1c(parsed_args): + '''Tests that the weather model gets passed through correctly''' + args, p = parsed_args + args.model = 'NCMR' + argDict = checkArgs(args, p) + assert argDict['weather_model']['type'].Model() == 'NCMR' + assert argDict['weather_model']['name'] == 'ncmr' + + +def test_models_1d(parsed_args): + '''Tests that the weather model gets passed through correctly''' + args, p = parsed_args + args.model = 'era-5' + argDict = checkArgs(args, p) + assert argDict['weather_model']['type'].Model() == 'ERA-5' + assert argDict['weather_model']['name'] == 'era5' + + +def test_models_1e(parsed_args): + '''Tests that the weather model gets passed through correctly''' + args, p = parsed_args + args.model = 'ERA-5' + argDict = checkArgs(args, p) + assert argDict['weather_model']['type'].Model() == 'ERA-5' + assert argDict['weather_model']['name'] == 'era5' + + +def test_models_1f(parsed_args): + '''Tests that the weather model gets passed through correctly''' + args, p = parsed_args + args.model = 'Era-5' + argDict = checkArgs(args, p) + assert argDict['weather_model']['type'].Model() == 'ERA-5' + assert argDict['weather_model']['name'] == 'era5' + + +def test_models_2(parsed_args): + '''Tests that unknown weather models get rejected''' + args, p = parsed_args + args.model = 'unknown' + with pytest.raises(NotImplementedError): + checkArgs(args, p) + + +def test_models_3a(parsed_args): + '''Tests that WRF weather models requires files''' + args, p = parsed_args + args.model = 'WRF' + with pytest.raises(RuntimeError): + checkArgs(args, p) + + +def test_models_3b(parsed_args): + '''Tests that HDF5 weather models requires files''' + args, p = parsed_args + args.model = 'HDF5' + with pytest.raises(RuntimeError): + checkArgs(args, p) + + +def test_models_3c(parsed_args): + '''Tests that WRF weather models requires files''' + args, p = parsed_args + args.model = 'WRF' + args.files = ['file1.wrf', 'file2.wrf'] + # argDict = checkArgs(args, p) + # TODO + assert True + + +def test_zref_1(parsed_args): + '''tests that default zref gets generated''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert argDict['zref'] == _ZREF + + +def test_zref_2(parsed_args): + '''tests that default zref gets generated''' + ztest = 20000 + args, p = parsed_args + args.zref = ztest + argDict = checkArgs(args, p) + assert argDict['zref'] == ztest + + +def test_parallel_1(parsed_args): + '''tests that parallel options are handled correctly''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert argDict['parallel'] == 1 + + +def test_parallel_2(parsed_args): + '''tests that parallel options are handled correctly''' + args, p = parsed_args + args.parallel = 'all' + argDict = checkArgs(args, p) + assert argDict['parallel'] == mp.cpu_count() + + +def test_parallel_3(parsed_args): + '''tests that parallel options are handled correctly''' + args, p = parsed_args + args.parallel = 2 + argDict = checkArgs(args, p) + assert argDict['parallel'] == 2 + + +def test_parallel_4(parsed_args): + '''tests that parallel options are handled correctly''' + args, p = parsed_args + args.parallel = 2000 + argDict = checkArgs(args, p) + assert argDict['parallel'] == mp.cpu_count() + + +def test_verbose_1(parsed_args): + '''tests that verbose option is handled correctly''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert not argDict['verbose'] + + +def test_verbose_2(parsed_args): + '''tests that verbose option is handled correctly''' + args, p = parsed_args + args.verbose = True + argDict = checkArgs(args, p) + assert argDict['verbose'] + + +def test_download_only_1(parsed_args): + '''tests that the download-only option is handled correctly''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert not argDict['download_only'] + + +def test_download_only_2(parsed_args): + '''tests that the download-only option is handled correctly''' + args, p = parsed_args + args.download_only = True + argDict = checkArgs(args, p) + assert argDict['download_only'] + + +def test_useWeatherNodes_1(parsed_args): + '''tests that the correct flag gets passed''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert argDict['flag'] == 'bounding_box' # default arguments use a bounding box + + +def test_filenames_1(parsed_args): + '''tests that the correct filenames are generated''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert 'Delay' not in argDict['wetFilenames'][0] + assert 'wet' in argDict['wetFilenames'][0] + assert 'hydro' in argDict['hydroFilenames'][0] + assert '20200103' in argDict['wetFilenames'][0] + assert '20200103' in argDict['hydroFilenames'][0] + assert len(argDict['hydroFilenames']) == 1 + + +def test_filenames_2(parsed_args): + '''tests that the correct filenames are generated''' + args, p = parsed_args + args.query_area = os.path.join(SCENARIO_2, 'stations.csv') + argDict = checkArgs(args, p) + assert 'Delay' in argDict['wetFilenames'][0] + assert '20200103' in argDict['wetFilenames'][0] + assert len(argDict['wetFilenames']) == 1 + + +def test_makeDelayFileNames_1(): + assert makeDelayFileNames(None, None, "h5", "name", "dir") == \ + ("dir/name_wet_ztd.h5", "dir/name_hydro_ztd.h5") + + +def test_makeDelayFileNames_2(): + assert makeDelayFileNames(None, (), "h5", "name", "dir") == \ + ("dir/name_wet_std.h5", "dir/name_hydro_std.h5") + + +def test_makeDelayFileNames_3(): + assert makeDelayFileNames(datetime.datetime(2020, 1, 1, 1, 2, 3), None, "h5", "model_name", "dir") == \ + ( + "dir/model_name_wet_20200101T010203_ztd.h5", + "dir/model_name_hydro_20200101T010203_ztd.h5" + ) + + +def test_makeDelayFileNames_4(): + assert makeDelayFileNames(datetime.datetime(1900, 12, 31, 1, 2, 3), "los", "h5", "model_name", "dir") == \ + ( + "dir/model_name_wet_19001231T010203_std.h5", + "dir/model_name_hydro_19001231T010203_std.h5" + ) + + +def test_model2module(): + model_module_name, model_obj = modelName2Module('ERA5') + assert model_obj().Model() == 'ERA-5' + + +def test_dem_1(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + argDict = checkArgs(args, p) + assert argDict['heights'][0] == 'skip' + assert argDict['heights'][1] is None + + +def test_dem_2(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.heightlvs = [10, 100, 1000] + argDict = checkArgs(args, p) + assert argDict['heights'][0] == 'lvs' + assert np.allclose(argDict['heights'][1], [10, 100, 1000]) + + +def test_dem_3(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.heightlvs = [10, 100, 1000] + args.query_area = os.path.join(SCENARIO_2, 'stations.csv') + argDict = checkArgs(args, p) + assert argDict['heights'][0] == 'lvs' + assert np.allclose(argDict['heights'][1], [10, 100, 1000]) + + +def test_dem_4(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.query_area = os.path.join(SCENARIO_2, 'stations.csv') + argDict = checkArgs(args, p) + assert argDict['heights'][0] == 'pandas' + assert argDict['heights'][1][0] == argDict['wetFilenames'][0] + + +def test_dem_5(parsed_args): + '''Test that passing a raster format with height levels throws an error''' + args, p = parsed_args + args.query_area = [os.path.join(SCENARIO_1, 'geom', 'lat.dat'), os.path.join(SCENARIO_1, 'geom', 'lat.dat')] + argDict = checkArgs(args, p) + assert argDict['heights'][0] == 'download' + assert argDict['heights'][1] == os.path.join(argDict['out'], 'geom', 'warpedDEM.dem') diff --git a/test/_entrypoints.py b/test/_entrypoints.py new file mode 100644 index 000000000..cd1eb7f61 --- /dev/null +++ b/test/_entrypoints.py @@ -0,0 +1,29 @@ + +def test_raider__main__2(script_runner): + ret = script_runner.run('generateGACOSVRT.py') + assert ret.success + + +def test_raider__main__3(script_runner): + ret = script_runner.run('prepARIA.py', '-h') + assert ret.success + + +def test_raider__main__4(script_runner): + ret = script_runner.run('raiderCombine.py', '-h') + assert ret.success + + +def test_raider__main__5(script_runner): + ret = script_runner.run('raider.py', '-h') + assert ret.success + + +def test_raider__main__6(script_runner): + ret = script_runner.run('raiderStats.py', '-h') + assert ret.success + + +def test_raider__main__7(script_runner): + ret = script_runner.run('raiderDownloadGNSS.py', '-h') + assert ret.success diff --git a/test/_scenario_1.py b/test/_scenario_1.py new file mode 100755 index 000000000..faaa61063 --- /dev/null +++ b/test/_scenario_1.py @@ -0,0 +1,144 @@ +import datetime +import os +import pytest + +import numpy as np + +from test import TEST_DIR, pushd + +from RAiDER.losreader import Zenith +from RAiDER.delay import main +from RAiDER.utilFcns import rio_open +from RAiDER.checkArgs import makeDelayFileNames +from RAiDER.cli.validators import modelName2Module + +SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_1") +_RTOL = 1e-2 + + +@pytest.mark.long +def test_tropo_delay_ERAI(tmp_path): + ''' + Scenario: + 1: Small area, ERAI, Zenith delay + ''' + core_test_tropo_delay(tmp_path, modelName="ERAI") + + +@pytest.mark.long +def test_tropo_delay_ERA5(tmp_path): + ''' + Scenario: + 1: Small area, ERA5, Zenith delay + ''' + core_test_tropo_delay(tmp_path, modelName="ERA5") + + +@pytest.mark.long +def test_tropo_delay_ERA5T(tmp_path): + ''' + Scenario: + 1: Small area, ERA5T, Zenith delay + ''' + core_test_tropo_delay(tmp_path, modelName="ERA5T") + + +@pytest.mark.long +def test_tropo_delay_MERRA2(tmp_path): + ''' + Scenario: + 1: Small area, MERRA2, Zenith delay + ''' + core_test_tropo_delay(tmp_path, modelName="MERRA2") + + +@pytest.mark.skip(reason="NCMR keeps hanging") +def test_tropo_delay_NCMR(tmp_path): + ''' + Scenario: + 1: Small area, NCMR, Zenith delay + ''' + core_test_tropo_delay(tmp_path, modelName="NCMR") + + +@pytest.mark.long +def test_tropo_delay_GMAO(tmp_path): + ''' + Scenario: + 1: Small area, GMAO, Zenith delay + ''' + core_test_tropo_delay(tmp_path, modelName="GMAO") + + +def core_test_tropo_delay(tmp_path, modelName): + ''' + Scenario: + 1: Small area, Zenith delay + ''' + lats = rio_open(os.path.join( + SCENARIO_DIR, 'geom', 'lat.dat' + )) + lons = rio_open(os.path.join( + SCENARIO_DIR, 'geom', 'lon.dat' + )) + + if modelName == 'ERAI': + time = datetime.datetime(2018, 1, 3, 23, 0) + elif modelName == 'NCMR': + time = datetime.datetime(2018, 7, 1, 0, 0) + else: + time = datetime.datetime(2020, 1, 3, 23, 0) + + wmLoc = os.path.join(SCENARIO_DIR, 'weather_files') + if not os.path.exists(wmLoc): + os.mkdir(wmLoc) + + _, model_obj = modelName2Module(modelName) + wet_file, hydro_file = makeDelayFileNames( + time, Zenith, "envi", modelName, tmp_path + ) + + with pushd(tmp_path): + # packing the dictionairy + args = {} + args['los'] = Zenith + args['lats'] = lats + args['lons'] = lons + args['ll_bounds'] = (15.75, 18.25, -103.24, -99.75) + args['heights'] = ("dem", os.path.join(TEST_DIR, "test_geom", "warpedDEM.dem")) + args['pnts_file'] = 'lat_query_points.h5' + args['flag'] = "files" + args['weather_model'] = {"type": model_obj(), "files": None, "name": modelName} + args['wmLoc'] = wmLoc + args['zref'] = 20000. + args['outformat'] = "envi" + args['times'] = time + args['out'] = tmp_path + args['download_only'] = False + args['wetFilenames'] = wet_file + args['hydroFilenames'] = hydro_file + args['verbose'] = True + + (_, _) = main(args) + + # get the results + wet = rio_open(wet_file) + hydro = rio_open(hydro_file) + true_wet = rio_open( + os.path.join( + SCENARIO_DIR, + modelName + "/wet.envi" + ), + userNDV=0. + ) + true_hydro = rio_open( + os.path.join( + SCENARIO_DIR, + modelName + "/hydro.envi" + ), + userNDV=0. + ) + + # get the true delay from the weather model + assert np.nanmax(np.abs((wet - true_wet) / true_wet)) < _RTOL + assert np.nanmax(np.abs((hydro - true_hydro) / true_hydro)) < _RTOL diff --git a/test/_scenario_2.py b/test/_scenario_2.py new file mode 100644 index 000000000..c02a77146 --- /dev/null +++ b/test/_scenario_2.py @@ -0,0 +1,69 @@ +# Unit and other tests +from datetime import datetime +import numpy as np +import os +import pandas as pd +import pytest +from shutil import copyfile +from test import TEST_DIR, pushd + +from RAiDER.delay import main +from RAiDER.losreader import Zenith +from RAiDER.cli.validators import modelName2Module + +SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_2") +_RTOL = 1e-2 + + +@pytest.mark.long +def test_computeDelay(tmp_path): + ''' + Scenario to use: + 2: GNSS station list + ''' + wetName = 'stations_with_Delays.csv' + wetFile = os.path.join(SCENARIO_DIR, wetName) + # Not used for station file input, only passed for consistent input arguments + hydroFile = wetFile + + true_delay = os.path.join(SCENARIO_DIR, 'ERA5_true_GNSS.csv') + + station_file = os.path.join(SCENARIO_DIR, 'stations.csv') + copyfile(station_file, wetFile) + stats = pd.read_csv(station_file).drop_duplicates(subset=["Lat", "Lon"]) + lats = stats['Lat'].values + lons = stats['Lon'].values + + _, model_obj = modelName2Module('ERA5') + + with pushd(tmp_path): + + # packing the dictionairy + args = {} + args['los'] = Zenith + args['lats'] = lats + args['lons'] = lons + args['ll_bounds'] = (33.746, 36.795, -118.312, -114.892) + args['heights'] = ('merge', [wetFile]) + args['pnts_file'] = 'ERA5_true_GNSS_query_points.h5' + args['flag'] = "station_file" + args['weather_model'] = {"type": model_obj(), "files": None, "name": "ERA5"} + args['wmLoc'] = None + args['zref'] = 20000. + args['outformat'] = "csv" + args['times'] = datetime(2020, 1, 3, 23, 0, 0) + args['out'] = tmp_path + args['download_only'] = False + args['wetFilenames'] = wetFile + args['hydroFilenames'] = hydroFile + args['verbose'] = True + + (_, _) = main(args) + + # get the results + est_delay = pd.read_csv(wetFile).drop_duplicates(subset=["Lat", "Lon"]) + true_delay = pd.read_csv(true_delay).drop_duplicates(subset=["Lat", "Lon"]) + + # get the true delay from the weather model + assert np.nanmax(np.abs((est_delay['wetDelay'].values - true_delay['wetDelay'].values) / true_delay['wetDelay'].values)) < _RTOL + assert np.nanmax(np.abs((est_delay['hydroDelay'].values - true_delay['hydroDelay'].values) / true_delay['hydroDelay'].values)) < _RTOL diff --git a/test/cli/_argument_parsers.py b/test/cli/_argument_parsers.py new file mode 100644 index 000000000..7082a0452 --- /dev/null +++ b/test/cli/_argument_parsers.py @@ -0,0 +1,118 @@ +from datetime import date, time + +import pytest + +import RAiDER.runProgram + + +@pytest.fixture +def delay_parser(): + return RAiDER.runProgram.create_parser() + + +@pytest.fixture +def stats_parser(): + pass + + +@pytest.fixture +def gnss_parser(): + pass + + +def test_delay_args(delay_parser): + args = delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--latlon', 'latfile.dat', 'lonfile.dat', + '--model', 'ERA5', + '--zref', '20000', + '-v', + '--out', 'test/scenario_1/' + ]) + + assert args.dateList == [date(2020, 1, 3)] + assert args.time == time(23, 0, 0) + assert args.query_area == ['latfile.dat', 'lonfile.dat'] + assert args.lineofsight is None + assert args.statevectors is None + assert args.dem is None + assert args.heightlvs is None + assert args.model == "ERA5" + assert args.files is None + assert args.wmLoc is None + assert args.zref == 20000.0 + assert args.outformat is None + assert args.out == 'test/scenario_1/' + assert args.download_only is False + assert args.verbose == 1 + + +def test_delay_los_mutually_exclusive(delay_parser): + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--lineofsight', 'losfile', + '--statevectors', 'statevectorfile' + ]) + + +def test_delay_aoi_mutually_exclusive(delay_parser): + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--bbox', '10', '20', '30', '40', + '--latlon', 'lat', 'lon', + '--station_file', 'station_file' + ]) + + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--bbox', '10', '20', '30', '40', + '--latlon', 'lat', 'lon', + ]) + + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--bbox', '10', '20', '30', '40', + '--station_file', 'station_file' + ]) + + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--latlon', 'lat', 'lon', + '--station_file', 'station_file' + ]) + + # AOI is required + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + ]) + + +def test_delay_model(delay_parser): + with pytest.raises(SystemExit): + delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--station_file', 'station_file', + '--model', 'FOOBAR' + ]) + + args = delay_parser.parse_args([ + '--date', '20200103', + '--time', '23:00:00', + '--station_file', 'station_file', + '--model', 'era-5' + ]) + assert args.model == "ERA5" diff --git a/test/cli/test_raiderDelay.py b/test/cli/test_raiderDelay.py new file mode 100644 index 000000000..446ee0b91 --- /dev/null +++ b/test/cli/test_raiderDelay.py @@ -0,0 +1,25 @@ +from argparse import ArgumentParser, ArgumentTypeError +from datetime import datetime, time + +from RAiDER.cli.raider import drop_nans + +def test_drop_nans(): + test_d = { + 'key1': 1, + 'key2': 'string', + 'key3': None, + 'key4': {'sub_key1': 1, 'sub_key2': None}, + } + + out_d = drop_nans(test_d) + + assert set(out_d.keys()) == set(['key1', 'key2', 'key4']) + + key_list = [] + for key, value in out_d.items(): + key_list.append(key) + if isinstance(value, dict): + for k, v in value.items(): + key_list.append(k) + + assert set(key_list) == set(['key1', 'key2', 'key4', 'sub_key1']) diff --git a/test/cli/test_validators.py b/test/cli/test_validators.py new file mode 100644 index 000000000..972c47ff8 --- /dev/null +++ b/test/cli/test_validators.py @@ -0,0 +1,144 @@ +from argparse import ArgumentParser, ArgumentTypeError +from datetime import datetime, time + +import pytest + +import numpy as np + +from RAiDER.cli.validators import ( + modelName2Module, getBufferedExtent, isOutside, isInside, + enforce_valid_dates as date_type, convert_time as time_type, + enforce_bbox, parse_dates +) + + +@pytest.fixture +def parser(): + return ArgumentParser() + + + +@pytest.fixture +def llsimple(): + lats = (10, 12) + lons = (-72, -74) + return lats, lons + + +@pytest.fixture +def latwrong(): + lats = (12, 10) + lons = (-72, -74) + return lats, lons + + +@pytest.fixture +def lonwrong(): + lats = (10, 12) + lons = (-72, -74) + return lats, lons + + +@pytest.fixture +def llarray(): + lats = np.arange(10, 12.1, 0.1) + lons = np.arange(-74, -71.9, 0.2) + return lats, lons + + + +def test_date_type(): + assert date_type("2020-10-1") == datetime(2020, 10, 1) + assert date_type("2020101") == datetime(2020, 10, 1) + + with pytest.raises(ValueError): + date_type("foobar") + + +@pytest.mark.parametrize("input,expected", ( + ("T23:00:01.000000", time(23, 0, 1)), + ("T23:00:01.000000", time(23, 0, 1)), + ("T230001.000000", time(23, 0, 1)), + ("230001.000000", time(23, 0, 1)), + ("T23:00:01", time(23, 0, 1)), + ("23:00:01", time(23, 0, 1)), + ("T230001", time(23, 0, 1)), + ("230001", time(23, 0, 1)), + ("T23:00", time(23, 0, 0)), + ("T2300", time(23, 0, 0)), + ("23:00", time(23, 0, 0)), + ("2300", time(23, 0, 0)) +)) +@pytest.mark.parametrize("timezone", ("", "z", "+0000")) +def test_time_type(input, timezone, expected): + assert time_type(input + timezone) == expected + + +def test_time_type_error(): + with pytest.raises(ValueError): + time_type("foobar") + + +def test_date_list_action(): + date_list = { + 'date_start':'20200101', + } + assert date_type(date_list['date_start']) == datetime(2020,1,1) + + + assert parse_dates(date_list) == [datetime(2020,1,1)] + + date_list['date_end'] = '20200103' + assert date_type(date_list['date_end']) == datetime(2020,1,3) + assert parse_dates(date_list) == [datetime(2020,1,1), datetime(2020,1,2), datetime(2020,1,3)] + + date_list['date_end'] = '20200112' + date_list['date_step'] = '5' + assert parse_dates(date_list) == [datetime(2020,1,1), datetime(2020,1,6), datetime(2020,1,11)] + + +def test_bbox_action(): + bbox_str = "45 46 -72 -70" + assert len(enforce_bbox(bbox_str)) == 4 + + assert enforce_bbox(bbox_str) == [45, 46, -72, -70] + + with pytest.raises(ValueError): + enforce_bbox("20 20 30 30") + with pytest.raises(ValueError): + enforce_bbox("30 100 20 40") + with pytest.raises(ValueError): + enforce_bbox("10 30 40 190") + + +def test_ll1(llsimple): + lats, lons = llsimple + assert np.allclose(getBufferedExtent(lats, lons), np.array([10, 12, -74, -72])) + + +def test_ll2(latwrong): + lats, lons = latwrong + assert np.allclose(getBufferedExtent(lats, lons), np.array([10, 12, -74, -72])) + + +def test_ll3(lonwrong): + lats, lons = lonwrong + assert np.allclose(getBufferedExtent(lats, lons), np.array([10, 12, -74, -72])) + + +def test_ll4(llarray): + lats, lons = llarray + assert np.allclose(getBufferedExtent(lats, lons), np.array([10, 12, -74, -72])) + + +def test_isOutside1(llsimple): + assert isOutside(getBufferedExtent(*llsimple), getBufferedExtent(*llsimple) + 1) + + +def test_isOutside2(llsimple): + assert not isOutside(getBufferedExtent(*llsimple), getBufferedExtent(*llsimple)) + + +def test_isInside(llsimple): + assert isInside(getBufferedExtent(*llsimple), getBufferedExtent(*llsimple)) + assert not isInside(getBufferedExtent(*llsimple), getBufferedExtent(*llsimple) + 1) diff --git a/test/scenario_0/geom/warpedDEM.dem b/test/scenario_0/geom/warpedDEM.dem new file mode 100644 index 000000000..c59b36dc6 --- /dev/null +++ b/test/scenario_0/geom/warpedDEM.dem @@ -0,0 +1,2 @@ +Lat,Lon,Hgt_m +-100.0,20.0,2907.47900390625 diff --git a/test/scenario_0/hgt.rdr b/test/scenario_0/hgt.rdr deleted file mode 100644 index 593f4708d..000000000 Binary files a/test/scenario_0/hgt.rdr and /dev/null differ diff --git a/test/scenario_0/hgt.rdr.aux.xml b/test/scenario_0/hgt.rdr.aux.xml deleted file mode 100644 index 9c772deb7..000000000 --- a/test/scenario_0/hgt.rdr.aux.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - BAND - - - 1 - 0 - 4 - ENVI Standard - 0 - bsq - 1 - 1 - - - - 0 - 0 - 0 - 0 - - - diff --git a/test/scenario_0/lat.rdr b/test/scenario_0/lat.rdr deleted file mode 100644 index d81254f9f..000000000 Binary files a/test/scenario_0/lat.rdr and /dev/null differ diff --git a/test/scenario_0/lat.rdr.aux.xml b/test/scenario_0/lat.rdr.aux.xml deleted file mode 100644 index ba2940605..000000000 --- a/test/scenario_0/lat.rdr.aux.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - BAND - - - 1 - 0 - 4 - ENVI Standard - 0 - bsq - 1 - 1 - - - - 20 - 20 - 20 - 0 - - - diff --git a/test/scenario_0/lon.rdr b/test/scenario_0/lon.rdr deleted file mode 100644 index 7a57d510a..000000000 Binary files a/test/scenario_0/lon.rdr and /dev/null differ diff --git a/test/scenario_0/lon.rdr.aux.xml b/test/scenario_0/lon.rdr.aux.xml deleted file mode 100644 index 90f3d6273..000000000 --- a/test/scenario_0/lon.rdr.aux.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - BAND - - - 1 - 0 - 4 - ENVI Standard - 0 - bsq - 1 - 1 - - - - -100 - -100 - -100 - 0 - - - diff --git a/test/scenario_0/station_file.txt b/test/scenario_0/station_file.txt new file mode 100644 index 000000000..2d0af0275 --- /dev/null +++ b/test/scenario_0/station_file.txt @@ -0,0 +1,2 @@ +ID,Lat,Lon,Hgt_m +TEST,20.,-100.,0. diff --git a/test/scenario_0/weather_files/ERA-5_2019_01_01_T02_00_00.nc b/test/scenario_0/weather_files/ERA-5_2019_01_01_T02_00_00.nc new file mode 100644 index 000000000..5cc2235f7 Binary files /dev/null and b/test/scenario_0/weather_files/ERA-5_2019_01_01_T02_00_00.nc differ diff --git a/test/scenario_0/weather_model_data.csv b/test/scenario_0/weather_model_data.csv new file mode 100644 index 000000000..0d8cffe8c --- /dev/null +++ b/test/scenario_0/weather_model_data.csv @@ -0,0 +1,29 @@ +Z,Q,P,T,wetRef,hydroRef,totalRef +-100.0,0.00345950666,100000.0,282.71797959,26.32862999885986,274.4784753786659,300.8071053775258 +123.68006799,0.00345950666,100000.0,282.71797959,26.32862999885986,274.4784753786659,300.8071053775258 +333.32107984,0.00345950666,97500.0,281.35077037,25.93863081966337,268.9169818177527,294.8556126374161 +547.32873038,0.00342566476,95000.0,279.98356115,25.28692592627421,263.30117274458416,288.58809867085836 +765.7038983,0.00334508358,92500.0,278.15074721,24.37995728425257,258.0615034113393,282.44146069559184 +989.17305489,0.00325314827,90000.0,276.46461222,23.36611536306203,252.6182263950078,275.98434175806983 +1217.01164394,0.0029791061,87500.0,275.43113124,20.96076011373678,246.52260510390371,267.4833652176405 +1450.67196044,0.00264245091,85000.0,273.79074931,18.28281846339846,240.9139102260781,259.1967286894766 +1689.4295493,0.00206184748,82500.0,271.90949095,14.039524786486911,235.4459926217592,249.4855174082461 +1934.73713426,0.00158784051,80000.0,270.12243008,10.624615163412141,229.82171447818777,240.4463296415999 +2188.04792126,0.00117115531,77500.0,268.66236906,7.674289151310374,223.84973455872793,231.5240237100383 +2447.18609583,0.000834610355,75000.0,267.4216536,5.3416209441185405,217.6338348690848,222.97545581320335 +2987.30649286,0.000326871721,70000.0,266.24149368,1.969078603837442,204.02529766937116,205.9943762732086 +3560.92165445,0.000100009778,65000.0,266.82820945,0.5564903760442554,189.03548505598235,189.5919754320266 +4176.03894047,7.99471571e-05,60000.0,266.70440704,0.4108355037007652,174.57529298725458,174.98612849095534 +4840.67331515,0.000333155069,55000.0,262.3215326,1.6248080696066896,162.70109272760476,164.32590079721146 +5559.21541078,0.000608409817,50000.0,256.51627613,2.825405341099266,151.25745853388474,154.082863874984 +6339.69635737,0.000288840929,45000.0,251.28697,1.2583925290330442,138.96462677710667,140.22301930613972 +7191.61241741,4.65462005e-05,40000.0,244.70929415,0.19010606029122196,126.8443853259343,127.03449138622553 +8130.29198184,3.7066061e-05,35000.0,238.11950719,0.13990597762452917,114.06037380351425,114.20027978113878 +9184.90913898,5.29397829e-05,30000.0,229.08058561,0.1850269326481093,101.62362706560046,101.80865399824857 +10384.68728154,2.08616365e-05,25000.0,219.13467681,0.06636982483301879,88.53003222680591,88.59640205163893 +11057.8956446,1.67829718e-05,22500.0,214.18527178,0.050287543610312804,81.51821016868988,81.56849771230019 +11793.85668035,1.34759464e-05,20000.0,209.55613821,0.03748536046070472,74.061299910228,74.09878527068871 +12624.65559248,1.0168921e-05,17500.0,210.45774271,0.024540004678788287,64.52601755171605,64.55055755639484 +13594.07298578,4.32650943e-06,15000.0,213.27155617,0.00871592943384299,54.578304810237746,54.58702073967159 +14749.60101055,3.00369927e-06,12500.0,215.97233265,0.004917782251142371,44.91316031539839,44.918078097649534 +16173.0927327,2.89346509e-06,10000.0,214.18258043,0.0038530275452162417,36.23077088912071,36.23462391666593 diff --git a/test/scenario_0/wmtype b/test/scenario_0/wmtype index ef3859dc3..925d57376 100644 --- a/test/scenario_0/wmtype +++ b/test/scenario_0/wmtype @@ -1,2 +1,3 @@ -erai -20180101004800 +pickle +20190101020000 +station_file diff --git a/test/scenario_1/ERA5/hydro.envi b/test/scenario_1/ERA5/hydro.envi new file mode 100755 index 000000000..f6ca5ff39 Binary files /dev/null and b/test/scenario_1/ERA5/hydro.envi differ diff --git a/test/scenario_0/hgt.hdr b/test/scenario_1/ERA5/hydro.hdr old mode 100644 new mode 100755 similarity index 80% rename from test/scenario_0/hgt.hdr rename to test/scenario_1/ERA5/hydro.hdr index 58ad111db..ef82c5600 --- a/test/scenario_0/hgt.hdr +++ b/test/scenario_1/ERA5/hydro.hdr @@ -1,6 +1,6 @@ ENVI -samples = 1 -lines = 1 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/scenario_1/ERA5/wet.envi b/test/scenario_1/ERA5/wet.envi new file mode 100755 index 000000000..ce0e6f262 Binary files /dev/null and b/test/scenario_1/ERA5/wet.envi differ diff --git a/test/scenario_0/lat.hdr b/test/scenario_1/ERA5/wet.hdr old mode 100644 new mode 100755 similarity index 80% rename from test/scenario_0/lat.hdr rename to test/scenario_1/ERA5/wet.hdr index 58ad111db..ef82c5600 --- a/test/scenario_0/lat.hdr +++ b/test/scenario_1/ERA5/wet.hdr @@ -1,6 +1,6 @@ ENVI -samples = 1 -lines = 1 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/scenario_1/ERA5T/hydro.envi b/test/scenario_1/ERA5T/hydro.envi new file mode 100644 index 000000000..14cbe2d8d Binary files /dev/null and b/test/scenario_1/ERA5T/hydro.envi differ diff --git a/test/scenario_0/lon.hdr b/test/scenario_1/ERA5T/hydro.hdr similarity index 80% rename from test/scenario_0/lon.hdr rename to test/scenario_1/ERA5T/hydro.hdr index 58ad111db..ef82c5600 100644 --- a/test/scenario_0/lon.hdr +++ b/test/scenario_1/ERA5T/hydro.hdr @@ -1,6 +1,6 @@ ENVI -samples = 1 -lines = 1 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/scenario_1/ERA5T/wet.envi b/test/scenario_1/ERA5T/wet.envi new file mode 100644 index 000000000..b1fd9562d Binary files /dev/null and b/test/scenario_1/ERA5T/wet.envi differ diff --git a/test/scenario_1/hgt.hdr b/test/scenario_1/ERA5T/wet.hdr similarity index 80% rename from test/scenario_1/hgt.hdr rename to test/scenario_1/ERA5T/wet.hdr index 58ad111db..ef82c5600 100644 --- a/test/scenario_1/hgt.hdr +++ b/test/scenario_1/ERA5T/wet.hdr @@ -1,6 +1,6 @@ ENVI -samples = 1 -lines = 1 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/scenario_1/ERAI/hydro.envi b/test/scenario_1/ERAI/hydro.envi new file mode 100644 index 000000000..e2b779ee6 Binary files /dev/null and b/test/scenario_1/ERAI/hydro.envi differ diff --git a/test/scenario_1/ERAI/hydro.hdr b/test/scenario_1/ERAI/hydro.hdr new file mode 100644 index 000000000..ef82c5600 --- /dev/null +++ b/test/scenario_1/ERAI/hydro.hdr @@ -0,0 +1,9 @@ +ENVI +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 diff --git a/test/scenario_1/ERAI/wet.envi b/test/scenario_1/ERAI/wet.envi new file mode 100644 index 000000000..604b1bec9 Binary files /dev/null and b/test/scenario_1/ERAI/wet.envi differ diff --git a/test/scenario_1/ERAI/wet.hdr b/test/scenario_1/ERAI/wet.hdr new file mode 100644 index 000000000..ef82c5600 --- /dev/null +++ b/test/scenario_1/ERAI/wet.hdr @@ -0,0 +1,9 @@ +ENVI +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 diff --git a/test/scenario_1/GMAO/hydro.envi b/test/scenario_1/GMAO/hydro.envi new file mode 100644 index 000000000..87196ab9d Binary files /dev/null and b/test/scenario_1/GMAO/hydro.envi differ diff --git a/test/scenario_1/GMAO/hydro.hdr b/test/scenario_1/GMAO/hydro.hdr new file mode 100644 index 000000000..9fd950e0e --- /dev/null +++ b/test/scenario_1/GMAO/hydro.hdr @@ -0,0 +1,14 @@ +ENVI +description = { +hydro.envi} +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 +band names = { +Band 1} +data ignore value = 0 diff --git a/test/scenario_1/GMAO/wet.envi b/test/scenario_1/GMAO/wet.envi new file mode 100644 index 000000000..bec021831 Binary files /dev/null and b/test/scenario_1/GMAO/wet.envi differ diff --git a/test/scenario_1/GMAO/wet.hdr b/test/scenario_1/GMAO/wet.hdr new file mode 100644 index 000000000..a141a6359 --- /dev/null +++ b/test/scenario_1/GMAO/wet.hdr @@ -0,0 +1,14 @@ +ENVI +description = { +wet.envi} +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 +band names = { +Band 1} +data ignore value = 0 diff --git a/test/scenario_1/HRES/hydro.envi b/test/scenario_1/HRES/hydro.envi new file mode 100644 index 000000000..47543c586 Binary files /dev/null and b/test/scenario_1/HRES/hydro.envi differ diff --git a/test/scenario_1/HRES/hydro.hdr b/test/scenario_1/HRES/hydro.hdr new file mode 100644 index 000000000..932e36ae4 --- /dev/null +++ b/test/scenario_1/HRES/hydro.hdr @@ -0,0 +1,14 @@ +ENVI +description = { +/Users/jlmd9g/software/RAiDER-1/test/scenario_1/HRES/t_hydro.envi} +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 +band names = { +Band 1} +data ignore value = 0 diff --git a/test/scenario_1/HRES/wet.envi b/test/scenario_1/HRES/wet.envi new file mode 100644 index 000000000..cbccb4ae8 Binary files /dev/null and b/test/scenario_1/HRES/wet.envi differ diff --git a/test/scenario_1/HRES/wet.hdr b/test/scenario_1/HRES/wet.hdr new file mode 100644 index 000000000..64a941ce6 --- /dev/null +++ b/test/scenario_1/HRES/wet.hdr @@ -0,0 +1,14 @@ +ENVI +description = { +/Users/jlmd9g/software/RAiDER-1/test/scenario_1/HRES/t_wet.envi} +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 +band names = { +Band 1} +data ignore value = 0 diff --git a/test/scenario_1/HRRR_tropo_20200101T120000_ztd.nc b/test/scenario_1/HRRR_tropo_20200101T120000_ztd.nc new file mode 100644 index 000000000..84e714271 Binary files /dev/null and b/test/scenario_1/HRRR_tropo_20200101T120000_ztd.nc differ diff --git a/test/scenario_1/MERRA2/hydro.envi b/test/scenario_1/MERRA2/hydro.envi new file mode 100644 index 000000000..d7548dca9 Binary files /dev/null and b/test/scenario_1/MERRA2/hydro.envi differ diff --git a/test/scenario_1/MERRA2/hydro.hdr b/test/scenario_1/MERRA2/hydro.hdr new file mode 100644 index 000000000..ef82c5600 --- /dev/null +++ b/test/scenario_1/MERRA2/hydro.hdr @@ -0,0 +1,9 @@ +ENVI +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 diff --git a/test/scenario_1/MERRA2/wet.envi b/test/scenario_1/MERRA2/wet.envi new file mode 100644 index 000000000..4e99ec185 Binary files /dev/null and b/test/scenario_1/MERRA2/wet.envi differ diff --git a/test/scenario_1/MERRA2/wet.hdr b/test/scenario_1/MERRA2/wet.hdr new file mode 100644 index 000000000..ef82c5600 --- /dev/null +++ b/test/scenario_1/MERRA2/wet.hdr @@ -0,0 +1,9 @@ +ENVI +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 diff --git a/test/scenario_1/NCMR/hydro.envi b/test/scenario_1/NCMR/hydro.envi new file mode 100644 index 000000000..f0a568aff Binary files /dev/null and b/test/scenario_1/NCMR/hydro.envi differ diff --git a/test/scenario_1/NCMR/hydro.hdr b/test/scenario_1/NCMR/hydro.hdr new file mode 100644 index 000000000..ef82c5600 --- /dev/null +++ b/test/scenario_1/NCMR/hydro.hdr @@ -0,0 +1,9 @@ +ENVI +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 diff --git a/test/scenario_1/NCMR/wet.envi b/test/scenario_1/NCMR/wet.envi new file mode 100644 index 000000000..ad59f0d7d Binary files /dev/null and b/test/scenario_1/NCMR/wet.envi differ diff --git a/test/scenario_1/NCMR/wet.hdr b/test/scenario_1/NCMR/wet.hdr new file mode 100644 index 000000000..ef82c5600 --- /dev/null +++ b/test/scenario_1/NCMR/wet.hdr @@ -0,0 +1,9 @@ +ENVI +samples = 15 +lines = 11 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 diff --git a/test/scenario_1/geom/lat.dat b/test/scenario_1/geom/lat.dat new file mode 100644 index 000000000..0d4770666 Binary files /dev/null and b/test/scenario_1/geom/lat.dat differ diff --git a/test/scenario_1/lat.hdr b/test/scenario_1/geom/lat.hdr similarity index 80% rename from test/scenario_1/lat.hdr rename to test/scenario_1/geom/lat.hdr index 58ad111db..ef82c5600 100644 --- a/test/scenario_1/lat.hdr +++ b/test/scenario_1/geom/lat.hdr @@ -1,6 +1,6 @@ ENVI -samples = 1 -lines = 1 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/scenario_1/geom/lon.dat b/test/scenario_1/geom/lon.dat new file mode 100644 index 000000000..8b36dd7a9 Binary files /dev/null and b/test/scenario_1/geom/lon.dat differ diff --git a/test/scenario_1/lon.hdr b/test/scenario_1/geom/lon.hdr similarity index 80% rename from test/scenario_1/lon.hdr rename to test/scenario_1/geom/lon.hdr index 58ad111db..ef82c5600 100644 --- a/test/scenario_1/lon.hdr +++ b/test/scenario_1/geom/lon.hdr @@ -1,6 +1,6 @@ ENVI -samples = 1 -lines = 1 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/scenario_1/hgt.rdr b/test/scenario_1/hgt.rdr deleted file mode 100644 index 593f4708d..000000000 Binary files a/test/scenario_1/hgt.rdr and /dev/null differ diff --git a/test/scenario_1/hgt.rdr.aux.xml b/test/scenario_1/hgt.rdr.aux.xml deleted file mode 100644 index 9c772deb7..000000000 --- a/test/scenario_1/hgt.rdr.aux.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - BAND - - - 1 - 0 - 4 - ENVI Standard - 0 - bsq - 1 - 1 - - - - 0 - 0 - 0 - 0 - - - diff --git a/test/scenario_1/lat.rdr b/test/scenario_1/lat.rdr deleted file mode 100644 index d81254f9f..000000000 Binary files a/test/scenario_1/lat.rdr and /dev/null differ diff --git a/test/scenario_1/lat.rdr.aux.xml b/test/scenario_1/lat.rdr.aux.xml deleted file mode 100644 index ba2940605..000000000 --- a/test/scenario_1/lat.rdr.aux.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - BAND - - - 1 - 0 - 4 - ENVI Standard - 0 - bsq - 1 - 1 - - - - 20 - 20 - 20 - 0 - - - diff --git a/test/scenario_1/lon.rdr b/test/scenario_1/lon.rdr deleted file mode 100644 index 7a57d510a..000000000 Binary files a/test/scenario_1/lon.rdr and /dev/null differ diff --git a/test/scenario_1/lon.rdr.aux.xml b/test/scenario_1/lon.rdr.aux.xml deleted file mode 100644 index 90f3d6273..000000000 --- a/test/scenario_1/lon.rdr.aux.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - BAND - - - 1 - 0 - 4 - ENVI Standard - 0 - bsq - 1 - 1 - - - - -100 - -100 - -100 - 0 - - - diff --git a/test/scenario_1/raider_example_1.yaml b/test/scenario_1/raider_example_1.yaml new file mode 100644 index 000000000..1bda013a5 --- /dev/null +++ b/test/scenario_1/raider_example_1.yaml @@ -0,0 +1,14 @@ +# vim: set filetype=yaml: + look_dir: right + date_group: # Groups mean that there are several different possible options, see documentation for details + date_start: 20200101 + time_group: + time: "12:00:00" # UTC time as a string + weather_model: HRRR # RAiDER supports several models, including ECMWF, HRRR, GMAO, and MERRA-2 + aoi_group: + bounding_box: 36 37 -92 -91 # bounding box uses the weather model grid nodes + height_group: + height_levels: 0 50 100 500 1000 # Return only these specific height levels + los_group: # absent other options ZTD is calculated + + diff --git a/test/scenario_1/wmtype b/test/scenario_1/wmtype deleted file mode 100644 index 4b8812e6a..000000000 --- a/test/scenario_1/wmtype +++ /dev/null @@ -1,2 +0,0 @@ -wrf -20180101004800 diff --git a/test/scenario_1/wrfout_d02_2010-06-24_13:16:00 b/test/scenario_1/wrfout_d02_2010-06-24_13:16:00 deleted file mode 100644 index db6b045ec..000000000 Binary files a/test/scenario_1/wrfout_d02_2010-06-24_13:16:00 and /dev/null differ diff --git a/test/scenario_1/wrfplev_d02_2010-06-24_13:16:00 b/test/scenario_1/wrfplev_d02_2010-06-24_13:16:00 deleted file mode 100644 index c2c7bca2c..000000000 Binary files a/test/scenario_1/wrfplev_d02_2010-06-24_13:16:00 and /dev/null differ diff --git a/test/scenario_2/raider_example_2.yaml b/test/scenario_2/raider_example_2.yaml new file mode 100644 index 000000000..c554925da --- /dev/null +++ b/test/scenario_2/raider_example_2.yaml @@ -0,0 +1,12 @@ +# vim: set filetype=yaml: + look_dir: right + date_group: + date_start: 20200101 + time_group: + time: "00:00:00" + weather_model: HRRR + aoi_group: + station_file: test/scenario_2/stations.csv + + + \ No newline at end of file diff --git a/test/scenario_2/stations.csv b/test/scenario_2/stations.csv new file mode 100644 index 000000000..9647b70d7 --- /dev/null +++ b/test/scenario_2/stations.csv @@ -0,0 +1,9 @@ +ID,Lat,Lon,Hgt_m +CAPE,33.746,-118.28,0.1 +FGNW,34.05,-118.259,0.1 +JPLT,34.201,-118.177,0.1 +MHMS,33.939,-118.244,0.1 +NVCO,36.795,-114.892,0.1 +NVTP,36.099,-115.329,0.1 +WLHG,36.152,-118.312,0.1 +WORG,35.695,-118.242,0.1 diff --git a/test/scenario_2/stations_with_Delays.csv b/test/scenario_2/stations_with_Delays.csv new file mode 100644 index 000000000..9647b70d7 --- /dev/null +++ b/test/scenario_2/stations_with_Delays.csv @@ -0,0 +1,9 @@ +ID,Lat,Lon,Hgt_m +CAPE,33.746,-118.28,0.1 +FGNW,34.05,-118.259,0.1 +JPLT,34.201,-118.177,0.1 +MHMS,33.939,-118.244,0.1 +NVCO,36.795,-114.892,0.1 +NVTP,36.099,-115.329,0.1 +WLHG,36.152,-118.312,0.1 +WORG,35.695,-118.242,0.1 diff --git a/test/scenario_3/HRRR_tropo_20181113T230000_std.nc b/test/scenario_3/HRRR_tropo_20181113T230000_std.nc new file mode 100644 index 000000000..8ba4eed64 Binary files /dev/null and b/test/scenario_3/HRRR_tropo_20181113T230000_std.nc differ diff --git a/test/scenario_3/S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942.EOF b/test/scenario_3/S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942.EOF new file mode 100644 index 000000000..cc0ac7ce3 --- /dev/null +++ b/test/scenario_3/S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942.EOF @@ -0,0 +1,121725 @@ + + + + + S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942 + Precise Orbit Ephemerides (POE) Orbit File + + Sentinel-1A + OPER + AUX_POEORB + + UTC=2018-11-12T22:59:42 + UTC=2018-11-14T00:59:42 + + 0001 + + OPOD + OPOD + 1.5.3 + UTC=2018-12-03T12:07:49 + + + + EARTH_FIXED + UTC + + + + + + TAI=2018-11-12T23:00:19.000000 + UTC=2018-11-12T22:59:42.000000 + UT1=2018-11-12T22:59:42.007459 + +24561 + -2081628.681153 + 6381582.222007 + 2231995.447037 + 805.966546 + 2737.136881 + -7041.537542 + NOMINAL + + + TAI=2018-11-12T23:00:29.000000 + UTC=2018-11-12T22:59:52.000000 + UT1=2018-11-12T22:59:52.007459 + +24561 + -2073432.667460 + 6408589.291855 + 2161455.309896 + 833.203147 + 2664.220014 + -7066.357037 + NOMINAL + + + TAI=2018-11-12T23:00:39.000000 + UTC=2018-11-12T23:00:02.000000 + UT1=2018-11-12T23:00:02.007459 + +24561 + -2064965.285362 + 6434865.494987 + 2090670.967443 + 860.239634 + 2590.964968 + -7090.378144 + NOMINAL + + + TAI=2018-11-12T23:00:49.000000 + UTC=2018-11-12T23:00:12.000000 + UT1=2018-11-12T23:00:12.007459 + +24561 + -2056228.553736 + 6460407.492520 + 2019650.417312 + 887.072466 + 2517.380329 + -7113.598127 + NOMINAL + + + TAI=2018-11-12T23:00:59.000000 + UTC=2018-11-12T23:00:22.000000 + UT1=2018-11-12T23:00:22.007459 + +24561 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mode 100644 index 000000000..41e960e17 --- /dev/null +++ b/test/scenario_3/S1_orbit_example.EOF @@ -0,0 +1,136 @@ + + + + + S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942 + Precise Orbit Ephemerides (POE) Orbit File + + Sentinel-1A + OPER + AUX_POEORB + + UTC=2018-11-12T22:59:42 + UTC=2018-11-14T00:59:42 + + 0001 + + OPOD + OPOD + 1.5.3 + UTC=2018-12-03T12:07:49 + + + + EARTH_FIXED + UTC + + + + + + TAI=2018-11-12T23:00:39.000000 + UTC=2018-11-12T23:00:02.000000 + UT1=2018-11-12T23:00:02.007459 + +24561 + -2064965.285362 + 6434865.494987 + 2090670.967443 + 860.239634 + 2590.964968 + -7090.378144 + NOMINAL + + + TAI=2018-11-12T23:00:49.000000 + UTC=2018-11-12T23:00:12.000000 + UT1=2018-11-12T23:00:12.007459 + +24561 + -2056228.553736 + 6460407.492520 + 2019650.417312 + 887.072466 + 2517.380329 + -7113.598127 + NOMINAL + + + TAI=2018-11-12T23:00:59.000000 + UTC=2018-11-12T23:00:22.000000 + UT1=2018-11-12T23:00:22.007459 + +24561 + -2047224.526705 + 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UTC=2018-11-12T23:01:12.000000 + UT1=2018-11-12T23:01:12.007458 + +24561 + -1998269.276601 + 6598065.082739 + 1589017.893976 + 1043.598837 + 2069.437298 + -7235.952940 + NOMINAL + + + + diff --git a/test/scenario_3/S1_sv_file.txt b/test/scenario_3/S1_sv_file.txt new file mode 100644 index 000000000..ae29b897f --- /dev/null +++ b/test/scenario_3/S1_sv_file.txt @@ -0,0 +1,8 @@ +2018-11-12T23:00:02.000000 -2064965.285362 6434865.494987 2090670.967443 860.239634 2590.964968 -7090.378144 +2018-11-12T23:00:12.000000 -2056228.553736 6460407.492520 2019650.417312 887.072466 2517.380329 -7113.598127 +2018-11-12T23:00:22.000000 -2047224.526705 6485212.031660 1948401.684024 913.698134 2443.474728 -7136.014344 +2018-11-12T23:00:32.000000 -2037955.293282 6509275.946120 1876932.818066 940.113169 2369.256838 -7157.624244 +2018-11-12T23:00:42.000000 -2028422.977002 6532596.156540 1805251.894958 966.314136 2294.735374 -7178.425371 +2018-11-12T23:00:52.000000 -2018629.735564 6555169.670917 1733367.014327 992.297636 2219.919093 -7198.415359 +2018-11-12T23:01:02.000000 -2008577.760461 6576993.585012 1661286.298987 1018.060311 2144.816789 -7217.591940 +2018-11-12T23:01:12.000000 -1998269.276601 6598065.082739 1589017.893976 1043.598837 2069.437298 -7235.952940 diff --git a/test/scenario_3/incorrect_file.txt b/test/scenario_3/incorrect_file.txt new file mode 100644 index 000000000..54e2b78c2 --- /dev/null +++ b/test/scenario_3/incorrect_file.txt @@ -0,0 +1 @@ +this is a dummy file diff --git a/test/scenario_3/raider_example_3.yaml b/test/scenario_3/raider_example_3.yaml new file mode 100644 index 000000000..b336ab452 --- /dev/null +++ b/test/scenario_3/raider_example_3.yaml @@ -0,0 +1,18 @@ +# vim: set filetype=yaml: +look_dir: right +date_group: + date_list: [20181113] +time_group: + time: "23:00:00" + end_time: +weather_model: HRRR +aoi_group: + bounding_box: 36.8 36.85 -76.15 -76.05 +height_group: + height_levels: 0 100 500 1000 +los_group: + ray_trace: True # Use projected slant delay by default + orbit_file: test/scenario_3/S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942.EOF +runtime_group: + output_projection: 4326 + cube_spacing_in_m: 5000.0 diff --git a/test/scenario_3/raider_example_4.yaml b/test/scenario_3/raider_example_4.yaml new file mode 100644 index 000000000..ce6dc46bf --- /dev/null +++ b/test/scenario_3/raider_example_4.yaml @@ -0,0 +1,17 @@ +# vim: set filetype=yaml: + look_dir: right + date_group: + date_list: [20181113] + time_group: + time: "23:00:00" + end_time: + weather_model: HRRR + aoi_group: + bounding_box: 36.8 36.85 -76.15 -76.05 + los_group: + ray_trace: False # Use projected slant delay by default + orbit_file: test/scenario_3/S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942.EOF + runtime_group: + output_projection: 4326 + cube_spacing_in_m: 250.0 + \ No newline at end of file diff --git a/test/scenario_5/wrfout_d01_2010-06-25_00:00:00 b/test/scenario_5/wrfout_d01_2010-06-25 similarity index 100% rename from test/scenario_5/wrfout_d01_2010-06-25_00:00:00 rename to test/scenario_5/wrfout_d01_2010-06-25 diff --git a/test/scenario_5/wrfplev_d01_2010-06-25_00:00:00 b/test/scenario_5/wrfplev_d01_2010-06-25 similarity index 100% rename from test/scenario_5/wrfplev_d01_2010-06-25_00:00:00 rename to test/scenario_5/wrfplev_d01_2010-06-25 diff --git a/test/test_dem.py b/test/test_dem.py new file mode 100644 index 000000000..c005ef3e4 --- /dev/null +++ b/test/test_dem.py @@ -0,0 +1,7 @@ +import os +import pytest + +import numpy as np + +from test import pushd + diff --git a/test/test_geom/lat.rdr.vrt b/test/test_geom/lat.rdr.vrt index df1c70bb6..1206f22d6 100644 --- a/test/test_geom/lat.rdr.vrt +++ b/test/test_geom/lat.rdr.vrt @@ -1,5 +1,12 @@ + + 21.493668454337 + 18.706121111652 + 15.763767828026 + 1.5837206786523 + 96.18 + 0 lat.rdr diff --git a/test/test_geom/lon.rdr.vrt b/test/test_geom/lon.rdr.vrt index cb13c3786..100435d6c 100644 --- a/test/test_geom/lon.rdr.vrt +++ b/test/test_geom/lon.rdr.vrt @@ -1,5 +1,12 @@ + + -98.241840984674 + -99.860320396801 + -101.6383676311 + 0.74882734423551 + 96.18 + 0 lon.rdr diff --git a/test/test_geom/test_era5.nc b/test/test_geom/test_era5.nc new file mode 100644 index 000000000..89d77b7a5 Binary files /dev/null and b/test/test_geom/test_era5.nc differ diff --git a/test/test_geom/test_era5t.nc b/test/test_geom/test_era5t.nc new file mode 100644 index 000000000..89d77b7a5 Binary files /dev/null and b/test/test_geom/test_era5t.nc differ diff --git a/test/test_geom/test_erai.nc b/test/test_geom/test_erai.nc new file mode 100644 index 000000000..2865fe82e Binary files /dev/null and b/test/test_geom/test_erai.nc differ diff --git a/test/test_geom/warpedDEM.dem b/test/test_geom/warpedDEM.dem index 352f19b2a..dd7d10e55 100644 Binary files a/test/test_geom/warpedDEM.dem and b/test/test_geom/warpedDEM.dem differ diff --git a/test/test_geom/warpedDEM.hdr b/test/test_geom/warpedDEM.hdr index c60fbad61..ef82c5600 100644 --- a/test/test_geom/warpedDEM.hdr +++ b/test/test_geom/warpedDEM.hdr @@ -1,6 +1,6 @@ ENVI -samples = 226 -lines = 45 +samples = 15 +lines = 11 bands = 1 header offset = 0 file type = ENVI Standard diff --git a/test/test_gnss.py b/test/test_gnss.py new file mode 100644 index 000000000..3b9a621ee --- /dev/null +++ b/test/test_gnss.py @@ -0,0 +1,101 @@ +import datetime +import os +import pytest + +import pandas as pd + +from test import pushd + +from RAiDER.gnss.processDelayFiles import ( + addDateTimeToFiles, + getDateTime, + concatDelayFiles +) + + +def file_len(fname): + with open(fname) as f: + for i, l in enumerate(f): + pass + return i + 1 + + +@pytest.fixture +def temp_file(): + df = pd.DataFrame( + { + 'ID': ['STAT1', 'STAT2', 'STAT3'], + 'Lat': [15.0, 20., 25.0], + 'Lon': [-100, -90., -85.], + 'totalDelay': [1., 1.5, 2.], + } + ) + return df + + +def test_getDateTime(): + f1 = '20080101T060000' + f2 = '20080101T560000' + f3 = '20080101T0600000' + f4 = '20080101_060000' + f5 = '2008-01-01T06:00:00' + assert getDateTime(f1) == datetime.datetime(2008, 1, 1, 6, 0, 0) + with pytest.raises(ValueError): + getDateTime(f2) + assert getDateTime(f3) == datetime.datetime(2008, 1, 1, 6, 0, 0) + with pytest.raises(AttributeError): + getDateTime(f4) + with pytest.raises(AttributeError): + getDateTime(f5) + + +def test_addDateTimeToFiles1(tmp_path, temp_file): + df = temp_file + + with pushd(tmp_path): + new_name = os.path.join(tmp_path, 'tmp.csv') + df.to_csv(new_name, index=False) + addDateTimeToFiles([new_name]) + df = pd.read_csv(new_name) + assert 'Datetime' not in df.columns + + +def test_addDateTimeToFiles2(tmp_path, temp_file): + f1 = '20080101T060000' + df = temp_file + + with pushd(tmp_path): + new_name = os.path.join( + tmp_path, + 'tmp' + f1 + '.csv' + ) + df.to_csv(new_name, index=False) + addDateTimeToFiles([new_name]) + df = pd.read_csv(new_name) + assert 'Datetime' in df.columns + + +def test_concatDelayFiles(tmp_path, temp_file): + f1 = '20080101T060000' + df = temp_file + + with pushd(tmp_path): + new_name = os.path.join( + tmp_path, + 'tmp' + f1 + '.csv' + ) + new_name2 = os.path.join( + tmp_path, + 'tmp' + f1 + '_2.csv' + ) + df.to_csv(new_name, index=False) + df.to_csv(new_name2, index=False) + file_length = file_len(new_name) + addDateTimeToFiles([new_name, new_name2]) + + out_name = os.path.join(tmp_path, 'out.csv') + concatDelayFiles( + [new_name, new_name2], + outName=out_name + ) + assert file_len(out_name) == file_length diff --git a/test/test_hdf5_parallel.py b/test/test_hdf5_parallel.py new file mode 100755 index 000000000..0cfde72ac --- /dev/null +++ b/test/test_hdf5_parallel.py @@ -0,0 +1,50 @@ +import os +from test import TEST_DIR, pushd +import pytest + +import numpy as np + +from RAiDER.delayFcns import get_delays + +# FIXME: Relying on prior setup to be performed in order for test to pass. +# This file should either by committed as test data, or set up by a fixture +# prior to the test running +POINTS_FILE = os.path.join( + TEST_DIR, + "scenario_1", + "geom", + "query_points.h5" +) +MODEL_FILE = os.path.join( + TEST_DIR, + "scenario_1", + "weather_files", + "ERA5_2020-01-03T23_00_00_15.75N_18.25N_-103.24E_-99.75E.h5" +) + + +@pytest.mark.skipif(~os.path.exists(MODEL_FILE) or + ~os.path.exists(POINTS_FILE), + reason="Will not pass until the test_scenario_*'s have run") +def test_get_delays_accuracy(tmp_path): + stepSize = 15.0 + interpType = 'rgi' + + with pushd(tmp_path): + delays_wet_1, delays_hydro_1 = get_delays( + stepSize, + POINTS_FILE, + MODEL_FILE, + interpType, + cpu_num=1 + ) + + delays_wet_4, delays_hydro_4 = get_delays( + stepSize, + POINTS_FILE, + MODEL_FILE, + interpType, + cpu_num=4 + ) + assert np.allclose(delays_wet_1, delays_wet_4) + assert np.allclose(delays_hydro_1, delays_hydro_4) diff --git a/test/test_integration.py b/test/test_integration.py new file mode 100644 index 000000000..6d7c222e5 --- /dev/null +++ b/test/test_integration.py @@ -0,0 +1,43 @@ +import numpy as np + +# The purpose of these tests is to verify that the axis parameter for trapz is +# equivalent to calling apply_along_axis(trapz, axis). + + +def test_integrate_along_axis(): + y = np.array([ + [[0, 1, 2], + [1, 2, 3]], + ]) + x = np.array([2, 3, 4]) + + for level in range(y.shape[2]): + assert np.allclose( + np.apply_along_axis(np.trapz, 2, y[..., level:], x=x[level:]), + np.trapz(y[..., level:], x[level:], axis=2) + ) + + +def test_integrate_along_axis_2(): + y = np.array([ + [[0, 1, 2], + [1, 2, 3]], + ]) + x = np.linspace(1, 5, num=y.shape[2]) + + for level in range(y.shape[2]): + assert np.allclose( + np.apply_along_axis(np.trapz, 2, y[..., level:], x=x[level:]), + np.trapz(y[..., level:], x[level:], axis=2) + ) + + +def test_integrate_along_axis_large(): + y = np.random.standard_normal(100_000).reshape(100, 100, 10) + x = np.linspace(0, 1000, num=y.shape[2]) + + for level in range(y.shape[2]): + assert np.allclose( + np.apply_along_axis(np.trapz, 2, y[..., level:], x=x[level:]), + np.trapz(y[..., level:], x[level:], axis=2) + ) diff --git a/test/test_interpolator.py b/test/test_interpolator.py new file mode 100644 index 000000000..c9ec478e5 --- /dev/null +++ b/test/test_interpolator.py @@ -0,0 +1,937 @@ +import numpy as np +import pytest +from scipy.interpolate import RegularGridInterpolator + +from RAiDER.interpolate import interpolate, interpolate_along_axis +from RAiDER.interpolator import RegularGridInterpolator as Interpolator, interpolateDEM +from RAiDER.interpolator import fillna3D, interp_along_axis, interpVector + + +@pytest.fixture +def nanArr(): + array = np.random.randn(2, 2, 3) + array[0, 0, 0] = np.nan + array[0, 0, 1] = np.nan + array[0, 0, 2] = np.nan + array[1, 0, 0] = np.nan + array[0, 1, 1] = np.nan + array[1, 1, 2] = np.nan + true_array = array.copy() + true_array[0, 0, 0] = np.nan + true_array[0, 0, 1] = np.nan + true_array[0, 0, 2] = np.nan + true_array[1, 0, 0] = true_array[1, 0, 1] + true_array[0, 1, 1] = (true_array[0, 1, 0] + true_array[0, 1, 2]) / 2 + true_array[1, 1, 2] = true_array[1, 1, 1] + return array, true_array + + +def test_interpVector(): + assert np.allclose( + interpVector( + np.array([ + 0, 1, 2, 3, 4, 5, + 0, 0.84147098, 0.90929743, 0.14112001, -0.7568025, -0.95892427, + 0.5, 1.5, 2.5, 3.5, 4.5 + ]), + 6 + ), + np.array([0.42073549, 0.87538421, 0.52520872, -0.30784124, -0.85786338]) + ) + + +def test_fillna3D(nanArr): + arr, tarr = nanArr + assert np.allclose(fillna3D(arr), tarr, equal_nan=True) + + +def test_interp_along_axis(): + z2 = np.tile(np.arange(100)[..., np.newaxis], (5, 1, 5)).swapaxes(1, 2) + zvals = 0.3 * z2 - 12.75 + + newz = np.tile( + np.array([1.5, 9.9, 15, 23.278, 39.99, 50.1])[..., np.newaxis], + (5, 1, 5) + ).swapaxes(1, 2) + corz = 0.3 * newz - 12.75 + + assert np.allclose(interp_along_axis(z2, newz, zvals, axis=2), corz) + + +def shuffle_along_axis(a, axis): + idx = np.random.rand(*a.shape).argsort(axis=axis) + return np.take_along_axis(a, idx, axis=axis) + + +def test_interpolate_along_axis(): + # Rejects scalar values + with pytest.raises(TypeError): + interpolate_along_axis(np.array(0), np.array(0), np.array(0)) + + # Rejects mismatched number of dimensions + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros(1), np.zeros(1), np.zeros((1, 1))) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros(1), np.zeros((1, 1)), np.zeros(1)) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros((1, 1)), np.zeros(1), np.zeros(1)) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros(1), np.zeros((1, 1)), np.zeros((1, 1))) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros((1, 1)), np.zeros((1, 1)), np.zeros(1)) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros((1, 1)), np.zeros(1), np.zeros((1, 1))) + + # Rejects mismatched shape for points and values + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros(1), np.zeros(2), np.zeros(1)) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros((9, 2)), np.zeros((9, 3)), np.zeros(1)) + + # Rejects bad axis + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros(1), np.zeros(1), np.zeros(1), axis=1) + with pytest.raises(TypeError): + interpolate_along_axis(np.zeros(1), np.zeros(1), np.zeros(1), axis=-2) + + # Rejects bad interp_points shape + with pytest.raises(TypeError): + interpolate_along_axis( + np.zeros((2, 2)), np.zeros((2, 2)), np.zeros((3, 2)) + ) + with pytest.raises(TypeError): + interpolate_along_axis( + np.zeros((2, 2)), np.zeros((2, 2)), np.zeros((2, 3)), + axis=0, max_threads=1 + ) + + +def test_interp_along_axis_1d(): + def f(x): + return 2 * x + + xs = np.array([1, 2, 3, 4]) + ys = f(xs) + + points = np.array([1.5, 3.1]) + + assert np.allclose( + interp_along_axis(xs, points, ys, axis=0), + 2 * points + ) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=0, max_threads=1), + 2 * points + ) + + +def test_interp_along_axis_1d_out_of_bounds(): + def f(x): + return 2 * x + + xs = np.array([1, 2, 3, 4]) + ys = f(xs) + + points = np.array([0, 5]) + + assert np.allclose( + interp_along_axis(xs, points, ys, axis=0), + np.array([np.nan, np.nan]), + equal_nan=True + ) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=0, + max_threads=1, fill_value=np.nan), + np.array([np.nan, np.nan]), + equal_nan=True + ) + + +def test_interp_along_axis_2d(): + def f(x): + return 2 * x + + xs = np.array([ + [1, 2, 3, 4], + [3, 4, 5, 6] + ]) + ys = f(xs) + + points = np.array([ + [1.5, 3.1, 3.6], + [3.5, 5.1, 5.2] + ]) + + assert np.allclose( + interp_along_axis(xs, points, ys, axis=1), + 2 * points + ) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=1), + 2 * points + ) + + +def test_interp_along_axis_2d_threads_edge_case(): + def f(x): + return 2 * x + + # Max of 4 threads but 5 rows to interpolate over. Each thread will get 2 + # rows which means only 3 threads will be used + max_threads = 4 + xs = np.array([ + [1, 2, 3, 4], + [3, 4, 5, 6], + [7, 8, 9, 10], + [11, 12, 13, 14], + [15, 16, 17, 18] + ]) + ys = f(xs) + + points = np.array([ + [1.5, 3.1, 3.6], + [3.5, 5.1, 5.2], + [7.5, 9.1, 9.9], + [11.1, 12.2, 13.3], + [15.1, 16.2, 17.3] + ]) + + assert np.allclose( + interp_along_axis(xs, points, ys, axis=1), + 2 * points + ) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=1, max_threads=max_threads), + 2 * points + ) + + +def test_interp_along_axis_3d(): + def f(x): + return 2 * x + + xs = np.array([ + [[1, 2, 3, 4], + [3, 4, 5, 6]], + + [[10, 11, 12, 13], + [21, 22, 23, 24]] + ]) + ys = f(xs) + + points = np.array([ + [[1.5, 3.1], + [3.5, 5.1]], + + [[10.3, 12.9], + [22.6, 22.1]] + ]) + + assert np.allclose( + interp_along_axis(xs, points, ys, axis=2), + 2 * points + ) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=2), + 2 * points + ) + + +def test_interp_along_axis_3d_axis1(): + def f(x): + return 2 * x + + xs = np.array([ + [[1, 2], + [3, 4]], + + [[10, 11], + [21, 22]] + ]) + ys = f(xs) + + points = np.array([ + [[1.5, 3.1], + [2.5, 2.1]], + + [[10.3, 12.9], + [15, 17]] + ]) + + assert np.allclose( + interp_along_axis(xs, points, ys, axis=1), + 2 * points + ) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=1), + 2 * points + ) + + +@pytest.mark.parametrize("num_points", (7, 200, 500)) +def test_interp_along_axis_3d_large(num_points): + def f(x): + return 2 * x + + # To scale values along axis 0 of a 3 dimensional array + scale = np.arange(1, 101).reshape((100, 1, 1)) + axis1 = np.arange(100) + axis2 = np.repeat(np.array([axis1]), 100, axis=0) + xs = np.repeat(np.array([axis2]), 100, axis=0) * scale + ys = f(xs) + + points = np.array([np.linspace(0, 99, num=num_points)]).repeat(100, axis=0) + points = np.repeat(np.array([points]), 100, axis=0) * scale + + ans = 2 * points + + assert np.allclose(interp_along_axis(xs, points, ys, axis=2), ans) + assert np.allclose(interpolate_along_axis(xs, ys, points, axis=2), ans) + assert np.allclose( + interpolate_along_axis(xs, ys, points, axis=2, assume_sorted=True), ans + ) + + +def test_interp_along_axis_3d_large_unsorted(): + def f(x): + return 2 * x + + # To scale values along axis 0 of a 3 dimensional array + scale = np.arange(1, 101).reshape((100, 1, 1)) + axis1 = np.arange(100) + axis2 = np.repeat(np.array([axis1]), 100, axis=0) + xs = np.repeat(np.array([axis2]), 100, axis=0) * scale + ys = f(xs) + + points = np.array([np.linspace(0, 99, num=300)]).repeat(100, axis=0) + points = np.repeat(np.array([points]), 100, axis=0) * scale + points = shuffle_along_axis(points, 2) + + ans = 2 * points + + assert np.allclose(interp_along_axis(xs, points, ys, axis=2), ans) + assert np.allclose(interpolate_along_axis(xs, ys, points, axis=2), ans) + + +def test_grid_dim_mismatch(): + with pytest.raises(TypeError): + interpolate( + points=(np.zeros((10,)), np.zeros((5,))), + values=np.zeros((1,)), + interp_points=np.zeros((1,)) + ) + + +def test_basic(): + ans = interpolate( + points=(np.array([0, 1]),), + values=np.array([0, 1]), + interp_points=np.array([[0.5]]), + max_threads=1, + assume_sorted=True + ) + + assert ans == np.array([0.5]) + + +def test_1d_out_of_bounds(): + ans = interpolate( + points=(np.array([0, 1]),), + values=np.array([0, 1]), + interp_points=np.array([[100]]), + max_threads=1, + assume_sorted=True + ) + + # Output is extrapolated + assert ans == np.array([100]) + + +def test_1d_fill_value(): + ans = interpolate( + points=(np.array([0, 1]),), + values=np.array([0, 1]), + interp_points=np.array([[100]]), + max_threads=1, + fill_value=np.nan, + assume_sorted=True + ) + + assert np.all(np.isnan(ans)) + + +def test_small(): + ans = interpolate( + points=(np.array([1, 2, 3, 4, 5, 6]),), + values=np.array([10, 9, 30, 10, 6, 1]), + interp_points=np.array([1.25, 2.9, 3.01, 5.7]).reshape(-1, 1) + ) + + assert ans.shape == (4,) + assert np.allclose(ans, np.array([9.75, 27.9, 29.8, 2.5]), atol=1e-15) + + +def test_small_not_sorted(): + ans = interpolate( + points=(np.array([1, 2, 3, 4, 5, 6]),), + values=np.array([10, 9, 30, 10, 6, 1]), + interp_points=np.array([2.9, 1.25, 5.7, 3.01]).reshape(-1, 1), + ) + + assert ans.shape == (4,) + assert np.allclose(ans, np.array([27.9, 9.75, 2.5, 29.8]), atol=1e-15) + + +def test_exact_points(): + ans = interpolate( + points=(np.array([1, 2, 3, 4, 5, 6]),), + values=np.array([10, 9, 30, 10, 6, 1]), + interp_points=np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1) + ) + + assert ans.shape == (6,) + assert np.allclose(ans, np.array([10, 9, 30, 10, 6, 1]), atol=1e-15) + + +def test_2d_basic(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + + values = (lambda x, y: x + y)( + *np.meshgrid(xs, ys, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=np.array([[0.5, 0.5]]) + ) + + assert ans == np.array([1]) + + +def test_2d_out_of_bounds(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + + values = (lambda x, y: x + y)( + *np.meshgrid(xs, ys, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=np.array([[100, 100]]) + ) + + # Output is extrapolated + assert ans == np.array([200]) + + +def test_2d_fill_value(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + + values = (lambda x, y: x + y)( + *np.meshgrid(xs, ys, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=np.array([[100, 100]]), + fill_value=np.nan + ) + + assert np.all(np.isnan(ans)) + + +def test_2d_square_small(): + def f(x, y): + return x ** 2 + 3 * y + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, atol=1e-15) + + +def test_2d_rectangle_small(): + def f(x, y): + return x ** 2 + 3 * y + + xs = np.linspace(0, 2000, 200) + ys = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, atol=1e-15) + + +def test_2d_rectangle_small_2(): + def f(x, y): + return x ** 2 + 3 * y + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 2000, 200) + + values = f(*np.meshgrid(xs, ys, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, atol=1e-15) + + +def test_2d_square_large(): + def f(x, y): + return x ** 2 + 3 * y + + xs = np.linspace(-10_000, 10_000, num=1_000) + ys = np.linspace(0, 20_000, num=1_000) + + values = f(*np.meshgrid(xs, ys, indexing="ij", sparse=True)) + num_points = 2_000_000 + points = np.stack(( + np.linspace(10, 990, num_points), + np.linspace(10, 890, num_points) + ), axis=-1) + + ans = interpolate( + points=(xs, ys), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, atol=1e-15) + + +def test_3d_basic(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + zs = np.array([0, 1]) + + values = (lambda x, y, z: x + y + z)( + *np.meshgrid(xs, ys, zs, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=np.array([[0.5, 0.5, 0.5]]), + assume_sorted=True + ) + + assert ans == np.array([1.5]) + + +def test_3d_out_of_bounds(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + zs = np.array([0, 1]) + + values = (lambda x, y, z: x + y + z)( + *np.meshgrid(xs, ys, zs, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=np.array([[100, 100, 100]]), + assume_sorted=True + ) + + # Output is extrapolated + assert ans == np.array([300]) + + +def test_3d_fill_value(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + zs = np.array([0, 1]) + + values = (lambda x, y, z: x + y + z)( + *np.meshgrid(xs, ys, zs, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=np.array([[100, 100, 100]]), + fill_value=np.nan, + assume_sorted=True + ) + + assert np.all(np.isnan(ans)) + + +def test_3d_cube_small(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5), + np.linspace(10, 780, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys, zs), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_3d_cube_small_not_sorted(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + points = np.stack(( + np.random.uniform(10, 990, 10), + np.random.uniform(10, 890, 10), + np.random.uniform(10, 780, 10) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=points, + ) + + rgi = RegularGridInterpolator((xs, ys, zs), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_3d_prism_small(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 2000, 200) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 50) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5), + np.linspace(10, 780, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys, zs), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_3d_prism_small_2(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 2000, 100) + ys = np.linspace(0, 1000, 200) + zs = np.linspace(0, 1000, 50) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5), + np.linspace(10, 780, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys, zs), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_3d_prism_small_3(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 2000, 50) + ys = np.linspace(0, 1000, 200) + zs = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5), + np.linspace(10, 780, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys, zs), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_3d_cube_large(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + num_points = 2_000_000 + points = np.stack(( + np.linspace(10, 990, num_points), + np.linspace(10, 890, num_points), + np.linspace(10, 780, num_points) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys, zs), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_4d_basic(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + zs = np.array([0, 1]) + ws = np.array([0, 1]) + + values = (lambda x, y, z, w: x + y + z + w)( + *np.meshgrid(xs, ys, zs, ws, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys, zs, ws), + values=values, + interp_points=np.array([[0.5, 0.5, 0.5, 0.5]]) + ) + + assert ans == np.array([2]) + + +def test_4d_out_of_bounds(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + zs = np.array([0, 1]) + ws = np.array([0, 1]) + + values = (lambda x, y, z, w: x + y + z + w)( + *np.meshgrid(xs, ys, zs, ws, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys, zs, ws), + values=values, + interp_points=np.array([[100, 100, 100, 100]]) + ) + + # Output is extrapolated + assert ans == np.array([400]) + + +def test_4d_fill_value(): + xs = np.array([0, 1]) + ys = np.array([0, 1]) + zs = np.array([0, 1]) + ws = np.array([0, 1]) + + values = (lambda x, y, z, w: x + y + z + w)( + *np.meshgrid(xs, ys, zs, ws, indexing="ij", sparse=True) + ) + + ans = interpolate( + points=(xs, ys, zs, ws), + values=values, + interp_points=np.array([[100, 100, 100, 100]]), + fill_value=np.nan + ) + + assert np.all(np.isnan(ans)) + + +def test_4d_cube_small(): + def f(x, y, z, w): + return x ** 2 + 3 * y - z * w + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 100) + ws = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, ws, indexing="ij", sparse=True)) + points = np.stack(( + np.linspace(10, 990, 5), + np.linspace(10, 890, 5), + np.linspace(10, 780, 5), + np.linspace(10, 670, 5) + ), axis=-1) + + ans = interpolate( + points=(xs, ys, zs, ws), + values=values, + interp_points=points, + assume_sorted=True + ) + + rgi = RegularGridInterpolator((xs, ys, zs, ws), values) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15) + + +def test_interpolate_wrapper(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + points_x = np.linspace(10, 1090, 5) + points_y = np.linspace(10, 890, 5) + points_z = np.linspace(10, 890, 5) + points = np.stack(( + points_x, + points_y, + points_z + ), axis=-1) + + interp = Interpolator((xs, ys, zs), values, fill_value=np.nan) + ans = interp(points) + ans2 = interp((points_x, points_y, points_z)) + rgi = RegularGridInterpolator((xs, ys, zs), values, bounds_error=False) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15, equal_nan=True) + assert np.allclose(ans2, ans_scipy, 1e-15, equal_nan=True) + + +def test_interpolate_wrapper2(): + def f(x, y, z): + return x ** 2 + 3 * y - z + + xs = np.linspace(0, 1000, 100) + ys = np.linspace(0, 1000, 100) + zs = np.linspace(0, 1000, 100) + + values = f(*np.meshgrid(xs, ys, zs, indexing="ij", sparse=True)) + nanInds = np.random.randint(0, 99, size=(100, 3)) + values[nanInds[:, 0], nanInds[:, 1], nanInds[:, 2]] = np.nan + + points_x = np.linspace(10, 1090, 5) + points_y = np.linspace(10, 890, 5) + points_z = np.linspace(10, 890, 5) + points = np.stack(( + points_x, + points_y, + points_z + ), axis=-1) + + interp = Interpolator((xs, ys, zs), values, fill_value=np.nan) + ans = interp(points) + ans2 = interp((points_x, points_y, points_z)) + rgi = RegularGridInterpolator((xs, ys, zs), values, bounds_error=False) + ans_scipy = rgi(points) + + assert np.allclose(ans, ans_scipy, 1e-15, equal_nan=True) + assert np.allclose(ans2, ans_scipy, 1e-15, equal_nan=True) + +# TODO: implement an interpolator test that is similar to test_scenario_1. +# Currently the scipy and C++ interpolators differ on that case. + + +def test_interpolateDEM(): + x = np.arange(10) + dem = np.outer(x, x) + extent = [0, 9, 0, 9] + out = interpolateDEM(dem, extent, np.array([[4.5, 4.5], [0.5, 0.5], [10, 10]])) + assert np.allclose(out, np.array([20.25, 0.25, np.nan]), equal_nan=True) diff --git a/test/test_llreader.py b/test/test_llreader.py new file mode 100644 index 000000000..dff8681a6 --- /dev/null +++ b/test/test_llreader.py @@ -0,0 +1,90 @@ +import os +import pytest + +import numpy as np +import pandas as pd + +from test import GEOM_DIR, TEST_DIR + +from RAiDER.cli.raider import create_parser + +from RAiDER.utilFcns import rio_open +from RAiDER.llreader import ( + StationFile, RasterRDR, BoundingBox, GeocodedFile, Geocube, + bounds_from_latlon_rasters, bounds_from_csv, get_bbox +) + +SCENARIO2_DIR = os.path.join(TEST_DIR, "scenario_2") +SCENARIO1_DIR = os.path.join(TEST_DIR, "scenario_1", "geom") + + +@pytest.fixture +def parser(): + return create_parser() + + +@pytest.fixture +def station_file(): + return os.path.join(SCENARIO2_DIR, 'stations.csv') + + +@pytest.fixture +def llfiles(): + return os.path.join(SCENARIO1_DIR, 'lat.dat'), os.path.join(SCENARIO1_DIR, 'lon.dat') + + +def test_latlon_reader(): + latfile = os.path.join(GEOM_DIR, 'lat.rdr') + lonfile = os.path.join(GEOM_DIR, 'lon.rdr') + lat_true = rio_open(latfile) + lon_true = rio_open(lonfile) + + query = RasterRDR(lat_file=latfile, lon_file=lonfile) + lats, lons = query.readLL() + assert lats.shape == (45, 226) + assert lons.shape == (45, 226) + + assert np.allclose(lat_true, lats, equal_nan=True) + assert np.allclose(lon_true, lons, equal_nan=True) + + # Hard code the lat/lon bounds to test against changing the files + bounds_true = [15.7637, 21.4936, -101.6384, -98.2418] + assert all([np.allclose(b, t, rtol=1e-4) for b, t in zip(query.bounds(), bounds_true)]) + + +def test_read_bbox(): + bbox = [20, 27, -115, -104] + query = BoundingBox(bbox) + assert query.type() == 'bounding_box' + assert query.bounds() == bbox + assert query.projection() == 'EPSG:4326' + + +def test_read_station_file(station_file): + query = StationFile(station_file) + lats, lons = query.readLL() + stats = pd.read_csv(station_file).drop_duplicates(subset=["Lat", "Lon"]) + + assert np.allclose(lats, stats['Lat'].values) + assert np.allclose(lons, stats['Lon'].values) + + assert query.projection() == 'EPSG:4326' + + # Hard code the lat/lon bounds to test against changing the files + bounds_true = [33.746, 36.795, -118.312, -114.892] + assert all([np.allclose(b, t, rtol=1e-4) for b, t in zip(query.bounds(), bounds_true)]) + + +def test_bounds_from_latlon_rasters(): + latfile = os.path.join(GEOM_DIR, 'lat.rdr') + lonfile = os.path.join(GEOM_DIR, 'lon.rdr') + _, snwe, _ = bounds_from_latlon_rasters(latfile, lonfile) + + bounds_true =[15.7637, 21.4936, -101.6384, -98.2418] + assert all([np.allclose(b, t, rtol=1e-4) for b, t in zip(snwe, bounds_true)]) + + +def test_bounds_from_csv(station_file): + bounds_true = [33.746, 36.795, -118.312, -114.892] + snwe = bounds_from_csv(station_file) + assert all([np.allclose(b, t) for b, t in zip(snwe, bounds_true)]) diff --git a/test/test_losreader.py b/test/test_losreader.py new file mode 100644 index 000000000..8f6b9c05a --- /dev/null +++ b/test/test_losreader.py @@ -0,0 +1,303 @@ +import datetime +import os +import pytest +from test import TEST_DIR + +import numpy as np + +from RAiDER.losreader import ( + read_ESA_Orbit_file, + read_txt_file, + cut_times, + inc_hd_to_enu, + get_sv, + getZenithLookVecs, + Conventional, + Zenith, +) + +SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_3") + + +@pytest.fixture +def svs(): + T = [ + datetime.datetime(2018, 11, 12, 23, 0, 2), + datetime.datetime(2018, 11, 12, 23, 0, 12), + datetime.datetime(2018, 11, 12, 23, 0, 22), + datetime.datetime(2018, 11, 12, 23, 0, 32), + datetime.datetime(2018, 11, 12, 23, 0, 42), + datetime.datetime(2018, 11, 12, 23, 0, 52), + datetime.datetime(2018, 11, 12, 23, 1, 2), + datetime.datetime(2018, 11, 12, 23, 1, 12), + ] + x = np.array([ + -2064965.285362, + -2056228.553736, + -2047224.526705, + -2037955.293282, + -2028422.977002, + -2018629.735564, + -2008577.760461, + -1998269.276601, + ]) + y = np.array([ + 6434865.494987, + 6460407.492520, + 6485212.031660, + 6509275.946120, + 6532596.156540, + 6555169.670917, + 6576993.585012, + 6598065.082739, + ]) + z = np.array([ + 2090670.967443, + 2019650.417312, + 1948401.684024, + 1876932.818066, + 1805251.894958, + 1733367.014327, + 1661286.298987, + 1589017.893976, + ]) + vx = np.array([ + 860.239634, + 887.072466, + 913.698134, + 940.113169, + 966.314136, + 992.297636, + 1018.060311, + 1043.598837, + ]) + vy = np.array([ + 2590.964968, + 2517.380329, + 2443.474728, + 2369.256838, + 2294.735374, + 2219.919093, + 2144.816789, + 2069.437298, + ]) + vz = np.array([ + -7090.378144, + -7113.598127, + -7136.014344, + -7157.624244, + -7178.425371, + -7198.415359, + -7217.591940, + -7235.952940, + ]) + return [T, x, y, z, vx, vy, vz] + + +def test_read_ESA_Orbit_file(svs): + true_svs = svs + filename = os.path.join(SCENARIO_DIR, 'S1_orbit_example.EOF') + svs = read_ESA_Orbit_file(filename) + assert [np.allclose( + [(x-y).total_seconds() for x, y in zip(svs[0], true_svs[0])], + np.zeros(len(svs[0])) + )] + assert [np.allclose(s, ts) for s, ts in zip(svs[1:], true_svs[1:])] + + +def test_read_txt_file(svs): + true_svs = svs + filename = os.path.join(SCENARIO_DIR, 'S1_sv_file.txt') + svs = read_txt_file(filename) + assert [np.allclose( + [(x-y).total_seconds() for x, y in zip(svs[0], true_svs[0])], + np.zeros(len(svs[0])) + )] + assert [np.allclose(s, ts) for s, ts in zip(svs[1:], true_svs[1:])] + + +def test_get_sv_1(svs): + true_svs = svs + filename = os.path.join(SCENARIO_DIR, 'S1_orbit_example.EOF') + svs = get_sv(filename, true_svs[0][0]) + assert [np.allclose( + [(x-y).total_seconds() for x, y in zip(svs[0], true_svs[0])], + np.zeros(len(svs[0])) + )] + assert [np.allclose(s, ts) for s, ts in zip(svs[1:], true_svs[1:])] + + +def test_get_sv_2(svs): + true_svs = svs + filename = os.path.join(SCENARIO_DIR, 'S1_sv_file.txt') + svs = get_sv(filename, true_svs[0][0]) + assert [np.allclose( + [(x-y).total_seconds() for x, y in zip(svs[0], true_svs[0])], + np.zeros(len(svs[0])) + )] + assert [np.allclose(s, ts) for s, ts in zip(svs[1:], true_svs[1:])] + + +def test_get_sv_3(svs): + true_svs = svs + filename = os.path.join(SCENARIO_DIR, 'incorrect_file.txt') + with pytest.raises(ValueError): + get_sv(filename, true_svs[0][0]) + + +def test_get_sv_4(svs): + true_svs = svs + filename = os.path.join(SCENARIO_DIR, 'no_exist.txt') + with pytest.raises(FileNotFoundError): + get_sv(filename, true_svs[0][0]) + + +def test_cut_times(svs): + true_svs = svs + assert all(cut_times(true_svs[0], true_svs[0][0])) + + +def test_cut_times_2(svs): + true_svs = svs + assert sum(cut_times(true_svs[0], true_svs[0][0], pad=5)) == 1 + + +def test_cut_times_3(svs): + true_svs = svs + assert np.sum(cut_times(true_svs[0], true_svs[0][4], pad=15)) == 3 + + +def test_cut_times_4(svs): + true_svs = svs + + assert np.sum(cut_times(true_svs[0], true_svs[0][0], pad=400)) == len(true_svs[0]) + + +def test_los_to_lv(): + with pytest.raises(ValueError): + inc_hd_to_enu(-10, 0) + + +def test_los_to_lv_2(): + assert np.allclose( + inc_hd_to_enu(0, 0), + np.array([0, 0, 1]) + ) + + +def test_los_to_lv_3(): + assert np.allclose( + inc_hd_to_enu(0, -180), + np.array([0, 0, 1]) + ) + + +def test_los_to_lv_3b(): + assert np.allclose( + inc_hd_to_enu(0, 18), + np.array([0, 0, 1]) + ) + + +def test_los_to_lv_3c(): + assert np.allclose( + inc_hd_to_enu(0, -18), + np.array([0, 0, 1]) + ) + + +def test_los_to_lv_4(): + assert np.allclose( + inc_hd_to_enu(35, 0), + np.array([0, np.sin(np.radians(35)), np.cos(np.radians(35))]) + ) + + +def test_los_to_lv_5(): + assert np.allclose( + inc_hd_to_enu(35, 180), + np.array([0, -np.sin(np.radians(35)), np.cos(np.radians(35))]) + ) + + +def test_los_to_lv_6(): + assert np.allclose( + inc_hd_to_enu(35, 90), + np.array([-np.sin(np.radians(35)), 0, np.cos(np.radians(35))]) + ) + + +def test_zenith_1(): + assert np.allclose( + getZenithLookVecs(np.array([0]), np.array([0]), np.array([0])), + np.array([1, 0, 0]) + ) + + +def test_zenith_2(): + assert np.allclose( + getZenithLookVecs(np.array([90]), np.array([0]), np.array([0])), + np.array([0, 0, 1]) + ) + + +def test_zenith_3(): + assert np.allclose( + getZenithLookVecs(np.array([-90]), np.array([0]), np.array([0])), + np.array([0, 0, -1]) + ) + + +def test_zenith_4(): + assert np.allclose( + getZenithLookVecs(np.array([0]), np.array([180]), np.array([0])), + np.array([-1, 0, 0]) + ) + + +def test_zenith_5(): + assert np.allclose( + getZenithLookVecs(np.array([0]), np.array([90]), np.array([0])), + np.array([0, 1, 0]) + ) + + +def test_zenith_6(): + assert np.allclose( + getZenithLookVecs(np.array([0]), np.array([0]), np.array([1000])), + np.array([1, 0, 0]) + ) + +def test_Zenith(): + lats = np.array([-90, 0, 0, 90]) + lons = np.array([-90, 0, 90, 180]) + hgts = np.array([-10, 0, 10, 1000]) + + unit_vecs = np.array([[0,0,-1], [1,0,0], [0,1,0], [0,0,1]]) + + z = Zenith() + with pytest.raises(RuntimeError): + z.setPoints(lats=None) + + z.setPoints(lats=lats, lons=lons, heights = hgts) + assert z._lats.shape == (4,) + assert z._lats.shape == z._lons.shape + assert np.allclose(z._heights, hgts) + + output = z(unit_vecs) + assert output.shape == (4,3) + assert np.allclose(output, unit_vecs) + +def test_Conventional(): + lats = np.array([-90, 0, 0, 90]) + lons = np.array([-90, 0, 90, 180]) + hgts = np.array([-10, 0, 10, 1000]) + + c = Conventional() + + with pytest.raises(ValueError): + c(np.ones(4)) + + c.setPoints(lats, lons, hgts) + with pytest.raises(ValueError): + c(np.ones(4)) diff --git a/test/test_raytracing.py b/test/test_raytracing.py new file mode 100644 index 000000000..d6c871977 --- /dev/null +++ b/test/test_raytracing.py @@ -0,0 +1,49 @@ +import datetime +import pytest +import numpy as np + +from RAiDER.losreader import Raytracing + + +def test_Raytracing(): + lats = np.array([-90, 0, 0, 90]) + lons = np.array([-90, 0, 90, 180]) + hgts = np.array([-10, 0, 10, 1000]) + + unit_vecs = np.array([[0,0,-1], [1,0,0], [0,1,0], [0,0,1]]) + + z = Raytracing() + with pytest.raises(RuntimeError): + z.setPoints(lats=None) + + z.setPoints(lats=lats, lons=lons, heights = hgts) + assert z._lats.shape == (4,) + assert z._lats.shape == z._lons.shape + assert np.allclose(z._heights, hgts) + + +def test_toa(): + lats = np.array([0, 0, 0, 0]) + lons = np.array([0, 180, 90, -90]) + hgts = np.array([0, 0, 0, 0]) + + z = Raytracing() + z.setPoints(lats=lats, lons=lons, heights=hgts) + + # Mock xyz + z._xyz = np.array([[6378137.0, 0.0, 0.0], + [-6378137.0, 0.0, 0.0], + [0.0, 6378137.0, 0.0], + [0.0, -6378137.0, 0.0]]) + z._look_vecs = np.array([[1, 0, 0], + [-1, 0, 0], + [0, 1, 0], + [0, -1, 0]]) + toppts = np.array([[6388137.0, 0.0, 0.0], + [-6388137.0, 0.0, 0.0], + [0.0, 6388137.0, 0.0], + [0.0, -6388137.0, 0.0]]) + + topxyz = z.getIntersectionWithHeight(10000.) + + assert np.allclose(topxyz, toppts) diff --git a/test/test_result_makePoints3D.txt b/test/test_result_makePoints3D.txt new file mode 100644 index 000000000..e13c50588 --- /dev/null +++ b/test/test_result_makePoints3D.txt @@ -0,0 +1,1620 @@ +0.000000000000000000e+00 +0.000000000000000000e+00 +0.000000000000000000e+00 +0.000000000000000000e+00 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use: - # 0: single point, ERAI, download DEM - # 1: single point, WRF, download DEM - # 2: - # 3: - # 4: Small area, ERAI - # 5: Small area, WRF, los available - # 6: Small area, ERA5, early date, Zenith - # 7: Small area, ERA5, late date, Zenith - # 8: Small area, ERAI, late date, Zenith - scenario = 'scenario_7' - - # Zenith or LOS? - useZen = True - ######################################### - - # load the weather model type and date for the given scenario - outdir = os.getcwd() + '/test/' - basedir = outdir + '/{}/'.format(scenario) - lines=[] - with open(os.path.join(basedir, 'wmtype'), 'r') as f: - for line in f: - lines.append(line.strip()) - wmtype = lines[0] - test_time = datetime.datetime.strptime(lines[1], '%Y%m%d%H%M%S') - - # get the data for the scenario - latfile = os.path.join(basedir, 'lat.rdr') - lonfile = os.path.join(basedir,'lon.rdr') - losfile = os.path.join(basedir,'los.rdr') - demfile = os.path.join(basedir,'warpedDEM.dem') - - if os.path.exists(demfile): - heights = ('dem', demfile) - else: - heights = ('download', None) - - if useZen: - los = None - else: - los = ('los', losfile) - - # load the weather model - wm = d4t.load_weather_model(wmtype) - - # test error messaging - def test_tropo_smallArea(self): - delay.tropo_delay(los = self.los, - lat = self.latfile, - lon = self.lonfile, - heights = self.heights, - weather = self.wm, - zref = 15000, - time = self.test_time, - out = self.outdir, - parallel=False, - verbose = True, - download_only = False) - - -# should have appropriate check here -# self.assertTrue(np.allclose(testData, refData,equal_nan = True)) - self.assertTrue(True) - -if __name__=='__main__': - - unittest.main() diff --git a/test/test_scenarios.py b/test/test_scenarios.py new file mode 100644 index 000000000..42adde142 --- /dev/null +++ b/test/test_scenarios.py @@ -0,0 +1,56 @@ +import os +import pytest +import subprocess + +from test import TEST_DIR + +import numpy as np +import xarray as xr + + +def test_scenario_1(): + SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_1") + test_path = os.path.join(SCENARIO_DIR, 'raider_example_1.yaml') + process = subprocess.run(['raider.py', test_path],stdout=subprocess.PIPE, universal_newlines=True,) + assert process.returncode == 0 + + new_data = xr.load_dataset('HRRR_tropo_20200101T120000_ztd.nc') + golden_data = xr.load_dataset(os.path.join(SCENARIO_DIR, 'HRRR_tropo_20200101T120000_ztd.nc')) + + assert np.allclose(golden_data['wet'], new_data['wet']) + assert np.allclose(golden_data['hydro'], new_data['hydro']) + + + # Clean up files + subprocess.run(['rm', '-f', './HRRR*']) + subprocess.run(['rm', '-rf', './weather_files']) + + +#TODO: Next release include GNSS station test +# @pytest.mark.long +# def test_scenario_2(): +# test_path = os.path.join(TEST_DIR, "scenario_2", 'raider_example_2.yaml') +# process = subprocess.run(['raider.py', test_path],stdout=subprocess.PIPE, universal_newlines=True) +# assert process.returncode == 0 + +# # Clean up files +# subprocess.run(['rm', '-f', './HRRR*']) +# subprocess.run(['rm', '-rf', './weather_files']) + + +def test_scenario_3(): + SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_3") + + test_path = os.path.join(SCENARIO_DIR, 'raider_example_3.yaml') + process = subprocess.run(['raider.py', test_path],stdout=subprocess.PIPE, universal_newlines=True) + assert process.returncode == 0 + + new_data = xr.load_dataset('HRRR_tropo_20181113T230000_std.nc') + golden_data = xr.load_dataset(os.path.join(SCENARIO_DIR, 'HRRR_tropo_20181113T230000_std.nc')) + + assert np.allclose(golden_data['wet'], new_data['wet']) + assert np.allclose(golden_data['hydro'], new_data['hydro']) + + # Clean up files + subprocess.run(['rm', '-f', './HRRR*']) + subprocess.run(['rm', '-rf', './weather_files']) diff --git a/test/test_util.py b/test/test_util.py new file mode 100644 index 000000000..4314a0999 --- /dev/null +++ b/test/test_util.py @@ -0,0 +1,474 @@ +import datetime +import h5py +import os +import pytest + +import numpy as np +import pyproj +import rasterio + +from test import TEST_DIR + +from RAiDER.utilFcns import ( + _least_nonzero, cosd, rio_open, sind, + writeArrayToRaster, writeResultsToHDF5, rio_profile, + rio_extents, getTimeFromFile, enu2ecef, ecef2enu, + transform_bbox, clip_bbox +) + + +_R_EARTH = 6378138 + +SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_1") + + +@pytest.fixture +def make_points_0d_data(): + return ( + np.stack([ + np.zeros(200), + np.zeros(200), + np.arange(0, 1000, 5) + ], + axis=-1 + ).T, + (1000., np.array([0., 0., 0.]), np.array([0., 0., 1.]), 5.) + ) + + +@pytest.fixture +def make_points_1d_data(): + ray1 = np.stack([ + np.zeros(200), + np.zeros(200), + np.arange(0, 1000, 5) + ], + axis=-1 + ).T + ray2 = np.stack([ + np.zeros(200), + np.arange(0, 1000, 5), + np.zeros(200), + ], + axis=-1 + ).T + rays = np.stack([ray1, ray2], axis=0) + + sp = np.array([[0., 0., 0.], + [0., 0., 0.]]) + slv = np.array([[0., 0., 1.], + [0., 1., 0.]]) + return rays, (1000., sp, slv, 5.) + + +@pytest.fixture +def make_points_2d_data(): + sp = np.zeros((2, 2, 3)) + slv = np.zeros((2, 2, 3)) + slv[0, 0, 0] = 1 + slv[0, 1, 1] = 1 + slv[1, 0, 2] = 1 + slv[1, 1, 0] = -1 + make_points_args = (20., sp, slv, 5) + + rays = np.array([[[[0., 5., 10., 15.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + [[0., 0., 0., 0.], + [0., 5., 10., 15.], + [0., 0., 0., 0.]]], + [[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 5., 10., 15.]], + [[0., -5., -10., -15.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]]) + + return rays, make_points_args + + +@pytest.fixture +def make_points_3d_data(): + sp = np.zeros((3, 3, 3, 3)) + sp[:, :, 1, 2] = 10 + sp[:, :, 2, 2] = 100 + slv = np.zeros((3, 3, 3, 3)) + slv[0, :, :, 2] = 1 + slv[1, :, :, 1] = 1 + slv[2, :, :, 0] = 1 + + make_points_args = (100., sp, slv, 5) + + df = np.loadtxt(os.path.join(TEST_DIR, "test_result_makePoints3D.txt")) + + return df.reshape((3, 3, 3, 3, 20)), make_points_args + + +def test_sind(): + assert np.allclose( + sind(np.array([0, 30, 90, 180])), + np.array([0, 0.5, 1, 0]) + ) + + +def test_cosd(): + assert np.allclose( + cosd(np.array([0, 60, 90, 180])), + np.array([1, 0.5, 0, -1]) + ) + + +def test_rio_open(): + out = rio_open(os.path.join(TEST_DIR, "test_geom", "lat.rdr"), False) + + assert np.allclose(out.shape, (45, 226)) + + +def test_writeResultsToHDF5(tmp_path): + lats = np.array([15.0, 15.5, 16.0, 16.5, 17.5, -40, 60, 90]) + lons = np.array([-100.0, -100.4, -91.2, 45.0, 0., -100, -100, -100]) + hgts = np.array([0., 1000., 10000., 0., 0., 0., 0., 0.]) + wet = np.zeros(lats.shape) + hydro = np.ones(lats.shape) + filename = str(tmp_path / 'dummy.hdf5') + + writeResultsToHDF5(lats, lons, hgts, wet, hydro, filename) + + with h5py.File(filename, 'r') as f: + assert np.allclose(np.array(f['lat']), lats) + assert np.allclose(np.array(f['hydroDelay']), hydro) + assert f.attrs['DelayType'] == 'Zenith' + + +def test_writeArrayToRaster(tmp_path): + array = np.transpose( + np.array([np.arange(0, 10)]) + ) * np.arange(0, 10) + filename = str(tmp_path / 'dummy.out') + + writeArrayToRaster(array, filename) + with rasterio.open(filename) as src: + band = src.read(1) + noval = src.nodatavals[0] + + + assert noval == 0 + assert np.allclose(band, array) + + +def test_makePoints0D_cython(make_points_0d_data): + from RAiDER.makePoints import makePoints0D + + true_ray, args = make_points_0d_data + + test_result = makePoints0D(*args) + assert np.allclose(test_result, true_ray) + + +def test_makePoints1D_cython(make_points_1d_data): + from RAiDER.makePoints import makePoints1D + + true_ray, args = make_points_1d_data + + test_result = makePoints1D(*args) + assert np.allclose(test_result, true_ray) + + +def test_makePoints2D_cython(make_points_2d_data): + from RAiDER.makePoints import makePoints2D + + true_ray, args = make_points_2d_data + + test_result = makePoints2D(*args) + assert np.allclose(test_result, true_ray) + + +def test_makePoints3D_Cython_values(make_points_3d_data): + from RAiDER.makePoints import makePoints3D + + true_rays, args = make_points_3d_data + + test_result = makePoints3D(*args) + + assert test_result.ndim == 5 + assert np.allclose(test_result, true_rays) + + +def test_least_nonzero(): + a = np.arange(20, dtype="float64").reshape(2, 2, 5) + a[0, 0, 0] = np.nan + a[1, 1, 0] = np.nan + + assert np.allclose( + _least_nonzero(a), + np.array([ + [1, 5], + [10, 16] + ]), + atol=1e-16 + ) + + +def test_least_nonzero_2(): + a = np.array([ + [[10., 5., np.nan], + [11., np.nan, 1], + [18, 17, 16]], + + [[np.nan, 12., 6.], + [np.nan, 13., 20.], + [np.nan, np.nan, np.nan]] + ]) + + assert np.allclose( + _least_nonzero(a), + np.array([ + [10, 11, 18], + [12, 13, np.nan] + ]), + atol=1e-16, + equal_nan=True + ) + + +def test_rio_extent(): + # Create a simple georeferenced test file + with rasterio.open("test.tif", mode="w", + width=11, height=11, count=1, + dtype=np.float64, crs=pyproj.CRS.from_epsg(4326), + transform=rasterio.Affine.from_gdal( + 17.0, 0.1, 0, 18.0, 0, -0.1 + )) as dst: + dst.write(np.random.randn(11, 11), 1) + profile = rio_profile("test.tif") + assert rio_extents(profile) == (17.0, 18.0, 17.0, 18.0) + + +def test_rio_extent2(): + with pytest.raises(AttributeError): + rio_profile(os.path.join(TEST_DIR, "test_geom", "lat.rdr")) + + +def test_getTimeFromFile(): + name1 = 'abcd_2020_01_01_T00_00_00jijk.xyz' + assert getTimeFromFile(name1) == datetime.datetime(2020, 1, 1, 0, 0, 0) + + +def test_project(): + # the true UTM coordinates are extracted from this website as an independent check: https://www.latlong.net/lat-long-utm.html + from RAiDER.utilFcns import project + # Hawaii + true_utm = (5, 'Q', 212721.65, 2192571.64) + tup = project((-155.742188, 19.808054)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # New Zealand + true_utm = (59, 'G', 645808.07, 5373216.94) + tup = project((172.754517, -41.779505)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # UK + true_utm = (30, 'U', 693205.98, 5742711.01) + tup = project((-0.197754, 51.801822)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # US + true_utm = (14, 'S', 640925.54, 4267877.48) + tup = project((-97.382813, 38.548165)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # China + true_utm = (48, 'S', 738881.72, 3734577.12) + tup = project((107.578125, 33.724340)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # South Africa + true_utm = (34, 'J', 713817.66, 6747653.92) + tup = project((23.203125, -29.382175)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # Argintina + true_utm = (19, 'H', 628210.60, 5581184.24) + tup = project((-67.500000, -39.909736)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + # Greenland + true_utm = (24, 'X', 475105.61, 8665516.77) + tup = project((-40.078125, 78.061989)) + assert np.allclose((tup[0], tup[2], tup[3]), (true_utm[0], true_utm[2], true_utm[3])) + assert tup[1] == true_utm[1] + + +def test_WGS84_to_UTM(): + from RAiDER.utilFcns import WGS84_to_UTM + + lats = np.array([38.0, 38.0, 38.0]) + lons = np.array([-97.0, -92.0, -87.0]) + + # true utm coodinates at local zones (14, 15, 16) + true_utm_local = np.array([[14, 675603.37, 4207702.37], [15, 587798.42, 4206286.76], [16, 500000.00, 4205815.02]]) + true_utm_local_letter = np.array(['S', 'S', 'S']) + + # true utm coordinates at the zone of the center (15) + # created using the following line + # pyproj.Proj(proj='utm', zone=15, ellps='WGS84')(lons,lats) + true_utm_common = np.array([[15, 148741.08527017, 4213370.735271454], [15, 587798.42, 4206286.76], [15, 1027018.2271954522, 4222839.127299805]]) + true_utm_common_letter = np.array(['S', 'S', 'S']) + + # use local UTM zones + Z, L, X, Y = WGS84_to_UTM(lons, lats) + cal_utm_local = np.array([Z, X, Y]).transpose() + assert np.allclose(true_utm_local, cal_utm_local) + assert np.all(true_utm_local_letter == L) + + # use common UTM zone + Z, L, X, Y = WGS84_to_UTM(lons, lats, common_center=True) + cal_utm_common = np.array([Z, X, Y]).transpose() + assert np.allclose(true_utm_common, cal_utm_common) + assert np.all(true_utm_common_letter == L) + + +@pytest.mark.skipif(True, reason='Need to ensure this file always get written before this executes') +def test_read_weather_model_file(): + # TODO: read_wm_file is undefined + weather_model_obj = read_wm_file( + os.path.join( + SCENARIO_DIR, + 'weather_files', + 'ERA5_2020_01_03_T23_00_00_15.75N_18.25N_103.24W_99.75W.nc' + ) + ) + assert weather_model_obj.Model() == 'ERA-5' + + +def test_enu2ecef_1(): + enu = np.array([0, 0, 1]) + llh = np.array([0, 0, 0]) + ecef = enu2ecef(enu[0], enu[1], enu[2], llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([1, 0, 0])) + + +def test_enu2ecef_2(): + enu = np.array([0, 0, 1]) + llh = np.array([0, 90, 0]) + ecef = enu2ecef(enu[0], enu[1], enu[2], llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 1, 0])) + + +def test_enu2ecef_3(): + enu = np.array([0, 0, 1]) + llh = np.array([0, -90, 0]) + ecef = enu2ecef(enu[0], enu[1], enu[2], llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, -1, 0])) + + +def test_enu2ecef_4(): + enu = np.array([0, 0, 1]) + llh = np.array([90, 0, 0]) + ecef = enu2ecef(enu[0], enu[1], enu[2], llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 0, 1])) + + +def test_enu2ecef_5(): + enu = np.array([0, 0, 1]) + llh = np.array([-90, 0, 0]) + ecef = enu2ecef(enu[0], enu[1], enu[2], llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 0, -1])) + + +def test_enu2ecef_6(): + enu = np.array([0, 1, 0]) + llh = np.array([0, 0, 0]) + ecef = enu2ecef(enu[0], enu[1], enu[2], llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 0, 1])) + + +def test_ecef2enu_1(): + enu = np.array([0, 0, 1]) + llh = np.array([0, 0, 0]) + enu = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(enu, np.array([0, 1, 0])) + + +def test_ecef2enu_2(): + enu = np.array([0, 0, 1]) + llh = np.array([0, 90, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 1, 0])) + + +def test_ecef2enu_3(): + enu = np.array([0, 0, 1]) + llh = np.array([0, -90, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 1, 0])) + + +def test_ecef2enu_4(): + enu = np.array([0, 0, 1]) + llh = np.array([90, 0, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 0, 1])) + + +def test_ecef2enu_5(): + enu = np.array([0, 0, 1]) + llh = np.array([-90, 0, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 0, -1])) + + +def test_ecef2enu_6(): + enu = np.array([0, 0, -1]) + llh = np.array([0, -180, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, -1, 0])) + + +def test_ecef2enu_7(): + enu = np.array([0, 0, 1]) + llh = np.array([0, -180, 1000]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([0, 1, 0])) + + +def test_ecef2enu_8(): + enu = np.array([1, 1, 0]) + llh = np.array([0, 0, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([1, 0, 1])) + + +def test_ecef2enu_9(): + enu = np.array([1, 1, 0]) + llh = np.array([0, 180, 0]) + ecef = ecef2enu(enu, llh[0], llh[1], llh[2]) + assert np.allclose(ecef, np.array([-1, 0, -1])) + + +def test_transform_bbox_1(): + wesn = [-77.0, -76.0, 34.0, 35.0] + snwe = wesn[2:] + wesn[:2] + + assert transform_bbox(wesn, src_crs=4326, dest_crs=4326) == snwe + + +def test_transform_bbox_2(): + wesn = [-77.0, -76.0, 34.0, 35.0] + + expected_snwe = [3762606.6598762725, + 3874870.6347308, + 315290.16886786406, + 408746.7471660769] + + snwe = transform_bbox(wesn, src_crs=4326, dest_crs=32618, margin=0.) + assert np.allclose(snwe, expected_snwe) + + assert clip_bbox(wesn, 0.01) == wesn diff --git a/test/time_scenario.py b/test/time_scenario.py deleted file mode 100644 index f1dead17d..000000000 --- a/test/time_scenario.py +++ /dev/null @@ -1,73 +0,0 @@ -# Unit and other tests -import datetime -import glob -from osgeo import gdal -import numpy as np -import traceback -import os - -import delay -import util -from . import data4tests as d4t - -def main(): - ######################################### - # Scenario to use: - # 0: single point, ERAI, download DEM - # 1: single point, WRF, download DEM - # 2: - # 3: - # 4: Small area, ERAI, los available - # 5: Small area, WRF, los available - scenario = 'scenario_5' - - # Zenith or LOS? - useZen = True - ######################################### - - # load the weather model type and date for the given scenario - outdir = os.getcwd() + '/test/' - basedir = outdir + '/{}/'.format(scenario) - lines=[] - with open(os.path.join(basedir, 'wmtype'), 'r') as f: - for line in f: - lines.append(line.strip()) - wmtype = lines[0] - test_time = datetime.datetime.strptime(lines[1], '%Y%m%d%H%M%S') - - # get the data for the scenario - latfile = os.path.join(basedir, 'lat.rdr') - lonfile = os.path.join(basedir,'lon.rdr') - losfile = os.path.join(basedir,'los.rdr') - demfile = os.path.join(basedir,'warpedDEM.dem') - - if os.path.exists(demfile): - heights = ('dem', demfile) - else: - heights = ('download', None) - - if useZen: - los = None - else: - los = ('los', losfile) - - # load the weather model - wm = d4t.load_weather_model(wmtype) - - # test error messaging - delay.tropo_delay(los =los, - lat = latfile, - lon = lonfile, - heights = heights, - weather = wm, - zref = 15000, - time =test_time, - out = outdir, - parallel=False, - verbose = True) - - - -if __name__=='__main__': - - main() diff --git a/test/weather_model/test_downloaders.py b/test/weather_model/test_downloaders.py new file mode 100644 index 000000000..6f86dc118 --- /dev/null +++ b/test/weather_model/test_downloaders.py @@ -0,0 +1,38 @@ +import os +import pytest + +import numpy as np + +from datetime import datetime +from test import TEST_DIR + +from RAiDER.models.era5 import ERA5 +from RAiDER.models.era5t import ERA5T +from RAiDER.models.erai import ERAI + +@pytest.mark.skip +def test_era5(): + wm = ERA5() + wm.fetch( + os.path.join(TEST_DIR, 'test_geom', 'test_era5.nc'), + np.array([10, 10.2, -72, -72]), + datetime(2020, 1, 1, 0, 0, 0) + ) + +@pytest.mark.skip +def test_era5t(): + wm = ERA5T() + wm.fetch( + os.path.join(TEST_DIR, 'test_geom', 'test_era5t.nc'), + np.array([10, 10.2, -72, -72]), + datetime(2020, 1, 1, 0, 0, 0) + ) + + +def test_erai(): + wm = ERAI() + wm.fetch( + os.path.join(TEST_DIR, 'test_geom', 'test_erai.nc'), + np.array([10, 10.2, -72, -72]), + datetime(2017, 1, 1, 0, 0, 0) + ) diff --git a/test/weather_model/test_processWM.py b/test/weather_model/test_processWM.py new file mode 100644 index 000000000..e0dbbb4e1 --- /dev/null +++ b/test/weather_model/test_processWM.py @@ -0,0 +1,51 @@ +import os +import pytest + +import numpy as np + +from test import TEST_DIR + +from RAiDER.models.era5 import ERA5 + + +SCENARIO_DIR = os.path.join(TEST_DIR, "scenario_1") + + +@pytest.fixture +def getWM(): + """The bounds of the ERA model are: + + BoundingBox(left=-103.7750015258789, + bottom=15.225000381469727, + right=-99.2750015258789, + top=18.725000381469727) + """ + wm = ERA5() + wm.files = [ + os.path.join( + SCENARIO_DIR, 'weather_files/ERA-5_2020_01_03_T23_00_00.nc' + ) + ] + return wm + + +def test_checkContainment(getWM): + if os.path.exists(getWM.files[0]): + wm = getWM + ll_bounds = (10, 20, -100, -100) + # outLats = np.linspace(10, 20) + # outLons = -100 * np.ones(outLats.shape) + + containment = wm.checkContainment(ll_bounds) + assert(~containment) + + +def test_checkContainment2(getWM): + if os.path.exists(getWM.files[0]): + wm = getWM + # outLats = np.linspace(17, 18) + # outLons = -100 * np.ones(outLats.shape) + ll_bounds = (17, 18, -100, -100) + + containment = wm.checkContainment(ll_bounds) + assert(containment) diff --git a/test/weather_model/test_weather_model.py b/test/weather_model/test_weather_model.py new file mode 100644 index 000000000..0cf5cc633 --- /dev/null +++ b/test/weather_model/test_weather_model.py @@ -0,0 +1,456 @@ +import datetime +import os +import operator +import pytest + +import isce3.ext.isce3 as isce +import numpy as np + +from functools import reduce +from isce3.core import DateTime, TimeDelta +from numpy import nan +from scipy.interpolate import RegularGridInterpolator as rgi +from pyproj import CRS + +from RAiDER.constants import _ZMIN, _ZREF +from RAiDER.delay import build_cube_ray +from RAiDER.losreader import state_to_los +from RAiDER.models.weatherModel import ( + WeatherModel, + find_svp, + make_raw_weather_data_filename, + make_weather_model_filename, +) +from RAiDER.models.erai import ERAI +from RAiDER.models.era5 import ERA5 +from RAiDER.models.era5t import ERA5T +from RAiDER.models.hres import HRES +from RAiDER.models.hrrr import HRRR +from RAiDER.models.gmao import GMAO +from RAiDER.models.merra2 import MERRA2 +from RAiDER.models.ncmr import NCMR + + +_LON0 = 0 +_LAT0 = 0 +_OMEGA = 0.1 / (180/np.pi) + + +@pytest.fixture +def erai(): + wm = ERAI() + return wm + + +@pytest.fixture +def era5(): + wm = ERA5() + return wm + + +@pytest.fixture +def era5t(): + wm = ERA5T() + return wm + + +@pytest.fixture +def hres(): + wm = HRES() + return wm + + +@pytest.fixture +def gmao(): + wm = GMAO() + return wm + + +@pytest.fixture +def merra2(): + wm = MERRA2() + return wm + + +@pytest.fixture +def hrrr(): + wm = HRRR() + return wm + + +@pytest.fixture +def ncmr(): + wm = NCMR() + return wm + + +def product(iterable): + return reduce(operator.mul, iterable, 1) + + +class MockWeatherModel(WeatherModel): + """Implement abstract methods for testing.""" + + def __init__(self): + super().__init__() + + self._k1 = 1 + self._k2 = 1 + self._k3 = 1 + + self._Name = "MOCK" + self._valid_range = (datetime.datetime(1970, 1, 1), "Present") + self._lag_time = datetime.timedelta(days=15) + + def _fetch(self, ll_bounds, time, out): + pass + + def load_weather(self, *args, **kwargs): + _N_Z = 32 + self._ys = np.arange(-2,3) + _LAT0 + self._xs = np.arange(-3,4) + _LON0 + self._zs = np.linspace(0, 1e5, _N_Z) + self._t = np.ones((len(self._ys), len(self._xs), _N_Z)) + self._e = self._t.copy() + self._e[:,3:,:] = 2 + + _p = np.arange(31, -1, -1) + self._p = np.broadcast_to(_p, self._t.shape) + + self._true_hydro_refr = np.broadcast_to(_p, (self._t.shape)) + self._true_wet_ztd = 1e-6 * 2 * np.broadcast_to(np.flip(self._zs), (self._t.shape)) + self._true_wet_ztd[:,3:] = 2 * self._true_wet_ztd[:,3:] + + self._true_hydro_ztd = np.zeros(self._t.shape) + for layer in range(len(self._zs)): + self._true_hydro_ztd[:,:,layer] = 1e-6 * 0.5 * (self._zs[-1] - self._zs[layer]) * _p[layer] + + self._true_wet_refr = 2 * np.ones(self._t.shape) + self._true_wet_refr[:,3:] = 4 + + def interpWet(self): + _ifWet = rgi((self._ys, self._xs, self._zs), self._true_wet_refr) + return _ifWet + def interpHydro(self): + _ifHydro = rgi((self._ys, self._xs, self._zs), self._true_hydro_refr) + return _ifHydro + + +@pytest.fixture +def model(): + return MockWeatherModel() + + +@pytest.fixture +def setup_fake_raytracing(): + '''This sets up a fake orbit for the weather model''' + lat0 = _LAT0 # degrees + lon0 = _LON0 + hsat = 700000. + omega = _OMEGA # degrees + Nvec = 30 + + t0 = DateTime("2017-02-12T01:12:30.0") + + elp = isce.core.Ellipsoid(6378137.,.0066943799901) + look = isce.core.LookSide.Left + + sat_hgt = elp.a + hsat + sat_lat = np.sin(np.radians(lat0)) + clat = np.cos(np.radians(lat0)) + + svs = [] + for k in range(Nvec): + dt = TimeDelta(100 * k) + lon = lon0 + omega * dt.total_seconds() + + pos = [] + pos.append(sat_hgt * clat * np.cos(np.radians(lon))) + pos.append(sat_hgt * clat * np.sin(np.radians(lon))) + pos.append(sat_hgt * sat_lat) + + vel = [] + vel.append(-omega * pos[1]) + vel.append(omega * pos[0]) + vel.append(0.) + + epoch = t0 + dt + + svs.append( + isce.core.StateVector(epoch,pos, vel) + ) + + orb = isce.core.Orbit(svs) + + return orb, look, elp, sat_hgt + +def solve(R, hsat, ellipsoid, side='left'): + # temp = 1.0 + hsat/ellipsoid.a + # temp1 = R/ellipsoid.a + # temp2 = R/(ellipsoid.a + hsat) + t2 = (np.square(hsat) + np.square(R)) - np.square(ellipsoid.a) + # cosang = 0.5 * (temp + (1.0/temp) - temp1 * temp2) + cosang = 0.5 * t2 / (R * hsat) + angdiff = np.arccos(cosang) + if side=='left': + x = _LAT0 + angdiff + else: + x = _LAT0 - angdiff + return x + + +def test_llhs(setup_fake_raytracing, model): + orb, look_dir, elp, sat_hgt = setup_fake_raytracing + llhs = [] + for k in range(20): + tinp = 5 + k * 2 + rng = 800000 + 1000 * k + expLon = _LON0 + _OMEGA * tinp + geocentricLat = solve(rng, sat_hgt, elp) + + xyz = [ + elp.a * np.cos(geocentricLat) * np.cos(expLon), + elp.a * np.cos(geocentricLat) * np.sin(expLon), + elp.a * np.sin(geocentricLat) + ] + llh = elp.xyz_to_lon_lat(xyz) + llhs.append(llh) + + assert len(llhs) == 20 + +@pytest.mark.skip +def test_build_cube_ray(setup_fake_raytracing, model): + orb, look_dir, elp, _ = setup_fake_raytracing + m = model + m.load_weather() + + ys = np.arange(-1,1) + _LAT0 + xs = np.arange(-1,1) + _LON0 + zs = np.arange(0, 1e5, 1e3) + + _Y, _X, _Z = np.meshgrid(ys, xs, zs) + + out_true = np.zeros(_Y.shape) + t0 = orb.start_time + tm1 = orb.end_time + ts = np.arange(t0, tm1 + orb.time.spacing, orb.time.spacing) + tlist = [orb.reference_epoch + isce.core.TimeDelta(dt) for dt in ts] + ts = np.broadcast_to(tlist, (1, len(tlist))).T + svs = np.hstack([ts, orb.position, orb.velocity]) + + #TODO: Check that the look vectors are not nans + lv, xyz = state_to_los(svs, np.stack([_Y.ravel(), _X.ravel(), _Z.ravel()], axis=-1),out="ecef") + out = build_cube_ray(xs, ys, zs, orb, look_dir, CRS(4326), CRS(4326), [m.interpWet(), m.interpHydro()], elp=elp) + assert out.shape == out_true.shape + + +def test_weatherModel_basic1(model): + wm = model + assert wm._zmin == _ZMIN + assert wm._zmax == _ZREF + assert wm.Model() == 'MOCK' + + # check some defaults + assert wm._humidityType == 'q' + + wm.setTime(datetime.datetime(2020, 1, 1, 6, 0, 0)) + assert wm._time == datetime.datetime(2020, 1, 1, 6, 0, 0) + + wm.setTime('2020-01-01T00:00:00') + assert wm._time == datetime.datetime(2020, 1, 1, 0, 0, 0) + + wm.setTime('19720229', fmt='%Y%m%d') # test a leap year + assert wm._time == datetime.datetime(1972, 2, 29, 0, 0, 0) + + with pytest.raises(RuntimeError): + wm.checkTime(datetime.datetime(1950, 1, 1)) + + wm.checkTime(datetime.datetime(2000, 1, 1)) + + with pytest.raises(RuntimeError): + wm.checkTime(datetime.datetime.now()) + + +def test_uniform_in_z_small(model): + # Uneven z spacing, but averages to [1, 2] + model._zs = np.array([ + [[1., 2.], + [0.9, 1.1]], + + [[1., 2.6], + [1.1, 2.3]] + ]) + model._xs = np.array([1, 2, 2]) + model._ys = np.array([2, 3, 2]) + model._p = np.arange(8).reshape(2, 2, 2) + model._t = model._p * 2 + model._e = model._p * 3 + model._lats = model._zs # for now just passing in dummy arrays for lats, lons + model._lons = model._zs + + model._uniform_in_z() + + # Note that when the lower bound is exactly equal we get a value, but + # when the upper bound is exactly equal we get the fill + interpolated = np.array([ + [[0, nan], + [2.5, nan]], + + [[4., 4.625], + [nan, 6.75]] + ]) + + assert np.allclose(model._p, interpolated, equal_nan=True, rtol=0) + assert np.allclose(model._t, interpolated * 2, equal_nan=True, rtol=0) + assert np.allclose(model._e, interpolated * 3, equal_nan=True, rtol=0) + + assert np.allclose(model._zs, np.array([1, 2]), rtol=0) + assert np.allclose(model._xs, np.array([1, 2]), rtol=0) + assert np.allclose(model._ys, np.array([2, 3]), rtol=0) + + +def test_uniform_in_z_large(model): + shape = (400, 500, 40) + x, y, z = shape + size = product(shape) + + # Uneven spacing that averages to approximately [1, 2, ..., 39, 40] + zlevels = np.arange(1, shape[-1] + 1) + model._zs = np.random.normal(1, 0.1, size).reshape(shape) * zlevels + model._xs = np.empty(0) # Doesn't matter + model._ys = np.empty(0) # Doesn't matter + model._p = np.tile(np.arange(y).reshape(-1, 1) * np.ones(z), (x, 1, 1)) + model._t = model._p * 2 + model._e = model._p * 3 + model._lats = model._zs # for now just passing in dummy arrays for lats, lons + model._lons = model._zs + + assert model._p.shape == shape + model._uniform_in_z() + + interpolated = np.tile(np.arange(y), (x, 1)) + + assert np.allclose(np.nanmean(model._p, axis=-1), + interpolated, equal_nan=True, rtol=0) + assert np.allclose(np.nanmean(model._t, axis=-1), + interpolated * 2, equal_nan=True, rtol=0) + assert np.allclose(np.nanmean(model._e, axis=-1), + interpolated * 3, equal_nan=True, rtol=0) + + assert np.allclose(model._zs, zlevels, atol=0.05, rtol=0) + + +def test_mwmf(): + name = 'ERA-5' + time = datetime.datetime(2020, 1, 1) + ll_bounds = (-90, 90, -180, 180) + assert make_weather_model_filename(name, time, ll_bounds) == \ + 'ERA-5_2020_01_01_T00_00_00_90S_90N_180W_180E.nc' + + +def test_mrwmf(): + outLoc = './' + name = 'ERA-5' + time = datetime.datetime(2020, 1, 1) + assert make_raw_weather_data_filename(outLoc, name, time) == \ + './ERA-5_2020_01_01_T00_00_00.nc' + + +def test_erai(erai): + wm = erai + assert wm._humidityType == 'q' + assert wm._Name == 'ERA-I' + assert wm._valid_range[0] == datetime.datetime(1979, 1, 1) + assert wm._valid_range[1] == datetime.datetime(2019, 8, 31) + assert wm._proj.to_epsg() == 4326 + + +def test_era5(era5): + wm = era5 + assert wm._humidityType == 'q' + assert wm._Name == 'ERA-5' + assert wm._valid_range[0] == datetime.datetime(1950, 1, 1) + assert wm._proj.to_epsg() == 4326 + + +def test_era5t(era5t): + wm = era5t + assert wm._humidityType == 'q' + assert wm._Name == 'ERA-5T' + assert wm._valid_range[0] == datetime.datetime(1950, 1, 1) + assert wm._proj.to_epsg() == 4326 + + +def test_hres(hres): + wm = hres + assert wm._humidityType == 'q' + assert wm._Name == 'HRES' + assert wm._valid_range[0] == datetime.datetime(1983, 4, 20) + assert wm._proj.to_epsg() == 4326 + assert wm._levels == 137 + + wm.update_a_b() + assert wm._levels == 91 + + +def test_gmao(gmao): + wm = gmao + assert wm._humidityType == 'q' + assert wm._Name == 'GMAO' + assert wm._valid_range[0] == datetime.datetime(2014, 2, 20) + assert wm._proj.to_epsg() == 4326 + + +def test_merra2(merra2): + wm = merra2 + assert wm._humidityType == 'q' + assert wm._Name == 'MERRA2' + assert wm._valid_range[0] == datetime.datetime(1980, 1, 1) + assert wm._proj.to_epsg() == 4326 + + +def test_hrrr(hrrr): + wm = hrrr + assert wm._humidityType == 'q' + assert wm._Name == 'HRRR' + assert wm._valid_range[0] == datetime.datetime(2016, 7, 15) + assert wm._proj.to_epsg() is None + + +def test_ncmr(ncmr): + wm = ncmr + assert wm._humidityType == 'q' + assert wm._Name == 'NCMR' + assert wm._valid_range[0] == datetime.datetime(2015, 12, 1) + + +def test_find_svp(): + t = np.arange(0, 100, 10) + 273.15 + svp_test = find_svp(t) + svp_true = np.array([ + 611.21, 1227.5981, 2337.2825, 4243.5093, + 7384.1753, 12369.2295, 20021.443, 31419.297, + 47940.574, 71305.16 + ]) + assert np.allclose(svp_test, svp_true) + + +def test_ztd(model): + m = model + m.load_weather() + + # wet refractivity will vary + m._get_wet_refractivity() + assert np.allclose(m._wet_refractivity, m._true_wet_refr) + + # hydro refractivity should be all the same + m._get_hydro_refractivity() + assert np.allclose( + m._hydrostatic_refractivity, + m._true_hydro_refr, + ) + + m._getZTD() + + assert np.allclose(m._wet_ztd, m._true_wet_ztd) + assert np.allclose(m._hydrostatic_ztd, m._true_hydro_ztd) diff --git a/tools/RAiDER/__init__.py b/tools/RAiDER/__init__.py index e69de29bb..9c933fb7a 100644 --- a/tools/RAiDER/__init__.py +++ b/tools/RAiDER/__init__.py @@ -0,0 +1,11 @@ +""" +Raytracing Atmospheric Delay Estimation for RADAR + +Copyright (c) 2019-2022, California Institute of Technology ("Caltech"). All rights reserved. +""" +from importlib.metadata import version + +__version__ = version(__name__) +__copyright__ = 'Copyright (c) 2019-2022, California Institute of Technology ("Caltech"). All rights reserved.' + +__all__ = ['__version__', '__copyright__'] diff --git a/tools/RAiDER/checkArgs.py b/tools/RAiDER/checkArgs.py index e37cc90d3..774b548f5 100644 --- a/tools/RAiDER/checkArgs.py +++ b/tools/RAiDER/checkArgs.py @@ -1,154 +1,99 @@ -import numpy as np +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ import os -from utils.util import gdal_trans -def checkArgs(args, p): +import pandas as pd + +from datetime import datetime + +from RAiDER.losreader import Zenith +from RAiDER.llreader import BoundingBox + + +def checkArgs(args): ''' - Helper fcn for checking argument compatibility and returns the + Helper fcn for checking argument compatibility and returns the correct variables ''' - import models - - if args.heightlvs is not None and args.outformat != 'hdf5': - raise ValueError('HDF5 must be used with height levels') - if args.area is None and args.bounding_box is None and args.wmnetcdf is None and args.station_file is None: - raise ValueError('You must specify one of the following: \n \ - (1) lat/lon files, (2) bounding box, (3) weather model files, or\n \ - (4) station file containing Lat and Lon columns') - - # Line of sight - if args.lineofsight is not None: - los = ('los', args.lineofsight) - elif args.statevectors is not None: - los = ('sv', args.statevectors) - else: - from utils.constants import Zenith - los = Zenith - - # Area - if args.area is not None: - lat, lon = args.area - lonFileName = '{}_Lon_{}.dat'.format(weather_model_name, - dt.strftime(time, '%Y_%m_%d_T%H_%M_%S')) - latFileName = '{}_Lat_{}.dat'.format(weather_model_name, - dt.strftime(time, '%Y_%m_%d_T%H_%M_%S')) - gdal_trans(lat, os.path.join(args.out, 'geom', latFileName), 'VRT') - gdal_trans(lon, os.path.join(args.out, 'geom', lonFileName), 'VRT') - - elif args.bounding_box is not None: - N,W,S,E = args.bounding_box - lat = np.array([float(N), float(S)]) - lon = np.array([float(E), float(W)]) - - if (lat[0] == lat[1]) | (lon[0]==lon[1]): - raise RuntimeError('You have passed a zero-size bounding box: {}'.format(args.bounding_box)) - - elif args.station_file is not None: - lat, lon = readLLFromStationFile(args.station_file) - - else: - lat = lon = None - - # DEM - if args.dem is not None: - heights = ('dem', args.dem) - elif args.heightlvs is not None: - heights = ('lvs', args.heightlvs) - else: - heights = ('download', None) - - # Weather - if args.model == 'WRF': - if args.wmnetcdf is not None: - p.error('Argument --wmnetcdf invalid with --model WRF') - if args.wrfmodelfiles is not None: - weathers = {'type': 'wrf', 'files': args.wrfmodelfiles, - 'name': 'wrf'} - else: - p.error('Argument --wrfmodelfiles required with --model WRF') - elif args.model=='ERA5' or args.model == 'ERA-5': - from models.era5 import ERA5 - weathers = {'type': ERA5(), 'files':None, 'name':'ERA-5'} - elif args.model=='pickle': - import pickle - weathers = {'type':'pickle', 'files': args.pickleFile, 'name': 'pickle'} - else: - model_module_name = mangle_model_to_module(args.model) - try: - import importlib - model_module = importlib.import_module(model_module_name) - except ImportError: - p.error("Couldn't find a module named {}, ".format(repr(model_module_name))+ - "needed to load weather model {}".format(repr(args.model))) - if args.wmnetcdf is not None: - weathers = {'type': model_module.Model(), 'files': args.wmnetcdf, - 'name': args.model} - elif args.time is None: - p.error('Must specify one of --wmnetcdf or --time (so I can ' - 'figure out what to download)') - elif lat is None: - p.error('Must specify one of --wmnetcdf or --area (so I can ' - 'figure out what to download)') + + ######################################################################################################################### + # Directories + if not os.path.exists(args.weather_model_directory): + os.mkdir(args.weather_model_directory) + + ######################################################################################################################### + # Date and Time parsing + args.date_list = [datetime.combine(d, args.time) for d in args.date_list] + + ######################################################################################################################### + # filenames + wetNames, hydroNames = [], [] + for d in args.date_list: + if not args.aoi is not BoundingBox: + if args.station_file is not None: + wetFilename = os.path.join( + args.output_directory, + '{}_Delay_{}.csv' + .format( + args.weather_model, + args.time.strftime('%Y%m%dT%H%M%S'), + ) + ) + hydroFilename = wetFilename + + # copy the input file to the output location for editing + indf = pd.read_csv(args.query_area).drop_duplicates(subset=["Lat", "Lon"]) + indf.to_csv(wetFilename, index=False) + + else: + wetNames.append(None) + hydroNames.append(None) else: - weathers = {'type': model_module.Model(), 'files': None, - 'name': args.model} - # zref - zref = args.zref - - # output file format - if args.heightlvs is not None: - if args.outformat.lower() != 'hdf5': - print("WARNING: input arguments require HDF5 output file type; changing outformat to HDF5") - outformat = 'hdf5' - elif args.station_file is not None: - if args.outformat.lower() != 'netcdf': - print("WARNING: input arguments require HDF5 output file type; changing outformat to HDF5") - outformat = 'netcdf' - else: - if args.outformat.lower() == 'hdf5': - print("WARNING: output require raster output file; changing outformat to ENVI") - outformat = 'ENVI' - else: - outformat = args.outformat.lower() - - # parallelization - parallel = True if not args.no_parallel else False - - # other - time = args.time - out = args.out - download_only = args.download_only - verbose = args.verbose - - if args.area is not None: - flag = 'files' - elif args.bounding_box is not None: - flag = 'bounding_box' - elif args.station_file is not None: - flag = 'station_file' - else: - flag = None - - return los, lat, lon, heights, flag, weathers, zref, outformat, time, out, download_only, parallel, verbose - - -def output_format(outformat): - """ - Reduce the outformat strings users can specifiy to a select consistent set that can be used for filename extensions. - """ - # convert the outformat to lower letters - outformat = outformat.lower() - - # capture few specific cases: - outformat_dict = {} - outformat_dict['hdf5'] = 'h5' - outformat_dict['hdf'] = 'h5' - outformat_dict['h5'] = 'h5' - outformat_dict['envi'] = 'envi' - - try: - outformat = outformat_dict[outformat] - except: - raise NotImplemented - return outformat + wetFilename, hydroFilename = makeDelayFileNames( + d, + args.los, + args.raster_format, + args.weather_model._dataset.upper(), + args.output_directory, + ) + + wetNames.append(wetFilename) + hydroNames.append(hydroFilename) + + args.wetFilenames = wetNames + args.hydroFilenames = hydroNames + + return args + + +def makeDelayFileNames(time, los, outformat, weather_model_name, out): + ''' + return names for the wet and hydrostatic delays. + + # Examples: + >>> makeDelayFileNames(datetime(2020, 1, 1, 0, 0, 0), None, "h5", "model_name", "some_dir") + ('some_dir/model_name_wet_00_00_00_ztd.h5', 'some_dir/model_name_hydro_00_00_00_ztd.h5') + >>> makeDelayFileNames(None, None, "h5", "model_name", "some_dir") + ('some_dir/model_name_wet_ztd.h5', 'some_dir/model_name_hydro_ztd.h5') + ''' + format_string = "{model_name}_{{}}_{time}{los}.{ext}".format( + model_name=weather_model_name, + time=time.strftime("%Y%m%dT%H%M%S_") if time is not None else "", + los="ztd" if (isinstance(los, Zenith) or los is None) else "std", + ext=outformat + ) + hydroname, wetname = ( + format_string.format(dtyp) for dtyp in ('hydro', 'wet') + ) + + hydro_file_name = os.path.join(out, hydroname) + wet_file_name = os.path.join(out, wetname) + return wet_file_name, hydro_file_name + + diff --git a/tools/RAiDER/cli/__init__.py b/tools/RAiDER/cli/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tools/RAiDER/cli/parser.py b/tools/RAiDER/cli/parser.py new file mode 100644 index 000000000..4684da73e --- /dev/null +++ b/tools/RAiDER/cli/parser.py @@ -0,0 +1,41 @@ +import os + +from RAiDER.cli.validators import BBoxAction, IntegerMappingType + +def add_cpus(parser): + parser.add_argument( + '--cpus', + help='The number of cpus to be used for multiprocessing or "all" for ' + 'all available cpus.', + type=IntegerMappingType(0, all=os.cpu_count()), + default=8, + ) + + +def add_verbose(parser): + parser.add_argument( + '--verbose', '-v', + help='Run in verbose mode', + action='count', + default=0 + ) + + +def add_out(parser): + parser.add_argument( + '--out', + help='Output directory', + default='.' + ) + + +def add_bbox(parser): + parser.add_argument( + '--bbox', '-b', + help="Bounding box", + nargs=4, + type=float, + dest='query_area', + action=BBoxAction, + metavar=('S', 'N', 'W', 'E') + ) diff --git a/tools/RAiDER/cli/raider.py b/tools/RAiDER/cli/raider.py new file mode 100644 index 000000000..b417732b7 --- /dev/null +++ b/tools/RAiDER/cli/raider.py @@ -0,0 +1,260 @@ +import argparse +import os +import shutil +import sys +import yaml +import re, glob + +import RAiDER +from RAiDER.constants import _ZREF, _CUBE_SPACING_IN_M +from RAiDER.logger import logger, logging +from RAiDER.cli.validators import (enforce_time, enforce_bbox, parse_dates, + get_query_region, get_heights, get_los, enforce_wm) + +from RAiDER.checkArgs import checkArgs +from RAiDER.delay import main as main_delay + + +HELP_MESSAGE = """ +Command line options for RAiDER processing. Default options can be found by running +raider.py --generate_config + +Download a weather model and calculate tropospheric delays +""" + +SHORT_MESSAGE = """ +Program to calculate troposphere total delays using a weather model +""" + +EXAMPLES = """ +Usage examples: +raider.py -g +raider.py customTemplatefile.cfg +""" + +class AttributeDict(dict): + __getattr__ = dict.__getitem__ + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + +DEFAULT_DICT = dict( + look_dir='right', + date_start=None, + date_end=None, + date_step=None, + date_list=None, + time=None, + end_time=None, + weather_model=None, + lat_file=None, + lon_file=None, + station_file=None, + bounding_box=None, + geocoded_file=None, + dem=None, + use_dem_latlon=False, + height_levels=None, + height_file_rdr=None, + ray_trace=False, + zref=_ZREF, + cube_spacing_in_m=_CUBE_SPACING_IN_M, # TODO - Where are these parsed? + los_file=None, + los_convention='isce', + los_cube=None, + orbit_file=None, + verbose=True, + raster_format='GTiff', + output_directory=os.getcwd(), + weather_model_directory=os.path.join( + os.getcwd(), + 'weather_files' + ), + output_projection='EPSG:4236', + ) + + +def create_parser(): + """Parse command line arguments using argparse.""" + p = argparse.ArgumentParser( + formatter_class = argparse.RawDescriptionHelpFormatter, + description = HELP_MESSAGE, + epilog = EXAMPLES, + ) + + p.add_argument( + 'customTemplateFile', nargs='?', + help='custom template with option settings.\n' + + "ignored if the default smallbaselineApp.cfg is input." + ) + + p.add_argument( + '-g', '--generate_template', + dest='generate_template', + action='store_true', + help='generate default template (if it does not exist) and exit.' + ) + + + p.add_argument( + '--download-only', + action='store_true', + help='only download a weather model.' + ) + + return p + + +def parseCMD(iargs=None): + """ + Parse command-line arguments and pass to delay.py + """ + + p = create_parser() + args = p.parse_args(args=iargs) + + args.argv = iargs if iargs else sys.argv[1:] + + # default input file + template_file = os.path.join( + os.path.dirname( + RAiDER.__file__ + ), + 'cli', 'raider.yaml' + ) + if '-g' in args.argv: + dst = os.path.join(os.getcwd(), 'raider.yaml') + shutil.copyfile( + template_file, + dst, + ) + + logger.info('Wrote %s', dst) + sys.exit(0) + + # check: existence of input template files + if (not args.customTemplateFile + and not os.path.isfile(os.path.basename(template_file)) + and not args.generate_template): + p.print_usage() + print(EXAMPLES) + + msg = "No template file found! It requires that either:" + msg += "\n a custom template file, OR the default template " + msg += "\n file 'raider.yaml' exists in current directory." + raise SystemExit(f'ERROR: {msg}') + + if args.customTemplateFile: + # check the existence + if not os.path.isfile(args.customTemplateFile): + raise FileNotFoundError(args.customTemplateFile) + + args.customTemplateFile = os.path.abspath(args.customTemplateFile) + + return args + + +def read_template_file(fname): + """ + Read the template file into a dictionary structure. + Parameters: fname - str, full path to the template file + delimiter - str, string to separate the key and value + skip_chars - list of str, skip certain charaters in values + Returns: template - dict, file content + Examples: template = read_template('raider.yaml') + + Modified from MintPy's 'read_template' + """ + with open(fname, 'r') as f: + try: + params = yaml.safe_load(f) + except yaml.YAMLError as exc: + print(exc) + raise ValueError('Something is wrong with the yaml file {}'.format(fname)) + + # Drop any values not specified + params = drop_nans(params) + + # Need to ensure that all the groups exist, even if they are not specified by the user + group_keys = ['date_group', 'time_group', 'aoi_group', 'height_group', 'los_group', 'runtime_group'] + for key in group_keys: + if not key in params.keys(): + params[key] = {} + + # Parse the user-provided arguments + template = DEFAULT_DICT + for key, value in params.items(): + if key == 'runtime_group': + for k, v in value.items(): + if v is not None: + template[k] = v + if key == 'weather_model': + template[key]= enforce_wm(value) + if key == 'time_group': + template.update(enforce_time(AttributeDict(value))) + if key == 'date_group': + template['date_list'] = parse_dates(AttributeDict(value)) + if key == 'aoi_group': + ## in case a DEM is passed and should be used + dct_temp = {**AttributeDict(value), + **AttributeDict(params['height_group'])} + template['aoi'] = get_query_region(AttributeDict(dct_temp)) + + if key == 'los_group': + template['los'] = get_los(AttributeDict(value)) + if key == 'look_dir': + if value.lower() not in ['right', 'left']: + raise ValueError(f"Unknown look direction {value}") + template['look_dir'] = value.lower() + + # Have to guarantee that certain variables exist prior to looking at heights + for key, value in params.items(): + if key == 'height_group': + template.update( + get_heights( + AttributeDict(value), + template['output_directory'], + template['station_file'], + template['bounding_box'], + ) + ) + return AttributeDict(template) + + +def drop_nans(d): + for key in list(d.keys()): + if d[key] is None: + del d[key] + elif isinstance(d[key], dict): + for k in list(d[key].keys()): + if d[key][k] is None: + del d[key][k] + return d + + +########################################################################## +def main(iargs=None): + # parse + inps = parseCMD(iargs) + + # Read the template file + params = read_template_file(inps.customTemplateFile) + + # Argument checking + params = checkArgs(params) + + params['download_only'] = inps.download_only + + if not params.verbose: + logger.setLevel(logging.INFO) + + + for t, w, f in zip( + params['date_list'], + params['wetFilenames'], + params['hydroFilenames'] + ): + try: + (_, _) = main_delay(t, w, f, params) + except RuntimeError: + logger.exception("Date %s failed", t) + continue diff --git a/tools/RAiDER/cli/raider.yaml b/tools/RAiDER/cli/raider.yaml new file mode 100644 index 000000000..1ebe8ee24 --- /dev/null +++ b/tools/RAiDER/cli/raider.yaml @@ -0,0 +1,127 @@ +# vim: set filetype=yaml: +##------------------------ raiderDelay.yaml ------------------------## +## +## There are three basic options for calculating tropospheric delays: +## 1. Calculate Zenith delays (ZTD; Default option if nothing else is passed) +## 2. Calculate slant delays (STD) by projecting ZTD using the incidence angle: +## STD = ZTD / cos(incidence_angle) +## 3. Calculate slant delays (STD) using raytracing: +## STD = 1e-6 * \int_H^{Top} N(l(x,y,z)) dl +## where H is the ground pixel elevation, "Top" is the top of the troposphere, +## N is the refractivity (point-wise delay), and l is a ray which is traced +## from the ground pixel to the top of the troposphere. +## +## In addition, RAiDER supports a number of options for specifying query points, +## heights, and different weather models. Full options are listed below. + +########## PARAMETERS +## Satellite look direction: +## Sentinel-1: right +## NISAR: left +look_dir: right + + +########## 1. Weather model +## REQUIRED: TRUE +## FORMATS: string, Select name from list below +## +## Currently implemented weather models include: +## ERA-5, ERA-5T, HRES, ERA-I, NCMR, HRRR, GMAO, MERRA-2 +## See https://github.com/dbekaert/RAiDER/blob/10686ee3f3533a33ca0788d866003c363f58fd5e/WeatherModels.md +## for more details and information on licensing +weather_model: + + +########## 2. Date +## REQUIRED: TRUE +## FORMATS: YYYYMMDD +## +## Dates can be a single date, two dates that define a set of dates every day, or two dates with an interval +## These will be ignored in the case that date_list is specified +date_group: + date_start: + date_end: + date_step: # date interval to download. Default is 1, i.e. every day between date_start and date_end + + ## Alternatively, you can also directly specify a comma-delimited list. + date_list: # e.g. [20200101, 20200201, 20200301] + + +########## 3. Time in UTC +## REQUIRED: TRUE +## FORMATS: HH:MM:SS; HH:MM +## +## Time is in UTC time and should be specified as HH:MM:SS or HH:MM +## The specified time should be the start time of the acquisition to within a minute +## end_time will be the end time of the acquition, if not supplied it will be assumed to be 30 seconds post start time +## For downloading weather models, RAiDER currently rounds to the nearest hour rather than interpolating +time_group: + time: + end_time: + + +########## 4. Area of Interest +## REQUIRED: FALSE +## FORMATS: string or list of floats +## +## There are several options for specifying query points +## 1. A bounding box in lat/lon specified as a space-separated list: South North West East +## 2. Specify a geocoded file, e.g. ARIA GUNW product, from which the AOI will be determined +## 3/4. lat/lon raster files (such as those produced by the ISCE software) +## 5. A comma-delimited file (station_file) containing at least the columns Lat and Lon, and optionally Hgt_m +aoi_group: + bounding_box: + geocoded_file: + lat_file: + lon_file: + station_file: + + +########## 5. Height info +## REQUIRED: FALSE +## FORMATS: None, string, list of floats +## +## Height information is used from one of the following possible sources: +## 1. (Default for bounding box) Weather model height levels (model-specific) +## 2. (Default for lat/lon points) Copernicus 30m DEM (GLO-30), downloaded on the fly +## 3. Georeferenced DEM +## NOTE: If "use_dem_latlon" is set to true, then delays will be calculated at the DEM pixels. +## 4. Height file in radar coordinates matching lat/lon input files (Query points Option 1) +## 5. List of height levels, which will be used for all input query points +height_group: + dem: + use_dem_latlon: False + height_file_rdr: + height_levels: + + +########## 6. Line-of-sight or zenith calculations +## REQUIRED: FALSE +## FORMATS: string +## +## ZTD calculation: No additional inputs required +## STD calculation: +los_group: + ray_trace: False # Use projected slant delay by default + zref: # Integration height. Only used in raytracing. + + # raster file in radar or geocoordinates + los_file: + los_convention: isce # can be "isce" or "hyp3", see *** for details + + # NETCDF (HDF5?) file containing look vectors, see *** for details + los_cube: + + # File containing orbit statevectors, see *** for details + orbit_file: + + +########## 7. Run-time parameters +## REQUIRED: FALSE +## +runtime_group: + verbose: True + raster_format: GTiff # Can be any rasterio-compatible format + output_directory: . # uses the runtime directory by default + weather_model_directory: # Defaults to /weather_files/ + output_projection: # Specify the PROJ-compatible projection of the output delays as an EPSG code diff --git a/tools/RAiDER/cli/statsPlot.py b/tools/RAiDER/cli/statsPlot.py new file mode 100755 index 000000000..9b0aa45ff --- /dev/null +++ b/tools/RAiDER/cli/statsPlot.py @@ -0,0 +1,2150 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Simran Sangha, Jeremy Maurer, & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +from RAiDER.logger import logger, logging +from RAiDER.cli.parser import add_cpus +from RAiDER.utilFcns import WGS84_to_UTM +from scipy import optimize +from scipy import sum as scipy_sum +from scipy.optimize import OptimizeWarning +from shapely.strtree import STRtree +from shapely.geometry import Point, Polygon +from matplotlib import pyplot as plt +import pandas as pd +import numpy as np +import rasterio +import argparse +import copy +import datetime as dt +import itertools +import multiprocessing +import os +import warnings + +import matplotlib as mpl +# must switch to Agg to avoid multiprocessing crashes +mpl.use('Agg') + + +def create_parser(): + """Parse command line arguments using argparse.""" + parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description=""" +Perform basic statistical analyses concerning the spatiotemporal distribution of zenith delays. + +Specifically, make any of the following specified plot(s): +scatterplot of station locations, total empirical and experimental variogram fits for data in each grid cell +(and for each valid time-slice if -variogram_per_timeslice specified), and gridded heatmaps of data, station distribution, +range and sill values associated with experimental variogram fits. The default is to generate all of these. + +Example call to plot gridded station mean delay in a specific time interval : +raiderStats.py -f -grid_delay_mean -ti '2016-01-01 2018-01-01' + +Example call to plot gridded station mean delay in a specific time interval with superimposed gridlines and station scatterplots : +raiderStats.py -f -grid_delay_mean -ti '2016-01-01 2018-01-01' --drawgridlines --stationsongrids + +Example call to plot gridded station variogram in a specific time interval and through explicitly the summer seasons: +raiderStats.py -f -grid_delay_mean -ti '2016-01-01 2018-01-01' --seasonalinterval '06-21 09-21' -variogramplot +""") + + # User inputs + userinps = parser.add_argument_group( + 'User inputs/options for which especially careful review is recommended') + userinps.add_argument('-f', '--file', dest='fname', + type=str, required=True, help='Final output file generated from downloadGNSSDelays.py which contains GPS zenith delays for a specified time period and spatial footprint. ') + userinps.add_argument('-c', '--column_name', dest='col_name', type=str, default='ZTD', + help='Name of the input column to plot. Input assumed to be in units of meters') + userinps.add_argument('-u', '--unit', dest='unit', type=str, default='m', + help='Specified output unit (as distance or time), by default m. Input unit assumed to be m following convention in downloadGNSSDelays.py. Refer to "convert_SI" for supported units. Note if you specify time unit here, you must specify input for "--obs_errlimit" to be in units of m') + userinps.add_argument('-w', '--workdir', dest='workdir', default='./', + help='Specify directory to deposit all outputs. Default is local directory where script is launched.') + add_cpus(userinps) + userinps.add_argument('-verbose', '--verbose', action='store_true', dest='verbose', + help="Run in verbose (debug) mode. Default False") + + # Spatiotemporal subset options + dtsubsets = parser.add_argument_group( + 'Controls for spatiotemporal subsetting.') + dtsubsets.add_argument('-b', '--bounding_box', dest='bounding_box', type=str, default=None, + help="Provide either valid shapefile or Lat/Lon Bounding SNWE. -- Example : '19 20 -99.5 -98.5'") + dtsubsets.add_argument('-sp', '--spacing', dest='spacing', type=float, default='1', + help='Specify spacing of grid-cells for statistical analyses. By default 1 deg.') + dtsubsets.add_argument('-ti', '--timeinterval', dest='timeinterval', type=str, default=None, + help="Subset in time by specifying earliest YYYY-MM-DD date followed by latest date YYYY-MM-DD. -- Example : '2016-01-01 2019-01-01'.") + dtsubsets.add_argument('-si', '--seasonalinterval', dest='seasonalinterval', type=str, default=None, + help="Subset in by an specific interval for each year by specifying earliest MM-DD time followed by latest MM-DD time. -- Example : '03-21 06-21'.") + dtsubsets.add_argument('-oe', '--obs_errlimit', dest='obs_errlimit', type=float, default='inf', + help="Observation error threshold to discard observations with large uncertainties.") + + # Plot formatting/options + pltformat = parser.add_argument_group( + 'Optional controls for plot formatting/options.') + pltformat.add_argument('-figdpi', '--figdpi', dest='figdpi', type=int, + default=100, help='DPI to use for saving figures') + pltformat.add_argument('-title', '--user_title', dest='user_title', type=str, + default=None, help='Specify custom title for plots.') + pltformat.add_argument('-fmt', '--plot_format', dest='plot_fmt', type=str, + default='png', help='Plot format to use for saving figures') + pltformat.add_argument('-cb', '--color_bounds', dest='cbounds', type=str, + default=None, help='List of two floats to use as color axis bounds') + pltformat.add_argument('-cp', '--colorpercentile', dest='colorpercentile', type=float, default=None, nargs=2, + help='Set low and upper percentile for plot colorbars. By default 25%% and 95%%, respectively.') + pltformat.add_argument('-cm', '--colormap', dest='usr_colormap', type=str, default='hot_r', + help='Specify matplotlib colorbar.') + pltformat.add_argument('-dt', '--densitythreshold', dest='densitythreshold', type=int, default='10', + help='For variogram plots, given grid-cell is only valid if it contains this specified threshold of stations. By default 10 stations.') + pltformat.add_argument('-sg', '--stationsongrids', dest='stationsongrids', action='store_true', + help='In gridded plots, superimpose your gridded array with a scatterplot of station locations.') + pltformat.add_argument('-dg', '--drawgridlines', dest='drawgridlines', + action='store_true', help='Draw gridlines on gridded plots.') + pltformat.add_argument('-tl', '--time_lines', dest='time_lines', + action='store_true', help='Draw central longitudinal lines with respect to datetime. Most useful for local-time analyses.') + pltformat.add_argument('-plotall', '--plotall', action='store_true', dest='plotall', + help="Generate all supported plots, including variogram plots.") + pltformat.add_argument('-min_span', '--min_span', dest='min_span', type=float, + default=[2, 0.6], nargs=2, help="Minimum TS span (years) and minimum fractional observations in span (fraction) imposed for seasonal amplitude/phase analyses to be performed for a given station.") + pltformat.add_argument('-period_limit', '--period_limit', dest='period_limit', type=float, + default=0., help="period limit (years) imposed for seasonal amplitude/phase analyses to be performed for a given station.") + + # All plot types + # Station scatter-plots + pltscatter = parser.add_argument_group( + 'Supported types of individual station scatter-plots.') + pltscatter.add_argument('-station_distribution', '--station_distribution', + action='store_true', dest='station_distribution', help="Plot station distribution.") + pltscatter.add_argument('-station_delay_mean', '--station_delay_mean', + action='store_true', dest='station_delay_mean', help="Plot station mean delay.") + pltscatter.add_argument('-station_delay_median', '--station_delay_median', + action='store_true', dest='station_delay_median', help="Plot station median delay.") + pltscatter.add_argument('-station_delay_stdev', '--station_delay_stdev', + action='store_true', dest='station_delay_stdev', help="Plot station delay stdev.") + pltscatter.add_argument('-station_seasonal_phase', '--station_seasonal_phase', + action='store_true', dest='station_seasonal_phase', help="Plot station delay phase/amplitude.") + pltscatter.add_argument('-phaseamp_per_station', '--phaseamp_per_station', + action='store_true', dest='phaseamp_per_station', help="Save debug figures of curve-fit vs data per station.") + + # Gridded plots + pltgrids = parser.add_argument_group('Supported types of gridded plots.') + pltgrids.add_argument('-grid_heatmap', '--grid_heatmap', action='store_true', + dest='grid_heatmap', help="Plot gridded station heatmap.") + pltgrids.add_argument('-grid_delay_mean', '--grid_delay_mean', action='store_true', + dest='grid_delay_mean', help="Plot gridded station-wise mean delay.") + pltgrids.add_argument('-grid_delay_median', '--grid_delay_median', action='store_true', + dest='grid_delay_median', help="Plot gridded station-wise median delay.") + pltgrids.add_argument('-grid_delay_stdev', '--grid_delay_stdev', action='store_true', + dest='grid_delay_stdev', help="Plot gridded station-wise delay stdev.") + pltgrids.add_argument('-grid_seasonal_phase', '--grid_seasonal_phase', action='store_true', + dest='grid_seasonal_phase', help="Plot gridded station-wise delay phase/amplitude.") + pltgrids.add_argument('-grid_delay_absolute_mean', '--grid_delay_absolute_mean', action='store_true', + dest='grid_delay_absolute_mean', help="Plot absolute gridded station mean delay.") + pltgrids.add_argument('-grid_delay_absolute_median', '--grid_delay_absolute_median', action='store_true', + dest='grid_delay_absolute_median', help="Plot absolute gridded station median delay.") + pltgrids.add_argument('-grid_delay_absolute_stdev', '--grid_delay_absolute_stdev', action='store_true', + dest='grid_delay_absolute_stdev', help="Plot absolute gridded station delay stdev.") + pltgrids.add_argument('-grid_seasonal_absolute_phase', '--grid_seasonal_absolute_phase', action='store_true', + dest='grid_seasonal_absolute_phase', help="Plot absolute gridded station delay phase/amplitude.") + pltgrids.add_argument('-grid_to_raster', '--grid_to_raster', action='store_true', + dest='grid_to_raster', help="Save gridded array as raster. May directly load/plot in successive script call.") + + # Variogram plots + pltvario = parser.add_argument_group('Supported types of variogram plots.') + pltvario.add_argument('-variogramplot', '--variogramplot', action='store_true', + dest='variogramplot', help="Plot gridded station variogram.") + pltvario.add_argument('-binnedvariogram', '--binnedvariogram', action='store_true', dest='binnedvariogram', + help="Apply experimental variogram fit to total binned empirical variograms for each time slice. Default is to pass total unbinned empiricial variogram.") + pltvario.add_argument('-variogram_per_timeslice', '--variogram_per_timeslice', action='store_true', dest='variogram_per_timeslice', + help="Generate variogram plots per gridded station AND time-slice.") + pltvario.add_argument('-variogram_errlimit', '--variogram_errlimit', dest='variogram_errlimit', type=float, default='inf', + help="Variogram RMSE threshold to discard grid-cells with large uncertainties.") + + return parser + + +def cmd_line_parse(iargs=None): + parser = create_parser() + return parser.parse_args(args=iargs) + + +def convert_SI(val, unit_in, unit_out): + ''' + Convert input to desired units + ''' + + SI = {'mm': 0.001, 'cm': 0.01, 'm': 1.0, 'km': 1000., + 'mm^2': 1e-6, 'cm^2': 1e-4, 'm^2': 1.0, 'km^2': 1e+6} + + # avoid conversion if output unit in time + if unit_out in ['minute', 'hour', 'day', 'year']: + # adjust if input isn't datetime, and assume it to be part of workflow + # e.g. sigZTD filter, already extracted datetime object + try: + return eval('val.apply(pd.to_datetime).dt.{}.astype(np.float).astype("Int32")'.format(unit_out)) + except BaseException: # TODO: Which error(s)? + return val + + # check if output spatial unit is supported + if unit_out not in SI: + raise Exception("User-specified output unit {} not recognized.".format(unit_out)) + + return val * SI[unit_in] / SI[unit_out] + + +def midpoint(p1, p2): + ''' + Calculate central longitude for '--time_lines' option + ''' + import math + + lat1, lon1, lat2, lon2 = map(math.radians, (p1[0], p1[1], p2[0], p2[1])) + dlon = lon2 - lon1 + dx = math.cos(lat2) * math.cos(dlon) + dy = math.cos(lat2) * math.sin(dlon) + lon3 = lon1 + math.atan2(dy, math.cos(lat1) + dx) + + return (int(math.degrees(lon3))) + + +def save_gridfile(df, gridfile_type, fname, plotbbox, spacing, unit, + colorbarfmt='%.2f', stationsongrids=False, time_lines=False, + dtype="float32", noData=np.nan): + ''' + Function to save gridded-arrays as GDAL-readable file. + ''' + # Pass metadata + metadata_dict = {} + metadata_dict['gridfile_type'] = gridfile_type + metadata_dict['plotbbox'] = ' '.join([str(i) for i in plotbbox]) + metadata_dict['spacing'] = str(spacing) + metadata_dict['unit'] = unit + if unit in ['minute', 'hour', 'day', 'year']: + colorbarfmt = '%1i' + metadata_dict['colorbarfmt'] = colorbarfmt + + if stationsongrids: + metadata_dict['stationsongrids'] = ' '.join([str(i) for i in stationsongrids]) + else: + metadata_dict['stationsongrids'] = 'False' + + if time_lines: + metadata_dict['time_lines'] = ' '.join([str(i) for i in time_lines]) + else: + metadata_dict['time_lines'] = 'False' + + # Write data to file + with rasterio.open(fname, mode="w", count=1, + width=df.shape[1], height=df.shape[0], + dtype=dtype, nodata=noData) as dst: + dst.write(df, 1) + dst.update_tags(1, **metadata_dict) + + # Finalize VRT + vrtname = fname + ".vrt" + rasterio.shutil.copy(fname, fname + ".vrt", driver="VRT") + + return + + +def load_gridfile(fname, unit): + ''' + Function to load gridded-arrays saved from previous runs. + ''' + + with rasterio.open(fname) as src: + grid_array = src.read().astype(float) + + # Read metadata variables needed for plotting + metadata_dict = src.tags() + + # Initiate no-data array to mask data + nodat_arr = [0, np.nan, np.inf] + if unit in ['minute', 'hour', 'day', 'year']: + nodat_arr = [np.nan, np.inf] + # set masked values as nans + for i in nodat_arr: + grid_array = np.ma.masked_where(grid_array == i, grid_array) + grid_array = np.ma.filled(grid_array, np.nan) + + # Make plotting command a global variable + gridfile_type = metadata_dict['gridfile_type'] + globals()[gridfile_type] = True + + plotbbox = [float(i) for i in metadata_dict['plotbbox'].split()] + spacing = float(metadata_dict['spacing']) + colorbarfmt = metadata_dict['colorbarfmt'] + inputunit = metadata_dict['unit'] + # adjust conversion if native units are squared + if '^2' in inputunit: + unit = unit.split('^2')[0] + '^2' + # convert to specified output unit + grid_array = convert_SI(grid_array, inputunit, unit) + + # Backwards compatible for cases where this key doesn't exist + try: + time_lines = metadata_dict['time_lines'] + except KeyError: + time_lines = False + + if metadata_dict['stationsongrids'] == 'False': + stationsongrids = False + else: + stationsongrids = [float(i) for i in metadata_dict['stationsongrids'.split()]] + + if metadata_dict['time_lines'] == 'False': + time_lines = False + else: + time_lines = [float(i) for i in metadata_dict['time_lines'].split()] + + return grid_array, plotbbox, spacing, colorbarfmt, stationsongrids, time_lines + + +class VariogramAnalysis(): + ''' + Class which ingests dataframe output from 'RaiderStats' class and performs variogram analysis. + ''' + + def __init__(self, filearg, gridpoints, col_name, unit='m', workdir='./', seasonalinterval=None, densitythreshold=10, binnedvariogram=False, numCPUs=8, variogram_per_timeslice=False, variogram_errlimit='inf'): + self.df = filearg + self.col_name = col_name + self.unit = unit + self.gridpoints = gridpoints + self.workdir = workdir + self.seasonalinterval = seasonalinterval + self.densitythreshold = densitythreshold + self.binnedvariogram = binnedvariogram + self.numCPUs = numCPUs + self.variogram_per_timeslice = variogram_per_timeslice + self.variogram_errlimit = float(variogram_errlimit) + + def _get_samples(self, data, Nsamp=1000): + ''' + pull samples from a 2D image for variogram analysis + ''' + import random + if len(data) < self.densitythreshold: + logger.warning('Less than {} points for this gridcell', self.densitythreshold) + logger.info('Will pass empty list') + d = [] + indpars = [] + else: + indpars = list(itertools.combinations(range(len(data)), 2)) + random.shuffle(indpars) + # subsample + Nvalidsamp = int(len(data) * (len(data) - 1) / 2) + # Only downsample if Nsamps>specified value + if Nvalidsamp > Nsamp: + indpars = indpars[:Nsamp] + d = np.array([[data[r[0]], data[r[1]]] for r in indpars]) + + return d, indpars + + def _get_XY(self, x2d, y2d, indpars): + ''' + Given a list of indices, return the x,y locations + from two matrices + ''' + x = np.array([[x2d[r[0]], x2d[r[1]]] for r in indpars]) + y = np.array([[y2d[r[0]], y2d[r[1]]] for r in indpars]) + + return x, y + + def _get_distances(self, XY): + ''' + Return the distances between each point in a list of points + ''' + from scipy.spatial.distance import cdist + return np.diag(cdist(XY[:, :, 0], XY[:, :, 1], metric='euclidean')) + + def _get_variogram(self, XY, xy=None): + ''' + Return variograms + ''' + return 0.5 * np.square(XY - xy) # XY = 1st col xy= 2nd col + + def _emp_vario(self, x, y, data, Nsamp=1000): + ''' + Compute empirical semivariance + ''' + # remove NaNs if possible + mask = ~np.isnan(data) + if False in mask: + data = data[mask] + x = x[mask] + y = y[mask] + + # deramp + temp1, temp2, x, y = WGS84_to_UTM(x, y, common_center=True) + A = np.array([x, y, np.ones(len(x))]).T + ramp = np.linalg.lstsq(A, data.T, rcond=None)[0] + data = data - (np.matmul(A, ramp)) + + samples, indpars = self._get_samples(data, Nsamp) + x, y = self._get_XY(x, y, indpars) + dists = self._get_distances( + np.array([[x[:, 0], y[:, 0]], [x[:, 1], y[:, 1]]]).T) + vario = self._get_variogram(samples[:, 0], samples[:, 1]) + + return dists, vario + + def _binned_vario(self, hEff, rawVario, xBin=None): + ''' + return a binned empirical variogram + ''' + if xBin is None: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="All-NaN slice encountered") + xBin = np.linspace(0, np.nanmax(hEff) * .67, 20) + + nBins = len(xBin) - 1 + hExp, expVario = [], [] + + for iBin in range(nBins): + iBinMask = np.logical_and(xBin[iBin] < hEff, hEff <= xBin[iBin + 1]) + # circumvent indexing + try: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="Mean of empty slice") + hExp.append(np.nanmean(hEff[iBinMask])) + expVario.append(np.nanmean(rawVario[iBinMask])) + except BaseException: # TODO: Which error(s)? + pass + + if False in ~np.isnan(hExp): + # NaNs present in binned histogram + hExp = [x for x in hExp if str(x) != 'nan'] + expVario = [x for x in expVario if str(x) != 'nan'] + + return np.array(hExp), np.array(expVario) + + def _fit_vario(self, dists, vario, model=None, x0=None, Nparm=None, ub=None): + ''' + Fit a variogram model to data + ''' + from scipy.optimize import least_squares + + def resid(x, d, v, m): + return (m(x, d) - v) + + if ub is None: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="All-NaN slice encountered") + ub = np.array([np.nanmax(dists) * 0.8, np.nanmax(vario) + * 0.8, np.nanmax(vario) * 0.8]) + + if x0 is None and Nparm is None: + raise RuntimeError( + 'Must specify either x0 or the number of model parameters') + if x0 is not None: + lb = np.zeros(len(x0)) + if Nparm is not None: + lb = np.zeros(Nparm) + x0 = (ub - lb) / 2 + bounds = (lb, ub) + + mask = np.isnan(dists) | np.isnan(vario) + d = dists[~mask].copy() + v = vario[~mask].copy() + + res_robust = least_squares(resid, x0, bounds=bounds, + loss='soft_l1', f_scale=0.1, + args=(d, v, model)) + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="All-NaN slice encountered") + d_test = np.linspace(0, np.nanmax(dists), 100) + # v_test is my y., # res_robust.x =a, b, c, where a = range, b = sill, and c = nugget model, d_test=x + v_test = model(res_robust.x, d_test) + + return res_robust, d_test, v_test + + # this would be expontential plus nugget + def __exponential__(self, parms, h, nugget=False): + ''' + returns a variogram model given a set of arguments and + key-word arguments + ''' + # a = range, b = sill, c = nugget model + a, b, c = parms + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="overflow encountered in true_divide") + if nugget: + return b * (1 - np.exp(-h / a)) + c + else: + return b * (1 - np.exp(-h / a)) + + # this would be gaussian plus nugget + def __gaussian__(self, parms, h): + ''' + returns a Gaussian variogram model + ''' + a, b, c = parms + return b * (1 - np.exp(-np.square(h) / (a**2))) + c + + def _append_variogram(self, grid_ind, grid_subset): + ''' + For a given grid-cell, iterate through time slices to generate/append empirical variogram(s) + ''' + # Comprehensive arrays recording data across all time epochs for given station + dists_arr = [] + vario_arr = [] + dists_binned_arr = [] + vario_binned_arr = [] + res_robust_arr = [] + d_test_arr = [] + v_test_arr = [] + for j in sorted(list(set(grid_subset['Date']))): + # If insufficient sample size, skip slice and record occurence + if len(np.array(grid_subset[grid_subset['Date'] == j][self.col_name])) < self.densitythreshold: + # Record skipped [gridnode, timeslice] + self.skipped_slices.append([grid_ind, j.strftime("%Y-%m-%d")]) + else: + self.gridcenterlist.append(['grid{} '.format( + grid_ind) + 'Lat:{} Lon:{}'.format( + str(self.gridpoints[grid_ind][1]), str(self.gridpoints[grid_ind][0]))]) + lonarr = np.array( + grid_subset[grid_subset['Date'] == j]['Lon']) + latarr = np.array( + grid_subset[grid_subset['Date'] == j]['Lat']) + delayarray = np.array( + grid_subset[grid_subset['Date'] == j][self.col_name]) + # fit empirical variogram for each time AND grid + dists, vario = self._emp_vario(lonarr, latarr, delayarray) + dists_binned, vario_binned = self._binned_vario( + dists, vario) + # fit experimental variogram for each time AND grid, model default is exponential + res_robust, d_test, v_test = self._fit_vario( + dists_binned, vario_binned, model=self.__exponential__, x0=None, Nparm=3) + # Plot empirical + experimental variogram for this gridnode and timeslice + if not os.path.exists(os.path.join(self.workdir, 'variograms/grid{}'.format(grid_ind))): + os.makedirs(os.path.join( + self.workdir, 'variograms/grid{}'.format(grid_ind))) + # Make variogram plots for each time-slice + if self.variogram_per_timeslice: + # Plot empirical variogram for this gridnode and timeslice + self.plot_variogram(grid_ind, j.strftime("%Y%m%d"), [self.gridpoints[grid_ind][1], self.gridpoints[grid_ind][0]], + workdir=os.path.join(self.workdir, 'variograms/grid{}'.format(grid_ind)), dists=dists, vario=vario, + dists_binned=dists_binned, vario_binned=vario_binned) + # Plot experimental variogram for this gridnode and timeslice + self.plot_variogram(grid_ind, j.strftime("%Y%m%d"), [self.gridpoints[grid_ind][1], self.gridpoints[grid_ind][0]], + workdir=os.path.join(self.workdir, 'variograms/grid{}'.format(grid_ind)), d_test=d_test, v_test=v_test, + res_robust=res_robust.x, dists_binned=dists_binned, vario_binned=vario_binned) + # append for plotting + self.good_slices.append([grid_ind, j.strftime("%Y%m%d")]) + dists_arr.append(dists) + vario_arr.append(vario) + dists_binned_arr.append(dists_binned) + vario_binned_arr.append(vario_binned) + res_robust_arr.append(res_robust.x) + d_test_arr.append(d_test) + v_test_arr.append(v_test) + # fit experimental variogram for each grid + if dists_binned_arr != []: + # TODO: need to change this from accumulating binned data to raw data + dists_arr = np.concatenate(dists_arr).ravel() + vario_arr = np.concatenate(vario_arr).ravel() + # if specified, passed binned empirical variograms + if self.binnedvariogram: + dists_binned_arr = np.concatenate(dists_binned_arr).ravel() + vario_binned_arr = np.concatenate(vario_binned_arr).ravel() + else: + # dists_binned_arr = dists_arr ; vario_binned_arr = vario_arr + dists_binned_arr, vario_binned_arr = self._binned_vario( + dists_arr, vario_arr) + TOT_res_robust, TOT_d_test, TOT_v_test = self._fit_vario( + dists_binned_arr, vario_binned_arr, model=self.__exponential__, x0=None, Nparm=3) + tot_timetag = self.good_slices[0][1] + '–' + self.good_slices[-1][1] + # Append TOT arrays + self.TOT_good_slices.append([grid_ind, tot_timetag]) + self.TOT_res_robust_arr.append(TOT_res_robust.x) + self.TOT_tot_timetag.append(tot_timetag) + var_rmse = np.sqrt(np.nanmean((TOT_res_robust.fun)**2)) + if var_rmse <= self.variogram_errlimit: + self.TOT_res_robust_rmse.append(var_rmse) + else: + self.TOT_res_robust_rmse.append(np.array(np.nan)) + # Plot empirical variogram for this gridnode + self.plot_variogram(grid_ind, tot_timetag, [self.gridpoints[grid_ind][1], self.gridpoints[grid_ind][0]], + workdir=os.path.join(self.workdir, 'variograms/grid{}'.format(grid_ind)), dists=dists_arr, vario=vario_arr, + dists_binned=dists_binned_arr, vario_binned=vario_binned_arr, seasonalinterval=self.seasonalinterval) + # Plot experimental variogram for this gridnode + self.plot_variogram(grid_ind, tot_timetag, [self.gridpoints[grid_ind][1], self.gridpoints[grid_ind][0]], + workdir=os.path.join(self.workdir, 'variograms/grid{}'.format(grid_ind)), d_test=TOT_d_test, v_test=TOT_v_test, + res_robust=TOT_res_robust.x, seasonalinterval=self.seasonalinterval, dists_binned=dists_binned_arr, vario_binned=vario_binned_arr) + # Record sparse grids which didn't have sufficient sample size of data through any of the timeslices + else: + self.sparse_grids.append(grid_ind) + + return self.TOT_good_slices, self.TOT_res_robust_arr, self.TOT_res_robust_rmse, self.gridcenterlist + + def create_variograms(self): + ''' + Iterate through grid-cells and time slices to generate empirical variogram(s) + ''' + # track data for plotting + self.TOT_good_slices = [] + self.TOT_res_robust_arr = [] + self.TOT_res_robust_rmse = [] + self.TOT_tot_timetag = [] + # track pass/rejected grids + self.sparse_grids = [] + self.good_slices = [] + self.skipped_slices = [] + # record grid-centers for lookup-table + self.gridcenterlist = [] + args = [] + for i in sorted(list(set(self.df['gridnode']))): + # pass subset of all stations corresponding to given grid-cell + grid_subset = self.df[self.df['gridnode'] == i] + args.append((i, grid_subset)) + # Parallelize iteration through all grid-cells and time slices + with multiprocessing.Pool(self.numCPUs) as multipool: + for i, j, k, l in multipool.starmap(self._append_variogram, args): + self.TOT_good_slices.extend(i) + self.TOT_res_robust_arr.extend(j) + self.TOT_res_robust_rmse.extend(k) + self.gridcenterlist.extend(l) + + # save grid-center lookup table + self.gridcenterlist = [list(i) for i in set(tuple(j) + for j in self.gridcenterlist)] + self.gridcenterlist.sort(key=lambda x: int(x[0][4:6])) + gridcenter = open( + (os.path.join(self.workdir, 'variograms/gridlocation_lookup.txt')), "w") + for element in self.gridcenterlist: + gridcenter.writelines("\n".join(element)) + gridcenter.write("\n") + gridcenter.close() + + TOT_grids = [i[0] for i in self.TOT_good_slices] + + return TOT_grids, self.TOT_res_robust_arr, self.TOT_res_robust_rmse + + def plot_variogram(self, gridID, timeslice, coords, workdir='./', d_test=None, v_test=None, res_robust=None, dists=None, vario=None, dists_binned=None, vario_binned=None, seasonalinterval=None): + ''' + Make empirical and/or experimental variogram fit plots + ''' + # If specified workdir doesn't exist, create it + if not os.path.exists(workdir): + os.mkdir(workdir) + + # make plot title + title_str = ' \nLat:{:.2f} Lon:{:.2f}\nTime:{}'.format( + coords[1], coords[0], str(timeslice)) + if seasonalinterval: + title_str += ' Season(mm/dd): {}/{} – {}/{}'.format(int(timeslice[4:6]), int( + timeslice[6:8]), int(timeslice[-4:-2]), int(timeslice[-2:])) + + if dists is not None and vario is not None: + # scale from m to user-defined units + dists = [convert_SI(i, 'm', self.unit) for i in dists] + plt.scatter(dists, vario, s=1, facecolor='0.5', label='raw') + if dists_binned is not None and vario_binned is not None: + # scale from m to user-defined units + dists_binned = [convert_SI(i, 'm', self.unit) for i in dists_binned] + plt.plot(dists_binned, vario_binned, 'bo', label='binned') + if res_robust is not None: + plt.axhline(y=res_robust[1], color='g', + linestyle='--', label='ɣ\u0332\u00b2({}\u00b2)'.format(self.unit)) + # scale from m to user-defined units + res_robust[0] = convert_SI(res_robust[0], 'm', self.unit) + plt.axvline(x=res_robust[0], color='c', + linestyle='--', label='h ({})'.format(self.unit)) + if d_test is not None and v_test is not None: + # scale from m to user-defined units + d_test = [convert_SI(i, 'm', self.unit) for i in d_test] + plt.plot(d_test, v_test, 'r-', label='experimental fit') + plt.xlabel('Distance ({})'.format(self.unit)) + plt.ylabel('Dissimilarity ({}\u00b2)'.format(self.unit)) + plt.legend(bbox_to_anchor=(1.02, 1), + loc='upper left', borderaxespad=0., framealpha=1.) + # Plot empirical variogram + if d_test is None and v_test is None: + plt.title('Empirical variogram' + title_str) + plt.tight_layout() + plt.savefig(os.path.join( + workdir, 'grid{}_timeslice{}_justEMPvariogram.eps'.format(gridID, timeslice))) + # Plot just experimental variogram + else: + plt.title('Experimental variogram' + title_str) + plt.tight_layout() + plt.savefig(os.path.join( + workdir, 'grid{}_timeslice{}_justEXPvariogram.eps'.format(gridID, timeslice))) + plt.close() + + return + + +class RaiderStats(object): + ''' + Class which loads standard weather model/GPS delay files and generates a series of user-requested statistics and graphics. + ''' + + # import dependencies + import glob + + def __init__(self, filearg, col_name, unit='m', workdir='./', bbox=None, spacing=1, timeinterval=None, seasonalinterval=None, + obs_errlimit='inf', time_lines=False, stationsongrids=False, station_seasonal_phase=False, cbounds=None, colorpercentile=[25, 95], + usr_colormap='hot_r', grid_heatmap=False, grid_delay_mean=False, grid_delay_median=False, grid_delay_stdev=False, + grid_seasonal_phase=False, grid_delay_absolute_mean=False, grid_delay_absolute_median=False, + grid_delay_absolute_stdev=False, grid_seasonal_absolute_phase=False, grid_to_raster=False, min_span=[2, 0.6], + period_limit=0.5, numCPUs=8, phaseamp_per_station=False): + self.fname = filearg + self.col_name = col_name + self.unit = unit + self.workdir = workdir + self.bbox = bbox + self.spacing = spacing + self.timeinterval = timeinterval + self.seasonalinterval = seasonalinterval + self.obs_errlimit = float(obs_errlimit) + self.time_lines = time_lines + self.stationsongrids = stationsongrids + self.station_seasonal_phase = station_seasonal_phase + self.cbounds = cbounds + self.colorpercentile = colorpercentile + self.usr_colormap = usr_colormap + self.grid_heatmap = grid_heatmap + self.grid_delay_mean = grid_delay_mean + self.grid_delay_median = grid_delay_median + self.grid_delay_stdev = grid_delay_stdev + self.grid_seasonal_phase = grid_seasonal_phase + self.grid_seasonal_amplitude = False + self.grid_seasonal_period = False + self.grid_seasonal_phase_stdev = False + self.grid_seasonal_amplitude_stdev = False + self.grid_seasonal_period_stdev = False + self.grid_seasonal_fit_rmse = False + self.grid_delay_absolute_mean = grid_delay_absolute_mean + self.grid_delay_absolute_median = grid_delay_absolute_median + self.grid_delay_absolute_stdev = grid_delay_absolute_stdev + self.grid_seasonal_absolute_phase = grid_seasonal_absolute_phase + self.grid_seasonal_absolute_amplitude = False + self.grid_seasonal_absolute_period = False + self.grid_seasonal_absolute_phase_stdev = False + self.grid_seasonal_absolute_amplitude_stdev = False + self.grid_seasonal_absolute_period_stdev = False + self.grid_seasonal_absolute_fit_rmse = False + self.grid_to_raster = grid_to_raster + self.min_span = min_span + self.period_limit = period_limit + self.numCPUs = numCPUs + self.phaseamp_per_station = phaseamp_per_station + self.grid_range = False + self.grid_variance = False + self.grid_variogram_rmse = False + + # create workdir if it doesn't exist + if not os.path.exists(self.workdir): + os.mkdir(self.workdir) + + # get colorbounds + if self.cbounds: + self.cbounds = [float(val) for val in self.cbounds.split()] + + # Pass color percentile and check for input error + if self.colorpercentile is None: + self.colorpercentile = [25, 95] + if self.colorpercentile[0] > self.colorpercentile[1]: + raise Exception('Input colorpercentile lower threshold {} higher than upper threshold {}'.format( + self.colorpercentile[0], self.colorpercentile[1])) + + # load dataframe directly if previously generated TIF grid-file + if self.fname.endswith('.tif'): + if 'grid_heatmap' in self.fname: + self.grid_heatmap, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_heatmap')[0] + if 'grid_delay_mean' in self.fname: + self.grid_delay_mean, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_delay_mean')[0] + if 'grid_delay_median' in self.fname: + self.grid_delay_median, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_delay_median')[0] + if 'grid_delay_stdev' in self.fname: + self.grid_delay_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_delay_stdev')[0] + if 'grid_seasonal_phase' in self.fname: + self.grid_seasonal_phase, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_phase')[0] + if 'grid_seasonal_period' in self.fname: + self.grid_seasonal_period, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_period')[0] + if 'grid_seasonal_amplitude' in self.fname: + self.grid_seasonal_amplitude, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_amplitude')[0] + if 'grid_seasonal_phase_stdev' in self.fname: + self.grid_seasonal_phase_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_phase_stdev')[0] + if 'grid_seasonal_amplitude_stdev' in self.fname: + self.grid_seasonal_amplitude_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_amplitude_stdev')[0] + if 'grid_seasonal_period_stdev' in self.fname: + self.grid_seasonal_period_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_period_stdev')[0] + if 'grid_seasonal_fit_rmse' in self.fname: + self.grid_seasonal_fit_rmse, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_fit_rmse')[0] + if 'grid_delay_absolute_mean' in self.fname: + self.grid_delay_absolute_mean, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_delay_absolute_mean')[0] + if 'grid_delay_absolute_median' in self.fname: + self.grid_delay_absolute_median, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_delay_absolute_median')[0] + if 'grid_delay_absolute_stdev' in self.fname: + self.grid_delay_absolute_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_delay_absolute_stdev')[0] + if 'grid_seasonal_absolute_phase' in self.fname: + self.grid_seasonal_absolute_phase, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_phase')[0] + if 'grid_seasonal_absolute_period' in self.fname: + self.grid_seasonal_absolute_period, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_period')[0] + if 'grid_seasonal_absolute_amplitude' in self.fname: + self.grid_seasonal_absolute_amplitude, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_amplitude')[0] + if 'grid_seasonal_absolute_phase_stdev' in self.fname: + self.grid_seasonal_absolute_phase_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_phase_stdev')[0] + if 'grid_seasonal_absolute_amplitude_stdev' in self.fname: + self.grid_seasonal_absolute_amplitude_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_amplitude_stdev')[0] + if 'grid_seasonal_absolute_period_stdev' in self.fname: + self.grid_seasonal_absolute_period_stdev, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_period_stdev')[0] + if 'grid_seasonal_absolute_fit_rmse' in self.fname: + self.grid_seasonal_absolute_fit_rmse, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_seasonal_absolute_fit_rmse')[0] + if 'grid_range' in self.fname: + self.grid_range, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_range')[0] + if 'grid_variance' in self.fname: + self.grid_variance, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_variance')[0] + if 'grid_variogram_rmse' in self.fname: + self.grid_variogram_rmse, self.plotbbox, self.spacing, self.colorbarfmt, self.stationsongrids, self.time_lines = load_gridfile(self.fname, self.unit) + self.col_name = os.path.basename(self.fname).split('_' + 'grid_variogram_rmse')[0] + # setup dataframe for statistical analyses (if CSV) + if self.fname.endswith('.csv'): + self.create_DF() + + def _get_extent(self): # dataset, spacing=1, userbbox=None + """ Get the bbox, spacing in deg (by default 1deg), optionally pass user-specified bbox. Output array in WESN degrees """ + extent = [np.floor(min(self.df['Lon'])), np.ceil(max(self.df['Lon'])), + np.floor(min(self.df['Lat'])), np.ceil(max(self.df['Lat']))] + if self.bbox is not None: + dfextents_poly = Polygon(np.column_stack((np.array([extent[0], extent[0], extent[1], extent[1], extent[0]]), + np.array([extent[2], extent[3], extent[3], extent[2], extent[2]])))) + userbbox_poly = Polygon(np.column_stack((np.array([self.bbox[2], self.bbox[3], self.bbox[3], self.bbox[2], self.bbox[2]]), + np.array([self.bbox[0], self.bbox[0], self.bbox[1], self.bbox[1], self.bbox[0]])))) + if userbbox_poly.intersects(dfextents_poly): + extent = [np.floor(self.bbox[2]), np.ceil(self.bbox[-1]), np.floor(self.bbox[0]), np.ceil(self.bbox[1])] + else: + raise Exception("User-specified bounds do not overlap with dataset bounds, adjust bounds and re-run program.") + if extent[0] < -180. or extent[1] > 180. or extent[2] < -90. or extent[3] > 90.: + raise Exception("Specified bounds exceed -180/180 lon and/or -90/90 lat, adjust bounds and re-run program.") + del dfextents_poly, userbbox_poly + + # ensure that extents do not exceed -180/180 lon and -90/90 lat + if extent[0] < -180.: + extent[0] = -180. + if extent[1] > 180.: + extent[1] = 180. + if extent[2] < -90.: + extent[2] = -90. + if extent[3] > 90.: + extent[3] = 90. + + # ensure even spacing, set spacing to 1 if specified spacing is not even multiple of bounds + if (extent[1] - extent[0]) % self.spacing != 0 or (extent[-1] - extent[-2]) % self.spacing: + logger.warning("User-specified spacing %s is not even multiple of bounds, resetting spacing to 1\N{DEGREE SIGN}", self.spacing) + self.spacing = 1 + + # Create corners of rectangle to be transformed to a grid + nw = [extent[0] + (self.spacing / 2), extent[-1] - (self.spacing / 2)] + se = [extent[1] - (self.spacing / 2), extent[2] + (self.spacing / 2)] + + # Store grid dimension [y,x] + grid_dim = [int((extent[1] - extent[0]) / self.spacing), + int((extent[-1] - extent[-2]) / self.spacing)] + + # Iterate over 2D area + gridpoints = [] + y_shape = [] + x_shape = [] + x = se[0] + while x >= nw[0]: + y = se[1] + while y <= nw[1]: + y_shape.append(y) + gridpoints.append([x, y]) + y += self.spacing + x_shape.append(x) + x -= self.spacing + gridpoints.reverse() + + return extent, grid_dim, gridpoints + + def _check_stationgrid_intersection(self, stat_ID): + ''' + Return index of grid cell which intersects with station + Note: Fast, but assumes station locations don't change + ''' + coord = Point((self.unique_points[1][self.unique_points[0].index( + stat_ID)], self.unique_points[2][self.unique_points[0].index(stat_ID)])) + # Get grid cell polygon which intersect with station coordinate + grid_int = self.polygon_tree.query(coord) + # Pass corresponding grid cell index + if grid_int: + return self.polygon_dict[id(grid_int[0])] + + return 'NaN' + + def _reader(self): + ''' + Read a input file + ''' + try: + data = pd.read_csv(self.fname, parse_dates=['Datetime']) + data['Date'] = data['Datetime'].apply(lambda x: x.date()) + data['Date'] = data['Date'].apply(lambda x: dt.datetime.strptime(x.strftime("%Y-%m-%d"), "%Y-%m-%d")) + except BaseException: + data = pd.read_csv(self.fname, parse_dates=['Date']) + + # check if user-specified key is valid + if self.col_name not in data.keys(): + raise Exception( + 'User-specified key {} not found in input file {}. Must specify valid key.' .format(self.col_name, self.fname)) + + # if user-specified key is the same as the 'Date' field, rename + if self.col_name == 'Date': + logger.warning('Input key {} same as "Date" field name, rename the former'.format(self.col_name)) + self.col_name += '_plot' + data[self.col_name] = data['Date'] + + # convert to specified output unit + inputunit = 'm' + data[self.col_name] = convert_SI(data[self.col_name], inputunit, self.unit) + # filter out obs by error + if 'sigZTD' in data.keys(): + data['sigZTD'] = convert_SI(data['sigZTD'], inputunit, self.unit) + self.obs_errlimit = convert_SI(self.obs_errlimit, inputunit, self.unit) + data = data[data['sigZTD'] <= self.obs_errlimit] + else: + logger.warning('Key "sigZTD" not found in dataset, cannot filter out obs by error') + + return data + + def create_DF(self): + ''' + Create dataframe. + ''' + # Open file + self.df = self._reader() + + # Filter dataframe + # drop all nans + self.df.dropna(how='any', inplace=True) + self.df.reset_index(drop=True, inplace=True) + # convert to datetime object + + # time-interval filter + if self.timeinterval: + self.timeinterval = [dt.datetime.strptime( + val, '%Y-%m-%d') for val in self.timeinterval.split()] + self.df = self.df[(self.df['Date'] >= self.timeinterval[0]) & ( + self.df['Date'] <= self.timeinterval[-1])] + + # seasonal filter + if self.seasonalinterval: + self.seasonalinterval = self.seasonalinterval.split() + # get day of year + self.seasonalinterval = [dt.datetime.strptime('2001-' + self.seasonalinterval[0], '%Y-%m-%d').timetuple( + ).tm_yday, dt.datetime.strptime('2001-' + self.seasonalinterval[-1], '%Y-%m-%d').timetuple().tm_yday] + # track input order and wrap around year if necessary + # e.g. month/day: 03/01 to 06/01 + if self.seasonalinterval[0] < self.seasonalinterval[1]: + # non leap-year + filtered_self = self.df[(not self.df['Date'].dt.is_leap_year) & ( + self.df['Date'].dt.dayofyear >= self.seasonalinterval[0]) & (self.df['Date'].dt.dayofyear <= self.seasonalinterval[-1])] + # leap-year + self.seasonalinterval = [i + 1 if i > + 59 else i for i in self.seasonalinterval] + self.df = filtered_self.append(self.df[(self.df['Date'].dt.is_leap_year) & ( + self.df['Date'].dt.dayofyear >= self.seasonalinterval[0]) & (self.df['Date'].dt.dayofyear <= self.seasonalinterval[-1])], ignore_index=True) + del filtered_self + # e.g. month/day: 12/01 to 03/01 + if self.seasonalinterval[0] > self.seasonalinterval[1]: + # non leap-year + filtered_self = self.df[(not self.df['Date'].dt.is_leap_year) & ( + self.df['Date'].dt.dayofyear >= self.seasonalinterval[-1]) & (self.df['Date'].dt.dayofyear <= self.seasonalinterval[0])] + # leap-year + self.seasonalinterval = [i + 1 if i > + 59 else i for i in self.seasonalinterval] + self.df = filtered_self.append(self.df[(self.df['Date'].dt.is_leap_year) & ( + self.df['Date'].dt.dayofyear >= self.seasonalinterval[-1]) & (self.df['Date'].dt.dayofyear <= self.seasonalinterval[0])], ignore_index=True) + del filtered_self + + # estimate central longitude lines if '--time_lines' specified + if self.time_lines and 'Datetime' in self.df.keys(): + self.df['Date_hr'] = self.df['Datetime'].dt.hour.astype( + np.float).astype("Int32") + # get list of unique times + all_hrs = sorted(set(self.df['Date_hr'])) + + # get central longitude bands associated with each time + central_points = [] + # if single time, avoid loop + if len(all_hrs) == 1: + central_points.append(([0, max(self.df['Lon'])], + [0, min(self.df['Lon'])])) + else: + for i in enumerate(all_hrs): + # last entry + if i[0] == len(all_hrs) - 1: + lons = self.df[self.df['Date_hr'] > all_hrs[i[0] - 1]] + # first entry + elif i[0] == 0: + lons = self.df[self.df['Date_hr'] < all_hrs[i[0] + 1]] + else: + lons = self.df[(self.df['Date_hr'] > all_hrs[i[0] - 1]) + & (self.df['Date_hr'] < all_hrs[i[0] + 1])] + central_points.append(([0, max(lons['Lon'])], + [0, min(lons['Lon'])])) + # get central longitudes + self.time_lines = [midpoint(i[0], i[1]) for i in central_points] + + # Get bbox, buffered by grid spacing. + # Check if bbox input is valid list. + if self.bbox is not None: + try: + self.bbox = [float(val) for val in self.bbox.split()] + except BaseException: + raise Exception( + 'Cannot understand the --bounding_box argument. String input is incorrect or path does not exist.') + self.plotbbox, self.grid_dim, self.gridpoints = self._get_extent() + + # generate list of grid-polygons + append_poly = [] + for i in self.gridpoints: + bbox = [i[1] - (self.spacing / 2), i[1] + (self.spacing / 2), + i[0] - (self.spacing / 2), i[0] + (self.spacing / 2)] + append_poly.append(Polygon(np.column_stack((np.array([bbox[2], bbox[3], bbox[3], bbox[2], bbox[2]]), + np.array([bbox[0], bbox[0], bbox[1], bbox[1], bbox[0]]))))) # Pass lons/lats to create polygon + + # Check for grid cell intersection with each station + idtogrid_dict = {} + self.unique_points = self.df.groupby(['ID', 'Lon', 'Lat']).size() + self.unique_points = [self.unique_points.index.get_level_values('ID').tolist(), self.unique_points.index.get_level_values( + 'Lon').tolist(), self.unique_points.index.get_level_values('Lat').tolist()] + # Initiate R-tree of gridded array domain + self.polygon_dict = dict((id(pt), i) for i, pt in enumerate(append_poly)) + self.polygon_tree = STRtree(append_poly) + for stat_ID in self.unique_points[0]: + grd_index = self._check_stationgrid_intersection(stat_ID) + idtogrid_dict[stat_ID] = grd_index + + # map gridnode dictionary to dataframe + self.df['gridnode'] = self.df['ID'].map(idtogrid_dict) + self.df = self.df[self.df['gridnode'].astype(str) != 'NaN'] + del self.unique_points, self.polygon_dict, self.polygon_tree, idtogrid_dict, append_poly + # sort by grid and date + self.df.sort_values(['gridnode', 'Date']) + + # If specified, pass station locations to superimpose on gridplots + if self.stationsongrids: + unique_points = self.df.groupby(['Lon', 'Lat']).size() + self.stationsongrids = [unique_points.index.get_level_values( + 'Lon').tolist(), unique_points.index.get_level_values('Lat').tolist()] + + # If specified, setup gridded array(s) + if self.grid_heatmap: + self.grid_heatmap = np.array([np.nan if i[0] not in self.df['gridnode'].values[:] else int(len(np.unique( + self.df['ID'][self.df['gridnode'] == i[0]]))) for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_heatmap' + '.tif') + save_gridfile(self.grid_heatmap, 'grid_heatmap', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%1i', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='int16', + noData=0) + + if self.grid_delay_mean: + # Take mean of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)[self.col_name].mean() + unique_points = unique_points.groupby(['gridnode'])[self.col_name].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_delay_mean = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_delay_mean' + '.tif') + save_gridfile(self.grid_delay_mean, 'grid_delay_mean', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + if self.grid_delay_median: + # Take mean of station-wise medians per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)[self.col_name].median() + unique_points = unique_points.groupby(['gridnode'])[self.col_name].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_delay_median = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_delay_median' + '.tif') + save_gridfile(self.grid_delay_median, 'grid_delay_median', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + if self.grid_delay_stdev: + # Take mean of station-wise stdev per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)[self.col_name].std() + unique_points = unique_points.groupby(['gridnode'])[self.col_name].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_delay_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_delay_stdev' + '.tif') + save_gridfile(self.grid_delay_stdev, 'grid_delay_stdev', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + if self.grid_delay_absolute_mean: + # Take mean of all data per gridcell + unique_points = self.df.groupby(['gridnode'])[self.col_name].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_delay_absolute_mean = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_delay_absolute_mean' + '.tif') + save_gridfile(self.grid_delay_absolute_mean, 'grid_delay_absolute_mean', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + if self.grid_delay_absolute_median: + # Take median of all data per gridcell + unique_points = self.df.groupby(['gridnode'])[self.col_name].median() + unique_points.dropna(how='any', inplace=True) + self.grid_delay_absolute_median = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_delay_absolute_median' + '.tif') + save_gridfile(self.grid_delay_absolute_median, 'grid_delay_absolute_median', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + if self.grid_delay_absolute_stdev: + # Take stdev of all data per gridcell + unique_points = self.df.groupby(['gridnode'])[self.col_name].std() + unique_points.dropna(how='any', inplace=True) + self.grid_delay_absolute_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_delay_absolute_stdev' + '.tif') + save_gridfile(self.grid_delay_absolute_stdev, 'grid_delay_absolute_stdev', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + # If specified, compute phase/amplitude fits + if self.station_seasonal_phase or self.grid_seasonal_phase or self.grid_seasonal_absolute_phase: + # Sort by coordinates + unique_points = self.df.sort_values(['ID', 'Date']) + unique_points['Date'] = [i.timestamp() for i in unique_points['Date']] + # Setup variables + self.ampfit = [] + self.phsfit = [] + self.periodfit = [] + self.ampfit_c = [] + self.phsfit_c = [] + self.periodfit_c = [] + self.seasonalfit_rmse = [] + args = [] + for i in sorted(list(set(unique_points['ID']))): + # pass all values corresponding to station (ID, data = y, time = x) + args.append((i, unique_points[unique_points['ID'] == i]['Date'].to_list(), unique_points[unique_points['ID'] == i][self.col_name].to_list(), self.min_span[0], self.min_span[1], self.period_limit)) + # Parallelize iteration through all grid-cells and time slices + with multiprocessing.Pool(self.numCPUs) as multipool: + for i, j, k, l, m, n, o in multipool.starmap(self._amplitude_and_phase, args): + self.ampfit.extend(i) + self.phsfit.extend(j) + self.periodfit.extend(k) + self.ampfit_c.extend(l) + self.phsfit_c.extend(m) + self.periodfit_c.extend(n) + self.seasonalfit_rmse.extend(o) + # map phase/amplitude fits dictionary to dataframe + self.phsfit = {k: v for d in self.phsfit for k, v in d.items()} + self.ampfit = {k: v for d in self.ampfit for k, v in d.items()} + self.periodfit = {k: v for d in self.periodfit for k, v in d.items()} + self.df['phsfit'] = self.df['ID'].map(self.phsfit) + # check if there are any valid data values + if self.df['phsfit'].isnull().values.all(axis=0): + raise Exception("No valid data values, adjust --min_span inputs for time span in years {} and/or fractional obs. {}". + format(self.min_span[0], self.min_span[1])) + self.df['ampfit'] = self.df['ID'].map(self.ampfit) + self.df['periodfit'] = self.df['ID'].map(self.periodfit) + self.phsfit_c = {k: v for d in self.phsfit_c for k, v in d.items()} + self.ampfit_c = {k: v for d in self.ampfit_c for k, v in d.items()} + self.periodfit_c = {k: v for d in self.periodfit_c for k, v in d.items()} + self.seasonalfit_rmse = {k: v for d in self.seasonalfit_rmse for k, v in d.items()} + self.df['phsfit_c'] = self.df['ID'].map(self.phsfit_c) + self.df['ampfit_c'] = self.df['ID'].map(self.ampfit_c) + self.df['periodfit_c'] = self.df['ID'].map(self.periodfit_c) + self.df['seasonalfit_rmse'] = self.df['ID'].map(self.seasonalfit_rmse) + # drop nan + self.df.dropna(how='any', inplace=True) + # If grid plots specified + if self.grid_seasonal_phase: + # Pass mean phase of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['phsfit'].mean() + unique_points = unique_points.groupby(['gridnode'])['phsfit'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_phase = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_phase' + '.tif') + save_gridfile(self.grid_seasonal_phase, 'grid_seasonal_phase', gridfile_name, self.plotbbox, self.spacing, + 'days', colorbarfmt='%.1i', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass mean amplitude of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['ampfit'].mean() + unique_points = unique_points.groupby(['gridnode'])['ampfit'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_amplitude = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_amplitude' + '.tif') + save_gridfile(self.grid_seasonal_amplitude, 'grid_seasonal_amplitude', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.3f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass mean period of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['periodfit'].mean() + unique_points = unique_points.groupby(['gridnode'])['periodfit'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_period = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_period' + '.tif') + save_gridfile(self.grid_seasonal_period, 'grid_seasonal_period', gridfile_name, self.plotbbox, self.spacing, + 'years', colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + ######################################################################################################################## + # Pass mean phase stdev of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['phsfit_c'].mean() + unique_points = unique_points.groupby(['gridnode'])['phsfit_c'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_phase_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_phase_stdev' + '.tif') + save_gridfile(self.grid_seasonal_phase_stdev, 'grid_seasonal_phase_stdev', gridfile_name, self.plotbbox, self.spacing, + 'days', colorbarfmt='%.1i', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass mean amplitude stdev of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['ampfit_c'].mean() + unique_points = unique_points.groupby(['gridnode'])['ampfit_c'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_amplitude_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_amplitude_stdev' + '.tif') + save_gridfile(self.grid_seasonal_amplitude_stdev, 'grid_seasonal_amplitude_stdev', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.3f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass mean period stdev of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['periodfit_c'].mean() + unique_points = unique_points.groupby(['gridnode'])['periodfit_c'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_period_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_period_stdev' + '.tif') + save_gridfile(self.grid_seasonal_period_stdev, 'grid_seasonal_period_stdev', gridfile_name, self.plotbbox, self.spacing, + 'years', colorbarfmt='%.2e', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass mean seasonal fit RMSE of station-wise means per gridcell + unique_points = self.df.groupby(['ID', 'Lon', 'Lat', 'gridnode'], as_index=False)['seasonalfit_rmse'].mean() + unique_points = unique_points.groupby(['gridnode'])['seasonalfit_rmse'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_fit_rmse = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_fit_rmse' + '.tif') + save_gridfile(self.grid_seasonal_fit_rmse, 'grid_seasonal_fit_rmse', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.3f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + ######################################################################################################################## + if self.grid_seasonal_absolute_phase: + # Pass absolute mean phase of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['phsfit'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_phase = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_phase' + '.tif') + save_gridfile(self.grid_seasonal_absolute_phase, 'grid_seasonal_absolute_phase', gridfile_name, self.plotbbox, self.spacing, + 'days', colorbarfmt='%.1i', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass absolute mean amplitude of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['ampfit'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_amplitude = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_amplitude' + '.tif') + save_gridfile(self.grid_seasonal_absolute_amplitude, 'grid_seasonal_absolute_amplitude', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.3f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass absolute mean period of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['periodfit'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_period = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_period' + '.tif') + save_gridfile(self.grid_seasonal_absolute_period, 'grid_seasonal_absolute_period', gridfile_name, self.plotbbox, self.spacing, + 'years', colorbarfmt='%.2f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + ######################################################################################################################## + # Pass absolute mean phase stdev of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['phsfit_c'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_phase_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_phase_stdev' + '.tif') + save_gridfile(self.grid_seasonal_absolute_phase_stdev, 'grid_seasonal_absolute_phase_stdev', gridfile_name, self.plotbbox, self.spacing, + 'days', colorbarfmt='%.1i', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass absolute mean amplitude stdev of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['ampfit_c'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_amplitude_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_amplitude_stdev' + '.tif') + save_gridfile(self.grid_seasonal_absolute_amplitude_stdev, 'grid_seasonal_absolute_amplitude_stdev', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.3f', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + # Pass absolute mean period stdev of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['periodfit_c'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_period_stdev = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_period_stdev' + '.tif') + save_gridfile(self.grid_seasonal_absolute_period_stdev, 'grid_seasonal_absolute_period_stdev', gridfile_name, self.plotbbox, self.spacing, + 'years', colorbarfmt='%.2e', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + # Pass absolute mean seasonal fit RMSE of all data per gridcell + unique_points = self.df.groupby(['gridnode'])['seasonalfit_rmse'].mean() + unique_points.dropna(how='any', inplace=True) + self.grid_seasonal_absolute_fit_rmse = np.array([np.nan if i[0] not in unique_points.index.get_level_values('gridnode').tolist( + ) else unique_points[i[0]] for i in enumerate(self.gridpoints)]).reshape(self.grid_dim).T + # If specified, save gridded array(s) + if self.grid_to_raster: + gridfile_name = os.path.join(self.workdir, self.col_name + '_' + 'grid_seasonal_absolute_fit_rmse' + '.tif') + save_gridfile(self.grid_seasonal_absolute_fit_rmse, 'grid_seasonal_absolute_fit_rmse', gridfile_name, self.plotbbox, self.spacing, + self.unit, colorbarfmt='%.2e', + stationsongrids=self.stationsongrids, + time_lines=self.time_lines, dtype='float32') + + def _amplitude_and_phase(self, station, tt, yy, min_span=2, min_frac=0.6, period_limit=0.): + ''' + Fit sin to the input time sequence, and return fitting parameters: + "amp", "omega", "phase", "offset", "freq", "period" and "fitfunc". + Minimum time span in years (min_span), minimum fractional observations in span (min_frac), + and period limit (period_limit) enforced for statistical analysis. + Source: https://stackoverflow.com/questions/16716302/how-do-i-fit-a-sine-curve-to-my-data-with-pylab-and-numpy + ''' + ampfit = {} + phsfit = {} + periodfit = {} + ampfit_c = {} + phsfit_c = {} + periodfit_c = {} + seasonalfit_rmse = {} + ampfit[station] = np.nan + phsfit[station] = np.nan + periodfit[station] = np.nan + ampfit_c[station] = np.nan + phsfit_c[station] = np.nan + periodfit_c[station] = np.nan + seasonalfit_rmse[station] = np.nan + # Fit with custom fit function with fixed period, if specified + if period_limit != 0: + # convert from years to radians/seconds + w = (1 / period_limit) * (1 / 31556952) * (2. * np.pi) + + def custom_sine_function_base(t, A, p, c): + return self._sine_function_base(t, A, w, p, c) + else: + def custom_sine_function_base(t, A, w, p, c): + return self._sine_function_base(t, A, w, p, c) + # If station TS does not span specified time period, pass NaNs + time_span_yrs = (max(tt) - min(tt)) / 31556952 + if time_span_yrs >= min_span and len(list(set(tt))) / (time_span_yrs * 365.25) >= min_frac: + tt = np.array(tt) + yy = np.array(yy) + ff = np.fft.fftfreq(len(tt), (tt[1] - tt[0])) # assume uniform spacing + Fyy = abs(np.fft.fft(yy)) + guess_freq = abs(ff[np.argmax(Fyy[1:]) + 1]) # excluding the zero period "peak", which is related to offset + guess_amp = np.std(yy) * 2.**0.5 + guess_offset = np.mean(yy) + guess = np.array([guess_amp, 2. * np.pi * guess_freq, 0., guess_offset]) + # Adjust frequency guess to reflect fixed period, if specified + if period_limit != 0: + guess = np.array([guess_amp, 0., guess_offset]) + # Catch warning where covariance cannot be estimated + # I.e. OptimizeWarning: Covariance of the parameters could not be estimated + with warnings.catch_warnings(): + warnings.simplefilter("error", OptimizeWarning) + try: + optimize_warning = False + try: + # Note, may have to adjust max number of iterations (maxfev) higher to avoid crashes + popt, pcov = optimize.curve_fit(custom_sine_function_base, tt, yy, p0=guess, maxfev=int(1e6)) + # If sparse input such that fittitng is not possible, pass NaNs + except TypeError: + self.ampfit.append(np.nan), self.phsfit.append(np.nan), self.periodfit.append(np.nan), \ + self.ampfit_c.append(np.nan), self.phsfit_c.append(np.nan), \ + self.periodfit_c.append(np.nan), self.seasonalfit_rmse.append(np.nan) + return self.ampfit, self.phsfit, self.periodfit, self.ampfit_c, \ + self.phsfit_c, self.periodfit_c, self.seasonalfit_rmse + except OptimizeWarning: + optimize_warning = True + warnings.simplefilter("ignore", OptimizeWarning) + popt, pcov = optimize.curve_fit(custom_sine_function_base, tt, yy, p0=guess, maxfev=int(1e6)) + print('OptimizeWarning: Covariance for station {} could not be estimated. Refer to debug figure here {} \ + '.format(station, os.path.join(self.workdir, 'phaseamp_per_station', 'station{}.png'.format(station)))) + pass + # Adjust expected output to reflect fixed period, if specified + if period_limit != 0: + A, p, c = popt + else: + A, w, p, c = popt + # convert from radians/seconds to years + f = (w / (2. * np.pi)) * (31556952) + f = 1 / f + + def fitfunc(t): + return A * np.sin(w * t + p) + c + # Outputs = "amp": A, "angular frequency": w, "phase": p, "offset": c, "freq": f, "period": 1./f, + # "fitfunc": fitfunc, "maxcov": np.max(pcov), "rawres": (guess,popt,pcov) + # Pass amplitude (specified units) and phase (days) and stdev + ampfit[station] = abs(A) + # Convert phase from rad to days, apply half wavelength shift if Amp is negative + if A < 0: + p += 3.14159 + phsfit[station] = (365.25 / 2) * np.sin(p) + periodfit[station] = f + # Catch warning where output is so small that it gets rounded to 0 + # I.e. RuntimeWarning: invalid value encountered in double_scalars + with np.errstate(invalid='raise'): + try: + # pass covariance for each parameter + ampfit_c[station] = pcov[0, 0]**0.5 + periodfit_c[station] = pcov[1, 1]**0.5 + phsfit_c[station] = pcov[2, 2]**0.5 + # pass RMSE of fit + seasonalfit_rmse[station] = yy - custom_sine_function_base(tt, *popt) + seasonalfit_rmse[station] = (scipy_sum(seasonalfit_rmse[station]**2) / + (seasonalfit_rmse[station].size - 2))**0.5 + except FloatingPointError: + pass + if self.phaseamp_per_station or optimize_warning: + # Debug plotting for each station + # convert time (datetime seconds) to absolute years for plotting + tt_plot = copy.deepcopy(tt) + tt_plot -= min(tt_plot) + tt_plot /= 31556952 + plt.plot(tt_plot, yy, "ok", label="input") + plt.xlabel("time (years)") + plt.ylabel("data ({})".format(self.unit)) + num_testpoints = len(tt) * 10 + if num_testpoints > 1000: + num_testpoints = 1000 + tt2 = np.linspace(min(tt), max(tt), num_testpoints) + # convert time to years for plotting + tt2_plot = copy.deepcopy(tt2) + tt2_plot -= min(tt2_plot) + tt2_plot /= 31556952 + plt.plot(tt2_plot, fitfunc(tt2), "r-", label="fit", linewidth=2) + plt.legend(loc="best") + if not os.path.exists(os.path.join(self.workdir, 'phaseamp_per_station')): + os.mkdir(os.path.join(self.workdir, 'phaseamp_per_station')) + plt.savefig(os.path.join(self.workdir, 'phaseamp_per_station', 'station{}.png'.format(station)), + format='png', bbox_inches='tight') + plt.close() + optimize_warning = False + + self.ampfit.append(ampfit) + self.phsfit.append(phsfit) + self.periodfit.append(periodfit) + self.ampfit_c.append(ampfit_c) + self.phsfit_c.append(phsfit_c) + self.periodfit_c.append(periodfit_c) + self.seasonalfit_rmse.append(seasonalfit_rmse) + + return self.ampfit, self.phsfit, self.periodfit, self.ampfit_c, \ + self.phsfit_c, self.periodfit_c, self.seasonalfit_rmse + + def _sine_function_base(self, t, A, w, p, c): + ''' + Base function for modeling sinusoidal amplitude/phase fits. + ''' + return A * np.sin(w * t + p) + c + + def __call__(self, gridarr, plottype, workdir='./', drawgridlines=False, colorbarfmt='%.2e', stationsongrids=None, resValue=5, plotFormat='pdf', userTitle=None): + ''' + Visualize a suite of statistics w.r.t. stations. Pass either a list of points or a gridded array as the first argument. Alternatively, you may superimpose your gridded array with a supplementary list of points by passing the latter through the stationsongrids argument. + ''' + from cartopy import crs as ccrs + from cartopy import feature as cfeature + from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter + from matplotlib import ticker as mticker + from mpl_toolkits.axes_grid1 import make_axes_locatable + + # If specified workdir doesn't exist, create it + if not os.path.exists(workdir): + os.mkdir(workdir) + + # Pass cbounds + cbounds = self.cbounds + # Initiate no-data array to mask data + nodat_arr = [0, np.nan, np.inf] + if self.unit in ['minute', 'hour', 'day', 'year']: + colorbarfmt = '%.1i' + nodat_arr = [np.nan, np.inf] + + fig, axes = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()}) + # by default set background to white + axes.add_feature(cfeature.NaturalEarthFeature( + 'physical', 'land', '50m', facecolor='white'), zorder=0) + axes.set_extent(self.plotbbox, ccrs.PlateCarree()) + # add coastlines + axes.coastlines(linewidth=0.2, color="gray", zorder=4) + cmap = copy.copy(mpl.cm.get_cmap(self.usr_colormap)) + # cmap.set_bad('black', 0.) + # extract all colors from the hot map + cmaplist = [cmap(i) for i in range(cmap.N)] + # create the new map + cmap = mpl.colors.LinearSegmentedColormap.from_list( + 'Custom cmap', cmaplist) + axes.set_xlabel('Longitude', weight='bold', zorder=2) + axes.set_ylabel('Latitude', weight='bold', zorder=2) + + # set ticks + axes.set_xticks(np.linspace( + self.plotbbox[0], self.plotbbox[1], 5), crs=ccrs.PlateCarree()) + axes.set_yticks(np.linspace( + self.plotbbox[2], self.plotbbox[3], 5), crs=ccrs.PlateCarree()) + lon_formatter = LongitudeFormatter( + number_format='.0f', degree_symbol='') + lat_formatter = LatitudeFormatter( + number_format='.0f', degree_symbol='') + axes.xaxis.set_major_formatter(lon_formatter) + axes.yaxis.set_major_formatter(lat_formatter) + + # draw central longitude lines corresponding to respective datetimes + if self.time_lines: + tl = axes.grid(axis='x', linewidth=1.5, + color='blue', alpha=0.5, linestyle='-', + zorder=3) + + # If individual stations passed + if isinstance(gridarr, list): + # spatial distribution of stations + if plottype == "station_distribution": + im = axes.scatter(gridarr[0], gridarr[1], zorder=1, s=0.5, + marker='.', color='b', transform=ccrs.PlateCarree()) + + # passing 3rd column as z-value + if len(gridarr) > 2: + # set land/water background to light gray/blue respectively so station point data can be seen + axes.add_feature(cfeature.NaturalEarthFeature( + 'physical', 'land', '50m', facecolor='#A9A9A9'), zorder=0) + axes.add_feature(cfeature.NaturalEarthFeature( + 'physical', 'ocean', '50m', facecolor='#ADD8E6'), zorder=0) + # set masked values as nans + zvalues = gridarr[2] + for i in nodat_arr: + zvalues = np.ma.masked_where(zvalues == i, zvalues) + zvalues = np.ma.filled(zvalues, np.nan) + # define the bins and normalize + if cbounds is None: + # avoid "ufunc 'isnan'" error by casting array as float + cbounds = [np.nanpercentile(zvalues.astype('float'), self.colorpercentile[0]), np.nanpercentile( + zvalues.astype('float'), self.colorpercentile[1])] + # if upper/lower bounds identical, overwrite lower bound as 75% of upper bound to avoid plotting ValueError + if cbounds[0] == cbounds[1]: + cbounds[0] *= 0.75 + cbounds.sort() + # adjust precision for colorbar if necessary + if (abs(np.nanmax(zvalues) - np.nanmin(zvalues)) < 1 and (np.nanmean(zvalues)) < 1) \ + or abs(np.nanmax(zvalues) - np.nanmin(zvalues)) > 500: + colorbarfmt = '%.2e' + + colorbounds = np.linspace(cbounds[0], cbounds[1], 256) + colorbounds = np.unique(colorbounds) + norm = mpl.colors.BoundaryNorm(colorbounds, cmap.N) + colorbounds_ticks = np.linspace(cbounds[0], cbounds[1], 10) + + # plot data and initiate colorbar + im = axes.scatter(gridarr[0], gridarr[1], c=zvalues, cmap=cmap, norm=norm, + zorder=1, s=0.5, marker='.', transform=ccrs.PlateCarree()) + # initiate colorbar and control height of colorbar + divider = make_axes_locatable(axes) + cax = divider.append_axes("right", size="5%", pad=0.05, axes_class=plt.Axes) + cbar_ax = fig.colorbar(im, spacing='proportional', + ticks=colorbounds_ticks, boundaries=colorbounds, format=colorbarfmt, pad=0.1, cax=cax) + + # If gridded area passed + else: + # set masked values as nans + for i in nodat_arr: + gridarr = np.ma.masked_where(gridarr == i, gridarr) + gridarr = np.ma.filled(gridarr, np.nan) + # set land/water background to light gray/blue respectively so grid cells can be seen + axes.add_feature(cfeature.NaturalEarthFeature( + 'physical', 'land', '50m', facecolor='#A9A9A9'), zorder=0) + axes.add_feature(cfeature.NaturalEarthFeature( + 'physical', 'ocean', '50m', facecolor='#ADD8E6'), zorder=0) + # define the bins and normalize + if cbounds is None: + cbounds = [np.nanpercentile(gridarr, self.colorpercentile[0]), np.nanpercentile( + gridarr, self.colorpercentile[1])] + # if upper/lower bounds identical, overwrite lower bound as 75% of upper bound to avoid plotting ValueError + if cbounds[0] == cbounds[1]: + cbounds[0] *= 0.75 + cbounds.sort() + # plot data and initiate colorbar + if (abs(np.nanmax(gridarr) - np.nanmin(gridarr)) < 1 and abs(np.nanmean(gridarr)) < 1) \ + or abs(np.nanmax(gridarr) - np.nanmin(gridarr)) > 500: + colorbarfmt = '%.2e' + + colorbounds = np.linspace(cbounds[0], cbounds[1], 256) + colorbounds = np.unique(colorbounds) + norm = mpl.colors.BoundaryNorm(colorbounds, cmap.N) + colorbounds_ticks = np.linspace(cbounds[0], cbounds[1], 10) + + # plot data + im = axes.imshow(gridarr, cmap=cmap, norm=norm, extent=self.plotbbox, + zorder=1, origin='upper', transform=ccrs.PlateCarree()) + # initiate colorbar and control height of colorbar + divider = make_axes_locatable(axes) + cax = divider.append_axes("right", size="5%", pad=0.05, axes_class=plt.Axes) + cbar_ax = fig.colorbar(im, spacing='proportional', ticks=colorbounds_ticks, + boundaries=colorbounds, format=colorbarfmt, pad=0.1, cax=cax) + + # superimpose your gridded array with a supplementary list of point, if specified + if self.stationsongrids: + axes.scatter(self.stationsongrids[0], self.stationsongrids[1], zorder=2, + s=0.5, marker='.', color='b', transform=ccrs.PlateCarree()) + + # draw gridlines, if specified + if drawgridlines: + gl = axes.gridlines(crs=ccrs.PlateCarree( + ), linewidth=0.5, color='black', alpha=0.5, linestyle='-', zorder=3) + gl.xlocator = mticker.FixedLocator(np.arange( + self.plotbbox[0], self.plotbbox[1] + self.spacing, self.spacing).tolist()) + gl.ylocator = mticker.FixedLocator(np.arange( + self.plotbbox[2], self.plotbbox[3] + self.spacing, self.spacing).tolist()) + + # Add labels to colorbar, if necessary + if 'cbar_ax' in locals(): + # experimental variogram fit sill heatmap + if plottype == "grid_variance": + cbar_ax.set_label(" ".join(plottype.replace('grid_', '').split('_')).title() + ' ({}\u00b2)'.format(self.unit), + rotation=-90, labelpad=10) + # specify appropriate units for mean/median/std/amplitude/experimental variogram fit heatmap + elif plottype == "grid_delay_mean" or plottype == "grid_delay_median" or plottype == "grid_delay_stdev" or \ + plottype == "grid_seasonal_amplitude" or plottype == "grid_range" or plottype == "station_delay_mean" or \ + plottype == "station_delay_median" or plottype == "station_delay_stdev" or \ + plottype == "station_seasonal_amplitude" or plottype == "grid_delay_absolute_mean" or \ + plottype == "grid_delay_absolute_median" or plottype == "grid_delay_absolute_stdev" or \ + plottype == "grid_seasonal_absolute_amplitude" or plottype == "grid_seasonal_amplitude_stdev" or \ + plottype == "grid_seasonal_absolute_amplitude_stdev" or plottype == "grid_seasonal_fit_rmse" or \ + plottype == "grid_seasonal_absolute_fit_rmse" or plottype == "grid_variogram_rmse": + # update label if sigZTD + if 'sig' in self.col_name: + cbar_ax.set_label("sig ZTD " + " ".join(plottype.replace('grid_', + '').replace('delay_', '').split('_')).title() + ' ({})'.format(self.unit), + rotation=-90, labelpad=10) + else: + cbar_ax.set_label(" ".join(plottype.replace('grid_', '').split('_')).title() + ' ({})'.format(self.unit), + rotation=-90, labelpad=10) + # specify appropriate units for phase heatmap (days) + elif plottype == "station_seasonal_phase" or plottype == "grid_seasonal_phase" or plottype == "grid_seasonal_absolute_phase" or \ + plottype == "grid_seasonal_absolute_phase_stdev" or plottype == "grid_seasonal_phase_stdev": + cbar_ax.set_label(" ".join(plottype.replace('grid_', '').split('_')).title() + ' ({})'.format('days'), + rotation=-90, labelpad=10) + # specify appropriate units for period heatmap (years) + elif plottype == "station_delay_period" or plottype == "grid_seasonal_period" or plottype == "grid_seasonal_absolute_period" or \ + plottype == "grid_seasonal_absolute_period_stdev" or plottype == "grid_seasonal_period_stdev": + cbar_ax.set_label(" ".join(plottype.replace('grid_', '').split('_')).title() + ' ({})'.format('years'), + rotation=-90, labelpad=10) + # gridmap of station density has no units + else: + cbar_ax.set_label(" ".join(plottype.replace('grid_', '').split('_')).title(), rotation=-90, labelpad=10) + + # Add title to plots, if specified + if userTitle: + axes.set_title(userTitle, zorder=2) + + # save/close figure + # cbar_ax.ax.locator_params(nbins=10) + # for label in cbar_ax.ax.xaxis.get_ticklabels()[::25]: + # label.set_visible(False) + plt.savefig(os.path.join(workdir, self.col_name + '_' + plottype + '.' + plotFormat), + format=plotFormat, bbox_inches='tight') + plt.close() + + return + + +def stats_analyses( + fname, + col_name, + unit, + workdir, + numCPUs, + verbose, + bbox, + spacing, + timeinterval, + seasonalinterval, + obs_errlimit, + figdpi, + user_title, + plot_fmt, + cbounds, + colorpercentile, + usr_colormap, + densitythreshold, + stationsongrids, + drawgridlines, + time_lines, + plotall, + station_distribution, + station_delay_mean, + station_delay_median, + station_delay_stdev, + station_seasonal_phase, + phaseamp_per_station, + grid_heatmap, + grid_delay_mean, + grid_delay_median, + grid_delay_stdev, + grid_seasonal_phase, + grid_delay_absolute_mean, + grid_delay_absolute_median, + grid_delay_absolute_stdev, + grid_seasonal_absolute_phase, + grid_to_raster, + min_span, + period_limit, + variogramplot, + binnedvariogram, + variogram_per_timeslice, + variogram_errlimit +): + ''' + Main workflow for generating a suite of plots to illustrate spatiotemporal distribution + and/or character of zenith delays + ''' + if verbose: + logger.setLevel(logging.DEBUG) + + # Control DPI for output figures + mpl.rcParams['savefig.dpi'] = figdpi + + # If user requests to generate all plots. + if plotall: + logger.info('"-plotall" == True. All plots will be made.') + station_distribution = True + station_delay_mean = True + station_delay_median = True + station_delay_stdev = True + station_seasonal_phase = True + grid_heatmap = True + grid_delay_mean = True + grid_delay_median = True + grid_delay_stdev = True + grid_seasonal_phase = True + grid_delay_absolute_mean = True + grid_delay_absolute_median = True + grid_delay_absolute_stdev = True + grid_seasonal_absolute_phase = True + variogramplot = True + + logger.info("***Stats Function:***") + # prep dataframe object for plotting/variogram analysis based off of user specifications + df_stats = RaiderStats(fname, col_name, unit, workdir, bbox, spacing, + timeinterval, seasonalinterval, obs_errlimit, time_lines, stationsongrids, station_seasonal_phase, cbounds, colorpercentile, + usr_colormap, grid_heatmap, grid_delay_mean, grid_delay_median, grid_delay_stdev, grid_seasonal_phase, + grid_delay_absolute_mean, grid_delay_absolute_median, grid_delay_absolute_stdev, + grid_seasonal_absolute_phase, grid_to_raster, min_span, period_limit, numCPUs, phaseamp_per_station) + + # Station plots + # Plot each individual station + if station_distribution: + logger.info("- Plot spatial distribution of stations.") + unique_points = df_stats.df.groupby(['Lon', 'Lat']).size() + df_stats([unique_points.index.get_level_values('Lon').tolist(), unique_points.index.get_level_values('Lat').tolist( + )], 'station_distribution', workdir=os.path.join(workdir, 'figures'), plotFormat=plot_fmt, userTitle=user_title) + # Plot mean delay per station + if station_delay_mean: + logger.info("- Plot mean delay for each station.") + unique_points = df_stats.df.groupby( + ['Lon', 'Lat'])[col_name].median() + unique_points.dropna(how='any', inplace=True) + df_stats([unique_points.index.get_level_values('Lon').tolist(), unique_points.index.get_level_values('Lat').tolist( + ), unique_points.values], 'station_delay_mean', workdir=os.path.join(workdir, 'figures'), plotFormat=plot_fmt, userTitle=user_title) + # Plot median delay per station + if station_delay_median: + logger.info("- Plot median delay for each station.") + unique_points = df_stats.df.groupby( + ['Lon', 'Lat'])[col_name].mean() + unique_points.dropna(how='any', inplace=True) + df_stats([unique_points.index.get_level_values('Lon').tolist(), unique_points.index.get_level_values('Lat').tolist( + ), unique_points.values], 'station_delay_median', workdir=os.path.join(workdir, 'figures'), plotFormat=plot_fmt, userTitle=user_title) + # Plot delay stdev per station + if station_delay_stdev: + logger.info("- Plot delay stdev for each station.") + unique_points = df_stats.df.groupby( + ['Lon', 'Lat'])[col_name].std() + unique_points.dropna(how='any', inplace=True) + df_stats([unique_points.index.get_level_values('Lon').tolist(), unique_points.index.get_level_values('Lat').tolist( + ), unique_points.values], 'station_delay_stdev', workdir=os.path.join(workdir, 'figures'), plotFormat=plot_fmt, userTitle=user_title) + # Plot delay phase/amplitude per station + if station_seasonal_phase: + logger.info("- Plot delay phase/amplitude for each station.") + # phase + unique_points_phase = df_stats.df.groupby( + ['Lon', 'Lat'])['phsfit'].mean() + unique_points_phase.dropna(how='any', inplace=True) + df_stats([unique_points_phase.index.get_level_values('Lon').tolist(), unique_points_phase.index.get_level_values('Lat').tolist( + ), unique_points_phase.values], 'station_seasonal_phase', workdir=os.path.join(workdir, 'figures'), + colorbarfmt='%.1i', plotFormat=plot_fmt, userTitle=user_title) + # amplitude + unique_points_amplitude = df_stats.df.groupby( + ['Lon', 'Lat'])['ampfit'].mean() + unique_points_amplitude.dropna(how='any', inplace=True) + df_stats([unique_points_amplitude.index.get_level_values('Lon').tolist(), unique_points_amplitude.index.get_level_values('Lat').tolist( + ), unique_points_amplitude.values], 'station_seasonal_amplitude', workdir=os.path.join(workdir, 'figures'), + colorbarfmt='%.3f', plotFormat=plot_fmt, userTitle=user_title) + # period + unique_points_period = df_stats.df.groupby( + ['Lon', 'Lat'])['periodfit'].mean() + df_stats([unique_points_period.index.get_level_values('Lon').tolist(), unique_points_period.index.get_level_values('Lat').tolist( + ), unique_points_period.values], 'station_delay_period', workdir=os.path.join(workdir, 'figures'), + colorbarfmt='%.2f', plotFormat=plot_fmt, userTitle=user_title) + + # Gridded station plots + # Plot density of stations for each gridcell + if isinstance(df_stats.grid_heatmap, np.ndarray): + logger.info("- Plot density of stations per gridcell.") + df_stats(df_stats.grid_heatmap, 'grid_heatmap', workdir=os.path.join(workdir, 'figures'), drawgridlines=drawgridlines, + colorbarfmt='%.1i', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of station-wise mean delay across each gridcell + if isinstance(df_stats.grid_delay_mean, np.ndarray): + logger.info("- Plot mean of station-wise mean delay across each gridcell.") + df_stats(df_stats.grid_delay_mean, 'grid_delay_mean', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of station-wise median delay across each gridcell + if isinstance(df_stats.grid_delay_median, np.ndarray): + logger.info("- Plot mean of station-wise median delay across each gridcell.") + df_stats(df_stats.grid_delay_median, 'grid_delay_median', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of station-wise stdev delay across each gridcell + if isinstance(df_stats.grid_delay_stdev, np.ndarray): + logger.info("- Plot mean of station-wise stdev delay across each gridcell.") + df_stats(df_stats.grid_delay_stdev, 'grid_delay_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of station-wise delay phase across each gridcell + if isinstance(df_stats.grid_seasonal_phase, np.ndarray): + logger.info("- Plot mean of station-wise delay phase across each gridcell.") + df_stats(df_stats.grid_seasonal_phase, 'grid_seasonal_phase', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.1i', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of station-wise delay amplitude across each gridcell + if isinstance(df_stats.grid_seasonal_amplitude, np.ndarray): + logger.info("- Plot mean of station-wise delay amplitude across each gridcell.") + df_stats(df_stats.grid_seasonal_amplitude, 'grid_seasonal_amplitude', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.3f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of station-wise delay period across each gridcell + if isinstance(df_stats.grid_seasonal_period, np.ndarray): + logger.info("- Plot mean of station-wise delay period across each gridcell.") + df_stats(df_stats.grid_seasonal_period, 'grid_seasonal_period', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean stdev of station-wise delay phase across each gridcell + if isinstance(df_stats.grid_seasonal_phase_stdev, np.ndarray): + logger.info("- Plot mean stdev of station-wise delay phase across each gridcell.") + df_stats(df_stats.grid_seasonal_phase_stdev, 'grid_seasonal_phase_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.1i', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean stdev of station-wise delay amplitude across each gridcell + if isinstance(df_stats.grid_seasonal_amplitude_stdev, np.ndarray): + logger.info("- Plot mean stdev of station-wise delay amplitude across each gridcell.") + df_stats(df_stats.grid_seasonal_amplitude_stdev, 'grid_seasonal_amplitude_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.3f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean stdev of station-wise delay period across each gridcell + if isinstance(df_stats.grid_seasonal_period_stdev, np.ndarray): + logger.info("- Plot mean stdev of station-wise delay period across each gridcell.") + df_stats(df_stats.grid_seasonal_period_stdev, 'grid_seasonal_period_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2e', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot mean of seasonal fit RMSE across each gridcell + if isinstance(df_stats.grid_seasonal_fit_rmse, np.ndarray): + logger.info("- Plot mean of seasonal fit RMSE across each gridcell.") + df_stats(df_stats.grid_seasonal_fit_rmse, 'grid_seasonal_fit_rmse', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.3f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute mean delay for each gridcell + if isinstance(df_stats.grid_delay_absolute_mean, np.ndarray): + logger.info("- Plot absolute mean delay per gridcell.") + df_stats(df_stats.grid_delay_absolute_mean, 'grid_delay_absolute_mean', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute median delay for each gridcell + if isinstance(df_stats.grid_delay_absolute_median, np.ndarray): + logger.info("- Plot absolute median delay per gridcell.") + df_stats(df_stats.grid_delay_absolute_median, 'grid_delay_absolute_median', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute stdev delay for each gridcell + if isinstance(df_stats.grid_delay_absolute_stdev, np.ndarray): + logger.info("- Plot absolute delay stdev per gridcell.") + df_stats(df_stats.grid_delay_absolute_stdev, 'grid_delay_absolute_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute delay phase for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_phase, np.ndarray): + logger.info("- Plot absolute delay phase per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_phase, 'grid_seasonal_absolute_phase', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.1i', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute delay amplitude for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_amplitude, np.ndarray): + logger.info("- Plot absolute delay amplitude per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_amplitude, 'grid_seasonal_absolute_amplitude', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.3f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute delay period for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_period, np.ndarray): + logger.info("- Plot absolute delay period per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_period, 'grid_seasonal_absolute_period', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute delay phase stdev for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_phase_stdev, np.ndarray): + logger.info("- Plot absolute delay phase stdev per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_phase_stdev, 'grid_seasonal_absolute_phase_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.1i', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute delay amplitude stdev for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_amplitude_stdev, np.ndarray): + logger.info("- Plot absolute delay amplitude stdev per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_amplitude_stdev, 'grid_seasonal_absolute_amplitude_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.3f', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute delay period stdev for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_period_stdev, np.ndarray): + logger.info("- Plot absolute delay period stdev per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_period_stdev, 'grid_seasonal_absolute_period_stdev', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2e', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + # Plot absolute mean seasonal fit RMSE for each gridcell + if isinstance(df_stats.grid_seasonal_absolute_fit_rmse, np.ndarray): + logger.info("- Plot absolute mean seasonal fit RMSE per gridcell.") + df_stats(df_stats.grid_seasonal_absolute_fit_rmse, 'grid_seasonal_absolute_fit_rmse', workdir=os.path.join(workdir, 'figures'), + drawgridlines=drawgridlines, colorbarfmt='%.2e', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + + # Perform variogram analysis + if variogramplot and not isinstance(df_stats.grid_range, np.ndarray) \ + and not isinstance(df_stats.grid_variance, np.ndarray) \ + and not isinstance(df_stats.grid_variogram_rmse, np.ndarray): + logger.info("***Variogram Analysis Function:***") + if unit in ['minute', 'hour', 'day', 'year']: + unit = 'm' + df_stats.unit = 'm' + logger.warning("Output unit {} specified for Variogram analysis. Reverted to meters".format(unit)) + make_variograms = VariogramAnalysis(df_stats.df, df_stats.gridpoints, col_name, unit, workdir, + df_stats.seasonalinterval, densitythreshold, binnedvariogram, + numCPUs, variogram_per_timeslice, variogram_errlimit) + TOT_grids, TOT_res_robust_arr, TOT_res_robust_rmse = make_variograms.create_variograms() + # get range + df_stats.grid_range = np.array([np.nan if i[0] not in TOT_grids else float(TOT_res_robust_arr[TOT_grids.index( + i[0])][0]) for i in enumerate(df_stats.gridpoints)]).reshape(df_stats.grid_dim).T + # convert range to specified output unit + df_stats.grid_range = convert_SI(df_stats.grid_range, 'm', unit) + # get sill + df_stats.grid_variance = np.array([np.nan if i[0] not in TOT_grids else float(TOT_res_robust_arr[TOT_grids.index( + i[0])][1]) for i in enumerate(df_stats.gridpoints)]).reshape(df_stats.grid_dim).T + # convert sill to specified output unit + df_stats.grid_range = convert_SI(df_stats.grid_range, 'm^2', unit.split('^2')[0] + '^2') + # get variogram rmse + df_stats.grid_variogram_rmse = np.array([np.nan if i[0] not in TOT_grids else float(TOT_res_robust_rmse[TOT_grids.index( + i[0])]) for i in enumerate(df_stats.gridpoints)]).reshape(df_stats.grid_dim).T + # convert range to specified output unit + df_stats.grid_variogram_rmse = convert_SI(df_stats.grid_variogram_rmse, 'm', unit) + # If specified, save gridded array(s) + if grid_to_raster: + # write range + gridfile_name = os.path.join(workdir, col_name + '_' + 'grid_range' + '.tif') + save_gridfile(df_stats.grid_range, 'grid_range', gridfile_name, df_stats.plotbbox, df_stats.spacing, + df_stats.unit, colorbarfmt='%1i', + stationsongrids=df_stats.stationsongrids, dtype='float32') + # write sill + gridfile_name = os.path.join(workdir, col_name + '_' + 'grid_variance' + '.tif') + save_gridfile(df_stats.grid_variance, 'grid_variance', gridfile_name, df_stats.plotbbox, df_stats.spacing, + df_stats.unit + '^2', colorbarfmt='%.3e', + stationsongrids=df_stats.stationsongrids, dtype='float32') + # write variogram rmse + gridfile_name = os.path.join(workdir, col_name + '_' + 'grid_variogram_rmse' + '.tif') + save_gridfile(df_stats.grid_variogram_rmse, 'grid_variogram_rmse', gridfile_name, df_stats.plotbbox, df_stats.spacing, + df_stats.unit, colorbarfmt='%.2e', + stationsongrids=df_stats.stationsongrids, dtype='float32') + + if isinstance(df_stats.grid_range, np.ndarray): + # plot range heatmap + logger.info("- Plot variogram range per gridcell.") + df_stats(df_stats.grid_range, 'grid_range', workdir=os.path.join(workdir, 'figures'), + colorbarfmt='%1i', drawgridlines=drawgridlines, stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + if isinstance(df_stats.grid_variance, np.ndarray): + # plot sill heatmap + logger.info("- Plot variogram sill per gridcell.") + df_stats(df_stats.grid_variance, 'grid_variance', workdir=os.path.join(workdir, 'figures'), drawgridlines=drawgridlines, + colorbarfmt='%.3e', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + if isinstance(df_stats.grid_variogram_rmse, np.ndarray): + # plot variogram rmse heatmap + logger.info("- Plot variogram RMSE per gridcell.") + df_stats(df_stats.grid_variogram_rmse, 'grid_variogram_rmse', workdir=os.path.join(workdir, 'figures'), drawgridlines=drawgridlines, + colorbarfmt='%.2e', stationsongrids=stationsongrids, plotFormat=plot_fmt, userTitle=user_title) + +def main(): + inps = cmd_line_parse() + + stats_analyses( + inps.fname, + inps.col_name, + inps.unit, + inps.workdir, + inps.cpus, + inps.verbose, + inps.bounding_box, + inps.spacing, + inps.timeinterval, + inps.seasonalinterval, + inps.obs_errlimit, + inps.figdpi, + inps.plot_fmt, + inps.cbounds, + inps.colorpercentile, + inps.usr_colormap, + inps.densitythreshold, + inps.stationsongrids, + inps.drawgridlines, + inps.time_lines, + inps.plotall, + inps.station_distribution, + inps.station_delay_mean, + inps.station_delay_median, + inps.station_delay_stdev, + inps.station_seasonal_phase, + inps.phaseamp_per_station, + inps.grid_heatmap, + inps.grid_delay_mean, + inps.grid_delay_median, + inps.grid_delay_stdev, + inps.grid_seasonal_phase, + inps.grid_delay_absolute_mean, + inps.grid_delay_absolute_median, + inps.grid_delay_absolute_stdev, + inps.grid_seasonal_absolute_phase, + inps.grid_to_raster, + inps.min_span, + inps.period_limit, + inps.variogramplot, + inps.binnedvariogram, + inps.variogram_per_timeslice, + inps.variogram_errlimit + ) + + +def main(): + inps = cmd_line_parse() + + stats_analyses( + inps.fname, + inps.col_name, + inps.unit, + inps.workdir, + inps.cpus, + inps.verbose, + inps.bounding_box, + inps.spacing, + inps.timeinterval, + inps.seasonalinterval, + inps.obs_errlimit, + inps.figdpi, + inps.user_title, + inps.plot_fmt, + inps.cbounds, + inps.colorpercentile, + inps.usr_colormap, + inps.densitythreshold, + inps.stationsongrids, + inps.drawgridlines, + inps.time_lines, + inps.plotall, + inps.station_distribution, + inps.station_delay_mean, + inps.station_delay_median, + inps.station_delay_stdev, + inps.station_seasonal_phase, + inps.phaseamp_per_station, + inps.grid_heatmap, + inps.grid_delay_mean, + inps.grid_delay_median, + inps.grid_delay_stdev, + inps.grid_seasonal_phase, + inps.grid_delay_absolute_mean, + inps.grid_delay_absolute_median, + inps.grid_delay_absolute_stdev, + inps.grid_seasonal_absolute_phase, + inps.grid_to_raster, + inps.min_span, + inps.period_limit, + inps.variogramplot, + inps.binnedvariogram, + inps.variogram_per_timeslice, + inps.variogram_errlimit + ) diff --git a/tools/RAiDER/cli/validators.py b/tools/RAiDER/cli/validators.py new file mode 100755 index 000000000..02793f122 --- /dev/null +++ b/tools/RAiDER/cli/validators.py @@ -0,0 +1,567 @@ +from argparse import Action, ArgumentError, ArgumentTypeError + +import importlib +import itertools +import os +import re + +import pandas as pd +import numpy as np + +from datetime import time, timedelta, datetime, date +from textwrap import dedent +from time import strptime + +from RAiDER.llreader import BoundingBox, Geocube, RasterRDR, StationFile, GeocodedFile, Geocube +from RAiDER.losreader import Zenith, Conventional, Raytracing +from RAiDER.utilFcns import rio_extents, rio_profile + +_BUFFER_SIZE = 0.2 # default buffer size in lat/lon degrees + +def enforce_wm(value): + model = value.upper().replace("-", "") + try: + _, model_obj = modelName2Module(model) + except ModuleNotFoundError: + raise NotImplementedError( + dedent(''' + Model {} is not yet fully implemented, + please contribute! + '''.format(model)) + ) + return model_obj() + + +def get_los(args): + if ('orbit_file' in args.keys()) and (args['orbit_file'] is not None): + if args.ray_trace: + los = Raytracing(args.orbit_file) + else: + los = Conventional(args.orbit_file) + elif ('los_file' in args.keys()) and (args['los_file'] is not None): + if args.ray_trace: + los = Raytracing(args.los_file, args.los_convention) + else: + los = Conventional(args.los_file, args.los_convention) + elif ('los_cube' in args.keys()) and (args['los_cube'] is not None): + raise NotImplementedError('LOS_cube is not yet implemented') +# if args.ray_trace: +# los = Raytracing(args.los_cube) +# else: +# los = Conventional(args.los_cube) + else: + los = Zenith() + + return los + + +def get_heights(args, out, station_file, bounding_box=None): + ''' + Parse the Height info and download a DEM if needed + ''' + dem_path = os.path.join(out, 'geom') + if not os.path.exists(dem_path): + os.mkdir(dem_path) + out = { + 'dem': None, + 'height_file_rdr': None, + 'height_levels': None, + } + + if 'dem' in args.keys(): + if (station_file is not None): + if 'Hgt_m' not in pd.read_csv(station_file): + out['dem'] = os.path.join(dem_path, 'GLO30.dem') + elif os.path.exists(args.dem): + out['dem'] = args['dem'] + if bounding_box is not None: + dem_bounds = rio_extents(rio_profile(args.dem)) + lats = dem_bounds[:2] + lons = dem_bounds[2:] + if isOutside( + bounding_box, + getBufferedExtent( + lats, + lons, + buf=_BUFFER_SIZE, + ) + ): + raise ValueError( + 'Existing DEM does not cover the area of the input lat/lon ' + 'points; either move the DEM, delete it, or change the input ' + 'points.' + ) + else: + pass # will download the dem later + + elif 'height_file_rdr' in args.keys(): + out['height_file_rdr'] = args.height_file_rdr + + elif 'height_levels' in args.keys(): + l = re.findall('[0-9]+', args.height_levels) + out['height_levels'] = [float(ll) for ll in l] + + else: + # download the DEM if needed + out['dem'] = os.path.join(dem_path, 'GLO30.dem') + + return out + + +def get_query_region(args): + ''' + Parse the query region from inputs + ''' + # Get bounds from the inputs + # make sure this is first + if ('use_dem_latlon' in args.keys()) and args['use_dem_latlon']: + query = GeocodedFile(args.dem, is_dem=True) + + elif 'lat_file' in args.keys(): + hgt_file = args.get('hgt_file_rdr', None) # only get it if exists + query = RasterRDR(args.lat_file, args.lon_file, hgt_file) + + elif 'station_file' in args.keys(): + query = StationFile(args.station_file) + + elif 'bounding_box' in args.keys(): + bbox = enforce_bbox(args.bounding_box) + if (np.min(bbox[0]) < -90) | (np.max(bbox[1]) > 90): + raise ValueError('Lats are out of N/S bounds; are your lat/lon coordinates switched? Should be SNWE') + query = BoundingBox(bbox) + + elif 'geocoded_file' in args.keys(): + query = GeocodedFile(args.geocoded_file, is_dem=False) + + ## untested + elif 'los_cube' in args.keys(): + query = Geocube(args.los_cube) + + else: + # TODO: Need to incorporate the cube + raise ValueError('No valid query points or bounding box found in the configuration file') + + + return query + + +def enforce_bbox(bbox): + """ + Enforce a valid bounding box + """ + bbox = [float(d) for d in bbox.strip().split()] + + # Check the bbox + if len(bbox) != 4: + raise ValueError("bounding box must have 4 elements!") + S, N, W, E = bbox + + if N <= S or E <= W: + raise ValueError('Bounding box has no size; make sure you use "S N W E"') + + for sn in (S, N): + if sn < -90 or sn > 90: + raise ValueError('Lats are out of S/N bounds (-90 to 90).') + + for we in (W, E): + if we < -180 or we > 180: + raise ValueError('Lons are out of W/E bounds (-180 to 180); Lons in the format of (0 to 360) are not supported.') + + return bbox + + +def parse_dates(arg_dict): + ''' + Determine the requested dates from the input parameters + ''' + + if 'date_list' in arg_dict.keys(): + l = arg_dict['date_list'] + if isinstance(l, str): + l = re.findall('[0-9]+', l) + L = [enforce_valid_dates(d) for d in l] + + else: + try: + start = arg_dict['date_start'] + except KeyError: + raise ValueError('Inputs must include either date_list or date_start') + start = enforce_valid_dates(start) + + if 'date_end' in arg_dict.keys(): + end = arg_dict['date_end'] + end = enforce_valid_dates(end) + else: + end = start + + if 'date_step' in arg_dict.keys(): + step = int(arg_dict['date_step']) + else: + step = 1 + + L = [start + timedelta(days=step) for step in range(0, (end - start).days + 1, step)] + + return L + + +def enforce_valid_dates(arg): + """ + Parse a date from a string in pseudo-ISO 8601 format. + """ + year_formats = ( + '%Y-%m-%d', + '%Y%m%d', + '%d', + '%j', + ) + + for yf in year_formats: + try: + return datetime.strptime(str(arg), yf) + except ValueError: + pass + + + raise ValueError( + 'Unable to coerce {} to a date. Try %Y-%m-%d'.format(arg) + ) + + +def enforce_time(arg_dict): + ''' + Parse an input time (required to be ISO 8601) + ''' + try: + arg_dict['time'] = convert_time(arg_dict['time']) + except KeyError: + raise ValueError('You must specify a "time" in the input config file') + + if 'end_time' in arg_dict.keys(): + arg_dict['end_time'] = convert_time(arg_dict['end_time']) + return arg_dict + + +def convert_time(inp): + time_formats = ( + '', + 'T%H:%M:%S.%f', + 'T%H%M%S.%f', + '%H%M%S.%f', + 'T%H:%M:%S', + '%H:%M:%S', + 'T%H%M%S', + '%H%M%S', + 'T%H:%M', + 'T%H%M', + '%H:%M', + 'T%H', + ) + timezone_formats = ( + '', + 'Z', + '%z', + ) + all_formats = map( + ''.join, + itertools.product(time_formats, timezone_formats) + ) + + for tf in all_formats: + try: + return time(*strptime(inp, tf)[3:6]) + except ValueError: + pass + + raise ValueError( + 'Unable to coerce {} to a time.'+ + 'Try T%H:%M:%S'.format(inp) + ) + + +def modelName2Module(model_name): + """Turn an arbitrary string into a module name. + Takes as input a model name, which hopefully looks like ERA-I, and + converts it to a module name, which will look like erai. I doesn't + always produce a valid module name, but that's not the goal. The + goal is just to handle common cases. + Inputs: + model_name - Name of an allowed weather model (e.g., 'era-5') + Outputs: + module_name - Name of the module + wmObject - callable, weather model object + """ + module_name = 'RAiDER.models.' + model_name.lower().replace('-', '') + model_module = importlib.import_module(module_name) + wmObject = getattr(model_module, model_name.upper().replace('-', '')) + return module_name, wmObject + + +def getBufferedExtent(lats, lons=None, buf=0.): + ''' + get the bounding box around a set of lats/lons + ''' + if lons is None: + lats, lons = lats[..., 0], lons[..., 1] + + try: + if (lats.size == 1) & (lons.size == 1): + out = [lats - buf, lats + buf, lons - buf, lons + buf] + elif (lats.size > 1) & (lons.size > 1): + out = [np.nanmin(lats), np.nanmax(lats), np.nanmin(lons), np.nanmax(lons)] + elif lats.size == 1: + out = [lats - buf, lats + buf, np.nanmin(lons), np.nanmax(lons)] + elif lons.size == 1: + out = [np.nanmin(lats), np.nanmax(lats), lons - buf, lons + buf] + except AttributeError: + if (isinstance(lats, tuple) or isinstance(lats, list)) and len(lats) == 2: + out = [min(lats) - buf, max(lats) + buf, min(lons) - buf, max(lons) + buf] + except Exception as e: + raise RuntimeError('Not a valid lat/lon shape or variable') + + return np.array(out) + + +def isOutside(extent1, extent2): + ''' + Determine whether any of extent1 lies outside extent2 + extent1/2 should be a list containing [lower_lat, upper_lat, left_lon, right_lon] + Equal extents are considered "inside" + ''' + t1 = extent1[0] < extent2[0] + t2 = extent1[1] > extent2[1] + t3 = extent1[2] < extent2[2] + t4 = extent1[3] > extent2[3] + if np.any([t1, t2, t3, t4]): + return True + return False + + +def isInside(extent1, extent2): + ''' + Determine whether all of extent1 lies inside extent2 + extent1/2 should be a list containing [lower_lat, upper_lat, left_lon, right_lon]. + Equal extents are considered "inside" + ''' + t1 = extent1[0] <= extent2[0] + t2 = extent1[1] >= extent2[1] + t3 = extent1[2] <= extent2[2] + t4 = extent1[3] >= extent2[3] + if np.all([t1, t2, t3, t4]): + return True + return False + + +## below are for downloadGNSSDelays +def date_type(arg): + """ + Parse a date from a string in pseudo-ISO 8601 format. + """ + year_formats = ( + '%Y-%m-%d', + '%Y%m%d', + '%d', + '%j', + ) + + for yf in year_formats: + try: + return date(*strptime(arg, yf)[0:3]) + except ValueError: + pass + + raise ArgumentTypeError( + 'Unable to coerce {} to a date. Try %Y-%m-%d'.format(arg) + ) + + +class MappingType(object): + """ + A type that maps arguments to constants. + + # Example + ``` + mapping = MappingType(foo=42, bar="baz").default(None) + assert mapping("foo") == 42 + assert mapping("bar") == "baz" + assert mapping("hello") is None + ``` + """ + UNSET = object() + + def __init__(self, **kwargs): + self.mapping = kwargs + self._default = self.UNSET + + def default(self, default): + """Set a default value if no mapping is found""" + self._default = default + return self + + def __call__(self, arg): + if arg in self.mapping: + return self.mapping[arg] + + if self._default is self.UNSET: + raise KeyError( + "Invalid choice '{}', must be one of {}".format( + arg, list(self.mapping.keys()) + ) + ) + + return self._default + + +class IntegerType(object): + """ + A type that converts arguments to integers. + + # Example + ``` + integer = IntegerType(0, 100) + assert integer("0") == 0 + assert integer("100") == 100 + integer("-10") # Raises exception + ``` + """ + + def __init__(self, lo=None, hi=None): + self.lo = lo + self.hi = hi + + def __call__(self, arg): + integer = int(arg) + + if self.lo is not None and integer < self.lo: + raise ArgumentTypeError("Must be greater than {}".format(self.lo)) + if self.hi is not None and integer > self.hi: + raise ArgumentTypeError("Must be less than {}".format(self.hi)) + + return integer + + +class IntegerMappingType(MappingType, IntegerType): + """ + An integer type that converts non-integer types through a mapping. + + # Example + ``` + integer = IntegerMappingType(0, 100, random=42) + assert integer("0") == 0 + assert integer("100") == 100 + assert integer("random") == 42 + ``` + """ + + def __init__(self, lo=None, hi=None, mapping={}, **kwargs): + IntegerType.__init__(self, lo, hi) + kwargs.update(mapping) + MappingType.__init__(self, **kwargs) + + def __call__(self, arg): + try: + return IntegerType.__call__(self, arg) + except ValueError: + return MappingType.__call__(self, arg) + + +class DateListAction(Action): + """An Action that parses and stores a list of dates""" + + def __init__( + self, + option_strings, + dest, + nargs=None, + const=None, + default=None, + type=None, + choices=None, + required=False, + help=None, + metavar=None + ): + if type is not date_type: + raise ValueError("type must be `date_type`!") + + super().__init__( + option_strings=option_strings, + dest=dest, + nargs=nargs, + const=const, + default=default, + type=type, + choices=choices, + required=required, + help=help, + metavar=metavar + ) + + def __call__(self, parser, namespace, values, option_string=None): + if len(values) > 3 or not values: + raise ArgumentError(self, "Only 1, 2 dates, or 2 dates and interval may be supplied") + + if len(values) == 2: + start, end = values + values = [start + timedelta(days=k) for k in range(0, (end - start).days + 1, 1)] + elif len(values) == 3: + start, end, stepsize = values + + if not isinstance(stepsize.day, int): + raise ArgumentError(self, "The stepsize should be in integer days") + + new_year = date(year=stepsize.year, month=1, day=1) + stepsize = (stepsize - new_year).days + 1 + + values = [start + timedelta(days=k) + for k in range(0, (end - start).days + 1, stepsize)] + + setattr(namespace, self.dest, values) + + +class BBoxAction(Action): + """An Action that parses and stores a valid bounding box""" + + def __init__( + self, + option_strings, + dest, + nargs=None, + const=None, + default=None, + type=None, + choices=None, + required=False, + help=None, + metavar=None + ): + if nargs != 4: + raise ValueError("nargs must be 4!") + + super().__init__( + option_strings=option_strings, + dest=dest, + nargs=nargs, + const=const, + default=default, + type=type, + choices=choices, + required=required, + help=help, + metavar=metavar + ) + + def __call__(self, parser, namespace, values, option_string=None): + S, N, W, E = values + + if N <= S or E <= W: + raise ArgumentError(self, 'Bounding box has no size; make sure you use "S N W E"') + + for sn in (S, N): + if sn < -90 or sn > 90: + raise ArgumentError(self, 'Lats are out of S/N bounds (-90 to 90).') + + for we in (W, E): + if we < -180 or we > 180: + raise ArgumentError(self, 'Lons are out of W/E bounds (-180 to 180); Lons in the format of (0 to 360) are not supported.') + + setattr(namespace, self.dest, values) diff --git a/tools/RAiDER/constants.py b/tools/RAiDER/constants.py index 56630e6a9..6f3b61575 100644 --- a/tools/RAiDER/constants.py +++ b/tools/RAiDER/constants.py @@ -1,11 +1,20 @@ -_ZMIN = -100 # minimum required height -_ZMAX = 15000 # maximum requierd height -_STEP = 15 # integration step size in meters +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import numpy as np -_g0 = 9.80665 +_ZMIN = np.float64(-100) # minimum required height +_ZREF = np.float64(15000) # maximum requierd height +_STEP = np.float64(15.0) # integration step size in meters -class Zenith: - """Special value indicating a look vector of "zenith".""" - pass +_CUBE_SPACING_IN_M = np.float(2000) # Horizontal spacing of cube +_g0 = np.float64(9.80665) +_RE = np.float64(6371008.7714) +R_EARTH_MAX = 6378137 +R_EARTH_MIN = 6356752 diff --git a/tools/RAiDER/delay.py b/tools/RAiDER/delay.py old mode 100644 new mode 100755 index ad951dc4e..10a957163 --- a/tools/RAiDER/delay.py +++ b/tools/RAiDER/delay.py @@ -1,537 +1,709 @@ -"""Compute the delay from a point to the transmitter. - -Dry and hydrostatic delays are calculated in separate functions. -Currently we take samples every _STEP meters, which causes either -inaccuracies or inefficiencies, and we no longer can integrate to -infinity. -""" - - -from osgeo import gdal -gdal.UseExceptions() - -# standard imports -import itertools -import numpy as np +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson, David Bekaert & Yang Lei +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ import os -import pyproj -#import queue -import threading -import sys - - -# local imports -import utils.constants as const -import utils.demdownload as dld -import utils.losreader as losreader -import utils.util as util -from utils.constants import Zenith -from utils.downloadWM import downloadWMFile as dwf -import h5py - -# Step in meters to use when integrating -_STEP = const._STEP -# Top of the troposphere -_ZREF = const._ZMAX - - -def _get_lengths(look_vecs): - ''' - Returns the lengths of a vector or set of vectors - ''' - if look_vecs is Zenith: - return _ZREF - - lengths = np.linalg.norm(look_vecs, axis=-1) - lengths[~np.isfinite(lengths)] = 0 - return lengths - - -def _getZenithLookVecs(lats, lons, heights, zref = _ZREF): - ''' - Returns look vectors when Zenith is used - ''' - return (np.array((util.cosd(lats)*util.cosd(lons), - util.cosd(lats)*util.sind(lons), - util.sind(lats))).T - * (zref - heights)[..., np.newaxis]) +import datetime +import h5py +import numpy as np +import xarray +from netCDF4 import Dataset +from pyproj import CRS, Transformer +from pyproj.exceptions import CRSError + +import isce3.ext.isce3 as isce +from RAiDER.constants import _STEP +from RAiDER.delayFcns import ( + getInterpolators, + calculate_start_points, + get_delays, +) +from RAiDER.dem import getHeights +from RAiDER.logger import logger +from RAiDER.llreader import BoundingBox +from RAiDER.losreader import Zenith, Conventional, Raytracing, get_sv, getTopOfAtmosphere +from RAiDER.processWM import prepareWeatherModel +from RAiDER.utilFcns import ( + writeDelays, projectDelays, writePnts2HDF5, + lla2ecef, transform_bbox, clip_bbox, rio_profile, +) + + +def tropo_delay_cube(dt, wf, args, model_file=None): + """ + raider cube generation function. + Same as tropo_delay() above. + """ + los = args['los'] + weather_model = args['weather_model'] + wmLoc = args['weather_model_directory'] + zref = args['zref'] + download_only = args['download_only'] + verbose = args['verbose'] + aoi = args['aoi'] + cube_spacing = args["cube_spacing_in_m"] + ll_bounds = aoi.bounds() -def _compute_ray(L, S, V, stepSize): - ''' - Compute and return points along a ray, given a total length, - start position (in x,y,z) and a unit look vector. - ''' - # Have to handle the case where there are invalid data try: - thisspace = np.arange(0, L, stepSize) - except ValueError: - thisspace = np.array([]) - ray = S + thisspace[..., np.newaxis]*V - return ray + crs = CRS(args['output_projection']) + except CRSError: + raise ValueError('output_projection argument is not a valid CRS specifier') + + # For testing multiprocessing + # TODO - move this to configuration + nproc = 1 + + # logging + logger.debug('Starting to run the weather model cube calculation') + logger.debug(f'Time: {dt}') + logger.debug(f'Max integration height is {zref:1.1f} m') + logger.debug(f'Output cube projection is {crs.to_wkt()}') + logger.debug(f'Output cube spacing is {cube_spacing} m') + + # We are using zenith model only for now + logger.debug('Beginning weather model pre-processing') + + # If weather model file is not provided + if model_file is None: + weather_model_file = prepareWeatherModel( + weather_model, + dt, + wmLoc=wmLoc, + ll_bounds=ll_bounds, + zref=zref, + download_only=download_only, + makePlots=verbose + ) + else: + weather_model_file = model_file + + # Determine the output grid extent here + wesn = ll_bounds[2:] + ll_bounds[:2] + out_snwe = transform_bbox( + wesn, src_crs=4326, dest_crs=crs + ) + + # Clip output grid to multiples of spacing + # If output is desired in degrees + if crs.axis_info[0].unit_name == "degree": + out_spacing = cube_spacing / 1.0e5 # Scale by 100km + out_snwe = clip_bbox(out_snwe, out_spacing) + else: + out_spacing = cube_spacing + out_snwe = clip_bbox(out_snwe, out_spacing) + logger.debug(f"Output SNWE: {out_snwe}") + logger.debug(f"Output cube spacing: {out_spacing}") -def _helper(tup): - return _compute_ray(tup[0], tup[1], tup[2], tup[3]) - #return _compute_ray(L, S, V, stepSize) + # Load downloaded weather model file to get projection info + with xarray.load_dataset(weather_model_file) as ds: + # Output grid points - North up grid + if args['height_levels'] is not None: + heights = args['height_levels'] + else: + heights = ds.z.values -def _get_rays_p(lengths, stepSize, start_positions, scaled_look_vecs, Nproc = 4): - import multiprocessing as mp + logger.debug(f'Output height range is {min(heights)} to {max(heights)}') - # setup for multiprocessing - data = zip(lengths, start_positions, scaled_look_vecs, [stepSize]*len(lengths)) + # Load CRS from weather model file + wm_proj = rio_profile(f"netcdf:{weather_model_file}:t")["crs"] + if wm_proj is None: + print("WARNING: I can't find a CRS in the weather model file, so I will assume you are using WGS84") + wm_proj = CRS.from_epsg(4326) + else: + wm_proj = CRS.from_wkt(wm_proj.to_wkt()) - pool = mp.Pool(Nproc) - positions_l = pool.map(helper, data) - return positions_l + # Build the output grid + zpts = np.array(heights) + xpts = np.arange(out_snwe[2], out_snwe[3] + out_spacing, out_spacing) + ypts = np.arange(out_snwe[1], out_snwe[0] - out_spacing, -out_spacing) + # If no orbit is provided + # Build zenith delay cube + if los.is_Zenith(): + out_type = "zenith" + out_filename = wf.replace("wet", "tropo") -def _get_rays_d(lengths, stepSize, start_positions, scaled_look_vecs, Nproc = 2): - import dask.bag as db - L = db.from_sequence(lengths) - S = db.from_sequence(start_positions) - Sv = db.from_sequence(scaled_look_vecs) - Ss = db.from_sequence([stepSize]*len(lengths)) + # Get ZTD interpolators + ifWet, ifHydro = getInterpolators(weather_model_file, "total") - # setup for multiprocessing - data = db.zip(L, S, Sv, Ss) + # Build cube + wetDelay, hydroDelay = build_cube( + xpts, ypts, zpts, + wm_proj, crs, + [ifWet, ifHydro]) - positions_l = db.map(helper, data) - return positions_l.compute() + else: + out_type = "slant range" + if not los.ray_trace(): + out_filename = wf.replace("_ztd", "_std").replace("wet", "tropo") + else: + out_filename = wf.replace("_ztd", "_ray").replace("wet", "tropo") + + if args["look_dir"].lower() not in ["right", "left"]: + raise ValueError( + f'Unknown look direction: {args["look_dir"]}' + ) + + # Get pointwise interpolators + ifWet, ifHydro = getInterpolators( + weather_model_file, + kind="pointwise", + shared=(nproc > 1), + ) + + if los.ray_trace(): + # Build cube + if nproc == 1: + wetDelay, hydroDelay = build_cube_ray( + xpts, ypts, zpts, + dt, args["los"]._file, args["look_dir"], + wm_proj, crs, + [ifWet, ifHydro]) + + ### Use multi-processing here + else: + # Pre-build output arrays + + # Create worker pool + + # Loop over heights + raise NotImplementedError + else: + raise NotImplementedError('Conventional STD is not yet implemented on the cube') + + # Write output file + # Modify this as needed for NISAR / other projects + ds = xarray.Dataset( + data_vars=dict( + wet=(["z", "y", "x"], + wetDelay, + {"units" : "m", + "description": f"wet {out_type} delay", + "grid_mapping": "cube_projection", + }), + hydro=(["z", "y", "x"], + hydroDelay, + {"units": "m", + "description": f"hydrostatic {out_type} delay", + "grid_mapping": "cube_projection", + }), + ), + coords=dict( + x=(["x"], xpts), + y=(["y"], ypts), + z=(["z"], zpts), + ), + attrs=dict( + Conventions="CF-1.7", + title="RAiDER geo cube", + source=os.path.basename(weather_model_file), + history=str(datetime.datetime.utcnow()) + " RAiDER", + description=f"RAiDER geo cube - {out_type}", + reference_time=str(args["time"]), + ), + ) + + # Write projection system mapping + ds["cube_projection"] = int() + for k, v in crs.to_cf().items(): + ds.cube_projection.attrs[k] = v + + # Write z-axis information + ds.z.attrs["axis"] = "Z" + ds.z.attrs["units"] = "m" + ds.z.attrs["description"] = "height above ellipsoid" + + # If in degrees + if crs.axis_info[0].unit_name == "degree": + ds.y.attrs["units"] = "degrees_north" + ds.y.attrs["standard_name"] = "latitude" + ds.y.attrs["long_name"] = "latitude" + + ds.x.attrs["units"] = "degrees_east" + ds.x.attrs["standard_name"] = "longitude" + ds.x.attrs["long_name"] = "longitude" + else: + ds.y.attrs["axis"] = "Y" + ds.y.attrs["standard_name"] = "projection_y_coordinate" + ds.y.attrs["long_name"] = "y-coordinate in projected coordinate system" + ds.y.attrs["units"] = "m" -def _get_rays(lengths, stepSize, start_positions, scaled_look_vecs): - ''' - Create the integration points for each ray path. - ''' - positions_l= [] - rayData = zip(lengths, start_positions, scaled_look_vecs) - for L, S, V in rayData: - positions_l.append(_compute_ray(L, S, V, stepSize)) + ds.x.attrs["axis"] = "X" + ds.x.attrs["standard_name"] = "projection_x_coordinate" + ds.x.attrs["long_name"] = "x-coordinate in projected coordinate system" + ds.x.attrs["units"] = "m" - return positions_l + ext = os.path.splitext(out_filename) + if ext not in '.nc .h5'.split(): + out_filename = f'{os.path.splitext(out_filename)[0]}.nc' + logger.debug('Invalid extension %s for cube. Defaulting to .nc', ext) -def _transform(ray, oldProj, newProj): - ''' - Transform a ray from one coordinate system to another - ''' - newRay = np.stack( - pyproj.transform( - oldProj, newProj, ray[:,0], ray[:,1], ray[:,2]) - ,axis = -1) - return newRay + if out_filename.endswith(".nc"): + ds.to_netcdf(out_filename, mode="w") + elif out_filename.endswith(".h5"): + ds.to_netcdf(out_filename, engine="h5netcdf", invalid_netcdf=True) + logger.info('Finished writing data to: %s', out_filename) + return -def _re_project(tup): - newPnt = _transform(tup[0],tup[1], tup[2]) - return newPnt -def f(x): - ecef = pyproj.Proj(proj='geocent') - return _transform(x, ecef, newProj) -def getIntFcn(weatherObj, itype = 'wet', interpType = 'scipy'): +def checkQueryPntsFile(pnts_file, query_shape): ''' - Function to create and return an Interpolator object + Check whether the query points file exists, and if it + does, check that the shapes are all consistent ''' - import interpolator as intprn - - ifFun = intprn.Interpolator() - ifFun.setPoints(*weatherObj.getPoints()) - ifFun.setProjection(weatherObj.getProjection()) - - if itype == 'wet': - ifFun.getInterpFcns(weatherObj.getWetRefractivity(), interpType = interpType) - elif itype == 'hydro': - ifFun.getInterpFcns(weatherObj.getHydroRefractivity(), interpType = interpType) - return ifFun - - -def _common_delay(weatherObj, lats, lons, heights, - look_vecs, zref = None, - stepSize = _STEP, interpType = 'rgi', - verbose = False, nproc = 8, useDask = False): - """ - This function calculates the line-of-sight vectors, estimates the point-wise refractivity - index for each one, and then integrates to get the total delay in meters. The point-wise - delay is calculated by interpolating the weatherObj, which contains a weather model with - wet and hydrostatic refractivity at each weather model grid node, to the points along - the ray. The refractivity is integrated along the ray to get the final delay. - - Inputs: - weatherObj - a weather model object - lats - Grid of latitudes for each ground point - lons - Grid of longitudes for each ground point - heights - Grid of heights for each ground point - look_vecs - Grid of look vectors (should be full-length) for each ground point - raytrace - If True, will use the raytracing method, if False, will use the Zenith - + projection method - stepSize - Integration step size in meters - intpType - Can be one of 'scipy': LinearNDInterpolator, or 'sane': _sane_interpolate. - Any other string will use the RegularGridInterpolate method - nproc - Number of parallel processes to use if useDask is True - useDask - use Dask to parallelize ray calculation - - Outputs: - delays - A list containing the wet and hydrostatic delays for each ground point in - meters. - """ - parThresh = 1e4 - - # If the number of points to interpolate are low, don't parallelize - if np.prod(lats.shape) < parThresh: - useDask = False - nproc = 1 - - # Determine if/what type of parallization to use - if useDask: - import dask.bag as db - elif nproc > 1: - import multiprocessing as mp - else: - pass - - if zref is None: - zref = _ZREF - - # If weather model nodes only are desired, the calculation is very quick - if look_vecs is Zenith: - _,_,zs = weatherObj.getPoints() - look_vecs = _getZenithLookVecs(lats, lons, heights, zref = zref) - wet_pw = weatherObj.getWetRefractivity() - hydro_pw= weatherObj.getHydroRefractivity() - wet_delays = _integrateZenith(zs, wet_pw) - hydro_delays = _integrateZenith(zs, hydro_pw) - return wet_delays,hydro_delays - - if verbose: - import time - print('_common_delay: Starting look vector calculation') - print('_common_delay: The integration stepsize is {} m'.format(stepSize)) - st = time.time() - - # Otherwise, set off on the interpolation road - mask = np.isnan(heights) - - # Get the integration points along the look vectors - # First get the length of each look vector, get integration steps along - # each, then get the unit vector pointing in the same direction - lengths = _get_lengths(look_vecs) - lengths[mask] = np.nan - start_positions = np.array(util.lla2ecef(lats, lons, heights)).T - scaled_look_vecs = look_vecs / lengths[..., np.newaxis] - positions_l= _get_rays(lengths, stepSize, start_positions, scaled_look_vecs) - - if verbose: - print('_common_delay: Finished _get_rays') - ft = time.time() - print('Ray initialization took {:4.2f} secs'.format(ft-st)) - print('_common_delay: Starting _re_project') - st = time.time() - - ecef = pyproj.Proj(proj='geocent') - newProj = weatherObj.getProjection() - if useDask: - if verbose: - print('Beginning re-projection using Dask') - Npart = min(len(positions_l)//100 + 1, 1000) - bag = [(pos, ecef, newProj) for pos in positions_l] - PntBag = db.from_sequence(bag, npartitions=Npart) - newPts = PntBag.map(_re_project).compute() - else: - if verbose: - print('Beginning re-projection without Dask') - newPts = list(map(f, positions_l)) - - newPts = [np.vstack([p[:,1], p[:,0], p[:,2]]).T for p in newPts] - - if verbose: - print('_common_delay: Finished re-projecting') - print('_common_delay: The size of look_vecs is {}'.format(np.shape(look_vecs))) - ft = time.time() - print('Re-projecting took {:4.2f} secs'.format(ft-st)) - print('_common_delay: Starting Interpolation') - st = time.time() - - # Define the interpolator objects - ifWet = getIntFcn(weatherObj,interpType =interpType) - ifHydro = getIntFcn(weatherObj,itype = 'hydro', interpType = interpType) - - # Depending on parallelization, do the interpolation - if useDask: - if verbose: - print('Beginning interpolation using Dask') - Npart = min(len(newPts)//100 + 1, 1000) - PntBag = db.from_sequence(newPts, npartitions=Npart) - wet_pw = PntBag.map(interpRay).compute() - hydro_pw = PntBag.map(interpRay).compute() - elif nproc > 1: - if verbose: - print('Beginning interpolation without Dask') - import multiprocessing as mp - pool = mp.Pool(12) - inp1 = zip([ifWet]*len(newPts), newPts) - inp2 = zip([ifHydro]*len(newPts), newPts) - - wet_pw = pool.map(interpRay,inp1) - hydro_pw = pool.map(interpRay, inp2) - else: - wet_pw, hydro_pw = [], [] - count = 0 - for pnt in newPts: - wet_pw.append(interpRay((ifWet, pnt))) - hydro_pw.append(interpRay((ifHydro, pnt))) - count = count+1 - - - if verbose: - print('_common_delay: Finished interpolation') - ft = time.time() - print('Interpolation took {:4.2f} secs'.format(ft-st)) - print('Average of {:1.6f} secs/ray'.format(.5*(ft-st)/len(newPts))) - print('_common_delay: finished point-wise delay calculations') - - # intergrate the point-wise delays to get total slant delay - delays = _integrateLOS(stepSize, wet_pw, hydro_pw) - - return delays - - -# call the interpolator on each ray -def interpRay(tup): - fcn, ray = tup - return fcn(ray)[0] - - -def _integrateLOS(stepSize, wet_pw, hydro_pw): - delays = [] - for d in (wet_pw, hydro_pw): - delays.append(_integrate_delays(stepSize, d)) - return delays + write_flag = True + if os.path.exists(pnts_file): + # Check whether the number of points is consistent with the new inputs + with h5py.File(pnts_file, 'r') as f: + if query_shape == tuple(f['lon'].attrs['Shape']): + write_flag = False + return write_flag -def _integrateZenith(zs, pw): - return 1e-6*np.trapz(pw, zs, axis = 2) - -def _integrate_delays(stepSize, refr): +def transformPoints(lats, lons, hgts, old_proj, new_proj): ''' - This function gets the actual delays by integrating the refractivity in - each node. Refractivity is given in the 'refr' variable. + Transform lat/lon/hgt data to an array of points in a new + projection + + Parameters + ---------- + lats - WGS-84 latitude (EPSG: 4326) + lons - ditto for longitude + hgts - Ellipsoidal height in meters + old_proj - the original projection of the points + new_proj - the new projection in which to return the points + + Returns + ------- + the array of query points in the weather model coordinate system (YX) ''' - delays = [] - for ray in refr: - delays.append(int_fcn(ray, stepSize)) - return delays - - -# integrate the delays to get overall delay -def int_fcn(y, dx): - return 1e-6*dx*np.nansum(y) - + t = Transformer.from_crs(old_proj, new_proj) -#TODO: the following three fcns are unclear if/how they are needed. -# likely need to see how they work with tropo_delay -def delay_over_area(weather, - lat_min, lat_max, lat_res, - lon_min, lon_max, lon_res, - ht_min, ht_max, ht_res, - los=Zenith, - parallel = True, verbose = False): - """Calculate (in parallel) the delays over an area.""" - lats = np.arange(lat_min, lat_max, lat_res) - lons = np.arange(lon_min, lon_max, lon_res) - hts = np.arange(ht_min, ht_max, ht_res) + # Flags for flipping inputs or outputs + in_flip = old_proj.axis_info[0].direction == "east" + out_flip = new_proj.axis_info[0].direction == "east" - if verbose: - print('delay_over_area: Size of lats: {}'.format(np.shape(lats))) - print('delay_over_area: Size of lons: {}'.format(np.shape(lons))) - print('delay_over_area: Size of hts: {}'.format(np.shape(hts))) - - # It's the cartesian product (thanks StackOverflow) - llas = np.array(np.meshgrid(lats, lons, hts)).T.reshape(-1, 3) - if verbose: - print('delay_over_area: Size of llas: {}'.format(np.shape(llas))) - - if verbose: - print('delay_over_area: running delay_from_grid') + if in_flip: + res = t.transform(lons, lats, hgts) + else: + res = t.transform(lats, lons, hgts) - return delay_from_grid(weather, llas, los, parallel=parallel, verbose = verbose) + if out_flip: + return np.stack((res[1], res[0], res[2]), axis=-1).T + else: + return np.stack(res, axis=-1).T -def delay_from_files(weather, lat, lon, ht, zref = None, parallel=False, los=Zenith, - raytrace=True, verbose = False): +def build_cube(xpts, ypts, zpts, model_crs, pts_crs, interpolators): """ - Read location information from files and calculate delay. + Iterate over interpolators and build a cube """ - if zref is None: - zref = _ZREF - - lats = util.gdal_open(lat) - lons = util.gdal_open(lon) - hts = util.gdal_open(ht) - - if los is not Zenith: - incidence, heading = util.gdal_open(los) - if raytrace: - los = losreader.los_to_lv( - incidence, heading, lats, lons, hts, zref).reshape(-1, 3) + # Create a regular 2D grid + xx, yy = np.meshgrid(xpts, ypts) + + # Output arrays + outputArrs = [np.zeros((zpts.size, ypts.size, xpts.size)) + for mm in range(len(interpolators))] + + # Assume all interpolators to be on same grid + zmin = min(interpolators[0].grid[2]) + zmax = max(interpolators[0].grid[2]) + + # print("Output grid: ") + # print("crs: ", pts_crs) + # print("X: ", xpts[0], xpts[-1]) + # print("Y: ", ypts[0], ypts[-1]) + + # ii = interpolators[0] + # print("Model grid: ") + # print("crs: ", model_crs) + # print("X: ", ii.grid[1][0], ii.grid[1][-1]) + # print("Y: ", ii.grid[0][0], ii.grid[0][-1]) + + # Loop over heights and compute delays + for ii, ht in enumerate(zpts): + + # Uncomment this line to clip heights for interpolator + # ht = max(zmin, min(ht, zmax)) + + # pts is in weather model system + if model_crs != pts_crs: + pts = np.transpose( + transformPoints( + yy, xx, np.full(yy.shape, ht), + pts_crs, model_crs, + ), (2, 1, 0)) else: - los = incidence.flatten() + pts = np.stack([yy, xx, np.full(yy.shape, ht)], axis=-1) - # We need the three to be the same shape so that we know what to - # reshape hydro and wet to. Plus, them being different sizes - # indicates a definite user error. - if not lats.shape == lons.shape == hts.shape: - raise ValueError('lat, lon, and ht should have the same shape, but ' + - 'instead lat had shape {}, lon had shape '.format(lats.shape) + - '{}, and ht had shape {}'.format(lons.shape,hts.shape)) + for mm, intp in enumerate(interpolators): + outputArrs[mm][ii,...] = intp(pts) - llas = np.stack((lats.flatten(), lons.flatten(), hts.flatten()), axis=1) - hydro, wet = delay_from_grid(weather, llas, los, - parallel=parallel, raytrace=raytrace, verbose = verbose) - hydro, wet = np.stack((hydro, wet)).reshape((2,) + lats.shape) - return hydro, wet + return outputArrs -def get_weather_and_nodes(model, filename, zmin=None): - """Look up weather information from a model and file. - - We use the module.load method to load the weather model file, but - we'll also create a weather model object for it. +def build_cube_ray(xpts, ypts, zpts, ref_time, orbit_file, look_dir, model_crs, + pts_crs, interpolators, outputArrs=None): """ - # TODO: Need to check how this fcn will fit into the new framework - xs, ys, proj, t, q, z, lnsp = model.load(filename) - return (reader.read_model_level(module, xs, ys, proj, t, q, z, lnsp, zmin), - xs, ys, proj) - -def tropo_delay(time, los = None, lats = None, lons = None, heights = None, - weather = None, zref = 15000, out = None, - parallel=True,verbose = False, download_only = False): - """Calculate troposphere delay from command-line arguments. - - We do a little bit of preprocessing, then call - _common_delay. + Iterate over interpolators and build a cube """ - from models.allowed import checkIfImplemented - from datetime import datetime as dt - from utils.llreader import getHeights - - if verbose: - print('type of time: {}'.format(type(time))) - print('Download-only is {}'.format(download_only)) - - # ensuring consistent file extensions - outformat = output_format(outformat) - - # the output folder where data is downloaded and delays are stored, default is same location - if out is None: - out = os.getcwd() - - # Make weather - weather_model, weather_files, weather_model_name = \ - weather['type'],weather['files'],weather['name'] - checkIfImplemented(weather_model_name) - # check whether weather model files are supplied - if weather_files is None: - download_flag, f = dwf(weather_model.Model(), time, out, verbose) - - # if no weather model files supplied, check the standard location - if download_flag: + # Some constants for this module + # TODO - Read this from constants or configuration + MAX_SEGMENT_LENGTH = 1000. + MAX_TROPO_HEIGHT = 50000. + + + # First load the state vectors into an isce orbit + orb = isce.core.Orbit([ + isce.core.StateVector( + isce.core.DateTime(row[0]), + row[1:4], row[4:7] + ) for row in np.stack( + get_sv(orbit_file, ref_time, pad=600), axis=-1 + ) + ]) + + # ISCE3 data structures + elp = isce.core.Ellipsoid() + dop = isce.core.LUT2d() + if look_dir.lower() == "right": + look = isce.core.LookSide.Right + elif look_dir.lower() == "left": + look = isce.core.LookSide.Left + else: + raise RuntimeError(f"Unknown look direction: {look_dir}") + logger.debug(f"Look direction: {look_dir}") + + # Get model heights in an array + # Assumption: All interpolators here are on the same grid + model_zs = interpolators[0].grid[2] + + # Create a regular 2D grid + xx, yy = np.meshgrid(xpts, ypts) + + # Output arrays + output_created_here = False + if outputArrs is None: + output_created_here = True + outputArrs = [np.zeros((zpts.size, ypts.size, xpts.size)) + for mm in range(len(interpolators))] + + # Various transformers needed here + epsg4326 = CRS.from_epsg(4326) + cube_to_llh = Transformer.from_crs(pts_crs, epsg4326, + always_xy=True) + ecef_to_model = Transformer.from_crs(CRS.from_epsg(4978), model_crs) + # For calling the interpolators + flip_xy = model_crs.axis_info[0].direction == "east" + + # Loop over heights of output cube and compute delays + for hh, ht in enumerate(zpts): + logger.info(f"Processing slice {hh+1} / {len(zpts)}: {ht}") + # Slices to fill on output + outSubs = [x[hh, ...] for x in outputArrs] + + # Step 1: transform points to llh and xyz + if pts_crs != epsg4326: + llh = list(cube_to_llh.transform(xx, yy, np.full(yy.shape, ht))) + else: + llh = [xx, yy, np.full(yy.shape, ht)] + + xyz = np.stack( + lla2ecef(llh[1], llh[0], np.full(yy.shape, ht)), + axis=-1) + + llh[0] = np.deg2rad(llh[0]) + llh[1] = np.deg2rad(llh[1]) + + # Step 2 - get LOS vectors for targets + # TODO - Modify when isce3 vectorization is available + los = np.full(xyz.shape, np.nan) + for ii in range(yy.shape[0]): + for jj in range(yy.shape[1]): + inp = np.array([ + llh[0][ii, jj], + llh[1][ii, jj], + ht]) + inp_xyz = xyz[ii, jj, :] + + if any(np.isnan(inp)) or any(np.isnan(inp_xyz)): + continue + + # Local normal vector + nv = elp.n_vector(inp[0], inp[1]) + + # Wavelength does not matter for + try: + aztime, slant_range = isce.geometry.geo2rdr( + inp, elp, orb, dop, 0.06, look, + threshold=1.0e-7, + maxiter=30, + delta_range=10.0) + sat_xyz, _ = orb.interpolate(aztime) + los[ii, jj, :] = (sat_xyz - inp_xyz) / slant_range + except Exception as e: + los[ii, jj, :] = np.nan + + # Free memory here + llh = None + + # Step 3 - Determine delays between each model height per ray + # Assumption: zpts (output cube) and model_zs (model) are assumed to be + # sorted in height + # We start integrating bottom up + low_xyz = None + high_xyz = None + cos_factor = None + for zz in range(model_zs.size-1): + # Low and High for model interval + low_ht = model_zs[zz] + high_ht = model_zs[zz + 1] + + # If high_ht < height of point - no contribution to integral + # If low_ht > max_tropo_height - no contribution to integral + if (high_ht <= ht) or (low_ht >= MAX_TROPO_HEIGHT): + continue + + # If low_ht < height of point - integral only up to height of point + if low_ht < ht: + low_ht = ht + + # If high_ht > max_tropo_height - integral only up to max tropo + # height + if high_ht > MAX_TROPO_HEIGHT: + high_ht = MAX_TROPO_HEIGHT + + # Continue only if needed - 1m troposphere does nothing + if np.abs(high_ht - low_ht) < 1.0: + continue + + # If high_xyz was defined, make new low_xyz - save computation + if high_xyz is not None: + low_xyz = high_xyz + else: + low_xyz = getTopOfAtmosphere(xyz, los, low_ht, factor=cos_factor) + + # Compute high_xyz + high_xyz = getTopOfAtmosphere(xyz, los, high_ht, factor=cos_factor) + + # Compute ray length + ray_length = np.linalg.norm(high_xyz - low_xyz, axis=-1) + + # Compute cos_factor for first iteration + if cos_factor is None: + cos_factor = (high_ht - low_ht) / ray_length + + # Determine number of parts to break ray into + try: + nParts = int(np.ceil(ray_length.max() / MAX_SEGMENT_LENGTH)) + 1 + except ValueError: + raise ValueError( + "geo2rdr did not converge. Check orbit coverage" + ) + + if (nParts == 1): + raise RuntimeError( + "Ray with one segment encountered" + ) + + # fractions + fracs = np.linspace(0., 1., num=nParts) + + # Integrate over the ray + for findex, ff in enumerate(fracs): + # Ray point in ECEF coordinates + pts_xyz = low_xyz + ff * (high_xyz - low_xyz) + + # Ray point in model coordinates + pts = ecef_to_model.transform( + pts_xyz[..., 0], + pts_xyz[..., 1], + pts_xyz[..., 2] + ) + + # Order for the interpolator + if flip_xy: + pts = np.stack((pts[1], pts[0], pts[2]), axis=-1) + else: + pts = np.stack(pts, axis=-1) + + # Trapezoidal integration with scaling + wt = 0.5 if findex in [0, fracs.size-1] else 1.0 + wt *= ray_length *1.0e-6 / (nParts - 1.0) + + # For each interpolator, integrate between levels + for mm, out in enumerate(outSubs): + val = interpolators[mm](pts) + + # TODO - This should not occur if there is enough padding in model + val[np.isnan(val)] = 0.0 + out += wt * val + + if output_created_here: + return outputArrs + + +############################################################################### +def main(dt, wetFilename, hydroFilename, args): + """ + raider main function for calculating delays. + + Parameterss + ---------- + args: dict Parameters and inputs needed for processing, + containing the following key-value pairs: + + los - tuple, Zenith class object, ('los', 2-band los file), or ('sv', orbit_file) + lats - ndarray or str + lons - ndarray or str + heights - see checkArgs for format + weather_model - type of weather model to use + wmLoc - Directory containing weather model files + zref - max integration height + outformat - File format to use for raster outputs + time - list of datetimes to calculate delays + download_only - Only download the raw weather model data and exit + wetFilename - + hydroFilename - + pnts_file - Input a points file from previous run + verbose - verbose printing + """ + # unpacking the dictionairy + los = args['los'] + heights = args['dem'] + weather_model = args['weather_model'] + wmLoc = args['weather_model_directory'] + zref = args['zref'] + outformat = args['raster_format'] + verbose = args['verbose'] + aoi = args['aoi'] + + download_only = args['download_only'] + + if los.ray_trace(): + ll_bounds = aoi.add_buffer(buffer=1) # add a buffer for raytracing + else: + ll_bounds = aoi.bounds() + + # logging + logger.debug('Starting to run the weather model calculation') + logger.debug('Time type: {}'.format(type(dt))) + logger.debug('Time: {}'.format(dt.strftime('%Y%m%d'))) + logger.debug('Max integration height is {:1.1f} m'.format(zref)) + + ########################################################### + # weather model calculation + logger.debug('Beginning weather model pre-processing') + + weather_model_file = prepareWeatherModel( + weather_model, + dt, + wmLoc=wmLoc, + ll_bounds=ll_bounds, # SNWE + zref=zref, + download_only=download_only, + makePlots=verbose, + ) + + if download_only: + logger.debug('Weather model has downloaded. Finished.') + return None, None + + + if aoi.type() == 'bounding_box' or \ + (args['height_levels'] and aoi.type() != 'station_file'): + # This branch is specifically for cube generation try: - weather_model.fetch(lats, lons, time, f) + tropo_delay_cube( + dt, wetFilename, args, + model_file=weather_model_file, + ) except Exception as e: - print('ERROR: Unable to download weather data') - print('Exception encountered: {}'.format(e)) - sys.exit(0) - - # exit on download if download_only requested - if download_only: - print('WARNING: download_only flag selected. I will only '\ - 'download the weather model, '\ - ' without doing any further processing.') - return None, None - - - # Load the weather model data - if weather_files is not None: - weather_model.load(*weather_files) - download_flag = False - elif weather_model_name == 'pickle': - weather_model = util.pickle_load(weather_files) + logger.error(e) + raise RuntimeError('Something went wrong in calculating delays on the cube') + return None, None + + ########################################################### + # Load the downloaded model file for CRS information + wm_proj = rio_profile(f"netcdf:{weather_model_file}:t")["crs"] + if wm_proj is None: + print("WARNING: I can't find a CRS in the weather model file, so I will assume you are using WGS84") + wm_proj = CRS.from_epsg(4326) else: - # output file for storing the weather model - #weather_model.load(f) - weather_model.load(f, lats = lats, lons = lons) # <-- this will trim the weather model to the lat/lon extents - if verbose: - print('Weather Model Name: {}'.format(wmName)) - #p = weather.plot(p) - - # Pull the lat/lon data if using the weather model - if lats is None or len(lats)==2: - lats,lons = weather_model.getLL() - lla = weather_model.getProjection() - util.writeLL(time, lats, lons,lla, weather_model_name, out) - - # check for compatilibility of the weather model locations and the input - if util.isOutside(util.getExtent(lats, lons), util.getExtent(*weather_model.getLL())): - print('WARNING: some of the requested points are outside of the existing \ - weather model; these will end up as NaNs') - - # Pull the DEM - if verbose: - print('Beginning DEM calculation') - demLoc = os.path.join(out, 'geom') - lats, lons, hgts = getHeights(lats, lons,heights, demLoc) - - # LOS check and load - if verbose: - print('Beginning LOS calculation') - if los is None: - los = Zenith - elif los is Zenith: - pass - else: - import utils.losreader as losr - los = losr.infer_los(los, lats, lons, hgts, zref) + wm_proj = CRS.from_wkt(wm_proj.to_wkt()) + #################################################################### + + #################################################################### + # Calculate delays + if isinstance(los, (Zenith, Conventional)): + # Start actual processing here + logger.debug("Beginning DEM calculation") + # Lats, Lons will be translated from file to array here if needed + + # read lats/lons + lats, lons = aoi.readLL() + hgts = aoi.readZ() + + los.setPoints(lats, lons, hgts) + + # Transform query points if needed + pnt_proj = CRS.from_epsg(4326) + if wm_proj != pnt_proj: + pnts = transformPoints( + lats, + lons, + hgts, + pnt_proj, + wm_proj, + ).T + else: + # interpolators require y, x, z + pnts = np.stack([lats, lons, hgts], axis=-1) - if los is Zenith: - raytrace = True - else: - raytrace = True - - util.checkShapes(los, lats, lons, hgts) - util.checkLOS(los, raytrace, np.prod(lats.shape)) - - # Save the shape so we can restore later, but flatten to make it - # easier to think about - llas = np.stack((lats, lons, hgts), axis=-1) - real_shape = llas.shape[:-1] - llas = llas.reshape(-1, 3) - lats, lons, hgts = np.moveaxis(llas, -1, 0) - - if verbose: - print('Beginning delay calculation') - # Call _common_delay to compute the hydrostatic and wet delays - if parallel: - useDask = True - nproc = 16 - else: - useDask = False - nproc = 1 - wet, hydro = _common_delay(weather_model, lats, lons, hgts, los, zref = zref,\ - nproc = nproc, useDask = useDask, verbose = verbose) - if verbose: - print('Finished delay calculation') - - # Restore shape - try: - hydro, wet = np.stack((hydro, wet)).reshape((2,) + real_shape) - except: - pass + # either way I'll need the ZTD + ifWet, ifHydro = getInterpolators(weather_model_file, 'total') + wetDelay = ifWet(pnts) + hydroDelay = ifHydro(pnts) - return wet, hydro + # return the delays (ZTD or STD) + wetDelay = los(wetDelay) + hydroDelay = los(hydroDelay) - + elif isinstance(los, Raytracing): + raise NotImplementedError + else: + raise ValueError("Unknown operation type") + + ########################################################### + # Write the delays to file + # Different options depending on the inputs + + if not isinstance(wetFilename, str): + wetFilename = wetFilename[0] + hydroFilename = hydroFilename[0] + + if aoi.type() == 'station_file': + wetFilename = f'{os.path.splitext(wetFilename)[0]}.csv' + + writeDelays( + aoi, + wetDelay, + hydroDelay, + wetFilename, + hydroFilename, + outformat=outformat, + ) + logger.info('Finished writing data to file') + + return wetDelay, hydroDelay diff --git a/tools/RAiDER/delayFcns.py b/tools/RAiDER/delayFcns.py new file mode 100755 index 000000000..ca1ebc347 --- /dev/null +++ b/tools/RAiDER/delayFcns.py @@ -0,0 +1,293 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson, David Bekaert & Yang Lei +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import itertools +import multiprocessing as mp +import xarray + +from netCDF4 import Dataset +import numpy as np +from pyproj import CRS, Transformer +from scipy.interpolate import RegularGridInterpolator + +from RAiDER.constants import _STEP +from RAiDER.interpolator import RegularGridInterpolator as Interpolator +from RAiDER.makePoints import makePoints1D + + +def calculate_start_points(x, y, z, ds): + ''' + Parameters + ---------- + wm_file: str - A file containing a regularized weather model. + Returns + ------- + SP: ndarray - a * x 3 array containing the XYZ locations of the pixels in ECEF coordinates. + Note the ordering of the array is [Y X Z] + ''' + [X, Y, Z] = np.meshgrid(x, y, z) + + try: + t = Transformer.from_crs(ds['CRS'], 4978, always_xy=True) # converts to WGS84 geocentric + except: + print("I can't find a CRS in the weather model file, so I will assume you are using WGS84") + t = Transformer.from_crs(4326, 4978, always_xy=True) # converts to WGS84 geocentric + + return np.moveaxis(np.array(t.transform(X, Y, Z)), 0, -1), np.stack([X, Y, Z], axis=-1) + + +def get_delays( + stepSize, + SP, + LOS, + wm_file, + cpu_num=0 +): + ''' + Create the integration points for each ray path. + ''' + ifWet, ifHydro = getInterpolators(wm_file) + + with xarray.load_dataset(wm_file) as f: + try: + wm_proj = f.attrs['CRS'] + except: + wm_proj = 4326 + print("I can't find a CRS in the weather model file, so I will assume you are using WGS84") + t = Transformer.from_crs(4326, 4978, always_xy=True) # converts to WGS84 geocentric + + in_shape = SP.shape[:-1] + chunkSize = in_shape + CHUNKS = chunk(in_shape, in_shape) + Nchunks = len(CHUNKS) + max_len = 15000 + stepSize = 100 + + chunk_inputs = [(kk, CHUNKS[kk], wm_proj, SP, LOS, + chunkSize, stepSize, ifWet, ifHydro, max_len, wm_file) for kk in range(Nchunks)] + + if Nchunks == 1: + delays = process_chunk(*chunk_inputs[0]) + else: + with mp.Pool() as pool: + individual_results = pool.starmap(process_chunk, chunk_inputs) + try: + delays = np.concatenate(individual_results) + except ValueError: + delays = np.concatenate(individual_results, axis=-1) + + wet_delay = delays[0, ...].reshape(in_shape) + hydro_delay = delays[1, ...].reshape(in_shape) + + return wet_delay, hydro_delay + + +def getInterpolators(wm_file, kind='pointwise', shared=False): + ''' + Read 3D gridded data from a processed weather model file and wrap it with + an interpolator + ''' + # Get the weather model data + with Dataset(wm_file, mode='r') as f: + xs_wm = np.array(f.variables['x'][:]) + ys_wm = np.array(f.variables['y'][:]) + zs_wm = np.array(f.variables['z'][:]) + + # Can get the point-wise or total delays, depending on what is requested + if kind == 'pointwise': + wet = np.array(f.variables['wet'][:]).transpose(1, 2, 0) + hydro = np.array(f.variables['hydro'][:]).transpose(1, 2, 0) + elif kind == 'total': + wet = np.array(f.variables['wet_total'][:]).transpose(1, 2, 0) + hydro = np.array(f.variables['hydro_total'][:]).transpose(1, 2, 0) + + # If shared interpolators are requested + # The arrays are not modified - so turning off lock for performance + if shared: + xs_wm = make_shared_raw(xs_wm) + ys_wm = make_shared_raw(ys_wm) + zs_wm = make_shared_raw(zs_wm) + wet = make_shared_raw(wet) + hydro = make_shared_raw(hydro) + + + ifWet = Interpolator((ys_wm, xs_wm, zs_wm), wet, fill_value=np.nan) + ifHydro = Interpolator((ys_wm, xs_wm, zs_wm), hydro, fill_value=np.nan) + + return ifWet, ifHydro + + +def make_shared_raw(inarr): + """ + Make numpy view array of mp.Array + """ + # Create flat shared array + shared_arr = mp.RawArray('d', inarr.size) + # Create a numpy view of it + shared_arr_np = np.ndarray(inarr.shape, dtype=np.float64, + buffer=shared_arr) + # Copy data to shared array + np.copyto(shared_arr_np, inarr) + + return shared_arr_np + + +def chunk(chunkSize, in_shape): + ''' + Create a set of indices to use as chunks + ''' + startInds = makeChunkStartInds(chunkSize, in_shape) + chunkInds = makeChunksFromInds(startInds, chunkSize, in_shape) + return chunkInds + + +def makeChunkStartInds(chunkSize, in_shape): + ''' + Create a list of start indices for chunking a numpy D-dimensional array. + Inputs: + chunkSize - length-D tuple containing chunk sizes + in_shape - length-D tuple containing the shape of the array to be chunked + Outputs + chunkInds - a list of length-D tuples, where each tuple is the starting + multi-index of each chunk + Example: + makeChunkStartInds((2,2,16), (4,8,16)) + Output: [(0, 0, 0), + (0, 2, 0), + (0, 4, 0), + (0, 6, 0), + (2, 0, 0), + (2, 2, 0), + (2, 4, 0), + (2, 6, 0)] + ''' + if len(in_shape) == 1: + chunkInds = [(i,) for i in range(0, in_shape[0], chunkSize[0])] + + elif len(in_shape) == 2: + chunkInds = [(i, j) for i, j in itertools.product(range(0, in_shape[0], chunkSize[0]), + range(0, in_shape[1], chunkSize[1]))] + elif len(in_shape) == 3: + chunkInds = [(i, j, k) for i, j, k in itertools.product(range(0, in_shape[0], chunkSize[0]), + range(0, in_shape[1], chunkSize[1]), + range(0, in_shape[2], chunkSize[2]))] + else: + raise NotImplementedError('makeChunkStartInds: ndim > 3 not supported') + + return chunkInds + + +def makeChunksFromInds(startInd, chunkSize, in_shape): + ''' + From a length-N list of tuples containing starting indices, + create a list of indices into chunks of a numpy D-dimensional array. + Inputs: + startInd - A length-N list of D-dimensional tuples containing the + starting indices of a set of chunks + chunkSize - A D-dimensional tuple containing chunk size in each dimension + in_shape - A D-dimensional tuple containing the size of each dimension + Outputs: + chunks - A length-N list of length-D lists, where each element of the + length-D list is a numpy array of indices + Example: + makeChunksFromInds([(0, 0), (0, 2), (2, 0), (2, 2)],(4,4),(2,2)) + Output: + [[np.array([0, 0, 1, 1]), np.array([0, 1, 0, 1])], + [np.array([0, 0, 1, 1]), np.array([2, 3, 2, 3])], + [np.array([2, 2, 3, 3]), np.array([0, 1, 0, 1])], + [np.array([2, 2, 3, 3]), np.array([2, 3, 2, 3])]] + ''' + indices = [] + for ci in startInd: + index = [] + for si, k, dim in zip(ci, chunkSize, range(len(chunkSize))): + if si + k > in_shape[dim]: + dend = in_shape[dim] + else: + dend = si + k + index.append(np.array(range(si, dend))) + indices.append(index) + + # Now create the index mesh (for Ndim > 1) + chunks = [] + if len(in_shape) > 1: + for index in indices: + chunks.append([np.array(g) for g in zip(*list(itertools.product(*index)))]) + else: + chunks = indices + + return chunks + + +def process_chunk(k, chunkInds, proj_wm, SP, SLV, chunkSize, stepSize, ifWet, ifHydro, max_len, wm_file): + """ + Perform the interpolation and integration over a single chunk. + """ + # Transformer from ECEF to weather model + t = Transformer.from_proj(4978, proj_wm, always_xy=True) + + # datatype must be specific for the cython makePoints* function + _DTYPE = np.float64 + + # H5PY does not support fancy indexing with tuples, hence this if/else check + if len(chunkSize) == 1: + row = chunkInds[0] + ray = makePoints1D(max_len, SP[row, :].astype(_DTYPE), SLV[row, :].astype(_DTYPE), stepSize) + elif len(chunkSize) == 2: + row, col = chunkInds + ray = makePoints1D(max_len, SP[row, col, :].astype(_DTYPE), SLV[row, col, :].astype(_DTYPE), stepSize) + elif len(chunkSize) == 3: + row, col, zind = chunkInds + ray = makePoints1D(max_len, SP[row, col, zind, :].astype(_DTYPE), SLV[row, col, zind, :].astype(_DTYPE), stepSize) + else: + raise RuntimeError('Data in more than 4 dimensions is not supported') + + ray_x, ray_y, ray_z = t.transform(ray[..., 0, :], ray[..., 1, :], ray[..., 2, :]) + delay_wet = interpolate2(ifWet, ray_x, ray_y, ray_z) + delay_hydro = interpolate2(ifHydro, ray_x, ray_y, ray_z) + int_delays = _integrateLOS(stepSize, delay_wet, delay_hydro) + + return int_delays + + +def interpolate2(fun, x, y, z): + ''' + helper function to make the interpolation step cleaner + ''' + in_shape = x.shape + out = fun((y.ravel(), x.ravel(), z.ravel())) # note that this re-ordering is on purpose to match the weather model + outData = out.reshape(in_shape) + return outData + + +def _integrateLOS(stepSize, wet_pw, hydro_pw, Npts=None): + delays = [] + for d in (wet_pw, hydro_pw): + if d.ndim == 1: + delays.append(np.array([int_fcn(d, stepSize)])) + else: + delays.append(_integrate_delays(stepSize, d, Npts)) + return np.stack(delays, axis=0) + + +def _integrate_delays(stepSize, refr, Npts=None): + ''' + This function gets the actual delays by integrating the refractivity in + each node. Refractivity is given in the 'refr' variable. + ''' + delays = [] + if Npts is not None: + for n, ray in zip(Npts, refr): + delays.append(int_fcn(ray, stepSize, n)) + else: + for ray in refr: + delays.append(int_fcn(ray, stepSize)) + return np.array(delays) + + +def int_fcn(y, dx, N=None): + return 1e-6 * dx * np.nansum(y[:N]) diff --git a/tools/RAiDER/dem.py b/tools/RAiDER/dem.py new file mode 100644 index 000000000..cd15939d8 --- /dev/null +++ b/tools/RAiDER/dem.py @@ -0,0 +1,89 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +from logging import warn +import os + +import numpy as np +import pandas as pd + +import rasterio +from dem_stitcher.stitcher import stitch_dem + +import RAiDER.utilFcns + +from RAiDER.interpolator import interpolateDEM +from RAiDER.logger import logger +from RAiDER.utilFcns import rio_open, rio_profile, rio_extents, get_file_and_band + + +def getHeights(ll_bounds, dem_type, dem_file, lats=None, lons=None): + ''' + Fcn to return heights from a DEM, either one that already exists + or will download one if needed. + ''' + # height_type, height_data = heights + if dem_type == 'hgt': + htinfo = get_file_and_band(dem_file) + hts = rio_open(htinfo[0], band=htinfo[1]) + + elif dem_type == 'csv': + # Heights are in the .csv file + hts = pd.read_csv(dem_file)['Hgt_m'].values + + elif dem_type == 'interpolate': + # heights will be vertically interpolated to the heightlvs + hts = None + + elif (dem_type == 'download') or (dem_type == 'dem'): + if ~os.path.exists(dem_file): + download_dem( + ll_bounds, + writeDEM = True, + outName=dem_file, + ) + #TODO: interpolate heights to query lats/lons + # Interpolate to the query points + hts = interpolateDEM( + dem_file, + lats, + lons, + ) + + return hts + + +def download_dem( + ll_bounds, + save_flag='new', + writeDEM=False, + outName='warpedDEM', + buf=0.02 +): + ''' Download a DEM if one is not already present. ''' + folder = os.path.dirname(outName) + # inExtent is SNWE + # dem-stitcher wants WSEN + bounds = [ + np.floor(ll_bounds[2]) - buf, np.floor(ll_bounds[0]) - buf, + np.ceil(ll_bounds[3]) + buf, np.ceil(ll_bounds[1]) + buf + ] + + zvals, metadata = stitch_dem( + bounds, + dem_name='glo_30', + dst_ellipsoidal_height=True, + dst_area_or_point='Area', + ) + if writeDEM: + dem_file = os.path.join(folder, 'GLO30_fullres_dem.tif') + with rasterio.open(dem_file, 'w', **metadata) as ds: + ds.write(zvals, 1) + ds.update_tags(AREA_OR_POINT='Point') + + return zvals, metadata + diff --git a/tools/RAiDER/demdownload.py b/tools/RAiDER/demdownload.py deleted file mode 100644 index 29e6db08a..000000000 --- a/tools/RAiDER/demdownload.py +++ /dev/null @@ -1,106 +0,0 @@ -import glob -import numpy as np -import os -from osgeo import gdal -import scipy.interpolate -from scipy.interpolate import RegularGridInterpolator as rgi - -import utils.util as util - -gdal.UseExceptions() - - -_world_dem = ('https://cloud.sdsc.edu/v1/AUTH_opentopography/Raster/' - 'SRTM_GL1_Ellip/SRTM_GL1_Ellip_srtm.vrt') - - -def download_dem(lats, lons, outLoc, save_flag= True, checkDEM = True, outName = 'warpedDEM.dem'): - ''' - Download a DEM if one is not already present. - ''' - gdalNDV = 0 - print('Getting the DEM') - - # Insert check for DEM noData values - if checkDEM: - lats[lats==0.] = np.nan - lons[lons==0.] = np.nan - - minlon = np.nanmin(lons) - 0.02 - maxlon = np.nanmax(lons) + 0.02 - minlat = np.nanmin(lats) - 0.02 - maxlat = np.nanmax(lats) + 0.02 - pixWidth = (maxlon - minlon)/lats.shape[1] - pixHeight = (maxlat - minlat)/lats.shape[1] - - # Make sure the DEM hasn't already been downloaded - outRasterName = os.path.join(outLoc, outName) - - if os.path.exists(outRasterName): - print('WARNING: DEM already exists in {}, checking shape'.format(outLoc)) - hgts = util.gdal_open(outRasterName) - if hgts.shape != lats.shape: - raise RuntimeError('Existing DEM does not cover the area of the input \n \ - lat/lon points; either move the DEM, delete it, or \n \ - change the inputs.') - hgts[hgts==0.] = np.nan - return hgts - - - # Specify filenames - memRaster = '/vsimem/warpedDEM' - inRaster ='/vsicurl/{}'.format(_world_dem) - - # Download and warp - print('Beginning DEM download and warping') - - wrpOpt = gdal.WarpOptions(outputBounds = (minlon, minlat,maxlon, maxlat)) - gdal.Warp(memRaster, inRaster, options = wrpOpt) - - print('DEM download finished') - - # Load the DEM data - try: - out = util.gdal_open(memRaster) - except: - raise RuntimeError('demdownload: Cannot open the warped file') - finally: - try: - gdal.Unlink('/vsimem/warpedDEM') - except: - pass - - # Flip the orientation, since GDAL writes top-bot - out = out[::-1] - - print('Beginning interpolation') - nPixLat = out.shape[0] - nPixLon = out.shape[1] - xlats = np.linspace(minlat, maxlat, nPixLat) - xlons = np.linspace(minlon, maxlon, nPixLon) - interpolator = rgi(points = (xlats, xlons),values = out, - method='linear', - bounds_error = False) - - outInterp = interpolator(np.stack((lats, lons), axis=-1)) - - # Need to ensure that noData values are consistently handled and - # can be passed on to GDAL - outInterp[np.isnan(outInterp)] = gdalNDV - outInterp[outInterp < -10000] = gdalNDV - - print('Interpolation finished') - - if save_flag: - print('Saving DEM to disk') - if outInterp.ndim==2: - - util.writeArrayToRaster(outInterp, outRasterName, noDataValue = gdalNDV) - - elif outInterp.ndim==1: - util.writeArrayToFile(lons, lats, outInterp, outRasterName, noDataValue = -9999) - else: - raise RuntimeError('Why is the DEM 3-dimensional?') - print('Finished saving DEM to disk') - - return outInterp diff --git a/tools/RAiDER/downloadWM.py b/tools/RAiDER/downloadWM.py deleted file mode 100644 index bef49a0b0..000000000 --- a/tools/RAiDER/downloadWM.py +++ /dev/null @@ -1,28 +0,0 @@ -# standard imports -import os -from datetime import datetime as dt - -# local imports -import utils.util as util - - -def downloadWMFile(weather_model_name, time, outLoc, verbose = False): - ''' - Check whether the output weather model exists, and - if not, download it. - ''' - util.mkdir('weather_files') - f = os.path.join(outLoc, 'weather_files', - '{}_{}.nc'.format(weather_model_name, - dt.strftime(time, '%Y_%m_%d_T%H_%M_%S'))) - - if verbose: - print('Storing weather model at: {}'.format(f)) - - download_flag = True - if os.path.exists(f): - print('WARNING: Weather model already exists, skipping download') - download_flag = False - - return download_flag, f - diff --git a/tools/RAiDER/ecmwf_downloader_fromWeb.py b/tools/RAiDER/ecmwf_downloader_fromWeb.py deleted file mode 100644 index 9348b6799..000000000 --- a/tools/RAiDER/ecmwf_downloader_fromWeb.py +++ /dev/null @@ -1,213 +0,0 @@ -#!/usr/bin/env python -# ------------------------------------------------------------------- -# - NAME: ERA5.py -# - AUTHOR: Reto Stauffer -# - DATE: 2017-01-05 -# ------------------------------------------------------------------- -# - DESCRIPTION: ERA5 data downloader (currently for the Alps, check -# lonmin, lonmax, latmin, latmax! -# ------------------------------------------------------------------- -# - EDITORIAL: 2017-01-05, RS: Created file on pc24-c707. -# ------------------------------------------------------------------- -# - L@ST MODIFIED: 2018-12-15 15:48 on marvin -# ------------------------------------------------------------------- - -import logging as log -log.basicConfig(format='%(levelname)s: %(message)s', level = log.DEBUG) - -# ------------------------------------------------------------------- -# Main script part -# ------------------------------------------------------------------- -if __name__ == "__main__": - - # Using sys.args to control the date which should be processed. - from optparse import OptionParser - import sys - usage = """Usage: - - {0:s} --years --parameter --level - {0:s} -y -p -l - - Downloading ERA5 reanalysis data from Copernicus Climate Data - Services. NOTE: Not yet made for ensemble ERA5! - - Requires `cdsapi` to be installed (python package to access - Copernicus Climate Data Services, CDS). - - How to install cdsapi and the required API key: - * https://cds.climate.copernicus.eu/api-how-to - - Available parameters for "single level" (-t/--type sf): - * https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form - - Available parameters for "pressure level" (-t/--type pl): - * https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form - - Usage examples: - - 10m wind speed for the year 2018 - >>> python {0:s} --years 2018 --parameter 10m_u_component_of_wind - - 10m wind speed for 2010 and 2018 - >>> python {0:s} --years 2010,2018 --parameter 10m_u_component_of_wind - - 10m wind speed for 2008, 2009, 2010, ..., 2018 - >>> python {0:s} --years 2008-2018 --parameter 10m_u_component_of_wind - - 700hPa geopotential height, 2018 - >>> python {0:s} --years 2018 --parameter geopotential --level 700 - - """.format(__file__) - parser = OptionParser(usage = usage) - parser.add_option("-y", "--years", dest = "years", type = "str", - help="years for which the data should be downloaded. " + \ - "Can either be a single year (e.g., 2010), a comma separated list " + \ - "(e.g., 2010,2011), or a sequence of the form - " + \ - "(e.g., 2010-2018; 2010 to 2018).") - parser.add_option("-p", "--parameter", dest = "param", type = "str", - help="the parameter/variable to be downloaded. Please " + \ - "check the cds website to see what's availabe.") - parser.add_option("-l", "--level", dest = "level", type = "int", default = None, - help="level, only for pressure level data. For \"single level data\" " + \ - "(e.g., surface level variables) do not set this parameter!") - parser.add_option("-t", "--test", dest = "test", default = False, action = "store_true", - help="development flag. If set, only one day (January 1th of the first " + \ - "year) will be downloaded before the script stops. " + \ - "The output file will be labeled with \"_test_\".") - (options,args) = parser.parse_args() - - - # Subsetting, currently hardcoded/fixed - lonmin = 2.0 - lonmax = 20.0 - latmin = 40.0 - latmax = 52.0 - the_subset = "/".join(["{:.2f}".format(x) for x in [latmax, lonmin, latmin, lonmax]]) - - # Output directory - datadir = "era5_data" - - # Missing date? - import re - from numpy import min, max, arange, unique, sort - if options.years == None or options.param == None: - parser.print_help(); sys.exit(9) - else: - if re.match("^[0-9]{4}$", options.years): - options.years = [int(options.years)] - elif re.match("^[0-9,]+$", options.years): - options.years = [int(x) for x in options.years.split(",")] - elif re.match("^[0-9]{4}-[0-9]{4}$", options.years): - options.years = [int(x) for x in options.years.split("-")] - options.years = list(arange(min(options.years), max(options.years)+1)) - else: - parser.print_help(); log.info("\n\n") - raise ValueError("Wrong format for -y/--years") - - # Sort and unique - options.years = list(sort(unique(options.years))) - options.years.reverse() - - # Minimum year - if min(options.years) < 1950: - raise ValueError("input years is definitively wrong as before 1950!") - - # Quick output - log.info("\n{:s}".format("".join(["-"]*60))) - log.info("Processing data for years: {:s}".format(", ".join([str(x) for x in options.years]))) - log.info("Parameter: {:s}".format(options.param)) - log.info("Subset: {:s}\n".format(the_subset)) - if options.level is None: - log.info("Single level parameter") - else: - log.info("Vertical level: {:d} hPa".format(options.level)) - if options.test: - log.info("TEST MODE ACTIVE: Only one day will be downloaded!") - log.info("{:s}\n".format("".join(["-"]*60))) - - # ------------------------------------------------------------- - # ------------------------------------------------------------- - # ------------------------------------------------------------- - # ------------------------------------------------------------- - - import sys, os - import datetime as dt - import subprocess as sub - from cdsapi import Client - from datetime import date - from numpy import arange - - # Output directory - tmp = "" if options.level is None else "_{:d}hPa".format(options.level) - paramdir = os.path.join(datadir, "{:s}{:s}".format(options.param,tmp)) - if not os.path.isdir(paramdir): - try: - os.makedirs(paramdir) - except: - raise Exception("not able to create directory \"{;s}\"".format(datadir)) - - # range(2017,2018) downloads data for 2017 - for year in options.years: - - if year > int(date.today().strftime("%Y")): continue - log.info("[!] Processing year {:04d}".format(year)) - - # ---------------------------------------------------------------- - # SURFACE DATA - # ---------------------------------------------------------------- - tmp = "" if options.level is None else "_{:d}hPa".format(options.level) - # File name without suffix - tmp = "ERA5_{:04d}{:s}_{:s}{:s}".format(year, \ - "_test" if options.test else "", options.param, tmp) - # Create the different file names we need - ncfile = os.path.join(paramdir, "{:s}.nc".format(tmp)) - - # If netcdf file exists: skip - if os.path.isfile(ncfile): - log.info("Output file \"{:s}\" exists, proceed with next ...".format(ncfile)) - else: - - # Request - log.info("Downloading: {:s}".format(ncfile)) - args = {"product_type" : "reanalysis", - "format" : "netcdf", - "area" : the_subset, - "variable" : options.param, - "year" : "{:04d}".format(year), - "month" : ["{:02d}".format(x) for x in range(1,13)], - "day" : ["{:02d}".format(x) for x in range(1,32)], - "time" : ["{:02d}:00".format(x) for x in range(0,24)]} - # Level - if not options.level == None: - args["pressure_level"] = "{:d}".format(options.level) - - # Test mode? - if options.test: - args["day"] = "01" - args["month"] = "01" - - for key,val in args.items(): - if isinstance(val, str): - log.info("{:20s} {:s}".format(key, val)) - else: - log.info("{:20s} {:s}".format(key, ", ".join(list(val))[0:40])) - - try: - server = Client() - if options.level: - cdstype = "reanalysis-era5-pressure-levels" - else: - cdstype = "reanalysis-era5-single-levels" - # Downloading data - server.retrieve(cdstype, args, ncfile) - log.info("[+] Everything seems to be fine for {0}".format(ncfile)) - - except Exception as e: - log.info(e) - log.info("[!] PROBLEMS DETECTED") - log.info(" cdsapi returned error code != 0") - log.info(" for file: {0}".format(ncfile)) - if options.test: log.info("\nTEST MODE: Stop here.\n"); sys.exit(0) - continue - - if options.test: log.info("\nTEST MODE: Stop here.\n"); sys.exit(0) diff --git a/tools/RAiDER/extra.py b/tools/RAiDER/extra.py deleted file mode 100644 index ead7985f4..000000000 --- a/tools/RAiDER/extra.py +++ /dev/null @@ -1,45 +0,0 @@ - - else: - weather, xs, ys, proj = weather_model.weather_and_nodes( - weather_files) - if lats is None: - def geo_info(ds): - ds.SetProjection(str(proj)) - ds.SetGeoTransform((xs[0], xs[1] - xs[0], 0, ys[0], 0, - ys[1] - ys[0])) - set_geo_info.append(geo_info) - lla = pyproj.Proj(proj='latlong') - xgrid, ygrid = np.meshgrid(xs, ys, indexing='ij') - lons, lats = pyproj.transform(proj, lla, xgrid, ygrid) - - - - - # use the weather model nodes in case the grid is not specififed - if lats is None: - # weather model projection - proj = weather_model.getProjection() - # weather model grid (3D cube= x,y,z) - xs = weather_model._xs - ys = weather_model._ys - # only using the grid from one dimension as z is a replicate - xs = xs[:,:,0] - ys = ys[:,:,0] - - # for geo transform make sure that we take right slice through the x and y grids - if len(np.unique(xs))==len(xs[:,0]): - xs_vector = xs[:,0] - ys_vector = np.transpose(ys[0,:]) - elif len(np.unique(xs))==len(xs[0,:]): - ys_vector = ys[:,0] - xs_vector = np.transpose(xs[0,:]) - - # code up the default projectoon to be WGS84 for local processing/ delay calculation - lla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84') - lons, lats = pyproj.transform(proj, lla,xs,ys) - - def geo_info(ds): - ds.SetProjection(str(proj)) - ds.SetGeoTransform((xs_vector[0], xs_vector[1] - xs_vector[0], 0, ys_vector[0], 0, ys_vector[1] - ys_vector[0])) - set_geo_info.append(geo_info) - diff --git a/tools/RAiDER/fileIO.py b/tools/RAiDER/fileIO.py deleted file mode 100644 index 4aa771397..000000000 --- a/tools/RAiDER/fileIO.py +++ /dev/null @@ -1,96 +0,0 @@ - -def writeWeatherLevelsToFile(): - ''' - Need to implement a separate file writing - routine for list-like outputs - ''' - if outformat == 'hdf5': - raise NotImplemented - else: - drv = gdal.GetDriverByName(outformat) - hydro_ds = drv.Create( - hydro_file_name, total_hydro.shape[2], - total_hydro.shape[1], len(hts), gdal.GDT_Float64) - for lvl, (hydro, ht) in enumerate(zip(total_hydro, hts), start=1): - band = hydro_ds.GetRasterBand(lvl) - band.SetDescription(str(ht)) - band.WriteArray(hydro) - for f in set_geo_info: - f(hydro_ds) - hydro_ds = None - - wet_ds = drv.Create( - wet_file_name, total_wet.shape[2], - total_wet.shape[1], len(hts), gdal.GDT_Float64) - for lvl, (wet, ht) in enumerate(zip(total_wet, hts), start=1): - band = wet_ds.GetRasterBand(lvl) - band.SetDescription(str(ht)) - band.WriteArray(wet) - for f in set_geo_info: - f(wet_ds) - wet_ds = None - - - -if outformat == 'hdf5' or outformat=='h5' or outformat =='hdf': - if os.path.isfile(hydro_file_name): - os.remove(hydro_file_name) - - # for hdf5 we will hard-code only the lon-lat grid for now - # for hdf and netcdf typically the dimensions are listed as z,y,x - # coordinates are provided as vectors for regular grids - - # hydro delay - hydro_ds = h5py.File(hydro_file_name,'w') - # generating hydro dataset - hdftotal_hydro = np.swapaxes(total_hydro,1,2) - dset = hydro_ds.create_dataset("hydro",data=hdftotal_hydro,fillvalue=0,compression="gzip") - dset.attrs["Description"]='Hydro delay in meters' - # generating lon lat dataset - if len(np.unique(lons))==len(lons[:,0]): - hdflons = lons[:,0] - hdflats = np.transpose(lats[0,:]) - dset = hydro_ds.create_dataset("lons",data=hdflons,compression="gzip") - dset = hydro_ds.create_dataset("lats",data=hdflats,compression="gzip") - elif len(np.unique(lons))==len(lons[0,:]): - hdflons = np.transpose(lons[0,:]) - hdflats = lats[:,0] - dset = hydro_ds.create_dataset("lons",data=hdflons,compression="gzip") - dset = hydro_ds.create_dataset("lats",data=hdflats,compression="gzip") - else: - dset = hydro_ds.create_dataset("lons_grid",data=lons,fillvalue=0,compression="gzip") - dset = hydro_ds.create_dataset("lats_grid",data=lats,fillvalue=0,compression="gzip") - # writing teh heights - dset = hydro_ds.create_dataset("heights",data=hts,compression="gzip") - # create the projection string - proj4= 'EPSG:{0}'.format(int(4326)) - hydro_ds.create_dataset('projection', data=[proj4.encode('utf-8')], dtype='S200') - # close the file - hydro_ds.close() - # hydro delay - wet_ds = h5py.File(wet_file_name,'w') - # generating hydro dataset - hdftotal_wet = np.swapaxes(total_wet,1,2) - dset = wet_ds.create_dataset("wet",data=hdftotal_wet,fillvalue=0,compression="gzip") - dset.attrs["Description"]='Wet delay in meters' - # generating lon lat dataset - if len(np.unique(lons))==len(lons[:,0]): - hdflons = lons[:,0] - hdflats = np.transpose(lats[0,:]) - dset = wet_ds.create_dataset("lons",data=hdflons,compression="gzip") - dset = wet_ds.create_dataset("lats",data=hdflats,compression="gzip") - elif len(np.unique(lons))==len(lons[0,:]): - hdflons = np.transpose(lons[0,:]) - hdflats = lats[:,0] - dset = wet_ds.create_dataset("lons",data=hdflons,compression="gzip") - dset = wet_ds.create_dataset("lats",data=hdflats,compression="gzip") - else: - dset = wet_ds.create_dataset("lons_grid",data=lons,fillvalue=0,compression="gzip") - dset = wet_ds.create_dataset("lats_grid",data=lats,fillvalue=0,compression="gzip") - # writing the heights - dset = wet_ds.create_dataset("heights",data=hts,compression="gzip") - # create teh projection string - wet_ds.create_dataset('projection', data=[proj4.encode('utf-8')], dtype='S200') - # close the file - wet_ds.close() - diff --git a/tools/RAiDER/getStationDelays.py b/tools/RAiDER/getStationDelays.py new file mode 100644 index 000000000..5b0cc4911 --- /dev/null +++ b/tools/RAiDER/getStationDelays.py @@ -0,0 +1,254 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Simran Sangha, & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import datetime as dt +import gzip +import io +import multiprocessing +import os +import zipfile + +import numpy as np +import pandas as pd +import requests + +from RAiDER.logger import logger + + +def get_delays_UNR(stationFile, filename, dateList, returnTime=None): + ''' + Parses and returns a dictionary containing either (1) all + the GPS delays, if returnTime is None, or (2) only the delay + at the closest times to to returnTime. + Inputs: + stationFile - a .gz station delay file + returnTime - specified time of GPS delay + Outputs: + a dict and CSV file containing the times and delay information + (delay in mm, delay uncertainty, delay gradients) + *NOTE: Due to a formatting error in the tropo SINEX files, the two tropospheric gradient columns + (TGNTOT and TGETOT) are interchanged, as are the formal error columns (_SIG). + Source —> http://geodesy.unr.edu/gps_timeseries/README_trop2.txt) + ''' + # Refer to the following sites to interpret stationFile variable names: + # ftp://igs.org/pub/data/format/sinex_tropo.txt + # http://geodesy.unr.edu/gps_timeseries/README_trop2.txt + # Wet and hydrostratic delays were derived as so: + # Constants —> k1 = 0.704, k2 = 0.776, k3 = 3739.0, m = 18.0152/28.9644, + # k2' = k2-(k1*m) = 0.33812796398337275, Rv = 461.5 J/(kg·K), ρl = 997 kg/m^3 + # Note wet delays passed here may be computed as so + # where PMV = precipitable water vapor, P = total atm pressure, Tm = mean temp of the column —> + # Wet zenith delay = 10^-6 ρlRv(k2' + k3/Tm) PMV + # Hydrostatic zenith delay = Total zenith delay - wet zenith delay = k1*(P/Tm) + # Source —> Hanssen, R. F. (2001) eqns. 6.2.7-10 + + # sort through station zip files + allstationTarfiles = [] + # if URL + if stationFile.startswith('http'): + r = requests.get(stationFile) + ziprepo = zipfile.ZipFile(io.BytesIO(r.content)) + # if downloaded file + else: + ziprepo = zipfile.ZipFile(stationFile) + # iterate through tarfiles + stationTarlist = sorted(ziprepo.namelist()) + + final_stationTarlist = [] + for j in stationTarlist: + # get the date of the file + time, yearFromFile, doyFromFile = get_date(os.path.basename(j).split('.')) + # check if in list of specified input dates + if time.strftime('%Y-%m-%d') not in dateList: + continue + final_stationTarlist.append(j) + f = gzip.open(ziprepo.open(j), 'rb') + # initialize variables + d, Sig, dwet, dhydro, timesList = [], [], [], [], [] + flag = False + for line in f.readlines(): + try: + line = line.decode('utf-8') + except UnicodeDecodeError: + line = line.decode('latin-1') + if flag: + # Do not attempt to read header + if 'SITE' in line: + continue + # Attempt to read data + try: + split_lines = line.split() + # units: mm, mm, mm, deg, deg, deg, deg, mm, mm, K + trotot, trototSD, trwet, tgetot, tgetotSD, tgntot, tgntotSD, wvapor, wvaporSD, mtemp = \ + [float(t) for t in split_lines[2:]] + except BaseException: # TODO: What error(s)? + continue + site = split_lines[0] + year, doy, seconds = [int(n) + for n in split_lines[1].split(':')] + # Break iteration if time from line in file does not match date reported in filename + if doy != doyFromFile: + logger.warning( + 'time %s from line in conflict with time %s from file ' + '%s, will continue reading next tarfile(s)', + doy, doyFromFile, j + ) + continue + # convert units from mm to m + d.append(trotot * 0.001) + Sig.append(trototSD * 0.001) + dwet.append(trwet * 0.001) + dhydro.append((trotot - trwet) * 0.001) + timesList.append(seconds) + if 'TROP/SOLUTION' in line: + flag = True + del f + # Break iteration if file contains no data. + if d == []: + logger.warning( + 'file %s for station %s is empty, will continue reading next ' + 'tarfile(s)', j, j.split('.')[0] + ) + continue + + # check for missing times + true_times = list(range(0, 86400, 300)) + if len(timesList) != len(true_times): + missing = [ + True if t not in timesList else False for t in true_times] + mask = np.array(missing) + delay, sig, wet_delay, hydro_delay = [np.full((288,), np.nan)] * 4 + delay[~mask] = d + sig[~mask] = Sig + wet_delay[~mask] = dwet + hydro_delay[~mask] = dhydro + times = true_times.copy() + else: + delay = np.array(d) + times = np.array(timesList) + sig = np.array(Sig) + wet_delay = np.array(dwet) + hydro_delay = np.array(dhydro) + + # if time not specified, pass all times + if returnTime is None: + filtoutput = {'ID': [site] * len(wet_delay), 'Date': [time] * len(wet_delay), 'ZTD': delay, 'wet_delay': wet_delay, + 'hydrostatic_delay': hydro_delay, 'times': times, 'sigZTD': sig} + filtoutput = [{key: value[k] for key, value in filtoutput.items()} + for k in range(len(filtoutput['ID']))] + else: + index = np.argmin(np.abs(np.array(timesList) - returnTime)) + filtoutput = [{'ID': site, 'Date': time, 'ZTD': delay[index], 'wet_delay': wet_delay[index], + 'hydrostatic_delay': hydro_delay[index], 'times': times[index], 'sigZTD': sig[index]}] + # setup pandas array and write output to CSV, making sure to update existing CSV. + filtoutput = pd.DataFrame(filtoutput) + if os.path.exists(filename): + filtoutput.to_csv(filename, index=False, mode='a', header=False) + else: + filtoutput.to_csv(filename, index=False) + + # record all used tar files + allstationTarfiles.extend([os.path.join(stationFile, k) + for k in stationTarlist]) + allstationTarfiles.sort() + del ziprepo + + return + + +def get_station_data(inFile, dateList, gps_repo=None, numCPUs=8, outDir=None, returnTime=None): + ''' + Pull tropospheric delay data for a given station name + ''' + if outDir is None: + outDir = os.getcwd() + + pathbase = os.path.join(outDir, 'GPS_delays') + if not os.path.exists(pathbase): + os.mkdir(pathbase) + + returnTime = seconds_of_day(returnTime) + # print warning if not divisible by 3 seconds + if returnTime % 3 != 0: + index = np.argmin( + np.abs(np.array(list(range(0, 86400, 300))) - returnTime)) + updatedreturnTime = str(dt.timedelta( + seconds=list(range(0, 86400, 300))[index])) + logger.warning( + 'input time %s not divisible by 3 seconds, so next closest time %s ' + 'will be chosen', returnTime, updatedreturnTime + ) + returnTime = updatedreturnTime + + # get list of station zip files + inFile_df = pd.read_csv(inFile) + stationFiles = inFile_df['path'].to_list() + del inFile_df + + if len(stationFiles) > 0: + outputfiles = [] + args = [] + # parse delays from UNR + if gps_repo == 'UNR': + for sf in stationFiles: + StationID = os.path.basename(sf).split('.')[0] + name = os.path.join(pathbase, StationID + '_ztd.csv') + args.append((sf, name, dateList, returnTime)) + outputfiles.append(name) + # Parallelize remote querying of zenith delays + with multiprocessing.Pool(numCPUs) as multipool: + multipool.starmap(get_delays_UNR, args) + + # confirm file exists (i.e. valid delays exists for specified time/region). + outputfiles = [i for i in outputfiles if os.path.exists(i)] + # Consolidate all CSV files into one object + if outputfiles == []: + raise Exception('No valid delays found for specified time/region.') + name = os.path.join(outDir, '{}combinedGPS_ztd.csv'.format(gps_repo)) + statsFile = pd.concat([pd.read_csv(i) for i in outputfiles]) + # drop all duplicate lines + statsFile.drop_duplicates(inplace=True) + # Convert the above object into a csv file and export + statsFile.to_csv(name, index=False, encoding="utf-8") + del statsFile + + # Add lat/lon/height info + origstatsFile = pd.read_csv(inFile) + statsFile = pd.read_csv(name) + statsFile = pd.merge(left=statsFile, right=origstatsFile[['ID', 'Lat', 'Lon', 'Hgt_m']], how='left', left_on='ID', right_on='ID') + # drop all lines with nans and sort by station ID and year + statsFile.dropna(how='any', inplace=True) + # drop all duplicate lines + statsFile.drop_duplicates(inplace=True) + statsFile.sort_values(['ID', 'Date']) + statsFile.to_csv(name, index=False) + del origstatsFile, statsFile + + +def get_date(stationFile): + ''' + extract the date from a station delay file + ''' + + # find the date info + year = int(stationFile[1]) + doy = int(stationFile[2]) + date = dt.datetime(year, 1, 1) + dt.timedelta(doy - 1) + + return date, year, doy + + +def seconds_of_day(returnTime): + ''' + Convert HH:MM:SS format time-tag to seconds of day. + ''' + if isinstance(returnTime, dt.time): + h, m, s = returnTime.hour, returnTime.minute, returnTime.second + else: + h, m, s = map(int, returnTime.split(":")) + + return h * 3600 + m * 60 + s diff --git a/tools/RAiDER/gnss/__init__.py b/tools/RAiDER/gnss/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tools/RAiDER/gnss/downloadGNSSDelays.py b/tools/RAiDER/gnss/downloadGNSSDelays.py new file mode 100755 index 000000000..971dc778b --- /dev/null +++ b/tools/RAiDER/gnss/downloadGNSSDelays.py @@ -0,0 +1,393 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Simran Sangha, & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import argparse +import itertools +import multiprocessing +import os +import pandas as pd +from textwrap import dedent + +from RAiDER.cli.parser import add_cpus, add_out, add_verbose +from RAiDER.cli.validators import DateListAction, date_type +from RAiDER.logger import logger, logging +from RAiDER.getStationDelays import get_station_data +from RAiDER.utilFcns import requests_retry_session + +# base URL for UNR repository +_UNR_URL = "http://geodesy.unr.edu/" + + +def create_parser(): + """Parse command line arguments using argparse.""" + p = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, + description=""" +Check for and download tropospheric zenith delays for a set of GNSS stations from UNR + +Example call to virtually access and append zenith delay information to a CSV table in specified output +directory, across specified range of time (in YYMMDD YYMMDD) and all available times of day, and confined to specified +geographic bounding box : +downloadGNSSdelay.py --out products -y 20100101 20141231 -b '39 40 -79 -78' + +Example call to virtually access and append zenith delay information to a CSV table in specified output +directory, across specified range of time (in YYMMDD YYMMDD) and specified time of day, and distributed globally : +downloadGNSSdelay.py --out products -y 20100101 20141231 --returntime '00:00:00' + + +Example call to virtually access and append zenith delay information to a CSV table in specified output +directory, across specified range of time in 12 day steps (in YYMMDD YYMMDD days) and specified time of day, and distributed globally : +downloadGNSSdelay.py --out products -y 20100101 20141231 12 --returntime '00:00:00' + +Example call to virtually access and append zenith delay information to a CSV table in specified output +directory, across specified range of time (in YYMMDD YYMMDD) and specified time of day, and distributed globally but restricted +to list of stations specified in input textfile : +downloadGNSSdelay.py --out products -y 20100101 20141231 --returntime '00:00:00' -f station_list.txt + +NOTE, following example call to physically download zenith delay information not recommended as it is not +necessary for most applications. +Example call to physically download and append zenith delay information to a CSV table in specified output +directory, across specified range of time (in YYMMDD YYMMDD) and specified time of day, and confined to specified +geographic bounding box : +downloadGNSSdelay.py --download --out products -y 20100101 20141231 --returntime '00:00:00' -b '39 40 -79 -78' +""") + + # Stations to check/download + area = p.add_argument_group( + 'Stations to check/download. Can be a lat/lon bounding box or file, or will run the whole world if not specified') + area.add_argument( + '--station_file', '-f', default=None, dest='station_file', + help=('Text file containing a list of 4-char station IDs separated by newlines')) + area.add_argument( + '-b', '--bounding_box', dest='bounding_box', type=str, default=None, + help="Provide either valid shapefile or Lat/Lon Bounding SNWE. -- Example : '19 20 -99.5 -98.5'") + area.add_argument( + '--gpsrepo', '-gr', default='UNR', dest='gps_repo', + help=('Specify GPS repository you wish to query. Currently supported archives: UNR.')) + + misc = p.add_argument_group("Run parameters") + add_out(misc) + + misc.add_argument( + '--date', dest='dateList', + help=dedent("""\ + Date to calculate delay. + Can be a single date, a list of two dates (earlier, later) with 1-day interval, or a list of two dates and interval in days (earlier, later, interval). + Example accepted formats: + YYYYMMDD or + YYYYMMDD YYYYMMDD + YYYYMMDD YYYYMMDD N + """), + nargs="+", + action=DateListAction, + type=date_type, + required=True + ) + + misc.add_argument( + '--returntime', dest='returnTime', + help="Return delays closest to this specified time. If not specified, the GPS delays for all times will be returned. Input in 'HH:MM:SS', e.g. '16:00:00'", + default=None) + + misc.add_argument( + '--download', + help='Physically download data. Note this option is not necessary to proceed with statistical analyses, as data can be handled virtually in the program.', + action='store_true', dest='download', default=False) + + add_cpus(misc) + add_verbose(misc) + + return p + + +def cmd_line_parse(iargs=None): + parser = create_parser() + return parser.parse_args(args=iargs) + + +def get_station_list(bbox=None, writeLoc=None, userstatList=None, name_appendix=''): + ''' + Creates a list of stations inside a lat/lon bounding box from a source + Inputs: + bbox - length-4 list of floats that describes a bounding box. Format is + S N W E + ''' + writeLoc = os.path.join(writeLoc or os.getcwd(), 'gnssStationList_overbbox' + name_appendix + '.csv') + + if userstatList: + userstatList = read_text_file(userstatList) + + statList = get_stats_by_llh(llhBox=bbox, userstatList=userstatList) + + # write to file and pass final stations list + statList.to_csv(writeLoc, index=False) + stations = list(statList['ID'].values) + + return stations, writeLoc + + +def get_stats_by_llh(llhBox=None, baseURL=_UNR_URL, userstatList=None): + ''' + Function to pull lat, lon, height, beginning date, end date, and number of solutions for stations inside the bounding box llhBox. + llhBox should be a tuple with format (lat1, lat2, lon1, lon2), where lat1, lon1 define the lower left-hand corner and lat2, lon2 + define the upper right corner. + ''' + if llhBox is None: + llhBox = [-90, 90, 0, 360] + + stationHoldings = '{}NGLStationPages/DataHoldings.txt'.format(baseURL) + # it's a file like object and works just like a file + + session = requests_retry_session() + data = session.get(stationHoldings) + stations = [] + for ind, line in enumerate(data.text.splitlines()): # files are iterable + if ind == 0: + continue + statID, lat, lon, height = get_ID(line) + # Only pass if in bbox + # And if user list of stations specified, only pass info for stations within list + if in_box(lat, lon, llhBox) and (not userstatList or statID in userstatList): + # convert lon into range [-180,180] + lon = fix_lons(lon) + stations.append({'ID': statID, 'Lat': lat, 'Lon': lon, 'Hgt_m': height}) + + logger.info('%d stations were found in %s', len(stations), llhBox) + stations = pd.DataFrame(stations) + # Report stations from user's list that do not cover bbox + if userstatList: + userstatList = [ + i for i in userstatList if i not in stations['ID'].to_list()] + if userstatList: + logger.warning( + "The following user-input stations are not covered by the input " + "bounding box %s: %s", + str(llhBox).strip('[]'), str(userstatList).strip('[]') + ) + + return stations + + +def download_tropo_delays(stats, years, gps_repo=None, writeDir='.', numCPUs=8, download=False): + ''' + Check for and download GNSS tropospheric delays from an archive. If download is True then + files will be physically downloaded, which again is not necessary as data can be virtually accessed. + ''' + + # argument checking + if not isinstance(stats, (list, str)): + raise TypeError('stats should be a string or a list of strings') + if not isinstance(years, (list, int)): + raise TypeError('years should be an int or a list of ints') + + # Iterate over stations and years and check or download data + stat_year_tup = itertools.product(stats, years) + stat_year_tup = ((*tup, writeDir, download) for tup in stat_year_tup) + # Parallelize remote querying of station locations + with multiprocessing.Pool(numCPUs) as multipool: + # only record valid path + if gps_repo == 'UNR': + results = [ + fileurl for fileurl in multipool.starmap(download_UNR, stat_year_tup) + if fileurl['path'] + ] + + # Write results to file + statDF = pd.DataFrame(results).set_index('ID') + statDF.to_csv(os.path.join(writeDir, '{}gnssStationList_overbbox_withpaths.csv'.format(gps_repo))) + + +def download_UNR(statID, year, writeDir='.', download=False, baseURL=_UNR_URL): + ''' + Download a zip file containing tropospheric delays for a given station and year + The URL format is http://geodesy.unr.edu/gps_timeseries/trop//..trop.zip + Inputs: + statID - 4-character station identifier + year - 4-numeral year + ''' + URL = "{0}gps_timeseries/trop/{1}/{1}.{2}.trop.zip".format( + baseURL, statID.upper(), year) + logger.debug('Currently checking station %s in %s', statID, year) + if download: + saveLoc = os.path.abspath(os.path.join( + writeDir, '{0}.{1}.trop.zip'.format(statID.upper(), year))) + filepath = download_url(URL, saveLoc) + else: + filepath = check_url(URL) + return {'ID': statID, 'year': year, 'path': filepath} + + +def download_url(url, save_path, chunk_size=2048): + ''' + Download a file from a URL. Modified from + https://stackoverflow.com/questions/9419162/download-returned-zip-file-from-url + ''' + session = requests_retry_session() + r = session.get(url, stream=True) + + if r.status_code == 404: + return '' + else: + logger.debug('Beginning download of %s to %s', url, save_path) + with open(save_path, 'wb') as fd: + for chunk in r.iter_content(chunk_size=chunk_size): + fd.write(chunk) + logger.debug('Completed download of %s to %s', url, save_path) + return save_path + + +def check_url(url): + ''' + Check whether a file exists at a URL. Modified from + https://stackoverflow.com/questions/9419162/download-returned-zip-file-from-url + ''' + session = requests_retry_session() + r = session.head(url) + if r.status_code == 404: + url = '' + return url + + +def read_text_file(filename): + ''' + Read a list of GNSS station names from a plain text file + ''' + with open(filename, 'r') as f: + return [line.strip() for line in f] + + +def in_box(lat, lon, llhbox): + ''' + Checks whether the given lat, lon pair are inside the bounding box llhbox + ''' + return lat < llhbox[1] and lat > llhbox[0] and lon < llhbox[3] and lon > llhbox[2] + + +def fix_lons(lon): + """ + Fix the given longitudes into the range ``[-180, 180]``. + """ + fixed_lon = ((lon + 180) % 360) - 180 + # Make the positive 180s positive again. + if fixed_lon == -180 and lon > 0: + fixed_lon *= -1 + return fixed_lon + + +def get_ID(line): + ''' + Pulls the station ID, lat, lon, and height for a given entry in the UNR text file + ''' + stat_id, lat, lon, height = line.split()[:4] + return stat_id, float(lat), float(lon), float(height) + + +def main(params=None): + """ + Main workflow for querying supported GPS repositories for zenith delay information. + """ + if params is not None: + inps = params + dateList = inps.date_list + returnTime = inps.time + else: + inps = cmd_line_parse() + dateList = inps.dateList + returnTime = inps.returnTime + + station_file = inps.station_file + bounding_box = inps.bounding_box + gps_repo = inps.gps_repo + out = inps.out + download = inps.download + cpus = inps.cpus + verbose = inps.verbose + + if verbose: + logger.setLevel(logging.DEBUG) + + # Create specified output directory if it does not exist. + if not os.path.exists(out): + os.mkdir(out) + + # Setup bounding box + if bounding_box: + if isinstance(bounding_box, str) and not os.path.isfile(bounding_box): + try: + bbox = [float(val) for val in bounding_box.split()] + except ValueError: + raise Exception( + 'Cannot understand the --bbox argument. String input is incorrect or path does not exist.') + elif isinstance(bounding_box, list): + bbox = bounding_box + + else: + raise Exception('Passing a file with a bounding box not yet supported.') + + long_cross_zero = 1 if bbox[2] * bbox[3] < 0 else 0 + + # if necessary, convert negative longitudes to positive + if bbox[2] < 0: + bbox[2] += 360 + + if bbox[3] < 0: + bbox[3] += 360 + + # If bbox not specified, query stations across the entire globe + else: + bbox = [-90, 90, 0, 360] + long_cross_zero = 1 + + # Handle station query + if long_cross_zero == 1: + bbox1 = bbox.copy() + bbox2 = bbox.copy() + bbox1[3] = 360.0 + bbox2[2] = 0.0 + stats1, origstatsFile1 = get_station_list(bbox=bbox1, writeLoc=out, userstatList=station_file, name_appendix='_a') + stats2, origstatsFile2 = get_station_list(bbox=bbox2, writeLoc=out, userstatList=station_file, name_appendix='_b') + stats = stats1 + stats2 + origstatsFile = origstatsFile1[:-6] + '.csv' + file_a = pd.read_csv(origstatsFile1) + file_b = pd.read_csv(origstatsFile2) + frames = [file_a, file_b] + result = pd.concat(frames, ignore_index=True) + result.to_csv(origstatsFile, index=False) + else: + if bbox[3] < bbox[2]: + bbox[3] = 360.0 + stats, origstatsFile = get_station_list( + bbox=bbox, writeLoc=out, userstatList=station_file) + + # iterate over years + years = list(set([i.year for i in dateList])) + download_tropo_delays( + stats, years, gps_repo=gps_repo, writeDir=out, download=download + ) + + # Add lat/lon info + origstatsFile = pd.read_csv(origstatsFile) + statsFile = pd.read_csv(os.path.join( + out, '{}gnssStationList_overbbox_withpaths.csv'.format(gps_repo))) + statsFile = pd.merge(left=statsFile, right=origstatsFile, + how='left', left_on='ID', right_on='ID') + statsFile.to_csv(os.path.join( + out, '{}gnssStationList_overbbox_withpaths.csv'.format(gps_repo)), index=False) + del origstatsFile, statsFile + + # Extract delays for each station + dateList = [k.strftime('%Y-%m-%d') for k in dateList] + get_station_data( + os.path.join(out, '{}gnssStationList_overbbox_withpaths.csv'.format(gps_repo)), + dateList, + gps_repo=gps_repo, + numCPUs=cpus, + outDir=out, + returnTime=returnTime + ) + + logger.debug('Completed processing') diff --git a/tools/RAiDER/gnss/processDelayFiles.py b/tools/RAiDER/gnss/processDelayFiles.py new file mode 100644 index 000000000..15e6288f0 --- /dev/null +++ b/tools/RAiDER/gnss/processDelayFiles.py @@ -0,0 +1,467 @@ +from textwrap import dedent +import argparse +import datetime +import glob +import os +import re +import math + +from tqdm import tqdm + +import pandas as pd +pd.options.mode.chained_assignment = None # default='warn' + + +def combineDelayFiles(outName, loc=os.getcwd(), source='model', ext='.csv', ref=None, col_name='ZTD'): + files = glob.glob(os.path.join(loc, '*' + ext)) + + if source == 'model': + print('Ensuring that "Datetime" column exists in files') + addDateTimeToFiles(files) + + # If single file, just copy source + if len(files) == 1: + if source == 'model': + import shutil + shutil.copy(files[0], outName) + else: + files = readZTDFile(files[0], col_name=col_name) + # drop all lines with nans + files.dropna(how='any', inplace=True) + # drop all duplicate lines + files.drop_duplicates(inplace=True) + files.to_csv(outName, index=False) + return + + print('Combining {} delay files'.format(source)) + try: + concatDelayFiles( + files, + sort_list=['ID', 'Datetime'], + outName=outName, + source=source + ) + except BaseException: + concatDelayFiles( + files, + sort_list=['ID', 'Date'], + outName=outName, + source=source, + ref=ref, + col_name=col_name + ) + + +def addDateTimeToFiles(fileList, force=False, verbose=False): + ''' Run through a list of files and add the datetime of each file as a column ''' + + print('Adding Datetime to delay files') + + for f in tqdm(fileList): + data = pd.read_csv(f) + + if 'Datetime' in data.columns and not force: + if verbose: + print( + 'File {} already has a "Datetime" column, pass' + '"force = True" if you want to override and ' + 're-process'.format(f) + ) + else: + try: + dt = getDateTime(f) + data['Datetime'] = dt + # drop all lines with nans + data.dropna(how='any', inplace=True) + # drop all duplicate lines + data.drop_duplicates(inplace=True) + data.to_csv(f, index=False) + except (AttributeError, ValueError): + print( + 'File {} does not contain datetime info, skipping' + .format(f) + ) + del data + + +def getDateTime(filename): + ''' Parse a datetime from a RAiDER delay filename ''' + filename = os.path.basename(filename) + dtr = re.compile(r'\d{8}T\d{6}') + dt = dtr.search(filename) + return datetime.datetime.strptime( + dt.group(), + '%Y%m%dT%H%M%S' + ) + + +def update_time(row, localTime_hrs): + '''Update with local origin time''' + localTime_estimate = row['Datetime'].replace(hour=localTime_hrs, + minute=0, second=0) + # determine if you need to shift days + time_shift = datetime.timedelta(days=0) + # round to nearest hour + days_diff = (row['Datetime'] - + datetime.timedelta(seconds=math.floor( + row['Localtime']) * 3600)).day - \ + localTime_estimate.day + # if lon <0, check if you need to add day + if row['Lon'] < 0: + # add day + if days_diff != 0: + time_shift = datetime.timedelta(days=1) + # if lon >0, check if you need to subtract day + if row['Lon'] > 0: + # subtract day + if days_diff != 0: + time_shift = -datetime.timedelta(days=1) + return localTime_estimate + datetime.timedelta(seconds=row['Localtime'] + * 3600) + time_shift + + +def pass_common_obs(reference, target, localtime=None): + '''Pass only observations in target spatiotemporally common to reference''' + if localtime: + return target[target['Datetime'].dt.date.isin(reference['Datetime'] + .dt.date) & + target['ID'].isin(reference['ID']) & + target[localtime].isin(reference[localtime])] + else: + return target[target['Datetime'].dt.date.isin(reference['Datetime'] + .dt.date) & + target['ID'].isin(reference['ID'])] + + +def concatDelayFiles( + fileList, + sort_list=['ID', 'Datetime'], + return_df=False, + outName=None, + source='model', + ref=None, + col_name='ZTD' +): + ''' + Read a list of .csv files containing the same columns and append them + together, sorting by specified columns + ''' + dfList = [] + + print('Concatenating delay files') + + for f in tqdm(fileList): + if source == 'model': + dfList.append(pd.read_csv(f, parse_dates=['Datetime'])) + else: + dfList.append(readZTDFile(f, col_name=col_name)) + # drop lines not found in reference file + if ref: + dfr = pd.read_csv(ref, parse_dates=['Datetime']) + for i in enumerate(dfList): + dfList[i[0]] = pass_common_obs(dfr, i[1]) + del dfr + + df_c = pd.concat( + dfList, + ignore_index=True + ).drop_duplicates().reset_index(drop=True) + df_c.sort_values(by=sort_list, inplace=True) + + print('Total number of rows in the concatenated file: {}'.format(df_c.shape[0])) + print('Total number of rows containing NaNs: {}'.format( + df_c[df_c.isna().any(axis=1)].shape[0] + ) + ) + + if return_df or outName is None: + return df_c + else: + # drop all lines with nans + df_c.dropna(how='any', inplace=True) + # drop all duplicate lines + df_c.drop_duplicates(inplace=True) + df_c.to_csv(outName, index=False) + + +def local_time_filter(raiderFile, ztdFile, dfr, dfz, localTime): + ''' + Convert to local-time reference frame WRT 0 longitude + ''' + localTime_hrs = int(localTime.split(' ')[0]) + localTime_hrthreshold = int(localTime.split(' ')[1]) + # with rotation rate and distance to 0 lon, get localtime shift WRT 00 UTC at 0 lon + # *rotation rate at given point = (360deg/23.9333333333hr) = 15.041782729825965 deg/hr + dfr['Localtime'] = (dfr['Lon'] / 15.041782729825965) + dfz['Localtime'] = (dfz['Lon'] / 15.041782729825965) + + # estimate local-times + dfr['Localtime'] = dfr.apply(lambda r: update_time(r, localTime_hrs), + axis=1) + dfz['Localtime'] = dfz.apply(lambda r: update_time(r, localTime_hrs), + axis=1) + + # filter out data outside of --localtime hour threshold + dfr['Localtime_u'] = dfr['Localtime'] + \ + datetime.timedelta(hours=localTime_hrthreshold) + dfr['Localtime_l'] = dfr['Localtime'] - \ + datetime.timedelta(hours=localTime_hrthreshold) + OG_total = dfr.shape[0] + dfr = dfr[(dfr['Datetime'] >= dfr['Localtime_l']) & + (dfr['Datetime'] <= dfr['Localtime_u'])] + + # only keep observation closest to Localtime + print('Total number of datapoints dropped in {} for not being within ' + '{} hrs of specified local-time {}: {} out of {}'.format( + raiderFile, localTime.split(' ')[1], localTime.split(' ')[0], + dfr.shape[0], OG_total)) + dfz['Localtime_u'] = dfz['Localtime'] + \ + datetime.timedelta(hours=localTime_hrthreshold) + dfz['Localtime_l'] = dfz['Localtime'] - \ + datetime.timedelta(hours=localTime_hrthreshold) + OG_total = dfz.shape[0] + dfz = dfz[(dfz['Datetime'] >= dfz['Localtime_l']) & + (dfz['Datetime'] <= dfz['Localtime_u'])] + # only keep observation closest to Localtime + print('Total number of datapoints dropped in {} for not being within ' + '{} hrs of specified local-time {}: {} out of {}'.format( + ztdFile, localTime.split(' ')[1], localTime.split(' ')[0], + dfz.shape[0], OG_total)) + + # drop all lines with nans + dfr.dropna(how='any', inplace=True) + dfz.dropna(how='any', inplace=True) + # drop all duplicate lines + dfr.drop_duplicates(inplace=True) + dfz.drop_duplicates(inplace=True) + # drop and rename columns + dfr.drop(columns=['Localtime_l', 'Localtime_u'], inplace=True) + dfz.drop(columns=['Localtime_l', 'Localtime_u'], inplace=True) + + return dfr, dfz + + +def mergeDelayFiles( + raiderFile, + ztdFile, + col_name='ZTD', + raider_delay='totalDelay', + outName=None, + localTime=None +): + ''' + Merge a combined RAiDER delays file with a GPS ZTD delay file + ''' + print('Merging delay files {} and {}'.format(raiderFile, ztdFile)) + dfr = pd.read_csv(raiderFile, parse_dates=['Datetime']) + # drop extra columns + expected_data_columns = ['ID', 'Lat', 'Lon', 'Hgt_m', 'Datetime', 'wetDelay', + 'hydroDelay', raider_delay] + dfr = dfr.drop(columns=[col for col in dfr if col not in + expected_data_columns]) + dfz = pd.read_csv(ztdFile, parse_dates=['Datetime']) + # drop extra columns + expected_data_columns = ['ID', 'Date', 'wet_delay', 'hydrostatic_delay', + 'times', 'sigZTD', 'Lat', 'Lon', 'Hgt_m', 'Datetime', + col_name] + dfz = dfz.drop(columns=[col for col in dfz if col not in + expected_data_columns]) + # only pass common locations and times + dfz = pass_common_obs(dfr, dfz) + dfr = pass_common_obs(dfz, dfr) + + # If specified, convert to local-time reference frame WRT 0 longitude + common_keys = ['Datetime', 'ID'] + if localTime is not None: + dfr, dfz = local_time_filter(raiderFile, ztdFile, dfr, dfz, localTime) + common_keys.append('Localtime') + # only pass common locations and times + dfz = pass_common_obs(dfr, dfz, localtime='Localtime') + dfr = pass_common_obs(dfz, dfr, localtime='Localtime') + + # drop all lines with nans + dfr.dropna(how='any', inplace=True) + dfz.dropna(how='any', inplace=True) + # drop all duplicate lines + dfr.drop_duplicates(inplace=True) + dfz.drop_duplicates(inplace=True) + + print('Beginning merge') + + dfc = dfr.merge( + dfz[common_keys + ['ZTD', 'sigZTD']], + how='left', + left_on=common_keys, + right_on=common_keys, + sort=True + ) + + # only keep observation closest to Localtime + if 'Localtime' in dfc.keys(): + dfc['Localtimediff'] = abs((dfc['Datetime'] - + dfc['Localtime']).dt.total_seconds() / 3600) + dfc = dfc.loc[dfc.groupby(['ID', 'Localtime']).Localtimediff.idxmin() + ].reset_index(drop=True) + dfc.drop(columns=['Localtimediff'], inplace=True) + + # estimate residual + dfc['ZTD_minus_RAiDER'] = dfc['ZTD'] - dfc[raider_delay] + + print('Total number of rows in the concatenated file: ' + '{}'.format(dfc.shape[0])) + print('Total number of rows containing NaNs: {}'.format( + dfc[dfc.isna().any(axis=1)].shape[0] + ) + ) + print('Merge finished') + + if outName is None: + return dfc + else: + # drop all lines with nans + dfc.dropna(how='any', inplace=True) + # drop all duplicate lines + dfc.drop_duplicates(inplace=True) + dfc.to_csv(outName, index=False) + + +def readZTDFile(filename, col_name='ZTD'): + ''' + Read and parse a GPS zenith delay file + ''' + try: + data = pd.read_csv(filename, parse_dates=['Date']) + times = data['times'].apply(lambda x: datetime.timedelta(seconds=x)) + data['Datetime'] = data['Date'] + times + except (KeyError, ValueError): + data = pd.read_csv(filename, parse_dates=['Datetime']) + + data.rename(columns={col_name: 'ZTD'}, inplace=True) + return data + + +def create_parser(): + """Parse command line arguments using argparse.""" + p = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, + description=dedent("""\ + Combine delay files from a weather model and GPS Zenith delays + Usage examples: + raiderCombine.py --raiderDir './*' --raider 'combined_raider_delays.csv' + raiderCombine.py --raiderDir ERA5/ --raider ERA5_combined_delays.csv --raider_column totalDelay --gnssDir GNSS/ --gnss UNRCombined_gnss.csv --column ZTD -o Combined_delays.csv + raiderCombine.py --raiderDir ERA5_2019/ --raider ERA5_combined_delays_2019.csv --raider_column totalDelay --gnssDir GNSS_2019/ --gnss UNRCombined_gnss_2019.csv --column ZTD -o Combined_delays_2019_UTTC18.csv --localtime '18:00:00 1' + """) + ) + + p.add_argument( + '--raider', dest='raider_file', + help=dedent("""\ + .csv file containing RAiDER-derived Zenith Delays. + Should contain columns "ID" and "Datetime" in addition to the delay column + If the file does not exist, I will attempt to create it from a directory of + delay files. + """), + required=True + ) + p.add_argument( + '--raiderDir', '-d', dest='raider_folder', + help=dedent("""\ + Directory containing RAiDER-derived Zenith Delay files. + Files should be named with a Datetime in the name and contain the + column "ID" as the delay column names. + """), + default=os.getcwd() + ) + p.add_argument( + '--gnssDir', '-gd', dest='gnss_folder', + help=dedent("""\ + Directory containing GNSS-derived Zenith Delay files. + Files should contain the column "ID" as the delay column names + and times should be denoted by the "Date" key. + """), + default=os.getcwd() + ) + + p.add_argument( + '--gnss', dest='gnss_file', + help=dedent("""\ + Optional .csv file containing GPS Zenith Delays. Should contain columns "ID", "ZTD", and "Datetime" + """), + default=None + ) + + p.add_argument( + '--raider_column', + '-r', + dest='raider_column_name', + help=dedent("""\ + Name of the column containing RAiDER delays. Only used with the "--gnss" option + """), + default='totalDelay' + ) + p.add_argument( + '--column', + '-c', + dest='column_name', + help=dedent("""\ + Name of the column containing GPS Zenith delays. Only used with the "--gnss" option + + """), + default='ZTD' + ) + + p.add_argument( + '--out', + '-o', + dest='out_name', + help=dedent("""\ + Name to use for the combined delay file. Only used with the "--gnss" option + + """), + default='Combined_delays.csv' + ) + + p.add_argument( + '--localtime', + '-lt', + dest='local_time', + help=dedent("""\ + "Optional control to pass only data at local-time (in integer hours) WRT user-defined time at 0 longitude (1st argument), + and within +/- specified hour threshold (2nd argument). + By default UTC is passed as is without local-time conversions. + Input in 'HH H', e.g. '16 1'" + + """), + default=None + ) + + return p + + +def parseCMD(): + """ + Parse command-line arguments and pass to delay.py + """ + + p = create_parser() + args = p.parse_args() + + if not os.path.exists(args.raider_file): + combineDelayFiles(args.raider_file, loc=args.raider_folder) + + if not os.path.exists(args.gnss_file): + combineDelayFiles(args.gnss_file, loc=args.gnss_folder, source='GNSS', + ref=args.raider_file, col_name=args.column_name) + + if args.gnss_file is not None: + mergeDelayFiles( + args.raider_file, + args.gnss_file, + col_name=args.column_name, + raider_delay=args.raider_column_name, + outName=args.out_name, + localTime=args.local_time + ) + +def main(): + parseCMD() diff --git a/tools/RAiDER/interpolator.py b/tools/RAiDER/interpolator.py index 715961eac..e5321d9d6 100644 --- a/tools/RAiDER/interpolator.py +++ b/tools/RAiDER/interpolator.py @@ -1,556 +1,124 @@ -"""Helper utilities for file readers.""" - +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ import numpy as np -import pyproj -from utils import util - -class Interpolator: - """ - Generic interpolator - - This model is based upon a linear interpolation scheme for pressure, - temperature, and relative humidity. - - """ - #TODO: handle 1-D, 2-D cases correctly - def __init__(self, xs = [], ys = [], zs = [], proj = None): - """Initialize a NetCDFModel.""" - self._xs = xs - self._ys = ys - self._zs = zs - self._getShape(xs, ys, zs) - self._Npoints = 0 - self.setPoints(xs, ys, zs) - self._proj = proj - self._fill_value = np.nan - self._interp = [] - self._getBBox() - - def __call__(self, newX): - ''' - Interpolate onto new coordinates - ''' - res = [] - for intFcn in self._interp: - res.append(intFcn(newX)) - return res - - def __repr__(self): - ''' - print method for Interpolator object - ''' - string = '='*6 + 'Interpolator Object' + '='*6 + '\n' - string += 'Total Number of points: {}\n'.format(len(self._xs)) - string += 'Number of points: {}\n'.format(self._Npoints) - string += 'Current Projection: {}\n'.format(self._proj.srs if self._proj.srs is not None else None) - string += 'Number of functions to interpolate: {}\n'.format(len(self._interp)) - string += '='*30 + '\n' - - s2 = '\n' - s2 += 'Lat/Lon bounding box: \n' - s2 += '\n' - s2 += '-------{:.2f}-------\n'.format(self._bbox[1][1]) - s2 += '| |\n' - s2 += '| |\n' - s2 += '| |\n' - s2 += '{:.2f} {:.2f}\n'.format(self._bbox[0][0], self._bbox[1][0]) - s2 += '| |\n' - s2 += '| |\n' - s2 += '| |\n' - s2 += '-------{:.2f}-------\n'.format(self._bbox[0][1]) - string += s2 - - return str(string) - - def _getShape(self, xs, ys, zs): - try: - self._shape = max(xs.shape, ys.shape, zs.shape) - except AttributeError: - self._shape = (len(xs), len(ys), len(zs)) - - def _getBBox(self): - if len(self._xs) > 0: - x1 = np.nanmin(self._xs) - x2 = np.nanmax(self._xs) - y1 = np.nanmin(self._ys) - y2 = np.nanmax(self._ys) - z1 = np.nanmin(self._zs) - z2 = np.nanmax(self._zs) - self._bbox = [(x1, y1, z1), (x2, y2, z2)] - else: - self._bbox = None +import pandas as pd - def setProjection(self, proj): - ''' - proj should be a prproj object - ''' - self._proj = proj - -# def reProject(self, new_proj): -# ''' -# Project a set of points into a different coordinate system. -# old_proj and new_proj should be pyproj projection objects. -# ''' -# self._xs, self._ys, self._zs = pyproj.transform(self._proj,new_proj, self._xs, self._ys, self._zs) -# self._proj = new_proj +from scipy.interpolate import interp1d, RegularGridInterpolator as rgi - def setPoints(self, xs, ys, zs): - ''' - Assign x, y, z points to use for interpolating - If the z-dimension is not to be used, set that dimension - to an empty list, e.g.: zs = [] - ''' - if xs is None: - xs, ys, zs = [], [], [] +from RAiDER.interpolate import interpolate - if isinstance(xs, list): - xs,ys,zs = np.array(xs),np.array(ys),np.array(zs) - self._getShape(xs, ys, zs) - self._Npoints = len(xs) - self._xs = xs.flatten() - self._ys = ys.flatten() - self._zs = zs.flatten() - self._getBBox() - - def checkPoint(self, pnts): - ''' - Checks whether the given point is within the bounding box - represented by the bounds on x, y, and z. - Inputs: - pnt - should be an Mx3 numpy array (M number of points, - 3 is the dimension. Should be ordered [x, y, z]) - Outputs: - inBBox - an Mx1 numpy array of True/False - ''' - xs, ys, zs = pnts[:,0], pnts[:,1], pnts[:,2] - ix = np.logical_and(np.less(self._bbox[0][0],xs), np.greater(self._bbox[1][0], xs)) - iy = np.logical_and(np.less(self._bbox[0][1],ys), np.greater(self._bbox[1][1], ys)) - iz = np.logical_and(np.less(self._bbox[0][2],zs), np.greater(self._bbox[1][2], zs)) - inBBox = np.logical_and.reduce((ix,iy,iz)) - return inBBox - - def printPoints(self): - for pnt in zip(self._xs, self._ys, self._zs): - print('{}\n'.format(pnt)) - - def getInterpFcns(self, *args, interpType = 'scipy', **kwargs): - ''' - return of list of interpolators, corresponding to the list of - inputs arguments - ''' - if interpType=='scipy': - self._make_scipy_interpolators(*args) - elif interpType=='_sane': - raise NotImplemented - #self._make_sane_interpolators(*args) - else: - self._make_3D_interpolators(*args) - - - def _make_scipy_interpolators(self, *args): - ''' - Interpolate a fcn or list of functions (contained - in *args) that are defined on a regular grid using - scipy. - ''' - from scipy.interpolate import LinearNDInterpolator as lndi - - points = np.stack([self._xs, self._ys, self._zs], axis = -1) - _interpolators = [] - for arg in args: - newArg = arg.flatten() - ix = np.isnan(newArg) - _interpolators.append(lndi(points[~ix], - newArg[~ix], - fill_value = self._fill_value - ) - ) - self._interp = _interpolators - - - def _make_3D_interpolators(self, *args): - ''' - Interpolate a fcn or list of functions (contained - in *args) that are defined on a regular grid. Use - the _sane_interpolate method. - ''' - _interpolators = [] - for arg in args: - _interpolators.append( - _interp3D(self._xs,self._ys, self._zs,arg, self._shape) - ) - self._interp = _interpolators - - def _make_sane_interpolators(self, *args, **kwargs): - pass - - -def _interp3D(xs, ys, zs, values, shape): - ''' - 3-D interpolation on a non-uniform grid, where z is non-uniform but x, y are uniform - ''' - from scipy.interpolate import RegularGridInterpolator as rgi - - # First interpolate uniformly in the z-direction - flipflag = False - Nfac = 10 - NzLevels = Nfac * shape[2] - nx, ny = shape[:2] - zmax = np.nanmax(zs) - zmin = np.nanmin(zs) - zshaped = np.reshape(zs, shape) - # if zs are upside down, flip to interpolate vertically - if np.nanmean(zshaped[..., 0]) > np.nanmean(zshaped[...,-1]): - flipflag = True - zshaped = np.flip(zshaped, axis = 2) - values = np.flip(values, axis = 2) - - dz = (zmax - zmin)/NzLevels - - # TODO: change to use mean height levels - zvalues = zmin + dz*np.arange(NzLevels) - - new_zs = np.tile(zvalues, (nx,ny,1)) - values = fillna3D(values) - - new_var = interp_along_axis(zshaped, new_zs, - values, axis = 2, - method='linear', pad = False) - # This assumes that the input data is in the correct projection; i.e. - # the native weather grid projection - xvalues = np.unique(xs) - yvalues = np.unique(ys) - - # TODO: temporarily switch x, y values until can confirm - interp= rgi((yvalues,xvalues, zvalues), new_var, - bounds_error=False, fill_value = np.nan) - return interp - -# TODO: Check whether running parallel_apply_along_axis with interp1D will work instead of this -def interp_along_axis(oldCoord, newCoord, data, axis = 2, inverse=False, method='linear', pad = False): - """ - - *** - The following was taken from https://stackoverflow.com/questions/ - 28934767/best-way-to-interpolate-a-numpy-ndarray-along-an-axis - *** - - Interpolate vertical profiles of 3-D data, e.g. of atmospheric - variables using vectorized numpy operations - - This function assumes that the x-xoordinate increases monotonically - - ps: - * Updated to work with irregularly spaced x-coordinate. - * Updated to work with irregularly spaced newx-coordinate - * Updated to easily inverse the direction of the x-coordinate - * Updated to fill with nans outside extrapolation range - * Updated to include a linear interpolation method as well - (it was initially written for a cubic function) - - Peter Kalverla - March 2018 - - *** - Modified by J. Maurer in Sept 2018. - Added a fillna function to pad nans outside the bounds of the - data with the closest non-nan value, and re-named inputs - *** - - -------------------- - More info: - Algorithm from: http://www.paulinternet.nl/?page=bicubic - It approximates y = f(x) = ax^3 + bx^2 + cx + d - where y may be an ndarray input vector - Returns f(newx) - - The algorithm uses the derivative f'(x) = 3ax^2 + 2bx + c - and uses the fact that: - f(0) = d - f(1) = a + b + c + d - f'(0) = c - f'(1) = 3a + 2b + c - - Rewriting this yields expressions for a, b, c, d: - a = 2f(0) - 2f(1) + f'(0) + f'(1) - b = -3f(0) + 3f(1) - 2f'(0) - f'(1) - c = f'(0) - d = f(0) - - These can be evaluated at two neighbouring points in x and - as such constitute the piecewise cubic interpolator. +class RegularGridInterpolator(object): + """ + Provides a wrapper around RAiDER.interpolate.interpolate with a similar + interface to scipy.interpolate.RegularGridInterpolator. """ - # View of x and y with axis as first dimension - if inverse: - _x = np.moveaxis(oldCoord, axis, 0)[::-1, ...] - _y = np.moveaxis(data, axis, 0)[::-1, ...] - _newx = np.moveaxis(newCoord, axis, 0)[::-1, ...] - else: - _y = np.moveaxis(data, axis, 0) - _x = np.moveaxis(oldCoord, axis, 0) - _newx = np.moveaxis(newCoord, axis, 0) - - # Sanity checks - if np.any(_newx[0] < _x[0]) or np.any(_newx[-1] > _x[-1]): - print('Values outside the valid range will be filled in '\ - 'with NaNs or the closest non-zero value (default)') - if np.any(np.diff(_x, axis=0) < 0): - print(_x[:,0,0]) - raise ValueError('x should increase monotonically') - if np.any(np.diff(_newx, axis=0) < 0): - print(newx) - raise ValueError('newx should increase monotonically') - - # Cubic interpolation needs the gradient of y in addition to its values - if method == 'cubic': - # For now, simply use a numpy function to get the derivatives - # This produces the largest memory overhead of the function and - # could alternatively be done in passing. - ydx = np.gradient(_y, axis=0, edge_order=2) - - # This will later be concatenated with a dynamic '0th' index - ind = [i for i in np.indices(_y.shape[1:])] - - # Allocate the output array - newdims = list(_y.shape) - newdims[0] = len(_newx) - newy = np.zeros(newdims) - - # set initial bounds - i_lower = np.zeros(_x.shape[1:], dtype=int) - i_upper = np.ones(_x.shape[1:], dtype=int) - x_lower = _x[0, ...] - x_upper = _x[1, ...] - - for i, xi in enumerate(_newx): - # Start at the 'bottom' of the array and work upwards - # This only works if x and newx increase monotonically - - # Update bounds where necessary and possible - needs_update = (xi > x_upper) & (i_upper+1 x_upper) & (i_upper+1 2: + in_shape = points.shape + interp_points = points.reshape((np.prod(points.shape[:-1]),) + (points.shape[-1],)) else: - newy[i, ...] = new_data + interp_points = points + in_shape = interp_points.shape - if inverse: - newy = newy[::-1, ...] + out = interpolate( + self.grid, + self.values, + interp_points, + fill_value=self.fill_value, + assume_sorted=self.assume_sorted, + max_threads=self.max_threads + ) + return out.reshape(in_shape[:-1]) - return np.moveaxis(newy, 0, axis) - - -def fillna(array): +def interp_along_axis(oldCoord, newCoord, data, axis=2, pad=False): ''' - Fcn to fill in NaNs in a 2-D array using nearest neighbors + DEPRECATED: Use RAiDER.interpolate.interpolate_along_axis instead (it is + much faster). This function now primarily exists to verify the behavior of + the new one. + + Interpolate an array of 3-D data along one axis. This function + assumes that the x-coordinate increases monotonically. ''' - from scipy.ndimage import distance_transform_edt as dte + if oldCoord.ndim > 1: + stackedData = np.concatenate([oldCoord, data, newCoord], axis=axis) + out = np.apply_along_axis(interpVector, axis=axis, arr=stackedData, Nx=oldCoord.shape[axis]) + else: + out = np.apply_along_axis(interpV, axis=axis, arr=data, old_x=oldCoord, new_x=newCoord, + left=np.nan, right=np.nan) - mask = np.isnan(array) - ind = dte(mask,return_distances=False,return_indices=True) - new_array = array[tuple(ind)] - return new_array + return out -def fillna3D(array, axis = 2): +def interpV(y, old_x, new_x, left=None, right=None, period=None): ''' - Fcn to fill in NaNs in a 3D array by interpolating over one axis only + Rearrange np.interp's arguments ''' - # Need to handle each axis - narr = np.moveaxis(array, axis, 2) - shape = narr.shape - y = narr.flatten() - - nans, x= nan_helper(y) - y[nans]= np.interp(x(nans), x(~nans), y[~nans]) - newy = np.reshape(y, shape) - final = np.moveaxis(newy, 2, axis) - return final - - -def nan_helper(y): - """Helper to handle indices and logical indices of NaNs. - - Input: - - y, 1d numpy array with possible NaNs - Output: - - nans, logical indices of NaNs - - index, a function, with signature indices= index(logical_indices), - to convert logical indices of NaNs to 'equivalent' indices - Example: - >>> # linear interpolation of NaNs - >>> nans, x= nan_helper(y) - >>> y[nans]= np.interp(x(nans), x(~nans), y[~nans]) - """ + return np.interp(new_x, old_x, y, left=left, right=right, period=period) - return np.isnan(y), lambda z: z.nonzero()[0] - -def _sane_interpolate(xs, ys, zs, values_list, old_proj, new_proj, zmin): +def interpVector(vec, Nx): ''' - do some interpolation + Interpolate data from a single vector containing the original + x, the original y, and the new x, in that order. Nx tells the + number of original x-points. ''' - # just a check for the consistency with Ray's old code: - ecef = pyproj.Proj(proj='geocent') - if old_proj != ecef: - import pdb - pdb.set_trace() - - NzLevels = 2 * zs.shape[0] - # First, find the maximum height - new_top = np.nanmax(zs) - - new_zs = np.linspace(zmin, new_top, NzLevels) - - inp_values = [np.zeros((len(new_zs),) + values.shape[1:]) - for values in values_list] - - # TODO: do without a for loop - for iv in range(len(values_list)): - for x in range(values_list[iv].shape[1]): - for y in range(values_list[iv].shape[2]): - not_nan = np.logical_not(np.isnan(zs[:, x, y])) - inp_values[iv][:, x, y] = scipy.interpolate.griddata( - zs[:, x, y][not_nan], - values_list[iv][:, x, y][not_nan], - new_zs, - method='cubic') - inp_values[iv] = _propagate_down(inp_values[iv]) - - interps = list() - for iv in range(len(values_list)): - # Indexing as height, ys, xs is a bit confusing, but it'll error - # if the sizes don't match, so we can be sure it's the correct - # order. - f = scipy.interpolate.RegularGridInterpolator((new_zs, ys, xs), - inp_values[iv], - bounds_error=False) - - # Python has some weird behavior here, eh? - def ggo(interp): - def go(pts): - xs, ys, zs = np.moveaxis(pts, -1, 0) - a, b, c = pyproj.transform(old_proj, new_proj, xs, ys, zs) - # Again we index as ys, xs - llas = np.stack((c, b, a), axis=-1) - return interp(llas) - return go - interps.append(ggo(f)) - - return interps - - -def _just_pull_down(a, direction=-1): - """Pull real values down to cover NaNs - - a might contain some NaNs which live under real values. We replace - those NaNs with actual values. a must be a 3D array. - """ - out = a.copy() - z, x, y = out.shape - for i in range(x): - for j in range(y): - held = None - if direction == 1: - r = range(z) - elif direction == -1: - r = range(z - 1, -1, -1) - else: - raise ValueError( - 'Unsupported direction. direction should be 1 or -1') - for k in r: - val = out[k][i][j] - if np.isnan(val) and held is not None: - out[k][i][j] = held - elif not np.isnan(val): - held = val - return out - - -def _propagate_down(a): - """Try to fill in NaN values in a.""" - out = np.zeros_like(a) - z, x, y = a.shape - xs = np.arange(x) - ys = np.arange(y) - xgrid, ygrid = np.meshgrid(xs, ys, indexing='ij') - points = np.stack((xgrid, ygrid), axis=-1) - for i in range(z): - nans = np.isnan(a[i]) - nonnan = np.logical_not(nans) - inpts = points[nonnan] - apts = a[i][nonnan] - outpoints = points.reshape(-1, 2) - try: - ans = scipy.interpolate.griddata(inpts, apts, outpoints, - method='nearest') - except ValueError: - # Likely there aren't any (non-nan) values here, but we'll - # copy over the whole thing to be safe. - ans = a[i] - out[i] = ans.reshape(out[i].shape) - # I honestly have no idea if this will work - return _just_pull_down(out) - - -# Minimum z (height) value on the earth - -def _least_nonzero(a): - """Fill in a flat array with the lowest nonzero value. - - Useful for interpolation below the bottom of the weather model. - """ - out = np.full(a.shape[1:], np.nan) - xlim, ylim = out.shape - zlim = len(a) - for x in range(xlim): - for y in range(ylim): - for z in range(zlim): - val = a[z][x][y] - if not np.isnan(val): - out[x][y] = val - break - return out - - - + x = vec[:Nx] + y = vec[Nx:2 * Nx] + xnew = vec[2 * Nx:] + f = interp1d(x, y, bounds_error=False, copy=False, assume_sorted=True) + return f(xnew) + + +def fillna3D(array, axis=-1): + + narr = np.moveaxis(array, axis, -1) + nars = narr.reshape((np.prod(narr.shape[:-1]),) + (narr.shape[-1],)) + dfd = pd.DataFrame(data=nars).interpolate(axis=1, limit_direction='both') + out = dfd.values.reshape(array.shape) + + return np.moveaxis(out, -1, axis) + + +def interpolateDEM(demRaster, extent, outLL, method='linear'): + ''' Interpolate a DEM raster to a set of lat/lon query points ''' + minlat, maxlat, minlon, maxlon = extent + nPixLat = demRaster.shape[0] + nPixLon = demRaster.shape[1] + xlats = np.linspace(minlat, maxlat, nPixLat) + xlons = np.linspace(minlon, maxlon, nPixLon) + interpolator = rgi( + points=(xlats, xlons), + values=demRaster, + method=method, + bounds_error=False + ) + outInterp = interpolator(outLL) + return outInterp diff --git a/tools/RAiDER/llreader.py b/tools/RAiDER/llreader.py index 6be59b36c..25b720b09 100644 --- a/tools/RAiDER/llreader.py +++ b/tools/RAiDER/llreader.py @@ -1,110 +1,231 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +from abc import abstractmethod +import os -import utils.demdownload as dld +import numpy as np +import pandas as pd -def readLL(lat, lon, flag): +from pyproj import CRS + +from RAiDER.dem import download_dem +from RAiDER.interpolator import interpolateDEM +from RAiDER.utilFcns import rio_extents, rio_open, rio_profile, rio_stats, get_file_and_band + + +class AOI(object): ''' - Parse lat/lon/height inputs and return - the appropriate outputs + This instantiates a generic AOI class object ''' - # Lats/Lons - if flag is None: - # They'll get set later with weather - lats = lons = None - latproj = lonproj = None - elif flag=='files': - # If they are files, open them - lats, latproj = util.gdal_open(lat, returnProj = True) - lons, lonproj = util.gdal_open(lon, returnProj = True) - elif flag=='bounding_box': - # assume that they are numbers/list/numpy array - lats = lat - lons = lon - latproj = lonproj = None - lon = lat = None - elif flag=='station_list': - lats = lat - lons = lon - latproj = lonproj = None - lon = lat = None - else: - raise RuntimeError('readLL: unknown flag') - - [lats, lons] = enforceNumpyArray(lats, lons) - - return lats, lons - - -def getHeights(lats, lons,heights, demLoc): - # Height - height_type, height_info = heights - if height_type == 'dem': - try: - hts = util.gdal_open(height_info) - except RuntimeError: - print('WARNING: File {} could not be opened. \n') - print('Proceeding with DEM download'.format(height_info)) - hts = dld.download_dem(lats, lons, demLoc) - elif height_type == 'lvs': - hts = height_info - latlist, lonlist, hgtlist = [], [], [] - for ht in hts: - latlist.append(lats.flatten()) - lonlist.append(lons.flatten()) - hgtlist.append(np.array([ht]*length(lats.flatten()))) - lats = np.array(latlist) - lons = np.array(lonlist) - hts = np.array(hgtlist) - - if height_type == 'download': - hts = dld.download_dem(lats, lons, demLoc) - - [lats, lons, hts] = enforceNumpyArray(lats, lons, hts) - - return lats, lons, hts - - -def setGeoInfo(lat, lon, latproj, lonproj): - # TODO: implement - # set_geo_info should be a list of functions to call on the dataset, - # and each will do some bit of work - set_geo_info = list() - if lat is not None: - def geo_info(ds): - ds.SetMetadata({'X_DATASET': os.path.abspath(lat), 'X_BAND': '1', - 'Y_DATASET': os.path.abspath(lon), 'Y_BAND': '1'}) - set_geo_info.append(geo_info) - # Is it ever possible that lats and lons will actually have embedded - # projections? - if latproj: - if outformat is not 'h5': - def geo_info(ds): - ds.SetProjection(latproj) + def __init__(self): + self._bounding_box = None + self._proj = CRS.from_epsg(4326) + + + def bounds(self): + return self._bounding_box + + + def projection(self): + return self._proj + + + def add_buffer(self, buffer): + ''' + Check whether an extra lat/lon buffer is needed for raytracing + ''' + # if raytracing, add a 1-degree buffer all around + ll_bounds = self._bounding_box.copy() + ll_bounds[0] = np.max([ll_bounds[0] - buffer, -90]) + ll_bounds[1] = np.min([ll_bounds[1] + buffer, 90]) + ll_bounds[2] = np.max([ll_bounds[2] - buffer,-180]) + ll_bounds[3] = np.min([ll_bounds[3] + buffer, 180]) + return ll_bounds + + +class StationFile(AOI): + '''Use a .csv file containing at least Lat, Lon, and optionally Hgt_m columns''' + def __init__(self, station_file): + AOI.__init__(self) + self._filename = station_file + self._bounding_box = bounds_from_csv(station_file) + + + def type(self): + return 'station_file' + + + def readLL(self): + df = pd.read_csv(self._filename).drop_duplicates(subset=["Lat", "Lon"]) + return df['Lat'].values, df['Lon'].values + + + def readZ(self): + df = pd.read_csv(self._filename) + if 'Hgt_m' in df.columns: + return df['Hgt_m'].values + else: + zvals, metadata = download_dem(self._bounding_box) + z_bounds = get_bbox(metadata) + z_out = interpolateDEM(zvals, z_bounds, self.readLL(), method='nearest') + df['Hgt_m'] = z_out + df.to_csv(self._filename, index=False) + self.__init__(self._filename) + return z_out + + +class RasterRDR(AOI): + def __init__(self, lat_file, lon_file=None, hgt_file=None, convention='isce'): + AOI.__init__(self) + # allow for 2-band lat/lon raster + if (lon_file is None): + self._file = lat_file else: - geo_info = None - elif lonproj: - def geo_info(ds): - ds.SetProjection(lonproj) - set_geo_info.append(geo_info) + self._latfile = lat_file + self._lonfile = lon_file + self._proj, self._bounding_box, _ = bounds_from_latlon_rasters(lat_file, lon_file) - return set_geo_info + # keep track of the height file + self._hgtfile = hgt_file + self._convention = convention -def enforceNumpyArray(*args): + def type(self): + return 'radar_rasters' + + + def readLL(self): + if self._latfile is not None: + return rio_open(self._latfile), rio_open(self._lonfile) + elif self._file is not None: + return rio_open(self._file) + else: + raise ValueError('lat/lon files are not defined') + + + def readZ(self): + if self._hgtfile is not None: + return rio_open(self._hgtfile) + else: + zvals, metadata = download_dem( + self._bounding_box, + writeDEM = True, + outName = os.path.join('GLO30_fullres_dem.tif'), + ) + z_bounds = get_bbox(metadata) + z_out = interpolateDEM(zvals, z_bounds, self.readLL(), method='nearest') + return z_out + + +class BoundingBox(AOI): + '''Parse a bounding box AOI''' + def __init__(self, bbox): + AOI.__init__(self) + self._bounding_box = bbox + + def type(self): + return 'bounding_box' + + +class GeocodedFile(AOI): + '''Parse a Geocoded file for coordinates''' + def __init__(self, filename, is_dem=False): + AOI.__init__(self) + self._filename = filename + self.p = rio_profile(filename) + self._bounding_box = rio_extents(self.p) + self._is_dem = is_dem + _, self._proj, self._gt = rio_stats(filename) + + + def type(self): + return 'geocoded_file' + + + def readLL(self): + # ll_bounds are SNWE + S, N, W, E = self._bounding_box + w, h = self.p['width'], self.p['height'] + px = (E - W) / w + py = (N - S) / h + x = np.array([W + (t * px) for t in range(w)]) + y = np.array([S + (t * py) for t in range(h)]) + X, Y = np.meshgrid(x,y) + return Y, X # lats, lons + + + def readZ(self): + if self._is_dem: + return rio_open(self._filename) + + else: + zvals, metadata = download_dem( + self._bounding_box, + writeDEM = True, + outName = os.path.join('GLO30_fullres_dem.tif'), + ) + z_bounds = get_bbox(metadata) + z_out = interpolateDEM(zvals, z_bounds, self.readLL(), method='nearest') + return z_out + + +class Geocube(AOI): + '''Parse a georeferenced data cube''' + def __init__(self): + AOI.__init__(self) + raise NotImplementedError + + def type(self): + return 'geocube' + + def readLL(self): + raise NotImplementedError + + +def bounds_from_latlon_rasters(latfile, lonfile): ''' - Enforce that a set of arguments are all numpy arrays. - Raise an error on failure. + Parse lat/lon/height inputs and return + the appropriate outputs ''' - return [checkArg(a) for a in args] + latinfo = get_file_and_band(latfile) + loninfo = get_file_and_band(lonfile) + lat_stats, lat_proj, _ = rio_stats(latinfo[0], band=latinfo[1]) + lon_stats, lon_proj, _ = rio_stats(loninfo[0], band=loninfo[1]) + + if lat_proj != lon_proj: + raise ValueError('Projection information for Latitude and Longitude files does not match') -def checkArg(arg): + # TODO - handle dateline crossing here + snwe = (lat_stats.min, lat_stats.max, + lon_stats.min, lon_stats.max) - if arg is None: - return None - else: - import numpy as np - try: - return np.array(arg) - except: - raise RuntimeError('checkArg: Cannot covert argument to numpy arrays') + fname = os.path.basename(latfile).split('.')[0] + return lat_proj, snwe, fname + +def bounds_from_csv(station_file): + ''' + station_file should be a comma-delimited file with at least "Lat" + and "Lon" columns, which should be EPSG: 4326 projection (i.e WGS84) + ''' + stats = pd.read_csv(station_file).drop_duplicates(subset=["Lat", "Lon"]) + if 'Hgt_m' in stats.columns: + use_csv_heights = True + snwe = [stats['Lat'].min(), stats['Lat'].max(), stats['Lon'].min(), stats['Lon'].max()] + return snwe + + +def get_bbox(p): + lon_w = p['transform'][2] + lat_n = p['transform'][5] + pix_lon = p['transform'][0] + pix_lat = p['transform'][4] + lon_e = lon_w + p['width'] * pix_lon + lat_s = lat_n + p['width'] * pix_lat + return lat_s, lat_n, lon_w, lon_e diff --git a/tools/RAiDER/logger.py b/tools/RAiDER/logger.py new file mode 100644 index 000000000..470e4d6db --- /dev/null +++ b/tools/RAiDER/logger.py @@ -0,0 +1,82 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Rohan Weeden +# Copyright 2020, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +Global logging configuration +""" +import logging +import os +import sys +from logging import FileHandler, Formatter, StreamHandler + +# Can change the default log location +_log_file_write_location = os.getcwd() + + +# Inspired by +# https://stackoverflow.com/questions/384076/how-can-i-color-python-logging-output +class UnixColorFormatter(Formatter): + yellow = "\x1b[33;21m" + red = "\x1b[31;21m" + bold_red = "\x1b[31;1m" + reset = "\x1b[0m" + + COLORS = { + logging.WARNING: yellow, + logging.ERROR: red, + logging.CRITICAL: bold_red + } + + def __init__(self, fmt=None, datefmt=None, style="%", use_color=True): + super().__init__(fmt, datefmt, style) + # Save the old function so we can call it later + self.__formatMessage = self.formatMessage + if use_color: + self.formatMessage = self.formatMessageColor + + def formatMessageColor(self, record): + message = self.__formatMessage(record) + color = self.COLORS.get(record.levelno) + if color: + message = "".join([color, message, self.reset]) + return message + + +class CustomFormatter(UnixColorFormatter): + """Adds levelname prefixes to the message on warning or above.""" + + def formatMessage(self, record): + message = super().formatMessage(record) + if record.levelno >= logging.WARNING: + message = ": ".join((record.levelname, message)) + return message + + +logger = logging.getLogger("RAiDER") +logger.setLevel(logging.DEBUG) + +stdout_handler = StreamHandler(sys.stdout) +stdout_handler.setFormatter(CustomFormatter(use_color=os.name != "nt")) +stdout_handler.setLevel(logging.DEBUG) + +debugfile_handler = FileHandler(os.path.join(_log_file_write_location, "debug.log")) +debugfile_handler.setFormatter(Formatter( + "[{asctime}] {funcName:>20}:{lineno:<5} {levelname:<10} {message}", + style="{" +)) +debugfile_handler.setLevel(logging.DEBUG) + +errorfile_handler = FileHandler(os.path.join(_log_file_write_location, "error.log")) +errorfile_handler.setFormatter(Formatter( + "[{asctime}] {funcName:>20}:{lineno:<5} {levelname:<10} {message}", + style="{" +)) +errorfile_handler.setLevel(logging.WARNING) + +logger.addHandler(stdout_handler) +logger.addHandler(errorfile_handler) +logger.addHandler(debugfile_handler) diff --git a/tools/RAiDER/losreader.py b/tools/RAiDER/losreader.py index fcc874091..98bd78b84 100644 --- a/tools/RAiDER/losreader.py +++ b/tools/RAiDER/losreader.py @@ -1,63 +1,355 @@ -import numpy as np - -import os.path +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Brett Buzzanga, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +import datetime import shelve -import utils.util as util +import xml.etree.ElementTree as ET +import numpy as np +from abc import ABC +from scipy.interpolate import interp1d + +import isce3.ext.isce3 as isce + +from RAiDER.utilFcns import ( + cosd, sind, rio_open, enu2ecef, lla2ecef, ecef2enu, ecef2lla +) +from RAiDER.constants import _ZREF + + +class LOS(ABC): + '''LOS Class definition for handling look vectors''' + + def __init__(self): + self._lats, self._lons, self._heights = None, None, None + self._look_vecs = None + self._ray_trace = False + self._is_zenith = False + + def setPoints(self, lats, lons=None, heights=None): + '''Set the pixel locations''' + if (lats is None) and (self._lats is None): + raise RuntimeError("You haven't given any point locations yet") + + # Will overwrite points by default + if lons is None: + llh = lats # assume points are [lats lons heights] + self._lats = llh[..., 0] + self._lons = llh[..., 1] + self._heights = llh[..., 2] + elif heights is None: + self._lats = lats + self._lons = lons + self._heights = np.zeros((len(lats), 1)) + else: + self._lats = lats + self._lons = lons + self._heights = heights + + def setTime(self, dt): + self._time = dt + + def is_Zenith(self): + return self._is_zenith + + def ray_trace(self): + return self._ray_trace + + +class Zenith(LOS): + """Special value indicating a look vector of "zenith".""" + def __init__(self): + LOS.__init__(self) + self._is_zenith = True + + def setLookVectors(self): + '''Set point locations and calculate Zenith look vectors''' + if self._lats is None: + raise ValueError('Target points not set') + if self._look_vecs is None: + self._look_vecs = getZenithLookVecs(self._lats, self._lons, self._heights) + + def __call__(self, delays): + '''Placeholder method for consistency with the other classes''' + return delays + + +class Conventional(LOS): + """ + Special value indicating that the zenith delay will + be projected using the standard cos(inc) scaling. + """ + + def __init__(self, filename=None, los_convention='isce', time=None, pad=None): + LOS.__init__(self) + self._file = filename + self._time = time + self._pad = pad + self._convention = los_convention + if self._convention == 'hyp3': + raise NotImplementedError() + + def __call__(self, delays): + '''Read the LOS file and convert it to look vectors''' + if self._lats is None: + raise ValueError('Target points not set') + if self._file is None: + raise ValueError('LOS file not set') + + try: + # if an ISCE-style los file is passed open it with GDAL + LOS_enu = inc_hd_to_enu(*rio_open(self._file)) + except OSError: + # Otherwise, treat it as an orbit / statevector file + svs = np.stack( + get_sv(self._file, self._time, self._pad), axis=-1 + ) + LOS_enu = state_to_los(svs, + [self._lats, self._lons, self._heights], + out="lookangle") + + if delays.shape == LOS_enu.shape: + return delays / LOS_enu + else: + return delays / LOS_enu[..., -1] + + +class Raytracing(LOS): + """ + Special value indicating that full raytracing will be + used to calculate slant delays. + + Get unit look vectors pointing from the ground (target) pixels to the sensor, + or to Zenith. Can be accomplished using an ISCE-style 2-band LOS file or a + file containing orbital statevectors. + + *NOTE*: + These line-of-sight vectors will NOT match ordinary LOS vectors for InSAR + because they are in an ECEF reference frame instead of a local ENU. This is done + because the construction of rays is done in ECEF rather than the local ENU. + + Parameters + ---------- + time: python datetime - user-requested query time. Must be + compatible with the orbit file passed. + Only required for a statevector file. + pad: int - integer number of seconds to pad around + the user-specified time; default 3 hours + Only required for a statevector file. + + Returns + ------- + look_vecs: ndarray - an x 3 array of unit look vectors, defined in + an Earth-centered, earth-fixed reference frame (ECEF). + Convention is vectors point from the target pixel to the + sensor. + lengths: ndarray - array of of the distnce from the surface to + the top of the troposphere (denoted by zref) + + Example: + -------- + >>> from RAiDER.losreader import Raytracing + >>> import numpy as np + +>>> #TODO + >>> # + """ + + def __init__(self, filename=None, los_convention='isce', time=None, pad=None): + '''read in and parse a statevector file''' + LOS.__init__(self) + self._ray_trace = True + self._file = filename + self._time = time + self._pad = pad + self._convention = los_convention + self._pad = 600 + if self._convention == 'hyp3': + raise NotImplementedError() + + def getLookVectors(self, time, pad=3 * 60): + ''' + Calculate look vectors for raytracing + ''' + if self._lats is None: + raise ValueError('Target points not set') + if self._file is None: + raise ValueError('LOS file not set') + + try: + # if an ISCE-style los file is provided, use GDAL + LOS_enu = inc_hd_to_enu(*rio_open(self._file)) + self._look_vecs = enu2ecef( + LOS_enu[..., 0], + LOS_enu[..., 1], + LOS_enu[..., 2], + self._lats, + self._lons, + self._heights + ) + self._xyz = np.stack( + lla2ecef([ + self._lats, + self._lons, + self._heights + ]), axis=-1 + ) + + except OSError: + # Otherwise treat it as a state vector file + svs = np.stack( + get_sv(self._file, self._time, self._pad), axis=-1 + ) + self._look_vecs, self._xyz = state_to_los( + svs, [self._lats, self._lons, self._heights], + out="ecef" + ) + + def getIntersectionWithHeight(self, height): + """ + This function computes the intersection point of a ray at a height + level + """ + # We just leverage the same code as finding top of atmosphere here + return getTopOfAtmosphere(self._xyz, self._look_vecs, height) + + def getIntersectionWithLevels(self, levels): + """ + This function returns the points at which rays intersect the + given height levels. This way we have same number of points in + each ray and only at level transitions. + + For targets that are above a given height level, the ray points are set + to nan to indicate that it does not contribute to the integration of + rays. + + Output: + rays: (self._lats.shape, len(levels), 3) + """ + rays = np.zeros(list(self._lats.shape) + [len(levels), 3]) + + # This can be further vectorized, if there is enough memory + for ind, z in enumerate(levels): + rays[..., ind, :] = self.getIntersectionWithHeight(z) + + # Set pixels above level to nan + value = rays[..., ind, :] + value[self._heights > z, :] = np.nan + + return rays + + def calculateDelays(self, delays): + ''' + Here "delays" is point-wise delays (i.e. refractivities), not + integrated ZTD/STD. + ''' + # Create rays (Use getIntersectionWithLevels above) + # Interpolate delays to rays + # Integrate along rays + # Return STD + raise NotImplementedError + + +def getZenithLookVecs(lats, lons, heights): + ''' + Returns look vectors when Zenith is used. -def state_to_los(t, x, y, z, vx, vy, vz, lats, lons, heights): - import Geo2rdr + Parameters + ---------- + lats/lons/heights: ndarray - Numpy arrays containing WGS-84 target locations - real_shape = lats.shape - lats = lats.flatten() - lons = lons.flatten() - heights = heights.flatten() + Returns + ------- + zenLookVecs: ndarray - (in_shape) x 3 unit look vectors in an ECEF reference frame + ''' + x = np.cos(np.radians(lats)) * np.cos(np.radians(lons)) + y = np.cos(np.radians(lats)) * np.sin(np.radians(lons)) + z = np.sin(np.radians(lats)) + + return np.stack([x, y, z], axis=-1) + + +def get_sv(los_file, ref_time, pad=3 * 60): + """ + Read an LOS file and return orbital state vectors + + Parameters + ---------- + los_file: str - user-passed file containing either look + vectors or statevectors for the sensor + ref_time: python datetime - User-requested datetime; if not encompassed + by the orbit times will raise a ValueError + pad: int - number of seconds to keep around the + requested time + + Returns + ------- + svs: 7 x 1 list of Nt x 1 ndarrays - the times, x/y/z positions and + velocities of the sensor for the given + window around the reference time + """ + try: + svs = read_txt_file(los_file) + except ValueError: + try: + svs = read_ESA_Orbit_file(los_file) + except BaseException: + try: + svs = read_shelve(los_file) + except BaseException: + raise ValueError( + 'get_sv: I cannot parse the statevector file {}'.format(los_file) + ) - geo2rdr_obj = Geo2rdr.PyGeo2rdr() - geo2rdr_obj.set_orbit(t, x, y, z, vx, vy, vz) + if ref_time: + idx = cut_times(svs[0], ref_time, pad=pad) + svs = [d[idx] for d in svs] - loss = np.zeros((3, len(lats))) - slant_ranges = np.zeros_like(lats) + return svs - for i, (lat, lon, height) in enumerate(zip(lats, lons, heights)): - height_array = np.array(((height,),)) - # Geo2rdr is picky about the type of height - height_array = height_array.astype(np.double) +def inc_hd_to_enu(incidence, heading): + ''' + Convert incidence and heading to line-of-sight vectors from the ground to the top of + the troposphere. - geo2rdr_obj.set_geo_coordinate(np.radians(lon), - np.radians(lat), - 1, 1, - height_array) - # compute the radar coordinate for each geo coordinate - geo2rdr_obj.geo2rdr() + Parameters + ---------- + incidence: ndarray - incidence angle in deg from vertical + heading: ndarray - heading angle in deg clockwise from north + lats/lons/heights: ndarray - WGS84 ellipsoidal target (ground pixel) locations - # get back the line of sight unit vector - los_x, los_y, los_z = geo2rdr_obj.get_los() - loss[:, i] = los_x, los_y, los_z + Returns + ------- + LOS: ndarray - (input_shape) x 3 array of unit look vectors in local ENU - # get back the slant ranges - slant_range = geo2rdr_obj.get_slant_range() - slant_ranges[i] = slant_range + Algorithm referenced from http://earthdef.caltech.edu/boards/4/topics/327 + ''' + if np.any(incidence < 0): + raise ValueError('inc_hd_to_enu: Incidence angle cannot be less than 0') - los = loss * slant_ranges + east = sind(incidence) * cosd(heading + 90) + north = sind(incidence) * sind(heading + 90) + up = cosd(incidence) - # Have to think about traversal order here. It's easy, though, since - # in both orders xs come first, followed by all ys, followed by all - # zs. - return los.reshape((3,) + real_shape) + return np.stack((east, north, up), axis=-1) def read_shelve(filename): - import isce - import iscesys.Component.ProductManager - + # TODO: docstring and unit tests with shelve.open(filename, 'r') as db: obj = db['frame'] numSV = len(obj.orbit.stateVectors) + if numSV == 0: + raise ValueError('read_shelve: the file has not statevectors') - t = np.ones(numSV) + t = [] x = np.ones(numSV) y = np.ones(numSV) z = np.ones(numSV) @@ -66,7 +358,7 @@ def read_shelve(filename): vz = np.ones(numSV) for i, st in enumerate(obj.orbit.stateVectors): - t[i] = st.time.second + st.time.minute*60.0 + t.append(st.time) x[i] = st.position[0] y[i] = st.position[1] z[i] = st.position[2] @@ -74,10 +366,27 @@ def read_shelve(filename): vy[i] = st.velocity[1] vz[i] = st.velocity[2] + t = np.array(t) return t, x, y, z, vx, vy, vz def read_txt_file(filename): + ''' + Read a 7-column text file containing orbit statevectors. Time + should be denoted as integer time in seconds since the reference + epoch (user-requested time). + + Parameters + ---------- + filename: str - user-supplied space-delimited text file with no header + containing orbital statevectors as 7 columns: + - time in seconds since the user-supplied epoch + - x / y / z locations in ECEF cartesian coordinates + - vx / vy / vz velocities in m/s in ECEF coordinates + Returns + svs: list - a length-7 list of numpy vectors containing the above + variables + ''' t = list() x = list() y = list() @@ -88,12 +397,14 @@ def read_txt_file(filename): with open(filename, 'r') as f: for line in f: try: - t_, x_, y_, z_, vx_, vy_, vz_ = line.split() + parts = line.strip().split() + t_ = datetime.datetime.fromisoformat(parts[0]) + x_, y_, z_, vx_, vy_, vz_ = [float(t) for t in parts[1:]] except ValueError: raise ValueError( - "I need {} to be a 7 column text file, with ".format(filename) + - "columns t, x, y, z, vx, vy, vz (Couldn't parse line " + - "{})".format(repr(line))) + "I need {} to be a 7 column text file, with ".format(filename) + + "columns t, x, y, z, vx, vy, vz (Couldn't parse line " + + "{})".format(repr(line))) t.append(t_) x.append(x_) y.append(y_) @@ -101,101 +412,267 @@ def read_txt_file(filename): vx.append(vx_) vy.append(vy_) vz.append(vz_) - return (np.array(t), np.array(x), np.array(y), np.array(z), np.array(vx), - np.array(vy), np.array(vz)) - -def read_xml_file(filename): - import isce - import iscesys.Component.ProductManager + if len(t) < 4: + raise ValueError('read_txt_file: File {} does not have enough statevectors'.format(filename)) - pm = iscesys.Component.ProductManager.ProductManager() - pm.configure() + return [np.array(a) for a in [t, x, y, z, vx, vy, vz]] - obj = pm.loadProduct(filename) - numSV = len(obj.orbit.stateVectors) - - t = np.ones(numSV) - x = np.ones(numSV) - y = np.ones(numSV) - z = np.ones(numSV) - vx = np.ones(numSV) - vy = np.ones(numSV) - vz = np.ones(numSV) - - for i, st in enumerate(obj.orbit.stateVectors): - t[i] = st.time.second + st.time.minute*60.0 - x[i] = st.position[0] - y[i] = st.position[1] - z[i] = st.position[2] - vx[i] = st.velocity[0] - vy[i] = st.velocity[1] - vz[i] = st.velocity[2] - - return t, x, y, z, vx, vy, vz - - -def infer_sv(los_file, lats, lons, heights): - """Infer the type of file to read, then read an LOS file.""" - _, ext = os.path.splitext(los_file) - if ext == '.txt': - svs = read_txt_file(los_file) - elif ext == '.xml': - svs = read_xml_file(los_file) +def read_ESA_Orbit_file(filename): + ''' + Read orbit data from an orbit file supplied by ESA + + Parameters + ---------- + filename: str - string of the orbit filename + + Returns + ------- + t: Nt x 1 ndarray - a numpy vector with Nt elements containing time + in python datetime + x, y, z: Nt x 1 ndarrays - x/y/z positions of the sensor at the times t + vx, vy, vz: Nt x 1 ndarrays - x/y/z velocities of the sensor at the times t + ''' + tree = ET.parse(filename) + root = tree.getroot() + data_block = root[1] + numOSV = len(data_block[0]) + + t = [] + x = np.ones(numOSV) + y = np.ones(numOSV) + z = np.ones(numOSV) + vx = np.ones(numOSV) + vy = np.ones(numOSV) + vz = np.ones(numOSV) + + for i, st in enumerate(data_block[0]): + t.append( + datetime.datetime.strptime( + st[1].text, + 'UTC=%Y-%m-%dT%H:%M:%S.%f' + ) + ) + + x[i] = float(st[4].text) + y[i] = float(st[5].text) + z[i] = float(st[6].text) + vx[i] = float(st[7].text) + vy[i] = float(st[8].text) + vz[i] = float(st[9].text) + t = np.array(t) + return [t, x, y, z, vx, vy, vz] + + +############################ +def state_to_los(svs, llh_targets, out="lookangle"): + ''' + Converts information from a state vector for a satellite orbit, given in terms of + position and velocity, to line-of-sight information at each (lon,lat, height) + coordinate requested by the user. + Parameters + ---------- + svs - t, x, y, z, vx, vy, vz - time, position, and velocity in ECEF of the sensor + llh_targets - lats, lons, heights - Ellipsoidal (WGS84) positions of target ground pixels + + Returns + ------- + LOS - * x 3 matrix of LOS unit vectors in ECEF (*not* ENU) + Example: + >>> import datetime + >>> import numpy + >>> from RAiDER.utilFcns import rio_open + >>> import RAiDER.losreader as losr + >>> lats, lons, heights = np.array([-76.1]), np.array([36.83]), np.array([0]) + >>> time = datetime.datetime(2018,11,12,23,0,0) + >>> # download the orbit file beforehand + >>> esa_orbit_file = 'S1A_OPER_AUX_POEORB_OPOD_20181203T120749_V20181112T225942_20181114T005942.EOF' + >>> svs = losr.read_ESA_Orbit_file(esa_orbit_file) + >>> LOS = losr.state_to_los(*svs, [lats, lons, heights], xyz) + ''' + if out not in ["lookangle", "ecef"]: + raise ValueError( + f"Output type can be lookangle or ecef - not {out}" + ) + + # check the inputs + if np.min(svs.shape) < 4: + raise RuntimeError( + 'state_to_los: At least 4 state vectors are required' + ' for orbit interpolation' + ) + + # Convert svs to isce3 orbit + orb = isce.core.Orbit([ + isce.core.StateVector( + isce.core.DateTime(row[0]), + row[1:4], row[4:7] + ) for row in svs + ]) + + # Flatten the input array for convenience + in_shape = llh_targets[0].shape + target_llh = np.stack([x.flatten() for x in llh_targets], axis=-1) + Npts = len(target_llh) + + # Iterate through targets and compute LOS + if out == "lookangle": + los_ang, slant_range = get_radar_pos(target_llh, orb, out="lookangle") + los_factor = np.cos(np.deg2rad(los_ang)).reshape(in_shape) + return los_factor + elif out == "ecef": + los_xyz, targ_xyz = get_radar_pos(target_llh, orb, out="ecef") + return los_xyz, targ_xyz else: - # Here's where things get complicated... Either it's a shelve - # file or the user messed up. For now we'll just try to read it - # as a shelve file, and throw whatever error that does, although - # the message might be sometimes misleading. - svs = read_shelve(los_file) - LOSs = state_to_los(*svs, lats = lats, lons = lons, heights = heights) - return LOSs - -def los_to_lv(incidence, heading, lats, lons, heights, zref, ranges=None): - # I'm looking at http://earthdef.caltech.edu/boards/4/topics/327 - a_0 = incidence - a_1 = heading - - east = util.sind(a_0)*util.cosd(a_1 + 90) - north = util.sind(a_0)*util.sind(a_1 + 90) - up = util.cosd(a_0) - - east, north, up = np.stack((east, north, up)) - - # Pick reasonable range to top of troposphere if not provided - if ranges is None: - ranges = (zref - heights) / up - - # Scale look vectors by range - east, north, up = np.stack((east, north, up)) * ranges - - x, y, z = util.enu2ecef( - east.flatten(), north.flatten(), up.flatten(), lats.flatten(), - lons.flatten(), heights.flatten()) + raise NotImplementedError("Unexpected logic in state_to_los") + + +def cut_times(times, ref_time, pad=3600 * 3): + """ + Slice the orbit file around the reference aquisition time. This is done + by default using a three-hour window, which for Sentinel-1 empirically + works out to be roughly the largest window allowed by the orbit time. + Parameters + ---------- + times: Nt x 1 ndarray - Vector of orbit times as datetime + ref_time: datetime - Reference time + pad: int - integer time in seconds to use as padding + Returns + ------- + idx: Nt x 1 logical ndarray - a mask of times within the padded request time. + """ + diff = np.array( + [(x - ref_time).total_seconds() for x in times] + ) + return np.abs(diff) < pad + + +def get_radar_pos(llh, orb, out="lookangle"): + ''' + Calculate the coordinate of the sensor in ECEF at the time corresponding to ***. + + Parameters + ---------- + orb: isce3.core.Orbit - Nt x 7 matrix of statevectors: [t x y z vx vy vz] + llh: ndarray - position of the target in LLH + out: str - either lookangle or ecef for vector + + Returns + ------- + if out == "lookangle" + los: ndarray - Satellite incidence angle + sr: ndarray - Slant range in meters + if out == "ecef" + los_xyz: ndarray - Satellite LOS in ECEF from target to satellite + targ_xyz: ndarray - Target XYZ positions in ECEF + ''' + if out not in ["lookangle", "ecef"]: + raise ValueError( + f"out kwarg must be lookangle or ecef - not {out}" + ) + + num_iteration = 30 + residual_threshold = 1.0e-7 + + # Get xyz positions of targets here + targ_xyz = np.stack( + lla2ecef(llh[:, 0], llh[:, 1], llh[:, 2]), axis=-1 + ) + + # Get some isce3 constants for this inversion + # TODO - Assuming right-looking for now + elp = isce.core.Ellipsoid() + dop = isce.core.LUT2d() + look = isce.core.LookSide.Right + + # Iterate for each point + # TODO - vectorize / parallelize + sr = np.empty((llh.shape[0],), dtype=np.float64) + if out == "lookangle": + output = np.empty((llh.shape[0],), dtype=np.float64) + else: + output = np.empty((llh.shape[0], 3), dtype=np.float64) + for ind, pt in enumerate(llh): + if not any(np.isnan(pt)): + # ISCE3 always uses xy convention + inp = np.array([np.deg2rad(pt[1]), + np.deg2rad(pt[0]), + pt[2]]) + # Local normal vector + nv = elp.n_vector(inp[0], inp[1]) + + # Wavelength does not matter for zero doppler + try: + aztime, slant_range = isce.geometry.geo2rdr( + inp, elp, orb, dop, 0.06, look, + threshold=residual_threshold, + maxiter=num_iteration, + delta_range=10.0) + sat_xyz, _ = orb.interpolate(aztime) + sr[ind] = slant_range + + delta = sat_xyz - targ_xyz[ind, :] + if out == "lookangle": + # TODO - if we only ever need cos(lookang), + # skip the arccos here and cos above + delta = delta / np.linalg.norm(delta) + output[ind] = np.rad2deg( + np.arccos(np.dot(delta, nv)) + ) + else: + output[ind, :] = (sat_xyz - targ_xyz) / slant_range + except Exception as e: + raise e + sat_xyz[ind, :] = np.nan + sr[ind] = np.nan + output[ind, ...] = np.nan + else: + sat_xyz[ind, :] = np.nan + sr[ind] = np.nan + output[ind, ...] = np.nan + + # If lookangle is requested + if out == "lookangle": + return output, sr + elif out == "ecef": + return output, targ_xyz + else: + raise NotImplementedError("Unexpected logic in get_radar_pos") - los = (np.stack((x, y, z), axis=-1) - - np.stack(util.lla2ecef( - lats.flatten(), lons.flatten(), heights.flatten()), axis=-1)) - los = los.reshape(east.shape + (3,)) - return los +def getTopOfAtmosphere(xyz, look_vecs, toaheight, factor=None): + """ + Get ray intersection at given height. + We use simple Newton-Raphson for this computation. This cannot be done + exactly since closed form expression from xyz to llh is super compliated. -def infer_los(los, lats, lons, heights, zref): - ''' - Helper function to deal with various LOS files supplied - ''' + If a factor (cos of inc angle) is provided - iterations are lot faster. + If factor is not provided solutions converges to + - 0.01 mm at heights near zero in 10 iterations + - 10 cm at heights above 40km in 10 iterations - los_type, los_file = los + If factor is know, we converge in 3 iterations to less than a micron. + """ + if factor is not None: + maxIter = 3 + else: + maxIter = 10 + factor = 1. - if los_type == 'sv': - LOS = infer_sv(los_file, lats, lons, heights) + # Guess top point + pos = xyz + toaheight * look_vecs - if los_type == 'los': - incidence, heading = util.gdal_open(los_file) - LOS = los_to_lv(incidence, heading, lats, lons, heights, zref) + for niter in range(10): + pos_llh = ecef2lla(pos[..., 0], pos[..., 1], pos[..., 2]) + pos = pos + look_vecs * ((toaheight - pos_llh[2])/factor)[..., None] - return LOS + # This is for debugging the approach + # print("Stats for TOA computation: ", toaheight, + # toaheight - np.nanmin(pos_llh[2]), + # toaheight - np.nanmax(pos_llh[2]), + # ) - raise ValueError("Unsupported los type '{}'".format(los_type)) + # The converged solution represents top of the rays + return pos diff --git a/tools/RAiDER/models/__init__.py b/tools/RAiDER/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tools/RAiDER/models/allowed.py b/tools/RAiDER/models/allowed.py old mode 100644 new mode 100755 index 77ff378c7..3670d1aaf --- a/tools/RAiDER/models/allowed.py +++ b/tools/RAiDER/models/allowed.py @@ -1,26 +1,8 @@ - -def AllowedModels(): - ''' - return a list of the implemented model types - ''' - allowedModels = [ - 'ERA-5', - 'ERA-I', - 'MERRA-2', - 'WRF', - 'HRRR', - 'pickle', - 'grib'] - - return allowedModels - - -def checkIfImplemented(modelName): - ''' - Check whether the input model name has been implemented - ''' - allowedWMTypes = AllowedModels() - if modelName not in allowedWMTypes: - raise RuntimeError('Weather model {} not allowed/implemented'.format(weather_fmt)) - - +ALLOWED_MODELS = [ + 'ERA5', + 'ERA5T', + 'HRRR', + 'GMAO', + 'HRES', + 'NCMR' +] diff --git a/tools/RAiDER/models/ecmwf.py b/tools/RAiDER/models/ecmwf.py old mode 100644 new mode 100755 index 55f9e93c1..f4d3d70b5 --- a/tools/RAiDER/models/ecmwf.py +++ b/tools/RAiDER/models/ecmwf.py @@ -1,121 +1,164 @@ -import datetime +from abc import abstractmethod +import datetime + import numpy as np -import pyproj +import xarray as xr + +from pyproj import CRS + +from RAiDER.logger import logger +from RAiDER import utilFcns as util +from RAiDER.models.model_levels import ( + LEVELS_137_HEIGHTS, + LEVELS_25_HEIGHTS, + A_137_HRES, + B_137_HRES, +) +from RAiDER.models.weatherModel import WeatherModel -import util -from models.weatherModel import WeatherModel class ECMWF(WeatherModel): ''' Implement ECMWF models ''' + def __init__(self): # initialize a weather model WeatherModel.__init__(self) # model constants - self._k1 = 0.776 # [K/Pa] - self._k2 = 0.233 # [K/Pa] - self._k3 = 3.75e3 # [K^2/Pa] + self._k1 = 0.776 # [K/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] + + self._time_res = 1 self._lon_res = 0.2 self._lat_res = 0.2 + self._proj = CRS.from_epsg(4326) + + self._model_level_type = 'ml' # Default + + def setLevelType(self, levelType): + '''Set the level type to model levels or pressure levels''' + if levelType in ['ml', 'pl']: + self._model_level_type = levelType + else: + raise RuntimeError('Level type {} is not recognized'.format(levelType)) + + if levelType == 'ml': + self.__model_levels__() + else: + self.__pressure_levels__() + def __pressure_levels__(self): + self._zlevels = np.flipud(LEVELS_25_HEIGHTS) + self._levels = len(self._zlevels) - def load_weather(self, filename): + def __model_levels__(self): + self._levels = 137 + self._zlevels = np.flipud(LEVELS_137_HEIGHTS) + self._a = A_137_HRES + self._b = B_137_HRES + + def load_weather(self, *args, **kwargs): ''' - Consistent class method to be implemented across all weather model types. - As a result of calling this method, all of the variables (x, y, z, p, q, - t, wet_refractivity, hydrostatic refractivity, e) should be fully - populated. + Consistent class method to be implemented across all weather model types. + As a result of calling this method, all of the variables (x, y, z, p, q, + t, wet_refractivity, hydrostatic refractivity, e) should be fully + populated. ''' - self._load_model_level(filename) + self._load_model_level(*self.files) def _load_model_level(self, fname): - from scipy.io import netcdf as nc - with nc.netcdf_file(fname, 'r', maskandscale=True) as f: - # 0,0 to get first time and first level - z = f.variables['z'][0][0].copy() - lnsp = f.variables['lnsp'][0][0].copy() - t = f.variables['t'][0].copy() - qq = f.variables['q'][0].copy() - lats = f.variables['latitude'][:].copy() - lons = f.variables['longitude'][:].copy() - self._levels = f.variables['level'][:].copy() + # read data from netcdf file + lats, lons, xs, ys, t, q, lnsp, z = self._makeDataCubes( + fname, + verbose=False + ) # ECMWF appears to give me this backwards if lats[0] > lats[1]: z = z[::-1] lnsp = lnsp[::-1] t = t[:, ::-1] - Q = qq[:, ::-1] + q = q[:, ::-1] lats = lats[::-1] # Lons is usually ok, but we'll throw in a check to be safe if lons[0] > lons[1]: z = z[..., ::-1] lnsp = lnsp[..., ::-1] t = t[..., ::-1] - Q = qq[..., ::-1] + q = q[..., ::-1] lons = lons[::-1] # pyproj gets fussy if the latitude is wrong, plus our # interpolator isn't clever enough to pick up on the fact that # they are the same lons[lons > 180] -= 360 - self._proj = pyproj.Proj(proj='latlong') self._t = t - self._q = Q - - geo_hgt,pres,hgt = self._calculategeoh(z, lnsp) + self._q = q + geo_hgt, pres, hgt = self._calculategeoh(z, lnsp) # re-assign lons, lats to match heights - _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], - hgt.shape) - _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], - hgt.shape) + _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], hgt.shape) + _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], hgt.shape) # ys is latitude self._get_heights(_lats, hgt) + h = self._zs.copy() # We want to support both pressure levels and true pressure grids. # If the shape has one dimension, we'll scale it up to act as a # grid, otherwise we'll leave it alone. if len(pres.shape) == 1: - self._p = np.broadcast_to(pres[:, np.newaxis, np.newaxis], - self._zs.shape) + self._p = np.broadcast_to(pres[:, np.newaxis, np.newaxis], self._zs.shape) else: self._p = pres # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) - self._p = np.transpose(self._p) - self._t = np.transpose(self._t) - self._q = np.transpose(self._q) - self._ys = np.transpose(_lats) - self._xs = np.transpose(_lons) - self._zs = np.transpose(self._zs) + self._p = np.transpose(self._p, (1, 2, 0)) + self._t = np.transpose(self._t, (1, 2, 0)) + self._q = np.transpose(self._q, (1, 2, 0)) + h = np.transpose(h, (1, 2, 0)) + self._lats = np.transpose(_lats, (1, 2, 0)) + self._lons = np.transpose(_lons, (1, 2, 0)) # Flip all the axis so that zs are in order from bottom to top - self._p = np.flip(self._p, axis = 2) - self._t = np.flip(self._t, axis = 2) - self._q = np.flip(self._q, axis = 2) - self._zs = np.flip(self._zs, axis = 2) - + # lats / lons are simply replicated to all heights so they don't need flipped + self._p = np.flip(self._p, axis=2) + self._t = np.flip(self._t, axis=2) + self._q = np.flip(self._q, axis=2) + self._ys = self._lats.copy() + self._xs = self._lons.copy() + self._zs = np.flip(h, axis=2) - - def fetch(self, lats, lons, time, out, Nextra = 2): + def _fetch(self, out): ''' Fetch a weather model from ECMWF ''' # bounding box plus a buffer - lat_min, lat_max, lon_min, lon_max = self._get_ll_bounds(lats, lons, Nextra) + lat_min, lat_max, lon_min, lon_max = self._ll_bounds # execute the search at ECMWF - self._get_from_ecmwf( - lat_min, lat_max, self._lat_res, lon_min, lon_max, self._lon_res, time, - out) + try: + self._get_from_ecmwf( + lat_min, + lat_max, + self._lat_res, + lon_min, + lon_max, + self._lon_res, + self._time, + out + ) + except Exception as e: + logger.warning('Query point bounds are {}/{}/{}/{}'.format(lat_min, lat_max, lon_min, lon_max)) + logger.warning('Query time: {}'.format(self._time)) + logger.exception(e) def _get_from_ecmwf(self, lat_min, lat_max, lat_step, lon_min, lon_max, - lon_step, time, out): + lon_step, time, out): import ecmwfapi server = ecmwfapi.ECMWFDataServer() @@ -125,7 +168,7 @@ def _get_from_ecmwf(self, lat_min, lat_max, lat_step, lon_min, lon_max, server.retrieve({ "class": self._classname, # ERA-Interim 'dataset': self._dataset, - "expver": "1", + "expver": "{}".format(self._expver), # They warn me against all, but it works well "levelist": 'all', "levtype": "ml", # Model levels @@ -145,57 +188,198 @@ def _get_from_ecmwf(self, lat_min, lat_max, lat_step, lon_min, lon_max, # be any of "3/6/9/12". "step": "0", # grid: Only regular lat/lon grids are supported. - "grid": '{}/{}'.format(lat_step,lon_step), - "area": '{}/{}/{}/{}'.format(lat_max,lon_min,lat_min,lon_max), # area: N/W/S/E + "grid": '{}/{}'.format(lat_step, lon_step), + "area": '{}/{}/{}/{}'.format(lat_max, lon_min, lat_min, lon_max), # area: N/W/S/E "format": "netcdf", "resol": "av", "target": out, # target: the name of the output file. }) - - def _get_from_cds(self, lat_min, lat_max, lat_step, lon_min, lon_max, - lon_step, acqTime, outname): + def _get_from_cds( + self, + lat_min, + lat_max, + lon_min, + lon_max, + acqTime, + outname + ): import cdsapi - - pls = ['1','2','3','5','7','10','20','30','50','70','100','125','150','175','200','225','250','300','350','400','450','500','550','600','650','700','750','775','800','825','850','875','900','925','950','975','1000'] - mls = np.arange(137) + 1 - c = cdsapi.Client(verify=0) - #corrected_date = util.round_date(time, datetime.timedelta(hours=6)) + if self._model_level_type == 'pl': - var = ['q','z','t'] - var = ['geopotential','relative_humidity','specific_humidity','temperature'] - levels = 'all' + var = ['z', 'q', 't'] levType = 'pressure_level' else: - var = ['lnsp', 'q','z','t'] - levels = mls + var = "129/130/133/152" # 'lnsp', 'q', 'z', 't' levType = 'model_level' - + bbox = [lat_max, lon_min, lat_min, lon_max] - + # round to the closest legal time + corrected_date = util.round_date(acqTime, datetime.timedelta(hours=self._time_res)) + + # I referenced https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5 dataDict = { - #"class": self._classname, - #'dataset': self._dataset, - #"expver": "{}".format(self._expver), - "product_type": "reanalysis", - "{}".format(levType):levels, + "class": "ea", + "expver": "1", + "levelist": 'all', "levtype": "{}".format(self._model_level_type), # 'ml' for model levels or 'pl' for pressure levels - 'variable':var, + 'param': var, "stream": "oper", "type": "an", - "year": "{}".format(acqTime.year), - "month": "{}".format(acqTime.month), - "day": "{}".format(acqTime.day), - "time": "{}".format(datetime.time.strftime(acqTime.time(), '%H:%M')), + "date": "{}".format(corrected_date.strftime('%Y-%m-%d')), + "time": "{}".format(datetime.time.strftime(corrected_date.time(), '%H:%M')), # step: With type=an, step is always "0". With type=fc, step can # be any of "3/6/9/12". "step": "0", - "area": bbox, + "area": bbox, + "grid": [0.25, .25], "format": "netcdf"} - print(dataDict) - c.retrieve('reanalysis-era5-pressure-levels',dataDict,outname) + try: + c.retrieve('reanalysis-era5-complete', dataDict, outname) + except Exception as e: + logger.warning('Query point bounds are {}/{} latitude and {}/{} longitude'.format(lat_min, lat_max, lon_min, lon_max)) + logger.warning('Query time: {}'.format(acqTime)) + logger.exception(e) + raise Exception + + def _download_ecmwf(self, lat_min, lat_max, lat_step, lon_min, lon_max, lon_step, time, out): + from ecmwfapi import ECMWFService + + server = ECMWFService("mars") + + # round to the closest legal time + corrected_date = util.round_date(time, datetime.timedelta(hours=self._time_res)) + + if self._model_level_type == 'ml': + param = "129/130/133/152" + else: + param = "129.128/130.128/133.128/152" + + server.execute( + { + 'class': self._classname, + 'dataset': self._dataset, + 'expver': "{}".format(self._expver), + 'resol': "av", + 'stream': "oper", + 'type': "an", + 'levelist': "all", + 'levtype': "{}".format(self._model_level_type), + 'param': param, + 'date': datetime.datetime.strftime(corrected_date, "%Y-%m-%d"), + 'time': "{}".format(datetime.time.strftime(corrected_date.time(), '%H:%M')), + 'step': "0", + 'grid': "{}/{}".format(lon_step, lat_step), + 'area': "{}/{}/{}/{}".format(lat_max, util.floorish(lon_min, 0.1), util.floorish(lat_min, 0.1), lon_max), + 'format': "netcdf", + }, + out + ) + + def _load_pressure_level(self, filename, *args, **kwargs): + with xr.open_dataset(filename) as block: + # Pull the data + z = np.squeeze(block['z'].values) + t = np.squeeze(block['t'].values) + q = np.squeeze(block['q'].values) + lats = np.squeeze(block.latitude.values) + lons = np.squeeze(block.longitude.values) + levels = np.squeeze(block.level.values) * 100 + + z = np.flip(z, axis=1) + + # ECMWF appears to give me this backwards + if lats[0] > lats[1]: + z = z[::-1] + t = t[:, ::-1] + q = q[:, ::-1] + lats = lats[::-1] + # Lons is usually ok, but we'll throw in a check to be safe + if lons[0] > lons[1]: + z = z[..., ::-1] + t = t[..., ::-1] + q = q[..., ::-1] + lons = lons[::-1] + # pyproj gets fussy if the latitude is wrong, plus our + # interpolator isn't clever enough to pick up on the fact that + # they are the same + lons[lons > 180] -= 360 + + self._t = t + self._q = q + + geo_hgt = z / self._g0 + + # re-assign lons, lats to match heights + _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], + geo_hgt.shape) + _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], + geo_hgt.shape) + + # correct heights for latitude + self._get_heights(_lats, geo_hgt) + + self._p = np.broadcast_to(levels[:, np.newaxis, np.newaxis], + self._zs.shape) + + # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) + self._p = np.transpose(self._p) + self._t = np.transpose(self._t) + self._q = np.transpose(self._q) + self._lats = np.transpose(_lats) + self._lons = np.transpose(_lons) + self._ys = self._lats.copy() + self._xs = self._lons.copy() + self._zs = np.transpose(self._zs) + + # check this + # data cube format should be lats,lons,heights + self._lats = self._lats.swapaxes(0, 1) + self._lons = self._lons.swapaxes(0, 1) + self._xs = self._xs.swapaxes(0, 1) + self._ys = self._ys.swapaxes(0, 1) + self._zs = self._zs.swapaxes(0, 1) + self._p = self._p.swapaxes(0, 1) + self._q = self._q.swapaxes(0, 1) + self._t = self._t.swapaxes(0, 1) + + # For some reason z is opposite the others + self._p = np.flip(self._p, axis=2) + self._t = np.flip(self._t, axis=2) + self._q = np.flip(self._q, axis=2) + + def _makeDataCubes(self, fname, verbose=False): + ''' + Create a cube of data representing temperature and relative humidity + at specified pressure levels + ''' + # get ll_bounds + S, N, W, E = self._ll_bounds + + with xr.open_dataset(fname) as ds: + ds = ds.assign_coords(longitude=(((ds.longitude + 180) % 360) - 180)) + + # mask based on query bounds + m1 = (S <= ds.latitude) & (N >= ds.latitude) + m2 = (W <= ds.longitude) & (E >= ds.longitude) + block = ds.where(m1 & m2, drop=True) + + # Pull the data + z = np.squeeze(block['z'].values)[0, ...] + t = np.squeeze(block['t'].values) + q = np.squeeze(block['q'].values) + lnsp = np.squeeze(block['lnsp'].values)[0, ...] + lats = np.squeeze(block.latitude.values) + lons = np.squeeze(block.longitude.values) + + xs = lons.copy() + ys = lats.copy() + if z.size == 0: + raise RuntimeError('There is no data in z, ' + 'you may have a problem with your mask') + return lats, lons, xs, ys, t, q, lnsp, z diff --git a/tools/RAiDER/models/era5.py b/tools/RAiDER/models/era5.py old mode 100644 new mode 100755 index cd029adf2..fd8f2a099 --- a/tools/RAiDER/models/era5.py +++ b/tools/RAiDER/models/era5.py @@ -1,9 +1,10 @@ -import datetime -import numpy as np -import pyproj +import datetime + +from pyproj import CRS + +from RAiDER.models.ecmwf import ECMWF +from RAiDER.logger import logger -import util -from ecmwf import ECMWF class ERA5(ECMWF): # I took this from @@ -11,152 +12,44 @@ class ERA5(ECMWF): def __init__(self): ECMWF.__init__(self) - self._humidityType = 'rh' - self._model_level_type = 'pl' # Default, pressure levels are 'pl' + self._humidityType = 'q' self._expver = '0001' self._classname = 'ea' self._dataset = 'era5' self._Name = 'ERA-5' + self._proj = CRS.from_epsg(4326) - self._valid_range = (datetime.date(1950,1,1),) # Tuple of min/max years where data is available. - self._lag_time = datetime.timedelta(days =30) # Availability lag time in days + # Tuple of min/max years where data is available. + self._valid_range = (datetime.datetime(1950, 1, 1), "Present") + # Availability lag time in days + self._lag_time = datetime.timedelta(days=30) - self._a = [0.000000, 2.000365, 3.102241, 4.666084, 6.827977, - 9.746966, 13.605424, 18.608931, 24.985718, 32.985710, - 42.879242, 54.955463, 69.520576, 86.895882, 107.415741, - 131.425507, 159.279404, 191.338562, 227.968948, 269.539581, - 316.420746, 368.982361, 427.592499, 492.616028, 564.413452, - 643.339905, 729.744141, 823.967834, 926.344910, 1037.201172, - 1156.853638, 1285.610352, 1423.770142, 1571.622925, 1729.448975, - 1897.519287, 2076.095947, 2265.431641, 2465.770508, 2677.348145, - 2900.391357, 3135.119385, 3381.743652, 3640.468262, 3911.490479, - 4194.930664, 4490.817383, 4799.149414, 5119.895020, 5452.990723, - 5798.344727, 6156.074219, 6526.946777, 6911.870605, 7311.869141, - 7727.412109, 8159.354004, 8608.525391, 9076.400391, 9562.682617, - 10065.978516, 10584.631836, 11116.662109, 11660.067383, 12211.547852, - 12766.873047, 13324.668945, 13881.331055, 14432.139648, 14975.615234, - 15508.256836, 16026.115234, 16527.322266, 17008.789063, 17467.613281, - 17901.621094, 18308.433594, 18685.718750, 19031.289063, 19343.511719, - 19620.042969, 19859.390625, 20059.931641, 20219.664063, 20337.863281, - 20412.308594, 20442.078125, 20425.718750, 20361.816406, 20249.511719, - 20087.085938, 19874.025391, 19608.572266, 19290.226563, 18917.460938, - 18489.707031, 18006.925781, 17471.839844, 16888.687500, 16262.046875, - 15596.695313, 14898.453125, 14173.324219, 13427.769531, 12668.257813, - 11901.339844, 11133.304688, 10370.175781, 9617.515625, 8880.453125, - 8163.375000, 7470.343750, 6804.421875, 6168.531250, 5564.382813, - 4993.796875, 4457.375000, 3955.960938, 3489.234375, 3057.265625, - 2659.140625, 2294.242188, 1961.500000, 1659.476563, 1387.546875, - 1143.250000, 926.507813, 734.992188, 568.062500, 424.414063, - 302.476563, 202.484375, 122.101563, 62.781250, 22.835938, - 3.757813, 0.000000, 0.000000] + # Default, need to change to ml + self.setLevelType('pl') - self._b = [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, - 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000007, - 0.000024, 0.000059, 0.000112, 0.000199, 0.000340, 0.000562, 0.000890, - 0.001353, 0.001992, 0.002857, 0.003971, 0.005378, 0.007133, 0.009261, - 0.011806, 0.014816, 0.018318, 0.022355, 0.026964, 0.032176, 0.038026, - 0.044548, 0.051773, 0.059728, 0.068448, 0.077958, 0.088286, 0.099462, - 0.111505, 0.124448, 0.138313, 0.153125, 0.168910, 0.185689, 0.203491, - 0.222333, 0.242244, 0.263242, 0.285354, 0.308598, 0.332939, 0.358254, - 0.384363, 0.411125, 0.438391, 0.466003, 0.493800, 0.521619, 0.549301, - 0.576692, 0.603648, 0.630036, 0.655736, 0.680643, 0.704669, 0.727739, - 0.749797, 0.770798, 0.790717, 0.809536, 0.827256, 0.843881, 0.859432, - 0.873929, 0.887408, 0.899900, 0.911448, 0.922096, 0.931881, 0.940860, - 0.949064, 0.956550, 0.963352, 0.969513, 0.975078, 0.980072, 0.984542, - 0.988500, 0.991984, 0.995003, 0.997630, 1.000000] - - def fetch(self, lats, lons, time, out, Nextra = 2): + def _fetch(self, out): ''' Fetch a weather model from ECMWF ''' # bounding box plus a buffer - lat_min, lat_max, lon_min, lon_max = self._get_ll_bounds(lats, lons, Nextra) + lat_min, lat_max, lon_min, lon_max = self._ll_bounds + time = self._time # execute the search at ECMWF - self._get_from_cds( - lat_min, lat_max, self._lat_res, lon_min, lon_max, self._lon_res, time, - out) - - def load_weather(self, f): - self._load_pressure_level(f) - - def _load_pressure_level(self, filename): - from scipy.io import netcdf as nc - with nc.netcdf_file( - filename, 'r', maskandscale=True) as f: - lats = f.variables['latitude'][:].copy() - lons = f.variables['longitude'][:].copy() - t = f.variables['t'][0].copy() - q = f.variables['q'][0].copy() - r = f.variables['r'][0].copy() - z = f.variables['z'][0].copy() - levels = f.variables['level'][:].copy()*100 - - # ECMWF appears to give me this backwards - if lats[0] > lats[1]: - z = z[::-1] - t = t[:, ::-1] - q = q[:, ::-1] - r = r[:, ::-1] - lats = lats[::-1] - # Lons is usually ok, but we'll throw in a check to be safe - if lons[0] > lons[1]: - z = z[..., ::-1] - t = t[..., ::-1] - q = q[..., ::-1] - r = r[..., ::-1] - lons = lons[::-1] - # pyproj gets fussy if the latitude is wrong, plus our - # interpolator isn't clever enough to pick up on the fact that - # they are the same - lons[lons > 180] -= 360 - self._proj = pyproj.Proj(proj='latlong') - - self._t = t - self._q = q - self._rh = r - - geo_hgt = z/self._g0 - - # re-assign lons, lats to match heights - _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], - geo_hgt.shape) - _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], - geo_hgt.shape) - - # correct heights for latitude - self._get_heights(_lats, geo_hgt) - - self._p = np.broadcast_to(levels[:, np.newaxis, np.newaxis], - self._zs.shape) - - # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) - self._p = np.transpose(self._p) - self._t = np.transpose(self._t) - self._q = np.transpose(self._q) - self._rh = np.transpose(self._rh) - self._ys = np.transpose(_lats) - self._xs = np.transpose(_lons) - self._zs = np.transpose(self._zs) - - # check this - self._xs = self._xs.swapaxes(0,1) - self._ys = self._ys.swapaxes(0,1) - self._zs = self._zs.swapaxes(0,1) - self._rh = self._rh.swapaxes(0,1) - self._p = self._p.swapaxes(0,1) - self._q = self._q.swapaxes(0,1) - self._t = self._t.swapaxes(0,1) - - # For some reason z is opposite the others - self._p = np.flip(self._p, axis = 2) - self._t = np.flip(self._t, axis = 2) - self._q = np.flip(self._q, axis = 2) - self._rh = np.flip(self._rh, axis = 2) - + try: + self._get_from_cds( + lat_min, lat_max, lon_min, lon_max, time, out) + except Exception as e: + logger.warning(e) + raise RuntimeError('Could not access or download from the CDS API') + + def load_weather(self, *args, **kwargs): + '''Load either pressure or model level data''' + if self._model_level_type == 'pl': + self._load_pressure_level(*self.files, *args, **kwargs) + elif self._model_level_type == 'ml': + self._load_model_level(*self.files, *args, **kwargs) + else: + raise RuntimeError( + '{} is not a valid model type'.format(self._model_level_type) + ) diff --git a/tools/RAiDER/models/era5t.py b/tools/RAiDER/models/era5t.py new file mode 100644 index 000000000..3e1c71df9 --- /dev/null +++ b/tools/RAiDER/models/era5t.py @@ -0,0 +1,17 @@ +import datetime + +from RAiDER.models.era5 import ERA5 + + +class ERA5T(ERA5): + # I took this from + # https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels. + def __init__(self): + ERA5.__init__(self) + + self._expver = '0005' + self._dataset = 'era5t' + self._Name = 'ERA-5T' + + self._valid_range = (datetime.datetime(1950, 1, 1), "Present") # Tuple of min/max years where data is available. + self._lag_time = datetime.timedelta(days=5) # Availability lag time in days diff --git a/tools/RAiDER/models/erai.py b/tools/RAiDER/models/erai.py old mode 100644 new mode 100755 index ddc99dcd7..8d587587b --- a/tools/RAiDER/models/erai.py +++ b/tools/RAiDER/models/erai.py @@ -1,63 +1,32 @@ -import datetime -import numpy as np -import pyproj +import datetime + +from RAiDER.models.ecmwf import ECMWF +from RAiDER.models.model_levels import A_ERAI, B_ERAI -import util -from ecmwf import ECMWF class ERAI(ECMWF): # A and B parameters to calculate pressures for model levels, # extracted from an ECMWF ERA-Interim GRIB file and then hardcoded here def __init__(self): ECMWF.__init__(self) - self._model_level_type = 'ml' # Default, pressure levels are 'pl' self._classname = 'ei' + self._expver = '0001' self._dataset = 'interim' self._Name = 'ERA-I' + self.setLevelType('ml') + + # Tuple of min/max years where data is available. + self._valid_range = ( + datetime.datetime(1979, 1, 1), + datetime.datetime(2019, 8, 31) + ) + + self._lag_time = datetime.timedelta(days=30) # Availability lag time in days - self._valid_range = (datetime.date(1950,1,1),) # Tuple of min/max years where data is available. - self._lag_time = datetime.timedelta(days =30) # Availability lag time in days + def __model_levels__(self): + self._levels = 60 + self._a = A_ERAI + self._b = B_ERAI - self._a = [0.0000000000e+000, 2.0000000000e+001, 3.8425338745e+001, - 6.3647796631e+001, 9.5636962891e+001, 1.3448330688e+002, - 1.8058435059e+002, 2.3477905273e+002, 2.9849584961e+002, - 3.7397192383e+002, 4.6461816406e+002, 5.7565112305e+002, - 7.1321801758e+002, 8.8366040039e+002, 1.0948347168e+003, - 1.3564746094e+003, 1.6806403809e+003, 2.0822739258e+003, - 2.5798886719e+003, 3.1964216309e+003, 3.9602915039e+003, - 4.9067070313e+003, 6.0180195313e+003, 7.3066328125e+003, - 8.7650546875e+003, 1.0376125000e+004, 1.2077445313e+004, - 1.3775324219e+004, 1.5379804688e+004, 1.6819472656e+004, - 1.8045183594e+004, 1.9027695313e+004, 1.9755109375e+004, - 2.0222203125e+004, 2.0429863281e+004, 2.0384480469e+004, - 2.0097402344e+004, 1.9584328125e+004, 1.8864750000e+004, - 1.7961359375e+004, 1.6899468750e+004, 1.5706449219e+004, - 1.4411125000e+004, 1.3043218750e+004, 1.1632757813e+004, - 1.0209500000e+004, 8.8023554688e+003, 7.4388046875e+003, - 6.1443164063e+003, 4.9417773438e+003, 3.8509133301e+003, - 2.8876965332e+003, 2.0637797852e+003, 1.3859125977e+003, - 8.5536181641e+002, 4.6733349609e+002, 2.1039389038e+002, - 6.5889236450e+001, 7.3677425385e+000, 0.0000000000e+000, - 0.0000000000e+000] - self._b = [0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, - 7.5823496445e-005, 4.6139489859e-004, 1.8151560798e-003, - 5.0811171532e-003, 1.1142909527e-002, 2.0677875727e-002, - 3.4121163189e-002, 5.1690407097e-002, 7.3533833027e-002, - 9.9674701691e-002, 1.3002252579e-001, 1.6438430548e-001, - 2.0247590542e-001, 2.4393314123e-001, 2.8832298517e-001, - 3.3515489101e-001, 3.8389211893e-001, 4.3396294117e-001, - 4.8477154970e-001, 5.3570991755e-001, 5.8616840839e-001, - 6.3554745913e-001, 6.8326860666e-001, 7.2878581285e-001, - 7.7159661055e-001, 8.1125342846e-001, 8.4737491608e-001, - 8.7965691090e-001, 9.0788388252e-001, 9.3194031715e-001, - 9.5182150602e-001, 9.6764522791e-001, 9.7966271639e-001, - 9.8827010393e-001, 9.9401944876e-001, 9.9763011932e-001, - 1.0000000000e+000] - + def __pressure_levels__(self): + raise RuntimeError('ERA-I does not use pressure levels, you need to use model levels') diff --git a/tools/RAiDER/models/generateGACOSVRT.py b/tools/RAiDER/models/generateGACOSVRT.py index cb86a4b07..60f703354 100644 --- a/tools/RAiDER/models/generateGACOSVRT.py +++ b/tools/RAiDER/models/generateGACOSVRT.py @@ -1,16 +1,16 @@ -#!/usr/bin/env python3 # # Author: Jeremy Maurer, building on a prior code by Ray # Copyright 2018 -def makeVRT(filename, dtype = 'Float32'): + +def makeVRT(filename, dtype='Float32'): ''' Use an RSC file to create a GDAL-compatible VRT file for opening GACOS weather model files ''' fields = readRSC(filename) - string = vrtStr(fields['XMAX'],fields['YMAX'], fields['X_FIRST'],fields['Y_FIRST'], fields['X_STEP'],fields['Y_STEP'],filename.replace('.rsc', ''), dtype = dtype) + string = vrtStr(fields['XMAX'], fields['YMAX'], fields['X_FIRST'], fields['Y_FIRST'], fields['X_STEP'], fields['Y_STEP'], filename.replace('.rsc', ''), dtype=dtype) writeStringToFile(string, filename.replace('.rsc', '').replace('.ztd', '') + '.vrt') - + def writeStringToFile(string, filename): ''' @@ -18,7 +18,7 @@ def writeStringToFile(string, filename): ''' with open(filename, 'w') as f: f.write(string) - + def readRSC(rscFilename): fields = {} @@ -27,17 +27,17 @@ def readRSC(rscFilename): fieldName, value = line.strip().split() fields[fieldName] = value return fields - -def vrtStr(xSize, ySize, lon1, lat1, lonStep, latStep, filename, dtype = 'Float32'): - string = ''' + +def vrtStr(xSize, ySize, lon1, lat1, lonStep, latStep, filename, dtype='Float32'): + string = f''' EPSG:4326 {lon1}, {lonStep}, 0.0000000000000000e+00, {lat1}, 0.0000000000000000e+00, {latStep} {filename} -'''.format(xSize=xSize, ySize=ySize, filename=filename, dtype=dtype, lon1 = lon1, lat1 = lat1) +''' return string @@ -52,14 +52,14 @@ def convertAllFiles(dirLoc): makeVRT(f) -if __name__=='__main__': - import sys - if len(sys.argv) == 2: - makeVRT(sys.argv[1]) - elif len(sys.argv) == 3: - convertAllFiles(sys.argv[1]) - print('Converting all RSC files in {}'.format(sys.argv[1])) - else: - print('Usage: ') - print('python3 generateGACOSVRT.py ') - sys.exit(0) +def main(): + import sys + if len(sys.argv) == 2: + makeVRT(sys.argv[1]) + elif len(sys.argv) == 3: + convertAllFiles(sys.argv[1]) + print('Converting all RSC files in {}'.format(sys.argv[1])) + else: + print('Usage: ') + print('python3 generateGACOSVRT.py ') + sys.exit(0) diff --git a/tools/RAiDER/models/gmao.py b/tools/RAiDER/models/gmao.py new file mode 100755 index 000000000..6c981129f --- /dev/null +++ b/tools/RAiDER/models/gmao.py @@ -0,0 +1,215 @@ +import os +import datetime as dt +import numpy as np +import shutil +import h5py +import pydap.cas.urs +import pydap.client +from pyproj import CRS + +from RAiDER.models.weatherModel import WeatherModel +from RAiDER.logger import logger +from RAiDER.utilFcns import writeWeatherVars2NETCDF4, round_time, requests_retry_session +from RAiDER.models.model_levels import ( + LEVELS_137_HEIGHTS, +) + + +class GMAO(WeatherModel): + # I took this from GMAO model level weblink + # https://opendap.nccs.nasa.gov/dods/GEOS-5/fp/0.25_deg/assim/inst3_3d_asm_Nv + def __init__(self): + # initialize a weather model + WeatherModel.__init__(self) + + self._humidityType = 'q' + self._model_level_type = 'ml' # Default, pressure levels are 'pl' + + self._classname = 'gmao' + self._dataset = 'gmao' + + # Tuple of min/max years where data is available. + self._valid_range = (dt.datetime(2014, 2, 20), "Present") + self._lag_time = dt.timedelta(hours=24.0) # Availability lag time in hours + + # model constants + self._k1 = 0.776 # [K/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] + + self._time_res = 1 + + # horizontal grid spacing + self._lat_res = 0.25 + self._lon_res = 0.3125 + self._x_res = 0.3125 + self._y_res = 0.25 + + self._zlevels = np.flipud(LEVELS_137_HEIGHTS) + + self._Name = 'GMAO' + self.files = None + self._bounds = None + + # Projection + self._proj = CRS.from_epsg(4326) + + def _fetch(self, out): + ''' + Fetch weather model data from GMAO + ''' + time = self._time + + # calculate the array indices for slicing the GMAO variable arrays + lat_min_ind = int((self._ll_bounds[0] - (-90.0)) / self._lat_res) + lat_max_ind = int((self._ll_bounds[1] - (-90.0)) / self._lat_res) + lon_min_ind = int((self._ll_bounds[2] - (-180.0)) / self._lon_res) + lon_max_ind = int((self._ll_bounds[3] - (-180.0)) / self._lon_res) + + T0 = dt.datetime(2017, 12, 1, 0, 0, 0) + # round time to nearest third hour + time1 = time + time = round_time(time, 3 * 60 * 60) + if not time1 == time: + logger.warning('Rounded given hour from %d to %d', time1.hour, time.hour) + + DT = time - T0 + time_ind = int(DT.total_seconds() / 3600.0 / 3.0) + + ml_min = 0 + ml_max = 71 + if time >= T0: + # open the dataset and pull the data + url = 'https://opendap.nccs.nasa.gov/dods/GEOS-5/fp/0.25_deg/assim/inst3_3d_asm_Nv' + session = pydap.cas.urs.setup_session('username', 'password', check_url=url) + ds = pydap.client.open_url(url, session=session) + qv = ds['qv'].array[ + time_ind, + ml_min:(ml_max + 1), + lat_min_ind:(lat_max_ind + 1), + lon_min_ind:(lon_max_ind + 1) + ].data[0] + + p = ds['pl'].array[ + time_ind, + ml_min:(ml_max + 1), + lat_min_ind:(lat_max_ind + 1), + lon_min_ind:(lon_max_ind + 1) + ].data[0] + t = ds['t'].array[ + time_ind, + ml_min:(ml_max + 1), + lat_min_ind:(lat_max_ind + 1), + lon_min_ind:(lon_max_ind + 1) + ].data[0] + h = ds['h'].array[ + time_ind, + ml_min:(ml_max + 1), + lat_min_ind:(lat_max_ind + 1), + lon_min_ind:(lon_max_ind + 1) + ].data[0] + + else: + root = 'https://portal.nccs.nasa.gov/datashare/gmao/geos-fp/das/Y{}/M{:02d}/D{:02d}' + base = f'GEOS.fp.asm.inst3_3d_asm_Nv.{time.strftime("%Y%m%d")}_{time.hour:02}00.V01.nc4' + url = f'{root.format(time.year, time.month, time.day)}/{base}' + f = '{}_raw{}'.format(*os.path.splitext(out)) + if not os.path.exists(f): + logger.info('Fetching URL: %s', url) + session = requests_retry_session() + resp = session.get(url, stream=True) + assert resp.ok, f'Could not access url for time: {time}' + with open(f, 'wb') as fh: + shutil.copyfileobj(resp.raw, fh) + else: + logger.warning('Weather model already exists, skipping download') + + with h5py.File(f, 'r') as ds: + q = ds['QV'][0, :, lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)] + p = ds['PL'][0, :, lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)] + t = ds['T'][0, :, lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)] + h = ds['H'][0, :, lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)] + os.remove(f) + + lats = np.arange( + (-90 + lat_min_ind * self._lat_res), + (-90 + (lat_max_ind + 1) * self._lat_res), + self._lat_res + ) + lons = np.arange( + (-180 + lon_min_ind * self._lon_res), + (-180 + (lon_max_ind + 1) * self._lon_res), + self._lon_res + ) + + try: + # Note that lat/lon gets written twice for GMAO because they are the same as y/x + writeWeatherVars2NETCDF4(self, lats, lons, h, qv, p, t, outName=out) + except Exception: + logger.exception("Unable to save weathermodel to file") + + def load_weather(self, f=None): + ''' + Consistent class method to be implemented across all weather model types. + As a result of calling this method, all of the variables (x, y, z, p, q, + t, wet_refractivity, hydrostatic refractivity, e) should be fully + populated. + ''' + if f is None: + f = self.files[0] + self._load_model_level(f) + + def _load_model_level(self, filename): + ''' + Get the variables from the GMAO link using OpenDAP + ''' + + # adding the import here should become absolute when transition to netcdf + from netCDF4 import Dataset + with Dataset(filename, mode='r') as f: + lons = np.array(f.variables['x'][:]) + lats = np.array(f.variables['y'][:]) + h = np.array(f.variables['H'][:]) + q = np.array(f.variables['QV'][:]) + p = np.array(f.variables['PL'][:]) + t = np.array(f.variables['T'][:]) + + # restructure the 3-D lat/lon/h in regular grid + _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], t.shape) + _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], t.shape) + + # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) + p = np.transpose(p) + q = np.transpose(q) + t = np.transpose(t) + h = np.transpose(h) + _lats = np.transpose(_lats) + _lons = np.transpose(_lons) + + # check this + # data cube format should be lats,lons,heights + p = p.swapaxes(0, 1) + q = q.swapaxes(0, 1) + t = t.swapaxes(0, 1) + h = h.swapaxes(0, 1) + _lats = _lats.swapaxes(0, 1) + _lons = _lons.swapaxes(0, 1) + + # For some reason z is opposite the others + p = np.flip(p, axis=2) + q = np.flip(q, axis=2) + t = np.flip(t, axis=2) + h = np.flip(h, axis=2) + _lats = np.flip(_lats, axis=2) + _lons = np.flip(_lons, axis=2) + + # assign the regular-grid (lat/lon/h) variables + + self._p = p + self._q = q + self._t = t + self._lats = _lats + self._lons = _lons + self._xs = _lons + self._ys = _lats + self._zs = h diff --git a/tools/RAiDER/models/grib.py b/tools/RAiDER/models/grib.py deleted file mode 100644 index b9864dc0b..000000000 --- a/tools/RAiDER/models/grib.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Reader for grib files. - -If indeed there is only one kind of grib file, this one reads it and -creates an object which respects the weather model interface. -""" - - -import pygrib - - -class GribModel: - """ - Grib weather model. - Implements point_dry_delay and point_hydrostatic_delay. - """ - def __init__(self): - raise NotImplemented - - def point_dry_delay(x, y, z): - """ - Calculate dry delay at a single geocentric point. - Internally, this interpolates the grib data before performing - the calculation. - """ - pass - - def point_hydrostatic_delay(x, y, z): - """ - Calculate hydrostatic delay at a single geocentric point. - """ - pass - - -def load(filename): - #with pygrib.open(filename, 'r') as f: - pass - - -def fetch(): - pass - diff --git a/tools/RAiDER/models/hres.py b/tools/RAiDER/models/hres.py new file mode 100755 index 000000000..4dfc45dd8 --- /dev/null +++ b/tools/RAiDER/models/hres.py @@ -0,0 +1,91 @@ +import datetime + +import numpy as np + +from pyproj import CRS + +from RAiDER.models.ecmwf import ECMWF +from RAiDER.models.weatherModel import WeatherModel +from RAiDER.models.model_levels import ( + LEVELS_91_HEIGHTS, + LEVELS_25_HEIGHTS, + A_91_HRES, + B_91_HRES, +) + + +class HRES(ECMWF): + ''' + Implement ECMWF models + ''' + + def __init__(self, level_type='ml'): + # initialize a weather model + WeatherModel.__init__(self) + + # model constants + self._k1 = 0.776 # [K/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] + + self._time_res = 6 + + # 9 km horizontal grid spacing. This is only used for extending the download-buffer, i.e. not in subsequent processing. + self._lon_res = 9. / 111 # 0.08108115 + self._lat_res = 9. / 111 # 0.08108115 + self._x_res = 9. / 111 # 0.08108115 + self._y_res = 9. / 111 # 0.08108115 + + self._humidityType = 'q' + # Default, pressure levels are 'pl' + self._expver = '1' + self._classname = 'od' + self._dataset = 'hres' + self._Name = 'HRES' + self._proj = CRS.from_epsg(4326) + + # Tuple of min/max years where data is available. + self._valid_range = (datetime.datetime(1983, 4, 20), "Present") + # Availability lag time in days + self._lag_time = datetime.timedelta(hours=6) + + self.setLevelType('ml') + + def update_a_b(self): + # Before 2013-06-26, there were only 91 model levels. The mapping coefficients below are extracted + # based on https://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels + self._levels = 91 + self._zlevels = np.flipud(LEVELS_91_HEIGHTS) + self._a = A_91_HRES + self._b = B_91_HRES + + def load_weather(self, filename=None): + ''' + Consistent class method to be implemented across all weather model types. + As a result of calling this method, all of the variables (x, y, z, p, q, + t, wet_refractivity, hydrostatic refractivity, e) should be fully + populated. + ''' + if filename is None: + filename = self.files[0] + + if self._model_level_type == 'ml': + if (self._time < datetime.datetime(2013, 6, 26, 0, 0, 0)): + self.update_a_b() + self._load_model_level(filename) + elif self._model_level_type == 'pl': + self._load_pressure_levels(filename) + + def _fetch(self,out): + ''' + Fetch a weather model from ECMWF + ''' + # bounding box plus a buffer + lat_min, lat_max, lon_min, lon_max = self._ll_bounds + time = self._time + + if (time < datetime.datetime(2013, 6, 26, 0, 0, 0)): + self.update_a_b() + + # execute the search at ECMWF + self._download_ecmwf(lat_min, lat_max, self._lat_res, lon_min, lon_max, self._lon_res, time, out) diff --git a/tools/RAiDER/models/hrrr.py b/tools/RAiDER/models/hrrr.py index a332ad5c8..f9e9c252a 100644 --- a/tools/RAiDER/models/hrrr.py +++ b/tools/RAiDER/models/hrrr.py @@ -1,45 +1,58 @@ +import datetime +import logging +import os +import shutil +import requests +import xarray - -import datetime import numpy as np -import os -import pyproj -import re -import sys -import utils.util as util -from models.weatherModel import WeatherModel +from herbie import Herbie +from pathlib import Path +from pyproj import CRS, Transformer + +from RAiDER.logger import logger +from RAiDER.utilFcns import rio_profile, rio_extents +from RAiDER.models.weatherModel import WeatherModel, transform_coords +from RAiDER.models.model_levels import ( + LEVELS_137_HEIGHTS, +) -def Model(): - return HRRR() class HRRR(WeatherModel): - # I took this from - # https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels. def __init__(self): # initialize a weather model WeatherModel.__init__(self) - self._humidityType = 'rh' - self._model_level_type = 'pl' # Default, pressure levels are 'pl' + self._humidityType = 'q' + self._model_level_type = 'pl' # Default, pressure levels are 'pl' self._expver = '0001' self._classname = 'hrrr' self._dataset = 'hrrr' - self._valid_range = (datetime.date(2018,7,15),) # Tuple of min/max years where data is available. - self._lag_time = datetime.timedelta(hours=3) # Availability lag time in days + self._time_res = 1 + + # Tuple of min/max years where data is available. + self._valid_range = (datetime.datetime(2016, 7, 15), "Present") + self._lag_time = datetime.timedelta(hours=3) # Availability lag time in days # model constants: TODO: need to update/double-check these self._k1 = 0.776 # [K/Pa] - self._k2 = 0.233 # [K/Pa] - self._k3 = 3.75e3 # [K^2/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] # 3 km horizontal grid spacing - self._lon_res = 3./111 - self._lat_res = 3./111 + self._lat_res = 3. / 111 + self._lon_res = 3. / 111 + self._x_res = 3. + self._y_res = 3. self._Nproc = 1 self._Name = 'HRRR' + self._Npl = 0 + self.files = None + self._bounds = None + self._zlevels = np.flipud(LEVELS_137_HEIGHTS) # Projection # See https://github.com/blaylockbk/pyBKB_v2/blob/master/demos/HRRR_earthRelative_vs_gridRelative_winds.ipynb and code lower down @@ -50,682 +63,216 @@ def __init__(self): lat0 = 38.5 lat1 = 38.5 lat2 = 38.5 - p1 = pyproj.Proj(proj='lcc', lat_1=lat1, - lat_2=lat2, lat_0=lat0, - lon_0=lon0, a=6370, b=6370, - towgs84=(0,0,0), no_defs=True) + x0 = 0 + y0 = 0 + earth_radius = 6371229 + p1 = CRS('+proj=lcc +lat_1={lat1} +lat_2={lat2} +lat_0={lat0} +lon_0={lon0} +x_0={x0} +y_0={y0} +a={a} +b={a} +units=m +no_defs'.format(lat1=lat1, lat2=lat2, lat0=lat0, lon0=lon0, x0=x0, y0=y0, a=earth_radius)) self._proj = p1 - - - def load(self, filename = None): - if self._p is not None: - pass - else: - self.load_weather(filename = filename, download_only = False, nProc = self._Nproc) - - - def fetch(self, lats, lons, time, out, Nextra = 2): + def _fetch(self, out): ''' Fetch weather model data from HRRR ''' # bounding box plus a buffer - lat_min, lat_max, lon_min, lon_max = self._get_ll_bounds(lats, lons, Nextra) + time = self._time - # execute the search at the HRRR archive (see documentation below) - self.load_weather(dateTime = time, filename = out, download_only = False, nProc = self._Nproc) - self._restrict_model(lat_min, lat_max, lon_min, lon_max) + self.files = self._download_hrrr_file(time, out) - def load_weather(self, dateTime = None, filename = None, download_only = False, nProc = 16): + def load_weather(self, *args, filename=None, **kwargs): ''' - Consistent class method to be implemented across all weather model types. - As a result of calling this method, all of the variables (x, y, z, p, q, - t, wet_refractivity, hydrostatic refractivity, e) should be fully - populated. + Load a weather model into a python weatherModel object, from self.files if no + filename is passed. ''' - if download_only: - self._download_pressure_levels(dateTime, filename, nProc = nProc) - elif filename is not None: - #TODO: either implement a NETCDF version of this, or handle both HDF5 and NETCDF - f = filename.replace('.nc', '.hdf5') - try: - self._load_pressure_levels(filename = f) - except: - self._download_pressure_levels(dateTime, filename=f, nProc = nProc) - self._load_pressure_levels(filename = f) - else: - self._load_pressure_levels(dateTime, nProc = nProc) - - self._find_e() - self._get_wet_refractivity() - self._get_hydro_refractivity() - - # adjust the grid based on the height data - self._adjust_grid() - + if filename is None: + filename = self.files + + # read data from grib file + ds = xarray.open_dataset(filename) + pl = np.array([self._convertmb2Pa(p) for p in ds.levels.values]) + xArr = ds['x'].values + yArr = ds['y'].values + lats = ds['lats'].values + lons = ds['lons'].values + temps = ds['t'].values.transpose(1, 2, 0) + qs = ds['q'].values.transpose(1, 2, 0) + geo_hgt = ds['z'].values.transpose(1, 2, 0) - def _restrict_model(self, lat_min, lat_max, lon_min, lon_max): - ''' - Restrict the HRRR model to the region of interest (ROI). If there are no - points left in the model (because e.g. the ROI is outside the US), raise - an exception. - ''' - - # Have to do some complicated stuff to get a nice square box without doing the whole US - self._xs = self._xs.swapaxes(0,1) - self._ys = self._ys.swapaxes(0,1) - self._zs = self._zs.swapaxes(0,1) - self._rh = self._rh.swapaxes(0,1) - self._p = self._p.swapaxes(0,1) - self._t = self._t.swapaxes(0,1) - - mask1 = (self._xs[...,0] > lon_min) & (self._xs[...,0] < lon_max) - mask3 = (self._ys[...,0] > lat_min) & (self._ys[...,0] < lat_max) - mask2 = np.sum(mask1, axis = 0).astype('bool') - mask4 = np.sum(mask3, axis = 1).astype('bool') - NptsX = self._xs.shape[1] - NptsY = self._xs.shape[0] - lonRange = np.arange(0,NptsX) - latRange = np.arange(0,NptsY) - lonx = lonRange[mask2] - latx = latRange[mask4] - zx = np.arange(0, self._zs.shape[2]) - - # if there are no points left, raise exception - if np.sum(mask2) == 0 or np.sum(mask4)==0: - raise RuntimeError('Region of interest is outside the region of the HRRR weather archive data') - - # otherwise subset the data - _xs = self._xs[np.ix_(latx,lonx, zx)] - _ys = self._ys[np.ix_(latx,lonx, zx)] - _zs = self._zs[np.ix_(latx,lonx, zx)] - _rh = self._rh[np.ix_(latx,lonx, zx)] - _t = self._t[np.ix_(latx,lonx, zx)] - _p = self._p[np.ix_(latx,lonx, zx)] - -#TODO: Will need to somehow handle the non-uniform grid for interpolation -# # Finally interpolate to a regular grid (need to avoid this in future) -# xbounds = (np.min(_xs), np.max(_xs)) -# ybounds = (np.min(_ys), np.max(_ys)) -# xstep = ystep = 0.03 # ~3 km grid spacing in degrees -# _xsN, _ysN = getNewXY(xbounds, ybounds, xstep, ystep) -# self._zs = interp2DLayers(_xs, _ys, _zs,_xsN, _ysN) -# self._rh = interp2DLayers(_xs, _ys, _rh,_xsN, _ysN) -# self._t = interp2DLayers(_xs, _ys, _t, _xsN, _ysN) -# self._p = interp2DLayers(_xs, _ys, _p, _xsN, _ysN) -# -# #self._wet_refractivity = interp2DLayers(_xs, _ys, _wrf, _xsN, _ysN) -# #self._hydrostatic_refractivity = interp2DLayers(_xs, _ys, _hrf, _xsN, _ysN) -# self._xs = np.broadcast_to(_xsN[..., np.newaxis], -# self._zs.shape) -# self._ys = np.broadcast_to(_ysN[..., np.newaxis], -# self._zs.shape) - - self._zs = _zs.copy() - self._ys = _ys.copy() - self._xs = _xs.copy() - self._rh = _rh.copy() - self._t = _t.copy() - self._p = _p.copy() - self._find_e_from_rh() - self._get_wet_refractivity() - self._get_hydro_refractivity() - - - def _download_pressure_levels(self, dateTime, filename, nProc = 16): - ''' - Save data from the HRRR archive: doi: 10.7278/S5JQ0Z5B, - citation: - Blaylock B., J. Horel and S. Liston, 2017: Cloud Archiving and - Data Mining of High Resolution Rapid Refresh Model Output. - Computers and Geosciences. 109, 43-50. - doi: 10.1016/j.cageo.2017.08.005. - ''' - import h5py - - if filename is None: - filename = 'hrrr_{}.hdf5'.format(dateTime.strftime('%Y%m%d')) - - # download the 'raw' data - lats, lons, temps, rhs, z, pl = makeDataCubes(dateTime, nProc = nProc) - Npl = len(pl) Ny, Nx = lats.shape - # save the data to an HDF5 file - today = datetime.datetime.today().date() - with h5py.File(filename, 'w') as f: - f.attrs['help'] = 'Raw weather model data from HRRR for {}, accessed {}'.format(dateTime,today) - - pld = f.create_dataset('Pressure_levels', (Npl, ), 'f') - pld.attrs['help'] = 'Pressure levels' - pld[:] = np.array(pl) - - lat = f.create_dataset('lats', (Ny, Nx), 'f') - lat.attrs['help'] = 'Latitude' - lat[:] = lats - - lon = f.create_dataset('lons', (Ny, Nx), 'f') - lon.attrs['help'] = 'Longitude' - lon[:] = lons - - temp = f.create_dataset('Temperature', (Ny, Nx, Npl), 'f') - temp.attrs['help'] = 'Temperature' - temp[:] = temps - - rh = f.create_dataset('Relative_humidity', (Ny, Nx, Npl), 'f') - rh.attrs['help'] = 'Relative humidity %' - rh[:] = rhs - - zs = f.create_dataset('Geopotential_height', (Ny, Nx, Npl), 'f') - zs.attrs['help'] = 'geopotential heights' - zs[:] = z - - - def _load_pressure_levels(self, dateTime = None, filename = None, nProc = 16): - ''' - Directly load the data from the HRRR archive: doi: 10.7278/S5JQ0Z5B, - citation: - Blaylock B., J. Horel and S. Liston, 2017: Cloud Archiving and - Data Mining of High Resolution Rapid Refresh Model Output. - Computers and Geosciences. 109, 43-50. - doi: 10.1016/j.cageo.2017.08.005. - ''' - try: - lats, lons, temps, rhs, geo_hgt, pl = self._load_pressure_levels_from_file(filename) - except: - lats, lons, temps, rhs, geo_hgt, pl = makeDataCubes(dateTime, nProc = nProc) - lons[lons > 180] -= 360 - self._proj = pyproj.Proj(proj='latlong') - # data cube format should be lons, lats, heights + # data cube format should be lats,lons,heights + _xs = np.broadcast_to(xArr[np.newaxis, :, np.newaxis], + geo_hgt.shape) + _ys = np.broadcast_to(yArr[:, np.newaxis, np.newaxis], + geo_hgt.shape) _lons = np.broadcast_to(lons[..., np.newaxis], - geo_hgt.shape) + geo_hgt.shape) _lats = np.broadcast_to(lats[..., np.newaxis], - geo_hgt.shape) + geo_hgt.shape) # correct for latitude self._get_heights(_lats, geo_hgt) self._t = temps - self._rh = rhs - + self._q = qs self._p = np.broadcast_to(pl[np.newaxis, np.newaxis, :], - self._zs.shape) - self._xs = _lons - self._ys = _lats - - # flip stuff around to match convention - self._p = np.flip(self._p, axis = 2) - self._t = np.flip(self._t, axis = 2) - self._zs = np.flip(self._zs, axis = 2) - self._rh = np.flip(self._rh, axis = 2) + geo_hgt.shape) + self._xs = _xs + self._ys = _ys + self._lats = _lats + self._lons = _lons - def _load_pressure_levels_from_file(self, fileName): + def _makeDataCubes(self, filename, out=None): ''' - Load pre-downloaded weather model from an HDF5 file + Create a cube of data representing temperature and relative humidity + at specified pressure levels ''' - import h5py - - # load the data - with h5py.File(fileName, 'r') as f: - pl = f['Pressure_levels'].value.copy() - z = f['Geopotential_height'].value.copy() - lats = f['lats'].value.copy() - lons = f['lons'].value.copy() - rh = f['Relative_humidity'].value.copy() - t = f['Temperature'].value.copy() - - return lats, lons, t, rh, z, pl - - -def makeDataCubes(dateTime = None, nProc = 16): - ''' - Create a cube of data representing temperature and relative humidity - at specified pressure levels - ''' - pl = getPresLevels() - - tempList = getTemps(dateTime, pl['Values'], nProc= nProc) - rhList = getRH(dateTime, pl['Values'], nProc= nProc) - zList = getZ(dateTime, pl['Values'], nProc= nProc) - - pl = [convertmb2Pa(p) for p in pl['Values']] + if out is None: + out = filename - try: - temps = stackList(tempList) - except: - raise RuntimeError('makeDataCubes: Something likely went wrong with the file download') - - rhs = stackList(rhList) - zs = stackList(zList) - - lats, lons = getLatLonsFromList(zList) - - return lats.T, lons.T, temps.swapaxes(0,1), rhs.swapaxes(0,1), zs.swapaxes(0,1), pl - - -def convertmb2Pa(pres): - return 100*pres - -def getLatLonsFromList(List): - return List[0]['lat'], List[0]['lon'] - -def stackList(List): - ''' - Take an input list of variable dictionaries and stack the data into a cube - ''' - dataList = [] - for d in List: - dataList.append(d['value']) - return np.stack(dataList, axis = 2) - - -def getPresLevels(): - presList = [int(v) for v in range(50, 1025, 25)] - presList.append(1013.2) - outDict = {'Values': presList, 'units': 'mb', 'Name': 'Pressure_levels'} - return outDict - - -def getTemps(dateTime = None, presLevels = None, nProc = 16): - dateTime = checkDateTime(dateTime) - varNames = ['TMP:{} mb'.format(val) for val in presLevels] - List = parfetch(dateTime, varNames, numProc = nProc) - return List - - -def getRH(dateTime = None, presLevels = None, nProc = 16): - dateTime = checkDateTime(dateTime) - varNames = ['RH:{} mb'.format(val) for val in presLevels] - List = parfetch(dateTime, varNames, numProc = nProc) - return List - - -def getZ(dateTime = None, presLevels = None, nProc = 16): - dateTime = checkDateTime(dateTime) - varNames = ['HGT:{} mb'.format(val) for val in presLevels] - List = parfetch(dateTime, varNames, numProc = nProc) - return List - - -def worker(tup): - ''' - Helper function for the parallel processing function parfetch - ''' - dateTime, var, fieldName = tup - res = get_hrrr_variable(dateTime, var, fxx=0, field=fieldName, model='hrrr') - return res - - -def parfetch(dateTime, varNames, fieldName = 'prs', numProc = 1): - import multiprocessing as mp - - tupList = [(dateTime, var, fieldName) for var in varNames] - if numProc > 1: - pool = mp.Pool() - individual_results = pool.map(worker, tupList) - pool.close() - pool.join() - else: - individual_results = [] - for tup in tupList: - print(('Currently fetching variable {}'.format(tup[1]))) - individual_results.append(worker(tup)) - - return individual_results - - -def checkDateTime(dateTime): - if dateTime is None: - dateTime = datetime.datetime(2016, 12, 5, 6) - return dateTime - - -# the following function is modified from the repository at -# https://github.com/blaylockbk/pyBKB_v2/tree/master/BB_downloads -def get_hrrr_variable(DATE, variable, - fxx=0, - model='hrrr', - field='sfc', - removeFile=False, - value_only=False, - verbose=True, - outDIR='./'): - """ - Uses cURL to grab the requested variable from a HRRR grib2 file in the - HRRR archive. Uses the the requested variable string to search the .idx - file and determine the byte range. When the byte range of a variable is - known, cURL is capable of downloading a single variable from a larger GRIB2 - file. This function packages the data in a dictionary. - Input: - DATE - The datetime(year, month, day, hour) for the HRRR file you - want. This is the same as the model run time, in UTC. - variable - A string describing the variable you are looking for in the - GRIB2 file. Refer to the .idx files. For example: - https://pando-rgw01.chpc.utah.edu/hrrr/sfc/20180101/hrrr.t00z.wrfsfcf00.grib2.idx - You want to put the variable short name and the level - information. For example, for 2m temperature: - - variable='TMP:2 m above ground' - fxx - The forecast hour you desire. Default is the analysis hour, - or f00. - model - The model you want. Options include ['hrrr', 'hrrrX', 'hrrrak'] - field - The file output type. Options include ['sfc', 'prs'] - removeFile - True: remove the GRIB2 file after it is downloaded - False: do not remove the GRIB2 file after it is downloaded - value_only - True: only return the values, not the lat/lon. - Returns output in 0.2 seconds - False: returns value and lat/lon, grib message, analysis and valid datetime. - Returns output in 0.75-1 seconds - verbose - Prints some diagnostics - outDIR - Specify where the downloaded data should be downloaded. - Default is the current directory. - Tips: - 1. The DATE you request represents the model run time. If you want to - retrieve the file based on the model's valid time, you need to - offset the DATE with the forecast lead time. For example: - VALID_DATE = datetime(year, month, day, hour) # We want the model data valid at this time - fxx = 15 # Forecast lead time - RUN_DATE = VALID_DATE-timedelta(hours=fxx) # The model run datetime that produced the data - zet_hrrr_variable(RUN_DATE, 'TMP:2 m', fxx=fxx) # The returned data will be a forecast for the requested valid time and lead time + # Get profile information from gdal + prof = rio_profile(str(filename)) - 2. You can request both U and V components at a level by using - variable='UVGRD:10 m' - This special request will return the U and V component winds - converted from grid-relative to earth-relative, as well as the - calculated wind speed. - Note: You can still get the grid-relative winds by requesting both - 'UGRD:10 m' and 'VGRD:10 m' individually. - """ - import pygrib - import urllib.request, urllib.error, urllib.parse - import ssl - - ## --- Catch Errors ------------------------------------------------------- - # Check that you requested the right model name and field name - if model not in ['hrrr', 'hrrrX', 'hrrrak']: - raise ValueError("Requested model must be 'hrrr', 'hrrrX', or 'hrrrak'") - if field not in ['prs', 'sfc']: - raise ValueError("Requested field must be 'prs' or 'sfc'. We do not store other fields in the archive") - - # Check that you requested the right forecasts available for the model - if model == 'hrrr' and fxx not in list(range(19)): - raise ValueError("HRRR: fxx must be between 0 and 18\nYou requested f%02d" % fxx) - elif model == 'hrrrX' and fxx != 0: - raise ValueError("HRRRx: fxx must be 0. We do not store other forecasts in the archive.\nYou requested f%02d" % fxx) - elif model == 'hrrrak' and fxx not in list(range(37)): - raise ValueError("HRRRak: fxx must be between 0 and 37\nYou requested f%02d" % fxx) - - # Check that the requested hour exists for the model - if model == 'hrrrak' and DATE.hour not in list(range(0,24,3)): - raise ValueError("HRRRak: DATE.hour must be 0, 3, 6, 9, 12, 15, 18, or 21\nYou requested %s" % DATE.hour) - - if verbose: - # Check that the request datetime has happened - if DATE > datetime.datetime.utcnow(): - print("Warning: The datetime you requested hasn't happened yet\nDATE: %s F%02d\n UTC: %s" % (DATE, fxx, datetime.datetime.utcnow())) - ## ---(Catch Errors)------------------------------------------------------- - - - ## --- Set Temporary File Name -------------------------------------------- - # Temporary file name has to be unique, or else when we use multiprocessing - # we might accidentally delete files before we are done with them. - outfile = '%stemp_%s_%s_f%02d_%s.grib2' % (outDIR, model, DATE.strftime('%Y%m%d%H'), fxx, variable[:3].replace(":", '')) - - if verbose is True: - print(' >> Dowloading tempfile: %s' % outfile) - - ## --- Requested Variable ------------------------------------------------- - # A special variable request is 'UVGRD:[level]' which will get both the U - # and V wind components converted to earth-relative direction in a single - # download. Since UGRD always proceeds VGRD, we will set the get_variable - # as UGRD. Else, set get_variable as variable. - if variable.split(':')[0] == 'UVGRD': - # We need both U and V to convert winds from grid-relative to earth-relative - get_variable = 'UGRD:' + variable.split(':')[1] - else: - get_variable = variable - - - ## --- Set Data Source ---------------------------------------------------- - """ - Dear User, - Only HRRR files are only downloaded and added to Pando every 3 hours. - That means if you are requesting data for today that hasn't been copied - to Pando yet, you will need to get it from the NOMADS website instead. - But good news! It's an easy fix. All we need to do is redirect you to the - NOMADS server. I'll check that the date you are requesting is not for - today's date. If it is, then I'll send you to NOMADS. Deal? :) - -Sincerely, Brian - """ - - # If the datetime requested is less than six hours ago, then the file is - # most likely on Pando. Else, download from NOMADS. - #if DATE+timedelta(hours=fxx) < datetime.utcnow()-timedelta(hours=6): - if DATE < datetime.datetime.utcnow()-datetime.timedelta(hours=12): - # Get HRRR from Pando - if verbose: - print("Oh, good, you requested a date that should be on Pando.") - grib2file = 'https://pando-rgw01.chpc.utah.edu/%s/%s/%s/%s.t%02dz.wrf%sf%02d.grib2' \ - % (model, field, DATE.strftime('%Y%m%d'), model, DATE.hour, field, fxx) - fileidx = grib2file+'.idx' - import pdb; pdb.set_trace() - else: - # Get operational HRRR from NOMADS - if model == 'hrrr': - if verbose: - print("/n---------------------------------------------------------------------------") - - print("!! Hey! You are requesting a date that is not on the Pando archive yet. !!") - print("!! That's ok, I'll redirect you to the NOMADS server. :) !!") - print("---------------------------------------------------------------------------\n") - #grib2file = 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%s/%s.t%02dz.wrf%sf%02d.grib2' \ - # % (DATE.strftime('%Y%m%d'), model, DATE.hour, field, fxx) - grib2file = 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%s/conus/hrrr.t%02dz.wrf%sf%02d.grib2' \ - % (DATE.strftime('%Y%m%d'), DATE.hour, field, fxx) - fileidx = grib2file+'.idx' - elif model == 'hrrrX': - print("\n-------------------------------------------------------------------------") - print("!! Sorry, I haven't download that Experimental HRRR run from ESRL yet !!") - print("!! Try again in a few hours. !!") - print("/n---------------------------------------------------------------------------") - print("!! Hey! You are requesting a date that is not on the Pando archive yet. !!") - print("!! That's ok, I'll redirect you to the PARALLEL NOMADS server. :) !!") - print("---------------------------------------------------------------------------\n") - grib2file = 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%s/alaska/hrrr.t%02dz.wrf%sf%02d.ak.grib2' \ - % (DATE.strftime('%Y%m%d'), DATE.hour, field, fxx) - fileidx = grib2file+'.idx' - - if verbose: - print('GRIB2 File: %s' % grib2file) - print(' .idx File: %s' % fileidx) - print("") - - - ## --- Download Requested Variable ---------------------------------------- - try: - ## 0) Read the grib2.idx file - try: - # ?? Ignore ssl certificate (else urllib2.openurl wont work). - # Depends on your version of python. - # See here: - # http://stackoverflow.com/questions/19268548/python-ignore-certicate-validation-urllib2 - ctx = ssl.create_default_context() - - ctx.check_hostname = False - ctx.verify_mode = ssl.CERT_NONE - idxpage = urllib.request.urlopen(fileidx, context=ctx) - except: - idxpage = urllib.request.urlopen(fileidx) - - lines = [t.decode('utf-8') for t in idxpage.readlines()] - - ## 1) Find the byte range for the requested variable. First find where - # in the .idx file the variable is located. We need the byte number - # The variable begins on. Keep a count (gcnt) of the line number so - # we can also get the beginning byte of the next variable. This is - # our byte range. - gcnt = 0 - for g in lines: - expr = re.compile(get_variable) - if expr.search(g): - if verbose is True: - print(' >> Matched a variable: ', g) - parts = g.split(':') - rangestart = parts[1] - if variable.split(':')[0] == 'UVGRD': - parts = lines[gcnt+2].split(':') # Grab range between U and V variables - else: - parts = lines[gcnt+1].split(':') # Grab range for requested variable only - rangeend = int(parts[1])-1 - if verbose is True: - print(' >> Byte Range:', rangestart, rangeend) - byte_range = str(rangestart) + '-' + str(rangeend) - gcnt += 1 - ## 2) When the byte range is discovered, use cURL to download the file. - os.system('curl -s -o %s --range %s %s' % (outfile, byte_range, grib2file)) - - - ## --- Convert winds to earth-relative -------------------------------- - # If the requested variable is 'UVGRD:[level]', then we have to change - # the wind direction from grid-relative to earth-relative. - # You can still get the grid-relative winds by requesting 'UGRD:[level]' - # and # 'VGRD:[level] independently. - # and # 'VGRD:[level] independently. - # !!! See more information on why/how to do this here: - # https://github.com/blaylockbk/pyBKB_v2/blob/master/demos/HRRR_earthRelative_vs_gridRelative_winds.ipynb - #from wind import wind_uv_to_spd - if variable.split(':')[0] == 'UVGRD': - if verbose: - print(' >> Converting winds to earth-relative') - wgrib2 = '/uufs/chpc.utah.edu/sys/installdir/wgrib2/2.0.2/wgrib2/wgrib2' - if model == 'hrrrak': - regrid = 'nps:225.000000:60.000000 185.117126:1299:3000.000000 41.612949:919:3000.000000' - if model == 'hrrr' or model == 'hrrrX': - regrid = 'lambert:262.500000:38.500000:38.500000:38.500000 237.280472:1799:3000.000000 21.138123:1059:3000.000000' - os.system('%s %s -new_grid_winds earth -new_grid %s %s.earth' % (wgrib2, outfile, regrid, outfile)) - os.system('rm -f %s' % outfile) # remove the original file - outfile = outfile+'.earth' # assign the `outfile`` as the regridded file - - - ## 3) Get data from the file, using pygrib and return what we want to use - grbs = pygrib.open(outfile) - - # Note: Returning only the variable value is a bit faster than returning - # the variable value with the lat/lon and other details. You can - # specify this when you call the function. - if value_only: - if variable.split(':')[0] == 'UVGRD': - return_this = {'UGRD': grbs[1].values, - 'VGRD': grbs[2].values, - 'SPEED': wind_uv_to_spd(grbs[1].values, grbs[2].values)} - else: - return_this = {'value': grbs[1].values} - if removeFile: - os.system('rm -f %s' % (outfile)) - return return_this - else: - if variable.split(':')[0] == 'UVGRD': - value1, lat, lon = grbs[1].data() - if model == 'hrrrak': - lon[lon>0] -= 360 - return_this = {'UGRD': value1, - 'VGRD': grbs[2].values, - 'SPEED': wind_uv_to_spd(value1, grbs[2].values), - 'lat': lat, - 'lon': lon, - 'valid': grbs[1].validDate, - 'anlys': grbs[1].analDate, - 'msg': [str(grbs[1]), str(grbs[2])], - 'name': [grbs[1].name, grbs[2].name], - 'units': [grbs[1].units, grbs[2].units], - 'level': [grbs[1].level, grbs[2].level], - 'URL': grib2file} - else: - value, lat, lon = grbs[1].data() - if model == 'hrrrak': - lon[lon>0] -= 360 - return_this = {'value': value, - 'lat': lat, - 'lon': lon, - 'valid': grbs[1].validDate, - 'anlys': grbs[1].analDate, - 'msg': str(grbs[1]), - 'name': grbs[1].name, - 'units': grbs[1].units, - 'level': grbs[1].level, - 'URL': grib2file} - if removeFile: - os.system('rm -f %s' % (outfile)) - - return return_this - - - except: - if verbose: - print(" _______________________________________________________________") - print(" !! Run Date Requested :", DATE, "F%02d" % fxx) - print(" !! Valid Date Requested :", DATE+datetime.timedelta(hours=fxx)) - print(" !! Valid Date Requested :", DATE+datetime.timedelta(hours=fxx)) - print(" !! Current UTC time :", datetime.datetime.utcnow()) - print(" !! ------------------------------------------------------------") - print(" !! ERROR downloading GRIB2:", grib2file) - print(" !! Is the variable right?", variable) - print(" !! Does the .idx file exist?", fileidx) - print(" ---------------------------------------------------------------") - return {'value' : np.nan, - 'lat' : np.nan, - 'lon' : np.nan, - 'valid' : np.nan, - 'anlys' : np.nan, - 'msg' : np.nan, - 'URL': grib2file} - - -def interp2D(tup): - from scipy.interpolate import griddata - - xs, ys, values, xnew, ynew = tup - new = griddata((xs, ys),values, (xnew, ynew), method = 'linear') - return new - -def interp2DLayers(xs, ys, values, xnew, ynew): - ''' - Implement a 2D interpolator to transform the non-uniform - HRRR grid to a uniform lat-long grid. This should be updated - in future to be avoided. - ''' - print('Interpolating to a uniform grid in 2D') - import multiprocessing as mp - - # set up the parallel interpolation - tupList = [] - for layerNum, layer in enumerate(np.moveaxis(values, 2, 0)): - tup = (xs[..., layerNum].flatten(), ys[..., layerNum].flatten(), - layer.flatten(),xnew, ynew) - tupList.append(tup) - - pool = mp.Pool(12) - newLayers = pool.map(interp2D, tupList) - # newLayers = [] - # for tup in tupList: - # newLayers.append(interp2D(tup)) - newVals = np.stack(newLayers, axis =2) - - return newVals - - -def getNewXY(xbounds, ybounds, xstep, ystep): - ''' - Get new uniform X,Y values from the bounds - ''' - xnew = np.arange(xbounds[0], xbounds[1], xstep) - ynew = np.arange(ybounds[0], ybounds[1], ystep) - [X, Y] = np.meshgrid(xnew, ynew) - - return X, Y - - + # Now get bounds + S, N, W, E = self._ll_bounds + + # open the dataset and pull the data + ds = xarray.open_dataset(filename, engine='cfgrib', filter_by_keys={'typeOfLevel': 'isobaricInhPa'}) + + # Determine mask based on query bounds + lats = ds["latitude"].to_numpy() + lons = ds["longitude"].to_numpy() + levels = ds["isobaricInhPa"].to_numpy() + shp = lats.shape + lons[lons > 180.0] -= 360. + m1 = (S <= lats) & (N >= lats) &\ + (W <= lons) & (E >= lons) + + if np.sum(m1) == 0: + raise RuntimeError('Area of Interest has no overlap with the HRRR model available extent') + + # Y extent + m1_y = np.argwhere(np.sum(m1, axis=1) != 0) + y_min = max(m1_y[0][0] - 2, 0) + y_max = min(m1_y[-1][0] + 3, shp[0]) + m1_y = None + + # X extent + m1_x = np.argwhere(np.sum(m1, axis=0) != 0) + x_min = max(m1_x[0][0] - 2, 0) + x_max = min(m1_x[-1][0] + 3, shp[1]) + m1_x = None + m1 = None + + # Coordinate arrays + # HRRR GRIB has data in south-up format + trans = prof["transform"].to_gdal() + xArr = trans[0] + (np.arange(x_min, x_max) + 0.5) * trans[1] + yArr = trans[3] + (prof["height"] * trans[5]) - (np.arange(y_min, y_max) + 0.5) * trans[5] + lats = lats[y_min:y_max, x_min:x_max] + lons = lons[y_min:y_max, x_min:x_max] + + # TODO: Remove this block once we know we handle + # different flavors of HRRR correctly + # self._proj currently used but these are available + # as attrs of ds["t"] for example + # llhtolcc = Transformer.from_crs(4326, self._proj) + # res = llhtolcc.transform(lats, lons) + # print("ERROR in X: ", np.abs(res[0] - xArr[None, :]).max()) + # print("ERROR in Y: ", np.abs(res[1] - yArr[:, None]).max()) + + # Data variables + t = ds['t'][:, y_min:y_max, x_min:x_max].to_numpy() + z = ds['gh'][:, y_min:y_max, x_min:x_max].to_numpy() + q = ds['q'][:, y_min:y_max, x_min:x_max].to_numpy() + ds.close() + + # This section is purely for flipping arrays as needed + # to match ECMWF reader is doing + # All flips are views - no extra memory use + # Lon -> From west to east + # Lat -> From south to north (ECMWF reads north to south and flips it + # load_weather) - So we do south to north here + # Pres -> High to Los - (ECWMF does now to high and flips it back) - so + # we do high to low + # Data is currently in [levels, y, x] order + flip_axes = [] + if levels[-1] > levels[0]: + flip_axes.append(0) + levels = np.flip(levels) + + if lats[0, 0] > lats[-1, 0]: + flip_axes.append(1) + lats = np.flip(lats, 0) + yArr = np.flip(yArr) + + if lons[0, 0] > lons[0, -1]: + flip_axes.append(2) + lons = np.flip(lons, 1) + xArr = np.flip(xArr) + + flip_axes = tuple(flip_axes) + if flip_axes: + t = np.flip(t, flip_axes) + z = np.flip(z, flip_axes) + q = np.flip(q, flip_axes) + + # Create output dataset + ds_new = xarray.Dataset( + data_vars=dict( + t= (["level", "y", "x"], t, + {"grid_mapping": "proj"}), + z= (["level", "y", "x"], z, + {"grid_mapping": "proj"}), + q= (["level", "y", "x"], q, + {"grid_mapping": "proj"}), + lats=(["y", "x"], lats), + lons=(["y", "x"], lons), + ), + coords=dict( + levels=(["level"], levels, + {"units": "millibars", + "long_name": "pressure_level", + "axis": "Z"}), + x=(["x"], xArr, + {"standard_name": "projection_x_coordinate", + "units": "m", + "axis": "X"}), + y=(["y"], yArr, + {"standard_name": "projection_y_coordinate", + "units": "m", + "axis": "Y"}), + ), + attrs={ + 'Conventions': 'CF-1.7', + 'Weather_model':'HRRR', + } + ) + + # Write projection of output + ds_new["proj"] = int() + for k, v in self._proj.to_cf().items(): + ds_new.proj.attrs[k] = v + + ds_new.to_netcdf(out) + + def _download_hrrr_file(self, DATE, out, model='hrrr', product='prs', fxx=0, verbose=False): + ''' + Download a HRRR model + ''' + H = Herbie( + DATE.strftime('%Y-%m-%d %H:%M'), + model=model, + product=product, + overwrite=False, + verbose=True, + save_dir=Path(os.path.dirname(out)), + ) + pf = H.download(":(SPFH|PRES|TMP|HGT):", verbose=verbose) + + self._makeDataCubes(pf, out) + + return out diff --git a/tools/RAiDER/models/merra2.py b/tools/RAiDER/models/merra2.py old mode 100644 new mode 100755 index a009508d9..c1b753b77 --- a/tools/RAiDER/models/merra2.py +++ b/tools/RAiDER/models/merra2.py @@ -1,100 +1,212 @@ -from models.weatherModel import WeatherModel +import os +import datetime as dt +import numpy as np +import pydap.cas.urs +import pydap.client + +from pyproj import CRS + +from RAiDER.models.weatherModel import WeatherModel +from RAiDER.logger import logger +from RAiDER.utilFcns import writeWeatherVars2NETCDF4, read_EarthData_loginInfo +from RAiDER.models.model_levels import ( + LEVELS_137_HEIGHTS, +) + + +# Path to Netrc file, can be controlled by env var +# Useful for containers - similar to CDSAPI_RC +EARTHDATA_RC = os.environ.get("EARTHDATA_RC", None) + + +def Model(): + return MERRA2() + class MERRA2(WeatherModel): - """Reader for MERRA-2 model. - - Only supports pressure levels right now, that may change someday. - """ - import urllib.request - import json - - _k1 = None - _k2 = None - _k3 = None - - _Name = 'MERRA-2' - - def fetch(self, lats, lons, time, out): - """Fetch MERRA-2.""" - # TODO: This function doesn't work right now. I'm getting a 302 - # Found response, with a message The document has moved. That's - # annoying, it'd be nice if pydap would just follow to the new - # page. I don't have time to debug this now, so I'll just drop - # this comment here. - import pydap.client - import pydap.cas.urs - import json - from scipy.io import netcdf as nc - - lat_min, lat_max, lon_min, lon_max = self._get_ll_bounds() - - url = ('https://goldsmr5.gesdisc.eosdis.nasa.gov:443/'\ - 'opendap/MERRA2/M2I6NPANA.5.12.4/1980/01/'\ - 'MERRA2_100.inst6_3d_ana_Np.19800101.nc4') - config = os.path.expandvars(os.path.join('$HOME', '.urs-auth')) - with open(config, 'r') as f: - j = json.load(f) - username = j['username'] - password = j['password'] - - session = pydap.cas.urs.setup_session( - username, password, check_url=url) - # TODO: probably use a context manager so it gets closed - dataset = pydap.client.open_url(url, session=session) - - # TODO: gotta figure out how to index as time - timeNum = 0 - - with nc.netcdf_file(out, 'w') as f: - def index(ds): - return ds[timeNum][:][lat_min:lat_max][lon_min:lon_max] - t = f.createVariable('T', float, []) - t[:] = index(dataset['T']) - lats = f.createVariable('lat', float, []) - lats[:] = dataset['lat'][lat_min:lat_max] - lons = f.createVariable('lon', float, []) - lons[:] = dataset['lon'][lon_min:lon_max] - q = f.createVariable('QV', float, []) - q[:] = index(dataset['QV']) - z = f.createVariable('H', float, []) - z[:] = index(dataset['H']) - p = f.createVariable('lev', float, []) - p[:] = dataset['lev'][:] - - def load_pressure_level(self, filename): - from scipy.io import netcdf as nc - with nc.netcdf_file( - filename, 'r', maskandscale=True) as f: - lats = f.variables['lat'][:].copy() - lons = f.variables['lon'][:].copy() - t = f.variables['T'][0].copy() - q = f.variables['QV'][0].copy() - z = f.variables['H'][0].copy() - p = f.variables['lev'][0].copy() - proj = pyproj.Proj('lla') - return lats, lons, proj, t, q, z, p - - def weather_and_nodes(self, filename): - return self.weather_and_nodes_from_pressure_levels(filename) - - def weather_and_nodes_from_pressure_levels(self, filename): - pass - - def _url_builder(self, time, lat_min, lat_step, lat_max, lon_min, lon_step, lon_max): - if lon_max < 0: - lon_max += 360 - if lon_min < 0: - lon_min += 360 - if lon_min > lon_max: - lon_max, lon_min = lon_min, lon_max - timeStr = '[0]' # TODO: change - latStr = '[{}:{}:{}]'.format(lat_min, lat_step, lat_max) - lonStr = '[{}:{}:{}]'.format(lon_min,lon_step, lon_max) # TODO: wrap - lvs = '[0:1:41]' - combined = '{}{}{}{}'.format(timeStr, lvs, latStr, lonStr) - return ('https://goldsmr5.gesdisc.eosdis.nasa.gov:443/opendap/MERRA2/' + - 'M2I6NPANA.5.12.4/{}/'.format(time.strftime("%Y/%m")) + - 'MERRA2_100.inst6_3d_ana_Np.{}.nc4.nc?'.format(time.strftime("%Y%m%d")) + - 'T{},H{},lat{},lev{},lon{},'.format(combined, combined, latStr, lvs, lonStr) + - 'time{}'.format(timeStr)) + # I took this from MERRA-2 model level weblink + # https://goldsmr5.gesdisc.eosdis.nasa.gov:443/opendap/MERRA2/M2I3NVASM.5.12.4/ + def __init__(self): + + import calendar + + # initialize a weather model + WeatherModel.__init__(self) + + self._humidityType = 'q' + self._model_level_type = 'ml' # Default, pressure levels are 'pl' + + self._classname = 'merra2' + self._dataset = 'merra2' + + # Tuple of min/max years where data is available. + utcnow = dt.datetime.utcnow() + enddate = dt.datetime(utcnow.year, utcnow.month, 15) - dt.timedelta(days=60) + enddate = dt.datetime(enddate.year, enddate.month, calendar.monthrange(enddate.year, enddate.month)[1]) + self._valid_range = (dt.datetime(1980, 1, 1), "Present") + lag_time = utcnow - enddate + self._lag_time = dt.timedelta(days=lag_time.days) # Availability lag time in days + self._time_res = 1 + + # model constants + self._k1 = 0.776 # [K/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] + + # horizontal grid spacing + self._lat_res = 0.5 + self._lon_res = 0.625 + self._x_res = 0.625 + self._y_res = 0.5 + + self._Name = 'MERRA2' + self.files = None + self._bounds = None + self._zlevels = np.flipud(LEVELS_137_HEIGHTS) + + # Projection + self._proj = CRS.from_epsg(4326) + + def _fetch(self, out): + ''' + Fetch weather model data from GMAO: note we only extract the lat/lon bounds for this weather model; fetching data is not needed here as we don't actually download any data using OpenDAP + ''' + time = self._time + + # check whether the file already exists + if os.path.exists(out): + return + + # calculate the array indices for slicing the GMAO variable arrays + lat_min_ind = int((self._ll_bounds[0] - (-90.0)) / self._lat_res) + lat_max_ind = int((self._ll_bounds[1] - (-90.0)) / self._lat_res) + lon_min_ind = int((self._ll_bounds[2] - (-180.0)) / self._lon_res) + lon_max_ind = int((self._ll_bounds[3] - (-180.0)) / self._lon_res) + + lats = np.arange( + (-90 + lat_min_ind * self._lat_res), + (-90 + (lat_max_ind + 1) * self._lat_res), + self._lat_res + ) + lons = np.arange( + (-180 + lon_min_ind * self._lon_res), + (-180 + (lon_max_ind + 1) * self._lon_res), + self._lon_res + ) + + if time.year < 1992: + url_sub = 100 + elif time.year < 2001: + url_sub = 200 + elif time.year < 2011: + url_sub = 300 + else: + url_sub = 400 + + T0 = dt.datetime(time.year, time.month, time.day, 0, 0, 0) + DT = time - T0 + time_ind = int(DT.total_seconds() / 3600.0 / 3.0) + + ml_min = 0 + ml_max = 71 + + # Earthdata credentials + earthdata_usr, earthdata_pwd = read_EarthData_loginInfo(EARTHDATA_RC) + + # open the dataset and pull the data + url = 'https://goldsmr5.gesdisc.eosdis.nasa.gov:443/opendap/MERRA2/M2I3NVASM.5.12.4/' + time.strftime('%Y/%m') + '/MERRA2_' + str(url_sub) + '.inst3_3d_asm_Nv.' + time.strftime('%Y%m%d') + '.nc4' + session = pydap.cas.urs.setup_session(earthdata_usr, earthdata_pwd, check_url=url) + ds = pydap.client.open_url(url, session=session) + + ############# The MERRA-2 server changes the pydap data retrieval format frequently between these two formats; so better to retain both of them rather than only using either one of them ############# + try: + q = ds['QV'].data[0][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + p = ds['PL'].data[0][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + t = ds['T'].data[0][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + h = ds['H'].data[0][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + except IndexError: + q = ds['QV'].data[time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + p = ds['PL'].data[time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + t = ds['T'].data[time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + h = ds['H'].data[time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + except AttributeError: + q = ds['QV'][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + p = ds['PL'][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + t = ds['T'][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + h = ds['H'][time_ind, ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)][0] + except BaseException: + logger.exception("MERRA-2: Unable to read weathermodel data") + ######################################################################################################################## + + try: + writeWeatherVars2NETCDF4(self, lats, lons, h, q, p, t, outName=out) + except Exception as e: + logger.debug(e) + logger.exception("MERRA-2: Unable to save weathermodel to file") + raise RuntimeError('MERRA-2 failed with the following error: {}'.format(e)) + + def load_weather(self, *args, f=None, **kwargs): + ''' + Consistent class method to be implemented across all weather model types. + As a result of calling this method, all of the variables (x, y, z, p, q, + t, wet_refractivity, hydrostatic refractivity, e) should be fully + populated. + ''' + if f is None: + f = self.files[0] + self._load_model_level(f) + + def _load_model_level(self, filename): + ''' + Get the variables from the GMAO link using OpenDAP + ''' + + # adding the import here should become absolute when transition to netcdf + from netCDF4 import Dataset + with Dataset(filename, mode='r') as f: + lons = np.array(f.variables['x'][:]) + lats = np.array(f.variables['y'][:]) + h = np.array(f.variables['H'][:]) + q = np.array(f.variables['QV'][:]) + p = np.array(f.variables['PL'][:]) + t = np.array(f.variables['T'][:]) + + # restructure the 3-D lat/lon/h in regular grid + _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], t.shape) + _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], t.shape) + + # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) + p = np.transpose(p) + q = np.transpose(q) + t = np.transpose(t) + h = np.transpose(h) + _lats = np.transpose(_lats) + _lons = np.transpose(_lons) + + # check this + # data cube format should be lats,lons,heights + p = p.swapaxes(0, 1) + q = q.swapaxes(0, 1) + t = t.swapaxes(0, 1) + h = h.swapaxes(0, 1) + _lats = _lats.swapaxes(0, 1) + _lons = _lons.swapaxes(0, 1) + + # For some reason z is opposite the others + p = np.flip(p, axis=2) + q = np.flip(q, axis=2) + t = np.flip(t, axis=2) + h = np.flip(h, axis=2) + _lats = np.flip(_lats, axis=2) + _lons = np.flip(_lons, axis=2) + + # assign the regular-grid (lat/lon/h) variables + self._p = p + self._q = q + self._t = t + self._lats = _lats + self._lons = _lons + self._xs = _lons + self._ys = _lats + self._zs = h diff --git a/tools/RAiDER/models/model_levels.py b/tools/RAiDER/models/model_levels.py new file mode 100644 index 000000000..f4063a737 --- /dev/null +++ b/tools/RAiDER/models/model_levels.py @@ -0,0 +1,507 @@ +''' +Pre-defined model levels and a, b constants for the different weather models + +**NOTE**: The fixed heights used here are from ECMWF's _geometric_ altitudes +(https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels), +where "geopotential altitude is calculated from a mathematical model that adjusts +the altitude to include the variation of gravity with height, while geometric +altitude is the standard direct vertical distance above mean sea level (MSL)." +- Wikipedia.org, https://en.wikipedia.org/wiki/International_Standard_Atmosphere +''' + +LEVELS_137_HEIGHTS = [ + 80301.65, + 74584.91, + 71918.79, + 69365.77, + 66906.53, + 64537.43, + 62254.39, + 60053.46, + 57930.78, + 55882.68, + 53905.62, + 51996.21, + 50159.36, + 48413.94, + 46756.98, + 45199.69, + 43738.55, + 42364.93, + 41071.2, + 39850.56, + 38696.94, + 37604.95, + 36569.72, + 35586.89, + 34652.52, + 33763.05, + 32915.27, + 32106.57, + 31330.96, + 30584.71, + 29866.09, + 29173.5, + 28505.47, + 27860.64, + 27237.73, + 26635.56, + 26053.04, + 25489.15, + 24942.93, + 24413.5, + 23900.02, + 23401.71, + 22917.85, + 22447.75, + 21990.82, + 21546.62, + 21114.77, + 20694.9, + 20286.66, + 19889.88, + 19503.09, + 19125.61, + 18756.34, + 18394.25, + 18038.35, + 17687.77, + 17341.62, + 16999.08, + 16659.55, + 16322.83, + 15988.88, + 15657.7, + 15329.24, + 15003.5, + 14680.44, + 14360.05, + 14042.3, + 13727.18, + 13414.65, + 13104.7, + 12797.3, + 12492.44, + 12190.1, + 11890.24, + 11592.86, + 11297.93, + 11005.69, + 10714.22, + 10422.64, + 10130.98, + 9839.26, + 9547.49, + 9255.7, + 8963.9, + 8672.11, + 8380.36, + 8088.67, + 7797.04, + 7505.51, + 7214.09, + 6922.8, + 6631.66, + 6340.68, + 6049.89, + 5759.3, + 5469.3, + 5180.98, + 4896.02, + 4615.92, + 4341.73, + 4074.41, + 3814.82, + 3563.69, + 3321.67, + 3089.25, + 2866.83, + 2654.69, + 2452.99, + 2261.8, + 2081.09, + 1910.76, + 1750.63, + 1600.44, + 1459.91, + 1328.7, + 1206.44, + 1092.73, + 987.15, + 889.29, + 798.72, + 715.02, + 637.76, + 566.54, + 500.95, + 440.61, + 385.16, + 334.24, + 287.52, + 244.69, + 205.44, + 169.51, + 136.62, + 106.54, + 79.04, + 53.92, + 30.96, + 10, + 0, + -10, + -20, + -50, + -100, + -200, + -300, + -500, +] + +A_ERAI = [ + 0.0000000000e+000, 2.0000000000e+001, 3.8425338745e+001, + 6.3647796631e+001, 9.5636962891e+001, 1.3448330688e+002, + 1.8058435059e+002, 2.3477905273e+002, 2.9849584961e+002, + 3.7397192383e+002, 4.6461816406e+002, 5.7565112305e+002, + 7.1321801758e+002, 8.8366040039e+002, 1.0948347168e+003, + 1.3564746094e+003, 1.6806403809e+003, 2.0822739258e+003, + 2.5798886719e+003, 3.1964216309e+003, 3.9602915039e+003, + 4.9067070313e+003, 6.0180195313e+003, 7.3066328125e+003, + 8.7650546875e+003, 1.0376125000e+004, 1.2077445313e+004, + 1.3775324219e+004, 1.5379804688e+004, 1.6819472656e+004, + 1.8045183594e+004, 1.9027695313e+004, 1.9755109375e+004, + 2.0222203125e+004, 2.0429863281e+004, 2.0384480469e+004, + 2.0097402344e+004, 1.9584328125e+004, 1.8864750000e+004, + 1.7961359375e+004, 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11901.339844, 11133.304688, 10370.175781, 9617.515625, 8880.453125, + 8163.375000, 7470.343750, 6804.421875, 6168.531250, 5564.382813, + 4993.796875, 4457.375000, 3955.960938, 3489.234375, 3057.265625, + 2659.140625, 2294.242188, 1961.500000, 1659.476563, 1387.546875, + 1143.250000, 926.507813, 734.992188, 568.062500, 424.414063, + 302.476563, 202.484375, 122.101563, 62.781250, 22.835938, + 3.757813, 0.000000, 0.000000 +] + +B_137_ERA5 = [ + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000007, + 0.000024, 0.000059, 0.000112, 0.000199, 0.000340, 0.000562, 0.000890, + 0.001353, 0.001992, 0.002857, 0.003971, 0.005378, 0.007133, 0.009261, + 0.011806, 0.014816, 0.018318, 0.022355, 0.026964, 0.032176, 0.038026, + 0.044548, 0.051773, 0.059728, 0.068448, 0.077958, 0.088286, 0.099462, + 0.111505, 0.124448, 0.138313, 0.153125, 0.168910, 0.185689, 0.203491, + 0.222333, 0.242244, 0.263242, 0.285354, 0.308598, 0.332939, 0.358254, + 0.384363, 0.411125, 0.438391, 0.466003, 0.493800, 0.521619, 0.549301, + 0.576692, 0.603648, 0.630036, 0.655736, 0.680643, 0.704669, 0.727739, + 0.749797, 0.770798, 0.790717, 0.809536, 0.827256, 0.843881, 0.859432, + 0.873929, 0.887408, 0.899900, 0.911448, 0.922096, 0.931881, 0.940860, + 0.949064, 0.956550, 0.963352, 0.969513, 0.975078, 0.980072, 0.984542, + 0.988500, 0.991984, 0.995003, 0.997630, 1.000000 +] + +# HRES PRESSURE LEVELS: +# 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa +LEVELS_25_HEIGHTS = [ + 48413.94, + 43738.55, + 39850.56, + 36569.72, + 33763.05, + 31330.96, + 26635.56, + 24413.5, + 20694.9, + 18394.25, + 16322.83, + 13727.18, + 11890.24, + 10422.64, + 9255.7, + 7214.09, + 5469.3, + 4074.41, + 3089.25, + 1910.76, + 1459.91, + 987.15, + 798.72, + 566.54, + 106.54, + -10, + -20, + -50, + -100, + -200, + -300, + -500, +] diff --git a/tools/RAiDER/models/ncmr.py b/tools/RAiDER/models/ncmr.py new file mode 100755 index 000000000..84fa62d35 --- /dev/null +++ b/tools/RAiDER/models/ncmr.py @@ -0,0 +1,227 @@ +""" +Created on Wed Sep 9 10:26:44 2020 @author: prashant +Modified by Yang Lei, GPS/Caltech +""" +import datetime +import os +import urllib.request + +import numpy as np + +from pyproj import CRS + +from RAiDER.models.weatherModel import WeatherModel +from RAiDER.logger import logger +from RAiDER.utilFcns import ( + writeWeatherVars2NETCDF4, + read_NCMR_loginInfo, + show_progress +) +from RAiDER.models.model_levels import ( + LEVELS_137_HEIGHTS, +) + + +class NCMR(WeatherModel): + ''' + Implement NCMRWF NCUM (named as NCMR) model in future + ''' + + def __init__(self): + # initialize a weather model + WeatherModel.__init__(self) + + self._humidityType = 'q' # q for specific humidity and rh for relative humidity + self._model_level_type = 'ml' # Default, pressure levels are 'pl', and model levels are "ml" + self._classname = 'ncmr' # name of the custom weather model + self._dataset = 'ncmr' # same name as above + self._Name = 'NCMR' # name of the new weather model (in Capital) + + # Tuple of min/max years where data is available. + self._valid_range = (datetime.datetime(2015, 12, 1), "Present") + # Availability lag time in days/hours + self._lag_time = datetime.timedelta(hours=6) + self._time_res = 1 + + # model constants + self._k1 = 0.776 # [K/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] + + # horizontal grid spacing + self._lon_res = .17578125 # grid spacing in longitude + self._lat_res = .11718750 # grid spacing in latitude + + self._x_res = .17578125 # same as longitude + self._y_res = .11718750 # same as latitude + + self._zlevels = np.flipud(LEVELS_137_HEIGHTS) + + self._bounds = None + + # Projection + self._proj = CRS.from_epsg(4326) + + def _fetch(self, out): + ''' + Fetch weather model data from NCMR: note we only extract the lat/lon bounds for this weather model; + fetching data is not needed here as we don't actually download data , data exist in same system + ''' + time = self._time + + # Auxillary function: + ''' + download data of the NCMR model and save it in desired location + ''' + self._files = self._download_ncmr_file(out, time, self._ll_bounds) + + def load_weather(self, filename=None, *args, **kwargs): + ''' + Load NCMR model variables from existing file + ''' + if filename is None: + filename = self.files[0] + + # bounding box plus a buffer + lat_min, lat_max, lon_min, lon_max = self._ll_bounds + self._bounds = (lat_min, lat_max, lon_min, lon_max) + + self._makeDataCubes(filename) + + def _download_ncmr_file(self, out, date_time, bounding_box): + ''' + Download weather model data (whole globe) from NCMR weblink, crop it to the region of interest, and save the cropped data as a standard .nc file of RAiDER (e.g. "NCMR_YYYY_MM_DD_THH_MM_SS.nc"); + Temporarily download data from NCMR ftp 'https://ftp.ncmrwf.gov.in/pub/outgoing/SAC/NCUM_OSF/' and copied in weather_models folder + ''' + + from netCDF4 import Dataset + + ############# Use these lines and modify the link when actually downloading NCMR data from a weblink ############# + url, username, password = read_NCMR_loginInfo() + filename = os.path.basename(out) + url = f'ftp://{username}:{password}@{url}/TEST/{filename}' + filepath = f'{out[:-3]}_raw.nc' + if not os.path.exists(filepath): + logger.info('Fetching URL: %s', url) + local_filename, headers = urllib.request.urlretrieve(url, filepath, show_progress) + else: + logger.warning('Weather model already exists, skipping download') + ######################################################################################################################## + + ############# For debugging: use pre-downloaded files; Remove/comment out it when actually downloading NCMR data from a weblink ############# +# filepath = os.path.dirname(out) + '/NCUM_ana_mdllev_20180701_00z.nc' + ######################################################################################################################## + + # calculate the array indices for slicing the GMAO variable arrays + lat_min_ind = int((self._bounds[0] - (-89.94141)) / self._lat_res) + lat_max_ind = int((self._bounds[1] - (-89.94141)) / self._lat_res) + if (self._bounds[2] < 0.0): + lon_min_ind = int((self._bounds[2] + 360.0 - (0.087890625)) / self._lon_res) + else: + lon_min_ind = int((self._bounds[2] - (0.087890625)) / self._lon_res) + if (self._bounds[3] < 0.0): + lon_max_ind = int((self._bounds[3] + 360.0 - (0.087890625)) / self._lon_res) + else: + lon_max_ind = int((self._bounds[3] - (0.087890625)) / self._lon_res) + + ml_min = 0 + ml_max = 70 + + with Dataset(filepath, 'r', maskandscale=True) as f: + lats = f.variables['latitude'][lat_min_ind:(lat_max_ind + 1)].copy() + if (self._bounds[2] * self._bounds[3] < 0): + lons1 = f.variables['longitude'][lon_min_ind:].copy() + lons2 = f.variables['longitude'][0:(lon_max_ind + 1)].copy() + lons = np.append(lons1, lons2) + else: + lons = f.variables['longitude'][lon_min_ind:(lon_max_ind + 1)].copy() + if (self._bounds[2] * self._bounds[3] < 0): + t1 = f.variables['air_temperature'][ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:].copy() + t2 = f.variables['air_temperature'][ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), 0:(lon_max_ind + 1)].copy() + t = np.append(t1, t2, axis=2) + else: + t = f.variables['air_temperature'][ml_min:(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)].copy() + + # Skipping first pressure levels (below 20 meter) + if (self._bounds[2] * self._bounds[3] < 0): + q1 = f.variables['specific_humidity'][(ml_min + 1):(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:].copy() + q2 = f.variables['specific_humidity'][(ml_min + 1):(ml_max + 1), lat_min_ind:(lat_max_ind + 1), 0:(lon_max_ind + 1)].copy() + q = np.append(q1, q2, axis=2) + else: + q = f.variables['specific_humidity'][(ml_min + 1):(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)].copy() + if (self._bounds[2] * self._bounds[3] < 0): + p1 = f.variables['air_pressure'][(ml_min + 1):(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:].copy() + p2 = f.variables['air_pressure'][(ml_min + 1):(ml_max + 1), lat_min_ind:(lat_max_ind + 1), 0:(lon_max_ind + 1)].copy() + p = np.append(p1, p2, axis=2) + else: + p = f.variables['air_pressure'][(ml_min + 1):(ml_max + 1), lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)].copy() + + level_hgt = f.variables['level_height'][(ml_min + 1):(ml_max + 1)].copy() + if (self._bounds[2] * self._bounds[3] < 0): + surface_alt1 = f.variables['surface_altitude'][lat_min_ind:(lat_max_ind + 1), lon_min_ind:].copy() + surface_alt2 = f.variables['surface_altitude'][lat_min_ind:(lat_max_ind + 1), 0:(lon_max_ind + 1)].copy() + surface_alt = np.append(surface_alt1, surface_alt2, axis=1) + else: + surface_alt = f.variables['surface_altitude'][lat_min_ind:(lat_max_ind + 1), lon_min_ind:(lon_max_ind + 1)].copy() + + hgt = np.zeros([len(level_hgt), len(surface_alt[:, 1]), len(surface_alt[1, :])]) + for i in range(len(level_hgt)): + hgt[i, :, :] = surface_alt[:, :] + level_hgt[i] + + lons[lons > 180] -= 360 + + ############# For debugging: comment it out when using pre-downloaded raw data files and don't want to remove them for test; Uncomment it when actually downloading NCMR data from a weblink ############# + os.remove(filepath) + ######################################################################################################################## + + try: + writeWeatherVars2NETCDF4(self, lats, lons, hgt, q, p, t, outName=out) + except Exception: + logger.exception("Unable to save weathermodel to file") + + def _makeDataCubes(self, filename): + ''' + Get the variables from the saved .nc file (named as "NCMR_YYYY_MM_DD_THH_MM_SS.nc") + ''' + from netCDF4 import Dataset + + # adding the import here should become absolute when transition to netcdf + with Dataset(filename, mode='r') as f: + lons = np.array(f.variables['x'][:]) + lats = np.array(f.variables['y'][:]) + hgt = np.array(f.variables['H'][:]) + q = np.array(f.variables['QV'][:]) + p = np.array(f.variables['PL'][:]) + t = np.array(f.variables['T'][:]) + + # re-assign lons, lats to match heights + _lons = np.broadcast_to(lons[np.newaxis, np.newaxis, :], + t.shape) + _lats = np.broadcast_to(lats[np.newaxis, :, np.newaxis], + t.shape) + + # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) + _lats = np.transpose(_lats) + _lons = np.transpose(_lons) + t = np.transpose(t) + q = np.transpose(q) + p = np.transpose(p) + hgt = np.transpose(hgt) + + # data cube format should be lats,lons,heights + p = p.swapaxes(0, 1) + q = q.swapaxes(0, 1) + t = t.swapaxes(0, 1) + hgt = hgt.swapaxes(0, 1) + _lats = _lats.swapaxes(0, 1) + _lons = _lons.swapaxes(0, 1) + + # assign the regular-grid variables + self._p = p + self._q = q + self._t = t + self._lats = _lats + self._lons = _lons + self._xs = _lons + self._ys = _lats + self._zs = hgt diff --git a/tools/RAiDER/models/plotWeather.py b/tools/RAiDER/models/plotWeather.py old mode 100644 new mode 100755 index 63a173b48..2873901b9 --- a/tools/RAiDER/models/plotWeather.py +++ b/tools/RAiDER/models/plotWeather.py @@ -1,143 +1,146 @@ """ This set of functions is designed to for plotting WeatherModel class objects. It is not designed to be used on its own apart -from this class. +from this class. """ -import matplotlib.pyplot as plt +from RAiDER.interpolator import RegularGridInterpolator as Interpolator from mpl_toolkits.axes_grid1 import make_axes_locatable as mal import numpy as np -import interpolator as intrp +import matplotlib.pyplot as plt +import matplotlib as mpl +mpl.use('Agg') -def plot_pqt(weatherObj, savefig = True, z1 = 500, z2 = 15000): + +def plot_pqt(weatherObj, savefig=True, z1=500, z2=15000): ''' Create a plot with pressure, temp, and humidity at two heights ''' - # Get the interpolator - intFcn= intrp.Interpolator() - intFcn.setPoints(*weatherObj.getPoints()) - intFcn.setProjection(weatherObj.getProjection()) - intFcn.getInterpFcns(weatherObj._p, weatherObj._e, weatherObj._t, interType = 'scipy') + # Get the interpolator + + intFcn_p = Interpolator((weatherObj._xs, weatherObj._ys, weatherObj._zs), weatherObj._p.swapaxes(0, 1)) + intFcn_e = Interpolator((weatherObj._xs, weatherObj._ys, weatherObj._zs), weatherObj._e.swapaxes(0, 1)) + intFcn_t = Interpolator((weatherObj._xs, weatherObj._ys, weatherObj._zs), weatherObj._t.swapaxes(0, 1)) # get the points needed - x = weatherObj._xs[:,:,0] - y = weatherObj._ys[:,:,0] + XY = np.meshgrid(weatherObj._xs, weatherObj._ys) + x = XY[0] + y = XY[1] z1a = np.zeros(x.shape) + z1 z2a = np.zeros(x.shape) + z2 - pts1 = np.stack((x.flatten(), y.flatten(), z1a.flatten()), axis = 1) - pts2 = np.stack((x.flatten(), y.flatten(), z2a.flatten()), axis = 1) + pts1 = np.stack((x.flatten(), y.flatten(), z1a.flatten()), axis=1) + pts2 = np.stack((x.flatten(), y.flatten(), z2a.flatten()), axis=1) - p1, e1, t1 = intFcn(pts1) - p2, e2, t2 = intFcn(pts2) - - # uncomment for debugging - #print(p1) - #print(p2) + p1 = intFcn_p(pts1) + e1 = intFcn_e(pts1) + t1 = intFcn_t(pts1) + p2 = intFcn_p(pts2) + e2 = intFcn_e(pts2) + t2 = intFcn_t(pts2) # Now get the data to plot - plots = [p1/1e2, e1/1e2, t1 - 273.15, p2/1e2, e2/1e2, t2 - 273.15] - - # titles - titles = ('P (hPa)', - 'E (hPa)'.format(z1), - 'T (C)', - '', - '', - '') + plots = [p1 / 1e2, e1 / 1e2, t1 - 273.15, p2 / 1e2, e2 / 1e2, t2 - 273.15] + # titles = ('P (hPa)', 'E (hPa)'.format(z1), 'T (C)', '', '', '') + titles = ('P (hPa)', 'E (hPa)', 'T (C)', '', '', '') # setup the plot - f = plt.figure(figsize = (10,6)) - xind = int(np.floor(weatherObj._xs.shape[0]/2)) - yind = int(np.floor(weatherObj._ys.shape[1]/2)) + f = plt.figure(figsize=(18, 14)) + f.suptitle('{} Pressure/Humidity/Temperature at height {}m and {}m (values should drop as elevation increases)'.format(weatherObj._Name, z1, z2)) + + xind = int(np.floor(weatherObj._xs.shape[0] / 2)) + yind = int(np.floor(weatherObj._ys.shape[0] / 2)) + # loop over each plot for ind, plot, title in zip(range(len(plots)), plots, titles): - sp = f.add_subplot(3,3,ind + 1) - sp.xaxis.set_ticklabels([]) - sp.yaxis.set_ticklabels([]) - im = sp.imshow(np.reshape(plot, x.shape), cmap='viridis') - sp.plot(xind, yind, 'ko', 'filled') + sp = f.add_subplot(3, 3, ind + 1) + im = sp.imshow(np.reshape(plot, x.shape), + cmap='viridis', + extent=[np.nanmin(x), np.nanmax(x), np.nanmin(y), np.nanmax(y)], + origin='lower') + sp.plot(x[yind, xind], y[yind, xind], 'ko') divider = mal(sp) cax = divider.append_axes("right", size="4%", pad=0.05) plt.colorbar(im, cax=cax) sp.set_title(title) - if ind==0: - sp.set_ylabel('{} m'.format(z1)) - if ind==3: - sp.set_ylabel('{} m'.format(z2)) + if ind == 0: + sp.set_ylabel('{} m\n'.format(z1)) + if ind == 3: + sp.set_ylabel('{} m\n'.format(z2)) # add plots that show each variable with height - zdata = weatherObj._zs[xind,yind,:]/1000 - sp = f.add_subplot(3,3,7) - sp.plot(weatherObj._p[xind,yind,:]/1e2, zdata) + zdata = weatherObj._zs[:] / 1000 + sp = f.add_subplot(3, 3, 7) + sp.plot(weatherObj._p[yind, xind, :] / 1e2, zdata) sp.set_ylabel('Height (km)') sp.set_xlabel('Pressure (hPa)') - - sp = f.add_subplot(3,3,8) - sp.plot(weatherObj._e[xind,yind,:]/100,zdata) - sp.yaxis.set_ticklabels([]) + + sp = f.add_subplot(3, 3, 8) + sp.plot(weatherObj._e[yind, xind, :] / 100, zdata) sp.set_xlabel('E (hPa)') - - sp = f.add_subplot(3,3,9) - sp.plot(weatherObj._t[xind,yind,:]- 273.15, zdata) - sp.yaxis.set_ticklabels([]) + + sp = f.add_subplot(3, 3, 9) + sp.plot(weatherObj._t[yind, xind, :] - 273.15, zdata) sp.set_xlabel('Temp (C)') - - plt.subplots_adjust(top=0.95, bottom=0.1, left=0.05, right=0.95, hspace=0.05, - wspace=0.3) + + plt.subplots_adjust(top=0.95, bottom=0.1, left=0.1, right=0.95, hspace=0.2, + wspace=0.3) if savefig: - plt.savefig('Weather_hgt{}_and_{}m.pdf'.format(z1, z2)) + plt.savefig('{}_weather_hgt{}_and_{}m.pdf'.format(weatherObj._Name, z1, z2)) return f -def plot_wh(weatherObj, savefig = True, z1 = 500, z2 = 15000): +def plot_wh(weatherObj, savefig=True, z1=500, z2=15000): ''' - Create a plot with wet refractivity and hydrostatic refractivity, + Create a plot with wet refractivity and hydrostatic refractivity, at two different heights ''' - # Get the interpolator - intFcn= intrp.Interpolator() - intFcn.setPoints(*weatherObj.getPoints()) - intFcn.setProjection(weatherObj.getProjection()) - intFcn.getInterpFcns(weatherObj._wet_refractivity, weatherObj._hydrostatic_refractivity, interType = 'scipy') + # Get the interpolator + intFcn_w = Interpolator((weatherObj._xs, weatherObj._ys, weatherObj._zs), weatherObj._wet_refractivity.swapaxes(0, 1)) + intFcn_h = Interpolator((weatherObj._xs, weatherObj._ys, weatherObj._zs), weatherObj._hydrostatic_refractivity.swapaxes(0, 1)) # get the points needed - x = weatherObj._xs[:,:,0] - y = weatherObj._ys[:,:,0] + XY = np.meshgrid(weatherObj._xs, weatherObj._ys) + x = XY[0] + y = XY[1] z1a = np.zeros(x.shape) + z1 z2a = np.zeros(x.shape) + z2 - pts1 = np.stack((x.flatten(), y.flatten(), z1a.flatten()), axis = 1) - pts2 = np.stack((x.flatten(), y.flatten(), z2a.flatten()), axis = 1) + pts1 = np.stack((x.flatten(), y.flatten(), z1a.flatten()), axis=1) + pts2 = np.stack((x.flatten(), y.flatten(), z2a.flatten()), axis=1) - w1, h1 = intFcn(pts1) - w2, h2 = intFcn(pts2) + w1 = intFcn_w(pts1) + h1 = intFcn_h(pts1) + w2 = intFcn_w(pts2) + h2 = intFcn_h(pts2) # Now get the data to plot plots = [w1, h1, w2, h2] # titles - titles = ('Wet refractivity ({:5.1f} m)'.format(z1), - 'Hydrostatic refractivity ({:5.1f} m)'.format(z1), - 'Wet refractivity ({:5.1f} m)'.format(z2), - 'Hydrostatic refractivity ({:5.1f} m)'.format(z2)) + titles = ('Wet refractivity {}'.format(z1), + 'Hydrostatic refractivity {}'.format(z1), + '{}'.format(z2), + '{}'.format(z2)) # setup the plot - f = plt.figure(figsize = (6,6)) + f = plt.figure(figsize=(14, 10)) + f.suptitle('{} Wet and Hydrostatic refractivity at height {}m and {}m'.format(weatherObj._Name, z1, z2)) # loop over each plot for ind, plot, title in zip(range(len(plots)), plots, titles): - sp = f.add_subplot(2,2,ind + 1) - sp.xaxis.set_ticklabels([]) - sp.yaxis.set_ticklabels([]) - im = sp.imshow(np.reshape(plot, x.shape), cmap='viridis') + sp = f.add_subplot(2, 2, ind + 1) + im = sp.imshow(np.reshape(plot, x.shape), cmap='viridis', extent=[np.nanmin(x), np.nanmax(x), np.nanmin(y), np.nanmax(y)], origin='lower') divider = mal(sp) cax = divider.append_axes("right", size="4%", pad=0.05) plt.colorbar(im, cax=cax) sp.set_title(title) + if ind == 0: + sp.set_ylabel('{} m\n'.format(z1)) + if ind == 2: + sp.set_ylabel('{} m\n'.format(z2)) if savefig: - plt.savefig('Refractivity_hgt{}_and_{}m.pdf'.format(z1, z2)) + plt.savefig('{}_refractivity_hgt{}_and_{}m.pdf'.format(weatherObj._Name, z1, z2)) return f - diff --git a/tools/RAiDER/models/template.py b/tools/RAiDER/models/template.py new file mode 100644 index 000000000..14dcf2ffd --- /dev/null +++ b/tools/RAiDER/models/template.py @@ -0,0 +1,164 @@ +from pyproj import CRS +import numpy as np +import datetime +from RAiDER.models.weatherModel import WeatherModel +from RAiDER.models.model_levels import ( + LEVELS_137_HEIGHTS, +) + + +class customModelReader(WeatherModel): + def __init__(self): + WeatherModel.__init__(self) + self._humidityType = 'q' # can be "q" (specific humidity) or "rh" (relative humidity) + self._model_level_type = 'pl' # Default, pressure levels are "pl", and model levels are "ml" + self._classname = 'abcd' # name of the custom weather model + self._dataset = 'abcd' # same name as above + + # Tuple of min/max years where data is available. + # valid range of the dataset. Users need to specify the start date and end date (can be "present") + self._valid_range = (datetime.datetime(2016, 7, 15), "Present") + # Availability lag time. Can be specified in hours "hours=3" or in days "days=3" + self._lag_time = datetime.timedelta(hours=3) + # Availabile time resolution; i.e. minimum rate model is available in hours. 1 is hourly + self._time_res = 1 + + # model constants (these three constants are borrowed from ECMWF model and currently + # set to be default for all other models, which may need to be double checked.) + self._k1 = 0.776 # [K/Pa] + self._k2 = 0.233 # [K/Pa] + self._k3 = 3.75e3 # [K^2/Pa] + + # horizontal grid spacing + self._lat_res = 3. / 111 # grid spacing in latitude + self._lon_res = 3. / 111 # grid spacing in longitude + self._x_res = 3. # x-direction grid spacing in the native weather model projection + # (if the projection is in lat/lon, it is the same as "self._lon_res") + self._y_res = 3. # y-direction grid spacing in the weather model native projection + # (if the projection is in lat/lon, it is the same as "self._lat_res") + + # zlevels specify fixed heights at which to interpolate the weather model variables + self._zlevels = np.flipud(LEVELS_137_HEIGHTS) + + self._Name = 'ABCD' # name of the custom weather model (better to be capitalized) + + # Projections in RAiDER are defined using pyproj (python wrapper around Proj) + # If the projection is defined with EPSG code, one can use "self._proj = CRS.from_epsg(4326)" + # to replace the following lines to get "self._proj". + # Below we show the example of HRRR model with the parameters of its Lambert Conformal Conic projection + lon0 = 262.5 + lat0 = 38.5 + lat1 = 38.5 + lat2 = 38.5 + x0 = 0 + y0 = 0 + earth_radius = 6371229 + p1 = CRS('+proj=lcc +lat_1={lat1} +lat_2={lat2} +lat_0={lat0} +lon_0={lon0} +x_0={x0} +y_0={y0} +a={a} +b={a} +units=m +no_defs'.format(lat1=lat1, lat2=lat2, lat0=lat0, lon0=lon0, x0=x0, y0=y0, a=earth_radius)) + self._proj = p1 + + def _fetch(self, out): + ''' + Fetch weather model data from the custom weather model "ABCD" + Inputs (no need to change in the custom weather model reader): + lats - latitude + lons - longitude + time - datatime object (year,month,day,hour,minute,second) + out - name of downloaded dataset file from the custom weather model server + Nextra - buffer of latitude/longitude for determining the bounding box + ''' + # Auxilliary function: + # download dataset of the custom weather model "ABCD" from a server and then save it to a file named out. + # This function needs to be writen by the users. For download from the weather model server, the weather model + # name, time and bounding box may be needed to retrieve the dataset; for cases where no file is actually + # downloaded, e.g. the GMAO and MERRA-2 models using OpenDAP, this function can be omitted leaving the data + # retrieval to the following "load_weather" function. + self._files = self._download_abcd_file(out, 'abcd', self._time, self._ll_bounds) + + def load_weather(self, filename): + ''' + Load weather model variables from the downloaded file named filename + Inputs: + filename - filename of the downloaded weather model file + ''' + + # Auxilliary function: + # read individual variables (in 3-D cube format with exactly the same dimension) from downloaded file + # This function needs to be writen by the users. For downloaded file from the weather model server, + # this function extracts the individual variables from the saved file named filename; + # for cases where no file is actually downloaded, e.g. the GMAO and MERRA-2 models using OpenDAP, + # this function retrieves the individual variables directly from the weblink of the weather model. + lats, lons, xs, ys, t, q, p, hgt = self._makeDataCubes(filename) + + # extra steps that may be needed to calculate topographic height and pressure level if not provided + # directly by the weather model through the above auxilliary function "self._makeDataCubes" + + # if surface pressure (in logarithm) is provided as "p" along with the surface geopotential "z" (needs to be + # added to the auxilliary function "self._makeDataCubes"), one can use the following line to convert to + # geopotential, pressure level and geopotential height; otherwise commented out + z, p, hgt = self._calculategeoh(z, p) # TODO: z is undefined + + # if the geopotential is provided as "z" (needs to be added to the auxilliary function "self._makeDataCubes"), + # one can use the following line to convert to geopotential height; otherwise, commented out + hgt = z / self._g0 + + # if geopotential height is provided/calculated as "hgt", one can use the following line to convert to + # topographic height, which is then automatically assigned to "self._zs"; otherwise commented out + self._get_heights(lats, hgt) + + # if topographic height is provided as "hgt", use the following line directly; otherwise commented out + self._zs = hgt + + ########### + + ######### output of the weather model reader for delay calculations (all in 3-D data cubes) ######## + + # _t: temperture + # _q: either relative or specific humidity + # _p: must be pressure level + # _xs: x-direction grid dimension of the native weather model coordinates (if in lat/lon, _xs = _lons) + # _ys: y-direction grid dimension of the native weather model coordinates (if in lat/lon, _ys = _lats) + # _zs: must be topographic height + # _lats: latitude + # _lons: longitude + self._t = t + self._q = q + self._p = p + self._xs = xs + self._ys = ys + self._lats = lats + self._lons = lons + + ########### + + def _download_abcd_file(self, out, model_name, date_time, bounding_box): + ''' + Auxilliary function: + Download weather model data from a server + Inputs: + out - filename for saving the retrieved data file + model_name - name of the custom weather model + date_time - datatime object (year,month,day,hour,minute,second) + bounding_box - lat/lon bounding box for the region of interest + Output: + out - returned filename from input + ''' + pass + + def _makeDataCubes(self, filename): + ''' + Auxilliary function: + Read 3-D data cubes from downloaded file or directly from weather model weblink (in which case, there is no + need to download and save any file; rather, the weblink needs to be hardcoded in the custom reader, e.g. GMAO) + Input: + filename - filename of the downloaded weather model file from the server + Outputs: + lats - latitude (3-D data cube) + lons - longitude (3-D data cube) + xs - x-direction grid dimension of the native weather model coordinates (3-D data cube; if in lat/lon, _xs = _lons) + ys - y-direction grid dimension of the native weather model coordinates (3-D data cube; if in lat/lon, _ys = _lats) + t - temperature (3-D data cube) + q - humidity (3-D data cube; could be relative humidity or specific humidity) + p - pressure level (3-D data cube; could be pressure level (preferred) or surface pressure) + hgt - height (3-D data cube; could be geopotential height or topographic height (preferred)) + ''' + pass diff --git a/tools/RAiDER/models/weatherModel.py b/tools/RAiDER/models/weatherModel.py old mode 100644 new mode 100755 index e702e2c35..3f0ec1cf7 --- a/tools/RAiDER/models/weatherModel.py +++ b/tools/RAiDER/models/weatherModel.py @@ -1,28 +1,33 @@ - -# standard imports import datetime -import numpy as np -import pyproj import os +from abc import ABC, abstractmethod -# local imports -import utils.constants as const -import models.plotWeather as plots -import utils.util as util -from utils.util import robmin, robmax - - -class ValidDateError(Exception): - def __init___(self,valid_range, badtime): - msg = 'ERROR: Valid date range for this weather model is {}-{}\n'.format(*valid_range) - msg += 'and the requested date is {}.'.format(time) - Exception.__init__(self,msg) - self.badtime= badtime - -class WeatherModel(): +import numpy as np +import netCDF4 +import rasterio +import xarray + +from pyproj import CRS +from shapely.geometry import box +from shapely.affinity import translate +from shapely.ops import unary_union + +from RAiDER.constants import _ZREF, _ZMIN, _g0 +from RAiDER import utilFcns as util +from RAiDER.interpolate import interpolate_along_axis +from RAiDER.interpolator import fillna3D +from RAiDER.logger import logger +from RAiDER.models import plotWeather as plots, weatherModel +from RAiDER.utilFcns import ( + robmax, robmin, write2NETCDF4core, calcgeoh, transform_coords +) + + +class WeatherModel(ABC): ''' Implement a generic weather model for getting estimated SAR delays ''' + def __init__(self): # Initialize model-specific constants/parameters self._k1 = None @@ -31,29 +36,47 @@ def __init__(self): self._humidityType = 'q' self._a = [] self._b = [] + + self.files = None + + self._time_res = None # time resolution of the weather model in hours + self._lon_res = None self._lat_res = None - self._classname = None + self._x_res = None + self._y_res = None + + self._classname = None self._dataset = None + self._model_level_type = 'ml' - self._valid_range = (datetime.date(1900,1,1),) # Tuple of min/max years where data is available. - self._lag_time = datetime.timedelta(days =30) # Availability lag time in days + + self._valid_range = ( + datetime.date(1900, 1, 1), + ) # Tuple of min/max years where data is available. + self._lag_time = datetime.timedelta(days=30) # Availability lag time in days + self._time = None + + self._bbox = None # Define fixed constants self._R_v = 461.524 - self._R_d = 287.053 - self._g0 = const._g0 # gravity constant - self._zmin = const._ZMIN # minimum integration height - self._zmax = const._ZMAX # max integration height - self._llaproj = pyproj.Proj(proj='latlong') - self._ecefproj = pyproj.Proj(proj='geocent') + self._R_d = 287.06 # in our original code this was 287.053 + self._g0 = _g0 # gravity constant + self._zmin = _ZMIN # minimum integration height + self._zmax = _ZREF # max integration height self._proj = None - # setup data structures + # setup data structures self._levels = [] - self._xs = np.empty((1, 1, 1)) # Use generic x/y/z instead of lon/lat/height + self._xs = np.empty((1, 1, 1)) # Use generic x/y/z instead of lon/lat/height self._ys = np.empty((1, 1, 1)) self._zs = np.empty((1, 1, 1)) + + self._lats = None + self._lons = None + self._ll_bounds = None + self._p = None self._q = None self._rh = None @@ -61,26 +84,18 @@ def __init__(self): self._e = None self._wet_refractivity = None self._hydrostatic_refractivity = None - self._svp = None + self._wet_ztd = None + self._hydrostatic_ztd = None - - def __repr__(self): + def __str__(self): string = '\n' string += '======Weather Model class object=====\n' - string += 'Number of points in Lon/Lat = {}/{}\n'.format(*self._xs.shape[:2]) - string += 'Total number of grid points (3D): {}\n'.format(np.prod(self._xs.shape)) + string += 'Weather model time: {}\n'.format(self._time) string += 'Latitude resolution: {}\n'.format(self._lat_res) string += 'Longitude resolution: {}\n'.format(self._lon_res) string += 'Native projection: {}\n'.format(self._proj) string += 'ZMIN: {}\n'.format(self._zmin) string += 'ZMAX: {}\n'.format(self._zmax) - string += 'Minimum/Maximum y (or latitude): {: 4.2f}/{: 4.2f}\n'\ - .format(robmin(self._ys), robmax(self._ys)) - string += 'Minimum/Maximum x (or longitude): {: 4.2f}/{: 4.2f}\n'\ - .format(robmin(self._xs), robmax(self._xs)) - string += 'Minimum/Maximum zs/heights: {: 10.2f}/{: 10.2f}\n'\ - .format(robmin(self._zs), robmax(self._zs)) - string += '=====================================\n' string += 'k1 = {}\n'.format(self._k1) string += 'k2 = {}\n'.format(self._k2) string += 'k3 = {}\n'.format(self._k3) @@ -91,185 +106,369 @@ def __repr__(self): string += '=====================================\n' string += 'A: {}\n'.format(self._a) string += 'B: {}\n'.format(self._b) + if self._p is not None: + string += 'Number of points in Lon/Lat = {}/{}\n'.format(*self._p.shape[:2]) + string += 'Total number of grid points (3D): {}\n'.format(np.prod(self._p.shape)) + if self._xs.size == 0: + string += 'Minimum/Maximum y: {: 4.2f}/{: 4.2f}\n'\ + .format(robmin(self._ys), robmax(self._ys)) + string += 'Minimum/Maximum x: {: 4.2f}/{: 4.2f}\n'\ + .format(robmin(self._xs), robmax(self._xs)) + string += 'Minimum/Maximum zs/heights: {: 10.2f}/{: 10.2f}\n'\ + .format(robmin(self._zs), robmax(self._zs)) + string += '=====================================\n' return str(string) - def plot(self, index): - import matplotlib.pyplot as plt - plt.plot(self._p[index, index, :], self._zs[index, index, :]) - plt.xlabel('Pressure') - plt.ylabel('Z') - plt.show() - def Model(self): return self._Name - def fetch(self, lats, lons, time, out): + def fetch(self, out, ll_bounds, time): + ''' + Checks the input datetime against the valid date range for the model and then + calls the model _fetch routine + + Parameters + ---------- + out - + ll_bounds - 4 x 1 array, SNWE + time = UTC datetime + ''' + self.checkTime(time) + self.set_latlon_bounds(ll_bounds) + self.setTime(time) + self._fetch(out) + + @abstractmethod + def _fetch(self, out): ''' Placeholder method. Should be implemented in each weather model type class ''' pass - def load(self, *args, lats = None, lons = None): + def setTime(self, time, fmt='%Y-%m-%dT%H:%M:%S'): + ''' Set the time for a weather model ''' + if isinstance(time, str): + self._time = datetime.datetime.strptime(time, fmt) + elif isinstance(time, datetime.datetime): + self._time = time + else: + raise ValueError('"time" must be a string or a datetime object') + + def get_latlon_bounds(self): + raise NotImplementedError + + def set_latlon_bounds(self, ll_bounds, Nextra=2): + ''' + Need to correct lat/lon bounds because not all of the weather models have valid + data exactly bounded by -90/90 (lats) and -180/180 (lons); for GMAO and MERRA2, + need to adjust the longitude higher end with an extra buffer; for other models, + the exact bounds are close to -90/90 (lats) and -180/180 (lons) and thus can be + rounded to the above regions (either in the downloading-file API or subsetting- + data API) without problems. + ''' + ex_buffer_lon_max = 0.0 + + if self._Name == 'GMAO' or self._Name == 'MERRA2': + ex_buffer_lon_max = self._lon_res + elif self._Name == 'HRRR': + Nextra = 6 # have a bigger buffer + + + # At boundary lats and lons, need to modify Nextra buffer so that the lats and lons do not exceed the boundary + S, N, W, E = ll_bounds + + # Adjust bounds if they get near the poles or IDL + S = np.max([S - Nextra * self._lat_res, -90.0 + Nextra * self._lat_res]) + N = np.min([N + Nextra * self._lat_res, 90.0 - Nextra * self._lat_res]) + W = np.max([W - Nextra * self._lon_res, -180.0 + Nextra * self._lon_res]) + E = np.min([E + Nextra * self._lon_res + ex_buffer_lon_max, 180.0 - Nextra * self._lon_res - ex_buffer_lon_max]) + + self._ll_bounds = np.array([S, N, W, E]) + + def load( + self, + outLoc, + *args, + ll_bounds=None, + _zlevels=None, + zref=_ZREF, + **kwargs + ): ''' - Calls the load_weather method. Each model class should define a load_weather - method appropriate for that class. 'args' should be one or more filenames. + Calls the load_weather method. Each model class should define a load_weather + method appropriate for that class. 'args' should be one or more filenames. ''' - self.load_weather(*args) - self._find_e() - self._get_wet_refractivity() - self._get_hydro_refractivity() - - # adjust the grid based on the height data - self._adjust_grid(lats, lons) + self.set_latlon_bounds(ll_bounds, Nextra=0) - def load_weather(self, filename): + # If the weather file has already been processed, do nothing + self._out_name = self.out_file(outLoc) + if os.path.exists(self._out_name): + return self._out_name + else: + # Load the weather just for the query points + self.load_weather(*args, **kwargs) + + # Process the weather model data + self._find_e() + self._uniform_in_z(_zlevels=_zlevels) + self._checkForNans() + self._get_wet_refractivity() + self._get_hydro_refractivity() + self._adjust_grid(ll_bounds) + + # Compute Zenith delays at the weather model grid nodes + self._getZTD(zref) + return None + + @abstractmethod + def load_weather(self, *args, **kwargs): ''' Placeholder method. Should be implemented in each weather model type class ''' pass - def plot(self, plotType = 'pqt', savefig = True): + def _get_time(self, filename=None): + if filename is None: + filename = self.files[0] + with netCDF4.Dataset(filename, mode='r') as f: + time = f.attrs['datetime'].copy() + self.time = datetime.datetime.strptime(time, "%Y_%m_%dT%H_%M_%S") + + def plot(self, plotType='pqt', savefig=True): ''' Plotting method. Valid plot types are 'pqt' ''' - if plotType=='pqt': + if plotType == 'pqt': plot = plots.plot_pqt(self, savefig) - plot.show() - elif plotType=='wh': + elif plotType == 'wh': plot = plots.plot_wh(self, savefig) - plot.show() else: raise RuntimeError('WeatherModel.plot: No plotType named {}'.format(plotType)) return plot - - def check(self, time): + + def checkTime(self, time): ''' Checks the time against the lag time and valid date range for the given model type ''' - import time - if time datetime.date.today() - self._lag_time: - raise ValidDateError(self._valid_range, time) - - def setLevelType(self, levelType = 'ml'): - ''' - Update the level type to use in fetching data from the weather models + logger.info( + 'Weather model %s is available from %s-%s', + self.Model(), self._valid_range[0], self._valid_range[1] + ) + if time < self._valid_range[0]: + raise RuntimeError("Weather model {} is not available at {}".format(self.Model(), time)) + if self._valid_range[1] is not None: + if self._valid_range[1] == 'Present': + pass + elif self._valid_range[1] < time: + raise RuntimeError("Weather model {} is not available at {}".format(self.Model(), time)) + if time > datetime.datetime.utcnow() - self._lag_time: + raise RuntimeError("Weather model {} is not available at {}".format(self.Model(), time)) + + def _convertmb2Pa(self, pres): + ''' + Convert pressure in millibars to Pascals ''' - self._model_level_type = levelType + return 100 * pres - def _get_heights(self, lats, geo_hgt, geo_ht_fill = np.nan): + def _get_heights(self, lats, geo_hgt, geo_ht_fill=np.nan): ''' Transform geo heights to actual heights ''' - geo_ht_fix = np.where(geo_hgt!= geo_ht_fill, geo_hgt, np.nan) - self._zs = util._geo_to_ht(lats, geo_ht_fix, self._g0) + geo_ht_fix = np.where(geo_hgt != geo_ht_fill, geo_hgt, np.nan) + self._zs = util._geo_to_ht(lats, geo_ht_fix) def _find_e(self): """Check the type of e-calculation needed""" - if self._humidityType=='rh': + if self._humidityType == 'rh': self._find_e_from_rh() - elif self._humidityType=='q': + elif self._humidityType == 'q': self._find_e_from_q() else: raise RuntimeError('Not a valid humidity type') + self._rh = None + self._q = None def _find_e_from_q(self): """Calculate e, partial pressure of water vapor.""" - self._find_svp() + svp = find_svp(self._t) # We have q = w/(w + 1), so w = q/(1 - q) - w = self._q/(1 - self._q) - self._e = w*self._R_v*(self._p - self._svp)/self._R_d + w = self._q / (1 - self._q) + self._e = w * self._R_v * (self._p - svp) / self._R_d def _find_e_from_rh(self): """Calculate partial pressure of water vapor.""" - self._find_svp() - self._e = self._rh/100 * self._svp + svp = find_svp(self._t) + self._e = self._rh / 100 * svp def _get_wet_refractivity(self): ''' Calculate the wet delay from pressure, temperature, and e ''' - self._wet_refractivity = self._k2*self._e/self._t+ self._k3*self._e/self._t**2 - + self._wet_refractivity = self._k2 * self._e / self._t + self._k3 * self._e / self._t**2 + def _get_hydro_refractivity(self): ''' Calculate the hydrostatic delay from pressure and temperature ''' - self._hydrostatic_refractivity = self._k1*self._p/self._t + self._hydrostatic_refractivity = self._k1 * self._p / self._t def getWetRefractivity(self): return self._wet_refractivity - + def getHydroRefractivity(self): return self._hydrostatic_refractivity - def _adjust_grid(self, lats, lons): + def _adjust_grid(self, ll_bounds=None): ''' - This function pads the weather grid with a level at self._zmin, if - it does not already go that low. It also removes levels that are - above self._zmax, since they are not needed. + This function pads the weather grid with a level at self._zmin, if + it does not already go that low. + <> + <> ''' + if self._zmin < np.nanmin(self._zs): # first add in a new layer at zmin - new_heights = np.zeros(self._zs.shape[:2]) + self._zmin - self._zs = np.concatenate( - (new_heights[:,:,np.newaxis], self._zs), axis = 2) - - # since xs/ys (or lons/lats) are the same for all z, just add an - # extra slice to match the new z shape - self._xs = np.concatenate((self._xs[:,:,0][...,np.newaxis],self._xs), axis = 2) - self._ys = np.concatenate((self._ys[:,:,0][...,np.newaxis],self._ys), axis = 2) - - # need to extrapolate the other variables down now - if self._humidityType == 'q': - self._q=util.padLower(self._q) - else: - self._rh=util.padLower(self._rh) - - self._p=util.padLower(self._p) - self._t=util.padLower(self._t) - self._e=util.padLower(self._e) - self._wet_refractivity=util.padLower(self._wet_refractivity) - self._hydrostatic_refractivity=util.padLower(self._hydrostatic_refractivity) + self._zs = np.insert(self._zs, 0, self._zmin) - # Now remove any model level fully above zmax - max_level_needed = util.getMaxModelLevel(self._zs, self._zmax, 'g') - levInd = range(0,max_level_needed + 1) - + self._p = util.padLower(self._p) + self._t = util.padLower(self._t) + self._e = util.padLower(self._e) + self._wet_refractivity = util.padLower(self._wet_refractivity) + self._hydrostatic_refractivity = util.padLower(self._hydrostatic_refractivity) + if ll_bounds is not None: + self._trimExtent(ll_bounds) - if self._humidityType == 'q': - self._q = self._q[...,levInd] - else: - self._rh = self._rh[...,levInd] - - self._zs = self._zs[...,levInd] - self._xs = self._xs[...,levInd] - self._ys = self._ys[...,levInd] - self._p = self._p[...,levInd] - self._t = self._t[...,levInd] - self._e = self._e[...,levInd] - self._wet_refractivity = self._wet_refractivity[...,levInd] - self._hydrostatic_refractivity=self._hydrostatic_refractivity[...,levInd] - - if lats is not None: - in_extent = self._getExtent(lats, lons) - self_extent = self._getExtent(self._ys, self._xs) - if self._isOutside(in_extent, self_extent): - raise RuntimeError('The weather model passed does not cover all of the \n \ - input points; you need to download a larger area.') - self._trimExtent(in_extent) - - - def _getExtent(self,lats, lons): + def _getZTD(self, zref=None): + ''' + Compute the full slant tropospheric delay for each weather model grid node, using the reference + height zref + ''' + if zref is None: + zref = self._zmax + + wet = self.getWetRefractivity() + hydro = self.getHydroRefractivity() + + # Get the integrated ZTD + wet_total, hydro_total = np.zeros(wet.shape), np.zeros(hydro.shape) + for level in range(wet.shape[2]): + wet_total[..., level] = 1e-6 * np.trapz( + wet[..., level:], x=self._zs[level:], axis=2 + ) + hydro_total[..., level] = 1e-6 * np.trapz( + hydro[..., level:], x=self._zs[level:], axis=2 + ) + self._hydrostatic_ztd = hydro_total + self._wet_ztd = wet_total + + def _getExtent(self, lats, lons): ''' get the bounding box around a set of lats/lons ''' - return [np.nanmin(lats), np.nanmax(lats), np.nanmin(lons), np.nanmax(lons)] + if (lats.size == 1) & (lons.size == 1): + return [lats - self._lat_res, lats + self._lat_res, lons - self._lon_res, lons + self._lon_res] + elif (lats.size > 1) & (lons.size > 1): + return [np.nanmin(lats), np.nanmax(lats), np.nanmin(lons), np.nanmax(lons)] + elif lats.size == 1: + return [lats - self._lat_res, lats + self._lat_res, np.nanmin(lons), np.nanmax(lons)] + elif lons.size == 1: + return [np.nanmin(lats), np.nanmax(lats), lons - self._lon_res, lons + self._lon_res] + else: + raise RuntimeError('Not a valid lat/lon shape') + + @property + def bbox(self) -> list: + """ + Obtains the bounding box of the weather model in lat/lon CRS. + Returns + ------- + list + xmin, ymin, xmax, ymax + + Raises + ------ + ValueError + When `self.files` is None. + """ + if self._bbox is None: + if self.files is None: + raise ValueError('Need to save weather model as netcdf') + weather_model_path = self.files[0] + with xarray.load_dataset(weather_model_path) as ds: + try: + xmin, xmax = ds.x.min(), ds.x.max() + ymin, ymax = ds.y.min(), ds.y.max() + except: + xmin, xmax = ds.longitude.min(), ds.longitude.max() + ymin, ymax = ds.latitude.min(), ds.latitude.max() + + wm_proj = self._proj + lons, lats = transform_coords(wm_proj, CRS(4326), [xmin, xmax], [ymin, ymax]) + self._bbox = [lons[0], lats[0], lons[1], lats[1]] + + return self._bbox + + + def checkContainment(self: weatherModel, + ll_bounds: np.ndarray, + buffer_deg: float = 1e-5) -> bool: + """" + Checks containment of weather model bbox of outLats and outLons + provided. + + Parameters + ---------- + weather_model : weatherModel + outLats : np.ndarray + An array of latitude points + outLons : np.ndarray + An array of longitude points + buffer_deg : float + For x-translates for extents that lie outside of world bounding box, + this ensures that translates have some overlap. The default is 1e-5 + or ~11.1 meters. + + Returns + ------- + bool + True if weather model contains bounding box of OutLats and outLons + and False otherwise. + """ + ymin_input, ymax_input, xmin_input, xmax_input = ll_bounds + input_box = box(xmin_input, ymin_input, xmax_input, ymax_input) + xmin, ymin, xmax, ymax = self.bbox + weather_model_box = box(xmin, ymin, xmax, ymax) + + world_box = box(-180, -90, 180, 90) + + # Logger + input_box_str = [f'{x:1.2f}' for x in [xmin_input, ymin_input, + xmax_input, ymax_input]] + weath_box_str = [f'{x:1.2f}' for x in [xmin, ymin, xmax, ymax]] + + weath_box_str = ', '.join(weath_box_str) + input_box_str = ', '.join(input_box_str) + + logger.info(f'Extent of the weather model is (xmin, ymin, xmax, ymax):' + f'{weath_box_str}') + logger.info(f'Extent of the input is (xmin, ymin, xmax, ymax): ' + f'{input_box_str}') + + # If the bounding box goes beyond the normal world extents + # Look at two x-translates, buffer them, and take their union. + if not world_box.contains(weather_model_box): + logger.info('Considering x-translates of weather model +/-360 ' + 'as bounding box outside of -180, -90, 180, 90') + translates = [weather_model_box.buffer(buffer_deg), + translate(weather_model_box, + xoff=360).buffer(buffer_deg), + translate(weather_model_box, + xoff=-360).buffer(buffer_deg) + ] + weather_model_box = unary_union(translates) + + return weather_model_box.contains(input_box) def _isOutside(self, extent1, extent2): ''' @@ -281,171 +480,385 @@ def _isOutside(self, extent1, extent2): t3 = extent1[2] < extent2[2] t4 = extent1[3] > extent2[3] if np.any([t1, t2, t3, t4]): - return True + return True return False - - def _trimExtent(self,extent): + def _trimExtent(self, extent): ''' get the bounding box around a set of lats/lons ''' - mask = (self._ys[:,:,0] > extent[0]) & (self._ys[:,:,0] < extent[1]) & \ - (self._xs[:,:,0] > extent[2]) & (self._xs[:,:,0] < extent[3]) - ma1 = np.sum(mask, axis = 1).astype('bool') - ma2 = np.sum(mask, axis = 0).astype('bool') - index1 = np.arange(len(ma1))[ma1][0] - index2 = np.arange(len(ma1))[ma1][-1] - index3 = np.arange(len(ma2))[ma2][0] - index4 = np.arange(len(ma2))[ma2][-1] + 1 - self._xs = self._xs[index1:index2,index3:index4,:] - self._ys = self._ys[index1:index2,index3:index4,...] - self._zs = self._zs[index1:index2,index3:index4,...] - self._p = self._p[index1:index2,index3:index4,...] - self._q = self._q[index1:index2,index3:index4,...] - self._rh = self._rh[index1:index2,index3:index4,...] - self._t = self._t[index1:index2,index3:index4,...] - self._e = self._e[index1:index2,index3:index4,...] - self._wet_refractivity = self._wet_refractivity[index1:index2,index3:index4,...] - self._hydrostatic_refractivity = self._hydrostatic_refractivity[index1:index2,index3:index4,:] - - - def _find_svp(self): - """ - Calculate standard vapor presure. Should be model-specific - """ - # From TRAIN: - # Could not find the wrf used equation as they appear to be - # mixed with latent heat etc. Istead I used the equations used - # in ERA-I (see IFS documentation part 2: Data assimilation - # (CY25R1)). Calculate saturated water vapour pressure (svp) for - # water (svpw) using Buck 1881 and for ice (swpi) from Alduchow - # and Eskridge (1996) euation AERKi - - # TODO: figure out the sources of all these magic numbers and move - # them somewhere more visible. - # TODO: (Jeremy) - Need to fix/get the equation for the other - # weather model types. Right now this will be used for all models, - # except WRF, which is yet to be implemented in my new structure. - t1 = 273.15 # O Celsius - t2 = 250.15 # -23 Celsius - - tref = self._t- t1 - wgt = (self._t - t2)/(t1 - t2) - svpw = (6.1121 * np.exp((17.502*tref)/(240.97 + tref))) - svpi = (6.1121 * np.exp((22.587*tref)/(273.86 + tref))) - - svp = svpi + (svpw - svpi)*wgt**2 - ix_bound1 =self._t > t1 - svp[ix_bound1] = svpw[ix_bound1] - ix_bound2 =self._t < t2 - svp[ix_bound2] = svpi[ix_bound2] - - self._svp = svp * 100 - - + lat = self._lats[:, :, 0] + lon = self._lons[:, :, 0] + lat[np.isnan(lat)] = np.nanmean(lat) + lon[np.isnan(lon)] = np.nanmean(lon) + mask = (lat >= extent[0]) & (lat <= extent[1]) & \ + (lon >= extent[2]) & (lon <= extent[3]) + ma1 = np.sum(mask, axis=1).astype('bool') + ma2 = np.sum(mask, axis=0).astype('bool') + if np.sum(ma1) == 0 and np.sum(ma2) == 0: + # Don't need to remove any points + return + + # indices of the part of the grid to keep + ny, nx, nz = self._p.shape + index1 = max(np.arange(len(ma1))[ma1][0] - 2, 0) + index2 = min(np.arange(len(ma1))[ma1][-1] + 2, ny) + index3 = max(np.arange(len(ma2))[ma2][0] - 2, 0) + index4 = min(np.arange(len(ma2))[ma2][-1] + 2, nx) + + # subset around points of interest + self._lons = self._lons[index1:index2, index3:index4, :] + self._lats = self._lats[index1:index2, index3:index4, ...] + self._xs = self._xs[index3:index4] + self._ys = self._ys[index1:index2] + self._p = self._p[index1:index2, index3:index4, ...] + self._t = self._t[index1:index2, index3:index4, ...] + self._e = self._e[index1:index2, index3:index4, ...] + + self._wet_refractivity = self._wet_refractivity[index1:index2, index3:index4, ...] + self._hydrostatic_refractivity = self._hydrostatic_refractivity[index1:index2, index3:index4, :] + def _calculategeoh(self, z, lnsp): ''' Function to calculate pressure, geopotential, and geopotential height - from the surface pressure and model levels provided by a weather model. + from the surface pressure and model levels provided by a weather model. The model levels are numbered from the highest eleveation to the lowest. - Inputs: + Inputs: self - weather model object with parameters a, b defined z - 3-D array of surface heights for the location(s) of interest lnsp - log of the surface pressure - Outputs: + Outputs: geopotential - The geopotential in units of height times acceleration - pressurelvs - The pressure at each of the model levels for each of + pressurelvs - The pressure at each of the model levels for each of the input points geoheight - The geopotential heights - ''' - geopotential = np.zeros_like(self._t) - pressurelvs = np.zeros_like(geopotential) - geoheight = np.zeros_like(geopotential) - - # surface pressure: pressure at the surface! - # Note that we integrate from the ground up, so from the largest model level to 0 - sp = np.exp(lnsp) - - # t should be structured [z, y, x] - levelSize = len(self._levels) - - if len(self._a) != levelSize + 1 or len(self._b) != levelSize + 1: - raise ValueError( - 'I have here a model with {} levels, but parameters a '.format(levelSize) + - 'and b have lengths {} and {} respectively. Of '.format(len(self._a),len(self._b)) + - 'course, these three numbers should be equal.') - - Ph_levplusone = self._a[levelSize] + (self._b[levelSize]*sp) - - # Integrate up into the atmosphere from *lowest level* - z_h = 0 # initial value - for lev, t_level, q_level in zip( - range(levelSize, 0, -1), self._t[::-1], self._q[::-1]): - - # lev is the level number 1-60, we need a corresponding index - # into ts and qs - #ilevel = levelSize - lev # << this was Ray's original, but is a typo - # because indexing like that results in pressure and height arrays that - # are in the opposite orientation to the t/q arrays. - ilevel = lev - 1 - - # compute moist temperature - t_level = t_level*(1 + 0.609133*q_level) - - # compute the pressures (on half-levels) - Ph_lev = self._a[lev-1] + (self._b[lev-1] * sp) - - pressurelvs[ilevel] = Ph_lev - - if lev == 1: - dlogP = np.log(Ph_levplusone/0.1) - alpha = np.log(2) - else: - dlogP = np.log(Ph_levplusone/Ph_lev) - dP = Ph_levplusone - Ph_lev - alpha = 1 - ((Ph_lev/dP)*dlogP) - - TRd = t_level*self._R_d - - # z_f is the geopotential of this full level - # integrate from previous (lower) half-level z_h to the full level - z_f = z_h + TRd*alpha - #geoheight[ilevel] = z_f/self._g0 - - # Geopotential (add in surface geopotential) - geopotential[ilevel] = z_f + z - geoheight[ilevel] = geopotential[ilevel]/self._g0 - - # z_h is the geopotential of 'half-levels' - # integrate z_h to next half level - z_h += TRd * dlogP - - Ph_levplusone = Ph_lev - - return geopotential, pressurelvs, geoheight - - def _get_ll_bounds(self, lats, lons, Nextra = 2): - ''' - returns the extents of lat/lon plus a buffer ''' - lat_min = np.nanmin(lats) - Nextra*self._lat_res - lat_max = np.nanmax(lats) + Nextra*self._lat_res - lon_min = np.nanmin(lons) - Nextra*self._lon_res - lon_max = np.nanmax(lons) + Nextra*self._lon_res + return calcgeoh(lnsp, self._t, self._q, z, self._a, self._b, self._R_d, self._levels) - return lat_min, lat_max, lon_min, lon_max def getProjection(self): ''' Returns the native weather projection, which should be a pyproj object - ''' + ''' return self._proj def getPoints(self): - return self._xs.copy(),self._ys.copy(), self._zs.copy() - - def getLL(self): - return self._ys[...,0].copy(), self._xs[...,0].copy() - + return self._xs.copy(), self._ys.copy(), self._zs.copy() + + def _uniform_in_z(self, _zlevels=None): + ''' + Interpolate all variables to a regular grid in z + ''' + nx, ny = self._p.shape[:2] + + # new regular z-spacing + if _zlevels is None: + try: + _zlevels = self._zlevels + except BaseException: + _zlevels = np.nanmean(self._zs, axis=(0, 1)) + new_zs = np.tile(_zlevels, (nx, ny, 1)) + + # re-assign values to the uniform z + self._t = interpolate_along_axis( + self._zs, self._t, new_zs, axis=2, fill_value=np.nan + ).astype(np.float32) + self._p = interpolate_along_axis( + self._zs, self._p, new_zs, axis=2, fill_value=np.nan + ).astype(np.float32) + self._e = interpolate_along_axis( + self._zs, self._e, new_zs, axis=2, fill_value=np.nan + ).astype(np.float32) + self._lats = interpolate_along_axis( + self._zs, self._lats, new_zs, axis=2, fill_value=np.nan + ).astype(np.float32) + self._lons = interpolate_along_axis( + self._zs, self._lons, new_zs, axis=2, fill_value=np.nan + ).astype(np.float32) + + self._zs = _zlevels + self._xs = np.unique(self._xs) + self._ys = np.unique(self._ys) + + def _checkForNans(self): + ''' + Fill in NaN-values + ''' + self._p = fillna3D(self._p) + self._t = fillna3D(self._t) + self._e = fillna3D(self._e) + + def out_file(self, outLoc): + f = make_weather_model_filename( + self._Name, + self._time, + self._ll_bounds, + ) + return os.path.join(outLoc, f) + + def filename(self, time=None, outLoc='weather_files'): + ''' + Create a filename to store the weather model + ''' + os.makedirs(outLoc, exist_ok=True) + if time is None: + if self._time is None: + raise ValueError('Time must be specified before the file can be written') + else: + time = self._time + f = make_raw_weather_data_filename( + outLoc, + self._Name, + time, + ) + + self.files = [f] + + def write( + self, + NoDataValue=-3.4028234e+38, + chunk=(1, 128, 128), + ): + ''' + By calling the abstract/modular netcdf writer + (RAiDER.utilFcns.write2NETCDF4core), write the weather model data + and refractivity to an NETCDF4 file that can be accessed by external programs. + ''' + # Generate the filename + mapping_name= get_mapping(self._proj) + + f = self._out_name + + dimidY, dimidX, dimidZ = self._t.shape + chunk_lines_Y = np.min([chunk[1], dimidY]) + chunk_lines_X = np.min([chunk[2], dimidX]) + ChunkSize = [1, chunk_lines_Y, chunk_lines_X] + + nc_outfile = netCDF4.Dataset(f, 'w', clobber=True, format='NETCDF4') + nc_outfile.setncattr('Conventions', 'CF-1.6') + nc_outfile.setncattr('datetime', datetime.datetime.strftime(self._time, "%Y_%m_%dT%H_%M_%S")) + nc_outfile.setncattr('date_created', datetime.datetime.now().strftime("%Y_%m_%dT%H_%M_%S")) + title = 'Weather model data and delay calculations' + nc_outfile.setncattr('title', title) + + tran = [self._xs[0], self._xs[1] - self._xs[0], 0.0, self._ys[0], 0.0, self._ys[1] - self._ys[0]] + dimension_dict = { + 'x': {'varname': 'x', + 'datatype': np.dtype('float64'), + 'dimensions': ('x'), + 'length': dimidX, + 'FillValue': None, + 'standard_name': 'projection_x_coordinate', + 'description': 'weather model native x', + 'dataset': self._xs, + 'units': 'degrees_east'}, + 'y': {'varname': 'y', + 'datatype': np.dtype('float64'), + 'dimensions': ('y'), + 'length': dimidY, + 'FillValue': None, + 'standard_name': 'projection_y_coordinate', + 'description': 'weather model native y', + 'dataset': self._ys, + 'units': 'degrees_north'}, + 'z': {'varname': 'z', + 'datatype': np.dtype('float32'), + 'dimensions': ('z'), + 'length': dimidZ, + 'FillValue': None, + 'standard_name': 'projection_z_coordinate', + 'description': 'vertical coordinate', + 'dataset': self._zs, + 'units': 'm'} + } + + dataset_dict = { + 'latitude': {'varname': 'latitude', + 'datatype': np.dtype('float64'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'latitude', + 'description': 'latitude', + 'dataset': self._lats.swapaxes(0, 2).swapaxes(1, 2), + 'units': 'degrees_north'}, + 'longitude': {'varname': 'longitude', + 'datatype': np.dtype('float64'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'longitude', + 'description': 'longitude', + 'dataset': self._lons.swapaxes(0, 2).swapaxes(1, 2), + 'units': 'degrees_east'}, + 't': {'varname': 't', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'temperature', + 'description': 'temperature', + 'dataset': self._t.swapaxes(0, 2).swapaxes(1, 2), + 'units': 'K'}, + 'p': {'varname': 'p', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'pressure', + 'description': 'pressure', + 'dataset': self._p.swapaxes(0, 2).swapaxes(1, 2), + 'units': 'Pa'}, + 'e': {'varname': 'e', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'humidity', + 'description': 'humidity', + 'dataset': self._e.swapaxes(0, 2).swapaxes(1, 2), + 'units': 'Pa'}, + 'wet': {'varname': 'wet', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'wet_refractivity', + 'description': 'wet_refractivity', + 'dataset': self._wet_refractivity.swapaxes(0, 2).swapaxes(1, 2)}, + 'hydro': {'varname': 'hydro', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'hydrostatic_refractivity', + 'description': 'hydrostatic_refractivity', + 'dataset': self._hydrostatic_refractivity.swapaxes(0, 2).swapaxes(1, 2)}, + 'wet_total': {'varname': 'wet_total', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'total_wet_refractivity', + 'description': 'total_wet_refractivity', + 'dataset': self._wet_ztd.swapaxes(0, 2).swapaxes(1, 2)}, + 'hydro_total': {'varname': 'hydro_total', + 'datatype': np.dtype('float32'), + 'dimensions': ('z', 'y', 'x'), + 'grid_mapping': mapping_name, + 'FillValue': NoDataValue, + 'ChunkSize': ChunkSize, + 'standard_name': 'total_hydrostatic_refractivity', + 'description': 'total_hydrostatic_refractivity', + 'dataset': self._hydrostatic_ztd.swapaxes(0, 2).swapaxes(1, 2)} + } + + nc_outfile = write2NETCDF4core( + nc_outfile, + dimension_dict, + dataset_dict, + tran, + mapping_name=mapping_name + ) + + nc_outfile.sync() # flush data to disk + nc_outfile.close() + return f + + +def make_weather_model_filename(name, time, ll_bounds): + if ll_bounds[0] < 0: + S = 'S' + else: + S = 'N' + if ll_bounds[1] < 0: + N = 'S' + else: + N = 'N' + if ll_bounds[2] < 0: + W = 'W' + else: + W = 'E' + if ll_bounds[3] < 0: + E = 'W' + else: + E = 'E' + return '{}_{}_{:.0f}{}_{:.0f}{}_{:.0f}{}_{:.0f}{}.nc'.format( + name, + time.strftime("%Y_%m_%d_T%H_%M_%S"), + np.ceil(np.abs(ll_bounds[0])), + S, + np.ceil(np.abs(ll_bounds[1])), + N, + np.ceil(np.abs(ll_bounds[2])), + W, + np.ceil(np.abs(ll_bounds[3])), + E + ) + + +def make_raw_weather_data_filename(outLoc, name, time): + ''' Filename generator for the raw downloaded weather model data ''' + f = os.path.join( + outLoc, + '{}_{}.{}'.format( + name, + datetime.datetime.strftime(time, '%Y_%m_%d_T%H_%M_%S'), + 'nc' + ) + ) + return f + + +def find_svp(t): + """ + Calculate standard vapor presure. Should be model-specific + """ + # From TRAIN: + # Could not find the wrf used equation as they appear to be + # mixed with latent heat etc. Istead I used the equations used + # in ERA-I (see IFS documentation part 2: Data assimilation + # (CY25R1)). Calculate saturated water vapour pressure (svp) for + # water (svpw) using Buck 1881 and for ice (swpi) from Alduchow + # and Eskridge (1996) euation AERKi + + # TODO: figure out the sources of all these magic numbers and move + # them somewhere more visible. + # TODO: (Jeremy) - Need to fix/get the equation for the other + # weather model types. Right now this will be used for all models, + # except WRF, which is yet to be implemented in my new structure. + t1 = 273.15 # O Celsius + t2 = 250.15 # -23 Celsius + + tref = t - t1 + wgt = (t - t2) / (t1 - t2) + svpw = (6.1121 * np.exp((17.502 * tref) / (240.97 + tref))) + svpi = (6.1121 * np.exp((22.587 * tref) / (273.86 + tref))) + + svp = svpi + (svpw - svpi) * wgt**2 + ix_bound1 = t > t1 + svp[ix_bound1] = svpw[ix_bound1] + ix_bound2 = t < t2 + svp[ix_bound2] = svpi[ix_bound2] + + svp = svp * 100 + return svp.astype(np.float32) + + +def get_mapping(proj): + '''Get CF-complient projection information from a proj''' + # In case of WGS-84 lat/lon, keep it simple + if proj.to_epsg()==4326: + return 'WGS84' + else: + return proj.to_wkt() diff --git a/tools/RAiDER/models/wrf.py b/tools/RAiDER/models/wrf.py index 5d24750da..d35f795bd 100644 --- a/tools/RAiDER/models/wrf.py +++ b/tools/RAiDER/models/wrf.py @@ -1,13 +1,13 @@ import numpy as np import scipy.io.netcdf as netcdf -import pyproj +from pyproj import CRS, Transformer -from models.weatherModel import WeatherModel +from RAiDER.models.weatherModel import WeatherModel -## Need to incorporate this snippet into this part of the code. -## was formally in delay.py -#if weather_model_name == 'wrf': +# Need to incorporate this snippet into this part of the code. +# was formally in delay.py +# if weather_model_name == 'wrf': # # Let lats and lons to weather model nodes if necessary # #TODO: Need to fix the case where lats are None, because # # the weather model need not be in latlong projection @@ -16,22 +16,25 @@ # class WRF(WeatherModel): ''' - WRF class definition, based on the WeatherModel base class. + WRF class definition, based on the WeatherModel base class. ''' - #TODO: finish implementing + # TODO: finish implementing + def __init__(self): WeatherModel.__init__(self) - - self._k1 = 0.776 # K/Pa - self._k2 = 0.233 # K/Pa - self._k3 = 3.75e3# K^2/Pa - + + self._k1 = 0.776 # K/Pa + self._k2 = 0.233 # K/Pa + self._k3 = 3.75e3 # K^2/Pa + # Currently WRF is using RH instead of Q to get E self._humidityType = 'rh' self._Name = 'WRF' + def _fetch(self): + pass - def load_weather(self, file1, file2): + def load_weather(self, file1, file2, *args, **kwargs): ''' Consistent class method to be implemented across all weather model types ''' @@ -41,13 +44,14 @@ def load_weather(self, file1, file2): except KeyError: self._get_wm_nodes(file2) self._read_netcdf(file1) - - # WRF doesn't give us the coordinates of the points in the native projection, - # only the coordinates in lat/long. Ray transformed these to the native + + # WRF doesn't give us the coordinates of the points in the native projection, + # only the coordinates in lat/long. Ray transformed these to the native # projection, then used an average to enforce a regular grid. It does matter # for the interpolation whether the grid is regular. - lla = pyproj.Proj(proj='latlong') - xs, ys = pyproj.transform(lla, self._proj, lons.flatten(), lats.flatten()) + lla = CRS.from_epsg(4326) + t = Transformer.from_proj(lla, self._proj) + xs, ys = t.transform(lons.flatten(), lats.flatten()) xs = xs.reshape(lons.shape) ys = ys.reshape(lats.shape) @@ -56,9 +60,9 @@ def load_weather(self, file1, file2): ys = np.mean(ys, axis=1) _xs = np.broadcast_to(xs[np.newaxis, np.newaxis, :], - self._p.shape) + self._p.shape) _ys = np.broadcast_to(ys[np.newaxis, :, np.newaxis], - self._p.shape) + self._p.shape) # Re-structure everything from (heights, lats, lons) to (lons, lats, heights) self._p = np.transpose(self._p) self._t = np.transpose(self._t) @@ -67,27 +71,25 @@ def load_weather(self, file1, file2): self._xs = np.transpose(_xs) self._zs = np.transpose(self._zs) - #TODO: Not sure if WRF provides this + # TODO: Not sure if WRF provides this self._levels = list(range(self._zs.shape[2])) - def _get_wm_nodes(self, nodeFile): with netcdf.netcdf_file(nodeFile, 'r', maskandscale=True) as outf: - lats = outf.variables['XLAT'][0].copy() # Takes only the first date! + lats = outf.variables['XLAT'][0].copy() # Takes only the first date! lons = outf.variables['XLONG'][0].copy() lons[lons > 180] -= 360 - - return lons, lats + return lons, lats - def _read_netcdf(self, weatherFile, defNul = None): + def _read_netcdf(self, weatherFile, defNul=None): """ Read weather variables from a netCDF file """ if defNul is None: defNul = np.nan - + # TODO: it'd be cool to use some kind of units package # TODO: extract partial pressure directly (q?) with netcdf.netcdf_file(weatherFile, 'r', maskandscale=True) as f: @@ -100,11 +102,11 @@ def _read_netcdf(self, weatherFile, defNul = None): lat0 = f.MOAD_CEN_LAT.copy() lat1 = f.TRUELAT1.copy() lat2 = f.TRUELAT2.copy() - + checkUnits(spvar.units.decode('utf-8'), 'pressure') checkUnits(temp.units.decode('utf-8'), 'temperature') - checkUnits(humid.units.decode('utf-8'),'relative humidity') - checkUnits(geohvar.units.decode('utf-8'), 'geopotential') + checkUnits(humid.units.decode('utf-8'), 'relative humidity') + checkUnits(geohvar.units.decode('utf-8'), 'geopotential') # _FillValue is not always set, but when it is we want to read it tNull = getNullValue(temp) @@ -113,9 +115,9 @@ def _read_netcdf(self, weatherFile, defNul = None): pNull = getNullValue(spvar) sp = spvar[0].copy() - temps= temp[0].copy() - humids= humid[0].copy() - geoh= geohvar[0].copy() + temps = temp[0].copy() + humids = humid[0].copy() + geoh = geohvar[0].copy() spvar = None temp = None @@ -125,55 +127,57 @@ def _read_netcdf(self, weatherFile, defNul = None): # Projection # See http://www.pkrc.net/wrf-lambert.html earthRadius = 6370e3 # <- note Ray had a bug here - p1 = pyproj.Proj(proj='lcc', lat_1=lat1, - lat_2=lat2, lat_0=lat0, - lon_0=lon0, a=earthRadius, b=earthRadius, - towgs84=(0,0,0), no_defs=True) + p1 = CRS(proj='lcc', lat_1=lat1, + lat_2=lat2, lat_0=lat0, + lon_0=lon0, a=earthRadius, b=earthRadius, + towgs84=(0, 0, 0), no_defs=True) self._proj = p1 - temps[temps==tNull] = np.nan - sp[sp==pNull] = np.nan - humids[humids==hNull] = np.nan - geoh[geoh==gNull] = np.nan + temps[temps == tNull] = np.nan + sp[sp == pNull] = np.nan + humids[humids == hNull] = np.nan + geoh[geoh == gNull] = np.nan self._t = temps - self._rh= humids + self._rh = humids - # Zs are problematic because any z below the topography is nan. - # For a temporary fix, I will assign any nan value to equal the - # nanmean of that level. - zmeans = np.nanmean(geoh, axis = (1,2)) + # Zs are problematic because any z below the topography is nan. + # For a temporary fix, I will assign any nan value to equal the + # nanmean of that level. + zmeans = np.nanmean(geoh, axis=(1, 2)) nz, ny, nx = geoh.shape Zmeans = np.tile(zmeans, (nx, ny, 1)) Zmeans = Zmeans.T ix = np.isnan(geoh) - geoh[ix]= Zmeans[ix] - self._zs= geoh + geoh[ix] = Zmeans[ix] + self._zs = geoh if len(sp.shape) == 1: self._p = np.broadcast_to( - sp[:, np.newaxis, np.newaxis],self._zs.shape) + sp[:, np.newaxis, np.newaxis], self._zs.shape) else: - self._p = sp - + self._p = sp + class UnitTypeError(Exception): ''' Define a unit type exception for easily formatting error messages for units ''' - def __init___(self,varName, unittype): + + def __init___(self, varName, unittype): msg = "Unknown units for {}: '{}'".format(varName, unittype) - Exception.__init__(self,msg) + Exception.__init__(self, msg) def checkUnits(unitCheck, varName): ''' Implement a check that the units are as expected ''' - unitDict = {'pressure': 'Pa', 'temperature':'K', 'relative humidity': '%', 'geopotential': 'm'} + unitDict = {'pressure': 'Pa', 'temperature': 'K', 'relative humidity': '%', 'geopotential': 'm'} if unitCheck != unitDict[varName]: - raise UnitTypeError(varName, unitCheck) + raise UnitTypeError(varName, unitCheck) + def getNullValue(var): ''' @@ -185,12 +189,9 @@ def getNullValue(var): # is to check if nodata is specified, and otherwise assume it's -999. _default_fill_value = -999 - try: var_fill = var._FillValue except AttributeError: var_fill = _default_fill_value return var_fill - - diff --git a/tools/RAiDER/prepFromAria.py b/tools/RAiDER/prepFromAria.py new file mode 100644 index 000000000..a4a3f1712 --- /dev/null +++ b/tools/RAiDER/prepFromAria.py @@ -0,0 +1,107 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer +# Copyright 2020, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import numpy as np +import rasterio +from RAiDER.utilFcns import rio_open, writeArrayToRaster + + +def parse_args(): + """Parse command line arguments using argparse.""" + import argparse + p = argparse.ArgumentParser( + description='Prepare files from ARIA-tools output to use with RAiDER') + + # Line of sight + p.add_argument( + '--incFile', '-i', type=str, + help='GDAL-readable raster image file of inclination', + metavar='INC', required=True) + p.add_argument( + '--azFile', '-a', type=str, + help='GDAL-readable raster image file of azimuth', + metavar='AZ', required=True) + + p.add_argument( + '--los_filename', '-f', default='los.rdr', type=str, dest='los_file', + help=('Output Line-of-sight filename')) + p.add_argument( + '--lat_filename', '-l', default='lat.rdr', type=str, dest='lat_file', + help=('Output latitude filename')) + p.add_argument( + '--lon_filename', '-L', default='lon.rdr', type=str, dest='lon_file', + help=('Output longitude filename')) + + p.add_argument( + '--format', '-t', default='ENVI', type=str, dest='fmt', + help=('Output file format')) + + return p.parse_args(), p + + +def makeLatLonGrid(inFile, lonFileName, latFileName, fmt='ENVI'): + ''' + Convert the geocoded grids to lat/lon files for input to RAiDER + ''' + ds = rasterio.open(inFile) + xSize = ds.width + ySize = ds.height + gt = ds.transform.to_gdal() + proj = ds.crs + + # Create the xy grid + xStart = gt[0] + yStart = gt[3] + xStep = gt[1] + yStep = gt[-1] + + xEnd = xStart + xStep * xSize - 0.5 * xStep + yEnd = yStart + yStep * ySize - 0.5 * yStep + + x = np.arange(xStart, xEnd, xStep) + y = np.arange(yStart, yEnd, yStep) + X, Y = np.meshgrid(x, y) + writeArrayToRaster(X, lonFileName, 0., fmt, proj, gt) + writeArrayToRaster(Y, latFileName, 0., fmt, proj, gt) + + +def makeLOSFile(incFile, azFile, fmt='ENVI', filename='los.rdr'): + ''' + Create a line-of-sight file from ARIA-derived azimuth and inclination files + ''' + az, az_prof = rio_open(azFile, returnProj=True) + az[az == 0] = np.nan + inc = rio_open(incFile) + + heading = 90 - az + heading[np.isnan(heading)] = 0. + + array_shp = np.shape(az)[:2] + + # Write the data to a file + with rasterio.open(filename, mode="w", count=2, + driver=fmt, width=array_shp[1], + height=array_shp[0], crs=az_prof.crs, + transform=az_prof.transform, + dtype=az.dtype, nodata=0.) as dst: + dst.write(inc, 1) + dst.write(heading, 2) + + return 0 + + +def prepFromAria(): + ''' + A command-line utility to convert ARIA standard product outputs from ARIA-tools to + RAiDER-compatible format + ''' + args, p = parse_args() + makeLOSFile(args.incFile, args.azFile, args.fmt, args.los_file) + makeLatLonGrid(args.incFile, args.lon_file, args.lat_file, args.fmt) + +def main(): + prepFromAria() diff --git a/tools/RAiDER/processWM.py b/tools/RAiDER/processWM.py new file mode 100755 index 000000000..ac944fa06 --- /dev/null +++ b/tools/RAiDER/processWM.py @@ -0,0 +1,190 @@ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Author: Jeremy Maurer, Raymond Hogenson & David Bekaert +# Copyright 2019, by the California Institute of Technology. ALL RIGHTS +# RESERVED. United States Government Sponsorship acknowledged. +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +import os +import numpy as np +from RAiDER.logger import logger +from RAiDER.utilFcns import getTimeFromFile +import matplotlib.pyplot as plt + + +def prepareWeatherModel( + weather_model, + time=None, + wmLoc=None, + ll_bounds=None, + zref=None, + download_only=False, + makePlots=False, + force_download=False, +): + ''' + Parse inputs to download and prepare a weather model grid for interpolation + ''' + # Ensure the file output location exists + if wmLoc is None: + wmLoc = os.path.join(os.getcwd(), 'weather_files') + os.makedirs(wmLoc, exist_ok=True) + + # check whether weather model files are supplied or should be downloaded + download_flag = True + if weather_model.files is None: + if time is None: + raise RuntimeError( + 'prepareWeatherModel: Either a file or a time must be specified' + ) + weather_model.filename(time, wmLoc) + if os.path.exists(weather_model.files[0]): + if not force_download: + logger.warning( + 'Weather model already exists, please remove it ("%s") if you want ' + 'to download a new one.', weather_model.files + ) + download_flag = False + else: + download_flag = False + + # if no weather model files supplied, check the standard location + if download_flag: + weather_model.fetch(*weather_model.files, ll_bounds, time) + else: + time = getTimeFromFile(weather_model.files[0]) + weather_model.setTime(time) + containment = weather_model.checkContainment(ll_bounds) + + if not containment: + logger.warning( + 'The weather model passed does not cover all of the input ' + 'points; you may need to download a larger area.' + ) + + # If only downloading, exit now + if download_only: + logger.warning( + 'download_only flag selected. No further processing will happen.' + ) + return None + + # Otherwise, load the weather model data + f = weather_model.load( + wmLoc, + ll_bounds = ll_bounds, + zref=zref, + ) + if f is not None: + logger.warning( + 'The processed weather model file already exists,' + ' so I will use that.' + ) + return f + + # Logging some basic info + logger.debug( + 'Number of weather model nodes: {}'.format( + np.prod(weather_model.getWetRefractivity().shape) + ) + ) + shape = weather_model.getWetRefractivity().shape + logger.debug(f'Shape of weather model: {shape}') + logger.debug( + 'Bounds of the weather model: %.2f/%.2f/%.2f/%.2f (SNWE)', + np.nanmin(weather_model._ys), np.nanmax(weather_model._ys), + np.nanmin(weather_model._xs), np.nanmax(weather_model._xs) + ) + logger.debug('Weather model: %s', weather_model.Model()) + logger.debug( + 'Mean value of the wet refractivity: %f', + np.nanmean(weather_model.getWetRefractivity()) + ) + logger.debug( + 'Mean value of the hydrostatic refractivity: %f', + np.nanmean(weather_model.getHydroRefractivity()) + ) + logger.debug(weather_model) + + if makePlots: + weather_model.plot('wh', True) + weather_model.plot('pqt', True) + plt.close('all') + + try: + f = weather_model.write() + return f + except Exception as e: + logger.exception("Unable to save weathermodel to file") + logger.exception(e) + raise RuntimeError("Unable to save weathermodel to file") + finally: + del weather_model + + +def checkBounds(weather_model, outLats, outLons): + '''Check the bounds of a weather model''' + ds = xr.load_dataset(weather_model.files[0]) # TODO: xr is undefined + coords = ds.coords # coords is dict-like + keys = [k for k in coords.keys()] + xc = coords[keys[0]] + yc = coords[keys[1]] + lat_bounds = [yc.min(), yc.max()] + lon_bounds = [xc.min(), xc.max()] + self_extent = lat_bounds + lon_bounds + in_extent = weather_model._getExtent(outLats, outLons) + + return in_extent, self_extent + + +def weather_model_debug( + los, + lats, + lons, + ll_bounds, + weather_model, + wmLoc, + zref, + time, + out, + download_only +): + """ + raiderWeatherModelDebug main function. + """ + + logger.debug('Starting to run the weather model calculation with debugging plots') + logger.debug('Time type: %s', type(time)) + logger.debug('Time: %s', time.strftime('%Y%m%d')) + + # location of the weather model files + logger.debug('Beginning weather model pre-processing') + logger.debug('Download-only is %s', download_only) + if wmLoc is None: + wmLoc = os.path.join(out, 'weather_files') + + # weather model calculation + # TODO: make_weather_model_filename is undefined + wm_filename = make_weather_model_filename( + weather_model['name'], + time, + ll_bounds + ) + weather_model_file = os.path.join(wmLoc, wm_filename) + + if not os.path.exists(weather_model_file): + prepareWeatherModel( + weather_model, + time, + wmLoc=wmLoc, + lats=lats, + lons=lons, + ll_bounds=ll_bounds, + zref=zref, + download_only=download_only, + makePlots=True + ) + try: + weather_model.write2NETCDF4(weather_model_file) + except Exception: + logger.exception("Unable to save weathermodel to file") diff --git a/tools/RAiDER/util.py b/tools/RAiDER/util.py deleted file mode 100644 index 6900877b7..000000000 --- a/tools/RAiDER/util.py +++ /dev/null @@ -1,559 +0,0 @@ -"""Geodesy-related utility functions.""" - - -from osgeo import gdal -import numpy as np -import pickle -import os - -gdal.UseExceptions() - - -# Top of the troposphere -zref = 15000 - - -def sind(x): - """Return the sine of x when x is in degrees.""" - return np.sin(np.radians(x)) - - -def cosd(x): - """Return the cosine of x when x is in degrees.""" - return np.cos(np.radians(x)) - - -def tand(x): - """Return degree tangent.""" - return np.tan(np.radians(x)) - - -def lla2ecef(lat, lon, height): - import pyproj - ecef = pyproj.Proj(proj='geocent') - lla = pyproj.Proj(proj='latlong') - - return pyproj.transform(lla, ecef, lon, lat, height) - - -def ecef2lla(x, y, z): - import pyproj - ecef = pyproj.Proj(proj='geocent') - lla = pyproj.Proj(proj='latlong') - lon, lat, height = pyproj.transform(ecef, lla, x, y, z) - return lat, lon, height - - -def enu2ecef(east, north, up, lat0, lon0, h0): - """Return ecef from enu coordinates.""" - # I'm looking at - # https://github.com/scivision/pymap3d/blob/master/pymap3d/__init__.py - x0, y0, z0 = lla2ecef(lat0, lon0, h0) - - t = cosd(lat0) * up - sind(lat0) * north - w = sind(lat0) * up + cosd(lat0) * north - - u = cosd(lon0) * t - sind(lon0) * east - v = sind(lon0) * t + cosd(lon0) * east - - my_ecef = np.stack((x0 + u, y0 + v, z0 + w)) - - return my_ecef - - -def lla2lambert(lat, lon, height=None): - import pyproj - lla = pyproj.Proj(proj='latlong') - lambert = pyproj.Proj( - '+proj=lcc +lat_1=30.0 +lat_2=60.0 +lat_0=18.500015 +lon_0=-100.2 ' - '+a=6370 +b=6370 +towgs84=0,0,0 +no_defs') - - if height is None: - return lla(lat, lon, errcheck=True) - return pyproj.transform(lla, lambert, lat, lon, height) - - -def state_to_los(t, x, y, z, vx, vy, vz, lats, lons, heights): - import Geo2rdr - - real_shape = lats.shape - lats = lats.flatten() - lons = lons.flatten() - heights = heights.flatten() - - geo2rdr_obj = Geo2rdr.PyGeo2rdr() - geo2rdr_obj.set_orbit(t, x, y, z, vx, vy, vz) - - loss = np.zeros((3, len(lats))) - slant_ranges = np.zeros_like(lats) - - for i, (lat, lon, height) in enumerate(zip(lats, lons, heights)): - height_array = np.array(((height,),)) - - # Geo2rdr is picky about the type of height - height_array = height_array.astype(np.double) - - geo2rdr_obj.set_geo_coordinate(np.radians(lon), - np.radians(lat), - 1, 1, - height_array) - # compute the radar coordinate for each geo coordinate - geo2rdr_obj.geo2rdr() - - # get back the line of sight unit vector - los_x, los_y, los_z = geo2rdr_obj.get_los() - loss[:, i] = los_x, los_y, los_z - - # get back the slant ranges - slant_range = geo2rdr_obj.get_slant_range() - slant_ranges[i] = slant_range - - los = loss * slant_ranges - - # Have to think about traversal order here. It's easy, though, since - # in both orders xs come first, followed by all ys, followed by all - # zs. - return los.reshape((3,) + real_shape) - - -def toXYZ(lats, lons, hts): - """Convert lat, lon, geopotential height to x, y, z in ECEF.""" - # Convert geopotential to geometric height. This comes straight from - # TRAIN - g0 = 9.80665 - # Map of g with latitude (I'm skeptical of this equation) - g = 9.80616*(1 - 0.002637*cosd(2*lats) + 0.0000059*(cosd(2*lats))**2) - Rmax = 6378137 - Rmin = 6356752 - Re = np.sqrt(1/(((cosd(lats)**2)/Rmax**2) + ((sind(lats)**2)/Rmin**2))) - - # Calculate Geometric Height, h - h = (hts*Re)/(g/g0*Re - hts) - return lla2ecef(lats, lons, h) - - -def big_and(*args): - result = args[0] - for a in args[1:]: - result = np.logical_and(result, a) - return result - - -def gdal_open(fname, returnProj = False): - if os.path.exists(fname + '.vrt'): - fname = fname + '.vrt' - try: - ds = gdal.Open(fname, gdal.GA_ReadOnly) - except: - raise RuntimeError('File {} could not be opened'.format(fname)) - proj = ds.GetProjection() - - val = [] - for band in range(ds.RasterCount): - b = ds.GetRasterBand(band + 1) # gdal counts from 1, not 0 - d = b.ReadAsArray() - try: - ndv = b.GetNoDataValue() - d[d==ndv]=np.nan - except: - print('NoDataValue attempt failed*******') - pass - val.append(d) - b = None - ds = None - - if len(val) > 1: - data = np.stack(val) - else: - data = val[0] - - if not returnProj: - return data - else: - return data, proj - - -def pickle_load(f): - with open(f, 'rb') as fil: - return pickle.load(fil) - -def pickle_dump(o, f): - with open(f, 'wb') as fil: - pickle.dump(o, fil) - - -def writeArrayToRaster(array, filename, noDataValue = 0, fmt = 'ENVI', proj = None, gt = None): - # write a numpy array to a GDAL-readable raster - import gdal - import numpy as np - array_shp = np.shape(array) - dType = array.dtype - if 'complex' in str(dType): - dType = gdal.GDT_CFloat32 - elif 'float' in str(dType): - dType = gdal.GDT_Float32 - else: - dType = gdal.GDT_Byte - - driver = gdal.GetDriverByName(fmt) - ds = driver.Create(filename, array_shp[1], array_shp[0], 1, dType) - if proj is not None: - ds.SetProjection(proj) - if gt is not None: - ds.SetGeoTransform(gt) - b1 = ds.GetRasterBand(1) - b1.WriteArray(array) - b1.SetNoDataValue(noDataValue) - ds = None - b1 = None - - -def writeArrayToFile(lats, lons, array, filename, noDataValue = -9999): - ''' - Write a single-dim array of values to a file - ''' - array[np.isnan(array)] = noDataValue - with open(filename, 'w') as f: - f.write('Lat,Lon,DEM_hgt_m\n') - for l, L, a in zip(lats, lons, array): - f.write('{},{},{}\n'.format(l, L, a)) - - -def round_date(date, precision): - import datetime - # First try rounding up - # Timedelta since the beginning of time - datedelta = datetime.datetime.min - date - # Round that timedelta to the specified precision - rem = datedelta % precision - # Add back to get date rounded up - round_up = date + rem - - # Next try rounding down - datedelta = date - datetime.datetime.min - rem = datedelta % precision - round_down = date - rem - - # It's not the most efficient to calculate both and then choose, but - # it's clear, and performance isn't critical here. - up_diff = round_up - date - down_diff = date - round_down - - return round_up if up_diff < down_diff else round_down - - -def _least_nonzero(a): - """Fill in a flat array with the lowest nonzero value. - - Useful for interpolation below the bottom of the weather model. - """ - out = np.full(a.shape[:2], np.nan) - xlim, ylim, zlim = np.shape(a) - for x in range(xlim): - for y in range(ylim): - for z in range(zlim): - val = a[x][y][z] - if not np.isnan(val): - out[x][y] = val - break - return out - - -def sind(x): - """Return the sine of x when x is in degrees.""" - return np.sin(np.radians(x)) - - -def cosd(x): - """Return the cosine of x when x is in degrees.""" - return np.cos(np.radians(x)) - - -def tand(x): - """Return degree tangent.""" - return np.tan(np.radians(x)) - - -def robmin(a): - ''' - Get the minimum of an array, accounting for empty lists - ''' - from numpy import nanmin as min - try: - return min(a) - except ValueError: - return 'N/A' - -def robmax(a): - ''' - Get the minimum of an array, accounting for empty lists - ''' - from numpy import nanmax as max - try: - return max(a) - except ValueError: - return 'N/A' - - -def _get_g_ll(lats): - ''' - Compute the variation in gravity constant with latitude - ''' - #TODO: verify these constants. In particular why is the reference g different from self._g0? - return 9.80616*(1 - 0.002637*cosd(2*lats) + 0.0000059*(cosd(2*lats))**2) - -def _get_Re(lats): - ''' - Returns the ellipsoid as a fcn of latitude - ''' - #TODO: verify constants, add to base class constants? - Rmax = 6378137 - Rmin = 6356752 - return np.sqrt(1/(((cosd(lats)**2)/Rmax**2) + ((sind(lats)**2)/Rmin**2))) - - -def _geo_to_ht(lats, hts, g0 = 9.80556): - """Convert geopotential height to altitude.""" - # Convert geopotential to geometric height. This comes straight from - # TRAIN - # Map of g with latitude (I'm skeptical of this equation - Ray) - g_ll = _get_g_ll(lats) - Re = _get_Re(lats) - - # Calculate Geometric Height, h - h = (hts*Re)/(g_ll/g0*Re - hts) - - return h - - -def padLower(invar): - ''' - add a layer of data below the lowest current z-level at height zmin - ''' - new_var = _least_nonzero(invar) - return np.concatenate((new_var[:,:,np.newaxis], invar), axis =2) - - -def testArr(arr, thresh, ttype): - ''' - Helper function for checking heights - ''' - if ttype=='g': - test = np.all(arr>thresh) - elif ttype =='l': - test = np.all(arr>> l = [1, 2, 3, 4] - >>> list(chunked(l, 4)) - [[1], [2], [3], [4]] - - >>> l = [1, 2, 3] - >>> list(chunked(l, 4)) - [[1], [2], [3], []] - - >>> l = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] - >>> list(chunked(l, 4)) - [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10]] - - """ - import math - chunksize = int(math.ceil(len(iterable) / n)) - return (iterable[i * chunksize:i * chunksize + chunksize] - for i in range(n)) - - -def makeDelayFileNames(time, los,outformat, weather_model_name, out): - ''' - return names for the wet and hydrostatic delays - ''' - str1 = time.isoformat() + "_" if time is not None else "" - str2 = "z" if los is None else "s" - str3 = 'td.{}'.format(outformat) - hydroname, wetname = ( - '{}_{}_'.format(weather_model_name, dtyp) + str1 + str2 + str3 - for dtyp in ('hydro', 'wet')) - - hydro_file_name = os.path.join(out, hydroname) - wet_file_name = os.path.join(out, wetname) - return wet_file_name, hydro_file_name - - -def mkdir(dirName): - try: - os.mkdir(dirName) - except FileExistsError: - pass - -def writeLL(time, lats, lons, llProj, weather_model_name, out): - ''' - If the weather model grid nodes are used, write the lat/lon values - out to a file - ''' - from datetime import datetime as dt - lonFileName = '{}_Lon_{}.dat'.format(weather_model_name, - dt.strftime(time, '%Y_%m_%d_T%H_%M_%S')) - latFileName = '{}_Lat_{}.dat'.format(weather_model_name, - dt.strftime(time, '%Y_%m_%d_T%H_%M_%S')) - - mkdir('geom') - - writeArrayToRaster(lons, os.path.join(out, 'geom', lonFileName)) - writeArrayToRaster(lats, os.path.join(out, 'geom', latFileName)) - - return latFileName, lonFileName - - -def checkShapes(los, lats, lons, hgts): - ''' - Make sure that by the time the code reaches here, we have a - consistent set of line-of-sight and position data. - ''' - from utils.constants import Zenith - test1 = hgts.shape == lats.shape == lons.shape - try: - test2 = los.shape[:-1] != hts.shape - except: - test2 = los is not Zenith - - if not test1 or test2: - raise ValueError( - 'I need lats, lons, heights, and los to all be the same shape. ' + - 'lats had shape {}, lons had shape {}, '.format(lats.shape, lons.shape)+ - 'heights had shape {}, and los was not Zenith'.format(hts.shape)) - - -def checkLOS(los, raytrace, Npts): - ''' - Check that los is either: - (1) Zenith, - (2) a set of scalar values of the same size as the number - of points, which represent the projection value), or - (3) a set of vectors, same number as the number of points. - ''' - from utils.constants import Zenith - # los can either be a bunch of vectors or a bunch of scalars. If - # raytrace, then it's vectors, otherwise scalars. (Or it's Zenith) - if los is not Zenith: - if raytrace: - los = los.reshape(-1, 3) - else: - los = los.flatten() - - if los is not Zenith and los.shape[0] != Npts: - raise RuntimeError('Found {} line-of-sight values and only {} points' - .format(los.shape[0], Npts)) - return los - - - -def readLLFromStationFile(fname): - ''' - Helper fcn for checking argument compatibility - ''' - try: - import pandas as pd - stats = pd.read_csv(fname) - return stats['Lat'].values,stats['Lon'].values - except: - lats, lons = [], [] - with open(fname, 'r') as f: - for i, line in enumerate(f): - if i == 0: - continue - lat, lon = [float(f) for f in line.split(',')[1:3]] - lats.append(lat) - lons.append(lon) - return lats, lons - - -def mangle_model_to_module(model_name): - """Turn an arbitrary string into a module name. - - Takes as input a model name, which hopefully looks like ERA-I, and - converts it to a module name, which will look like erai. I doesn't - always produce a valid module name, but that's not the goal. The - goal is just to handle common cases. - """ - return 'models.' + model_name.lower().replace('-', '') - - -def gdal_trans(f1, f2, fmt = 'VRT'): - ''' - translate a file from one location to another using GDAL - ''' - ds1 = gdal.Open(f1) - if ds1 is None: - raise RuntimeError('Could not open the file {}'.format(f1)) - ds2 = gdal.Translate(f2, ds1, format = fmt) - if ds2 is None: - raise RuntimeError('Could not translate the file {} to {}'.format(f1, f2)) - ds1 = None - ds2 = None - - -def isOutside(extent1, extent2): - ''' - Determine whether any of extent1 lies outside extent2 - extent1/2 should be a list containing [lower_lat, upper_lat, left_lon, right_lon] - ''' - t1 = extent1[0] < extent2[0] - t2 = extent1[1] > extent2[1] - t3 = extent1[2] < extent2[2] - t4 = extent1[3] > extent2[3] - if np.any([t1, t2, t3, t4]): - return True - return False - - -def getExtent(lats, lons=None): - ''' - get the bounding box around a set of lats/lons - ''' - if lons is None: - ds = gdal.Open(lats, gdal.GA_ReadOnly) - trans = ds.GetGeoTransform() - # W E S N - extent = [trans[0], trans[0] + ds.RasterXSize * trans[1], - trans[3] + ds.RasterYSize*trans[5], trans[3]] - if shrink is not None: - delW, delE, delS, delN = shrink - extent = [extent[0] + delW, extent[1] - delE, extent[2] + delS, extent[3] - delN] - del ds - return extent - - else: - return [np.nanmin(lats), np.nanmax(lats), np.nanmin(lons), np.nanmax(lons)] - - -def setLLds(infile, latfile, lonfile): - ''' - Use a lat/lon file to set the x/y coordinates of infile - ''' - from osgeo import gdal, osr - ds = gdal.Open(infile, gdal.GA_ReadOnly) - ds.SetMetadata({'X_DATASET': os.path.abspath(latfile), 'X_BAND': '1', - 'Y_DATASET': os.path.abspath(lonfile), 'Y_BAND': '1'}) - - srs = osr.SpatialReference() - srs.ImportFromEPSG(4326) - ds.SetProjection(srs.ExportToWkt()) - del ds - diff --git a/tools/RAiDER/utilFcns.py b/tools/RAiDER/utilFcns.py new file mode 100755 index 000000000..e5cfb86f2 --- /dev/null +++ b/tools/RAiDER/utilFcns.py @@ -0,0 +1,1075 @@ +"""Geodesy-related utility functions.""" +import multiprocessing as mp +import os +import re + +from datetime import datetime, timedelta + +import h5py +import xarray as xr +import numpy as np +from numpy import ndarray +import pandas as pd +import pyproj +from pyproj import Transformer +import progressbar +import rasterio + +from RAiDER.constants import ( + _g0 as g0, + R_EARTH_MAX as Rmax, + R_EARTH_MIN as Rmin, +) +from RAiDER.logger import logger + + +pbar = None + + +def projectDelays(delay, inc): + '''Project zenith delays to LOS''' + return delay / cosd(inc) + + +def floorish(val, frac): + '''Round a value to the lower fractional part''' + return val - (val % frac) + + +def sind(x): + """Return the sine of x when x is in degrees.""" + return np.sin(np.radians(x)) + + +def cosd(x): + """Return the cosine of x when x is in degrees.""" + return np.cos(np.radians(x)) + + +def lla2ecef(lat, lon, height): + T = Transformer.from_crs(4326, 4978, always_xy=True) + + return T.transform(lon, lat, height) + + +def ecef2lla(x, y, z): + T = Transformer.from_crs(4978, 4326, always_xy=True) + + return T.transform(x, y, z) + + +def enu2ecef( + east: ndarray, + north: ndarray, + up: ndarray, + lat0: ndarray, + lon0: ndarray, + h0: ndarray, +): + """ + Parameters + ---------- + e1 : float + target east ENU coordinate (meters) + n1 : float + target north ENU coordinate (meters) + u1 : float + target up ENU coordinate (meters) + Results + ------- + u : float + v : float + w : float + """ + t = cosd(lat0) * up - sind(lat0) * north + w = sind(lat0) * up + cosd(lat0) * north + + u = cosd(lon0) * t - sind(lon0) * east + v = sind(lon0) * t + cosd(lon0) * east + + return np.stack((u, v, w), axis=-1) + + +def ecef2enu(xyz, lat, lon, height): + '''Convert ECEF xyz to ENU''' + x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2] + + t = cosd(lon) * x + sind(lon) * y + + e = -sind(lon) * x + cosd(lon) * y + n = -sind(lat) * t + cosd(lat) * z + u = cosd(lat) * t + sind(lat) * z + return np.stack((e, n, u), axis=-1) + + +def rio_profile(fname): + ## need to access subdataset directly + if os.path.basename(fname).startswith('S1-GUNW'): + fname = os.path.join(f'NETCDF:"{fname}":science/grids/data/unwrappedPhase') + with rasterio.open(fname) as src: + profile = src.profile + + elif os.path.exists(fname + '.vrt'): + fname = fname + '.vrt' + + with rasterio.open(fname) as src: + profile = src.profile + # if 'S1-GUNW' in fname: + # profile['length'] = profile['width'] + # profile['width'] = profile['height'] + + if profile["crs"] is None: + raise AttributeError( + f"{fname} does not contain geotransform information" + ) + + return profile + + +def rio_extents(profile): + """ Get a bounding box in SNWE from a rasterio profile """ + gt = profile["transform"].to_gdal() + xSize = profile["width"] + ySize = profile["height"] + + if profile["crs"] is None or not gt: + raise AttributeError('Profile does not contain geotransform information') + W, E = gt[0], gt[0] + (xSize - 1) * gt[1] + (ySize - 1) * gt[2] + N, S = gt[3], gt[3] + (xSize - 1) * gt[4] + (ySize - 1) * gt[5] + return S, N, W, E + + +def rio_open(fname, returnProj=False, userNDV=None, band=None): + if os.path.exists(fname + '.vrt'): + fname = fname + '.vrt' + + with rasterio.open(fname) as src: + profile = src.profile + + # For all bands + nodata = src.nodatavals + + # If user requests a band + if band is not None: + ndv = nodata[band - 1] + data = src.read(band).squeeze() + nodataToNan(data, [userNDV, nodata[band - 1]]) + + else: + data = src.read().squeeze() + if data.ndim > 2: + for bnd in range(data.shape[0]): + val = data[bnd, ...] + nodataToNan(val, [userNDV, nodata[bnd]]) + else: + nodataToNan(data, list(nodata) + [userNDV]) + + + if data.ndim > 2: + dlist = [] + for k in range(data.shape[0]): + dlist.append(data[k,...].copy()) + data = dlist + + if not returnProj: + return data + + else: + return data, profile + + +def nodataToNan(inarr, listofvals): + """ + Setting values to nan as needed + """ + inarr = inarr.astype(float) # nans cannot be integers (i.e. in DEM) + for val in listofvals: + if val is not None: + inarr[inarr == val] = np.nan + + +def rio_stats(fname, band=1, userNDV=None): + if os.path.basename(fname).startswith('S1-GUNW'): + fname = os.path.join(f'NETCDF:"{fname}":science/grids/data/unwrappedPhase') + + if os.path.exists(fname + '.vrt'): + fname = fname + '.vrt' + + # Turn off PAM to avoid creating .aux.xml files + with rasterio.Env(GDAL_PAM_ENABLED="NO"): + with rasterio.open(fname) as src: + gt = src.transform.to_gdal() + proj = src.crs + stats = src.statistics(band) + + return stats, proj, gt + + +def get_file_and_band(filestr): + """ + Support file;bandnum as input for filename strings + """ + parts = filestr.split(";") + + # Defaults to first band if no bandnum is provided + if len(parts) == 1: + return filestr.strip(), 1 + elif len(parts) == 2: + return parts[0].strip(), int(parts[1].strip()) + else: + raise ValueError( + f"Cannot interpret {filestr} as valid filename" + ) + + +def writeResultsToHDF5(lats, lons, hgts, wet, hydro, filename, delayType=None): + ''' + write a 1-D array to a NETCDF5 file + ''' + if delayType is None: + delayType = "Zenith" + + with h5py.File(filename, 'w') as f: + f['lat'] = lats + f['lon'] = lons + f['hgts'] = hgts + f['wetDelay'] = wet + f['hydroDelay'] = hydro + f['wetDelayUnit'] = "m" + f['hydroDelayUnit'] = "m" + f['hgtsUnit'] = "m" + f.attrs['DelayType'] = delayType + + +def writeArrayToRaster(array, filename, noDataValue=0., fmt='ENVI', proj=None, gt=None): + ''' + write a numpy array to a GDAL-readable raster + ''' + array_shp = np.shape(array) + if array.ndim != 2: + raise RuntimeError('writeArrayToRaster: cannot write an array of shape {} to a raster image'.format(array_shp)) + + # Data type + if "complex" in str(array.dtype): + dtype = np.complex64 + elif "float" in str(array.dtype): + dtype = np.float32 + else: + dtype = np.uint8 + + # Geotransform + trans = None + if gt is not None: + trans = rasterio.Affine.from_gdal(*gt) + + with rasterio.open(filename, mode="w", count=1, + width=array_shp[1], height=array_shp[0], + dtype=dtype, crs=proj, nodata=noDataValue, + driver=fmt, transform=trans) as dst: + dst.write(array, 1) + + +def writeArrayToFile(lats, lons, array, filename, noDataValue=-9999): + ''' + Write a single-dim array of values to a file + ''' + array[np.isnan(array)] = noDataValue + with open(filename, 'w') as f: + f.write('Lat,Lon,Hgt_m\n') + for lat, lon, height in zip(lats, lons, array): + f.write('{},{},{}\n'.format(lat, lon, height)) + + +def round_date(date, precision): + # First try rounding up + # Timedelta since the beginning of time + datedelta = datetime.min - date + # Round that timedelta to the specified precision + rem = datedelta % precision + # Add back to get date rounded up + round_up = date + rem + + # Next try rounding down + datedelta = date - datetime.min + rem = datedelta % precision + round_down = date - rem + + # It's not the most efficient to calculate both and then choose, but + # it's clear, and performance isn't critical here. + up_diff = round_up - date + down_diff = date - round_down + + return round_up if up_diff < down_diff else round_down + + +def _least_nonzero(a): + """Fill in a flat array with the first non-nan value in the last dimension. + + Useful for interpolation below the bottom of the weather model. + """ + mgrid_index = tuple(slice(None, d) for d in a.shape[:-1]) + return a[tuple(np.mgrid[mgrid_index]) + ((~np.isnan(a)).argmax(-1),)] + + +def robmin(a): + ''' + Get the minimum of an array, accounting for empty lists + ''' + try: + return np.nanmin(a) + except ValueError: + return 'N/A' + + +def robmax(a): + ''' + Get the minimum of an array, accounting for empty lists + ''' + try: + return np.nanmax(a) + except ValueError: + return 'N/A' + + +def _get_g_ll(lats): + ''' + Compute the variation in gravity constant with latitude + ''' + # TODO: verify these constants. In particular why is the reference g different from self._g0? + return 9.80616 * (1 - 0.002637 * cosd(2 * lats) + 0.0000059 * (cosd(2 * lats))**2) + + +def _get_Re(lats): + ''' + Returns the ellipsoid as a fcn of latitude + ''' + # TODO: verify constants, add to base class constants? + return np.sqrt(1 / (((cosd(lats)**2) / Rmax**2) + ((sind(lats)**2) / Rmin**2))) + + +def _geo_to_ht(lats, hts): + """Convert geopotential height to altitude.""" + # Convert geopotential to geometric height. This comes straight from + # TRAIN + # Map of g with latitude (I'm skeptical of this equation - Ray) + g_ll = _get_g_ll(lats) + Re = _get_Re(lats) + + # Calculate Geometric Height, h + h = (hts * Re) / (g_ll / g0 * Re - hts) + # from metpy + # return (geopotential * Re) / (g0 * Re - geopotential) + + return h + + +def padLower(invar): + ''' + add a layer of data below the lowest current z-level at height zmin + ''' + new_var = _least_nonzero(invar) + return np.concatenate((new_var[:, :, np.newaxis], invar), axis=2) + + +def checkShapes(los, lats, lons, hts): + ''' + Make sure that by the time the code reaches here, we have a + consistent set of line-of-sight and position data. + ''' + from RAiDER.losreader import Zenith + test1 = hts.shape == lats.shape == lons.shape + try: + test2 = los.shape[:-1] == hts.shape + except AttributeError: + test2 = los is Zenith + + if not test1 and test2: + raise ValueError( + 'I need lats, lons, heights, and los to all be the same shape. ' + + 'lats had shape {}, lons had shape {}, '.format(lats.shape, lons.shape) + + 'heights had shape {}, and los was not Zenith'.format(hts.shape)) + + +def checkLOS(los, Npts): + ''' + Check that los is either: + (1) Zenith, + (2) a set of scalar values of the same size as the number + of points, which represent the projection value), or + (3) a set of vectors, same number as the number of points. + ''' + from RAiDER.losreader import Zenith + + # los is a bunch of vectors or Zenith + if los is not Zenith: + los = los.reshape(-1, 3) + + if los is not Zenith and los.shape[0] != Npts: + raise RuntimeError('Found {} line-of-sight values and only {} points' + .format(los.shape[0], Npts)) + return los + + +def read_hgt_file(filename): + ''' + Read height data from a comma-delimited file + ''' + data = pd.read_csv(filename) + hgts = data['Hgt_m'].values + return hgts + + +def round_time(dt, roundTo=60): + ''' + Round a datetime object to any time lapse in seconds + dt: datetime.datetime object + roundTo: Closest number of seconds to round to, default 1 minute. + Source: https://stackoverflow.com/questions/3463930/how-to-round-the-minute-of-a-datetime-object/10854034#10854034 + ''' + seconds = (dt.replace(tzinfo=None) - dt.min).seconds + rounding = (seconds + roundTo / 2) // roundTo * roundTo + return dt + timedelta(0, rounding - seconds, -dt.microsecond) + + +def writeDelays(aoi, wetDelay, hydroDelay, + wetFilename, hydroFilename=None, + outformat=None, proj=None, gt=None, ndv=0.): + ''' + Write the delay numpy arrays to files in the format specified + ''' + + # Need to consistently handle noDataValues + wetDelay[np.isnan(wetDelay)] = ndv + hydroDelay[np.isnan(hydroDelay)] = ndv + + # Do different things, depending on the type of input + if aoi.type() == 'station_file': + try: + df = pd.read_csv(aoi._filename) + except ValueError: + df = pd.read_csv(aoi._filename) + + df['wetDelay'] = wetDelay + df['hydroDelay'] = hydroDelay + df['totalDelay'] = wetDelay + hydroDelay + df.to_csv(wetFilename, index=False) + + else: + writeArrayToRaster( + wetDelay, + wetFilename, + noDataValue=ndv, + fmt=outformat, + proj=proj, + gt=gt + ) + writeArrayToRaster( + hydroDelay, + hydroFilename, + noDataValue=ndv, + fmt=outformat, + proj=proj, + gt=gt + ) + + +def getTimeFromFile(filename): + ''' + Parse a filename to get a date-time + ''' + fmt = '%Y_%m_%d_T%H_%M_%S' + p = re.compile(r'\d{4}_\d{2}_\d{2}_T\d{2}_\d{2}_\d{2}') + try: + out = p.search(filename).group() + return datetime.strptime(out, fmt) + except BaseException: # TODO: Which error(s)? + raise RuntimeError('The filename for {} does not include a datetime in the correct format'.format(filename)) + + +def writePnts2HDF5(lats, lons, hgts, los, lengths, outName='testx.h5', chunkSize=None, noDataValue=0., epsg=4326): + ''' + Write query points to an HDF5 file for storage and access + ''' + projname = 'projection' + + # converts from WGS84 geodetic to WGS84 geocentric + t = Transformer.from_crs(epsg, 4978, always_xy=True) + + checkLOS(los, np.prod(lats.shape)) + in_shape = lats.shape + + # create directory if needed + os.makedirs(os.path.abspath(os.path.dirname(outName)), exist_ok=True) + + # Set up the chunking + if chunkSize is None: + chunkSize = getChunkSize(in_shape) + + with h5py.File(outName, 'w') as f: + f.attrs['Conventions'] = np.string_("CF-1.8") + + x = f.create_dataset('lon', data=lons, chunks=chunkSize, fillvalue=noDataValue) + y = f.create_dataset('lat', data=lats, chunks=chunkSize, fillvalue=noDataValue) + z = f.create_dataset('hgt', data=hgts, chunks=chunkSize, fillvalue=noDataValue) + los = f.create_dataset( + 'LOS', + data=los, + chunks=chunkSize + (3,), + fillvalue=noDataValue + ) + lengths = f.create_dataset( + 'Rays_len', + data=lengths, + chunks=x.chunks, + fillvalue=noDataValue + ) + sp_data = np.stack(t.transform(lons, lats, hgts), axis=-1).astype(np.float64) + sp = f.create_dataset( + 'Rays_SP', + data=sp_data, + chunks=chunkSize + (3,), + fillvalue=noDataValue + ) + + x.attrs['Shape'] = in_shape + y.attrs['Shape'] = in_shape + z.attrs['Shape'] = in_shape + los.attrs['Shape'] = in_shape + (3,) + lengths.attrs['Shape'] = in_shape + lengths.attrs['Units'] = 'm' + sp.attrs['Shape'] = in_shape + (3,) + f.attrs['ChunkSize'] = chunkSize + f.attrs['NoDataValue'] = noDataValue + + # CF 1.8 Convention stuff + crs = pyproj.CRS.from_epsg(epsg) + projds = f.create_dataset(projname, (), dtype='i') + projds[()] = epsg + + # WGS84 ellipsoid + projds.attrs['semi_major_axis'] = 6378137.0 + projds.attrs['inverse_flattening'] = 298.257223563 + projds.attrs['ellipsoid'] = np.string_("WGS84") + projds.attrs['epsg_code'] = epsg + # TODO - Remove the wkt version after verification + projds.attrs['spatial_ref'] = np.string_(crs.to_wkt("WKT1_GDAL")) + + # Geodetic latitude / longitude + if epsg == 4326: + # Set up grid mapping + projds.attrs['grid_mapping_name'] = np.string_('latitude_longitude') + projds.attrs['longitude_of_prime_meridian'] = 0.0 + + x.attrs['standard_name'] = np.string_("longitude") + x.attrs['units'] = np.string_("degrees_east") + y.attrs['standard_name'] = np.string_("latitude") + y.attrs['units'] = np.string_("degrees_north") + z.attrs['standard_name'] = np.string_("height") + z.attrs['units'] = np.string_("m") + else: + raise NotImplementedError + + los.attrs['grid_mapping'] = np.string_(projname) + sp.attrs['grid_mapping'] = np.string_(projname) + lengths.attrs['grid_mapping'] = np.string_(projname) + + f.attrs['NumRays'] = len(x) + f['Rays_len'].attrs['MaxLen'] = np.nanmax(lengths) + + +# Part of the following UTM and WGS84 converter is borrowed from https://gist.github.com/twpayne/4409500 +# Credits go to Tom Payne + +_projections = {} + + +def zone(coordinates): + if 56 <= coordinates[1] < 64 and 3 <= coordinates[0] < 12: + return 32 + if 72 <= coordinates[1] < 84 and 0 <= coordinates[0] < 42: + if coordinates[0] < 9: + return 31 + elif coordinates[0] < 21: + return 33 + elif coordinates[0] < 33: + return 35 + return 37 + return int((coordinates[0] + 180) / 6) + 1 + + +def letter(coordinates): + return 'CDEFGHJKLMNPQRSTUVWXX'[int((coordinates[1] + 80) / 8)] + + +def project(coordinates, z=None, l=None): + if z is None: + z = zone(coordinates) + if l is None: + l = letter(coordinates) + if z not in _projections: + _projections[z] = pyproj.Proj(proj='utm', zone=z, ellps='WGS84') + x, y = _projections[z](coordinates[0], coordinates[1]) + if y < 0: + y += 10000000 + return z, l, x, y + + +def unproject(z, l, x, y): + if z not in _projections: + _projections[z] = pyproj.Proj(proj='utm', zone=z, ellps='WGS84') + if l < 'N': + y -= 10000000 + lng, lat = _projections[z](x, y, inverse=True) + return (lng, lat) + + +def WGS84_to_UTM(lon, lat, common_center=False): + shp = lat.shape + lon = np.ravel(lon) + lat = np.ravel(lat) + if common_center: + lon0 = np.median(lon) + lat0 = np.median(lat) + z0, l0, x0, y0 = project((lon0, lat0)) + Z = lon.copy() + L = np.zeros(lon.shape, dtype=' 180 + outLons = inLons + outLons[mask] = outLons[mask] - 360 + return outLons + + +def read_NCMR_loginInfo(filepath=None): + + from pathlib import Path + + if filepath is None: + filepath = str(Path.home()) + '/.ncmrlogin' + + f = open(filepath, 'r') + lines = f.readlines() + url = lines[0].strip().split(': ')[1] + username = lines[1].strip().split(': ')[1] + password = lines[2].strip().split(': ')[1] + + return url, username, password + + +def read_EarthData_loginInfo(filepath=None): + + from netrc import netrc + + urs_usr, _, urs_pwd = netrc().hosts["urs.earthdata.nasa.gov"] + return urs_usr, urs_pwd + + +def show_progress(block_num, block_size, total_size): + global pbar + if pbar is None: + pbar = progressbar.ProgressBar(maxval=total_size) + pbar.start() + + downloaded = block_num * block_size + if downloaded < total_size: + pbar.update(downloaded) + else: + pbar.finish() + pbar = None + + +def getChunkSize(in_shape): + '''Create a reasonable chunk size''' + minChunkSize = 100 + maxChunkSize = 1000 + cpu_count = mp.cpu_count() + chunkSize = tuple( + max( + min(maxChunkSize, s // cpu_count), + min(s, minChunkSize) + ) for s in in_shape + ) + return chunkSize + + +def calcgeoh(lnsp, t, q, z, a, b, R_d, num_levels): + ''' + Calculate pressure, geopotential, and geopotential height + from the surface pressure and model levels provided by a weather model. + The model levels are numbered from the highest eleveation to the lowest. + Parameters + ---------- + lnsp: ndarray - [y, x] array of log surface pressure + t: ndarray - [z, y, x] cube of temperatures + q: ndarray - [z, y, x] cube of specific humidity + geopotential: ndarray - [z, y, x] cube of geopotential values + a: ndarray - [z] vector of a values + b: ndarray - [z] vector of b values + num_levels: int - integer number of model levels + Returns + ------- + geopotential - The geopotential in units of height times acceleration + pressurelvs - The pressure at each of the model levels for each of + the input points + geoheight - The geopotential heights + ''' + geopotential = np.zeros_like(t) + pressurelvs = np.zeros_like(geopotential) + geoheight = np.zeros_like(geopotential) + + # log surface pressure + # Note that we integrate from the ground up, so from the largest model level to 0 + sp = np.exp(lnsp) + + if len(a) != num_levels + 1 or len(b) != num_levels + 1: + raise ValueError( + 'I have here a model with {} levels, but parameters a '.format(num_levels) + + 'and b have lengths {} and {} respectively. Of '.format(len(a), len(b)) + + 'course, these three numbers should be equal.') + + # Integrate up into the atmosphere from *lowest level* + z_h = 0 # initial value + for lev, t_level, q_level in zip( + range(num_levels, 0, -1), t[::-1], q[::-1]): + + # lev is the level number 1-60, we need a corresponding index + # into ts and qs + # ilevel = num_levels - lev # << this was Ray's original, but is a typo + # because indexing like that results in pressure and height arrays that + # are in the opposite orientation to the t/q arrays. + ilevel = lev - 1 + + # compute moist temperature + t_level = t_level * (1 + 0.609133 * q_level) + + # compute the pressures (on half-levels) + Ph_lev = a[lev - 1] + (b[lev - 1] * sp) + Ph_levplusone = a[lev] + (b[lev] * sp) + + pressurelvs[ilevel] = Ph_lev # + Ph_levplusone) / 2 # average pressure at half-levels above and below + + if lev == 1: + dlogP = np.log(Ph_levplusone / 0.1) + alpha = np.log(2) + else: + dlogP = np.log(Ph_levplusone) - np.log(Ph_lev) + alpha = 1 - ((Ph_lev / (Ph_levplusone - Ph_lev)) * dlogP) + + TRd = t_level * R_d + + # z_f is the geopotential of this full level + # integrate from previous (lower) half-level z_h to the full level + z_f = z_h + TRd * alpha + z + + # Geopotential (add in surface geopotential) + geopotential[ilevel] = z_f + geoheight[ilevel] = geopotential[ilevel] / g0 + + # z_h is the geopotential of 'half-levels' + # integrate z_h to next half level + z_h += TRd * dlogP + + return geopotential, pressurelvs, geoheight + + +def transform_coords(proj1, proj2, x, y): + """ + Transform coordinates from proj1 to proj2 (can be EPSG or crs from proj). + e.g. x, y = transform_coords(4326, 4087, lon, lat) + """ + transformer = Transformer.from_crs(proj1, proj2, always_xy=True) + return transformer.transform(x, y) diff --git a/tools/bin/raiderDelay.py b/tools/bin/raiderDelay.py deleted file mode 100755 index fe03adf10..000000000 --- a/tools/bin/raiderDelay.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python3 -""" -usage: tropo_delay [-h] [--lineofsight LOS | --statevectors STATEVECTORS] - [--area LAT LONG | --bounding_box N W S E | --station_file STATION_FILE] - [--dem DEM | --heightlvs HEIGHTLVS [HEIGHTLVS ...]] --time - TIME [--model MODEL] [--pickleFile PICKLEFILE] - [--wrfmodelfiles OUT PLEV] [--wmnetcdf WMNETCDF] - [--zref ZREF] [--outformat OUTFORMAT] [--out OUT] - [--model_location WMLOC] [--no_parallel] [--download_only] - [--verbose] - -Calculate tropospheric delay from a weather model - -optional arguments: - -h, --help show this help message and exit - --lineofsight LOS, -l LOS - GDAL-readable line-of-sight file - --statevectors STATEVECTORS, -s STATEVECTORS - An ISCE XML or shelve file containing state vectors - specifying the orbit of the sensor - --area LAT LONG, -a LAT LONG - GDAL-readable longitude and latitude files to specify - the region over which to calculate delay. Delay will - be calculated at weather model nodes if unspecified - --bounding_box N W S E, -bb N W S E - Bounding box, given as N W S E - --station_file STATION_FILE - CSV file containing a list of stations, with at least the - columns "Lat" and "Lon" - --dem DEM, -d DEM DEM file. DEM will be downloaded if not specified - --heightlvs HEIGHTLVS [HEIGHTLVS ...] - Delay will be calculated at each of these heights - across all of the specified area - --time TIME Fetch weather model data at this (ISO 8601 format) - time - --model MODEL Weather model to use - --pickleFile PICKLEFILE - Pickle file to load - --wmnetcdf WMNETCDF Weather model netcdf file. Should have q, t, z, lnsp - as variables - --zref ZREF, -z ZREF Height limit when integrating (meters) (default: - 15000) - --outformat OUTFORMAT - Output file format; GDAL-compatible for DEM, HDF5 for - height levels (default: ENVI) - --out OUT Output file directory - --model_location WMLOC - Directory where weather model files are stored - --no_parallel, -p Do not run operation in parallel? Default False. - Recommend only True for verbose (debug) mode - --download_only Download weather model only without processing? - Default False - --verbose, -v Run in verbose (debug) mode? Default False - -WRF: - Arguments for when --model WRF is specified - - --wrfmodelfiles OUT PLEV - WRF model files -""" - - -import argparse -import datetime -import itertools -import os - -# Local imports -import delay -import pdb - -def read_date(s): - """Read a date from a string in pseudo-ISO 8601 format.""" - year_formats = ( - '%Y-%m-%d', - '%Y%m%d', - '%Y-%m', - '%Y', # I don't think anyone would ever want just a year - ) - time_formats = ( - '', - 'T%H:%M:%S.%f', - 'T%H:%M:%S', - 'T%H%M%S.%f', - 'T%H%M%S', - 'T%H:%M', - 'T%H%M', - 'T%H', - ) - timezone_formats = ( - '', - 'Z', - '%z', - ) - all_formats = map( - ''.join, - itertools.product(year_formats, time_formats, timezone_formats)) - date = None - for date_format in all_formats: - try: - date = datetime.datetime.strptime(s, date_format) - except ValueError: - continue - if date is None: - raise ValueError( - 'Unable to coerce {} to a date. Try %Y-%m-%dT%H:%M:%S.%f%z'.format(s)) - - return date - - -def parse_args(): - """Parse command line arguments using argparse.""" - p = argparse.ArgumentParser( - description='Calculate tropospheric delay from a weather model') - - p.add_argument( - '--time', - help='Fetch weather model data at this (ISO 8601 format) time', - type=read_date, required=True) - - # Line of sight - los = p.add_mutually_exclusive_group() - los.add_argument( - '--lineofsight', '-l', - help='GDAL-readable line-of-sight file', - metavar='LOS', default=None) - los.add_argument( - '--statevectors', '-s', default=None, - help=('An ISCE XML or shelve file containing state vectors specifying ' - 'the orbit of the sensor')) - - # Area - area = p.add_mutually_exclusive_group() - area.add_argument( - '--area', '-a', nargs=2,default = None, - help=('GDAL-readable longitude and latitude files to specify the ' - 'region over which to calculate delay. Delay will be ' - 'calculated at weather model nodes if unspecified'), - metavar=('LAT', 'LONG')) - - # model BBOX - p.add_argument( - '--modelBBOX', '-modelbb', nargs=4, - help='BBOX of the model to be downloaded, given as N W S E, if not givem defualts in following order: lon-lat derived BBOX, or full world', - metavar=('N', 'W', 'S', 'E')) - area.add_argument( - '--station_file',default = None, type=str, dest='station_file', - help=('CSV file containing a list of stations, with at least ' - 'the columns "Lat" and "Lon"')) - - # heights - heights = p.add_mutually_exclusive_group() - heights.add_argument( - '--dem', '-d', default=None, - help='DEM file. DEM will be downloaded if not specified') - heights.add_argument( - '--heightlvs', default=None, - help=('Delay will be calculated at each of these heights across ' - 'all of the specified area'), - nargs='+', type=float) - - # Weather model - p.add_argument( - '--model', - help='Weather model to use', - default='ERA-5') - p.add_argument( - '--pickleFile', - help='Pickle file to load', - default=None) - - wrf = p.add_argument_group( - title='WRF', - description='Arguments for when --model WRF is specified') - wrf.add_argument( - '--wrfmodelfiles', nargs=2, - help='WRF model files', - metavar=('OUT', 'PLEV')) - - p.add_argument( - '--wmnetcdf', - help=('Weather model netcdf file. Should have q, t, z, lnsp as ' - 'variables')) - - # Height max - p.add_argument( - '--zref', '-z', - help=('Height limit when integrating (meters) ' - '(default: %(default)s)'), - type=int, default=15000) - - p.add_argument( - '--outformat', help='Output file format; GDAL-compatible for DEM, HDF5 for height levels (default: ENVI)', - default='ENVI') - - p.add_argument('--out', help='Output file directory', default='.') - - p.add_argument('--no_parallel', '-p', action='store_true',dest='no_parallel', default = False, help='Do not run operation in parallel? Default False. Recommend only True for verbose (debug) mode') - - p.add_argument('--download_only', action='store_true',dest='download_only', default = False, help='Download weather model only without processing? Default False') - - p.add_argument('--verbose', '-v', action='store_true',dest='verbose', default = False, help='Run in verbose (debug) mode? Default False') - - return p.parse_args(), p - - -def writeDelays(wetDelay, hydroDelay, time, los, - out, outformat, weather_model_name, - proj = None, gt = None): - ''' - Write the delay numpy arrays to files in the format specified - ''' - import numpy as np - from utils.util import makeDelayFileNames as mdf, writeArrayToRaster as watr - - # Use zero for nodata - wetDelay[np.isnan(wetDelay)] = 0. - hydroDelay[np.isnan(hydroDelay)] = 0. - - # For later - wetFilename, hydroFilename = \ - mdf(time, los, outformat, weather_model_name, out) - - watr(wetDelay, wetFilename, noDataValue = 0., - fmt = outformat, proj = proj, gt = gt) - watr(hydroDelay, hydroFilename, noDataValue = 0., - fmt = outformat, proj = proj, gt = gt) - - - -def getTropoDelay(): - """tropo_delay main function. - - We'll parse arguments and call delay.py. - """ - from utils.util import mkdir - from utils.checkArgs import checkArgs - import utils.llreader as llr - - args, p = parse_args() - - mkdir(os.path.join(args.out, 'geom')) - mkdir(os.path.join(args.out, 'weather_files')) - - # Argument checking - los, lat, lon, heights, flag, weather_model, zref, outformat, \ - time, out, download_only, parallel, verbose = checkArgs(args, p) - - if verbose: - print('Starting to run the weather model calculation') - print('Time type: {}'.format(type(time))) - print('Time: {}'.format(time.strftime('%Y%m%d'))) - print('Parallel is {}'.format(parallel)) - - lats, lons= llr.readLL(lat, lon, flag) - - wetDelay, hydroDelay = \ - delay.tropo_delay(time, los, lats, lons, heights, - weather_model, zref, out, - parallel=parallel, verbose = verbose, - download_only = download_only) - - writeDelays(wetDelay, hydroDelay, time, los, - out, outformat, weather_model['name'], - proj = None, gt = None) - -if __name__ == '__main__': - getTropoDelay() - diff --git a/tools/bindings/geometry/cpp/CMakeLists.txt b/tools/bindings/geometry/cpp/CMakeLists.txt deleted file mode 100644 index 96e8e57c6..000000000 --- a/tools/bindings/geometry/cpp/CMakeLists.txt +++ /dev/null @@ -1,61 +0,0 @@ -cmake_minimum_required (VERSION 2.6) -project(geometrylib) - -if (APPLE) - set(CMAKE_MACOSX_RPATH 1) -endif(APPLE) - -if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE Release) -endif() - -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lstdc++ -fPIC") -set(CMAKE_CXX_FLAGS_DEBUG "-g") -set(CMAKE_CXX_FLAGS_RELEASE "-O3") - -# Set compile flags -set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS}") # -lblitz") - -# These are used to specify directories for build outputs -#set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY lib) -#set(CMAKE_LIBRARY_OUTPUT_DIRECTORY lib) -#set(CMAKE_RUNTIME_OUTPUT_DIRECTORY bin) - -# Set the install prefix if not specified on command line -if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) - set(CMAKE_INSTALL_PREFIX "." CACHE PATH "..." FORCE) -endif(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) - -# Find all the library directories -file(GLOB libraries classes/*) -# Find all the library files -foreach(library ${libraries}) - #message(${library}) - file(GLOB these_sources ${library}/*cc) - set(library_sources "${library_sources};${these_sources}") - file(GLOB these_headers ${library}/*h) - set(library_headers "${library_headers};${these_headers}") -endforeach() -# Set up build - -#add_library(geometry STATIC ${library_sources}) -add_library(geometry SHARED ${library_sources}) -target_link_libraries(geometry) # netcdf netcdf_c++ gsl gslcblas) # blitz) -target_include_directories(geometry PUBLIC ${libraries}) -# This allows the headers to be installed -set_target_properties(geometry PROPERTIES PUBLIC_HEADER "${library_headers}") -# Set up install path -install(TARGETS geometry DESTINATION lib PUBLIC_HEADER DESTINATION include) - -# Find all the mains -#file(GLOB main_files mains/*cc) -#foreach(main ${main_files}) - #message(${main}) - # Just the name of file, without path or extension -# get_filename_component(exe_name ${main} NAME_WE) - # Set up build -# add_executable(${exe_name} ${main}) -# target_link_libraries(${exe_name} geometry) - # Set up install path -# install(TARGETS ${exe_name} DESTINATION bin) -#endforeach() diff --git a/tools/bindings/geometry/cpp/classes/Geometry/Geometry.cc b/tools/bindings/geometry/cpp/classes/Geometry/Geometry.cc deleted file mode 100644 index a994f9d43..000000000 --- a/tools/bindings/geometry/cpp/classes/Geometry/Geometry.cc +++ /dev/null @@ -1,251 +0,0 @@ -/* - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -*/ - -#include "Geometry.h" - -void Geo2rdr::set_orbit(int nr_state_vec, - double *t, - double *x, - double *y, - double *z, - double *vx, - double *vy, - double *vz) -{ - - this->nr_state_vectors = nr_state_vec; - std::vector zeros(nr_state_vectors, 0); - orbit.populate(zeros, zeros, zeros, zeros, zeros, zeros, zeros); - for (int i=0; ilon0 = lon_first; - this->lat0 = lat_first; - this->delta_lon = lon_step; - this->delta_lat = lat_step; - this->nr_lines = length; - this->nr_pixels = width; - - heights = new double*[nr_lines]; - heights[0] = hgt; - for (int line=1; line residual_threshold) - { - //std::cout << ii << std::endl; - st = orbit.get_statevector(t); - E1 = st.velocity.x*(xyz.x - st.position.x) + st.velocity.y*(xyz.y - st.position.y) + st.velocity.z*(xyz.z - st.position.z); - dE1 = -pow(st.velocity.x,2) - pow(st.velocity.y,2) - pow(st.velocity.z,2); - dt = -E1/dE1; - t = t+dt; - - //slant_range = sqrt(pow(xyz.x - st.position.x ,2) + pow(xyz.y - st.position.y, 2) + pow(xyz.z - st.position.z, 2)); - - //std::cout << setprecision(10) << " ii, dt: " << ii << " , " << dt << " , " << slant_range << std::endl; - ii++; - - } - //std::cout << "azimuth time : " << t << std::endl; - sensor_xyz.x = st.position.x; - sensor_xyz.y = st.position.y; - sensor_xyz.z = st.position.z; - //double t_azimuth = t; - slant_range = sqrt(pow(xyz.x - st.position.x ,2) + pow(xyz.y - st.position.y, 2) + pow(xyz.z - st.position.z, 2)); - - //double t_isce = 3118.661048; - //statevector st_i = orbit.get_statevector(t_isce); - //double rng_isce = sqrt(pow(xyz.x - st_i.position.x ,2) + pow(xyz.y - st_i.position.y, 2) + pow(xyz.z - st_i.position.z, 2)); - //std::cout << setprecision(10) << "range with isce az time: " << rng_isce << std::endl; -} - - - -void Geo2rdr::geo2rdr() -{ - - std::cout << "geo2rdr" << std::endl; - - ellipsoid elp; - elp.wgs84(); - elp.info(); - int middle_stvec = nr_state_vectors/2; - double t0 = orbit.t[5]; // middle_stvec]; - - // - //statevector st = orbit.get_statevector(t0); - //std::cout << " state vector at : " << t0 << std::endl; - //std::cout << std::setprecision(10) << st.position.x << " , " << st.position.y << " , "<< st.position.z << std::endl; - - // - - double lat; - double lon; - double height; - point xyz; - point sensor_xyz; - pixel rdr_pixel; - double this_range; - range = new double[nr_lines*nr_pixels]; - los_x = new double[nr_lines*nr_pixels]; - los_y = new double[nr_lines*nr_pixels]; - los_z = new double[nr_lines*nr_pixels]; - for (int line=0; line < nr_lines; line++){ - for (int pixel=0; pixel -#include -#include -#include -#include "Orbit.h" -#include "Ellipsoid.h" - -using namespace std; - -const double PI = 3.141592653589793; - -class Geo2rdr -{ - - public: - Geo2rdr(void) {}; - ~Geo2rdr(void) {}; - - static Geo2rdr* instance(void) - { - if (self == NULL) {self = new Geo2rdr();} - return self; - }; - - virtual void set_orbit(int nr_state_vectors, - double* t, - double* x, - double* y, - double* z, - double* vx, - double* vy, - double* vz); - - virtual void set_geo_coordinate(double lon_first, double lat_first, - double lon_step, double lat_step, - int length, int width, - double* hgt); - - point xyz2llh(point, ellipsoid); - point llh2xyz(point, ellipsoid); - pixel get_radar_coordinate( Orbit &orbit, point &xyz, double t0); - //virtual void get_radar_coordinate( Orbit& orbit, point& xyz, point& sensor_xyz, double& range, double t0); - virtual void get_radar_coordinate( point& xyz, point& sensor_xyz, double& range, double t0); - - //void geo2rdr( Orbit &orbit, int nr_state_vectors, double lat0, double lon0, double delta_lat, double delta_lon, double* heights, int nr_lines, int nr_pixels, double* range, double* azimuth); - - virtual void geo2rdr(); - virtual void get_los(double** ux, double** uy,double** uz, int* length, int* width); - virtual void get_sensor_xyz(double** sensor_x, double** sensor_y, double** sensor_z); - virtual void get_range(double** range, int* length, int* width); - virtual void get_azimuth_time(double** az_time); - // ------------------------------------------------ - // data members - Orbit orbit; - int nr_state_vectors; - int nr_lines; - int nr_pixels; - double lat0, lon0, delta_lat, delta_lon; - double **heights; - double *range; - double *azimuth; - double *los_x; - double *los_y; - double *los_z; - - private: - static Geo2rdr* self; - - Geo2rdr(const Geo2rdr&) {assert(false);} - - void operator=(const Geo2rdr&) {assert(false);} - -}; -#endif diff --git a/tools/bindings/geometry/cpp/classes/Orbit/Orbit.cc b/tools/bindings/geometry/cpp/classes/Orbit/Orbit.cc deleted file mode 100644 index 713e8a7cd..000000000 --- a/tools/bindings/geometry/cpp/classes/Orbit/Orbit.cc +++ /dev/null @@ -1,111 +0,0 @@ -/* - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -*/ - -#include -#include -#include -#include "Orbit.h" - -using namespace std; -typedef std::vector< double > dVec; - - - -void Orbit::populate(dVec &T, dVec &X, dVec &Y, dVec &Z, dVec &Vx, dVec &Vy, dVec &Vz) -{ - t = T; - x = X; - y = Y; - z = Z; - vx = Vx; - vy = Vy; - vz = Vz; - -} - -void Orbit::interpolate() -{ - dx = Orbit::spline(t, x); - dy = Orbit::spline(t, y); - dz = Orbit::spline(t, z); - dvx = Orbit::spline(t, vx); - dvy = Orbit::spline(t, vy); - dvz = Orbit::spline(t, vz); - -} - -statevector Orbit::get_statevector(double &target_time) -{ - statevector st; - st.position.x = Orbit::splint(t, x, dx, target_time); - st.position.y = Orbit::splint(t, y, dy, target_time); - st.position.z = Orbit::splint(t, z, dz, target_time); - st.velocity.x = Orbit::splint(t, vx, dvx, target_time); - st.velocity.y = Orbit::splint(t, vy, dvy, target_time); - st.velocity.z = Orbit::splint(t, vz, dvz, target_time); - - st.t = target_time; - return st; -} - -std::vector Orbit::spline(std::vector &x, std::vector &y) -{ - int n = x.size(); - double sig, p; - - std::vector y2(n, 0); - std::vector u(n, 0); - y2[0] = 0; - u[0] = 0; - - - for (int i=1; i0; k--) - { - y2[k] = y2[k]*y2[k+1] + u[k]; - } - - return y2; - -} - -double Orbit::splint(std::vector &xa, std::vector &ya, std::vector &y2a, double &x) -{ - int n = xa.size(); - int klo=0; - int khi=n-1; - int k; - while (khi-klo > 1) - { - k = int((khi+klo)/2); - - if (xa[k]>x){ - khi=k; - } - else{ - klo=k; - }; - } - - - double h = xa[khi]-xa[klo]; - double a = (xa[khi]-x)/h; - double b = (x-xa[klo])/h; - double y = a*ya[klo] + b*ya[khi] + ((pow(a,3)-a)*y2a[klo] + (pow(b,3)-b)*y2a[khi])*(pow(h,2))/6; - - return y; - -} - diff --git a/tools/bindings/geometry/cpp/classes/Orbit/Orbit.h b/tools/bindings/geometry/cpp/classes/Orbit/Orbit.h deleted file mode 100644 index 7c5aa3eba..000000000 --- a/tools/bindings/geometry/cpp/classes/Orbit/Orbit.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -*/ - -#ifndef ORBIT_H -#define ORBIT_H - -#include -#include -#include -#include "StateVector.h" - -using namespace std; -typedef std::vector< double > dVec; - -class Orbit{ - - /* A class to reprtesent orbit of a platform. Orbit object (an instance - of this class) contains state vectors in discrete time. - The class contains a method for interpolating the orbit and a method - to extract state vector at a given time from interpolated orbit. - */ - - public: - - Orbit(void) {}; - ~Orbit(void) {}; - - dVec t; - dVec x; - dVec y; - dVec z; - dVec vx; - dVec vy; - dVec vz; - - dVec dx; - dVec dy; - dVec dz; - dVec dvx; - dVec dvy; - dVec dvz; - - public: - Orbit (dVec&, dVec&, dVec&, dVec&, dVec&, dVec&, dVec&); - void populate(dVec&, dVec&, dVec&, dVec&, dVec&, dVec&, dVec&); - void interpolate(); - statevector get_statevector(double&); - private: - std::vector spline(std::vector&, std::vector&); - double splint(std::vector&, std::vector&, std::vector&, double&); - -}; - -#endif - diff --git a/tools/bindings/geometry/cpp/classes/Orbit/StateVector.h b/tools/bindings/geometry/cpp/classes/Orbit/StateVector.h deleted file mode 100644 index be47572d3..000000000 --- a/tools/bindings/geometry/cpp/classes/Orbit/StateVector.h +++ /dev/null @@ -1,25 +0,0 @@ -/* - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -*/ - -#ifndef STATEVECTOR_H -#define STATEVECTOR_H - -#include "Point.h" - -class statevector { - /*Class to define a platform statevector, - * t: time at which the state vector of the platform is given - * position: cartesian coordinate of the platform position (x , y, z) - * velocity: velocity of the platform in all three axes (vx, vy, vz) - */ - public: - double t; - point position; - point velocity; -}; - -#endif diff --git a/tools/bindings/geometry/cpp/classes/Utility/Ellipsoid.h b/tools/bindings/geometry/cpp/classes/Utility/Ellipsoid.h deleted file mode 100644 index 3a9334765..000000000 --- a/tools/bindings/geometry/cpp/classes/Utility/Ellipsoid.h +++ /dev/null @@ -1,30 +0,0 @@ -/* - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -*/ - -#ifndef ELLIPSOID_H -#define ELLIPSOID_H - -struct ellipsoid { - /* A structure representing an ellipsoid which approximates a planet. - * */ - double a; - double b; - double f; - double e2 ; - ellipsoid () {a=0.0;b=0.0;f=0.0; e2 = 0.0;}; - ellipsoid (double x, double y) {a=x;b=y;f = (a-b)/a; e2 = 2.0*f-f*f;}; - void wgs84() {a=6378137.0;b=6356752.314245312;f=(a-b)/a; e2 = 2.0*f-f*f;}; - void info(){ - cout << "ellipsoid:" << endl; - cout << "semi major axis a: " << a << endl; - cout << "semi minor axis b: " << b << endl; - cout << "eccentricity e2: " << e2 << endl; - } - -}; - -#endif diff --git a/tools/bindings/geometry/cpp/classes/Utility/Point.h b/tools/bindings/geometry/cpp/classes/Utility/Point.h deleted file mode 100644 index 3f7e31386..000000000 --- a/tools/bindings/geometry/cpp/classes/Utility/Point.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -*/ - -#ifndef POINT_H -#define POINT_H - -#include -#include -#include - -class pixel { - /*A structure to define a pixel in radar range-Doppler coordinate system - * * represented by time (seconds) and range (slant range in meters); - * */ - public: - - double time; - double range; - public: - - pixel () { time=0; range=0;} - pixel (double a, double b) { time=a; range=b; } - pixel (double *a) { time=a[0]; range=a[1]; } - ~pixel () {} - -}; - -class point { - /* A class representing a 3D point with x, y and z coordinates. - * */ - friend std::ostream& operator<< (std::ostream&, point); - friend std::istream& operator>> (std::istream&, point &); - - public: // members - - double x; - double y; - double z; - - public : - - point () { x=0; y=0; z=0; } - point (double a, double b, double c) { x=a; y=b; z=c; } - point (double *a) { x=a[0]; y=a[1]; z=a[2]; } - ~point () {} - - point unity (); - double magnitude () { return sqrt(x*x + y*y + z*z); } - - -}; - -#endif - - diff --git a/tools/bindings/geometry/cython/Geo2rdr/ArrayWrapper.pxd b/tools/bindings/geometry/cython/Geo2rdr/ArrayWrapper.pxd deleted file mode 100644 index 0284be88a..000000000 --- a/tools/bindings/geometry/cython/Geo2rdr/ArrayWrapper.pxd +++ /dev/null @@ -1,29 +0,0 @@ -''' - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - 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PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) -#else - #define CYTHON_PEP393_ENABLED 0 - #define PyUnicode_1BYTE_KIND 1 - #define PyUnicode_2BYTE_KIND 2 - #define PyUnicode_4BYTE_KIND 4 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) - #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) - #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) -#endif -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) -#else - #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ - PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) - #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) - #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) - #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) -#endif -#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) -#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) -#else - #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) -#endif -#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) - #define PyObject_ASCII(o) PyObject_Repr(o) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBaseString_Type PyUnicode_Type - #define PyStringObject PyUnicodeObject - #define PyString_Type PyUnicode_Type - #define PyString_Check PyUnicode_Check - #define PyString_CheckExact PyUnicode_CheckExact - #define PyObject_Unicode PyObject_Str -#endif -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) - #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) -#else - #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) - #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) -#endif -#ifndef PySet_CheckExact - #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) -#endif -#if CYTHON_ASSUME_SAFE_MACROS - #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) -#else - #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyIntObject PyLongObject - #define PyInt_Type PyLong_Type - #define PyInt_Check(op) PyLong_Check(op) - #define PyInt_CheckExact(op) PyLong_CheckExact(op) - #define PyInt_FromString PyLong_FromString - #define PyInt_FromUnicode PyLong_FromUnicode - #define PyInt_FromLong PyLong_FromLong - #define PyInt_FromSize_t PyLong_FromSize_t - #define PyInt_FromSsize_t PyLong_FromSsize_t - #define PyInt_AsLong PyLong_AsLong - #define PyInt_AS_LONG PyLong_AS_LONG - #define PyInt_AsSsize_t PyLong_AsSsize_t - #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask - #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask - #define PyNumber_Int PyNumber_Long -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBoolObject PyLongObject -#endif -#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY - #ifndef PyUnicode_InternFromString - #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) - #endif -#endif -#if PY_VERSION_HEX < 0x030200A4 - typedef long Py_hash_t; - #define __Pyx_PyInt_FromHash_t PyInt_FromLong - #define __Pyx_PyInt_AsHash_t PyInt_AsLong -#else - #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t - #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t -#endif -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) -#else - #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) -#endif -#if CYTHON_USE_ASYNC_SLOTS - #if PY_VERSION_HEX >= 0x030500B1 - #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods - #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) - #else - #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) - #endif -#else - #define __Pyx_PyType_AsAsync(obj) NULL -#endif -#ifndef __Pyx_PyAsyncMethodsStruct - typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; - } __Pyx_PyAsyncMethodsStruct; -#endif - -#if defined(WIN32) || defined(MS_WINDOWS) - #define _USE_MATH_DEFINES -#endif -#include -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif -#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) -#define __Pyx_truncl trunc -#else -#define __Pyx_truncl truncl -#endif - - -#define __PYX_ERR(f_index, lineno, Ln_error) \ -{ \ - __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ -} - -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#endif - -#define __PYX_HAVE__Geo2rdr -#define __PYX_HAVE_API__Geo2rdr -/* Early includes */ -#include "ios" -#include "new" -#include "stdexcept" -#include "typeinfo" -#include "Geometry.h" -#include -#include -#include "numpy/arrayobject.h" -#include "numpy/ufuncobject.h" -#ifdef _OPENMP -#include -#endif /* _OPENMP */ - -#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) - #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); -#define __Pyx_PySequence_Tuple(obj)\ - (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); 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-#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* PyObject_GenericGetAttrNoDict.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr -#endif - -/* PyObject_GenericGetAttr.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr -#endif - -/* SetupReduce.proto */ -static int __Pyx_setup_reduce(PyObject* type_obj); - -/* Import.proto */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); - -/* CLineInTraceback.proto */ -#ifdef CYTHON_CLINE_IN_TRACEBACK -#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) -#else -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); -#endif - -/* CodeObjectCache.proto */ -typedef struct { - PyCodeObject* code_object; - int code_line; -} __Pyx_CodeObjectCacheEntry; -struct __Pyx_CodeObjectCache { - int count; - int max_count; - __Pyx_CodeObjectCacheEntry* entries; -}; -static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); -static PyCodeObject *__pyx_find_code_object(int code_line); -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); - -/* AddTraceback.proto */ -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename); - -/* BufferStructDeclare.proto */ -typedef struct { - Py_ssize_t shape, strides, suboffsets; -} __Pyx_Buf_DimInfo; -typedef struct { - size_t refcount; - Py_buffer pybuffer; -} __Pyx_Buffer; -typedef struct { - __Pyx_Buffer *rcbuffer; - char *data; - __Pyx_Buf_DimInfo diminfo[8]; -} __Pyx_LocalBuf_ND; - -#if PY_MAJOR_VERSION < 3 - static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); - static void __Pyx_ReleaseBuffer(Py_buffer *view); -#else - #define __Pyx_GetBuffer PyObject_GetBuffer - #define __Pyx_ReleaseBuffer PyBuffer_Release -#endif - - -/* CppExceptionConversion.proto */ -#ifndef __Pyx_CppExn2PyErr -#include -#include -#include -#include -static void __Pyx_CppExn2PyErr() { - try { - if (PyErr_Occurred()) - ; // let the latest Python exn pass through and ignore the current one - else - throw; - } catch (const std::bad_alloc& exn) { - PyErr_SetString(PyExc_MemoryError, exn.what()); - } catch (const std::bad_cast& exn) { - PyErr_SetString(PyExc_TypeError, exn.what()); - } catch (const std::bad_typeid& exn) { - PyErr_SetString(PyExc_TypeError, exn.what()); - } catch (const std::domain_error& exn) { - PyErr_SetString(PyExc_ValueError, exn.what()); - } catch (const std::invalid_argument& exn) { - PyErr_SetString(PyExc_ValueError, exn.what()); - } catch (const std::ios_base::failure& exn) { - PyErr_SetString(PyExc_IOError, exn.what()); - } catch (const std::out_of_range& exn) { - PyErr_SetString(PyExc_IndexError, exn.what()); - } catch (const std::overflow_error& exn) { - PyErr_SetString(PyExc_OverflowError, exn.what()); - } catch (const std::range_error& exn) { - PyErr_SetString(PyExc_ArithmeticError, exn.what()); - } catch (const std::underflow_error& exn) { - PyErr_SetString(PyExc_ArithmeticError, exn.what()); - } catch (const std::exception& exn) { - PyErr_SetString(PyExc_RuntimeError, exn.what()); - } - catch (...) - { - PyErr_SetString(PyExc_RuntimeError, "Unknown exception"); - } -} -#endif - -/* RealImag.proto */ -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - #define __Pyx_CREAL(z) ((z).real()) - #define __Pyx_CIMAG(z) ((z).imag()) - #else - #define __Pyx_CREAL(z) (__real__(z)) - #define __Pyx_CIMAG(z) (__imag__(z)) - #endif -#else - #define __Pyx_CREAL(z) ((z).real) - #define __Pyx_CIMAG(z) ((z).imag) -#endif -#if defined(__cplusplus) && CYTHON_CCOMPLEX\ - && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) - #define __Pyx_SET_CREAL(z,x) ((z).real(x)) - #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) -#else - #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) - #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) -#endif - -/* Arithmetic.proto */ -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq_float(a, b) ((a)==(b)) - #define __Pyx_c_sum_float(a, b) ((a)+(b)) - #define __Pyx_c_diff_float(a, b) ((a)-(b)) - #define __Pyx_c_prod_float(a, b) ((a)*(b)) - #define __Pyx_c_quot_float(a, b) ((a)/(b)) - #define __Pyx_c_neg_float(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero_float(z) ((z)==(float)0) - #define __Pyx_c_conj_float(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs_float(z) (::std::abs(z)) - #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero_float(z) ((z)==0) - #define __Pyx_c_conj_float(z) (conjf(z)) - #if 1 - #define __Pyx_c_abs_float(z) (cabsf(z)) - #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); - #endif -#endif - -/* Arithmetic.proto */ -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq_double(a, b) ((a)==(b)) - #define __Pyx_c_sum_double(a, b) ((a)+(b)) - #define __Pyx_c_diff_double(a, b) ((a)-(b)) - #define __Pyx_c_prod_double(a, b) ((a)*(b)) - #define __Pyx_c_quot_double(a, b) ((a)/(b)) - #define __Pyx_c_neg_double(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero_double(z) ((z)==(double)0) - #define __Pyx_c_conj_double(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs_double(z) (::std::abs(z)) - #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero_double(z) ((z)==0) - #define __Pyx_c_conj_double(z) (conj(z)) - #if 1 - #define __Pyx_c_abs_double(z) (cabs(z)) - #define __Pyx_c_pow_double(a, b) (cpow(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); - #endif -#endif - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* FastTypeChecks.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); -#else -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) -#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) -#endif -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* PyIdentifierFromString.proto */ -#if !defined(__Pyx_PyIdentifier_FromString) -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) -#else - #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) -#endif -#endif - -/* ModuleImport.proto */ -static PyObject *__Pyx_ImportModule(const char *name); - -/* TypeImport.proto */ -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - - -/* Module declarations from 'cython' */ - -/* Module declarations from 'cpython.buffer' */ - -/* Module declarations from 'libc.string' */ - -/* Module declarations from 'libc.stdio' */ - -/* Module declarations from '__builtin__' */ - -/* Module declarations from 'cpython.type' */ -static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; - -/* Module declarations from 'cpython' */ - -/* Module declarations from 'cpython.object' */ - -/* Module declarations from 'cpython.ref' */ - -/* Module declarations from 'cpython.mem' */ - -/* Module declarations from 'numpy' */ - -/* Module declarations from 'numpy' */ -static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; -static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; -static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; -static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; -static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; -static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ -static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/ - -/* Module declarations from 'ArrayWrapper' */ -static CYTHON_INLINE PyObject *__pyx_f_12ArrayWrapper_pointer_to_array2D(void *, int *, int); /*proto*/ -static CYTHON_INLINE PyObject *__pyx_f_12ArrayWrapper_pointer_to_double2D(void *, int *); /*proto*/ -static CYTHON_INLINE PyObject *__pyx_f_12ArrayWrapper_numpy_own_array(PyArrayObject *); /*proto*/ - -/* Module declarations from 'Geo2rdr' */ -static PyTypeObject *__pyx_ptype_7Geo2rdr_PyGeo2rdr = 0; -static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t = { "float64_t", NULL, sizeof(__pyx_t_5numpy_float64_t), { 0 }, 0, 'R', 0, 0 }; -#define __Pyx_MODULE_NAME "Geo2rdr" -extern int __pyx_module_is_main_Geo2rdr; -int __pyx_module_is_main_Geo2rdr = 0; - -/* Implementation of 'Geo2rdr' */ -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin_RuntimeError; -static PyObject *__pyx_builtin_ImportError; -static const char __pyx_k_x[] = "x"; -static const char __pyx_k_y[] = "y"; -static const char __pyx_k_z[] = "z"; -static const char __pyx_k_np[] = "np"; -static const char __pyx_k_vx[] = "vx"; -static const char __pyx_k_vy[] = "vy"; -static const char __pyx_k_vz[] = "vz"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_name[] = "__name__"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_numpy[] = "numpy"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_times[] = "times"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_reduce[] = "__reduce__"; -static const char __pyx_k_heights[] = "heights"; -static const char __pyx_k_getstate[] = "__getstate__"; -static const char __pyx_k_lat_step[] = "lat_step"; -static const char __pyx_k_lon_step[] = "lon_step"; -static const char __pyx_k_setstate[] = "__setstate__"; -static const char __pyx_k_TypeError[] = "TypeError"; -static const char __pyx_k_lat_first[] = "lat_first"; -static const char __pyx_k_lon_first[] = "lon_first"; -static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_ImportError[] = "ImportError"; -static const char __pyx_k_RuntimeError[] = "RuntimeError"; -static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; -static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; -static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; -static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; -static const char __pyx_k_Copyright_c_2018_Authors_s_Here[] = "\n\nCopyright (c) 2018-\nAuthors(s): Heresh Fattahi\n\n"; -static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; -static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; -static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; -static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; -static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; -static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; -static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; -static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; -static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; -static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; -static PyObject *__pyx_n_s_ImportError; -static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; -static PyObject *__pyx_n_s_RuntimeError; -static PyObject *__pyx_n_s_TypeError; -static PyObject *__pyx_n_s_ValueError; -static PyObject *__pyx_n_s_cline_in_traceback; -static PyObject *__pyx_n_s_getstate; -static PyObject *__pyx_n_s_heights; -static PyObject *__pyx_n_s_import; -static PyObject *__pyx_n_s_lat_first; -static PyObject *__pyx_n_s_lat_step; -static PyObject *__pyx_n_s_lon_first; -static PyObject *__pyx_n_s_lon_step; -static PyObject *__pyx_n_s_main; -static PyObject *__pyx_n_s_name; -static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; -static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; -static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; -static PyObject *__pyx_n_s_np; -static PyObject *__pyx_n_s_numpy; -static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; -static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; -static PyObject *__pyx_n_s_range; -static PyObject *__pyx_n_s_reduce; 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- } - if (exact) { - more_or_less = "exactly"; - } - PyErr_Format(PyExc_TypeError, - "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", - func_name, more_or_less, num_expected, - (num_expected == 1) ? "" : "s", num_found); -} - -/* KeywordStringCheck */ -static int __Pyx_CheckKeywordStrings( - PyObject *kwdict, - const char* function_name, - int kw_allowed) -{ - PyObject* key = 0; - Py_ssize_t pos = 0; -#if CYTHON_COMPILING_IN_PYPY - if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0)) - goto invalid_keyword; - return 1; -#else - while (PyDict_Next(kwdict, &pos, &key, 0)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_Check(key))) - #endif - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } - if ((!kw_allowed) && unlikely(key)) - goto invalid_keyword; - return 1; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - return 0; -#endif -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif - return 0; -} - -/* RaiseDoubleKeywords */ -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -/* ParseKeywords */ -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - while (PyDict_Next(kwds, &pos, &key, &value)) { - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = (**name == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* ArgTypeTest */ -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) -{ - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - else if (exact) { - #if PY_MAJOR_VERSION == 2 - if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(__Pyx_TypeCheck(obj, type))) return 1; - } - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); - return 0; -} - -/* IsLittleEndian */ -static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) -{ - union { - uint32_t u32; - uint8_t u8[4]; - } S; - S.u32 = 0x01020304; - return S.u8[0] == 4; -} - -/* BufferFormatCheck */ -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t < '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? "'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - CYTHON_FALLTHROUGH; - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - CYTHON_FALLTHROUGH; - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} - -/* BufferGetAndValidate */ - static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (unlikely(info->buf == NULL)) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} -static void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static int __Pyx__GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - buf->buf = NULL; - if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) { - __Pyx_ZeroBuffer(buf); - return -1; - } - if (unlikely(buf->ndim != nd)) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if (unlikely((unsigned)buf->itemsize != dtype->size)) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_SafeReleaseBuffer(buf); - return -1; -} - -/* BufferIndexError */ - static void __Pyx_RaiseBufferIndexError(int axis) { - PyErr_Format(PyExc_IndexError, - "Out of bounds on buffer access (axis %d)", axis); -} - -/* PyErrFetchRestore */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -/* PyObjectCall */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* RaiseException */ - #if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* PyCFunctionFastCall */ - #if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - int flags = PyCFunction_GET_FLAGS(func); - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { - return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL); - } else { - return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs); - } -} -#endif - -/* PyFunctionFastCall */ - #if CYTHON_FAST_PYCALL -#include "frameobject.h" -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = f->f_localsplus; - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif -#endif - -/* PyObjectCallMethO */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif - if (likely(PyCFunction_Check(func))) { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* DictGetItem */ - #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY -static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { - PyObject *value; - value = PyDict_GetItemWithError(d, key); - if (unlikely(!value)) { - if (!PyErr_Occurred()) { - PyObject* args = PyTuple_Pack(1, key); - if (likely(args)) - PyErr_SetObject(PyExc_KeyError, args); - Py_XDECREF(args); - } - return NULL; - } - Py_INCREF(value); - return value; -} -#endif - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* SaveResetException */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if PY_VERSION_HEX >= 0x030700A2 - *type = tstate->exc_state.exc_type; - *value = tstate->exc_state.exc_value; - *tb = tstate->exc_state.exc_traceback; - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - #endif - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if PY_VERSION_HEX >= 0x030700A2 - tmp_type = tstate->exc_state.exc_type; - tmp_value = tstate->exc_state.exc_value; - tmp_tb = tstate->exc_state.exc_traceback; - tstate->exc_state.exc_type = type; - tstate->exc_state.exc_value = value; - tstate->exc_state.exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; icurexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - if (unlikely(PyTuple_Check(err))) - return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); - return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { -#endif - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if PY_VERSION_HEX >= 0x030700A2 - tmp_type = tstate->exc_state.exc_type; - tmp_value = tstate->exc_state.exc_value; - tmp_tb = tstate->exc_state.exc_traceback; - tstate->exc_state.exc_type = local_type; - tstate->exc_state.exc_value = local_value; - tstate->exc_state.exc_traceback = local_tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* PyObject_GenericGetAttrNoDict */ - #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'%.50s' object has no attribute '%U'", - tp->tp_name, attr_name); -#else - "'%.50s' object has no attribute '%.400s'", - tp->tp_name, PyString_AS_STRING(attr_name)); -#endif - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { - PyObject *descr; - PyTypeObject *tp = Py_TYPE(obj); - if (unlikely(!PyString_Check(attr_name))) { - return PyObject_GenericGetAttr(obj, attr_name); - } - assert(!tp->tp_dictoffset); - descr = _PyType_Lookup(tp, attr_name); - if (unlikely(!descr)) { - return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); - } - Py_INCREF(descr); - #if PY_MAJOR_VERSION < 3 - if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) - #endif - { - descrgetfunc f = Py_TYPE(descr)->tp_descr_get; - if (unlikely(f)) { - PyObject *res = f(descr, obj, (PyObject *)tp); - Py_DECREF(descr); - return res; - } - } - return descr; -} -#endif - -/* PyObject_GenericGetAttr */ - #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { - if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { - return PyObject_GenericGetAttr(obj, attr_name); - } - return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); -} -#endif - -/* SetupReduce */ - static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD; -#else - if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD; -#endif -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD; - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD; - setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD; - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD; - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto GOOD; -BAD: - if (!PyErr_Occurred()) - PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); - ret = -1; -GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* CLineInTraceback */ - #ifndef CYTHON_CLINE_IN_TRACEBACK -static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) { - PyObject *use_cline; - PyObject *ptype, *pvalue, *ptraceback; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject **cython_runtime_dict; -#endif - if (unlikely(!__pyx_cython_runtime)) { - return c_line; - } - __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); -#if CYTHON_COMPILING_IN_CPYTHON - cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); - if (likely(cython_runtime_dict)) { - use_cline = __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback); - } else -#endif - { - PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); - if (use_cline_obj) { - use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; - Py_DECREF(use_cline_obj); - } else { - PyErr_Clear(); - use_cline = NULL; - } - } - if (!use_cline) { - c_line = 0; - PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); - } - else if (PyObject_Not(use_cline) != 0) { - c_line = 0; - } - __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); - return c_line; -} -#endif - -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ - #include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - if (c_line) { - c_line = __Pyx_CLineForTraceback(tstate, c_line); - } - py_code = __pyx_find_code_object(c_line ? -c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); - } - py_frame = PyFrame_New( - tstate, /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if ((0)) {} - else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); - view->obj = NULL; - Py_DECREF(obj); -} -#endif - - - /* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = 1.0 / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = 1.0 / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(a, a); - case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); - case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = powf(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2f(0, -1); - } - } else { - r = __Pyx_c_abs_float(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = 1.0 / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = 1.0 / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0, -1); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* FastTypeChecks */ - #if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = a->tp_base; 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PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; - if (!res) { - res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } - return res; -} -#endif -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) { - if (likely(err == exc_type)) return 1; - if (likely(PyExceptionClass_Check(err))) { - return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type); - } - return PyErr_GivenExceptionMatches(err, exc_type); -} -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) { - if (likely(err == exc_type1 || err == exc_type2)) return 1; - if (likely(PyExceptionClass_Check(err))) { - return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2); - } - return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2)); -} -#endif - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* ModuleImport */ - #ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -/* TypeImport */ - #ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. 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" - "The ability to return an instance of a strict subclass of int " - "is deprecated, and may be removed in a future version of Python.", - Py_TYPE(result)->tp_name)) { - Py_DECREF(result); - return NULL; - } - return result; - } -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - type_name, type_name, Py_TYPE(result)->tp_name); - Py_DECREF(result); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x) || PyLong_Check(x))) -#else - if (likely(PyLong_Check(x))) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = m->nb_int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = m->nb_long(x); - } - #else - if (likely(m && m->nb_int)) { - name = "int"; - res = m->nb_int(x); - } - #endif -#else - if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { - res = PyNumber_Int(x); - } -#endif - if (likely(res)) { -#if PY_MAJOR_VERSION < 3 - if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { -#else - if (unlikely(!PyLong_CheckExact(res))) { -#endif - return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(x); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { - return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/tools/bindings/geometry/cython/Geo2rdr/Geo2rdr.pxd b/tools/bindings/geometry/cython/Geo2rdr/Geo2rdr.pxd deleted file mode 100644 index 143bf0d39..000000000 --- a/tools/bindings/geometry/cython/Geo2rdr/Geo2rdr.pxd +++ /dev/null @@ -1,32 +0,0 @@ -''' - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -''' - -cdef extern from 'Geometry.h': - cdef cppclass Geo2rdr: - Geo2rdr() except + - - void set_orbit(int nr_state_vectors, - double* t, - double* x, - double* y, - double* z, - double* vx, - double* vy, - double* vz) - - void set_geo_coordinate(double lon_first, double lat_first, - double lon_step, double lat_step, - int length, int width, - double* heights) - - void geo2rdr() - - void get_los(double** ux, double** uy,double** uz, int* dim1, int* dim2) - - void get_range(double** rng, int* dim1, int* dim2) - - diff --git a/tools/bindings/geometry/cython/Geo2rdr/Geo2rdr.pyx b/tools/bindings/geometry/cython/Geo2rdr/Geo2rdr.pyx deleted file mode 100644 index 13870494d..000000000 --- a/tools/bindings/geometry/cython/Geo2rdr/Geo2rdr.pyx +++ /dev/null @@ -1,84 +0,0 @@ -''' - -Copyright (c) 2018- -Authors(s): Heresh Fattahi - -''' - -import cython -import numpy as np -cimport numpy as np -from Geo2rdr cimport Geo2rdr - -cimport ArrayWrapper - -np.import_array () - -cdef class PyGeo2rdr: - ''' - Python wrapper for Geo2rdr - - ''' - cdef Geo2rdr* c_geo2rdr - - def __cinit__(self): - self.c_geo2rdr = new Geo2rdr() - - def __dealloc__ (self): - del self.c_geo2rdr - - - def set_geo_coordinate(self, lon_first, lat_first, - lon_step, lat_step, - np.ndarray[double, ndim=2, mode="c"] heights not None): - - self.c_geo2rdr.set_geo_coordinate ( - lon_first, lat_first, - lon_step, lat_step, - heights.shape[0], heights.shape[1], - &heights[0,0]) - - def set_orbit(self, np.ndarray[double,ndim=1,mode="c"] times not None, - np.ndarray[double,ndim=1,mode="c"] x not None, - np.ndarray[double,ndim=1,mode="c"] y not None, - np.ndarray[double,ndim=1,mode="c"] z not None, - np.ndarray[double,ndim=1,mode="c"] vx not None, - np.ndarray[double,ndim=1,mode="c"] vy not None, - np.ndarray[double,ndim=1,mode="c"] vz not None): - - nr_state_vectors = len(x) - self.c_geo2rdr.set_orbit(nr_state_vectors, - ×[0], - &x[0], - &y[0], - &z[0], - &vx[0], - &vy[0], - &vz[0]) - - def geo2rdr(self): - self.c_geo2rdr.geo2rdr() - - def get_los(self): - cdef double* los_x - cdef double* los_y - cdef double* los_z - cdef int[2] dim - self.c_geo2rdr.get_los(&los_x, &los_y, &los_z, &dim[0], &dim[1]) - np_los_x = ArrayWrapper.pointer_to_double2D (los_x, dim) - np_los_y = ArrayWrapper.pointer_to_double2D (los_y, dim) - np_los_z = ArrayWrapper.pointer_to_double2D (los_z, dim) - ArrayWrapper.numpy_own_array (np_los_x) - ArrayWrapper.numpy_own_array (np_los_y) - ArrayWrapper.numpy_own_array (np_los_z) - return np_los_x, np_los_y, np_los_z - - def get_slant_range(self): - cdef double* rng - cdef int[2] dim - self.c_geo2rdr.get_range(&rng, &dim[0], &dim[1]) - np_rng = ArrayWrapper.pointer_to_double2D (rng, dim) - ArrayWrapper.numpy_own_array (np_rng) - return np_rng - - diff --git a/tools/bindings/geometry/cython/setup.py b/tools/bindings/geometry/cython/setup.py deleted file mode 100644 index 1d48002c9..000000000 --- a/tools/bindings/geometry/cython/setup.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -from distutils.core import setup -from distutils.extension import Extension -import numpy -from Cython.Build import cythonize -#source_dir = "/home/fattahi/tools/geometry/Geo2rdr/cpp/classes" -GEOMETRY_DIR = os.environ['GEOMETRY_DIR'] #"/home/fattahi/tools/ray_tracing/raytracing_current/Geometry" -GEOMETRY_LIB_DIR = os.environ['GEOMETRY_LIB_DIR'] #"/home/fattahi/tools/ray_tracing/build" -obj_files = ['Geometry'] - -source_files = [os.path.join(GEOMETRY_DIR,"cpp/classes", f, f+'.cc') for f in obj_files] -source_files = source_files + [os.path.join(GEOMETRY_DIR,'cython/Geo2rdr/Geo2rdr.pyx')] - -cls_dirs = [os.path.join(GEOMETRY_DIR, "cpp/classes/Geometry"), - os.path.join(GEOMETRY_DIR, "cpp/classes/Orbit"), - os.path.join(GEOMETRY_DIR, "cpp/classes/Utility")] - -#inc_dirs = [os.path.join(GEOMETRY_DIR,"cpp/classes", f, f+'.cc') for f in obj_files] -# Pin the os.env variables for the compiler to be g++ (otherwise it calls gcc which throws warnings) -os.environ["CC"] = 'g++' -os.environ["CXX"] = 'g++' - -extensions = [ - Extension( - name="Geo2rdr", - sources=source_files, - include_dirs=[numpy.get_include()] + cls_dirs, - extra_compile_args=['-std=c++11'], - extra_link_args=['-lm'], - library_dirs=[GEOMETRY_LIB_DIR], - libraries=['geometry'], - language="c++") -] - -setup( - name="Geometry", - version="0.0", - ext_modules=cythonize(extensions, quiet=True, nthreads=8) - ) - diff --git a/tools/bindings/interpolate/src/interpolate.cpp b/tools/bindings/interpolate/src/interpolate.cpp new file mode 100644 index 000000000..0f97b496e --- /dev/null +++ b/tools/bindings/interpolate/src/interpolate.cpp @@ -0,0 +1,332 @@ +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// +// Author: Rohan Weeden +// Copyright 2020, by the California Institute of Technology. ALL RIGHTS +// RESERVED. United States Government Sponsorship acknowledged. +// +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +#include "assert.h" +#include "stdio.h" +#include "interpolate.h" + +#include + +// data_zs must have length data_x_N * data_y_N +// out must have length N +// interpolation_points must have length 2N +void interpolate_2d( + double * data_xs, + size_t data_x_N, + double * data_ys, + size_t data_y_N, + double * data_zs, + double * interpolation_points, + double * out, + size_t N, + std::optional fill_value, + bool assume_sorted +) { + size_t lox = 0; + size_t loy = 0; + for (size_t i = 0; i < N; i++) { + double x = interpolation_points[i * 2]; + double y = interpolation_points[i * 2 + 1]; + size_t hix, hiy; + if (assume_sorted) { + hix = find_left(data_xs + lox, data_xs + data_x_N, x) + lox; + hiy = find_left(data_ys + loy, data_ys + data_y_N, y) + loy; + } else { + hix = bisect_left(data_xs, data_xs + data_x_N, x); + hiy = bisect_left(data_ys, data_ys + data_y_N, y); + } + + if (fill_value.has_value()) { + if (fill_out_of_bounds(hix, 1, data_x_N - 1, *fill_value, &out[i])) { + continue; + } + if (fill_out_of_bounds(hiy, 1, data_y_N - 1, *fill_value, &out[i])) { + continue; + } + } + else { + hix = clamp_bounds(hix, 1, data_x_N - 1); + hiy = clamp_bounds(hiy, 1, data_y_N - 1); + } + + lox = hix - 1; + loy = hiy - 1; + + // https://en.wikipedia.org/wiki/Bilinear_interpolation + double x0 = data_xs[lox], + y0 = data_ys[loy], + x1 = data_xs[hix], + y1 = data_ys[hiy], + // Outputs + z00 = data_zs[lox * data_y_N + loy], + z01 = data_zs[lox * data_y_N + hiy], + z10 = data_zs[hix * data_y_N + loy], + z11 = data_zs[hix * data_y_N + hiy]; + + double dx = x1 - x0, + dy = y1 - y0, + dist_x0 = x - x0, + dist_x1 = x1 - x, + dist_y0 = y - y0, + dist_y1 = y1 - y; + + out[i] = ( + dist_x1 * (z00 * dist_y1 + z01 * dist_y0) + + dist_x0 * (z10 * dist_y1 + z11 * dist_y0) + ) / (dx * dy); + } +} + +void interpolate_3d( + double * data_xs, + size_t data_x_N, + double * data_ys, + size_t data_y_N, + double * data_zs, + size_t data_z_N, + double * data_ws, + double * interpolation_points, + double * out, + size_t N, + std::optional fill_value, + bool assume_sorted +) { + size_t lox = 0; + size_t loy = 0; + size_t loz = 0; + for (size_t i = 0; i < N; i++) { + double x = interpolation_points[i * 3]; + double y = interpolation_points[i * 3 + 1]; + double z = interpolation_points[i * 3 + 2]; + size_t hix, hiy, hiz; + if (assume_sorted) { + hix = find_left(data_xs + lox, data_xs + data_x_N, x) + lox; + hiy = find_left(data_ys + loy, data_ys + data_y_N, y) + loy; + hiz = find_left(data_zs + loz, data_zs + data_z_N, z) + loz; + } else { + hix = bisect_left(data_xs, data_xs + data_x_N, x); + hiy = bisect_left(data_ys, data_ys + data_y_N, y); + hiz = bisect_left(data_zs, data_zs + data_z_N, z); + } + if (fill_value.has_value()) { + if (fill_out_of_bounds(hix, 1, data_x_N - 1, *fill_value, &out[i])) { + continue; + } + if (fill_out_of_bounds(hiy, 1, data_y_N - 1, *fill_value, &out[i])) { + continue; + } + if (fill_out_of_bounds(hiz, 1, data_z_N - 1, *fill_value, &out[i])) { + continue; + } + } + else { + hix = clamp_bounds(hix, 1, data_x_N - 1); + hiy = clamp_bounds(hiy, 1, data_y_N - 1); + hiz = clamp_bounds(hiz, 1, data_z_N - 1); + } + + lox = hix - 1; + loy = hiy - 1; + loz = hiz - 1; + + // https://en.wikipedia.org/wiki/Trilinear_interpolation + size_t data_yz_N = data_y_N * data_z_N; + double x0 = data_xs[lox], + y0 = data_ys[loy], + z0 = data_zs[loz], + x1 = data_xs[hix], + y1 = data_ys[hiy], + z1 = data_zs[hiz], + // Outputs + w000 = data_ws[lox * data_yz_N + loy * data_z_N + loz], + w001 = data_ws[lox * data_yz_N + loy * data_z_N + hiz], + w010 = data_ws[lox * data_yz_N + hiy * data_z_N + loz], + w011 = data_ws[lox * data_yz_N + hiy * data_z_N + hiz], + w100 = data_ws[hix * data_yz_N + loy * data_z_N + loz], + w101 = data_ws[hix * data_yz_N + loy * data_z_N + hiz], + w110 = data_ws[hix * data_yz_N + hiy * data_z_N + loz], + w111 = data_ws[hix * data_yz_N + hiy * data_z_N + hiz]; + + double dx = x1 - x0, + dy = y1 - y0, + dz = z1 - z0, + dist_x0 = x - x0, + dist_x1 = x1 - x, + dist_y0 = y - y0, + dist_y1 = y1 - y, + dist_z0 = z - z0, + dist_z1 = z1 - z; + + out[i] = ( + dist_x1 * ( + dist_y1 * (dist_z1 * w000 + dist_z0 * w001) + + dist_y0 * (dist_z1 * w010 + dist_z0 * w011) + ) + + dist_x0 * ( + dist_y1 * (dist_z1 * w100 + dist_z0 * w101) + + dist_y0 * (dist_z1 * w110 + dist_z0 * w111) + ) + ) / (dx * dy * dz); + } +} + +void interpolate( + const std::vector> &grid, + const slice &values, + const slice &interpolation_points, + slice &out, + std::optional fill_value, + bool assume_sorted +) { + // Only up to 64 dimensions is supported because at that point we run out + // of space for our bitmask. However, this should be suitable for any + // practical use case. + assert(grid.size() < 64); + + size_t dimensions = grid.size(); + size_t num_points = out.size; + + std::vector los(dimensions); + std::vector his(dimensions); + + std::vector lower_bounds(dimensions); + std::vector upper_bounds(dimensions); + std::vector lower_dist(dimensions); + std::vector upper_dist(dimensions); + + std::vector corner_points(1 << dimensions); + + for (size_t i = 0; i < num_points; i++) { + double total_volume = 1; + for (size_t dim = 0; dim < dimensions; dim++) { + auto xs = grid[dim]; + double x = interpolation_points.ptr[i * dimensions + dim]; + size_t hi; + if (assume_sorted) { + hi = find_left(xs.ptr + los[dim], xs.ptr + xs.size, x) + los[dim]; + } else { + hi = bisect_left(xs.ptr, xs.ptr + xs.size, x); + } + if (fill_value.has_value()) { + if (fill_out_of_bounds(hi, 1, xs.size - 1, *fill_value, &out.ptr[i])) { + // continue the outer loop + goto NEXT_POINT; + } + } + else { + hi = clamp_bounds(hi, 1, xs.size - 1); + } + size_t lo = hi - 1; + + los[dim] = lo; + his[dim] = hi; + + double x0 = xs.ptr[lo]; + double x1 = xs.ptr[hi]; + + lower_bounds[dim] = x0; + upper_bounds[dim] = x1; + total_volume *= x1 - x0; + lower_dist[dim] = x - x0; + upper_dist[dim] = x1 - x; + } + + out.ptr[i] = 0; + for (unsigned long j = 0; j < corner_points.size(); j++) { + size_t index = 0; + for (size_t dim = 0; dim < dimensions; dim++) { + index += ( + (j >> (dim)) & 1 ? his[dim] : los[dim] + ); + index *= (dim + 1 < dimensions ? grid[dim + 1].size : 1); + } + double term = values.ptr[index]; + for (size_t dim = 0; dim < dimensions; dim++) { + term *= (j >> (dim)) & 1 ? lower_dist[dim] : upper_dist[dim]; + } + out.ptr[i] += term; + } + + out.ptr[i] /= total_volume; +NEXT_POINT: ; + } +} + +void interpolate_1d_along_axis( + const py::buffer_info grid, + const py::buffer_info values, + const py::buffer_info interp_points, + py::buffer_info out, + size_t axis, + std::optional fill_value, + bool assume_sorted +) { + size_t dimensions = interp_points.ndim; + const std::vector &interp_shape = interp_points.shape; + const std::vector &interp_strides = interp_points.strides; + ssize_t interp_axis_stride = interp_strides[axis]; + size_t interp_axis_size = (size_t) interp_shape[axis]; + + auto grid_strides = grid.strides; + ssize_t grid_axis_stride = grid_strides[axis]; + size_t grid_axis_size = (size_t) grid.shape[axis]; + + std::vector index(dimensions); + + const double *grid_ptr = (double*) grid.ptr; + const double *values_ptr = (double*) values.ptr; + const double *interp_ptr = (double*) interp_points.ptr; + double *out_ptr = (double*) out.ptr; + + // Iterate over the starting indices. This counts over the shape skipping + // the dimension 'axis'. + bool done = false; + while (!done) { + // precompute the strided offsets + size_t interp_offset_base = 0; + size_t grid_offset_base = 0; + for (size_t dim = 0; dim < dimensions; dim++) { + if (dim != axis) { + interp_offset_base += index[dim] * interp_strides[dim]; + grid_offset_base += index[dim] * grid_strides[dim]; + } + } + // interp_points and out have the same structure + axis_iterator interp_begin_axis(interp_ptr, dimensions, axis, interp_axis_stride, interp_offset_base); + axis_iterator out_begin_axis(out_ptr, dimensions, axis, interp_axis_stride, interp_offset_base); + // grid and values have the same structure + axis_iterator grid_begin_axis(grid_ptr, dimensions, axis, grid_axis_stride, grid_offset_base); + axis_iterator values_begin_axis(values_ptr, dimensions, axis, grid_axis_stride, grid_offset_base); + + interpolate_1d( + grid_begin_axis, + grid_axis_size, + values_begin_axis, + interp_begin_axis, + out_begin_axis, + interp_axis_size, + fill_value, + assume_sorted + ); + + // Find the next index + done = true; + for (size_t i = dimensions; i > 0; i--) { + size_t dim = i - 1; + if (dim == axis) { + continue; + } + if (index[dim] + 1 < (size_t) interp_shape[dim]) { + index[dim] += 1; + done = false; + break; + } + index[dim] = 0; + } + } +} diff --git a/tools/bindings/interpolate/src/interpolate.h b/tools/bindings/interpolate/src/interpolate.h new file mode 100644 index 000000000..a42a29306 --- /dev/null +++ b/tools/bindings/interpolate/src/interpolate.h @@ -0,0 +1,271 @@ +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// +// Author: Rohan Weeden +// Copyright 2020, by the California Institute of Technology. ALL RIGHTS +// RESERVED. United States Government Sponsorship acknowledged. +// +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +#include +#include + +#include +#include + +#include "sys/types.h" +#include "assert.h" + +#ifndef PY_FAST_INTERP_H +#define PY_FAST_INTERP_H + +namespace py = pybind11; + +template +inline size_t bisect_left(RAIter begin, RAIter end, double x) { + size_t left = 0; + size_t right = std::distance(begin, end); + + while (right != left) { + size_t mid = (left + right) / 2; + if (x < begin[mid]) { + right = mid; + } else { + left = mid + 1; + } + } + + return right; +} + +// Like bisect_left but optimized for when we expect the target to appear near +// the start of the array. +// +// Tries a small scan first and then switches to bisection +template +inline size_t find_left(RAIter begin, RAIter end, double x) { + size_t left = 0; + size_t data_N = std::distance(begin, end); + + for (; left < 5; left++) { + if (left == data_N || x < begin[left]) { + return left; + } + } + + return bisect_left(begin + left, end, x) + left; +} + +template +inline bool fill_out_of_bounds(size_t x, size_t lo, size_t hi, T fill_value, T * out) { + if (x < lo || x > hi) { + *out = fill_value; + return true; + } + return false; +} + +inline size_t clamp_bounds(size_t x, size_t lo, size_t hi) { + if (x < lo) { + x = lo; + } else if (x > hi) { + x = hi; + } + return x; +} + +// TODO: Don't store grid points as an array, just derive them from a formula? +// TODO: Same for interpolation points? +template +void interpolate_1d( + const RAIter data_xs, + size_t data_N, + const RAIter data_ys, + const RAIter xs, + RAIter out, + size_t N, + std::optional fill_value, + bool assume_sorted +) { + size_t lo = 0; + for (size_t i = 0; i < N; i++) { + T x = xs[i]; + size_t hi; + if (assume_sorted) { + hi = find_left(data_xs + lo, data_xs + data_N, x) + lo; + } else { + hi = bisect_left(data_xs, data_xs + data_N, x); + } + if (fill_value.has_value()) { + if (fill_out_of_bounds(hi, 1, data_N - 1, *fill_value, &out[i])) { + continue; + } + } + else { + hi = clamp_bounds(hi, 1, data_N - 1); + } + + lo = hi - 1; + + T x0 = data_xs[lo], + x1 = data_xs[hi], + // Output + y0 = data_ys[lo], + y1 = data_ys[hi]; + + T slope = (y1 - y0) / (x1 - x0); + out[i] = y0 + slope * (x - x0); + } +} + +void interpolate_2d( + double * data_xs, + size_t data_x_N, + double * data_ys, + size_t data_y_N, + double * data_zs, + double * interpolation_points, + double * out, + size_t N, + std::optional fill_value, + bool assume_sorted +); + +void interpolate_3d( + double * data_xs, + size_t data_x_N, + double * data_ys, + size_t data_y_N, + double * data_zs, + size_t data_z_N, + double * data_ws, + double * interpolation_points, + double * out, + size_t N, + std::optional fill_value, + bool assume_sorted +); + +template +struct slice { + size_t size; + T * ptr; +}; + +// Any dimension, but slower +void interpolate( + const std::vector> &grid, + const slice &values, + const slice &interpolation_points, + slice &out, + std::optional fill_value, + bool assume_sorted +); + +// Helper for handling the striding required to iterate along a given axis +template +class axis_iterator { + const unsigned char *data; + size_t ndim; + size_t axis; + size_t axis_stride; + size_t offset_base = 0; + size_t position = 0; +public: + axis_iterator( + const T *data, + size_t ndim, + size_t axis, + const size_t * index, + const ssize_t * strides + ) : data((unsigned char*) data), ndim(ndim), axis(axis) { + assert(axis < ndim); + axis_stride = strides[axis]; + for (size_t dim = 0; dim < ndim; dim++) { + if (dim != axis) { + offset_base += index[dim] * strides[dim]; + } + } + } + + axis_iterator( + const py::array_t &array, + size_t axis, + const size_t * index + ) : axis_iterator( + array.data(), + (size_t) array.ndim(), + axis, + index, + array.strides() + ) { assert(array.ndim() > 0); } + + axis_iterator( + const T *data, + size_t ndim, + size_t axis, + size_t axis_stride, + size_t offset_base + ) : data((unsigned char*) data), + ndim(ndim), + axis(axis), + axis_stride(axis_stride), + offset_base(offset_base) { } + + axis_iterator& operator++() { + position += 1; + return *this; + } + axis_iterator operator++(int) { + axis_iterator retval = *this; + ++(*this); + return retval; + } + axis_iterator& operator+=(size_t n) { + this->position += n; + return *this; + } + axis_iterator operator+(size_t n) const { + axis_iterator next(*this); + next += n; + return next; + } + long operator-(axis_iterator &other) const { + return ((ssize_t) position) - other.position; + } + bool operator==(axis_iterator &other) const { + return data == other.data + && ndim == other.ndim + && axis == other.axis + && axis_stride == other.axis_stride + && offset_base == other.offset_base + && position == other.position; + } + bool operator!=(axis_iterator &other) const { + return !(*this == other); + } + T operator*() { + return (*this)[position]; + } + T & operator[](const ssize_t &n) const { + size_t offset = offset_base + (position + n) * axis_stride; + return *((T*) (data + offset)); + } + // iterator traits + using difference_type = long; + using value_type = T; + using pointer = const T*; + using reference = const T&; + using iterator_category = std::random_access_iterator_tag; +}; + +void interpolate_1d_along_axis( + const py::buffer_info grid, + const py::buffer_info values, + const py::buffer_info interp_points, + py::buffer_info out, + size_t axis, + std::optional fill_value, + bool assume_sorted +); + +#endif diff --git a/tools/bindings/interpolate/src/module.cpp b/tools/bindings/interpolate/src/module.cpp new file mode 100644 index 000000000..d5284aadb --- /dev/null +++ b/tools/bindings/interpolate/src/module.cpp @@ -0,0 +1,494 @@ +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// +// Author: Rohan Weeden +// Copyright 2020, by the California Institute of Technology. ALL RIGHTS +// RESERVED. United States Government Sponsorship acknowledged. +// +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +#include +#include +#include + +#include +#include +#include +#include + +#include "interpolate.h" + + +namespace py = pybind11; + +PYBIND11_MODULE(interpolate, m) { + m.doc() = "Fast linear interpolator over a regular grid"; + + m.def("interpolate", []( + std::vector> points, + py::array_t values, + py::array_t interp_points, + std::optional fill_value, + bool assume_sorted, + size_t max_threads + ) { + size_t num_dims = points.size(); + + if (values.ndim() == 0 || interp_points.ndim() == 0) { + throw py::type_error("Only arrays are supported, not scalar values!"); + } + + for (auto arr : points) { + if (arr.ndim() != 1) { + throw py::type_error("'points' must be a list of 1D arrays!"); + } + } + + if (num_dims != values.ndim()) { + std::stringstream ss; + ss << "Dimension mismatch! Grid is " << num_dims + << "D but values are " << values.ndim() << "D!"; + throw py::type_error(ss.str()); + } + + if (interp_points.ndim() != 2) { + throw py::type_error("'interp_points' should have shape (N, ndim)."); + } + + size_t interp_dims = interp_points.shape()[1]; + if (num_dims != interp_dims) { + std::stringstream ss; + ss << "Dimension mismatch! Grid is " << num_dims + << "D but interpolation points are " << interp_dims << "D!"; + throw py::type_error(ss.str()); + } + size_t num_elements = interp_points.shape()[0]; + double * out = new double[num_elements]; + + auto values_info = values.request(); + auto interp_points_info = interp_points.request(); + + + // Reasonable thread defaults based on profiling. It seems that spawning + // threads in powers of 2 yields optimal performance. + size_t desired_threads; + if (num_elements < 10000) { + desired_threads = 1; + } else if (num_elements < 4000000) { + desired_threads = 2; + } else if (num_elements < 160000000){ + desired_threads = 4; + } else { + desired_threads = 8; + } + size_t num_threads = std::min(desired_threads, max_threads); + if (num_threads == 0) { + num_threads = 1; + } + size_t stride = (num_elements / num_threads); + if (stride * num_threads < num_elements) { + stride += 1; + } + + double * values_ptr = (double *) values_info.ptr, + * interp_points_ptr = (double *) interp_points_info.ptr; + + if (num_dims == 1) { + auto xs_info = points[0].request(); + + double * xs_ptr = (double *) xs_info.ptr; + + if (num_threads == 1) { + interpolate_1d( + xs_ptr, + points[0].size(), + values_ptr, + interp_points_ptr, + out, + num_elements, + fill_value, + assume_sorted + ); + } else { + std::vector> tasks; + + for (size_t i = 0; i < num_threads; i++) { + size_t index = i * stride; + tasks.push_back( + std::async( + &interpolate_1d, + xs_ptr, + xs_info.shape[0], + values_ptr, + &interp_points_ptr[index], + &out[index], + index + stride < num_elements ? stride : num_elements - index, + fill_value, + assume_sorted + ) + ); + } + for (auto &future : tasks) { + std::move(future); + } + } + } else if (num_dims == 2) { + auto xs_info = points[0].request(); + auto ys_info = points[1].request(); + + double * xs_ptr = (double *) xs_info.ptr, + * ys_ptr = (double *) ys_info.ptr; + + if (num_threads == 1) { + interpolate_2d( + xs_ptr, + points[0].size(), + ys_ptr, + points[1].size(), + values_ptr, + interp_points_ptr, + out, + num_elements, + fill_value, + assume_sorted + ); + } else { + std::vector> tasks; + + for (size_t i = 0; i < num_threads; i++) { + size_t index = i * stride; + tasks.push_back( + std::async( + &interpolate_2d, + xs_ptr, + points[0].size(), + ys_ptr, + points[1].size(), + values_ptr, + &interp_points_ptr[index * num_dims], + &out[index], + index + stride < num_elements ? stride : num_elements - index, + fill_value, + assume_sorted + ) + ); + } + for (auto &future : tasks) { + std::move(future); + } + } + } else if (num_dims == 3) { + auto xs_info = points[0].request(); + auto ys_info = points[1].request(); + auto zs_info = points[2].request(); + + double * xs_ptr = (double *) xs_info.ptr, + * ys_ptr = (double *) ys_info.ptr, + * zs_ptr = (double *) zs_info.ptr; + + if (num_threads == 1) { + interpolate_3d( + xs_ptr, + points[0].size(), + ys_ptr, + points[1].size(), + zs_ptr, + points[2].size(), + values_ptr, + interp_points_ptr, + out, + num_elements, + fill_value, + assume_sorted + ); + } else { + std::vector> tasks; + + for (size_t i = 0; i < num_threads; i++) { + size_t index = i * stride; + tasks.push_back( + std::async( + &interpolate_3d, + xs_ptr, + points[0].size(), + ys_ptr, + points[1].size(), + zs_ptr, + points[2].size(), + values_ptr, + &interp_points_ptr[index * num_dims], + &out[index], + index + stride < num_elements ? stride : num_elements - index, + fill_value, + assume_sorted + ) + ); + } + for (auto &future : tasks) { + std::move(future); + } + } + } else { + std::vector> grid; + for (auto axis : points) { + auto info = axis.request(); + grid.push_back(slice { + (size_t) info.shape[0], + (double *) info.ptr + }); + } + slice values_slice = { + values_info.size / sizeof(double), + values_ptr + }; + slice interpolation_points_slice = { + values_info.size / sizeof(double), + interp_points_ptr + }; + slice out_slice = {num_elements, out}; + + interpolate( + grid, + values_slice, + interpolation_points_slice, + out_slice, + fill_value, + assume_sorted + ); + } + + + py::capsule free_when_done(out, [](void *f) { + double *out = reinterpret_cast(f); + delete[] out; + }); + + return py::array_t( + {num_elements}, // Shape + {sizeof(double)}, // Strides + out, // the data pointer + free_when_done + ); // numpy array references this parent + }, + R"pbdoc( + Linear interpolator in any dimension. Arguments are similar to + scipy.interpolate.RegularGridInterpolator + + :param points: Tuple of N axis coordinates specifying the grid. + :param values: Nd array containing the grid point values. + :param interp_points: List of points to interpolate, should have + dimension (x, N). If this list is guaranteed to be sorted make sure + to use the `assume_sorted` option. + :param fill_value: The value to return for interpolation points + outside of the grid range. + :param assume_sorted: Enable optimization when the list of interpolation + points is sorted. + :param max_threads: Limit the number of threads to a certain amount. + Note: The number of threads will always be one of {1, 2, 4, 8} + )pbdoc", + py::arg("points"), + py::arg("values"), + py::arg("interp_points"), + py::arg("fill_value") = std::nullopt, + py::arg("assume_sorted") = false, + py::arg("max_threads") = 8 + ); + + m.def("interpolate_along_axis", []( + py::array_t points, + py::array_t values, + py::array_t interp_points, + ssize_t axis_in, + std::optional fill_value, + bool assume_sorted, + size_t max_threads + ) { + py::buffer_info points_info = points.request(); + py::buffer_info values_info = values.request(); + py::buffer_info interp_points_info = interp_points.request(); + + if (values.ndim() == 0 || interp_points.ndim() == 0) { + throw py::type_error("Only arrays are supported, not scalar values!"); + } + + if (points.ndim() != values.ndim() || points.ndim() != interp_points.ndim()) { + throw py::type_error( + "'points', 'values' and 'interp_points' must all have the " + "same number of dimensions!" + ); + } + size_t dimensions = (size_t) points.ndim(); + + for (size_t i = 0; i < dimensions; i++) { + if (points.shape(i) != values.shape(i)) { + throw py::type_error("'points' and 'values' must have the same shape!"); + } + } + + size_t num_threads = std::max((size_t) 1, max_threads); + + if (axis_in < 0) { axis_in += dimensions; } + if (axis_in >= dimensions || axis_in < 0) { + throw py::type_error("'axis' out of range!"); + } else if (axis_in == 0 && max_threads > 1) { + throw std::runtime_error( + "Cannot interpolate along axis 0 with multiple threads!" + ); + } + size_t axis = (size_t) axis_in; + + for (size_t i = 0; i < dimensions; i++) { + if (i != axis && interp_points_info.shape[i] != points_info.shape[i]) { + std::stringstream ss; + ss << "Dimension mismatch at axis " << i << "! 'points' is " + << points_info.shape[i] << " but interp_points is " + << interp_points_info.shape[i] << "!"; + throw py::type_error(ss.str()); + } + } + + + size_t interp_points_size = 1; + for (size_t i = 0; i < dimensions; i++) { + interp_points_size *= interp_points_info.shape[i]; + } + double *out = new double[interp_points_size]; + + py::capsule free_when_done(out, [](void *f) { + double *out = reinterpret_cast(f); + delete[] out; + }); + + std::vector shape(interp_points_info.shape); + std::vector strides(interp_points_info.strides); + + auto out_array = py::array_t( + shape, // Shape + strides, // Strides + out, // the data pointer + free_when_done + ); // numpy array references this parent + + py::buffer_info out_info = out_array.request(); + + num_threads = std::min(num_threads, (size_t) points_info.shape[0]); + + size_t thread_stride = points_info.shape[0] / num_threads; + if (thread_stride * num_threads < points_info.shape[0]) { + thread_stride += 1; + } + + + if (num_threads == 1) { + interpolate_1d_along_axis( + std::move(points_info), + std::move(values_info), + std::move(interp_points_info), + std::move(out_info), + axis, + fill_value, + assume_sorted + ); + } else { + std::vector> tasks; + + for (size_t i = 0; i < num_threads; i++) { + size_t index = i * thread_stride; + if (index >= points_info.shape[0]) { + break; + } + size_t num_elements = + index + thread_stride < points_info.shape[0] + ? thread_stride : points_info.shape[0] - index; + + if (num_elements == 0) { + break; + } + + std::vector points_view_shape(points_info.shape); + points_view_shape[0] = num_elements; + py::buffer_info points_view_info( + (unsigned char *) points_info.ptr + index * points_info.strides[0], + points_info.itemsize, + points_info.format, + points_info.ndim, + points_view_shape, + std::vector(points_info.strides) + ); + std::vector values_view_shape(values_info.shape); + values_view_shape[0] = num_elements; + py::buffer_info values_view_info( + (unsigned char *) values_info.ptr + index * values_info.strides[0], + values_info.itemsize, + values_info.format, + values_info.ndim, + values_view_shape, + std::vector(values_info.strides) + ); + std::vector interp_points_view_shape(interp_points_info.shape); + interp_points_view_shape[0] = num_elements; + py::buffer_info interp_points_view_info( + (unsigned char *) interp_points_info.ptr + index * interp_points_info.strides[0], + interp_points_info.itemsize, + interp_points_info.format, + interp_points_info.ndim, + interp_points_view_shape, + std::vector(interp_points_info.strides) + ); + std::vector out_view_shape(out_info.shape); + out_view_shape[0] = num_elements; + py::buffer_info out_view_info( + (unsigned char *) out_info.ptr + index * out_info.strides[0], + out_info.itemsize, + out_info.format, + out_info.ndim, + out_view_shape, + std::vector(out_info.strides) + ); + tasks.push_back( + std::async( + &interpolate_1d_along_axis, + std::move(points_view_info), + std::move(values_view_info), + std::move(interp_points_view_info), + std::move(out_view_info), + axis, + fill_value, + assume_sorted + ) + ); + } + for (auto &future : tasks) { + std::move(future); + } + } + + return out_array; + }, + R"pbdoc( + 1D linear interpolator along a specific axis. + + :param points: N-dimensional x coordinates. Axis specified by + 'axis' must contain at least 2 points. + :param values: N-dimensional y values. Must have the same shape as + 'points'. + :param interp_points: N-dimensional x coordinates to interpolate at. + The shape may only differ from that of 'points' at the axis + specified by 'axis'. For example if 'points' has shape (1, 2, 3) + and 'axis' is 2, then and shape like (1, 2, X) is valid. + :param axis: The axis to interpolate along. + :param fill_value: The value to return for interpolation points + outside of the grid range. + :param assume_sorted: Enable optimization when the list of interpolation + points is sorted along the axis of interpolation. + :param max_threads: Limit the number of threads to a certain amount. + )pbdoc", + py::arg("points"), + py::arg("values"), + py::arg("interp_points"), + py::arg("axis") = -1, + py::arg("fill_value") = std::nullopt, + py::arg("assume_sorted") = false, + py::arg("max_threads") = 8, + py::return_value_policy::move + ); +} diff --git a/tools/bindings/interpolate/src/tests.cpp b/tools/bindings/interpolate/src/tests.cpp new file mode 100644 index 000000000..34919afa0 --- /dev/null +++ b/tools/bindings/interpolate/src/tests.cpp @@ -0,0 +1,23 @@ +#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do this in one cpp file +#include + +#include "interpolate.h" + + +TEST_CASE( "test_bisect_left", "[bisect_left]" ) { + std::vector list = {1., 2., 3., 4.}; + REQUIRE( bisect_left(list.begin(), list.end(), 0.5) == 0 ); + REQUIRE( bisect_left(list.begin(), list.end(), 1.5) == 1 ); + REQUIRE( bisect_left(list.begin(), list.end(), 2.1) == 2 ); + REQUIRE( bisect_left(list.begin(), list.end(), 3.99) == 3 ); + REQUIRE( bisect_left(list.begin(), list.end(), 4.2) == 4 ); +} + +TEST_CASE( "test_find_left", "[find_left]" ) { + std::vector list = {1., 2., 3., 4.}; + REQUIRE( find_left(list.begin(), list.end(), 0.5) == 0 ); + REQUIRE( find_left(list.begin(), list.end(), 1.5) == 1 ); + REQUIRE( find_left(list.begin(), list.end(), 2.1) == 2 ); + REQUIRE( find_left(list.begin(), list.end(), 3.99) == 3 ); + REQUIRE( find_left(list.begin(), list.end(), 4.2) == 4 ); +} diff --git a/tools/bindings/utils/makePoints.pyx b/tools/bindings/utils/makePoints.pyx new file mode 100644 index 000000000..9cb4b502e --- /dev/null +++ b/tools/bindings/utils/makePoints.pyx @@ -0,0 +1,148 @@ +import numpy as np +cimport numpy as cnp + +DTYPE = np.float64 + +cimport cython + +ctypedef double npy_float64 +ctypedef float npy_float32 +ctypedef npy_float64 float64_t +ctypedef npy_float32 float32_t + +@cython.boundscheck(False) # turn off array bounds check +@cython.wraparound(False) # turn off negative indices ([-1,-1]) +def makePoints0D(double max_len, cnp.ndarray[double, ndim=1] Rays_SP, cnp.ndarray[double, ndim=1] Rays_SLV, double stepSize): + ''' + Fast cython code to create the rays needed for ray-tracing. + Inputs: + max_len: maximum length of the rays + Rays_SP: 1 x 3 numpy array of the location of the ground pixels in an earth-centered, + earth-fixed coordinate system + Rays_SLV: 1 x 3 numpy array of the look vectors pointing from the ground pixel to the sensor + stepSize: Distance between points along the ray-path + Output: + ray: a 3 x Npts array containing the rays tracing a path from the ground pixels, along the + line-of-sight vectors, up to the maximum length specified. + ''' + cdef int k3, k4 + cdef int Npts + if max_len % stepSize != 0: + Npts = int(max_len//stepSize) + 1 + else: + Npts = int(max_len//stepSize) + + cdef cnp.ndarray[npy_float64, ndim = 2, mode = 'c'] ray = np.empty((3, Npts), dtype=np.float64) + cdef cnp.ndarray[npy_float64, ndim = 1, mode = 'c'] basespace = np.arange(0, max_len+stepSize, stepSize) + + for k3 in range(3): + for k4 in range(Npts): + ray[k3, k4] = Rays_SP[k3] + basespace[k4]*Rays_SLV[k3] + return ray + +@cython.boundscheck(False) # turn off array bounds check +@cython.wraparound(False) # turn off negative indices ([-1,-1]) +def makePoints1D(double max_len, cnp.ndarray[double, ndim=2] Rays_SP, cnp.ndarray[double, ndim=2] Rays_SLV, double stepSize): + ''' + Fast cython code to create the rays needed for ray-tracing. + Inputs: + max_len: maximum length of the rays + Rays_SP: Nx x 3 numpy array of the location of the ground pixels in an earth-centered, + earth-fixed coordinate system + Rays_SLV: Nx x 3 numpy array of the look vectors pointing from the ground pixel to the sensor + stepSize: Distance between points along the ray-path + Output: + ray: a Nx x 3 x Npts array containing the rays tracing a path from the ground pixels, along the + line-of-sight vectors, up to the maximum length specified. + ''' + cdef int k1, k3, k4 + cdef int Npts + + if max_len % stepSize != 0: + Npts = int(max_len//stepSize) + 1 + else: + Npts = int(max_len//stepSize) + + cdef int nrow = Rays_SP.shape[0] + cdef cnp.ndarray[npy_float64, ndim = 3, mode = 'c'] ray = np.empty((nrow, 3, Npts), dtype=np.float64) + cdef cnp.ndarray[npy_float64, ndim = 1, mode = 'c'] basespace = np.arange(0, max_len+stepSize, stepSize) + + for k1 in range(nrow): + for k3 in range(3): + for k4 in range(Npts): + ray[k1, k3, k4] = Rays_SP[k1, k3] + basespace[k4]*Rays_SLV[k1, k3] + return ray + + +@cython.boundscheck(False) # turn off array bounds check +@cython.wraparound(False) # turn off negative indices ([-1,-1]) +def makePoints2D(double max_len, cnp.ndarray[double, ndim=3] Rays_SP, cnp.ndarray[double, ndim=3] Rays_SLV, double stepSize): + ''' + Fast cython code to create the rays needed for ray-tracing. + Inputs: + max_len: maximum length of the rays + Rays_SP: Nx x Ny x 3 numpy array of the location of the ground pixels in an earth-centered, + earth-fixed coordinate system + Rays_SLV: Nx x Ny x 3 numpy array of the look vectors pointing from the ground pixel to the sensor + stepSize: Distance between points along the ray-path + Output: + ray: a Nx x Ny x 3 x Npts array containing the rays tracing a path from the ground pixels, along the + line-of-sight vectors, up to the maximum length specified. + ''' + cdef int k1, k2, k3, k4 + + cdef int Npts + if max_len % stepSize != 0: + Npts = int(max_len//stepSize) + 1 + else: + Npts = int(max_len//stepSize) + + cdef int nrow = Rays_SP.shape[0] + cdef int ncol = Rays_SP.shape[1] + cdef cnp.ndarray[npy_float64, ndim = 4, mode = 'c'] ray = np.empty((nrow, ncol, 3, Npts), dtype=np.float64) + cdef cnp.ndarray[npy_float64, ndim = 1, mode = 'c'] basespace = np.arange(0, max_len+stepSize, stepSize) + + for k1 in range(nrow): + for k2 in range(ncol): + for k3 in range(3): + for k4 in range(Npts): + ray[k1, k2, k3, k4] = Rays_SP[k1, k2, k3] + basespace[k4]*Rays_SLV[k1, k2, k3] + return ray + + +@cython.boundscheck(False) # turn off array bounds check +@cython.wraparound(False) # turn off negative indices ([-1,-1]) +def makePoints3D(double max_len, cnp.ndarray[double, ndim=4] Rays_SP, cnp.ndarray[double, ndim=4] Rays_SLV, double stepSize): + ''' + Fast cython code to create the rays needed for ray-tracing + Inputs: + max_len: maximum length of the rays + Rays_SP: Nx x Ny x Nz x 3 numpy array of the location of the ground pixels in an earth-centered, + earth-fixed coordinate system + Rays_SLV: Nx x Ny x Nz x 3 numpy array of the look vectors pointing from the ground pixel to the sensor + stepSize: Distance between points along the ray-path + Output: + ray: a Nx x Ny x Nz x 3 x Npts array containing the rays tracing a path from the ground pixels, along the + line-of-sight vectors, up to the maximum length specified. + ''' + cdef int k1, k2, k2a, k3, k4 + + cdef int Npts + if max_len % stepSize != 0: + Npts = int(max_len//stepSize) + 1 + else: + Npts = int(max_len//stepSize) + + cdef int nrow = Rays_SP.shape[0] + cdef int ncol = Rays_SP.shape[1] + cdef int nz = Rays_SP.shape[2] + cdef cnp.ndarray[npy_float64, ndim = 5, mode = 'c'] ray = np.empty((nrow, ncol, nz, 3, Npts), dtype=np.float64) + cdef cnp.ndarray[npy_float64, ndim = 1, mode = 'c'] basespace = np.arange(0, max_len+stepSize, stepSize) + + for k1 in range(nrow): + for k2 in range(ncol): + for k2a in range(nz): + for k3 in range(3): + for k4 in range(Npts): + ray[k1, k2, k2a, k3, k4] = Rays_SP[k1, k2, k2a, k3] + basespace[k4]*Rays_SLV[k1, k2, k2a, k3] + return ray