pax_global_header00006660000000000000000000000064144462703360014523gustar00rootroot0000000000000052 comment=a8b68a35f5f87c401c1c9258958113b45d656f31 pygac-1.7.2/000077500000000000000000000000001444627033600126355ustar00rootroot00000000000000pygac-1.7.2/.bumpversion.cfg000066400000000000000000000001441444627033600157440ustar00rootroot00000000000000[bumpversion] current_version = 1.4.0 commit = True tag = True [bumpversion:file:pygac/version.py] pygac-1.7.2/.github/000077500000000000000000000000001444627033600141755ustar00rootroot00000000000000pygac-1.7.2/.github/workflows/000077500000000000000000000000001444627033600162325ustar00rootroot00000000000000pygac-1.7.2/.github/workflows/ci.yaml000066400000000000000000000023501444627033600175110ustar00rootroot00000000000000name: CI on: [push, pull_request] jobs: test: runs-on: ${{ matrix.os }} continue-on-error: ${{ matrix.experimental }} strategy: fail-fast: true matrix: os: ["ubuntu-latest"] python-version: ["3.8", "3.9", "3.10"] experimental: [false] env: PYTHON_VERSION: ${{ matrix.python-version }} OS: ${{ matrix.os }} ACTIONS_ALLOW_UNSECURE_COMMANDS: true PYGAC_CONFIG_FILE: etc/pygac.cfg.template steps: - name: Checkout source uses: actions/checkout@v2 - name: Setup Conda Environment uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: ${{ matrix.python-version }} environment-file: continuous_integration/environment.yaml activate-environment: test-environment - name: Install pygac shell: bash -l {0} run: | pip install --no-deps -e . - name: Run unit tests shell: bash -l {0} run: | pytest --cov=pygac pygac/tests --cov-report=xml - name: Upload unittest coverage uses: codecov/codecov-action@v2 with: file: ./coverage.xml env_vars: OS,PYTHON_VERSION pygac-1.7.2/.github/workflows/deploy-sdist.yaml000066400000000000000000000007601444627033600215410ustar00rootroot00000000000000name: Deploy sdist on: release: types: - published jobs: test: runs-on: ubuntu-latest steps: - name: Checkout source uses: actions/checkout@v2 - name: Create sdist shell: bash -l {0} run: python setup.py sdist - name: Publish package to PyPI if: github.event.action == 'published' uses: pypa/gh-action-pypi-publish@v1.4.1 with: user: __token__ password: ${{ secrets.pypi_password }} pygac-1.7.2/.gitignore000066400000000000000000000001351444627033600146240ustar00rootroot00000000000000build doc/build *.so *.pyc *~ dist pygac.egg-info etc/*.cfg gapfilled_tles/ pygac/version.py pygac-1.7.2/.landscape.yaml000066400000000000000000000002751444627033600155350ustar00rootroot00000000000000doc-warnings: yes test-warnings: yes strictness: high max-line-length: 120 autodetect: yes ignore-paths: - doc - etc ignore-patterns: - ^example/doc_.*\.py$ - (^|/)doc(/|$) pygac-1.7.2/.pre-commit-config.yaml000066400000000000000000000003371444627033600171210ustar00rootroot00000000000000exclude: '^$' fail_fast: false repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v2.2.3 hooks: - id: flake8 additional_dependencies: [flake8-docstrings, flake8-debugger, flake8-bugbear] pygac-1.7.2/.readthedocs.yml000066400000000000000000000005521444627033600157250ustar00rootroot00000000000000# Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details version: 2 # Build documentation in the docs/ directory with Sphinx sphinx: configuration: doc/source/conf.py fail_on_warning: true build: os: "ubuntu-20.04" tools: python: "mambaforge-4.10" conda: environment: doc/rtd_environment.yaml pygac-1.7.2/.stickler.yml000066400000000000000000000001361444627033600152560ustar00rootroot00000000000000linters: flake8: fixer: true python: 3 config: setup.cfg fixers: enable: true pygac-1.7.2/CHANGELOG.md000066400000000000000000000306731444627033600144570ustar00rootroot00000000000000## Version 1.7.2 (2023/06/26) ### Pull Requests Merged #### Bugs fixed * [PR 120](https://github.com/pytroll/pygac/pull/120) - Compatibility with numpy v1.24 #### Features added * [PR 122](https://github.com/pytroll/pygac/pull/122) - Don't use deprecated distutils module. #### Documentation changes * [PR 123](https://github.com/pytroll/pygac/pull/123) - Update supported data formats in documentation ([2494](https://github.com/pytroll/satpy/issues/2494)) In this release 3 pull requests were closed. ## Version 1.7.1 (2022/12/09) ### Pull Requests Merged #### Bugs fixed * [PR 119](https://github.com/pytroll/pygac/pull/119) - Fix the documentation to include FRAC and light cleanup #### Documentation changes * [PR 119](https://github.com/pytroll/pygac/pull/119) - Fix the documentation to include FRAC and light cleanup In this release 2 pull requests were closed. ## Version 1.7.0 (2022/10/31) ### Issues Closed * [Issue 112](https://github.com/pytroll/pygac/issues/112) - Usage documentation uses wrong function name for 'get_reader_class' ([PR 113](https://github.com/pytroll/pygac/pull/113) by [@mraspaud](https://github.com/mraspaud)) * [Issue 78](https://github.com/pytroll/pygac/issues/78) - Handle completely missing ICT or space counts ([PR 117](https://github.com/pytroll/pygac/pull/117) by [@mraspaud](https://github.com/mraspaud)) * [Issue 22](https://github.com/pytroll/pygac/issues/22) - Thermal calibration error ([PR 117](https://github.com/pytroll/pygac/pull/117) by [@mraspaud](https://github.com/mraspaud)) In this release 3 issues were closed. ### Pull Requests Merged #### Bugs fixed * [PR 117](https://github.com/pytroll/pygac/pull/117) - Fix calibration when 3b is totally deactivated ([78](https://github.com/pytroll/pygac/issues/78), [22](https://github.com/pytroll/pygac/issues/22)) * [PR 116](https://github.com/pytroll/pygac/pull/116) - Fix typos and deprecations * [PR 113](https://github.com/pytroll/pygac/pull/113) - Fix usage typo ([112](https://github.com/pytroll/pygac/issues/112)) #### Features added * [PR 118](https://github.com/pytroll/pygac/pull/118) - Bump up python versions * [PR 115](https://github.com/pytroll/pygac/pull/115) - Add support for frac data In this release 5 pull requests were closed. ## Version 1.6.0 (2022/08/04) ### Issues Closed * [Issue 107](https://github.com/pytroll/pygac/issues/107) - Fix sun-earth distance correction * [Issue 104](https://github.com/pytroll/pygac/issues/104) - Links to POD/KLM user guides are mixed up ([PR 105](https://github.com/pytroll/pygac/pull/105) by [@mraspaud](https://github.com/mraspaud)) * [Issue 103](https://github.com/pytroll/pygac/issues/103) - API is missing on readthedocs * [Issue 40](https://github.com/pytroll/pygac/issues/40) - Calculated sun-earth distance correction factor once and add as attribute In this release 4 issues were closed. ### Pull Requests Merged #### Features added * [PR 109](https://github.com/pytroll/pygac/pull/109) - Add new unpublished METOPC VIS calibration coefficients from Patmos-x #### Documentation changes * [PR 105](https://github.com/pytroll/pygac/pull/105) - Fix the pod and klm guide links ([104](https://github.com/pytroll/pygac/issues/104)) In this release 2 pull requests were closed. ## Version 1.5.0 (2022/01/10) ### Issues Closed * [Issue 94](https://github.com/pytroll/pygac/issues/94) - Method get_tle_lines() raises index error when tle lines are empty strings * [Issue 90](https://github.com/pytroll/pygac/issues/90) - Change in masked scanlines * [Issue 87](https://github.com/pytroll/pygac/issues/87) - Unit test discovery ([PR 98](https://github.com/pytroll/pygac/pull/98) by [@sfinkens](https://github.com/sfinkens)) * [Issue 85](https://github.com/pytroll/pygac/issues/85) - Update documentation * [Issue 80](https://github.com/pytroll/pygac/issues/80) - Reduce rounding error in POD reader adjust clock drift ([PR 84](https://github.com/pytroll/pygac/pull/84) by [@carloshorn](https://github.com/carloshorn)) * [Issue 76](https://github.com/pytroll/pygac/issues/76) - IndexError in PODReader._adjust_clock_drift ([PR 84](https://github.com/pytroll/pygac/pull/84) by [@carloshorn](https://github.com/carloshorn)) * [Issue 74](https://github.com/pytroll/pygac/issues/74) - Remove Cython dependency ([PR 83](https://github.com/pytroll/pygac/pull/83) by [@carloshorn](https://github.com/carloshorn)) * [Issue 46](https://github.com/pytroll/pygac/issues/46) - Pypi and presentation work * [Issue 43](https://github.com/pytroll/pygac/issues/43) - Links to POD/KLM user guides are broken * [Issue 39](https://github.com/pytroll/pygac/issues/39) - Update release in preparation for conda-forge package In this release 10 issues were closed. ### Pull Requests Merged #### Bugs fixed * [PR 91](https://github.com/pytroll/pygac/pull/91) - Invert bit position in quality flags * [PR 84](https://github.com/pytroll/pygac/pull/84) - Refactor clock drift ([82](https://github.com/pytroll/pygac/issues/82), [81](https://github.com/pytroll/pygac/issues/81), [80](https://github.com/pytroll/pygac/issues/80), [76](https://github.com/pytroll/pygac/issues/76)) * [PR 79](https://github.com/pytroll/pygac/pull/79) - Fix tests on bigendian platforms * [PR 75](https://github.com/pytroll/pygac/pull/75) - Typo #### Features added * [PR 98](https://github.com/pytroll/pygac/pull/98) - Switch to pytest for unit tests ([87](https://github.com/pytroll/pygac/issues/87)) * [PR 92](https://github.com/pytroll/pygac/pull/92) - Allow PathLike objects * [PR 89](https://github.com/pytroll/pygac/pull/89) - Add github workflow * [PR 86](https://github.com/pytroll/pygac/pull/86) - add METOP C coefficients * [PR 84](https://github.com/pytroll/pygac/pull/84) - Refactor clock drift ([82](https://github.com/pytroll/pygac/issues/82), [81](https://github.com/pytroll/pygac/issues/81), [80](https://github.com/pytroll/pygac/issues/80), [76](https://github.com/pytroll/pygac/issues/76)) * [PR 83](https://github.com/pytroll/pygac/pull/83) - Replace cython filter ([74](https://github.com/pytroll/pygac/issues/74)) In this release 10 pull requests were closed. ## Version 1.4.0 (2020/08/06) ### Issues Closed * [Issue 66](https://github.com/pytroll/pygac/issues/66) - Typos in calibration coefficients ([PR 67](https://github.com/pytroll/pygac/pull/67)) * [Issue 62](https://github.com/pytroll/pygac/issues/62) - Computation of Earth scene radiance ([PR 58](https://github.com/pytroll/pygac/pull/58)) * [Issue 60](https://github.com/pytroll/pygac/issues/60) - Improve readability of quality indicators bit unpacking ([PR 72](https://github.com/pytroll/pygac/pull/72)) * [Issue 57](https://github.com/pytroll/pygac/issues/57) - channel 4 BT to radiance conversion ([PR 67](https://github.com/pytroll/pygac/pull/67)) * [Issue 54](https://github.com/pytroll/pygac/issues/54) - Function check_file_version should not be part of pygac-run ([PR 55](https://github.com/pytroll/pygac/pull/55)) * [Issue 47](https://github.com/pytroll/pygac/issues/47) - Fix reading of renamed files In this release 6 issues were closed. ### Pull Requests Merged #### Bugs fixed * [PR 73](https://github.com/pytroll/pygac/pull/73) - Fix azimuth encoding * [PR 67](https://github.com/pytroll/pygac/pull/67) - Correct coefficients ([66](https://github.com/pytroll/pygac/issues/66), [57](https://github.com/pytroll/pygac/issues/57)) #### Features added * [PR 72](https://github.com/pytroll/pygac/pull/72) - Quality indicators ([60](https://github.com/pytroll/pygac/issues/60)) * [PR 71](https://github.com/pytroll/pygac/pull/71) - Expose new metadata * [PR 70](https://github.com/pytroll/pygac/pull/70) - Remove config dependency from reader class * [PR 58](https://github.com/pytroll/pygac/pull/58) - export coefficients to json file ([62](https://github.com/pytroll/pygac/issues/62)) * [PR 55](https://github.com/pytroll/pygac/pull/55) - Refactor gac-run ([54](https://github.com/pytroll/pygac/issues/54)) In this release 7 pull requests were closed. ## Version v1.3.1 (2020/02/07) ### Issues Closed * [Issue 52](https://github.com/pytroll/pygac/issues/52) - Allow gzip input files ([PR 53](https://github.com/pytroll/pygac/pull/53)) * [Issue 49](https://github.com/pytroll/pygac/issues/49) - Calibration Coeffs Patmos-X 2017 ([PR 50](https://github.com/pytroll/pygac/pull/50)) In this release 2 issues were closed. ### Pull Requests Merged #### Bugs fixed * [PR 50](https://github.com/pytroll/pygac/pull/50) - Fix typo in MetOp-B calibration ([49](https://github.com/pytroll/pygac/issues/49)) * [PR 48](https://github.com/pytroll/pygac/pull/48) - Update metadata when reading lonlat also #### Features added * [PR 53](https://github.com/pytroll/pygac/pull/53) - Allow gzip compressed files as input for klm and pod readers read method ([52](https://github.com/pytroll/pygac/issues/52)) * [PR 51](https://github.com/pytroll/pygac/pull/51) - Use the data format name to id ARS headered files In this release 4 pull requests were closed. ## Version 1.3.0 (2019/12/05) ### Pull Requests Merged #### Features added * [PR 45](https://github.com/pytroll/pygac/pull/45) - Add LAC support ([5](https://github.com/pytroll/pygac/issues/5)) * [PR 42](https://github.com/pytroll/pygac/pull/42) - Update documentation * [PR 41](https://github.com/pytroll/pygac/pull/41) - Add meta_data dictionary to reader class. In this release 3 pull requests were closed. ## Version 1.2.1 (2019/11/15) ### Pull Requests Merged #### Bugs fixed * [PR 37](https://github.com/pytroll/pygac/pull/37) - Fixing geotiepoints attribute error for python2.7 * [PR 36](https://github.com/pytroll/pygac/pull/36) - Fix update of missing scanlines #### Features added * [PR 38](https://github.com/pytroll/pygac/pull/38) - Fix tests for python 3 In this release 3 pull requests were closed. ## Version 1.2.0 (2019/10/17) ### Issues Closed * [Issue 33](https://github.com/pytroll/pygac/issues/33) - Make use of pytroll geotiepoints instead of using a deprecated copy * [Issue 7](https://github.com/pytroll/pygac/issues/7) - Project URL points to wrong domain In this release 2 issues were closed. ### Pull Requests Merged #### Bugs fixed * [PR 30](https://github.com/pytroll/pygac/pull/30) - Feature update angles computation #### Features added * [PR 35](https://github.com/pytroll/pygac/pull/35) - Changed azimuth angle range to ]-180, 180] * [PR 34](https://github.com/pytroll/pygac/pull/34) - Use the geotiepoints library * [PR 32](https://github.com/pytroll/pygac/pull/32) - Updated documentation about azimuth angles * [PR 31](https://github.com/pytroll/pygac/pull/31) - Refactor I/O In this release 5 pull requests were closed. ## Version 1.1.0 (2019/06/12) ### Issues Closed * [Issue 23](https://github.com/pytroll/pygac/issues/23) - Add support for Python3 * [Issue 20](https://github.com/pytroll/pygac/issues/20) - IndexError if orbit time beyond TLE range ([PR 21](https://github.com/pytroll/pygac/pull/21)) * [Issue 18](https://github.com/pytroll/pygac/issues/18) - Error in pygac data zenith angle * [Issue 16](https://github.com/pytroll/pygac/issues/16) - Unit test failure In this release 4 issues were closed. ### Pull Requests Merged #### Bugs fixed * [PR 27](https://github.com/pytroll/pygac/pull/27) - Add get_times to angles computation * [PR 24](https://github.com/pytroll/pygac/pull/24) - Fix unit tests * [PR 21](https://github.com/pytroll/pygac/pull/21) - Fix TLE line identification ([20](https://github.com/pytroll/pygac/issues/20)) * [PR 17](https://github.com/pytroll/pygac/pull/17) - Python 3 compatibiity * [PR 15](https://github.com/pytroll/pygac/pull/15) - Fix selection of user defined scanlines #2 * [PR 14](https://github.com/pytroll/pygac/pull/14) - Fix gac-run * [PR 9](https://github.com/pytroll/pygac/pull/9) - Fixes: Dependency and docs * [PR 3](https://github.com/pytroll/pygac/pull/3) - Feature clock fixed nan tsm scanline timestamp #### Features added * [PR 28](https://github.com/pytroll/pygac/pull/28) - Simplify TLE computation * [PR 26](https://github.com/pytroll/pygac/pull/26) - Python-3 Compatibility * [PR 25](https://github.com/pytroll/pygac/pull/25) - Fix style * [PR 19](https://github.com/pytroll/pygac/pull/19) - Add support for TIROS-N * [PR 13](https://github.com/pytroll/pygac/pull/13) - Update installation.rst with TLE files * [PR 11](https://github.com/pytroll/pygac/pull/11) - Add scanline timestamps to qualflags file * [PR 10](https://github.com/pytroll/pygac/pull/10) - Usability improvements * [PR 6](https://github.com/pytroll/pygac/pull/6) - Add new attributes to /how * [PR 4](https://github.com/pytroll/pygac/pull/4) - Improve interface of gac_run.py In this release 17 pull requests were closed. pygac-1.7.2/LICENSE.txt000066400000000000000000001045131444627033600144640ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . pygac-1.7.2/MANIFEST.in000066400000000000000000000001721444627033600143730ustar00rootroot00000000000000include etc/* include gapfilled_tles/* include bin/pygac-* include LICENSE.txt include README.md include pygac/version.py pygac-1.7.2/README.md000066400000000000000000000011401444627033600141100ustar00rootroot00000000000000pygac ===== [![Build](https://github.com/pytroll/pygac/actions/workflows/ci.yaml/badge.svg)](https://github.com/pytroll/pygac/actions/workflows/ci.yaml) [![Coverage](https://codecov.io/gh/pytroll/pygac/branch/main/graph/badge.svg?token=DQMgf2LxuM)](https://codecov.io/gh/pytroll/pygac) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5832775.svg)](https://doi.org/10.5281/zenodo.5832775) Pygac is a Python package to read, calibrate and navigate data from the AVHRR instrument onboard NOAA and MetOp satellites in GAC and LAC format. The documentation is available at https://pygac.readthedocs.io/. pygac-1.7.2/RELEASING.md000066400000000000000000000033751444627033600145000ustar00rootroot00000000000000# Releasing Pygac 1. checkout main branch 2. pull from repo 3. run the unittests 4. run `loghub`. Replace and with proper values. To get the previous version run `git tag` and select the most recent with highest version number. ``` loghub pytroll/pygac -u -st v -plg bug "Bugs fixed" -plg enhancement "Features added" -plg documentation "Documentation changes" -plg backwards-incompatibility "Backwards incompatible changes" ``` This command will create a CHANGELOG.temp file which need to be added to the top of the CHANGLOG.md file. The same content is also printed to terminal, so that can be copy-pasted, too. Remember to update also the version number to the same given in step 5. Don't forget to commit CHANGELOG.md! 5. Create a tag with the new version number, starting with a 'v', eg: ``` git tag -a v -m "Version " ``` For example if the previous tag was `v0.9.0` and the new release is a patch release, do: ``` git tag -a v0.9.1 -m "Version 0.9.1" ``` See [semver.org](http://semver.org/) on how to write a version number. 6. push changes to github `git push --follow-tags` 7. Verify github action unittests passed. 8. Create a "Release" on GitHub by going to https://github.com/pytroll/pygac/releases and clicking "Draft a new release". On the next page enter the newly created tag in the "Tag version" field, "Version X.Y.Z" in the "Release title" field, and paste the markdown from the changelog (the portion under the version section header) in the "Describe this release" box. Finally click "Publish release". 9. Verify the GitHub actions for deployment succeed and the release is on PyPI. pygac-1.7.2/bin/000077500000000000000000000000001444627033600134055ustar00rootroot00000000000000pygac-1.7.2/bin/pygac-convert-patmosx-coefficients000077500000000000000000000333031444627033600222460ustar00rootroot00000000000000#!/usr/bin/env python # Author(s): # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Convert PATMOS-x calibration file tarballs to PyGAC calibration json format. The official tarballs are available on the PATMOS-x webpage "https://cimss.ssec.wisc.edu/patmosx/avhrr_cal.html". """ import argparse import pathlib import datetime as dt import tarfile import re import json import logging from scipy.optimize import bisect class PatmosxReader: """Read PATMOS-x coefficient files tarballs.""" # regular expression with named capturing groups to read an entire patmosx file regex = re.compile( r'\s*(?P\w+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+),?\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'(?:[a-z]+[^\n]*\n)?' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)\s*(?P[eE0-9\.-]+)[^\n]*\n' r'(?:\s*(?P[eE0-9\.-]+),\s*(?P[eE0-9\.-]+),\s*(?P[eE0-9\.-]+),\s*(?P[eE0-9\.-]+)[^\n]*\n)?' r'(?:\s*(?P[eE0-9\.-]+),\s*(?P[eE0-9\.-]+),\s*(?P[eE0-9\.-]+),\s*(?P[eE0-9\.-]+)[^\n]*\n)?' r'(?:\![^v][^\n]*\n)*' r'(?:\!(?Pv\w+))?' ) def __init__(self, tarball): self.tarball = tarball self.coeffs = [] with tarfile.open(tarball) as archive: for tarinfo in archive: if tarinfo.isfile(): # open satellite specific coefficient file filename = tarinfo.name fileobj = archive.extractfile(filename) content = fileobj.read().decode() match = self.regex.match(content) sat_coeffs = {key: self.parse_types(value) for key, value in match.groupdict().items()} self.coeffs.append(sat_coeffs) def __iter__(self): yield from self.coeffs # noqa @staticmethod def parse_types(value): """parse the types of coefficients""" try: try: _value = int(value) except ValueError: _value = float(value) except ValueError: _value = value except TypeError: _value = None return _value class Translator: """Translate PATMOS-x coefficients to PyGAC format.""" sat_names = {"m01": "metopb", "m02": "metopa", "n05": "tirosn", "m03": "metopc"} sat_names.update({"n{0:02d}".format(i): "noaa{0}".format(i) for i in range(6,20)}) description = { "visible": { "channels": ['1', '2', '3a'], "coefficients": { 'dark_count': "instrument counts under dark conditions []", "gain_switch": "dual-gain switch count, set to 'null' for single-gain instruments []", "s0": "single-gain calibration slope at launch date [%]", "s1": "linear single-gain calibration slope parameter [% years^{-1}]", "s2": "quadratic single-gain calibration slope parameter [% years^{-2}]", "date_of_launch": "timestamp of launch date [UTC]" }, "method": 'Heidinger, A.K., W.C. Straka III, C.C. Molling, J.T. Sullivan, and X. Wu, 2010: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record. International Journal of Remote Sensing, 31:6493-6517' }, "thermal": { "channels": ['3b', '4', '5'], "coefficients": { 'centroid_wavenumber': "centroid wavenumber [cm^{-1}]", "b0": "constant non-linear radiance correction coefficient [mW m^{-2} sr cm^{-1}]", "b1": "linear non-linear radiance correction coefficient []", "b2": "quadratic non-linear radiance correction coefficient [(mW^{-1} m^2 sr^{-1} cm)]", "space_radiance": "radiance of space [mW m^{-2} sr cm^{-1}]", "to_eff_blackbody_intercept": "thermal channel temperature to effective blackbody temperature intercept [K]", "to_eff_blackbody_slope": "thermal channel temperature to effective blackbody temperature slope []", "d0": "constant thermometer counts to temperature conversion coefficient [K]", "d1": "linear thermometer counts to temperature conversion coefficient [K]", "d2": "quadratic thermometer counts to temperature conversion coefficient [K]", "d3": "cubic thermometer counts to temperature conversion coefficient [K]", "d4": "quartic thermometer counts to temperature conversion coefficient [K]" }, "method": "Goodrum, G., Kidwell, K.B. and W. Winston, 2000: NOAA KLM User's Guide. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data and Information Service; Walton, C. C., J. T. Sullivan, C. R. N. Rao, and M. P. Weinreb, 1998: Corrections for detector nonlinearities and calibration inconsistencies of the infrared channels of the Advanced Very High Resolution Radiometer. J. Geophys. Res., 103, 3323-3337; Trishchenko, A.P., 2002: Removing Unwanted Fluctuations in the AVHRR Thermal Calibration Data Using Robust Techniques. Journal of Atmospheric and Oceanic Technology, 19:1939-1954" } } def __init__(self, patmosx_coeffs): self.coeffs = {"description": self.description} for patmosx_sat_coeffs in patmosx_coeffs: sat_name = self.sat_names[patmosx_sat_coeffs["sat_name"]] pygac_sat_coeffs = self.convert(patmosx_sat_coeffs) self.coeffs[sat_name] = pygac_sat_coeffs @classmethod def convert(cls, patmosx_sat_coeffs): pygac_sat_coeffs = {} # visible calibration for ch in ("1", "2", "3a"): s0l = patmosx_sat_coeffs['ch{0}_low_gain_S0'.format(ch)] s0h = patmosx_sat_coeffs['ch{0}_high_gain_S0'.format(ch)] if s0l == s0h: gain_switch = None s0 = s0l else: gain_switch = patmosx_sat_coeffs['ch{0}_gain_switches_count'.format(ch)] s0 = cls.find_s0(s0l, s0h, ch) pygac_sat_coeffs["channel_{0}".format(ch)] = { "dark_count": float(patmosx_sat_coeffs['ch{0}_dark_count'.format(ch)]), "gain_switch": gain_switch, "s0": s0, "s1": patmosx_sat_coeffs['ch{0}_high_gain_S1'.format(ch)], "s2": patmosx_sat_coeffs['ch{0}_high_gain_S2'.format(ch)] } date_of_launch = cls.float2date(patmosx_sat_coeffs['date_of_launch']) pygac_sat_coeffs['date_of_launch'] = date_of_launch.strftime("%Y-%m-%dT%H:%M:%S.%fZ") # thermal channels for ch in ("3b", "4", "5"): pygac_sat_coeffs["channel_{0}".format(ch)] = { "b0": patmosx_sat_coeffs['ch{0}_b0'.format(ch)], "b1": patmosx_sat_coeffs['ch{0}_b1'.format(ch)], "b2": patmosx_sat_coeffs['ch{0}_b2'.format(ch)], "centroid_wavenumber": patmosx_sat_coeffs['nu_{0}'.format(ch)], "space_radiance": patmosx_sat_coeffs['ch{0}_Ns'.format(ch)], "to_eff_blackbody_intercept": (-patmosx_sat_coeffs['a1_{0}'.format(ch)] / patmosx_sat_coeffs['a2_{0}'.format(ch)]), "to_eff_blackbody_slope": 1/patmosx_sat_coeffs['a2_{0}'.format(ch)] } for t in range(1, 5): pygac_sat_coeffs["thermometer_{0}".format(t)] = { "d{0}".format(d): float(patmosx_sat_coeffs["PRT{0}_{1}".format(t, d)]) for d in range(5) } return pygac_sat_coeffs @staticmethod def find_s0(s0_low, s0_high, ch): """Find the single-gain calibration slope at launch date. Arguments s0_low - low gain calibration slope at launch date s0_high - high gain calibration slope at launch date ch - channel name ("1", "2", "3a") Note: In case of a single-gain instrument, s0_low is equal to s0_high. """ if s0_low == s0_high: # single gain case return s0_low if ch == '3a': g_low, g_high = 0.25, 1.75 else: g_low, g_high = 0.5, 1.5 # Note: the PATMOS-x coefficients are rounded to three decimals. def diff(s0): return s0_low - round(s0*g_low, 3) + s0_high - round(s0*g_high, 3) s0_l = s0_low / g_low s0_h = s0_high / g_high if diff(s0_l) == 0: s0 = s0_l elif diff(s0_h) == 0: s0 = s0_h else: s0 = bisect(diff, s0_l, s0_h) return s0 @staticmethod def float2date(date_float): """Convert date float to date. Argument date_float (float) - date as year Return date (datetime.datetime) - date Note This is the reverse function of date2float. """ year = int(date_float) days_in_year = (dt.datetime(year+1, 1, 1) - dt.datetime(year, 1, 1)).days seconds = date_float*days_in_year*24*3600 - year*days_in_year*24*3600 diff = dt.timedelta(seconds=seconds) date = dt.datetime(year, 1, 1) + diff return date def save(self, filepath): """Save coefficients as PyGAC json file.""" with open(filepath, mode='w') as json_file: json.dump(self.coeffs, json_file, indent=4, sort_keys=True) if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('tarball', type=str, help='path to PATMOS-x coefficients tarball') parser.add_argument('-o', '--output', type=str, metavar="JSON", help='path to PyGAC json file, defaults to tarball path with suffix ".json"') parser.add_argument('-v', '--verbose', action='store_true', help='explain what is being done') args = parser.parse_args() if args.verbose: loglevel = logging.INFO else: loglevel = logging.WARNING logging.basicConfig(level=loglevel, format='[%(asctime)s] %(message)s') tarball = pathlib.Path(args.tarball) logging.info('Read PATMOS-x tarball "%s".', tarball) patmosx_coeffs = PatmosxReader(tarball) logging.info('Translate PATMOS-x coefficients to PyGAC format.') pygac_coeffs = Translator(patmosx_coeffs) output = args.output or tarball.with_suffix(".json") logging.info('Write PyGAC calibration json file "%s".', output) pygac_coeffs.save(output) logging.info('Done!') pygac-1.7.2/bin/pygac-run000077500000000000000000000134411444627033600152430ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2012, 2014 Abhay Devasthale and Martin Raspaud # Author(s): # Abhay Devasthale # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Read a gac file. """ import argparse import logging import os import sys import tarfile from datetime import datetime from functools import wraps import pygac if sys.version_info.major < 3: class FileNotFoundError(OSError): pass logger = logging.getLogger("pygac") verbosity2loglevel = { 1: logging.INFO, 2: logging.DEBUG } class MyFormatter(logging.Formatter): converter = datetime.fromtimestamp def formatTime(self, record, datefmt=None): ct = self.converter(record.created) if datefmt: s = ct.strftime(datefmt) else: t = ct.strftime("%Y-%m-%d %H:%M:%S") s = "%s.%03d" % (t, record.msecs) return s def str2scanline(string): """Convert string to scanline. Make sure, the scanline is not negative. Args: string (str): String to be converted Returns: int: Scanline """ integer = int(string) if integer < 0: raise argparse.ArgumentTypeError('Scanlines must be >= 0') return integer def validate_args(args): if args.end_line > 0 and args.start_line > args.end_line: raise ValueError('Start Scanline > End Scanline') if args.config and not os.path.isfile(args.config): raise FileNotFoundError( 'The provided config file "%s" does not exist!' % args.config) if args.verbose > 0: # redirect the log messages to the standart output ch = logging.StreamHandler() formatter = MyFormatter( '[ %(levelname)s %(name)s %(asctime)s] %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) # set the log-level depending on user verbosity verbosity = min(len(verbosity2loglevel), args.verbose) logger.setLevel(verbosity2loglevel[verbosity]) def tarfile_walker(path): """Yield all files from a tar archive. Args: path (str): Path to archive Yields: filename: name of the extracted file fileobj: corresponding file object """ with tarfile.open(path) as archive: for tarinfo in archive: if tarinfo.isfile(): filename = tarinfo.name fileobj = archive.extractfile(filename) yield filename, fileobj def logged_trial(processor, debug=False): """Decorator to logs exceptions during processing.""" @wraps(processor) def wrapper(filename, *args, **kwargs): try: processor(filename, *args, **kwargs) except Exception: logger.exception('Could not process "%s"' % str(filename)) if debug: raise return wrapper if __name__ == "__main__": parser = argparse.ArgumentParser( description='Read, calibrate and navigate NOAA AVHRR GAC data') parser.add_argument('path', type=str, help='Path to GAC file(s) to be processed' ' (Can be a file, directory or tar-archive)') parser.add_argument('start_line', type=str2scanline, help='First scanline to be processed (0-based)') parser.add_argument('end_line', type=str2scanline, help='Last scanline to be processed (0-based, ' 'set to 0 for the last available scanline)') parser.add_argument('-c', '--config', type=str, help='pygac config file to be used') parser.add_argument('-v', '--verbose', action='count', default=0, help='Explain what is being done, frequent occurrence' ' increases details') parser.add_argument('--debug', action='store_true', help="If given, stop on the first exception, otherwise" " log exception and continue with next file") parser.add_argument('--version', action='version', version='pygac %s' % pygac.__version__) args = parser.parse_args() validate_args(args) if args.config: pygac.read_config_file(args.config) processor = logged_trial(pygac.process_file, debug=args.debug) path = args.path start_line = args.start_line end_line = args.end_line if os.path.isfile(path): if tarfile.is_tarfile(path): logger.info('Open archive "%s"' % str(path)) for filename, fileobj in tarfile_walker(path): processor(filename, start_line, end_line, fileobj=fileobj) else: filename = path # Raise exceptions in case of a single file. pygac.process_file(filename, start_line, end_line) elif os.path.isdir(path): logger.info('Open directory "%s"' % str(path)) for basename in os.listdir(path): filename = os.path.join(path, basename) processor(filename, start_line, end_line) else: raise FileNotFoundError( 'The provided path "%s" is neither a file nor a directory!' % args.path) pygac-1.7.2/changelog.rst000066400000000000000000000207751444627033600153310ustar00rootroot00000000000000Changelog ========= %%version%% (unreleased) ------------------------ - Update changelog. [Martin Raspaud] - Bump version: 1.0.0 → 1.0.1. [Martin Raspaud] - Use version number from version.py. [Martin Raspaud] - Update changelog. [Martin Raspaud] - Bump version: 0.1.0 → 1.0.0. [Martin Raspaud] - Do style cleanup for gac_pod. [Martin Raspaud] - Allow PYGAC_CONFIG_FILE to be missing. [Martin Raspaud] - Merge branch 'master' into develop. [Adam.Dybbroe] - Merge branch 'pre-master' [Adam.Dybbroe] - Merge branch 'feature-clock' into pre-master. [Adam.Dybbroe] - Time stamp for mid-morning orbit investigated. [Abhay Devasthale] - Time stamp for mid-night orbit investigated. [Abhay Devasthale] - The support for NOAA-6 and -8 added. [Abhay Devasthale] - Calibration support for NOAA-6 and -8 added. [Abhay Devasthale] - Merge branch 'feature-clock' into pre-master. [Martin Raspaud] - Changed noaa07 and noaa09 to noaa7 and noaa9 resp. [Abhay Devasthale] - I/O part updated. PyGAC will first cut the start and end of the orbit if invalid geolocation is present and THEN apply user provided start and end scanline numbers to output the orbit. [Abhay Devasthale] - Absolute value of minimum scanline number used to remove negative scan line numbers. [Abhay Devasthale] - Modifications to cut out orbits that contain invalid lat/lon values at the start and the end of the tracks. [Abhay Devasthale] - NOAA-11 IndexError messages fixed. Scan line number format changed from unsigned to signed integer. [Abhay Devasthale] - Update to handle out of range values of time stamps. [Abhay Devasthale] - Updated launch dates to match Andy's. Also changes calibration coefficients for MetOps. [Abhay Devasthale] - Bug fix while subsetting channel 2. [Abhay Devasthale] - Better handling of incorrect time stamps in the L1b data. Correct reorganizing of scanlines if the orbit is missing starting scanlines. Minor bug fixes in the header reading. [Abhay Devasthale] - Merge branch 'feature-clock' of github.com:adybbroe/pygac into feature-clock. [Martin Raspaud] - Changed handeling of buggy lat,lons and scanline numbers (Abhay) [Nina.Hakansson] - Bugfix: masking invalid brightness temperatures. [Nina.Hakansson] Before values set to -32001 where multiplied with 100 and saved as in16 and got actual values -32768. - Merge branch 'feature-clock' of ssh://github.com/adybbroe/pygac into feature-clock. [Nina.Hakansson] - Rpy fix for pod-satellites (Abhay) [Nina.Hakansson] - Fixed the unit tests. [Martin Raspaud] - Don't use pod's fixed_error_correction rpy parameters. [Martin Raspaud] Use klm's constant_[roll,pitch,yaw]_attitude_error parameters instead. - Fix the rpy problem for older pod scenes. [Martin Raspaud] - Pep8ify. [Adam Dybbroe] - Updated lauch dates for several satellites. [Sara.Hornquist] - Quick fix to handle channel 3a/3b switch. [Nina.Hakansson] Maybe better to not interpolate. - Corrected launch date for metopb. [Sara.Hornquist] - Channel 3a/3b transition set to missing data. [Nina.Hakansson] - Adding metopb to gac_klm.py. [Nina.Hakansson] - Bugfix: apply earth-sun distance correction. [Nina.Hakansson] - If all pixels will be masked out, stop processing; i.e. do not produce output files. [Sara.Hornquist] - When using startline/endline, set times in the filenames that corresponds to the actual start and end time of the selected part of the orbit. Do not use the line numbers in the filenames any more. (changes from Abhay) [Sara.Hornquist] - Merge branch 'feature-clock' of ssh://github.com/adybbroe/pygac into feature-clock. [Sara.Hornquist] - Remove obsolete hdf5 files. [Adam Dybbroe] - Bug fix, one attribute was misspelled. [Sara.Hornquist] - Corrections in writing products, in order to match PPS v2014: - Make sure the string attribute has got the right format. - Changed nodata used for lat/lon. (the old nodata was inside valid range) [Sara.Hornquist] - Fix readme file. [Adam Dybbroe] - Gain factor applied to lat lon values. [Abhay Devasthale] - Minor correction in calibration file. NOAA-7 amd MetOp-A tested. [Abhay Devasthale] - Channel 3 BTs over the Antarctica corrected. [Abhay Devasthale] - Minor changes to output file name. [Abhay Devasthale] - Filenaming and quality flag changes. [Abhay Devasthale] * Filenaming convention is changed to harmonize with pps convention. * Quality flag file updated. - Merge branch 'feature-clock' of github.com:adybbroe/pygac into feature-clock. [Abhay Devasthale] Conflicts: pygac/gac_pod.py - Fix the clock drift correction. [Martin Raspaud] - Remove testing 2.6 in travis until scipy dependency is removed. [Martin Raspaud] - Try another hdf5 dev package. [Martin Raspaud] - Fix to allow h5py to build on 2.6. [Martin Raspaud] - Don't raise an error if PYGAC_CONFIG_FILE doesn't point to a file. [Martin Raspaud] - Fixing travis for 2.6. [Martin Raspaud] - Recent changes to GAC IO. [Abhay Devasthale] - Bugfixing. [Martin Raspaud] - Bugfix calibration coefficients. [Martin Raspaud] - Added missing calibration coefficients. [Martin Raspaud] - Add the gac reader generic class. [Martin Raspaud] - CI on 2.6 and add the PYGAC env var. [Martin Raspaud] - Completing calibration coefficients. [Martin Raspaud] - Finished factorizing, hopefully. [Martin Raspaud] - Add slerp tests. [Martin Raspaud] - Numpy 1.8.0 needed at least. [Martin Raspaud] - Revamped tests. [Martin Raspaud] - Implemented clock drift for pod. [Martin Raspaud] - Add slerp computations. [Martin Raspaud] - Add a simple clock drift adjustment (line shifting) [Martin Raspaud] - WIP: Update calibration coeffs. [Martin Raspaud] - Finish factorizing code for calibration. Some calibration coeffs missing. [Martin Raspaud] - WIP: Clock drift and refactoring. [Martin Raspaud] - Cleaning, and beginning of refactoring. [Martin Raspaud] - Supplements A, B and C added. [abhaydd] - Updating documentation. [abhaydd] - Updating pygac api documentation. [abhaydd] - Updated text on command-line usage. [abhaydd] - Update usage.rst. [abhaydd] - Update usage.rst. [abhaydd] - Bugfix. [Adam Dybbroe] - Added for scipy dependency. [Adam Dybbroe] - Added requirements file, for Travis... [Adam Dybbroe] - Added support for travis. [Adam Dybbroe] - Added buttons on readme page for code health etc. [Adam Dybbroe] - Added customization support for Landscape. [Adam Dybbroe] - Smoothing window for thermal channel calibration adjusted. [Abhay.Devasthale] - Updates on time information in output files. No 10th seconds, and seconds-since-1970 is now properly set. [Sara.Hornquist] - Merge branch 'pre-master' of github.com:adybbroe/pygac into pre- master. [Sara.Hornquist] - Dumping of debugging info on screen is avoided in gac_pod.py. [Abhay.Devasthale] - Update in output files: attribute what/time do not have tenth-of- second any more. [Sara.Hornquist] - Updated documentation on filenames. [Sara.Hornquist] - Negative reflectances replaced by MISSING_DATA. [Abhay.Devasthale] - Replaced nighttime reflectances with MISSING_DATA. [Abhay.Devasthale] - POD: Refined the tle search to get the nearest match. [Martin Raspaud] In the case of old satellites, the tle data can be quite scarse. For that reason, the find_tle_index function was enhanced to provide the closest match to the required date. - Bugfix in pod, and cleanup. [Martin Raspaud] - A correct determination of which sensor was generating each prt has been implemented, allowing the data to miss scanlines. It is based on the scanline numbers provided in the data - The pod data is also cleaned up before after reading. - The code has been cleaned up a little, to follow python standards. - Remove astronomy.py, depend on pyorbital instead. [Martin Raspaud] - Added h5py as a requirement in setup. [Adam Dybbroe] - Merge branch 'pre-master' of github.com:adybbroe/pygac into pre- master. [Adam Dybbroe] - Add some test scripts, and remove test data. [Martin Raspaud] - Added documentation. [Abhay.Devasthale] - Update api.rst. [abhaydd] - Update api.rst. [abhaydd] - Update api.rst. [abhaydd] - Update api.rst. [abhaydd] - Minor editorial. [Adam Dybbroe] - Fixing Manifest and setup. [Adam Dybbroe] - Updated usage docs. [Adam Dybbroe] - Adding a bit of documentation and the test case. [Adam Dybbroe] - Add empty (sphinx) docs. [Adam Dybbroe] - Adding configuration and logging. [Adam Dybbroe] - Merge branch 'master' into develop. [Adam Dybbroe] - Changed readme. [Adam Dybbroe] - Making a python package out of it. [Adam Dybbroe] - Initial commit. [Adam Dybbroe] pygac-1.7.2/continuous_integration/000077500000000000000000000000001444627033600174465ustar00rootroot00000000000000pygac-1.7.2/continuous_integration/environment.yaml000066400000000000000000000004541444627033600227010ustar00rootroot00000000000000name: test-environment channels: - conda-forge dependencies: - numpy - scipy - bottleneck - python-dateutil - hdf5 - h5py - packaging - pytest - pytest-cov - pip - pip: - docutils - pyorbital - python-geotiepoints - trollimage - pyspectral - pyorbital pygac-1.7.2/doc/000077500000000000000000000000001444627033600134025ustar00rootroot00000000000000pygac-1.7.2/doc/Makefile000066400000000000000000000107561444627033600150530ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = build # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " text to make text files" @echo " man to make manual pages" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: -rm -rf $(BUILDDIR)/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/pygac.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/pygac.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/pygac" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/pygac" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." make -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." pygac-1.7.2/doc/rtd_environment.yaml000066400000000000000000000007041444627033600175040ustar00rootroot00000000000000name: readthedocs channels: - conda-forge dependencies: - python=3.9 - numpy - scipy - bottleneck - python-dateutil - hdf5 - h5py - graphviz - setuptools - setuptools_scm - setuptools_scm_git_archive - sphinx - sphinx_rtd_theme - sphinxcontrib-apidoc - pip - pip: - docutils - pyorbital - python-geotiepoints - trollimage - pyspectral - pyorbital - .. # relative path to the pygac project pygac-1.7.2/doc/source/000077500000000000000000000000001444627033600147025ustar00rootroot00000000000000pygac-1.7.2/doc/source/_static/000077500000000000000000000000001444627033600163305ustar00rootroot00000000000000pygac-1.7.2/doc/source/_static/.gitkeep000066400000000000000000000000001444627033600177470ustar00rootroot00000000000000pygac-1.7.2/doc/source/api.rst000066400000000000000000000026571444627033600162170ustar00rootroot00000000000000The Pygac API ============= .. inheritance-diagram:: pygac.gac_pod.GACPODReader pygac.gac_klm.GACKLMReader pygac.lac_pod.LACPODReader pygac.lac_klm.LACKLMReader Reader Base Classes ------------------- Common functionality shared by multiple readers. Reader ~~~~~~ .. automodule:: pygac.reader :members: :undoc-members: GAC format reader ~~~~~~~~~~~~~~~~~ .. automodule:: pygac.gac_reader :members: :undoc-members: LAC format reader ~~~~~~~~~~~~~~~~~ .. automodule:: pygac.lac_reader :members: :undoc-members: POD series reader ~~~~~~~~~~~~~~~~~ .. automodule:: pygac.pod_reader :members: :undoc-members: :exclude-members: tsm_affected_intervals KLM series reader ~~~~~~~~~~~~~~~~~ .. automodule:: pygac.klm_reader :members: :undoc-members: :exclude-members: tsm_affected_intervals Actual Reader Implementations ----------------------------- Actual reader implementations building upon the base classes. GAC POD reader ~~~~~~~~~~~~~~ .. automodule:: pygac.gac_pod :members: :undoc-members: GAC KLM reader ~~~~~~~~~~~~~~ .. automodule:: pygac.gac_klm :members: :undoc-members: LAC POD reader ~~~~~~~~~~~~~~ .. automodule:: pygac.lac_pod :members: :undoc-members: LAC KLM reader ~~~~~~~~~~~~~~ .. automodule:: pygac.lac_klm :members: :undoc-members: Calibration ----------- .. automodule:: pygac.calibration :members: :undoc-members: pygac-1.7.2/doc/source/conf.py000066400000000000000000000160031444627033600162010ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # pygac documentation build configuration file, created by # sphinx-quickstart on Thu May 1 12:56:29 2014. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.inheritance_diagram'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'pygac' copyright = u'2014, Abhay Devasthale, Martin Raspaud and Adam Dybbroe' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1' # The full version, including alpha/beta/rc tags. release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'pygacdoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'pygac.tex', u'pygac Documentation', u'Abhay Devasthale, Martin Raspaud and Adam Dybbroe', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. 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"²è+šŒU¯½ôËó;þ6­6Xe ™£Š_òUú’kÿà*wÎLLtïö$o¦ÕR #ˆz ó 0ë“‚YÂRÌ3@,dˆ…̱ 2ÄBf€XÈ  ™b!3@,dˆ…̱p¿)p$®Z»3-ûòµ'ô1êAÓžy°¢õ2 ˜g€#q%ßlþøðŦf««f<ßUÖ0ÏG²èj ÷<²`ñ¬l¡:|‹à@|ý•Zλ›C“À<ˆ«½¢3çn|jêÆòî=xüìç’‡ùaÜ‘/|wà@Š™oo‰ªSûy2µ§Ó7¿õäÌ »ÿ³(ÆU¸È2IÑ-<þ>ëŸÑáWóã×ïË_3CLa'#H…qQ»± ÃËA>à@†’OT9""suö†2¼ïŽI†|á»ÇáMÅŸ¾˜º§BÑM£lг¡“o~%¶«p)d8£y`å÷'—\©®mdÜ}ý½\°_Jæà`Š®Ý0·p8žb!3@,dˆ…̱p ì,88XXÎó °…B¡Óé…¥íÒéô žÁ$˜g€Ý$&ÆîÚµ×b±+Ú¦P(c…¥p·Bf€ÝDDô‹ˆè','‚}S 2ÄBf€X Ïó²6ì=k€œM U ‹Úu™ÿã°o ÄBf€XÈ  ™b!3@,dˆ…̱ 2ÄBf€Xÿ¨º<kÉý™IEND®B`‚pygac-1.7.2/doc/source/index.rst000066400000000000000000000012001444627033600165340ustar00rootroot00000000000000.. pygac documentation master file, created by sphinx-quickstart on Thu May 1 12:56:29 2014. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Pygac's documentation ===================== Pygac is a Python package to read, calibrate and navigate data from the AVHRR instrument onboard NOAA and MetOp satellites in GAC and LAC format. Table of Contents ----------------- .. toctree:: :maxdepth: 2 introduction installation usage methods api legacy Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` pygac-1.7.2/doc/source/installation.rst000066400000000000000000000025161444627033600201410ustar00rootroot00000000000000Installation ------------ You can install the latest stable release of the software via the python package index (pypi) .. code-block:: bash pip install pygac TLE files ~~~~~~~~~ The pygac package requires Two-Line Element files stored per-satellite in files with names such as TLE_noaa19.txt. The contents should be the historical TLEs, i.e. a concatenation of just lines 1 and 2 without the satellite name. For example .. code-block:: 1 23455U 94089A 01122.93455091 .00000622 00000-0 36103-3 0 7210 2 23455 99.1771 113.3063 0008405 277.6106 82.4106 14.12671703326608 1 23455U 94089A 01326.97611660 .00000739 00000-0 42245-3 0 9806 2 23455 99.1886 322.4670 0009980 66.2863 293.9354 14.12871991355419 etc These can be downloaded from CelesTrak via the `special data request form`_. .. _special data request form: https://celestrak.com/NORAD/archives/request.php Development ~~~~~~~~~~~ For development clone the repository from github and install pygac in editable mode. .. code-block:: bash git clone git://github.com/pytroll/pygac cd pygac pip install -e .[dev] It is recommended to activate `pre-commit`_ checks. .. code-block:: bash pre-commit install The test suite can be run using pytest. .. code-block:: bash pytest -vs pygac/tests .. _pre-commit: https://pre-commit.com/ pygac-1.7.2/doc/source/introduction.rst000066400000000000000000000031231444627033600201540ustar00rootroot00000000000000Introduction ============ Supported Data Format --------------------- Pygac reads AVHRR GAC (Global Area Coverage), LAC (Local Area Coverage) and FRAC (Full Resolution Area Coverage) level 1b data from NOAA, which is described in the `POD`_ (NOAA-14 and before) and `KLM`_ (NOAA-15 and following) user guides. The data can be obtained from `NOAA CLASS`_, where you can also find a comprehensive `introduction`_. .. note:: Pygac can only read AVHRR data with 10 bits/pixel. This can be specified in your NOAA CLASS order or user preferences (see screenshot below). .. image:: images/noaa_class_preferences.png :width: 350 :alt: Screenshot of AVHRR data extraction preferences .. _NOAA CLASS: https://www.class.noaa.gov/ .. _POD: https://www.ncei.noaa.gov/pub/data/satellite/publications/podguides/TIROS-N%20thru%20N-14/ .. _KLM: https://www.ncei.noaa.gov/pub/data/satellite/publications/podguides/N-15%20thru%20N-19/ .. _introduction: https://www.class.noaa.gov/release/data_available/avhrr/index.htm Supported Sensors ----------------- Pygac currently supports AVHRR generations 1-3 onboard NOAA (TIROS-N, NOAA-6 and onwards) and MetOp satellites. .. _here: https://www.avl.class.noaa.gov/release/data_available/avhrr/index.htm Related Projects ---------------- - `pygac-fdr`_: Generate a fundamental data record of AVHRR GAC data using Pygac. - `level1c4pps`_: Prepare AVHRR GAC data for `NWCSAF/PPS`_ using Pygac. .. _level1c4pps: https://github.com/foua-pps/level1c4pps .. _NWCSAF/PPS: https://www.nwcsaf.org/16 .. _pygac-fdr: https://github.com/pytroll/pygac-fdr pygac-1.7.2/doc/source/legacy.rst000066400000000000000000000361371444627033600167120ustar00rootroot00000000000000Legacy Output ============= GAC I/O module -------------- The I/O module generates three HDF5 files, one containing reflectances, brightness temperatures, and lat/lon information. The other output file contains solar and satellite zenith and azimuth angles. And the third file contains quality flags. The output file name format is: ``ECC_GAC_avhrr_satellitename_99999_yyyymmddThhmmsstZ_yyyymmddThhmmsstZ.h5`` and ``ECC_GAC_sunsatangles_satellitename_99999_yyyymmddThhmmsstZ_yyyymmddThhmmsstZ.h5`` and ``ECC_GAC_qualflags_satellitename_99999_yyyymmddThhmmsstZ_yyyymmddThhmmsstZ.h5`` where, ``ECC``: ESA CCI Clouds (This prefix can be changed/specified by the user) ``avhrr``: denoting that it contains reflectances and BTs ``sunsatangles``: denoting that it contains angles ``qualflags``: denoting that it contains quality flag information ``yyyymmddThhmmsstZ``: yy:year, mm:month, dd:day, hh:hour, mm:min, ss:sec, t:tenth of second (for the start and the end of the orbit). Letters ``T`` and ``Z`` are separators for time info. The value of ``99999`` is currently used instead of providing actual orbit number. Appendices A, B and C provide detailed format of these files, including variable names, scaling, etc. The start and end times in the header and in actual L1b data can be different for orbits. The mismatch can range from few milliseconds to days. It was decided to trust the time stamps in L1b data in Pygac. After reorganizing based on scanline numbers (see issue highlighted above), the time stamps from the first and the last scanlines are taken as start and end times. In some orbits, the latitude and longitude information contains corrupt values for a part/s of the orbit and these scanlines are not flagged in the corresponding scanline-by-scaline quality flags. Currently, Pygac uses only a simple if_else construct to constrain valid range. Further improvement could be done using extra- and interpolation techniques. For extremely warm and cold temperatures, the channel 3b is saturating producing irrelevant brightness temperatures. Such saturation is often not flagged in quality information. Pygac currently uses a simple if_else construct to constrain valid range of Bts (170.0K 1.1.0). Previous versions used azimuth +/-180 degrees, which correspond to degrees clockwise from south. All angles are converted to degrees. All azimuth angles are converted to range ]-180, 180] (2019 October version > 1.1.0 ). Note that ]-180, 180] is an open interval. .. _get_alt_az: https://pyorbital.readthedocs.io/en/latest/#pyorbital.astronomy.get_alt_az .. _get_observer_look: https://pyorbital.readthedocs.io/en/latest/#pyorbital.orbital.Orbital.get_observer_look Correction of Satellite Location -------------------------------- Whenever possible, Pygac uses RPY corrections along with other orbital parameters to compute accurate satellite location (e.g. instead of assuming constant altitude). However, RPY corrections are not available for all NOAA satellites. In case of the majority of the POD family satellites, these corrections are set to zero. Correction of Scanline Timestamps --------------------------------- The geolocation in Pygac depends on accurate scanline timestamps. However, these may be corrupt, especially for older sensors. Assuming a constant scanning rate, Pygac attempts to fix them using extrapolation based on the scan line number and a reference time. Finding the right reference time is difficult due to the multitude of possible timestamp corruptions. But the combination of the following three options proved to be a robust reference in many situations: Timestamp of the first scanline, median time offset of all scanlines and header timestamp. See :meth:`pygac.reader.Reader.correct_times_median` and :meth:`pygac.reader.Reader.correct_times_thresh` for details. Finally, not only timestamps but also scanline numbers may be corrupt. Therefor lines with erroneous scanline numbers are removed before extrapolation, see :meth:`pygac.reader.Reader.correct_scan_line_numbers`. Scan-Motor-Issue ---------------- Between 2001 and 2004 GAC data from NOAA-14, NOAA-15, and NOAA-16 frequently contain a significant amount of noise towards an edge of the swath. As reported by `Schlundt et al (2017)`_, section 5.2, this is probably caused by a temporary scan-motor issue. Pygac tries to identify and mask affected pixels. .. _Schlundt et al (2017): https://climate.esa.int/media/documents/Cloud_Technical-Report-AVHRR-GAC-FCDR-generation_v1.0.pdf pygac-1.7.2/doc/source/usage.rst000066400000000000000000000067701444627033600165520ustar00rootroot00000000000000Usage ----- Through Satpy ~~~~~~~~~~~~~ The preferred way of using Pygac is through `Satpy`_. Results are returned as dask-friendly `xarray`_ DataArrays with proper dataset/coordinate names and additional metadata. It is also possible to select a user-defined range of scanlines. Furthermore, Satpy provides many options for resampling, visualizing and saving the data. .. code-block:: python import satpy # Channel set for KLM satellites. For POD satellites the channels are # ['1', '2', '3', '4', '5']. channels = ['1', '2', '3a', '3b', '4', '5'] ancillary = ['solar_zenith_angle', 'sensor_zenith_angle', 'solar_azimuth_angle', 'sensor_azimuth_angle', 'sun_sensor_azimuth_difference_angle', 'qual_flags', 'latitude', 'longitude'] scene = satpy.Scene(filenames=['NSS.GHRR.NP.D15361.S0121.E0315.B3547172.SV'], reader='avhrr_l1b_gaclac', reader_kwargs={'tle_dir': '/path/to/tle/', 'tle_name': 'TLE_%(satname)s.txt'}) scene.load(channels + ancillary) For a list of Satpy reader keyword arguments see `satpy.readers.avhrr_l1b_gaclac`_ and for further Pygac reader keyword arguments see :class:`pygac.reader.Reader`. Especially it is possible to choose a different version of calibration coefficients or even specify your own. .. _Satpy: https://satpy.readthedocs.io .. _xarray: https://xarray.pydata.org .. _satpy.readers.avhrr_l1b_gaclac: https://satpy.readthedocs.io/en/stable/api/satpy.readers.avhrr_l1b_gaclac.html?highlight=avhrr_l1b_gaclac .. _example notebook: https://github.com/pytroll/pytroll-examples/blob/main/satpy/avhrr_l1b_gaclac.ipynb Direct Usage ~~~~~~~~~~~~ Alternatively you can also use Pygac directly. .. code-block:: python from pygac import get_reader_class filename = 'NSS.GHRR.NP.D15361.S0121.E0315.B3547172.SV' reader_cls = get_reader_class(filename) reader = reader_cls(tle_dir='/path/to/tle', tle_name='TLE_%(satname)s.txt') reader.read(filename) channels = reader.get_calibrated_channels() lons, lats = reader.get_lonlat() scanline_times = reader.get_times() bad_quality_lines = reader.mask Legacy CLI ~~~~~~~~~~ .. note:: Usage of the legacy command line program ``pygac-run`` is deprecated in favour of the above options. There is also a legacy command line program ``pygac-run`` which saves the results to HDF5 and requires a configuration file. Copy the template file ``etc/pygac.cfg.template`` to ``pygac.cfg`` and place it in a directory as you please. Set the environment variable ``PYGAC_CONFIG_FILE`` pointing to the file. e.g. .. code-block:: bash PYGAC_CONFIG_FILE=/home/user/pygac.cfg; export PYGAC_CONFIG_FILE Also adapt the configuration file to your needs. The ``tledir`` parameter should be set to where your Two Line Element (TLE) files are located. Then call ``pygac-run`` on a GAC/LAC file. .. code-block:: bash pygac-run testdata/NSS.GHRR.NL.D02187.S1904.E2058.B0921517.GC 0 0 The last two digits are the start and end scanline numbers, thus specifying the portion of the GAC orbit that user wants to process. The first scanline number starts at 0. If zeroes are specified at both locations, then the entire orbit will be processed. The result will be three hdf5 files, one with the calibrated AVHRR data, the other with sun-satellite viewing geometry data and this third with scanline quality information. pygac-1.7.2/etc/000077500000000000000000000000001444627033600134105ustar00rootroot00000000000000pygac-1.7.2/etc/pygac.cfg.template000066400000000000000000000001761444627033600170120ustar00rootroot00000000000000[tle] tledir = /path/to/gapfilled/tles tlename = TLE_%(satname)s.txt [output] output_dir = /tmp output_file_prefix = ECC_GAC pygac-1.7.2/gapfilled_tles/000077500000000000000000000000001444627033600156135ustar00rootroot00000000000000pygac-1.7.2/gapfilled_tles/TLE_noaa16.txt000066400000000000000000047744331444627033600201730ustar00rootroot000000000000001 26536U 00055A 00265.61930214 .00000000 00000-0 00000+0 0 58 2 26536 98.7900 210.3675 0010476 274.6981 85.3501 14.10895524 30 1 26536U 00055A 00265.76113541 .00000000 00000-0 00000+0 0 61 2 26536 98.7896 210.5061 0010542 273.6655 86.3817 14.10891736 50 1 26536U 00055A 00265.76707352 -.00020078 00000-0 -11203-1 0 13 2 26536 98.7886 210.5136 0009705 275.1802 115.0094 14.10880075 42 1 26536U 00055A 00267.10855521 .00000000 00000-0 00000+0 0 73 2 26536 98.7896 211.8250 0010380 269.7771 90.2710 14.10888681 230 1 26536U 00055A 00267.17950142 .00005737 00000-0 32198-2 0 28 2 26536 98.7914 211.8957 0009561 273.3396 86.8630 14.10896289 244 1 26536U 00055A 00267.46313939 .00000000 00000-0 00000+0 0 96 2 26536 98.7895 212.1719 0010391 269.1950 90.8528 14.10888698 284 1 26536U 00055A 00267.74680648 .00000000 00000-0 00000+0 0 103 2 26536 98.7894 212.4494 0010428 268.5020 91.5451 14.10888805 326 1 26536U 00055A 00267.81772330 .00000000 00000-0 00000+0 0 112 2 26536 98.7899 212.5191 0010425 268.2766 91.7705 14.10888789 333 1 26536U 00055A 00267.95955713 .00000000 00000-0 00000+0 0 126 2 26536 98.7901 212.6579 0010410 267.7158 92.3314 14.10888639 350 1 26536U 00055A 00268.17230780 .00000000 00000-0 00000+0 0 132 2 26536 98.7903 212.8662 0010397 267.2640 92.7834 14.10888553 381 1 26536U 00055A 00268.38505837 .00000000 00000-0 00000+0 0 177 2 26536 98.7898 213.0738 0010381 266.5800 93.4675 14.10888531 415 1 26536U 00055A 00268.45608683 -.00082297 00000-0 -46414-1 0 23 2 26536 98.7888 213.1439 0008932 273.1275 87.4688 14.10851396 429 1 26536U 00055A 00269.44880446 .00001714 00000-0 97981-3 0 190 2 26536 98.7880 214.1149 0009149 264.8638 95.1492 14.10886134 563 1 26536U 00055A 00270.79622936 .00000000 00000-0 00000+0 0 210 2 26536 98.7889 215.4321 0010341 260.3163 99.7321 14.10890406 757 1 26536U 00055A 00270.86714691 .00000000 00000-0 00000+0 0 229 2 26536 98.7898 215.5023 0010416 259.8428 100.2049 14.10888833 764 1 26536U 00055A 00271.50539150 .00000621 00000-0 37043-3 0 226 2 26536 98.7898 216.1283 0009682 258.4126 101.6155 14.10892311 851 1 26536U 00055A 00274.48388387 .00000629 00000-0 37479-3 0 232 2 26536 98.7903 219.0432 0009713 249.8868 110.1275 14.10896360 1271 1 26536U 00055A 00278.38428297 .00000772 00000-0 45446-3 0 243 2 26536 98.7910 222.8607 0009699 238.3042 121.7454 14.10903728 1820 1 26536U 00055A 00278.38428297 .00000772 00000-0 45446-3 0 254 2 26536 98.7910 222.8607 0009699 238.3042 121.7454 14.10903728 1820 1 26536U 00055A 00282.35557167 .00000615 00000-0 36676-3 0 289 2 26536 98.7909 226.7490 0009636 226.6187 133.3997 14.10908393 2388 1 26536U 00055A 00282.35557167 .00000615 00000-0 36676-3 0 302 2 26536 98.7909 226.7490 0009636 226.6187 133.3997 14.10908393 2388 1 26536U 00055A 00285.26312341 .00000648 00000-0 38534-3 0 321 2 26536 98.7917 229.5955 0009775 217.5542 142.5023 14.10912717 2794 1 26536U 00055A 00285.26312341 .00000648 00000-0 38534-3 0 343 2 26536 98.7917 229.5955 0009775 217.5542 142.5023 14.10912717 2794 1 26536U 00055A 00285.26312341 .00000648 00000-0 38534-3 0 354 2 26536 98.7917 229.5955 0009775 217.5542 142.5023 14.10912717 2794 1 26536U 00055A 00290.79453118 .00000539 00000-0 32448-3 0 386 2 26536 98.7927 235.0114 0009950 200.8541 159.2009 14.10918615 3572 1 26536U 00055A 00290.79453118 .00000539 00000-0 32448-3 0 409 2 26536 98.7927 235.0114 0009950 200.8541 159.2009 14.10918615 3572 1 26536U 00055A 00292.56741928 .00000446 00000-0 27238-3 0 414 2 26536 98.7930 236.7475 0010039 195.4610 164.6138 14.10919435 3822 1 26536U 00055A 00292.56741928 .00000446 00000-0 27238-3 0 436 2 26536 98.7930 236.7475 0010039 195.4610 164.6138 14.10919435 3822 1 26536U 00055A 00292.56741928 .00000446 00000-0 27238-3 0 447 2 26536 98.7930 236.7475 0010039 195.4610 164.6138 14.10919435 3822 1 26536U 00055A 00295.33311829 .00000492 00000-0 29832-3 0 452 2 26536 98.7937 239.4565 0010122 187.1415 172.9741 14.10922973 4210 1 26536U 00055A 00295.33311829 .00000492 00000-0 29832-3 0 474 2 26536 98.7937 239.4565 0010122 187.1415 172.9741 14.10922973 4210 1 26536U 00055A 00295.33311829 .00000492 00000-0 29832-3 0 496 2 26536 98.7937 239.4565 0010122 187.1415 172.9741 14.10922973 4210 1 26536U 00055A 00295.33311829 .00000492 00000-0 29832-3 0 508 2 26536 98.7937 239.4565 0010122 187.1415 172.9741 14.10922973 4210 1 26536U 00055A 00301.36089324 .00000412 00000-0 25346-3 0 510 2 26536 98.7944 245.3610 0010495 169.1737 190.9908 14.10929651 5064 1 26536U 00055A 00301.36089324 .00000412 00000-0 25346-3 0 521 2 26536 98.7944 245.3610 0010495 169.1737 190.9908 14.10929651 5064 1 26536U 00055A 00301.36089324 .00000412 00000-0 25346-3 0 565 2 26536 98.7944 245.3610 0010495 169.1737 190.9908 14.10929651 5064 1 26536U 00055A 00305.47394449 .00000596 00000-0 35591-3 0 575 2 26536 98.7960 249.3899 0010565 157.4054 202.7945 14.10936241 5645 1 26536U 00055A 00305.47394449 .00000596 00000-0 35591-3 0 597 2 26536 98.7960 249.3899 0010565 157.4054 202.7945 14.10936241 5645 1 26536U 00055A 00305.47394449 .00000596 00000-0 35591-3 0 609 2 26536 98.7960 249.3899 0010565 157.4054 202.7945 14.10936241 5645 1 26536U 00055A 00305.89943105 .00000831 00000-0 46328-3 0 572 2 26536 98.7964 249.8073 0010792 160.3332 199.8748 14.10937990 5708 1 26536U 00055A 00308.38142872 .00000828 00000-0 48527-3 0 615 2 26536 98.7971 252.2380 0010769 149.2681 210.9531 14.10943359 6052 1 26536U 00055A 00311.28889574 .00000564 00000-0 33840-3 0 658 2 26536 98.7976 255.0876 0010927 140.9859 219.1900 14.10945884 6460 1 26536U 00055A 00311.28889574 .00000564 00000-0 33840-3 0 669 2 26536 98.7976 255.0876 0010927 140.9859 219.1900 14.10945884 6460 1 26536U 00055A 00314.40911236 .00001014 00000-0 58858-3 0 686 2 26536 98.7978 258.1443 0011066 132.4667 227.7834 14.10954267 6909 1 26536U 00055A 00314.40911236 .00001014 00000-0 58858-3 0 697 2 26536 98.7978 258.1443 0011066 132.4667 227.7834 14.10954267 6909 1 26536U 00055A 00314.40911236 .00001014 00000-0 58858-3 0 709 2 26536 98.7978 258.1443 0011066 132.4667 227.7834 14.10954267 6909 1 26536U 00055A 00314.40911236 .00001014 00000-0 58858-3 0 721 2 26536 98.7978 258.1443 0011066 132.4667 227.7834 14.10954267 6909 1 26536U 00055A 00319.16029794 -.00000043 00000-0 00000+0 0 744 2 26536 98.8016 262.8012 0011324 119.5338 240.6552 14.10957318 7570 1 26536U 00055A 00319.16029794 -.00000043 00000-0 00000-0 0 745 2 26536 98.8016 262.8012 0011324 119.5338 240.6552 14.10957318 7570 1 26536U 00055A 00319.16029794 -.00000043 00000-0 00000-0 0 756 2 26536 98.8016 262.8012 0011324 119.5338 240.6552 14.10957318 7570 1 26536U 00055A 00319.16029794 -.00000043 00000-0 00000-0 0 767 2 26536 98.8016 262.8012 0011324 119.5338 240.6552 14.10957318 7570 1 26536U 00055A 00322.42232311 .00000205 00000-0 13819-3 0 787 2 26536 98.7999 265.9960 0011351 110.3846 249.8716 14.10959573 8038 1 26536U 00055A 00325.32976319 .00000407 00000-0 25044-3 0 813 2 26536 98.8003 268.8452 0011419 102.8236 257.4512 14.10963698 8444 1 26536U 00055A 00325.32976319 .00000407 00000-0 25044-3 0 824 2 26536 98.8003 268.8452 0011419 102.8236 257.4512 14.10963698 8444 1 26536U 00055A 00325.32976319 .00000407 00000-0 25044-3 0 835 2 26536 98.8003 268.8452 0011419 102.8236 257.4512 14.10963698 8444 1 26536U 00055A 00325.32976319 .00000407 00000-0 25044-3 0 857 2 26536 98.8003 268.8452 0011419 102.8236 257.4512 14.10963698 8444 1 26536U 00055A 00330.29366411 .00000476 00000-0 28928-3 0 877 2 26536 98.8039 273.7150 0011722 90.7584 269.5180 14.10969482 9141 1 26536U 00055A 00330.29366411 .00000476 00000-0 28928-3 0 899 2 26536 98.8039 273.7150 0011722 90.7584 269.5180 14.10969482 9141 1 26536U 00055A 00332.99224537 .00000917 00000-0 53437-3 0 905 2 26536 98.8004 276.3573 0011400 84.6438 295.4444 14.10975582 9525 1 26536U 00055A 00332.99224537 .00000917 00000-0 53437-3 0 927 2 26536 98.8004 276.3573 0011400 84.6438 295.4444 14.10975582 9525 1 26536U 00055A 00335.39935637 .00001032 00000-0 59827-3 0 933 2 26536 98.8022 278.7163 0011501 77.4501 282.8189 14.10982967 9867 1 26536U 00055A 00335.39935637 .00001032 00000-0 59827-3 0 944 2 26536 98.8022 278.7163 0011501 77.4501 282.8189 14.10982967 9867 1 26536U 00055A 00339.44132673 .00000868 00000-0 50703-3 0 976 2 26536 98.8031 282.6772 0011536 66.1536 294.0517 14.10988950 10431 1 26536U 00055A 00339.44132673 .00000868 00000-0 50703-3 0 987 2 26536 98.8031 282.6772 0011536 66.1536 294.0517 14.10988950 10431 1 26536U 00055A 00339.44132673 .00000868 00000-0 50703-3 0 998 2 26536 98.8031 282.6772 0011536 66.1536 294.0517 14.10988950 10431 1 26536U 00055A 00342.34870918 .00000663 00000-0 39295-3 0 1012 2 26536 98.8032 285.5264 0011486 57.6925 302.4959 14.10991062 10848 1 26536U 00055A 00342.34870918 .00000663 00000-0 39295-3 0 1023 2 26536 98.8032 285.5264 0011486 57.6925 302.4959 14.10991062 10848 1 26536U 00055A 00342.34870918 .00000663 00000-0 39295-3 0 1034 2 26536 98.8032 285.5264 0011486 57.6925 302.4959 14.10991062 10848 1 26536U 00055A 00345.39791229 .00000514 00000-0 31005-3 0 1057 2 26536 98.8037 288.5160 0011500 50.1061 310.0889 14.10993269 11273 1 26536U 00055A 00345.39791229 .00000514 00000-0 31005-3 0 1079 2 26536 98.8037 288.5160 0011500 50.1061 310.0889 14.10993269 11273 1 26536U 00055A 00348.30528523 .00000445 00000-0 27152-3 0 1080 2 26536 98.8043 291.3673 0011653 41.7052 318.4988 14.10996065 11680 1 26536U 00055A 00348.30528523 .00000445 00000-0 27152-3 0 1091 2 26536 98.8043 291.3673 0011653 41.7052 318.4988 14.10996065 11680 1 26536U 00055A 00348.30528523 .00000445 00000-0 27152-3 0 1103 2 26536 98.8043 291.3673 0011653 41.7052 318.4988 14.10996065 11680 1 26536U 00055A 00351.42538181 .00000010 00000-0 29725-4 0 1120 2 26536 98.8062 294.4271 0011896 33.1468 326.9923 14.10994025 12121 1 26536U 00055A 00351.42538181 .00000010 00000-0 29725-4 0 1131 2 26536 98.8062 294.4271 0011896 33.1468 326.9923 14.10994025 12121 1 26536U 00055A 00354.47458394 .00000279 00000-0 17924-3 0 1152 2 26536 98.8052 297.4165 0011360 26.6749 333.5519 14.10998417 12551 1 26536U 00055A 00354.47458394 .00000279 00000-0 17924-3 0 1163 2 26536 98.8052 297.4165 0011360 26.6749 333.5519 14.10998417 12551 1 26536U 00055A 00354.47458394 .00000279 00000-0 17924-3 0 1174 2 26536 98.8052 297.4165 0011360 26.6749 333.5519 14.10998417 12551 1 26536U 00055A 00354.47458394 .00000279 00000-0 17924-3 0 1185 2 26536 98.8052 297.4165 0011360 26.6749 333.5519 14.10998417 12551 1 26536U 00055A 00359.36744284 .00000606 00000-0 36121-3 0 1223 2 26536 98.8062 302.2146 0011319 13.1515 347.0801 14.11007291 13245 1 26536U 00055A 00359.36744284 .00000606 00000-0 36121-3 0 1234 2 26536 98.8062 302.2146 0011319 13.1515 347.0801 14.11007291 13245 1 26536U 00055A 00359.36744284 .00000606 00000-0 36121-3 0 1256 2 26536 98.8062 302.2146 0011319 13.1515 347.0801 14.11007291 13245 1 26536U 00055A 00363.62206414 .00000560 00000-0 33555-3 0 1277 2 26536 98.8072 306.3880 0011052 2.0309 358.0800 14.11012020 13841 1 26536U 00055A 00363.62206414 .00000560 00000-0 33555-3 0 1301 2 26536 98.8072 306.3880 0011052 2.0309 358.0800 14.11012020 13841 1 26536U 00055A 01002.16033173 .00000697 00000-0 41184-3 0 1321 2 26536 98.8084 310.8397 0010834 349.6906 10.4402 14.11019835 14486 1 26536U 00055A 01002.16033173 .00000697 00000-0 41184-3 0 1332 2 26536 98.8084 310.8397 0010834 349.6906 10.4402 14.11019835 14486 1 26536U 00055A 01002.16033173 .00000697 00000-0 41184-3 0 1343 2 26536 98.8084 310.8397 0010834 349.6906 10.4402 14.11019835 14486 1 26536U 00055A 01005.42218579 .00000558 00000-0 33424-3 0 1366 2 26536 98.8081 314.0379 0010892 340.2366 19.7960 14.11021950 14943 1 26536U 00055A 01005.42218579 .00000558 00000-0 33424-3 0 1377 2 26536 98.8081 314.0379 0010892 340.2366 19.7960 14.11021950 14943 1 26536U 00055A 01005.42218579 .00000558 00000-0 33424-3 0 1412 2 26536 98.8081 314.0379 0010892 340.2366 19.7960 14.11021950 14943 1 26536U 01007.47855059 .00000602 00000-0 35879-3 0 155 2 26536 98.8093 316.0570 0010453 335.2099 24.6617 14.11022877 15237 1 26536U 00055A 01009.46405976 .00000429 00000-0 26250-3 0 1426 2 26536 98.8086 318.0029 0010534 328.9508 31.0678 14.11024334 15514 1 26536U 00055A 01009.46405976 .00000429 00000-0 26250-3 0 1437 2 26536 98.8086 318.0029 0010534 328.9508 31.0678 14.11024334 15514 1 26536U 00055A 01009.46405976 .00000429 00000-0 26250-3 0 1448 2 26536 98.8086 318.0029 0010534 328.9508 31.0678 14.11024334 15514 1 26536U 00055A 01009.46405976 .00000429 00000-0 26250-3 0 1460 2 26536 98.8086 318.0029 0010534 328.9508 31.0678 14.11024334 15514 1 26536U 00055A 01013.43501805 .00000422 00000-0 25870-3 0 1471 2 26536 98.8094 321.8980 0010516 317.5138 42.5197 14.11027918 16070 1 26536U 00055A 01013.43501805 .00000422 00000-0 25870-3 0 1505 2 26536 98.8094 321.8980 0010516 317.5138 42.5197 14.11027918 16070 1 26536U 00055A 01016.41322571 .00000365 00000-0 22730-3 0 1516 2 26536 98.8097 324.8190 0010435 308.8192 51.1888 14.11029602 16493 1 26536U 00055A 01016.41322571 .00000365 00000-0 22730-3 0 1538 2 26536 98.8097 324.8190 0010435 308.8192 51.1888 14.11029602 16493 1 26536U 00055A 01016.41322571 .00000365 00000-0 22730-3 0 1549 2 26536 98.8097 324.8190 0010435 308.8192 51.1888 14.11029602 16493 1 26536U 00055A 01019.39143329 .00000350 00000-0 21871-3 0 1551 2 26536 98.8103 327.7414 0010248 299.9258 60.0934 14.11031968 16913 1 26536U 00055A 01019.39143329 .00000350 00000-0 21871-3 0 1562 2 26536 98.8103 327.7414 0010248 299.9258 60.0934 14.11031968 16913 1 26536U 00055A 01020.34419961 .00000451 00000-0 27472-3 0 1584 2 26536 98.8112 328.6776 0010214 297.0483 220.0680 14.11033932 17040 1 26536U 00055A 01020.34419961 .00000451 00000-0 27472-3 0 1595 2 26536 98.8112 328.6776 0010214 297.0483 220.0680 14.11033932 17040 1 26536U 00055A 01020.34419961 .00000451 00000-0 27472-3 0 1618 2 26536 98.8112 328.6776 0010214 297.0483 220.0680 14.11033932 17040 1 26536U 00055A 01024.35509004 .00000387 00000-0 23954-3 0 1621 2 26536 98.8114 332.6118 0010103 285.2988 74.6847 14.11036565 17619 1 26536U 00055A 01024.35509004 .00000387 00000-0 23954-3 0 1632 2 26536 98.8114 332.6118 0010103 285.2988 74.6847 14.11036565 17619 1 26536U 00055A 01029.38965222 .00000431 00000-0 26398-3 0 1677 2 26536 98.8119 337.5520 0009969 269.8946 90.1055 14.11041175 18321 1 26536U 00055A 01029.38965222 .00000431 00000-0 26398-3 0 1688 2 26536 98.8119 337.5520 0009969 269.8946 90.1055 14.11041175 18321 1 26536U 00055A 01029.38965222 .00000431 00000-0 26398-3 0 1699 2 26536 98.8119 337.5520 0009969 269.8946 90.1055 14.11041175 18321 1 26536U 00055A 01029.38965222 .00000431 00000-0 26398-3 0 1712 2 26536 98.8119 337.5520 0009969 269.8946 90.1055 14.11041175 18321 1 26536U 00055A 01033.28965153 .00000424 00000-0 25996-3 0 1725 2 26536 98.8127 341.3783 0009915 257.6833 102.3220 14.11044761 18879 1 26536U 00055A 01033.28965153 .00000424 00000-0 25996-3 0 1736 2 26536 98.8127 341.3783 0009915 257.6833 102.3220 14.11044761 18879 1 26536U 00055A 01033.28965153 .00000424 00000-0 25996-3 0 1769 2 26536 98.8127 341.3783 0009915 257.6833 102.3220 14.11044761 18879 1 26536U 00055A 01037.26055028 .00000387 00000-0 23918-3 0 1773 2 26536 98.8128 345.2747 0009905 245.6743 114.3250 14.11047652 19430 1 26536U 00055A 01037.26055028 .00000387 00000-0 23918-3 0 1784 2 26536 98.8128 345.2747 0009905 245.6743 114.3250 14.11047652 19430 1 26536U 00055A 01037.26055028 .00000387 00000-0 23918-3 0 1795 2 26536 98.8128 345.2747 0009905 245.6743 114.3250 14.11047652 19430 1 26536U 00055A 01037.26055028 .00000387 00000-0 23918-3 0 1807 2 26536 98.8128 345.2747 0009905 245.6743 114.3250 14.11047652 19430 1 26536U 00055A 01040.30963147 .00000481 00000-0 29153-3 0 1819 2 26536 98.8130 348.2676 0009824 236.1119 123.9246 14.11051502 19866 1 26536U 00055A 01043.28779028 .00000363 00000-0 22610-3 0 1856 2 26536 98.8127 351.1900 0009867 227.1692 132.8419 14.11052780 20286 1 26536U 00055A 01043.28779028 .00000363 00000-0 22610-3 0 1878 2 26536 98.8127 351.1900 0009867 227.1692 132.8419 14.11052780 20286 1 26536U 00055A 01043.28779028 .00000363 00000-0 22610-3 0 1889 2 26536 98.8127 351.1900 0009867 227.1692 132.8419 14.11052780 20286 1 26536U 00055A 01046.26595463 .00000336 00000-0 21110-3 0 1894 2 26536 98.8132 354.1125 0009912 217.9599 142.0787 14.11054647 20700 1 26536U 00055A 01046.26595463 .00000336 00000-0 21110-3 0 1906 2 26536 98.8132 354.1125 0009912 217.9599 142.0787 14.11054647 20700 1 26536U 00055A 01046.26595463 .00000336 00000-0 21110-3 0 1917 2 26536 98.8132 354.1125 0009912 217.9599 142.0787 14.11054647 20700 1 26536U 00055A 01050.23683043 .00000258 00000-0 16759-3 0 1940 2 26536 98.8137 358.0115 0010242 204.5893 155.5059 14.11058661 21267 1 26536U 00055A 01050.23683044 .00000258 00000-0 16759-3 0 1952 2 26536 98.8137 358.0115 0010242 204.5893 155.5059 14.11058661 21267 1 26536U 00055A 01051.30045014 .00000264 00000-0 17064-3 0 1968 2 26536 98.8143 359.0555 0010247 201.6096 158.4609 14.11059078 21414 1 26536U 00055A 01051.30045014 .00000264 00000-0 17064-3 0 1980 2 26536 98.8143 359.0555 0010247 201.6096 158.4609 14.11059078 21414 1 26536U 00055A 01054.27859870 .00000180 00000-0 12437-3 0 2002 2 26536 98.8141 1.9773 0010306 192.8831 167.1929 14.11059664 21830 1 26536U 00055A 01054.27859870 .00000180 00000-0 12437-3 0 2013 2 26536 98.8141 1.9773 0010306 192.8831 167.1929 14.11059664 21830 1 26536U 00055A 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.00000301 00000-0 18869-3 0 1729 2 26536 98.8990 328.4682 0011039 33.7527 326.4312 14.11887676119978 1 26536U 00055A 03020.66852292 .00000283 00000-0 17908-3 0 1732 2 26536 98.8991 329.1712 0011048 31.7636 328.4200 14.11888160120070 1 26536U 00055A 03021.02285524 .00000135 00000-0 98331-4 0 1748 2 26536 98.8997 329.5229 0011084 30.8026 329.3738 14.11887414120129 1 26536U 00055A 03021.80238780 .00000085 00000-0 70731-4 0 1755 2 26536 98.9001 330.2967 0011122 28.3978 331.7788 14.11887450120239 1 26536U 00055A 03022.51105326 .00000005 00000-0 26884-4 0 1766 2 26536 98.9003 330.9999 0011085 26.6024 333.5709 14.11887135120338 1 26536U 00055A 03022.93625244 .00000043 00000-0 47738-4 0 1772 2 26536 98.9003 331.4217 0011125 25.1805 334.9913 14.11887391120395 1 26536U 00055A 03023.50319273 .00000263 00000-0 14403-3 0 1774 2 26536 98.8993 331.9846 0010928 23.5519 336.6637 14.11888733120471 1 26536U 00055A 03023.64491715 .00000024 00000-0 37543-4 0 1788 2 26536 98.9002 332.1247 0011102 23.2012 336.9647 14.11887391120496 1 26536U 00055A 03024.07011636 .00000119 00000-0 89158-4 0 1791 2 26536 98.9001 332.5467 0011054 21.9216 338.2462 14.11888047120556 1 26536U 00055A 03024.77878002 .00000285 00000-0 18002-3 0 1801 2 26536 98.9001 333.2498 0011030 19.9875 340.1755 14.11889204120654 1 26536U 00055A 03025.55830932 .00000356 00000-0 21927-3 0 1818 2 26536 98.8997 334.0234 0011000 17.7039 342.4514 14.11890150120761 1 26536U 00055A 03025.91264109 .00000356 00000-0 21927-3 0 1826 2 26536 98.8997 334.3749 0011000 16.7029 343.4503 14.11890433120812 1 26536U 00055A 03026.69216933 .00000540 00000-0 31999-3 0 1830 2 26536 98.8990 335.1486 0010908 14.6886 345.4592 14.11892190120920 1 26536U 00055A 03027.82602835 .00000473 00000-0 28327-3 0 1843 2 26536 98.8992 336.2742 0010708 11.6721 348.4670 14.11893203121086 1 26536U 00055A 03028.60556725 .00000405 00000-0 22189-3 0 1852 2 26536 98.8998 337.0474 0010785 9.7134 350.4714 14.11893154121193 1 26536U 00055A 03028.81816603 .00000396 00000-0 21696-3 0 1861 2 26536 98.9003 337.2588 0010759 9.0497 351.1335 14.11893205121223 1 26536U 00055A 03028.95988854 .00000301 00000-0 18875-3 0 1876 2 26536 98.8994 337.3995 0010586 8.7367 351.3982 14.11893304121246 1 26536U 00055A 03029.66855068 .00000156 00000-0 10958-3 0 1877 2 26536 98.8994 338.1026 0010600 6.7828 353.3448 14.11892902121340 1 26536U 00055A 03030.80241149 -.00000017 00000-0 15025-4 0 1883 2 26536 98.8994 339.2277 0010658 3.7132 356.4103 14.11892375121502 1 26536U 00055A 03031.58194079 -.00000042 00000-0 12246-5 0 1890 2 26536 98.8992 340.0008 0010674 1.5943 358.5260 14.11892218121611 1 26536U 00055A 03031.93627207 -.00000048 00000-0 -19377-5 0 1904 2 26536 98.8994 340.3527 0010781 0.7472 359.3715 14.11892327121669 1 26536U 00055A 03032.64493454 -.00000048 00000-0 -19377-5 0 1919 2 26536 98.8994 341.0557 0010781 358.7455 1.3687 14.11892322121765 1 26536U 00055A 03034.55832330 -.00000048 00000-0 -19377-5 0 1933 2 26536 98.9002 342.9540 0010459 353.0099 7.0979 14.11892210122036 1 26536U 00055A 03034.55832330 -.00000048 00000-0 -19377-5 0 1944 2 26536 98.9002 342.9540 0010459 353.0099 7.0979 14.11892210122036 1 26536U 00055A 03035.55044986 -.00004605 00000-0 -24974-2 0 1957 2 26536 98.9016 343.9400 0010215 350.3920 9.7328 14.11890667122176 1 26536U 00055A 03035.90477657 .00000551 00000-0 32552-3 0 1968 2 26536 98.9009 344.2908 0010370 349.0782 11.0326 14.11897499122228 1 26536U 00055A 03036.68429901 .00000551 00000-0 32552-3 0 1985 2 26536 98.9009 345.0642 0010370 346.8761 13.2139 14.11898425122332 1 26536U 00055A 03037.03862870 .00000513 00000-0 30487-3 0 1973 2 26536 98.9007 345.4155 0010120 346.1122 13.9774 14.11898685122385 1 26536U 00055A 03037.81815439 .00000282 00000-0 17884-3 0 1988 2 26536 98.9017 346.1891 0009986 343.4959 16.5876 14.11898191122493 1 26536U 00055A 03038.95201141 -.00000029 00000-0 86137-5 0 1993 2 26536 98.9018 347.3143 0010051 340.2331 19.8447 14.11897058122650 1 26536U 00055A 03038.95201141 -.00000029 00000-0 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168.3095 191.8311 14.12195064220812 1 26536U 00055A 05004.41429105 .00000246 00000-0 15841-3 0 1735 2 26536 98.9925 322.1159 0010705 165.8371 194.3098 14.12195113220942 1 26536U 00055A 05004.91024893 .00000223 00000-0 14590-3 0 1741 2 26536 98.9924 322.6137 0010773 164.5566 195.5938 14.12195296221016 1 26536U 00055A 05005.33535552 .00000202 00000-0 13465-3 0 1744 2 26536 98.9923 323.0401 0010796 163.4831 196.6699 14.12195476221074 1 26536U 00055A 05005.76046201 .00000078 00000-0 66935-4 0 1752 2 26536 98.9927 323.4664 0010776 162.3894 197.7609 14.12194819221138 1 26536U 00055A 05006.11471853 -.00000028 00000-0 92058-5 0 1759 2 26536 98.9930 323.8219 0010864 161.3957 198.7598 14.12194357221189 1 26536U 00055A 05006.82323042 -.00000159 00000-0 -61842-4 0 1766 2 26536 98.9936 324.5324 0011055 159.8833 200.2768 14.12193679221283 1 26536U 00055A 05007.31918894 -.00000212 00000-0 -91062-4 0 1763 2 26536 98.9933 325.0298 0011122 158.6262 201.5375 14.12193337221351 1 26536U 00055A 05007.31918894 -.00000212 00000-0 -91062-4 0 1774 2 26536 98.9933 325.0298 0011122 158.6262 201.5375 14.12193337221351 1 26536U 00055A 05007.88599866 -.00000134 00000-0 -48432-4 0 1787 2 26536 98.9931 325.5982 0011134 157.1176 203.0521 14.12193660221433 1 26536U 00055A 05008.31110529 -.00000106 00000-0 -33039-4 0 1787 2 26536 98.9929 326.0245 0011134 155.8430 204.3277 14.12193825221497 1 26536U 00055A 05008.73621216 -.00000051 00000-0 -32734-5 0 1795 2 26536 98.9928 326.4508 0011111 154.6244 205.5473 14.12193935221557 1 26536U 00055A 05008.73621216 -.00000051 00000-0 -32734-5 0 1807 2 26536 98.9928 326.4508 0011111 154.6244 205.5473 14.12193935221557 1 26536U 00055A 05011.57025924 .00000073 00000-0 63999-4 0 1816 2 26536 98.9929 329.2931 0010865 145.8248 214.3823 14.12195778221958 1 26536U 00055A 05012.49131640 .00000307 00000-0 19157-3 0 1828 2 26536 98.9928 330.2175 0011081 144.2750 215.9159 14.12197949222087 1 26536U 00055A 05012.84557132 .00000205 00000-0 13594-3 0 1832 2 26536 98.9933 330.5729 0011106 143.2749 216.9163 14.12197638222133 1 26536U 00055A 05013.27067762 .00000167 00000-0 11516-3 0 1834 2 26536 98.9937 330.9992 0011135 142.0890 218.1066 14.12197705222196 1 26536U 00055A 05013.76663445 .00000154 00000-0 10847-3 0 1841 2 26536 98.9945 331.4974 0011276 140.8921 219.3066 14.12197794222267 1 26536U 00055A 05013.83749529 .00000279 00000-0 15183-3 0 1832 2 26536 98.9937 331.5701 0010950 139.8710 220.3755 14.12198258222273 1 26536U 00055A 05014.12088930 .00000062 00000-0 58386-4 0 1841 2 26536 98.9951 331.8526 0011326 139.9660 220.2336 14.12197500222318 1 26536U 00055A 05014.68769738 -.00000034 00000-0 62838-5 0 1853 2 26536 98.9956 332.4214 0011288 138.3040 221.8954 14.12196897222395 1 26536U 00055A 05015.46705949 -.00000192 00000-0 -80171-4 0 1859 2 26536 98.9955 333.2036 0011288 136.0980 224.1074 14.12196143222509 1 26536U 00055A 05015.46705949 -.00000192 00000-0 -80171-4 0 1860 2 26536 98.9955 333.2036 0011288 136.0980 224.1074 14.12196143222509 1 26536U 00055A 05015.89216610 -.00000124 00000-0 -43042-4 0 1871 2 26536 98.9955 333.6305 0011263 135.0820 225.1297 14.12196515222564 1 26536U 00055A 05016.31727165 -.00000108 00000-0 -34074-4 0 1878 2 26536 98.9953 334.0569 0011270 133.9180 226.2936 14.12196645222626 1 26536U 00055A 05016.81322854 -.00000064 00000-0 -10187-4 0 1880 2 26536 98.9957 334.5547 0011284 132.7838 227.4286 14.12196674222693 1 26536U 00055A 05017.23833460 -.00000099 00000-0 -29074-4 0 1880 2 26536 98.9951 334.9813 0011238 131.2336 228.9791 14.12196483222755 1 26536U 00055A 05017.73429208 -.00000003 00000-0 22652-4 0 1896 2 26536 98.9949 335.4788 0011215 129.7045 230.5126 14.12196851222821 1 26536U 00055A 05018.51365150 .00000128 00000-0 93989-4 0 1896 2 26536 98.9946 336.2608 0011231 128.2466 231.9720 14.12197903222933 1 26536U 00055A 05019.36386191 .00000275 00000-0 17403-3 0 1909 2 26536 98.9937 337.1141 0011200 125.9718 234.2516 14.12199298223057 1 26536U 00055A 05019.78896670 .00000380 00000-0 23118-3 0 1911 2 26536 98.9934 337.5404 0011200 124.7625 235.4612 14.12200025223112 1 26536U 00055A 05020.49747375 .00000609 00000-0 35589-3 0 1917 2 26536 98.9932 338.2512 0011237 123.0762 237.1499 14.12201911223210 1 26536U 00055A 05020.92257798 .00000572 00000-0 33581-3 0 1920 2 26536 98.9934 338.6773 0011244 121.6511 238.5733 14.12202155223272 1 26536U 00055A 05021.27683208 .00000489 00000-0 29067-3 0 1929 2 26536 98.9936 339.0328 0011251 120.8384 239.3890 14.12202235223320 1 26536U 00055A 05021.70193647 .00000352 00000-0 21605-3 0 1930 2 26536 98.9935 339.4592 0011237 119.6472 240.5777 14.12201800223385 1 26536U 00055A 05022.41044533 .00000163 00000-0 11326-3 0 1937 2 26536 98.9934 340.1702 0011262 117.7023 242.5288 14.12201355223480 1 26536U 00055A 05022.41044533 .00000163 00000-0 11326-3 0 1948 2 26536 98.9934 340.1702 0011262 117.7023 242.5288 14.12201355223480 1 26536U 00055A 05022.83554998 .00000027 00000-0 39026-4 0 1953 2 26536 98.9934 340.5965 0011268 116.0734 244.1564 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313.1642 0010443 348.7668 11.3292 14.12390268320436 1 26536U 00055A 06344.75521778 .00000378 00000-0 22986-3 0 1823 2 26536 99.0932 313.6678 0010443 347.4895 12.6003 14.12390596320504 1 26536U 00055A 06345.18026420 .00000394 00000-0 23872-3 0 1828 2 26536 99.0934 314.0990 0010442 346.2585 13.8305 14.12391042320561 1 26536U 00055A 06345.60531015 .00000299 00000-0 18694-3 0 1837 2 26536 99.0936 314.5304 0010424 344.9275 15.1565 14.12390867320622 1 26536U 00055A 06346.45540322 .00000155 00000-0 10892-3 0 1835 2 26536 99.0938 315.3929 0010383 342.5993 17.4820 14.12390690320747 1 26536U 00055A 06346.80960839 .00000095 00000-0 76357-4 0 1846 2 26536 99.0939 315.7522 0010410 341.4685 18.6086 14.12390420320791 1 26536U 00055A 06347.23465506 .00000032 00000-0 42437-4 0 1848 2 26536 99.0936 316.1833 0010423 340.4269 19.6484 14.12390221320858 1 26536U 00055A 06347.58886068 -.00000050 00000-0 -20088-5 0 1858 2 26536 99.0937 316.5428 0010392 339.3297 20.7438 14.12389823320900 1 26536U 00055A 06348.43895440 -.00000136 00000-0 -48563-4 0 1859 2 26536 99.0936 317.4052 0010363 337.0876 22.9841 14.12389426321023 1 26536U 00055A 06348.43895440 -.00000136 00000-0 -48563-4 0 1860 2 26536 99.0936 317.4052 0010363 337.0876 22.9841 14.12389426321023 1 26536U 00055A 06348.79315973 -.00000161 00000-0 -62097-4 0 1874 2 26536 99.0940 317.7645 0010378 336.0673 23.9999 14.12389118321077 1 26536U 00055A 06349.14736563 -.00000161 00000-0 -62426-4 0 1872 2 26536 99.0939 318.1238 0010358 335.0223 25.0453 14.12389089321127 1 26536U 00055A 06349.57241223 -.00000155 00000-0 -59030-4 0 1884 2 26536 99.0938 318.5549 0010323 333.8014 26.2638 14.12389035321186 1 26536U 00055A 06350.42250511 -.00000022 00000-0 13048-4 0 1881 2 26536 99.0940 319.4172 0010241 331.1274 28.9360 14.12389796321308 1 26536U 00055A 06350.77670995 .00000029 00000-0 40589-4 0 1898 2 26536 99.0937 319.7765 0010269 330.2791 29.7791 14.12389973321358 1 26536U 00055A 06351.13091543 .00000173 00000-0 11863-3 0 1897 2 26536 99.0934 320.1359 0010225 328.8179 31.2429 14.12390821321401 1 26536U 00055A 06351.91016541 .00000296 00000-0 18547-3 0 1913 2 26536 99.0933 320.9264 0010182 326.7581 33.2953 14.12391862321511 1 26536U 00055A 06352.33521123 .00000393 00000-0 23790-3 0 1900 2 26536 99.0935 321.3576 0010146 325.4144 34.6396 14.12392725321576 1 26536U 00055A 06352.90193812 .00000345 00000-0 21209-3 0 1914 2 26536 99.0927 321.9325 0010134 323.8677 36.1782 14.12392837321654 1 26536U 00055A 06353.25614333 .00000381 00000-0 23140-3 0 1915 2 26536 99.0929 322.2917 0010099 322.7752 37.2726 14.12393285321703 1 26536U 00055A 06353.25614333 .00000381 00000-0 23140-3 0 1926 2 26536 99.0929 322.2917 0010099 322.7752 37.2726 14.12393285321703 1 26536U 00055A 06353.25614333 .00000381 00000-0 23140-3 0 1937 2 26536 99.0929 322.2917 0010099 322.7752 37.2726 14.12393285321703 1 26536U 00055A 06353.75202955 .00000348 00000-0 21365-3 0 1968 2 26536 99.0928 322.7948 0010115 321.3588 38.6848 14.12393488321774 1 26536U 00055A 06354.17707519 .00000280 00000-0 17661-3 0 1960 2 26536 99.0928 323.2259 0010110 320.1601 39.8811 14.12393436321836 1 26536U 00055A 06354.60212098 .00000197 00000-0 13196-3 0 1978 2 26536 99.0928 323.6572 0010070 318.7428 41.2964 14.12393218321893 1 26536U 00055A 06355.38137209 .00000013 00000-0 31915-4 0 1971 2 26536 99.0930 324.4479 0010027 316.4518 43.5855 14.12392590322008 1 26536U 00055A 06355.38137209 .00000013 00000-0 31915-4 0 1982 2 26536 99.0930 324.4479 0010027 316.4518 43.5855 14.12392590322008 1 26536U 00055A 06355.80641798 -.00000027 00000-0 10412-4 0 1998 2 26536 99.0935 324.8791 0010017 315.2261 44.8083 14.12392311322069 1 26536U 00055A 06356.16062322 -.00000085 00000-0 -20925-4 0 1993 2 26536 99.0936 325.2383 0010081 314.7677 45.2658 14.12392096322117 1 26536U 00055A 06356.58566945 -.00000182 00000-0 -73534-4 0 2004 2 26536 99.0937 325.6695 0010086 313.4816 46.5500 14.12391557322171 1 26536U 00055A 06357.43576191 -.00000193 00000-0 -79642-4 0 2001 2 26536 99.0941 326.5318 0010060 311.0820 48.9487 14.12391413322293 1 26536U 00055A 06357.86080798 -.00000166 00000-0 -64798-4 0 2018 2 26536 99.0944 326.9629 0010041 309.6585 50.3700 14.12391319322352 1 26536U 00055A 06358.21501315 -.00000099 00000-0 -28459-4 0 2010 2 26536 99.0945 327.3222 0010034 308.4698 51.5593 14.12391623322408 1 26536U 00055A 06358.64005885 -.00000109 00000-0 -34142-4 0 2027 2 26536 99.0951 327.7537 0010020 307.3087 52.7163 14.12391456322469 1 26536U 00055A 06359.41930967 -.00000043 00000-0 17630-5 0 2021 2 26536 99.0951 328.5442 0010006 304.6717 55.3527 14.12391958322579 1 26536U 00055A 06359.84435539 .00000106 00000-0 82751-4 0 2038 2 26536 99.0952 328.9753 0010048 303.6790 56.3447 14.12392717322638 1 26536U 00055A 06360.34024166 .00000216 00000-0 14229-3 0 2031 2 26536 99.0953 329.4782 0010019 301.9884 58.0343 14.12393473322702 1 26536U 00055A 06360.90696846 .00000315 00000-0 19582-3 0 2041 2 26536 99.0951 330.0533 0010037 300.4877 59.5318 14.12394266322785 1 26536U 00055A 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335.5972 14.12598539535885 1 26536U 00055A 11044.83263236 .00000313 00000-0 19431-3 0 1894 2 26536 99.1538 72.7970 0010695 23.2732 336.8900 14.12598528535941 1 26536U 00055A 11045.18678547 .00000266 00000-0 16870-3 0 1899 2 26536 99.1535 73.1589 0010731 22.2781 337.8836 14.12598489535993 1 26536U 00055A 11045.96592222 .00000067 00000-0 61073-4 0 1918 2 26536 99.1541 73.9549 0010657 20.1854 339.9707 14.12597706536104 1 26536U 00055A 11046.32007572 -.00000074 00000-0 -14630-4 0 1904 2 26536 99.1538 74.3168 0010728 19.1772 340.9778 14.12597183536152 1 26536U 00055A 11046.74505986 -.00000096 00000-0 -26862-4 0 1917 2 26536 99.1537 74.7509 0010723 18.1252 342.0293 14.12597035536219 1 26536U 00055A 11047.52419699 -.00000177 00000-0 -70538-4 0 1918 2 26536 99.1536 75.5469 0010775 15.8436 344.3071 14.12596671536323 1 26536U 00055A 11047.87835017 -.00000154 00000-0 -58267-4 0 1923 2 26536 99.1536 75.9085 0010765 14.9083 345.2396 14.12596570536371 1 26536U 00055A 11048.30333418 -.00000165 00000-0 -63932-4 0 1927 2 26536 99.1533 76.3426 0010795 13.6903 346.4560 14.12596482536437 1 26536U 00055A 11048.72831806 -.00000172 00000-0 -67752-4 0 1930 2 26536 99.1534 76.7766 0010754 12.6718 347.4721 14.12596329536493 1 26536U 00055A 11049.50745486 -.00000109 00000-0 -33613-4 0 1934 2 26536 99.1535 77.5726 0010728 10.5143 349.6259 14.12596624536600 1 26536U 00055A 11049.50745486 -.00000109 00000-0 -33613-4 0 1945 2 26536 99.1535 77.5726 0010728 10.5143 349.6259 14.12596624536600 1 26536U 00055A 11049.93243854 -.00000119 00000-0 -39360-4 0 1951 2 26536 99.1535 78.0065 0010725 9.3635 350.7720 14.12596399536662 1 26536U 00055A 11050.28659207 -.00000047 00000-0 -49518-7 0 1953 2 26536 99.1533 78.3683 0010654 8.2810 351.8572 14.12596870536710 1 26536U 00055A 11050.78240573 -.00000009 00000-0 20499-4 0 1962 2 26536 99.1529 78.8743 0010614 7.0172 353.1148 14.12597006536784 1 26536U 00055A 11051.56154140 .00000174 00000-0 11933-3 0 1967 2 26536 99.1530 79.6699 0010523 4.6874 355.4415 14.12598305536893 1 26536U 00055A 11051.91569373 .00000250 00000-0 16018-3 0 1979 2 26536 99.1528 80.0318 0010536 3.7195 356.4054 14.12598745536948 1 26536U 00055A 11052.34067686 .00000354 00000-0 21627-3 0 1974 2 26536 99.1526 80.4659 0010450 2.4076 357.7182 14.12599630537005 1 26536U 00055A 11052.83648945 .00000342 00000-0 20980-3 0 1980 2 26536 99.1529 80.9725 0010437 1.1039 359.0141 14.12599859537077 1 26536U 00055A 11053.26147250 .00000348 00000-0 21316-3 0 1981 2 26536 99.1527 81.4065 0010404 359.9897 0.1275 14.12600216537137 1 26536U 00055A 11053.68645516 .00000221 00000-0 14453-3 0 1990 2 26536 99.1528 81.8407 0010391 358.6032 1.5082 14.12599822537191 1 26536U 00055A 11054.46559133 .00000066 00000-0 60610-4 0 1990 2 26536 99.1523 82.6367 0010340 356.4550 3.6532 14.12599357537304 1 26536U 00055A 11054.89057462 -.00000029 00000-0 95912-5 0 2002 2 26536 99.1526 83.0708 0010322 355.1200 4.9844 14.12598846537367 1 26536U 00055A 11055.31555828 -.00000176 00000-0 -69710-4 0 2009 2 26536 99.1523 83.5052 0010389 354.1468 5.9554 14.12598213537423 1 26536U 00055A 11055.66971162 -.00000218 00000-0 -92515-4 0 2017 2 26536 99.1527 83.8673 0010329 353.2146 6.8882 14.12597903537470 1 26536U 00055A 11056.51967862 -.00000219 00000-0 -93171-4 0 2014 2 26536 99.1525 84.7355 0010389 350.8582 9.2400 14.12597718537598 1 26536U 00055A 11056.51967862 -.00000219 00000-0 -93171-4 0 2025 2 26536 99.1525 84.7355 0010389 350.8582 9.2400 14.12597718537598 1 26536U 00055A 11056.94466214 -.00000192 00000-0 -78308-4 0 2033 2 26536 99.1528 85.1696 0010372 349.6643 10.4306 14.12597567537658 1 26536U 00055A 11057.36964587 -.00000198 00000-0 -81962-4 0 2032 2 26536 99.1527 85.6039 0010367 348.4380 11.6559 14.12597490537717 1 26536U 00055A 11057.72379872 -.00000238 00000-0 -10332-3 0 2047 2 26536 99.1528 85.9657 0010339 347.4163 12.6737 14.12597113537760 1 26536U 00055A 11058.14878268 -.00000169 00000-0 -66326-4 0 2045 2 26536 99.1528 86.3997 0010306 346.0202 14.0713 14.12597429537822 1 26536U 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242.9195 0010489 219.4800 140.7192 14.11319060 56397 1 26536U 00055A 01302.31652132 .00001570 00000-0 89239-3 0 5589 2 26536 98.8395 242.9195 0010489 219.4800 140.7192 14.11319060 56397 1 26536U 00055A 01303.69701525 .00001326 00000-0 75727-3 0 5601 2 26536 98.8394 249.2044 0010657 201.3535 158.6300 14.11334348 57296 1 26536U 00055A 01304.69701525 .00001326 00000-0 75727-3 0 5602 2 26536 98.8394 249.2044 0010657 201.3535 158.6300 14.11334348 57296 1 26536U 00055A 03033.64493454 -.00000048 00000-0 -19377-5 0 1910 2 26536 98.8994 341.0557 0010781 358.7455 1.3687 14.11892322121765 1 26536U 00055A 03105.57305823 -.00000046 00000-0 -10084-5 0 2908 2 26536 98.9098 52.4770 0010847 145.9495 214.2375 14.11935421131919 1 26536U 00055A 03246.73105806 -.00000044 00000-0 41911-6 0 5174 2 26536 98.9216 192.8446 0011392 108.2516 251.9895 14.11996387151838 1 26536U 00055A 03283.93287809 -.00000045 00000-0 -13713-6 0 5701 2 26536 98.9250 229.8721 0010928 11.0565 349.0863 14.12012136157081 1 26536U 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99.0146 158.5189 0010261 343.4599 16.6236 14.12255393248538 1 26536U 00055A 05201.79267973 .00000352 00000-0 21585-3 0 4416 2 26536 99.0150 161.4408 0010012 334.8498 25.2449 14.12258686248941 1 26536U 00055A 05224.26766343 -.00000046 00000-0 -31950-6 0 4678 2 26536 99.0163 182.0427 0009725 274.2212 85.7848 14.12262444251838 1 26536U 00055A 05287.39253671 -.00000050 00000-0 -22772-5 0 5651 2 26536 99.0294 245.5989 0011411 94.8973 265.3506 14.12279183260741 1 26536U 00055A 05288.93486787 .00000099 00000-0 78477-4 0 5684 2 26536 99.0285 249.1688 0011319 84.5170 275.7289 14.12282639261248 1 26536U 00055A 06030.99928532 -.00000002 00000-0 23897-4 0 7307 2 26536 99.0484 355.1287 0010770 145.6278 214.5555 14.12311658276078 1 26536U 00055A 06110.34522608 -.00000042 00000-0 21447-5 0 8483 2 26536 99.0614 75.2874 0009829 288.6429 71.3675 14.12331643287276 1 26536U 00055A 07039.97814278 .00000375 00000-0 22848-3 0 2778 2 26536 99.1023 13.7704 0010740 173.3696 186.7645 14.12404753328862 1 26536U 00055A 07096.01269642 -.00000167 00000-0 -65673-4 0 3614 2 26536 99.1111 70.7024 0010857 23.3186 336.8474 14.12414945336776 pygac-1.7.2/pygac/000077500000000000000000000000001444627033600137405ustar00rootroot00000000000000pygac-1.7.2/pygac/__init__.py000066400000000000000000000030371444627033600160540ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Adam.Dybbroe # Author(s): # Adam.Dybbroe # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . try: from pygac.version import version as __version__ # noqa except ModuleNotFoundError: raise ModuleNotFoundError( "No module named pygac.version. This could mean " "you didn't install 'pygac' properly. Try reinstalling ('pip " "install').") import logging from pygac.configuration import get_config, read_config_file # noqa from pygac.runner import get_reader_class, process_file # noqa # add a NullHandler to prevent messages in sys.stderr if the using application does # not use logging, but pygac makes logging calls of severity WARNING and greater. # See https://docs.python.org/3/howto/logging.html (Configuring Logging for a Library) logging.getLogger('pygac').addHandler(logging.NullHandler()) pygac-1.7.2/pygac/calibration.py000066400000000000000000000636031444627033600166110ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2014-2015, 2019 Pytroll Developers # Author(s): # Martin Raspaud # Abhay Devasthale # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Calibration coefficients and generic calibration functions """ from __future__ import division from enum import Enum import sys import logging import numpy as np import json import hashlib import warnings import datetime as dt from collections import namedtuple from pkg_resources import resource_filename LOG = logging.getLogger(__name__) class CoeffStatus(Enum): """Indicates the status of calibration coefficients.""" NOMINAL = 'nominal' PROVISIONAL = 'provisional' EXPERIMENTAL = 'experimental' class Calibrator(object): """Factory class to create namedtuples holding the calibration coefficients. Attributes: fields: coefficient names Calibrator: namedtuple constructor default_coeffs: dictonary containing default values for all spacecrafts """ version_hashs = { '963af9b66268475ed500ad7b37da33c5': { 'name': 'PATMOS-x, v2017r1', 'status': CoeffStatus.NOMINAL }, '689386c822de18a07194ac7fd71652ea': { 'name': 'PATMOS-x, v2017r1, with provisional coefficients for MetOp-C', 'status': CoeffStatus.PROVISIONAL } } fields = [ "dark_count", "gain_switch", "s0", "s1", "s2", "b", # "b0", "b1", "b2", "centroid_wavenumber", "space_radiance", "to_eff_blackbody_intercept", "to_eff_blackbody_slope", "date_of_launch", "d", "spacecraft", "version" ] Calibrator = namedtuple('Calibrator', fields) default_coeffs = None default_file = None default_version = None def __new__(cls, spacecraft, custom_coeffs=None, coeffs_file=None): """Creates a namedtuple for calibration coefficients of a given spacecraft Args: spacecraft (str): spacecraft name in pygac convention custom_coeffs (dict): custom coefficients (optional) coeffs_file (str): path to coefficents file (optional) Returns: calibrator (namedtuple): calibration coefficients Note: The coefficients in coeffs_file serve as default values if no custom_coeffs are given. If omitted, Calibrator uses the PyGAC internal defaults. """ if cls.default_coeffs is None or cls.default_file != coeffs_file: cls.default_file = coeffs_file cls.default_coeffs, cls.default_version = cls.read_coeffs(coeffs_file) if custom_coeffs: LOG.info('Using following custom coefficients "%s".', custom_coeffs) customs = custom_coeffs or {} defaults = cls.default_coeffs[spacecraft] coeffs = defaults.copy() coeffs.update(customs) # transpose the coefficient order from channel - coeff to coeff - channel # and store as arrays for vectorized calls of calibration functions arraycoeffs = dict.fromkeys(cls.fields) # visible channels for key in ("dark_count", "gain_switch", "s0", "s1", "s2"): arraycoeffs[key] = np.array([ coeffs[channel][key] for channel in ('channel_1', 'channel_2', 'channel_3a') ], dtype=float) # thermal channels for key in ("centroid_wavenumber", "space_radiance", "to_eff_blackbody_intercept", "to_eff_blackbody_slope"): arraycoeffs[key] = np.array([ coeffs[channel][key] for channel in ('channel_3b', 'channel_4', 'channel_5') ], dtype=float) arraycoeffs["b"] = np.array([ [ coeffs[channel][key] for key in ("b0", "b1", "b2") ] for channel in ('channel_3b', 'channel_4', 'channel_5') ], dtype=float) # thermometers # Note, that "thermometer_0" does not exists, and is filled with zeros to # account for the PRT reset every fifth scanline arraycoeffs["d"] = np.array([ [ coeffs.get("thermometer_{0}".format(t), {}).get("d{0}".format(d), 0.0) for t in range(5) ] for d in range(5) ], dtype=float) # parse date of launch date_of_launch_str = coeffs["date_of_launch"].replace('Z', '+00:00') if sys.version_info < (3, 7): # Note that here any time information is lost import dateutil.parser date_of_launch = dateutil.parser.parse(date_of_launch_str) else: # datetime.fromisoformat() was introduced in Python-3.7 date_of_launch = dt.datetime.fromisoformat( date_of_launch_str).astimezone(dt.timezone.utc) # remove time zone information (easier to handle in calculations) arraycoeffs["date_of_launch"] = date_of_launch.replace(tzinfo=None) arraycoeffs["spacecraft"] = spacecraft arraycoeffs["version"] = cls.default_version if custom_coeffs: arraycoeffs["version"] = None # create namedtuple calibrator = cls.Calibrator(**arraycoeffs) return calibrator @classmethod def read_coeffs(cls, coeffs_file): """Read calibration coefficients for all satellites from file. Args: coeffs_file (str): path to coefficients file Returns: coeffs (dict): dictionary containing coefficients for all satellites version (str): version of the coefficients (None if unknown) """ if coeffs_file: LOG.info('Read calibration coefficients from "%s"', coeffs_file) else: LOG.debug("Read PyGAC internal calibration coefficients.") coeffs_file = resource_filename('pygac', 'data/calibration.json') with open(coeffs_file, mode='rb') as json_file: content = json_file.read() coeffs = json.loads(content) version = cls._get_coeffs_version(content) return coeffs, version @classmethod def _get_coeffs_version(cls, coeff_file_content): """Determine coefficient version.""" md5_hash = hashlib.md5(coeff_file_content) digest = md5_hash.hexdigest() version_dict = cls.version_hashs.get( digest, {'name': None, 'status': None} ) version = version_dict['name'] status = version_dict['status'] if version is None: warning = "Unknown calibration coefficients version!" warnings.warn(warning, RuntimeWarning) LOG.warning(warning) else: LOG.info('Identified calibration coefficients version "%s".', version) if status != CoeffStatus.NOMINAL: warning = 'Using {} calibration coefficients'.format(status) warnings.warn(warning, RuntimeWarning) LOG.warning(warning) return version @staticmethod def date2float(date, decimals=5): """Convert date to year float. Argument date (datetime.datetime) - date decimals (int or None) - rounding precision if None, do not round (default=5) Return date_float (float) - date as year Note rounding to the 5th decimal reproduces the original float values from patmos-x Example: date2float('2000-07-02') == 2000.5 because the 2nd of July was the middle day of the leap year 2000 """ year = date.year days_in_year = (dt.datetime(year+1, 1, 1) - dt.datetime(year, 1, 1)).days diff = date - dt.datetime(year, 1, 1) seconds = diff.total_seconds() date_float = date.year + seconds/(days_in_year*24*3600) if decimals is not None: date_float = round(date_float, decimals) return date_float def calibrate_solar(counts, chan, year, jday, cal, corr=1): """Do the solar calibration and return scaled radiance. Arguments: counts (array) - raw counts for the given channels (options 1, 2[, 3A if available & active]) chan (array) - pygac internal channel index array year (int) - year jday (int) - day of year cal (namedtuple) - spacecraft specific calibration coefficients, see Calibrator Optional: corr (float) - depricated - reflectance correction multiplier (default = 1) Returns: r_cal (array) - scaled radiance Note: This function and documentation follows the time-dependent solar calibration as described in: Heidinger, A.K., W.C. Straka III, C.C. Molling, J.T. Sullivan, and X. Wu, (2010). "Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record.", International Journal of Remote Sensing, 31:6493 - 6517. """ # Step 1. Obtain the calibration slope using equation (6) in Heidinger et al 2010 # S(t) = S_0*(100 + S_1*t + S_2*t^2) / 100, # where t is the time since launch expressed as years, S(t) is the calibration slope # and S_0, S_1 and S_2 are the coefficients of the quadratic fit. # See also section "Fitting of Calibration Slope Equations" in PATMOS-x documentation # (CDRP-ATBD-0184 Rev. 2 03/29/2018 page 19) # Note that the this implementation does not take leap years into account! # Using datetime objects would include it automatically # sensing_date = dt.strptime('{0}.{1}'.format(year, jday), '%Y.%j') # delta = sensing_date - cal.date_of_launch # t = delta.total_seconds() / 31557600 # divided by seconds of a Julian year l_date = Calibrator.date2float(cal.date_of_launch) t = (year + jday / 365.0) - l_date # Note: splitting the calibration slope is needed to reproduce old results and may disappear in future, # because actually there is only one set of slope parameters defined for single-gain counts # as described in Heidinger et al. 2010. See Step 2 for more information. # Note that in case of a single-gain instrument, all gain_switch parameters are set to NaN. if np.isnan(cal.gain_switch).all(): glow = ghigh = np.ones(3) else: glow = np.array([0.5, 0.5, 0.25]) ghigh = np.array([1.5, 1.5, 1.75]) # especially the rounding to three digits is crutial to exectly reproduce the original PATMOS-x values. al, bl, cl = np.round(glow*cal.s0, 3), cal.s1, cal.s2 ah, bh, ch = np.round(ghigh*cal.s0, 3), cal.s1, cal.s2 # apply slope equation for low and high gain coefficients stl = (al[chan] * (100.0 + bl[chan] * t + cl[chan] * t * t)) / 100.0 sth = (ah[chan] * (100.0 + bh[chan] * t + ch[chan] * t * t)) / 100.0 # Step 2. Calculate the scaled radiance using equation (1) in Heidinger et al 2010 # R_cal = S*(C-D), # where R_cal is the value generated from the calibration and is referred to as a # scaled radiance, S is the calibration slope, C the measured count and D the dark count. # This equation is only valid for single-gain instruments. Starting with the AVHRR/3 series # (from NOAA-15 onwards), the channel-1, 2 and 3a require a dual-gain calibration. # The conversion for channel-1 and 2 is given in the appendix, equation (A1) and (A2). # In general, these equations can be written as # C(C_dg) = D + G_low*(C_dg-D), if C_dg <= B_dg # C(C_dg) = C(B_dg) + G_high*(C_dg-B_dg), otherwise # where C_dg is the measured dual-gain counts, B_dg is the dual-gain switch and G_low/high # are the gain factors for the low and high count region. # Quote from the book "Remote Sensing Time Series: Revealing Land Surface Dynamics": # > Another change in design of the AVHRR/3 instrument was the introduction of a dual-gain feature # > for the reflective channels 1, 2 and 3A. In order to improve the radiometric resolution of the # > instrument for low reflectance targets, the dynamic range of the instrument was divided equally # > in two ranges, i.e. nominally from 0 to 500 counts and from 500 to 1,000 counts. For channels 1 # > and 2 half of the available Digital Number (DN) range is assigned to the low albedo range from # > 0 to 25% with the other half to the high albedo range from 26 to 100%. This allows for an increase # > in the radiometric resolution for dark targets. For channel 3A, the split between low and high # > albedo range is set at 12.5% albedo (Rao and Sullivan 2001). # The gain factors are given by the ratio of the fraction of albedo range to the fraction of count range # for the given count region. From the information given by the book quote, we get # G_low = 25% / 50% = 0.5 for channel-1 and 2 # G_low = 12.5% / 50% = 0.25 for channel-3a # G_high = (100% - 25%) / (100% - 50%) = 1.5 for channel-1 and 2 # G_high = (100% - 12.5%) / (100% - 50%) = 1.75 for channel-3a # Inserting the converted dual-gain counts into equation (1) yields the scaled radiance equation, which # is a continuous piecewise linear function of two line segments. # R_cal # ^ * R_cal = S*G_low*(C_dg-D), if C_dg <= B_dg # | * R_cal = S*G_low*(B_dg-D) + S*G_high*(C_dg-B_dg), otherwise # | * # R_cal(B_dg)|--------* # | * | # 0 +--*-------------> C_dg # * D B_dg # Note, that in the former implementation, there was a distinction beteen low and high gain slopes # given by S_low/high = S*G_low/high. which only affects S0 in equation (6) in Heidinger et al 2010. # Furthermore, the implementation allows for an additional correction factor corr which defaults to 1. (depricated) d = cal.dark_count[chan] b_dg = cal.gain_switch[chan] # Note that in case of a single-gain instrument, all gain_switch parameters are set to NaN. if not np.isnan(cal.gain_switch).all(): r_cal = np.where( counts <= b_dg, (counts - d)*stl, (b_dg - d)*stl + (counts - b_dg)*sth ) else: r_cal = stl*(counts - d) # apply depricated correction if corr != 1: warnings.warn( "Using the 'corr' argument is depricated in favor of making the units" " of the function result clear. Please make any unit conversion outside this function.", DeprecationWarning ) r_cal *= corr # Mask negative scaled radiances r_cal[r_cal < 0] = np.nan return r_cal def calibrate_thermal(counts, prt, ict, space, line_numbers, channel, cal): """Do the thermal calibration and return brightness temperatures (K). Arguments: counts (array) - counts for the given channel (options: 3B (if active), 4, 5) prt (array) - counts of the Platinum Resistance Thermometers (PRT) ict (array) - counts of the In-orbit Calibration Targets (ICT) space (array) - counts of cold space line_numbers (array) - line number index channel (array) - pygac internal channel index array cal (namedtuple) - spacecraft specific calibration coefficients, see Calibrator Note: This function and documentation follows steps 1 to 4 from the KLM User's Guide (Robel, J. (2009). NOAA KLM user's guide with NOAA-N,-P supplement. NOAA KLM Users Guide - August 2014 Revision) section 7.1.2.4 "Steps to Calibrate the AVHRR Thermal Channels", and the smoothing approach by Trishchenko (2002). The correction method for the non-linear response of the Mercury-Cadmium-Telluride detectors used for channels 4 and 5 is based on Walton et al. (1998) """ # Shift channel index by three to obtain thermal channels [3b, 4, 5]. chan = channel - 3 lines, columns = counts.shape[:2] # Step 1. The temperature of the internal blackbody target is measured by four platinum resistance # thermometers (PRT)s. In each scanline, data words 18, 19 and 20 in the HRPT minor frame format contain # three readings from one of the four PRTs. (See Section 4.1.3) A different PRT is sampled each scanline; # every fifth scanline all three PRT values are set equal to 0 to indicate that a set of four PRTs has # just been sampled. The count value CPRT of each PRT is converted to temperature TPRT by the formula # T_PRT = d0 + d1*C_PRT + d2*C_PRT^2 + d3*C_PRT^3 + d4*C_PRT^4 (7.1.2.4-1) # The coefficients d0, d1, d2, d3 and d4 vary slightly for each PRT. Values for the coefficients are # found in Appendix D, in Table D.1-8 for NOAA-15 (coefficients d3 and d4 are 0 for NOAA-15), # Table D.2-9 for NOAA-16, Table D.3-3 for NOAA-17 and Table D.4-3 for NOAA-18. To # calculate the internal blackbody temperature TBB, NESDIS uses the simple average # T_BB = (T_PRT1 + T_PRT2 + T_PRT3 + T_PRT4)/4 (7.1.2.4-2) # Find the corresponding PRT values for a given line number # Note that the prt values are the average value of the three readings from one of the four # PRTs. See reader.get_telemetry implementations. prt_threshold = 50 # empirically found and set by Abhay Devasthale offset = 0 for i, prt_val in enumerate(prt): # According to the KLM Guide the fill value between PRT measurments is 0, but we search # for the first measurment gap using the threshold. Is this on purpose? if prt_val < prt_threshold: offset = i break # get the PRT index, iprt equals to 0 corresponds to the measurement gaps iprt = (line_numbers - line_numbers[0] + 5 - offset) % 5 # fill measured values below threshold by interpolation ifix = np.where(np.logical_and(iprt == 1, prt < prt_threshold)) if len(ifix[0]): inofix = np.where(np.logical_and(iprt == 1, prt > prt_threshold)) prt[ifix] = np.interp(ifix[0], inofix[0], prt[inofix]) ifix = np.where(np.logical_and(iprt == 2, prt < prt_threshold)) if len(ifix[0]): inofix = np.where(np.logical_and(iprt == 2, prt > prt_threshold)) prt[ifix] = np.interp(ifix[0], inofix[0], prt[inofix]) ifix = np.where(np.logical_and(iprt == 3, prt < prt_threshold)) if len(ifix[0]): inofix = np.where(np.logical_and(iprt == 3, prt > prt_threshold)) prt[ifix] = np.interp(ifix[0], inofix[0], prt[inofix]) ifix = np.where(np.logical_and(iprt == 4, prt < prt_threshold)) if len(ifix[0]): inofix = np.where(np.logical_and(iprt == 4, prt > prt_threshold)) prt[ifix] = np.interp(ifix[0], inofix[0], prt[inofix]) # calculate PRT temperature using equation (7.1.2.4-1) KLM Guide # Tprt = d0 + d1*Cprt + d2*Cprt^2 + d3*Cprt^3 + d4*Cprt^4 # Note: First dimension of cal.d are the five coefficient indicees tprt = np.polynomial.polynomial.polyval(prt, cal.d[:, iprt], tensor=False) # Note: the KLM Guide proposes to calculate the mean temperature using equation (7.1.2.4-2). # PyGAC follows the smoothing approach by Trishchenko (2002), i.e. # filling the zeros that mark a complete set of thermometer measurements # by interpolation, and then using a weighting function (so far only equal # weighting) to convolve the temperatures to calculate a moving average of a given window size. # The same averaging technique is applied for ICTs and Space counts. zeros = iprt == 0 nonzeros = np.logical_not(zeros) tprt[zeros] = np.interp((zeros).nonzero()[0], (nonzeros).nonzero()[0], tprt[nonzeros]) # Thresholds to flag missing/wrong data for interpolation ict_threshold = 100 space_threshold = 100 if channel == 3: zeros = ict < ict_threshold nonzeros = np.logical_not(zeros) ict[zeros] = np.interp((zeros).nonzero()[0], (nonzeros).nonzero()[0], ict[nonzeros]) zeros = space < space_threshold nonzeros = np.logical_not(zeros) space[zeros] = np.interp((zeros).nonzero()[0], (nonzeros).nonzero()[0], space[nonzeros]) # convolving and smoothing PRT, ICT and SPACE values if lines > 51: wlength = 51 # empirically found and set by Abhay Devasthale else: wlength = 3 weighting_function = np.ones(wlength, dtype=float) / wlength tprt_convolved = np.convolve(tprt, weighting_function, 'same') ict_convolved = np.convolve(ict, weighting_function, 'same') space_convolved = np.convolve(space, weighting_function, 'same') # take care of the beginning and end tprt_convolved[0:(wlength - 1) // 2] = tprt_convolved[(wlength - 1) // 2] ict_convolved[0:(wlength - 1) // 2] = ict_convolved[(wlength - 1) // 2] space_convolved[0:(wlength - 1) // 2] = space_convolved[(wlength - 1) // 2] tprt_convolved[-(wlength - 1) // 2:] = tprt_convolved[-((wlength + 1) // 2)] ict_convolved[-(wlength - 1) // 2:] = ict_convolved[-((wlength + 1) // 2)] space_convolved[-(wlength - 1) // 2:] = space_convolved[-((wlength + 1) // 2)] new_tprt = np.transpose(np.tile(tprt_convolved, (columns, 1))) new_ict = np.transpose(np.tile(ict_convolved, (columns, 1))) new_space = np.transpose(np.tile(space_convolved, (columns, 1))) # Step 2. The radiance NBB sensed in each thermal AVHRR channel from the internal blackbody # at temperature TBB is the weighted mean of the Planck function over the spectral response of the # channel. [...]. Each thermal channel has one equation, which uses a centroid wavenumber labmda_c and an # "effective" blackbody temperature TBB*. The two steps are: # TsBB = A + B*TBB (7.1.2.4-3) # NBB = c1*nu_e^3/(exp(c2*nu_e/TsBB) - 1) (7.1.2.4-3) # where the constants of the Planck function are defined as c1 = 2*h*c^2, c2 = h*c/k_B. # constatns c1 = 1.1910427e-5 # mW/m^2/sr/cm^{-4} c2 = 1.4387752 # cm K # coefficients A = cal.to_eff_blackbody_intercept[chan] B = cal.to_eff_blackbody_slope[chan] nu_c = cal.centroid_wavenumber[chan] nS = cal.space_radiance[chan] b = cal.b[chan, 0:3] # the second index are the three polynomial coefficients # variables tBB = new_tprt cS = new_space cE = counts.astype(float) cBB = new_ict tsBB = A + B*tBB nBB_num = c1 * nu_c**3 nBB = nBB_num / (np.exp((c2 * nu_c) / tsBB) - 1.0) # Step 3. Output from the two in-orbit calibration targets is used to compute a linear estimate of # the Earth scene radiance NE. Each scanline, the AVHRR views the internal blackbody target and # outputs 10 count values for each of the three thermal channel detectors; these are found in words # 23 to 52 in the HRPT data stream. When the AVHRR views cold space, 10 counts from each of # the five channel sensors are output and placed into words 52 to 102. (Table 4.1.3-1 describes # how these data are multiplexed.) Count values for each channel are averaged together to smooth # our random noise; often counts from five consecutive scanlines are averaged because it takes five # lines to obtain a set of all four PRT measurements. The average blackbody count CBB and the # average space count CS, together with blackbody radiance NBB and space radiance NS, explained # in the next paragraph, are used to compute the linear radiance estimate NLIN, # NLIN = NS + (NBB - NS)*(CS - CE)/(CS - CBB) (7.1.2.4-5) # where CE is the AVHRR count output when it views one of the reference Earth targets. # While the detector in channel 3B has a linear response, the Mercury-Cadmium-Telluride detectors used # for channels 4 and 5 have a nonlinear response to incoming radiance. Pre-launch laboratory measurements show that: # a. scene radiance is a slightly nonlinear (quadratic) function of AVHRR output count, # b. the nonlinearity depends on the AVHRR operating temperature. # It is assumed that the nonlinear response will persist in orbit. For the NOAA KLM series of # satellites, NESDIS uses a radiance-based nonlinear correction method. In this method, the linear # radiance estimate is first computed using a non-zero radiance of space, the NS term in Equation # 7.1.2.4-5. Then, the linear radiance value is input into a quadratic equation to generate the # nonlinear radiance correction NCOR: # NCOR = b0 + b1*NLIN + b2*NLIN^2 (7.1.2.4-6) # Finally, the Earth scene radiance is obtained by adding NCOR to NLIN # NE = NLIN + NCOR # Note: For channel 3B, the non-linear correction coefficients are set to zero to use the same equation. Nlin = nS + (nBB - nS)*(cS - cE)/(cS - cBB) Ncor = np.polynomial.polynomial.polyval(Nlin, b[0:3], tensor=False) Ne = Nlin + Ncor # Step 4. Data users often convert the computed Earth scene radiance value NE into an equivalent # blackbody temperature TE. This temperature is defined by simple inverting the steps used to # calculate the radiance NE sensed by an AVHRR channel from an emitting blackbody at a # temperature TE. The two-step process is: # TsE = c2*nu_c / ln(1 + (c1*nu_c^3/NE)) (7.1.2.4-8) # TE = (TsE - A)/B (7.1.2.4-9) tsE = c2*nu_c / np.log(1.0 + nBB_num / Ne) bt = (tsE - A) / B # Why do we do this on channel 3b? if chan == 0: bt = np.where((counts - new_space) >= 0, 0.0, bt) # Mask values outside valid range bt = np.where(np.logical_or(bt < 170.0, bt > 350.0), np.nan, bt) return bt pygac-1.7.2/pygac/clock_offsets_converter.py000066400000000000000000000254311444627033600212320ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Martin Raspaud # Author(s): # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Converts text to python """ from datetime import datetime txt = {"noaa14": """95013 223633 -0.37 95213 233350 0.59 95214 011409 -0.93 95365 225835 0.04 95365 211723 0.53 96079 234708 1.07 96080 012736 -0.43 96222 230133 0.59 96223 004223 0.07 96289 223559 0.58 96290 001652 -0.93 97126 223140 0.69 97127 001236 0.18 97182 001344 0.64 97182 015422 1.63 97196 230953 1.75 97197 005044 0.25 97231 232830 0.55 97232 010911 0.03 97302 000502 0.63 97302 014537 -0.90 98111 234827 0.55 98112 012906 0.04 98181 223817 0.62 98181 001812 -0.88 98342 230000 0.45 98343 000000 -0.05 98365 202823 0.14 98365 220000 -0.34 99131 205700 0.72 99132 002800 0.22 99222 235900 0.97 99223 010000 -0.52 99310 235900 0.15 99311 000000 -0.65 00018 235900 -0.02 00019 000000 -0.50 00238 235900 0.70""", "noaa12": """92001 100717 -0.214742484714 92085 201923 0.659529247721 92085 201923 0.659529247721 92182 201100 0.379947500166 92182 201100 0.379947500166 92143 020248 -0.0575787620387 92143 020248 -0.0575787620387 92183 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005747 -0.693724861601 96313 093417 0.0453934348631 96313 093417 0.0453934348631 97093 101211 0.0168789226547 97093 101211 0.0168789226547 97093 101211 0.0168789226547 97093 101211 0.0168789226547 97181 230200 0.07963445895 97181 230200 0.07963445895 97182 003500 -0.764519227361 97182 003500 -0.764519227361 97282 020852 -0.0541122818126 97282 020852 -0.0541122818126 97235 232100 -0.745022654355 97235 232100 -0.745022654355 97334 234704 -0.121024921026 97334 234704 -0.121024921026 97335 031035 -0.131177187257 97335 031035 -0.131177187257 98083 052322 -0.288495446794 98083 052322 -0.288495446794 98256 093733 -0.175770660865 98256 093733 -0.175770660865 98236 085900 0.19721669783 98236 085900 0.19721669783 98237 065201 0.379949295221 98237 065201 0.379949295221 98348 225656 -0.347935041683""", "noaa11": """88270 175315 0.10 88362 225730 0.55 88363 003825 -0.45 89108 223157 0.55 89109 001247 -1.21 89283 223149 0.92 89284 001246 -0.99 90001 005132 0.19 90001 062225 -0.81 90086 225343 0.51 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93235 005344 -0.21 93271 221249 0.68 93272 000413 -0.33 93313 235403 0.70 93314 013043 -0.33 93355 234331 0.73 93356 012432 -0.28 94032 233238 0.77 94033 011326 -0.24 94067 230533 0.64 94068 004655 -0.38 94109 224303 0.69 94110 003436 -0.34 94144 221719 0.57 94145 000747 -0.43 94181 230407 0.50 94182 005611 -1.51 94209 173715 -0.81 94209 204026 0.23 94221 232635 0.54 94222 010716 -0.45 94256 224431 0.45 94257 003856 -0.59 94298 223205 0.53 94299 002843 -0.47 94340 221659 0.65 94341 001032 -0.38 95012 164514 0.60 95013 194752 -0.36 95052 231636 0.67 95053 010842 -0.33 95094 225950 0.77 95095 005202 -0.23 95208 163447 2.81 95208 163450 2.81 96004 165442 7.36""", "noaa7": """81179 000000 0.1 82003 235959 0.8 82004 000000 0.8 82053 235959 1.1 82054 000000 -0.5 82181 235959 0.1 82182 000000 -0.6 83025 235959 0.3 83026 000000 -0.1 83181 235959 0.5 83182 000000 1.5 83193 235959 1.6 83194 000000 -0.7 84081 235959 0.3 84082 000000 -1.1 84342 235959 -0.1 84343 000000 -0.9 84366 235959 -0.8 85001 000000 0.0 85107 235959 0.4 85108 000000 0.2 85136 235959 0.3 85137 000000 -1.1 85181 235959 -1.0 85182 000000 -0.0 85304 235959 0.4 85305 000000 -0.3 86148 235959 0.3 86149 000000 -1.3 86158 235959 -1.3""", "noaa9": """86014 183754 -0.53 86051 090207 -0.80 86051 184443 0.45 86190 092344 -0.76 86190 190707 0.46 86308 195123 -0.80 86309 081339 0.47 87048 210941 -0.84 87049 075241 0.40 87139 195622 -0.85 87140 081825 0.39 87216 210921 -0.75 87217 075309 0.48 87293 204300 -0.77 87294 090756 0.48 87365 225918 -0.79 88001 004020 0.21 88061 234258 -0.89 88062 012352 0.36 88125 000338 -0.87 88125 014432 0.39 88187 224121 -0.87 88188 002232 0.40 88250 225846 -0.93 88251 022040 0.32 88306 224954 -0.89 88307 003033 0.35 88363 001536 -0.88 88363 015925 0.37 89023 110528 -0.25 89024 091318 0.75 89094 111829 -0.91 89095 035705 0.31 89143 031928 -0.85 89144 030810 0.36 89192 111852 -0.79 89193 035610 0.46 89242 232725 -0.80 89243 024905 0.45 89290 035408 -0.79 89291 020143 0.45 89338 101232 -0.81 89339 025034 0.41 89365 212451 -0.30 90001 023815 0.69 90058 102159 -0.87 90059 030319 0.36 90107 105326 -0.99 90108 014914 0.25 90149 221415 -0.93 90150 033528 0.31 90191 222735 -0.85 90192 033935 0.36 90233 110326 -0.81 90234 034148 0.40 90275 110402 -0.79 90276 020138 0.42 90314 095839 -0.72 90320 232628 0.33 90365 224854 -1.03 91001 022659 -0.07 91022 214259 -0.71 91023 030313 0.52 91064 231826 -0.76 91065 025737 0.47 91106 044021 -0.82 91107 024821 0.39 91142 223525 -0.72 91142 224834 0.16 91176 034520 -0.88 91177 033300 0.31 91211 031322 -0.78 91213 043140 0.39 91231 225648 -0.21 91270 042933 -0.24 91288 224028 -0.86 91289 053517 0.36 91331 000157 -1.03 91331 051428 0.44 92007 233938 -0.95 92008 063157 0.52 92049 231312 -0.90 92050 042700 0.58 92090 195517 -0.84 92091 055256 0.75 92133 221715 -0.77 92134 065146 0.70 92182 234047 -0.99 92183 134449 -0.01 92207 120034 -0.87 92211 124842 0.45 92236 141404 -0.42 92246 233007 0.71 92287 231141 -0.78 92288 060512 0.68 92329 143633 -0.79 92330 002139 0.69 93005 234707 -0.87 93006 064126 0.63 93047 231538 -0.91 93048 060831 0.57 93089 175433 -0.96 93090 002050 0.52 93136 193915 -1.26 93137 020351 0.25 93166 195650 -0.88 93167 004310 0.62 93182 060334 0.00 93182 131313 1.02 93213 181559 -0.18 93243 011321 1.22 93292 194948 -0.72 93293 021545 0.77 93341 210417 -1.15 93343 013534 0.83 94025 203714 -1.04 94026 012315 0.42 94060 194824 -0.95 94061 072640 0.53 94101 020148 -1.03 94103 013547 0.35 94137 200139 -1.02 94138 022803 0.48 94181 235541 -1.28 94182 030236 0.20 94209 193554 -0.94 94210 020305 0.07 94235 204323 -1.01 94236 012744 0.53 94277 214407 -1.15 94278 022908 0.30 94305 204327 -0.82 94306 012723 0.67 94347 150149 -1.06 94348 004630 0.42 95019 202513 -1.11 95020 010905 0.41 95052 195749 -0.97 95053 004426 0.50 95087 190612 -0.90 95088 013146 0.56 95115 212721 -0.62 95116 021019 0.89 95150 203430 -0.57 95151 030113 0.91 95201 193323 -1.24 95202 020016 0.74 95214 200458 0.21"""} sat = None def get_offsets(sat): """Get the clock drift offsets for sat. """ errors = [] offsets = txt[sat] for line in offsets.split("\n"): elts = line.split() errors.append((datetime.strptime("".join(elts[:2]), "%y%j%H%M%S"), float(elts[2]))) errors.append((datetime.strptime("".join(elts[3:5]), "%y%j%H%M%S"), float(elts[5]))) return zip(*errors) pygac-1.7.2/pygac/configuration.py000066400000000000000000000065031444627033600171650ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2020 Pygac Developers # Author(s): # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Module configuration class. Read and manage module configuration. """ import logging import os import sys try: import configparser except ImportError: import ConfigParser as configparser if sys.version_info.major < 3: class FileNotFoundError(OSError): pass LOG = logging.getLogger(__name__) class Configuration(configparser.ConfigParser, object): """Configuration container for pygac.""" config_file = '' def read(self, config_file): """Read and parse the configuration file Args: config_file (str): path to the config file Note: In contrast to the parent method, this implementation only accepts a single file and raises an exception if the given file is not readable. """ if not os.path.isfile(config_file): raise FileNotFoundError( 'Given config path "%s" is not a file!' % config_file) try: super(Configuration, self).read(config_file) except configparser.NoSectionError: LOG.error('Failed reading configuration file: "%s"' % config_file) raise self.config_file = config_file def get(self, *args, **kwargs): """python 2 compatibility for fallback attribute""" if sys.version_info.major < 3: if 'fallback' in kwargs: fallback = kwargs.pop('fallback') else: fallback = None try: value = super(Configuration, self).get(*args, **kwargs) except (configparser.NoSectionError, configparser.NoOptionError): value = fallback else: value = super().get(*args, **kwargs) return value _config = Configuration() def get_config(initialized=True): """Retrun the module configuration. Args: initialized (bool): if true ensure that the configuration has been initialized (default: True) """ global _config if initialized and not _config.sections(): LOG.info('Configuration has not been initialized. Use' ' environment variable "PYGAC_CONFIG_FILE"') try: config_file = os.environ["PYGAC_CONFIG_FILE"] except KeyError: LOG.error('Environment variable PYGAC_CONFIG_FILE not set!') raise _config.read(config_file) return _config def read_config_file(config_file): """Read a given config file.""" config = get_config(initialized=False) config.read(config_file) pygac-1.7.2/pygac/correct_tsm_issue.py000066400000000000000000000472251444627033600200600ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # Cornelia Schlundt # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """NOAA-14, 15 and 16 suffer from temporary scan motor issue i.e. parts of an orbit contain corrupt data. This module identifies affected pixels and flags them with fill values.""" import numpy as np import bottleneck as bn import datetime TSM_AFFECTED_INTERVALS_POD = { 3: [(datetime.datetime(2001, 10, 19, 4, 50), datetime.datetime(2001, 10, 19, 13, 38)), (datetime.datetime(2001, 10, 19, 16, 58), datetime.datetime(2001, 10, 19, 18, 7)), (datetime.datetime(2001, 10, 19, 23, 55), datetime.datetime(2001, 10, 27, 3, 19)), (datetime.datetime(2001, 10, 27, 4, 53), datetime.datetime(2001, 10, 31, 21, 7)), (datetime.datetime(2001, 10, 31, 20, 58), datetime.datetime(2001, 11, 13, 12, 46)), (datetime.datetime(2001, 11, 13, 13, 25), datetime.datetime(2001, 11, 21, 23, 40)), (datetime.datetime(2001, 11, 22, 1, 14), datetime.datetime(2001, 11, 25, 6, 1)), (datetime.datetime(2001, 11, 25, 11, 3), datetime.datetime(2001, 11, 25, 13, 8)), (datetime.datetime(2001, 11, 26, 10, 51), datetime.datetime(2001, 11, 26, 12, 55)), (datetime.datetime(2001, 11, 26, 15, 49), datetime.datetime(2001, 11, 26, 19, 26)), (datetime.datetime(2001, 11, 26, 20, 36), datetime.datetime(2001, 11, 26, 22, 31)), (datetime.datetime(2001, 11, 27, 0, 5), datetime.datetime(2001, 12, 2, 23, 7)), (datetime.datetime(2001, 12, 3, 0, 41), datetime.datetime(2001, 12, 15, 0, 2)), (datetime.datetime(2001, 12, 15, 1, 30), datetime.datetime(2001, 12, 15, 3, 26)), (datetime.datetime(2001, 12, 15, 5, 2), datetime.datetime(2001, 12, 15, 10, 36)), (datetime.datetime(2001, 12, 15, 12, 2), datetime.datetime(2001, 12, 15, 13, 53)), (datetime.datetime(2001, 12, 15, 15, 21), datetime.datetime(2001, 12, 15, 18, 56)), (datetime.datetime(2001, 12, 15, 23, 37), datetime.datetime(2001, 12, 16, 1, 33)), (datetime.datetime(2002, 1, 1, 10, 14), datetime.datetime(2002, 1, 10, 1, 29)), (datetime.datetime(2002, 7, 26, 14, 40), datetime.datetime(2002, 7, 27, 13, 6)), (datetime.datetime(2002, 7, 29, 5, 26), datetime.datetime(2002, 8, 8, 0, 2)), (datetime.datetime(2002, 8, 8, 1, 32), datetime.datetime(2002, 8, 12, 22, 51)), (datetime.datetime(2002, 8, 13, 0, 25), datetime.datetime(2002, 9, 30, 4, 8)) ] # NOAA-14 } TSM_AFFECTED_INTERVALS_KLM = { 4: [(datetime.datetime(2001, 1, 8, 23, 55), datetime.datetime(2001, 1, 9, 23, 47)), (datetime.datetime(2001, 1, 10, 1, 14), datetime.datetime(2001, 1, 11, 1, 6)), (datetime.datetime(2001, 1, 23, 18, 32), datetime.datetime(2001, 1, 26, 0, 25)), (datetime.datetime(2001, 1, 30, 10, 59), datetime.datetime(2001, 1, 30, 17, 58)), (datetime.datetime(2001, 2, 2, 2, 42), datetime.datetime(2001, 2, 8, 11, 10)), (datetime.datetime(2001, 2, 11, 6, 3), datetime.datetime(2001, 2, 16, 21, 32)), (datetime.datetime(2001, 4, 24, 5, 25), datetime.datetime(2001, 4, 24, 9, 18)), (datetime.datetime(2001, 4, 24, 10, 53), datetime.datetime(2001, 4, 25, 8, 55)), (datetime.datetime(2001, 4, 26, 2, 59), datetime.datetime(2001, 4, 26, 8, 25)), (datetime.datetime(2001, 4, 26, 18, 22), datetime.datetime(2001, 4, 26, 20, 18)), (datetime.datetime(2001, 4, 26, 23, 5), datetime.datetime(2001, 4, 27, 8, 2)), (datetime.datetime(2001, 4, 28, 22, 23), datetime.datetime(2001, 4, 29, 0, 14)), (datetime.datetime(2001, 4, 29, 15, 33), datetime.datetime(2001, 4, 29, 17, 28)), (datetime.datetime(2001, 4, 29, 20, 33), datetime.datetime(2001, 4, 30, 8, 35)), (datetime.datetime(2001, 4, 30, 13, 35), datetime.datetime(2001, 4, 30, 18, 46)), (datetime.datetime(2001, 4, 30, 20, 11), datetime.datetime(2001, 5, 1, 8, 11)), (datetime.datetime(2001, 5, 2, 2, 23), datetime.datetime(2001, 5, 2, 5, 58)), (datetime.datetime(2001, 5, 2, 9, 22), datetime.datetime(2001, 5, 2, 22, 45)), (datetime.datetime(2001, 5, 3, 0, 8), datetime.datetime(2001, 5, 3, 9, 14)), (datetime.datetime(2001, 5, 8, 10, 34), datetime.datetime(2001, 5, 8, 14, 8)), (datetime.datetime(2001, 5, 8, 15, 30), datetime.datetime(2001, 5, 9, 15, 34)), (datetime.datetime(2001, 5, 10, 0, 50), datetime.datetime(2001, 5, 10, 4, 35)), (datetime.datetime(2001, 5, 10, 13, 7), datetime.datetime(2001, 5, 10, 21, 48)), (datetime.datetime(2001, 5, 10, 22, 47), datetime.datetime(2001, 5, 11, 0, 42)), (datetime.datetime(2001, 5, 11, 3, 57), datetime.datetime(2001, 5, 11, 7, 43)), (datetime.datetime(2001, 5, 11, 11, 5), datetime.datetime(2001, 5, 11, 17, 55)), (datetime.datetime(2001, 5, 11, 19, 21), datetime.datetime(2001, 5, 12, 7, 19)), (datetime.datetime(2001, 5, 12, 8, 56), datetime.datetime(2001, 5, 14, 6, 26)), (datetime.datetime(2001, 5, 14, 21, 32), datetime.datetime(2001, 5, 14, 23, 11)), (datetime.datetime(2001, 5, 15, 4, 6), datetime.datetime(2001, 5, 15, 7, 52)), (datetime.datetime(2001, 5, 15, 17, 59), datetime.datetime(2001, 5, 15, 19, 44)), (datetime.datetime(2001, 5, 15, 21, 19), datetime.datetime(2001, 5, 16, 7, 29)), (datetime.datetime(2001, 5, 16, 17, 26), datetime.datetime(2001, 5, 16, 19, 22)), (datetime.datetime(2001, 5, 16, 23, 50), datetime.datetime(2001, 5, 17, 8, 55)), (datetime.datetime(2001, 5, 17, 15, 26), datetime.datetime(2001, 5, 17, 17, 18)), (datetime.datetime(2001, 5, 17, 20, 25), datetime.datetime(2001, 5, 18, 6, 36)), (datetime.datetime(2001, 6, 5, 16, 34), datetime.datetime(2001, 6, 5, 21, 46)), (datetime.datetime(2001, 6, 6, 0, 35), datetime.datetime(2001, 6, 6, 7, 51)), (datetime.datetime(2001, 8, 4, 5, 2), datetime.datetime(2001, 8, 4, 8, 54)), (datetime.datetime(2001, 8, 6, 21, 17), datetime.datetime(2001, 8, 7, 7, 37)), (datetime.datetime(2001, 8, 7, 19, 16), datetime.datetime(2001, 8, 8, 12, 31)), (datetime.datetime(2001, 8, 8, 15, 33), datetime.datetime(2001, 8, 8, 22, 13)), (datetime.datetime(2001, 8, 23, 0, 58), datetime.datetime(2001, 8, 23, 15, 18)), (datetime.datetime(2001, 8, 23, 21, 30), datetime.datetime(2001, 8, 24, 7, 50)), (datetime.datetime(2001, 8, 24, 9, 24), datetime.datetime(2001, 8, 25, 0, 25)), (datetime.datetime(2001, 8, 25, 3, 50), datetime.datetime(2001, 8, 25, 11, 5)), (datetime.datetime(2001, 8, 25, 12, 28), datetime.datetime(2001, 8, 25, 17, 39)), (datetime.datetime(2001, 8, 25, 20, 54), datetime.datetime(2001, 8, 26, 8, 52)), (datetime.datetime(2001, 8, 26, 18, 41), datetime.datetime(2001, 8, 27, 4, 51)), (datetime.datetime(2001, 8, 27, 23, 2), datetime.datetime(2001, 8, 28, 0, 58)), (datetime.datetime(2001, 8, 28, 4, 14), datetime.datetime(2001, 8, 28, 7, 59)), (datetime.datetime(2001, 8, 28, 14, 49), datetime.datetime(2001, 8, 28, 16, 31)), (datetime.datetime(2001, 8, 28, 17, 58), datetime.datetime(2001, 8, 29, 17, 48)), (datetime.datetime(2001, 8, 29, 22, 19), datetime.datetime(2001, 8, 30, 0, 11)), (datetime.datetime(2001, 8, 30, 5, 7), datetime.datetime(2001, 8, 31, 13, 46)), (datetime.datetime(2001, 8, 31, 18, 37), datetime.datetime(2001, 8, 31, 22, 11)), (datetime.datetime(2001, 8, 31, 23, 10), datetime.datetime(2001, 9, 1, 8, 8)), (datetime.datetime(2001, 9, 1, 11, 29), datetime.datetime(2001, 9, 1, 13, 23)), (datetime.datetime(2001, 9, 1, 14, 58), datetime.datetime(2001, 9, 1, 16, 39)), (datetime.datetime(2001, 9, 1, 18, 13), datetime.datetime(2001, 9, 1, 23, 3)), (datetime.datetime(2001, 9, 2, 0, 28), datetime.datetime(2001, 9, 2, 7, 44)), (datetime.datetime(2001, 9, 2, 16, 5), datetime.datetime(2001, 9, 2, 18, 8)), (datetime.datetime(2001, 9, 2, 22, 26), datetime.datetime(2001, 9, 3, 9, 10)), (datetime.datetime(2001, 9, 3, 12, 22), datetime.datetime(2001, 9, 4, 5, 8)), (datetime.datetime(2001, 9, 7, 2, 4), datetime.datetime(2001, 9, 7, 9, 19)), (datetime.datetime(2001, 9, 7, 10, 54), datetime.datetime(2001, 9, 7, 19, 23)), (datetime.datetime(2001, 9, 7, 23, 51), datetime.datetime(2001, 9, 8, 20, 40)), (datetime.datetime(2001, 9, 8, 23, 29), datetime.datetime(2001, 9, 10, 22, 58)), (datetime.datetime(2001, 9, 11, 0, 25), datetime.datetime(2001, 9, 11, 4, 16)), (datetime.datetime(2001, 9, 11, 5, 35), datetime.datetime(2001, 9, 11, 12, 55)), (datetime.datetime(2001, 10, 25, 17, 39), datetime.datetime(2001, 10, 26, 0, 8)), (datetime.datetime(2002, 3, 17, 14, 29), datetime.datetime(2002, 3, 18, 2, 4)), (datetime.datetime(2002, 3, 18, 5, 11), datetime.datetime(2002, 3, 18, 7, 15)), (datetime.datetime(2002, 3, 18, 18, 53), datetime.datetime(2002, 3, 18, 20, 48)) ], # NOAA-15 2: [(datetime.datetime(2004, 1, 14, 14, 13), datetime.datetime(2004, 1, 14, 22, 46)), (datetime.datetime(2004, 1, 15, 15, 45), datetime.datetime(2004, 1, 15, 19, 3)), (datetime.datetime(2004, 1, 15, 22, 28), datetime.datetime(2004, 1, 16, 2, 6)), (datetime.datetime(2004, 1, 16, 5, 29), datetime.datetime(2004, 1, 16, 22, 23)), (datetime.datetime(2004, 1, 16, 23, 58), datetime.datetime(2004, 1, 18, 0, 1)), (datetime.datetime(2004, 1, 18, 1, 23), datetime.datetime(2004, 1, 18, 8, 45)), (datetime.datetime(2004, 1, 18, 10, 6), datetime.datetime(2004, 1, 18, 23, 42)), (datetime.datetime(2004, 1, 19, 1, 11), datetime.datetime(2004, 1, 19, 15, 12)), (datetime.datetime(2004, 1, 19, 19, 44), datetime.datetime(2004, 1, 19, 23, 30)), (datetime.datetime(2004, 1, 20, 0, 59), datetime.datetime(2004, 1, 20, 4, 52)), (datetime.datetime(2004, 1, 20, 9, 56), datetime.datetime(2004, 1, 20, 15, 1)), (datetime.datetime(2004, 1, 20, 16, 37), datetime.datetime(2004, 1, 21, 4, 40)), (datetime.datetime(2004, 1, 21, 11, 13), datetime.datetime(2004, 1, 21, 16, 39)), (datetime.datetime(2004, 1, 22, 0, 35), datetime.datetime(2004, 1, 22, 6, 18)), (datetime.datetime(2004, 1, 22, 12, 42), datetime.datetime(2004, 1, 23, 2, 27)), (datetime.datetime(2004, 1, 23, 10, 50), datetime.datetime(2004, 1, 23, 22, 45)), (datetime.datetime(2004, 1, 24, 0, 12), datetime.datetime(2004, 1, 24, 5, 54)), (datetime.datetime(2004, 3, 16, 22, 38), datetime.datetime(2004, 3, 17, 22, 41)), (datetime.datetime(2004, 3, 18, 0, 1), datetime.datetime(2004, 3, 18, 5, 44)), (datetime.datetime(2004, 3, 20, 18, 13), datetime.datetime(2004, 3, 20, 23, 42)), (datetime.datetime(2004, 3, 21, 2, 59), datetime.datetime(2004, 3, 21, 5, 3)), (datetime.datetime(2004, 3, 21, 10, 9), datetime.datetime(2004, 3, 21, 23, 29)), (datetime.datetime(2004, 3, 22, 0, 58), datetime.datetime(2004, 3, 22, 6, 41)), (datetime.datetime(2004, 3, 22, 9, 55), datetime.datetime(2004, 3, 22, 15, 0)), (datetime.datetime(2004, 3, 22, 16, 26), datetime.datetime(2004, 3, 22, 23, 18)), (datetime.datetime(2004, 3, 23, 0, 46), datetime.datetime(2004, 3, 23, 4, 39)), (datetime.datetime(2004, 3, 23, 6, 14), datetime.datetime(2004, 3, 23, 11, 27)), (datetime.datetime(2004, 3, 23, 12, 53), datetime.datetime(2004, 3, 23, 16, 38)), (datetime.datetime(2004, 3, 23, 19, 20), datetime.datetime(2004, 3, 24, 6, 17)), (datetime.datetime(2004, 3, 24, 7, 43), datetime.datetime(2004, 3, 25, 6, 5)), (datetime.datetime(2004, 3, 25, 20, 46), datetime.datetime(2004, 3, 25, 22, 50)), (datetime.datetime(2004, 3, 26, 0, 11), datetime.datetime(2004, 3, 26, 5, 54)), (datetime.datetime(2004, 3, 26, 10, 39), datetime.datetime(2004, 3, 26, 14, 15)), (datetime.datetime(2004, 3, 28, 8, 40), datetime.datetime(2004, 4, 1, 23, 5)), (datetime.datetime(2004, 4, 2, 0, 32), datetime.datetime(2004, 4, 2, 22, 53)), (datetime.datetime(2004, 4, 3, 0, 21), datetime.datetime(2004, 4, 4, 22, 31)), (datetime.datetime(2004, 4, 5, 0, 6), datetime.datetime(2004, 4, 5, 22, 19)), (datetime.datetime(2004, 4, 5, 23, 55), datetime.datetime(2004, 4, 6, 5, 23)), (datetime.datetime(2004, 4, 6, 18, 21), datetime.datetime(2004, 4, 6, 23, 49)), (datetime.datetime(2004, 4, 7, 13, 24), datetime.datetime(2004, 4, 7, 15, 20)), (datetime.datetime(2004, 4, 7, 16, 45), datetime.datetime(2004, 4, 7, 23, 38)), (datetime.datetime(2004, 4, 8, 1, 6), datetime.datetime(2004, 4, 9, 10, 7)), (datetime.datetime(2004, 4, 21, 2, 1), datetime.datetime(2004, 4, 21, 17, 41)), (datetime.datetime(2004, 4, 21, 18, 51), datetime.datetime(2004, 4, 21, 22, 38)), (datetime.datetime(2004, 4, 22, 0, 13), datetime.datetime(2004, 4, 22, 5, 43)), (datetime.datetime(2004, 4, 24, 21, 49), datetime.datetime(2004, 4, 24, 23, 47)), (datetime.datetime(2004, 4, 25, 1, 14), datetime.datetime(2004, 4, 25, 20, 3)), (datetime.datetime(2004, 4, 26, 2, 51), datetime.datetime(2004, 4, 26, 11, 43)), (datetime.datetime(2004, 4, 26, 21, 27), datetime.datetime(2004, 4, 26, 23, 22)), (datetime.datetime(2004, 4, 27, 0, 50), datetime.datetime(2004, 4, 27, 4, 43)), (datetime.datetime(2004, 4, 27, 8, 0), datetime.datetime(2004, 4, 27, 10, 3)), (datetime.datetime(2004, 4, 27, 12, 57), datetime.datetime(2004, 4, 27, 21, 28)), (datetime.datetime(2004, 4, 28, 0, 38), datetime.datetime(2004, 4, 28, 4, 32)), (datetime.datetime(2004, 4, 28, 11, 5), datetime.datetime(2004, 4, 28, 16, 22)), (datetime.datetime(2004, 4, 29, 0, 26), datetime.datetime(2004, 4, 29, 14, 30)), (datetime.datetime(2004, 5, 2, 8, 44), datetime.datetime(2004, 5, 2, 23, 55)), (datetime.datetime(2004, 5, 3, 1, 23), datetime.datetime(2004, 5, 4, 3, 16)), (datetime.datetime(2004, 5, 4, 4, 50), datetime.datetime(2004, 5, 4, 8, 35)), (datetime.datetime(2004, 5, 4, 10, 9), datetime.datetime(2004, 5, 4, 23, 32)), (datetime.datetime(2004, 5, 5, 1, 0), datetime.datetime(2004, 5, 5, 3, 4)), (datetime.datetime(2004, 5, 5, 6, 27), datetime.datetime(2004, 5, 5, 10, 13)), (datetime.datetime(2004, 5, 5, 11, 27), datetime.datetime(2004, 5, 5, 13, 22)), (datetime.datetime(2004, 5, 8, 9, 23), datetime.datetime(2004, 5, 8, 16, 9)), (datetime.datetime(2004, 5, 9, 7, 22), datetime.datetime(2004, 5, 9, 10, 55)), (datetime.datetime(2004, 5, 10, 22, 8), datetime.datetime(2004, 5, 11, 3, 38)), (datetime.datetime(2004, 5, 12, 21, 44), datetime.datetime(2004, 5, 13, 3, 13)), (datetime.datetime(2004, 5, 13, 6, 37), datetime.datetime(2004, 5, 13, 10, 21)), (datetime.datetime(2004, 5, 13, 11, 35), datetime.datetime(2004, 5, 13, 15, 12)), (datetime.datetime(2004, 5, 13, 18, 2), datetime.datetime(2004, 5, 14, 1, 13)), (datetime.datetime(2004, 5, 14, 6, 25), datetime.datetime(2004, 5, 14, 11, 39)), (datetime.datetime(2004, 5, 14, 13, 5), datetime.datetime(2004, 5, 14, 16, 41)), (datetime.datetime(2004, 5, 15, 6, 14), datetime.datetime(2004, 5, 15, 16, 30)), (datetime.datetime(2004, 5, 15, 19, 22), datetime.datetime(2004, 5, 15, 21, 24)), (datetime.datetime(2004, 5, 19, 22, 9), datetime.datetime(2004, 5, 20, 22, 9)), (datetime.datetime(2004, 5, 21, 8, 28), datetime.datetime(2004, 5, 21, 10, 19)) ] # NOAA-16 } def _rolling_window(array, size): """Create an array view of rolling windows. Args: array (np.ndarray): 2D array size (tuple): window size Returns: windows (np.ndarray): 4D array of rolling windows the first dimensions are the window location on the 2D array, the last two dimensions are the location inside the window. """ shape = ((array.shape[0] - size[0] + 1, array.shape[1] - size[1] + 1,) + size) strides = array.strides + array.strides return np.lib.stride_tricks.as_strided(array, shape=shape, strides=strides) def std_filter(data, box_size): """Filter a 2D array using a standard deviation kernel. Args: data (np.ndarray): 2D array to be filtered box_size (int): Specifies the boxsize. Must be odd. Returns: np.ndarray: The filtered array """ size = (box_size, box_size) border = box_size // 2 # need to surround the data with NaNs to calculate values at the boundary padded_data = np.pad( data, (border, border), mode='constant', constant_values=np.nan ) windows = _rolling_window(padded_data, size) n3, n2, n1, n0 = windows.shape # flatten the windows for bottleneck function call windows = windows.reshape((n3, n2, n1*n0)) return bn.nanstd(windows, axis=2) def get_tsm_idx(ch1, ch2, ch4, ch5): """Determine indices of TSM affected pixels.""" # absolute difference because ch1 is very similar to ch2 abs_d12 = abs(ch1 - ch2) # relative difference because ch4 and ch5 differ rel_d45 = 100.0*(ch4 - ch5) / ch5 # standard deviation of abs_d12 and rel_d45 box_size = 3 std_d12 = std_filter(abs_d12, box_size) std_d45 = std_filter(rel_d45, box_size) # replace NaNs to avoid "RuntimeWarning: invalid value encountered in greater" std_d12 = np.nan_to_num(std_d12, copy=False) std_d45 = np.nan_to_num(std_d45, copy=False) # using ch1, ch2, ch4, ch5 in combination # all channels seems to be affected throughout the whole orbit, # independent of VIS and NIR or day and night idx = np.where((std_d12 > 2.0) & (std_d45 > 2.0)) return idx pygac-1.7.2/pygac/data/000077500000000000000000000000001444627033600146515ustar00rootroot00000000000000pygac-1.7.2/pygac/data/calibration.json000066400000000000000000001173321444627033600200420ustar00rootroot00000000000000{ "description": { "thermal": { "channels": [ "3b", "4", "5" ], "coefficients": { "b0": "constant non-linear radiance correction coefficient [mW m^{-2} sr cm^{-1}]", "b1": "linear non-linear radiance correction coefficient []", "b2": "quadratic non-linear radiance correction coefficient [(mW^{-1} m^2 sr^{-1} cm)]", "centroid_wavenumber": "centroid wavenumber [cm^{-1}]", "d0": "constant thermometer counts to temperature conversion coefficient [K]", "d1": "linear thermometer counts to temperature conversion coefficient [K]", "d2": "quadratic thermometer counts to temperature conversion coefficient [K]", "d3": "cubic thermometer counts to temperature conversion coefficient [K]", "d4": "quartic thermometer counts to temperature conversion coefficient [K]", "space_radiance": "radiance of space [mW m^{-2} sr cm^{-1}]", "to_eff_blackbody_intercept": "thermal channel temperature to effective blackbody temperature intercept [K]", "to_eff_blackbody_slope": "thermal channel temperature to effective blackbody temperature slope []" }, "method": "Goodrum, G., Kidwell, K.B. and W. Winston, 2000: NOAA KLM User's Guide. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data and Information Service; Walton, C. C., J. T. Sullivan, C. R. N. Rao, and M. P. Weinreb, 1998: Corrections for detector nonlinearities and calibration inconsistencies of the infrared channels of the Advanced Very High Resolution Radiometer. J. Geophys. Res., 103, 3323-3337; Trishchenko, A.P., 2002: Removing Unwanted Fluctuations in the AVHRR Thermal Calibration Data Using Robust Techniques. Journal of Atmospheric and Oceanic Technology, 19:1939-1954" }, "visible": { "channels": [ "1", "2", "3a" ], "coefficients": { "dark_count": "instrument counts under dark conditions []", "date_of_launch": "timestamp of launch date [UTC]", "gain_switch": "dual-gain switch count, set to 'null' for single-gain instruments []", "s0": "single-gain calibration slope at launch date [%]", "s1": "linear single-gain calibration slope parameter [% years^{-1}]", "s2": "quadratic single-gain calibration slope parameter [% years^{-2}]" }, "method": "Heidinger, A.K., W.C. Straka III, C.C. Molling, J.T. Sullivan, and X. Wu, 2010: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record. International Journal of Remote Sensing, 31:6493-6517" } }, "metopa": { "channel_1": { "dark_count": 40.43, "gain_switch": 501.0, "s0": 0.11266666666666668, "s1": 0.609, "s2": -0.029 }, "channel_2": { "dark_count": 39.75, "gain_switch": 500.0, "s0": 0.13266666666666668, "s1": 0.98, "s2": -0.016 }, "channel_3a": { "dark_count": 41.8, "gain_switch": 502.0, "s0": 0.1217142857142857, "s1": 1.224, "s2": -0.033 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2687.0392, "space_radiance": 0.0, "to_eff_blackbody_intercept": 2.0582306816399316, "to_eff_blackbody_slope": 0.9965700053555672 }, "channel_4": { "b0": 5.44, "b1": -0.10152, "b2": 0.00046964, "centroid_wavenumber": 927.2763, "space_radiance": -4.98, "to_eff_blackbody_intercept": 0.564181969408163, "to_eff_blackbody_slope": 0.998493273650062 }, "channel_5": { "b0": 3.84, "b1": -0.06249, "b2": 0.00025239, "centroid_wavenumber": 837.80762, "space_radiance": -3.4, "to_eff_blackbody_intercept": 0.3842947903481519, 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"noaa12": { "channel_1": { "dark_count": 41.0, "gain_switch": null, "s0": 0.121, "s1": 2.032, "s2": -0.032 }, "channel_2": { "dark_count": 40.0, "gain_switch": null, "s0": 0.148, "s1": 1.323, "s2": -0.008 }, "channel_3a": { "dark_count": 40.0, "gain_switch": null, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2651.7708, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.8995562357304514, "to_eff_blackbody_slope": 0.9969990329109382 }, "channel_4": { "b0": 5.11, "b1": -0.1107, "b2": 0.0005968, "centroid_wavenumber": 922.36261, "space_radiance": -5.51, "to_eff_blackbody_intercept": 0.6329612453773935, "to_eff_blackbody_slope": 0.9982953109270609 }, "channel_5": { "b0": 1.91, "b1": -0.037, "b2": 0.0001775, "centroid_wavenumber": 838.02678, "space_radiance": -2.51, "to_eff_blackbody_intercept": 0.4103730120125729, "to_eff_blackbody_slope": 0.9988004406707545 }, "date_of_launch": "1991-05-14T22:02:38.400002Z", "thermometer_1": 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1.47656e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.59258, "d1": 0.050966, "d2": 1.47656e-06, "d3": 0.0, "d4": 0.0 } }, "noaa16": { "channel_1": { "dark_count": 39.3, "gain_switch": 498.96, "s0": 0.11, "s1": 0.839, "s2": -0.051 }, "channel_2": { "dark_count": 38.9, "gain_switch": 500.17, "s0": 0.11933333333333333, "s1": 0.786, "s2": -0.031 }, "channel_3a": { "dark_count": 38.4, "gain_switch": 499.43, "s0": 0.10685714285714286, "s1": 0.29, "s2": -0.294 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2681.254, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.674558933750318, "to_eff_blackbody_slope": 0.9982713932554388 }, "channel_4": { "b0": 2.96, "b1": -0.05411, "b2": 0.00024532, "centroid_wavenumber": 922.3479, "space_radiance": -2.467, "to_eff_blackbody_intercept": 0.5555332488394067, "to_eff_blackbody_slope": 0.9985101230454039 }, "channel_5": { "b0": 2.25, "b1": -0.03665, "b2": 0.00014854, "centroid_wavenumber": 834.61814, "space_radiance": -2.009, "to_eff_blackbody_intercept": 0.4138044554994394, "to_eff_blackbody_slope": 0.9987848783170394 }, "date_of_launch": "2000-09-21T13:04:30.719994Z", "thermometer_1": { "d0": 276.355, "d1": 0.05562, "d2": -1.59e-05, "d3": 2.486e-08, "d4": -1.199e-11 }, "thermometer_2": { "d0": 276.142, "d1": 0.05605, "d2": -1.707e-05, "d3": 2.595e-08, "d4": -1.224e-11 }, "thermometer_3": { "d0": 275.996, "d1": 0.05486, "d2": -1.223e-05, "d3": 1.862e-08, "d4": -8.53e-12 }, "thermometer_4": { "d0": 276.132, "d1": 0.05494, "d2": -1.344e-05, "d3": 2.112e-08, "d4": -1.001e-11 } }, "noaa17": { "channel_1": { "dark_count": 39.99, "gain_switch": 501.12, "s0": 0.11466666666666665, "s1": 1.007, "s2": -0.044 }, "channel_2": { "dark_count": 39.09, "gain_switch": 500.73, "s0": 0.14, "s1": 1.474, "s2": -0.118 }, "channel_3a": { "dark_count": 42.09, "gain_switch": 501.37, "s0": 0.11942857142857143, "s1": 2.787, "s2": -0.292 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2669.1414, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.695762344709997, "to_eff_blackbody_slope": 0.997334722687091 }, "channel_4": { "b0": 8.22, "b1": -0.15795, "b2": 0.00075579, "centroid_wavenumber": 928.29959, "space_radiance": -8.55, "to_eff_blackbody_intercept": 0.5654877558672039, "to_eff_blackbody_slope": 0.9984818084103121 }, "channel_5": { "b0": 4.31, "b1": -0.07318, "b2": 0.00030976, "centroid_wavenumber": 840.20289, "space_radiance": -3.97, "to_eff_blackbody_intercept": 0.37224447975949276, "to_eff_blackbody_slope": 0.9989170740000766 }, "date_of_launch": "2002-06-24T21:05:28.319992Z", "thermometer_1": { "d0": 276.628, "d1": 0.05098, "d2": 1.371e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.538, "d1": 0.05098, "d2": 1.371e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.761, "d1": 0.05097, "d2": 1.369e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.66, "d1": 0.051, "d2": 1.348e-06, "d3": 0.0, "d4": 0.0 } }, "noaa18": { "channel_1": { "dark_count": 39.44, "gain_switch": 500.54, "s0": 0.114, "s1": 0.603, "s2": -0.0 }, "channel_2": { "dark_count": 39.4, "gain_switch": 500.4, "s0": 0.128, "s1": 0.632, "s2": 0.045 }, "channel_3a": { "dark_count": 37.51, "gain_switch": 500.56, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2660.6468, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.7173477182782537, "to_eff_blackbody_slope": 0.9971448750791857 }, "channel_4": { "b0": 5.82, "b1": -0.11069, "b2": 0.00052337, "centroid_wavenumber": 928.73452, "space_radiance": -5.53, "to_eff_blackbody_intercept": 0.5461660253184831, "to_eff_blackbody_slope": 0.9985440229601218 }, "channel_5": { "b0": 2.67, "b1": -0.0436, "b2": 0.00017715, "centroid_wavenumber": 834.08306, "space_radiance": -2.22, "to_eff_blackbody_intercept": 0.3989160707985957, "to_eff_blackbody_slope": 0.9988289729121578 }, "date_of_launch": "2005-05-20T21:42:28.799988Z", "thermometer_1": { "d0": 276.601, "d1": 0.0509, "d2": 1.657e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.683, "d1": 0.05101, "d2": 1.482e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.565, "d1": 0.05117, "d2": 1.313e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.615, "d1": 0.05103, "d2": 1.484e-06, "d3": 0.0, "d4": 0.0 } }, "noaa19": { "channel_1": { "dark_count": 38.8, "gain_switch": 496.43, "s0": 0.108, "s1": 0.626, "s2": -0.044 }, "channel_2": { "dark_count": 39.0, "gain_switch": 500.37, "s0": 0.122, "s1": 0.95, "s2": -0.039 }, "channel_3a": { "dark_count": 39.4, "gain_switch": 496.11, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2670.2425, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.6820200170457578, "to_eff_blackbody_slope": 0.9974112191806167 }, "channel_4": { "b0": 5.7, "b1": -0.11187, "b2": 0.00054668, "centroid_wavenumber": 927.92374, "space_radiance": -5.49, "to_eff_blackbody_intercept": 0.39366677255917354, "to_eff_blackbody_slope": 0.9986718662850276 }, "channel_5": { "b0": 3.58, "b1": -0.05991, "b2": 0.00024985, "centroid_wavenumber": 831.28619, "space_radiance": -3.39, "to_eff_blackbody_intercept": 0.2633947633588976, "to_eff_blackbody_slope": 0.9990463103920997 }, "date_of_launch": "2009-02-05T00:57:36.000000Z", "thermometer_1": { "d0": 276.6067, "d1": 0.051111, "d2": 1.405783e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.6119, "d1": 0.05109, "d2": 1.496037e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.6311, "d1": 0.051033, "d2": 1.49699e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.6268, "d1": 0.051058, "d2": 1.49311e-06, "d3": 0.0, "d4": 0.0 } }, "noaa6": { "channel_1": { "dark_count": 39.44, "gain_switch": null, "s0": 0.113, "s1": 0.9, "s2": 0.0 }, "channel_2": { "dark_count": 39.4, "gain_switch": null, "s0": 0.128, "s1": 0.699, "s2": 0.0 }, "channel_3a": { "dark_count": 37.51, "gain_switch": null, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2671.5433, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.7624057951236716, "to_eff_blackbody_slope": 0.9975631527305099 }, "channel_4": { "b0": 2.24, "b1": -0.03964, "b2": 0.00016925, "centroid_wavenumber": 913.46088, "space_radiance": -3.26, "to_eff_blackbody_intercept": 0.5032756477395923, "to_eff_blackbody_slope": 0.9986426449170288 }, "channel_5": { "b0": 2.24, "b1": -0.03964, "b2": 0.00016925, "centroid_wavenumber": 913.46088, "space_radiance": -3.26, "to_eff_blackbody_intercept": 0.5032756477395923, "to_eff_blackbody_slope": 0.9986426449170288 }, "date_of_launch": "1979-06-28T20:23:59.999992Z", "thermometer_1": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 } }, "noaa7": { "channel_1": { "dark_count": 36.0, "gain_switch": null, "s0": 0.115, "s1": 3.792, "s2": -0.269 }, "channel_2": { "dark_count": 37.0, "gain_switch": null, "s0": 0.127, "s1": 2.685, "s2": -0.101 }, "channel_3a": { "dark_count": 39.0, "gain_switch": null, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2684.5233, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.9431412686479361, "to_eff_blackbody_slope": 0.9970825364982062 }, "channel_4": { "b0": 5.25, "b1": -0.10217, "b2": 0.0004819, "centroid_wavenumber": 928.23757, "space_radiance": -5.16, "to_eff_blackbody_intercept": 0.5273396378823769, "to_eff_blackbody_slope": 0.9985980681720933 }, "channel_5": { "b0": 3.93, "b1": -0.06317, "b2": 0.0002425, "centroid_wavenumber": 841.52137, "space_radiance": -4.28, "to_eff_blackbody_intercept": 0.4050927062086506, "to_eff_blackbody_slope": 0.9988224881686979 }, "date_of_launch": "1981-06-23T21:15:50.400009Z", "thermometer_1": { "d0": 277.099, "d1": 0.05048, "d2": 2.823e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.734, "d1": 0.05069, "d2": 2.493e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.876, "d1": 0.05148, "d2": 1.04e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.16, "d1": 0.05128, "d2": 1.414e-06, "d3": 0.0, "d4": 0.0 } }, "noaa8": { "channel_1": { "dark_count": 39.44, "gain_switch": null, "s0": 0.119, "s1": 6.065, "s2": 0.0 }, "channel_2": { "dark_count": 39.4, "gain_switch": null, "s0": 0.136, "s1": 7.248, "s2": 0.0 }, "channel_3a": { "dark_count": 37.51, "gain_switch": null, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2651.3776, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.7721113578458658, "to_eff_blackbody_slope": 0.9975798712323902 }, "channel_4": { "b0": 2.24, "b1": -0.03964, "b2": 0.00016925, "centroid_wavenumber": 915.3033, "space_radiance": -3.26, "to_eff_blackbody_intercept": 0.49950763272635035, "to_eff_blackbody_slope": 0.9986558092807081 }, "channel_5": { "b0": 2.24, "b1": -0.03964, "b2": 0.00016925, "centroid_wavenumber": 915.3033, "space_radiance": -3.26, "to_eff_blackbody_intercept": 0.49950763272635035, "to_eff_blackbody_slope": 0.9986558092807081 }, "date_of_launch": "1983-03-29T23:09:36.000000Z", "thermometer_1": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 } }, "noaa9": { "channel_1": { "dark_count": 38.0, "gain_switch": null, "s0": 0.108, "s1": 4.255, "s2": 0.64 }, "channel_2": { "dark_count": 40.0, "gain_switch": null, "s0": 0.122, "s1": 0.31, "s2": 0.642 }, "channel_3a": { "dark_count": 38.0, "gain_switch": null, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2690.0451, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.8778246397589067, "to_eff_blackbody_slope": 0.9971105729816139 }, "channel_4": { "b0": 5.24, "b1": -0.1136, "b2": 0.0006033, "centroid_wavenumber": 930.5023, "space_radiance": -5.53, "to_eff_blackbody_intercept": 0.5108402897268406, "to_eff_blackbody_slope": 0.99864483895354 }, "channel_5": { "b0": 2.42, "b1": -0.0469, "b2": 0.0002198, "centroid_wavenumber": 845.75, "space_radiance": -3.06, "to_eff_blackbody_intercept": 0.3877802982856218, "to_eff_blackbody_slope": 0.9988802552338829 }, "date_of_launch": "1984-12-12T23:13:55.200005Z", "thermometer_1": { "d0": 277.018, "d1": 0.05128, "d2": 0.0, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.75, "d1": 0.05128, "d2": 0.0, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.862, "d1": 0.05128, "d2": 0.0, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.546, "d1": 0.05128, "d2": 0.0, "d3": 0.0, "d4": 0.0 } }, "tirosn": { "channel_1": { "dark_count": 39.44, "gain_switch": null, "s0": 0.115, "s1": 5.11, "s2": 0.0 }, "channel_2": { "dark_count": 39.4, "gain_switch": null, "s0": 0.133, "s1": 0.717, "s2": 0.0 }, "channel_3a": { "dark_count": 37.51, "gain_switch": null, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.00195, "b1": -0.015, "b2": 0.011, "centroid_wavenumber": 2655.7409, "space_radiance": -0.0039, "to_eff_blackbody_intercept": 1.645107312780676, "to_eff_blackbody_slope": 0.9979149564899099 }, "channel_4": { "b0": 6.13, "b1": -0.131942, "b2": 0.000673193, "centroid_wavenumber": 913.05397, "space_radiance": -8.13, "to_eff_blackbody_intercept": 0.5305934198578978, "to_eff_blackbody_slope": 0.9985677542700504 }, "channel_5": { "b0": 6.13, "b1": -0.131942, "b2": 0.000673193, "centroid_wavenumber": 913.05397, "space_radiance": -8.13, "to_eff_blackbody_intercept": 0.5305934198578978, "to_eff_blackbody_slope": 0.9985677542700504 }, "date_of_launch": "1978-10-13T19:04:47.999992Z", "thermometer_1": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.659, "d1": 0.051275, "d2": 1.363e-06, "d3": 0.0, "d4": 0.0 } } } pygac-1.7.2/pygac/gac_calibration.py_old000066400000000000000000000502131444627033600202520ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Martin Raspaud, Abhay Devasthale # Author(s): # Martin Raspaud # Abhay Devasthale # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Calibration coefficients and generic calibration functions """ import numpy as np coeffs = { 'metop02': {'ah': [0.1665, 0.1905, 0.217], 'al': [0.0555, 0.0635, 0.031], 'bh': [1.797, 2.149, 4.11], 'bl': [1.797, 2.149, 4.11], 'c_dark': [40.43, 39.75, 41.8], 'c_s': [501.0, 500.0, 502.0], 'ch': [-0.352, -0.225, 0.0], 'cl': [-0.352, -0.225, 0.0], 'l_date': 2006.77, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.6194, 0.050919, 1.471E-06, 0.0, 0.0], [276.6511, 0.050892, 1.489E-06, 0.0, 0.0], [276.6597, 0.050845, 1.521E-06, 0.0, 0.0], [276.3685, 0.050992, 1.482E-06, 0.0, 0.0]]), 'n_s': np.array([0.0, -4.98, -3.40]), 'c_wn': np.array([2687.0392, 927.27630, 837.80762]), 'a': np.array([2.0653147, 0.56503332, 0.38472766]), 'b': np.array([1.0 / 1.0034418, 1.0 / 1.0015090, 1.0 / 1.0011264]), 'b0': np.array([0.0, 5.44, 3.84]), 'b1': np.array([1 - 0.0, 1 - 0.10152, 1 - 0.06249]), 'b2': np.array([0.0, 0.00046964, 0.00025239]), }, 'noaa07': {'ah': np.array([0.111206, 0.107664, 0.0]), 'al': np.array([0.111206, 0.107664, 0.0]), 'bh': np.array([8.8581, 20.7843, 0.0]), 'bl': np.array([8.8581, 20.7843, 0.0]), 'c_dark': np.array([36.0, 37.0, 39.0]), 'ch': np.array([-1.11938, -4.32167, 0.0]), 'cl': np.array([-1.11938, -4.32167, 0.0]), 'l_date': 1981.4764, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [277.099, 5.048E-2, 2.823E-6, 0, 0], [276.734, 5.069E-2, 2.493E-6, 0, 0], [276.876, 5.148E-2, 1.040E-6, 0, 0], [276.160, 5.128E-2, 1.414E-6, 0, 0]]), 'n_s': np.array([0.0, -5.16, -4.28]), 'c_wn': np.array([2684.5233, 928.23757, 841.52137]), 'a': np.array([1.94882690, 0.52807997, 0.40557027]), 'b': np.array([1.0 / 1.0029260, 1.0 / 1.0014039, 1.0 / 1.0011789]), 'b1': np.array([1.0, 0.89783, 0.93683]), 'b2': np.array([0.0, 0.0004819, 0.0002425]), 'b0': np.array([0.0, 5.25, 3.93]), }, 'noaa09': {'ah': np.array([0.109312, 0.11601, 0.0]), 'al': np.array([0.109312, 0.11601, 0.0]), 'bh': np.array([5.66526, 3.93889, 0.0]), 'bl': np.array([5.66526, 3.93889, 0.0]), 'c_dark': np.array([38.0, 40.0, 38.0]), 'ch': np.array([0.27152, -0.258047, 0.0]), 'cl': np.array([0.27152, -0.258047, 0.0]), 'l_date': 1984.948, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [277.018000, 0.051280, 0.0, 0, 0], [276.750000, 0.051280, 0.0, 0, 0], [276.862000, 0.051280, 0.0, 0, 0], [276.546000, 0.051280, 0.0, 0, 0]]), 'n_s': np.array([0.0, -5.530, -3.06]), 'c_wn': np.array([2690.0451, 930.50230, 845.75000]), 'a': np.array([1.8832662, 0.5115335, 0.3882150]), 'b': np.array([1.0 / 1.0028978, 1.0 / 1.0013570, 1.0 / 1.0011210]), 'b1': np.array([1.0, 0.88643, 0.95311]), 'b2': np.array([0.0, 0.0006033, 0.0002198]), 'b0': np.array([0.0, 5.24, 2.42]), }, 'noaa10': {'ah': np.array([0.114803, 0.132682, 0.0]), 'al': np.array([0.114803, 0.132682, 0.0]), 'bh': np.array([3.43456, 0.00926103, 0.0]), 'bl': np.array([3.43456, 0.00926103, 0.0]), 'c_dark': np.array([39.44, 39.40, 37.51]), 'ch': np.array([-0.384635, 0.137333, 0.0]), 'cl': np.array([-0.384635, 0.137333, 0.0]), 'l_date': 1986.712, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.659, 0.051275, 1.363e-06, 0, 0], [276.659, 0.051275, 1.363e-06, 0, 0], [276.659, 0.051275, 1.363e-06, 0, 0], [276.659, 0.051275, 1.363e-06, 0, 0]]), 'n_s': np.array([0.0, 0.0, 0.0]), 'c_wn': np.array([2672.6392, 910.51930, 910.51930]), 'a': np.array([1.80168640, 0.45707890, 0.45707890]), 'b': np.array([1.0 / 1.0026383, 1.0 / 1.0012338, 1.0 / 1.0012338]), 'b1': np.array([1.0, 1.0, 1.0]), 'b2': np.array([0.0, 0.0, 0.0]), 'b0': np.array([0.0, 0.0, 0.0]), }, 'noaa11': {'ah': np.array([0.114786, 0.115219, 0.0]), 'al': np.array([0.114786, 0.115219, 0.0]), 'bh': np.array([-1.82974, -1.39495, 0.0]), 'bl': np.array([-1.82974, -1.39494, 0.0]), 'c_dark': np.array([40.0, 40.0, 40.0]), 'ch': np.array([0.412551, 0.236978, 0.0]), 'cl': np.array([0.412551, 0.236978, 0.0]), 'l_date': 1988.7064, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0]]), 'n_s': np.array([0.0, -8.055, -3.51]), 'c_wn': np.array([2680.05, 927.462, 840.746]), 'a': np.array([1.738973, 0.321199, 0.048652]), 'b': np.array([1.0 / 1.003354, 1.0 / 1.001213, 1.0 / 1.000664]), 'b1': np.array([1.0, 0.84120, 0.94598]), 'b2': np.array([0.0, 0.0008739, 0.0002504]), 'b0': np.array([0.0, 7.21, 2.92]), }, 'noaa12': {'ah': np.array([0.118059, 0.135684, 0.0]), 'al': np.array([0.118059, 0.135684, 0.0]), 'bh': np.array([4.88848, 4.67098, 0.0]), 'bl': np.array([4.88848, 4.67098, 0.0]), 'c_dark': np.array([41.0, 40.0, 40.0]), 'ch': np.array([-0.347769, -0.399447, 0.0]), 'cl': np.array([-0.347769, -0.399447, 0.0]), 'l_date': 1991.3669, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0]]), 'n_s': np.array([0.0, -5.510, -2.51]), 'c_wn': np.array([2651.7708, 922.36261, 838.02678]), 'a': np.array([1.90527390, 0.63404209, 0.41086587]), 'b': np.array([1.0 / 1.0030100, 1.0 / 1.0017076, 1.0 / 1.0012010]), 'b1': np.array([1.0, 0.88929, 0.96299]), 'b2': np.array([0.0, 0.0005968, 0.0001775]), 'b0': np.array([0.0, 5.11, 1.91]), }, 'noaa14': {'ah': np.array([0.121408, 0.144515, 0.0]), 'al': np.array([0.121408, 0.144515, 0.0]), 'bh': np.array([4.07313, 0.885331, 0.0]), 'bl': np.array([4.07313, 0.885331, 0.0]), 'c_dark': np.array([41.0, 41.0, 39.0]), 'ch': np.array([-0.375215, 0.233727, 0.0]), 'cl': np.array([-0.375215, 0.233727, 0.0]), 'l_date': 1994.9699, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0], [276.597, 0.051275, 1.363e-06, 0, 0]]), 'n_s': np.array([0.0069, -4.05, -2.29]), 'c_wn': np.array([2654.25, 928.349, 833.040]), 'a': np.array([1.885330, 0.308384, 0.022171]), 'b': np.array([1.0 / 1.003839, 1.0 / 1.001443, 1.0 / 1.000538]), 'b1': np.array([1.00359, 0.92378, 0.96194]), 'b2': np.array([0.0, 0.000382, 0.0001742]), 'b0': np.array([-0.0031, 3.72, 2.00])}, 'noaa15': {'ah': np.array([0.1815, 0.2025, 0.1846]), 'al': np.array([0.0605, 0.0675, 0.0275]), 'bh': np.array([0.447, 0.035, 0.0]), 'bl': np.array([0.447, 0.035, 0.0]), 'c_dark': np.array([39.0, 40.0, 39.0]), 'c_s': np.array([500.0, 500.0, 500.0]), 'ch': np.array([-0.060, 0.007, 0.0]), 'cl': np.array([-0.060, 0.007, 0.0]), 'l_date': 1998.3641, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.60157, 0.051045, 1.36328E-06, 0.0, 0.0], [276.62531, 0.050909, 1.47266E-06, 0.0, 0.0], [276.67413, 0.050907, 1.47656E-06, 0.0, 0.0], [276.59258, 0.050966, 1.47656E-06, 0.0, 0.0]]), 'n_s': np.array([0.0, -4.50, -3.61]), 'c_wn': np.array([2695.9743, 925.4075, 839.8979]), 'a': np.array([1.624481, 0.338243, 0.304856]), 'b': np.array([1.0 / 1.001989, 1.0 / 1.001283, 1.0 / 1.000977]), 'b0': np.array([0.0, 4.76, 3.83]), 'b1': np.array([1 - 0.0, 1 - 0.0932, 1 - 0.0659]), 'b2': np.array([0.0, 0.0004524, 0.0002811]), }, 'noaa16': {'ah': np.array([0.168, 0.174, 0.2013]), 'al': np.array([0.056, 0.058, 0.0288]), 'bh': np.array([0.306, 0.586, -0.810]), 'bl': np.array([0.306, 0.586, -0.81]), 'c_dark': np.array([39.3, 38.9, 38.4]), 'c_s': np.array([498.96, 500.17, 499.43]), 'ch': np.array([0.0250, 0.036, 0.0]), 'cl': np.array([0.0250, 0.036, 0.0]), 'l_date': 2000.7228, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.355, 5.562E-02, -1.590E-05, 2.486E-08, -1.199E-11], [276.142, 5.605E-02, -1.707E-05, 2.595E-08, -1.224E-11], [275.996, 5.486E-02, -1.223E-05, 1.862E-08, -0.853E-11], [276.132, 5.494E-02, -1.344E-05, 2.112E-08, -1.001E-11]]), 'n_s': np.array([0.0, -2.467, -2.009]), 'c_wn': np.array([2681.2540, 922.34790, 834.61814]), 'a': np.array([1.6774586, 0.55636216, 0.41430789]), 'b': np.array([1.0 / 1.0017316, 1.0 / 1.0014921, 1.0 / 1.0012166]), 'b0': np.array([0.0, 2.96, 2.25]), 'b1': np.array([1 - 0.0, 1 - 0.05411, 1 - 0.03665]), 'b2': np.array([0.0, 0.00024532, 0.00014854]), }, 'noaa17': {'ah': np.array([0.1725, 0.1950, 0.2153]), 'al': np.array([0.0575, 0.0650, 0.0308]), 'bh': np.array([1.707, 3.117, 4.06]), 'bl': np.array([1.707, 3.117, 4.06]), 'c_dark': np.array([39.99, 39.09, 42.09]), 'c_s': np.array([501.12, 500.73, 501.37]), 'ch': np.array([-0.151, -0.265, -0.37]), 'cl': np.array([-0.151, -0.265, -0.37]), 'l_date': 2002.47912, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.628, 0.05098, 1.371E-06, 0.0, 0.0], [276.538, 0.05098, 1.371E-06, 0.0, 0.0], [276.761, 0.05097, 1.369E-06, 0.0, 0.0], [276.660, 0.05100, 1.348E-06, 0.0, 0.0]]), 'n_s': np.array([0.0, -8.55, -3.97]), 'c_wn': np.array([2669.1414, 928.29959, 840.20289]), 'a': np.array([1.70002941, 0.56634758, 0.37264803]), 'b': np.array([1.0 / 1.0026724, 1.0 / 1.0015205, 1.0 / 1.0010841]), 'b0': np.array([0.0, 8.22, 4.31]), 'b1': np.array([1 - 0.0, 1 - 0.15795, 1 - 0.07318]), 'b2': np.array([0.0, 0.00075579, 0.00030976]), }, 'noaa18': {'ah': np.array([0.1665, 0.1785, 0.1849]), 'al': np.array([0.0555, 0.0595, 0.0262]), 'bh': np.array([3.068, 4.541, 0.0]), 'bl': np.array([3.068, 4.541, 0.0]), 'c_dark': np.array([39.44, 39.40, 37.51]), 'c_s': np.array([500.54, 500.40, 500.56]), 'ch': np.array([-0.443, -0.611, 0.0]), 'cl': np.array([-0.443, -0.611, 0.0]), 'l_date': 2005.18891, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.601, 0.05090, 1.657E-06, 0.0, 0.0], [276.683, 0.05101, 1.482E-06, 0.0, 0.0], [276.565, 0.05117, 1.313E-06, 0.0, 0.0], [276.615, 0.05103, 1.484E-06, 0.0, 0.0]]), 'n_s': np.array([0.0, -5.53, -2.22]), 'c_wn': np.array([2660.6468, 928.73452, 834.08306]), 'a': np.array([1.7222650, 0.54696239, 0.39938376]), 'b': np.array([1.0 / 1.0028633, 1.0 / 1.0014581, 1.0 / 1.0011724]), 'b0': np.array([0.0, 5.82, 2.67]), 'b1': np.array([1 - 0.0, 1 - 0.11069, 1 - 0.04360]), 'b2': np.array([0.0, 0.00052337, 0.00017715]), }, 'noaa19': {'ah': np.array([0.1680, 0.1755, 0.1880]), 'al': np.array([0.0560, 0.0585, 0.0272]), 'bh': np.array([-5.985, 2.263, 0.0]), 'bl': np.array([-5.985, 2.263, 0.0]), 'c_dark': np.array([38.8, 39.00, 39.4]), 'c_s': np.array([496.43, 500.37, 496.11]), 'ch': np.array([-8.687, 0.748, 0.0]), 'cl': np.array([-8.687, 0.748, 0.0]), 'l_date': 2009.096, 'd': np.array([[0, 0, 0, 0, 0], # reset prt [276.6067, 0.051111, 1.405783e-06, 0, 0], [276.6119, 0.051090, 1.496037e-06, 0, 0], [276.6311, 0.051033, 1.496990e-06, 0, 0], [276.6268, 0.051058, 1.493110e-06, 0, 0]]), 'n_s': np.array([0.0, -5.49, -3.39]), 'c_wn': np.array([2670.2425, 927.92374, 831.28619]), 'a': np.array([1.6863857, 0.39419031, 0.26364620]), 'b': np.array([1.0 / 1.0025955, 1.0 / 1.0013299, 1.0 / 1.0009546]), 'b0': np.array([0.0, 5.70, 3.58]), 'b1': np.array([1 - 0.0, 1 - 0.11187, 1 - 0.05991]), 'b2': np.array([0.0, 0.00054668, 0.00024985])}} class Calibrator(object): def __init__(self, spacecraft): self.ah = None self.al = None self.bh = None self.bl = None self.ch = None self.cl = None self.c_s = None self.c_dark = None self.l_date = None self.d = None self.n_s = None self.c_wn = None self.a = None self.b = None self.b0 = None self.b1 = None self.b2 = None self.__dict__.update(coeffs[spacecraft]) def calibrate_solar(counts, chan, year, jday, spacecraft, corr=1): """Do the solar calibration and return reflectance (between 0 and 100). """ cal = Calibrator(spacecraft) t = (year + jday / 365.0) - cal.l_date stl = (cal.al[chan] * (100.0 + cal.bl[chan] * t + cal.cl[chan] * t * t)) / 100.0 sth = (cal.ah[chan] * (100.0 + cal.bh[chan] * t + cal.ch[chan] * t * t)) / 100.0 if cal.c_s is not None: return np.where(counts <= cal.c_s[chan], (counts - cal.c_dark[chan]) * stl, (cal.c_s[chan] - cal.c_dark[chan]) * stl + (counts - cal.c_s[chan]) * sth) else: return (counts - cal.c_dark[chan]) * stl * corr def calibrate_thermal(counts, prt, ict, space, line_numbers, channel, spacecraft): """Do the thermal calibration and return brightness temperatures (K). """ cal = Calibrator(spacecraft) chan = channel - 3 lines, columns = counts.shape[:2] offset = 0 for i, prt_val in enumerate(prt): if prt_val < 50: offset = i break iprt = (line_numbers - line_numbers[0] + 5 - offset) % 5 tprt = (cal.d[iprt, 0] + prt * (cal.d[iprt, 1] + prt * (cal.d[iprt, 2] + prt * (cal.d[iprt, 3] + prt * (cal.d[iprt, 4]))))) zeros = iprt == 0 nonzeros = np.logical_not(zeros) tprt[zeros] = np.interp((zeros).nonzero()[0], (nonzeros).nonzero()[0], tprt[nonzeros]) # convolving and smoothing PRT, ICT and SPACE values if lines > 51: wlength = 51 else: wlength = 3 weighting_function = np.ones(wlength, dtype=float) / wlength tprt_convolved = np.convolve(tprt, weighting_function, 'same') ict_convolved = np.convolve(ict, weighting_function, 'same') space_convolved = np.convolve(space, weighting_function, 'same') # take care of the beginning and end tprt_convolved[0:(wlength - 1) / 2] = tprt_convolved[(wlength - 1) / 2] ict_convolved[0:(wlength - 1) / 2] = ict_convolved[(wlength - 1) / 2] space_convolved[0:(wlength - 1) / 2] = space_convolved[(wlength - 1) / 2] tprt_convolved[-(wlength - 1) / 2:] = tprt_convolved[-((wlength + 1) / 2)] ict_convolved[-(wlength - 1) / 2:] = ict_convolved[-((wlength + 1) / 2)] space_convolved[-(wlength - 1) / 2:] = \ space_convolved[-((wlength + 1) / 2)] new_tprt = np.transpose(np.tile(tprt_convolved, (columns, 1))) new_ict = np.transpose(np.tile(ict_convolved, (columns, 1))) new_space = np.transpose(np.tile(space_convolved, (columns, 1))) # calibrating thermal channel tBB = new_tprt tsBB = cal.a[chan] + cal.b[chan] * tBB nBB_num = (1.1910427 * 0.000010) * cal.c_wn[chan] ** 3 nBB = nBB_num / (np.exp((1.4387752 * cal.c_wn[chan]) / tsBB) - 1.0) Nlin = (cal.n_s[chan] + (((nBB - cal.n_s[chan]) * (new_space - counts)) / (new_space - new_ict))) Ncor = cal.b0[chan] + Nlin * (cal.b1[chan] + cal.b2[chan] * Nlin) Ne = Ncor tsE = ((1.4387752 * cal.c_wn[chan]) / np.log(1.0 + nBB_num / Ne)) bt = (tsE - cal.a[chan]) / cal.b[chan] return bt pygac-1.7.2/pygac/gac_io.py000066400000000000000000000653641444627033600155510ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2012, 2014 Abhay Devasthale # Author(s): # Abhay Devasthale # Adam Dybbroe # Sara Hornquist # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . import calendar import datetime import logging import os import time import h5py import numpy as np from pygac.utils import slice_channel, strip_invalid_lat, check_user_scanlines LOG = logging.getLogger(__name__) MISSING_DATA = -32001 MISSING_DATA_LATLON = -999999 def save_gac(satellite_name, xutcs, lats, lons, ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi, qual_flags, start_line, end_line, gac_file, meta_data, output_file_prefix, avhrr_dir, qual_dir, sunsatangles_dir): midnight_scanline = meta_data['midnight_scanline'] miss_lines = meta_data['missing_scanlines'] corr = meta_data['sun_earth_distance_correction_factor'] last_scan_line_number = qual_flags[-1, 0] # Strip invalid coordinates first_valid_lat, last_valid_lat = strip_invalid_lat(lats) if first_valid_lat > start_line: LOG.info('New start_line chosen (due to invalid lat/lon ' 'info) = ' + str(first_valid_lat)) if end_line > last_valid_lat: LOG.info('New end_line chosen (due to invalid lat/lon ' 'info) = ' + str(last_valid_lat)) # Check user-defined scanlines start_line, end_line = check_user_scanlines( start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) # Slice data using new start/end lines _, miss_lines, midnight_scanline = slice_channel( np.zeros(lats.shape), start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat, qual_flags=qual_flags, miss_lines=miss_lines, midnight_scanline=midnight_scanline) ref1, _, _ = slice_channel(ref1, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) ref2, _, _ = slice_channel(ref2, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) ref3, _, _ = slice_channel(ref3, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) bt3, _, _ = slice_channel(bt3, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) bt4, _, _ = slice_channel(bt4, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) bt5, _, _ = slice_channel(bt5, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) sun_zen, _, _ = slice_channel(sun_zen, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) sun_azi, _, _ = slice_channel(sun_azi, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) sat_zen, _, _ = slice_channel(sat_zen, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) sat_azi, _, _ = slice_channel(sat_azi, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) rel_azi, _, _ = slice_channel(rel_azi, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) lons, _, _ = slice_channel(lons, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) lats, _, _ = slice_channel(lats, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) qual_flags, _, _ = slice_channel(qual_flags, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) xutcs, _, _ = slice_channel(xutcs, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat) total_number_of_scan_lines = lats.shape[0] # Reading time from the body of the gac file start = xutcs[0].astype(datetime.datetime) end = xutcs[-1].astype(datetime.datetime) startdate = start.strftime("%Y%m%d") starttime = start.strftime("%H%M%S%f")[:-5] enddate = end.strftime("%Y%m%d") endtime = end.strftime("%H%M%S%f")[:-5] # Apply scaling & offset bt3 -= 273.15 bt4 -= 273.15 bt5 -= 273.15 for array in [bt3, bt4, bt5, ref1, ref2, ref3, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi]: array *= 100.0 for array in [lats, lons]: array *= 1000.0 # Replace NaN with fill values for array in [ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi]: array[np.isnan(array)] = MISSING_DATA for array in [lats, lons]: array[np.isnan(array)] = MISSING_DATA_LATLON avhrrGAC_io(satellite_name, xutcs, startdate, enddate, starttime, endtime, lats, lons, ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi, qual_flags, start_line, end_line, total_number_of_scan_lines, last_scan_line_number, corr, gac_file, midnight_scanline, miss_lines, output_file_prefix, avhrr_dir, qual_dir, sunsatangles_dir) def avhrrGAC_io(satellite_name, xutcs, startdate, enddate, starttime, endtime, arrLat_full, arrLon_full, ref1, ref2, ref3, bt3, bt4, bt5, arrSZA, arrSTZ, arrSAA, arrSTA, arrRAA, qual_flags, start_line, end_line, total_number_of_scan_lines, last_scan_line_number, corr, gac_file, midnight_scanline, miss_lines, output_file_prefix, avhrr_dir, qual_dir, sunsatangles_dir): # Calculate start and end time in sec1970 t_obj = time.strptime(startdate + starttime[0:6], "%Y%m%d%H%M%S") starttime_sec1970 = calendar.timegm(t_obj) t_obj = time.strptime(enddate + endtime[0:6], "%Y%m%d%H%M%S") endtime_sec1970 = calendar.timegm(t_obj) LOG.info('Output file prefix = ' + str(output_file_prefix)) LOG.info('AVHRR data will be written to ' + str(avhrr_dir)) ofn = os.path.join(avhrr_dir, (output_file_prefix + '_avhrr_' + satellite_name + '_99999_' + startdate + 'T' + starttime + 'Z_' + enddate + 'T' + endtime + 'Z.h5')) LOG.info('Filename: ' + str(os.path.basename(ofn))) fout = h5py.File(ofn, "w") dset1 = fout.create_dataset("/image1/data", dtype='int16', data=ref1) dset2 = fout.create_dataset("/image2/data", dtype='int16', data=ref2) dset3 = fout.create_dataset("/image3/data", dtype='int16', data=bt3) dset4 = fout.create_dataset("/image4/data", dtype='int16', data=bt4) dset5 = fout.create_dataset("/image5/data", dtype='int16', data=bt5) dset6 = fout.create_dataset("/image6/data", dtype='int16', data=ref3) dset7 = fout.create_dataset("/where/lat/data", dtype='int32', data=arrLat_full) dset8 = fout.create_dataset("/where/lon/data", dtype='int32', data=arrLon_full) del dset8 channellist = [] channellist.append("channel1".encode('utf8')) channellist.append("channel2".encode('utf8')) channellist.append("channel3b".encode('utf8')) channellist.append("channel4".encode('utf8')) channellist.append("channel5".encode('utf8')) channellist.append("channel3a".encode('utf8')) dset10 = fout.create_dataset("/how/channel_list", data=channellist) del dset10 # Attributes directly on highest level groups g1 = fout.require_group("/image1") g2 = fout.require_group("/image2") g3 = fout.require_group("/image3") g4 = fout.require_group("/image4") g5 = fout.require_group("/image5") g6 = fout.require_group("/image6") g7 = fout.require_group("/where") g1.attrs["channel"] = np.string_("1") g1.attrs["description"] = np.string_("AVHRR ch1") g2.attrs["channel"] = np.string_("2") g2.attrs["description"] = np.string_("AVHRR ch2") g3.attrs["channel"] = np.string_("3b") g3.attrs["description"] = np.string_("AVHRR ch3b") g4.attrs["channel"] = np.string_("4") g4.attrs["description"] = np.string_("AVHRR ch4") g5.attrs["channel"] = np.string_("5") g5.attrs["description"] = np.string_("AVHRR ch5") g6.attrs["channel"] = np.string_("3a") g6.attrs["description"] = np.string_("AVHRR ch3a") g7.attrs["num_of_pixels"] = np.int32(arrSZA.shape[1]) g7.attrs["num_of_lines"] = np.int32(arrSZA.shape[0]) g7.attrs["xscale"] = np.float32(0.0) g7.attrs["yscale"] = np.float32(0.0) g7.attrs["start_line"] = start_line g7.attrs["end_line"] = end_line # Attributes in the 'what' groups g1 = fout.create_group("/image1/what") g2 = fout.create_group("/image2/what") g3 = fout.create_group("/image3/what") g4 = fout.create_group("/image4/what") g5 = fout.create_group("/image5/what") g6 = fout.create_group("/image6/what") g7 = fout.create_group("/where/lat/what") g8 = fout.create_group("/where/lon/what") g9 = fout.create_group("/what") g1.attrs["product"] = np.string_("SATCH") g1.attrs["quantity"] = np.string_("REFL") g1.attrs["dataset_name"] = np.string_('Channel 1 reflectance') g1.attrs["units"] = np.string_('%') g1.attrs["gain"] = np.float32(0.01) g1.attrs["offset"] = np.float32(0.0) g1.attrs["missingdata"] = np.int32(MISSING_DATA) g1.attrs["nodata"] = np.int32(MISSING_DATA) g1.attrs["starttime"] = np.string_(starttime[0:6]) g1.attrs["endtime"] = np.string_(endtime[0:6]) g1.attrs["startdate"] = np.string_(startdate) g1.attrs["enddate"] = np.string_(enddate) g2.attrs["product"] = np.string_("SATCH") g2.attrs["quantity"] = np.string_("REFL") g2.attrs["dataset_name"] = np.string_('Channel 2 reflectance') g2.attrs["units"] = np.string_('%') g2.attrs["gain"] = np.float32(0.01) g2.attrs["offset"] = np.float32(0.0) g2.attrs["missingdata"] = np.int32(MISSING_DATA) g2.attrs["nodata"] = np.int32(MISSING_DATA) g2.attrs["starttime"] = np.string_(starttime[0:6]) g2.attrs["endtime"] = np.string_(endtime[0:6]) g2.attrs["startdate"] = np.string_(startdate) g2.attrs["enddate"] = np.string_(enddate) g6.attrs["product"] = np.string_("SATCH") g6.attrs["quantity"] = np.string_("REFL") g6.attrs["dataset_name"] = np.string_('Channel 3a reflectance') g6.attrs["units"] = np.string_('%') g6.attrs["gain"] = np.float32(0.01) g6.attrs["offset"] = np.float32(0.0) g6.attrs["missingdata"] = np.int32(MISSING_DATA) g6.attrs["nodata"] = np.int32(MISSING_DATA) g6.attrs["starttime"] = np.string_(starttime[0:6]) g6.attrs["endtime"] = np.string_(endtime[0:6]) g6.attrs["startdate"] = np.string_(startdate) g6.attrs["enddate"] = np.string_(enddate) g3.attrs["product"] = np.string_("SATCH") g3.attrs["quantity"] = np.string_("TB") g3.attrs["dataset_name"] = np.string_('Channel 3b brightness temperature') g3.attrs["units"] = np.string_('K') g3.attrs["gain"] = np.float32(0.01) g3.attrs["offset"] = np.float32(273.15) g3.attrs["missingdata"] = np.int32(MISSING_DATA) g3.attrs["nodata"] = np.int32(MISSING_DATA) g3.attrs["starttime"] = np.string_(starttime[0:6]) g3.attrs["endtime"] = np.string_(endtime[0:6]) g3.attrs["startdate"] = np.string_(startdate) g3.attrs["enddate"] = np.string_(enddate) g4.attrs["product"] = np.string_("SATCH") g4.attrs["quantity"] = np.string_("TB") g4.attrs["dataset_name"] = np.string_('Channel 4 brightness temperature') g4.attrs["units"] = np.string_('K') g4.attrs["gain"] = np.float32(0.01) g4.attrs["offset"] = np.float32(273.15) g4.attrs["missingdata"] = np.int32(MISSING_DATA) g4.attrs["nodata"] = np.int32(MISSING_DATA) g4.attrs["starttime"] = np.string_(starttime[0:6]) g4.attrs["endtime"] = np.string_(endtime[0:6]) g4.attrs["startdate"] = np.string_(startdate) g4.attrs["enddate"] = np.string_(enddate) g5.attrs["product"] = np.string_("SATCH") g5.attrs["quantity"] = np.string_("TB") g5.attrs["dataset_name"] = np.string_('Channel 5 brightness temperature') g5.attrs["units"] = np.string_('K') g5.attrs["gain"] = np.float32(0.01) g5.attrs["offset"] = np.float32(273.15) g5.attrs["missingdata"] = np.int32(MISSING_DATA) g5.attrs["nodata"] = np.int32(MISSING_DATA) g5.attrs["starttime"] = np.string_(starttime[0:6]) g5.attrs["endtime"] = np.string_(endtime[0:6]) g5.attrs["startdate"] = np.string_(startdate) g5.attrs["enddate"] = np.string_(enddate) g7.attrs["dataset_name"] = np.string_('Latitude') g7.attrs["units"] = np.string_('Deg') g7.attrs["gain"] = np.float32(0.0010) g7.attrs["offset"] = np.float32(0.0) g7.attrs["missingdata"] = np.int32(MISSING_DATA_LATLON) g7.attrs["nodata"] = np.int32(MISSING_DATA_LATLON) g7.attrs["starttime"] = np.string_(starttime[0:6]) g7.attrs["endtime"] = np.string_(endtime[0:6]) g7.attrs["startdate"] = np.string_(startdate) g7.attrs["enddate"] = np.string_(enddate) g8.attrs["dataset_name"] = np.string_('Longitude') g8.attrs["units"] = np.string_('Deg') g8.attrs["gain"] = np.float32(0.0010) g8.attrs["offset"] = np.float32(0.0) g8.attrs["missingdata"] = np.int32(MISSING_DATA_LATLON) g8.attrs["nodata"] = np.int32(MISSING_DATA_LATLON) g8.attrs["starttime"] = np.string_(starttime[0:6]) g8.attrs["endtime"] = np.string_(endtime[0:6]) g8.attrs["startdate"] = np.string_(startdate) g8.attrs["enddate"] = np.string_(enddate) g9.attrs["object"] = np.string_("SATP") g9.attrs["sets"] = np.int32(len(channellist)) g9.attrs["version"] = np.string_("H5rad ?.?") g9.attrs["date"] = np.string_(startdate) g9.attrs["time"] = np.string_(starttime[0:6]) # Attributes in the 'how' groups g1 = fout.create_group("/image1/how") g2 = fout.create_group("/image2/how") g3 = fout.create_group("/image3/how") g4 = fout.create_group("/image4/how") g5 = fout.create_group("/image5/how") g6 = fout.create_group("/image6/how") g10 = fout.require_group("/how") # SHq: Is the sun_earth_distance correction applied? g1.attrs["sun_earth_distance_correction_applied"] = np.string_("TRUE") g1.attrs["sun_earth_distance_correction_factor"] = corr g2.attrs["sun_earth_distance_correction_applied"] = np.string_("TRUE") g2.attrs["sun_earth_distance_correction_factor"] = corr # No attributes on 'how' for image3,4,5 g6.attrs["sun_earth_distance_correction_applied"] = np.string_("TRUE") g6.attrs["sun_earth_distance_correction_factor"] = corr # We do not know much about how; mostly use no-data g10.attrs["yaw_error"] = 0.0 g10.attrs["roll_error"] = 0.0 g10.attrs["pitch_error"] = 0.0 g10.attrs["startepochs"] = starttime_sec1970 g10.attrs["endepochs"] = endtime_sec1970 g10.attrs["platform"] = np.string_(satellite_name) g10.attrs["instrument"] = np.string_("avhrr") g10.attrs["orbit_number"] = np.int32(99999) g10.attrs["gac_file"] = np.string_(gac_file) g10.attrs["software"] = np.string_("pyGAC") g10.attrs["version"] = np.string_("1.0") fout.close() LOG.info('Sun and Satellite viewing angles will be ' + 'written to ' + str(sunsatangles_dir)) ofn = os.path.join(sunsatangles_dir, (output_file_prefix + '_sunsatangles_' + satellite_name + '_99999_' + startdate + 'T' + starttime + 'Z_' + enddate + 'T' + endtime + 'Z.h5')) LOG.info('Filename: ' + str(os.path.basename(ofn))) fout = h5py.File(ofn, "w") dset1 = fout.create_dataset("/image1/data", dtype='int16', data=arrSZA) dset2 = fout.create_dataset("/image2/data", dtype='int16', data=arrSTZ) dset3 = fout.create_dataset("/image3/data", dtype='int16', data=arrRAA) dset4 = fout.create_dataset("/image4/data", dtype='int16', data=arrSAA) dset5 = fout.create_dataset("/image5/data", dtype='int16', data=arrSTA) dset6 = fout.create_dataset("/where/lat/data", dtype='int32', data=arrLat_full) dset7 = fout.create_dataset("/where/lon/data", dtype='int32', data=arrLon_full) del dset4, dset5, dset6, dset7 # Attributes directly on highest level groups g1 = fout.require_group("/image1") g2 = fout.require_group("/image2") g3 = fout.require_group("/image3") g4 = fout.require_group("/image4") g5 = fout.require_group("/image5") g6 = fout.require_group("/where") g1.attrs["description"] = np.string_('Solar zenith angle') g2.attrs["description"] = np.string_('Satellite zenith angle') g3.attrs["description"] = np.string_( 'Relative satellite-sun azimuth angle') g4.attrs["description"] = np.string_('Solar azimuth angle') g5.attrs["description"] = np.string_('Satellite azimuth angle') g6.attrs["num_of_pixels"] = np.int32(arrSZA.shape[1]) g6.attrs["num_of_lines"] = np.int32(arrSZA.shape[0]) g6.attrs["xscale"] = np.float32(0.0) g6.attrs["yscale"] = np.float32(0.0) g6.attrs["start_line"] = start_line g6.attrs["end_line"] = end_line # Attributes in the 'what' groups + 'how' g1 = fout.create_group("/image1/what") g2 = fout.create_group("/image2/what") g3 = fout.create_group("/image3/what") g4 = fout.create_group("/image4/what") g5 = fout.create_group("/image5/what") g6 = fout.create_group("/where/lat/what") g7 = fout.create_group("/where/lon/what") g8 = fout.create_group("/what") g9 = fout.create_group("/how") g1.attrs["product"] = np.string_("SUNZ") g1.attrs["quantity"] = np.string_("DEG") g1.attrs["dataset_name"] = np.string_('Solar zenith angle') g1.attrs["units"] = np.string_('Deg') g1.attrs["gain"] = np.float32(0.01) g1.attrs["offset"] = np.float32(0.0) g1.attrs["missingdata"] = np.int32(MISSING_DATA) g1.attrs["nodata"] = np.int32(MISSING_DATA) g1.attrs["starttime"] = np.string_(starttime[0:6]) g1.attrs["endtime"] = np.string_(endtime[0:6]) g1.attrs["startdate"] = np.string_(startdate) g1.attrs["enddate"] = np.string_(enddate) g2.attrs["product"] = np.string_("SATZ") g2.attrs["quantity"] = np.string_("DEG") g2.attrs["dataset_name"] = np.string_('Satellite zenith angle') g2.attrs["units"] = np.string_('Deg') g2.attrs["gain"] = np.float32(0.01) g2.attrs["offset"] = np.float32(0.0) g2.attrs["missingdata"] = np.int32(MISSING_DATA) g2.attrs["nodata"] = np.int32(MISSING_DATA) g2.attrs["starttime"] = np.string_(starttime[0:6]) g2.attrs["endtime"] = np.string_(endtime[0:6]) g2.attrs["startdate"] = np.string_(startdate) g2.attrs["enddate"] = np.string_(enddate) g3.attrs["product"] = np.string_("SSAZD") g3.attrs["quantity"] = np.string_("DEG") g3.attrs["dataset_name"] = np.string_( 'Relative satellite-sun azimuth angle') g3.attrs["units"] = np.string_('Deg') g3.attrs["gain"] = np.float32(0.01) g3.attrs["offset"] = np.float32(0.0) g3.attrs["missingdata"] = np.int32(MISSING_DATA) g3.attrs["nodata"] = np.int32(MISSING_DATA) g3.attrs["starttime"] = np.string_(starttime[0:6]) g3.attrs["endtime"] = np.string_(endtime[0:6]) g3.attrs["startdate"] = np.string_(startdate) g3.attrs["enddate"] = np.string_(enddate) g4.attrs["product"] = np.string_("SUNA") g4.attrs["quantity"] = np.string_("DEG") g4.attrs["dataset_name"] = np.string_('Solar azimuth angle') g4.attrs["units"] = np.string_('Deg') g4.attrs["gain"] = np.float32(0.01) g4.attrs["offset"] = np.float32(0.0) g4.attrs["missingdata"] = np.int32(MISSING_DATA) g4.attrs["nodata"] = np.int32(MISSING_DATA) g4.attrs["starttime"] = np.string_(starttime[0:6]) g4.attrs["endtime"] = np.string_(endtime[0:6]) g4.attrs["startdate"] = np.string_(startdate) g4.attrs["enddate"] = np.string_(enddate) g5.attrs["product"] = np.string_("SATA") g5.attrs["quantity"] = np.string_("DEG") g5.attrs["dataset_name"] = np.string_('Satellite azimuth angle') g5.attrs["units"] = np.string_('Deg') g5.attrs["gain"] = np.float32(0.01) g5.attrs["offset"] = np.float32(0.0) g5.attrs["missingdata"] = np.int32(MISSING_DATA) g5.attrs["nodata"] = np.int32(MISSING_DATA) g5.attrs["starttime"] = np.string_(starttime[0:6]) g5.attrs["endtime"] = np.string_(endtime[0:6]) g5.attrs["startdate"] = np.string_(startdate) g5.attrs["enddate"] = np.string_(enddate) g6.attrs["dataset_name"] = np.string_('Latitude') g6.attrs["units"] = np.string_('Deg') g6.attrs["gain"] = np.float32(0.0010) g6.attrs["offset"] = np.float32(0.0) g6.attrs["missingdata"] = np.int32(MISSING_DATA_LATLON) g6.attrs["nodata"] = np.int32(MISSING_DATA_LATLON) g6.attrs["starttime"] = np.string_(starttime[0:6]) g6.attrs["endtime"] = np.string_(endtime[0:6]) g6.attrs["startdate"] = np.string_(startdate) g6.attrs["enddate"] = np.string_(enddate) g7.attrs["dataset_name"] = np.string_('Longitude') g7.attrs["units"] = np.string_('Deg') g7.attrs["gain"] = np.float32(0.0010) g7.attrs["offset"] = np.float32(0.0) g7.attrs["missingdata"] = np.int32(MISSING_DATA_LATLON) g7.attrs["nodata"] = np.int32(MISSING_DATA_LATLON) g7.attrs["starttime"] = np.string_(starttime[0:6]) g7.attrs["endtime"] = np.string_(endtime[0:6]) g7.attrs["startdate"] = np.string_(startdate) g7.attrs["enddate"] = np.string_(enddate) g8.attrs["object"] = np.string_("SATP") g8.attrs["sets"] = np.int32(5) g8.attrs["version"] = np.string_("H5rad ?.?") g8.attrs["date"] = np.string_(startdate) g8.attrs["time"] = np.string_(starttime[0:6]) # We do not know much about how; mostly use no-data g9.attrs["yaw_error"] = 0.0 g9.attrs["roll_error"] = 0.0 g9.attrs["pitch_error"] = 0.0 g9.attrs["startepochs"] = starttime_sec1970 g9.attrs["endepochs"] = endtime_sec1970 g9.attrs["platform"] = np.string_(satellite_name) g9.attrs["instrument"] = np.string_("avhrr") g9.attrs["orbit_number"] = np.int32(99999) g9.attrs["gac_file"] = np.string_(gac_file) g9.attrs["software"] = np.string_("pyGAC") g9.attrs["version"] = np.string_("1.0") fout.close() LOG.info('Quality flags will be ' + 'written to ' + str(qual_dir)) ofn = os.path.join(qual_dir, (output_file_prefix + '_qualflags_' + satellite_name + '_99999_' + startdate + 'T' + starttime + 'Z_' + enddate + 'T' + endtime + 'Z.h5')) LOG.info('Filename: ' + str(os.path.basename(ofn))) fout = h5py.File(ofn, "w") g1 = fout.require_group("/qual_flags") dset1 = g1.create_dataset("data", dtype='int16', data=qual_flags) del dset1 g1.attrs["product"] = np.string_("QFLAG") g1.attrs["quantity"] = np.string_("INT") g1.attrs["dataset_name"] = np.string_('Scanline quality flags') g1.attrs["units"] = np.string_('None') g1.attrs["gain"] = np.int32(1) g1.attrs["offset"] = np.int32(0) g1.attrs["missingdata"] = np.int32(MISSING_DATA) g1.attrs["nodata"] = np.int32(MISSING_DATA) g1.attrs["starttime"] = np.string_(starttime[0:6]) g1.attrs["endtime"] = np.string_(endtime[0:6]) g1.attrs["startdate"] = np.string_(startdate) g1.attrs["enddate"] = np.string_(enddate) g1.attrs["gac_file"] = np.string_(gac_file) g1.attrs["total_number_of_data_records"] = total_number_of_scan_lines g1.attrs["last_scan_line_number"] = last_scan_line_number g2 = fout.require_group("/ancillary") dset2 = g2.create_dataset("missing_scanlines", dtype='int16', data=miss_lines) del dset2 dset3 = g2.create_dataset("scanline_timestamps", dtype='int64', data=xutcs.astype('int64')) dset3.attrs['units'] = 'Milliseconds since 1970-01-01 00:00:00 UTC' dset3.attrs['calendar'] = 'standard' g2.attrs["midnight_scanline"] = np.string_(midnight_scanline) fout.close() pygac-1.7.2/pygac/gac_io.py_old000066400000000000000000000452041444627033600163760ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2012, 2014 Abhay Devasthale # Author(s): # Abhay Devasthale # Adam Dybbroe # Sara Hornquist # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . import h5py import numpy as np import time import calendar import logging LOG = logging.getLogger(__name__) import ConfigParser import os try: CONFIG_FILE = os.environ['PYGAC_CONFIG_FILE'] except KeyError: LOG.exception('Environment variable PYGAC_CONFIG_FILE not set!') raise if not os.path.exists(CONFIG_FILE) or not os.path.isfile(CONFIG_FILE): raise IOError(str(CONFIG_FILE) + " pointed to by the environment " + "variable PYGAC_CONFIG_FILE is not a file or does not exist!") conf = ConfigParser.ConfigParser() try: conf.read(CONFIG_FILE) except ConfigParser.NoSectionError: LOG.exception('Failed reading configuration file: ' + str(CONFIG_FILE)) raise options = {} for option, value in conf.items('output', raw=True): options[option] = value OUTDIR = options['output_dir'] OUTPUT_FILE_PREFIX = options['output_file_prefix'] SUNSATANGLES_DIR = os.environ.get('SM_SUNSATANGLES_DIR', OUTDIR) AVHRR_DIR = os.environ.get('SM_AVHRR_DIR', OUTDIR) QUAL_DIR = os.environ.get('SM_AVHRR_DIR', OUTDIR) MISSING_DATA = -32001 def save_gac(satellite_name, start, end, lats, lons, ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi, mask, qual_flags, switch=None): bt3 -= 273.15 bt4 -= 273.15 bt5 -= 273.15 sat_azi -= 180.0 rel_azi = abs(rel_azi) rel_azi = 180.0 - rel_azi for array in [ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi]: array *= 100 for array in [lats, lons, ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi]: array[mask] = MISSING_DATA for ref in [ref1, ref2, ref3]: ref[ref < 0] = MISSING_DATA if switch is not None: ref3[switch == 0] = MISSING_DATA bt3[switch == 1] = MISSING_DATA startdate = start.strftime("%Y%m%d") starttime = start.strftime("%H%M%S%f")[:-5] enddate = end.strftime("%Y%m%d") endtime = end.strftime("%H%M%S%f")[:-5] avhrrGAC_io(satellite_name, startdate, enddate, starttime, endtime, lats, lons, ref1, ref2, ref3, bt3, bt4, bt5, sun_zen, sat_zen, sun_azi, sat_azi, rel_azi, qual_flags) def avhrrGAC_io(satellite_name, startdate, enddate, starttime, endtime, arrLat_full, arrLon_full, ref1, ref2, ref3, bt3, bt4, bt5, arrSZA, arrSTZ, arrSAA, arrSTA, arrRAA, qual_flags): import os # Calculate start and end time in sec1970 t_obj = time.strptime(startdate + starttime[0:6], "%Y%m%d%H%M%S") starttime_sec1970 = calendar.timegm(t_obj) t_obj = time.strptime(enddate + endtime[0:6], "%Y%m%d%H%M%S") endtime_sec1970 = calendar.timegm(t_obj) LOG.info('Output file prefix = ' + str(OUTPUT_FILE_PREFIX)) LOG.info('AVHRR data will be written to ' + str(AVHRR_DIR)) ofn = os.path.join(AVHRR_DIR, (OUTPUT_FILE_PREFIX + '_avhrr_' + satellite_name + '_99999_' + startdate + 'T' + starttime + 'Z_' + enddate + 'T' + endtime + 'Z.h5')) LOG.info('Filename: ' + str(os.path.basename(ofn))) fout = h5py.File(ofn, "w") dset1 = fout.create_dataset("/image1/data", dtype='int16', data=ref1) dset2 = fout.create_dataset("/image2/data", dtype='int16', data=ref2) dset3 = fout.create_dataset("/image3/data", dtype='int16', data=bt3) dset4 = fout.create_dataset("/image4/data", dtype='int16', data=bt4) dset5 = fout.create_dataset("/image5/data", dtype='int16', data=bt5) dset6 = fout.create_dataset("/image6/data", dtype='int16', data=ref3) dset7 = fout.create_dataset("/where/lat/data", dtype='int32', data=arrLat_full) dset8 = fout.create_dataset("/where/lon/data", dtype='int32', data=arrLon_full) channellist = [] channellist.append("channel1") channellist.append("channel2") channellist.append("channel3b") channellist.append("channel4") channellist.append("channel5") channellist.append("channel3a") dset10 = fout.create_dataset("/how/channel_list", data=channellist) # Attributes directly on highest level groups g1 = fout.require_group("/image1") g2 = fout.require_group("/image2") g3 = fout.require_group("/image3") g4 = fout.require_group("/image4") g5 = fout.require_group("/image5") g6 = fout.require_group("/image6") g7 = fout.require_group("/where") g1.attrs["channel"] = "1" g1.attrs["description"] = "AVHRR ch1" g2.attrs["channel"] = "2" g2.attrs["description"] = "AVHRR ch2" g3.attrs["channel"] = "3b" g3.attrs["description"] = "AVHRR ch3b" g4.attrs["channel"] = "4" g4.attrs["description"] = "AVHRR ch4" g5.attrs["channel"] = "5" g5.attrs["description"] = "AVHRR ch5" g6.attrs["channel"] = "3a" g6.attrs["description"] = "AVHRR ch3a" g7.attrs["num_of_pixels"] = np.int32(arrSZA.shape[1]) g7.attrs["num_of_lines"] = np.int32(arrSZA.shape[0]) g7.attrs["xscale"] = np.float32(0.0) # PPS says 1100.0, is that really g7.attrs["yscale"] = np.float32(0.0) # true for GAC? /SHq # Attributes in the 'what' groups g1 = fout.create_group("/image1/what") g2 = fout.create_group("/image2/what") g3 = fout.create_group("/image3/what") g4 = fout.create_group("/image4/what") g5 = fout.create_group("/image5/what") g6 = fout.create_group("/image6/what") g7 = fout.create_group("/where/lat/what") g8 = fout.create_group("/where/lon/what") g9 = fout.create_group("/what") g1.attrs["product"] = "SATCH" g1.attrs["quantity"] = "REFL" g1.attrs["dataset_name"] = 'Channel 1 reflectance' g1.attrs["units"] = '%' g1.attrs["gain"] = np.float32(0.01) g1.attrs["offset"] = np.float32(0.0) g1.attrs["missingdata"] = np.int32(-32001) g1.attrs["nodata"] = np.int32(-32001) g1.attrs["starttime"] = starttime[0:6] g1.attrs["endtime"] = endtime[0:6] g1.attrs["startdate"] = startdate g1.attrs["enddate"] = enddate g2.attrs["product"] = "SATCH" g2.attrs["quantity"] = "REFL" g2.attrs["dataset_name"] = 'Channel 2 reflectance' g2.attrs["units"] = '%' g2.attrs["gain"] = np.float32(0.01) g2.attrs["offset"] = np.float32(0.0) g2.attrs["missingdata"] = np.int32(-32001) g2.attrs["nodata"] = np.int32(-32001) g2.attrs["starttime"] = starttime[0:6] g2.attrs["endtime"] = endtime[0:6] g2.attrs["startdate"] = startdate g2.attrs["enddate"] = enddate g6.attrs["product"] = "SATCH" g6.attrs["quantity"] = "REFL" g6.attrs["dataset_name"] = 'Channel 3a reflectance' g6.attrs["units"] = '%' g6.attrs["gain"] = np.float32(0.01) g6.attrs["offset"] = np.float32(0.0) g6.attrs["missingdata"] = np.int32(-32001) g6.attrs["nodata"] = np.int32(-32001) g6.attrs["starttime"] = starttime[0:6] g6.attrs["endtime"] = endtime[0:6] g6.attrs["startdate"] = startdate g6.attrs["enddate"] = enddate g3.attrs["product"] = "SATCH" g3.attrs["quantity"] = "TB" g3.attrs["dataset_name"] = 'Channel 3b brightness temperature' g3.attrs["units"] = 'K' g3.attrs["gain"] = np.float32(0.01) g3.attrs["offset"] = np.float32(273.15) g3.attrs["missingdata"] = np.int32(-32001) g3.attrs["nodata"] = np.int32(-32001) g3.attrs["starttime"] = starttime[0:6] g3.attrs["endtime"] = endtime[0:6] g3.attrs["startdate"] = startdate g3.attrs["enddate"] = enddate g4.attrs["product"] = "SATCH" g4.attrs["quantity"] = "TB" g4.attrs["dataset_name"] = 'Channel 4 brightness temperature' g4.attrs["units"] = 'K' g4.attrs["gain"] = np.float32(0.01) g4.attrs["offset"] = np.float32(273.15) g4.attrs["missingdata"] = np.int32(-32001) g4.attrs["nodata"] = np.int32(-32001) g4.attrs["starttime"] = starttime[0:6] g4.attrs["endtime"] = endtime[0:6] g4.attrs["startdate"] = startdate g4.attrs["enddate"] = enddate g5.attrs["product"] = "SATCH" g5.attrs["quantity"] = "TB" g5.attrs["dataset_name"] = 'Channel 5 brightness temperature' g5.attrs["units"] = 'K' g5.attrs["gain"] = np.float32(0.01) g5.attrs["offset"] = np.float32(273.15) g5.attrs["missingdata"] = np.int32(-32001) g5.attrs["nodata"] = np.int32(-32001) g5.attrs["starttime"] = starttime[0:6] g5.attrs["endtime"] = endtime[0:6] g5.attrs["startdate"] = startdate g5.attrs["enddate"] = enddate g7.attrs["dataset_name"] = 'Latitude' g7.attrs["units"] = 'Deg' g7.attrs["gain"] = np.float32(0.010) g7.attrs["offset"] = np.float32(0.0) g7.attrs["missingdata"] = np.int32(-32001) g7.attrs["nodata"] = np.int32(-32001) g7.attrs["starttime"] = starttime[0:6] g7.attrs["endtime"] = endtime[0:6] g7.attrs["startdate"] = startdate g7.attrs["enddate"] = enddate g8.attrs["dataset_name"] = 'Longitude' g8.attrs["units"] = 'Deg' g8.attrs["gain"] = np.float32(0.010) g8.attrs["offset"] = np.float32(0.0) g8.attrs["missingdata"] = np.int32(-32001) g8.attrs["nodata"] = np.int32(-32001) g8.attrs["starttime"] = starttime[0:6] g8.attrs["endtime"] = endtime[0:6] g8.attrs["startdate"] = startdate g8.attrs["enddate"] = enddate g9.attrs["object"] = "SATP" g9.attrs["sets"] = np.int32(len(channellist)) g9.attrs["version"] = "H5rad ?.?" g9.attrs["date"] = startdate g9.attrs["time"] = starttime[0:6] # Attributes in the 'how' groups g1 = fout.create_group("/image1/how") g2 = fout.create_group("/image2/how") g3 = fout.create_group("/image3/how") g4 = fout.create_group("/image4/how") g5 = fout.create_group("/image5/how") g6 = fout.create_group("/image6/how") g10 = fout.require_group("/how") # SHq: Is the sun_earth_distance correction applied? g1.attrs["sun_earth_distance_correction_applied"] = "TRUE" g1.attrs["sun_earth_distance_correction_factor"] = np.int32(1.0) g2.attrs["sun_earth_distance_correction_applied"] = "TRUE" g2.attrs["sun_earth_distance_correction_factor"] = np.int32(1.0) # No attributes on 'how' for image3,4,5 g6.attrs["sun_earth_distance_correction_applied"] = "TRUE" g6.attrs["sun_earth_distance_correction_factor"] = np.int32(1.0) # We do not know much about how; mostly use no-data g10.attrs["yaw_error"] = 0.0 g10.attrs["roll_error"] = 0.0 g10.attrs["pich_error"] = 0.0 g10.attrs["startepochs"] = starttime_sec1970 g10.attrs["endepochs"] = endtime_sec1970 g10.attrs["platform"] = satellite_name g10.attrs["instrument"] = "avhrr" g10.attrs["orbit_number"] = np.int32(99999) g10.attrs["software"] = "pyGAC" g10.attrs["version"] = "1.0" fout.close() LOG.info('Sun and Satellite viewing angles will be ' + 'written to ' + str(SUNSATANGLES_DIR)) ofn = os.path.join(SUNSATANGLES_DIR, (OUTPUT_FILE_PREFIX + '_sunsatangles_' + satellite_name + '_99999_' + startdate + 'T' + starttime + 'Z_' + enddate + 'T' + endtime + 'Z.h5')) LOG.info('Filename: ' + str(os.path.basename(ofn))) fout = h5py.File(ofn, "w") dset1 = fout.create_dataset("/image1/data", dtype='int16', data=arrSZA) dset2 = fout.create_dataset("/image2/data", dtype='int16', data=arrSTZ) dset3 = fout.create_dataset("/image3/data", dtype='int16', data=arrRAA) dset4 = fout.create_dataset("/image4/data", dtype='int16', data=arrSAA) dset5 = fout.create_dataset("/image5/data", dtype='int16', data=arrSTA) dset6 = fout.create_dataset("/where/lat/data", dtype='int32', data=arrLat_full) dset7 = fout.create_dataset("/where/lon/data", dtype='int32', data=arrLon_full) # Attributes directly on highest level groups g1 = fout.require_group("/image1") g2 = fout.require_group("/image2") g3 = fout.require_group("/image3") g4 = fout.require_group("/image4") g5 = fout.require_group("/image5") g6 = fout.require_group("/where") g1.attrs["description"] = 'Solar zenith angle' g2.attrs["description"] = 'Satellite zenith angle' g3.attrs["description"] = 'Relative satellite-sun azimuth angle' g4.attrs["description"] = 'Solar azimuth angle' g5.attrs["description"] = 'Satellite azimuth angle' g6.attrs["num_of_pixels"] = np.int32(arrSZA.shape[1]) g6.attrs["num_of_lines"] = np.int32(arrSZA.shape[0]) g6.attrs["xscale"] = np.float32(0.0) # PPS says 1100.0, is that really g6.attrs["yscale"] = np.float32(0.0) # true for GAC? /SHq # Attributes in the 'what' groups + 'how' g1 = fout.create_group("/image1/what") g2 = fout.create_group("/image2/what") g3 = fout.create_group("/image3/what") g4 = fout.create_group("/image4/what") g5 = fout.create_group("/image5/what") g6 = fout.create_group("/where/lat/what") g7 = fout.create_group("/where/lon/what") g8 = fout.create_group("/what") g9 = fout.create_group("/how") g1.attrs["product"] = "SUNZ" g1.attrs["quantity"] = "DEG" g1.attrs["dataset_name"] = 'Solar zenith angle' g1.attrs["units"] = 'Deg' g1.attrs["gain"] = np.float32(0.01) g1.attrs["offset"] = np.float32(0.0) g1.attrs["missingdata"] = np.int32(-32001) g1.attrs["nodata"] = np.int32(-32001) g1.attrs["starttime"] = starttime[0:6] g1.attrs["endtime"] = endtime[0:6] g1.attrs["startdate"] = startdate g1.attrs["enddate"] = enddate g2.attrs["product"] = "SATZ" g2.attrs["quantity"] = "DEG" g2.attrs["dataset_name"] = 'Satellite zenith angle' g2.attrs["units"] = 'Deg' g2.attrs["gain"] = np.float32(0.01) g2.attrs["offset"] = np.float32(0.0) g2.attrs["missingdata"] = np.int32(-32001) g2.attrs["nodata"] = np.int32(-32001) g2.attrs["starttime"] = starttime[0:6] g2.attrs["endtime"] = endtime[0:6] g2.attrs["startdate"] = startdate g2.attrs["enddate"] = enddate g3.attrs["product"] = "SSAZD" g3.attrs["quantity"] = "DEG" g3.attrs["dataset_name"] = 'Relative satellite-sun azimuth angle' g3.attrs["units"] = 'Deg' g3.attrs["gain"] = np.float32(0.01) g3.attrs["offset"] = np.float32(0.0) g3.attrs["missingdata"] = np.int32(-32001) g3.attrs["nodata"] = np.int32(-32001) g3.attrs["starttime"] = starttime[0:6] g3.attrs["endtime"] = endtime[0:6] g3.attrs["startdate"] = startdate g3.attrs["enddate"] = enddate g4.attrs["product"] = "SUNA" g4.attrs["quantity"] = "DEG" g4.attrs["dataset_name"] = 'Solar azimuth angle' g4.attrs["units"] = 'Deg' g4.attrs["gain"] = np.float32(0.01) g4.attrs["offset"] = np.float32(180.0) g4.attrs["missingdata"] = np.int32(-32001) g4.attrs["nodata"] = np.int32(-32001) g4.attrs["starttime"] = starttime[0:6] g4.attrs["endtime"] = endtime[0:6] g4.attrs["startdate"] = startdate g4.attrs["enddate"] = enddate g5.attrs["product"] = "SATA" g5.attrs["quantity"] = "DEG" g5.attrs["dataset_name"] = 'Satellite azimuth angle' g5.attrs["units"] = 'Deg' g5.attrs["gain"] = np.float32(0.01) g5.attrs["offset"] = np.float32(180.0) g5.attrs["missingdata"] = np.int32(-32001) g5.attrs["nodata"] = np.int32(-32001) g5.attrs["starttime"] = starttime[0:6] g5.attrs["endtime"] = endtime[0:6] g5.attrs["startdate"] = startdate g5.attrs["enddate"] = enddate g6.attrs["dataset_name"] = 'Latitude' g6.attrs["units"] = 'Deg' g6.attrs["gain"] = np.float32(0.010) g6.attrs["offset"] = np.float32(0.0) g6.attrs["missingdata"] = np.int32(-32001) g6.attrs["nodata"] = np.int32(-32001) g6.attrs["starttime"] = starttime[0:6] g6.attrs["endtime"] = endtime[0:6] g6.attrs["startdate"] = startdate g6.attrs["enddate"] = enddate g7.attrs["dataset_name"] = 'Longitude' g7.attrs["units"] = 'Deg' g7.attrs["gain"] = np.float32(0.010) g7.attrs["offset"] = np.float32(0.0) g7.attrs["missingdata"] = np.int32(-32001) g7.attrs["nodata"] = np.int32(-32001) g7.attrs["starttime"] = starttime[0:6] g7.attrs["endtime"] = endtime[0:6] g7.attrs["startdate"] = startdate g7.attrs["enddate"] = enddate g8.attrs["object"] = "SATP" g8.attrs["sets"] = np.int32(5) g8.attrs["version"] = "H5rad ?.?" g8.attrs["date"] = startdate g8.attrs["time"] = starttime[0:6] # We do not know much about how; mostly use no-data g9.attrs["yaw_error"] = 0.0 g9.attrs["roll_error"] = 0.0 g9.attrs["pich_error"] = 0.0 g9.attrs["startepochs"] = starttime_sec1970 g9.attrs["endepochs"] = endtime_sec1970 g9.attrs["platform"] = satellite_name g9.attrs["instrument"] = "avhrr" g9.attrs["orbit_number"] = np.int32(99999) g9.attrs["software"] = "pyGAC" g9.attrs["version"] = "1.0" fout.close() LOG.info('Quality flags will be ' + 'written to ' + str(QUAL_DIR)) ofn = os.path.join(QUAL_DIR, (OUTPUT_FILE_PREFIX + '_qualflags_' + satellite_name + '_99999_' + startdate + 'T' + starttime + 'Z_' + enddate + 'T' + endtime + 'Z.h5')) LOG.info('Filename: ' + str(os.path.basename(ofn))) fout = h5py.File(ofn, "w") dset1 = fout.create_dataset("/qual_flags/data", dtype='int8', data=qual_flags) g1 = fout.require_group("/qual_flags") g1.attrs["product"] = "QFLAG" g1.attrs["quantity"] = "INT" g1.attrs["dataset_name"] = 'Scanline quality flags' g1.attrs["units"] = 'None' g1.attrs["gain"] = np.int32(1) g1.attrs["offset"] = np.int32(0) g1.attrs["missingdata"] = np.int32(-32001) g1.attrs["nodata"] = np.int32(-32001) g1.attrs["starttime"] = starttime[0:6] g1.attrs["endtime"] = endtime[0:6] g1.attrs["startdate"] = startdate g1.attrs["enddate"] = enddate fout.close() pygac-1.7.2/pygac/gac_klm.py000066400000000000000000000244411444627033600157140ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Abhay Devasthale and Martin Raspaud # Author(s): # Abhay Devasthale # Martin Raspaud # Adam Dybbroe # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Reader for GAC KLM data.""" from __future__ import print_function import logging import numpy as np from pygac.gac_reader import GACReader from pygac.klm_reader import KLMReader, main_klm LOG = logging.getLogger(__name__) # video data object scanline = np.dtype([("scan_line_number", ">u2"), ("scan_line_year", ">u2"), ("scan_line_day_of_year", ">u2"), ("satellite_clock_drift_delta", ">i2"), ("scan_line_utc_time_of_day", ">u4"), ("scan_line_bit_field", ">u2"), ("zero_fill0", ">i2", (5, )), # QUALITY INDICATORS ("quality_indicator_bit_field", ">u4"), ("scan_line_quality_flags", [("reserved", ">u1"), ("time_problem_code", ">u1"), ("calibration_problem_code", ">u1"), ("earth_location_problem_code", ">u1")]), ("calibration_quality_flags", ">u2", (3, )), ("count_of_bit_errors_in_frame_sync", ">u2"), ("zero_fill1", ">i4", (2, )), # CALIBRATION COEFFICIENTS ("visible_operational_cal_ch_1_slope_1", ">i4"), ("visible_operational_cal_ch_1_intercept_1", ">i4"), ("visible_operational_cal_ch_1_slope_2", ">i4"), ("visible_operational_cal_ch_1_intercept_2", ">i4"), ("visible_operational_cal_ch_1_intersection", ">i4"), ("visible_test_cal_ch_1_slope_1", ">i4"), ("visible_test_cal_ch_1_intercept_1", ">i4"), ("visible_test_cal_ch_1_slope_2", ">i4"), ("visible_test_cal_ch_1_intercept_2", ">i4"), ("visible_test_cal_ch_1_intersection", ">i4"), ("visible_prelaunch_cal_ch_1_slope_1", ">i4"), ("visible_prelaunch_cal_ch_1_intercept_1", ">i4"), ("visible_prelaunch_cal_ch_1_slope_2", ">i4"), ("visible_prelaunch_cal_ch_1_intercept_2", ">i4"), ("visible_prelaunch_cal_ch_1_intersection", ">i4"), ("visible_operational_cal_ch_2_slope_1", ">i4"), ("visible_operational_cal_ch_2_intercept_1", ">i4"), ("visible_operational_cal_ch_2_slope_2", ">i4"), ("visible_operational_cal_ch_2_intercept_2", ">i4"), ("visible_operational_cal_ch_2_intersection", ">i4"), ("visible_test_cal_ch_2_slope_1", ">i4"), ("visible_test_cal_ch_2_intercept_1", ">i4"), ("visible_test_cal_ch_2_slope_2", ">i4"), ("visible_test_cal_ch_2_intercept_2", ">i4"), ("visible_test_cal_ch_2_intersection", ">i4"), ("visible_prelaunch_cal_ch_2_slope_1", ">i4"), ("visible_prelaunch_cal_ch_2_intercept_1", ">i4"), ("visible_prelaunch_cal_ch_2_slope_2", ">i4"), ("visible_prelaunch_cal_ch_2_intercept_2", ">i4"), ("visible_prelaunch_cal_ch_2_intersection", ">i4"), ("visible_operational_cal_ch_3a_slope_1", ">i4"), ("visible_operational_cal_ch_3a_intercept_1", ">i4"), ("visible_operational_cal_ch_3a_slope_2", ">i4"), ("visible_operational_cal_ch_3a_intercept_2", ">i4"), ("visible_operational_cal_ch_3a_intersection", ">i4"), ("visible_test_cal_ch_3a_slope_1", ">i4"), ("visible_test_cal_ch_3a_intercept_1", ">i4"), ("visible_test_cal_ch_3a_slope_2", ">i4"), ("visible_test_cal_ch_3a_intercept_2", ">i4"), ("visible_test_cal_ch_3a_intersection", ">i4"), ("visible_prelaunch_cal_ch_3a_slope_1", ">i4"), ("visible_prelaunch_cal_ch_3a_intercept_1", ">i4"), ("visible_prelaunch_cal_ch_3a_slope_2", ">i4"), ("visible_prelaunch_cal_ch_3a_intercept_2", ">i4"), ("visible_prelaunch_cal_ch_3a_intersection", ">i4"), ("ir_operational_cal_ch_3b_coefficient_1", ">i4"), ("ir_operational_cal_ch_3b_coefficient_2", ">i4"), ("ir_operational_cal_ch_3b_coefficient_3", ">i4"), ("ir_test_cal_ch_3b_coefficient_1", ">i4"), ("ir_test_cal_ch_3b_coefficient_2", ">i4"), ("ir_test_cal_ch_3b_coefficient_3", ">i4"), ("ir_operational_cal_ch_4_coefficient_1", ">i4"), ("ir_operational_cal_ch_4_coefficient_2", ">i4"), ("ir_operational_cal_ch_4_coefficient_3", ">i4"), ("ir_test_cal_ch_4_coefficient_1", ">i4"), ("ir_test_cal_ch_4_coefficient_2", ">i4"), ("ir_test_cal_ch_4_coefficient_3", ">i4"), ("ir_operational_cal_ch_5_coefficient_1", ">i4"), ("ir_operational_cal_ch_5_coefficient_2", ">i4"), ("ir_operational_cal_ch_5_coefficient_3", ">i4"), ("ir_test_cal_ch_5_coefficient_1", ">i4"), ("ir_test_cal_ch_5_coefficient_2", ">i4"), ("ir_test_cal_ch_5_coefficient_3", ">i4"), # NAVIGATION ("computed_yaw_steering", ">i2", (3,)), # only in version 5 ("total_applied_attitude_correction", ">i2", (3,)), # only in version 5 ("navigation_status_bit_field", ">u4"), ("time_associated_with_tip_euler_angles", ">u4"), ("tip_euler_angles", ">i2", (3, )), ("spacecraft_altitude_above_reference_ellipsoid", ">u2"), ("angular_relationships", ">i2", (153, )), ("zero_fill3", ">i2", (3, )), ("earth_location", ">i4", (102, )), ("zero_fill4", ">i4", (2, )), # HRPT MINOR FRAME TELEMETRY ("frame_sync", ">u2", (6, )), ("id", ">u2", (2, )), ("time_code", ">u2", (4, )), ('telemetry', [("ramp_calibration", '>u2', (5, )), ("PRT", '>u2', (3, )), ("ch3_patch_temp", '>u2'), ("spare", '>u2'), ]), ("back_scan", ">u2", (30, )), ("space_data", ">u2", (50, )), ("sync_delta", ">u2"), ("zero_fill5", ">i2"), # AVHRR SENSOR DATA ("sensor_data", ">u4", (682, )), ("zero_fill6", ">i4", (2, )), # DIGITAL B TELEMETRY ("invalid_word_bit_flags1", ">u2"), ("avhrr_digital_b_data", ">u2"), ("zero_fill7", ">i4", (3, )), # ANALOG HOUSEKEEPING DATA (TIP) ("invalid_word_bit_flags2", ">u4"), ("patch_temperature_range", ">u1"), ("patch_temperature_extended", ">u1"), ("patch_power", ">u1"), ("radiator_temperature", ">u1"), ("blackbody_temperature_1", ">u1"), ("blackbody_temperature_2", ">u1"), ("blackbody_temperature_3", ">u1"), ("blackbody_temperature_4", ">u1"), ("electronics_current", ">u1"), ("motor_current", ">u1"), ("earth_shield_position", ">u1"), ("electronics_temperature", ">u1"), ("cooler_housing_temperature", ">u1"), ("baseplate_temperature", ">u1"), ("motor_housing_temperature", ">u1"), ("a/d_converter_temperature", ">u1"), ("detector_#4_bias_voltage", ">u1"), ("detector_#5_bias_voltage", ">u1"), ("blackbody_temperature_channel3b", ">u1"), ("blackbody_temperature_channel4", ">u1"), ("blackbody_temperature_channel5", ">u1"), ("reference_voltage", ">u1"), ("zero_fill8", ">i2", (3, )), # CLOUDS FROM AVHRR (CLAVR) ("reserved0", ">u4"), ("reserved1", ">u4"), ("reserved2", ">u2", (52, )), # FILLER ("zero_fill9", ">i4", (112, ))]) class GACKLMReader(GACReader, KLMReader): """The GAC KLM reader class. The offset attribute tells where in the file the scanline data starts. """ def __init__(self, *args, **kwargs): """Init the GAC KLM reader.""" GACReader.__init__(self, *args, **kwargs) self.scanline_type = scanline self.offset = 4608 def main(filename, start_line, end_line): """Generate a l1c file.""" return main_klm(GACKLMReader, filename, start_line, end_line) if __name__ == "__main__": import sys main(sys.argv[1], sys.argv[2], sys.argv[3]) pygac-1.7.2/pygac/gac_pod.py000066400000000000000000000050771444627033600157170ustar00rootroot00000000000000#!/usr/bin/python # Copyright (c) 2014-2016. # # Author(s): # Abhay Devasthale # Adam Dybbroe # Sajid Pareeth # Martin Raspaud # This work was done in the framework of ESA-CCI-Clouds phase I # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Reader for GAC POD data.""" import logging import numpy as np from pygac.pod_reader import PODReader, main_pod from pygac.gac_reader import GACReader LOG = logging.getLogger(__name__) scanline = np.dtype([("scan_line_number", ">i2"), ("time_code", ">u2", (3, )), ("quality_indicators", ">u4"), ("calibration_coefficients", ">i4", (10, )), ("number_of_meaningful_zenith_angles_and_earth_location_appended", ">u1"), ("solar_zenith_angles", "i1", (51, )), ("earth_location", ">i2", (102, )), ("telemetry", ">u4", (35, )), ("sensor_data", ">u4", (682, )), ("add_on_zenith", ">u2", (10, )), ("clock_drift_delta", ">u2"), ("spare3", "u2", (11, ))]) class GACPODReader(GACReader, PODReader): """The GAC POD reader class. The `scan_points` attributes provides the position of the longitude and latitude points to compute relative to the full swath width. The offset attribute tells where in the file the scanline data starts. """ def __init__(self, *args, **kwargs): """Init the GAC POD reader.""" GACReader.__init__(self, *args, **kwargs) self.scanline_type = scanline self.offset = 3220 self.scan_points = np.arange(3.5, 2048, 5) def main(filename, start_line, end_line): """Generate a l1c file.""" return main_pod(GACPODReader, filename, start_line, end_line) if __name__ == "__main__": import sys main(sys.argv[1], sys.argv[2], sys.argv[3]) pygac-1.7.2/pygac/gac_reader.py000066400000000000000000000040251444627033600163670ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014, 2015 Martin Raspaud # Author(s): # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Generic reader for GAC data. Can't be used as is, has to be subclassed to add specific read functions. """ import logging from pygac.reader import Reader, ReaderError import pygac.pygac_geotiepoints as gtp LOG = logging.getLogger(__name__) class GACReader(Reader): """Reader for GAC data.""" # Scanning frequency (scanlines per millisecond) scan_freq = 2.0 / 1000.0 # Max scanlines max_scanlines = 15000 def __init__(self, *args, **kwargs): """Init the GAC reader.""" super(GACReader, self).__init__(*args, **kwargs) self.scan_width = 409 self.lonlat_interpolator = gtp.gac_lat_lon_interpolator @classmethod def _validate_header(cls, header): """Check if the header belongs to this reader.""" # call super to enter the Method Resolution Order (MRO) super(GACReader, cls)._validate_header(header) LOG.debug("validate header") data_set_name = header['data_set_name'].decode() # split header into parts creation_site, transfer_mode, platform_id = ( data_set_name.split('.')[:3]) if transfer_mode != 'GHRR': raise ReaderError('Improper transfer mode "%s"!' % transfer_mode) pygac-1.7.2/pygac/investigate_bts1.m000066400000000000000000000013411444627033600173700ustar00rootroot00000000000000clear all; close all; fn='temp1.h5'; prt=hdf5read(fn,'prt'); iprt=hdf5read(fn,'iprt'); lines=hdf5read(fn,'lines'); counts=hdf5read(fn,'counts'); space=hdf5read(fn,'space'); nlin=hdf5read(fn,'nlin'); ncor=hdf5read(fn,'ncor'); tse=hdf5read(fn,'tse'); hold on; i=find(iprt==1); plot(lines(i),prt(i),'r-'); i=find(iprt==2); plot(lines(i),prt(i),'g-'); i=find(iprt==3); plot(lines(i),prt(i),'b-'); i=find(iprt==4); plot(lines(i),prt(i),'k-'); plot(lines(1:length(lines)-1),diff(lines)+230,'c-'); i=find(iprt==0); plot(lines(i),prt(i)+250,'m-'); figure; subplot(4,1,1); imagesc(counts-space); colorbar; subplot(4,1,2); imagesc(nlin); colorbar; subplot(4,1,3); imagesc(ncor); colorbar; subplot(4,1,4); imagesc(tse); colorbar; pygac-1.7.2/pygac/klm_reader.py000066400000000000000000001363031444627033600164250ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014-2019 # Author(s): # Abhay Devasthale # Sajid Pareeth # Martin Raspaud # Adam Dybbroe # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Read KLM data. Reads L1b GAC/LAC data from KLM series of satellites (NOAA-15 and later). Format specification can be found in section 8 of the `KLM user guide`_. .. _KLM user guide: https://www.ncei.noaa.gov/pub/data/satellite/publications/podguides/N-15%20thru%20N-19/ """ import datetime import logging try: from enum import IntFlag except ImportError: # python version < 3.6, use a simple object without nice representation IntFlag = object import numpy as np from pygac.correct_tsm_issue import TSM_AFFECTED_INTERVALS_KLM, get_tsm_idx from pygac.reader import Reader, ReaderError from pygac.utils import file_opener LOG = logging.getLogger(__name__) class KLM_QualityIndicator(IntFlag): """Quality Indicators. Source: KLM guide - Table 8.3.1.3.3.1-1. Format of packed LAC/HRPT Data Sets (Version 2, pre-April 28, 2005). - Table 8.3.1.3.3.2-1. Format of LAC/HRPT Data Record for NOAA-N (Version 5, post-November 14, 2006, all spacecraft). - Table 8.3.1.4.3.1-1. Format of packed GAC Data Record for NOAA KLM (Version 2, pre-April 28, 2005). - Table 8.3.1.4.3.2-1. Format of GAC Data Record for NOAA-N (Version 4, post-January 25, 2006, all spacecraft). Notes: - Table 8.3.1.3.3.1-1. and Table 8.3.1.4.3.1-1. define bit: 21 as "frame sync word not valid" - Table 8.3.1.3.3.2-1. and Table 8.3.1.4.3.2-1. define bit: 21 as "flywheeling detected during this frame" """ FATAL_FLAG = 2**31 # Data should not be used for product generation TIME_ERROR = 2**30 # Time sequence error detected within this scan DATA_GAP = 2**29 # Data gap precedes this scan CALIBRATION = 2**28 # Insufficient data for calibration NO_EARTH_LOCATION = 2**27 # Earth location data not available CLOCK_UPDATE = 2**26 # First good time following a clock update (nominally 0) INSTRUMENT_CHANGE = 2**25 # Instrument status changed with this scan BIT_SYNC_STATUS = 2**24 # Sync lock dropped during this frame SYNC_ERROR = 2**23 # Frame sync word error greater than zero FRAME_SYNC_LOCK = 2**22 # Frame sync previously dropped lock SYNC_INVALID = 2**21 # Frame sync word not valid FLYWHEELING = 2**21 # Flywheeling detected during this frame BIT_SLIPPAGE = 2**20 # Bit slippage detected during this frame # Note: Bit 19 - 9 are not defined for KLMs TIP_PARITY = 2**8 # TIP parity error detected # Reflected Sunlight (RS) detected (solar blackbody contamination) CH_3B_RS = 2**7 CH_3B_RS_ANOMALY = 2**6 CH_3_CONTAMINATION = CH_3B_RS | CH_3B_RS_ANOMALY # POD compatible alias CH_4_RS = 2**5 CH_4_RS_ANOMALY = 2**4 CH_4_CONTAMINATION = CH_4_RS | CH_4_RS_ANOMALY # POD compatible alias CH_5_RS = 2**3 CH_5_RS_ANOMALY = 2**2 CH_5_CONTAMINATION = CH_5_RS | CH_5_RS_ANOMALY # POD compatible alias DATA_JITTER = 2**1 # Resync occurred on this frame PSEUDO_NOISE = 2**0 # Pseudo noise occurred on this frame # header object header = np.dtype([("data_set_creation_site_id", "S3"), ("ascii_blank_=_x20", "S1"), ("noaa_level_1b_format_version_number", ">u2"), ("noaa_level_1b_format_version_year", ">u2"), ("noaa_level_1b_format_version_day_of_year", ">u2"), ("reserved_for_logical_record_length", ">u2"), ("reserved_for_block_size", ">u2"), ("count_of_header_records", ">u2"), ("zero_fill0", ">i2", (3, )), ("data_set_name", "S42"), ("processing_block_identification", "S8"), ("noaa_spacecraft_identification_code", ">u2"), ("instrument_id", ">u2"), ("data_type_code", ">u2"), ("tip_source_code", ">u2"), ("start_of_data_set_day_count_starting_from_0_at_00h,_1_jan_1950", ">u4"), ("start_of_data_set_year", ">u2"), ("start_of_data_set_day_of_year", ">u2"), ("start_of_data_set_utc_time_of_day", ">u4"), ("end_of_data_set_day_count_starting_from_0_at_00h,_1_jan_1950", ">u4"), ("end_of_data_set_year", ">u2"), ("end_of_data_set_day_of_year", ">u2"), ("end_of_data_set_utc_time_of_day", ">u4"), ("year_of_last_cpids_update", ">u2"), ("day_of_year_of_last_cpids_update", ">u2"), ("zero_fill1", ">i2", (4, )), # DATA SET QUALITY INDICATORS ("instrument_status", ">u4"), ("zero_fill2", ">i2"), ("record_number_of_status_change", ">u2"), ("second_instrument_status", ">u4"), ("count_of_data_records", ">u2"), ("count_of_calibrated,_earth_located_scan_lines", ">u2"), ("count_of_missing_scan_lines", ">u2"), ("count_of_data_gaps", ">u2"), # only in version 5 ("count_of_data_frames_without_frame_sync_word", ">u2"), ("count_of_pacs_detected_tip_parity_errors", ">u2"), # only in version 5 ("sum_of_all_auxiliary_sync_errors_detected", ">u2"), ("time_sequence_error", ">u2"), ("time_sequence_error_code", ">u2"), ("socc_clock_update_indicator", ">u2"), ("earth_location_error_indicator", ">u2"), ("earth_location_error_code", ">u2"), ("pacs_status_bit_field", ">u2"), ("data_source", ">u2"), ("zero_fill3", ">i4"), ("reserved_for_the_ingester", "S8"), ("reserved_for_decommutation", "S8"), ("zero_fill4", ">i2", (5, )), # CALIBRATION ("ramp_auto_calibration_indicators_bit_field", ">u2"), ("year_of_most_recent_solar_channel_calibration", ">u2"), ("day_of_year_of_most_recent_solar_channel_calibration", ">u2"), ("primary_calibration_algorithm_id", ">u2"), ("primary_calibration_algorithm_selected_options", ">u2"), ("secondary_calibration_algorithm_id", ">u2"), ("secondary_calibration_algorithm_selected_options", ">u2"), ("ir_target_temperature_1_conversion_coefficient_1", ">i2"), ("ir_target_temperature_1_conversion_coefficient_2", ">i2"), ("ir_target_temperature_1_conversion_coefficient_3", ">i2"), ("ir_target_temperature_1_conversion_coefficient_4", ">i2"), ("ir_target_temperature_1_conversion_coefficient_5", ">i2"), ("ir_target_temperature_1_conversion_coefficient_6", ">i2"), ("ir_target_temperature_2_conversion_coefficient_1", ">i2"), ("ir_target_temperature_2_conversion_coefficient_2", ">i2"), ("ir_target_temperature_2_conversion_coefficient_3", ">i2"), ("ir_target_temperature_2_conversion_coefficient_4", ">i2"), ("ir_target_temperature_2_conversion_coefficient_5", ">i2"), ("ir_target_temperature_2_conversion_coefficient_6", ">i2"), ("ir_target_temperature_3_conversion_coefficient_1", ">i2"), ("ir_target_temperature_3_conversion_coefficient_2", ">i2"), ("ir_target_temperature_3_conversion_coefficient_3", ">i2"), ("ir_target_temperature_3_conversion_coefficient_4", ">i2"), ("ir_target_temperature_3_conversion_coefficient_5", ">i2"), ("ir_target_temperature_3_conversion_coefficient_6", ">i2"), ("ir_target_temperature_4_conversion_coefficient_1", ">i2"), ("ir_target_temperature_4_conversion_coefficient_2", ">i2"), ("ir_target_temperature_4_conversion_coefficient_3", ">i2"), ("ir_target_temperature_4_conversion_coefficient_4", ">i2"), ("ir_target_temperature_4_conversion_coefficient_5", ">i2"), ("ir_target_temperature_4_conversion_coefficient_6", ">i2"), ("zero_fill5", ">i4", (2, )), # RADIANCE CONVERSION ("ch_1_solar_filtered_irradiance_in_wavelength", ">i4"), ("ch_1_equivalent_filter_width_in_wavelength", ">i4"), ("ch_2_solar_filtered_irradiance_in_wavelength", ">i4"), ("ch_2_equivalent_filter_width_in_wavelength", ">i4"), ("ch_3a_solar_filtered_irradiance_in_wavelength", ">i4"), ("ch_3a_equivalent_filter_width_in_wavelength", ">i4"), ("ch_3b_central_wavenumber", ">i4"), ("ch_3b_constant_1", ">i4"), ("ch_3b_constant_2", ">i4"), ("ch_4_central_wavenumber", ">i4"), ("ch_4_constant_1", ">i4"), ("ch_4_constant_2", ">i4"), ("ch_5_central_wavenumber", ">i4"), ("ch_5_constant_1", ">i4"), ("ch_5_constant_2", ">i4"), ("zero_fill6", ">i4", (3, )), # NAVIGATION ("reference_ellipsoid_model_id", "S8"), ("nadir_earth_location_tolerance", ">u2"), ("earth_location_bit_field", ">u2"), ("zero_fill7", ">i2"), ("constant_roll_attitude_error", ">i2"), ("constant_pitch_attitude_error", ">i2"), ("constant_yaw_attitude_error", ">i2"), ("epoch_year_for_orbit_vector", ">u2"), ("day_of_epoch_year_for_orbit_vector", ">u2"), ("epoch_utc_time_of_day_for_orbit_vector", ">u4"), ("semi-major_axis", ">i4"), ("eccentricity", ">i4"), ("inclination", ">i4"), ("argument_of_perigee", ">i4"), ("right_ascension_of_the_ascending_node", ">i4"), ("mean_anomaly", ">i4"), ("position_vector_x_component", ">i4"), ("position_vector_y_component", ">i4"), ("position_vector_z_component", ">i4"), ("velocity_vector_x-dot_component", ">i4"), ("velocity_vector_y-dot_component", ">i4"), ("velocity_vector_z-dot_component", ">i4"), ("earth_sun_distance_ratio", ">u4"), ("zero_fill8", ">i4", (4, ))]) # ANALOG TELEMETRY CONVERSION analog_telemetry_v2 = np.dtype([("patch_temperature_conversion_coefficient_1", ">i2"), ("patch_temperature_conversion_coefficient_2", ">i2"), ("patch_temperature_conversion_coefficient_3", ">i2"), ("patch_temperature_conversion_coefficient_4", ">i2"), ("patch_temperature_conversion_coefficient_5", ">i2"), ("reserved0", ">i2"), ("patch_temperature_extended_conversion_coefficient_1", ">i2"), ("patch_temperature_extended_conversion_coefficient_2", ">i2"), ("patch_temperature_extended_conversion_coefficient_3", ">i2"), ("patch_temperature_extended_conversion_coefficient_4", ">i2"), ("patch_temperature_extended_conversion_coefficient_5", ">i2"), ("reserved1", ">i2"), ("patch_power_conversion_coefficient_1", ">i2"), ("patch_power_conversion_coefficient_2", ">i2"), ("patch_power_conversion_coefficient_3", ">i2"), ("patch_power_conversion_coefficient_4", ">i2"), ("patch_power_conversion_coefficient_5", ">i2"), ("reserved2", ">i2"), ("radiator_temperature_conversion_coefficient_1", ">i2"), ("radiator_temperature_conversion_coefficient_2", ">i2"), ("radiator_temperature_conversion_coefficient_3", ">i2"), ("radiator_temperature_conversion_coefficient_4", ">i2"), ("radiato r_temperature_conversion_coefficient_5", ">i2"), ("reserved3", ">i2"), ("blackbody_temperature_1_conversion_coefficient_1", ">i2"), ("blackbody_temperature_1_conversion_coefficient_2", ">i2"), ("blackbody_temperature_1_conversion_coefficient_3", ">i2"), ("blackbody_temperature_1_conversion_coefficient_4", ">i2"), ("blackbody_temperature_1_conversion_coefficient_5", ">i2"), ("reserved4", ">i2"), ("blackbody_temperature_2_conversion_coefficient_1", ">i2"), ("blackbody_temperature_2_conversion_coefficient_2", ">i2"), ("blackbody_temperature_2_conversion_coefficient_3", ">i2"), ("blackbody_temperature_2_conversion_coefficient_4", ">i2"), ("blackbody_temperature_2_conversion_coefficient_5", ">i2"), ("reserved5", ">i2"), ("blackbody_temperature_3_conversion_coefficient_1", ">i2"), ("blackbody_temperature_3_conversion_coefficient_2", ">i2"), ("blackbody_temperature_3_conversion_coefficient_3", ">i2"), ("blackbody_temperature_3_conversion_coefficient_4", ">i2"), ("blackbody_temperature_3_conversion_coefficient_5", ">i2"), ("reserved6", ">i2"), ("blackbody_temperature_4_conversion_coefficient_1", ">i2"), ("blackbody_temperature_4_conversion_coefficient_2", ">i2"), ("blackbody_temperature_4_conversion_coefficient_3", ">i2"), ("blackbody_temperature_4_conversion_coefficient_4", ">i2"), ("blackbody_temperature_4_conversion_coefficient_5", ">i2"), ("reserved7", ">i2"), ("electronics_current_conversion_coefficient_1", ">i2"), ("electronics_current_conversion_coefficient_2", ">i2"), ("electronics_current_conversion_coefficient_3", ">i2"), ("electronics_current_conversion_coefficient_4", ">i2"), ("electronics_current_conversion_coefficient_5", ">i2"), ("reserved8", ">i2"), ("motor_current_conversion_coefficient_1", ">i2"), ("motor_current_conversion_coefficient_2", ">i2"), ("motor_current_conversion_coefficient_3", ">i2"), ("motor_current_conversion_coefficient_4", ">i2"), ("motor_current_conversion_coefficient_5", ">i2"), ("reserved9", ">i2"), ("earth_shield_position_conversion_coefficient_1", ">i2"), ("earth_shield_position_conversion_coefficient_2", ">i2"), ("earth_shield_position_conversion_coefficient_3", ">i2"), ("earth_shield_position_conversion_coefficient_4", ">i2"), ("earth_shield_position_conversion_coefficient_5", ">i2"), ("reserved10", ">i2"), ("electronics_temperature_conversion_coefficient_1", ">i2"), ("electronics_temperature_conversion_coefficient_2", ">i2"), ("electronics_temperature_conversion_coefficient_3", ">i2"), ("electronics_temperature_conversion_coefficient_4", ">i2"), ("electronics_temperature_conversion_coefficient_5", ">i2"), ("reserved11", ">i2"), ("cooler_housing_temperature_conversion_coefficient_1", ">i2"), ("cooler_housing_temperature_conversion_coefficient_2", ">i2"), ("cooler_housing_temperature_conversion_coefficient_3", ">i2"), ("cooler_housing_temperature_conversion_coefficient_4", ">i2"), ("cooler_housing_temperature_conversion_coefficient_5", ">i2"), ("reserved12", ">i2"), ("baseplate_temperature_conversion_coefficient_1", ">i2"), ("baseplate_temperature_conversion_coefficient_2", ">i2"), ("baseplate_temperature_conversion_coefficient_3", ">i2"), ("baseplate_temperature_conversion_coefficient_4", ">i2"), ("baseplate_temperature_conversion_coefficient_5", ">i2"), ("reserved13", ">i2"), ("motor_housing_temperature_conversion_coefficient_1", ">i2"), ("motor_housing_temperature_conversion_coefficient_2", ">i2"), ("motor_housing_temperature_conversion_coefficient_3", ">i2"), ("motor_housing_temperature_conversion_coefficient_4", ">i2"), ("motor_housing_temperature_conversion_coefficient_5", ">i2"), ("reserved14", ">i2"), ("a/d_converter_temperature_conversion_coefficient_1", ">i2"), ("a/d_converter_temperature_conversion_coefficient_2", ">i2"), ("a/d_converter_temperature_conversion_coefficient_3", ">i2"), ("a/d_converter_temperature_conversion_coefficient_4", ">i2"), ("a/d_converter_temperature_conversion_coefficient_5", ">i2"), ("reserved15", ">i2"), ("detector_#4_bias_voltage_conversion_coefficient_1", ">i2"), ("detector_#4_bias_voltage_conversion_coefficient_2", ">i2"), ("detector_#4_bias_voltage_conversion_coefficient_3", ">i2"), ("detector_#4_bias_voltage_conversion_coefficient_4", ">i2"), ("detector_#4_bias_voltage_conversion_coefficient_5", ">i2"), ("reserved16", ">i2"), ("detector_#5_bias_voltage_conversion_coefficient_1", ">i2"), ("detector_#5_bias_voltage_conversion_coefficient_2", ">i2"), ("detector_#5_bias_voltage_conversion_coefficient_3", ">i2"), ("detector_#5_bias_voltage_conversion_coefficient_4", ">i2"), ("detector_#5_bias_voltage_conversion_coefficient_5", ">i2"), ("reserved17", ">i2"), ("channel_3b_blackbody_view_conversion_coefficient_1", ">i2"), ("channel_3b_blackbody_view_conversion_coefficient_2", ">i2"), ("channel_3b_blackbody_view_conversion_coefficient_3", ">i2"), ("channel_3b_blackbody_view_conversion_coefficient_4", ">i2"), ("channel_3b_blackbody_view_conversion_coefficient_5", ">i2"), ("reserved18", ">i2"), ("channel_4_blackbody_view_conversion_coefficient_1", ">i2"), ("channel_4_blackbody_view_conversion_coefficient_2", ">i2"), ("channel_4_blackbody_view_conversion_coefficient_3", ">i2"), ("channel_4_blackbody_view_conversion_coefficient_4", ">i2"), ("channel_4_blackbody_view_conversion_coefficient_5", ">i2"), ("reserved19", ">i2"), ("channel_5_blackbody_view_conversion_coefficient_1", ">i2"), ("channel_5_blackbody_view_conversion_coefficient_2", ">i2"), ("channel_5_blackbody_view_conversion_coefficient_3", ">i2"), ("channel_5_blackbody_view_conversion_coefficient_4", ">i2"), ("channel_5_blackbody_view_conversion_coefficient_5", ">i2"), ("reserved20", ">i2"), ("reference_voltage_conversion_coefficient_1", ">i2"), ("reference_voltage_conversion_coefficient_2", ">i2"), ("reference_voltage_conversion_coefficient_3", ">i2"), ("reference_voltage_conversion_coefficient_4", ">i2"), ("reference_voltage_conversion_coefficient_5", ">i2"), ("reserved21", ">i2")]) # FILLER # ("zero_fill9", ">i4", (4608 - 688, ))]) analog_telemetry_v5 = np.dtype([("patch_temperature_conversion_coefficient_1", ">i4"), ("patch_temperature_conversion_coefficient_2", ">i4"), ("patch_temperature_conversion_coefficient_3", ">i4"), ("patch_temperature_conversion_coefficient_4", ">i4"), ("patch_temperature_conversion_coefficient_5", ">i4"), ("patch_temperature_conversion_coefficient_6", ">i4"), ("patch_temperature_extended_conversion_coefficient_1", ">i4"), ("patch_temperature_extended_conversion_coefficient_2", ">i4"), ("patch_temperature_extended_conversion_coefficient_3", ">i4"), ("patch_temperature_extended_conversion_coefficient_4", ">i4"), ("patch_temperature_extended_conversion_coefficient_5", ">i4"), ("patch_temperature_extended_conversion_coefficient_6", ">i4"), ("patch_power_conversion_coefficient_1", ">i4"), ("patch_power_conversion_coefficient_2", ">i4"), ("patch_power_conversion_coefficient_3", ">i4"), ("patch_power_conversion_coefficient_4", ">i4"), ("patch_power_conversion_coefficient_5", ">i4"), ("patch_power_conversion_coefficient_6", ">i4"), ("radiator_temperature_conversion_coefficient_1", ">i4"), ("radiator_temperature_conversion_coefficient_2", ">i4"), ("radiator_temperature_conversion_coefficient_3", ">i4"), ("radiator_temperature_conversion_coefficient_4", ">i4"), ("radiator_temperature_conversion_coefficient_5", ">i4"), ("radiator_temperature_conversion_coefficient_6", ">i4"), ("blackbody_temperature_1_conversion_coefficient_1", ">i4"), ("blackbody_temperature_1_conversion_coefficient_2", ">i4"), ("blackbody_temperature_1_conversion_coefficient_3", ">i4"), ("blackbody_temperature_1_conversion_coefficient_4", ">i4"), ("blackbody_temperature_1_conversion_coefficient_5", ">i4"), ("blackbody_temperature_1_conversion_coefficient_6", ">i4"), ("blackbody_temperature_2_conversion_coefficient_1", ">i4"), ("blackbody_temperature_2_conversion_coefficient_2", ">i4"), ("blackbody_temperature_2_conversion_coefficient_3", ">i4"), ("blackbody_temperature_2_conversion_coefficient_4", ">i4"), ("blackbody_temperature_2_conversion_coefficient_5", ">i4"), ("blackbody_temperature_2_conversion_coefficient_6", ">i4"), ("blackbody_temperature_3_conversion_coefficient_1", ">i4"), ("blackbody_temperature_3_conversion_coefficient_2", ">i4"), ("blackbody_temperature_3_conversion_coefficient_3", ">i4"), ("blackbody_temperature_3_conversion_coefficient_4", ">i4"), ("blackbody_temperature_3_conversion_coefficient_5", ">i4"), ("blackbody_temperature_3_conversion_coefficient_6", ">i4"), ("blackbody_temperature_4_conversion_coefficient_1", ">i4"), ("blackbody_temperature_4_conversion_coefficient_2", ">i4"), ("blackbody_temperature_4_conversion_coefficient_3", ">i4"), ("blackbody_temperature_4_conversion_coefficient_4", ">i4"), ("blackbody_temperature_4_conversion_coefficient_5", ">i4"), ("blackbody_temperature_4_conversion_coefficient_6", ">i4"), ("electronics_current_conversion_coefficient_1", ">i4"), ("electronics_current_conversion_coefficient_2", ">i4"), ("electronics_current_conversion_coefficient_3", ">i4"), ("electronics_current_conversion_coefficient_4", ">i4"), ("electronics_current_conversion_coefficient_5", ">i4"), ("electronics_current_conversion_coefficient_6", ">i4"), ("motor_current_conversion_coefficient_1", ">i4"), ("motor_current_conversion_coefficient_2", ">i4"), ("motor_current_conversion_coefficient_3", ">i4"), ("motor_current_conversion_coefficient_4", ">i4"), ("motor_current_conversion_coefficient_5", ">i4"), ("motor_current_conversion_coefficient_6", ">i4"), ("earth_shield_position_conversion_coefficient_1", ">i4"), ("earth_shield_position_conversion_coefficient_2", ">i4"), ("earth_shield_position_conversion_coefficient_3", ">i4"), ("earth_shield_position_conversion_coefficient_4", ">i4"), ("earth_shield_position_conversion_coefficient_5", ">i4"), ("earth_shield_position_conversion_coefficient_6", ">i4"), ("electronics_temperature_conversion_coefficient_1", ">i4"), ("electronics_temperature_conversion_coefficient_2", ">i4"), ("electronics_temperature_conversion_coefficient_3", ">i4"), ("electronics_temperature_conversion_coefficient_4", ">i4"), ("electronics_temperature_conversion_coefficient_5", ">i4"), ("electronics_temperature_conversion_coefficient_6", ">i4"), ("cooler_housing_temperature_conversion_coefficient_1", ">i4"), ("cooler_housing_temperature_conversion_coefficient_2", ">i4"), ("cooler_housing_temperature_conversion_coefficient_3", ">i4"), ("cooler_housing_temperature_conversion_coefficient_4", ">i4"), ("cooler_housing_temperature_conversion_coefficient_5", ">i4"), ("cooler_housing_temperature_conversion_coefficient_6", ">i4"), ("baseplate_temperature_conversion_coefficient_1", ">i4"), ("baseplate_temperature_conversion_coefficient_2", ">i4"), ("baseplate_temperature_conversion_coefficient_3", ">i4"), ("baseplate_temperature_conversion_coefficient_4", ">i4"), ("baseplate_temperature_conversion_coefficient_5", ">i4"), ("baseplate_temperature_conversion_coefficient_6", ">i4"), ("motor_housing_temperature_conversion_coefficient_1", ">i4"), ("motor_housing_temperature_conversion_coefficient_2", ">i4"), ("motor_housing_temperature_conversion_coefficient_3", ">i4"), ("motor_housing_temperature_conversion_coefficient_4", ">i4"), ("motor_housing_temperature_conversion_coefficient_5", ">i4"), ("motor_housing_temperature_conversion_coefficient_6", ">i4"), ("a/d_converter_temperature_conversion_coefficient_1", ">i4"), ("a/d_converter_temperature_conversion_coefficient_2", ">i4"), ("a/d_converter_temperature_conversion_coefficient_3", ">i4"), ("a/d_converter_temperature_conversion_coefficient_4", ">i4"), ("a/d_converter_temperature_conversion_coefficient_5", ">i4"), ("a/d_converter_temperature_conversion_coefficient_6", ">i4"), ("detector_#4_bias_voltage_conversion_coefficient_1", ">i4"), ("detector_#4_bias_voltage_conversion_coefficient_2", ">i4"), ("detector_#4_bias_voltage_conversion_coefficient_3", ">i4"), ("detector_#4_bias_voltage_conversion_coefficient_4", ">i4"), ("detector_#4_bias_voltage_conversion_coefficient_5", ">i4"), ("detector_#4_bias_voltage_conversion_coefficient_6", ">i4"), ("detector_#5_bias_voltage_conversion_coefficient_1", ">i4"), ("detector_#5_bias_voltage_conversion_coefficient_2", ">i4"), ("detector_#5_bias_voltage_conversion_coefficient_3", ">i4"), ("detector_#5_bias_voltage_conversion_coefficient_4", ">i4"), ("detector_#5_bias_voltage_conversion_coefficient_5", ">i4"), ("detector_#5_bias_voltage_conversion_coefficient_6", ">i4"), ("blackbody_temperature_channel3b_conversion_coefficient_1", ">i4"), ("blackbody_temperature_channel3b_conversion_coefficient_2", ">i4"), ("blackbody_temperature_channel3b_conversion_coefficient_3", ">i4"), ("blackbody_temperature_channel3b_conversion_coefficient_4", ">i4"), ("blackbody_temperature_channel3b_conversion_coefficient_5", ">i4"), ("blackbody_temperature_channel3b_conversion_coefficient_6", ">i4"), ("blackbody_temperature_channel4_conversion_coefficient_1", ">i4"), ("blackbody_temperature_channel4_conversion_coefficient_2", ">i4"), ("blackbody_temperature_channel4_conversion_coefficient_3", ">i4"), ("blackbody_temperature_channel4_conversion_coefficient_4", ">i4"), ("blackbody_temperature_channel4_conversion_coefficient_5", ">i4"), ("blackbody_temperature_channel4_conversion_coefficient_6", ">i4"), ("blackbody_temperature_channel5_conversion_coefficient_1", ">i4"), ("blackbody_temperature_channel5_conversion_coefficient_2", ">i4"), ("blackbody_temperature_channel5_conversion_coefficient_3", ">i4"), ("blackbody_temperature_channel5_conversion_coefficient_4", ">i4"), ("blackbody_temperature_channel5_conversion_coefficient_5", ">i4"), ("blackbody_temperature_channel5_conversion_coefficient_6", ">i4"), ("reference_voltage_conversion_coefficient_1", ">i4"), ("reference_voltage_conversion_coefficient_2", ">i4"), ("reference_voltage_conversion_coefficient_3", ">i4"), ("reference_voltage_conversion_coefficient_4", ">i4"), ("reference_voltage_conversion_coefficient_5", ">i4"), ("reference_voltage_conversion_coefficient_6", ">i4"), # METOP MANEUVERS IDENTIFICATION ("start_of_maneuver_year", ">u2"), ("start_of_maneuver_day", ">u2"), ("start_of_maneuver_utc_time_of_day", ">u4"), ("end_of_manuever_year", ">u2"), ("end_of_maneuver_day", ">u2"), ("end_of_maneuver_utc_time_of_day", ">u4"), ("change_in_spacecraft_velocity", ">i4"), ("change in spacecraft_mass", ">u4"), # CLOUDS FROM AVHRR(CLAVR) ("clavr_status_bit_field", ">u2"), ("zero_fill9", ">i2")]) ars_header = np.dtype([('COST_number', 'S6'), ('SAA_number', 'S8'), ('order_creation_year', 'S4'), ('order_creation_day_of_year', 'S3'), ('processing_site_code', 'S1'), ('processing_software', 'S8'), # data selection criteria ('data_set_name', 'S42'), ('ascii_blank_', 'S2'), ('select_flag', 'S1'), ('beginning_latitude', 'S3'), ('ending_latitude', 'S3'), ('beginning_longitude', 'S4'), ('ending_longitude', 'S4'), ('start_hour', 'S2'), ('start_minute', 'S2'), ('number_of_minutes', 'S3'), ('appended_data_flag', 'S1'), ('channel_select_flag', 'S1', (20, )), # dataset summary ('ascii_blank__', 'S29'), ('ascend_descend_flag', 'S1'), ('first_latitude', 'S3'), ('last_latitude', 'S3'), ('first_longitude', 'S4'), ('last_longitude', 'S4'), ('data_format', 'S20'), ('size_of_record', 'S6'), ('number_of_records', 'S6'), # filler ('ascii_blank', 'S319') ]) class KLMReader(Reader): """Reader for KLM data.""" spacecraft_names = {4: 'noaa15', 2: 'noaa16', 6: 'noaa17', 7: 'noaa18', 8: 'noaa19', 12: 'metopa', 11: 'metopb', 13: 'metopc', } spacecrafts_orbital = {4: 'noaa 15', 2: 'noaa 16', 6: 'noaa 17', 7: 'noaa 18', 8: 'noaa 19', 12: 'metop 02', 11: 'metop 01', 13: 'metop 03', } tsm_affected_intervals = TSM_AFFECTED_INTERVALS_KLM QFlag = KLM_QualityIndicator _quality_indicators_key = "quality_indicator_bit_field" def read(self, filename, fileobj=None): """Read the data. Args: filename: Path to GAC/LAC file fileobj: An open file object to read from. (optional) Returns: header: numpy record array The header metadata scans: numpy record array The scanlines """ # Note that np.fromfile does not work with gzip.GzipFile # objects (numpy version 1.16.4), because it restricts the # file objects to (io.FileIO, io.BufferedReader, io.BufferedWriter) # see: numpy.compat.py3k.isfileobj self.filename = filename LOG.info('Reading %s', self.filename) with file_opener(fileobj or filename) as fd_: self.ars_head, self.head = self.read_header( filename, fileobj=fd_) if self.ars_head: ars_offset = ars_header.itemsize else: ars_offset = 0 self.header_version = self.head[ "noaa_level_1b_format_version_number"] if self.header_version >= 5: self.analog_telemetry, = np.frombuffer( fd_.read(analog_telemetry_v5.itemsize), dtype=analog_telemetry_v5, count=1) else: self.analog_telemetry, = np.frombuffer( fd_.read(analog_telemetry_v2.itemsize), dtype=analog_telemetry_v2, count=1) # LAC: 1, GAC: 2, ... self.data_type = self.head['data_type_code'] # read until end of file fd_.seek(self.offset + ars_offset, 0) buffer = fd_.read() count = self.head["count_of_data_records"] self._read_scanlines(buffer, count) self.correct_scan_line_numbers() self.spacecraft_id = self.head["noaa_spacecraft_identification_code"] self.spacecraft_name = self.spacecraft_names[self.spacecraft_id] @classmethod def read_header(cls, filename, fileobj=None): """Read the file header. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) Returns: archive_header (struct): archive header header (struct): file header """ with file_opener(fileobj or filename) as fd_: # read ars_header if present _ars_head, = np.frombuffer( fd_.read(ars_header.itemsize), dtype=ars_header, count=1) if _ars_head['data_format'].startswith(b'NOAA Level 1b'): ars_head = _ars_head.copy() else: fd_.seek(0) ars_head = None # need to copy frombuffer to have write access on head head, = np.frombuffer( fd_.read(header.itemsize), dtype=header, count=1).copy() head = cls._correct_data_set_name(head, filename) cls._validate_header(head) return ars_head, head @classmethod def _validate_header(cls, header): """Check if the header belongs to this reader.""" # call super to enter the Method Resolution Order (MRO) super(KLMReader, cls)._validate_header(header) LOG.debug("validate header") data_set_name = header['data_set_name'].decode() # split header into parts creation_site, transfer_mode, platform_id = ( data_set_name.split('.')[:3]) allowed_ids = ['NK', 'NL', 'NM', 'NN', 'NP', 'M1', 'M2', 'M3'] if platform_id not in allowed_ids: raise ReaderError('Improper platform id "%s"!' % platform_id) def get_telemetry(self): """Get the telemetry. Returns: prt_counts: np.array ict_counts: np.array space_counts: np.array """ prt_counts = np.mean(self.scans["telemetry"]['PRT'], axis=1) # getting ICT counts ict_counts = np.zeros((len(self.scans), 3)) ict_counts[:, 0] = np.mean(self.scans["back_scan"][:, 0::3], axis=1) ict_counts[:, 1] = np.mean(self.scans["back_scan"][:, 1::3], axis=1) ict_counts[:, 2] = np.mean(self.scans["back_scan"][:, 2::3], axis=1) # getting space counts space_counts = np.zeros((len(self.scans), 3)) space_counts[:, 0] = np.mean(self.scans["space_data"][:, 2::5], axis=1) space_counts[:, 1] = np.mean(self.scans["space_data"][:, 3::5], axis=1) space_counts[:, 2] = np.mean(self.scans["space_data"][:, 4::5], axis=1) return prt_counts, ict_counts, space_counts def _get_lonlat(self): """Get the longitudes and latitudes.""" lats = self.scans["earth_location"][:, 0::2] / 1e4 lons = self.scans["earth_location"][:, 1::2] / 1e4 return lons, lats def get_header_timestamp(self): """Get the timestamp from the header. Returns: A datetime object containing the timestamp from the header. Raises: A ValueError if the timestamp is corrupt. """ year = self.head['start_of_data_set_year'] jday = self.head['start_of_data_set_day_of_year'] msec = self.head['start_of_data_set_utc_time_of_day'] try: return self.to_datetime(self.to_datetime64(year=year, jday=jday, msec=msec)) except ValueError as err: raise ValueError('Corrupt header timestamp: {0}'.format(err)) def _get_times(self): """Get the times of the scanlines.""" year = self.scans["scan_line_year"] jday = self.scans["scan_line_day_of_year"] msec = self.scans["scan_line_utc_time_of_day"] return year, jday, msec def get_ch3_switch(self): """Channel 3 identification. 0: Channel 3b (Brightness temperature) 1: Channel 3a (Reflectance) 2: Transition (No data) """ return self.scans["scan_line_bit_field"][:] & 3 def _get_ir_channels_to_calibrate(self): ir_channels_to_calibrate = [3, 4, 5] if np.all(self.get_ch3_switch() != 0): ir_channels_to_calibrate = [4, 5] return ir_channels_to_calibrate def postproc(self, channels): """Apply KLM specific postprocessing. Masking out 3a/3b/transition. """ switch = self.get_ch3_switch() channels[:, :, 2][switch == 0] = np.nan channels[:, :, 3][switch == 1] = np.nan channels[:, :, 2][switch == 2] = np.nan channels[:, :, 3][switch == 2] = np.nan def _adjust_clock_drift(self): """Adjust the geolocation to compensate for the clock error. Note: Clock drift correction is only applied to POD satellites. On the KLM series, the clock is updated daily. """ def get_tsm_pixels(self, channels): """Determine pixels affected by the scan motor issue. Uses channels 1, 2, 4 and 5. Neither 3a, nor 3b. """ return get_tsm_idx(channels[:, :, 0], channels[:, :, 1], channels[:, :, 4], channels[:, :, 5]) def main_klm(reader_cls, filename, start_line, end_line): """Generate a l1c file.""" tic = datetime.datetime.now() reader = reader_cls.fromfile(filename) reader.save(int(start_line), int(end_line)) LOG.info("pygac took: %s", str(datetime.datetime.now() - tic)) pygac-1.7.2/pygac/lac_klm.py000066400000000000000000000245631444627033600157260ustar00rootroot00000000000000#!/usr/bin/python # Copyright (c) 2014-2019 # # Author(s): # Abhay Devasthale # Sajid Pareeth # Martin Raspaud # Adam Dybbroe # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Reader for LAC KLM data.""" import logging import numpy as np from pygac.klm_reader import KLMReader, main_klm from pygac.lac_reader import LACReader LOG = logging.getLogger(__name__) # video data object scanline = np.dtype([("scan_line_number", ">u2"), ("scan_line_year", ">u2"), ("scan_line_day_of_year", ">u2"), ("satellite_clock_drift_delta", ">i2"), ("scan_line_utc_time_of_day", ">u4"), ("scan_line_bit_field", ">u2"), ("zero_fill0", ">i2", (5, )), # QUALITY INDICATORS ("quality_indicator_bit_field", ">u4"), ("scan_line_quality_flags", [("reserved", ">u1"), ("time_problem_code", ">u1"), ("calibration_problem_code", ">u1"), ("earth_location_problem_code", ">u1")]), ("calibration_quality_flags", ">u2", (3, )), ("count_of_bit_errors_in_frame_sync", ">u2"), ("zero_fill1", ">i4", (2, )), # CALIBRATION COEFFICIENTS ("visible_operational_cal_ch_1_slope_1", ">i4"), ("visible_operational_cal_ch_1_intercept_1", ">i4"), ("visible_operational_cal_ch_1_slope_2", ">i4"), ("visible_operational_cal_ch_1_intercept_2", ">i4"), ("visible_operational_cal_ch_1_intersection", ">i4"), ("visible_test_cal_ch_1_slope_1", ">i4"), ("visible_test_cal_ch_1_intercept_1", ">i4"), ("visible_test_cal_ch_1_slope_2", ">i4"), ("visible_test_cal_ch_1_intercept_2", ">i4"), ("visible_test_cal_ch_1_intersection", ">i4"), ("visible_prelaunch_cal_ch_1_slope_1", ">i4"), ("visible_prelaunch_cal_ch_1_intercept_1", ">i4"), ("visible_prelaunch_cal_ch_1_slope_2", ">i4"), ("visible_prelaunch_cal_ch_1_intercept_2", ">i4"), ("visible_prelaunch_cal_ch_1_intersection", ">i4"), ("visible_operational_cal_ch_2_slope_1", ">i4"), ("visible_operational_cal_ch_2_intercept_1", ">i4"), ("visible_operational_cal_ch_2_slope_2", ">i4"), ("visible_operational_cal_ch_2_intercept_2", ">i4"), ("visible_operational_cal_ch_2_intersection", ">i4"), ("visible_test_cal_ch_2_slope_1", ">i4"), ("visible_test_cal_ch_2_intercept_1", ">i4"), ("visible_test_cal_ch_2_slope_2", ">i4"), ("visible_test_cal_ch_2_intercept_2", ">i4"), ("visible_test_cal_ch_2_intersection", ">i4"), ("visible_prelaunch_cal_ch_2_slope_1", ">i4"), ("visible_prelaunch_cal_ch_2_intercept_1", ">i4"), ("visible_prelaunch_cal_ch_2_slope_2", ">i4"), ("visible_prelaunch_cal_ch_2_intercept_2", ">i4"), ("visible_prelaunch_cal_ch_2_intersection", ">i4"), ("visible_operational_cal_ch_3a_slope_1", ">i4"), ("visible_operational_cal_ch_3a_intercept_1", ">i4"), ("visible_operational_cal_ch_3a_slope_2", ">i4"), ("visible_operational_cal_ch_3a_intercept_2", ">i4"), ("visible_operational_cal_ch_3a_intersection", ">i4"), ("visible_test_cal_ch_3a_slope_1", ">i4"), ("visible_test_cal_ch_3a_intercept_1", ">i4"), ("visible_test_cal_ch_3a_slope_2", ">i4"), ("visible_test_cal_ch_3a_intercept_2", ">i4"), ("visible_test_cal_ch_3a_intersection", ">i4"), ("visible_prelaunch_cal_ch_3a_slope_1", ">i4"), ("visible_prelaunch_cal_ch_3a_intercept_1", ">i4"), ("visible_prelaunch_cal_ch_3a_slope_2", ">i4"), ("visible_prelaunch_cal_ch_3a_intercept_2", ">i4"), ("visible_prelaunch_cal_ch_3a_intersection", ">i4"), ("ir_operational_cal_ch_3b_coefficient_1", ">i4"), ("ir_operational_cal_ch_3b_coefficient_2", ">i4"), ("ir_operational_cal_ch_3b_coefficient_3", ">i4"), ("ir_test_cal_ch_3b_coefficient_1", ">i4"), ("ir_test_cal_ch_3b_coefficient_2", ">i4"), ("ir_test_cal_ch_3b_coefficient_3", ">i4"), ("ir_operational_cal_ch_4_coefficient_1", ">i4"), ("ir_operational_cal_ch_4_coefficient_2", ">i4"), ("ir_operational_cal_ch_4_coefficient_3", ">i4"), ("ir_test_cal_ch_4_coefficient_1", ">i4"), ("ir_test_cal_ch_4_coefficient_2", ">i4"), ("ir_test_cal_ch_4_coefficient_3", ">i4"), ("ir_operational_cal_ch_5_coefficient_1", ">i4"), ("ir_operational_cal_ch_5_coefficient_2", ">i4"), ("ir_operational_cal_ch_5_coefficient_3", ">i4"), ("ir_test_cal_ch_5_coefficient_1", ">i4"), ("ir_test_cal_ch_5_coefficient_2", ">i4"), ("ir_test_cal_ch_5_coefficient_3", ">i4"), # NAVIGATION ("computed_yaw_steering", ">i2", (3,)), # only in version 5 ("total_applied_attitude_correction", ">i2", (3,)), # only in version 5 ("navigation_status_bit_field", ">u4"), ("time_associated_with_tip_euler_angles", ">u4"), ("tip_euler_angles", ">i2", (3, )), ("spacecraft_altitude_above_reference_ellipsoid", ">u2"), ("angular_relationships", ">i2", (153, )), ("zero_fill2", ">i2", (3, )), ("earth_location", ">i4", (102, )), ("zero_fill3", ">i4", (2, )), # HRPT MINOR FRAME TELEMETRY ("frame_sync", ">u2", (6, )), ("id", ">u2", (2, )), ("time_code", ">u2", (4, )), ('telemetry', [("ramp_calibration", '>u2', (5, )), ("PRT", '>u2', (3, )), ("ch3_patch_temp", '>u2'), ("spare", '>u2'), ]), ("back_scan", ">u2", (30, )), ("space_data", ">u2", (50, )), ("sync_delta", ">u2"), ("zero_fill4", ">i2"), # EARTH OBSERVATIONS ("sensor_data", ">u4", (3414,)), ("zero_fill5", ">i4", (2,)), # DIGITAL B HOUSEKEEPING TELEMETRY ("digital_b_telemetry_update_flags", ">u2"), ("avhrr_digital_b_data", ">u2"), ("zero_fill6", ">i4", (3,)), # ANALOG HOUSEKEEPING DATA (TIP) ("analog_telemetry_update_flags", ">u4"), ("patch_temperature_range", ">u1"), ("patch_temperature_extended", ">u1"), ("patch_power", ">u1"), ("radiator_temperature", ">u1"), ("blackbody_temperature_1", ">u1"), ("blackbody_temperature_2", ">u1"), ("blackbody_temperature_3", ">u1"), ("blackbody_temperature_4", ">u1"), ("electronics_current", ">u1"), ("motor_current", ">u1"), ("earth_shield_position", ">u1"), ("electronics_temperature", ">u1"), ("cooler_housing_temperature", ">u1"), ("baseplate_temperature", ">u1"), ("motor_housing_temperature", ">u1"), ("a/d_converter_temperature", ">u1"), ("detector_#4_bias_voltage", ">u1"), ("detector_#5_bias_voltage", ">u1"), ("blackbody_temperature_channel3b", ">u1"), ("blackbody_temperature_channel4", ">u1"), ("blackbody_temperature_channel5", ">u1"), ("reference_voltage", ">u1"), ("zero_fill7", ">i2", (3,)), # CLOUDS FROM AVHRR (CLAVR) ("reserved_clavr_status_bit_field", ">u4"), ("reserved_clavr", ">u4"), ("reserved_clavr_ccm", ">u2", (256,)), # FILLER ("zero_fill8", ">i4", (94,))]) class LACKLMReader(LACReader, KLMReader): """The LAC KLM reader. The offset attribute tells where in the file the scanline data starts. """ def __init__(self, *args, **kwargs): """Init the LAC KLM reader.""" LACReader.__init__(self, *args, **kwargs) self.scanline_type = scanline self.offset = 15872 # packed: 15872 # self.offset = 22528 # unpacked: 22528 def main(filename, start_line, end_line): """Generate a l1c file.""" return main_klm(LACKLMReader, filename, start_line, end_line) if __name__ == "__main__": import sys main(sys.argv[1], sys.argv[2], sys.argv[3]) pygac-1.7.2/pygac/lac_pod.py000066400000000000000000000050151444627033600157140ustar00rootroot00000000000000#!/usr/bin/python # Copyright (c) 2014-2019. # # Author(s): # Abhay Devasthale # Adam Dybbroe # Sajid Pareeth # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Reader for LAC POD data.""" import logging import numpy as np from pygac.pod_reader import PODReader, main_pod from pygac.lac_reader import LACReader LOG = logging.getLogger(__name__) scanline = np.dtype([("scan_line_number", ">i2"), ("time_code", ">u2", (3, )), ("quality_indicators", ">u4"), ("calibration_coefficients", ">i4", (10, )), ("number_of_meaningful_zenith_angles_and_earth_location_appended", ">u1"), ("solar_zenith_angles", "i1", (51, )), ("earth_location", ">i2", (102, )), ("telemetry", ">u4", (35, )), ("sensor_data", ">u4", (3414, )), ("add_on_zenith", ">u2", (10, )), ("clock_drift_delta", ">u2"), # only in newest version ("spare3", "u2", (337, ))]) class LACPODReader(LACReader, PODReader): """The LAC POD reader. The `scan_points` attributes provides the position of the longitude and latitude points to compute relative to the full swath width. The offset attribute tells where in the file the scanline data starts. """ def __init__(self, *args, **kwargs): """Init the LAC POD reader.""" LACReader.__init__(self, *args, **kwargs) self.scanline_type = scanline self.offset = 14800 self.scan_points = np.arange(2048) def main(filename, start_line, end_line): """Generate a l1c file.""" return main_pod(LACPODReader, filename, start_line, end_line) if __name__ == "__main__": import sys main(sys.argv[1], sys.argv[2], sys.argv[3]) pygac-1.7.2/pygac/lac_reader.py000066400000000000000000000040531444627033600163750ustar00rootroot00000000000000#!/usr/bin/python # Copyright (c) 2014-2019. # # Author(s): # Abhay Devasthale # Adam Dybbroe # Sajid Pareeth # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """The LAC reader.""" import logging from pygac.reader import Reader, ReaderError import pygac.pygac_geotiepoints as gtp LOG = logging.getLogger(__name__) class LACReader(Reader): """Reader for LAC data.""" # Scanning frequency (scanlines per millisecond) scan_freq = 6.0 / 1000.0 # Max scanlines max_scanlines = 65535 def __init__(self, *args, **kwargs): """Init the LAC reader.""" super(LACReader, self).__init__(*args, **kwargs) self.scan_width = 2048 self.lonlat_interpolator = gtp.lac_lat_lon_interpolator @classmethod def _validate_header(cls, header): """Check if the header belongs to this reader.""" # call super to enter the Method Resolution Order (MRO) super(LACReader, cls)._validate_header(header) LOG.debug("validate header") data_set_name = header['data_set_name'].decode() # split header into parts creation_site, transfer_mode, platform_id = ( data_set_name.split('.')[:3]) if transfer_mode not in ["LHRR", "HRPT", "FRAC"]: raise ReaderError('Improper transfer mode "%s"!' % transfer_mode) pygac-1.7.2/pygac/pod_reader.py000066400000000000000000000607171444627033600164310ustar00rootroot00000000000000#!/usr/bin/python # Copyright (c) 2014-2019 Pygac developers # # Author(s): # Abhay Devasthale # Adam Dybbroe # Sajid Pareeth # Martin Raspaud # Carlos Horn # This work was done in the framework of ESA-CCI-Clouds phase I # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """POD file reading. Reads L1b GAC/LAC data from POD series of satellites (NOAA-14 and earlier). Format specification can be found in chapters 2 & 3 of the `POD user guide`_. .. _POD user guide: https://www.ncei.noaa.gov/pub/data/satellite/publications/podguides/TIROS-N%20thru%20N-14/ """ import datetime import logging try: from enum import IntFlag except ImportError: # python version < 3.6, use a simple object without nice representation IntFlag = object import numpy as np from pyorbital.geoloc_instrument_definitions import avhrr_gac from pyorbital.geoloc import compute_pixels, get_lonlatalt from pygac.clock_offsets_converter import get_offsets from pygac.correct_tsm_issue import TSM_AFFECTED_INTERVALS_POD, get_tsm_idx from pygac.reader import Reader, ReaderError, NoTLEData from pygac.slerp import slerp from pygac.utils import file_opener LOG = logging.getLogger(__name__) class POD_QualityIndicator(IntFlag): """Quality Indicators. Source: POD guide Table 3.1.2.1-2. Format of quality indicators. """ # POD guide Table 3.1.2.1-2. Format of quality indicators. FATAL_FLAG = 2**31 # Data should not be used for product generation TIME_ERROR = 2**30 # A time sequence error was detected while Processing # this frame DATA_GAP = 2**29 # A gap precedes this frame DATA_JITTER = 2**28 # Resync occurred on this frame CALIBRATION = 2**27 # Insufficient data for calibration NO_EARTH_LOCATION = 2**26 # Earth location data not available ASCEND_DESCEND = 2**25 # AVHRR Earth location indication of Ascending (=0) # or descending (=1) data PSEUDO_NOISE = 2**24 # Pseudo Noise (P/N) occurred (=1) on the frame, # data not used for calibration computations BIT_SYNC_STATUS = 2**23 # Drop lock during frame SYNC_ERROR = 2**22 # Frame Sync word error greater than zero FRAME_SYNC_LOCK = 2**21 # Frame Sync previously dropped lock FLYWHEELING = 2**20 # Flywheeling detected during this frame BIT_SLIPPAGE = 2**19 # Bit slippage detected during this frame # Solar blackbody contamination indicator # 0 = no correction # 1 = solar contamination corrected CH_3_CONTAMINATION = 2**18 # Channel 3 solar blackbody contamination CH_4_CONTAMINATION = 2**17 # Channel 4 solar blackbody contamination CH_5_CONTAMINATION = 2**16 # Channel 5 solar blackbody contamination # TIP Parity TIP_PARITY_1 = 2**15 # In first minor frame TIP_PARITY_2 = 2**14 # In second minor frame TIP_PARITY_3 = 2**13 # In third minor frame TIP_PARITY_4 = 2**12 # In fourth minor frame TIP_PARITY_5 = 2**11 # In fifth minor frame # Note: Bit 10 to 8, and 1 to 0 are spare bits. 7 to 2 define # "SYNC ERRORS - Number of bit errors in frame sync" (6 bit integer?) # common header header0 = np.dtype([("noaa_spacecraft_identification_code", ">u1"), ("data_type_code", ">u1"), ("start_time", ">u2", (3, )), ("number_of_scans", ">u2"), ("end_time", ">u2", (3, ))]) # For L1B data before September 8, 1992 # http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/podug/html/k/app-k.htm header1 = np.dtype([("noaa_spacecraft_identification_code", ">u1"), ("data_type_code", ">u1"), ("start_time", ">u2", (3, )), ("number_of_scans", ">u2"), ("end_time", ">u2", (3, )), ("processing_block_id", "S7"), ("ramp_auto_calibration", ">u1"), ("number_of_data_gaps", ">u2"), ("dacs_quality", ">u1", (6, )), ("calibration_parameter_id", ">i2"), ("dacs_status", ">u1"), ("spare1", ">i1", (5, )), ("data_set_name", "S44")]) # For L1B data between October 21, 1992 to November 15, 1994 # http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/podug/html/l/app-l.htm header2 = np.dtype([("noaa_spacecraft_identification_code", ">u1"), ("data_type_code", ">u1"), ("start_time", ">u2", (3, )), ("number_of_scans", ">u2"), ("end_time", ">u2", (3, )), ("processing_block_id", "S7"), ("ramp_auto_calibration", ">u1"), ("number_of_data_gaps", ">u2"), ("dacs_quality", ">u1", (6, )), ("calibration_parameter_id", ">i2"), ("dacs_status", ">u1"), ("spare1", ">i1", (5, )), ("data_set_name", "S42"), ("blankfill", "S2"), ("julian_year_of_epoch", ">u2"), ("julian_day_of_epoch", ">u2"), ("millisecond_utc_epoch_time_of_day", ">u4"), # Keplerian orbital elements ("semi_major_axis", ">f8"), ("eccentricity", ">f8"), ("inclination", ">f8"), ("argument_of_perigee", ">f8"), ("right_ascension", ">f8"), ("mean_anomaly", ">f8"), # cartesian inertial true date of elements ("x_component_of_position_vector", ">f8"), ("y_component_of_position_vector", ">f8"), ("z_component_of_position_vector", ">f8"), ("x_dot_component_of_position_vector", ">f8"), ("y_dot_component_of_position_vector", ">f8"), ("z_dot_component_of_position_vector", ">f8")]) # For L1B data post November 15, 1994 # http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/podug/html/c2/sec2-0.htm header3 = np.dtype([("noaa_spacecraft_identification_code", ">u1"), ("data_type_code", ">u1"), ("start_time", ">u2", (3, )), ("number_of_scans", ">u2"), ("end_time", ">u2", (3, )), ("processing_block_id", "S7"), ("ramp_auto_calibration", ">u1"), ("number_of_data_gaps", ">u2"), ("dacs_quality", ">u1", (6, )), ("calibration_parameter_id", ">i2"), ("dacs_status", ">u1"), ("reserved_for_mounting_and_fixed_attitude_correction_indicator", ">i1"), ("nadir_earth_location_tolerance", ">i1"), ("spare1", ">i1"), ("start_of_data_set_year", ">u2"), ("data_set_name", "S44"), ("year_of_epoch", ">u2"), ("julian_day_of_epoch", ">u2"), ("millisecond_utc_epoch_time_of_day", ">u4"), # Keplerian orbital elements ("semi_major_axis", ">i4"), ("eccentricity", ">i4"), ("inclination", ">i4"), ("argument_of_perigee", ">i4"), ("right_ascension", ">i4"), ("mean_anomaly", ">i4"), # cartesian inertial true date of elements ("x_component_of_position_vector", ">i4"), ("y_component_of_position_vector", ">i4"), ("z_component_of_position_vector", ">i4"), ("x_dot_component_of_position_vector", ">i4"), ("y_dot_component_of_position_vector", ">i4"), ("z_dot_component_of_position_vector", ">i4"), # future use ("yaw_fixed_error_correction", ">i2"), ("roll_fixed_error_correction", ">i2"), ("pitch_fixed_error_correction", ">i2")]) # archive header tbm_header = np.dtype([('fill', 'S30'), ('data_set_name', 'S44'), ('select_flag', 'S1'), ('beginning_latitude', 'S3'), ('ending_latitude', 'S3'), ('beginning_longitude', 'S4'), ('ending_longitude', 'S4'), ('start_hour', 'S2'), ('start_minute', 'S2'), ('number_of_minutes', 'S3'), ('appended_data_flag', 'S1'), ('channel_select_flag', 'S1', (20, )), ('sensor_data_word_size', 'S2'), ('fill2', 'S3')]) class PODReader(Reader): """The POD reader.""" spacecrafts_orbital = {25: 'tiros n', 2: 'noaa 6', 4: 'noaa 7', 6: 'noaa 8', 7: 'noaa 9', 8: 'noaa 10', 1: 'noaa 11', 5: 'noaa 12', 3: 'noaa 14', } spacecraft_names = {25: 'tirosn', 2: 'noaa6', 4: 'noaa7', 6: 'noaa8', 7: 'noaa9', 8: 'noaa10', 1: 'noaa11', 5: 'noaa12', 3: 'noaa14', } tsm_affected_intervals = TSM_AFFECTED_INTERVALS_POD QFlag = POD_QualityIndicator _quality_indicators_key = "quality_indicators" def correct_scan_line_numbers(self): """Correct the scan line numbers.""" # Perform common corrections first. super(PODReader, self).correct_scan_line_numbers() # cleaning up the data min_scanline_number = np.amin( np.absolute(self.scans["scan_line_number"][:])) if self.scans["scan_line_number"][0] == self.scans["scan_line_number"][-1] + 1: while self.scans["scan_line_number"][0] != min_scanline_number: self.scans = np.roll(self.scans, -1) else: while self.scans["scan_line_number"][0] != min_scanline_number: self.scans = self.scans[1:] self.scans = self.scans[self.scans["scan_line_number"] != 0] def read(self, filename, fileobj=None): """Read the data. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) Returns: header: numpy record array The header metadata scans: numpy record array The scanlines """ self.filename = filename LOG.info('Reading %s', self.filename) # choose the right header depending on the date with file_opener(fileobj or filename) as fd_: self.tbm_head, self.head = self.read_header( filename, fileobj=fd_) if self.tbm_head: tbm_offset = tbm_header.itemsize else: tbm_offset = 0 # read scan lines until end of file fd_.seek(self.offset + tbm_offset, 0) buffer = fd_.read() count = self.head["number_of_scans"] self._read_scanlines(buffer, count) year, jday, _ = self.decode_timestamps(self.head["start_time"]) start_date = (datetime.date(year, 1, 1) + datetime.timedelta(days=int(jday) - 1)) self.correct_scan_line_numbers() self.spacecraft_id = self.head["noaa_spacecraft_identification_code"] if self.spacecraft_id == 1 and start_date < datetime.date(1982, 1, 1): self.spacecraft_id = 25 self.spacecraft_name = self.spacecraft_names[self.spacecraft_id] LOG.info( "Reading %s data", self.spacecrafts_orbital[self.spacecraft_id]) return self.head, self.scans @classmethod def read_header(cls, filename, fileobj=None): """Read the file header. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) Returns: archive_header (struct): archive header header (struct): file header """ # choose the right header depending on the date with file_opener(fileobj or filename) as fd_: # read tbm_header if present _tbm_head, = np.frombuffer( fd_.read(tbm_header.itemsize), dtype=tbm_header, count=1) try: data_set_name = _tbm_head['data_set_name'].decode() except UnicodeDecodeError: data_set_name = '---' allowed_empty = (42*b'\x00' + b' ') if (cls.data_set_pattern.match(data_set_name) or (_tbm_head['data_set_name'] == allowed_empty)): tbm_head = _tbm_head.copy() tbm_offset = tbm_header.itemsize else: fd_.seek(0) tbm_head = None tbm_offset = 0 # read header head0, = np.frombuffer( fd_.read(header0.itemsize), dtype=header0, count=1) year, jday, _ = cls.decode_timestamps(head0["start_time"]) start_date = (datetime.date(year, 1, 1) + datetime.timedelta(days=int(jday) - 1)) if start_date < datetime.date(1992, 9, 8): header = header1 elif start_date <= datetime.date(1994, 11, 15): header = header2 else: header = header3 fd_.seek(tbm_offset, 0) # need to copy frombuffer to have write access on head head, = np.frombuffer( fd_.read(header.itemsize), dtype=header, count=1).copy() head = cls._correct_data_set_name(head, filename) cls._validate_header(head) return tbm_head, head @classmethod def _validate_header(cls, header): """Check if the header belongs to this reader.""" # call super to enter the Method Resolution Order (MRO) super(PODReader, cls)._validate_header(header) LOG.debug("validate header") data_set_name = header['data_set_name'].decode() # split header into parts creation_site, transfer_mode, platform_id = ( data_set_name.split('.')[:3]) allowed_ids = ['TN', 'NA', 'NB', 'NC', 'ND', 'NE', 'NF', 'NG', 'NH', 'NI', 'NJ'] if platform_id not in allowed_ids: raise ReaderError('Improper platform id "%s"!' % platform_id) def get_header_timestamp(self): """Get the timestamp from the header. Returns: A datetime object containing the timestamp from the header. Raises: A ValueError if the timestamp is corrupt. """ year, jday, msec = self.decode_timestamps(self.head["start_time"]) try: return self.to_datetime(self.to_datetime64(year=year, jday=jday, msec=msec)) except ValueError as err: raise ValueError('Corrupt header timestamp: {0}'.format(err)) @staticmethod def decode_timestamps(encoded): """Decode timestamps. Returns: year day of year milliseconds since 00:00 """ ndims = len(encoded.shape) if ndims == 1: # Single header timestamp enc0 = encoded[0] enc1 = encoded[1] enc2 = encoded[2] elif ndims == 2: # Scanline timestamps enc0 = encoded[:, 0] enc1 = encoded[:, 1] enc2 = encoded[:, 2] else: raise ValueError('Invalid timestamp dimension') year = enc0 >> 9 year = np.where(year > 75, year + 1900, year + 2000) jday = (enc0 & 0x1FF) msec = ((np.uint32(enc1 & 2047) << 16) | (np.uint32(enc2))) return year, jday, msec def _get_times(self): return self.decode_timestamps(self.scans["time_code"]) def _compute_missing_lonlat(self, missed_utcs): """Compute lon lat values using pyorbital.""" tic = datetime.datetime.now() scan_rate = datetime.timedelta(milliseconds=1/self.scan_freq).total_seconds() sgeom = avhrr_gac(missed_utcs.astype(datetime.datetime), self.scan_points, frequency=scan_rate) t0 = missed_utcs[0].astype(datetime.datetime) s_times = sgeom.times(t0) tle1, tle2 = self.get_tle_lines() rpy = self.get_attitude_coeffs() pixels_pos = compute_pixels((tle1, tle2), sgeom, s_times, rpy) pos_time = get_lonlatalt(pixels_pos, s_times) missed_lons, missed_lats = pos_time[:2] pixels_per_line = self.lats.shape[1] missed_lons = missed_lons.reshape(-1, pixels_per_line) missed_lats = missed_lats.reshape(-1, pixels_per_line) toc = datetime.datetime.now() LOG.warning("Computation of geolocation: %s", str(toc - tic)) return missed_lons, missed_lats def _adjust_clock_drift(self): """Adjust the geolocation to compensate for the clock error.""" tic = datetime.datetime.now() self.get_times() try: error_utcs, clock_error = get_offsets(self.spacecraft_name) except KeyError: LOG.info("No clock drift info available for %s", self.spacecraft_name) return error_utcs = np.array(error_utcs, dtype='datetime64[ms]') # interpolate to get the clock offsets at the scan line utcs # the clock_error is given in seconds, so offsets are in seconds, too. offsets = np.interp(self.utcs.astype(np.uint64), error_utcs.astype(np.uint64), clock_error) LOG.info("Adjusting for clock drift of %s to %s seconds.", str(min(offsets)), str(max(offsets))) # For the interpolation of geolocations, we need to prepend/append # lines. The conversion from offset to line numbers is given by the # scan rate = 1/scan frequency. scan_rate = datetime.timedelta(milliseconds=1/self.scan_freq) offset_lines = offsets / scan_rate.total_seconds() # To avoid trouble with missing lines, we will construct an # array that covers the whole interpolation range. scan_lines = self.scans["scan_line_number"] shifted_lines = scan_lines - offset_lines shifted_lines_floor = np.floor(shifted_lines).astype(int) # compute the line range, note that the max requires a "+1" # to cover the interpolation range because of the floor. min_line = min(scan_lines.min(), shifted_lines_floor.min()) max_line = max(scan_lines.max(), shifted_lines_floor.max()+1) num_lines = max_line - min_line + 1 missed_lines = np.setdiff1d(np.arange(min_line, max_line+1), scan_lines) missed_utcs = ((missed_lines - scan_lines[0])*np.timedelta64(scan_rate, "ms") + self.utcs[0]) # calculate the missing geo locations try: missed_lons, missed_lats = self._compute_missing_lonlat(missed_utcs) except NoTLEData as err: LOG.warning('Cannot perform clock drift correction: %s', str(err)) return # create arrays of lons and lats for interpolation. The locations # correspond to not yet corrected utcs, i.e. the time difference from # one line to the other should be equal to the scan rate. pixels_per_line = self.lats.shape[1] complete_lons = np.full((num_lines, pixels_per_line), np.nan, dtype=np.float64) complete_lats = np.full((num_lines, pixels_per_line), np.nan, dtype=np.float64) complete_lons[scan_lines - min_line] = self.lons complete_lats[scan_lines - min_line] = self.lats complete_lons[missed_lines - min_line] = missed_lons complete_lats[missed_lines - min_line] = missed_lats # perform the slerp interpolation to the corrected utc times slerp_t = shifted_lines - shifted_lines_floor # in [0, 1) slerp_res = slerp(complete_lons[shifted_lines_floor - min_line, :], complete_lats[shifted_lines_floor - min_line, :], complete_lons[shifted_lines_floor - min_line + 1, :], complete_lats[shifted_lines_floor - min_line + 1, :], slerp_t[:, np.newaxis, np.newaxis]) # set corrected values self.lons = slerp_res[:, :, 0] self.lats = slerp_res[:, :, 1] self.utcs -= (offsets * 1000).astype('timedelta64[ms]') toc = datetime.datetime.now() LOG.debug("clock drift adjustment took %s", str(toc - tic)) def _get_lonlat(self): lats = self.scans["earth_location"][:, 0::2] / 128.0 lons = self.scans["earth_location"][:, 1::2] / 128.0 return lons, lats def get_telemetry(self): """Get the telemetry. Returns: prt_counts: np.array ict_counts: np.array space_counts: np.array """ number_of_scans = self.scans["telemetry"].shape[0] decode_tele = np.zeros((int(number_of_scans), 105)) decode_tele[:, ::3] = (self.scans["telemetry"] >> 20) & 1023 decode_tele[:, 1::3] = (self.scans["telemetry"] >> 10) & 1023 decode_tele[:, 2::3] = self.scans["telemetry"] & 1023 prt_counts = np.mean(decode_tele[:, 17:20], axis=1) # getting ICT counts ict_counts = np.zeros((int(number_of_scans), 3)) ict_counts[:, 0] = np.mean(decode_tele[:, 22:50:3], axis=1) ict_counts[:, 1] = np.mean(decode_tele[:, 23:51:3], axis=1) ict_counts[:, 2] = np.mean(decode_tele[:, 24:52:3], axis=1) # getting space counts space_counts = np.zeros((int(number_of_scans), 3)) space_counts[:, 0] = np.mean(decode_tele[:, 54:100:5], axis=1) space_counts[:, 1] = np.mean(decode_tele[:, 55:101:5], axis=1) space_counts[:, 2] = np.mean(decode_tele[:, 56:102:5], axis=1) return prt_counts, ict_counts, space_counts @staticmethod def _get_ir_channels_to_calibrate(): ir_channels_to_calibrate = [3, 4, 5] return ir_channels_to_calibrate def postproc(self, channels): """No POD specific postprocessing to be done.""" pass def get_tsm_pixels(self, channels): """Determine pixels affected by the scan motor issue. Uses channels 1, 2, 4 and 5. Neither 3a, nor 3b. """ return get_tsm_idx(channels[:, :, 0], channels[:, :, 1], channels[:, :, 3], channels[:, :, 4]) def _get_calibrated_channels_uniform_shape(self): """Prepare the channels as input for gac_io.save_gac.""" _channels = self.get_calibrated_channels() # prepare input # maybe there is a better (less memory requiring) method shape = _channels.shape[:-1] + (6,) # empty_like does not know the kwarg shape in older versions channels = np.empty(shape, dtype=_channels.dtype) channels[:, :, 0] = _channels[:, :, 0] channels[:, :, 1] = _channels[:, :, 1] channels[:, :, 2] = np.nan channels[:, :, 3] = _channels[:, :, 2] channels[:, :, 4] = _channels[:, :, 3] channels[:, :, 5] = _channels[:, :, 4] return channels def main_pod(reader_cls, filename, start_line, end_line): """Generate a l1c file.""" tic = datetime.datetime.now() reader = reader_cls.fromfile(filename) reader.save(int(start_line), int(end_line)) LOG.info("pygac took: %s", str(datetime.datetime.now() - tic)) pygac-1.7.2/pygac/pygac_geotiepoints.py000066400000000000000000000042771444627033600202200ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2010-2012. # Author(s): # Adam Dybbroe # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Interpolation of geographical tiepoints.""" import geotiepoints as gtp import numpy as np def gac_lat_lon_interpolator(lons_subset, lats_subset): """Interpolate lat-lon values in the AVHRR GAC data. Each GAC row has 409 pixels. But lat-lon values are provided for every eigth pixel, ranging from 5 to 405. Interpolate to full resolution. """ cols_subset = np.arange(4, 405, 8) cols_full = np.arange(409) return lat_lon_interpolator(lons_subset, lats_subset, cols_subset, cols_full) def lac_lat_lon_interpolator(lons_subset, lats_subset): """Interpolate lat-lon values in the AVHRR LAC data.""" cols_subset = np.arange(24, 2048, 40) cols_full = np.arange(2048) return lat_lon_interpolator(lons_subset, lats_subset, cols_subset, cols_full) def lat_lon_interpolator(lons_subset, lats_subset, cols_subset, cols_full): """Interpolate lat-lon values in the AVHRR data.""" lines = lats_subset.shape[0] rows_subset = np.arange(lines) rows_full = np.arange(lines) along_track_order = 1 cross_track_order = 3 satint = gtp.SatelliteInterpolator((lons_subset, lats_subset), (rows_subset, cols_subset), (rows_full, cols_full), along_track_order, cross_track_order) return satint.interpolate() pygac-1.7.2/pygac/reader.py000066400000000000000000001272251444627033600155650ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2014, 2019 Pygac Developers # Author(s): # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Generic reader for GAC and LAC data. Can't be used as is, has to be subclassed to add specific read functions. """ from abc import ABCMeta, abstractmethod import datetime import logging import numpy as np import os import re import six import types import warnings import pyorbital from pygac.utils import (centered_modulus, calculate_sun_earth_distance_correction, get_absolute_azimuth_angle_diff) from pyorbital.orbital import Orbital from pyorbital import astronomy from pygac.calibration import Calibrator, calibrate_solar, calibrate_thermal from pygac import gac_io from packaging.version import Version LOG = logging.getLogger(__name__) # rpy values from # here:http://yyy.rsmas.miami.edu/groups/rrsl/pathfinder/Processing/proc_app_a.html rpy_coeffs = { 'noaa7': {'roll': 0.000, 'pitch': 0.000, 'yaw': 0.000, }, 'noaa9': {'roll': 0.000, 'pitch': 0.0025, 'yaw': 0.000, }, 'noaa10': {'roll': 0.000, 'pitch': 0.000, 'yaw': 0.000, }, 'noaa11': {'roll': -0.0019, 'pitch': -0.0037, 'yaw': 0.000, }, 'noaa12': {'roll': 0.000, 'pitch': 0.000, 'yaw': 0.000, }, 'noaa14': {'roll': 0.000, 'pitch': 0.000, 'yaw': 0.000, }} class ReaderError(ValueError): """Raised in Reader.read if the given file does not correspond to it.""" pass class NoTLEData(IndexError): """Raised if no TLE data available within time range.""" class Reader(six.with_metaclass(ABCMeta)): """Reader for GAC and LAC format, POD and KLM platforms.""" # data set header format, see _validate_header for more details data_set_pattern = re.compile( r'\w{3}\.\w{4}\.\w{2}.D\d{5}\.S\d{4}\.E\d{4}\.B\d{7}\.\w{2}') def __init__(self, interpolate_coords=True, adjust_clock_drift=True, tle_dir=None, tle_name=None, tle_thresh=7, creation_site=None, custom_calibration=None, calibration_file=None): """Init the reader. Args: interpolate_coords: Interpolate coordinates from tiepoint grid to all pixels. adjust_clock_drift: Adjust the geolocation to compensate for the clock error (POD satellites only). tle_dir: Directory holding TLE files tle_name: Filename pattern of TLE files. tle_thresh: Maximum number of days between observation and nearest TLE creation_site: The three-letter identifier of the creation site (eg 'NSS') custom_calibration: dictionary with a subset of user defined satellite specific calibration coefficients calibration_file: path to json file containing default calibrations """ self.meta_data = {} self.interpolate_coords = interpolate_coords self.adjust_clock_drift = adjust_clock_drift self.tle_dir = tle_dir self.tle_name = tle_name self.tle_thresh = tle_thresh self.creation_site = (creation_site or 'NSS').encode('utf-8') self.custom_calibration = custom_calibration self.calibration_file = calibration_file self.head = None self.scans = None self.spacecraft_name = None self.spacecraft_id = None self.utcs = None self.lats = None self.lons = None self.tle_lines = None self.filename = None self._mask = None self._rpy = None @property def times(self): """Get the UTCs as datetime.datetime.""" return self.to_datetime(self.utcs) @property def filename(self): """Get the property 'filename'.""" return self._filename @filename.setter def filename(self, filepath): """Set the property 'filename'.""" if filepath is None: self._filename = None else: filepath = os.fspath(filepath) match = self.data_set_pattern.search(filepath) if match: self._filename = match.group() else: self._filename = os.path.basename(filepath) @property def calibration(self): """Get the property 'calibration'.""" calibration = Calibrator( self.spacecraft_name, custom_coeffs=self.custom_calibration, coeffs_file=self.calibration_file ) return calibration @abstractmethod def read(self, filename, fileobj=None): # pragma: no cover """Read the GAC/LAC data. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) """ raise NotImplementedError @classmethod @abstractmethod def read_header(cls, filename, fileobj=None): # pragma: no cover """Read the file header. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) Returns: archive_header (struct): archive header header (struct): file header Note: This is a classmethod to avoid throwaway instances while checking if the reader corresponds to the input file. """ raise NotImplementedError @classmethod def _correct_data_set_name(cls, header, filename): """Replace invalid data_set_name from header with filename. Args: header (struct): file header filename (str): path to file """ filename = str(filename) data_set_name = header['data_set_name'].decode(errors='ignore') if not cls.data_set_pattern.match(data_set_name): LOG.debug('The data_set_name in header %s does not match.' ' Use filename instead.' % header['data_set_name']) match = cls.data_set_pattern.search(filename) if match: data_set_name = match.group() LOG.debug("Set data_set_name, to filename %s" % data_set_name) header['data_set_name'] = data_set_name.encode() else: LOG.debug("header['data_set_name']=%s; filename='%s'" % (header['data_set_name'], filename)) raise ReaderError('Cannot determine data_set_name!') return header @classmethod def _validate_header(cls, header): """Check if the header belongs to this reader. Note: according to https://www1.ncdc.noaa.gov/pub/data/satellite/ publications/podguides/TIROS-N%20thru%20N-14/pdf/NCDCPOD2.pdf and https://www1.ncdc.noaa.gov/pub/data/satellite/ publications/podguides/N-15%20thru%20N-19/pdf/ 2.5%20Section%208.0%20NOAA%20Level%201B%20Database.pdf the data set name splits into PROCESSING-CENTER.DATA-TYPE.SPACECRAFT-UNIQUE-ID. YEAR-DAY.START-TIME.STOP-TIME.PROCESSING-BLOCK-ID.SOURCE This should be sufficient information to determine the reader. Global Area Coverage (GAC): DATA-TYPE = GHRR Local Area Coverage (LAC): DATA-TYPE = LHRR Polar Orbiter Data (POD): SPACECRAFT-UNIQUE-ID in [TN, NA, NB, NC, ND, NE, NF, NG, NH, NI, NJ] NOAA-K, -L, -M system, but also newer satellites (KLM): SPACECRAFT-UNIQUE-ID in [NK, NL, NM, NN, NP, M2, M1] """ # This method does not need to be implemented in all subclasses. # It is intended for cooperative multiple inheritance, i.e. # each child class which implements this method, should call the # super method to enter into the method resolution order. # See https://docs.python.org/3/library/functions.html#super # second use case "diamond diagrams". # Check if the data set name matches the pattern LOG.debug("validate header") data_set_name = header['data_set_name'].decode(errors='ignore') if not cls.data_set_pattern.match(data_set_name): raise ReaderError('Data set name %s does not match!' % header['data_set_name']) def _read_scanlines(self, buffer, count): """Read the scanlines from the given buffer. Args: buffer (bytes, bytearray): buffer to read from count (int): number of expected scanlines """ # Calculate the actual number of complete scanlines. The integer divisoin # may strip a potentially incomplete line at the end of the file. line_count = len(buffer) // self.scanline_type.itemsize if line_count != count: LOG.warning( "Expected %d scan lines, but found %d!" % (count, line_count)) warnings.warn("Unexpected number of scanlines!", category=RuntimeWarning) self.scans = np.frombuffer( buffer, dtype=self.scanline_type, count=line_count) @classmethod def can_read(cls, filename, fileobj=None): """Read the GAC/LAC data. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) Retruns: result (bool): True if the reader can read the input """ if fileobj: pos = fileobj.tell() try: archive_header, header = cls.read_header( filename, fileobj=fileobj) result = True except (ReaderError, ValueError) as exception: LOG.debug("%s failed to read the file! %s" % (cls.__name__, repr(exception))) result = False finally: if fileobj: fileobj.seek(pos) return result @classmethod def fromfile(cls, filename, fileobj=None): """Create Reader from file, alternative constructor. Args: filename (str): Path to GAC/LAC file Kwargs: fileobj (file object): Open file object to read from Note: The fileobj is useful when dealing with tar archives, where the filename is given by the tarinfo object, but the extracted file object's property 'name' is set to the filename of the archive. """ instance = cls() instance.read(filename, fileobj=fileobj) return instance def _get_calibrated_channels_uniform_shape(self): """Prepare the channels as input for gac_io.save_gac.""" channels = self.get_calibrated_channels() assert channels.shape[-1] == 6 return channels def save(self, start_line, end_line, output_file_prefix="PyGAC", output_dir="./", avhrr_dir=None, qual_dir=None, sunsatangles_dir=None): """Convert the Reader instance content into hdf5 files.""" avhrr_dir = avhrr_dir or output_dir qual_dir = qual_dir or output_dir sunsatangles_dir = sunsatangles_dir or output_dir self.get_lonlat() channels = self._get_calibrated_channels_uniform_shape() sat_azi, sat_zen, sun_azi, sun_zen, rel_azi = self.get_angles() qual_flags = self.get_qual_flags() if (np.all(self.mask)): print("ERROR: All data is masked out. Stop processing") raise ValueError("All data is masked out.") gac_io.save_gac( self.spacecraft_name, self.utcs, self.lats, self.lons, channels[:, :, 0], channels[:, :, 1], channels[:, :, 2], channels[:, :, 3], channels[:, :, 4], channels[:, :, 5], sun_zen, sat_zen, sun_azi, sat_azi, rel_azi, qual_flags, start_line, end_line, self.filename, self.meta_data, output_file_prefix, avhrr_dir, qual_dir, sunsatangles_dir ) @abstractmethod def get_header_timestamp(self): # pragma: no cover """Read start timestamp from the header. Returns: datetime.datetime: Start timestamp """ raise NotImplementedError def get_counts(self): """Get the counts. Returns: np.array: The counts, with channel 3a and 3b split if necessary. """ packed_data = self.scans["sensor_data"] counts = np.zeros((len(self.scans), self.scan_width * 5)) counts_nb = (self.scan_width * 5) // 3 remainder = (self.scan_width * 5) % 3 if remainder == 0: nb1 = nb2 = nb3 = counts_nb elif remainder == 1: nb1 = counts_nb + 1 nb2 = nb3 = counts_nb elif remainder == 2: nb1 = nb2 = counts_nb + 1 nb3 = counts_nb counts[:, 0::3] = ((packed_data >> 20) & 1023)[:, :nb1] counts[:, 1::3] = ((packed_data >> 10) & 1023)[:, :nb2] counts[:, 2::3] = (packed_data & 1023)[:, :nb3] counts = counts.reshape((-1, self.scan_width, 5)) try: switch = self.get_ch3_switch() except AttributeError: return counts else: channels = np.zeros((len(self.scans), self.scan_width, 6), dtype=counts.dtype) channels[:, :, :2] = counts[:, :, :2] channels[:, :, -2:] = counts[:, :, -2:] channels[:, :, 2][switch == 1] = counts[:, :, 2][switch == 1] channels[:, :, 3][switch == 0] = counts[:, :, 2][switch == 0] return channels @abstractmethod def _get_times(self): # pragma: no cover """Specify how to read scanline timestamps from GAC data. Returns: int: year int: day of year int: milliseconds since 00:00 """ raise NotImplementedError def get_times(self): """Read scanline timestamps and try to correct invalid values. Note: Also sets self.utcs and self.times! Returns: UTC timestamps """ if self.utcs is None: # Read timestamps year, jday, msec = self._get_times() # Correct invalid values year, jday, msec = self.correct_times_median(year=year, jday=jday, msec=msec) self.utcs = self.to_datetime64(year=year, jday=jday, msec=msec) self.correct_times_thresh() return self.utcs @staticmethod def to_datetime64(year, jday, msec): """Convert timestamps to numpy.datetime64. Args: year: Year jday: Day of the year (1-based) msec: Milliseconds since 00:00 Returns: numpy.datetime64: Converted timestamps """ return (year.astype(str).astype('datetime64[Y]') + (jday - 1).astype('timedelta64[D]') + msec.astype('timedelta64[ms]')) @staticmethod def to_datetime(datetime64): """Convert numpy.datetime64 to datetime.datetime. Args: datetime64 (numpy.datetime64): Numpy timestamp to be converted. Returns: datetime.datetime: Converted timestamp """ return datetime64.astype(datetime.datetime) def lineno2msec(self, scan_line_number): """Compute ideal scanline timestamp based on the scanline number. Assumes a constant scanning frequency. Args: scan_line_number: Specifies the scanline number (1-based) Returns: Corresponding timestamps in milliseconds since 1970-01-01 00:00, i.e. the first scanline has timestamp 0. """ return (scan_line_number - 1) / self.scan_freq def get_sun_earth_distance_correction(self): """Get the julian day and the sun-earth distance correction.""" self.get_times() jday = self.times[0].timetuple().tm_yday return calculate_sun_earth_distance_correction(jday) def update_meta_data(self): """Add some metd data to the meta_data dicitonary.""" if 'sun_earth_distance_correction_factor' not in self.meta_data: self.meta_data['sun_earth_distance_correction_factor'] = ( self.get_sun_earth_distance_correction()) if 'midnight_scanline' not in self.meta_data: self.meta_data['midnight_scanline'] = self.get_midnight_scanline() if 'missing_scanlines' not in self.meta_data: self.meta_data['missing_scanlines'] = self.get_miss_lines() if 'gac_header' not in self.meta_data: self.meta_data['gac_header'] = self.head.copy() self.meta_data['calib_coeffs_version'] = self.calibration.version def get_calibrated_channels(self): """Calibrate and return the channels.""" channels = self.get_counts() self.get_times() times = self.times self.update_meta_data() year = times[0].year delta = times[0].date() - datetime.date(year, 1, 1) jday = delta.days + 1 corr = self.meta_data['sun_earth_distance_correction_factor'] calibration_coeffs = self.calibration # how many reflective channels are there ? tot_ref = channels.shape[2] - 3 channels[:, :, 0:tot_ref] = calibrate_solar( channels[:, :, 0:tot_ref], np.arange(tot_ref), year, jday, calibration_coeffs, corr ) prt, ict, space = self.get_telemetry() ir_channels_to_calibrate = self._get_ir_channels_to_calibrate() for chan in ir_channels_to_calibrate: channels[:, :, chan - 6] = calibrate_thermal( channels[:, :, chan - 6], prt, ict[:, chan - 3], space[:, chan - 3], self.scans["scan_line_number"], chan, calibration_coeffs ) # Mask out corrupt values channels[self.mask] = np.nan # Apply KLM/POD specific postprocessing self.postproc(channels) # Mask pixels affected by scan motor issue if self.is_tsm_affected(): LOG.info('Correcting for temporary scan motor issue') self.mask_tsm_pixels(channels) return channels @abstractmethod def get_telemetry(self): # pragma: no cover """KLM/POD specific readout of telemetry.""" raise NotImplementedError def get_lonlat(self): """Compute lat/lon coordinates. TODO: Switch to faster interpolator? """ if self.lons is None and self.lats is None: self.lons, self.lats = self._get_lonlat() self.update_meta_data() # Interpolate from every eighth pixel to all pixels. if self.interpolate_coords: self.lons, self.lats = self.lonlat_interpolator( self.lons, self.lats) # Adjust clock drift if self.adjust_clock_drift: self._adjust_clock_drift() # Mask out corrupt scanlines self.lons[self.mask] = np.nan self.lats[self.mask] = np.nan # Mask values outside the valid range self.lats[np.fabs(self.lats) > 90.0] = np.nan self.lons[np.fabs(self.lons) > 180.0] = np.nan return self.lons, self.lats @abstractmethod def _get_lonlat(self): # pragma: no cover """KLM/POD specific readout of lat/lon coordinates.""" raise NotImplementedError @property def mask(self): """Mask for corrupt scanlines.""" if self._mask is None: self._mask = self._get_corrupt_mask() return self._mask @property @abstractmethod def QFlag(self): # pragma: no cover """KLM/POD specific quality indicators.""" raise NotImplementedError @property @abstractmethod def _quality_indicators_key(self): # pragma: no cover raise NotImplementedError def _get_corrupt_mask(self, flags=None): """Readout of corrupt scanline mask. Args: flags (QFlag.flag): An "ORed" bitmask that defines corrupt values. Defauts to (QFlag.FATAL_FLAG | QFlag.CALIBRATION | QFlag.NO_EARTH_LOCATION) Note: The Quality flags mapping (QFlag) is KLM/POD specific. """ QFlag = self.QFlag if flags is None: flags = QFlag.FATAL_FLAG | QFlag.CALIBRATION | QFlag.NO_EARTH_LOCATION return (self.scans[self._quality_indicators_key] & int(flags)).astype(bool) def get_qual_flags(self): """Read quality flags.""" number_of_scans = self.scans["telemetry"].shape[0] qual_flags = np.zeros((int(number_of_scans), 7)) qual_flags[:, 0] = self.scans["scan_line_number"] qual_flags[:, 1] = self._get_corrupt_mask(flags=self.QFlag.FATAL_FLAG) qual_flags[:, 2] = self._get_corrupt_mask(flags=self.QFlag.CALIBRATION) qual_flags[:, 3] = self._get_corrupt_mask(flags=self.QFlag.NO_EARTH_LOCATION) qual_flags[:, 4] = self._get_corrupt_mask(flags=self.QFlag.CH_3_CONTAMINATION) qual_flags[:, 5] = self._get_corrupt_mask(flags=self.QFlag.CH_4_CONTAMINATION) qual_flags[:, 6] = self._get_corrupt_mask(flags=self.QFlag.CH_5_CONTAMINATION) return qual_flags @abstractmethod def postproc(self, channels): # pragma: no cover """Apply KLM/POD specific postprocessing.""" raise NotImplementedError @abstractmethod def _adjust_clock_drift(self): # pragma: no cover """Adjust clock drift.""" raise NotImplementedError @staticmethod def tle2datetime64(times): """Convert TLE timestamps to numpy.datetime64. Args: times (float): TLE timestamps as %y%j.1234, e.g. 18001.25 """ # Convert %y%j.12345 to %Y%j.12345 (valid for 1950-2049) times = np.where(times > 50000, times + 1900000, times + 2000000) # Convert float to datetime64 doys = (times % 1000).astype('int') - 1 years = (times // 1000).astype('int') msecs = np.rint(24 * 3600 * 1000 * (times % 1)) times64 = ( years - 1970).astype('datetime64[Y]').astype('datetime64[ms]') times64 += doys.astype('timedelta64[D]') times64 += msecs.astype('timedelta64[ms]') return times64 def get_tle_file(self): """Find TLE file for the current satellite.""" tle_dir, tle_name = self.tle_dir, self.tle_name if tle_dir is None: raise RuntimeError("TLE directory not specified!") if tle_name is None: raise RuntimeError("TLE name not specified!") values = {"satname": self.spacecraft_name, } tle_filename = os.path.join(tle_dir, tle_name % values) LOG.info('TLE filename = ' + str(tle_filename)) return tle_filename def read_tle_file(self, tle_filename): """Read TLE file.""" with open(tle_filename, 'r') as fp_: return fp_.readlines() def get_tle_lines(self): """Find closest two line elements (TLEs) for the current orbit. Raises: IndexError, if the closest TLE is more than :meth:`pygac.GACReader.tle_thresh` days apart """ if self.tle_lines is not None: return self.tle_lines self.get_times() tle_data = self.read_tle_file(self.get_tle_file()) sdate = self.utcs[0] dates = self.tle2datetime64( np.array([float(line[18:32]) for line in tle_data[::2]])) # Find index "iindex" such that dates[iindex-1] < sdate <= dates[iindex] # Notes: # 1. If sdate < dates[0] then iindex = 0 # 2. If sdate > dates[-1] then iindex = len(dates), beyond the right boundary! iindex = np.searchsorted(dates, sdate) if iindex in (0, len(dates)): if iindex == len(dates): # Reset index if beyond the right boundary (see note 2. above) iindex -= 1 elif abs(sdate - dates[iindex - 1]) < abs(sdate - dates[iindex]): # Choose the closest of the two surrounding dates iindex -= 1 # Make sure the TLE we found is within the threshold delta_days = abs(sdate - dates[iindex]) / np.timedelta64(1, 'D') if delta_days > self.tle_thresh: raise NoTLEData( "Can't find tle data for %s within +/- %d days around %s" % (self.spacecraft_name, self.tle_thresh, sdate)) if delta_days > 3: LOG.warning("Found TLE data for %s that is %f days appart", sdate, delta_days) else: LOG.debug("Found TLE data for %s that is %f days appart", sdate, delta_days) # Select TLE data tle1 = tle_data[iindex * 2] tle2 = tle_data[iindex * 2 + 1] self.tle_lines = tle1, tle2 return tle1, tle2 def get_sat_angles(self): """Get satellite angles. Returns: Azimuth, elevation (degrees) """ try: return self._get_sat_angles_with_tle() except NoTLEData: LOG.warning( 'No TLE data available. Falling back to approximate ' 'calculation of satellite angles.' ) return self._get_sat_angles_without_tle() def _get_sat_angles_with_tle(self): tle1, tle2 = self.get_tle_lines() orb = Orbital(self.spacecrafts_orbital[self.spacecraft_id], line1=tle1, line2=tle2) sat_azi, sat_elev = orb.get_observer_look(self.times[:, np.newaxis], self.lons, self.lats, 0) return sat_azi, sat_elev def _get_sat_angles_without_tle(self): """Get satellite angles using lat/lon from data to approximate satellite postition instead of TLE.""" from pyorbital.orbital import get_observer_look as get_observer_look_no_tle LOG.warning('Approximating satellite height to 850km (TIROS-N OSCAR)!') sat_alt = 850.0 # km TIROS-N OSCAR mid_column = int(0.5*self.lons.shape[1]) sat_azi, sat_elev = get_observer_look_no_tle( self.lons[:, mid_column][:, np.newaxis], self.lats[:, mid_column][:, np.newaxis], # approximate satellite position sat_alt, # approximate satellite altitude self.times[:, np.newaxis], self.lons, self.lats, 0) # Sometimes (pyorbital <= 1.6.1) the get_observer_look_not_tle returns nodata instead of 90. # Problem solved with https://github.com/pytroll/pyorbital/pull/77 if Version(pyorbital.__version__) <= Version('1.6.1'): sat_elev[:, mid_column] = 90 return sat_azi, sat_elev def get_angles(self): """Get azimuth and zenith angles. Azimuth angle definition is the same as in pyorbital, but with different units (degrees not radians for sun azimuth angles) and different ranges. Returns: sat_azi: satellite azimuth angle degree clockwise from north in range ]-180, 180] sat_zenith: satellite zenith angles in degrees in range [0,90] sun_azi: sun azimuth angle degree clockwise from north in range ]-180, 180] sun_zenith: sun zenith angles in degrees in range [0,90] rel_azi: absolute azimuth angle difference in degrees between sun and sensor in range [0, 180] """ self.get_times() self.get_lonlat() times = self.times sat_azi, sat_elev = self.get_sat_angles() sat_zenith = 90 - sat_elev sun_zenith = astronomy.sun_zenith_angle(times[:, np.newaxis], self.lons, self.lats) alt, sun_azi = astronomy.get_alt_az(times[:, np.newaxis], self.lons, self.lats) del alt sun_azi = np.rad2deg(sun_azi) rel_azi = get_absolute_azimuth_angle_diff(sun_azi, sat_azi) # Scale angles range to half open interval ]-180, 180] sat_azi = centered_modulus(sat_azi, 360.0) sun_azi = centered_modulus(sun_azi, 360.0) # Mask corrupt scanlines for arr in (sat_azi, sat_zenith, sun_azi, sun_zenith, rel_azi): arr[self.mask] = np.nan return sat_azi, sat_zenith, sun_azi, sun_zenith, rel_azi def correct_times_median(self, year, jday, msec): """Replace invalid timestamps with statistical estimates (using median). Assumes that the majority of timestamps is ok. Args: year: Year jday: Day of the year msec: Milliseconds since 00:00 Returns: Corrected year Corrected day of the year Corrected milliseconds """ # Estimate ideal timestamps based on the scanline number. Still without # offset, e.g. the first scanline has timestamp 1970-01-01 00:00 msec_lineno = self.lineno2msec(self.scans["scan_line_number"]) jday = np.where(np.logical_or(jday < 1, jday > 366), np.median(jday), jday) if_wrong_jday = np.ediff1d(jday, to_begin=0) jday = np.where(if_wrong_jday < 0, max(jday), jday) if_wrong_msec = np.where(msec < 1) if_wrong_msec = if_wrong_msec[0] if len(if_wrong_msec) > 0: if if_wrong_msec[0] != 0: msec = msec[0] + msec_lineno else: msec0 = np.median(msec - msec_lineno) msec = msec0 + msec_lineno if_wrong_msec = np.ediff1d(msec, to_begin=0) msec = np.where(np.logical_and(np.logical_or(if_wrong_msec < -1000, if_wrong_msec > 1000), if_wrong_jday != 1), msec[0] + msec_lineno, msec) # checking if year value is out of valid range if_wrong_year = np.where( np.logical_or(year < 1978, year > datetime.datetime.now().year)) if_wrong_year = if_wrong_year[0] if len(if_wrong_year) > 0: # if the first scanline has valid time stamp if if_wrong_year[0] != 0: year = year[0] jday = jday[0] msec = msec[0] + msec_lineno # Otherwise use median time stamp else: year = np.median(year) jday = np.median(jday) msec0 = np.median(msec - msec_lineno) msec = msec0 + msec_lineno return year.astype(int), jday.astype(int), msec def correct_scan_line_numbers(self): """Remove scanlines with corrupted scanline numbers. This includes: - Scanline numbers outside the valid range - Scanline numbers deviating more than a certain threshold from the ideal case (1,2,3,...N) Example files having corrupt scanline numbers: - NSS.GHRR.NJ.D96144.S2000.E2148.B0720102.GC - NSS.GHRR.NJ.D96064.S0043.E0236.B0606162.WI - NSS.GHRR.NJ.D99286.S1818.E2001.B2466869.WI Returns: Intermediate and final results (for plotting purpose) """ along_track = np.arange(1, len(self.scans["scan_line_number"])+1) results = {'along_track': along_track, 'n_orig': self.scans['scan_line_number'].copy()} # Remove scanlines whose scanline number is outside the valid range within_range = np.logical_and(self.scans["scan_line_number"] < self.max_scanlines, self.scans["scan_line_number"] >= 0) self.scans = self.scans[within_range] # Remove scanlines deviating more than a certain threshold from the # ideal case (1,2,3,...N). ideal = np.arange(1, len(self.scans["scan_line_number"])+1) # ... Estimate possible offset (in case some scanlines are missing in # the beginning of the scan) offsets = self.scans["scan_line_number"] - ideal med_offset = np.median(offsets) # ... Compute difference to ideal case (1,2,3,...N) + offset diffs = np.abs(self.scans["scan_line_number"] - (ideal + med_offset)) # ... Remove those scanlines whose difference is larger than a certain # threshold. For the threshold computation we only regard nonzero # differences. nz_diffs = diffs[diffs > 0] if len(nz_diffs) < 50: # Not enough differences for reliable statistics. Use fixed # threshold. thresh = 500 else: mean_nz_diffs = np.mean(nz_diffs) std_nz_diffs = np.std(nz_diffs) med_nz_diffs = np.median(nz_diffs) mad_nz_diffs = np.median(np.abs(nz_diffs - med_nz_diffs)) if mean_nz_diffs / float(med_nz_diffs) < 3: # Relatively small variation, keep (almost) everything thresh = mean_nz_diffs + 3*std_nz_diffs else: # Large variation, filter more aggressively. Use median and # median absolute deviation (MAD) as they are less sensitive to # outliers. However, allow differences < 500 scanlines as they # occur quite often. thresh = max(500, med_nz_diffs + 3*mad_nz_diffs) self.scans = self.scans[diffs <= thresh] LOG.debug('Removed %s scanline(s) with corrupt scanline numbers', str(len(along_track) - len(self.scans))) results.update({'n_corr': self.scans['scan_line_number'], 'within_range': within_range, 'diffs': diffs, 'thresh': thresh, 'nz_diffs': nz_diffs}) return results def correct_times_thresh(self, max_diff_from_t0_head=6*60*1000, min_frac_near_t0_head=0.01, max_diff_from_ideal_t=10*1000): """Correct corrupted timestamps using a threshold approach. The threshold approach is based on the scanline number and the header timestamp. It also works if the majority of scanlines has corrupted timestamps. The header timestamp is used as a guideline to estimate the offset between timestamps computed from the scanline number and the actual scanline timestamps in the data. If header timestamp and scanline timestamps do not match, no correction is applied. Once the offset has been estimated, one can calculate the ideal timestamps based on the scanline number. Timestamps deviating more than a certain threshold from the ideal timestamps are replaced by the ideal timestamps. Example files having corrupt timestamps: - NSS.GHRR.NA.D81193.S2329.E0116.B1061214.WI - NSS.GHRR.NL.D01035.S2342.E0135.B0192627.WI Args: max_diff_from_t0_head (int): Threshold for offset estimation: A scanline timestamp matches the header timestamp t0_head if it is within the interval [t0_head - max_diff_from_t0_head, t0_head + max_diff_from_t0_head] around the header timestamp. min_frac_near_t0_head (float): Specifies the minimum fraction of scanline timestamps matching the header timestamp required for applying the correction. max_diff_from_ideal_t (float): Threshold for timestamp correction: If a scanline timestamp deviates more than max_diff_from_ideal_t from the ideal timestamp, it is regarded as corrupt and will be replaced with the ideal timestamp. Returns: Intermediate and final results (for plotting purpose) """ results = {} dt64_msec = ">M8[ms]" # Check whether scanline number increases monotonically nums = self.scans["scan_line_number"] results.update({'t': self.utcs.copy(), 'n': nums}) if np.any(np.diff(nums) < 0): LOG.error("Cannot perform timestamp correction. Scanline number " "does not increase monotonically.") results['fail_reason'] = "Scanline number jumps backwards" return results # Convert time to milliseconds since 1970-01-01 t = self.utcs.astype("i8") try: t0_head = np.array([self.get_header_timestamp().isoformat()], dtype="datetime64[ms]").astype("i8")[0] except ValueError as err: LOG.error("Cannot perform timestamp correction: %s", err) return # Compute ideal timestamps based on the scanline number. Still # without offset, i.e. scanline 0 has timestamp 1970-01-01 00:00 tn = self.lineno2msec(nums) # Try to determine the timestamp t0 of the first scanline. Since none # of the actual timestamps is trustworthy, use the header timestamp # as a guideline. However, the header timestamp may also be corrupted, # so we only apply corrections if there is a minimum fraction of # scanlines whose timestamps match the header timestamp. # # 1) Compute offsets between actual timestamps and idealized timestamps offsets = t - tn # 2) If the offsets of a certain minimum fraction of scanlines are # within a certain interval around the header timestamp, estimate # t0 by calculating the median offset among these timestamps. If not, # we do not have reliable information and cannot proceed. near_t0_head = np.where( np.fabs(offsets - t0_head) <= max_diff_from_t0_head)[0] results.update({'offsets': offsets, 't0_head': t0_head, 'max_diff_from_t0_head': max_diff_from_t0_head}) if near_t0_head.size / float(nums.size) >= min_frac_near_t0_head: t0 = np.median(offsets[near_t0_head]) else: LOG.error("Timestamp mismatch. Cannot perform correction.") results['fail_reason'] = "Timestamp mismatch" return results # Add estimated offset to the ideal timestamps tn += t0 # Replace timestamps deviating more than a certain threshold from the # ideal timestamp with the ideal timestamp. corrupt_lines = np.where(np.fabs(t - tn) > max_diff_from_ideal_t) self.utcs[corrupt_lines] = tn[corrupt_lines].astype(dt64_msec) LOG.debug("Corrected %s timestamp(s)", str(len(corrupt_lines[0]))) results.update({'tn': tn, 'tcorr': self.utcs, 't0': t0}) return results @property @abstractmethod def tsm_affected_intervals(self): # pragma: no cover """Specify time intervals being affected by the scan motor problem. Returns: dict: Affected time intervals. A dictionary containing a list of (start, end) tuples for each affected platform. Both start and end must be datetime.datetime objects. """ raise NotImplementedError def is_tsm_affected(self): """Determine whether this orbit is affected by the scan motor problem. Returns: bool: True if the orbit is affected, False otherwise. """ self.get_times() ts, te = self.to_datetime(self.utcs[[0, -1]]) try: for interval in self.tsm_affected_intervals[self.spacecraft_id]: if ts >= interval[0] and te <= interval[1]: # Found a matching interval return True # No matching interval, orbit is not affected return False except KeyError: # Platform is not affected at all return False def get_midnight_scanline(self): """Find the scanline where the UTC date increases by one day. Returns: int: The midnight scanline if it exists and is unique. None, else. """ self.get_times() d0 = np.datetime64(datetime.date(1970, 1, 1), 'D') days = (self.utcs.astype('datetime64[D]') - d0).astype(int) incr = np.where(np.diff(days) == 1)[0] if len(incr) != 1: if len(incr) > 1: LOG.warning('Unable to determine midnight scanline: ' 'UTC date increases more than once. ') return None else: return incr[0] def get_miss_lines(self): """Find missing scanlines. I.e. scanlines which were dropped for some reason or were never recorded. Returns: Indices of missing scanlines """ # Compare scanline number against the ideal case (1, 2, 3, ...) and # find the missing line numbers. ideal = set(range(1, self.scans['scan_line_number'][-1] + 1)) missing = sorted(ideal.difference(set(self.scans['scan_line_number']))) return np.array(missing, dtype=int) def mask_tsm_pixels(self, channels): """Mask pixels affected by the scan motor issue.""" idx = self.get_tsm_pixels(channels) channels[idx] = np.nan @abstractmethod def get_tsm_pixels(self, channels): # pragma: no cover """Determine pixels affected by the scan motor issue. Channel selection is POD/KLM specific. """ raise NotImplementedError def get_attitude_coeffs(self): """Return the roll, pitch, yaw values.""" if self._rpy is None: if "constant_yaw_attitude_error" in self.head.dtype.fields: rpy = np.deg2rad([self.head["constant_roll_attitude_error"] / 1e3, self.head["constant_pitch_attitude_error"] / 1e3, self.head["constant_yaw_attitude_error"] / 1e3]) else: try: # This needs to be checked thoroughly first # rpy_spacecraft = rpy_coeffs[self.spacecraft_name] # rpy = np.array([rpy_spacecraft['roll'], # rpy_spacecraft['pitch'], # rpy_spacecraft['yaw']]) # LOG.debug("Using static attitude correction") raise KeyError except KeyError: LOG.debug("Not applying attitude correction") rpy = np.zeros(3) LOG.info("Using rpy: %s", str(rpy)) self._rpy = rpy return self._rpy def inherit_doc(cls): """Make a class method inherit its docstring from the parent class. Copied from http://stackoverflow.com/a/8101598/5703449 . """ for name, func in vars(cls).items(): if isinstance(func, types.FunctionType) and not func.__doc__: for parent in cls.__bases__: parfunc = getattr(parent, name, None) if parfunc and getattr(parfunc, '__doc__', None): func.__doc__ = parfunc.__doc__ break return cls pygac-1.7.2/pygac/runner.py000066400000000000000000000102141444627033600156210ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2014, 2019 Pygac Developers # Author(s): # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Processing utilities for GAC/LAC KLM/POD files. Functions: process_file: allows to process a given input file get_reader_class: allows to select the appropriate reader class for a given file. """ import datetime import logging import os from pygac.gac_klm import GACKLMReader from pygac.gac_pod import GACPODReader from pygac.lac_klm import LACKLMReader from pygac.lac_pod import LACPODReader from pygac.utils import file_opener from pygac.configuration import get_config LOG = logging.getLogger(__name__) _reader_classes = [GACKLMReader, LACKLMReader, GACPODReader, LACPODReader] def get_reader_class(filename, fileobj=None): """Return the reader class that can read the GAC/LAC KLM/POD file. Args: filename (str): Path to GAC/LAC file fileobj: An open file object to read from. (optional) """ found_reader = False for index, Reader in enumerate(_reader_classes): if Reader.can_read(filename, fileobj=fileobj): LOG.debug("%s can read the file." % Reader.__name__) found_reader = True break if not found_reader: raise ValueError('Unable to read the file "%s"' % filename) # Move the Reader in front of _reader_classes. Chance is high that the # next file is of the same kind. _reader_classes.insert(0, _reader_classes.pop(index)) return Reader def process_file(filename, start_line, end_line, fileobj=None): """Read, calibrate and navigate NOAA AVHRR GAC/LAC POD/KLM data. It creates three hdf5 files in the output location given by the pygac config file. The three files contain the avhrr data, quality flags, and sunsatangles. Argsuments filename (str): Path to GAC/LAC file start_line (int): First scanline to be processed (0-based) end_line (int): Last scanline to be processed (0-based), set to 0 for the last available scanline fileobj: An open file object to read from. (optional) Note This function expects an initialized config file. """ tic = datetime.datetime.now() LOG.info("Process file: %s", str(filename)) # reader specific values config = get_config() tle_dir = config.get('tle', 'tledir', raw=True) tle_name = config.get('tle', 'tlename', raw=True) coeffs_file = config.get("calibration", "coeffs_file", fallback='') # output specific values output_dir = config.get('output', 'output_dir', raw=True) output_file_prefix = config.get('output', 'output_file_prefix', raw=True) avhrr_dir = os.environ.get('SM_AVHRR_DIR') qual_dir = os.environ.get('SM_AVHRR_DIR') sunsatangles_dir = os.environ.get('SM_SUNSATANGLES_DIR') # Keep the file open while searching for the reader class and later # creation of the instance. with file_opener(fileobj or filename) as open_file: reader_cls = get_reader_class(filename, fileobj=open_file) reader = reader_cls( tle_dir=tle_dir, tle_name=tle_name, calibration_file=coeffs_file ) reader.read(filename, fileobj=fileobj) reader.save( start_line, end_line, output_file_prefix=output_file_prefix, output_dir=output_dir, avhrr_dir=avhrr_dir, qual_dir=qual_dir, sunsatangles_dir=sunsatangles_dir ) LOG.info("Processing took: %s", str(datetime.datetime.now() - tic)) pygac-1.7.2/pygac/slerp.py000066400000000000000000000030071444627033600154370ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Martin Raspaud # Author(s): # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """slerp implementation in numpy """ import numpy as np def tocart(lon, lat): rlat = np.deg2rad(lat) rlon = np.deg2rad(lon) x__ = np.cos(rlat) * np.cos(rlon) y__ = np.cos(rlat) * np.sin(rlon) z__ = np.sin(rlat) return np.dstack((x__, y__, z__)) def toll(arr): lat = np.arcsin(arr.take(2, -1)) lon = np.arctan2(arr.take(1, -1), arr.take(0, -1)) return np.rad2deg(np.dstack((lon.squeeze(), lat.squeeze()))) def dot(a, b): return np.sum(a * b, -1) def slerp(lon0, lat0, lon1, lat1, t): cp0 = tocart(lon0, lat0) cp1 = tocart(lon1, lat1) omega = np.arccos(dot(cp0, cp1))[:, :, np.newaxis] return toll(np.sin((1 - t) * omega) / np.sin(omega) * cp0 + np.sin(t * omega) / np.sin(omega) * cp1) pygac-1.7.2/pygac/tests/000077500000000000000000000000001444627033600151025ustar00rootroot00000000000000pygac-1.7.2/pygac/tests/__init__.py000066400000000000000000000015331444627033600172150ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2013, 2014 Martin Raspaud # Author(s): # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """The tests package.""" pygac-1.7.2/pygac/tests/test_angles.py000066400000000000000000000053001444627033600177620ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014-2019 Pytroll Developers # Author(s): # Nina Hakansson # Adam Dybbroe # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test function for the angle calculation.""" import unittest import numpy as np from pygac.utils import (get_absolute_azimuth_angle_diff, centered_modulus) class TestAngles(unittest.TestCase): """Test function for the angle calculation.""" def test_azidiff_angle(self): """Test function for the azidiff angle.""" sat_az = np.ma.array([[48.0, 56.0, 64.0, 72.0], [80.0, 88.0, 96.0, 104.0], [-80.0, -88.0, -96.0, -104.0], [-180.0, -188.0, -196.0, -204.0]], mask=False) sun_az = np.ma.array([[148.0, 156.0, 164.0, 172.0], [180.0, 188.0, 196.0, 204.0], [180.0, 188.0, 196.0, 204.0], [185.0, 193.0, 201.0, 209.0]], mask=False) res = np.ma.array([[100., 100., 100., 100.], [100., 100., 100., 100.], [100., 84., 68., 52.], [5., 21., 37., 53.]], mask=False) rel_azi = get_absolute_azimuth_angle_diff(sat_az, sun_az) np.testing.assert_allclose(rel_azi, res) def test_centered_modulus(self): """Test centered_modulus.""" angles = np.ma.array( [[180.0, -180.0, -179.9, 201.0], [80.0, 360.0, -360.0, 604.0], [-80.0, -88.0, -796.0, -104.0], [-3.0, -188.0, -196.0, -204.0]], mask=False) expected = np.ma.array( [[180.0, 180.0, -179.9, -159.0], [80.0, 0.0, 0.0, -116.0], [-80.0, -88.0, -76.0, -104.0], [-3.0, 172.0, 164.0, 156.0]], mask=False) transformed = centered_modulus(angles, 360.0) np.testing.assert_allclose(transformed, expected) pygac-1.7.2/pygac/tests/test_calibrate_klm.py000066400000000000000000000122441444627033600213070ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014-2019 Pytroll Developers # Author(s): # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test function for the POD calibration. """ import unittest try: import mock except ImportError: from unittest import mock import numpy as np from pygac.calibration import Calibrator, calibrate_solar, calibrate_thermal class TestGenericCalibration(unittest.TestCase): def test_calibration_vis(self): counts = np.array([[0, 0, 0, 0, 0, 512, 512, 512, 512, 512, 1023, 1023, 1023, 1023, 1023], [41, 41, 41, 41, 41, 150, 150, 150, 150, 150, 700, 700, 700, 700, 700]]) year = 2010 jday = 1 spacecraft_id = "noaa19" cal = Calibrator(spacecraft_id) corr = 1 channel = 0 ref1 = calibrate_solar(counts[:, channel::5], channel, year, jday, cal, corr) channel = 1 ref2 = calibrate_solar(counts[:, channel::5], channel, year, jday, cal, corr) channel = 2 data = np.ma.array(counts[:, channel::5], mask=True) ref3 = calibrate_solar(data, channel, year, jday, cal, corr) expected = (np.array([[np.nan, 27.37909518, 110.60103456], [0.11943135, 6.03671211, 57.99695154]]), np.array([[np.nan, 3.05229160e+01, 1.24811455e+02], [1.23011792e-01, 6.82715447e+00, 6.52122414e+01]]), np.array([[0., 523.41775, 1034.41775], [41., 150., 711.41775]])) np.testing.assert_allclose(ref1, expected[0]) np.testing.assert_allclose(ref2, expected[1]) np.testing.assert_allclose(ref3, expected[2]) def test_calibration_ir(self): counts = np.array([[0, 0, 612, 0, 0, 512, 512, 487, 512, 512, 923, 923, 687, 923, 923], [41, 41, 634, 41, 41, 150, 150, 461, 150, 150, 700, 700, 670, 700, 700], [241, 241, 656, 241, 241, 350, 350, 490, 350, 350, 600, 600, 475, 600, 600]]) prt_counts = np.array([0, 230, 230]) ict_counts = np.array([[745.3, 397.9, 377.8], [744.8, 398.1, 378.4], [745.7, 398., 378.3]]) space_counts = np.array([[987.3, 992.5, 989.4], [986.9, 992.8, 989.6], [986.3, 992.3, 988.9]]) spacecraft_id = "noaa19" cal = Calibrator(spacecraft_id) ch3 = calibrate_thermal(counts[:, 2::5], prt_counts, ict_counts[:, 0], space_counts[:, 0], line_numbers=np.array([1, 2, 3]), channel=3, cal=cal) expected_ch3 = np.array([[298.36742, 305.248478, 293.238328], [296.960275, 306.493766, 294.488956], [295.476935, 305.101309, 305.829827]]) np.testing.assert_allclose(expected_ch3, ch3) ch4 = calibrate_thermal(counts[:, 3::5], prt_counts, ict_counts[:, 1], space_counts[:, 1], line_numbers=np.array([1, 2, 3]), channel=4, cal=cal) expected_ch4 = np.array([[326.576534, 275.348988, 197.688755], [323.013104, 313.207077, 249.36352], [304.58091, 293.579308, 264.0631]]) np.testing.assert_allclose(expected_ch4, ch4) ch5 = calibrate_thermal(counts[:, 4::5], prt_counts, ict_counts[:, 2], space_counts[:, 2], line_numbers=np.array([1, 2, 3]), channel=5, cal=cal) expected_ch5 = np.array([[326.96161, 272.090164, 188.267991], [323.156317, 312.673269, 244.184452], [303.439383, 291.649444, 259.973091]]) np.testing.assert_allclose(expected_ch5, ch5) pygac-1.7.2/pygac/tests/test_calibrate_pod.py000066400000000000000000000122151444627033600213040ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014, 2015 Martin Raspaud # Author(s): # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test function for the POD calibration. """ import unittest try: import mock except ImportError: from unittest import mock import numpy as np from pygac.calibration import Calibrator, calibrate_solar, calibrate_thermal class TestGenericCalibration(unittest.TestCase): def test_calibration_vis(self): counts = np.array([[0, 0, 0, 0, 0, 512, 512, 512, 512, 512, 1023, 1023, 1023, 1023, 1023], [41, 41, 41, 41, 41, 150, 150, 150, 150, 150, 700, 700, 700, 700, 700]]) year = 1997 jday = 196 spacecraft_id = "noaa14" cal = Calibrator(spacecraft_id) corr = 1 channel = 0 ref1 = calibrate_solar(counts[:, channel::5], channel, year, jday, cal, corr) channel = 1 ref2 = calibrate_solar(counts[:, channel::5], channel, year, jday, cal, corr) channel = 2 data = np.ma.array(counts[:, channel::5], mask=True) ref3 = calibrate_solar(data, channel, year, jday, cal, corr) expected = (np.array([[np.nan, 60.891074, 126.953364], [0., 14.091565, 85.195791]]), np.array([[np.nan, 72.98262, 152.16334], [0., 16.889821, 102.113687]]), np.array([[-32001., -32001., -32001.], [-32001., -32001., -32001.]])) np.testing.assert_allclose(ref1, expected[0]) np.testing.assert_allclose(ref2, expected[1]) np.testing.assert_allclose(ref3.filled(-32001), expected[2]) def test_calibration_ir(self): counts = np.array([[0, 0, 612, 0, 0, 512, 512, 487, 512, 512, 923, 923, 687, 923, 923], [41, 41, 634, 41, 41, 150, 150, 461, 150, 150, 700, 700, 670, 700, 700], [241, 241, 656, 241, 241, 350, 350, 490, 350, 350, 600, 600, 475, 600, 600]]) prt_counts = np.array([0, 230, 230]) ict_counts = np.array([[745.3, 397.9, 377.8], [744.8, 398.1, 378.4], [745.7, 398., 378.3]]) space_counts = np.array([[987.3, 992.5, 989.4], [986.9, 992.8, 989.6], [986.3, 992.3, 988.9]]) spacecraft_id = "noaa14" cal = Calibrator(spacecraft_id) ch3 = calibrate_thermal(counts[:, 2::5], prt_counts, ict_counts[:, 0], space_counts[:, 0], line_numbers=np.array([1, 2, 3]), channel=3, cal=cal) expected_ch3 = np.array([[298.28466, 305.167571, 293.16182], [296.878502, 306.414234, 294.410224], [295.396779, 305.020259, 305.749526]]) np.testing.assert_allclose(expected_ch3, ch3) ch4 = calibrate_thermal(counts[:, 3::5], prt_counts, ict_counts[:, 1], space_counts[:, 1], line_numbers=np.array([1, 2, 3]), channel=4, cal=cal) expected_ch4 = np.array([[325.828062, 275.414804, 196.214709], [322.359517, 312.785057, 249.380649], [304.326806, 293.490822, 264.148021]]) np.testing.assert_allclose(expected_ch4, ch4) ch5 = calibrate_thermal(counts[:, 4::5], prt_counts, ict_counts[:, 2], space_counts[:, 2], line_numbers=np.array([1, 2, 3]), channel=5, cal=cal) expected_ch5 = np.array([[326.460316, 272.146547, 187.434456], [322.717606, 312.388155, 244.241633], [303.267012, 291.590832, 260.05426]]) np.testing.assert_allclose(expected_ch5, ch5) pygac-1.7.2/pygac/tests/test_calibration_coefficients.py000066400000000000000000000151361444627033600235310ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2014-2020 Pytroll Developers # Author(s): # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test function for the calibration coeffictions handling. """ import sys import unittest try: import mock except ImportError: from unittest import mock import numpy as np from pygac.calibration import Calibrator, calibrate_solar, CoeffStatus # dummy user json file including only noaa19 data with changed channel 1 coefficients user_json_file = b"""{ "noaa19": { "channel_1": { "dark_count": 0, "gain_switch": 1000, "s0": 2, "s1": 0, "s2": 0 }, "channel_2": { "dark_count": 39.0, "gain_switch": 500.37, "s0": 0.122, "s1": 0.95, "s2": -0.039 }, "channel_3a": { "dark_count": 39.4, "gain_switch": 496.11, "s0": 0.1, "s1": 0.0, "s2": 0.0 }, "channel_3b": { "b0": 0.0, "b1": 0.0, "b2": 0.0, "centroid_wavenumber": 2670.2425, "space_radiance": 0.0, "to_eff_blackbody_intercept": 1.6863857, "to_eff_blackbody_slope": 0.9974112191806167 }, "channel_4": { "b0": 5.7, "b1": -0.11187000000000002, "b2": 0.00054668, "centroid_wavenumber": 927.92374, "space_radiance": -5.49, "to_eff_blackbody_intercept": 0.39419031, "to_eff_blackbody_slope": 0.9986718662850276 }, "channel_5": { "b0": 3.58, "b1": -0.05991000000000002, "b2": 0.00024985, "centroid_wavenumber": 831.28619, "space_radiance": -3.39, "to_eff_blackbody_intercept": 0.2636462, "to_eff_blackbody_slope": 0.9990463103920997 }, "date_of_launch": "2009-02-05T00:57:36.000000Z", "thermometer_1": { "d0": 276.6067, "d1": 0.051111, "d2": 1.405783e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_2": { "d0": 276.6119, "d1": 0.05109, "d2": 1.496037e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_3": { "d0": 276.6311, "d1": 0.051033, "d2": 1.49699e-06, "d3": 0.0, "d4": 0.0 }, "thermometer_4": { "d0": 276.6268, "d1": 0.051058, "d2": 1.49311e-06, "d3": 0.0, "d4": 0.0 } } }""" class TestCalibrationCoefficientsHandling(unittest.TestCase): @mock.patch('pygac.calibration.open', mock.mock_open(read_data=user_json_file)) def test_user_coefficients_file(self): if sys.version_info.major < 3: cal = Calibrator('noaa19', coeffs_file="/path/to/unknow/defaults.json") else: with self.assertWarnsRegex(RuntimeWarning, "Unknown calibration coefficients version!"): cal = Calibrator('noaa19', coeffs_file="/path/to/unknow/defaults.json") self.assertEqual(cal.dark_count[0], 0) self.assertEqual(cal.gain_switch[0], 1000) self.assertEqual(cal.s0[0], 2) self.assertEqual(cal.s1[0], 0) self.assertEqual(cal.s2[0], 0) # check that the version is set to None if an unknown file is used if sys.version_info.major > 2: self.assertIsNone(cal.version) def test_custom_coefficients(self): custom_coeffs = { "channel_1": { "dark_count": 0, "gain_switch": 1000, "s0": 2, "s1": 0, "s2": 0 } } # The coefficients are choosen to preserve the counts of channel 1 if counts are less than 1000 counts = np.arange(10) year = 2010 jday = 1 spacecraft_id = "noaa19" channel = 0 cal = Calibrator(spacecraft_id, custom_coeffs=custom_coeffs) scaled_radiance = calibrate_solar(counts, channel, year, jday, cal) np.testing.assert_allclose(scaled_radiance, counts) # check that the version is set to None if custom coeffs are used if sys.version_info.major > 2: self.assertIsNone(cal.version) @unittest.skipIf(sys.version_info.major < 3, "Skipped in python2!") def test_vis_deprecation_warning(self): counts = np.arange(10) year = 2010 jday = 1 spacecraft_id = "noaa19" channel = 0 corr = 2 message = ( "Using the 'corr' argument is depricated in favor of making the units" " of the function result clear. Please make any unit conversion outside this function." ) cal = Calibrator(spacecraft_id) with self.assertWarnsRegex(DeprecationWarning, message): calibrate_solar(counts, channel, year, jday, cal, corr=corr) # check that the version is set in this case self.assertIsNotNone(cal.version) def test_default_coeffs(self): """Test identification of default coefficients.""" _, version = Calibrator.read_coeffs(None) self.assertIsNotNone(version) @unittest.skipIf(sys.version_info.major < 3, "Skipped in python2!") def test_read_coeffs_warnings(self): """Test warnings issued by Calibrator.read_coeffs.""" version_dicts = [ # Non-nominal coefficients {'name': 'v123', 'status': CoeffStatus.PROVISIONAL}, # Unknown coefficients {'name': None, 'status': None} ] with mock.patch.object(Calibrator, 'version_hashs') as version_hashs: for version_dict in version_dicts: version_hashs.get.return_value = version_dict with self.assertWarns(RuntimeWarning): Calibrator.read_coeffs(None) pygac-1.7.2/pygac/tests/test_io.py000066400000000000000000000226261444627033600171320ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test the I/O.""" import unittest try: import mock except ImportError: from unittest import mock import numpy as np import numpy.testing import pygac.gac_io as gac_io import pygac.utils as utils class TestIO(unittest.TestCase): """Test the gac_io module.""" longMessage = True def test_strip_invalid_lat(self): """Test stripping the invalid lats.""" lats = np.array([np.nan, 1, np.nan, 2, np.nan]) start, end = utils.strip_invalid_lat(lats) self.assertEqual(start, 1) self.assertEqual(end, 3) def test_update_scanline(self): """Test updating the scanlines.""" test_data = [{'new_start_line': 100, 'new_end_line': 200, 'scanline': 110, 'scanline_exp': 10}, {'new_start_line': 100, 'new_end_line': 200, 'scanline': 90, 'scanline_exp': None}, {'new_start_line': 100, 'new_end_line': 200, 'scanline': 210, 'scanline_exp': None}] for t in test_data: scanline_exp = t.pop('scanline_exp') scanline = utils._update_scanline(**t) self.assertEqual(scanline, scanline_exp) def test_update_missing_scanlines(self): """Test updating the missing scanlines.""" qual_flags = np.array([[1, 2, 4, 5, 6, 8, 9, 11, 12]]).transpose() miss_lines = np.array([3, 7, 10]) test_data = [{'start_line': 0, 'end_line': 8, 'miss_lines_exp': [3, 7, 10]}, {'start_line': 3, 'end_line': 6, 'miss_lines_exp': [1, 2, 3, 4, 7, 10, 11, 12]}] for t in test_data: miss_lines_exp = t.pop('miss_lines_exp') miss_lines = utils._update_missing_scanlines( miss_lines=miss_lines, qual_flags=qual_flags, **t) numpy.testing.assert_array_equal(miss_lines, miss_lines_exp) # If intersection of miss_lines and qual_flags is not empty # (here: extra "1" in miss_lines), make sure that items are # not added twice. miss_lines = utils._update_missing_scanlines( miss_lines=np.array([1, 3, 7, 10]), qual_flags=qual_flags, start_line=3, end_line=6) numpy.testing.assert_array_equal(miss_lines, [1, 2, 3, 4, 7, 10, 11, 12]) def test_slice(self): """Test slices.""" ch = np.array([[1, 2, 3, 4, 5]]).transpose() sliced_exp = np.array([[2, 3, 4]]).transpose() # Without update sliced = utils._slice(ch, start_line=1, end_line=3) numpy.testing.assert_array_equal(sliced, sliced_exp) # With update sliced, updated = utils._slice(ch, start_line=1, end_line=3, update=[0, 2, 4, None]) numpy.testing.assert_array_equal(sliced, sliced_exp) numpy.testing.assert_array_equal(updated, [None, 1, None, None]) # Make sure slice is a copy ch += 1 numpy.testing.assert_array_equal(sliced, sliced_exp) def test_slice_channel(self): """Test selection of user defined scanlines. Scanline Nr: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Missing: X X X X Inv. lat/lon: X X X X Before stripping of invalid lats -------------------------------- idx: 0 1 2 3 4 5 6 7 8 9 data: 1 2 3 4 5 6 7 8 9 10 After stripping of invalid lats ------------------------------- idx: 0 1 2 3 4 5 data: 3 4 5 6 7 8 => All missing lines: [1, 2, 3, 4, 11, 12, 13, 14] """ # Define test data ch = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).transpose() miss_lines = np.array([1, 4, 11, 12]) # never recorded qualflags = np.array( [[2, 3, 5, 6, 7, 8, 9, 10, 13, 14]]).transpose() # scanline number midn_line = 3 first_valid_lat = 2 last_valid_lat = 7 start_line = 1 end_line = 4 # Without lat stripping sliced_ref = np.array([[2, 3, 4, 5]]).transpose() sliced, miss_lines_new, midn_line_new = utils.slice_channel( ch, start_line=start_line, end_line=end_line, miss_lines=miss_lines, midnight_scanline=midn_line, qual_flags=qualflags) numpy.testing.assert_array_equal(sliced, sliced_ref) numpy.testing.assert_array_equal(miss_lines_new, miss_lines) self.assertEqual(midn_line_new, 2) # With lat stripping sliced_ref = np.array([[4, 5, 6, 7]]).transpose() miss_lines_ref = np.array([1, 2, 3, 4, 11, 12, 13, 14]) sliced, miss_lines_new, midn_line_new = utils.slice_channel( ch, start_line=start_line, end_line=end_line, first_valid_lat=first_valid_lat, last_valid_lat=last_valid_lat, miss_lines=miss_lines, midnight_scanline=midn_line, qual_flags=qualflags) numpy.testing.assert_array_equal(sliced, sliced_ref) numpy.testing.assert_array_equal(miss_lines_new, miss_lines_ref) self.assertEqual(midn_line_new, 0) def test_check_user_scanlines(self): """Check the scanlines.""" # All scanlines start, end = utils.check_user_scanlines(0, 0, 100, 200) self.assertEqual(start, 0) self.assertEqual(end, 100) start, end = utils.check_user_scanlines(0, 0, None, None, 100) self.assertEqual(start, 0) self.assertEqual(end, 99) # Valid scanlines start, end = utils.check_user_scanlines(10, 20, 100, 200) self.assertEqual(start, 10) self.assertEqual(end, 20) start, end = utils.check_user_scanlines(10, 20, None, None, 100) self.assertEqual(start, 10) self.assertEqual(end, 20) # Invalid scanlines start, end = utils.check_user_scanlines(10, 110, 100, 200) self.assertEqual(start, 10) self.assertEqual(end, 100) start, end = utils.check_user_scanlines(10, 110, None, None, 100) self.assertEqual(start, 10) self.assertEqual(end, 99) self.assertRaises(ValueError, gac_io.check_user_scanlines, 110, 120, 100, 200) self.assertRaises(ValueError, gac_io.check_user_scanlines, 110, 120, None, None, 100) @mock.patch('pygac.gac_io.strip_invalid_lat') @mock.patch('pygac.gac_io.avhrrGAC_io') @mock.patch('pygac.gac_io.slice_channel') @mock.patch('pygac.gac_io.check_user_scanlines') def test_save_gac(self, check_user_scanlines, slice_channel, avhrr_gac_io, strip_invalid_lat): """Test saving.""" # Test scanline selection mm = mock.MagicMock() kwargs = dict( satellite_name=mm, xutcs=mm, lats=mm, lons=mm, ref1=mm, ref2=mm, ref3=mm, bt3=mm, bt4=mm, bt5=mm, sun_zen=mm, sat_zen=mm, sun_azi=mm, sat_azi=mm, rel_azi=mm, qual_flags=mm, gac_file=mm, meta_data=mm, output_file_prefix=mm, avhrr_dir=mm, qual_dir=mm, sunsatangles_dir=mm ) slice_channel.return_value = mm, 'miss', 'midnight' strip_invalid_lat.return_value = 0, 0 check_user_scanlines.return_value = 'start', 'end' gac_io.save_gac(start_line=0, end_line=0, **kwargs) slice_channel.assert_called_with(mock.ANY, start_line='start', end_line='end', first_valid_lat=mock.ANY, last_valid_lat=mock.ANY ) expected_args = [ mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, mock.ANY, 'midnight', 'miss', mock.ANY, mock.ANY, mock.ANY, mock.ANY ] avhrr_gac_io.assert_called_with(*expected_args) pygac-1.7.2/pygac/tests/test_klm.py000066400000000000000000000157331444627033600173070ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2013, 2014 Martin Raspaud # Author(s): # Martin Raspaud # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test the GAC KLM reader.""" import datetime as dt from unittest import mock import numpy as np import numpy.testing from pygac.gac_klm import GACKLMReader from pygac.klm_reader import header from pygac.lac_klm import LACKLMReader, scanline from pygac.tests.utils import CalledWithArray class TestKLM: """Test the klm reader.""" def setup(self): """Set up the tests.""" self.reader = GACKLMReader() def test_get_lonlat(self): """Test readout of lon/lat coordinates.""" earth_loc = 1e4 * np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) self.reader.scans = {'earth_location': earth_loc} lons_exp = np.array([[2, 4], [6, 8]]) lats_exp = np.array([[1, 3], [5, 7]]) lons, lats = self.reader._get_lonlat() numpy.testing.assert_array_equal(lons, lons_exp) numpy.testing.assert_array_equal(lats, lats_exp) def test_get_header_timestamp(self): """Test readout of header timestamp.""" self.reader.head = { 'start_of_data_set_year': np.array([2019]), 'start_of_data_set_day_of_year': np.array([123]), 'start_of_data_set_utc_time_of_day': np.array([123456]) } time = self.reader.get_header_timestamp() assert time == dt.datetime(2019, 5, 3, 0, 2, 3, 456000) def test_get_times(self): """Test readout of scanline timestamps.""" self.reader.scans = {'scan_line_year': 1, 'scan_line_day_of_year': 2, 'scan_line_utc_time_of_day': 3} assert self.reader._get_times() == (1, 2, 3) def test_get_ch3_switch(self): """Test channel 3 identification.""" self.reader.scans = { 'scan_line_bit_field': np.array([1, 2, 3, 4, 5, 6])} switch_exp = np.array([1, 2, 3, 0, 1, 2]) numpy.testing.assert_array_equal( self.reader.get_ch3_switch(), switch_exp) def test_postproc(self): """Test KLM specific postprocessing.""" self.reader.scans = { 'scan_line_bit_field': np.array([0, 1, 2])} channels = np.array([[[1., 2., 3., 4.], [1., 2., 3., 4.]], [[1., 2., 3., 4.], [1., 2., 3., 4.]], [[1., 2., 3, 4.], [1., 2., 3, 4.]]]) # (lines, pixels, channels) masked_exp = np.array([[[1., 2., np.nan, 4.], [1., 2., np.nan, 4.]], [[1., 2., 3., np.nan], [1., 2., 3., np.nan]], [[1., 2., np.nan, np.nan], [1., 2., np.nan, np.nan]]]) self.reader.postproc(channels) # masks in-place numpy.testing.assert_array_equal(channels, masked_exp) def test_quality_indicators(self): """Test the quality indicator unpacking.""" reader = self.reader QFlag = reader.QFlag quality_indicators = np.array([ 0, # nothing flagged -1, # everything flagged QFlag.CALIBRATION | QFlag.NO_EARTH_LOCATION, QFlag.TIME_ERROR | QFlag.DATA_GAP, QFlag.FATAL_FLAG ], dtype=np.uint32) reader.scans = {self.reader._quality_indicators_key: quality_indicators} # test mask, i.e. QFlag.FATAL_FLAG | QFlag.CALIBRATION | QFlag.NO_EARTH_LOCATION expected_mask = np.array([False, True, True, False, True], dtype=bool) numpy.testing.assert_array_equal(reader.mask, expected_mask) # test individual flags assert reader._get_corrupt_mask(flags=QFlag.FATAL_FLAG).any() # count the occurence (everything flagged and last entrance => 2) assert reader._get_corrupt_mask(flags=QFlag.FATAL_FLAG).sum() == 2 class TestGACKLM: """Tests for gac klm.""" def setup(self): """Set up the tests.""" self.reader = GACKLMReader() @mock.patch('pygac.klm_reader.get_tsm_idx') def test_get_tsm_pixels(self, get_tsm_idx): """Test channel set used for TSM correction.""" ones = np.ones((409, 100)) zeros = np.zeros(ones.shape) ch1 = 1*ones ch2 = 2*ones ch4 = 4*ones ch5 = 5*ones channels = np.dstack((ch1, ch2, zeros, zeros, ch4, ch5)) self.reader.get_tsm_pixels(channels) get_tsm_idx.assert_called_with(CalledWithArray(ch1), CalledWithArray(ch2), CalledWithArray(ch4), CalledWithArray(ch5)) class TestLACKLM: """Tests for lac klm.""" def setup(self): """Set up the tests.""" self.reader = LACKLMReader() self.reader.scans = np.ones(100, dtype=scanline) self.reader.head = np.ones(1, dtype=header)[0] self.reader.spacecraft_id = 12 self.reader.head["noaa_spacecraft_identification_code"] = self.reader.spacecraft_id self.reader.spacecraft_name = "metopa" self.reader.scans["scan_line_number"] = np.arange(100) # PRT self.reader.scans["telemetry"]["PRT"] = 400 self.reader.scans["telemetry"]["PRT"][0::5, :] = 0 def test_get_ch3_switch(self): """Test channel 3 identification.""" self.reader.scans = { 'scan_line_bit_field': np.array([1, 2, 3, 4, 5, 6])} switch_exp = np.array([1, 2, 3, 0, 1, 2]) numpy.testing.assert_array_equal( self.reader.get_ch3_switch(), switch_exp) def test_calibrate_channels(self): """Test channel calibration.""" # ICT self.reader.scans["back_scan"] = 400 self.reader.scans["back_scan"][0::5, :] = 0 # Space self.reader.scans["space_data"] = 400 self.reader.scans["space_data"][0::5, :] = 0 assert np.any(np.isfinite(self.reader.get_calibrated_channels())) def test_calibrate_inactive_3b(self): """Test calibration of an inactive 3b.""" channels = self.reader.get_calibrated_channels() assert np.all(np.isnan(channels[:, :, 3])) pygac-1.7.2/pygac/tests/test_pod.py000066400000000000000000000314141444627033600173000ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test module for the pod reading.""" import datetime as dt import unittest import numpy as np import numpy.testing import sys try: from unittest import mock except ImportError: import mock from pygac.clock_offsets_converter import txt as clock_offsets_txt from pygac.reader import ReaderError, NoTLEData from pygac.gac_pod import GACPODReader from pygac.lac_pod import LACPODReader from pygac.tests.utils import CalledWithArray class TestPOD(unittest.TestCase): """Test the POD GAC reader.""" longMessage = True def setUp(self): """Set up the test.""" self.reader = GACPODReader() # python 2 compatibility if sys.version_info.major < 3: self.assertRaisesRegex = self.assertRaisesRegexp def test__validate_header(self): """Test the header validation""" filename = b'NSS.GHRR.TN.D80001.S0332.E0526.B0627173.WI' head = {'data_set_name': filename} GACPODReader._validate_header(head) # wrong name pattern with self.assertRaisesRegex(ReaderError, 'Data set name .* does not match!'): head = {'data_set_name': b'abc.txt'} GACPODReader._validate_header(head) # wrong platform name = b'NSS.GHRR.NL.D02187.S1904.E2058.B0921517.GC' with self.assertRaisesRegex(ReaderError, 'Improper platform id "NL"!'): head = {'data_set_name': name} GACPODReader._validate_header(head) # wrong transfer mode name = filename.replace(b'GHRR', b'LHRR') with self.assertRaisesRegex(ReaderError, 'Improper transfer mode "LHRR"!'): head = {'data_set_name': name} GACPODReader._validate_header(head) # other change reader head = {'data_set_name': name} LACPODReader._validate_header(head) @mock.patch('pygac.reader.Reader.get_calibrated_channels') def test__get_calibrated_channels_uniform_shape(self, get_channels): """Test the uniform shape as required by gac_io.save_gac.""" channels = np.arange(2*2*5, dtype=float).reshape((2, 2, 5)) get_channels.return_value = channels uniform_channels = self.reader._get_calibrated_channels_uniform_shape() self.assertTrue(np.isnan(uniform_channels[:, :, 2]).all()) self.assertTrue(uniform_channels[:, :, [0, 1, 3, 4, 5]].sum() == channels.sum()) def test_decode_timestamps(self): """Test POD timestamp decoding.""" # Reference timestamps, one before 2000 one after 2000 t2000_ref = (2001, 335, 53644260) t1900_ref = (1983, 336, 35058207) # Encoded version t2000_enc = np.array([847, 818, 35812]) t1900_enc = np.array([42832, 534, 61983]) # Test whether PODReader decodes them correctly self.assertEqual(GACPODReader.decode_timestamps(t2000_enc), t2000_ref, msg='Timestamp after 2000 was decoded incorrectly') self.assertEqual(GACPODReader.decode_timestamps(t1900_enc), t1900_ref, msg='Timestamp before 2000 was decoded incorrectly') @mock.patch('pygac.gac_pod.GACPODReader.decode_timestamps') def test_get_header_timestamp(self, decode_timestamps): """Test readout of header timestamp.""" self.reader.head = {'start_time': 123} decode_timestamps.return_value = np.array( [2019]), np.array([123]), np.array([123456]) time = self.reader.get_header_timestamp() decode_timestamps.assert_called_with(123) self.assertEqual(time, dt.datetime(2019, 5, 3, 0, 2, 3, 456000)) @mock.patch('pygac.gac_pod.GACPODReader.decode_timestamps') def test_get_times(self, decode_timestamps): """Test getting times.""" self.reader.scans = {'time_code': 123} self.reader._get_times() decode_timestamps.assert_called_with(123) def test_get_lonlat(self): """Test readout of lon/lat coordinates.""" earth_loc = 128 * np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) self.reader.scans = {'earth_location': earth_loc} lons_exp = np.array([[2, 4], [6, 8]]) lats_exp = np.array([[1, 3], [5, 7]]) lons, lats = self.reader._get_lonlat() numpy.testing.assert_array_equal(lons, lons_exp) numpy.testing.assert_array_equal(lats, lats_exp) @mock.patch('pygac.pod_reader.get_tsm_idx') def test_get_tsm_pixels(self, get_tsm_idx): """Test channel set used for TSM correction.""" ones = np.ones((409, 100)) zeros = np.zeros(ones.shape) ch1 = 1*ones ch2 = 2*ones ch4 = 4*ones ch5 = 5*ones channels = np.dstack((ch1, ch2, zeros, ch4, ch5)) self.reader.get_tsm_pixels(channels) get_tsm_idx.assert_called_with(CalledWithArray(ch1), CalledWithArray(ch2), CalledWithArray(ch4), CalledWithArray(ch5)) def test_quality_indicators(self): """Test the quality indicator unpacking.""" reader = self.reader QFlag = reader.QFlag quality_indicators = np.array([ 1, # 00...001 QFlag.FATAL_FLAG, # 100...00 QFlag.CALIBRATION | QFlag.NO_EARTH_LOCATION, QFlag.TIME_ERROR | QFlag.DATA_GAP, ], dtype='>u4') # check if the bits look as expected bits = np.unpackbits(quality_indicators.view(np.uint8)).reshape((-1, 32)) # For a big endian integer, the number 1 fills only the last of the 32 bits self.assertEqual(bits[0].sum(), 1) # only one bit is filled self.assertEqual(bits[0][-1], 1) # the last bit is filled # The fatal flag fills only the first bit self.assertEqual(bits[1].sum(), 1) # only one bit is filled self.assertEqual(bits[1][0], 1) # the first bit is filled # setup reader and test reader.scans = {self.reader._quality_indicators_key: quality_indicators} # default mask is QFlag.FATAL_FLAG | QFlag.CALIBRATION | QFlag.NO_EARTH_LOCATION expected_mask = np.array([False, True, True, False], dtype=bool) numpy.testing.assert_array_equal(reader.mask, expected_mask) # test individual flags expected_mask = np.array([False, False, False, True], dtype=bool) numpy.testing.assert_array_equal( reader._get_corrupt_mask(flags=QFlag.TIME_ERROR), expected_mask ) # test combination of flags expected_mask = np.array([False, False, True, True], dtype=bool) flags = QFlag.DATA_GAP | QFlag.NO_EARTH_LOCATION numpy.testing.assert_array_equal( reader._get_corrupt_mask(flags=flags), expected_mask ) @mock.patch('pygac.pod_reader.get_lonlatalt') @mock.patch('pygac.pod_reader.compute_pixels') @mock.patch('pygac.reader.Reader.get_tle_lines') @mock.patch('pygac.pod_reader.avhrr_gac') def test__adjust_clock_drift(self, avhrr_gac, get_tle_lines, compute_pixels, get_lonlatalt): """Test the clock drift adjustment.""" sat_name = "fake_sat" reader = self.reader # We construct the following input # the scan lines do not start with 1 and have a gap scan_lines = np.array([15, 16, 17, 18, 22, 23, 24, 25]) scan_rate = 0.5 # seconds/line # the first valid scans starts 1980-01-01 (without clock correction) # which leads to the following utcs for the given scan lines # ['1980-01-01T00:00:00.000', '1980-01-01T00:00:00.500', # '1980-01-01T00:00:01.000', '1980-01-01T00:00:01.500', # '1980-01-01T00:00:03.500', '1980-01-01T00:00:04.000', # '1980-01-01T00:00:04.500', '1980-01-01T00:00:05.000'] scan_utcs = ( (1000 * scan_rate * (scan_lines - scan_lines[0])).astype('timedelta64[ms]') + np.datetime64("1980", "ms") ) # For the geolocations, we assume an artificial swath of two pixels width # from north to south with constant lon, lat difference of 3deg for simplicity # lons = [[0, 3], [0, 3], [0, 3], [0, 3], # [0, 3], [0, 3], [0, 3], [0, 3]], # lats = [[45, 45], [48, 48], [51, 51], [54, 54], # [66, 66], [69, 69], [72, 72], [75, 75]] scan_angle = 3.0 # deg scan_lons, scan_lats = np.meshgrid(scan_angle*np.arange(2), scan_angle*scan_lines) # we assume a constant clock offset of 3.75 seconds # which should lead to the following adjustment on utcs # ['1979-12-31T23:59:56.250', '1979-12-31T23:59:56.750', # '1979-12-31T23:59:57.250', '1979-12-31T23:59:57.750', # '1979-12-31T23:59:59.750', '1980-01-01T00:00:00.250', # '1980-01-01T00:00:00.750', '1980-01-01T00:00:01.250'] offset = 3.75 scan_offsets = offset*np.ones_like(scan_lines, dtype=float) # seconds expected_utcs = scan_utcs - (1000*scan_offsets).astype('timedelta64[ms]') # the adjustment of geolocations should keep the lons unchanged, # but should shift the lats by scan_angel * offset / scan_rate # = 3deg/line * 3.75sec / 0.5sec/line = 22.5deg # [[22.5, 22.5], [25.5, 25.5], [28.5, 28.5], [31.5, 31.5], # [43.5, 43.5], [46.5, 46.5], [49.5, 49.5], [52.5, 52.5]] expected_lons = scan_lons lats_shift = scan_angle*offset/scan_rate expected_lats = scan_lats - lats_shift # prepare the reader reader.scans = {"scan_line_number": scan_lines} reader.utcs = scan_utcs reader.lons = scan_lons reader.lats = scan_lats reader.spacecraft_name = sat_name # prepare offsets clock_offsets_txt[sat_name] = ("75001 000000 {offset} " "85001 000000 {offset}").format(offset=offset) # set attitude coeffs reader._rpy = np.zeros(3) # set mocks for reader._compute_missing_lonlat call sgeom = mock.Mock() sgeom.times.return_value = [None] avhrr_gac.return_value = sgeom get_tle_lines.return_value = [None, None] compute_pixels.return_value = None # for the following mock, we need to know the missing values in advanced, # which we do, because we can convert the offset seconds into line number. offset_lines = offset / scan_rate min_line = np.floor(scan_lines[0] - offset_lines).astype(int) max_line = scan_lines[-1] missed_lines = np.setdiff1d(np.arange(min_line, max_line+1), scan_lines) n_missed = len(missed_lines) missed_lons = np.tile([0., scan_angle], n_missed) missed_lats = np.repeat(scan_angle*missed_lines, 2) get_lonlatalt.return_value = [missed_lons, missed_lats] # adjust clock drift reader._adjust_clock_drift() # check output # use allclose for geolocations, because the slerp interpolation # includes a transormation to cartesian coordinates and back to lon, lats. numpy.testing.assert_array_equal(reader.utcs, expected_utcs) numpy.testing.assert_allclose(reader.lons, expected_lons) numpy.testing.assert_allclose(reader.lats, expected_lats) # undo changes to clock_offsets_txt clock_offsets_txt.pop(sat_name) @mock.patch('pygac.pod_reader.get_offsets') @mock.patch('pygac.reader.Reader.get_tle_lines') def test__adjust_clock_drift_without_tle(self, get_tle_lines, get_offsets): """Test that clockdrift adjustment can handle missing TLE data.""" reader = self.reader reader.utcs = np.zeros(10, dtype='datetime64[ms]') reader.scans = {"scan_line_number": np.arange(10)} get_offsets.return_value = np.zeros(10), np.zeros(10) get_tle_lines.side_effect = NoTLEData('No TLE data available') reader._adjust_clock_drift() # should pass without errors pygac-1.7.2/pygac/tests/test_reader.py000066400000000000000000000724721444627033600177710ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test the readers.""" import datetime import os import sys import unittest try: import mock except ImportError: from unittest import mock import numpy as np import numpy.testing from pygac.gac_reader import GACReader, ReaderError from pygac.lac_reader import LACReader from pygac.pod_reader import POD_QualityIndicator from pygac.gac_pod import scanline from pygac.reader import NoTLEData class TestPath(os.PathLike): """Fake path class.""" def __init__(self, path): """Initialize the path.""" self.path = str(path) def __fspath__(self): """Return the path.""" return self.path class FakeGACReader(GACReader): """Fake GAC reader class.""" QFlag = POD_QualityIndicator _quality_indicators_key = "quality_indicators" tsm_affected_intervals = {None: []} along_track = 3 across_track = 4 def __init__(self): """Initialize the fake reader.""" super().__init__() self.scan_width = self.across_track scans = np.zeros(self.along_track, dtype=scanline) scans["scan_line_number"] = np.arange(self.along_track) scans["sensor_data"] = 128 self.scans = scans self.head = {'foo': 'bar'} self.spacecraft_name = 'noaa6' def _get_times(self): year = np.full(self.along_track, 1970, dtype=int) jday = np.full(self.along_track, 1, dtype=int) msec = 1000 * np.arange(1, self.along_track+1, dtype=int) return year, jday, msec def get_header_timestamp(self): """Get the header timestamp.""" return datetime.datetime(1970, 1, 1) def get_telemetry(self): """Get the telemetry.""" prt = 51 * np.ones(self.along_track) # prt threshold is 50 ict = 101 * np.ones((self.along_track, 3)) # ict threshold is 100 space = 101 * np.ones((self.along_track, 3)) # space threshold is 100 return prt, ict, space def _adjust_clock_drift(self): pass def _get_lonlat(self): pass @staticmethod def _get_ir_channels_to_calibrate(): return [3, 4, 5] def postproc(self, channels): """Postprocess the data.""" pass def read(self, filename, fileobj=None): """Read the data.""" pass @classmethod def read_header(cls, filename, fileobj=None): """Read the header.""" pass def get_tsm_pixels(self, channels): """Get the tsm pixels.""" pass class TestGacReader(unittest.TestCase): """Test the common GAC Reader.""" longMessage = True @mock.patch.multiple('pygac.gac_reader.GACReader', __abstractmethods__=set()) @mock.patch('pygac.gac_reader.gtp.gac_lat_lon_interpolator') def setUp(self, interpolator, *mocks): """Set up the tests.""" self.interpolator = interpolator self.reader = GACReader() # python 2 compatibility if sys.version_info.major < 3: self.assertRaisesRegex = self.assertRaisesRegexp def test_filename(self): """Test the setter of the filename property.""" # test path with .gz extension filename = 'NSS.GHRR.TN.D80001.S0332.E0526.B0627173.WI' filepath = '/path/to/' + filename + '.gz' self.reader.filename = filepath self.assertEqual(self.reader.filename, filename) self.reader.filename = None self.assertIsNone(self.reader.filename) self.reader.filename = TestPath(filepath) self.assertEqual(self.reader.filename, filename) @unittest.skipIf(sys.version_info.major < 3, "Skipped in python2!") def test__read_scanlines(self): """Test the scanline extraction.""" self.reader.scanline_type = np.dtype([ ('a', 'S2'), ('b', 'M8[ms]") # Add some corruptions msecs[3000:] += 1800 * 1000 msecs[1000] += 24*3600*1000 msecs[2000] -= 24*3600*1000 # Mock reader get_header_timestamp.return_value = header_time self.reader.utcs = msecs.astype(">M8[ms]") self.reader.scans = np.array(scan_line_numbers, dtype=[("scan_line_number", ">u2")]) # Test correction self.reader.correct_times_thresh() numpy.testing.assert_array_equal(self.reader.utcs, utcs_expected) def test_calculate_sun_earth_distance_correction(self): """Test the calculate sun earth distance correction method.""" self.reader.utcs = np.array([315748035469, 315748359969, 315751135469, 315754371969, 315754371969]).astype('datetime64[ms]') corr = self.reader.get_sun_earth_distance_correction() numpy.testing.assert_almost_equal(corr, 0.96660494, decimal=7) @mock.patch('pygac.reader.Reader.get_sun_earth_distance_correction') @mock.patch('pygac.reader.Reader.get_midnight_scanline') @mock.patch('pygac.reader.Reader.get_miss_lines') @mock.patch('pygac.reader.Reader.calibration', new_callable=mock.PropertyMock) def test_update_metadata(self, calibration, get_miss_lines, get_midnight_scanline, get_sun_earth_distance_correction): """Test updating the metadata.""" get_miss_lines.return_value = 'miss_lines' get_midnight_scanline.return_value = 'midn_line' get_sun_earth_distance_correction.return_value = 'factor' self.reader.head = {'foo': 'bar'} calibration.return_value = mock.MagicMock(version='version') self.reader.update_meta_data() mda_exp = {'midnight_scanline': 'midn_line', 'missing_scanlines': 'miss_lines', 'sun_earth_distance_correction_factor': 'factor', 'gac_header': {'foo': 'bar'}, 'calib_coeffs_version': 'version'} self.assertDictEqual(self.reader.meta_data, mda_exp) def _get_scanline_numbers(scanlines_along_track): """Create artificial scanline numbers with some corruptions. Returns: Corrupted and corrected scanline numbers. """ scans = np.zeros(scanlines_along_track, dtype=[("scan_line_number", ">u2")]) scans["scan_line_number"] = np.arange(1, scanlines_along_track + 1) # ... with 500 missing scanlines at scanline 8000 scans["scan_line_number"][8000:] += 500 corrected = scans["scan_line_number"].copy() # ... and some spikes here and there scans["scan_line_number"][3000] += 1E4 scans["scan_line_number"][9000] -= 1E4 corrected = np.delete(corrected, [3000, 9000]) return scans, corrected class TestLacReader(unittest.TestCase): """Test the common LAC Reader.""" longMessage = True @mock.patch.multiple('pygac.lac_reader.LACReader', __abstractmethods__=set()) def setUp(self, *mocks): """Set up the tests.""" self.reader = LACReader() def test_lac_reader_accepts_FRAC(self): """Test the header validation.""" head = {'data_set_name': b'NSS.FRAC.M1.D19115.S2352.E0050.B3425758.SV'} self.reader._validate_header(head) def test_correct_scan_line_numbers(self): """Test scanline number correction.""" scans, expected = _get_scanline_numbers(22000) self.reader.scans = scans self.reader.correct_scan_line_numbers() numpy.testing.assert_array_equal(self.reader.scans['scan_line_number'], expected) pygac-1.7.2/pygac/tests/test_slerp.py000066400000000000000000000105221444627033600176400ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Martin Raspaud # Author(s): # Martin Raspaud # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test the slerp module """ import unittest from pygac.slerp import slerp import numpy as np class TestSlerp(unittest.TestCase): def test_slerp(self): lon0, lat0 = (0, 0) lon1, lat1 = (0, 1) self.assertTrue( np.allclose(slerp(lon0, lat0, lon1, lat1, 0.5), (0, 0.5))) def test_slerp_datum(self): lon0, lat0 = (183, 0) lon1, lat1 = (179, 0) res = slerp(lon0, lat0, lon1, lat1, 0.5) res %= 360 self.assertTrue( np.allclose(res, (181, 0))) def test_slerp_pole(self): lon0, lat0 = (0, 89) lon1, lat1 = (180, 89) res = slerp(lon0, lat0, lon1, lat1, 0.5) self.assertTrue( np.allclose(res[:, :, 1], 90)) lon0, lat0 = (-90, 89) lon1, lat1 = (90, 89) res = slerp(lon0, lat0, lon1, lat1, 0.5) self.assertTrue( np.allclose(res[:, :, 1], 90)) lon0, lat0 = (0, 89) lon1, lat1 = (180, 87) res = slerp(lon0, lat0, lon1, lat1, 0.5) self.assertTrue( np.allclose(res, (180, 89))) def test_slerp_vec(self): lon0 = np.array([[0, 0], [0, 0]]) lat0 = np.array([[0, 0], [0, 0]]) lon1 = np.array([[0, 0], [0, 0]]) lat1 = np.array([[1, 1], [1, 1]]) res = slerp(lon0, lat0, lon1, lat1, 0.5) self.assertTrue(np.allclose(res[:, :, 0], 0)) self.assertTrue(np.allclose(res[:, :, 1], 0.5)) lon0 = np.array([[183, 0], [-90, 0]]) lat0 = np.array([[0, 89], [89, 89]]) lon1 = np.array([[179, 180], [90, 180]]) lat1 = np.array([[0, 89], [89, 87]]) res = slerp(lon0, lat0, lon1, lat1, 0.5) self.assertTrue(np.allclose(res[0, 0, :] % 360, (181, 0))) self.assertTrue(np.allclose(res[0, 1, 1], 90)) self.assertTrue(np.allclose(res[1, 0, 1], 90)) self.assertTrue(np.allclose(res[1, 1, :], (180, 89))) def test_slerp_tvec(self): lon0 = np.array([[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]) lat0 = np.array([[0, 0], [5, 0], [10, 0], [15, 0], [20, 0], [25, 0], [30, 0]]) lon1 = np.array([[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]) lat1 = np.array([[45, 45], [45, 45], [45, 45], [45, 45], [45, 45], [45, 45], [45, 45]]) t = np.array([[0.5, 0, 0.2, 0.4, 0.6, 0.8, 1]]).T t = t[:, :, np.newaxis] res = slerp(lon0, lat0, lon1, lat1, t) expected = np.array([[22.5, 22.5], [5., 0.], [17., 9.], [27., 18.], [35., 27.], [41., 36.], [45., 45.]]) self.assertTrue(np.allclose(res[:, :, 0], 0)) self.assertTrue(np.allclose(res[:, :, 1], expected)) pygac-1.7.2/pygac/tests/test_tsm.py000066400000000000000000000061111444627033600173150ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test TSM module.""" import unittest import pygac.correct_tsm_issue as tsm import numpy as np import numpy.testing class TSMTest(unittest.TestCase): """Test TSM module.""" def test_get_tsm_idx(self): """Test identification of TSM affected pixels.""" cols = np.arange(409) rows = np.arange(3000) mcols, mrows = np.meshgrid(cols, rows) # Create dummy reflectances in [0, 1] and BTs [170, 350] refl = np.sin(mrows / float(rows.size)) * \ np.cos(mcols / float(cols.size)) bt = np.cos(mrows / float(rows.size)) * \ np.sin(mcols / float(cols.size)) bt = 170 + (350 - 170) * bt ch1 = refl.copy() ch2 = refl.copy() ch4 = bt.copy() ch5 = bt.copy() # Add rectangle with noise to channel 1 and 4 noise_cols, noise_rows = np.meshgrid(np.arange(200, 300), np.arange(1000, 2000)) for ch in [ch1, ch4]: ch[noise_rows, noise_cols] += 1000 + 1000*np.random.rand( noise_rows.shape[0], noise_rows.shape[1]) # We expect the filter to also detect the edges of the noisy # rectangle: At the edges there is a transition from zero to # nonzero channel diff which leads to an increased 3x3 standard # deviation. noise_cols_exp, noise_rows_exp = np.meshgrid(np.arange(199, 301), np.arange(999, 2001)) idx = tsm.get_tsm_idx(ch1, ch2, ch4, ch5) numpy.testing.assert_array_equal(idx[0], noise_rows_exp.ravel()) numpy.testing.assert_array_equal(idx[1], noise_cols_exp.ravel()) def test_std_filter(self): """Test standard deviation filter.""" # Define test data data = np.array([ [2., 2., 2., 2.], [2., np.nan, np.nan, 2.], [np.nan, 3., 3., np.nan], [3., 2., 2., 3.], ]) # Define reference filtered_ref = np.sqrt(np.array([ [0., 0., 0., 0.], [0.1875, 2./9., 2./9., 0.1875], [0.25, 0.25, 0.25, 0.25], [2./9., 0.24, 0.24, 2./9.] ])) # Apply filter and compare results against reference filtered = tsm.std_filter(data=data, box_size=3) numpy.testing.assert_array_equal(filtered, filtered_ref) pygac-1.7.2/pygac/tests/test_utils.py000066400000000000000000000075211444627033600176600ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2013, 2014 Martin Raspaud # Author(s): # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test pygac.utils module """ import gzip import io import os import unittest from unittest import mock import numpy as np from pygac.utils import file_opener, calculate_sun_earth_distance_correction class TestUtils(unittest.TestCase): """Test pygac.utils functions""" longMessage = True @mock.patch('pygac.utils.open', mock.MagicMock(return_value=io.BytesIO(b'file content'))) def test_file_opener_1(self): """Test if a file is redirected correctly through file_opener.""" with file_opener('path/to/file') as f: content = f.read() self.assertEqual(content, b'file content') def test_file_opener_2(self): """Test file_opener with file objects and compression""" # prepare test normal_message = b'normal message' gzip_message_decoded = b'gzip message' with io.BytesIO() as f: with gzip.open(f, mode='wb') as g: g.write(gzip_message_decoded) f.seek(0) gzip_message_encoded = f.read() # on normal file (check also if it remains open) with io.BytesIO(normal_message) as f: with file_opener(f) as g: message = g.read() self.assertFalse(f.closed) self.assertEqual(message, normal_message) # on gzip file with io.BytesIO(gzip_message_encoded) as f: with file_opener(f) as g: message = g.read() self.assertEqual(message, gzip_message_decoded) @mock.patch('pygac.utils.open', mock.MagicMock(side_effect=FileNotFoundError)) def test_file_opener_3(self): """Test file_opener with PathLike object""" # prepare test class RawBytes(os.PathLike): def __init__(self, filename, raw_bytes): self.filename = str(filename) self.raw_bytes = raw_bytes def __fspath__(self): return self.filename def open(self): return io.BytesIO(self.raw_bytes) filename = '/path/to/file' file_bytes = b'TestTestTest' test_pathlike = RawBytes(filename, file_bytes) with file_opener(test_pathlike) as f: content = f.read() self.assertEqual(content, file_bytes) # test with lazy loading open method (open only in context) class RawBytesLazy(RawBytes): def open(self): self.lazy_opener_mock = mock.MagicMock() self.lazy_opener_mock.__enter__.return_value = io.BytesIO(self.raw_bytes) return self.lazy_opener_mock test_pathlike = RawBytesLazy(filename, file_bytes) with file_opener(test_pathlike) as f: content = f.read() self.assertEqual(content, file_bytes) test_pathlike.lazy_opener_mock.__exit__.assert_called_once_with(None, None, None) def test_calculate_sun_earth_distance_correction(self): """Test function for the sun distance corretction.""" corr = calculate_sun_earth_distance_correction(3) np.testing.assert_almost_equal(corr, 0.96660494, decimal=7) pygac-1.7.2/pygac/tests/utils.py000066400000000000000000000020611444627033600166130ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """Test utilities.""" import numpy as np class CalledWithArray(object): """Adapter for arrays in mock.assert_called_with().""" def __init__(self, array): self.array = array def __repr__(self): return repr(self.array) def __eq__(self, other): return np.all(self.array == other) pygac-1.7.2/pygac/utils.py000066400000000000000000000260561444627033600154630ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author(s): # Stephan Finkensieper # Carlos Horn # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . import gzip import io import logging from contextlib import contextmanager, nullcontext import numpy as np LOG = logging.getLogger(__name__) def gzip_inspected(open_file): """Try to gzip decompress the file object if applicable.""" try: file_object = gzip.GzipFile(mode='rb', fileobj=open_file) file_object.read(1) except OSError: file_object = open_file finally: file_object.seek(0) return file_object @contextmanager def file_opener(file): if isinstance(file, io.IOBase) and file.seekable(): # avoid closing the file using nullcontext open_file = nullcontext(file) elif hasattr(file, 'open'): try: open_file = file.open(mode='rb') except TypeError: open_file = file.open() else: open_file = open(file, mode='rb') # set open_file into context in case of lazy loading in __enter__ method. with open_file as file_object: yield gzip_inspected(file_object) def get_absolute_azimuth_angle_diff(sat_azi, sun_azi): """Calculates absolute azimuth difference angle. """ rel_azi = abs(sat_azi - sun_azi) rel_azi = rel_azi % 360 # Not using np.where to avoid copying array rel_azi[rel_azi > 180] = 360.0 - rel_azi[rel_azi > 180] return rel_azi def centered_modulus(array, divisor): """Transform array to half open range ]-divisor/2, divisor/2].""" arr = array % divisor arr[arr > divisor / 2] -= divisor return arr def calculate_sun_earth_distance_correction(jday): """Calculate the sun earth distance correction. In 2008 3-4 different equations of ESD were considered. This one was chosen as it at the time gave reflectances most closely matching the PATMOS-x data provided then by Andy Heidinger. Formula might need to be reconsidered if jday is updated to a float. """ # Earth-Sun distance correction factor corr = 1.0 - 0.0334 * np.cos(2.0 * np.pi * (jday - 2) / 365.25) return corr def check_user_scanlines(start_line, end_line, first_valid_lat=None, last_valid_lat=None, along_track=None): """Check user-defined scanlines. Can be used by both pygac and satpy. Args: start_line: User-defined start line (afer stripping, if enabled) end_line: User-defined end line (afer stripping, if enabled) first_valid_lat: First scanline with valid latitudes last_valid_lat: Last scanline with valid latitudes along_track: Number of scanlines (only needed if stripping is disabled) """ if first_valid_lat is not None and last_valid_lat is not None: num_valid_lines = last_valid_lat - first_valid_lat + 1 else: if along_track is None: raise ValueError('Need along_track') num_valid_lines = along_track start_line = int(start_line) end_line = int(end_line) if end_line == 0: # If the user specifies 0 as the last scanline, process all # scanlines with valid coordinates end_line = num_valid_lines - 1 elif end_line >= num_valid_lines: end_line = num_valid_lines - 1 LOG.warning('Given end line exceeds scanline range, resetting ' 'to {}'.format(end_line)) if start_line > num_valid_lines: raise ValueError('Given start line {} exceeds scanline range {}' .format(start_line, num_valid_lines)) return start_line, end_line def strip_invalid_lat(lats): """Strip invalid latitudes at the end and beginning of the orbit.""" no_wrong_lat = np.where(np.logical_not(np.isnan(lats))) return min(no_wrong_lat[0]), max(no_wrong_lat[0]) def slice_channel(ch, start_line, end_line, first_valid_lat=None, last_valid_lat=None, midnight_scanline=None, miss_lines=None, qual_flags=None): """Slice channel data using user-defined start/end line. If valid_lat_start/end are given, strip scanlines with invalid coordinates at the beginning and end of the orbit. Can be used by both pygac and satpy. Args: ch: Channel data start_line: User-defined start line (afer stripping, if enabled) end_line: User-defined end line (after stripping, if enabled) first_valid_lat: First scanline with valid latitudes last_valid_lat: Last scanline with valid latitudes. midnight_scanline: If given, update midnight scanline to the new scanline range. miss_lines: If given, update list of missing lines with the ones that have been stripped due to invalid coordinates qual_flags: Quality flags, needed to updated missing lines. """ if first_valid_lat is not None and last_valid_lat is not None: # Strip invalid coordinates and update midnight scanline as well as # user-defined start/end lines ch, updated = _slice(ch, start_line=first_valid_lat, end_line=last_valid_lat, update=[midnight_scanline]) midnight_scanline = updated[0] # Reset user-defined end line, if it has been removed end_line = min(end_line, ch.shape[0] - 1) start_line = min(start_line, ch.shape[0] - 1) # Update missing scanlines if miss_lines is not None: miss_lines = _update_missing_scanlines( miss_lines=miss_lines, qual_flags=qual_flags, start_line=first_valid_lat, end_line=last_valid_lat) # Slice data using user-defined start/end lines ch_slc, updated = _slice(ch, start_line=start_line, end_line=end_line, update=[midnight_scanline]) midnight_scanline = updated[0] return ch_slc, miss_lines, midnight_scanline def _slice(ch, start_line, end_line, update=None): """Slice the given channel. Args: start_line: New start line end_line: New end line update: List of scanlines to be updated to the new range """ # Slice data using new start/end lines if len(ch.shape) == 1: ch_slc = ch[start_line:end_line + 1].copy() else: ch_slc = ch[start_line:end_line + 1, :].copy() if update: updated = [_update_scanline(l, start_line, end_line) if l is not None else None for l in update] return ch_slc, updated return ch_slc def _update_scanline(scanline, new_start_line, new_end_line): """Update the given scanline to the new range. Set scanline to None if it lies outside the new range. """ scanline -= new_start_line num_lines = new_end_line - new_start_line + 1 if scanline < 0 or scanline >= num_lines: scanline = None return scanline def _update_missing_scanlines(miss_lines, qual_flags, start_line, end_line): """Add scanlines excluded by slicing to the list of missing scanlines. Args: miss_lines: List of missing scanlines qual_flags: Quality flags start_line: New start line of the slice end_line: New end line of the slice """ return np.sort(np.unique( qual_flags[0:start_line, 0].tolist() + miss_lines.tolist() + qual_flags[end_line + 1:, 0].tolist() )) def plot_correct_times_thresh(res, filename=None): """Visualize results of GACReader.correct_times_thresh.""" import matplotlib.pyplot as plt t = res['t'] tcorr = res.get('tcorr') n = res['n'] offsets = res.get('offsets') t0_head = res.get('t0_head') max_diff_from_t0_head = res.get('max_diff_from_t0_head') fail_reason = res.get('fail_reason', 'Failed for unknown reason') # Setup figure along_track = np.arange(t.size) _, (ax0, ax1, ax2) = plt.subplots(nrows=3, sharex=True, figsize=(8, 10)) # Plot original vs corrected timestamps ax0.plot(along_track, t, "b-", label="original") if tcorr is not None: ax0.plot(along_track, tcorr, color="red", linestyle="--", label="corrected") else: ax0.set_title(fail_reason) ax0.set_ylabel("Time") ax0.set_ylim(t.min() - np.timedelta64(30, "m"), t.max() + np.timedelta64(30, "m")) ax0.legend(loc="best") # Plot offset (original time - ideal time) if offsets is not None: ax1.plot(along_track, offsets) ax1.fill_between( along_track, t0_head - np.ones(along_track.size) * max_diff_from_t0_head, t0_head + np.ones(along_track.size) * max_diff_from_t0_head, facecolor="g", alpha=0.33) ax1.axhline(y=t0_head, color="g", linestyle="--", label="Header timestamp") ax1.set_ylim(t0_head - 5 * max_diff_from_t0_head, t0_head + 5 * max_diff_from_t0_head) ax1.set_ylabel("Offset t-tn [ms]") ax1.legend(loc="best") # Plot scanline number ax2.plot(along_track, n) ax2.set_ylabel("Scanline number") ax2.set_xlabel("Along Track") if filename: plt.savefig(filename, bbox_inches="tight", dpi=100) else: plt.show() def plot_correct_scanline_numbers(res, filename=None): """Visualize results of GACReader.correct_scanline_numbers.""" import matplotlib.pyplot as plt along_track = res['along_track'] n_orig = res['n_orig'] n_corr = res['n_corr'] within_range = res['within_range'] thresh = res['thresh'] diffs = res['diffs'] nz_diffs = res['nz_diffs'] # Setup figure _, (ax0, ax1) = plt.subplots(nrows=2) # Plot original vs corrected scanline numbers ax0.plot(along_track, n_orig, "b-", label="original") along_track_corr = along_track.copy() along_track_corr = along_track_corr[within_range] along_track_corr = along_track_corr[diffs <= thresh] ax0.plot(along_track_corr, n_corr, "r--", label="corrected") ax0.set_ylabel("Scanline Number") ax0.set_xlabel("Along Track") ax0.legend(loc="best") # Plot difference from ideal ax1.plot(np.arange(len(nz_diffs)), nz_diffs) ax1.axhline(thresh, color="r", label="thresh={0:.2f}" .format(thresh)) ax1.set_xlabel("Index") ax1.set_ylabel("nonzero |n - n'|") ax1.legend() plt.tight_layout() if filename: plt.savefig(filename, bbox_inches='tight') else: plt.show() pygac-1.7.2/pyproject.toml000066400000000000000000000003161444627033600155510ustar00rootroot00000000000000[build-system] requires = ["setuptools>=45", "wheel", "setuptools_scm[toml]>=6.2", 'setuptools_scm_git_archive'] build-backend = "setuptools.build_meta" [tool.setuptools_scm] write_to = "pygac/version.py" pygac-1.7.2/requirements.txt000066400000000000000000000001231444627033600161150ustar00rootroot00000000000000numpy>=1.6.1 h5py>=2.0.1 scipy>=0.8.0 python-geotiepoints>=1.1.8 packaging>=14.0 pygac-1.7.2/setup.cfg000066400000000000000000000001551444627033600144570ustar00rootroot00000000000000[options] setup_requires = setuptools_scm setuptools_scm_git_archive [flake8] max-line-length = 120 pygac-1.7.2/setup.py000066400000000000000000000052341444627033600143530ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2014 Abhay Devasthale, Adam.Dybbroe, Martin Raspaud # Author(s): # Adam.Dybbroe # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . """The setup module.""" from setuptools import setup import os if __name__ == '__main__': with open("README.md", "r") as fd: long_description = fd.read() requirements = ['docutils>=0.3', 'numpy>=1.8.0', 'pyorbital>=v0.3.2', 'h5py>=2.0.1', 'scipy>=0.8.0', 'python-geotiepoints>=1.1.8', 'bottleneck>=1.0.0'] extras_require = { 'dev': ['pytest', 'pre-commit', 'flake8'] } setup(name='pygac', description='NOAA AVHRR GAC/LAC reader and calibration', author='The Pytroll Team', author_email='pytroll@googlegroups.com', classifiers=["Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 " + "or later (GPLv3+)", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Scientific/Engineering"], url="https://github.com/pytroll/pygac", long_description=long_description, long_description_content_type='text/markdown', license='GPLv3', packages=['pygac'], package_data={'pygac': ['data/calibration.json']}, # Project should use reStructuredText, so ensure that the docutils get # installed or upgraded on the target machine install_requires=requirements, extras_require=extras_require, scripts=[os.path.join('bin', item) for item in os.listdir('bin')], data_files=[('etc', ['etc/pygac.cfg.template']), ('gapfilled_tles', ['gapfilled_tles/TLE_noaa16.txt'])], python_requires='>=3.8', zip_safe=False )