diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 000000000..5d3a56beb --- /dev/null +++ b/.coveragerc @@ -0,0 +1,7 @@ +[run] +branch = True +source = icepyx +omit = + setup.py + test/* + doc/* diff --git a/.github/workflows/binderbot.yaml b/.github/workflows/binderbot.yaml new file mode 100644 index 000000000..7c95fdb5e --- /dev/null +++ b/.github/workflows/binderbot.yaml @@ -0,0 +1,80 @@ +name: Binderbot +on: + # Trigger the workflow on push or pull request, + # but only for the master branch + push: + branches: + - enable-gallery + - development + - master + paths: + - 'examples/*.ipynb' + - 'binder-gallery.yaml' + - 'doc/source/_static/icepyx_logo.png' + pull_request: + branches: + - enable-gallery + - development + - master + paths: + - 'examples/*.ipynb' + - 'binder-gallery.yaml' + - 'doc/source/_static/icepyx_logo.png' + +jobs: + build: + name: Binderbot Build + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: parse config + id: parse + uses: CumulusDS/get-yaml-paths-action@v0.0.3 + with: + file: binder-gallery.yaml + gallery_repository: gallery_repository + binder_url: binder_url + binder_repo: binder_repo + binder_ref: binder_ref + binderbot_target_branch: binderbot_target_branch + - name: Verify output + run: | + echo gallery_repository: "${{ steps.parse.outputs.gallery_repository }}" + echo binder_url: "${{ steps.parse.outputs.binder_url }}" + echo binder_repo: "${{ steps.parse.outputs.binder_repo }}" + echo binder_ref: "${{ steps.parse.outputs.binder_ref }}" + echo binderbot_target_branch: "${{ steps.parse.outputs.binderbot_target_branch }}" + - uses: actions/setup-python@v1 + with: + python-version: '3.8' + architecture: 'x64' + - name: Install Binderbot from github master + run: pip install git+https://github.com/pangeo-gallery/binderbot.git + - name: Create Clean Branch + run: | + git checkout --orphan ${{ steps.parse.outputs.binderbot_target_branch }} + git rm --cached -r . + - name: Build notebooks using Binderbot + run: | + python -m binderbot.cli \ + --binder-url ${{ steps.parse.outputs.binder_url }} \ + --repo ${{ steps.parse.outputs.binder_repo }} \ + --ref ${{ steps.parse.outputs.binder_ref }} \ + examples/*.ipynb + - name: Commit files + if: github.ref == 'refs/heads/master' + run: | + git config --local user.email "action@github.com" + git config --local user.name "GitHub Action" + git add binder-gallery.yaml doc/source/_static/icepyx_logo.png examples/*.ipynb + git commit -m "Binderbot output" + - name: Push commit + if: github.ref == 'refs/heads/master' + uses: ad-m/github-push-action@master + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + branch: ${{ steps.parse.outputs.binderbot_target_branch }} + force: true + - name: Trigger repository dispatch on Pangeo Gallery + if: github.ref == 'refs/heads/master' + run: curl -f https://pangeo-gallery-bot.herokuapp.com/gallery/submodule-dispatch/${{ github.repository }} diff --git a/.github/workflows/flake8_action.yml b/.github/workflows/flake8_action.yml new file mode 100644 index 000000000..b9f5e06eb --- /dev/null +++ b/.github/workflows/flake8_action.yml @@ -0,0 +1,18 @@ +# use the flake8 linter to annotate improperly formatted code +# from: https://github.com/marketplace/actions/run-flake8-on-your-pr-with-annotations +name: Run flake8 linter on PRs + +on: + pull_request: + branches: + - enable-gallery + - development + - master +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Annotate PR after running flake8 + uses: TrueBrain/actions-flake8@master + with: + max_line_length: 88 diff --git a/.github/workflows/lint_yaml.yaml b/.github/workflows/lint_yaml.yaml new file mode 100644 index 000000000..297ad2465 --- /dev/null +++ b/.github/workflows/lint_yaml.yaml @@ -0,0 +1,27 @@ +name: Lint Yaml +on: + push: + branches: + - enable-gallery + - development + - master + paths: + - binder-gallery.yaml + pull_request: + branches: + - enable-gallery + - development + - master + paths: + - binder-gallery.yaml + +jobs: + build: + name: Lint Yaml + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: jacobtomlinson/gha-lint-yaml@master + with: + path: binder-gallery.yaml + strict: true diff --git a/.mailmap b/.mailmap new file mode 100644 index 000000000..84f19933f --- /dev/null +++ b/.mailmap @@ -0,0 +1,22 @@ +Jessica Scheick +Jessica Scheick JessicaS11 +Jessica Scheick Jessica + +Zheng Liu +Zheng Liu liuzheng-arctic + +Raphael Hagen raphael +Fernando Perez +Shashank Bhushan +Anthony Arendt +Scott Henderson +Wei Ji +Tom Johnson +Bruce Wallin <35819999+wallinb@users.noreply.github.com> +Tyler Sutterley +Amy Steiker <47193922+asteiker@users.noreply.github.com> asteiker +David Shean +Anna Valentine <65192768+annavalentine@users.noreply.github.com> +Bidhyananda Yadav +Friedrich Knuth + diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..777350a71 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,5 @@ +repos: +- repo: https://github.com/psf/black + rev: stable + hooks: + - id: black \ No newline at end of file diff --git a/.travis.yml b/.travis.yml index 2038aef2a..b222ef720 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,21 +1,44 @@ language: python +install: + - pip install -r requirements.txt -r requirements-dev.txt + - pip install -e . + stages: - name: basic tests - if: type = push + if: type = push OR type = pull_request - name: behind Earthdata if: branch = master OR commit_message =~ nsidc_tests OR type = cron -#ultimately move basic tests list into a separate script with the list of files to run (then this can be put into a matrix and paralellized as well) +#ultimately move basic tests list into a separate script with the list of files to run (then this can be put into a matrix and parallellized as well) jobs: include: - stage: basic tests - script: pytest icepyx/tests/test_APIformatting.py - icepyx/tests/test_geospatial.py - icepyx/tests/test_is2ref.py - icepyx/tests/test_validate_inputs.py - + script: pytest icepyx/ --ignore icepyx/tests/behind_NSIDC_API_login.py + after_success: codecov + # - stage: behind Earthdata - # script: + # script: # - export NSIDC_LOGIN=$NSIDC_LOGIN # - pytest icepyx/tests/behind_NSIDC_API_login.py + +deploy: + - provider: pypi + server: https://test.pypi.org/legacy/ + username: "__token__" + password: $test_PyPI_API_Token + on: + branch: development + if: commit_message =~ test_pypi + skip_existing: true + distributions: "sdist bdist_wheel" + +# NOTE: the API token on Travis must be set to ALL branches, because a tag build is not considered to be on the master branch: https://travis-ci.community/t/pypi-upload-with-api-token-not-working/5338/5 +- provider: pypi + username: "__token__" + password: $PyPI_API_Token + on: + branch: master + tags: true + skip_existing: true + distributions: "sdist bdist_wheel" diff --git a/CONTRIBUTORS.rst b/CONTRIBUTORS.rst index b9f7fbf35..da8dd4ef4 100644 --- a/CONTRIBUTORS.rst +++ b/CONTRIBUTORS.rst @@ -3,15 +3,18 @@ Project Contributors The following people have made contributions to the project (in alphabetical order by last name) and are considered "The icepyx Developers": -* [Anthony Arendt](https://github.com/aaarendt/) - University of Washington -* [Shashank Bhushan](https://github.com/ShashankBice) - University of Washington -* [Raphael Hagen](https://github.com/norlandrhagen) - University of Washington -* [Scott Henderson](https://github.com/scottyhq) - University of Washington -* [Zheng Liu](https://github.com/liuzheng-arctic) - University of Washington -* [Joakim Meyer](https://github.com/jomey) - University of Utah -* [Fernando Perez](https://github.com/fperez) - University of California, Berkeley -* [Jessica Scheick](https://github.com/jessicas11) - Unaffiliated (ORCID: [0000-0002-3421-4459](https://www.orcid.org/0000-0002-3421-4459)) -* [Ben Smith](https://github.com/smithb) - University of Washington -* [Amy Steiker](https://github.com/asteiker) - NSIDC, University of Colorado -* [Bidhyananda Yadav](https://github.com/bidhya) - Ohio State University +* `Anthony Arendt `_ - University of Washington +* `Shashank Bhushan `_ - University of Washington +* `Raphael Hagen `_ - University of Washington +* `Scott Henderson `_ - University of Washington +* `Thomas Johnson `_ - University College London +* `Zheng Liu `_ - University of Washington +* `Joachim Meyer `_ - University of Utah +* `Fernando Perez `_ - University of California, Berkeley +* `Jessica Scheick `_ - Unaffiliated (ORCID: `0000-0002-3421-4459 `_) +* `David Shean `_ - University of Washington +* `Ben Smith `_ - University of Washington +* `Amy Steiker `_ - NSIDC, University of Colorado +* `Bidhyananda Yadav `_ - Ohio State University +* `Anna Valentine `_ - Colorado School of Mines diff --git a/README.rst b/README.rst index 2b6d80f88..f71137600 100644 --- a/README.rst +++ b/README.rst @@ -3,7 +3,7 @@ icepyx **Python tools for obtaining and working with ICESat-2 data** -|Documentation Status| |GitHub license| |Travis CI Status| +|Documentation Status| |GitHub license| |Travis CI Status| |Code Coverage| .. |Documentation Status| image:: https://readthedocs.org/projects/icepyx/badge/?version=latest :target: http://icepyx.readthedocs.io/?badge=latest @@ -14,6 +14,9 @@ icepyx .. |Travis CI Status| image:: https://travis-ci.org/icesat2py/icepyx.svg?branch=master :target: https://travis-ci.org/icesat2py/icepyx +.. |Code Coverage| image:: https://codecov.io/gh/icesat2py/icepyx/branch/development/graph/badge.svg + :target: https://codecov.io/gh/icesat2py/icepyx + Origin and Purpose ------------------ icepyx is both a software library and a community composed of ICESat-2 data users, developers, and the scientific community. We are working together to develop a shared library of resources - including existing resources, new code, tutorials, and use-cases/examples - that simplify the process of querying, obtaining, analyzing, and manipulating ICESat-2 datasets to enable scientific discovery. @@ -26,35 +29,56 @@ This project combines and generalizes these scripts into a unified framework, ma .. _`zipped file`: https://github.com/icesat2py/icepyx/archive/master.zip +.. _`Fiona`: https://pypi.org/project/Fiona/ Installation ------------ -Currently icepyx is only available for use as a github repository. -The contents of the repository can be download as a `zipped file`_ or cloned. +The simplest way to install icepyx is using pip. + +.. code-block:: + + pip install icepyx + + +Windows users will need to first install `Fiona`_, please look at the instructions there. Windows users may consider installing Fiona using pipwin + +.. code-block:: + + pip install pipwin + pipwin install Fiona -To use icepyx, fork this repo to your own account, then ``git clone`` the repo onto your system. -Provided the location of the repo is part of your $PYTHONPATH, -you should simply be able to add ``import icepyx`` to your Python document. +Currently, updated packages are not automatically generated with each build. This means it is possible that pip will not install the latest release of icepyx. In this case, icepyx is also available for use via the GitHub repository. The contents of the repository can be download as a `zipped file`_ or cloned. + +To use icepyx this way, fork this repo to your own account, then git clone the repo onto your system. To clone the repository: -.. code-block:: none +.. code-block:: git clone https://github.com/icesat2py/icepyx.git -Future developments of icepyx may include pip and conda as simplified installation options. +Provided the location of the repo is part of your $PYTHONPATH, you should simply be able to add import icepyx to your Python document. +Alternatively, in a command line or terminal, navigate to the folder in your cloned repository containing setup.py and run + +.. code-block:: + + pip install -e + + +Future developments of icepyx may include conda as another simplified installation option. Examples (Jupyter Notebooks) ---------------------------- -.. _`ICESat-2_DAAC_DataAccess_Example`: ICESat-2_DAAC_DataAccess_Example.ipynb +Listed below are example jupyter-notebooks +`ICESat-2_DAAC_DataAccess_Example `_ -Listed below are example jupyter-notebooks +`ICESat-2_DAAC_DataAccess2_Subsetting `_ -ICESat-2_DAAC_DataAccess_Example_ +`ICESat-2_DEM_comparison_Colombia_working `_ Citing icepyx @@ -72,7 +96,7 @@ Come join the conversation at: https://discourse.pangeo.io/. Search for "icesat-2" under the "science" topic to find us. .. _`icepyx`: https://github.com/icesat2py/icepyx -.. _`contribution guidelines`: ./doc/source/development/contribution_guidelines.rst +.. _`contribution guidelines`: ./doc/source/contributing/contribution_guidelines.rst Contribute ---------- @@ -82,4 +106,4 @@ Check out our `contribution guidelines`_ to see how you can contribute. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. |Contributor Covenant| .. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg - :target: code_of_conduct.md \ No newline at end of file + :target: code_of_conduct.md diff --git a/binder-gallery.yaml b/binder-gallery.yaml new file mode 100644 index 000000000..4e3b0631a --- /dev/null +++ b/binder-gallery.yaml @@ -0,0 +1,10 @@ +--- +name: icepyx examples gallery +description: >- + Examples using icepyx Python tools for obtaining and working with + ICESat-2 data +gallery_repository: pangeo-gallery/pangeo-gallery +binder_url: "https://mybinder.org" +binder_repo: salvis2/icepyx +binder_ref: enable-gallery +binderbot_target_branch: binderbot-built diff --git a/binder/Dockerfile b/binder/Dockerfile new file mode 100644 index 000000000..826ac39a7 --- /dev/null +++ b/binder/Dockerfile @@ -0,0 +1 @@ +FROM uwhackweeks/icesat2:2020.06.15 diff --git a/doc/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb b/doc/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb deleted file mode 100644 index ec632cc53..000000000 --- a/doc/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb +++ /dev/null @@ -1,5341 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comparing ICESat-2 Altimetry Elevations with DEM\n", - "### Example Notebook\n", - "This notebook compares elevations from ICESat-2 to those from a DEM.\n", - "\n", - "#### Credits\n", - "* notebook by: [Jessica Scheick](https://github.com/JessicaS11) and [Shashank Bhushan](https://github.com/ShashankBice)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup\n", - "##### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", - "##### For full functionality,\n", - "- Please install [icepyx](https://github.com/icesat2py/icepyx), [topolib](https://github.com/ICESAT-2HackWeek/topohack), [contextily](https://github.com/darribas/contextily) using `git clone xxxxx`, `pip install -e .` workflow (see below; **you must restart your kernel after installing the packages**)\n", - "- Download [NASA ASP](https://github.com/NeoGeographyToolkit/StereoPipeline) tar ball and unzip, we execute the commands from the notebook, using the path to the untared bin folder for the given commands." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Obtaining file:///home/jovyan/contextily\n", - "Requirement already satisfied: geopy in /srv/conda/envs/notebook/lib/python3.8/site-packages (from contextily==1.0rc2) (1.21.0)\n", - "Requirement already satisfied: matplotlib in /srv/conda/envs/notebook/lib/python3.8/site-packages (from contextily==1.0rc2) (3.2.1)\n", - "Requirement already satisfied: mercantile in /srv/conda/envs/notebook/lib/python3.8/site-packages (from contextily==1.0rc2) (1.1.2)\n", - "Requirement already satisfied: pillow in /srv/conda/envs/notebook/lib/python3.8/site-packages (from contextily==1.0rc2) (7.1.1)\n", - "Requirement already satisfied: rasterio in /srv/conda/envs/notebook/lib/python3.8/site-packages (from contextily==1.0rc2) (1.1.3)\n", - "Requirement already satisfied: requests in /srv/conda/envs/notebook/lib/python3.8/site-packages (from contextily==1.0rc2) (2.23.0)\n", - 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"Successfully installed topolib\n", - "Obtaining file:///home/jovyan/icepyx\n", - "Installing collected packages: icepyx\n", - " Attempting uninstall: icepyx\n", - " Found existing installation: icepyx 0.0.1\n", - " Uninstalling icepyx-0.0.1:\n", - " Successfully uninstalled icepyx-0.0.1\n", - " Running setup.py develop for icepyx\n", - "Successfully installed icepyx\n" - ] - } - ], - "source": [ - "%%bash\n", - "cd ~\n", - "# git clone https://github.com/icesat2py/icepyx.git\n", - "# git clone https://github.com/ICESAT-2HackWeek/topohack.git\n", - "# git clone https://github.com/darribas/contextily.git\n", - "\n", - "cd contextily\n", - "pip install -e .\n", - "cd ../topohack\n", - "pip install -e .\n", - "cd ../icepyx\n", - "pip install -e ." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan\n" - ] - } - ], - "source": [ - "%cd ~\n", - "#needs to be wherever icepyx, contextily, and topolib are installed in the previous step (ideally $HOME)\n", - "# %pwd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", - "- Along track heights for canopy (land and vegitation) and terrain\n", - "- Terrain heights provided are aggregated over every 100 m along track interval, output contains \"h_te_best_fit: height from best fit algorithm for all photons in the range\", median height and others. Here we use h_te_best_fit.\n", - "- See this preliminary introduction and quality assessment [paper](https://www.mdpi.com/2072-4292/11/14/1721) for more detail" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import packages, including icepyx" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from icepyx import icesat2data as ipd\n", - "import os\n", - "import shutil\n", - "import h5py\n", - "import xarray as xr\n", - "# depedencies\n", - "import getpass\n", - "#from topolib.subsetDat import subsetBBox;\n", - "from topolib import icesat2_data\n", - "import glob\n", - "import rasterio\n", - "from topolib import gda_lib\n", - "from topolib import dwnldArctic\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "from multiprocessing import Pool\n", - "import contextily as ctx\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/doc/examples\n" - ] - } - ], - "source": [ - "%cd ~/icepyx/doc/examples/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocess #1\n", - "- Download using icepyx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Create an ICESat-2 data object with the desired search parameters\n", - "- See the ICESat-2 DAAC Data Access notebook for more details on downloading data from the NSIDC" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "region_a = ipd.Icesat2Data('ATL08', [-73.9, 10.7, -73.4, 11.1], ['2018-12-01','2019-09-01'], \\\n", - " start_time='00:00:00', end_time='23:59:59')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Finding and downloading data\n", - "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login (available for free on their website). This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. The token is attached to the data object and stored, but the session must be passed to the download function. Then we can order the granules." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Log in to Earthdata" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Earthdata Login password: ········\n" - ] - } - ], - "source": [ - "earthdata_uid = 'Jessica.scheick'\n", - "email = 'jessica.scheick@maine.edu'\n", - "region_a.earthdata_login(earthdata_uid, email)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Number of available granules': 20,\n", - " 'Average size of granules (MB)': 21.710584306709997,\n", - " 'Total size of all granules (MB)': 434.21168613419997}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#search for available granules\n", - "region_a.avail_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - 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"metadata": {}, - "source": [ - "#### Place the order" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'short_name': 'ATL08', 'version': '002', 'temporal': '2018-12-01T00:00:00Z,2019-09-01T23:59:59Z', 'bounding_box': '-73.9,10.7,-73.4,11.1', 'page_size': 10, 'page_num': 2, 'request_mode': 'async', 'include_meta': 'Y', 'agent': 'NO'}\n", - "[]\n", - "order ID: 5000000645675\n", - "Data request 1 is submitting...\n", - "Initial request status is processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: complete\n", - "[]\n", - "order ID: 5000000645676\n", - "Data request 2 is submitting...\n", - "Initial request status is processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: complete\n" - ] - } - ], - "source": [ - "region_a.order_granules(subset=False)\n", - "#region_a.order_granules(verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['5000000645675', '5000000645676']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#view a short list of order IDs\n", - "region_a.granules.orderIDs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the order\n", - "Finally, we can download our order to a specified directory (which needs to have a full path but doesn't have to point to an existing directory) and the download status will be printed as the program runs. Additional information is again available by using the optional boolean keyword 'verbose'." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "wd=%pwd\n", - "path = wd + '/download'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beginning download of zipped output...\n", - "Data request 5000000645675 of 2 order(s) is complete.\n", - "Beginning download of zipped output...\n", - "Data request 5000000645676 of 2 order(s) is complete.\n" - ] - } - ], - "source": [ - "region_a.download_granules(path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Clean up the download folder by removing individual order folders:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#Clean up Outputs folder by removing individual granule folders \n", - "for root, dirs, files in os.walk(path, topdown=False):\n", - " for file in files:\n", - " try:\n", - " shutil.move(os.path.join(root, file), path)\n", - " except OSError:\n", - " pass\n", - " \n", - "for root, dirs, files in os.walk(path):\n", - " for name in dirs:\n", - " os.rmdir(os.path.join(root, name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocess #2\n", - "- Convert data into geopandas dataframe, which allows for doing basing geospatial opertaions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# glob to list of files (run block of code creating wd and path variables if starting processing here)\n", - "ATL08_list = sorted(glob.glob(path+'/*.h5'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Examine content of 1 ATLO8 hdf file" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Keys: \n" - ] - } - ], - "source": [ - "filename = ATL08_list[5]\n", - "with h5py.File(filename, 'r') as f:\n", - " # List all groups\n", - " pairs=[1, 2, 3]\n", - " beams=['l','r']\n", - " print(\"Keys: %s\" % f.keys())\n", - " a_group_key = list(f.keys())[0]\n", - "#" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/home/jovyan/icepyx/doc/examples/download/ATL08_20181203060400_10020107_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20181207174958_10710101_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190101044008_00570207_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190105162606_01260201_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190130031618_04990207_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190203030801_05600207_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190203150218_05680201_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190304014401_10020207_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190308132958_10710201_002_01.h5',\n", - " '/home/jovyan/icepyx/doc/examples/download/ATL08_20190402002000_00570307_002_01.h5']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ATL08_list" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# dict containing data entries to retrive\n", - "dataset_dict = {'land_segments':['delta_time','longitude','latitude','atl06_quality_summary','quality','terrain_flg'], 'land_segments/terrain':['h_te_best_fit']}" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "#gda_lib.ATL08_to_dict(ATL08_list[0],dataset_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "## the data can be converted to geopandas dataframe, see ATL08_2_gdf function in topolib gda_lib\n", - "temp_gdf = gda_lib.ATL08_2_gdf(ATL08_list[0],dataset_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " delta_time longitude latitude terrain_flg h_te_best_fit pair beam \\\n", - "0 2.905231e+07 -72.512772 22.623270 0.0 -44.644165 1.0 0.0 \n", - "1 2.905231e+07 -72.512863 22.622368 1.0 -44.511307 1.0 0.0 \n", - "2 2.905231e+07 -72.512955 22.621466 0.0 -44.777416 1.0 0.0 \n", - "3 2.905231e+07 -72.513054 22.620565 0.0 -44.896725 1.0 0.0 \n", - "4 2.905231e+07 -72.513145 22.619663 1.0 -44.382587 1.0 0.0 \n", - "\n", - " p_b geometry \n", - "0 1.0_0.0 POINT (-72.51277160644531 22.62327003479004) \n", - "1 1.0_0.0 POINT (-72.51286315917969 22.62236785888672) \n", - "2 1.0_0.0 POINT (-72.51295471191406 22.6214656829834) \n", - "3 1.0_0.0 POINT (-72.51305389404297 22.62056541442871) \n", - "4 1.0_0.0 POINT (-72.51314544677734 22.61966323852539) " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "temp_gdf.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "46580" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(temp_gdf)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "colombia_crs = {'init':'epsg:32618'}\n", - "plot_web = {'init':'epsg:3857'}" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['delta_time', 'longitude', 'latitude', 'terrain_flg', 'h_te_best_fit',\n", - " 'pair', 'beam', 'p_b', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_gdf.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert the list of hdf5 files into more familiar Pandas Dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_list = [(gda_lib.ATL08_2_gdf(x,dataset_dict)) for x in ATL08_list]\n", - "gdf_colombia = gda_lib.concat_gdf(gdf_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Preprocess #3\n", - "- Visualise data footprints" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(10,10))\n", - "temp_web = gdf_colombia.to_crs(plot_web)\n", - "clim = np.percentile(temp_web['h_te_best_fit'].values,(2,98))\n", - "temp_web.plot('h_te_best_fit',ax=ax,s=3,legend=True,cmap='inferno',vmin=clim[0],vmax=clim[1])\n", - "ctx.add_basemap(ax=ax)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### We will use the TANDEM-X Global DEM for our comparison. The resolution of the globally avaialable product is 90 m, with *horizontal* and *vertical* accuracy better than 2 to 3 m.\n", - "- TANDEM-X DEM for the region was downloaded and preprocessed, filtered using scripts from the [tandemx](https://github.com/dshean/tandemx) repository" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "dem_file = os.path.join(wd,'supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea.tif')\n", - "hs_file = os.path.splitext(dem_file)[0]+'_hs.tif'\n", - "dem_ds = rasterio.open(dem_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0...10...20...30...40...50...60...70...80...90...100 - done.\n" - ] - } - ], - "source": [ - "! gdaldem hillshade $dem_file $hs_file" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "hs_ds = rasterio.open(hs_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def gdf_on_raster(gdf,ds,ax,hs_ds=None,cmap='inferno'):\n", - " gdf = gdf.to_crs(ds.crs)\n", - " xmin,ymin,xmax,ymax = ds.bounds\n", - " ndv = gda_lib.get_ndv(ds)\n", - " img = ds.read(1)\n", - " img = np.ma.masked_less_equal(img,ndv)\n", - " clim = np.nanpercentile(img,(2,98))\n", - " if hs_ds:\n", - " hs = hs_ds.read(1)\n", - " ndv = gda_lib.get_ndv(hs_ds)\n", - " hs = np.ma.masked_less_equal(hs,ndv)\n", - " ax.imshow(hs,cmap='gray',extent=[xmin,xmax,ymin,ymax])\n", - " im = ax.imshow(img,alpha=0.6,cmap=cmap,extent=[xmin,xmax,ymin,ymax])\n", - " print(clim)\n", - " else:\n", - " im = ax.imshow(img,cmap=cmap,vmin=clim[0],vmax=clim[1],extent=[xmin,xmax,ymin,ymax])\n", - " gdf.plot('p_b',ax=ax,s=1)\n", - " plt.colorbar(im,ax=ax,extend='both',label='Elevation (m)')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "xmin,ymin,xmax,ymax = dem_ds.bounds" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "## Filter points based on DEM extent" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_colombia['x_atc'] = gdf_colombia['delta_time']\n", - "gdf_colombia_dem_extent = gdf_colombia.to_crs(dem_ds.crs).cx[xmin:xmax,ymin:ymax]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-32767. 2951.64549805]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(10,5))\n", - "gdf_on_raster(gdf_colombia_dem_extent,dem_ds,ax,hs_ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 1\n", - "- This contains demonstration of elevation profile along 1 track, which has 6 beams" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/doc/examples/download/ATL08_20190105162606_01260201_002_01.h5\n", - "[-32767. 2951.64549805]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "### Picking out 1 track\n", - "### check with different ATLO8 inputs\n", - "test_track = ATL08_list[3]\n", - "print(test_track)\n", - "test_gdf = gda_lib.ATL08_2_gdf(test_track,dataset_dict).to_crs(dem_ds.crs).cx[xmin:xmax,ymin:ymax]\n", - "fig,ax = plt.subplots(figsize=(10,5))\n", - "gdf_on_raster(test_gdf,dem_ds,ax,hs_ds)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['1.0_0.0', '1.0_1.0', '2.0_0.0', '2.0_1.0', '3.0_0.0', '3.0_1.0'],\n", - " dtype=object)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Working with track from 20190105 to show how we can use this to plot elevation values along the collect just by using ICESat-2\n", - "np.unique(test_gdf['p_b'].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Limit analysis to 1 pair beam combination\n", - "mask = test_gdf['p_b']== '1.0_0.0'\n", - "test_gdf_pb = test_gdf[mask]\n", - "fig,ax = plt.subplots(figsize=(5,4))\n", - "plot_var = test_gdf_pb['h_te_best_fit'].values\n", - "ax.scatter(np.arange(len(plot_var)),plot_var,s=1)\n", - "ax.set_xlabel('Along-track id')\n", - "ax.set_ylabel('ATL08-Terrain Height')\n", - "ax.grid('--')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#or do it for all the 6 tracks\n", - "track_identifier = list(np.unique(test_gdf['p_b'].values))\n", - "fig,axa = plt.subplots(3,2,figsize=(10,10))\n", - "ax = axa.ravel()\n", - "for idx,track in enumerate(track_identifier):\n", - " mask = test_gdf['p_b']== track\n", - " test_gdf_pb = test_gdf[mask]\n", - " plot_var = test_gdf_pb['h_te_best_fit'].values\n", - " ax[idx].scatter(np.arange(len(plot_var)),plot_var,s=1)\n", - " ax[idx].set_xlabel('Along-track id')\n", - " ax[idx].set_ylabel('ATL08-Terrain Height')\n", - " ax[idx].grid('--')\n", - " ax[idx].set_title('Track: {} Beam: {}'.format(track.split('_',15)[0],track.split('_',15)[1]))\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 2:\n", - "- Compare ICESat-2 Elevation with that of reference DEM (in this case TANDEM-X)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sample elevations from DEM at ATLO8-locations using nearest neighbour algorithm " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "del_time,elev = gda_lib.sample_near_nbor(dem_ds,gdf_colombia_dem_extent)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_colombia_dem_extent['dem_z'] = elev" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot elevation differences (ICESat-2 minus TANDEM-X) as a function of elevation\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Elevation difference (m)')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "gdf_colombia_dem_extent['z_diff'] = gdf_colombia_dem_extent['h_te_best_fit'] - gdf_colombia_dem_extent['dem_z']\n", - "fig,ax = plt.subplots(figsize=(5,4))\n", - "# Sort elevation values\n", - "gdf_colombia_dem_extent.sort_values(by='dem_z',inplace=True)\n", - "gdf_colombia_dem_extent_filt = gdf_colombia_dem_extent[gdf_colombia_dem_extent['z_diff']<1000]\n", - "ax.scatter(gdf_colombia_dem_extent_filt.dem_z.values,gdf_colombia_dem_extent_filt.z_diff.values,s=1)\n", - "ax.set_ylim(-50,50)\n", - "ax.set_xlabel('Elevation (TANDEM-X) (m)')\n", - "ax.set_ylabel('Elevation difference (m)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- The difference above might be noise or real signal (\" the dates of ICESAT-2 footprints are between December to March 2018-2019, while TANDEM-X contains a mosaic of elevations between 2012-2014\")\n", - "- It's hard to make out anything from the above plot, let's try a box plot" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'ICESat-2 minus TANDEM-X (m)')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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s2TLmzp1LbW2tMZkmsYrXQyQUXG1tLc3NzdFQcFVVVUEXrc9TWlrKnXfeSXZ2NgDZ2dnceeedxsf2ezH9Ei0pKWH9+vUce+yxiAjHHnss69evN+rfGuntX7x4Mc899xyLFy+mqqrKqPKdNWsWBw8e5N1330VVeffddzl48KDR1ueWLVs48cQTY/JOPPHEvvs1Gs/+EHQCTgRe86SPgZuB7wHvefIvSHQu69XQP2V6R8pFOvNMB0IPYshwUHETcnJyYrwacnJyjF/boEJumrDx9soWr6puVdWTVfVkYDJwEFjhbv5JZJuqPpts2aWlpWzcuJG1a9eyceNGGwA9BfDLxrpjxw5mzZoV06qfNWuWUf/WIEwq1dXVPPHEExw+fJja2loOHz7ME088kbxRXe2QStMq9YXOtXOAv6nq9r58oS3+Ul1dzeOPPx7TuVZbW2u0cw3ggQceOGqyS5MEES5xy5Yt7NixgwkTJkQHp8ydO9eosn///fd54IEHYgbELFiwgK9//evGZJqk04pXRHJxZvdtALapaquxUsVyOeA1WFWIyNXAK8AtqrrXp3JY+hBBtATD4TCNjY0xeY2NjUY7nYxPUROHkSNHMnfu3KM8VExOcTRu3DgKCwvZuHFjzIu0r3Z8d3hHiMgQ4EagFBgA7AYygXwReQn4maoa61YUkQHANOA2N2sJ8AMcZ+0fAIuAsjjHXQ9cD5Cfn09dXV2XZR84cKBbx/UEKzN5jB49mrvvvpuJEydG5W3YsIHRo0cbk93S0oKqUlpayq5duzj22GNRVVpaWozJLCgoYMaMGZSVlfHOO+8wevRorrzySgoKCozJbGxspKWlhddee42xY8fy9ttv09jYSFpamjGZl1xyCTNmzOA73/kOY8eO5Sc/+Qk/+tGPmDlzpvF7ycj9Gs/wq0c6udYAVwFD42ybDPw3MLOjc/QkARcDq9vZVgRsTHQOGySnf8oMYgBFfwiXqBrcbMpBdXz3u0DowGPAtZ71As/yN4HHEp2jLyjeIG6o/iAzCHl+Tynvxc/oZEF4GPTF+6c9xdsVG+9n3FZm9BhV/XW3m9qJ5Q0EzgVu8GQvFJGTcUwN29ps65MEMX9VUDJnz55NdnY2qk50stmzZxuVWVpaSmlpadQm6CfOM5e6+O1h4Pc9a1xePG3cNgHLcDqzHgT+n5uWdebYoFNvb/EG4YcZ1LQ4fdEPsyv0J1OD37Mp+/2cJOu/pCemBmBzZ/brjam3K94goi5FBhR4lWBkoIEpAF29erWqHqnn6tWrUyo6mYhE7Z6RlJGRYfS6eknlxoLfyj5Zz0h7irezAyheFBGzA877KUEMbR0wYACVlZUx05pUVlYyYMAAYzL9JoihtOD0+Ofm5gKQm5t7lHtZKhDE0PqRI0cyZ86cmP9zzpw5xlzYjD8j8bRx2wR8HvgI2Ar8GfgL8OfOHBt06u0t3iB630UkrkyTLbPCwkItKCiIkVlQUGDM1BBEqwyIG+ydFAsLqer/14Tf0w2JSNwh58lq8XZW8b6J4087FhgTSZ05NujU2xWvqv83cRC2yP4yy/Cpp54a42Z16qmnpqTi9VumiKiI6IgRIzQUCumIESOieSYoLCyMBu6P3K9DhgzpsqLvqeJd15n9emPqC4rXb5lBzbTh5wsmiJcLrl3XqxwieX6Qyo0FEdHy8nJVPVLP8vJyo4o3GV9oPVW8PwNqcEawfSWSOnNs0Mkq3vikehS2ioqKmE6uSKqoqDAmMyIjYl6I/Kaa4g3CXxmI29ll6tomqzOvPcXb2c61LKAROA+4yE0XdvJYiwXwN2D3/fffD8CgQYNifiP5JnGet9T15Z0zZw719fW89957tLa28t5771FfX8+cOXOMyczIyODMM8+ksrKSqVOnUllZyZlnnklGRoYRed7YEJFIhYWFhXaW4c4m2+I9miBMDX7LBHTixIkxLZaJEycabX0CWlpaGiOztLQ05Vq8uK35/Pz8mF+T9ayoqFAR0XA4rICGw2EVEWNfMIHOMgx8F8jrYPvZwIUdnSPoZBXv0fSHQRuA5uTkxPRK5+TkGFe8+fn5MQ9rfn5+Sire9PT0GA+DyLIpIoo3MmtzWlqaUcWrGuAswzhBav4ArAV+BMwB/h14GMel7CfA8I7OEXSyivdoghh55LcLGwF0dHlnoIgoe9MzUHhJ5endMzIydMaMGTH37IwZMzQjI8OYzAiBBckBPgV8HSc8483AVCCrM8cGnfqC4g3CJ9Lv4bt+PzgRRRAvmSKoIDl+3z+RFqe3xRtpiZqUGc+v1vT/GWiQHFV9A3ijM/taukYQAWvA/yAnjY2N1NTUEAqFaG1t5a9//SubN2+OvNhTgtLSUl544QXuv/9+Wltb2blzJ9ddd53R/zGo+6elpYWWlhYAmpqajMnxsn//frKzs6P36v79+43J6hVBcvpy6u0t3qDGvfsdT9Vrm2v7awJAQ6FQzCiyUChkvIUURAzgIEboRa6v99fktcVn80ayrit9MR5vMlJvV7xBjLAKwtQQeUj8srkCOmDAgJjP4QEDBhhVDsXFxTp9+vSYF9r06dONv0SDGKEX7yVqWvFGOiojyWTHZbKua3uKt9uzDItIX5gos9cTRJAcgEOHDlFWVsbUqVMpKyvj0KFDRuWBM+tuZmYmqkpmZmZ0Nl5THD58mLy8PADy8vI4fPiwUXmbNm3iySefZOjQoYgIQ4cO5cknn2TTpk3GZAZ1/6Snp0dNDS0tLaSnpxuVB/DBBx9QXl7OU089RXl5OR988IExWcavazxtHEnAU8SJyQB8kU5Mu9MbUm9v8Xo7ZLydBqaD5GRlZcW0HrKysoyHhczJyYlpZZt07wqHw5qdnR1zXbOzszUcDhuRp+rUMTMzM0ZmZmZmypk3CCAYEG1a1t51EwTtxzsDeAuoAtJxZhn+BfASMLmjY3uacGaY+AvwWqTwQB7OPHBvuL+5ic7TlxSvXz3haWlpce2fpuytqv6bGoJ6uQQRnSwIr4Z4AWtMK15Ac3NzVUQ0NzfXFy+VQPx4neMYAtyLE6FsO87svZLouJ4mV/EOa5O3ELjVXb4VWJDoPL1d8QbVOTJw4MAYZT9w4EDjPq5ZWVkxNtesrCxjduW8vLy4yiEvL8+IPFXnup599tkxD+vZZ5+dkgMopk2bFmPLnjZtmnHFO2nSpJhrO2nSpJRWvKcDG3CC5LwO/BuQnui4nqZ2FO9W3AkvgQJga6Lz9HbFG1TnSEQBRpLpkUd+m1TC4bDm5eXFfCrm5eUZNzWkpaXFtHhN+7d68XOySz9j46o61zYcDsdc28jwYROYNjV02EEmIj8HJgHfUNUXRSQbuB34k4jcrKqrOzq+hyiw2v2EuVdV7wPyVXUnztXeKSLHtlPu63Fa5uTn51NXV9dl4QcOHOjWcV1l9OjR3H333UycODEqc8OGDYwePdqo/KamJnJycjhw4ACDBg2K+kSakllQUMCYMWN45ZVXANi2bRunnHIKBQUFRmQ2NzdTVFTE2WefHc2bNGkS69evN1bHYcOGsX//fubMmRP1/QyHw+Tm5vpyL/l1z44YMYIdO3ZE15uammhqamL8+PHG5Kenp3PiiSdy66230tTURHp6Op/+9KfZunWrEZnz5s1jypQplJWV8c477zB69GimTJnCvHnzKCgo6LmAeNpYj7Qwvwmkxck/Cfjfjo7taQJGur/HAn/CmQVjX5t99iY6T29v8QYVYg+f7WUVFRXRT37vr6mx9qFQKBrABdf1SESMfkn0l+nd23ZyRZLJPgK/g+Qka4g73XEnU9WfqGqLN09ERqjqX1T1cz1X+x3Kft/93QWsAE4FPhCRArccBcCuZMv1M3RhhMbGxpgQe37M03Xaaadx8OBBVJWDBw9y2mmnGZV3zz33MHDgwBh3soEDB3LPPfcYkRe5wSMuRx988IH3hW2E0tJS7rzzzqibXHZ2NnfeeaexEWQiEpNKSkqOyjNBxI3MO7ecN98kzc3NMb+mGDBgABUVFTFzrlVUVPg755o3Aeu7ekw3ZGQDOZ7lF4Av4QTq8XauLUx0rq60eINwzfFOMRKxfXZnipGuQACda+Bv5C4CiNXQkUyTciOMmfu0cRmqR2zZ3vvHtC07HA7HHRBjymZvepbh7ijFDV09phsyPoFjXvgTsAmocvOPwYmU9ob7227IykjqiuINysNg8ODBMYp38ODBRm/i7OzsqKnB+5udnW1MJj73+EceTu+DY7oD0YtfSjAImZEXSdv7x4+Xml+j5ZIVxrQ9xdud0WfGQ/ir6lvAP8XJ/wdwjim5W7ZsYcqUKTF5U6ZMYcuWLaZEApCWlsayZcuiHTKXXnqpUXm5ubk0NTWxd+9eAPbu3cuAAQOin4ymWLduHeXl5cyfP59nn32WJUuWGJXX0tLChg0bGD9+PH/+8599+RTuT3jvH7/wjpYzSVVVFTfccAOHDh2itbWV119/nbvuuot77703KedP5NWQFyf7sUi+qu5JSil6CZFhgiUlJdE8P4ZfHj58mLKyMrZv386YMWOMD2197733CIdj/3pV5b333jMmMxwOk5aWxs9//nOWLFlCeno6GRkZRh+gtLS0mF7wtLQ0WltbjcmzmCcrK4tDhw6RmZlJQ0ODMTkvvPAC9fX1DB8+nF27dpGXl8fu3bt54YUXkmKzTxSr4UPckWNuetWTXumx9F5GVVUVM2fOpLa2lubmZmpra5k5cyZVVVVG5dbX17Nt2zZUlW3btlFfX29UHjguQLm5uYhItAVskpaWFnJychg1ahShUIhRo0aRk5NjTPFmZ2fT1NQUM+daU1OT8fgQ/YVQKNThuikOHz6MqhpvnNx///2cfvrp7Nu3D1Vl3759nH766Umbsy+RqWExcBbOLBTLgeddu0VKEnmTVVZWsmXLFsaNG0d1dbXxuLiqSm5uLh999BFDhgxh7969RuPjRv7CAQMGoKrRnlqTf+348eOZPn06K1euBBzFOGPGjOh6srn//vspKyuL+RzOzMz0ZbLL/sCgQYPIy8uLfqXt2bOHjz/+2LjcYcOGsWvXLoYNG2Y0SE5jYyMvvfQSCxcuZPz48WzevDnqn50U4hl+vQkQoAS4D6f1uxAYm+i43pJ6ux8vxE4D7l03KdPPgCNeOfFSqsj0ksqda94ARBGvBpMBiIK6f6ZNm6aqR3RBd4ZF092wkO7xtTjzrd0DXIsTncySJLRNS7PtuglaWlqin4ehUMiorTVys9XU1FBcXAwSori4mJqaGmN19d7kY+Y+Ha9BYekGIkJzc3PUPNba2ho1j5nyrVXVqInIe88CRk1Hzz77LD/+8Y85dOgQP/7xj3n22WeTdu4OFa+IZIvIFSLyBPAsMAiYpKr2ey3JREwLpqfg8RIxMSTNKTwBpaWlbNy4kTFznmTjxo1GTTgWM0ReXBUVFWRkZACQkZFBRUWF0Rfa/fffT1ZWVrRztLW1laysLKOmo+bmZm655RbOP/98brnlluiLJRnPaKIW7y6clu4LwCKcEJGfFZGviMhXeizdEiVy0/rVGhORaPDzQ4cO+arwLX2fxYsXc+jQIcbMfZpDhw6xePFio/JKS0tZunRpzBfT0qVLjb28KyoqCIfDLFq0iOO++T8sWrSIcDictBdMos61X+LYUT7tJi8K/LrHJehlLF++nOrq6mjnWlVVVUq2zDIzM8nPz48GAPnggw+MuudYLD2ltLSU0tJSim59ho3zv2xUVuRFMm/ePBobG5mXkcGsWbOS9oLpUPGq6teTIqWPsHz5cmbPnh21G9XX1zN79mzA7IytfhJp2TY0NLBt2zaA6K/FYjnC4sWLWbx4MUW3PsO2JCv6RDbe//Ysz26z7YGklqQXMGfOnKg/a+RzoqmpiTlz5gRZrKQSsc+JCGlpaYAz0EBEqKioCLh0Fkv/IJGN9/Oe5WvabPtMkssSODt27IhGClN3JFdjY2NM7NFUYPHixdx4443R0WvhcJgbb7zRuJ3OYrE4JFK80s5yytLQ0MD8+fNZtWoV8+fP983u6bdXg9+dIxaL5QiJOtdCIpKLo6AjyxHNkGa0ZAGRmZnJ4sWLo51OmZmZvkx9HhnBFvm1WCypSyLFOwQnLkNE2a73bEtJ7dDY2HnDjx8AACAASURBVEhDQwOtra00NDQYDUrubd16/RO9260StlhSj0ReDUU+laPXEA6H2bPHCbq2Z88ewuGwsQAyqtpuxCzTo8ksFktwJBwyLCJhEblIRL7jpi+LSHfi+HYaETlORGpFZIuIbIp4VIjI90TkPRF5zU0XJFt2ZOK+tsum+MY3vhHXw+Ab3/iGUbkWiyU4EsXjHQnUAjtxpngX4ELgxyJSou68aAZoBm5R1fUikgO8KiJr3G0/UdX/MiTXdyKdWvfffz8tLS2Ew2Guu+4629llsaQwiVq8/wksUdWzVPWbqnqzqn4B+Clwh6lCqepOVV3vLu8HtgCjTMkLGuthYLH0LxIp3tNU9b/bZqrqXYDZaWldRKQImAj80c2qEJE/i8gy18vCYrFY+hSJbLUdObEeTGZB4iEig4BfATer6scisgT4AY5HxQ9wAveUxTnueuB6gPz8fOrq6npclmScozfJ6W8y+0Mdrcw+JC9ekF5PzNK3gK/ESZcCf+vo2J4mIB14DvhWO9uLgI2JztOVQOgEGDhbNbWDZwcpsz/U0crsnfLo5izDvwMuamfb77un6hMjjoPrUmCLqv7Yk1+gqjvd1UuAjabKYLFYLKZI5Md7rV8FacOZwFXAX0TkNTdvHlAqIifjtEK3ATcEUzyLxWLpPoncya7uYLOq6sNJLk/kxM8TPzZE8ubesFgsloBIZGr4bJw8wTE/jAKMKF6LxWJJZRKZGiojy67ddQYwF3gJqDZbNIvFYklNEg79dYcHfx24BceX9l9UdavhclksFkvKksjGeyMwG1gLfElVt/tSKovFYklhErV4F+PMNDwFeMoTxlBwOtdSbhYKi8ViMU0ixTvWl1JYLBZLPyJR51pc04KInAlcAdxoolAWi8WSynQ6rq47cOEK4KvA28CvTRXKYrFYUplEnWsnAJcDpcA/gMcBUdUSH8pmsVgsKUmiFu9fgf8FLlLVNwFE5JvGS2WxWCwpTCLFeylOi7dWRH4DPEY/mebdYumN/NPtq/moof3pqIpufSZu/pCsdP70H+f1GZmpTqLOtRXAChHJBqYD3wTy3bi4K1R1tQ9ltFgsLh81NLFt/pfjbqurq+Oss86Ku6095dgbZSZS9B2du68o+0Q23rCqNqtqPfAo8KiI5AGXAbcCVvFaLJak0pGih2CUfUfn7Y6yT2RqeBmY5M1Q1T3AvW7qN4hIJAC7xQLYT/BUoruteuiesk+keK0918UqXUtbgvjst6QGiRTvcBH5VnsbvbNDWCxB4venosXSExIp3jRgEL2o5SsiXwLuxCnbz1V1frLOrap44lHE5Fs6TxCdI35/KlosPSGR4t2pqt/3pSSdQETSgJ8C5wI7gP8TkSdVdXOyZESUbNGtz3Ro4O9L+G2LDKJzxGLpS/Q1G++pwJuq+haAiDwGXAwkTfGaJogOGWuLtFh6F4kU7zm+lKLzjALe9azvAP45oLJ0C6sELRZLogEUe/wqSCeJ1wI/ygArItcD1wPk5+dTV1cX92Q3rq2nvgNTZHvKLjsdfnpOdsLCtkd75Tlw4EC72zo6rjfK7Og4v2UmkheEzL70X+aMu5WTHry1/R0ebO84qKvr3nOS6vdPp6OT9RJ2AMd51guB99vupKr3AfcBnHLKKdpeKzK0/SRyulmQs876S/cO/M0z7bZqO+wE6uC4XiczwXF+y0zUuea7zL70XwL7b53f7a+0s67phswA7p+c7SdR2dH8Ov/o4NhxXdcHnVK87pDhBlVtdSOWfRpYpaodd10nn/8DPiUiY4H3cOJIXNHdk+3f0v0bymKxmCFhCxs6bGVD1zvFu6sLwMwAigi/Bz4nIrk486+9AnwNZ9Zh31DVZhGpAJ7DcSdbpqqb/CyDxRIkPfns745CCoKOlCCkRqOos4pXVPWgiMwEFqvqQhHZYLJg7aGqzwLPBiHbYgka+5WWGnRa8YrI6Tgt3JldPLZX0+EN+Zv2XbssFoulu3RWed4M3IYTCnKTiHwCqDVXLH/o6HMmlQZQWCyW3kWnFK+q/g74nWf9LeAmU4VKZfqDjS4IuntdnWPBXluLn3TWq6GWOP6yqnp20kuU4gRho/Nb2feHXmmwL1FL9+msqeHbnuVMnCmBmpNfnODxBsmRBc5vXw+S47ey7w+90mA7uizdp7OmhlfbZP1BRH4Xd+c+TLzIZJH8vq58LRZL76GzpoY8z2oImAyMMFIii8XS67DeP8mls6aGV3FsvIJjYnibI25l/QLb6rX0V/qL9093Xi7QvRdMZ00NY7t85hTDKl2LxT8S2sGT3Mr2++XS6UEQInIGUOQ9RlUfSmppLBZLvyeRkkuFVnZnbbwPA58EXgNa3GwFrOK1WCyWLtLZFu8pwHi139sWi8XSYzqreDfieDHsNFgWi8XSCayHQd+ns4p3GLBZRF4GGiOZqjrNSKlSHPvgWLpLf/EwSHU6q3i/Z7IQ/Qn74Fgslq4EybH0YfpDK9tPP0yLpSd0qHhF5HlVnSIi+4kNkiOAqurgZBdIRH4EXAQcBv4GXKuq+0SkCNgCbHV3fUlVZyVbfioSRCs71f0wLZaekGiW4Snub3fnhOwOa4Db3Gl+FuDEAZ7rbvubqp7sY1ks3aA/+GFG6A9fEpbk05UBFLk4M/x6B1CsT3aBVHW1Z/Ul4F+SLcNiSQa2lW3pLp0dQPED4OvAW0Crm62A6Xi8ZcDjnvWx7lxvHwPfVdX/jXeQiFwPXA+Qn5/f5Tnv45GMc/QmOf1NZn+oo5XZh+SpasKEY1cd0Jl9O3m+3+L4BrdNF3v2qQJW4Ey0CZABHOMuTwbeBQYnkjV58mTtLDgvk7jJD8bMfdoXOf1NZn+oo5XZO+UBr2gcvdSVARRDgV090vIuqvrFjraLyDXAhcA5buFR1UZcH2JVfVVE/gacgDPVvMVisfQZOqt47wA2iMhGDA+gEJEv4XSmfUFVD3ryhwN7VLXFnWzzUzimD4vFYulTdFbxPggsAP7CERuvKe7GMSuscWeEiLiNfR74vog04wTqmaWqewyXxWKxWJJOZxXvh6p6l9GSuKjq8e3k/wr4lR9lsFgsFpN0egYKEbkDeJJYU0PS3cksFosl1ems4p3o/p7myfPDncxisVhSjs7GaigxXRCLxWLpL4SCLoDFYrH0N6zitVgsFp+xitdisVh8JqHiFZHBIvLJOPmfMVMki8ViSW06VLwi8lXgr8CvRGSTiHzWs/kBkwWzWCyWVCVRi3ceMFmdGLjXAg+LyFfcbWK0ZBaLxZKiJFK8aaq6E0BVXwZKgCoRuYnYGSksFks/orKykszMTLYvuJDMzEwqKyuDLlKfIpHi3e+177pKuAS4GCg2WTCLxdL7EBFEhLvvvpvGRmcQa2NjI3fffTdubBVLJ0ikeMtpY1JQ1Y+BL+EEKbdYLP0IVY0q2PLycp566inKy8sBrOLtAh0qXlX9k6q+6c0TkQtVtUlVHzVbNIvF0htRVcaNG8eyZcu46KKLWLZsGePGjYtMYGCMVDJvdMeP9/tJL4XFYulTbNmyhaFDhyIiDB06lC1bthiTlYrmjU5Pdumhb9a0F3PMMcewZ48TWlgWQF5eHv/4xz8CLpXF0jEffPBBzK8pVJW0tDRaW1tJS0ujpaUl+hsKmRkD1lahy4Kjy9QTOlVqEcnwrN4QJy9piMj3ROQ9EXnNTRd4tt0mIm+KyFYRmWpCvt94lW6EPXv2cMwxxwRUIoul99Ha2oqIMGzYMACGDRuGiNDaamZeBlWloqKCjAxHzWVkZFBRUeGdN7JHdPZ18aKnQC+3zTPAT1T1ZDc9CyAi44HLcbwpvgT8TETSDJbBF9oq3UT5Fkt/JRwOk5WVhYiQlZVFONydD/bOUVlZyc9+9rMYc8rPfvazpNmVE41cGyEik4EsEZkoIpPcdBYwMCkl6DwXA4+paqOqvg28CZyaTAEiQl5eXkxeXl6eMTtSovP2VfuVpX9QVFTEww8/TFFRkS/ympqa+OijjxARPvroI5qamozJuueee8jKyiIrKwsgunzPPfck5fyJXhlTga8DhcCPPfn7cUa1maJCRK7GmUH4FlXdC4wCXvLss8PNSxqqyp49ewiFQrS2thIKhYy2PL2uOe1tt1h6K9u2beOqq67yVebevXtjfk3R3NzMMcccw7Jly6I25dLSUurr65Ny/g4Vr6o+CDwoIpe6c54lBRH5LTAizqYqYAnwA5yRcT8AFuH4DMfTUHE1k4hcD1wPkJ+fT11dXZfKN2TIEPbt28eQIUOif3BXz5EM/JKZynULSl5/khm0fFMyMzMzmTp1Kk1NTaSnpzNy5MjkyYsYixMl4MvAHODfI6mzx3Y3AUXARnf5NuA2z7bngNMTnWPy5MnaWXAUuY4YMUJDoZCOGDEimmeKyPnjJT8YM/dpX+QEKbM/1NFPmUHcs4CGw2FNT09XQNPT0zUcDhuTGanPoEGDVER00KBB3aoj8IrG0Uud9Wq4B/gaUInT8rwMGNNlLd85WQWe1UuAje7yk8DlIpIhImOBTwEvtz2+p6SlpfH3v/+d1tZW/v73v5OW1uf77+KSSs7olv5BVlYWo0aNIhQKMWrUqKj91SQNDQ2oKg0NDUk9b2e9Gs5Q1auBvap6O3A6cFxSS3KEhSLyFxH5M05ciG8CqOom4BfAZuA3wI2q2pJs4S0tLUybNo0VK1Ywbdo0WlqSLiJwKisr4zqjW+Vr6a2Ew+Go65i6fR+tra1GPRsGDx7McccdRygU4rjjjmPw4MFJO3dnFW9E3R8UkZFAEzA2aaXwoKpXqepJqvoZVZ2mbnQ0d1u1qn5SVU9U1VUm5AM8+eSTXHLJJTz55JOmRARGZARQPNrLt1jikZubG/NrklmzZnHw4EHeffddVJV3332XgwcPMmvWLGMyi4uL2blzJ62trezcuZPi4uTFBeus4n1aRIYCPwLWA9uA5UkrRS8j8hY1+TYNikhrwWLpKX55GACcccYZDBo0KDpSLRQKMWjQIM444wwj8rKzs3nxxRcpKyvjqaeeoqysjBdffJHs7OyknL9TildVf6Cq+9TxbBgDfFpV/z0pJehlDBw4kNWrV7NmzRpWr17NwIF+uyv7h58tliBYvnw5EyZMYPvCaUyYMIHly1O2rZDyVFdXc9NNN3HCCScQCoU44YQTuOmmm6iurjYiLzc3l7S0NJYsWcJFF13EkiVLSEtLS9qz0mGTzp3q511V/bu7fjVwKbBdRL6nqik3vKqhoYFzzz036rtnakhib8DPFovfLF++nKuuuipqo9+0aVPU57S0tDTIoqUMgwYN4sCBA9Ffk2zevJn6+voYv9qysjK2b99uRN6OHTsIhULk5+eza9cujj32WHbv3s2OHTuScv5ELd57gcMAIvJ5YD7wEPARcF9SStCLCIfDDBgwIOZzZsCAASlpcggCP1ugV1999VEdoy0tLVx99dXGZPpNJGpXJG1fcOFReSaJKFvTShdgwIABVFZWUlJSQjgcpqSkhMrKSgYMGGBEnohQUlISjQkxbNgwSkpKknZNOzP1T6RV+zXgPlX9lar+G3B8UkrQixg8eDBNTU3RYcJ5eXk0NTUltTezv7J8+XKuueYaNm3aBNrKpk2buOaaa4wp3+bm5nbzU2UotqpSU1NDTk4O6enpAKSnp5OTk0NNTY1xe37E1dIPl8vDhw9z9913U1tbS3NzM7W1tdx9990cPnzYiDxVpa6ujrKyMp555hnKysqoq6tL3jWN59wbSTg+tGF3+a/A573bOjq2t6SuDKAQEc3JyYlx0s7JyVER6fQ5ugquU3YoFIr5JYUGbXQkz7TM8vJyfeqpp7S8vDwlB6bk5eVpWlqaLlq0SFetWqWLFi3StLQ0zcvLMyYzLS0t7v+YlpZmTGZxcbFWVVVpcXGxhkKhmHUTiIjm5eXF1C8vL6/LuoB2BlAkUrxVwB+AJ4ANgLj5xwN/6OjY3pK6ongzMjJ00aJFqqpaW1urqqqLFi3SjIyMTp+jq/itkPqLzMi5i4qKNBQKaVFRUUoqXkCvuOKKGIV0xRVXpNR/qapaU1Ojw4cPj/k/hw8frjU1NUbkRepzxhln6C9/+Us944wzkjpyLVGshmoRWQsUAKvdE4Fjokg5b/vDhw+zePFiJk6cSEtLC7W1tSxevNjY50x/RERQ1eivabZt2xbzm4qsXbuW5cuXxwRzMU1+fj779u2jsbGRjIwMhg4dajwgegQ/7htwoq+9+uqrXHbZZWRkZFBUVJS0+yhRWMizVfUlVV0BHBvJV9XXceIopBTjx49nxowZVFZWMnXqVCorK5kxYwbjx48PumgpQ+Sh8evh6Q8cPHiww3UT7N69m6FDhwIwdOhQdu/ebVRedXU1jz/+OG+//Tbr1q3j7bff5vHHHzfmTgbOPbpq1SrWrFnDqlWrknrPJuqu/y9gkrv8K88ywHeBXyetJL2AqqoqZs+eTXZ2NqpKfX099913H3feeWfQRbNY2qW+vp7S0tKo21OyQhd2RCQYeSgUigYnN8mWLVuYMmVKTN6UKVOMzfUmIgwaNIjzzz8/2qo//vjjffNqkHaW462nFKnS823xl6lTpxIKhdi+4EJCoRBTp5qdoaq4uJhp06axb98+VJV9+/Yxbdq0pA5vjUdLSwsffvghra2tfPjhh8ZjmowbN47nn38+Ju/5559n3LhxRuRNmDCBTZs2MXXqVFasWMHUqVPZtGkTEyZMSI6AeIbfSALWx1uOt95bU1c614qLi3XdunWqeqRzbd26dcZ6TlWPGPFT2ashCJmRc+fm5mooFNLc3FzjdTzvvPOiMr2/5513njGZNTU1OnbsWF23bp2uWbNG161bp2PHjjXW6aTqXFsRifkPI+um8LuexcXFOnbs2Jg6jh07tsu6gG56NezDCcf4lGc5sr63o2N7S+qK4g2FQnr48GFVPaJ4Dx8+rKFQqNPn6Cr9QQkGIbO31dG0wo8oPhExquhVneckFArFuLBF8kxSU1MT471h8uUiInEVvV/uZF/oKHV0bG9JfaXF21sURKrIbNsS87bQTAFoTk5OjMtTTk6OUZkVFRUqIlHf2rS0NBURraioMCYzIsf7H0bW/SDybJokIyNDZ8yYEaPoZ8yY0WXX0u4q3uHA+Dj5xcDwjo7tLakrijeoz7aglKD3YU1FxSsiMYNhTH8OR+QUFRWpiGhRUVFUvimCaH1G/re2JhXTitfvFq+IxMxGE8nrCt1VvI/Fa9niTIJZ09GxvSV1RfGq+vvnqganeOO1klJN8Qb1Qps2bZquWLFCp02b5ovM/Pz8mPpF1k3KzMzMjGmgZGZmGpXpd6MoHA5rdnZ2zEs0Oztbw+Fwl87TXcW7qYNtRoYMA48Dr7lpG/Cam1+EE5A9su2ezpyvq4o3gh+fM6pHWknelpnpVpKIaHFxsWZkZCigGRkZWlxc7MvQaL8Vr9+dln53OgWl7PPy8mKUUmR4rSn8NgOCM/eiV9FH5mDs4nm6Nedaeje3dRtV/ZqqnqyqJ+P4Dnt9hf8W2aaq5kLP+8h5551HU1NT1B2npaWFpqYmzjvvPGMyzz33XDZt2sTAgQMJhUIMHDiQTZs2ce655xqTGcGvGMDtuQP64SaYn5+PiJCfn29cVoQ//vGPXHLJJfzxj380LktEmDhxItnZ2YgI2dnZTJw40ei19duPF+Daa6+NGUx17bXXJu/k8bSxHml9PgNcECf/fGBVR8f2NOH4Cb8LfEqPtHi73Mru7aaGmpqauB0VJuXW1NTEtABxW4SpZMsGorPQRpLJWWlVk/d52hX8vq6qR9zm2gYgMulN4XeQnMLCwrgt3sLCwi6dh26aGk4AXgcewInNUAk86Oad0NGxPU3A572FdhVvPU6wnt8Bn+vMeXp751oQ0aWys7PjPjjZ2dnGZAaheHNycmL+S9MeBiISNd9EUkZGhnETjt+diKr+u7BVVFRoOByOeU7C4bAx7w1vUJ7IS7Q7QXnaU7yJguS8LiInAVcAkSEbvwNuUNVDHR3bESLyW2BEnE1VqvqEu1xK7LxuO4HRqvoPEZkMrBSRYlX9OM75rweuB+ezr66urlPlmjdvHjfddBMiwqFDhxg0aBCVlZXMmzePgoKCxCfoBnv27OGGG25g0qRJHDhwgEmTJvGv//qv3HvvvZ0ud1epr69n4sSJrFq1invvvZfRo0czceJENmzYYExmhAEDBnD48OHoL2C0nr/85S85++yzWbduXXQorSl5kZkYcnNz2bdvH0OHDmXfvn0MGjTI6HVVVT772c9SXl7OkiVLeOGFFwBz9QS47bbbuO2226IzUJiW9/TTT3P55ZezePFi3nnnHUaPHs3ll1/O008/zaWXXpp0eQUFBdxwww088sgjURPKDTfcQEFBQXLqGU8bB51wYkh8ABR2sE8dcEqic/WFARTPPvtsjMxnn33WeOfI4MGDY97mgwcPTjmvhkmTJsW0yiZNmmTc1JCXlxfTys7LyzNuavCGvPSu+4FfndBBPJsRelJHutPiFZH97p951Cb3jzU1NcMXgb+qanSCIxEZDuxR1RYR+QTwKeCtZAqNjAcvKSmJ5pkcDw7OdEOXXXYZw4cPZ/v27YwZM4bdu3cbn27o448/pr6+HlVnqmzTY+39Ji8vj/Xr10fXVZX169eTl5dnTGZzczOLFi2isrKSLVu2MG7cOBYtWpTcTpk2FBYW0tjYyLp166JhIWfMmEFhYaExmUEQxLNplHjaOOiEY1Oe1SbvUmAT8CecKeYv6sy5umrj9TPYsmow4/txW0ZZWVkqIpqVleWLCxL0PLB0Z2nbeRhJJltIQQTSD+Kejcj1uxPa7/6XCCZavIErWdOpu4q3Jwb1rlBcXKynnHJKzCfxKaecYnyY8qhRo2Jkjho1KqV8XCPn97PTye8OoAj9RQn6Xc8IVvEaVrxBxGpIVjCOrkCSnMO7KhOcUVUiEjPaypQ8vwczqDrK1zswxbTS9eKXvdVv1662+FXPZMhrT/EmGkDRrwjCSXvAgAFUVFTETFtdUVFhbNpqcOzK+/fvp6ysjKlTp1JWVsb+/ft9mcber6nHwWlUTJs2jRUrVjBt2jSnpWGYM844g+OPP55QKMTxxx/PGWecYVym32zevJm77rqL119/ndbWVl5//XXuuusuNm/eHHTR+gxW8XrwO9gyHJnnzTtttel53mbNmkVDQwOHDjkegYcOHaKhoYFZs8wOBhQRdu/eTWtrK7t37/ZF+a5Zs4avfOUrrFmzxris5cuXM3v27KjbWn19PbNnzzY2hX2EyspKMjMzKSkpITMzk8pKs9Mhigj19fXMnz+fVatWMX/+fOrr6+3kAV0hXjM4lVJvH0BRXFys06dPj/k8nT59uvHPtpNOOinmM/ykk04yKs9vWzYBmBoKCwu1oKAg5v4pKCjo8minrhCEXRl3sI23Qy8yKMcPUsHUELhiNJ16+5DhIB6cioqKaKg7769pmX7WE/wPPgTo6tWrVfXIw7p69WqjMoPwpCAAP3AvVvH2gdTbo5MF4dUQhKO/3y37SIu+bSB0ky37IBQvoPX19TEy6+vrjcv0ezi2qvVq6FOptyteIG5L0PSD4/doORGJ66pnynujsLAw2sKNpPT0dKOf/YWFhTpkyJCYT/AhQ4YYlZmsmRK6Aq4/tNdDJeI3bYpU8+O1nWsBIyJcd911fOtb3yIzM5NvfetbXHfddcY7KjZu3NjherJJS0ujubmZZcuWsXr1apYtW0ZzczNpaWlG5O3YsYNvf/vbFBcXEwqFKC4u5tvf/jY7duxIfHA3mT59Ovv376ehoYHW1lYaGhrYv38/06dPNybzC1/4Ao8++ijvv/8+AO+//z6PPvooX/jCF4zJDGJm4+rqapYuXRrj/bN06VKqq6uNyTRKPG2cSqkvtHiLiopi3uSmx9rn5eVFWyreX5MR0YBoazDS4h0yZIhRP16/fZWD8G8tLCzUgQMHxtiyBw4caLSVHUTrMxQK6UMPPRRzbR966KE+G6shcMVoOvV2xRvEp2JQEyRGOmQin+EmO2TC4bBmZGTEKKSMjAyjduyggiz5bVdW9d/eGoTHSATfg+RYzHPddddxzz33sGDBAsaPH8/mzZuZO3euUZ/a2tpaLr74YlatWkVLSwvhcJjzzz+f2tpaYzLD4TCtra2A87IHaG1tNTZoo7m5mebmZkIhx5oWmdnDJOPGjeP2229n5cqV0SA506dP77uBXDqgtLSU0tJS6urqOOuss3yRGblv2lvvU8TTxqmUenuLV9X/oNJBDVOO58KGQVNDZmZmjGnD9ISMETe9tp1OJr8kkjVTQnfxMyxkKpkabOdawCxfvpw33niDtWvXsmbNGtauXcsbb7xhdLRTEMOUMzIyKC0t5ZhjjgHgmGOOobS0lIyMDGMyhw4dGtOZN3ToUGOyAFauXMngwYPJyspCRMjKymLw4MGsXLnSmMyFCxfS0tJCWVkZ5513HmVlZbS0tLBw4UJjMoNg3LhxFBYWsnHjRtauXcvGjRspLCzsu18T8bRxKqXe3uINokMm0gJs26FnssUrIjps2LAYG++wYcOMyQT09NNPj/EbPv3001NuAIVqcP6tqv49J6nmTha4YjSdevvItSCUYFC97/G8Gkx9Egc1r1wQijeC3yO6/JZpB1D0odTbYzUEFTzb73r63SsdhJtVf7G39jeZKaN4gctwZpNopc28acBtwJvAVmCqJ38y8Bd3212AdEaWjccbH79bD353joRCIS0vL48xNZSXlxvtjAkikL6X/tD6VLWKtyeKdxxwIm0mrATG40ztkwGMBf4GpLnbXgZOx5nvbRVwfmdk9fbJLvtLUGm/X2pB+X32B4UUpL1VNTUUbyBeDaq6RVW3xtl0MfCYqjaq6ts4rdtTRaQAGKyqL7qVeQhI+jjMIOLxVlVVUVNTw+LFi3nuuedYvHgxNTU16tHnpQAAC8JJREFUVFVVGZMZBFVVVcycOTMm7vDMmTON1tO5VdpfN0FpaWlMz3tpaalxmX6TcsN3A0D8uBnbFS5SB3xbVV9x1+8GXlLVR9z1pTit223AfFX9opv/OWCuql7YznmvB64HyM/Pn/zYY491qjxr165l6dKlfOc732Hs2LG8/fbb/OhHP2LmzJmcc845PalqQrmPPPII77zzDqNHj+bKK680Ks/LgQMHGDRokC+y/KznOeecw9y5c1m+fHlUXmlpKQsWLGDt2rVGZHrx87r6LfOcc87hueeeIxwOR2U2NzczderUlLy2PZFXUlLyqqqectSGeM3gZCTgt8DGOOlizz51xJoafgpc6VlfijO78GeB33ryPwc81Zly9HavBi/9oaPCL5lB2Ou9pOp1Ve1/17ZPDRlWt3XaRXYAx3nWC4H33fzCOPlJJ4ihkJbkEzFtLF26lJaWlqhpw34O9xx7bXtOb4vV8CRQIyI/BkYCnwJeVtUWEdkvIqcBfwSuBhYHWE5LLydiW62srIzGTaiurk5Jm6vf2GvbcwJRvCJyCY7iHA48IyKvqepUVd0kIr8ANgPNwI2q2uIeVg48AGTh2H1X+V9yS1/Cfr2Yw17bnhGI4lXVFcCKdrZVA0d9s6jTATfBcNEsFovFODZIjsVisfiMVbwWi8XiM1bxWiwWi89YxWuxWCw+E+jINT8Qkd3A9m4cOgz4MMnFsTKDkdkf6mhl9k55Y1R1eNvMlFe83UVEXtF4Q/2szD4nsz/U0crsW/KsqcFisVh8xipei8Vi8RmreNvnPiszZWT2hzpamX1InrXxWiwWi8/YFq/FYrH4jFW8cRCRL4nIVhF5U0RuTeJ5t4nIX0TkNRGJBH/PE5E1IvKG+5vr2f82twxbRWRqJ2UsE5FdIrLRk9dlGSIy2S3rmyJyl4hIF2V+T0Tec+v6mohckCyZInKciNSKyBYR2SQis03XswOZJuuZKSIvi8ifXJm3+1DP9mQaq6e7b5qIbBCRp03XsQOZRusYQ7wgvf05AWk4c719AhiAMwfc+CSdexswrE3eQuBWd/lWYIG73O78cwlkfB6YBGzsiQy6MMddOzK/hzO7SNt9eywTKAAmucs5wOvueY3VswOZJuspwCB3OR0nJOpphuvZnkxj9XT3/RZQAzztxz3bjkyjdfQm2+I9mlOBN1X1LVU9DDyGMxecKS4GHnSXH+TIXHJx559LdDJV/T2wpycypItz3LUjsz16LFNVd6rqend5P7AFGGWynh3INFlPVdUD7mq6m9RwPduTaayeIlIIfBn4eZvzGrtn25FprI5tsYr3aEYB73rWd9DxA9YVFFgtIq+KMy8cQL6q7gTn4QaONVCOrsoY5S73VHaFiPxZHFNE5FMxqTJFpAiYiNMy86WebWSCwXq6n8OvAbuANapqvJ7tyDRZz/8G5gCtnjzT/2U8meDDPQtW8cYjno0mWa4fZ6rqJOB84EYR+XxA5UgkIxmylwCfBE4GdgKLki1TRAYBvwJuVtWPO9rVoEyj9VTVFlU9GWe6q1NFpKOY1CZlGqmniFwI7FLVV9srT9tDeiIvgUzj92wEq3iPpr1533qMqr7v/u7CCQR/KvCB+8mC+7vLQDm6KqPHc9yp6gfuA9wK3M8RM0lSZIpIOo4CfFRVf+1mG61nPJmm6xlBVffhTA77JdP1jCfTYD3PBKaJyDYcs97ZIvKI4TrGlenXfwnYzrW2CWdWjrdwjOiRzrXiJJw3G8jxLL+A8xD9iNhOhIXucjGxBv236ETnmntsEbEdXV2WAfwfTqdKpNPggi7KLPAsfxPHRpYUme72h4D/bpNvrJ4dyDRZz+HAUHc5C/hf4ELD9WxPprF6es57Fkc6uozfs3FkGq9j9Pw9VSipmIALcHqt/wZUJemcn3D/vD8BmyLnBY4B1gJvuL95nmOq3DJspZO9pcBynM+kJpw38szuyABOATa62+7GHWzTBZkPA38B/owziWlBsmQCU3A+6f4MvOamC0zWswOZJuv5GWCDe+6NwL93955Jgkxj9fTsfxZHlKDRe7YdmcbrGEl25JrFYrH4jLXxWiwWi89YxWuxWCw+YxWvxWKx+IxVvBaLxeIzVvFaLBaLz1jFa7FYLD5jFa+lXUSkxRMi7zVxQ2SKSJ2IJHXyPxG5WUQGetafFZGhSTz31SLyU7cem0WkwVOvf3H3C4vIhyJyR5vj68QN4+munyIide7yWSLykRtecKuI/N4dkhrZt22owddEZKh7nIrITM++E928b8epw3Ei8raI5Lnrue76GBEZLiK/6aD+BZHQh124Zv8lImd35RhL5wkHXQBLr6ZBnTH7fnAz8AhwEEBVL+h4984hImGgDCek40NuXhGO03zbup2H4yD/VRGZp7FO7seKyPmquiqOmP9V1Qvdc58MrBSRBlVd627/iar+V5tygeOs/zVgqZt9Oc4Am6NQ1XdFZAkwH7je/b1PVbe759spImeq6h/iHP4tnCGwXWGxe8y6Lh5n6QS2xWvpESJynoi8KCLrReSXIjJIRM4XkV949jlLRJ5yl5eIyCsSG2T7JmAkUCsitW7eNhEZ5i5/S0Q2uulmN69InKDk97vnWi0iWXGKeDawXlWbO1GdUuBO4B2cYaBefgR8N9EJVPU14PtARSfkvQNkiki+OJr4SzjDTtvjJ8Bp7jWYwpEgLgArgRntHHcp8BsAEfm6iKwUkafcFnOFe303iMhLkRa1q9CPEZERnaiHpYtYxWvpiKw2n8hf8250FeN3gS+qE3XtFZzW1RocBZHt7vo14HF3uUpVT8EZmvoFEfmMqt6FE1ykRFVL2siYDFwL/DOOMrxORCa6mz8F/FRVi4F9OAqmLWcCCSNfuUr7HOBpnCHQpW12eRFoFJGStsfGYT3wac/6Nz3XsLbNvv8DXAac4R7X2N5JVbU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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dem_bins = list(np.arange(0,5500,500))\n", - "# mask out differences larger than 100 m ?\n", - "filt_lim = (-100,100)\n", - "mask = (gdf_colombia_dem_extent['z_diff']<=100) & (gdf_colombia_dem_extent['z_diff']>=-100)\n", - "gdf_colombia_dem_extent_filt_box = gdf_colombia_dem_extent[mask]\n", - "gdf_colombia_dem_extent_filt_box['bin'] = pd.cut(gdf_colombia_dem_extent_filt_box['dem_z'],bins=dem_bins)\n", - "fig,ax = plt.subplots(figsize=(5,4))\n", - "gdf_colombia_dem_extent_filt_box.boxplot(column='z_diff',by='bin',ax=ax)\n", - "ax.set_xlabel('Elevation (TANDEM-X) (m)')\n", - "ax.set_xticklabels(dem_bins)\n", - "#ax.set_ylabel('Elevation difference (m)')\n", - "ax.set_title('')\n", - "ax.set_ylabel('ICESat-2 minus TANDEM-X (m)')\n", - "#plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- The x labels in the plot are lower intervals of boxplots, we see that the median differences are close to zero for most elevation ranges with a maximum difference of -10 m. Also, we see a constant negative bias in all the elevation difference. This might be due to a bias present between the 2 sources. This bias maybe due to offset between the 2 datasets which might come down after coregistration." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 3\n", - "- Application of ICESat-2 as control surface for DEMs coregistration\n", - "- Or, to find offsets and align ICESat-2 tracks to a control surface" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Going fancy, include only if you want to :)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Application of ICESat-2 as control for DEM co-registration ?\n", - "- Can use point cloud alignment techniques to align DEMs to points, for now as a starting point we can use the transformation matrix to inform on the horizontal and vertical offset between ICESat-2 tracks and DEMs\n", - "- We will be using a flavor of Iterative Closest Point alignment algorithm, implemented in [Ames Stereo Pipeline](https://github.com/NeoGeographyToolkit/StereoPipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['delta_time', 'longitude', 'latitude', 'terrain_flg', 'h_te_best_fit',\n", - " 'pair', 'beam', 'p_b', 'geometry', 'x_atc', 'dem_z', 'z_diff'],\n", - " dtype='object')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf_colombia_dem_extent.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "### Save the geodataframe in the specified way as expected by Ames Stereo Pipline\n", - "icesat2_pc = '/home/jovyan/icesat2/icesat2_colombia_pc.csv' \n", - "gdf_colombia_dem_extent[['latitude','longitude','h_te_best_fit']].to_csv(icesat2_pc,header=False,index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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delta_timelongitudelatitudeterrain_flgh_te_best_fitpairbeamp_bgeometryx_atcdem_zz_diff
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" - ], - "text/plain": [ - " delta_time longitude latitude terrain_flg h_te_best_fit pair \\\n", - "831 2.905249e+07 -73.689125 11.268554 1.0 -3.830631 1.0 \n", - "306 3.405362e+07 -73.087708 11.822446 0.0 -16.512878 1.0 \n", - "307 3.405362e+07 -73.087807 11.821545 0.0 -16.170166 1.0 \n", - "308 3.405362e+07 -73.087898 11.820642 0.0 -16.313721 1.0 \n", - "309 3.405362e+07 -73.087997 11.819741 0.0 -17.072906 1.0 \n", - "\n", - " beam p_b geometry x_atc \\\n", - "831 0.0 1.0_0.0 POINT (643085.4287785299 1245993.700456058) 2.905249e+07 \n", - "306 0.0 1.0_0.0 POINT (708341.3413268254 1307632.688194632) 3.405362e+07 \n", - "307 0.0 1.0_0.0 POINT (708331.213868225 1307532.908917141) 3.405362e+07 \n", - "308 0.0 1.0_0.0 POINT (708321.9185105585 1307433.029837989) 3.405362e+07 \n", - "309 0.0 1.0_0.0 POINT (708311.7908113913 1307333.250599968) 3.405362e+07 \n", - "\n", - " dem_z z_diff \n", - "831 -32767.0 32763.169369 \n", - "306 -32767.0 32750.487122 \n", - "307 -32767.0 32750.829834 \n", - "308 -32767.0 32750.686279 \n", - "309 -32767.0 32749.927094 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf_colombia_dem_extent.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "### Save the geodataframe in the specified way as expected by Ames Stereo Pipline\n", - "icesat2_pc = '/home/jovyan/icesat2/icesat2_colombia_pc.csv'\n", - "pc_rename_dict = {'latitude':'lat','longitude':'lon','h_te_best_fit':'height_above_datum'}\n", - "gdf_colombia_dem_extent = gdf_colombia_dem_extent.rename(columns=pc_rename_dict)\n", - "#gdf_colombia_dem_extent['height_above_datum'] = gdf_colombia_dem_extent['h_te_best_fit']\n", - "\n", - "gdf_colombia_dem_extent[['lon','lat','height_above_datum']].to_csv(icesat2_pc,header=True,index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "!exportPATH=\"/home/jovyan/icesat2/StereoPipeline/bin:$PATH\"" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "download\n" - ] - } - ], - "source": [ - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t--> Setting number of processing threads to: 4\n", - "Writing log info to: /home/jovyan/icesat2/align/run-log-pc_align-01-29-2108-383.txt\n", - "Will use datum (for CSV files): Geodetic Datum --> Name: WGS_1984 Spheroid: WGS 84 Semi-major axis: 6378137 Semi-minor axis: 6356752.3142451793 Meridian: Greenwich at 0 Proj4 Str: +proj=longlat +datum=WGS84 +no_defs\n", - "Computing the intersection of the bounding boxes of the reference and source points using 9000000 sample points.\n", - "Reference box: (Origin: (-74.111, 9.99494) width: 1.0286 height: 1.8794)\n", - "Source box: (Origin: (-75.0001, 9.99947) width: 3.00066 height: 2.00111)\n", - "Intersection reference box: (Origin: (-74.111, 9.99947) width: 1.0286 height: 1.87486)\n", - "Intersection source box: (Origin: (-74.111, 9.99947) width: 1.0286 height: 1.87486)\n", - "Intersection of bounding boxes took 7.56439 [s]\n", - "Reading: /home/jovyan/icesat2/icesat2_columbia_pc.csv\n", - "Loaded points: 47714\n", - "Loading the reference point cloud took 0.184672 [s]\n", - "Reading: /home/jovyan/icesat2/TDM1_DEM_90m_columbia_DEM_masked_aea.tif\n", - " --> [********************************************************] Complete!\n", - "Loaded points: 1926617\n", - "Loading the source point cloud took 1.91502 [s]\n", - "Data shifted internally by subtracting: Vector3(1.77143e+06,-6.01232e+06,1.1803e+06)\n", - "Building the reference cloud tree.\n", - "Reference point cloud processing took 0.010831 [s]\n", - "Filtering gross outliers\n", - "Filtering gross outliers took 0.448045 [s]\n", - "Reducing number of source points to 831\n", - "Number of errors: 831\n", - "Input: error percentile of smallest errors (meters): 16%: 5.28881, 50%: 7.71163, 84%: 9.29089\n", - "Input: mean of smallest errors (meters): 25%: 4.57134, 50%: 5.78683, 75%: 6.64005, 100%: 7.34246\n", - "Initial error computation took 0.000312 [s]\n", - "writing to /home/jovyan/icesat2/align/run-iterationInfo.csv\n", - "Match ratio: 0.750903\n", - "Alignment took 0.042786 [s]\n", - "Number of errors: 831\n", - "Output: error percentile of smallest errors (meters): 16%: 4.6739, 50%: 8.66104, 84%: 12.116\n", - "Output: mean of smallest errors (meters): 25%: 4.08223, 50%: 5.67732, 75%: 7.11268, 100%: 8.49464\n", - "Final error computation took 0.000236 [s]\n", - "Alignment transform (origin is planet center):\n", - " 1 0 0 4.22524\n", - " 0 1 0 3.35328\n", - " 0 0 1 0.19915\n", - " 0 0 0 1\n", - "Centroid of source points (Cartesian, meters): Vector3(1.77469e+06,-6.01717e+06,1.15123e+06)\n", - "Centroid of source points (lat,lon,z): Vector3(10.4671,-73.5672,742.776)\n", - "\n", - "Translation vector (Cartesian, meters): Vector3(4.22524,3.35328,0.19915)\n", - "Translation vector (North-East-Down, meters): Vector3(0.562998,5.00126,1.95122)\n", - "Translation vector magnitude (meters): 5.39785\n", - "Maximum displacement of points between the source cloud with any initial transform applied to it and the source cloud after alignment to the reference: 5.39785 m\n", - "Translation vector (lat,lon,z): Vector3(5.0893e-06,4.5677e-05,-1.95122)\n", - "\n", - "Transform scale - 1 = 0\n", - "Euler angles (degrees): Vector3(0,-0,0)\n", - "Euler angles (North-East-Down, degrees): Vector3(0,-0,-3.87536e-16)\n", - "Axis of rotation and angle (degrees): Vector3(-nan,-nan,-nan) 0\n", - "Writing: /home/jovyan/icesat2/align/run-transform.txt\n", - "Writing: /home/jovyan/icesat2/align/run-inverse-transform.txt\n", - "Writing: /home/jovyan/icesat2/align/run-trans_source.tif\n", - " --> [********************************************************] Complete!\n", - "Writing: /home/jovyan/icesat2/align/run-beg_errors.csv\n", - "Writing: /home/jovyan/icesat2/align/run-end_errors.csv\n", - "Writing: /home/jovyan/icesat2/align/run-iterationInfo.csv\n", - "Saving to disk took 6.49591 [s]\n" - ] - } - ], - "source": [ - "align_fol = '/home/jovyan/icesat2/align/run'\n", - "#max-displacement is set to 10, given ICESat-2 reported operational accuracy\n", - "pc_align_opts=\"--csv-format '1:lon 2:lat 3:height_above_datum' --max-displacement 10 --save-transformed-source-points --alignment-method point-to-point --datum WGS84\"\n", - "!/home/jovyan/icesat2/StereoPipeline/bin/pc_align $pc_align_opts $icesat2_pc $dem_file -o $align_fol" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Alignment results suggest that there is an offset of ~5.4 m between the ICESat-2 points and TANDEM-X DEM, so that could have contributed to the offsets which we see above" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t--> Setting number of processing threads to: 4\n", - "Writing log info to: /home/jovyan/icesat2/align/run-trans_source-log-point2dem-01-29-2110-464.txt\n", - "The point cloud files must have an equal number of channels which must be 4 or 6 to be able to remove outliers.\n", - "Statistics: [********************************************************] Complete!\n", - "QuadTree: [**********************************************************] Complete!\n", - "\t-- Starting DEM rasterization --\n", - "\t--> DEM spacing: 90 pt/px\n", - "\t or: 0.0111111 px/pt\n", - "Creating output file that is Vector2(3656,2479) px.\n", - "Writing: /home/jovyan/icesat2/align/run-trans_source-DEM.tif\n", - "DEM: [***************************************************************] Complete!\n", - "Percentage of valid pixels = 0.670479\n" - ] - } - ], - "source": [ - "##Lets rerun the analysis with the new DEM to see if the alignment improved anything or not\n", - "## Regrid the transformed pointcloud into DEM at 90 m posting\n", - "!/home/jovyan/icesat2/StereoPipeline/bin/point2dem --tr 90 --t_srs EPSG:32618 $align_fol-trans_source.tif" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_colombia_dem_extent = gdf_colombia_dem_extent.loc[:,~gdf_colombia_dem_extent.columns.duplicated()]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-16.466503143310547" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf_colombia_dem_extent['height_above_datum'].values[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " if sys.path[0] == '':\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'ICESat-2 minus TANDEM-X DEM after coregistration (m)')" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "trans_dem_file = '/home/jovyan/icesat2/align/run-trans_source-DEM.tif'\n", - "trans_dem_ds = rasterio.open(trans_dem_file)\n", - "del_time,elev = gda_lib.sample_near_nbor(trans_dem_ds,gdf_colombia_dem_extent)\n", - "gdf_colombia_dem_extent['trans_dem_z'] = elev\n", - "dem_bins = list(np.arange(0,5500,500))\n", - "# mask out differences larger than 100 m ?\n", - "filt_lim = (-100,100)\n", - "gdf_colombia_dem_extent['trans_z_diff'] = gdf_colombia_dem_extent.height_above_datum - gdf_colombia_dem_extent.trans_dem_z\n", - "\n", - "mask = (gdf_colombia_dem_extent['trans_z_diff']<=100) & (gdf_colombia_dem_extent['trans_z_diff']>=-100)\n", - "gdf_colombia_dem_extent_filt_box = gdf_colombia_dem_extent[mask]\n", - "gdf_colombia_dem_extent_filt_box['bin'] = pd.cut(gdf_colombia_dem_extent_filt_box['dem_z'],bins=dem_bins)\n", - "fig,ax = plt.subplots(figsize=(5,4))\n", - "gdf_colombia_dem_extent_filt_box.boxplot(column='trans_z_diff',by='bin',ax=ax)\n", - "ax.set_xlabel('Elevation (TANDEM-X) (m)')\n", - "ax.set_xticklabels(dem_bins)\n", - "ax.set_title('')\n", - "ax.set_ylabel('ICESat-2 minus TANDEM-X DEM after coregistration (m)')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- We see that after coregistration, the bias reduces to an extent. Note that this is a very preliminary analysis, results will be better after filtering the ATL08 points based on quality metrics and finding truly static surfaces (snow free during acquisiton time of ICESat-2 points)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/source/_static/icepyx_class_diagram.png b/doc/source/_static/icepyx_class_diagram.png deleted file mode 100644 index 17a96e98b..000000000 Binary files a/doc/source/_static/icepyx_class_diagram.png and /dev/null differ diff --git a/doc/source/community/contact.rst b/doc/source/community/contact.rst index 0589fe16d..ba1de118b 100644 --- a/doc/source/community/contact.rst +++ b/doc/source/community/contact.rst @@ -2,8 +2,22 @@ Contact Us ========== -Working with ICESat-2 data and have ideas you want to share? -Have a great suggestion or recommendation of something you'd like to see -implemented and want to find out if others would like that tool too? -Come join the conversation at: https://discourse.pangeo.io/. -Search for "icesat-2" under the "science" topic to find us. \ No newline at end of file +The best way to contact us depends on what information you're looking for. + +* Need help installing, running, or using `icepyx`? Add a new topic to ask for help on `Discourse `_ (after reviewing the documentation and existing topics, of course, to see if they answer your question!) or attend one of our regular virtual meetings (details below). +* Found a bug or have a feature request? Post an issue on `GitHub `_! +* Have an idea you'd like to discuss? Start a conversation on `Discourse `_ or attend one of our regular virtual meetings (details below). +* Want to get involved? Do one or more of the above, or reach out to one of the dev team members individually. We're excited to hear your thoughts and provide help! + + +Regular Meeting Schedule +------------------------ +Our team (developers, users, scientists, educators) meets regularly via Zoom to provide support, troubleshoot issues, and plan development. +We meet on: + +* the second Tuesday of the month at 4pm GMT (12pm Eastern, 9am Pacific) +* the fourth Monday of the month at 8pm GMT (4pm Eastern, 1pm Pacific) + +Additional information about logging in to the meetings can be found on `this Discourse post `_. + +Absolutely NO previous software development experience is necessary to attend any meeting. Think of them more like coffee hour mixed with office hours than a conference call. We look forward to seeing you there! \ No newline at end of file diff --git a/doc/source/conf.py b/doc/source/conf.py index 861271b8b..fff656138 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -12,7 +12,9 @@ # import os import sys -sys.path.insert(0, os.path.abspath('../..')) + +sys.path.insert(0, os.path.abspath("../..")) +sys.path.insert(0, os.path.abspath("../sphinxext")) import datetime import icepyx @@ -21,9 +23,9 @@ # -- Project information ----------------------------------------------------- -project = 'icepyx' +project = "icepyx" year = datetime.date.today().year -copyright = '2019-{}, The icepyx Developers'.format(year) +copyright = "2019-{}, The icepyx Developers".format(year) # -- General configuration --------------------------------------------------- @@ -35,24 +37,25 @@ "sphinx.ext.autosectionlabel", "numpydoc", "nbsphinx", - "recommonmark" - + "recommonmark", + "contributors", # custom extension, from pandas ] + source_suffix = { - '.rst': 'restructuredtext', - '.txt': 'markdown', - '.md': 'markdown', + ".rst": "restructuredtext", + ".txt": "markdown", + ".md": "markdown", } # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['**.ipynb_checkpoints'] +exclude_patterns = ["**.ipynb_checkpoints"] # location of master document (by default sphinx looks for contents.rst) -master_doc = 'index' +master_doc = "index" # -- Configuration options --------------------------------------------------- @@ -68,24 +71,30 @@ # html_theme = 'alabaster' html_theme = "sphinx_rtd_theme" html_theme_options = { - 'logo_only': True, - 'display_version': False, - 'prev_next_buttons_location': None, - 'navigation_depth': 4, - 'collapse_navigation': True + "logo_only": True, + "display_version": False, + "prev_next_buttons_location": None, + "navigation_depth": 4, + "collapse_navigation": True, } -html_logo = '_static/icepyx_v2_oval_orig_nobackgr.png' -html_favicon = '_static/icepyx_v2_oval_tiny-uml_nobackgr.png' -html_static_path = ['_static'] +html_logo = "_static/icepyx_v2_oval_orig_nobackgr.png" +html_favicon = "_static/icepyx_v2_oval_tiny-uml_nobackgr.png" +html_static_path = ["_static"] html_context = { - 'menu_links_name': '', - 'menu_links': [ - (' icepyx Github', 'https://github.com/icesat2py/icepyx'), - (' Pangeo Discourse', 'https://discourse.pangeo.io/t/icepyx-python-tools-for-icesat-2-data/404/2') - - ] + "menu_links_name": "", + "menu_links": [ + ( + ' icepyx Github', + "https://github.com/icesat2py/icepyx", + ), + ( + ' Pangeo Discourse', + "https://discourse.pangeo.io/t/icepyx-python-tools-for-icesat-2-data/404/2", + ), + ], } + def setup(app): app.add_stylesheet("style.css") diff --git a/doc/source/contributing/contribution_guidelines.rst b/doc/source/contributing/contribution_guidelines.rst index 5feacc95c..e2c5912fb 100644 --- a/doc/source/contributing/contribution_guidelines.rst +++ b/doc/source/contributing/contribution_guidelines.rst @@ -34,27 +34,28 @@ Find the *Issues* tab at the top of GitHub repository and click *New Issue* butt Questions and Help ------------------ -Please do not create issues to ask for help. A faster way to reach the community is through our Science/ICESat-2 subcategory on the `Pangeo discourse `_ page. We are excited to have you join an existing conversation or start a new post! Please note that a github login is required to post on the discourse page. +Please do not create issues to ask for help. A faster way to reach the community is through our Science/ICESat-2 subcategory on the `Pangeo discourse `_ page. We are excited to have you join an existing conversation or start a new post! Please note that a GitHub login is required to post on the discourse page. Other Resources ^^^^^^^^^^^^^^^ -- We are currently working on compiling a list of resources for working with ICESat-2 data (in the meantime, check out the `2019 ICESat-2 Hackweek repo `_ and `consider applying for this year's Hackweek `_) +- Check out our :ref:`ICESat-2 Open-Source Resources Guide` for a host of tools and code for getting and working with ICESat-2 data +- The `2019 ICESat-2 Hackweek Tutorial repo `_ and [in progress] `2020 ICESat-2 Hackweek Tutorial repo `_ are great resources for learning basic Python and development skills, learning about ICESat-2 data, setting up a computational environment, and finding and analyzing ICESat-2 datasets. - A great set of `interactive tutorials `_ for learning and practicing using git - Let us know about the helpful tools you've found by posting on our discourse forum as indicated above (`Questions and Help`_)! Adding Examples --------------- -We are delighted you'd like to contribute your `icepyx `_ example! Examples may be in the form of executable scripts or interactive Jupyter Notebooks. Please make sure that each example has a descriptive name so someone not familiar with your project understands its general behavior. Fully working examples should be submitted using a pull request to the "examples" branch, following the steps outlined below for `Contributing Code`_. +We are delighted you'd like to contribute your `icepyx `_ example! Examples may be in the form of executable scripts or interactive Jupyter Notebooks. Please make sure that each example has a descriptive name so someone not familiar with your project understands its general behavior. Fully working examples should be submitted using a pull request to the "development" branch, following the steps outlined below for `Contributing Code`_. Contributing Code ----------------- -We follow a standard git workflow for code changes and additions. All submitted code, including our own, goes through the pull request process; no changes are pushed directly to the *master* branch. This allows our continuous integration (testing) process to ensure that the code is up to our standards and passes all of our tests (i.e. doesn't break what's already there and working). It also means that the *master* branch remains stable even as new features are being developed. +We follow a standard git workflow for code changes and additions. All submitted code, including our own, goes through the pull request process; no changes are pushed directly to the *master* or *development* branches. This allows our continuous integration (testing) process to ensure that the code is up to our standards and passes all of our tests (i.e. doesn't break what's already there and working). By having a *development* branch for daily work, we enable the *master* branch to remain stable between releases even as new features are being added. First Steps ^^^^^^^^^^^ -Before you even begin writing code, please first check out our issues page. Someone may already be working on the same problem, and you may be able to contribute directly to their efforts. If not, create a new issue to describe what you plan to do. +Before you begin writing code, please first check out our issues page. Someone may already be working on the same problem, and you may be able to contribute directly to their efforts. If not, create a new issue to describe what you plan to do. General Guidelines ^^^^^^^^^^^^^^^^^^ @@ -75,14 +76,15 @@ We encourage users to follow the `git pull request workflow " ] diff --git a/doc/source/getting_started/citation_link.rst b/doc/source/getting_started/citation_link.rst index 1f8f6be6c..c7f2c41ee 100644 --- a/doc/source/getting_started/citation_link.rst +++ b/doc/source/getting_started/citation_link.rst @@ -1,4 +1 @@ -Citing icepyx -============= - .. include:: ../../../CITATION.rst \ No newline at end of file diff --git a/doc/source/getting_started/example_link.rst b/doc/source/getting_started/example_link.rst index 8241e6e4d..b4a7d005b 100644 --- a/doc/source/getting_started/example_link.rst +++ b/doc/source/getting_started/example_link.rst @@ -1,4 +1,4 @@ Examples ======== -.. include:: ../../examples/examples.rst +.. include:: ../../../examples/examples.rst diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst index 04cf021f7..b6b584275 100644 --- a/doc/source/getting_started/install.rst +++ b/doc/source/getting_started/install.rst @@ -2,7 +2,7 @@ .. _`zipped file`: https://github.com/icesat2py/icepyx/archive/master.zip - +.. _`Fiona`: https://pypi.org/project/Fiona/ @@ -10,21 +10,37 @@ Installation ============ +The simplest way to install icepyx is using pip. + +.. code-block:: + + pip install icepyx + + +Windows users will need to first install `Fiona`_, please look at the instructions there. Windows users may consider installing Fiona using pipwin -Currently icepyx is only available for use as a github repository. -The contents of the repository can be download as a `zipped file`_ or cloned. +.. code-block:: -To use icepyx, fork this repo to your own account, then ``git clone`` the repo onto your system. -Provided the location of the repo is part of your $PYTHONPATH, -you should simply be able to add ``import icepyx`` to your Python document. + pip install pipwin + pipwin install Fiona + +Currently, updated packages are not automatically generated with each build. This means it is possible that pip will not install the latest release of icepyx. In this case, icepyx is also available for use via the GitHub repository. The contents of the repository can be download as a `zipped file`_ or cloned. + +To use icepyx this way, fork this repo to your own account, then git clone the repo onto your system. To clone the repository: -.. code-block:: none +.. code-block:: git clone https://github.com/icesat2py/icepyx.git +Provided the location of the repo is part of your $PYTHONPATH, you should simply be able to add import icepyx to your Python document. +Alternatively, in a command line or terminal, navigate to the folder in your cloned repository containing setup.py and run + +.. code-block:: + + pip install -e -Future developments of icepyx may include pip and conda as simplified installation options. +Future developments of icepyx may include conda as another simplified installation option. \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index e7e2dd0b3..c4d967311 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -25,6 +25,7 @@ icepyx is both a software library and a community composed of ICESat-2 data user .. user_guide/Gallery user_guide/documentation/icepyx + user_guide/changelog/index .. toctree:: :maxdepth: 2 diff --git a/doc/source/user_guide/changelog/index.rst b/doc/source/user_guide/changelog/index.rst new file mode 100644 index 000000000..3d984f0d2 --- /dev/null +++ b/doc/source/user_guide/changelog/index.rst @@ -0,0 +1,30 @@ +.. _release: + +icepyx ChangeLog +================ + +This is the list of changes made to icepyx in between each release. Full details can be found in the `commit logs `_. + +Latest Release (Version 0.3.0) +------------------------------ + +.. toctree:: + :maxdepth: 2 + + v0.3.0 + +Version 0.2-alpha +----------------- + +.. toctree:: + :maxdepth: 2 + + v0.2-alpha + +Version 0.1-alpha +----------------- + +.. toctree:: + :maxdepth: 2 + + v0.1-alpha \ No newline at end of file diff --git a/doc/source/user_guide/changelog/template.rst b/doc/source/user_guide/changelog/template.rst new file mode 100644 index 000000000..0db507f23 --- /dev/null +++ b/doc/source/user_guide/changelog/template.rst @@ -0,0 +1,53 @@ +.. _whatsnew_030: + +What's new in 0.3.0-alpha (?? DATE ??) +----------------------------------- + +These are the changes in icepyx 0.3.0-alpha See :ref:`release` for a full changelog +including other versions of icepyx. + + +New Features +~~~~~~~~~~~~ + +- +- + +Bug fixes +~~~~~~~~~ + +- +- + + +Deprecations +~~~~~~~~~~~~ + +- +- + + +Maintenance +^^^^^^^^^^^ + +- +- + + +Documentation +^^^^^^^^^^^^^ + +- +- + + +Other +^^^^^ +- +- + + +Contributors +~~~~~~~~~~~~ + +.. contributors:: v0.3.0..v0.3.1|HEAD \ No newline at end of file diff --git a/doc/source/user_guide/changelog/v0.1-alpha.rst b/doc/source/user_guide/changelog/v0.1-alpha.rst new file mode 100644 index 000000000..25eb1ed3c --- /dev/null +++ b/doc/source/user_guide/changelog/v0.1-alpha.rst @@ -0,0 +1,54 @@ +.. _whatsnew_010: + +What's new in v0.1-alpha (7 April 2020) +-------------------------------------- + +This was the first official "release" of icepyx, after it had been in development since Fall 2019. + +This changelog captures the general features of icepyx functionality at the time of this initial release, rather than providing a detailed account of all development steps and changes that were made. + + + + +Features +~~~~~~~~ + +- Functionality to query and order data from NSIDC using their built-in API and NASA's CMR API +- Visualization of input spatial parameters +- Enable subsetting using NSIDC subsetter +- Variable and variable path viewing and manipulation +- Set up continuous integration testing with Travis + + +Bug fixes +~~~~~~~~~ + +- No known bugs at release + + +Deprecations +~~~~~~~~~~~~ + +- is2class became icesat2data + + +Documentation +^^^^^^^^^^^^^ + +- Example usage notebooks + - using `icepyx` to access ICESat-2 data + - subsetting using the NSIDC subsetter + - comparing ATLAS altimeter and DEM data in Colombia +- Generate documentation using Sphinx and automate building/updating to ReadtheDocs + + +Other +^^^^^ +- Develop attribution and contribution guidelines +- Provide ICESat-2 Resources Guide + + +Contributors +~~~~~~~~~~~~ + +.. contributors:: 12ba33d..v0.1-alpha \ No newline at end of file diff --git a/doc/source/user_guide/changelog/v0.2-alpha.rst b/doc/source/user_guide/changelog/v0.2-alpha.rst new file mode 100644 index 000000000..f43f5eefb --- /dev/null +++ b/doc/source/user_guide/changelog/v0.2-alpha.rst @@ -0,0 +1,55 @@ +.. _whatsnew_020: + +What's new in v0.2-alpha (6 May 2020) +----------------------------------- + +These are the changes in pandas v0.2-alpha See :ref:`release` for a full changelog +including other versions of icepyx. + + +New Features +~~~~~~~~~~~~ + +- Ongoing work to refactor the icesat2data class into a more pythonic, modular structure + + - Create `Earthdata` login class object and call as an attribute of an icesat2data object + - Move API (NSIDC and CMR) formatting functions to a separate module, `APIformatting` + - Create ICESat-2 reference function module, `is2ref` + - Create `Granules` class to get/order/download granules and call as an attribute of the icesat2data object + - Create `Variables` class to interface with ICESat-2 nested variables + - Create `Parameters` class for managing API inputs within `APIformatting` module + +- allow installation with pip and git + +Bug fixes +~~~~~~~~~ + +- Polygon handling will now put polygon coordinates into the correct order for submitting to CMR API + + +Deprecations +~~~~~~~~~~~~ + +- icesat2data class was refactored - access to some functionality changed + + +Maintenance +^^^^^^^^^^^ + +- Update examples to work with refactored code +- Update and expand tests for refactored code + + +Documentation +^^^^^^^^^^^^^ + +- Generate and include a UML diagram +- Update documentation to reflect refactored code + + - Separate into icesat2data API and component classes + + +Contributors +~~~~~~~~~~~~ + +.. contributors:: v0.1-alpha..v0.2-alpha \ No newline at end of file diff --git a/doc/source/user_guide/changelog/v0.3.0.rst b/doc/source/user_guide/changelog/v0.3.0.rst new file mode 100644 index 000000000..ec25bf1de --- /dev/null +++ b/doc/source/user_guide/changelog/v0.3.0.rst @@ -0,0 +1,65 @@ +.. _whatsnew_030: + +What's new in v0.3.0 (?? August 2020) +----------------------------------- + +This is a summary of the changes in icepyx v0.3.0. See :ref:`release` for a full changelog +including other versions of icepyx. Note that during this time period we transitioned to master + development branches, with mandatory squash commits to the development branch from working branches in order to simplify the git history. + + +New Features +~~~~~~~~~~~~ + +- allow data querying using tracks and cycles +- transition to use of `query` class object +- add Black pre-commit hook and flake8 for code formatting and style consistency +- created a development branch, enabling master to be the stable release branch +- add icepyx release to PyPI, thereby enabling non-dev installs with pip +- add code coverage badge for testing +- enable alternative Earthdata authentication with netrc +- automatically unzip downloaded files into a single directory +- save order IDs and enable restart of download for previously ordered data +- option to suppress order status emails from NSIDC +- display variables in a dictionary format +- overall, the variables class was overhauled: generalized, improved, and tested + +Bug fixes +~~~~~~~~~ + +- update bounding box assertions to allow crossing dateline +- add try/except for gaierror +- automatically order polygon vertices properly for submission to CMR and NSIDC APIs +- fix index error due to NSIDC metadata changes +- skip straight to variable subsetting without needing to manually run data search first + + +Deprecations +~~~~~~~~~~~~ + +- `icesat2data` class is deprecated. The existing functionality to search and obtain data has been migrated to the `query` class. A new class will be created for subsequent steps of working with data. +- inclusive flag for `variable.append` and `variable.remove` methods has been removed + + +Maintenance +^^^^^^^^^^^ + +- add PyPI building to Travis for new releases +- update class architecture diagram and add to documentation page +- refactor test suite into multiple modules + + +Documentation +^^^^^^^^^^^^^ + +- update and improve installation instructions (especially for Windows users) +- review and update all docstrings (including examples) +- move examples to top level directory for easy finding (and make development notebooks harder to find) +- create subsetting workflow example Jupyter noteobok +- improve explanations in introductory example notebook +- reorganized documentation structure to be more intuitive (and categorized) + + +Contributors +~~~~~~~~~~~~ + +.. contributors:: v0.2-alpha..v0.3.0a|HEAD \ No newline at end of file diff --git a/doc/source/user_guide/documentation/components.rst b/doc/source/user_guide/documentation/components.rst index 3a7b4f298..3ab00051c 100644 --- a/doc/source/user_guide/documentation/components.rst +++ b/doc/source/user_guide/documentation/components.rst @@ -1,4 +1,4 @@ -Icesat2Data Components +Query Components ====================== diff --git a/doc/source/user_guide/documentation/icepyx.rst b/doc/source/user_guide/documentation/icepyx.rst index 9aef43e1f..29c5f109e 100644 --- a/doc/source/user_guide/documentation/icepyx.rst +++ b/doc/source/user_guide/documentation/icepyx.rst @@ -1,5 +1,5 @@ -icepyx API Reference -==================== +icepyx Documentation (API Reference) +==================================== @@ -11,5 +11,5 @@ icepyx class diagram illustrating the library's public-facing classes, their att .. toctree:: - icesat2data + query components diff --git a/doc/source/user_guide/documentation/icepyx_architecture.wsd b/doc/source/user_guide/documentation/icepyx_architecture.wsd index 8ecc86a53..8b2667bac 100644 --- a/doc/source/user_guide/documentation/icepyx_architecture.wsd +++ b/doc/source/user_guide/documentation/icepyx_architecture.wsd @@ -1,7 +1,7 @@ @startuml -title **icepyx Class Diagram** +title **icepyx Class Diagram** -class Icesat2Data { +class Query { reqparams extent_type subsetparams @@ -157,25 +157,25 @@ package "Format for API (apifmt)" { } -Icesat2Data *-left- Granules -Icesat2Data *-right- Variables +Query *-left- Granules +Query *-right- Variables -'Icesat2Data -right-> about_dataset : is2ref.about_dataset -'Icesat2Data -right-> _get_custom_options : is2ref._get_custom_options -'Icesat2Data -right-> _validate_dataset : is2ref._validate_dataset -Icesat2Data -right-> "ICESat-2 Reference (is2ref)" +'Query -right-> about_dataset : is2ref.about_dataset +'Query -right-> _get_custom_options : is2ref._get_custom_options +'Query -right-> _validate_dataset : is2ref._validate_dataset +Query -right-> "ICESat-2 Reference (is2ref)" -Icesat2Data -right-> "Validation (val)" -'Icesat2Data --> dset_version : val.dset_version -'Icesat2Data --> spatial : val.spatial -'Icesat2Data --> temporal: val.temporal +Query -right-> "Validation (val)" +'Query --> dset_version : val.dset_version +'Query --> spatial : val.spatial +'Query --> temporal: val.temporal -Icesat2Data -left-> "Format for API (apifmt)" -'Icesat2Data --> build_CMR_params: apifmt.build_CMR_params -'Icesat2Data --> build_reqconfig_params : apifmt.build_reqconfig_params -' Icesat2Data -left-> APIformatting +Query -left-> "Format for API (apifmt)" +'Query --> build_CMR_params: apifmt.build_CMR_params +'Query --> build_reqconfig_params : apifmt.build_reqconfig_params +' Query -left-> APIformatting -Icesat2Data .down. Earthdata +Query .down. Earthdata Variables -down-> _get_custom_options diff --git a/doc/source/user_guide/documentation/icepyx_architecture_user.wsd b/doc/source/user_guide/documentation/icepyx_architecture_user.wsd index 6c1519805..80e57bcf8 100644 --- a/doc/source/user_guide/documentation/icepyx_architecture_user.wsd +++ b/doc/source/user_guide/documentation/icepyx_architecture_user.wsd @@ -1,7 +1,7 @@ @startuml -title **icepyx Class Diagram** +title **icepyx Class Diagram** -class Icesat2Data { +class Query { reqparams extent_type subsetparams @@ -157,25 +157,25 @@ package "Format for API (apifmt)" { } -Icesat2Data *-left- Granules -Icesat2Data *-right- Variables +Query *-left- Granules +Query *-right- Variables -''Icesat2Data -right-> about_dataset : is2ref.about_dataset -''Icesat2Data -right-> _get_custom_options : is2ref._get_custom_options -''Icesat2Data -right-> _validate_dataset : is2ref._validate_dataset -Icesat2Data -right-> "ICESat-2 Reference (is2ref)" +''Query -right-> about_dataset : is2ref.about_dataset +''Query -right-> _get_custom_options : is2ref._get_custom_options +''Query -right-> _validate_dataset : is2ref._validate_dataset +Query -right-> "ICESat-2 Reference (is2ref)" -Icesat2Data -right-> "Validation (val)" -''Icesat2Data --> dset_version : val.dset_version -''Icesat2Data --> spatial : val.spatial -''Icesat2Data --> temporal: val.temporal +Query -right-> "Validation (val)" +''Query --> dset_version : val.dset_version +''Query --> spatial : val.spatial +''Query --> temporal: val.temporal -Icesat2Data -left-> "Format for API (apifmt)" -''Icesat2Data --> build_CMR_params: apifmt.build_CMR_params -''Icesat2Data --> build_reqconfig_params : apifmt.build_reqconfig_params -'' Icesat2Data -left-> APIformatting +Query -left-> "Format for API (apifmt)" +''Query --> build_CMR_params: apifmt.build_CMR_params +''Query --> build_reqconfig_params : apifmt.build_reqconfig_params +'' Query -left-> APIformatting -Icesat2Data .down. Earthdata +Query .down. Earthdata ' Variables -down-> _get_custom_options diff --git a/doc/source/user_guide/documentation/icepyx_class_diagram.png b/doc/source/user_guide/documentation/icepyx_class_diagram.png index db709efe4..a98b53605 100644 Binary files a/doc/source/user_guide/documentation/icepyx_class_diagram.png and b/doc/source/user_guide/documentation/icepyx_class_diagram.png differ diff --git a/doc/source/user_guide/documentation/icepyx_class_diagram_full.png b/doc/source/user_guide/documentation/icepyx_class_diagram_full.png index e285e4eab..84efb7c7a 100644 Binary files a/doc/source/user_guide/documentation/icepyx_class_diagram_full.png and b/doc/source/user_guide/documentation/icepyx_class_diagram_full.png differ diff --git a/doc/source/user_guide/documentation/icesat2data.rst b/doc/source/user_guide/documentation/icesat2data.rst deleted file mode 100644 index 0165deab5..000000000 --- a/doc/source/user_guide/documentation/icesat2data.rst +++ /dev/null @@ -1,50 +0,0 @@ -Icesat2Data Class -================= - -.. currentmodule:: icepyx.icesat2data - - -Constructor ------------ - -.. autosummary:: - :toctree: ../_icepyx/ - - Icesat2Data - - -Attributes ----------- - -.. autosummary:: - :toctree: ../_icepyx/ - - Icesat2Data.CMRparams - Icesat2Data.dataset - Icesat2Data.dataset_version - Icesat2Data.dates - Icesat2Data.end_time - Icesat2Data.file_vars - Icesat2Data.granules - Icesat2Data.order_vars - Icesat2Data.reqparams - Icesat2Data.spatial_extent - Icesat2Data.subsetparams - Icesat2Data.start_time - - -Methods -------- - -.. autosummary:: - :toctree: ../_icepyx/ - - Icesat2Data.avail_granules - Icesat2Data.dataset_all_info - Icesat2Data.dataset_summary_info - Icesat2Data.download_granules - Icesat2Data.earthdata_login - Icesat2Data.latest_version - Icesat2Data.order_granules - Icesat2Data.show_custom_options - Icesat2Data.visualize_spatial_extent \ No newline at end of file diff --git a/doc/source/user_guide/documentation/query.rst b/doc/source/user_guide/documentation/query.rst new file mode 100644 index 000000000..4fb29cfb1 --- /dev/null +++ b/doc/source/user_guide/documentation/query.rst @@ -0,0 +1,52 @@ +Query Class +================= + +.. currentmodule:: icepyx.query + + +Constructor +----------- + +.. autosummary:: + :toctree: ../_icepyx/ + + Query + + +Attributes +---------- + +.. autosummary:: + :toctree: ../_icepyx/ + + Query.CMRparams + Query.cycles + Query.dataset + Query.dataset_version + Query.dates + Query.end_time + Query.file_vars + Query.granules + Query.orbit_number + Query.order_vars + Query.reqparams + Query.spatial_extent + Query.subsetparams + Query.start_time + Query.tracks + +Methods +------- + +.. autosummary:: + :toctree: ../_icepyx/ + + Query.avail_granules + Query.dataset_all_info + Query.dataset_summary_info + Query.download_granules + Query.earthdata_login + Query.latest_version + Query.order_granules + Query.show_custom_options + Query.visualize_spatial_extent diff --git a/doc/sphinxext/announce.py b/doc/sphinxext/announce.py new file mode 100644 index 000000000..21bf7a69e --- /dev/null +++ b/doc/sphinxext/announce.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +# -*- encoding:utf-8 -*- +""" +Script to generate contributor and pull request lists + +This script generates contributor and pull request lists for release +announcements using Github v3 protocol. Use requires an authentication token in +order to have sufficient bandwidth, you can get one following the directions at +`_ +Don't add any scope, as the default is read access to public information. The +token may be stored in an environment variable as you only get one chance to +see it. + +Usage:: + + $ ./scripts/announce.py + +The output is utf8 rst. + +Custom extension from the Pandas library: https://github.com/pandas-dev/pandas/blob/1.1.x/doc/sphinxext/announce.py +Copied 10 August 2020 and subsequently modified. +Specifically, get_authors was adjusted to check for a .mailmap file and use the git through the command line in order to utilize it if present. Using a mailmap file is currently not possible in gitpython (from git import Repo), and the recommended solution is to bring in the mailmap file yourself and use it to modify the author list (i.e. replicate the functionality that already exists in git). This felt a bit out of time-scope for right now. Alternatively, the git-fame library (imported as gitfame) uses the mailmap file and compiles statistics, but the python wrapper for this command line tool was taking forever. So, I've reverted to using os.system to use git behind the scenes instead. + +Dependencies +------------ + +- gitpython +- pygithub + +Some code was copied from scipy `tools/gh_lists.py` and `tools/authors.py`. + +Examples +-------- + +From the bash command line with $GITHUB token. + + $ ./scripts/announce.py $GITHUB v1.11.0..v1.11.1 > announce.rst + +""" +import codecs +import os +import re +import textwrap + +from git import Repo + +UTF8Writer = codecs.getwriter("utf8") +this_repo = Repo(os.path.join(os.path.dirname(__file__), "..", "..")) + +author_msg = """\ +A total of %d people contributed to this release. People with a +"+" by their names contributed for the first time. +""" + +pull_request_msg = """\ +A total of %d pull requests were merged for this release. +""" + + +def get_authors(revision_range): + pat = "^.*\\t(.*)$" + lst_release, cur_release = [r.strip() for r in revision_range.split("..")] + + if "|" in cur_release: + # e.g. v1.0.1|HEAD + maybe_tag, head = cur_release.split("|") + assert head == "HEAD" + if maybe_tag in this_repo.tags: + cur_release = maybe_tag + else: + cur_release = head + revision_range = f"{lst_release}..{cur_release}" + + # authors, in current release and previous to current release. + # We need two passes over the log for cur and prev, one to get the + # "Co-authored by" commits, which come from backports by the bot, + # and one for regular commits. + if ".mailmap" in os.listdir(this_repo.git.working_dir): + + xpr = re.compile(r"Co-authored-by: (?P[^<]+) ") + + gitcur = list(os.popen("git shortlog -s " + revision_range).readlines()) + cur = [] + for n in gitcur: + n = re.search(r".*?\t(.*)\n.*", n).group(1) + cur.append(n) + cur = set(cur) + + gitpre = list(os.popen("git shortlog -s " + lst_release).readlines()) + pre = [] + for n in gitpre: + n = re.search(r".*?\t(.*)\n.*", n).group(1) + pre.append(n) + pre = set(pre) + + else: + + xpr = re.compile(r"Co-authored-by: (?P[^<]+) ") + cur = set( + xpr.findall( + this_repo.git.log("--grep=Co-authored", "--pretty=%b", revision_range) + ) + ) + cur |= set(re.findall(pat, this_repo.git.shortlog("-se", revision_range), re.M)) + + pre = set( + xpr.findall( + this_repo.git.log("--grep=Co-authored", "--pretty=%b", lst_release) + ) + ) + pre |= set(re.findall(pat, this_repo.git.shortlog("-se", lst_release), re.M)) + + # Homu is the author of auto merges, clean him out. + # cur.discard("Homu") + # pre.discard("Homu") + + # Append '+' to new authors. + authors = [s + " +" for s in cur - pre] + [s for s in cur & pre] + authors.sort() + return authors + + +def get_pull_requests(repo, revision_range): + prnums = [] + + # From regular merges + merges = this_repo.git.log("--oneline", "--merges", revision_range) + issues = re.findall("Merge pull request \\#(\\d*)", merges) + prnums.extend(int(s) for s in issues) + + # From Homu merges (Auto merges) + issues = re.findall("Auto merge of \\#(\\d*)", merges) + prnums.extend(int(s) for s in issues) + + # From fast forward squash-merges + commits = this_repo.git.log( + "--oneline", "--no-merges", "--first-parent", revision_range + ) + issues = re.findall("^.*\\(\\#(\\d+)\\)$", commits, re.M) + prnums.extend(int(s) for s in issues) + + # get PR data from github repo + prnums.sort() + prs = [repo.get_pull(n) for n in prnums] + return prs + + +def build_components(revision_range, heading="Contributors"): + lst_release, cur_release = [r.strip() for r in revision_range.split("..")] + authors = get_authors(revision_range) + + return { + "heading": heading, + "author_message": author_msg % len(authors), + "authors": authors, + } + + +def build_string(revision_range, heading="Contributors"): + components = build_components(revision_range, heading=heading) + components["uline"] = "=" * len(components["heading"]) + components["authors"] = "* " + "\n* ".join(components["authors"]) + + # Don't change this to an fstring. It breaks the formatting. + tpl = textwrap.dedent( + """\ + {heading} + {uline} + + {author_message} + {authors}""" + ).format(**components) + return tpl + + +def main(revision_range): + # document authors + text = build_string(revision_range) + print(text) + + +if __name__ == "__main__": + from argparse import ArgumentParser + + parser = ArgumentParser(description="Generate author lists for release") + parser.add_argument("revision_range", help="..") + args = parser.parse_args() + main(args.revision_range) + + +""" +Early attempts at implementing use of mailmap within this script: + +cur = re.compile('|'.join(map(re.escape, ['<*>']))) +# ------------ +with open(os.path.join(os.path.dirname('[fill in here]'), "[repo_name]/.mailmap")) as f: + l = [line.rstrip('\n') for line in f] + +m={} +for line in l: + try: + m.update(dict(line.split('> '))) + except ValueError: + m.update({line:''}) +""" diff --git a/doc/sphinxext/contributors.py b/doc/sphinxext/contributors.py new file mode 100644 index 000000000..976ae0a5a --- /dev/null +++ b/doc/sphinxext/contributors.py @@ -0,0 +1,56 @@ +"""Sphinx extension for listing code contributors to a release. + +Custom extension from the Pandas library: https://github.com/pandas-dev/pandas/blob/1.1.x/doc/sphinxext/contributors.py +Copied 10 August 2020. + +Usage:: + .. contributors:: v0.23.0..v0.23.1 +This will be replaced with a message indicating the number of +code contributors and commits, and then list each contributor +individually. For development versions (before a tag is available) +use:: + .. contributors:: v0.23.0..v0.23.1|HEAD +While the v0.23.1 tag does not exist, that will use the HEAD of the +branch as the end of the revision range. +""" +from announce import build_components +from docutils import nodes +from docutils.parsers.rst import Directive +import git + + +class ContributorsDirective(Directive): + required_arguments = 1 + name = "contributors" + + def run(self): + range_ = self.arguments[0] + if range_.endswith("x..HEAD"): + return [nodes.paragraph(), nodes.bullet_list()] + try: + components = build_components(range_) + except git.GitCommandError as exc: + return [ + self.state.document.reporter.warning( + f"Cannot find contributors for range {repr(range_)}: {exc}", + line=self.lineno, + ) + ] + else: + message = nodes.paragraph() + message += nodes.Text(components["author_message"]) + + listnode = nodes.bullet_list() + + for author in components["authors"]: + para = nodes.paragraph() + para += nodes.Text(author) + listnode += nodes.list_item("", para) + + return [message, listnode] + + +def setup(app): + app.add_directive("contributors", ContributorsDirective) + + return {"version": "0.1", "parallel_read_safe": True, "parallel_write_safe": True} diff --git a/doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb b/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb similarity index 86% rename from doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb rename to examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb index f4db346c2..6561f8dba 100644 --- a/doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb +++ b/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb @@ -37,11 +37,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "from icepyx import icesat2data as ipd\n", + "import icepyx as ipx\n", "\n", "import numpy as np\n", "import xarray as xr\n", @@ -56,40 +56,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create an icesat2data object and log in to Earthdata\n", + "### Create a query object and log in to Earthdata\n", "\n", "For this example, we'll be working with a sea ice dataset (ATL09) for an area along West Greenland (Disko Bay)." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "region_a = ipd.Icesat2Data('ATL09',[-55, 68, -48, 71],['2019-02-22','2019-02-28'], \\\n", + "region_a = ipx.Query('ATL09',[-55, 68, -48, 71],['2019-02-22','2019-02-28'], \\\n", " start_time='00:00:00', end_time='23:59:59')" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "Enter a properly formatted email address", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregion_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mearthdata_login\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'username'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'email'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/icepyx/core/icesat2data.py\u001b[0m in \u001b[0;36mearthdata_login\u001b[0;34m(self, uid, email)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0mcapability_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'https://n5eil02u.ecs.nsidc.org/egi/capabilities/{self.dataset}.{self._version}.xml'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEarthdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0memail\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcapability_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 575\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_email\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0memail\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/icepyx/core/Earthdata.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, uid, email, capability_url, pswd)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Enter your login user id as a string\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'[^@]+@[^@]+\\.[^@]+'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0memail\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Enter a properly formatted email address\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAssertionError\u001b[0m: Enter a properly formatted email address" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "region_a.earthdata_login('username','email')" ] @@ -143,8 +129,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About Data Variables in an Icesat2Data object\n", - "There are two possible variable parameters associated with each ```icesat2data``` object.\n", + "## About Data Variables in a query object\n", + "There are two possible variable parameters associated with each ```query``` object.\n", "1. `order_vars`, which is for interacting with variables during data querying, ordering, and downloading activities. `order_vars.wanted` holds the user's list to be submitted to the NSIDC subsetter and download a smaller, reproducible dataset.\n", "2. `file_vars`, which is for interacting with variables associated with local files [not yet implemented].\n", "\n", diff --git a/doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb b/examples/ICESat-2_DAAC_DataAccess_Example.ipynb similarity index 89% rename from doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb rename to examples/ICESat-2_DAAC_DataAccess_Example.ipynb index c2fc8b147..39e1a788b 100644 --- a/doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb +++ b/examples/ICESat-2_DAAC_DataAccess_Example.ipynb @@ -27,7 +27,7 @@ "metadata": {}, "outputs": [], "source": [ - "from icepyx import icesat2data as ipd\n", + "import icepyx as ipx\n", "import os\n", "import shutil\n", "%matplotlib inline" @@ -41,7 +41,7 @@ "\n", "The entire process of getting ICESat-2 data (from query to download) can ultimately be accomplished in three minimal lines of code:\n", "\n", - "`region_a = ipd.Icesat2Data(short_name, spatial_extent, date_range)`\n", + "`region_a = ipx.Query(short_name, spatial_extent, date_range)`\n", "\n", "`region_a.earthdata_login(earthdata_uid, email)`\n", "\n", @@ -124,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "region_a = ipd.Icesat2Data(short_name, spatial_extent, date_range)" + "region_a = ipx.Query(short_name, spatial_extent, date_range)" ] }, { @@ -145,7 +145,9 @@ "print(region_a.start_time)\n", "print(region_a.end_time)\n", "print(region_a.dataset_version)\n", - "print(region_a.spatial_extent)\n", + "print(region_a.cycles)\n", + "print(region_a.tracks)\n", + "# print(region_a.spatial_extent)\n", "region_a.visualize_spatial_extent()" ] }, @@ -167,15 +169,46 @@ "metadata": {}, "outputs": [], "source": [ - "region_a = ipd.Icesat2Data(short_name, spatial_extent, date_range, \\\n", - " start_time='03:30:00', end_time='21:30:00', version='001')\n", + "region_a = ipx.Query(short_name, spatial_extent, date_range, \\\n", + " start_time='03:30:00', end_time='21:30:00', version='002')\n", "\n", "print(region_a.dataset)\n", "print(region_a.dates)\n", "print(region_a.start_time)\n", "print(region_a.end_time)\n", "print(region_a.dataset_version)\n", - "print(region_a.spatial_extent)" + "print(region_a.cycles)\n", + "print(region_a.tracks)\n", + "# print(region_a.spatial_extent)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, there are also two optional inputs to allow the user to search based on orbital parameters. Note that your search will return a no-data error if your cycles or tracks are not withint your spatial extent.\n", + "- `cycles` = Which orbital cycle of the dataset to use, input as a numerical string or a list of strings. If no input is given, this value defaults to all available cycles within the search parameters. An orbital cycle refers to the 91-day repeat period of the ICESat-2 orbit.\n", + "- `tracks` = Which [Reference Ground Track (RGT)](https://icesat-2.gsfc.nasa.gov/science/specs) of the dataset to use, input as a numerical string or a list of strings. If no input is given, this value defaults to all available RGTs within the spatial and temporal search parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region_a = ipx.Query(short_name, spatial_extent, date_range, \\\n", + " cycles='02', tracks=['0849','0902'])\n", + "\n", + "print(region_a.dataset)\n", + "print(region_a.dates)\n", + "print(region_a.start_time)\n", + "print(region_a.end_time)\n", + "print(region_a.dataset_version)\n", + "print(region_a.cycles)\n", + "print(region_a.tracks)\n", + "print(region_a.orbit_number)\n", + "# print(region_a.spatial_extent)" ] }, { @@ -191,7 +224,7 @@ "metadata": {}, "outputs": [], "source": [ - "# region_a = ipd.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-01','2019-02-28'], \\\n", + "# region_a = ipx.Query('ATL06',[-55, 68, -48, 71],['2019-02-01','2019-02-28'], \n", "# start_time='00:00:00', end_time='23:59:59', version='002')" ] }, @@ -293,7 +326,7 @@ "metadata": {}, "source": [ "### Downloading the found granules\n", - "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login. This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. Once you have successfully logged in for a given icesat2data instance, the token and session will be passed behind the scenes as needed for you to order and download data. Passwords are entered but not shown or stored in plain text by the system." + "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login. This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. Once you have successfully logged in for a given query instance, the token and session will be passed behind the scenes as needed for you to order and download data. Passwords are entered but not shown or stored in plain text by the system." ] }, { @@ -418,33 +451,6 @@ "region_a.download_granules(path)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Clean up the download folder by removing individual order folders:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Clean up Outputs folder by removing individual granule folders \n", - "\n", - "for root, dirs, files in os.walk(path, topdown=False):\n", - " for file in files:\n", - " try:\n", - " shutil.move(os.path.join(root, file), path)\n", - " except OSError:\n", - " pass\n", - " \n", - "for root, dirs, files in os.walk(path):\n", - " for name in dirs:\n", - " os.rmdir(os.path.join(root, name))" - ] - }, { "cell_type": "code", "execution_count": null, @@ -469,7 +475,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb b/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb new file mode 100644 index 000000000..a7c7b0aab --- /dev/null +++ b/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb @@ -0,0 +1,896 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing ICESat-2 Altimetry Elevations with DEM\n", + "### Example Notebook\n", + "This notebook compares elevations from ICESat-2 to those from a DEM.\n", + "\n", + "#### Credits\n", + "* notebook by: [Jessica Scheick](https://github.com/JessicaS11) and [Shashank Bhushan](https://github.com/ShashankBice)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup\n", + "##### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", + "##### For full functionality,\n", + "- Please install [icepyx](https://github.com/icesat2py/icepyx), [topolib](https://github.com/ICESAT-2HackWeek/topohack), [contextily](https://github.com/darribas/contextily) using `git clone xxxxx`, `pip install -e .` workflow (see below; **you must restart your kernel after installing the packages**)\n", + "- Download [NASA ASP](https://github.com/NeoGeographyToolkit/StereoPipeline) tar ball and unzip, we execute the commands from the notebook, using the path to the untared bin folder for the given commands." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "cd ~\n", + "# git clone https://github.com/icesat2py/icepyx.git\n", + "# git clone https://github.com/ICESAT-2HackWeek/topohack.git\n", + "# git clone https://github.com/darribas/contextily.git\n", + "\n", + "cd contextily\n", + "pip install -e .\n", + "cd ../topohack\n", + "pip install -e .\n", + "cd ../icepyx\n", + "pip install -e ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%cd ~\n", + "#needs to be wherever icepyx, contextily, and topolib are installed in the previous step (ideally $HOME)\n", + "# %pwd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", + "- Along track heights for canopy (land and vegitation) and terrain\n", + "- Terrain heights provided are aggregated over every 100 m along track interval, output contains \"h_te_best_fit: height from best fit algorithm for all photons in the range\", median height and others. Here we use h_te_best_fit.\n", + "- See this preliminary introduction and quality assessment [paper](https://www.mdpi.com/2072-4292/11/14/1721) for more detail" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages, including icepyx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import icepyx as ipx\n", + "import os\n", + "import shutil\n", + "import h5py\n", + "import xarray as xr\n", + "# depedencies\n", + "import getpass\n", + "#from topolib.subsetDat import subsetBBox;\n", + "from topolib import icesat2_data\n", + "import glob\n", + "import rasterio\n", + "from topolib import gda_lib\n", + "from topolib import dwnldArctic\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "from multiprocessing import Pool\n", + "import contextily as ctx\n", + "import pandas as pd\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%cd ~/icepyx/doc/examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocess #1\n", + "- Download using icepyx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Create an ICESat-2 data object with the desired search parameters\n", + "- See the ICESat-2 DAAC Data Access notebook for more details on downloading data from the NSIDC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region_a = ipx.Query('ATL08', [-73.9, 10.7, -73.4, 11.1], ['2018-12-01','2019-09-01'], \\\n", + " start_time='00:00:00', end_time='23:59:59')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finding and downloading data\n", + "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login (available for free on their website). This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. The token is attached to the data object and stored, but the session must be passed to the download function. Then we can order the granules." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Log in to Earthdata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "earthdata_uid = 'Jessica.scheick'\n", + "email = 'jessica.scheick@maine.edu'\n", + "region_a.earthdata_login(earthdata_uid, email)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#search for available granules\n", + "region_a.avail_granules()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "region_a.granules.avail" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Place the order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region_a.order_granules(subset=False)\n", + "#region_a.order_granules(verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#view a short list of order IDs\n", + "region_a.granules.orderIDs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Download the order\n", + "Finally, we can download our order to a specified directory (which needs to have a full path but doesn't have to point to an existing directory) and the download status will be printed as the program runs. Additional information is again available by using the optional boolean keyword 'verbose'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wd=%pwd\n", + "path = wd + '/download'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region_a.download_granules(path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Clean up the download folder by removing individual order folders:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Clean up Outputs folder by removing individual granule folders \n", + "for root, dirs, files in os.walk(path, topdown=False):\n", + " for file in files:\n", + " try:\n", + " shutil.move(os.path.join(root, file), path)\n", + " except OSError:\n", + " pass\n", + " \n", + "for root, dirs, files in os.walk(path):\n", + " for name in dirs:\n", + " os.rmdir(os.path.join(root, name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocess #2\n", + "- Convert data into geopandas dataframe, which allows for doing basing geospatial opertaions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# glob to list of files (run block of code creating wd and path variables if starting processing here)\n", + "ATL08_list = sorted(glob.glob(path+'/*.h5'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examine content of 1 ATLO8 hdf file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filename = ATL08_list[5]\n", + "with h5py.File(filename, 'r') as f:\n", + " # List all groups\n", + " pairs=[1, 2, 3]\n", + " beams=['l','r']\n", + " print(\"Keys: %s\" % f.keys())\n", + " a_group_key = list(f.keys())[0]\n", + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ATL08_list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dict containing data entries to retrive\n", + "dataset_dict = {'land_segments':['delta_time','longitude','latitude','atl06_quality_summary','quality','terrain_flg'], 'land_segments/terrain':['h_te_best_fit']}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#gda_lib.ATL08_to_dict(ATL08_list[0],dataset_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## the data can be converted to geopandas dataframe, see ATL08_2_gdf function in topolib gda_lib\n", + "temp_gdf = gda_lib.ATL08_2_gdf(ATL08_list[0],dataset_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_gdf.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(temp_gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colombia_crs = {'init':'epsg:32618'}\n", + "plot_web = {'init':'epsg:3857'}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_gdf.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert the list of hdf5 files into more familiar Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_list = [(gda_lib.ATL08_2_gdf(x,dataset_dict)) for x in ATL08_list]\n", + "gdf_colombia = gda_lib.concat_gdf(gdf_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess #3\n", + "- Visualise data footprints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(figsize=(10,10))\n", + "temp_web = gdf_colombia.to_crs(plot_web)\n", + "clim = np.percentile(temp_web['h_te_best_fit'].values,(2,98))\n", + "temp_web.plot('h_te_best_fit',ax=ax,s=3,legend=True,cmap='inferno',vmin=clim[0],vmax=clim[1])\n", + "ctx.add_basemap(ax=ax)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We will use the TANDEM-X Global DEM for our comparison. The resolution of the globally avaialable product is 90 m, with *horizontal* and *vertical* accuracy better than 2 to 3 m.\n", + "- TANDEM-X DEM for the region was downloaded and preprocessed, filtered using scripts from the [tandemx](https://github.com/dshean/tandemx) repository" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dem_file = os.path.join(wd,'supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea.tif')\n", + "hs_file = os.path.splitext(dem_file)[0]+'_hs.tif'\n", + "dem_ds = rasterio.open(dem_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! gdaldem hillshade $dem_file $hs_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hs_ds = rasterio.open(hs_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gdf_on_raster(gdf,ds,ax,hs_ds=None,cmap='inferno'):\n", + " gdf = gdf.to_crs(ds.crs)\n", + " xmin,ymin,xmax,ymax = ds.bounds\n", + " ndv = gda_lib.get_ndv(ds)\n", + " img = ds.read(1)\n", + " img = np.ma.masked_less_equal(img,ndv)\n", + " clim = np.nanpercentile(img,(2,98))\n", + " if hs_ds:\n", + " hs = hs_ds.read(1)\n", + " ndv = gda_lib.get_ndv(hs_ds)\n", + " hs = np.ma.masked_less_equal(hs,ndv)\n", + " ax.imshow(hs,cmap='gray',extent=[xmin,xmax,ymin,ymax])\n", + " im = ax.imshow(img,alpha=0.6,cmap=cmap,extent=[xmin,xmax,ymin,ymax])\n", + " print(clim)\n", + " else:\n", + " im = ax.imshow(img,cmap=cmap,vmin=clim[0],vmax=clim[1],extent=[xmin,xmax,ymin,ymax])\n", + " gdf.plot('p_b',ax=ax,s=1)\n", + " plt.colorbar(im,ax=ax,extend='both',label='Elevation (m)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xmin,ymin,xmax,ymax = dem_ds.bounds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Filter points based on DEM extent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia['x_atc'] = gdf_colombia['delta_time']\n", + "gdf_colombia_dem_extent = gdf_colombia.to_crs(dem_ds.crs).cx[xmin:xmax,ymin:ymax]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(figsize=(10,5))\n", + "gdf_on_raster(gdf_colombia_dem_extent,dem_ds,ax,hs_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Section 1\n", + "- This contains demonstration of elevation profile along 1 track, which has 6 beams" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Picking out 1 track\n", + "### check with different ATLO8 inputs\n", + "test_track = ATL08_list[3]\n", + "print(test_track)\n", + "test_gdf = gda_lib.ATL08_2_gdf(test_track,dataset_dict).to_crs(dem_ds.crs).cx[xmin:xmax,ymin:ymax]\n", + "fig,ax = plt.subplots(figsize=(10,5))\n", + "gdf_on_raster(test_gdf,dem_ds,ax,hs_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Working with track from 20190105 to show how we can use this to plot elevation values along the collect just by using ICESat-2\n", + "np.unique(test_gdf['p_b'].values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Limit analysis to 1 pair beam combination\n", + "mask = test_gdf['p_b']== '1.0_0.0'\n", + "test_gdf_pb = test_gdf[mask]\n", + "fig,ax = plt.subplots(figsize=(5,4))\n", + "plot_var = test_gdf_pb['h_te_best_fit'].values\n", + "ax.scatter(np.arange(len(plot_var)),plot_var,s=1)\n", + "ax.set_xlabel('Along-track id')\n", + "ax.set_ylabel('ATL08-Terrain Height')\n", + "ax.grid('--')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#or do it for all the 6 tracks\n", + "track_identifier = list(np.unique(test_gdf['p_b'].values))\n", + "fig,axa = plt.subplots(3,2,figsize=(10,10))\n", + "ax = axa.ravel()\n", + "for idx,track in enumerate(track_identifier):\n", + " mask = test_gdf['p_b']== track\n", + " test_gdf_pb = test_gdf[mask]\n", + " plot_var = test_gdf_pb['h_te_best_fit'].values\n", + " ax[idx].scatter(np.arange(len(plot_var)),plot_var,s=1)\n", + " ax[idx].set_xlabel('Along-track id')\n", + " ax[idx].set_ylabel('ATL08-Terrain Height')\n", + " ax[idx].grid('--')\n", + " ax[idx].set_title('Track: {} Beam: {}'.format(track.split('_',15)[0],track.split('_',15)[1]))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Section 2:\n", + "- Compare ICESat-2 Elevation with that of reference DEM (in this case TANDEM-X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sample elevations from DEM at ATLO8-locations using nearest neighbour algorithm " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "del_time,elev = gda_lib.sample_near_nbor(dem_ds,gdf_colombia_dem_extent)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia_dem_extent['dem_z'] = elev" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot elevation differences (ICESat-2 minus TANDEM-X) as a function of elevation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia_dem_extent['z_diff'] = gdf_colombia_dem_extent['h_te_best_fit'] - gdf_colombia_dem_extent['dem_z']\n", + "fig,ax = plt.subplots(figsize=(5,4))\n", + "# Sort elevation values\n", + "gdf_colombia_dem_extent.sort_values(by='dem_z',inplace=True)\n", + "gdf_colombia_dem_extent_filt = gdf_colombia_dem_extent[gdf_colombia_dem_extent['z_diff']<1000]\n", + "ax.scatter(gdf_colombia_dem_extent_filt.dem_z.values,gdf_colombia_dem_extent_filt.z_diff.values,s=1)\n", + "ax.set_ylim(-50,50)\n", + "ax.set_xlabel('Elevation (TANDEM-X) (m)')\n", + "ax.set_ylabel('Elevation difference (m)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The difference above might be noise or real signal (\" the dates of ICESAT-2 footprints are between December to March 2018-2019, while TANDEM-X contains a mosaic of elevations between 2012-2014\")\n", + "- It's hard to make out anything from the above plot, let's try a box plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dem_bins = list(np.arange(0,5500,500))\n", + "# mask out differences larger than 100 m ?\n", + "filt_lim = (-100,100)\n", + "mask = (gdf_colombia_dem_extent['z_diff']<=100) & (gdf_colombia_dem_extent['z_diff']>=-100)\n", + "gdf_colombia_dem_extent_filt_box = gdf_colombia_dem_extent[mask]\n", + "gdf_colombia_dem_extent_filt_box['bin'] = pd.cut(gdf_colombia_dem_extent_filt_box['dem_z'],bins=dem_bins)\n", + "fig,ax = plt.subplots(figsize=(5,4))\n", + "gdf_colombia_dem_extent_filt_box.boxplot(column='z_diff',by='bin',ax=ax)\n", + "ax.set_xlabel('Elevation (TANDEM-X) (m)')\n", + "ax.set_xticklabels(dem_bins)\n", + "#ax.set_ylabel('Elevation difference (m)')\n", + "ax.set_title('')\n", + "ax.set_ylabel('ICESat-2 minus TANDEM-X (m)')\n", + "#plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The x labels in the plot are lower intervals of boxplots, we see that the median differences are close to zero for most elevation ranges with a maximum difference of -10 m. Also, we see a constant negative bias in all the elevation difference. This might be due to a bias present between the 2 sources. This bias maybe due to offset between the 2 datasets which might come down after coregistration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Section 3\n", + "- Application of ICESat-2 as control surface for DEMs coregistration\n", + "- Or, to find offsets and align ICESat-2 tracks to a control surface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Going fancy, include only if you want to :)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Application of ICESat-2 as control for DEM co-registration ?\n", + "- Can use point cloud alignment techniques to align DEMs to points, for now as a starting point we can use the transformation matrix to inform on the horizontal and vertical offset between ICESat-2 tracks and DEMs\n", + "- We will be using a flavor of Iterative Closest Point alignment algorithm, implemented in [Ames Stereo Pipeline](https://github.com/NeoGeographyToolkit/StereoPipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia_dem_extent.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Save the geodataframe in the specified way as expected by Ames Stereo Pipline\n", + "icesat2_pc = '/home/jovyan/icesat2/icesat2_colombia_pc.csv' \n", + "gdf_colombia_dem_extent[['latitude','longitude','h_te_best_fit']].to_csv(icesat2_pc,header=False,index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia_dem_extent.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Save the geodataframe in the specified way as expected by Ames Stereo Pipline\n", + "icesat2_pc = '/home/jovyan/icesat2/icesat2_colombia_pc.csv'\n", + "pc_rename_dict = {'latitude':'lat','longitude':'lon','h_te_best_fit':'height_above_datum'}\n", + "gdf_colombia_dem_extent = gdf_colombia_dem_extent.rename(columns=pc_rename_dict)\n", + "#gdf_colombia_dem_extent['height_above_datum'] = gdf_colombia_dem_extent['h_te_best_fit']\n", + "\n", + "gdf_colombia_dem_extent[['lon','lat','height_above_datum']].to_csv(icesat2_pc,header=True,index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!exportPATH=\"/home/jovyan/icesat2/StereoPipeline/bin:$PATH\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "align_fol = '/home/jovyan/icesat2/align/run'\n", + "#max-displacement is set to 10, given ICESat-2 reported operational accuracy\n", + "pc_align_opts=\"--csv-format '1:lon 2:lat 3:height_above_datum' --max-displacement 10 --save-transformed-source-points --alignment-method point-to-point --datum WGS84\"\n", + "!/home/jovyan/icesat2/StereoPipeline/bin/pc_align $pc_align_opts $icesat2_pc $dem_file -o $align_fol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Alignment results suggest that there is an offset of ~5.4 m between the ICESat-2 points and TANDEM-X DEM, so that could have contributed to the offsets which we see above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##Lets rerun the analysis with the new DEM to see if the alignment improved anything or not\n", + "## Regrid the transformed pointcloud into DEM at 90 m posting\n", + "!/home/jovyan/icesat2/StereoPipeline/bin/point2dem --tr 90 --t_srs EPSG:32618 $align_fol-trans_source.tif" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia_dem_extent = gdf_colombia_dem_extent.loc[:,~gdf_colombia_dem_extent.columns.duplicated()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_colombia_dem_extent['height_above_datum'].values[5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trans_dem_file = '/home/jovyan/icesat2/align/run-trans_source-DEM.tif'\n", + "trans_dem_ds = rasterio.open(trans_dem_file)\n", + "del_time,elev = gda_lib.sample_near_nbor(trans_dem_ds,gdf_colombia_dem_extent)\n", + "gdf_colombia_dem_extent['trans_dem_z'] = elev\n", + "dem_bins = list(np.arange(0,5500,500))\n", + "# mask out differences larger than 100 m ?\n", + "filt_lim = (-100,100)\n", + "gdf_colombia_dem_extent['trans_z_diff'] = gdf_colombia_dem_extent.height_above_datum - gdf_colombia_dem_extent.trans_dem_z\n", + "\n", + "mask = (gdf_colombia_dem_extent['trans_z_diff']<=100) & (gdf_colombia_dem_extent['trans_z_diff']>=-100)\n", + "gdf_colombia_dem_extent_filt_box = gdf_colombia_dem_extent[mask]\n", + "gdf_colombia_dem_extent_filt_box['bin'] = pd.cut(gdf_colombia_dem_extent_filt_box['dem_z'],bins=dem_bins)\n", + "fig,ax = plt.subplots(figsize=(5,4))\n", + "gdf_colombia_dem_extent_filt_box.boxplot(column='trans_z_diff',by='bin',ax=ax)\n", + "ax.set_xlabel('Elevation (TANDEM-X) (m)')\n", + "ax.set_xticklabels(dem_bins)\n", + "ax.set_title('')\n", + "ax.set_ylabel('ICESat-2 minus TANDEM-X DEM after coregistration (m)')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- We see that after coregistration, the bias reduces to an extent. Note that this is a very preliminary analysis, results will be better after filtering the ATL08 points based on quality metrics and finding truly static surfaces (snow free during acquisiton time of ICESat-2 points)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/examples/examples.rst b/examples/examples.rst similarity index 54% rename from doc/examples/examples.rst rename to examples/examples.rst index 15410c079..65ce3a920 100644 --- a/doc/examples/examples.rst +++ b/examples/examples.rst @@ -5,8 +5,8 @@ Example Notebooks Listed below are example jupyter-notebooks -`ICESat-2_DAAC_DataAccess_Example `_ +`ICESat-2_DAAC_DataAccess_Example `_ -`ICESat-2_DAAC_DataAccess2_Subsetting `_ +`ICESat-2_DAAC_DataAccess2_Subsetting `_ -`ICESat-2_DEM_comparison_Colombia_working `_ +`ICESat-2_DEM_comparison_Colombia_working `_ diff --git a/examples/supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea.tif b/examples/supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea.tif new file mode 100644 index 000000000..19372def8 Binary files /dev/null and b/examples/supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea.tif differ diff --git a/examples/supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea_hs.tif b/examples/supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea_hs.tif new file mode 100644 index 000000000..c17285052 Binary files /dev/null and b/examples/supporting_files/TDM1_DEM_90m_colombia_DEM_masked_aea_hs.tif differ diff --git a/doc/examples/supporting_files/data-access_PineIsland/CITATIONS.txt b/examples/supporting_files/data-access_PineIsland/CITATIONS.txt similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/CITATIONS.txt rename to examples/supporting_files/data-access_PineIsland/CITATIONS.txt diff --git a/doc/examples/supporting_files/data-access_PineIsland/README.txt b/examples/supporting_files/data-access_PineIsland/README.txt similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/README.txt rename to examples/supporting_files/data-access_PineIsland/README.txt diff --git a/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.dbf b/examples/supporting_files/data-access_PineIsland/glims_polygons.dbf similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/glims_polygons.dbf rename to examples/supporting_files/data-access_PineIsland/glims_polygons.dbf diff --git a/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.kml b/examples/supporting_files/data-access_PineIsland/glims_polygons.kml similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/glims_polygons.kml rename to examples/supporting_files/data-access_PineIsland/glims_polygons.kml diff --git a/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.prj b/examples/supporting_files/data-access_PineIsland/glims_polygons.prj similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/glims_polygons.prj rename to examples/supporting_files/data-access_PineIsland/glims_polygons.prj diff --git a/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.shp b/examples/supporting_files/data-access_PineIsland/glims_polygons.shp similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/glims_polygons.shp rename to examples/supporting_files/data-access_PineIsland/glims_polygons.shp diff --git a/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.shx b/examples/supporting_files/data-access_PineIsland/glims_polygons.shx similarity index 100% rename from doc/examples/supporting_files/data-access_PineIsland/glims_polygons.shx rename to examples/supporting_files/data-access_PineIsland/glims_polygons.shx diff --git a/icepyx/__init__.py b/icepyx/__init__.py index efae9241b..c6a0b6d0d 100644 --- a/icepyx/__init__.py +++ b/icepyx/__init__.py @@ -1 +1 @@ -from .core import icesat2data \ No newline at end of file +from icepyx.core.query import Query diff --git a/icepyx/core/APIformatting.py b/icepyx/core/APIformatting.py index 0e89b011c..621954bdf 100644 --- a/icepyx/core/APIformatting.py +++ b/icepyx/core/APIformatting.py @@ -1,4 +1,4 @@ -#Generate and format information for submitting to API (CMR and NSIDC) +# Generate and format information for submitting to API (CMR and NSIDC) import datetime as dt import geopandas as gpd @@ -6,15 +6,17 @@ from shapely.geometry import Polygon from shapely.geometry.polygon import orient import fiona -fiona.drvsupport.supported_drivers['LIBKML'] = 'rw' + +fiona.drvsupport.supported_drivers["LIBKML"] = "rw" # ---------------------------------------------------------------------- # parameter-specific formatting for display # or input to a set of API parameters (CMR or NSIDC) -def _fmt_temporal(start,end,key): + +def _fmt_temporal(start, end, key): """ - Format the start and end dates and times into a temporal CMR search + Format the start and end dates and times into a temporal CMR search or subsetting key value. Parameters @@ -34,14 +36,25 @@ def _fmt_temporal(start,end,key): assert isinstance(start, dt.datetime) assert isinstance(end, dt.datetime) - assert key in ['time', 'temporal'], 'An invalid time key was submitted for formatting.' - - if key == 'temporal': - fmt_timerange = start.strftime('%Y-%m-%dT%H:%M:%SZ') +',' + end.strftime('%Y-%m-%dT%H:%M:%SZ') - elif key == 'time': - fmt_timerange = start.strftime('%Y-%m-%dT%H:%M:%S') +',' + end.strftime('%Y-%m-%dT%H:%M:%S') - - return {key:fmt_timerange} + assert key in [ + "time", + "temporal", + ], "An invalid time key was submitted for formatting." + + if key == "temporal": + fmt_timerange = ( + start.strftime("%Y-%m-%dT%H:%M:%SZ") + + "," + + end.strftime("%Y-%m-%dT%H:%M:%SZ") + ) + elif key == "time": + fmt_timerange = ( + start.strftime("%Y-%m-%dT%H:%M:%S") + + "," + + end.strftime("%Y-%m-%dT%H:%M:%S") + ) + + return {key: fmt_timerange} def _fmt_spatial(ext_type, extent): @@ -66,30 +79,33 @@ def _fmt_spatial(ext_type, extent): """ - #CMR keywords: ['bounding_box', 'polygon'] - #subsetting keywords: ['bbox','Boundingshape'] - assert ext_type in ['bounding_box', 'polygon'] or ext_type in ['bbox','Boundingshape'],\ - "Invalid spatial extent type." + # CMR keywords: ['bounding_box', 'polygon'] + # subsetting keywords: ['bbox','Boundingshape'] + assert ext_type in ["bounding_box", "polygon"] or ext_type in [ + "bbox", + "Boundingshape", + ], "Invalid spatial extent type." - if ext_type in ['bounding_box', 'bbox']: - fmt_extent = ','.join(map(str, extent)) + if ext_type in ["bounding_box", "bbox"]: + fmt_extent = ",".join(map(str, extent)) - - elif ext_type == 'polygon': - #Simplify polygon. The larger the tolerance value, the more simplified the polygon. See Bruce Wallin's function to do this + elif ext_type == "polygon": + # Simplify polygon. The larger the tolerance value, the more simplified the polygon. See Bruce Wallin's function to do this poly = extent.simplify(0.05, preserve_topology=False) poly = orient(poly, sign=1.0) - #Format dictionary to polygon coordinate pairs for API submission - polygon = (','.join([str(c) for xy in zip(*poly.exterior.coords.xy) for c in xy])).split(",") + # Format dictionary to polygon coordinate pairs for API submission + polygon = ( + ",".join([str(c) for xy in zip(*poly.exterior.coords.xy) for c in xy]) + ).split(",") extent = [float(i) for i in polygon] - fmt_extent = ','.join(map(str, extent)) + fmt_extent = ",".join(map(str, extent)) - #DevNote: this elif currently does not have a test (seems like it would just be testing geopandas?) - elif ext_type == 'Boundingshape': + # DevNote: this elif currently does not have a test (seems like it would just be testing geopandas?) + elif ext_type == "Boundingshape": poly = orient(extent, sign=1.0) fmt_extent = gpd.GeoSeries(poly).to_json() - fmt_extent = fmt_extent.replace(' ', '') #remove spaces for API call + fmt_extent = fmt_extent.replace(" ", "") # remove spaces for API call return {ext_type: fmt_extent} @@ -97,20 +113,21 @@ def _fmt_spatial(ext_type, extent): def _fmt_var_subset_list(vdict): """ Return the NSIDC-API subsetter formatted coverage string for variable subset request. - + Parameters ---------- vdict : dictionary Dictionary containing variable names as keys with values containing a list of paths to those variables (so each variable key may have multiple paths, e.g. for multiple beams) - """ - - subcover = '' + """ + + subcover = "" for vn in vdict.keys(): vpaths = vdict[vn] - for vpath in vpaths: subcover += '/'+vpath+',' - + for vpath in vpaths: + subcover += "/" + vpath + "," + return subcover[:-1] @@ -139,18 +156,18 @@ def combine_params(*param_dicts): 'page_size': 10, 'page_num': 1} """ - params={} + params = {} for dictionary in param_dicts: params.update(dictionary) return params # ---------------------------------------------------------------------- -#DevNote: Currently, this class is not tested!! -#DevGoal: this could be expanded, similar to the variables class, to provide users with valid options if need be -#DevGoal: currently this does not do much by way of checking/formatting of other subsetting options (reprojection or formats) -#it would be great to incorporate that so that people can't just feed any keywords in... -class Parameters(): +# DevNote: Currently, this class is not tested!! +# DevGoal: this could be expanded, similar to the variables class, to provide users with valid options if need be +# DevGoal: currently this does not do much by way of checking/formatting of other subsetting options (reprojection or formats) +# it would be great to incorporate that so that people can't just feed any keywords in... +class Parameters: """ Build and update the parameter lists needed to submit a data order @@ -158,7 +175,7 @@ class Parameters(): ---------- partype : string Type of parameter list. Must be one of ['CMR','required','subset'] - + values : dictionary, default None Dictionary of already-formatted parameters, if there are any, to avoid re-creating them. @@ -169,16 +186,22 @@ class Parameters(): """ def __init__(self, partype, values=None, reqtype=None): - - assert partype in ['CMR','required','subset'], "You need to submit a valid parametery type." + + assert partype in [ + "CMR", + "required", + "subset", + ], "You need to submit a valid parametery type." self.partype = partype - - if partype == 'required': - assert reqtype in ['search','download'], "A valid require parameter type is needed" + + if partype == "required": + assert reqtype in [ + "search", + "download", + ], "A valid require parameter type is needed" self._reqtype = reqtype - - self._fmted_keys = values if values is not None else {} + self._fmted_keys = values if values is not None else {} @property def poss_keys(self): @@ -187,9 +210,9 @@ def poss_keys(self): Possible input keys depend on the parameter type (partype). """ - if not hasattr(self,'_poss_keys'): + if not hasattr(self, "_poss_keys"): self._get_possible_keys() - + return self._poss_keys # @property @@ -198,77 +221,109 @@ def poss_keys(self): # self._wanted = [] # return self._wanted - + @property def fmted_keys(self): """ Returns the dictionary of formated keys associated with the parameter object. - """ + """ return self._fmted_keys - def _get_possible_keys(self): """ Use the parameter type to get a list of possible parameter keys. """ - - if self.partype == 'CMR': - self._poss_keys = {'default': ['short_name','version','temporal'], 'spatial': ['bounding_box','polygon'], 'optional': []} - elif self.partype == 'required': - self._poss_keys = {'search': ['page_size','page_num'], 'download': ['page_size','page_num','request_mode','token','email','include_meta']} - elif self.partype == 'subset': - self._poss_keys = {'default': ['time'],'spatial': ['bbox','Boundingshape'],'optional': ['format','projection','projection_parameters','Coverage']} + if self.partype == "CMR": + self._poss_keys = { + "default": ["short_name", "version", "temporal"], + "spatial": ["bounding_box", "polygon"], + "optional": ["orbit_number"], + } + elif self.partype == "required": + self._poss_keys = { + "search": ["page_size", "page_num"], + "download": [ + "page_size", + "page_num", + "request_mode", + "token", + "email", + "include_meta", + ], + } + elif self.partype == "subset": + self._poss_keys = { + "default": ["time"], + "spatial": ["bbox", "Boundingshape"], + "optional": [ + "format", + "projection", + "projection_parameters", + "Coverage", + ], + } def _check_valid_keys(self): """ Checks that any keys passed in with values are valid keys. """ - + # if self._wanted == None: # raise ValueError("No desired parameter list was passed") - + val_list = list(set(val for lis in self.poss_keys.values() for val in lis)) - + for key in self.fmted_keys.keys(): - assert key in val_list, "An invalid key (" + key + ") was passed. Please remove it using `del`" + assert key in val_list, ( + "An invalid key (" + key + ") was passed. Please remove it using `del`" + ) - def check_req_values(self): """ Check that all of the required keys have values, if the key was passed in with the values parameter. """ - - assert self.partype == 'required', "You cannot call this function for your parameter type" + + assert ( + self.partype == "required" + ), "You cannot call this function for your parameter type" reqkeys = self.poss_keys[self._reqtype] - + if all(keys in self.fmted_keys.keys() for keys in reqkeys): - assert all(self.fmted_keys.get(key, -9999) != -9999 for key in reqkeys),"One of your formated parameters is missing a value" + assert all( + self.fmted_keys.get(key, -9999) != -9999 for key in reqkeys + ), "One of your formated parameters is missing a value" return True else: return False - def check_values(self): """ Check that the non-required keys have values, if the key was passed in with the values parameter. """ - assert self.partype != 'required', "You cannot call this function for your parameter type" - - default_keys = self.poss_keys['default'] - spatial_keys = self.poss_keys['spatial'] + assert ( + self.partype != "required" + ), "You cannot call this function for your parameter type" + + default_keys = self.poss_keys["default"] + spatial_keys = self.poss_keys["spatial"] if all(keys in self._fmted_keys.keys() for keys in default_keys): - assert all(self.fmted_keys.get(key, -9999) != -9999 for key in default_keys),"One of your formated parameters is missing a value" - - #not the most robust check, but better than nothing... + assert all( + self.fmted_keys.get(key, -9999) != -9999 for key in default_keys + ), "One of your formated parameters is missing a value" + + # not the most robust check, but better than nothing... if any(keys in self._fmted_keys.keys() for keys in spatial_keys): - assert any(self.fmted_keys.get(key, -9999) != -9999 for key in default_keys),"One of your formated parameters is missing a value" + assert any( + self.fmted_keys.get(key, -9999) != -9999 for key in default_keys + ), "One of your formated parameters is missing a value" return True - else: return False + else: + return False else: return False @@ -281,8 +336,8 @@ def build_params(self, **kwargs): ---------- **kwargs Keyword inputs containing the needed information to build the parameter list, depending on - parameter type, if the already formatted key:value is not submitted as a kwarg. - May include optional keyword arguments to be passed to the subsetter. Valid keywords + parameter type, if the already formatted key:value is not submitted as a kwarg. + May include optional keyword arguments to be passed to the subsetter. Valid keywords are time, bbox OR Boundingshape, format, projection, projection_parameters, and Coverage. Keyword argument inputs for 'CMR' may include: dataset, version, start, end, extent_type, spatial_extent @@ -291,70 +346,80 @@ def build_params(self, **kwargs): """ - if not kwargs: kwargs={} + if not kwargs: + kwargs = {} else: self._check_valid_keys() - - if self.partype == 'required': - if self.check_req_values==True and kwargs=={}: pass + + if self.partype == "required": + if self.check_req_values == True and kwargs == {}: + pass else: reqkeys = self.poss_keys[self._reqtype] - defaults={'page_size':10,'page_num':1,'request_mode':'async','include_meta':'Y'} + defaults = { + "page_size": 10, + "page_num": 1, + "request_mode": "async", + "include_meta": "Y", + } for key in reqkeys: if key in kwargs: - self._fmted_keys.update({key:kwargs[key]}) - # elif key in defaults: - # if key is 'page_num': - # pnum = math.ceil(len(is2obj.granules)/reqparams['page_size']) - # if pnum > 0: - # reqparams.update({key:pnum}) - # else: - # reqparams.update({key:defaults[key]}) + self._fmted_keys.update({key: kwargs[key]}) + # elif key in defaults: + # if key is 'page_num': + # pnum = math.ceil(len(is2obj.granules)/reqparams['page_size']) + # if pnum > 0: + # reqparams.update({key:pnum}) + # else: + # reqparams.update({key:defaults[key]}) elif key in defaults: - self._fmted_keys.update({key:defaults[key]}) + self._fmted_keys.update({key: defaults[key]}) else: pass - self._fmted_keys['page_num'] = 1 + self._fmted_keys["page_num"] = 1 else: - if self.check_values==True and kwargs==None: pass + if self.check_values == True and kwargs == None: + pass else: - default_keys = self.poss_keys['default'] - spatial_keys = self.poss_keys['spatial'] - opt_keys = self.poss_keys['optional'] + default_keys = self.poss_keys["default"] + spatial_keys = self.poss_keys["spatial"] + opt_keys = self.poss_keys["optional"] for key in default_keys: if key in self._fmted_keys.values(): assert self._fmted_keys[key] else: - if key == 'short_name': - self._fmted_keys.update({key:kwargs['dataset']}) - elif key == 'version': - self._fmted_keys.update({key:kwargs['version']}) - elif key == 'temporal' or key == 'time': - self._fmted_keys.update(_fmt_temporal(kwargs['start'],kwargs['end'],key)) - - + if key == "short_name": + self._fmted_keys.update({key: kwargs["dataset"]}) + elif key == "version": + self._fmted_keys.update({key: kwargs["version"]}) + elif key == "temporal" or key == "time": + self._fmted_keys.update( + _fmt_temporal(kwargs["start"], kwargs["end"], key) + ) + for key in opt_keys: - if key == 'Coverage' and key in kwargs.keys(): - #DevGoal: make there be an option along the lines of Coverage=default, which will get the default variables for that dataset without the user having to input is2obj.build_wanted_wanted_var_list as their input value for using the Coverage kwarg - self._fmted_keys.update({key:_fmt_var_subset_list(kwargs[key])}) + if key == "Coverage" and key in kwargs.keys(): + # DevGoal: make there be an option along the lines of Coverage=default, which will get the default variables for that dataset without the user having to input is2obj.build_wanted_wanted_var_list as their input value for using the Coverage kwarg + self._fmted_keys.update( + {key: _fmt_var_subset_list(kwargs[key])} + ) elif key in kwargs: - self._fmted_keys.update({key:kwargs[key]}) + self._fmted_keys.update({key: kwargs[key]}) else: pass - - + if any(keys in self._fmted_keys for keys in spatial_keys): pass else: - if self.partype =='CMR': - k = kwargs['extent_type'] - elif self.partype == 'subset': - if kwargs['extent_type'] == 'bounding_box': - k = 'bbox' - elif kwargs['extent_type'] == 'polygon': - k = 'Boundingshape' - - self._fmted_keys.update(_fmt_spatial(k,kwargs['spatial_extent'])) + if self.partype == "CMR": + k = kwargs["extent_type"] + elif self.partype == "subset": + if kwargs["extent_type"] == "bounding_box": + k = "bbox" + elif kwargs["extent_type"] == "polygon": + k = "Boundingshape" + + self._fmted_keys.update(_fmt_spatial(k, kwargs["spatial_extent"])) diff --git a/icepyx/core/Earthdata.py b/icepyx/core/Earthdata.py index f34be4063..2b6b86fe2 100644 --- a/icepyx/core/Earthdata.py +++ b/icepyx/core/Earthdata.py @@ -5,8 +5,8 @@ import re import json -#DevNote: currently this class is not tested -class Earthdata(): +# DevNote: currently this class is not tested +class Earthdata: """ Initiate an Earthdata session for interacting with the NSIDC DAAC. @@ -28,15 +28,13 @@ class Earthdata(): """ def __init__( - self, - uid, - email, - capability_url, - pswd=None, + self, uid, email, capability_url, pswd=None, ): assert isinstance(uid, str), "Enter your login user id as a string" - assert re.match(r'[^@]+@[^@]+\.[^@]+',email), "Enter a properly formatted email address" + assert re.match( + r"[^@]+@[^@]+\.[^@]+", email + ), "Enter a properly formatted email address" self.netrc = None self.uid = uid @@ -45,32 +43,48 @@ def __init__( self.pswd = pswd def _start_session(self): - #Request CMR token using Earthdata credentials - token_api_url = 'https://cmr.earthdata.nasa.gov/legacy-services/rest/tokens' + # Request CMR token using Earthdata credentials + token_api_url = "https://cmr.earthdata.nasa.gov/legacy-services/rest/tokens" hostname = socket.gethostname() ip = socket.gethostbyname(hostname) + # try initially with machine hostname + # revert to using localhost if gaierror exception + try: + ip = socket.gethostbyname(socket.gethostname()) + except: + ip = socket.gethostbyname("localhost") + - data = {'token': {'username': self.uid, 'password': self.pswd,\ - 'client_id': 'NSIDC_client_id','user_ip_address': ip} + data = { + "token": { + "username": self.uid, + "password": self.pswd, + "client_id": "NSIDC_client_id", + "user_ip_address": ip, + } } response = None - response = requests.post(token_api_url, json=data, headers={'Accept': 'application/json'}) + response = requests.post( + token_api_url, json=data, headers={"Accept": "application/json"} + ) - #check for a valid login + # check for a valid login try: - json.loads(response.content)['token'] + json.loads(response.content)["token"] except KeyError: try: - print(json.loads(response.content)['errors']) + print(json.loads(response.content)["errors"]) except KeyError: - print("There are no error messages, but an Earthdata login token was not successfully generated") + print( + "There are no error messages, but an Earthdata login token was not successfully generated" + ) - token = json.loads(response.content)['token']['id'] + token = json.loads(response.content)["token"]["id"] session = requests.session() s = session.get(self.capability_url) - response = session.get(s.url,auth=(self.uid,self.pswd)) + response = session.get(s.url, auth=(self.uid, self.pswd)) self.session = session @@ -102,19 +116,19 @@ def login(self): """ try: - url = 'urs.earthdata.nasa.gov' - self.uid,_,self.pswd = netrc.netrc(self.netrc).authenticators(url) + url = "urs.earthdata.nasa.gov" + self.uid, _, self.pswd = netrc.netrc(self.netrc).authenticators(url) session = self._start_session() except: - self.pswd = getpass.getpass('Earthdata Login password: ') + self.pswd = getpass.getpass("Earthdata Login password: ") for i in range(5): try: session = self._start_session() break except KeyError: self.uid = input("Please re-enter your Earthdata user ID: ") - self.pswd = getpass.getpass('Earthdata Login password: ') + self.pswd = getpass.getpass("Earthdata Login password: ") i += 1 else: raise RuntimeError("You could not successfully log in to Earthdata") @@ -122,7 +136,7 @@ def login(self): return self.session -#DevGoal: try turning this into a class that uses super... an initial attempt at portions of this is below +# DevGoal: try turning this into a class that uses super... an initial attempt at portions of this is below """ class Earthdata(requests.Session): diff --git a/icepyx/core/geospatial.py b/icepyx/core/geospatial.py index 7282cd39f..6e53c0cee 100644 --- a/icepyx/core/geospatial.py +++ b/icepyx/core/geospatial.py @@ -1,9 +1,9 @@ import geopandas as gpd from shapely.geometry import Polygon - -#DevGoal: need to update the spatial_extent docstring to describe coordinate order for input + +# DevGoal: need to update the spatial_extent docstring to describe coordinate order for input def geodataframe(extent_type, spatial_extent, file=False): - """ + """ Return a geodataframe of the spatial extent Parameters @@ -24,48 +24,64 @@ def geodataframe(extent_type, spatial_extent, file=False): See Also -------- - icepyx.icesat2data.Icesat2Data + icepyx.Query Examples -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a = icepyx.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) >>> gdf = geospatial.geodataframe(reg_a.extent_type, reg_a._spat_extent) >>> gdf.geometry 0 POLYGON ((-55.00000 68.00000, -55.00000 71.000... Name: geometry, dtype: geometry """ - if extent_type == 'bounding_box': - boxx = [spatial_extent[0], spatial_extent[0], spatial_extent[2],\ - spatial_extent[2], spatial_extent[0]] - boxy = [spatial_extent[1], spatial_extent[3], spatial_extent[3],\ - spatial_extent[1], spatial_extent[1]] - #DevGoal: check to see that the box is actually correctly constructed; have not checked actual location of test coordinates - gdf = gpd.GeoDataFrame(geometry=[Polygon(list(zip(boxx,boxy)))]) + if extent_type == "bounding_box": + boxx = [ + spatial_extent[0], + spatial_extent[0], + spatial_extent[2], + spatial_extent[2], + spatial_extent[0], + ] + boxy = [ + spatial_extent[1], + spatial_extent[3], + spatial_extent[3], + spatial_extent[1], + spatial_extent[1], + ] + # DevGoal: check to see that the box is actually correctly constructed; have not checked actual location of test coordinates + gdf = gpd.GeoDataFrame(geometry=[Polygon(list(zip(boxx, boxy)))]) - #DevGoal: Currently this if/else within this elif are not tested... - #DevGoal: the crs setting and management needs to be improved - elif extent_type == 'polygon' and file==False: - #DevGoal: look into when/if this if is even called. I think all the incoming spatial_extents without a file will be floats... - #DEL: I don't think this if is ever used, so long as the spatial extent always comes in as a list of floats - # if isinstance(spatial_extent,str): - # print('this string instance is needed') - # spat_extent = spatial_extent.split(',') - # spatial_extent_geom = Polygon(zip(spat_extent[0::2], spat_extent[1::2])) - if isinstance(spatial_extent, Polygon): - spatial_extent_geom = spatial_extent - else: - spatial_extent_geom = Polygon(zip(spatial_extent[0::2], spatial_extent[1::2])) #spatial_extent - - gdf = gpd.GeoDataFrame(index=[0],crs='epsg:4326', geometry=[spatial_extent_geom]) + # DevGoal: Currently this if/else within this elif are not tested... + # DevGoal: the crs setting and management needs to be improved + elif extent_type == "polygon" and file == False: + # DevGoal: look into when/if this if is even called. I think all the incoming spatial_extents without a file will be floats... + # DEL: I don't think this if is ever used, so long as the spatial extent always comes in as a list of floats + # if isinstance(spatial_extent,str): + # print('this string instance is needed') + # spat_extent = spatial_extent.split(',') + # spatial_extent_geom = Polygon(zip(spat_extent[0::2], spat_extent[1::2])) + if isinstance(spatial_extent, Polygon): + spatial_extent_geom = spatial_extent + else: + spatial_extent_geom = Polygon( + zip(spatial_extent[0::2], spatial_extent[1::2]) + ) # spatial_extent - #DevGoal: Currently this elif isn't tested... - elif extent_type == 'polygon' and file==True: - gdf = gpd.read_file(spatial_extent) + gdf = gpd.GeoDataFrame( + index=[0], crs="epsg:4326", geometry=[spatial_extent_geom] + ) - else: - raise TypeError("Your spatial extent type is not an accepted input and a geodataframe cannot be constructed") - #DevNote: can't get test for this else to pass if print the extent_type in the string... - # raise TypeError("Your spatial extent type (" + extent_type + ") is not an accepted input and a geodataframe cannot be constructed") + # DevGoal: Currently this elif isn't tested... + elif extent_type == "polygon" and file == True: + gdf = gpd.read_file(spatial_extent) + + else: + raise TypeError( + "Your spatial extent type is not an accepted input and a geodataframe cannot be constructed" + ) + # DevNote: can't get test for this else to pass if print the extent_type in the string... + # raise TypeError("Your spatial extent type (" + extent_type + ") is not an accepted input and a geodataframe cannot be constructed") - return gdf \ No newline at end of file + return gdf diff --git a/icepyx/core/granules.py b/icepyx/core/granules.py index ee9b80402..82dfef31d 100644 --- a/icepyx/core/granules.py +++ b/icepyx/core/granules.py @@ -1,6 +1,7 @@ import requests import time import io +import re import json import numpy as np import os @@ -10,52 +11,94 @@ import icepyx.core.APIformatting as apifmt + def info(grans): """ - Return some basic summary information about a set of granules for an - icesat2data object. Granule info may be from a list of those available + Return some basic summary information about a set of granules for an + query object. Granule info may be from a list of those available from NSIDC (for ordering/download) or a list of granules present on the file system. """ - assert len(grans)>0, "Your data object has no granules associated with it" + assert len(grans) > 0, "Your data object has no granules associated with it" gran_info = {} - gran_info.update({'Number of available granules': len(grans)}) + gran_info.update({"Number of available granules": len(grans)}) - gran_sizes = [float(gran['granule_size']) for gran in grans] - gran_info.update({'Average size of granules (MB)': np.mean(gran_sizes)}) - gran_info.update({'Total size of all granules (MB)': sum(gran_sizes)}) + gran_sizes = [float(gran["granule_size"]) for gran in grans] + gran_info.update({"Average size of granules (MB)": np.mean(gran_sizes)}) + gran_info.update({"Total size of all granules (MB)": sum(gran_sizes)}) return gran_info -#DevNote: currently this fn is not tested -def gran_IDs(grans): + +# DevNote: currently this fn is not tested +# DevNote: could add flag to separate ascending and descending orbits based on ATL03 granule region +def gran_IDs(grans, ids=True, cycles=False, tracks=False): """ - Returns a list of the granule IDs for the granule dictionary. + Returns a list of granule information for the granule dictionary. Granule info may be from a list of those available from NSIDC (for ordering/download) or a list of granules present on the file system. + + Parameters + ---------- + ids: boolean, default True + Return a list of the available granule IDs for the granule dictionary + cycles : boolean, default False + Return a list of the available orbital cycles for the granule dictionary + tracks : boolean, default Fal + Return a list of the available Reference Ground Tracks (RGTs) for the granule dictionary """ - assert len(grans)>0, "Your data object has no granules associated with it" + assert len(grans) > 0, "Your data object has no granules associated with it" + # regular expression for extracting parameters from file names + rx = re.compile('(ATL\d{2})(-\d{2})?_(\d{4})(\d{2})(\d{2})(\d{2})(\d{2})' + '(\d{2})_(\d{4})(\d{2})(\d{2})_(\d{3})_(\d{2})(.*?).(.*?)$') gran_ids = [] + gran_cycles = [] + gran_tracks = [] for gran in grans: - gran_ids.append(gran['producer_granule_id']) - - return gran_ids - - -#DevGoal: this will be a great way/place to manage data from the local file system -#where the user already has downloaded data! -#DevNote: currently this class is not tested -class Granules(): + producer_granule_id = gran["producer_granule_id"] + gran_ids.append(producer_granule_id) + # PRD: ICESat-2 product + # HEM: Sea Ice Hemisphere flag + # YY,MM,DD,HH,MN,SS: Year, Month, Day, Hour, Minute, Second + # TRK: Reference Ground Track (RGT) + # CYCL: Orbital Cycle + # GRAN: Granule region (1-14) + # RL: Data Release + # VERS: Product Version + # AUX: Auxiliary flags + # SFX: Suffix (h5) + PRD,HEM,YY,MM,DD,HH,MN,SS,TRK,CYCL,GRAN,RL,VERS,AUX,SFX = \ + rx.findall(producer_granule_id).pop() + gran_cycles.append(CYCL) + gran_tracks.append(TRK) + # list of granule parameters + gran_list = [] + # granule IDs + if ids: + gran_list.append(gran_ids) + # orbital cycles + if cycles: + gran_list.append(gran_cycles) + # reference ground tracks (RGTs) + if tracks: + gran_list.append(gran_tracks) + # return the list of granule parameters + return gran_list + +# DevGoal: this will be a great way/place to manage data from the local file system +# where the user already has downloaded data! +# DevNote: currently this class is not tested +class Granules: """ Interact with ICESat-2 data granules. This includes finding, ordering, and downloading them as well as (not yet implemented) getting already - downloaded granules into the icesat2data object. + downloaded granules into the query object. Returns ------- Granules object """ - + def __init__( self, # avail=[], @@ -68,13 +111,13 @@ def __init__( # self.orderIDs = orderIDs # self.files = files # session = session - + # ---------------------------------------------------------------------- # Methods def get_avail(self, CMRparams, reqparams): """ - Get a list of available granules for the icesat2data object's parameters. + Get a list of available granules for the query object's parameters. Generates the `avail` attribute of the granules object. Parameters @@ -87,57 +130,72 @@ def get_avail(self, CMRparams, reqparams): Notes ----- - This function is used by icesat2data.Icesat2Data.avail_granules(), which automatically + This function is used by query.Query.avail_granules(), which automatically feeds in the required parameters. - + See Also -------- APIformatting.Parameters - icesat2data.Icesat2Data.avail_granules + query.Query.avail_granules """ - assert CMRparams is not None and reqparams is not None, "Missing required input parameter dictionaries" + assert ( + CMRparams is not None and reqparams is not None + ), "Missing required input parameter dictionaries" - # if not hasattr(self, 'avail'): - self.avail=[] + # if not hasattr(self, 'avail'): + self.avail = [] - granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules' + granule_search_url = "https://cmr.earthdata.nasa.gov/search/granules" - headers={'Accept': 'application/json'} - #DevGoal: check the below request/response for errors and show them if they're there; then gather the results - #note we should also do this whenever we ping NSIDC-API - make a function to check for errors + headers = {"Accept": "application/json"} + # DevGoal: check the below request/response for errors and show them if they're there; then gather the results + # note we should also do this whenever we ping NSIDC-API - make a function to check for errors while True: - response = requests.get(granule_search_url, headers=headers,\ - params=apifmt.combine_params(CMRparams,\ - {k: reqparams[k] for k in ('page_size','page_num')})) + response = requests.get( + granule_search_url, + headers=headers, + params=apifmt.combine_params( + CMRparams, {k: reqparams[k] for k in ("page_size", "page_num")} + ), + ) results = json.loads(response.content) # print(results) - - if len(results['feed']['entry']) == 0: + + if len(results["feed"]["entry"]) == 0: # Out of results, so break out of loop break # Collect results and increment page_num - self.avail.extend(results['feed']['entry']) - reqparams['page_num'] += 1 + self.avail.extend(results["feed"]["entry"]) + reqparams["page_num"] += 1 - #DevNote: The above calculated page_num is wrong when mod(granule number, page_size)=0. + # DevNote: The above calculated page_num is wrong when mod(granule number, page_size)=0. # print(reqparams['page_num']) - reqparams['page_num'] = int(np.ceil(len(self.avail)/reqparams['page_size'])) - - assert len(self.avail)>0, "Your search returned no results; try different search parameters" - - - #DevNote: currently, default subsetting DOES NOT include variable subsetting, only spatial and temporal - #DevGoal: add kwargs to allow subsetting and more control over request options. - def place_order(self, CMRparams, reqparams, subsetparams, verbose, - subset=True, session=None, geom_filepath=None): #, **kwargs): + reqparams["page_num"] = int(np.ceil(len(self.avail) / reqparams["page_size"])) + + assert ( + len(self.avail) > 0 + ), "Your search returned no results; try different search parameters" + + # DevNote: currently, default subsetting DOES NOT include variable subsetting, only spatial and temporal + # DevGoal: add kwargs to allow subsetting and more control over request options. + def place_order( + self, + CMRparams, + reqparams, + subsetparams, + verbose, + subset=True, + session=None, + geom_filepath=None, + ): # , **kwargs): """ - Place an order for the available granules for the icesat2data object. + Place an order for the available granules for the query object. Adds the list of zipped files (orders) to the granules data object (which is - stored as the `granules` attribute of the icesat2data object). + stored as the `granules` attribute of the query object). You must be logged in to Earthdata to use this function. Parameters @@ -149,7 +207,7 @@ def place_order(self, CMRparams, reqparams, subsetparams, verbose, or downloading from NSIDC. subsetparams : dictionary Dictionary of properly formatted subsetting parameters. An empty dictionary - is passed as input here when subsetting is set to False in icesat2data methods. + is passed as input here when subsetting is set to False in query methods. verbose : boolean, default False Print out all feedback available from the order process. Progress information is automatically printed regardless of the value of verbose. @@ -161,151 +219,173 @@ def place_order(self, CMRparams, reqparams, subsetparams, verbose, granules. This eliminates false-positive granules returned by the metadata-level search) session : requests.session object A session object authenticating the user to order data using their Earthdata login information. - The session object will automatically be passed from the icesat2data object if you + The session object will automatically be passed from the query object if you have successfully logged in there. geom_filepath : string, default None String of the full filename and path when the spatial input is a file. - + Notes ----- - This function is used by icesat2data.Icesat2Data.order_granules(), which automatically + This function is used by query.Query.order_granules(), which automatically feeds in the required parameters. - + See Also -------- - icesat2data.Icesat2Data.order_granules + query.Query.order_granules """ if session is None: - raise ValueError("Don't forget to log in to Earthdata using is2_data.earthdata_login(uid, email)") - - base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request' - #DevGoal: get the base_url from the granules? + raise ValueError( + "Don't forget to log in to Earthdata using is2_data.earthdata_login(uid, email)" + ) + base_url = "https://n5eil02u.ecs.nsidc.org/egi/request" + # DevGoal: get the base_url from the granules? - self.get_avail(CMRparams, reqparams) #this way the reqparams['page_num'] is updated + self.get_avail( + CMRparams, reqparams + ) # this way the reqparams['page_num'] is updated if subset is False: - request_params = apifmt.combine_params(CMRparams, reqparams, {'agent':'NO'}) + request_params = apifmt.combine_params( + CMRparams, reqparams, {"agent": "NO"} + ) else: - request_params = apifmt.combine_params(CMRparams, reqparams, subsetparams) - - order_fn = '.order_restart' - - print('Total number of data order requests is ',request_params['page_num'], ' for ',len(self.avail), ' granules.') - #DevNote/05/27/20/: Their page_num values are the same, but use the combined version anyway. - #I'm switching back to reqparams, because that value is not changed by the for loop. I shouldn't cause an issue either way, but I've had issues with mutable types in for loops elsewhere. - for i in range(reqparams['page_num']): -# for i in range(request_params['page_num']): + request_params = apifmt.combine_params(CMRparams, reqparams, subsetparams) + + order_fn = ".order_restart" + + print( + "Total number of data order requests is ", + request_params["page_num"], + " for ", + len(self.avail), + " granules.", + ) + # DevNote/05/27/20/: Their page_num values are the same, but use the combined version anyway. + # I'm switching back to reqparams, because that value is not changed by the for loop. I shouldn't cause an issue either way, but I've had issues with mutable types in for loops elsewhere. + for i in range(reqparams["page_num"]): + # for i in range(request_params['page_num']): page_val = i + 1 - - print('Data request ', page_val, ' of ', reqparams['page_num'],' is submitting to NSIDC') - request_params.update( {'page_num': page_val} ) - #DevNote: earlier versions of the code used a file upload+post rather than putting the geometries - #into the parameter dictionaries. However, this wasn't working with shapefiles, but this more general - #solution does, so the geospatial parameters are included in the parameter dictionaries. + print( + "Data request ", + page_val, + " of ", + reqparams["page_num"], + " is submitting to NSIDC", + ) + request_params.update({"page_num": page_val}) + + # DevNote: earlier versions of the code used a file upload+post rather than putting the geometries + # into the parameter dictionaries. However, this wasn't working with shapefiles, but this more general + # solution does, so the geospatial parameters are included in the parameter dictionaries. request = session.get(base_url, params=request_params) - - #DevGoal: use the request response/number to do some error handling/give the user better messaging for failures + + # DevGoal: use the request response/number to do some error handling/give the user better messaging for failures # print(request.content) - root=ET.fromstring(request.content) - #print([subset_agent.attrib for subset_agent in root.iter('SubsetAgent')]) + root = ET.fromstring(request.content) + # print([subset_agent.attrib for subset_agent in root.iter('SubsetAgent')]) if verbose is True: - print('Request HTTP response: ', request.status_code) + print("Request HTTP response: ", request.status_code) # print('Order request URL: ', request.url) - # Raise bad request: Loop will stop for bad response code. + # Raise bad request: Loop will stop for bad response code. request.raise_for_status() esir_root = ET.fromstring(request.content) if verbose is True: - print('Order request URL: ', request.url) - print('Order request response XML content: ', request.content) + print("Order request URL: ", request.url) + print("Order request response XML content: ", request.content) - #Look up order ID + # Look up order ID orderlist = [] for order in esir_root.findall("./order/"): # if verbose is True: # print(order) orderlist.append(order.text) orderID = orderlist[0] - print('order ID: ', orderID) + print("order ID: ", orderID) - #Create status URL - statusURL = base_url + '/' + orderID + # Create status URL + statusURL = base_url + "/" + orderID if verbose is True: - print('status URL: ', statusURL) + print("status URL: ", statusURL) - #Find order status + # Find order status request_response = session.get(statusURL) if verbose is True: - print('HTTP response from order response URL: ', request_response.status_code) + print( + "HTTP response from order response URL: ", + request_response.status_code, + ) - # Raise bad request: Loop will stop for bad response code. + # Raise bad request: Loop will stop for bad response code. request_response.raise_for_status() request_root = ET.fromstring(request_response.content) statuslist = [] for status in request_root.findall("./requestStatus/"): statuslist.append(status.text) status = statuslist[0] - print('Initial status of your order request at NSIDC is: ', status) - - #Continue loop while request is still processing - while status == 'pending' or status == 'processing': - print('Your order status is still ', status, ' at NSIDC. Please continue waiting... this may take a few moments.') + print("Initial status of your order request at NSIDC is: ", status) + + # Continue loop while request is still processing + while status == "pending" or status == "processing": + print( + "Your order status is still ", + status, + " at NSIDC. Please continue waiting... this may take a few moments.", + ) # print('Status is not complete. Trying again') time.sleep(10) loop_response = session.get(statusURL) - # Raise bad request: Loop will stop for bad response code. + # Raise bad request: Loop will stop for bad response code. loop_response.raise_for_status() loop_root = ET.fromstring(loop_response.content) - #find status + # find status statuslist = [] for status in loop_root.findall("./requestStatus/"): statuslist.append(status.text) status = statuslist[0] # print('Retry request status is: ', status) - if status == 'pending' or status == 'processing': + if status == "pending" or status == "processing": continue - #Order can either complete, complete_with_errors, or fail: - # Provide complete_with_errors error message: - if status == 'complete_with_errors' or status == 'failed': + # Order can either complete, complete_with_errors, or fail: + # Provide complete_with_errors error message: + if status == "complete_with_errors" or status == "failed": messagelist = [] for message in loop_root.findall("./processInfo/"): messagelist.append(message.text) - print('Your order is: ', status) - print('NSIDC provided these error messages:') + print("Your order is: ", status) + print("NSIDC provided these error messages:") pprint.pprint(messagelist) - if status == 'complete' or status == 'complete_with_errors': - print('Your order is:', status) - if not hasattr(self,'orderIDs'): - self.orderIDs=[] + if status == "complete" or status == "complete_with_errors": + print("Your order is:", status) + if not hasattr(self, "orderIDs"): + self.orderIDs = [] self.orderIDs.append(orderID) - else: print('Request failed.') - - #DevGoal: save orderIDs more frequently than just at the end for large orders (e.g. for len(reqparams['page_num']) > 5 or 10 or something) - #Save orderIDs to file to avoid resubmitting order in case kernel breaks down. - # save orderIDs for every 5 orders when more than 10 orders are submitted. - # DevNote: These numbers are hard coded for now. Consider to allow user to set them in future? - if reqparams['page_num']>=10 and i%5==0: - with open(order_fn,'w') as fid: - json.dump({'orderIDs':self.orderIDs},fid) - - # --- Output the final orderIDs - with open(order_fn,'w') as fid: - json.dump({'orderIDs':self.orderIDs},fid) + else: + print("Request failed.") + # DevGoal: save orderIDs more frequently than just at the end for large orders (e.g. for len(reqparams['page_num']) > 5 or 10 or something) + # Save orderIDs to file to avoid resubmitting order in case kernel breaks down. + # save orderIDs for every 5 orders when more than 10 orders are submitted. + # DevNote: These numbers are hard coded for now. Consider to allow user to set them in future? + if reqparams["page_num"] >= 10 and i % 5 == 0: + with open(order_fn, "w") as fid: + json.dump({"orderIDs": self.orderIDs}, fid) + # --- Output the final orderIDs + with open(order_fn, "w") as fid: + json.dump({"orderIDs": self.orderIDs}, fid) return self.orderIDs - def download(self, verbose, path, session=None, restart=False): """ Downloads the data for the object's orderIDs, which are generated by ordering data @@ -320,85 +400,95 @@ def download(self, verbose, path, session=None, restart=False): String with complete path to desired download directory and location. session : requests.session object A session object authenticating the user to download data using their Earthdata login information. - The session object will automatically be passed from the icesat2data object if you + The session object will automatically be passed from the query object if you have successfully logged in there. restart : boolean, default False Restart your download if it has been interrupted. If the kernel has been restarted, but you successfully - completed your order, you will need to re-initialize your icesat2data class object and log in to Earthdata + completed your order, you will need to re-initialize your query class object and log in to Earthdata and can then skip immediately to the download_granules method with restart=True. Notes ----- - This function is used by icesat2data.Icesat2Data.download_granules(), which automatically + This function is used by query.Query.download_granules(), which automatically feeds in the required parameters. - + See Also -------- - icesat2data.Icesat2Data.download_granules + query.Query.download_granules """ """ extract : boolean, default False Unzip the downloaded granules. """ - #Note: need to test these checks still + # Note: need to test these checks still if session is None: - raise ValueError("Don't forget to log in to Earthdata using is2_data.earthdata_login(uid, email)") - #DevGoal: make this a more robust check for an active session - - #DevNote: this will replace any existing orderIDs with the saved list (could create confusion depending on whether download was interrupted or kernel restarted) - order_fn = '.order_restart' - if os.path.exists( order_fn ): - with open(order_fn,'r') as fid: + raise ValueError( + "Don't forget to log in to Earthdata using is2_data.earthdata_login(uid, email)" + ) + # DevGoal: make this a more robust check for an active session + + # DevNote: this will replace any existing orderIDs with the saved list (could create confusion depending on whether download was interrupted or kernel restarted) + order_fn = ".order_restart" + if os.path.exists(order_fn): + with open(order_fn, "r") as fid: order_dat = json.load(fid) - self.orderIDs = order_dat['orderIDs'] - - if not hasattr(self,'orderIDs') or len(self.orderIDs)==0: - raise ValueError('Please confirm that you have submitted a valid order and it has successfully completed.') - - #DevNote: Temporary. Hard code the orderID info files here. order_fn should be consistent with place_order. - - downid_fn = '.download_ID' - + self.orderIDs = order_dat["orderIDs"] + + if not hasattr(self, "orderIDs") or len(self.orderIDs) == 0: + raise ValueError( + "Please confirm that you have submitted a valid order and it has successfully completed." + ) + + # DevNote: Temporary. Hard code the orderID info files here. order_fn should be consistent with place_order. + + downid_fn = ".download_ID" + i_order = 0 - - if restart: - print('Restarting download ... ') - - # --- update the starting point of download list - if os.path.exists( downid_fn ): - order_start = str(int( np.loadtxt(downid_fn) ) ) - i_order = self.orderIDs.index(order_start) + 1 - + + if restart: + print("Restarting download ... ") + + # --- update the starting point of download list + if os.path.exists(downid_fn): + order_start = str(int(np.loadtxt(downid_fn))) + i_order = self.orderIDs.index(order_start) + 1 + for order in self.orderIDs[i_order:]: - downloadURL = 'https://n5eil02u.ecs.nsidc.org/esir/' + order + '.zip' - #DevGoal: get the download_url from the granules + downloadURL = "https://n5eil02u.ecs.nsidc.org/esir/" + order + ".zip" + # DevGoal: get the download_url from the granules - if verbose is True: - print('Zip download URL: ', downloadURL) - print('Beginning download of zipped output...') + print("Zip download URL: ", downloadURL) + print("Beginning download of zipped output...") zip_response = session.get(downloadURL) # Raise bad request: Loop will stop for bad response code. zip_response.raise_for_status() - print('Data request', order, 'of ', len(self.orderIDs[i_order:]), ' order(s) is downloaded.') - - #DevGoal: move this option back out to the is2class level and implement it in an alternate way? - # #Note: extract the dataset to save it locally - # if extract is True: + print( + "Data request", + order, + "of ", + len(self.orderIDs[i_order:]), + " order(s) is downloaded.", + ) + + # DevGoal: move this option back out to the is2class level and implement it in an alternate way? + # #Note: extract the dataset to save it locally + # if extract is True: with zipfile.ZipFile(io.BytesIO(zip_response.content)) as z: for zfile in z.filelist: # Remove the subfolder name from the filepath zfile.filename = os.path.basename(zfile.filename) z.extract(member=zfile, path=path) - + # update the current finished order id and save to file - with open(downid_fn,'w') as fid: - fid.write( order ) - - # remove orderID and download id files at the end - if os.path.exists( order_fn ): os.remove(order_fn) - if os.path.exists( downid_fn ): os.remove(downid_fn) - - print('Download complete') - \ No newline at end of file + with open(downid_fn, "w") as fid: + fid.write(order) + + # remove orderID and download id files at the end + if os.path.exists(order_fn): + os.remove(order_fn) + if os.path.exists(downid_fn): + os.remove(downid_fn) + + print("Download complete") diff --git a/icepyx/core/icesat2data.py b/icepyx/core/icesat2data.py index f8a7e36e1..cebce4160 100644 --- a/icepyx/core/icesat2data.py +++ b/icepyx/core/icesat2data.py @@ -1,764 +1,11 @@ -import datetime as dt -import os -import requests -import json import warnings -import pprint -import time -import geopandas as gpd -import matplotlib.pyplot as plt -from icepyx.core.Earthdata import Earthdata -import icepyx.core.APIformatting as apifmt -import icepyx.core.is2ref as is2ref -import icepyx.core.granules as granules -from icepyx.core.granules import Granules as Granules -#QUESTION: why doesn't from granules import Granules as Granules work, since granules=icepyx.core.granules? -# from icepyx.core.granules import Granules -from icepyx.core.variables import Variables as Variables -import icepyx.core.geospatial as geospatial -import icepyx.core.validate_inputs as val -#DevGoal: update docs throughout to allow for polygon spatial extent -#Note: add files to docstring once implemented -#DevNote: currently this class is not tested -class Icesat2Data(): - """ - ICESat-2 Data object to query, obtain, and perform basic operations on - available ICESat-2 datasets using temporal and spatial input parameters. - Allows the easy input and formatting of search parameters to match the - NASA NSIDC DAAC and (development goal-not yet implemented) conversion to multiple data types. +class Icesat2Data: + def __init__(self,): - Parameters - ---------- - dataset : string - ICESat-2 dataset ID, also known as "short name" (e.g. ATL03). - Available datasets can be found at: https://nsidc.org/data/icesat-2/data-sets - spatial_extent : list or string - Spatial extent of interest, provided as a bounding box, list of polygon coordinates, or - geospatial polygon file. - Bounding box coordinates should be provided in decimal degrees as - [lower-left-longitude, lower-left-latitute, upper-right-longitude, upper-right-latitude]. - Polygon coordinates should be provided as coordinate pairs in decimal degrees as - [(longitude1, latitude1), (longitude2, latitude2), ... (longitude_n,latitude_n), (longitude1,latitude1)] - or - [longitude1, latitude1, longitude2, latitude2, ... longitude_n,latitude_n, longitude1,latitude1]. - Your list must contain at least four points, where the first and last are identical. - DevGoal: adapt code so the polygon is automatically closed if need be - Geospatial polygon files are entered as strings with the full file path and - must contain only one polygon with the area of interest. - Currently supported formats are: kml, shp, and gpkg - date_range : list of 'YYYY-MM-DD' strings - Date range of interest, provided as start and end dates, inclusive. - The required date format is 'YYYY-MM-DD' strings, where - YYYY = 4 digit year, MM = 2 digit month, DD = 2 digit day. - Currently, a list of specific dates (rather than a range) is not accepted. - DevGoal: accept date-time objects, dicts (with 'start_date' and 'end_date' keys, and DOY inputs). - DevGoal: allow searches with a list of dates, rather than a range. - start_time : HH:mm:ss, default 00:00:00 - Start time in UTC/Zulu (24 hour clock). If None, use default. - DevGoal: check for time in date-range date-time object, if that's used for input. - end_time : HH:mm:ss, default 23:59:59 - End time in UTC/Zulu (24 hour clock). If None, use default. - DevGoal: check for time in date-range date-time object, if that's used for input. - version : string, default most recent version - Dataset version, given as a 3 digit string. If no version is given, the current - version is used. - - Returns - ------- - icesat2data object - - Examples - -------- - Initializing Icesat2Data with a bounding box. - - >>> reg_a_bbox = [-55, 68, -48, 71] - >>> reg_a_dates = ['2019-02-20','2019-02-28'] - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06', reg_a_bbox, reg_a_dates) - >>> reg_a - - - Initializing Icesat2Data with a list of polygon vertex coordinate pairs. - - >>> reg_a_poly = [(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)] - >>> reg_a_dates = ['2019-02-20','2019-02-28'] - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06', reg_a_poly, reg_a_dates) - >>> reg_a - - - Initializing Icesat2Data with a geospatial polygon file. - - >>> aoi = '/User/name/location/aoi.shp' - >>> reg_a_dates = ['2019-02-22','2019-02-28'] - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06', aoi, reg_a_dates) - >>> reg_a - - """ - - - # ---------------------------------------------------------------------- - # Constructors - - def __init__( - self, - dataset = None, - spatial_extent = None, - date_range = None, - start_time = None, - end_time = None, - version = None, - files = None, - ): - - # warnings.filterwarnings("always") - # warnings.warn("Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.") - - if (dataset is None or spatial_extent is None or date_range is None) and files is None: - raise ValueError("Please provide the required inputs. Use help([function]) to view the function's documentation") - - if files is not None: - self._source = 'files' - # self.file_vars = Variables(self._source) - else: - self._source = 'order' - # self.order_vars = Variables(self._source) - # self.variables = Variables(self._source) - - self._dset = is2ref._validate_dataset(dataset) - - self.extent_type, self._spat_extent, self._geom_filepath = val.spatial(spatial_extent) - - self._start, self._end = val.temporal(date_range, start_time, end_time) - - self._version = val.dset_version(self.latest_version(), version) - - - # ---------------------------------------------------------------------- - # Properties - - @property - def dataset(self): - """ - Return the short name dataset ID string associated with the icesat2data object. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.dataset - 'ATL06' - """ - return self._dset - - @property - def dataset_version(self): - """ - Return the dataset version of the data object. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.dataset_version - '003' - - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') - >>> reg_a.dataset_version - '001' - """ - return self._version - - @property - def spatial_extent(self): - """ - Return an array showing the spatial extent of the icesat2data object. - Spatial extent is returned as an input type (which depends on how - you initially entered your spatial data) followed by the geometry data. - Bounding box data is [lower-left-longitude, lower-left-latitute, upper-right-longitude, upper-right-latitude]. - Polygon data is [[array of longitudes],[array of corresponding latitudes]]. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.spatial_extent - ['bounding box', [-55, 68, -48, 71]] - - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)],['2019-02-20','2019-02-28']) - >>> reg_a.spatial_extent - ['polygon', [-55.0, 68.0, -55.0, 71.0, -48.0, 71.0, -48.0, 68.0, -55.0, 68.0]] - """ - - - if self.extent_type == 'bounding_box': - return ['bounding box', self._spat_extent] - elif self.extent_type == 'polygon': - # return ['polygon', self._spat_extent] - # Note: self._spat_extent is a shapely geometry object - return ['polygon', self._spat_extent.exterior.coords.xy] - else: - return ['unknown spatial type', None] - - @property - def dates(self): - """ - Return an array showing the date range of the icesat2data object. - Dates are returned as an array containing the start and end datetime objects, inclusive, in that order. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.dates - ['2019-02-20', '2019-02-28'] - """ - return [self._start.strftime('%Y-%m-%d'), self._end.strftime('%Y-%m-%d')] #could also use self._start.date() - - @property - def start_time(self): - """ - Return the start time specified for the start date. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.start_time - '00:00:00' - - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], start_time='12:30:30') - >>> reg_a.start_time - '12:30:30' - """ - return self._start.strftime('%H:%M:%S') - - @property - def end_time(self): - """ - Return the end time specified for the end date. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.end_time - '23:59:59' - - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], end_time='10:20:20') - >>> reg_a.end_time - '10:20:20' - """ - return self._end.strftime('%H:%M:%S') - - @property - def CMRparams(self): - """ - Display the CMR key:value pairs that will be submitted. It generates the dictionary if it does not already exist. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.CMRparams - {'short_name': 'ATL06', - 'version': '002', - 'temporal': '2019-02-20T00:00:00Z,2019-02-28T23:59:59Z', - 'bounding_box': '-55,68,-48,71'} - """ - - if not hasattr(self, '_CMRparams'): - self._CMRparams = apifmt.Parameters('CMR') - # print(self._CMRparams) - # print(self._CMRparams.fmted_keys) - - if self._CMRparams.fmted_keys == {}: - self._CMRparams.build_params(dataset=self.dataset, version=self._version,\ - start=self._start, end=self._end, extent_type=self.extent_type, spatial_extent=self._spat_extent) - - return self._CMRparams.fmted_keys - - @property - def reqparams(self): - """ - Display the required key:value pairs that will be submitted. It generates the dictionary if it does not already exist. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.reqparams - {'page_size': 10, 'page_num': 1} - - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - >>> reg_a.order_granules() - >>> reg_a.reqparams - {'page_size': 10, 'page_num': 1, 'request_mode': 'async', 'include_meta': 'Y'} - """ - - if not hasattr(self, '_reqparams'): - self._reqparams = apifmt.Parameters('required', reqtype='search') - self._reqparams.build_params() - - return self._reqparams.fmted_keys - - # @property - #DevQuestion: if I make this a property, I get a "dict" object is not callable when I try to give input kwargs... what approach should I be taking? - def subsetparams(self, **kwargs): - """ - Display the subsetting key:value pairs that will be submitted. It generates the dictionary if it does not already exist - and returns an empty dictionary if subsetting is set to False during ordering. - - Parameters - ---------- - **kwargs : key-value pairs - Additional parameters to be passed to the subsetter. - By default temporal and spatial subset keys are passed. - Acceptable key values are ['format','projection','projection_parameters','Coverage']. - At this time (2020-05), only variable ('Coverage') parameters will be automatically formatted. - - See Also - -------- - order_granules - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.subsetparams() - {'time': '2019-02-20T00:00:00,2019-02-28T23:59:59', 'bbox': '-55,68,-48,71'} - """ - if not hasattr(self, '_subsetparams'): self._subsetparams = apifmt.Parameters('subset') - - if self._subsetparams==None and not kwargs: - return {} - else: - if self._subsetparams==None: self._subsetparams = apifmt.Parameters('subset') - if self._geom_filepath is not None: - self._subsetparams.build_params(geom_filepath = self._geom_filepath, \ - start=self._start, end=self._end, extent_type=self.extent_type, \ - spatial_extent=self._spat_extent, **kwargs) - else: - self._subsetparams.build_params(start=self._start, end=self._end,\ - extent_type=self.extent_type, spatial_extent=self._spat_extent, **kwargs) - - return self._subsetparams.fmted_keys - - #DevGoal: add to tests - #DevGoal: add statements to the following vars properties to let the user know if they've got a mismatched source and vars type - @property - def order_vars(self): - """ - Return the order variables object. - This instance is generated when data is ordered from the NSIDC. - - See Also - -------- - variables.Variables - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - >>> reg_a.order_vars - - """ - - if not hasattr(self, '_order_vars'): - if self._source == 'order': - #DevGoal: check for active session here - if hasattr(self, '_cust_options'): - self._order_vars = Variables(self._source, session=self._session, dataset=self.dataset, avail=self._cust_options['variables']) - else: - self._order_vars = Variables(self._source, session=self._session, dataset=self.dataset, version=self._version) - - # I think this is where property setters come in, and one should be used here? Right now order_vars.avail is only filled in - #if _cust_options exists when the class is initialized, but not if _cust_options is filled in prior to another call to order_vars - # if self._order_vars.avail == None and hasattr(self, '_cust_options'): - # print('got into the loop') - # self._order_vars.avail = self._cust_options['variables'] - - return self._order_vars - - - @property - def file_vars(self): - """ - Return the file variables object. - This instance is generated when files are used to create the data object (not yet implemented). - - See Also - -------- - variables.Variables - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - >>> reg_a.file_vars - - """ - - if not hasattr(self, '_file_vars'): - if self._source == 'file': - self._file_vars = Variables(self._source, dataset=self.dataset) - - return self._file_vars - - @property - def granules(self): - """ - Return the granules object, which provides the underlying funtionality for searching, ordering, - and downloading granules for the specified dataset. Users are encouraged to use the built in wrappers - rather than trying to access the granules object themselves. - - See Also - -------- - avail_granules - order_granules - download_granules - granules.Granules - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.granules - - """ - - if not hasattr(self, '_granules'): - self._granules = Granules() - elif self._granules==None: - self._granules = Granules() - - return self._granules - - - # ---------------------------------------------------------------------- - # Methods - Get and display neatly information at the dataset level - - def dataset_summary_info(self): - """ - Display a summary of selected metadata for the specified version of the dataset - of interest (the collection). - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.dataset_summary_info() - dataset_id : ATLAS/ICESat-2 L3A Land Ice Height V002 - short_name : ATL06 - version_id : 002 - time_start : 2018-10-14T00:00:00.000Z - coordinate_system : CARTESIAN - summary : This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. - orbit_parameters : {'swath_width': '36.0', 'period': '94.29', 'inclination_angle': '92.0', 'number_of_orbits': '0.071428571', 'start_circular_latitude': '0.0'} - """ - if not hasattr(self, '_about_dataset'): self._about_dataset=is2data(self._dset) - summ_keys = ['dataset_id', 'short_name', 'version_id', 'time_start', 'coordinate_system', 'summary', 'orbit_parameters'] - for key in summ_keys: - print(key,': ',self._about_dataset['feed']['entry'][-1][key]) - - def dataset_all_info(self): - """ - Display all metadata about the dataset of interest (the collection). - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.dataset_all_info() - {very long prettily-formatted dictionary output} - - """ - if not hasattr(self, '_about_dataset'): self._about_dataset=is2data(self._dset) - pprint.pprint(self._about_dataset) - - def latest_version(self): - """ - Determine the most recent version available for the given dataset. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.latest_version() - '003' - """ - if not hasattr(self, '_about_dataset'): self._about_dataset=is2ref.about_dataset(self._dset) - return max([entry['version_id'] for entry in self._about_dataset['feed']['entry']]) - - def show_custom_options(self, dictview=False): - """ - Display customization/subsetting options available for this dataset. - - Parameters - ---------- - dictview : boolean, default False - Show the variable portion of the custom options list as a dictionary with key:value - pairs representing variable:paths-to-variable rather than as a long list of full - variable paths. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - >>> reg_a.show_custom_options(dictview=True): - Subsetting options - [{'id': 'ICESAT2', - 'maxGransAsyncRequest': '2000', - 'maxGransSyncRequest': '100', - 'spatialSubsetting': 'true', - 'spatialSubsettingShapefile': 'true', - 'temporalSubsetting': 'true', - 'type': 'both'}] - Data File Formats (Reformatting Options) - ['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3'] - Reprojection Options - [] - Data File (Reformatting) Options Supporting Reprojection - ['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3', 'No reformatting'] - Data File (Reformatting) Options NOT Supporting Reprojection - [] - Data Variables (also Subsettable) - ['ancillary_data/atlas_sdp_gps_epoch', - 'ancillary_data/control', - 'ancillary_data/data_end_utc', - . - . - . - 'quality_assessment/gt3r/signal_selection_source_fraction_3'] - """ - headers=['Subsetting options', 'Data File Formats (Reformatting Options)', 'Reprojection Options', - 'Data File (Reformatting) Options Supporting Reprojection', - 'Data File (Reformatting) Options NOT Supporting Reprojection', - 'Data Variables (also Subsettable)'] - keys=['options', 'fileformats', 'reprojectionONLY', 'formatreproj', 'noproj', 'variables'] - - try: - all(key in self._cust_options.keys() for key in keys) - except AttributeError or KeyError: - self._cust_options=is2ref._get_custom_options(self._session, self.dataset, self._version) - - for h,k in zip(headers,keys): - print(h) - if k=='variables' and dictview: - vgrp,paths = Variables.parse_var_list(self._cust_options[k]) - pprint.pprint(vgrp) - else: - pprint.pprint(self._cust_options[k]) - - - - # ---------------------------------------------------------------------- - # Methods - Login and Granules (NSIDC-API) - - def earthdata_login(self,uid,email): - """ - Log in to NSIDC EarthData to access data. Generates the needed session and token for most - data searches and data ordering/download. - - Parameters - ---------- - uid : string - Earthdata login user ID - email : string - Email address. NSIDC will automatically send you emails about the status of your order. - - See Also - -------- - Earthdata.Earthdata - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - """ - - capability_url = f'https://n5eil02u.ecs.nsidc.org/egi/capabilities/{self.dataset}.{self._version}.xml' - self._session = Earthdata(uid,email,capability_url).login() - self._email = email - - #DevGoal: check to make sure the see also bits of the docstrings work properly in RTD - def avail_granules(self, ids=False): - """ - Obtain information about the available granules for the icesat2data - object's parameters. By default, a complete list of available granules is - obtained and stored in the object, but only summary information is returned. - A list of granule IDs can be obtained using the boolean trigger. - - Parameters - ---------- - ids : boolean, default False - Indicates whether the function should return summary granule information (default) - or a list of granule IDs. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.avail_granules() - {'Number of available granules': 4, - 'Average size of granules (MB)': 48.975419759750004, - 'Total size of all granules (MB)': 195.90167903900002} - - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.avail_granules(ids=True) - - """ - -# REFACTOR: add test to make sure there's a session - if not hasattr(self, '_granules'): self.granules - try: self.granules.avail - except AttributeError: - self.granules.get_avail(self.CMRparams,self.reqparams) - - if ids==True: - return granules.gran_IDs(self.granules.avail) - else: - return granules.info(self.granules.avail) - - - - - #DevGoal: display output to indicate number of granules successfully ordered (and number of errors) - #DevGoal: deal with subset=True for variables now, and make sure that if a variable subset Coverage kwarg is input it's successfully passed through all other functions even if this is the only one run. - def order_granules(self, verbose=False, subset=True, email=True, **kwargs): - """ - Place an order for the available granules for the icesat2data object. - - Parameters - ---------- - verbose : boolean, default False - Print out all feedback available from the order process. - Progress information is automatically printed regardless of the value of verbose. - subset : boolean, default True - Apply subsetting to the data order from the NSIDC, returning only data that meets the - subset parameters. Spatial and temporal subsetting based on the input parameters happens - by default when subset=True, but additional subsetting options are available. - Spatial subsetting returns all data that are within the area of interest (but not complete - granules. This eliminates false-positive granules returned by the metadata-level search) - email: boolean, default True - Have NSIDC auto-send order status email updates to indicate order status as pending/completed. - **kwargs : key-value pairs - Additional parameters to be passed to the subsetter. - By default temporal and spatial subset keys are passed. - Acceptable key values are ['format','projection','projection_parameters','Coverage']. - The variable 'Coverage' list should be constructed using the `order_vars.wanted` attribute of the object. - At this time (2020-05), only variable ('Coverage') parameters will be automatically formatted. - - See Also - -------- - granules.place_order - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - >>> reg_a.order_granules() - order ID: [###############] - [order status output] - error messages: - [if any were returned from the NSIDC subsetter, e.g. No data found that matched subset constraints.] - . - . - . - Retry request status is: complete - """ - - if not hasattr(self, 'reqparams'): self.reqparams - - if self._reqparams._reqtype == 'search': - self._reqparams._reqtype = 'download' - - - if 'email' in self._reqparams.fmted_keys.keys() or email==False: - self._reqparams.build_params(**self._reqparams.fmted_keys) - else: - self._reqparams.build_params(**self._reqparams.fmted_keys, email=self._email) - - - if subset is False: - self._subsetparams=None - elif subset==True and hasattr(self, '_subsetparams') and self._subsetparams==None: - del self._subsetparams - - #REFACTOR: add checks here to see if the granules object has been created, and also if it already has a list of avail granules (if not, need to create one and add session) - if not hasattr(self, '_granules'): self.granules - self._granules.place_order(self.CMRparams, self.reqparams, self.subsetparams(**kwargs), verbose, subset, session=self._session, geom_filepath=self._geom_filepath) - - - #DevGoal: put back in the kwargs here so that people can just call download granules with subset=False! - def download_granules(self, path, verbose=False, subset=True, restart=False, **kwargs): #, extract=False): - """ - Downloads the data ordered using order_granules. - - Parameters - ---------- - path : string - String with complete path to desired download location. - verbose : boolean, default False - Print out all feedback available from the order process. - Progress information is automatically printed regardless of the value of verbose. - subset : boolean, default True - Apply subsetting to the data order from the NSIDC, returning only data that meets the - subset parameters. Spatial and temporal subsetting based on the input parameters happens - by default when subset=True, but additional subsetting options are available. - Spatial subsetting returns all data that are within the area of interest (but not complete - granules. This eliminates false-positive granules returned by the metadata-level search) - restart: boolean, default false - If previous download was terminated unexpectedly. Run again with restart set to True to continue. - **kwargs : key-value pairs - Additional parameters to be passed to the subsetter. - By default temporal and spatial subset keys are passed. - Acceptable key values are ['format','projection','projection_parameters','Coverage']. - The variable 'Coverage' list should be constructed using the `order_vars.wanted` attribute of the object. - At this time (2020-05), only variable ('Coverage') parameters will be automatically formatted. - - See Also - -------- - granules.download - """ - """ - extract : boolean, default False - Unzip the downloaded granules. - - Examples - -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) - >>> reg_a.earthdata_login(user_id,user_email) - Earthdata Login password: ········ - >>> reg_a.download_granules('/path/to/download/folder') - Beginning download of zipped output... - Data request [##########] of x order(s) is complete. - """ - - # if not os.path.exists(path): - # os.mkdir(path) - # os.chdir(path) - - if not hasattr(self, '_granules'): self.granules - - if restart == True: - pass - else: - if not hasattr(self._granules, 'orderIDs') or len(self._granules.orderIDs)==0: self.order_granules(verbose=verbose, subset=subset, **kwargs) - - self._granules.download(verbose, path, session=self._session, restart=restart) - - - #DevGoal: add testing? What do we test, and how, given this is a visualization. - #DevGoal(long term): modify this to accept additional inputs, etc. - #DevGoal: move this to it's own module for visualizing, etc. - #DevGoal: see Amy's data access notebook for a zoomed in map - implement here? - def visualize_spatial_extent(self): #additional args, basemap, zoom level, cmap, export - """ - Creates a map displaying the input spatial extent - - Examples - -------- - >>> icepyx.icesat2data.Icesat2Data('ATL06','path/spatialfile.shp',['2019-02-22','2019-02-28']) - >>> reg_a.visualize_spatial_extent - [visual map output] - """ - - world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) - f, ax = plt.subplots(1, figsize=(12, 6)) - world.plot(ax=ax, facecolor='lightgray', edgecolor='gray') - geospatial.geodataframe(self.extent_type,self._spat_extent).plot(ax=ax, color='#FF8C00',alpha = 0.7) - plt.show() + warnings.filterwarnings("always") + warnings.warn( + "DEPRECATED. Please use icepyx.Query to create a download data object (all other functionality is the same)", + DeprecationWarning, + ) diff --git a/icepyx/core/is2ref.py b/icepyx/core/is2ref.py index 0bd9cca56..90a77c612 100644 --- a/icepyx/core/is2ref.py +++ b/icepyx/core/is2ref.py @@ -4,8 +4,9 @@ import icepyx -#ICESat-2 specific reference functions -#options to get customization options for ICESat-2 data (though could be used generally) +# ICESat-2 specific reference functions +# options to get customization options for ICESat-2 data (though could be used generally) + def _validate_dataset(dataset): """ @@ -13,127 +14,204 @@ def _validate_dataset(dataset): """ if isinstance(dataset, str): dataset = str.upper(dataset) - assert dataset in ['ATL01','ATL02', 'ATL03', 'ATL04','ATL06', 'ATL07', 'ATL08', 'ATL09', 'ATL10', \ - 'ATL12', 'ATL13'],\ - "Please enter a valid dataset" + assert dataset in [ + "ATL01", + "ATL02", + "ATL03", + "ATL04", + "ATL06", + "ATL07", + "ATL08", + "ATL09", + "ATL10", + "ATL12", + "ATL13", + ], "Please enter a valid dataset" else: raise TypeError("Please enter a dataset string") return dataset -#DevGoal: See if there's a way to dynamically get this list so it's automatically updated - -#DevNote: test for this function is commented out; dates in some of the values were causing the test to fail... + + +# DevGoal: See if there's a way to dynamically get this list so it's automatically updated + +# DevNote: test for this function is commented out; dates in some of the values were causing the test to fail... def about_dataset(dset): """ Ping Earthdata to get metadata about the dataset of interest (the collection). See Also -------- - icesat2data.Icesat2Data.dataset_all_info + query.Query.dataset_all_info """ - cmr_collections_url = 'https://cmr.earthdata.nasa.gov/search/collections.json' - response = requests.get(cmr_collections_url, params={'short_name': dset}) + cmr_collections_url = "https://cmr.earthdata.nasa.gov/search/collections.json" + response = requests.get(cmr_collections_url, params={"short_name": dset}) results = json.loads(response.content) return results -#DevGoal: use a mock of this output to test later functions, such as displaying options and widgets, etc. +# DevGoal: use a mock of this output to test later functions, such as displaying options and widgets, etc. def _get_custom_options(session, dataset, version): """ Get lists of what customization options are available for the dataset from NSIDC. """ - cust_options={} - + cust_options = {} + if session is None: - raise ValueError("Don't forget to log in to Earthdata using is2_data.earthdata_login(uid, email)") + raise ValueError( + "Don't forget to log in to Earthdata using is2_data.earthdata_login(uid, email)" + ) - capability_url = f'https://n5eil02u.ecs.nsidc.org/egi/capabilities/{dataset}.{version}.xml' + capability_url = ( + f"https://n5eil02u.ecs.nsidc.org/egi/capabilities/{dataset}.{version}.xml" + ) response = session.get(capability_url) root = ET.fromstring(response.content) # collect lists with each service option - subagent = [subset_agent.attrib for subset_agent in root.iter('SubsetAgent')] - cust_options.update({'options':subagent}) + subagent = [subset_agent.attrib for subset_agent in root.iter("SubsetAgent")] + cust_options.update({"options": subagent}) # reformatting - formats = [Format.attrib for Format in root.iter('Format')] - format_vals = [formats[i]['value'] for i in range(len(formats))] - format_vals.remove('') - cust_options.update({'fileformats':format_vals}) + formats = [Format.attrib for Format in root.iter("Format")] + format_vals = [formats[i]["value"] for i in range(len(formats))] + format_vals.remove("") + cust_options.update({"fileformats": format_vals}) # reprojection only applicable on ICESat-2 L3B products, yet to be available. # reformatting options that support reprojection - normalproj = [Projections.attrib for Projections in root.iter('Projections')] + normalproj = [Projections.attrib for Projections in root.iter("Projections")] normalproj_vals = [] - normalproj_vals.append(normalproj[0]['normalProj']) - format_proj = normalproj_vals[0].split(',') - format_proj.remove('') - format_proj.append('No reformatting') - cust_options.update({'formatreproj':format_proj}) - - #reprojection options - projections = [Projection.attrib for Projection in root.iter('Projection')] + normalproj_vals.append(normalproj[0]["normalProj"]) + format_proj = normalproj_vals[0].split(",") + format_proj.remove("") + format_proj.append("No reformatting") + cust_options.update({"formatreproj": format_proj}) + + # reprojection options + projections = [Projection.attrib for Projection in root.iter("Projection")] proj_vals = [] for i in range(len(projections)): - if (projections[i]['value']) != 'NO_CHANGE' : - proj_vals.append(projections[i]['value']) - cust_options.update({'reprojectionONLY':proj_vals}) + if (projections[i]["value"]) != "NO_CHANGE": + proj_vals.append(projections[i]["value"]) + cust_options.update({"reprojectionONLY": proj_vals}) # reformatting options that do not support reprojection no_proj = [i for i in format_vals if i not in format_proj] - cust_options.update({'noproj':no_proj}) + cust_options.update({"noproj": no_proj}) # variable subsetting - vars_raw = [] + vars_raw = [] + def get_varlist(elem): childlist = list(elem) - if len(childlist)==0 and elem.tag=='SubsetVariable': - vars_raw.append(elem.attrib['value']) + if len(childlist) == 0 and elem.tag == "SubsetVariable": + vars_raw.append(elem.attrib["value"]) for child in childlist: get_varlist(child) + get_varlist(root) - vars_vals = [v.replace(':', '/') if v.startswith('/') == False else v.replace('/:','') for v in vars_raw] - cust_options.update({'variables':vars_vals}) + vars_vals = [ + v.replace(":", "/") if v.startswith("/") == False else v.replace("/:", "") + for v in vars_raw + ] + cust_options.update({"variables": vars_vals}) return cust_options -#DevGoal: populate this with default variable lists for all of the datasets! -#DevGoal: add a test for this function (to make sure it returns the right list, but also to deal with dataset not being in the list, though it should since it was checked as valid earlier...) + +# DevGoal: populate this with default variable lists for all of the datasets! +# DevGoal: add a test for this function (to make sure it returns the right list, but also to deal with dataset not being in the list, though it should since it was checked as valid earlier...) def _default_varlists(dataset): """ Return a list of default variables to select and send to the NSIDC subsetter. """ - common_list = ['delta_time','latitude','longitude'] - - if dataset == 'ATL06': - return common_list + ['h_li','h_li_sigma','atl06_quality_summary','segment_id','sigma_geo_h', - 'x_atc', 'y_atc','seg_azimuth','sigma_geo_at','sigma_geo_xt', 'dh_fit_dx','dh_fit_dx_sigma', - 'h_mean', 'dh_fit_dy','h_rms_misfit','h_robust_sprd','n_fit_photons','signal_selection_source', - 'snr_significance','w_surface_window_final','bsnow_conf','bsnow_h','cloud_flg_asr', - 'cloud_flg_atm','r_eff','tide_ocean'] - - - elif dataset == 'ATL07': - return common_list + ['seg_dist_x', - 'height_segment_height','height_segment_length_seg','height_segment_ssh_flag', - 'height_segment_type', 'height_segment_quality', 'height_segment_confidence' ] - - - elif dataset == 'ATL09': - return common_list + ['bsnow_h','bsnow_dens','bsnow_con','bsnow_psc','bsnow_od', - 'cloud_flag_asr','cloud_fold_flag','cloud_flag_atm', - 'column_od_asr','column_od_asr_qf', - 'layer_attr','layer_bot','layer_top','layer_flag','layer_dens','layer_ib', - 'msw_flag','prof_dist_x','prof_dist_y','apparent_surf_reflec'] - - - elif dataset == 'ATL10': - return common_list + ['seg_dist_x','lead_height','lead_length', - 'beam_fb_height', 'beam_fb_length', 'beam_fb_confidence', 'beam_fb_quality_flag', - 'height_segment_height','height_segment_length_seg','height_segment_ssh_flag', - 'height_segment_type', 'height_segment_confidence'] + common_list = ["delta_time", "latitude", "longitude"] + + if dataset == "ATL06": + return common_list + [ + "h_li", + "h_li_sigma", + "atl06_quality_summary", + "segment_id", + "sigma_geo_h", + "x_atc", + "y_atc", + "seg_azimuth", + "sigma_geo_at", + "sigma_geo_xt", + "dh_fit_dx", + "dh_fit_dx_sigma", + "h_mean", + "dh_fit_dy", + "h_rms_misfit", + "h_robust_sprd", + "n_fit_photons", + "signal_selection_source", + "snr_significance", + "w_surface_window_final", + "bsnow_conf", + "bsnow_h", + "cloud_flg_asr", + "cloud_flg_atm", + "r_eff", + "tide_ocean", + ] + + elif dataset == "ATL07": + return common_list + [ + "seg_dist_x", + "height_segment_height", + "height_segment_length_seg", + "height_segment_ssh_flag", + "height_segment_type", + "height_segment_quality", + "height_segment_confidence", + ] + + elif dataset == "ATL09": + return common_list + [ + "bsnow_h", + "bsnow_dens", + "bsnow_con", + "bsnow_psc", + "bsnow_od", + "cloud_flag_asr", + "cloud_fold_flag", + "cloud_flag_atm", + "column_od_asr", + "column_od_asr_qf", + "layer_attr", + "layer_bot", + "layer_top", + "layer_flag", + "layer_dens", + "layer_ib", + "msw_flag", + "prof_dist_x", + "prof_dist_y", + "apparent_surf_reflec", + ] + + elif dataset == "ATL10": + return common_list + [ + "seg_dist_x", + "lead_height", + "lead_length", + "beam_fb_height", + "beam_fb_length", + "beam_fb_confidence", + "beam_fb_quality_flag", + "height_segment_height", + "height_segment_length_seg", + "height_segment_ssh_flag", + "height_segment_type", + "height_segment_confidence", + ] else: - print("THE REQUESTED DATASET DOES NOT YET HAVE A DEFAULT LIST SET UP. ONLY DELTA_TIME, LATITUTDE, AND LONGITUDE WILL BE RETURNED") - return common_list \ No newline at end of file + print( + "THE REQUESTED DATASET DOES NOT YET HAVE A DEFAULT LIST SET UP. ONLY DELTA_TIME, LATITUTDE, AND LONGITUDE WILL BE RETURNED" + ) + return common_list diff --git a/icepyx/core/query.py b/icepyx/core/query.py new file mode 100644 index 000000000..54112863a --- /dev/null +++ b/icepyx/core/query.py @@ -0,0 +1,926 @@ +import datetime as dt +import os +import requests +import json +import warnings +import pprint +import time +import geopandas as gpd +import matplotlib.pyplot as plt + +from icepyx.core.Earthdata import Earthdata +import icepyx.core.APIformatting as apifmt +import icepyx.core.is2ref as is2ref +import icepyx.core.granules as granules +from icepyx.core.granules import Granules as Granules + +# QUESTION: why doesn't from granules import Granules as Granules work, since granules=icepyx.core.granules? +# from icepyx.core.granules import Granules +from icepyx.core.variables import Variables as Variables +import icepyx.core.geospatial as geospatial +import icepyx.core.validate_inputs as val + +# DevGoal: update docs throughout to allow for polygon spatial extent +# Note: add files to docstring once implemented +# DevNote: currently this class is not tested +class Query: + """ + ICESat-2 Data object to query, obtain, and perform basic operations on + available ICESat-2 datasets using temporal and spatial input parameters. + Allows the easy input and formatting of search parameters to match the + NASA NSIDC DAAC and (development goal-not yet implemented) conversion to multiple data types. + + Parameters + ---------- + dataset : string + ICESat-2 dataset ID, also known as "short name" (e.g. ATL03). + Available datasets can be found at: https://nsidc.org/data/icesat-2/data-sets + spatial_extent : list or string + Spatial extent of interest, provided as a bounding box, list of polygon coordinates, or + geospatial polygon file. + Bounding box coordinates should be provided in decimal degrees as + [lower-left-longitude, lower-left-latitute, upper-right-longitude, upper-right-latitude]. + Polygon coordinates should be provided as coordinate pairs in decimal degrees as + [(longitude1, latitude1), (longitude2, latitude2), ... (longitude_n,latitude_n), (longitude1,latitude1)] + or + [longitude1, latitude1, longitude2, latitude2, ... longitude_n,latitude_n, longitude1,latitude1]. + Your list must contain at least four points, where the first and last are identical. + DevGoal: adapt code so the polygon is automatically closed if need be + Geospatial polygon files are entered as strings with the full file path and + must contain only one polygon with the area of interest. + Currently supported formats are: kml, shp, and gpkg + date_range : list of 'YYYY-MM-DD' strings + Date range of interest, provided as start and end dates, inclusive. + The required date format is 'YYYY-MM-DD' strings, where + YYYY = 4 digit year, MM = 2 digit month, DD = 2 digit day. + Currently, a list of specific dates (rather than a range) is not accepted. + DevGoal: accept date-time objects, dicts (with 'start_date' and 'end_date' keys, and DOY inputs). + DevGoal: allow searches with a list of dates, rather than a range. + start_time : HH:mm:ss, default 00:00:00 + Start time in UTC/Zulu (24 hour clock). If None, use default. + DevGoal: check for time in date-range date-time object, if that's used for input. + end_time : HH:mm:ss, default 23:59:59 + End time in UTC/Zulu (24 hour clock). If None, use default. + DevGoal: check for time in date-range date-time object, if that's used for input. + version : string, default most recent version + Dataset version, given as a 3 digit string. If no version is given, the current + version is used. + cycle : string, default all available orbital cycles + Dataset cycle, given as a 2 digit string. If no cycle is given, all available + cycles are used. + track : string, default all available reference ground tracks (RGTs) + Dataset track, given as a 4 digit string. If no track is given, all available + reference ground tracks are used. + + Returns + ------- + query object + + Examples + -------- + Initializing Query with a bounding box. + + >>> reg_a_bbox = [-55, 68, -48, 71] + >>> reg_a_dates = ['2019-02-20','2019-02-28'] + >>> reg_a = icepyx.query.Query('ATL06', reg_a_bbox, reg_a_dates) + >>> reg_a + + + Initializing Query with a list of polygon vertex coordinate pairs. + + >>> reg_a_poly = [(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)] + >>> reg_a_dates = ['2019-02-20','2019-02-28'] + >>> reg_a = icepyx.query.Query('ATL06', reg_a_poly, reg_a_dates) + >>> reg_a + + + Initializing Query with a geospatial polygon file. + + >>> aoi = '/User/name/location/aoi.shp' + >>> reg_a_dates = ['2019-02-22','2019-02-28'] + >>> reg_a = icepyx.query.Query('ATL06', aoi, reg_a_dates) + >>> reg_a + + """ + + # ---------------------------------------------------------------------- + # Constructors + + def __init__( + self, + dataset=None, + spatial_extent=None, + date_range=None, + start_time=None, + end_time=None, + version=None, + cycles=None, + tracks=None, + orbit_number=None, + files=None, + ): + + # warnings.filterwarnings("always") + # warnings.warn("Please note: as of 2020-05-05, a major reorganization of the core icepyx.query code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.") + + if ( + dataset is None or spatial_extent is None or date_range is None + ) and files is None: + raise ValueError( + "Please provide the required inputs. Use help([function]) to view the function's documentation" + ) + + if files is not None: + self._source = "files" + # self.file_vars = Variables(self._source) + else: + self._source = "order" + # self.order_vars = Variables(self._source) + # self.variables = Variables(self._source) + + self._dset = is2ref._validate_dataset(dataset) + + self.extent_type, self._spat_extent, self._geom_filepath = val.spatial( + spatial_extent + ) + + self._start, self._end = val.temporal(date_range, start_time, end_time) + + self._version = val.dset_version(self.latest_version(), version) + + # list of CMR orbit number parameters + self._orbit_number = [] + # get list of available ICESat-2 cycles and tracks + all_cycles,all_tracks = self.avail_granules(ids=False,cycles=True,tracks=True) + self._cycles = val.cycles(all_cycles, cycles) + self._tracks = val.tracks(all_tracks, tracks) + # build list of available CMR orbit number if reducing by cycle or RGT + if cycles or tracks: + # for each available cycle of interest + for c in self.cycles: + # for each available track of interest + for t in self.tracks: + self._orbit_number.append(int(t) + (int(c)-1)*1387 + 201) + # update the CMR parameters for orbit_number + self.CMRparams['orbit_number'] = self.orbit_number + # update required parameters (number of pages) + self._reqparams.build_params() + # update the list of available granules + self.granules.get_avail(self.CMRparams, self.reqparams) + + # ---------------------------------------------------------------------- + # Properties + + @property + def dataset(self): + """ + Return the short name dataset ID string associated with the query object. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.dataset + 'ATL06' + """ + return self._dset + + @property + def dataset_version(self): + """ + Return the dataset version of the data object. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.dataset_version + '003' + + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') + >>> reg_a.dataset_version + '001' + """ + return self._version + + @property + def spatial_extent(self): + """ + Return an array showing the spatial extent of the query object. + Spatial extent is returned as an input type (which depends on how + you initially entered your spatial data) followed by the geometry data. + Bounding box data is [lower-left-longitude, lower-left-latitute, upper-right-longitude, upper-right-latitude]. + Polygon data is [[array of longitudes],[array of corresponding latitudes]]. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.spatial_extent + ['bounding box', [-55, 68, -48, 71]] + + >>> reg_a = icepyx.query.Query('ATL06',[(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)],['2019-02-20','2019-02-28']) + >>> reg_a.spatial_extent + ['polygon', [-55.0, 68.0, -55.0, 71.0, -48.0, 71.0, -48.0, 68.0, -55.0, 68.0]] + """ + + if self.extent_type == "bounding_box": + return ["bounding box", self._spat_extent] + elif self.extent_type == "polygon": + # return ['polygon', self._spat_extent] + # Note: self._spat_extent is a shapely geometry object + return ["polygon", self._spat_extent.exterior.coords.xy] + else: + return ["unknown spatial type", None] + + @property + def dates(self): + """ + Return an array showing the date range of the query object. + Dates are returned as an array containing the start and end datetime objects, inclusive, in that order. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.dates + ['2019-02-20', '2019-02-28'] + """ + return [ + self._start.strftime("%Y-%m-%d"), + self._end.strftime("%Y-%m-%d"), + ] # could also use self._start.date() + + @property + def start_time(self): + """ + Return the start time specified for the start date. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.start_time + '00:00:00' + + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], start_time='12:30:30') + >>> reg_a.start_time + '12:30:30' + """ + return self._start.strftime("%H:%M:%S") + + @property + def end_time(self): + """ + Return the end time specified for the end date. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.end_time + '23:59:59' + + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], end_time='10:20:20') + >>> reg_a.end_time + '10:20:20' + """ + return self._end.strftime("%H:%M:%S") + + @property + def cycles(self): + """ + Return the unique ICESat-2 orbital cycle. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.cycles + ['02'] + """ + return sorted(set(self._cycles)) + + @property + def tracks(self): + """ + Return the unique ICESat-2 Reference Ground Tracks + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.tracks + ['0841', '0849', '0902', '0910'] + """ + return sorted(set(self._tracks)) + + @property + def orbit_number(self): + """ + Return the ICESat-2 CMR orbit number + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.orbit_number + """ + return ",".join(map(str,self._orbit_number)) + + + @property + def CMRparams(self): + """ + Display the CMR key:value pairs that will be submitted. It generates the dictionary if it does not already exist. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.CMRparams + {'short_name': 'ATL06', + 'version': '002', + 'temporal': '2019-02-20T00:00:00Z,2019-02-28T23:59:59Z', + 'bounding_box': '-55,68,-48,71'} + """ + + if not hasattr(self, "_CMRparams"): + self._CMRparams = apifmt.Parameters("CMR") + # print(self._CMRparams) + # print(self._CMRparams.fmted_keys) + + # dictionary of optional CMR parameters + kwargs = {} + if self._orbit_number: + kwargs['orbit_number'] = self.orbit_number + + if self._CMRparams.fmted_keys == {}: + self._CMRparams.build_params( + dataset=self.dataset, + version=self._version, + start=self._start, + end=self._end, + extent_type=self.extent_type, + spatial_extent=self._spat_extent, + ) + + return self._CMRparams.fmted_keys + + @property + def reqparams(self): + """ + Display the required key:value pairs that will be submitted. It generates the dictionary if it does not already exist. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.reqparams + {'page_size': 10, 'page_num': 1} + + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + >>> reg_a.order_granules() + >>> reg_a.reqparams + {'page_size': 10, 'page_num': 1, 'request_mode': 'async', 'include_meta': 'Y'} + """ + + if not hasattr(self, "_reqparams"): + self._reqparams = apifmt.Parameters("required", reqtype="search") + self._reqparams.build_params() + + return self._reqparams.fmted_keys + + # @property + # DevQuestion: if I make this a property, I get a "dict" object is not callable when I try to give input kwargs... what approach should I be taking? + def subsetparams(self, **kwargs): + """ + Display the subsetting key:value pairs that will be submitted. It generates the dictionary if it does not already exist + and returns an empty dictionary if subsetting is set to False during ordering. + + Parameters + ---------- + **kwargs : key-value pairs + Additional parameters to be passed to the subsetter. + By default temporal and spatial subset keys are passed. + Acceptable key values are ['format','projection','projection_parameters','Coverage']. + At this time (2020-05), only variable ('Coverage') parameters will be automatically formatted. + + See Also + -------- + order_granules + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.subsetparams() + {'time': '2019-02-20T00:00:00,2019-02-28T23:59:59', 'bbox': '-55,68,-48,71'} + """ + if not hasattr(self, "_subsetparams"): + self._subsetparams = apifmt.Parameters("subset") + + if self._subsetparams == None and not kwargs: + return {} + else: + if self._subsetparams == None: + self._subsetparams = apifmt.Parameters("subset") + if self._geom_filepath is not None: + self._subsetparams.build_params( + geom_filepath=self._geom_filepath, + start=self._start, + end=self._end, + extent_type=self.extent_type, + spatial_extent=self._spat_extent, + **kwargs, + ) + else: + self._subsetparams.build_params( + start=self._start, + end=self._end, + extent_type=self.extent_type, + spatial_extent=self._spat_extent, + **kwargs, + ) + + return self._subsetparams.fmted_keys + + # DevGoal: add to tests + # DevGoal: add statements to the following vars properties to let the user know if they've got a mismatched source and vars type + @property + def order_vars(self): + """ + Return the order variables object. + This instance is generated when data is ordered from the NSIDC. + + See Also + -------- + variables.Variables + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + >>> reg_a.order_vars + + """ + + if not hasattr(self, "_order_vars"): + if self._source == "order": + # DevGoal: check for active session here + if hasattr(self, "_cust_options"): + self._order_vars = Variables( + self._source, + session=self._session, + dataset=self.dataset, + avail=self._cust_options["variables"], + ) + else: + self._order_vars = Variables( + self._source, + session=self._session, + dataset=self.dataset, + version=self._version, + ) + + # I think this is where property setters come in, and one should be used here? Right now order_vars.avail is only filled in + # if _cust_options exists when the class is initialized, but not if _cust_options is filled in prior to another call to order_vars + # if self._order_vars.avail == None and hasattr(self, '_cust_options'): + # print('got into the loop') + # self._order_vars.avail = self._cust_options['variables'] + + return self._order_vars + + @property + def file_vars(self): + """ + Return the file variables object. + This instance is generated when files are used to create the data object (not yet implemented). + + See Also + -------- + variables.Variables + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + >>> reg_a.file_vars + + """ + + if not hasattr(self, "_file_vars"): + if self._source == "file": + self._file_vars = Variables(self._source, dataset=self.dataset) + + return self._file_vars + + @property + def granules(self): + """ + Return the granules object, which provides the underlying funtionality for searching, ordering, + and downloading granules for the specified dataset. Users are encouraged to use the built in wrappers + rather than trying to access the granules object themselves. + + See Also + -------- + avail_granules + order_granules + download_granules + granules.Granules + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.granules + + """ + + if not hasattr(self, "_granules"): + self._granules = Granules() + elif self._granules == None: + self._granules = Granules() + + return self._granules + + # ---------------------------------------------------------------------- + # Methods - Get and display neatly information at the dataset level + + def dataset_summary_info(self): + """ + Display a summary of selected metadata for the specified version of the dataset + of interest (the collection). + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.dataset_summary_info() + dataset_id : ATLAS/ICESat-2 L3A Land Ice Height V002 + short_name : ATL06 + version_id : 002 + time_start : 2018-10-14T00:00:00.000Z + coordinate_system : CARTESIAN + summary : This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. + orbit_parameters : {'swath_width': '36.0', 'period': '94.29', 'inclination_angle': '92.0', 'number_of_orbits': '0.071428571', 'start_circular_latitude': '0.0'} + """ + if not hasattr(self, "_about_dataset"): + self._about_dataset = is2data(self._dset) + summ_keys = [ + "dataset_id", + "short_name", + "version_id", + "time_start", + "coordinate_system", + "summary", + "orbit_parameters", + ] + for key in summ_keys: + print(key, ": ", self._about_dataset["feed"]["entry"][-1][key]) + + def dataset_all_info(self): + """ + Display all metadata about the dataset of interest (the collection). + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.dataset_all_info() + {very long prettily-formatted dictionary output} + + """ + if not hasattr(self, "_about_dataset"): + self._about_dataset = is2data(self._dset) + pprint.pprint(self._about_dataset) + + def latest_version(self): + """ + Determine the most recent version available for the given dataset. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.latest_version() + '003' + """ + if not hasattr(self, "_about_dataset"): + self._about_dataset = is2ref.about_dataset(self._dset) + return max( + [entry["version_id"] for entry in self._about_dataset["feed"]["entry"]] + ) + + def show_custom_options(self, dictview=False): + """ + Display customization/subsetting options available for this dataset. + + Parameters + ---------- + dictview : boolean, default False + Show the variable portion of the custom options list as a dictionary with key:value + pairs representing variable:paths-to-variable rather than as a long list of full + variable paths. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + >>> reg_a.show_custom_options(dictview=True): + Subsetting options + [{'id': 'ICESAT2', + 'maxGransAsyncRequest': '2000', + 'maxGransSyncRequest': '100', + 'spatialSubsetting': 'true', + 'spatialSubsettingShapefile': 'true', + 'temporalSubsetting': 'true', + 'type': 'both'}] + Data File Formats (Reformatting Options) + ['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3'] + Reprojection Options + [] + Data File (Reformatting) Options Supporting Reprojection + ['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3', 'No reformatting'] + Data File (Reformatting) Options NOT Supporting Reprojection + [] + Data Variables (also Subsettable) + ['ancillary_data/atlas_sdp_gps_epoch', + 'ancillary_data/control', + 'ancillary_data/data_end_utc', + . + . + . + 'quality_assessment/gt3r/signal_selection_source_fraction_3'] + """ + headers = [ + "Subsetting options", + "Data File Formats (Reformatting Options)", + "Reprojection Options", + "Data File (Reformatting) Options Supporting Reprojection", + "Data File (Reformatting) Options NOT Supporting Reprojection", + "Data Variables (also Subsettable)", + ] + keys = [ + "options", + "fileformats", + "reprojectionONLY", + "formatreproj", + "noproj", + "variables", + ] + + try: + all(key in self._cust_options.keys() for key in keys) + except AttributeError or KeyError: + self._cust_options = is2ref._get_custom_options( + self._session, self.dataset, self._version + ) + + for h, k in zip(headers, keys): + print(h) + if k == "variables" and dictview: + vgrp, paths = Variables.parse_var_list(self._cust_options[k]) + pprint.pprint(vgrp) + else: + pprint.pprint(self._cust_options[k]) + + # ---------------------------------------------------------------------- + # Methods - Login and Granules (NSIDC-API) + + def earthdata_login(self, uid, email): + """ + Log in to NSIDC EarthData to access data. Generates the needed session and token for most + data searches and data ordering/download. + + Parameters + ---------- + uid : string + Earthdata login user ID + email : string + Email address. NSIDC will automatically send you emails about the status of your order. + + See Also + -------- + Earthdata.Earthdata + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + """ + + capability_url = f"https://n5eil02u.ecs.nsidc.org/egi/capabilities/{self.dataset}.{self._version}.xml" + self._session = Earthdata(uid, email, capability_url).login() + self._email = email + + # DevGoal: check to make sure the see also bits of the docstrings work properly in RTD + def avail_granules(self, ids=False, cycles=False, tracks=False): + """ + Obtain information about the available granules for the query + object's parameters. By default, a complete list of available granules is + obtained and stored in the object, but only summary information is returned. + Lists of granule IDs, cycles and RGTs can be obtained using the boolean triggers. + + Parameters + ---------- + ids : boolean, default False + Indicates whether the function should return a list of granule IDs. + + cycles : boolean, default False + Indicates whether the function should return a list of orbital cycles. + + tracks : boolean, default False + Indicates whether the function should return a list of RGTs. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.avail_granules() + {'Number of available granules': 4, + 'Average size of granules (MB)': 48.975419759750004, + 'Total size of all granules (MB)': 195.90167903900002} + + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.avail_granules(ids=True) + >>> reg_a.avail_granules(cycles=True) + ['02'] + >>> reg_a.avail_granules(tracks=True) + ['0841', '0849', '0902', '0910'] + """ + + # REFACTOR: add test to make sure there's a session + if not hasattr(self, "_granules"): + self.granules + try: + self.granules.avail + except AttributeError: + self.granules.get_avail(self.CMRparams, self.reqparams) + + if ids or cycles or tracks: + # list of outputs in order of ids, cycles, tracks + return granules.gran_IDs(self.granules.avail, ids=ids, + cycles=cycles, tracks=tracks) + else: + return granules.info(self.granules.avail) + + # DevGoal: display output to indicate number of granules successfully ordered (and number of errors) + # DevGoal: deal with subset=True for variables now, and make sure that if a variable subset Coverage kwarg is input it's successfully passed through all other functions even if this is the only one run. + def order_granules(self, verbose=False, subset=True, email=True, **kwargs): + """ + Place an order for the available granules for the query object. + + Parameters + ---------- + verbose : boolean, default False + Print out all feedback available from the order process. + Progress information is automatically printed regardless of the value of verbose. + subset : boolean, default True + Apply subsetting to the data order from the NSIDC, returning only data that meets the + subset parameters. Spatial and temporal subsetting based on the input parameters happens + by default when subset=True, but additional subsetting options are available. + Spatial subsetting returns all data that are within the area of interest (but not complete + granules. This eliminates false-positive granules returned by the metadata-level search) + email: boolean, default True + Have NSIDC auto-send order status email updates to indicate order status as pending/completed. + **kwargs : key-value pairs + Additional parameters to be passed to the subsetter. + By default temporal and spatial subset keys are passed. + Acceptable key values are ['format','projection','projection_parameters','Coverage']. + The variable 'Coverage' list should be constructed using the `order_vars.wanted` attribute of the object. + At this time (2020-05), only variable ('Coverage') parameters will be automatically formatted. + + See Also + -------- + granules.place_order + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + >>> reg_a.order_granules() + order ID: [###############] + [order status output] + error messages: + [if any were returned from the NSIDC subsetter, e.g. No data found that matched subset constraints.] + . + . + . + Retry request status is: complete + """ + + if not hasattr(self, "reqparams"): + self.reqparams + + if self._reqparams._reqtype == "search": + self._reqparams._reqtype = "download" + + if "email" in self._reqparams.fmted_keys.keys() or email == False: + self._reqparams.build_params(**self._reqparams.fmted_keys) + else: + self._reqparams.build_params( + **self._reqparams.fmted_keys, email=self._email + ) + + if subset is False: + self._subsetparams = None + elif ( + subset == True + and hasattr(self, "_subsetparams") + and self._subsetparams == None + ): + del self._subsetparams + + # REFACTOR: add checks here to see if the granules object has been created, and also if it already has a list of avail granules (if not, need to create one and add session) + if not hasattr(self, "_granules"): + self.granules + self._granules.place_order( + self.CMRparams, + self.reqparams, + self.subsetparams(**kwargs), + verbose, + subset, + session=self._session, + geom_filepath=self._geom_filepath, + ) + + # DevGoal: put back in the kwargs here so that people can just call download granules with subset=False! + def download_granules( + self, path, verbose=False, subset=True, restart=False, **kwargs + ): # , extract=False): + """ + Downloads the data ordered using order_granules. + + Parameters + ---------- + path : string + String with complete path to desired download location. + verbose : boolean, default False + Print out all feedback available from the order process. + Progress information is automatically printed regardless of the value of verbose. + subset : boolean, default True + Apply subsetting to the data order from the NSIDC, returning only data that meets the + subset parameters. Spatial and temporal subsetting based on the input parameters happens + by default when subset=True, but additional subsetting options are available. + Spatial subsetting returns all data that are within the area of interest (but not complete + granules. This eliminates false-positive granules returned by the metadata-level search) + restart: boolean, default false + If previous download was terminated unexpectedly. Run again with restart set to True to continue. + **kwargs : key-value pairs + Additional parameters to be passed to the subsetter. + By default temporal and spatial subset keys are passed. + Acceptable key values are ['format','projection','projection_parameters','Coverage']. + The variable 'Coverage' list should be constructed using the `order_vars.wanted` attribute of the object. + At this time (2020-05), only variable ('Coverage') parameters will be automatically formatted. + + See Also + -------- + granules.download + """ + """ + extract : boolean, default False + Unzip the downloaded granules. + + Examples + -------- + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a.earthdata_login(user_id,user_email) + Earthdata Login password: ········ + >>> reg_a.download_granules('/path/to/download/folder') + Beginning download of zipped output... + Data request [##########] of x order(s) is complete. + """ + + # if not os.path.exists(path): + # os.mkdir(path) + # os.chdir(path) + + if not hasattr(self, "_granules"): + self.granules + + if restart == True: + pass + else: + if ( + not hasattr(self._granules, "orderIDs") + or len(self._granules.orderIDs) == 0 + ): + self.order_granules(verbose=verbose, subset=subset, **kwargs) + + self._granules.download(verbose, path, session=self._session, restart=restart) + + # DevGoal: add testing? What do we test, and how, given this is a visualization. + # DevGoal(long term): modify this to accept additional inputs, etc. + # DevGoal: move this to it's own module for visualizing, etc. + # DevGoal: see Amy's data access notebook for a zoomed in map - implement here? + def visualize_spatial_extent( + self, + ): # additional args, basemap, zoom level, cmap, export + """ + Creates a map displaying the input spatial extent + + Examples + -------- + >>> icepyx.query.Query('ATL06','path/spatialfile.shp',['2019-02-22','2019-02-28']) + >>> reg_a.visualize_spatial_extent + [visual map output] + """ + + world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")) + f, ax = plt.subplots(1, figsize=(12, 6)) + world.plot(ax=ax, facecolor="lightgray", edgecolor="gray") + geospatial.geodataframe(self.extent_type, self._spat_extent).plot( + ax=ax, color="#FF8C00", alpha=0.7 + ) + plt.show() diff --git a/icepyx/core/validate_inputs.py b/icepyx/core/validate_inputs.py index eb1420608..0a4a30751 100644 --- a/icepyx/core/validate_inputs.py +++ b/icepyx/core/validate_inputs.py @@ -2,10 +2,12 @@ import os import warnings import geopandas as gpd +import numpy as np import icepyx.core.APIformatting as apifmt import icepyx.core.geospatial as geospatial + def dset_version(latest_vers, version): """ Check if the submitted dataset version is valid, and warn the user if a newer version is available. @@ -14,7 +16,7 @@ def dset_version(latest_vers, version): vers = latest_vers else: if isinstance(version, str): - assert int(version)>0, "Version number must be positive" + assert int(version) > 0, "Version number must be positive" vers_length = 3 vers = version.zfill(vers_length) else: @@ -23,35 +25,110 @@ def dset_version(latest_vers, version): if int(vers) < int(latest_vers): warnings.filterwarnings("always") warnings.warn("You are using an old version of this dataset") - + return vers -#DevGoal: clean up; turn into classes (see validate_inputs_classes.py) +def cycles(all_cycles, cycles): + """ + Check if the submitted cycle is valid, and warn the user if not available. + """ + if cycles is None: + cycle_list = all_cycles + else: + cycle_length = 2 + if isinstance(cycles, str): + assert int(cycles) > 0, "Cycle number must be positive" + cycle_list = [cycles.zfill(cycle_length)] + elif isinstance(cycles, list): + cycle_list = [] + for c in cycles: + assert int(c) > 0, "Cycle number must be positive" + cycle_list.append(c.zfill(cycle_length)) + else: + raise TypeError("Please enter the cycle number as a list or string") + + if not set(all_cycles) & set(cycle_list): + warnings.filterwarnings("always") + warnings.warn("Listed cycle is not presently available") + + return cycle_list + + +def tracks(all_tracks, tracks): + """ + Check if the submitted RGT is valid, and warn the user if not available. + """ + if tracks is None: + track_list = all_tracks + else: + track_length = 4 + if isinstance(tracks, str): + assert int(tracks) > 0, "Reference Ground Track must be positive" + track_list = [tracks.zfill(track_length)] + elif isinstance(tracks, list): + track_list = [] + for t in tracks: + assert int(t) > 0, "Reference Ground Track must be positive" + track_list.append(t.zfill(track_length)) + else: + raise TypeError( + "Please enter the Reference Ground Track as a list or string" + ) + + if not set(all_tracks) & set(track_list): + warnings.filterwarnings("always") + warnings.warn("Listed Reference Ground Track is not available") + + return track_list + + +# DevGoal: clean up; turn into classes (see validate_inputs_classes.py) def spatial(spatial_extent): """ - Validate the input spatial extent and return the needed parameters to the icesat2data object. + Validate the input spatial extent and return the needed parameters to the query object. """ if isinstance(spatial_extent, list): - #bounding box - if len(spatial_extent)==4 and all(type(i) in [int, float] for i in spatial_extent): + # bounding box + if len(spatial_extent) == 4 and all( + type(i) in [int, float] for i in spatial_extent + ): assert -90 <= spatial_extent[1] <= 90, "Invalid latitude value" assert -90 <= spatial_extent[3] <= 90, "Invalid latitude value" - assert -180 <= spatial_extent[0] <= 360, "Invalid longitude value" #tighten these ranges depending on actual allowed inputs - assert -180 <= spatial_extent[2] <= 360, "Invalid longitude value" - assert spatial_extent[0] <= spatial_extent[2], "Invalid bounding box longitudes" - assert spatial_extent[1] <= spatial_extent[3], "Invalid bounding box latitudes" + assert ( + -180 <= spatial_extent[0] <= 180 + ), "Invalid longitude value" # tighten these ranges depending on actual allowed inputs + assert -180 <= spatial_extent[2] <= 180, "Invalid longitude value" + if np.sign(spatial_extent[0]) != np.sign(spatial_extent[2]): + assert ( + spatial_extent[0] >= spatial_extent[2] + ), "Invalid bounding box longitudes" + else: + assert ( + spatial_extent[0] <= spatial_extent[2] + ), "Invalid bounding box longitudes" + assert ( + spatial_extent[1] <= spatial_extent[3] + ), "Invalid bounding box latitudes" _spat_extent = spatial_extent - extent_type = 'bounding_box' - - #user-entered polygon as list of lon, lat coordinate pairs + extent_type = "bounding_box" + + # user-entered polygon as list of lon, lat coordinate pairs elif all(type(i) in [list, tuple] for i in spatial_extent): - assert len(spatial_extent)>=4, "Your spatial extent polygon has too few vertices" - assert spatial_extent[0][0] == spatial_extent[-1][0], "Starting longitude doesn't match ending longitude" - assert spatial_extent[0][1] == spatial_extent[-1][1], "Starting latitude doesn't match ending latitude" - polygon = (','.join([str(c) for xy in spatial_extent for c in xy])).split(",") - - extent_type = 'polygon' + assert ( + len(spatial_extent) >= 4 + ), "Your spatial extent polygon has too few vertices" + assert ( + spatial_extent[0][0] == spatial_extent[-1][0] + ), "Starting longitude doesn't match ending longitude" + assert ( + spatial_extent[0][1] == spatial_extent[-1][1] + ), "Starting latitude doesn't match ending latitude" + polygon = (",".join([str(c) for xy in spatial_extent for c in xy])).split( + "," + ) + + extent_type = "polygon" polygon = [float(i) for i in polygon] gdf = geospatial.geodataframe(extent_type, polygon, file=False) @@ -62,13 +139,21 @@ def spatial(spatial_extent): # #DevGoal: properly format this input type (and any polygon type) so that it is clockwise (and only contains 1 pole)!! # warnings.warn("this type of input is not yet well handled and you may not be able to find data") - #user-entered polygon as a single list of lon and lat coordinates + # user-entered polygon as a single list of lon and lat coordinates elif all(type(i) in [int, float] for i in spatial_extent): - assert len(spatial_extent)>=8, "Your spatial extent polygon has too few vertices" - assert len(spatial_extent)%2 == 0, "Your spatial extent polygon list should have an even number of entries" - assert spatial_extent[0] == spatial_extent[-2], "Starting longitude doesn't match ending longitude" - assert spatial_extent[1] == spatial_extent[-1], "Starting latitude doesn't match ending latitude" - extent_type = 'polygon' + assert ( + len(spatial_extent) >= 8 + ), "Your spatial extent polygon has too few vertices" + assert ( + len(spatial_extent) % 2 == 0 + ), "Your spatial extent polygon list should have an even number of entries" + assert ( + spatial_extent[0] == spatial_extent[-2] + ), "Starting longitude doesn't match ending longitude" + assert ( + spatial_extent[1] == spatial_extent[-1] + ), "Starting latitude doesn't match ending latitude" + extent_type = "polygon" polygon = [float(i) for i in spatial_extent] gdf = geospatial.geodataframe(extent_type, polygon, file=False) @@ -76,70 +161,84 @@ def spatial(spatial_extent): # _spat_extent = polygon - else: - raise ValueError('Your spatial extent does not meet minimum input criteria') - - #DevGoal: write a test for this? - #make sure there is nothing set to _geom_filepath since its existence determines later steps - try: del _geom_filepath - except: UnboundLocalError - - #DevGoal: revisit this section + geospatial.geodataframe. There might be some good ways to combine the functionality in these checks with that + raise ValueError("Your spatial extent does not meet minimum input criteria") + + # DevGoal: write a test for this? + # make sure there is nothing set to _geom_filepath since its existence determines later steps + try: + del _geom_filepath + except: + UnboundLocalError + + # DevGoal: revisit this section + geospatial.geodataframe. There might be some good ways to combine the functionality in these checks with that elif isinstance(spatial_extent, str): - assert os.path.exists(spatial_extent), "Check that the path and filename of your geometry file are correct" - #DevGoal: more robust polygon inputting (see Bruce's code): correct for clockwise/counterclockwise coordinates, deal with simplification, etc. - if spatial_extent.split('.')[-1] in ['kml','shp','gpkg']: - extent_type = 'polygon' + assert os.path.exists( + spatial_extent + ), "Check that the path and filename of your geometry file are correct" + # DevGoal: more robust polygon inputting (see Bruce's code): correct for clockwise/counterclockwise coordinates, deal with simplification, etc. + if spatial_extent.split(".")[-1] in ["kml", "shp", "gpkg"]: + extent_type = "polygon" gdf = geospatial.geodataframe(extent_type, spatial_extent, file=True) # print(gdf.iloc[0].geometry) - #DevGoal: does the below line mandate that only the first polygon will be read? Perhaps we should require files containing only one polygon? - #RAPHAEL - It only selects the first polygon if there are multiple. Unless we can supply the CMR params with muliple polygon inputs we should probably req a single polygon. + # DevGoal: does the below line mandate that only the first polygon will be read? Perhaps we should require files containing only one polygon? + # RAPHAEL - It only selects the first polygon if there are multiple. Unless we can supply the CMR params with muliple polygon inputs we should probably req a single polygon. _spat_extent = gdf.iloc[0].geometry # _spat_extent = apifmt._fmt_polygon(spatial_extent) _geom_filepath = spatial_extent else: - raise TypeError('Input spatial extent file must be a kml, shp, or gpkg') + raise TypeError("Input spatial extent file must be a kml, shp, or gpkg") - #DevGoal: currently no specific test for this if statement... - if '_geom_filepath' not in locals(): _geom_filepath = None + # DevGoal: currently no specific test for this if statement... + if "_geom_filepath" not in locals(): + _geom_filepath = None return extent_type, _spat_extent, _geom_filepath + def temporal(date_range, start_time, end_time): """ - Validate the input temporal parameters and return the needed parameters to the icesat2data object. + Validate the input temporal parameters and return the needed parameters to the query object. """ if isinstance(date_range, list): - if len(date_range)==2: - _start = dt.datetime.strptime(date_range[0], '%Y-%m-%d') - _end = dt.datetime.strptime(date_range[1], '%Y-%m-%d') + if len(date_range) == 2: + _start = dt.datetime.strptime(date_range[0], "%Y-%m-%d") + _end = dt.datetime.strptime(date_range[1], "%Y-%m-%d") assert _start.date() <= _end.date(), "Your date range is invalid" else: - raise ValueError("Your date range list is the wrong length. It should have start and end dates only.") - -#DevGoal: accept more date/time input formats -# elif isinstance(date_range, date-time object): -# print('it is a date-time object') -# elif isinstance(date_range, dict): -# print('it is a dictionary. now check the keys for start and end dates') + raise ValueError( + "Your date range list is the wrong length. It should have start and end dates only." + ) + # DevGoal: accept more date/time input formats + # elif isinstance(date_range, date-time object): + # print('it is a date-time object') + # elif isinstance(date_range, dict): + # print('it is a dictionary. now check the keys for start and end dates') if start_time is None: - _start = _start.combine(_start.date(),dt.datetime.strptime('00:00:00', '%H:%M:%S').time()) + _start = _start.combine( + _start.date(), dt.datetime.strptime("00:00:00", "%H:%M:%S").time() + ) else: if isinstance(start_time, str): - _start = _start.combine(_start.date(),dt.datetime.strptime(start_time, '%H:%M:%S').time()) + _start = _start.combine( + _start.date(), dt.datetime.strptime(start_time, "%H:%M:%S").time() + ) else: raise TypeError("Please enter your start time as a string") if end_time is None: - _end = _start.combine(_end.date(),dt.datetime.strptime('23:59:59', '%H:%M:%S').time()) + _end = _start.combine( + _end.date(), dt.datetime.strptime("23:59:59", "%H:%M:%S").time() + ) else: if isinstance(end_time, str): - _end = _start.combine(_end.date(),dt.datetime.strptime(end_time, '%H:%M:%S').time()) + _end = _start.combine( + _end.date(), dt.datetime.strptime(end_time, "%H:%M:%S").time() + ) else: raise TypeError("Please enter your end time as a string") - + return _start, _end diff --git a/icepyx/core/variables.py b/icepyx/core/variables.py index 31e885a2b..faa2cc0ca 100644 --- a/icepyx/core/variables.py +++ b/icepyx/core/variables.py @@ -1,15 +1,14 @@ - import numpy as np import os import pprint import icepyx.core.is2ref as is2ref -#DEVGOAL: use h5py to simplify some of these tasks, if possible! +# DEVGOAL: use h5py to simplify some of these tasks, if possible! -#REFACTOR: class needs better docstrings -#DevNote: currently this class is not tested -class Variables(): +# REFACTOR: class needs better docstrings +# DevNote: currently this class is not tested +class Variables: """ Get, create, interact, and manipulate lists of variables and variable paths contained in ICESat-2 datasets. @@ -19,14 +18,14 @@ class Variables(): vartype : string One of ['order', 'file'] to indicate the source of the input variables. This field will be auto-populated when a variable object is created as an - attribute of an icesat2data object. + attribute of a query object. avail : dictionary, default None Dictionary (key:values) of available variable names (keys) and paths (values). wanted : dictionary, default None As avail, but for the desired list of variables session : requests.session object A session object authenticating the user to download data using their Earthdata login information. - The session object will automatically be passed from the icesat2data object if you + The session object will automatically be passed from the query object if you have successfully logged in there. dataset : string, default None Properly formatted string specifying a valid ICESat-2 dataset @@ -36,38 +35,45 @@ class Variables(): For vartype file, a path to a directory or single input source files (not yet implemented) """ - def __init__(self, vartype, avail=None, wanted=None, session=None, - dataset=None, version=None, source=None): - - assert vartype in ['order','file'], "Please submit a valid variables type flag" - + def __init__( + self, + vartype, + avail=None, + wanted=None, + session=None, + dataset=None, + version=None, + source=None, + ): + + assert vartype in ["order", "file"], "Please submit a valid variables type flag" + self._vartype = vartype self.dataset = dataset self._avail = avail self.wanted = wanted self._session = session - #DevGoal: put some more/robust checks here to assess validity of inputs + # DevGoal: put some more/robust checks here to assess validity of inputs - if self._vartype == 'order': + if self._vartype == "order": if self._avail == None: self._version = version - elif self._vartype == 'file': - #DevGoal: check that the list or string are valid dir/files + elif self._vartype == "file": + # DevGoal: check that the list or string are valid dir/files self.source = source - # @property # def wanted(self): # return self._wanted - + def avail(self, options=False, internal=False): """ Get the list of available variables and variable paths from the input dataset Examples -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') >>> reg_a.earthdata_login(user_id,user_email) Earthdata Login password: ········ >>> reg_a.order_vars.avail() @@ -83,23 +89,25 @@ def avail(self, options=False, internal=False): # if hasattr(self, '_avail'): # return self._avail # else: - if not hasattr(self, '_avail') or self._avail==None: - if self._vartype == 'order': - self._avail = is2ref._get_custom_options(self._session, self.dataset, self._version)['variables'] + if not hasattr(self, "_avail") or self._avail == None: + if self._vartype == "order": + self._avail = is2ref._get_custom_options( + self._session, self.dataset, self._version + )["variables"] - elif self._vartype == 'file': + elif self._vartype == "file": self._avail = None - if options==True: - vgrp, paths = self.parse_var_list(self._avail) + if options == True: + vgrp, paths = self.parse_var_list(self._avail) allpaths = [] [allpaths.extend(np.unique(np.array(paths[p]))) for p in range(len(paths))] allpaths = np.unique(allpaths) - if internal==False: - print('var_list inputs: ' + ', '.join(vgrp.keys())) - print('keyword_list and beam_list inputs: ' + ', '.join(allpaths)) - elif internal==True: - return vgrp,allpaths + if internal == False: + print("var_list inputs: " + ", ".join(vgrp.keys())) + print("keyword_list and beam_list inputs: " + ", ".join(allpaths)) + elif internal == True: + return vgrp, allpaths else: return self._avail @@ -110,7 +118,7 @@ def parse_var_list(varlist): Examples -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') >>> reg_a.earthdata_login(user_id,user_email) Earthdata Login password: ········ >>> var_dict, paths = reg_a.order_vars.parse_var_list(reg_a.order_vars.avail()) @@ -169,32 +177,33 @@ def parse_var_list(varlist): # create a dictionary of variable names and paths vgrp = {} - num = np.max([v.count('/') for v in varlist]) - # print('max needed: ' + str(num)) + num = np.max([v.count("/") for v in varlist]) + # print('max needed: ' + str(num)) paths = [[] for i in range(num)] - - #print(self._cust_options['variables']) + + # print(self._cust_options['variables']) for vn in varlist: - vpath,vkey = os.path.split(vn) - #print('path '+ vpath + ', key '+vkey) + vpath, vkey = os.path.split(vn) + # print('path '+ vpath + ', key '+vkey) if vkey not in vgrp.keys(): vgrp[vkey] = [vn] else: vgrp[vkey].append(vn) if vpath: - j=0 - for d in vpath.split('/'): - paths[j].append(d) - j=j+1 - for i in range(j,num): - paths[i].append('none') - i=i+1 - + j = 0 + for d in vpath.split("/"): + paths[j].append(d) + j = j + 1 + for i in range(j, num): + paths[i].append("none") + i = i + 1 + return vgrp, paths - - def _check_valid_lists(self, vgrp, allpaths, var_list=None, beam_list=None, keyword_list=None): + def _check_valid_lists( + self, vgrp, allpaths, var_list=None, beam_list=None, keyword_list=None + ): """ Check that the user is requesting valid paths and/or variables for their dataset. @@ -218,111 +227,115 @@ def _check_valid_lists(self, vgrp, allpaths, var_list=None, beam_list=None, keyw List of user requested variable path keywords """ - # check if the list of variables, if specified, are available in the dataset + # check if the list of variables, if specified, are available in the dataset if var_list is not None: for var_id in var_list: if var_id not in vgrp.keys(): - err_msg_varid = "Invalid variable name: " + var_id + '. ' - err_msg_varid = err_msg_varid + 'Please select from this list: ' - err_msg_varid = err_msg_varid + ', '.join(vgrp.keys()) + err_msg_varid = "Invalid variable name: " + var_id + ". " + err_msg_varid = err_msg_varid + "Please select from this list: " + err_msg_varid = err_msg_varid + ", ".join(vgrp.keys()) raise ValueError(err_msg_varid) - - #DevGoal: is there a way to not have this hard-coded in? + + # DevGoal: is there a way to not have this hard-coded in? # check if the list of beams, if specified, are available in the dataset - if self.dataset=='ATL09': - beam_avail = ['profile_'+str(i+1) for i in range(3)] + if self.dataset == "ATL09": + beam_avail = ["profile_" + str(i + 1) for i in range(3)] else: - beam_avail = ['gt'+str(i+1)+'l' for i in range(3)] - beam_avail = beam_avail + ['gt'+str(i+1)+'r' for i in range(3)] + beam_avail = ["gt" + str(i + 1) + "l" for i in range(3)] + beam_avail = beam_avail + ["gt" + str(i + 1) + "r" for i in range(3)] if beam_list is not None: for beam_id in beam_list: if beam_id not in beam_avail: - err_msg_beam = "Invalid beam_id: " + beam_id + '. ' - err_msg_beam = err_msg_beam + 'Please select from this list: ' - err_msg_beam = err_msg_beam + ', '.join(beam_avail) + err_msg_beam = "Invalid beam_id: " + beam_id + ". " + err_msg_beam = err_msg_beam + "Please select from this list: " + err_msg_beam = err_msg_beam + ", ".join(beam_avail) raise ValueError(err_msg_beam) - #check if keywords, if specified, are available for the dataset + # check if keywords, if specified, are available for the dataset if keyword_list is not None: for kw in keyword_list: -# assert kw in allpaths, "Invalid keyword. Please select from: " + ', '.join(allpaths) - - #DevGoal: update here to not include profiles/beams in the allpaths list + # assert kw in allpaths, "Invalid keyword. Please select from: " + ', '.join(allpaths) + + # DevGoal: update here to not include profiles/beams in the allpaths list if kw not in allpaths: - err_msg_kw = "Invalid keyword: " + kw + '. ' - err_msg_kw = err_msg_kw + 'Please select from this list: ' - err_msg_kw = err_msg_kw + ', '.join(np.unique(np.array(allpaths))) + err_msg_kw = "Invalid keyword: " + kw + ". " + err_msg_kw = err_msg_kw + "Please select from this list: " + err_msg_kw = err_msg_kw + ", ".join(np.unique(np.array(allpaths))) raise ValueError(err_msg_kw) - + def _get_sum_varlist(self, var_list, all_vars, defaults): - ''' + """ Get the list of variables to add or iterate through, depending on function inputs. - ''' + """ sum_varlist = [] - if defaults==True: + if defaults == True: sum_varlist = sum_varlist + is2ref._default_varlists(self.dataset) if var_list is not None: for vn in var_list: - if vn not in sum_varlist: sum_varlist.append(vn) - if len(sum_varlist)==0: + if vn not in sum_varlist: + sum_varlist.append(vn) + if len(sum_varlist) == 0: sum_varlist = all_vars return sum_varlist - + @staticmethod def _get_combined_list(beam_list, keyword_list): - ''' + """ Get the combined list of beams and/or keywords to add or iterate through. - ''' + """ combined_list = [] - if beam_list==None: - combined_list = keyword_list - elif keyword_list==None: + if beam_list == None: + combined_list = keyword_list + elif keyword_list == None: combined_list = beam_list else: combined_list = keyword_list + beam_list - + return combined_list @staticmethod def _iter_vars(sum_varlist, req_vars, vgrp): - ''' + """ Iterate through the wanted variables supplied in sum_varlist and add them and their paths to the list of requested variables. - ''' + """ for vn in sum_varlist: req_vars[vn] = vgrp[vn] return req_vars def _iter_paths(self, sum_varlist, req_vars, vgrp, beam_list, keyword_list): - ''' + """ Iterate through the list of paths for each variable in sum_varlist. Add the paths that have matches to combined_list the dictionary of requested variables. - ''' + """ combined_list = self._get_combined_list(beam_list, keyword_list) - + for vkey in sum_varlist: for vpath in vgrp[vkey]: - vpath_kws = vpath.split('/') - + vpath_kws = vpath.split("/") + try: for bkw in beam_list: if bkw in vpath_kws: for kw in keyword_list: if kw in vpath_kws: - if vkey not in req_vars: req_vars[vkey] = [] - if vpath not in req_vars[vkey]: req_vars[vkey].append(vpath) + if vkey not in req_vars: + req_vars[vkey] = [] + if vpath not in req_vars[vkey]: + req_vars[vkey].append(vpath) except TypeError: for kw in combined_list: if kw in vpath_kws: - if vkey not in req_vars: req_vars[vkey] = [] - if vpath not in req_vars[vkey]: req_vars[vkey].append(vpath) + if vkey not in req_vars: + req_vars[vkey] = [] + if vpath not in req_vars[vkey]: + req_vars[vkey].append(vpath) return req_vars - - #DevGoal: we can ultimately add an "interactive" trigger that will open the not-yet-made widget. Otherwise, it will use the var_list passed by the user/defaults + # DevGoal: we can ultimately add an "interactive" trigger that will open the not-yet-made widget. Otherwise, it will use the var_list passed by the user/defaults def append(self, defaults=False, var_list=None, beam_list=None, keyword_list=None): - ''' + """ Add to the list of desired variables using user specified beams and variable list. A pregenerated default variable list can be used by setting defaults to True. Note: The calibrated backscatter cab_prof is not in the default list for ATL09 @@ -356,7 +369,7 @@ def append(self, defaults=False, var_list=None, beam_list=None, keyword_list=Non Examples -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) >>> reg_a.earthdata_login(user_id,user_email) Earthdata Login password: ········ @@ -375,43 +388,58 @@ def append(self, defaults=False, var_list=None, beam_list=None, keyword_list=Non To add all variables and paths in ancillary_data >>> reg_a.order_vars.append(keyword_list=['ancillary_data']) - ''' + """ + + assert not ( + defaults == False + and var_list == None + and beam_list == None + and keyword_list == None + ), "You must enter parameters to add to a variable subset list. If you do not want to subset by variable, ensure your is2.subsetparams dictionary does not contain the key 'Coverage'." - assert not (defaults==False and var_list==None and beam_list==None and keyword_list==None), \ - "You must enter parameters to add to a variable subset list. If you do not want to subset by variable, ensure your is2.subsetparams dictionary does not contain the key 'Coverage'." - req_vars = {} # if not hasattr(self, 'avail') or self.avail==None: self.get_avail() - # vgrp, paths = self.parse_var_list(self.avail) + # vgrp, paths = self.parse_var_list(self.avail) # allpaths = [] # [allpaths.extend(np.unique(np.array(paths[p]))) for p in range(len(paths))] - vgrp,allpaths = self.avail(options=True, internal=True) + vgrp, allpaths = self.avail(options=True, internal=True) self._check_valid_lists(vgrp, allpaths, var_list, beam_list, keyword_list) - #add the mandatory variables to the data object - nec_varlist = ['sc_orient','sc_orient_time','atlas_sdp_gps_epoch','data_start_utc','data_end_utc', - 'granule_start_utc','granule_end_utc','start_delta_time','end_delta_time'] - - if not hasattr(self, 'wanted') or self.wanted==None: + # add the mandatory variables to the data object + nec_varlist = [ + "sc_orient", + "sc_orient_time", + "atlas_sdp_gps_epoch", + "data_start_utc", + "data_end_utc", + "granule_start_utc", + "granule_end_utc", + "start_delta_time", + "end_delta_time", + ] + + if not hasattr(self, "wanted") or self.wanted == None: for varid in nec_varlist: req_vars[varid] = vgrp[varid] self.wanted = req_vars - #DEVGOAL: add a secondary var list to include uncertainty/error information for lower level data if specific data variables have been specified... + # DEVGOAL: add a secondary var list to include uncertainty/error information for lower level data if specific data variables have been specified... - #generate a list of variable names to include, depending on user input + # generate a list of variable names to include, depending on user input sum_varlist = self._get_sum_varlist(var_list, vgrp.keys(), defaults) - - #Case only variables (but not keywords or beams) are specified - if beam_list==None and keyword_list==None: + + # Case only variables (but not keywords or beams) are specified + if beam_list == None and keyword_list == None: req_vars.update(self._iter_vars(sum_varlist, req_vars, vgrp)) - - #Case a beam and/or keyword list is specified (with or without variables) + + # Case a beam and/or keyword list is specified (with or without variables) else: - req_vars.update(self._iter_paths(sum_varlist, req_vars, vgrp, beam_list, keyword_list)) - + req_vars.update( + self._iter_paths(sum_varlist, req_vars, vgrp, beam_list, keyword_list) + ) + # update the data object variables for vkey in req_vars.keys(): # add all matching keys and paths for new variables @@ -419,12 +447,12 @@ def append(self, defaults=False, var_list=None, beam_list=None, keyword_list=Non self.wanted[vkey] = req_vars[vkey] else: for vpath in req_vars[vkey]: - if vpath not in self.wanted[vkey]: self.wanted[vkey].append(vpath) - - - #DevGoal: we can ultimately add an "interactive" trigger that will open the not-yet-made widget. Otherwise, it will use the var_list passed by the user/defaults + if vpath not in self.wanted[vkey]: + self.wanted[vkey].append(vpath) + + # DevGoal: we can ultimately add an "interactive" trigger that will open the not-yet-made widget. Otherwise, it will use the var_list passed by the user/defaults def remove(self, all=False, var_list=None, beam_list=None, keyword_list=None): - ''' + """ Remove the variables and paths from the wanted list using user specified beam, keyword, and variable lists. @@ -454,7 +482,7 @@ def remove(self, all=False, var_list=None, beam_list=None, keyword_list=None): Examples -------- - >>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) + >>> reg_a = icepyx.query.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) >>> reg_a.earthdata_login(user_id,user_email) Earthdata Login password: ········ @@ -473,50 +501,58 @@ def remove(self, all=False, var_list=None, beam_list=None, keyword_list=None): To remove all variables and paths in ancillary_data >>> reg_a.order_vars.remove(keyword_list=['ancillary_data']) - ''' + """ - if not hasattr(self, 'wanted') or self.wanted==None: - raise ValueError("You must construct a wanted variable list in order to remove values from it.") + if not hasattr(self, "wanted") or self.wanted == None: + raise ValueError( + "You must construct a wanted variable list in order to remove values from it." + ) - assert not (all==False and var_list==None and beam_list==None and keyword_list==None), \ - "You must specify which variables/paths/beams you would like to remove from your wanted list." + assert not ( + all == False + and var_list == None + and beam_list == None + and keyword_list == None + ), "You must specify which variables/paths/beams you would like to remove from your wanted list." - # if not hasattr(self, 'avail'): self.get_avail() - # vgrp, paths = self.parse_var_list(self.avail) - # # vgrp, paths = self.parse_var_list(self._cust_options['variables']) + # vgrp, paths = self.parse_var_list(self.avail) + # # vgrp, paths = self.parse_var_list(self._cust_options['variables']) # allpaths = [] # [allpaths.extend(np.unique(np.array(paths[p]))) for p in range(len(paths))] # allpaths = np.unique(allpaths) # self._check_valid_lists(vgrp, allpaths, var_list, beam_list, keyword_list) - if all==True: - try: self.wanted=None + if all == True: + try: + self.wanted = None except NameError: pass - + else: - #Case only variables (but not keywords or beams) are specified - if beam_list==None and keyword_list==None: + # Case only variables (but not keywords or beams) are specified + if beam_list == None and keyword_list == None: for vn in var_list: - try: del self.wanted[vn] - except KeyError: pass - - - #DevGoal: Do we want to enable the user to remove mandatory variables (how it's written now)? - #Case a beam and/or keyword list is specified (with or without variables) - else: + try: + del self.wanted[vn] + except KeyError: + pass + + # DevGoal: Do we want to enable the user to remove mandatory variables (how it's written now)? + # Case a beam and/or keyword list is specified (with or without variables) + else: combined_list = self._get_combined_list(beam_list, keyword_list) - if var_list==None: var_list=self.wanted.keys() - + if var_list == None: + var_list = self.wanted.keys() + # nec_varlist = ['sc_orient','atlas_sdp_gps_epoch','data_start_utc','data_end_utc', # 'granule_start_utc','granule_end_utc','start_delta_time','end_delta_time'] - for vkey in tuple(var_list): #self.wanted.keys()): + for vkey in tuple(var_list): # self.wanted.keys()): for vpath in tuple(self.wanted[vkey]): - vpath_kws = vpath.split('/') - + vpath_kws = vpath.split("/") + try: for bkw in beam_list: if bkw in vpath_kws: @@ -529,9 +565,8 @@ def remove(self, all=False, var_list=None, beam_list=None, keyword_list=None): if kw in vpath_kws and vkey in var_list: self.wanted[vkey].remove(vpath) - try: - if self.wanted[vkey] == []: del self.wanted[vkey] + if self.wanted[vkey] == []: + del self.wanted[vkey] except KeyError: - pass - \ No newline at end of file + pass diff --git a/icepyx/tests/behind_NSIDC_API_login.py b/icepyx/tests/behind_NSIDC_API_login.py index 7e34fda77..9d4bb96df 100644 --- a/icepyx/tests/behind_NSIDC_API_login.py +++ b/icepyx/tests/behind_NSIDC_API_login.py @@ -1,640 +1,659 @@ -from icepyx import is2class as ipd +import icepyx as ipx import pytest import warnings -#from unittest.mock import patch -#import mock -#import builtins -#import getpass + +# from unittest.mock import patch +# import mock +# import builtins +# import getpass import os -#test avail data and subsetting success for each input type (kml, shp, list of coords, bbox) -#check that agent key is added in event of no subsetting -#check that downloaded data is subset +# test avail data and subsetting success for each input type (kml, shp, list of coords, bbox) +# check that agent key is added in event of no subsetting +# check that downloaded data is subset + @pytest.fixture -def reg_a(scope='module'): - return ipd.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-22','2019-02-28']) +def reg_a(scope="module"): + return ipx.Query("ATL06", [-55, 68, -48, 71], ["2019-02-22", "2019-02-28"]) + + +# @patch('my_module.__get_input', return_value='y') -#@patch('my_module.__get_input', return_value='y') @pytest.fixture -def session(reg_a, scope='module'): - return reg_a._start_earthdata_session('icepyx_devteam', 'icepyx.dev@gmail.com', os.getenv('NSIDC_LOGIN')) +def session(reg_a, scope="module"): + return reg_a._start_earthdata_session( + "icepyx_devteam", "icepyx.dev@gmail.com", os.getenv("NSIDC_LOGIN") + ) + -#QUESTION: should we be testing to make sure the session starts? If so, how? The below doesn't work because the 'requests.sessions.Session' isn't recognized... is this a case where I'd need to have a mock session to compare it to? +# QUESTION: should we be testing to make sure the session starts? If so, how? The below doesn't work because the 'requests.sessions.Session' isn't recognized... is this a case where I'd need to have a mock session to compare it to? # def test_earthdata_session_started(session): # assert isinstance(session, 'requests.sessions.Session') import icepyx.core.is2ref as is2ref + ########## _get_custom_options ########## -#TestQuestion: should this use a mock rather than an actual call to NSIDC? -#TestQuestion: is there a better way to deal with this than having the super long dictionary copied here? +# TestQuestion: should this use a mock rather than an actual call to NSIDC? +# TestQuestion: is there a better way to deal with this than having the super long dictionary copied here? def test_get_custom_options_output(session): - obs = is2ref._get_custom_options(session, 'ATL06','002') - exp = {'options' : [{'id': 'ICESAT2', - 'maxGransAsyncRequest': '2000', - 'maxGransSyncRequest': '100', - 'spatialSubsetting': 'true', - 'spatialSubsettingShapefile': 'true', - 'temporalSubsetting': 'true', - 'type': 'both'}], - 'fileformats' : ['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3'], - 'formatreproj' : [], - 'reprojectionONLY' : ['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3', 'No reformatting'], - 'noproj' : [], - 'variables' : - ['ancillary_data/atlas_sdp_gps_epoch', - 'ancillary_data/control', - 'ancillary_data/data_end_utc', - 'ancillary_data/data_start_utc', - 'ancillary_data/end_cycle', - 'ancillary_data/end_delta_time', - 'ancillary_data/end_geoseg', - 'ancillary_data/end_gpssow', - 'ancillary_data/end_gpsweek', - 'ancillary_data/end_orbit', - 'ancillary_data/end_region', - 'ancillary_data/end_rgt', - 'ancillary_data/granule_end_utc', - 'ancillary_data/granule_start_utc', - 'ancillary_data/qa_at_interval', - 'ancillary_data/release', - 'ancillary_data/start_cycle', - 'ancillary_data/start_delta_time', - 'ancillary_data/start_geoseg', - 'ancillary_data/start_gpssow', - 'ancillary_data/start_gpsweek', - 'ancillary_data/start_orbit', - 'ancillary_data/start_region', - 'ancillary_data/start_rgt', - 'ancillary_data/version', - 'ancillary_data/land_ice/dt_hist', - 'ancillary_data/land_ice/fit_maxiter', - 'ancillary_data/land_ice/fpb_maxiter', - 'ancillary_data/land_ice/maxiter', - 'ancillary_data/land_ice/max_res_ids', - 'ancillary_data/land_ice/min_dist', - 'ancillary_data/land_ice/min_gain_th', - 'ancillary_data/land_ice/min_n_pe', - 'ancillary_data/land_ice/min_n_sel', - 'ancillary_data/land_ice/min_signal_conf', - 'ancillary_data/land_ice/n_hist', - 'ancillary_data/land_ice/nhist_bins', - 'ancillary_data/land_ice/n_sigmas', - 'ancillary_data/land_ice/proc_interval', - 'ancillary_data/land_ice/rbin_width', - 'ancillary_data/land_ice/sigma_beam', - 'ancillary_data/land_ice/sigma_tx', - 'ancillary_data/land_ice/t_dead', - 'ancillary_data/land_ice/win_nsig', - 'gt1l/land_ice_segments/atl06_quality_summary', - 'gt1l/land_ice_segments/delta_time', - 'gt1l/land_ice_segments/h_li', - 'gt1l/land_ice_segments/h_li_sigma', - 'gt1l/land_ice_segments/latitude', - 'gt1l/land_ice_segments/longitude', - 'gt1l/land_ice_segments/segment_id', - 'gt1l/land_ice_segments/sigma_geo_h', - 'gt1l/land_ice_segments/bias_correction/fpb_mean_corr', - 'gt1l/land_ice_segments/bias_correction/fpb_mean_corr_sigma', - 'gt1l/land_ice_segments/bias_correction/fpb_med_corr', - 'gt1l/land_ice_segments/bias_correction/fpb_med_corr_sigma', - 'gt1l/land_ice_segments/bias_correction/fpb_n_corr', - 'gt1l/land_ice_segments/bias_correction/med_r_fit', - 'gt1l/land_ice_segments/bias_correction/tx_mean_corr', - 'gt1l/land_ice_segments/bias_correction/tx_med_corr', - 'gt1l/land_ice_segments/dem/dem_flag', - 'gt1l/land_ice_segments/dem/dem_h', - 'gt1l/land_ice_segments/dem/geoid_h', - 'gt1l/land_ice_segments/fit_statistics/dh_fit_dx', - 'gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma', - 'gt1l/land_ice_segments/fit_statistics/dh_fit_dy', - 'gt1l/land_ice_segments/fit_statistics/h_expected_rms', - 'gt1l/land_ice_segments/fit_statistics/h_mean', - 'gt1l/land_ice_segments/fit_statistics/h_rms_misfit', - 'gt1l/land_ice_segments/fit_statistics/h_robust_sprd', - 'gt1l/land_ice_segments/fit_statistics/n_fit_photons', - 'gt1l/land_ice_segments/fit_statistics/n_seg_pulses', - 'gt1l/land_ice_segments/fit_statistics/sigma_h_mean', - 'gt1l/land_ice_segments/fit_statistics/signal_selection_source', - 'gt1l/land_ice_segments/fit_statistics/signal_selection_source_status', - 'gt1l/land_ice_segments/fit_statistics/snr', - 'gt1l/land_ice_segments/fit_statistics/snr_significance', - 'gt1l/land_ice_segments/fit_statistics/w_surface_window_final', - 'gt1l/land_ice_segments/geophysical/bckgrd', - 'gt1l/land_ice_segments/geophysical/bsnow_conf', - 'gt1l/land_ice_segments/geophysical/bsnow_h', - 'gt1l/land_ice_segments/geophysical/bsnow_od', - 'gt1l/land_ice_segments/geophysical/cloud_flg_asr', - 'gt1l/land_ice_segments/geophysical/cloud_flg_atm', - 'gt1l/land_ice_segments/geophysical/dac', - 'gt1l/land_ice_segments/geophysical/e_bckgrd', - 'gt1l/land_ice_segments/geophysical/msw_flag', - 'gt1l/land_ice_segments/geophysical/neutat_delay_total', - 'gt1l/land_ice_segments/geophysical/r_eff', - 'gt1l/land_ice_segments/geophysical/solar_azimuth', - 'gt1l/land_ice_segments/geophysical/solar_elevation', - 'gt1l/land_ice_segments/geophysical/tide_earth', - 'gt1l/land_ice_segments/geophysical/tide_equilibrium', - 'gt1l/land_ice_segments/geophysical/tide_load', - 'gt1l/land_ice_segments/geophysical/tide_ocean', - 'gt1l/land_ice_segments/geophysical/tide_pole', - 'gt1l/land_ice_segments/ground_track/ref_azimuth', - 'gt1l/land_ice_segments/ground_track/ref_coelv', - 'gt1l/land_ice_segments/ground_track/seg_azimuth', - 'gt1l/land_ice_segments/ground_track/sigma_geo_at', - 'gt1l/land_ice_segments/ground_track/sigma_geo_xt', - 'gt1l/land_ice_segments/ground_track/x_atc', - 'gt1l/land_ice_segments/ground_track/y_atc', - 'gt1l/residual_histogram/bckgrd_per_bin', - 'gt1l/residual_histogram/count', - 'gt1l/residual_histogram/delta_time', - 'gt1l/residual_histogram/dh', - 'gt1l/residual_histogram/ds_segment_id', - 'gt1l/residual_histogram/lat_mean', - 'gt1l/residual_histogram/lon_mean', - 'gt1l/residual_histogram/pulse_count', - 'gt1l/residual_histogram/segment_id_list', - 'gt1l/residual_histogram/x_atc_mean', - 'gt1l/segment_quality/delta_time', - 'gt1l/segment_quality/record_number', - 'gt1l/segment_quality/reference_pt_lat', - 'gt1l/segment_quality/reference_pt_lon', - 'gt1l/segment_quality/segment_id', - 'gt1l/segment_quality/signal_selection_source', - 'gt1l/segment_quality/signal_selection_status/signal_selection_status_all', - 'gt1l/segment_quality/signal_selection_status/signal_selection_status_backup', - 'gt1l/segment_quality/signal_selection_status/signal_selection_status_confident', - 'gt1r/land_ice_segments/atl06_quality_summary', - 'gt1r/land_ice_segments/delta_time', - 'gt1r/land_ice_segments/h_li', - 'gt1r/land_ice_segments/h_li_sigma', - 'gt1r/land_ice_segments/latitude', - 'gt1r/land_ice_segments/longitude', - 'gt1r/land_ice_segments/segment_id', - 'gt1r/land_ice_segments/sigma_geo_h', - 'gt1r/land_ice_segments/bias_correction/fpb_mean_corr', - 'gt1r/land_ice_segments/bias_correction/fpb_mean_corr_sigma', - 'gt1r/land_ice_segments/bias_correction/fpb_med_corr', - 'gt1r/land_ice_segments/bias_correction/fpb_med_corr_sigma', - 'gt1r/land_ice_segments/bias_correction/fpb_n_corr', - 'gt1r/land_ice_segments/bias_correction/med_r_fit', - 'gt1r/land_ice_segments/bias_correction/tx_mean_corr', - 'gt1r/land_ice_segments/bias_correction/tx_med_corr', - 'gt1r/land_ice_segments/dem/dem_flag', - 'gt1r/land_ice_segments/dem/dem_h', - 'gt1r/land_ice_segments/dem/geoid_h', - 'gt1r/land_ice_segments/fit_statistics/dh_fit_dx', - 'gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma', - 'gt1r/land_ice_segments/fit_statistics/dh_fit_dy', - 'gt1r/land_ice_segments/fit_statistics/h_expected_rms', - 'gt1r/land_ice_segments/fit_statistics/h_mean', - 'gt1r/land_ice_segments/fit_statistics/h_rms_misfit', - 'gt1r/land_ice_segments/fit_statistics/h_robust_sprd', - 'gt1r/land_ice_segments/fit_statistics/n_fit_photons', - 'gt1r/land_ice_segments/fit_statistics/n_seg_pulses', - 'gt1r/land_ice_segments/fit_statistics/sigma_h_mean', - 'gt1r/land_ice_segments/fit_statistics/signal_selection_source', - 'gt1r/land_ice_segments/fit_statistics/signal_selection_source_status', - 'gt1r/land_ice_segments/fit_statistics/snr', - 'gt1r/land_ice_segments/fit_statistics/snr_significance', - 'gt1r/land_ice_segments/fit_statistics/w_surface_window_final', - 'gt1r/land_ice_segments/geophysical/bckgrd', - 'gt1r/land_ice_segments/geophysical/bsnow_conf', - 'gt1r/land_ice_segments/geophysical/bsnow_h', - 'gt1r/land_ice_segments/geophysical/bsnow_od', - 'gt1r/land_ice_segments/geophysical/cloud_flg_asr', - 'gt1r/land_ice_segments/geophysical/cloud_flg_atm', - 'gt1r/land_ice_segments/geophysical/dac', - 'gt1r/land_ice_segments/geophysical/e_bckgrd', - 'gt1r/land_ice_segments/geophysical/msw_flag', - 'gt1r/land_ice_segments/geophysical/neutat_delay_total', - 'gt1r/land_ice_segments/geophysical/r_eff', - 'gt1r/land_ice_segments/geophysical/solar_azimuth', - 'gt1r/land_ice_segments/geophysical/solar_elevation', - 'gt1r/land_ice_segments/geophysical/tide_earth', - 'gt1r/land_ice_segments/geophysical/tide_equilibrium', - 'gt1r/land_ice_segments/geophysical/tide_load', - 'gt1r/land_ice_segments/geophysical/tide_ocean', - 'gt1r/land_ice_segments/geophysical/tide_pole', - 'gt1r/land_ice_segments/ground_track/ref_azimuth', - 'gt1r/land_ice_segments/ground_track/ref_coelv', - 'gt1r/land_ice_segments/ground_track/seg_azimuth', - 'gt1r/land_ice_segments/ground_track/sigma_geo_at', - 'gt1r/land_ice_segments/ground_track/sigma_geo_xt', - 'gt1r/land_ice_segments/ground_track/x_atc', - 'gt1r/land_ice_segments/ground_track/y_atc', - 'gt1r/residual_histogram/bckgrd_per_bin', - 'gt1r/residual_histogram/count', - 'gt1r/residual_histogram/delta_time', - 'gt1r/residual_histogram/dh', - 'gt1r/residual_histogram/ds_segment_id', - 'gt1r/residual_histogram/lat_mean', - 'gt1r/residual_histogram/lon_mean', - 'gt1r/residual_histogram/pulse_count', - 'gt1r/residual_histogram/segment_id_list', - 'gt1r/residual_histogram/x_atc_mean', - 'gt1r/segment_quality/delta_time', - 'gt1r/segment_quality/record_number', - 'gt1r/segment_quality/reference_pt_lat', - 'gt1r/segment_quality/reference_pt_lon', - 'gt1r/segment_quality/segment_id', - 'gt1r/segment_quality/signal_selection_source', - 'gt1r/segment_quality/signal_selection_status/signal_selection_status_all', - 'gt1r/segment_quality/signal_selection_status/signal_selection_status_backup', - 'gt1r/segment_quality/signal_selection_status/signal_selection_status_confident', - 'gt2l/land_ice_segments/atl06_quality_summary', - 'gt2l/land_ice_segments/delta_time', - 'gt2l/land_ice_segments/h_li', - 'gt2l/land_ice_segments/h_li_sigma', - 'gt2l/land_ice_segments/latitude', - 'gt2l/land_ice_segments/longitude', - 'gt2l/land_ice_segments/segment_id', - 'gt2l/land_ice_segments/sigma_geo_h', - 'gt2l/land_ice_segments/bias_correction/fpb_mean_corr', - 'gt2l/land_ice_segments/bias_correction/fpb_mean_corr_sigma', - 'gt2l/land_ice_segments/bias_correction/fpb_med_corr', - 'gt2l/land_ice_segments/bias_correction/fpb_med_corr_sigma', - 'gt2l/land_ice_segments/bias_correction/fpb_n_corr', - 'gt2l/land_ice_segments/bias_correction/med_r_fit', - 'gt2l/land_ice_segments/bias_correction/tx_mean_corr', - 'gt2l/land_ice_segments/bias_correction/tx_med_corr', - 'gt2l/land_ice_segments/dem/dem_flag', - 'gt2l/land_ice_segments/dem/dem_h', - 'gt2l/land_ice_segments/dem/geoid_h', - 'gt2l/land_ice_segments/fit_statistics/dh_fit_dx', - 'gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma', - 'gt2l/land_ice_segments/fit_statistics/dh_fit_dy', - 'gt2l/land_ice_segments/fit_statistics/h_expected_rms', - 'gt2l/land_ice_segments/fit_statistics/h_mean', - 'gt2l/land_ice_segments/fit_statistics/h_rms_misfit', - 'gt2l/land_ice_segments/fit_statistics/h_robust_sprd', - 'gt2l/land_ice_segments/fit_statistics/n_fit_photons', - 'gt2l/land_ice_segments/fit_statistics/n_seg_pulses', - 'gt2l/land_ice_segments/fit_statistics/sigma_h_mean', - 'gt2l/land_ice_segments/fit_statistics/signal_selection_source', - 'gt2l/land_ice_segments/fit_statistics/signal_selection_source_status', - 'gt2l/land_ice_segments/fit_statistics/snr', - 'gt2l/land_ice_segments/fit_statistics/snr_significance', - 'gt2l/land_ice_segments/fit_statistics/w_surface_window_final', - 'gt2l/land_ice_segments/geophysical/bckgrd', - 'gt2l/land_ice_segments/geophysical/bsnow_conf', - 'gt2l/land_ice_segments/geophysical/bsnow_h', - 'gt2l/land_ice_segments/geophysical/bsnow_od', - 'gt2l/land_ice_segments/geophysical/cloud_flg_asr', - 'gt2l/land_ice_segments/geophysical/cloud_flg_atm', - 'gt2l/land_ice_segments/geophysical/dac', - 'gt2l/land_ice_segments/geophysical/e_bckgrd', - 'gt2l/land_ice_segments/geophysical/msw_flag', - 'gt2l/land_ice_segments/geophysical/neutat_delay_total', - 'gt2l/land_ice_segments/geophysical/r_eff', - 'gt2l/land_ice_segments/geophysical/solar_azimuth', - 'gt2l/land_ice_segments/geophysical/solar_elevation', - 'gt2l/land_ice_segments/geophysical/tide_earth', - 'gt2l/land_ice_segments/geophysical/tide_equilibrium', - 'gt2l/land_ice_segments/geophysical/tide_load', - 'gt2l/land_ice_segments/geophysical/tide_ocean', - 'gt2l/land_ice_segments/geophysical/tide_pole', - 'gt2l/land_ice_segments/ground_track/ref_azimuth', - 'gt2l/land_ice_segments/ground_track/ref_coelv', - 'gt2l/land_ice_segments/ground_track/seg_azimuth', - 'gt2l/land_ice_segments/ground_track/sigma_geo_at', - 'gt2l/land_ice_segments/ground_track/sigma_geo_xt', - 'gt2l/land_ice_segments/ground_track/x_atc', - 'gt2l/land_ice_segments/ground_track/y_atc', - 'gt2l/residual_histogram/bckgrd_per_bin', - 'gt2l/residual_histogram/count', - 'gt2l/residual_histogram/delta_time', - 'gt2l/residual_histogram/dh', - 'gt2l/residual_histogram/ds_segment_id', - 'gt2l/residual_histogram/lat_mean', - 'gt2l/residual_histogram/lon_mean', - 'gt2l/residual_histogram/pulse_count', - 'gt2l/residual_histogram/segment_id_list', - 'gt2l/residual_histogram/x_atc_mean', - 'gt2l/segment_quality/delta_time', - 'gt2l/segment_quality/record_number', - 'gt2l/segment_quality/reference_pt_lat', - 'gt2l/segment_quality/reference_pt_lon', - 'gt2l/segment_quality/segment_id', - 'gt2l/segment_quality/signal_selection_source', - 'gt2l/segment_quality/signal_selection_status/signal_selection_status_all', - 'gt2l/segment_quality/signal_selection_status/signal_selection_status_backup', - 'gt2l/segment_quality/signal_selection_status/signal_selection_status_confident', - 'gt2r/land_ice_segments/atl06_quality_summary', - 'gt2r/land_ice_segments/delta_time', - 'gt2r/land_ice_segments/h_li', - 'gt2r/land_ice_segments/h_li_sigma', - 'gt2r/land_ice_segments/latitude', - 'gt2r/land_ice_segments/longitude', - 'gt2r/land_ice_segments/segment_id', - 'gt2r/land_ice_segments/sigma_geo_h', - 'gt2r/land_ice_segments/bias_correction/fpb_mean_corr', - 'gt2r/land_ice_segments/bias_correction/fpb_mean_corr_sigma', - 'gt2r/land_ice_segments/bias_correction/fpb_med_corr', - 'gt2r/land_ice_segments/bias_correction/fpb_med_corr_sigma', - 'gt2r/land_ice_segments/bias_correction/fpb_n_corr', - 'gt2r/land_ice_segments/bias_correction/med_r_fit', - 'gt2r/land_ice_segments/bias_correction/tx_mean_corr', - 'gt2r/land_ice_segments/bias_correction/tx_med_corr', - 'gt2r/land_ice_segments/dem/dem_flag', - 'gt2r/land_ice_segments/dem/dem_h', - 'gt2r/land_ice_segments/dem/geoid_h', - 'gt2r/land_ice_segments/fit_statistics/dh_fit_dx', - 'gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma', - 'gt2r/land_ice_segments/fit_statistics/dh_fit_dy', - 'gt2r/land_ice_segments/fit_statistics/h_expected_rms', - 'gt2r/land_ice_segments/fit_statistics/h_mean', - 'gt2r/land_ice_segments/fit_statistics/h_rms_misfit', - 'gt2r/land_ice_segments/fit_statistics/h_robust_sprd', - 'gt2r/land_ice_segments/fit_statistics/n_fit_photons', - 'gt2r/land_ice_segments/fit_statistics/n_seg_pulses', - 'gt2r/land_ice_segments/fit_statistics/sigma_h_mean', - 'gt2r/land_ice_segments/fit_statistics/signal_selection_source', - 'gt2r/land_ice_segments/fit_statistics/signal_selection_source_status', - 'gt2r/land_ice_segments/fit_statistics/snr', - 'gt2r/land_ice_segments/fit_statistics/snr_significance', - 'gt2r/land_ice_segments/fit_statistics/w_surface_window_final', - 'gt2r/land_ice_segments/geophysical/bckgrd', - 'gt2r/land_ice_segments/geophysical/bsnow_conf', - 'gt2r/land_ice_segments/geophysical/bsnow_h', - 'gt2r/land_ice_segments/geophysical/bsnow_od', - 'gt2r/land_ice_segments/geophysical/cloud_flg_asr', - 'gt2r/land_ice_segments/geophysical/cloud_flg_atm', - 'gt2r/land_ice_segments/geophysical/dac', - 'gt2r/land_ice_segments/geophysical/e_bckgrd', - 'gt2r/land_ice_segments/geophysical/msw_flag', - 'gt2r/land_ice_segments/geophysical/neutat_delay_total', - 'gt2r/land_ice_segments/geophysical/r_eff', - 'gt2r/land_ice_segments/geophysical/solar_azimuth', - 'gt2r/land_ice_segments/geophysical/solar_elevation', - 'gt2r/land_ice_segments/geophysical/tide_earth', - 'gt2r/land_ice_segments/geophysical/tide_equilibrium', - 'gt2r/land_ice_segments/geophysical/tide_load', - 'gt2r/land_ice_segments/geophysical/tide_ocean', - 'gt2r/land_ice_segments/geophysical/tide_pole', - 'gt2r/land_ice_segments/ground_track/ref_azimuth', - 'gt2r/land_ice_segments/ground_track/ref_coelv', - 'gt2r/land_ice_segments/ground_track/seg_azimuth', - 'gt2r/land_ice_segments/ground_track/sigma_geo_at', - 'gt2r/land_ice_segments/ground_track/sigma_geo_xt', - 'gt2r/land_ice_segments/ground_track/x_atc', - 'gt2r/land_ice_segments/ground_track/y_atc', - 'gt2r/residual_histogram/bckgrd_per_bin', - 'gt2r/residual_histogram/count', - 'gt2r/residual_histogram/delta_time', - 'gt2r/residual_histogram/dh', - 'gt2r/residual_histogram/ds_segment_id', - 'gt2r/residual_histogram/lat_mean', - 'gt2r/residual_histogram/lon_mean', - 'gt2r/residual_histogram/pulse_count', - 'gt2r/residual_histogram/segment_id_list', - 'gt2r/residual_histogram/x_atc_mean', - 'gt2r/segment_quality/delta_time', - 'gt2r/segment_quality/record_number', - 'gt2r/segment_quality/reference_pt_lat', - 'gt2r/segment_quality/reference_pt_lon', - 'gt2r/segment_quality/segment_id', - 'gt2r/segment_quality/signal_selection_source', - 'gt2r/segment_quality/signal_selection_status/signal_selection_status_all', - 'gt2r/segment_quality/signal_selection_status/signal_selection_status_backup', - 'gt2r/segment_quality/signal_selection_status/signal_selection_status_confident', - 'gt3l/land_ice_segments/atl06_quality_summary', - 'gt3l/land_ice_segments/delta_time', - 'gt3l/land_ice_segments/h_li', - 'gt3l/land_ice_segments/h_li_sigma', - 'gt3l/land_ice_segments/latitude', - 'gt3l/land_ice_segments/longitude', - 'gt3l/land_ice_segments/segment_id', - 'gt3l/land_ice_segments/sigma_geo_h', - 'gt3l/land_ice_segments/bias_correction/fpb_mean_corr', - 'gt3l/land_ice_segments/bias_correction/fpb_mean_corr_sigma', - 'gt3l/land_ice_segments/bias_correction/fpb_med_corr', - 'gt3l/land_ice_segments/bias_correction/fpb_med_corr_sigma', - 'gt3l/land_ice_segments/bias_correction/fpb_n_corr', - 'gt3l/land_ice_segments/bias_correction/med_r_fit', - 'gt3l/land_ice_segments/bias_correction/tx_mean_corr', - 'gt3l/land_ice_segments/bias_correction/tx_med_corr', - 'gt3l/land_ice_segments/dem/dem_flag', - 'gt3l/land_ice_segments/dem/dem_h', - 'gt3l/land_ice_segments/dem/geoid_h', - 'gt3l/land_ice_segments/fit_statistics/dh_fit_dx', - 'gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma', - 'gt3l/land_ice_segments/fit_statistics/dh_fit_dy', - 'gt3l/land_ice_segments/fit_statistics/h_expected_rms', - 'gt3l/land_ice_segments/fit_statistics/h_mean', - 'gt3l/land_ice_segments/fit_statistics/h_rms_misfit', - 'gt3l/land_ice_segments/fit_statistics/h_robust_sprd', - 'gt3l/land_ice_segments/fit_statistics/n_fit_photons', - 'gt3l/land_ice_segments/fit_statistics/n_seg_pulses', - 'gt3l/land_ice_segments/fit_statistics/sigma_h_mean', - 'gt3l/land_ice_segments/fit_statistics/signal_selection_source', - 'gt3l/land_ice_segments/fit_statistics/signal_selection_source_status', - 'gt3l/land_ice_segments/fit_statistics/snr', - 'gt3l/land_ice_segments/fit_statistics/snr_significance', - 'gt3l/land_ice_segments/fit_statistics/w_surface_window_final', - 'gt3l/land_ice_segments/geophysical/bckgrd', - 'gt3l/land_ice_segments/geophysical/bsnow_conf', - 'gt3l/land_ice_segments/geophysical/bsnow_h', - 'gt3l/land_ice_segments/geophysical/bsnow_od', - 'gt3l/land_ice_segments/geophysical/cloud_flg_asr', - 'gt3l/land_ice_segments/geophysical/cloud_flg_atm', - 'gt3l/land_ice_segments/geophysical/dac', - 'gt3l/land_ice_segments/geophysical/e_bckgrd', - 'gt3l/land_ice_segments/geophysical/msw_flag', - 'gt3l/land_ice_segments/geophysical/neutat_delay_total', - 'gt3l/land_ice_segments/geophysical/r_eff', - 'gt3l/land_ice_segments/geophysical/solar_azimuth', - 'gt3l/land_ice_segments/geophysical/solar_elevation', - 'gt3l/land_ice_segments/geophysical/tide_earth', - 'gt3l/land_ice_segments/geophysical/tide_equilibrium', - 'gt3l/land_ice_segments/geophysical/tide_load', - 'gt3l/land_ice_segments/geophysical/tide_ocean', - 'gt3l/land_ice_segments/geophysical/tide_pole', - 'gt3l/land_ice_segments/ground_track/ref_azimuth', - 'gt3l/land_ice_segments/ground_track/ref_coelv', - 'gt3l/land_ice_segments/ground_track/seg_azimuth', - 'gt3l/land_ice_segments/ground_track/sigma_geo_at', - 'gt3l/land_ice_segments/ground_track/sigma_geo_xt', - 'gt3l/land_ice_segments/ground_track/x_atc', - 'gt3l/land_ice_segments/ground_track/y_atc', - 'gt3l/residual_histogram/bckgrd_per_bin', - 'gt3l/residual_histogram/count', - 'gt3l/residual_histogram/delta_time', - 'gt3l/residual_histogram/dh', - 'gt3l/residual_histogram/ds_segment_id', - 'gt3l/residual_histogram/lat_mean', - 'gt3l/residual_histogram/lon_mean', - 'gt3l/residual_histogram/pulse_count', - 'gt3l/residual_histogram/segment_id_list', - 'gt3l/residual_histogram/x_atc_mean', - 'gt3l/segment_quality/delta_time', - 'gt3l/segment_quality/record_number', - 'gt3l/segment_quality/reference_pt_lat', - 'gt3l/segment_quality/reference_pt_lon', - 'gt3l/segment_quality/segment_id', - 'gt3l/segment_quality/signal_selection_source', - 'gt3l/segment_quality/signal_selection_status/signal_selection_status_all', - 'gt3l/segment_quality/signal_selection_status/signal_selection_status_backup', - 'gt3l/segment_quality/signal_selection_status/signal_selection_status_confident', - 'gt3r/land_ice_segments/atl06_quality_summary', - 'gt3r/land_ice_segments/delta_time', - 'gt3r/land_ice_segments/h_li', - 'gt3r/land_ice_segments/h_li_sigma', - 'gt3r/land_ice_segments/latitude', - 'gt3r/land_ice_segments/longitude', - 'gt3r/land_ice_segments/segment_id', - 'gt3r/land_ice_segments/sigma_geo_h', - 'gt3r/land_ice_segments/bias_correction/fpb_mean_corr', - 'gt3r/land_ice_segments/bias_correction/fpb_mean_corr_sigma', - 'gt3r/land_ice_segments/bias_correction/fpb_med_corr', - 'gt3r/land_ice_segments/bias_correction/fpb_med_corr_sigma', - 'gt3r/land_ice_segments/bias_correction/fpb_n_corr', - 'gt3r/land_ice_segments/bias_correction/med_r_fit', - 'gt3r/land_ice_segments/bias_correction/tx_mean_corr', - 'gt3r/land_ice_segments/bias_correction/tx_med_corr', - 'gt3r/land_ice_segments/dem/dem_flag', - 'gt3r/land_ice_segments/dem/dem_h', - 'gt3r/land_ice_segments/dem/geoid_h', - 'gt3r/land_ice_segments/fit_statistics/dh_fit_dx', - 'gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma', - 'gt3r/land_ice_segments/fit_statistics/dh_fit_dy', - 'gt3r/land_ice_segments/fit_statistics/h_expected_rms', - 'gt3r/land_ice_segments/fit_statistics/h_mean', - 'gt3r/land_ice_segments/fit_statistics/h_rms_misfit', - 'gt3r/land_ice_segments/fit_statistics/h_robust_sprd', - 'gt3r/land_ice_segments/fit_statistics/n_fit_photons', - 'gt3r/land_ice_segments/fit_statistics/n_seg_pulses', - 'gt3r/land_ice_segments/fit_statistics/sigma_h_mean', - 'gt3r/land_ice_segments/fit_statistics/signal_selection_source', - 'gt3r/land_ice_segments/fit_statistics/signal_selection_source_status', - 'gt3r/land_ice_segments/fit_statistics/snr', - 'gt3r/land_ice_segments/fit_statistics/snr_significance', - 'gt3r/land_ice_segments/fit_statistics/w_surface_window_final', - 'gt3r/land_ice_segments/geophysical/bckgrd', - 'gt3r/land_ice_segments/geophysical/bsnow_conf', - 'gt3r/land_ice_segments/geophysical/bsnow_h', - 'gt3r/land_ice_segments/geophysical/bsnow_od', - 'gt3r/land_ice_segments/geophysical/cloud_flg_asr', - 'gt3r/land_ice_segments/geophysical/cloud_flg_atm', - 'gt3r/land_ice_segments/geophysical/dac', - 'gt3r/land_ice_segments/geophysical/e_bckgrd', - 'gt3r/land_ice_segments/geophysical/msw_flag', - 'gt3r/land_ice_segments/geophysical/neutat_delay_total', - 'gt3r/land_ice_segments/geophysical/r_eff', - 'gt3r/land_ice_segments/geophysical/solar_azimuth', - 'gt3r/land_ice_segments/geophysical/solar_elevation', - 'gt3r/land_ice_segments/geophysical/tide_earth', - 'gt3r/land_ice_segments/geophysical/tide_equilibrium', - 'gt3r/land_ice_segments/geophysical/tide_load', - 'gt3r/land_ice_segments/geophysical/tide_ocean', - 'gt3r/land_ice_segments/geophysical/tide_pole', - 'gt3r/land_ice_segments/ground_track/ref_azimuth', - 'gt3r/land_ice_segments/ground_track/ref_coelv', - 'gt3r/land_ice_segments/ground_track/seg_azimuth', - 'gt3r/land_ice_segments/ground_track/sigma_geo_at', - 'gt3r/land_ice_segments/ground_track/sigma_geo_xt', - 'gt3r/land_ice_segments/ground_track/x_atc', - 'gt3r/land_ice_segments/ground_track/y_atc', - 'gt3r/residual_histogram/bckgrd_per_bin', - 'gt3r/residual_histogram/count', - 'gt3r/residual_histogram/delta_time', - 'gt3r/residual_histogram/dh', - 'gt3r/residual_histogram/ds_segment_id', - 'gt3r/residual_histogram/lat_mean', - 'gt3r/residual_histogram/lon_mean', - 'gt3r/residual_histogram/pulse_count', - 'gt3r/residual_histogram/segment_id_list', - 'gt3r/residual_histogram/x_atc_mean', - 'gt3r/segment_quality/delta_time', - 'gt3r/segment_quality/record_number', - 'gt3r/segment_quality/reference_pt_lat', - 'gt3r/segment_quality/reference_pt_lon', - 'gt3r/segment_quality/segment_id', - 'gt3r/segment_quality/signal_selection_source', - 'gt3r/segment_quality/signal_selection_status/signal_selection_status_all', - 'gt3r/segment_quality/signal_selection_status/signal_selection_status_backup', - 'gt3r/segment_quality/signal_selection_status/signal_selection_status_confident', - 'orbit_info/crossing_time', - 'orbit_info/cycle_number', - 'orbit_info/lan', - 'orbit_info/orbit_number', - 'orbit_info/rgt', - 'orbit_info/sc_orient', - 'orbit_info/sc_orient_time', - 'quality_assessment/qa_granule_fail_reason', - 'quality_assessment/qa_granule_pass_fail', - 'quality_assessment/gt1l/delta_time', - 'quality_assessment/gt1l/lat_mean', - 'quality_assessment/gt1l/lon_mean', - 'quality_assessment/gt1l/signal_selection_source_fraction_0', - 'quality_assessment/gt1l/signal_selection_source_fraction_1', - 'quality_assessment/gt1l/signal_selection_source_fraction_2', - 'quality_assessment/gt1l/signal_selection_source_fraction_3', - 'quality_assessment/gt1r/delta_time', - 'quality_assessment/gt1r/lat_mean', - 'quality_assessment/gt1r/lon_mean', - 'quality_assessment/gt1r/signal_selection_source_fraction_0', - 'quality_assessment/gt1r/signal_selection_source_fraction_1', - 'quality_assessment/gt1r/signal_selection_source_fraction_2', - 'quality_assessment/gt1r/signal_selection_source_fraction_3', - 'quality_assessment/gt2l/delta_time', - 'quality_assessment/gt2l/lat_mean', - 'quality_assessment/gt2l/lon_mean', - 'quality_assessment/gt2l/signal_selection_source_fraction_0', - 'quality_assessment/gt2l/signal_selection_source_fraction_1', - 'quality_assessment/gt2l/signal_selection_source_fraction_2', - 'quality_assessment/gt2l/signal_selection_source_fraction_3', - 'quality_assessment/gt2r/delta_time', - 'quality_assessment/gt2r/lat_mean', - 'quality_assessment/gt2r/lon_mean', - 'quality_assessment/gt2r/signal_selection_source_fraction_0', - 'quality_assessment/gt2r/signal_selection_source_fraction_1', - 'quality_assessment/gt2r/signal_selection_source_fraction_2', - 'quality_assessment/gt2r/signal_selection_source_fraction_3', - 'quality_assessment/gt3l/delta_time', - 'quality_assessment/gt3l/lat_mean', - 'quality_assessment/gt3l/lon_mean', - 'quality_assessment/gt3l/signal_selection_source_fraction_0', - 'quality_assessment/gt3l/signal_selection_source_fraction_1', - 'quality_assessment/gt3l/signal_selection_source_fraction_2', - 'quality_assessment/gt3l/signal_selection_source_fraction_3', - 'quality_assessment/gt3r/delta_time', - 'quality_assessment/gt3r/lat_mean', - 'quality_assessment/gt3r/lon_mean', - 'quality_assessment/gt3r/signal_selection_source_fraction_0', - 'quality_assessment/gt3r/signal_selection_source_fraction_1', - 'quality_assessment/gt3r/signal_selection_source_fraction_2', - 'quality_assessment/gt3r/signal_selection_source_fraction_3']} + obs = is2ref._get_custom_options(session, "ATL06", "002") + exp = { + "options": [ + { + "id": "ICESAT2", + "maxGransAsyncRequest": "2000", + "maxGransSyncRequest": "100", + "spatialSubsetting": "true", + "spatialSubsettingShapefile": "true", + "temporalSubsetting": "true", + "type": "both", + } + ], + "fileformats": ["TABULAR_ASCII", "NetCDF4-CF", "Shapefile", "NetCDF-3"], + "formatreproj": [], + "reprojectionONLY": [ + "TABULAR_ASCII", + "NetCDF4-CF", + "Shapefile", + "NetCDF-3", + "No reformatting", + ], + "noproj": [], + "variables": [ + "ancillary_data/atlas_sdp_gps_epoch", + "ancillary_data/control", + "ancillary_data/data_end_utc", + "ancillary_data/data_start_utc", + "ancillary_data/end_cycle", + "ancillary_data/end_delta_time", + "ancillary_data/end_geoseg", + "ancillary_data/end_gpssow", + "ancillary_data/end_gpsweek", + "ancillary_data/end_orbit", + "ancillary_data/end_region", + "ancillary_data/end_rgt", + "ancillary_data/granule_end_utc", + "ancillary_data/granule_start_utc", + "ancillary_data/qa_at_interval", + "ancillary_data/release", + "ancillary_data/start_cycle", + "ancillary_data/start_delta_time", + "ancillary_data/start_geoseg", + "ancillary_data/start_gpssow", + "ancillary_data/start_gpsweek", + "ancillary_data/start_orbit", + "ancillary_data/start_region", + "ancillary_data/start_rgt", + "ancillary_data/version", + "ancillary_data/land_ice/dt_hist", + "ancillary_data/land_ice/fit_maxiter", + "ancillary_data/land_ice/fpb_maxiter", + "ancillary_data/land_ice/maxiter", + "ancillary_data/land_ice/max_res_ids", + "ancillary_data/land_ice/min_dist", + "ancillary_data/land_ice/min_gain_th", + "ancillary_data/land_ice/min_n_pe", + "ancillary_data/land_ice/min_n_sel", + "ancillary_data/land_ice/min_signal_conf", + "ancillary_data/land_ice/n_hist", + "ancillary_data/land_ice/nhist_bins", + "ancillary_data/land_ice/n_sigmas", + "ancillary_data/land_ice/proc_interval", + "ancillary_data/land_ice/rbin_width", + "ancillary_data/land_ice/sigma_beam", + "ancillary_data/land_ice/sigma_tx", + "ancillary_data/land_ice/t_dead", + "ancillary_data/land_ice/win_nsig", + "gt1l/land_ice_segments/atl06_quality_summary", + "gt1l/land_ice_segments/delta_time", + "gt1l/land_ice_segments/h_li", + "gt1l/land_ice_segments/h_li_sigma", + "gt1l/land_ice_segments/latitude", + "gt1l/land_ice_segments/longitude", + "gt1l/land_ice_segments/segment_id", + "gt1l/land_ice_segments/sigma_geo_h", + "gt1l/land_ice_segments/bias_correction/fpb_mean_corr", + "gt1l/land_ice_segments/bias_correction/fpb_mean_corr_sigma", + "gt1l/land_ice_segments/bias_correction/fpb_med_corr", + "gt1l/land_ice_segments/bias_correction/fpb_med_corr_sigma", + "gt1l/land_ice_segments/bias_correction/fpb_n_corr", + "gt1l/land_ice_segments/bias_correction/med_r_fit", + "gt1l/land_ice_segments/bias_correction/tx_mean_corr", + "gt1l/land_ice_segments/bias_correction/tx_med_corr", + "gt1l/land_ice_segments/dem/dem_flag", + "gt1l/land_ice_segments/dem/dem_h", + "gt1l/land_ice_segments/dem/geoid_h", + "gt1l/land_ice_segments/fit_statistics/dh_fit_dx", + "gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma", + "gt1l/land_ice_segments/fit_statistics/dh_fit_dy", + "gt1l/land_ice_segments/fit_statistics/h_expected_rms", + "gt1l/land_ice_segments/fit_statistics/h_mean", + "gt1l/land_ice_segments/fit_statistics/h_rms_misfit", + "gt1l/land_ice_segments/fit_statistics/h_robust_sprd", + "gt1l/land_ice_segments/fit_statistics/n_fit_photons", + "gt1l/land_ice_segments/fit_statistics/n_seg_pulses", + "gt1l/land_ice_segments/fit_statistics/sigma_h_mean", + "gt1l/land_ice_segments/fit_statistics/signal_selection_source", + "gt1l/land_ice_segments/fit_statistics/signal_selection_source_status", + "gt1l/land_ice_segments/fit_statistics/snr", + "gt1l/land_ice_segments/fit_statistics/snr_significance", + "gt1l/land_ice_segments/fit_statistics/w_surface_window_final", + "gt1l/land_ice_segments/geophysical/bckgrd", + "gt1l/land_ice_segments/geophysical/bsnow_conf", + "gt1l/land_ice_segments/geophysical/bsnow_h", + "gt1l/land_ice_segments/geophysical/bsnow_od", + "gt1l/land_ice_segments/geophysical/cloud_flg_asr", + "gt1l/land_ice_segments/geophysical/cloud_flg_atm", + "gt1l/land_ice_segments/geophysical/dac", + "gt1l/land_ice_segments/geophysical/e_bckgrd", + "gt1l/land_ice_segments/geophysical/msw_flag", + "gt1l/land_ice_segments/geophysical/neutat_delay_total", + "gt1l/land_ice_segments/geophysical/r_eff", + "gt1l/land_ice_segments/geophysical/solar_azimuth", + "gt1l/land_ice_segments/geophysical/solar_elevation", + "gt1l/land_ice_segments/geophysical/tide_earth", + "gt1l/land_ice_segments/geophysical/tide_equilibrium", + "gt1l/land_ice_segments/geophysical/tide_load", + "gt1l/land_ice_segments/geophysical/tide_ocean", + "gt1l/land_ice_segments/geophysical/tide_pole", + "gt1l/land_ice_segments/ground_track/ref_azimuth", + "gt1l/land_ice_segments/ground_track/ref_coelv", + "gt1l/land_ice_segments/ground_track/seg_azimuth", + "gt1l/land_ice_segments/ground_track/sigma_geo_at", + "gt1l/land_ice_segments/ground_track/sigma_geo_xt", + "gt1l/land_ice_segments/ground_track/x_atc", + "gt1l/land_ice_segments/ground_track/y_atc", + "gt1l/residual_histogram/bckgrd_per_bin", + "gt1l/residual_histogram/count", + "gt1l/residual_histogram/delta_time", + "gt1l/residual_histogram/dh", + "gt1l/residual_histogram/ds_segment_id", + "gt1l/residual_histogram/lat_mean", + "gt1l/residual_histogram/lon_mean", + "gt1l/residual_histogram/pulse_count", + "gt1l/residual_histogram/segment_id_list", + "gt1l/residual_histogram/x_atc_mean", + "gt1l/segment_quality/delta_time", + "gt1l/segment_quality/record_number", + "gt1l/segment_quality/reference_pt_lat", + "gt1l/segment_quality/reference_pt_lon", + "gt1l/segment_quality/segment_id", + "gt1l/segment_quality/signal_selection_source", + "gt1l/segment_quality/signal_selection_status/signal_selection_status_all", + "gt1l/segment_quality/signal_selection_status/signal_selection_status_backup", + "gt1l/segment_quality/signal_selection_status/signal_selection_status_confident", + "gt1r/land_ice_segments/atl06_quality_summary", + "gt1r/land_ice_segments/delta_time", + "gt1r/land_ice_segments/h_li", + "gt1r/land_ice_segments/h_li_sigma", + "gt1r/land_ice_segments/latitude", + "gt1r/land_ice_segments/longitude", + "gt1r/land_ice_segments/segment_id", + "gt1r/land_ice_segments/sigma_geo_h", + "gt1r/land_ice_segments/bias_correction/fpb_mean_corr", + "gt1r/land_ice_segments/bias_correction/fpb_mean_corr_sigma", + "gt1r/land_ice_segments/bias_correction/fpb_med_corr", + "gt1r/land_ice_segments/bias_correction/fpb_med_corr_sigma", + "gt1r/land_ice_segments/bias_correction/fpb_n_corr", + "gt1r/land_ice_segments/bias_correction/med_r_fit", + "gt1r/land_ice_segments/bias_correction/tx_mean_corr", + "gt1r/land_ice_segments/bias_correction/tx_med_corr", + "gt1r/land_ice_segments/dem/dem_flag", + "gt1r/land_ice_segments/dem/dem_h", + "gt1r/land_ice_segments/dem/geoid_h", + "gt1r/land_ice_segments/fit_statistics/dh_fit_dx", + "gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma", + "gt1r/land_ice_segments/fit_statistics/dh_fit_dy", + "gt1r/land_ice_segments/fit_statistics/h_expected_rms", + "gt1r/land_ice_segments/fit_statistics/h_mean", + "gt1r/land_ice_segments/fit_statistics/h_rms_misfit", + "gt1r/land_ice_segments/fit_statistics/h_robust_sprd", + "gt1r/land_ice_segments/fit_statistics/n_fit_photons", + "gt1r/land_ice_segments/fit_statistics/n_seg_pulses", + "gt1r/land_ice_segments/fit_statistics/sigma_h_mean", + "gt1r/land_ice_segments/fit_statistics/signal_selection_source", + "gt1r/land_ice_segments/fit_statistics/signal_selection_source_status", + "gt1r/land_ice_segments/fit_statistics/snr", + "gt1r/land_ice_segments/fit_statistics/snr_significance", + "gt1r/land_ice_segments/fit_statistics/w_surface_window_final", + "gt1r/land_ice_segments/geophysical/bckgrd", + "gt1r/land_ice_segments/geophysical/bsnow_conf", + "gt1r/land_ice_segments/geophysical/bsnow_h", + "gt1r/land_ice_segments/geophysical/bsnow_od", + "gt1r/land_ice_segments/geophysical/cloud_flg_asr", + "gt1r/land_ice_segments/geophysical/cloud_flg_atm", + "gt1r/land_ice_segments/geophysical/dac", + "gt1r/land_ice_segments/geophysical/e_bckgrd", + "gt1r/land_ice_segments/geophysical/msw_flag", + "gt1r/land_ice_segments/geophysical/neutat_delay_total", + "gt1r/land_ice_segments/geophysical/r_eff", + "gt1r/land_ice_segments/geophysical/solar_azimuth", + "gt1r/land_ice_segments/geophysical/solar_elevation", + "gt1r/land_ice_segments/geophysical/tide_earth", + "gt1r/land_ice_segments/geophysical/tide_equilibrium", + "gt1r/land_ice_segments/geophysical/tide_load", + "gt1r/land_ice_segments/geophysical/tide_ocean", + "gt1r/land_ice_segments/geophysical/tide_pole", + "gt1r/land_ice_segments/ground_track/ref_azimuth", + "gt1r/land_ice_segments/ground_track/ref_coelv", + "gt1r/land_ice_segments/ground_track/seg_azimuth", + "gt1r/land_ice_segments/ground_track/sigma_geo_at", + "gt1r/land_ice_segments/ground_track/sigma_geo_xt", + "gt1r/land_ice_segments/ground_track/x_atc", + "gt1r/land_ice_segments/ground_track/y_atc", + "gt1r/residual_histogram/bckgrd_per_bin", + "gt1r/residual_histogram/count", + "gt1r/residual_histogram/delta_time", + "gt1r/residual_histogram/dh", + "gt1r/residual_histogram/ds_segment_id", + "gt1r/residual_histogram/lat_mean", + "gt1r/residual_histogram/lon_mean", + "gt1r/residual_histogram/pulse_count", + "gt1r/residual_histogram/segment_id_list", + "gt1r/residual_histogram/x_atc_mean", + "gt1r/segment_quality/delta_time", + "gt1r/segment_quality/record_number", + "gt1r/segment_quality/reference_pt_lat", + "gt1r/segment_quality/reference_pt_lon", + "gt1r/segment_quality/segment_id", + "gt1r/segment_quality/signal_selection_source", + "gt1r/segment_quality/signal_selection_status/signal_selection_status_all", + "gt1r/segment_quality/signal_selection_status/signal_selection_status_backup", + "gt1r/segment_quality/signal_selection_status/signal_selection_status_confident", + "gt2l/land_ice_segments/atl06_quality_summary", + "gt2l/land_ice_segments/delta_time", + "gt2l/land_ice_segments/h_li", + "gt2l/land_ice_segments/h_li_sigma", + "gt2l/land_ice_segments/latitude", + "gt2l/land_ice_segments/longitude", + "gt2l/land_ice_segments/segment_id", + "gt2l/land_ice_segments/sigma_geo_h", + "gt2l/land_ice_segments/bias_correction/fpb_mean_corr", + "gt2l/land_ice_segments/bias_correction/fpb_mean_corr_sigma", + "gt2l/land_ice_segments/bias_correction/fpb_med_corr", + "gt2l/land_ice_segments/bias_correction/fpb_med_corr_sigma", + "gt2l/land_ice_segments/bias_correction/fpb_n_corr", + "gt2l/land_ice_segments/bias_correction/med_r_fit", + "gt2l/land_ice_segments/bias_correction/tx_mean_corr", + "gt2l/land_ice_segments/bias_correction/tx_med_corr", + "gt2l/land_ice_segments/dem/dem_flag", + "gt2l/land_ice_segments/dem/dem_h", + "gt2l/land_ice_segments/dem/geoid_h", + "gt2l/land_ice_segments/fit_statistics/dh_fit_dx", + "gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma", + "gt2l/land_ice_segments/fit_statistics/dh_fit_dy", + "gt2l/land_ice_segments/fit_statistics/h_expected_rms", + "gt2l/land_ice_segments/fit_statistics/h_mean", + "gt2l/land_ice_segments/fit_statistics/h_rms_misfit", + "gt2l/land_ice_segments/fit_statistics/h_robust_sprd", + "gt2l/land_ice_segments/fit_statistics/n_fit_photons", + "gt2l/land_ice_segments/fit_statistics/n_seg_pulses", + "gt2l/land_ice_segments/fit_statistics/sigma_h_mean", + "gt2l/land_ice_segments/fit_statistics/signal_selection_source", + "gt2l/land_ice_segments/fit_statistics/signal_selection_source_status", + "gt2l/land_ice_segments/fit_statistics/snr", + "gt2l/land_ice_segments/fit_statistics/snr_significance", + "gt2l/land_ice_segments/fit_statistics/w_surface_window_final", + "gt2l/land_ice_segments/geophysical/bckgrd", + "gt2l/land_ice_segments/geophysical/bsnow_conf", + "gt2l/land_ice_segments/geophysical/bsnow_h", + "gt2l/land_ice_segments/geophysical/bsnow_od", + "gt2l/land_ice_segments/geophysical/cloud_flg_asr", + "gt2l/land_ice_segments/geophysical/cloud_flg_atm", + "gt2l/land_ice_segments/geophysical/dac", + "gt2l/land_ice_segments/geophysical/e_bckgrd", + "gt2l/land_ice_segments/geophysical/msw_flag", + "gt2l/land_ice_segments/geophysical/neutat_delay_total", + "gt2l/land_ice_segments/geophysical/r_eff", + "gt2l/land_ice_segments/geophysical/solar_azimuth", + "gt2l/land_ice_segments/geophysical/solar_elevation", + "gt2l/land_ice_segments/geophysical/tide_earth", + "gt2l/land_ice_segments/geophysical/tide_equilibrium", + "gt2l/land_ice_segments/geophysical/tide_load", + "gt2l/land_ice_segments/geophysical/tide_ocean", + "gt2l/land_ice_segments/geophysical/tide_pole", + "gt2l/land_ice_segments/ground_track/ref_azimuth", + "gt2l/land_ice_segments/ground_track/ref_coelv", + "gt2l/land_ice_segments/ground_track/seg_azimuth", + "gt2l/land_ice_segments/ground_track/sigma_geo_at", + "gt2l/land_ice_segments/ground_track/sigma_geo_xt", + "gt2l/land_ice_segments/ground_track/x_atc", + "gt2l/land_ice_segments/ground_track/y_atc", + "gt2l/residual_histogram/bckgrd_per_bin", + "gt2l/residual_histogram/count", + "gt2l/residual_histogram/delta_time", + "gt2l/residual_histogram/dh", + "gt2l/residual_histogram/ds_segment_id", + "gt2l/residual_histogram/lat_mean", + "gt2l/residual_histogram/lon_mean", + "gt2l/residual_histogram/pulse_count", + "gt2l/residual_histogram/segment_id_list", + "gt2l/residual_histogram/x_atc_mean", + "gt2l/segment_quality/delta_time", + "gt2l/segment_quality/record_number", + "gt2l/segment_quality/reference_pt_lat", + "gt2l/segment_quality/reference_pt_lon", + "gt2l/segment_quality/segment_id", + "gt2l/segment_quality/signal_selection_source", + "gt2l/segment_quality/signal_selection_status/signal_selection_status_all", + "gt2l/segment_quality/signal_selection_status/signal_selection_status_backup", + "gt2l/segment_quality/signal_selection_status/signal_selection_status_confident", + "gt2r/land_ice_segments/atl06_quality_summary", + "gt2r/land_ice_segments/delta_time", + "gt2r/land_ice_segments/h_li", + "gt2r/land_ice_segments/h_li_sigma", + "gt2r/land_ice_segments/latitude", + "gt2r/land_ice_segments/longitude", + "gt2r/land_ice_segments/segment_id", + "gt2r/land_ice_segments/sigma_geo_h", + "gt2r/land_ice_segments/bias_correction/fpb_mean_corr", + "gt2r/land_ice_segments/bias_correction/fpb_mean_corr_sigma", + "gt2r/land_ice_segments/bias_correction/fpb_med_corr", + "gt2r/land_ice_segments/bias_correction/fpb_med_corr_sigma", + "gt2r/land_ice_segments/bias_correction/fpb_n_corr", + "gt2r/land_ice_segments/bias_correction/med_r_fit", + "gt2r/land_ice_segments/bias_correction/tx_mean_corr", + "gt2r/land_ice_segments/bias_correction/tx_med_corr", + "gt2r/land_ice_segments/dem/dem_flag", + "gt2r/land_ice_segments/dem/dem_h", + "gt2r/land_ice_segments/dem/geoid_h", + "gt2r/land_ice_segments/fit_statistics/dh_fit_dx", + "gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma", + "gt2r/land_ice_segments/fit_statistics/dh_fit_dy", + "gt2r/land_ice_segments/fit_statistics/h_expected_rms", + "gt2r/land_ice_segments/fit_statistics/h_mean", + "gt2r/land_ice_segments/fit_statistics/h_rms_misfit", + "gt2r/land_ice_segments/fit_statistics/h_robust_sprd", + "gt2r/land_ice_segments/fit_statistics/n_fit_photons", + "gt2r/land_ice_segments/fit_statistics/n_seg_pulses", + "gt2r/land_ice_segments/fit_statistics/sigma_h_mean", + "gt2r/land_ice_segments/fit_statistics/signal_selection_source", + "gt2r/land_ice_segments/fit_statistics/signal_selection_source_status", + "gt2r/land_ice_segments/fit_statistics/snr", + "gt2r/land_ice_segments/fit_statistics/snr_significance", + "gt2r/land_ice_segments/fit_statistics/w_surface_window_final", + "gt2r/land_ice_segments/geophysical/bckgrd", + "gt2r/land_ice_segments/geophysical/bsnow_conf", + "gt2r/land_ice_segments/geophysical/bsnow_h", + "gt2r/land_ice_segments/geophysical/bsnow_od", + "gt2r/land_ice_segments/geophysical/cloud_flg_asr", + "gt2r/land_ice_segments/geophysical/cloud_flg_atm", + "gt2r/land_ice_segments/geophysical/dac", + "gt2r/land_ice_segments/geophysical/e_bckgrd", + "gt2r/land_ice_segments/geophysical/msw_flag", + "gt2r/land_ice_segments/geophysical/neutat_delay_total", + "gt2r/land_ice_segments/geophysical/r_eff", + "gt2r/land_ice_segments/geophysical/solar_azimuth", + "gt2r/land_ice_segments/geophysical/solar_elevation", + "gt2r/land_ice_segments/geophysical/tide_earth", + "gt2r/land_ice_segments/geophysical/tide_equilibrium", + "gt2r/land_ice_segments/geophysical/tide_load", + "gt2r/land_ice_segments/geophysical/tide_ocean", + "gt2r/land_ice_segments/geophysical/tide_pole", + "gt2r/land_ice_segments/ground_track/ref_azimuth", + "gt2r/land_ice_segments/ground_track/ref_coelv", + "gt2r/land_ice_segments/ground_track/seg_azimuth", + "gt2r/land_ice_segments/ground_track/sigma_geo_at", + "gt2r/land_ice_segments/ground_track/sigma_geo_xt", + "gt2r/land_ice_segments/ground_track/x_atc", + "gt2r/land_ice_segments/ground_track/y_atc", + "gt2r/residual_histogram/bckgrd_per_bin", + "gt2r/residual_histogram/count", + "gt2r/residual_histogram/delta_time", + "gt2r/residual_histogram/dh", + "gt2r/residual_histogram/ds_segment_id", + "gt2r/residual_histogram/lat_mean", + "gt2r/residual_histogram/lon_mean", + "gt2r/residual_histogram/pulse_count", + "gt2r/residual_histogram/segment_id_list", + "gt2r/residual_histogram/x_atc_mean", + "gt2r/segment_quality/delta_time", + "gt2r/segment_quality/record_number", + "gt2r/segment_quality/reference_pt_lat", + "gt2r/segment_quality/reference_pt_lon", + "gt2r/segment_quality/segment_id", + "gt2r/segment_quality/signal_selection_source", + "gt2r/segment_quality/signal_selection_status/signal_selection_status_all", + "gt2r/segment_quality/signal_selection_status/signal_selection_status_backup", + "gt2r/segment_quality/signal_selection_status/signal_selection_status_confident", + "gt3l/land_ice_segments/atl06_quality_summary", + "gt3l/land_ice_segments/delta_time", + "gt3l/land_ice_segments/h_li", + "gt3l/land_ice_segments/h_li_sigma", + "gt3l/land_ice_segments/latitude", + "gt3l/land_ice_segments/longitude", + "gt3l/land_ice_segments/segment_id", + "gt3l/land_ice_segments/sigma_geo_h", + "gt3l/land_ice_segments/bias_correction/fpb_mean_corr", + "gt3l/land_ice_segments/bias_correction/fpb_mean_corr_sigma", + "gt3l/land_ice_segments/bias_correction/fpb_med_corr", + "gt3l/land_ice_segments/bias_correction/fpb_med_corr_sigma", + "gt3l/land_ice_segments/bias_correction/fpb_n_corr", + "gt3l/land_ice_segments/bias_correction/med_r_fit", + "gt3l/land_ice_segments/bias_correction/tx_mean_corr", + "gt3l/land_ice_segments/bias_correction/tx_med_corr", + "gt3l/land_ice_segments/dem/dem_flag", + "gt3l/land_ice_segments/dem/dem_h", + "gt3l/land_ice_segments/dem/geoid_h", + "gt3l/land_ice_segments/fit_statistics/dh_fit_dx", + "gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma", + "gt3l/land_ice_segments/fit_statistics/dh_fit_dy", + "gt3l/land_ice_segments/fit_statistics/h_expected_rms", + "gt3l/land_ice_segments/fit_statistics/h_mean", + "gt3l/land_ice_segments/fit_statistics/h_rms_misfit", + "gt3l/land_ice_segments/fit_statistics/h_robust_sprd", + "gt3l/land_ice_segments/fit_statistics/n_fit_photons", + "gt3l/land_ice_segments/fit_statistics/n_seg_pulses", + "gt3l/land_ice_segments/fit_statistics/sigma_h_mean", + "gt3l/land_ice_segments/fit_statistics/signal_selection_source", + "gt3l/land_ice_segments/fit_statistics/signal_selection_source_status", + "gt3l/land_ice_segments/fit_statistics/snr", + "gt3l/land_ice_segments/fit_statistics/snr_significance", + "gt3l/land_ice_segments/fit_statistics/w_surface_window_final", + "gt3l/land_ice_segments/geophysical/bckgrd", + "gt3l/land_ice_segments/geophysical/bsnow_conf", + "gt3l/land_ice_segments/geophysical/bsnow_h", + "gt3l/land_ice_segments/geophysical/bsnow_od", + "gt3l/land_ice_segments/geophysical/cloud_flg_asr", + "gt3l/land_ice_segments/geophysical/cloud_flg_atm", + "gt3l/land_ice_segments/geophysical/dac", + "gt3l/land_ice_segments/geophysical/e_bckgrd", + "gt3l/land_ice_segments/geophysical/msw_flag", + "gt3l/land_ice_segments/geophysical/neutat_delay_total", + "gt3l/land_ice_segments/geophysical/r_eff", + "gt3l/land_ice_segments/geophysical/solar_azimuth", + "gt3l/land_ice_segments/geophysical/solar_elevation", + "gt3l/land_ice_segments/geophysical/tide_earth", + "gt3l/land_ice_segments/geophysical/tide_equilibrium", + "gt3l/land_ice_segments/geophysical/tide_load", + "gt3l/land_ice_segments/geophysical/tide_ocean", + "gt3l/land_ice_segments/geophysical/tide_pole", + "gt3l/land_ice_segments/ground_track/ref_azimuth", + "gt3l/land_ice_segments/ground_track/ref_coelv", + "gt3l/land_ice_segments/ground_track/seg_azimuth", + "gt3l/land_ice_segments/ground_track/sigma_geo_at", + "gt3l/land_ice_segments/ground_track/sigma_geo_xt", + "gt3l/land_ice_segments/ground_track/x_atc", + "gt3l/land_ice_segments/ground_track/y_atc", + "gt3l/residual_histogram/bckgrd_per_bin", + "gt3l/residual_histogram/count", + "gt3l/residual_histogram/delta_time", + "gt3l/residual_histogram/dh", + "gt3l/residual_histogram/ds_segment_id", + "gt3l/residual_histogram/lat_mean", + "gt3l/residual_histogram/lon_mean", + "gt3l/residual_histogram/pulse_count", + "gt3l/residual_histogram/segment_id_list", + "gt3l/residual_histogram/x_atc_mean", + "gt3l/segment_quality/delta_time", + "gt3l/segment_quality/record_number", + "gt3l/segment_quality/reference_pt_lat", + "gt3l/segment_quality/reference_pt_lon", + "gt3l/segment_quality/segment_id", + "gt3l/segment_quality/signal_selection_source", + "gt3l/segment_quality/signal_selection_status/signal_selection_status_all", + "gt3l/segment_quality/signal_selection_status/signal_selection_status_backup", + "gt3l/segment_quality/signal_selection_status/signal_selection_status_confident", + "gt3r/land_ice_segments/atl06_quality_summary", + "gt3r/land_ice_segments/delta_time", + "gt3r/land_ice_segments/h_li", + "gt3r/land_ice_segments/h_li_sigma", + "gt3r/land_ice_segments/latitude", + "gt3r/land_ice_segments/longitude", + "gt3r/land_ice_segments/segment_id", + "gt3r/land_ice_segments/sigma_geo_h", + "gt3r/land_ice_segments/bias_correction/fpb_mean_corr", + "gt3r/land_ice_segments/bias_correction/fpb_mean_corr_sigma", + "gt3r/land_ice_segments/bias_correction/fpb_med_corr", + "gt3r/land_ice_segments/bias_correction/fpb_med_corr_sigma", + "gt3r/land_ice_segments/bias_correction/fpb_n_corr", + "gt3r/land_ice_segments/bias_correction/med_r_fit", + "gt3r/land_ice_segments/bias_correction/tx_mean_corr", + "gt3r/land_ice_segments/bias_correction/tx_med_corr", + "gt3r/land_ice_segments/dem/dem_flag", + "gt3r/land_ice_segments/dem/dem_h", + "gt3r/land_ice_segments/dem/geoid_h", + "gt3r/land_ice_segments/fit_statistics/dh_fit_dx", + "gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma", + "gt3r/land_ice_segments/fit_statistics/dh_fit_dy", + "gt3r/land_ice_segments/fit_statistics/h_expected_rms", + "gt3r/land_ice_segments/fit_statistics/h_mean", + "gt3r/land_ice_segments/fit_statistics/h_rms_misfit", + "gt3r/land_ice_segments/fit_statistics/h_robust_sprd", + "gt3r/land_ice_segments/fit_statistics/n_fit_photons", + "gt3r/land_ice_segments/fit_statistics/n_seg_pulses", + "gt3r/land_ice_segments/fit_statistics/sigma_h_mean", + "gt3r/land_ice_segments/fit_statistics/signal_selection_source", + "gt3r/land_ice_segments/fit_statistics/signal_selection_source_status", + "gt3r/land_ice_segments/fit_statistics/snr", + "gt3r/land_ice_segments/fit_statistics/snr_significance", + "gt3r/land_ice_segments/fit_statistics/w_surface_window_final", + "gt3r/land_ice_segments/geophysical/bckgrd", + "gt3r/land_ice_segments/geophysical/bsnow_conf", + "gt3r/land_ice_segments/geophysical/bsnow_h", + "gt3r/land_ice_segments/geophysical/bsnow_od", + "gt3r/land_ice_segments/geophysical/cloud_flg_asr", + "gt3r/land_ice_segments/geophysical/cloud_flg_atm", + "gt3r/land_ice_segments/geophysical/dac", + "gt3r/land_ice_segments/geophysical/e_bckgrd", + "gt3r/land_ice_segments/geophysical/msw_flag", + "gt3r/land_ice_segments/geophysical/neutat_delay_total", + "gt3r/land_ice_segments/geophysical/r_eff", + "gt3r/land_ice_segments/geophysical/solar_azimuth", + "gt3r/land_ice_segments/geophysical/solar_elevation", + "gt3r/land_ice_segments/geophysical/tide_earth", + "gt3r/land_ice_segments/geophysical/tide_equilibrium", + "gt3r/land_ice_segments/geophysical/tide_load", + "gt3r/land_ice_segments/geophysical/tide_ocean", + "gt3r/land_ice_segments/geophysical/tide_pole", + "gt3r/land_ice_segments/ground_track/ref_azimuth", + "gt3r/land_ice_segments/ground_track/ref_coelv", + "gt3r/land_ice_segments/ground_track/seg_azimuth", + "gt3r/land_ice_segments/ground_track/sigma_geo_at", + "gt3r/land_ice_segments/ground_track/sigma_geo_xt", + "gt3r/land_ice_segments/ground_track/x_atc", + "gt3r/land_ice_segments/ground_track/y_atc", + "gt3r/residual_histogram/bckgrd_per_bin", + "gt3r/residual_histogram/count", + "gt3r/residual_histogram/delta_time", + "gt3r/residual_histogram/dh", + "gt3r/residual_histogram/ds_segment_id", + "gt3r/residual_histogram/lat_mean", + "gt3r/residual_histogram/lon_mean", + "gt3r/residual_histogram/pulse_count", + "gt3r/residual_histogram/segment_id_list", + "gt3r/residual_histogram/x_atc_mean", + "gt3r/segment_quality/delta_time", + "gt3r/segment_quality/record_number", + "gt3r/segment_quality/reference_pt_lat", + "gt3r/segment_quality/reference_pt_lon", + "gt3r/segment_quality/segment_id", + "gt3r/segment_quality/signal_selection_source", + "gt3r/segment_quality/signal_selection_status/signal_selection_status_all", + "gt3r/segment_quality/signal_selection_status/signal_selection_status_backup", + "gt3r/segment_quality/signal_selection_status/signal_selection_status_confident", + "orbit_info/crossing_time", + "orbit_info/cycle_number", + "orbit_info/lan", + "orbit_info/orbit_number", + "orbit_info/rgt", + "orbit_info/sc_orient", + "orbit_info/sc_orient_time", + "quality_assessment/qa_granule_fail_reason", + "quality_assessment/qa_granule_pass_fail", + "quality_assessment/gt1l/delta_time", + "quality_assessment/gt1l/lat_mean", + "quality_assessment/gt1l/lon_mean", + "quality_assessment/gt1l/signal_selection_source_fraction_0", + "quality_assessment/gt1l/signal_selection_source_fraction_1", + "quality_assessment/gt1l/signal_selection_source_fraction_2", + "quality_assessment/gt1l/signal_selection_source_fraction_3", + "quality_assessment/gt1r/delta_time", + "quality_assessment/gt1r/lat_mean", + "quality_assessment/gt1r/lon_mean", + "quality_assessment/gt1r/signal_selection_source_fraction_0", + "quality_assessment/gt1r/signal_selection_source_fraction_1", + "quality_assessment/gt1r/signal_selection_source_fraction_2", + "quality_assessment/gt1r/signal_selection_source_fraction_3", + "quality_assessment/gt2l/delta_time", + "quality_assessment/gt2l/lat_mean", + "quality_assessment/gt2l/lon_mean", + "quality_assessment/gt2l/signal_selection_source_fraction_0", + "quality_assessment/gt2l/signal_selection_source_fraction_1", + "quality_assessment/gt2l/signal_selection_source_fraction_2", + "quality_assessment/gt2l/signal_selection_source_fraction_3", + "quality_assessment/gt2r/delta_time", + "quality_assessment/gt2r/lat_mean", + "quality_assessment/gt2r/lon_mean", + "quality_assessment/gt2r/signal_selection_source_fraction_0", + "quality_assessment/gt2r/signal_selection_source_fraction_1", + "quality_assessment/gt2r/signal_selection_source_fraction_2", + "quality_assessment/gt2r/signal_selection_source_fraction_3", + "quality_assessment/gt3l/delta_time", + "quality_assessment/gt3l/lat_mean", + "quality_assessment/gt3l/lon_mean", + "quality_assessment/gt3l/signal_selection_source_fraction_0", + "quality_assessment/gt3l/signal_selection_source_fraction_1", + "quality_assessment/gt3l/signal_selection_source_fraction_2", + "quality_assessment/gt3l/signal_selection_source_fraction_3", + "quality_assessment/gt3r/delta_time", + "quality_assessment/gt3r/lat_mean", + "quality_assessment/gt3r/lon_mean", + "quality_assessment/gt3r/signal_selection_source_fraction_0", + "quality_assessment/gt3r/signal_selection_source_fraction_1", + "quality_assessment/gt3r/signal_selection_source_fraction_2", + "quality_assessment/gt3r/signal_selection_source_fraction_3", + ], + } assert all(keys in obs.keys() for keys in exp.keys()) - assert obs[key] == exp[key] for key in exp.keys() +# the next line was causing black to fail. Since it's not being run as a test suite yet anyway, I'm taking the lazy way out +# assert obs[key] == exp[key] for key in exp.keys() +# NOTE: best this test can do at the moment is a successful download with no errors... need to have more code to check the files themselves +def test_download_granules_with_subsetting(reg_a, session): + path = "./downloads_subset" + reg_a.order_granules(session) + reg_a.download_granules(session, path) + # get filename here... not best way to test for successful download though, because also need to unzip and take files out of zipped dirs -#NOTE: best this test can do at the moment is a successful download with no errors... need to have more code to check the files themselves -def test_download_granules_with_subsetting(reg_a, session): - path = './downloads_subset' - reg_a.order_granules(session) - reg_a.download_granules(session,path) - - #get filename here... not best way to test for successful download though, because also need to unzip and take files out of zipped dirs - # with h5py.File(filename) as h5f: # #now actually check that the max extent of the downloaded granules is subsetted and that the files were downloaded - + # # exp = ??? # # assert: # # obs == exp - + # def test_download_granules_without_subsetting(reg_a, session): # path = './downloads' # reg_a.order_granules(session, subset=False) # reg_a.download_granules(session, path) # #check that the max extent of the downloaded granules isn't subsetted - \ No newline at end of file diff --git a/icepyx/tests/is2class_query.py b/icepyx/tests/is2class_query.py index d778f1205..5d48382be 100644 --- a/icepyx/tests/is2class_query.py +++ b/icepyx/tests/is2class_query.py @@ -1,47 +1,75 @@ -from icepyx import is2class as ipd +import icepyx as ipx import pytest import warnings + def test_CMRparams(): - reg_a = ipd.Icesat2Data('ATL06',[-64, 66, -55, 72],['2019-02-22','2019-02-28']) + reg_a = ipx.Query("ATL06", [-64, 66, -55, 72], ["2019-02-22", "2019-02-28"]) reg_a.build_CMR_params() obs_keys = reg_a.CMRparams.keys() - exp_keys_all = ['short_name','version','temporal'] - exp_keys_any = ['bounding_box','polygon'] - + exp_keys_all = ["short_name", "version", "temporal"] + exp_keys_any = ["bounding_box", "polygon"] + assert all(keys in obs_keys for keys in exp_keys_all) assert any(key in obs_keys for key in exp_keys_any) - + + def test_reqconfig_params(): - reg_a = ipd.Icesat2Data('ATL06',[-64, 66, -55, 72],['2019-02-22','2019-02-28']) - - #test for search params - reg_a.build_reqconfig_params('search') + reg_a = ipx.Query("ATL06", [-64, 66, -55, 72], ["2019-02-22", "2019-02-28"]) + + # test for search params + reg_a.build_reqconfig_params("search") obs_keys = reg_a.reqparams.keys() - exp_keys_all = ['page_size','page_num'] + exp_keys_all = ["page_size", "page_num"] assert all(keys in obs_keys for keys in exp_keys_all) - #test for download params - reg_a.reqparams=None - reg_a.build_reqconfig_params('download') - reg_a.reqparams.update({'token':'','email':''}) + # test for download params + reg_a.reqparams = None + reg_a.build_reqconfig_params("download") + reg_a.reqparams.update({"token": "", "email": ""}) obs_keys = reg_a.reqparams.keys() - exp_keys_all = ['page_size','page_num','request_mode','token','email','include_meta'] + exp_keys_all = [ + "page_size", + "page_num", + "request_mode", + "token", + "email", + "include_meta", + ] assert all(keys in obs_keys for keys in exp_keys_all) - + + def test_properties(): - reg_a = ipd.Icesat2Data('ATL06',[-64, 66, -55, 72],['2019-02-22','2019-02-28'],\ - start_time='03:30:00', end_time='21:30:00', version='2') - obs_list = [reg_a.dataset, reg_a.dates, reg_a.start_time, reg_a.end_time, reg_a.dataset_version, reg_a.spatial_extent] - exp_list = ['ATL06',['2019-02-22', '2019-02-28'], '03:30:00', '21:30:00', '002', ['bounding box', [-64, 66, -55, 72]]] - - for obs, expected in zip(obs_list,exp_list): - assert obs == expected + reg_a = ipx.Query( + "ATL06", + [-64, 66, -55, 72], + ["2019-02-22", "2019-02-28"], + start_time="03:30:00", + end_time="21:30:00", + version="2", + ) + obs_list = [ + reg_a.dataset, + reg_a.dates, + reg_a.start_time, + reg_a.end_time, + reg_a.dataset_version, + reg_a.spatial_extent, + ] + exp_list = [ + "ATL06", + ["2019-02-22", "2019-02-28"], + "03:30:00", + "21:30:00", + "002", + ["bounding box", [-64, 66, -55, 72]], + ] -#BestPractices: should do additional properties tests for each potential property type (e.g. spatial extent can have type bounding_box or polygon) + for obs, expected in zip(obs_list, exp_list): + assert obs == expected +# BestPractices: should do additional properties tests for each potential property type (e.g. spatial extent can have type bounding_box or polygon) - -#check that search results are correct (spatially, temporally, match actually available data) \ No newline at end of file +# check that search results are correct (spatially, temporally, match actually available data) diff --git a/icepyx/tests/test_APIformatting.py b/icepyx/tests/test_APIformatting.py index 802cfc872..16e559771 100644 --- a/icepyx/tests/test_APIformatting.py +++ b/icepyx/tests/test_APIformatting.py @@ -6,62 +6,1023 @@ import icepyx.core.APIformatting as apifmt -#DevNote: is this a situation where you'd ideally build a test class, since you're just repeating the -#test function with different inputs? Especially for the _fmt_spaital, where there's >2 tests? +# DevNote: is this a situation where you'd ideally build a test class, since you're just repeating the +# test function with different inputs? Especially for the _fmt_spaital, where there's >2 tests? -#CMR temporal and spatial formats --> what's the best way to compare formatted text? character by character comparison of strings? +# CMR temporal and spatial formats --> what's the best way to compare formatted text? character by character comparison of strings? ########## _fmt_temporal ########## def test_time_fmt(): - obs = apifmt._fmt_temporal(dt.datetime(2019,1,11,12,30,30), dt.datetime(2020,10,31,1,15), 'time') - exp = {'time': '2019-01-11T12:30:30,2020-10-31T01:15:00'} + obs = apifmt._fmt_temporal( + dt.datetime(2019, 1, 11, 12, 30, 30), dt.datetime(2020, 10, 31, 1, 15), "time" + ) + exp = {"time": "2019-01-11T12:30:30,2020-10-31T01:15:00"} assert obs == exp + def test_temporal_fmt(): - obs = apifmt._fmt_temporal(dt.datetime(2019,1,11,12,30,30), dt.datetime(2020,10,31,1,15), 'temporal') - exp = {'temporal': '2019-01-11T12:30:30Z,2020-10-31T01:15:00Z'} + obs = apifmt._fmt_temporal( + dt.datetime(2019, 1, 11, 12, 30, 30), + dt.datetime(2020, 10, 31, 1, 15), + "temporal", + ) + exp = {"temporal": "2019-01-11T12:30:30Z,2020-10-31T01:15:00Z"} assert obs == exp ########## _fmt_spatial ########## def test_bounding_box_fmt(): - obs = apifmt._fmt_spatial('bounding_box', [-55, 68, -48, 71]) - exp = {'bounding_box': '-55,68,-48,71'} + obs = apifmt._fmt_spatial("bounding_box", [-55, 68, -48, 71]) + exp = {"bounding_box": "-55,68,-48,71"} assert obs == exp + def test_bbox_fmt(): - obs = apifmt._fmt_spatial('bbox', [-55, 68, -48, 71]) - exp = {'bbox': '-55,68,-48,71'} + obs = apifmt._fmt_spatial("bbox", [-55, 68, -48, 71]) + exp = {"bbox": "-55,68,-48,71"} assert obs == exp + def test_polygon_fmt(): - poly = Polygon([[-86.622742, -74.908126, 0.0], [-86.602149, -74.998483, 0.0], [-86.671945, -74.999545, 0.0], [-86.667881, -75.01762, 0.0], [-86.737771, -75.018662, 0.0], [-86.717729, -75.109052, 0.0], [-86.788057, -75.110077, 0.0], [-86.780144, -75.14624, 0.0], [-86.850654, -75.147247, 0.0], [-86.835058, -75.219586, 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-74.729523, 0.0], + [-86.792609, -74.765656, 0.0], + [-86.723889, -74.764633, 0.0], + [-86.716003, -74.800766, 0.0], + [-86.647127, -74.799719, 0.0], + [-86.63091, -74.871988, 0.0], + [-86.561712, -74.870913, 0.0], + [-86.553377, -74.907049, 0.0], + [-86.622742, -74.908126, 0.0], + ] + ) + obs = apifmt._fmt_spatial("polygon", poly) + exp = { + "polygon": "-86.622742,-74.908126,-86.561712,-74.870913,-86.868859,-74.730522,-86.962905,-74.605038,-89.02594,-74.316754,-89.630517,-74.192147,-89.830808,-74.065919,-90.746478,-73.956258,-91.668214,-74.023169,-92.049815,-73.929387,-93.420791,-73.929327,-93.997163,-73.882768,-94.277701,-73.714183,-95.133017,-73.966355,-96.513501,-74.127404,-99.889802,-74.085347,-100.114438,-74.019422,-100.355131,-74.080906,-100.462734,-74.240864,-100.827076,-74.373988,-101.795349,-74.369597,-102.424826,-74.497263,-101.188725,-74.7179,-101.564382,-75.02971,-103.37484,-75.273725,-103.914847,-75.426057,-104.012128,-75.5223,-103.029452,-75.748774,-102.350567,-75.749245,-101.837882,-75.943066,-101.899461,-76.014086,-101.280944,-76.192769,-101.325735,-76.246168,-101.190803,-76.27106,-101.250474,-76.342292,-101.175067,-76.345822,-101.402436,-76.52035,-101.326063,-76.523929,-101.449791,-76.666392,-101.310795,-76.691373,-101.357407,-76.744819,-101.217404,-76.769752,-101.295133,-76.85887,-101.058051,-76.962123,-100.447336,-77.117686,-98.433698,-77.320866,-97.28308,-77.355688,-97.491148,-77.423178,-96.514174,-77.485919,-96.552494,-77.558236,-96.384656,-77.562336,-96.441516,-77.670857,-97.139363,-77.836566,-97.193451,-77.926901,-97.64271,-78.080044,-96.297869,-78.388943,-96.327803,-78.44329,-95.721466,-78.511065,-95.748962,-78.565482,-94.940425,-78.617072,-94.988611,-78.726066,-94.911669,-78.763976,-95.609268,-78.843079,-95.637038,-78.897535,-95.37191,-78.9391,-95.693408,-79.006456,-95.269903,-79.124145,-95.323729,-79.233172,-95.430206,-79.249633,-95.155505,-79.291032,-95.191045,-79.363748,-94.81352,-79.406486,-94.847075,-79.479253,-94.747448,-79.48078,-94.772403,-79.535367,-93.90411,-79.638844,-93.843651,-79.749409,-93.967323,-79.802836,-93.788723,-79.87821,-93.816393,-79.951128,-93.230546,-80.085534,-91.707475,-79.87748,-91.801545,-79.822143,-91.488897,-79.805457,-91.465152,-79.641131,-90.447349,-79.5894,-90.545492,-79.534464,-90.042319,-79.37062,-90.140775,-79.334083,-90.041814,-79.24285,-88.982186,-79.076903,-90.230262,-78.914333,-90.32191,-78.804808,-90.689626,-78.676516,-91.150024,-78.638589,-92.035347,-78.414844,-92.106013,-78.30491,-91.651645,-78.271472,-91.365784,-78.127206,-91.188783,-78.128018,-91.090167,-78.019109,-90.737076,-77.983849,-90.909191,-77.946905,-90.732603,-77.911009,-90.727088,-77.819973,-91.070502,-77.800626,-91.14118,-77.636469,-91.90279,-77.613923,-91.984627,-77.595116,-91.972963,-77.522365,-92.466819,-77.463587,-92.199521,-77.374914,-92.352136,-77.300761,-92.335283,-77.209895,-91.434206,-77.234653,-91.426015,-77.16193,-91.015545,-77.145686,-91.008355,-77.054784,-91.086397,-77.018096,-91.647835,-76.97871,-91.640906,-76.924199,-91.873848,-76.868024,-91.779021,-76.759619,-90.823937,-76.710073,-90.345113,-76.52953,-86.988029,-75.856983,-86.945563,-75.711143,-86.872234,-75.710165,-87.034102,-75.63967,-86.965004,-75.620616,-87.075115,-75.440545,-87.003154,-75.439609,-87.021872,-75.349129,-86.835058,-75.219586,-86.850654,-75.147247,-86.717729,-75.109052,-86.737771,-75.018662,-86.602149,-74.998483,-86.622742,-74.908126" + } assert obs == exp ########## _fmt_var_subset_list ########## def test_var_subset_list_fmt(): - obs = apifmt._fmt_var_subset_list({'atlas_sdp_gps_epoch': ['ancillary_data/atlas_sdp_gps_epoch'], - 'data_end_utc': ['ancillary_data/data_end_utc'], - 'data_start_utc': ['ancillary_data/data_start_utc'], - 'end_delta_time': ['ancillary_data/end_delta_time'], - 'granule_end_utc': ['ancillary_data/granule_end_utc'], - 'granule_start_utc': ['ancillary_data/granule_start_utc'], - 'latitude': ['profile_2/high_rate/latitude', 'profile_2/low_rate/latitude'], - 'sc_orient': ['orbit_info/sc_orient'], - 'start_delta_time': ['ancillary_data/start_delta_time']}) - exp = '/ancillary_data/atlas_sdp_gps_epoch,/ancillary_data/data_end_utc,/ancillary_data/data_start_utc,/ancillary_data/end_delta_time,/ancillary_data/granule_end_utc,/ancillary_data/granule_start_utc,/profile_2/high_rate/latitude,/profile_2/low_rate/latitude,/orbit_info/sc_orient,/ancillary_data/start_delta_time' + obs = apifmt._fmt_var_subset_list( + { + "atlas_sdp_gps_epoch": ["ancillary_data/atlas_sdp_gps_epoch"], + "data_end_utc": ["ancillary_data/data_end_utc"], + "data_start_utc": ["ancillary_data/data_start_utc"], + "end_delta_time": ["ancillary_data/end_delta_time"], + "granule_end_utc": ["ancillary_data/granule_end_utc"], + "granule_start_utc": ["ancillary_data/granule_start_utc"], + "latitude": ["profile_2/high_rate/latitude", "profile_2/low_rate/latitude"], + "sc_orient": ["orbit_info/sc_orient"], + "start_delta_time": ["ancillary_data/start_delta_time"], + } + ) + exp = "/ancillary_data/atlas_sdp_gps_epoch,/ancillary_data/data_end_utc,/ancillary_data/data_start_utc,/ancillary_data/end_delta_time,/ancillary_data/granule_end_utc,/ancillary_data/granule_start_utc,/profile_2/high_rate/latitude,/profile_2/low_rate/latitude,/orbit_info/sc_orient,/ancillary_data/start_delta_time" assert obs == exp ########## combine_params ########## def test_combine_params(): - dict1 = {'key1': 0, 'key2': 1} - dict2 = {'key3':10} - obs = apifmt.combine_params(dict1,dict2) - expected = {'key1': 0, 'key2': 1, 'key3':10} + dict1 = {"key1": 0, "key2": 1} + dict2 = {"key3": 10} + obs = apifmt.combine_params(dict1, dict2) + expected = {"key1": 0, "key2": 1, "key3": 10} assert obs == expected @@ -72,28 +1033,36 @@ def test_combine_params(): # def CMRparams(scope='module'): # return apifmt.Parameters('CMR') + def test_CMRparams_no_other_inputs(): - CMRparams = apifmt.Parameters('CMR') - #TestQuestion: the next statement essentially tests _get_possible_keys as well, so how would I test them independently? - assert CMRparams.poss_keys == {'default': ['short_name','version','temporal'], 'spatial': ['bounding_box','polygon'], 'optional': []} + CMRparams = apifmt.Parameters("CMR") + # TestQuestion: the next statement essentially tests _get_possible_keys as well, so how would I test them independently? + assert CMRparams.poss_keys == { + "default": ["short_name", "version", "temporal"], + "spatial": ["bounding_box", "polygon"], + "optional": ["orbit_number"], + } assert CMRparams.fmted_keys == {} assert CMRparams._check_valid_keys - #Note: this test must be done before the next one - if CMRparams.partype == 'required': + # Note: this test must be done before the next one + if CMRparams.partype == "required": assert CMRparams.check_req_values() == False else: assert CMRparams.check_values() == False - - CMRparams.build_params(dataset='ATL06', version='003', - start=dt.datetime(2019, 2, 20, 0, 0), - end=dt.datetime(2019, 2, 24, 23, 59, 59), - extent_type='bounding_box', - spatial_extent=[-55, 68, -48, 71]) + + CMRparams.build_params( + dataset="ATL06", + version="003", + start=dt.datetime(2019, 2, 20, 0, 0), + end=dt.datetime(2019, 2, 24, 23, 59, 59), + extent_type="bounding_box", + spatial_extent=[-55, 68, -48, 71], + ) obs_fmted_params = CMRparams.fmted_keys - exp_fmted_params = {'short_name': 'ATL06', 'version': '003', - 'temporal': '2019-02-20T00:00:00Z,2019-02-24T23:59:59Z', - 'bounding_box': '-55,68,-48,71'} + exp_fmted_params = { + "short_name": "ATL06", + "version": "003", + "temporal": "2019-02-20T00:00:00Z,2019-02-24T23:59:59Z", + "bounding_box": "-55,68,-48,71", + } assert obs_fmted_params == exp_fmted_params - - - \ No newline at end of file diff --git a/icepyx/tests/test_geospatial.py b/icepyx/tests/test_geospatial.py index 31dbd38f3..9d7f86b13 100644 --- a/icepyx/tests/test_geospatial.py +++ b/icepyx/tests/test_geospatial.py @@ -8,15 +8,15 @@ ########## geodataframe ########## def test_gdf_from_bbox(): - obs = geospatial.geodataframe('bounding_box',[-55, 68, -48, 71]) + obs = geospatial.geodataframe("bounding_box", [-55, 68, -48, 71]) geom = [Polygon(list(zip([-55, -55, -48, -48, -55], [68, 71, 71, 68, 68])))] exp = gpd.GeoDataFrame(geometry=geom) - #DevNote: this feels like a questionable test to me, since it specifies the first entry (though there should only be one) + # DevNote: this feels like a questionable test to me, since it specifies the first entry (though there should only be one) assert obs.geometry[0] == exp.geometry[0] -#TestQuestions: 1) Do these need to be tested? -#2) Is the best way to test them with lengthy inputs and seeing if the gdfs are the same? +# TestQuestions: 1) Do these need to be tested? +# 2) Is the best way to test them with lengthy inputs and seeing if the gdfs are the same? # def test_gdf_from_strpoly(): # def test_gdf_from_filepoly(): @@ -24,7 +24,7 @@ def test_gdf_from_bbox(): def test_bad_extent_input(): ermsg = "Your spatial extent type is not an accepted input and a geodataframe cannot be constructed" - #DevNote: can't get the test to pass if the extent_type is included. Not sure why the strings "don't match" + # DevNote: can't get the test to pass if the extent_type is included. Not sure why the strings "don't match" # ermsg = "Your spatial extent type (polybox) is not an accepted input and a geodataframe cannot be constructed" with pytest.raises(TypeError, match=ermsg): - geospatial.geodataframe('polybox',[1,2,3,4]) \ No newline at end of file + geospatial.geodataframe("polybox", [1, 2, 3, 4]) diff --git a/icepyx/tests/test_granules.py b/icepyx/tests/test_granules.py index d173d69cd..a1f0ff21f 100644 --- a/icepyx/tests/test_granules.py +++ b/icepyx/tests/test_granules.py @@ -1,15 +1,14 @@ import pytest import warnings -from icepyx.core import icesat2data as ipd +import icepyx as ipx from icepyx.core import granules as granules from icepyx.core.granules import Granules as Granules - # @pytest.fixture # def reg_a(): -# return ipd.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-22','2019-02-28']) +# return ipx.Query('ATL06',[-55, 68, -48, 71],['2019-02-22','2019-02-28']) # #@patch('my_module.__get_input', return_value='y') @@ -18,397 +17,591 @@ # return reg_a._start_earthdata_session('icepyx_devteam', 'icepyx.dev@gmail.com', os.getenv('NSIDC_LOGIN')) -#DevNote: clearly there's a better way that doesn't make the function so long... what is it? +# DevNote: clearly there's a better way that doesn't make the function so long... what is it? def test_granules_info(): - # reg_a = ipd.Icesat2Data('ATL06', [-55, 68, -48, 71], ['2019-02-20','2019-02-24'], version='3') + # reg_a = ipx.Query('ATL06', [-55, 68, -48, 71], ['2019-02-20','2019-02-24'], version='3') # granules = reg_a.granules.avail - granules = [{'producer_granule_id': 'ATL06_20190221121851_08410203_003_01.h5', - 'time_start': '2019-02-21T12:19:05.000Z', - 'orbit': {'ascending_crossing': '-40.35812957405553', - 'start_lat': '59.5', - 'start_direction': 'A', - 'end_lat': '80', - 'end_direction': 'A'}, - 'updated': '2020-05-04T15:43:02.942Z', - 'orbit_calculated_spatial_domains': [{'equator_crossing_date_time': '2019-02-21T12:03:18.922Z', - 'equator_crossing_longitude': '-40.35812957405553', - 'orbit_number': '2429'}], - 'dataset_id': 'ATLAS/ICESat-2 L3A Land Ice Height V003', - 'data_center': 'NSIDC_ECS', - 'title': 'SC:ATL06.003:177534295', - 'coordinate_system': 'ORBIT', - 'time_end': '2019-02-21T12:24:16.000Z', - 'id': 'G1723268629-NSIDC_ECS', - 'original_format': 'ISO-SMAP', - 'granule_size': '50.3300800323', - 'browse_flag': True, - 'polygons': [['60.188087866839815 -48.12471565111877 79.13565976324539 -56.91308349854652 79.82054625244331 -57.75066986682175 79.88471463831527 -55.94835931630358 79.19580392788636 -55.21962622534677 60.21083561664105 -47.47451382423887 60.188087866839815 -48.12471565111877']], - 'collection_concept_id': 'C1706333750-NSIDC_ECS', - 'online_access_flag': True, - 'links': [{'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#', - 'type': 'application/x-hdfeos', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP7/ATLAS/ATL06.003/2019.02.21/ATL06_20190221121851_08410203_003_01.h5'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.default.default1.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.default.default2.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1l.atl06_quality_summary.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1l.h_li.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1l.h_li_sigma.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1l.n_fit_photons.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1l.signal_selection_source.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1r.atl06_quality_summary.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1r.h_li.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1r.h_li_sigma.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1r.n_fit_photons.jpg'}, - {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#', - 'type': 'image/jpeg', - 'hreflang': 'en-US', - 'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.16/ATL06_20190221121851_08410203_003_01_BRW.gt1r.signal_selection_source.jpg'}, - {'rel': 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"http://esipfed.org/ns/fedsearch/1.1/data#", + "hreflang": "en-US", + "href": "https://search.earthdata.nasa.gov/search/granules?p=C1706333750-NSIDC_ECS&q=atl06%20v003&m=-29.109278436791882!-59.86889648437499!1!1!0!0%2C2&tl=1572814258!4!!", + }, + { + "inherited": True, + "length": "0.0KB", + "rel": "http://esipfed.org/ns/fedsearch/1.1/data#", + "hreflang": "en-US", + "href": "https://openaltimetry.org/", + }, + { + "inherited": True, + "rel": "http://esipfed.org/ns/fedsearch/1.1/metadata#", + "hreflang": "en-US", + "href": "https://doi.org/10.5067/ATLAS/ATL06.003", + }, + { + "inherited": True, + "rel": "http://esipfed.org/ns/fedsearch/1.1/documentation#", + "hreflang": "en-US", + "href": "https://doi.org/10.5067/ATLAS/ATL06.003", + }, + ], + }, + ] + obs = granules.info(grans) + exp = { + "Number of available granules": 2, + "Average size of granules (MB)": 46.49339485165, + "Total size of all granules (MB)": 92.9867897033, + } + assert obs == exp def test_no_granules_in_search_results(): ermsg = "Your search returned no results; try different search parameters" with pytest.raises(AssertionError, match=ermsg): - ipd.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-20'], version='2').avail_granules() + ipx.Query( + "ATL06", [-55, 68, -48, 71], ["2019-02-20", "2019-02-20"], version="2" + ).avail_granules() + def test_correct_granule_list_returned(): - reg_a = ipd.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='2') + reg_a = ipx.Query( + "ATL06", [-55, 68, -48, 71], ["2019-02-20", "2019-02-28"], version="2" + ) + reg_a.avail_granules() - obs_grans = [gran['producer_granule_id'] for gran in reg_a.granules] - exp_grans = ['ATL06_20190221121851_08410203_002_01.h5', 'ATL06_20190222010344_08490205_002_01.h5', 'ATL06_20190225121032_09020203_002_01.h5', 'ATL06_20190226005526_09100205_002_01.h5'] - - assert set(obs_grans) == set(exp_grans) \ No newline at end of file + obs_grans = [gran["producer_granule_id"] for gran in reg_a.granules.avail] + exp_grans = [ + "ATL06_20190221121851_08410203_002_01.h5", + "ATL06_20190222010344_08490205_002_01.h5", + "ATL06_20190225121032_09020203_002_01.h5", + "ATL06_20190226005526_09100205_002_01.h5", + ] + + assert set(obs_grans) == set(exp_grans) diff --git a/icepyx/tests/test_is2ref.py b/icepyx/tests/test_is2ref.py index 94c0f7bc0..3112be7c2 100644 --- a/icepyx/tests/test_is2ref.py +++ b/icepyx/tests/test_is2ref.py @@ -5,26 +5,28 @@ ########## _validate_dataset ########## def test_lowercase_dataset(): - lcds = 'atl03' + lcds = "atl03" obs = is2ref._validate_dataset(lcds) - expected = 'ATL03' + expected = "ATL03" assert obs == expected + def test_num_dataset(): dsnum = 6 ermsg = "Please enter a dataset string" with pytest.raises(TypeError, match=ermsg): is2ref._validate_dataset(dsnum) - + + def test_bad_dataset(): - wrngds = 'atl-6' + wrngds = "atl-6" ermsg = "Please enter a valid dataset" with pytest.raises(AssertionError, match=ermsg): is2ref._validate_dataset(wrngds) ########## about_dataset ########## -#Note: requires internet connection +# Note: requires internet connection # def test_dataset_info(): # obs = is2ref.about_dataset('ATL06') # print(obs) @@ -151,16 +153,36 @@ def test_bad_dataset(): ########## _get_custom_options ########## -#Note: requires internet connection + active NSIDC session -#Thus, the tests for this function are in the test_behind_NSIDC_API_login suite +# Note: requires internet connection + active NSIDC session +# Thus, the tests for this function are in the test_behind_NSIDC_API_login suite ########## _default_varlists ########## def test_ATL09_default_varlist(): - obs = is2ref._default_varlists('ATL09') - expected = ['delta_time','latitude','longitude', 'bsnow_h','bsnow_dens','bsnow_con','bsnow_psc','bsnow_od', - 'cloud_flag_asr','cloud_fold_flag','cloud_flag_atm', - 'column_od_asr','column_od_asr_qf', - 'layer_attr','layer_bot','layer_top','layer_flag','layer_dens','layer_ib', - 'msw_flag','prof_dist_x','prof_dist_y','apparent_surf_reflec'] - assert obs == expected \ No newline at end of file + obs = is2ref._default_varlists("ATL09") + expected = [ + "delta_time", + "latitude", + "longitude", + "bsnow_h", + "bsnow_dens", + "bsnow_con", + "bsnow_psc", + "bsnow_od", + "cloud_flag_asr", + "cloud_fold_flag", + "cloud_flag_atm", + "column_od_asr", + "column_od_asr_qf", + "layer_attr", + "layer_bot", + "layer_top", + "layer_flag", + "layer_dens", + "layer_ib", + "msw_flag", + "prof_dist_x", + "prof_dist_y", + "apparent_surf_reflec", + ] + assert obs == expected diff --git a/icepyx/tests/test_validate_inputs.py b/icepyx/tests/test_validate_inputs.py index f8bcc918c..c0da6a764 100644 --- a/icepyx/tests/test_validate_inputs.py +++ b/icepyx/tests/test_validate_inputs.py @@ -8,119 +8,176 @@ ########## dset_version ########## def test_version_setting_to_latest(): - obs = val.dset_version('010',None) - expected = '010' + obs = val.dset_version("010", None) + expected = "010" assert obs == expected + def test_neg_version_str_given(): ermesg = "Version number must be positive" with pytest.raises(AssertionError, match=ermesg): - val.dset_version('010', '-12') + val.dset_version("010", "-12") + def test_short_version_str_given(): - obs = val.dset_version('001', '2') - expected = '002' + obs = val.dset_version("001", "2") + expected = "002" assert obs == expected + def test_int_version(): ermesg = "Please enter the version number as a string" with pytest.raises(TypeError, match=ermesg): - val.dset_version('010', 12) + val.dset_version("010", 12) + def test_old_version(): wrng = "You are using an old version of this dataset" with pytest.warns(UserWarning, match=wrng): - val.dset_version('003','001') - + val.dset_version("003", "001") + ########## spatial ########## def test_intlist_bbox(): obs = val.spatial([-64, 66, -55, 72]) - expected = ['bounding_box', [-64, 66, -55, 72], None] + expected = ["bounding_box", [-64, 66, -55, 72], None] for i in range(len(expected)): assert obs[i] == expected[i] + def test_floatlist_bbox(): obs = val.spatial([-64.2, 66.2, -55.5, 72.5]) - expected = ['bounding_box', [-64.2, 66.2, -55.5, 72.5], None] + expected = ["bounding_box", [-64.2, 66.2, -55.5, 72.5], None] for i in range(len(expected)): assert obs[i] == expected[i] + def test_list_latlon_pairs(): out = val.spatial([[-55, 68], [-55, 71], [-48, 71], [-48, 68], [-55, 68]]) - obs = [out[0], out[1].exterior.coords.xy[0].tolist(), out[1].exterior.coords.xy[1].tolist(), out[2]] + obs = [ + out[0], + out[1].exterior.coords.xy[0].tolist(), + out[1].exterior.coords.xy[1].tolist(), + out[2], + ] # expected = ['polygon', [-55.0, 68.0, -55.0, 71.0, -48.0, 71.0, -48.0, 68.0, -55.0, 68.0], None] # expected = ['polygon', [[-55.0, 68.0], [-55.0, 71.0], [-48.0, 71.0], [-48.0, 68.0], [-55.0, 68.0]], None] - expected = ['polygon', [-55.0, -55.0, -48.0, -48.0, -55.0], [68.0, 71.0, 71.0, 68.0, 68.0], None] + expected = [ + "polygon", + [-55.0, -55.0, -48.0, -48.0, -55.0], + [68.0, 71.0, 71.0, 68.0, 68.0], + None, + ] for i in range(len(expected)): assert obs[i] == expected[i] + def test_tuple_latlon_pairs(): out = val.spatial([(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)]) - obs = [out[0], out[1].exterior.coords.xy[0].tolist(), out[1].exterior.coords.xy[1].tolist(), out[2]] - expected = ['polygon', [-55.0, -55.0, -48.0, -48.0, -55.0], [68.0, 71.0, 71.0, 68.0, 68.0], None] + obs = [ + out[0], + out[1].exterior.coords.xy[0].tolist(), + out[1].exterior.coords.xy[1].tolist(), + out[2], + ] + expected = [ + "polygon", + [-55.0, -55.0, -48.0, -48.0, -55.0], + [68.0, 71.0, 71.0, 68.0, 68.0], + None, + ] for i in range(len(expected)): assert obs[i] == expected[i] + def test_intlist_latlon_coords(): out = val.spatial([-55, 68, -55, 71, -48, 71, -48, 68, -55, 68]) - obs = [out[0], out[1].exterior.coords.xy[0].tolist(), out[1].exterior.coords.xy[1].tolist(), out[2]] - expected = ['polygon', [-55.0, -55.0, -48.0, -48.0, -55.0], [68.0, 71.0, 71.0, 68.0, 68.0], None] + obs = [ + out[0], + out[1].exterior.coords.xy[0].tolist(), + out[1].exterior.coords.xy[1].tolist(), + out[2], + ] + expected = [ + "polygon", + [-55.0, -55.0, -48.0, -48.0, -55.0], + [68.0, 71.0, 71.0, 68.0, 68.0], + None, + ] for i in range(len(expected)): assert obs[i] == expected[i] + def test_floatlist_latlon_coords(): out = val.spatial([-55.0, 68.7, -55.0, 71, -48, 71, -48, 68.7, -55.0, 68.7]) - obs = [out[0], out[1].exterior.coords.xy[0].tolist(), out[1].exterior.coords.xy[1].tolist(), out[2]] - expected = ['polygon', [-55.0, -55.0, -48.0, -48.0, -55.0], [68.7, 71.0, 71.0, 68.7, 68.7], None] + obs = [ + out[0], + out[1].exterior.coords.xy[0].tolist(), + out[1].exterior.coords.xy[1].tolist(), + out[2], + ] + expected = [ + "polygon", + [-55.0, -55.0, -48.0, -48.0, -55.0], + [68.7, 71.0, 71.0, 68.7, 68.7], + None, + ] for i in range(len(expected)): assert obs[i] == expected[i] + def test_spat_input_type(): - ermsg = 'Your spatial extent does not meet minimum input criteria' + ermsg = "Your spatial extent does not meet minimum input criteria" with pytest.raises(ValueError, match=ermsg): val.spatial([-64, 66, (-55, 72), 72]) + def test_poly_spat_file_input(): ermsg = "Check that the path and filename of your geometry file are correct" with pytest.raises(AssertionError, match=ermsg): - val.spatial('file_path/name/invalid_type.txt') + val.spatial("file_path/name/invalid_type.txt") -#should there be additional tests on the valid file type if-else? If so, is this were we'd use a mock to assume -#a valid (existing) file has been passed? Otherwise we'll never get passed the assert statement... + +# should there be additional tests on the valid file type if-else? If so, is this were we'd use a mock to assume +# a valid (existing) file has been passed? Otherwise we'll never get passed the assert statement... ########## temporal ########## def test_date_range_order(): ermsg = "Your date range is invalid" with pytest.raises(AssertionError, match=ermsg): - val.temporal(['2019-03-22','2019-02-28'], None, None) + val.temporal(["2019-03-22", "2019-02-28"], None, None) + def test_bad_date_range(): ermsg = "Your date range list is the wrong length. It should have start and end dates only." with pytest.raises(ValueError, match=ermsg): - val.temporal(['2019-02-22'], None, None) + val.temporal(["2019-02-22"], None, None) + def test_time_defaults(): - obs_st, obs_end = val.temporal(['2019-02-22','2019-02-28'], None, None) - exp_start = dt.datetime(2019,2,22,00,00,00) - exp_end = dt.datetime(2019,2,28,23,59,59) + obs_st, obs_end = val.temporal(["2019-02-22", "2019-02-28"], None, None) + exp_start = dt.datetime(2019, 2, 22, 00, 00, 00) + exp_end = dt.datetime(2019, 2, 28, 23, 59, 59) assert obs_st == exp_start assert obs_end == exp_end + def test_time_validstr(): - obs_st, obs_end = val.temporal(['2019-02-22','2019-02-28'], '13:50:59', '23:15:00') - exp_start = dt.datetime(2019,2,22,13,50,59) - exp_end = dt.datetime(2019,2,28,23,15) + obs_st, obs_end = val.temporal(["2019-02-22", "2019-02-28"], "13:50:59", "23:15:00") + exp_start = dt.datetime(2019, 2, 22, 13, 50, 59) + exp_end = dt.datetime(2019, 2, 28, 23, 15) assert obs_st == exp_start assert obs_end == exp_end + def test_starttime_validstr(): ermsg = "Please enter your start time as a string" with pytest.raises(TypeError, match=ermsg): - val.temporal(['2019-02-22','2019-02-28'], 121500, None) + val.temporal(["2019-02-22", "2019-02-28"], 121500, None) + def test_endtime_validstr(): ermsg = "Please enter your end time as a string" with pytest.raises(TypeError, match=ermsg): - val.temporal(['2019-02-22','2019-02-28'], '00:15:00', 235959) + val.temporal(["2019-02-22", "2019-02-28"], "00:15:00", 235959) diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 000000000..ff9ff2086 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,5 @@ +[pytest] +minversion = 2.0 +norecursedirs = .git +python_files = test*.py +addopts = --cov=./ diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 000000000..f28d0e70f --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,4 @@ +codecov +pre_commit +pytest>=4.6 +pytest-cov diff --git a/requirements-docs.txt b/requirements-docs.txt index 341ea897b..ea2890790 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,3 +1,5 @@ +gitpython nbsphinx numpydoc +pygithub -r requirements.txt diff --git a/requirements.txt b/requirements.txt index 807d6262d..3fd176fc7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,5 +4,6 @@ geopandas h5py matplotlib numpy +pre-commit requests shapely diff --git a/setup.py b/setup.py index d45dea393..b82495305 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ setuptools.setup( name="icepyx", - version="0.2.0", + version="0.3.0", author="The icepyx Developers", author_email="jbscheick@gmail.com", maintainer="Jessica Scheick",