diff --git a/.circleci/config.yml b/.circleci/config.yml index fde8c81..a4e79b0 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -14,6 +14,7 @@ jobs: - setup_remote_docker - run: name: Build and save Docker image + working_directory: ~/tutorials/binder command: | DOCKER_IMAGE=nsidc/tutorials @@ -32,7 +33,7 @@ jobs: docker save -o build/saved-docker-image.tar ${DOCKER_IMAGE}:${TAG} echo "${DOCKER_IMAGE}:${TAG}" > build/docker-image-name.txt - persist_to_workspace: - root: ~/tutorials + root: ~/tutorials/binder paths: - build @@ -41,7 +42,7 @@ jobs: steps: - setup_remote_docker - attach_workspace: - at: ~/tutorials + at: ~/tutorials/binder - deploy: name: Publish Docker image command: | diff --git a/.github/workflows/scan_notebooks.py b/.github/workflows/scan_notebooks.py index 5c098fb..80ad668 100644 --- a/.github/workflows/scan_notebooks.py +++ b/.github/workflows/scan_notebooks.py @@ -26,9 +26,9 @@ def ensure_environment(notebook, libraries): def scan_notebook(notebook_path): notebook_path = notebook_path.replace(" ", "\\ ") - command = f"pipreqsnb {notebook_path} --print" + command = f"pipreqsnb {notebook_path} --print --debug" - libraries = [] + libraries = set() p = subprocess.Popen( command, shell=True, @@ -42,8 +42,10 @@ def scan_notebook(notebook_path): parsed_out = inline.decode("UTF-8").lower() if "==" in parsed_out: library = parsed_out.split("==")[0] - libraries.append(library) - print(libraries) + if library.lower() == "gdal": + library = "osgeo" + libraries.add(library) + print(list(libraries)) ensure_environment(notebook_path, libraries) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 0648a2a..e747699 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,72 +1,84 @@ name: Test Notebooks on: - push: pull_request: + paths: + - notebooks/** + - binder/** + - '.github/workflows/' types: [opened, synchronize] +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: - posix: - name: Runtime (${{ matrix.os }}, Mambaforge) + linux: + name: Linux tests + runs-on: ubuntu-latest + strategy: + fail-fast: true + steps: + - uses: actions/checkout@v3 + - name: "Install Conda environment with Micromamba" + uses: mamba-org/setup-micromamba@v1 + with: + cache-env: true + environment-file: binder/conda-linux-64.lock + environment-name: nsidc-tutorials + + - name: "Verify micromamba" + shell: bash -l {0} + run: | + micromamba info + jupyter kernelspec list + which python + conda list | sort + + - name: "Verify that libraries used in notebooks are present in the environment" + shell: bash -l {0} + run: | + find notebooks -type f -iname \*.ipynb -not -path '*\.ipynb_checkpoints/*' -not -path '*/iceflow/*' | xargs -I % python .github/workflows/scan_notebooks.py -n % + + - name: "Setup Quarto" + uses: quarto-dev/quarto-actions/setup@v2 + with: + # version: 1.1.251 + version: 1.3.361 + + - name: "Execute notebooks with Quarto" + env: + EARTHDATA_USERNAME: ${{ secrets.EARTHDATA_USERNAME }} + EARTHDATA_PASSWORD: ${{ secrets.EARTHDATA_PASSWORD }} + shell: bash -l {0} + run: | + find notebooks -type f -name "*.ipynb" -not -path '*\.ipynb_checkpoints/*' -not -path '*endered*' -not -path '*SnowEx*' | xargs -I F quarto render "F" --execute-daemon-restart + + + win-osx: + name: Runtime (${{ matrix.os }}, micromamba) runs-on: ${{ matrix.os }}-latest strategy: fail-fast: false matrix: - os: ["ubuntu", "macos"] + os: ["windows", "macos"] include: - - os: ubuntu - environment-file: binder/conda-linux-64.lock - miniforge-variant: Mambaforge - miniforge-version: latest + - os: windows + environment-file: binder/conda-win-64.lock - os: macos environment-file: binder/conda-osx-64.lock - miniforge-variant: Mambaforge - miniforge-version: latest steps: - - uses: actions/checkout@v2 - - uses: conda-incubator/setup-miniconda@v2 + - uses: actions/checkout@v3 + - name: "Install Conda environment with Micromamba" + uses: mamba-org/setup-micromamba@v1 with: + cache-env: true environment-file: ${{ matrix.environment-file }} - miniforge-variant: ${{ matrix.miniforge-variant }} - miniforge-version: ${{ matrix.miniforge-version }} - use-mamba: true + environment-name: nsidc-tutorials - - name: Bash + - name: "Verify micromamba" shell: bash -l {0} run: | - conda info - conda list + micromamba info + jupyter kernelspec list printenv | sort - find notebooks -type f -iname \*.ipynb -not -path '*\.ipynb_checkpoints/*' -not -path "*/iceflow/*" | xargs -I % python .github/workflows/scan_notebooks.py -n % - - - # windows: - # name: Runtime Windows Mambaforge - # runs-on: windows-latest - # strategy: - # fail-fast: false - # steps: - # - uses: actions/checkout@v2 - # - uses: conda-incubator/setup-miniconda@v2 - # with: - # environment-file: binder/conda-win-64.lock - # miniforge-variant: Mambaforge - # miniforge-version: latest - # use-mamba: true - - # - name: PowerShell - # shell: powershell - # run: | - # conda info - # conda list - - # - name: PowerShell Core - # shell: pwsh - # run: | - # conda info - # conda list - - # - name: Cmd.exe - # shell: cmd /C CALL {0} - # run: >- - # conda info && conda list diff --git a/.gitignore b/.gitignore index 028b5aa..9ed8e86 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ MANIFEST notebook/Outputs build +bin dist _build docs/man/*.gz diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index bb4072f..0000000 --- a/Dockerfile +++ /dev/null @@ -1,22 +0,0 @@ -FROM jupyter/base-notebook:2022-02-17 - -USER root - -# Install all OS dependencies for notebook server that starts but lacks all -# features (e.g., download as all possible file formats) -ENV DEBIAN_FRONTEND noninteractive -RUN apt-get update \ - && apt-get install -yq --no-install-recommends git jq \ - && apt-get clean && apt autoremove && rm -rf /var/lib/apt - -USER $NB_UID - -ADD binder/environment.yml $HOME -RUN mamba env update -f environment.yml -n base && conda clean --force --yes --all && rm $HOME/environment.yml && \ -npm cache clean --force && \ -rm -rf /opt/conda/lib/python3.9/site-packages/awscli/examples \ -&& find /opt/conda/ -follow -type f -name '*.a' -delete \ -&& find /opt/conda/ -follow -type f -name '*.pyc' -delete \ -&& fix-permissions /home/$NB_USER && fix-permissions - -ENTRYPOINT ["jupyter", "lab","--ip=0.0.0.0","--allow-root"] diff --git a/README.md b/README.md index 9825641..fc9a908 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nsidc/NSIDC-Data-Tutorial/main?urlpath=lab/tree/notebooks) -[![CircleCI](https://circleci.com/gh/nsidc/NSIDC-Data-Tutorials.svg?style=svg)](https://circleci.com/gh/nsidc/NSIDC-Data-Tutorials) +[![Test Notebooks](https://github.com/nsidc/NSIDC-Data-Tutorials/actions/workflows/test.yml/badge.svg)](https://github.com/nsidc/NSIDC-Data-Tutorials/actions/workflows/test.yml) ## Summary @@ -42,6 +42,17 @@ The notebooks on this project demonstrate how to search and access ITS_LIVE velo Originally presented to the UWG (User Working Group) in May 2022, this tutorial demonstrates how to search for ICESat-2 data hosted in the Earthdata Cloud and how to directly access it from an Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instance using the `earthaccess` package. +### [MEaSUREs](./notebooks/measures) + +**Download, crop, resample, and plot multiple GeoTIFFs** + +This tutorial guides you through programmatically accessing and downloading GeoTIFF files from the NSIDC DAAC to your local computer. We then crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF, then plot one on top of the other. + +We will use two data sets from the NASA [MEaSUREs](https://nsidc.org/data/measures) (Making Earth System data records for Use in Research Environments) program as an example: + +* [MEaSUREs Greenland Ice Mapping Project (GrIMP) Digital Elevation Model from GeoEye and WorldView Imagery, Version 2 (NSIDC-0715)](https://nsidc.org/data/nsidc-0715/versions/2) +* [MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR, Version 4 (NSIDC-0481)](https://nsidc.org/data/nsidc-0481/versions/4) + ## Usage with Binder The Binder button above allows you to explore and run the notebook in a shared cloud computing environment without the need to install dependencies on your local machine. Note that this option will not directly download data to your computer; instead the data will be downloaded to the cloud environment. diff --git a/binder/Dockerfile b/binder/Dockerfile new file mode 100644 index 0000000..7fdd717 --- /dev/null +++ b/binder/Dockerfile @@ -0,0 +1,6 @@ +FROM pangeo/base-image:2023.01.13 + + +USER $NB_UID + +ENTRYPOINT ["jupyter", "lab","--ip=0.0.0.0","--allow-root"] diff --git a/binder/conda-linux-64.lock b/binder/conda-linux-64.lock index 31a0ea9..033ce6c 100644 --- a/binder/conda-linux-64.lock +++ b/binder/conda-linux-64.lock @@ -1,34 +1,33 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: e68f2de02fbfaf4dde040136c11d2ed31916ba37bc7fbdd0d4071ed10e739b81 +# input_hash: b054bec068535d773fab22d5d999ba249fa828f48fc7a9add1cd7575a26c9120 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 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+https://conda.anaconda.org/conda-forge/noarch/jupyter-offlinenotebook-0.2.2-pyh1d7be83_0.tar.bz2#fe55056ce4bc4bd4953ba440270735fb +https://conda.anaconda.org/conda-forge/noarch/jupyterlab-3.6.4-pyhd8ed1ab_0.conda#e66d4a6e36fcc92fe266932bf85fd35a +https://conda.anaconda.org/conda-forge/noarch/vaex-jupyter-0.8.1-pyhd8ed1ab_0.conda#f2e69dc140bdbc5672cc73036a6a6fa4 +https://conda.anaconda.org/conda-forge/noarch/icepyx-0.7.0-pyhd8ed1ab_0.conda#03cf6dbe3a74ae873628057eb1167d80 +https://conda.anaconda.org/conda-forge/noarch/sidecar-0.5.2-pyhd8ed1ab_0.conda#bcf479b100d93fd9ffbe513d257f8a5a +https://conda.anaconda.org/conda-forge/noarch/vaex-4.16.0-pyhd8ed1ab_0.conda#e4cf56f2102cc3e4d026a51041da76cb +# pip pyreadline3 @ https://files.pythonhosted.org/packages/56/fc/a3c13ded7b3057680c8ae95a9b6cc83e63657c38e0005c400a5d018a33a7/pyreadline3-3.4.1-py3-none-any.whl#sha256=b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb +# pip verboselogs @ https://files.pythonhosted.org/packages/b8/9d/c5c3cb0093642042fd604b53928ac65e7650fdc5a8a97814288e29beab84/verboselogs-1.7-py2.py3-none-any.whl#sha256=d63f23bf568295b95d3530c6864a0b580cec70e7ff974177dead1e4ffbc6ff49 +# pip humanfriendly @ https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl#sha256=1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477 +# pip coloredlogs @ https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl#sha256=612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934 +# pip property-manager @ https://files.pythonhosted.org/packages/b1/9f/be7d2ab00492710007cee08d2577f0176f3cf41c6b769963dc709bbdf653/property_manager-3.0-py2.py3-none-any.whl#sha256=d5d648053e669cf9cb2f157e4a41ef46174eaa8ee13bfa49e1530c17d65bfe45 +# pip executor @ https://files.pythonhosted.org/packages/cf/1b/cad3a5e33cba6982fd5df15c0ac531caed56b167c3981bd4e3625024edac/executor-23.2-py2.py3-none-any.whl#sha256=f2ea8cf92a1570a9898d5915b9620033aa9d4789eaf27e8a915d91dc8ba83dd9 +# pip awscliv2 @ https://files.pythonhosted.org/packages/8e/b0/507b940b0e6f9b54592ddc55e5b1895127f9d1cc54764deff124a0efbc07/awscliv2-2.2.0-py3-none-any.whl#sha256=78de01b4d7a996da97b88c06197ba6cf3d9cbc0d15cc25289d34daa70c65dee5 diff --git a/binder/env-lock.yml b/binder/env-lock.yml index 0c6d75d..3418925 100644 --- a/binder/env-lock.yml +++ b/binder/env-lock.yml @@ -2,6 +2,10 @@ name: env-lock channels: - conda-forge dependencies: + - python=3.9 + # https://github.com/conda/conda-lock/issues/381 + - conda=22.9 - conda-lock=1.2.1 - mamba>=1.0 + - lockfile - pip diff --git a/binder/environment.yml b/binder/environment.yml index 16f5a9b..b97d87d 100644 --- a/binder/environment.yml +++ b/binder/environment.yml @@ -3,7 +3,6 @@ channels: - conda-forge dependencies: - python=3.9 -# - pangeo-notebook - fsspec>=2022.10 - gdal=3.6 - xarray @@ -14,11 +13,10 @@ dependencies: - geopandas - h5py - h5netcdf -# - netcdf4~=1.6.2 -- netcdf4 +- netcdf4=1.6.2 - geopy - pyresample -- earthaccess>=0.5.1 +- earthaccess>=0.5.2 - fiona - zarr - ipympl @@ -33,14 +31,15 @@ dependencies: - intake-stac - jupyter-offlinenotebook - sidecar -- icepyx - geoviews - pipreqsnb -- conda-lock=1.2.1 +- conda-lock>=1.2.1 - mamba>=1.0 - pip - pip: - - awscli + - git+https://github.com/icesat2py/icepyx.git + - awscliv2 platforms: - linux-64 -- osx-6 +- osx-64 +- win-64 diff --git a/binder/postBuild b/binder/postBuild new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data Rendered.ipynb b/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data Rendered.ipynb index f0b5478..f68a9b6 100644 --- a/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data Rendered.ipynb +++ b/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data Rendered.ipynb @@ -601,7 +601,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data.ipynb b/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data.ipynb index c9cc70d..3755295 100755 --- a/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data.ipynb +++ b/notebooks/ICESat-2_MODIS_Arctic_Sea_Ice/Customize and Access Data.ipynb @@ -485,7 +485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb index 65bb7f1..37a48a0 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb @@ -16,7 +16,10 @@ "4. Read CSV and GeoTIFF formatted data using geopandas and rasterio libraries.\n", "5. Subset data based on buffered area.\n", "5. Extract and visualize raster values at point locations.\n", - "6. Save output as shapefile for further GIS analysis.\n" + "6. Save output as shapefile for further GIS analysis.\n", + "\n", + "---\n", + "\n" ] }, { @@ -82,7 +85,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import os\n", @@ -144,7 +149,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "polygon_filepath = str(os.getcwd() + '/Data/nsidc-polygon.json') # Note: A shapefile or other vector-based spatial data format could be substituted here.\n", @@ -173,7 +180,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "temporal = '2017-01-01T00:00:00Z,2017-12-31T23:59:59Z' # Set temporal range" @@ -191,7 +200,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "data_dict = { 'snowex': {'short_name': 'SNEX17_GPR','version': '2','polygon': polygon,'temporal':temporal},\n", @@ -212,7 +223,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "for k, v in data_dict.items(): fn.granule_info(data_dict[k])" @@ -230,7 +243,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "search_df = fn.time_overlap(data_dict)\n", @@ -253,7 +268,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": true, + "tags": [] }, "outputs": [], "source": [ @@ -299,7 +315,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "bounds = poly.bounds # Get polygon bounds to be used as bounding box input\n", @@ -324,7 +342,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request' # Set NSIDC data access base URL\n", @@ -349,19 +369,23 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "path = str(os.getcwd() + '/Data')\n", + "from pathlib import Path\n", + "\n", + "path = Path(\".\") / \"Data\"\n", + "\n", "if not os.path.exists(path):\n", + " print(f\"creating data directory: {path}\")\n", " os.mkdir(path)\n", - "os.chdir(path)\n", - "#fn.cmr_download(urls)\n", - "#fn.cmr_download(api_request)\n", - "\n", "\n", + "print(f\"Downloading data from S3 to {path}\")\n", + "os.chdir(path)\n", "# pull data from staged bucket for demonstration\n", - "!aws --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory" + "!awscliv2 --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory" ] }, { @@ -377,11 +401,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "snowex_path = './SnowEx17_GPR_Version2_Week1.csv' # Define local filepath\n", - "df = pd.read_csv(snowex_path, sep='\\t') \n", + "snowex_path = 'SnowEx17_GPR_Version2_Week1.csv' # Define local filepath\n", + "print(snowex_path, os.getcwd())\n", + "df = pd.read_csv(snowex_path, sep='\\t')\n", "df.head()" ] }, @@ -756,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb index d2d7cf8..0adf60f 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb @@ -16,7 +16,11 @@ "4. Read CSV and GeoTIFF formatted data using geopandas and rasterio libraries.\n", "5. Subset data based on buffered area.\n", "5. Extract and visualize raster values at point locations.\n", - "6. Save output as shapefile for further GIS analysis.\n" + "6. Save output as shapefile for further GIS analysis.\n", + "\n", + "\n", + "---\n", + "\n" ] }, { @@ -82,7 +86,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import os\n", @@ -732,16 +738,16 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 2, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "download: s3://snowex-aso-modis-tutorial-data/MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif to ./MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif\n", - "download: s3://snowex-aso-modis-tutorial-data/SnowEx17_GPR_Version2_Week1.csv to ./SnowEx17_GPR_Version2_Week1.csv\n", - "download: s3://snowex-aso-modis-tutorial-data/ASO_3M_SD_USCOGM_20170208.tif to ./ASO_3M_SD_USCOGM_20170208.tif\n" + "zsh:1: command not found: aws\n" ] } ], @@ -755,7 +761,7 @@ "\n", "\n", "# pull data from staged bucket for demonstration\n", - "!aws --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory" + "!awscliv2 --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory" ] }, { @@ -1798,7 +1804,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/iceflow/0_introduction.ipynb b/notebooks/iceflow/0_introduction.ipynb index 9270510..dc9e506 100644 --- a/notebooks/iceflow/0_introduction.ipynb +++ b/notebooks/iceflow/0_introduction.ipynb @@ -11,7 +11,6 @@ "### Point Cloud Data Access\n", "\n", "\n", - "---\n", "
\n", "\n", "
\n", @@ -361,7 +360,9 @@ "metadata": {}, "outputs": [], "source": [ - "orders = client.place_data_orders(params=my_params)\n", + "orders = []\n", + "# IMPORTANT: uncomment the following line to place the order\n", + "# orders = client.place_data_orders(params=my_params)\n", "orders" ] }, @@ -431,7 +432,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# ICESat and ICESat-2\n", @@ -448,17 +451,22 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "# ICESat-2 + ICESat/GLAS \n", - "orders = client.place_data_orders(params=my_params)" + "# IMPORTANT: uncomment the following line to place the order\n", + "\n", + "# orders = client.place_data_orders(params=my_params)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Downloading the data, ICESat-2 will be downloaded right away.\n", @@ -791,12 +799,12 @@ "outputs": [], "source": [ "# We group our dataframe by year\n", - "glas_by_year = glas_gdf.groupby([(glas_gdf.index.year)])\n", + "glas_by_year = glas_gdf.groupby([glas_gdf.index.year])\n", "\n", "for key, group in glas_by_year:\n", " group.plot(column='elevation',\n", " markersize=0.5,\n", - " label=key,\n", + " label=key[0],\n", " legend=True,\n", " legend_kwds={'label':f'GLAH06 {key} elevation (meters)'})" ] diff --git a/notebooks/iceflow/1_widget.ipynb b/notebooks/iceflow/1_widget.ipynb index be71dcf..c56713d 100644 --- a/notebooks/iceflow/1_widget.ipynb +++ b/notebooks/iceflow/1_widget.ipynb @@ -62,7 +62,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Note:** To view the widget output you need to enable Jupyter's log console. You can activate the console by clicking the right-most icon at the bottom of your browser or by slecting “View” >> “Show log console” on the menu bar." + "**Note:** To view the widget output you need to enable Jupyter's log console. You can activate the console by clicking the right-most icon at the bottom of your browser or by slecting “View” >> “Show log console” on the menu bar.\n", + "\n", + "Let's start loading the user interface. Next, we will explain all options and user interface components.\n", + "vertical = Sidecar widget, horizontal = render the widget in this notebook. Note that depending on your screen size and resolution the 'vertical' display option may not work correctly. This is a current bug in the jupyter-widget that can not be solved within the scope of IceFlow." ] }, { @@ -73,10 +76,6 @@ }, "outputs": [], "source": [ - "# Let's start loading the user interface. Next, we will explain all options and user interface components.\n", - "# vertical = Sidecar widget, horizontal = render the widget in this notebook.\n", - "# Note that depending on your screen size and resolution the 'vertical' display option may not work correctly.\n", - "# This is a current bug in the jupyter-widget that can not be solved within the scope of IceFlow.\n", "ui.display_map('horizontal', extra_layers=True)" ] }, @@ -85,6 +84,8 @@ "metadata": {}, "source": [ "#### IceFlow user interface (UI) components\n", + "\n", + "\n", "This user interface uses [*ipyleaflet*](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a) which allows us to draw\n", "polygons or bounding boxes to delimit our area of interest. We can also edit and delete these geometries using the widget controls in the map.\n", "
\n", diff --git a/notebooks/iceflow/2_api.ipynb b/notebooks/iceflow/2_api.ipynb index 8ea5d4b..34f5a91 100644 --- a/notebooks/iceflow/2_api.ipynb +++ b/notebooks/iceflow/2_api.ipynb @@ -102,17 +102,23 @@ "granules = ifc.query_cmr(my_params)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we order the data, this is an asynchronous process. Asynchronous downloads allow concurrent requests to be queued and processed without the need for a continuous connection. The maximum granule count per asynchronous request is 2000. When the order is complete.\n", + "Now you can proceed to download the IceFlow data granule." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Now we order the data, this is an asynchronous process. \n", - "# Asynchronous downloads allow concurrent requests to be queued and processed without the need for a continuous connection. \n", - "# The maximum granule count per asynchronous request is 2000. When the order is complete\n", - "# Now you can proceed to download the IceFlow data granule.\n", - "orders = ifc.place_data_orders(my_params)\n", + "orders = []\n", + "# Uncomment the following line to place the orders!\n", + "# orders = ifc.place_data_orders(my_params)\n", "orders" ] }, diff --git a/notebooks/iceflow/3_dataviz.ipynb b/notebooks/iceflow/3_dataviz.ipynb index 6b284aa..593cdfb 100644 --- a/notebooks/iceflow/3_dataviz.ipynb +++ b/notebooks/iceflow/3_dataviz.ipynb @@ -22,7 +22,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import warnings\n", @@ -52,7 +54,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "%%time\n", @@ -73,7 +77,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "%%time\n", @@ -105,16 +111,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "my_df.widget.heatmap(my_df[df_key['longitude']], \n", - " my_df[df_key['latitude']],\n", - " what=vaex.stat.mean(my_df[df_key['elevation']]),\n", - " shape=512, \n", - " figsize=(10,6),\n", - " limits='minmax',\n", - " colormap='inferno')" + "# There is a bug in Vaex that needs to be fixed: https://github.com/vaexio/vaex/pull/2353\n", + "\n", + "# my_df.widget.heatmap(my_df[df_key['longitude']], \n", + "# my_df[df_key['latitude']],\n", + "# what=vaex.stat.mean(my_df[df_key['elevation']]),\n", + "# shape=512, \n", + "# figsize=(10,6),\n", + "# limits='minmax',\n", + "# colormap='inferno')" ] }, { @@ -127,7 +137,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "%matplotlib widget\n", @@ -161,7 +173,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "%matplotlib widget\n", diff --git a/notebooks/iceflow/4_time_series_tutorial.ipynb b/notebooks/iceflow/4_time_series_tutorial.ipynb index 9e29ad7..0d5476c 100644 --- a/notebooks/iceflow/4_time_series_tutorial.ipynb +++ b/notebooks/iceflow/4_time_series_tutorial.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "subslide" } }, "source": [ "# *IceFlow & icepyx*: Altimetry Time Series Tutorial\n", - "### NASA Earthdata Webinar - April 2021\n", + "### NASA Earthdata Webinar - April 2021\n", "\n", "This tutorial demonstrates how to harmonize several NASA altimetry data sets with varying temporal coverage, formats, and coordinate reference frames using the IceFlow and icepyx Python tools. Please refer to the 0_introduction.ipynb notebook for detailed information on IceFlow and the data sets you will be exploring in this tutorial. \n", "\n", @@ -25,17 +25,7 @@ "Jessica Scheick, *University of New Hampshire*, Durham, New Hampshire
\n", "Nicholas Kotlinski & Amy Steiker, *NASA National Snow and Ice Data Center DAAC*, Boulder, Colorado, USA\n", "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ + "\n", "#### Running this tutorial locally\n", "\n", "To run this notebook locally, you must first set up your computing environment. Please see the [repository readme](https://github.com/nsidc/NSIDC-Data-Tutorials#usage-with-binder) for instructions on several ways (using Binder, Docker, or Conda) to do this." @@ -165,7 +155,7 @@ } }, "source": [ - "---\n", + "\n", "#### 2.3. Accessing Data with the *IceFlow* Access Widget\n", "The *IceFlow* access widget is a user interface tool to visualize flightpaths from IceBridge, draw a region of interest, set spatio-temporal parameters and place data orders to the *IceFlow* API and *icepyx* package without the need to write code.\n", "The output of the operations performed in the widget can be seen in the log window (right-most icon at the bottom left corner of your browser.) \n", @@ -231,7 +221,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Only ICESat/GLAS and ICESat-2 data are included above due to the high density of ATM data available from IceBridge. Here we order a smaller subset of ATM data to decrease the order and download times. Again, the number of files and volume of ATM data from each IceBridge dataset is outputted below." + "\n", + "Only ICESat/GLAS and ICESat-2 data are included above due to the high density of ATM data available from IceBridge. Here we order a smaller subset of ATM data to decrease the \n", + "order and download times. Again, the number of files and volume of ATM data from each IceBridge dataset is outputted below.\n" ] }, { @@ -359,7 +351,7 @@ } }, "source": [ - "---\n", + "\n", "#### 2.5. Downloading ICESat-2 data [directly] with ***icepyx***\n", "Behind the scenes, *IceFlow* is using the [*icepyx*](https://icepyx.readthedocs.io/en/latest/) Python package to download ICESat-2 data. *icepyx* is a standalone library that includes its own examples and documentation and welcomes contributions from data users (no previous GitHub or software development experience required!). Thus, it has a lot of additional functionality for querying, subsetting, ordering, and downloading ICESat-2 datasets (with in-the-works additions for data ingest into multiple formats), including making it easier to programmatically download data from multiple regions. \n", "\n", @@ -688,17 +680,6 @@ "source": [ "tsgdf.plot(x=\"time_col\",y=\"elevation\", kind=\"scatter\");" ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "---" - ] } ], "metadata": { diff --git a/notebooks/iceflow/README.md b/notebooks/iceflow/README.md index a716489..f248b2c 100644 --- a/notebooks/iceflow/README.md +++ b/notebooks/iceflow/README.md @@ -1,18 +1,18 @@ # IceFlow Point Cloud Data Access -## Summary +## Summary The IceFlow python library simplifies accessing and combining data from several of NASA's cryospheric altimetry missions, including ICESat/GLAS, Operation IceBridge, and ICESat-2. In particular, IceFlow harmonizes the various file formats and georeferencing parameters across several of the missions' data sets, allowing you to analyze data across the multi-decadal time series. The contents of the IceFlow folder include the IceFlow library itself, along with several Jupyter Notebooks that provide data access and harmonization using IceFlow. If you are new to IceFlow, we recommend starting at [0_introduction.ipynb](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/iceflow/0_introduction.ipynb), which provides a descriptive background on the data, as well as both map widget-based and programmatic-based options for accessing data from IceFlow. -## Setup +## Setup To run the notebooks provided in this folder, please see the [NSIDC-Data-Tutorials repository readme](https://github.com/nsidc/NSIDC-Data-Tutorials#readme) for instructions on several ways (using Binder, Docker, or Conda) to do this. ## Key Learning Objectives -1. Learn the basics about the data sets (pre-IceBridge, IceBridge, ICESat/GLAS and ICESat-2) served by the IceFlow library. +1. Learn the basics about the data sets (pre-IceBridge, IceBridge, ICESat/GLAS and ICESat-2) served by the IceFlow library. 2. Learn to access these data sets using the IceFlow user interface widget. diff --git a/notebooks/iceflow/iceflow/is2.py b/notebooks/iceflow/iceflow/is2.py index 4c6297e..2c5578a 100644 --- a/notebooks/iceflow/iceflow/is2.py +++ b/notebooks/iceflow/iceflow/is2.py @@ -1,5 +1,4 @@ import icepyx as ipx -from icepyx.core import Earthdata class is2: @@ -8,21 +7,13 @@ class is2: """ def __init__(self, credentials): self.credentials = credentials - cap_url = 'https://n5eil02u.ecs.nsidc.org/egi/capabilities/ATL06.005.xml' - self._session = Earthdata.Earthdata( - uid=credentials['username'], - email=credentials['email'], - pswd=credentials['password'], - capability_url=cap_url) - self._session._start_session() def query(self, parameters): dataset = parameters['dataset'] date_range = [parameters['start'], parameters['end']] bounding_box = [round(float(coord), 4) for coord in parameters['bbox'].split(',')] query = ipx.Query(dataset, bounding_box, date_range) - query._email = self.credentials['email'] - query._session = self._session.session + query.earthdata_login() return query def simplify_atl03(self, files, variables, filters): diff --git a/notebooks/measures/README.md b/notebooks/measures/README.md new file mode 100644 index 0000000..52d1a04 --- /dev/null +++ b/notebooks/measures/README.md @@ -0,0 +1,20 @@ +## Search, Download and Plot multiple GeoTIFFs + +### Summary +In this tutorial we demonstrate how to programmatically access and download GeoTIFF files from the NSIDC DAAC data to your local computer. We then walk through the steps for cropping and resampling one GeoTIFF based on the extent and pixel size of another GeoTIFF, with the end goal of plotting one on top of the other. + +We use two data sets from the NASA [MEaSUREs](https://nsidc.org/data/measures) (Making Earth System data records for Use in Research Environments) program as an example: + +* [MEaSUREs Greenland Ice Mapping Project (GrIMP) Digital Elevation Model from GeoEye and WorldView Imagery, Version 2 (NSIDC-0715)](https://nsidc.org/data/nsidc-0715/versions/2) +* [MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR, Version 4 (NSIDC-0481)](https://nsidc.org/data/nsidc-0481/versions/4) + + +### Set up + +To run the notebook provided in this folder, please see the [NSIDC-Data-Tutorials repository readme](https://github.com/nsidc/NSIDC-Data-Tutorials#readme) for instructions on several ways (using Binder, Docker, or Conda) to do this. + +### Key Learning Objectives + +1. Use the `earthaccess` library for authentication and to programmatically search for and download NSIDC DAAC data that meet specified spatial and temporal requirements. +2. Use the `gdal` and `osr` modules from the `osgeo` package to crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF. +3. Use `rasterio`, `numpy` and `matplotlib` libraries to overlay one GeoTIFF on top of another. diff --git a/notebooks/measures/Search_Download_and_Plot_GeoTIFFs.ipynb b/notebooks/measures/Search_Download_and_Plot_GeoTIFFs.ipynb new file mode 100644 index 0000000..770efa9 --- /dev/null +++ b/notebooks/measures/Search_Download_and_Plot_GeoTIFFs.ipynb @@ -0,0 +1,565 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1341755d", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "\n", + "\n", + "# **Download, Crop, Resample and Plot Multiple GeoTIFFs - Using MEaSUREs data sets as an example**" + ] + }, + { + "cell_type": "markdown", + "id": "38830c9d", + "metadata": {}, + "source": [ + "## **1. Tutorial Overview**\n", + "\n", + "This tutorial guides you through programmatically accessing and downloading GeoTIFF files from the NSIDC DAAC to your local computer. We then crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF, then plot one on top of the other. \n", + "\n", + "We will use two data sets from the NASA [MEaSUREs](https://nsidc.org/data/measures) (Making Earth System data records for Use in Research Environments) program as an example:\n", + "\n", + "* [MEaSUREs Greenland Ice Mapping Project (GrIMP) Digital Elevation Model from GeoEye and WorldView Imagery, Version 2 (NSIDC-0715)](https://nsidc.org/data/nsidc-0715/versions/2)\n", + "* [MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR, Version 4 (NSIDC-0481)](https://nsidc.org/data/nsidc-0481/versions/4)" + ] + }, + { + "cell_type": "markdown", + "id": "9bb1a111", + "metadata": {}, + "source": [ + "### **Credits**\n", + "\n", + "This tutorial was developed by Jennifer Roebuck of NSIDC.\n", + "\n", + "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues)." + ] + }, + { + "cell_type": "markdown", + "id": "f7d173fc", + "metadata": {}, + "source": [ + "### **Objectives** \n", + "\n", + "1. Use the `earthaccess` library for authentication and to programmatically search for and download NSIDC DAAC data that meet specified spatial and temporal requirements. \n", + "2. Use the `gdal` and `osr` modules from the `osgeo` package to crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF.\n", + "3. Use `rasterio`, `numpy` and `matplotlib` libraries to overlay one GeoTIFF on top of another.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5cee718b", + "metadata": {}, + "source": [ + "### **Prerequisites**\n", + "\n", + "To run this tutorial you will need:\n", + "1. An Earthdata Login for authentication and downloading the data. It is completely free. If you don't have one, you can register for one [here](https://urs.earthdata.nasa.gov/). \n", + "2. A .netrc file. We recommend using a .netrc file for storing your Earthdata Login username and password. Instructions for setting one up can be found in Step 1 in this [Programmatic Data Access Guide](https://nsidc.org/data/user-resources/help-center/programmatic-data-access-guide#anchor-0). If you don't want to set one up, you will be prompted for our username and password during the tutorial.\n", + "3. A basic understanding of python may also be helpful for this tutorial, but not necessary." + ] + }, + { + "cell_type": "markdown", + "id": "25584ec8", + "metadata": {}, + "source": [ + "### **Example of end product**\n", + "\n", + "At the end of this tutorial, you will have produced a figure similar to the one below, which overlays velocity data on top of a digital elevation model:\n", + "\n", + "
\n", + " \n", + "
\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "30fbaa69", + "metadata": {}, + "source": [ + "### **Time requirement**\n", + "\n", + "This tutorial will take approximately 30 minutes to complete. \n" + ] + }, + { + "cell_type": "markdown", + "id": "90d526d0", + "metadata": {}, + "source": [ + "## **2. Tutorial Steps**\n", + "\n", + "### **Import libraries and classes**\n", + "\n", + "We will use the following libraries:\n", + "1. `earthaccess` to authenticate, search and download NSIDC DAAC data \n", + "2. `os` to list all the files we have downloaded \n", + "3. `osgeo.gdal` and `osgeo.osr` to crop and resample one of the GeoTIFFs \n", + "4. `rasterio` and `numpy` to read the GeoTIFFs and set up a grid for plotting the data. \n", + "5. `matplotlib` for plotting the data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da4e53c4", + "metadata": {}, + "outputs": [], + "source": [ + "import earthaccess\n", + "import os\n", + "from osgeo import gdal, osr\n", + "import rasterio\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from affine import Affine" + ] + }, + { + "cell_type": "markdown", + "id": "a94373e3", + "metadata": {}, + "source": [ + "### **Authentication**\n", + "\n", + "We need to set up our authentication using our Earthdata Login credentials and the `login` method from `earthaccess`. If we have a .netrc with our Earthdata login credentials, we can just run the cell below and it will automatically authenticate. If we don't have a .netrc, we will be prompted to enter our Earthdata Login username and password. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "257a1e51", + "metadata": {}, + "outputs": [], + "source": [ + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "markdown", + "id": "6aad881a", + "metadata": {}, + "source": [ + "### **Search for data using spatial and temporal filters**\n", + "This tutorial assumes we already know which data sets we would like to download (that have data in GeoTIFF format). Each data set at NSIDC has a data set ID; it can be found after the title on the data set landing page. \n", + "\n", + "We will look at two data sets focused on Greenland: a Digital Elevation Model (DEM) ([NSIDC-0715](https://nsidc.org/data/nsidc-0715)) and velocity at glacier sites ([NSIDC-0481](https://nsidc.org/data/nsidc-0481)). We will use the `search_data` method from the `earthaccess` library and the following variables to search for granules within these data sets and it will output the number of granules that meet the search criteria for each data set:\n", + "\n", + "* `short_name` - this is the data set ID e.g., NSIDC-0715, NSIDC-0481. It can be found in the data set title on the data set landing page.\n", + "* `version` - data set version number, also included in the data set title.\n", + "* `cloud_hosted` - NSIDC is in the process of migrating data sets to the cloud. The data sets we are interested in are currently still archived on-prem so will set this to False.\n", + "* `bounding_box` - sets a spatial filter by specifying latitude and longitude in the following order: W, S, E, and N.\n", + "* `temporal` - sets a temporal filter by specifying a start and end date in the format YYYY-MM-DD.\n", + "* `count` - this sets the maximum number of granules that will be returned in the search\n", + "\n", + "**NOTE** The data sets and the spatial and temporal filters used here are just examples. If there are other data sets in GeoTIFF format or different spatial and temporal filters we wish to try, then we can just modify the `short_name`, `bounding_box` and `temporal` variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62b87b2e", + "metadata": {}, + "outputs": [], + "source": [ + "#Search for DEM files\n", + "print('For NSIDC-0715')\n", + "results_dem = earthaccess.search_data(\n", + " short_name='NSIDC-0715',\n", + " version='2',\n", + " cloud_hosted=False,\n", + " bounding_box=(-33.45,68.29,-31.41,69.26),\n", + " temporal=('2015-12-01','2015-12-31'),\n", + " count=100\n", + ")\n", + "\n", + "#Search for velocity data \n", + "print('For NSIDC-0481')\n", + "results_vel = earthaccess.search_data(\n", + " short_name='NSIDC-0481',\n", + " version='4',\n", + " cloud_hosted=False,\n", + " bounding_box=(-33.45,68.29,-31.41,69.26),\n", + " temporal=('2017-01-01','2017-12-31'),\n", + " count=100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f58d1b42", + "metadata": {}, + "source": [ + "### **Download the data**\n", + "Now we have found granules that meet our search criteria we can download them using the `download` method from `earthaccess`. Note that for these particular data sets within each granule there are multiple files. So even though 1 granule was found for the DEM data set, 6 files will be downloaded as the one granule represents one tile of the DEM (tile_4_2) and for each tile there are 6 files: \n", + "\n", + "* _tile_4_2_30m_dem_v02.0.tif_ - the DEM\n", + "* _tile_4_2_30m_count_v02.0.tif_ - number of individual DEMs used to estimate height at each pixel\n", + "* _tile_4_2_30m_mindate_v02.0.tif_ - date of the most recent DEM at each pixel\n", + "* _tile_4_2_30m_maxdate_v02.0.tif_ - date of the oldest DEM at each pixel\n", + "* _tile_4_2_30m_mad_v02.0.tif_ - median absolute deviation (mad) of DEM heights at each pixel\n", + "* _tile_4_2_30m_browse_v02.0.tif_ - DEM hillshade image (this is the one we will use for plotting) \n", + "\n", + "For the velocity data there are 20 granules, one for each time period, and there are 5 files within each granule, for example for the time period 16 July to 27 July 2017:\n", + "\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_vv_v04.0.tif_ - velocity magnitude \n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_vx_v04.0.tif_ - x component of velocity\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_vy_v04.0.tif_ - y component of velocity\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_ex_v04.0.tif_ - error of x component\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_ey_v04.0.tif_ - error of y component \n", + "\n", + "Further details can be found in the data set user guides for the [DEM](https://nsidc.org/sites/default/files/documents/user-guide/nsidc-0715-v002-userguide.pdf) and [velocity](https://nsidc.org/sites/default/files/documents/user-guide/nsidc-0481-v004-userguide.pdf) data sets. \n", + "\n", + "The progress and size of each order will be printed out.\n", + "\n", + "We will create a folder called 'outputs' to store the downloaded files. This folder will be created in the folder where we are currently running this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55d7625f", + "metadata": {}, + "outputs": [], + "source": [ + "#Set up an outputs folder to download the data to\n", + "path = str(os.getcwd() + '/outputs')\n", + "if not os.path.exists(path):\n", + " os.mkdir(path)\n", + "\n", + "#Download the DEM granules \n", + "dem_files = earthaccess.download(results_dem, path)\n", + "\n", + "#Download the velocity granules\n", + "vel_files = earthaccess.download(results_vel, path)\n" + ] + }, + { + "cell_type": "markdown", + "id": "db0008d8", + "metadata": {}, + "source": [ + "### **Check the files that have been downloaded**\n", + "We will list all the DEM and velocity files that were downloaded, as this is needed for the next steps. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0095777f", + "metadata": {}, + "outputs": [], + "source": [ + "dir_list = os.listdir(path)\n", + "\n", + "print('Files in outputs folder')\n", + "\n", + "for x in dir_list:\n", + " if x.endswith('.tif'):\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "id": "5afa949b", + "metadata": {}, + "source": [ + "### **Select and read in the DEM file and velocity file**\n", + "\n", + "Based on the list of filenames above, we will select the GeoTIFFs that we wish to plot and input them into the cell below. We will use the 'browse' GeoTIFF of the DEM tile, as that provides the best continuous surface for visual display. For the velocity we will plot the velocity magnitude, which is denoted by 'vv' in the filename, and we will plot the velocity covering the time period 07 August to 18 August 2017. \n", + "\n", + "The DEM GeoTIFF covers a larger area than the velocity GeoTIFF, and we will be cropping the DEM GeoTIFF to the extent of the velocity GeoTIFF, so we will also set a filename for the cropped DEM GeoTIFF. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36027699", + "metadata": {}, + "outputs": [], + "source": [ + "vel_fp = str(path + '/TSX_E68.80N_07Aug17_18Aug17_19-41-22_vv_v04.0.tif')\n", + "\n", + "dem_fp = str(path + '/tile_4_2_30m_browse_v02.0.tif')\n", + "\n", + "dem_crop = str(path + '/dem_crop_100.tif')" + ] + }, + { + "cell_type": "markdown", + "id": "39178989", + "metadata": {}, + "source": [ + "### **Crop and resample DEM file based on velocity file extent and pixel size**\n", + "\n", + "We will use the `open` method from the `gdal` module to read the velocity GeoTIFF and get the extent and pixel size, and we will use the `SpatialReference` method from the `osr` module to get the projection information. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16184745", + "metadata": {}, + "outputs": [], + "source": [ + "#open the velocity GeoTIFF that we specified above\n", + "vel_raster = gdal.Open(vel_fp)\n", + "\n", + "#get the grid information (extent and pixel size) from the velocity file\n", + "geoTransform = vel_raster.GetGeoTransform()\n", + "\n", + "#get the projection information (EPSG code) from the velocity file\n", + "proj=osr.SpatialReference(wkt=vel_raster.GetProjection())" + ] + }, + { + "cell_type": "markdown", + "id": "80107a09-2f45-408f-90fc-85fa5923e890", + "metadata": {}, + "source": [ + "We will now assign the information, retrieved in the cell above, to variables that can be used to crop and downsample the DEM GeoTIFF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2db7449-d293-4d9f-a3ff-4a721c7bf6be", + "metadata": {}, + "outputs": [], + "source": [ + "#assign the EPSG code to a variable that will be used by GDAL to ensure the output file is in the same projection\n", + "epsg = 'EPSG:' + proj.GetAttrValue('AUTHORITY',1)\n", + "\n", + "# assign the pixel size and extent info to variables that will be used to crop and downsample the data \n", + "pixelSizeX = geoTransform[1]\n", + "pixelSizeY = geoTransform[5]\n", + "minx = geoTransform[0]\n", + "maxy = geoTransform[3]\n", + "maxx = minx + pixelSizeX * vel_raster.RasterXSize\n", + "miny = maxy + pixelSizeY * vel_raster.RasterYSize" + ] + }, + { + "cell_type": "markdown", + "id": "babecd8c-5ebf-4e96-ae4a-b6eff4110216", + "metadata": {}, + "source": [ + "Now we will use these variables with the `Warp` method from the `gdal` module to crop and downsample the DEM GeoTIFF and save it as new file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e0f586b-55d7-4eea-8193-a70c0d887ed4", + "metadata": {}, + "outputs": [], + "source": [ + "#set up the keyword arguments needed for the Warp method to crop and downsample the DEM geotiff\n", + "kwargs = { 'format': 'GTiff', 'outputBounds': [minx, miny, maxx, maxy], 'outputBoundsSRS': epsg, 'xRes': pixelSizeX, 'yRes': pixelSizeY}\n", + "\n", + "#use the Warp method to crop and downsample the DEM GeoTIFF and save it as a new file\n", + "ds = gdal.Warp(dem_crop, dem_fp, **kwargs)\n", + "ds=None" + ] + }, + { + "cell_type": "markdown", + "id": "c743d341", + "metadata": {}, + "source": [ + "### **Set up the grids to plot the DEM and velocity data**\n", + "To plot the cropped and downsampled DEM with the velocity data, we need to set up a grid. We will read in the data and metadata using the `open` and `meta` methods, then we will use the `transform` or `affine` methods to map the image coordinates to world coordinates, all methods from the `rasterio` package. This information will then be used to generate x and y grid locations using the `arrange` method from the `numpy` package. It will also mask the no data values from the velocity GeoTIFF to produce a nicer plot. The cell below will print out the metadata from both files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15fc1119", + "metadata": {}, + "outputs": [], + "source": [ + "# read in subsetted and resampled DEM\n", + "dem_src = rasterio.open(dem_crop)\n", + "\n", + "# print out metadata information\n", + "for k in dem_src.meta:\n", + " print(k,dem_src.meta[k])\n", + "\n", + "# Retrieve the affine transformation\n", + "if isinstance(dem_src.transform, Affine):\n", + " transform = dem_src.transform\n", + "else:\n", + " transform = dem_src.affine\n", + "\n", + "N = dem_src.width\n", + "M = dem_src.height\n", + "dx = transform.a\n", + "dy = transform.e\n", + "minx = transform.c\n", + "maxy = transform.f\n", + "\n", + "# Read the image data, flip upside down if necessary\n", + "dem_crop_in = dem_src.read(1)\n", + "if dy < 0:\n", + " dy = -dy\n", + " dem_crop_in = np.flip(dem_crop_in, 0)\n", + "\n", + "#Uncomment the line below if you wish to see the min/max DEM values\n", + "#print('Data minimum, maximum = ', np.amin(data_in), np.amax(data_in))\n", + "\n", + "# Generate X and Y grid locations\n", + "xdata = minx + dx/2 + dx*np.arange(N)\n", + "ydata = maxy - dy/2 - dy*np.arange(M-1,-1,-1)\n", + "\n", + "dem_extent = [xdata[0], xdata[-1], ydata[0], ydata[-1]]\n", + "\n", + "### read in the velocity data\n", + "vel_data = rasterio.open(vel_fp)\n", + "\n", + "for k in vel_data.meta:\n", + " print(k,vel_data.meta[k])\n", + "\n", + "# Retrieve the affine transformation\n", + "if isinstance(vel_data.transform, Affine):\n", + " transform = vel_data.transform\n", + "else:\n", + " transform = vel_data.affine\n", + "\n", + "N2 = vel_data.width\n", + "M2 = vel_data.height\n", + "dx2 = transform.a\n", + "dy2 = transform.e\n", + "minx2 = transform.c\n", + "maxy2 = transform.f\n", + "\n", + "# Read the image data, flip upside down if necessary\n", + "vel_in = vel_data.read(1)\n", + "if dy2 < 0:\n", + " dy2 = -dy2\n", + " vel_in = np.flip(vel_in, 0)\n", + "\n", + "#Uncomment the line below if you wish to see the min/max velocity values\n", + "#print('Data minimum, maximum = ', np.amin(vel_in), np.amax(vel_in))\n", + "\n", + "# Generate X and Y grid locations\n", + "xdata2 = minx2 + dx2/2 + dx2*np.arange(N2)\n", + "ydata2 = maxy2 - dy2/2 - dy2*np.arange(M2-1,-1,-1)\n", + "\n", + "vel_extent = [xdata2[0], xdata2[-1], ydata2[0], ydata2[-1]]\n", + "\n", + "#Need to mask the no data values in the velocity data\n", + "vel_masked = np.ma.masked_where(vel_in == -1.0, vel_in, copy=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2febb071", + "metadata": {}, + "source": [ + "### **Plot the DEM and velocity data**\n", + "Now we can use the `matplotlib` library to plot the DEM with the velocity data on top. We will set the transparency of the velocity layer so we can see the DEM underneath. There is also an option to save the figure as .png; we can uncomment the last line if we want to save the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29fa16b7", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.figure(figsize=(8,8))\n", + "fig = plt.imshow(dem_crop_in, extent=dem_extent, origin='lower', cmap='gray')\n", + "fig2 = plt.imshow(vel_masked, extent=vel_extent, origin='lower', cmap='terrain', alpha=0.8)\n", + "plt.title('Velocity and DEM')\n", + "plt.xlabel('X (km)')\n", + "plt.ylabel('Y (km)')\n", + "cb = plt.colorbar(fig2, shrink=0.5)\n", + "cb.set_label('Velocity Magnitude (m/yr)')\n", + "\n", + "#Option to save the figure\n", + "#plt.savefig(\"velocity.png\", dpi=300, bbox_inches='tight', pad_inches=0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "60b01514-2442-4a51-9d50-e24eeda6550f", + "metadata": {}, + "source": [ + "**Optional** - if you wish to clean up the outputs folder and remove all the downloaded .tif files then you can uncomment the lines in the following cell (remove the '#') and run it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6baa3e22-12f9-4adf-b96c-ec4413ccd6d0", + "metadata": {}, + "outputs": [], + "source": [ + "#for x in dir_list:\n", + "# if x.endswith('.tif'):\n", + "# os.remove(os.path.join(path, x))" + ] + }, + { + "cell_type": "markdown", + "id": "9ff31ea8", + "metadata": {}, + "source": [ + "## **3. Learning Outcomes**\n", + "\n", + "* Search and download NSIDC DAAC data using `earthaccess`\n", + "* Crop and resample GeoTIFF using `gdal` and `osr`\n", + "* Overlay one GeoTIFF on another in a plot using `rasterio`, `numpy` and `matplotlib`" + ] + }, + { + "cell_type": "markdown", + "id": "182024ce", + "metadata": {}, + "source": [ + "## **4. Additional Resources**\n", + "\n", + "* Further details on the `earthacess` library can be found [here](https://github.com/nsidc/earthaccess)\n", + "* Further details on data available from NSIDC can be found [here](https://nsidc.org/data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "470d00ef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/measures/Search_Download_and_Plot_GeoTIFFs_rendered.ipynb b/notebooks/measures/Search_Download_and_Plot_GeoTIFFs_rendered.ipynb new file mode 100644 index 0000000..edbe82e --- /dev/null +++ b/notebooks/measures/Search_Download_and_Plot_GeoTIFFs_rendered.ipynb @@ -0,0 +1,838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1341755d", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "\n", + "\n", + "# **Download, Crop, Resample and Plot Multiple GeoTIFFs - Using MEaSUREs data sets as an example**" + ] + }, + { + "cell_type": "markdown", + "id": "38830c9d", + "metadata": {}, + "source": [ + "## **1. Tutorial Overview**\n", + "\n", + "This tutorial guides you through programmatically accessing and downloading GeoTIFF files from the NSIDC DAAC to your local computer. We then crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF, then plot one on top of the other. \n", + "\n", + "We will use two data sets from the NASA [MEaSUREs](https://nsidc.org/data/measures) (Making Earth System data records for Use in Research Environments) program as an example:\n", + "\n", + "* [MEaSUREs Greenland Ice Mapping Project (GrIMP) Digital Elevation Model from GeoEye and WorldView Imagery, Version 2 (NSIDC-0715)](https://nsidc.org/data/nsidc-0715/versions/2)\n", + "* [MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR, Version 4 (NSIDC-0481)](https://nsidc.org/data/nsidc-0481/versions/4)" + ] + }, + { + "cell_type": "markdown", + "id": "9bb1a111", + "metadata": {}, + "source": [ + "### **Credits**\n", + "\n", + "This tutorial was developed by Jennifer Roebuck of NSIDC.\n", + "\n", + "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues)." + ] + }, + { + "cell_type": "markdown", + "id": "f7d173fc", + "metadata": {}, + "source": [ + "### **Objectives** \n", + "\n", + "1. Use the `earthaccess` library for authentication and to programmatically search for and download NSIDC DAAC data that meet specified spatial and temporal requirements. \n", + "2. Use the `gdal` and `osr` modules from the `osgeo` package to crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF.\n", + "3. Use `rasterio`, `numpy` and `matplotlib` libraries to overlay one GeoTIFF on top of another.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5cee718b", + "metadata": {}, + "source": [ + "### **Prerequisites**\n", + "\n", + "To run this tutorial you will need:\n", + "1. An Earthdata Login for authentication and downloading the data. It is completely free. If you don't have one, you can register for one [here](https://urs.earthdata.nasa.gov/). \n", + "2. A .netrc file. We recommend using a .netrc file for storing your Earthdata Login username and password. Instructions for setting one up can be found in Step 1 in this [Programmatic Data Access Guide](https://nsidc.org/data/user-resources/help-center/programmatic-data-access-guide#anchor-0). If you don't want to set one up, you will be prompted for our username and password during the tutorial.\n", + "3. A basic understanding of python may also be helpful for this tutorial, but not necessary." + ] + }, + { + "cell_type": "markdown", + "id": "25584ec8", + "metadata": {}, + "source": [ + "### **Example of end product**\n", + "\n", + "At the end of this tutorial, you will have produced a figure similar to the one below, which overlays velocity data on top of a digital elevation model:\n", + "\n", + "
\n", + " \n", + "
\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "30fbaa69", + "metadata": {}, + "source": [ + "### **Time requirement**\n", + "\n", + "This tutorial will take approximately 30 minutes to complete. \n" + ] + }, + { + "cell_type": "markdown", + "id": "90d526d0", + "metadata": {}, + "source": [ + "## **2. Tutorial Steps**\n", + "\n", + "### **Import libraries and classes**\n", + "\n", + "We will use the following libraries:\n", + "1. `earthaccess` to authenticate, search and download NSIDC DAAC data \n", + "2. `os` to list all the files we have downloaded \n", + "3. `osgeo.gdal` and `osgeo.osr` to crop and resample one of the GeoTIFFs \n", + "4. `rasterio` and `numpy` to read the GeoTIFFs and set up a grid for plotting the data. \n", + "5. `matplotlib` for plotting the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "da4e53c4", + "metadata": {}, + "outputs": [], + "source": [ + "import earthaccess\n", + "import os\n", + "from osgeo import gdal, osr\n", + "import rasterio\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from affine import Affine" + ] + }, + { + "cell_type": "markdown", + "id": "a94373e3", + "metadata": {}, + "source": [ + "### **Authentication**\n", + "\n", + "We need to set up our authentication using our Earthdata Login credentials and the `login` method from `earthaccess`. If we have a .netrc with our Earthdata login credentials, we can just run the cell below and it will automatically authenticate. If we don't have a .netrc, we will be prompted to enter our Earthdata Login username and password. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "257a1e51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)\n", + "You're now authenticated with NASA Earthdata Login\n", + "Using token with expiration date: 05/13/2023\n", + "Using .netrc file for EDL\n" + ] + } + ], + "source": [ + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "markdown", + "id": "6aad881a", + "metadata": {}, + "source": [ + "### **Search for data using spatial and temporal filters**\n", + "This tutorial assumes we already know which data sets we would like to download (that have data in GeoTIFF format). Each data set at NSIDC has a data set ID; it can be found after the title on the data set landing page. \n", + "\n", + "We will look at two data sets focused on Greenland: a Digital Elevation Model (DEM) ([NSIDC-0715](https://nsidc.org/data/nsidc-0715)) and velocity at glacier sites ([NSIDC-0481](https://nsidc.org/data/nsidc-0481)). We will use the `search_data` method from the `earthaccess` library and the following variables to search for granules within these data sets and it will output the number of granules that meet the search criteria for each data set:\n", + "\n", + "* `short_name` - this is the data set ID e.g., NSIDC-0715, NSIDC-0481. It can be found in the data set title on the data set landing page.\n", + "* `version` - data set version number, also included in the data set title.\n", + "* `cloud_hosted` - NSIDC is in the process of migrating data sets to the cloud. The data sets we are interested in are currently still archived on-prem so will set this to False.\n", + "* `bounding_box` - sets a spatial filter by specifying latitude and longitude in the following order: W, S, E, and N.\n", + "* `temporal` - sets a temporal filter by specifying a start and end date in the format YYYY-MM-DD.\n", + "* `count` - this sets the maximum number of granules that will be returned in the search\n", + "\n", + "**NOTE** The data sets and the spatial and temporal filters used here are just examples. If there are other data sets in GeoTIFF format or different spatial and temporal filters we wish to try, then we can just modify the `short_name`, `bounding_box` and `temporal` variables." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "62b87b2e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For NSIDC-0715\n", + "Granules found: 1\n", + "For NSIDC-0481\n", + "Granules found: 20\n" + ] + } + ], + "source": [ + "#Search for DEM files\n", + "print('For NSIDC-0715')\n", + "results_dem = earthaccess.search_data(\n", + " short_name='NSIDC-0715',\n", + " version='2',\n", + " cloud_hosted=False,\n", + " bounding_box=(-33.45,68.29,-31.41,69.26),\n", + " temporal=('2015-12-01','2015-12-31'),\n", + " count=100\n", + ")\n", + "\n", + "#Search for velocity data \n", + "print('For NSIDC-0481')\n", + "results_vel = earthaccess.search_data(\n", + " short_name='NSIDC-0481',\n", + " version='4',\n", + " cloud_hosted=False,\n", + " bounding_box=(-33.45,68.29,-31.41,69.26),\n", + " temporal=('2017-01-01','2017-12-31'),\n", + " count=100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f58d1b42", + "metadata": {}, + "source": [ + "### **Download the data**\n", + "Now we have found granules that meet our search criteria we can download them using the `download` method from `earthaccess`. Note that for these particular data sets within each granule there are multiple files. So even though 1 granule was found for the DEM data set, 6 files will be downloaded as the one granule represents one tile of the DEM (tile_4_2) and for each tile there are 6 files: \n", + "\n", + "* _tile_4_2_30m_dem_v02.0.tif_ - the DEM\n", + "* _tile_4_2_30m_count_v02.0.tif_ - number of individual DEMs used to estimate height at each pixel\n", + "* _tile_4_2_30m_mindate_v02.0.tif_ - date of the most recent DEM at each pixel\n", + "* _tile_4_2_30m_maxdate_v02.0.tif_ - date of the oldest DEM at each pixel\n", + "* _tile_4_2_30m_mad_v02.0.tif_ - median absolute deviation (mad) of DEM heights at each pixel\n", + "* _tile_4_2_30m_browse_v02.0.tif_ - DEM hillshade image (this is the one we will use for plotting) \n", + "\n", + "For the velocity data there are 20 granules, one for each time period, and there are 5 files within each granule, for example for the time period 16 July to 27 July 2017:\n", + "\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_vv_v04.0.tif_ - velocity magnitude \n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_vx_v04.0.tif_ - x component of velocity\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_vy_v04.0.tif_ - y component of velocity\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_ex_v04.0.tif_ - error of x component\n", + "* _TSX_E68.80N_16Jul17_27Jul17_19-41-20_ey_v04.0.tif_ - error of y component \n", + "\n", + "Further details can be found in the data set user guides for the [DEM](https://nsidc.org/sites/default/files/documents/user-guide/nsidc-0715-v002-userguide.pdf) and [velocity](https://nsidc.org/sites/default/files/documents/user-guide/nsidc-0481-v004-userguide.pdf) data sets. \n", + "\n", + "The progress and size of each order will be printed out.\n", + "\n", + "We will create a folder called 'outputs' to store the downloaded files. This folder will be created in the folder where we are currently running this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "55d7625f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Getting 1 granules, approx download size: 0.88 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3387b71aef504d3ebf27bf3b4c686dcc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "SUBMITTING | : 0%| | 0/6 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "plt.figure(figsize=(8,8))\n", + "fig = plt.imshow(dem_crop_in, extent=dem_extent, origin='lower', cmap='gray')\n", + "fig2 = plt.imshow(vel_masked, extent=vel_extent, origin='lower', cmap='terrain', alpha=0.8)\n", + "plt.title('Velocity and DEM')\n", + "plt.xlabel('X (km)')\n", + "plt.ylabel('Y (km)')\n", + "cb = plt.colorbar(fig2, shrink=0.5)\n", + "cb.set_label('Velocity Magnitude (m/yr)')\n", + "\n", + "#Option to save the figure\n", + "#plt.savefig(\"velocity.png\", dpi=300, bbox_inches='tight', pad_inches=0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "60b01514-2442-4a51-9d50-e24eeda6550f", + "metadata": {}, + "source": [ + "**Optional** - if you wish to clean up the outputs folder and remove all the downloaded .tif files then you can uncomment the lines in the following cell (remove the '#') and run it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6baa3e22-12f9-4adf-b96c-ec4413ccd6d0", + "metadata": {}, + "outputs": [], + "source": [ + "#for x in dir_list:\n", + "# if x.endswith('.tif'):\n", + "# os.remove(os.path.join(path, x))" + ] + }, + { + "cell_type": "markdown", + "id": "9ff31ea8", + "metadata": {}, + "source": [ + "## **3. Learning Outcomes**\n", + "\n", + "* Search and download NSIDC DAAC data using `earthaccess`\n", + "* Crop and resample GeoTIFF using `gdal` and `osr`\n", + "* Overlay one GeoTIFF on another in a plot using `rasterio`, `numpy` and `matplotlib`" + ] + }, + { + "cell_type": "markdown", + "id": "182024ce", + "metadata": {}, + "source": [ + "## **4. Additional Resources**\n", + "\n", + "* Further details on the `earthacess` library can be found [here](https://github.com/nsidc/earthaccess)\n", + "* Further details on data available from NSIDC can be found [here](https://nsidc.org/data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "470d00ef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/measures/environment/environment.yml b/notebooks/measures/environment/environment.yml new file mode 100644 index 0000000..7817d6f --- /dev/null +++ b/notebooks/measures/environment/environment.yml @@ -0,0 +1,11 @@ +name: measures +channels: +- conda-forge +dependencies: +- python=3.9 +- gdal=3.6 +- matplotlib-base>=3.5 +- numpy=1.23.5 +- earthaccess>=0.5.1 +- rasterio~=1.3.6 +- affine diff --git a/notebooks/measures/img/example_geotiff_plot.png b/notebooks/measures/img/example_geotiff_plot.png new file mode 100644 index 0000000..bc04872 Binary files /dev/null and b/notebooks/measures/img/example_geotiff_plot.png differ diff --git a/notebooks/measures/img/nsidc_logo.png b/notebooks/measures/img/nsidc_logo.png new file mode 100644 index 0000000..35a06e0 Binary files /dev/null and b/notebooks/measures/img/nsidc_logo.png differ