From e198d3faa45813733e2bb4e366a1157b584a6c69 Mon Sep 17 00:00:00 2001 From: Philipp Rudiger Date: Tue, 29 Dec 2020 17:10:10 +0100 Subject: [PATCH] Updated glaciers notebook [build:glaciers] (#122) --- .github/workflows/test.yml | 15 +- glaciers/anaconda-project-lock.yml | 664 ----------------------------- glaciers/anaconda-project.yml | 21 +- glaciers/glaciers.ipynb | 281 +++++------- glaciers/screenshot.png | Bin 0 -> 218744 bytes 5 files changed, 129 insertions(+), 852 deletions(-) delete mode 100644 glaciers/anaconda-project-lock.yml create mode 100644 glaciers/screenshot.png diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 49e56d7ce..322dc9272 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -17,23 +17,19 @@ jobs: - uses: actions/checkout@v2 with: fetch-depth: "100" - - uses: actions/setup-python@v2 - with: - python-version: 3.7 + - name: record changed + run: | + git fetch origin master + git diff origin/master ${{ github.sha }} --name-only > .diff - uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" - - name: Fetch unshallow - run: git fetch --prune --tags --unshallow - name: conda setup run: | eval "$(conda shell.bash hook)" conda config --set always_yes True conda install -c pyviz "pyctdev>=0.5" doit ecosystem_setup - - name: record changed - run: | - git diff ${{ github.event.before }}...${{ github.event.after }} --name-only > .diff - name: doit test_project run: | eval "$(conda shell.bash hook)" @@ -62,9 +58,6 @@ jobs: - uses: actions/checkout@v2 with: fetch-depth: "100" - - uses: actions/setup-python@v2 - with: - python-version: 3.7 - uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" diff --git a/glaciers/anaconda-project-lock.yml b/glaciers/anaconda-project-lock.yml deleted file mode 100644 index 02cf359cb..000000000 --- a/glaciers/anaconda-project-lock.yml +++ /dev/null @@ -1,664 +0,0 @@ -# This is an Anaconda project lock file. -# The lock file locks down exact versions of all your dependencies. -# -# In most cases, this file is automatically maintained by the `anaconda-project` command or GUI tools. -# It's best to keep this file in revision control (such as git or svn). -# The file is in YAML format, please see http://www.yaml.org/start.html for more. -# - -# -# Set to false to ignore locked versions. -# -locking_enabled: true - -# -# A key goes in here for each env spec. -# -env_specs: - test: - locked: true - env_spec_hash: 28f7a2b122e2de30bf06427b0abbd79c3927715e - platforms: - - linux-64 - - osx-64 - - win-64 - packages: - all: - - atomicwrites=1.4.0=py_0 - - attrs=19.3.0=py_0 - - backcall=0.2.0=py_0 - - beautifulsoup4=4.9.1=py36_0 - - blas=1.0=mkl - - bleach=3.1.5=py_0 - - bokeh=1.4.0=py36_0 - - ca-certificates=2020.6.24=0 - - certifi=2020.6.20=py36_0 - - chardet=3.0.4=py36_1003 - - click=7.1.2=py_0 - - cloudpickle=1.4.1=py_0 - - colorcet=2.0.2=py_0 - - contextvars=2.4=py_0 - - dask-core=2.19.0=py_0 - - dask=2.19.0=py_0 - - datashader=0.7.0=py_0 - - datashape=0.5.4=py36_1 - - decorator=4.4.2=py_0 - - defusedxml=0.6.0=py_0 - - distributed=2.19.0=py36_0 - - entrypoints=0.3=py36_0 - - fsspec=0.7.4=py_0 - - geoviews-core=1.6.2=py_0 - - heapdict=1.0.1=py_0 - - holoviews=1.12.3=py_2 - - idna=2.10=py_0 - - imageio=2.8.0=py_0 - - importlib-metadata=1.7.0=py36_0 - - importlib_metadata=1.7.0=0 - - ipykernel=5.1.0=py36h39e3cac_0 - - ipython=7.16.1=py36h5ca1d4c_0 - - ipython_genutils=0.2.0=py36_0 - - jedi=0.17.1=py36_0 - - jinja2=2.11.2=py_0 - - jsonschema=3.2.0=py36_0 - - jupyter_client=6.1.3=py_0 - - jupyter_core=4.6.3=py36_0 - - markdown=3.1.1=py36_0 - - matplotlib=3.2.2=0 - - more-itertools=8.4.0=py_0 - - multipledispatch=0.6.0=py36_0 - - nbconvert=5.6.1=py36_0 - - nbformat=5.0.7=py_0 - - nbsmoke=0.2.8=py36_0 - - networkx=2.4=py_0 - - notebook=5.7.8=py36_0 - - olefile=0.46=py36_0 - - owslib=0.19.2=py_1 - - packaging=20.4=py_0 - - pandoc=2.9.2.1=0 - - pandocfilters=1.4.2=py36_1 - - panel=0.6.0=h39e3cac_0 - - param=1.9.3=py_0 - - parso=0.7.0=py_0 - - partd=1.1.0=py_0 - - pickleshare=0.7.5=py36_0 - - pip=20.1.1=py36_1 - - pluggy=0.13.1=py36_0 - - prometheus_client=0.8.0=py_0 - - prompt-toolkit=3.0.5=py_0 - - py=1.9.0=py_0 - - pycparser=2.20=py_0 - - pyct=0.4.6=py36_0 - - pyepsg=0.4.0=py36_0 - - pyflakes=2.2.0=py_0 - - pygments=2.6.1=py_0 - - pyopenssl=19.1.0=py36_0 - - pyparsing=2.4.7=py_0 - - pyshp=2.1.0=py_0 - - pysocks=1.7.1=py36_0 - - pytest=4.4.1=py36_0 - - python-dateutil=2.8.1=py_0 - - pytz=2020.1=py_0 - - pyviz_comms=0.7.2=py_0 - - requests=2.24.0=py_0 - - send2trash=1.5.0=py36_0 - - setuptools=47.3.1=py36_0 - - six=1.15.0=py_0 - - sortedcontainers=2.2.2=py_0 - - soupsieve=2.0.1=py_0 - - tblib=1.6.0=py_0 - - terminado=0.8.3=py36_0 - - toolz=0.10.0=py_0 - - traitlets=4.3.3=py36_0 - - urllib3=1.25.9=py_0 - - wcwidth=0.2.5=py_0 - - webencodings=0.5.1=py36_1 - - wheel=0.34.2=py36_0 - - xarray=0.15.1=py_0 - - zict=2.0.0=py_0 - - zipp=3.1.0=py_0 - unix: - - pexpect=4.8.0=py36_0 - - ptyprocess=0.6.0=py36_0 - linux-64: - - _libgcc_mutex=0.1=main - - brotlipy=0.7.0=py36h7b6447c_1000 - - cartopy=0.17.0=py36h3e2e9bd_1 - - cffi=1.14.0=py36he30daa8_1 - - cryptography=2.9.2=py36h1ba5d50_0 - - cycler=0.10.0=py36_0 - - cytoolz=0.10.1=py36h7b6447c_0 - - dbus=1.13.16=hb2f20db_0 - - expat=2.2.9=he6710b0_2 - - fontconfig=2.13.0=h9420a91_0 - - freetype=2.10.2=h5ab3b9f_0 - - geos=3.8.0=he6710b0_0 - - glib=2.65.0=h3eb4bd4_0 - - gst-plugins-base=1.14.0=hbbd80ab_1 - - gstreamer=1.14.0=hb31296c_0 - - icu=58.2=he6710b0_3 - - immutables=0.14=py36h7b6447c_0 - - intel-openmp=2020.1=217 - - jpeg=9b=h024ee3a_2 - - kiwisolver=1.2.0=py36hfd86e86_0 - - krb5=1.18.2=h173b8e3_0 - - ld_impl_linux-64=2.33.1=h53a641e_7 - - libcurl=7.71.0=h20c2e04_0 - - libedit=3.1.20191231=h7b6447c_0 - - libffi=3.3=he6710b0_1 - - libgcc-ng=9.1.0=hdf63c60_0 - - libgfortran-ng=7.3.0=hdf63c60_0 - - libllvm9=9.0.1=h4a3c616_0 - - libpng=1.6.37=hbc83047_0 - - libsodium=1.0.18=h7b6447c_0 - - libssh2=1.9.0=h1ba5d50_1 - - libstdcxx-ng=9.1.0=hdf63c60_0 - - libtiff=4.1.0=h2733197_1 - - libuuid=1.0.3=h1bed415_2 - - libxcb=1.13=h1bed415_1 - - libxml2=2.9.10=he19cac6_1 - - llvmlite=0.33.0=py36hd408876_0 - - locket=0.2.0=py36_1 - - lz4-c=1.9.2=he6710b0_0 - - markupsafe=1.1.1=py36h7b6447c_0 - - matplotlib-base=3.2.2=py36hef1b27d_0 - - mistune=0.8.4=py36h7b6447c_0 - - mkl-service=2.3.0=py36he904b0f_0 - - mkl=2020.1=217 - - mkl_fft=1.1.0=py36h23d657b_0 - - mkl_random=1.1.1=py36h0573a6f_0 - - msgpack-python=1.0.0=py36hfd86e86_1 - - ncurses=6.2=he6710b0_1 - - numba=0.50.1=py36h0573a6f_0 - - numpy-base=1.18.5=py36hde5b4d6_0 - - numpy=1.18.5=py36ha1c710e_0 - - openssl=1.1.1g=h7b6447c_0 - - pandas=1.0.5=py36h0573a6f_0 - - pcre=8.44=he6710b0_0 - - pillow=7.1.2=py36hb39fc2d_0 - - proj=7.0.1=h59a7b90_1 - - psutil=5.7.0=py36h7b6447c_0 - - pykdtree=1.3.1=py36hdd07704_2 - - pyproj=2.6.1.post1=py36h61f852b_1 - - pyqt=5.9.2=py36h05f1152_2 - - pyrsistent=0.16.0=py36h7b6447c_0 - - python=3.6.10=h7579374_2 - - pywavelets=1.1.1=py36h7b6447c_0 - - pyyaml=5.3.1=py36h7b6447c_1 - - pyzmq=19.0.1=py36he6710b0_1 - - qt=5.9.7=h5867ecd_1 - - readline=8.0=h7b6447c_0 - - scikit-image=0.16.2=py36h0573a6f_0 - - scipy=1.5.0=py36h0b6359f_0 - - shapely=1.7.0=py36h98ec03d_0 - - sip=4.19.8=py36hf484d3e_0 - - sqlite=3.32.3=h62c20be_0 - - tbb=2020.0=hfd86e86_0 - - testpath=0.3.1=py36_0 - - tk=8.6.10=hbc83047_0 - - tornado=5.1.1=py36h7b6447c_0 - - xz=5.2.5=h7b6447c_0 - - yaml=0.2.5=h7b6447c_0 - - zeromq=4.3.2=he6710b0_2 - - zlib=1.2.11=h7b6447c_3 - - zstd=1.4.4=h0b5b093_3 - osx-64: - - appnope=0.1.0=py36hf537a9a_0 - - brotlipy=0.7.0=py36haf1e3a3_1000 - - cartopy=0.17.0=py36h1d108a8_1 - - cffi=1.14.0=py36hc512035_1 - - cryptography=2.9.2=py36ha12b0ac_0 - - cycler=0.10.0=py36hfc81398_0 - - cytoolz=0.10.1=py36h1de35cc_0 - - freetype=2.10.2=ha233b18_0 - - geos=3.8.0=hb1e8313_0 - - immutables=0.14=py36haf1e3a3_0 - - intel-openmp=2019.4=233 - - jpeg=9b=he5867d9_2 - - kiwisolver=1.2.0=py36h04f5b5a_0 - - krb5=1.18.2=h75d18d8_0 - - libcurl=7.71.0=h8a08a2b_0 - - libcxx=10.0.0=1 - - libedit=3.1.20191231=haf1e3a3_0 - - libffi=3.3=h0a44026_1 - - libgfortran=3.0.1=h93005f0_2 - - libllvm9=9.0.1=h21ff451_0 - - libpng=1.6.37=ha441bb4_0 - - libsodium=1.0.18=h1de35cc_0 - - libssh2=1.9.0=ha12b0ac_1 - - libtiff=4.1.0=hcb84e12_1 - - llvm-openmp=10.0.0=h28b9765_0 - - llvmlite=0.33.0=py36h98b8051_0 - - locket=0.2.0=py36hca03003_1 - - lz4-c=1.9.2=h0a44026_0 - - markupsafe=1.1.1=py36h1de35cc_0 - - matplotlib-base=3.2.2=py36h5670ca0_0 - - mistune=0.8.4=py36h1de35cc_0 - - mkl-service=2.3.0=py36hfbe908c_0 - - mkl=2019.4=233 - - mkl_fft=1.1.0=py36hc64f4ea_0 - - mkl_random=1.1.1=py36h959d312_0 - - msgpack-python=1.0.0=py36h04f5b5a_1 - - ncurses=6.2=h0a44026_1 - - numba=0.50.1=py36h959d312_0 - - numpy-base=1.18.5=py36h3304bdc_0 - - numpy=1.18.5=py36h55a193a_0 - - openssl=1.1.1g=h1de35cc_0 - - pandas=1.0.5=py36h959d312_0 - - pillow=7.1.2=py36h4655f20_0 - - proj=7.0.1=hdb716b3_1 - - psutil=5.7.0=py36h1de35cc_0 - - pykdtree=1.3.1=py36h1d22016_2 - - pyproj=2.6.1.post1=py36ha9dc991_1 - - pyrsistent=0.16.0=py36h1de35cc_0 - - python=3.6.10=hf48f09d_2 - - pywavelets=1.1.1=py36h1de35cc_0 - - pyyaml=5.3.1=py36haf1e3a3_1 - - pyzmq=19.0.1=py36hb1e8313_1 - - readline=8.0=h1de35cc_0 - - scikit-image=0.16.2=py36h6c726b0_0 - - scipy=1.5.0=py36h912ce22_0 - - shapely=1.7.0=py36h727f1cc_0 - - sqlite=3.32.3=hffcf06c_0 - - tbb=2020.0=h04f5b5a_0 - - testpath=0.3.1=py36h625a49b_0 - - tk=8.6.10=hb0a8c7a_0 - - tornado=5.1.1=py36h1de35cc_0 - - xz=5.2.5=h1de35cc_0 - - yaml=0.2.5=haf1e3a3_0 - - zeromq=4.3.2=hb1e8313_2 - - zlib=1.2.11=h1de35cc_3 - - zstd=1.4.4=h1990bb4_3 - win-64: - - brotlipy=0.7.0=py36he774522_1000 - - cartopy=0.17.0=py36hd77ba2b_1 - - cffi=1.14.0=py36h7a1dbc1_0 - - colorama=0.4.3=py_0 - - cryptography=2.9.2=py36h7a1dbc1_0 - - cycler=0.10.0=py36h009560c_0 - - cytoolz=0.10.1=py36he774522_0 - - freetype=2.10.2=hd328e21_0 - - geos=3.8.0=h33f27b4_0 - - icc_rt=2019.0.0=h0cc432a_1 - - icu=58.2=ha925a31_3 - - immutables=0.14=py36he774522_0 - - intel-openmp=2020.1=216 - - jpeg=9b=hb83a4c4_2 - - kiwisolver=1.2.0=py36h74a9793_0 - - libllvm9=9.0.1=h21ff451_0 - - libpng=1.6.37=h2a8f88b_0 - - libsodium=1.0.18=h62dcd97_0 - - libtiff=4.1.0=h56a325e_1 - - llvmlite=0.33.0=py36ha925a31_0 - - locket=0.2.0=py36hfed976d_1 - - lz4-c=1.9.2=h62dcd97_0 - - m2w64-gcc-libgfortran=5.3.0=6 - - m2w64-gcc-libs-core=5.3.0=7 - - m2w64-gcc-libs=5.3.0=7 - - m2w64-gmp=6.1.0=2 - - m2w64-libwinpthread-git=5.0.0.4634.697f757=2 - - markupsafe=1.1.1=py36he774522_0 - - matplotlib-base=3.2.2=py36h64f37c6_0 - - mistune=0.8.4=py36he774522_0 - - mkl-service=2.3.0=py36hb782905_0 - - mkl=2020.1=216 - - mkl_fft=1.1.0=py36h45dec08_0 - - mkl_random=1.1.1=py36h47e9c7a_0 - - msgpack-python=1.0.0=py36h74a9793_1 - - msys2-conda-epoch=20160418=1 - - numba=0.50.1=py36h47e9c7a_0 - - numpy-base=1.18.5=py36hc3f5095_0 - - numpy=1.18.5=py36h6530119_0 - - openssl=1.1.1g=he774522_0 - - pandas=1.0.5=py36h47e9c7a_0 - - pillow=7.1.2=py36hcc1f983_0 - - proj=6.2.1=h9f7ef89_0 - - psutil=5.7.0=py36he774522_0 - - pykdtree=1.3.1=py36h8c2d366_2 - - pyproj=2.6.1.post1=py36hcfa1391_1 - - pyqt=5.9.2=py36h6538335_2 - - pyrsistent=0.16.0=py36he774522_0 - - python=3.6.10=h9f7ef89_2 - - pywavelets=1.1.1=py36he774522_0 - - pywin32=227=py36he774522_1 - - pywinpty=0.5.7=py36_0 - - pyyaml=5.3.1=py36he774522_1 - - pyzmq=19.0.1=py36ha925a31_1 - - qt=5.9.7=vc14h73c81de_0 - - scikit-image=0.16.2=py36h47e9c7a_0 - - scipy=1.5.0=py36h9439919_0 - - shapely=1.7.0=py36h210f175_0 - - sip=4.19.8=py36h6538335_0 - - sqlite=3.32.3=h2a8f88b_0 - - tbb=2020.0=h74a9793_0 - - testpath=0.3.1=py36h2698cfe_0 - - tk=8.6.10=he774522_0 - - tornado=5.1.1=py36hfa6e2cd_0 - - vc=14.1=h0510ff6_4 - - vs2015_runtime=14.16.27012=hf0eaf9b_2 - - win_inet_pton=1.1.0=py36_0 - - wincertstore=0.2=py36h7fe50ca_0 - - winpty=0.4.3=4 - - xz=5.2.5=h62dcd97_0 - - yaml=0.2.5=he774522_0 - - zeromq=4.3.2=ha925a31_2 - - zlib=1.2.11=h62dcd97_4 - - zstd=1.4.4=ha9fde0e_3 - default: - locked: true - env_spec_hash: 4dd16646d36994afdcf59e58cdf69855afc7d260 - platforms: - - linux-64 - - osx-64 - - win-64 - packages: - all: - - attrs=19.3.0=py_0 - - backcall=0.2.0=py_0 - - blas=1.0=mkl - - bleach=3.1.5=py_0 - - bokeh=1.4.0=py36_0 - - ca-certificates=2020.6.24=0 - - certifi=2020.6.20=py36_0 - - chardet=3.0.4=py36_1003 - - click=7.1.2=py_0 - - cloudpickle=1.4.1=py_0 - - colorcet=2.0.2=py_0 - - contextvars=2.4=py_0 - - dask-core=2.19.0=py_0 - - dask=2.19.0=py_0 - - datashader=0.7.0=py_0 - - datashape=0.5.4=py36_1 - - decorator=4.4.2=py_0 - - defusedxml=0.6.0=py_0 - - distributed=2.19.0=py36_0 - - entrypoints=0.3=py36_0 - - fsspec=0.7.4=py_0 - - geoviews-core=1.6.2=py_0 - - heapdict=1.0.1=py_0 - - holoviews=1.12.3=py_2 - - idna=2.10=py_0 - - imageio=2.8.0=py_0 - - importlib-metadata=1.7.0=py36_0 - - importlib_metadata=1.7.0=0 - - ipykernel=5.1.0=py36h39e3cac_0 - - ipython=7.16.1=py36h5ca1d4c_0 - - ipython_genutils=0.2.0=py36_0 - - jedi=0.17.1=py36_0 - - jinja2=2.11.2=py_0 - - jsonschema=3.2.0=py36_0 - - jupyter_client=6.1.3=py_0 - - jupyter_core=4.6.3=py36_0 - - markdown=3.1.1=py36_0 - - matplotlib=3.2.2=0 - - multipledispatch=0.6.0=py36_0 - - nbconvert=5.6.1=py36_0 - - nbformat=5.0.7=py_0 - - networkx=2.4=py_0 - - notebook=5.7.8=py36_0 - - olefile=0.46=py36_0 - - owslib=0.19.2=py_1 - - packaging=20.4=py_0 - - pandoc=2.9.2.1=0 - - pandocfilters=1.4.2=py36_1 - - panel=0.6.0=h39e3cac_0 - - param=1.9.3=py_0 - - parso=0.7.0=py_0 - - partd=1.1.0=py_0 - - pickleshare=0.7.5=py36_0 - - pip=20.1.1=py36_1 - - prometheus_client=0.8.0=py_0 - - prompt-toolkit=3.0.5=py_0 - - pycparser=2.20=py_0 - - pyct=0.4.6=py36_0 - - pyepsg=0.4.0=py36_0 - - pygments=2.6.1=py_0 - - pyopenssl=19.1.0=py36_0 - - pyparsing=2.4.7=py_0 - - pyshp=2.1.0=py_0 - - pysocks=1.7.1=py36_0 - - python-dateutil=2.8.1=py_0 - - pytz=2020.1=py_0 - - pyviz_comms=0.7.2=py_0 - - requests=2.24.0=py_0 - - send2trash=1.5.0=py36_0 - - setuptools=47.3.1=py36_0 - - six=1.15.0=py_0 - - sortedcontainers=2.2.2=py_0 - - tblib=1.6.0=py_0 - - terminado=0.8.3=py36_0 - - toolz=0.10.0=py_0 - - traitlets=4.3.3=py36_0 - - urllib3=1.25.9=py_0 - - wcwidth=0.2.5=py_0 - - webencodings=0.5.1=py36_1 - - wheel=0.34.2=py36_0 - - xarray=0.15.1=py_0 - - zict=2.0.0=py_0 - - zipp=3.1.0=py_0 - unix: - - pexpect=4.8.0=py36_0 - - ptyprocess=0.6.0=py36_0 - linux-64: - - _libgcc_mutex=0.1=main - - brotlipy=0.7.0=py36h7b6447c_1000 - - cartopy=0.17.0=py36h3e2e9bd_1 - - cffi=1.14.0=py36he30daa8_1 - - cryptography=2.9.2=py36h1ba5d50_0 - - cycler=0.10.0=py36_0 - - cytoolz=0.10.1=py36h7b6447c_0 - - dbus=1.13.16=hb2f20db_0 - - expat=2.2.9=he6710b0_2 - - fontconfig=2.13.0=h9420a91_0 - - freetype=2.10.2=h5ab3b9f_0 - - geos=3.8.0=he6710b0_0 - - glib=2.65.0=h3eb4bd4_0 - - gst-plugins-base=1.14.0=hbbd80ab_1 - - gstreamer=1.14.0=hb31296c_0 - - icu=58.2=he6710b0_3 - - immutables=0.14=py36h7b6447c_0 - - intel-openmp=2020.1=217 - - jpeg=9b=h024ee3a_2 - - kiwisolver=1.2.0=py36hfd86e86_0 - - krb5=1.18.2=h173b8e3_0 - - ld_impl_linux-64=2.33.1=h53a641e_7 - - libcurl=7.71.0=h20c2e04_0 - - libedit=3.1.20191231=h7b6447c_0 - - libffi=3.3=he6710b0_1 - - libgcc-ng=9.1.0=hdf63c60_0 - - libgfortran-ng=7.3.0=hdf63c60_0 - - libllvm9=9.0.1=h4a3c616_0 - - libpng=1.6.37=hbc83047_0 - - libsodium=1.0.18=h7b6447c_0 - - libssh2=1.9.0=h1ba5d50_1 - - libstdcxx-ng=9.1.0=hdf63c60_0 - - libtiff=4.1.0=h2733197_1 - - libuuid=1.0.3=h1bed415_2 - - libxcb=1.13=h1bed415_1 - - libxml2=2.9.10=he19cac6_1 - - llvmlite=0.33.0=py36hd408876_0 - - locket=0.2.0=py36_1 - - lz4-c=1.9.2=he6710b0_0 - - markupsafe=1.1.1=py36h7b6447c_0 - - matplotlib-base=3.2.2=py36hef1b27d_0 - - mistune=0.8.4=py36h7b6447c_0 - - mkl-service=2.3.0=py36he904b0f_0 - - mkl=2020.1=217 - - mkl_fft=1.1.0=py36h23d657b_0 - - mkl_random=1.1.1=py36h0573a6f_0 - - msgpack-python=1.0.0=py36hfd86e86_1 - - ncurses=6.2=he6710b0_1 - - numba=0.50.1=py36h0573a6f_0 - - numpy-base=1.18.5=py36hde5b4d6_0 - - numpy=1.18.5=py36ha1c710e_0 - - openssl=1.1.1g=h7b6447c_0 - - pandas=1.0.5=py36h0573a6f_0 - - pcre=8.44=he6710b0_0 - - pillow=7.1.2=py36hb39fc2d_0 - - proj=7.0.1=h59a7b90_1 - - psutil=5.7.0=py36h7b6447c_0 - - pykdtree=1.3.1=py36hdd07704_2 - - pyproj=2.6.1.post1=py36h61f852b_1 - - pyqt=5.9.2=py36h05f1152_2 - - pyrsistent=0.16.0=py36h7b6447c_0 - - python=3.6.10=h7579374_2 - - pywavelets=1.1.1=py36h7b6447c_0 - - pyyaml=5.3.1=py36h7b6447c_1 - - pyzmq=19.0.1=py36he6710b0_1 - - qt=5.9.7=h5867ecd_1 - - readline=8.0=h7b6447c_0 - - scikit-image=0.16.2=py36h0573a6f_0 - - scipy=1.5.0=py36h0b6359f_0 - - shapely=1.7.0=py36h98ec03d_0 - - sip=4.19.8=py36hf484d3e_0 - - sqlite=3.32.3=h62c20be_0 - - tbb=2020.0=hfd86e86_0 - - testpath=0.3.1=py36_0 - - tk=8.6.10=hbc83047_0 - - tornado=5.1.1=py36h7b6447c_0 - - xz=5.2.5=h7b6447c_0 - - yaml=0.2.5=h7b6447c_0 - - zeromq=4.3.2=he6710b0_2 - - zlib=1.2.11=h7b6447c_3 - - zstd=1.4.4=h0b5b093_3 - osx-64: - - appnope=0.1.0=py36hf537a9a_0 - - brotlipy=0.7.0=py36haf1e3a3_1000 - - cartopy=0.17.0=py36h1d108a8_1 - - cffi=1.14.0=py36hc512035_1 - - cryptography=2.9.2=py36ha12b0ac_0 - - cycler=0.10.0=py36hfc81398_0 - - cytoolz=0.10.1=py36h1de35cc_0 - - freetype=2.10.2=ha233b18_0 - - geos=3.8.0=hb1e8313_0 - - immutables=0.14=py36haf1e3a3_0 - - intel-openmp=2019.4=233 - - jpeg=9b=he5867d9_2 - - kiwisolver=1.2.0=py36h04f5b5a_0 - - krb5=1.18.2=h75d18d8_0 - - libcurl=7.71.0=h8a08a2b_0 - - libcxx=10.0.0=1 - - libedit=3.1.20191231=haf1e3a3_0 - - libffi=3.3=h0a44026_1 - - libgfortran=3.0.1=h93005f0_2 - - libllvm9=9.0.1=h21ff451_0 - - libpng=1.6.37=ha441bb4_0 - - libsodium=1.0.18=h1de35cc_0 - - libssh2=1.9.0=ha12b0ac_1 - - libtiff=4.1.0=hcb84e12_1 - - llvm-openmp=10.0.0=h28b9765_0 - - llvmlite=0.33.0=py36h98b8051_0 - - locket=0.2.0=py36hca03003_1 - - lz4-c=1.9.2=h0a44026_0 - - markupsafe=1.1.1=py36h1de35cc_0 - - matplotlib-base=3.2.2=py36h5670ca0_0 - - mistune=0.8.4=py36h1de35cc_0 - - mkl-service=2.3.0=py36hfbe908c_0 - - mkl=2019.4=233 - - mkl_fft=1.1.0=py36hc64f4ea_0 - - mkl_random=1.1.1=py36h959d312_0 - - msgpack-python=1.0.0=py36h04f5b5a_1 - - ncurses=6.2=h0a44026_1 - - numba=0.50.1=py36h959d312_0 - - numpy-base=1.18.5=py36h3304bdc_0 - - numpy=1.18.5=py36h55a193a_0 - - openssl=1.1.1g=h1de35cc_0 - - pandas=1.0.5=py36h959d312_0 - - pillow=7.1.2=py36h4655f20_0 - - proj=7.0.1=hdb716b3_1 - - psutil=5.7.0=py36h1de35cc_0 - - pykdtree=1.3.1=py36h1d22016_2 - - pyproj=2.6.1.post1=py36ha9dc991_1 - - pyrsistent=0.16.0=py36h1de35cc_0 - - python=3.6.10=hf48f09d_2 - - pywavelets=1.1.1=py36h1de35cc_0 - - pyyaml=5.3.1=py36haf1e3a3_1 - - pyzmq=19.0.1=py36hb1e8313_1 - - readline=8.0=h1de35cc_0 - - scikit-image=0.16.2=py36h6c726b0_0 - - scipy=1.5.0=py36h912ce22_0 - - shapely=1.7.0=py36h727f1cc_0 - - sqlite=3.32.3=hffcf06c_0 - - tbb=2020.0=h04f5b5a_0 - - testpath=0.3.1=py36h625a49b_0 - - tk=8.6.10=hb0a8c7a_0 - - tornado=5.1.1=py36h1de35cc_0 - - xz=5.2.5=h1de35cc_0 - - yaml=0.2.5=haf1e3a3_0 - - zeromq=4.3.2=hb1e8313_2 - - zlib=1.2.11=h1de35cc_3 - - zstd=1.4.4=h1990bb4_3 - win-64: - - brotlipy=0.7.0=py36he774522_1000 - - cartopy=0.17.0=py36hd77ba2b_1 - - cffi=1.14.0=py36h7a1dbc1_0 - - colorama=0.4.3=py_0 - - cryptography=2.9.2=py36h7a1dbc1_0 - - cycler=0.10.0=py36h009560c_0 - - cytoolz=0.10.1=py36he774522_0 - - freetype=2.10.2=hd328e21_0 - - geos=3.8.0=h33f27b4_0 - - icc_rt=2019.0.0=h0cc432a_1 - - icu=58.2=ha925a31_3 - - immutables=0.14=py36he774522_0 - - intel-openmp=2020.1=216 - - jpeg=9b=hb83a4c4_2 - - kiwisolver=1.2.0=py36h74a9793_0 - - libllvm9=9.0.1=h21ff451_0 - - libpng=1.6.37=h2a8f88b_0 - - libsodium=1.0.18=h62dcd97_0 - - libtiff=4.1.0=h56a325e_1 - - llvmlite=0.33.0=py36ha925a31_0 - - locket=0.2.0=py36hfed976d_1 - - lz4-c=1.9.2=h62dcd97_0 - - m2w64-gcc-libgfortran=5.3.0=6 - - m2w64-gcc-libs-core=5.3.0=7 - - m2w64-gcc-libs=5.3.0=7 - - m2w64-gmp=6.1.0=2 - - m2w64-libwinpthread-git=5.0.0.4634.697f757=2 - - markupsafe=1.1.1=py36he774522_0 - - matplotlib-base=3.2.2=py36h64f37c6_0 - - mistune=0.8.4=py36he774522_0 - - mkl-service=2.3.0=py36hb782905_0 - - mkl=2020.1=216 - - mkl_fft=1.1.0=py36h45dec08_0 - - mkl_random=1.1.1=py36h47e9c7a_0 - - msgpack-python=1.0.0=py36h74a9793_1 - - msys2-conda-epoch=20160418=1 - - numba=0.50.1=py36h47e9c7a_0 - - numpy-base=1.18.5=py36hc3f5095_0 - - numpy=1.18.5=py36h6530119_0 - - openssl=1.1.1g=he774522_0 - - pandas=1.0.5=py36h47e9c7a_0 - - pillow=7.1.2=py36hcc1f983_0 - - proj=6.2.1=h9f7ef89_0 - - psutil=5.7.0=py36he774522_0 - - pykdtree=1.3.1=py36h8c2d366_2 - - pyproj=2.6.1.post1=py36hcfa1391_1 - - pyqt=5.9.2=py36h6538335_2 - - pyrsistent=0.16.0=py36he774522_0 - - python=3.6.10=h9f7ef89_2 - - pywavelets=1.1.1=py36he774522_0 - - pywin32=227=py36he774522_1 - - pywinpty=0.5.7=py36_0 - - pyyaml=5.3.1=py36he774522_1 - - pyzmq=19.0.1=py36ha925a31_1 - - qt=5.9.7=vc14h73c81de_0 - - scikit-image=0.16.2=py36h47e9c7a_0 - - scipy=1.5.0=py36h9439919_0 - - shapely=1.7.0=py36h210f175_0 - - sip=4.19.8=py36h6538335_0 - - sqlite=3.32.3=h2a8f88b_0 - - tbb=2020.0=h74a9793_0 - - testpath=0.3.1=py36h2698cfe_0 - - tk=8.6.10=he774522_0 - - tornado=5.1.1=py36hfa6e2cd_0 - - vc=14.1=h0510ff6_4 - - vs2015_runtime=14.16.27012=hf0eaf9b_2 - - win_inet_pton=1.1.0=py36_0 - - wincertstore=0.2=py36h7fe50ca_0 - - winpty=0.4.3=4 - - xz=5.2.5=h62dcd97_0 - - yaml=0.2.5=he774522_0 - - zeromq=4.3.2=ha925a31_2 - - zlib=1.2.11=h62dcd97_4 - - zstd=1.4.4=ha9fde0e_3 diff --git a/glaciers/anaconda-project.yml b/glaciers/anaconda-project.yml index 1529c153c..0ab257e9e 100644 --- a/glaciers/anaconda-project.yml +++ b/glaciers/anaconda-project.yml @@ -1,6 +1,6 @@ # To reproduce: install 'anaconda-project', then 'anaconda-project run' name: glaciers -description: Glaciers explorer using Datashader +description: Glaciers explorer using Datashader and Panel maintainers: - philippjfr labels: @@ -10,19 +10,18 @@ labels: user_fields: [labels, skip, maintainers] -channels: [] +channels: +- pyviz packages: &pkgs -- python=3.6 -- bokeh=1.4.0 +- python=3.7 +- bokeh=2.2.3 - notebook=5.7.8 -- ipykernel=5.1.0 -- tornado<6 -- pyviz_comms=0.7.2 -- datashader=0.7.0 -- holoviews=1.12.3 -- geoviews-core=1.6.2 -- panel=0.6.0 +- pyviz_comms=2.0.0 +- datashader=0.11.1 +- holoviews=1.14.1 +- panel=0.10.2 +- spatialpandas=0.3.6 dependencies: *pkgs diff --git a/glaciers/glaciers.ipynb b/glaciers/glaciers.ipynb index 2f85f38d4..b3b2dc643 100644 --- a/glaciers/glaciers.ipynb +++ b/glaciers/glaciers.ipynb @@ -6,11 +6,11 @@ "source": [ "## Glaciers explorer using Datashader\n", "\n", - "\n", + "\n", "\n", "This notebook provides an annotated [HoloViews](https://holoviews.org)+[Panel](https://panel.pyviz.org) implementation of a [dashboard originally developed in Plotly+Dash](https://github.com/OGGM/OGGM-Dash/blob/master/apps/explore/app.py) for viewing data about the Earth's glaciers from the [Open Global Glacier Model](https://oggm.org). To run it, first:\n", "\n", - " conda install -c pyviz pandas=0.24 param=1.9.0 panel=0.5.1 holoviews=1.12.2 geoviews=1.6.2 datashader=0.7.0\n", + " conda install -c pyviz pandas=0.24 param=1.10.0 panel=0.10.1 holoviews=1.13.5 datashader=0.11.1\n", "\n", "Next, save the [data file](https://cluster.klima.uni-bremen.de/~fmaussion/misc/oggm_glacier_explorer.csv) as `data/oggm_glacier_explorer.csv` (and gzip it if desired).\n", "\n", @@ -27,20 +27,25 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np, pandas as pd, cartopy.crs as ccrs\n", - "import holoviews as hv, geoviews as gv, datashader as ds\n", + "import numpy as np\n", + "import pandas as pd\n", + "import holoviews as hv\n", + "import datashader as ds\n", + "import panel as pn\n", "\n", "from colorcet import bmy\n", "from holoviews.operation.datashader import rasterize, datashade\n", "\n", - "hv.extension('bokeh', width=100)" + "hv.extension('bokeh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load the data" + "## Load the data\n", + "\n", + "Here we will load the glaciers data and project the latitudes and longitudes to Google Mercator coordinates, which will allow us to plot it on top of a tile source. We use the `pn.state.as_cached` function to cache the data to ensure that only the first visitor to our app has to load the data." ] }, { @@ -49,8 +54,13 @@ "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv('data/oggm_glacier_explorer.csv')\n", - "df['latdeg'] = df.cenlat\n", + "def load_data():\n", + " df = pd.read_csv('data/oggm_glacier_explorer.csv')\n", + " df['latdeg'] = df.cenlat\n", + " df['x'], df['y'] = ds.utils.lnglat_to_meters(df.cenlon, df.cenlat)\n", + " return df\n", + "\n", + "df = pn.state.as_cached('glaciers', load_data)\n", "\n", "df.tail()" ] @@ -72,12 +82,13 @@ "metadata": {}, "outputs": [], "source": [ - "data = gv.Points(df, [('cenlon', 'Longitude'), ('cenlat', 'Latitude')],\n", + "data = hv.Dataset(df, [('x', 'Longitude'), ('y', 'Latitude')],\n", " [('avg_prcp', 'Annual Precipitation (mm/yr)'),\n", " ('area_km2', 'Area'), ('latdeg', 'Latitude (deg)'),\n", " ('avg_temp_at_mean_elev', 'Annual Temperature at avg. altitude'), \n", " ('mean_elev', 'Elevation')])\n", "total_area = df.area_km2.sum()\n", + "\n", "print(data, len(data), total_area)" ] }, @@ -85,18 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we've declared that `cenlon` and `cenlat` (the lat,lon location of the center of the glacier) are the [\"key dimensions\"](http://holoviews.org/getting_started/Tabular_Datasets.html#Tabular) (independent values that specify which glacier this is), and the rest are \"value dimensions\" (various dependent values characterizing that particular sample).\n", - "\n", - "To make it faster to work with this data in plots, let's project all the lat,lon values into a coordinate system that can be displayed on top of tile-based online maps. We'll still need the original latitudes in degrees for some purposes, so we saved those above in a column `latdeg`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = gv.Dataset(gv.operation.project_points(data))" + "Here we've declared that `x` and `y` (the projected lat,lon location of the center of the glacier) are the [\"key dimensions\"](http://holoviews.org/getting_started/Tabular_Datasets.html#Tabular) (independent values that specify which glacier this is), and the rest are \"value dimensions\" (various dependent values characterizing that particular sample)." ] }, { @@ -118,7 +118,7 @@ "prcp_kw = dict(num_bins=50, adjoin=False, normed=False, bin_range=data.range('avg_prcp'))\n", "\n", "size_opts = dict(min_height=400, min_width=600, responsive=True)\n", - "geo_opts = dict(size_opts, cmap=bmy, global_extent=False, logz=True, colorbar=True)\n", + "geo_opts = dict(size_opts, cmap=bmy, logz=True, colorbar=True, xlabel='', ylabel='')\n", "elev_opts = dict(size_opts, show_grid=True)\n", "temp_opts = dict(size_opts, fill_color='#f1948a', default_tools=[], toolbar=None, alpha=1.0)\n", "prcp_opts = dict(size_opts, fill_color='#85c1e9', default_tools=[], toolbar=None, alpha=1.0)" @@ -128,7 +128,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using these options with HoloViews and GeoViews, we can plot various combinations of the variables of interest:" + "Using these options with HoloViews, we can plot various combinations of the variables of interest:" ] }, { @@ -137,8 +137,8 @@ "metadata": {}, "outputs": [], "source": [ - "geo_bg = gv.tile_sources.EsriImagery.options(alpha=0.6, bgcolor=\"black\")\n", - "geopoints = data.to(gv.Points, ['cenlon', 'cenlat'], ['area_km2'], []).options(**geo_opts).redim.range(area_km2=(0, 3000))\n", + "geo_bg = hv.element.tiles.EsriImagery().opts(alpha=0.6, bgcolor=\"black\")\n", + "geopoints = hv.Points(data, vdims=['area_km2']).opts(**geo_opts)\n", "\n", "(geo_bg*rasterize(geopoints, **geo_kw).options(**geo_opts) + \n", " datashade(data.to(hv.Scatter, 'mean_elev','latdeg', []), **elev_kw).options(**elev_opts) + \n", @@ -163,12 +163,10 @@ "metadata": {}, "outputs": [], "source": [ - "def geo(data): return gv.Points(data, crs=ccrs.GOOGLE_MERCATOR).options(alpha=1)\n", + "def geo(data): return hv.Points(data).options(alpha=1)\n", "def elev(data): return data.to(hv.Scatter, 'mean_elev', 'latdeg', [])\n", "def temp(data): return data.hist('avg_temp_at_mean_elev', **temp_kw).options(**temp_opts)\n", - "def prcp(data): return data.hist('avg_prcp', **prcp_kw).options(**prcp_opts)\n", - "def count(data): return hv.Div('

Glaciers selected: {}'.format(len(data)) + \"
\" +\n", - " 'Area: {:.0f} km² ({:.1f}%)'.format(np.sum(data['area_km2']), np.sum(data['area_km2']) / total_area * 100)).options(height=40)" + "def prcp(data): return data.hist('avg_prcp', **prcp_kw).options(**prcp_opts)" ] }, { @@ -188,8 +186,8 @@ "metadata": {}, "outputs": [], "source": [ - "static_geo = rasterize(geo(data), **geo_kw).options(alpha=0.1, tools=['hover', 'box_select'], active_tools=['box_select'], **geo_opts)\n", - "static_elev = datashade(elev(data), **elev_kw).options(alpha=0.1, tools=[ 'box_select'], active_tools=['box_select'], **elev_opts)\n", + "static_geo = rasterize(geo(data), **geo_kw).options(alpha=0.1, tools=['hover'], active_tools=['box_select'], **geo_opts)\n", + "static_elev = datashade(elev(data), **elev_kw).options(alpha=0.1, active_tools=['box_select'], **elev_opts)\n", "static_temp = temp(data).options(alpha=0.1)\n", "static_prcp = prcp(data).options(alpha=0.1)" ] @@ -207,60 +205,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Selections\n", + "## Add linked selections\n", "\n", - "Now that we have some static plots for the background and some functions to create plots dynamically, we can set up selections linked between each of the static plots and determining which data is shown dynamically. We'll be using [HoloViews streams](http://holoviews.org/user_guide/Custom_Interactivity.html), which allow any of a variety of data producers to be linked to any of a variety of data consumers. First, we'll define functions for selecting by 1D or 2D value range:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def combine_selections(**kwargs):\n", - " \"\"\"\n", - " Combines selections on all available plots into a single selection by index.\n", - " \"\"\"\n", - " if all(not v for v in kwargs.values()):\n", - " return slice(None)\n", - " selection = {}\n", - " for key, bounds in kwargs.items():\n", - " if bounds is None:\n", - " continue\n", - " elif len(bounds) == 2:\n", - " selection[key] = bounds\n", - " else:\n", - " xbound, ybound = key.split('__')\n", - " selection[xbound] = bounds[0], bounds[2]\n", - " selection[ybound] = bounds[1], bounds[3]\n", - " return sorted(set(data.select(**selection).data.index))\n", - "\n", - "def select_data(**kwargs):\n", - " return data.iloc[combine_selections(**kwargs)] if kwargs else data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, `select_data` will accept various keyword arguments that specify 2D or 1D bounds with which to filter the data. A 2D bound uses four values and selects on the values of two columns (here with values in Web Mercator coordinates):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "select_data(cenlon__cenlat=(3000000, 9000000, 5000000, 11000000)).dframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each additional selection will further narrow down the items selected. Here we further select by a 1D bound on temperature, to select only those with an average temperature below -2 degrees:" + "All we have to do to add linked selections to these static plots is make a `hv.link_selections` instance and apply it to our plots:" ] }, { @@ -269,37 +216,21 @@ "metadata": {}, "outputs": [], "source": [ - "select_data(cenlon__cenlat=(3000000, 9000000, 5000000, 11000000), avg_temp_at_mean_elev=(-5,-2)).dframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Separately, we can instantiate some Stream-based selection objects provided by HoloViews that capture user selection/bounds events on each of the static plots:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from holoviews.streams import Stream, BoundsXY, BoundsX\n", + "ls = hv.link_selections.instance()\n", "\n", - "geo_bounds = BoundsXY(source=static_geo, rename={'bounds': 'cenlon__cenlat'})\n", - "elev_bounds = BoundsXY(source=static_elev, rename={'bounds': 'mean_elev__latdeg'})\n", - "temp_bounds = BoundsX( source=static_temp, rename={'boundsx': 'avg_temp_at_mean_elev'})\n", - "prcp_bounds = BoundsX( source=static_prcp, rename={'boundsx': 'avg_prcp'})\n", + "geomap = geo_bg * ls(static_geo)\n", + "elevation = ls(static_elev)\n", + "temperature = ls(static_temp)\n", + "precipitation = ls(static_prcp)\n", "\n", - "selections = [geo_bounds, elev_bounds, temp_bounds, prcp_bounds]" + "(geomap + elevation + temperature + precipitation).cols(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can make dynamic versions of the above static plots that first compute the appropriate subset of the data from all the selection objects, then call the plotting functions above to generate the plot for just that subset of the data:" + "Let's also create a pane that renders the count of total selections:" ] }, { @@ -308,24 +239,26 @@ "metadata": {}, "outputs": [], "source": [ - "dyn_data = hv.DynamicMap(select_data, streams=selections)\n", + "def count(data): \n", + " selected_area = np.sum(data['area_km2'])\n", + " selected_percentage = selected_area / total_area * 100\n", + " return pn.pane.Markdown(\n", + " '## Glaciers selected: {} | Area: {:.0f} km² ({:.1f}%)'.format(\n", + " len(data), selected_area, selected_percentage),\n", + " align='center', width=500\n", + " )\n", "\n", - "dyn_geo = rasterize(dyn_data.apply(geo), **geo_kw).options( **geo_opts)\n", - "dyn_elev = datashade(dyn_data.apply(elev), **elev_kw).options(**elev_opts)\n", - "dyn_temp = dyn_data.apply(temp)\n", - "dyn_prcp = dyn_data.apply(prcp)\n", - "dyn_count = dyn_data.apply(count)" + "dynamic_count = pn.bind(count, ls.selection_param(df))\n", + "pn.panel(dynamic_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Again, we could view these dynamic plots on their own:\n", - "\n", - " (geo_bg*dyn_geo + dyn_elev + dyn_temp + dyn_prcp).cols(2)\n", + "## Dashboard\n", "\n", - "But to show the selections relative to the static plots, we'll overlay the dynamic plots onto the static plots:" + "The code and plots above should be fine for exploring this data in a notebook, but let's go further and make a shareable dashboard using [Panel](http://panel.pyviz.org). Panel lets us add arbitrary custom functionality, such as a button to reset the selections by calling `clear_selections` which sets the selection_expr to `None`:" ] }, { @@ -334,44 +267,20 @@ "metadata": {}, "outputs": [], "source": [ - "geomap = geo_bg * static_geo * dyn_geo\n", - "elevation = static_elev * dyn_elev\n", - "temperature = static_temp * dyn_temp\n", - "precipitation = static_prcp * dyn_prcp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could now view these four overlaid plots at once and see how all the selections work (being sure to choose the `box-select` tool rather than the default `box-zoom` tool before selecting):\n", + "pn.extension()\n", "\n", - " (geomap + elevation + temperature + precipitation).cols(2)\n", + "def clear_selections(event):\n", + " ls.selection_expr = None\n", "\n", - "If you create that plot and try it out, you'll see that each time a selection is made, it reduces the set of glaciers included. So we need one more function that will allow us to reset to the initial state by clearing all the selections:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def clear_selections(arg=None):\n", - " geo_bounds.update(bounds=None)\n", - " elev_bounds.update(bounds=None)\n", - " temp_bounds.update(boundsx=None)\n", - " prcp_bounds.update(boundsx=None)\n", - " Stream.trigger(selections)" + "clear_button = pn.widgets.Button(name='Clear selection', align='center')\n", + "clear_button.param.watch(clear_selections, 'clicks');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Dashboard\n", - "\n", - "The code and plots above should be fine for exploring this data in a notebook, but let's go further and make a shareable dashboard using [Panel](http://panel.pyviz.org). Panel lets us add arbitrary custom functionality, such as a button to reset the selections by calling `clear_selections`:" + "And we can add static text, Markdown, or HTML items like a title, instructions, and logos:" ] }, { @@ -380,18 +289,20 @@ "metadata": {}, "outputs": [], "source": [ - "import panel as pn\n", - "pn.extension()\n", - "\n", - "clear_button = pn.widgets.Button(name='Clear selection')\n", - "clear_button.param.watch(clear_selections, 'clicks');" + "title = '

World glaciers explorer
'\n", + "instruction = 'Box-select on each plot to subselect; clear selection to reset.
' + \\\n", + " 'See the Jupyter notebook source code for how to build apps like this!'\n", + "oggm_url = 'https://raw.githubusercontent.com/OGGM/oggm/master/docs/_static/logos/oggm_s_alpha.png'\n", + "oggm_logo = ''.format(oggm_url)\n", + "pn_url = 'http://panel.pyviz.org/_static/logo_stacked.png'\n", + "pn_logo = ''.format(pn_url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "And we can add static text, Markdown, or HTML items like a title, instructions, and logos:" + "If you want detailed control over the formatting, you could define these items in a separate [Jinja2 template](http://bokeh.pydata.org/en/latest/docs/user_guide/server.html#building-bokeh-applications). But here, let's put it all together using Panel Row and Column objects, which can display objects and plots from many different libraries, including the HoloViews objects used here. You'll then get an app with widgets and plots usable from within the notebook:" ] }, { @@ -400,18 +311,21 @@ "metadata": {}, "outputs": [], "source": [ - "title = '
World glaciers explorer
'\n", - "instruction = 'Box-select on each plot to subselect; clear selection to reset.
' + \\\n", - " 'See the Jupyter notebook source code for how to build apps like this!'\n", - "oggm_logo = ''\n", - "pn_logo = ''" + "header = pn.Row(pn.Pane(oggm_logo), pn.layout.Spacer(width=30), \n", + " pn.Column(pn.Pane(title, width=400), pn.Pane(instruction, width=500)),\n", + " pn.layout.HSpacer(), pn.Column(dynamic_count, pn.layout.Spacer(height=20), clear_button), \n", + " pn.Pane(pn_logo, width=140))\n", + "\n", + "content = pn.Column(header, pn.Row(geomap, elevation), pn.Row(temperature, precipitation), width_policy='max', height_policy='max')\n", + "\n", + "content" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you want detailed control over the formatting, you could define these items in a separate [Jinja2 template](http://bokeh.pydata.org/en/latest/docs/user_guide/server.html#building-bokeh-applications). But here, let's put it all together using Panel Row and Column objects, which can display objects and plots from many different libraries, including the HoloViews objects used here. You'll then get an app with widgets and plots usable from within the notebook:" + "Lastly we will build a Template to give this dashboard a more polished look and feel when deployed, reflecting the image shown at the top of the notebook:" ] }, { @@ -420,12 +334,27 @@ "metadata": {}, "outputs": [], "source": [ - "header = pn.Row(pn.Pane(oggm_logo), pn.layout.Spacer(width=30), \n", - " pn.Column(pn.Pane(title, width=400), pn.Pane(instruction, width=500)),\n", - " pn.layout.HSpacer(), pn.Column(pn.Pane(dyn_count), pn.layout.Spacer(height=20), clear_button), \n", - " pn.Pane(pn_logo, width=140))\n", + "template = pn.template.MaterialTemplate(title='World Glaciers Explorer')\n", + "\n", + "template.header.append(\n", + " pn.Row(\n", + " pn.layout.HSpacer(),\n", + " dynamic_count,\n", + " clear_button,\n", + " )\n", + ")\n", + "\n", + "template.sidebar.extend([\n", + " pn.pane.PNG(oggm_url, width=250),\n", + " pn.pane.Markdown(instruction, width=250, height=100),\n", + " pn.pane.PNG(pn_url, width=200, align='center')\n", + "])\n", "\n", - "pn.Column(header, pn.Row(geomap, elevation), pn.Row(temperature, precipitation), width_policy='max', height_policy='max').servable()" + "template.main.append(\n", + " (geomap + elevation + temperature + precipitation).cols(2)\n", + ")\n", + "\n", + "template.servable();" ] }, { @@ -439,11 +368,31 @@ } ], "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", - "pygments_lexer": "ipython3" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/glaciers/screenshot.png 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