Base Conda Environments#

cisl-cloud-base#

The NCAR JupyterHub has a custom conda environment, cisl-cloud-base, as the default base environment. This environment has been put together based on input from users, referencing other production Jupyter images, and requirements that were set to deliver to users.

Note

This is just a default environment that provides common packages to try and enable users to get started quickly. Custom environments are supported and documentation on how to implement them can be found here

List of Packages used#

An up to date list of packages and versions can be found directly at this link to the file in GitHub

cisl-cloud-base package list

  • argopy=0.1.14

  • arm_pyart=1.15.0=py310h1fa729e_0

  • astropy=5.3.1=py310h278f3c1_0

  • beautifulsoup4=4.12.2=pyha770c72_0

  • bokeh=3.1.1=pyhd8ed1ab_0

  • boto3=1.28.2=pyhd8ed1ab_0

  • bottleneck=1.3.7=py310h0a54255_0

  • ca-certificates=2023.7.22=hbcca054_0

  • cartopy=0.22.0=py310hcc13569_1

  • cdsapi=0.6.1=pyhd8ed1ab_0

  • celluloid=0.2.0=pyhd8ed1ab_0

  • certifi=2023.7.22=pyhd8ed1ab_0

  • cf-units=3.2.0=py310h278f3c1_0

  • cfgrib=0.9.10.4=pyhd8ed1ab_0

  • click=8.1.4=unix_pyh707e725_0

  • cmocean=3.0.3=pyhd8ed1ab_0

  • dask=2023.7.0=pyhd8ed1ab_0

  • dask-gateway=2023.1.1=pyh8af1aa0_0

  • dask-jobqueue=0.8.2=pyhd8ed1ab_0

  • dask-ml=2023.3.24=pyhd8ed1ab_1

  • datashader=0.15.1=pyhd8ed1ab_0

  • descartes=1.1.0=py_4

  • docopt=0.6.2=py_1

  • erddapy=2.2.0=pyhd8ed1ab_0

  • esmpy=8.4.2=pyhc1e730c_1

  • fiona=1.9.4=py310h111440e_0

  • flox=0.7.2=pyhd8ed1ab_0

  • folium=0.14.0=pyhd8ed1ab_0

  • gdal=3.7.3=py310h5c4b078_5

  • geocat-comp=2023.06.1=pyha770c72_0

  • geocat-viz=2023.07.0=pyhd8ed1ab_0

  • geocube=0.4.2=pyhd8ed1ab_1

  • geopandas=0.14.1

  • geopy=2.4.0

  • geoviews=1.10.0=pyhd8ed1ab_0

  • ghp-import=2.1.0=pyhd8ed1ab_0

  • globus-cli=3.15.0=pyhd8ed1ab_0

  • globus-sdk=3.21.0=pyhd8ed1ab_0

  • gsw=3.6.17=py310h278f3c1_0

  • h5netcdf=1.2.0=pyhd8ed1ab_0

  • h5py=3.10.0=nompi_py310ha2ad45a_100

  • holoviews=1.18.1=pyhd8ed1ab_0

  • hvplot=0.8.4=pyhd8ed1ab_1

  • intake=0.7.0=pyhd8ed1ab_0

  • intake-esm=2023.7.7=pyhd8ed1ab_0

  • intake-thredds=2022.8.19=pyhd8ed1ab_0

  • intake-xarray=0.7.0=pyhd8ed1ab_0

  • ipympl=0.9.3=pyhd8ed1ab_0

  • ipykernel=6.24.0=pyh71e2992_0

  • ipywidgets-bokeh=1.5.0

  • iris=3.6.1=pyha770c72_0

  • jupyter_bokeh=3.0.7

  • jupyter-book=0.15.1=pyhd8ed1ab_0

  • jupyter-panel-proxy=0.1.0

  • ldcpy=0.17=py310h5764c6d_1

  • libblas=3.9.0=17_linux64_blis

  • matplotlib=3.7.2=py310hff52083_0

  • metpy=1.5.1=pyhd8ed1ab_0

  • mpi4py=3.1.4=py310h37cc914_0

  • nbstripout=0.6.1=pyhd8ed1ab_0

  • nc-time-axis=1.4.1=pyhd8ed1ab_0

  • netcdf4=1.6.4=nompi_py310hba70d50_103

  • numba=0.57.1=py310h0f6aa51_0

  • numcodecs=0.11.0=py310heca2aa9_1

  • numexpr=2.8.4=py310h690d005_100

  • numpy=1.24.4=py310ha4c1d20_0

  • ocgis=2.1.1=py_1

  • pandas=2.0.3=py310h7cbd5c2_1

  • panel=1.2.3=pyhd8ed1ab_0

  • papermill=2.3.4=pyhd8ed1ab_0

  • pillow=10.0.1=py310h01dd4db_2

  • pop-tools=2023.6.0=pyhd8ed1ab_0

  • pyarrow=14.0.1=py310hf9e7431_1_cpu

  • pydap=3.4.0=pyhd8ed1ab_0

  • pygraphviz=1.11=py310h91ff30a_0

  • pygrib=2.1.4=py310hdcc264a_7

  • pyhdf=0.11.3=py310h3532cbf_0

  • pylint=2.17.4=pyhd8ed1ab_0

  • pynco=1.1.0=pyhd8ed1ab_1

  • pyspharm=1.0.9=py310h19f2f35_1008

  • pystac=1.9.0=pyhd8ed1ab_0

  • pystac-client=0.7.5=pyhd8ed1ab_0

  • pytables=3.9.1=py310h374b01c_0

  • pyqt=5.15.7=py310hab646b1_3

  • python=3.10.12=hd12c33a_0_cpython

  • python-graphviz=0.20.1=pyh22cad53_0

  • python-wget=3.2=py_0

  • rasterio=1.3.9=py310h6a913dc_0

  • rechunker=0.5.2=pyhd8ed1ab_1

  • rio-cogeo=5.0.0=pyhd8ed1ab_0

  • rioxarray=0.15.0=pyhd8ed1ab_0

  • satpy=0.44.0=pyhd8ed1ab_0

  • scikit-image=0.21.0=py310hc6cd4ac_0

  • scikit-learn=1.3.0=py310hf7d194e_0

  • scipy=1.11.1=py310ha4c1d20_0

  • seaborn=0.12.2=hd8ed1ab_0

  • seawater=3.3.4=py_1

  • shapely=2.0.2=py310h7dcad9a_0

  • siphon=0.9=pyhd8ed1ab_2

  • statsmodels=0.14.0=py310h278f3c1_1

  • tobac=1.4.2=pyhd8ed1ab_0

  • widgetsnbextension=4.0.8=pyhd8ed1ab_0

  • windspharm=1.7.0=py310hff52083_1004

  • wrf-python=1.3.4.1=py310h3254323_3

  • xarray=2023.6.0=pyhd8ed1ab_0

  • xesmf=0.7.1=pyhd8ed1ab_0

  • xgcm=0.8.1=pyhd8ed1ab_0

  • xrft=1.0.1=pyhd8ed1ab_0

  • zarr=2.15.0=pyhd8ed1ab_0

NPL#

We also include the NCAR Python Library (NPL) conda environment and Python Kernel to users. This is a copy of the packages utilized for NPL that is hosted on HPC. We did have to upgrade a few specific versions to address vulnerabilities. For the most part versions will match what is used on HPC JupyterHub.

List of Packages used#

An up to date list of packages and versions can be found directly at this link to the file in GitHub

npl-2023b package list

  • arm_pyart=1.15.0=py310h1fa729e_0

  • astropy=5.3.1=py310h278f3c1_0

  • bokeh=3.1.1=pyhd8ed1ab_0

  • boto3=1.28.2=pyhd8ed1ab_0

  • bottleneck=1.3.7=py310h0a54255_0

  • ca-certificates=2023.7.22=hbcca054_0

  • cartopy=0.22.0=py310hcc13569_1

  • cdsapi=0.6.1=pyhd8ed1ab_0

  • celluloid=0.2.0=pyhd8ed1ab_0

  • certifi=2023.7.22=pyhd8ed1ab_0

  • cf-units=3.2.0=py310h278f3c1_0

  • cfgrib=0.9.10.4=pyhd8ed1ab_0

  • click=8.1.4=unix_pyh707e725_0

  • cmocean=3.0.3=pyhd8ed1ab_0

  • dask-jobqueue=0.8.2=pyhd8ed1ab_0

  • dask-labextension=6.1.0=pyhd8ed1ab_0

  • dask-mpi=2022.4.0=pyh458ca06_2

  • dask=2023.7.0=pyhd8ed1ab_0

  • datashader=0.15.1=pyhd8ed1ab_0

  • descartes=1.1.0=py_4

  • docopt=0.6.2=py_1

  • eccodes=2.32.1=h35c6de3_0

  • esmpy=8.4.2=pyhc1e730c_1

  • fiona=1.9.4=py310h111440e_0

  • flox=0.7.2=pyhd8ed1ab_0

  • folium=0.14.0=pyhd8ed1ab_0

  • gdal=3.7.3=py310h5c4b078_5

  • geocat-comp=2023.06.1=pyha770c72_0

  • geocat-viz=2023.07.0=pyhd8ed1ab_0

  • geoviews=1.10.0=pyhd8ed1ab_0

  • ghp-import=2.1.0=pyhd8ed1ab_0

  • globus-cli=3.15.0=pyhd8ed1ab_0

  • gsw=3.6.17=py310h278f3c1_0

  • h5netcdf=1.2.0=pyhd8ed1ab_0

  • h5py=3.10.0=nompi_py310ha2ad45a_100

  • hvplot=0.8.4=pyhd8ed1ab_1

  • intake-esm=2023.7.7=pyhd8ed1ab_0

  • intake-xarray=0.7.0=pyhd8ed1ab_0

  • intake=0.7.0=pyhd8ed1ab_0

  • ipykernel=6.24.0=pyh71e2992_0

  • ipympl=0.9.3=pyhd8ed1ab_0

  • ipywidgets=8.0.7=pyhd8ed1ab_0

  • iris=3.6.1=pyha770c72_0

  • jupyter-book=0.15.1=pyhd8ed1ab_0

  • jupyter-server-proxy=4.0.0=pyhd8ed1ab_0

  • jupyterlab=4.0.2=pyhd8ed1ab_0

  • ldcpy=0.17=py310h5764c6d_1

  • libblas=3.9.0=17_linux64_blis

  • matplotlib=3.7.2=py310hff52083_0

  • mpi4py=3.1.4=py310h37cc914_0

  • nc-time-axis=1.4.1=pyhd8ed1ab_0

  • ncar-jobqueue=2021.4.14=pyh44b312d_0

  • netcdf4=1.6.4=nompi_py310hba70d50_103

  • numba=0.57.1=py310h0f6aa51_0

  • numcodecs=0.11.0=py310heca2aa9_1

  • numexpr=2.8.4=py310h690d005_100

  • numpy=1.24.4=py310ha4c1d20_0

  • ocgis=2.1.1=py_1

  • openssl=3.1.4=hd590300_0

  • pandas=2.0.3=py310h7cbd5c2_1

  • papermill=2.3.4=pyhd8ed1ab_0

  • pillow=10.0.1=py310h01dd4db_2

  • pop-tools=2023.6.0=pyhd8ed1ab_0

  • pyarrow=14.0.1=py310hf9e7431_1_cpu

  • pydap=3.4.0=pyhd8ed1ab_0

  • pygraphviz=1.11=py310h91ff30a_0

  • pygrib=2.1.4=py310hdcc264a_7

  • pyhdf=0.11.3=py310h3532cbf_0

  • pylint=2.17.4=pyhd8ed1ab_0

  • pynco=1.1.0=pyhd8ed1ab_1

  • pyqt=5.15.7=py310hab646b1_3

  • pyspharm=1.0.9=py310h19f2f35_1008

  • pytables=3.9.1=py310h374b01c_0

  • python-graphviz=0.20.1=pyh22cad53_0

  • python-wget=3.2=py_0

  • python=3.10.12=hd12c33a_0_cpython

  • scikit-image=0.21.0=py310hc6cd4ac_0

  • scikit-learn=1.3.0=py310hf7d194e_0

  • scipy=1.11.1=py310ha4c1d20_0

  • seaborn=0.12.2=hd8ed1ab_0

  • seawater=3.3.4=py_1

  • shapely=2.0.2=py310h7dcad9a_0

  • statsmodels=0.14.0=py310h278f3c1_1

  • tobac=1.4.2=pyhd8ed1ab_0

  • widgetsnbextension=4.0.8=pyhd8ed1ab_0

  • windspharm=1.7.0=py310hff52083_1004

  • wrf-python=1.3.4.1=py310h3254323_3

  • xarray=2023.6.0=pyhd8ed1ab_0

  • xesmf=0.7.1=pyhd8ed1ab_0

  • xgcm=0.8.1=pyhd8ed1ab_0

  • xrft=1.0.1=pyhd8ed1ab_0

  • zarr=2.15.0=pyhd8ed1ab_0

r-4.3#

We provide a base R environment with a packages installed based off what is provided to users on HPC JupyterHub.

List of Packages used#

An up to date list of packages and versions can be found directly at this link to the file in GitHub

r-4.3 package list

  • r=4.3=r43hd8ed1ab_1007

  • r-irkernel=1.3.2=r43h785f33e_1

  • ca-certificates=2023.7.22=hbcca054_0

  • openssl=3.1.4=hd590300_0

  • r-terra=1.7_55=r43h25a7ac2_0

  • r-rgdal=1.6_7=r43haac4566_0

  • r-rnetcdf=2.6_2=r43h3183d2a_4

  • r-ggplot2=3.4.4=r43hc72bb7e_0

  • r-lubridate=1.9.3=r43h57805ef_0

  • r-randomforest=4.7_1.1=r43h61816a4_2

  • r-rgooglemaps=1.5.1=r43hc72bb7e_0

  • r-lava=1.7.3=r43hc72bb7e_0

  • r-beanplot=1.3.1=r43ha770c72_2

  • r-cdft=1.2=r43hc72bb7e_2

  • r-corrplot=0.92=r43hc72bb7e_2

  • r-dt=0.28=r43hc72bb7e_1

  • r-ellipse=0.5.0=r43hc72bb7e_0

  • r-energy=1.7_11=r43ha503ecb_1

  • r-fields=15.2=r43h61816a4_0

  • r-fitdistrplus=1.1_11=r43hc72bb7e_1

  • r-moments=0.14.1=r43hc72bb7e_2

  • r-pcict=0.5_4.4=r43h57805ef_1

  • r-proj=0.4.0=r43h57805ef_2

  • r-prroc=1.3.1=r43hc72bb7e_1005

  • r-pscl=1.5.5.1=r43hd590300_1

  • r-qgraph=1.9.8=r43ha503ecb_0

  • r-quantreg=5.97=r43hd9ac46e_0

  • r-roxygen2=7.2.3=r43ha503ecb_1

  • r-udunits2=0.13.2.1=r43h57805ef_3

  • r-zoo=1.8_12=r43h57805ef_1

  • r-clipr=0.8.0=r43hc72bb7e_2

  • r-readr=2.1.4=r43ha503ecb_1

  • r-curl=5.1.0=r43hf9611b0_0

  • r-lmoments=1.3_1=r43h7ce84a7_5

  • r-statmod=1.5.0=r43hd8f1df9_1

  • r-zip=2.3.0=r43h57805ef_1

  • r-distillery=1.2_1=r43h785f33e_2

  • r-nloptr=2.0.3=r43hcf54a89_2

  • r-extremes=2.1_3=r43h1df0287_1

  • r-climate4r.climdex=0.2.3=r43ha770c72_0

  • r-climate4r.datasets=0.0.1=r43ha770c72_2

  • r-climate4r.indices=0.3.1=r43ha770c72_0

  • r-s2dv=2.0.0=r43hc72bb7e_0

  • jupyter_client=8.6.0=pyhd8ed1ab_0

  • r-dataretrieval=2.7.14=r43h785f33e_0

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