Jupyterlab workbench supporting visual exploration and classification of high dimensional sensor data.
> conda create --prefix /explore/nobackup/projects/ilab/conda/envs/spectraclass -c conda-forge python=3.9 mamba
> conda activate spectraclass
> mamba install -c conda-forge ipympl jupytext pyepsg ipysheet tensorflow h5py pythreejs nb_conda_kernels nodejs jupyterlab jupyterlab_server ipywidgets numpy xarray matplotlib rasterio scipy scikit-learn dask netcdf4 scikit-image numba gdal owslib rioxarray cartopy shapely bottleneck geopandas
> conda create --name spectraclass.hv -c pyviz -c conda-forge mamba holoviews geopandas geoviews hvplot
> conda create --prefix /explore/nobackup/projects/ilab/conda/envs/spectraclass.hv -c pyviz -c conda-forge mamba holoviews geopandas geoviews hvplot
> conda activate spectraclass.hv
> mamba install -c pyviz -c conda-forge ipympl jupytext pyepsg ipysheet tensorflow h5py pythreejs nb_conda_kernels nodejs jupyterlab jupyterlab_server rasterio dask netcdf4 scikit-image numba owslib rioxarray bottleneck
The x-ray application requires the following additional packages:
> mamba install -c conda-forge jupyter_bokeh
$ git clone https://github.com/nasa-nccs-cds/spectraclass.git
$ cd spectraclass
$ pip install .
For example, with DESIS data:
gdaltindex -t_srs EPSG:32618 image_index_srs.shp *-SPECTRAL_IMAGE.tif
When actively developing your extension, build Jupyter Lab with the command:
$ jupyter lab --watch
This takes a minute or so to get started, but then automatically rebuilds JupyterLab when your javascript changes.
Note on first jupyter lab --watch, you may need to touch a file to get Jupyter Lab to open.