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scottyhq / LANDSAT_8_C1_ARCHIVE.csv
Created August 24, 2018 05:02
Comparison of USGS Landsat archive vs Landsat on AWS
Landsat Product Identifier Landsat Scene Identifier Acquisition Date Collection Category Collection Number WRS Path WRS Row Target WRS Path Target WRS Row Nadir/Off Nadir Roll Angle Date L-1 Generated Start Time Stop Time Station Identifier Day/Night Indicator Land Cloud Cover Scene Cloud Cover Ground Control Points Model Ground Control Points Version Geometric RMSE Model (meters) Geometric RMSE Model X Geometric RMSE Model Y Image Quality Processing Software Version Sun Elevation L1 Sun Azimuth L1 TIRS SSM Model Data Type Level-1 Sensor Identifier Panchromatic Lines Panchromatic Samples Reflective Lines Reflective Samples Thermal Lines Thermal Samples Map Projection Level-1 UTM Zone Datum Ellipsoid Grid Cell Size Panchromatic Grid Cell Size Reflective Grid Cell Size Thermal Bias Parameter File Name OLI Bias Parameter File Name TIRS Calibration Parameter File RLUT File Name Center Latitude Center Longitude UL Corner Lat UL Corner Long UR Corner Lat UR Corner Long LL Corner Lat LL Corner Long LR Corner Lat L
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scottyhq / 0-load-hdf5.ipynb
Created February 20, 2019 20:15
experimenting with remote HDF5 files in xarray
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scottyhq / s3fs-vs-gcsfs.ipynb
Created February 27, 2019 01:03
testing netcdf4 access with s3fs versus gcsfs
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scottyhq / 2-GRFN-archive-hawaii.ipynb
Created August 7, 2018 22:15
grfn-archive-notebook
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scottyhq / tiledb-experiments.ipynb
Created August 28, 2019 04:39
Trying out tiledb with python, gdal, xarray
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scottyhq / atl06-example.ipynb
Created September 26, 2019 03:41
basic loading and visualizing of icesat2 atl06
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scottyhq / vrt-cloud-rasterio-gdal.ipynb
Created June 5, 2018 21:26
exploring VRT format with GDAL and rasterio
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scottyhq / xarray-temporal-fit.ipynb
Created December 4, 2018 23:44
polynomial fit with xarray in time
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scottyhq / gpu-spot-nodegroup.yml
Created May 10, 2020 22:19
create gpu nodegroup on spot instances with eksctl
# https://eksctl.io/usage/gpu-support/
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
name: pangeo
region: us-west-2
nodeGroups:
- name: user-gpu-spot
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scottyhq / customenv.md
Last active June 1, 2020 21:48
custom environments on jupyterhub

Most JupyterHub systems are configured to wipe all directories except for /home/jovyan every time your server shuts down. This is by design to keep file systems from bloating and to allow your computing environment to be run on different physical machines. /home/jovyan should be kept small, with just your text files, github repositories with code, and perhaps a small amount of data. If the default JupyterHub conda environment is missing packages you can either update the environment each time (run conda install mypackage in a terminal) or you can install entirely custom conda environment as explained below.

1. Open a terminal in your Jupyterlab session. You’ll see that the (notebook) environment is activated by default:
(notebook) jovyan@jupyter-scottyhq:~$ conda info -e
# conda environments:
#
base                     /srv/conda
notebook              *  /srv/conda/envs/notebook