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Tutorial update
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dshean committed Jun 20, 2019
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35 changes: 21 additions & 14 deletions shean_ICESat-2_hackweek_tutorial_GeospatialAnalysis.ipynb
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"*Not intended for trillions of ATL03 points\n",
"\n",
"ICESat-2 hackweek \n",
"June 18, 2019 \n",
"June 19, 2019 \n",
"David Shean"
]
},
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"\n",
"## The Scientific Python landscape\n",
"\n",
"* Python, iPython, Jupyter\n",
"* Python\n",
"* iPython, Jupyter\n",
"* NumPy, Pandas, Matplotlib, SciPy\n",
"* xarray, scikit-learn, \n",
"* xarray, scikit-image, scikit-learn\n",
"\n",
"One interpretation of this stack:\n",
"\n",
"![2017 Scientific Python Stack](https://ryanwingate.com/assets/images/python/python-1-1.png)\n",
"Slide from Jake VanderPlas’s presentation at PyCon 2017, entitled “The Unexpected Effectiveness of Python in Science.”\n",
"\n",
"## The Geospatial Python landscape\n",
"* GDAL/OGR, GEOS, PROJ\n",
"* shapely, fiona\n",
"* geopandas, rasterio, Ccrtopy\n",
"* shapely, fiona, pyproj\n",
"* geopandas, rasterio, cartopy\n",
"\n",
"[Insert shiny new figure here]\n",
"\n",
"## Complementary approaches for ICESat-2 data\n",
"\n",
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" * Basic array manipulations\n",
" * Array size limited by memory\n",
"2. Higher-level data science - *analysis, interpetation, and visualization*\n",
" * NumPy under the hood, convenience/functionality comes with small performance hit\n",
" * NumPy under the hood\n",
" * Convenience and flexibility comes with small performance hit\n",
" * Labels make life easier (don't need to remember integer indices)\n",
"\n",
"At the end of the day, most of us just want/need (x,y,z,t) with ICESat.\n",
"Here, we're going to explore #2 with an existing dataset.\n",
"\n",
"As with all things in the *nix/open-source/Python world, there are always multiple approaches that can be used to accomplish the same goals. The user must decide on an approach based on complexity, time constraints, etc.\n",
"At the end of the day, most of us just want/need (x,y,z,t) with ICESat-2.\n",
"\n",
"Here, we're going to explore #2 with an existing dataset."
"As with all things in the *nix/open-source/Python world, there are always multiple approaches that can be used to accomplish the same goals. The user must decide on an approach based on complexity, time constraints, etc."
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Wait a minute, I thought this was an ICESat-2 hackweek!\n",
"## Wait, I thought this was an ICESat-2 hackweek?\n",
"\n",
"Note that we could (and should!) swap similarly processed/filtered ATL06 points over CONUS for this tutorial. I did not have time to do this before the hackweek, but it would make a great project (nudge). After opening this updated file with Pandas, it should mostly be a matter of updating field names throughout the notebook."
]
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"metadata": {},
"outputs": [],
"source": [
"glas_df = pd.read_csv(glas_fn)\n",
"#That's it!"
"glas_df = pd.read_csv(glas_fn)"
]
},
{
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"\n",
"Note that leaflet uses tiled basemaps: https://en.wikipedia.org/wiki/Tiled_web_map \n",
"\n",
"Default projection is Web Mercator (EPSG:3857): https://en.wikipedia.org/wiki/Web_Mercator_projection. This works well for lower latitudes, but not the polar regions. I know QGIS can reproject tiled basemaps on the fly - I haven't tested with ipyleaflet."
"Default projection is Web Mercator (EPSG:3857): https://en.wikipedia.org/wiki/Web_Mercator_projection. This works well for lower latitudes, but not the polar regions. I know QGIS can reproject tiled basemaps on the fly - I haven't tested with ipyleaflet.\n",
"\n",
"FYI, `folium` provides similar functionality outside of the iPython/Jupyter stack: https://python-visualization.github.io/folium/"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 155,
"metadata": {},
"outputs": [],
"source": [
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