#!/usr/bin/env python
# coding: utf-8

# ## SlideRule ATL06 Computation: Interactive Tutorial
# ### IPython Widgets Example
# 
# ### Purpose
# Demonstrate common uses of the `ipysliderule` module

# #### Load necessary packages

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import warnings
warnings.filterwarnings('ignore') # turn off warnings for demo

from sliderule import icesat2, ipysliderule, io, sliderule
import geopandas
import logging

get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


# ### Initiate SlideRule API
# - Sets the URL for accessing the SlideRule service
# - Builds a table of servers available for processing data

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# set the url for the sliderule service
# set the logging level
icesat2.init("slideruleearth.io", loglevel=logging.WARNING)


# ### Set options for making science data processing requests to SlideRule
# 
# SlideRule follows a streamlined version of the [ATL06 land ice height algorithm](https://nsidc.org/sites/nsidc.org/files/technical-references/ICESat2_ATL06_ATBD_r005.pdf).
# 
# SlideRule also can use different sources for photon classification before calculating the average segment height.  
# This is useful for example, in cases where there may be a vegetated canopy affecting the spread of the photon returns.
# - ATL03 photon confidence values, based on algorithm-specific classification types for land, ocean, sea-ice, land-ice, or inland water
# - [ATL08 Land and Vegetation Height product](https://nsidc.org/data/atl08) photon classification
# - Experimental YAPC (Yet Another Photon Classification) photon-density-based classification

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# display widgets for setting SlideRule parameters
SRwidgets = ipysliderule.widgets()
SRwidgets.VBox(SRwidgets.atl06())


# ### Interactive Mapping with Leaflet
# 
# Interactive maps within the SlideRule python API are built upon [ipyleaflet](https://ipyleaflet.readthedocs.io).
# 
# #### Leaflet Basemaps and Layers
# 
# There are 3 projections available within SlideRule for mapping ([Global](https://epsg.io/3857), [North](https://epsg.io/5936) and [South](https://epsg.io/3031)).  There are also contextual layers available for each projection.
# 
# <table>
#   <tbody>
#     <tr>
#       <th align='center' max-width="30%"><a href="https://epsg.io/3857">Global (Web Mercator, EPSG:3857)</a></th>
#       <th align='center' max-width="30%"><a href="https://epsg.io/5936">North (Alaska Polar Stereographic, EPSG:5936)</a></th>
#       <th align='center' max-width="30%"><a href="https://epsg.io/3031">South (Antarctic Polar Stereographic, EPSG:3031)</a></th>
#     </tr>
#     <tr>
#       <td align='left' valign='top' width="30%">
#         <ul style="line-height: 1.5em">
#             <li><a href="https://apps.nationalmap.gov/3depdem/">USGS 3DEP Hillshade</a></li>
#             <li><a href="https://asterweb.jpl.nasa.gov/gdem.asp">ASTER GDEM Hillshade</a></li>
#             <li><a href="https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9">ESRI Imagery</a></li>
#             <li><a href="http://glims.colorado.edu/glacierdata/">Global Land Ice Measurements from Space (GLIMS)</a></li>
#             <li><a href="https://www.glims.org/RGI/">Randolph Glacier Inventory (RGI)</a></li>
#         </ul>
#        </td>
#        <td align='left' valign='top' width="30%">
#         <ul style="line-height: 1.5em">
#             <li><a href="http://goto.arcgisonline.com/maps/Arctic_Imagery">ESRI Imagery</a></li>
#             <li><a href="https://www.pgc.umn.edu/data/arcticdem">ArcticDEM</a></li>
#         </ul>
#        </td>
#        <td align='left' valign='top' width="30%">
#         <ul style="line-height: 1.5em">
#             <li><a href="https://lima.usgs.gov/">Landsat Image Mosaic of Antarctica (LIMA)</a></li>
#             <li><a href="https://nsidc.org/data/nsidc-0280">MODIS Mosaic of Antarctica (MOA)</a></li>
#             <li><a href="https://nsidc.org/data/NSIDC-0103">Radarsat Antarctic Mapping Project (RAMP)</a></li>
#             <li><a href="https://www.pgc.umn.edu/data/rema">Reference Elevation Model of Antarctica (REMA)</a></li>
#         </ul>
#       </td>
#     </tr>
#   </tbody>
# </table>
# 
# In addition, most [xyzservice providers](https://xyzservices.readthedocs.io/en/stable/introduction.html) can be added as contextual layers to the global Web Mercator maps

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SRwidgets.VBox([
    SRwidgets.projection,
    SRwidgets.layers,
    SRwidgets.raster_functions
])


# ### Select regions of interest for submitting to SlideRule
# 
# Here, we create polygons or bounding boxes for our regions of interest.  
# This map is also our viewer for inspecting our SlideRule ICESat-2 data returns.

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# create ipyleaflet map in specified projection
m = ipysliderule.leaflet(SRwidgets.projection.value)
m.map


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m.add_layer(
    layers=SRwidgets.layers.value,
    rendering_rule=SRwidgets.rendering_rule
)


# ### Build and transmit requests to SlideRule
# 
# - SlideRule will query the [NASA Common Metadata Repository (CMR)](https://cmr.earthdata.nasa.gov/) for ATL03 data within our region of interest
# - When using the `icesat2` asset, the ICESat-2 ATL03 data are then accessed from the NSIDC AWS s3 bucket in `us-west-2`
# - The ATL03 granules is spatially subset within SlideRule to our exact region of interest
# - SlideRule then uses our specified parameters to calculate average height segments from the ATL03 data in parallel
# - The completed data is streamed concurrently back and combined into a geopandas GeoDataFrame within the Python client

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get_ipython().run_cell_magic('time', '', '# build sliderule parameters using latest values from widget\nparms = SRwidgets.build_atl06()\n\n# clear existing geodataframe results\nelevations = [sliderule.emptyframe()]\n\n# for each region of interest\nsliderule.logger.warning(\'No valid regions to run\') if not m.regions else None\nfor poly in m.regions:\n    # add polygon from map to sliderule parameters\n    parms["poly"] = poly\n    # make the request to the SlideRule (ATL06-SR) endpoint\n    # and pass it the request parameters to request ATL06 Data\n    elevations.append(icesat2.atl06p(parms))\ngdf = geopandas.pd.concat(elevations)\n')


# ### Review GeoDataFrame output
# Can inspect the columns, number of returns and returns at the top of the GeoDataFrame.
# 
# See the [ICESat-2 documentation](https://slideruleearth.io/rtd/user_guide/ICESat-2.html#elevations) for descriptions of each column

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print(f'Returned {gdf.shape[0]} records')
gdf.head()


# ### Add GeoDataFrame to map
# 
# For stability of the leaflet map, SlideRule will as a default limit the plot to have up to 10000 points from the GeoDataFrame
# 
# GeoDataFrames can be plotted in any available [matplotlib colormap](https://matplotlib.org/stable/tutorials/colors/colormaps.html)

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SRwidgets.VBox([
    SRwidgets.variable,
    SRwidgets.cmap,
    SRwidgets.reverse,
])


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get_ipython().run_line_magic('matplotlib', 'inline')
# ATL06-SR fields for hover tooltip
fields = gdf.leaflet.default_atl06_fields()
gdf.leaflet.GeoData(m.map, column_name=SRwidgets.variable.value, cmap=SRwidgets.colormap,
    max_plot_points=10000, tooltip=True, colorbar=True, fields=fields)
# install handlers and callbacks
gdf.leaflet.set_observables(SRwidgets)
gdf.leaflet.add_selected_callback(SRwidgets.atl06_click_handler)
m.add_region_callback(gdf.leaflet.handle_region)


# ### Create plots for a single track
# - cycles: Will plot all available cycles of data returned by SlideRule for a single RGT and ground track
# - scatter: Will plot data returned by SlideRule for a single RGT, ground track and cycle
# - (to select a track from the leaflet plot above, click on one of the plotted elevations and the RGT and Cycle will automatically get populated below)
# 
# The cycles plots should only be used in regions with [repeat Reference Ground Track (RGT) pointing](https://icesat-2.gsfc.nasa.gov/science/specs)

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SRwidgets.VBox([
    SRwidgets.plot_kind,
    SRwidgets.rgt,
    SRwidgets.ground_track,
    SRwidgets.cycle,
])


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get_ipython().run_line_magic('matplotlib', 'widget')
# default is to skip cycles with significant off-pointing
gdf.icesat2.plot(kind=SRwidgets.plot_kind.value, cycle_start=3,
    legend=True, legend_frameon=False, **SRwidgets.plot_kwargs)


# ### Save GeoDataFrame to output file

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display(SRwidgets.filesaver)


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# append sliderule api version to attributes
version = sliderule.get_version()
parms['version'] = version['icesat2']['version']
parms['commit'] = version['icesat2']['commit']
# save to file in format (HDF5 or netCDF)
io.to_file(gdf, SRwidgets.file,
    format=SRwidgets.format,
    parameters=parms,
    regions=m.regions,
    verbose=True)


# ### Read GeoDataFrame from input file

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display(SRwidgets.fileloader)


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# read from file in format (HDF5 or netCDF)
gdf,parms,regions = io.from_file(SRwidgets.file,
    format=SRwidgets.format,
    return_parameters=True,
    return_regions=True)


# ### Review GeoDataFrame input from file

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gdf.head()


# ### Set parameters and add saved regions to map

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SRwidgets.set_values(parms)
m.add_region(regions)


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