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Philippe May, 06/05/2019 17:14

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h1. Live
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While the primary intention is use a database for all layers, Gisaf has the capability to display layers directly from GeoPandas GeodataFrames.
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In this case, they can also be updated dynamically, adding animation capabilities to the maps.
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This can be used for, eg:
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* displaying text (eg. temperatures, well levels)
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* moving elements
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* results of computations and analysis...
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h2. Using live directly from a Python script on the Gisaf server
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Eg:
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<pre><code class="python">
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#!/usr/bin/env python
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from asyncio import run
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import geopandas as gpd
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from shapely.geometry import Point
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from gisaf.live import live_server
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async def run(gs):
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    gdf = gpd.GeoDataFrame(
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        data={
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            'geometry': [
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                Point(12.01, 79.81)
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            ]
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        },
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        crs='epsg:4326'
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    )
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    await live_server.publish_gdf('FooLayer', gdf)
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async def main():
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    await live_server.create_connections()
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    await run(gs)
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if __name__ == '__main__':
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    run(main())
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</code></pre>
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Explanations:
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1. Initialize the connection with @live_server.create_connections()@.
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2. Publish a geo dataframe with @live_server.publish_gdf('name of the layer', gdf)@
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h2. From Jupyter notebooks
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Quite similarly to the case above, jupyter notebooks (running on a different machine) can be used to publish and control live layers. See the examples in @Templates/gisaf_live_templates@.
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h2. Architecture
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Gisaf live layers use a redis data store for:
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1. Storage of the live layers
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2. Publish/subscribe for live updates.
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The live updates are sent through a websocket, initiated by the clients (web browsers).
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Moreover, Gisaf exposes an HTTP API for external control of the live layers, eg. by Jupyter notebooks running on another server.