OpenEnergy Platform



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__copyright__ = "Reiner Lemoine Institut, Zentrum für nachhaltige Energiesysteme Flensburg"
__license__   = "GNU Affero General Public License Version 3 (AGPL-3.0)"
__url__       = "https://github.com/openego/data_processing/blob/master/LICENSE"
__author__    = "wolfbunke, Ludee"

Tutorial - How to work with the OpenEnergy Platform (OEP)

This is an important information!

This is an information!

This is your task!

This tutorial gives you an overview of the OpenEnergy Platform and how you can work with the REST-full-HTTP API in Python.
The full API documentaion can be found on ReadtheDocs.io.

Part III

0 Setup token
1 Select data
2 Make a pandas dataframe
3 Plot a dataframe (geo plot)

Part III

0. Setup token

Do not push your token to GitHub!



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import requests
import pandas as pd
from IPython.core.display import HTML

# oedb
oep_url= 'http://oep.iks.cs.ovgu.de/'

# token
your_token = ''

1. Select data



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import geopandas as gpd
from shapely.geometry import Point
import shapely.wkt
from shapely import wkb
from geoalchemy2.shape import to_shape



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# select powerplant data
schema = 'supply'
table = 'ego_dp_conv_powerplant'
where = 'version=v0.2.10'

conv_powerplants = requests.get(oep_url+'/api/v0/schema/'+schema+'/tables/'+table+'/rows/?where='+where, )
conv_powerplants.status_code

**200** succesfully selected data!



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# select borders
schema = 'boundaries'
table = 'bkg_vg250_2_lan_mview'

vg = requests.get(oep_url+'/api/v0/schema/'+schema+'/tables/'+table+'/rows/')
vg.status_code

**200** succesfully selected data!

2. Make a pandas dataframe



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df_pp = pd.DataFrame(conv_powerplants.json())
df_vg = pd.DataFrame(vg.json())

Let's take a look into our data



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df_pp.info()



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df_pp.columns



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#df_pp
df_vg

3. Plot a dataframe (geo plot)



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import geopandas as gpd
import shapely
import matplotlib.pyplot as plt
%matplotlib inline



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# transform WKB to WKT / Geometry
df_pp['geom'] = df_pp['geom'].apply(lambda x:shapely.wkb.loads(x, hex=True))
df_vg['geom'] = df_vg['geom'].apply(lambda x:shapely.wkb.loads(x, hex=True))



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# plot powerplants
crs = {'init' :'epsg:4326'}
gdf_pp = gpd.GeoDataFrame(df_pp, crs=crs, geometry=df_pp.geom)
base = gdf_pp.plot(color='white', edgecolor='black',figsize=(8, 8))
gdf_pp.plot(ax=base)
plt.show()



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# plot borders
crs = {'init' :'epsg:3035'}
gdf_vg = gpd.GeoDataFrame(df_pp, crs=crs, geometry=df_vg.geom)
base = gdf_vg.plot(color='white', edgecolor='black',figsize=(8, 8))
gdf_vg.plot(ax=base)

plt.show()



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# transform WKB to WKT / Geometry
crs1 = {'init' :'epsg:4326'}
crs2 = {'init' :'epsg:3035'}

gdf_pp = gpd.GeoDataFrame(df_pp, crs=crs1, geometry=df_pp.geom)
gdf_vg = gpd.GeoDataFrame(df_vg, crs=crs2, geometry=df_vg.geom)

base = gdf_vg.plot(color='white', edgecolor='black',figsize=(10, 10))

gdf_pp.plot(ax=base)
#gdf_vg.plot(ax=base)

plt.show()

bug under ubuntu



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from shapely import geos
from geoalchemy2.shape import to_shape
from shapely.geometry import Point

from ipywidgets import widgets
from IPython.display import display
from IPython.core.display import HTML
from geoalchemy2 import Geometry, WKTElement
import requests
import pandas as pd
import mplleaflet



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plants_data = requests.get(oep_url+'/api/v0/schema/model_draft/tables/ego_dp_supply_conv_powerplant/rows/?where=scenario=Status+Quo&limit=910',)
regions  =  requests.get(oep_url+'/api/v0/schema/model_draft/tables/renpass_gis_parameter_region/rows/?where=stat_level=999',)
regions.status_code
plants_data.status_code



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sq_plants = pd.DataFrame(plants_data.json())
renpass_region_df = pd.DataFrame(regions.json())

# transform WKB to WKT / Geometry
crs = {'init' :'epsg:4326'}

sq_plants['geom'] =sq_plants['geom'].apply(lambda x:shapely.wkb.loads(x, hex=True))
renpass_region_df['geom'] =renpass_region_df['geom'].apply(lambda x:shapely.wkb.loads(x, hex=True))

gdf_plants = gpd.GeoDataFrame(sq_plants, crs=crs, geometry=sq_plants.geom)
gdf_regions = gpd.GeoDataFrame(renpass_region_df, crs=crs, geometry=renpass_region_df.geom)

base = gdf_regions.plot(color='white', edgecolor='black',figsize=(10, 10))

gdf_plants.plot(ax=base)


plt.show()

Point Plot



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import folium
from folium import plugins
import matplotlib.pyplot as plt
%matplotlib inline



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# define map region
map = folium.Map(location=[51, 9], zoom_start=6)



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# Use column lon / lat in order to plot map
for name, row in df_pp.iloc[:1000].iterrows():
    folium.Marker([row["lat"], row["lon"]], popup=row["type"] ).add_to(map)
#map.create_map('plants.html')
map

Heat plot for locations



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stops_heatmap = folium.Map(location=[51, 9], zoom_start=6)
stops_heatmap.add_child(plugins.HeatMap([[row["lat"], row["lon"]] for capacity, row in df_pp.iloc[:1000].iterrows()]))
stops_heatmap.save("heatmap.html")
stops_heatmap

Make some statistics

Make an interesting API-example you need!



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