Tutorial: API tutorial 3 - Plot data and spatial data
OpenEnergy PlatformPermalink
API tutorial 3 - Plot data and spatial dataPermalink
Repository: https://github.com/openego/oedialect
Please report bugs and improvements here: https://github.com/OpenEnergyPlatform/examples/issues
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In [1]:
__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"
IntroductionPermalink
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 - How to work with the OpenEnergy Platform (OEP)Permalink
0 Setup token
1 Select data
2 Make a pandas dataframe
3 Plot a dataframe (geo plot)
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import requests
import pandas as pd
from IPython.core.display import HTML
from token_config import oep_url, get_oep_token
# token
your_token = get_oep_token()
1. Select dataPermalink
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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
Response [200] succesfully selected data!
Response [404] table doesn't exist!
Response [404] table doesn't exist!
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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
Response [200] succesfully selected data!
Response [404] table doesn't exist!
Response [404] table doesn't exist!
2. Make a pandas dataframePermalink
Create pandas dataframes for each data set returned as API result!
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# Create dataframe from json format
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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# Show metadata for a specific dataframe.
df_pp.info()
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# List all column names for a specific dataframe.
df_pp.columns
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#Print the df_pp dataframe as table.
df_vg
3. Plot a dataframe (geo plot)Permalink
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import geopandas as gpd
import shapely
import matplotlib.pyplot as plt
%matplotlib inline
If we want to apply a change to every entity in a column we can use Pandas apply function.
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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))
Let's plot our data!
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# plot powerplants
crs = {'init' :'epsg:4326'}
gdf_pp = gpd.GeoDataFrame(df_pp, crs=crs, geometry=df_pp.geom)
base1 = gdf_pp.plot(color='white', edgecolor='black',figsize=(8, 8))
gdf_pp.plot(ax=base1, color='green')
plt.show()
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# plot borders
crs = {'init' :'epsg:4326'}
gdf_vg = gpd.GeoDataFrame(df_vg, geometry=df_vg.geom)
base2 = gdf_vg.plot(color='white', edgecolor='black',figsize=(8, 8))
gdf_vg.plot(ax=base2)
plt.show()
Now we can create a map with two layers.
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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, marker='o', markersize=5)
# gdf_vg.plot(ax=base)
plt.show()
This plot does not display the data corretly. We need to change the Projections(crs) to something similar.
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
Response [200] succesfully selected data!
Response [404] table doesn't exist!
Response [404] table doesn't exist!
Let´s transform the crs and plot the data again.
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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 PlotPermalink
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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 gdf_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 locationsPermalink
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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 statisticsPermalink
Make an interesting API-example you need!
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If you find bugs or if you have ideas to improve the Open Energy Platform, you are
welcome to add your comments to the existing issues on GitHub.
You can also fork the project and get involved.
Please note that the platform is still under construction and therefore the design of this page is still highly volatile!