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#!/usr/bin/python3 |
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# -*- coding: utf-8 -*- |
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from datetime import datetime |
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import logging |
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from pytz import timezone |
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from shapely.wkt import loads as wkt_loads |
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import numpy as np |
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import pandas as pd |
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import feedinlib.weather as weather |
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from . import tools |
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def get_weather(conn, geometry, year): |
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r""" |
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Get the weather data for the given geometry and create an oemof |
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weather object. |
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""" |
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rename_dc = { |
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'ASWDIFD_S': 'dhi', |
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'ASWDIR_S': 'dirhi', |
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'PS': 'pressure', |
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'T_2M': 'temp_air', |
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'WSS_10M': 'v_wind', |
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'Z0': 'z0'} |
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if geometry.geom_type in ['Polygon', 'MultiPolygon']: |
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# Create MultiWeather |
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# If polygon covers only one data set switch to SingleWeather |
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sql_part = """ |
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SELECT sp.gid, ST_AsText(point.geom), ST_AsText(sp.geom) |
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FROM coastdat.cosmoclmgrid AS sp |
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JOIN coastdat.spatial AS point ON (sp.gid=point.gid) |
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WHERE st_intersects(ST_GeomFromText('{wkt}',4326), sp.geom) |
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""".format(wkt=geometry.wkt) |
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df = fetch_raw_data(sql_weather_string(year, sql_part), conn, geometry) |
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obj = create_multi_weather(df, rename_dc) |
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elif geometry.geom_type == 'Point': |
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# Create SingleWeather |
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sql_part = """ |
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SELECT sp.gid, ST_AsText(point.geom), ST_AsText(sp.geom) |
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FROM coastdat.cosmoclmgrid AS sp |
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JOIN coastdat.spatial AS point ON (sp.gid=point.gid) |
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WHERE st_contains(sp.geom, ST_GeomFromText('{wkt}',4326)) |
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""".format(wkt=geometry.wkt) |
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df = fetch_raw_data(sql_weather_string(year, sql_part), conn, geometry) |
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obj = create_single_weather(df, rename_dc) |
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else: |
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logging.error('Unknown geometry type: {0}'.format(geometry.geom_type)) |
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obj = None |
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return obj |
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def sql_weather_string(year, sql_part): |
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""" |
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Creates an sql-string to read all datasets within a given geometry. |
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""" |
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# TODO@Günni. Replace sql-String with alchemy/GeoAlchemy |
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# Create string parts for where conditions |
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return ''' |
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SELECT tsptyti.*, y.leap |
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FROM coastdat.year as y |
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INNER JOIN ( |
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SELECT tsptyd.*, sc.time_id |
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FROM coastdat.scheduled as sc |
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INNER JOIN ( |
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SELECT tspty.*, dt.name, dt.height |
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FROM coastdat.datatype as dt |
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INNER JOIN ( |
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SELECT tsp.*, typ.type_id |
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FROM coastdat.typified as typ |
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INNER JOIN ( |
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SELECT spl.*, t.tsarray, t.id |
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FROM coastdat.timeseries as t |
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INNER JOIN ( |
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SELECT sps.*, l.data_id |
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FROM ( |
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{sql_part} |
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) as sps |
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INNER JOIN coastdat.located as l |
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ON (sps.gid = l.spatial_id)) as spl |
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ON (spl.data_id = t.id)) as tsp |
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ON (tsp.id = typ.data_id)) as tspty |
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ON (tspty.type_id = dt.id)) as tsptyd |
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ON (tsptyd.id = sc.data_id))as tsptyti |
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ON (tsptyti.time_id = y.year) |
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where y.year = '{year}' |
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;'''.format(year=year, sql_part=sql_part) |
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def fetch_raw_data(sql, connection, geometry): |
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""" |
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Fetch the coastdat2 from the database, adapt it to the local time zone |
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and create a time index. |
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""" |
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tmp_dc = {} |
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weather_df = pd.DataFrame( |
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connection.execute(sql).fetchall(), columns=[ |
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'gid', 'geom_point', 'geom_polygon', 'data_id', 'time_series', |
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'dat_id', 'type_id', 'type', 'height', 'year', 'leap_year']).drop( |
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'dat_id', 1) |
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# Get the timezone of the geometry |
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tz = tools.tz_from_geom(connection, geometry) |
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for ix in weather_df.index: |
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# Convert the point of the weather location to a shapely object |
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weather_df.loc[ix, 'geom_point'] = wkt_loads( |
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weather_df['geom_point'][ix]) |
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# Roll the dataset forward according to the timezone, because the |
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# dataset is based on utc (Berlin +1, Kiev +2, London +0) |
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utc = timezone('utc') |
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offset = int(utc.localize(datetime(2002, 1, 1)).astimezone( |
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timezone(tz)).strftime("%z")[:-2]) |
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# Get the year and the length of the data array |
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db_year = weather_df.loc[ix, 'year'] |
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db_len = len(weather_df['time_series'][ix]) |
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# Set absolute time index for the data sets to avoid errors. |
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tmp_dc[ix] = pd.Series( |
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np.roll(np.array(weather_df['time_series'][ix]), offset), |
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index=pd.date_range(pd.datetime(db_year, 1, 1, 0), |
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periods=db_len, freq='H', tz=tz)) |
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weather_df['time_series'] = pd.Series(tmp_dc) |
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return weather_df |
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def create_single_weather(df, rename_dc): |
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"""Create an oemof weather object for the given geometry""" |
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my_weather = weather.FeedinWeather() |
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data_height = {} |
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name = None |
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# Create a pandas.DataFrame with the time series of the weather data set |
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weather_df = pd.DataFrame(index=df.time_series.iloc[0].index) |
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for row in df.iterrows(): |
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key = rename_dc[row[1].type] |
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weather_df[key] = row[1].time_series |
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data_height[key] = row[1].height if not np.isnan(row[1].height) else 0 |
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name = row[1].gid |
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my_weather.data = weather_df |
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my_weather.timezone = weather_df.index.tz |
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my_weather.longitude = df.geom_point.iloc[0].x |
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my_weather.latitude = df.geom_point.iloc[0].y |
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my_weather.geometry = df.geom_point.iloc[0] |
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my_weather.data_height = data_height |
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my_weather.name = name |
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return my_weather |
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def create_multi_weather(df, rename_dc): |
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"""Create a list of oemof weather objects if the given geometry is a polygon |
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""" |
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weather_list = [] |
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# Create a pandas.DataFrame with the time series of the weather data set |
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# for each data set and append them to a list. |
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for gid in df.gid.unique(): |
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gid_df = df[df.gid == gid] |
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obj = create_single_weather(gid_df, rename_dc) |
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weather_list.append(obj) |
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return weather_list |
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oemof /
oemof.db
