data.datasets.ch4_prod.load_biogas_generators() - Code Metrics - Inspection of "Move and merge the two assigne_gas_bus_id function..." - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — dev (#840)

unknown

created 2022-07-15 10:20 UTC

data.datasets.ch4_prod.load_biogas_generators() B

↳ Parent: data.datasets.ch4_prod

Complexity

Conditions

Size

Total Lines	104
Code Lines	53

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	53
dl	0
loc	104
rs	8.5381
c	0
b	0
f	0
cc	3
nop	1

How to fix Long Method

# -*- coding: utf-8 -*-
"""
The central module containing all code dealing with importing CH4 production data
"""
from pathlib import Path
from urllib.request import urlretrieve
import ast

import geopandas as gpd
import numpy as np
import pandas as pd

from egon.data import config, db
from egon.data.config import settings
from egon.data.datasets import Dataset
from egon.data.datasets.scenario_parameters import get_sector_parameters


class CH4Production(Dataset):
    def __init__(self, dependencies):
        super().__init__(
            name="CH4Production",
            version="0.0.7",
            dependencies=dependencies,
            tasks=(import_gas_generators),
        )


def load_NG_generators(scn_name):
    """Define the natural CH4 production units in Germany

    Parameters
    ----------
    scn_name : str
        Name of the scenario.
    Returns
    -------
    CH4_generators_list :
        Dataframe containing the natural gas production units in Germany

    """
    # read carrier information from scnario parameter data
    scn_params = get_sector_parameters("gas", scn_name)

    target_file = (
        Path(".")
        / "datasets"
        / "gas_data"
        / "data"
        / "IGGIELGN_Productions.csv"
    )

    NG_generators_list = pd.read_csv(
        target_file,
        delimiter=";",
        decimal=".",
        usecols=["lat", "long", "country_code", "param"],
    )

    NG_generators_list = NG_generators_list[
        NG_generators_list["country_code"].str.match("DE")
    ]

    # Cut data to federal state if in testmode
    NUTS1 = []
    for index, row in NG_generators_list.iterrows():
        param = ast.literal_eval(row["param"])
        NUTS1.append(param["nuts_id_1"])
    NG_generators_list = NG_generators_list.assign(NUTS1=NUTS1)

    boundary = settings()["egon-data"]["--dataset-boundary"]
    if boundary != "Everything":
        map_states = {
            "Baden-Württemberg": "DE1",
            "Nordrhein-Westfalen": "DEA",
            "Hessen": "DE7",
            "Brandenburg": "DE4",
            "Bremen": "DE5",
            "Rheinland-Pfalz": "DEB",
            "Sachsen-Anhalt": "DEE",
            "Schleswig-Holstein": "DEF",
            "Mecklenburg-Vorpommern": "DE8",
            "Thüringen": "DEG",
            "Niedersachsen": "DE9",
            "Sachsen": "DED",
            "Hamburg": "DE6",
            "Saarland": "DEC",
            "Berlin": "DE3",
            "Bayern": "DE2",
        }

        NG_generators_list = NG_generators_list[
            NG_generators_list["NUTS1"].isin([map_states[boundary], np.nan])
        ]

    NG_generators_list = NG_generators_list.rename(
        columns={"lat": "y", "long": "x"}
    )
    NG_generators_list = gpd.GeoDataFrame(
        NG_generators_list,
        geometry=gpd.points_from_xy(
            NG_generators_list["x"], NG_generators_list["y"]
        ),
    )
    NG_generators_list = NG_generators_list.rename(
        columns={"geometry": "geom"}
    ).set_geometry("geom", crs=4326)

    # Insert p_nom
    p_nom = []
    for index, row in NG_generators_list.iterrows():
        param = ast.literal_eval(row["param"])
        p_nom.append(param["max_supply_M_m3_per_d"])

    conversion_factor = 437.5  # MCM/day to MWh/h
    NG_generators_list["p_nom"] = [i * conversion_factor for i in p_nom]

    # Add missing columns
    NG_generators_list["marginal_cost"] = scn_params["marginal_cost"]["CH4"]

    # Remove useless columns
    NG_generators_list = NG_generators_list.drop(
        columns=["x", "y", "param", "country_code", "NUTS1"]
    )

    return NG_generators_list


def load_biogas_generators(scn_name):
    """Define the biogas production units in Germany

    Parameters
    ----------
    scn_name : str
        Name of the scenario.
    Returns
    -------
    CH4_generators_list :
        Dataframe containing the biogas production units in Germany

    """
    # read carrier information from scnario parameter data
    scn_params = get_sector_parameters("gas", scn_name)

    # Download file
    basename = "Biogaspartner_Einspeiseatlas_Deutschland_2021.xlsx"
    url = (
        "https://www.biogaspartner.de/fileadmin/Biogaspartner/Dokumente/Einspeiseatlas/"
        + basename
    )
    target_file = Path(".") / "datasets" / "gas_data" / basename

    urlretrieve(url, target_file)

    # Read-in data from csv-file
    biogas_generators_list = pd.read_excel(
        target_file,
        usecols=["Koordinaten", "Einspeisung Biomethan [(N*m^3)/h)]"],
    )

    x = []
    y = []
    for index, row in biogas_generators_list.iterrows():
        coordinates = row["Koordinaten"].split(",")
        y.append(coordinates[0])
        x.append(coordinates[1])
    biogas_generators_list["x"] = x
    biogas_generators_list["y"] = y

    biogas_generators_list = gpd.GeoDataFrame(
        biogas_generators_list,
        geometry=gpd.points_from_xy(
            biogas_generators_list["x"], biogas_generators_list["y"]
        ),
    )
    biogas_generators_list = biogas_generators_list.rename(
        columns={"geometry": "geom"}
    ).set_geometry("geom", crs=4326)

    # Connect to local database
    engine = db.engine()

    # Cut data to federal state if in testmode
    boundary = settings()["egon-data"]["--dataset-boundary"]
    if boundary != "Everything":
        db.execute_sql(
            """
              DROP TABLE IF EXISTS grid.egon_biogas_generator CASCADE;
            """
        )
        biogas_generators_list.to_postgis(
            "egon_biogas_generator",
            engine,
            schema="grid",
            index=False,
            if_exists="replace",
        )

        sql = """SELECT *
            FROM grid.egon_biogas_generator, boundaries.vg250_sta_union  as vg
            WHERE ST_Transform(vg.geometry,4326) && egon_biogas_generator.geom
            AND ST_Contains(ST_Transform(vg.geometry,4326), egon_biogas_generator.geom)"""

        biogas_generators_list = gpd.GeoDataFrame.from_postgis(
            sql, con=engine, geom_col="geom", crs=4326
        )
        biogas_generators_list = biogas_generators_list.drop(
            columns=["id", "bez", "area_ha", "geometry"]
        )
        db.execute_sql(
            """
              DROP TABLE IF EXISTS grid.egon_biogas_generator CASCADE;
            """
        )

    # Insert p_nom
    conversion_factor = 0.01083  # m^3/h to MWh/h
    biogas_generators_list["p_nom"] = [
        i * conversion_factor
        for i in biogas_generators_list["Einspeisung Biomethan [(N*m^3)/h)]"]
    ]

    # Add missing columns
    biogas_generators_list["marginal_cost"] = scn_params["marginal_cost"][
        "biogas"
    ]

    # Remove useless columns
    biogas_generators_list = biogas_generators_list.drop(
        columns=["x", "y", "Koordinaten", "Einspeisung Biomethan [(N*m^3)/h)]"]
    )
    return biogas_generators_list


def import_gas_generators(scn_name="eGon2035"):
    """Insert list of gas production units in database

    Parameters
    ----------
    scn_name : str
        Name of the scenario.
    """
    # Connect to local database
    engine = db.engine()

    # Select source and target from dataset configuration
    source = config.datasets()["gas_prod"]["source"]
    target = config.datasets()["gas_prod"]["target"]

    # Clean table
    db.execute_sql(
        f"""
        DELETE FROM {target['stores']['schema']}.{target['stores']['table']}
        WHERE "carrier" = 'CH4' AND
        scn_name = '{scn_name}' AND bus not IN (
            SELECT bus_id FROM {source['buses']['schema']}.{source['buses']['table']}
            WHERE scn_name = '{scn_name}' AND country != 'DE'
        );
        """
    )

    CH4_generators_list = pd.concat(
        [load_NG_generators(scn_name), load_biogas_generators(scn_name)]
    )

    # Add missing columns
    c = {"scn_name": scn_name, "carrier": "CH4"}
    CH4_generators_list = CH4_generators_list.assign(**c)

    # Match to associated CH4 bus
    CH4_generators_list = db.assign_gas_bus_id(
        CH4_generators_list, scn_name, "CH4"
    )

    # Remove useless columns
    CH4_generators_list = CH4_generators_list.drop(columns=["geom", "bus_id"])

    # Aggregate ch4 productions with same properties at the same bus
    CH4_generators_list = (
        CH4_generators_list.groupby(
            ["bus", "carrier", "scn_name", "marginal_cost"]
        )
        .agg({"p_nom": "sum"})
        .reset_index(drop=False)
    )

    new_id = db.next_etrago_id("generator")
    CH4_generators_list["generator_id"] = range(
        new_id, new_id + len(CH4_generators_list)
    )

    # Insert data to db
    CH4_generators_list.to_sql(
        target["stores"]["table"],
        engine,
        schema=target["stores"]["schema"],
        index=False,
        if_exists="append",
    )


1			# -- coding: utf-8 --
2			"""
3			The central module containing all code dealing with importing CH4 production data
4			"""
5			from pathlib import Path
6			from urllib.request import urlretrieve
7			import ast
8
9			import geopandas as gpd
10			import numpy as np
11			import pandas as pd
12
13			from egon.data import config, db
14			from egon.data.config import settings
15			from egon.data.datasets import Dataset
16			from egon.data.datasets.scenario_parameters import get_sector_parameters
17
18
19			class CH4Production(Dataset):
20			def __init__(self, dependencies):
21			super().__init__(
22			name="CH4Production",
23			version="0.0.7",
24			dependencies=dependencies,
25			tasks=(import_gas_generators),
26			)
27
28
29			def load_NG_generators(scn_name):
30			"""Define the natural CH4 production units in Germany
31
32			Parameters
33			----------
34			scn_name : str
35			Name of the scenario.
36			Returns
37			-------
38			CH4_generators_list :
39			Dataframe containing the natural gas production units in Germany
40
41			"""
42			# read carrier information from scnario parameter data
43			scn_params = get_sector_parameters("gas", scn_name)
44
45			target_file = (
46			Path(".")
47			/ "datasets"
48			/ "gas_data"
49			/ "data"
50			/ "IGGIELGN_Productions.csv"
51			)
52
53			NG_generators_list = pd.read_csv(
54			target_file,
55			delimiter=";",
56			decimal=".",
57			usecols=["lat", "long", "country_code", "param"],
58			)
59
60			NG_generators_list = NG_generators_list[
61			NG_generators_list["country_code"].str.match("DE")
62			]
63
64			# Cut data to federal state if in testmode
65			NUTS1 = []
66			for index, row in NG_generators_list.iterrows():
67			param = ast.literal_eval(row["param"])
68			NUTS1.append(param["nuts_id_1"])
69			NG_generators_list = NG_generators_list.assign(NUTS1=NUTS1)
70
71			boundary = settings()["egon-data"]["--dataset-boundary"]
72			if boundary != "Everything":
73			map_states = {
74			"Baden-Württemberg": "DE1",
75			"Nordrhein-Westfalen": "DEA",
76			"Hessen": "DE7",
77			"Brandenburg": "DE4",
78			"Bremen": "DE5",
79			"Rheinland-Pfalz": "DEB",
80			"Sachsen-Anhalt": "DEE",
81			"Schleswig-Holstein": "DEF",
82			"Mecklenburg-Vorpommern": "DE8",
83			"Thüringen": "DEG",
84			"Niedersachsen": "DE9",
85			"Sachsen": "DED",
86			"Hamburg": "DE6",
87			"Saarland": "DEC",
88			"Berlin": "DE3",
89			"Bayern": "DE2",
90			}
91
92			NG_generators_list = NG_generators_list[
93			NG_generators_list["NUTS1"].isin([map_states[boundary], np.nan])
94			]
95
96			NG_generators_list = NG_generators_list.rename(
97			columns={"lat": "y", "long": "x"}
98			)
99			NG_generators_list = gpd.GeoDataFrame(
100			NG_generators_list,
101			geometry=gpd.points_from_xy(
102			NG_generators_list["x"], NG_generators_list["y"]
103			),
104			)
105			NG_generators_list = NG_generators_list.rename(
106			columns={"geometry": "geom"}
107			).set_geometry("geom", crs=4326)
108
109			# Insert p_nom
110			p_nom = []
111			for index, row in NG_generators_list.iterrows():
112			param = ast.literal_eval(row["param"])
113			p_nom.append(param["max_supply_M_m3_per_d"])
114
115			conversion_factor = 437.5 # MCM/day to MWh/h
116			NG_generators_list["p_nom"] = [i * conversion_factor for i in p_nom]
117
118			# Add missing columns
119			NG_generators_list["marginal_cost"] = scn_params["marginal_cost"]["CH4"]
120
121			# Remove useless columns
122			NG_generators_list = NG_generators_list.drop(
123			columns=["x", "y", "param", "country_code", "NUTS1"]
124			)
125
126			return NG_generators_list
127
128
129			def load_biogas_generators(scn_name):
130			"""Define the biogas production units in Germany
131
132			Parameters
133			----------
134			scn_name : str
135			Name of the scenario.
136			Returns
137			-------
138			CH4_generators_list :
139			Dataframe containing the biogas production units in Germany
140
141			"""
142			# read carrier information from scnario parameter data
143			scn_params = get_sector_parameters("gas", scn_name)
144
145			# Download file
146			basename = "Biogaspartner_Einspeiseatlas_Deutschland_2021.xlsx"
147			url = (
148			"https://www.biogaspartner.de/fileadmin/Biogaspartner/Dokumente/Einspeiseatlas/"
149			+ basename
150			)
151			target_file = Path(".") / "datasets" / "gas_data" / basename
152
153			urlretrieve(url, target_file)
154
155			# Read-in data from csv-file
156			biogas_generators_list = pd.read_excel(
157			target_file,
158			usecols=["Koordinaten", "Einspeisung Biomethan [(N*m^3)/h)]"],
159			)
160
161			x = []
162			y = []
163			for index, row in biogas_generators_list.iterrows():
164			coordinates = row["Koordinaten"].split(",")
165			y.append(coordinates[0])
166			x.append(coordinates[1])
167			biogas_generators_list["x"] = x
168			biogas_generators_list["y"] = y
169
170			biogas_generators_list = gpd.GeoDataFrame(
171			biogas_generators_list,
172			geometry=gpd.points_from_xy(
173			biogas_generators_list["x"], biogas_generators_list["y"]
174			),
175			)
176			biogas_generators_list = biogas_generators_list.rename(
177			columns={"geometry": "geom"}
178			).set_geometry("geom", crs=4326)
179
180			# Connect to local database
181			engine = db.engine()
182
183			# Cut data to federal state if in testmode
184			boundary = settings()["egon-data"]["--dataset-boundary"]
185			if boundary != "Everything":
186			db.execute_sql(
187			"""
188			DROP TABLE IF EXISTS grid.egon_biogas_generator CASCADE;
189			"""
190			)
191			biogas_generators_list.to_postgis(
192			"egon_biogas_generator",
193			engine,
194			schema="grid",
195			index=False,
196			if_exists="replace",
197			)
198
199			sql = """SELECT *
200			FROM grid.egon_biogas_generator, boundaries.vg250_sta_union as vg
201			WHERE ST_Transform(vg.geometry,4326) && egon_biogas_generator.geom
202			AND ST_Contains(ST_Transform(vg.geometry,4326), egon_biogas_generator.geom)"""
203
204			biogas_generators_list = gpd.GeoDataFrame.from_postgis(
205			sql, con=engine, geom_col="geom", crs=4326
206			)
207			biogas_generators_list = biogas_generators_list.drop(
208			columns=["id", "bez", "area_ha", "geometry"]
209			)
210			db.execute_sql(
211			"""
212			DROP TABLE IF EXISTS grid.egon_biogas_generator CASCADE;
213			"""
214			)
215
216			# Insert p_nom
217			conversion_factor = 0.01083 # m^3/h to MWh/h
218			biogas_generators_list["p_nom"] = [
219			i * conversion_factor
220			for i in biogas_generators_list["Einspeisung Biomethan [(N*m^3)/h)]"]
221			]
222
223			# Add missing columns
224			biogas_generators_list["marginal_cost"] = scn_params["marginal_cost"][
225			"biogas"
226			]
227
228			# Remove useless columns
229			biogas_generators_list = biogas_generators_list.drop(
230			columns=["x", "y", "Koordinaten", "Einspeisung Biomethan [(N*m^3)/h)]"]
231			)
232			return biogas_generators_list
233
234
235			def import_gas_generators(scn_name="eGon2035"):
236			"""Insert list of gas production units in database
237
238			Parameters
239			----------
240			scn_name : str
241			Name of the scenario.
242			"""
243			# Connect to local database
244			engine = db.engine()
245
246			# Select source and target from dataset configuration
247			source = config.datasets()["gas_prod"]["source"]
248			target = config.datasets()["gas_prod"]["target"]
249
250			# Clean table
251			db.execute_sql(
252			f"""
253			DELETE FROM {target['stores']['schema']}.{target['stores']['table']}
254			WHERE "carrier" = 'CH4' AND
255			scn_name = '{scn_name}' AND bus not IN (
256			SELECT bus_id FROM {source['buses']['schema']}.{source['buses']['table']}
257			WHERE scn_name = '{scn_name}' AND country != 'DE'
258			);
259			"""
260			)
261
262			CH4_generators_list = pd.concat(
263			[load_NG_generators(scn_name), load_biogas_generators(scn_name)]
264			)
265
266			# Add missing columns
267			c = {"scn_name": scn_name, "carrier": "CH4"}
268			CH4_generators_list = CH4_generators_list.assign(**c)
269
270			# Match to associated CH4 bus
271			CH4_generators_list = db.assign_gas_bus_id(
272			CH4_generators_list, scn_name, "CH4"
273			)
274
275			# Remove useless columns
276			CH4_generators_list = CH4_generators_list.drop(columns=["geom", "bus_id"])
277
278			# Aggregate ch4 productions with same properties at the same bus
279			CH4_generators_list = (
280			CH4_generators_list.groupby(
281			["bus", "carrier", "scn_name", "marginal_cost"]
282			)
283			.agg({"p_nom": "sum"})
284			.reset_index(drop=False)
285			)
286
287			new_id = db.next_etrago_id("generator")
288			CH4_generators_list["generator_id"] = range(
289			new_id, new_id + len(CH4_generators_list)
290			)
291
292			# Insert data to db
293			CH4_generators_list.to_sql(
294			target["stores"]["table"],
295			engine,
296			schema=target["stores"]["schema"],
297			index=False,
298			if_exists="append",
299			)
300

openego / eGon-data

Pull Request — dev (#840)

data.datasets.ch4_prod.load_biogas_generators() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

Duplication Side-by-Side

Filter issues like