scenario_builder.demand.get_heat_profiles_deflex() - Code Metrics - Inspection of "Add deflex scenario builder" - reegis/scenario_builder - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#2)

by Uwe

created 2021-02-03 18:04 UTC

scenario_builder.demand.get_heat_profiles_deflex() C

↳ Parent: scenario_builder.demand

Complexity

Conditions

Size

Total Lines	82
Code Lines	41

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	10
eloc	41
nop	5
dl	0
loc	82
rs	5.9999
c	0
b	0
f	0

How to fix Long Method Complexity

"""Create a basic scenario from the internal data structure.

SPDX-FileCopyrightText: 2016-2019 Uwe Krien <[email protected]>

SPDX-License-Identifier: MIT
"""


import calendar
import logging
import os

import pandas as pd
from reegis import demand_elec
from reegis import demand_heat

from scenario_builder import config as cfg


def get_heat_profiles_deflex(
    deflex_geo, year, time_index=None, weather_year=None, keep_unit=False
):
    """

    Parameters
    ----------
    year
    deflex_geo
    time_index
    weather_year
    keep_unit

    Returns
    -------

    """
    # separate_regions=keep all demand connected to the region
    separate_regions = cfg.get_list("creator", "separate_heat_regions")
    # Add lower and upper cases to be not case sensitive
    separate_regions = [x.upper() for x in separate_regions] + [
        x.lower() for x in separate_regions
    ]

    # add second fuel to first
    # combine_fuels = cfg.get_dict("combine_heat_fuels")
    combine_fuels = {"natural gas": "gas"}

    # fuels to be dissolved per region
    region_fuels = cfg.get_list("creator", "local_fuels")

    fn = os.path.join(
        cfg.get("paths", "demand"),
        "heat_profiles_{year}_{map}".format(year=year, map=deflex_geo.name),
    )

    demand_region = (
        demand_heat.get_heat_profiles_by_region(
            deflex_geo, year, to_csv=fn, weather_year=weather_year
        )
        .groupby(level=[0, 1], axis=1)
        .sum()
    )

    # Decentralised demand is combined to a nation-wide demand if not part
    # of region_fuels.
    regions = list(
        set(demand_region.columns.get_level_values(0).unique()) - set(separate_regions)
    )

    # If region_fuels is 'all' fetch all fuels to be local.
    if "all" in region_fuels:
        region_fuels = demand_region.columns.get_level_values(1).unique()

    for fuel in demand_region.columns.get_level_values(1).unique():
        demand_region["DE_demand", fuel] = 0

    for region in regions:
        for f1, f2 in combine_fuels.items():
            demand_region[region, f1] += demand_region[region, f2]
            demand_region.drop((region, f2), axis=1, inplace=True)
        cols = list(set(demand_region[region].columns) - set(region_fuels))
        for col in cols:
            demand_region["DE_demand", col] += demand_region[region, col]
            demand_region.drop((region, col), axis=1, inplace=True)

    if time_index is not None:
        demand_region.index = time_index

    if not keep_unit:
        msg = "The unit of the source is 'TJ'. " "Will be divided by {0} to get 'MWh'."
        converter = 0.0036
        demand_region = demand_region.div(converter)
        logging.debug(msg.format(converter))

    demand_region.sort_index(1, inplace=True)

    for c in demand_region.columns:
        if demand_region[c].sum() == 0:
            demand_region.drop(c, axis=1, inplace=True)

    return demand_region


def scenario_demand(regions, year, name, opsd_version=None, weather_year=None):
    """

    Parameters
    ----------
    regions
    year
    name
    opsd_version
    weather_year

    Returns
    -------

    Examples
    --------
    >>> from reegis import geometries  # doctest: +SKIP
    >>> fs=geometries.get_federal_states_polygon()  # doctest: +SKIP
    >>> my_demand=scenario_demand(regions, 2014, "de21")  # doctest: +SKIP
    >>> int(my_demand["DE01", "district heating"].sum())  # doctest: +SKIP
    18639262
    >>> int(my_demand["DE05", "electrical_load"].sum())  # doctest: +SKIP
    10069304

    """
    demand_series = scenario_elec_demand(
        pd.DataFrame(), regions, year, name, weather_year=weather_year,
        version=opsd_version
    )
    if cfg.get("creator", "heat"):
        demand_series = scenario_heat_demand(
            demand_series, regions, year, weather_year=weather_year
        )
    return demand_series


def scenario_heat_demand(table, regions, year, weather_year=None):
    """

    Parameters
    ----------
    table
    regions
    year
    weather_year

    Returns
    -------

    """
    idx = table.index  # Use the index of the existing time series
    table = pd.concat(
        [
            table,
            get_heat_profiles_deflex(regions, year, idx, weather_year=weather_year),
        ],
        axis=1,
    )
    return table.sort_index(1)


def scenario_elec_demand(table, regions, year, name, version=None, weather_year=None):
    """

    Parameters
    ----------
    table
    regions
    year
    name
    weather_year

    Returns
    -------

    """
    if weather_year is None:
        demand_year = year
    else:
        demand_year = weather_year

    df = demand_elec.get_entsoe_profile_by_region(
        regions, demand_year, name, annual_demand="bmwi", version=version
    )
    df = pd.concat([df], axis=1, keys=["electrical_load"]).swaplevel(0, 1, 1)
    df = df.reset_index(drop=True)
    if not calendar.isleap(year) and len(df) > 8760:
        df = df.iloc[:8760]
    return pd.concat([table, df], axis=1).sort_index(1)


if __name__ == "__main__":
    pass


1			"""Create a basic scenario from the internal data structure.
2
3			SPDX-FileCopyrightText: 2016-2019 Uwe Krien <[email protected]>
4
5			SPDX-License-Identifier: MIT
6			"""
7
8
9			import calendar
10			import logging
11			import os
12
13			import pandas as pd
14			from reegis import demand_elec
15			from reegis import demand_heat
16
17			from scenario_builder import config as cfg
18
19
20			def get_heat_profiles_deflex(
21			deflex_geo, year, time_index=None, weather_year=None, keep_unit=False
22			):
23			"""
24
25			Parameters
26			----------
27			year
28			deflex_geo
29			time_index
30			weather_year
31			keep_unit
32
33			Returns
34			-------
35
36			"""
37			# separate_regions=keep all demand connected to the region
38			separate_regions = cfg.get_list("creator", "separate_heat_regions")
39			# Add lower and upper cases to be not case sensitive
40			separate_regions = [x.upper() for x in separate_regions] + [
41			x.lower() for x in separate_regions
42			]
43
44			# add second fuel to first
45			# combine_fuels = cfg.get_dict("combine_heat_fuels")
46			combine_fuels = {"natural gas": "gas"}
47
48			# fuels to be dissolved per region
49			region_fuels = cfg.get_list("creator", "local_fuels")
50
51			fn = os.path.join(
52			cfg.get("paths", "demand"),
53			"heat_profiles_{year}_{map}".format(year=year, map=deflex_geo.name),
54			)
55
56			demand_region = (
57			demand_heat.get_heat_profiles_by_region(
58			deflex_geo, year, to_csv=fn, weather_year=weather_year
59			)
60			.groupby(level=[0, 1], axis=1)
61			.sum()
62			)
63
64			# Decentralised demand is combined to a nation-wide demand if not part
65			# of region_fuels.
66			regions = list(
67			set(demand_region.columns.get_level_values(0).unique()) - set(separate_regions)
68			)
69
70			# If region_fuels is 'all' fetch all fuels to be local.
71			if "all" in region_fuels:
72			region_fuels = demand_region.columns.get_level_values(1).unique()
73
74			for fuel in demand_region.columns.get_level_values(1).unique():
75			demand_region["DE_demand", fuel] = 0
76
77			for region in regions:
78			for f1, f2 in combine_fuels.items():
79			demand_region[region, f1] += demand_region[region, f2]
80			demand_region.drop((region, f2), axis=1, inplace=True)
81			cols = list(set(demand_region[region].columns) - set(region_fuels))
82			for col in cols:
83			demand_region["DE_demand", col] += demand_region[region, col]
84			demand_region.drop((region, col), axis=1, inplace=True)
85
86			if time_index is not None:
87			demand_region.index = time_index
88
89			if not keep_unit:
90			msg = "The unit of the source is 'TJ'. " "Will be divided by {0} to get 'MWh'."
91			converter = 0.0036
92			demand_region = demand_region.div(converter)
93			logging.debug(msg.format(converter))
94
95			demand_region.sort_index(1, inplace=True)
96
97			for c in demand_region.columns:
98			if demand_region[c].sum() == 0:
99			demand_region.drop(c, axis=1, inplace=True)
100
101			return demand_region
102
103
104			def scenario_demand(regions, year, name, opsd_version=None, weather_year=None):
105			"""
106
107			Parameters
108			----------
109			regions
110			year
111			name
112			opsd_version
113			weather_year
114
115			Returns
116			-------
117
118			Examples
119			--------
120			>>> from reegis import geometries # doctest: +SKIP
121			>>> fs=geometries.get_federal_states_polygon() # doctest: +SKIP
122			>>> my_demand=scenario_demand(regions, 2014, "de21") # doctest: +SKIP
123			>>> int(my_demand["DE01", "district heating"].sum()) # doctest: +SKIP
124			18639262
125			>>> int(my_demand["DE05", "electrical_load"].sum()) # doctest: +SKIP
126			10069304
127
128			"""
129			demand_series = scenario_elec_demand(
130			pd.DataFrame(), regions, year, name, weather_year=weather_year,
131			version=opsd_version
132			)
133			if cfg.get("creator", "heat"):
134			demand_series = scenario_heat_demand(
135			demand_series, regions, year, weather_year=weather_year
136			)
137			return demand_series
138
139
140			def scenario_heat_demand(table, regions, year, weather_year=None):
141			"""
142
143			Parameters
144			----------
145			table
146			regions
147			year
148			weather_year
149
150			Returns
151			-------
152
153			"""
154			idx = table.index # Use the index of the existing time series
155			table = pd.concat(
156			[
157			table,
158			get_heat_profiles_deflex(regions, year, idx, weather_year=weather_year),
159			],
160			axis=1,
161			)
162			return table.sort_index(1)
163
164
165			def scenario_elec_demand(table, regions, year, name, version=None, weather_year=None):
166			"""
167
168			Parameters
169			----------
170			table
171			regions
172			year
173			name
174			weather_year
175
176			Returns
177			-------
178
179			"""
180			if weather_year is None:
181			demand_year = year
182			else:
183			demand_year = weather_year
184
185			df = demand_elec.get_entsoe_profile_by_region(
186			regions, demand_year, name, annual_demand="bmwi", version=version
187			)
188			df = pd.concat([df], axis=1, keys=["electrical_load"]).swaplevel(0, 1, 1)
189			df = df.reset_index(drop=True)
190			if not calendar.isleap(year) and len(df) > 8760:
191			df = df.iloc[:8760]
192			return pd.concat([table, df], axis=1).sort_index(1)
193
194
195			if __name__ == "__main__":
196			pass
197

reegis / scenario_builder

Pull Request — master (#2)

scenario_builder.demand.get_heat_profiles_deflex() C

Complexity

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Duplication

Importance

How to fix Long Method Complexity

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