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 2021-02-12 08:13 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 config as cfg
from reegis import demand_elec
from reegis import demand_heat


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

reegis / scenario_builder

Pull Request — master (#2)

scenario_builder.demand.get_heat_profiles_deflex() C

Complexity

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Duplication

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How to fix Long Method Complexity

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