motorized_individual_travel.download_and_preprocess() - Code Metrics - Inspection of "save simbev metadata" - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — dev (#978)

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created 2022-10-28 12:14 UTC

download_and_preprocess() B

↳ Parent: motorized_individual_travel

Complexity

Conditions

Size

Total Lines	72
Code Lines	40

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	40
dl	0
loc	72
rs	8.92
c	0
b	0
f	0
cc	4
nop	0

How to fix Long Method

"""
Motorized Individual Travel (MIT)

Main module for preparation of model data (static and timeseries) for
motorized individual travel.

**Contents of this module**
* Creation of DB tables
* Download and preprocessing of vehicle registration data from KBA and BMVI
* Calculate number of electric vehicles and allocate on different spatial
  levels. See :py:mod:`egon.data.metadata`
* Extract and write pre-generated trips to DB

**Configuration**

The config of this dataset can be found in *datasets.yml* in section
*emobility_mit*.

**Scenarios and variations**

* Scenario overview
* Change scenario variation for 2050: adjust in
emobility_mit->scenario->variation->eGon100RE

**Trip data**

The electric vehicles' trip data for each scenario have been generated using
`simBEV <https://github.com/rl-institut/simbev/>`_. The methodical background
is given in its `documentation <https://simbev.readthedocs.io>`_.

6 different vehicle types are used:
* Battery Electric Vehicle (BEV): mini, medium, luxury
* Plug-in Hybrid Electric Vehicle (PHEV): mini, medium, luxury

.. csv-table:: EV types
    :header: "Tecnnology", "Size", "Max. charging capacity slow [kW]",
             "Max. charging capacity fast [kW]", "Battery capacity [kWh]",
             "Energy consumption [kWh/km]"
    :widths: 10, 10, 30, 30, 25, 30

    "BEV", "mini", 11, 120, 60, 0.1397
    "BEV", "medium", 22, 350, 90, 0.1746
    "BEV", "luxury", 50, 350, 110, 0.2096
    "PHEV", "mini", 3.7, 40, 14, 0.1425
    "PHEV", "medium", 11, 40, 20, 0.1782
    "PHEV", "luxury", 11, 120, 30, 0.2138

The complete tech data and assumptions of the run can be found in the metadata:
*<WORKING_DIRECTORY>/data_bundle_egon_data/emobility/mit_trip_data/<SCENARIO>/
metadata_simbev_run.json*.efficiency_fixed

* explain scenario parameters

* run params (all in meta file?)

**EV allocation**

The EVs per registration district (Zulassungsbezirk) is taken from KBA's
vehicle registration data. The numbers per EV type (BEV and PHEV)

* RegioStaR7
* scenario parameters: shares

**Further notes**

* Sanity checks

**Model paametrization**

**Example queries**


"""

from pathlib import Path
from urllib.request import urlretrieve
import os
import tarfile

from airflow.operators.python_operator import PythonOperator
from psycopg2.extensions import AsIs, register_adapter
import numpy as np
import pandas as pd

from egon.data import db, subprocess
from egon.data.datasets import Dataset
from egon.data.datasets.emobility.motorized_individual_travel.db_classes import (  # noqa: E501
    EgonEvCountMunicipality,
    EgonEvCountMvGridDistrict,
    EgonEvCountRegistrationDistrict,
    EgonEvMetadata,
    EgonEvMvGridDistrict,
    EgonEvPool,
    EgonEvTrip,
)
from egon.data.datasets.emobility.motorized_individual_travel.ev_allocation import (  # noqa: E501
    allocate_evs_numbers,
    allocate_evs_to_grid_districts,
)
from egon.data.datasets.emobility.motorized_individual_travel.helpers import (
    COLUMNS_KBA,
    DATA_BUNDLE_DIR,
    DATASET_CFG,
    MVGD_MIN_COUNT,
    TESTMODE_OFF,
    TRIP_COLUMN_MAPPING,
    WORKING_DIR,
)
from egon.data.datasets.emobility.motorized_individual_travel.model_timeseries import (  # noqa: E501
    delete_model_data_from_db,
    generate_model_data_bunch,
    generate_model_data_eGon100RE_remaining,
    generate_model_data_eGon2035_remaining,
    read_simbev_metadata_file,
)


# ========== Register np datatypes with SQLA ==========
def adapt_numpy_float64(numpy_float64):
    return AsIs(numpy_float64)


def adapt_numpy_int64(numpy_int64):
    return AsIs(numpy_int64)


register_adapter(np.float64, adapt_numpy_float64)
register_adapter(np.int64, adapt_numpy_int64)
# =====================================================


def create_tables():
    """Create tables for electric vehicles

    Returns
    -------
    None
    """

    engine = db.engine()
    EgonEvCountRegistrationDistrict.__table__.drop(
        bind=engine, checkfirst=True
    )
    EgonEvCountRegistrationDistrict.__table__.create(
        bind=engine, checkfirst=True
    )
    EgonEvCountMunicipality.__table__.drop(bind=engine, checkfirst=True)
    EgonEvCountMunicipality.__table__.create(bind=engine, checkfirst=True)
    EgonEvCountMvGridDistrict.__table__.drop(bind=engine, checkfirst=True)
    EgonEvCountMvGridDistrict.__table__.create(bind=engine, checkfirst=True)
    EgonEvPool.__table__.drop(bind=engine, checkfirst=True)
    EgonEvPool.__table__.create(bind=engine, checkfirst=True)
    EgonEvTrip.__table__.drop(bind=engine, checkfirst=True)
    EgonEvTrip.__table__.create(bind=engine, checkfirst=True)
    EgonEvMvGridDistrict.__table__.drop(bind=engine, checkfirst=True)
    EgonEvMvGridDistrict.__table__.create(bind=engine, checkfirst=True)
    EgonEvMetadata.__table__.drop(bind=engine, checkfirst=True)
    EgonEvMetadata.__table__.create(bind=engine, checkfirst=True)

    # Create dir for results, if it does not exist
    result_dir = WORKING_DIR / Path("results")
    result_dir.mkdir(exist_ok=True, parents=True)


def download_and_preprocess():
    """Downloads and preprocesses data from KBA and BMVI

    Returns
    -------
    pandas.DataFrame
        Vehicle registration data for registration district
    pandas.DataFrame
        RegioStaR7 data
    """

    mit_sources = DATASET_CFG["original_data"]["sources"]

    # Create the folder, if it does not exist
    if not os.path.exists(WORKING_DIR):
        os.mkdir(WORKING_DIR)

    ################################
    # Download and import KBA data #
    ################################
    url = mit_sources["KBA"]["url"]
    file = WORKING_DIR / mit_sources["KBA"]["file"]
    if not os.path.isfile(file):
        urlretrieve(url, file)

    kba_data = pd.read_excel(
        file,
        sheet_name=mit_sources["KBA"]["sheet"],
        usecols=mit_sources["KBA"]["columns"],
        skiprows=mit_sources["KBA"]["skiprows"],
    )
    kba_data.columns = COLUMNS_KBA
    kba_data.replace(
        " ",
        np.nan,
        inplace=True,
    )
    kba_data = kba_data.dropna()
    kba_data[
        ["ags_reg_district", "reg_district"]
    ] = kba_data.reg_district.str.split(
        " ",
        1,
        expand=True,
    )
    kba_data.ags_reg_district = kba_data.ags_reg_district.astype("int")

    kba_data.to_csv(
        WORKING_DIR / mit_sources["KBA"]["file_processed"], index=None
    )

    #######################################
    # Download and import RegioStaR7 data #
    #######################################

    url = mit_sources["RS7"]["url"]
    file = WORKING_DIR / mit_sources["RS7"]["file"]
    if not os.path.isfile(file):
        urlretrieve(url, file)

    rs7_data = pd.read_excel(file, sheet_name=mit_sources["RS7"]["sheet"])

    rs7_data["ags_district"] = (
        rs7_data.gem_20.multiply(1 / 1000).apply(np.floor).astype("int")
    )
    rs7_data = rs7_data.rename(
        columns={"gem_20": "ags", "RegioStaR7": "rs7_id"}
    )
    rs7_data.rs7_id = rs7_data.rs7_id.astype("int")

    rs7_data.to_csv(
        WORKING_DIR / mit_sources["RS7"]["file_processed"], index=None
    )


def extract_trip_file():
    """Extract trip file from data bundle"""
    trip_dir = DATA_BUNDLE_DIR / Path("mit_trip_data")

    for scenario_name in ["eGon2035", "eGon100RE"]:
        print(f"SCENARIO: {scenario_name}")
        trip_file = trip_dir / Path(
            DATASET_CFG["original_data"]["sources"]["trips"][scenario_name][
                "file"
            ]
        )

        tar = tarfile.open(trip_file)
        if os.path.isfile(trip_file):
            tar.extractall(trip_dir)
        else:
            raise FileNotFoundError(
                f"Trip file {trip_file} not found in data bundle."
            )


def write_evs_trips_to_db():
    """Write EVs and trips generated by simBEV from data bundle to database
    table
    """

    def import_csv(f):
        df = pd.read_csv(f, usecols=TRIP_COLUMN_MAPPING.keys())
        df["rs7_id"] = int(f.parent.name)
        df["simbev_ev_id"] = "_".join(f.name.split("_")[0:3])
        return df

    for scenario_name in ["eGon2035", "eGon100RE"]:
        print(f"SCENARIO: {scenario_name}")
        trip_dir_name = Path(
            DATASET_CFG["original_data"]["sources"]["trips"][scenario_name][
                "file"
            ].split(".")[0]
        )

        trip_dir_root = DATA_BUNDLE_DIR / Path("mit_trip_data", trip_dir_name)

        if TESTMODE_OFF:
            trip_files = list(trip_dir_root.glob("*/*.csv"))
        else:
            # Load only 1000 EVs per region if in test mode
            trip_files = [
                list(rdir.glob("*.csv"))[:1000]
                for rdir in [_ for _ in trip_dir_root.iterdir() if _.is_dir()]
            ]
            # Flatten
            trip_files = [i for sub in trip_files for i in sub]

        # Read, concat and reorder cols
        print(f"Importing {len(trip_files)} EV trip CSV files...")
        trip_data = pd.concat(map(import_csv, trip_files))
        trip_data.rename(columns=TRIP_COLUMN_MAPPING, inplace=True)
        trip_data = trip_data.reset_index().rename(
            columns={"index": "simbev_event_id"}
        )
        cols = ["rs7_id", "simbev_ev_id", "simbev_event_id"] + list(
            TRIP_COLUMN_MAPPING.values()
        )
        trip_data.index.name = "event_id"
        trip_data = trip_data[cols]

        # Extract EVs from trips
        evs_unique = trip_data[["rs7_id", "simbev_ev_id"]].drop_duplicates()
        evs_unique = evs_unique.reset_index().drop(columns=["event_id"])
        evs_unique.index.name = "ev_id"

        # Add EV id to trip DF
        trip_data["egon_ev_pool_ev_id"] = pd.merge(
            trip_data, evs_unique.reset_index(), on=["rs7_id", "simbev_ev_id"]
        )["ev_id"]

        # Split simBEV id into type and id
        evs_unique[["type", "simbev_ev_id"]] = evs_unique[
            "simbev_ev_id"
        ].str.rsplit("_", 1, expand=True)
        evs_unique.simbev_ev_id = evs_unique.simbev_ev_id.astype(int)
        evs_unique["scenario"] = scenario_name

        trip_data.drop(columns=["rs7_id", "simbev_ev_id"], inplace=True)
        trip_data["scenario"] = scenario_name
        trip_data.sort_index(inplace=True)

        # Write EVs to DB
        print("Writing EVs to DB pool...")
        evs_unique.to_sql(
            name=EgonEvPool.__table__.name,
            schema=EgonEvPool.__table__.schema,
            con=db.engine(),
            if_exists="append",
            index=True,
        )

        # Write trips to CSV and import to DB
        print("Writing EV trips to CSV file...")
        trip_file = WORKING_DIR / f"trip_data_{scenario_name}.csv"
        trip_data.to_csv(trip_file)

        # Get DB config
        docker_db_config = db.credentials()
        host = ["-h", f"{docker_db_config['HOST']}"]
        port = ["-p", f"{docker_db_config['PORT']}"]
        pgdb = ["-d", f"{docker_db_config['POSTGRES_DB']}"]
        user = ["-U", f"{docker_db_config['POSTGRES_USER']}"]
        command = [
            "-c",
            rf"\copy {EgonEvTrip.__table__.schema}.{EgonEvTrip.__table__.name}"
            rf"({','.join(trip_data.reset_index().columns)})"
            rf" FROM '{str(trip_file)}' DELIMITER ',' CSV HEADER;",
        ]

        print("Importing EV trips from CSV file to DB...")
        subprocess.run(
            ["psql"] + host + port + pgdb + user + command,
            env={"PGPASSWORD": docker_db_config["POSTGRES_PASSWORD"]},
        )

        os.remove(trip_file)


def write_metadata_to_db():
    """
    Write used SimBEV metadata per scenario to database.
    """
    for scenario_name in ["eGon2035", "eGon100RE"]:
        meta_run_config = read_simbev_metadata_file(
            scenario_name, "config"
        ).loc["basic"]

        dtypes = {
            "scenario": str,
            "eta_cp": float,
            "stepsize": int,
            "start_date": np.datetime64,
            "end_date": np.datetime64,
            "soc_min": float,
            "grid_timeseries": bool,
            "grid_timeseries_by_usecase": bool,
        }

        meta_run_config = (
            meta_run_config.to_frame()
            .T.assign(scenario=scenario_name)[dtypes.keys()]
            .astype(dtypes)
        )

        meta_run_config.to_sql(
            name=EgonEvMetadata.__table__.name,
            schema=EgonEvMetadata.__table__.schema,
            con=db.engine(),
            if_exists="append",
            index=False,
        )


class MotorizedIndividualTravel(Dataset):
    def __init__(self, dependencies):
        def generate_model_data_tasks(scenario_name):
            """Dynamically generate tasks for model data creation.

            The goal is to speed up the creation of model timeseries. However,
            the exact number of parallel task cannot be determined during the
            DAG building as the number of grid districts (MVGD) is calculated
            within another pipeline task.
            Approach: assuming an approx. count of `mvgd_min_count` of 3700,
            the majority of the MVGDs can be parallelized. The remainder is
            handled subsequently in XXX.
            The number of parallel tasks is defined via parameter
            `parallel_tasks` in the dataset config `datasets.yml`.

            Parameters
            ----------
            scenario_name : str
                Scenario name

            Returns
            -------
            set of functools.partial
                The tasks. Each element is of
                :func:`egon.data.datasets.emobility.motorized_individual_travel.model_timeseries.generate_model_data`
            """
            parallel_tasks = DATASET_CFG["model_timeseries"].get(
                "parallel_tasks", 1
            )
            mvgd_bunch_size = divmod(MVGD_MIN_COUNT, parallel_tasks)[0]

            tasks = set()
            for _ in range(parallel_tasks):
                bunch = range(_ * mvgd_bunch_size, (_ + 1) * mvgd_bunch_size)
                tasks.add(
                    PythonOperator(
                        task_id=(
                            f"generate_model_data_"
                            f"{scenario_name}_"
                            f"bunch{bunch[0]}-{bunch[-1]}"
                        ),
                        python_callable=generate_model_data_bunch,
                        op_kwargs={
                            "scenario_name": scenario_name,
                            "bunch": bunch,
                        },
                    )
                )

            if scenario_name == "eGon2035":
                tasks.add(generate_model_data_eGon2035_remaining)
            elif scenario_name == "eGon100RE":
                tasks.add(generate_model_data_eGon100RE_remaining)
            return tasks

        super().__init__(
            name="MotorizedIndividualTravel",
            version="0.0.6",
            dependencies=dependencies,
            tasks=(
                create_tables,
                {
                    (download_and_preprocess, allocate_evs_numbers),
                    (extract_trip_file, write_evs_trips_to_db),
                },
                allocate_evs_to_grid_districts,
                delete_model_data_from_db,
                {
                    *generate_model_data_tasks(scenario_name="eGon2035"),
                    *generate_model_data_tasks(scenario_name="eGon100RE"),
                },
                write_metadata_to_db,
            ),
        )


1			"""
2			Motorized Individual Travel (MIT)
3
4			Main module for preparation of model data (static and timeseries) for
5			motorized individual travel.
6
7			Contents of this module
8			* Creation of DB tables
9			* Download and preprocessing of vehicle registration data from KBA and BMVI
10			* Calculate number of electric vehicles and allocate on different spatial
11			levels. See :py:mod:`egon.data.metadata`
12			* Extract and write pre-generated trips to DB
13
14			Configuration
15
16			The config of this dataset can be found in datasets.yml in section
17			emobility_mit.
18
19			Scenarios and variations
20
21			* Scenario overview
22			* Change scenario variation for 2050: adjust in
23			emobility_mit->scenario->variation->eGon100RE
24
25			Trip data
26
27			The electric vehicles' trip data for each scenario have been generated using
28			`simBEV <https://github.com/rl-institut/simbev/>`_. The methodical background
29			is given in its `documentation <https://simbev.readthedocs.io>`_.
30
31			6 different vehicle types are used:
32			* Battery Electric Vehicle (BEV): mini, medium, luxury
33			* Plug-in Hybrid Electric Vehicle (PHEV): mini, medium, luxury
34
35			.. csv-table:: EV types
36			:header: "Tecnnology", "Size", "Max. charging capacity slow [kW]",
37			"Max. charging capacity fast [kW]", "Battery capacity [kWh]",
38			"Energy consumption [kWh/km]"
39			:widths: 10, 10, 30, 30, 25, 30
40
41			"BEV", "mini", 11, 120, 60, 0.1397
42			"BEV", "medium", 22, 350, 90, 0.1746
43			"BEV", "luxury", 50, 350, 110, 0.2096
44			"PHEV", "mini", 3.7, 40, 14, 0.1425
45			"PHEV", "medium", 11, 40, 20, 0.1782
46			"PHEV", "luxury", 11, 120, 30, 0.2138
47
48			The complete tech data and assumptions of the run can be found in the metadata:
49			*<WORKING_DIRECTORY>/data_bundle_egon_data/emobility/mit_trip_data/<SCENARIO>/
50			metadata_simbev_run.json*.efficiency_fixed
51
52			* explain scenario parameters
53
54			* run params (all in meta file?)
55
56			EV allocation
57
58			The EVs per registration district (Zulassungsbezirk) is taken from KBA's
59			vehicle registration data. The numbers per EV type (BEV and PHEV)
60
61			* RegioStaR7
62			* scenario parameters: shares
63
64			Further notes
65
66			* Sanity checks
67
68			Model paametrization
69
70			Example queries
71
72
73			"""
74
75			from pathlib import Path
76			from urllib.request import urlretrieve
77			import os
78			import tarfile
79
80			from airflow.operators.python_operator import PythonOperator
81			from psycopg2.extensions import AsIs, register_adapter
82			import numpy as np
83			import pandas as pd
84
85			from egon.data import db, subprocess
86			from egon.data.datasets import Dataset
87			from egon.data.datasets.emobility.motorized_individual_travel.db_classes import ( # noqa: E501
88			EgonEvCountMunicipality,
89			EgonEvCountMvGridDistrict,
90			EgonEvCountRegistrationDistrict,
91			EgonEvMetadata,
92			EgonEvMvGridDistrict,
93			EgonEvPool,
94			EgonEvTrip,
95			)
96			from egon.data.datasets.emobility.motorized_individual_travel.ev_allocation import ( # noqa: E501
97			allocate_evs_numbers,
98			allocate_evs_to_grid_districts,
99			)
100			from egon.data.datasets.emobility.motorized_individual_travel.helpers import (
101			COLUMNS_KBA,
102			DATA_BUNDLE_DIR,
103			DATASET_CFG,
104			MVGD_MIN_COUNT,
105			TESTMODE_OFF,
106			TRIP_COLUMN_MAPPING,
107			WORKING_DIR,
108			)
109			from egon.data.datasets.emobility.motorized_individual_travel.model_timeseries import ( # noqa: E501
110			delete_model_data_from_db,
111			generate_model_data_bunch,
112			generate_model_data_eGon100RE_remaining,
113			generate_model_data_eGon2035_remaining,
114			read_simbev_metadata_file,
115			)
116
117
118			# ========== Register np datatypes with SQLA ==========
119			def adapt_numpy_float64(numpy_float64):
120			return AsIs(numpy_float64)
121
122
123			def adapt_numpy_int64(numpy_int64):
124			return AsIs(numpy_int64)
125
126
127			register_adapter(np.float64, adapt_numpy_float64)
128			register_adapter(np.int64, adapt_numpy_int64)
129			# =====================================================
130
131
132			def create_tables():
133			"""Create tables for electric vehicles
134
135			Returns
136			-------
137			None
138			"""
139
140			engine = db.engine()
141			EgonEvCountRegistrationDistrict.__table__.drop(
142			bind=engine, checkfirst=True
143			)
144			EgonEvCountRegistrationDistrict.__table__.create(
145			bind=engine, checkfirst=True
146			)
147			EgonEvCountMunicipality.__table__.drop(bind=engine, checkfirst=True)
148			EgonEvCountMunicipality.__table__.create(bind=engine, checkfirst=True)
149			EgonEvCountMvGridDistrict.__table__.drop(bind=engine, checkfirst=True)
150			EgonEvCountMvGridDistrict.__table__.create(bind=engine, checkfirst=True)
151			EgonEvPool.__table__.drop(bind=engine, checkfirst=True)
152			EgonEvPool.__table__.create(bind=engine, checkfirst=True)
153			EgonEvTrip.__table__.drop(bind=engine, checkfirst=True)
154			EgonEvTrip.__table__.create(bind=engine, checkfirst=True)
155			EgonEvMvGridDistrict.__table__.drop(bind=engine, checkfirst=True)
156			EgonEvMvGridDistrict.__table__.create(bind=engine, checkfirst=True)
157			EgonEvMetadata.__table__.drop(bind=engine, checkfirst=True)
158			EgonEvMetadata.__table__.create(bind=engine, checkfirst=True)
159
160			# Create dir for results, if it does not exist
161			result_dir = WORKING_DIR / Path("results")
162			result_dir.mkdir(exist_ok=True, parents=True)
163
164
165			def download_and_preprocess():
166			"""Downloads and preprocesses data from KBA and BMVI
167
168			Returns
169			-------
170			pandas.DataFrame
171			Vehicle registration data for registration district
172			pandas.DataFrame
173			RegioStaR7 data
174			"""
175
176			mit_sources = DATASET_CFG["original_data"]["sources"]
177
178			# Create the folder, if it does not exist
179			if not os.path.exists(WORKING_DIR):
180			os.mkdir(WORKING_DIR)
181
182			################################
183			# Download and import KBA data #
184			################################
185			url = mit_sources["KBA"]["url"]
186			file = WORKING_DIR / mit_sources["KBA"]["file"]
187			if not os.path.isfile(file):
188			urlretrieve(url, file)
189
190			kba_data = pd.read_excel(
191			file,
192			sheet_name=mit_sources["KBA"]["sheet"],
193			usecols=mit_sources["KBA"]["columns"],
194			skiprows=mit_sources["KBA"]["skiprows"],
195			)
196			kba_data.columns = COLUMNS_KBA
197			kba_data.replace(
198			" ",
199			np.nan,
200			inplace=True,
201			)
202			kba_data = kba_data.dropna()
203			kba_data[
204			["ags_reg_district", "reg_district"]
205			] = kba_data.reg_district.str.split(
206			" ",
207			1,
208			expand=True,
209			)
210			kba_data.ags_reg_district = kba_data.ags_reg_district.astype("int")
211
212			kba_data.to_csv(
213			WORKING_DIR / mit_sources["KBA"]["file_processed"], index=None
214			)
215
216			#######################################
217			# Download and import RegioStaR7 data #
218			#######################################
219
220			url = mit_sources["RS7"]["url"]
221			file = WORKING_DIR / mit_sources["RS7"]["file"]
222			if not os.path.isfile(file):
223			urlretrieve(url, file)
224
225			rs7_data = pd.read_excel(file, sheet_name=mit_sources["RS7"]["sheet"])
226
227			rs7_data["ags_district"] = (
228			rs7_data.gem_20.multiply(1 / 1000).apply(np.floor).astype("int")
229			)
230			rs7_data = rs7_data.rename(
231			columns={"gem_20": "ags", "RegioStaR7": "rs7_id"}
232			)
233			rs7_data.rs7_id = rs7_data.rs7_id.astype("int")
234
235			rs7_data.to_csv(
236			WORKING_DIR / mit_sources["RS7"]["file_processed"], index=None
237			)
238
239
240			def extract_trip_file():
241			"""Extract trip file from data bundle"""
242			trip_dir = DATA_BUNDLE_DIR / Path("mit_trip_data")
243
244			for scenario_name in ["eGon2035", "eGon100RE"]:
245			print(f"SCENARIO: {scenario_name}")
246			trip_file = trip_dir / Path(
247			DATASET_CFG["original_data"]["sources"]["trips"][scenario_name][
248			"file"
249			]
250			)
251
252			tar = tarfile.open(trip_file)
253			if os.path.isfile(trip_file):
254			tar.extractall(trip_dir)
255			else:
256			raise FileNotFoundError(
257			f"Trip file {trip_file} not found in data bundle."
258			)
259
260
261			def write_evs_trips_to_db():
262			"""Write EVs and trips generated by simBEV from data bundle to database
263			table
264			"""
265
266			def import_csv(f):
267			df = pd.read_csv(f, usecols=TRIP_COLUMN_MAPPING.keys())
268			df["rs7_id"] = int(f.parent.name)
269			df["simbev_ev_id"] = "_".join(f.name.split("_")[0:3])
270			return df
271
272			for scenario_name in ["eGon2035", "eGon100RE"]:
273			print(f"SCENARIO: {scenario_name}")
274			trip_dir_name = Path(
275			DATASET_CFG["original_data"]["sources"]["trips"][scenario_name][
276			"file"
277			].split(".")[0]
278			)
279
280			trip_dir_root = DATA_BUNDLE_DIR / Path("mit_trip_data", trip_dir_name)
281
282			if TESTMODE_OFF:
283			trip_files = list(trip_dir_root.glob("/.csv"))
284			else:
285			# Load only 1000 EVs per region if in test mode
286			trip_files = [
287			list(rdir.glob("*.csv"))[:1000]
288			for rdir in [_ for _ in trip_dir_root.iterdir() if _.is_dir()]
289			]
290			# Flatten
291			trip_files = [i for sub in trip_files for i in sub]
292
293			# Read, concat and reorder cols
294			print(f"Importing {len(trip_files)} EV trip CSV files...")
295			trip_data = pd.concat(map(import_csv, trip_files))
296			trip_data.rename(columns=TRIP_COLUMN_MAPPING, inplace=True)
297			trip_data = trip_data.reset_index().rename(
298			columns={"index": "simbev_event_id"}
299			)
300			cols = ["rs7_id", "simbev_ev_id", "simbev_event_id"] + list(
301			TRIP_COLUMN_MAPPING.values()
302			)
303			trip_data.index.name = "event_id"
304			trip_data = trip_data[cols]
305
306			# Extract EVs from trips
307			evs_unique = trip_data[["rs7_id", "simbev_ev_id"]].drop_duplicates()
308			evs_unique = evs_unique.reset_index().drop(columns=["event_id"])
309			evs_unique.index.name = "ev_id"
310
311			# Add EV id to trip DF
312			trip_data["egon_ev_pool_ev_id"] = pd.merge(
313			trip_data, evs_unique.reset_index(), on=["rs7_id", "simbev_ev_id"]
314			)["ev_id"]
315
316			# Split simBEV id into type and id
317			evs_unique[["type", "simbev_ev_id"]] = evs_unique[
318			"simbev_ev_id"
319			].str.rsplit("_", 1, expand=True)
320			evs_unique.simbev_ev_id = evs_unique.simbev_ev_id.astype(int)
321			evs_unique["scenario"] = scenario_name
322
323			trip_data.drop(columns=["rs7_id", "simbev_ev_id"], inplace=True)
324			trip_data["scenario"] = scenario_name
325			trip_data.sort_index(inplace=True)
326
327			# Write EVs to DB
328			print("Writing EVs to DB pool...")
329			evs_unique.to_sql(
330			name=EgonEvPool.__table__.name,
331			schema=EgonEvPool.__table__.schema,
332			con=db.engine(),
333			if_exists="append",
334			index=True,
335			)
336
337			# Write trips to CSV and import to DB
338			print("Writing EV trips to CSV file...")
339			trip_file = WORKING_DIR / f"trip_data_{scenario_name}.csv"
340			trip_data.to_csv(trip_file)
341
342			# Get DB config
343			docker_db_config = db.credentials()
344			host = ["-h", f"{docker_db_config['HOST']}"]
345			port = ["-p", f"{docker_db_config['PORT']}"]
346			pgdb = ["-d", f"{docker_db_config['POSTGRES_DB']}"]
347			user = ["-U", f"{docker_db_config['POSTGRES_USER']}"]
348			command = [
349			"-c",
350			rf"\copy {EgonEvTrip.__table__.schema}.{EgonEvTrip.__table__.name}"
351			rf"({','.join(trip_data.reset_index().columns)})"
352			rf" FROM '{str(trip_file)}' DELIMITER ',' CSV HEADER;",
353			]
354
355			print("Importing EV trips from CSV file to DB...")
356			subprocess.run(
357			["psql"] + host + port + pgdb + user + command,
358			env={"PGPASSWORD": docker_db_config["POSTGRES_PASSWORD"]},
359			)
360
361			os.remove(trip_file)
362
363
364			def write_metadata_to_db():
365			"""
366			Write used SimBEV metadata per scenario to database.
367			"""
368			for scenario_name in ["eGon2035", "eGon100RE"]:
369			meta_run_config = read_simbev_metadata_file(
370			scenario_name, "config"
371			).loc["basic"]
372
373			dtypes = {
374			"scenario": str,
375			"eta_cp": float,
376			"stepsize": int,
377			"start_date": np.datetime64,
378			"end_date": np.datetime64,
379			"soc_min": float,
380			"grid_timeseries": bool,
381			"grid_timeseries_by_usecase": bool,
382			}
383
384			meta_run_config = (
385			meta_run_config.to_frame()
386			.T.assign(scenario=scenario_name)[dtypes.keys()]
387			.astype(dtypes)
388			)
389
390			meta_run_config.to_sql(
391			name=EgonEvMetadata.__table__.name,
392			schema=EgonEvMetadata.__table__.schema,
393			con=db.engine(),
394			if_exists="append",
395			index=False,
396			)
397
398
399			class MotorizedIndividualTravel(Dataset):
400			def __init__(self, dependencies):
401			def generate_model_data_tasks(scenario_name):
402			"""Dynamically generate tasks for model data creation.
403
404			The goal is to speed up the creation of model timeseries. However,
405			the exact number of parallel task cannot be determined during the
406			DAG building as the number of grid districts (MVGD) is calculated
407			within another pipeline task.
408			Approach: assuming an approx. count of `mvgd_min_count` of 3700,
409			the majority of the MVGDs can be parallelized. The remainder is
410			handled subsequently in XXX.
411			The number of parallel tasks is defined via parameter
412			`parallel_tasks` in the dataset config `datasets.yml`.
413
414			Parameters
415			----------
416			scenario_name : str
417			Scenario name
418
419			Returns
420			-------
421			set of functools.partial
422			The tasks. Each element is of
423			:func:`egon.data.datasets.emobility.motorized_individual_travel.model_timeseries.generate_model_data`
424			"""
425			parallel_tasks = DATASET_CFG["model_timeseries"].get(
426			"parallel_tasks", 1
427			)
428			mvgd_bunch_size = divmod(MVGD_MIN_COUNT, parallel_tasks)[0]
429
430			tasks = set()
431			for _ in range(parallel_tasks):
432			bunch = range(_ * mvgd_bunch_size, (_ + 1) * mvgd_bunch_size)
433			tasks.add(
434			PythonOperator(
435			task_id=(
436			f"generate_model_data_"
437			f"{scenario_name}_"
438			f"bunch{bunch[0]}-{bunch[-1]}"
439			),
440			python_callable=generate_model_data_bunch,
441			op_kwargs={
442			"scenario_name": scenario_name,
443			"bunch": bunch,
444			},
445			)
446			)
447
448			if scenario_name == "eGon2035":
449			tasks.add(generate_model_data_eGon2035_remaining)
450			elif scenario_name == "eGon100RE":
451			tasks.add(generate_model_data_eGon100RE_remaining)
452			return tasks
453
454			super().__init__(
455			name="MotorizedIndividualTravel",
456			version="0.0.6",
457			dependencies=dependencies,
458			tasks=(
459			create_tables,
460			{
461			(download_and_preprocess, allocate_evs_numbers),
462			(extract_trip_file, write_evs_trips_to_db),
463			},
464			allocate_evs_to_grid_districts,
465			delete_model_data_from_db,
466			{
467			*generate_model_data_tasks(scenario_name="eGon2035"),
468			*generate_model_data_tasks(scenario_name="eGon100RE"),
469			},
470			write_metadata_to_db,
471			),
472			)
473

openego / eGon-data

Pull Request — dev (#978)

download_and_preprocess() B

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