data.datasets.electricity_demand_timeseries.tools.timeit() - Code Metrics - Inspection of "Desaggregate heat pumps" - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — dev (#905)

unknown

created 2023-01-10 13:11 UTC

timeit() A

↳ Parent: data.datasets.electricity_demand_timeseries.tools

Complexity

Conditions

Size

Total Lines	15
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	9
dl	0
loc	15
rs	9.95
c	0
b	0
f	0
cc	1
nop	1

from io import StringIO
import csv
import time

from shapely.geometry import Point
import geopandas as gpd
import numpy as np
import pandas as pd

from egon.data import db, logger

engine = db.engine()


def timeit(func):
    """
    Decorator for measuring function's running time.
    """

    def measure_time(*args, **kw):
        start_time = time.time()
        result = func(*args, **kw)
        print(
            "Processing time of %s(): %.2f seconds."
            % (func.__qualname__, time.time() - start_time)
        )
        return result

    return measure_time


def random_point_in_square(geom, tol):
    """
    Generate a random point within a square

    Parameters
    ----------
    geom: gpd.Series
        Geometries of square
    tol: float
        tolerance to square bounds

    Returns
    -------
    points: gpd.Series
        Series of random points
    """
    # cell bounds - half edge_length to not build buildings on the cell border
    xmin = geom.bounds["minx"] + tol / 2
    xmax = geom.bounds["maxx"] - tol / 2
    ymin = geom.bounds["miny"] + tol / 2
    ymax = geom.bounds["maxy"] - tol / 2

    # generate random coordinates within bounds - half edge_length
    x = (xmax - xmin) * np.random.rand(geom.shape[0]) + xmin
    y = (ymax - ymin) * np.random.rand(geom.shape[0]) + ymin

    points = pd.Series([Point(cords) for cords in zip(x, y)])
    points = gpd.GeoSeries(points, crs="epsg:3035")

    return points


# distribute amenities evenly
def specific_int_until_sum(s_sum, i_int):
    """
    Generate list `i_int` summing to `s_sum`. Last value will be <= `i_int`
    """
    list_i = [] if [s_sum % i_int] == [0] else [s_sum % i_int]
    list_i += s_sum // i_int * [i_int]
    return list_i


def random_ints_until_sum(s_sum, m_max):
    """
    Generate non-negative random integers < `m_max` summing to `s_sum`.
    """
    list_r = []
    while s_sum > 0:
        r = np.random.randint(1, m_max + 1)
        r = r if r <= m_max and r < s_sum else s_sum
        list_r.append(r)
        s_sum -= r
    return list_r


def write_table_to_postgis(gdf, table, engine=db.engine(), drop=True):
    """
    Helper function to append df data to table in db. Only predefined columns
    are passed. Error will raise if column is missing. Dtype of columns are
    taken from table definition.

    Parameters
    ----------
    gdf: gpd.DataFrame
        Table of data
    table: declarative_base
        Metadata of db table to export to
    engine:
        connection to database db.engine()
    drop: bool
        Drop table before appending

    """

    # Only take in db table defined columns
    columns = [column.key for column in table.__table__.columns]
    gdf = gdf.loc[:, columns]

    if drop:
        table.__table__.drop(bind=engine, checkfirst=True)
        table.__table__.create(bind=engine)

    dtypes = {
        i: table.__table__.columns[i].type
        for i in table.__table__.columns.keys()
    }

    # Write new buildings incl coord into db
    gdf.to_postgis(
        name=table.__tablename__,
        con=engine,
        if_exists="append",
        schema=table.__table_args__["schema"],
        dtype=dtypes,
    )


def psql_insert_copy(table, conn, keys, data_iter):
    """
    Execute SQL statement inserting data

    Parameters
    ----------
    table : pandas.io.sql.SQLTable
    conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
    keys : list of str
        Column names
    data_iter : Iterable that iterates the values to be inserted
    """
    # gets a DBAPI connection that can provide a cursor
    dbapi_conn = conn.connection
    with dbapi_conn.cursor() as cur:
        s_buf = StringIO()
        writer = csv.writer(s_buf)
        writer.writerows(data_iter)
        s_buf.seek(0)

        columns = ", ".join('"{}"'.format(k) for k in keys)
        if table.schema:
            table_name = "{}.{}".format(table.schema, table.name)
        else:
            table_name = table.name

        sql = "COPY {} ({}) FROM STDIN WITH CSV".format(table_name, columns)
        cur.copy_expert(sql=sql, file=s_buf)


def write_table_to_postgres(
    df, db_table, drop=False, index=False, if_exists="append"
):
    """
    Helper function to append df data to table in db. Fast string-copy is used.
    Only predefined columns are passed. If column is missing in dataframe a
    warning is logged. Dtypes of columns are taken from table definition. The
    writing process happens in a scoped session.

    Parameters
    ----------
    df: pd.DataFrame
        Table of data
    db_table: declarative_base
        Metadata of db table to export to
    drop: boolean, default False
        Drop db-table before appending
    index: boolean, default False
        Write DataFrame index as a column.
    if_exists: {'fail', 'replace', 'append'}, default 'append'
        - fail: If table exists, do nothing.
        - replace: If table exists, drop it, recreate it, and insert data.
        - append: If table exists, insert data. Create if does not exist.

    """
    logger.info("Write table to db")
    # Only take in db table defined columns and dtypes
    columns = {
        column.key: column.type for column in db_table.__table__.columns
    }

    # Take only the columns defined in class
    # pandas raises an error if column is missing
    try:
        df = df.loc[:, columns.keys()]
    except KeyError:
        same = df.columns.intersection(columns.keys())
        missing = same.symmetric_difference(df.columns)
        logger.warning(f"Columns: {missing.values} missing!")
        df = df.loc[:, same]

    if drop:
        db_table.__table__.drop(bind=engine, checkfirst=True)
        db_table.__table__.create(bind=engine)
    else:
        db_table.__table__.create(bind=engine, checkfirst=True)

    with db.session_scope() as session:
        df.to_sql(
            name=db_table.__table__.name,
            schema=db_table.__table__.schema,
            con=session.connection(),
            if_exists=if_exists,
            index=index,
            method=psql_insert_copy,
            dtype=columns,
        )


1			from io import StringIO
2			import csv
3			import time
4
5			from shapely.geometry import Point
6			import geopandas as gpd
7			import numpy as np
8			import pandas as pd
9
10			from egon.data import db, logger
11
12			engine = db.engine()
13
14
15			def timeit(func):
16			"""
17			Decorator for measuring function's running time.
18			"""
19
20			def measure_time(args, *kw):
21			start_time = time.time()
22			result = func(args, *kw)
23			print(
24			"Processing time of %s(): %.2f seconds."
25			% (func.__qualname__, time.time() - start_time)
26			)
27			return result
28
29			return measure_time
30
31
32			def random_point_in_square(geom, tol):
33			"""
34			Generate a random point within a square
35
36			Parameters
37			----------
38			geom: gpd.Series
39			Geometries of square
40			tol: float
41			tolerance to square bounds
42
43			Returns
44			-------
45			points: gpd.Series
46			Series of random points
47			"""
48			# cell bounds - half edge_length to not build buildings on the cell border
49			xmin = geom.bounds["minx"] + tol / 2
50			xmax = geom.bounds["maxx"] - tol / 2
51			ymin = geom.bounds["miny"] + tol / 2
52			ymax = geom.bounds["maxy"] - tol / 2
53
54			# generate random coordinates within bounds - half edge_length
55			x = (xmax - xmin) * np.random.rand(geom.shape[0]) + xmin
56			y = (ymax - ymin) * np.random.rand(geom.shape[0]) + ymin
57
58			points = pd.Series([Point(cords) for cords in zip(x, y)])
59			points = gpd.GeoSeries(points, crs="epsg:3035")
60
61			return points
62
63
64			# distribute amenities evenly
65			def specific_int_until_sum(s_sum, i_int):
66			"""
67			Generate list `i_int` summing to `s_sum`. Last value will be <= `i_int`
68			"""
69			list_i = [] if [s_sum % i_int] == [0] else [s_sum % i_int]
70			list_i += s_sum // i_int * [i_int]
71			return list_i
72
73
74			def random_ints_until_sum(s_sum, m_max):
75			"""
76			Generate non-negative random integers < `m_max` summing to `s_sum`.
77			"""
78			list_r = []
79			while s_sum > 0:
80			r = np.random.randint(1, m_max + 1)
81			r = r if r <= m_max and r < s_sum else s_sum
82			list_r.append(r)
83			s_sum -= r
84			return list_r
85
86
87			def write_table_to_postgis(gdf, table, engine=db.engine(), drop=True):
88			"""
89			Helper function to append df data to table in db. Only predefined columns
90			are passed. Error will raise if column is missing. Dtype of columns are
91			taken from table definition.
92
93			Parameters
94			----------
95			gdf: gpd.DataFrame
96			Table of data
97			table: declarative_base
98			Metadata of db table to export to
99			engine:
100			connection to database db.engine()
101			drop: bool
102			Drop table before appending
103
104			"""
105
106			# Only take in db table defined columns
107			columns = [column.key for column in table.__table__.columns]
108			gdf = gdf.loc[:, columns]
109
110			if drop:
111			table.__table__.drop(bind=engine, checkfirst=True)
112			table.__table__.create(bind=engine)
113
114			dtypes = {
115			i: table.__table__.columns[i].type
116			for i in table.__table__.columns.keys()
117			}
118
119			# Write new buildings incl coord into db
120			gdf.to_postgis(
121			name=table.__tablename__,
122			con=engine,
123			if_exists="append",
124			schema=table.__table_args__["schema"],
125			dtype=dtypes,
126			)
127
128
129			def psql_insert_copy(table, conn, keys, data_iter):
130			"""
131			Execute SQL statement inserting data
132
133			Parameters
134			----------
135			table : pandas.io.sql.SQLTable
136			conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
137			keys : list of str
138			Column names
139			data_iter : Iterable that iterates the values to be inserted
140			"""
141			# gets a DBAPI connection that can provide a cursor
142			dbapi_conn = conn.connection
143			with dbapi_conn.cursor() as cur:
144			s_buf = StringIO()
145			writer = csv.writer(s_buf)
146			writer.writerows(data_iter)
147			s_buf.seek(0)
148
149			columns = ", ".join('"{}"'.format(k) for k in keys)
150			if table.schema:
151			table_name = "{}.{}".format(table.schema, table.name)
152			else:
153			table_name = table.name
154
155			sql = "COPY {} ({}) FROM STDIN WITH CSV".format(table_name, columns)
156			cur.copy_expert(sql=sql, file=s_buf)
157
158
159			def write_table_to_postgres(
160			df, db_table, drop=False, index=False, if_exists="append"
161			):
162			"""
163			Helper function to append df data to table in db. Fast string-copy is used.
164			Only predefined columns are passed. If column is missing in dataframe a
165			warning is logged. Dtypes of columns are taken from table definition. The
166			writing process happens in a scoped session.
167
168			Parameters
169			----------
170			df: pd.DataFrame
171			Table of data
172			db_table: declarative_base
173			Metadata of db table to export to
174			drop: boolean, default False
175			Drop db-table before appending
176			index: boolean, default False
177			Write DataFrame index as a column.
178			if_exists: {'fail', 'replace', 'append'}, default 'append'
179			- fail: If table exists, do nothing.
180			- replace: If table exists, drop it, recreate it, and insert data.
181			- append: If table exists, insert data. Create if does not exist.
182
183			"""
184			logger.info("Write table to db")
185			# Only take in db table defined columns and dtypes
186			columns = {
187			column.key: column.type for column in db_table.__table__.columns
188			}
189
190			# Take only the columns defined in class
191			# pandas raises an error if column is missing
192			try:
193			df = df.loc[:, columns.keys()]
194			except KeyError:
195			same = df.columns.intersection(columns.keys())
196			missing = same.symmetric_difference(df.columns)
197			logger.warning(f"Columns: {missing.values} missing!")
198			df = df.loc[:, same]
199
200			if drop:
201			db_table.__table__.drop(bind=engine, checkfirst=True)
202			db_table.__table__.create(bind=engine)
203			else:
204			db_table.__table__.create(bind=engine, checkfirst=True)
205
206			with db.session_scope() as session:
207			df.to_sql(
208			name=db_table.__table__.name,
209			schema=db_table.__table__.schema,
210			con=session.connection(),
211			if_exists=if_exists,
212			index=index,
213			method=psql_insert_copy,
214			dtype=columns,
215			)
216

openego / eGon-data

Pull Request — dev (#905)

timeit() A

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