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Merge pull request 'feat/add-data' (#3) from feat/add-data into main

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Reviewed-on: #3
This commit was merged in pull request #3.
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quadfaselt
2024-08-31 12:43:11 +00:00
parent c31a57f537 61b238e87b
commit 6a1a5c11f0
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"""Global configuration for data preprocessing."""
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent.parent
DATA_DIR = BASE_DIR / "data"
APP_DIR = BASE_DIR / "app"

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import json
from datetime import datetime
from typing import Any
class DateTimeEncoder(json.JSONEncoder):
"""A custom JSON encoder that handles datetime objects.
This encoder extends the json.JSONEncoder class to provide serialization
support for datetime objects, converting them to ISO format strings.
Methods
-------
default(obj: Any) -> str
Encode datetime objects as ISO format strings.
"""
def default(self, obj: Any) -> str:
"""Encode the given object as a JSON-serializable type.
Parameters
----------
obj : Any
The object to encode.
Returns
-------
str
The ISO format string if the object is a datetime, otherwise
delegates to the superclass encoder.
"""
if isinstance(obj, datetime):
return obj.isoformat()
return super().default(obj)

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"""Module to randomly generate a JSON file with customer data for the Avacon app."""
import json
import math
import random
from datetime import datetime, timedelta
from typing import Dict, List, Tuple
import pandas as pd
from config import DATA_DIR
from data_utils import DateTimeEncoder
def generate_readings(num_customers: int) -> List[Dict[str, Tuple[str, List[datetime], List[int]]]]:
"""Generate simulated meter readings for a specified number of customers.
This function creates synthetic data for both natural gas and electricity
meter readings. It simulates readings based on average consumption patterns
in Germany, applying random variations to model real-world scenarios.
Parameters
----------
num_customers : int
The number of customers for which to generate meter readings.
Returns
-------
List[Dict[str, Tuple[str, List[datetime], List[int]]]]
A list of dictionaries, where each dictionary represents a customer and
contains:
- 'electricity': A tuple of (meter number, list of reading dates, list of readings)
- 'gas': A tuple of (meter number, list of reading dates, list of readings)
"""
# NOTE: Of course, natural gas and electricity consumption depend on various factors, such as
# size of the household, age of the building, insulation, etc. For the sake of this example,
# we will simply take the known average value and sample from a Gaussian distribution around
# this value, with a standard deviation of 10% of the mean.
# For the mean we assumed the average size of a flat in Germany (90m²) and the average
# consumption of 140 kWh/m², which we need to convert to m³ for the meter reading. To do this,
# I am using the calorific value of natural gas. I assumed a value around 11.215 kWh/m³. I also
# need to account for slight pressure differences using the conversion factor ("Zustandszahl").
mean_natural_gas = 12600 # in kWh
calorific_value = 11.215 # in kWh/m³
conversion_factor = 0.9692
mean_cubic = mean_natural_gas / (calorific_value * conversion_factor)
# For electricity, we take as average consumption the one of a 2-person household in Germany.
mean_electricity = 3500 # in kWh
readings = []
# For each customer, generate between 1 and 10 readings (we assume that natural gas and
# electricity are always read at the same time)
for _ in range(num_customers):
# The initial reading of the customers meter
gas_reading = random.randint(1000, 60000)
elt_reading = random.randint(1000, 600_000)
# Create an avacon-style meter number
gas_meter_number = generate_meter_number()
elt_meter_number = generate_meter_number()
num_readings = random.randint(1, 10)
# Get initial timestamp: Assuming that each reading takes place once a year around a similar
# date, we just take today's date and subtract a number of years corresponding to the number
# of readings
init_date = generate_past_date_with_variance(num_readings)
tmp_gas_dates: list[datetime] = []
tmp_elt_dates: list[datetime] = []
tmp_gas_readings = []
tmp_elt_readings = []
for j in range(num_readings):
time_diff = 0
if j > 0:
time_diff = 365 + random.randint(-50, 50)
gas_date = tmp_gas_dates[-1] + timedelta(days=time_diff) if j > 0 else init_date
# Electricity is around a similar date as natural gas
elt_date = gas_date + timedelta(days=random.randint(-10, 10))
# Generate random readings
gas_reading += int(random.gauss(mean_cubic, mean_cubic * 0.1))
elt_reading += int(random.gauss(mean_electricity, mean_electricity * 0.1))
# Append to temporary lists
tmp_gas_dates.append(gas_date)
tmp_elt_dates.append(elt_date)
tmp_gas_readings.append(gas_reading)
tmp_elt_readings.append(elt_reading)
# Append to final list
full_readings_dict = {
"electricity": (elt_meter_number, tmp_elt_dates, tmp_elt_readings),
"gas": (gas_meter_number, tmp_gas_dates, tmp_gas_readings),
}
readings.append(full_readings_dict)
return readings
def generate_past_date_with_variance(years_ago):
# Get current date (ignoring time)
current_date = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
# Subtract the specified number of years
past_date = current_date.replace(year=current_date.year - years_ago)
# Generate a random number of days between -50 and 50
days_variance = random.randint(-50, 50)
# Apply the variance
final_date = past_date + timedelta(days=days_variance)
return final_date
def weighted_random_int(min_value: int = 1, max_value: int = 120) -> int:
"""Generate a random integer with a logarithmic distribution.
This function produces random integers between min_value and max_value (inclusive),
with a distribution skewed towards lower numbers. It uses a logarithmic
transformation to achieve this weighted distribution.
Parameters
----------
min_value : int, optional
The minimum value of the range (inclusive). Default is 1.
max_value : int, optional
The maximum value of the range (inclusive). Default is 120.
Returns
-------
int
A random integer between min_value and max_value, with a distribution
skewed towards lower numbers.
"""
r = random.random()
# Apply a logarithmic transformation to skew towards lower numbers
value = math.exp(r * math.log(max_value - min_value + 1)) + min_value - 1
# Round down to the nearest integer
return int(math.floor(value))
def generate_meter_number() -> str:
"""Generate a random meter number in a specific format.
This function creates a meter number string in the format "X.YYY.ZZZ.Q",
where X and Q are single digits, and YYY and ZZZ are three-digit numbers.
Returns
-------
str
A randomly generated meter number string in the format "X.YYY.ZZZ.Q".
"""
return (
f"{random.randint(1, 9)}.{random.randint(100, 999)}."
f"{random.randint(100, 999)}.{random.randint(1, 9)}"
)
if __name__ == "__main__":
# Generate data for app
# Number of customers to generate
num_customers = 1000
# Load base data file
# NOTE: All of this information is publicly available, see readme for sources of information
# that were used
with open(DATA_DIR / "base_data.json", "r") as file:
base_data = json.load(file)
zip_results = base_data["zips"]
# Create weighted population-weighted sample of customers
df = pd.DataFrame(zip_results)
df["weight"] = df["population"] / df["population"].sum()
selected_zips = df.sample(n=num_customers, weights="weight", replace=True)
# Generate customer names
print("Generating customers data...")
c_given_names = random.choices(base_data["given_names"], k=num_customers)
c_surnames = random.choices(base_data["surnames"], k=num_customers)
# Generate addresses
print("Generating address data...")
c_streets = random.choices(base_data["streets"], k=num_customers)
# For street numbers, we just generate a random number between 1 and 120, weighted such that the
# lower numbers are more likely to occur
house_numbers = [weighted_random_int(1, 120) for _ in range(num_customers)]
# Finally, create meter readings
print("Generating meter readings...")
readings = generate_readings(num_customers)
# Create a final list of customers and store as JSON
print("Creating final JSON file...")
customers = []
for i in range(num_customers):
customers.append(
{
"given_name": c_given_names[i],
"surname": c_surnames[i],
"street": c_streets[i],
"house_number": house_numbers[i],
"city": selected_zips.iloc[i]["city"],
"zip_code": selected_zips.iloc[i]["zip_codes"],
"longitude": float(selected_zips.iloc[i]["longitude"]),
"latitude": float(selected_zips.iloc[i]["latitude"]),
"readings_elt": readings[i]["electricity"],
"readings_gas": readings[i]["gas"],
}
)
with open(DATA_DIR / "customers.json", "w") as file:
json.dump(customers, file, indent=4, ensure_ascii=False, cls=DateTimeEncoder)

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"""Get base data (Addresses, Names) to form the foundation of the customer generation process."""
import json
from pathlib import Path
from typing import Any, Dict, List, Union
import requests
from config import DATA_DIR
def read_file_to_list(file_path: Union[str, Path]) -> List[str]:
"""Read the contents of a file and return them as a list of strings.
This function opens the specified file, reads all lines, and returns
them as a list where each element is a line from the file.
Parameters
----------
file_path : str
The path to the file to be read.
Returns
-------
List[str]
A list containing the lines of the file as strings.
"""
with open(file_path, "r") as file:
return file.readlines()
def get_datalist(file_path: Path) -> List[str]:
"""Read a file and return its contents as a list of stripped strings.
This function reads the contents of the specified file, removes leading
and trailing whitespace from each line, and returns the resulting lines
as a list of strings.
Parameters
----------
file_path : Path
The path to the file to be read, as a pathlib.Path object.
Returns
-------
List[str]
A list containing the stripped lines of the file as strings.
"""
data_list = read_file_to_list(file_path)
for idx in range(len(data_list)):
data_list[idx] = data_list[idx].strip()
return data_list
def select_zips() -> Dict[str, List]:
"""Select and retrieve information about zip codes within the Avacon Netz area.
This function reads a list of German zip codes, filters them based on
geographical boundaries approximating the Avacon Netz coverage area,
and retrieves additional information for each selected zip code using
a public API.
The function uses predefined latitude and longitude ranges to determine
if a zip code falls within the Avacon Netz area.
Returns
-------
Dict[str, List]
A dictionary containing the following keys, each associated with a list:
- 'latitude': Latitudes of selected zip codes
- 'longitude': Longitudes of selected zip codes
- 'population': Population counts for selected zip codes
- 'city': City names for selected zip codes
- 'zip_codes': Selected zip codes
Notes
-----
- This function requires an internet connection to access the API.
- The function assumes the existence of a global constant DATA_DIR
pointing to the directory containing the 'zip_codes.txt' file.
- The API used is 'https://gvz.tuerantuer.org/api/administrative_divisions/'.
"""
lat_list = []
lon_list = []
pop_list = []
selected_zips = []
city_list = []
# Get list of all zip codes in Germany
zip_list = read_file_to_list(DATA_DIR / "zip_codes.txt")
# Geographical settings:
# The main idea is to roughly describe the Avacon Netz area, then use an API to go through all
# zip codes in Germany and only keep those that are inside the longitude and latitude range.
# We use the same API to get the actual latutide and longitude for each zip code and the total
# population so that we can use this information to weight the sampling of customers.
# Approximate Northernmost point of Avacon Netz coverage near Geesthacht to southernmost point
# in south Harz mountains close to Ilfeld
min_lat, max_lat = 51.618147, 53.432608
# Approximate Westernmost point of Avacon Netz coverage near Vechta to easternmost point near
# Reuden/Anhalt
min_lon, max_lon = 8.476919, 12.270417
for zip_code in zip_list:
if zip_code in selected_zips:
continue
# Get additional data by using this public API
response = requests.get(
f"https://gvz.tuerantuer.org/api/administrative_divisions/?search={zip_code}"
)
if response.status_code == 200:
response_json = response.json()
if response_json["count"] > 0:
zip_data = response_json["results"][0]
lat = zip_data["latitude"]
lon = zip_data["longitude"]
# Check if the zip code is within the Avacon Netz area
if (min_lat <= lat <= max_lat) and (min_lon <= lon <= max_lon):
num_associated_zips = len(zip_data["zip_codes"])
selected_zips += zip_data["zip_codes"] # Add any associated zip-codes
lat_list += [lat] * num_associated_zips
lon_list += [lon] * num_associated_zips
pop_list += [zip_data["citizens_total"]] * num_associated_zips
city_list += [zip_data["office_city"]] * num_associated_zips
else:
print(f"No data found for {zip_code}")
result = {
"latitude": lat_list,
"longitude": lon_list,
"population": pop_list,
"city": city_list,
"zip_codes": selected_zips,
}
return result
if __name__ == "__main__":
data: Dict[str, Union[list[Any], Dict[str, list[Any]]]] = {}
# Get given names and surnames
data["surnames"] = get_datalist(DATA_DIR / "names.txt")
data["given_names"] = get_datalist(DATA_DIR / "female_gnames.txt") + get_datalist(
DATA_DIR / "male_gnames.txt"
)
# Get list of street names
data["streets"] = get_datalist(DATA_DIR / "street_names.txt")
# Get list of all zip codes and select those in the Avacon Netz area.
# Also include geographical and population data, which is needed for sampling later.
data["zips"] = select_zips()
# Save data to file
with open(DATA_DIR / "base_data.json", "w") as file:
json.dump(data, file, indent=4, ensure_ascii=False)

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"""Script to create the SQL schema in Azure SQL Database."""
import os
import pyodbc
schema_creation = """
CREATE TABLE Addresses (
ID INT PRIMARY KEY IDENTITY(1,1),
StreetName NVARCHAR(100),
HouseNumber NVARCHAR(10),
City NVARCHAR(50),
PostalCode NVARCHAR(10),
Longitude FLOAT,
Latitude FLOAT
);
CREATE TABLE Meters (
ID INT PRIMARY KEY IDENTITY(1,1),
Signature NVARCHAR(11),
MeterType NVARCHAR(3),
AddressID INT,
FOREIGN KEY (AddressID) REFERENCES Addresses(ID)
);
CREATE TABLE Customers (
ID INT PRIMARY KEY IDENTITY(1,1),
FirstName NVARCHAR(100),
LastName NVARCHAR(100),
GasMeterID INT,
EltMeterID INT,
FOREIGN KEY (GasMeterID) REFERENCES Meters(ID),
FOREIGN KEY (EltMeterID) REFERENCES Meters(ID)
);
CREATE TABLE Readings (
ID INT PRIMARY KEY IDENTITY(1,1),
CustomerID INT,
MeterID INT,
ReadingDate DATE,
ReadingValue INT,
FOREIGN KEY (CustomerID) REFERENCES Customers(ID),
FOREIGN KEY (MeterID) REFERENCES Meters(ID)
);
"""
def create_tables(cursor: pyodbc.Cursor, schema: str) -> None:
"""Create database tables based on the provided schema.
This function takes a database schema as a string and executes each table
creation statement separately. It handles cases where tables already exist
and reports this information.
Parameters
----------
cursor : pyodbc.Cursor
A pyodbc cursor object used to execute SQL commands.
schema : str
A string containing SQL statements for table creation, separated by
semicolons.
Returns
-------
None
This function doesn't return any value.
Raises
------
pyodbc.ProgrammingError
If there's an error in executing the SQL statements, other than
"table already exists".
Notes
-----
- The function assumes the existence of a global 'conn' object for
committing changes to the database.
- It prints a message if a table already exists instead of raising an error.
"""
for table_creation in schema.split(";"):
if table_creation.strip():
try:
cursor.execute(table_creation)
conn.commit()
except pyodbc.ProgrammingError as e:
if "There is already an object named" in str(e):
print(f"Table already exists: {table_creation.split('(')[0].split()[-1]}")
else:
raise
if __name__ == "__main__":
# Connect to the database
connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
try:
conn = pyodbc.connect(connection_string)
cursor = conn.cursor()
print("Connected to Azure SQL Database successfully!")
# Example query
create_tables(cursor, schema_creation)
except pyodbc.Error as e:
print(f"Error connecting to Azure SQL Database: {e}")
finally:
if "cursor" in locals():
cursor.close()
if "conn" in locals():
conn.close()

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"""Script to insert the preprocessed data into the Azure SQL Database."""
import json
import os
from datetime import datetime
from typing import List, Tuple
import pyodbc
from config import DATA_DIR
def insert_addresses(
cursor: pyodbc.Cursor, addresses: List[Tuple[str, str, str, str, float, float]]
) -> None:
"""Insert multiple addresses into the Addresses table using pyodbc.
Parameters
----------
cursor : pyodbc.Cursor
A pyodbc cursor object used to execute SQL commands.
addresses : List[Tuple[str, str, str, str, float, float]]
A list of tuples, where each tuple represents an address with the following elements:
(StreetName, HouseNumber, City, PostalCode, Longitude, Latitude).
Returns
-------
None
This function doesn't return any value.
"""
for address in addresses:
cursor.execute(
"""
INSERT INTO Addresses (StreetName, HouseNumber, City, PostalCode, Longitude, Latitude)
VALUES (?, ?, ?, ?, ?, ?);
""",
address,
)
conn.commit()
def insert_meters(cursor: pyodbc.Cursor, meter_data: List[Tuple[str, str, int]]) -> None:
"""Insert multiple meters into the Meters table in a database using pyodbc.
Parameters
----------
cursor : pyodbc.Cursor
A pyodbc cursor object used to execute SQL commands.
meter_data : List[Tuple[str, str, int]]
A list of tuples, where each tuple represents a meter with the following elements:
(Signature, MeterType, AddressID).
Returns
-------
None
This function doesn't return any value.
"""
for meter in meter_data:
cursor.execute(
"""
INSERT INTO Meters (Signature, MeterType, AddressID)
VALUES (?, ?, ?);
""",
meter,
)
conn.commit()
def insert_customers(cursor: pyodbc.Cursor, customer_data: List[Tuple[str, str, int, int]]) -> None:
"""Insert multiple customers into the Customers table in a database using pyodbc.
Parameters
----------
cursor : pyodbc.Cursor
A pyodbc cursor object used to execute SQL commands.
customer_data : List[Tuple[str, str, int, int]]
A list of tuples, where each tuple represents a customer with the following elements:
(FirstName, LastName, GasMeterID, EltMeterID).
Returns
-------
None
This function doesn't return any value.
"""
for customer in customer_data:
cursor.execute(
"""
INSERT INTO Customers (FirstName, LastName, GasMeterID, EltMeterID)
VALUES (?, ?, ?, ?);
""",
customer,
)
conn.commit()
def insert_readings(
cursor: pyodbc.Cursor, readings_data: List[Tuple[int, int, datetime, int]]
) -> None:
"""Insert multiple readings into the Readings table in a database using pyodbc.
Parameters
----------
cursor : pyodbc.Cursor
A pyodbc cursor object used to execute SQL commands.
readings_data : List[Tuple[int, int, datetime, int]]
A list of tuples, where each tuple represents a reading with the following elements:
(CustomerID, MeterID, ReadingDate, ReadingValue).
Returns
-------
None
This function doesn't return any value.
"""
for reading in readings_data:
cursor.execute(
"""
INSERT INTO Readings (CustomerID, MeterID, ReadingDate, ReadingValue)
VALUES (?, ?, ?, ?);
""",
reading,
)
conn.commit()
if __name__ == "__main__":
conn_str = os.environ.get("AZURE_SQL_CONNECTION_STRING")
try:
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
print("Connected to Azure SQL Database successfully!")
except pyodbc.Error as e:
print(f"Error connecting to Azure SQL Database: {e}")
# Load generated customer data
with open(DATA_DIR / "customers.json", "r") as file:
customers = json.load(file)
# Insert data into the database
# start with addresses
full_addr_list = []
for c in customers:
full_addr_list.append(
(
c["street"],
c["house_number"],
c["city"],
c["zip_code"],
c["longitude"],
c["latitude"],
)
)
unique_addresses = list(set(full_addr_list))
address_ids = []
for i in range(len(customers)):
address_ids.append(unique_addresses.index(full_addr_list[i]) + 1)
gas_meter_data = []
elt_meter_data = []
gas_readings_data = []
elt_readings_data = []
customer_data = []
gas_id = 1
elt_id = len(customers) + 1
for c_idx, c in enumerate(customers):
customer_data.append((c["given_name"], c["surname"], c_idx + 1, c_idx + 1))
for r in range(len(c["readings_gas"][1])):
if r == 0:
gas_meter_data.append((c["readings_gas"][0], "GAS", address_ids[c_idx]))
elt_meter_data.append((c["readings_elt"][0], "ELT", address_ids[c_idx]))
gas_readings_data.append(
(
c_idx + 1,
gas_id,
datetime.fromisoformat(c["readings_gas"][1][r]),
c["readings_gas"][2][r],
)
) # CustomerID, MeterID, ReadingDate, ReadingValue
elt_readings_data.append(
(
c_idx + 1,
elt_id,
datetime.fromisoformat(c["readings_elt"][1][r]),
c["readings_elt"][2][r],
)
)
gas_id += 1
elt_id += 1
print("Inserting addresses into the database...")
insert_addresses(cursor, unique_addresses)
print("Inserting gas meter data into the database...")
insert_meters(cursor, gas_meter_data)
print("Inserting electricity meter data into the database...")
insert_meters(cursor, elt_meter_data)
print("Inserting customer data into the database...")
insert_customers(cursor, customer_data)
print("Inserting gas readings into the database...")
insert_readings(cursor, gas_readings_data)
print("Inserting electricity readings into the database...")
insert_readings(cursor, elt_readings_data)
print("Data successfully inserted into the database!")
if "cursor" in locals():
cursor.close()
if "conn" in locals():
conn.close()

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"""Script to test connection to Azure SQL Database."""
import os
import pyodbc
def test_connection() -> bool:
"""Test the connection to Azure SQL Database.
This function attempts to establish a connection to an Azure SQL Database
using a connection string stored in the environment variable
'AZURE_SQL_CONNECTION_STRING'. It executes a simple query to verify
the connection and prints the result of the connection attempt.
Returns
-------
bool
True if the connection was successful and closed properly,
False if there was an error connecting to the database.
Notes
-----
- The function requires the 'AZURE_SQL_CONNECTION_STRING' environment
variable to be set with a valid connection string.
- It uses pyodbc to establish the database connection.
- The function prints success or error messages to stdout.
- In case of a successful connection, it executes "SELECT @@version;"
as a test query.
- The function ensures that both the cursor and the connection are
closed after the operation, regardless of its success or failure.
"""
connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
try:
conn = pyodbc.connect(connection_string)
cursor = conn.cursor()
print("Connected to Azure SQL Database successfully!")
# Example query
cursor.execute("SELECT @@version;")
except pyodbc.Error as e:
print(f"Error connecting to Azure SQL Database: {e}")
return False
finally:
if "cursor" in locals():
cursor.close()
if "conn" in locals():
conn.close()
return True
if __name__ == "__main__":
test_connection()

198
poetry.lock generated
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@@ -802,6 +802,67 @@ files = [
{file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
]
[[package]]
name = "numpy"
version = "2.1.0"
description = "Fundamental package for array computing in Python"
optional = false
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version = "8.3.2"
@@ -1045,6 +1219,17 @@ files = [
[package.extras]
cli = ["click (>=5.0)"]
[[package]]
name = "pytz"
version = "2024.1"
description = "World timezone definitions, modern and historical"
optional = false
python-versions = "*"
files = [
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version = "2024.1"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
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name = "urllib3"
version = "2.2.2"
@@ -1598,4 +1794,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "1ace87aaeab8e9964d3f2e843eb5a0d15c1506bb0d1871dc41a9a38f5ed3a352"
content-hash = "3f76ad143c2f6e35163d1bc8d123b97b9bf2a96f86914d240e0c7da2226f1d85"

2
pyproject.toml
View File

@@ -32,6 +32,8 @@ python = "^3.10"
plotly = "^5.23.0"
dash = "^2.17.1"
gunicorn = "^23.0.0"
pyodbc = "^5.1.0"
pandas = "^2.2.2"
[tool.poetry.group.docs.dependencies]
mkdocs = "^1.6.0"