# -*- coding: utf-8 -*-
"""
Pytest configuration and shared fixtures for tests.
"""
# Standard
import tempfile
from pathlib import Path
# Third-Party
import numpy as np
import pandas as pd
import pytest
@pytest.fixture
def sample_dataframe():
"""Create a sample DataFrame for testing."""
np.random.seed(42)
return pd.DataFrame(
{
"id": range(1, 101),
"name": [f"Person_{i}" for i in range(1, 101)],
"age": np.random.randint(18, 80, 100),
"income": np.random.normal(50000, 15000, 100),
"category": np.random.choice(["A", "B", "C"], 100),
"score": np.random.uniform(0, 100, 100),
"date": pd.date_range("2023-01-01", periods=100, freq="D"),
"active": np.random.choice([True, False], 100),
}
)
@pytest.fixture
def sample_csv_file(sample_dataframe):
"""Create a temporary CSV file for testing."""
with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as f:
sample_dataframe.to_csv(f.name, index=False)
yield f.name
Path(f.name).unlink()
@pytest.fixture
def sample_json_file(sample_dataframe):
"""Create a temporary JSON file for testing."""
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
sample_dataframe.to_json(f.name, orient="records")
yield f.name
Path(f.name).unlink()
@pytest.fixture
def time_series_dataframe():
"""Create a sample time series DataFrame."""
dates = pd.date_range("2023-01-01", periods=365, freq="D")
np.random.seed(42)
# Create trend component
trend = np.linspace(100, 120, 365)
# Create seasonal component (yearly)
seasonal = 10 * np.sin(2 * np.pi * np.arange(365) / 365)
# Create noise
noise = np.random.normal(0, 5, 365)
# Combine components
values = trend + seasonal + noise
return pd.DataFrame(
{
"date": dates,
"value": values,
"category": np.random.choice(["A", "B", "C"], 365),
}
)
@pytest.fixture
def stock_dataframe():
"""Create a sample stock price DataFrame."""
dates = pd.date_range("2023-01-01", periods=252, freq="B") # Business days
np.random.seed(42)
symbols = ["AAPL", "GOOGL", "MSFT", "TSLA"]
data = []
for symbol in symbols:
# Generate random walk for stock prices
returns = np.random.normal(0.001, 0.02, len(dates))
prices = 100 * np.cumprod(1 + returns)
for i, (date, price) in enumerate(zip(dates, prices, strict=False)):
# Add some randomness to high/low
high = price * np.random.uniform(1.01, 1.05)
low = price * np.random.uniform(0.95, 0.99)
open_price = price * np.random.uniform(0.98, 1.02)
volume = np.random.randint(1000000, 10000000)
data.append(
{
"date": date,
"symbol": symbol,
"open": open_price,
"high": high,
"low": low,
"close": price,
"volume": volume,
}
)
return pd.DataFrame(data)
@pytest.fixture
def temp_directory():
"""Create a temporary directory for test files."""
with tempfile.TemporaryDirectory() as temp_dir:
yield Path(temp_dir)
@pytest.fixture
def config_dict():
"""Return a test configuration dictionary."""
return {
"max_datasets": 10,
"max_memory_mb": 100,
"max_download_size_mb": 50,
"timeout_seconds": 10,
"plot_output_dir": "./test_plots",
"plot_style": "default",
"max_query_results": 1000,
}
