Enhancing Signal Data: A Dask-Powered Approach with DSP for Feature Extraction and Parquet File Integration

# Add new feature column to parquet file

# !pip install -U dask partd pandas pyarrow numpy
import dask.dataframe as dd
import numpy as np
from scipy.signal import find_peaks

# Step 2: Load the Existing Parquet File
# Replace 'existing_file.parquet' with your actual file path
df = dd.read_parquet('existing_file.parquet')

# Step 3: Define Signal Processing Function
def process_signal(row):
    # Extract relevant columns for signal processing
    signal_data = row['your_signal_column']

    # Apply signal processing function
    peaks, _ = find_peaks(signal_data)  # Example signal processing function

    # You can have more features based on your requirements
    mean_value = np.mean(signal_data)
    std_dev = np.std(signal_data)

    return peaks, mean_value, std_dev

# Step 4: Apply Signal Processing Function
# The meta argument is essential for Dask to infer the output types of the apply function
df[['peaks', 'mean_value', 'std_dev']] = df.apply(
    process_signal, axis=1, meta=('x', 'object')
)

# Step 5: Write the Updated DataFrame to Parquet
# Replace 'new_file.parquet' with your desired output file path
df.to_parquet('new_file.parquet', engine='pyarrow')

In this example, the process_signal function returns a tuple (peaks, mean_value, std_dev). The apply function is used to apply this function to each row of the DataFrame, and the result is assigned to three new columns ('peaks', 'mean_value', and 'std_dev').

Make sure to adapt the signal processing function (process_signal) based on your actual requirements. Also, adjust column names and types accordingly. This is a simplified example, and you may need to modify it based on the characteristics of your data and the specific signal processing operations you want to perform.