Create data_pipeline.py

Author: KOSASIH

src/neuromorphic_analytics/data_pipeline.py

@@ -0,0 +1,76 @@
+# src/neuromorphic_analytics/data_pipeline.py
+
+import numpy as np
+import logging
+
+# Set up logging for the Data Pipeline
+logger = logging.getLogger(__name__)
+
+class DataPipeline:
+    def __init__(self, data_sources):
+        """
+        Initialize the Data Pipeline.
+
+        Parameters:
+        - data_sources (list): List of data sources to be processed (e.g., Market Analysis, IoT).
+        """
+        self.data_sources = data_sources
+        logger.info("Data Pipeline initialized with sources: %s", self.data_sources)
+
+    def collect_data(self):
+        """
+        Collect data from the defined sources.
+
+        Returns:
+        - list: Collected raw data from all sources.
+        """
+        raw_data = []
+        for source in self.data_sources:
+            data = self._fetch_data_from_source(source)
+            raw_data.extend(data)
+            logger.info("Collected data from %s: %s", source, data)
+        return raw_data
+
+    def _fetch_data_from_source(self, source):
+        """
+        Simulate fetching data from a specific source.
+
+        Parameters:
+        - source (str): The data source to fetch data from.
+
+        Returns:
+        - list: Simulated data from the source.
+        """
+        # In a real implementation, this would involve API calls, database queries, etc.
+        # Here we simulate data fetching with random numbers for demonstration purposes.
+        simulated_data = np.random.rand(5).tolist()  # Simulate 5 data points
+        return simulated_data
+
+    def preprocess_data(self, raw_data):
+        """
+        Preprocess the collected raw data.
+
+        Parameters:
+        - raw_data (list): The raw data to preprocess.
+
+        Returns:
+        - list: Preprocessed data ready for analysis.
+        """
+        logger.info("Starting data preprocessing...")
+        preprocessed_data = [self._normalize(data) for data in raw_data]
+        logger.info("Data preprocessing completed.")
+        return preprocessed_data
+
+    def _normalize(self, data):
+        """
+        Normalize a single data point.
+
+        Parameters:
+        - data (float): The data point to normalize.
+
+        Returns:
+        - float: Normalized data point.
+        """
+        normalized_data = (data - np.min(data)) / (np.max(data) - np.min(data)) if np.max(data) != np.min(data) else 0
+        logger.debug("Normalized data point: %s to %s", data, normalized_data)
+        return normalized_data