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Add example scripts for running traces and testing OpenTelemetry integration for all functions in the project
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‎examples/run_all_traces.py‎

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"""
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Run all instrumented functions and display their trace outputs.
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This script demonstrates all functions decorated with @trace_function
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and shows their OpenTelemetry trace spans in the console.
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"""
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import sys
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from pathlib import Path
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# Add project root to path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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import numpy as np
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import pandas as pd
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from src.telemetry import setup_telemetry
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from src.numerical.optimization import gradient_descent
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from src.algorithms.graph import graph_traversal, find_node_clusters, PathFinder, calculate_node_betweenness
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from src.algorithms.dynamic_programming import fibonacci, matrix_sum, matrix_chain_order, coin_change, knapsack
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from src.data_processing.dataframe import dataframe_filter, groupby_mean, dataframe_merge
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from src.statistics.descriptive import describe, correlation
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# Initialize OpenTelemetry with console exporter
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print("=" * 80)
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print("Initializing OpenTelemetry...")
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print("=" * 80)
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setup_telemetry(
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service_name="optimize-me",
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service_version="0.1.0",
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exporter_type="console",
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)
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print("\n" + "=" * 80)
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print("RUNNING ALL INSTRUMENTED FUNCTIONS")
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print("=" * 80)
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print("\nTraces will appear as JSON objects below each function call.\n")
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# ============================================================================
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# Numerical Optimization
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# ============================================================================
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print("\n--- Numerical Optimization ---")
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print("Running gradient_descent...")
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X = np.array([[1, 2], [3, 4], [5, 6]])
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y = np.array([1, 2, 3])
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weights = gradient_descent(X, y, learning_rate=0.01, iterations=100)
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print(f"Result: {weights}\n")
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# ============================================================================
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# Graph Algorithms
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# ============================================================================
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print("\n--- Graph Algorithms ---")
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print("Running graph_traversal...")
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graph = {1: {2, 3}, 2: {4}, 3: {4}, 4: {}}
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visited = graph_traversal(graph, 1)
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print(f"Result: {visited}\n")
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print("Running find_node_clusters...")
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nodes = [{"id": 1}, {"id": 2}, {"id": 3}, {"id": 4}]
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edges = [{"source": 1, "target": 2}, {"source": 3, "target": 4}]
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clusters = find_node_clusters(nodes, edges)
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print(f"Result: {clusters}\n")
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print("Running PathFinder.find_shortest_path...")
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path_finder = PathFinder({"A": ["B", "C"], "B": ["D"], "C": ["D"], "D": []})
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path = path_finder.find_shortest_path("A", "D")
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print(f"Result: {path}\n")
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print("Running calculate_node_betweenness...")
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nodes_list = ["A", "B", "C", "D"]
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edges_list = [{"source": "A", "target": "B"}, {"source": "B", "target": "C"}, {"source": "C", "target": "D"}]
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betweenness = calculate_node_betweenness(nodes_list, edges_list)
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print(f"Result: {betweenness}\n")
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# ============================================================================
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# Dynamic Programming
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# ============================================================================
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print("\n--- Dynamic Programming ---")
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print("Running fibonacci...")
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fib_result = fibonacci(10)
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print(f"Result: {fib_result}\n")
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print("Running matrix_sum...")
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matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
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matrix_result = matrix_sum(matrix)
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print(f"Result: {matrix_result}\n")
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print("Running matrix_chain_order...")
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matrices = [(10, 20), (20, 30), (30, 40)]
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chain_result = matrix_chain_order(matrices)
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print(f"Result: {chain_result}\n")
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print("Running coin_change...")
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coins = [1, 2, 5]
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amount = 5
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coin_result = coin_change(coins, amount, 0)
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print(f"Result: {coin_result}\n")
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print("Running knapsack...")
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weights = [10, 20, 30]
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values = [60, 100, 120]
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capacity = 50
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knapsack_result = knapsack(weights, values, capacity, len(weights))
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print(f"Result: {knapsack_result}\n")
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# ============================================================================
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# Data Processing
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# ============================================================================
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print("\n--- Data Processing ---")
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print("Running dataframe_filter...")
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df = pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": [10, 20, 30, 40, 50]})
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filtered = dataframe_filter(df, "A", 3)
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print(f"Result:\n{filtered}\n")
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print("Running groupby_mean...")
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df_group = pd.DataFrame({
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"group": ["A", "A", "B", "B", "C"],
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"value": [10, 20, 30, 40, 50]
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})
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grouped = groupby_mean(df_group, "group", "value")
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print(f"Result: {grouped}\n")
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print("Running dataframe_merge...")
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df_left = pd.DataFrame({"id": [1, 2, 3], "name": ["Alice", "Bob", "Charlie"]})
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df_right = pd.DataFrame({"id": [2, 3, 4], "age": [25, 30, 35]})
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merged = dataframe_merge(df_left, df_right, "id", "id")
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print(f"Result:\n{merged}\n")
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# ============================================================================
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# Statistics
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# ============================================================================
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print("\n--- Statistics ---")
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print("Running describe...")
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series = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
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stats = describe(series)
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print(f"Result: {stats}\n")
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print("Running correlation...")
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df_corr = pd.DataFrame({
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"x": [1, 2, 3, 4, 5],
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"y": [2, 4, 6, 8, 10],
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"z": [1, 3, 5, 7, 9]
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})
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corr_result = correlation(df_corr)
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print(f"Result: {corr_result}\n")
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print("=" * 80)
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print("ALL FUNCTIONS EXECUTED - Check the JSON trace spans above!")
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print("=" * 80)
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print("\nEach function call above generated a trace span with:")
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print(" - Function name")
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print(" - Execution time (start_time, end_time)")
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print(" - Captured arguments (for gradient_descent: iterations, learning_rate)")
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print(" - Status (OK or ERROR)")
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print(" - Service information (service.name, service.version)")
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print("\n")
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‎examples/run_custom_traces.py‎

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"""
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Custom script to run specific functions and see their trace outputs.
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Modify this script to call any functions you want to trace.
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"""
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import sys
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from pathlib import Path
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# Add project root to path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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import numpy as np
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import pandas as pd
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from src.telemetry import setup_telemetry
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# Import the functions you want to test
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from src.numerical.optimization import gradient_descent
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from src.algorithms.graph import graph_traversal
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from src.algorithms.dynamic_programming import fibonacci
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# Add more imports as needed...
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# ============================================================================
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# Initialize OpenTelemetry
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# ============================================================================
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setup_telemetry(
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service_name="optimize-me",
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service_version="0.1.0",
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exporter_type="console", # Change to "otlp" for production
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)
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# ============================================================================
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# Call your functions here - each will generate a trace span
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# ============================================================================
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print("Running gradient_descent...")
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X = np.array([[1, 2], [3, 4]])
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y = np.array([1, 2])
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result = gradient_descent(X, y, learning_rate=0.01, iterations=50)
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print(f"Result: {result}\n")
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print("Running fibonacci...")
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fib_result = fibonacci(8)
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print(f"Result: {fib_result}\n")
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print("Running graph_traversal...")
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graph = {1: {2, 3}, 2: {4}, 3: {4}, 4: {}}
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visited = graph_traversal(graph, 1)
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print(f"Result: {visited}\n")
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# Add more function calls here to see their traces...
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print("\nDone! Check the JSON trace spans above each function result.")
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