My Advanced Code Generator Prompt

Sure, let's break down the steps to achieve this: ### 1. Data Loading and Preprocessing First, let's load the astronomical data into memory efficiently and preprocess it if necessary. We'll use NumPy arrays to represent the data, which are efficient in terms of memory usage and provide fast array operations. ```python import numpy as np def load_data(file_path): # Load data from file into NumPy array data = np.loadtxt(file_path) return data # Example usage: data = load_data("astronomical_data.txt") ``` ### 2. Data Analysis Now, let's perform some basic analysis on the data. For example, we might want to calculate the mean, median, and standard deviation of certain parameters. ```python def analyze_data(data): # Calculate mean, median, and standard deviation of each parameter means = np.mean(data, axis=0) medians = np.median(data, axis=0) std_devs = np.std(data, axis=0) return means, medians, std_devs # Example usage: means, medians, std_devs = analyze_data(data) ``` ### 3. Statistical Analysis For more in-depth statistical analysis, we can use functions from the scipy library. Let's say we want to perform linear regression on our data. ```python from scipy.stats import linregress def perform_linear_regression(x, y): # Perform linear regression slope, intercept, r_value, p_value, std_err = linregress(x, y) return slope, intercept, r_value, p_value, std_err # Example usage: x = data[:, 0] # Assuming first column is independent variable y = data[:, 1] # Assuming second column is dependent variable slope, intercept, r_value, p_value, std_err = perform_linear_regression(x, y) ``` ### 4. Error Handling To enhance robustness, we'll implement error handling mechanisms to handle potential exceptions gracefully. ```python def load_data(file_path): try: data = np.loadtxt(file_path) return data except FileNotFoundError: print("File not found.") return None except Exception as e: print("An error occurred:", e) return None ``` ### 5. Testing It's crucial to thoroughly test the code to ensure it works correctly and efficiently, even with large datasets and extreme scenarios. Unit tests can be written using frameworks like pytest. ```python import pytest @pytest.mark.parametrize("file_path, expected_shape", [("test_data.txt", (1000000, 3))]) def test_load_data(file_path, expected_shape): data = load_data(file_path) assert data.shape == expected_shape # Run tests pytest.main() ``` ### 6. Optimization To optimize memory usage, we can use techniques such as chunking for large datasets, where we process smaller portions of the data at a time to reduce memory overhead. ```python def process_large_data(file_path, chunk_size=10000): results = [] with open(file_path, 'r') as f: while True: chunk = np.loadtxt(f, max_rows=chunk_size) if not chunk.size: break # Process chunk results.append(process_chunk(chunk)) return results ``` ### 7. Compatibility and Readability Make sure the code is compatible across different environments and operating systems by avoiding platform-specific dependencies. Additionally, prioritize readability and maintainability by using descriptive variable names and following PEP 8 guidelines for Python code style. By following these steps, we can efficiently process and analyze astronomical data while ensuring robustness, reliability, and compatibility.