{"library":"pywavelets","code":"import pywt\nimport numpy as np\n\ndata = np.array([1, 2, 3, 4, 5, 6])\nwavelet = 'db1'\n\n# Perform a single-level Discrete Wavelet Transform (DWT)\ncA, cD = pywt.dwt(data, wavelet)\n\nprint(f\"Original data: {data}\")\nprint(f\"Approximation coefficients (cA): {cA}\")\nprint(f\"Detail coefficients (cD): {cD}\")\n\n# Perform inverse DWT to reconstruct the signal\nreconstructed_data = pywt.idwt(cA, cD, wavelet)\nprint(f\"Reconstructed data: {reconstructed_data}\")","lang":"python","description":"This quickstart demonstrates a basic 1D Discrete Wavelet Transform (DWT) and its inverse using the 'db1' (Daubechies 1) wavelet. It shows how to decompose a signal into approximation (cA) and detail (cD) coefficients, and then reconstruct the original signal.","tag":null,"tag_description":null,"last_tested":"2026-04-24","results":[{"runtime":"python:3.10-alpine","exit_code":0},{"runtime":"python:3.10-slim","exit_code":0},{"runtime":"python:3.11-alpine","exit_code":0},{"runtime":"python:3.11-slim","exit_code":0},{"runtime":"python:3.12-alpine","exit_code":0},{"runtime":"python:3.12-slim","exit_code":0},{"runtime":"python:3.13-alpine","exit_code":0},{"runtime":"python:3.13-slim","exit_code":0},{"runtime":"python:3.9-alpine","exit_code":0},{"runtime":"python:3.9-slim","exit_code":0}]}