{"library":"lmfit","title":"LMFit","description":"LMFit is a Python library providing a high-level interface for non-linear least-squares minimization and curve fitting. It extends and builds upon `scipy.optimize` by introducing `Parameter` objects, which simplify the process of defining, constraining, and managing fitting variables. Currently at version 1.3.4, `lmfit` maintains an active development cycle with regular releases addressing bug fixes, new features, and dependency updates.","language":"python","status":"active","last_verified":"Fri May 15","install":{"commands":["pip install lmfit"],"cli":null},"imports":["from lmfit import Model","from lmfit import Parameters","from lmfit import minimize"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom lmfit import Model\n\n# 1. Generate some data\nx = np.linspace(0, 10, 100)\ny_true = 3.0 * np.exp(-0.5 * x) + 2.0\nnp.random.seed(0)\ny_data = y_true + np.random.normal(0, 0.2, x.shape)\n\n# 2. Define your model function\ndef exponential_decay(x, amplitude, decay, offset):\n    return amplitude * np.exp(-decay * x) + offset\n\n# 3. Create a Model instance from your function\nexp_model = Model(exponential_decay)\n\n# 4. Create initial parameters with guess() or manually\n# guess() method often requires x and y data for good initial estimates\nparams = exp_model.make_params(amplitude=5., decay=0.1, offset=1.)\n\n# Or, for more refined control:\n# params = exp_model.guess(y_data, x=x)\n\n# Optionally set bounds or fix parameters\nparams['amplitude'].set(min=0.0)\nparams['decay'].set(min=0.0)\n\n# 5. Fit the model to the data\nresult = exp_model.fit(y_data, params, x=x)\n\n# 6. Print the fitting report\nprint(result.fit_report())\n\n# You can also access best-fit parameters, statistics, etc.\n# print(f\"Best-fit amplitude: {result.params['amplitude'].value:.3f}\")\n# print(f\"Reduced Chi-square: {result.redchi:.3f}\")","lang":"python","description":"This quickstart demonstrates fitting an exponential decay model to noisy data using `lmfit.Model`. It covers defining the model function, creating `Model` and `Parameters` objects, setting initial guesses and constraints, performing the fit, and reporting the results.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-15","installed_version":"1.3.4","pypi_latest":"1.3.4","is_stale":false,"summary":{"python_range":"3.10–3.9","success_rate":100,"avg_install_s":7.5,"avg_import_s":2.53,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":2.02,"mem_mb":42.8,"disk_size":"232.8M"},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":7.2,"import_time_s":1.47,"mem_mb":42.8,"disk_size":"224M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":3.55,"mem_mb":52.1,"disk_size":"248.4M"},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":7.2,"import_time_s":3.14,"mem_mb":52.1,"disk_size":"237M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":3.14,"mem_mb":51.3,"disk_size":"234.3M"},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":7.4,"import_time_s":3.18,"mem_mb":51.3,"disk_size":"223M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":2.71,"mem_mb":51.5,"disk_size":"233.1M"},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":7.6,"import_time_s":2.81,"mem_mb":51.5,"disk_size":"222M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":1.66,"mem_mb":38.9,"disk_size":"235.0M"},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"lmfit","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":8.1,"import_time_s":1.63,"mem_mb":39,"disk_size":"231M"}]}}