# trspecfit examples **trspecfit** fits multi-component spectral models to 1D energy-resolved and 2D time- and energy-resolved spectroscopy data: modular lineshapes, time dynamics, convolution kernels, parameter profiles, and lmfit-based fitting with confidence intervals and MCMC. The pages here are the package's example notebooks, executed end to end, so you can read the plots, fit reports and tables without installing anything. They were run with trspecfit {{ trspecfit_version }} (git ref {{ trspecfit_ref }}) on {{ build_date }}. Every page has a download button for the executed notebook. To run them yourself: ```bash python -m pip install "trspecfit[lab]" ``` [Source on GitHub](https://github.com/InfinityMonkeyAtWork/time-resolved-spectroscopy-fit) · [Documentation](https://time-resolved-spectroscopy-fit.readthedocs.io/) · [PyPI](https://pypi.org/project/trspecfit/) Pick the notebook that matches what you are trying to do; each one is self-contained. The fitting workflows are numbered in blocks: 0x single-file fitting skills, 1x post-fit work, 2x multi-file workflows. % The example tables, the data generator list and the sidebar entries follow % here at build time, generated by scripts/build_site.py from the examples % tree (see its docstring for the layout it expects). ## Fitting workflows | Notebook | What you learn | |---|---| | [01 basic fitting](examples/fitting_workflows/01_basic_fitting/example.ipynb) | Load data, define a model, fit baseline + 2D, visualize results. | | [02 dependent parameters](examples/fitting_workflows/02_dependent_parameters/example.ipynb) | Link parameters with expressions and physical constraints. | | [03 multi cycle dynamics](examples/fitting_workflows/03_multi_cycle_dynamics/example.ipynb) | Multi-cycle dynamics with subcycles and frequency. | | [04 parameter profiles](examples/fitting_workflows/04_parameter_profiles/example.ipynb) | Depth-dependent parameters via profile functions (with optional time-dependence). | | [10 model comparison](examples/fitting_workflows/10_model_comparison/example.ipynb) | Compare two models on the same file (baseline / SbS / 2D). | | [11 save load export](examples/fitting_workflows/11_save_load_export/example.ipynb) | `FitResults` HDF5 round-trip, CSV/PNG export, "ship just the winners". | | [12 uncertainty mcmc](examples/fitting_workflows/12_uncertainty_mcmc/example.ipynb) | Declare the noise model, then three tiers of parameter uncertainty — `stderr`, profiled CIs, and MCMC — checked against truth and the Cramér-Rao bound. | | [13 missing noise model](examples/fitting_workflows/13_missing_noise_model/example.ipynb) | What the `unknown` default does to `stderr`, profiled CIs and MCMC on the same data as 12, and the Gaussian fallback. | | [20 multi file independent fit](examples/fitting_workflows/20_multi_file_independent_fit/example.ipynb) | Multi-file workspace, per-file independent fits (bridge to shared-parameter fitting). | | [21 multi file shared fit](examples/fitting_workflows/21_multi_file_shared_fit/example.ipynb) | Multi-file workspace, shared-parameter fits across files. | ## Synthetic data | Notebook | What you learn | |---|---| | [01 simulator](examples/synthetic_data/01_simulator/example.ipynb) | Generate synthetic noisy data from a known ground truth. | | [02 ml training data](examples/synthetic_data/02_ml_training_data/example.ipynb) | Sweep parameter space for ML training datasets. | ## Data generators Some examples ship synthetic data made by a notebook in their `data/` directory from truth model files, which those pages show. The build re-runs the generators and checks that the regenerated data matches what the examples were fitted to. - [01 basic fitting](examples/fitting_workflows/01_basic_fitting/data/generate_data.ipynb) - [03 multi cycle dynamics](examples/fitting_workflows/03_multi_cycle_dynamics/data/generate_data.ipynb) - [04 parameter profiles](examples/fitting_workflows/04_parameter_profiles/data/generate_data.ipynb) - [21 multi file shared fit](examples/fitting_workflows/21_multi_file_shared_fit/data/generate_data.ipynb) ```{toctree} :hidden: :caption: Fitting workflows 01 basic fitting 02 dependent parameters 03 multi cycle dynamics 04 parameter profiles 10 model comparison 11 save load export 12 uncertainty mcmc 13 missing noise model 20 multi file independent fit 21 multi file shared fit ``` ```{toctree} :hidden: :caption: Synthetic data 01 simulator 02 ml training data ``` ```{toctree} :hidden: :caption: Data generators 01 basic fitting 03 multi cycle dynamics 04 parameter profiles 21 multi file shared fit ```