{{ title }}

{{ description }}

{% if is_best_fit %}

This is the best fit of the minimizers used.

{% endif %}
{% if list_params %} {% set initial_list = initial_guess.split(", ") %} {% set fitted_list = min_params.split(", ") %} {% for init in initial_list %} {% set fit = fitted_list[loop.index0] %} {% set init_key, init_value = init.split("=") %} {% set fit_key, fit_value = fit.split("=") %} {% endfor %} {% else %} {% endif %}
Problem Outline
Number of parameters {{ n_params }}
Number of data points {{ n_data_points }}
Minimizer {{ minimizer }}
Fitting Metrics
Accuracy {{ accuracy }}
Runtime (mean) {{ mean_runtime }} seconds
Runtime (minimum) {{ minimum_runtime }} seconds
Runtime (maximum) {{ maximum_runtime }} seconds
Runtime (first) {{ first_runtime }} seconds
Runtime (median) {{ median_runtime }} seconds
Runtime (harmonic) {{ harmonic_runtime }} seconds
Runtime (trim) {{ trim_runtime }} seconds
Energy Usage {{ energy }}
Number of iterations {{ iteration_count }}
Number of function evaluations {{ func_evals }}
Function
Form {{ equation }}
Parameters
Name Initial value Fitted value Change (Δ)
{{ init_key.strip() }} {{ init_value.strip() }} {{ fit_value.strip() }} {{ fit_value.strip()|float - init_value.strip()|float }}
Too many parameters to display
{% if fitted_plot_available %} {% else %}

{{ fitted_plot }}

{% endif %} {% if pdf_plot_available %}

Estimated posterior pdf of each parameter

The vertical red line on each pdf shows the Scipy curve fit parameter estimate, with the dashed vertical red lines indicating the 2 sigma error of the fit. The area between the dashed lines is used to calculate the confidence in the MCMC fit.

{% else %}

{{ pdf_plot }}

{% endif %}