Getting Started

What is Graphical Curve Fit for Python ?

Graphical Curve Fit for Python is a graphical tool designed for scientists, engineers, and students to explore curve fitting strategies interactively.

Unlike libraries such as lmfit, emcee, or , scipy.odr this application is not a backend library. Instead, it offers a full graphical interface for visualising, adjusting, and comparing fits, especially when testing hypotheses, discovering models.

Behind the scenes, the fitting itself is handled by scientific libraries:

  • lmfit for least-squares optimization and model abstraction,
  • scipy.odr for orthogonal distance regression,
  • emcee for Bayesian sampling using MCMC.

Graphical Curve Fit for Python acts as a frontend to these tools, with a focus on interactivity, model clarity, and visual output.

Launching the Application

Once installed (see Installation), launch the application from your terminal:

gcfpy

You should see the main interface open within a few seconds.

Default View on Launch

Main Interface

Upon startup, the application opens with:

  • A menu that allows you to load data, add tabs, and manage dock visibility,
  • A toolbar toggle plots and views of the fits, apply x selection, activate compare dock.
  • A Fit tab (you can open multiple), each representing an independent session,

  • Two dock widgets:

  • Data Plot: central plot of data and fitted models,

  • Fit Formula: input area for model equations,

Basic Workflow

1. Load a Dataset

From the top menu, choose:

File > Load Data

Selecting a .csv file with at least X and Y columns is required. Optional columns such as Z, X_err, and Y_err are also supported. Some data are available in examples/data.

Once loaded:

  • The X/Y Data dock is populated.
  • The data appears in the plot area.
  • Fit actions are enabled.

data loaded

When data is loaded, two additional docks become visible:

  • The Fit Control dock, which allows running the fit defined in the Fit Formula dock
  • The X/Y Data dock, which displays the loaded dataset in table format

In the toolbar, several options become available:

  • Xmin/Xmax selection to restrict the fit to a specific x-range
  • Smoothing tools to process noisy data
  • Error handling that automatically enables weighting if X_err or Y_err columns are present in the data

2. Enter a Formula

In the Fit Formula dock, enter your model using a Python-like syntax. For example:

y = a * x + b

The application automatically extracts the parameters (a, b) and updates the fitting configuration. You can use mathematical functions like sin, exp, log, abs, and more.

Physical constants can also be added to the formula; see the list and syntax in Fit Formula

Advanced options like importing/exporting formulas or editing them via GUI are also available.

3. Run the Fit

To perform a fit:

  • Click the Fit button on the toolbar,

  • Or open the Fit Control dock to select:

  • The fitting method (Least Squares, ODR, MCMC),

  • Parameter settings and bounds,
  • Weighting strategies.

data loaded

Once the fit is complete:

  • The result appears in the Fit Results dock,
  • The fitted curve

In the toolbar, several options for different plots become available when the fit is made.