complex-curve-fit-gui 1.1.5

GUI for lmfit using matplotlib

Installation

You can install the complex-curve-fit-gui from PyPi:

pip install complex-curve-fit-gui

The GUI is supported on Python 3.7 and above.
Note: only complex-curve-fit-gui is installed without any required dependencies. Depending on if you are using pip or conda to manage your environment you should manually install the following additional packages:

• Using pip:

  pip install numpy scipy matplotlib PyQt5
  

• Using conda (required for M1 Mac):

  conda install numpy scipy matplotlib qtpy pyqt
  

Getting Started

1. If on MacOS M1 a conda environment will be needed
2. import the gui: from curvefitgui import curve_fit_gui
3. define x and y data as 1 dimensional numpy arrays of equal length xdata = np.array([1, 2, 3, 4, 5]) ydata = np.array([-3.5, -2.4, -1, 0.5, 1.8])
4. Call curve_fit_gui:

   • Set first parameter to None to be able to select predefined functions

   • For example:

     curve_fit_gui(None,xdata,ydata)

5. Optionally disable gui and immediately get fit results as tuple (popt, pcov, result) where popt is an array of the best-fit parameters, pcov is an array of the confidence intervals, and result is an lmfit ModelResult object. Model Result curve_fit_gui(function,xdata,ydata, showgui=False)

Open Multiple Windows

• Appending & to the end of the command used to run program will give access to current terminal, allowing for another window to be openned. Ex: python3 curve.py&

Plot multiple dataset on single window

• Use the keyword argument add and format argument as an array of tuples i.e [(np.array, np.array, string)] where the first index is xdata, the next in y-data, and the last is a label for the data

curve_fit_gui function:

This starts up the GUI. The call signature is curve_fit_gui(f,xdata,ydata,xerr=None -> [optional],yerr=None -> [optional],p0=None -> [optional],xlabel="x-axis" -> [optional],ylabel="y-axis" -> [optional],absolute_sigma=False -> [optional],jac=None -> [optional],showgui=True -> [optional],**kwargs, -> [optional])

• f : callable the fit function which if set to None, gives option of selection predefined fit functions
• xdata : 1-D numpy array x-coordinates of the data
• ydata : 1-D numpy array y-coordinates of the data
• yerr : 1-D numpy array, optional (default:None) error/uncertainty in y-values used for weighted fit with a relative weight defined as 1/yerr**2 (for compatibility also the use of the keyword sigma can be used for the same)
• xerr : 1-D numpy array, optional (default:None) error in x-values. For plotting errorbars only and ignored during fitting
• xlabel : string, optional (default:'x-values') x-axis title in the plot
• ylabel : string, optional (default:'y-values') y-axis title in the plot
• title : string, optional data plot title
• p0 : array-like, optional initial values for fit parameters, if not specified 1 is used for each parameter
• showgui : boolean, optional (default=True) if True, the gui is shown, otherwise not
• absolute_sigma : boolean, optional see doc-string scipy.optimize.curve_fit()
• kwargs keyword arguments for compatibility (e.g. you can use sigma to specify the error in y)

Return Data

• The curve fit function returns a tuple with three elements. 1. Array containing the fit parameters 2. Array containing the standard error (+/-) 3. lmfit Model_Result object pertaining to the best fit, reference here: https://lmfit.github.io/lmfit-py/model.html#the-modelresult-class

User defined functions

• To make a user defined function create a function and pass to curve_fit_gui as f. For example:

      def linear(x, a, b, c): 
             return y = a * x + b

      curve_fit_gui(linear, xdata, ydata)

User defined complex functions

• When defining a complex function add the complex=True keyword argument and use 1j in the equation. For example for Euler's equation e^(ix)cs: return a * (np.exp(1j * (x)))

Customizing Data Plot

The plots are customizable from inside the gui and also from the curve_fit_gui function call by using keyword arguments or kwargs. The options available from a matplotlib plot are also available for the main data plot For example: curve_fit_gui(None, xdata, ydata, markerfacecolor="None", linestyle="-" For a list of optional keyword arguments visit the matplotlib documentation on this page under the list of available Line2D properties: matplotlib.pyplot.plot

• Note: Use full names i.e. linewidth not lw
• In addition, title can be set using title keyword argument i.e curve_fit_gui(...,...,...,title="More Data")

Customizing Fitline:

Options for customizing fitline inlclude color, linestyle, and thickness. The values are the same from matplotlib, however the argument is preceded with fitline_. For example curve_fit_gui(None, xdata, ydata, fitline_color="red", fitline_linestle="-"). The possible customizations for fitline are: - fitline_color - fitline_linestyle: supported values are '-', '--', '-.', ':', 'None', ' ', '', 'solid', 'dashed', 'dashdot', 'dotted' - fitline_label - fitline_linewidth

GUI controls

1. Data plot: A matplotlib plot that shows the data as solid dots and both y-error and x-error errorbars if provided. A fitted curve as a dashed line is shown if a fit is performed.
2. Residual plot A matplotlib plot that shows the residuals as the difference between the measured and fitted values:
3. Model settings: Here you can enter inital values for the fitparameters. By ticking the checkbox fix you can set a parameter to fixed:e.g. the parameter is not optimised during the fit.
4. Weight settings: If error data on the y-values are passed using the keyword argument yerr you can use the dropdownbox to set how the error data is treated:
   • None: the error data is ignored
   • Relative: Use the error data for a relative weight. Corresponds to setting scipy's curve_fit() function keyword absolute_sigma = False.
   • Standard deviation: Treat the error data as being standard deviations. Corresponds to setting scipy's curve_fit() function keyword absolute_sigma = True.
5. Evaluate: Use this button to compute the model function given the current values of the parameters (set in the model settings panel)
6. Fit: Performs the fit and updates the parameter values.
7. Report: When a fit is performed, the results are shown here. The information on the model is actually the provided docstring of the function f that is passed to the curvefitgui function.
8. Quit: Quits the gui and returns a tuple of best-fit parameters, confidence interval, and Model Result object from lmfit. Model Result
9. Toolbar: This is the standard matplotlib toolbar to adjust some plot properties and provides zoom/pan and save options.

• Customize Change visual features of the plot such as marker type or color and axis titles
• Pan and Zoom Shift and zoom on plot
• Left and Right Undo and redo actions for shift and zoom
• Home Return to original plot view

10. FitTextbox: This textbox is generated if a valid fit is performed. It can be moved by the mouse to any convenient positions in the plot.
11. Range Selector Activates/deactivates the range-selector. The range-selector allows to select a datarange used for fitting. Only datapoints that are within the two vertical dashed lines are considered during fitting. The lines can be moved using the mouse.