README.md

pyplotfit

dependencies / used modules

• matplotlib.pyplot
• matplotlib.patches
• numpy
• math
• scipy
• inspect

HOW-TO

read datas

First of all reading your Dataset. According to our experiment, we designed a method for reading the output file of an Gamma-Ray-Detektor. Feel free to add or change other reading functions in datasetfunctions.py.

dataset = datasetfunctions.readfile("EU152.Spe")

The output of readfile(filename) is an dictionary.

dataset = {  
	  'name': filename.split('.')[0],  
	  'time': int(measuretime),  
	  'channel': channel,  
	  'counts': counts  
}

realize plot

In the next step you are able to create an Plot()-, Scatter()- or Errorbar()-Object. The only required parameters are the datas for x and y. For example:

plot = plot.Errorbar(X, Y)

All other parameters are not required or there are default settings. In depends on the chosen plot style you are able to set ... (with default values)

Plot(
	x: list (required)
	y: list (required)
	dataLabel=None: str 
	axisLabel=None: list
	xAxis=None: list
	lineType="--": str
	lineColor="blue": str 
	grid=True: bool
	extraLegendComponent=None: str
)

Scatter(
	x: list (required)
	y: list (required)
	dataLabel=None: str 
	axisLabel=None: list
	xAxis=None: list
	lineType="--": str
	lineColor="blue": str 
	grid=True: bool
	extraLegendComponent=None: str
	marker=None: str
	edgecolors=None: str
	linewidths=None: str
)

Errorbar(
	x: list (required)
	y: list (required)
	dataLabel=None: str 
	axisLabel=None: list
	xAxis=None: list
	lineType="--": str
	lineColor="blue": str 
	grid=True: bool
	extraLegendComponent=None: str
	yerr=None: list (same dimension like y required)
	xerr=None: list (same dimension like x required) 
	elinewidth=1.5: float
	markersize=5: float  
	ecolor="lightgray": str 
	fmt="x": str
)

add fits

If you want, you can add fits to an specific area. Therefore you create an fit() object.

GaussFit = fit.Fit(plot, X, Y, 3, upperLimit=670, lowerLimit=650, initialGuesses=[1000, 660, 1, 1, 1])

Required Arguments are the plot object, the x- and y-datas and the fit index. All avaible fits will be printed in the console with the current index.

List of all key numbers of available fits:
1: doublegaussianwithlinearunderground
2: exp
3: gaussianwithlinearunderground
4: linear

Not required arguments are upper- and lower limits for the fit area and the initial guesses. But maybe the fit wont show if you dont set them.

Example

import src.datasetfunctions as dsf
import src.fit as fit
import src.plot as plot

if __name__ == '__main__':

    # read your file and set x and y
    dataset = dsf.readSpeFile("Sichel-Tanne.Spe")
    X, Y = dataset["channel"], dataset["counts"]

    # change scale and calculate statistical error
    X = dsf.calibrate_dataSets(X, [0.39, 18.62])
    yerr = dsf.calculatestatisticalerrors(Y)

    # create plot object
    plot = plot.Errorbar(X, Y, xAxis=[625, 700], axisLabel=["Ereignisse N", "Energie E [keV]"], ecolor="grey")
    plot.setDataLabel("Energiekalibrierung")

    # create fit object
    GaussFit = fit.Fit(plot, X, Y, 3, upperLimit=670, lowerLimit=650, initialGuesses=[1000, 660, 1, 1, 1])

    # show everything you done
    plot.showPlot()

Console output:

Calculated fit-params:
        c1  =  1012.3287915580813 with standard deviation +/- 8.20387656143891
        mu1  =  661.0887096784303 with standard deviation +/- 0.011160440691047291
        sigma1  =  1.2134294338415335 with standard deviation +/- 0.011929200090804748
        a  =  -0.2220767579949974 with standard deviation +/- 0.3694342003581278
        b  =  155.6003162319009 with standard deviation +/- 243.63893229660619