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: straxisLabel=None: listxAxis=None: listlineType="--": strlineColor="blue": strgrid=True: boolextraLegendComponent=None: str
)
Scatter(
x: list (required)
y: list (required)
dataLabel=None: straxisLabel=None: listxAxis=None: listlineType="--": strlineColor="blue": strgrid=True: boolextraLegendComponent=None: strmarker=None: stredgecolors=None: strlinewidths=None: str
)
Errorbar(
x: list (required)
y: list (required)
dataLabel=None: straxisLabel=None: listxAxis=None: listlineType="--": strlineColor="blue": strgrid=True: boolextraLegendComponent=None: stryerr=None: list (samedimensionlikeyrequired)
xerr=None: list (samedimensionlikexrequired) elinewidth=1.5: floatmarkersize=5: floatecolor="lightgray": strfmt="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.

Listofallkeynumbersofavailablefits:
1: doublegaussianwithlinearunderground2: exp3: gaussianwithlinearunderground4: 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

importsrc.datasetfunctionsasdsfimportsrc.fitasfitimportsrc.plotasplotif__name__=='__main__':
# read your file and set x and ydataset=dsf.readSpeFile("Sichel-Tanne.Spe")
X, Y=dataset["channel"], dataset["counts"]
# change scale and calculate statistical errorX=dsf.calibrate_dataSets(X, [0.39, 18.62])
yerr=dsf.calculatestatisticalerrors(Y)
# create plot objectplot=plot.Errorbar(X, Y, xAxis=[625, 700], axisLabel=["Ereignisse N", "Energie E [keV]"], ecolor="grey")
plot.setDataLabel("Energiekalibrierung")
# create fit objectGaussFit=fit.Fit(plot, X, Y, 3, upperLimit=670, lowerLimit=650, initialGuesses=[1000, 660, 1, 1, 1])
# show everything you doneplot.showPlot()

Console output:

Calculatedfit-params:
c1=1012.3287915580813withstandarddeviation+/-8.20387656143891mu1=661.0887096784303withstandarddeviation+/-0.011160440691047291sigma1=1.2134294338415335withstandarddeviation+/-0.011929200090804748a=-0.2220767579949974withstandarddeviation+/-0.3694342003581278b=155.6003162319009withstandarddeviation+/-243.63893229660619