diff --git a/README.md b/README.md
index c0660dec6b17c021e06b463d9e10e24927f22584..5ea9bfc601f5f8abd87e7d98a71c793edddfe941 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,15 @@ years code examples for [ROOT](https://root.cern.ch/) have been provided.
Material on the usage of python was missing. The code examples shown in this
repository follow the examples shown in the ROOT introductions.
+# Table of Contents
+1. [Installation](#installation)
+2. [Prerequisites and Structure](#prerequisites-and-structure)
+3. ['Hello World' Example](#hello-world-example)
+4. [Numpy Arrays](#numpy-arrays)
+5. [Plotting Functions](#numpy-arrays)
+6. [Plotting Data Points](#plotting-data-points)
+7. [Reading, Plotting and Fitting Experimental Data](#reading-plotting-and-fitting-experimental-data)
+
# Installation
To get started with python for data analysis in the advanced laboratories you
need the python interpreter. In this document we will use `python3`. The
@@ -245,7 +254,7 @@ $ cp func_plot.py data_plot.py
-->
We generate the pseudo data points by adding random deviations to the expected
-y-values. Lets assume we measured data points for all half-integer x-values.
+y-values. Lets assume we measured data points for all quarter-integer x-values.
<!-- append data_plot.py -->
```python
x_data = np.array([-2.5, -2, -1.5, -1, -.5, 0, .5, 1, 1.5, 2, 2.5, 3])
@@ -299,3 +308,207 @@ measurement.png
After running `data_plot.py` you should have a plot similar to this.
+# Reading, Plotting and Fitting Experimental data
+We are given with experimental data from a radioactive decay in this example.
+The experimental setup consisted of a radioactive probe, a detector and a
+multi-channel-analyzer. The recorded data in `decay.txt` consist of two
+tab-separated columns. The first column is called channel. Each channel
+corresponds to a certain energy range. The multi-channel-analyzer maintains a
+counter for each channel. For each recorded decay the internal counter which
+corresponds to the measured energy is incremented. The seconds column stored
+these counts. Open the file with out favorite text editor and have a look at the
+data.
+
+As usual, create the file `decay.py` and the import statements, which we need
+for this example.
+
+<!-- Add additional files for non-X11 environment in CI -->
+<!-- write decay.py
+```python
+import matplotlib
+matplotlib.use('Agg')
+```
+-->
+<!-- write decay.py -->
+```python
+import numpy as np
+import scipy.optimize # provides fit routines
+import scipy.stats # provides statistical tests and distributions
+import matplotlib.pyplot as plt
+```
+
+To inspect the provided data, we can plot the raw data points first. Numpy
+provides the function `loadtxt`, which read a whitespace-separated file into a
+numpy array. The function returns a two dimensional array. The outer array has
+one entry for each line, and the inner two entries in our case, one for the
+channel and the other one for the event count. We can use `transpose()` to flip
+the matrix, such that the outer array has two entries, one with an array of
+channel values and the other one with event counts. Since our measured event
+counts stem from radioactive decay, we know that the event counts follow a
+Poisson distribution. Therefore, the uncertainty of the events counts is simply
+the square root of the number of events.
+
+<!-- append decay.py -->
+```python
+channel, count = np.loadtxt("decay.txt").transpose()
+s_count = np.sqrt(count)
+plt.errorbar(channel, count, s_count, fmt='.k', capsize=0, label="Data")
+plt.savefig("decay_raw.eps")
+```
+<!-- append decay.py
+```
+plt.savefig("decay_raw.png")
+```
+-->
+<!-- console
+```
+$ python3 decay.py
+```
+-->
+
+The plot of the raw data is shown below. The plot shows the channel on the
+x-axis and the number of events per bin on the y-axis. From the experimental setup we expect a
+linearly rising background plus a Gaussian peak.
+
+
+Judging from the plot, it looks like the assumed model describes the data. We
+would like this model to our data to determine the optimal value of the model
+parameter and their uncertainties (and the covariance matrix). Lets give a more
+formal version of the expected model
+```math
+ n(c) = A exp\left(-\frac{(c-m)^2}{2 s^2}\right) + y_0 + bc,
+```
+where $`n(c)`$ is the expected number of events in channel $`c`$; $`A, m`$
+and $`s`$ are the height, center and width of the Gaussian respectively. The
+parameters $`y_0`$ and $`b`$ are the usual parameter of a linear curve which is
+assumed to describe our background. The model can be implemented in python as a
+function. The first parameter should be the x value, all following arguments are
+free parameters of the model. The return value corresponds to the y value, in
+our case the expected number of events.
+
+<!-- append decay.py -->
+```
+def model(channel, m, s, A, y0, b):
+ return A * np.exp(-0.5 * (channel - m)**2 / s**2) + y0 + b * channel
+```
+
+Please note that we are going to make an approximation with this definiton.
+Strictly speaking, comparing thhe return values of our model to the measured
+count is not correct. The variable channel corresponds to the radiation energy
+measured with the setup. Lets assume channel $`c_i`$ corresponds to energy
+$`E_i`$. If we measure $`n_i`$ events in channel $`c_i`$, this means that we
+have measured $`n_i`$ in the energy interval $`[\frac{1}{2}(E_{i-1} + E_i),
+\frac{1}{2}(E_i} + E_{i+}]`$. The proper way is to integrate our continuous
+function $`n(c)`$ in each bin $`[c_{i} - \frac{1}{2}, c_{i} } \frac{1}{2}]`$. The
+prodcedure show here is a good approximation, if the function can be considered
+is linear within each bin. However, the parameter $`A`$ and $`b`$ are not normalized to
+the bin width in this case.
+
+
+To fit this model to our experimental data, we can use the function `curve_fit`
+provided by the scipy package. The function `curve_fit` performs a least square
+fit and returns the optimal parameters and the covariance matrix. The fit might
+not converge on its one. We can guide optimization procedure by providing
+suitable start values of the free parameters. From the plot I read off a height
+$`A=50`$, a center $`m=60`$ and a width $`s = 10`$ for the Gaussian part and
+$`y_0 = 20`$, $`b = 1`$ for the linear part. These values don't have to be
+accurate. They should be a rough estimation, this is usually enough to get a
+stable fit result.
+
+<!-- append decay.py -->
+```
+p0 = (60, 10, 50, 20, 1)
+popt, pcov = scipy.optimize.curve_fit(model, channel, count, p0, np.sqrt(count))
+```
+
+To visualize the fitted model, we need to evaluate our model with the optimized
+parameters `popt`. We are now ready to add the fitted curve to the plot and save
+it.
+
+<!-- append decay.py -->
+```
+fit_count = model(channel, *popt)
+plt.plot(channel, model(channel, *popt), label="Linear + Gauss")
+
+plt.xlabel("Channel")
+plt.ylabel("Counts")
+plt.ylim(0, 1.1 * max(count))
+plt.legend()
+plt.savefig("decay.eps")
+```
+<!-- append decay.py
+```
+plt.savefig("decay.png")
+```
+-->
+<!-- console
+```
+$ python3 decay.py
+```
+-->
+The result should look like this.
+
+
+Usually we want to measure some quantity with the experimental setup. For this
+we need the optimized parameters and the covariance matrix returned by the
+fit. Lets assume we are interested in the best fit value of the parameters and
+their uncertainties. We can add the following print outs, to display this kind
+of information.
+
+<!-- append decay.py -->
+```python
+print("Optimal parameters:")
+print(" m = %g +- %g" % (popt[0], np.sqrt(pcov[0][0])))
+print(" s = %g +- %g" % (popt[1], np.sqrt(pcov[1][1])))
+print(" A = %g +- %g" % (popt[2], np.sqrt(pcov[2][2])))
+print(" y0 = %g +- %g" % (popt[3], np.sqrt(pcov[3][3])))
+print(" b = %g +- %g" % (popt[4], np.sqrt(pcov[4][4])))
+print()
+```
+
+A $`\chi^2`$-test can also be performed, to assess the goodness of this fit. In
+a counting experiment like this one, we can rely on scipy's `chisquare`, which
+returns the $`\chi^2`$ and the $`p`$-value. The `chisquare` method assumes, that
+the uncertainties are the square root of the expectation. If this is not the
+case, we have to compute the $`\chi^2`$ manually. The following example shows
+both examples. The print statements produce the same output. Please note, that
+we have five degrees of freedom, since we have five free parameters in our
+model.
+
+<!-- append decay.py -->
+```python
+print("chi^2 from scipy:")
+chi2, p = scipy.stats.chisquare(count, fit_count, ddof=5)
+print(" chi2 / ndf = %g / %d" % (chi2, len(count) - 6))
+print(" p-value = %g" % p)
+
+print()
+
+print("Manual chi^2 test:")
+uncertainty = np.sqrt(fit_count)
+chi2 = ((count - fit_count)**2 / uncertainty**2).sum()
+p = scipy.stats.distributions.chi2.sf(chi2, len(count) - 6)
+print(" chi2 / ndf = %g / %d" % (chi2, len(count) - 6))
+print(" p-value = %g" % p)
+
+```
+
+If you run the `decay.py` you should see the following fit results.
+<!-- console_output -->
+```bash
+$ python3 decay.py
+Optimal parameters:
+ m = 61.756 +- 0.696195
+ s = 8.59757 +- 0.708479
+ A = 43.2461 +- 3.24047
+ y0 = 20.6589 +- 1.0885
+ b = 0.841156 +- 0.0193703
+
+chi^2 from scipy:
+ chi2 / ndf = 120.577 / 122
+ p-value = 0.519418
+
+Manual chi^2 test:
+ chi2 / ndf = 120.577 / 122
+ p-value = 0.519418
+```
diff --git a/decay.txt b/decay.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b8892c8429aeb8aca18051a479a118736211a696
--- /dev/null
+++ b/decay.txt
@@ -0,0 +1,128 @@
+0 21
+1 19
+2 18
+3 23
+4 37
+5 30
+6 23
+7 28
+8 32
+9 24
+10 32
+11 32
+12 39
+13 32
+14 31
+15 37
+16 32
+17 31
+18 36
+19 30
+20 35
+21 39
+22 39
+23 39
+24 30
+25 45
+26 50
+27 46
+28 49
+29 53
+30 41
+31 45
+32 57
+33 48
+34 39
+35 65
+36 61
+37 53
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+39 51
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+42 54
+43 71
+44 64
+45 69
+46 59
+47 81
+48 73
+49 93
+50 68
+51 80
+52 98
+53 88
+54 102
+55 99
+56 106
+57 113
+58 95
+59 107
+60 107
+61 117
+62 106
+63 107
+64 115
+65 125
+66 127
+67 136
+68 120
+69 110
+70 99
+71 106
+72 103
+73 96
+74 92
+75 107
+76 93
+77 86
+78 72
+79 108
+80 92
+81 94
+82 83
+83 101
+84 110
+85 105
+86 110
+87 94
+88 87
+89 106
+90 74
+91 92
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+93 98
+94 102
+95 94
+96 114
+97 91
+98 117
+99 107
+100 100
+101 97
+102 106
+103 91
+104 94
+105 96
+106 98
+107 128
+108 120
+109 105
+110 104
+111 129
+112 126
+113 120
+114 115
+115 122
+116 127
+117 131
+118 142
+119 128
+120 117
+121 139
+122 128
+123 122
+124 123
+125 129
+126 137
+127 110