This tutorial covers different methods of analysing data *without* running GST. So far, there's only one, which checks for consistency between two (or more) datasets, called "Data Set Comparison".

This method declares that two or more `DataSet`

s are "consistent" if the observed counts for the same gate strings across the data sets are all consistent with being generated by the same underlying gateset. This protocol can be used to test for, among other things, drift and crosstalk. It can also be used
to compare an experimental dataset to an "ideal" dataset.

In [1]:

```
from __future__ import division, print_function
import pygsti
import numpy as np
import scipy
from scipy import stats
from pygsti.construction import std1Q_XYI
```

Let's first compare two `Dataset`

objects where the underlying gate sets are the same. The data sets we'll use will be GST datasets (which allows us to do some nice visualization), but arbitrary datasets will work in general, provided that the gate sequences across the datasets are the same.

In [2]:

```
#Let's make our underlying gate set have a little bit of random unitary noise.
gs_exp_0 = std1Q_XYI.gs_target.copy()
gs_exp_0 = gs_exp_0.randomize_with_unitary(.01,seed=0)
```

In [3]:

```
germs = std1Q_XYI.germs
fiducials = std1Q_XYI.fiducials
max_lengths = [1,2,4,8,16,32,64,128,256]
gate_sequences = pygsti.construction.make_lsgst_experiment_list(std1Q_XYI.gates,fiducials,fiducials,germs,max_lengths)
```

In [4]:

```
#Generate the data for the two datasets, using the same gate set, with 100 repetitions of each sequence.
N=100
DS_0 = pygsti.construction.generate_fake_data(gs_exp_0,gate_sequences,N,'binomial',seed=10)
DS_1 = pygsti.construction.generate_fake_data(gs_exp_0,gate_sequences,N,'binomial',seed=20)
```

In [5]:

```
#Let's compare the two datasets.
comparator_0_1 = pygsti.objects.DataComparator([DS_0,DS_1])
```

In [6]:

```
#Let's get the report from the comparator.
comparator_0_1.report(confidence_level=0.95)
```

In [7]:

```
#Create a workspace to show plots
w = pygsti.report.Workspace()
w.init_notebook_mode(connected=False, autodisplay=True)
```