Minor cleanup event sampling tutorial
@@ -7,7 +7,7 @@ Learn to sampling events from a given sky model and IRFs.
Prerequisites
-------------
-To understand how to generate a model and a `MapDataset` and how to fit
+To understand how to generate a model and a `~gammapy.datasets.MapDataset` and how to fit
the data, please refer to the `~gammapy.modeling.models.SkyModel` and
:doc:`/tutorials/analysis-3d/simulate_3d` tutorial.
@@ -22,7 +22,7 @@ event-sampler and how to obtain an output photon event list.
The core of the event sampling lies into the Gammapy
`~gammapy.datasets.MapDatasetEventSampler` class, which is based on
the inverse cumulative distribution function `(Inverse
-CDF) <https://en.wikipedia.org/wiki/Cumulative_distribution_function#Inverse_distribution_function_(quantile_function)>`__. # noqa: E501
+CDF) <https://en.wikipedia.org/wiki/Cumulative_distribution_function#Inverse_distribution_function_(quantile_function)>`__.
The `~gammapy.datasets.MapDatasetEventSampler` takes in input a
`~gammapy.datasets.Dataset` object containing the spectral, spatial
@@ -46,13 +46,13 @@ Objective
---------
Describe the process of sampling events from a given Sky model and
-obtaining an output event-list.
+obtain an output event-list.
Proposed approach
-----------------
-In this section, we will show how to define an observation and to create
+In this section, we will show how to define an observation and create
a Dataset object. These are both necessary for the event sampling. Then,
we will define the Sky model from which we sample events.
@@ -63,7 +63,7 @@ In this tutorial, we propose examples for sampling events of:
source <#time-variable-source-using-a-lightcurve>`__
- `an extended source using a template
map <#extended-source-using-a-template>`__
-- `a set of observations <#simulate-mutiple-event-lists>`__
+- `a set of observations <#simulate-multiple-event-lists>`__
We will work with the following functions and classes:
@@ -262,7 +262,7 @@ print(dataset.models)
# The next step shows how to sample the events with the
# `~gammapy.datasets.MapDatasetEventSampler` class. The class requests a
# random number seed generator (that we set with `random_state=0`), the
-# `~gammapy.datasets.Dataset` and the `gammapy.data.Observations`
+# `~gammapy.datasets.Dataset` and the `~gammapy.data.Observations`
# object. From the latter, the
# `~gammapy.datasets.MapDatasetEventSampler` class takes all the meta
# data information.
