Merge pull request #100 from cyrraz/documentation-small-fix

Small documentation update

docs/advanced/other_advanced.rst

@@ -20,40 +20,38 @@ In this example, we will compare two tuples of histograms and use pull and ratio
 
 First, we make the histograms and scale them. Then, we plot the histograms and the comparison plots on different axes:
 
-.. literalinclude:: ../examples/advanced/1d_comparison_advanced.py
-   :language: python
-   :start-after: ###
-
 .. image:: ../img/1d_comparison_advanced.svg
    :alt: Advanced hist comparison
    :width: 500
 
+.. literalinclude:: ../examples/advanced/1d_comparison_advanced.py
+   :language: python
+   :start-after: ###
+
 
 .. _advanced-asymmetry-label:
 Advanced example using asymmetry comparison
 ===========================================
 
 This example shows how to plot an asymmetry plot between two histograms and two functions. Information on how to convert a function from an external fitting package to an object that can be used by ``plothist`` can be found in :ref:`plot-fit-result-label`.
 
-.. literalinclude:: ../examples/advanced/asymmetry_comparison_advanced.py
-   :language: python
-   :start-after: ###
-
 .. image:: ../img/asymmetry_comparison_advanced.svg
    :alt: Advanced asymmetry comparison
    :width: 500
 
+.. literalinclude:: ../examples/advanced/asymmetry_comparison_advanced.py
+   :language: python
+   :start-after: ###
 
 Flatten 2D variable
 ===================
 
 Compare data and stacked histogram for a flatten 2D variable:
 
-.. literalinclude:: ../examples/advanced/model_examples_flatten2D.py
-    :language: python
-    :start-after: ###
-
 .. image:: ../img/model_examples_flatten2D.svg
    :alt: Data/model comparison, flatten variable
    :width: 500
 
+.. literalinclude:: ../examples/advanced/model_examples_flatten2D.py
+    :language: python
+    :start-after: ###

docs/basics/variable_registry.rst

@@ -210,7 +210,7 @@ To plot multiple variables using the ``variable_manager``, you can use the follo
     update_variable_registry_ranges(df, variable_keys)
 
     # Add custom info
-    update_variable_registry({"text": "simulation"}, variable_keys)
+    update_variable_registry({"text": "my analysis"}, variable_keys)
 
     for variable_key in variable_keys:
         variable = get_variable_from_registry(variable_key)
@@ -224,7 +224,7 @@ To plot multiple variables using the ``variable_manager``, you can use the follo
         ax.set_xlim(variable["range"])
         ax.set_ylabel("Entries")
 
-        add_text(variable["text"], ax=ax)
+        add_text(variable["text"], ax=ax, x="right")
 
         fig.savefig(f"{variable_key}.png", bbox_inches="tight")
 

docs/examples/1d_hist/1d_comparison_asymmetry.py

@@ -29,8 +29,8 @@
     h3,
     xlabel=name,
     ylabel="Entries",
-    h1_label="$h_1$",
-    h2_label="$h_2$",
+    h1_label="$h_2$",
+    h2_label="$h_3$",
     comparison="asymmetry",  # <--
 )
 

docs/examples/1d_hist/1d_comparison_relative_difference.py

@@ -29,8 +29,8 @@
     h3,
     xlabel=name,
     ylabel="Entries",
-    h1_label="$\mathbf{H\,\,1}$",
-    h2_label="$\mathbf{H\,\,2}$",
+    h1_label="$\mathbf{H\,\,2}$",
+    h2_label="$\mathbf{H\,\,3}$",
     comparison="relative_difference",  # <--
 )
 

docs/examples/2d_hist/2d_hist_simple_discrete_colormap.py

@@ -20,10 +20,10 @@
 
 h = make_2d_hist([df[name_x][:nentries], df[name_y][:nentries]], bins=[50, 50])
 
+###
 from matplotlib.colors import ListedColormap
 from plothist import get_color_palette
 
-###
 # 0 entries will be white, the rest will have one color from the plasma colormap per entry value
 cmap = ListedColormap(
     ["white"] + list(get_color_palette("plasma", int(h.values().max()) * 2 - 1))