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+"""
+.. _ref_plottype:
+
+Plot types
+==========
+
+This example shows how to start an Ansys Dynamic Reporting
+service via a Docker image and create different plot items.
+The example focuses on showing how to use the API to generate
+different plot types.
+
+.. note::
+   This example assumes that you do not have a local Ansys installation but
+   are starting an Ansys Dynamic Reporting Service via a Docker image on
+   a new database.
+
+"""
+
+###############################################################################
+# Start an Ansys Dynamic Reporting service
+# ----------------------------------------
+#
+# Start an Ansys Dynamic Reporting service via a Docker image on a new
+# database. The path for the database directory must be to an empty directory.
+
+import numpy as np
+
+import ansys.dynamicreporting.core as adr
+
+db_dir = r"C:\tmp\new_database"
+adr_service = adr.Service(ansys_installation="docker", db_directory=db_dir)
+session_guid = adr_service.start(create_db=True)
+
+###############################################################################
+# Create a simple table
+# ---------------------
+#
+# Let us start by creating a simple table and visualizing it. Create a table
+# with 5 columns and 3 rows.
+
+simple_table = adr_service.create_item(obj_name="Simple Table", source="Documentation")
+simple_table.item_table = np.array(
+    [[0, 1, 2, 3, 4], [0, 3, 6, 9, 12], [0, 1, 4, 9, 16]], dtype="|S20"
+)
+simple_table.labels_row = ["X", "line", "square"]
+
+###############################################################################
+# You can use the labels_row attribute to set the row labels. Use the visualize
+# method on the object to see its representation. By default, it will be displayed
+# as a table
+
+simple_table.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/simpletable.png
+#
+# Visualize as a line plot
+# ------------------------
+#
+# Let us know create a new item that is the same as the previous simple table,
+# but this time we will set the plot attribute to line to visualize the values
+# as two line plots, and we will use the xaxis attribute to set which row should
+# be used as the X axis. We can also control the formatting and the title of the
+# axis separately with the *axis_format and *title attributes, as done below.
+# The result can be seen in the following image.
+
+line_plot = adr_service.create_item(obj_name="Line Plot", source="Documentation")
+line_plot.item_table = np.array([[0, 1, 2, 3, 4], [0, 3, 6, 9, 12], [0, 1, 4, 9, 16]], dtype="|S20")
+line_plot.labels_row = ["X", "line", "square"]
+line_plot.plot = "line"
+line_plot.xaxis = "X"
+line_plot.yaxis_format = "floatdot0"
+line_plot.xaxis_format = "floatdot1"
+line_plot.xtitle = "x"
+line_plot.ytitle = "f(x)"
+line_plot.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/line_plot.png
+#
+# Visualize as a bar plot
+# -----------------------
+#
+# Next, we will see how to create a bar plot, and decorate it with the same
+# attributes used in the previous code snippet. See the following image for
+# the resulting visualization.
+
+bar_plot = adr_service.create_item(obj_name="Bar Plot", source="Documentation")
+bar_plot.item_table = np.array([[0, 1, 2, 3, 4], [0.3, 0.5, 0.7, 0.6, 0.3]], dtype="|S20")
+bar_plot.plot = "bar"
+bar_plot.labels_row = ["ics", "my variable"]
+bar_plot.xaxis_format = "floatdot0"
+bar_plot.yaxis_format = "floatdot2"
+bar_plot.xaxis = "ics"
+bar_plot.yaxis = "my variable"
+bar_plot.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/bar_plot.png
+#
+# Visualize a pie chart
+# ---------------------
+#
+# Next supported plot type is the pie chart. Please see the following code snippet
+# to generate the pie chart as in the following image.
+
+
+pie_plot = adr_service.create_item(obj_name="Pie Plot", source="Documentation")
+pie_plot.item_table = np.array([[10, 20, 50, 20]], dtype="|S20")
+pie_plot.plot = "pie"
+pie_plot.labels_column = ["Bar", "Triangle", "Quad", "Penta"]
+pie_plot.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/pie_plot.png
+#
+# Visualize a heatmap
+# -------------------
+#
+# Heatmaps are plots where at each (X,Y) position is associated the value of a
+# variable, colored according to a legend. Here the snippet on how to create
+# a heatmap representation - please note how nan values are also supported,
+# resulting in empty cells.
+
+heatmap = adr_service.create_item(obj_name="Heatmap", source="Documentation")
+heatmap.item_table = np.array(
+    [
+        [0.00291, 0.01306, 0.02153, 0.01306, 0.00291],
+        [0.01306, 0.05854, 0.09653, 0.05854, 0.01306],
+        [0.02153, 0.09653, np.nan, 0.09653, 0.02153],
+        [0.01306, 0.05854, 0.09653, 0.05854, 0.01306],
+        [0.00291, 0.01306, 0.02153, 0.01306, 0.00291],
+    ],
+    dtype="|S20",
+)
+heatmap.plot = "heatmap"
+heatmap.format = "floatdot0"
+heatmap.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/heatmap.png
+#
+# Visualize a parallel coordinate plot
+# ------------------------------------
+#
+# Parallel coordinate plots are especially useful when analyzing data coming
+# from multiple runs. Place in each raw the values of variables for a given
+# simulation. Each column is a different variable. The parallel coordinate
+# plot allows you to visualize all this data in a way that stresses
+# correlations between variables and runs.
+
+parallel = adr_service.create_item()
+parallel.item_table = np.array(
+    [
+        [54.2, 12.3, 1.45e5],
+        [72.3, 9.3, 4.34e5],
+        [45.4, 10.8, 8.45e4],
+        [67.4, 12.2, 2.56e5],
+        [44.8, 13.5, 9.87e4],
+    ],
+    dtype="|S20",
+)
+parallel.labels_column = ["Temperature", "Max. Pressure", "Max. Work"]
+parallel.plot = "parallel"
+parallel.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/parallel_coord.png
+#
+# Visualize a Sankey diagram
+# --------------------------
+#
+# A Sankey diagram allows you to visualize the relationship between
+# different elements. For this reprenstation, place the information
+# inside a squared table.
+
+sankey_plot = adr_service.create_item()
+sankey_plot.item_table = np.array(
+    [
+        [0, 0, 8, 2, 0, 0],
+        [0, 0, 0, 4, 0, 0],
+        [0, 0, 0, 0, 8, 0],
+        [0, 0, 0, 0, 5, 1],
+        [0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0],
+    ],
+    dtype="|S20",
+)
+sankey_plot.labels_row = ["A", "B", "C", "D", "E", "F"]
+sankey_plot.labels_column = ["A", "B", "C", "D", "E", "F"]
+sankey_plot.plot = "sankey"
+sankey_plot.visualize()
+
+
+###############################################################################
+#
+# .. image:: /_static/sankey.png
+#
+# Close the service
+# -----------------
+#
+# Close the Ansys Dynamic Reporting service. The database with the items that
+# were created remains on disk.
+
+# sphinx_gallery_thumbnail_path = '_static/01_connect_3.png'
+adr_service.stop()