release 0.5.1

.github/workflows/ci_cd.yml

@@ -51,7 +51,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.9', '3.10', '3.11', '3.12']
         should-release:
           - ${{ github.event_name == 'push' && contains(github.ref, 'refs/tags') }}
         exclude:
@@ -73,7 +73,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-latest ]
-        python-version: [ '3.8', '3.9', '3.10', '3.11' ]
+        python-version: [ '3.9', '3.10', '3.11', '3.12' ]
 
     steps:
       - uses: actions/checkout@v4
@@ -120,6 +120,7 @@ jobs:
         with:
           python-version: ${{ env.MAIN_PYTHON_VERSION }}
           check-links: false
+          sphinxopts: '-j auto'
 
   package:
     name: Package library

.github/workflows/nightly-docs.yml

@@ -23,6 +23,7 @@ jobs:
         with:
           python-version: ${{ env.MAIN_PYTHON_VERSION }}
           check-links: false
+          sphinxopts: '-j auto'
 
   docs_upload:
     needs: docs_build

.github/workflows/nightly.yml

@@ -30,7 +30,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: [ '3.9', '3.10', '3.11', '3.12']
     steps:
       - name: Build wheelhouse and perform smoke test
         uses: ansys/actions/build-wheelhouse@v4
@@ -47,7 +47,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-latest ]
-        python-version: [ '3.8', '3.9', '3.10', '3.11' ]
+        python-version: [ '3.9', '3.10', '3.11', '3.12' ]
 
     steps:
       - uses: actions/checkout@v4

README.rst

@@ -66,7 +66,7 @@ To reach the project support team, email `[email protected] <pyansys.core@a
 
 Installation
 ------------
-The ``pydynamicreporting`` package supports Python 3.7 through 3.11 on
+The ``pydynamicreporting`` package supports Python 3.9 through 3.12 on
 Windows and Linux. It is currently available on the PyPi
 `repository <https://pypi.org/project/ansys-dynamicreporting-core/>`_.
 
@@ -203,7 +203,7 @@ PyDynamicReporting is licensed under the MIT license.
 
 PyDynamicReporting makes no commercial claim over Ansys whatsoever.
 This library extends the functionality of Ansys Dynamic Reporting by
-adding a Python interface to Ansys Dynamic Reproting without changing
+adding a Python interface to Ansys Dynamic Reporting without changing
 the core behavior or license of the original software. The use of
 PyDynamicReporting requires a legally licensed copy of an Ansys product
 that supports Ansys Dynamic Reporting.

codegen/adr_utils.txt

@@ -60,15 +60,15 @@ def get_logger(logfile=None):
             The logger object.
     """
     logger = logging.getLogger()
-    logger.setLevel(logging.DEBUG)
+    logger.setLevel(logging.ERROR)
     if logfile is None:
         # Logging for Python APIs should be disabled by default
         ch = logging.NullHandler()
     elif logfile=='stdout':
         ch = logging.StreamHandler(sys.stdout)
     else:
         ch = logging.FileHandler(logfile)
-    ch.setLevel(logging.DEBUG)
+    ch.setLevel(logging.ERROR)
     formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
     ch.setFormatter(formatter)
     logger.addHandler(ch)

codegen/pyadritem.txt

@@ -95,7 +95,7 @@ class Item:
         self.item_image = None
         """Image object (Image and PNG binary files)"""
         self.item_scene = None
-        """3D scene (AVZ, PLY, SCDOC, and STL files)"""
+        """3D scene (AVZ, PLY, SCDOC, GLB, and STL files)"""
         # Attributes for the table items
         self.table_attr = table_attr
         self.item_table = None

doc/.vale.ini

@@ -20,7 +20,7 @@ WordTemplate = \b(?:%s)\b
 Packages = Google
 
 # Define the Ansys vocabulary
-Vocab = ANSYS
+Vocab = ANSYS, Polyflow
 
 [*.{md,rst}]
 

doc/source/class_documentation.rst

@@ -3,11 +3,10 @@ API reference
 *************
 
 Ansys Dynamic Reporting contains a low-level API that allows the user to access
-a;; the available features and properties in full detail. While this low-level
+all the available features and properties in full detail. While this low-level
 API is very powerful, it can also be quite complex to use and it requires a
 steep learning curve. For a comprehensive description of this API, see
-`External Python API <https://nexusdemo.ensight.com/docs/html/Nexus.html?ExternalPythonAPI.html>`_
-in the documentation for Ansys Dynamic Reporting.
+the section :ref:`Low Level Python API <lowlevel>`.
 
 The goal of PyDynamicReporting is to provide an easier, more Pythonic way to
 start or connect to an Ansys Dynamic Reporting service so that you do not need
@@ -30,9 +29,14 @@ class to create, query, and modify items.
 Lastly, you create and use ``Report`` instances to access reports in Ansys
 Dynamic Reporting.
 
+
 .. autosummary::
    :toctree: _autosummary/
 
    ansys.dynamicreporting.core.Item
    ansys.dynamicreporting.core.Service
    ansys.dynamicreporting.core.Report
+
+.. toctree::
+   lowlevelapi/index.rst
+

doc/source/conf.py

@@ -74,7 +74,7 @@
 
 # Intersphinx mapping
 intersphinx_mapping = {
-    "python": ("https://docs.python.org/3", None),
+    "python": ("https://docs.python.org/3.11", None),
     # kept here as an example
     # "scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
     # "numpy": ("https://numpy.org/devdocs", None),

doc/source/examples_source/00-basic/02-plottype.py

@@ -0,0 +1,218 @@
+"""
+.. _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()