Multiple retrieval of material properties using `connect`

[germa89]

Before submitting the issue

• I have checked for Compatibility issues
• I have searched among the existing issues
• I am using a Python virtual environment

Description of the bug

My goal is to extract all the properties of a given material.

It seems you cannot get all the material properties at once (really?). So you need to iterate over all the names knowing before hand their labels. So I am doing:

full_mesh = model.metadata.meshed_region
mats = full_mesh.property_field("mat")

mat_prop = dpf.operators.result.mapdl_material_properties()
mat_prop.inputs.materials.connect(mats)

props = tuple(["DENS", "EX"])
mat_prop.inputs.properties_name.connect(props) # error
mat_prop.inputs.data_sources.connect(model)

prop_field = mat_prop.outputs.properties_value.get_data()

However the line mat_prop.inputs.properties_name.connect(props) gives the following error:

      5 mat_prop.inputs.materials.connect(mats)
      7 props = tuple(["DENS", "EX"])
----> 8 mat_prop.inputs.properties_name.connect(props) # error
      9 mat_prop.inputs.data_sources.connect(model)
     11 prop_field = mat_prop.outputs.properties_value.get_data()

File ~/.venv/lib/python3.11/site-packages/ansys/dpf/core/inputs.py:139, in Input.connect(self, inpt)
    134     err_str = (
    135         f"The input operator for the {self._spec.name} pin must be "
    136         "one of the following types:\n"
    137     )
    138     err_str += "\n".join([f"- {py_type}" for py_type in self._python_expected_types])
--> 139     raise TypeError(err_str)
    141 from ansys.dpf.core.results import Result
    143 if isinstance(inpt, _Outputs):

TypeError: The input operator for the properties_name pin must be one of the following types:
- str
- Vector<String>

It seems you cannot use a list of strings as input of connect method.

I did try to use ansys.dpf.gate.dpf_vector.DPFVectorString class, but it gives NotImplementedError:

>>> gate.dpf_vector.DPFVectorString("DENS", "EX")
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File ~/.venv/lib/python3.11/site-packages/ansys/dpf/gate/dpf_vector.py:41, in DPFVectorBase.__init__(self, client, api)
     40     else:
---> 41         self._internal_obj = self.dpf_vector_api.dpf_vector_new_for_object(client)
     42 except AttributeError:

AttributeError: 'str' object has no attribute 'dpf_vector_new_for_object'

During handling of the above exception, another exception occurred:

NotImplementedError                       Traceback (most recent call last)
Cell In[80], line 1
----> 1 gate.dpf_vector.DPFVectorString("DENS", "EX")

File ~/.venv/lib/python3.11/site-packages/ansys/dpf/gate/dpf_vector.py:262, in DPFVectorString.__init__(self, client, api)
    261 def __init__(self, client=None, api=dpf_vector_capi.DpfVectorCAPI):
--> 262     super().__init__(client, api)
    263     self._array = MutableListString()

File ~/.venv/lib/python3.11/site-packages/ansys/dpf/gate/dpf_vector.py:43, in DPFVectorBase.__init__(self, client, api)
     41         self._internal_obj = self.dpf_vector_api.dpf_vector_new_for_object(client)
     42 except AttributeError:
---> 43     raise NotImplementedError

NotImplementedError: 

Hence, the question is how to retrieve all the properties of a given material in the most efficient way?

Steps To Reproduce

NA

Which Operating System causes the issue?

MacOS

Which DPF/Ansys version are you using?

Ansys 2025 R2

Which Python version causes the issue?

3.11

Installed packages

Packages

accessible-pygments==0.0.5
aiohappyeyeballs==2.6.1
aiohttp==3.11.18
aiosignal==1.3.2
alabaster==1.0.0
ansys-api-mapdl==0.5.2
ansys-api-platform-instancemanagement==1.1.3
ansys-dpf-core==0.13.6
-e git+https://github.com/ansys/pymapdl.git@749ef517136fa54e135d4274068307a1d8bb42fa#egg=ansys_mapdl_core
ansys-mapdl-reader==0.55.0
ansys-math-core==0.2.2
ansys-platform-instancemanagement==1.1.2
ansys-sphinx-theme==1.4.4
ansys-tools-path==0.7.1
ansys-tools-visualization-interface==0.9.1
anyio==4.9.0
appdirs==1.4.4
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==3.0.0
async-lru==2.0.5
attrs==24.2.0
autopep8==2.3.2
babel==2.17.0
beautifulsoup4==4.13.4
bleach==6.2.0
certifi==2025.4.26
cffi==1.17.1
cfgv==3.4.0
charset-normalizer==3.4.2
click==8.2.0
colorama==0.4.6
comm==0.2.2
contourpy==1.3.2
coverage==7.8.0
cycler==0.12.1
debugpy==1.8.14
decorator==5.2.1
defusedxml==0.7.1
distlib==0.3.9
docutils==0.21.2
executing==2.2.0
fastjsonschema==2.21.1
filelock==3.18.0
fonttools==4.58.0
fqdn==1.5.1
frozenlist==1.6.0
geomdl==5.3.1
gprof2dot==2025.4.14
grpcio==1.70.0
h11==0.16.0
httpcore==1.0.9
httpx==0.28.1
identify==2.6.10
idna==3.10
imageio==2.37.0
imageio-ffmpeg==0.6.0
imagesize==1.4.1
importlib_metadata==8.7.0
iniconfig==2.1.0
ipydatawidgets==4.3.5
ipykernel==6.29.5
ipython==9.2.0
ipython_pygments_lexers==1.1.1
ipywidgets==8.1.7
isoduration==20.11.0
jedi==0.19.2
Jinja2==3.1.6
json5==0.12.0
jsonpointer==3.0.0
jsonschema==4.23.0
jsonschema-specifications==2025.4.1
jupyter==1.1.1
jupyter-console==6.6.3
jupyter-events==0.12.0
jupyter-lsp==2.2.5
jupyter-sphinx==0.5.3
jupyter_client==8.6.3
jupyter_core==5.7.2
jupyter_server==2.16.0
jupyter_server_terminals==0.5.3
jupyterlab==4.4.2
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.15
kiwisolver==1.4.8
MarkupSafe==3.0.2
matplotlib==3.10.3
matplotlib-inline==0.1.7
mistune==3.1.3
more-itertools==10.7.0
msgpack==1.1.0
multidict==6.4.4
narwhals==1.40.0
nbclient==0.10.2
nbconvert==7.16.6
nbformat==5.10.4
nest-asyncio==1.6.0
nodeenv==1.9.1
notebook==7.4.2
notebook_shim==0.2.4
numpy==2.2.6
numpydoc==1.8.0
overrides==7.7.0
packaging==25.0
pandas==2.2.3
pandocfilters==1.5.1
parso==0.8.4
pdf2image==1.17.0
pexpect==4.9.0
pillow==11.2.1
platformdirs==4.3.8
plotly==6.0.1
pluggy==1.6.0
pooch==1.8.2
pre_commit==4.2.0
prometheus_client==0.22.0
prompt_toolkit==3.0.51
propcache==0.3.1
protobuf==4.25.7
psutil==7.0.0
ptyprocess==0.7.0
pure_eval==0.2.3
pyansys-tools-report==0.8.2
pyansys-tools-versioning==0.6.0
pycodestyle==2.13.0
pycparser==2.22
pydata-sphinx-theme==0.16.1
pyfakefs==5.8.0
Pygments==2.19.1
pyiges==0.3.1
pypandoc==1.15
pyparsing==3.2.3
pytest==8.3.5
pytest-cov==6.1.1
pytest-profiling==1.8.1
pytest-random-order==1.1.1
pytest-reportlog==0.4.0
pytest-rerunfailures==15.1
pytest-sphinx==0.6.3
pytest-timeout==2.4.0
pytest_pyvista==0.1.9
python-dateutil==2.9.0.post0
python-json-logger==3.3.0
pythreejs==2.4.2
pytz==2025.2
pyvista==0.45.2
PyYAML==6.0.2
pyzmq==26.4.0
referencing==0.36.2
requests==2.32.3
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
roman-numerals-py==3.1.0
rpds-py==0.25.0
scipy==1.15.3
scooby==0.10.1
Send2Trash==1.8.3
six==1.17.0
sniffio==1.3.1
snowballstemmer==2.2.0
soupsieve==2.7
Sphinx==8.2.3
sphinx-autobuild==2024.10.3
sphinx-autodoc-typehints==3.1.0
sphinx-copybutton==0.5.2
sphinx-gallery==0.19.0
sphinx-jinja==2.0.2
sphinx-notfound-page==1.1.0
sphinx_design==0.6.1
sphinxcontrib-applehelp==2.0.0
sphinxcontrib-devhelp==2.0.0
sphinxcontrib-htmlhelp==2.1.0
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==2.0.0
sphinxcontrib-serializinghtml==2.0.0
sphinxcontrib-websupport==2.0.0
sphinxemoji==0.3.1
stack-data==0.6.3
starlette==0.46.2
tabulate==0.9.0
terminado==0.18.1
tinycss2==1.4.0
tornado==6.5
tqdm==4.67.1
traitlets==5.14.3
traittypes==0.2.1
trame==3.9.0
trame-client==3.9.0
trame-common==0.2.0
trame-server==3.4.0
trame-vtk==2.8.15
trame-vuetify==3.0.1
types-python-dateutil==2.9.0.20250516
typing_extensions==4.13.2
tzdata==2025.2
uri-template==1.3.0
urllib3==2.4.0
uvicorn==0.34.2
virtualenv==20.31.2
vtk==9.4.2
watchfiles==1.0.5
wcwidth==0.2.13
webcolors==24.11.1
webencodings==0.5.1
websocket-client==1.8.0
websockets==15.0.1
widgetsnbextension==4.0.14
wslink==2.3.4
yarl==1.20.0
zipp==3.21.0

[PProfizi]

Hi @germa89, thanks for raising the issue. I'll take a look at the implementation of the operator to see why it does not accept a vector a strings despite it being documented as an accepted type.

[PProfizi]

Alright I have reproduced and found the issue.
The problem is the treatment of vector as an accepted input type by the Python module of the operator.

You can workaround this using the Operator.connect method directly as shown below:

import ansys.dpf.core as dpf
from ansys.dpf.core import examples


model = dpf.Model(examples.find_multishells_rst())

full_mesh = model.metadata.meshed_region

mats = full_mesh.property_field("mat")

mat_prop = dpf.operators.result.mapdl_material_properties()
mat_prop.inputs.data_sources.connect(model)
mat_prop.inputs.materials.connect(mats)

props = ["DENS", "EX"]
mat_prop.connect(0, props) # error

prop_fc = mat_prop.outputs.properties_value()
print(prop_fc)
print(prop_fc[0]) 

result in:

DPF  Fields Container
  with 2 field(s)
  defined on labels: DENS EX 

  with:
  - field 0 {DENS:  1, EX:  0} with Material location, 1 components and 12 entities.
  - field 1 {DENS:  0, EX:  1} with Material location, 1 components and 12 entities.

DPF  Field
  Location: Material
  Unit: 
  12 entities 
  Data: 1 components and 12 elementary data 

  IDs                   data
  ------------          ----------
  2                     7.850000e+03   
                        
  3                     7.850000e+03   
                        
  5                     0.000000e+00   
                        
  ...

[PProfizi]

I'll work on fixing this exposure in Python of list[str] as an accepted input type for this operator.

[PProfizi]

You also mentioned the need to be able to know which material property "keys" are available?

Do you mean you'd like a way to know if DENS is available? Isn't it always available?

[germa89]

Hi @PProfizi

thank you a lot for your prompt reply! It is very helpful.

The code you shared works.

You also mentioned the need to be able to know which material property "keys" are available?

Do you mean you'd like a way to know if DENS is available? Isn't it always available?

Yes. I am intending to replicate this reader method:

https://github.com/ansys/pymapdl-reader/blob/362631aac4703dad8cc203110518c0d3194d6ea5/ansys/mapdl/reader/rst.py#L481-L602

Hence I would like to get an dict-like data structure like:

{
     %material_id%: {
          '%property%': %value%
     }
}

For instance:

{
     1: {
          'EX': 1E9,
          'DENS': 3400,
     },
     3: {
          'EX': 1.5E9,
          'DENS': 3500,
          'KXX': 100,
     },
}

Because of the general nature of the API, I dont want to assume the presence of any specific property.

Ideally I would like to get all the properties for each material. The code you posted gives me each material value for each property which could be fine too if there is no other option.

So I guess I will be needing:

1. Function to list all the materials defined (their ids should be fine)
2. Function to get all the properties defined for a given material.

This way I can build up that dictionary easily.

[germa89]

Also I noticed that if the value is not defined, when I query it it returns zero. In most of the physical entities, having a zero value is like having nothing. But from the user perspective, there is a difference between having zero value and not defined value.

Is there anything I can do to know if a value is set to zero or not set at all?

[PProfizi]

Also I noticed that if the value is not defined, when I query it it returns zero. In most of the physical entities, having a zero value is like having nothing. But from the user perspective, there is a difference between having zero value and not defined value.

Is there anything I can do to know if a value is set to zero or not set at all?

That is a very good remark and a very good question!

Returning 0 is not a valid way of dealing with undefined values, especially not here. We should return an empty field instead.
I'll start a dedicated issue on that but I think this is going to require changes to the operator server-side.