Repository: qiskit-community/qiskit-metal (language: python)

Issue:
Display option name and option value of failed runs in the Sweeper class' `run_sweep` method
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Please be as descriptive as possible, including: what is expected, why is this feature needed, what is the objective, etc.

### What is the feature being requested?

Currently, when a run fails during the execution of Sweeper class' `run_sweep` method, the warning message, , does not tell which option name or option value that failed.

https://github.com/Qiskit/qiskit-metal/blob/fdc6df69ecb1e9a4ada03f519228a16903f0ec7d/qiskit_metal/analyses/sweep_and_optimize/sweeper.py#L166-L168

One way to trigger this warning is to abort the simulation in Ansys when the `run_sweep` class is executing.

### What are use cases for this feature?

It will be useful to add which option name + option value (key) combination that didn't execute properly for reference in case the user wants to run the sweeper again for the failed option values. 

### Proposed idea

I made a change in my local Qiskit Metal code as following to display the option name and option value that failed in the warning message.

```self.design.logger.warning(f'For class {self.parent.__class__.__name__}, option_name={".".join(option_path)}, key={item}, run() did not execute as expected: {message}')```

The necessary variables are available within the `iterate_option_sweep` method.

### Miscellaneous

Slack conversation at https://qiskit.slack.com/archives/C01R8KP5WP7/p1673986921233929



Discussion / hints:

Fix patch:
diff --git a/README_Tutorials.md b/README_Tutorials.md
index 59c9ccfe5..0001d9568 100644
--- a/README_Tutorials.md
+++ b/README_Tutorials.md
@@ -38,4 +38,5 @@ Join the [Qiskit](https://ibm.co/joinqiskitslack) `#metal` slack channel to requ
 | Mar 24, 2022 | Tutorial | [Time Evolution for the CR gate](https://www.youtube.com/watch?v=icDryjZrUQ4)|
 | Aug 4, 2022 | Tutorial | [Open-Source Rendering and Simulation Solution for Qiskit Metal](https://youtu.be/sA0uXtCAhWw)|
 | Aug 11, 2022 | Tutorial | [Qiskit Metal E21. Quantum Spice Web App - Sneak Peak](https://youtu.be/3UE57DaG-HI)|
+| Feb 2, 2023 | Tutorial | [Qiskit Metal E22. Open-Source Simulation Renderers for Metal — Gmsh and ElmerFEM](https://youtu.be/84j3l_9fHko)|
 | 2022 &rightarrow; ... | TBD | Topics to potentially schedule:<ul><li>Analysis deep dives (as needed)</li><li>What happens frequently when analyzing a chip.</li><li>Metal use cases: Common problems with pins, ports, fillet matches meander distance</li><li>GDS fabrication option, use cases</li><li>QRenderers: How to use, modify and add new renderers</li><li>New QAnalysis/QRenderer structure and usage</li><li>...</li></ul> |
diff --git a/docs/index.rst b/docs/index.rst
index dce135a64..475fba449 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -4,8 +4,8 @@ Qiskit Metal | Quantum Device Design & Analysis (Q-EDA) |version|
 
 .. attention::
 
-    This is an alpha version of Qiskit Metal, the code is still under development. 
-    Please let us know about anything you might want us to add or elaborate upon in the 
+    This is an alpha version of Qiskit Metal, the code is still under development.
+    Please let us know about anything you might want us to add or elaborate upon in the
     Slack channel `#metal <https://qiskit.slack.com/archives/C01R8KP5WP7>`_ in the `qiskit workspace <https://ibm.co/joinqiskitslack>`_.
 
 .. hint::
@@ -22,7 +22,7 @@ Qiskit Metal | Quantum Device Design & Analysis (Q-EDA) |version|
 .. rubric:: **About**
 
 **Qiskit Metal | for quantum device design & analysis (`Qiskit Metal`)**
-is an open-source framework (and library) for the design of superconducting quantum 
+is an open-source framework (and library) for the design of superconducting quantum
 chips and devices.
 Call it quantum EDA (Q-EDA) and analysis. Qiskit Metal is:
 
@@ -34,21 +34,20 @@ Call it quantum EDA (Q-EDA) and analysis. Qiskit Metal is:
 * Community-oriented and tutorial-driven, join the `#metal <https://qiskit.slack.com/archives/C01R8KP5WP7>`_ Slack.
 
 
-Use the slack channel.  Join `qiskit slack <https://ibm.co/joinqiskitslack>`_ and 
-then join the `#metal <https://qiskit.slack.com/archives/C01R8KP5WP7>`_ channel to communicate with the developers and other participants.  
+Use the slack channel.  Join `qiskit slack <https://ibm.co/joinqiskitslack>`_ and
+then join the `#metal <https://qiskit.slack.com/archives/C01R8KP5WP7>`_ channel to communicate with the developers and other participants.
 You may also use this channel to learn about upcoming weekly tutorials and to inquire about potential collaborations.
 
 
 .. rubric:: **Live tutorials and Q&A**:
-We host live tutorials and Q&A sessions, `sign up here <https://airtable.com/shrxQEgKqZCf319F3>`_ to receive an invite to the upcoming sessions.  
-For past sessions & videos, see `here <https://github.com/Qiskit/qiskit-metal/blob/main/README_Tutorials.md>`_.
+We host live tutorials and Q&A sessions.  Announcements for future tutorials are posted in the #metal channel.
 
 .. rubric:: **Qiskit Metal Vision**:
 
-Designing quantum devices is the bedrock of the quantum ecosystem, 
+Designing quantum devices is the bedrock of the quantum ecosystem,
 but it is a difficult, multi-step process that connects traditionally disparate worlds.
-Metal is automating and streamlining this process. 
-Our vision is to develop a community-driven universal platform capable 
+Metal is automating and streamlining this process.
+Our vision is to develop a community-driven universal platform capable
 of orchestrating quantum chip development from concept to fabrication in
 a simple and open framework.
 
diff --git a/docs/tut/4-Analysis/4.21-Capacitance-matrix.ipynb b/docs/tut/4-Analysis/4.21-Capacitance-matrix.ipynb
index 5104fdf49..9580cc44c 100644
--- a/docs/tut/4-Analysis/4.21-Capacitance-matrix.ipynb
+++ b/docs/tut/4-Analysis/4.21-Capacitance-matrix.ipynb
@@ -158,7 +158,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -294,31 +294,31 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO 08:08AM [connect_project]: Connecting to Ansys Desktop API...\n",
-      "INFO 08:08AM [load_ansys_project]: \tOpened Ansys App\n",
-      "INFO 08:08AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
-      "INFO 08:08AM [load_ansys_project]: \tOpened Ansys Project\n",
-      "\tFolder:    C:/Ansoft/\n",
-      "\tProject:   Project23\n",
-      "INFO 08:08AM [connect_design]: No active design found (or error getting active design).\n",
-      "INFO 08:08AM [connect]: \t Connected to project \"Project23\". No design detected\n",
-      "INFO 08:08AM [connect_design]: \tOpened active design\n",
+      "INFO 05:12PM [connect_project]: Connecting to Ansys Desktop API...\n",
+      "INFO 05:12PM [load_ansys_project]: \tOpened Ansys App\n",
+      "INFO 05:12PM [load_ansys_project]: \tOpened Ansys Desktop v2021.2.0\n",
+      "INFO 05:12PM [load_ansys_project]: \tOpened Ansys Project\n",
+      "\tFolder:    J:/Ansoft/\n",
+      "\tProject:   Project5\n",
+      "INFO 05:12PM [connect_design]: \tOpened active design\n",
       "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
-      "WARNING 08:08AM [connect_setup]: \tNo design setup detected.\n",
-      "WARNING 08:08AM [connect_setup]: \tCreating Q3D default setup.\n",
-      "INFO 08:08AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
-      "INFO 08:08AM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
-      "INFO 08:08AM [analyze]: Analyzing setup sweeper_q3d_setup\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpqt5sgbte.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp5ks6ofb0.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpxblcplxt.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpuhodx2js.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp70ngvsc6.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpum8bsgyx.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpc82acfxx.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpkuctcf1q.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmptslx7ez1.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpmom37xqf.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
+      "INFO 05:12PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
+      "INFO 05:12PM [connect]: \tConnected to project \"Project5\" and design \"GetCapacitance_q3d\" 😀 \n",
+      "\n",
+      "INFO 05:12PM [connect_design]: \tOpened active design\n",
+      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
+      "INFO 05:12PM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
+      "INFO 05:12PM [analyze]: Analyzing setup sweeper_q3d_setup\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpasduwpn9.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmprvnct2sk.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpj_89pabg.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmplp2v7wsi.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpr1583hvq.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpcv77_j8q.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmptehz4r2y.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpv2q0vlmb.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpfyfnx19b.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
+      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpj_fn6u7x.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
      ]
     },
     {
@@ -367,20 +367,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO 08:09AM [connect_design]: \tOpened active design\n",
+      "INFO 05:13PM [connect_design]: \tOpened active design\n",
       "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
-      "INFO 08:09AM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
-      "INFO 08:09AM [analyze]: Analyzing setup sweeper_q3d_setup\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpwe7jn05r.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpcrkv_5jk.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpaf2kqdlm.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpfxlsiiss.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp0p60le17.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpqlpr1t42.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpcmz4ifz4.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp_crzbc6u.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmptqdktbym.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
-      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpzrm77gi5.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
+      "INFO 05:13PM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
+      "INFO 05:13PM [analyze]: Analyzing setup sweeper_q3d_setup\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpczb1qvs3.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpefiqwjuk.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpb7_eoa61.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpcjsjfzlz.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmptyounyv7.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpuqz5ofoi.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpghifad5m.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpu9yh6bfc.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpipgeyx_p.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
+      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpczimzks4.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
      ]
     },
     {
@@ -429,20 +429,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO 08:09AM [connect_design]: \tOpened active design\n",
+      "INFO 05:14PM [connect_design]: \tOpened active design\n",
       "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
-      "INFO 08:09AM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
-      "INFO 08:09AM [analyze]: Analyzing setup sweeper_q3d_setup\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpa_vi_96w.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp67kj69wt.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpdr77b6cg.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp7bokcgn7.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp8hut452g.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpycxt1u0b.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpvjp_qeb4.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpfph35ir7.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmphwbh3l79.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
-      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmparw9bhfb.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
+      "INFO 05:15PM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
+      "INFO 05:15PM [analyze]: Analyzing setup sweeper_q3d_setup\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp4w4vtfuv.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpd317tmlb.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpi6iwsdi9.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp8l_x9l2z.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp1q5inamq.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp9t4l6dmy.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp2sgn1_3h.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp482h5dug.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmphez85936.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
+      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpayf6wj5t.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
      ]
     },
     {
@@ -647,7 +647,7 @@
     {
      "data": {
       "text/plain": [
-       "dict_keys(['sim_setup_name', 'cap_matrix', 'units', 'cap_all_passes'])"
+       "dict_keys(['sim_setup_name', 'cap_matrix', 'units', 'cap_all_passes', 'is_converged'])"
       ]
      },
      "execution_count": 16,
@@ -659,10 +659,38 @@
     "sweep_data['20um']['sim_variables'].keys()"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A boolean flag indicating whether a sweep has converged can be obtained from the `is_converged` key within the `sim_variables`. For instance, the following code retrieves the convergence result for the sweep parameter, 20um. The convergence is extracted from the Ansys Q3D renderer."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sweep_data['20um']['sim_variables']['is_converged']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -673,8 +701,8 @@
       "\n",
       "key=20um\n",
       "option_name['20um']['option_name']=pad_gap\n",
-      "variables={'lumped_oscillator': {'fQ': 5.266513054956589, 'EC': 284.4904739015804, 'EJ': 13.616300010297985, 'alpha': -328.02763545950194, 'dispersion': 16.25804359817505, 'gbus': array([108.06151986, -82.02115476,  70.10660922]), 'chi_in_MHz': array([-2.19607046, -5.70438759, -2.76340023])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha dispersion  \\\n",
-      "1    5.6945  336.195588  13.6163 -394.022958  73.505735   \n",
+      "variables={'lumped_oscillator': {'fQ': 5.266513054956589, 'EC': 284.4904739015804, 'EJ': 13.616300010297985, 'alpha': -328.02763545951046, 'dispersion': 16.25804376029968, 'gbus': array([108.06151986, -82.02115476,  70.10660922]), 'chi_in_MHz': array([-2.19607046, -5.70438759, -2.76340023])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha dispersion  \\\n",
+      "1  5.694647  336.214178  13.6163 -394.047036  73.540872   \n",
       "2  5.597383  324.026735  13.6163 -378.317097  53.282182   \n",
       "3  5.486697   310.47202  13.6163 -360.949348  36.405901   \n",
       "4  5.417928  302.217736  13.6163 -350.437946  28.505987   \n",
@@ -684,7 +712,7 @@
       "8  5.266513  284.490474  13.6163 -328.027635  16.258044   \n",
       "\n",
       "                                                gbus  \\\n",
-      "1  [102.84576293087251, -71.14320976656029, 74.94...   \n",
+      "1  [102.81449386064145, -70.90109936803726, 74.96...   \n",
       "2  [107.77093095701179, -80.12086229266069, 65.69...   \n",
       "3  [105.54815295436816, -80.44038588561413, 67.29...   \n",
       "4  [104.90594389814552, -79.89581633369512, 68.00...   \n",
@@ -694,14 +722,14 @@
       "8  [108.06151985814759, -82.02115475685893, 70.10...   \n",
       "\n",
       "                                          chi_in_MHz    χr MHz      gr MHz  \n",
-      "1  [-3.810201304704236, -18.694204087958646, -9.7...  3.810201  102.845763  \n",
-      "2  [-3.575143906278386, -15.48527763225972, -5.54...  3.575144  107.770931  \n",
-      "3  [-2.888576221585544, -10.445672739152624, -4.2...  2.888576  105.548153  \n",
-      "4  [-2.5743348275854756, -8.277915423269258, -3.6...  2.574335  104.905944  \n",
-      "5  [-2.415933356695892, -7.106373744825816, -3.16...  2.415933  106.338976  \n",
-      "6  [-2.301822505955287, -6.324281107517073, -2.95...  2.301823  107.129811  \n",
-      "7  [-2.2529865943189042, -6.001303111687617, -2.8...  2.252987  107.701239  \n",
-      "8  [-2.1960704619822486, -5.704387588554083, -2.7...  2.196070  108.061520  }\n",
+      "1  [-3.8088097481735166, -18.580483394229866, -9....  3.808810  102.814494  \n",
+      "2  [-3.575143906278383, -15.485277632259882, -5.5...  3.575144  107.770931  \n",
+      "3  [-2.8885762215855513, -10.445672739152704, -4....  2.888576  105.548153  \n",
+      "4  [-2.5743348275853832, -8.277915423268908, -3.6...  2.574335  104.905944  \n",
+      "5  [-2.4159333566958128, -7.106373744825535, -3.1...  2.415933  106.338976  \n",
+      "6  [-2.3018225059552377, -6.324281107516913, -2.9...  2.301823  107.129811  \n",
+      "7  [-2.2529865943188865, -6.001303111687568, -2.8...  2.252987  107.701239  \n",
+      "8  [-2.1960704619822993, -5.704387588554193, -2.7...  2.196070  108.061520  }\n",
       "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
       "bus1_connector_pad_Q1                  50.68289               -0.45605   \n",
       "bus2_connector_pad_Q1                  -0.45605               54.98763   \n",
@@ -724,18 +752,18 @@
       "ground_main_plane                        -37.12178  \n",
       "pad_bot_Q1                               -19.15111  \n",
       "pad_top_Q1                                -2.31960  \n",
-      "readout_connector_pad_Q1                  60.98654  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4147670e-14, -3.9492000e-16, -3.1041150e-14, -1.4496900e-15,\n",
-      "        -1.0486820e-14, -1.7218000e-16],\n",
-      "       [-3.9492000e-16,  4.6459810e-14, -3.2923590e-14, -1.1642650e-14,\n",
-      "        -4.3180000e-16, -3.3660000e-16],\n",
-      "       [-3.1041150e-14, -3.2923590e-14,  2.3575625e-13, -2.7950670e-14,\n",
-      "        -3.5372900e-14, -3.4016130e-14],\n",
-      "       [-1.4496900e-15, -1.1642650e-14, -2.7950670e-14,  8.8123550e-14,\n",
-      "        -2.9463910e-14, -1.5955250e-14],\n",
-      "       [-1.0486820e-14, -4.3180000e-16, -3.5372900e-14, -2.9463910e-14,\n",
-      "         7.9772790e-14, -2.0327400e-15],\n",
-      "       [-1.7218000e-16, -3.3660000e-16, -3.4016130e-14, -1.5955250e-14,\n",
-      "        -2.0327400e-15,  5.3205750e-14]]), 2: array([[ 4.5639150e-14, -4.5303000e-16, -3.0625850e-14, -1.4292000e-15,\n",
+      "readout_connector_pad_Q1                  60.98654  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4197190e-14, -3.9163000e-16, -3.1057080e-14, -1.4393800e-15,\n",
+      "        -1.0445580e-14, -1.7242000e-16],\n",
+      "       [-3.9163000e-16,  4.6460040e-14, -3.2925350e-14, -1.1641860e-14,\n",
+      "        -4.2839000e-16, -3.3658000e-16],\n",
+      "       [-3.1057080e-14, -3.2925350e-14,  2.3578951e-13, -2.7958200e-14,\n",
+      "        -3.5407050e-14, -3.4017410e-14],\n",
+      "       [-1.4393800e-15, -1.1641860e-14, -2.7958200e-14,  8.8124720e-14,\n",
+      "        -2.9463370e-14, -1.5955740e-14],\n",
+      "       [-1.0445580e-14, -4.2839000e-16, -3.5407050e-14, -2.9463370e-14,\n",
+      "         7.9743910e-14, -2.0364200e-15],\n",
+      "       [-1.7242000e-16, -3.3658000e-16, -3.4017410e-14, -1.5955740e-14,\n",
+      "        -2.0364200e-15,  5.3205550e-14]]), 2: array([[ 4.5639150e-14, -4.5303000e-16, -3.0625850e-14, -1.4292000e-15,\n",
       "        -1.2108900e-14, -2.1362000e-16],\n",
       "       [-4.5303000e-16,  4.9745810e-14, -3.3732750e-14, -1.1903450e-14,\n",
       "        -1.7305600e-15, -1.0127300e-15],\n",
@@ -812,23 +840,23 @@
       "       [-1.3444720e-14, -1.9069600e-15, -3.7244960e-14, -3.6179440e-14,\n",
       "         9.3774090e-14, -2.3196000e-15],\n",
       "       [-2.1628000e-16, -1.0396900e-15, -3.7121780e-14, -1.9151110e-14,\n",
-      "        -2.3196000e-15,  6.0986540e-14]])}}\n",
+      "        -2.3196000e-15,  6.0986540e-14]])}, 'is_converged': True}\n",
       "\n",
       "key=30um\n",
       "option_name['30um']['option_name']=pad_gap\n",
-      "variables={'lumped_oscillator': {'fQ': 5.462896549506973, 'EC': 307.6008059702114, 'EJ': 13.616300010297985, 'alpha': -357.2874591478059, 'dispersion': 33.47434948539734, 'gbus': array([116.21339556, -87.83512791,  75.52064749]), 'chi_in_MHz': array([ -3.37823347, -11.51952754,  -5.08308206])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha dispersion  \\\n",
-      "1  5.830122  353.625282  13.6163 -416.708817  113.11692   \n",
-      "2  5.745499  342.689918  13.6163 -402.449428   86.65132   \n",
-      "3  5.652966  330.959668  13.6163 -387.251884   64.14433   \n",
-      "4  5.600191  324.374985  13.6163 -378.765066  53.788833   \n",
-      "5  5.539417  316.886639  13.6163 -369.151877  43.731781   \n",
-      "6  5.501965  312.321897  13.6163 -363.311786   38.40525   \n",
-      "7  5.476807  309.277112  13.6163 -359.424666  35.161001   \n",
-      "8  5.462897  307.600806  13.6163 -357.287459  33.474349   \n",
+      "variables={'lumped_oscillator': {'fQ': 5.46289654950697, 'EC': 307.6008059702114, 'EJ': 13.616300010297985, 'alpha': -357.28745914780166, 'dispersion': 33.47434947013855, 'gbus': array([116.21339556, -87.83512791,  75.52064749]), 'chi_in_MHz': array([ -3.37823347, -11.51952754,  -5.08308206])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha  dispersion  \\\n",
+      "1  5.830279  353.645796  13.6163  -416.73565  113.172147   \n",
+      "2  5.745499  342.689905  13.6163 -402.449411   86.651292   \n",
+      "3  5.652966  330.959668  13.6163 -387.251884    64.14433   \n",
+      "4  5.600191  324.374985  13.6163 -378.765066   53.788833   \n",
+      "5  5.539417  316.886639  13.6163 -369.151877   43.731781   \n",
+      "6  5.501965  312.321897  13.6163 -363.311786    38.40525   \n",
+      "7  5.476807  309.277112  13.6163 -359.424666   35.161001   \n",
+      "8  5.462897  307.600806  13.6163 -357.287459   33.474349   \n",
       "\n",
       "                                                gbus  \\\n",
-      "1  [109.02524713531591, -74.94480873602234, 76.41...   \n",
-      "2  [112.91991484159519, -82.54641088589537, 69.19...   \n",
+      "1  [108.98992719353839, -74.66540858425307, 76.43...   \n",
+      "2  [112.91989864116363, -82.54641626955339, 69.19...   \n",
       "3  [112.00253623300011, -84.53126333113148, 72.24...   \n",
       "4  [111.7129552679439, -84.54971279864021, 73.324...   \n",
       "5  [113.80880482565388, -85.29604218811512, 73.28...   \n",
@@ -837,14 +865,14 @@
       "8  [116.21339555843822, -87.83512791363434, 75.52...   \n",
       "\n",
       "                                          chi_in_MHz    χr MHz      gr MHz  \n",
-      "1  [-5.399387143023984, -47.010676103849015, -16....  5.399387  109.025247  \n",
-      "2  [-5.002679690889769, -32.8443439546258, -9.922...  5.002680  112.919915  \n",
-      "3  [-4.221519632060214, -21.76025416309242, -7.93...  4.221520  112.002536  \n",
-      "4  [-3.8586174786699887, -17.438517118939547, -6....  3.858617  111.712955  \n",
-      "5  [-3.640928569155065, -14.097167414381117, -5.8...  3.640929  113.808805  \n",
-      "6  [-3.510312154331424, -12.695294731236526, -5.4...  3.510312  115.010965  \n",
-      "7  [-3.4230978902406988, -11.925426143748691, -5....  3.423098  115.762798  \n",
-      "8  [-3.378233467896828, -11.519527540166955, -5.0...  3.378233  116.213396  }\n",
+      "1  [-5.3973914316567875, -46.71741582928083, -16....  5.397391  108.989927  \n",
+      "2  [-5.002677392991025, -32.844329514281235, -9.9...  5.002677  112.919899  \n",
+      "3  [-4.221519632060279, -21.760254163093062, -7.9...  4.221520  112.002536  \n",
+      "4  [-3.8586174786700056, -17.438517118939654, -6....  3.858617  111.712955  \n",
+      "5  [-3.640928569154806, -14.09716741437999, -5.85...  3.640929  113.808805  \n",
+      "6  [-3.510312154331441, -12.695294731236578, -5.4...  3.510312  115.010965  \n",
+      "7  [-3.423097890240712, -11.925426143748664, -5.1...  3.423098  115.762798  \n",
+      "8  [-3.378233467896783, -11.519527540166766, -5.0...  3.378233  116.213396  }\n",
       "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
       "bus1_connector_pad_Q1                  50.59475               -0.42373   \n",
       "bus2_connector_pad_Q1                  -0.42373               54.94375   \n",
@@ -867,24 +895,24 @@
       "ground_main_plane                        -37.11942  \n",
       "pad_bot_Q1                               -19.21035  \n",
       "pad_top_Q1                                -2.22435  \n",
-      "readout_connector_pad_Q1                  60.91761  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4164070e-14, -3.5989000e-16, -3.1093760e-14, -1.3814300e-15,\n",
-      "        -1.0566140e-14, -1.6608000e-16],\n",
-      "       [-3.5989000e-16,  4.6488900e-14, -3.2942860e-14, -1.1565090e-14,\n",
-      "        -5.7897000e-16, -3.1986000e-16],\n",
-      "       [-3.1093760e-14, -3.2942860e-14,  2.3763876e-13, -2.8915460e-14,\n",
-      "        -3.6226730e-14, -3.4078370e-14],\n",
-      "       [-1.3814300e-15, -1.1565090e-14, -2.8915460e-14,  8.5774560e-14,\n",
-      "        -2.6142050e-14, -1.6056500e-14],\n",
-      "       [-1.0566140e-14, -5.7897000e-16, -3.6226730e-14, -2.6142050e-14,\n",
-      "         7.7480320e-14, -1.9372900e-15],\n",
-      "       [-1.6608000e-16, -3.1986000e-16, -3.4078370e-14, -1.6056500e-14,\n",
-      "        -1.9372900e-15,  5.3242550e-14]]), 2: array([[ 4.6325230e-14, -4.2047000e-16, -3.1584610e-14, -1.4162100e-15,\n",
+      "readout_connector_pad_Q1                  60.91761  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4218330e-14, -3.5664000e-16, -3.1113930e-14, -1.3706900e-15,\n",
+      "        -1.0520740e-14, -1.6641000e-16],\n",
+      "       [-3.5664000e-16,  4.6489100e-14, -3.2944760e-14, -1.1564330e-14,\n",
+      "        -5.7548000e-16, -3.1984000e-16],\n",
+      "       [-3.1113930e-14, -3.2944760e-14,  2.3767564e-13, -2.8923580e-14,\n",
+      "        -3.6263220e-14, -3.4079430e-14],\n",
+      "       [-1.3706900e-15, -1.1564330e-14, -2.8923580e-14,  8.5775550e-14,\n",
+      "        -2.6142010e-14, -1.6057020e-14],\n",
+      "       [-1.0520740e-14, -5.7548000e-16, -3.6263220e-14, -2.6142010e-14,\n",
+      "         7.7450100e-14, -1.9410100e-15],\n",
+      "       [-1.6641000e-16, -3.1984000e-16, -3.4079430e-14, -1.6057020e-14,\n",
+      "        -1.9410100e-15,  5.3242360e-14]]), 2: array([[ 4.6325230e-14, -4.2047000e-16, -3.1584610e-14, -1.4162100e-15,\n",
       "        -1.1900660e-14, -2.0238000e-16],\n",
-      "       [-4.2047000e-16,  4.9779490e-14, -3.3812720e-14, -1.1965030e-14,\n",
+      "       [-4.2047000e-16,  4.9779490e-14, -3.3812720e-14, -1.1965040e-14,\n",
       "        -1.6715600e-15, -1.0030400e-15],\n",
       "       [-3.1584610e-14, -3.3812720e-14,  2.3584731e-13, -3.0625210e-14,\n",
       "        -3.6991100e-14, -3.4724940e-14],\n",
-      "       [-1.4162100e-15, -1.1965030e-14, -3.0625210e-14,  8.9142120e-14,\n",
+      "       [-1.4162100e-15, -1.1965040e-14, -3.0625210e-14,  8.9142120e-14,\n",
       "        -2.6367590e-14, -1.6742180e-14],\n",
       "       [-1.1900660e-14, -1.6715600e-15, -3.6991100e-14, -2.6367590e-14,\n",
       "         8.1202660e-14, -1.9176400e-15],\n",
@@ -955,12 +983,12 @@
       "       [-1.349320e-14, -1.838080e-15, -3.822709e-14, -3.061516e-14,\n",
       "         8.915073e-14, -2.224350e-15],\n",
       "       [-2.066400e-16, -1.031450e-15, -3.711942e-14, -1.921035e-14,\n",
-      "        -2.224350e-15,  6.091761e-14]])}}\n",
+      "        -2.224350e-15,  6.091761e-14]])}, 'is_converged': True}\n",
       "\n",
       "key=40um\n",
       "option_name['40um']['option_name']=pad_gap\n",
-      "variables={'lumped_oscillator': {'fQ': 5.603196288676893, 'EC': 324.7478590723875, 'EJ': 13.616300010297985, 'alpha': -379.24480934899753, 'dispersion': 54.335718358039856, 'gbus': array([122.75192426, -92.44407275,  79.74179062]), 'chi_in_MHz': array([ -4.68116172, -21.09933964,  -8.31554677])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha  dispersion  \\\n",
-      "1  5.942275   368.42715  13.6163 -436.152747  159.132464   \n",
+      "variables={'lumped_oscillator': {'fQ': 5.603196288676896, 'EC': 324.7478590723875, 'EJ': 13.616300010297985, 'alpha': -379.2448093489956, 'dispersion': 54.33571833992004, 'gbus': array([122.75192426, -92.44407275,  79.74179062]), 'chi_in_MHz': array([ -4.68116172, -21.09933964,  -8.31554677])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha  dispersion  \\\n",
+      "1  5.942448  368.450236  13.6163 -436.183203   159.21458   \n",
       "2  5.860089  357.545759  13.6163 -421.842741  124.079999   \n",
       "3   5.77753   346.80562  13.6163 -407.805744   95.939037   \n",
       "4  5.737369  341.649788  13.6163 -401.097753   84.424981   \n",
@@ -970,7 +998,7 @@
       "8  5.603196  324.747859  13.6163 -379.244809   54.335718   \n",
       "\n",
       "                                                gbus  \\\n",
-      "1  [114.42468061343125, -78.23514734277781, 77.94...   \n",
+      "1  [114.38520672445735, -77.91536562297485, 77.96...   \n",
       "2  [118.14507620202656, -86.1504566631403, 72.301...   \n",
       "3  [117.78733127044518, -88.41648711373728, 75.26...   \n",
       "4  [117.84135564008048, -88.71652047403923, 76.68...   \n",
@@ -980,14 +1008,14 @@
       "8  [122.75192426481861, -92.44407275077926, 79.74...   \n",
       "\n",
       "                                          chi_in_MHz    χr MHz      gr MHz  \n",
-      "1  [-7.298600527505494, -187.3189810121671, -29.6...  7.298601  114.424681  \n",
-      "2  [-6.688603442516156, -79.71703699688189, -17.0...  6.688603  118.145076  \n",
-      "3  [-5.749406339167914, -45.519940778626875, -13....  5.749406  117.787331  \n",
-      "4  [-5.3738345109544365, -36.26780028757956, -11....  5.373835  117.841356  \n",
-      "5  [-5.12495836977476, -28.483809227385823, -9.94...  5.124958  120.256220  \n",
-      "6  [-4.907144357646663, -24.287140896629243, -9.1...  4.907144  121.569483  \n",
-      "7  [-4.764435821161131, -22.38230347227866, -8.52...  4.764436  122.236283  \n",
-      "8  [-4.681161717420851, -21.099339639730726, -8.3...  4.681162  122.751924  }\n",
+      "1  [-7.295942608032298, -186.41543152823883, -29....  7.295943  114.385207  \n",
+      "2  [-6.68860344251652, -79.71703699689004, -17.06...  6.688603  118.145076  \n",
+      "3  [-5.749406339167892, -45.51994077862497, -13.2...  5.749406  117.787331  \n",
+      "4  [-5.373834510954572, -36.26780028758106, -11.8...  5.373835  117.841356  \n",
+      "5  [-5.124958369774344, -28.48380922738183, -9.94...  5.124958  120.256220  \n",
+      "6  [-4.907144357646617, -24.28714089662891, -9.12...  4.907144  121.569483  \n",
+      "7  [-4.764435821161295, -22.382303472279492, -8.5...  4.764436  122.236283  \n",
+      "8  [-4.6811617174208555, -21.099339639730957, -8....  4.681162  122.751924  }\n",
       "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
       "bus1_connector_pad_Q1                  50.64383               -0.39535   \n",
       "bus2_connector_pad_Q1                  -0.39535               55.01423   \n",
@@ -1010,18 +1038,18 @@
       "ground_main_plane                        -37.20723  \n",
       "pad_bot_Q1                               -19.29195  \n",
       "pad_top_Q1                                -2.13165  \n",
-      "readout_connector_pad_Q1                  60.96517  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4182950e-14, -3.2910000e-16, -3.1155100e-14, -1.3180500e-15,\n",
-      "        -1.0630010e-14, -1.6021000e-16],\n",
-      "       [-3.2910000e-16,  4.6523900e-14, -3.2982350e-14, -1.1519720e-14,\n",
-      "        -6.7360000e-16, -3.0400000e-16],\n",
-      "       [-3.1155100e-14, -3.2982350e-14,  2.3943871e-13, -2.9786690e-14,\n",
-      "        -3.7040360e-14, -3.4155240e-14],\n",
-      "       [-1.3180500e-15, -1.1519720e-14, -2.9786690e-14,  8.4031320e-14,\n",
-      "        -2.3505740e-14, -1.6139880e-14],\n",
-      "       [-1.0630010e-14, -6.7360000e-16, -3.7040360e-14, -2.3505740e-14,\n",
-      "         7.5765500e-14, -1.8454500e-15],\n",
-      "       [-1.6021000e-16, -3.0400000e-16, -3.4155240e-14, -1.6139880e-14,\n",
-      "        -1.8454500e-15,  5.3281010e-14]]), 2: array([[ 4.5616390e-14, -3.9686000e-16, -3.0842440e-14, -1.3883800e-15,\n",
+      "readout_connector_pad_Q1                  60.96517  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4242490e-14, -3.2589000e-16, -3.1180050e-14, -1.3069000e-15,\n",
+      "        -1.0580130e-14, -1.6062000e-16],\n",
+      "       [-3.2589000e-16,  4.6524070e-14, -3.2984390e-14, -1.1518980e-14,\n",
+      "        -6.7004000e-16, -3.0398000e-16],\n",
+      "       [-3.1180050e-14, -3.2984390e-14,  2.3947968e-13, -2.9795340e-14,\n",
+      "        -3.7079460e-14, -3.4156100e-14],\n",
+      "       [-1.3069000e-15, -1.1518980e-14, -2.9795340e-14,  8.4032180e-14,\n",
+      "        -2.3506090e-14, -1.6140410e-14],\n",
+      "       [-1.0580130e-14, -6.7004000e-16, -3.7079460e-14, -2.3506090e-14,\n",
+      "         7.5733560e-14, -1.8492400e-15],\n",
+      "       [-1.6062000e-16, -3.0398000e-16, -3.4156100e-14, -1.6140410e-14,\n",
+      "        -1.8492400e-15,  5.3280840e-14]]), 2: array([[ 4.5616390e-14, -3.9686000e-16, -3.0842440e-14, -1.3883800e-15,\n",
       "        -1.1984460e-14, -1.9837000e-16],\n",
       "       [-3.9686000e-16,  4.9820940e-14, -3.3872680e-14, -1.2002070e-14,\n",
       "        -1.6197500e-15, -1.0311400e-15],\n",
@@ -1098,7 +1126,7 @@
       "       [-1.3556460e-14, -1.7674500e-15, -3.9138300e-14, -2.6862540e-14,\n",
       "         8.6265150e-14, -2.1316500e-15],\n",
       "       [-1.9817000e-16, -1.0218200e-15, -3.7207230e-14, -1.9291950e-14,\n",
-      "        -2.1316500e-15,  6.0965170e-14]])}}\n"
+      "        -2.1316500e-15,  6.0965170e-14]])}, 'is_converged': True}\n"
      ]
     }
    ],
@@ -1153,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1162,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1171,7 +1199,7 @@
        "True"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,7 +1220,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "metal-torch",
    "language": "python",
    "name": "python3"
   },
@@ -1206,7 +1234,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0cc2a9121c76659a7ef66132cd751167ab445a9a4740c688857ba2d4daf3a404"
+   }
   }
  },
  "nbformat": 4,
diff --git a/environment.yml b/environment.yml
index 3b30aff19..3c3e0b23b 100644
--- a/environment.yml
+++ b/environment.yml
@@ -15,9 +15,10 @@ dependencies:
   - pyepr-quantum>=0.8.5.7
   - pygments
   - pyside2
+  - qdarkstyle
   - qutip
   - scipy
-  - shapely==1.8.2
+  - shapely
   - jupyter
   - scqubits
   - pip
diff --git a/qiskit_metal/analyses/simulation/lumped_elements.py b/qiskit_metal/analyses/simulation/lumped_elements.py
index 66d66f628..54c5ea882 100644
--- a/qiskit_metal/analyses/simulation/lumped_elements.py
+++ b/qiskit_metal/analyses/simulation/lumped_elements.py
@@ -59,7 +59,7 @@ class LumpedElementsSim(QSimulation):
     """Default setup."""
 
     # supported labels for data generated from the simulation
-    data_labels = ['cap_matrix', 'cap_all_passes', 'units']
+    data_labels = ['cap_matrix', 'cap_all_passes', 'units', 'is_converged']
     """Default data labels."""
 
     def __init__(self, design: 'QDesign' = None, renderer_name: str = 'q3d'):
@@ -94,6 +94,8 @@ def _get_results_from_renderer(self):
             # extract the capacitance matrices for all passes
             self.capacitance_all_passes, _ = self.renderer.get_capacitance_all_passes(
             )
+            # extract convergence
+            self.is_converged = self.renderer.get_convergence()
         else:
             self.logger.error(
                 "Please initialize renderer before trying to load the simulation results."
@@ -216,3 +218,26 @@ def units(self, data: str):
                 {type(data)})
             return
         self.set_data('units', data)
+
+    @property
+    def is_converged(self) -> bool:
+        """Getter
+
+        Returns:
+            bool: Boolean indicating whether simulation has converged
+        """
+        return self.get_data('is_converged')
+
+    @is_converged.setter
+    def is_converged(self, data: bool):
+        """Setter
+
+        Args:
+            data (bool): Sets convergence of simulation for
+        """
+        if not isinstance(data, bool):
+            self.logger.warning(
+                'Unsupported type %s. Only accepts boolean. Please try again.',
+                {type(data)})
+            return
+        self.set_data('is_converged', data)
diff --git a/qiskit_metal/analyses/sweep_and_optimize/sweeper.py b/qiskit_metal/analyses/sweep_and_optimize/sweeper.py
index ebd436c9e..3d9bbc491 100644
--- a/qiskit_metal/analyses/sweep_and_optimize/sweeper.py
+++ b/qiskit_metal/analyses/sweep_and_optimize/sweeper.py
@@ -164,8 +164,9 @@ def iterate_option_sweep(self, args: list, all_dicts: Dict,
                 template = "An exception of type {0} occurred. Arguments:\n{1!r}"
                 message = template.format(type(ex).__name__, ex.args)
                 self.design.logger.warning(
-                    f'For class {self.parent.__class__.__name__}, run() did not execute as expected: {message}'
-                )
+                    f'For class {self.parent.__class__.__name__}, '
+                    f'option_name={".".join(option_path)}, key={item}, '
+                    f'run() did not execute as expected: {message}')
 
             self.populate_all_sweep(all_sweep, item, args[1])
 
diff --git a/qiskit_metal/renderers/renderer_ansys/q3d_renderer.py b/qiskit_metal/renderers/renderer_ansys/q3d_renderer.py
index 5017ef8db..a2e4b15e7 100644
--- a/qiskit_metal/renderers/renderer_ansys/q3d_renderer.py
+++ b/qiskit_metal/renderers/renderer_ansys/q3d_renderer.py
@@ -12,7 +12,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-from typing import List, Union
+from typing import List, Tuple, Union
 
 import pandas as pd
 from collections import defaultdict
@@ -443,6 +443,158 @@ def lumped_oscillator_vs_passes(self, *args, **kwargs):
             'This method is deprecated. Change your scripts to use'
             'analysis.quantitative.capacitance_lom.run_lom()')
 
+    def get_convergence(self) -> bool:
+        """Extracts convergence from Ansys simulation result
+        """
+        # If 'LastAdaptive' is used, then the pass_number won't affect anything.
+        # If 'AdaptivePass' is used, then the pass_number is used.
+        convergence_df, convergence_txt = self._pinfo.setup.get_convergence()
+        target, current, pass_min = self._parse_text_from_q3d_convergence(
+            convergence_txt)
+        is_converged = self._test_if_q3d_analysis_converged(
+            target, current, pass_min)
+
+        return is_converged
+
+    def _test_if_q3d_analysis_converged(cls, target: float, current: float,
+                                        passes_min: int) -> Union[bool, None]:
+        """Use solution-data from Ansys-Q3d to determine if converged.
+
+        Args:
+            target (float): Delta percentage for target. Default is None.
+            current (float): Delta percentage for current. Default is None.
+            passes_min (int): Regarding convergence, minimum number of passes.
+              Default is None.
+
+        Returns:
+            Union[bool, None]: Had solution data converged.  Default is None.
+        """
+
+        if None not in (target, current, passes_min):
+            # Confirm that all three numbers have an value.
+            if current <= target and passes_min > 1:
+                is_converged = True
+                return is_converged
+            is_converged = False
+            return is_converged
+
+        is_converged = None
+        return is_converged
+
+    def _parse_text_from_q3d_convergence(
+            self,
+            gui_text: str) -> Tuple[Union[None, float], Union[None, float]]:
+        """Parse gui_text using a priori known formatting. Ansys-Q3D
+        solution-data provides gui_text.
+
+        Args:
+            gui_text (str): From Ansys-GUI-SolutionData.
+
+        Returns:
+            1st Union[None, float]: Delta percentage for target. Default is None.
+            2nd Union[None, float]: Delta percentage for current. Default is None.
+        """
+
+        text_list = gui_text.splitlines()
+
+        # Find Target information in text.
+        target_all = [string for string in text_list if 'Target' in string]
+
+        # Find Current information in text.
+        current_all = [string for string in text_list if 'Current' in string]
+
+        # Find Minimum number of passes from solution-data.
+        min_passes_all = [string for string in text_list if 'Minimum' in string]
+
+        target = self._extract_target_delta(target_all)
+        current = self._extract_current_delta(current_all)
+        min_passes = self._extract_min_passes(min_passes_all)
+
+        return target, current, min_passes
+
+    def _extract_min_passes(self, min_passes_all: list) -> Union[None, float]:
+        """Given a pre-formatted list, search and return the "Minimum Number
+        Of Passes."
+
+        Args:
+            min_passes_all (list): Result of search through string returned from Ansys-Q3D.
+
+        Returns:
+            Union[None, float]: Regarding convergence, minimum number of passes.
+              Default is None.
+        """
+
+        min_num_of_passes = None
+        if len(min_passes_all) == 1:
+            if min_passes_all[0]:
+                _, _, min_passes_str = min_passes_all[0].partition(':')
+                try:
+                    min_num_of_passes = int(min_passes_str)
+                except ValueError:
+                    self.design.logger.warning(
+                        f'Target={min_passes_str} in GUI is not an int.'
+                        'Force Minimum Number Of Passes to be None.')
+        else:
+            self.design.logger.warning(
+                'Either could not find Minimum Number of Passes '
+                'information or too many entries in text. '
+                'Force Minimum Number of Passes to be None.')
+        return min_num_of_passes
+
+    def _extract_target_delta(self, target_all: list) -> Union[None, float]:
+        """Given a pre-formatted list, search and return the target-delta
+        percentage for convergence.
+
+        Args:
+            target_all (list): Result of search through string returned from Ansys-Q3D.
+
+        Returns:
+            Union[None, float]: Delta percentage for target. Default is None.
+        """
+
+        target = None
+        if len(target_all) == 1:
+            if target_all[0]:
+                _, _, target_str = target_all[0].partition(':')
+                try:
+                    target = float(target_str)
+                except ValueError:
+                    self.design.logger.warning(
+                        f'Target={target_str} in GUI is not a float.'
+                        'Force Target Delta to be None.')
+        else:
+            self.design.logger.warning(
+                'Either could not find Target Delta information or too many '
+                'entries in text. Force Target Delta to be None.')
+        return target
+
+    def _extract_current_delta(self, current_all: list) -> Union[None, float]:
+        """Given a pre-formatted list, search and return the current-delta
+        percentage for convergence.
+
+        Args:
+            current_all (list): Result of search through string returned from Ansys-Q3D.
+
+        Returns:
+            Union[None, float]: Delta percentage for current. Default is None.
+        """
+
+        current = None
+        if len(current_all) == 1:
+            if current_all[0]:
+                _, _, current_str = current_all[0].partition(':')
+                try:
+                    current = float(current_str)
+                except ValueError:
+                    self.design.logger.warning(
+                        f'Target={current_str} in GUI is not a float.'
+                        'Force Current Delta to be None.')
+        else:
+            self.design.logger.warning(
+                'Either could not find Current Delta information or too many '
+                'entries in text. Force Current Delta to be None.')
+        return current
+
     def plot_convergence_main(self, RES: pd.DataFrame):
         """Plot alpha and frequency versus pass number, as well as convergence
         of delta (in %).
diff --git a/requirements.txt b/requirements.txt
index df3683ed4..28bce209e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,9 +10,10 @@ pint
 pyEPR-quantum>=0.8.5.7
 pygments
 pyside2
+qdarkstyle
 qutip
 scipy
-shapely==1.8.2 #using conda on Windows OS: pip install of shapely will fail on import for missing geos_c.dll, if <= v1.7.x. For as long as a 1.8.0 version is not released (already patched in github), you will need to run `conda install shapely` (before installing qiskit_metal).
+shapely
 scqubits
 gmsh
 pyaedt   #since conda-forge doesn't have pyaedt, we have to manually pip install or completely switch to virtualenv .  virtualenv uses less memory than conda. 
diff --git a/tutorials/4 Analysis/C. Parametric sweeps/4.21 Capacitance matrix.ipynb b/tutorials/4 Analysis/C. Parametric sweeps/4.21 Capacitance matrix.ipynb
index 058de2406..8f951f7c2 100644
--- a/tutorials/4 Analysis/C. Parametric sweeps/4.21 Capacitance matrix.ipynb
+++ b/tutorials/4 Analysis/C. Parametric sweeps/4.21 Capacitance matrix.ipynb
@@ -308,6 +308,23 @@
     "sweep_data['20um']['sim_variables'].keys()"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A boolean flag indicating whether a sweep has converged can be obtained from the `is_converged` key within the `sim_variables`. For instance, the following code retrieves the convergence result for the sweep parameter, 20um. The convergence is extracted from the Ansys Q3D renderer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep_data['20um']['sim_variables']['is_converged']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -391,7 +408,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "metal-torch",
    "language": "python",
    "name": "python3"
   },
@@ -405,7 +422,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.10.9 | packaged by conda-forge | (main, Jan 11 2023, 15:15:40) [MSC v.1916 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0cc2a9121c76659a7ef66132cd751167ab445a9a4740c688857ba2d4daf3a404"
+   }
   }
  },
  "nbformat": 4,


Test patch:
diff --git a/qiskit_metal/tests/test_renderers.py b/qiskit_metal/tests/test_renderers.py
index 56e6d7f12..8bfd5f47b 100644
--- a/qiskit_metal/tests/test_renderers.py
+++ b/qiskit_metal/tests/test_renderers.py
@@ -20,6 +20,7 @@
 """Qiskit Metal unit tests analyses functionality."""
 
 import unittest
+from unittest.mock import MagicMock
 import matplotlib.pyplot as _plt
 
 from qiskit_metal import designs
@@ -525,6 +526,73 @@ def test_renderer_gds_check_either_cheese(self):
         self.assertEqual(renderer._check_either_cheese('main', 1), 1)
         self.assertEqual(renderer._check_either_cheese('fake', 0), 5)
 
+    def test_successful_get_convergence(self):
+        """Test get_convergence returns the correct Boolean when converged"""
+
+        convergence_txt = '''Setup : Setup
+        \nProblem Type : CG\n
+        \n==================\n
+        Number of Passes\n
+        Completed : 15\n
+        Maximum   : 20\n
+        Minimum   : 2\n
+        ==================\n
+        Criterion : Delta %\n
+        Target    : 0.1\n
+        Current   : 0.042998\n
+        ==================\n
+        Pass|# Triangle| Delta %|\n
+        1|       108|     N/A|\n
+        2|       326|   1.739|\n
+        3|       966|  1.3044|\n
+        4|      2358|  1.2871|\n
+        5|      3040|  1.0324|\n
+        6|      4038| 0.53456|\n
+        7|      5470| 0.50188|\n
+        8|      7440| 0.28342|\n
+        9|     10122| 0.33775|\n
+        10|     13768| 0.10967|\n
+        11|     18498| 0.11437|\n
+        12|     25044| 0.13151|\n
+        13|     33562| 0.13472|\n
+        14|     44028| 0.07696|\n
+        15|     57390|0.042998|\n\n'''
+        design = designs.DesignPlanar()
+        renderer = QQ3DRenderer(design, initiate=False)
+        renderer._pinfo = MagicMock()
+        renderer._pinfo.setup = MagicMock()
+        renderer._pinfo.setup.get_convergence = MagicMock()
+        renderer._pinfo.setup.get_convergence.return_value = (None,
+                                                              convergence_txt)
+        self.assertTrue(renderer.get_convergence())
+
+    def test_unsuccessful_get_convergence(self):
+        """Test get_convergence returns the correct Boolean when not converged"""
+
+        convergence_txt = ''''Setup : Setup\n
+            Problem Type : CG\n\n
+            ==================\n
+            Number of Passes\n
+            Completed : 2\n
+            Maximum   : 2\n
+            Minimum   : 2\n
+            ==================\n
+            Criterion : Delta %\n
+            Target    : 0.1\n
+            Current   : 1.739\n
+            ==================\n
+            Pass|# Triangle|Delta %|\n
+            1|       108|    N/A|\n
+            2|       326|  1.739|\n\n'''
+        design = designs.DesignPlanar()
+        renderer = QQ3DRenderer(design, initiate=False)
+        renderer._pinfo = MagicMock()
+        renderer._pinfo.setup = MagicMock()
+        renderer._pinfo.setup.get_convergence = MagicMock()
+        renderer._pinfo.setup.get_convergence.return_value = (None,
+                                                              convergence_txt)
+        self.assertFalse(renderer.get_convergence())
+
 
 if __name__ == '__main__':
     unittest.main(verbosity=2)