Add form-csv to yaml converter

formresponse_to_yaml.py

@@ -0,0 +1,59 @@
+import pandas as pd
+import yaml
+
+csv_file = "OPL_form.csv"
+yaml_file = "problems.yaml"
+
+
+translations = {
+    # "Timestamp", 
+    #  "Submitter Name", 
+    #  "Submitter Email",
+       "Short name of Suite / Problem / Generator" : "name", 
+       "Type" : "suite/generator/single",
+       "Types of input variables" : "variable type",
+       "Number of Input variables (number or range or 'scalable')" : "dimensionality",
+       "Number of Objectives (number or range or 'scalable')" : "objectives",
+       "Problem Characteristics [Constrained]":"constraints",
+       "Problem Characteristics [Dynamic]":"dynamic", 
+       "Problem Characteristics [Noisy]":"noise",
+       "Problem Characteristics [Multi-modal]":"multimodal",
+    #    "Problem Characteristics [Partial evaluations possible]",
+       "Problem Characteristics [Multiple fidelities]":"multi-fidelity", 
+       "Problem Source":"source (real-world/artificial)",
+       "Link to Implementation" :"implementation", 
+       "Short description of problem(s)":"textual description",
+    #    "Do you wish to provide some more detailed information about the proposed problems?",
+    #    "Full name of suite", 
+    #    "Constraint Properties", 
+    #    "Number of constraints",
+    #    "Type of Dynamicism", 
+    #    "Form of noise model", 
+    #    "Type of noise space",
+    #    "Other noise properties", 
+    #    "Description of multimodality",
+       "Citation / reference": "reference", 
+    #    "Key challenges / characteristics",
+    #    "Scientific motivation for the proposed suite / problem / generator",
+    #    "Limitations of  the proposed suite / problem / generator",
+    #    "Implemenation languages", 
+    #    "Links to implementations",
+    #    "Approximate time to evaluate a single solution (or times if e.g. multi-fidelity)",
+    #    "Links to examples of usage of the proposed suite / problem / generator",
+       "Other relevant information":"other info", 
+    #    "Feedback about the form"
+}
+
+# Read the csv file
+data = pd.read_csv(csv_file)
+
+# Handle empty cells being read as 'NaN', by emptying them again
+data = data.fillna("")
+data2 = data.rename(columns=translations)
+data2.drop(columns=[col for col in data2.columns if col not in translations.values()], inplace=True)
+data2 = data2[data2['name'] != 'test']
+
+# Write the yaml file
+with open(yaml_file, "a") as out_file:
+    yaml.dump(data2.to_dict(orient="records"), out_file,
+              sort_keys=False)  # Prevent columns being reordered alphabetically

problems.yaml

@@ -900,3 +900,21 @@
   implementation: https://zenodo.org/records/8307853
   source (real-world/artificial): 'real-world'
   textual description: ''
+- name: MECHBench
+  suite/generator/single: Problem Suite
+  variable type: Continuous
+  dimensionality: scalable'
+  objectives: 1
+  constraints: Present
+  dynamic: Not Present
+  noise: Not Present
+  multimodal: Present
+  multi-fidelity: Not Present
+  source (real-world/artificial): Real-World Application
+  implementation: https://github.com/BayesOptApp/MECHBench
+  textual description: This is a set of problems with inspiration from Structural
+    Mechanics Design Optimization. The suite comprises three physical models, from
+    which the user may define different kind of problems which impact the final design
+    output.
+  reference: https://arxiv.org/abs/2511.10821
+  other info: ''