Port over fmudesign

pyproject.toml

@@ -79,10 +79,13 @@ dependencies = [
     "resfo-utilities>=0.5.0",
     "natsort>=8.4.0",
     "shapely>=2.1.2",
+    "probabilit>=0.4.2",
+    "openpyxl>=3.1.5",
 ]
 
 [project.scripts]
 ert = "ert.__main__:main"
+fmudesign = "ert.config.fmudesign.fmudesignrunner:main"
 "fm_dispatch.py" = "_ert.forward_model_runner.fm_dispatch:main"
 everest = "everest.bin.main:start_everest"
 everserver = "everest.detached.everserver:main"

src/ert/config/fmudesign/__init__.py

@@ -0,0 +1,10 @@
+from ._designsummary import summarize_design
+from ._excel_to_dict import excel_to_dict, inputdict_to_yaml
+from .create_design import DesignMatrix
+
+__all__ = [
+    "DesignMatrix",
+    "excel_to_dict",
+    "inputdict_to_yaml",
+    "summarize_design",
+]

src/ert/config/fmudesign/_designsummary.py

@@ -0,0 +1,116 @@
+"""Module for summarizing design set up for one by one sensitivities"""
+
+import pandas as pd
+
+
+def _get_sensitivity_type(senscase: str) -> str:
+    """Determine sensitivity type based on the case name"""
+    sensitivity_types = {"p10_p90": "mc", "ref": "ref"}
+    return sensitivity_types.get(senscase.lower(), "scalar")
+
+
+def summarize_design(filename: str, sheetname: str = "DesignSheet01") -> pd.DataFrame:
+    """
+     Summarizes the design set up for one by one sensitivities
+     specified in a design matrix on standard fmu format.
+
+    Args:
+        filename (str): Name of excel or csv file containing designmatrix
+            for one by one sensitivities on standard FMU format.
+        sheetname (str): Name of sheet in excel workbook which
+            contains the designmatrix (only for excel input). Defaults to
+            'DesignSheet01'.
+
+    Returns:
+        pd.DataFrame: Summary of sensitivities,
+        corresponding realisation numbers,
+        senstype('mc' or 'scalar')
+        and senscase (name of high and low cases).
+        Each row represents one sensitivity
+        with 1-2 cases (low/high).
+        Column names are ['sensno', 'sensname',
+        'senstype', 'casename1', 'startreal1', 'endreal1',
+        'casename2', 'startreal2', 'endreal2']
+
+    Example:
+        >> from semeio.fmudesign import summarize_design
+        >> designname = 'design_filename.xlsx'
+        >> designsheet = 'DesignSheet01'
+        >> designtable = summarize_design(designname, designsheet)
+
+    """
+
+    # Read design matrix
+    if str(filename).endswith(".xlsx"):
+        # Drop empty rows or columns that have been read in
+        # due to having background colour/formatting
+        dgn = (
+            pd.read_excel(filename, sheetname, engine="openpyxl")
+            .dropna(axis=0, how="all")
+            .loc[:, lambda df: ~df.columns.str.contains("^Unnamed")]
+        )
+    elif str(filename).endswith(".csv"):
+        dgn = pd.read_csv(filename)
+    else:
+        raise ValueError(
+            "Design matrix must be on Excel or csv format"
+            " and filename must end with .xlsx or .csv"
+        )
+
+    # Initialize results DataFrame with same columns
+    designsummary = pd.DataFrame(
+        columns=[
+            "sensno",
+            "sensname",
+            "senstype",
+            "casename1",
+            "startreal1",
+            "endreal1",
+            "casename2",
+            "startreal2",
+            "endreal2",
+        ]
+    )
+
+    # Get unique sensitivity names in order of appearance
+    sensnames = dgn["SENSNAME"].unique()
+
+    for sensno, sensname in enumerate(sensnames):
+        sens_group = dgn[dgn["SENSNAME"] == sensname].copy()
+        # Get cases in order of appearance
+        cases = (
+            sens_group.drop_duplicates("SENSCASE")[["SENSCASE"]].to_numpy().flatten()
+        )
+
+        # First case
+        case1_data = sens_group[sens_group["SENSCASE"] == cases[0]]
+        casename1 = cases[0]
+        startreal1 = case1_data["REAL"].min()
+        endreal1 = case1_data["REAL"].max()
+
+        # Handle second case if it exists
+        if len(cases) > 1:
+            case2_data = sens_group[sens_group["SENSCASE"] == cases[1]]
+            casename2 = cases[1]
+            startreal2 = case2_data["REAL"].min()
+            endreal2 = case2_data["REAL"].max()
+        else:
+            casename2 = None
+            startreal2 = None
+            endreal2 = None
+
+        senstype = _get_sensitivity_type(cases[0])
+        # Add row to results
+        designsummary.loc[sensno] = [
+            sensno,
+            sensname,
+            senstype,
+            casename1,
+            startreal1,
+            endreal1,
+            casename2,
+            startreal2,
+            endreal2,
+        ]
+
+    return designsummary