Remove dependency on cost_model_queries

pyproject.toml

@@ -5,14 +5,14 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "cost-model-queries==0.0.12",
     "geopandas>=1.0.1",
     "ipython>=9.5.0",
     "netcdf4>=1.7.2",
     "numpy>=2.2.4",
     "pandas>=2.2.3",
     "pywin32>=310",
     "pyyaml>=6.0.2",
+    "salib>=1.5.2",
     "sphinx>=8.2.3",
     "sphinx-autoapi>=3.6.0",
 ]

src/cost_calculations.py

@@ -4,11 +4,11 @@
 import json
 import pandas as pd
 
-from cost_model_queries.sampling.sampling_functions import (
+from sampling import (
     problem_spec,
     convert_factor_types,
     sample_deployment_cost,
-    sample_production_cost
+    sample_production_cost,
 )
 
 def load_config():

src/sampling.py

@@ -0,0 +1,254 @@
+import win32com.client
+from SALib import ProblemSpec
+import numpy as np
+import pandas as pd
+
+
+def calculate_deployment_cost(wb, factor_spec, factors):
+    """
+    Calculates set up and operational costs in the deployment cost model (wb), given a set of parameters to sample.
+
+    Parameters
+    ----------
+        wb : Workbook
+            The cost model as an excel workbook
+        factor_spec : dataframe
+            The factor specification, as loaded from the config.csv
+        factors : dataframerow
+            Row of a pandas dataframe with factors to sample
+
+    Returns
+    -------
+        Cost: float
+            Operational cost
+        setupCost: float
+            Setup cost
+    """
+    reef_key = ["Moore", "Davies", "Swains", "Keppel"]
+    port = factors["port"].iloc[0]
+
+    for _, factor_row in factor_spec[
+        (factor_spec.factor_names != "Cost") & (factor_spec.factor_names != "setupCost")
+    ].iterrows():
+        ws = wb.Sheets(factor_row.sheet)
+        if factor_row.factor_names == "distance_from_port":
+            ws.Cells(factor_row.cell_row + port, factor_row.cell_col).Value = factors[
+                factor_row.factor_names
+            ].iloc[0]
+        elif factor_row.factor_names == "port":
+            ws.Cells(factor_row.cell_row, factor_row.cell_col).Value = reef_key[
+                port - 1
+            ]
+        else:
+            ws.Cells(factor_row.cell_row, factor_row.cell_col).Value = factors[
+                factor_row.factor_names
+            ].iloc[0]
+
+    ws = wb.Sheets("Dashboard")
+    ws.EnableCalculation = True
+    ws.Calculate()
+
+    # get the new output
+    cost_cells = factor_spec.loc[factor_spec.factor_names == "Cost"]
+    setupcost_cells = factor_spec.loc[factor_spec.factor_names == "setupCost"]
+    Cost = ws.Cells(cost_cells.cell_row.iloc[0], cost_cells.cell_col.iloc[0]).Value
+    setupCost = ws.Cells(
+        setupcost_cells.cell_row.iloc[0], setupcost_cells.cell_col.iloc[0]
+    ).Value
+
+    return [Cost, setupCost]
+
+
+def calculate_production_cost(wb, factor_spec, factors):
+    """
+    Calculates set up and operational costs in the production cost model (wb), given a set of parameters to sample.
+
+    Parameters
+    ----------
+        wb : Workbook
+            The cost model as an excel workbook
+        factor_spec : dataframe
+            factor specification, as loaded from the config.csv
+        factors : dataframerow
+            Row of a pandas dataframe with factors to sample
+
+    Returns
+    -------
+        Cost: float
+            Operational cost
+        setupCost: float
+            Setup cost
+    """
+    for _, factor_row in factor_spec[
+        (factor_spec.factor_names != "Cost") & (factor_spec.factor_names != "setupCost")
+    ].iterrows():
+        ws = wb.Sheets(factor_row.sheet)
+
+        ws.Cells(factor_row.cell_row, factor_row.cell_col).Value = factors[
+            factor_row.factor_names
+        ].iloc[0]
+
+    ws = wb.Sheets("Dashboard")
+    ws.EnableCalculation = True
+    ws.Calculate()
+
+    # get the new output
+    cost_cells = factor_spec.loc[factor_spec.factor_names == "Cost"]
+    setupcost_cells = factor_spec.loc[factor_spec.factor_names == "setupCost"]
+    Cost = ws.Cells(cost_cells.cell_row.iloc[0], cost_cells.cell_col.iloc[0]).Value
+    setupCost = ws.Cells(
+        setupcost_cells.cell_row.iloc[0], setupcost_cells.cell_col.iloc[0]
+    ).Value
+
+    return [Cost, setupCost]
+
+
+def load_config(config_filepath="config.csv"):
+    """
+    Load configuration file for model sampling
+
+    Parameters
+    ----------
+        config_filepath : str
+            String specifying filepath of config file, default is the default package config file
+    """
+    return pd.read_csv(config_filepath)
+
+
+def problem_spec(cost_type, config_filepath="config.csv"):
+    """
+    Create a problem specification for sampling using SALib.
+
+    Parameters
+    ----------
+        cost_type : str
+            String specifying cost model type, "production_params" or "deployment_params"
+        config_filepath : str
+            String specifying filepath of config file, default is the default package config file
+
+    Returns
+    -------
+        sp: dict
+            ProblemSpec for sampling with SALib
+        factor_spec : dataframe
+            factor specification, as loaded from the config.csv
+    """
+    if (cost_type != "production") & (cost_type != "deployment"):
+        raise ValueError("Non-existent parameter type")
+
+    factor_specs = load_config(config_filepath=config_filepath)
+    factor_specs = factor_specs[factor_specs.cost_type == cost_type]
+    factor_ranges = [
+        factor_specs[["range_lower", "range_upper"]].iloc[k].values
+        for k in range(factor_specs.shape[0])
+    ]
+
+    problem_dict = {
+        "num_vars": factor_specs.shape[0],
+        "names": [name for name in factor_specs.factor_names],
+        "bounds": factor_ranges,
+    }
+    return ProblemSpec(problem_dict), factor_specs
+
+
+def convert_factor_types(factors_df, is_cat):
+    """
+    SALib samples floats, so convert categorical variables to integers by taking the ceiling.
+
+    Parameters
+    ----------
+        factors_df : dataframe
+            A dataframe of sampled factors
+        is_cat : list{bool}
+            Boolian vector specifian whether each factor is categorical
+
+    Returns:
+        factors_df: Updated sampled factor dataframe with categorical factors as integers
+    """
+    for ic_ind, ic in enumerate(is_cat):
+        if ic:
+            factors_df[factors_df.columns[ic_ind]] = np.ceil(
+                factors_df[factors_df.columns[ic_ind]]
+            ).astype(int)
+
+    return factors_df
+
+
+def _sample_cost(wb_file_path, factors_df, factor_spec, calculate_cost):
+    """
+    Sample a cost model.
+
+    Parameters
+    ----------
+        wb_file_path : str
+            Filepath to a cost model as an excel workbook
+        factors_df : dataframe
+            Dataframe of factors to input in the cost model
+        factor_spec : dataframe
+            factor specification, as loaded from the config.csv
+        calculate_cost: function
+            Function to use to sample cost
+
+    Returns
+    -------
+        factors_df : dataframe
+            Updated sampled factor dataframe with costs added
+    """
+    xlApp = win32com.client.Dispatch("Excel.Application")  # Open workbook
+    wb = xlApp.Workbooks.Open(wb_file_path)
+
+    total_cost = np.zeros((factors_df.shape[0], 2))
+    for idx_n in range(len(total_cost)):
+        total_cost[idx_n, :] = calculate_cost(wb, factor_spec, factors_df.iloc[[idx_n]])
+
+    factors_df.loc[:, "Cost"] = total_cost[:, 0]
+    factors_df.loc[:, "setupCost"] = total_cost[:, 1]
+
+    wb.Close(True)  # Close workbook
+    return factors_df
+
+
+def sample_deployment_cost(wb_file_path, factors_df, factor_spec):
+    """
+    Sample the deployment cost model.
+
+    Parameters
+    ----------
+        wb_file_path : str
+            Filepath to a cost model as an excel workbook
+        factors_df : dataframe
+            Dataframe of factors to input in the cost model
+        factor_spec : dataframe
+            factor specification, as loaded from the config.csv
+
+    Returns
+    -------
+        factors_df : dataframe
+            Updated sampled factor dataframe with costs added
+    """
+    return _sample_cost(
+        wb_file_path, factors_df, factor_spec, calculate_deployment_cost
+    )
+
+
+def sample_production_cost(wb_file_path, factors_df, factor_spec):
+    """
+    Sample the production cost model.
+
+    Parameters
+    ----------
+        wb_file_path : Workbook file path
+            A cost model as an excel workbook
+        factors_df : dataframe
+            Dataframe of factors to input in the cost model
+        factor_spec : dataframe
+            Factor specification, as loaded from the config.csv
+
+    Returns
+    -------
+        factors_df : dataframe
+            Updated sampled factor dataframe with costs added
+    """
+    return _sample_cost(
+        wb_file_path, factors_df, factor_spec, calculate_production_cost
+    )