diff --git a/landbosse/model/ErectionCost.py b/landbosse/model/ErectionCost.py
index 53257d6c..d2059a32 100644
--- a/landbosse/model/ErectionCost.py
+++ b/landbosse/model/ErectionCost.py
@@ -865,9 +865,10 @@ def aggregate_erection_costs(self):
 
         Returns
         -------
-        (pd.DataFrame, pd.DataFrame)
-            Two dataframes: First, utilizing the same crane for base and topping.
-            Second, utilizing separate cranes for base and topping
+        Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]
+            Two dataframes: First, utilizing the separate cranes for base and topping.
+            Second, utilizing same crane for base and topping,
+            Third is crew cost.
         """
         join_wind_operation = self.output_dict['join_wind_operation']
         overtime_multiplier = self.input_dict['overtime_multiplier']
@@ -925,6 +926,7 @@ def aggregate_erection_costs(self):
         crew_cost_grouped = crew_cost.groupby(['Crew type ID', 'Operation']).sum().reset_index()
 
         # merge crane data with grouped crew costs
+
         possible_crane_cost = pd.merge(possible_crane_cost, crew_cost_grouped, on=['Crew type ID', 'Operation'])
 
         # calculate labor costs
diff --git a/post_processing_scripts/extract_crane_choice.py b/post_processing_scripts/extract_crane_choice.py
new file mode 100644
index 00000000..417f5d1f
--- /dev/null
+++ b/post_processing_scripts/extract_crane_choice.py
@@ -0,0 +1,119 @@
+import pandas as pd
+
+# The extended_landbosse_details dataframe includes all the details along side
+# all the project list inputs
+print("Reading extended details...")
+df = pd.read_csv("extended_landbosse_details.csv")
+
+# Extract the crane choice data
+print("Selecting erection data...")
+erection_df = df.query("`Module` == 'ErectionCost'")[[
+    "Project ID with serial",
+    "Variable name",
+    "Numeric value",
+    "Non-numeric value",
+    "Number of turbines",
+    "Turbine rating MW",
+    "Hub height m",
+    "Labor cost multiplier",
+    "Crane breakdown fraction",
+    "Breakpoint between base and topping (percent)",
+    "Total project construction time (months)"
+]]
+
+aligned_erection_rows = []
+
+print("Selecting unique projects...")
+unique_project_id_with_serial = erection_df['Project ID with serial'].unique()
+
+print("Rearranging crane detail data from rows into columns...")
+for project_id_with_serial in unique_project_id_with_serial:
+    print(f"\t{project_id_with_serial}")
+
+    # Crane data output
+    crane_data_df = erection_df.query(
+        "`Project ID with serial` == @project_id_with_serial and `Variable name` == 'crane_data_output: crane_boom_operation_concat - variable - value'"
+    )
+    top_wind_multiplier_row = crane_data_df[crane_data_df["Non-numeric value"].str.contains("Top - Wind multiplier")]
+    base_wind_multiplier_row = crane_data_df[crane_data_df["Non-numeric value"].str.contains("Base - Wind multiplier")]
+    offload_wind_multiplier_row = crane_data_df[crane_data_df["Non-numeric value"].str.contains("Offload - Wind multiplier")]
+    top_wind_multiplier = top_wind_multiplier_row["Numeric value"].values[0]
+    offload_wind_multiplier = offload_wind_multiplier_row["Numeric value"].values[0]
+    if len(base_wind_multiplier_row) > 0:
+        base_wind_multiplier = base_wind_multiplier_row["Numeric value"].values[0]
+    else:
+        base_wind_multiplier = None
+
+    # Re-using crane_data_df from above, operation time for all turbines
+    top_operation_time_hours_row = crane_data_df[
+        crane_data_df["Non-numeric value"].str.contains("Top - Operation time all turbines hrs")]
+    offload_operation_time_hours_row = crane_data_df[
+        crane_data_df["Non-numeric value"].str.contains("Offload - Operation time all turbines hrs")]
+    base_operation_time_hours_row = crane_data_df[
+        crane_data_df["Non-numeric value"].str.contains("Base - Operation time all turbines hrs")]
+    top_operation_time_hours = top_operation_time_hours_row["Numeric value"].values[0]
+    offload_operation_time_hours = offload_operation_time_hours_row["Numeric value"].values[0]
+    if len(base_operation_time_hours_row) > 0:
+        base_operation_time_hours = base_operation_time_hours_row["Numeric value"].values[0]
+    else:
+        base_operation_time_hours = None
+
+    # Crane cost details
+    crane_cost_df = erection_df.query(
+        "`Project ID with serial` == @project_id_with_serial and `Variable name` == 'crane_cost_details: Operation ID - Type of cost - Cost'"
+    )
+    top_total_cost_row = crane_cost_df[crane_cost_df["Non-numeric value"].str.contains("Top - Total cost USD")]
+    base_total_cost_row = crane_cost_df[crane_cost_df["Non-numeric value"].str.contains("Base - Total cost USD")]
+    offload_total_cost_row = crane_cost_df[crane_cost_df["Non-numeric value"].str.contains("Offload - Total cost USD")]
+    top_total_cost = top_total_cost_row["Numeric value"].values[0]
+    offload_total_cost = offload_total_cost_row["Numeric value"].values[0]
+    if len(base_total_cost_row) > 0:
+        base_total_cost = base_total_cost_row["Numeric value"].values[0]
+    else:
+        base_total_cost = None
+
+    # Crane choice
+    crane_choice_rows_df = erection_df.query(
+        "`Project ID with serial` == @project_id_with_serial and `Variable name` == 'crane_choice: Crew name - Boom system - Operation'"
+    )
+    top_row = crane_choice_rows_df[crane_choice_rows_df["Non-numeric value"].str.contains("Top")]
+    base_row = crane_choice_rows_df[crane_choice_rows_df["Non-numeric value"].str.contains("Base")]
+    offload_row = crane_choice_rows_df[crane_choice_rows_df["Non-numeric value"].str.contains("Offload")]
+    offload = " ".join(offload_row["Non-numeric value"].values[0].split(" - ")[:-1])
+    top = " ".join(top_row["Non-numeric value"].values[0].split(" - ")[:-1])
+    if len(base_row) > 0:
+        base = " ".join(base_row["Non-numeric value"].values[0].split(" - ")[:-1])
+    else:
+        base = None
+
+    aligned_erection_row = {
+        "Project ID with serial": project_id_with_serial,
+        "Number of turbines": top_row["Number of turbines"].values[0],
+        "Breakpoint between base and topping (percent)": \
+            top_row["Breakpoint between base and topping (percent)"].values[0],
+        "Turbine rating MW": top_row["Turbine rating MW"].values[0],
+        "Crane breakdown fraction": top_row["Crane breakdown fraction"].values[0],
+        "Labor cost multiplier": top_row["Labor cost multiplier"].values[0],
+        "Hub height m": top_row["Hub height m"].values[0],
+        "Base crane choice": base,
+        "Offload crane choice": offload,
+        "Top crane choice": top,
+        "Base total cost":
+            round(float(base_total_cost), 0) if base_total_cost is not None else None,
+        "Offload total cost": round(float(offload_total_cost), 0),
+        "Top total cost": round(float(top_total_cost), 0),
+        "Base wind multiplier":
+            round(float(base_wind_multiplier), 2) if base_wind_multiplier is not None else None,
+        "Offload wind multiplier": round(float(offload_wind_multiplier), 2),
+        "Top wind multiplier": round(float(top_wind_multiplier), 2),
+        "Base operation time all turbines (hours)":
+            round(float(base_operation_time_hours), 0) if base_operation_time_hours is not None else None,
+        "Offload operation time all turbines (hours)": round(float(offload_operation_time_hours), 0),
+        "Top operation time all turbines (hours)": round(float(top_operation_time_hours), 0)
+    }
+
+    aligned_erection_rows.append(aligned_erection_row)
+
+print("Writing crane choices...")
+aligned_crane_choice_df = pd.DataFrame(aligned_erection_rows)
+aligned_crane_choice_df.to_csv("crane_details.csv", index=False)
diff --git a/post_processing_scripts/sql_queries/everything_against_baseline.sql b/post_processing_scripts/sql_queries/everything_against_baseline.sql
deleted file mode 100644
index 77d5e20c..00000000
--- a/post_processing_scripts/sql_queries/everything_against_baseline.sql
+++ /dev/null
@@ -1,47 +0,0 @@
-WITH baseline AS (SELECT
-	 "Project ID with serial" AS "Baseline project ID with serial",
-	 "Module",
-	 "Type of cost",
-	 ROUND("Cost per turbine"::numeric, 0) AS "Baseline $/turbine",
-	 ROUND("Cost per project"::numeric, 0) AS "Baseline $/project",
-	 ROUND("Cost per kW"::numeric, 0) AS "Baseline $/kW",
-	 "Turbine rating MW" AS "Baseline turbine rating (MW)",
-	 "Hub height m" AS "Baseline hub height",
-	 "Labor cost multiplier" AS "Baseline labor cost multiplier",
-	 "Crane breakdown fraction" AS "Baseline crane breakdown fraction",
-	 ROUND("Number of turbines"::numeric * "Turbine rating MW"::numeric, -1) AS "Baseline plant size (MW)"
-FROM
-	landbosse_costs
-WHERE
-	ROUND("Number of turbines"::numeric * "Turbine rating MW"::numeric, -1) = 150
-	AND "Hub height m" = 90
-	AND "Turbine rating MW" = 2.5
-	AND "Crane breakdown fraction" = 0
-	AND "Labor cost multiplier" = 1.0
-ORDER BY 1, 2, 3)
-
-SELECT
-	lc."Project ID with serial",
-	lc."Module",
-	lc."Type of cost",
-	ROUND(lc."Cost per turbine"::numeric, 0) AS "$/turbine",
-	ROUND(lc."Cost per project"::numeric, 0) AS "$/project",
-	ROUND(lc."Cost per kW"::numeric, 0) AS "$/kW",
-	b."Baseline $/turbine",
-	b."Baseline $/project",
-	b."Baseline $/kW",
-	lc."Turbine rating MW" AS "Turbine rating (MW)",
-	lc."Hub height m",
-	lc."Labor cost multiplier",
-	lc."Crane breakdown fraction",
-	ROUND(lc."Number of turbines"::numeric * lc."Turbine rating MW"::numeric, -1) AS "Plant size (MW)",
-	b."Baseline turbine rating (MW)",
-	b."Baseline hub height",
-	b."Baseline labor cost multiplier",
-	b."Baseline crane breakdown fraction",
-	b."Baseline plant size (MW)"
-FROM landbosse_costs lc
-JOIN baseline b
-	ON b."Module" = lc."Module"
-	AND b."Type of cost" = lc."Type of cost"
-ORDER BY 1, 2, 3;
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 5677de0b..477a0c94 100644
--- a/setup.py
+++ b/setup.py
@@ -28,8 +28,6 @@
         'scipy',
         'xlsxwriter',
         'xlrd',
-        'psycopg2-binary',
-        'sqlalchemy',
         'pytest'
     ],
     command_options={