Merge pull request #159 from WISDEM/release-2.3.3

Release 2.3.3

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

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)

setup.py

@@ -28,8 +28,6 @@
         'scipy',
         'xlsxwriter',
         'xlrd',
-        'psycopg2-binary',
-        'sqlalchemy',
         'pytest'
     ],
     command_options={