Merge pull request #158 from WISDEM/release-v2.3.2

Release v2.3.2

.travis.yml

@@ -4,10 +4,9 @@ python:
 
 # command to install dependencies
 install:
-  - pip install pandas==0.25.2
+  - pip install pandas
   - pip install numpy
-  - pip install sympy
-  - pip install shapely
+  - pip install scipy
   - pip install xlsxwriter
   - pip install xlrd
   - pip install psycopg2-binary

installation_instructions/macos_developer.md

@@ -131,8 +131,8 @@ The file named `projects_list.xlsx` must keep the same name. The names of the pr
 Suppose that you have created the directory structure on your desktop as mentioned above in step 2 "Create a folder structure". You can set the environment variables and execute LandBOSSE with a couple easy commands:
 
 ```
-cd ~/Desktop/landbosse
-LANDBOSSE_INPUT_DIR=~/Desktop/landbosse/input LANDBOSSE_OUTPUT_DIR=~/Desktop/landbosse/output python LandBOSSE/main.py
+cd ~/Desktop/landbosse/LandBOSSE
+python main.py -i ~/Desktop/landbosse/input -o ~/Desktop/landbosse/output 
 ```
 
 The file will be produced with a filename like:

landbosse/excelio/XlsxReader.py

@@ -592,7 +592,7 @@ def apply_cost_and_scaling_modifications_to_project_parameters(self, project_par
 
         number_of_access_roads = 0.0 if project_size_MW <= 20 else ceil(0.0052 * project_size_MW + 0.7917)
         number_of_highway_permits = ceil(0.2 * project_parameters['Number of turbines'])
-        if flag_use_user_homerun is 1:
+        if flag_use_user_homerun == 1:
              project_parameters['Combined Homerun Trench Length to Substation (km)'] = 0.1776 * project_size_MW - 2.551
 
         # 10 deliveries per week for 1.5 MW machines

landbosse/model/ErectionCost.py

@@ -1,7 +1,5 @@
 import pandas as pd
 import numpy as np
-from shapely.geometry import Point
-from shapely.geometry.polygon import Polygon
 from math import ceil
 
 from .CostModule import CostModule
@@ -15,6 +13,40 @@
 m_per_ft = 0.3048
 
 
+class Point(object):
+    def __init__(self, x, y):
+        if type(x) == type(pd.Series()):
+            self.x = float(x.values[0])
+            self.y = float(y.values[0])
+        elif type(x) == type(np.array([])):
+            self.x = float(x[0])
+            self.y = float(y[0])
+        elif type(x) == type(int(0)):
+            self.x = float(x)
+            self.y = float(y)
+        elif type(x) == type(float(0.0)):
+            self.x = x
+            self.y = y
+        else:
+            raise ValueError(type(x))
+
+def ccw(A,B,C):
+    return (C.y-A.y) * (B.x-A.x) > (B.y-A.y) * (C.x-A.x)
+
+# Return true if line segments AB and CD intersect
+def intersect(A,B,C,D):
+    return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)
+
+def point_in_polygon(pt, poly):
+    result = False
+    maxx = float(np.r_[pt.x, np.array([m.x for m in poly])].max())
+    for i in range(len(poly)-1):
+        if intersect(poly[i], poly[i+1], pt, Point(1.1*maxx, pt.y)):
+            result = not result
+    if intersect(poly[-1], poly[0], pt, Point(1.1*maxx, pt.y)):
+        result = not result
+    return result
+
 class ErectionCost(CostModule):
     """
     ErectionCost.py
@@ -643,7 +675,7 @@ def calculate_crane_lift_polygons(self, crane_grouped):
             setup_time = max(crane['Setup time hr'])
             breakdown_time = max(crane['Breakdown time hr'])
             crew_type = crane.loc[0, 'Crew type ID'] # For every crane/boom combo the crew is the same, so we can just take first crew.
-            polygon = Polygon([(0, 0), (0, max(y)), (min(x), max(y)), (max(x), min(y)), (max(x), 0)])
+            polygon = [Point(0, 0), Point(0, max(y)), Point(min(x), max(y)), Point(max(x), min(y)), Point(max(x), 0)]
             df = pd.DataFrame([[equipment_name,
                                 equipment_id,
                                 crane_name,
@@ -726,7 +758,7 @@ def calculate_component_lift_max_wind_speed(self, *, component_group, crane_poly
                     point = Point(component_only['Mass tonne'] / 2, (component_only['Section height m'] + component_only['Offload hook height m']))
                 else:
                     point = Point(component_only['Mass tonne'], (component_only['Lift height m'] + component_only['Offload hook height m']))
-                crane['Lift boolean {component}'.format(component=component)] = polygon.contains(point)
+                crane['Lift boolean {component}'.format(component=component)] = point_in_polygon(point, polygon)
 
             # Transform the "Lift boolean" indexes in the series to a list of booleans
             # that signify if the crane can lift a component.

landbosse/model/FoundationCost.py

@@ -4,8 +4,7 @@
 import pandas as pd
 import numpy as np
 import math
-from sympy.solvers import solve
-from sympy import Symbol
+from scipy.optimize import root_scalar
 
 from .WeatherDelay import WeatherDelay as WD
 from .CostModule import CostModule
@@ -343,41 +342,30 @@ def calculate_foundation_load(self, foundation_load_input_data, foundation_load_
         if (r_test_gapping / 3) < e:
             r_gapping = 0
         else:
-            r_g = Symbol('r_g', real=True, positive=True)
-            foundation_vol = np.pi * r_g ** 2 * foundation_load_input_data['depth']
-            v_1 = (foundation_vol * (vol_fraction_fill * unit_weight_fill + vol_fraction_concrete * unit_weight_concrete) + f_dead)
-            e = m_tot / v_1
-            r_gapping = solve(e * 3 - r_g, r_g)
-            if len(r_gapping) > 0:
-                r_gapping = max(r_gapping)
-            else:
-                r_gapping = 0
+            def r_g(x):
+                foundation_vol = np.pi * x** 2 * foundation_load_input_data['depth']
+                v_1 = (foundation_vol * (vol_fraction_fill * unit_weight_fill + vol_fraction_concrete * unit_weight_concrete) + f_dead)
+                e = m_tot / v_1
+                return (e * 3 - x)
+            result = root_scalar(r_g, method='brentq', bracket=[0.9*r_overturn, 50], xtol=1e-4, maxiter=50)
+            r_gapping = result.root
+            if not result.converged:
+                raise ValueError(f'Warning {self.project_name} calculate_foundation_load r_gapping solve failed, {result.flag}')
 
         r_test_bearing = max(r_test_gapping, r_gapping)
 
         # calculate foundation radius based on bearing pressure
+        def r_b(x):
+            foundation_vol = np.pi * r_test_bearing ** 2 * foundation_load_input_data['depth']
+            v_1 = (foundation_vol * (vol_fraction_fill * unit_weight_fill + vol_fraction_concrete * unit_weight_concrete) + f_dead)
+            e = m_tot / v_1
+            a_eff = v_1 / bearing_pressure
+            return (2 * (x ** 2 - e * (x ** 2 - e ** 2) ** 0.5) - a_eff)
+        result = root_scalar(r_b, method='brentq', bracket=[0.9*r_overturn, 50], xtol=1e-10, maxiter=50)
+        r_bearing = result.root
 
-        # Restrict r_b to only real numbers. Positive solutions for r_b are
-        # selected below
-        r_b = Symbol('r_b', real=True)
-
-        foundation_vol = np.pi * r_test_bearing ** 2 * foundation_load_input_data['depth']
-        v_1 = (foundation_vol * (vol_fraction_fill * unit_weight_fill + vol_fraction_concrete * unit_weight_concrete) + f_dead)
-        e = m_tot / v_1
-        a_eff = v_1 / bearing_pressure
-        r_bearing = solve(2 * (r_b ** 2 - e * (r_b ** 2 - e ** 2) ** 0.5) - a_eff, r_b)
-
-        # Select only positive solutions to r_b. This is selected by max(). If there are
-        # not positive solutions to r_b, that means something is wrong with the foundation
-        # parameters. In that case, generate a warning below.
-
-        if len(r_bearing) > 0:
-            r_bearing = max(r_bearing)
-        else:
-            r_bearing = 0
-
-        if r_bearing < 0:
-            raise ValueError(f'Warning {self.project_name} calculate_foundation_load r_bearing is negative, r_bearing={r_bearing}')
+        if not result.converged:
+            raise ValueError(f'Warning {self.project_name} calculate_foundation_load r_bearing solve failed, {result.flag}')
 
         # pick the largest foundation radius based on all 4 foundation design criteria: moment, gapping, bearing, slipping
         r_choosen = max(r_bearing, r_overturn, r_slipping, r_gapping)
@@ -778,12 +766,6 @@ def outputs_for_detailed_tab(self, input_dict, output_dict):
             'variable_df_key_col_name': 'Radius',
             'value': float(self.output_dict['Radius_m'])
         })
-        result.append({
-            'unit': 'm^3',
-            'type': 'variable',
-            'variable_df_key_col_name': 'foundation_volume_concrete_m3_per_turbine',
-            'value': float(self.output_dict['foundation_volume_concrete_m3_per_turbine'])
-        })
         result.append({
             'unit': 'short_ton',
             'type': 'variable',

post_processing_scripts/extended_landbosse_output_to_csv_and_pgsql.py

@@ -33,7 +33,7 @@
 join_landbosse_output_costs.to_csv("extended_landbosse_costs.csv", index=False)
 join_landbosse_output_details.to_csv("extended_landbosse_details.csv", index=False)
 
-load_into_database_enabled = True
+load_into_database_enabled = False
 if load_into_database_enabled:
     print("Load into database...")
 

setup.py

@@ -23,11 +23,9 @@
     test_suite='nose.collector',
     tests_require=['nose'],
     install_requires=[
-        'pandas==0.25.2',
+        'pandas',
         'numpy',
-        'sympy',
         'scipy',
-        'shapely',
         'xlsxwriter',
         'xlrd',
         'psycopg2-binary',