def objfnc(x):
global best_fitness
global best_solution
global list_of_best_fitness
# Convert input into index_parameter_matrix
x = np.asarray(x).round()
parameter_matrix = x.reshape(-1, 2)
index_matrix = np.arange(1, len(parameter_matrix) + 1).reshape(-1, 1)
index_parameter_matrix = np.concatenate((index_matrix, parameter_matrix),
axis=1)
# Prepare avoided points (River Cells)
rivercells = extract_rivercells()
# Check if wells satisfy constraints. Return zero if they do not
for well in parameter_matrix:
# Check if the well is too close to the river
if n_nearest_dist(rivercells, well, 0) < 1.0:
return 0
# Check if the well is too close to another well:
if n_nearest_dist(parameter_matrix, well, 1) < 1.0:
return 0
# Prepare new GWM files using the index-parameter matrix
write_supply2decvar(index_parameter_matrix)
write_supply2hedcon(index_parameter_matrix)
# Run GWM
# print("Running gwm on: ", x, end=" ")
try:
run_gwm()
except:
print("Error with run_gwm")
return 0
# Define ordered list of wells
wells = ["Q1", "Q2", "Q3", "Q4"]
# Read in the new fitness vector and return the negative sum
totalfitness = -read_fitness_array(wells).sum().round()
# print("fitness: ", totalfitness, end=" ")
# Save best
if totalfitness < -best_fitness:
best_fitness = -totalfitness
best_solution = parameter_matrix
print(best_fitness)
print(best_solution)
list_of_best_fitness.append(best_fitness)
return totalfitness
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from GWM_Manipulator import extract_rivercells
# Streamflow Constraint
strmflw_cnstrnt = np.array([[14, 14], [14, 21], [13, 23], [15, 26]])
# Load in potentiometric surface
surf = np.loadtxt("Supply2_PotenSurf.txt")
surf = surf.reshape(25, 30)
print(surf, surf.shape)
# Setup empty grid model
data = np.ones((25, 30)) * np.nan # Rows, Cols
# put river cells in matrix
rivercells = extract_rivercells()
for rivercell in rivercells:
data[rivercell[0] - 1, rivercell[1] - 1] = 0
print("data")
print(data)
print(data.shape)
# make a figure + axes
fig, ax = plt.subplots(1, 1, tight_layout=True, figsize=(8, 5.5))
# make color map
river_cmap = matplotlib.colors.ListedColormap(['aqua'])
hk_cmap = matplotlib.colors.ListedColormap(['r', 'b'])
surf_cmap = plt.get_cmap('hsv', 14)
# set the 'bad' values (nan) to be white and transparent
river_cmap.set_bad(color='w', alpha=0)
# draw the grid