def plot_production_history_with_fit_and_predict():
df = pd.read_csv(predict_file)
starting_index = producer_starting_indicies[1]
producer = producers[1][starting_index:]
injectors_tmp = [injector[starting_index:] for injector in injectors]
X, y = production_rate_dataset(producer, *injectors_tmp)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.5,
shuffle=False)
crmp = CRMP().fit(X_train, y_train)
for i in range(len(producer_names)):
producer_df = producer_rows_from_df(df, i + 1)
starting_index = producer_starting_indicies[i]
producer = producers[i][starting_index:]
injectors_tmp = [injector[starting_index:] for injector in injectors]
X, y = production_rate_dataset(producer, *injectors_tmp)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.5,
shuffle=False)
producer_length = len(producer)
t = np.linspace(1, producer_length, producer_length)
train_length = len(y_train)
train_time = t[:train_length]
test_time = t[train_length:][1:]
empty = []
plt.plot(empty, empty, c='r', label='Fit')
plt.plot(empty, empty, c='g', label='Predict')
plt.plot(t, producer, c='k')
for index, row in producer_df.iterrows():
tau = row['tau_final']
f1 = row['f1_final']
f2 = row['f2_final']
f3 = row['f3_final']
f4 = row['f4_final']
crmp.tau_ = tau
crmp.gains_ = [f1, f2, f3, f4]
# Fitting
y_hat = crmp.predict(X_train)
plt.plot(train_time, y_hat, '--', alpha=0.02, c='r', linewidth=2)
# Prediction
y_hat = crmp.predict(X_test)
plt.plot(test_time, y_hat, ':', alpha=0.02, c='g', linewidth=2)
plt.vlines(test_time[0],
0,
1.1 * max(producer),
linewidth=2,
alpha=0.8)
plot_helper(FIG_DIR,
title=producer_names[i],
xlabel='Time [days]',
ylabel='Production Rate [bbls/day]',
legend=True,
save=True)
def objective_function_contour_plot():
for i in range(number_of_producers):
producer = i + 1
producer_df = producer_rows_from_df(objective_function_df, producer)
x, y, z = contour_params(producer_df,
x_column='f1',
y_column='tau',
z_column='MSE')
plt.contourf(x, y, z, 15, alpha=1.0)
plt.colorbar()
title = 'CRMP: Producer {} Objective Function'.format(producer)
x_true, y_true = true_params[producer]
actual = plt.scatter(x_true,
y_true,
s=100,
c='r',
label='True Value',
alpha=0.4)
plt.legend(handles=[actual], loc='upper left')
plt.tight_layout()
plt.ylim(0, 100)
plot_helper(FIG_DIR,
title=title,
xlabel=xlabel,
ylabel=ylabel,
save=True)
def smallest_error_by_well():
objective_function_file = INPUTS['crmp']['crmp']['predict'][
'objective_function']
objective_function_df = pd.read_csv(objective_function_file)
for i in range(number_of_producers):
producer = i + 1
producer_rows_df = producer_rows_from_df(objective_function_df,
producer)
min_mse = producer_rows_df['MSE'].min()
print(min_mse)
print(np.log(min_mse))
print()
print()
print()
def parameter_convergence():
for i in range(len(producers)):
plt.figure(figsize=[7, 4.8])
producer = i + 1
producer_rows_df = producer_rows_from_df(
fitting_sensitivity_analysis_df, producer)
x, y = initial_and_final_params_from_df(producer_rows_df)
x_true, y_true = true_params[producer]
for j in range(len(x)):
initial = plt.scatter(x[j][0],
y[j][0],
s=40,
c='g',
marker='o',
label='Initial')
final = plt.scatter(x[j][1],
y[j][1],
s=40,
c='r',
marker='x',
label='Final')
plt.plot(x[j], y[j], c='k', alpha=0.15)
actual = plt.scatter(x_true,
y_true,
s=200,
c='b',
marker='X',
label='True Value')
# actual = plt.scatter(
# x_true, y_true, s=100, c='r', label='Actual', alpha=0.5
# )
title = 'CRMP: Producer {} Initial Parameter Values with Convergence'.format(
producer)
plt.legend(handles=[actual, initial, final],
bbox_to_anchor=(1.04, 1),
loc="upper left")
plt.xlim(0, 1)
plt.ylim(0, 100)
plt.tight_layout()
plot_helper(FIG_DIR,
title=title,
xlabel=xlabel,
ylabel=ylabel,
save=True)
def gradient_across_parameter_space_prediction_data():
for i in range(number_of_producers):
producer = i + 1
producer_df = producer_rows_from_df(objective_function_df, producer)
x, y, z = contour_params(producer_df,
x_column='f1',
y_column='tau',
z_column='MSE')
dz = np.gradient(z)[0]
plt.contourf(x, y, dz, 15, alpha=1.0)
plt.colorbar()
title = 'CRMP: Producer {} ln(Gradient) Across Parameter Space for MSEs from Prediction'.format(
producer)
plt.tight_layout()
plt.ylim(0, 100)
plot_helper(FIG_DIR,
title=title,
xlabel=xlabel,
ylabel=ylabel,
save=True)
def fitted_params_and_mean_squared_error_fitting():
for i in range(len(producers)):
producer = i + 1
producer_rows_df = producer_rows_from_df(
fitting_sensitivity_analysis_df, producer)
x, y, z = contour_params(producer_rows_df,
x_column='f1_initial',
y_column='tau_initial',
z_column='MSE')
plt.contourf(x, y, z)
plt.colorbar()
x, y = true_params[producer]
actual = plt.scatter(x, y, c='red', label='Actual')
plt.legend(handles=[actual])
title = 'CRMP Producer {}: Fitted Parameter Values with ln(MSE) from Fitting'.format(
producer)
plot_helper(FIG_DIR,
title=title,
xlabel=xlabel,
ylabel=ylabel,
save=True)
def constraint_analysis():
df = pd.read_csv(predict_output_file)
length = len(p0s)
print(length)
print()
for i in range(len(producer_names)):
print(producer_names[i])
constraint_violation_counter = 0
sum_of_gains = 0
producer_df = producer_rows_from_df(df, i + 1)
for index, row in producer_df.iterrows():
f1 = row['f1_final']
f2 = row['f2_final']
f3 = row['f3_final']
f4 = row['f4_final']
gains = f1 + f2 + f3 + f4
sum_of_gains += gains
if gains > 1.01:
constraint_violation_counter += 1
print('Constraint Violations: ', constraint_violation_counter)
print('Average Gains Sum: ', (sum_of_gains / length))
print()
def converged_parameter_statistics():
df = pd.read_csv(predict_output_file)
for i in range(len(producer_names)):
producer_df = producer_rows_from_df(df, i + 1)
print(producer_names[i])
print('tau: ', producer_df['tau_final'].mean())
print('tau std: ', producer_df['tau_final'].std())
print('f1: ', producer_df['f1_final'].mean())
print('f1 std: ', producer_df['f1_final'].std())
print('f2: ', producer_df['f2_final'].mean())
print('f2 std: ', producer_df['f2_final'].std())
print('f3: ', producer_df['f3_final'].mean())
print('f3 std: ', producer_df['f3_final'].std())
print('f4: ', producer_df['f4_final'].mean())
print('f4 std: ', producer_df['f4_final'].std())
print('MSE: ', producer_df['MSE'].mean())
print('MSE std: ', producer_df['MSE'].std())
print()
print()
print()
print()
print()
print()