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From SW's laptop

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sanginnwoo 2022-12-07 23:31:23 +09:00
parent 7ba5eefc4c
commit a748d20de2
2 changed files with 308 additions and 229 deletions
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202
controller.py
View File

@ -5,8 +5,10 @@ Sang Inn Woo, Ph.D. @ Incheon National University
Starting Date: 2022-11-10
"""
import psycopg2 as pg2
import sys
import numpy as np
import settle_prediction_steps_main
import matplotlib.pyplot as plt
'''
@ -20,81 +22,159 @@ fill_height: height of surcharge fill
nod: number of date
'''
def settlement_prediction(point_name):
# connect the database
connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432")
# connect the database
connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432")
# set cursor
cursor = connection.cursor()
# set cursor
cursor = connection.cursor()
# select all monitoring points
postgres_select_query = """SELECT * FROM apptb_surset01"""
cursor.execute(postgres_select_query)
point_record = cursor.fetchall()
# select monitoring data for the monitoring point
postgres_select_query = """SELECT * FROM apptb_surset02 WHERE cons_code='""" \
+ point_name + """' ORDER BY nod ASC"""
cursor.execute(postgres_select_query)
monitoring_record = cursor.fetchall()
# for a monitoring point, set name
point_name = point_record[0][3]
# initialize time, surcharge, and settlement lists
time = []
surcharge = []
settlement = []
# select monitoring data for the monitoring point
postgres_select_query = """SELECT * FROM apptb_surset02 WHERE cons_code='""" \
+ point_name + """' ORDER BY nod ASC"""
cursor.execute(postgres_select_query)
monitoring_record = cursor.fetchall()
# fill lists
for row in monitoring_record:
settlement.append(float(row[6]))
surcharge.append(float(row[8]))
time.append(float(row[12]))
# initialize time, surcharge, and settlement lists
time = []
surcharge = []
settlement = []
# convert lists to np arrays
settlement = np.array(settlement)
surcharge = np.array(surcharge)
time = np.array(time)
# fill list
for row in monitoring_record:
settlement.append(float(row[6]))
surcharge.append(float(row[8]))
time.append(float(row[12]))
# run the settlement prediction and get results
results = settle_prediction_steps_main.run_settle_prediction(point_name=point_name,
np_time=time,
np_surcharge=surcharge,
np_settlement=settlement,
final_step_predict_percent=90,
additional_predict_percent=300,
plot_show=False,
print_values=False,
run_original_hyperbolic=True,
run_nonlinear_hyperbolic=True,
run_weighted_nonlinear_hyperbolic=True,
run_asaoka=True,
run_step_prediction=True,
asaoka_interval=3)
# convert lists to np arrays
settlement = np.array(settlement)
surcharge = np.array(surcharge)
time = np.array(time)
# if there are prediction data for the given data point, delete it first
postgres_delete_query = """DELETE FROM apptb_pred02 WHERE cons_code='""" + point_name + """'"""
cursor.execute(postgres_delete_query)
connection.commit()
# run the settlement prediction and get results
results = settle_prediction_steps_main.run_settle_prediction(point_name=point_name,
np_time=time,
np_surcharge=surcharge,
np_settlement=settlement,
final_step_predict_percent=90,
additional_predict_percent=300,
plot_show=True,
print_values=True,
run_original_hyperbolic=True,
run_nonlinear_hyperbolic=True,
run_weighted_nonlinear_hyperbolic=True,
run_asaoka=True,
run_step_prediction=True,
asaoka_interval=3)
# insert predicted settlement into database
for i in range(5):
# if there are prediction data for the given data point, delete it first
postgres_delete_query = """DELETE FROM apptb_pred02 WHERE cons_code='""" + point_name + """'"""
cursor.execute(postgres_delete_query)
connection.commit()
# get time and settlement arrays
time = results[2 * i]
predicted_settlement = results[2 * i + 1]
time = results[0]
predicted_settlement = results[1]
# for each prediction time
for j in range(len(time)):
for i in range(5):
# construct insert query
postgres_insert_query \
= """INSERT INTO apptb_pred02 (cons_code, prediction_progress_days, predicted_settlement, prediction_method) """\
+ """VALUES (%s, %s, %s, %s)"""
time = results[2 * i]
predicted_settlement = results[2 * i + 1]
# set data to insert
record_to_insert = (point_name, time[j], predicted_settlement[j], i)
for j in range(len(time)):
# execute the insert query
cursor.execute(postgres_insert_query, record_to_insert)
postgres_insert_query \
= """INSERT INTO apptb_pred02 (cons_code, prediction_progress_days, predicted_settlement, prediction_method) """\
+ """VALUES (%s, %s, %s, %s)"""
record_to_insert = (point_name, time[j], predicted_settlement[j], i)
cursor.execute(postgres_insert_query, record_to_insert)
connection.commit()
# commit changes
connection.commit()
def read_database_and_plot(point_name):
# connect the database
connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432")
# set cursor
cursor = connection.cursor()
# select monitoring data for the monitoring point
postgres_select_query = """SELECT * FROM apptb_surset02 WHERE cons_code='""" \
+ point_name + """' ORDER BY nod ASC"""
cursor.execute(postgres_select_query)
monitoring_record = cursor.fetchall()
# initialize time, surcharge, and settlement lists
time_monitored = []
surcharge_monitored = []
settlement_monitored = []
# fill lists
for row in monitoring_record:
settlement_monitored.append(float(row[6]))
surcharge_monitored.append(float(row[8]))
time_monitored.append(float(row[12]))
# convert lists to np arrays
settlement_monitored = np.array(settlement_monitored)
surcharge_monitored = np.array(surcharge_monitored)
time_monitored = np.array(time_monitored)
prediction_method = 0
# select monitoring data for the monitoring point
postgres_select_query = """SELECT prediction_progress_days, predicted_settlement """ \
+ """FROM apptb_pred02 WHERE cons_code= '""" + point_name \
+ """' and prediction_method = """ + str(prediction_method) \
+ """ ORDER BY prediction_progress_days ASC"""
cursor.execute(postgres_select_query)
prediction_record = cursor.fetchall()
# initialize time, surcharge, and settlement lists
time_predicted = []
settlement_predicted = []
# fill lists
for row in prediction_record:
time_predicted.append(float(row[0]))
settlement_predicted.append(float(row[1]))
# convert lists to np arrays
settlement_predicted = np.array(settlement_predicted)
time_predicted = np.array(time_predicted)
# 그래프 크기, 서브 그래프 개수 및 비율 설정
fig, axes = plt.subplots(2, 1, figsize=(12, 9), gridspec_kw={'height_ratios': [1, 3]})
# 성토고 그래프 표시
axes[0].plot(time_monitored, surcharge_monitored, color='black', label='surcharge height')
# 성토고 그래프 설정
axes[0].set_ylabel("Surcharge height (m)", fontsize=15)
axes[0].set_xlim(left=0)
axes[0].grid(color="gray", alpha=.5, linestyle='--')
axes[0].tick_params(direction='in')
# 계측 및 예측 침하량 표시
axes[1].scatter(time_monitored, -settlement_monitored, s=50,
facecolors='white', edgecolors='black', label='measured data')
axes[1].plot(time_predicted, -settlement_predicted,
linestyle='--', color='red', label='Original Hyperbolic')
# python3 controller.py 1_SP-5
if __name__ == '__main__':
args = sys.argv[1:]
point_name = args[0]
#settlement_prediction(point_name=point_name)
read_database_and_plot(point_name=point_name)

335
settle_prediction_steps_main.py
View File

@ -570,183 +570,182 @@ def run_settle_prediction(point_name,
print("Error in Final Settlement (Asaoka): %0.3f" % final_error_asaoka)
# ==========================================
# Post-Processing #2 : 그래프 작성
# ==========================================
# 그래프 크기, 서브 그래프 개수 및 비율 설정
fig, axes = plt.subplots(2, 1, figsize=(12, 9), gridspec_kw={'height_ratios': [1, 3]})
# 성토고 그래프 표시
axes[0].plot(time, surcharge, color='black', label='surcharge height')
# 성토고 그래프 설정
axes[0].set_ylabel("Surcharge height (m)", fontsize=15)
axes[0].set_xlim(left=0)
axes[0].grid(color="gray", alpha=.5, linestyle='--')
axes[0].tick_params(direction='in')
# 계측 및 예측 침하량 표시
axes[1].scatter(time[0:settle.size], -settle, s=50,
facecolors='white', edgecolors='black', label='measured data')
axes[1].plot(time_hyper, -sp_hyper_original,
linestyle='--', color='red', label='Original Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_nonlinear,
linestyle='--', color='green', label='Nonlinear Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_weight_nonlinear,
linestyle='--', color='blue', label='Nonlinear Hyperbolic (Weighted)')
axes[1].plot(time_asaoka, -sp_asaoka,
linestyle='--', color='orange', label='Asaoka')
axes[1].plot(time[step_start_index[0]:], -sp_step[step_start_index[0]:],
linestyle='--', color='navy', label='Nonlinear + Step Loading')
# 침하량 그래프 설정
axes[1].set_xlabel("Time (day)", fontsize=15)
axes[1].set_ylabel("Settlement (cm)", fontsize=15)
axes[1].set_ylim(top=0)
axes[1].set_ylim(bottom=-1.5 * settle.max())
axes[1].set_xlim(left=0)
axes[1].grid(color="gray", alpha=.5, linestyle='--')
axes[1].tick_params(direction='in')
# 범례 표시
axes[1].legend(loc=1, ncol=3, frameon=True, fontsize=10)
# 예측 데이터 사용 범위 음영 처리 - 단계성토
plt.axvspan(time[step_start_index[0]], final_step_predict_end_date,
alpha=0.1, color='grey', hatch='//')
# 예측 데이터 사용 범위 음영 처리 - 기존 및 비선형 쌍곡선
plt.axvspan(final_step_start_date, final_step_predict_end_date,
alpha=0.1, color='grey', hatch='\\')
# 예측 데이터 사용 범위 표시 화살표 세로 위치 설정
arrow1_y_loc = 1.3 * min(-settle)
arrow2_y_loc = 1.4 * min(-settle)
# 화살표 크기 설정
arrow_head_width = 0.03 * max(settle)
arrow_head_length = 0.01 * max(time)
# 예측 데이터 사용 범위 화살표 처리 - 단계성토
axes[1].arrow(time[step_start_index[0]], arrow1_y_loc,
final_step_predict_end_date - time[step_start_index[0]], 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
axes[1].arrow(final_step_predict_end_date, arrow1_y_loc,
time[step_start_index[0]] - final_step_predict_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
# 예측 데이터 사용 범위 화살표 처리 - 기존 및 비선형 쌍곡선
axes[1].arrow(final_step_start_date, arrow2_y_loc,
final_step_predict_end_date - final_step_start_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
axes[1].arrow(final_step_predict_end_date, arrow2_y_loc,
final_step_start_date - final_step_predict_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
# Annotation 표시용 공간 설정
space = max(time) * 0.01
# 예측 데이터 사용 범위 범례 표시 - 단계성토
plt.annotate('Data Range Used (Nonlinear + Step Loading)', xy=(final_step_predict_end_date, arrow1_y_loc),
xytext=(final_step_predict_end_date + space, arrow1_y_loc),
horizontalalignment='left', verticalalignment='center')
# 예측 데이터 사용 범위 범례 표시 - 기존 및 비선형 쌍곡선
plt.annotate('Data Range Used (Hyperbolic and Asaoka)', xy=(final_step_predict_end_date, arrow1_y_loc),
xytext=(final_step_predict_end_date + space, arrow2_y_loc),
horizontalalignment='left', verticalalignment='center')
# RMSE 산정 범위 표시 화살표 세로 위치 설정
arrow3_y_loc = 0.55 * min(-settle)
# RMSE 산정 범위 화살표 표시
axes[1].arrow(final_step_predict_end_date, arrow3_y_loc,
final_step_end_date - final_step_predict_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
axes[1].arrow(final_step_end_date, arrow3_y_loc,
final_step_predict_end_date - final_step_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
# RMSE 산정 범위 세로선 설정
axes[1].axvline(x=final_step_end_date, color='silver', linestyle=':')
# RMSE 산정 범위 범례 표시
plt.annotate('RMSE Estimation Section', xy=(final_step_end_date, arrow3_y_loc),
xytext=(final_step_end_date + space, arrow3_y_loc),
horizontalalignment='left', verticalalignment='center')
# RMSE 출력
mybox = {'facecolor': 'white', 'edgecolor': 'black', 'boxstyle': 'round', 'alpha': 0.2}
plt.text(max(time) * 1.04, 0.20 * min(-settle),
r"$\bf{Root\ Mean\ Squared\ Error}$"
+ "\n" + "Original Hyperbolic: %0.3f" % rmse_hyper_original
+ "\n" + "Nonlinear Hyperbolic: %0.3f" % rmse_hyper_nonlinear
+ "\n" + "Nonlinear Hyperbolic (Weighted): %0.3f" % rmse_hyper_weight_nonlinear
+ "\n" + "Asaoka: %0.3f" % rmse_asaoka
+ "\n" + "Nonlinear + Step Loading: %0.3f" % rmse_step,
color='r', horizontalalignment='right',
verticalalignment='top', fontsize='10', bbox=mybox)
# (최종 계측 침하량 - 예측값) 출력
plt.text(max(time) * 1.04, 0.55 * min(-settle),
r"$\bf{Error\ in\ Final\ Settlement}$"
+ "\n" + "Original Hyperbolic: %0.3f" % final_error_hyper_original
+ "\n" + "Nonlinear Hyperbolic: %0.3f" % final_error_hyper_nonlinear
+ "\n" + "Nonlinear Hyperbolic (Weighted): %0.3f" % final_error_hyper_weight_nonlinear
+ "\n" + "Asaoka: %0.3f" % final_error_asaoka
+ "\n" + "Nonlinear + Step Loading: %0.3f" % final_error_step,
color='r', horizontalalignment='right',
verticalalignment='top', fontsize='10', bbox=mybox)
# 파일 이름만 추출
filename = os.path.basename(point_name)
# 그래프 제목 표시
plt.title(filename + ": up to %i%% data used in the final step" % final_step_predict_percent)
# 그래프 저장 (SVG 및 PNG)
# plt.savefig(output_dir + '/' + filename +' %i percent (SVG).svg' %final_step_predict_percent, bbox_inches='tight')
#plt.savefig(output_dir + '/' + filename + ' %i percent (PNG).png' % final_step_predict_percent, bbox_inches='tight')
# 그래프 출력
# 만약 그래프 도시가 필요할 경우,
if plot_show:
plt.show()
# 그래프 닫기 (메모리 소모 방지)
plt.close()
# 그래프 크기, 서브 그래프 개수 및 비율 설정
fig, axes = plt.subplots(2, 1, figsize=(12, 9), gridspec_kw={'height_ratios': [1, 3]})
# 예측 완료 표시
print("Settlement prediction is done for " + filename +
" with " + str(final_step_predict_percent) + "% data usage")
# 성토고 그래프 표시
axes[0].plot(time, surcharge, color='black', label='surcharge height')
# 단계 성토 고려 여부 표시
is_multi_step = True
if len(step_start_index) == 1:
is_multi_step = False
# 성토고 그래프 설정
axes[0].set_ylabel("Surcharge height (m)", fontsize=15)
axes[0].set_xlim(left=0)
axes[0].grid(color="gray", alpha=.5, linestyle='--')
axes[0].tick_params(direction='in')
# 반환
# 계측 및 예측 침하량 표시
axes[1].scatter(time[0:settle.size], -settle, s=50,
facecolors='white', edgecolors='black', label='measured data')
axes[1].plot(time_hyper, -sp_hyper_original,
linestyle='--', color='red', label='Original Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_nonlinear,
linestyle='--', color='green', label='Nonlinear Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_weight_nonlinear,
linestyle='--', color='blue', label='Nonlinear Hyperbolic (Weighted)')
axes[1].plot(time_asaoka, -sp_asaoka,
linestyle='--', color='orange', label='Asaoka')
axes[1].plot(time[step_start_index[0]:], -sp_step[step_start_index[0]:],
linestyle='--', color='navy', label='Nonlinear + Step Loading')
axes[1].plot(time_hyper, -sp_hyper_original,
linestyle='--', color='red', label='Original Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_nonlinear,
linestyle='--', color='green', label='Nonlinear Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_weight_nonlinear,
linestyle='--', color='blue', label='Nonlinear Hyperbolic (Weighted)')
axes[1].plot(time_asaoka, -sp_asaoka,
linestyle='--', color='orange', label='Asaoka')
axes[1].plot(time[step_start_index[0]:], -sp_step[step_start_index[0]:],
linestyle='--', color='navy', label='Nonlinear + Step Loading')
# 침하량 그래프 설정
axes[1].set_xlabel("Time (day)", fontsize=15)
axes[1].set_ylabel("Settlement (cm)", fontsize=15)
axes[1].set_ylim(top=0)
axes[1].set_ylim(bottom=-1.5 * settle.max())
axes[1].set_xlim(left=0)
axes[1].grid(color="gray", alpha=.5, linestyle='--')
axes[1].tick_params(direction='in')
# 범례 표시
axes[1].legend(loc=1, ncol=3, frameon=True, fontsize=10)
# 예측 데이터 사용 범위 음영 처리 - 단계성토
plt.axvspan(time[step_start_index[0]], final_step_predict_end_date,
alpha=0.1, color='grey', hatch='//')
# 예측 데이터 사용 범위 음영 처리 - 기존 및 비선형 쌍곡선
plt.axvspan(final_step_start_date, final_step_predict_end_date,
alpha=0.1, color='grey', hatch='\\')
# 예측 데이터 사용 범위 표시 화살표 세로 위치 설정
arrow1_y_loc = 1.3 * min(-settle)
arrow2_y_loc = 1.4 * min(-settle)
# 화살표 크기 설정
arrow_head_width = 0.03 * max(settle)
arrow_head_length = 0.01 * max(time)
# 예측 데이터 사용 범위 화살표 처리 - 단계성토
axes[1].arrow(time[step_start_index[0]], arrow1_y_loc,
final_step_predict_end_date - time[step_start_index[0]], 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
axes[1].arrow(final_step_predict_end_date, arrow1_y_loc,
time[step_start_index[0]] - final_step_predict_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
# 예측 데이터 사용 범위 화살표 처리 - 기존 및 비선형 쌍곡선
axes[1].arrow(final_step_start_date, arrow2_y_loc,
final_step_predict_end_date - final_step_start_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
axes[1].arrow(final_step_predict_end_date, arrow2_y_loc,
final_step_start_date - final_step_predict_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
# Annotation 표시용 공간 설정
space = max(time) * 0.01
# 예측 데이터 사용 범위 범례 표시 - 단계성토
plt.annotate('Data Range Used (Nonlinear + Step Loading)', xy=(final_step_predict_end_date, arrow1_y_loc),
xytext=(final_step_predict_end_date + space, arrow1_y_loc),
horizontalalignment='left', verticalalignment='center')
# 예측 데이터 사용 범위 범례 표시 - 기존 및 비선형 쌍곡선
plt.annotate('Data Range Used (Hyperbolic and Asaoka)', xy=(final_step_predict_end_date, arrow1_y_loc),
xytext=(final_step_predict_end_date + space, arrow2_y_loc),
horizontalalignment='left', verticalalignment='center')
# RMSE 산정 범위 표시 화살표 세로 위치 설정
arrow3_y_loc = 0.55 * min(-settle)
# RMSE 산정 범위 화살표 표시
axes[1].arrow(final_step_predict_end_date, arrow3_y_loc,
final_step_end_date - final_step_predict_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
axes[1].arrow(final_step_end_date, arrow3_y_loc,
final_step_predict_end_date - final_step_end_date, 0,
head_width=arrow_head_width, head_length=arrow_head_length,
color='black', length_includes_head='True')
# RMSE 산정 범위 세로선 설정
axes[1].axvline(x=final_step_end_date, color='silver', linestyle=':')
# RMSE 산정 범위 범례 표시
plt.annotate('RMSE Estimation Section', xy=(final_step_end_date, arrow3_y_loc),
xytext=(final_step_end_date + space, arrow3_y_loc),
horizontalalignment='left', verticalalignment='center')
# RMSE 출력
mybox = {'facecolor': 'white', 'edgecolor': 'black', 'boxstyle': 'round', 'alpha': 0.2}
plt.text(max(time) * 1.04, 0.20 * min(-settle),
r"$\bf{Root\ Mean\ Squared\ Error}$"
+ "\n" + "Original Hyperbolic: %0.3f" % rmse_hyper_original
+ "\n" + "Nonlinear Hyperbolic: %0.3f" % rmse_hyper_nonlinear
+ "\n" + "Nonlinear Hyperbolic (Weighted): %0.3f" % rmse_hyper_weight_nonlinear
+ "\n" + "Asaoka: %0.3f" % rmse_asaoka
+ "\n" + "Nonlinear + Step Loading: %0.3f" % rmse_step,
color='r', horizontalalignment='right',
verticalalignment='top', fontsize='10', bbox=mybox)
# (최종 계측 침하량 - 예측값) 출력
plt.text(max(time) * 1.04, 0.55 * min(-settle),
r"$\bf{Error\ in\ Final\ Settlement}$"
+ "\n" + "Original Hyperbolic: %0.3f" % final_error_hyper_original
+ "\n" + "Nonlinear Hyperbolic: %0.3f" % final_error_hyper_nonlinear
+ "\n" + "Nonlinear Hyperbolic (Weighted): %0.3f" % final_error_hyper_weight_nonlinear
+ "\n" + "Asaoka: %0.3f" % final_error_asaoka
+ "\n" + "Nonlinear + Step Loading: %0.3f" % final_error_step,
color='r', horizontalalignment='right',
verticalalignment='top', fontsize='10', bbox=mybox)
# 파일 이름만 추출
filename = os.path.basename(point_name)
# 그래프 제목 표시
plt.title(filename + ": up to %i%% data used in the final step" % final_step_predict_percent)
# 그래프 저장 (SVG 및 PNG)
# plt.savefig(output_dir + '/' + filename +' %i percent (SVG).svg' %final_step_predict_percent, bbox_inches='tight')
#plt.savefig(output_dir + '/' + filename + ' %i percent (PNG).png' % final_step_predict_percent, bbox_inches='tight')
# 그래프 출력
if plot_show:
plt.show()
# 그래프 닫기 (메모리 소모 방지)
plt.close()
# 예측 완료 표시
print("Settlement prediction is done for " + filename +
" with " + str(final_step_predict_percent) + "% data usage")
# 단계 성토 고려 여부 표시
is_multi_step = True
if len(step_start_index) == 1:
is_multi_step = False
# 반환
axes[1].plot(time_hyper, -sp_hyper_original,
linestyle='--', color='red', label='Original Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_nonlinear,
linestyle='--', color='green', label='Nonlinear Hyperbolic')
axes[1].plot(time_hyper, -sp_hyper_weight_nonlinear,
linestyle='--', color='blue', label='Nonlinear Hyperbolic (Weighted)')
axes[1].plot(time_asaoka, -sp_asaoka,
linestyle='--', color='orange', label='Asaoka')
axes[1].plot(time[step_start_index[0]:], -sp_step[step_start_index[0]:],
linestyle='--', color='navy', label='Nonlinear + Step Loading')
return [time_hyper, sp_hyper_original,
time_hyper, sp_hyper_nonlinear,
@ -756,7 +755,7 @@ def run_settle_prediction(point_name,
rmse_hyper_original, rmse_hyper_nonlinear, rmse_hyper_weight_nonlinear,
rmse_asaoka, rmse_step,
final_error_hyper_original, final_error_hyper_nonlinear, final_error_hyper_weight_nonlinear,
final_error_asaoka, final_error_step, is_multi_step]
final_error_asaoka, final_error_step]
'''
run_settle_prediction(input_file='data/2-5_No.39.csv',