From SW's laptop
sanginnwoo
parent
7ba5eefc4c
commit
a748d20de2
158
controller.py
158
controller.py
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@ -5,8 +5,10 @@ Sang Inn Woo, Ph.D. @ Incheon National University
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Starting Date: 2022-11-10
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"""
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import psycopg2 as pg2
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import sys
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import numpy as np
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import settle_prediction_steps_main
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import matplotlib.pyplot as plt
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'''
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@ -20,52 +22,45 @@ fill_height: height of surcharge fill
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nod: number of date
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'''
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def settlement_prediction(point_name):
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# connect the database
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connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432")
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# connect the database
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connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432")
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# set cursor
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cursor = connection.cursor()
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# set cursor
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cursor = connection.cursor()
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# select all monitoring points
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postgres_select_query = """SELECT * FROM apptb_surset01"""
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cursor.execute(postgres_select_query)
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point_record = cursor.fetchall()
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# for a monitoring point, set name
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point_name = point_record[0][3]
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# select monitoring data for the monitoring point
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postgres_select_query = """SELECT * FROM apptb_surset02 WHERE cons_code='""" \
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# select monitoring data for the monitoring point
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postgres_select_query = """SELECT * FROM apptb_surset02 WHERE cons_code='""" \
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+ point_name + """' ORDER BY nod ASC"""
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cursor.execute(postgres_select_query)
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monitoring_record = cursor.fetchall()
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cursor.execute(postgres_select_query)
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monitoring_record = cursor.fetchall()
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# initialize time, surcharge, and settlement lists
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time = []
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surcharge = []
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settlement = []
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# initialize time, surcharge, and settlement lists
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time = []
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surcharge = []
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settlement = []
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# fill list
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for row in monitoring_record:
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# fill lists
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for row in monitoring_record:
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settlement.append(float(row[6]))
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surcharge.append(float(row[8]))
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time.append(float(row[12]))
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# convert lists to np arrays
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settlement = np.array(settlement)
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surcharge = np.array(surcharge)
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time = np.array(time)
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# convert lists to np arrays
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settlement = np.array(settlement)
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surcharge = np.array(surcharge)
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time = np.array(time)
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# run the settlement prediction and get results
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results = settle_prediction_steps_main.run_settle_prediction(point_name=point_name,
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# run the settlement prediction and get results
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results = settle_prediction_steps_main.run_settle_prediction(point_name=point_name,
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np_time=time,
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np_surcharge=surcharge,
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np_settlement=settlement,
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final_step_predict_percent=90,
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additional_predict_percent=300,
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plot_show=True,
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print_values=True,
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plot_show=False,
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print_values=False,
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run_original_hyperbolic=True,
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run_nonlinear_hyperbolic=True,
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run_weighted_nonlinear_hyperbolic=True,
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@ -73,28 +68,113 @@ results = settle_prediction_steps_main.run_settle_prediction(point_name=point_na
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run_step_prediction=True,
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asaoka_interval=3)
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# if there are prediction data for the given data point, delete it first
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postgres_delete_query = """DELETE FROM apptb_pred02 WHERE cons_code='""" + point_name + """'"""
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cursor.execute(postgres_delete_query)
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connection.commit()
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# if there are prediction data for the given data point, delete it first
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postgres_delete_query = """DELETE FROM apptb_pred02 WHERE cons_code='""" + point_name + """'"""
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cursor.execute(postgres_delete_query)
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connection.commit()
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time = results[0]
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predicted_settlement = results[1]
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for i in range(5):
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# insert predicted settlement into database
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for i in range(5):
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# get time and settlement arrays
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time = results[2 * i]
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predicted_settlement = results[2 * i + 1]
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# for each prediction time
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for j in range(len(time)):
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# construct insert query
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postgres_insert_query \
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= """INSERT INTO apptb_pred02 (cons_code, prediction_progress_days, predicted_settlement, prediction_method) """\
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+ """VALUES (%s, %s, %s, %s)"""
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# set data to insert
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record_to_insert = (point_name, time[j], predicted_settlement[j], i)
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# execute the insert query
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cursor.execute(postgres_insert_query, record_to_insert)
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connection.commit()
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# commit changes
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connection.commit()
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def read_database_and_plot(point_name):
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# connect the database
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connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432")
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# set cursor
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cursor = connection.cursor()
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# select monitoring data for the monitoring point
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postgres_select_query = """SELECT * FROM apptb_surset02 WHERE cons_code='""" \
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+ point_name + """' ORDER BY nod ASC"""
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cursor.execute(postgres_select_query)
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monitoring_record = cursor.fetchall()
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# initialize time, surcharge, and settlement lists
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time_monitored = []
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surcharge_monitored = []
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settlement_monitored = []
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# fill lists
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for row in monitoring_record:
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settlement_monitored.append(float(row[6]))
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surcharge_monitored.append(float(row[8]))
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time_monitored.append(float(row[12]))
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# convert lists to np arrays
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settlement_monitored = np.array(settlement_monitored)
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surcharge_monitored = np.array(surcharge_monitored)
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time_monitored = np.array(time_monitored)
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prediction_method = 0
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# select monitoring data for the monitoring point
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postgres_select_query = """SELECT prediction_progress_days, predicted_settlement """ \
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+ """FROM apptb_pred02 WHERE cons_code= '""" + point_name \
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+ """' and prediction_method = """ + str(prediction_method) \
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+ """ ORDER BY prediction_progress_days ASC"""
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cursor.execute(postgres_select_query)
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prediction_record = cursor.fetchall()
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# initialize time, surcharge, and settlement lists
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time_predicted = []
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settlement_predicted = []
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# fill lists
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for row in prediction_record:
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time_predicted.append(float(row[0]))
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settlement_predicted.append(float(row[1]))
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# convert lists to np arrays
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settlement_predicted = np.array(settlement_predicted)
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time_predicted = np.array(time_predicted)
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# 그래프 크기, 서브 그래프 개수 및 비율 설정
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fig, axes = plt.subplots(2, 1, figsize=(12, 9), gridspec_kw={'height_ratios': [1, 3]})
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# 성토고 그래프 표시
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axes[0].plot(time_monitored, surcharge_monitored, color='black', label='surcharge height')
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# 성토고 그래프 설정
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axes[0].set_ylabel("Surcharge height (m)", fontsize=15)
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axes[0].set_xlim(left=0)
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axes[0].grid(color="gray", alpha=.5, linestyle='--')
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axes[0].tick_params(direction='in')
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# 계측 및 예측 침하량 표시
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axes[1].scatter(time_monitored, -settlement_monitored, s=50,
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facecolors='white', edgecolors='black', label='measured data')
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axes[1].plot(time_predicted, -settlement_predicted,
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linestyle='--', color='red', label='Original Hyperbolic')
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# python3 controller.py 1_SP-5
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if __name__ == '__main__':
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args = sys.argv[1:]
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point_name = args[0]
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#settlement_prediction(point_name=point_name)
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read_database_and_plot(point_name=point_name)
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@ -570,14 +570,13 @@ def run_settle_prediction(point_name,
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print("Error in Final Settlement (Asaoka): %0.3f" % final_error_asaoka)
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# ==========================================
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# Post-Processing #2 : 그래프 작성
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# ==========================================
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# 만약 그래프 도시가 필요할 경우,
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if plot_show:
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# 그래프 크기, 서브 그래프 개수 및 비율 설정
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fig, axes = plt.subplots(2, 1, figsize=(12, 9), gridspec_kw={'height_ratios': [1, 3]})
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@ -756,7 +755,7 @@ def run_settle_prediction(point_name,
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rmse_hyper_original, rmse_hyper_nonlinear, rmse_hyper_weight_nonlinear,
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rmse_asaoka, rmse_step,
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final_error_hyper_original, final_error_hyper_nonlinear, final_error_hyper_weight_nonlinear,
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final_error_asaoka, final_error_step, is_multi_step]
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final_error_asaoka, final_error_step]
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'''
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run_settle_prediction(input_file='data/2-5_No.39.csv',
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