From SW's laptop
sanginnwoo
parent
a748d20de2
commit
261f1d8e95
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@ -38,16 +38,8 @@ for input_file in input_files:
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for i in range(20, 100, 10):
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# 침하 예측을 수행하고 반환값 저장
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return_values = settle_prediction_steps_main.\
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run_settle_prediction(input_file=input_file, output_dir=output_dir,
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final_step_predict_percent=i,
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additional_predict_percent=100,
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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_step_prediction=True,
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settle_unit=settle_unit)
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return_values = settle_prediction_steps_main. \
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run_settle_prediction(,
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# 데이터프레임에 일단 및 다단 성토를 포함한 예측의 에러를 저장
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df_overall.loc[len(df_overall.index)] = [input_file, i, return_values[0], return_values[1],
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@ -25,7 +25,8 @@ nod: number of date
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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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#connection = pg2.connect("host=localhost dbname=postgres user=postgres password=lab36981 port=5432") # local
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connection = pg2.connect("host=192.168.0.13 dbname=sgis user=sgis password=sgis port=5432") # ICTWay internal
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# set cursor
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cursor = connection.cursor()
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@ -43,9 +44,9 @@ def settlement_prediction(point_name):
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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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settlement.append(float(row[5]))
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surcharge.append(float(row[7]))
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time.append(float(row[1]))
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# convert lists to np arrays
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settlement = np.array(settlement)
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@ -53,19 +54,14 @@ def settlement_prediction(point_name):
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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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np_time=time,
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np_surcharge=surcharge,
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np_settlement=settlement,
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results = settle_prediction_steps_main.run_settle_prediction(point_name=point_name, np_time=time,
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np_surcharge=surcharge, 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=False,
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print_values=False,
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run_original_hyperbolic=True,
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additional_predict_percent=300, plot_show=False,
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print_values=False, 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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run_asaoka=True,
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run_step_prediction=True,
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run_asaoka=True, 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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@ -73,6 +69,14 @@ def settlement_prediction(point_name):
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cursor.execute(postgres_delete_query)
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connection.commit()
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# prediction method code
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# 0: original hyperbolic method
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# 1: nonlinear hyperbolic method
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# 2: weighted nonlinear hyperbolic method
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# 3: Asaoka method
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# 4: Step loading
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# 5: temp
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# insert predicted settlement into database
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for i in range(5):
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@ -85,7 +89,8 @@ def settlement_prediction(point_name):
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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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= """INSERT INTO apptb_pred02 """ \
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+ """(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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@ -128,8 +133,18 @@ def read_database_and_plot(point_name):
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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 code
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# 0: original hyperbolic method
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# 1: nonlinear hyperbolic method
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# 2: weighted nonlinear hyperbolic method
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# 3: Asaoka method
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# 4: Step loading
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# 5: temp
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# temporarily set the prediction method as 0
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prediction_method = 0
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# select monitoring data for the monitoring point
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# select predicted 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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@ -169,12 +184,10 @@ def read_database_and_plot(point_name):
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linestyle='--', color='red', label='Original Hyperbolic')
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# python3 controller.py 1_SP-5
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# script to call: python3 controller.py [business_code] [cons_code]
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# for example:
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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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settlement_prediction(point_name=point_name)
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# read_database_and_plot(point_name=point_name) #DB 입력 결과 확인 시에 활성화 / 평소에는 비활성화
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@ -79,19 +79,12 @@ def run_settle_prediction_from_file(input_file, output_dir,
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settle = data['Settlement'].to_numpy()
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surcharge = data['Surcharge'].to_numpy()
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run_settle_prediction(point_name=input_file,
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np_time=time,
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np_surcharge=surcharge,
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np_settlement=settle,
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run_settle_prediction(point_name=input_file, np_time=time, np_surcharge=surcharge, np_settlement=settle,
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final_step_predict_percent=final_step_predict_percent,
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additional_predict_percent=additional_predict_percent,
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plot_show=plot_show,
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print_values=print_values,
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run_original_hyperbolic=run_original_hyperbolic,
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run_nonlinear_hyperbolic=run_nonlinear_hyperbolic,
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run_weighted_nonlinear_hyperbolic='False',
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run_asaoka=run_asaoka,
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run_step_prediction=run_step_prediction,
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additional_predict_percent=additional_predict_percent, plot_show=plot_show,
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print_values=print_values, run_original_hyperbolic=run_original_hyperbolic,
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run_nonlinear_hyperbolic=run_nonlinear_hyperbolic, run_weighted_nonlinear_hyperbolic='False',
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run_asaoka=run_asaoka, run_step_prediction=run_step_prediction,
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asaoka_interval=asaoka_interval)
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def run_settle_prediction(point_name,
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@ -101,10 +94,10 @@ def run_settle_prediction(point_name,
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print_values,
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run_original_hyperbolic='True',
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run_nonlinear_hyperbolic='True',
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run_weighted_nonlinear_hyperbolic='False',
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run_weighted_nonlinear_hyperbolic='True',
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run_asaoka = 'True',
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run_step_prediction='True',
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asaoka_interval = 3):
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asaoka_interval = 5):
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# ====================
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# 파일 읽기, 데이터 설정
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@ -757,6 +750,11 @@ def run_settle_prediction(point_name,
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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]
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#def run_postprocessing_error(point_name, np_time, np_surcharge, np_settlement):
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# a = a + 1
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#def run_postprocessing_graph(point_name, np_time, np_surcharge, np_)
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#
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'''
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run_settle_prediction(input_file='data/2-5_No.39.csv',
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output_dir='output',
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