From SW's laptop
sanginnwoo
parent
ec16712498
commit
7ba5eefc4c
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@ -52,6 +52,49 @@ for row in monitoring_record:
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surcharge.append(float(row[8]))
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time.append(float(row[12]))
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print(settlement)
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print(surcharge)
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print(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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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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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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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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time = results[0]
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predicted_settlement = results[1]
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for i in range(5):
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time = results[2 * i]
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predicted_settlement = results[2 * i + 1]
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for j in range(len(time)):
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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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record_to_insert = (point_name, time[j], predicted_settlement[j], i)
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cursor.execute(postgres_insert_query, record_to_insert)
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connection.commit()
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@ -79,7 +79,8 @@ 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(np_time=time,
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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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final_step_predict_percent=final_step_predict_percent,
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@ -93,7 +94,8 @@ def run_settle_prediction_from_file(input_file, output_dir,
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run_step_prediction=run_step_prediction,
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asaoka_interval=asaoka_interval)
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def run_settle_prediction(np_time, np_surcharge, np_settlement,
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def run_settle_prediction(point_name,
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np_time, np_surcharge, np_settlement,
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final_step_predict_percent, additional_predict_percent,
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plot_show,
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print_values,
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@ -510,9 +512,9 @@ def run_settle_prediction(np_time, np_surcharge, np_settlement,
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# ==========
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# 에러 산정
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# ==========
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# ==============================
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# Post-Processing #1 : 에러 산정
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# ==============================
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# RMSE 계산 데이터 구간 설정 (계측)
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sm_rmse = settle[final_step_predict_end_index:final_step_monitor_end_index]
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@ -572,9 +574,9 @@ def run_settle_prediction(np_time, np_surcharge, np_settlement,
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# =====================
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# Post-Processing
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# =====================
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# ==========================================
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# Post-Processing #2 : 그래프 작성
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# ==========================================
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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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@ -708,14 +710,14 @@ def run_settle_prediction(np_time, np_surcharge, np_settlement,
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verticalalignment='top', fontsize='10', bbox=mybox)
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# 파일 이름만 추출
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filename = os.path.basename(input_file)
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filename = os.path.basename(point_name)
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# 그래프 제목 표시
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plt.title(filename + ": up to %i%% data used in the final step" % final_step_predict_percent)
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# 그래프 저장 (SVG 및 PNG)
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# plt.savefig(output_dir + '/' + filename +' %i percent (SVG).svg' %final_step_predict_percent, bbox_inches='tight')
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plt.savefig(output_dir + '/' + filename + ' %i percent (PNG).png' % final_step_predict_percent, bbox_inches='tight')
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#plt.savefig(output_dir + '/' + filename + ' %i percent (PNG).png' % final_step_predict_percent, bbox_inches='tight')
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# 그래프 출력
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if plot_show:
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@ -734,7 +736,25 @@ def run_settle_prediction(np_time, np_surcharge, np_settlement,
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is_multi_step = False
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# 반환
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return [rmse_hyper_original, rmse_hyper_nonlinear, rmse_hyper_weight_nonlinear, rmse_asaoka, rmse_step,
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axes[1].plot(time_hyper, -sp_hyper_original,
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linestyle='--', color='red', label='Original Hyperbolic')
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axes[1].plot(time_hyper, -sp_hyper_nonlinear,
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linestyle='--', color='green', label='Nonlinear Hyperbolic')
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axes[1].plot(time_hyper, -sp_hyper_weight_nonlinear,
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linestyle='--', color='blue', label='Nonlinear Hyperbolic (Weighted)')
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axes[1].plot(time_asaoka, -sp_asaoka,
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linestyle='--', color='orange', label='Asaoka')
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axes[1].plot(time[step_start_index[0]:], -sp_step[step_start_index[0]:],
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linestyle='--', color='navy', label='Nonlinear + Step Loading')
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return [time_hyper, sp_hyper_original,
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time_hyper, sp_hyper_nonlinear,
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time_hyper, sp_hyper_weight_nonlinear,
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time_asaoka, sp_asaoka,
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time[step_start_index[0]:], -sp_step[step_start_index[0]:],
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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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