From f82dca2f1cda9f3aba883657552ecb90856a1f55 Mon Sep 17 00:00:00 2001
From: Shan Yang <839651726@qq.com>
Date: Thu, 16 Oct 2025 16:28:07 +0800
Subject: [PATCH 1/2] narp integration
integration of narp
---
NARP/Pipfile | 11 +
NARP/__init__.py | 30 +
NARP/common/__init__.py | 0
NARP/common/utils.py | 69 +
NARP/conftest.py | 21 +
NARP/docs/data.rst | 11 +
NARP/docs/index.rst | 20 +
NARP/docs/installation.rst | 13 +
NARP/docs/manual.rst | 10 +
NARP/docs/summary.rst | 33 +
NARP/docs/usage.rst | 136 +
NARP/mcp_jobs/jobs_index.json | 2 +
NARP/mcp_server/README.md | 15 +
NARP/mcp_server/__init__.py | 6 +
NARP/mcp_server/manager.py | 237 +
NARP/mcp_server/server.py | 34 +
NARP/mcp_server/worker.py | 198 +
NARP/narp_mcp.py | 181 +
NARP/output.txt | 600 ++
NARP/reliabilityassessment/__init__.py | 0
.../data_processing/__init__.py | 0
.../data_processing/dataf1.py | 25 +
.../data_processing/datax.py | 250 +
.../data_processing/dpeak.py | 68 +
.../data_processing/gsm.py | 156 +
.../data_processing/input_processing.py | 173 +
.../data_processing/pind.py | 69 +
.../data_processing/plhr.py | 28 +
.../data_processing/pmlolp.py | 57 +
.../data_processing/pmsc.py | 871 ++
.../data_processing/readInputB.py | 545 +
.../data_processing/resca.py | 107 +
.../data_processing/smaint.py | 98 +
.../data_processing/tests/ZZDD.csv | 1 +
.../data_processing/tests/ZZFC.csv | 1 +
.../data_processing/tests/ZZLD.csv | 3 +
.../data_processing/tests/ZZMC.csv | 2 +
.../data_processing/tests/ZZOD.csv | 1 +
.../data_processing/tests/ZZTC.csv | 3 +
.../data_processing/tests/ZZTD.csv | 2 +
.../data_processing/tests/ZZUD.csv | 3 +
.../data_processing/tests/__init__.py | 0
.../tests/ground_truth_for_xldnew.txt | 8760 +++++++++++++++++
.../data_processing/tests/test_dataf1.py | 283 +
.../data_processing/tests/test_dataf1_new.py | 249 +
.../data_processing/tests/test_datax.py | 180 +
.../data_processing/tests/test_dpeak.py | 36 +
.../data_processing/tests/test_gsm.py | 27 +
.../data_processing/tests/test_pind.py | 45 +
.../data_processing/tests/test_pmlolp.py | 68 +
.../data_processing/tests/test_pmsc.py | 325 +
.../data_processing/tests/test_readInputB.py | 79 +
.../data_processing/tests/test_resca.py | 40 +
.../data_processing/tests/test_smaint.py | 77 +
.../data_processing/tests/test_smaint_new.py | 78 +
.../data_processing/tests/test_wpeakf.py | 23 +
.../data_processing/tests/test_xldnew.py | 16 +
.../data_processing/tests/test_xporta.py | 29 +
.../tests/testdata_input_processing/BLPA | 5 +
.../tests/testdata_input_processing/BN | 5 +
.../tests/testdata_input_processing/CAPCON | 160 +
.../tests/testdata_input_processing/CAPOWN | 5 +
.../tests/testdata_input_processing/DERATE | 160 +
.../tests/testdata_input_processing/DYLOAD | 5 +
.../tests/testdata_input_processing/FCTERR | 5 +
.../tests/testdata_input_processing/FU | 5 +
.../testdata_input_processing/HRLOAD_XPOS | 5 +
.../testdata_input_processing/ID_modified | 160 +
.../tests/testdata_input_processing/INHBT1 | 5 +
.../tests/testdata_input_processing/INHBT2 | 5 +
.../tests/testdata_input_processing/INTCH | 60 +
.../tests/testdata_input_processing/INTCHR | 60 +
.../tests/testdata_input_processing/JCRIT | 0
.../tests/testdata_input_processing/JENT | 5 +
.../tests/testdata_input_processing/JPLOUT | 52 +
.../tests/testdata_input_processing/LEEI | 3655 +++++++
.../tests/testdata_input_processing/LINENO | 5 +
.../tests/testdata_input_processing/LP | 5 +
.../tests/testdata_input_processing/MAXDAY | 5 +
.../tests/testdata_input_processing/MAXHR | 5 +
.../tests/testdata_input_processing/MAXRAN | 5 +
.../tests/testdata_input_processing/MINRAN | 5 +
.../tests/testdata_input_processing/MXPLHR | 365 +
.../tests/testdata_input_processing/NAMA | 5 +
.../tests/testdata_input_processing/NOGEN | 5 +
.../tests/testdata_input_processing/PKLOAD | 5 +
.../tests/testdata_input_processing/PROBG | 160 +
.../tests/testdata_input_processing/PROBL | 6 +
.../tests/testdata_input_processing/QTR | 3 +
.../tests/testdata_input_processing/RATES | 160 +
.../tests/testdata_input_processing/SUSTAT | 6 +
.../testdata_input_processing/SUSTAT_RESCA | 6 +
.../tests/testdata_input_processing/WPEAK | 5 +
.../tests/testdata_input_processing/ZZDD.csv | 1 +
.../tests/testdata_input_processing/ZZFC.csv | 1 +
.../tests/testdata_input_processing/ZZLD.csv | 6 +
.../tests/testdata_input_processing/ZZMC.csv | 2 +
.../tests/testdata_input_processing/ZZOD.csv | 1 +
.../tests/testdata_input_processing/ZZTC.csv | 3 +
.../tests/testdata_input_processing/ZZTD.csv | 6 +
.../tests/testdata_input_processing/ZZUD.csv | 161 +
.../tests/testdata_pmlolp/KA_modified | 5000 ++++++++++
.../tests/testdata_pmlolp/PA_modified | 5000 ++++++++++
.../data_processing/tests/testdata_pmlolp/RD | 500 +
.../tests/testdata_pmsc/case1/DERATE | 160 +
.../tests/testdata_pmsc/case1/ID | 160 +
.../tests/testdata_pmsc/case1/ID_modified | 160 +
.../tests/testdata_pmsc/case1/INHBT1 | 5 +
.../tests/testdata_pmsc/case1/INHBT2 | 5 +
.../tests/testdata_pmsc/case1/MAXRAN | 5 +
.../tests/testdata_pmsc/case1/MINRAN | 5 +
.../tests/testdata_pmsc/case1/NAMU | 160 +
.../tests/testdata_pmsc/case1/NUMP | 160 +
.../tests/testdata_pmsc/case1/PKLOAD | 5 +
.../tests/testdata_pmsc/case1/PROBG | 160 +
.../tests/testdata_pmsc/case1/RATES | 160 +
.../tests/testdata_pmsc/case1/WPEAK | 5 +
.../tests/testdata_pmsc/case2/DERATE | 160 +
.../tests/testdata_pmsc/case2/ID | 160 +
.../tests/testdata_pmsc/case2/ID_modified | 160 +
.../tests/testdata_pmsc/case2/INHBT1 | 5 +
.../tests/testdata_pmsc/case2/INHBT2 | 5 +
.../tests/testdata_pmsc/case2/MAXRAN | 5 +
.../tests/testdata_pmsc/case2/MINRAN | 5 +
.../tests/testdata_pmsc/case2/NAMU | 160 +
.../tests/testdata_pmsc/case2/NUMP | 160 +
.../tests/testdata_pmsc/case2/PKLOAD | 5 +
.../tests/testdata_pmsc/case2/PROBG | 160 +
.../tests/testdata_pmsc/case2/RATES | 160 +
.../tests/testdata_pmsc/case2/WPEAK | 5 +
.../tests/testdata_pmsc/case3/DERATE | 160 +
.../tests/testdata_pmsc/case3/ID | 160 +
.../tests/testdata_pmsc/case3/ID_modified | 160 +
.../tests/testdata_pmsc/case3/INHBT1 | 5 +
.../tests/testdata_pmsc/case3/INHBT2 | 5 +
.../tests/testdata_pmsc/case3/MAXRAN | 5 +
.../tests/testdata_pmsc/case3/MINRAN | 5 +
.../tests/testdata_pmsc/case3/NAMU | 160 +
.../tests/testdata_pmsc/case3/NUMP | 160 +
.../tests/testdata_pmsc/case3/PKLOAD | 5 +
.../tests/testdata_pmsc/case3/PROBG | 160 +
.../tests/testdata_pmsc/case3/RATES | 160 +
.../tests/testdata_pmsc/case3/WPEAK | 5 +
.../tests/testdata_pmsc/case4/DERATE | 160 +
.../tests/testdata_pmsc/case4/ID | 160 +
.../tests/testdata_pmsc/case4/ID_modified | 160 +
.../tests/testdata_pmsc/case4/INHBT1 | 5 +
.../tests/testdata_pmsc/case4/INHBT2 | 5 +
.../tests/testdata_pmsc/case4/MAXRAN | 5 +
.../tests/testdata_pmsc/case4/MINRAN | 5 +
.../tests/testdata_pmsc/case4/NAMU | 160 +
.../tests/testdata_pmsc/case4/NUMP | 160 +
.../tests/testdata_pmsc/case4/PKLOAD | 5 +
.../tests/testdata_pmsc/case4/PROBG | 160 +
.../tests/testdata_pmsc/case4/RATES | 160 +
.../tests/testdata_pmsc/case4/WPEAK | 5 +
.../tests/testdata_pmsc/case5/DERATE | 160 +
.../tests/testdata_pmsc/case5/ID | 160 +
.../tests/testdata_pmsc/case5/ID_modified | 160 +
.../tests/testdata_pmsc/case5/INHBT1 | 5 +
.../tests/testdata_pmsc/case5/INHBT2 | 5 +
.../tests/testdata_pmsc/case5/MAXRAN | 5 +
.../tests/testdata_pmsc/case5/MINRAN | 5 +
.../tests/testdata_pmsc/case5/NAMU | 160 +
.../tests/testdata_pmsc/case5/NUMP | 160 +
.../tests/testdata_pmsc/case5/PKLOAD | 5 +
.../tests/testdata_pmsc/case5/PROBG | 160 +
.../tests/testdata_pmsc/case5/RATES | 160 +
.../tests/testdata_pmsc/case5/WPEAK | 5 +
.../tests/testdata_smaint/DERATE | 160 +
.../data_processing/tests/testdata_smaint/ID | 160 +
.../tests/testdata_smaint/ID_mod | 160 +
.../tests/testdata_smaint/INHBT1 | 5 +
.../tests/testdata_smaint/INHBT2 | 5 +
.../tests/testdata_smaint/JPLOUT | 52 +
.../tests/testdata_smaint/MAXRAN | 5 +
.../tests/testdata_smaint/MINRAN | 5 +
.../tests/testdata_smaint/NAMU | 160 +
.../tests/testdata_smaint/NUMP | 160 +
.../tests/testdata_smaint/PKLOAD | 5 +
.../tests/testdata_smaint/PROBG | 160 +
.../tests/testdata_smaint/RATES | 160 +
.../tests/testdata_smaint/WPEAK | 5 +
.../tests/unit_test_input_for_xldnew | 1462 +++
.../data_processing/wpeakf.py | 48 +
.../data_processing/xldnew.py | 45 +
.../data_processing/xporta.py | 42 +
.../data_processing/xports.py | 20 +
.../integration_test/INPUTB | 315 +
.../integration_test/INPUTC | 98 +
.../integration_test/LEEI | 3655 +++++++
.../integration_test/OUTPUT_fortran | 845 ++
.../integration_test/ZZDD.csv | 1 +
.../integration_test/ZZFC.csv | 1 +
.../integration_test/ZZLD.csv | 6 +
.../integration_test/ZZMC.csv | 2 +
.../integration_test/ZZOD.csv | 1 +
.../integration_test/ZZTC.csv | 3 +
.../integration_test/ZZTD.csv | 6 +
.../integration_test/ZZUD.csv | 161 +
.../integration_test/output_python.txt | 508 +
.../test_integration_result_comparison.py | 85 +
.../monte_carlo/__init__.py | 0
.../monte_carlo/admitb.py | 31 +
.../monte_carlo/admitm.py | 17 +
.../monte_carlo/admitr.py | 85 +
.../monte_carlo/admref.py | 12 +
.../monte_carlo/assign.py | 87 +
.../monte_carlo/conls.py | 240 +
.../monte_carlo/connls.py | 249 +
.../monte_carlo/contrl.py | 388 +
.../monte_carlo/cvchk.py | 138 +
.../monte_carlo/dgeco.py | 144 +
.../monte_carlo/dgedi.py | 149 +
.../monte_carlo/dgefa.py | 135 +
.../monte_carlo/events.py | 372 +
.../monte_carlo/filem.py | 102 +
.../monte_carlo/findld.py | 17 +
.../monte_carlo/findtn.py | 28 +
NARP/reliabilityassessment/monte_carlo/flo.py | 34 +
.../monte_carlo/gstate.py | 81 +
.../reliabilityassessment/monte_carlo/hour.py | 251 +
.../monte_carlo/hrstat.py | 132 +
.../monte_carlo/initfl.py | 25 +
.../monte_carlo/initl.py | 120 +
.../reliabilityassessment/monte_carlo/intm.py | 143 +
.../monte_carlo/line_prob_sampling.py | 132 +
.../reliabilityassessment/monte_carlo/linp.py | 144 +
.../monte_carlo/lstate.py | 48 +
NARP/reliabilityassessment/monte_carlo/mc.py | 14 +
.../monte_carlo/narpMain.py | 315 +
NARP/reliabilityassessment/monte_carlo/net.py | 86 +
.../monte_carlo/pkstat.py | 124 +
.../monte_carlo/quartr.py | 36 +
NARP/reliabilityassessment/monte_carlo/rc.py | 15 +
.../monte_carlo/remove.py | 49 +
.../monte_carlo/report.py | 628 ++
NARP/reliabilityassessment/monte_carlo/rm.py | 12 +
.../monte_carlo/rstart.py | 25 +
.../monte_carlo/seeder.py | 36 +
.../monte_carlo/sumcap.py | 38 +
.../monte_carlo/tests/__init__.py | 0
.../monte_carlo/tests/test_admitb.py | 11 +
.../monte_carlo/tests/test_admitr.py | 28 +
.../monte_carlo/tests/test_assign.py | 79 +
.../monte_carlo/tests/test_conls.py | 208 +
.../monte_carlo/tests/test_connls.py | 214 +
.../monte_carlo/tests/test_cvchk.py | 51 +
.../monte_carlo/tests/test_cvchk_new.py | 42 +
.../monte_carlo/tests/test_dgeco.py | 58 +
.../monte_carlo/tests/test_dgedi.py | 55 +
.../monte_carlo/tests/test_dgefa.py | 67 +
.../monte_carlo/tests/test_filem.py | 64 +
.../monte_carlo/tests/test_filem_new.py | 220 +
.../monte_carlo/tests/test_findtn.py | 51 +
.../monte_carlo/tests/test_flo.py | 150 +
.../monte_carlo/tests/test_gstate.py | 55 +
.../monte_carlo/tests/test_hrstat.py | 191 +
.../monte_carlo/tests/test_hrstat_new.py | 259 +
.../monte_carlo/tests/test_initl.py | 62 +
.../tests/test_line_prob_sampling.py | 93 +
.../monte_carlo/tests/test_linp.py | 124 +
.../monte_carlo/tests/test_mc.py | 56 +
.../monte_carlo/tests/test_net.py | 202 +
.../monte_carlo/tests/test_pkstat.py | 155 +
.../monte_carlo/tests/test_pkstat_new.py | 202 +
.../monte_carlo/tests/test_rc.py | 21 +
.../monte_carlo/tests/test_remove.py | 17 +
.../monte_carlo/tests/test_remove_new.py | 109 +
.../monte_carlo/tests/test_report.py | 199 +
.../monte_carlo/tests/test_rm.py | 55 +
.../monte_carlo/tests/test_sumcap.py | 17 +
.../monte_carlo/tests/test_theta.py | 15 +
.../monte_carlo/tests/test_thetac.py | 15 +
.../monte_carlo/tests/test_tm1.py | 82 +
.../monte_carlo/tests/test_tm2.py | 410 +
.../monte_carlo/tests/test_transm.py | 1160 +++
.../monte_carlo/tests/test_week.py | 50 +
.../monte_carlo/tests/test_ximpa.py | 31 +
.../monte_carlo/tests/test_ximpar.py | 32 +
.../monte_carlo/tests/test_year.py | 3238 ++++++
.../monte_carlo/tests/testdata_conls/case1/A | 200 +
.../monte_carlo/tests/testdata_conls/case1/B | 200 +
.../monte_carlo/tests/testdata_conls/case1/B1 | 200 +
.../monte_carlo/tests/testdata_conls/case1/BC | 5 +
.../tests/testdata_conls/case1/BLP | 5 +
.../monte_carlo/tests/testdata_conls/case1/BN | 5 +
.../monte_carlo/tests/testdata_conls/case1/BS | 200 +
.../tests/testdata_conls/case1/IBAS | 250 +
.../tests/testdata_conls/case1/INJ | 5 +
.../tests/testdata_conls/case1/INJB | 5 +
.../tests/testdata_conls/case1/LOD | 5 +
.../monte_carlo/tests/testdata_conls/case1/LP | 5 +
.../monte_carlo/tests/testdata_conls/case1/LT | 5 +
.../tests/testdata_conls/case1/TAB | 200 +
.../tests/testdata_conls/case1/XOB | 250 +
.../tests/testdata_conls/case1/XOBI | 2 +
.../monte_carlo/tests/testdata_conls/case2/A | 200 +
.../monte_carlo/tests/testdata_conls/case2/B | 200 +
.../monte_carlo/tests/testdata_conls/case2/B1 | 200 +
.../monte_carlo/tests/testdata_conls/case2/BC | 5 +
.../tests/testdata_conls/case2/BLP | 5 +
.../monte_carlo/tests/testdata_conls/case2/BN | 5 +
.../monte_carlo/tests/testdata_conls/case2/BS | 200 +
.../tests/testdata_conls/case2/IBAS | 250 +
.../tests/testdata_conls/case2/INJ | 5 +
.../tests/testdata_conls/case2/INJB | 5 +
.../tests/testdata_conls/case2/LOD | 5 +
.../monte_carlo/tests/testdata_conls/case2/LP | 5 +
.../monte_carlo/tests/testdata_conls/case2/LT | 5 +
.../tests/testdata_conls/case2/TAB | 200 +
.../tests/testdata_conls/case2/XOB | 250 +
.../tests/testdata_conls/case2/XOBI | 2 +
.../monte_carlo/tests/testdata_conls/case3/A | 200 +
.../monte_carlo/tests/testdata_conls/case3/B | 200 +
.../monte_carlo/tests/testdata_conls/case3/B1 | 200 +
.../monte_carlo/tests/testdata_conls/case3/BC | 5 +
.../tests/testdata_conls/case3/BLP | 5 +
.../monte_carlo/tests/testdata_conls/case3/BN | 5 +
.../monte_carlo/tests/testdata_conls/case3/BS | 200 +
.../tests/testdata_conls/case3/IBAS | 250 +
.../tests/testdata_conls/case3/INJ | 5 +
.../tests/testdata_conls/case3/INJB | 5 +
.../tests/testdata_conls/case3/LOD | 5 +
.../monte_carlo/tests/testdata_conls/case3/LP | 5 +
.../monte_carlo/tests/testdata_conls/case3/LT | 5 +
.../tests/testdata_conls/case3/TAB | 200 +
.../tests/testdata_conls/case3/XOB | 250 +
.../tests/testdata_conls/case3/XOBI | 2 +
.../monte_carlo/tests/testdata_connls/case1/A | 200 +
.../monte_carlo/tests/testdata_connls/case1/B | 200 +
.../tests/testdata_connls/case1/B1 | 200 +
.../tests/testdata_connls/case1/BC | 5 +
.../tests/testdata_connls/case1/BLP | 5 +
.../tests/testdata_connls/case1/BN | 5 +
.../tests/testdata_connls/case1/BS | 200 +
.../tests/testdata_connls/case1/IBAS | 250 +
.../tests/testdata_connls/case1/INJ | 5 +
.../tests/testdata_connls/case1/INJB | 5 +
.../tests/testdata_connls/case1/LOD | 5 +
.../tests/testdata_connls/case1/LP | 5 +
.../tests/testdata_connls/case1/LT | 5 +
.../tests/testdata_connls/case1/TAB | 200 +
.../tests/testdata_connls/case1/XOB | 250 +
.../tests/testdata_connls/case1/XOBI | 2 +
.../monte_carlo/tests/testdata_connls/case2/A | 200 +
.../monte_carlo/tests/testdata_connls/case2/B | 200 +
.../tests/testdata_connls/case2/B1 | 200 +
.../tests/testdata_connls/case2/BC | 5 +
.../tests/testdata_connls/case2/BLP | 5 +
.../tests/testdata_connls/case2/BN | 5 +
.../tests/testdata_connls/case2/BS | 200 +
.../tests/testdata_connls/case2/IBAS | 250 +
.../tests/testdata_connls/case2/INJ | 5 +
.../tests/testdata_connls/case2/INJB | 5 +
.../tests/testdata_connls/case2/LOD | 5 +
.../tests/testdata_connls/case2/LP | 5 +
.../tests/testdata_connls/case2/LT | 5 +
.../tests/testdata_connls/case2/TAB | 200 +
.../tests/testdata_connls/case2/XOB | 250 +
.../tests/testdata_connls/case2/XOBI | 2 +
.../monte_carlo/tests/testdata_connls/case3/A | 200 +
.../monte_carlo/tests/testdata_connls/case3/B | 200 +
.../tests/testdata_connls/case3/B1 | 200 +
.../tests/testdata_connls/case3/BC | 5 +
.../tests/testdata_connls/case3/BLP | 5 +
.../tests/testdata_connls/case3/BN | 5 +
.../tests/testdata_connls/case3/BS | 200 +
.../tests/testdata_connls/case3/IBAS | 250 +
.../tests/testdata_connls/case3/INJ | 5 +
.../tests/testdata_connls/case3/INJB | 5 +
.../tests/testdata_connls/case3/LOD | 5 +
.../tests/testdata_connls/case3/LP | 5 +
.../tests/testdata_connls/case3/LT | 5 +
.../tests/testdata_connls/case3/TAB | 200 +
.../tests/testdata_connls/case3/XOB | 250 +
.../tests/testdata_connls/case3/XOBI | 2 +
.../monte_carlo/tests/testdata_dgeco/A1 | 30 +
.../tests/testdata_dgeco/A1_modified | 30 +
.../monte_carlo/tests/testdata_dgeco/A2 | 30 +
.../tests/testdata_dgeco/A2_modified | 30 +
.../monte_carlo/tests/testdata_dgeco/A3 | 30 +
.../tests/testdata_dgeco/A3_modified | 30 +
.../monte_carlo/tests/testdata_dgeco/IPVT1 | 30 +
.../monte_carlo/tests/testdata_dgeco/IPVT2 | 30 +
.../monte_carlo/tests/testdata_dgeco/IPVT3 | 30 +
.../monte_carlo/tests/testdata_dgedi/A1 | 30 +
.../tests/testdata_dgedi/A1_modified | 30 +
.../monte_carlo/tests/testdata_dgedi/A2 | 30 +
.../tests/testdata_dgedi/A2_modified | 30 +
.../monte_carlo/tests/testdata_dgedi/A3 | 30 +
.../tests/testdata_dgedi/A3_modified | 30 +
.../monte_carlo/tests/testdata_dgedi/IPVT1 | 30 +
.../monte_carlo/tests/testdata_dgedi/IPVT2 | 30 +
.../monte_carlo/tests/testdata_dgedi/IPVT3 | 30 +
.../monte_carlo/tests/testdata_dgefa/A | 30 +
.../monte_carlo/tests/testdata_dgefa/A2 | 30 +
.../monte_carlo/tests/testdata_dgefa/A3 | 30 +
.../tests/testdata_dgefa/A_modified | 30 +
.../tests/testdata_dgefa/A_modified2 | 30 +
.../tests/testdata_dgefa/A_modified3 | 30 +
.../monte_carlo/tests/testdata_dgefa/IPVT | 30 +
.../monte_carlo/tests/testdata_dgefa/IPVT2 | 30 +
.../monte_carlo/tests/testdata_dgefa/IPVT3 | 30 +
.../tests/testdata_filem/case1/ATRIB | 2 +
.../tests/testdata_filem/case1/EVNTS | 103 +
.../tests/testdata_filem/case1/EVNTS_mod | 103 +
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.../monte_carlo/theta.py | 13 +
.../monte_carlo/thetac.py | 18 +
NARP/reliabilityassessment/monte_carlo/tm1.py | 49 +
NARP/reliabilityassessment/monte_carlo/tm2.py | 307 +
.../monte_carlo/transm.py | 144 +
.../reliabilityassessment/monte_carlo/week.py | 49 +
.../monte_carlo/ximpa.py | 17 +
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NARP/requirements.txt | 5 +
NARP/setup.py | 13 +
NARP/tox.ini | 28 +
1911 files changed, 85305 insertions(+)
create mode 100644 NARP/Pipfile
create mode 100644 NARP/__init__.py
create mode 100644 NARP/common/__init__.py
create mode 100644 NARP/common/utils.py
create mode 100644 NARP/conftest.py
create mode 100644 NARP/docs/data.rst
create mode 100644 NARP/docs/index.rst
create mode 100644 NARP/docs/installation.rst
create mode 100644 NARP/docs/manual.rst
create mode 100644 NARP/docs/summary.rst
create mode 100644 NARP/docs/usage.rst
create mode 100644 NARP/mcp_jobs/jobs_index.json
create mode 100644 NARP/mcp_server/README.md
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create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/SWNTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/SWNTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/XNEWA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/XNEWA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/XNEWP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/XNEWP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case5/ZB
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/ATRIB
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/ATRIB_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/BB
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/DPLOLE
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/DPLOLE_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/EUES
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/EUES_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/EVNTS
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/EVNTS_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/HLOLE
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/HLOLE_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLGPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLSPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LOLTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LSFLG
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/LT
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNGPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/MGNTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/NAMA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNGPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNSHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNSHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNSHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNSHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNSPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNSPA_mod
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create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SGNTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLGPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLSPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SOLTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SUSTAT
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SUSTAT_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLGPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLSPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWLTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNGPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNSPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTHA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTHA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTHP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTHP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTPA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTPA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTPP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/SWNTPP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/XNEWA
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/XNEWA_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/XNEWP
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/XNEWP_mod
create mode 100644 NARP/reliabilityassessment/monte_carlo/tests/testdata_year/case6/ZB
create mode 100644 NARP/reliabilityassessment/monte_carlo/theta.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/thetac.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/tm1.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/tm2.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/transm.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/week.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/ximpa.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/ximpar.py
create mode 100644 NARP/reliabilityassessment/monte_carlo/year.py
create mode 100644 NARP/requirements.txt
create mode 100644 NARP/setup.py
create mode 100644 NARP/tox.ini
diff --git a/NARP/Pipfile b/NARP/Pipfile
new file mode 100644
index 0000000..9478db9
--- /dev/null
+++ b/NARP/Pipfile
@@ -0,0 +1,11 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+black = "*"
+pytest = "*"
+
+[packages]
+numpy = "~=1.20"
diff --git a/NARP/__init__.py b/NARP/__init__.py
new file mode 100644
index 0000000..dbd5902
--- /dev/null
+++ b/NARP/__init__.py
@@ -0,0 +1,30 @@
+"""
+NARP reliability assessment integration for PowerMCP.
+
+Re-exports the MCP server instance and tool functions from :mod:`NARP.narp_mcp`
+so callers can simply import ``NARP`` and access the registered tools.
+"""
+
+from .narp_mcp import (
+ mcp,
+ submit_simulation,
+ run_simulation_sync,
+ get_job_status,
+ get_job_result,
+ get_job_summary,
+ list_jobs,
+ cancel_job,
+ validate_input,
+)
+
+__all__ = [
+ "mcp",
+ "submit_simulation",
+ "run_simulation_sync",
+ "get_job_status",
+ "get_job_result",
+ "get_job_summary",
+ "list_jobs",
+ "cancel_job",
+ "validate_input",
+]
diff --git a/NARP/common/__init__.py b/NARP/common/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/NARP/common/utils.py b/NARP/common/utils.py
new file mode 100644
index 0000000..2162563
--- /dev/null
+++ b/NARP/common/utils.py
@@ -0,0 +1,69 @@
+from dataclasses import dataclass
+from typing import Callable, Dict, Any, Optional
+import functools
+from mcp.server.fastmcp import FastMCP
+
+
+
+
+def power_mcp_tool(mcp: FastMCP):
+ """
+ Decorator to register a PowerMCP tool.
+
+ A decorator that wraps a function and transforms the result to a dictionary if it is an instance of PowerError.
+ It also adds the function to the MCP server automatically.
+
+ Args:
+ mcp: The MCP server to register the tool to.
+
+ Example:
+ @power_mcp_tool(mcp)
+ def my_tool(x: int) -> str:
+ return str(x)
+ """
+ def decorator(func):
+
+ @mcp.tool()
+ @functools.wraps(func)
+ def wrapper(*args, **kwargs):
+ result = func(*args, **kwargs)
+ return _transform_power_mcp_result(result)
+ return wrapper
+ return decorator
+
+
+
+def _transform_power_mcp_result(result: Any) -> Any:
+ """
+ Transforms the result of a function to a dictionary if it is an instance of PowerError.
+ """
+ if isinstance(result, PowerError):
+ base: Dict[str, Any] = {
+ "status": result.status,
+ "message": result.message
+ }
+ if result.info:
+ # We have some additional data, we will add the keys.
+ for key, value in result.info.items():
+ base[key] = value
+ return base
+ else:
+ return result
+
+@dataclass
+class PowerError:
+ """
+ A custom error class that is used to return errors from the MCP server.
+ It is used to return errors in a consistent format.
+ The info field is used to return additional data about the error.
+
+ Attributes:
+ status: The status of the error.
+ message: The message of the error.
+ info: Additional data about the error, this is a dictionary of key-value pairs,
+ and is added alongside the other fields. For instance, if this field were to be {"hello": "world"},
+ then the returned object would be {"status": "error", "message": "An error occurred", "hello": "world"}
+ """
+ status: str
+ message: str
+ info: Optional[Dict[str, Any]] = None
diff --git a/NARP/conftest.py b/NARP/conftest.py
new file mode 100644
index 0000000..e446d0a
--- /dev/null
+++ b/NARP/conftest.py
@@ -0,0 +1,21 @@
+import pytest
+
+
+def pytest_addoption(parser):
+ parser.addoption(
+ "--runslow", action="store_true", default=False, help="run slow tests"
+ )
+
+
+def pytest_configure(config):
+ config.addinivalue_line("markers", "slow: mark test as slow to run")
+
+
+def pytest_collection_modifyitems(config, items):
+ if config.getoption("--runslow"):
+ # --runslow given in cli: do not skip slow tests
+ return
+ skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+ for item in items:
+ if "slow" in item.keywords:
+ item.add_marker(skip_slow)
diff --git a/NARP/docs/data.rst b/NARP/docs/data.rst
new file mode 100644
index 0000000..3283dce
--- /dev/null
+++ b/NARP/docs/data.rst
@@ -0,0 +1,11 @@
+Capabilities and Data
+---------------------
+The reliability assessment program reads in and interprets generation properties,
+ownership, firm contracts, transmission link data between areas, and other important
+power system data with simulation settings on an area basis. Then the program calculates
+key reliability indices according to industry standards, including LOLE (Loss of Load
+Expectations), Hourly LOLE (HLOLE in short), and EUE (Expected Unserved Energy, in kWh or
+MWh) for electric systems. Those reliability indices measure the availability of
+generation and transmission capacity given a preset table of failure probabilities. It
+also creates a planned generation unit maintenance library and the probabilistic
+distribution of each defined outage for the users to assess the system reliability risk.
diff --git a/NARP/docs/index.rst b/NARP/docs/index.rst
new file mode 100644
index 0000000..981fc68
--- /dev/null
+++ b/NARP/docs/index.rst
@@ -0,0 +1,20 @@
+Reliability Assessment
+======================
+Collaborators: Dr. Chanan Singh, Dr. Yongli Zhu
+
+.. contents:: :local:
+
+.. include::
+ summary.rst
+
+.. include::
+ data.rst
+
+.. include::
+ manual.rst
+
+.. include::
+ installation.rst
+
+.. include::
+ usage.rst
diff --git a/NARP/docs/installation.rst b/NARP/docs/installation.rst
new file mode 100644
index 0000000..d65893f
--- /dev/null
+++ b/NARP/docs/installation.rst
@@ -0,0 +1,13 @@
+Installation
+------------
+
+This project requires Python 3.8 and relies on the ``numpy`` and ``pandas`` packages
+both available on `PyPi `_. We recommend that you use ``pipenv`` to
+install the dependencies in an isolated environment
+
++ Clone or fork the source code from
+ `GitHub `_
++ Install dependencies using the **Pipfile** or *requirements.txt* files located at the
+ root of the package, e.g., using ``pip install -r requirements.txt``
++ Install the ``reliabilityassessment`` package as a third-party library in your working
+ environment with ``pip install .``
\ No newline at end of file
diff --git a/NARP/docs/manual.rst b/NARP/docs/manual.rst
new file mode 100644
index 0000000..63469c0
--- /dev/null
+++ b/NARP/docs/manual.rst
@@ -0,0 +1,10 @@
+User Manual
+-----------
+The **reliability-assessment** project is available on GitHub
+(https://github.com/Breakthrough-Energy/reliability-assessment).
+The package can be used as a standalone application with separate
+Input/Output pipeline, to maintain the integrity and performance
+inherited from the original NARP software.
+
+A short guide of the usage of the ``reliabilityassessment``
+package can be found below.
diff --git a/NARP/docs/summary.rst b/NARP/docs/summary.rst
new file mode 100644
index 0000000..98968b3
--- /dev/null
+++ b/NARP/docs/summary.rst
@@ -0,0 +1,33 @@
+Summary
+-------
+
+Reliability performance is the most important criteria for any electric power system
+planning and operation activity to comply with. This becomes increasingly challenging
+as more intermittent energy resources are added to the system with an accelerated pace
+of the retirement of fossil fuel generation. In addition, more frequent extreme weather
+events and climate change also leads to more severe blackouts, as measured by both the
+time duration of the blackout and the magnitude of affected demand. Understanding the
+reliability performance of the existing system and accordingly planning for a reliable
+future energy system requires a comprehensive reliability assessment module with
+flexible demand and variable generation that can assess the fundamental elements of a
+system’s reliability, including:
+
++ Generator’s availability and capacity contribution to system adequacy;
++ System resource balancing capability to satisfy demand needs with renewable generation;
++ Risk of load interruptions under modeled events and outages.
+
+The objective of this project is to develop and translate an add-on module embedded
+into the modeling framework to enable the simulation of reliability performance metrics
+of an electric system’s transmission and generation equipment to identify future
+planning needs. Underlying this project is the reliability assessment currently used as
+the industry standard – General Electric’s Multi Area Reliability Simulation (GE-MARS).
+GE-MARS calculates reliability performance indices in a multi-area interconnected
+electric power system to evaluate system risk and guide generation capacity planning.
+The program is based on a Monte Carlo simulation approach to reflect the effects of
+random events such as generator and transmission link failures as well as deterministic
+operating rules and policies. The original concept and codes came from
+Prof. Chanan Singh, currently at Texas A&M University. This project ports an original
+Fortran-based power system reliability assessment program ('NARP') into a modern
+language (Python and Julia) and publishes it as an open-source tool as the final
+deliverable, not only for industry application but also for the research community
+to continue building upon.
diff --git a/NARP/docs/usage.rst b/NARP/docs/usage.rst
new file mode 100644
index 0000000..1665073
--- /dev/null
+++ b/NARP/docs/usage.rst
@@ -0,0 +1,136 @@
+Running the Program
+-------------------
+
+Before running the program, the user needs to prepare several raw input files which
+defines the system for reliability assessment and certain simulation settings. These
+inputs are described below in :numref:`major_input` and are defined in a csv file
+format except for the LEEI file which defines the load profile data and that follows
+the classic EEI format that has been used in many industry applications like PROMOD.
+A simulation-ready example is available for reference in
+**reliabilityassessment/data_processing/tests/**
+
+The program has automatic error checking for data anomalies to improve the
+robustness of user defined inputs given the simulation needs. There are other
+miscellaneous inputs that are needed for simulation settings or for changing the case
+definition (file **InputC**), which are all included in the tests. Below are
+descriptions focusing on the data inputs for the modeled system. Please note that
+among all the inputs, ZZMC, ZZUD, ZZLD, ZZTD, and ZZTC are required to run a
+simulation while others are optional.
+
+.. _major_input:
+
+.. table:: Major input data on model system
+
+ +-------+--------+------------------------------------------------------------+
+ | Input | Format | Description |
+ +=======+========+============================================================+
+ | ZZTC | csv | Simulation title card |
+ +-------+--------+------------------------------------------------------------+
+ | ZZLD | csv | System data, including: |
+ | | | Area name; |
+ | | | Peak demand; |
+ | | | Forecast uncertainty; |
+ | | | Outage window; |
+ | | | Forbidden period; |
+ | | | Flow constraints. |
+ +-------+--------+------------------------------------------------------------+
+ | ZZUD | csv | Unit data, including: |
+ | | | Unit Name; |
+ | | | Serial number; |
+ | | | Location; |
+ | | | Capacity; |
+ | | | Outage rate; |
+ | | | Derated outage rate; |
+ | | | Percentage derating due to partial failure; |
+ | | | Option to predetermine or auto-schedule; |
+ | | | Beginning week and Duration of the first and second weeks. |
+ +-------+--------+------------------------------------------------------------+
+ | ZZFC | csv | Firm contracts, specifying the firm interchanges of power |
+ | | | between areas. |
+ +-------+--------+------------------------------------------------------------+
+ | ZZOD | csv | Unit ownership data in percentage |
+ +-------+--------+------------------------------------------------------------+
+ | ZZTD | csv | Line data, including: |
+ | | | Location of areas; |
+ | | | Admittance (negative number); |
+ | | | Capacity of the line in the forward direction; |
+ | | | Capacity of the line in the reverse direction; |
+ | | | Probability of the line in each state (six states in total |
+ | | | for each line) |
+ +-------+--------+------------------------------------------------------------+
+ | ZZDD | csv | Line derating data. |
+ +-------+--------+------------------------------------------------------------+
+ | ZZMC | csv | Miscellaneous program settings, including: |
+ | | | Seed (Loss sharing included); |
+ | | | Definition of seasons; |
+ | | | Convergence test parameters; |
+ | | | Frequency of drawing of the status of generators and |
+ | | | transmission lines, either daily or hourly; |
+ | | | Frequency of data collection, either daily peak or hourly; |
+ | | | Specifications for the probability distribution of EUE |
+ | | | (e.g.upper limit); |
+ | | | Printout options. |
+ +-------+--------+------------------------------------------------------------+
+ | LEEI | EEI | Load profile data. |
+ +-------+--------+------------------------------------------------------------+
+
+Unlike other modules, the modeling framework is not required to execute this module as
+a standalone reliability assessment analysis. However, it is recommended to have a
+complete energy system simulation package to accommodate the reliability module as a
+key piece of an overall planning study;
+
+Similar to GE-MARS or other industry simulation application, this module is area-based,
+meaning that it is designed for zonal reliability analysis. Nodal models with full
+granularity need to be translated into zonal scale first in order to be able to use
+this module.
+
+After preparing all the required inputs, navigate to the folder where all input data
+are files, specify the path and run:
+
+.. code::
+
+ INPUT_DIR = "absolute path of the input data”
+ narp_main(INPUT_DIR).
+
+The command triggers input data processing function ``dataf1`` followed by Monte-Carlo
+function``contrl``. Data processing is a one-time processing while Monte-Carlo is a
+repeated iterations thus convergence needs to be watched. While the program is
+progressing, it will report the iteration convergence index information in the
+terminal as shown below:
+
+.. code::
+
+ n-th REPLICATION; SUM=143.0 NEW=0.0 MEAN=0.43202416918429004 CONVERGENCE=3.702682434420153
+
+When the ``CONVERGENCE`` index number drops to below than 1, it indicates that the
+program has reached convergence tolerance and finished evaluation. If it won’t reduce
+to less than 1 within defined number of iterations, it indicates that either the system
+has issues, or the simulation needs to be checked. The user should follow the below
+steps to identify the problem and solutions:
+
+1. Increase number of max iteration limit (default to 9999) and re-run;
+2. If it converges, stop and check results. If it still not converges, keep increasing
+ iteration limit until it is reasonably large;
+3. If it won’t converge with a reasonably large max # of iterations, check input data
+ and look for anomalies;
+4. If input data is correct without anomalies, then it indicates the system may have
+ significant reliability problems that block the program from converging. Sometimes
+ there may be numeric issues within the simulation rather than real system problems
+ that the user may need to further verify before using the results.
+
+After the running is finished and converged successfully, a txt file will be
+generated in the root directory. The contains reliability statistics for all the
+areas are printed out in a table format.
+
+The Expected outputs from the program will mainly include three parts:
+
++ Unit planned maintenance generated with Monte-Carlo simulation. This measures the
+ expected availability of generation units in each area, and can be used as an input
+ for follow-up studies or simulations to support generation planning.
++ Reliability indices measuring the risk of interruptions serving load for each area
+ of the system: LOLE (Loss of Load Expectations), Hourly LOLE (HLOLE in short), EUE
+ (Expected Unserved Energy, in kWh or MWh) for electric systems.
++ Probability distribution histogram of outage events for each area. This indicates
+ the severity and probability of outages and is a key metric of risk-based
+ reliability planning studies.
+
diff --git a/NARP/mcp_jobs/jobs_index.json b/NARP/mcp_jobs/jobs_index.json
new file mode 100644
index 0000000..7a73a41
--- /dev/null
+++ b/NARP/mcp_jobs/jobs_index.json
@@ -0,0 +1,2 @@
+{
+}
\ No newline at end of file
diff --git a/NARP/mcp_server/README.md b/NARP/mcp_server/README.md
new file mode 100644
index 0000000..1d24b5d
--- /dev/null
+++ b/NARP/mcp_server/README.md
@@ -0,0 +1,15 @@
+Minimal local MCP server skeleton for reliability-assessment.
+
+Usage:
+
+from mcp_server.manager import JobManager
+jm = JobManager()
+job_id = jm.submit_simulation('reliabilityassessment/integration_test')
+status = jm.get_job_status(job_id)
+# wait for completion then
+result = jm.get_job_result(job_id)
+
+Notes:
+- jobs stored in sqlite at mcp_jobs.sqlite
+- job working dirs under ./mcp_jobs
+- parse_output is minimal and should be extended for production
diff --git a/NARP/mcp_server/__init__.py b/NARP/mcp_server/__init__.py
new file mode 100644
index 0000000..5ca60a6
--- /dev/null
+++ b/NARP/mcp_server/__init__.py
@@ -0,0 +1,6 @@
+"""Minimal local MCP server package for reliability-assessment.
+Expose JobManager for local programmatic use.
+"""
+from .manager import JobManager
+
+__all__ = ["JobManager"]
diff --git a/NARP/mcp_server/manager.py b/NARP/mcp_server/manager.py
new file mode 100644
index 0000000..de4e7f6
--- /dev/null
+++ b/NARP/mcp_server/manager.py
@@ -0,0 +1,237 @@
+import os
+import shutil
+import json
+import uuid
+import time
+from concurrent.futures import ThreadPoolExecutor
+import threading
+from pathlib import Path
+from typing import Optional
+import logging
+
+from .worker import worker_run, parse_output
+
+logger = logging.getLogger(__name__)
+
+
+class JobManager:
+ def __init__(self, db_path: str = "mcp_jobs.sqlite", work_root: Optional[str] = None, max_workers: int = 1):
+ # db_path parameter kept for compatibility but not used in the in-memory implementation
+ self.db_path = db_path
+
+ # Ensure work_root is located inside the project directory so results are always
+ # written to the repository's mcp_jobs folder regardless of where JobManager is instantiated.
+ # Project root is the parent of the package directory (two levels up from this file).
+ project_root = Path(__file__).resolve().parents[1]
+ if work_root:
+ wr = Path(work_root)
+ # If user passed a relative path, resolve it relative to project root
+ if not wr.is_absolute():
+ wr = project_root / wr
+ self.work_root = wr.resolve()
+ else:
+ # default to the project's mcp_jobs folder
+ self.work_root = (project_root / "mcp_jobs").resolve()
+
+ # create the work_root directory if it doesn't exist
+ self.work_root.mkdir(parents=True, exist_ok=True)
+
+ # maintain an index file so users can quickly locate job folders
+ self.index_file = self.work_root / "jobs_index.json"
+ self._index = {}
+ if self.index_file.exists():
+ try:
+ self._index = json.loads(self.index_file.read_text(encoding="utf-8"))
+ except Exception:
+ self._index = {}
+
+ # in-memory job store
+ self._jobs = {} # job_id -> job dict
+ self._lock = threading.Lock()
+
+ # use threads so worker can update in-memory state via callback
+ self.executor = ThreadPoolExecutor(max_workers=max_workers)
+ self._futures = {}
+
+ def _write_index(self):
+ try:
+ self.index_file.write_text(json.dumps(self._index, indent=2), encoding="utf-8")
+ except Exception:
+ pass
+
+ def _write_metadata(self, job: dict):
+ """Persist a job's metadata into its job_dir/metadata.json (best-effort)."""
+ try:
+ jd = job.get("job_dir")
+ if not jd:
+ return
+ p = Path(jd)
+ p.mkdir(parents=True, exist_ok=True)
+ meta_file = p / "metadata.json"
+ # prepare a serializable copy
+ to_write = {
+ k: job.get(k) for k in [
+ "id", "status", "params", "job_dir", "created_at", "started_at", "finished_at", "result", "error"
+ ]
+ }
+ meta_file.write_text(json.dumps(to_write, indent=2), encoding="utf-8")
+ except Exception:
+ # metadata persistence is best-effort; don't raise
+ pass
+
+ def _insert_job(self, job_id: str, params: dict, job_dir: str):
+ # ensure job_dir is absolute
+ job_dir = str(Path(job_dir).resolve())
+ with self._lock:
+ self._jobs[job_id] = {
+ "id": job_id,
+ "status": "pending",
+ "params": params,
+ "job_dir": job_dir,
+ "created_at": time.time(),
+ "started_at": None,
+ "finished_at": None,
+ "result": None,
+ "error": None,
+ }
+ # persist initial metadata
+ self._write_metadata(self._jobs[job_id])
+ # update index for quick lookup
+ self._index[job_id] = job_dir
+ self._write_index()
+ logger.info(f"Created job {job_id} at {job_dir}")
+
+ def _update_job(self, job_id: str, **fields):
+ with self._lock:
+ job = self._jobs.get(job_id)
+ if not job:
+ return
+ for k, v in fields.items():
+ job[k] = v
+ # persist after update
+ self._write_metadata(job)
+
+ def submit_simulation(self, test_dir: str, params: Optional[dict] = None) -> dict:
+ """Submit an async job. Returns a dict with job_id and absolute paths for job_dir, output and log."""
+ params = params or {}
+ job_id = f"job_{uuid.uuid4().hex[:8]}"
+ job_dir_path = self.work_root / job_id
+ job_dir = str(job_dir_path.resolve())
+ # copy inputs into job folder (create target)
+ shutil.copytree(test_dir, job_dir)
+ self._insert_job(job_id, params, job_dir)
+ # submit to thread pool and provide update callback
+ future = self.executor.submit(worker_run, job_id, job_dir, params, self._update_job)
+ self._futures[job_id] = future
+ # provide absolute paths for output and log files so callers can locate them immediately
+ output_path = str(Path(job_dir) / "output_python.txt")
+ log_path = str(Path(job_dir) / "run.log")
+ return {
+ "job_id": job_id,
+ "job_dir": job_dir,
+ "output_path": output_path,
+ "log_path": log_path,
+ }
+
+ def run_simulation_sync(self, test_dir: str, params: Optional[dict] = None) -> dict:
+ params = params or {}
+ job_id = f"job_{uuid.uuid4().hex[:8]}"
+ job_dir_path = self.work_root / job_id
+ job_dir = str(job_dir_path.resolve())
+ shutil.copytree(test_dir, job_dir)
+ self._insert_job(job_id, params, job_dir)
+ # run directly (in current thread)
+ try:
+ worker_run(job_id, job_dir, params, self._update_job)
+ with self._lock:
+ job = self._jobs.get(job_id)
+ return job.copy() if job else {"job_id": job_id, "status": "completed", "result": None}
+ except Exception as e:
+ # persist failure
+ self._update_job(job_id, status="failed", error=str(e), finished_at=time.time())
+ return {"job_id": job_id, "status": "failed", "error": str(e)}
+
+ def get_job_status(self, job_id: str) -> dict:
+ with self._lock:
+ job = self._jobs.get(job_id)
+ if not job:
+ raise KeyError(job_id)
+ return {
+ "job_id": job["id"],
+ "status": job["status"],
+ "created_at": job["created_at"],
+ "started_at": job["started_at"],
+ "finished_at": job["finished_at"],
+ "error": job["error"],
+ "job_dir": job["job_dir"],
+ }
+
+ def get_job_result(self, job_id: str) -> dict:
+ """
+ Return the raw output_python.txt contents for a job if present.
+
+ Returns a dict with job_id, output_path and output (text). If the file is not
+ present returns a dict with status 'no_output' and the expected output_path.
+ """
+ with self._lock:
+ job = self._jobs.get(job_id)
+ if not job:
+ raise KeyError(job_id)
+ job_dir = job.get("job_dir")
+
+ out_path = Path(job_dir) / "output_python.txt"
+ if out_path.exists():
+ try:
+ text = out_path.read_text(encoding="utf-8", errors="ignore")
+ except Exception:
+ # best-effort fallback
+ try:
+ text = out_path.read_text(encoding="utf-8", errors="replace")
+ except Exception:
+ text = ""
+ return {"job_id": job_id, "output_path": str(out_path), "output": text}
+ return {"job_id": job_id, "status": "no_output", "output_path": str(out_path)}
+
+ def get_job_summary(self, job_id: str) -> dict:
+ """
+ Return a parsed summary (TABLE 12 / TABLE 13) from output_python.txt if present.
+
+ Does not persist result.json; returns parsed dict or a 'no_output' status.
+ """
+ with self._lock:
+ job = self._jobs.get(job_id)
+ if not job:
+ raise KeyError(job_id)
+ job_dir = job.get("job_dir")
+
+ out_path = Path(job_dir) / "output_python.txt"
+ if out_path.exists():
+ parsed = parse_output(str(out_path))
+ return {"job_id": job_id, "job_dir": job_dir, "summary": parsed}
+ return {"job_id": job_id, "status": "no_output", "output_path": str(out_path)}
+
+ def list_jobs(self):
+ with self._lock:
+ jobs = list(self._jobs.values())
+ # sort by created_at desc
+ jobs = sorted(jobs, key=lambda j: j.get("created_at", 0), reverse=True)
+ return [{"job_id": j["id"], "status": j["status"], "created_at": j["created_at"], "job_dir": j["job_dir"]} for j in jobs]
+
+ def cancel_job(self, job_id: str) -> bool:
+ # best-effort: if future exists, attempt to cancel
+ fut = self._futures.get(job_id)
+ if fut:
+ ok = fut.cancel()
+ if ok:
+ self._update_job(job_id, status="cancelled", finished_at=time.time())
+ return ok
+ # otherwise mark cancelled in memory
+ with self._lock:
+ job = self._jobs.get(job_id)
+ if job:
+ job["status"] = "cancelled"
+ job["finished_at"] = time.time()
+ # persist
+ self._write_metadata(job)
+ return True
+ return False
diff --git a/NARP/mcp_server/server.py b/NARP/mcp_server/server.py
new file mode 100644
index 0000000..8d5712f
--- /dev/null
+++ b/NARP/mcp_server/server.py
@@ -0,0 +1,34 @@
+"""
+Compatibility wrapper that preserves the legacy ``NARP.mcp_server.server`` import path.
+
+The canonical MCP tool definitions now live in :mod:`NARP.narp_mcp`; this module simply
+re-exports them so existing entry points keep working.
+"""
+
+from ..narp_mcp import (
+ mcp,
+ submit_simulation,
+ run_simulation_sync,
+ get_job_status,
+ get_job_result,
+ get_job_summary,
+ list_jobs,
+ cancel_job,
+ validate_input,
+)
+
+__all__ = [
+ "mcp",
+ "submit_simulation",
+ "run_simulation_sync",
+ "get_job_status",
+ "get_job_result",
+ "get_job_summary",
+ "list_jobs",
+ "cancel_job",
+ "validate_input",
+]
+
+
+if __name__ == "__main__":
+ mcp.run(transport="stdio")
diff --git a/NARP/mcp_server/worker.py b/NARP/mcp_server/worker.py
new file mode 100644
index 0000000..af4c268
--- /dev/null
+++ b/NARP/mcp_server/worker.py
@@ -0,0 +1,198 @@
+import os
+import time
+import json
+import subprocess
+from pathlib import Path
+import sys
+
+
+def _call_update(target, job_id: str, **fields):
+ """Call update target, which may be a callable(update_job_id, **fields) or None.
+
+ If target is None, attempt to write metadata.json into the job folder if available
+ via fields.get('job_dir'). This keeps worker usable even without a manager callback.
+ """
+ if callable(target):
+ try:
+ target(job_id, **fields)
+ return
+ except TypeError:
+ try:
+ target(job_id, fields)
+ return
+ except Exception:
+ pass
+ # fallback: write metadata.json if job_dir provided
+ jd = fields.get("job_dir")
+ if jd:
+ try:
+ p = Path(jd) / "metadata.json"
+ # read existing metadata if present
+ meta = {}
+ if p.exists():
+ try:
+ meta = json.loads(p.read_text(encoding="utf-8"))
+ except Exception:
+ meta = {}
+ # update fields
+ meta.update(fields)
+ p.write_text(json.dumps(meta, indent=2), encoding="utf-8")
+ except Exception:
+ pass
+
+
+def worker_run(job_id: str, job_dir: str, params: dict, updater=None):
+ """Worker executed in separate thread/process to run narpMain and capture outputs.
+
+ updater: optional callable(job_id, **fields) used to update manager's in-memory state.
+ """
+ job_dir = Path(job_dir)
+ log_path = job_dir / "run.log"
+ out_path = job_dir / "output_python.txt"
+ result_path = job_dir / "result.json"
+
+ _call_update(updater, job_id, job_dir=str(job_dir), status="running", started_at=time.time())
+
+ try:
+ # Ensure the subprocess can import the local package by adding the project root
+ # to PYTHONPATH. Project root is the parent of this file's parent (two levels up).
+ project_root = Path(__file__).resolve().parents[1]
+ env = os.environ.copy()
+ prev = env.get("PYTHONPATH", "")
+ env["PYTHONPATH"] = str(project_root) + (os.pathsep + prev if prev else "")
+ # Ensure Python child process is unbuffered so logs flush promptly
+ env["PYTHONUNBUFFERED"] = "1"
+
+ # Run the narpMain script directly if available, otherwise fall back to -m invocation.
+ narp_py = project_root / "reliabilityassessment" / "monte_carlo" / "narpMain.py"
+ if narp_py.exists():
+ cmd = [sys.executable, str(narp_py), job_dir.as_posix()]
+ else:
+ cmd = [sys.executable, "-m", "reliabilityassessment.monte_carlo.narpMain", job_dir.as_posix()]
+
+ with open(log_path, "wb") as logf:
+ # Prevent child from reading stdin (important when server runs over stdio)
+ proc = subprocess.Popen(cmd, stdout=logf, stderr=logf, stdin=subprocess.DEVNULL, env=env)
+ ret = proc.wait()
+ if ret != 0:
+ raise RuntimeError(f"narpMain failed with code {ret}; see {log_path}")
+
+ # Do not parse output here. Worker will mark job completed and provide paths;
+ # parsing can be done on-demand by calling get_job_result which uses parse_output.
+ _call_update(
+ updater,
+ job_id,
+ job_dir=str(job_dir),
+ status="completed",
+ finished_at=time.time(),
+ output_path=str(out_path),
+ log_path=str(log_path),
+ )
+ except Exception as e:
+ _call_update(updater, job_id, job_dir=str(job_dir), status="failed", finished_at=time.time(), error=str(e))
+ raise
+
+
+def parse_output(output_file: str) -> dict:
+ """Parse TABLE 12 and TABLE 13 from the narp output into a structured dict.
+
+ Output structure:
+ {
+ "summary": { "A1": {"peak":..., "installed":..., "PCT_RES":..., "HLOLE":..., "EUE":..., "LOLE":...}, ... },
+ "areas": [ {"area": 1, "HLOLE":..., "XLOL_hourly":..., "EUE":..., "LOLE":..., "XLOL_peak":...}, ... ]
+ }
+
+ The parser makes best-effort numeric extraction and is resilient to spacing differences.
+ """
+ import re
+
+ res = {"summary": {}, "areas": []}
+ txt = ""
+ try:
+ with open(output_file, "r", encoding="utf-8", errors="ignore") as f:
+ txt = f.read()
+ except Exception:
+ return res
+
+ # --- TABLE 12 parsing: area-level hourly/peak statistics ---
+ m12 = re.search(r"TABLE\s*12", txt, flags=re.I)
+ if m12:
+ # stop at TABLE 13 or next TABLE occurrence
+ tail = txt[m12.end():]
+ end_marker = re.search(r"TABLE\s*13|TABLE\s{3}13|TABLE\s+13", tail, flags=re.I)
+ block = tail[:end_marker.start()] if end_marker else tail[:4000]
+ for ln in block.splitlines():
+ s = ln.strip()
+ # lines that start with area number e.g. '1 AV' or '2 AV'
+ if re.match(r"^\d+\s+\w", s):
+ # collect numeric tokens from the line
+ toks = re.split(r"\s+", s)
+ nums = []
+ for t in toks[1:]:
+ try:
+ # accept ints and floats, strip commas
+ v = float(t.replace(',', ''))
+ nums.append(v)
+ except Exception:
+ continue
+ if len(nums) >= 4:
+ try:
+ area = int(re.match(r"^(\d+)", s).group(1))
+ # map numeric columns heuristically:
+ # nums[0]=HLOLE, nums[1]=XLOL_hourly, nums[2]=EUE, nums[3]=LOLE, nums[4]=XLOL_peak (if present)
+ hlole = nums[0]
+ xlol_h = nums[1] if len(nums) > 1 else None
+ eue = nums[2] if len(nums) > 2 else None
+ lole = nums[3] if len(nums) > 3 else None
+ xlol_p = nums[4] if len(nums) > 4 else None
+ res["areas"].append({
+ "area": area,
+ "HLOLE": hlole,
+ "XLOL_hourly": xlol_h,
+ "EUE": eue,
+ "LOLE": lole,
+ "XLOL_peak": xlol_p,
+ })
+ except Exception:
+ continue
+
+ # --- TABLE 13 parsing: area summary rows like 'A1 ...' ---
+ m13 = re.search(r"TABLE\s{0,3}13|TABLE\s13", txt, flags=re.I)
+ if m13:
+ tail = txt[m13.end():]
+ block = tail[:3000]
+ for ln in block.splitlines():
+ s = ln.strip()
+ # lines starting with A1, A2, etc.
+ if re.match(r"^A\d+", s, flags=re.I):
+ toks = re.split(r"\s+", s)
+ nums = []
+ for t in toks[1:]:
+ try:
+ v = float(t.replace(',', ''))
+ nums.append(v)
+ except Exception:
+ continue
+ # heuristic mapping based on observed layout:
+ # nums[0]=PEAK, nums[1]=INSTALLED, nums[2]=PCT_RES, nums[3]=HLOLE_MAGN,
+ # nums[4]=HLOLE_PCT_SD, nums[5]=EUE_MAGN, nums[6]=EUE_PCT_SD, nums[7]=LOLE_MAGN, nums[8]=LOLE_PCT_SD
+ try:
+ area_label = toks[0]
+ entry = {}
+ if len(nums) > 0:
+ entry["peak"] = nums[0]
+ if len(nums) > 1:
+ entry["installed"] = nums[1]
+ if len(nums) > 2:
+ entry["PCT_RES"] = nums[2]
+ if len(nums) > 3:
+ entry["HLOLE"] = nums[3]
+ if len(nums) > 5:
+ entry["EUE"] = nums[5]
+ if len(nums) > 7:
+ entry["LOLE"] = nums[7]
+ res["summary"][area_label] = entry
+ except Exception:
+ continue
+
+ return res
diff --git a/NARP/narp_mcp.py b/NARP/narp_mcp.py
new file mode 100644
index 0000000..67fa6a8
--- /dev/null
+++ b/NARP/narp_mcp.py
@@ -0,0 +1,181 @@
+"""
+PowerMCP integration layer for the NARP reliability assessment tools.
+
+This module mirrors the style used by the other integrations (e.g. pandapower) by
+exposing a `FastMCP` instance alongside decorated tool functions. The actual job
+execution logic lives in :mod:`NARP.mcp_server.manager` and :mod:`NARP.mcp_server.worker`.
+"""
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from common.utils import PowerError, power_mcp_tool
+from mcp.server.fastmcp import FastMCP
+
+from .mcp_server.manager import JobManager
+
+logger = logging.getLogger(__name__)
+logger.info("Initializing NARP Reliability Assessment MCP tools")
+
+# Public MCP server instance matching the repository convention.
+mcp = FastMCP("NARP Reliability Assessment Server")
+
+# Reuse a single job manager instance so that tooling retains job history.
+_job_manager = JobManager(max_workers=1)
+
+# Required input artefacts expected by the native NARP workflow.
+_REQUIRED_INPUT_FILES = (
+ "ZZMC.csv",
+ "ZZUD.csv",
+ "ZZLD.csv",
+ "ZZTD.csv",
+ "ZZTC.csv",
+ "LEEI",
+)
+
+
+def _coerce_params(params: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+ """Return a mutable params dictionary for downstream calls."""
+ return dict(params) if params else {}
+
+
+@power_mcp_tool(mcp)
+def submit_simulation(test_dir: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+ """
+ Submit an asynchronous simulation job.
+
+ Args:
+ test_dir: Path to a directory containing the NARP input files.
+ params: Optional runtime parameters to pass through to the worker.
+ """
+ payload = _coerce_params(params)
+ logger.info("Submitting NARP simulation for %s", test_dir)
+ try:
+ return _job_manager.submit_simulation(test_dir, payload)
+ except Exception as exc:
+ logger.exception("Failed to submit NARP simulation from %s", test_dir)
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def run_simulation_sync(test_dir: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+ """
+ Run a simulation synchronously (blocking until completion).
+
+ Returns the final job metadata – callers can inspect ``result`` and ``status`` fields.
+ """
+ payload = _coerce_params(params)
+ logger.info("Running synchronous NARP simulation for %s", test_dir)
+ try:
+ result = _job_manager.run_simulation_sync(test_dir, payload)
+ return {"result": result}
+ except Exception as exc:
+ logger.exception("Synchronous NARP simulation failed for %s", test_dir)
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def get_job_status(job_id: str) -> Dict[str, Any]:
+ """Return the status metadata for a previously submitted job."""
+ try:
+ job = _job_manager.get_job_status(job_id)
+ return {"job": job}
+ except KeyError:
+ logger.warning("Requested status for unknown NARP job %s", job_id)
+ return PowerError(status="error", message=f"Job not found: {job_id}")
+ except Exception as exc:
+ logger.exception("Failed to retrieve status for NARP job %s", job_id)
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def get_job_result(job_id: str) -> Dict[str, Any]:
+ """
+ Retrieve the raw ``output_python.txt`` content for a job.
+
+ The worker writes this file into the job directory after completion.
+ """
+ try:
+ result = _job_manager.get_job_result(job_id)
+ return {"result": result}
+ except KeyError:
+ logger.warning("Requested result for unknown NARP job %s", job_id)
+ return PowerError(status="error", message=f"Job not found: {job_id}")
+ except Exception as exc:
+ logger.exception("Failed to retrieve result for NARP job %s", job_id)
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def get_job_summary(job_id: str) -> Dict[str, Any]:
+ """
+ Return the parsed TABLE 12 / TABLE 13 summary extracted from ``output_python.txt``.
+
+ Parsing is performed lazily using :func:`NARP.mcp_server.worker.parse_output`.
+ """
+ try:
+ summary = _job_manager.get_job_summary(job_id)
+ return summary
+ except KeyError:
+ logger.warning("Requested summary for unknown NARP job %s", job_id)
+ return PowerError(status="error", message=f"Job not found: {job_id}")
+ except Exception as exc:
+ logger.exception("Failed to retrieve summary for NARP job %s", job_id)
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def list_jobs(limit: int = 100) -> Dict[str, Any]:
+ """List the most recent jobs tracked by the manager (sorted newest first)."""
+ try:
+ jobs = _job_manager.list_jobs()
+ return {"jobs": jobs[:limit]}
+ except Exception as exc:
+ logger.exception("Failed to list NARP jobs")
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def cancel_job(job_id: str) -> Dict[str, Any]:
+ """Attempt to cancel a running or pending job."""
+ try:
+ cancelled = _job_manager.cancel_job(job_id)
+ return {"cancelled": bool(cancelled)}
+ except Exception as exc:
+ logger.exception("Failed to cancel NARP job %s", job_id)
+ return PowerError(status="error", message=str(exc))
+
+
+@power_mcp_tool(mcp)
+def validate_input(test_dir: str) -> Dict[str, Any]:
+ """
+ Validate that the directory contains the files required by the NARP pipeline.
+ """
+ try:
+ td = Path(test_dir)
+ missing = [name for name in _REQUIRED_INPUT_FILES if not td.joinpath(name).exists()]
+ if missing:
+ return {"valid": False, "missing": missing}
+ return {"valid": True}
+ except Exception as exc:
+ logger.exception("Failed to validate NARP input directory %s", test_dir)
+ return PowerError(status="error", message=str(exc))
+
+
+__all__ = [
+ "mcp",
+ "submit_simulation",
+ "run_simulation_sync",
+ "get_job_status",
+ "get_job_result",
+ "get_job_summary",
+ "list_jobs",
+ "cancel_job",
+ "validate_input",
+]
+
+
+if __name__ == "__main__":
+ mcp.run(transport="stdio")
diff --git a/NARP/output.txt b/NARP/output.txt
new file mode 100644
index 0000000..5bb5f62
--- /dev/null
+++ b/NARP/output.txt
@@ -0,0 +1,600 @@
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 1
+
+
+ --------------------------------
+
+ TABLE 7
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 2
+
+
+ --------------------------------
+
+ TABLE 8
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 3
+
+
+ --------------------------------
+
+ TABLE 9
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 4
+
+
+ --------------------------------
+
+ TABLE 10
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 5
+
+
+ --------------------------------
+
+ TABLE 11
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 1
+
+
+ --------------------------------
+
+ TABLE 7
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 2
+
+
+ --------------------------------
+
+ TABLE 8
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 3
+
+
+ --------------------------------
+
+ TABLE 9
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 4
+
+
+ --------------------------------
+
+ TABLE 10
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 5
+
+
+ --------------------------------
+
+ TABLE 11
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
diff --git a/NARP/reliabilityassessment/__init__.py b/NARP/reliabilityassessment/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/NARP/reliabilityassessment/data_processing/__init__.py b/NARP/reliabilityassessment/data_processing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/NARP/reliabilityassessment/data_processing/dataf1.py b/NARP/reliabilityassessment/data_processing/dataf1.py
new file mode 100644
index 0000000..d948a31
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/dataf1.py
@@ -0,0 +1,25 @@
+from reliabilityassessment.data_processing.input_processing import input_processing
+from reliabilityassessment.data_processing.pind import pind
+from reliabilityassessment.data_processing.readInputB import readInputB
+
+
+def dataf1(filepaths):
+ """
+ Data file reading and preprocessing
+
+ :param list filepaths: the filepath to file 'INPUTB' and 'LEEI' respectively
+ :return: (*tuple*) -- the return values of 'input_processing' together with
+ the reference bus number and the list of area names.
+ """
+
+ NR = 1 - 1 # 0-based index in Python
+ inputB_dict = readInputB(filepaths[0])
+ NAMU, NUMP = zip(*map(lambda x: (x[1:5], x[5:-1]), inputB_dict["ZZUD"]["NAT"]))
+ NAMA = inputB_dict["ZZLD"]["NAR"]
+ NAMA = [e.strip("'") + " " for e in NAMA]
+ inputB_dict_nameToInt = pind(inputB_dict)
+
+ return input_processing(inputB_dict_nameToInt, filepaths[1], NAMU, NUMP) + (
+ NR,
+ NAMA,
+ )
diff --git a/NARP/reliabilityassessment/data_processing/datax.py b/NARP/reliabilityassessment/data_processing/datax.py
new file mode 100644
index 0000000..0f8e215
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/datax.py
@@ -0,0 +1,250 @@
+from copy import deepcopy
+
+import numpy as np
+
+
+def datax(inputB_dict):
+ """
+ Reads all data except the hourly load data
+ and creates appropriate arrays.
+
+ :param dict inputB_dict: a compound python dictionary stores
+ all the parsed data from the INPUTB file.
+
+ :return: (*tuple*) a series of numpy ndarrays;
+ please refer to the 'variable description list.xlsx' file at
+ https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+ """
+
+ # --------------------------------------------------------------
+ # Process ZZMC card data and creates appropriate arrays
+ # please refer to the 'variable description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ # QTR -- array of hourly index corresponding to the three quarterly change timing.
+ QTR = np.zeros(3)
+ QTR[0] = inputB_dict["ZZMC"]["IW1"] * 168 + 0.5
+ QTR[1] = inputB_dict["ZZMC"]["IW2"] * 168 + 0.5
+ QTR[2] = inputB_dict["ZZMC"]["IW3"] * 168 + 0.5
+
+ # --------------------------------------------------------------
+ # Process ZZLD data card and creates appropriate arrays
+ # please refer to the 'variable description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ NOAREA = len(inputB_dict["ZZLD"]["RATES"]) # number of areas
+ PKLOAD = deepcopy(
+ inputB_dict["ZZLD"]["RATES"][:, 0]
+ ) # Annual peak in the area, in MW.
+ FU = deepcopy(inputB_dict["ZZLD"]["RATES"][:, 1]) # forecasting uncertainty
+ MINRAN = deepcopy(
+ inputB_dict["ZZLD"]["ID"][:, 0]
+ ) # BEG WK: Beginning week of the outage window
+ MINRAN -= 1 # 0-based index in Python!
+ MAXRAN = deepcopy(
+ inputB_dict["ZZLD"]["ID"][:, 1]
+ ) # END WK: Ending week of the outage window
+ MAXRAN -= 1 # 0-based index in Python!
+ INHBT1 = deepcopy(
+ inputB_dict["ZZLD"]["ID"][:, 2]
+ ) # BEG WK: Beginning week of the Forbidden Period.
+ INHBT1 -= 1 # 0-based index in Python!
+ INHBT2 = deepcopy(
+ inputB_dict["ZZLD"]["ID"][:, 3]
+ ) # END WK: Ending week of the Forbidden Period.
+ INHBT2 -= 1 # 0-based index in Python!
+
+ # BN: a helper array storing area related info.
+ BN = np.zeros((NOAREA, 5))
+
+ if FU.any():
+ NORR = 3 - 1 # 0-based index in Python
+ NFCST = 5
+ else:
+ NORR = 1 - 1 # 0-based index in Python
+ NFCST = 1
+
+ BN[:, 0] = [i for i in range(NOAREA)]
+ BN[:, 1] = 0
+ BN[:, 2] = 0
+ BN[:, 3] = deepcopy(inputB_dict["ZZLD"]["RATES"][:, 2]) # Sum of Flows Constraint.
+ BN[:, 4] = deepcopy(inputB_dict["ZZLD"]["RATES"][:, 2]) # Sum of Flows Constraint.
+
+ # SUSTAT: An area-related 2D array holding annual peak (MW) and certain statistics
+ # Note: its last row is kept for the usage of final total statistics.
+ SUSTAT = np.zeros((NOAREA + 1, 6))
+ SUSTAT[:NOAREA, 0] = deepcopy(inputB_dict["ZZLD"]["RATES"][:, 0])
+
+ # FCTERR: tiers of load-power forecasting errors
+ FCTERR = np.zeros((NOAREA, 5)) #
+ FCTERR[:, 2] = 1.0
+ FCTERR[:, 3] = 1.0 - FU / 100.0
+ FCTERR[:, 1] = 1.0 + FU / 100.0
+ FCTERR[:, 0] = 1.0 + FU * 2.5 / 100.0
+ FCTERR[:, 4] = 1.0 - FU * 2.5 / 100.0
+
+ # --------------------------------------------------------------
+ # Read ZZUD card (gen. unit data) and create appropriate arrays.
+ # please refer to the 'variable description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ NUNITS = len(inputB_dict["ZZUD"]["SNRI"])
+ CAPOWN = np.zeros((NOAREA, NUNITS)) # capacity ownership shares
+ CAPCON = np.zeros((NUNITS,), dtype=int) # capacity ownrship condition
+ NOGEN = np.zeros((NOAREA,), dtype=int) # number of gen units
+ PROBG = np.zeros((NUNITS, 2)) # sampling probabilities of gen.unit capacities
+ DERATE = np.zeros((NUNITS,)) # derated unit power
+
+ CAPCON[inputB_dict["ZZUD"]["NAT"]] = deepcopy(inputB_dict["ZZUD"]["NAR"])
+ CAPOWN[inputB_dict["ZZUD"]["NAR"], inputB_dict["ZZUD"]["NAT"]] = 1
+ NOGEN = np.bincount(inputB_dict["ZZUD"]["NAR"])
+ PROBG[:, 0] = 1 - inputB_dict["ZZUD"]["HRLOAD"][[4, 5], :].sum(axis=0)
+ PROBG[:, 1] = 1 - inputB_dict["ZZUD"]["HRLOAD"][5, :]
+ DERATE = 1 - inputB_dict["ZZUD"]["HRLOAD"][6, :] / 100.0
+
+ # --------------------------------------------------------------
+ # Read contract interchanges between areas and create appropriate arrays.
+ # please refer to the 'variable description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ # JENT(J1,J2)-- matrix indicating areas which have interchange
+ # if "-1", means none (use "0" here will cause bug since "0-based array index" in Python)
+ # otherwise, means the integer index L used in INTCH(L,N).
+ JENT = -1 * np.ones((NOAREA, NOAREA), dtype=int)
+ MAX_CONTRACTS = 60 # the maximum possible count of fixed contracts; can be adjusted
+
+ # INTCH(L,N): contract interchanges MW between areas indicated by pointer L, day N.
+ INTCH = np.zeros((MAX_CONTRACTS, 365))
+ # INTCHR: recording the from and to area no. for each contract
+ INTCHR = np.zeros((MAX_CONTRACTS, 2))
+ if inputB_dict["ZZFC"] and len(inputB_dict["ZZFC"]["SNRI"]) > 0:
+ for i, (src, dst) in enumerate(
+ dict.fromkeys(zip(inputB_dict["ZZFC"]["NAR"]), inputB_dict["ZZFC"]["NAE"])
+ ):
+ JENT[src, dst] = i
+ INTCHR[i, :] = src, dst
+ beg_day, end_day = (
+ inputB_dict["ZZFC"]["BEG DAY"][i],
+ inputB_dict["ZZFC"]["END DAY"][i],
+ )
+ INTCH[i, beg_day : end_day + 1] = inputB_dict["ZZFC"]["MW"][i]
+
+ # --------------------------------------------------------------
+ # Read ownership of units and create appropriate arrays.
+ # please refer to the 'variable description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ if inputB_dict["ZZOD"] and len(inputB_dict["ZZOD"]["SNRI"]) > 0:
+ for i in range(len(inputB_dict["ZZOD"]["SNRI"])):
+ for j in range(NOAREA): # CAPOWN.shape = (NOAREA, NUNITS)
+ genUnitIdx = inputB_dict["ZZOD"]["NAT"][i]
+ # CAPOWN(j,i),i.e.,the fraction of unit i owned by area j
+ CAPOWN[j, genUnitIdx] = inputB_dict["ZZOD"]["PERCENT"][i, j] / 100.0
+
+ # --------------------------------------------------------------
+ # Read line data and create appropriate arrays.
+ # please refer to the 'varaible description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ NLINES = len(inputB_dict["ZZTD"]["SNRI"]) # total number of lines
+ # LINENO(j1,j2) line number between area j1 and j2.
+ LINENO = (-1) * np.ones((NOAREA, NOAREA), dtype=int)
+ # LP is a helper array for storing line related parameters
+ LP = (-1) * np.ones((NLINES, 3), dtype=int)
+ LP[:, 0] = deepcopy(inputB_dict["ZZTD"]["LineID"]) # idx (int) for the line
+ LP[:, 1] = deepcopy(inputB_dict["ZZTD"]["NAR"]) # idx (int) for 'from area'
+ LP[:, 2] = deepcopy(inputB_dict["ZZTD"]["NAE"])
+ LINENO[LP[:, 1], LP[:, 2]] = LP[:, 0] # idx (int) for 'to area'
+
+ # Convert discrete probability to cumulative probability
+ # PROBL(k,l) cumulative probability that line l is in state k, k=0,...5
+ PROBL = np.add.accumulate(inputB_dict["ZZTD"]["PROBT"].T)
+
+ BLPA = np.zeros((NLINES, 18))
+ BLPA[:, ::3] = deepcopy(inputB_dict["ZZTD"]["ADM"])
+ BLPA[:, ::3][BLPA[:, ::3] == 0] = -0.5
+ BLPA[:, 1::3] = deepcopy(inputB_dict["ZZTD"]["CAP"])
+ BLPA[:, 1::3][BLPA[:, 1::3] == 0] = 1
+ BLPA[:, 2::3] = deepcopy(inputB_dict["ZZTD"]["CAPR"])
+ BLPA[:, 2::3][BLPA[:, 2::3] == 0] = 1
+
+ # Read critical unit data and create appropriate arrays.
+ # please refer to the 'variable description list.xlsx' file at
+ # https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ MXCRIT = 0 # total (maximum) number of critical units
+ JCRIT = -1 * np.ones(MXCRIT) # critical (affecting) unit id
+
+ # Create array 'ID':
+ NUNITS = len(PROBG)
+ ID = np.zeros((NUNITS, 8), dtype=int)
+ ID[:, 0] = deepcopy(inputB_dict["ZZUD"]["NAT"])
+ ID[:, 1] = deepcopy(inputB_dict["ZZUD"]["NAT"])
+ ID[:, 2] = deepcopy(inputB_dict["ZZUD"]["NAR"])
+ ID[:, 3] = deepcopy(inputB_dict["ZZUD"]["ID"][:, 1]) - 1 # to 0-based index
+ ID[:, 4] = deepcopy(inputB_dict["ZZUD"]["ID"][:, 2])
+ ID[:, 5] = deepcopy(inputB_dict["ZZUD"]["ID"][:, 3]) - 1 # to 0-based index
+ ID[:, 6] = deepcopy(inputB_dict["ZZUD"]["ID"][:, 4])
+ ID[:, 7] = deepcopy(inputB_dict["ZZUD"]["ID"][:, 0])
+
+ NLS = inputB_dict["ZZMC"]["NLS"] # indicator of load sharing mode
+ IOI = inputB_dict["ZZMC"]["IOI"]
+ IOJ = inputB_dict["ZZMC"]["IOJ"]
+ KVLOC = inputB_dict["ZZMC"]["KVLOC"] - 1 # 0-based index in Python
+ KVSTAT = inputB_dict["ZZMC"]["KVSTAT"]
+ KVTYPE = inputB_dict["ZZMC"]["KVTYPE"]
+ KVWHEN = inputB_dict["ZZMC"]["KVWHEN"]
+ KWHERE = inputB_dict["ZZMC"]["KWHERE"]
+ CVTEST = inputB_dict["ZZMC"]["CVTEST"]
+ MAXEUE = inputB_dict["ZZMC"]["MAXEUE"]
+ JSTEP = int(inputB_dict["ZZMC"]["JSTEP"])
+ JFREQ = int(inputB_dict["ZZMC"]["JFREQ"])
+ FINISH = inputB_dict["ZZMC"]["FINISH"] * 8760.0
+ INTV = inputB_dict["ZZMC"]["INTV"]
+ INTVT = INTV
+
+ return (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ ID,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ )
diff --git a/NARP/reliabilityassessment/data_processing/dpeak.py b/NARP/reliabilityassessment/data_processing/dpeak.py
new file mode 100644
index 0000000..115da37
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/dpeak.py
@@ -0,0 +1,68 @@
+import numpy as np
+
+
+def _dpeak(HRLOAD, hour_within_day=24):
+ """
+ Find daily peaks (MW) and hour (index) of those peaks
+
+ :param numpy.ndarray HRLOAD: 2D array of hourly load data
+ (0-dim: area index, 1-dim: hour index (1~8760 hr))
+
+ return (*tuple*) -- a tuple of multiple arrays, i.e.,
+ DYLOAD: 2D array, daily peak load amount (MW)
+ MAXHR: 2D array, the hour-index of the daily peak load for each day of each area
+ (in the range of 0 to 8759; note that the python index starts from 0)
+ MAXDAY: 1D array, the day-index of the daily peak load for each area
+ """
+ NOAREA = HRLOAD.shape[0]
+ day_within_year = HRLOAD.shape[1] // hour_within_day
+ DYLOAD = np.zeros((NOAREA, day_within_year))
+ MAXHR = np.zeros((NOAREA, day_within_year), dtype=int)
+ MAXDAY = np.zeros(NOAREA, dtype=int)
+
+ for areaIdx in range(NOAREA):
+ for dayIdx in range(day_within_year):
+ XMAX = float("-inf")
+ for j in range(hour_within_day):
+ j1 = dayIdx * hour_within_day + j
+ if HRLOAD[areaIdx, j1] <= XMAX:
+ continue
+ XMAX = HRLOAD[areaIdx, j1]
+ MAXHR[areaIdx, dayIdx] = j1
+ DYLOAD[areaIdx, dayIdx] = XMAX
+
+ for areaIdx in range(NOAREA):
+ XMAX = float("-inf")
+ for dayIdx in range(day_within_year):
+ if DYLOAD[areaIdx, dayIdx] > XMAX:
+ XMAX = DYLOAD[areaIdx, dayIdx]
+ MAXDAY[areaIdx] = dayIdx
+
+ return MAXHR, DYLOAD, MAXDAY
+
+
+def dpeak(HRLOAD, hour_within_day=24):
+ """
+ Find daily peaks (MW) and hour (index) of those peaks
+
+ :param numpy.ndarray HRLOAD: 2D array of hourly load data
+ (0-dim: area index, 1-dim: hour index (1~8760 hr))
+
+ return (*tuple*) -- a tuple of multiple arrays, i.e.,
+ DYLOAD: 2D array, daily peak load amount (MW)
+ MAXHR: 2D array, the hour-index of the daily peak load for each day of each area
+ (in the range of 0 to 8759; note that the python index starts from 0)
+ MAXDAY: 1D array, the day-index of the daily peak load for each area
+ """
+ NOAREA = HRLOAD.shape[0]
+
+ # reshape (area, hour_within_year) to (area, hour_within_day, day_within_year)
+ # note: the 'order' parameter should be 'F' to keep the original internal ordering.
+ reshaped = HRLOAD.reshape((NOAREA, hour_within_day, -1), order="F")
+ day_within_year = reshaped.shape[2]
+ # i.e., HRLOAD .shape[1] / hour_within_day
+ DYLOAD = reshaped.max(axis=1)
+ MAXHR = reshaped.argmax(axis=1) + hour_within_day * np.arange(day_within_year)
+ MAXDAY = DYLOAD.argmax(axis=1)
+
+ return MAXHR, DYLOAD, MAXDAY
diff --git a/NARP/reliabilityassessment/data_processing/gsm.py b/NARP/reliabilityassessment/data_processing/gsm.py
new file mode 100644
index 0000000..9e0d75b
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/gsm.py
@@ -0,0 +1,156 @@
+import numpy as np
+
+
+def gsm(N, KT, KP, P1, P2, P3, PA, KA):
+ """
+ Add a 2-state or a 3-state unit model to the generation capacity-outage model
+ based on the "unit addition algorithm".
+
+ Its high-level idea and illustration can be partially found in these slides:
+ https://www.dropbox.com/s/krcse3mjuh97i88/module_6-1.pdf?dl=0
+
+ :param int N: total number of starting states
+ :param int KT: rounded (nearest integer) full capacity of the added gen unit
+ :param int KP: rounded (nearest integer) derated capacity of the added gen unit
+ :param float P1: exact prob. of the I-th gen unit at its full capacity
+ :param float P2: exact prob. of the I-th gen unit at its derated capacity
+ :param float P3: exact prob. of the I-th gen unit in total loss (i.e. zero MW)
+ :param numpy.ndarray PA: CUT OFF probability in the CAP outage table
+ :param numpy.ndarray KA: CUT OFF capacity in the CAP outage table
+ :return: (*int*) -- updated total number of states
+
+ .. note:: KA and PA are modified in-place
+ .. todo:: Check the physical meaning of input parameter N and update doc strings
+ """
+ INGS = 5000
+ INGS1 = INGS - 50
+
+ # Create outage states after unit addition
+ KO = np.zeros((5000, 3), dtype=int) # for local use only
+
+ KO[:, 0] = KA
+ KO[:, 1] = KA + KP
+ KO[:, 2] = KA + KT
+ P = PA.copy()
+
+ # Possibly, N,NN,NNN represents certain counts of some states,
+ # rather than the array "index"
+ # NN = N + 1 # NN and NNN may have physical meanings
+ # NNN = NN + 1
+ KO[N : N + 2, :] = 10**6
+ P[N : N + 2] = 0.0
+
+ # Select outage states in ascending order of magnitude and
+ # calculate their probabilities and frequencies
+ L = 0 # 1 in original Fortran
+ KA[0] = 0
+ PA[0] = 1.0
+ J = np.array([1, KP == 0, 0])
+ while J.min() < N and PA[L] > 0.1e-08 and L < INGS1:
+ L += 1
+ KA[L] = KO[J, [0, 1, 2]].min()
+ PA[L] = np.dot(P[J], [P1, P2, P3])
+ J[KO[J, [0, 1, 2]] == KO[J, [0, 1, 2]].min()] += 1
+
+ N = L + 1 # =L in original Fortran
+
+ if L >= INGS1: # > in original Fortran
+ print(
+ "to prevent array overflow, CUT OFF PROB. "
+ "in CAP outage table reduced to %13.6f" % (PA[L])
+ )
+
+ PA[N] = 0.0
+
+ return N
+
+
+def _gsm(N, KT, KP, P1, P2, P3, PA, KA):
+ """
+ Add a 2-state or a 3-state unit model to the generation capacity-outage model
+
+ Its high-level idea and illustration can be partially found in these slides:
+ https://www.dropbox.com/s/krcse3mjuh97i88/module_6-1.pdf?dl=0
+
+ :param int N: total number of starting states
+ :param int KT: rounded (nearest integer) full capacity of the added gen unit
+ :param int KP: rounded (nearest integer) derated capacity of the added gen unit
+ :param float P1: exact prob. of the I-th gen unit at its full capacity
+ :param float P2: exact prob. of the I-th gen unit at its derated capacity
+ :param float P3: exact prob. of the I-th gen unit in total loss (i.e. zero MW)
+ :param numpy.ndarray PA: CUT OFF probability in the CAP outage table
+ :param numpy.ndarray KA: CUT OFF capacity in the CAP outage table
+ :return: (*int*) -- updated total number of states
+
+ .. note:: KA and PA are modified in-place
+ .. todo:: Check the physical meaning of input parameter N and update doc strings
+ """
+ INGS = 5000
+ INGS1 = INGS - 50
+
+ # Create outage states after unit addition
+ KO = np.zeros((5000, 3), dtype=int) # for local use only
+ P = np.zeros((5000, 1)) # for local use only
+
+ for i in range(N):
+ KO[i, 0] = KA[i]
+ KO[i, 1] = KA[i] + KP
+ KO[i, 2] = KA[i] + KT
+ P[i] = PA[i]
+
+ # Possibly, N,NN,NNN represents certain counts of some states,
+ # rather than the array "index"
+ NN = N + 1
+ NNN = NN + 1
+
+ for i in range(NN - 1, NNN):
+ KO[i, 0] = 1000000
+ KO[i, 1] = 1000000
+ KO[i, 2] = 1000000
+ P[i] = 0.0
+
+ # Select outage states in ascending order of magnitude and
+ # calculate their probabilities and frequencies
+ L = 0 # 1 in original Fortran
+ J1 = 1
+ J2 = 0
+ if KP == 0:
+ J2 += 1
+ J3 = 0
+ KA[L] = 0
+ PA[L] = 1.0
+
+ while True:
+ L += 1
+ KCH = min(KO[J1, 0], KO[J2, 1], KO[J3, 2])
+ KA[L] = KCH
+ PA[L] = P[J1] * P1 + P[J2] * P2 + P[J3] * P3
+ if KCH == KO[J1, 0]:
+ J1 += 1
+ if KCH == KO[J2, 1]:
+ J2 += 1
+ if KCH == KO[J3, 2]:
+ J3 += 1
+ JM = min(J1, J2, J3)
+
+ if JM >= N: # > in original Fortran
+ break
+
+ if PA[L] <= 0.1e-08:
+ break
+
+ if L >= INGS1: # > in original Fortran
+ break
+
+ N = L + 1 # =L in original Fortran
+
+ if L >= INGS1: # > in original Fortran
+ print(
+ "to prevent array overflow, CUT OFF PROB. "
+ "in CAP outage table reduced to %13.6f" % (PA[L])
+ )
+
+ NN = N + 1
+ PA[NN - 1] = 0.0 # PA[NN] = 0.0 in original Fortran
+
+ return N
diff --git a/NARP/reliabilityassessment/data_processing/input_processing.py b/NARP/reliabilityassessment/data_processing/input_processing.py
new file mode 100644
index 0000000..395c90a
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/input_processing.py
@@ -0,0 +1,173 @@
+from copy import deepcopy
+
+from reliabilityassessment.data_processing.datax import datax
+from reliabilityassessment.data_processing.dpeak import dpeak
+from reliabilityassessment.data_processing.plhr import plhr
+from reliabilityassessment.data_processing.resca import resca
+from reliabilityassessment.data_processing.smaint import smaint
+from reliabilityassessment.data_processing.wpeakf import wpeakf
+from reliabilityassessment.data_processing.xldnew import xldnew
+from reliabilityassessment.data_processing.xporta import xporta
+from reliabilityassessment.data_processing.xports import xports
+
+
+def input_processing(inputB_dict_nameToInt, filePathToLEEI, NAMU, NUMP):
+ """
+ Apply further pre-processing on the loaded data and create/update certain arrays.
+
+ :param dict inputB_dict_nameToInt: a compound dictionary stores all the parsed
+ data from the INPUTB file
+ :param str filePathToLEEI: path to file 'LEEI'
+ :param list NAMU: list of strings, gen unit names
+ :param list NUMP: list of strings, gen plant names
+ :return: (*tuple*) -- a series of numpy arrays
+
+ .. note:: detailed variable info see ``variable description list.xlsx`` at
+ https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+ """
+
+ (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ ID,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ ) = datax(inputB_dict_nameToInt)
+
+ RATES = deepcopy(inputB_dict_nameToInt["ZZUD"]["HRLOAD"][0:4, :].T)
+
+ # Read the hourly load data from the file 'LEEI'
+ HRLOAD = xldnew(filePathToLEEI, PKLOAD)
+
+ # Add interchanges
+ xporta(JENT, INTCH, HRLOAD)
+
+ # Find daily peaks and hour of daily peaks
+ MAXHR, DYLOAD, MAXDAY = dpeak(HRLOAD, hour_within_day=24)
+
+ # Finds weekly peaks (MW)
+ WPEAK = wpeakf(DYLOAD)
+
+ # Find hour of pool daily peaks
+ MXPLHR = plhr(HRLOAD)
+
+ # Remove interchanges and restore hrload
+ xports(JENT, INTCH, HRLOAD)
+
+ # Calculate the total owned cap and interchange at peak
+ resca(SUSTAT, MAXDAY, QTR, CAPOWN, RATES, JENT, INTCH)
+
+ # Prepare jplout - table of planned outages of units
+ # * return values are subjected to change *
+ IREPM = inputB_dict_nameToInt["ZZMC"]["IREPM"]
+ ITAB = 6
+ JPLOUT, ITAB = smaint(
+ NOAREA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ return (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ RATES,
+ ID,
+ HRLOAD,
+ MAXHR,
+ DYLOAD,
+ MAXDAY,
+ WPEAK,
+ MXPLHR,
+ JPLOUT,
+ ITAB,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ )
diff --git a/NARP/reliabilityassessment/data_processing/pind.py b/NARP/reliabilityassessment/data_processing/pind.py
new file mode 100644
index 0000000..abf3263
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/pind.py
@@ -0,0 +1,69 @@
+from copy import deepcopy
+
+import numpy as np
+
+
+def pind(inputB_dict):
+ """
+ Mapping name strings (of the areas, gen units, transmission link (lines)) to integer (indices)
+
+ :param dict inputB_dict: resulted dictionary from :py:func: `readInputB`
+
+ :return: (*dict*) inputB_dict_nameToInt -- A dictionary storing the same content as the 'inputB_dict',
+ except that all string-type 'names' are mapped to interger indices
+ based on their showing orders in the original INPUTB file.
+ """
+ inputB_dict_nameToInt = deepcopy(inputB_dict)
+
+ # For ZZLD:
+ NAR = inputB_dict_nameToInt["ZZLD"]["NAR"]
+ unique_list = list(dict.fromkeys(NAR))
+ map_AreaNameToInt = dict(zip(unique_list, range(len(unique_list))))
+ inputB_dict_nameToInt["ZZLD"]["NAR"] = np.array(
+ [map_AreaNameToInt[key] for key in NAR]
+ )
+
+ # For ZZUD:
+ NAT = inputB_dict_nameToInt["ZZUD"]["NAT"]
+ unique_list = list(dict.fromkeys(NAT))
+ map_GenUnitNameToInt = dict(zip(unique_list, range(len(unique_list))))
+ inputB_dict_nameToInt["ZZUD"]["NAT"] = np.array(
+ [map_GenUnitNameToInt[key] for key in NAT]
+ )
+
+ NAR = inputB_dict_nameToInt["ZZUD"]["NAR"]
+ inputB_dict_nameToInt["ZZUD"]["NAR"] = np.array(
+ [map_AreaNameToInt[key] for key in NAR]
+ )
+
+ # For ZZTD:
+ inputB_dict_nameToInt["ZZTD"]["LineID"] -= 1
+
+ NAR = inputB_dict_nameToInt["ZZTD"]["NAR"]
+ inputB_dict_nameToInt["ZZTD"]["NAR"] = np.array(
+ [map_AreaNameToInt[key] for key in NAR]
+ )
+
+ NAE = inputB_dict_nameToInt["ZZTD"]["NAE"]
+ inputB_dict_nameToInt["ZZTD"]["NAE"] = np.array(
+ [map_AreaNameToInt[key] for key in NAE]
+ )
+
+ # For ZZFC:
+ NAR = inputB_dict_nameToInt["ZZFC"]["NAR"]
+ inputB_dict_nameToInt["ZZFC"]["NAR"] = np.array(
+ [map_AreaNameToInt[key] for key in NAR]
+ )
+
+ NAE = inputB_dict_nameToInt["ZZFC"]["NAE"]
+ inputB_dict_nameToInt["ZZFC"]["NAE"] = np.array(
+ [map_AreaNameToInt[key] for key in NAE]
+ )
+
+ # For ZZOD:
+ NAT = inputB_dict_nameToInt["ZZOD"]["NAT"]
+ inputB_dict_nameToInt["ZZOD"]["NAT"] = np.array(
+ [map_GenUnitNameToInt[key] for key in NAT]
+ )
+
+ return inputB_dict_nameToInt
diff --git a/NARP/reliabilityassessment/data_processing/plhr.py b/NARP/reliabilityassessment/data_processing/plhr.py
new file mode 100644
index 0000000..d229832
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/plhr.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+
+def plhr(HRLOAD):
+ """
+ Find peak hours for all areas.
+
+ :param numpy.ndarray HRLOAD: 2D array of hourly load data (0-dim: area,
+ 1-dim: hour, 1~8760)
+ :return: (*numpy.ndarray*) -- MXPLHR, integer array of peak hour for all areas
+ with shape (365,)
+
+ .. note:: Number of days in a year and number of hours for each day is hardcoded
+ by 365 and 24, given the Monte Carlo simulation in reliability analysis
+ simulates a statistic year and calculates average reliability index,
+ which gives a general measure of the studied system.
+ """
+
+ # sum across all areas
+ summed_load = HRLOAD.sum(axis=0)
+ # reshape 8760-length 1D array to N x 24 (N computed automatically)
+ reshaped_load = summed_load.reshape((-1, 24))
+ # select the index of the greatest value for each day
+ daily_peak_hour = reshaped_load.argmax(axis=1)
+ # translate hour-of-day to hour-of-year
+ MXPLHR = daily_peak_hour + 24 * np.arange(len(daily_peak_hour))
+
+ return MXPLHR
diff --git a/NARP/reliabilityassessment/data_processing/pmlolp.py b/NARP/reliabilityassessment/data_processing/pmlolp.py
new file mode 100644
index 0000000..7128bd4
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/pmlolp.py
@@ -0,0 +1,57 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.gsm import gsm
+
+
+def pmlolp(CAPLOS, RD, NGU):
+ """
+ Computes the capacity outage probability table
+ (for computing the 'M' factor used in function 'pmsc')
+
+ Its high-level idea and illustration can be partially found in these slides:
+ https://www.dropbox.com/s/krcse3mjuh97i88/module_6-1.pdf?dl=0
+
+ :param numpy.ndarray CAPLOS: 1D array of assumed capacity loss of each gen unit
+ :param numpy.ndarray RD: 2D array of mixed information of each gen unit in the
+ specific area with shape (NUNITS, 3):
+ RD(I,0): exact prob. of the I-th gen unit at its derated capacity
+ RD(I,1): exact prob. of the I-th gen unit in total loss (i.e., zero MW)
+ RD(I,2): the derated capacity (MW) of the I-th gen unit
+ I is the internal ordering of the specific area.
+ :param int NGU: total number of gen units in the specific area.
+ :return: (*tuple*) -- a tuple of arrays (NGS, KA, PA):
+ NGS: int, the total number of generation states after adding a specific gen unit
+ KA: 1D array CUT-OFF capacity in the CAP-outage-table
+ PA: 1D array CUT-OFF probability in the CAP-outage-table
+ """
+
+ INC = 10
+ XINC = 5.5
+
+ # (INGS) 5000 : the possible maximum total number of system generation states
+ KA = np.zeros(5000)
+ PA = np.zeros(5000)
+
+ PA[0] = 1.0
+ NGS = 1
+
+ # add units to capacity outage probability table
+ for NT in range(NGU):
+ # update the system capacity up to this unit
+ KT = int(CAPLOS[NT] + XINC)
+ KT -= KT % INC
+
+ # compute the derating and round off to 'INC''
+ KP = int(KT * RD[NT, 2] + XINC)
+ KP -= KP % INC
+
+ P2, P3 = RD[NT, :2]
+ P1 = 1 - P2 - P3
+
+ # IRAT = P1 * 10**6 (might go back to this logic)
+ # if IRAT != 0: (might go back to this logic)
+ if P1 > 1e-6:
+ NGS = gsm(NGS, KT, KP, P1, P2, P3, PA, KA)
+
+ # return when all units have been added
+ return NGS, KA, PA
diff --git a/NARP/reliabilityassessment/data_processing/pmsc.py b/NARP/reliabilityassessment/data_processing/pmsc.py
new file mode 100644
index 0000000..8c51e70
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/pmsc.py
@@ -0,0 +1,871 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.pmlolp import pmlolp
+
+
+def pmsc(
+ IA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+):
+ """
+ Automatic schedule of planned maintenance
+
+ :param int IA: integer index of a specific area
+ :param numpy.ndarray ID:shape (NUNITS, 8)
+ ID(I,K), K=0: unit number (0-based)
+ K=1: plant number (0-based)
+ K=2: area of location (0-based)
+ K=3: starting week of first planned outage (0-based)
+ K=4: duration of first planned outage in weeks
+ K=5: starting week of second planned outage (0-based)
+ K=6: duration of second planned outage in weeks
+ K=7: 1 if maintenance is pre-scheduled;
+ 0 if set automatically by the program
+ :param int ITAB: the table index (created so far)
+ :param numpy.ndarray RATES: original rating data of each unit for four seasons with
+ shape (NUNITS, 4)
+ :param numpy.ndarray PROBG: 2D array of accumulated probability for each capacity
+ tier of each unit
+ :param numpy.ndarray DERATE: array of the derated capacity of each unit
+ :param np.ndarray PKLOAD: array of user-defined annual peak of each area.
+ :param numpy.ndarray WPEAK: 2D array, weekly peak load amount (MW)
+ 1st dim: areaIdx, 2nd dim: weekIdx
+ :param int IREPM: indicator for whether printing the reuslt of maintenance not
+ :param numpy.ndarray MINRAN: beginning week of the maintenance (outage) window of
+ each area with shape (NOAREA,)
+ :param numpy.ndarray MAXRAN: ending week of maintenance (outage) window of
+ each area with shape (NOAREA,)
+ :param numpy.ndarray INHBT1: beginning week of the forbidden (inhibt) period of
+ each area with shape (NOAREA,)
+ :param numpy.ndarray INHBT2: ending week of the forbidden (inhibt) period of
+ each area with shape (NOAREA,)
+ :param numpy.ndarray NAMU: name string of each generator unit with shape (NUNITS,)
+ (in Fortran, it is double-typed as also 'int' but not used)
+ :param numpy.ndarray NUMP: name string of each generator plant with shape (NUNITS,)
+ (in Fortran, it is double-typed as also 'int' but not used)
+ :return: (*int*) -- ITAB
+
+ .. note:: 1. Global array 'ID' is modified in-place
+ 2. Annotations for certain intermediate (local) variables/arrays:
+ SCHLOS SCHEDULED LOSS DUE TO MAINTENANCE
+ DURLOS LOSS DUE TO MAINTENANCE
+ SDLOLP SCHEDULED LOSS X DURATION X PROB. OF FORCED OUT
+ LOCREW LOCATION & CREW
+ LOAD WEEKLY PEAK LOAD
+ WEEKS ASSIGNED TIME FOR MAINTENANCE
+ CULOLP CUMMLOLP
+ """
+
+ ITC = ITAB
+ INUO = 500 # the possible maximum number of units
+ INPO = 150 # the possible maximum number of plants
+
+ N = 0 # N is the highest plant number
+
+ NUNITS = ID.shape[0]
+
+ # Below are all local arrays created for the conveneice in intermedaite computation
+ INDEX = np.zeros((INUO, 2)) # maybe use "-1" for intialization
+ INDEX1 = np.zeros(INUO) # maybe use "-1" for intialization
+ P = np.ones(INUO)
+ NR0 = np.zeros(NUNITS, dtype=int)
+ ID1 = np.zeros((1 + NUNITS, 8), dtype=int)
+ CAPLOS = np.zeros(NUNITS)
+ RD = np.zeros((NUNITS, 3))
+ DURLOS = np.zeros((NUNITS, 2))
+ LOCREW = np.zeros(NUNITS)
+ IDT = np.zeros((NUNITS, 8))
+ RDT = np.zeros((NUNITS, 3))
+ CAPLO = np.zeros(NUNITS)
+ DURLO = np.zeros((NUNITS, 2))
+ LOCRE = np.zeros(NUNITS)
+ WEEKS = [""] * 52
+ A = "AA"
+ B = ".."
+ AB = "PP"
+ CHECK = np.zeros((INPO, 52))
+ LOAD = np.zeros(52)
+ EFLOAD = np.zeros(52)
+ MWTOT = np.zeros(NUNITS)
+ MWI = np.zeros(NUNITS)
+ MWEEK = np.zeros(NUNITS)
+ NPSU = np.zeros(NUNITS)
+ SCHLOS = np.zeros(NUNITS) # SCHEDULED LOSS DUE TO MAINTENANCE;
+
+ # Note that 'NGU' means the total number of gen units used in this function
+ # but 0 in original Fortran code, it also uses sometimes as "index" for arrays
+ # since Fortran is 1-based indexing.
+ NGU = -1
+
+ for i in range(NUNITS):
+ if ID[i, 2] != IA:
+ continue
+
+ NGU += 1
+ N = max(N, ID[i, 1])
+
+ NR0[NGU] = ID[i, 0]
+ ID1[NGU, 0] = NGU
+ ID1[NGU, 1:8] = ID[i, 1:8]
+
+ CAPLOS[NGU] = max(RATES[i, :4])
+ RD[NGU, 1] = 1.0 - PROBG[i, 1]
+ RD[NGU, 0] = PROBG[i, 1] - PROBG[i, 0]
+ RD[NGU, 2] = 1 - DERATE[i]
+
+ NGU += 1 # restoring its literal meaning, i.e.the total number of gen units used in this function
+
+ if NGU == 0:
+ return ITAB
+
+ DURLOS[:NGU, :2] = ID1[:NGU, [4, 6]].copy()
+ ID1[:NGU, [4, 6]] *= 168
+ ID1[:NGU, [3, 5]] = ID1[:NGU, [3, 5]] * 168 - 168
+ LOCREW[:NGU] = ID1[:NGU, 1].copy()
+
+ # G: approximately peak load. This number is used to ensure that load plus capacity on planned maintenance
+ # does not exceed this value during any week of the year
+ # CAP1, CAP2: these values of capacity are used to compute the slope of the capacity outage probability table
+ # IOPT: 0 if departure rates are used
+ # 1 if dfor and for are used
+ # >1 if dafor is used
+
+ CAP = sum(CAPLOS[:NGU])
+ CAP1 = CAP / 20
+ CAP2 = CAP / 5
+ G = PKLOAD[IA]
+ M = NGU # M is the total number of generating units considerred in this function
+ i1 = 0
+ # arrange the sequence of units such that for a given plant the units are
+ # arranged in the descending order of capacity*planned maintenance duration
+ for i in range(N):
+ while True:
+ IC = 0 # (possibly) a flag varaiable
+ CMAX = -5.0
+
+ for j in np.where(ID1[:M, 1] == i)[0]:
+ IC = 1
+ CDUR = CAPLOS[j] * max(DURLOS[j, :2])
+ if CDUR > CMAX:
+ CMAX, JT = CDUR, j
+
+ if IC == 0:
+ break
+
+ i1 += 1
+ IDT[i1, :8] = ID1[JT, :8].copy()
+
+ ID1[JT, 1] = -1 # plant index
+ RDT[i1, :3] = RD[JT, :3].copy()
+
+ CAPLO[i1] = CAPLOS[JT]
+ DURLO[i1, 0] = DURLOS[JT, 0]
+ DURLO[i1, 1] = DURLOS[JT, 1]
+ LOCRE[i1] = LOCREW[JT]
+
+ ID1[:M, :8] = IDT[:M, :8].copy()
+ RD[:M, :3] = RDT[:M, :3].copy()
+ CAPLOS[:M] = CAPLO[:M].copy()
+ DURLOS[:M, :2] = DURLO[:M, :2].copy()
+ LOCREW[:M] = LOCRE[:M].copy()
+
+ ID1[M, 0] = 2000
+
+ # call subroutine pmlolp to create capacity outage probability table
+ NGS, KA, PA = pmlolp(CAPLOS, RD, NGU)
+
+ # calculate the 'M factor' ,ie, the slope of capacity outage probability table
+ for i in range(NGS):
+ if KA[i] >= CAP1:
+ break
+
+ UL = PA[i]
+ CAP1 = KA[i]
+
+ for i in range(NGS):
+ if KA[i] >= CAP2:
+ break
+
+ XLL = PA[i]
+ CAP2 = KA[i]
+
+ XM = 5000 # the "M factor"
+ if XLL >= 0.0000001:
+ DENOM = np.log(UL / XLL)
+ if DENOM >= 0.0000001:
+ XM = (CAP2 - CAP1) / DENOM # XM is the so-called 'M factor'
+ # print("M factor = %f\n"%(XM))
+
+ # Read scheduled loss, duration loss, previous maint. weeks, minimum range,
+ # max. range, weekly predicted max. load, inhbted periods.
+ # To save computing time the data is arranged by maintenance-crew units.
+ # All generating units in a particular location-crew is arranged
+ # in sequence based on the largest schedule maintenance x duration
+ WEEKS[:52] = B
+ CHECK[:INPO, :52] = 0.0
+ LOAD[:52] = WPEAK[IA, :52].copy()
+ EFLOAD[:52] = LOAD[:52].copy()
+
+ MWTOT[:N] = 0
+ MWEEK[:M] = 0
+
+ f = open("output.txt", "a") # can put earlier
+ if IREPM != 0:
+ f.write("\n WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING \n")
+ for i in range(52):
+ if i % 13 == 0:
+ f.write("\n")
+ f.write(" %.4f" % (LOAD[i]))
+
+ NPS = -1 # 0 in Fortran
+ for i in range(M):
+ if ID1[i, 7] == 0:
+ break
+ NPS += 1
+ NPSU[NPS] = ID1[i, 0]
+ NPS += 1 # restoring the meainig of certain length
+
+ i = -1 # 0 in Fortran
+ LTEM = 0
+ for j in range(M): # goto flag #52
+ R = 1 - P[j] + P[j] * np.exp(CAPLOS[j] / XM)
+ SCHLOS[j] = CAPLOS[j] - XM * np.log(R)
+ if SCHLOS[j] <= 0:
+ SCHLOS[j] = CAPLOS[j]
+ if LTEM != LOCREW[j]:
+ i += 1
+ LTEM = LOCREW[j]
+ MWTOT[j] = 0
+ MWI[j] = LOCREW[j]
+
+ if NPS > 0: # in Fortran " NPS != 0 "
+ gotoflag11 = False
+ for ii in range(NPS):
+ IN1 = NPSU[ii]
+ if ID1[j, 0] == IN1:
+ gotoflag11 = True
+ break
+
+ if gotoflag11 is False:
+ MWEEK[j] = SCHLOS[j] * (DURLOS[j, 0] + DURLOS[j, 1])
+ MWTOT[i] += MWEEK[j]
+ continue
+
+ if ID1[j, 0] != IN1:
+ MWEEK[j] = SCHLOS[j] * (DURLOS[j, 0] + DURLOS[j, 1])
+ MWTOT[i] += MWEEK[j]
+ continue
+
+ if ID1[j, 3] != -1 or ID1[j, 4] != 0: # ID1[j, 3] != 0 in Fotran
+ M1 = (
+ ID1[j, 3] // 168 + 1
+ ) # maybe remvoe "+1" here; ID1[j, 3] / 168 + 1 in Fortran
+ M2 = M1 + DURLOS[j, 0] - 1
+ IPN = i
+ M2 = min(M2, 51) # maybe 52; need to check
+ if M2 != -1: # originally in Fortran "if M2 != 0"
+ CHECK[IPN, M1 : M2 + 1] = LOCREW[j]
+ EFLOAD[M1 : M2 + 1] += SCHLOS[j]
+ LOAD[M1 : M2 + 1] += CAPLOS[j]
+ INDEX[j, 0] = M1
+ INDEX1[j] = 0 # originally in Fortran "1"
+ else:
+ INDEX1[j] = 0 # originally in Fortran "1"
+
+ if ID1[j, 5] != -1 or ID1[j, 6] != 0:
+ M3 = (
+ ID1[j, 5] // 168 + 1
+ ) # maybe remvoe "+1" here; ID1[j, 5] / 168 + 1 in Fortran
+ M4 = M3 + DURLOS[j, 1] - 1
+ M4 = min(M4, 51) # maybe 52; need to check
+ if M4 != -1: # originally in Fortran "if M4 != 0"
+ CHECK[IPN, M3 : M4 + 1] = LOCREW[j]
+ EFLOAD[M3 : M4 + 1] += SCHLOS[j]
+ LOAD[M3 : M4 + 1] += CAPLOS[j]
+ INDEX[j, 1] = M3
+ continue
+
+ MWEEK[j] = SCHLOS[j] * (DURLOS[j, 0] + DURLOS[j, 1])
+ MWTOT[i] += MWEEK[j]
+
+ ITC += 1 # increase the table no. by one
+
+ f = open("output.txt", "a") # can put earlier
+
+ if IREPM != 0:
+ # 323
+ f.write("\n ")
+ for _ in range(50):
+ f.write(" ")
+ f.write(
+ " \n\t\t\t\t\t\t\t\t\t\t\t\t PLANNED MAINTENANCE SCHEDULE FOR AREA %3d \n"
+ % (IA + 1)
+ )
+ f.write("\n")
+
+ # 523
+ f.write("\n ")
+ for _ in range(51):
+ f.write(" ")
+ for _ in range(32):
+ f.write("-")
+ f.write("\n")
+
+ # 190
+ f.write("\n ")
+ for _ in range(63):
+ f.write(" ")
+ f.write("TABLE %4d \n\n" % (ITC))
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 802
+ f.write("\n | | |")
+ for _ in range(107):
+ f.write(" ")
+ f.write("|")
+
+ # 803
+ f.write("\n | | |")
+ for _ in range(50):
+ f.write(" ")
+ f.write("MONTHS")
+ for _ in range(51):
+ f.write(" ")
+ f.write("|")
+
+ # 802
+ f.write("\n | | |")
+ for _ in range(107):
+ f.write(" ")
+ f.write("|")
+
+ # 811
+ f.write("\n | | |")
+ for _ in range(107):
+ f.write("-")
+ f.write("|")
+
+ # 804
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 805
+ f.write(
+ "\n | UNIT | CAP | JAN FEB MAR | APR MAY JUN |"
+ "JUL AUG SEP | OCT NOV DEC |"
+ )
+
+ # 806
+ f.write("\n | NAME | (MW) |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 807
+ f.write(
+ "\n | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | "
+ "7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |"
+ )
+
+ # 809
+ f.write(
+ "\n | | 1 | 2 | 3"
+ " | 4 5 |"
+ )
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 804
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # schedule first the plant with the largest total schedule MW loss x duration.
+ # Find the largest MWTOT(i)
+ i = N
+ for LM in range(N): # 89
+ MAX1 = 0
+
+ for K in range(N):
+ if MAX1 >= MWTOT[K]:
+ continue
+ MAX1 = MWTOT[K]
+ i = K
+
+ # Create MWEEK(J)
+ K1 = MWI[i]
+ MWEEK[:M] = 0
+
+ for K2 in np.where(ID1[:M, 1] == K1)[0]:
+ MWEEK[K2] = SCHLOS[K2] * (DURLOS[K2, 0] + DURLOS[K2, 1])
+
+ # Clear the MWTOT(i), since no longer required
+ if MWTOT[i] == 0:
+ continue
+
+ MWTOT[i] = 0
+ # Set the index for MWEEK(J), J=N1,N2
+
+ N1 = 0 # in Fortran, 1; need to check
+ N2 = -1 # in Fortran,0; need to check
+ for j in range(M):
+ if MWEEK[j] != 0:
+ N2 = j
+ if MWEEK[j] == 0 and N2 == -1:
+ N1 += 1
+
+ # Schedule all generators within the same plant
+ gotoFlag18 = True
+ while gotoFlag18:
+ gotoFlag18 = False
+
+ for JA in range(N1, N2): # flag 30 ; maybe N2 + 1
+ j = JA
+ J4 = JA
+
+ # Set index for scheduling within the flexible maintenance period,
+ # between min. and max. range
+ if MWEEK[JA] == 0:
+ continue
+
+ gotoFlag191 = True
+ while gotoFlag191: # flag 191
+ gotoFlag191 = False
+ MT = 0
+ IMT = 0
+ I5 = 2
+ if ID1[j, 4] > ID1[j, 6]:
+ I5 = 1
+ if ID1[j, 4] == 0 or ID1[j, 6] == 0:
+ I5 = 0
+
+ DURL = DURLOS[j, 0]
+ if DURLOS[j, 1] > DURL:
+ DURL = DURLOS[j, 1]
+
+ MAXR = MAXRAN[IA]
+ MINR = MINRAN[IA]
+
+ if I5 != 0:
+ if I5 != 2:
+ MAXRAN[IA] = INHBT1[IA]
+ else:
+ MINRAN[IA] = INHBT2[IA]
+
+ while True: # 19
+ I1 = MINRAN[IA]
+
+ IM = DURL - 1
+ IT = MAXRAN[IA] - IM
+ IX = 53 - DURL - 1 # originally in Fortran "53-DURL"
+ if I1 <= 0:
+ I1 = 1
+ IX = min(IX, IT)
+ ITE = 10**6
+ IC = 0
+
+ for J1 in range(I1, IX + 1):
+ IR = SCHLOS[j]
+ IS = J1 + IM
+ if J1 >= INHBT1[IA] - IM and IS <= INHBT2[IA] + IM:
+ continue
+ if LOCREW[j] == CHECK[i, J1] or LOCREW[j] == CHECK[i, IS]:
+ continue
+
+ breakFlag = False
+ for L in range(J1, IS + 1):
+ if IR + EFLOAD[L] >= G:
+ breakFlag = True
+ break
+ if breakFlag:
+ continue
+
+ if IS + 1 > J1:
+ IC = 1
+ IR += sum(EFLOAD[J1 : IS + 1])
+
+ if ITE < IR:
+ continue
+ ITE = IR
+ M1 = J1
+
+ MAXRAN[IA] = MAXR
+ MINRAN[IA] = MINR
+
+ if I5 != 0:
+ # I6=I5 # I6 never used in original Fortran
+ IC = 0
+ MT = M1
+ IMT = IM
+ DURL = DURLOS[j, 0]
+ if DURLOS[j, 1] <= DURL:
+ DURL = DURLOS[j, 1]
+ if I5 != 2:
+ MINRAN[IA] = INHBT2[IA]
+ else:
+ MAXRAN[IA] = INHBT1[IA]
+ I5 = 0
+ continue # GO TO 19
+
+ # IC is the flag to check whether the schedule is feasible.
+ if IC == 0:
+ while True:
+ J4 -= 1
+ if J4 <= -1: # <=0 in Fotran
+ IU0 = ID1[JA, 0]
+ IU0 = NR0[IU0]
+ IBG1N = (
+ ID1[JA, 3] // 168 + 1
+ ) # maybe remvoe "+1" here; ID1[JA, 3] / 168 + 1 in Fortran
+ IBG2N = (
+ ID1[JA, 5] // 168 + 1
+ ) # maybe remvoe "+1" here; ID1[JA, 5] / 168 + 1 in Fortran
+ ID1R = (
+ ID1[JA, 4] // 168
+ ) # ID1[JA, 4] / 168 in Fortran
+ ID2R = (
+ ID1[JA, 6] // 168
+ ) # ID1[JA, 6] / 168 in Fortran
+
+ f.write(
+ " SCHEDULE FOR GENERATOR ** %s %s ** IS NOT FEASIBLE"
+ "\n 'THIS UNIT IS SCHEDULED WITH PRESPECIFIED PARAMETERS AS FOLLOWS:\n"
+ " BEGINING FIRST OUTAGE = %2d DURATION = %2d\n"
+ " BEGINING SECOND OUTAGE = %2d DURATION = %2d\n"
+ % (
+ NAMU[IU0],
+ NUMP[IU0],
+ IBG1N,
+ ID1R,
+ IBG2N,
+ ID2R,
+ )
+ )
+
+ MWEEK[JA] = 0
+ INDEX[j, 0] = 0
+ INDEX[j, 1] = 0
+
+ gotoFlag18 = True # GO TO 18
+ break
+
+ # Cancel the previous scheduled generator, try to schedule the
+ # Non-feasible one. Interchange order of the scheduling.
+ if MWEEK[J4] == 0:
+ continue
+
+ if INDEX[J4, 0] == 0 and INDEX[J4, 1] == 0:
+ continue
+ else:
+ break
+
+ for ci in [0, 1]:
+ if INDEX[J4, ci] != 0: # maybe -1
+ NC1 = INDEX[J4, ci]
+ NC2 = NC1 + DURLOS[J4, ci] - 1
+ CHECK[i, NC1 : NC2 + 1] = 0
+ LOAD[NC1 : NC2 + 1] -= CAPLOS[J4]
+ EFLOAD[NC1 : NC2 + 1] -= SCHLOS[J4]
+ WEEKS[NC1 : NC2 + 1] = B
+ INDEX[J4, ci] = 0
+
+ gotoFlag191 = True
+ break
+
+ if gotoFlag18:
+ break
+
+ if gotoFlag191:
+ break
+
+ # INDEX(j) is the starting scheduled maintenance week for generator j
+ IMM = IM + 1
+ IMM = IMM * 168
+
+ if IMM != ID1[j, 4]:
+ M3 = MT
+ IM3 = IMT
+ M4 = M1
+ IM4 = IM
+ else:
+ M3 = M1
+ IM3 = IM
+ M4 = MT
+ IM4 = IMT
+
+ INDEX[j, 0] = M3
+ ID1[j, 3] = (M3 - 1) * 168
+ INDEX1[j] = 0
+ M2 = M3 + IM3
+
+ if M2 >= M3 and M3 != 0:
+ CHECK[i, M3 : M2 + 1] = LOCREW[j]
+ EFLOAD[M3 : M2 + 1] += SCHLOS[j]
+ LOAD[M3 : M2 + 1] += CAPLOS[j]
+
+ INDEX[j, 1] = M4
+ ID1[j, 5] = (M4 - 1) * 168
+ M2 = M4 + IM4
+
+ if M2 >= M4 and M4 != 0:
+ CHECK[i, M4 : M2 + 1] = LOCREW[j]
+ EFLOAD[M4 : M2 + 1] += SCHLOS[j]
+ LOAD[M4 : M2 + 1] += CAPLOS[j]
+
+ while True:
+ J4 += 1
+ if J4 > JA:
+ gotoFlag30 = True
+ break # GO TO 30
+ j -= 1
+ if MWEEK[j] == 0:
+ continue
+
+ if gotoFlag30:
+ break
+
+ while True:
+ J4 += 1
+ if J4 > JA:
+ gotoFlag30 = True
+ break # GO TO 30
+ j -= 1
+ if MWEEK[j] != 0:
+ break
+
+ if gotoFlag30:
+ break
+
+ # end of while-loop 19
+
+ if gotoFlag18:
+ break
+
+ if gotoFlag191:
+ continue
+
+ # end of while-loop 191
+
+ if gotoFlag18:
+ break
+
+ if gotoFlag30:
+ continue
+ # end of for-loop 30
+
+ if gotoFlag18:
+ continue
+ # end of for-loop 89
+
+ # Print output of the scheduled generators of the plant
+ NTI = 0
+ NMAX = 40
+ WEEKS = [" "] * 52
+ for i in range(M):
+ JB = i
+ if INDEX[JB, 0] == 0 and INDEX[JB, 1] == 0:
+ continue
+
+ for ci in [0, 1]:
+ if INDEX[JB, ci] != 0:
+ MC1 = INDEX[JB, ci]
+ MC2 = MC1 + DURLOS[JB, ci] - 1
+ MC2 = min(MC2, 51)
+ WEEKS[MC1 : MC2 + 1] = A if INDEX1[JB] == 0 else AB
+
+ IU0 = ID1[JB, 0]
+ IU0 = NR0[IU0]
+
+ if IREPM != 0:
+ f.write("\n | %s%s | %4d |\n" % (NAMU[IU0], NUMP[IU0], CAPLOS[JB]))
+
+ for KI in range(4):
+ for KJ in range(13):
+ f.write("%s " % (WEEKS[(KI - 1) * 13 + KJ]))
+ f.write("|")
+
+ WEEKS[:52] = B
+
+ NTI += 1
+ if NTI <= NMAX:
+ continue
+
+ NMAX += 40
+ if IREPM != 0:
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 190
+ for _ in range(63):
+ f.write("\n ")
+ f.write("TABLE %4d \n\n" % (ITC))
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 802
+ f.write("\n | | |")
+ for _ in range(107):
+ f.write(" ")
+ f.write("|")
+
+ # 803
+ f.write("\n | | |")
+ for _ in range(50):
+ f.write(" ")
+ f.write("MONTHS")
+ for _ in range(51):
+ f.write(" ")
+ f.write("|")
+
+ # 802
+ f.write("\n | | |")
+ for _ in range(107):
+ f.write(" ")
+ f.write("|")
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 804
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 805
+ f.write(
+ "\n | UNIT | CAP | JAN FEB MAR | APR MAY JUN |"
+ "JUL AUG SEP | OCT NOV DEC |"
+ )
+
+ # 806
+ f.write("\n | NAME | (MW) |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 807
+ f.write(
+ "\n | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | "
+ "7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |"
+ )
+
+ # 804
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 804
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ if IREPM != 0:
+ # 804
+ f.write("\n | | |")
+ for _ in range(4):
+ for KJ in range(26):
+ f.write(" ")
+ f.write("|")
+
+ # 801
+ f.write("\n ")
+ for _ in range(123):
+ f.write("-")
+ f.write("\n | | |")
+
+ # 258
+ f.write("\n 1")
+ for _ in range(10):
+ f.write("\n")
+ f.write(
+ "\t\t\t\t WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE \n"
+ )
+
+ # 290
+ f.write("0\n")
+ for KI in range(4):
+ for KJ in range(13):
+ f.write(" %.4f" % (EFLOAD[KI * 13 + KJ]))
+ f.write("\n")
+
+ # 268
+ f.write("0\n\n\n\n\n\t\t\t\t WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE \n")
+
+ # 290
+ f.write("0\n")
+ for KI in range(4):
+ for KJ in range(13):
+ f.write(" %.4f" % (LOAD[KI * 13 + KJ]))
+ f.write("\n")
+
+ f.close()
+
+ ITAB = ITC
+ for i in range(NGU):
+ IND = ID1[i, 0]
+ IND = NR0[IND]
+ ID[IND, 3] = (
+ ID1[i, 3] // 168 + 1
+ ) # maybe remvoe "+1" here; ID1[i, 3] / 168 + 1 in Fortran
+ ID[IND, 5] = (
+ ID1[i, 5] // 168 + 1
+ ) # maybe remvoe "+1" here; ID1[i, 3] / 168 + 1 in Fortran
+
+ return ITAB
diff --git a/NARP/reliabilityassessment/data_processing/readInputB.py b/NARP/reliabilityassessment/data_processing/readInputB.py
new file mode 100644
index 0000000..f7ef13a
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/readInputB.py
@@ -0,0 +1,545 @@
+import os
+
+import numpy as np
+import pandas as pd
+
+
+def read_card_ZZTC(filepath):
+ """
+ ZZTC: title card
+ The purpose of this card is to provide a title in the final output. Data inserted
+ under this card is simply reproduced. This is the only formatted card in this file.
+
+ :param str filepath: root file path for input csvs.
+ :return: (*string*) -- A string describes some basic information about
+ the studied power system (e.g., how many areas).
+ """
+ df_ZZTC = pd.read_csv(os.path.join(filepath, "ZZTC.csv"), header=None).to_numpy()
+ data = ". ".join([s[0] for s in df_ZZTC])
+ return data
+
+
+def read_card_ZZMC(filepath):
+ """
+ ZZMC: miscellaneous card.
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+ """
+
+ df_ZZMC = pd.read_csv(os.path.join(filepath, "ZZMC.csv"))
+ data = dict()
+
+ """
+ The basic seed used by the program to create seeds for generators and
+ transmission lines, a 7-digit integer.
+ This may be useless in Python since random number can be easily generated.
+ Will remove it later
+ """
+ data["JSEED"] = df_ZZMC["SEED"].item() # e.g. 345237
+
+ """
+ Load-loss sharing:
+ 0 --"Yes"; 1 --"No"
+ """
+ data["NLS"] = df_ZZMC["LS"].item() # 1
+
+ """
+ Ending weeks for the 1st (beginning week is assumed to be the 1st week),
+ 2nd, 3rd seasons. The ending weeks of the 4th season is assumed as 52
+ """
+ # e.g. #[13, 26, 39]
+ (
+ data["IW1"],
+ data["IW2"],
+ data["IW3"],
+ ) = (
+ df_ZZMC["END WK1"].item(),
+ df_ZZMC["END WK2"].item(),
+ df_ZZMC["END WK3"].item(),
+ )
+
+ # ----------Vars for convergence test--------------
+ """
+ 1: based on area-statistics. The area is specified by the variable KVL below
+ 2: based on pool statistics
+ """
+ data["KWHERE"] = df_ZZMC["WHERE"].item() # e.g. 1
+
+ """
+ 1: Hourly statistics are to be used for convergence
+ 2: Peak statistic are to be used for convergence"""
+ data["KVWHEN"] = df_ZZMC["WHEN"].item() # e.g. 1
+
+ """
+ 1: LOLE is to be used for convegence
+ hourly LOLE for 'WHEN = 1'
+ peak LOLE for 'WHEN = 2'
+ 2: EUE is to be used for convegence ( for 'WHEN = 1' only)
+ """
+ data["KVSTAT"] = df_ZZMC["KVS"].item() # e.g. 1
+
+ """
+ 1: Weighted average statistcs (cosniderring forecast error)
+ is to be used for convegence
+ 2: Use only the No-forecast-error statistics
+ """
+ data["KVTYPE"] = df_ZZMC["KVT"].item() # e.g. 2
+
+ """
+ Area number (used only if 'WHERE'=1) for convergence
+ """
+ data["KVLOC"] = df_ZZMC["KVL"].item() # e.g. 1
+
+ """
+ 0: Program stops when standard deviation of the
+ tested statistics < (0.025 * mean); i.e. the true value lies
+ between pos/neg 5% of the estimate with confidence level at 95%.
+
+ >0: Program stops when standard deviation of the
+ tested statistics < (CVT * mean); i.e. the true value lies
+ between pos/neg (2 CVT)% of the estimate with confidence level at 95%.
+ """
+ data["CVTEST"] = df_ZZMC["CVT"].item() # e.g. 0.025
+
+ """
+ Time to terminate simulation if no convergence.
+ Should be an INT type (years)
+ """
+ data["FINISH"] = df_ZZMC["FIN"].item() # e.g. 9999
+
+ # ---- Other variables
+ """
+ 1: Stats are collected every hour
+ 24: Daily peak stats only are collected
+ """
+ data["JSTEP"] = df_ZZMC["STEP"].item() # e.g. 1
+
+ """
+ 1: Hourly Monte Carlo draws of generator ;and line status are used
+ 24: Daily Monte Carlo draws are used
+ """
+ data["JFREQ"] = df_ZZMC["FREQ"].item() # e.g. 1
+
+ """
+ Defines the upper limit of the prob distribution of
+ Expected unserved Energy, in MWHRS.
+ Note: the original Fortran code set it as int type,
+ but float type is used here since it may be more reasonable
+ """
+ data["MAXEUE"] = df_ZZMC["MAXE"].item() # e.g. 1000
+
+ """
+ 0: Transmission mod results are not printed.
+ 1: Transmission mod results are printed into file TRAOUT.
+ """
+ data["IOI"] = df_ZZMC["II"].item() # e.g. 0
+
+ """
+ 0: Only final statistics are printed.
+ 1: In addition to final statistics, results after each
+ replication are also printed.
+ """
+ data["IOJ"] = df_ZZMC["IJ"].item() # e.g. 0
+
+ """
+ 0: Delete DUMP after the run.
+ 1: Retain DUMP after the run.
+ """
+ data["IREM"] = df_ZZMC["IR"].item() # e.g. 1
+
+ """
+ Interval, in number of replications, for storing
+ """
+ data["INTV"] = df_ZZMC["IN"].item() # e.g. 5
+
+ """
+ 0: Input data from INPUTB and INPUTC is not printed.
+ 1: Input data is printed."""
+ data["IREPD"] = df_ZZMC["D"].item() # 1
+
+ """
+ 0: Planned outage schedules are not printed.
+ 1: Planned outage schedules are printed.
+ """
+ data["IREPM"] = df_ZZMC["M"].item() # e.g. 1
+
+ return data
+
+
+def read_card_ZZLD(filepath):
+ """
+ ZZLD: system data card
+ Area names are given in four letters. The purpose of this card is to specify
+ data applicable to a given area.
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+ """
+ df_ZZLD = pd.read_csv(os.path.join(filepath, "ZZLD.csv"))
+ data = dict()
+
+ RATES = np.zeros((len(df_ZZLD), 3))
+ ID = np.zeros((len(df_ZZLD), 4)).astype(int)
+
+ """
+ Serial Number.
+ The serial number in this and subsequent types of cards identifies
+ each entry uniquely and specifies the sequence of data entries.
+ After reading, the data entries are arranged by the program in the
+ sequence specified under SN. The serial number can have up to three
+ decimal places. This he.lps in deleting lines and inserting new lines
+ through INPUTC file.
+ """
+ data["SNRI"] = df_ZZLD["SN"].astype(int).to_numpy()
+
+ """
+ The name of the area. It can have up to four letters and
+ must be enclosed by quotes.
+ """
+ data["NAR"] = df_ZZLD["AREA NAME"].to_numpy()
+
+ """
+ Annual peak in the area, in MW.
+ """
+ RATES[:, 0] = df_ZZLD["PEAK (MW)"].to_numpy()
+
+ """
+ Load forecast uncertainty (LFU).
+ One standard deviation expressed as percentage of the mean
+ """
+ RATES[:, 1] = df_ZZLD["LFU"].to_numpy()
+
+ """
+ Outage Window.
+ The weeks during which planned maintenance can be performed.
+ BEG WK: Beginning week of the outage window
+ END WK: Ending week of the outage window
+ """
+ ID[:, 0], ID[:, 1] = (
+ df_ZZLD["OUTAGE BEG WK"].to_numpy(),
+ df_ZZLD["OUTAGE END WK"].to_numpy(),
+ )
+
+ """
+ Forbidden Period.
+ A part of the outage window during which planned maintenance
+ is NOT allowed.
+ BEG WK: Beginning week of the Forbidden Period.
+ END WK: Ending week of the Forbidden Period.
+ """
+ ID[:, 2], ID[:, 3] = (
+ df_ZZLD["FORBIDDEN BEG WK"].to_numpy(),
+ df_ZZLD["FORBIDDEN END WK"].to_numpy(),
+ )
+
+ """
+ Sum of Flows Constraint.
+ The algebraic sum of flows at this node is not to exceed this value.
+ """
+ RATES[:, 2] = df_ZZLD["SUM OF FLOWS CONSTRAINT"].to_numpy()
+
+ data["RATES"] = RATES
+ data["ID"] = ID
+
+ return data
+
+
+def read_card_ZZUD(filepath):
+ """
+ ZZUD: generator unit data card
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+ """
+
+ df_ZZUD = pd.read_csv(os.path.join(filepath, "ZZUD.csv"))
+ data = dict()
+
+ # Note: the original Fortran code set the HRLOAD array as INT type;
+ # Here, I use float type for it.
+ HRLOAD = np.zeros((7, len(df_ZZUD)))
+ ID_ZZUD = np.zeros((len(df_ZZUD), 5)).astype(int)
+
+ """
+ Serial number (refet to ZZLD)
+ """
+ data["SNRI"] = df_ZZUD["SN"].astype(int).to_numpy()
+
+ """
+ Unit name is six alphanumeric characters. The first four letters are the plant
+ number and the next two numbers identify the unit.
+ """
+ data["NAT"] = df_ZZUD["NAME"].to_numpy()
+
+ """
+ Area of location
+ """
+ data["NAR"] = df_ZZUD["LOC"].to_numpy()
+
+ """
+ Unit capacity, MW, in the ith season (see ZZLD)
+ """
+ # CAP1, CAP2, CAP3, CAP4
+ HRLOAD[0, :], HRLOAD[1, :], HRLOAD[2, :], HRLOAD[3, :] = (
+ df_ZZUD["CAP1"].to_numpy(),
+ df_ZZUD["CAP2"].to_numpy(),
+ df_ZZUD["CAP3"].to_numpy(),
+ df_ZZUD["CAP4"].to_numpy(),
+ )
+
+ """
+ Derated forced outage rate (DFOR)
+ """
+ HRLOAD[4, :] = df_ZZUD["DFOR"].to_numpy()
+
+ """
+ Forced outage rate(FOR)
+ """
+ HRLOAD[5, :] = df_ZZUD["FOR"].to_numpy()
+
+ """
+ Percent derating (DER) due to partial failure. This is
+ different from the seasonal derating which can be
+ specified using CAPI.
+ """
+ HRLOAD[6, :] = df_ZZUD["DER"].to_numpy()
+
+ """
+ Predetermined (1) or automatic (0) scheduling.
+ """
+ ID_ZZUD[:, 0] = df_ZZUD["P/A"].to_numpy()
+
+ # Notes:
+ # 1. In scheduling the planned outages, the program does not
+ # take out two units belonging to one plant at the same
+ # time. Also the program can accept up to two planned
+ # outages per year for each unit.
+
+ # 2. The program schedules units for planned outage by
+ # adjusting them into valleys in the load cycle. Therefore
+ # for any area having only generation, planned outages must
+ # be predetermined by the user.
+ #
+ # 3. If the program cannot schedule a unit in the automatic
+ # mode, it will print a message to indicate this and
+ # schedule the unit using B1,D1l and B2,D2 parameters.
+ """
+ Beginning week and duration of the first outage.
+ In the automatic mode Bl is ignored.(B1, D1)
+ """
+ ID_ZZUD[:, 1], ID_ZZUD[:, 2] = df_ZZUD["B1"].to_numpy(), df_ZZUD["D1"].to_numpy()
+
+ """
+ Beginning week and duration of second outage. In
+ the automatic mode, B2 is ignored.(B2, D2)
+ """
+ ID_ZZUD[:, 3], ID_ZZUD[:, 4] = df_ZZUD["B2"].to_numpy(), df_ZZUD["D2"].to_numpy()
+
+ data["ID"] = ID_ZZUD
+ data["HRLOAD"] = HRLOAD
+
+ return data
+
+
+def read_card_ZZTD(filepath):
+ """
+ ZZTD: line data card
+ The purpose of this card is to specify the data for transmission links.
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+ """
+
+ df_ZZTD = pd.read_csv(os.path.join(filepath, "ZZTD.csv"))
+ data = dict()
+
+ ADM = np.zeros((len(df_ZZTD), 6))
+ CAP, CAPR = np.zeros((len(df_ZZTD), 6)), np.zeros((len(df_ZZTD), 6))
+ PROBT = np.zeros((len(df_ZZTD), 6))
+
+ """
+ Serial number
+ """
+ data["SNRI"] = df_ZZTD["SN"].to_numpy()
+
+ """
+ Line Id
+ """
+ data["LineID"] = df_ZZTD["LINE NUMBER"].to_numpy()
+
+ """
+ Name of From area
+ """
+ data["NAR"] = df_ZZTD["FROM AREA"].to_numpy()
+
+ """
+ Name of To area
+ """
+ data["NAE"] = df_ZZTD["TO AREA"].to_numpy()
+
+ tmp = df_ZZTD["META DATA"].to_numpy()
+ for i in range(len(tmp)):
+ tmp_ = np.array(list(map(float, tmp[i].strip().split(",")))).reshape((6, 4))
+ ADM[i, :] = tmp_[:, 0]
+ CAP[i, :] = tmp_[:, 1]
+ CAPR[i, :] = tmp_[:, 2]
+ PROBT[i, :] = tmp_[:, 3]
+
+ data["ADM"] = ADM
+ data["CAP"] = CAP
+ data["CAPR"] = CAPR
+ data["PROBT"] = PROBT
+
+ return data
+
+
+def read_card_ZZFC(filepath):
+ """
+ ZZFC: firm contracts data card
+ This card is to specify firm interchanges of power between areas. The following
+ format is used for input of these interchanges
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+
+ ** ZZFC data is not required and is given now **.
+ Default values (zero , empty , None, etc.) will be given here
+ """
+
+ df_ZZFC = pd.read_csv(os.path.join(filepath, "ZZFC.csv"))
+ data = dict()
+
+ """
+ Serial number
+ """
+ data["SNRI"] = df_ZZFC["SN"].to_numpy()
+
+ """
+ Name of From area sending power
+ """
+ data["NAR"] = df_ZZFC["FROM AREA"].to_numpy()
+
+ """
+ Name of To area receiving power
+ """
+ data["NAE"] = df_ZZFC["TO AREA"].to_numpy()
+
+ """
+ Beginning and ending days (1 to 365) of the contract.
+ """
+ data["BEG DAY"] = df_ZZFC["BEG DAY"].to_numpy()
+ data["END DAY"] = df_ZZFC["END DAY"].to_numpy()
+
+ """
+ Exchanged power (MW) of the firm interchange (power) during the period.
+ """
+ data["MW"] = df_ZZFC["MW"].to_numpy()
+
+ return data
+
+
+def read_card_ZZOD(filepath):
+ """
+ ZZOD: unit ownership data card
+ This card is to specify the joint ownership of a unit by several areas.
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+
+ ** ZZOD data is NOT required and is not given now **.
+ Default values (zero , empty , None, etc.) will be used here
+ """
+
+ df_ZZOD = pd.read_csv(os.path.join(filepath, "ZZOD.csv"))
+ data = dict()
+
+ """
+ Serial number
+ """
+ data["SNRI"] = df_ZZOD["SN"].to_numpy()
+
+ """
+ Name of the (jointly owned) unit
+ """
+ data["NAT"] = df_ZZOD["UNIT NAME"].to_numpy()
+
+ """
+ Percentage ownership by each area
+ """
+ data["PERCENT"] = df_ZZOD["PERCENT OWNED BY AREA"].to_numpy()
+
+ return data
+
+
+def read_card_ZZDD(filepath):
+ """
+ ZZDD: line derarting data card
+ Status of certain units can effect the line rating. For a given line, four units
+ can be specified in a combination such that if all the units in the combination
+ are down, then the line ratings will be multiplied by the derating factors. For
+ a given line, more than one combination can be specified.
+
+ :param str filepath: root file path for input csvs.
+ :return: (*dict*) -- a dictionary contains info from the raw dataframe with
+ designated keys.
+
+ ** ZZDD data is NOT required and is not given now **.
+ Default values (zero , empty , None, etc.) will be used here
+ """
+
+ df_ZZDD = pd.read_csv(os.path.join(filepath, "ZZDD.csv"))
+ data = dict()
+
+ """
+ Serial number
+ """
+ data["SNRI"] = df_ZZDD["SN"].to_numpy()
+
+ """
+ Line number
+ """
+ data["LineID"] = df_ZZDD["LINE NUMBER"].to_numpy()
+
+ """
+ Four related generator unit name
+ """
+ data["unit1_name"] = df_ZZDD["UNIT1"].to_numpy()
+ data["unit2_name"] = df_ZZDD["UNIT2"].to_numpy()
+ data["unit3_name"] = df_ZZDD["UNIT3"].to_numpy()
+ data["unit4_name"] = df_ZZDD["UNIT4"].to_numpy()
+
+ """
+ Derate factors
+ """
+ data["forward_derate"] = df_ZZDD["FORWARD"].to_numpy()
+ data["backward_derate"] = df_ZZDD["BACKWARD"].to_numpy()
+
+ return data
+
+
+def readInputB(filepath):
+ """read and parse raw text data from csv files.
+
+ :param str filepath: root file path for input csvs.
+
+ :return: (*dict*) -- A nested Python dictionary for each data card (INPUTB file).
+ Originally, each data card contains either a single value or multiple arrays.
+ """
+ inputB_dict = dict()
+ inputB_dict["ZZTC"] = read_card_ZZTC(filepath)
+ inputB_dict["ZZMC"] = read_card_ZZMC(filepath)
+ inputB_dict["ZZLD"] = read_card_ZZLD(filepath)
+ inputB_dict["ZZUD"] = read_card_ZZUD(filepath)
+ inputB_dict["ZZTD"] = read_card_ZZTD(filepath)
+ inputB_dict["ZZFC"] = read_card_ZZFC(filepath)
+ inputB_dict["ZZOD"] = read_card_ZZOD(filepath)
+ inputB_dict["ZZDD"] = read_card_ZZDD(filepath)
+
+ return inputB_dict
diff --git a/NARP/reliabilityassessment/data_processing/resca.py b/NARP/reliabilityassessment/data_processing/resca.py
new file mode 100644
index 0000000..8e53df1
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/resca.py
@@ -0,0 +1,107 @@
+import numpy as np
+
+
+def resca(SUSTAT, MAXDAY, QTR, CAPOWN, RATES, JENT, INTCH):
+ """
+ Calculate the total owned capacity with peak interchange and seasonal effects
+
+ :param numpy.ndarray SUSTAT: statistics of each area with shape (NOAREA+1, 6)
+ SUSTAT(J,0): total load (MW), initialized by PKLOAD(J)
+ SUSTAT(J,1): total generation (MW)
+ SUSTAT(J,2): loss of power (MW), XLOL
+ SUSTAT(J,3): intermediate value (MW),
+ e.g.: (SUSTAT(I,2)-SUSTAT(I,1))/SUSTAT(I,1)*100,
+ i.e., total owned net capacity
+ SUSTAT(J,4): loss of load probability, XLOLP
+ SUSTAT(J,5): loss of energy (MWh), EUE (expected
+ unserved energy), the last row is kept for total
+ statistic purpose.
+ :param numpy.ndarray MAXDAY: index of the day in a year with maximum daily peak,
+ shape (NOAREA, )
+ :param numpy.ndarray QTR: quarterly index (in units of hours) with shape (3, )
+ e.g. [13*168+0.5, 13*2*168+0.5, 13*3*168+0.5]
+ :param numpy.ndarray CAPOWN: shape (NOAREA, NUNITS), the (j, i)th element means
+ the fraction of unit i owned by area j.
+ :param numpy.ndarray RATES: shape (NUNITS, 4), (i, j)th element means the rated
+ power (MW) of unit i in the j-th season.
+ :param numpy.ndarray JENT: 2D array of pointer identifying a specific contract with
+ shape (NOAREA, NOAREA), the (i, j)th element means the contract (no.)
+ between area i and area j.
+ :param numpy.ndarray INTCH: 2D array of the contract content with shape (total
+ number of contracts, 365), the (JENT[i, j], k)th element means the contracted
+ power (MW) on the kth day from area i to area j.
+
+ .. note:: SUSTAT is modified in place.
+ """
+ NUINTS = CAPOWN.shape[1]
+ NOAREA = SUSTAT.shape[0] - 1
+ for i in range(NOAREA):
+ IDAY = MAXDAY[i]
+ IPER = np.argmax(QTR > IDAY * 24)
+ for j in range(NUINTS):
+ SUSTAT[i, 1] += CAPOWN[i, j] * RATES[j, IPER]
+
+ SUSTAT[i, 1] -= INTCH[JENT[i, :][JENT[i, :] != -1], IDAY].sum()
+ SUSTAT[i, 1] += INTCH[JENT[:, i][JENT[:, i] != -1], IDAY].sum()
+
+
+def _resca(SUSTAT, MAXDAY, QTR, CAPOWN, RATES, JENT, INTCH):
+ """
+ Calculate the total owned capacity with peak interchange and seasonal effects
+
+ :param numpy.ndarray SUSTAT: statistics of each area with shape (NOAREA+1, 6)
+ SUSTAT(J,0): total load (MW), initialized by PKLOAD(J)
+ SUSTAT(J,1): total generation (MW)
+ SUSTAT(J,2): loss of power (MW), XLOL
+ SUSTAT(J,3): intermediate value (MW),
+ e.g.: (SUSTAT(I,2)-SUSTAT(I,1))/SUSTAT(I,1)*100,
+ i.e., total owned net capacity
+ SUSTAT(J,4): loss of load probability, XLOLP
+ SUSTAT(J,5): loss of energy (MWh), EUE (expected
+ unserved energy), the last row is kept for total
+ statistic purpose.
+ :param numpy.ndarray MAXDAY: index of the day in a year with maximum daily peak,
+ shape (NOAREA, )
+ :param numpy.ndarray QTR: quarterly index (in units of hours) with shape (3, )
+ e.g. [13*168+0.5, 13*2*168+0.5, 13*3*168+0.5]
+ :param numpy.ndarray CAPOWN: shape (NOAREA, NUNITS), the (j, i)th element means
+ the fraction of unit i owned by area j.
+ :param numpy.ndarray RATES: shape (NUNITS, 4), (i, j)th element means the rated
+ power (MW) of unit i in the j-th season.
+ :param numpy.ndarray JENT: 2D array of pointer identifying a specific contract with
+ shape (NOAREA, NOAREA), the (i, j)th element means the contract (no.)
+ between area i and area j.
+ :param numpy.ndarray INTCH: 2D array of the contract content with shape (total
+ number of contracts, 365), the (JENT[i, j], k)th element means the contracted
+ power (MW) on the kth day from area i to area j.
+
+ .. note:: SUSTAT is modified in place.
+ """
+
+ NOAREA = SUSTAT.shape[0] - 1
+ for i in range(NOAREA):
+ IDAY = MAXDAY[i]
+ IHR = IDAY * 24
+ IPER = 0
+
+ if IHR > QTR[0]:
+ IPER = 1
+
+ if IHR > QTR[1]:
+ IPER = 2
+
+ if IHR > QTR[2]:
+ IPER = 3
+
+ NUINTS = CAPOWN.shape[1]
+ for j in range(NUINTS):
+ SUSTAT[i, 1] += CAPOWN[i, j] * RATES[j, IPER]
+
+ for j1 in range(NOAREA):
+ if JENT[i, j1] != -1:
+ IP = JENT[i, j1]
+ SUSTAT[i, 1] -= INTCH[IP, IDAY]
+
+ if JENT[j1, i] != -1:
+ IP = JENT[j1, i]
+ SUSTAT[i, 1] += INTCH[IP, IDAY]
diff --git a/NARP/reliabilityassessment/data_processing/smaint.py b/NARP/reliabilityassessment/data_processing/smaint.py
new file mode 100644
index 0000000..e08224d
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/smaint.py
@@ -0,0 +1,98 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.pmsc import pmsc
+
+
+def smaint(
+ NOAREA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+):
+ """
+ Obtain the (gen unit) maintenance schedule table ('JPLOUT') for each area
+
+ :param int NOAREA: total number of areas
+ :param numpy.ndarray ID:shape (NUNITS, 8)
+ ID(I,K), K=0: unit number (0-based)
+ K=1: plant number (0-based)
+ K=2: area of location (0-based)
+ K=3: starting week of first planned outage (0-based)
+ K=4: duration of first planned outage in weeks
+ K=5: starting week of second planned outage (0-based)
+ K=6: duration of second planned outage in weeks
+ K=7: 1 if maintenance is pre-scheduled;
+ 0 if set automatically by the program
+ :param int ITAB: index number of printed tables in the final output file
+ :param numpy.ndarray RATES: original rating data (MW) of each unit for four seasons
+ with shape (NUNITS, 4)
+ :param numpy.ndarray PROBG: 2D array of accumulated probability for each capacity
+ tier of each unit
+ :param numpy.ndarray DERATE: array of the derated capacity of each unit
+ :param numpy.ndarray PKLOAD: array of the user-defined annual peak of each area.
+ :param numpy.ndarray WPEAK: 2D array, weekly peak load amount (MW)
+ 1st dim: areaIdx, 2nd dim: weekIdx
+ :param int IREPM: indicator of printing maintenance result (if exists) or not
+ :param int MINRAN: beginning week of the outage window
+ :param int MAXRAN: ending week of the outage window
+ :param int INHBT1: beginning week of the forbidden period.
+ :param int INHBT2: ending week of the forbidden period.
+ :param list NAMU: list of strings for generator unit name
+ :param list NUMP: list of strings for generator plant name
+ :return: (*tuple*) -- JPLOUT: table of planned outages of units, with shape (52,
+ NUINTS+1) 1-dim: week index; 2-dim: total number
+ of gen units scheduled for maintenance in the
+ corresponding week followed by a sequence of 0-based
+ generator unit numbers
+ ITAB: updated index number of printed tables
+
+ .. note:: ID is modified in place
+ """
+
+ NUNITS = ID.shape[0]
+ for areaIdx in range(NOAREA):
+ # automatic scheduling of planned maintenance
+ # for the specified area here; pmsc will update array ID
+ ITAB = pmsc(
+ areaIdx,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ JPLOUT = (-1) * np.ones((52, 1 + NUNITS), dtype=int)
+ JPLOUT[:, 0] = 0
+
+ for i in range(NUNITS):
+ for wi in {3, 5}:
+ if ID[i, wi] != -1:
+ j1 = ID[i, wi]
+ # the index of last week of a simulation year is 51
+ j2 = min(51, j1 + ID[i, wi + 1])
+ # increase total number of units under maintenance for the weeks
+ JPLOUT[j1:j2, 0] += 1
+ JPLOUT[np.arange(j1, j2), JPLOUT[j1:j2, 0]] = int(ID[i, 0])
+
+ return JPLOUT, ITAB
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZDD.csv b/NARP/reliabilityassessment/data_processing/tests/ZZDD.csv
new file mode 100644
index 0000000..cd34ea4
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZDD.csv
@@ -0,0 +1 @@
+SN,LINE NUMBER,UNIT1,UNIT2,UNIT3,UNIT4,FORWARD,BACKWARD
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZFC.csv b/NARP/reliabilityassessment/data_processing/tests/ZZFC.csv
new file mode 100644
index 0000000..c3c724e
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZFC.csv
@@ -0,0 +1 @@
+SN,FROM AREA,TO AREA,BEG DAY,END DAY,MW
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZLD.csv b/NARP/reliabilityassessment/data_processing/tests/ZZLD.csv
new file mode 100644
index 0000000..1120373
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZLD.csv
@@ -0,0 +1,3 @@
+SN,AREA NAME,PEAK (MW),LFU,OUTAGE BEG WK,OUTAGE END WK,FORBIDDEN BEG WK,FORBIDDEN END WK,SUM OF FLOWS CONSTRAINT
+1,'A1',3000,0,1,52,31,32,30000
+2,'A2',3000,0,1,52,31,32,30000
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZMC.csv b/NARP/reliabilityassessment/data_processing/tests/ZZMC.csv
new file mode 100644
index 0000000..c6f4b0f
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZMC.csv
@@ -0,0 +1,2 @@
+SEED,LS,END WK1,END WK2,END WK3,WHERE,WHEN,KVS,KVT,KVL,CVT,FIN,STEP,FREQ,MAXE,II,IJ,IR,IN,D,M
+345237,1,13,26,39,1,1,1,2,1,0.025,9999,1,1,1000,0,0,1,5,1,1
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZOD.csv b/NARP/reliabilityassessment/data_processing/tests/ZZOD.csv
new file mode 100644
index 0000000..264a5ff
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZOD.csv
@@ -0,0 +1 @@
+SN,UNIT NAME,PERCENT OWNED BY AREA
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZTC.csv b/NARP/reliabilityassessment/data_processing/tests/ZZTC.csv
new file mode 100644
index 0000000..52407e8
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZTC.csv
@@ -0,0 +1,3 @@
+THIS IS A FIVE AREA SYSTEM
+EACH AREA IS IDENTICAL TO THE IEEE-RTS
+PLANNED MAINTENANCE IS CONSIDERED
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZTD.csv b/NARP/reliabilityassessment/data_processing/tests/ZZTD.csv
new file mode 100644
index 0000000..9aae382
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZTD.csv
@@ -0,0 +1,2 @@
+SN,LINE NUMBER,FROM AREA,TO AREA,META DATA
+1,1,'A1','A2',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/ZZUD.csv b/NARP/reliabilityassessment/data_processing/tests/ZZUD.csv
new file mode 100644
index 0000000..184cf65
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ZZUD.csv
@@ -0,0 +1,3 @@
+SN,NAME,LOC,CAP1,CAP2,CAP3,CAP4,DFOR,FOR,DER,P/A,B1,D1,B2,D2
+1,'A10101','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+2,'A20101','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/__init__.py b/NARP/reliabilityassessment/data_processing/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/NARP/reliabilityassessment/data_processing/tests/ground_truth_for_xldnew.txt b/NARP/reliabilityassessment/data_processing/tests/ground_truth_for_xldnew.txt
new file mode 100644
index 0000000..971f8ee
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/ground_truth_for_xldnew.txt
@@ -0,0 +1,8760 @@
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diff --git a/NARP/reliabilityassessment/data_processing/tests/test_dataf1.py b/NARP/reliabilityassessment/data_processing/tests/test_dataf1.py
new file mode 100644
index 0000000..a7f88e7
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_dataf1.py
@@ -0,0 +1,283 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.data_processing.dataf1 import dataf1
+
+TEST_DIR = Path(__file__).parent.absolute()
+
+
+def smaint_mock(
+ areaIdx,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+):
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/ID_modified")
+ ID[:] = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JPLOUT")
+ JPLOUT = loadtxt(FileNameAndPath).astype(int)
+ JPLOUT[:, 1:] -= 1 # 0-based index
+
+ ITAB = 11
+ return JPLOUT, ITAB
+
+
+def test_dataf1(mocker):
+ mocker.patch(
+ "reliabilityassessment.data_processing.input_processing.smaint",
+ side_effect=smaint_mock,
+ )
+
+ filepaths = [
+ Path(TEST_DIR, "testdata_input_processing"),
+ Path(TEST_DIR, "testdata_input_processing/LEEI"),
+ ]
+
+ (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ RATES,
+ ID,
+ HRLOAD,
+ MAXHR,
+ DYLOAD,
+ MAXDAY,
+ WPEAK,
+ MXPLHR,
+ JPLOUT,
+ ITAB,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ NR,
+ NAMA,
+ ) = dataf1(filepaths)
+
+ NORR_true = 1 - 1 # 0-based index in Python
+ NFCST_true = 1
+ NOAREA_true = 5
+ MXCRIT_true = 0
+ ITAB_true = 11
+
+ assert NORR == NORR_true
+ assert NFCST == NFCST_true
+ assert NOAREA == NOAREA_true
+ assert MXCRIT == MXCRIT_true
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/QTR")
+ QTR_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(QTR, QTR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/PKLOAD")
+ PKLOAD_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(PKLOAD, PKLOAD_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/FU")
+ FU_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FU, FU_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MINRAN")
+ MINRAN_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(MINRAN, MINRAN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MAXRAN")
+ MAXRAN_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(MAXRAN, MAXRAN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INHBT1")
+ INHBT1_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(INHBT1, INHBT1_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INHBT2")
+ INHBT2_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(INHBT2, INHBT2_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/BN")
+ BN_true = loadtxt(FileNameAndPath)
+ BN_true[:, 0] -= 1 # 0-based index in python!
+ np.testing.assert_array_almost_equal(BN, BN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/SUSTAT_RESCA")
+ SUSTAT_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SUSTAT, SUSTAT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/FCTERR")
+ FCTERR_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FCTERR, FCTERR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/CAPCON")
+ CAPCON_true = loadtxt(FileNameAndPath).astype(int)
+ CAPCON_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(CAPCON, CAPCON_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/CAPOWN")
+ CAPOWN_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(CAPOWN, CAPOWN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/NOGEN")
+ NOGEN_true = loadtxt(FileNameAndPath).astype(
+ int
+ ) # total number of gen units of each area
+ np.testing.assert_array_equal(NOGEN, NOGEN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/PROBG")
+ PROBG_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(PROBG, PROBG_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/DERATE")
+ DERATE_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(DERATE, DERATE_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JENT")
+ JENT_true = loadtxt(FileNameAndPath).astype(int)
+ JENT_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(JENT, JENT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INTCH")
+ INTCH_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(INTCH, INTCH_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INTCHR")
+ INTCHR_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(INTCHR, INTCHR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/LP")
+ LP_true = loadtxt(FileNameAndPath).astype(int)
+ LP_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(LP, LP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/LINENO")
+ LINENO_true = loadtxt(FileNameAndPath).astype(int)
+ LINENO_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(LINENO, LINENO_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/PROBL")
+ PROBL_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(PROBL, PROBL_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/BLPA")
+ BLPA_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BLPA, BLPA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JCRIT")
+ JCRIT_true = (
+ np.array([]) if MXCRIT == 0 else -1 + loadtxt(FileNameAndPath).astype(int)
+ ) # 0-based index in python!
+ np.testing.assert_array_equal(JCRIT, JCRIT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MAXHR")
+ MAXHR_true = loadtxt(FileNameAndPath).astype(int)
+ MAXHR_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(MAXHR, MAXHR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MAXDAY")
+ MAXDAY_true = loadtxt(FileNameAndPath).astype(int)
+ MAXDAY_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(MAXDAY, MAXDAY_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/DYLOAD")
+ DYLOAD_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(DYLOAD, DYLOAD_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/WPEAK")
+ WPEAK_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(WPEAK, WPEAK_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MXPLHR")
+ MXPLHR_true = loadtxt(FileNameAndPath).astype(int)
+ MXPLHR_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(MXPLHR, MXPLHR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/HRLOAD_XPOS")
+ HRLOAD_XPOS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(HRLOAD, HRLOAD_XPOS_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/RATES")
+ RATES_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(RATES, RATES_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ np.testing.assert_array_equal(ID, ID_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JPLOUT")
+ JPLOUT_true = loadtxt(FileNameAndPath).astype(int)
+ JPLOUT_true[:, 1:] -= 1 # 0-based index
+ np.testing.assert_array_equal(JPLOUT, JPLOUT_true)
+
+ assert NLS == 1
+ assert IOI == 0
+ assert IOJ == 0
+ assert KVLOC == 1 - 1 # 0-based index in Python;
+ assert KVSTAT == 1
+ assert KVTYPE == 2
+ assert KVWHEN == 1
+ assert KWHERE == 1
+ assert CVTEST == 0.025
+ assert MAXEUE == 1000
+ assert JSTEP == 1
+ assert JFREQ == 1
+ assert FINISH == 9999 * 8760.0
+ assert INTV == 5
+ assert INTVT == 5
+
+ assert NR == 1 - 1 # 0-based index
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/NAMA")
+ with open(FileNameAndPath, "r") as file:
+ NAMA_true = [e.strip("\n") for e in file.readlines()]
+ assert NAMA == NAMA_true
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_dataf1_new.py b/NARP/reliabilityassessment/data_processing/tests/test_dataf1_new.py
new file mode 100644
index 0000000..8cd405a
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_dataf1_new.py
@@ -0,0 +1,249 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.data_processing.dataf1 import dataf1
+
+TEST_DIR = Path(__file__).parent.absolute()
+
+
+def test_dataf1_new():
+ filepaths = [
+ Path(TEST_DIR, "testdata_input_processing"),
+ Path(TEST_DIR, "testdata_input_processing/LEEI"),
+ ]
+
+ (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ RATES,
+ ID,
+ HRLOAD,
+ MAXHR,
+ DYLOAD,
+ MAXDAY,
+ WPEAK,
+ MXPLHR,
+ JPLOUT,
+ ITAB,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ NR,
+ NAMA,
+ ) = dataf1(filepaths)
+
+ NORR_true = 1 - 1 # 0-based index in Python
+ NFCST_true = 1
+ NOAREA_true = 5
+ MXCRIT_true = 0
+ ITAB_true = 11
+
+ assert NORR == NORR_true
+ assert NFCST == NFCST_true
+ assert NOAREA == NOAREA_true
+ assert MXCRIT == MXCRIT_true
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/QTR")
+ QTR_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(QTR, QTR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/PKLOAD")
+ PKLOAD_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(PKLOAD, PKLOAD_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/FU")
+ FU_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FU, FU_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MINRAN")
+ MINRAN_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(MINRAN, MINRAN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MAXRAN")
+ MAXRAN_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(MAXRAN, MAXRAN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INHBT1")
+ INHBT1_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(INHBT1, INHBT1_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INHBT2")
+ INHBT2_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(INHBT2, INHBT2_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/BN")
+ BN_true = loadtxt(FileNameAndPath)
+ BN_true[:, 0] -= 1 # 0-based index in python!
+ np.testing.assert_array_almost_equal(BN, BN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/SUSTAT_RESCA")
+ SUSTAT_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SUSTAT, SUSTAT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/FCTERR")
+ FCTERR_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FCTERR, FCTERR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/CAPCON")
+ CAPCON_true = loadtxt(FileNameAndPath).astype(int)
+ CAPCON_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(CAPCON, CAPCON_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/CAPOWN")
+ CAPOWN_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(CAPOWN, CAPOWN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/NOGEN")
+ NOGEN_true = loadtxt(FileNameAndPath).astype(
+ int
+ ) # total number of gen units of each area
+ np.testing.assert_array_equal(NOGEN, NOGEN_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/PROBG")
+ PROBG_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(PROBG, PROBG_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/DERATE")
+ DERATE_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(DERATE, DERATE_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JENT")
+ JENT_true = loadtxt(FileNameAndPath).astype(int)
+ JENT_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(JENT, JENT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INTCH")
+ INTCH_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(INTCH, INTCH_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/INTCHR")
+ INTCHR_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(INTCHR, INTCHR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/LP")
+ LP_true = loadtxt(FileNameAndPath).astype(int)
+ LP_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(LP, LP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/LINENO")
+ LINENO_true = loadtxt(FileNameAndPath).astype(int)
+ LINENO_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(LINENO, LINENO_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/PROBL")
+ PROBL_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(PROBL, PROBL_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/BLPA")
+ BLPA_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BLPA, BLPA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JCRIT")
+ JCRIT_true = (
+ np.array([]) if MXCRIT == 0 else -1 + loadtxt(FileNameAndPath).astype(int)
+ ) # 0-based index in python!
+ np.testing.assert_array_equal(JCRIT, JCRIT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MAXHR")
+ MAXHR_true = loadtxt(FileNameAndPath).astype(int)
+ MAXHR_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(MAXHR, MAXHR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MAXDAY")
+ MAXDAY_true = loadtxt(FileNameAndPath).astype(int)
+ MAXDAY_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(MAXDAY, MAXDAY_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/DYLOAD")
+ DYLOAD_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(DYLOAD, DYLOAD_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/WPEAK")
+ WPEAK_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(WPEAK, WPEAK_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/MXPLHR")
+ MXPLHR_true = loadtxt(FileNameAndPath).astype(int)
+ MXPLHR_true -= 1 # 0-based index in python!
+ np.testing.assert_array_equal(MXPLHR, MXPLHR_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/HRLOAD_XPOS")
+ HRLOAD_XPOS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(HRLOAD, HRLOAD_XPOS_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/RATES")
+ RATES_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(RATES, RATES_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ np.testing.assert_array_equal(ID, ID_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/JPLOUT")
+ JPLOUT_true = loadtxt(FileNameAndPath).astype(int)
+ JPLOUT_true[:, 1:] -= 1 # 0-based index
+ np.testing.assert_array_equal(JPLOUT, JPLOUT_true)
+
+ assert NLS == 1
+ assert IOI == 0
+ assert IOJ == 0
+ assert KVLOC == 1 - 1 # 0-based index in Python;
+ assert KVSTAT == 1
+ assert KVTYPE == 2
+ assert KVWHEN == 1
+ assert KWHERE == 1
+ assert CVTEST == 0.025
+ assert MAXEUE == 1000
+ assert JSTEP == 1
+ assert JFREQ == 1
+ assert FINISH == 9999 * 8760.0
+ assert INTV == 5
+ assert INTVT == 5
+
+ assert NR == 1 - 1 # 0-based index
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_input_processing/NAMA")
+ with open(FileNameAndPath, "r") as file:
+ NAMA_true = [e.strip("\n") for e in file.readlines()]
+ assert NAMA == NAMA_true
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_datax.py b/NARP/reliabilityassessment/data_processing/tests/test_datax.py
new file mode 100644
index 0000000..d120e36
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_datax.py
@@ -0,0 +1,180 @@
+from pathlib import Path
+
+import numpy as np
+
+from reliabilityassessment.data_processing.datax import datax
+from reliabilityassessment.data_processing.pind import pind
+from reliabilityassessment.data_processing.readInputB import readInputB
+
+
+def test_datax():
+ TEST_DIR = str(Path(__file__).parent.absolute())
+ inputB_dict = readInputB(TEST_DIR)
+ inputB_dict_nameToInt = pind(inputB_dict)
+
+ (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ ID,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ ) = datax(inputB_dict_nameToInt)
+
+ QTR_ = np.array([13 * 168 + 0.5, 26 * 168 + 0.5, 39 * 168 + 0.5])
+ np.testing.assert_array_equal(QTR_, QTR)
+
+ NORR_ = 1 - 1 # 0-based index in Python
+ assert NORR_ == NORR
+ NFCST_ = 1
+ assert NFCST_ == NFCST
+ NOAREA_ = 2
+ assert NOAREA_ == NOAREA
+
+ PKLOAD_ = np.array([3000.0, 3000.0])
+ np.testing.assert_array_equal(PKLOAD_, PKLOAD)
+
+ FU_ = np.array([0.0, 0.0])
+ np.testing.assert_array_equal(FU_, FU)
+
+ MINRAN_ = -1 + np.array([1, 1], dtype=int) # 0-based inbdex in Python!
+ np.testing.assert_array_equal(MINRAN_, MINRAN)
+ MAXRAN_ = -1 + np.array([52, 52], dtype=int) # 0-based inbdex in Python!
+ np.testing.assert_array_equal(MAXRAN_, MAXRAN)
+
+ INHBT1_ = -1 + np.array([31, 31], dtype=int) # 0-based inbdex in Python!
+ np.testing.assert_array_equal(INHBT1_, INHBT1)
+ INHBT2_ = -1 + np.array([32, 32], dtype=int) # 0-based inbdex in Python!
+ np.testing.assert_array_equal(INHBT2_, INHBT2)
+
+ BN_ = np.array([[0, 0, 0, 30000.0, 30000.0], [1, 0, 0, 30000.0, 30000.0]])
+ np.testing.assert_array_equal(BN_, BN)
+
+ SUSTAT_ = np.array(
+ [[3000.0, 0, 0, 0, 0, 0], [3000.0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
+ )
+ np.testing.assert_array_equal(SUSTAT_, SUSTAT)
+
+ FCTERR_ = np.array([[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0]])
+ np.testing.assert_array_equal(FCTERR_, FCTERR)
+
+ CAPCON_ = np.array([0, 1], dtype=int)
+ np.testing.assert_array_equal(CAPCON_, CAPCON)
+
+ CAPOWN_ = np.array([[1, 0], [0, 1]])
+ np.testing.assert_array_equal(CAPOWN_, CAPOWN)
+
+ NOGEN_ = np.array([1, 1], dtype=int)
+ np.testing.assert_array_equal(NOGEN_, NOGEN)
+
+ PROBG_ = np.array([[0.98, 0.98], [0.98, 0.98]])
+ np.testing.assert_array_equal(PROBG_, PROBG)
+
+ DERATE_ = np.array([1.0, 1.0])
+ np.testing.assert_array_equal(DERATE_, DERATE)
+
+ JENT_ = np.array([[-1, -1], [-1, -1]], dtype=int)
+ np.testing.assert_array_equal(JENT_, JENT)
+
+ INTCH_ = np.zeros((60, 365)) # 60 is the maium posobel conut of fixed contracts
+ np.testing.assert_array_equal(INTCH_, INTCH)
+
+ INTCHR_ = np.zeros((60, 2)) # 60 is the maium posobel conut of fixed contracts
+ np.testing.assert_array_equal(INTCHR_, INTCHR)
+
+ LP_ = np.array([[0, 0, 1]], dtype=int)
+ np.testing.assert_array_equal(LP_, LP)
+
+ LINENO_ = np.array([[-1, 0], [-1, -1]], dtype=int)
+ np.testing.assert_array_equal(LINENO_, LINENO)
+
+ PROBL_ = np.array([[0.9216], [0.9984], [1.0], [1.0], [1.0], [1.0]])
+ np.testing.assert_array_equal(PROBL_, PROBL)
+
+ BLPA_ = np.array(
+ [
+ [
+ -120.0,
+ 300.0,
+ 300.0,
+ -60.0,
+ 150.0,
+ 150.0,
+ -0.5,
+ 1.0,
+ 1.0,
+ -80.0,
+ 150.0,
+ 150.0,
+ -40.0,
+ 100.0,
+ 100.0,
+ -20.0,
+ 50.0,
+ 50.0,
+ ]
+ ]
+ )
+ np.testing.assert_array_equal(BLPA_, BLPA)
+
+ MXCRIT_ = 0
+ assert MXCRIT_ == MXCRIT
+
+ JCRIT_ = []
+ np.testing.assert_array_equal(JCRIT_, JCRIT)
+
+ ID_ = np.array([[0, 0, 0, -1, 0, -1, 0, 0], [1, 1, 1, -1, 0, -1, 0, 0]], dtype=int)
+ np.testing.assert_array_equal(ID_, ID)
+
+ assert NLS == 1
+ assert IOI == 0
+ assert IOJ == 0
+ assert KVLOC == 1 - 1 # 0-based index in Python;
+ assert KVSTAT == 1
+ assert KVTYPE == 2
+ assert KVWHEN == 1
+ assert KWHERE == 1
+ assert CVTEST == 0.025
+ assert MAXEUE == 1000
+ assert JSTEP == 1
+ assert JFREQ == 1
+ assert FINISH == 9999 * 8760.0
+ assert INTV == 5
+ assert INTVT == 5
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_dpeak.py b/NARP/reliabilityassessment/data_processing/tests/test_dpeak.py
new file mode 100644
index 0000000..ff002db
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_dpeak.py
@@ -0,0 +1,36 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.dpeak import _dpeak, dpeak
+
+
+def test_dpeak():
+ # vanilla version
+ HRLOAD = np.array(
+ [
+ [1.5, 2.5, 3.5, 3.6, 2.6, 1.6],
+ [4.5, 5.5, 6.5, 6.6, 5.6, 4.6],
+ [7.5, 8.5, 9.5, 9.6, 8.6, 7.6],
+ [10.5, 11.5, 12.5, 12.6, 11.6, 10.6],
+ ]
+ )
+
+ MAXHR, DYLOAD, MAXDAY = _dpeak(HRLOAD, hour_within_day=3)
+ np.testing.assert_array_equal(
+ MAXHR, np.array([[2, 3], [2, 3], [2, 3], [2, 3]], dtype=int)
+ )
+ np.testing.assert_array_equal(
+ DYLOAD, np.array([[3.5, 3.6], [6.5, 6.6], [9.5, 9.6], [12.5, 12.6]])
+ )
+ np.testing.assert_array_equal(MAXDAY, np.array([1, 1, 1, 1], dtype=int))
+
+ # vectorized version
+ # Note: the dummy input 'hour_within_day' is set for unit test purpose;
+ # in real data, it always equals to 24
+ MAXHR, DYLOAD, MAXDAY = dpeak(HRLOAD, hour_within_day=3)
+ np.testing.assert_array_equal(
+ MAXHR, np.array([[2, 3], [2, 3], [2, 3], [2, 3]], dtype=int)
+ )
+ np.testing.assert_array_equal(
+ DYLOAD, np.array([[3.5, 3.6], [6.5, 6.6], [9.5, 9.6], [12.5, 12.6]])
+ )
+ np.testing.assert_array_equal(MAXDAY, np.array([1, 1, 1, 1], dtype=int))
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_gsm.py b/NARP/reliabilityassessment/data_processing/tests/test_gsm.py
new file mode 100644
index 0000000..978b898
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_gsm.py
@@ -0,0 +1,27 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.gsm import gsm
+
+
+def test_gsm():
+ N = 1
+ KT = 10
+ KP = 0
+ P1 = 0.98
+ P2 = 0.0
+ P3 = 2e-2
+ KA = np.zeros(5000, dtype=int)
+ PA = np.zeros(5000)
+ PA[0] = 1.0
+
+ N_true = 2
+ PA_true = np.zeros(5000)
+ PA_true[:2] = [1.0, 0.02]
+ KA_true = np.zeros(5000, dtype=int)
+ KA_true[1] = 10
+
+ N = gsm(N, KT, KP, P1, P2, P3, PA, KA)
+
+ assert N == N_true
+ np.testing.assert_array_almost_equal(PA, PA_true)
+ np.testing.assert_array_almost_equal(KA, KA_true)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_pind.py b/NARP/reliabilityassessment/data_processing/tests/test_pind.py
new file mode 100644
index 0000000..82c8c0b
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_pind.py
@@ -0,0 +1,45 @@
+from pathlib import Path
+
+import numpy as np
+
+from reliabilityassessment.data_processing.pind import pind
+from reliabilityassessment.data_processing.readInputB import readInputB
+
+
+def test_pind():
+ TEST_DIR = str(Path(__file__).parent.absolute())
+ inputB_dict = readInputB(TEST_DIR)
+
+ inputB_dict_nameToInt = pind(inputB_dict)
+
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZLD"]["NAR"], np.array([0, 1])
+ )
+
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZUD"]["NAT"], np.array([0, 1])
+ )
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZUD"]["NAR"], np.array([0, 1])
+ )
+
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZTD"]["LineID"], np.array([0], dtype=int)
+ )
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZTD"]["NAR"], np.array([0], dtype=int)
+ )
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZTD"]["NAE"], np.array([1], dtype=int)
+ )
+
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZFC"]["NAR"], np.array([], dtype=int)
+ )
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZFC"]["NAE"], np.array([], dtype=int)
+ )
+
+ np.testing.assert_array_equal(
+ inputB_dict_nameToInt["ZZOD"]["NAT"], np.array([], dtype=int)
+ )
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_pmlolp.py b/NARP/reliabilityassessment/data_processing/tests/test_pmlolp.py
new file mode 100644
index 0000000..16fa16a
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_pmlolp.py
@@ -0,0 +1,68 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.data_processing.pmlolp import pmlolp
+
+
+def test_pmlolp():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ CAPLOS = np.zeros(500, dtype=int)
+ CAPLOS[:32] = np.array(
+ [
+ 12,
+ 12,
+ 12,
+ 12,
+ 12,
+ 20,
+ 20,
+ 20,
+ 20,
+ 50,
+ 50,
+ 50,
+ 50,
+ 50,
+ 50,
+ 76,
+ 76,
+ 76,
+ 76,
+ 100,
+ 100,
+ 100,
+ 155,
+ 155,
+ 155,
+ 155,
+ 197,
+ 197,
+ 197,
+ 350,
+ 400,
+ 400,
+ ],
+ dtype=int,
+ )
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmlolp/RD")
+ RD = loadtxt(FileNameAndPath)
+
+ NGS_true = 213
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmlolp/PA_modified")
+ PA_true = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmlolp/KA_modified")
+ KA_true = loadtxt(FileNameAndPath, dtype=int)
+
+ NGU = 32
+
+ NGS, KA, PA = pmlolp(CAPLOS, RD, NGU)
+
+ assert NGS == NGS_true
+ np.testing.assert_array_almost_equal(KA, KA_true)
+ np.testing.assert_array_almost_equal(PA, PA_true, decimal=4)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_pmsc.py b/NARP/reliabilityassessment/data_processing/tests/test_pmsc.py
new file mode 100644
index 0000000..f859990
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_pmsc.py
@@ -0,0 +1,325 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.data_processing.pmsc import pmsc
+
+
+def test_pmsc():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # --------------------------case1-------------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/ID")
+ ID = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/RATES")
+ RATES = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/PROBG")
+ PROBG = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/DERATE")
+ DERATE = loadtxt(FileNameAndPath).astype(int)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/PKLOAD")
+ PKLOAD = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/WPEAK")
+ WPEAK = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/MINRAN")
+ MINRAN = loadtxt(FileNameAndPath)
+ MINRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/MAXRAN")
+ MAXRAN = loadtxt(FileNameAndPath)
+ MAXRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/INHBT1")
+ INHBT1 = loadtxt(FileNameAndPath)
+ INHBT1 -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/INHBT2")
+ INHBT2 = loadtxt(FileNameAndPath)
+ INHBT2 -= 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/NAMU")
+ NAMU = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/NUMP")
+ NUMP = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+
+ IA = 1 - 1 # in Python, 0-based integer index!
+ ITAB = 6 # in Python, 0-based integer index!
+ IREPM = 1 # this variable does not stands for an integer index.
+
+ ITAB = pmsc(
+ IA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ ITAB_true = 7
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case1/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+
+ np.testing.assert_array_equal(ID, ID_true)
+
+ # --------------------------case2-------------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/ID")
+ ID = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/RATES")
+ RATES = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/PROBG")
+ PROBG = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/DERATE")
+ DERATE = loadtxt(FileNameAndPath).astype(int)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/PKLOAD")
+ PKLOAD = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/WPEAK")
+ WPEAK = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/MINRAN")
+ MINRAN = loadtxt(FileNameAndPath)
+ MINRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/MAXRAN")
+ MAXRAN = loadtxt(FileNameAndPath)
+ MAXRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/INHBT1")
+ INHBT1 = loadtxt(FileNameAndPath)
+ INHBT1 -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/INHBT2")
+ INHBT2 = loadtxt(FileNameAndPath)
+ INHBT2 -= 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/NAMU")
+ NAMU = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/NUMP")
+ NUMP = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+
+ IA = 2 - 1 # in Python, 0-based integer index!
+ ITAB = 7 # in Python, 0-based integer index!
+ IREPM = 1 # this variable does not stands for an integer index.
+
+ ITAB = pmsc(
+ IA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ ITAB_true = 8
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case2/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+
+ np.testing.assert_array_equal(ID, ID_true)
+
+ # --------------------------case3-------------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/ID")
+ ID = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/RATES")
+ RATES = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/PROBG")
+ PROBG = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/DERATE")
+ DERATE = loadtxt(FileNameAndPath).astype(int)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/PKLOAD")
+ PKLOAD = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/WPEAK")
+ WPEAK = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/MINRAN")
+ MINRAN = loadtxt(FileNameAndPath)
+ MINRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/MAXRAN")
+ MAXRAN = loadtxt(FileNameAndPath)
+ MAXRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/INHBT1")
+ INHBT1 = loadtxt(FileNameAndPath)
+ INHBT1 -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/INHBT2")
+ INHBT2 = loadtxt(FileNameAndPath)
+ INHBT2 -= 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/NAMU")
+ NAMU = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/NUMP")
+ NUMP = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+
+ IA = 3 - 1 # in Python, 0-based integer index!
+ ITAB = 8 # in Python, 0-based integer index!
+ IREPM = 1 # this variable does not stands for an integer index.
+
+ ITAB = pmsc(
+ IA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ ITAB_true = 9
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case3/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+
+ np.testing.assert_array_equal(ID, ID_true)
+
+ # --------------------------case4-------------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/ID")
+ ID = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/RATES")
+ RATES = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/PROBG")
+ PROBG = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/DERATE")
+ DERATE = loadtxt(FileNameAndPath).astype(int)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/PKLOAD")
+ PKLOAD = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/WPEAK")
+ WPEAK = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/MINRAN")
+ MINRAN = loadtxt(FileNameAndPath)
+ MINRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/MAXRAN")
+ MAXRAN = loadtxt(FileNameAndPath)
+ MAXRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/INHBT1")
+ INHBT1 = loadtxt(FileNameAndPath)
+ INHBT1 -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/INHBT2")
+ INHBT2 = loadtxt(FileNameAndPath)
+ INHBT2 -= 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/NAMU")
+ NAMU = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/NUMP")
+ NUMP = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+
+ IA = 4 - 1 # in Python, 0-based integer index!
+ ITAB = 9 # in Python, 0-based integer index!
+ IREPM = 1 # this variable does not stands for an integer index.
+
+ ITAB = pmsc(
+ IA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ ITAB_true = 10
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case4/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+
+ np.testing.assert_array_equal(ID, ID_true)
+
+ # --------------------------case5-------------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/ID")
+ ID = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/RATES")
+ RATES = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/PROBG")
+ PROBG = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/DERATE")
+ DERATE = loadtxt(FileNameAndPath).astype(int)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/PKLOAD")
+ PKLOAD = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/WPEAK")
+ WPEAK = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/MINRAN")
+ MINRAN = loadtxt(FileNameAndPath)
+ MINRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/MAXRAN")
+ MAXRAN = loadtxt(FileNameAndPath)
+ MAXRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/INHBT1")
+ INHBT1 = loadtxt(FileNameAndPath)
+ INHBT1 -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/INHBT2")
+ INHBT2 = loadtxt(FileNameAndPath)
+ INHBT2 -= 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/NAMU")
+ NAMU = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/NUMP")
+ NUMP = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+
+ IA = 5 - 1 # in Python, 0-based integer index!
+ ITAB = 10 # in Python, 0-based integer index!
+ IREPM = 1 # this variable does not stands for an integer index.
+
+ ITAB = pmsc(
+ IA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ ITAB_true = 11
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_pmsc/case5/ID_modified")
+ ID_true = loadtxt(FileNameAndPath).astype(int)
+ ID_true[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+
+ np.testing.assert_array_equal(ID, ID_true)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_readInputB.py b/NARP/reliabilityassessment/data_processing/tests/test_readInputB.py
new file mode 100644
index 0000000..be6abaf
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_readInputB.py
@@ -0,0 +1,79 @@
+from pathlib import Path
+
+import numpy as np
+
+from reliabilityassessment.data_processing.readInputB import readInputB
+
+
+def test_readInputB():
+ TEST_DIR = str(Path(__file__).parent.absolute())
+ inputB_dict = readInputB(TEST_DIR)
+
+ # assertion for values in ZZMC data card
+ assert inputB_dict["ZZMC"]["JSEED"] == 345237
+ assert inputB_dict["ZZMC"]["NLS"] == 1
+ assert inputB_dict["ZZMC"]["IW1"] == 13
+ assert inputB_dict["ZZMC"]["IW2"] == 26
+ assert inputB_dict["ZZMC"]["IW3"] == 39
+ assert inputB_dict["ZZMC"]["KWHERE"] == 1
+ assert inputB_dict["ZZMC"]["KVWHEN"] == 1
+ assert inputB_dict["ZZMC"]["KVSTAT"] == 1
+ assert inputB_dict["ZZMC"]["KVTYPE"] == 2
+ assert inputB_dict["ZZMC"]["KVLOC"] == 1
+ assert inputB_dict["ZZMC"]["CVTEST"] == 0.025
+ assert inputB_dict["ZZMC"]["FINISH"] == 9999
+ assert inputB_dict["ZZMC"]["JSTEP"] == 1
+ assert inputB_dict["ZZMC"]["JFREQ"] == 1
+ assert inputB_dict["ZZMC"]["MAXEUE"] == 1000
+ assert inputB_dict["ZZMC"]["IOI"] == 0
+ assert inputB_dict["ZZMC"]["IOJ"] == 0
+ assert inputB_dict["ZZMC"]["IREM"] == 1
+ assert inputB_dict["ZZMC"]["INTV"] == 5
+ assert inputB_dict["ZZMC"]["IREPD"] == 1
+ assert inputB_dict["ZZMC"]["IREPM"] == 1
+
+ # assertion for values in ZZLD data card
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["SNRI"], [1, 2])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["NAR"], ["'A1'", "'A2'"])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["RATES"][:, 0], [3000, 3000])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["RATES"][:, 1], [0.0, 0.0])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["RATES"][:, 2], [30000, 30000])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["ID"][:, 0], [1, 1])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["ID"][:, 1], [52, 52])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["ID"][:, 2], [31, 31])
+ np.testing.assert_array_equal(inputB_dict["ZZLD"]["ID"][:, 3], [32, 32])
+
+ # assertion for values in ZZUD data card
+ np.testing.assert_array_equal(inputB_dict["ZZUD"]["SNRI"], [1, 2])
+ np.testing.assert_array_equal(inputB_dict["ZZUD"]["NAT"], ["'A10101'", "'A20101'"])
+ np.testing.assert_array_equal(inputB_dict["ZZUD"]["NAR"], ["'A1'", "'A2'"])
+ np.testing.assert_array_equal(
+ inputB_dict["ZZUD"]["HRLOAD"][:, 0], [12.0, 12.0, 12.0, 12.0, 0.0, 0.02, 0.0]
+ )
+ np.testing.assert_array_equal(
+ inputB_dict["ZZUD"]["HRLOAD"][:, 1], [12.0, 12.0, 12.0, 12.0, 0.0, 0.02, 0.0]
+ )
+ np.testing.assert_array_equal(
+ inputB_dict["ZZUD"]["ID"][0, :], [0.0, 0.0, 0.0, 0.0, 0.0]
+ )
+ np.testing.assert_array_equal(
+ inputB_dict["ZZUD"]["ID"][1, :], [0.0, 0.0, 0.0, 0.0, 0.0]
+ )
+
+ # assertion for values in ZZTD data card
+ np.testing.assert_array_equal(inputB_dict["ZZTD"]["SNRI"], [1])
+ np.testing.assert_array_equal(inputB_dict["ZZTD"]["LineID"], [1])
+ np.testing.assert_array_equal(inputB_dict["ZZTD"]["NAR"][0], ["'A1'"])
+ np.testing.assert_array_equal(inputB_dict["ZZTD"]["NAE"][0], ["'A2'"])
+ np.testing.assert_array_equal(
+ inputB_dict["ZZTD"]["ADM"][0, :], [-120.0, -60.0, 0.0, -80.0, -40.0, -20.0]
+ )
+ np.testing.assert_array_equal(
+ inputB_dict["ZZTD"]["CAP"][0, :], [300.0, 150.0, 0.0, 150.0, 100.0, 50.0]
+ )
+ np.testing.assert_array_equal(
+ inputB_dict["ZZTD"]["CAPR"][0, :], [300.0, 150.0, 0.0, 150.0, 100.0, 50.0]
+ )
+ np.testing.assert_array_equal(
+ inputB_dict["ZZTD"]["PROBT"][0, :], [0.9216, 0.0768, 0.0016, 0.0, 0.0, 0.0]
+ )
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_resca.py b/NARP/reliabilityassessment/data_processing/tests/test_resca.py
new file mode 100644
index 0000000..f95934f
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_resca.py
@@ -0,0 +1,40 @@
+import copy
+
+import numpy as np
+
+from reliabilityassessment.data_processing.resca import _resca, resca
+
+
+def test_resca():
+ np.random.seed(1)
+
+ NOAREA = 3
+ NUNITS = 5
+
+ JENT = (-1) * np.ones((NOAREA, NOAREA), dtype=int)
+ NO_CONTRACTS = 0
+ JENT[1, 0] = 0
+ NO_CONTRACTS += 1
+ JENT[2, 1] = 1
+ NO_CONTRACTS += 1
+
+ # NO_CONTRACTS = 2
+ INTCH = np.zeros((NO_CONTRACTS, 365))
+ INTCH[0, 0:30] = 110.5 # MW
+ INTCH[1, 20:80] = 120.5 # MW
+
+ MAXDAY = np.array([1, 92, 183])
+ SUSTAT = np.random.random((NOAREA, 6))
+ SUSTAT = np.vstack([SUSTAT, [0, 0, 0, 0, 0, 0]])
+ QTR = np.array([13 * 168 + 0.5, 13 * 2 * 168 + 0.5, 13 * 3 * 168 + 0.5])
+ RATES = np.random.random((NUNITS, 3))
+ CAPOWN = np.array([[1.0, 0.5, 0, 0, 0], [0, 0.5, 1.0, 0.4, 0], [0, 0, 0, 0.6, 1.0]])
+
+ SUSTAT_copy = copy.deepcopy(SUSTAT)
+ SUSTAT_1_truth = np.array([111.84484222, 1.46481165, 2.36289247, 0.0])
+
+ _resca(SUSTAT, MAXDAY, QTR, CAPOWN, RATES, JENT, INTCH)
+ np.testing.assert_array_almost_equal(SUSTAT[:, 1], SUSTAT_1_truth)
+
+ resca(SUSTAT_copy, MAXDAY, QTR, CAPOWN, RATES, JENT, INTCH)
+ np.testing.assert_array_almost_equal(SUSTAT_copy[:, 1], SUSTAT_1_truth)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_smaint.py b/NARP/reliabilityassessment/data_processing/tests/test_smaint.py
new file mode 100644
index 0000000..863c586
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_smaint.py
@@ -0,0 +1,77 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.smaint import smaint
+
+
+def pmsc_mock(
+ areaIdx,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+):
+ ID[:] = np.array([[1, 1, 1, 0, 3, 0, 0, 0], [0, 0, 0, 1, 4, 0, 0, 0]], dtype=int)
+ ITAB = 1
+ return ITAB
+
+
+def test_smaint(mocker):
+ mocker.patch(
+ "reliabilityassessment.data_processing.smaint.pmsc", side_effect=pmsc_mock
+ )
+
+ NOAREA = 2
+ ID = np.zeros((2, 8), dtype=int)
+ NUNITS = ID.shape[0]
+
+ ITAB = 0
+ RATES = np.array([[12.0, 12.0, 12.0, 12.0], [12.0, 12.0, 12.0, 12.0]])
+ PROBG = np.array([[0.98, 0.98], [0.98, 0.98]])
+ DERATE = np.array([1.0, 1.0])
+ PKLOAD = np.array([3000.0, 3000.0])
+ WPEAK_1 = np.hstack((np.ones(51) * 1.0, 101.0))
+ WPEAK_2 = np.hstack((np.ones(51) * 1.0, 101.0))
+ WPEAK = np.vstack((WPEAK_1, WPEAK_2))
+ IREPM = 1
+ MINRAN, MAXRAN = np.array([1, 1], dtype=int), np.array([52, 52], dtype=int)
+ INHBT1, INHBT2 = np.array([31, 31], dtype=int), np.array([32, 32], dtype=int)
+ NAMU = ["A101", "A201"]
+ NUMP = ["01", "01"]
+
+ JPLOUT, ITAB = smaint(
+ NOAREA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ JPLOUT_true = (-1) * np.ones((52, 1 + NUNITS), dtype=int)
+ JPLOUT_true[:, 0] = 0
+ JPLOUT_true[:5, 0] = [1, 2, 2, 1, 1]
+ JPLOUT_true[:5, 1] = [1, 1, 1, 0, 0]
+ JPLOUT_true[1:3, 2] = 0
+ ITAB_true = 1
+
+ assert ITAB == ITAB_true
+ np.testing.assert_almost_equal(JPLOUT_true, JPLOUT)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_smaint_new.py b/NARP/reliabilityassessment/data_processing/tests/test_smaint_new.py
new file mode 100644
index 0000000..d7cb442
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_smaint_new.py
@@ -0,0 +1,78 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.data_processing.smaint import smaint
+
+
+def test_smaint_new():
+ TEST_DIR = Path(__file__).parent.absolute()
+ # TEST_DIR = "C:\\Users\\zylpa\\Dropbox\\PostdocTAMU\\Code\\PyREL"
+
+ NOAREA = 5
+ ITAB, IREPM = 6, 1
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/ID")
+ ID = loadtxt(FileNameAndPath).astype(int)
+ ID[:, [0, 1, 2, 3, 5]] -= 1 # 0-based index
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/RATES")
+ RATES = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/PROBG")
+ PROBG = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/DERATE")
+ DERATE = loadtxt(FileNameAndPath).astype(int)
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/PKLOAD")
+ PKLOAD = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/WPEAK")
+ WPEAK = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/MINRAN")
+ MINRAN = loadtxt(FileNameAndPath)
+ MINRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/MAXRAN")
+ MAXRAN = loadtxt(FileNameAndPath)
+ MAXRAN -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/INHBT1")
+ INHBT1 = loadtxt(FileNameAndPath)
+ INHBT1 -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/INHBT2")
+ INHBT2 = loadtxt(FileNameAndPath)
+ INHBT2 -= 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/NAMU")
+ NAMU = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/NUMP")
+ NUMP = loadtxt(FileNameAndPath, dtype=str, unpack=False)
+
+ JPLOUT, ITAB = smaint(
+ NOAREA,
+ ID,
+ ITAB,
+ RATES,
+ PROBG,
+ DERATE,
+ PKLOAD,
+ WPEAK,
+ IREPM,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ NAMU,
+ NUMP,
+ )
+
+ ITAB_true = 11
+ assert ITAB == ITAB_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_smaint/JPLOUT")
+ JPLOUT_true = loadtxt(FileNameAndPath).astype(int)
+ JPLOUT_true[:, 1:] -= 1 # 0-based index
+ NUNITS = PROBG.shape[0]
+ # the original Fortran version uses a magic size “120” for the 2nd-dim of JPLOUT;
+ # which is not as robust as what we used in the Python verison, i.e., NUINTS+1;
+ # Thus, a slight adjustment is needed for a fair comparison here.
+ JPLOUT_true = np.hstack(
+ (JPLOUT_true, -1 * np.ones((52, NUNITS + 1 - 120), dtype=int))
+ )
+ np.testing.assert_almost_equal(JPLOUT_true, JPLOUT)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_wpeakf.py b/NARP/reliabilityassessment/data_processing/tests/test_wpeakf.py
new file mode 100644
index 0000000..86d85c6
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_wpeakf.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+from reliabilityassessment.data_processing.wpeakf import _wpeakf, wpeakf
+
+
+def test_wpeakf():
+ x = np.linspace(1.0, 0.1, 7)
+ # array([1. , 0.85, 0.7 , 0.55, 0.4 , 0.25, 0.1 ])
+ y = np.flip(x)
+ # array([0.1 , 0.25, 0.4 , 0.55, 0.7 , 0.85, 1. ])
+ x1 = np.hstack((np.tile(x, 52), 101.0))
+ y1 = np.hstack((np.tile(y, 52), 101.0))
+ DYLOAD = np.vstack((x1, y1))
+
+ WPEAK_truth_1 = np.hstack((np.ones(51) * 1.0, 101.0))
+ WPEAK_truth_2 = np.hstack((np.ones(51) * 1.0, 101.0))
+ WPEAK_truth = np.vstack((WPEAK_truth_1, WPEAK_truth_2))
+
+ _WPEAK = _wpeakf(DYLOAD) # vanilla verison
+ np.testing.assert_array_equal(_WPEAK, WPEAK_truth)
+
+ WPEAK = wpeakf(DYLOAD) # vectorized version
+ np.testing.assert_array_equal(WPEAK, WPEAK_truth)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_xldnew.py b/NARP/reliabilityassessment/data_processing/tests/test_xldnew.py
new file mode 100644
index 0000000..80c0b0d
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_xldnew.py
@@ -0,0 +1,16 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.data_processing.xldnew import xldnew
+
+TEST_DIR = Path(__file__).parent.absolute()
+
+
+def test_xldnew():
+ PKLOAD = np.array([6000, 9000]).T
+ FileNameAndPath = Path(TEST_DIR, "unit_test_input_for_xldnew")
+ HRLOAD = xldnew(FileNameAndPath, PKLOAD)
+ HRLOAD_truth = loadtxt(Path(TEST_DIR, "ground_truth_for_xldnew.txt"), unpack=False)
+ np.testing.assert_array_almost_equal(HRLOAD, HRLOAD_truth.T)
diff --git a/NARP/reliabilityassessment/data_processing/tests/test_xporta.py b/NARP/reliabilityassessment/data_processing/tests/test_xporta.py
new file mode 100644
index 0000000..e5c8168
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/test_xporta.py
@@ -0,0 +1,29 @@
+from copy import deepcopy
+
+import numpy as np
+
+from reliabilityassessment.data_processing.xporta import _xporta, xporta
+
+
+def test_xporta():
+ NOAREA = 3
+
+ JENT = (-1) * np.ones((NOAREA, NOAREA), dtype=int)
+ NO_CONTRACTS = 0
+ JENT[1, 0] = 0
+ NO_CONTRACTS += 1
+ JENT[2, 1] = 1
+ NO_CONTRACTS += 1
+
+ # NO_CONTRACTS = 2
+ INTCH = np.zeros((NO_CONTRACTS, 365))
+ INTCH[0, 0:30] = 110.5 # MW
+ INTCH[1, 20:80] = 120.5 # MW
+
+ HRLOAD = np.random.random((NOAREA, 8760))
+ HRLOAD1 = deepcopy(HRLOAD)
+ np.testing.assert_array_equal(HRLOAD, HRLOAD1) # make sure the copy works
+
+ _xporta(JENT, INTCH, HRLOAD) # original version
+ xporta(JENT, INTCH, HRLOAD1) # vectorized version
+ np.testing.assert_array_equal(HRLOAD, HRLOAD1)
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/BLPA b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/BLPA
new file mode 100644
index 0000000..9ce8d54
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/BLPA
@@ -0,0 +1,5 @@
+ -120.00000000 300.00000000 300.00000000 -60.00000000 150.00000000 150.00000000 -0.50000000 1.00000000 1.00000000 -80.00000000 150.00000000 150.00000000 -40.00000000 100.00000000 100.00000000 -20.00000000 50.00000000 50.00000000
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/FCTERR b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/FCTERR
new file mode 100644
index 0000000..9c7b228
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/FCTERR
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/FU b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/FU
new file mode 100644
index 0000000..28007d7
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/FU
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/HRLOAD_XPOS b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/HRLOAD_XPOS
new file mode 100644
index 0000000..36f241d
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/HRLOAD_XPOS
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ID_modified b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ID_modified
new file mode 100644
index 0000000..a1e529c
--- /dev/null
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LEEI b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LEEI
new file mode 100644
index 0000000..fd83e8d
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LEEI
@@ -0,0 +1,3655 @@
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LINENO b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LINENO
new file mode 100644
index 0000000..1ba7e22
--- /dev/null
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LP b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/LP
new file mode 100644
index 0000000..4add319
--- /dev/null
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MAXDAY b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MAXDAY
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--- /dev/null
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MAXHR b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MAXHR
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MAXRAN b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MAXRAN
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MINRAN b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/MINRAN
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/NAMA b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/NAMA
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/NOGEN b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/NOGEN
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/PROBL b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/PROBL
new file mode 100644
index 0000000..9e6fc00
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/QTR b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/QTR
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/RATES b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/RATES
new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/SUSTAT b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/SUSTAT
new file mode 100644
index 0000000..785de34
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/SUSTAT
@@ -0,0 +1,6 @@
+ 3000.000000 0.000000 0.000000 0.000000 0.000000 0.000000
+ 3000.000000 0.000000 0.000000 0.000000 0.000000 0.000000
+ 3000.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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+ 3000.000000 0.000000 0.000000 0.000000 0.000000 0.000000
+ 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/SUSTAT_RESCA b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/SUSTAT_RESCA
new file mode 100644
index 0000000..64ba297
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/SUSTAT_RESCA
@@ -0,0 +1,6 @@
+ 3000.000000 3405.000000 0.000000 0.000000 0.000000 0.000000
+ 3000.000000 3405.000000 0.000000 0.000000 0.000000 0.000000
+ 3000.000000 3405.000000 0.000000 0.000000 0.000000 0.000000
+ 3000.000000 3405.000000 0.000000 0.000000 0.000000 0.000000
+ 3000.000000 3405.000000 0.000000 0.000000 0.000000 0.000000
+ 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/WPEAK b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/WPEAK
new file mode 100644
index 0000000..bf8b801
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/WPEAK
@@ -0,0 +1,5 @@
+ 2585.000000 2700.000000 2634.000000 2501.000000 2640.000000 2523.000000 2495.000000 2418.000000 2219.000000 2210.000000 2144.000000 2180.000000 2111.000000 2250.000000 2162.000000 2399.000000 2261.000000 2510.000000 2609.000000 2640.000000 2567.000000 2433.000000 2700.000000 2661.000000 2688.000000 2583.000000 2265.000000 2448.000000 2403.000000 2640.000000 2165.000000 2328.000000 2399.000000 2186.000000 2177.000000 2114.000000 2340.000000 2084.000000 2171.000000 2171.000000 2229.000000 2231.000000 2399.000000 2643.000000 2655.000000 2726.000000 2819.000000 2670.000000 2825.000000 2909.000000 3000.000000 2855.000000
+ 2585.000000 2700.000000 2634.000000 2501.000000 2640.000000 2523.000000 2495.000000 2418.000000 2219.000000 2210.000000 2144.000000 2180.000000 2111.000000 2250.000000 2162.000000 2399.000000 2261.000000 2510.000000 2609.000000 2640.000000 2567.000000 2433.000000 2700.000000 2661.000000 2688.000000 2583.000000 2265.000000 2448.000000 2403.000000 2640.000000 2165.000000 2328.000000 2399.000000 2186.000000 2177.000000 2114.000000 2340.000000 2084.000000 2171.000000 2171.000000 2229.000000 2231.000000 2399.000000 2643.000000 2655.000000 2726.000000 2819.000000 2670.000000 2825.000000 2909.000000 3000.000000 2855.000000
+ 2585.000000 2700.000000 2634.000000 2501.000000 2640.000000 2523.000000 2495.000000 2418.000000 2219.000000 2210.000000 2144.000000 2180.000000 2111.000000 2250.000000 2162.000000 2399.000000 2261.000000 2510.000000 2609.000000 2640.000000 2567.000000 2433.000000 2700.000000 2661.000000 2688.000000 2583.000000 2265.000000 2448.000000 2403.000000 2640.000000 2165.000000 2328.000000 2399.000000 2186.000000 2177.000000 2114.000000 2340.000000 2084.000000 2171.000000 2171.000000 2229.000000 2231.000000 2399.000000 2643.000000 2655.000000 2726.000000 2819.000000 2670.000000 2825.000000 2909.000000 3000.000000 2855.000000
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+ 2585.000000 2700.000000 2634.000000 2501.000000 2640.000000 2523.000000 2495.000000 2418.000000 2219.000000 2210.000000 2144.000000 2180.000000 2111.000000 2250.000000 2162.000000 2399.000000 2261.000000 2510.000000 2609.000000 2640.000000 2567.000000 2433.000000 2700.000000 2661.000000 2688.000000 2583.000000 2265.000000 2448.000000 2403.000000 2640.000000 2165.000000 2328.000000 2399.000000 2186.000000 2177.000000 2114.000000 2340.000000 2084.000000 2171.000000 2171.000000 2229.000000 2231.000000 2399.000000 2643.000000 2655.000000 2726.000000 2819.000000 2670.000000 2825.000000 2909.000000 3000.000000 2855.000000
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZDD.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZDD.csv
new file mode 100644
index 0000000..cd34ea4
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZDD.csv
@@ -0,0 +1 @@
+SN,LINE NUMBER,UNIT1,UNIT2,UNIT3,UNIT4,FORWARD,BACKWARD
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZFC.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZFC.csv
new file mode 100644
index 0000000..c3c724e
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZFC.csv
@@ -0,0 +1 @@
+SN,FROM AREA,TO AREA,BEG DAY,END DAY,MW
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZLD.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZLD.csv
new file mode 100644
index 0000000..10a8ae2
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZLD.csv
@@ -0,0 +1,6 @@
+SN,AREA NAME,PEAK (MW),LFU,OUTAGE BEG WK,OUTAGE END WK,FORBIDDEN BEG WK,FORBIDDEN END WK,SUM OF FLOWS CONSTRAINT
+1,'A1',3000,0,1,52,31,32,30000
+2,'A2',3000,0,1,52,31,32,30000
+3,'A3',3000,0,1,52,31,32,30000
+4,'A4',3000,0,1,52,31,32,30000
+5,'A5',3000,0,1,52,31,32,30000
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZMC.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZMC.csv
new file mode 100644
index 0000000..c6f4b0f
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZMC.csv
@@ -0,0 +1,2 @@
+SEED,LS,END WK1,END WK2,END WK3,WHERE,WHEN,KVS,KVT,KVL,CVT,FIN,STEP,FREQ,MAXE,II,IJ,IR,IN,D,M
+345237,1,13,26,39,1,1,1,2,1,0.025,9999,1,1,1000,0,0,1,5,1,1
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZOD.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZOD.csv
new file mode 100644
index 0000000..264a5ff
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZOD.csv
@@ -0,0 +1 @@
+SN,UNIT NAME,PERCENT OWNED BY AREA
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZTC.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZTC.csv
new file mode 100644
index 0000000..52407e8
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZTC.csv
@@ -0,0 +1,3 @@
+THIS IS A FIVE AREA SYSTEM
+EACH AREA IS IDENTICAL TO THE IEEE-RTS
+PLANNED MAINTENANCE IS CONSIDERED
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZTD.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZTD.csv
new file mode 100644
index 0000000..66f1bf2
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZTD.csv
@@ -0,0 +1,6 @@
+SN,LINE NUMBER,FROM AREA,TO AREA,META DATA
+1,1,'A1','A2',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+2,2,'A2','A3',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+3,3,'A3','A4',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+4,4,'A4','A5',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+5,5,'A5','A1',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZUD.csv b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZUD.csv
new file mode 100644
index 0000000..9789474
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/tests/testdata_input_processing/ZZUD.csv
@@ -0,0 +1,161 @@
+SN,NAME,LOC,CAP1,CAP2,CAP3,CAP4,DFOR,FOR,DER,P/A,B1,D1,B2,D2
+1,'A10101','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+2,'A10201','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+3,'A10301','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+4,'A10401','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+5,'A10501','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+6,'A10601','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+7,'A10701','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+8,'A10801','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+9,'A10901','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+10,'A11001','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+11,'A11101','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+12,'A11201','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+13,'A11301','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+14,'A11401','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+15,'A11501','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+16,'A11601','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+17,'A11701','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+18,'A11801','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+19,'A11901','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+20,'A12001','A1',100,100,100,100,0,0.04,0,0,0,0,0,0
+21,'A12101','A1',100,100,100,100,0,0.04,0,0,0,0,0,0
+22,'A12201','A1',100,100,100,100,0,0.04,0,0,0,0,0,0
+23,'A12301','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+24,'A12401','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+25,'A12501','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+26,'A12601','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+27,'A12701','A1',197,197,197,197,0,0.05,0,0,0,0,0,0
+28,'A12801','A1',197,197,197,197,0,0.05,0,0,0,0,0,0
+29,'A12901','A1',197,197,197,197,0,0.05,0,0,0,0,0,0
+30,'A13001','A1',350,350,350,350,0,0.08,0,0,0,0,0,0
+31,'A13101','A1',400,400,400,400,0,0.12,0,0,0,0,0,0
+32,'A13201','A1',400,400,400,400,0,0.12,0,0,0,0,0,0
+33,'A20101','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+34,'A20201','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+35,'A20301','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+36,'A20401','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+37,'A20501','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+38,'A20601','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+39,'A20701','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+40,'A20801','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+41,'A20901','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+42,'A21001','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+43,'A21101','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+44,'A21201','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+45,'A21301','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+46,'A21401','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+47,'A21501','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+48,'A21601','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+49,'A21701','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+50,'A21801','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+51,'A21901','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+52,'A22001','A2',100,100,100,100,0,0.04,0,0,0,0,0,0
+53,'A22101','A2',100,100,100,100,0,0.04,0,0,0,0,0,0
+54,'A22201','A2',100,100,100,100,0,0.04,0,0,0,0,0,0
+55,'A22301','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
+56,'A22401','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
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+58,'A22601','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
+59,'A22701','A2',197,197,197,197,0,0.05,0,0,0,0,0,0
+60,'A22801','A2',197,197,197,197,0,0.05,0,0,0,0,0,0
+61,'A22901','A2',197,197,197,197,0,0.05,0,0,0,0,0,0
+62,'A23001','A2',350,350,350,350,0,0.08,0,0,0,0,0,0
+63,'A23101','A2',400,400,400,400,0,0.12,0,0,0,0,0,0
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_pmlolp/KA_modified b/NARP/reliabilityassessment/data_processing/tests/testdata_pmlolp/KA_modified
new file mode 100644
index 0000000..fee31dd
--- /dev/null
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_pmlolp/PA_modified b/NARP/reliabilityassessment/data_processing/tests/testdata_pmlolp/PA_modified
new file mode 100644
index 0000000..64b9151
--- /dev/null
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new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_pmsc/case3/DERATE b/NARP/reliabilityassessment/data_processing/tests/testdata_pmsc/case3/DERATE
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new file mode 100644
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_smaint/RATES b/NARP/reliabilityassessment/data_processing/tests/testdata_smaint/RATES
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diff --git a/NARP/reliabilityassessment/data_processing/tests/testdata_smaint/WPEAK b/NARP/reliabilityassessment/data_processing/tests/testdata_smaint/WPEAK
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+ 1637 1511 1427 1385 1343 1364 1385 1469 1679 1847 1889 1910
+ 1889 1847 1826 1826 1910 2099 2078 2036 1973 1931 1826 1700
+ 1595 1472 1390 1349 1308 1329 1349 1431 1636 1799 1840 1861
+ 1840 1799 1779 1779 1861 2045 2024 1983 1922 1881 1779 1656
+ 1757 1652 1573 1547 1547 1573 1940 2255 2491 2517 2517 2491
+ 2491 2491 2439 2465 2596 2622 2622 2517 2386 2176 1914 1652
+ 1889 1776 1691 1663 1663 1691 2086 2425 2678 2707 2707 2678
+ 2678 2678 2622 2650 2791 2819 2819 2707 2566 2340 2058 1776
+ 1851 1741 1658 1630 1630 1658 2045 2376 2625 2653 2653 2625
+ 2625 2625 2570 2597 2735 2763 2763 2653 2514 2293 2017 1741
+ 1813 1705 1624 1597 1597 1624 2003 2328 2571 2598 2598 2571
+ 2571 2571 2517 2544 2680 2707 2707 2598 2463 2246 1976 1705
+ 1776 1670 1590 1563 1563 1590 1961 2279 2518 2544 2544 2518
+ 2518 2518 2465 2491 2624 2650 2650 2544 2412 2200 1935 1670
+ 1693 1563 1476 1433 1389 1411 1433 1519 1737 1910 1954 1975
+ 1954 1910 1889 1889 1975 2171 2149 2106 2041 1997 1889 1758
+ 1649 1522 1438 1395 1353 1374 1395 1480 1691 1861 1903 1924
+ 1903 1861 1840 1840 1924 2114 2093 2051 1988 1945 1840 1713
+ 1663 1564 1489 1465 1465 1489 1837 2135 2358 2383 2383 2358
+ 2358 2358 2309 2334 2458 2483 2483 2383 2259 2060 1812 1564
+ 1788 1682 1601 1575 1575 1601 1975 2296 2536 2563 2563 2536
+ 2536 2536 2483 2509 2643 2670 2670 2563 2429 2216 1949 1682
+ 1753 1648 1569 1543 1543 1569 1936 2250 2485 2511 2511 2485
+ 2485 2485 2433 2459 2590 2616 2616 2511 2381 2171 1910 1648
+ 1717 1614 1537 1512 1512 1537 1896 2204 2435 2460 2460 2435
+ 2435 2435 2383 2409 2537 2563 2563 2460 2332 2127 1871 1614
+ 1681 1581 1505 1480 1480 1505 1857 2158 2384 2409 2409 2384
+ 2384 2384 2334 2359 2484 2509 2509 2409 2283 2083 1832 1581
+ 1603 1480 1398 1356 1315 1336 1356 1439 1644 1809 1850 1870
+ 1850 1809 1788 1788 1870 2055 2035 1994 1932 1891 1788 1665
+ 1561 1441 1361 1321 1281 1301 1321 1401 1601 1762 1802 1822
+ 1802 1762 1742 1742 1822 2002 1982 1942 1882 1842 1742 1622
+ 1760 1655 1576 1550 1550 1576 1944 2260 2496 2523 2523 2496
+ 2496 2496 2444 2470 2601 2628 2628 2523 2391 2181 1918 1655
+ 1893 1780 1695 1667 1667 1695 2091 2430 2684 2712 2712 2684
+ 2684 2684 2628 2656 2797 2825 2825 2712 2571 2345 2062 1780
+ 1855 1744 1661 1633 1633 1661 2049 2381 2631 2658 2658 2631
+ 2631 2631 2575 2603 2741 2769 2769 2658 2520 2298 2021 1744
+ 1817 1709 1627 1600 1600 1627 2007 2333 2577 2604 2604 2577
+ 2577 2577 2523 2550 2685 2712 2712 2604 2468 2251 1980 1709
+ 1779 1673 1593 1567 1567 1593 1965 2284 2523 2550 2550 2523
+ 2523 2523 2470 2497 2629 2656 2656 2550 2417 2204 1939 1673
+ 1697 1566 1479 1436 1392 1414 1436 1523 1740 1914 1958 1980
+ 1958 1914 1893 1893 1980 2176 2154 2110 2045 2001 1893 1762
+ 1653 1526 1441 1398 1356 1377 1398 1483 1695 1865 1907 1928
+ 1907 1865 1843 1843 1928 2119 2098 2055 1992 1949 1843 1716
+ 1813 1704 1623 1596 1596 1623 2002 2327 2570 2598 2598 2570
+ 2570 2570 2516 2543 2679 2706 2706 2598 2462 2246 1975 1704
+ 1949 1833 1745 1716 1716 1745 2153 2502 2764 2793 2793 2764
+ 2764 2764 2706 2735 2880 2909 2909 2793 2648 2415 2124 1833
+ 1910 1796 1711 1682 1682 1711 2110 2452 2709 2737 2737 2709
+ 2709 2709 2652 2680 2823 2851 2851 2737 2595 2366 2081 1796
+ 1871 1759 1676 1648 1648 1676 2067 2402 2653 2681 2681 2653
+ 2653 2653 2598 2625 2765 2793 2793 2681 2542 2318 2039 1759
+ 1832 1723 1641 1613 1613 1641 2024 2352 2598 2625 2625 2598
+ 2598 2598 2543 2571 2708 2735 2735 2625 2489 2270 1996 1723
+ 1747 1613 1523 1478 1434 1456 1478 1568 1792 1971 2016 2039
+ 2016 1971 1949 1949 2039 2240 2218 2173 2106 2061 1949 1814
+ 1702 1571 1484 1440 1396 1418 1440 1527 1745 1920 1964 1986
+ 1964 1920 1898 1898 1986 2182 2160 2117 2051 2007 1898 1767
+ 1869 1757 1673 1646 1646 1673 2064 2399 2650 2678 2678 2650
+ 2650 2650 2594 2622 2762 2789 2789 2678 2538 2315 2036 1757
+ 2009 1890 1799 1769 1769 1799 2219 2579 2849 2879 2879 2849
+ 2849 2849 2789 2819 2969 3000 3000 2879 2730 2489 2189 1890
+ 1969 1852 1763 1734 1734 1763 2175 2528 2792 2822 2822 2792
+ 2792 2792 2734 2763 2910 2939 2939 2822 2675 2440 2146 1852
+ 1929 1814 1727 1699 1699 1727 2131 2476 2735 2764 2764 2735
+ 2735 2735 2678 2707 2851 2879 2879 2764 2620 2390 2102 1814
+ 1889 1776 1691 1663 1663 1691 2086 2425 2678 2707 2707 2678
+ 2678 2678 2622 2650 2791 2819 2819 2707 2566 2340 2058 1776
+ 1801 1663 1570 1524 1478 1501 1524 1616 1847 2032 2078 2102
+ 2078 2032 2009 2009 2102 2310 2286 2240 2171 2125 2009 1871
+ 1754 1619 1529 1484 1439 1462 1484 1574 1799 1979 2024 2047
+ 2024 1979 1957 1957 2047 2250 2227 2182 2114 2069 1957 1822
+ 1779 1673 1593 1567 1567 1593 1965 2284 2523 2549 2549 2523
+ 2523 2523 2470 2496 2629 2656 2656 2549 2417 2204 1938 1673
+ 1913 1799 1713 1685 1685 1713 2113 2456 2713 2741 2741 2713
+ 2713 2713 2656 2684 2827 2855 2855 2741 2598 2370 2084 1799
+ 1875 1763 1679 1651 1651 1679 2071 2407 2658 2686 2686 2658
+ 2658 2658 2602 2630 2770 2798 2798 2686 2546 2323 2043 1763
+ 1836 1727 1645 1617 1617 1645 2028 2357 2604 2632 2632 2604
+ 2604 2604 2549 2577 2714 2741 2741 2632 2495 2275 2001 1727
+ 1798 1691 1610 1583 1583 1610 1986 2308 2550 2577 2577 2550
+ 2550 2550 2496 2523 2657 2684 2684 2577 2443 2228 1959 1691
+ 1715 1583 1495 1451 1407 1429 1451 1539 1759 1935 1979 2001
+ 1979 1935 1913 1913 2001 2199 2177 2133 2067 2023 1913 1781
+ 1670 1542 1456 1413 1370 1392 1413 1499 1713 1884 1927 1949
+ 1927 1884 1863 1863 1949 2141 2120 2077 2013 1970 1863 1735
+ 1321 1321 1321 1321 1321 1321 1601 1601 1601 1601 1601 1850
+ 1850 1850 1850 2027 2027 2027 2195 2195 2356 2356 2496 2496
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/data_processing/wpeakf.py b/NARP/reliabilityassessment/data_processing/wpeakf.py
new file mode 100644
index 0000000..f49ea79
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/wpeakf.py
@@ -0,0 +1,48 @@
+import numpy as np
+
+
+# vanilla version
+def _wpeakf(DYLOAD):
+ """
+ Finds weekly peaks (MW)
+
+ :param numpy.ndarray DYLOAD: 2D array, daily peak load amount (MW)
+ 1st dim: areaIdx, 2nd dim: dayIdx
+
+ :return: (*numpy.ndarray*) WPEAK -- 2D array, weekly peak load amount (MW)
+ 1st dim: areaIdx, 2nd dim: weekIdx
+ """
+ NOAREA = DYLOAD.shape[0]
+ WPEAK = np.zeros((NOAREA, 52))
+ # 52 weeks in one year is a fixed value in our program
+
+ for areaIdx in range(NOAREA):
+ for weekIdx in range(52):
+ XMAX = float("-Inf")
+ JW = 7
+ if (
+ weekIdx == 51
+ ): # since 365 day in one year is a fixed value in our program;
+ JW = 8 # thus, the 'last week' has '8 days'
+ for j in range(JW):
+ j1 = weekIdx * 7 + j
+ if DYLOAD[areaIdx, j1] > XMAX:
+ XMAX = DYLOAD[areaIdx, j1]
+ WPEAK[areaIdx, weekIdx] = XMAX
+ return WPEAK
+
+
+# vectorized version
+def wpeakf(DYLOAD):
+ """
+ Finds weekly peaks (MW)
+
+ :param numpy.ndarray DYLOAD: 2D array, daily peak load amount (MW)
+ 1st dim: areaIdx, 2nd dim: dayIdx
+
+ :return: (*numpy.ndarray*) WPEAK -- 2D array, weekly peak load amount (MW)
+ 1st dim: areaIdx, 2nd dim: weekIdx
+ """
+ WPEAK = np.reshape(DYLOAD[:, :-1], (DYLOAD.shape[0], 52, 7)).max(axis=2)
+ WPEAK[:, -1] = np.maximum(WPEAK[:, -1], DYLOAD[:, -1])
+ return WPEAK
diff --git a/NARP/reliabilityassessment/data_processing/xldnew.py b/NARP/reliabilityassessment/data_processing/xldnew.py
new file mode 100644
index 0000000..9d4f510
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/xldnew.py
@@ -0,0 +1,45 @@
+import numpy as np
+
+
+def xldnew(FileNameAndPath, PKLOAD):
+ """
+ Read and parse raw text data from the input file 'LEEI' (load profile in EEI format)
+ and normalize to per unit load profile. Then, re-scale it based on the user-defined
+ annual peak of each area.
+
+ :param str FileNameAndPath: full file path for the input file 'LEEI '
+ :param np.arrays PKLOAD: array of user-defined annual peak of each area.
+ :return: (*np.arrays*) HRLOAD -- 2D array of hourly load data
+ (0-dim: area index, 1-dim: hour index)
+
+ .. note:: the hardcoded number "8760" is OK in this code package.
+ Because for Monte Carlo Simulation in reliability analysis area, researchers does not
+ try to simulate "real-life year", but purely for statistic purpose. In fact,
+ each reliability index is eventually an averaged number. So, the leap year is not
+ a concern in this area.
+ """
+
+ # Read file LEEI and parsing
+ with open(FileNameAndPath, "r") as f:
+ Lines = f.readlines()
+
+ HRLOAD = np.zeros((len(PKLOAD), 8760))
+
+ for line in Lines:
+ if line[:4] == "AREA":
+ areaIdx = int(line[6]) - 1
+ hourIdx = 0
+ else:
+ HRLOAD[areaIdx, hourIdx : hourIdx + 12] = list(
+ map(float, line.strip().split())
+ )[-12:]
+ hourIdx += 12
+
+ # Normalize and re-scale the annual load data of each area
+ HRLOAD = (
+ HRLOAD
+ / np.amax(HRLOAD, axis=1, initial=0.001)[:, np.newaxis]
+ * PKLOAD[:, np.newaxis]
+ )
+
+ return HRLOAD
diff --git a/NARP/reliabilityassessment/data_processing/xporta.py b/NARP/reliabilityassessment/data_processing/xporta.py
new file mode 100644
index 0000000..8c98f2d
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/xporta.py
@@ -0,0 +1,42 @@
+import numpy as np
+
+
+# vectorized version
+def xporta(JENT, INTCH, HRLOAD):
+ """
+ Add power interchanges based on contracts to hourly load for each area
+
+ :param numpy.ndarray JENT: 2D array of contract number with shape (NOAREA, NOAREA)
+ the (i,j)-th element specifies the contract number between area-i and area-j.
+ :param numpy.ndarray INTCH: 2D array of the contract content with shape
+ (total number of contracts, 365). (``JENT[i,j]``, k)-th element means the
+ contracted power (MW) on the kth day from area-i to area-j.
+ :param numpy.ndarray HRLOAD: 2D array of hourly load (MW) with shape (NOAREA, 8760)
+
+ .. note:: All input arrays are modified in-place;
+ Contracts always specify daily power transfer from high-indexed area
+ to low-indexed area.
+ """
+ for j1, j2 in zip(*np.nonzero(JENT != -1)):
+ contract_id = JENT[j1, j2]
+ for day in np.nonzero(INTCH[contract_id])[0]:
+ HRLOAD[j1, day * 24 : (day + 1) * 24] += INTCH[contract_id, day]
+ HRLOAD[j2, day * 24 : (day + 1) * 24] -= INTCH[contract_id, day]
+
+
+# original version
+def _xporta(JENT, INTCH, HRLOAD):
+ NOAREA = HRLOAD.shape[0]
+ for j1 in range(NOAREA):
+ for j2 in range(NOAREA):
+ if JENT[j1, j2] == -1:
+ continue
+ contractIdx = JENT[j1, j2]
+ for k in range(365):
+ if INTCH[contractIdx, k] == 0:
+ continue
+ k1 = k * 24
+ k2 = k1 + 24
+ for k3 in range(k1, k2):
+ HRLOAD[j1, k3] = HRLOAD[j1, k3] + INTCH[contractIdx, k]
+ HRLOAD[j2, k3] = HRLOAD[j2, k3] - INTCH[contractIdx, k]
diff --git a/NARP/reliabilityassessment/data_processing/xports.py b/NARP/reliabilityassessment/data_processing/xports.py
new file mode 100644
index 0000000..bff2f3a
--- /dev/null
+++ b/NARP/reliabilityassessment/data_processing/xports.py
@@ -0,0 +1,20 @@
+from reliabilityassessment.data_processing.xporta import xporta
+
+
+def xports(JENT, INTCH, HRLOAD):
+ """
+ Remove power interchanges in contracts from hourly load for each area
+
+ :param numpy.ndarray JENT: 2D array of contract number with shape (NOAREA, NOAREA)
+ the (i,j)-th element specifies the contract number between area-i and area-j.
+ :param numpy.ndarray INTCH: 2D array of the contract content with shape
+ (total number of contracts, 365). (``JENT[i,j]``, k)-th element means the
+ contracted power (MW) on the kth day from area-i to area-j.
+ :param numpy.ndarray HRLOAD: 2D array of hourly load (MW) with shape (NOAREA, 8760)
+
+ .. note:: All input arrays are modified in-place;
+ Contracts always specify daily power transfer from high-indexed area
+ to low-indexed area.
+ """
+ # Undo operations in function `xporta` simply by passing reversed contracts instead
+ xporta(JENT, -INTCH, HRLOAD)
diff --git a/NARP/reliabilityassessment/integration_test/INPUTB b/NARP/reliabilityassessment/integration_test/INPUTB
new file mode 100644
index 0000000..93a1e47
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/INPUTB
@@ -0,0 +1,315 @@
+NOTE:ANY INFORMATION BEFORE ZZTC AND AFTER ZZND IS IGNORED.
+ZZTC:TITLE CARD
+S.N. DESCRIPTION
+ 1 THIS IS A FIVE AREA SYSTEM
+ 2 EACH AREA IS IDENTICAL TO THE IEEE-RTS
+ 3 PLANNED MAINTENANCE IS CONSIDERED
+
+
+ZZMC:MISCELLANEOUS. LOSS SHARING=0 NO LOSS SHARING=1
+SEED LS END WKS WHERE WHEN KVS KVT KVL CVT FIN STEP FREQ MAXE II IJ IR IN D M
+ W1 W2 W3
+
+123456 1 13 26 39 1 1 1 2 1 .025 9999 1 1 1000 0 0 1 5 1 1
+
+
+ZZLD:SYSTEM DATA. AREA NAME IN FOUR LETTERS.
+S.N. AREA NAME PEAK LFU OUTAGE WINDOW FORBIDDEN PERIOD SUM OF FLOWS
+ (MW) % BEG WK END WK BEG WK END WK CONSTRAINT
+
+ 1. 'A1' 3000 0 1 52 31 32 30000.
+
+ 2. 'A2' 3000 0 1 52 31 32 30000.
+
+ 3. 'A3' 3000 0 1 52 31 32 30000.
+
+ 4. 'A4' 3000 0 1 52 31 32 30000.
+
+ 5. 'A5' 3000 0 1 52 31 32 30000.
+
+
+
+ZZUD:UNIT DATA
+UN NAME -UNIT NAME IN SIX LETTERS.FIRST FOUR LETTERS ARE THE PLANT NUMBER
+AND NEXT TWO NUMBERS IDENTIFY THE UNIT.
+LOC - AREA OF LOCATION,FOUR LETTERS.
+CAPI - UNIT CAPACITY DURING ITH SEASON.
+DFOR - DERATED FORCED OUTAGE RATE.
+FOR - FORCED OUTAGE RATE.
+DER - PERCENT DERATING.
+P/A -PREDETERMINED(1) OR AUTOMATIC(0) SCHEDULING.
+B1,D1 - BEGINNING WEEK AND DURATION OF FIRST OUTAGE.
+B2,D2 -BEGINNING WEEK AND DURATION OF SECOND OUTAGE.
+S.N. UN NAME LOC CAP1 CAP2 CAP3 CAP4 DFOR FOR DER P/A B1 D1 B2 D2
+
+ 1 'A10101' 'A1' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 2 'A10201' 'A1' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 3 'A10301' 'A1' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 4 'A10401' 'A1' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 5 'A10501' 'A1' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 6 'A10601' 'A1' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 7 'A10701' 'A1' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 8 'A10801' 'A1' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 9 'A10901' 'A1' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 10 'A11001' 'A1' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 11 'A11101' 'A1' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 12 'A11201' 'A1' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 13 'A11301' 'A1' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 14 'A11401' 'A1' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 15 'A11501' 'A1' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 16 'A11601' 'A1' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 17 'A11701' 'A1' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 18 'A11801' 'A1' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 19 'A11901' 'A1' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 20 'A12001' 'A1' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 21 'A12101' 'A1' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 22 'A12201' 'A1' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 23 'A12301' 'A1' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 24 'A12401' 'A1' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 25 'A12501' 'A1' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 26 'A12601' 'A1' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 27 'A12701' 'A1' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 28 'A12801' 'A1' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 29 'A12901' 'A1' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 30 'A13001' 'A1' 350 350 350 350 0. 0.080 0 0 0 0 0 0
+ 31 'A13101' 'A1' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+ 32 'A13201' 'A1' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+ 33 'A20101' 'A2' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 34 'A20201' 'A2' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 35 'A20301' 'A2' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 36 'A20401' 'A2' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 37 'A20501' 'A2' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 38 'A20601' 'A2' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 39 'A20701' 'A2' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 40 'A20801' 'A2' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 41 'A20901' 'A2' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 42 'A21001' 'A2' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 43 'A21101' 'A2' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 44 'A21201' 'A2' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 45 'A21301' 'A2' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 46 'A21401' 'A2' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 47 'A21501' 'A2' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 48 'A21601' 'A2' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 49 'A21701' 'A2' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 50 'A21801' 'A2' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 51 'A21901' 'A2' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 52 'A22001' 'A2' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 53 'A22101' 'A2' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 54 'A22201' 'A2' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 55 'A22301' 'A2' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 56 'A22401' 'A2' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 57 'A22501' 'A2' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 58 'A22601' 'A2' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 59 'A22701' 'A2' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 60 'A22801' 'A2' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 61 'A22901' 'A2' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 62 'A23001' 'A2' 350 350 350 350 0. 0.080 0 0 0 0 0 0
+ 63 'A23101' 'A2' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+ 64 'A23201' 'A2' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+ 65 'A30101' 'A3' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 66 'A30201' 'A3' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 67 'A30301' 'A3' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 68 'A30401' 'A3' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 69 'A30501' 'A3' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 70 'A30601' 'A3' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 71 'A30701' 'A3' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 72 'A30801' 'A3' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 73 'A30901' 'A3' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+ 74 'A31001' 'A3' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 75 'A31101' 'A3' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 76 'A31201' 'A3' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 77 'A31301' 'A3' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 78 'A31401' 'A3' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 79 'A31501' 'A3' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+ 80 'A31601' 'A3' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 81 'A31701' 'A3' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 82 'A31801' 'A3' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 83 'A31901' 'A3' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+ 84 'A32001' 'A3' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 85 'A32101' 'A3' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 86 'A32201' 'A3' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+ 87 'A32301' 'A3' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 88 'A32401' 'A3' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 89 'A32501' 'A3' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 90 'A32601' 'A3' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+ 91 'A32701' 'A3' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 92 'A32801' 'A3' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 93 'A32901' 'A3' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+ 94 'A33001' 'A3' 350 350 350 350 0. 0.080 0 0 0 0 0 0
+ 95 'A33101' 'A3' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+ 96 'A33201' 'A3' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+ 97 'A40101' 'A4' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 98 'A40201' 'A4' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+ 99 'A40301' 'A4' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+100 'A40401' 'A4' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+101 'A40501' 'A4' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+102 'A40601' 'A4' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+103 'A40701' 'A4' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+104 'A40801' 'A4' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+105 'A40901' 'A4' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+106 'A41001' 'A4' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+107 'A41101' 'A4' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+108 'A41201' 'A4' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+109 'A41301' 'A4' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+110 'A41401' 'A4' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+111 'A41501' 'A4' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+112 'A41601' 'A4' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+113 'A41701' 'A4' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+114 'A41801' 'A4' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+115 'A41901' 'A4' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+116 'A42001' 'A4' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+117 'A42101' 'A4' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+118 'A42201' 'A4' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+119 'A42301' 'A4' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+120 'A42401' 'A4' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+121 'A42501' 'A4' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+122 'A42601' 'A4' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+123 'A42701' 'A4' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+124 'A42801' 'A4' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+125 'A42901' 'A4' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+126 'A43001' 'A4' 350 350 350 350 0. 0.080 0 0 0 0 0 0
+127 'A43101' 'A4' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+128 'A43201' 'A4' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+129 'A50101' 'A5' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+130 'A50201' 'A5' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+131 'A50301' 'A5' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+132 'A50401' 'A5' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+133 'A50501' 'A5' 12 12 12 12 0. 0.020 0 0 0 0 0 0
+134 'A50601' 'A5' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+135 'A50701' 'A5' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+136 'A50801' 'A5' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+137 'A50901' 'A5' 20 20 20 20 0. 0.100 0 0 0 0 0 0
+138 'A51001' 'A5' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+139 'A51101' 'A5' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+140 'A51201' 'A5' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+141 'A51301' 'A5' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+142 'A51401' 'A5' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+143 'A51501' 'A5' 50 50 50 50 0. 0.010 0 0 0 0 0 0
+144 'A51601' 'A5' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+145 'A51701' 'A5' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+146 'A51801' 'A5' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+147 'A51901' 'A5' 76 76 76 76 0. 0.020 0 0 0 0 0 0
+148 'A52001' 'A5' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+149 'A52101' 'A5' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+150 'A52201' 'A5' 100 100 100 100 0. 0.040 0 0 0 0 0 0
+151 'A52301' 'A5' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+152 'A52401' 'A5' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+153 'A52501' 'A5' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+154 'A52601' 'A5' 155 155 155 155 0. 0.040 0 0 0 0 0 0
+155 'A52701' 'A5' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+156 'A52801' 'A5' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+157 'A52901' 'A5' 197 197 197 197 0. 0.050 0 0 0 0 0 0
+158 'A53001' 'A5' 350 350 350 350 0. 0.080 0 0 0 0 0 0
+159 'A53101' 'A5' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+160 'A53201' 'A5' 400 400 400 400 0. 0.120 0 0 0 0 0 0
+
+
+
+ZZFC:FIRM CONTRACTS. AREA INDICATED BY FOUR LETTERS.
+S.N. FROM AREA TO AREA BEG DAY END DAY MW
+
+
+ZZOD:UNIT OWNERSHIP DATA
+S.N. UN NAME PERCENT OWNERSHIP OF AREA
+ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
+
+
+ZZTD:LINE DATA
+S.N. LINE NUMBER FROM TO ADM CAP CAPR PROB (STATE 1)
+ ADM CAP CAPR PROB (STATE 2)
+ ADM CAP CAPR PROB (STATE 3)
+ ADM CAP CAPR PROB (STATE 4)
+ ADM CAP CAPR PROB (STATE 5)
+ ADM CAP CAPR PROB (STATE 6)
+
+ 1 1 'A1' 'A2' -120. 300. 300. 0.9216
+ -60. 150. 150. 0.0768
+ 0. 0. 0. 0.0016
+ -80. 150. 150. 0.0000
+ -40. 100. 100. 0.0000
+ -20. 50. 50. 0.0000
+ 2 2 'A2' 'A3' -120. 300. 300. 0.9216
+ -60. 150. 150. 0.0768
+ 0. 0. 0. 0.0016
+ -80. 150. 150. 0.0000
+ -40. 100. 100. 0.0000
+ -20. 50. 50. 0.0000
+ 3 3 'A3' 'A4' -120. 300. 300. 0.9216
+ -60. 150. 150. 0.0768
+ 0. 0. 0. 0.0016
+ -80. 150. 150. 0.0000
+ -40. 100. 100. 0.0000
+ -20. 50. 50. 0.0000
+ 4 4 'A4' 'A5' -120. 300. 300. 0.9216
+ -60. 150. 150. 0.0768
+ 0. 0. 0. 0.0016
+ -80. 150. 150. 0.0000
+ -40. 100. 100. 0.0000
+ -20. 50. 50. 0.0000
+ 5 5 'A5' 'A1' -120. 300. 300. 0.9216
+ -60. 150. 150. 0.0768
+ 0. 0. 0. 0.0016
+ -80. 150. 150. 0.0000
+ -40. 100. 100. 0.0000
+ -20. 50. 50. 0.0000
+
+
+ZZDD:LINE DERATING DATA
+S.N. LINE NUMBER UNITS DERATING THIS LINE DERATING FACTORS
+ UNIT1 UNIT2 UNIT3 UNIT4 FORWARD BACKWARD
+
+
+ZZND:TERMINATING CARD
+
+DESCRIPTION OF VARIABLES IN CARD ZZMC
+-------------------------------------
+-------------------------------------
+ VARIABLES FOR CONVERGENCE TEST:
+ KWHERE : 1 = AREA (TO BE SPECIFIED)
+ 2 = POOL
+
+ WVWHEN : 1 = HOURLY STATISTICS
+ 2 = PEAK STATISTICS
+
+ KVSTAT : 1 = LOLE
+ 2 = EUE (USED ONLY WITH HOURLY STATS)
+
+ KVTYPE : 1 = WEIGHTED AVERAGE OF STATS, CONSIDERING FORECAST ERROR)
+ 2 = USE ONLY THE NO FORECAST ERROR STATISTIC
+
+ KVLOC : AREA NUMBER (USED ONLY IF KWHERE = 1)
+
+ CVTEST : 0 = STOP WHEN STANDARD DEVIATION OF THE TESTED STATISTIC
+ IS LESS THAN (.025*MEAN),I.E.,WHEN WE ARE 95% CONFIDENT
+ THE ESTIMATE IS WITHIN 5% OF TRUE VALUE.
+ >0 = STOP WHEN STANDARD DEVIATION IS LESS THAN CVTEST.
+
+ FIN : TIME TO END SIMULATION IF NO CONVERGENCE. IT SHOULD BE
+ MULTIPLE OF 8760.
+
+ OTHER VARIABLES:
+ STEP : 1 = COLLECT STATS EVERY HOUR.
+ 24 = DAILY PEAK STATS ONLY.
+
+ FREQ : 1 = USE HOURLY MONTE CARLO DRAWS OF GENERATOR AND LINE STATUS.
+ 24 = USE DAILY MONTE CARLO DRAWS.
+
+ MAXE : DEFINES THE UPPER LIMIT OF THE PROB DISTRIBUTION OF EUE.
+
+ II : 0 = DO NOT PRINT TRANSMISSION MOD RESULTS.
+ 1 = PRINT TRANSMISSION MOD RESULTS.
+
+ IJ : 0 = DO NOT PRINT AFTER EACH REPLICATION
+
+ IR : 0 =DELETE DUMP AFTER THE RUN
+ 1 = RETAIN DUMP AFTER THE RUN
+
+ IN : INTERNAL, IN NUMBER OF REPLICATIONS, FOR STORING
+ THE CONTENTS OF ARRAYS FOR RESTART.
+ 1 = PRINT AFTER EACH REPLICATION
+
+ D : 0 = DO NOT PRINT INPUT DATA
+ 1 = PRINT INPUT DATA
+
+ M : 0 = DO NOT PRINT PLANNED OUTAGE SCHEDULES
+ 1 = PRINT PLANNED OUTAGE SCHEDULES
diff --git a/NARP/reliabilityassessment/integration_test/INPUTC b/NARP/reliabilityassessment/integration_test/INPUTC
new file mode 100644
index 0000000..695347d
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/INPUTC
@@ -0,0 +1,98 @@
+NOTE:ANY INFORMATION INCLUDED BEFORE ZZTC AND AFTER ZZND IS IGNORED BY THE
+PROGRAM.
+ZZTC:TITLE CARD
+S.N. DESCRIPTION [new STUART NELSON LINK Z & CAPS]
+
+ZZMC:MISCELLANEOUS. LOSS SHARING=0 NO LOSS SHARING=1
+SEED LS END WKS WHERE WHEN KVS KVT KVL CVT FIN STEP FREQ MAXE II IJ IR IN D M
+ W1 IW2 IW3
+
+ZZLD:SYSTEM DATA. AREA NAME IN FOUR LETTERS.
+S.N. AREA NAME PEAK LFU OUTAGE WINDOW FORBIDDEN PERIOD SUM OF FLOWS
+ (MW) % BEG WK END WK BEG WK END WK CONSTRAINT
+
+ZZUD:UNIT DATA
+UN NAME - UNIT NAME IN SIX LETTERS. FIRST FOUR LETTERS ARE THE PLANT NUMBER
+AND NEXT TWO NUMBERS IDENTIFY THE UNIT.
+LOC - AREA OF LOCATION, FOUR LETTERS.
+CAPI - UNIT CAPACITY DURING I-TH SEASON.
+DFOR - DERATED FORCED OUTAGE RATE.
+FOR - FORCED OUTAGE RATE.
+DER - PERCENT DERATING.
+P/A - PREDETERMINED (1) OR AUTOMATIC (0) SCHEDULING.
+B1, D1 - BEGINNING WEEK AND DURATION OF FIRST OUTAGE.
+B2, D2 - BEGINNING WEEK AND DURATION OF SECOND OUTAGE.
+S.N. UN NAME LOC CAP1 CAP2 CAP3 CAP4 DFOR FOR DER P/A B1 D1 B2 D2
+
+ZZFC:FIRM CONTRACTS. AREA INDICATED BY FOUR LETTERS. CONTRACT LOAD FLOW
+S.N. FROM AREA TO AREA BEG DAY END DAY MW MW
+
+ZZOD:UNIT OWNERSHIP DATA
+S.N. UN NAME PERCENT OWNERSHIP OF AREA
+ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
+
+ZZTD:LINE DATA
+S.N. LINE NUMBER FROM TO ADM CAP CAPR PROB (STATE 1)
+ ADM CAP CAPR PROB (STATE 2)
+ ADM CAP CAPR PROB (STATE 3)
+ ADM CAP CAPR PROB (STATE 4)
+ ADM CAP CAPR PROB (STATE 5)
+ ADM CAP CAPR PROB (STATE 6)
+
+ZZDD:LINE DERATING DATA
+S.N. LINE NUMBER UNITS DERATING THIS LINE DERATING FACTORS
+ UNIT1 UNIT2 UNIT3 UNIT4 FORWARD BACKWARD
+
+ZZND:TERMINATING CARD
+
+DESCRIPTION OF VARIABLES IN CARD ZZMC
+-------------------------------------
+ VARIABLES FOR CONVERGENCE TEST:
+ KWHERE : 1 = AREA (TO BE SPECIFIED)
+ 2 = POOL
+
+ WVWHEN : 1 = HOURLY STATISTICS
+ 2 = PEAK STATISTICS
+
+ KVSTAT : 1 = LOLE
+ 2 = EUE (USED ONLY WITH HOURLY STATS)
+
+ KVTYPE : 1 = WEIGHTED AVERAGE OF STATS, CONSIDERING FORECAST ERROR)
+ 2 = USE ONLY THE NO FORECAST ERROR STATISTIC
+
+ KVLOC : AREA NUMBER (USED ONLY IF KWHERE = 1)
+
+ CVTEST : 0 = STOP WHEN STANDARD DEVIATION OF THE TESTED STATISTIC
+ IS LESS THAN (.025*MEAN),I.E.,WHEN WE ARE 95% CONFIDENT
+ THE ESTIMATE IS WITHIN 5% OF TRUE VALUE.
+ >0 = STOP WHEN STANDARD DEVIATION IS LESS THAN CVTEST.
+
+ FIN : TIME TO END SIMULATION IF NO CONVERGENCE. IT SHOULD BE
+ MULTIPLE OF 8760.
+
+ OTHER VARIABLES:
+ STEP : 1 = COLLECT STATS EVERY HOUR.
+ 24 = DAILY PEAK STATS ONLY.
+
+ FREQ : 1 = USE HOURLY MONTE CARLO DRAWS OF GENERATOR AND LINE STATUS.
+ 24 = USE DAILY MONTE CARLO DRAWS.
+
+ MAXE : DEFINES THE UPPER LIMIT OF THE PROB DISTRIBUTION OF EUE.
+
+ II : 0 = DO NOT PRINT TRANSMISSION MOD RESULTS.
+ 1 = PRINT TRANSMISSION MOD RESULTS.
+
+ IJ : 0 = DO NOT PRINT AFTER EACH REPLICATION
+
+ IR : 0 =DELETE DUMP AFTER THE RUN
+ 1 = RETAIN DUMP AFTER THE RUN
+
+ IN : INTERNAL, IN NUMBER OF REPLICATIONS, FOR STORING
+ THE CONTENTS OF ARRAYS FOR RESTART.
+ 1 = PRINT AFTER EACH REPLICATION
+
+ D : 0 = DO NOT PRINT INPUT DATA
+ 1 = PRINT INPUT DATA
+
+ M : 0 = DO NOT PRINT PLANNED OUTAGE SCHEDULES
+ 1 = PRINT PLANNED OUTAGE SCHEDULES
diff --git a/NARP/reliabilityassessment/integration_test/LEEI b/NARP/reliabilityassessment/integration_test/LEEI
new file mode 100644
index 0000000..fd83e8d
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/LEEI
@@ -0,0 +1,3655 @@
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+ 1321 1321 1321 1321 1321 1321 1601 1601 1601 1601 1601 1850
+ 1850 1850 1850 2027 2027 2027 2195 2195 2356 2356 2496 2496
diff --git a/NARP/reliabilityassessment/integration_test/OUTPUT_fortran b/NARP/reliabilityassessment/integration_test/OUTPUT_fortran
new file mode 100644
index 0000000..4bd6cea
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/OUTPUT_fortran
@@ -0,0 +1,845 @@
+ THIS IS A FIVE AREA SYSTEM
+ EACH AREA IS IDENTICAL TO THE IEEE-RTS
+ PLANNED MAINTENANCE IS CONSIDERED
+1
+ SUMMARY OF OPTIONS :
+
+ SEED FOR RANDOM NUMBER GENERATOR = 123456
+
+ LOSS SHARING = NO
+
+ ENDING WEEK FOR FIRST SEASON = 13
+
+ ENDING WEEK FOR SECOND SEASON =26
+
+ ENDING WEEK FOR THIRD SEASON = 39
+
+ SPECIFICATIONS FOR CONVERGENCE TEST :
+
+ * PERCENT ERROR FOR CONVERGENCE (CVT) : 2.50
+ * CONVERGENCE BASED ON STATISTICS OF AREA 1
+ * HOURLY LOLE IS USED FOR CONVERGENCE
+ * STATISTICS FOR BASE FORECAST USED FOR CONVERGENCE
+ * REPLICATIONS TO END SIMULATION IF NO CONVERGENCE :9999
+
+
+ OTHER VARIABLES
+
+
+ * STATISTCS ARE COLLECTED EVERY HOUR
+ * MONTE CARLO DRAWS ARE PERFORMED EVERY HOUR
+ * RESULTS OF TRANSMISSION MODULE ARE NOT PRINTED
+ * RESULTS AFTER EACH REPLICATION ARE NOT PRINTED
+ * UPPER LIMIT OF EUE PROB DISTRIBUTION = 1000 MW
+ * THIS IS A NEW RUN.
+
+1
+
+
+
+
+
+ TABLE 1
+ SYSTEM DATA
+ -----------
+
+ AREA PEAK LOAD FORECAST OUTAGE WINDOW FORBIDDEN PERIOD SUM OF FLOWS
+ MW UNCERTAINTY (%) BEG WK END WK BEG WK END WK CONSTRAINT
+ A1 3000. 0.00 1 52 31 32 30000.
+ A2 3000. 0.00 1 52 31 32 30000.
+ A3 3000. 0.00 1 52 31 32 30000.
+ A4 3000. 0.00 1 52 31 32 30000.
+ A5 3000. 0.00 1 52 31 32 30000.
+1
+
+
+
+
+
+ TABLE 2
+ UNIT DATA
+ --------
+
+ UNIT NAME LOCATION CAPACITY FOR SEASON DFOR FOR DERAT. PRESCH. FIRST OUTAGE SECOND OUTAGE
+ 1 2 3 4 % OR AUTO ST DUR ST DUR
+
+ A10101 A1 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A10201 A1 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A10301 A1 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A10401 A1 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A10501 A1 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A10601 A1 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A10701 A1 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A10801 A1 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A10901 A1 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A11001 A1 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A11101 A1 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A11201 A1 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A11301 A1 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A11401 A1 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A11501 A1 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A11601 A1 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A11701 A1 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A11801 A1 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A11901 A1 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A12001 A1 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A12101 A1 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A12201 A1 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A12301 A1 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A12401 A1 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A12501 A1 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A12601 A1 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A12701 A1 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A12801 A1 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A12901 A1 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A13001 A1 350. 350. 350. 350. .0000 .0800 0. A 0 0 0 0
+ A13101 A1 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A13201 A1 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A20101 A2 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A20201 A2 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A20301 A2 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A20401 A2 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A20501 A2 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A20601 A2 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A20701 A2 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A20801 A2 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A20901 A2 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A21001 A2 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A21101 A2 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A21201 A2 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A21301 A2 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A21401 A2 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A21501 A2 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A21601 A2 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A21701 A2 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A21801 A2 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A21901 A2 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A22001 A2 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A22101 A2 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A22201 A2 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A22301 A2 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A22401 A2 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A22501 A2 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A22601 A2 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A22701 A2 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A22801 A2 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A22901 A2 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A23001 A2 350. 350. 350. 350. .0000 .0800 0. A 0 0 0 0
+ A23101 A2 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A23201 A2 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A30101 A3 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A30201 A3 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A30301 A3 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A30401 A3 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A30501 A3 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A30601 A3 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A30701 A3 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A30801 A3 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A30901 A3 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A31001 A3 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A31101 A3 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A31201 A3 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A31301 A3 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A31401 A3 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A31501 A3 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A31601 A3 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A31701 A3 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A31801 A3 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A31901 A3 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A32001 A3 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A32101 A3 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A32201 A3 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A32301 A3 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A32401 A3 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A32501 A3 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A32601 A3 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A32701 A3 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A32801 A3 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A32901 A3 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A33001 A3 350. 350. 350. 350. .0000 .0800 0. A 0 0 0 0
+ A33101 A3 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A33201 A3 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A40101 A4 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A40201 A4 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A40301 A4 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A40401 A4 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A40501 A4 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A40601 A4 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A40701 A4 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A40801 A4 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A40901 A4 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A41001 A4 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A41101 A4 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A41201 A4 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A41301 A4 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A41401 A4 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A41501 A4 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A41601 A4 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A41701 A4 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A41801 A4 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A41901 A4 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A42001 A4 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A42101 A4 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A42201 A4 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A42301 A4 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A42401 A4 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A42501 A4 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A42601 A4 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A42701 A4 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A42801 A4 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A42901 A4 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A43001 A4 350. 350. 350. 350. .0000 .0800 0. A 0 0 0 0
+ A43101 A4 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A43201 A4 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A50101 A5 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A50201 A5 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A50301 A5 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A50401 A5 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A50501 A5 12. 12. 12. 12. .0000 .0200 0. A 0 0 0 0
+ A50601 A5 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A50701 A5 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A50801 A5 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A50901 A5 20. 20. 20. 20. .0000 .1000 0. A 0 0 0 0
+ A51001 A5 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A51101 A5 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A51201 A5 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A51301 A5 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A51401 A5 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A51501 A5 50. 50. 50. 50. .0000 .0100 0. A 0 0 0 0
+ A51601 A5 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A51701 A5 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A51801 A5 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A51901 A5 76. 76. 76. 76. .0000 .0200 0. A 0 0 0 0
+ A52001 A5 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A52101 A5 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A52201 A5 100. 100. 100. 100. .0000 .0400 0. A 0 0 0 0
+ A52301 A5 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A52401 A5 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A52501 A5 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A52601 A5 155. 155. 155. 155. .0000 .0400 0. A 0 0 0 0
+ A52701 A5 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A52801 A5 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A52901 A5 197. 197. 197. 197. .0000 .0500 0. A 0 0 0 0
+ A53001 A5 350. 350. 350. 350. .0000 .0800 0. A 0 0 0 0
+ A53101 A5 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+ A53201 A5 400. 400. 400. 400. .0000 .1200 0. A 0 0 0 0
+1
+
+
+
+
+
+ TABLE 3
+ FIRM CONTRACTS
+ -------------
+
+ FROM TO BEG DAY END DAY MW
+
+1
+
+
+
+
+
+ TABLE 4
+ OWNERSHIP DATA
+ -------------
+
+ UNIT NAME PERCENT OWNERSHIP OF AREA
+
+ 1 2 3 4 5
+1
+
+
+
+
+
+ TABLE 5
+ LINE DATA
+ ---------
+
+ LINE NO FROM TO STATE ADMITANCE CAPACITY(+) CAPACITY(-) PROBABILITY
+
+ PER UNIT
+ 1 A1 A2 1 -120.000 300.0 300.0 0.92160
+ 2 -60.000 150.0 150.0 0.07680
+ 3 0.000 0.0 0.0 0.00160
+ 4 -80.000 150.0 150.0 0.00000
+ 5 -40.000 100.0 100.0 0.00000
+ 6 -20.000 50.0 50.0 0.00000
+ 2 A2 A3 1 -120.000 300.0 300.0 0.92160
+ 2 -60.000 150.0 150.0 0.07680
+ 3 0.000 0.0 0.0 0.00160
+ 4 -80.000 150.0 150.0 0.00000
+ 5 -40.000 100.0 100.0 0.00000
+ 6 -20.000 50.0 50.0 0.00000
+ 3 A3 A4 1 -120.000 300.0 300.0 0.92160
+ 2 -60.000 150.0 150.0 0.07680
+ 3 0.000 0.0 0.0 0.00160
+ 4 -80.000 150.0 150.0 0.00000
+ 5 -40.000 100.0 100.0 0.00000
+ 6 -20.000 50.0 50.0 0.00000
+ 4 A4 A5 1 -120.000 300.0 300.0 0.92160
+ 2 -60.000 150.0 150.0 0.07680
+ 3 0.000 0.0 0.0 0.00160
+ 4 -80.000 150.0 150.0 0.00000
+ 5 -40.000 100.0 100.0 0.00000
+ 6 -20.000 50.0 50.0 0.00000
+ 5 A5 A1 1 -120.000 300.0 300.0 0.92160
+ 2 -60.000 150.0 150.0 0.07680
+ 3 0.000 0.0 0.0 0.00160
+ 4 -80.000 150.0 150.0 0.00000
+ 5 -40.000 100.0 100.0 0.00000
+ 6 -20.000 50.0 50.0 0.00000
+1
+
+
+
+
+
+ TABLE 6
+ LINE DERATING DATA
+ -------------------
+
+ LINE UNITS DERATING THIS LINE DERATING FACTORS
+ FORWARD BACKWARD
+
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1 PLANNED MAINTENANCE SCHEDULE FOR AREA 1
+
+ --------------------------------
+ TABLE 7
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN | JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3| 4 5 6 7 8 9 0 1 2 3 4 5 6| 7 8 9 0 1 2 3 4 5 6 7 8 9| 0 1 2 3 4 5 6 7 8 9 0 1 2|
+ | | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD +EFFECTIVE CAPACITY (MW)) ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1 PLANNED MAINTENANCE SCHEDULE FOR AREA 2
+
+ --------------------------------
+ TABLE 8
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN | JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3| 4 5 6 7 8 9 0 1 2 3 4 5 6| 7 8 9 0 1 2 3 4 5 6 7 8 9| 0 1 2 3 4 5 6 7 8 9 0 1 2|
+ | | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD +EFFECTIVE CAPACITY (MW)) ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1 PLANNED MAINTENANCE SCHEDULE FOR AREA 3
+
+ --------------------------------
+ TABLE 9
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN | JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3| 4 5 6 7 8 9 0 1 2 3 4 5 6| 7 8 9 0 1 2 3 4 5 6 7 8 9| 0 1 2 3 4 5 6 7 8 9 0 1 2|
+ | | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD +EFFECTIVE CAPACITY (MW)) ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1 PLANNED MAINTENANCE SCHEDULE FOR AREA 4
+
+ --------------------------------
+ TABLE 10
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN | JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3| 4 5 6 7 8 9 0 1 2 3 4 5 6| 7 8 9 0 1 2 3 4 5 6 7 8 9| 0 1 2 3 4 5 6 7 8 9 0 1 2|
+ | | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD +EFFECTIVE CAPACITY (MW)) ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1 PLANNED MAINTENANCE SCHEDULE FOR AREA 5
+
+ --------------------------------
+ TABLE 11
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN | JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3| 4 5 6 7 8 9 0 1 2 3 4 5 6| 7 8 9 0 1 2 3 4 5 6 7 8 9| 0 1 2 3 4 5 6 7 8 9 0 1 2|
+ | | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD +EFFECTIVE CAPACITY (MW)) ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+
+0
+ 2585 2700 2634 2501 2640 2523 2495 2418 2219 2210 2144 2180 2111
+ 2250 2162 2399 2261 2510 2609 2640 2567 2433 2700 2661 2688 2583
+ 2265 2448 2403 2640 2165 2328 2399 2186 2177 2114 2340 2084 2171
+ 2171 2229 2231 2399 2643 2655 2726 2819 2670 2825 2909 3000 2855
+1
+ TABLE 12
+
+ FINAL RESULTS AFTER 3549 REPLICATIONS
+
+ AREA FORECAST HOURLY STATISTICS PEAK STATISTICS REMARKS
+ NO NO HLOLE XLOL EUE LOLE XLOL
+ (HRS/YR) (MW) (MWH) (DAYS/YR) (MW)
+ 1 AV 0.10 138.32 14. 0.04 146.55 GC
+ 1 AV 0.33 105.58 35. 0.06 111.66 TC
+ 1 AV 0.43 113.43 49. 0.10 124.41 GT
+
+
+ 2 AV 0.11 132.71 15. 0.03 136.35 GC
+ 2 AV 0.34 104.25 35. 0.06 110.96 TC
+ 2 AV 0.45 111.33 50. 0.09 119.96 GT
+
+
+ 3 AV 0.10 138.85 14. 0.04 140.14 GC
+ 3 AV 0.39 105.35 41. 0.08 101.73 TC
+ 3 AV 0.49 112.46 55. 0.11 114.50 GT
+
+
+ 4 AV 0.12 136.37 16. 0.04 138.33 GC
+ 4 AV 0.33 103.52 34. 0.06 120.93 TC
+ 4 AV 0.44 112.21 50. 0.10 127.54 GT
+
+
+ 5 AV 0.11 132.19 14. 0.04 130.19 GC
+ 5 AV 0.30 97.22 29. 0.06 111.50 TC
+ 5 AV 0.40 106.49 43. 0.09 118.75 GT
+
+
+
+ POOL STATISTICS
+
+
+ AV 0.245 300.90 74. 0.083 306.10 GC
+ AV 1.586 109.22 173. 0.297 119.83 TC
+ AV 1.831 134.89 247. 0.379 160.38 GT
+1
+ TABLE 13
+ SUMMARY OF RESULTS
+
+ AREA PEAK INSTALLED AREA HLOLE EUE LOLE
+ ---------------- ---------------- ----------------
+ CAPACITY PCT RES MAGN PCT SD MAGN PCT SD MAGN PCT SD
+ A1 3000. 3405. 13.5 0.430 2.499 49. 4. 0.102 5.229
+ A2 3000. 3405. 13.5 0.452 2.530 50. 4. 0.095 5.457
+ A3 3000. 3405. 13.5 0.491 2.406 55. 3. 0.113 4.981
+ A4 3000. 3405. 13.5 0.444 2.537 50. 4. 0.101 5.270
+ A5 3000. 3405. 13.5 0.402 2.583 43. 4. 0.093 5.449
+
+ ERCOT 15000. 17025. 13.5 1.831 1.235 247. 2. 0.379 2.686
+1 TABLE 14
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 1
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+ NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+
+ 0 3204. 0.903 2283. 0.643 0 2660. 0.7495
+ 1 329. 0.093 1036. 0.292 50 263. 0.0741
+ 2 16. 0.005 200. 0.056 100 197. 0.0555
+ 3 0. 0.000 29. 0.008 150 133. 0.0375
+ 4 0. 0.000 1. 0.000 200 82. 0.0231
+ 5 0. 0.000 0. 0.000 250 61. 0.0172
+ 6 0. 0.000 0. 0.000 300 52. 0.0147
+ 7 0. 0.000 0. 0.000 350 40. 0.0113
+ 8 0. 0.000 0. 0.000 400 19. 0.0054
+ 9 0. 0.000 0. 0.000 450 14. 0.0039
+ 10 0. 0.000 0. 0.000 500 8. 0.0023
+ 11 0. 0.000 0. 0.000 550 10. 0.0028
+ 12 0. 0.000 0. 0.000 600 3. 0.0008
+ 13 0. 0.000 0. 0.000 650 2. 0.0006
+ 14 0. 0.000 0. 0.000 700 0. 0.0000
+ 15 0. 0.000 0. 0.000 750 1. 0.0003
+ 16 0. 0.000 0. 0.000 800 3. 0.0008
+ 17 0. 0.000 0. 0.000 850 0. 0.0000
+ 18 0. 0.000 0. 0.000 900 0. 0.0000
+ 19 0. 0.000 0. 0.000 950 0. 0.0000
+ 20 0. 0.000 0. 0.000 1000 0. 0.0000
+ 21 0. 0.000 0. 0.000 1050 1. 0.0003
+1 TABLE 15
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 2
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+ NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+
+ 0 3229. 0.910 2269. 0.639 0 2657. 0.7487
+ 1 304. 0.086 1009. 0.284 50 277. 0.0781
+ 2 16. 0.005 225. 0.063 100 166. 0.0468
+ 3 0. 0.000 39. 0.011 150 133. 0.0375
+ 4 0. 0.000 7. 0.002 200 88. 0.0248
+ 5 0. 0.000 0. 0.000 250 75. 0.0211
+ 6 0. 0.000 0. 0.000 300 41. 0.0116
+ 7 0. 0.000 0. 0.000 350 32. 0.0090
+ 8 0. 0.000 0. 0.000 400 29. 0.0082
+ 9 0. 0.000 0. 0.000 450 19. 0.0054
+ 10 0. 0.000 0. 0.000 500 12. 0.0034
+ 11 0. 0.000 0. 0.000 550 5. 0.0014
+ 12 0. 0.000 0. 0.000 600 5. 0.0014
+ 13 0. 0.000 0. 0.000 650 6. 0.0017
+ 14 0. 0.000 0. 0.000 700 0. 0.0000
+ 15 0. 0.000 0. 0.000 750 1. 0.0003
+ 16 0. 0.000 0. 0.000 800 0. 0.0000
+ 17 0. 0.000 0. 0.000 850 2. 0.0006
+ 18 0. 0.000 0. 0.000 900 1. 0.0003
+ 19 0. 0.000 0. 0.000 950 0. 0.0000
+ 20 0. 0.000 0. 0.000 1000 0. 0.0000
+ 21 0. 0.000 0. 0.000 1050 0. 0.0000
+1 TABLE 16
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 3
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+ NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+
+ 0 3169. 0.893 2185. 0.616 0 2578. 0.7264
+ 1 361. 0.102 1037. 0.292 50 255. 0.0719
+ 2 18. 0.005 281. 0.079 100 203. 0.0572
+ 3 1. 0.000 42. 0.012 150 148. 0.0417
+ 4 0. 0.000 3. 0.001 200 106. 0.0299
+ 5 0. 0.000 1. 0.000 250 92. 0.0259
+ 6 0. 0.000 0. 0.000 300 53. 0.0149
+ 7 0. 0.000 0. 0.000 350 46. 0.0130
+ 8 0. 0.000 0. 0.000 400 23. 0.0065
+ 9 0. 0.000 0. 0.000 450 16. 0.0045
+ 10 0. 0.000 0. 0.000 500 8. 0.0023
+ 11 0. 0.000 0. 0.000 550 12. 0.0034
+ 12 0. 0.000 0. 0.000 600 4. 0.0011
+ 13 0. 0.000 0. 0.000 650 2. 0.0006
+ 14 0. 0.000 0. 0.000 700 1. 0.0003
+ 15 0. 0.000 0. 0.000 750 1. 0.0003
+ 16 0. 0.000 0. 0.000 800 1. 0.0003
+ 17 0. 0.000 0. 0.000 850 0. 0.0000
+ 18 0. 0.000 0. 0.000 900 0. 0.0000
+ 19 0. 0.000 0. 0.000 950 0. 0.0000
+ 20 0. 0.000 0. 0.000 1000 0. 0.0000
+ 21 0. 0.000 0. 0.000 1050 0. 0.0000
+1 TABLE 17
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 4
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+ NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+
+ 0 3207. 0.904 2274. 0.641 0 2662. 0.7501
+ 1 327. 0.092 1023. 0.288 50 257. 0.0724
+ 2 14. 0.004 211. 0.059 100 189. 0.0533
+ 3 1. 0.000 33. 0.009 150 115. 0.0324
+ 4 0. 0.000 7. 0.002 200 107. 0.0301
+ 5 0. 0.000 1. 0.000 250 68. 0.0192
+ 6 0. 0.000 0. 0.000 300 46. 0.0130
+ 7 0. 0.000 0. 0.000 350 40. 0.0113
+ 8 0. 0.000 0. 0.000 400 23. 0.0065
+ 9 0. 0.000 0. 0.000 450 12. 0.0034
+ 10 0. 0.000 0. 0.000 500 11. 0.0031
+ 11 0. 0.000 0. 0.000 550 6. 0.0017
+ 12 0. 0.000 0. 0.000 600 3. 0.0008
+ 13 0. 0.000 0. 0.000 650 2. 0.0006
+ 14 0. 0.000 0. 0.000 700 2. 0.0006
+ 15 0. 0.000 0. 0.000 750 3. 0.0008
+ 16 0. 0.000 0. 0.000 800 3. 0.0008
+ 17 0. 0.000 0. 0.000 850 0. 0.0000
+ 18 0. 0.000 0. 0.000 900 0. 0.0000
+ 19 0. 0.000 0. 0.000 950 0. 0.0000
+ 20 0. 0.000 0. 0.000 1000 0. 0.0000
+ 21 0. 0.000 0. 0.000 1050 0. 0.0000
+1 TABLE 18
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 5
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+ NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+
+ 0 3231. 0.910 2357. 0.664 0 2731. 0.7695
+ 1 306. 0.086 976. 0.275 50 258. 0.0727
+ 2 12. 0.003 198. 0.056 100 194. 0.0547
+ 3 0. 0.000 18. 0.005 150 114. 0.0321
+ 4 0. 0.000 0. 0.000 200 84. 0.0237
+ 5 0. 0.000 0. 0.000 250 60. 0.0169
+ 6 0. 0.000 0. 0.000 300 30. 0.0085
+ 7 0. 0.000 0. 0.000 350 29. 0.0082
+ 8 0. 0.000 0. 0.000 400 19. 0.0054
+ 9 0. 0.000 0. 0.000 450 14. 0.0039
+ 10 0. 0.000 0. 0.000 500 5. 0.0014
+ 11 0. 0.000 0. 0.000 550 3. 0.0008
+ 12 0. 0.000 0. 0.000 600 3. 0.0008
+ 13 0. 0.000 0. 0.000 650 0. 0.0000
+ 14 0. 0.000 0. 0.000 700 0. 0.0000
+ 15 0. 0.000 0. 0.000 750 2. 0.0006
+ 16 0. 0.000 0. 0.000 800 2. 0.0006
+ 17 0. 0.000 0. 0.000 850 1. 0.0003
+ 18 0. 0.000 0. 0.000 900 0. 0.0000
+ 19 0. 0.000 0. 0.000 950 0. 0.0000
+ 20 0. 0.000 0. 0.000 1000 0. 0.0000
+ 21 0. 0.000 0. 0.000 1050 0. 0.0000
diff --git a/NARP/reliabilityassessment/integration_test/ZZDD.csv b/NARP/reliabilityassessment/integration_test/ZZDD.csv
new file mode 100644
index 0000000..cd34ea4
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZDD.csv
@@ -0,0 +1 @@
+SN,LINE NUMBER,UNIT1,UNIT2,UNIT3,UNIT4,FORWARD,BACKWARD
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/integration_test/ZZFC.csv b/NARP/reliabilityassessment/integration_test/ZZFC.csv
new file mode 100644
index 0000000..c3c724e
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZFC.csv
@@ -0,0 +1 @@
+SN,FROM AREA,TO AREA,BEG DAY,END DAY,MW
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/integration_test/ZZLD.csv b/NARP/reliabilityassessment/integration_test/ZZLD.csv
new file mode 100644
index 0000000..10a8ae2
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZLD.csv
@@ -0,0 +1,6 @@
+SN,AREA NAME,PEAK (MW),LFU,OUTAGE BEG WK,OUTAGE END WK,FORBIDDEN BEG WK,FORBIDDEN END WK,SUM OF FLOWS CONSTRAINT
+1,'A1',3000,0,1,52,31,32,30000
+2,'A2',3000,0,1,52,31,32,30000
+3,'A3',3000,0,1,52,31,32,30000
+4,'A4',3000,0,1,52,31,32,30000
+5,'A5',3000,0,1,52,31,32,30000
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/integration_test/ZZMC.csv b/NARP/reliabilityassessment/integration_test/ZZMC.csv
new file mode 100644
index 0000000..c6f4b0f
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZMC.csv
@@ -0,0 +1,2 @@
+SEED,LS,END WK1,END WK2,END WK3,WHERE,WHEN,KVS,KVT,KVL,CVT,FIN,STEP,FREQ,MAXE,II,IJ,IR,IN,D,M
+345237,1,13,26,39,1,1,1,2,1,0.025,9999,1,1,1000,0,0,1,5,1,1
diff --git a/NARP/reliabilityassessment/integration_test/ZZOD.csv b/NARP/reliabilityassessment/integration_test/ZZOD.csv
new file mode 100644
index 0000000..264a5ff
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZOD.csv
@@ -0,0 +1 @@
+SN,UNIT NAME,PERCENT OWNED BY AREA
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/integration_test/ZZTC.csv b/NARP/reliabilityassessment/integration_test/ZZTC.csv
new file mode 100644
index 0000000..52407e8
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZTC.csv
@@ -0,0 +1,3 @@
+THIS IS A FIVE AREA SYSTEM
+EACH AREA IS IDENTICAL TO THE IEEE-RTS
+PLANNED MAINTENANCE IS CONSIDERED
diff --git a/NARP/reliabilityassessment/integration_test/ZZTD.csv b/NARP/reliabilityassessment/integration_test/ZZTD.csv
new file mode 100644
index 0000000..66f1bf2
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZTD.csv
@@ -0,0 +1,6 @@
+SN,LINE NUMBER,FROM AREA,TO AREA,META DATA
+1,1,'A1','A2',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+2,2,'A2','A3',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+3,3,'A3','A4',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+4,4,'A4','A5',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
+5,5,'A5','A1',"-120. ,300. ,300. ,0.9216,-60. ,150. ,150. ,0.0768, 0. ,0. ,0. ,0.0016,-80. ,150. ,150. ,0.0000,-40. ,100. ,100. ,0.0000,-20. ,50. ,50.,0.0000"
\ No newline at end of file
diff --git a/NARP/reliabilityassessment/integration_test/ZZUD.csv b/NARP/reliabilityassessment/integration_test/ZZUD.csv
new file mode 100644
index 0000000..9789474
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/ZZUD.csv
@@ -0,0 +1,161 @@
+SN,NAME,LOC,CAP1,CAP2,CAP3,CAP4,DFOR,FOR,DER,P/A,B1,D1,B2,D2
+1,'A10101','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+2,'A10201','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+3,'A10301','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+4,'A10401','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+5,'A10501','A1',12,12,12,12,0,0.02,0,0,0,0,0,0
+6,'A10601','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+7,'A10701','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+8,'A10801','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+9,'A10901','A1',20,20,20,20,0,0.1,0,0,0,0,0,0
+10,'A11001','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+11,'A11101','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+12,'A11201','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+13,'A11301','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+14,'A11401','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+15,'A11501','A1',50,50,50,50,0,0.01,0,0,0,0,0,0
+16,'A11601','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+17,'A11701','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+18,'A11801','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+19,'A11901','A1',76,76,76,76,0,0.02,0,0,0,0,0,0
+20,'A12001','A1',100,100,100,100,0,0.04,0,0,0,0,0,0
+21,'A12101','A1',100,100,100,100,0,0.04,0,0,0,0,0,0
+22,'A12201','A1',100,100,100,100,0,0.04,0,0,0,0,0,0
+23,'A12301','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+24,'A12401','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+25,'A12501','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+26,'A12601','A1',155,155,155,155,0,0.04,0,0,0,0,0,0
+27,'A12701','A1',197,197,197,197,0,0.05,0,0,0,0,0,0
+28,'A12801','A1',197,197,197,197,0,0.05,0,0,0,0,0,0
+29,'A12901','A1',197,197,197,197,0,0.05,0,0,0,0,0,0
+30,'A13001','A1',350,350,350,350,0,0.08,0,0,0,0,0,0
+31,'A13101','A1',400,400,400,400,0,0.12,0,0,0,0,0,0
+32,'A13201','A1',400,400,400,400,0,0.12,0,0,0,0,0,0
+33,'A20101','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+34,'A20201','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+35,'A20301','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+36,'A20401','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+37,'A20501','A2',12,12,12,12,0,0.02,0,0,0,0,0,0
+38,'A20601','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+39,'A20701','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+40,'A20801','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+41,'A20901','A2',20,20,20,20,0,0.1,0,0,0,0,0,0
+42,'A21001','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+43,'A21101','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+44,'A21201','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+45,'A21301','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+46,'A21401','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+47,'A21501','A2',50,50,50,50,0,0.01,0,0,0,0,0,0
+48,'A21601','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+49,'A21701','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+50,'A21801','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+51,'A21901','A2',76,76,76,76,0,0.02,0,0,0,0,0,0
+52,'A22001','A2',100,100,100,100,0,0.04,0,0,0,0,0,0
+53,'A22101','A2',100,100,100,100,0,0.04,0,0,0,0,0,0
+54,'A22201','A2',100,100,100,100,0,0.04,0,0,0,0,0,0
+55,'A22301','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
+56,'A22401','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
+57,'A22501','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
+58,'A22601','A2',155,155,155,155,0,0.04,0,0,0,0,0,0
+59,'A22701','A2',197,197,197,197,0,0.05,0,0,0,0,0,0
+60,'A22801','A2',197,197,197,197,0,0.05,0,0,0,0,0,0
+61,'A22901','A2',197,197,197,197,0,0.05,0,0,0,0,0,0
+62,'A23001','A2',350,350,350,350,0,0.08,0,0,0,0,0,0
+63,'A23101','A2',400,400,400,400,0,0.12,0,0,0,0,0,0
+64,'A23201','A2',400,400,400,400,0,0.12,0,0,0,0,0,0
+65,'A30101','A3',12,12,12,12,0,0.02,0,0,0,0,0,0
+66,'A30201','A3',12,12,12,12,0,0.02,0,0,0,0,0,0
+67,'A30301','A3',12,12,12,12,0,0.02,0,0,0,0,0,0
+68,'A30401','A3',12,12,12,12,0,0.02,0,0,0,0,0,0
+69,'A30501','A3',12,12,12,12,0,0.02,0,0,0,0,0,0
+70,'A30601','A3',20,20,20,20,0,0.1,0,0,0,0,0,0
+71,'A30701','A3',20,20,20,20,0,0.1,0,0,0,0,0,0
+72,'A30801','A3',20,20,20,20,0,0.1,0,0,0,0,0,0
+73,'A30901','A3',20,20,20,20,0,0.1,0,0,0,0,0,0
+74,'A31001','A3',50,50,50,50,0,0.01,0,0,0,0,0,0
+75,'A31101','A3',50,50,50,50,0,0.01,0,0,0,0,0,0
+76,'A31201','A3',50,50,50,50,0,0.01,0,0,0,0,0,0
+77,'A31301','A3',50,50,50,50,0,0.01,0,0,0,0,0,0
+78,'A31401','A3',50,50,50,50,0,0.01,0,0,0,0,0,0
+79,'A31501','A3',50,50,50,50,0,0.01,0,0,0,0,0,0
+80,'A31601','A3',76,76,76,76,0,0.02,0,0,0,0,0,0
+81,'A31701','A3',76,76,76,76,0,0.02,0,0,0,0,0,0
+82,'A31801','A3',76,76,76,76,0,0.02,0,0,0,0,0,0
+83,'A31901','A3',76,76,76,76,0,0.02,0,0,0,0,0,0
+84,'A32001','A3',100,100,100,100,0,0.04,0,0,0,0,0,0
+85,'A32101','A3',100,100,100,100,0,0.04,0,0,0,0,0,0
+86,'A32201','A3',100,100,100,100,0,0.04,0,0,0,0,0,0
+87,'A32301','A3',155,155,155,155,0,0.04,0,0,0,0,0,0
+88,'A32401','A3',155,155,155,155,0,0.04,0,0,0,0,0,0
+89,'A32501','A3',155,155,155,155,0,0.04,0,0,0,0,0,0
+90,'A32601','A3',155,155,155,155,0,0.04,0,0,0,0,0,0
+91,'A32701','A3',197,197,197,197,0,0.05,0,0,0,0,0,0
+92,'A32801','A3',197,197,197,197,0,0.05,0,0,0,0,0,0
+93,'A32901','A3',197,197,197,197,0,0.05,0,0,0,0,0,0
+94,'A33001','A3',350,350,350,350,0,0.08,0,0,0,0,0,0
+95,'A33101','A3',400,400,400,400,0,0.12,0,0,0,0,0,0
+96,'A33201','A3',400,400,400,400,0,0.12,0,0,0,0,0,0
+97,'A40101','A4',12,12,12,12,0,0.02,0,0,0,0,0,0
+98,'A40201','A4',12,12,12,12,0,0.02,0,0,0,0,0,0
+99,'A40301','A4',12,12,12,12,0,0.02,0,0,0,0,0,0
+100,'A40401','A4',12,12,12,12,0,0.02,0,0,0,0,0,0
+101,'A40501','A4',12,12,12,12,0,0.02,0,0,0,0,0,0
+102,'A40601','A4',20,20,20,20,0,0.1,0,0,0,0,0,0
+103,'A40701','A4',20,20,20,20,0,0.1,0,0,0,0,0,0
+104,'A40801','A4',20,20,20,20,0,0.1,0,0,0,0,0,0
+105,'A40901','A4',20,20,20,20,0,0.1,0,0,0,0,0,0
+106,'A41001','A4',50,50,50,50,0,0.01,0,0,0,0,0,0
+107,'A41101','A4',50,50,50,50,0,0.01,0,0,0,0,0,0
+108,'A41201','A4',50,50,50,50,0,0.01,0,0,0,0,0,0
+109,'A41301','A4',50,50,50,50,0,0.01,0,0,0,0,0,0
+110,'A41401','A4',50,50,50,50,0,0.01,0,0,0,0,0,0
+111,'A41501','A4',50,50,50,50,0,0.01,0,0,0,0,0,0
+112,'A41601','A4',76,76,76,76,0,0.02,0,0,0,0,0,0
+113,'A41701','A4',76,76,76,76,0,0.02,0,0,0,0,0,0
+114,'A41801','A4',76,76,76,76,0,0.02,0,0,0,0,0,0
+115,'A41901','A4',76,76,76,76,0,0.02,0,0,0,0,0,0
+116,'A42001','A4',100,100,100,100,0,0.04,0,0,0,0,0,0
+117,'A42101','A4',100,100,100,100,0,0.04,0,0,0,0,0,0
+118,'A42201','A4',100,100,100,100,0,0.04,0,0,0,0,0,0
+119,'A42301','A4',155,155,155,155,0,0.04,0,0,0,0,0,0
+120,'A42401','A4',155,155,155,155,0,0.04,0,0,0,0,0,0
+121,'A42501','A4',155,155,155,155,0,0.04,0,0,0,0,0,0
+122,'A42601','A4',155,155,155,155,0,0.04,0,0,0,0,0,0
+123,'A42701','A4',197,197,197,197,0,0.05,0,0,0,0,0,0
+124,'A42801','A4',197,197,197,197,0,0.05,0,0,0,0,0,0
+125,'A42901','A4',197,197,197,197,0,0.05,0,0,0,0,0,0
+126,'A43001','A4',350,350,350,350,0,0.08,0,0,0,0,0,0
+127,'A43101','A4',400,400,400,400,0,0.12,0,0,0,0,0,0
+128,'A43201','A4',400,400,400,400,0,0.12,0,0,0,0,0,0
+129,'A50101','A5',12,12,12,12,0,0.02,0,0,0,0,0,0
+130,'A50201','A5',12,12,12,12,0,0.02,0,0,0,0,0,0
+131,'A50301','A5',12,12,12,12,0,0.02,0,0,0,0,0,0
+132,'A50401','A5',12,12,12,12,0,0.02,0,0,0,0,0,0
+133,'A50501','A5',12,12,12,12,0,0.02,0,0,0,0,0,0
+134,'A50601','A5',20,20,20,20,0,0.1,0,0,0,0,0,0
+135,'A50701','A5',20,20,20,20,0,0.1,0,0,0,0,0,0
+136,'A50801','A5',20,20,20,20,0,0.1,0,0,0,0,0,0
+137,'A50901','A5',20,20,20,20,0,0.1,0,0,0,0,0,0
+138,'A51001','A5',50,50,50,50,0,0.01,0,0,0,0,0,0
+139,'A51101','A5',50,50,50,50,0,0.01,0,0,0,0,0,0
+140,'A51201','A5',50,50,50,50,0,0.01,0,0,0,0,0,0
+141,'A51301','A5',50,50,50,50,0,0.01,0,0,0,0,0,0
+142,'A51401','A5',50,50,50,50,0,0.01,0,0,0,0,0,0
+143,'A51501','A5',50,50,50,50,0,0.01,0,0,0,0,0,0
+144,'A51601','A5',76,76,76,76,0,0.02,0,0,0,0,0,0
+145,'A51701','A5',76,76,76,76,0,0.02,0,0,0,0,0,0
+146,'A51801','A5',76,76,76,76,0,0.02,0,0,0,0,0,0
+147,'A51901','A5',76,76,76,76,0,0.02,0,0,0,0,0,0
+148,'A52001','A5',100,100,100,100,0,0.04,0,0,0,0,0,0
+149,'A52101','A5',100,100,100,100,0,0.04,0,0,0,0,0,0
+150,'A52201','A5',100,100,100,100,0,0.04,0,0,0,0,0,0
+151,'A52301','A5',155,155,155,155,0,0.04,0,0,0,0,0,0
+152,'A52401','A5',155,155,155,155,0,0.04,0,0,0,0,0,0
+153,'A52501','A5',155,155,155,155,0,0.04,0,0,0,0,0,0
+154,'A52601','A5',155,155,155,155,0,0.04,0,0,0,0,0,0
+155,'A52701','A5',197,197,197,197,0,0.05,0,0,0,0,0,0
+156,'A52801','A5',197,197,197,197,0,0.05,0,0,0,0,0,0
+157,'A52901','A5',197,197,197,197,0,0.05,0,0,0,0,0,0
+158,'A53001','A5',350,350,350,350,0,0.08,0,0,0,0,0,0
+159,'A53101','A5',400,400,400,400,0,0.12,0,0,0,0,0,0
+160,'A53201','A5',400,400,400,400,0,0.12,0,0,0,0,0,0
diff --git a/NARP/reliabilityassessment/integration_test/output_python.txt b/NARP/reliabilityassessment/integration_test/output_python.txt
new file mode 100644
index 0000000..69f2ad5
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/output_python.txt
@@ -0,0 +1,508 @@
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 1
+
+
+ --------------------------------
+
+ TABLE 7
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 2
+
+
+ --------------------------------
+
+ TABLE 8
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 3
+
+
+ --------------------------------
+
+ TABLE 9
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 4
+
+
+ --------------------------------
+
+ TABLE 10
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ WEEKLY PEAK LOAD BEFORE MAINTENANCE SCHEDULING
+
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ PLANNED MAINTENANCE SCHEDULE FOR AREA 5
+
+
+ --------------------------------
+
+ TABLE 11
+
+
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | |
+ | | | MONTHS |
+ | | | |
+ | | |-----------------------------------------------------------------------------------------------------------|
+ | | | | | | |
+ | UNIT | CAP | JAN FEB MAR | APR MAY JUN |JUL AUG SEP | OCT NOV DEC |
+ | NAME | (MW) | | | | |
+ | | | 1 2 3 4 5 6 7 8 9 0 1 2 3 | 4 5 6 7 8 9 0 1 2 3 4 5 6 | 7 8 9 0 1 2 3 4 5 6 7 8 9 | 0 1 2 3 4 5 6 7 8 9 0 1 2 |
+ | | 1 | 2 | 3 | 4 5 |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ | | | | | | |
+ | | | | | | |
+ ---------------------------------------------------------------------------------------------------------------------------
+ | | |
+ 1
+
+
+
+
+
+
+
+
+
+ WEEKLY PEAK LOAD + EFFECTIVE CAPACITY (MW) ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+0
+
+
+
+
+ WEEKLY PEAK LOAD + CAPACITY ON MAINTENANCE
+0
+ 2585.0000 2700.0000 2634.0000 2501.0000 2640.0000 2523.0000 2495.0000 2418.0000 2219.0000 2210.0000 2144.0000 2180.0000 2111.0000
+ 2250.0000 2162.0000 2399.0000 2261.0000 2510.0000 2609.0000 2640.0000 2567.0000 2433.0000 2700.0000 2661.0000 2688.0000 2583.0000
+ 2265.0000 2448.0000 2403.0000 2640.0000 2165.0000 2328.0000 2399.0000 2186.0000 2177.0000 2114.0000 2340.0000 2084.0000 2171.0000
+ 2171.0000 2229.0000 2231.0000 2399.0000 2643.0000 2655.0000 2726.0000 2819.0000 2670.0000 2825.0000 2909.0000 3000.0000 2855.0000
+
+ TABLE 12
+
+ FINAL RESULTS AFTER 3549 REPLICATIONS
+
+ AREA FORECAST HOURLY STATISTICS PEAK STATISTICS REMARKS
+ NO NO HLOLE XLOL EUE LOLE XLOL
+ (HRS/YR) (MW) (MWH) (DAYS/YR) (MW)
+ 1 AV 0.09 130.54 11 0.03 134.15 GC
+ 1 AV 0.34 109.04 37 0.07 120.08 TC
+ 1 AV 0.43 113.42 49 0.10 124.41 GT
+
+ 2 AV 0.08 100.55 8 0.02 115.48 GC
+ 2 AV 0.00 0.00 0 0.00 0.00 TC
+ 2 AV 0.08 100.55 8 0.02 115.48 GT
+
+ 3 AV 0.08 121.87 10 0.03 118.08 GC
+ 3 AV 0.41 110.53 45 0.08 113.27 TC
+ 3 AV 0.49 112.42 55 0.11 114.46 GT
+
+ 4 AV 0.10 127.34 12 0.03 125.80 GC
+ 4 AV 0.35 107.82 38 0.07 128.25 TC
+ 4 AV 0.44 112.04 50 0.10 127.52 GT
+
+ 5 AV 0.09 123.54 11 0.03 113.22 GC
+ 5 AV 0.31 101.56 32 0.06 121.36 TC
+ 5 AV 0.40 106.57 43 0.09 118.72 GT
+
+
+ POOL STATISTICS
+
+ AV 0.001 252.00 0 0.000 195.00 GC
+ AV 0.000 1.00 0 0.000 0.00 TC
+ AV 0.001 168.33 0 0.000 195.00 GT
+
+ TABLE 13
+
+ SUMMARY OF RESULTS
+
+ AREA PEAK INSTALLED AREA HLOLE EUE LOLE
+ ---------------- ---------------- ----------------
+ CAPACITY PCT RES MAGN PCT SD MAGN PCT SD MAGN PCT SD
+ A1 3000 3405 13.5 0.430 2.499 49 4 0.102 5.229
+ A2 3000 3405 13.5 0.078 5.891 8 9 0.023 10.915
+ A3 3000 3405 13.5 0.491 2.406 55 3 0.113 4.981
+ A4 3000 3405 13.5 0.444 2.537 50 4 0.101 5.270
+ A5 3000 3405 13.5 0.402 2.584 43 4 0.093 5.449
+
+ ERCOT 15000 17025 13.5 0.001 3858.468 0 3723 0.000 3889.730
+
+ TABLE 14
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 1
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+ 0 3204 0.903 2283 0.643 0 2660 0.7495
+ 1 329 0.093 1036 0.292 50 263 0.0741
+ 2 16 0.005 200 0.056 100 197 0.0555
+ 3 0 0.000 29 0.008 150 133 0.0375
+ 4 0 0.000 1 0.000 200 82 0.0231
+ 5 0 0.000 0 0.000 250 61 0.0172
+ 6 0 0.000 0 0.000 300 52 0.0147
+ 7 0 0.000 0 0.000 350 40 0.0113
+ 8 0 0.000 0 0.000 400 19 0.0054
+ 9 0 0.000 0 0.000 450 14 0.0039
+ 10 0 0.000 0 0.000 500 8 0.0023
+ 11 0 0.000 0 0.000 550 10 0.0028
+ 12 0 0.000 0 0.000 600 3 0.0008
+ 13 0 0.000 0 0.000 650 2 0.0006
+ 14 0 0.000 0 0.000 700 0 0.0000
+ 15 0 0.000 0 0.000 750 1 0.0003
+ 16 0 0.000 0 0.000 800 3 0.0008
+ 17 0 0.000 0 0.000 850 0 0.0000
+ 18 0 0.000 0 0.000 900 0 0.0000
+ 19 0 0.000 0 0.000 950 0 0.0000
+ 20 0 0.000 0 0.000 1000 0 0.0000
+ 21 0 0.000 0 0.000 1050 1 0.0003
+
+
+ TABLE 15
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 2
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+ 0 3467 0.977 3277 0.923 0 3398 0.9575
+ 1 82 0.023 266 0.075 50 56 0.0158
+ 2 0 0.000 6 0.002 100 28 0.0079
+ 3 0 0.000 0 0.000 150 18 0.0051
+ 4 0 0.000 0 0.000 200 16 0.0045
+ 5 0 0.000 0 0.000 250 17 0.0048
+ 6 0 0.000 0 0.000 300 7 0.0020
+ 7 0 0.000 0 0.000 350 3 0.0008
+ 8 0 0.000 0 0.000 400 4 0.0011
+ 9 0 0.000 0 0.000 450 1 0.0003
+ 10 0 0.000 0 0.000 500 0 0.0000
+ 11 0 0.000 0 0.000 550 0 0.0000
+ 12 0 0.000 0 0.000 600 0 0.0000
+ 13 0 0.000 0 0.000 650 0 0.0000
+ 14 0 0.000 0 0.000 700 0 0.0000
+ 15 0 0.000 0 0.000 750 1 0.0003
+ 16 0 0.000 0 0.000 800 0 0.0000
+ 17 0 0.000 0 0.000 850 0 0.0000
+ 18 0 0.000 0 0.000 900 0 0.0000
+ 19 0 0.000 0 0.000 950 0 0.0000
+ 20 0 0.000 0 0.000 1000 0 0.0000
+ 21 0 0.000 0 0.000 1050 0 0.0000
+
+
+ TABLE 16
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 3
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+ 0 3169 0.893 2185 0.616 0 2578 0.7264
+ 1 361 0.102 1037 0.292 50 255 0.0719
+ 2 18 0.005 281 0.079 100 203 0.0572
+ 3 1 0.000 42 0.012 150 148 0.0417
+ 4 0 0.000 3 0.001 200 106 0.0299
+ 5 0 0.000 1 0.000 250 92 0.0259
+ 6 0 0.000 0 0.000 300 53 0.0149
+ 7 0 0.000 0 0.000 350 47 0.0132
+ 8 0 0.000 0 0.000 400 22 0.0062
+ 9 0 0.000 0 0.000 450 16 0.0045
+ 10 0 0.000 0 0.000 500 8 0.0023
+ 11 0 0.000 0 0.000 550 12 0.0034
+ 12 0 0.000 0 0.000 600 4 0.0011
+ 13 0 0.000 0 0.000 650 2 0.0006
+ 14 0 0.000 0 0.000 700 1 0.0003
+ 15 0 0.000 0 0.000 750 1 0.0003
+ 16 0 0.000 0 0.000 800 1 0.0003
+ 17 0 0.000 0 0.000 850 0 0.0000
+ 18 0 0.000 0 0.000 900 0 0.0000
+ 19 0 0.000 0 0.000 950 0 0.0000
+ 20 0 0.000 0 0.000 1000 0 0.0000
+ 21 0 0.000 0 0.000 1050 0 0.0000
+
+
+ TABLE 17
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 4
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+ 0 3207 0.904 2274 0.641 0 2662 0.7501
+ 1 327 0.092 1023 0.288 50 258 0.0727
+ 2 14 0.004 211 0.059 100 189 0.0533
+ 3 1 0.000 33 0.009 150 116 0.0327
+ 4 0 0.000 7 0.002 200 106 0.0299
+ 5 0 0.000 1 0.000 250 67 0.0189
+ 6 0 0.000 0 0.000 300 46 0.0130
+ 7 0 0.000 0 0.000 350 40 0.0113
+ 8 0 0.000 0 0.000 400 23 0.0065
+ 9 0 0.000 0 0.000 450 12 0.0034
+ 10 0 0.000 0 0.000 500 11 0.0031
+ 11 0 0.000 0 0.000 550 6 0.0017
+ 12 0 0.000 0 0.000 600 3 0.0008
+ 13 0 0.000 0 0.000 650 2 0.0006
+ 14 0 0.000 0 0.000 700 2 0.0006
+ 15 0 0.000 0 0.000 750 3 0.0008
+ 16 0 0.000 0 0.000 800 3 0.0008
+ 17 0 0.000 0 0.000 850 0 0.0000
+ 18 0 0.000 0 0.000 900 0 0.0000
+ 19 0 0.000 0 0.000 950 0 0.0000
+ 20 0 0.000 0 0.000 1000 0 0.0000
+ 21 0 0.000 0 0.000 1050 0 0.0000
+
+
+ TABLE 18
+
+ PROBABILITY DISTRIBUTIONS FOR AREA 5
+
+
+ DAILY PEAK LOLES PER YEAR HOURLY LOLES PER YEAR ANNUAL UNSERVED ENERGY (MWH)
+
+NUMBER OBSERVATIONS PROBABILITY OBSERVATIONS PROBABILITY LIMIT (MWH) OBSERVATIONS PROBABILITY
+ 0 3231 0.910 2357 0.664 0 2731 0.7695
+ 1 306 0.086 975 0.275 50 258 0.0727
+ 2 12 0.003 199 0.056 100 193 0.0544
+ 3 0 0.000 18 0.005 150 114 0.0321
+ 4 0 0.000 0 0.000 200 84 0.0237
+ 5 0 0.000 0 0.000 250 60 0.0169
+ 6 0 0.000 0 0.000 300 30 0.0085
+ 7 0 0.000 0 0.000 350 30 0.0085
+ 8 0 0.000 0 0.000 400 19 0.0054
+ 9 0 0.000 0 0.000 450 14 0.0039
+ 10 0 0.000 0 0.000 500 5 0.0014
+ 11 0 0.000 0 0.000 550 3 0.0008
+ 12 0 0.000 0 0.000 600 3 0.0008
+ 13 0 0.000 0 0.000 650 0 0.0000
+ 14 0 0.000 0 0.000 700 0 0.0000
+ 15 0 0.000 0 0.000 750 2 0.0006
+ 16 0 0.000 0 0.000 800 2 0.0006
+ 17 0 0.000 0 0.000 850 1 0.0003
+ 18 0 0.000 0 0.000 900 0 0.0000
+ 19 0 0.000 0 0.000 950 0 0.0000
+ 20 0 0.000 0 0.000 1000 0 0.0000
+ 21 0 0.000 0 0.000 1050 0 0.0000
diff --git a/NARP/reliabilityassessment/integration_test/test_integration_result_comparison.py b/NARP/reliabilityassessment/integration_test/test_integration_result_comparison.py
new file mode 100644
index 0000000..baecb9b
--- /dev/null
+++ b/NARP/reliabilityassessment/integration_test/test_integration_result_comparison.py
@@ -0,0 +1,85 @@
+from pathlib import Path
+
+import numpy as np
+
+
+def test_integration_result_comparison():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # from reliabilityassessment.monte_carlo.narpMain import narpMain
+ # run "narpMain(TEST_DIR)" externally. Then move the output file
+ # to this folder (if needed) and rename it as "output_python.txt".
+ # read Python output
+ python_result_path = Path(TEST_DIR, "output_python.txt")
+ f1 = open(python_result_path, "r")
+
+ lole_obs, lole_prob = np.zeros((22, 5)), np.zeros((22, 5))
+ hlole_obs, hlole_prob = np.zeros((22, 5)), np.zeros((22, 5))
+ eue_obs, eue_prob = np.zeros((22, 5)), np.zeros((22, 5))
+
+ cnt = 1
+ k = 0
+ while k < 5:
+ s = f1.readline()
+ while "PROBABILITY DISTRIBUTIONS FOR AREA" not in s.strip():
+ s = f1.readline()
+ cnt += 1
+ while "NUMBER" not in s:
+ s = f1.readline()
+ cnt += 1
+ for i in range(22):
+ s = f1.readline().strip().split()
+ cnt += 1
+ lole_obs[i][k], lole_prob[i][k] = s[1:3]
+ hlole_obs[i][k], hlole_prob[i][k] = s[3:5]
+ eue_obs[i][k], eue_prob[i][k] = s[6:8]
+ k += 1
+ f1.close()
+
+ # run "NARP.exe" externally. Then move the output file
+ # to this folder (if needed) and rename it as "OUTPUT_fortran".
+ # read Fortran output
+ fortran_result_path = Path(TEST_DIR, "OUTPUT_fortran")
+ f2 = open(fortran_result_path, "r")
+
+ LOLE_OBS, LOLE_PROB = np.zeros((22, 5)), np.zeros((22, 5))
+ HLOLE_OBS, HLOLE_PROB = np.zeros((22, 5)), np.zeros((22, 5))
+ EUE_OBS, EUE_PROB = np.zeros((22, 5)), np.zeros((22, 5))
+
+ cnt = 1
+ k = 0
+ while k < 5:
+ s = f2.readline()
+ while "PROBABILITY DISTRIBUTIONS FOR AREA" not in s.strip():
+ s = f2.readline()
+ cnt += 1
+ while "NUMBER" not in s:
+ s = f2.readline()
+ cnt += 1
+ f2.readline()
+ for i in range(22):
+ s = f2.readline().strip().split()
+ cnt += 1
+ LOLE_OBS[i][k], LOLE_PROB[i][k] = s[1:3]
+ HLOLE_OBS[i][k], HLOLE_PROB[i][k] = s[3:5]
+ EUE_OBS[i][k], EUE_PROB[i][k] = s[6:8]
+ k += 1
+ f2.close()
+
+ # Compare the values of the probability (PDF) tables :
+ sum_LOLE_prob = 0
+ sum_HLOLE_prob = 0
+ sum_EUE_prob = 0
+ for k in range(5):
+ sum_LOLE_prob += np.linalg.norm(LOLE_PROB[:, k] - lole_prob[:, k])
+ sum_HLOLE_prob += np.linalg.norm(HLOLE_PROB[:, k] - hlole_prob[:, k])
+ sum_EUE_prob += np.linalg.norm(EUE_PROB[:, k] - eue_prob[:, k])
+ rmse_LOLE_prob = sum_LOLE_prob / 5
+ rmse_HLOLE_prob = sum_HLOLE_prob / 5
+ rmse_EUE_prob = sum_EUE_prob / 5
+ print(rmse_LOLE_prob)
+ print(rmse_HLOLE_prob)
+ print(rmse_EUE_prob)
+ assert rmse_LOLE_prob < 0.1
+ assert rmse_HLOLE_prob < 0.1
+ assert rmse_EUE_prob < 0.1
diff --git a/NARP/reliabilityassessment/monte_carlo/__init__.py b/NARP/reliabilityassessment/monte_carlo/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/NARP/reliabilityassessment/monte_carlo/admitb.py b/NARP/reliabilityassessment/monte_carlo/admitb.py
new file mode 100644
index 0000000..90a57c3
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/admitb.py
@@ -0,0 +1,31 @@
+import numpy as np
+
+
+def admitb(LP, BLP):
+ """
+ Construct the admittance matrix (B) from the line data array (BLP)
+
+ :param numpy.ndarray LP: 2D array with shape (NLINES, 3)
+ LP[I, 0]: line number, set to I
+ LP[I, 1]: starting node
+ LP[I, 2]: ending node
+ :param numpy.ndarray BLP: 2D array with shape (NLINES, 3)
+ BLP[I, 0]: admittance of the line at the Ith entry of LP
+ BLP[I, 1]: capacity (MW)
+ BLP[I, 2]: backward capacity (MW)
+ :return: (*numpy.ndarray*) -- BB: 2D array with shape (NOAREA, NOAREA)
+ B Matrix in DC power flow
+ """
+
+ NLINES = LP.shape[0]
+ NOAREA = 1 + LP[:, 1:].max()
+ BB = np.zeros((NOAREA, NOAREA))
+ for i in range(NLINES):
+ j = LP[i, 1] # from-bus (area) id
+ k = LP[i, 2] # to-bus (area) id
+ BB[j, k] += BLP[i, 0] * (-1.0)
+ BB[k, j] = BB[j, k]
+ BB[j, j] += BLP[i, 0]
+ BB[k, k] += BLP[i, 0]
+
+ return BB
diff --git a/NARP/reliabilityassessment/monte_carlo/admitm.py b/NARP/reliabilityassessment/monte_carlo/admitm.py
new file mode 100644
index 0000000..b5f4167
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/admitm.py
@@ -0,0 +1,17 @@
+def admitm(BB, NII, NJJ, BIJ):
+ """
+ Modify the admittance matrix by an adding an extra admittance
+ on a specified branch
+
+ :param numpy.ndarray BB: 2D array with shape (NOAREA, NOAREA), the admittance
+ matrix in DC power flow
+ :param int NII: specify the from-bus of the impacted branch
+ :param int NJJ: specify the to-bus of the impacted branch
+ :param float BIJ: the amount of admittance value to add on
+
+ .. note:: BB is modified in place
+ """
+ BB[NII, NJJ] -= BIJ
+ BB[NJJ, NII] -= BIJ
+ BB[NII, NII] += BIJ
+ BB[NJJ, NJJ] += BIJ
diff --git a/NARP/reliabilityassessment/monte_carlo/admitr.py b/NARP/reliabilityassessment/monte_carlo/admitr.py
new file mode 100644
index 0000000..75e2955
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/admitr.py
@@ -0,0 +1,85 @@
+import numpy as np
+
+
+# vanilla version
+def _admitr(BB, BN, NR):
+ """
+ Remove REF bus (area) from the admittance matrix
+
+ :param numpy.ndarray BB: 2D array with shape (NOAREA, NOAREA) the admittance
+ matrix 'B' used in DC power flow, in particular, it contains the entire
+ admittance matrix upon input and the REF bus is removed at output
+ :param numpy.ndarray BN: 2D array with shape (NOAREA, 5)
+ BN[I, 0]: area (i.e., bus) number
+ BN[I, 1]: load (MW) at area I; will be overridden by net
+ injection after calling :py:func: `tm2`
+ BN[I, 2]: net injection (generation)(MW) at area I; will be
+ overridden by load curtailment after calling
+ :py:func: `tm2`
+ BN[I, 3]: max power flow (MW) allowed at area I
+ BN[I, 4]: a helper value to store modified
+ "constraint on sum of flows" at the area I
+ :param int NR: index number of the reference node.
+ :return: (*tuple*) -- a pair of numpy arrays, BB and LT
+ BB: 2D array with shape (NOAREA, NOAREA)
+ the 'B matrix without reference bus' in DC power flow
+ LT: 1D array with shape (NOAREA, )
+ array recording the original bus no.
+ """
+
+ NN = BB.shape[0] # total number of buses (i.e., areas)
+ NX = NN - 1
+
+ BT = np.zeros((NX, NX))
+ LT = np.zeros((NN,), dtype=int)
+
+ ii = 0
+ for i in range(NN):
+ if i == NR:
+ continue
+ LT[ii] = BN[i, 0]
+ iii = 0
+ for j in range(NN):
+ if j == NR:
+ continue
+ BT[ii, iii] = BB[i, j]
+ iii += 1
+ ii += 1
+ LT[NX] = NR
+
+ BB[:NX, :NX] = BT[:, :]
+ BB[-1, :] = 0
+ BB[0:, -1] = 0
+
+ return BB, LT
+
+
+# vectorized version
+def admitr(BB, BN, NR):
+ """
+ Remove REF bus (area) from the admittance matrix
+
+ :param numpy.ndarray BB: 2D array with shape (NOAREA, NOAREA) the admittance
+ matrix 'B' used in DC power flow, in particular, it contains the entire
+ admittance matrix upon input and the REF bus is removed at output
+ :param numpy.ndarray BN: 2D array with shape (NOAREA, 5)
+ BN[I, 0]: area (i.e., bus) number
+ BN[I, 1]: load (MW) at area I; will be overridden by net
+ injection after calling :py:func: `tm2`
+ BN[I, 2]: net injection (generation)(MW) at area I; will be
+ overridden by load curtailment after calling
+ :py:func: `tm2`
+ BN[I, 3]: max power flow (MW) allowed at area I
+ BN[I, 4]: a helper value to store modified
+ "constraint on sum of flows" at the area I
+ :param int NR: index number of the reference node.
+ :return: (*tuple*) -- a pair of numpy arrays, BB and LT
+ BB: 2D array with shape (NOAREA, NOAREA)
+ the 'B matrix without reference bus' in DC power flow
+ LT: 1D array with shape (NOAREA, )
+ array recording the original bus no.
+ """
+ LT = np.append(np.delete(BN[:, 0], NR), BN[NR, 0]).astype(int)
+ BB = np.delete(np.delete(BB, obj=NR, axis=0), obj=NR, axis=1)
+ BB = np.pad(BB, ((0, 1), (0, 1)))
+ return BB, LT
diff --git a/NARP/reliabilityassessment/monte_carlo/admref.py b/NARP/reliabilityassessment/monte_carlo/admref.py
new file mode 100644
index 0000000..a51e467
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/admref.py
@@ -0,0 +1,12 @@
+def admref(BB, NII, BIJ):
+ """
+ Update a specific diagonal element of the (admittance) matrix BB
+
+ :param numpy.ndarray BB: 2D array with shape (NOAREA, NOAREA), B Matrix (i.e.
+ admittance matrix) in DC power flow
+ :param int NII: specify the position of the diagonal element
+ :param float BIJ: the amount of admittance value to add on
+
+ .. note:: BB is modified in place
+ """
+ BB[NII, NII] += BIJ
diff --git a/NARP/reliabilityassessment/monte_carlo/assign.py b/NARP/reliabilityassessment/monte_carlo/assign.py
new file mode 100644
index 0000000..a4a5c31
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/assign.py
@@ -0,0 +1,87 @@
+import numpy as np
+
+
+def _assign(INJ, LOD, LT, NN, NLS):
+ """
+ Assigns injections at buses so as to balance positive and negative injections
+
+ :param numpy.ndarray INJ: 1D array of input injections
+ :param numpy.ndarray LOD: 1D array of loads with shape (NLINES, )
+ :param numpy.ndarray LT: 1D array with shape (NN, ) (i.e., NOAREA)
+ LT[I] contains the actual node index corresponding to node I of the reduced
+ admittance matrix
+ :param int NN: user-specified dimension for the arrays
+ :param int NLS: indicator of loss sharing mode, 0: LOSS SHARING, 1: NO LOSS SHARING
+ :return: (*tuple*) -- a pair of numpy arrays, INJ1: vector of modified injections
+ with shape (NN, ), LODC: vector of load curtailments with shape (NN, )
+ """
+
+ LODC = np.zeros(NN)
+ INJ1 = INJ[:NN].copy()
+
+ SUMN = 0.0
+ SUMP = 0.0
+
+ SUMN = sum(e for e in INJ1 if e <= 0)
+ SUMP = sum(e for e in INJ1 if e > 0)
+
+ SUMN = abs(SUMN)
+ if SUMN <= SUMP:
+ INJ1 = [e * SUMN / SUMP if e > 0 else e for e in INJ1]
+ return INJ1, LODC
+
+ if NLS != 0:
+ for i in range(NN):
+ if INJ1[i] > 0:
+ continue
+ PINT = INJ1[i]
+ INJ1[i] = INJ1[i] * SUMP / SUMN
+ J = LT[i]
+ LODC[J] = INJ1[i] - PINT
+ return INJ1, LODC
+
+ SUML = sum(LOD[:NN])
+
+ DIFF = SUMN - SUMP
+ LODC[LT[:NN]] = LOD[LT[:NN]] * DIFF / SUML
+ INJ1[:NN] += LODC[LT[:NN]]
+
+ return INJ1, LODC
+
+
+# vectorized version:
+def assign(INJ, LOD, LT, NN, NLS):
+ """
+ Assigns injections at buses so as to balance positive and negative injections
+
+ :param numpy.ndarray INJ: 1D array of input injections
+ :param numpy.ndarray LOD: 1D array of loads with shape (NLINES, )
+ :param numpy.ndarray LT: 1D array with shape (NN, ) (i.e., NOAREA)
+ LT[I] contains the actual node index corresponding to node I of the reduced
+ admittance matrix
+ :param int NN: user-specified dimension for the arrays
+ :param int NLS: indicator of loss sharing mode, 0: LOSS SHARING, 1: NO LOSS SHARING
+ :return: (*tuple*) -- a pair of numpy arrays, INJ1: vector of modified injections
+ with shape (NN, ), LODC: vector of load curtailments with shape (NN, )
+ """
+
+ LODC = np.zeros(NN)
+ INJ1 = INJ[:NN].copy()
+
+ SUMN = abs(sum(INJ1[INJ1 <= 0]))
+ SUMP = sum(INJ1[INJ1 > 0])
+
+ SUMN = abs(SUMN)
+ if SUMN <= SUMP: # (Positive injection is larger)
+ INJ1[INJ1 > 0] *= SUMN / SUMP if SUMP > 0 else 1
+ elif NLS != 0: # (Negative injection is larger and no loss sharing)
+ PINT = INJ1[INJ1 <= 0].copy()
+ INJ1[INJ1 <= 0] *= SUMP / SUMN
+ LODC[LT[INJ1 <= 0]] = INJ1[INJ1 <= 0] - PINT
+ else: # (Negative injection is larger and loss sharing)
+ SUML = sum(LOD[:NN])
+ DIFF = SUMN - SUMP
+ LODC[LT] = LOD[LT] * DIFF / SUML
+ INJ1 += LODC[LT]
+
+ return INJ1, LODC
diff --git a/NARP/reliabilityassessment/monte_carlo/conls.py b/NARP/reliabilityassessment/monte_carlo/conls.py
new file mode 100644
index 0000000..a8a18e5
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/conls.py
@@ -0,0 +1,240 @@
+import numpy as np
+
+
+def conls(BC, INJ, NX, NR, LT, BLP, LP, BN, LOD, NLS):
+ """
+ Update the coefficient-matrices of constraints, and other quantities, for the LP-based DCOPF
+ (load shedding) under the "load-sharing" mode (in short, "LS")
+
+ :param numpy.ndarray BC: shape (NOAREA, NOAREA)
+ the realistic size is determined on-the-fly
+ a matrix used in LP-based DCOPF
+ :param numpy.ndarray INJ: shape (NOAREA,)
+ the realistic size is determined on-the-fly
+ a vector used in LP-based DCOPF
+ (possibly) related to the injection at each bus(i.e., "area")
+ :param int NX: an integer used in LP-based DCOPF
+ (possibly) related to the variables/constraint dimensions in LP-based DCOPF
+ :param int NR: an integer used in LP-based DCOPF
+ (possibly) means the index of the reference bus (area) in power flow computation
+ :param numpy.ndarray LT: 1D array with initial shape (NOAREA,)
+ LT[I] contains the actual node index corresponding to node I
+ of the reduced admittance matrix here;
+ :param numpy.ndarray BLP: 2D array with shape (NLINES, 3)
+ BLP[I, 0]: admittance of the line at the I-th entry of LP
+ BLP[I, 1]: capacity (MW)
+ BLP[I, 2]: backward capacity (MW)
+ :param numpy.ndarray LP: 2D array with shape (NLINES, 3)
+ LP[I, 0]: line number, set to I
+ LP[I, 1]: starting node
+ LP[I, 2]: ending node
+ :param numpy.ndarray BN: 2D array with shape (NOAREA, 5)
+ BN[I, 0]: area (bus/node) number
+ BN[I, 1]: area load, overridden by net injection after
+ calling :py:func: `tm2`
+ BN[I, 2]: area net injection, overridden by load
+ curtailment after calling :py:func: `tm2`
+ BN[I, 3]: area constraint on total power flow
+ BN[I, 4]: a helper value storing the modified "constraint on
+ sum of flows at this area"
+ :param numpy.ndarray LOD: 1D array with shape (NLINES, 3)
+ vector of loads
+ :param int NLS: an indicator of loss sharing mode,
+ 0 LOSS SHARING
+ 1 NO LOSS SHARING
+ :return: (*tuple*) -- a tuple of three scalars, M, N, N1;
+ M: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of constraints
+ N: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of "decision variables" in the LP
+ N1: an integer used in LP-based DCOPF
+ A: array with initial shape (200, 250),
+ the realistic size is determined on-the-fly
+ a matrix used in LP-based DCOPF
+ (possibly) means the coefficient matrix of all constraints in LP
+ XOB: initial shape (250, )
+ the realistic size is determined on-the-fly
+ a vector used in linear programming(LP)-based DCOPF
+ values are either 0 or 1; 'float' type in original Fortran code.
+ XOBI: initial shape (2, 250)
+ the realistic size is determined on-the-fly
+ a matrix used in linear programming(LP)-based DCOPF
+ values are either 0 or 1; 'float' type in original Fortran code.
+ IBAS: 1D integer array with initial shape (250, ) (possibly (M,))
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ (possibly) means the indices of the "basis-vector" used in LP
+ BS: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ B: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a vector used in linear programming(LP)-based DCOPF
+ B1: 1D array with initial shape (200, ) (possibly (M,))
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ TAB: initial shape (200, 250) (possibly (M, N))
+ the realistic size is determined on-the-fly
+ a matrix used in linear programming(LP)-based DCOPF
+ And modify several arrays in place.
+ """
+
+ NX1 = NX + 1
+ NP1 = 3 * NX + 1
+ NP = 2 * NX + 1
+ NP2 = NP1 + NX1
+ NMAX1 = 250
+ NMAX2 = 200
+
+ # Index-related array must be initialized "-1"!
+ IEQ = (-1) * np.ones(NX + 1, dtype=int) # originally in Fortran np.zeros(20)
+ # maybe also Ok to initialize it by np.zeros(NOAREA) (check later)
+
+ XOB = np.zeros(NMAX1)
+ XOBI = np.zeros((2, NMAX1))
+ A = np.zeros((NMAX2, NMAX1))
+ B = np.zeros(NMAX2)
+ BS = np.zeros(NMAX2)
+
+ IEQ[LT[:NX]] = range(NX)
+
+ IEQ[NR] = NX
+ NXD = 2 * NX
+
+ for i in range(NX):
+ JJ = 0
+ for j in range(NX):
+ A[i, JJ] = BC[i, j]
+ A[i, JJ + 1] = -BC[i, j]
+ JJ += 2
+ A[i, NXD + i] = -1.0
+ j = LT[i]
+ B[i] = -LOD[j]
+ A[i, NP1 + i] = -1.0
+
+ NX1 = NX + 1
+ NL = BLP.shape[0]
+ for i in range(NL):
+ for j in range(1, 3):
+ if LP[i, j] == NR:
+ I1 = IEQ[LP[i, 3 - j]] * 2
+ A[NX, I1] += BLP[i, 0]
+ A[NX, I1 + 1] -= BLP[i, 0]
+
+ A[NX, NX + NP - 1] = 1.0 # A[NX1, NX + NP] in original Fortran
+ B[NX] = -LOD[NR] # B[NX1] in original Fortran
+ A[NX, NP1 + NX] = 1.0 # A[NX1, NP1 + NX1] in original Fortran
+ A[NX, :NP2] = -A[NX, :NP2]
+
+ NXD = 2 * NX
+ XOB[NXD : NXD + NX1] = 1.0
+
+ for i in range(NX1):
+ A[i + NX1, NP2 + i] = 1.0
+ A[i + NX1, NXD + i] = 1.0
+ j = LT[i]
+ B[i + NX1] = LOD[j]
+
+ NX2 = 2 * NX1
+ NP2 = 2 * NX + 2 * NX1
+ for i in range(NX1):
+ A[i + NX2, NP1 + i] = 1.0
+ A[i + NX2, NP2 + NX1 + i] = 1.0
+ j = LT[i]
+ B[i + NX2] = LOD[j] + INJ[i]
+
+ NX3 = 3 * NX1
+ NP22 = NP2 + 2 * NX1
+ NP3 = NP22 + NL
+
+ for i in range(NL):
+ I1 = NX3 + i
+ I2 = I1 + NL
+ J1 = LP[i, 1]
+ J2 = LP[i, 2]
+ J4 = IEQ[J1]
+ J5 = IEQ[J2]
+ J42 = J4 * 2 + 1 # J4 * 2 in original Fortran
+ J41 = J42 - 1
+ J52 = J5 * 2 + 1 # J5 * 2 in original Fortran
+ J51 = J52 - 1
+ if J4 < NX: # "if J4 <= NX" in original Fortran
+ A[I1, J41] = BLP[i, 0]
+ A[I1, J42] = -BLP[i, 0]
+ A[I2, J41] = -BLP[i, 0]
+ A[I2, J42] = BLP[i, 0]
+ if J5 < NX: # "if J5 <= NX" in original Fortran
+ A[I1, J51] = -BLP[i, 0]
+ A[I1, J52] = BLP[i, 0]
+ A[I2, J51] = BLP[i, 0]
+ A[I2, J52] = -BLP[i, 0]
+ A[I1, NP22 + i] = 1.0
+ A[I2, NP3 + i] = 1.0
+ B[I1] = BLP[i, 1]
+ B[I2] = BLP[i, 2]
+
+ NXL = NP3 + NL
+ NXD1 = NXD + 1
+ M = 3 * NX1 + 2 * NL
+ N = NXL
+
+ for i in range(NX1):
+ for j in range(NXD):
+ K = LT[i]
+ A[i + M, j] = A[i, j]
+ B[i + M] = BN[K, 3]
+ A[i + M + NX1, j] = -A[i, j]
+ B[i + M + NX1] = BN[K, 4]
+ A[i + M, i + N] = 1.0
+ A[i + M + NX1, i + N + NX1] = 1.0
+
+ M += 2 * NX1
+ N += 2 * NX1
+
+ B1 = np.zeros(
+ M
+ ) # in Fortran, B1 is by default 'int' type with size 'NMAX2', which is no need here.
+ B1[:M] = B[:M]
+
+ B[:NX1] = B[:NX1].clip(max=0.0)
+
+ A[np.where(B[:M] < 0), :N] *= -1
+ B[:M] = abs(B[:M])
+
+ # for i in range(NX1):
+ # A[i, i + N] = 1.0
+ # XOBI[0, i + N] = 1.0
+ A[tuple([np.arange(NX1), np.arange(N, N + NX1)])] = 1.0
+ XOBI[0, N : N + NX1] = 1.0
+ XOBI[1, :N] = XOB[:N]
+
+ N += NX1
+ NXD2 = NXD1 + 2 * NX1
+ II = -1
+
+ # Index-related array must be initialized "-1"!
+ IBAS = (-1) * np.ones(M, dtype=int) # size = NMAX1 in original Fortran
+
+ for i in range(NXD2 - 1, N):
+ II += 1
+ IBAS[II] = i
+
+ TAB = np.zeros((M, N)) # np.zeros((NMAX2, NMAX1)) in original Fortran
+
+ NX2 = NX1 + 1
+ j = -1
+ for i in range(NX2 - 1, M):
+ j += 1
+ TAB[j, :N] = A[i, :N]
+ BS[j] = B[i]
+
+ for i in range(NX1):
+ j += 1
+ TAB[j, :N] = A[i, :N]
+ BS[j] = B[i]
+
+ A[:M, :N] = TAB[:M, :N]
+
+ N1 = N - NX1
+
+ return M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB
diff --git a/NARP/reliabilityassessment/monte_carlo/connls.py b/NARP/reliabilityassessment/monte_carlo/connls.py
new file mode 100644
index 0000000..49f537b
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/connls.py
@@ -0,0 +1,249 @@
+import numpy as np
+
+
+def connls(BC, INJ, INJB, NX, NR, LT, BLP, LP, BN, LOD, NLS):
+ """
+ Update the coefficient-matrices of constraints, and other quantities, for the LP-based DCOPF
+ (load shedding) under the "no-load-sharing" mode (in short, "NLS")
+
+ :param numpy.ndarray BC: shape (NOAREA, NOAREA)
+ the realistic size is determined on-the-fly
+ a matrix used in LP-based DCOPF
+ :param numpy.ndarray INJ: shape (NOAREA,)
+ the realistic size is determined on-the-fly
+ a vector used in LP-based DCOPF
+ (possibly) related to the injection at each bus(i.e., "area")
+ :param numpy.ndarray INJB: shape (NOAREA,)
+ the realistic size is determined on-the-fly
+ a vector used in LP-based DCOPF
+ (possibly) related to the injection at each bus(i.e., "area")
+ :param int NX: an integer used in LP-based DCOPF
+ (possibly) related to the variables/constraint dimensions in LP-based DCOPF
+ :param int NR: an integer used in LP-based DCOPF
+ (possibly) means the index of the reference bus (area) in power flow computation
+ :param numpy.ndarray LT: 1D array with initial shape (NOAREA,)
+ LT[I] contains the actual node index corresponding to node I
+ of the reduced admittance matrix here;
+ :param numpy.ndarray BLP: 2D array with shape (NLINES, 3)
+ BLP[I, 0]: admittance of the line at the I-th entry of LP
+ BLP[I, 1]: capacity (MW)
+ BLP[I, 2]: backward capacity (MW)
+ :param numpy.ndarray LP: 2D array with shape (NLINES, 3)
+ LP[I, 0]: line number, set to I
+ LP[I, 1]: starting node
+ LP[I, 2]: ending node
+ :param numpy.ndarray BN: 2D array with shape (NOAREA, 5)
+ BN[I, 0]: area (bus/node) number
+ BN[I, 1]: area load, overridden by net injection after
+ calling :py:func: `tm2`
+ BN[I, 2]: area net injection, overridden by load
+ curtailment after calling :py:func: `tm2`
+ BN[I, 3]: area constraint on total power flow
+ BN[I, 4]: a helper value storing the modified "constraint on
+ sum of flows at this area"
+ :param numpy.ndarray LOD: 1D array with shape (NLINES, 3)
+ vector of loads
+ :param int NLS: an indicator of loss sharing mode,
+ 0 LOSS SHARING
+ 1 NO LOSS SHARING
+
+ :return: (*tuple*) -- a tuple of three scalars, M, N, N1;
+ M: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of constraints
+ N: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of "decision variables" in the LP
+ N1: an integer used in LP-based DCOPF
+
+ A: array with initial shape (200, 250),
+ the realistic size is determined on-the-fly
+ a matrix used in LP-based DCOPF
+ (possibly) means the coefficient matrix of all constraints in LP
+
+ XOB: initial shape (250, )
+ the realistic size is determined on-the-fly
+ a vector used in linear programming(LP)-based DCOPF
+ values are either 0 or 1; 'float' type in original Fortran code.
+
+ XOBI: initial shape (2, 250)
+ the realistic size is determined on-the-fly
+ a matrix used in linear programming(LP)-based DCOPF
+ values are either 0 or 1; 'float' type in original Fortran code.
+
+ IBAS: 1D integer array with initial shape (250, ) (possibly (M,))
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ (possibly) means the indices of the "basis-vector" used in LP
+ BS: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+
+ B: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a vector used in linear programming(LP)-based DCOPF
+
+ B1: 1D array with initial shape (200, ) (possibly (M,))
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+
+ TAB: initial shape (200, 250) (possibly (M, N))
+ the realistic size is determined on-the-fly
+ a matrix used in linear programming(LP)-based DCOPF
+
+ And modify several arrays in place.
+ """
+
+ NMAX2 = 200
+ NMAX1 = 250
+
+ # Index-related array must be initialized "-1"!
+ IEQ = (-1) * np.ones(NX + 1, dtype=int) # originally in Fortran np.zeros(20)
+ # maybe also Ok to initialize it by np.zeros(NOAREA) (check later)
+
+ XOB = np.zeros(NMAX1)
+ XOBI = np.zeros((2, NMAX1))
+ A = np.zeros((NMAX2, NMAX1))
+ B = np.zeros(NMAX2)
+ BS = np.zeros(NMAX2)
+
+ IEQ[LT[:NX]] = range(NX)
+
+ IEQ[NR] = NX
+ NXD = 2 * NX
+
+ for i in range(NX):
+ JJ = 0
+ for j in range(NX):
+ A[i, JJ] = BC[i, j]
+ A[i, JJ + 1] = -BC[i, j]
+ JJ += 2
+ A[i, NXD + i] = -1.0
+ B[i] = INJ[i]
+
+ NX1 = NX + 1
+ NL = BLP.shape[0]
+ for i in range(NL):
+ for j in range(1, 3):
+ if LP[i, j] == NR:
+ I1 = IEQ[LP[i, 3 - j]] * 2
+ A[NX, I1] += BLP[i, 0]
+ A[NX, I1 + 1] -= BLP[i, 0]
+
+ NP = 2 * NX + 1 # check here; maybe no need +1?
+ A[NX, NX + NP - 1] = 1 # A[NX1, NX + NP] in original Fortran
+ B[NX] = INJB[NR] # B[NX1] in original Fortran
+
+ NP1 = 3 * NX + 1
+ A[NX, :NP1] = -A[NX, :NP1]
+
+ if B[NX] <= 0: # B[NX1] in original Fortran
+ XOB[NP1 - 1] = 1.0 # XOB[NP1] in original Fortran
+
+ NXD = 2 * NX
+ for i in range(NX1):
+ if B[i] <= 0:
+ XOB[NXD + i] = 1.0
+
+ NP1 = 3 * NX + 1
+
+ for i in range(NX1):
+ A[i + NX1, NP1 + i] = 1
+ A[i + NX1, NXD + i] = 1
+
+ if NLS != 0:
+ B[NX1 : NX1 + NX1] = abs(B[:NX1])
+ else:
+ B[NX1 : NX1 + NX1] = B[NX1 : NX1 + NX1].clip(min=0) + LOD[LT[:NX1]]
+
+ NX2 = 2 * NX1
+ NP2 = 2 * NX + 2 * NX1
+ NP3 = NP2 + NL
+
+ for i in range(NL):
+ I1 = NX2 + i
+ I2 = I1 + NL
+ J1 = LP[i, 1]
+ J2 = LP[i, 2]
+ J4 = IEQ[J1]
+ J5 = IEQ[J2]
+ J42 = J4 * 2 + 1 # J4 * 2 in original Fortran
+ J41 = J42 - 1
+ J52 = J5 * 2 + 1 # J5 * 2 in original Fortran
+ J51 = J52 - 1
+ if J4 < NX: # "if J4 <= NX" in original Fortran
+ A[I1, J41] = BLP[i, 0]
+ A[I1, J42] = -BLP[i, 0]
+ A[I2, J41] = -BLP[i, 0]
+ A[I2, J42] = BLP[i, 0]
+ if J5 < NX: # "if J5 <= NX" in original Fortran
+ A[I1, J51] = -BLP[i, 0]
+ A[I1, J52] = BLP[i, 0]
+ A[I2, J51] = BLP[i, 0]
+ A[I2, J52] = -BLP[i, 0]
+ A[I1, NP2 + i] = 1
+ A[I2, NP3 + i] = 1
+ B[I1] = BLP[i, 1]
+ B[I2] = BLP[i, 2]
+
+ NXL = NP3 + NL
+ NXD1 = NXD + 1
+ M = 2 * NX1 + 2 * NL
+ N = NXL
+
+ for i in range(NX1):
+ for j in range(NXD):
+ K = LT[i]
+ A[i + M, j] = A[i, j]
+ B[i + M] = BN[K, 4]
+ A[i + M + NX1, j] = -A[i, j]
+ B[i + M + NX1] = BN[K, 3]
+ A[i + M, i + N] = 1.0
+ A[i + M + NX1, i + N + NX1] = 1.0
+
+ M += 2 * NX1
+ N += 2 * NX1
+
+ B1 = np.zeros(
+ M
+ ) # in Fortran, B1 is by default 'int' type with size 'NMAX2', which is no need here.
+ B1[:M] = B[:M]
+
+ B[:NX1] = B[:NX1].clip(max=0.0)
+
+ A[np.where(B[:M] < 0), :N] *= -1
+ B[:M] = abs(B[:M])
+
+ for i in range(NX1):
+ A[i, i + N] = 1.0
+ XOBI[0, i + N] = 1.0
+
+ XOBI[1, :N] = XOB[:N]
+
+ N += NX1
+ NXD2 = NXD1 + NX1
+ II = -1 # II = 0 in original Fortran
+
+ # Index-related array must be initialized "-1"!
+ IBAS = (-1) * np.ones(M, dtype=int) # size = NMAX1 in original Fortran
+ for i in range(NXD2 - 1, N):
+ II += 1
+ IBAS[II] = i
+
+ TAB = np.zeros((M, N)) # np.zeros((NMAX2, NMAX1)) in original Fortran
+
+ NX2 = NX1 + 1
+ j = -1 # j = 0 in original Fortran
+ for i in range(NX2 - 1, M):
+ j += 1
+ TAB[j, :N] = A[i, :N]
+ BS[j] = B[i]
+
+ for i in range(NX1):
+ j += 1
+ TAB[j, :N] = A[i, :N]
+ BS[j] = B[i]
+
+ A[:M, :N] = TAB[:M, :N]
+
+ N1 = N - NX1
+
+ return M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB
diff --git a/NARP/reliabilityassessment/monte_carlo/contrl.py b/NARP/reliabilityassessment/monte_carlo/contrl.py
new file mode 100644
index 0000000..3e9cb9f
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/contrl.py
@@ -0,0 +1,388 @@
+from reliabilityassessment.monte_carlo.events import events
+from reliabilityassessment.monte_carlo.remove import remove
+from reliabilityassessment.monte_carlo.report import report
+
+
+def contrl(
+ RFLAG,
+ CLOCK,
+ NUMINQ,
+ IPOINT,
+ EVNTS,
+ ATRIB,
+ FINISH,
+ ITAB,
+ INDX,
+ SUSTAT,
+ DPLOLE,
+ EUES,
+ HLOLE,
+ LSTEP,
+ NAMA,
+ NFCST,
+ PLNDST,
+ JHOUR,
+ JPLOUT,
+ IQ,
+ RATES,
+ NHRSYR,
+ JSTEP,
+ JFREQ,
+ MXPLHR,
+ PROBG,
+ RATING,
+ DERATE,
+ CAPOWN,
+ CAPCON,
+ PROBL,
+ JENT,
+ INTCH,
+ MXCRIT,
+ JCRIT,
+ HRLOAD,
+ FCTERR,
+ LSFLG,
+ LINENO,
+ LP,
+ LT,
+ BB,
+ ZB,
+ BLP0,
+ BLPA,
+ IOI,
+ BN,
+ NR,
+ NLS,
+ CVTEST,
+ SSQ,
+ NORR,
+ XLAST,
+ INTV,
+ INTVT,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ LOLGHA,
+ LOLGPA,
+ LOLGHP,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGPA,
+ MGNGHP,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+ IGSEED,
+ ILSEED,
+):
+ """
+ Call function 'events' to pull the next event from the list and check its time
+ stamp, if the whole simulation ends, function 'report' will be called instead.
+
+ :param numpy.ndarray ATRIB: events attribute vector of length 2
+ ATRIB[0] -- global simulation clock (in unit: hour)
+ ATRIB[1] -- int, simulation event type
+ 1: hourly
+ 2: weekly
+ 3: quarterly
+ 4: yearly
+ :param int NUMINQ: up-to-date total number of event entries (inquires)
+ :param int IPOINT: pointer of the first (already/previously stored) entry
+ initial value = -1
+ :param numpy.ndarray EVNTS: 1D array of events
+ :param int RFLAG: indicator of whether or not to generate an output report
+ with all the so-far obtained reliability assessment results
+ """
+
+ print("In function 'contrl': ")
+
+ while True:
+ if IPOINT == -1: # 0 in original Fortran
+ print("Error: Pointer to event list is -1!")
+ return
+
+ if CLOCK > EVNTS[IPOINT + 1]:
+ print("Error: next event to occur is scheduled prior to the current time!")
+ print(
+ "ATRIB[0] (the time clock this event will occur (unit: hr)) is: %.2f"
+ % (ATRIB[0])
+ )
+ print("ATRIB[1] (the type of this event) is: %d" % (ATRIB[1]))
+ return
+
+ if RFLAG == 1:
+ return
+
+ if EVNTS[IPOINT + 1] > (FINISH + 1.0):
+ IYEAR = CLOCK // 8760 # IYEAR = int(CLOCK / 8760)
+ ITAB = report(
+ IYEAR,
+ ITAB,
+ INDX,
+ SUSTAT,
+ DPLOLE,
+ EUES,
+ HLOLE,
+ LSTEP,
+ NAMA,
+ NFCST,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+ )
+ return
+ else:
+ MFA, NUMINQ, IPOINT = remove(NUMINQ, ATRIB, EVNTS, IPOINT)
+ CLOCK = ATRIB[0]
+ NUMBER = int(ATRIB[1]) # simulation type
+
+ (
+ RFLAG,
+ IPOINT,
+ MFA,
+ NUMINQ,
+ SSQ,
+ XLAST,
+ INTVT,
+ ITAB,
+ IQ,
+ JHOUR,
+ ) = events(
+ NUMBER,
+ RFLAG,
+ CLOCK,
+ ATRIB,
+ MFA,
+ NUMINQ,
+ IPOINT,
+ EVNTS,
+ PLNDST,
+ JHOUR,
+ JPLOUT,
+ IQ,
+ RATES,
+ NHRSYR,
+ JSTEP,
+ JFREQ,
+ MXPLHR,
+ PROBG,
+ RATING,
+ DERATE,
+ CAPOWN,
+ CAPCON,
+ PROBL,
+ JENT,
+ INTCH,
+ MXCRIT,
+ JCRIT,
+ NFCST,
+ HRLOAD,
+ FCTERR,
+ LSFLG,
+ LOLGHA,
+ LOLGPA,
+ LOLGHP,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGPA,
+ MGNGHP,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ LINENO,
+ LP,
+ LT,
+ BB,
+ ZB,
+ BLP0,
+ BLPA,
+ IOI,
+ BN,
+ NR,
+ NLS,
+ CVTEST,
+ DPLOLE,
+ EUES,
+ FINISH,
+ HLOLE,
+ NAMA,
+ SSQ,
+ SUSTAT,
+ XLAST,
+ INTV,
+ INTVT,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ LSTEP,
+ NORR,
+ INDX,
+ ITAB,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+ IGSEED,
+ ILSEED,
+ )
diff --git a/NARP/reliabilityassessment/monte_carlo/cvchk.py b/NARP/reliabilityassessment/monte_carlo/cvchk.py
new file mode 100644
index 0000000..75cb245
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/cvchk.py
@@ -0,0 +1,138 @@
+def cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ):
+ """
+ Check the convergence of the sequential Monte Carlo simulation. The formulation of
+ convergence criteria refers to the slide in the following directory
+ https://www.dropbox.com/s/mbooxvj85jb2oi2/module_4-3.pdf?dl=0, on page 25,
+ 26 and 31.
+
+ :param int CLOCK: global time clock used in the simulation
+ :param int FINISH: number of hours to terminate if fails to converge, typically
+ integer multiples of the total hours of a year
+ :param int IYEAR: integer index of the year
+ :param float CVTEST: threshold value for convergence, defaults to 0.025 if 0 is set
+ :param float SUM: cumulative quantities for a specific reliability index
+ :param float XLAST: reliability index of the last simulation year
+ :param float SSQ: cumulative square error of the specific reliability index
+ :return: (*tuple*) -- a tuple of three scalars, RFLAG: an integer indicator of
+ “early return or not” and updated SSQ, XLAST
+
+ .. note:: Variable names may not be the same as the ones in the reference slides
+ """
+
+ RFLAG = int(CLOCK >= FINISH)
+
+ DIFF = SUM - XLAST
+ XLAST = SUM
+ SSQ = SSQ + DIFF**2
+
+ # window size for statistics cannot be less than 5 years (simulation runs)
+ # assume IYEAR is 0 based here
+ if IYEAR < 5:
+ return RFLAG, SSQ, XLAST
+
+ XMEAN = SUM / IYEAR
+
+ XX = CVTEST * XMEAN if CVTEST else 0.025 * XMEAN
+
+ STDERR = pow(((SSQ / IYEAR) - XMEAN**2) / IYEAR, 0.5)
+
+ # how many multiples of our "COV" to the given threshold
+ XKCPXX = STDERR / XX if XX else 0.0
+
+ print(
+ f"{IYEAR}-th REPLICATION; SUM={SUM} NEW={DIFF} MEAN={XMEAN} CONVERGENCE="
+ f"{XKCPXX}"
+ )
+
+ # edge case, to avoid permanent-loop
+ if XMEAN == 0.0:
+ return RFLAG, SSQ, XLAST
+
+ # The following part exists in the original Fortran code, but since 'STDERR'
+ # will not be negative based on theoretical proof, so we comment here
+ # if STDERR < 0.0:
+ # return RFLAG, SSQ, XLAST
+
+ if STDERR < XX:
+ RFLAG = 1
+ print("**************** SIMULATION CONVERGED ****************")
+ print(f" CLOCK = {CLOCK} STD = {STDERR} TEST = {XX}")
+
+ return RFLAG, SSQ, XLAST
+
+
+def _cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ):
+ """
+ Check the convergence of the sequential Monte Carlo simulation. The formulation of
+ convergence criteria refers to the slide in the following directory
+ https://www.dropbox.com/s/mbooxvj85jb2oi2/module_4-3.pdf?dl=0, on page 25,
+ 26 and 31.
+
+ :param int CLOCK: global time clock used in the simulation
+ :param int FINISH: number of hours to terminate if fails to converge, typically
+ integer multiples of the total hours of a year
+ :param int IYEAR: integer index of the year
+ :param float CVTEST: threshold value for convergence, defaults to 0.025 if 0 is set
+ :param float SUM: cumulative quantities for a specific reliability index
+ :param float XLAST: reliability index of the last simulation year
+ :param float SSQ: cumulative square error of the specific reliability index
+ :return: (*tuple*) -- a tuple of three scalars, RFLAG: an integer indicator of
+ “early return or not” and updated SSQ, XLAST
+
+ .. note:: Variable names may not be the same as the ones in the reference slides
+ """
+
+ RFLAG = 0
+
+ if CLOCK >= FINISH:
+ RFLAG = 1
+
+ DIFF = SUM - XLAST
+ XLAST = SUM
+ SSQ = SSQ + DIFF**2
+
+ if (
+ IYEAR < 5
+ ): # window size for statistics cannot be less than 5 years (simulation runs)
+ return RFLAG, SSQ, XLAST
+
+ XMEAN = SUM / IYEAR
+ if CVTEST == 0.0:
+ XX = 0.025 * XMEAN # 0.025 is a default value for CVTEST
+ else:
+ XX = CVTEST * XMEAN
+
+ VAR = (SSQ / IYEAR) - (XMEAN**2)
+ STD = pow(VAR, 0.5)
+
+ STDERR = STD / (IYEAR**0.5)
+ XKCPXX = 0.0
+ if XX != 0.0:
+ XKCPXX = STDERR / XX # how many multiples of our "COV" to the given threshold
+
+ print(
+ IYEAR,
+ " -th REPLICATION; SUM=",
+ SUM,
+ " NEW=",
+ DIFF,
+ " MEAN=",
+ XMEAN,
+ " CONVERGENCE=",
+ XKCPXX,
+ )
+
+ if XMEAN == 0.0: # edge case, to avoid permanent-loop
+ return RFLAG, SSQ, XLAST
+
+ # The following part exists in the original Fortran code, but since 'STDERR'
+ # will not be negative based on theoretical proof, so we comment here
+ # if STDERR < 0.0:
+ # return RFLAG, SSQ, XLAST
+
+ if STDERR < XX:
+ RFLAG = 1
+ print("**************** SIMULATION CONVERGED ****************")
+ print(" CLOCK = ", CLOCK, "' STD = ", STDERR, " TEST = ", XX)
+
+ return RFLAG, SSQ, XLAST
diff --git a/NARP/reliabilityassessment/monte_carlo/dgeco.py b/NARP/reliabilityassessment/monte_carlo/dgeco.py
new file mode 100644
index 0000000..3ee955e
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/dgeco.py
@@ -0,0 +1,144 @@
+from reliabilityassessment.monte_carlo.dgefa import dgefa
+
+
+def dgeco(A, N):
+ """
+ Factors a double precision matrix by Gaussian elimination
+ and estimates the matrix's condition number (though not used).
+
+ :param numpy.ndarray A: the matrix to be factored
+ :param int N: the dimension of matrix A
+ :return: (*numpy.ndarray*) -- IPVT: an integer vector of pivot indices with
+ shape (N, )
+
+ .. note:: 1. Matrix A is modified in place.
+ 2. If `RCOND` is not set, :py:func: `dgefa` is slightly faster.
+ 3. `RCOND` and `Z` are designed to investigate the root cause of
+ potential numerical failures during the matrix factorization (inversion),
+ which are not used for now. The logic is kept for references only.
+
+ RCOND: a float to estimate the reciprocal condition of A. For the
+ system A*x = b , relative perturbations in A and b of size epsilon may
+ cause relative perturbations in x of size epsilon/RCOND. if RCOND is
+ so small that the logical expression 1.0 + RCOND == 1.0 is true,
+ then A may be singular to working precision. In particular, RCOND is
+ zero if the exact singularity is detected or the estimate underflows.
+
+ Z: a temporal numpy array for intermediate calculation usage. If A is
+ close to a singular matrix, then Z is an approximate null vector in the
+ sense that Norm(A*Z) = RCOND * norm(A) * norm(Z).
+ """
+ # Some intermediate variables or original returning variables are never used outside
+ # e.g. YNORM, ANORM, RCOND and Z
+ # Currently still keep them in case of any special debugging need in the future.
+
+ # Compute 1-norm of A, i.e., is the maximum abs. column sum
+ # ANORM = 0.0
+ # for j in range(N):
+ # ANORM = max(ANORM, sum(abs(A[:, j])))
+
+ # Factorization by calling dgefa
+ # The 1st return parameter 'INFO' is not used anywhere.
+ # Currently still keep it as a placeholder for future debugging purposes
+ _, IPVT = dgefa(A, N)
+
+ # EK = 1.0
+ # Z = np.zeros(N)
+ # for k in range(N):
+ # if Z[k] != 0.0:
+ # EK = abs(EK) if Z[k] <= 0 else -abs(EK)
+ # if abs(EK - Z[k]) > abs(A[k, k]):
+ # S = abs(A[k, k]) / abs(EK - Z[k])
+ # Z *= S
+ # EK = S * EK
+
+ # WK = EK - Z[k]
+ # WKM = -EK - Z[k]
+ # S = abs(WK)
+ # SM = abs(WKM)
+ # if A[k, k] != 0.0:
+ # WK = WK / A[k, k]
+ # WKM = WKM / A[k, k]
+ # else:
+ # WK = 1.0
+ # WKM = 1.0
+
+ # KP1 = k + 1
+ # if KP1 <= N - 1:
+ # for j in range(KP1, N): # in original Fortran: from KP1 to N (included)
+ # SM += abs(Z[j] + WKM * A[k, j])
+ # Z[j] += WK * A[k, j]
+ # S += abs(Z[j])
+ # if S < SM:
+ # T = WKM - WK
+ # WK = WKM
+ # # in original Fortran: from KP1 to N (included)
+ # for j in range(KP1, N):
+ # Z[j] += T * A[k, j]
+ # Z[k] = WK
+
+ # S = 1.0 / sum(Z)
+ # Z *= S
+
+ # # L, U stands for the lower and upper-triangular decomposition of A
+ # # Y, V are the names for certain intermediate matrices during computation
+ # # W stands for (possibly) the "old" /"initial" value of the Z vector
+
+ # # Solve TRANS(L)*Y = W
+ # for KB in range(N):
+ # K = N - 1 - KB
+ # if K < N - 1:
+ # Z[K] += np.dot(
+ # A[K + 1 : N, K], Z[K + 1 : N]
+ # ) # DDOT(N-1-K,A(K+1,K),1,Z(K+1),1)
+ # if abs(Z[K]) > 1.0:
+ # S = 1.0 / abs(Z[K])
+ # Z *= S
+ # L = IPVT[K]
+ # Z[L], Z[K] = Z[K], Z[L]
+
+ # S = 1.0 / sum(Z)
+ # Z *= S
+
+ # YNORM = 1.0
+ # # Solve L*V = Y
+ # for K in range(N):
+ # L = IPVT[K]
+ # T = Z[L]
+ # Z[L], Z[K] = Z[K], Z[L]
+ # if K < N - 1:
+ # Z[K + 1 : N] += T * A[K + 1 : N, K] # DAXPY(N-1-K,T,A(K+1,K),1,Z(K+1),1)
+ # if abs(Z[K]) > 1.0:
+ # S = 1.0 / abs(Z[K])
+ # Z *= S
+ # YNORM = S * YNORM
+
+ # S = 1.0 / sum(Z)
+ # Z *= S
+ # YNORM = S * YNORM
+
+ # # Solve U*Z = V
+ # for KB in range(N):
+ # K = N - 1 - KB
+ # if abs(Z[K]) > abs(A[K, K]):
+ # S = abs(A[K, K]) / abs(Z[K])
+ # Z *= S
+ # YNORM = S * YNORM
+ # if A[K, K] != 0.0:
+ # Z[K] = Z[K] / A[K, K]
+ # if A[K, K] == 0.0:
+ # Z[K] = 1.0
+ # T = -Z[K]
+ # Z[0:K] += T * A[0:K, K] # DAXPY(K-1,T,A(1,K),1,Z(1),1)
+
+ # # Make ZNORM = 1.0
+ # S = 1.0 / sum(Z)
+ # Z *= S
+ # YNORM = S * YNORM
+
+ # if ANORM != 0.0:
+ # RCOND = YNORM / ANORM
+ # else:
+ # RCOND = 0.0
+
+ return IPVT
diff --git a/NARP/reliabilityassessment/monte_carlo/dgedi.py b/NARP/reliabilityassessment/monte_carlo/dgedi.py
new file mode 100644
index 0000000..10d730a
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/dgedi.py
@@ -0,0 +1,149 @@
+import numpy as np
+
+
+# simplified version without redundant variables
+def dgedi(A, N, IPVT):
+ """
+ Computes the determinant and inverse of a matrix using the factors computed by
+ :py:func: `dgeco`
+
+ :param numpy.ndarray A: the input matrix
+ :param int N: the dimension of matrix A
+ :param (*numpy.ndarray*) -- IPVT, an integer vector of pivot indices with shape
+ (N, ) from :py:func:`dgeco` or :py:func:`dgefa`
+
+ .. note:: 1. Matrix A is modified in place.
+ 2. A will be inversed or unchanged based on different situations.
+ 3. An error will be thrown if trying to inverse the matrix with
+ diagonal zeros, which shouldn't happen if :py:func:`dgeco` sets
+ RCOND > 0.0 or :py:func:`dgefa` sets INFO == 0.
+ """
+ # WORK is a local vector of shape (N, )
+ WORK = np.zeros(N)
+
+ # Compute "inverse(U)". U is the upper triangular part of LU decomposition,
+ # which is implicitly stored “in place” in the original matrix A
+ for K in range(N):
+ A[K, K] = 1.0 / A[K, K]
+ A[:K, K] *= -A[K, K] # DSCAL[K-1,T,A(1,K),1) in Fortran where T = -A[K, K]
+
+ T = A[K, K + 1 : N].copy()
+ A[K, K + 1 : N] = 0.0
+ A[: K + 1, K + 1 : N] += np.outer(
+ A[: K + 1, K], T
+ ) # DAXPY[K,T,A(1,K),1,A(1,J),1) in Fortran
+
+ # Form "inverse(U)*inverse(L)". L is the lower triangular part of LU decomposition.
+ # Similar to the above U, L is stored implicitly in A.
+ if N > 1:
+ for KB in range(1, N):
+ K = N - 1 - KB
+ WORK[K + 1 : N] = A[K + 1 : N, K]
+ A[K + 1 : N, K] = 0.0
+
+ # DAXPY(N,T,A(1,J),1,A(1,K),1) in Fortran
+ # where T runs through WORK[K + 1 : N]
+ A[:N, K] += (A[:N, K + 1 : N] * WORK[K + 1 : N]).sum(axis=1)
+ L = IPVT[K]
+
+ # DSWAP(N,A(1,K),1,A(1,L),1) in Fortran
+ if L != K:
+ A[:N, [K, L]] = A[:N, [L, K]]
+
+
+# original version with unused variables such as DET and JOB
+def _dgedi(A, N, IPVT, JOB):
+ """
+ Computes the determinant and inverse of a matrix
+ using the factors computed by dgeco
+
+ :param numpy.ndarray A: the matrix to be factored
+ :param int N: the order of the matrix A
+ :param numpy.ndarray IPVT: the (integer) pivot vector from dgeco or dgefa
+ shape (N,)
+
+ :param int JOB: 1 both determinant and inverse
+ 2 inverse only
+ 3 determinant only
+
+ .. note:: 1) arrays are modified in place.
+ 2) on return, A will be inverse of the original matrix if requested
+ otherwise unchanged.
+ 3) DET and WORK are not used outside
+ :param float DET: the determinant of the original matrix if requested.
+ Otherwise not referenced.
+ determinant = det(1) * 10.0**det(2)
+ with 1.0 <= abs(det(1)) < 10.0, or det(1) == 0.0
+ :param numpy.ndarray WORK: a vector of which the contents will be destroyed.
+ shape (N,)
+
+ 4) Error condition: a "division by zero" will occur if the input factor
+ contains a zero on the diagonal, and the inverse is requested.
+ It will not occur if the functions are called correctly
+ and if dgeco has set (achieve?) RCOND > 0.0,
+ or dgefa has set (achieve?) INFO == 0
+ """
+ DET = np.zeros(2)
+ WORK = np.zeros(N)
+
+ # Compute determinant
+ if JOB == 1 or JOB == 3:
+ DET[0] = 1.0
+ DET[1] = 0.0
+ TEN = 10.0
+ for i in range(1, N + 1):
+ if IPVT[i] != i:
+ DET[0] = -DET[0]
+ DET[0] = A[i, i] * DET[0]
+
+ if DET[0] == 0.0:
+ break
+
+ while abs(DET(1)) < 1.0:
+ DET[0] *= TEN
+ DET[1] -= 1.0
+
+ while abs(DET(1)) >= TEN:
+ DET[0] /= TEN
+ DET[1] += 1.0
+
+ # If no need to compute inverse, then return
+ if JOB == 3:
+ return
+
+ # Compute "inverse(U)". U is the upper triangular part of LU decomposition,
+ # which is implicitly stored “in place” in the original matrix A
+ for K in range(N):
+ A[K, K] = 1.0 / A[K, K]
+ T = -A[K, K]
+ A[:K, K] *= T # DSCAL[K-1,T,A(1,K),1) in Fortran
+ KP1 = K + 1
+ if N > KP1:
+ for J in range(KP1, N): # in original Fortran: from KP1 to N (included)
+ T = A[K, J]
+ A[K, J] = 0.0
+ A[: K + 1, J] += (
+ T * A[: K + 1, K]
+ ) # DAXPY[K,T,A(1,K),1,A(1,J),1) in Fortran
+
+ # Form "inverse(U)*inverse(L)". L is the lower triangular part of LU decomposition.
+ # Similar to the above U, L is stored implicitly in A.
+ if N <= 1:
+ return
+ for KB in range(1, N):
+ K = N - 1 - KB
+ KP1 = K + 1
+ for i in range(KP1, N):
+ WORK[i] = A[i, K]
+ A[i, K] = 0.0
+ for J in range(KP1, N):
+ T = WORK[J]
+ A[:N, K] += T * A[:N, J] # DAXPY(N,T,A(1,J),1,A(1,K),1) in Fortran
+ L = IPVT[K]
+ if L != K:
+ A[:N, K], A[:N, L] = (
+ A[:N, L],
+ A[:N, K].copy(),
+ ) # DSWAP(N,A(1,K),1,A(1,L),1) in Fortran
+
+ return
diff --git a/NARP/reliabilityassessment/monte_carlo/dgefa.py b/NARP/reliabilityassessment/monte_carlo/dgefa.py
new file mode 100644
index 0000000..0e12641
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/dgefa.py
@@ -0,0 +1,135 @@
+import numpy as np
+
+
+# original version
+def _dgefa(A, N):
+ """
+ Factors a double precision matrix by Gaussian elimination.
+
+ :param numpy.ndarray A: the matrix to be factored
+ :param int N: the dimension of the matrix A
+ :return: (*tuple*) -- INFO: an integer with 0 indicates normal value and K if
+ U[K,K] == 0.0, where U is the upper triangular part in the LU decomposition;
+ IPVT: an integer vector of pivot indices with shape (N, )
+
+ .. note:: 1. Matrix A is modified in place.
+ 2. INFO indicates whether :py:func: `dgedi` will have a zero divisor,
+ in which case RCOND is used in :py:func: `degco` as a singularity
+ indicator. Note that :py:func: `dgefa` is slightly faster if RCOND is
+ not set.
+ 3. A will be modified into an upper triangular matrix with the multipliers
+ used to obtain it. The factorization can be written as A = L*U,
+ where L is a product of permutation and unit lower triangular matrix
+ and U is upper triangular.
+ 4. A common practice in the realistic implementation of LU decomposition:
+ L, U (or LDU with a diagonal matrix D if needed) are not necessarily
+ stored explicitly but directly manipulating matrix A so that L and U
+ are both stored in modified matrix A.
+ 5. :py:func: `dgefa` is usually called by :py:func: `dgeco`, but it can be
+ called directly to improve the computational performance if RCOND is
+ not set. T(dgeco) = (1 + 9/n) * T(dgefa) where T indicates run time.
+ """
+
+ IPVT = np.zeros(N, dtype=int)
+
+ # Gaussian elimination with partial pivoting
+ INFO = 0
+ for K in range(N - 1):
+ KP1 = K + 1
+
+ # Find L = "pivot index"
+ L = (
+ np.argmax(abs(A[K:N, K])) + K
+ ) # L = IDAMAX(N-K+1,A(K,K),1) + K - 1 in Fortran
+ # The meaning of "+k-1" means move K-1 steps; since we use a 0-based index here for K,
+ # thus, using "+K" here is correct
+
+ IPVT[K] = L
+
+ # zero pivot implies this column is already triangularized
+ if A[L, K] != 0.0:
+ # interchange if necessary
+ if L != K:
+ A[L, K], A[K, K] = A[K, K], A[L, K]
+
+ # compute multipliers
+ T = -1.0 / A[K, K]
+ A[K + 1 : N, K] *= T # DSCAL(N-K,T,A[K+1,K],1) in Fortran
+ # A[K + 1 : N, K] in Fortran
+
+ # row elimination with column indexing
+ for J in range(KP1, N): # in Fortran: from KP1 to N (included)
+ T = A[L, J]
+ if L != K:
+ A[L, J] = A[K, J]
+ A[K, J] = T
+ A[K + 1 : N, J] += (
+ T * A[K + 1 : N, K]
+ ) # DAXPY[N-K,T,A[K+1,K],1,A[K+1,J],1) in Fortran
+ # A[K + 1 : N, K]in Fortran
+
+ else:
+ INFO = K
+
+ IPVT[N - 1] = N - 1 # in original Fortran: IPVT[N] = N
+ if A[N - 1, N - 1] == 0.0: # IN original Fortran A[N,N]
+ INFO = N - 1
+
+ return INFO, IPVT
+
+
+# vectorized version
+def dgefa(A, N):
+ """
+ Factors a double precision matrix by Gaussian elimination.
+
+ :param numpy.ndarray A: the matrix to be factored
+ :param int N: the dimension of the matrix A
+ :return: (*tuple*) -- INFO: an integer with 0 indicates normal value and K if
+ U[K,K] == 0.0, where U is the upper triangular part in the LU decomposition;
+ IPVT: an integer vector of pivot indices with shape (N, )
+
+ .. note:: 1. Matrix A is modified in place.
+ 2. INFO indicates whether :py:func: `dgedi` will have a zero divisor,
+ in which case RCOND is used in :py:func: `degco` as a singularity
+ indicator. Note that :py:func: `dgefa` is slightly faster if RCOND is
+ not set.
+ 3. A will be modified into an upper triangular matrix with the multipliers
+ used to obtain it. The factorization can be written as A = L*U,
+ where L is a product of permutation and unit lower triangular matrix
+ and U is upper triangular.
+ 4. A common practice in the realistic implementation of LU decomposition:
+ L, U (or LDU with a diagonal matrix D if needed) are not necessarily
+ stored explicitly but directly manipulating matrix A so that L and U
+ are both stored in modified matrix A.
+ 5. :py:func: `dgefa` is usually called by :py:func: `dgeco`, but it can be
+ called directly to improve the computational performance if RCOND is
+ not set. T(dgeco) = (1 + 9/n) * T(dgefa) where T indicates run time.
+ """
+
+ IPVT = np.zeros(N, dtype=int)
+
+ # Gaussian elimination with partial pivoting
+ INFO = 0
+ for K in range(N - 1):
+ # Find pivot index L
+ L = np.argmax(abs(A[K:N, K])) + K
+ # L = IDAMAX(N-K+1, A(K,K), 1) + K - 1 in Fortran, index shifting by K here
+
+ IPVT[K] = L
+
+ # Zero pivot implies no operations are needed to apply
+ if A[L, K] != 0.0:
+ if L != K:
+ A[[L, K], K:] = A[[K, L], K:]
+ A[K + 1 : N, K] *= -1.0 / A[K, K]
+ A[K + 1 : N, K + 1 : N] += np.outer(A[K + 1 : N, K], A[K, K + 1 : N])
+
+ else:
+ INFO = K
+
+ IPVT[N - 1] = N - 1
+ if A[N - 1, N - 1] == 0.0:
+ INFO = N - 1
+
+ return INFO, IPVT
diff --git a/NARP/reliabilityassessment/monte_carlo/events.py b/NARP/reliabilityassessment/monte_carlo/events.py
new file mode 100644
index 0000000..c7f4549
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/events.py
@@ -0,0 +1,372 @@
+from reliabilityassessment.monte_carlo.hour import hour
+from reliabilityassessment.monte_carlo.quartr import quartr
+from reliabilityassessment.monte_carlo.week import week
+from reliabilityassessment.monte_carlo.year import year
+
+
+def events(
+ NUMBER,
+ RFLAG,
+ CLOCK,
+ ATRIB,
+ MFA,
+ NUMINQ,
+ IPOINT,
+ EVNTS,
+ PLNDST,
+ JHOUR,
+ JPLOUT,
+ IQ,
+ RATES,
+ NHRSYR,
+ JSTEP,
+ JFREQ,
+ MXPLHR,
+ PROBG,
+ RATING,
+ DERATE,
+ CAPOWN,
+ CAPCON,
+ PROBL,
+ JENT,
+ INTCH,
+ MXCRIT,
+ JCRIT,
+ NFCST,
+ HRLOAD,
+ FCTERR,
+ LSFLG,
+ LOLGHA,
+ LOLGPA,
+ LOLGHP,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGPA,
+ MGNGHP,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ LINENO,
+ LP,
+ LT,
+ BB,
+ ZB,
+ BLP0,
+ BLPA,
+ IOI,
+ BN,
+ NR,
+ NLS,
+ CVTEST,
+ DPLOLE,
+ EUES,
+ FINISH,
+ HLOLE,
+ NAMA,
+ SSQ,
+ SUSTAT,
+ XLAST,
+ INTV,
+ INTVT,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ LSTEP,
+ NORR,
+ INDX,
+ ITAB,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+ IGSEED,
+ ILSEED,
+):
+ """
+ Simulation schedule for different time-scale of events
+
+ :param: int NUMBER: Simulation type indicator
+ :return: (*tuple*) -- a series of numpy arrays
+
+ .. note:: detailed variable info see ``variable description list.xlsx`` at
+ https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+ """
+ # print("Events called with argument = %d" % (NUMBER))
+
+ assert NUMBER in (1, 2, 3, 4), "The argument 'NUMBER' is invalid! Program aborted."
+
+ if NUMBER == 1:
+ # simulate hourly event
+ (NUMINQ, MFA, IPOINT, JHOURT) = hour(
+ ATRIB,
+ CLOCK,
+ MFA,
+ JSTEP,
+ JFREQ,
+ NUMINQ,
+ IPOINT,
+ EVNTS,
+ MXPLHR,
+ PROBG,
+ RATING,
+ DERATE,
+ PLNDST,
+ CAPOWN,
+ CAPCON,
+ PROBL,
+ JENT,
+ INTCH,
+ MXCRIT,
+ JCRIT,
+ NFCST,
+ HRLOAD,
+ FCTERR,
+ LSFLG,
+ LOLGHA,
+ LOLGPA,
+ LOLGHP,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGPA,
+ MGNGHP,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ LINENO,
+ LP,
+ LT,
+ BB,
+ ZB,
+ BLP0,
+ BLPA,
+ IOI,
+ BN,
+ NR,
+ NLS,
+ IGSEED,
+ ILSEED,
+ )
+
+ JHOUR = JHOURT
+ return (
+ RFLAG,
+ IPOINT,
+ MFA,
+ NUMINQ,
+ SSQ,
+ XLAST,
+ INTVT,
+ ITAB,
+ IQ,
+ JHOUR,
+ )
+ elif NUMBER == 2:
+ # simulate weekly event
+ IPOINT, MFA, NUMINQ = week(
+ CLOCK, ATRIB, MFA, NUMINQ, IPOINT, EVNTS, PLNDST, JHOUR, JPLOUT
+ )
+ return (
+ RFLAG,
+ IPOINT,
+ MFA,
+ NUMINQ,
+ SSQ,
+ XLAST,
+ INTVT,
+ ITAB,
+ IQ,
+ JHOUR,
+ )
+ elif NUMBER == 3:
+ # simulate quarterly event
+ IQ, IPOINT, MFA, NUMINQ, RATING[:] = quartr(
+ IQ, RATES, CLOCK, ATRIB, NHRSYR, MFA, NUMINQ, IPOINT, EVNTS
+ )
+ return (
+ RFLAG,
+ IPOINT,
+ MFA,
+ NUMINQ,
+ SSQ,
+ XLAST,
+ INTVT,
+ ITAB,
+ IQ,
+ JHOUR,
+ )
+ else: # NUMBER == 4:
+ # simulate yearly event
+ IPOINT, MFA, NUMINQ, SSQ, XLAST, RFLAG, INTVT, ITAB = year(
+ ATRIB,
+ CLOCK,
+ CVTEST,
+ DPLOLE,
+ EUES,
+ EVNTS,
+ FINISH,
+ HLOLE,
+ IPOINT,
+ MFA,
+ NUMINQ,
+ RFLAG,
+ LSFLG,
+ NAMA,
+ SSQ,
+ SUSTAT,
+ XLAST,
+ BB,
+ LT,
+ ZB,
+ INTV,
+ INTVT,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ LSTEP,
+ NFCST,
+ NORR,
+ INDX,
+ ITAB,
+ LOLGHA,
+ LOLGHP,
+ LOLGPA,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGHP,
+ MGNGPA,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+ )
+ return (
+ RFLAG,
+ IPOINT,
+ MFA,
+ NUMINQ,
+ SSQ,
+ XLAST,
+ INTVT,
+ ITAB,
+ IQ,
+ JHOUR,
+ )
diff --git a/NARP/reliabilityassessment/monte_carlo/filem.py b/NARP/reliabilityassessment/monte_carlo/filem.py
new file mode 100644
index 0000000..9c1b211
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/filem.py
@@ -0,0 +1,102 @@
+def filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS):
+ """
+ Append a new event into the event list, maintain chronological order and update
+ corresponding pointers
+
+ :param int MFA: pointer of the first available (i.e. empty) entry in the event list
+ :param numpy.ndarray ATRIB: events attribute vector of length 2
+ ATRIB[0] -- global simulation clock (in unit: hour)
+ ATRIB[1] -- int, simulation event type
+ 1: hourly
+ 2: weekly
+ 3: quarterly
+ 4: yearly
+ :param int NUMINQ: up-to-date total number of event entries (inquires)
+ :param int IPOINT: pointer of the first (already/previously stored) entry in the
+ event list, defaults to -1 if the list is empty
+ :param numpy.ndarray EVNTS: 1D array of events
+ :return: (*tuple*) -- updated NUMINQ, MFA and IPOINT
+
+ .. note:: EVNTS is modified in place
+ """
+
+ K = 0 # dummy pointer used to record predecessor
+
+ # insert new entry
+ NEW = MFA
+ EVNTS[NEW + 1] = ATRIB[0]
+ EVNTS[NEW + 2] = ATRIB[1]
+ NUMINQ = NUMINQ + 1
+
+ # re-define pointer of first available (i.e. empty) entry (position)
+ MFA = int(EVNTS[MFA])
+
+ if IPOINT > -1: # event list 1 contains entries, synchronize
+ j = IPOINT
+
+ # new entry goes into middle of event list
+ if ATRIB[0] > EVNTS[j + 1]:
+ while ATRIB[0] > EVNTS[j + 1]:
+ K = j
+ j = int(EVNTS[j])
+ if j == -1: # new entry becomes last in event list
+ EVNTS[K] = NEW
+ EVNTS[NEW] = -1
+ return NUMINQ, MFA, IPOINT
+ # file into middle - set successor pointer.
+ EVNTS[NEW] = j
+ # set predecessor's successor pointer
+ EVNTS[K] = NEW
+ return NUMINQ, MFA, IPOINT
+ else:
+ # new entry becomes first entry
+ # reset first pointer and define successor
+ IPOINT = NEW
+ EVNTS[NEW] = j
+ return NUMINQ, MFA, IPOINT
+ else: # event list is empty, set first pointer to new entry, successor to -1
+ IPOINT = NEW
+ EVNTS[NEW] = -1
+ return NUMINQ, MFA, IPOINT
+
+
+# def filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS):
+# """
+# Append a new event into the event list, maintain chronological order and update
+# corresponding pointers
+
+# :param int MFA: pointer of the first available (i.e. empty) entry in the event list
+# :param numpy.ndarray ATRIB: events attribute vector of length 2
+# ATRIB[0] -- global simulation clock (in unit: hour)
+# ATRIB[1] -- int, simulation type
+# 0: hourly
+# 1: weekly
+# 2: quarterly
+# 3: yearly
+# :param int NUMINQ: up-to-date total number of event entries (inquires)
+# :param int IPOINT: pointer of the first (already/previously stored) entry in the
+# event list, defaults to -1 if the list is empty
+# :param numpy.ndarray EVNTS: 1D array of events
+# :return: (*tuple*) -- updated NUMINQ, MFA and IPOINT
+
+# .. note:: EVNTS is modified in place
+# """
+
+# NUMINQ += 1
+# EVNTS[MFA + 1 : MFA + 3] = ATRIB
+# time_rank = EVNTS[: NUMINQ * 4][1::4].argsort().argsort()
+# # the new event is the first entry in chronological order
+# if time_rank[-1] == 0:
+# EVNTS[MFA] = IPOINT
+# IPOINT = MFA
+# # the new event is the last entry in chronological order
+# elif time_rank[-1] == NUMINQ - 1:
+# EVNTS[MFA] = -1
+# EVNTS[np.where(time_rank == NUMINQ - 2)[0][0] * 4] = MFA
+# # the new event is one of the middle entries in chronological order
+# else:
+# EVNTS[MFA] = np.where(time_rank == time_rank[-1] + 1)[0][0] * 4
+# EVNTS[np.where(time_rank == time_rank[-1] - 1)[0][0] * 4] = MFA
+# MFA += 4
+
+# return NUMINQ, MFA, IPOINT
diff --git a/NARP/reliabilityassessment/monte_carlo/findld.py b/NARP/reliabilityassessment/monte_carlo/findld.py
new file mode 100644
index 0000000..5ded662
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/findld.py
@@ -0,0 +1,17 @@
+def findld(NFACT, JHOUR, HRLOAD, FCTERR):
+ """
+ Determine the load needs of each area under the specific load forecast error factor
+
+ :param: int NFACT: the index of the forecast error factor to be used
+ :param: int JHOUR: the hour index in the annual load profile
+ :param: numpy.ndarray HRLOAD: 2D array with shape (NOAREA, 8760), the hourly
+ annual load profile
+ :param: numpy.ndarray FCTERR: 2D array with shape shape (NOAREA, 5), the load
+ forecast error factors
+ :return: (*numpy.ndarray*) -- 1D array with shape (NOAREA, ), the capacity required
+ for each area at the hour indexed by JOUHR, i.e. CAPREQ(J) passed to
+ transmission module
+
+ .. note:: in the original Fortran code, CAPREQ is annotated as "LOAD IN AREA J"
+ """
+ return HRLOAD[:, JHOUR] * FCTERR[:, NFACT]
diff --git a/NARP/reliabilityassessment/monte_carlo/findtn.py b/NARP/reliabilityassessment/monte_carlo/findtn.py
new file mode 100644
index 0000000..a1a2ece
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/findtn.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+
+def findtn(JENT, INTCH, JDAY):
+ """
+ Determine (net) inter-area transferred power on a specific JDAY for an
+ interested pair of areas based on fixed contracts (if there exists)
+
+ :param numpy.ndarray JENT: 2D array of pointer identifying a specific contract,
+ the (i,j)-th element means the contract (no.) between area-i and area-j,
+ shape: (NOAREA, NOAREA)
+ :param numpy.ndarray INTCH: 2D array of the contract content, the (JENT[i,j],
+ k)-th element means the contracted power (MW) on the kth day from area-i to
+ area-j, shape: (total numebr of contracts, 365)
+ :param int JDAY: integer index for the day
+ :return: (*numpy.ndarray*) TRNSFR: 1D array with shape (NOAREA, ), the ith
+ element means the power transferred-out from this area-i to the rest of pool
+
+ .. note:: TRANSFERS treated as from higher to lower area number
+ """
+
+ NOAREA = JENT.shape[0]
+ TRNSFR = np.zeros((NOAREA,))
+ for j1, j2 in zip(*np.nonzero(JENT != -1)):
+ JPOINT = JENT[j1, j2]
+ TRNSFR[j1] += INTCH[JPOINT, JDAY]
+ TRNSFR[j2] -= INTCH[JPOINT, JDAY]
+ return TRNSFR
diff --git a/NARP/reliabilityassessment/monte_carlo/flo.py b/NARP/reliabilityassessment/monte_carlo/flo.py
new file mode 100644
index 0000000..3cf4dee
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/flo.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+
+def flo(LP, BLP, THET):
+ """
+ Compute line power flows and bus (power) injection
+
+ :param numpy.ndarray LP: 2D array with shape (NLINES, 3)
+ LP[I, 0]: line number, set to I
+ LP[I, 1]: starting node
+ LP[I, 2]: ending node
+ :param numpy.ndarray BLP: 2D array with shape (NLINES, 3)
+ BLP[I, 0]: admittance of the line at the Ith entry of LP
+ BLP[I, 1]: capacity (MW)
+ BLP[I, 2]: backward capacity (MW)
+ :param numpy.ndarray THET: bus (area) angle, shape: (NOAREA, )
+ :return: (*tuple*) -- SFLOW: vector of bus (area) injection power (MW), shape:
+ (NOAREA, )
+ FLOW: vector of line power flows (MW), shape: (NLINES, )
+ """
+
+ NLINES = LP.shape[0]
+ NOAREA = len(THET)
+ SFLOW = np.zeros((NOAREA,))
+ FLOW = np.zeros((NLINES,))
+
+ for i in range(NLINES):
+ j = LP[i, 1] # from bus (area)
+ k = LP[i, 2] # to bus (area)
+ FLOW[i] = (THET[j] - THET[k]) * BLP[i, 0]
+ SFLOW[k] += FLOW[i]
+ SFLOW[j] -= FLOW[i]
+
+ return SFLOW, FLOW
diff --git a/NARP/reliabilityassessment/monte_carlo/gstate.py b/NARP/reliabilityassessment/monte_carlo/gstate.py
new file mode 100644
index 0000000..7e61224
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/gstate.py
@@ -0,0 +1,81 @@
+from bisect import bisect_left
+
+import numpy as np
+
+
+def gstate(IGSEED, PROBG, RATING, DERATE, PLNDST):
+ """
+ It samples the state of available capacity for each generator up to NUNITS
+
+ :param numpy.ndarray IGSEED: list of seeded random numbers for generator state
+ sampling
+ :param numpy.ndarray PROBG: 2D array of accumulated probability for each capacity
+ tier of each unit
+ :param numpy.ndarray RATING: array of the fully rated capacity of each unit
+ :param numpy.ndarray DERATE: array of the derated capacity of each unit
+ :param numpy.ndarray PLNDST: array of the on/off status (due to planned
+ maintenance) of each unit
+ :return: (*tuple*) -- PCTAVL: array of unadjusted power capacity (in p.u.
+ value) of each unit; AVAIL: array of the finalized power capacity (
+ in nominal value) of each unit
+ .. note:: the purpose of using 'IGSEED' is to mimic the random number generating
+ procedure in Fortran to guarantee the same pseudo random sequence being
+ generated.
+ """
+ NUNITS = len(PROBG)
+ # print("Enter subroutine gstate, total number of Gen units is %d" % (NUNITS))
+
+ PCTAVL = np.zeros(NUNITS)
+ AVAIL = np.zeros(NUNITS)
+
+ for i in range(NUNITS):
+ # Draw a random number for each gen unit
+ IGSEED[i] *= 65539
+ if IGSEED[i] < 0:
+ IGSEED[i] += 2147483647
+ IGSEED[i] += 1
+ YFL = float(IGSEED[i])
+ YFL *= 0.4656613e-9
+ RANDGi = YFL
+
+ # Determine the State and Available Capacity for Each Generator.
+ if RANDGi < PROBG[i, 0]:
+ PCTAVL[i] = 1.0 # normalized value for gen. capacity is used here
+ elif RANDGi < PROBG[i, 1]:
+ PCTAVL[i] = DERATE[i] # normalized value for gen. capacity is used here
+ else:
+ PCTAVL[i] = 0
+
+ AVAIL[i] = PCTAVL[i] * RATING[i] * PLNDST[i]
+
+ return PCTAVL, AVAIL
+
+
+def _gstate(PROBG, RATING, DERATE, PLNDST, rng=np.random.default_rng()):
+ """
+ Sample the state of available capacity for each generator up to NUNITS
+
+ :param numpy.ndarray PROBG: 2D array of accumulated probability for each capacity
+ tier of each unit
+ :param numpy.ndarray RATING: array of the fully rated capacity of each unit
+ :param numpy.ndarray DERATE: array of the derated capacity of each unit
+ :param numpy.ndarray PLNDST: array of the on/off status (due to planned maintenance)
+ of each unit
+ :return: (*tuple*) -- a pair of numpy.ndarray objects for unadjusted power
+ capacity in p.u. of each unit and finalized power capacity in MW of each unit.
+ """
+
+ NUNITS = len(PROBG)
+ # print("Enter function gstate, total number of Gen units is %d" % (NUNITS))
+
+ PCTAVL = np.zeros((NUNITS, 1))
+ AVAIL = np.zeros((NUNITS, 1))
+
+ # Vectorized operation utilizing the bisect API
+ aux = {i: {0: 1, 1: d, 2: 0} for i, d in enumerate(DERATE)}
+ PCTAVL = np.array(
+ [aux[i][bisect_left(PROBG[i], x)] for i, x in enumerate(rng.random(len(PROBG)))]
+ )
+ AVAIL = (PCTAVL * RATING * PLNDST).reshape(-1, 1)
+
+ return PCTAVL, AVAIL
diff --git a/NARP/reliabilityassessment/monte_carlo/hour.py b/NARP/reliabilityassessment/monte_carlo/hour.py
new file mode 100644
index 0000000..f4a6bc0
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/hour.py
@@ -0,0 +1,251 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.filem import filem
+from reliabilityassessment.monte_carlo.findld import findld
+from reliabilityassessment.monte_carlo.findtn import findtn
+from reliabilityassessment.monte_carlo.gstate import gstate
+from reliabilityassessment.monte_carlo.hrstat import hrstat
+from reliabilityassessment.monte_carlo.lstate import lstate
+from reliabilityassessment.monte_carlo.pkstat import pkstat
+from reliabilityassessment.monte_carlo.sumcap import sumcap
+from reliabilityassessment.monte_carlo.transm import transm
+
+
+def hour(
+ ATRIB,
+ CLOCK,
+ MFA,
+ JSTEP,
+ JFREQ,
+ NUMINQ,
+ IPOINT,
+ EVNTS,
+ MXPLHR,
+ PROBG,
+ RATING,
+ DERATE,
+ PLNDST,
+ CAPOWN,
+ CAPCON,
+ PROBL,
+ JENT,
+ INTCH,
+ MXCRIT,
+ JCRIT,
+ NFCST,
+ HRLOAD,
+ FCTERR,
+ LSFLG,
+ LOLGHA,
+ LOLGPA,
+ LOLGHP,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGPA,
+ MGNGHP,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ LINENO,
+ LP,
+ LT,
+ BB,
+ ZB,
+ BLP0,
+ BLPA,
+ IOI,
+ BN,
+ NR,
+ NLS,
+ IGSEED,
+ ILSEED,
+):
+ """
+ Schedule hourly simulation events
+
+ :return: (*tuple*)
+ .. note:: 1) For descriptions of input and output variables, please refer to `variable
+ descriptions.xlsx` in the project Dropbox folder at:
+ https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+ 2) arrays are modified in-place.
+ 3) EVNTS is modified in place
+ """
+ # print("Entering subroutine hour")
+
+ IFLAG, JFLAG = 0, 0
+
+ ATRIB[0] = CLOCK + JSTEP
+
+ # may need modify here: some retun var can be implciit
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ # ..............UPDATE HOUR NUMBER.............
+ DXX = 8760
+ JHOUR = -1 + int(CLOCK % DXX) # DMOD(CLOCK,DXX) in original Fortran
+ # need to minus 1 due to 0-based index in Python!
+
+ # need to double check
+ JDAY = np.floor(JHOUR / 24).astype(
+ int
+ ) # original Fortran, (JHOUR-.1)/24 + 1, since 1-based
+
+ JHOURT = JHOUR
+ if JSTEP == 24: # if only daily peak stats are collected
+ JHOUR = MXPLHR[JDAY] # MXPLHR stores the pool peak hour
+
+ # ......Draw generator states and sum capacities by area...............
+ NOAREA = CAPOWN.shape[0]
+ if JHOUR % JFREQ == 0 or JFREQ == 1:
+ PCTAVL, AVAIL = gstate(IGSEED, PROBG, RATING, DERATE, PLNDST)
+ CAPAVL, SYSOWN, SYSCON, TRNSFJ = sumcap(AVAIL, CAPOWN, CAPCON)
+ LNSTAT = lstate(ILSEED, PROBL)
+ TRNSFR = findtn(JENT, INTCH, JDAY)
+
+ # STMULT(I,1) IS FORWARD DIRECTION ADJUSTMENT
+ # STMULT(I,2) IS BACKWARD DIRECTION ADJUSTMENT
+ NLINES = len(LNSTAT)
+ STMULT = np.ones((NLINES, 2), dtype=float)
+
+ JPNT = 0 # in original Fortran, 1
+
+ for i in range(MXCRIT):
+ if JCRIT[JPNT] == 0:
+ JPNT = JPNT + 1
+ else:
+ NOCRIT = JCRIT[JPNT + 1]
+ CRTVAL = 0.0
+ for ii in range(NOCRIT):
+ if 0 < JCRIT[JPNT + 1 + ii] <= 500 - 1:
+ CRTVAL += AVAIL[JCRIT[JPNT + 1 + ii]]
+ else:
+ print("\n Logic error happens regarding the JCRIT array ...\n")
+
+ if CRTVAL == 0.0:
+ STMULT[JCRIT[JPNT], 0] = float(JCRIT[JPNT + NOCRIT + 3]) / 100.0
+ STMULT[JCRIT[JPNT], 1] = float(JCRIT[JPNT + NOCRIT + 4]) / 100.0
+
+ JPNT += NOCRIT + 5
+
+ for NST in range(NFCST):
+ CAPREQ = findld(NST, JHOUR, HRLOAD, FCTERR)
+
+ FLAG = 0
+ MARGIN = (SYSCON[:NOAREA] - CAPREQ[:NOAREA]).astype(int)
+ FLAG += sum(MARGIN < 0)
+ IT = np.where(MARGIN < 0)[0][-1]
+
+ if FLAG == 0:
+ # print('Returning: No Loss')
+ return (
+ NUMINQ,
+ MFA,
+ IPOINT,
+ JHOURT,
+ )
+
+ NSHRT = FLAG
+ LNCAP = np.zeros(NLINES)
+
+ if NSHRT != NOAREA:
+ if FLAG > 1:
+ pass # as the original Fortran logic
+ else:
+ NEED = abs(MARGIN[IT])
+ for j in np.where(MARGIN > 0)[0]:
+ N = LINENO[j, IT]
+ if N > -1:
+ M = (LNSTAT[N] - 1) * 3 + 1
+ XX = BLPA[N, M + (LP[N, 1] != j)]
+ LNCAP[N] = int(XX)
+ MXHELP = min(MARGIN[j], LNCAP[N])
+ NEED -= MXHELP
+
+ TEMPFL = int(any(MARGIN < 0))
+
+ JFLAG = 0
+ JFLAG, FLOW, SADJ = transm(
+ IFLAG,
+ JFLAG,
+ CLOCK,
+ JHOUR,
+ IOI,
+ BN,
+ BLPA,
+ BLP0,
+ BB,
+ LT,
+ ZB,
+ LP,
+ NR,
+ STMULT,
+ LNSTAT,
+ NLS,
+ TRNSFR,
+ TRNSFJ,
+ SYSCON,
+ CAPREQ,
+ PLNDST,
+ PCTAVL,
+ )
+
+ if IFLAG == 0:
+ IFLAG = 1
+ if JFLAG == 0:
+ return (
+ NUMINQ,
+ MFA,
+ IPOINT,
+ JHOURT,
+ )
+ TEMPFL = 0.0
+
+ MARGIN = (-SADJ[:NOAREA]).astype(int)
+ TEMPFL = int(any(MARGIN < 0))
+
+ if TEMPFL == 0:
+ return (
+ NUMINQ,
+ MFA,
+ IPOINT,
+ JHOURT,
+ )
+
+ hrstat(
+ NST,
+ MARGIN,
+ LSFLG,
+ LOLGHA,
+ LOLGHP,
+ LOLTHA,
+ LOLTHP,
+ MGNGHA,
+ MGNGHP,
+ MGNTHA,
+ MGNTHP,
+ )
+ if JHOUR == MXPLHR[JDAY]:
+ pkstat(
+ NST,
+ MARGIN,
+ LOLGPA,
+ LOLGPP,
+ LOLTPA,
+ LOLTPP,
+ MGNGPA,
+ MGNGPP,
+ MGNTPA,
+ MGNTPP,
+ )
+
+ return (
+ NUMINQ,
+ MFA,
+ IPOINT,
+ JHOURT,
+ )
diff --git a/NARP/reliabilityassessment/monte_carlo/hrstat.py b/NARP/reliabilityassessment/monte_carlo/hrstat.py
new file mode 100644
index 0000000..1346bb9
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/hrstat.py
@@ -0,0 +1,132 @@
+def hrstat(
+ NST, MARGIN, LSFLG, LOLGHA, LOLGHP, LOLTHA, LOLTHP, MGNGHA, MGNGHP, MGNTHA, MGNTHP
+):
+ """
+ Collect hourly statistics at area level and pool level
+
+ :param int NST: indicating the NST-th load-forecasting level
+ :param numpy.ndarray MARGIN: 1D array with shape (NOAREA, ), the (original)
+ generation margin of each area after deducting the load in that area
+ :param numpy.ndarray LSFLG: 1D array with shape (NOAREA, ), the total count of
+ (hourly) events that "generation < load" happened in each area
+ :param numpy.ndarray LOLTHP to MGNGHP:
+ please refer to the following naming rules for those
+ 6-digit statistic-related quantities:
+
+ The first three digits identify the type of statistic
+ LOL means this is annual loss of load statistic of some type
+ MGN means this is annual sum of negative margins (EUE)
+ SOL means this is the cumulative sum, all years, LOLE
+ SNG means this is the cumulative sum, all years, EUE
+
+ The fourth digit identifies the cause of an outage
+ T is transmission
+ G is generation
+ S is sum of t & g
+
+ The fifth digit indicates whether the stat is hourly or peak
+ H is for hourly stats
+ P is for peak stats
+
+ The sixth digit is for area or pool
+ A is area
+ P is pool
+
+ Examples:
+ SGNSPA is the cumulative sum, EUE, total of t&g, peak, area
+ LOLTHP is the annual loss of load, transmission, hourly, pool
+
+ LOLTHP, LOLGHP, MGNTHP, MGNGHP: shape (5, )
+ LOLTHA, MGNTHA, LOLGHA, MGNGHA: shape (NOAREA, 5)
+
+ .. note:: LOLTPP, LOLGPP, LOLTPA, MGNTPA, MGNTPP, LOLGPA, MGNGPA, MGNGPP are
+ modified in place. Besides, to exactly match the final statistics results of
+ the Fortran code in the integration test, "int-conversion" logic is kept
+ for now
+ """
+ SUM = sum(MARGIN)
+
+ # Somebody has a loss, else we wouldn't be here
+ # Update pool peak LOLE
+ if SUM >= 0.0:
+ LOLTHP[NST] += 1
+ else:
+ LOLGHP[NST] += 1
+
+ for j, X in enumerate(MARGIN):
+ if X < 0.0:
+ LSFLG[j] += 1
+ if SUM > 0.0:
+ LOLTHA[j, NST] += 1
+ MGNTHA[j, NST] += int(-X)
+ MGNTHP[NST] += int(-X)
+ else:
+ LOLGHA[j, NST] += 1
+ MGNGHA[j, NST] += int(-X)
+ MGNGHP[NST] += int(-X)
+
+
+def _hrstat(
+ NST, MARGIN, LSFLG, LOLGHA, LOLGHP, LOLTHA, LOLTHP, MGNGHA, MGNGHP, MGNTHA, MGNTHP
+):
+ """
+ Collect hourly statistics at area level and pool level
+
+ :param int NST: indicating the NST-th load-forecasting level
+ :param numpy.ndarray MARGIN: 1D array with shape (NOAREA, ), the (original)
+ generation margin of each area after deducting the load in that area
+ :param numpy.ndarray LSFLG: 1D array with shape (NOAREA, ), the total count of
+ (hourly) events that "generation < load" happened in each area
+ :param numpy.ndarray LOLTHP to MGNGHP:
+ please refer to the following naming rules for those
+ 6-digit statistic-related quantities:
+
+ The first three digits identify the type of statistic
+ LOL means this is annual loss of load statistic of some type
+ MGN means this is annual sum of negative margins (EUE)
+ SOL means this is the cumulative sum, all years, LOLE
+ SNG means this is the cumulative sum, all years, EUE
+
+ The fourth digit identifies the cause of an outage
+ T is transmission
+ G is generation
+ S is sum of t & g
+
+ The fifth digit indicates whether the stat is hourly or peak
+ H is for hourly stats
+ P is for peak stats
+
+ The sixth digit is for area or pool
+ A is area
+ P is pool
+
+ Examples:
+ SGNSPA is the cumulative sum, EUE, total of t&g, peak, area
+ LOLTHP is the annual loss of load, transmission, hourly, pool
+
+ LOLTHP, LOLGHP, MGNTHP, MGNGHP: shape (5, )
+ LOLTHA, MGNTHA, LOLGHA, MGNGHA: shape (NOAREA, 5)
+
+ .. note:: LOLTPP, LOLGPP, LOLTPA, MGNTPA, MGNTPP, LOLGPA, MGNGPA, MGNGPP are
+ modified in place. Besides, to exactly match the final statistics results of
+ the Fortran code in the integration test, "int-conversion" logic is kept
+ for now
+ """
+ SUM = sum(MARGIN)
+
+ # Somebody has a loss, else we wouldn't be here
+ # Update pool peak LOLE
+ if SUM >= 0.0:
+ LOLTHP[NST] += 1
+ else:
+ LOLGHP[NST] += 1
+
+ LSFLG[MARGIN < 0] += 1
+ if SUM > 0.0:
+ LOLTHA[:, NST][MARGIN < 0] += 1
+ MGNTHA[:, NST][MARGIN < 0] -= MARGIN[MARGIN < 0].astype(int)
+ MGNTHP[NST] -= int(MARGIN[MARGIN < 0].sum())
+ else:
+ LOLGHA[:, NST][MARGIN < 0] += 1
+ MGNGHA[:, NST][MARGIN < 0] -= MARGIN[MARGIN < 0].astype(int)
+ MGNGHP[NST] -= int(MARGIN[MARGIN < 0].sum())
diff --git a/NARP/reliabilityassessment/monte_carlo/initfl.py b/NARP/reliabilityassessment/monte_carlo/initfl.py
new file mode 100644
index 0000000..4dacd6e
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/initfl.py
@@ -0,0 +1,25 @@
+import numpy as np
+
+
+def initfl():
+ """
+ Initialize array EVNTS and several related pointers
+
+ return (*tuple*) -- MFA: pointer of first available (i.e. unoccupied) slot
+ in the event list
+ IPOINT: pointer of the first already occupied slot
+ (i.e. an stored entry) in the event list initial
+ value = -1
+ EVNTS: 1D array of event list
+ """
+
+ MFA = 0
+ IPOINT = -1
+ # this value is predefined and adjustable; it won't change during simulation.
+ MXCELL = 100
+ EVNTS = np.zeros(MXCELL)
+
+ # array slots starting from MXCELL – 4 (1-based index) are 0 after initialization.
+ EVNTS[: MXCELL - 4 : 4] = np.arange(0, MXCELL - 4, 4) + 4
+
+ return MFA, IPOINT, EVNTS
diff --git a/NARP/reliabilityassessment/monte_carlo/initl.py b/NARP/reliabilityassessment/monte_carlo/initl.py
new file mode 100644
index 0000000..d94c98c
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/initl.py
@@ -0,0 +1,120 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.filem import filem
+
+
+def _initl(JSTEP, EVNTS, IPOINT, MFA, NUMINQ, QTR):
+ """
+ Initialization for the Monte Carlo Simulation
+
+ :param int JSTEP: frequency of simulation statistics collection, 1: hourly 24:
+ daily, i.e. only peak stats are collected
+ :param numpy.ndarray EVNTS: 1D array of events
+ :param int IPOINT: pointer of the first (already/previously stored) entry in the
+ event list, defaults to -1 if the list is empty
+ :param int MFA: pointer of the first available (i.e. empty) entry in the event list
+ :param int NUMINQ: up-to-date total number of event entries (inquires)
+ :param numpy.ndarray QTR: first hour index of the last three quarters of a year
+ i.e. [13*168+0.5, 13*2*168+0.5, 13*3*168+0.5]
+ :return: (*tuple*) -- ATRIB: events attribute vector of length 2
+ ATRIB[0] -- global simulation clock (in unit: hour)
+ ATRIB[1] -- int, simulation event type
+ 1: hourly
+ 2: weekly
+ 3: quarterly
+ 4: yearly
+ CLOCK: global time clock used in sequential Monte Carlo
+ simulation
+ updated IPOINT, MFA, and NUMINQ
+
+ .. note:: EVNTS is modified in place
+ """
+
+ print("Entering function initl")
+ CLOCK = 0.0
+
+ # Schedule all quarterly state changes
+ ATRIB = np.zeros(2)
+ ATRIB[1] = 3
+ ATRIB[0] = 0.5
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ ATRIB[0] = QTR[0]
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ ATRIB[0] = QTR[1]
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ ATRIB[0] = QTR[2]
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ # Do Not Put Anything Between These Two 'filem' calls. Need The 8760.
+ # Schedule First Call To Year (comment by Dr. Singh)
+ ATRIB[0] = 8760.0
+ ATRIB[1] = 4
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ # Schedule First Call To SBRTNE Week To Schedule Planned Outage
+ ATRIB[0] = 0.5
+ ATRIB[1] = 2
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ # Schedule First Monte Carlo Draw
+ ATRIB[1] = 1
+ ATRIB[0] = JSTEP
+ # Call the entry point function of Monte Carlo simulation
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ return ATRIB, CLOCK, IPOINT, MFA, NUMINQ
+
+
+def initl(JSTEP, EVNTS):
+ """
+ Initialization for the Monte Carlo Simulation
+
+ :param int JSTEP: frequency of simulation statistics collection, 1: hourly 24:
+ daily, i.e. only peak stats are collected
+ :param numpy.ndarray EVNTS: 1D array of events
+ :return: (*tuple*) -- ATRIB: events attribute vector of length 2
+ ATRIB[0] -- global simulation clock (in unit: hour)
+ ATRIB[1] -- int, simulation type
+ 0: hourly
+ 1: weekly
+ 2: quarterly
+ 3: yearly
+ CLOCK: global time clock used in sequential Monte Carlo
+ simulation
+ IPOINT: pointer of the first (already/previously stored) entry
+ in the event list, defaults to -1 if the list is empty
+ MFA: pointer of the first available (i.e. empty) entry in the
+ event list
+ NUMINQ: up-to-date total number of event entries (inquires)
+
+ .. note:: EVNTS is modified in place
+ """
+ # fmt: off
+ EVNTS[:28] = np.array(
+ [
+ 24, 0.5, 3, 0.0, # noqa: E241 1st quarterly state change
+ 8.0, 2184.5, 3, 0.0, # noqa: E241 2nd quarterly state change
+ 12.0, 4368.5, 3, 0.0, # noqa: E241 3rd quarterly state change
+ 16.0, 6552.5, 3, 0.0, # noqa: E241 4th quarterly state change
+ -1.0, 8760.0, 4, 0.0, # noqa: E241 first call to subroutine "year"
+ 0, 0.5, 2, 0.0, # noqa: E241 first call to subroutine "week"
+ 4.0, JSTEP, 1, 0.0, # noqa: E241 first Monte Carlo draw
+ ]
+ )
+ # fmt: on
+ ATRIB = np.array([JSTEP, 1.0])
+ CLOCK = 0.0
+ IPOINT = 20
+ MFA = 28
+ NUMINQ = 7
+
+ return ATRIB, CLOCK, IPOINT, MFA, NUMINQ
diff --git a/NARP/reliabilityassessment/monte_carlo/intm.py b/NARP/reliabilityassessment/monte_carlo/intm.py
new file mode 100644
index 0000000..d61d7f2
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/intm.py
@@ -0,0 +1,143 @@
+import pickle
+
+
+def intm(
+ ATRIB,
+ CLOCK,
+ IPOINT,
+ XLAST,
+ SSQ,
+ MFA,
+ NUMINQ,
+ ITAB,
+ INTVT,
+ EVNTS,
+ IGSEED,
+ ILSEED,
+ LT,
+ BB,
+ ZB,
+ SOLTHA,
+ SOLGHA,
+ SOLSHA,
+ SGNTHA,
+ SGNGHA,
+ SGNSHA,
+ SOLTPA,
+ SOLGPA,
+ SOLSPA,
+ SGNTPA,
+ SGNGPA,
+ SGNSPA,
+ SWLSHA,
+ SWLGHA,
+ SWLTHA,
+ SWNSHA,
+ SWNGHA,
+ SWNTHA,
+ SWLSPA,
+ SWLGPA,
+ SWLTPA,
+ SWNSPA,
+ SWNGPA,
+ SWNTPA,
+ SOLTHP,
+ SOLGHP,
+ SOLSHP,
+ SGNTHP,
+ SGNGHP,
+ SGNSHP,
+ SOLTPP,
+ SOLGPP,
+ SOLSPP,
+ SGNTPP,
+ SGNGPP,
+ SGNSPP,
+ HLOLE,
+ DPLOLE,
+ EUES,
+ XNEWA,
+ XNEWP,
+ WOLSHA,
+ LSFLG,
+):
+ """
+ Save intermediate results into a pickle file, DUMP.pkl, in case of unintentional or
+ intentional interruption.
+
+ .. note:: For descriptions of input variables, please refer to `variable
+ descriptions.xlsx` in the project Dropbox folder.
+ .. todo::
+ 1. Once the main loop of the sequential Monte Carlo simulation is
+ implemented, refactor/remove this function according to the call signature
+ such as directly save input variables into the caching file within the scope.
+ 2. If this function is kept during future refactor, update the doc strings of
+ input variables according to the final version of the project doc.
+ 3. If this function is kept during future refactor, make the caching file
+ name capable to be specified so that multiple users can run this module on a
+ cluster with intermediate results caching due to interruptions.
+ """
+
+ snapshot_data = {
+ "ATRIB": ATRIB[1],
+ "CLOCK": CLOCK,
+ "IPOINT": IPOINT,
+ "XLAST": XLAST,
+ "SSQ": SSQ,
+ "MFA": MFA,
+ "NUMINQ": NUMINQ,
+ "ITAB": ITAB,
+ "INTVT": INTVT,
+ "EVNTS": EVNTS,
+ "IGSEED": IGSEED,
+ "ILSEED": ILSEED,
+ "LT": LT,
+ "BB": BB,
+ "ZB": ZB,
+ "SOLTHA": SOLTHA,
+ "SOLGHA": SOLGHA,
+ "SOLSHA": SOLSHA,
+ "SGNTHA": SGNTHA,
+ "SGNGHA": SGNGHA,
+ "SGNSHA": SGNSHA,
+ "SOLTPA": SOLTPA,
+ "SOLGPA": SOLGPA,
+ "SOLSPA": SOLSPA,
+ "SGNTPA": SGNTPA,
+ "SGNGPA": SGNGPA,
+ "SGNSPA": SGNSPA,
+ "SWLSHA": SWLSHA,
+ "SWLGHA": SWLGHA,
+ "SWLTHA": SWLTHA,
+ "SWNSHA": SWNSHA,
+ "SWNGHA": SWNGHA,
+ "SWNTHA": SWNTHA,
+ "SWLSPA": SWLSPA,
+ "SWLGPA": SWLGPA,
+ "SWLTPA": SWLTPA,
+ "SWNSPA": SWNSPA,
+ "SWNGPA": SWNGPA,
+ "SWNTPA": SWNTPA,
+ "SOLTHP": SOLTHP,
+ "SOLGHP": SOLGHP,
+ "SOLSHP": SOLSHP,
+ "SGNTHP": SGNTHP,
+ "SGNGHP": SGNGHP,
+ "SGNSHP": SGNSHP,
+ "SOLTPP": SOLTPP,
+ "SOLGPP": SOLGPP,
+ "SOLSPP": SOLSPP,
+ "SGNTPP": SGNTPP,
+ "SGNGPP": SGNGPP,
+ "SGNSPP": SGNSPP,
+ "HLOLE": HLOLE,
+ "DPLOLE": DPLOLE,
+ "EUES": EUES,
+ "XNEWA": XNEWA,
+ "XNEWP": XNEWP,
+ "WOLSHA": WOLSHA,
+ "LSFLG": LSFLG,
+ }
+
+ with open("DUMP.pkl", "wb") as fp:
+ pickle.dump(snapshot_data, fp)
diff --git a/NARP/reliabilityassessment/monte_carlo/line_prob_sampling.py b/NARP/reliabilityassessment/monte_carlo/line_prob_sampling.py
new file mode 100644
index 0000000..6ddca1e
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/line_prob_sampling.py
@@ -0,0 +1,132 @@
+import math
+
+import numpy as np
+
+
+def lstate(PROBL, test_seed=None):
+ """Establish the line state according the probability of the line being in
+ each particular state. This vectorized implementation allows the line state
+ probabilities to be different for each line, and assumes that the lines can be
+ in up to 6 states overall.
+
+ :param numpy.ndarray PROBL: cumulative capacity probability of each line
+ :raises TypeError: if PROBL is not a numpy array
+ :return: (*numpy.ndarray*) -- line state value (in integer) of each line
+ """
+ if not isinstance(PROBL, np.ndarray):
+ raise TypeError("PROBL must be a numpy array")
+
+ NLINES = PROBL.shape[0]
+ line_states = np.array([1, 2, 3, 4, 5, 6])
+
+ # set random seed for reproducibility
+ if test_seed is not None:
+ rng = np.random.default_rng(seed=test_seed)
+ else:
+ rng = np.random.default_rng()
+
+ rand_mat = rng.random(NLINES)
+ LNSTAT = np.zeros(NLINES, dtype=np.int32)
+
+ for i in range(line_states.size):
+ mask = (LNSTAT == 0) & (rand_mat < PROBL[:, i])
+ LNSTAT[mask] = line_states[i]
+ return LNSTAT
+
+
+def lstate_original(NLINES, PROBL, test_seed=None):
+ """Establishes the line state according the probability of the line being in
+ each particular state. This implementation assumes the lines can be in up to 6 states overall.
+
+ :param int NLINES: total number of lines
+ :param numpy.ndarray PROBL: array of the cumulative capacity probability of each line
+ :raises TypeError:
+ NLINES must be an integer
+ PROBL must be a numpy array
+ :raises ValueError: NLINES must be positive
+ :raises Exception: PROBL values must sum to 1
+ :return: (*numpy.ndarray*) -- array of line state value (in integer) of each line
+ """
+
+ if not isinstance(NLINES, int):
+ raise TypeError("NLINES must be an integer")
+
+ if not NLINES > 0:
+ raise ValueError("NLINES must be positive")
+
+ if not isinstance(PROBL, np.ndarray):
+ raise TypeError("PROBL must be a numpy array")
+
+ # set random seed for reproducibility
+ if test_seed is not None:
+ rng = np.random.default_rng(seed=test_seed)
+ else:
+ rng = np.random.default_rng()
+
+ # recover original probabilities
+ PROB = np.concatenate(([PROBL[0]], np.diff(PROBL)))
+ if not math.isclose(np.sum(PROB), 1):
+ raise Exception("PROB values must must sum to 1")
+
+ LNSTAT = np.zeros(NLINES, dtype=np.int32)
+ for i in range(NLINES):
+ # Draw a random number for each line
+ RANDL = rng.random()
+
+ # Determine the State and Available Capacity for Each Generator.
+ if RANDL <= PROBL[0]:
+ LNSTAT[i] = 1
+ elif RANDL <= PROBL[1]:
+ LNSTAT[i] = 2
+ elif RANDL <= PROBL[2]:
+ LNSTAT[i] = 3
+ elif RANDL <= PROBL[3]:
+ LNSTAT[i] = 4
+ elif RANDL <= PROBL[4]:
+ LNSTAT[i] = 5
+ else:
+ LNSTAT[i] = 6
+ return LNSTAT
+
+
+def discrete_prob_gen(NLINES, PROBL, test_seed=None):
+ """Establishes the line state according the probability of the line being in
+ each particular state. This implementation makes use of folllowing built-in numpy
+ function:
+ https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html
+ This implementation assumes that the line state probabilities are the same for
+ every line, and the lines can be in up to 6 states overall.
+
+ :param int NLINES: total number of lines
+ :param numpy.ndarray PROBL: array of predefined capacity probability of each line
+ :raises TypeError:
+ NLINES must be an integer
+ PROBL must be a numpy array
+ :raises ValueError: NLINES must be positive
+ :raises Exception: PROBL values must sum to 1
+ :return: (*numpy.ndarray*) -- array of line state value (in integer) of each line
+ """
+
+ if not isinstance(NLINES, int):
+ raise TypeError("NLINES must be an integer")
+
+ if not NLINES > 0:
+ raise ValueError("NLINES must be positive")
+
+ if not isinstance(PROBL, np.ndarray):
+ raise TypeError("PROBL must be a numpy array")
+
+ # recover original probabilities
+ PROB = np.concatenate(([PROBL[0]], np.diff(PROBL)))
+ if not math.isclose(np.sum(PROB), 1):
+ raise Exception("PROB values must must sum to 1")
+
+ # set random seed for reproducibility
+ if test_seed is not None:
+ rng = np.random.default_rng(seed=test_seed)
+ else:
+ rng = np.random.default_rng()
+
+ line_states = np.array([1, 2, 3, 4, 5, 6])
+
+ return rng.choice(line_states, NLINES, p=PROB)
diff --git a/NARP/reliabilityassessment/monte_carlo/linp.py b/NARP/reliabilityassessment/monte_carlo/linp.py
new file mode 100644
index 0000000..77ec183
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/linp.py
@@ -0,0 +1,144 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.dgeco import dgeco
+from reliabilityassessment.monte_carlo.dgedi import dgedi
+from reliabilityassessment.monte_carlo.mc import mc
+from reliabilityassessment.monte_carlo.rc import rc
+from reliabilityassessment.monte_carlo.rm import rm
+
+
+def linp(M, N, A, XOB, XOBI, IBAS, BS, LCLOCK, N1):
+ """
+ A customized version of linear programming (LP).
+
+ :param int M: total number of constraints (possibly)
+ :param int N: total number of decision variables (possibly)
+ :param numpy.ndarray A: coefficient matrix of all constraints (possibly) with
+ initial shape (200, 250), the actual size is determined on-the-fly
+ :param numpy.ndarray XOB: 1D binary vector with initial shape (250, ), the actual
+ size is determined on-the-fly (possibly (N, ))
+ :param numpy.ndarray XOBI: 2D binary vector with initial shape (2, 250),
+ the actual size is determined on-the-fly (possibly (2, N))
+ :param numpy.ndarray IBAS: indices of basis-vector (possibly) with initial shape
+ (250, ), the actual size is determined on-the-fly (possibly (M, ))
+ :param numpy.ndarray BS: 1D array with initial shape (200, ) the actual size is
+ determined on-the-fly (possibly shape (M, ))
+ :param float LCLOCK: the simulation time clock
+ :param int N1: an integer used in LP-based DCOPF
+ :return: (*tuple*) -- updated N and TAB, a matrix used in LP with initial shape
+ (200, 250), the actual size is determined on-the-fly
+
+ .. note:: 1. Arrays are modified in place.
+ 2. Inputs, outputs and internal logic may not be exactly the same as
+ that in standard LP APIs in SciPy/NumPy or MATLAB.
+ """
+
+ IBASF = np.zeros(N, dtype=int) # np.zeros(250, dtype=int) in original Fortran
+ CB = np.zeros(M) # np.zeros(200) in original Fortran
+ BS1 = BS[:M].copy() # np.zeros(200) in original Fortran
+
+ IR = 0
+ INV = 0
+ XOB[:N] = XOBI[0, :N].copy()
+
+ IPH = 1
+ TAB = np.identity(M) # size was (200, 250) in original Fortran
+
+ IBASF[IBAS[:M]] = 1
+ CB[:M] = XOB[IBAS[:M]]
+
+ while True: # 110 while loop
+ N = N1
+ PY = rm(M, CB, TAB)
+
+ gotoFlag1000 = False
+ while True: # 111 while loop
+ CM = 0.0
+ for i in np.where(IBASF[:N] != 1)[0]:
+ CC = XOB[i] - rc(M, PY, A, i)
+ if abs(CC) < 1e-7:
+ CC = 0.0
+ if abs(CC - CM) < 1e-7:
+ continue
+ if CC < CM:
+ IND, CM = i, CC
+
+ if CM < 0:
+ break # GO TO 400
+
+ if IR == 1:
+ gotoFlag1000 = True
+ break # GO TO 1000
+
+ IMAX = 0
+ TABT = A[:M, IBAS[:M]]
+
+ IPVT = dgeco(TABT, M)
+ dgedi(TABT, M, IPVT)
+
+ PY = rm(M, CB, TABT)
+
+ for i in np.where(IBASF[:N] != 0)[0]:
+ CC = abs(XOB[i] - rc(M, PY, A, i))
+ if CC < 1e-08:
+ continue
+ IMAX = 500
+
+ if IMAX != 0: # i.e. == 500
+ f = open("extra.txt", "w")
+ f.write("\n INVERSIONS = %2d LCLOCK = %6d\n" % (INV, LCLOCK))
+ f.close()
+ INV += 1
+
+ BS[:M] = 0.0
+ BS[:M] += TABT @ BS1
+ TAB = TABT.copy()
+
+ IR = 1
+ # GO TO 111 while loop
+
+ # 400 CONTINUE
+ if gotoFlag1000 is False:
+ IR = 0
+ P = mc(M, IND, TAB, A)
+ ICOUN = 0
+
+ for i in np.where(P[:M] > 1e-07)[0]:
+ BNUM = BS[i] if abs(BS[i]) >= 1e-13 else 0.0
+ RAT = BNUM / P[i]
+ ICOUN += 1
+ if ICOUN == 1:
+ RAT1, IPIV = RAT, i
+ continue
+ if RAT < RAT1:
+ RAT1, IPIV = RAT, i
+ continue
+ if IPH != 2 and BS[i] == 0 and XOB[IBAS[i]] == 1:
+ IPIV = i
+
+ for i in np.where(P[:M] != 0)[0]:
+ if i == IPIV:
+ continue
+ TAB[i, :M] -= TAB[IPIV, :M] * P[i] / P[IPIV]
+ BS[i] -= BS[IPIV] * P[i] / P[IPIV]
+
+ TAB[IPIV, :M] /= P[IPIV]
+
+ BS[IPIV] /= P[IPIV]
+ CB[IPIV] = XOB[IND]
+ K1 = IBAS[IPIV]
+ IBASF[K1] = 0
+ IBASF[IND] = 1
+ IBAS[IPIV] = IND
+ # GO TO 110 while loop
+ else: # 1000 CONTINUE
+ if IPH == 2:
+ break # GO TO 2000 (exit)
+ # N -= NXT # this comment exists in the original Fortran code.
+ XOB[:N] = XOBI[1, :N].copy()
+ CB[:M] = XOB[IBAS[:M]]
+ IPH = 2
+ # GO TO 110 while loop
+
+ # 2000 (exit)
+ return N, TAB
diff --git a/NARP/reliabilityassessment/monte_carlo/lstate.py b/NARP/reliabilityassessment/monte_carlo/lstate.py
new file mode 100644
index 0000000..a8f7726
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/lstate.py
@@ -0,0 +1,48 @@
+import numpy as np
+
+
+def lstate(ILSEED, PROBL):
+ """Establishes the line state according the probability of the line being in
+ each particular state. This implementation assumes the lines can be in up to 6
+ states overall.
+
+ :param array ILSEED: list of seeded random numbers for line state sampling
+ :param numpy.ndarray PROBL: array of the cumulative capacity probability of
+ each line
+ :return: (*numpy.ndarray*) -- array of line state value (in integer) of each line
+ .. note:: the purpose of using 'ILSEED' is to mimic the random number generating
+ procedure in Fortran to guarantee the same pseudo random sequence being
+ generated.
+ """
+
+ if not isinstance(PROBL, np.ndarray):
+ raise TypeError("PROBL must be a numpy array")
+
+ NLINES = PROBL.shape[1]
+ LNSTAT = np.zeros(NLINES, dtype=np.int32)
+
+ for i in range(NLINES):
+ # Draw a random number for each line
+ ILSEED[i] *= 65539
+ if ILSEED[i] < 0:
+ ILSEED[i] += 2147483647
+ ILSEED[i] += 1
+ YFL = float(ILSEED[i])
+ YFL *= 0.4656613e-9
+ RANDLi = YFL
+
+ # Determine the State of each line.
+ if RANDLi <= PROBL[0, i]:
+ LNSTAT[i] = 1
+ elif RANDLi <= PROBL[1, i]:
+ LNSTAT[i] = 2
+ elif RANDLi <= PROBL[2, i]:
+ LNSTAT[i] = 3
+ elif RANDLi <= PROBL[3, i]:
+ LNSTAT[i] = 4
+ elif RANDLi <= PROBL[4, i]:
+ LNSTAT[i] = 5
+ else:
+ LNSTAT[i] = 6
+
+ return LNSTAT
diff --git a/NARP/reliabilityassessment/monte_carlo/mc.py b/NARP/reliabilityassessment/monte_carlo/mc.py
new file mode 100644
index 0000000..50ad218
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/mc.py
@@ -0,0 +1,14 @@
+def mc(M, IN, TAB, A):
+ """
+ 2D Matrix right-multiplication by a vector, which is a specified column of
+ another matrix.
+
+ :param int M: the specified range for the matrix (to be multiplied)
+ :param int IN: the specified index of column of matrix A
+ :param numpy.ndarray TAB: the input matrix (to be multiplied)
+ the row/col size of which could be larger than M.
+ :param numpy.ndarray A: the input matrix (to be extracted one specific column)
+ the row/col size of which could be larger than M.
+ :return: (*numpy.ndarray*) -- the result vector
+ """
+ return TAB[:M, :M] @ A[:M, IN]
diff --git a/NARP/reliabilityassessment/monte_carlo/narpMain.py b/NARP/reliabilityassessment/monte_carlo/narpMain.py
new file mode 100644
index 0000000..cdaa8b6
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/narpMain.py
@@ -0,0 +1,315 @@
+from pathlib import Path
+
+import numpy as np
+
+from reliabilityassessment.data_processing.dataf1 import dataf1
+from reliabilityassessment.monte_carlo.contrl import contrl
+from reliabilityassessment.monte_carlo.initfl import initfl
+from reliabilityassessment.monte_carlo.initl import initl
+from reliabilityassessment.monte_carlo.seeder import seeder
+
+
+def narpMain(TEST_DIR):
+ """
+ Main entry function of the whole NARP program
+
+ ..note: This python program is based on a previous Fortran program originally
+ developed by Dr. Chanan Singh (Texas A&M) and the Associated Power Analysts, Inc.
+ """
+ print("\n NARP Python Version-0, 01/01/2023 \n")
+ print("\n call function initfl \n")
+ MFA, IPOINT, EVNTS = initfl()
+
+ print("\n call function dataf1 \n")
+ filepaths = [
+ Path(TEST_DIR),
+ Path(TEST_DIR, "LEEI"),
+ ]
+
+ (
+ QTR,
+ NORR,
+ NFCST,
+ NOAREA,
+ PKLOAD,
+ FU,
+ MINRAN,
+ MAXRAN,
+ INHBT1,
+ INHBT2,
+ BN,
+ SUSTAT,
+ FCTERR,
+ CAPCON,
+ CAPOWN,
+ NOGEN,
+ PROBG,
+ DERATE,
+ JENT,
+ INTCH,
+ INTCHR,
+ LP,
+ LINENO,
+ PROBL,
+ BLPA,
+ MXCRIT,
+ JCRIT,
+ RATES,
+ ID,
+ HRLOAD,
+ MAXHR,
+ DYLOAD,
+ MAXDAY,
+ WPEAK,
+ MXPLHR,
+ JPLOUT,
+ ITAB,
+ NLS,
+ IOI,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ CVTEST,
+ MAXEUE,
+ JSTEP,
+ JFREQ,
+ FINISH,
+ INTV,
+ INTVT,
+ NR,
+ NAMA,
+ ) = dataf1(filepaths)
+
+ LSTEP = int(MAXEUE // 20)
+ NUNITS = PROBG.shape[0]
+ NLINES = LP.shape[0]
+ NOAREA = HRLOAD.shape[0]
+ # seeding:
+ JSEED = 123456
+ IGSEED, ILSEED = seeder(JSEED, NUNITS, NLINES)
+ # ----------------------------Finish data processing part--------------------------
+
+ # ----------------------------Begin Monte Carlo simulation part --------------------------
+ ATRIB, CLOCK, IPOINT, MFA, NUMINQ = initl(JSTEP, EVNTS)
+ IQ = 0 # initialize the quarter index to be 0 (i.e., the 1st quarter)
+ JHOUR = 0 # initialize the hour index
+ SSQ = 0.0
+ XLAST = 0.0 # initial values for convergence-related quantities
+ NHRSYR = 8760
+ RFLAG = 0 # initialize the normal return flag
+ INDX = 0
+ if JSTEP == 24:
+ INDX = 1
+
+ HLOLE = np.zeros((NOAREA, 22))
+ DPLOLE = np.zeros((NOAREA, 22))
+ EUES = np.zeros((NOAREA, 22))
+ PLNDST = np.zeros(NUNITS)
+ LSFLG = np.zeros(NOAREA)
+ LT = np.zeros(NOAREA, dtype=int)
+ BB = np.zeros((NOAREA, NOAREA))
+ ZB = np.zeros((NOAREA, NOAREA))
+ BLP0 = np.zeros(NLINES)
+ RATING = np.zeros(NUNITS)
+
+ LOLGHA = np.zeros((NOAREA, 5))
+ LOLGPA = np.zeros((NOAREA, 5))
+ LOLGHP = np.zeros(5)
+ LOLGPP = np.zeros(5)
+ LOLTHA = np.zeros((NOAREA, 5))
+ LOLTHP = np.zeros(5)
+ LOLTPA = np.zeros((NOAREA, 5))
+ LOLTPP = np.zeros(5)
+ MGNGHA = np.zeros((NOAREA, 5))
+ MGNGPA = np.zeros((NOAREA, 5))
+ MGNGHP = np.zeros(5)
+ MGNGPP = np.zeros(5)
+ MGNTHA = np.zeros((NOAREA, 5))
+ MGNTHP = np.zeros(5)
+ MGNTPA = np.zeros((NOAREA, 5))
+ MGNTPP = np.zeros(5)
+ SGNGHA = np.zeros((NOAREA, 5))
+ SGNGHP = np.zeros(5)
+ SGNGPA = np.zeros((NOAREA, 5))
+ SGNGPP = np.zeros(5)
+ SGNSHA = np.zeros((NOAREA, 5))
+ SGNSHP = np.zeros(5)
+ SGNSPA = np.zeros((NOAREA, 5))
+ SGNSPP = np.zeros(5)
+ SGNTHA = np.zeros((NOAREA, 5))
+ SGNTHP = np.zeros(5)
+ SGNTPA = np.zeros((NOAREA, 5))
+ SGNTPP = np.zeros(5)
+ SOLGHA = np.zeros((NOAREA, 5))
+ SOLGHP = np.zeros(5)
+ SOLGPA = np.zeros((NOAREA, 5))
+ SOLGPP = np.zeros(5)
+ SOLSHA = np.zeros((NOAREA, 5))
+ SOLSHP = np.zeros(5)
+ SOLSPA = np.zeros((NOAREA, 5))
+ SOLSPP = np.zeros(5)
+ SOLTHA = np.zeros((NOAREA, 5))
+ SOLTHP = np.zeros(5)
+ SOLTPA = np.zeros((NOAREA, 5))
+ SOLTPP = np.zeros(5)
+ SWLGHA = np.zeros(NOAREA)
+ SWLGHP = 0
+ SWLGPA = np.zeros(NOAREA)
+ SWLGPP = 0
+ SWLSHA = np.zeros(NOAREA)
+ SWLSHP = 0
+ SWLSPA = np.zeros(NOAREA)
+ SWLSPP = 0
+ SWLTHA = np.zeros(NOAREA)
+ SWLTHP = 0
+ SWLTPA = np.zeros(NOAREA)
+ SWLTPP = 0
+ SWNGHA = np.zeros(NOAREA)
+ SWNGHP = 0
+ SWNGPA = np.zeros(NOAREA)
+ SWNGPP = 0
+ SWNSHA = np.zeros(NOAREA)
+ SWNSHP = 0
+ SWNSPA = np.zeros(NOAREA)
+ SWNSPP = 0
+ SWNTHA = np.zeros(NOAREA)
+ SWNTHP = 0
+ SWNTPA = np.zeros(NOAREA)
+ SWNTPP = 0
+ XNEWA = np.zeros((NOAREA, 3))
+ XNEWP = np.zeros(3)
+
+ contrl(
+ RFLAG,
+ CLOCK,
+ NUMINQ,
+ IPOINT,
+ EVNTS,
+ ATRIB,
+ FINISH,
+ ITAB,
+ INDX,
+ SUSTAT,
+ DPLOLE,
+ EUES,
+ HLOLE,
+ LSTEP,
+ NAMA,
+ NFCST,
+ PLNDST,
+ JHOUR,
+ JPLOUT,
+ IQ,
+ RATES,
+ NHRSYR,
+ JSTEP,
+ JFREQ,
+ MXPLHR,
+ PROBG,
+ RATING,
+ DERATE,
+ CAPOWN,
+ CAPCON,
+ PROBL,
+ JENT,
+ INTCH,
+ MXCRIT,
+ JCRIT,
+ HRLOAD,
+ FCTERR,
+ LSFLG,
+ LINENO,
+ LP,
+ LT,
+ BB,
+ ZB,
+ BLP0,
+ BLPA,
+ IOI,
+ BN,
+ NR,
+ NLS,
+ CVTEST,
+ SSQ,
+ NORR,
+ XLAST,
+ INTV,
+ INTVT,
+ IOJ,
+ KVLOC,
+ KVSTAT,
+ KVTYPE,
+ KVWHEN,
+ KWHERE,
+ LOLGHA,
+ LOLGPA,
+ LOLGHP,
+ LOLGPP,
+ LOLTHA,
+ LOLTHP,
+ LOLTPA,
+ LOLTPP,
+ MGNGHA,
+ MGNGPA,
+ MGNGHP,
+ MGNGPP,
+ MGNTHA,
+ MGNTHP,
+ MGNTPA,
+ MGNTPP,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+ IGSEED,
+ ILSEED,
+ )
diff --git a/NARP/reliabilityassessment/monte_carlo/net.py b/NARP/reliabilityassessment/monte_carlo/net.py
new file mode 100644
index 0000000..3438c75
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/net.py
@@ -0,0 +1,86 @@
+# vanilla version
+def _net(B, B1, RES, IBAS, BS, M, N):
+ """
+ Construct the result array based on the outcome of LP
+
+ :param numpy.ndarray B: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a vector used in linear programming(LP)-based DCOPF
+ :param numpy.ndarray B1: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ :param numpy.ndarray RES: 2D array with shape (250, 3)
+ a helper array used in linear programming-based DCOPF
+ means the result from the (LP)-based DCOPF
+ [:,0]: the load curtailments (based on the LP outcome)
+ [:,1]: unknown
+ [:,2]: defined not used in the Fortran code
+ :param numpy.ndarray IBAS: 1D array with initial shape (250, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ (possibly) means the indices of the "basis-vector" used in LP
+ :param numpy.ndarray BS: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ :param int M: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of constraints
+ :param int N: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of "decision variables" in the LP
+ """
+
+ NX1 = N - M # NX1 is local
+
+ for i in range(N):
+ for j in range(3):
+ RES[i, j] = 0
+
+ for i in range(M):
+ k = IBAS[i]
+ if k > NX1:
+ RES[k, 0] = -BS[i]
+ continue
+ RES[k, 0] = BS[i]
+
+ for i in range(M):
+ i1 = i + NX1
+ if B1[i] < 0:
+ RES[i1, 1] = 1.0
+ RES[i1, 0] += B[i]
+
+
+def net(B, B1, RES, IBAS, BS, M, N):
+ """
+ Construct the result array based on the outcome of LP
+
+ :param numpy.ndarray B: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a vector used in linear programming(LP)-based DCOPF
+ :param numpy.ndarray B1: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ :param numpy.ndarray RES: 2D array with shape (250, 3)
+ a helper array used in linear programming-based DCOPF
+ means the result from the (LP)-based DCOPF
+ [:,0]: the load curtailments (based on the LP outcome)
+ [:,1]: unknown
+ [:,2]: defined not used in the Fortran code
+ :param numpy.ndarray IBAS: 1D array with initial shape (250, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ (possibly) means the indices of the "basis-vector" used in LP
+ :param numpy.ndarray BS: 1D array with initial shape (200, )
+ the realistic size is determined on-the-fly
+ a helper vector used in linear programming-based DCOPF
+ :param int M: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of constraints
+ :param int N: used in linear programming(LP)-based DCOPF
+ (possibly) means the total number of "decision variables" in the LP
+ """
+
+ RES[:N, :3] = 0
+
+ RES[IBAS[:M], 0] = BS[:M]
+ RES[IBAS[:M][IBAS[:M] > N - M], 0] *= -1
+
+ RES[N - M : N, 1][B1[:M] < 0] = 1
+ RES[N - M : N, 0] += B[:M]
diff --git a/NARP/reliabilityassessment/monte_carlo/pkstat.py b/NARP/reliabilityassessment/monte_carlo/pkstat.py
new file mode 100644
index 0000000..3d56fc0
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/pkstat.py
@@ -0,0 +1,124 @@
+def _pkstat(
+ NST, MARGIN, LOLGPA, LOLGPP, LOLTPA, LOLTPP, MGNGPA, MGNGPP, MGNTPA, MGNTPP
+):
+ """
+ Collect (daily) peak statistics at area level and pool level
+
+ :param int NST: indicating the NST-th load-forecasting level
+ :param numpy.ndarray MARGIN: 1D array with shape (NOAREA, ) the (original)
+ generation margin of each area after deducting the load in that area
+ :param numpy.ndarray LOLTPP to MGNGPP:
+ please refer to the following naming rules for those
+ 6-digit statistic-related quantities:
+
+ The first three digits identify the type of statistic
+ LOL means this is the annual loss of load statistics
+ MGN means this is the annual sum of negative margins, EUE
+ SOL means this is the cumulative sum, all years, LOLE
+ SNG means this is the cumulative sum, all years, EUE
+
+ The fourth digit identifies the cause of the outage
+ T is transmission
+ G is generation
+ S is the sum of t & g
+
+ The fifth digit indicates whether the stat is hourly or peak
+ H is for hourly stats
+ P is for peak stats
+
+ The sixth digit is for area or pool
+ A is area
+ P is pool
+
+ Examplex:
+ SGNSPA is the cumulative sum, EUE, total of t&g, peak, area
+ LOLTHP is the annual loss of load, transmission, hourly, pool
+
+ LOLTPP, LOLGPP, MGNTPP, MGNGPP: shape (5, )
+ LOLTPA, MGNTPA, LOLGPA, MGNGPA: shape (NOAREA, 5)
+
+ .. note:: LOLTPP, LOLGPP, LOLTPA, MGNTPA, MGNTPP, LOLGPA, MGNGPA, MGNGPP are
+ modified in place. Besides, to exactly match the final statistics results of
+ the Fortran code in the integration test, "int-conversion" logic is kept
+ for now
+ """
+ SUM = sum(MARGIN)
+
+ # Some area has a loss, else we wouldn't be here
+ # Update pool peak LOLE
+ if SUM >= 0:
+ LOLTPP[NST] += 1
+ else:
+ LOLGPP[NST] += 1
+
+ for j, X in enumerate(MARGIN):
+ if X < 0.0:
+ if SUM > 0.0:
+ LOLTPA[j, NST] += 1
+ MGNTPA[j, NST] -= int(X)
+ MGNTPP[NST] -= int(X)
+ else:
+ LOLGPA[j, NST] += 1
+ MGNGPA[j, NST] -= int(X)
+ MGNGPP[NST] -= int(X)
+
+
+def pkstat(NST, MARGIN, LOLGPA, LOLGPP, LOLTPA, LOLTPP, MGNGPA, MGNGPP, MGNTPA, MGNTPP):
+ """
+ Collect (daily) peak statistics at area level and pool level
+
+ :param int NST: indicating the NST-th load-forecasting level
+ :param numpy.ndarray MARGIN: 1D array with shape (NOAREA, ) the (original)
+ generation margin of each area after deducting the load in that area
+ :param numpy.ndarray LOLTPP to MGNGPP:
+ please refer to the following naming rules for those
+ 6-digit statistic-related quantities:
+
+ The first three digits identify the type of statistic
+ LOL means this is the annual loss of load statistics
+ MGN means this is the annual sum of negative margins, EUE
+ SOL means this is the cumulative sum, all years, LOLE
+ SNG means this is the cumulative sum, all years, EUE
+
+ The fourth digit identifies the cause of the outage
+ T is transmission
+ G is generation
+ S is the sum of t & g
+
+ The fifth digit indicates whether the stat is hourly or peak
+ H is for hourly stats
+ P is for peak stats
+
+ The sixth digit is for area or pool
+ A is area
+ P is pool
+
+ Examplex:
+ SGNSPA is the cumulative sum, EUE, total of t&g, peak, area
+ LOLTHP is the annual loss of load, transmission, hourly, pool
+
+ LOLTPP, LOLGPP, MGNTPP, MGNGPP: shape (5, )
+ LOLTPA, MGNTPA, LOLGPA, MGNGPA: shape (NOAREA, 5)
+
+ .. note:: LOLTPP, LOLGPP, LOLTPA, MGNTPA, MGNTPP, LOLGPA, MGNGPA, MGNGPP are
+ modified in place. Besides, to exactly match the final statistics results of
+ the Fortran code in the integration test, "int-conversion" logic is kept
+ for now
+ """
+ SUM = sum(MARGIN)
+
+ # Some area has a loss, else we wouldn't be here
+ # Update pool peak LOLE
+ if SUM >= 0:
+ LOLTPP[NST] += 1
+ else:
+ LOLGPP[NST] += 1
+
+ if SUM > 0.0:
+ LOLTPA[:, NST][MARGIN < 0] += 1
+ MGNTPA[:, NST][MARGIN < 0] -= MARGIN[MARGIN < 0].astype(int)
+ MGNTPP[NST] -= MARGIN[MARGIN < 0].sum().astype(int)
+ else:
+ LOLGPA[:, NST][MARGIN < 0] += 1
+ MGNGPA[:, NST][MARGIN < 0] -= MARGIN[MARGIN < 0].astype(int)
+ MGNGPP[NST] -= MARGIN[MARGIN < 0].sum().astype(int)
diff --git a/NARP/reliabilityassessment/monte_carlo/quartr.py b/NARP/reliabilityassessment/monte_carlo/quartr.py
new file mode 100644
index 0000000..2e0a791
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/quartr.py
@@ -0,0 +1,36 @@
+from reliabilityassessment.monte_carlo.filem import filem
+
+
+def quartr(IQ, RATES, CLOCK, ATRIB, NHRSYR, MFA, NUMINQ, IPOINT, EVNTS):
+ """
+ Schedule quarterly simulation events
+
+ :param int IQ: index of quarters (0, 1, 2, 3)
+ :param numpy.ndarray RATES: original rating data of each unit for four seasons with
+ shape (NUNITS, 4)
+ :param float CLOCK: (global) time clock used in sequential Monte Carlo simulation
+ :param numpy.ndarray ATRIB: events attribute vector of length 2
+ ATRIB[0] -- global simulation clock (in unit: hour)
+ ATRIB[1] -- int, simulation type
+ 0: hourly
+ 1: weekly
+ 2: quarterly
+ 3: yearly
+ :param int NHRSYR: 8760, number of hours in a typical simulation year
+ :param int MFA: pointer of the first available (i.e. empty) entry in the event list
+ :param int NUMINQ: up-to-date total number of event entries (inquires)
+ :param int IPOINT: pointer of the first (already/previously stored) entry in the
+ event list, defaults to -1 if the list is empty
+ :param numpy.ndarray EVNTS: 1D array of events
+ :return: (*tuple*) -- updated IQ, IPOINT, MFA, NUMINQ and RATING, 1D numpy array
+ with shape (NUNITS, ) that gives the actual rating of each unit in Monte
+ Carlo simulation
+
+ .. note:: ATRIB, EVNTS are modified in place
+ """
+ IQ = (IQ + 1) % 4
+ RATING = RATES[:, IQ].copy()
+ ATRIB[0] = CLOCK + NHRSYR
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ return IQ, IPOINT, MFA, NUMINQ, RATING
diff --git a/NARP/reliabilityassessment/monte_carlo/rc.py b/NARP/reliabilityassessment/monte_carlo/rc.py
new file mode 100644
index 0000000..fa74101
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/rc.py
@@ -0,0 +1,15 @@
+import numpy as np
+
+
+def rc(M, PY, A, IND):
+ """
+ A vector dot-producting a specified column of a matrix
+
+ :param int M: the specified range for the matrix (to be multiplied)
+ :param numpy.ndarray PY: the input 1D vector with shape (M, )
+ :param numpy.ndarray A: the input matrix (to be multiplied)
+ the row/col size of which could be larger than M.
+ :param int IND: the specified column index of matrix A
+ :return: (*float*) -- the product result
+ """
+ return np.dot(PY[:M], A[:M, IND])
diff --git a/NARP/reliabilityassessment/monte_carlo/remove.py b/NARP/reliabilityassessment/monte_carlo/remove.py
new file mode 100644
index 0000000..157c700
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/remove.py
@@ -0,0 +1,49 @@
+import sys
+
+
+def remove(NUMINQ, ATRIB, EVNTS, IPOINT):
+ """
+ Remove an event from event list
+
+ :param int NUMINQ: up-to-date total number of event entries (inquires)
+ :param numpy.ndarray ATRIB: events attribute vector of length 2
+ ATRIB[0] -- global simulation clock (in unit: hour)
+ ATRIB[1] -- int, simulation event type
+ 1: hourly
+ 2: weekly
+ 3: quarterly
+ 4: yearly
+ :param numpy.ndarray EVNTS: 1D array of events
+ :param int IPOINT: pointer of the first (already/previously stored) entry;
+ initial value = -1
+ :return: (*tuple*) -- MFA: pointer of first available (i.e. empty) slot in the
+ event list. IPOINT, NUMINQ: see above
+
+ .. note:: ATRIB and EVNTS are modified in place
+ """
+
+ # print("Remove an event from the event list")
+
+ if NUMINQ <= 0:
+ print("Error in 'remove', the event list is empty!")
+ sys.exit() # abort the whole program
+ elif IPOINT < 0:
+ print("Error in 'remove', IPOINT is negative!")
+ return # just return
+ else:
+ # Remove entry from events array
+ ATRIB[0] = EVNTS[IPOINT + 1]
+ ATRIB[1] = EVNTS[IPOINT + 2]
+
+ NUMINQ -= 1
+
+ # Update event list pointers...First update MFA
+ MFA = IPOINT
+ EVNTS[IPOINT + 1] = 0.0
+ EVNTS[IPOINT + 2] = 0.0
+
+ # First (still existing) (i.e. unvisited) position pointer;
+ # item is always removed from this position (when called)
+ IPOINT = int(EVNTS[IPOINT])
+
+ return MFA, NUMINQ, IPOINT
diff --git a/NARP/reliabilityassessment/monte_carlo/report.py b/NARP/reliabilityassessment/monte_carlo/report.py
new file mode 100644
index 0000000..119f343
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/report.py
@@ -0,0 +1,628 @@
+import numpy as np
+
+
+def report(
+ IYEAR,
+ ITAB,
+ INDX,
+ SUSTAT,
+ DPLOLE,
+ EUES,
+ HLOLE,
+ LSTEP,
+ NAMA,
+ NFCST,
+ SGNGHA,
+ SGNGHP,
+ SGNGPA,
+ SGNGPP,
+ SGNSHA,
+ SGNSHP,
+ SGNSPA,
+ SGNSPP,
+ SGNTHA,
+ SGNTHP,
+ SGNTPA,
+ SGNTPP,
+ SOLGHA,
+ SOLGHP,
+ SOLGPA,
+ SOLGPP,
+ SOLSHA,
+ SOLSHP,
+ SOLSPA,
+ SOLSPP,
+ SOLTHA,
+ SOLTHP,
+ SOLTPA,
+ SOLTPP,
+ SWLGHA,
+ SWLGHP,
+ SWLGPA,
+ SWLGPP,
+ SWLSHA,
+ SWLSHP,
+ SWLSPA,
+ SWLSPP,
+ SWLTHA,
+ SWLTHP,
+ SWLTPA,
+ SWLTPP,
+ SWNGHA,
+ SWNGHP,
+ SWNGPA,
+ SWNGPP,
+ SWNSHA,
+ SWNSHP,
+ SWNSPA,
+ SWNSPP,
+ SWNTHA,
+ SWNTHP,
+ SWNTPA,
+ SWNTPP,
+ XNEWA,
+ XNEWP,
+):
+ """
+ Post-process the simulation results and write to the final output file
+
+ .. note:: 1. The inputs and outputs are massive scalars, 1D and 2D numpy.ndarrays.
+ For descriptions of input and output variables, please refer to `variable descriptions.xlsx.`
+ in the project Dropbox folder: https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+ 2. Modify array SUSTAT in-place
+ 3. For arrays LOLGHA to WOLTPP:
+ please refer to the following naming rules for those
+ 6-digit statistic-related quantities:
+ The first three digits identify the type of statistic
+ LOL means this is an annual loss of load statistic of some type
+ MGN means this is the annual sum of negative margins (EUE)
+ SOL means this is the cumulative sum, all years, LOLE
+ SNG means this is the cumulative sum, all years, EUE
+ The fourth digit identifies the cause of the outage
+ T is transmission
+ G is generation
+ S is sum statistics of T & G
+ The fifth digit indicates whether the stat is hourly or peak
+ H is for hourly stats
+ P is for peak stats
+ The sixth digit is for area or pool
+ A is area
+ P is pool
+ Examples:
+ SGNSPA is the cumulative sum, EUE, total of T & G, peak, area
+ LOLTHP is the annual loss of load, transmission, hourly, pool
+ """
+
+ XG, XT, XS, XA, FH, SH = "GC", "TC", "GT", "AV", "ERCO", "T"
+
+ f = open("output.txt", "a")
+
+ ITAB += 1
+ f.write("\n TABLE %d\n" % (ITAB))
+
+ f.write("\n\t\t\t FINAL RESULTS AFTER %d REPLICATIONS \n" % (IYEAR))
+
+ if INDX != 1:
+ f.write(
+ "\n AREA FORECAST HOURLY STATISTICS PEAK STATISTICS REMARKS"
+ )
+ f.write("\n NO NO HLOLE XLOL EUE LOLE XLOL")
+ f.write("\n (HRS/YR) (MW) (MWH) (DAYS/YR) (MW)")
+ else:
+ f.write("\n AREA FORECAST PEAK STATISTICS REMARKS")
+ f.write("\n NO NO LOLE XLOL")
+ f.write("\n (DAYS/YR) MW")
+
+ XYEAR = IYEAR
+ NOAREA = SUSTAT.shape[0] - 1
+ SSQA = np.zeros((NOAREA, 3))
+ for J in range(NOAREA):
+ if NFCST != 1:
+ for N in range(NFCST):
+ if SOLGHA[J, N] > 0:
+ XMGN = SGNGHA[J, N] / SOLGHA[J, N]
+ else:
+ XMGN = 0.0
+
+ XLOL = SOLGHA[J, N] / XYEAR
+ EUE = SGNGHA[J, N] / XYEAR
+ if SOLGPA[J, N] > 0:
+ XMGNP = SGNGPA[J, N] / SOLGPA[J, N]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SOLGPA[J, N] / XYEAR
+ # EUEP = SGNGPA[J,N]/XYEAR # may have future usage
+ if INDX != 1:
+ f.write(
+ "\n %2d %2d %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (J + 1, N + 1, XLOL, XMGN, EUE, XLOLP, XMGNP, XG)
+ )
+ else:
+ f.write(
+ "\n %2d %2d %8.2f %8.2f %s"
+ % (J + 1, N + 1, XLOLP, XMGNP, XG)
+ )
+
+ if SOLTHA[J, N] > 0:
+ XMGN = SGNTHA[J, N] / SOLTHA[J, N]
+ else:
+ XMGN = 0.0
+
+ XLOL = SOLTHA[J, N] / XYEAR
+ EUE = SGNTHA[J, N] / XYEAR
+ if SOLTPA[J, N] > 0:
+ XMGNP = SGNTPA[J, N] / SOLTPA[J, N]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SOLTPA[J, N] / XYEAR
+ # EUEP = SGNTPA[J,N]/XYEAR # may have future usage
+ if INDX != 1:
+ f.write(
+ "\n %2d %2d %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (J + 1, N + 1, XLOL, XMGN, EUE, XLOLP, XMGNP, XT)
+ )
+ else:
+ f.write(
+ "\n %2d %2d %8.2f %8.2f %s"
+ % (J + 1, N + 1, XLOLP, XMGNP, XT)
+ )
+
+ if SOLSHA[J, N] > 0.0:
+ XMGN = SGNSHA[J, N] / SOLSHA[J, N]
+ else:
+ XMGN = 0.0
+
+ XLOL = SOLSHA[J, N] / XYEAR
+ EUE = SGNSHA[J, N] / XYEAR
+
+ if SOLSPA[J, N] > 0:
+ XMGNP = SGNSPA[J, N] / SOLSPA[J, N]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SOLSPA[J, N] / XYEAR
+ # EUEP = SGNSPA[J,N]/XYEAR # may have future usage
+ if INDX != 1:
+ f.write(
+ "\n %2d %2d %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (J + 1, N + 1, XLOL, XMGN, EUE, XLOLP, XMGNP, XS)
+ )
+ else:
+ f.write(
+ "\n %2d %2d %8.2f %8.2f %s"
+ % (J + 1, N + 1, XLOLP, XMGNP, XS)
+ )
+
+ if SWLGHA[J] > 0.0:
+ XMGN = SWNGHA[J] / SWLGHA[J]
+ else:
+ XMGN = 0.0
+
+ XLOL = SWLGHA[J] / XYEAR
+ EUE = SWNGHA[J] / XYEAR
+
+ if SWLGPA[J] > 0.0:
+ XMGNP = SWNGPA[J] / SWLGPA[J]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SWLGPA[J] / XYEAR
+ # EUEP = SWNGPA[J]/XYEAR # may have future usage
+ if INDX != 1:
+ f.write(
+ "\n %2d %s %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (J + 1, XA, XLOL, XMGN, EUE, XLOLP, XMGNP, XG)
+ )
+ else:
+ f.write(
+ "\n %2d %s %8.2f %8.2f %s"
+ % (J + 1, XA, XLOLP, XMGNP, XG)
+ )
+
+ if SWLTHA[J] > 0.0:
+ XMGN = SWNTHA[J] / SWLTHA[J]
+ else:
+ XMGN = 0.0
+
+ XLOL = SWLTHA[J] / XYEAR
+ EUE = SWNTHA[J] / XYEAR
+
+ if SWLTPA[J] > 0.0:
+ XMGNP = SWNTPA[J] / SWLTPA[J]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SWLTPA[J] / XYEAR
+ # EUEP = SWNTPA[J]/XYEAR # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %2d %s %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (J + 1, XA, XLOL, XMGN, EUE, XLOLP, XMGNP, XT)
+ )
+ else:
+ f.write(
+ "\n %2d %s %8.2f %8.2f %s"
+ % (J + 1, XA, XLOLP, XMGNP, XT)
+ )
+
+ if SWLSHA[J] > 0.0:
+ XMGN = SWNSHA[J] / SWLSHA[J]
+ else:
+ XMGN = 0.0
+
+ XLOL = SWLSHA[J] / XYEAR
+ SSQA[J, 0] = XNEWA[J, 0] / XYEAR - XLOL**2
+ EUE = SWNSHA[J] / XYEAR
+ SSQA[J, 1] = XNEWA[J, 1] / XYEAR - EUE**2
+
+ if SWLSPA[J] > 0.0:
+ XMGNP = SWNSPA[J] / SWLSPA[J]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SWLSPA[J] / XYEAR
+ SSQA[J, 2] = XNEWA[J, 2] / XYEAR - XLOLP**2
+ # EUEP = SWNSPA[J]/XYEAR # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %2d %s %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (J + 1, XA, XLOL, XMGN, EUE, XLOLP, XMGNP, XS)
+ )
+ else:
+ f.write(
+ "\n %2d %s %8.2f %8.2f %s"
+ % (J + 1, XA, XLOLP, XMGNP, XS)
+ )
+
+ f.write("\n")
+
+ SUSTAT[J, 3] = XLOL
+ SUSTAT[J, 4] = XLOLP
+ SUSTAT[J, 5] = EUE
+
+ # Then, for Pool Statistics:
+ f.write("\n\n POOL STATISTICS \n")
+ if NFCST != 1:
+ for N in range(NFCST):
+ if SOLGHP[N] > 0:
+ XMGN = SGNGHP[N] / SOLGHP[N]
+ else:
+ XMGN = 0.0
+ XLOL = SOLGHP[N] / XYEAR
+ EUE = SGNGHP[N] / XYEAR
+ if SOLGPP[N] > 0:
+ XMGNP = SGNGPP[N] / SOLGPP[N]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SOLGPP[N] / XYEAR
+ # EUEP = 0.0 # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %2d %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (N + 1, XLOL, XMGN, EUE, XLOLP, XMGNP, XG)
+ )
+ else:
+ f.write(
+ "\n %2d %8.2f %8.2f %s"
+ % (N + 1, XLOLP, XMGNP, XG)
+ )
+
+ if SOLTHP[N] > 0:
+ XMGN = SGNTHP[N] / SOLTHP[N]
+ else:
+ XMGN = 0.0
+
+ XLOL = SOLTHP[N] / XYEAR
+ EUE = SGNTHP[N] / XYEAR
+
+ if SOLTPP[N] > 0:
+ XMGNP = SGNTPP[N] / SOLTPP[N]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SOLTPP[N] / XYEAR
+ # EUEP = 0.0 # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %2d %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (N + 1, XLOL, XMGN, EUE, XLOLP, XMGNP, XT)
+ )
+ else:
+ f.write(
+ "\n %2d %8.2f %8.2f %s"
+ % (N + 1, XLOLP, XMGNP, XT)
+ )
+
+ if SOLSHP[N] > 0:
+ XMGN = SGNSHP[N] / SOLSHP[N]
+ else:
+ XMGN = 0.0
+
+ XLOL = SOLSHP[N] / XYEAR
+ EUE = SGNSHP[N] / XYEAR
+
+ if SOLSPP[N] > 0:
+ XMGNP = SGNSPP[N] / SOLSPP[N]
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SOLSPP[N] / XYEAR
+ # EUEP = 0.0 # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %2d %7.2f %8.2f %7.0f %7.2f %8.2f %s"
+ % (N + 1, XLOL, XMGN, EUE, XLOLP, XMGNP, XS)
+ )
+ else:
+ f.write(
+ "\n %2d %8.2f %8.2f %s"
+ % (N + 1, XLOLP, XMGNP, XS)
+ )
+
+ if SWLGHP > 0.0:
+ XMGN = SWNGHP / SWLGHP
+ else:
+ XMGN = 0.0
+
+ XLOL = SWLGHP / XYEAR
+ EUE = SWNGHP / XYEAR
+
+ if SWLGPP > 0.0:
+ XMGNP = SWNGPP / SWLGPP
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SWLGPP / XYEAR
+ # EUEP = 0.0 # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %s %7.3f %8.2f %7.0f %7.3f %8.2f %s"
+ % (XA, XLOL, XMGN, EUE, XLOLP, XMGNP, XG)
+ )
+ else:
+ f.write(
+ "\n %s %8.2f %8.2f %s" % (XA, XLOLP, XMGNP, XG)
+ )
+
+ if SWLTHP > 0.0:
+ XMGN = SWNTHP / SWLTHP
+ else:
+ XMGN = 0.0
+
+ XLOL = SWLTHP / XYEAR
+ EUE = SWNTHP / XYEAR
+
+ if SWLTPP > 0:
+ XMGNP = SWNTPP / SWLTPP
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SWLTPP / XYEAR
+ # EUEP = 0.0 # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %s %7.3f %8.2f %7.0f %7.3f %8.2f %s"
+ % (XA, XLOL, XMGN, EUE, XLOLP, XMGNP, XT)
+ )
+ else:
+ f.write(
+ "\n %s %8.2f %8.2f %s" % (XA, XLOLP, XMGNP, XT)
+ )
+
+ if SWLSHP > 0.0:
+ XMGN = SWNSHP / SWLSHP
+ else:
+ XMGN = 0.0
+
+ XLOL = SWLSHP / XYEAR
+ SSQP = np.zeros((3,))
+ SSQP[0] = XNEWP[0] / XYEAR - XLOL**2
+ EUE = SWNSHP / XYEAR
+ SSQP[1] = XNEWP[1] / XYEAR - EUE**2
+
+ if SWLSPP > 0.0:
+ XMGNP = SWNSPP / SWLSPP
+ else:
+ XMGNP = 0.0
+
+ XLOLP = SWLSPP / XYEAR
+ SSQP[2] = XNEWP[2] / XYEAR - XLOLP**2
+ # EUEP = 0.0 # may have future usage
+
+ if INDX != 1:
+ f.write(
+ "\n %s %7.3f %8.2f %7.0f %7.3f %8.2f %s"
+ % (XA, XLOL, XMGN, EUE, XLOLP, XMGNP, XS)
+ )
+ else:
+ f.write(
+ "\n %s %8.2f %8.2f %s" % (XA, XLOLP, XMGNP, XS)
+ )
+
+ for IAR in range(NOAREA):
+ for J in range(3):
+ SSQA[IAR, J] = (SSQA[IAR, J] / XYEAR) ** 0.5
+
+ for J in range(3):
+ SSQP[J] = (SSQP[J] / XYEAR) ** 0.5
+
+ SUSTAT[NOAREA, 3] = XLOL
+ SUSTAT[NOAREA, 4] = XLOLP
+ SUSTAT[NOAREA, 5] = EUE
+
+ ITAB += 1
+ f.write("\n\n\t\t\t\t TABLE %4d \n" % (ITAB))
+ f.write("\n \t\t\t\t\t\t\t\t\t\t SUMMARY OF RESULTS \n\n")
+
+ if INDX != 1:
+ f.write(
+ " AREA PEAK INSTALLED AREA HLOLE EUE LOLE\n"
+ )
+ f.write(
+ " ---------------- ---------------- ----------------\n"
+ )
+ f.write(
+ " CAPACITY PCT RES MAGN PCT SD MAGN PCT SD MAGN PCT SD"
+ )
+
+ else:
+ f.write(" AREA PEAK INSTALLED AREA ")
+ f.write(
+ " ----------------\n"
+ )
+ f.write(
+ " CAPACITY PCT RES MAGN PCT SD"
+ )
+
+ for i in range(NOAREA):
+ if SUSTAT[i, 0] > 0:
+ SUSTAT[i, 2] = (SUSTAT[i, 1] - SUSTAT[i, 0]) / SUSTAT[i, 0] * 100
+
+ if INDX != 1:
+ SSHL = 0.0
+ SSEU = 0.0
+ SSLO = 0.0
+ if SUSTAT[i, 3] > 0:
+ SSHL = SSQA[i, 0] / SUSTAT[i, 3] * 100
+ if SUSTAT[i, 5] > 0:
+ SSEU = SSQA[i, 1] / SUSTAT[i, 5] * 100
+ if SUSTAT[i, 4] > 0:
+ SSLO = SSQA[i, 2] / SUSTAT[i, 4] * 100
+
+ f.write(
+ "\n %s %7.0f %8.0f %5.1f %7.3f %7.3f %8.0f %8.0f %7.3f %7.3f"
+ % (
+ NAMA[i],
+ SUSTAT[i, 0],
+ SUSTAT[i, 1],
+ SUSTAT[i, 2],
+ SUSTAT[i, 3],
+ SSHL,
+ SUSTAT[i, 5],
+ SSEU,
+ SUSTAT[i, 4],
+ SSLO,
+ )
+ )
+ else:
+ SSLO = 0
+ if SUSTAT[i, 4] > 0:
+ SSLO = SSQA[i, 2] / SUSTAT[i, 4] * 100
+ f.write(
+ "\n %s %7.0f %8.0f %5.1f %7.3f %7.3f"
+ % (
+ NAMA[i],
+ SUSTAT[i, 0],
+ SUSTAT[i, 1],
+ SUSTAT[i, 2],
+ SUSTAT[i, 4],
+ SSLO,
+ )
+ )
+
+ NO1 = NOAREA
+ SUSTAT[NO1, 0] += SUSTAT[i, 0]
+ SUSTAT[NO1, 1] += SUSTAT[i, 1]
+
+ if SUSTAT[i, 0] > 0:
+ SUSTAT[NO1, 2] = (SUSTAT[i, 1] - SUSTAT[i, 0]) / SUSTAT[i, 0] * 100
+
+ if INDX != 1:
+ SSHL = 0.0
+ SSEU = 0.0
+ SSLO = 0.0
+ if SUSTAT[NO1, 3] > 0:
+ SSHL = SSQP[0] / SUSTAT[NO1, 3] * 100.0
+ if SUSTAT[NO1, 5] > 0:
+ SSEU = SSQP[1] / SUSTAT[NO1, 5] * 100.0
+ if SUSTAT[NO1, 4] > 0:
+ SSLO = SSQP[2] / SUSTAT[NO1, 4] * 100.0
+ f.write(
+ "\n\n %s%s %7.0f %8.0f %5.1f %7.3f %7.3f %8.0f %8.0f %7.3f %7.3f"
+ % (
+ FH,
+ SH,
+ SUSTAT[NO1, 0],
+ SUSTAT[NO1, 1],
+ SUSTAT[NO1, 2],
+ SUSTAT[NO1, 3],
+ SSHL,
+ SUSTAT[NO1, 5],
+ SSEU,
+ SUSTAT[NO1, 4],
+ SSLO,
+ )
+ )
+ else:
+ SSLO = 0.0
+ if SUSTAT[NO1, 4] > 0:
+ SSLO = SSQP[2] / SUSTAT[NO1, 4] * 100.0
+ f.write(
+ "\n\n %s%s %7.0f %8.0f %5.1f %7.3f %7.3f"
+ % (
+ FH,
+ SH,
+ SUSTAT[NO1, 0],
+ SUSTAT[NO1, 1],
+ SUSTAT[NO1, 2],
+ SUSTAT[NO1, 4],
+ SSLO,
+ )
+ )
+
+ SUMHL, SUMDP, SUMEUE = np.zeros(NOAREA), np.zeros(NOAREA), np.zeros(NOAREA)
+ for j in range(22):
+ for i in range(NOAREA):
+ SUMHL[i] += HLOLE[i, j]
+ SUMDP[i] += DPLOLE[i, j]
+ SUMEUE[i] += EUES[i, j]
+
+ PHL, PDP, PEUE = (
+ np.zeros((NOAREA, 22)),
+ np.zeros((NOAREA, 22)),
+ np.zeros((NOAREA, 22)),
+ )
+ for j in range(22):
+ for i in range(NOAREA):
+ PHL[i, j] = HLOLE[i, j] / SUMHL[i]
+ PDP[i, j] = DPLOLE[i, j] / SUMHL[i]
+ PEUE[i, j] = EUES[i, j] / SUMEUE[i]
+
+ for i in range(NOAREA):
+ ITAB += 1
+ f.write(
+ "\n\n\t\t\t\t\t\t\t\t\t\t\t TABLE %5d\n\n"
+ "\t\t\t\t\t\t\t\t PROBABILITY DISTRIBUTIONS FOR AREA %3d\n\n\n"
+ "\t\t DAILY PEAK LOLES PER YEAR \t\t\t HOURLY LOLES PER YEAR \t\t\t\t ANNUAL UNSERVED ENERGY (MWH)\n\n"
+ "NUMBER\t OBSERVATIONS\t PROBABILITY"
+ "\t OBSERVATIONS\t PROBABILITY\t"
+ "LIMIT (MWH)\t OBSERVATIONS\t PROBABILITY \n" % (ITAB, i + 1)
+ )
+ for j in range(22):
+ LIMIT = j * LSTEP
+ f.write(
+ "%4d\t\t%6.0f\t\t\t%5.3f\t\t\t%6.0f\t\t\t%5.3f\t\t\t%6d\t\t%6.0f\t\t\t %6.4f\n"
+ % (
+ j,
+ DPLOLE[i, j],
+ PDP[i, j],
+ HLOLE[i, j],
+ PHL[i, j],
+ LIMIT,
+ EUES[i, j],
+ PEUE[i, j],
+ )
+ )
+
+ f.close() # close "output" file
+ return ITAB
diff --git a/NARP/reliabilityassessment/monte_carlo/rm.py b/NARP/reliabilityassessment/monte_carlo/rm.py
new file mode 100644
index 0000000..2e717f0
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/rm.py
@@ -0,0 +1,12 @@
+def rm(M, CB, TAB):
+ """
+ 2D Matrix left-multiplication by a vector within a specified range
+
+ :param int M: the specified range for the matrix (to be multiplied)
+ :param numpy.ndarray CB: the input vector (as the be left-multiplier)
+ the length of which could be larger than M.
+ :param numpy.ndarray TAB: the input matrix (to be multiplied)
+ the row/col size of which could be larger than M.
+ :return: (*numpy.ndarray*) -- 1D array with shape (M, )
+ """
+ return CB[:M] @ TAB[:M, :M]
diff --git a/NARP/reliabilityassessment/monte_carlo/rstart.py b/NARP/reliabilityassessment/monte_carlo/rstart.py
new file mode 100644
index 0000000..6684f78
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/rstart.py
@@ -0,0 +1,25 @@
+import pickle
+
+
+def rstart(DUMPED_file):
+ """
+ Load (if exists) the 'DUMP' file (previously saved intermediate data and results)
+ which is used to resume the Monte Carlo Simulation in external call
+
+ :param str DUMPED_file: file path of the pickle file storing previously obtained
+ intermediate data and results
+ :return: (*dict*) -- snapshot_data: a python dictionary storing previously obtained
+ intermediate data and results, which contains multiple scalars, 1D and 2D
+ numpy.ndarrays. For detailed description see 'variable descriptions.xlsx' at:
+ https://www.dropbox.com/s/eahg8x584s9pg4j/variable%20descriptions.xlsx?dl=0
+
+ .. note:: In external call, the returned dict object will be handled via the
+ following way to load all variables into memory:
+ for key, val in snapshot_data.items():
+ exec(key + '=val')
+ Using string evaluation is risky and this is subject to refactor in the future.
+ """
+ with open(DUMPED_file, "rb") as f:
+ snapshot_data = pickle.load(f)
+
+ return snapshot_data
diff --git a/NARP/reliabilityassessment/monte_carlo/seeder.py b/NARP/reliabilityassessment/monte_carlo/seeder.py
new file mode 100644
index 0000000..bc85c13
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/seeder.py
@@ -0,0 +1,36 @@
+import numpy as np
+
+
+def seeder(JSEED, NUNITS, NLINES):
+ """
+ Initialize the lists of random number seeds for generator units and lines.
+
+ :param int JSEED: the random number seed
+ :param int NUNITS: the total number of generator units
+ :param int NLINES: the total number of lines
+ :return: (*tuple*) -- IGSEED: list of seeded random numbers for generator state
+ sampling; ILSEED: list of seeded random numbers for line state sampling
+ """
+ IGSEED = np.zeros(1 + NUNITS, dtype=np.int32)
+ ILSEED = np.zeros(1 + NLINES, dtype=np.int32)
+ # Seeding random number for Each Generator
+ IGSEED[0] = JSEED
+ for i in range(NUNITS):
+ IGSEED[i] = IGSEED[i] * 9649599
+ if IGSEED[i] < 0:
+ IGSEED[i] += 2147483647
+ IGSEED[i] += 1
+ j = i + 1
+ IGSEED[j] = IGSEED[i]
+
+ # Continue, seeding Lines
+ ILSEED[0] = IGSEED[NUNITS - 1]
+ for i in range(NLINES):
+ ILSEED[i] = ILSEED[i] * 9649599
+ if ILSEED[i] < 0:
+ ILSEED[i] += 2147483647
+ ILSEED[i] += 1
+ j = i + 1
+ ILSEED[j] = ILSEED[i]
+
+ return IGSEED, ILSEED
diff --git a/NARP/reliabilityassessment/monte_carlo/sumcap.py b/NARP/reliabilityassessment/monte_carlo/sumcap.py
new file mode 100644
index 0000000..3d8264e
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/sumcap.py
@@ -0,0 +1,38 @@
+import numpy as np
+
+
+def sumcap(AVAIL, CAPOWN, CAPCON):
+ """
+ Determine the total available capacity for each system (area).
+
+ :param numpy.ndarray AVAIL: array of the finalized power capacity
+ (in nominal value) of each unit
+ :param numpy.ndarray CAPOWN: 2D array, the (j,i) th element means
+ the fraction of unit i owned by area j.
+ :param numpy.ndarray CAPCON: 1D array of length NUINT; the CAPCON[i] means the area
+ that the ith generator physically resides in.
+ :return: (*tuple*) -- CAPAVL: 2D array, the (j,i) th element means the
+ the fraction (available) capacity of the ith
+ generator owned by the jth area
+ SYSOWN: the genuine/net capacity owned by each area.
+ SYSCON: the literal capacity owned by each area.
+ TRNSFJ: the capacity transferred out to the rest area(s)
+ from the current system (area)
+ """
+
+ # Split Capacity by Ownership.
+ NOAREA, NUNITS = CAPOWN.shape
+
+ CAPAVL = np.multiply(CAPOWN, AVAIL.T)
+
+ SYSOWN = np.sum(CAPAVL, axis=1)
+
+ SYSCON = np.zeros(NOAREA)
+
+ for i in range(NUNITS):
+ j = CAPCON[i]
+ SYSCON[j] = SYSCON[j] + AVAIL[i]
+
+ TRNSFJ = SYSCON - SYSOWN
+
+ return CAPAVL, SYSOWN, SYSCON, TRNSFJ
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/__init__.py b/NARP/reliabilityassessment/monte_carlo/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_admitb.py b/NARP/reliabilityassessment/monte_carlo/tests/test_admitb.py
new file mode 100644
index 0000000..c19c730
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_admitb.py
@@ -0,0 +1,11 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.admitb import admitb
+
+
+def test_admitb():
+ LP = np.array([[0, 0, 1], [1, 1, 2], [2, 2, 0]], dtype=int)
+ BLP = np.array([[-120, 300, 300], [-60, 150, 150], [-80, 100, 100]])
+ BB_ = np.array([[-200.0, 120, 80], [120, -180, 60], [80, 60, -140]])
+ BB = admitb(LP, BLP)
+ np.testing.assert_array_almost_equal(BB, BB_)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_admitr.py b/NARP/reliabilityassessment/monte_carlo/tests/test_admitr.py
new file mode 100644
index 0000000..86f7402
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_admitr.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.admitr import _admitr, admitr
+
+
+def test_admitr():
+ BB = np.array([[1.5, -0.5, -0.5], [-0.5, 2.5, -1.0], [-0.5, -1.0, 3.0]])
+ BN = np.array(
+ [[0, 50.0, 100.0, 100.0], [1, 50.0, 100.0, 100.0], [2, 50.0, 100.0, 100.0]]
+ )
+ NR = 1 # delibrately use 1 here since this is an artificially created test case
+
+ # vanilla version
+ BB, LT = _admitr(BB, BN, NR)
+ np.testing.assert_array_equal(
+ BB, np.array([[1.5, -0.5, 0.0], [-0.5, 3.0, 0.0], [0.0, 0.0, 0.0]])
+ )
+ np.testing.assert_array_equal(LT, np.array([0, 2, 1], dtype=int))
+
+ # vectorized version
+ BB = np.array([[1.5, -0.5, -0.5], [-0.5, 2.5, -1.0], [-0.5, -1.0, 3.0]])
+
+ BB, LT = admitr(BB, BN, NR)
+ # np.testing.assert_array_equal(BB, np.array([[1.5, -0.5], [-0.5, 3.0]]))
+ np.testing.assert_array_equal(
+ BB, np.array([[1.5, -0.5, 0.0], [-0.5, 3.0, 0.0], [0.0, 0.0, 0.0]])
+ )
+ np.testing.assert_array_equal(LT, np.array([0, 2, 1], dtype=int))
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_assign.py b/NARP/reliabilityassessment/monte_carlo/tests/test_assign.py
new file mode 100644
index 0000000..7ca9f72
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_assign.py
@@ -0,0 +1,79 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.assign import assign
+
+
+def test_assign():
+ # --------------- test case-1 -----------
+ NN, NLS = 5, 1
+
+ INJ = np.zeros(20)
+ LOD = np.zeros(20)
+
+ LT = np.zeros(20, dtype=int)
+ LT[:5] = [2, 3, 4, 5, 1]
+ LT[:5] -= 1 # be careful: 0-based index in Python
+
+ INJ1, LODC = assign(INJ, LOD, LT, NN, NLS)
+
+ INJ1_true = np.zeros(5)
+
+ np.testing.assert_array_almost_equal(INJ1, INJ1_true)
+
+ LODC_true = np.zeros(5)
+
+ np.testing.assert_array_almost_equal(LODC, LODC_true)
+
+ # --------------- test case-2 -----------
+ NN, NLS = 5, 1
+
+ INJ = np.zeros(20)
+ INJ[:NN] = [-162.00, 814.00, 414.00, 314.00, 432.00]
+
+ LOD = np.zeros(20)
+ LOD[:NN] = [2591.00, 2591.00, 2591.00, 2591.00, 2591.00]
+
+ LT = np.zeros(20, dtype=int)
+ LT[:5] = [2, 3, 4, 5, 1]
+ LT[:5] -= 1 # be careful: 0-based index in Python
+
+ INJ1, LODC = assign(INJ, LOD, LT, NN, NLS)
+
+ INJ1_true = np.array([-162.000000, 66.802429, 33.975685, 25.768997, 35.452888])
+
+ np.testing.assert_array_almost_equal(INJ1, INJ1_true, decimal=5)
+
+ LODC_true = np.zeros(5)
+
+ np.testing.assert_array_almost_equal(LODC, LODC_true, decimal=5)
+
+ # --------------- test case-3 -----------
+ NN, NLS = 5, 1
+
+ INJ = np.zeros(20)
+ INJ[:NN] = [620.00, -40.000000, 668.00, 495.000000, 845.00]
+
+ LOD = np.zeros(20)
+ LOD[:NN] = [2540.00, 2540.00, 2540.00, 2540.00, 2540.00]
+
+ LT = np.zeros(20, dtype=int)
+ LT[:5] = [2, 3, 4, 5, 1]
+ LT[:5] -= 1 # be careful: 0-based index in Python
+
+ INJ1, LODC = assign(INJ, LOD, LT, NN, NLS)
+
+ INJ1_true = np.array(
+ [
+ 9.436834,
+ -40.000000,
+ 10.167428,
+ 7.534246,
+ 12.861491,
+ ]
+ )
+
+ np.testing.assert_array_almost_equal(INJ1, INJ1_true, decimal=5)
+
+ LODC_true = np.zeros(5)
+
+ np.testing.assert_array_almost_equal(LODC, LODC_true, decimal=5)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_conls.py b/NARP/reliabilityassessment/monte_carlo/tests/test_conls.py
new file mode 100644
index 0000000..21cb00c
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_conls.py
@@ -0,0 +1,208 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.conls import conls
+
+
+def test_conls():
+ NX, NR, NLS = 4, 1, 0
+ # NL = 5 # not used
+ NR -= 1 # 0-based index in Python!
+
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # -----------------------Test case 1 ----------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/BC")
+ BC = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/INJ")
+ INJ = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/LT")
+ LT = loadtxt(FileNameAndPath).astype(int)
+ LT -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/BLP")
+ BLP = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/BN")
+ BN = loadtxt(FileNameAndPath)
+ BN[:, 0] -= 1 # 0-based index in python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/LOD")
+ LOD = loadtxt(FileNameAndPath)
+
+ M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB = conls(
+ BC, INJ, NX, NR, LT, BLP, LP, BN, LOD, NLS
+ )
+
+ M_true, N_true, N1_true = 35, 53, 48
+ assert M == M_true
+ assert N == N_true
+ assert N1 == N1_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/A")
+ A_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/XOB")
+ XOB_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOB, XOB_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/XOBI")
+ XOBI_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOBI, XOBI_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/IBAS")
+ IBAS_true = loadtxt(FileNameAndPath).astype(int)
+ IBAS_true -= 1 # 0-based index in Python!
+ IBAS_true = IBAS_true[:M]
+ np.testing.assert_array_equal(IBAS, IBAS_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/BS")
+ BS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BS, BS_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/B")
+ B_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(B, B_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/B1")
+ B1_true = loadtxt(FileNameAndPath)
+ B1_true = B1_true[:M]
+ np.testing.assert_array_almost_equal(B1, B1_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case1/TAB")
+ TAB_true = loadtxt(FileNameAndPath)
+ TAB_true = TAB_true[:M, :N]
+ np.testing.assert_array_almost_equal(TAB, TAB_true, decimal=5)
+
+ # -----------------------Test case 2 ----------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/BC")
+ BC = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/INJ")
+ INJ = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/LT")
+ LT = loadtxt(FileNameAndPath).astype(int)
+ LT -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/BLP")
+ BLP = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/BN")
+ BN = loadtxt(FileNameAndPath)
+ BN[:, 0] -= 1 # 0-based index in python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/LOD")
+ LOD = loadtxt(FileNameAndPath)
+
+ M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB = conls(
+ BC, INJ, NX, NR, LT, BLP, LP, BN, LOD, NLS
+ )
+
+ M_true, N_true, N1_true = 35, 53, 48
+ assert M == M_true
+ assert N == N_true
+ assert N1 == N1_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/A")
+ A_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/XOB")
+ XOB_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOB, XOB_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/XOBI")
+ XOBI_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOBI, XOBI_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/IBAS")
+ IBAS_true = loadtxt(FileNameAndPath).astype(int)
+ IBAS_true -= 1 # 0-based index in Python!
+ IBAS_true = IBAS_true[:M]
+ np.testing.assert_array_equal(IBAS, IBAS_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/BS")
+ BS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BS, BS_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/B")
+ B_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(B, B_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/B1")
+ B1_true = loadtxt(FileNameAndPath)
+ B1_true = B1_true[:M]
+ np.testing.assert_array_almost_equal(B1, B1_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case2/TAB")
+ TAB_true = loadtxt(FileNameAndPath)
+ TAB_true = TAB_true[:M, :N]
+ np.testing.assert_array_almost_equal(TAB, TAB_true, decimal=5)
+
+ # -----------------------Test case 3 ----------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/BC")
+ BC = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/INJ")
+ INJ = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/LT")
+ LT = loadtxt(FileNameAndPath).astype(int)
+ LT -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/BLP")
+ BLP = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/BN")
+ BN = loadtxt(FileNameAndPath)
+ BN[:, 0] -= 1 # 0-based index in python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/LOD")
+ LOD = loadtxt(FileNameAndPath)
+
+ M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB = conls(
+ BC, INJ, NX, NR, LT, BLP, LP, BN, LOD, NLS
+ )
+
+ M_true, N_true, N1_true = 35, 53, 48
+ assert M == M_true
+ assert N == N_true
+ assert N1 == N1_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/A")
+ A_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/XOB")
+ XOB_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOB, XOB_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/XOBI")
+ XOBI_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOBI, XOBI_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/IBAS")
+ IBAS_true = loadtxt(FileNameAndPath).astype(int)
+ IBAS_true -= 1 # 0-based index in Python!
+ IBAS_true = IBAS_true[:M]
+ np.testing.assert_array_equal(IBAS, IBAS_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/BS")
+ BS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BS, BS_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/B")
+ B_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(B, B_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/B1")
+ B1_true = loadtxt(FileNameAndPath)
+ B1_true = B1_true[:M]
+ np.testing.assert_array_almost_equal(B1, B1_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_conls/case3/TAB")
+ TAB_true = loadtxt(FileNameAndPath)
+ TAB_true = TAB_true[:M, :N]
+ np.testing.assert_array_almost_equal(TAB, TAB_true, decimal=5)
+
+ return
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_connls.py b/NARP/reliabilityassessment/monte_carlo/tests/test_connls.py
new file mode 100644
index 0000000..9b6a6cb
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_connls.py
@@ -0,0 +1,214 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.connls import connls
+
+
+def test_connls():
+ NX, NR, NLS = 4, 1, 1
+ # NL = 5 # not used
+ NR -= 1 # 0-based index in Python!
+
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # -----------------------Test case 1 ----------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/BC")
+ BC = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/INJ")
+ INJ = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/INJB")
+ INJB = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/LT")
+ LT = loadtxt(FileNameAndPath).astype(int)
+ LT -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/BLP")
+ BLP = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/BN")
+ BN = loadtxt(FileNameAndPath)
+ BN[:, 0] -= 1 # 0-based index in python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/LOD")
+ LOD = loadtxt(FileNameAndPath)
+
+ M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB = connls(
+ BC, INJ, INJB, NX, NR, LT, BLP, LP, BN, LOD, NLS
+ )
+
+ M_true, N_true, N1_true = 30, 43, 38
+ assert M == M_true
+ assert N == N_true
+ assert N1 == N1_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/A")
+ A_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/XOB")
+ XOB_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOB, XOB_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/XOBI")
+ XOBI_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOBI, XOBI_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/IBAS")
+ IBAS_true = loadtxt(FileNameAndPath).astype(int)
+ IBAS_true -= 1 # 0-based index in Python!
+ IBAS_true = IBAS_true[:M]
+ np.testing.assert_array_equal(IBAS, IBAS_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/BS")
+ BS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BS, BS_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/B")
+ B_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(B, B_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/B1")
+ B1_true = loadtxt(FileNameAndPath)
+ B1_true = B1_true[:M]
+ np.testing.assert_array_almost_equal(B1, B1_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case1/TAB")
+ TAB_true = loadtxt(FileNameAndPath)
+ TAB_true = TAB_true[:M, :N]
+ np.testing.assert_array_almost_equal(TAB, TAB_true, decimal=5)
+
+ # -----------------------Test case 2 ----------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/BC")
+ BC = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/INJ")
+ INJ = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/INJB")
+ INJB = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/LT")
+ LT = loadtxt(FileNameAndPath).astype(int)
+ LT -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/BLP")
+ BLP = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/BN")
+ BN = loadtxt(FileNameAndPath)
+ BN[:, 0] -= 1 # 0-based index in python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/LOD")
+ LOD = loadtxt(FileNameAndPath)
+
+ M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB = connls(
+ BC, INJ, INJB, NX, NR, LT, BLP, LP, BN, LOD, NLS
+ )
+
+ M_true, N_true, N1_true = 30, 43, 38
+ assert M == M_true
+ assert N == N_true
+ assert N1 == N1_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/A")
+ A_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/XOB")
+ XOB_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOB, XOB_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/XOBI")
+ XOBI_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOBI, XOBI_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/IBAS")
+ IBAS_true = loadtxt(FileNameAndPath).astype(int)
+ IBAS_true -= 1 # 0-based index in Python!
+ IBAS_true = IBAS_true[:M]
+ np.testing.assert_array_equal(IBAS, IBAS_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/BS")
+ BS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BS, BS_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/B")
+ B_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(B, B_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/B1")
+ B1_true = loadtxt(FileNameAndPath)
+ B1_true = B1_true[:M]
+ np.testing.assert_array_almost_equal(B1, B1_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case2/TAB")
+ TAB_true = loadtxt(FileNameAndPath)
+ TAB_true = TAB_true[:M, :N]
+ np.testing.assert_array_almost_equal(TAB, TAB_true, decimal=5)
+
+ # -----------------------Test case 3 ----------------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/BC")
+ BC = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/INJ")
+ INJ = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/INJB")
+ INJB = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/LT")
+ LT = loadtxt(FileNameAndPath).astype(int)
+ LT -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/BLP")
+ BLP = loadtxt(FileNameAndPath)
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in Python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/BN")
+ BN = loadtxt(FileNameAndPath)
+ BN[:, 0] -= 1 # 0-based index in python!
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/LOD")
+ LOD = loadtxt(FileNameAndPath)
+
+ M, N, N1, A, XOB, XOBI, IBAS, BS, B, B1, TAB = connls(
+ BC, INJ, INJB, NX, NR, LT, BLP, LP, BN, LOD, NLS
+ )
+
+ M_true, N_true, N1_true = 30, 43, 38
+ assert M == M_true
+ assert N == N_true
+ assert N1 == N1_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/A")
+ A_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/XOB")
+ XOB_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOB, XOB_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/XOBI")
+ XOBI_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(XOBI, XOBI_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/IBAS")
+ IBAS_true = loadtxt(FileNameAndPath).astype(int)
+ IBAS_true -= 1 # 0-based index in Python!
+ IBAS_true = IBAS_true[:M]
+ np.testing.assert_array_equal(IBAS, IBAS_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/BS")
+ BS_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(BS, BS_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/B")
+ B_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(B, B_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/B1")
+ B1_true = loadtxt(FileNameAndPath)
+ B1_true = B1_true[:M]
+ np.testing.assert_array_almost_equal(B1, B1_true, decimal=5)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_connls/case3/TAB")
+ TAB_true = loadtxt(FileNameAndPath)
+ TAB_true = TAB_true[:M, :N]
+ np.testing.assert_array_almost_equal(TAB, TAB_true, decimal=5)
+
+ return
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_cvchk.py b/NARP/reliabilityassessment/monte_carlo/tests/test_cvchk.py
new file mode 100644
index 0000000..834f88e
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_cvchk.py
@@ -0,0 +1,51 @@
+from reliabilityassessment.monte_carlo.cvchk import cvchk
+
+
+def test_cvchk():
+ CLOCK = 12.5
+ FINISH = 9999 * 8760
+ SUM = 3.0
+ XLAST = 2.0
+ SSQ = 3.0
+ IYEAR = 4
+ CVTEST = 0.05
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 0
+ assert SSQ == 4.0
+ assert XLAST == 3.0
+
+ CLOCK = 9999 * 8760 + 1
+ FINISH = 9999 * 8760
+ SUM = 3.0
+ XLAST = 2.0
+ SSQ = 3.0
+ IYEAR = 4
+ CVTEST = 0.05
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 1
+ assert SSQ == 4.0
+ assert XLAST == 3.0
+
+ CLOCK = 12.5
+ FINISH = 9999 * 8760
+ SUM = 3.0
+ XLAST = 2.0
+ SSQ = 3.0
+ IYEAR = 5
+ CVTEST = 0.05
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 0
+ assert SSQ == 4.0
+ assert XLAST == 3.0
+
+ CLOCK = 12.5
+ FINISH = 9999 * 8760
+ SUM = 1.0
+ XLAST = 1.0
+ SSQ = 0.3
+ IYEAR = 5
+ CVTEST = 0.5
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 1
+ assert SSQ == 0.3
+ assert XLAST == 1.0
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_cvchk_new.py b/NARP/reliabilityassessment/monte_carlo/tests/test_cvchk_new.py
new file mode 100644
index 0000000..5734ebb
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_cvchk_new.py
@@ -0,0 +1,42 @@
+from reliabilityassessment.monte_carlo.cvchk import cvchk
+
+
+def test_cvchk_new():
+ # ---- example test case 1 from real run ------
+ CLOCK = 8760.0
+ FINISH = 87591240.0 # i.e. 9999 * 8760
+ SUM = 0.0
+ XLAST = 0.0
+ SSQ = 0.0
+ IYEAR = 1
+ CVTEST = 0.025
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 0
+ assert SSQ == 0.0
+ assert XLAST == 0.0
+
+ # ---- example test case 2 from real run ------
+ CLOCK = 43800.0
+ FINISH = 87591240.0 # i.e. 9999 * 8760
+ SUM = 1.0
+ XLAST = 1.0
+ SSQ = 1.0
+ IYEAR = 5
+ CVTEST = 0.025
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 0
+ assert SSQ == 1.0
+ assert XLAST == 1.0
+
+ # ---- example test case 3 from real run ------
+ CLOCK = 52569.0
+ FINISH = 87591240.0 # i.e. 9999 * 8760
+ SUM = 2.0
+ XLAST = 1.0
+ SSQ = 1.0
+ IYEAR = 6
+ CVTEST = 0.025
+ RFLAG, SSQ, XLAST = cvchk(CLOCK, FINISH, IYEAR, CVTEST, SUM, XLAST, SSQ)
+ assert RFLAG == 0
+ assert SSQ == 2.0
+ assert XLAST == 2.0
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_dgeco.py b/NARP/reliabilityassessment/monte_carlo/tests/test_dgeco.py
new file mode 100644
index 0000000..82e2b14
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_dgeco.py
@@ -0,0 +1,58 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.dgeco import dgeco
+
+
+def test_dgeco():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # ------ test case 1--------------
+ N = 30
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/A1")
+ A = loadtxt(FileNameAndPath)
+
+ IPVT = dgeco(A, N)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/IPVT1")
+ IPVT_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(IPVT, IPVT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/A1_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true)
+
+ # ------ test case 2--------------
+ N = 30
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/A2")
+ A = loadtxt(FileNameAndPath)
+
+ IPVT = dgeco(A, N)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/IPVT2")
+ IPVT_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(IPVT, IPVT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/A2_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true)
+
+ # ------ test case 3--------------
+ N = 30
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/A3")
+ A = loadtxt(FileNameAndPath)
+
+ IPVT = dgeco(A, N)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/IPVT3")
+ IPVT_true = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+ np.testing.assert_array_equal(IPVT, IPVT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgeco/A3_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_dgedi.py b/NARP/reliabilityassessment/monte_carlo/tests/test_dgedi.py
new file mode 100644
index 0000000..51c972b
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_dgedi.py
@@ -0,0 +1,55 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.dgedi import dgedi
+
+
+def test_dgedi():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # ------ test case 1--------------
+ N = 30
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/A1")
+ A = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/IPVT1")
+ IPVT = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+
+ dgedi(A, N, IPVT)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/A1_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true, decimal=4)
+
+ # ------ test case 2--------------
+ N = 30
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/A2")
+ A = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/IPVT2")
+ IPVT = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+
+ dgedi(A, N, IPVT)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/A2_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true, decimal=4)
+
+ # ------ test case 3--------------
+ N = 30
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/A3")
+ A = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/IPVT3")
+ IPVT = -1 + loadtxt(FileNameAndPath).astype(int) # 0-based index in python!
+
+ dgedi(A, N, IPVT)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgedi/A3_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true, decimal=4)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_dgefa.py b/NARP/reliabilityassessment/monte_carlo/tests/test_dgefa.py
new file mode 100644
index 0000000..491c5f6
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_dgefa.py
@@ -0,0 +1,67 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.dgefa import dgefa
+
+
+def test_dgefa():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # ------ test case 1--------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/A")
+ A = loadtxt(FileNameAndPath)
+
+ N = 30
+ INFO, IPVT = dgefa(A, N)
+
+ INFO_true = 0
+ assert INFO == INFO_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/IPVT")
+ IPVT_true = loadtxt(FileNameAndPath).astype(int)
+ IPVT_true -= 1 # 0-based index in Python
+ np.testing.assert_array_equal(IPVT, IPVT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/A_modified")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true)
+
+ # ------ test case 2--------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/A2")
+ A = loadtxt(FileNameAndPath)
+
+ N = 30
+ INFO, IPVT = dgefa(A, N)
+
+ INFO_true = 0
+ assert INFO == INFO_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/IPVT2")
+ IPVT_true = loadtxt(FileNameAndPath).astype(int)
+ IPVT_true -= 1 # 0-based index in Python
+ np.testing.assert_array_equal(IPVT, IPVT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/A_modified2")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true)
+
+ # ------ test case 3--------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/A3")
+ A = loadtxt(FileNameAndPath)
+
+ N = 30
+ INFO, IPVT = dgefa(A, N)
+
+ INFO_true = 0
+ assert INFO == INFO_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/IPVT3")
+ IPVT_true = loadtxt(FileNameAndPath).astype(int)
+ IPVT_true -= 1 # 0-based index in Python
+ np.testing.assert_array_equal(IPVT, IPVT_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_dgefa/A_modified3")
+ A_modified_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(A, A_modified_true)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_filem.py b/NARP/reliabilityassessment/monte_carlo/tests/test_filem.py
new file mode 100644
index 0000000..443aa00
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_filem.py
@@ -0,0 +1,64 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.filem import filem
+
+
+def test_filem():
+ # Note that: based on the original logic and design of EVNT list (array),
+ # its length is multiple of 4 and its last 4 slots are always zeros;
+
+ # case-1: the event list is empty
+ MFA = 0
+ NUMINQ = 0
+ IPOINT = -1
+ EVNTS = np.array([4, 0.0, 0, 0, 8, 0.0, 0, 0, 12, 0.0, 0, 0, 0, 0.0, 0, 0])
+ ATRIB = np.array([1.5, 3])
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+ assert NUMINQ == 1
+ assert MFA == 4
+ assert IPOINT == 0
+ EVNTS_true = np.array([-1, 1.5, 3, 0, 8, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0])
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # case-2: new entry is the first
+ MFA = 4
+ NUMINQ = 1
+ IPOINT = 0
+ EVNTS = np.array([-1, 1.5, 3, 0, 8, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0])
+ ATRIB = np.array([0.5, 2])
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+ assert NUMINQ == 2
+ assert MFA == 8
+ assert IPOINT == 4
+ EVNTS_true = np.array([-1, 1.5, 3, 0, 0, 0.5, 2, 0, 12, 0, 0, 0, 0, 0, 0, 0])
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # case-3: new entry is the last
+ MFA = 8
+ NUMINQ = 2
+ IPOINT = 4
+ EVNTS = np.array([-1, 1.5, 3, 0, 0, 0.5, 2, 0, 12, 0, 0, 0, 0, 0, 0, 0])
+ ATRIB = np.array([2.5, 1])
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+ assert NUMINQ == 3
+ assert MFA == 12
+ assert IPOINT == 4
+ EVNTS_true = np.array([8, 1.5, 3, 0, 0, 0.5, 2, 0, -1, 2.5, 1, 0, 0, 0, 0, 0])
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # case-4: new entry is in the middle
+ MFA = 8
+ NUMINQ = 2
+ IPOINT = 4
+ EVNTS = np.array([-1, 1.5, 3, 0, 0, 0.5, 2, 0, 12, 0, 0, 0, 0, 0, 0, 0])
+ ATRIB = np.array([1.0, 1])
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+ assert NUMINQ == 3
+ assert MFA == 12
+ assert IPOINT == 4
+ EVNTS_true = np.array([-1, 1.5, 3, 0, 8, 0.5, 2, 0, 0, 1.0, 1, 0, 0, 0, 0, 0])
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_filem_new.py b/NARP/reliabilityassessment/monte_carlo/tests/test_filem_new.py
new file mode 100644
index 0000000..2b4b6a1
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_filem_new.py
@@ -0,0 +1,220 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.filem import filem
+
+
+def test_filem_new():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # --------- case1 --------------
+ MFA = 1 - 1 # 0-based index in Python
+ NUMINQ = 0
+ IPOINT = 0 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case1/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case1/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 5 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 1
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 1 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case1/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # ---------- case2 --------------
+ MFA = 5 - 1 # 0-based index in Python
+ NUMINQ = 1
+ IPOINT = 1 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case2/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case2/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 9 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 2
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 1 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case2/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # ---------- case3 --------------
+ MFA = 9 - 1 # 0-based index in Python
+ NUMINQ = 2
+ IPOINT = 1 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case3/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case3/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 13 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 3
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 1 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case3/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # ---------- case4 --------------
+ MFA = 13 - 1 # 0-based index in Python
+ NUMINQ = 3
+ IPOINT = 1 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case4/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case4/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 17 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 4
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 1 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case4/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # ---------- case5 --------------
+ MFA = 17 - 1 # 0-based index in Python
+ NUMINQ = 4
+ IPOINT = 1 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case5/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case5/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 21 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 5
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 1 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case5/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # ---------- case6 --------------
+ MFA = 21 - 1 # 0-based index in Python
+ NUMINQ = 5
+ IPOINT = 1 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case6/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case6/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 25 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 6
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 21 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case6/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
+
+ # ---------- case7 --------------
+ MFA = 25 - 1 # 0-based index in Python
+ NUMINQ = 6
+ IPOINT = 21 - 1 # 0-based index in Python
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case7/ATRIB")
+ ATRIB = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case7/EVNTS")
+ EVNTS = loadtxt(FileNameAndPath)
+ EVNTS = EVNTS[:-3]
+ EVNTS[-4] = 0
+ EVNTS[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+
+ NUMINQ, MFA, IPOINT = filem(MFA, ATRIB, NUMINQ, IPOINT, EVNTS)
+
+ MFA_true = 29 - 1 # 0-based index in Python
+ assert MFA == MFA_true
+ NUMINQ_true = 7
+ assert NUMINQ == NUMINQ_true
+ IPOINT_true = 21 - 1 # 0-based index in Python
+ assert IPOINT == IPOINT_true
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_filem/case7/EVNTS_mod")
+ EVNTS_true = loadtxt(FileNameAndPath)
+ EVNTS_true = EVNTS_true[:-3]
+ EVNTS_true[-4] = 0
+ EVNTS_true[0 : 100 - 4 : 4] -= 1 # 0-based index in Python
+ np.testing.assert_array_almost_equal(EVNTS, EVNTS_true)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_findtn.py b/NARP/reliabilityassessment/monte_carlo/tests/test_findtn.py
new file mode 100644
index 0000000..69e25db
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_findtn.py
@@ -0,0 +1,51 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.findtn import findtn
+
+
+def test_findtn():
+ NOAREA = 3
+
+ JENT = (-1) * np.ones((NOAREA, NOAREA), dtype=int)
+ NO_CONTRACTS = 0
+ JENT[1, 0] = 0
+ NO_CONTRACTS += 1
+ JENT[2, 1] = 1
+ NO_CONTRACTS += 1
+
+ # NO_CONTRACTS = 2
+ INTCH = np.zeros((NO_CONTRACTS, 365))
+ INTCH[0, 0:30] = 110.5 # MW
+ INTCH[1, 20:80] = 120.5 # MW
+
+ JDAY = 5
+ TRNSFR_truth = np.zeros((NOAREA,))
+ TRNSFR_truth[0] = -110.5
+ TRNSFR_truth[1] = 110.5
+ TRNSFR_truth[2] = 0.0
+ TRNSFR = findtn(JENT, INTCH, JDAY)
+ np.testing.assert_array_almost_equal(TRNSFR, TRNSFR_truth)
+
+ JDAY = 25
+ TRNSFR_truth = np.zeros((NOAREA,))
+ TRNSFR_truth[0] = -110.5
+ TRNSFR_truth[1] = -10.0
+ TRNSFR_truth[2] = 120.5
+ TRNSFR = findtn(JENT, INTCH, JDAY)
+ np.testing.assert_array_almost_equal(TRNSFR, TRNSFR_truth)
+
+ JDAY = 50
+ TRNSFR_truth = np.zeros((NOAREA,))
+ TRNSFR_truth[0] = 0.0
+ TRNSFR_truth[1] = -120.5
+ TRNSFR_truth[2] = 120.5
+ TRNSFR = findtn(JENT, INTCH, JDAY)
+ np.testing.assert_array_almost_equal(TRNSFR, TRNSFR_truth)
+
+ JDAY = 100
+ TRNSFR_truth = np.zeros((NOAREA,))
+ TRNSFR_truth[0] = 0.0
+ TRNSFR_truth[1] = 0.0
+ TRNSFR_truth[2] = 0.0
+ TRNSFR = findtn(JENT, INTCH, JDAY)
+ np.testing.assert_array_almost_equal(TRNSFR, TRNSFR_truth)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_flo.py b/NARP/reliabilityassessment/monte_carlo/tests/test_flo.py
new file mode 100644
index 0000000..6368d54
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_flo.py
@@ -0,0 +1,150 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.flo import flo
+
+
+def test_flo():
+ # NOAREA = 3
+ # NLINES = 3
+ # SFLOW = np.zeros((NOAREA,))
+ # FLOW = np.zeros((NLINES,))
+ LP = np.array([[0, 0, 1], [1, 1, 2], [2, 2, 0]], dtype=int)
+ BLP = np.array([[-120, 300, 300], [-60, 150, 150], [-80, 100, 100]])
+ THET = np.array([0.1, 0.2, 0.3]) # shape: (NOAREA,)
+
+ FLOW_ = np.array([12.0, 6.0, -16.0])
+ SFLOW_ = np.array([-28.0, 6.0, 22.0])
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_)
+
+ # NOAREA = 3
+ # NLINES = 2
+ # SFLOW = np.zeros((NOAREA,))
+ # FLOW = np.zeros((NLINES,))
+ LP = np.array([[0, 0, 1], [1, 2, 0]], dtype=int)
+ BLP = np.array([[-120, 300, 300], [-80, 100, 100]])
+ THET = np.array([0.1, 0.2, 0.3]) # shape: (NOAREA,)
+
+ FLOW_ = np.array([12.0, -16.0])
+ SFLOW_ = np.array([-28.0, 12.0, 16.0])
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_)
+
+ # ---------------- Extra unit test cases added in Oct. 2022-------------
+ # NOAREA = 5
+ # NLINES = 5
+ # SFLOW = np.zeros((NOAREA,))
+ # FLOW = np.zeros((NLINES,))
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # --------------- case 1 --------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case1/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case1/BLP")
+ BLP = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case1/THET")
+ THET = loadtxt(FileNameAndPath)
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case1/FLOW")
+ FLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case1/SFLOW")
+ SFLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_true)
+
+ # --------------- case 2 --------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case2/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case2/BLP")
+ BLP = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case2/THET")
+ THET = loadtxt(FileNameAndPath)
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case2/FLOW")
+ FLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case2/SFLOW")
+ SFLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_true, decimal=4)
+
+ # --------------- case 3 --------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case3/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case3/BLP")
+ BLP = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case3/THET")
+ THET = loadtxt(FileNameAndPath)
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case3/FLOW")
+ FLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case3/SFLOW")
+ SFLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_true, decimal=4)
+
+ # --------------- case 4 --------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case4/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case4/BLP")
+ BLP = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case4/THET")
+ THET = loadtxt(FileNameAndPath)
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case4/FLOW")
+ FLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case4/SFLOW")
+ SFLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_true, decimal=4)
+
+ # --------------- case 5 --------------
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case5/LP")
+ LP = loadtxt(FileNameAndPath).astype(int)
+ LP -= 1 # 0-based index in python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case5/BLP")
+ BLP = loadtxt(FileNameAndPath)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case5/THET")
+ THET = loadtxt(FileNameAndPath)
+
+ SFLOW, FLOW = flo(LP, BLP, THET)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case5/FLOW")
+ FLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(FLOW, FLOW_true, decimal=4)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_flo/case5/SFLOW")
+ SFLOW_true = loadtxt(FileNameAndPath)
+ np.testing.assert_array_almost_equal(SFLOW, SFLOW_true, decimal=4)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_gstate.py b/NARP/reliabilityassessment/monte_carlo/tests/test_gstate.py
new file mode 100644
index 0000000..70ba1ba
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_gstate.py
@@ -0,0 +1,55 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.gstate import _gstate
+
+
+def test_gstate():
+ # ---------------case1----------------------
+ NUNITS = 2
+ PROBG = np.array(
+ [
+ [0.2, 0.5],
+ [0.1, 0.7],
+ ]
+ )
+ RATING = np.array([150, 250])
+ DERATE = np.array([0.35, 0.15])
+ PLNDST = np.array([1, 1])
+ rng_42 = np.random.default_rng(seed=42)
+ # act
+ # random numbers are generated for seed 42
+ PCTAVL, AVAIL = _gstate(PROBG, RATING, DERATE, PLNDST, rng=rng_42)
+ # assert
+ assert PCTAVL.size == NUNITS
+ assert AVAIL.size == NUNITS
+ # unit 1 random #: 0.7739560485559633
+ assert PCTAVL[0] == 0
+ assert AVAIL[0] == 0
+ # unit 2 random #: 0.4388784397520523
+ assert PCTAVL[1] == 0.15
+ assert AVAIL[1] == 37.5
+
+ # ---------------case2----------------------
+ NUNITS = 2
+ PROBG = np.array(
+ [
+ [0.2, 0.2],
+ [0.7, 0.7],
+ ]
+ )
+ RATING = np.array([150, 250])
+ DERATE = np.array([0.0, 0.0])
+ PLNDST = np.array([1, 1])
+ rng_52 = np.random.default_rng(seed=52)
+ # act
+ # random numbers are generated for seed 42
+ PCTAVL, AVAIL = _gstate(PROBG, RATING, DERATE, PLNDST, rng=rng_52)
+ # assert
+ assert PCTAVL.size == NUNITS
+ assert AVAIL.size == NUNITS
+ # unit 1 random #: 0.7739560485559633
+ assert PCTAVL[0] == 0
+ assert AVAIL[0] == 0
+ # unit 2 random #: 0.4388784397520523
+ assert PCTAVL[1] == 1
+ assert AVAIL[1] == 250
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_hrstat.py b/NARP/reliabilityassessment/monte_carlo/tests/test_hrstat.py
new file mode 100644
index 0000000..87688c9
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_hrstat.py
@@ -0,0 +1,191 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.hrstat import _hrstat, hrstat
+
+
+def test_hrstat():
+ np.random.seed(1)
+
+ NST = 1
+ MARGIN = np.array([60, -70, 80])
+ NOAREA = len(MARGIN)
+ LOLTHP = np.random.randint(0, 100, (5,))
+ LOLGHP = np.random.randint(0, 100, (5,))
+ LOLTHA = np.random.randint(0, 100, (NOAREA, 5))
+ MGNTHA = 200 * np.random.rand(NOAREA, 5)
+ MGNTHP = 600 * np.random.rand(
+ 5,
+ )
+
+ LOLGHA = np.random.randint(0, 100, (NOAREA, 5))
+ MGNGHA = 1000 * np.random.rand(NOAREA, 5)
+ MGNGHP = 3000 * np.random.rand(5)
+
+ LSFLG = np.random.randint(0, 100, (NOAREA,))
+
+ _hrstat(
+ NST,
+ MARGIN,
+ LSFLG,
+ LOLGHA,
+ LOLGHP,
+ LOLTHA,
+ LOLTHP,
+ MGNGHA,
+ MGNGHP,
+ MGNTHA,
+ MGNTHP,
+ )
+
+ LSFLG_true = np.array([52, 86, 70], dtype=int)
+ np.testing.assert_array_equal(LSFLG, LSFLG_true)
+
+ LOLTHP_true = np.array([37, 13, 72, 9, 75])
+ np.testing.assert_array_almost_equal(LOLTHP, LOLTHP_true)
+
+ LOLGHP_true = np.array([5, 79, 64, 16, 1])
+ np.testing.assert_array_almost_equal(LOLGHP, LOLGHP_true)
+
+ LOLTHA_true = np.array(
+ [[76, 71, 6, 25, 50], [20, 18 + 1, 84, 11, 28], [29, 14, 50, 68, 87]]
+ )
+ np.testing.assert_array_almost_equal(LOLTHA, LOLTHA_true)
+
+ MGNTHA_true = np.array(
+ [
+ [91.4409616, 86.13971344, 187.82555788, 155.67784727, 143.19410319],
+ [
+ 160.55150079,
+ 18.56016173 - int(MARGIN[1]),
+ 103.63050979,
+ 173.0040504,
+ 165.82938147,
+ ],
+ [165.92067188, 54.60999484, 11.84864026, 134.10560801, 118.61310366],
+ ]
+ )
+ np.testing.assert_array_almost_equal(MGNTHA, MGNTHA_true)
+
+ MGNTHP_true = np.array(
+ [
+ 402.99245845,
+ 247.07272738 - int(MARGIN[1]),
+ 118.53053879,
+ 173.77778433,
+ 85.27208128,
+ ]
+ )
+ np.testing.assert_array_almost_equal(MGNTHP, MGNTHP_true)
+
+ LOLGHA_true = np.array(
+ [[26, 52, 80, 41, 82], [15, 64, 68, 25, 98], [87, 7, 26, 25, 22]]
+ )
+ np.testing.assert_array_almost_equal(LOLGHA, LOLGHA_true)
+
+ MGNGHA_true = np.array(
+ [
+ [908.59550309, 293.61414837, 287.77533859, 130.02857212, 19.36695787],
+ [678.83553294, 211.628116, 265.54665937, 491.57315928, 53.36254512],
+ [574.11760549, 146.72857491, 589.3055369, 699.75836002, 102.33442883],
+ ]
+ )
+ np.testing.assert_array_almost_equal(MGNGHA, MGNGHA_true)
+
+ MGNGHP_true = np.array(
+ [1242.16796346, 2083.20047318, 1242.53780858, 149.86037684, 1607.68921775]
+ )
+ np.testing.assert_array_almost_equal(MGNGHP, MGNGHP_true)
+
+ # ----------------------------------------------------------------
+ # test for the vectorized version
+ np.random.seed(1)
+
+ NST = 1
+ MARGIN = np.array([60, -70, 80])
+ NOAREA = len(MARGIN)
+ LOLTHP = np.random.randint(0, 100, (5,))
+ LOLGHP = np.random.randint(0, 100, (5,))
+ LOLTHA = np.random.randint(0, 100, (NOAREA, 5))
+ MGNTHA = 200 * np.random.rand(NOAREA, 5)
+ MGNTHP = 600 * np.random.rand(
+ 5,
+ )
+
+ LOLGHA = np.random.randint(0, 100, (NOAREA, 5))
+ MGNGHA = 1000 * np.random.rand(NOAREA, 5)
+ MGNGHP = 3000 * np.random.rand(5)
+
+ LSFLG = np.random.randint(0, 100, (NOAREA,))
+
+ hrstat(
+ NST,
+ MARGIN,
+ LSFLG,
+ LOLGHA,
+ LOLGHP,
+ LOLTHA,
+ LOLTHP,
+ MGNGHA,
+ MGNGHP,
+ MGNTHA,
+ MGNTHP,
+ )
+
+ LSFLG_true = np.array([52, 86, 70], dtype=int)
+ np.testing.assert_array_equal(LSFLG, LSFLG_true)
+
+ LOLTHP_true = np.array([37, 13, 72, 9, 75])
+ np.testing.assert_array_almost_equal(LOLTHP, LOLTHP_true)
+
+ LOLGHP_true = np.array([5, 79, 64, 16, 1])
+ np.testing.assert_array_almost_equal(LOLGHP, LOLGHP_true)
+
+ LOLTHA_true = np.array(
+ [[76, 71, 6, 25, 50], [20, 18 + 1, 84, 11, 28], [29, 14, 50, 68, 87]]
+ )
+ np.testing.assert_array_almost_equal(LOLTHA, LOLTHA_true)
+
+ MGNTHA_true = np.array(
+ [
+ [91.4409616, 86.13971344, 187.82555788, 155.67784727, 143.19410319],
+ [
+ 160.55150079,
+ 18.56016173 - int(MARGIN[1]),
+ 103.63050979,
+ 173.0040504,
+ 165.82938147,
+ ],
+ [165.92067188, 54.60999484, 11.84864026, 134.10560801, 118.61310366],
+ ]
+ )
+ np.testing.assert_array_almost_equal(MGNTHA, MGNTHA_true)
+
+ MGNTHP_true = np.array(
+ [
+ 402.99245845,
+ 247.07272738 - int(MARGIN[1]),
+ 118.53053879,
+ 173.77778433,
+ 85.27208128,
+ ]
+ )
+ np.testing.assert_array_almost_equal(MGNTHP, MGNTHP_true)
+
+ LOLGHA_true = np.array(
+ [[26, 52, 80, 41, 82], [15, 64, 68, 25, 98], [87, 7, 26, 25, 22]]
+ )
+ np.testing.assert_array_almost_equal(LOLGHA, LOLGHA_true)
+
+ MGNGHA_true = np.array(
+ [
+ [908.59550309, 293.61414837, 287.77533859, 130.02857212, 19.36695787],
+ [678.83553294, 211.628116, 265.54665937, 491.57315928, 53.36254512],
+ [574.11760549, 146.72857491, 589.3055369, 699.75836002, 102.33442883],
+ ]
+ )
+ np.testing.assert_array_almost_equal(MGNGHA, MGNGHA_true)
+
+ MGNGHP_true = np.array(
+ [1242.16796346, 2083.20047318, 1242.53780858, 149.86037684, 1607.68921775]
+ )
+ np.testing.assert_array_almost_equal(MGNGHP, MGNGHP_true)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_hrstat_new.py b/NARP/reliabilityassessment/monte_carlo/tests/test_hrstat_new.py
new file mode 100644
index 0000000..4ddf26e
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_hrstat_new.py
@@ -0,0 +1,259 @@
+from pathlib import Path
+
+import numpy as np
+from numpy import loadtxt
+
+from reliabilityassessment.monte_carlo.hrstat import hrstat
+
+
+def test_hrstat_new():
+ TEST_DIR = Path(__file__).parent.absolute()
+
+ # ------ case1 --------------
+ NST = 1 - 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LSFLG")
+ LSFLG = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MARGIN")
+ MARGIN = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLTHP")
+ LOLTHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLGHP")
+ LOLGHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLTHA")
+ LOLTHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNTHA")
+ MGNTHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNTHP")
+ MGNTHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLGHA")
+ LOLGHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNGHA")
+ MGNGHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNGHP")
+ MGNGHP = loadtxt(FileNameAndPath).astype(int)
+
+ hrstat(
+ NST,
+ MARGIN,
+ LSFLG,
+ LOLGHA,
+ LOLGHP,
+ LOLTHA,
+ LOLTHP,
+ MGNGHA,
+ MGNGHP,
+ MGNTHA,
+ MGNTHP,
+ )
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LSFLG_mod")
+ LSFLG_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LSFLG, LSFLG_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLTHP_mod")
+ LOLTHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLTHP, LOLTHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLGHP_mod")
+ LOLGHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLGHP, LOLGHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLTHA_mod")
+ LOLTHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLTHA, LOLTHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNTHA_mod")
+ MGNTHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNTHA, MGNTHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNTHP_mod")
+ MGNTHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNTHP, MGNTHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/LOLGHA_mod")
+ LOLGHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLGHA, LOLGHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNGHA_mod")
+ MGNGHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNGHA, MGNGHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case1/MGNGHP_mod")
+ MGNGHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNGHP, MGNGHP_true)
+
+ # ------ case2 --------------
+ NST = 1 - 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LSFLG")
+ LSFLG = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MARGIN")
+ MARGIN = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLTHP")
+ LOLTHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLGHP")
+ LOLGHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLTHA")
+ LOLTHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNTHA")
+ MGNTHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNTHP")
+ MGNTHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLGHA")
+ LOLGHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNGHA")
+ MGNGHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNGHP")
+ MGNGHP = loadtxt(FileNameAndPath).astype(int)
+
+ hrstat(
+ NST,
+ MARGIN,
+ LSFLG,
+ LOLGHA,
+ LOLGHP,
+ LOLTHA,
+ LOLTHP,
+ MGNGHA,
+ MGNGHP,
+ MGNTHA,
+ MGNTHP,
+ )
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LSFLG_mod")
+ LSFLG_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LSFLG, LSFLG_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLTHP_mod")
+ LOLTHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLTHP, LOLTHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLGHP_mod")
+ LOLGHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLGHP, LOLGHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLTHA_mod")
+ LOLTHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLTHA, LOLTHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNTHA_mod")
+ MGNTHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNTHA, MGNTHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNTHP_mod")
+ MGNTHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNTHP, MGNTHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/LOLGHA_mod")
+ LOLGHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLGHA, LOLGHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNGHA_mod")
+ MGNGHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNGHA, MGNGHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case2/MGNGHP_mod")
+ MGNGHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNGHP, MGNGHP_true)
+
+ # ------ case3 --------------
+ NST = 1 - 1 # 0-based index in Python!
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LSFLG")
+ LSFLG = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MARGIN")
+ MARGIN = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLTHP")
+ LOLTHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLGHP")
+ LOLGHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLTHA")
+ LOLTHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNTHA")
+ MGNTHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNTHP")
+ MGNTHP = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLGHA")
+ LOLGHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNGHA")
+ MGNGHA = loadtxt(FileNameAndPath).astype(int)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNGHP")
+ MGNGHP = loadtxt(FileNameAndPath).astype(int)
+
+ hrstat(
+ NST,
+ MARGIN,
+ LSFLG,
+ LOLGHA,
+ LOLGHP,
+ LOLTHA,
+ LOLTHP,
+ MGNGHA,
+ MGNGHP,
+ MGNTHA,
+ MGNTHP,
+ )
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LSFLG_mod")
+ LSFLG_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LSFLG, LSFLG_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLTHP_mod")
+ LOLTHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLTHP, LOLTHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLGHP_mod")
+ LOLGHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLGHP, LOLGHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLTHA_mod")
+ LOLTHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLTHA, LOLTHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNTHA_mod")
+ MGNTHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNTHA, MGNTHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNTHP_mod")
+ MGNTHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNTHP, MGNTHP_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/LOLGHA_mod")
+ LOLGHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(LOLGHA, LOLGHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNGHA_mod")
+ MGNGHA_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNGHA, MGNGHA_true)
+
+ FileNameAndPath = Path(TEST_DIR, "testdata_hrstat/case3/MGNGHP_mod")
+ MGNGHP_true = loadtxt(FileNameAndPath).astype(int)
+ np.testing.assert_array_equal(MGNGHP, MGNGHP_true)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_initl.py b/NARP/reliabilityassessment/monte_carlo/tests/test_initl.py
new file mode 100644
index 0000000..843565f
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_initl.py
@@ -0,0 +1,62 @@
+import numpy as np
+
+from reliabilityassessment.monte_carlo.initl import _initl
+
+
+def test_initl():
+ EVNTS = np.zeros(32)
+ EVNTS[: 32 - 4 : 4] = np.arange(0, 32 - 4, 4) + 4
+ MFA = 0
+ IPOINT = -1
+ NUMINQ = 0
+ JSTEP = 1
+ QTR = np.array([13 * 168 + 0.5, 13 * 2 * 168 + 0.5, 13 * 3 * 168 + 0.5])
+
+ ATRIB, CLOCK, IPOINT, MFA, NUMINQ = _initl(JSTEP, EVNTS, IPOINT, MFA, NUMINQ, QTR)
+
+ ATRIB_truth = np.array([1.0, 1])
+ np.testing.assert_array_almost_equal(ATRIB_truth, ATRIB)
+
+ assert CLOCK == 0.0
+ assert IPOINT == 20
+ assert MFA == 28
+ assert NUMINQ == 7
+
+ EVNTS_truth = np.array(
+ [
+ 24,
+ 0.5,
+ 3,
+ 0,
+ 8,
+ 2184.5,
+ 3,
+ 0,
+ 12,
+ 4368.5,
+ 3,
+ 0,
+ 16,
+ 6552.5,
+ 3,
+ 0,
+ -1,
+ 8760.0,
+ 4,
+ 0,
+ 0,
+ 0.5,
+ 2,
+ 0,
+ 4,
+ 1.0,
+ 1,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ ]
+ )
+
+ np.testing.assert_array_almost_equal(EVNTS_truth, EVNTS)
diff --git a/NARP/reliabilityassessment/monte_carlo/tests/test_line_prob_sampling.py b/NARP/reliabilityassessment/monte_carlo/tests/test_line_prob_sampling.py
new file mode 100644
index 0000000..f6c5cb3
--- /dev/null
+++ b/NARP/reliabilityassessment/monte_carlo/tests/test_line_prob_sampling.py
@@ -0,0 +1,93 @@
+import numpy as np
+import pytest
+
+from reliabilityassessment.monte_carlo.line_prob_sampling import (
+ discrete_prob_gen,
+ lstate,
+ lstate_original,
+)
+
+
+# The following three tests are probabilistic which rely on the law of large numbers.
+# If a test failure occurs, run again and see if the test passes
+@pytest.mark.slow
+def test_lstate():
+ # arrange
+ NLINES = 1000000
+ PROB = np.array([0.9216, 0.0768, 0.0016, 0.0000, 0.0000, 0.0000])
+ PROBL = np.cumsum(PROB)
+ PROBL_mat = np.tile(PROBL, (NLINES, 1))
+ # act
+ LNSTAT = lstate(PROBL_mat, test_seed=42)
+ # assert
+ assert LNSTAT.size == NLINES
+ LNSTAT_bins = np.bincount(LNSTAT)
+ print(f"LNSTAT_bins={LNSTAT_bins}")
+ assert LNSTAT_bins.size <= PROBL.size
+ for i in range(LNSTAT_bins.size - 1):
+ assert PROB[i] == pytest.approx(LNSTAT_bins[i + 1] / NLINES, 0.05)
+
+
+@pytest.mark.slow
+def test_lstate_original():
+ # arrange
+ NLINES = 1000000
+ PROB = np.array([0.9216, 0.0768, 0.0016, 0.0000, 0.0000, 0.0000])
+ PROBL = np.cumsum(PROB)
+ # act
+ LNSTAT = lstate_original(NLINES, PROBL, test_seed=42)
+ # assert
+ assert LNSTAT.size == NLINES
+ LNSTAT_bins = np.bincount(LNSTAT)
+ print(f"LNSTAT_bins={LNSTAT_bins}")
+ assert LNSTAT_bins.size <= PROBL.size
+ for i in range(LNSTAT_bins.size - 1):
+ assert PROB[i] == pytest.approx(LNSTAT_bins[i + 1] / NLINES, 0.05)
+
+
+@pytest.mark.slow
+def test_generate_discrete_probability_generator():
+ # arrange
+ NLINES = 1000000
+ PROB = np.array([0.9216, 0.0768, 0.0016, 0.0000, 0.0000, 0.0000])
+ PROBL = np.cumsum(PROB)
+ # act
+ LNSTAT = discrete_prob_gen(NLINES, PROBL, test_seed=42)
+ # assert
+ assert LNSTAT.size == NLINES
+ LNSTAT_bins = np.bincount(LNSTAT)
+ print(f"LNSTAT_bins={LNSTAT_bins}")
+ assert LNSTAT_bins.size <