diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 87de83e..d82f1e4 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -9,8 +9,8 @@ on: - 'v[0-9]+.[0-9]+.[0-9]+*' jobs: - test-suite: + if: github.event.pull_request.draft == false runs-on: ${{ matrix.os }} strategy: matrix: @@ -75,4 +75,4 @@ jobs: uses: pypa/gh-action-pypi-publish@master with: user: __token__ - password: ${{ secrets.PYPI_DEPLOYMENT_TOKEN }} \ No newline at end of file + password: ${{ secrets.PYPI_DEPLOYMENT_TOKEN }} diff --git a/btrdbextras/__init__.py b/btrdbextras/__init__.py index 5a9dad2..4e1439e 100644 --- a/btrdbextras/__init__.py +++ b/btrdbextras/__init__.py @@ -1,5 +1,5 @@ from .conn import Connection -__version__ = 'v5.11.9' +__version__ = '5.11.9' __all__ = ["__version__", "Connection"] diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv new file mode 100644 index 0000000..ba14e1e --- /dev/null +++ b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv @@ -0,0 +1,16 @@ +SourceBus, 200, 400 +650, 200, 350 +RG60, 200, 300 +646, 0, 250 +645, 100, 250 +632, 200, 250 +633, 350, 250 +634, 400, 250 +670, 200, 200 +611, 0, 100 +684, 100, 100 +671, 200, 100 +692, 250, 100 +675, 400, 100 +652, 100, 0 +680, 200, 0 diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss new file mode 100644 index 0000000..6be3991 --- /dev/null +++ b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss @@ -0,0 +1,177 @@ +Clear +Set DefaultBaseFrequency=60 + +! +! This script is based on a script developed by Tennessee Tech Univ students +! Tyler Patton, Jon Wood, and David Woods, April 2009 +! + +new circuit.IEEE13Nodeckt +~ basekv=115 pu=1.0001 phases=3 bus1=SourceBus +~ Angle=30 ! advance angle 30 deg so result agree with published angle +~ MVAsc3=20000 MVASC1=21000 ! stiffen the source to approximate inf source + + + +!SUB TRANSFORMER DEFINITION +! Although this data was given, it does not appear to be used in the test case results +! The published test case starts at 1.0 per unit at Bus 650. To make this happen, we will change the impedance +! on the transformer to something tiny by dividing by 1000 using the DSS in-line RPN math +New Transformer.Sub Phases=3 Windings=2 XHL=(8 1000 /) +~ wdg=1 bus=SourceBus conn=delta kv=115 kva=5000 %r=(.5 1000 /) +~ wdg=2 bus=650 conn=wye kv=4.16 kva=5000 %r=(.5 1000 /) + +! FEEDER 1-PHASE VOLTAGE REGULATORS +! Define low-impedance 2-wdg transformer + +New Transformer.Reg1 phases=1 bank=reg1 XHL=0.01 kVAs=[1666 1666] +~ Buses=[650.1 RG60.1] kVs=[2.4 2.4] %LoadLoss=0.01 +new regcontrol.Reg1 transformer=Reg1 winding=2 vreg=122 band=2 ptratio=20 ctprim=700 R=3 X=9 + +New Transformer.Reg2 phases=1 bank=reg1 XHL=0.01 kVAs=[1666 1666] +~ Buses=[650.2 RG60.2] kVs=[2.4 2.4] %LoadLoss=0.01 +new regcontrol.Reg2 transformer=Reg2 winding=2 vreg=122 band=2 ptratio=20 ctprim=700 R=3 X=9 + +New Transformer.Reg3 phases=1 bank=reg1 XHL=0.01 kVAs=[1666 1666] +~ Buses=[650.3 RG60.3] kVs=[2.4 2.4] %LoadLoss=0.01 +new regcontrol.Reg3 transformer=Reg3 winding=2 vreg=122 band=2 ptratio=20 ctprim=700 R=3 X=9 + + +!TRANSFORMER DEFINITION +New Transformer.XFM1 Phases=3 Windings=2 XHL=2 +~ wdg=1 bus=633 conn=Wye kv=4.16 kva=500 %r=.55 XHT=1 +~ wdg=2 bus=634 conn=Wye kv=0.480 kva=500 %r=.55 XLT=1 + + +!LINE CODES +redirect IEEELineCodes.dss + +// these are local matrix line codes +// corrected 9-14-2011 +New linecode.mtx601 nphases=3 BaseFreq=60 +~ rmatrix = (0.3465 | 0.1560 0.3375 | 0.1580 0.1535 0.3414 ) +~ xmatrix = (1.0179 | 0.5017 1.0478 | 0.4236 0.3849 1.0348 ) +~ units=mi +New linecode.mtx602 nphases=3 BaseFreq=60 +~ rmatrix = (0.7526 | 0.1580 0.7475 | 0.1560 0.1535 0.7436 ) +~ xmatrix = (1.1814 | 0.4236 1.1983 | 0.5017 0.3849 1.2112 ) +~ units=mi +New linecode.mtx603 nphases=2 BaseFreq=60 +~ rmatrix = (1.3238 | 0.2066 1.3294 ) +~ xmatrix = (1.3569 | 0.4591 1.3471 ) +~ units=mi +New linecode.mtx604 nphases=2 BaseFreq=60 +~ rmatrix = (1.3238 | 0.2066 1.3294 ) +~ xmatrix = (1.3569 | 0.4591 1.3471 ) +~ units=mi +New linecode.mtx605 nphases=1 BaseFreq=60 +~ rmatrix = (1.3292 ) +~ xmatrix = (1.3475 ) +~ units=mi + +/*********** Original 606 Linecode ********************* + +You have to use this to match Kersting's results: + +New linecode.mtx606 nphases=3 BaseFreq=60 +~ rmatrix = (0.7982 | 0.3192 0.7891 | 0.2849 0.3192 0.7982 ) +~ xmatrix = (0.4463 | 0.0328 0.4041 | -0.0143 0.0328 0.4463 ) +~ Cmatrix = [257 | 0 257 | 0 0 257] ! <--- This is too low by 1.5 +~ units=mi + +Corrected mtx606 Feb 3 2016 by RDugan + +The new LineCode.606 is computed using the following CN cable definition and +LineGeometry definition: + +New CNDATA.250_1/3 k=13 DiaStrand=0.064 Rstrand=2.816666667 epsR=2.3 +~ InsLayer=0.220 DiaIns=1.06 DiaCable=1.16 Rac=0.076705 GMRac=0.20568 diam=0.573 +~ Runits=kft Radunits=in GMRunits=in + +New LineGeometry.606 nconds=3 nphases=3 units=ft +~ cond=1 cncable=250_1/3 x=-0.5 h= -4 +~ cond=2 cncable=250_1/3 x=0 h= -4 +~ cond=3 cncable=250_1/3 x=0.5 h= -4 + +****End Comment******/ + +New Linecode.mtx606 nphases=3 Units=mi +~ Rmatrix=[0.791721 |0.318476 0.781649 |0.28345 0.318476 0.791721 ] +~ Xmatrix=[0.438352 |0.0276838 0.396697 |-0.0184204 0.0276838 0.438352 ] +~ Cmatrix=[383.948 |0 383.948 |0 0 383.948 ] +New linecode.mtx607 nphases=1 BaseFreq=60 +~ rmatrix = (1.3425 ) +~ xmatrix = (0.5124 ) +~ cmatrix = [236] +~ units=mi + + +!LOAD DEFINITIONS +New Load.671 Bus1=671.1.2.3 Phases=3 Conn=Delta Model=1 kV=4.16 kW=1155 kvar=660 +New Load.634a Bus1=634.1 Phases=1 Conn=Wye Model=1 kV=0.277 kW=160 kvar=110 +New Load.634b Bus1=634.2 Phases=1 Conn=Wye Model=1 kV=0.277 kW=120 kvar=90 +New Load.634c Bus1=634.3 Phases=1 Conn=Wye Model=1 kV=0.277 kW=120 kvar=90 +New Load.645 Bus1=645.2 Phases=1 Conn=Wye Model=1 kV=2.4 kW=170 kvar=125 +New Load.646 Bus1=646.2.3 Phases=1 Conn=Delta Model=2 kV=4.16 kW=230 kvar=132 +New Load.692 Bus1=692.3.1 Phases=1 Conn=Delta Model=5 kV=4.16 kW=170 kvar=151 +New Load.675a Bus1=675.1 Phases=1 Conn=Wye Model=1 kV=2.4 kW=485 kvar=190 +New Load.675b Bus1=675.2 Phases=1 Conn=Wye Model=1 kV=2.4 kW=68 kvar=60 +New Load.675c Bus1=675.3 Phases=1 Conn=Wye Model=1 kV=2.4 kW=290 kvar=212 +New Load.611 Bus1=611.3 Phases=1 Conn=Wye Model=5 kV=2.4 kW=170 kvar=80 +New Load.652 Bus1=652.1 Phases=1 Conn=Wye Model=2 kV=2.4 kW=128 kvar=86 +New Load.670a Bus1=670.1 Phases=1 Conn=Wye Model=1 kV=2.4 kW=17 kvar=10 +New Load.670b Bus1=670.2 Phases=1 Conn=Wye Model=1 kV=2.4 kW=66 kvar=38 +New Load.670c Bus1=670.3 Phases=1 Conn=Wye Model=1 kV=2.4 kW=117 kvar=68 + +!CAPACITOR DEFINITIONS +New Capacitor.Cap1 Bus1=675 phases=3 kVAR=600 kV=4.16 +New Capacitor.Cap2 Bus1=611.3 phases=1 kVAR=100 kV=2.4 + +!Bus 670 is the concentrated point load of the distributed load on line 632 to 671 located at 1/3 the distance from node 632 + +!LINE DEFINITIONS +New Line.650632 Phases=3 Bus1=RG60.1.2.3 Bus2=632.1.2.3 LineCode=mtx601 Length=2000 units=ft +New Line.632670 Phases=3 Bus1=632.1.2.3 Bus2=670.1.2.3 LineCode=mtx601 Length=667 units=ft +New Line.670671 Phases=3 Bus1=670.1.2.3 Bus2=671.1.2.3 LineCode=mtx601 Length=1333 units=ft +New Line.671680 Phases=3 Bus1=671.1.2.3 Bus2=680.1.2.3 LineCode=mtx601 Length=1000 units=ft +New Line.632633 Phases=3 Bus1=632.1.2.3 Bus2=633.1.2.3 LineCode=mtx602 Length=500 units=ft +New Line.632645 Phases=2 Bus1=632.3.2 Bus2=645.3.2 LineCode=mtx603 Length=500 units=ft +New Line.645646 Phases=2 Bus1=645.3.2 Bus2=646.3.2 LineCode=mtx603 Length=300 units=ft +New Line.692675 Phases=3 Bus1=692.1.2.3 Bus2=675.1.2.3 LineCode=mtx606 Length=500 units=ft +New Line.671684 Phases=2 Bus1=671.1.3 Bus2=684.1.3 LineCode=mtx604 Length=300 units=ft +New Line.684611 Phases=1 Bus1=684.3 Bus2=611.3 LineCode=mtx605 Length=300 units=ft +New Line.684652 Phases=1 Bus1=684.1 Bus2=652.1 LineCode=mtx607 Length=800 units=ft + + +!SWITCH DEFINITIONS +New Line.671692 Phases=3 Bus1=671 Bus2=692 Switch=y r1=1e-4 r0=1e-4 x1=0.000 x0=0.000 c1=0.000 c0=0.000 + +Set Voltagebases=[115, 4.16, .48] +calcv +Solve +BusCoords IEEE13Node_BusXY.csv + +!--------------------------------------------------------------------------------------------------------------------------------------------------- +!----------------Show some Results ----------------------------------------------------------------------------------------------------------------- +!--------------------------------------------------------------------------------------------------------------------------------------------------- + + +// Show Voltages LN Nodes +// Show Currents Elem +// Show Powers kVA Elem +// Show Losses +// Show Taps + +!--------------------------------------------------------------------------------------------------------------------------------------------------- +!--------------------------------------------------------------------------------------------------------------------------------------------------- +! Alternate Solution Script +! To force the taps to be same as published results, set the transformer taps manually and disable the controls +!--------------------------------------------------------------------------------------------------------------------------------------------------- +/* +Transformer.Reg1.Taps=[1.0 1.0625] +Transformer.Reg2.Taps=[1.0 1.0500] +Transformer.Reg3.Taps=[1.0 1.06875] +Set Controlmode=OFF + +Solve +*/ diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.dss b/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.dss new file mode 100644 index 0000000..1ca26b6 --- /dev/null +++ b/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.dss @@ -0,0 +1,213 @@ +! this file was corrected 9/16/2010 to match the values in Kersting's files + + + +! These line codes are used in the 123-bus circuit + +New linecode.1 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0312137 0.0901946 | 0.0306264 0.0316143 0.0889665 ) +!!!~ xmatrix = (0.20744 | 0.0935314 0.200783 | 0.0760312 0.0855879 0.204877 ) +!!!~ cmatrix = (2.90301 | -0.679335 3.15896 | -0.22313 -0.481416 2.8965 ) +~ rmatrix = [0.086666667 | 0.029545455 0.088371212 | 0.02907197 0.029924242 0.087405303] +~ xmatrix = [0.204166667 | 0.095018939 0.198522727 | 0.072897727 0.080227273 0.201723485] +~ cmatrix = [2.851710072 | -0.920293787 3.004631862 | -0.350755566 -0.585011253 2.71134756] + +New linecode.2 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0901946 | 0.0316143 0.0889665 | 0.0312137 0.0306264 0.088205 ) +!!!~ xmatrix = (0.200783 | 0.0855879 0.204877 | 0.0935314 0.0760312 0.20744 ) +!!!~ cmatrix = (3.15896 | -0.481416 2.8965 | -0.679335 -0.22313 2.90301 ) +~ rmatrix = [0.088371212 | 0.02992424 0.087405303 | 0.029545455 0.02907197 0.086666667] +~ xmatrix = [0.198522727 | 0.080227273 0.201723485 | 0.095018939 0.072897727 0.204166667] +~ cmatrix = [3.004631862 | -0.585011253 2.71134756 | -0.920293787 -0.350755566 2.851710072] + +New linecode.3 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0889665 | 0.0306264 0.088205 | 0.0316143 0.0312137 0.0901946 ) +!!!~ xmatrix = (0.204877 | 0.0760312 0.20744 | 0.0855879 0.0935314 0.200783 ) +!!!~ cmatrix = (2.8965 | -0.22313 2.90301 | -0.481416 -0.679335 3.15896 ) + +~ rmatrix = [0.087405303 | 0.02907197 0.086666667 | 0.029924242 0.029545455 0.088371212] +~ xmatrix = [0.201723485 | 0.072897727 0.204166667 | 0.080227273 0.095018939 0.198522727] +~ cmatrix = [2.71134756 | -0.350755566 2.851710072 | -0.585011253 -0.920293787 3.004631862] + +New linecode.4 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0889665 | 0.0316143 0.0901946 | 0.0306264 0.0312137 0.088205 ) +!!!~ xmatrix = (0.204877 | 0.0855879 0.200783 | 0.0760312 0.0935314 0.20744 ) +!!!~ cmatrix = (2.8965 | -0.481416 3.15896 | -0.22313 -0.679335 2.90301 ) +~ rmatrix = [0.087405303 | 0.029924242 0.088371212 | 0.02907197 0.029545455 0.086666667] +~ xmatrix = [0.201723485 | 0.080227273 0.198522727 | 0.072897727 0.095018939 0.204166667] +~ cmatrix = [2.71134756 | 0.585011253 3.004631862 | -0.350755566 -0.920293787 2.851710072] + +New linecode.5 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0901946 | 0.0312137 0.088205 | 0.0316143 0.0306264 0.0889665 ) +!!!~ xmatrix = (0.200783 | 0.0935314 0.20744 | 0.0855879 0.0760312 0.204877 ) +!!!~ cmatrix = (3.15896 | -0.679335 2.90301 | -0.481416 -0.22313 2.8965 ) + +~ rmatrix = [0.088371212 | 0.029545455 0.086666667 | 0.029924242 0.02907197 0.087405303] +~ xmatrix = [0.198522727 | 0.095018939 0.204166667 | 0.080227273 0.072897727 0.201723485] +~ cmatrix = [3.004631862 | -0.920293787 2.851710072 | -0.585011253 -0.350755566 2.71134756] + +New linecode.6 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0306264 0.0889665 | 0.0312137 0.0316143 0.0901946 ) +!!!~ xmatrix = (0.20744 | 0.0760312 0.204877 | 0.0935314 0.0855879 0.200783 ) +!!!~ cmatrix = (2.90301 | -0.22313 2.8965 | -0.679335 -0.481416 3.15896 ) +~ rmatrix = [0.086666667 | 0.02907197 0.087405303 | 0.029545455 0.029924242 0.088371212] +~ xmatrix = [0.204166667 | 0.072897727 0.201723485 | 0.095018939 0.080227273 0.198522727] +~ cmatrix = [2.851710072 | -0.350755566 2.71134756 | -0.920293787 -0.585011253 3.004631862] +New linecode.7 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0306264 0.0889665 ) +!!!~ xmatrix = (0.20744 | 0.0760312 0.204877 ) +!!!~ cmatrix = (2.75692 | -0.326659 2.82313 ) +~ rmatrix = [0.086666667 | 0.02907197 0.087405303] +~ xmatrix = [0.204166667 | 0.072897727 0.201723485] +~ cmatrix = [2.569829596 | -0.52995137 2.597460011] +New linecode.8 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0306264 0.0889665 ) +!!!~ xmatrix = (0.20744 | 0.0760312 0.204877 ) +!!!~ cmatrix = (2.75692 | -0.326659 2.82313 ) +~ rmatrix = [0.086666667 | 0.02907197 0.087405303] +~ xmatrix = [0.204166667 | 0.072897727 0.201723485] +~ cmatrix = [2.569829596 | -0.52995137 2.597460011] +New linecode.9 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.10 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.11 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.12 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.291814 | 0.101656 0.294012 | 0.096494 0.101656 0.291814 ) +!!!~ xmatrix = (0.141848 | 0.0517936 0.13483 | 0.0401881 0.0517936 0.141848 ) +!!!~ cmatrix = (53.4924 | 0 53.4924 | 0 0 53.4924 ) +~ rmatrix = [0.288049242 | 0.09844697 0.29032197 | 0.093257576 0.09844697 0.288049242] +~ xmatrix = [0.142443182 | 0.052556818 0.135643939 | 0.040852273 0.052556818 0.142443182] +~ cmatrix = [33.77150149 | 0 33.77150149 | 0 0 33.77150149] + +! These line codes are used in the 34-node test feeder + +New linecode.300 nphases=3 basefreq=60 ! ohms per 1000ft Corrected 11/30/05 +~ rmatrix = [0.253181818 | 0.039791667 0.250719697 | 0.040340909 0.039128788 0.251780303] !ABC ORDER +~ xmatrix = [0.252708333 | 0.109450758 0.256988636 | 0.094981061 0.086950758 0.255132576] +~ CMATRIX = [2.680150309 | -0.769281006 2.5610381 | -0.499507676 -0.312072984 2.455590387] +New linecode.301 nphases=3 basefreq=60 +~ rmatrix = [0.365530303 | 0.04407197 0.36282197 | 0.04467803 0.043333333 0.363996212] +~ xmatrix = [0.267329545 | 0.122007576 0.270473485 | 0.107784091 0.099204545 0.269109848] +~ cmatrix = [2.572492163 | -0.72160598 2.464381882 | -0.472329395 -0.298961096 2.368881119] +New linecode.302 nphases=1 basefreq=60 +~ rmatrix = (0.530208 ) +~ xmatrix = (0.281345 ) +~ cmatrix = (2.12257 ) +New linecode.303 nphases=1 basefreq=60 +~ rmatrix = (0.530208 ) +~ xmatrix = (0.281345 ) +~ cmatrix = (2.12257 ) +New linecode.304 nphases=1 basefreq=60 +~ rmatrix = (0.363958 ) +~ xmatrix = (0.269167 ) +~ cmatrix = (2.1922 ) + + +! This may be for the 4-node test feeder, but is not actually referenced. +! instead, the 4Bus*.dss files all use the wiredata and linegeometry inputs +! to calculate these matrices from physical data. + +New linecode.400 nphases=3 BaseFreq=60 +~ rmatrix = (0.088205 | 0.0312137 0.0901946 | 0.0306264 0.0316143 0.0889665 ) +~ xmatrix = (0.20744 | 0.0935314 0.200783 | 0.0760312 0.0855879 0.204877 ) +~ cmatrix = (2.90301 | -0.679335 3.15896 | -0.22313 -0.481416 2.8965 ) + +! These are for the 13-node test feeder + +New linecode.601 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0674673 | 0.0312137 0.0654777 | 0.0316143 0.0306264 0.0662392 ) +!!!~ xmatrix = (0.195204 | 0.0935314 0.201861 | 0.0855879 0.0760312 0.199298 ) +!!!~ cmatrix = (3.32591 | -0.743055 3.04217 | -0.525237 -0.238111 3.03116 ) +~ rmatrix = [0.065625 | 0.029545455 0.063920455 | 0.029924242 0.02907197 0.064659091] +~ xmatrix = [0.192784091 | 0.095018939 0.19844697 | 0.080227273 0.072897727 0.195984848] +~ cmatrix = [3.164838036 | -1.002632425 2.993981593 | -0.632736516 -0.372608713 2.832670203] +New linecode.602 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.144361 | 0.0316143 0.143133 | 0.0312137 0.0306264 0.142372 ) +!!!~ xmatrix = (0.226028 | 0.0855879 0.230122 | 0.0935314 0.0760312 0.232686 ) +!!!~ cmatrix = (3.01091 | -0.443561 2.77543 | -0.624494 -0.209615 2.77847 ) +~ rmatrix = [0.142537879 | 0.029924242 0.14157197 | 0.029545455 0.02907197 0.140833333] +~ xmatrix = [0.22375 | 0.080227273 0.226950758 | 0.095018939 0.072897727 0.229393939] +~ cmatrix = [2.863013423 | -0.543414918 2.602031589 | -0.8492585 -0.330962141 2.725162768] +New linecode.603 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.254472 | 0.0417943 0.253371 ) +!!!~ xmatrix = (0.259467 | 0.0912376 0.261431 ) +!!!~ cmatrix = (2.54676 | -0.28882 2.49502 ) +~ rmatrix = [0.251780303 | 0.039128788 0.250719697] +~ xmatrix = [0.255132576 | 0.086950758 0.256988636] +~ cmatrix = [2.366017603 | -0.452083836 2.343963508] +New linecode.604 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.253371 | 0.0417943 0.254472 ) +!!!~ xmatrix = (0.261431 | 0.0912376 0.259467 ) +!!!~ cmatrix = (2.49502 | -0.28882 2.54676 ) +~ rmatrix = [0.250719697 | 0.039128788 0.251780303] +~ xmatrix = [0.256988636 | 0.086950758 0.255132576] +~ cmatrix = [2.343963508 | -0.452083836 2.366017603] +New linecode.605 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.606 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.152193 | 0.0611362 0.15035 | 0.0546992 0.0611362 0.152193 ) +!!!~ xmatrix = (0.0825685 | 0.00548281 0.0745027 | -0.00339824 0.00548281 0.0825685 ) +!!!~ cmatrix = (72.7203 | 0 72.7203 | 0 0 72.7203 ) +~ rmatrix = [0.151174242 | 0.060454545 0.149450758 | 0.053958333 0.060454545 0.151174242] +~ xmatrix = [0.084526515 | 0.006212121 0.076534091 | -0.002708333 0.006212121 0.084526515] +~ cmatrix = [48.67459408 | 0 48.67459408 | 0 0 48.67459408] +New linecode.607 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.255799 ) +!!!~ xmatrix = (0.092284 ) +!!!~ cmatrix = (50.7067 ) +~ rmatrix = [0.254261364] +~ xmatrix = [0.097045455] +~ cmatrix = [44.70661522] + +! These are for the 37-node test feeder, all underground + +New linecode.721 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0554906 | 0.0127467 0.0501597 | 0.00640446 0.0127467 0.0554906 ) +!!!~ xmatrix = (0.0372331 | -0.00704588 0.0358645 | -0.00796424 -0.00704588 0.0372331 ) +!!!~ cmatrix = (124.851 | 0 124.851 | 0 0 124.851 ) +~ rmatrix = [0.055416667 | 0.012746212 0.050113636 | 0.006382576 0.012746212 0.055416667] +~ xmatrix = [0.037367424 | -0.006969697 0.035984848 | -0.007897727 -0.006969697 0.037367424] +~ cmatrix = [80.27484728 | 0 80.27484728 | 0 0 80.27484728] +New linecode.722 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0902251 | 0.0309584 0.0851482 | 0.0234946 0.0309584 0.0902251 ) +!!!~ xmatrix = (0.055991 | -0.00646552 0.0504025 | -0.0117669 -0.00646552 0.055991 ) +!!!~ cmatrix = (93.4896 | 0 93.4896 | 0 0 93.4896 ) +~ rmatrix = [0.089981061 | 0.030852273 0.085 | 0.023371212 0.030852273 0.089981061] +~ xmatrix = [0.056306818 | -0.006174242 0.050719697 | -0.011496212 -0.006174242 0.056306818] +~ cmatrix = [64.2184109 | 0 64.2184109 | 0 0 64.2184109] +New linecode.723 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.247572 | 0.0947678 0.249104 | 0.0893782 0.0947678 0.247572 ) +!!!~ xmatrix = (0.126339 | 0.0390337 0.118816 | 0.0279344 0.0390337 0.126339 ) +!!!~ cmatrix = (58.108 | 0 58.108 | 0 0 58.108 ) +~ rmatrix = [0.245 | 0.092253788 0.246628788 | 0.086837121 0.092253788 0.245] +~ xmatrix = [0.127140152 | 0.039981061 0.119810606 | 0.028806818 0.039981061 0.127140152] +~ cmatrix = [37.5977112 | 0 37.5977112 | 0 0 37.5977112] +New linecode.724 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.399883 | 0.101765 0.402011 | 0.0965199 0.101765 0.399883 ) +!!!~ xmatrix = (0.146325 | 0.0510963 0.139305 | 0.0395402 0.0510963 0.146325 ) +!!!~ cmatrix = (46.9685 | 0 46.9685 | 0 0 46.9685 ) +~ rmatrix = [0.396818182 | 0.098560606 0.399015152 | 0.093295455 0.098560606 0.396818182] +~ xmatrix = [0.146931818 | 0.051856061 0.140113636 | 0.040208333 0.051856061 0.146931818] +~ cmatrix = [30.26701029 | 0 30.26701029 | 0 0 30.26701029] diff --git a/btrdbextras/opendss_ingest/README.md b/btrdbextras/opendss_ingest/README.md new file mode 100644 index 0000000..e9704bf --- /dev/null +++ b/btrdbextras/opendss_ingest/README.md @@ -0,0 +1,10 @@ +# OpenDSS Simulation Ingestor + +## What is OpenDSS? +[OpenDSS](https://www.epri.com/pages/sa/opendss) is an open source tool for simulating electrical distribution networks. For us, it may be useful for generating realistic grid data from particular physical contexts---for example, measurements on two ends of a line, or two ends of a transformer---without any data privacy concerns. In this sense, it is especially useful for the Dominion Apps project, for which we would otherwise need to prototype and test on Dominion's PMU data, access to which is restricted. + +## This Repo +This repo contains notebooks demonstrating how to run simulations (ie powerflow solutions) and retrieve data via OpenDSS's Python API, called [`OpenDSSDirect`](https://dss-extensions.org/OpenDSSDirect.py/index.html). The notebooks work with IEEE network models that come prepackaged with OpenDSS and have also been committed to this repo under the `Models` folder. + +### Getting Started +To get started, install OpenDSS on your local machine from [here](https://sourceforge.net/projects/electricdss/files/). Next, install `OpenDSSDirect` following the instructions [here](https://dss-extensions.org/OpenDSSDirect.py/notebooks/Installation.html). If installation has been successful, you should be able to run the notebooks in this repo. It is recommended you begin with `Intro to Simulation with OpenDSS` which will introduce the main steps in any simulation, and demonstrate how to obtain the resulting data. diff --git a/btrdbextras/opendss_ingest/__init__.py b/btrdbextras/opendss_ingest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb b/btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb new file mode 100644 index 0000000..f0ec190 --- /dev/null +++ b/btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb @@ -0,0 +1,5042 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import opendssdirect as dss\n", + "import matplotlib.pyplot as plt\n", + "from tqdm.notebook import tqdm_notebook, tqdm\n", + "import btrdb as db\n", + "import uuid\n", + "from btrdb.utils.timez import datetime_to_ns\n", + "import simulation_utils as sims\n", + "from datetime import datetime, timedelta\n", + "\n", + "%matplotlib inline\n", + "\n", + "import importlib\n", + "\n", + "importlib.reload(sims);" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to the database\n", + "conn = db.connect(profile=\"collab\")" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "model_loc = \"./Models/13Bus/IEEE13Nodeckt.dss\"\n", + "dss.run_command(\"Redirect \" + model_loc);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create output streams\n", + "The following cells create the output streams or retrieve them if they have already been created. " + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "prefix = \"simulated/ieee13\"\n", + "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nstreams = len(collections)\n", + "print(\"Creating\", nstreams, \"streams\")\n", + "for i in range(nstreams):\n", + " print(collections[i] + \"/\" + names[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 234 streams under simulated/ieee13\n", + "Found 234 streams. Created 0 streams.\n" + ] + } + ], + "source": [ + "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate simulated measurements\n", + "The following cell generates data that will be converted into streams. \n", + "For convenience, we back-calculate the number of samples from a user specified sample rate (`fs`) and simulation duration (`start_time` to `end_time`). However, keep in mind that the simulation has no inherent sense of time - we are abitrarily assigning timestamps to each simulation result. " + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We will generate 1800 samples.\n" + ] + } + ], + "source": [ + "# The number of samples to generate\n", + "start_time = datetime(2022, 1, 1, 0, 0, 0)\n", + "end_time = datetime(2022, 1, 1, 0, 1, 0)\n", + "fs = 30 # Hz\n", + "T = int(((end_time - start_time).total_seconds()) * fs)\n", + "print(\"We will generate\", T, \"samples.\")\n", + "\n", + "# Generate the nanosecond timestamps for the data\n", + "start_ns = datetime_to_ns(start_time)\n", + "end_ns = datetime_to_ns(end_time)\n", + "timestamps = np.arange(start_ns, end_ns, 1e9 / 30, dtype=\"int\")" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the original loads\n", + "load, load_names = sims.get_loads()\n", + "nloads = len(load_names)\n", + "\n", + "# Generate the randomized scaling factors\n", + "mu = 1.1\n", + "sig = 0.1\n", + "s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n", + "\n", + "# Generate the new load values\n", + "new_load = s * load[:, np.newaxis]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Running simulation: 0%| | 0/1799 [00:00]" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(timestamps, V[\"646/VCM\"])\n", + "plt.plot(timestamps, V[\"646/VBM\"])\n", + "\n", + "plt.figure()\n", + "plt.plot(timestamps, V[\"646/VCA\"])\n", + "plt.plot(timestamps, V[\"646/VBA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of streams simulated:\n", + "234\n" + ] + } + ], + "source": [ + "print(\"Number of streams simulated:\")\n", + "print(len(V.keys()) + len(I.keys()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Push data to streams" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Put voltage data into the corresponding stream\n", + "sims.add_all_data(timestamps, V, streams_dict, prefix)\n", + "# Put current data into the corresponding stream\n", + "sims.add_all_data(timestamps, I, streams_dict, prefix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add long period of data\n", + "The following cell runs a loop which will generate and push data over a much longer period. \n", + "The purpose of the loop is to avoid generating all the data at once - instead, the full time period is divided into smaller time chunks, with data generated and inserted for each chunk sequentially. \n", + "\n", + "The code is divided into two cells below - the first is the initialization step. The second runs the loop. If there is an error during the loop, the second cell can be re-run and will pick up where it left off. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Collection prefix in which data will be added\n", + "prefix = \"simulated/ieee13\"\n", + "# Get the desired output streams to which data will be pushed\n", + "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)\n", + "# If the desired streams exist, retrieve them. Otherwise create them.\n", + "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)\n", + "\n", + "\n", + "# Get the original loads\n", + "load, load_names = sims.get_loads()\n", + "nloads = len(load_names)\n", + "\n", + "# Simulation time window - the FULL time range over which we want to generate data\n", + "start_time = datetime(2022, 1, 2, 0, 0, 0)\n", + "end_time = datetime(2022, 1, 3, 0, 0, 0)\n", + "fs = 30 # Hz\n", + "Ttotal = int(((end_time - start_time).total_seconds()) * fs)\n", + "print(\"We will generate\", Ttotal, \"samples.\")\n", + "\n", + "# The simulation time step - this is the amount of data we insert at once.\n", + "step = timedelta(minutes=5)\n", + "nsteps = int((end_time - start_time) / step)\n", + "\n", + "# Create progress bar\n", + "pbar = tqdm(total=nsteps, desc=\"Adding simulated data\")\n", + "\n", + "t0 = start_time" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c1f55605fad2421f944ee0fc466c5752", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Adding simulated data: 0%| | 0/110 [00:00", + "image/png": 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Po8a48PlTSMl2m2FA7ujn8aivFLvR5TWifMGYozPR8opUmNLA/ghMVwoY3f9y2WTdX1rn9HRoQrSKHoXbBBojIZICBUxPqESKqoD58ssvKS0tZfDgwdhsNmw2G4cOHeLee+9l6NChAOTl5VFaGth4x+12U1FRQV5ennFMSUlJwDHq/+qYYBISEkhPTw/46Y38+oPtPPDetgAT14mI16ux0mQKdXk06loJ62ua5m9kl+KPgORl+HwwXWDkPW5EYPwCZnwnl1KrBShcjrut+D0wzQVM8KDKw+W6gFFDEDfHmIAJ74HpnllISsBYrZYeb+QNZeIFvw+mI0Zep9tLqWmqu0rNBlNu+n20ho2qz3h2qt0wVTs93i7xRdU0uoy+L5dM7G98rzoShVHiOJxPSKWQMnwCZnz/DKBnGHmjKmCuv/56tmzZwqZNm4yf/Px87rvvPj755BMAZs6cSVVVFevXrzf+bvHixXi9XmbMmGEcs2zZMlwuvyJctGgRY8aMoU+fPtE85ZhDLahHY7wRVEfZUeSgusEVUAHTmlGvzunB5dGjNFnJfgGTmxbdcQLrD1WwYn9ZyNvKDQ+M//HH5qX7jXGdIKJUCPhQeV1U0g3F1SEiML7XM/g9UAbeWb65T5uPVHX48buS4jAemK5vZBdYhQREtYfQL9/dyuVPLw+44HeUUCZegMFZegSmqANrWFF1A+aAazgfjFlQR8vIq8RSdkpCwPvRFVGYVQcqcHs1hmYnMyQ7xRAVVQ3tjy6p866sd4b0AaomdiqSpiIwh8rru32IZZsFTG1trSFOAAoKCti0aROHDx8mOzubk046KeAnPj6evLw8xowZA8C4ceO48MILufXWW1mzZg3Lly/nzjvv5JprrjFKrq+77jrsdju33HIL27dv58033+SJJ57gnnvuid4zj1FU46re2vU0Ulb6/C8zhmcbTelaCxGrBSzBZjUuRAA5UewF0+T2cMOLa7jpX2tDNhlTC20/UwQmyR5nNPjqjCiM2kG5PFqHha/Hqxk73zyzgEkNE4HxpZAu9u0Wi6obu3RwZkcJjsAkxHezidceQsB0MAKjaRpvrzvC5sIq7n9na5tm6xRW1IcUc40uf1rD7IEBv/8sUt9aKI5UBn6Owwkvc1l/Rx4v1H1mp9qJs1pM85A6/2KuIu9njuoHmF7LDogzJWC8WuDIAEVwCqlPip183/ehu7tBt1nArFu3jilTpjBlyhQA7rnnHqZMmcKDDz4Y8X3Mnz+fsWPHcv7553PxxRdzxhlnBPR4ycjI4NNPP6WgoIBp06Zx77338uCDDwb0ijkR0TTNUNrBF4oTDZU+mjk82/gSVzW0/JoEVyAp1EDHaEQ/Civq9Zy0x8u+482/3OUhUkgA4/P1sGxnLAjmioWO+iXKapvweDXirJYAH0+4idSFFfqFZkxeGqN9U5RjxQdT73Qb0au8oBRSd/eBAUxDHTv2mVGfV4BPd5Tw5trI/IN7Smo467El3Pnaxma3qYuexQKp9sAITDSiBkeDBUyI9bDR5QmoeouWiVdVNinvXFeWUisD75mj9Iim8vJ1RJzVNvn/NlSarToohQT+9aq7fTC21g8J5JxzzmmTQj948GCz32VlZfHaa6+1+Hcnn3wyX375ZVtPr1dT2+Q2zLvRyufGIi6Pl9VKwIzI5rOduj+qtS+xul196RWqn0k0UkgHTOWh+4/XMW1IVsB5q3Mwp5BAvxh9uLlzFgTzJNkDx+s4d0z770tFqXLSEoiz+o36Rhm16XPpdHs55qtsGJyVzJTBmewqrmFTYRVzJjT3snm9GlZr15r/W0I919QEm+Hj6A4B4/FqxuOlJPgjh0oQltU2UVXvNOZ7tZXg3ftvPtzBjOHZRlQwHCv2lemVZUermt2m0g5pCbZm76mx4eiAoDhS1XoEJniNjEYKSdM0435V5V1aoo2i6s5PIRVW1FNQVkec1WI0lFPveXUrm7eWMJ93KJEXqhx+fH46n+0s6XYfjMxCiiHMKZITWcBsPVpNndNDRlI84/unR7wgqtuzUgIX+tyM6KWQzEbZ4CoLtYBaLTS72Az3XSyiPeTOHMrXz69jlR9FQZ4QhX8itf89OFql+xSS4uPom2pn0sBMILSR9401hxnzwP9YsqvzJ+tGSrD/BSDJri+ZXemBUU3swG/iVf9WFT0dMcSq6EVeeiIzh2fT4PJw9xsbW+2boi5ex2uamnmrwhl4wd9EsiNVQSoCEx+ni6NQHpjgrtDRaJPgaHT7fXQpSsB0TS8YFX2ZOjjTeMzoRGD8n6+KulAppOZeJn8lkggYIULMX/gTOYWk/C8zh2djtVqMBbEyxJfPjLq4BqeQlIk3Gikks4A5ENSsS1UgZaUERi/M5xStPL0iOKfd0Uok5V/JSw8UMGoitb7A6xczVUI9KEsfOTBpUCagj30wtwFodHn486d7cHk0Fu0MrD7sToL9L2DqA9OFHhiVPrJZLSTYApdsVdHTkVb5Slj3TbPzl6snkZ5oY/ORap78fG+Lf7fNd/Hyav7PtqImRA8YRUaS+qx3IALjM4erCr5Q62FZUDozGhEYFelJS7AZnwWVQursidTB/hcwrxtRisCEeI1CRWCUgNlXWtOhEu6OIgImhjB7PIK9BicSqsInOIzaugem5RRSWa2zw1/GQAETeFEpCxojYEaVdUd7sqwK5fvPqWMCJlwEJiMp3ijpVIugMvAOztIn9o7OTSPZHkdtkzvgtXl/01GjsuNgD+ppEqpcvDtSSOYS6uD+WqOiUJJsFvb5mUk8fOVEAJ5eso91BytC/k2T28PeEr/3pigoeukI0QNGEanpviWUGf1kX1QvVApJRWDUSxaNzUF5UPoIMJl4O0fA1DvdrNhXxvJ9gf4XgEzlJ2pl89YStabzDhWlCuWBGZCZREZSPC6Pxp6S7jPyioCJIYJTSG3xIvUWGl0e1h3Ux0nMUgImKbIFMVwKKSvFboSig3eSbcUsYA5X1AeE4cuDxgiYUaKqusEV1SaFavFRu8Si6kbqne1faMNFYOKsFkNIqkXQH4FJNo45aYBu/tvoSyN5vRrPLztg3E9PEjBq9EL/gBRS15dRqwoks4FXMTIKAqYy6Htx6aR8vjFlAF4NnggThdlTXIvb9DktCRYwQZUrZtTnpLad7f09Xs1I7508UP88heoDo75vg/ron79obA5CmfA7I4V0uLye3y3YweV//4qJD33Kdf9cjaPRTWZyvCHaADJTItu8haPR5TdwQ5gITIj30jyItjs78oqAiSHMrn2XR6MmxubKRIONh6tocnvpm5pgLN6RhlH9EZhAAWOxWMgxesG0P41U2+Q2Sozj4yy4PFpAJ9qyED1gFJm+sLqm+Xev0UAtPoOzko2d78Gy+pb+pEXURT04AgOmbry1oSMwAFN8aSTlg1myu5T9x+uMyMax6sYu77ESjpAemG5MIZkNvIpoCBgVgTEL+9vOHg7AuoOVIUXGtiCzefMIjOodEiqF5P9de6IwJY5G3F4Nm6mZX6gp6Cpaol6jaKSQVBTV/Fp1xkTqB97fxotfFbDZl27tn5HIZZPyeWbu1CDzfMc8MMGVU6G8leHey+tnDuF3V5zEacOz2/XY0UAETAxRFfThOhF7wajy6Vkjso1weqRGtkojVN58UTWGOnbAyKuiB9kpdkbm6BUi5pSNv39E8wiM3WY1dtjRTCOZu2iqqpKO+GBUpVZwBAaad+MNJWCUD0Y1tFPRl+tnDjEuBIfK2y+wokkoD4w5hdRVEdDgMQJmRvoG6x2tajCETlsxIjAmYT86J42MpHgaXJ6QRs3garlg4e838TY/5zirxXiv21M9o3rA5GcmkeNL/1bUO5tFLtX3TQkYsz+rvagLvHkTYpRRRyhgnlu6n6ufW9li2bXq7fPLi8ey/BfnsfL+83ny2ilGQ0hFRyu6gkVXqLUnVAoJ9N5O1582hMHZyc3+pqsQARNDBPdNKD8BfTArg/wv4PePVLcagQlt4gVzKXX7BYzqsTKsbwojfEPrDpiqfo63kEICf3VGNI28ZgPesL76Qh7szYkUTdNajsAElVIHp5AAJvsEzK6iGtYUVLC6oAKb1cJ3Tx9qEljRmdnUUYwITLp/6naiL4Xk1QgIvXcmoXrAKPqk2I2LaXtnC6kLvXm8htVq4ZSheguANQXlzf5m21H9AqtSOMERmJoWUkjmx2rPZ/1olf65GpCZRFayHYtFj1wGX3zV+ji8b4rhg+noPKTy2sAeMGBKITVFdt/zlh9kzcEKvtobult3Vb2T475I7nUzhhiVZqHIiDB9Ho5g0RUcgXG6vYbfK7ijck9ABEwMEfwhDRU27c2U1TYZc0BmmQSM8sC0thgak6hTmgsYI4VU035RWHDcL2DUyPnQEZjQ/Tqi0VUzGHMJpJoE3N4IjKPBbcwAyg0VgTGVUlfXu4zHVh4E0KMZ/dIScHs1fv7fLQBcNimf/hlJDDUETPdHYJrcHiOSFCoCA9Do7FoBk2IPfQHpaBop2AOjmDFMCZhAI6/b42VXsS5gzh+bCzRvQRDcfj4Yw3jfHgHji8AM6JOELc5qfG+C10P1/5z0BNOFvoOTm0OYeNsykdrt8RrVjruLQ5tf1fuYn5EYUrSaUWtZVb2rXRHBYNEVvIaafT2hSuK7GxEwMURwuPVE6wXz1rojuL0akwZmMCTb32RLLYaOxpYNsBWdnUIq9wmYfv4IjHlXrDww/cJEYDKjUBIZjDmFpHrNtLcbr6rK6ZMcb5SQmsk2TaRW6aN+aQkBYxssFovRD0YJqVvP0v0WQ33vaU8w8qqLZILNGlC1Fh9nxebzIHRVJZIxRiDMxWxkB0upw7UXONUkYMwzcg6U1dHo8pJij+O04foxxWFSSOEiMP5NR9s/66oCSUUm1OcuuBLJHC0Jjg62l1AmXiUyIimjPl7bhHopw1XvqPdxpK9RYUuotczpCT+RuyWU6ErzPYfg18dhuj249UNPQARMDKF2K+oLcyL1gvF6NV5bcwiAuacNCbhNXWA0LbDrrJlGl8e44ITqWGqkkDrQC0YJg+F9Uxjet3kEpqy1FFIUykuDqTankFRa63htu3Zr/vRR6JB2VggBY/a/KKYMzjT+feaovoYR04gQlXe/gFnrKx+eOCCjWemyfyJ11wgYfwqpuWgEGOXzW7U/AqN/RoIjMBPy00m2x+FodLPbdLFV/pfx+enk+0REsaMx4DMVqneIGf9nvf0emIF9fALGFw0pM62HmqYFREv8PrmOChjffaaYIzCRVyGZU20qihXM3hL9fVQl8i2RFB+HPc43zLYdz02lkJSPxdHoCmhKaKwfYYRodyMCJoZQHhi10J9IEZgv95VRWNFAWqKNS0/OD7gtPs5q7CDCLYhKFJgNhGZyOzjQUdM0CnzRlmF9U433qNyXTtE0LfIUUjQjMKad8NBs3QvgaHS367OjKqyU2AvGb+JtalHATDKVgd7mi75Az4rAGM0SRzSvsFA+mK6LwIQ38ULHUkgerxa2vYAtzsq0IX2AwDSS8r9MyM8wTLROtzcg/RBuErUiWikk8EdDzBGY2ia30dMpOyXBeG4d9ZeF6gPTlhSSeX05WB56EObeUl0sRiJgLBaLIc7a81oq0aXEYPAm0BHUhqGnIQKmi/jNh9v50esbm7XcbgtqoRnh81ecSAJm/io9+nLV1IEBKQlFRiuVSH4Db3yzHTX4BUxpO+chVdQ5cTS6sVhgSHYyKQk2o1Jnf1kt1Q0uo29G6wImmiZefVHNSNLTPvm+6El7fDDlRiO+lgVMZZ2LwsrmBl7FtCF9OGlAOrPH5XLGSH9VhfLAlNY0tbuiJhpomsaKlgRMvL5sdpWAqYtQwBwqr6PJ3bZzqm5wGSmN4AaPENoHoyIwE/LTSbDFGdEIFaEDk4k3TASmvS3wNc0/UX1gpv7Z6ut7fHMvGLU2JtvjSLLHhZzV1VY8Xs1YR8wm3vR2RmA8Xi2k8VoJ0VG5rQsYMFcitX3dUOI4M8lu+ITMGyi1AQquQOopiIDpAirrnLy0/CAfbD7G5iPtG9anaZrxAVVehhMlhVRU3WAMbJw7Y3DIY1orJ1TG2HAD75SAqWlyt+viqQRBfkaS4Q9RUZj9pbVG/4i0RBsJttCpAP9IhOi9r/4QsC3gnNrTkdfvKQj9GprLqI0KpD7N001J9jgW3HUm/7xxeoCYzEiKN+6joyMPOsKBsjpKa5qw26xMHdyn2e1GCqmLesGoWUjhDJ05aQmkJdrwam1/3dQFPT3RRnxc88vBqcN0Abe6oAJN09A0zSirVk0JDf+YyQfTUh8Y8H9f21pGXVbrpMntxWLxP64/AuMMOE6/TX8cv9m1/d+tynonmqZ39jX76FR0otHlbbVMuyho1lmwkbem0WWInJH9WvfAgHnz1vbnZnhgEm2mFLBfCEkKSWBnkT/XuepA85LESKhzeowd/HAjAhMbZdSapvH0kn1hywZb4401hXg1fTc4KoyxrbUdnfp9KAMv6BcHdYFoTym1uYRaoSJlB8rqWjXwQieZeIN2wsM6YOQNFT434w/TOw1Dc6gUUksM9eXiD3ajD0alj6YOzgxpVu7qcQKtmXgtFku700jhKpAUJw/MwG6zUlbbREFZHYUVDdQ0urHbrMZjqiotdeGNpPTW+L62sQW+moGUl56I3TcXyvDAmARMcLmzPwLT/uimUW6ebMdmEnuppvRKa71ginxri/Kt7A4y8qr3LyctwRAmrWH4idrRAFM1Q01NtBn3Y45StSZEuxsRMF3ADpOAUYtjW1E7B7vNauR+Y6WR3eYj1Tz2yW7+772tbf5bt8fLG2sPA83Nu2ZajcC00ANGofL5wRUVkVAQQsAMN5lmW0u/6OcWfROvI6gJ1fAO9FopC9EDw4y6CHq8GoUV+k6zrU2uVBqpO30w/maJfUPentjFAqY1Ey/4/RLKABopRgVSGAGTGB9n9O5ZU1BhdOAdm5dmRGyC/WPmVEq4qFF7xXpwBRL4P4/mvlhKbKseOVkp7Y9SGPdpDGMNfK3i46yGqG3NB6Neo1OG6ZG94AjM3jamj8C09rUjcltrRGDiAzYgCkkhCQGzItYdqmhznhr8F7XMpHh/2WCMzEMq9QmCo5UNAeWYkfD5rlJKHE1kp9iZMyE37HGtGdkqw5SKmsnrgA/mYEgBoy9C+4/7IzDhohfmc4tWBEbTNJOZ0heB8Z1TRzww4Z5Dgi0u4IJlj7Mak74jZVh29/aC0TSNVS34X8A/D6mrxgm05oEBk5G3jc3s1PciXFoQAn0w244q/0uGcbuKwKiLs/rMpSbYAiIVZlQZdbiqwXAEG3gB+qU17wMTHIGJRnSzrIXXKtWYSN3y81Gv0TmjcwDYUxw6AqMqyyKhNf9fSyixmZZgC+kTCk5B9zREwHQB5ghMo8vLJl8ztrZgCJjkeOMC0uT2UteFM1naizp3t1drs29n/mo9+vKt6YPCekeg9QXKmIOUEn4nYewkOxKB6WcSMD4xc6i8zkhLtRSBMYuwaAjTOqfH6IujQsDqnA6W17d5aKTa4bb0HMy704FZSVjb2DtCvX7dlULaU1JLeZ2TpPi4gGopM11dRt1aFRKYSqnbGIEpj0DYq34wqwsqDP/LhPx04/bg701NC2MEFO0V6y1GYGqbR2CyjAhMx5tEVrTQBiGSSiSPVzPWgbNG9wP02V9mEacmfI+MoAJJ0ZFxArWmFFKo16i1cvjuRgRMJ9Pk9hiqeqqv/8XKdvhg1LTRzGQ7yXabUQkRC2kk84j2tvhLDpXXsWzPcSwWuO7U0OZdRWvpl6oIUkhqIW6rB8br1QwBM9wUgRmQmUSCzYrLo7HJN7ywpQiMWkDa0pRq65FqfvvhDqpDPG+1+NjjrMbnJT8zCbvNitPt5ViQobAlNE0zdmYtRpFMAqat/hfo/lJqNapi+tA+hscimK72wLQ0SkChLngFZXVtqnRUF6twHhiAqYP7EGe1cLSqgdW+sQLKwAvQ31fZZkRgIvBNqI1Eo8vbJiHo7wHj/2ypz2Od02NExYL7tbR16GFRdQMvLDsQIApa8oBF0gumvLYJt1fDaoER/VLI90Wu9pp8MEYKqU0CpgMeGJOJV313KwJSSP4qxp6ICJhOZm+JPnY+Iymeq6YNBNrngzGnkCB03renUtkOAfPV3jK+O28tAGeN6teql8LYhYSpagg1sC6Y9s5DKnI00uT2Eh9nCdgZWq0WI6W04XAl0HL0Iik+zrhoRlru+aeFu/jX8gIWbD3W7DZz+FdV+8RZLYZRti1GXn0Qnh6xaeliZw6vm0cIRMpQU4VdW9MLoAuthduK2t3Pp6XyaYXRB6bLRgm0bOIFXSwnxltxerwUVkYuTNXFKpwHRj2uEiyNLi9xVgtj8/wpjrxmKaTW0w7mzq5t8XyFSiGlJtiM741aD4Ojhf6qJ1dEAu+xT3bz8Mc7ufWVdUbK36hsCuEBi2Qi9THf65Obnogtzspo32u4y5dGqne6jQhTuGKFUGQktT89VmNK96m1MWQERgTMiYmqQBrXP42ZvrHjGw9XtTn8rBZzlWYwdz3t6VSZnP+tpWeKqhu447UNfOfF1Rw4XkffVDv3zRnT6mNktFLVUFEf+PqFIs+IwLRNFKoZSIOykpvl/FUlkpoh1LeF6IXFYmmTkdfj9Ud2iqqav67hwr9GJVIb/BIqPJ+WEL4MHALFTXsiMKkJNvql6ReI9kRhXl5xkB+8uoHffbSjzX/r9Wqs9vU7Ud/VUHRlBMbp9hpDI1PDzEICXSyrz9reMC3qQ2FEYFoQ9uD3wYA+AdtcnaUETE2Tm9omd0RpB4vFYmzGwm06gjH3gDFvFCwWi6kXjH5fwX6tjKR4/0DHCISx6sS89mAlv3p3m68RZXgfmzGRuoUWDMVBg1DH+ASMMvIeOF6HpumbgJY2CcF0xPzvT/fFmyIwzRvZhZtp1d2IgOlklP9lfP8MhvVNITc9AafHy4ZDlW26HxXKVF4Pc8+NruT9TUd55OOdbTLjBkRgWtgZv7b6MOf/ZSkfbSnCaoGbZg3l83vPCQhXh6O1PHBVBDvNnHZ241UVPeb0kWJEv8DftRSBgbZ5A/YfrzUWzNIQIxCCDbyK4e0w8rZWQq0wL7yhmthFgjLyttUH4/J4eeHLAqB9fW52FDmobnCRmmBjYgufua70wJh7EqW0UIUE7TPyqotVS98LwJhMDYH+F9BFp+qEXVzd2OocJEVrm45gHA1u4/MePKE5uBtvcLTEFmc1BFVrXpHSmkYKKxqwWMBqgbfWH+HFrwr834EQr1VaQuspJFVmrkzPY3xRFlVKrTrwtsX/Au3vcaNpmvF6ppnKqAMiMBG+l92FCJhOxhyBsVgsRmlmW30wlUERhOxuiMBomsav3tvGP5YdaNP5m3cG4SIwJY5GfvXeVuqdHqYN6cOCu87kocsmRJx7bS3H7a9CaiEC41tYSmsa22SiDdUDRqHEgqI1AdOWmS1mM3hpiCna4ZpQDTNKqdsgYEIMsQtFRyMwAEP76n/X1kqpBVuOGTv04+2YaaVSu6cOywpbPQNdW4WkLjCJ8dYWzwn8vom2GHlVL6nWdvynDPU39JsQQtzlmtJIZl9FS7TVfKq6O/dNtTfrxq2EdXmtE6+pY6454hmqUVsoNhyqAmB0Thr/d8l4AP7w8U5jLQ/1HYjExKs2RnnpuvgyR2A0TfPPQGpDCTX414zqBlebNpYNLo/RhdnsgVFrpaZpAZ28eyIiYDoRTdOMEurxvl2LCk2vaKMPxu+BCYzAdKWAOVLZYHxBt7Sho3CgiTd0eqagrA6vBoOyknjr+zON1ytSVGSqweVptjN2e7xGNKIlE69qMufyaG16Xf0l1M0XnuFBEZhIIxiRhIM3+tJHELr0O1z415hK3YYoRVmIIXahCIzAhB762BrqdWxLCknTNP6x9IDx//I6Z5vHdihR3lL6CLq2D0xrXXjNtCcCo6IfrQmYzGQ704b0wWIJTCcpjFJqR2PElSttNZ+GSh8pVKSlrK6J6gb/VHpzZClUo7ZQbPT51aYO6cPNpw/l29MH4dUwjPWhvsP+MurwAiY4AjOiXypxVgvVDS5Ka5pMBt7I/S/gvyZ4tdbLuM2otdxq0aOKKo1Y45sj1ejypy8lAtNL8Hi1iE24R6sacDS6iY+zGB9KZQ7cXFjVppb11UYVks8DY9pxdBXmcvCtR6si/ruqCEy8qrpgSFZKm0tvQfdmqD8LNn+aF8iWdhJ2m9XYsbXFBxOqiZ3C/LsEm7XVC1FmiF4M4VALLYSOwIRrQqXO6WhVQ0TzW8DsKWg5AmOu+khrZ+nlMBWBKY+8F8wXu4+zq7iGFHscNqsFTQvszNoabo/XmPfTkoEXuieF1JKBVzHSNJU6kp14k9tjRHha88AAPDt3Ku/cPitkSjfPSL82tDrIUdFW82koA6+ir6kXjDLwZiTFB4xHiDTiowz3UwdnYrFY+N0VJ3GqKYXWN4SJN5IqJCMC4xMwifFxhqF+V3GNqQdM2yIwdpuVFF9Eqi29YMwGXovFQnpSvLGGVjU4jfUjzmox7r+nIQKmDagUyrUvrOJfXxW0evzOIj2nOaJfquGSH5SVzIDMJNxejXVt8ME0r0JSF7quq0IyN+SLNAJjnuEE4VNIqkX4wBCLUyRYrZawvWDUgpWeGL6xliInrW2l1E63v+ojONoC+sKmqpv6piaEHCRpxm/Ia3mRrWtys8dk1iyva2oWcQiXQspOTTCiMF/sPt7i45jvH1o2IYNeXpuaYONsX5+L9qAqkQqO1wak8jRNY93BipCpm2eX7gfguhmDjTRdKF9QOLYeraa2yU1GUjzj+rcc/Uuyd90wR2OMQAsGXsWQ7GRsVgv1To/Rsr4lzBPaI5k2nJOeyJQQs6HAVInUjghMqBYAoVCbnFARmL6mXjDBc5CMxwtRJhyM0+011rapvkncdpuVZ78zlfH905k1IjukMIskhXTMZ+LNz/Q3dxybp3/Wth6p4pDP8zWyjSkkME/3jly0mw28oH8OjDW0zhUQwW1t3eouRMC0AU3z775+u2AHj32yq0WvRHD6SDHLt8NrSzm1iiJkGFVI+he2K1NI5gjMkcqGgMZR4ahpchsznEBfNEPtXP39HdonYCD8fBW1K4nE2R9qMF1LFFbqDeGS7XHkpIWOTgz3pURau/hD5BOptx6txqvpM1PifBGHYEN3S/04LpqYB8D/thW1ek7QvK9GOHLTE1n3q9n89duTI7rfUAzJ0gWMo9Ed8Dr8/qOdfPO5lVz2968C0ksbDleypqCC+DgLN58xzBgJ0ZYomkofzRiWZZT3hiPR1vURmEhSSPFxVkP8RVKJVGHyhbUn6mnGXEodqfGzLX4vgKNV+iYnZApJRaTrnP6xHUGRkkiqdXYUOWhye8lMjg8w5WenJvDRj87gtVtPC3kx95dRh75vr6mJXV6G//xH+4y8/9tWjFfTo0YtzUsLR2udyENhNvAqzGm2nm7gBREwbcJqtfDA18cZZb1PL9nPL9/dGraj6Y4iXcmPD9rRqRB1pEZYPYoR2Igt1ACzzkaZ2NRat/Vo61EYZQhLjPc3UwslDvwRmPYZPyF8iFgt1OEmUZvJbeM8JFVCPaxvSthdiorMtGbgNZ9ja4u6Kp+eNqSPKe0VeM4tzTG56KT+ACzZdZx6Z+upTBWByYrgOSTGx3Vox5ZkjzN8Aio9t2J/GS/6op57S2u57O9fsWyPHj36hy/6cvnkAfTPSDKEZFsiMGt96aPTWvG/gKkPTJdEYFQKKbIQvko/bDdFS8MRyXiNSMkzdeON1MTrjxq0zQMTao1Qqc0yUwopbASmhU2fqg6dMiiz2We4pc+0imKEK6Mur3Pi8mhYLARsdJSRV71fo3JS2/XdaU9n41Dvk3keUnUP78ILImDajMVi4Y5zR/LINyZitcDrawq5Y/6GkLsxlUIKJ2C2HqmKyHRV7/QYTcQ6swppw+FKVuwPPTG6usFlRElUG+ytEaSRjOhHst3U6bb5zjgqERijr0Tga6p26+bQbThaOsdQqAvs0BD+F4UKu0cSGlZD51pb1FUF0uRBmUbaK9jI62hhjsmE/HQGZyXT4PKwNII0kn9X2/GLXSSYO/I6Gl3c99YWAC6blM/UwZk4Gt3c9NIaHv5oB5/uKAHgB2cPB6BfmNejJdQFZJJvcGFLGH1guqAKqS0eGPB/N+evOoTT3bKJuTyCLryREhCBifDC15aogab5B4SG8sBkG31gwqeQQjVqC8bvfwmdKgtHaykk5X/pl5oQ4MsZkxdo2G1rBZKiXRGYxubRPfM8pJ5egQQiYNrNtacO5pm5U7HHWVm4vZjb/r0+wDhX0+jicIUeVQjOqffPSGJodjJezb/za4kqU0t4tXiqRafB5YnKQup0e7nxxTXc8OIaiqqbd/JU0ZcBmUmcMVIvBd8SSQTG1L8m3Kwht8drOPQ7EoEJF70wStnzWq9saus4gWV79Yt/S8a7K6cM4L+3z+Qns0e3en+RRmA2FuoLrS5gVMQh8ILd0g7KYrEYaaSPtraeRvL3gWl7eLs9GFOpy+v47Yc7OFrVwOCsZB75xkRev+00rp4+EK8GL3xZgKbB7HG5hok13OsRjuM1TZTWNGGxENBhNhx+E2/nd+JtSwoJ9M9av7QEjlU38v6moy0ea3SnjoKAUeME9AiIz3PWyoWvLVGDI5UNVDe4iI+zhDTLq+hmRZ3TGJyaFZRCiuS7tdG3MZg2pK0CRpl4QwsYtaaqyKJicFayEZkGvxG7rWRG6J0zU2OkkPzvk3keUiQdlbsbETAd4MKT+jPv5lNIio9j2Z7jvLW+0LhNtYfOz0gM2SRqZht8MOpDmZEcb4QXUxNs2OMC22d3hIPldYZf5au9zaMwSgSMz083dqlbjlRFfO59UuL94iCoUVyxoxGPVyM+zhLWRxIJ4XLc6r1ozZwJ5m68rQuY7ceq+XJvGVYLXDV1YNjj4qwWpg3JCuheGo4+EewSi6obKHE0EWe1MHFghuH5CE6Z1LQyx+RiXxpp8a7SFv0cbo/XWPRbKwOPFsp/8M6Go7y9/ggWCzx+9SRSfJ2A/3TVyfzmsgmGX+X2c4Ybf6tej0h7wajP9rC+KRFFOpKikEIqrIhsmGZtBGMEzCTGx/G9M4YBurG5pccwPDBREDB9kuONQoUmX+Snte6tmW0oo1Yp03H900N+j9SF1+PVjA7TwZ4zf3ok9OOVOBo5WtWA1RJZJM6MEpi1Te6Qr7natPXPCIwexVktAWXTba1AUkTqnTOj/DqpZg+MyeiszNWSQurFzBrRl3sv0HfWj/xvl7EoKANvuIumyrWvOdh6BKY6qAIJ9B10NHvB7DU1v1q+r7mAMT+f8f3TsVr0NEtrF3plqO2TbCcvjL/EXF3QETNhcCMm0CNLqjxxbP/Wdzc5bZiH9E9f19eLJ/Zvd9fZYJQIq3N6wqYAVPpodG4ayXabP2VSEy6FFHoBOnlgBgMyk6h3eli6J3waqbLehaaBxRIdv0QkDDWVegN8/6wRTDeVslosFm6cNZSPf3Qmb952GtOG+G/LCfN6hMPfLTuy3kMdTSG9uuoQZz66hKv/sbLV+2hrCglg7mlDSE+0ceB4HZ9uLw57XKRjBCLBYrEY4l/RWhm9uXKmtcaRm30CZnIYYWG3WQ3BtMe3lgXPLFLp2XARGOV/GZOX3qbXGwJ9JKF8MMeqAkuozZjTSO1PIbXdA6NSSGmm52pOs7XkoespiICJAjfNGsrYvDSq6l08unAXEL4CSaFac28/5mi1H4zaoQRfPKI5TkC1sQZYvr+82YJiXuRTEmxG06zWfDCVJvNxuBRSqAmz7SEjhAdmX6k+TDMt0RayeiEYtQiX1TpxtdAI7VhVAx9u1gcofv+sER057QDSE029GMIsRmo3OsU33VwZj82eD49XM0LE4XbCFouFi07yVSO1kEYyDLzJ9lYrdKKF6gUDelrnJ18bFfK4MXlpzAgy3hoppAg9MK19V4MxN7JrS8dm9Vi/XaDPaVp/qJIfv7GxxSiJP4UUeR+O1AQbN84aCsAzX+wPe46RjhGIFLOASYy3hp3mrVBi3eXRWp2+rj7zkwZmhj3GnEaC5tFCdZEPN9Bx/SF//5e2khgfZ0TEQ1UiFYdJIYF/pEBqgq2ZCIwUtbltywDUUCZeFRWrqHf5qxhFwPRubHFWfn/FSQC8sbaQ9Ycq2VnccgQmPzOJAZlJeLyaYRwLR6UphWRGfUErolCJZI7AHDd1hQR9xoy6Xc1BmTggE2jdB+MXMPH+Vv3NBIzuFWpv51ZFqCqkXcV+/0sk7v4+yXbi4/TjWtrBv7S8ALdXY+bwbCYObH1WU6RYrRZDiIULB28M2o2qiIM5ZWJeRFtagC6aqKeRPttZakzdDSZ4MF5XMCgrmczkeOxxVh6/enKLAySDUVG0stqmiBq6bT8WulowHOY29k2tGGXN1Dvd3Pn6BpxuL5MHZWK3Wfl0RwkPfbA9rMiobUcEBvRNVWK8la1Hq/kqREQVzGMEonOBMkcXIkk7JJku+i1FDlweL9t871FLqZ3gKr/gFJK6yGta6At9ew28ipaMvEXV4SMwqt/MpEEZ7a7e69NKdCkUtSH8VWYPTHUPH+QIImCixvShWVw9XfdB/N+7Ww3fRUuL4qm+ltxrD7YsYIKb2CmimkLyRWCSfYuz2Qez/3gtTo+XtASbUSU0aZB+0d7aig/GP8PJHlBqaSZaEZhQ85DMs6giwWq1tNrMztHo4vU1ut/ptrOGhzymI/RJCR8Odnu8RtRriiFgmptW1e4p2R4XUPUQzJRBmeSlJ1Lb5A7pfQIMU2RwSL4zSbDF8c7ts/j4x2e0eayE3jAQ3F6txaZloIsKNcsq4giMKbLQljTSr9/fzoHjdeSmJ/Cvm07hb9+ejMUC/151iOdMYxDMtGWUgJns1ASuOWUwAM8s2R/ymApjjEB03ldzdCGSXbvFYomoemZPSQ2NLi9pibaQA1MVwQI7+POqD3TUX8fgzUGT28O2o/paMbWNBl5FSxOpw3lgQDcMv37rafzt21Pa9bhAQAO6SHEENbKDwFJz6QNzgvGLi8aRmRzPruIanG4vKfa4FgfaqTRSa5VISglnJocWMB1NIbk8XqMc+JvTdBFm9sGY/S9qh6Cm9W45Ut1iGL3KVOlgLlE2/01Hu/AqQnWjVEJybIS7a/CnZMJNzn599WFqm9yMyknlnDHt7zgbjpaMvLtLamhweUhLsDHCNyjSb1r1Rxwi7eFgtVq40JdG+nhraL+EisBkdWEEBvRBmO2pyoiPsxq5/NbSSPogPV30KOHaGrY4qxE5iNTI+97Go7y1/ghWCzxxzRSyUuxcPLE/D/iGBf5p4S7e29i8aqgtnXiDufWs4disFlYeKA8Z5Y2mBwb8FXwQ+a69TwS9YMzpo5Y8cmYBE2eKZJrJCrM52H7MgdPjJSvFbrT3byvhxglomtZsDlIwM0dk068DBQztSSEZEZjEEB6YehEwJxxZKXZ+fuFY4/9j+6e3+IU7dZiu9DcWVrbYs6HKVIpsJlrjBA6V1+Py6N1kvzVtEACrCyoMD0ioKMa4/unYrBbK65wcC3Oh18/NL77Uhdbp9gbsgKLRA0Y9BuiLoRJI/nOPXMC01I3X6fby0vKDgH6B6IwW2y1N1laL+cmDMozPVqiIQ1sMeBf70kiLdhSH/ByqCF9X9YCJBv0ibGa3o6ht/heFKn0NFjCHy+u5ed5a7np9I098tpePtxaxfF8Z//fuVgDuOm9UQLO8m88YZlQN3ff2Zg4HzX9qj4lXMSAziSumDACaR2E0zf9Z6ROlFFJbIzDgT4u3lPpQBl4V9Q2HOeLSJ9kecu0NZfQHv4FXzT9qD+FSSJX1LuN7ldtOj0trKCFY6xvEGAm1ITww6vWpd3oM8d+rqpCWLVvGpZdeSn5+PhaLhffee8+4zeVy8fOf/5yJEyeSkpJCfn4+N9xwA8eOHQu4j4qKCubOnUt6ejqZmZnccsst1NYGTlDdsmULZ555JomJiQwaNIhHH320fc+wi/n29EGGN+GkVhbFEf1S6ZMcT6PLn+MNhZFCauaBUfM/OhaB2edLH43MSWVCfjp9kuOpbXIbZdKhFvnE+DijDXZLaSRzB+EEW5yxA1KNnaLVA0Y9BugX8tomN8dr9KZWFguMboO7X+3Ei0Ps3j/cfIxiRyM5aQlcPjm/Q+cbjpYqCswN7BShIg4tNbELZtqQPvRLS8DR6A7ZyNDf2bTrUkgdJSc9skokw8DbBoELplLqoBTS2+sLWbyrlA83H+Ovn+3hh/M3MPefq6lzejh1WBZ3nTey2X398uJxTBqYgcujsbogsK1CW/vABPODs3WD+Wc7SwJGf9Sbqtyi0QcGINckYCId5BnJROrNhfraOHlQy6kds+cl3NiOcL1nVP+XcLOeIkG9R8ETqVUPmL6pCa0am9tLelI8SndVNUR2PTBMvAn+9yo90WYY9dV3p1dVIdXV1TFp0iSefvrpZrfV19ezYcMGHnjgATZs2MA777zD7t27ueyyywKOmzt3Ltu3b2fRokUsWLCAZcuWcdtttxm3OxwOLrjgAoYMGcL69et57LHHeOihh3j++efb8RS7FqvVwt+vm8ItZwzj+2e3XJ1isViM0tCW0kh+D0znVCGpssNROWlYrRZmjdAb1X21V69G8i/ygTsgtSNqabCjuQoJTI3ifDtj1QPGHmdt1wwQM0n2OBJ8C0RVvcsw8A7LTiG5DSH4cBEYTdN44Uvdq3DT6UPbZCxtCy0NdFQG3ilBi3lwxKEtbcDjrBYunKCnkRb5utqaCdfZtCejfEHHWxMw7YzAqFLqYOOzWvRPH5nNVVMHMmlQJqkJegXcE9dMDjlM1Gq1GIJ0X2ngRq6towSCGZmTalS5rDGtMSqqlmDzN8fsKAERmAhTSGpNqwqzhtU2udnj22C1GoExrR/hPqv+TrOBgslfgdR+ARMuhVRU1XL6KBqYU2aRduMNlUKyWCzNql17ciO7Np/ZRRddxEUXXRTytoyMDBYtWhTwu7///e+ceuqpHD58mMGDB7Nz504WLlzI2rVrmT59OgBPPfUUF198MX/+85/Jz89n/vz5OJ1O/vWvf2G325kwYQKbNm3i8ccfDxA6PZWBfZJ54OvjIzr21KFZLNpRwtqDFWEFj1LUzSIwUTLxqooj1YPg9JF9+cgX+v72KYOorHfpDZeCohgTB2TyOoVhZyI1ujxGt9JMX5g6Lz2BnUV+f4nRA6ZPx3rAKPok2yl2NFJZ7zTSR5H0fzGTG6YXzPZjDnYV15AUH8fcGUM6fK7h6BOm4Zaj0cV+X5OuyUGlnjnpiewqrjEuoG3t4XDqsCz+vepQyBk65d1g4u0o/lLq8Ckkj1djV5hxH61hlFI7A8P1SjBdenI+15yqm2hVOrOl1MRIn8gwV/9pmtbhCAzAacOz2F1Sw+qCCqPqrMI0RiBaadB+qQlYLeDVIk8hqXUhXARm65FqNE1Ph7XmUTIPGg33WfWP6vCvmYfL6yl2NBJntbQqkloiXAqpyBji2HkCBnQfTFW9KyIB4/FFqaH5zKqslHjDuA+9LIXUVqqrq3W3eWYmACtXriQzM9MQLwCzZ8/GarWyevVq45izzjoLu93/gZwzZw67d++msrLlip1Y4xRfJdK6Q5VhSz7VBzL4YtTWKqRw968m16oukGpUwMbCStb6Gu2N6JfSrAPmyQNbNvKq6IvNajGaJRkzUxyBAqaj/heF2QejLk6RjBAwE65fjWr2dvrIvp0aVg03lHJLob6YD+yT1KxkNDco4tDWHg6qhf6ekppmnxMV4YtkmnZPIZJxAgfL62hweUiMt4ZsT98S5l4wZtTjmQ2ZFoulVZGgvnvmfkyNLi/qrWiPB0ah+uSsMg2PrTCZ66OFLc5qPO9IL3qtjRPYFKH/BSKLwGQmN18zX119CNCFXlsitcGEm0jdUg+YaNKWZnZ1pgGuweLYHIGx26wRdRDvLjpVwDQ2NvLzn/+ca6+9lvR0/SJSXFxMTk5OwHE2m42srCyKi4uNY3JzcwOOUf9XxwTT1NSEw+EI+IkFJuSnkxQfR1W9i33Ha5vdrmmasTtpHoHRv7C1Te6wPTxAN57Ofnwp335+ZTOh4fZ4jTJS1dJ6cHYyg7KScHk0Xl5xEAi9Qx2dm4bdZqW6wT/3yYx5CrRawINLlKNVgaTINJkCdxgRmPYJmOAKFiVgzu6EyiMz5pH2ZjaZ5h8FY4wTcASnkCJbkIf2TcEeZ6Xe6TFEpcLfByaGIjAReGBUanRsXnqbG/QlhREwx0MImEhQfrIjlQ3GdHC1Q7ZY/O0N2oNq17CruMb4TFXWRV/AAOT5yoQjTTsY1TNhogabI2hgpzCnoLPDPK/gKqS6JjevrzkMwM2nD4vonMNhTKRu5oEJX0IdTVpKPQejokT2uOYCxfyZ6Mn+F+hEAeNyubj66qvRNI1nn322sx7G4JFHHiEjI8P4GTRoUKc/ZjSIj7MydUgmEJijVjS6vIbZLlRu0uZbeFuKwhRW1rOvtJa1ByubpQgKKxtwur0kxlsDRMTpPh/MOl9uOFQVj91mNX4fygejIkd9TMLLPLUWotcDRqFeo+M1TUa6JZIBfWaUgKltchsXkZpGl1GpcPaozhUwmWFKSzcZZsbMZn/jF4aBKaRIIzDxcVZG+KIAyjsEehpQvQbRvth1JjkRVCG11/8CfhNvo8nE6/VqRui9rQImK8VOdoodTYP9pfqGwqhAsts6lObpm5pgRHjUGmPMQYryaIizR/UlwWaNSHBA61GDzb4CgXAjBMyY18NwYju4wu/t9UeoaXQzvG8K547JCfk3kRIuhVTcSgl1tAi3boTCmEQdYoNjrnbtyU3soJMEjBIvhw4dYtGiRUb0BSAvL4/S0tKA491uNxUVFeTl5RnHlJQEmgnV/9Uxwdx///1UV1cbP4WFhSGP64lMH6Ia2jUXMOqLHR9nabYLs1gshl+ipUok822f7wx87feU+CuQzB6U031pJEW4Rf5kXz+YUD6YYAMvmIcl6gt99CMw+mOtP1SJy6MFNN+LlNQEmxFWVZGiFfvLcXs1hvVNYXA7+0RESqiwuqZpRjg9tIAJvGC3NgcpFEro7S72pzFU+ig+ztLjFzMzxjykoJ5DZpSYn9AeARMiAlPV4MLty/m0xy80MiiN1FEDr5nTgtJIFZ0UgbnngjFseegCThoQmZekpUZ2JY5GiqobsVqI6P4sFouROgoXgTH3WPJ6NV5ars8z++7pQzvswQs3kbq4hS680SSzhfYLwdQ2qSZ2zb/T5s7MPbkHDHSCgFHiZe/evXz22WdkZwfOKZk5cyZVVVWsX7/e+N3ixYvxer3MmDHDOGbZsmW4XP43YtGiRYwZM4Y+fUK7xBMSEkhPTw/4iRWMjrwhIjB+/0tos10kRl5zn5jFuwKFoap6GBXUMGzWiMD3LVwfFdVGP9RMpEpj1or/S5CbHpxC6hwPjFqox/ZPa9fuNbiZnUofnTWqb9i/iRbq9apucBl+lKNVDZTVNmGzWkIu5v6J1E3G30LbDHhqqNyuEpOAMRl4O6PnTWehXo8mt7dZWauivSXUENoDo8RjVoq9XeWyyiSvjLwd6QETzIzh+hqz2rfGhNpcRIu2VOcZfq8QJl4l2EfnpkX8GkwckInNagm7XplTSJ/vKuVgeT0ZSfFcNS38NPlIUdEMh8kDo2kax7rIAxPOOxcK9Z0IZQ43fyZ6XQqptraWTZs2sWnTJgAKCgrYtGkThw8fxuVy8c1vfpN169Yxf/58PB4PxcXFFBcX43TqL+q4ceO48MILufXWW1mzZg3Lly/nzjvv5JprriE/X++rcd1112G327nlllvYvn07b775Jk888QT33HNP9J55D2LKYP1Ld6y60YhIKMJVICkiMfKay6w3H6kOqMzYa4rAmMlOTTAW9py0hGamUYUKTatOvmYqQ4Sp1S6kvM5Jg9MTtR4wiuAQcVsa2Jkxl3trmsayLvK/gL+01Kv5F0PVC2Ns/7SQpjrzBGZN04wFqi0L0JgWIjCxVEINusBQu8vjIdJIpTWNlNU2YbXoHpi2kmT3NbIzpZAM/0s7vUJqE6HmjrV3jEAoZgzTNyS7ih1U1Tv9EZhufl/Nvo1g83hrE6hD8ex3prLy/vPDTofPNAkm1RLh2lMHd8i8qwiVQqpucBmVmJ3VxE6RGUFTQEVtCwLGHJXryRVI0A4Bs27dOqZMmcKUKfrchnvuuYcpU6bw4IMPcvToUT744AOOHDnC5MmT6d+/v/GzYsUK4z7mz5/P2LFjOf/887n44os544wzAnq8ZGRk8Omnn1JQUMC0adO49957efDBB2OihLo9JNttTPDtqoPTSNVh5iApIukFEzzscfEufxpJ7faUidDM6SP1Ra8lEaCqN4odjc2aelWG6CDcxzegD/T8drR6wCiCuxW35+IE/lRXcXUTB8rqOFLZgD3OGtBFtbOw26zGwqIuNC0ZeMHvuXC6vTga3G1qZKdQKaSCsjrDFB6LBl5FS1OpVfRlWN+UgOGMkaJSSI2uEAKmnS3h1WZgn5FCav8YgWD6pSUwMicVTdOjMGpmTrTGCLQX1YnXq2FMT1f4K5AyI76/eFMlVCjURV7TdD9QnNXCDTOj0xIhVBWS2qBlpdg7vZqnLR4Y/yTq5tcV83TyntwDBtrRB+acc85pcfZNJOPls7KyeO2111o85uSTT+bLL79s6+nFLKcO7cPmwirWHqzkyin+cKa/Ain0QtPX6MYbvtpCiZvEeCuNLi+f7yrlmlMH4/FqphRS8061N58xjIPl9S0OLMxMtpOZrPcfOFheFyB2Qpl4LRYLOekJHKlsMJpHRasHjP5Yga9TpEMcg8kxpbpU9OWUYX2islOLhExfN2QVSdrUSjVGYnwc6Yk2HI1uSmsa25VCyktPNO5jX2ktE/IzjM9VLI0RUOSkJbL/eF3ISiS/gbd9fT9CeWA6KmBG+lJIhyvqaXR5oppCApgxLIt9pbWsPlAR9TEC7SXBFkeyPY56p4eqeqcRMfR6NaMwoC0RmNaI9w10VBHKiyf2Jz8zOulrowqpyW1cB7f5vIGdnT4CczSrgx6Y3pxCEjqHcB15w40RUESSQlK3XXyS3sTqq71lNLo8HKmsp8ntxW6zhgy59s9I4oUbphtDJ8MxNFuPwhwMSiMZefagi5+KbigBEy3/CwS+ThaLPy3SVvJMzez8/pfOTx8pzPlsl8drmKSnBDWwM6NC1IW+9xX8O9xIsFgsRsRKpZFiNYUEZl9Q8xRSR/wvAIkhRgkoAZPTTgHTLzWBzOR4vJo+Ad7fxC46O3ezkbezyqjbg4oum82nB8pqqW1ykxQfF3Jz1RHM69EtZ3SsdNqMEgNeDX7z4Q7O+fMX3Pf2FiC6a1w4VOo5khRSTYg5SIpenUISOgclEvaW1gaIEWOQY0dSSL7bzhjVl/4ZiTS4PKw8UG7k2kf0S21zHwwzKo1UUB4sYFQEJnCRVDNTOkPAmKM9Q9s4QsCMEgOHK+oNQ3BX+F8U5oqCPSU1NLq8pCXYGN43/GKuLtgqqmaxQGobn7/qWqwEjCoLzoqhLryKFlNIHSihhtARmFBN7NqCxWIxpZFqTVVIUYrA+Iy8O4sd/kZ23ZxCgtBT5NVsookDMkKOX+gIaj2aNqRPVKM7SfFxxjo6b8VBDpXXk2CzMntcLj/52uioPU44zBVdbk/LAx1rWjLxBqSQRMAIEZCVYjfSL+9tPGr8vrUITCRVSP5ddALnjdV7HXy+s8Tkf+nYDidsBMYw8Qaeu4rAqDRHtAy8EJhqa2v/FzNKZG0/5qDR5SU3PcGYKdMVKGFaVe8MOYE6FMrIqwRMWoKtzak5oxJJRWBicA6SwmxsNlPvdBum8/ZGYDrDAwMw0mTkjcYYATM5aYkM75eCpukeEAifmu5KQpVSv73+CKCnbaPNxAEZWCxwx7ktz6prKxaLhQtPyiMjKZ7LJ+fzzNypbHjga/zzxunt9uK1hZz0BNITbTg9Xv762Z4Wj/WPEWh+XUmxxxk+RUkhCRHzndP02SnzVhzE43PkqyqkjDALTURVSEYprJ3zx+kCZvHO0mYjBNrL0L66AAmuRApl4gW/gFFENYVk+sK1twIJmlcMnDWqX5eWEZt7wYSaQB0KFXFQwrQt6SNFcC8YNYk6lsYIKMKlkHYcc6Bpun+svWLDmEZtFjC1HatCgsCRAoaJN0oCBvzVSKCnDzprOnJbCC7/XX+oktUFFcTHWfjOadGfOfarr49j2X3nct7Y3NYPbiNPXzeVzb++gCeumcLFE/tH9b1rjQRbHA9fORGAZ77Yb3j3QqGMxqEa2en9xfS1Q1JIQsR8Y8pAMpLiOVxRz+c79X4toYywZvq1MnVX0zRDSGSl2Jk1oi+J8VaOVTfyme8xRuZ0LLJgpJDK/CXgbo/XCFMGn7u6sCiiKWBscVZj7lJHIjDBPoazRndd+gj8u9KKOpepgV3Lu1H1WVARmPYsPqoardjRSHW9yx+BicEUUr8w85CUp+nUDuzuE40IjD9UH40IjLkXTLRNvKDP+1H0BP8LNG/A9uwX+wG4YvKATmm/n2CLC1tmHetcOimf62YMRtPgJ29uCjvM1IjAhPlsnTc2l35pCe1OsXYVImB6EEn2OK6boUdh/uXrEKnSLMqgFUxOiLb3ZhyNblwePZqjSvnUsEblxA+eMt1WhvoETFltk6HsVfWUxdI8DNk8AhPdxeT0kX3pm2pv1XzcEvFxViPqYLX4B1x2FWpXeqSy3piR1dpAO/VZUMKxPQImLTGeAb6qjF3Fjl6RQjoe5IFR3ag7sgM3PDA+E2+jy2N8V1ubmtwSqhfMofJ6Y+MRLRMvENAGoDOa2LUHJWCqG3S/12c7S7BY4PtnRzfFc6Lw4NfHMzYvjfI6Jz9+Y5MRzTfTkokX4JFvTGT1/ef3GJEbDhEwPYwbZg4hzmph1YEKth+rNqVhQl+MzG3vQ6ltlVpKsccZu8bzx/kXbnuclSEd3I2kJ8YbXpxD5XoURvlf0hPjm5nwzC21o9kDRqGaWQVXP7UVlUY6eWBmh++rraj3e01BBZoGAzKTWr0wBkeN2pu/VpGrdYcqcfrMgLEYgVGRvpomtyE0iqsb2VHkwGKBczpgyk4M8sAos7M9ztqh3hm56QmkJdrweDWjBDeaEZjc9EQjYtpTLk7mdOlzvujLnPF5zZprCpGRGB/H03OnkmyPY+WBcp78fG+zY1pqZKeIVmuLzkQETA+jf0YSF0/Uy51fWn7QNEog/MUoJ0yoHPxjBMwdN5WRF2B4v5SouPyHGmkk3QdT2ULqy+wviWYPGIXFYiE+Cs9Jha+7On0E/ouLKoeOpFoiWMC090KqjLwr9pcBuvhtT7O37iYtwUZivP45UD4Y1cRx8qDMsN2lIyG4CsmcPuqIV8pciaS+Q9H2Uag0Uk+JwKi1bccxB+9vPgbA7edI9KUjjOiXysNXngTAk4v3stpXSalwtNDILpYQAdMDufn0oQC8v+mocQELF4EB/06zJEQERqUAzGWwuemJTPR1/o3WLie4EimcgRf0HYJ6Pl3RH6G93HXeSK49dTC3nB69XhGREnxxiUjABKXm2mvAUwJm3UG9zL272823F4vF0qwSSc0CO98k4tuDMUogSMD07YD/RRE8lyxaVUiKuTOGMGVwJldNHRDV+20v6rO+t7QWj1fj9JHZbeq+K4TmyikD+cbUAWgavLLqUMBtLTWyiyVEwPRApgzuw5TBmYZ3xWa1tLiIqYhGqH4XKoUUPJ316lMGAXB2lKILw1Qlkq8XTJXJOByKXN+FpScLmEmDMnnkGxPbVc3TUYIF6+QWGtgpUhNspJgiJe1PIenGPSWeYzF9pDD3gml0eVi+T9+JnttBAZMY5IFRFUjtbWJnJtiTFo1RAmZOGpDBuz88nVld7OsKR3A34B+eM7KbzqT38e3p+jq/+kC50R3Y5fEa5nMRMEKncLNp15+ZHN9iWNqfQgoRgQkjYL4zYzCrf3k+34zCFFbwp5AOBqWQwkWOVJ+VaBt4ewvmCEyc1cJJEba8N0dh2tuEani/FOLj/J+3WCyhVphLqVceKKfB5SEvPbHd/V8UKoXU5Pbi9WrG5qEjFUiK4KhotCMwPY0MU4HCyQMzmDWi8+eNnShMHpxJgs1KWa3TqE6sNQ2b7Moy785ABEwP5cKT8oz5Ga3tpI3JySEiMEYKKegiZLFYyE1PjFpvE2UMPBhk4g2XZ79ySj7D+6Ywe1z0ezH0BpJNzaTG5KZF7EExX0Db64GJj7Myop//IhrbERh/Cmmxqj4al9Phz735/Wh0e6LSA0YxKqhhYkoUq5B6Imaf3A/PGdGl/ZZ6Owm2OKYP1dsFrPT5YFS1amK8NSpewe4kts++FxMfZ+WGmUOB1nd15sGDwSgTb3AEJtooD0xFnZPqBpd/DlKYCMyVUway+KfntHtWUW/H3EwqkvSRwpzC6EgXTfP7Eosl1Ip+phSSMvCeN6Zj6SOARJtfVDQ4PVHpAaPIz0gMSAXG+i65NbJS7HxjygAum5TPBePzuvt0eh0zfaXzK/frAsbRqPwvsW3ghXZMoxa6ju+ePpQmt6dVn0pOC83syuuam3g7g5QEGzlpCZTWNHGwrM6UQordi1930yfZTomjiclhJlCHwlxq3ZEumoECJpYjMPq5r9xfxrHqRhJsVk6PgvfDarWQYLPS5PbS4IqugLFYLIzMTWNzYRU23+P0ZiwWC49/e3J3n0avZeYI/xBPr1czUkjhmtjFEr37mxHjJMbHcffs0UwZ3HLH0NwWIzChPTCdgeGDKa9r1cQrtM53Tx/KGSP7MmdC5LtSc5fjjgxiM3cxjm0PjP7dOFatfzdmjciOWkm4up9Gk4CJhokX/CMFUhJsklIROsTJAzNJio+jst7F7pKaVpvYxRIiYHoBatGsc3qadeOtqOs6ITEs298LRj1uS+XfQst8+5TBvPq9GW2qgjJfQDsWgfGbXGNZhAYLivM6WH1kRhl566OcQgK/gOntBl6h84mPsxo+mFUHyo1rRKg5SLGGCJheQEqYbryapplSSF0XgSkoqzPNcIrdi18sYm4S2BEPTH5GouFf6p/R/tb43U2wgOlo+bQZJWBKHE1Gx+KONMczo1J4sgEQooFKI63cX06NMQcp9j9bsS/BBEBPHdQed1PiaGK4r4KkzunBqXp5dEEaYJhpKrWahSQCpmtRF+z4OIvRhbY9WCwW/nbNFPaX1nZ42Gd30ifZjs1qwe3VGJuXFtWyfdUL5nCFXnmXnmgzftdRzhzVjx+dNzJgdpEgtBdl5F1dUGE0CewNEZjYfwYCoDeGO3C8LqAXTIWvhDox3kpylJthhUJFYHYV1xgDxGQH2bWM6JfKpZPyGZKV3GHvxNmj+0Wt0WF3YbVa6JeWQFF1Y1TTR+D3wBT6BExwJ+SOEGe1cM8FY6J2f8KJzcQBGaQm2KhucLGmoALoHelJSSH1EoyGXaZeMGVGCXXXVJEMydIFjIr6JJsGSApdg9Vq4alrp/DTOXLxU0wb0ge7zcqlk/Kjer9JQRGYaA8lFYRoYYuzcorqB+Mrp06XCIzQUwhViaQiMF3VxyPJHkf/jESKfBUfkj4SegJ//tYkHA2uqEZIACNFZwiYKBl4BaEzmDkimyW7jxt+rd6QQpIITC9BeR9KTL1gurICSaEa2oGkj4SeQWJ8XNTFi7pf8KeQRMAIPZmZwwP7H/WGRnYiYHoJOcZAR38EpisrkBTKB9PVjysIXY15HhJErweMIHQG4/PTA3q/iAdG6DHkGgMdzRGYrhkjYEZVIoF04RV6N8EN8SQCI/Rk4qwWZgzLMv4vjeyEHkMoD0xXjREwY04hhZuDJAi9gaR4ETBCbGEuyxcBI/QYVBVSvakbb1eOEVAM62v2wEgERui9BFfYiYARejqqoR1Aai9oZCcCppeQbLcZw7lUFKY7TLyDspJR7UckAiP0ZpqlkKSMWujhjMtLZ0BmEnablbwY7rCtiP0YkmCQk55AzXE3JY5GRvRLpdxXRp3VhcP4EuPjyM9I4mhVg5h4hV6NOYVks1qkbYDQ47FaLbzzw1nUNbk7NGqkpyARmF5ETpquqNVguXKfibdvF3pgAM4fl0NivJWTB2Z26eMKQldiFjB9UxOwWmVqtNDzyU1PNMbNxDoSgelF5Pp8MCWORuqdbhpdenlnV0ZgAH5z2QR+efE46cIr9GoSTSkk8b8IQtcjEZhehL8SqclIH9ltVlLsXSskLBaLiBeh12OOwIiAEYSuRwRML6KfqReMuQKpo0P9BEFojlnASBM7Qeh6RMD0Isy9YLqjAkkQTiSS7P7lUyIwgtD1iIDpReSaxgl0xxgBQTiRSJQUkiB0KyJgehE5phRSeW3XjxEQhBOJAAEjPWAEocsRAdOLMHfjPeSbkNuVYwQE4URCTLyC0L2IgOlFJNttxnyLXUUOALK7uIRaEE4UAk28sd/VVBBijTYLmGXLlnHppZeSn5+PxWLhvffeC7hd0zQefPBB+vfvT1JSErNnz2bv3r0Bx1RUVDB37lzS09PJzMzklltuoba2NuCYLVu2cOaZZ5KYmMigQYN49NFH2/7sTkCUD2ZXcQ0gKSRB6CxSEmykJthItscZ0U9BELqONguYuro6Jk2axNNPPx3y9kcffZQnn3yS5557jtWrV5OSksKcOXNobPRPSZ47dy7bt29n0aJFLFiwgGXLlnHbbbcZtzscDi644AKGDBnC+vXreeyxx3jooYd4/vnn2/EUTyyUD6be6QHExCsInYXdZmX+92bw+q2nSd8jQegG2tyJ96KLLuKiiy4KeZumafztb3/jV7/6FZdffjkAr7zyCrm5ubz33ntcc8017Ny5k4ULF7J27VqmT58OwFNPPcXFF1/Mn//8Z/Lz85k/fz5Op5N//etf2O12JkyYwKZNm3j88ccDhI7QHBWBUUgKSRA6j0mDMrv7FAThhCWqHpiCggKKi4uZPXu28buMjAxmzJjBypUrAVi5ciWZmZmGeAGYPXs2VquV1atXG8ecddZZ2O3+i++cOXPYvXs3lZWVIR+7qakJh8MR8HMiEhzKFhOvIAiC0BuJqoApLi4GIDc3N+D3ubm5xm3FxcXk5OQE3G6z2cjKygo4JtR9mB8jmEceeYSMjAzjZ9CgQR1/QjFIsJlQUkiCIAhCb6TXVCHdf//9VFdXGz+FhYXdfUrdQq4pAhMfZyE9UeZ1CoIgCL2PqAqYvLw8AEpKSgJ+X1JSYtyWl5dHaWlpwO1ut5uKioqAY0Ldh/kxgklISCA9PT3g50TE7IHpkyxzkARBEITeSVQFzLBhw8jLy+Pzzz83fudwOFi9ejUzZ84EYObMmVRVVbF+/XrjmMWLF+P1epkxY4ZxzLJly3C5XMYxixYtYsyYMfTp0yeap9zrMA+Vk/SRIAiC0Ftps4Cpra1l06ZNbNq0CdCNu5s2beLw4cNYLBbuvvtufv/73/PBBx+wdetWbrjhBvLz87niiisAGDduHBdeeCG33nora9asYfny5dx5551cc8015OfnA3Dddddht9u55ZZb2L59O2+++SZPPPEE99xzT9SeeG/F7IGRCiRBEASht9Jmg8S6des499xzjf8rUXHjjTcyb948fvazn1FXV8dtt91GVVUVZ5xxBgsXLiQx0X9hnT9/PnfeeSfnn38+VquVq666iieffNK4PSMjg08//ZQ77riDadOm0bdvXx588EEpoY6AJHsc6Yk2HI1usqUCSRAEQeilWDRN07r7JDoDh8NBRkYG1dXVJ5wfZvbjS9lXWstNs4by0GUTuvt0BEEQBCFiIr1+95oqJMGPqkSSMQKCIAhCb0UETC9k4oBMAMbnn1iRJ0EQBOHEQZqE9ELumzOGuTMGMygrubtPRRAEQRA6BYnA9ELirBYRL4IgCEKvRgSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5hABIwiCIAhCzCECRhAEQRCEmEMEjCAIgiAIMYcIGEEQBEEQYg4RMIIgCIIgxBwiYARBEARBiDlEwAiCIAiCEHOIgBEEQRAEIeYQASMIgiAIQswhAkYQBEEQhJhDBIwgCIIgCDGHCBhBEARBEGIOETCCIAiCIMQcImAEQRAEQYg5RMAIgiAIghBziIARBEEQBCHmEAEjCIIgCELMIQJGEARBEISYQwSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5hABIwiCIAhCzCECRhAEQRCEmEMEjCAIgiAIMUfUBYzH4+GBBx5g2LBhJCUlMWLECH73u9+haZpxjKZpPPjgg/Tv35+kpCRmz57N3r17A+6noqKCuXPnkp6eTmZmJrfccgu1tbXRPl1BEARBEGKQqAuYP/3pTzz77LP8/e9/Z+fOnfzpT3/i0Ucf5amnnjKOefTRR3nyySd57rnnWL16NSkpKcyZM4fGxkbjmLlz57J9+3YWLVrEggULWLZsGbfddlu0T1cQBEEQhBjEoplDI1Hg61//Orm5ubz44ovG76666iqSkpJ49dVX0TSN/Px87r33Xn76058CUF1dTW5uLvPmzeOaa65h586djB8/nrVr1zJ9+nQAFi5cyMUXX8yRI0fIz89v9TwcDgcZGRlUV1eTnp4ezacoCIIgCEInEen1O+oRmFmzZvH555+zZ88eADZv3sxXX33FRRddBEBBQQHFxcXMnj3b+JuMjAxmzJjBypUrAVi5ciWZmZmGeAGYPXs2VquV1atXh3zcpqYmHA5HwI8gCIIgCL0TW7Tv8Be/+AUOh4OxY8cSFxeHx+Ph4YcfZu7cuQAUFxcDkJubG/B3ubm5xm3FxcXk5OQEnqjNRlZWlnFMMI888gi/+c1vov10BEEQBEHogUQ9AvOf//yH+fPn89prr7FhwwZefvll/vznP/Pyyy9H+6ECuP/++6murjZ+CgsLO/XxBEEQBEHoPqIegbnvvvv4xS9+wTXXXAPAxIkTOXToEI888gg33ngjeXl5AJSUlNC/f3/j70pKSpg8eTIAeXl5lJaWBtyv2+2moqLC+PtgEhISSEhIiPbTEQRBEAShBxL1CEx9fT1Wa+DdxsXF4fV6ARg2bBh5eXl8/vnnxu0Oh4PVq1czc+ZMAGbOnElVVRXr1683jlm8eDFer5cZM2ZE+5QFQRAEQYgxoh6BufTSS3n44YcZPHgwEyZMYOPGjTz++OPcfPPNAFgsFu6++25+//vfM2rUKIYNG8YDDzxAfn4+V1xxBQDjxo3jwgsv5NZbb+W5557D5XJx5513cs0110RUgSQIgiAIQu8m6gLmqaee4oEHHuCHP/whpaWl5Ofn8/3vf58HH3zQOOZnP/sZdXV13HbbbVRVVXHGGWewcOFCEhMTjWPmz5/PnXfeyfnnn4/VauWqq67iySefjPbpCoIgCIIQg0S9D0xPQfrACIIgCELs0W19YARBEARBEDobETCCIAiCIMQcImAEQRAEQYg5RMAIgiAIghBziIARBEEQBCHmEAEjCIIgCELMIQJGEARBEISYQwSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5hABIwiCIAhCzCECRhAEQRCEmEMEjCAIgiAIMYcIGEEQBEEQYg4RMIIgCIIgxBwiYARBEARBiDlEwAiCIAiCEHOIgBEEQRAEIeYQASMIgiAIQswhAkYQBEEQhJhDBIwgCIIgCDGHCBhBEARBEGIOETCCIAiCIMQcImAEQRAEQYg5RMAIgiAIghBziIARBEEQBCHmEAEjCIIgCELMIQJGEARBEISYQwSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5ugUAXP06FG+853vkJ2dTVJSEhMnTmTdunXG7Zqm8eCDD9K/f3+SkpKYPXs2e/fuDbiPiooK5s6dS3p6OpmZmdxyyy3U1tZ2xukKgiAIghBjRF3AVFZWcvrppxMfH8///vc/duzYwV/+8hf69OljHPPoo4/y5JNP8txzz7F69WpSUlKYM2cOjY2NxjFz585l+/btLFq0iAULFrBs2TJuu+22aJ+uIAiCIAgxiEXTNC2ad/iLX/yC5cuX8+WXX4a8XdM08vPzuffee/npT38KQHV1Nbm5ucybN49rrrmGnTt3Mn78eNauXcv06dMBWLhwIRdffDFHjhwhPz+/1fNwOBxkZGRQXV1Nenp69J6gIAiCIAidRqTX76hHYD744AOmT5/Ot771LXJycpgyZQovvPCCcXtBQQHFxcXMnj3b+F1GRgYzZsxg5cqVAKxcuZLMzExDvADMnj0bq9XK6tWrQz5uU1MTDocj4EcQBEEQhN5J1AXMgQMHePbZZxk1ahSffPIJt99+Oz/60Y94+eWXASguLgYgNzc34O9yc3ON24qLi8nJyQm43WazkZWVZRwTzCOPPEJGRobxM2jQoGg/NUEQBEEQeghRFzBer5epU6fyhz/8gSlTpnDbbbdx66238txzz0X7oQK4//77qa6uNn4KCws79fEEQRAEQeg+oi5g+vfvz/jx4wN+N27cOA4fPgxAXl4eACUlJQHHlJSUGLfl5eVRWloacLvb7aaiosI4JpiEhATS09MDfgRBEARB6J1EXcCcfvrp7N69O+B3e/bsYciQIQAMGzaMvLw8Pv/8c+N2h8PB6tWrmTlzJgAzZ86kqqqK9evXG8csXrwYr9fLjBkzon3KgiAIgiDEGLZo3+FPfvITZs2axR/+8Aeuvvpq1qxZw/PPP8/zzz8PgMVi4e677+b3v/89o0aNYtiwYTzwwAPk5+dzxRVXAHrE5sILLzRSTy6XizvvvJNrrrkmogokQRAEQRB6N1EvowZYsGAB999/P3v37mXYsGHcc8893Hrrrcbtmqbx61//mueff56qqirOOOMMnnnmGUaPHm0cU1FRwZ133smHH36I1Wrlqquu4sknnyQ1NTWic5AyakEQBEGIPSK9fneKgOkJiIARBEEQhNij2/rACIIgCIIgdDYiYARBEARBiDlEwAiCIAiCEHOIgBEEQRAEIeYQASMIgiAIQswhAkYQBEEQhJhDBIwgCIIgCDGHCBhBEARBEGIOETCCIAiCIMQcImAEQRAEQYg5RMAIgiAIghBziIARBEEQBCHmEAEjCIIgCELMIQJGEARBEISYQwSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5hABIwiCIAhCzCECRhAEQRCEmEMEjCAIgiAIMYcIGEEQBEEQYg4RMIIgCIIgxBwiYARBEARBiDlEwAiCIAiCEHOIgBEEQRAEIeYQASMIgiAIQswhAkYQBEEQhJhDBIwgCIIgCDGHCBhBEARBEGIOETCCIAiCIMQcImAEQRAEQYg5RMAIgiAIghBziIARBEEQBCHm6HQB88c//hGLxcLdd99t/K6xsZE77riD7OxsUlNTueqqqygpKQn4u8OHD3PJJZeQnJxMTk4O9913H263u7NPVxAEQRCEGKBTBczatWv5xz/+wcknnxzw+5/85Cd8+OGHvPXWWyxdupRjx47xjW98w7jd4/FwySWX4HQ6WbFiBS+//DLz5s3jwQcf7MzTFQRBEAQhRug0AVNbW8vcuXN54YUX6NOnj/H76upqXnzxRR5//HHOO+88pk2bxksvvcSKFStYtWoVAJ9++ik7duzg1VdfZfLkyVx00UX87ne/4+mnn8bpdHbWKQuCIAiCECN0moC54447uOSSS5g9e3bA79evX4/L5Qr4/dixYxk8eDArV64EYOXKlUycOJHc3FzjmDlz5uBwONi+fXvIx2tqasLhcAT8CIIgCILQO7F1xp2+8cYbbNiwgbVr1za7rbi4GLvdTmZmZsDvc3NzKS4uNo4xixd1u7otFI888gi/+c1vonD2giAIgiD0dKIegSksLOTHP/4x8+fPJzExMdp3H5b777+f6upq46ewsLDLHlsQBEEQhK4l6gJm/fr1lJaWMnXqVGw2GzabjaVLl/Lkk09is9nIzc3F6XRSVVUV8HclJSXk5eUBkJeX16wqSf1fHRNMQkIC6enpAT+CIAiCIPROoi5gzj//fLZu3cqmTZuMn+nTpzN37lzj3/Hx8Xz++efG3+zevZvDhw8zc+ZMAGbOnMnWrVspLS01jlm0aBHp6emMHz8+2qcsCIIgCEKMEXUPTFpaGieddFLA71JSUsjOzjZ+f8stt3DPPfeQlZVFeno6d911FzNnzuS0004D4IILLmD8+PFcf/31PProoxQXF/OrX/2KO+64g4SEhGifsiAIgiAIMUanmHhb469//StWq5WrrrqKpqYm5syZwzPPPGPcHhcXx4IFC7j99tuZOXMmKSkp3Hjjjfz2t7/tjtMVBEEQBKGHYdE0Tevuk+gMHA4HGRkZVFdXix9GEARBEGKESK/fMgtJEARBEISYQwSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5hABIwiCIAhCzCECRhAEQRCEmEMEjCAIgiAIMYcIGEEQBEEQYg4RMIIgCIIgxBwiYARBEARBiDlEwAiCIAiCEHOIgBEEQRAEIeYQASMIgiAIQswhAkYQBEEQhJhDBIwgCIIgCDGHCBhBEARBEGIOETCCIAiCIMQcImAEQRAEQYg5RMAIgiAIghBziIARBEEQBCHmEAEjCIIgCELMIQJGEARBEISYQwSMIAiCIAgxhwgYQRAEQRBiDhEwgiAIgiDEHCJgBEEQBEGIOUTACIIgCIIQc4iAEQRBEAQh5hABIwiCIAhCzGHr7hM4USncVcEXr+5i4NgsTrtiOEmp9pDHORvd2OxxWK2WLj5DQRAEQei5iIDpBqpK6/nk+W001bvZ8dUx9m8o5bQrRjD+jHysVgter8bBLWVs/eIIR3ZVMv7MfM6dO7a7T1sQBEEQegwiYLoYZ6Obj5/dSlO9m36D0/B6NMqP1rL0td3s+OoYQ0/uy64VRdRUNBp/s3tlMadfNRJ7orxdQvdx/HAN/3tuKxPOymfahUO7+3SECNA0jSX/3oXb5eW868dis8d19ykJQtQQD0wXonk1PntpB5VFdaRk2LnkjpO5+pfTOePqUdgT4zh+uIa1CwqoqWgkIcXG1DmDSe+biMft5dC28u4+/V6F1+Ol3uHs7tOIGTweL5+/spOaikbWfnSQuuqm7j4lIQLKjtSyc0URe9eW8OmL2/F6vN19Sl1K2ZEaXvzpl3z20g40Tevu0xGijGzpu5C1HxVQsLkMq83ChT+YSEpGAgCTzhvEqOm5rH5/P5Ul9Yyblc+o6Tm+3ZKFDZ8cYv+G44yantu9T6CXUFVSz0fPbMFR3sDld08hf2Rmd59SMzRNY82HBRzaVs55N4yl78C0Tn280kMOPn95J5NnD2bcrP7Nbt/8eSHlR2oB8Li8bP6skFlXjezUcxI6zsEtZca/CzaXsfSNPZxz3Rgsls7z1NVVN1FxtI6KojrKj9VSVVzPkInZXR61czs9fPriDhprXexeXUzeiAxOOmtA2GPjbFYsXeA1/Oo/ezm8o5w5t51Edn5qu++n3uHkwKbjjJmRR3xC5JG13auKqK1qYsrXBmONi+0YRtTP/pFHHuGUU04hLS2NnJwcrrjiCnbv3h1wTGNjI3fccQfZ2dmkpqZy1VVXUVJSEnDM4cOHueSSS0hOTiYnJ4f77rsPt9sd7dPtMvZvLGXtRwcBOHfuWPKGZQTcnpxu59zrx/GNn05j3Kz+Rqh3xNR+ABzaVobL6enSc+5qyo7UUn28vlMf49D2ct764zqqSurxujXWvb+zUx8P9GjPvvWlNDVE/vnd+sUR1n18kOOHa/jgiU1UFtdB9VF4/lz47KGon+O6jw9ScayOJf/eScHm4wG3OcoaWPthAQCjTtFF9NZlR2msdUX9PE5UjuyqYPPiQjxRjpAoATN8Sj+wwI4vj7F2QUFUH8PM6g8PMO/ny/ngyU189dZedi4vomh/NWs+KMDZ2LXr98p391NZVIfVpouSr97aS/mx2mbHHd1TybxfLOf1366m4lhdp55TRVEdmxcXUllczwdPbKL6eEO77kfzavzvua0sfW13m6JLu1cX89m8nax67wCLXtoR8xG5qAuYpUuXcscdd7Bq1SoWLVqEy+XiggsuoK7O/8H4yU9+wocffshbb73F0qVLOXbsGN/4xjeM2z0eD5dccglOp5MVK1bw8ssvM2/ePB588MFon26X4HZ5WPLyNgBOPm8gY2c23+GGo9/gNNKyE3E7vRRur4jqefWkkGrJQQf/+cNa3npkHQ210U/taJrGxkWH+ejvm3E2uMmJ34cFD4V7Gyg95Ij645lZ+/FBPnlhG5+9tCOi4w/vKOer/+wFICktnoYaF+//dSPVb/wCjm2A5U9A5UFAX3w3fHqoQ+K2rrqJg1v1FKWmwaf/3E7xgWrf/zWWvr4bt8vLgNGZfO274+k7KBV3k4fNiwvb/ZjN2PBveHoG7F4YvfvsqWgaOI6B14PX42Xlu/t4/2+b+Oo/e9mw8FDUHqauuonSQzUAnHXNaM6+dgwAaz86yLZlR6P2OArNq7Hdd7/p/ZIYPrkf0y8eSkqGHa9Xo2h/ddQfMxyHt5ezZckRAC7+wckMHp+Fx+Vl0Yvbcbv835XDO8pZ8NRmmurdVBbX8/af1nFg0/Fwd9thNn7if3/rq5188MRG6qrano7d/tUx4zt6YNNxNn/e+nfx+OEalry6y/j/vnWlfDZvJ15vZNeBuuqmiI/tKqIuYBYuXMhNN93EhAkTmDRpEvPmzePw4cOsX78egOrqal588UUef/xxzjvvPKZNm8ZLL73EihUrWLVqFQCffvopO3bs4NVXX2Xy5MlcdNFF/O53v+Ppp5/G6Yw938LhDYdoaoRUaxmnn9m2C43FYtF3T8C+DaVRO6f1Cw/y4k+/ZPfq4g7dj6O8gf/8YS0bPmn/wutxe1ny751oXo2mejfrfJGqaOHxeFn88k5W/HcfmgbjBhXyjaz7GZ24DIANH++P6uOZaWpws2WxvpAe3FJmLDrhqCyu45MXtqNpMPa0PK799Qyy8lOoq3by/uZLqPFkg+alZOGbvP+3jbz3+EZWvrOfNR8c8N9JG4Xp7lXFaF6NnKHpDDkpG7fLy0fPbKGqpJ6960o4vL2COJuVc+aOxWK1MP2ioQBsWXKkTVGlsGx+Az64E47vgrdvhpLtIQ/zejVKDznaHKWoq2qicGdF9y6+tcdhy1vw3g/h8fHw+DjqXv4u7/9tIxs+OWwcpiJhkVJT0RhW8CvfXM6QNFIyEjjprAFMv2QoAMte383S13dTuKsialGfkoMOGmpc2JNsXPfQDC76wURmXDacQROyATi6uzIqj9MaDTVOPn9Zj6xOPHcgQ07K5rwbx5GUFk/50TpWvKN/3w9uKeOjZ7bgdnkZPCGbAWMycTV5+N9zW1n9wQG0KH9eaioa2bNGzzRc9IOJpPdLwlHWyPtPbGpTNLOuuomV7+rPYcCYTABWvrO/RYHYUOPk4+e24HF5GXJSNhd+/ySsVgt715bw+cs7Wv1uFGwp45VfruDtP66jsa7nRF47PQFWXa2/qFlZWQCsX78el8vF7NmzjWPGjh3L4MGDWblyJQArV65k4sSJ5Ob6PR9z5szB4XCwfXvoxa2pqQmHwxHw01PYt1SPvoxIXI516/w2//3IqTkAHNxahsfV8cVmzYICVr13gKY6N0tf301tZfsNmWs/0tMcK9/dz/6N7RNYGz45RPnROmx2/eO4belRqkqjl0ra+Olhdq0qxmK1cMbleZzruY84i5spWYsA2L+5Qk/RdALblh7BabrIrzYLjSAa61x8/OxWnA1u8oanc87csSSl2rns+gwybEXUeHN4v/YvfFz5c95eegpHdlUaOfutXxyltrIR6srg2Vkw7+vQ0PoFQ9M0dq4oAmDCmflc8L0J9BucRmOtiw//vtmIBE27aAiZuckADJ/cjz55yTgb3Gz94ki7XxsAdn6oX9QBUvqBqw5evxbqA6ONTQ1uPnp6C289so4Pn9wcsItujY+f3cIHT2zincfWU360eQqhU6kro/HlGzj88FUce+MvHF+7gqpKjcNNk/nP2ss4trea+MQ45tx6EkNP7ovXo7H4363vijVNY/Pnhbzyf/pFJdTrodJHQ0/ua/zu1K8PY8KZ+Wia/j374G+beOm+r/jsz+9zdP5fwBv+dW2sc1F2pMb/i6Ya+PBu2PZfQL/IAQyekEWcyVsxcHQm0AYBU7YPVjyli9ljGyP7Gx+aprHk1V3UO5z06Z/CrCtHAJCSkcB5N4wDYOuSI3z11l7+99xWvG6N4ZP7cfHtE7nsR5OZdN4gQBeSHz27hab66F2sN312GK9XY8CYTIZP7sflP55MSmYClUV1fPjUpoB1oiWWv7UXZ4NexXrZrUMYNb0fXq/GJy9so6GmuZj1eLx88sI2aiuayMhJ4ms3j2fElBwuuHUCFquFPatLWPJK+M9cbWUTi1/eidejcfxwDR89vbnL04Hh6FQB4/V6ufvuuzn99NM56aSTACguLsZut5OZmRlwbG5uLsXFxcYxZvGible3heKRRx4hIyPD+Bk0aFCUn01oXE4PHz+7RY8ghNj5up0eCg7qTepGJi6HzW+Cp21fityh6aRk2HE1eijc2bE00poFBUYOPDldv88v39wT2R97vbDsMf2C01Sr7yhW+d+Pz1/eGV4IVB4CZ/PbKo7Vse5/BwE4N/tfDE7bjderseq96ERFaioaWf+x7/6/M4ZJtjeweJtg4KlkX3ATQxPWoIzSUUHTYOlj8NlDuBoajdDujMuGYY2zcGRXZciF3Ovx8uk/t1FVUk9qVgIX/eBk4uKt4HGT8tkPubzPA6QlOKhuyKCg6TQseBg70sF3fnsa+aMy8bi9usfqs19D6Q44+CXM/xY0tXzBLtpXTVVJPbaEOEZOy8GeaOPrd04ivW8ijuMNNNS46JOXzNQ5Q4y/sVgtTPNFYTZ/XoirqWUxUVPRyJYlhRRsKaP6eIN/Z7t/sX6R0jwweS78cDVkDoGqQ/DWTeDRF8nq4w3899H1HN6uRxSO7q7k039GVlFTUVRnpFFKCvQ05eoPDgRsBDRNw7H5K6rf+jXUtk2EF+2r4siuMN/JwjWUP3E181d9gw8rH+Ldij/wn/LHmV/2DB9W/pp6bxbZtoNcfdYKRk7L4exrR2NPjKOkwMHWJeGFodfj5cs39/LVW3tBA0dZI7tXBa6Lbpd/rRg60S9gLBYLZ183hkvuOJlxs/qTmBpPU72b3fvSeO/LKRz+5OOQj+lsdPPfR9fz5sNr/Tv9T34J61+Cd74PxVv9gsn0eAADxvQB9BRG2Ihd6S745P/gyanw92nw6a90YfT6dc3EbEvsXFGkF0rEWfjazeOxFSyCv58Cb32XoYMbOfm8gYD+ufV6NUadkssFFzUR98bVWD/5BWdcOYjZN40jLt7Koa3lvPnwWkoKIt8MN9a5QlboNdQ62fHVMQCmzRkKQHrfJC778WQSU+IpPVTDgqc3typiDm0vZ++6UiwWOHfcaqx/Hs45Ry8m015KXVUTix57D++uTwL+ZsXb+zi6p4r4xDguvv1kEpLjAXQRc4suYnatKubzl3f4o3ENlVC0Bc3t4bN5O2isc5GVn0JCso3iAw7+99zWNm0iOotOFTB33HEH27Zt44033ujMhwHg/vvvp7q62vgpLIxifr4Flr+1l4LNZexYXsSxPVXNbj+0aidur520uFJy045DXSnsXdSmx7BYLQz3RWHaG+UAWPPhAUO8zLxyBJf+aDJWq4UDm45zYGPzvG9jnYtty47qOVqPC979Piz+PWyaD4t/z8ZPfTuK0Znkj8rE1aiHX5up87K98NQ0eGambkT14fVqLHl1J163xpC0XYzyvs8s+zNY8LJ/w/FW0y2RsPztfbhdXvqPzGDspARY+6J+w5n3wuTrmJapL9h7VhcH9N5pN1/8EZb8Hr76Kzuee4qGGhfpfROZOmcI48/IB/QojFnsal6Nxa/sonBnJTa7lUt+eDLJ6b7OzF89DkfXk5bs5vIfTyJveAYjh9VyTd+7OT/+IdKzEjjtCn2XuXP5MarWLtb/LiEdjqyFN64DV/jntWO5vqiOmp5j9BlKTrdz6V36wmqxWjhn7ljibIFLxajpOaT3TaSx1sX2L1v2U3z20g6+fHMvHz+zhVcfWMnzdy/lrd98wdYXXgKPE8ZdBpc+CSnZcO3rEJ8CBUth0YMc21vJ239cZ7QeOOua0cTZrBRsLmPJq7taDfPv96Vd+w+EYZP0CMe6jw/y5sNr+PzlHbz1yFpe+PEX/PtZJ69+fjZf/uVVtAhTKlUl9bz7+Ebe/9smVryzz38umgarnqXqhZt5/8gdNGrpJKdaychJIiXDjj0xDlu8lXFj67kq++dkbvkjrHyG1D6JRmXXqvf3hzR4OhvdfPzcVj3yZfGLgw2fHg4QdEd3V+F2ekntk0DfQYGVLhaLhaET+3LeDeP47qNncOWMpQxLWA3A4o+9IVMEy/+7j6qSetB0gzn7PoMNr+g3el043vg5FcfqsFgtDDkpO+BvU/skktEvCU2Dor1VzV/ImmL45/mw8u9QsR+s8TD8HOgzFGqOwft3RpQWra1s0kUdcNrXB9Bv4y/h9W9D2R7Y/g78/VRm5S2k78AUAMaeksnsrKeJe+l82LcI1vwD5l3MmPEaV903jfS+idSUN/LOY+vZ9NnhFj2DHo+XjYsO88ovV/DK/61gz5pAQbllyRHcTi/9BqcxcFwf4/dZ/VO49EeTsCfZKNpXzft/2xg2neRyelj6ml4Qc/JZfem342EA7JqDC9MexkYThaV9+fjZzfzvgXm89YdVzPvFcsMLNPum8WT1Twm4z5HTcvjazeONSMz/ntuKq6oMnj8H/nEmGx/6CUd3V2KLhwtvPYmv3zUJW0IcR3bpm4hom87bSqcJmDvvvJMFCxawZMkSBg4caPw+Ly8Pp9NJVVVVwPElJSXk5eUZxwRXJan/q2OCSUhIID09PeCns9m/sZTtXx4z/r/ps8PNjtn3lW6aGplbiGXqd3wHtj2NNMLngynYXNbmD43H7WXle/uNKqiZ3xjB1DlD6DswlckXDAZg2Ru7A9R/+dFa3vrjOpa+tpv3/7oB1/wbYet/wKJXR9WveJ0dX+lfjOkXD2XOrSeRkmGnsrieJf/eFfhl3/YOeF36zvqVy4xd7tYvjlB8wEF8nJOzE/+MJS2X7CwXY5P0i/CKt3Y1WzQqi+vYv6GUvetK2L26mF0ri9i9upjGWqculFY+A//+Brx2DUdWb2H/Bn23ctY1o7Gs/Sc4ayH3JBg9BxLTyTv1FAbYt+L1hn7/2sTWt2HpH/XXHDsb9+pRiykX6OWK0y8aSly8laL91Rzeoe8qNU1j2Rt72L1aT3Fd8L2T/CXTxzbC0j/p/77kz2QMH8ZVP5vGnJ+cT1ZqDVQWwJ6F9B+RwdCTstA0WF17DUz5Dlz/LthTdSHw9s0ho35NDW72r9ffi/Gn5wfclpmbzNzfnsZ1v55B/qjMZn9rjbMaZbEbFx0Ouxs7friGY3ursFotZA9MxWqz4HZ6KS3ysqzqu+xN/z5c9U+I83V0yJ0AVz4LwM7FO3j/rxtorHORMySNb91/ChPPGcgF3/PtGlcWs/ztfS1eWPat8z2/mie5aOgbzLl1Aknp+ud018piSg/V4HJqWNFfny1Fk/n0zwsiStWuX3jQEC0bPz3MJ//cjrvGAW9/F8eCx3m/7EEavH3IHpDEtb85ne/8diY3/ekMbv3b2Xz/qXM47+6vEz/7fv3OPrkftr7N+NPzGTA6E7fTyxfz/Z//xjoXR3dX8u5fNnBoazlx8VYuvPUkLvnhySSmxOM43sD+Df5NiIqGDJnYt8WSaWvNMfILn+ZrmY+TGXeUOlcaS19cFXBMwebj7DCtc/s3lNLwru+8J10LKf04eFSvquw/IoPElPhmj6OE1pE9IdJIa57H1eTE23c8fOtl+NkBuOF9uPoViLPD7o9g7T9beisA+PLNPbgaPeQNsDB517d8AssCp94Gg2eCq464xQ9wZfpPufLcLZxXfDnWzb61ePzlkNQHjq6H58+hn2UnV//fqYyYmoPXq7H87X18/MyWkCmawl0VvPm7Naz47z5cTR68bo1F/9rBuo8Pomkazka3EVGbOmcIFo9LT/X6yBmSzhU/mUJiqh6JeffxDXoUxxO4EVy7oICa8kZSsxI4NfNdcNVD/hT48Wayb36Ss8/SI0WHmqZz4PhgSg/XU1fVhMWib1iHj0+GXR/BBz+Cj35qRMRHTc/l4h9MNKJOHzy8kMby45S6RrC67OsAnJn0d/q8OZO8kte45AcTjE3E4pd3Rt0r1Bai3gdG0zTuuusu3n33Xb744guGDRsWcPu0adOIj4/n888/56qrrgJg9+7dHD58mJkzZwIwc+ZMHn74YUpLS8nJ0SMPixYtIj09nfHjx0f7lNtFTUUjS/6ti5NR03PYu76Ug1vLqSiqM1Suq9HNwUJ99zPytKEw+SR9l7FnoW7qS+3nv0NN01MzhavglFth2k1gTzZu7j8yk6Q0Gw01bo6++TSDv3kz2APVdDBej5fdq4tZ++F+air1BXrmlcOZeoE/HXDKxUPZv76U6uMNrHpvP2ddO4b9G0r57OWduH2pgcqSBpY5hnF+diJc/W/Y8R6bv7TgcUPu0FQGjOmDxWJhzm0Tee8vG9i3vpScoelMnj1IXzx3LdAfzBoP5ftwvnQ1Zee8wqr39WjQrJR/kWavgW9/BIkZnPrCtew9fCZFBVCw7gjDTxnE8cM1rPv4YNgKgVRbJRdlPExOvJ568mhxLFtxMTCQk84aQN9+FpivXxg58x5Qi/optzL1q9s46pzIji+PMv3ioWHnUrXIkfXw/h36v2fdxa6KGdQVp5BiLWdc4zzgV6RkJjDx7AFs+qyQ1e8fYPD4LFa+u1+vCLHA7JvGMUz5Fbxe+PDH4HXD+Ctg4rf8j2VP0T8fy5+AVc/A2IuZMXwDB7cNZl/jmUwdO4p+AwfBtW/Aq1fpF4D3fghX/gOs/j3L3rUluF1e+vRPIXdYc8GfmBIf8mIEgLOeMX13sDbJSW01bF+0m0kXN/9uqkqlkdNz+NrNE/AWrqf6pdvZWnUmW+svYcmhS+hX7iHTnDEefzk7Bz3G4rV6NGLE4CrO/8mZxCfq5zJ8cj/Ou2Esn8/byebFhSSk2DjlkmHBD035sVoqiuqw4mJYwmosq5cwcnojAx98hG3LivB6vGTt/wdZZQvIyLKxP+t2Pl81jH0FqTQ8vpKLfjSDhKTQS2T18Xp2r9Y3VVPnDGHTZ4fZv6GUul3rOCtpIwurfkutty998pK57MdTw7+OZ/xEj0Cs+Qe8cxuWfZ9zzsV38kaBlSO7KnnnsQ3UVjYG+NSS0uK5+IcnG60YTj5vIGs+LGD9wkOMnO73ywEMnZjd/DHNrPw7eF3EDz2D2cN28N9VeezbAcPWFjP6lDzqHU6jcmXy1wZzdHclxw/XsLt0LJMHuuCSv8CBLzj4jB4ZGDoodMpywJhMdnx1rHn61FmHc/WrvFn2N5zVeVxw3hQGJfo+i/0nwdd+Cwt/oaeXBp8GeRMD/97jhooDHFi5mwObUrBaPJzTeC8WzyHIGKyL4aFn6Ovr5tfh0wewV2whv2KL/vf5U+Cix2DQKVBRoEcsS3fAvEtIuORx5tz6HbZ/2Yev/rOXg1vL+dd9X5GUbic9O5H07ERcTR6jgi8x2crM4WuprElm06FxrP7gAI6yBjJzk2mqd5ORk8Tw0VY9ulG+D25fAX31z3i/wWlcec9UPnhiIxXH6nj3t4u4LP2XNJz/Nw6UDaFg03Eqi3Vf4NlX9sf+6XP6+Z9zvx6p6jOUsSNAG3qMqv0FpB54k5SGnaTGlZM2/hSSjpfBn74Ejym9dWwDXPcfSOnL0JP7ctmPJ/PxE6sprsnn3cZH8KQNwYuXETkHGWdfCVX1sPAXDOz/Ohd+8zH+95969qwpISMnmVO/3vz71xVEPQJzxx138Oqrr/Laa6+RlpZGcXExxcXFNDTo4dCMjAxuueUW7rnnHpYsWcL69ev57ne/y8yZMznttNMAuOCCCxg/fjzXX389mzdv5pNPPuFXv/oVd9xxBwkJCdE+5Tbj9XXUbap3kzMkjfNvGm9ceMzlbIeWrcOt2UmPK6XfWRdCzjjIn6pflLa8GXina16Aza9BxQF9N/bEyfoFqqkWyvZi/eR+hms+0+naI7DoQeqqm9j46WHe+uM63vrjOj6ft4P1C/WL/O6Vx3j9V0tY/MouaipdJFsrOC/9Sabuvw72LzEe1maP45y5ennl1mVH+XzeDhY+vw13k4cBI1O4cOgbWPCwq+F8dk9+G0ZfQOMZD7G1/mIApg1ca+zw+o/I4PRvjQJgxX/38fyPlvLv//uSd7Z/m4VV9/Fe/H+Zd/wlXtj+IO8+vQ93k4f8+O1MSPpUXwgHnQr9xpD63ZeYnKHncZfPX8tHj37Gf/6w1hAvOf0aGZBxlIEJWxls30CatZRadx/eKf8DO1LugAt+z9bEu6h0DyTRUs2ptuf0C31DJWSN0AWBImcsg8Zk0M+2H7dLMyqG2kT1EXjjWnA3wugL8Z77EBu26ReOySnvE7fiMd2UiH6xi0/Quy5/9MwWNn6qR33OuW4Mo081RRd3fQhFm8Gepr82wbvoU2/To2EHv4S9n9F304OMSvwSgFWf+jwDw86Eq1+hyjuQ/WsP0/j6beD0m6N3+tJH40/vH1ljM1cjrHoWXrkc/jSUuDeuYnr8vwBY99E+nMf2BRxeV93E3rX6RX7S+YPg6Aasr15JH+9uzpiwg/wRabiaPCx8YRtuUxn4vvWlLFmnL+wnJ3/InKabiX/n+gAvxNjT+nOG77O25sMCivZVNTtdFV0anLCJhPyRgAXWvUjiZz9h+oWDODX1DUZWPUdWYhlx17zM6Btu4+vj3iPeUs/Rgibe/fP6sCWu6xceQvNqDJ6QzcwrR3DZDX1IsNZTXD+Y/5T/FYcnl/R+SVx+9xR/OjAUFgtc+EfdA6R5YPNrZL4xixmD9IKG4gPVhnhJy05k5LQcvvnz6f4+UqU7mTg1jviEOMqP1nJoWznlR2uprWzCFm9l4Jg+4R4Z6sph/Tz932f+hNw513JK6n8AWPbaLmoqGln875001LjIHpDKaZcNZ8Jo/XXeXv81tMufBnsKziFzOOo6GYChBb/Vzb1BDBitn0fZkdrAFNWm19hVOQ2Hpz+NjRY+fGoTGz455I+qzfgBjJqjX3jfvlmPGjjrYft7uk/qj4NxPnUWyxbq15fJye+SHX9Ij0LevlwXL+p1nnwd3LVO3yD2GweXPw3fW6yLF4CsYXDLIhh3qZ7a/OBOLGue56SzBvDNX0wjK1/fMDY4nJQUONi7Tt+0WiwwceAu5qbOZXzZ7zm96ZecNfAjLBbdk6Mqhqae2xfr/CugdLv+fLYEWiuy8lO48qfTSMtKoLoumVeL/sLbr8KGhYeoLK7HarUw+WuDGVo1zx99GXVBwH2Mm5XPzOtPZ+IvH2X4eTPIiT9A0t439JSfp0n3mE2/2R9tevECXbgB+TULuDL9pyRbK6hwDaK6Qk9BnvPz67H8bB9c8jgkZkDRZoYuvZDZUzeTPSCZCWcGRm+7EosW5WYg4RbCl156iZtuugnQG9nde++9vP766zQ1NTFnzhyeeeaZgPTQoUOHuP322/niiy9ISUnhxhtv5I9//CM2W2RBI4fDQUZGBtXV1VFPJ639qIA1HxYQnxDHt391Chn9kjm2r4p3/7yBOJuVG/4wi+R0Owt/M5/9Rf2ZOnQ7M39xl++PX4SP7tG/QD9cqX+xSnfqqtzdCCdfA4dX6ukW0C9gTn1BKGyaxAeVD5FgqSHPvpvDzumtpoYTLQ6mpr7DSSOKiK/aA41V+g2j5sAFv4N+unj5/OUd7Frpz9tOOrsvs8p/gLVsO2ubbmJN5eXYEuK4+v7p7N9QyuoPCsi2HeTb/X6G5ftLjJ2Rpml89Z+9Rt41HEnWKnLi93FW+vOkn3YFXPLngNude1fx6l+LaPDqC7UFDyMTv2J66ttk2Uz3nT6QpmEX89neCzh4QE9vjT0tj/2bjuNq9HBu+jOMTzZ5ji77O0y9PvBkdn7Ivpef5pOqn2GxwPk3jWfMjNCpymC8tRXUvngD1rLt2HOGYvve++zdUs9nL+0gMTWeGy5ZR/zSh/SDR34NBkxj9cHprFvt75w566qRTPnaYNOdeuDZ0+H4Tjj753DuL0M/+Fvf1XP7cQngaaIqezav77wTr1fj/BvH4Shv5MDGUsqP6qHieEs9E3PWM+m2m6jXsnjz92uxxlm46Y+nk5TWStSpbJ9+wSjZ6v9d+gC8w87j9SVnUOXMYXqfD5lx1w36wore1GzdRwfJG57BVXMt8MoV0FQNg06D77xNXYOdNx9eQ0ONyxhYemhbOR8/uwWvR2P86f05Z+wqLAt/ri++GYPhW/Ng4DTjFBa/spOdK4roPzKDK++dGrD+vPbgl1SWujg/4wnG/vRRKNqi+7g0DwyeBYdX6Ade+Q+YdI3+76rDHP/btXxYeg8N3j6k903kGz+dRkqmf+PkKGtg/oOr8Ho1rvrZNPIsm+GN71BRl8aC6t9Q48oiNSuBK++dSnp2Usuvq5kj62H5X2HnAryahR0NX4NBp5F90XfJGpDWPBp0YCn8+wqIS2B5zjw2bbDTf0QGgydksfqDAoae3JdLfnhy+Mdb/DAse1SPdNy2FCwWvC9dyn83XEKpazQpmQnUVTURZ7Pyrfunk53lwvn3c5h34A+4tCSu+MkUBozpw771pXzywjYy40uYm/0DmHQdTL5W97s5joCjCAbP5LV3BlFZXM9FP5jI8Mn99D44T05n/t77cHjyyB6YanR7HjE1h/NuGKv7surK9O9DbTH0GwtVh/ULuI9ltT9ga+0c0pNrufYbRdiGnQIDpkb+ugfj9cKSh+HLP+tR45sXwsDpaJre5qGmvBFHWQOOoyU0bfuMkbXz6Bt/UP/b0Rfp3rP6Mg4mXMEnRTfhdmqkZMRz/fDfEle0Bqw2fRObPQruXNtsc1K79mPen1dGlWcg8ZYGhvQ5xLDLr2DIxH4kaNXwt5P1ar1r34QxF7b8XA6t1DfG2SNh9IXQd7T+eGV79VR79WG9+u/c/4P//Qw8ThzTH+TDtadTW9HIpT+aRP4okwiuLYWF98O2t/WXKm0w1sv/CiNnhzmB9hHp9TvqAqan0FkCpmh3Ke8+sQ3Nq4f8x5ymN6XTNI23/7Se0oMOpl8ylCnn5fPST5fg1hK4+jte+p3he4MbquAvY3Sxcuti3Y/xwnlQsk3/EMx92xeh+Y/+Bao4AFhg9IV4pn2Pl56301Tnz43mDU1lzKwBJKbEU1lcR+W+g1TuO4DTm8S4tOWcPCsd+4zv6N6C+gq9imjN8/pjWOJg1l1wzv00NsXpF5NaF+d+ayBjtt2g7xRS8/DesIAP5tdwdHcV2QNSqKty0ljn4oJxSxhV+ST0nwzf+9zvY0CvvqqrdlL3+o+pP1ZI3egbSBh/Dpm5yfThAAlvXg6N1TDkDLjhPYhrHmLfu/BLli5wMDR9N9P6LaWPvUQ/75R+MGq2/oXMGQ8WC5pXY/3CQ6z+8AD4PtE5Q9L45tV1WN65VTdPpw+EH20EW9DF2uNG+9skFh+5gl0N54MFzrt+LONmNd9ZOMobKNpTQemWnZQeKKPMkYFbC4wKWix6xHrGZcOZftEQvXPu8r8Ztzd5k/n38X/QpKUyfXZfZnwz6CKz5S1453v6bufHWyApM/SHsXAtvOj7XFmscOtivliSFODL0m+ykJIKtQ79hbFZnKT3TaTiuJcRU/tx4W0Tg+85kK1v6+ksZy0kZ+sG6JGzjcVw3/I9fPLvI9gsDVzf/16Sr3sa95BzeOX+FTTUuphzbjEjD/wiQLyQoPt8CndU8MFTm0CDybMHsXXpUTwur5FysloteiTqPzfqnh+rDTL8nrpaVyavHvg/PF4bl9xxslEBU36sljd+uwYrLm4+9UUSbtYjC+x4H96+RfdkAZz6fbj40cDnu3E+jv8+xPuVv8HhydN3xvf600BfzN/F9i+PMWhcHy47dx+8d7t+f4NnUn/Jy+zd5mT4lH6kZSW2/LqG4/geWPEEbHpdF1tf/xtM/27gMQ2V+kXdoRuo6zx9eKX8n3i9VhLivTS5rJz7nbGGcbwZTTXw1wn6d/BbL8OEK/Tf7/qYqlfv5c3yx43P9RnfGsWkM7Pgv9+D3R+zpPFn7KiayciTMzlp/2tszvgaBcfimXyqldMPX4HxBQxiab832bbVzsRzB3LWt0fDzg858PJT/K/qFyQk27jxkdPZvbqYL9/cg9ejkZWfwpgZeaRk2Elu2E3yZ3eRFnccu7UBMgfD+Csozvw6/32pDjS47O7JDBqb1b7XPBhNg7du1D8vGYPg+8sg2XTflYfg5Uv9G83xl8NZ9+kbueN79ChlzTGOJ57BuuT/4yTPywyqfgOSsvTU7suX6qL89hX62mzmv7fi3PwBZflzyS3/L3GuKv2+z/sVLPq1vpb0nwy3fdE8MtsWaoph/jeh2LQpGft1uPrfeDVwNnhITA2T+tz7mb4RrzoU+PmJEiJgOkHAaF6NN372LhW1mYxOXMrXMv+mL6hxdhg4nX2Df8cnb1aQmBrPrFkNLP7URkZ8CXP/+k0sNtMH4b/fg61vwfRbID5Jz0MnZ8PtKyHNZAbwuHU1nzFA/8KiG1+3f1nI0Ib3GWN9nz4zL/FHL45t1Pt/OGv1MOg3XtDvP5iyfbDoAdjtK5nMHgWXP01T36lo9ZUkvnWlLqhS8+CmBdB3FHXVTbz5e323DJDRL4nrfjoU6zMz9AvT134Lp/848HHqK+CxEaB54ceb9VytomSH7s2YfkvgwtBBDm0vZ9GL23E5PVx571Q91F5Toou2sZeE35l9+TjaZ79lacOP2e44G4CzrxtjzE4pPlDNxo/3cGCbAwhcNKwWNxq2gGhYcrqd6x6aYZQscmwTFK6Goxvg2EaOFzmp8eQwbJgLy80f+98njxuePlWvxjjvATjrpy0/4RfOh6O+sPglf6auqok3fr8GV6OHQeOzGD65H8Mm9SUhycbBlTtZ9/Y6Shv8AuDSuyYxeEIYn4SrAf73c9jwsv7/Iafrhtv0wIuipmm8/chqSg/XMzH5I87KnMdO21wWH76CVOtxru/3A6wWbzPxolj9wQHW+UrdQfdtXPiDiQG9RGis1qtRdn7Q7DRX1NzAxroryR6Yyrd/eQoWq4XV7+1m3cKjDE1YwyU/nARjLvL/wZ5P9O/goFP1i0mweNY0eGMum/ZsZP3xR2h0p5M7LJ3L755CY52LVx9YidejceV308lfeIEuMiZcCVc8B/HtFC2hWP6k/j21JeobhLyT/Le9fYu+C84aDiPOg7X/ZEn1D9jRMMc45KY/nW7MW2t+30/Aogf1nfkda8Dqiwh6vfDUVLYdHcVSx+0MGp/FpdckY3nrBj0iaLVx/MIP+c+/GrFqHmat+D9Wz3gAly2FK+6ZwoCiF/T1LCVHX7fSB0J9Oez5H/ucZ/NJxd1k5adw7YMz4MU5vLPpUopc45l64RBm+qrpivZXs/AfW8MOW01Js5A1MJM+/VMo3FFBZXE9Y07LY/ZNUfZHNlbrkfGKA3qq5to3dQ9Z+X5doFQXQp9hcM385iKk8iC8fJlP4FgADRIy4MYPIH+yXh6++yM462dw3v/5/87VAI+N1NfwWz7T//6/t+j38Y0X9I1EpNGXiJ6jA/5zPRz4Qt8M3vJps+9nWJz1egR48tyOCakQiIDppAhM1bw7WbUpl/PSn9Z3Aia81iRerZ5HTZ0de5wTp8fOtNEHOO2e7wXeyf4leujXlgRu331c+0bgItsaB77Qv0QAN34IafnwrzlQXwZDz9QjOa0tprs+ggX36KFZLHq++dBXuiJPyYGbPoJ+o43DD+8o58MnNwP4d3cb/q13UbWnwl0bAgXYptf03WnuRLj9q8ifWwdpqnfRVO8mvW8bwvcNlfDiBWjH9/BVzc1sqb8U0Adtluwvo/iQ/73Ojd9NblIhOSNzyTntdDInTAWrBY/bi6vRg7PRQ1JavFGWHArX8d24XppDcn0lnHQVXPWivghsfFU3Aydn69GXhFaGvVUcgN3/g2nfNUzfzkY3Fosl5IA3rdFB4T9/w+Y9/Umw1jD7a/VY5/webEEXumMb4d3b9YsWFjjrpziHzaX4kUfIvOIK0i++OODwI7sqeP9vm7BaPFyXfSf/q/oZ5e5hzOzzH6aOL9HFy8wfhlwcvV6ND57YyNHdVQwYk8nX75hkzAILPHlN79Zr9lg4jtL41t38u+TvOLUUvnbLeEZNz+X1X3xKZXU8s/NfZcyvXvBfoBXuJn3jEWbhPVS0kSs+uZ4hNTlcsufnOF3xDBrXh7SsRHYsL2LA6AyuSPmJHh0adyl865UAg3RU8Hr1MuC9n+rRrluX6J8HFaGzxOkXnIHTYe0/KZ73BP91/xUsVtIch/jmeVtIHp2vb7LiU3TfQ3IfSMzUzaq1JboPZMp3Ah939T/gfz+jPHkWmRfeRtyCu/Q0dmoufGse2uCZvHbXB1S50+h7fBNl/SZjt8Mtfz0n9HBAjxte/zYNe1bzr1K99Prmn6TieOV23i5/DGsc3PDw6QFpurrqJrYtPUpNeSN11U3UO5zUVTcFRJ8ViSnxXPebGe0z37dG0Rb452w9WnL+r/UIxSuXQU2RvvG78YNmgt7AcUxfo8v26Ovj9e/5/TZb/gPv3No8jbRzAbw5Vxd+P9mm//7Du/V+O4poRF/MuJ2w/3PdJJ3UgmeqCxEB01keGLdTN3h5XXppqsel+0o+fQD2f87mukv4qsYvWL59Wxp9p54SeB9er27SrfYZfqffDF//a9vPRX2wM/+/vfsOj6poGzj825rd9N4JvXcIHQQFBGnSLIgIotIFLBQFREXp+CpIU5qIig0pKr0jvYN0CIR00ttutpz5/jiymJdiQJQ3fHNfVy5l9+TsZLacZ2eeeSZKHbXNilXns3v/DKYi/s2WDFj3tjpPep1H0B/BS8WbDj+9O4H0hDwadimrfkNWFLWGQ8JhqPMCdJp14+DlPdUVSM1Hw6Nv3f3fdx/Zk5LQBwWh0d1h11a7RR2J2fkf9mY/y+G8G/tzabFT0byNWmVi8G/dS/1Gdotpr6LIseXw0vqXOJd+loaWfJ7IzaNl3cF4Nhuh1svJioXHP1Cn9/4JivOPOf4Z6r/D66i5JX4l1dfzzhnqVOP16bqun+MMrMPlZ57FdvkyOj8/ym3dgtZUOEBePfMoV0+lExAoSEvVoDdo6D2pCaYiXFjsNidxp9MpUdn/1sHLnfw2k4M/HWVf7vN4+2lp078O308+iA4bfXvEYGw+8O7OB8w8PJPPT3wOwJir/mQlj8fxp9zTzo/HEHH8dXWab/CBwoH7/ZSXBvOaqBfMms+p+VBzm6ijni3eghajAXUU7Er3ThxUmpAcUp+yF1dSIedXSj9+Dc3t4irvCBh69OYp1YJcdbuDgj/VYSrZBLovBq8Q0r9cxuFFWzlbsafr7hLmFDp+9AznMs4RnxtP88jm6P4cNFqzYGEblp/uR5qjFG0qb+RSjBvnrY9QsUEorV4s2uhJQb6djKR80hPzyEjMIzvNSrVmEZSocv9GcQGsp0+TtXoNfj2exXhtK6wZqk7Tmv3UEaWgymrw4hl85xPlpaqjvxXbqSMvQPLUaeTt2kmJKvsxmCyFp5Guj6w1GgJt1Dov2C2woPWN/LMe36KUbEH2L79i/f13AgcPQh/wF6vNihkZwPyDSbxCiJuTlYWAI19iW/s+S+M+okB44mu8xnMfP4XmVt/Mtk5Sa4YElFfnV/+0ZLrIrNlq2fjrgZB/Wei7vvDy7KI6vxF+fk2d7nl+BQRXKvrvxu5VR3/QwICd6jywLR+mllFHmPrvhLA7JBP+g4QQpM6ZQ+qsT/Fo2pQSn82/5fPhVJxsjN1IKe9SVHJqKVg6hJ27yhPr3Ywy+t3UqxuLx+OvqsP1f+Obj12xM2jTIPYmFq61YVQEj+h9efnqWaoa/dVcnXt5TdyNcxvgp35qEGvyUb9hHv5CHVUAdV6//UcIow+xr/Qjf++NNoe++y7m7k8y7rdx/JbwG3WC61BP34zcb2689qo9EkHz524Ogu87RcG+rCdf7u2ORfHDx1chK1NLafNB2k3sd/scotudTii0+bENSXlqUnt5m42P0+rwS1w/FIcgvJSJLo6u6mu70yw1cP8nXf4Nvuigvjd9otQANyJafa//kXeWu2MHV/v1RzG5o3vhacSybyC/AL9O5RlZIw2Ek/lOf8yWLHVq12GFdtOgevdbP+aGsa6VczR+FVq+Czo9ebt3E/tKPxzo2d18Og5FfS9VuLyMj19OJDZPTbBvX6Y9Hzb5sHAQk3GZnZM+43h2K0q57edKQV0EOp5+ux5BUUWctvgX5O3fT9yAgSj5+Wh9fIj8aAYeyUtvrBoKrQ69VqmFF++S7coVLrZ9AoTAs6I3JWqfuTGNZLfA1LLqFNHLm9WRtetSL8DittgM5cmwPUrWjytw/rFNj0/3boR/8MH9+NP/ZxT1+n3f68A87PKPHOHaR/8hfPo0DH/e7kCjgTovYCz7GDVnf8n+2AZUrmW4dfAC0HS4OpxepdO9X6hM3tBpJizrrn4T6PUTwuxP5vLl5O7cRcDLL+Feu3bRzlW+NWLoMYRwoP3v6YS/EtVQXZp8aqVar+GFVWqZeIdF/cD979oN95H13DlSpkzFo1lT/J9/Hs2fVqkJRSFlyhTSv1CHrfN27SJj+XL8n3uu0Dnic+N5e+fbHE45jE5oePdqPSr9kEJEwVUi2AI6Lby8AMo1+lttFULw3u732Ju4F7PezORmkzmbcZa1J74ghjw2KVlsDw/hvfDH6XiL10RWQRZLTy0lKS8JRSg4hRNFKPi5+dGvRj+C3IseuAohEOVboe2/U11dFH9QTcoDdYqh/Qyo1g0BJI1/l/y9e9G6u+PdoQOZ333HtcWLeN9zNcfS1Hoa2+O2s53ttAroTbk0Nc/oetn2e5VqUWuZBJoDb3m/IhQWnVxEXE4cQzt8QL3zU9hx7VmyMtX3XNmKursOXgAOJB0gKS8JL4MXDsXBeSMkspkn6jfkyLWmNHWfC8kWdaq2dq+/PuHfVaoJtHhbrfCcFQsGd+j6mSt4EYpCyscfAxDY4xlCXh9JRngpkt59l5SNl4kpqZDpqeGTKp0ZXX900R7zkRHql7JSzVy5FgUxMcQNfw2cTgK6dCKigi9X9mWjUZyExB3F7ZwNtygTDsXBL5d+QYuWCU0m3Ahi/EoR0bodx3+0cbmgPgARFXz/p4KX3J07iRvyKqKgAI27O0pWFrH9+hPyxnD8yqai0emh89x7zttLX/qlq6pw7tlssoNMeJ9aqY6snd+oBi8+JSCibqHfc+gCSLrSlpzNW0Co00n6oCAc166RvXoNwa+/jt6/cJvSrekcSDrAmfQzeBu9CXEPIcQjhFCPUELcQ9Bri//lX47A3AWhKJzr2AHlYgy6gAAiPvoIjwb1b3lc6qVEAsuEuzbbg5tHbvLsebjr3YtWg+NO0i6CZzCWszEkvfc+1usbXup0BA0dSsArL98+kPpDQm4Cw7YOI9+ez8zHZlLWt+wtj8v8aSUFV2IIHjQYjfFPQ88Zl9U9R5w2NcHs1Cp1WqrhIGg76e/9fbehWK3EdOuO7aJaZ8GtSmXC3nsfc/VqCIeDxHHvkPXTTwB4NH+EvO070JjNlFm9CmOJEgghWH1xNZP2TyLPnkdUuo5Xfi6g4vXK+PVq4uUbTM7GjWi9vSm1/BvcypS55/bOPTaXOUfnoNPomPnYTB6JfAQA4bBz7uvOfJp7hm0eauDySvVXGFJ7CFqNFiEEv8b8ytQDU0m33npfmDI+ZVjcdjH+pr/+YL2afZWhW4eiCIX5recT6uav7qG0d666sqvjx+ClLiNPW7yElClTQKslcs5s3KPrca5Fc8jNY0p3Leer+jK24VgS8xLZnbCbC1eu0O74QK76nuH5QS1pENbgnvrqas5Vnvn5GawOK2MbjqVr+a6F7rc77Yz5bQxrY9YCEOwezJSyvTm90EC2M0SdPno7CmNU0RM7hRA409KY+es4Tp/awWOmGti9PZhr3ku0sDDrWrqaLL1/vrp0fdAeFP/SaNDc83s43ZrO9qvb2XJ1CxaHhb7V+tI4vPHNBypOdcXIxS3qtgt1e7vuyl67lvjXXkfr4UHZTRvR+/khnE5Odu2A/uxldlTV8GknNYhY+PhC6ofd/Jl1J5bjx8n46muy165F2GyYa9UifMkCBq0cSdUd7THnn6T53sVkd3+M6uOn8VvCb4zYPgKncNKpbCfeb/y+K4ix5tlZ+MYOrifCtxtU40bxxr9QcP48wm7H9DeLmdqTU7CePIFbhQoYIiNdz132+g3Ev/km2O14Nm9O+LSpJH84kaxVqwDw6dqV0HfHozXePCWqWK3Y4+Oxx8WhCwjEXK3qTcc4MzM5/+hjCIsF90YNyd+zF73ZSZl2KeiG7lanbX9foY54Pf4BFocFk86ELSaGq/0HYP9jexyPJk3w6/kcns2bc/mZZ7GePEnQ8GEEDhjAsWvHWBuzlv1J+zmfcf62fRBkDuKDph/c8rUmhGDNpTVczrrMy9Vfxt3wD48E34KcQvqHppBmrnmb8tN+olQKKFoNzv49qDLkLfS6W0ezSkEBWStXkb5kCWg0lJg3F21kOLOOzGLxycWU9C7Jk+WepFPZTgS7/8V86m04MjK49tF/yPzhBxACxcNMYklPIk6phd/cGzUkfMoUDMGFz389oDqXcY6BGweSYlELf/m4+TCv1TyqBVYrdOz5ae/jXKQOo17oXo9mY2fia/K9ccKN76irGwLKq8nElgw1l6ZUUy5mXiTNkoZBZ8CoNWLQGTDrzAS6B2LW30Wy7Z8kT55C+pIl6P74wFays0Grxa9nTxxJSeRs3Ag6HWEffoBPp07E9u5D/oEDuNerh2nuVKYcnMrGK2qNmOevRtHp+zgosGExaviipYYdtQ0MrtKfljN2YD1yFEOJEpT6dnmhbzoFzgLOpJ+hWkC1wsPl/2X1xdWM2aWuNhjXcBxPV3y68AHWLJRdHzNLb2HBlV8AaBnVkkG1BjHj4Ax2J6g1S8r6lKVD2Q7oNXq0Gi1ajZYlvy8hOT+Zin4VWdhmIT5uPrdtx5n0MwzYOIA0q1o9tJxvOZa0XaL+ji2/0GhgzpYtxA1W96EJeWs0/r17cyrtFBtG9qLNb/lcKOVG1eUrKON7I6izOCx8tux1fszdiS44iB86/kCA+e6G2u2KnT7r+nD82nHXbU9VeIrR9Udj1BnJteUyfNtw9iXuQ6/RE+oRSlxuHDqNjoGWjjiOPErFoDO0mqDucm1PScF+5Qrm6OjbBhrpS5eS8vEniPybd0FXNHA+HKoH5RIRmo/J1wEtx5Ma/QIDNg7AKZzMbTWXUI9b1w5KtaRyNOUoBc4CbE4bNqeNbFs2vyX8xpGUIyii8LYFj5d8nBH1Rtx8PodN/aLwp8R64XBwqUNHbJcvE/jqEIIGq9WgswqyGDanPSPmp6EFto5qyVztdsI9wvmx0494Gm8khwtF4eymH4g9+hsWCrAIG3miACxW6h3Jw3DuiutYc82aRH46i9lXv2bBiQX46vz50mMAlrfexxAVRdn169BoNGy4vIGRO0biFE6eLPsk7zd5H+0fyTjffrif1Ku5+ASb6fluw0Jf8v6bIzWV7F9+IXPlKgpOnwbA74VeBL/xBtq7LGqqWK2kLVxI2ucLEFZ1fzBdUCDudepiCA8n/YsvQFEwt23Fj8+UYFvSTl6o0ovHdueTMm0aKApaHx90np5oTCZyKSDdnklQng5d2p/yhbRaSi79Avfo6EKPn/rZ51z76CPcKlak1LfLudTpSeyxsfiVzyV0yAtwcJFa3+aVLXxwdS3fnv2W+lfdGPyDBbNVIS/IE93kt6ndpIvrnFmrV5MwchT64GDSl02k37ZBhV5P5XzLUSu4FhaHheS8ZJLzk0nOS8am2NCg4aXqLzG41mDXaExsdizv7XmP/Un7AajsX5lPW356z9emeyUDmH8ogHlr51tsOvcLL6110Pyk2nWHKhs588qj1CjTiLqh0ZTxKYOSm0vGN8tJX7oUZ+qNfS80wYHMfymMTZwudF6tRkvj8Mb0qNTD9c38r9iTksj46isyvv1OvXgDsU3LMqHOZbLc4dHjgr4bFdzsoPHzxa9LVxxJSdji47DHxSMsFgqeacvAElvItudSzrccJp2Jk2kncde7M/OxmTQIa4CiKOx6+xWCVu6+8dg6eKefBy2aPk/vqr3xM/mpiXoz66jBC6g1D948z96Ug/Tf2P+mD+rrvI3ehHiEEOweTHRIND0q9cDDcOdtEvL27ye2dx8Qgsh5czFXq0bylKlkr1njOkYYdIj33yCyXVe8jd6cObkd5/OvoitwsLC1lvXRWgxCx5TfqxG55hAAHo0b4zb2dT68PJ+tV9WKxVW0kYxdmIM+KQ1znTpELV6EMOhZc2kNs4/OJikviRaRLZjRYgZG3c3fzrZd3cZrW1/DIRz0rdaX1+q+dse/bc3FNYzfPR67ciNr1Kg10r9mf16s+iKG/0oejsmKoc+6PqRb06kRWIPPHv+sUP85UlPJP3iIcxU8eHXvCHLtuVTwq0CGNYNrlmvUDanL/NbzcdOpFwShKKQvWkTKfz4Gp5P89s347bmqnEg7yZGUI3hkWJk914lOgVLff4+5+o1AN3XuXK59MpNrgQaG9lVoFNWM2S1nuy5eAJeyLjFx30SCzcGMbTgWs95M5nffk7F8OT4dOrC8eg7zTi/Ay+BF9wrdWfL7EgSCGkE1GNNgDON3j+dM+hnMejMft/iYWsG1+GDvB6y5pD73LY1NGNNqBEFBZdULfOfO2C5cJHjECAJe6qsWW4zfRTnfcoR5hpG7fTtX+w9Q/3aNhnRPQbafGzWqPIotJoaCs2cL9bdHSQM+H39Jv4sTOZV2ClBHwJa0XaK+D/7kQNIBhm4ZSq79Ron9oExBi+MKad4a9lXUEBVRhUejHiXTmsnys8tRhIJZb2ZQzUH0rNITg1Z9voUQiPx8NCaTKxk94ZulZL03CZ2fH2U3bkTnqT7vI7ePZO3ltby+2Z2G+7PRBQextoqNzaVyqdOsG+82eQ9nbi7pK37gyuL5eCRm3vb1qOh1+LVvj99zz2GqUYN9Sfvot6EfAsGM5jNoFdSEc40aI2w2Sq9aiamimve0/vJ6Ru0YhVM46Va+G+MbjUej0XBkQyy7V1yg1Yu3LhgpFIW8nTvJ+GY5uTt3gvOPKs0GA9jV94RbxYpETJ+GW/nyt233dTaHDcvGzaRMm4YjIVE9VUQE9pQU1/muS29Vh7cbx5Juv9EfzSOb87amHTlvvY+SlcXtaD080Hp44EhJwRAZSemVK13Ph7DZuNCqNY6UFMImqyv58nbvJrbvS4Cg1OMZmP2t4BvFkR5f8MK63rQ6ovDSegWdgDORMK2bDouHgU8e++TG6K3NxvmWLXFeS2VBV082VLTSJLwJXcp3oV5ovVuOyFodVqYdmMZ359TaSLUDazHB53l2mGOZefozrE4rJp0Jk95EZkEmIe4hfFplPJ7zvsez5WP4du78l33+d8kA5h9M4s0qyGJX3E6SvlpK/eUn0P9xXVYAux4ceg1uTg36PzaE04WGEtCrF4nff43ucjyZHjDtBU/6dhyPzWlj1YVVHE45jLtVYHTAyLaT6Fi2420f33LiJOlffEH2unXgUJcVasuVZtHjOn71uQxA65KtOZpyFOPVFIatclLqDptY76mkYdeLtflP2znotXqGbR3GvsR9GLQGxtUfg+2jedTYribnbetWlnqxBjwOnOFUCXivpw6TwZ0R9UbwVIWnblQaBqjVk5x2U+i6uitJeUmEuIfgpnPDptiwO+3kO/KxXF9G/if+Jn9eqf4KT1d8+pYBgTM3l5gnO2OPj8f3qe6ETZhAti2bn87/xKGfF9F5ZQre+TCjq5aTpdQLp5vOjQJnAW0OKry0UcFqgK+GVKLvbhOafUcBCOjfn6Chr6LR6RBC8POln5l+cDrp1nQiUgWTvgST1Ym1cU2mt8rjuP1yoXY1jWjKx49+7AoEAH449wMT9k5AEQpPlHqCyY9MBruD7NWrsSck4tGoIebatQvl7gAcSTnC8K3DSbemUz+0PuMajiM8x0DShPexx8ej0epAp0Oj1aL19MQSXYmRhlXEmHOpG1KXua3moktKJ33RQjJ/XIEoKCA+QMOMLlqCq9ZlVstZJOYm0mddH3LtubQu2Zppj0xDZGUTP3o0+dt3ALCzqoY57bU4dTe+JTcKa8SYDZ7k/7wW73btiPhIXc2UtmABKdNnuI5b0N7IhhoKb0a/Se+qvRFCsOriKibum+h63qv7VOLDfaWw/vSz6/eSfeGrFlq69pvGE2XasTNuJ+9uHEGp8zmUThJcCNcQWzmAWW3nUjVAHaoXQvDThZ+YuG8iBc4CIj0jmdNqDn6bjpA45o86GxoNoZ/8h+nuO1h9cTV+bn58WXsG1hdeRcnKwvfZZxhfL5Y9qQcYXGswA2qqQc2eIz/z3RejqHdRQ+2LCigCp1bDujqwqaU/Dk8TyfnJVA2oyoLHF7hGNzZf2czIHSOxKTaivKIoYQym4ZYk6m6MdX02oNfj2bw5Ph3a496gAedjDvHN9plkxl0iIBvKFfgQTUl0Kek4EpMQNrU2isZsxu6mw5mbi5sDDj1dg6pDRlMrqBbrLq9j5I6R6DQ6ljWdi9vLY1wXboBUL/CqXQe3A6fAoo5E5BshvnIAJoMZN0WHAR35tjy2BV5jS00N3ev1ZXjd4WQWZNJ9dXeuWa7RvUJ3xjcaD8DVQYPJ3bKFwCFDCBoy2PVY62LWMWrnKBSh8Fb9t3iu8nMIRWDNsxeq/pxuTceenY1h3U7Sv1qG/cqNjVVNNWvg8+STeD/xBNbjx0l4ewzOtDQ0bm4EjxyB37PPFlpdKITgQvwJju/4gbQDewg5Hk+FePUy5wzyI2zUaPzbd0QUFJB3/Cgpe3eQdfggv3kn8lmdDNBoKO1TmhYlWrDs1DLsih1/kz/v134LbcI1Fh6ahyUvE5NTS5QpguOaOLL9TUzrOJfaXpWJ6fQk9oQEfLp3wzhmOL9c+oWAbScoO+sXnAE+iO/nEhVQBh83H+LfeI3sX9bh5mundOtr5JfsxYJTJwn/PYVqV9Q2F7RqyPkBrdmW/Bs74nbgpnNjXqt5RIeqIzyJn84k89O5nA+DZa9XZ+kTSzHp/7oe0frL65m6+R36rsihzkVBbBBMeFZHpXINGd94PBo0DNo8iPT4S0z4ShCaroBeT+kVP2KqUOEvz/93yADmHwxg/iznyCFiX38NbeLNmwzGBsLqhlr2VTVQMqAsSfHnGPuNQw0mfH0ovWQJxlKlyN22jaQV32H7bS8KgunPmhg75Fsq+BV+kQghSHr3PTK/vbGPknu9esS1q81I53dkO3PxNnrzYdMPaVGiBXbFztbYrfx4cjlBP+8jMFuQ4qshxQdSfDWUSRK8tF5Br4CxSiWi5szFEBqKzWlj1I5R7Duzkee2KbQ8JlCAqwOe4PFh03EmJHKxY0dEvoXV3SJYViEZDRqmN5/O4yUeg/mPqFV8n/+RsUnbWHVxFZGekfzY6cdC86lCCHLtuSTnJZOSn8KVnCt8ffprMhJjqHpFUC/eTG1LEGFNWhHwZGfcyqp5OQljx5L1w48YIiKwLJrIqsQNrL642nVR9DV40zAwmiRHOnG5ca5kUJPORMPQBvSefwmPEzGukrkak4nwiR/eVNsE1DylxScXs/TUUspeyOPtb9X+yjHBitbuVOo1hDL+ZXlj2xtYnVYahTXik8c+waQzMefYHOYdUzdd61KuC2NqvUnejytJW7AQR8qNiFLr44Nns2Z4tmiB12OPonVX+yjNkkZMVgx1Q+pScOYMsf364byWelMb/+xihI7dFQXlUvXUP2lD98d10qYHowPsRi1h775HUFd19cn+xP0M2DQAu2Knv+5Rmszbiyk1B5sOFrfWsqWWltK+ZageWJ0aQTWoHlidSv6VKDhzhpguXUGno9yG9eRs2kTyJHUnbnPt2liOHMEW6EOfF3PBaOCz1p/x4/kf+eWSOj1WN6QuabHneXl5OhUSAI0G9+6dSVi3Gt8cp+s87g3qk79nL5YTJ9Ql+9f5eOPXvgM+T3bCVKOGa2robPpZhm0dRnxuPP5aL+Ys0KJNTsNYujS2mBjsRi1jn9MQE6ZB7xBM+UZPibgCTDVq4DZvKm3XdEQgWNdtHRGeEa7XaedVnbmUdYlxES/jv2A1EUfVasfCxwv9M08y3bKGE/65VC5Tnzkt57Dm0ho+2PsBilB4rMRjjFfakz5luiuPwVy3Lkpu7k2jO/ci2Rdef0WHXa9eeFMtqeTYchhYcyCDag3CmZND7tat5GzaTMb2LegLbtRSiQuAnQ09af7yeFpW7lDovEIIFpxYwMwjMwFoV7odufZcdsTtoIxPGZZ3WO6a/s38aSWJb72FW8WKlFm18sY5FIVfZ73J2tj1HK1g4JPOC6kbUrfQYxzc9g37Fk6h8XEb5j9q12k8PfDr/hS+Tz91U96ZIzWVhLfeJm/nTtdtNncjVg8D+e46FKuF0GR7oY3+CvSwqqGW1Q016M0eVA6ozLX8ayTkJuAQN/rD2+jNoFqDeLri0xi0Bs6mn+WtXW/dlE9SxqcME5tOpIJfBYZuHcqu+F14GDz4vPXnlImxuEaHP37azO4yNqYsdlI6Gb5pruWnxlpMOhMzWsygsbkqF1s2R7E60egUhLNwrmLQsKEEDBiARqPBrth5fevrbIvbhofBg4VtFlLFvwoT1r5J5xG/YnCCx+JZRDUqWll/69lzXB48EBF3o3J3QXgAVb78FmOE+trPSInlxDNdCEq8MbWaWi6QY+89jZ+7P/4mf2oG1STc8/7uhyQDmH8pgAH1Tark5SEKCrDl53I+6Xd+T/udfYY4jqUdd+UbAPQI68Az885iO3UarZeXmrOSW3gH1xwTzHo1ijm9fsDLeCNDP3X+Z1z7z39Aq8W7Q3v8X+jNV2IPnxz+BIAaQTWY/sh0wjzDbmrj1eyrHEw+yO9pv3Mq7RRn089iU2z0U5rS5rNjODMy0AcFEdCvHwXnzpJ36BD2S+omX4oGdGOHUannANf50r/4guRJk9F6efHzhNYsSV6NUWtkYZuF1NAHYTu2g/16wQcHJiK0GqY+Op2q/lUQdrv6Y7MjrBYcqak4rqWq/01JwXL8OLZLl27Zz/llQvGu1xDHtysRGpj9Uig7gm5c0Mv5luP5ys/Tvkz7Qt9Ars//hnqEYtKbsF29yqUnOyPy89GHh1Fi9mxMlSvf8TlOyU9hztE5/L7tR15e5yQqRX3bmGvVIvSdcZzQJfHehpHoc63UNVcgROfHgfi96BRoFfEozfVVyPj6a5xp6mtBHxKCuU5t8nfvcS2HBNAFBhI0ZAi+3bu5RmXy9u4lbvAQlLw83CpUIHjECNBqQFEQTif2hARy1m8gf/9+1wqH646V0rCysYargRombAog7JQaOPk+1Z3AgQOxnj7N71t/JHHPVsongE5Aoh8seTaQZo++QLcK3W6bGHzlxRfJ37MXt8qVXfkJgYMGEdC/Hxcfb4MjOZndz1Tm4zI3Pvx1Gh2Daw2mh6MOcUOHIlLTyTXBZ908cdavzvHY/fQ86sUTu62IP0YHrtOXKklW6UC8jsegpN1IZnarUpmo+fPRB6mrsNIsaQzdMpSItUd5cZOCI8CHkFXfsvel7pQ/m0uGp4b0WSNJmPMpzQ7kYXHXU2nVz3yVsYGZR2ZSL7Qei9osKvTY3539jgl7J2DUGrEpNmpehpG7gzBcSSx0XIoPpIZ7YLHnYy4QhGl8CXJ64IiPdz3vIaNH4dW2LRqNBuu5c2T/8ivZP/+MPT4erY8PhtBQ9KEh2P292FRwjGPaBFJ9NETXbEu72s/y4fZ3SEmNxcOupWepblSo9SgrUjay/vJ6VxBfNaAqX7b70jX9dF1+bibvzuqK54UkTkdpCG3WmrGNxt0xT2n1xdWM/22860Jv1Br5uv3XVPS/sUTemZnJuSZNwemk7Pp1GEuWRCkoIGH0aHLWrgPAoYVT5Yw0fP4NQus1I3fLFuK+/RJjbLLrPHEBsDZay45qGqpG1mVkvZFUDbw5IVZxOvl6wvNUWXHUFfT8t6wAE0qVcgTXb0pKvdJsthxj29Vtrly/64xaI5FekTQOb0z/Gv0L5/Wh5rnNPDyTpaeWokFDn6p9GFx7sGuk1eqwMnjzYPYn7cfb6E2vKr2wffIZrXdbyHSHTV2j6L4sFrtRy4wxFblCOmnWNIxaI7NbzabStytJnKcmCluMGk6WhNAWbWjRfTjGkiULtcXqsDJo8yAOJB3A182XLuW6sPj3xQz8ReHR40qhEdE7yfrlFxLHjkNYLOjDw0l5uT2Bn69BSUxCHx5GycWL0QUEEtu3L9bjx8n3MTGlg53R3zsx22B+Wy2ba6vB1sSmE+84Y3AvZADzLwYwdyKEIDEvkeOpxwk0BRIdGo0zJ4erL7+C5Zhab0MfHoZP+w54tWlD3DtjcZw6w+Vg2PJ2K6a2mYlGo1Ez5IeppfpD3x2P19PdmbRvkmse8/nKz/N69Os3fWDdjt1p55rlGmEeYdjj44kbOJCC8xduPrBUCUJfex2/NoXLVgun05UB7/n440zt6CR57zZandLT7JwO8m5OhiwyjQZjxQrElvNmm+485X7PotYl4ZqqA1hTX8OXLXWY9WZalGhB9/LdqRdar8irQfL27iV3x04CXup7V0WgEnITMAodmh/XkjpzFsotkj7vxBARQUC/fvh06YzWaEQ4nViOHSN36zayf/0V+x8XOmOZMgS/8TqK1UrC6LfAbse9Xj0iZ3+K7javZ8e1a2Rv3EjWpo1YPY3wbCeoWAaBwMPgQZRHJKlz55E6e/ZNgc51J2v64D7uTR6v/ORNuTb/7XrtkesCXnmFoNdfQ6PRkLH8W5LefRdtgD9vDDZzxa4GkFMfmUr5s3nEDR6MsNnQly3NrKc92MoZQA1wlj6xlMpKKGkLF+DMyMSjQX08GjfGEK5+yxMOB3l79pC1ajU5mzYhrFZMVatS8sulrtGr/Ox0zrR8FHOOjflttRxuGIA1O52JyyAixYk+OBhHSgoKMOlpLRWfeJb9Sfu5nH2Z9xu/T5fyXQr9rfn2fFr/0Jpsm5prNrbBWJ4u142slSvJ/e03rKdOFZr2uIleT0Cf3gQOHIjW4+b8LiEEwma7KTHVoTiYf3w+84/NR/xpj6FgczBTm08tNJqRa8tl7eW1nEw9Sb8a/VwjSP/tQsYFPj36Ka1LtqZd6XZFes/sjt/Na9teI9+R75oK+m/XA9rgEW/i260bcUNeJf/gQTAYMESVwH7x1l9MbHqIrR1G9d7D+S08mw1XNnI45TAAXgYvFrVdRCX/wnWpvjr9FZP3T0YvNDwV+gRBdjO+BTp8rFp83Xyp3LQjXmFRNz2WIhROpZ3iYuZFwjzCiPKOItg9uFCO1u2cST+DTqOjvN/NeTf59nz6bezHsWvqZ7rBIZi+VEdYsk2t0Kwo+D3Xg9B33sGu2Hlj2xtsvboVs97M/MfmUP6XH/jCmcDnhsOUDazItx2+ve0y5zx7Hi+vf5mTaSddt40L6EX1NxerI6KbN2EIDUUoCgVnz2I5ehRHRgZKVjbO7Gwc166Rt0utjO7RuDHhM6aj9/PDnphI7It9sV2+jD4oCENUFJZDh9D5+BD15VIOe6aSt+xbIhdtwOZu5PvxzUgw5TOk1hDqhPyNzTNvQQYw/yMBzO0oeXlkrlyJqUIFzHXrupY525OSON+1M5r0LPZU0qD7YCRPi2iu9OqFsFrxe6EX3iOGM2rHKLbFbUODhlH1R9Gzcs+/eMQ7c+bmkjJtOgUXL2CuWRP3unUx166N3u/2paWtZ84Q0607OJ3oggILTW8UmHQUaJwYhBZ3rUnN1dHr0RgMrh+t0YguMBB9UJD6ExiIsUxpPOrVQ+frC6gf7BczL7Ln9Hoy1v5K5N4Y8kwaDg1pTpsKHWke2fyBLPMD9blKnjyFnHXqN0ytuztOTzNxmkwsBkEp/7L4egSg0enRmNzweqwlPh07oDHcOjAQNhsZ335H6pw5ODMyCt3n1bYt4VMm3/XKi1vJ272b+JGjcKamYixXFvfadTDXqYOmRmW8y1QochAohCDmyc4UnDuHf58+BI8a6fpdYbNxsV177HFxGIe8xG+PBvNkuSfRHznN1f4DEAUFeLZoQfj06dhNOsbsGsOGKxt4o+4b9KnWp8h/i+3KFS4/2wNnRgaeLVoQ+eksNHq9K5k4L8Sbl3vn4dRpKOldktlV3qXgpddcifW5z7fjpRIbXcGBWW9m69Nbb5lEPu/YPGYfnc0r1V9haJ2hN93vzMnhwM7v2Lnza2pFRNOgbAu0np5oPTwxloh0jRDdi32J+xi9czSpllSahDdhYrOJRVoyfz/F5cQRmxNLo7BGt3yNpH/9NcnvT8CtfHmEomC7eBGtpyeRn87Co2FDrhzbxfI5r1LnpJXINDgTAdtraAnt2I0RLcYXWsWXlJfEqB2jOJxyGH+TP4vbLqaMjzqVtDthNwM3DUQRN/Kr/hdk27IZsnkI8bnx9Kvej/bOKlx9tqeaKKzRUHbtrxhLlQLA5rTx6pZX2Z2wG0+DJ29Ev8H7e95HIFjcZrErv+V2Mq2Z9FnXh4tZF2lbqi1TH5lK7Au9yT9wAM+WLdGaTOTt2YMz/dZlF+CPLxzDhxXKH3KkphL7Yl8KzqujploPD6KWLMZcXa3lJRwOLj/9DNZTp/Bu356IGdP/Zq/dmgxg/scDmDvJP3yEmBeeR+tQ+LWejpYXTLhl5JEXXYnM9wey6PQSTqSewE3nxuRmk2lV8v5uZX43UmbMIO3zBQBoPD3ZXQnWVbRwpgTotAaWtV/mSrS8H/Lt6ojHgwpabkXJy0NjNLoCk6yCLJzCec8XGGdODmkLFpK+ZAmioAC/Xr0IeWv0X9byuRvC6USxWNB5/sVeS3/BnpREwYWLeDRpfNNFLXPlShJHv4XWx4dymzb+kcfTH2GxqMHGzE8K1RLKKsi64xLw28k/coTYPi8iCgrw7fEsQUOHcrH14yi5uYTPmM6OSgrHrh1jUK1B+Jv8sRw/TtyQV3GPjiZ82lSWnlnG9IPqB3GHMh2Y1OzWdYsUoXAt/xohHv/QtgF/Iasgi7PpZ4kOjS7SiMG/zZ6cwoXmzV3/1oeGUmL+fEwVb+Ty7Y7fzcBNA9A5FOx6DYNrDaZ/jf63DIiub7lxOv00we7BfNH2C5zCyXO/PEe2LZtOZTvxQZMP/n4drfvo+uX0eptSP/+cazM+wqtNGyI/+bjQsRaHhYGbBnIo+ZDrtnal2zHlkSlFeqxsWzYHkw7SLKIZBp2BnE2biBtSePsRjbs77nXqYAgLQ+fjjdbbB523F6bKlTHXrHnL8zoyMogbPISCCxeInDXrplpnlpO/c/npp0FRKPH553g2a1qk9t4NGcAU4wAGIOPb70gaP97179ggGNdLh8VNfWP4uPkw67FZ1A4uYqXdf4iw2cj45hv0ISF4Pvool63x9Pq1F9m2bAbVHMTAWne/D42ksqekYI+Px1yr1v/Uh3RRCaeTSx07Ybt0Ca82bcjduRORn49Hs2ZEzv70lgXB7lX2hg3EDxsOQuBWvjwF58/jVqkSpVf8eMvATyiK63YhBNMOTmPF+RUsbLPwvgbc/99c7vEcliNHcKtYkRLz52EIvXmZ9PIzy/n8xOcMqDlAXbl4BxnWDF5c9yIXsy4S6RmJQWcgJiuGGoE1WNR2UaEVf/+LhBBYjh7FVKHCLacO8+x59NvQj+Opx3HXu7Omy5p7rrkinE4SRozEnpCAe6OGeDZujLlmzcIFR++i3cJuv+17NGniRDKWfokhMpIya1ajNd9bLa/bkQFMMQ9gAOLefYec5d9j9XJjxagGpPioL3gfNx/ejH6T0j6lH3QTbyk2O5aTqSdpU6rNHYu7SQ+/7HXriB9+o+6NR+NGRM6Zc9NGkPdD+tKlJE+8MXoSOW8uXi1aFPn3b7nHmXRXCi5cIHfbNnyfeQad1/3ZIiAlP4Xea3sTl6uWcgg2B7O8w/K72jrjf1lWQRYLTyykcURjGoY1fNDNKRJnbh6X2rfHkZx809L5+0EGMA9BACMcDrLXrcdcqybGyL+3r4wkPQhCUYjp1p2C06dxr1+fEvPn3fdva3+WPGkS6V8sxRxdl5JffikDkodEfG48L657kWxbNgseX1CoSrj0YORs2kTO1q0Ev/nmHXMl74UMYB6CAEaSHga22Fhyt+/At2uXWw6j309CUcjbswdTlSr3/UNVerBsThsWh+We8qSk4kUGMDKAkSRJkqRip6jX7/+9VHZJkiRJkqS/IAMYSZIkSZKKHRnASJIkSZJU7MgARpIkSZKkYkcGMJIkSZIkFTsygJEkSZIkqdiRAYwkSZIkScWODGAkSZIkSSp2ZAAjSZIkSVKxIwMYSZIkSZKKHRnASJIkSZJU7MgARpIkSZKkYkcGMJIkSZIkFTv6B92Af8r1Tbazs7MfcEskSZIkSSqq69ft69fx23loA5icnBwASpQo8YBbIkmSJEnS3crJycHHx+e292vEX4U4xZSiKCQkJODl5YVGo7lv583OzqZEiRJcvXoVb2/v+3Ze6Wayr/9dsr//PbKv/z2yr/8996uvhRDk5OQQHh6OVnv7TJeHdgRGq9USGRn5j53f29tbvhn+JbKv/12yv/89sq//PbKv/z33o6/vNPJynUzilSRJkiSp2JEBjCRJkiRJxY4MYO6Sm5sb48ePx83N7UE35aEn+/rfJfv73yP7+t8j+/rf82/39UObxCtJkiRJ0sNLjsBIkiRJklTsyABGkiRJkqRiRwYwkiRJkiQVOzKAkSRJkiSp2JEBzF2aPXs2pUqVwmQy0aBBA/bv3/+gm1TsTZo0iXr16uHl5UVwcDCdO3fm7NmzhY6xWq0MHjyYgIAAPD096datG8nJyQ+oxQ+PyZMno9FoGD58uOs22df3T3x8PM8//zwBAQGYzWaqV6/OwYMHXfcLIXjnnXcICwvDbDbTqlUrzp8//wBbXDw5nU7GjRtH6dKlMZvNlC1blgkTJhTaS0f29b3ZsWMHHTt2JDw8HI1Gw8qVKwvdX5R+TU9Pp2fPnnh7e+Pr68tLL71Ebm7u32+ckIps+fLlwmg0ikWLFonff/9dvPLKK8LX11ckJyc/6KYVa23atBGLFy8WJ0+eFEePHhXt2rUTUVFRIjc313XMgAEDRIkSJcTmzZvFwYMHRcOGDUXjxo0fYKuLv/3794tSpUqJGjVqiGHDhrlul319f6Snp4uSJUuKPn36iH379olLly6J9evXiwsXLriOmTx5svDx8RErV64Ux44dE506dRKlS5cWFovlAba8+Pnwww9FQECA+Pnnn0VMTIz4/vvvhaenp/jkk09cx8i+vje//vqrGDNmjFixYoUAxE8//VTo/qL0a9u2bUXNmjXF3r17xc6dO0W5cuVEjx49/nbbZABzF+rXry8GDx7s+rfT6RTh4eFi0qRJD7BVD5+UlBQBiO3btwshhMjMzBQGg0F8//33rmNOnz4tALFnz54H1cxiLScnR5QvX15s3LhRNG/e3BXAyL6+f0aNGiWaNm162/sVRRGhoaFi2rRprtsyMzOFm5ub+Oabb/6NJj402rdvL/r27Vvotq5du4qePXsKIWRf3y//HcAUpV9PnTolAHHgwAHXMWvXrhUajUbEx8f/rfbIKaQistlsHDp0iFatWrlu02q1tGrVij179jzAlj18srKyAPD39wfg0KFD2O32Qn1fqVIloqKiZN/fo8GDB9O+fftCfQqyr++n1atXEx0dzVNPPUVwcDC1a9fm888/d90fExNDUlJSob728fGhQYMGsq/vUuPGjdm8eTPnzp0D4NixY+zatYsnnngCkH39TylKv+7ZswdfX1+io6Ndx7Rq1QqtVsu+ffv+1uM/tJs53m+pqak4nU5CQkIK3R4SEsKZM2ceUKsePoqiMHz4cJo0aUK1atUASEpKwmg04uvrW+jYkJAQkpKSHkAri7fly5dz+PBhDhw4cNN9sq/vn0uXLjF37lxef/113n77bQ4cOMDQoUMxGo307t3b1Z+3+kyRfX13Ro8eTXZ2NpUqVUKn0+F0Ovnwww/p2bMngOzrf0hR+jUpKYng4OBC9+v1evz9/f9238sARvqfMnjwYE6ePMmuXbsedFMeSlevXmXYsGFs3LgRk8n0oJvzUFMUhejoaCZOnAhA7dq1OXnyJPPmzaN3794PuHUPl++++46vvvqKr7/+mqpVq3L06FGGDx9OeHi47OuHmJxCKqLAwEB0Ot1NqzGSk5MJDQ19QK16uAwZMoSff/6ZrVu3EhkZ6bo9NDQUm81GZmZmoeNl39+9Q4cOkZKSQp06ddDr9ej1erZv387MmTPR6/WEhITIvr5PwsLCqFKlSqHbKleuTGxsLICrP+Vnyt83YsQIRo8ezbPPPkv16tXp1asXr732GpMmTQJkX/9TitKvoaGhpKSkFLrf4XCQnp7+t/teBjBFZDQaqVu3Lps3b3bdpigKmzdvplGjRg+wZcWfEIIhQ4bw008/sWXLFkqXLl3o/rp162IwGAr1/dmzZ4mNjZV9f5datmzJiRMnOHr0qOsnOjqanj17uv5f9vX90aRJk5vKAZw7d46SJUsCULp0aUJDQwv1dXZ2Nvv27ZN9fZfy8/PRagtfznQ6HYqiALKv/ylF6ddGjRqRmZnJoUOHXMds2bIFRVFo0KDB32vA30oB/n9m+fLlws3NTSxZskScOnVK9OvXT/j6+oqkpKQH3bRibeDAgcLHx0ds27ZNJCYmun7y8/NdxwwYMEBERUWJLVu2iIMHD4pGjRqJRo0aPcBWPzz+vApJCNnX98v+/fuFXq8XH374oTh//rz46quvhLu7u1i2bJnrmMmTJwtfX1+xatUqcfz4cfHkk0/Kpb33oHfv3iIiIsK1jHrFihUiMDBQjBw50nWM7Ot7k5OTI44cOSKOHDkiAPHRRx+JI0eOiCtXrgghitavbdu2FbVr1xb79u0Tu3btEuXLl5fLqB+EWbNmiaioKGE0GkX9+vXF3r17H3STij3glj+LFy92HWOxWMSgQYOEn5+fcHd3F126dBGJiYkPrtEPkf8OYGRf3z9r1qwR1apVE25ubqJSpUris88+K3S/oihi3LhxIiQkRLi5uYmWLVuKs2fPPqDWFl/Z2dli2LBhIioqSphMJlGmTBkxZswYUVBQ4DpG9vW92bp16y0/n3v37i2EKFq/pqWliR49eghPT0/h7e0tXnzxRZGTk/O326YR4k+lCiVJkiRJkooBmQMjSZIkSVKxIwMYSZIkSZKKHRnASJIkSZJU7MgARpIkSZKkYkcGMJIkSZIkFTsygJEkSZIkqdiRAYwkSZIkScWODGAkSZIkSSp2ZAAjSZIkSVKxIwMYSZIkSZKKHRnASJIkSZJU7MgARpIkSZKkYuf/AEVCDpQPNVyzAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T = 100 # Number of time points to simulate\n", + "\n", + "# Get the original loads\n", + "load, load_names = sims.get_loads()\n", + "nloads = len(load_names)\n", + "\n", + "# Generate the randomized scaling factors\n", + "mu = 1.1\n", + "sig = 0.1\n", + "s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n", + "\n", + "# Generate the new load values by scaling the original loads with randomized s\n", + "new_load = s * load[:, np.newaxis]\n", + "\n", + "# Visualize some of the load curves\n", + "plt.plot(new_load[0:5, :].T);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to iteratively run the simulation for each load setting and retrieve the resulting voltages and currents...\n", + "\n", + "The function `simulate_network` does this all for you. It has the signature `simulate_network(loads, load_names, contypes=['Line', 'Transformer'])`. The first argument is the desired load values in an $n\\times T$ matrix (as created above), where $n$ is the number of loads we want to set. The second argument is the names of the loads to set (as returned by `get_loads()` above). Finally, the optional argument `contypes` specifies which types of connections we want to return current data for. \n", + "\n", + "The function returns dictionaries of voltage and current, with the sames keys as `v2dict` and `i2dict` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.140034Z", + "start_time": "2023-11-22T02:55:26.046946Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "Running simulation: 0%| | 0/99 [00:00", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "key_list = [key for key in V.keys()]\n", + "\n", + "plt.plot(V[key_list[0]])\n", + "plt.title(key_list[0]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And one of the resulting current magnitudes. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.482497Z", + "start_time": "2023-11-22T02:55:26.358654Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "key_list = [key for key in I.keys()]\n", + "\n", + "plt.plot(I[key_list[0]])\n", + "plt.title(key_list[0]);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/btrdbextras/opendss_ingest/opendss_ingestor.py b/btrdbextras/opendss_ingest/opendss_ingestor.py new file mode 100644 index 0000000..a2deb17 --- /dev/null +++ b/btrdbextras/opendss_ingest/opendss_ingestor.py @@ -0,0 +1,210 @@ +import argparse +import os +import time +from datetime import datetime +from typing import List, Tuple + +import numpy as np +import opendssdirect as dss +import pyarrow as pa +from btrdb import connect as btrdb_connect +from btrdb.stream import Stream,StreamSet, INSERT_BATCH_SIZE +from btrdb.utils.timez import datetime_to_ns, ns_delta + +import btrdbextras.opendss_ingest.simulation_utils as sims + +MODEL_REL_PATH = os.path.dirname(__file__) + + +def initialize_simulation(model_loc:str) -> Tuple[np.ndarray, List[str]]: + """ + Initializes the simulation by activating the model in OpenDSS and + retrieving the simulated loads. + + Parameters + ---------- + model_loc : str + The file path of the model to be activated in OpenDSS. + + Returns + ------- + load : np.ndarray + An array of loads retrieved from the model. + load_names : list of str + An array of load names corresponding to the loads in the model. + + """ + # Activate the model in OpenDSS + dss.run_command("Redirect " + model_loc) + load, load_names = sims.get_loads() + return load, load_names + + +def generate_scaling(mu, sig, size): + """ + Generates a random scaling factor based on a normal distribution with mean `mu` + and standard deviation `sig`. The number of scaling factors generated is determined by `size`. + + Parameters + ---------- + mu : float + The mean of the normal distribution. + sig : float + The standard deviation of the normal distribution. + size : int + The number of scaling factors to generate. + + Returns + ------- + ndarray + An array of scaling factors generated from the normal distribution. + + """ + # TODO: add more types of scaling as additive noise/signal? + return np.random.normal(loc=mu, scale=sig, size=size) + + +def simulate_event(value, continue_event:bool=False): + + event_type = np.random.choice(np.arange(10)) # 10% probability of evnet? + + if event_type == 0 and continue_event: # where the values for V and I are + # 0.0 + value = 0.0 + elif event_type == 1: # with no data at all + value = None + elif event_type == 2: # value is out of bounds + value = np.random.choice([np.random.uniform(low=-10, high=-1), + np.random.uniform(low=1e15, high=1e20)]) + else: + pass + return value + +def run_simulation(start_ns, end_ns, collection_prefix, + fs=30, conn=None, + model_location=None): + """ + Runs a simulation from `start_ns` to `end_ns` with the given `collection_prefix`. + The simulation is initialized using a model obtained from `model_location`. + If `model_location` is not provided, it defaults to 'Models/13Bus/IEEE13Nodeckt.dss'. + The simulation uses frequency `fs` and a database connection `conn`. + + Parameters + ---------- + start_ns : int + The start time of the simulation in nanoseconds. + end_ns : int + The end time of the simulation in nanoseconds. + collection_prefix : str + The prefix for the name of the data collection for the simulation. + fs : int, optional + The frequency of the simulation, defaults to 30. + conn : obj, optional + The database connection used for the simulation, defaults to None. + model_location : str, optional + The file location of the model to be used for the simulation. + Defaults to 'Models/13Bus/IEEE13Nodeckt.dss' if not specified. + + """ + model_location = (model_location + if model_location is not None + else os.path.join(MODEL_REL_PATH, + 'Models/13Bus/IEEE13Nodeckt.dss')) + # Initialize simulation parameters + load, load_names = initialize_simulation(model_location) + timestamps = np.arange(start_ns, end_ns, 1e9 // fs, dtype="int") + scale = generate_scaling(1.1, 0.1, [len(load_names), timestamps.size]) + new_load = scale * load[:, np.newaxis] + collections, names, tags, annotations = sims.get_stream_info( + base_col=collection_prefix + ) + streams_dict = sims.create_streams( + collection_prefix, collections, names, tags, annotations, conn + ) + streamset = StreamSet(list(streams_dict.values())) + _stream_info = lambda s: "/".join( + [s.collection.replace( + collection_prefix + '/', '' + ), s.name] + ) + _values = lambda s, cont_event: simulate_event( + (V.get(_stream_info(s)) or I.get(_stream_info(s)))[0], + cont_event) + prev_timestamp = None + # For example, event lasts for at most 100 timestamps + continue_event_ind_left = np.random.randint(1, 100) + # Run simulation + for i in range(new_load.shape[1]): + V, I = sims.simulate_network(new_load[:, [i]], load_names) + if continue_event_ind_left == 0: + continue_event_ind_left = np.random.randint(1, 100) + else: + continue_event_ind_left -= 1 + now = datetime_to_ns(datetime.utcnow()) + _datamap_gen = conn._executor.map( + lambda s: (s._uuid, + dict(time=now, + value=_values(s, + continue_event_ind_left > 0) + )), + streamset._streams + ) + data_map = {} + for k,v in _datamap_gen: + if v['value'] is not None: + data_map[k] = pa.Table.from_pylist([v]) + _streamset = StreamSet([Stream(conn, uuid=k) for k in + data_map.keys()]) + _streamset.arrow_insert(data_map) + if i % INSERT_BATCH_SIZE == 0: + [stream.flush() for stream in streamset] + print("Streamset flushed.") + time.sleep(round(1/fs,ndigits=5)) + # print(prev_timestamp, now) if i % 10 == 0 else None + prev_timestamp = now + +def main(collection_prefix, start_time, end_time, fs, profile): + conn = btrdb_connect(profile=profile) + run_simulation(start_time, end_time, collection_prefix, fs, conn) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Simulate network.") + parser.add_argument( + "-s", + "--start_ns", + default=datetime_to_ns(datetime.utcnow()), + type=int, + help=("Start time in nanoseconds. " "(default: %(default))"), + ) + parser.add_argument( + "-e", + "--end_ns", + type=int, + help="End " "time " "in nanoseconds relative to Jan 1, 2023.", + ) + parser.add_argument( + "--frequency", default=30, type=int, + help="Sampling frequency in Hz" + ) + parser.add_argument( + "--duration_days", + default=1, + type=int, + help="Duration in hours relative to start.", + ) + parser.add_argument( + "--collection_prefix", default="simulated/ieee13", help="Collection prefix" + ) + parser.add_argument( + "--profile", default="ni4ai", help="BTRDB profile name (default: %(default))" + ) + + args = parser.parse_args() + if args.end_ns is None: + print("end_ns is not given, using default value for duration") + args.end_ns = args.start_ns + ns_delta(days=args.duration_days) + + main( + args.collection_prefix, args.start_ns, args.end_ns, args.frequency, args.profile + ) diff --git a/btrdbextras/opendss_ingest/requirements.txt b/btrdbextras/opendss_ingest/requirements.txt new file mode 100644 index 0000000..d959997 --- /dev/null +++ b/btrdbextras/opendss_ingest/requirements.txt @@ -0,0 +1,4 @@ +OpenDSSDirect.py[extras] + +numpy>=1.24.3 +tqdm>=4.66.1 diff --git a/btrdbextras/opendss_ingest/simulation_utils.py b/btrdbextras/opendss_ingest/simulation_utils.py new file mode 100644 index 0000000..0119851 --- /dev/null +++ b/btrdbextras/opendss_ingest/simulation_utils.py @@ -0,0 +1,569 @@ +import uuid +from typing import Dict, List, Optional, Tuple + +import numpy as np +import opendssdirect as dss +from btrdb import BTrDB +from btrdb.stream import StreamSet +from pandas import DataFrame +from tqdm.auto import tqdm + +PHASE_LETTERS = ["A", "B", "C"] + + +def v2dict(bus_names: List[str]) -> Dict[str, float]: + """ + Returns the voltage data on each phase of the buses in bus_names. + + Parameters + ---------- + bus_names : List[str] + Buses for which to return voltage data. + + Returns + ------- + Dict[str, float] + V : dictionary of real values. The keys are the stream collection/name for the data. + The collection / stream name encodes the bus, phase, and quantity + which are formatted by the method get_voltage_stream_column_name. + """ + # Instantiate the dict of results + V = {} + + # Iterate through the buses + for bus in bus_names: + # Set the current bus to be "active" + dss.Circuit.SetActiveBus(bus) + + # Get the phases at this bus + phases = dss.Bus.Nodes() + nphases = len(phases) + + # Get all voltages at this bus + # This is a real array of size nphases * 2 - + # each pair is the re & imag part of the voltage + busvolt = dss.Bus.Voltages() + + # Get the voltage at each phase + for pidx in range(nphases): + # We don't want to save any phase 0 data + if phases[pidx] == 0: + continue + + voltage = busvolt[pidx * 2] + 1j * busvolt[pidx * 2 + 1] + + # Save the magnitude/angle data + for is_mag in (True, False): + col, name = get_voltage_stream_column_name( + bus, PHASE_LETTERS[phases[pidx] - 1], is_mag + ) + V[col + "/" + name] = (np.abs(voltage) + if is_mag else + np.angle(voltage, deg=True)) + + return V + + +def i2dict(con_names: List[str]) -> Dict[str, float]: + """ + Returns the complex currents on each phase at each end of each connector. + The order of results matches the input names. + + Parameters + ---------- + con_names : List[str] + Connectors for which to return current data + + Returns + ------- + Dict[str, float] + I - dictionary of real values. The keys are the stream collection/name for the data. + The collection / stream name encodes the connector, end, phase, and quantity + which are formatted by the method get_lineflow_stream_colname. + """ + + ncons = len(con_names) + # Get the ends of each connector + con_ends = get_conn_ends(con_names) + + # Instantiate the dict of results + I = {} + + for cidx in range(ncons): + # Set the current connector to be "active" + dss.Circuit.SetActiveElement(con_names[cidx]) + + # Get the phases on the connector + # this is the phases of each terminal at each end + phases = dss.CktElement.NodeOrder() + nphases = int(len(phases) / 2) + + # Get the currents on each phase at each end of the connector + # This is a real array of size nphases * 2 * 2 - + # each pair is the re & imag part of the current + coni = dss.CktElement.Currents() + + for end in range(len(con_ends[cidx])): + for pidx in range(nphases): + # We don't want to save any phase 0 information which corresponds to the grounding connection + if phases[pidx] == 0: + continue + # Construct the complex current. + current = ( + coni[2 * (end * nphases + pidx)] + + 1j * coni[2 * (end * nphases + pidx) + 1] + ) + # Save the magnitude data + col, name = get_lineflow_stream_colname( + con_names[cidx], + con_ends[cidx][end], + PHASE_LETTERS[phases[pidx] - 1], + True, + ) + I[col + "/" + name] = np.abs(current) + # Save the angle data + col, name = get_lineflow_stream_colname( + con_names[cidx], + con_ends[cidx][end], + PHASE_LETTERS[phases[pidx] - 1], + False, + ) + I[col + "/" + name] = np.angle(current, deg=True) + return I + + +def simulate_network( + loads: np.ndarray, + load_names: List[str], + con_types: Optional[List[str]] = ["Line", "Transformer"], +) -> Dict[str, np.ndarray]: + """ + Simulates the network for all the values of load in the input loads. + + Parameters + ---------- + loads : np.ndarray + n x T matrix of floats. This is the load values to set and simulate. + load_names : List[str] + The names of the loads whose values are to be set to those in loads. + con_types : Optional[List[str]] + The connector types for which to return current data. + + Returns + ------- + Dict[str, np.ndarray] + V : Dictionary of real length T array. Values are voltage magnitude + & angle time series generated by simulation. + The keys are the stream collection/name for the data. The collection + / stream name encodes the bus, + phase, and quantity which is formatted by the method get_voltage_stream_colname. + + I : Dictionary of real length T array. Values are current magnitude + & angle time series generated by simulation. + The keys are the stream collection/name for the data. The collection + / stream name encodes the + connector, end, phase, and quantity which is formatted by the method + get_lineflow_stream_colname. + """ + [n, T] = loads.shape + + # Get the buses and connectors + bus_names = get_buses() + con_names = get_connectors(con_types) + con_ends = get_conn_ends(con_names) + + V = {} + I = {} + + # if is_initial_run: + # # Run the first simulation to get the keys for the output dictionary + # # Set the new load values + # set_loads(loads[:, 0], load_names) + # # Solve the power flow + # dss.Solution.Solve() + # # Get the data + # vdata = v2dict(bus_names) + # idata = i2dict(con_names) + # # Save the voltage data + # for key, val in vdata.items(): + # V[key] = np.nan * np.ones(T) + # V[key][0] = val + # # Save the current data + # for key, val in idata.items(): + # I[key] = np.nan * np.ones(T) + # I[key][0] = val + + # Iterate through rest of the times + prog_bar = tqdm(range(0, T), desc="Running simulation", leave=False) + for t in prog_bar: + set_loads(loads[:, t], load_names) + dss.Solution.Solve() # TODO: explore incremental solution in opendss + vdata = v2dict(bus_names) + idata = i2dict(con_names) + + for key, val in vdata.items(): + if t == 0: + V[key] = np.nan * np.ones(T) + V[key][t] = val + for key, val in idata.items(): + if t == 0: + I[key] = np.nan * np.ones(T) + I[key][t] = val + + return V, I + + +############################################################################### +# Methods related to streams that we will create & push data to. +############################################################################### + + +def get_stream_info( + base_col="simulated", +) -> Tuple[List[str], List[str], List[Dict[str, str]], List[Dict[str, str]]]: + """ + Returns collection names, tags, and annotations + for all the streams we want to create to hold + voltage and current data across the network. + + Parameters + ---------- + base_col : str + The base collection level under which we want all the simulated streams to be organized. + + Returns + ------- + collections: List[str] + names : List[str] + tags : List[Dict[str, str]] + annotations : List[Dict[str, str]] + """ + phases = PHASE_LETTERS + + # The lists to store all results + collections = [] + names = [] + tags = [] + annotations = [] + + ## Get information for streams of bus voltages + # Get the names of all buses + bus_names = dss.Circuit.AllBusNames() + # Iterate over all buses and determine streams for each + for bus in bus_names: + # Set the bus to "active" + dss.Circuit.SetActiveBus(bus) + # Get the basekV of this bus + basekV = dss.Bus.kVBase() + # Get phases at this bus + busphases = dss.Bus.Nodes() + for p in busphases: + # We don't want to save any phase 0 information + if p == 0: + continue + # Magnitude stream + cM, nM, tM, aM = get_voltage_stream_info(bus, phases[p - 1], True, basekV) + # Angle stream + cA, nA, tA, aA = get_voltage_stream_info(bus, phases[p - 1], False, basekV) + # Save results + collections.append(base_col + "/" + cM) + collections.append(base_col + "/" + cA) + names.append(nM) + names.append(nA) + tags.append(tM) + tags.append(tA) + annotations.append(aM) + annotations.append(aA) + + ## Get information for the streams of connection currents + # Get the names of all connectors + con_names = get_connectors() + for con in con_names: + # Set the current connector to be "active" + dss.Circuit.SetActiveElement(con) + + # Get the buses that this connector connects + # (the split removes terminals indicating the phases at each end + # so three-phase busX.1.2.3 becomes busX) + to = dss.CktElement.BusNames()[0].split(".")[0] + frm = dss.CktElement.BusNames()[1].split(".")[0] + # Check that to and frm are different (these can be the same for capacitors) + if to == frm: + ends = [to] + else: + ends = [to, frm] + + # Get the phases on the line + # this is the phases of each terminal at each end + conphases = dss.CktElement.NodeOrder() + nphases = int(len(conphases) / 2) + + for end in ends: + for p in conphases[0:nphases]: + # We don't want to save any phase 0 information + if p == 0: + continue + + # Magnitude stream + cM, nM, tM, aM = get_lineflow_stream_info(con, end, phases[p - 1], True) + # Angle stream + cA, nA, tA, aA = get_lineflow_stream_info( + con, end, phases[p - 1], False + ) + # Save results + collections.append(base_col + "/" + cM) + collections.append(base_col + "/" + cA) + names.append(nM) + names.append(nA) + tags.append(tM) + tags.append(tA) + annotations.append(aM) + annotations.append(aA) + + return collections, names, tags, annotations + + +def get_existing_streams(col_prefix, conn): + """Get the existing streams under the base collection col_prefix""" + streams = conn.streams_in_collection(col_prefix) + # Build the dictionary of the streams + streams_dict = {} + for stream in streams: + streams_dict[stream.collection + "/" + stream.name] = stream + print("Found", len(streams_dict.keys()), "streams under", col_prefix) + return streams_dict + + +def create_streams( + col_prefix: str, + collections: List, + names, + tags, + annotations, + conn: BTrDB, + verbose: bool = False, +): + """ + Given a set of collections, names, tags, and annotations for intended streams, check if + they exist. If not, create them. + + Returns + ------- + existing : dict + A dictionary capturing all the intended streams. Keys are the collection/name of the stream, + values are stream objects. + """ + + existing = get_existing_streams(col_prefix, conn) + + # Iterate through the desired streams and check if they exist already. If not + # create them. + nstreams = len(collections) + nexisting = 0 + ncreated = 0 + for i in range(nstreams): + stream_info = collections[i] + "/" + names[i] + if stream_info in existing: + if verbose: + print(stream_info, "already exists.") + nexisting += 1 + pass + else: + stream_id = uuid.uuid4() + + stream = conn.create( + uuid=stream_id, + collection=collections[i], + tags=tags[i], + annotations=annotations[i], + ) + + existing[stream_info] = stream + if verbose: + print("Created", stream_info, ", uuid:", stream_id) + ncreated += 1 + print("Found", nexisting, "streams. Created", ncreated, "streams.") + return existing + + +def get_lineflow_stream_info(line_name, line_end, phase, ismag): + if ismag: + unit = "amps" + else: + unit = "degrees" + collection, name = get_lineflow_stream_colname(line_name, line_end, phase, ismag) + + tags = {"name": name, "unit": unit} + annotations = {"phase": phase} + + return collection, name, tags, annotations + + +def get_lineflow_stream_colname(line_name, line_end, phase, ismag): + collection = line_name + "/" + line_end + if ismag: + lastltr = "M" + else: + lastltr = "A" + + name = "I" + phase + lastltr + return collection, name + + +def get_voltage_stream_column_name(bus_name, phase, ismag): + collection = bus_name + if ismag: + lastltr = "M" + else: + lastltr = "A" + + name = "V" + phase + lastltr + return collection, name + + +def get_voltage_stream_info(bus_name, phase, ismag, basekV): + if ismag: + unit = "volts" + else: + unit = "degrees" + collection, name = get_voltage_stream_column_name(bus_name, phase, ismag) + tags = {"name": name, "unit": unit} + annotations = {"phase": phase, "basekV": str(basekV)} + + return collection, name, tags, annotations + + +def add_all_data(times, data_dict, streams_dict, base_col): + """ + Add data to each stream. + + Parameters + ---------- + times : list of ints + The timestamps for the data to be added (one set of times for all data) + + data_dict : dict of arrays + The dictionary containing data to be added. Keys are the collection/name of + the stream to which data is to be added. Values are arrays of floats to add. + + streams_dict : dict of stream objects + keys are the collection/name of each stream. values are the stream objects. + + base_col : string + base collection prefix under which all streams can be found. + """ + # Create progress bar + nstreams = len(data_dict) + pbar = tqdm(total=nstreams, desc="Pushing data to streams", leave=False) + + for key, value in data_dict.items(): + stream_info = f"{base_col}/{key}" + if stream_info in streams_dict: + add_to_stream(streams_dict[stream_info], times, value) + else: + print(f"WARNING: {stream_info} not found") + pbar.update(1) + pbar.close() + + +def add_to_stream(stream, times, values): + """ + Given times and values, put them in the required tuple format and + add them to the stream. + """ + if len(times) != len(values): + print("WARNING: times & values not same size") + + payload = list(zip(times, values)) + stream.insert(payload, merge="replace") + + +def ingest_streamset(streamset:StreamSet, data: DataFrame, period_ns: int): + while True: + for row in data.iterrows(): + streamset.insert(row.to_dict()) + pass + + +############################################################################### +# Convenient wrappers to get model information +############################################################################### + +def get_buses(): + """A convenient wrapper to retrieve all buses in the system.""" + return dss.Circuit.AllBusNames() + + +def get_connectors(qualified=["Line", "Transformer"]): + """This method returns all connection elements of the "qualified" types""" + connectors = [] + pds = dss.PDElements.AllNames() + for pd in pds: + # Need to split the name to get the element type + if pd.split(".")[0] in qualified: + connectors.append(pd) + return connectors + + +def get_conn_ends(con_names): + """The list of lists with names of connectors ends""" + con_ends = [] + for con in con_names: + # Set the current connector to be "active" + dss.Circuit.SetActiveElement(con) + # Get the buses that this connector connects + # (the split removes terminals indicating the phases at each end + # so three phase busX.1.2.3 becomes busX) + to = dss.CktElement.BusNames()[0].split(".")[0] + frm = dss.CktElement.BusNames()[1].split(".")[0] + # Check for to and frm being identical - can happen with capacitors + if to == frm: + ends = [to] + else: + ends = [to, frm] + con_ends.append(ends) + return con_ends + + +def get_loads() -> Tuple[np.ndarray, List[str]]: + """Get all the loads in the network""" + load_names = dss.Loads.AllNames() + nloads = len(load_names) + + load = np.zeros([nloads]) + # Get the initial loads + for i in range(nloads): + dss.Loads.Name(load_names[i]) + load[i] = dss.Loads.kW() + + return load, load_names + + +def set_loads(load, load_names): + """Set the value of load load_names[i] to load[i]""" + nloads = len(load_names) + for i in range(nloads): + # Set this load to be active + dss.Loads.Name(load_names[i]) + # Set the load + dss.Loads.kW(load[i]) + + +def get_nbuses(): + """Get the number of buses in the network""" + return len(dss.Circuit.AllBusNames()) + + +def get_nlines(): + """Get the number of lines in the network""" + return len(dss.Lines.AllNames()) + + +def get_nconnectors(contypes=["Line", "Transformer"]): + """Get the number of connectors in the network""" + return len(get_connectors(qualified=contypes)) + + +def get_nloads(): + """Get the number of loads in the network""" + return len(dss.Loads.AllNames()) diff --git a/requirements.txt b/requirements.txt index a613877..0dcdf97 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,11 +6,11 @@ grpcio-tools>=1.16.1 dill==0.3.2 # BTrDB -btrdb>=5.11.7 +btrdb>=5.31.0 # Utilities and helpers tabulate==0.8.9 certifi # Readthedocs -sphinx_glpi_theme \ No newline at end of file +sphinx_glpi_theme diff --git a/tests/test_opendss_ingestor.py b/tests/test_opendss_ingestor.py new file mode 100644 index 0000000..75b79d9 --- /dev/null +++ b/tests/test_opendss_ingestor.py @@ -0,0 +1,41 @@ +import os + +import btrdb +from btrdb.utils.timez import to_nanoseconds + +from btrdbextras.opendss_ingest.opendss_ingestor import ( + MODEL_REL_PATH,initialize_simulation,run_simulation +) + + +class TestOpendssIngestor: + def test_initialize_simulation(self): + # Arrange + mock_model_loc = os.path.join( + MODEL_REL_PATH, + "Models/13Bus/IEEE13Nodeckt.dss" + ) + load, load_names = ([1155., 160., 120., 120., 170., 230., 170., + 485., 68., 290., 170., 128., 17., 66., 117.], + ['671', '634a', '634b', '634c', '645', '646', '692', + '675a', '675b', '675c', '611', '652', '670a', '670b', + '670c']) + # Act + results = initialize_simulation(mock_model_loc) + assert results[0].tolist() == load + assert results[-1] == load_names + + def test_simulate_network(self): + # load, load_names = ([1155., 160., 120., 120., 170., 230., 170., + # 485., 68., 290., 170., 128., 17., 66., 117.], + # ['671', '634a', '634b', '634c', '645', '646', + # '692', + # '675a', '675b', '675c', '611', '652', '670a', + # '670b', + # '670c']) + start_time = to_nanoseconds('2023-01-01 00:00:00') + end_time = to_nanoseconds('2023-01-01 00:01:00') + db = btrdb.connect(profile='ni4ai') + run_simulation(start_time, end_time, + collection_prefix='simulated/ieee13', fs=30, conn=db) + assert True