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import os
-from pathlib import Path
-import PV_ICE
-import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np
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+
+SIPS: QUIP SERIES¶
This journal explores the case studies of the SIPS 2025 project understanding the implications of repowering and module reuse. This compliments analyses conducted in SAM and PV Watts. Below are the Residential, Commercial, and Utility scale case studies
-testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
-baselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines')
-supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
-resultsfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial'/ 'USHistoryResults')
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-cwd=os.getcwd()
-print(os.getcwd())
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-C:\Users\hmirletz\Documents\GitHub\PV_ICE\docs\dev -
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-In [2]:
+In [ ]:
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-PV_ICE.__version__
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+if not os.path.exists(testfolder):
+ os.makedirs(testfolder)
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+cwd = os.getcwd() #grabs current working directory
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+testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'MESC-NRELStdScens')
+inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')
+baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
+supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
+#altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
-Out[2]:
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+#setup
+import numpy as np
+import pandas as pd
+import os,sys
+from pathlib import Path
+import matplotlib.pyplot as plt
+import plotly.express as px
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-''
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+import PV_ICE
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-In [3]:
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@@ -14684,19 +14625,15 @@
MATERIALS = ['glass']#,'aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']
-moduleFile = os.path.join(baselinesfolder, 'TEST_baseline_modules_mass_US.csv')
-#newmodfilesPAth = os.path.join(supportMatfolder,'Calculations-Installs-Subset-CommUtility.xlsx')
+print("Python version ", sys.version)
+print("Pandas version ", pd.__version__)
+print("pyplot ", plt.matplotlib.__version__)
+print("PV_ICE version ", PV_ICE.__version__)
+
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+In [ ]:
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@@ -14704,100 +14641,32 @@
r1 = PV_ICE.Simulation(name='sim1', path=testfolder)
-r1.createScenario(name='test', massmodulefile=moduleFile) #create the scenario, name and mod file attach
-for mat in MATERIALS:
- materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
- r1.scenario['test'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
+sim1 = PV_ICE.Simulation(name='SIPS', path=testfolder)
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-path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP -Baseline folder directed to default: C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines -No energy module file passed. If desired, pass one of the following options: ['baseline_modules_energy.csv', 'baseline_modules_energy_CdTe.csv'] --
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-r1.calculateMassFlow()
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Residential Case Study¶
This case study considers a residential system where the PV system is not at technical end of life (installed 2015), but the roof needs replacing in 2024. The owner has the option of keeping the old system or replacing the PV system.
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>>>> Calculating Material Flows <<<< - -Working on Scenario: test -******************** -Finished Area+Power Generation Calculations -==> Working on Material : glass --
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-In [6]:
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-usyearlyr1, uscumr1 = r1.aggregateResults()
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+In [ ]:
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@@ -14805,60 +14674,23 @@
r1.scenario['test'].dataOut_m.keys()
+#c-Si
+MATERIALS = ['glass','aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']
+moduleFile = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')
+#CdTe
+#MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte', 'copper_cdte', 'encapsulant_cdte','cadmium','tellurium']
+#moduleFile_CdTe = os.path.join(baselinesfolder, 'baseline_modules_mass_US_CdTe.csv')
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-Out[7]:
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-Index(['Area', 'Cumulative_Active_Area', 'EOL_BadStatus', 'EOL_Landfill0', - 'EOL_PATHS', 'EOL_PG', 'Effective_Capacity_[W]', 'Landfill_0_ProjLife', - 'MerchantTail_Area', 'MerchantTail_[W]', 'ModuleTotal_MFG', 'P2_stored', - 'P3_reMFG', 'P4_recycled', 'PB1_landfill', 'PB2_stored', 'PB3_reMFG', - 'PB3_reMFG_unyield', 'PB3_reMFG_yield', 'PB4_recycled', 'PG1_landfill', - 'PG2_stored', 'PG3_reMFG', 'PG3_reMFG_unyield', 'PG3_reMFG_yield', - 'PG4_recycled', 'Power_Degraded_[W]', 'Repaired_Area', 'Repaired_[W]', - 'Resold_Area', 'Resold_[W]', 'WeibullParams', - 'Yearly_Sum_Area_EOLby_Degradation', 'Yearly_Sum_Area_EOLby_Failure', - 'Yearly_Sum_Area_EOLby_ProjectLifetime', 'Yearly_Sum_Area_PathsBad', - 'Yearly_Sum_Area_PathsGood', 'Yearly_Sum_Area_atEOL', - 'Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure', - 'Yearly_Sum_Power_EOLby_ProjectLifetime', 'Yearly_Sum_Power_PathsBad', - 'Yearly_Sum_Power_PathsGood', 'Yearly_Sum_Power_atEOL', - 'irradiance_stc'], - dtype='object')-
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+In [ ]:
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@@ -14866,40 +14698,19 @@
'Yearly_Sum_Power_EOLby_Degradation',
-'Yearly_Sum_Power_EOLby_Failure',
-'Yearly_Sum_Power_EOLby_ProjectLifetime',
-'Yearly_Sum_Power_PathsBad',
-'Yearly_Sum_Power_PathsGood',
-'Yearly_Sum_Power_atEOL'
+#residential system parameters
+resi_sys1_size = 0.00552 #MW, first system on roof (degrades to 5 kW in 2024 after 9 years (2024-2015))
+resi_sys1_deg = 1.1 #%/yr, corresponds to 20 yr life
+resi_sys1_life = 20 #years of life
+resi_sys1_life_repower = 9 #years of life but remove for repowering
+
+resi_sys2_size = 0.0073 #MW, 2nd system on roof
+resi_sys2_deg = 0.7 #%/yr, corresponds to 30 yr life
+resi_sys2_life = 30 #years of life
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-Out[8]:
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-'Yearly_Sum_Power_atEOL'-
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+In [ ]:
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@@ -14912,10 +14723,14 @@
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yspeol_deg = r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_Degradation']
+#Residential case study
+sim1.createScenario(name='Resi_keep', massmodulefile=moduleFile) #create the scenario, name and mod file attach
+for mat in MATERIALS:
+ materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ sim1.scenario['Resi_keep'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
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+In [ ]:
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yspeol_fail =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_Failure']
+#Residential case study
+sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile) #create the scenario, name and mod file attach
+for mat in MATERIALS:
+ materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
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+yspeol_plife =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_ProjectLifetime']
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idx_temp = pd.RangeIndex(start=2024,stop=2051,step=1) #create the index +CdTeRamp = pd.DataFrame(index=idx_temp, columns=['CdTedeploy[MWdc]'], dtype=float) +CdTeRamp.loc[2024] = 14000 +CdTeRamp.loc[2030] = 50000#22000 +CdTeRamp_full = round(CdTeRamp.interpolate(),0)
+CdTeRamp_full¶
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+In [ ]:
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yspeol_sum =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_atEOL']
+#Modify the Scenario
+sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #
+#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life
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+In [ ]:
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plt.plot(yspeol_deg, label='deg')
-plt.plot(yspeol_fail, label='fail')
-plt.plot(yspeol_plife, label='life')
-plt.plot(yspeol_sum, ls=':', label='sum')
-plt.legend()
+#Modify the Scenario
+sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #
+#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life_repower
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+sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'new_Installed_Capacity_[MW]'] = resi_sys2_size
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_degradation'] = resi_sys2_deg
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_lifetime'] = resi_sys2_life
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-<matplotlib.legend.Legend at 0x1a30cb5d890>-
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yspeol = pd.concat([yspeol_deg,yspeol_fail,yspeol_plife,yspeol_sum],axis=1)
-yspeol
+#do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?
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-| - | Yearly_Sum_Power_EOLby_Degradation | -Yearly_Sum_Power_EOLby_Failure | -Yearly_Sum_Power_EOLby_ProjectLifetime | -Yearly_Sum_Power_atEOL | -
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| 0 | -0.000000e+00 | -0.000000e+00 | -0.0 | -0.000000e+00 | -
| 1 | -0.000000e+00 | -6.866359e-01 | -0.0 | -6.866359e-01 | -
| 2 | -0.000000e+00 | -2.831734e+01 | -0.0 | -2.831734e+01 | -
| 3 | -0.000000e+00 | -2.485886e+02 | -0.0 | -2.485886e+02 | -
| 4 | -0.000000e+00 | -1.158294e+03 | -0.0 | -1.158294e+03 | -
| 5 | -0.000000e+00 | -3.813970e+03 | -0.0 | -3.813970e+03 | -
| 6 | -0.000000e+00 | -1.008092e+04 | -0.0 | -1.008092e+04 | -
| 7 | -0.000000e+00 | -2.289102e+04 | -0.0 | -2.289102e+04 | -
| 8 | -0.000000e+00 | -4.649511e+04 | -0.0 | -4.649511e+04 | -
| 9 | -0.000000e+00 | -8.668822e+04 | -0.0 | -8.668822e+04 | -
| 10 | -0.000000e+00 | -1.509730e+05 | -0.0 | -1.509730e+05 | -
| 11 | -0.000000e+00 | -2.486091e+05 | -0.0 | -2.486091e+05 | -
| 12 | -0.000000e+00 | -3.904739e+05 | -0.0 | -3.904739e+05 | -
| 13 | -0.000000e+00 | -5.886399e+05 | -0.0 | -5.886399e+05 | -
| 14 | -0.000000e+00 | -8.555594e+05 | -0.0 | -8.555594e+05 | -
| 15 | -0.000000e+00 | -1.202760e+06 | -0.0 | -1.202760e+06 | -
| 16 | -0.000000e+00 | -1.639007e+06 | -0.0 | -1.639007e+06 | -
| 17 | -0.000000e+00 | -2.168002e+06 | -0.0 | -2.168002e+06 | -
| 18 | -0.000000e+00 | -2.785889e+06 | -0.0 | -2.785889e+06 | -
| 19 | -0.000000e+00 | -3.479076e+06 | -0.0 | -3.479076e+06 | -
| 20 | -0.000000e+00 | -4.223139e+06 | -0.0 | -4.223139e+06 | -
| 21 | -0.000000e+00 | -4.983659e+06 | -0.0 | -4.983659e+06 | -
| 22 | -0.000000e+00 | -5.719628e+06 | -0.0 | -5.719628e+06 | -
| 23 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 24 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 25 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 26 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 27 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 28 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 29 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 30 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 31 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 32 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 33 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 34 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 35 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 36 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 37 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 38 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 39 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 40 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 41 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 42 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 43 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 44 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 45 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 46 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 47 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 48 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 49 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 50 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 51 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 52 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 53 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 54 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
| 55 | -2.494077e+06 | -5.719628e+06 | -0.0 | -8.213705e+06 | -
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-In [15]:
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-yspeol_MW = yspeol/1000000#), index = usyearlyr1.index)
-yspeol_MW_cumu = yspeol_MW.cumsum()
-yspeol_MW_cumu.index = usyearlyr1.index
-#yspeol_MW_cumu
-decomm_cap = usyearlyr1.filter(like='Decomm')
-compare_decom = pd.concat([yspeol_MW_cumu,decomm_cap],axis=1)
-compare_decom.tail()
-
+
+
Commercial Case Study¶
This case study considers a set of commerical PV installations.
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| - | Yearly_Sum_Power_EOLby_Degradation | -Yearly_Sum_Power_EOLby_Failure | -Yearly_Sum_Power_EOLby_ProjectLifetime | -Yearly_Sum_Power_atEOL | -DecommisionedCapacity_sim1_test_[MW] | -
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| year | -- | - | - | - | - |
| 2046 | -72.328234 | -194.476022 | -0.0 | -266.804256 | -341.370469 | -
| 2047 | -74.822311 | -200.195649 | -0.0 | -275.017960 | -351.370469 | -
| 2048 | -77.316388 | -205.915277 | -0.0 | -283.231665 | -361.370469 | -
| 2049 | -79.810465 | -211.634905 | -0.0 | -291.445370 | -371.370469 | -
| 2050 | -82.304542 | -217.354532 | -0.0 | -299.659074 | -381.370469 | -
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plt.plot(compare_decom)
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-[<matplotlib.lines.Line2D at 0x1a30cc64390>, - <matplotlib.lines.Line2D at 0x1a30cd24a90>, - <matplotlib.lines.Line2D at 0x1a30cd24e50>, - <matplotlib.lines.Line2D at 0x1a30cd24dd0>, - <matplotlib.lines.Line2D at 0x1a30cd254d0>]-
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decomm_cap.diff()
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-| - | DecommisionedCapacity_sim1_test_[MW] | -
|---|---|
| year | -- |
| 1995 | -NaN | -
| 1996 | -0.100001 | -
| 1997 | -0.199028 | -
| 1998 | -0.297258 | -
| 1999 | -0.395195 | -
| 2000 | -0.493899 | -
| 2001 | -0.595227 | -
| 2002 | -0.702085 | -
| 2003 | -0.818668 | -
| 2004 | -0.950675 | -
| 2005 | -1.105453 | -
| 2006 | -1.292034 | -
| 2007 | -1.520979 | -
| 2008 | -1.803935 | -
| 2009 | -2.152815 | -
| 2010 | -2.578488 | -
| 2011 | -3.088950 | -
| 2012 | -3.687055 | -
| 2013 | -4.368071 | -
| 2014 | -5.117577 | -
| 2015 | -5.910465 | -
| 2016 | -6.711880 | -
| 2017 | -7.480730 | -
| 2018 | -10.000000 | -
| 2019 | -10.000000 | -
| 2020 | -10.000000 | -
| 2021 | -10.000000 | -
| 2022 | -10.000000 | -
| 2023 | -10.000000 | -
| 2024 | -10.000000 | -
| 2025 | -10.000000 | -
| 2026 | -10.000000 | -
| 2027 | -10.000000 | -
| 2028 | -10.000000 | -
| 2029 | -10.000000 | -
| 2030 | -10.000000 | -
| 2031 | -10.000000 | -
| 2032 | -10.000000 | -
| 2033 | -10.000000 | -
| 2034 | -10.000000 | -
| 2035 | -10.000000 | -
| 2036 | -10.000000 | -
| 2037 | -10.000000 | -
| 2038 | -10.000000 | -
| 2039 | -10.000000 | -
| 2040 | -10.000000 | -
| 2041 | -10.000000 | -
| 2042 | -10.000000 | -
| 2043 | -10.000000 | -
| 2044 | -10.000000 | -
| 2045 | -10.000000 | -
| 2046 | -10.000000 | -
| 2047 | -10.000000 | -
| 2048 | -10.000000 | -
| 2049 | -10.000000 | -
| 2050 | -10.000000 | -
+
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-
In [18]:
+In [ ]:
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plt.plot(usyearlyr1.filter(like='Waste'))
-plt.legend(usyearlyr1.filter(like='Waste').keys())
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-<matplotlib.legend.Legend at 0x1a30cc85850>-
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In [19]:
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+
usyearlyr1.filter(like='Waste')
-
+
+
Utility Case Study¶
This case study considers a set of utility scale PV installations.
-
-
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-Out[19]:
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-| - | WasteAll_glass_sim1_test_[Tonnes] | -WasteAll_Module_sim1_test_[Tonnes] | -WasteEOL_glass_sim1_test_[Tonnes] | -WasteEOL_Module_sim1_test_[Tonnes] | -WasteMFG_glass_sim1_test_[Tonnes] | -WasteMFG_Module_sim1_test_[Tonnes] | -
|---|---|---|---|---|---|---|
| year | -- | - | - | - | - | - |
| 1995 | -20.210526 | -20.210526 | -0.000000 | -0.000000 | -20.210526 | -20.210526 | -
| 1996 | -19.892295 | -19.892295 | -0.000044 | -0.000044 | -19.892250 | -19.892250 | -
| 1997 | -19.616101 | -19.616101 | -0.001848 | -0.001848 | -19.614253 | -19.614253 | -
| 1998 | -19.360267 | -19.360267 | -0.016348 | -0.016348 | -19.343919 | -19.343919 | -
| 1999 | -19.157638 | -19.157638 | -0.076703 | -0.076703 | -19.080935 | -19.080935 | -
| 2000 | -19.079252 | -19.079252 | -0.254246 | -0.254246 | -18.825006 | -18.825006 | -
| 2001 | -19.252246 | -19.252246 | -0.676395 | -0.676395 | -18.575851 | -18.575851 | -
| 2002 | -19.956205 | -19.956205 | -1.545806 | -1.545806 | -18.410398 | -18.410398 | -
| 2003 | -21.407715 | -21.407715 | -3.159848 | -3.159848 | -18.247867 | -18.247867 | -
| 2004 | -24.017046 | -24.017046 | -5.928866 | -5.928866 | -18.088180 | -18.088180 | -
| 2005 | -28.322026 | -28.322026 | -10.390762 | -10.390762 | -17.931263 | -17.931263 | -
| 2006 | -34.995025 | -34.995025 | -17.217979 | -17.217979 | -17.777046 | -17.777046 | -
| 2007 | -44.836641 | -44.836641 | -27.211182 | -27.211182 | -17.625459 | -17.625459 | -
| 2008 | -58.748500 | -58.748500 | -41.272065 | -41.272065 | -17.476435 | -17.476435 | -
| 2009 | -77.676259 | -77.676259 | -60.346349 | -60.346349 | -17.329910 | -17.329910 | -
| 2010 | -102.514189 | -102.514189 | -85.328367 | -85.328367 | -17.185822 | -17.185822 | -
| 2011 | -133.652690 | -133.652690 | -116.922122 | -116.922122 | -16.730568 | -16.730568 | -
| 2012 | -172.031056 | -172.031056 | -155.462361 | -155.462361 | -16.568694 | -16.568694 | -
| 2013 | -216.819410 | -216.819410 | -200.714147 | -200.714147 | -16.105263 | -16.105263 | -
| 2014 | -267.080725 | -267.080725 | -251.689830 | -251.689830 | -15.390895 | -15.390895 | -
| 2015 | -320.223355 | -320.223355 | -306.543473 | -306.543473 | -13.679882 | -13.679882 | -
| 2016 | -375.182901 | -375.182901 | -362.613902 | -362.613902 | -12.568998 | -12.568998 | -
| 2017 | -428.688224 | -428.688224 | -416.674306 | -416.674306 | -12.013918 | -12.013918 | -
| 2018 | -623.268730 | -623.268730 | -612.513091 | -612.513091 | -10.755639 | -10.755639 | -
| 2019 | -614.458976 | -614.458976 | -604.413917 | -604.413917 | -10.045060 | -10.045060 | -
| 2020 | -606.463431 | -606.463431 | -596.986195 | -596.986195 | -9.477236 | -9.477236 | -
| 2021 | -598.425793 | -598.425793 | -589.898611 | -589.898611 | -8.527181 | -8.527181 | -
| 2022 | -591.302103 | -591.302103 | -583.126572 | -583.126572 | -8.175531 | -8.175531 | -
| 2023 | -583.802493 | -583.802493 | -576.626711 | -576.626711 | -7.175783 | -7.175783 | -
| 2024 | -576.963531 | -576.963531 | -570.334241 | -570.334241 | -6.629290 | -6.629290 | -
| 2025 | -570.695624 | -570.695624 | -564.724969 | -564.724969 | -5.970655 | -5.970655 | -
| 2026 | -564.001701 | -564.001701 | -558.932081 | -558.932081 | -5.069621 | -5.069621 | -
| 2027 | -557.423514 | -557.423514 | -552.887434 | -552.887434 | -4.536080 | -4.536080 | -
| 2028 | -550.529332 | -550.529332 | -546.523913 | -546.523913 | -4.005419 | -4.005419 | -
| 2029 | -542.988428 | -542.988428 | -539.783283 | -539.783283 | -3.205145 | -3.205145 | -
| 2030 | -343.518088 | -343.518088 | -340.880813 | -340.880813 | -2.637275 | -2.637275 | -
| 2031 | -338.638740 | -338.638740 | -336.028174 | -336.028174 | -2.610567 | -2.610567 | -
| 2032 | -333.521340 | -333.521340 | -330.924320 | -330.924320 | -2.597020 | -2.597020 | -
| 2033 | -328.206654 | -328.206654 | -325.622114 | -325.622114 | -2.584540 | -2.584540 | -
| 2034 | -321.320325 | -321.320325 | -318.747350 | -318.747350 | -2.572975 | -2.572975 | -
| 2035 | -316.255674 | -316.255674 | -313.693469 | -313.693469 | -2.562204 | -2.562204 | -
| 2036 | -309.856819 | -309.856819 | -307.304691 | -307.304691 | -2.552128 | -2.552128 | -
| 2037 | -306.082971 | -306.082971 | -303.540307 | -303.540307 | -2.542663 | -2.542663 | -
| 2038 | -298.903317 | -298.903317 | -296.369574 | -296.369574 | -2.533743 | -2.533743 | -
| 2039 | -294.109264 | -294.109264 | -291.583954 | -291.583954 | -2.525310 | -2.525310 | -
| 2040 | -293.103954 | -293.103954 | -290.586640 | -290.586640 | -2.517314 | -2.517314 | -
| 2041 | -289.519637 | -289.519637 | -287.009924 | -287.009924 | -2.509713 | -2.509713 | -
| 2042 | -287.966956 | -287.966956 | -285.464484 | -285.464484 | -2.502472 | -2.502472 | -
| 2043 | -287.466292 | -287.466292 | -284.970733 | -284.970733 | -2.495559 | -2.495559 | -
| 2044 | -286.072961 | -286.072961 | -283.584015 | -283.584015 | -2.488946 | -2.488946 | -
| 2045 | -287.188917 | -287.188917 | -284.706308 | -284.706308 | -2.482609 | -2.482609 | -
| 2046 | -284.609605 | -284.609605 | -282.133079 | -282.133079 | -2.476526 | -2.476526 | -
| 2047 | -283.770910 | -283.770910 | -281.300231 | -281.300231 | -2.470679 | -2.470679 | -
| 2048 | -281.580696 | -281.580696 | -279.115646 | -279.115646 | -2.465050 | -2.465050 | -
| 2049 | -279.789975 | -279.789975 | -277.330351 | -277.330351 | -2.459624 | -2.459624 | -
| 2050 | -279.225361 | -279.225361 | -276.770974 | -276.770974 | -2.454387 | -2.454387 | -
diff --git a/docs/dev/SIPS2025-CaseStudies.ipynb b/docs/dev/SIPS2025-CaseStudies.ipynb
new file mode 100644
index 00000000..87e55ef6
--- /dev/null
+++ b/docs/dev/SIPS2025-CaseStudies.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "badb9b1e-e3ab-46ab-835d-46aec0dc01ac",
+ "metadata": {},
+ "source": [
+ "# SIPS: QUIP SERIES\n",
+ "This journal explores the case studies of the SIPS 2025 project understanding the implications of repowering and module reuse. This compliments analyses conducted in SAM and PV Watts. Below are the Residential, Commercial, and Utility scale case studies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51c46971-8cdf-4836-8418-0469123f3ab9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#setup\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import os,sys\n",
+ "from pathlib import Path\n",
+ "import matplotlib.pyplot as plt\n",
+ "import plotly.express as px\n",
+ "\n",
+ "import PV_ICE\n",
+ "\n",
+ "cwd = os.getcwd() #grabs current working directory\n",
+ "\n",
+ "testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'MESC-NRELStdScens')\n",
+ "inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')\n",
+ "baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')\n",
+ "supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')\n",
+ "#altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')\n",
+ "\n",
+ "if not os.path.exists(testfolder):\n",
+ " os.makedirs(testfolder)\n",
+ "\n",
+ "print(\"Python version \", sys.version)\n",
+ "print(\"Pandas version \", pd.__version__)\n",
+ "print(\"pyplot \", plt.matplotlib.__version__)\n",
+ "print(\"PV_ICE version \", PV_ICE.__version__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "613f82f3-f393-43dc-9386-0b9329ab0804",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sim1 = PV_ICE.Simulation(name='SIPS', path=testfolder)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a43f9553-be86-4a17-92d2-b32c65992b15",
+ "metadata": {},
+ "source": [
+ "## Residential Case Study\n",
+ "This case study considers a residential system where the PV system is not at technical end of life (installed 2015), but the roof needs replacing in 2024. The owner has the option of keeping the old system or replacing the PV system."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93a52417-0815-46cc-8b4f-c86fc2b6e556",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#c-Si\n",
+ "MATERIALS = ['glass','aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']\n",
+ "moduleFile = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')\n",
+ "#CdTe\n",
+ "#MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte', 'copper_cdte', 'encapsulant_cdte','cadmium','tellurium']\n",
+ "#moduleFile_CdTe = os.path.join(baselinesfolder, 'baseline_modules_mass_US_CdTe.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ee5a3e3-29a3-4eea-94c5-e5ae75fb1da6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#residential system parameters\n",
+ "resi_sys1_size = 0.00552 #MW, first system on roof (degrades to 5 kW in 2024 after 9 years (2024-2015))\n",
+ "resi_sys1_deg = 1.1 #%/yr, corresponds to 20 yr life\n",
+ "resi_sys1_life = 20 #years of life\n",
+ "resi_sys1_life_repower = 9 #years of life but remove for repowering\n",
+ "\n",
+ "resi_sys2_size = 0.0073 #MW, 2nd system on roof\n",
+ "resi_sys2_deg = 0.7 #%/yr, corresponds to 30 yr life\n",
+ "resi_sys2_life = 30 #years of life"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "efedbc4b-0216-48aa-a884-dbc8ce255975",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Residential case study\n",
+ "sim1.createScenario(name='Resi_keep', massmodulefile=moduleFile) #create the scenario, name and mod file attach\n",
+ "for mat in MATERIALS:\n",
+ " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
+ " sim1.scenario['Resi_keep'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7be01ab2-6824-4687-a1b8-23b68dbeb4ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Residential case study\n",
+ "sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile) #create the scenario, name and mod file attach\n",
+ "for mat in MATERIALS:\n",
+ " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
+ " sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "68bf3167-c4a5-4da3-91ed-57f0f9bacd5f",
+ "metadata": {},
+ "source": [
+ "idx_temp = pd.RangeIndex(start=2024,stop=2051,step=1) #create the index\n",
+ "CdTeRamp = pd.DataFrame(index=idx_temp, columns=['CdTe_deploy_[MWdc]'], dtype=float)\n",
+ "CdTeRamp.loc[2024] = 14000\n",
+ "CdTeRamp.loc[2030] = 50000#22000\n",
+ "CdTeRamp_full = round(CdTeRamp.interpolate(),0)\n",
+ "#CdTeRamp_full"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61014a62-9b16-431d-9c5e-3aa2f19acef6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Modify the Scenario \n",
+ "sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #\n",
+ "#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015\n",
+ "sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size\n",
+ "sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg\n",
+ "sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ac8fa8a-7c2b-40ee-8b64-5c1e34594d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Modify the Scenario \n",
+ "sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #\n",
+ "#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015\n",
+ "sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size\n",
+ "sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg\n",
+ "sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life_repower\n",
+ "\n",
+ "sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'new_Installed_Capacity_[MW]'] = resi_sys2_size\n",
+ "sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_degradation'] = resi_sys2_deg\n",
+ "sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_lifetime'] = resi_sys2_life"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6268306d-58bc-4065-9810-0871f6871744",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b229bce9-496d-4f55-b8a1-2fd1158b3a75",
+ "metadata": {},
+ "source": [
+ "# Commercial Case Study\n",
+ "This case study considers a set of commerical PV installations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5601df4c-6556-4b71-8922-6a05dbe9ccc6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76aec89f-0650-43c9-94db-b3f70de82ef9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bf115fa7-ac29-41c2-bfcd-7b8dc1beb64b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "afcc89b5-fccf-4c63-91e7-194446218e18",
+ "metadata": {},
+ "source": [
+ "# Utility Case Study\n",
+ "This case study considers a set of utility scale PV installations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3210d68d-f56b-4da3-b500-8073b707a2d5",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/dev/SIPS2025-CaseStudies.py b/docs/dev/SIPS2025-CaseStudies.py
new file mode 100644
index 00000000..1cd669a2
--- /dev/null
+++ b/docs/dev/SIPS2025-CaseStudies.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # SIPS: QUIP SERIES
+# This journal explores the case studies of the SIPS 2025 project understanding the implications of repowering and module reuse. This compliments analyses conducted in SAM and PV Watts. Below are the Residential, Commercial, and Utility scale case studies
+
+# In[ ]:
+
+
+#setup
+import numpy as np
+import pandas as pd
+import os,sys
+from pathlib import Path
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+import PV_ICE
+
+cwd = os.getcwd() #grabs current working directory
+
+testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'MESC-NRELStdScens')
+inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')
+baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
+supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
+#altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
+
+if not os.path.exists(testfolder):
+ os.makedirs(testfolder)
+
+print("Python version ", sys.version)
+print("Pandas version ", pd.__version__)
+print("pyplot ", plt.matplotlib.__version__)
+print("PV_ICE version ", PV_ICE.__version__)
+
+
+# In[ ]:
+
+
+sim1 = PV_ICE.Simulation(name='SIPS', path=testfolder)
+
+
+# ## Residential Case Study
+# This case study considers a residential system where the PV system is not at technical end of life (installed 2015), but the roof needs replacing in 2024. The owner has the option of keeping the old system or replacing the PV system.
+
+# In[ ]:
+
+
+#c-Si
+MATERIALS = ['glass','aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']
+moduleFile = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')
+#CdTe
+#MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte', 'copper_cdte', 'encapsulant_cdte','cadmium','tellurium']
+#moduleFile_CdTe = os.path.join(baselinesfolder, 'baseline_modules_mass_US_CdTe.csv')
+
+
+# In[ ]:
+
+
+#residential system parameters
+resi_sys1_size = 0.00552 #MW, first system on roof (degrades to 5 kW in 2024 after 9 years (2024-2015))
+resi_sys1_deg = 1.1 #%/yr, corresponds to 20 yr life
+resi_sys1_life = 20 #years of life
+resi_sys1_life_repower = 9 #years of life but remove for repowering
+
+resi_sys2_size = 0.0073 #MW, 2nd system on roof
+resi_sys2_deg = 0.7 #%/yr, corresponds to 30 yr life
+resi_sys2_life = 30 #years of life
+
+
+# In[ ]:
+
+
+#Residential case study
+sim1.createScenario(name='Resi_keep', massmodulefile=moduleFile) #create the scenario, name and mod file attach
+for mat in MATERIALS:
+ materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ sim1.scenario['Resi_keep'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
+
+
+# In[ ]:
+
+
+#Residential case study
+sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile) #create the scenario, name and mod file attach
+for mat in MATERIALS:
+ materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
+
+
+# idx_temp = pd.RangeIndex(start=2024,stop=2051,step=1) #create the index
+# CdTeRamp = pd.DataFrame(index=idx_temp, columns=['CdTe_deploy_[MWdc]'], dtype=float)
+# CdTeRamp.loc[2024] = 14000
+# CdTeRamp.loc[2030] = 50000#22000
+# CdTeRamp_full = round(CdTeRamp.interpolate(),0)
+# #CdTeRamp_full
+
+# In[ ]:
+
+
+#Modify the Scenario
+sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #
+#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life
+
+
+# In[ ]:
+
+
+#Modify the Scenario
+sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #
+#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life_repower
+
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'new_Installed_Capacity_[MW]'] = resi_sys2_size
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_degradation'] = resi_sys2_deg
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_lifetime'] = resi_sys2_life
+
+
+# In[ ]:
+
+
+#do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?
+
+
+# # Commercial Case Study
+# This case study considers a set of commerical PV installations.
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# # Utility Case Study
+# This case study considers a set of utility scale PV installations.
+
+# In[ ]:
+
+
+
+
diff --git a/docs/dev/Untitled.ipynb b/docs/dev/Untitled.ipynb
deleted file mode 100644
index a640f142..00000000
--- a/docs/dev/Untitled.ipynb
+++ /dev/null
@@ -1,2122 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "d60db3d1-02df-4861-a0be-fb88226ffdb3",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\docs\\dev\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "from pathlib import Path\n",
- "import PV_ICE\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "\n",
- "testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')\n",
- "baselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines')\n",
- "supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')\n",
- "resultsfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial'/ 'USHistoryResults')\n",
- "\n",
- "cwd=os.getcwd()\n",
- "print(os.getcwd())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "19661f71-b66a-422b-9d63-7d45a2af9528",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "''"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "PV_ICE.__version__"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "efe189a5-77b9-43a0-aa70-1ae8cad0dc20",
- "metadata": {},
- "outputs": [],
- "source": [
- "MATERIALS = ['glass']#,'aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']\n",
- "moduleFile = os.path.join(baselinesfolder, 'TEST_baseline_modules_mass_US.csv')\n",
- "#newmodfilesPAth = os.path.join(supportMatfolder,'Calculations-Installs-Subset-CommUtility.xlsx')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "84dd7254-14a6-4adb-be0b-5d67d9f8aba2",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "path = C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\TEMP\n",
- "Baseline folder directed to default: C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\baselines\n",
- "No energy module file passed. If desired, pass one of the following options: ['baseline_modules_energy.csv', 'baseline_modules_energy_CdTe.csv']\n"
- ]
- }
- ],
- "source": [
- "r1 = PV_ICE.Simulation(name='sim1', path=testfolder)\n",
- "r1.createScenario(name='test', massmodulefile=moduleFile) #create the scenario, name and mod file attach\n",
- "for mat in MATERIALS:\n",
- " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
- " r1.scenario['test'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "8a1a9c69-4242-4ac9-85f1-4bad985b1fb4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- ">>>> Calculating Material Flows <<<<\n",
- "\n",
- "Working on Scenario: test\n",
- "********************\n",
- "Finished Area+Power Generation Calculations\n",
- "==> Working on Material : glass\n"
- ]
- }
- ],
- "source": [
- "r1.calculateMassFlow()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "914366b4-3a2b-4918-acf1-c14107d34250",
- "metadata": {},
- "outputs": [],
- "source": [
- "usyearlyr1, uscumr1 = r1.aggregateResults()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "eb2cf213-03cb-4661-8f5b-5d9e1ae2fce1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Area', 'Cumulative_Active_Area', 'EOL_BadStatus', 'EOL_Landfill0',\n",
- " 'EOL_PATHS', 'EOL_PG', 'Effective_Capacity_[W]', 'Landfill_0_ProjLife',\n",
- " 'MerchantTail_Area', 'MerchantTail_[W]', 'ModuleTotal_MFG', 'P2_stored',\n",
- " 'P3_reMFG', 'P4_recycled', 'PB1_landfill', 'PB2_stored', 'PB3_reMFG',\n",
- " 'PB3_reMFG_unyield', 'PB3_reMFG_yield', 'PB4_recycled', 'PG1_landfill',\n",
- " 'PG2_stored', 'PG3_reMFG', 'PG3_reMFG_unyield', 'PG3_reMFG_yield',\n",
- " 'PG4_recycled', 'Power_Degraded_[W]', 'Repaired_Area', 'Repaired_[W]',\n",
- " 'Resold_Area', 'Resold_[W]', 'WeibullParams',\n",
- " 'Yearly_Sum_Area_EOLby_Degradation', 'Yearly_Sum_Area_EOLby_Failure',\n",
- " 'Yearly_Sum_Area_EOLby_ProjectLifetime', 'Yearly_Sum_Area_PathsBad',\n",
- " 'Yearly_Sum_Area_PathsGood', 'Yearly_Sum_Area_atEOL',\n",
- " 'Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure',\n",
- " 'Yearly_Sum_Power_EOLby_ProjectLifetime', 'Yearly_Sum_Power_PathsBad',\n",
- " 'Yearly_Sum_Power_PathsGood', 'Yearly_Sum_Power_atEOL',\n",
- " 'irradiance_stc'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "r1.scenario['test'].dataOut_m.keys()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "588df476-191e-4601-997c-496e15fff5bf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Yearly_Sum_Power_atEOL'"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "'Yearly_Sum_Power_EOLby_Degradation',\n",
- "'Yearly_Sum_Power_EOLby_Failure',\n",
- "'Yearly_Sum_Power_EOLby_ProjectLifetime',\n",
- "'Yearly_Sum_Power_PathsBad',\n",
- "'Yearly_Sum_Power_PathsGood',\n",
- "'Yearly_Sum_Power_atEOL'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "85ce6e1b-760c-459b-a738-585b7047777c",
- "metadata": {},
- "outputs": [],
- "source": [
- "yspeol_deg = r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_Degradation']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "5a08dc29-3c73-49e7-bd9a-d2fc6e116563",
- "metadata": {},
- "outputs": [],
- "source": [
- "yspeol_fail =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_Failure']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "ffd9f3dd-6236-4a0c-ac52-917c39e986d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "yspeol_plife =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_ProjectLifetime']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "4fffff2e-d579-4387-8c9f-ad08ab86c356",
- "metadata": {},
- "outputs": [],
- "source": [
- "yspeol_sum =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_atEOL']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "a4d0ccf4-c03f-4549-8b07-bcb10751cac6",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(yspeol_deg, label='deg')\n",
- "plt.plot(yspeol_fail, label='fail')\n",
- "plt.plot(yspeol_plife, label='life')\n",
- "plt.plot(yspeol_sum, ls=':', label='sum')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "d1695354-2e33-4bd5-b643-4313d5473030",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ",\n",
- " ,\n",
- " ,\n",
- " ,\n",
- " ]"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " Yearly_Sum_Power_EOLby_Degradation Yearly_Sum_Power_EOLby_Failure \\\n",
- "0 0.000000e+00 0.000000e+00 \n",
- "1 0.000000e+00 6.866359e-01 \n",
- "2 0.000000e+00 2.831734e+01 \n",
- "3 0.000000e+00 2.485886e+02 \n",
- "4 0.000000e+00 1.158294e+03 \n",
- "5 0.000000e+00 3.813970e+03 \n",
- "6 0.000000e+00 1.008092e+04 \n",
- "7 0.000000e+00 2.289102e+04 \n",
- "8 0.000000e+00 4.649511e+04 \n",
- "9 0.000000e+00 8.668822e+04 \n",
- "10 0.000000e+00 1.509730e+05 \n",
- "11 0.000000e+00 2.486091e+05 \n",
- "12 0.000000e+00 3.904739e+05 \n",
- "13 0.000000e+00 5.886399e+05 \n",
- "14 0.000000e+00 8.555594e+05 \n",
- "15 0.000000e+00 1.202760e+06 \n",
- "16 0.000000e+00 1.639007e+06 \n",
- "17 0.000000e+00 2.168002e+06 \n",
- "18 0.000000e+00 2.785889e+06 \n",
- "19 0.000000e+00 3.479076e+06 \n",
- "20 0.000000e+00 4.223139e+06 \n",
- "21 0.000000e+00 4.983659e+06 \n",
- "22 0.000000e+00 5.719628e+06 \n",
- "23 2.494077e+06 5.719628e+06 \n",
- "24 2.494077e+06 5.719628e+06 \n",
- "25 2.494077e+06 5.719628e+06 \n",
- "26 2.494077e+06 5.719628e+06 \n",
- "27 2.494077e+06 5.719628e+06 \n",
- "28 2.494077e+06 5.719628e+06 \n",
- "29 2.494077e+06 5.719628e+06 \n",
- "30 2.494077e+06 5.719628e+06 \n",
- "31 2.494077e+06 5.719628e+06 \n",
- "32 2.494077e+06 5.719628e+06 \n",
- "33 2.494077e+06 5.719628e+06 \n",
- "34 2.494077e+06 5.719628e+06 \n",
- "35 2.494077e+06 5.719628e+06 \n",
- "36 2.494077e+06 5.719628e+06 \n",
- "37 2.494077e+06 5.719628e+06 \n",
- "38 2.494077e+06 5.719628e+06 \n",
- "39 2.494077e+06 5.719628e+06 \n",
- "40 2.494077e+06 5.719628e+06 \n",
- "41 2.494077e+06 5.719628e+06 \n",
- "42 2.494077e+06 5.719628e+06 \n",
- "43 2.494077e+06 5.719628e+06 \n",
- "44 2.494077e+06 5.719628e+06 \n",
- "45 2.494077e+06 5.719628e+06 \n",
- "46 2.494077e+06 5.719628e+06 \n",
- "47 2.494077e+06 5.719628e+06 \n",
- "48 2.494077e+06 5.719628e+06 \n",
- "49 2.494077e+06 5.719628e+06 \n",
- "50 2.494077e+06 5.719628e+06 \n",
- "51 2.494077e+06 5.719628e+06 \n",
- "52 2.494077e+06 5.719628e+06 \n",
- "53 2.494077e+06 5.719628e+06 \n",
- "54 2.494077e+06 5.719628e+06 \n",
- "55 2.494077e+06 5.719628e+06 \n",
- "\n",
- " Yearly_Sum_Power_EOLby_ProjectLifetime Yearly_Sum_Power_atEOL \n",
- "0 0.0 0.000000e+00 \n",
- "1 0.0 6.866359e-01 \n",
- "2 0.0 2.831734e+01 \n",
- "3 0.0 2.485886e+02 \n",
- "4 0.0 1.158294e+03 \n",
- "5 0.0 3.813970e+03 \n",
- "6 0.0 1.008092e+04 \n",
- "7 0.0 2.289102e+04 \n",
- "8 0.0 4.649511e+04 \n",
- "9 0.0 8.668822e+04 \n",
- "10 0.0 1.509730e+05 \n",
- "11 0.0 2.486091e+05 \n",
- "12 0.0 3.904739e+05 \n",
- "13 0.0 5.886399e+05 \n",
- "14 0.0 8.555594e+05 \n",
- "15 0.0 1.202760e+06 \n",
- "16 0.0 1.639007e+06 \n",
- "17 0.0 2.168002e+06 \n",
- "18 0.0 2.785889e+06 \n",
- "19 0.0 3.479076e+06 \n",
- "20 0.0 4.223139e+06 \n",
- "21 0.0 4.983659e+06 \n",
- "22 0.0 5.719628e+06 \n",
- "23 0.0 8.213705e+06 \n",
- "24 0.0 8.213705e+06 \n",
- "25 0.0 8.213705e+06 \n",
- "26 0.0 8.213705e+06 \n",
- "27 0.0 8.213705e+06 \n",
- "28 0.0 8.213705e+06 \n",
- "29 0.0 8.213705e+06 \n",
- "30 0.0 8.213705e+06 \n",
- "31 0.0 8.213705e+06 \n",
- "32 0.0 8.213705e+06 \n",
- "33 0.0 8.213705e+06 \n",
- "34 0.0 8.213705e+06 \n",
- "35 0.0 8.213705e+06 \n",
- "36 0.0 8.213705e+06 \n",
- "37 0.0 8.213705e+06 \n",
- "38 0.0 8.213705e+06 \n",
- "39 0.0 8.213705e+06 \n",
- "40 0.0 8.213705e+06 \n",
- "41 0.0 8.213705e+06 \n",
- "42 0.0 8.213705e+06 \n",
- "43 0.0 8.213705e+06 \n",
- "44 0.0 8.213705e+06 \n",
- "45 0.0 8.213705e+06 \n",
- "46 0.0 8.213705e+06 \n",
- "47 0.0 8.213705e+06 \n",
- "48 0.0 8.213705e+06 \n",
- "49 0.0 8.213705e+06 \n",
- "50 0.0 8.213705e+06 \n",
- "51 0.0 8.213705e+06 \n",
- "52 0.0 8.213705e+06 \n",
- "53 0.0 8.213705e+06 \n",
- "54 0.0 8.213705e+06 \n",
- "55 0.0 8.213705e+06 "
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "yspeol = pd.concat([yspeol_deg,yspeol_fail,yspeol_plife,yspeol_sum],axis=1)\n",
- "yspeol"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "8496d458-12a5-40cb-b7c3-db6da777340f",
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "| \n", - " | Yearly_Sum_Power_EOLby_Degradation | \n", - "Yearly_Sum_Power_EOLby_Failure | \n", - "Yearly_Sum_Power_EOLby_ProjectLifetime | \n", - "Yearly_Sum_Power_atEOL | \n", - "
|---|---|---|---|---|
| 0 | \n", - "0.000000e+00 | \n", - "0.000000e+00 | \n", - "0.0 | \n", - "0.000000e+00 | \n", - "
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| 2 | \n", - "0.000000e+00 | \n", - "2.831734e+01 | \n", - "0.0 | \n", - "2.831734e+01 | \n", - "
| 3 | \n", - "0.000000e+00 | \n", - "2.485886e+02 | \n", - "0.0 | \n", - "2.485886e+02 | \n", - "
| 4 | \n", - "0.000000e+00 | \n", - "1.158294e+03 | \n", - "0.0 | \n", - "1.158294e+03 | \n", - "
| 5 | \n", - "0.000000e+00 | \n", - "3.813970e+03 | \n", - "0.0 | \n", - "3.813970e+03 | \n", - "
| 6 | \n", - "0.000000e+00 | \n", - "1.008092e+04 | \n", - "0.0 | \n", - "1.008092e+04 | \n", - "
| 7 | \n", - "0.000000e+00 | \n", - "2.289102e+04 | \n", - "0.0 | \n", - "2.289102e+04 | \n", - "
| 8 | \n", - "0.000000e+00 | \n", - "4.649511e+04 | \n", - "0.0 | \n", - "4.649511e+04 | \n", - "
| 9 | \n", - "0.000000e+00 | \n", - "8.668822e+04 | \n", - "0.0 | \n", - "8.668822e+04 | \n", - "
| 10 | \n", - "0.000000e+00 | \n", - "1.509730e+05 | \n", - "0.0 | \n", - "1.509730e+05 | \n", - "
| 11 | \n", - "0.000000e+00 | \n", - "2.486091e+05 | \n", - "0.0 | \n", - "2.486091e+05 | \n", - "
| 12 | \n", - "0.000000e+00 | \n", - "3.904739e+05 | \n", - "0.0 | \n", - "3.904739e+05 | \n", - "
| 13 | \n", - "0.000000e+00 | \n", - "5.886399e+05 | \n", - "0.0 | \n", - "5.886399e+05 | \n", - "
| 14 | \n", - "0.000000e+00 | \n", - "8.555594e+05 | \n", - "0.0 | \n", - "8.555594e+05 | \n", - "
| 15 | \n", - "0.000000e+00 | \n", - "1.202760e+06 | \n", - "0.0 | \n", - "1.202760e+06 | \n", - "
| 16 | \n", - "0.000000e+00 | \n", - "1.639007e+06 | \n", - "0.0 | \n", - "1.639007e+06 | \n", - "
| 17 | \n", - "0.000000e+00 | \n", - "2.168002e+06 | \n", - "0.0 | \n", - "2.168002e+06 | \n", - "
| 18 | \n", - "0.000000e+00 | \n", - "2.785889e+06 | \n", - "0.0 | \n", - "2.785889e+06 | \n", - "
| 19 | \n", - "0.000000e+00 | \n", - "3.479076e+06 | \n", - "0.0 | \n", - "3.479076e+06 | \n", - "
| 20 | \n", - "0.000000e+00 | \n", - "4.223139e+06 | \n", - "0.0 | \n", - "4.223139e+06 | \n", - "
| 21 | \n", - "0.000000e+00 | \n", - "4.983659e+06 | \n", - "0.0 | \n", - "4.983659e+06 | \n", - "
| 22 | \n", - "0.000000e+00 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "5.719628e+06 | \n", - "
| 23 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 24 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 25 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 26 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 27 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 28 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 29 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 30 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 31 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 32 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 33 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 34 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 35 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 36 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 37 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 38 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 39 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 40 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 41 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 42 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 43 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 44 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 45 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 46 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 47 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 48 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 49 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 50 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 51 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 52 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 53 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 54 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
| 55 | \n", - "2.494077e+06 | \n", - "5.719628e+06 | \n", - "0.0 | \n", - "8.213705e+06 | \n", - "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " Yearly_Sum_Power_EOLby_Degradation Yearly_Sum_Power_EOLby_Failure \\\n",
- "year \n",
- "2046 72.328234 194.476022 \n",
- "2047 74.822311 200.195649 \n",
- "2048 77.316388 205.915277 \n",
- "2049 79.810465 211.634905 \n",
- "2050 82.304542 217.354532 \n",
- "\n",
- " Yearly_Sum_Power_EOLby_ProjectLifetime Yearly_Sum_Power_atEOL \\\n",
- "year \n",
- "2046 0.0 266.804256 \n",
- "2047 0.0 275.017960 \n",
- "2048 0.0 283.231665 \n",
- "2049 0.0 291.445370 \n",
- "2050 0.0 299.659074 \n",
- "\n",
- " DecommisionedCapacity_sim1_test_[MW] \n",
- "year \n",
- "2046 341.370469 \n",
- "2047 351.370469 \n",
- "2048 361.370469 \n",
- "2049 371.370469 \n",
- "2050 381.370469 "
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "yspeol_MW = yspeol/1000000#), index = usyearlyr1.index)\n",
- "yspeol_MW_cumu = yspeol_MW.cumsum()\n",
- "yspeol_MW_cumu.index = usyearlyr1.index\n",
- "#yspeol_MW_cumu\n",
- "decomm_cap = usyearlyr1.filter(like='Decomm')\n",
- "compare_decom = pd.concat([yspeol_MW_cumu,decomm_cap],axis=1)\n",
- "compare_decom.tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "1d812cbc-eb0a-4c6d-9900-03680ffe90ab",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[| \n", - " | Yearly_Sum_Power_EOLby_Degradation | \n", - "Yearly_Sum_Power_EOLby_Failure | \n", - "Yearly_Sum_Power_EOLby_ProjectLifetime | \n", - "Yearly_Sum_Power_atEOL | \n", - "DecommisionedCapacity_sim1_test_[MW] | \n", - "
|---|---|---|---|---|---|
| year | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " |
| 2046 | \n", - "72.328234 | \n", - "194.476022 | \n", - "0.0 | \n", - "266.804256 | \n", - "341.370469 | \n", - "
| 2047 | \n", - "74.822311 | \n", - "200.195649 | \n", - "0.0 | \n", - "275.017960 | \n", - "351.370469 | \n", - "
| 2048 | \n", - "77.316388 | \n", - "205.915277 | \n", - "0.0 | \n", - "283.231665 | \n", - "361.370469 | \n", - "
| 2049 | \n", - "79.810465 | \n", - "211.634905 | \n", - "0.0 | \n", - "291.445370 | \n", - "371.370469 | \n", - "
| 2050 | \n", - "82.304542 | \n", - "217.354532 | \n", - "0.0 | \n", - "299.659074 | \n", - "381.370469 | \n", - "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(compare_decom)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "ca63ad8d-9c90-40f6-bf2e-9208b51ddc65",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
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- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
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- "data": {
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pv2CvMAUz6TjxV/Ue1Oz0Lh7e/hYquSA8+USAIgjCHV1NTWbfW+2okWjFuT6tifroS0sX6aEo0OdzcN03XFv1M9UPX0JtSqpsegRUzxNNp46opBxqnFtBVUzD5xdIVvzhHIFT+DsENwq3XOEF4QlVlvu3yGUuCJVM1vXLWOlMeXgeZqJAS1OpbWnefSh0H8qNq/9wYNkc2LCdasm5BPyRBn98S4aDkoOtQ7GuG4zf5ThqF/xJk8xtsHYbiRtqkdVwIA069EOltrH06QhCpSNahwlCJZObnob1I0wUWBG4VKlGu+EzaLflINbffcbZqKfItFfgnG2kxsbj+M1cxcXf9KxR9WSXXTv0kjW1ChNpcmAM6VNC2Bv7PtfT/rH0aQhCpSJqUAShktFlpmFroUSBFUFQ47YENW6LXpdLwuqFZKxahe+xq1Q/nwNLd5FrAxuaNkUZ6E5L3U48uY7nuQXo/vcV+92jqNpxFL7BDS19GoLwxBM1KIJQyRRkXcUuz1SD4uhRcYa5f9TUNna06jWCTj/uxHPLGs73ac1VN2vsdFBr5zmClhzkyG53fshryx/UxEZRQLPra/FdHsbh6VGc2LcJyWi8+4EEQbgvopGsIFQyO78aRZWZGwDwit+Em9bPwiWqOAyGQg5vWsrl75fhe+iSnLAwTw3nGmrR+BiItD4qd1M+ZR1MVqOhNGjfB2uV2oIlF4THg+jFIwjCHW2d+QrVvzqCUQG1jv4hbqx3cPXSGQ4umYVm/S48rv47Ym2yn4aM2o5EOfyJo7Vp/iWFF8nB/WnQ9R009o6WKrIgVHgiQBEE4Y42jGtPwI8XydJA08MVN1FgRWE0Gjm85TtSl8Wa1apk2Cs4V9+VhtoLhNhkAHANZ07XfJ36z4/EzsHZgqUWhIqpLPdv0QZFECoZQ24mADl2IsPvvVAqlTTu2Jfnlv2K58ZVJL3UjBuOSpxzJBrsvU7hGnvWHK7J9htaXKUMmv89h/yZT7Hv24/Iybph6eILwmNLBCiCUNnkmZLn5duLTnxl5eVXm04TY2m8K4Er/+lPcg1HrCSolZhL1U1K4uJ8WZfijb0hk+Zn56GfVY+9Sz4kOzPd0kUXhMeOCFAEobLR6QAocLC1cEEeX2obO9q89h6R63/Hatl8zkTURKcC77RCauyAwxuqsfpvbwz6HFokfUbBp/XYF/sf8nKyLF10QXhsiABFECoZhc405rvRWSTHKw/BTdrRecEv+G3fwvlX25DuZIVzjkTIAfjn/7xYc9SbqzkFND/3P45+87aliysIjw0RoAhCJZKfmw2mChSULi4WLcuTxtXDh47/XUiTnb+TNrYP/1TXoDZArRNQ+IsbG/dqyU5NtHQxBeGxIQIUQahEMq5fBp3pY6+u8uTm4bEktY0dYW/8l2e3HEA3fxxJDb0wAv7nlWQfzbR08QThsSECFEGoRLKvX8bqZh4etXsVC5fmyaZUKmnY/hU6rYjn9GutAbDJfexGdRAEixEBiiBUIrk3rqC6GaBoKkmiwIrAyacmALZ5khgeXxDukQhQBKES0WWlYXszD4+9R1ULl6bycPczBSiafAV5uaInjyDcCxGgCEIlYsi6it3NTMZOlThR4KPmUd0UoNjnwY2rqRYujSA8HkSAIgiViC4jFY3e9H8XL1/LFqYScdX6A2AlwZVkkV5AEO6FCFAEoRLR37gEgEEBTm7iEc+jorFzIl9l+v+1f05btjCC8JgQAYogVCIF2dcByNaYepkIj06O5ua/aRcsWxBBeEyIbyhBqEQMOaZxOHJFosBHLk9j+rrVpadZuCSC8HgQAYogVCJSvilRoM5eZeGSVD75NwOUQpE4UBDuiQhQBKEy0ZlayIpEgY+eXnMzKMwW3YwF4V6IAEUQKgnJaESpMw0SZnRxtHBpKh+DvSkoVObmW7gkgvB4EAGKIFQSuTmZSDrTIG1Wbq4WLk3lIznaA2CVX2DhkgjC40EEKIJQSWRcuwy6ojw8IlHgo6Z0dgFAlWewbEEE4TEhAhRBqCRy0i9jfTMPj41IFPjI2biZgkJ1vkgYKAj3QgQoglBJ5N1IkxMF2opEgY+c/c2Re21EExRBuCciQBGESkKfdQXbmwGKvbsYRfZRc/UJBECTB/m52RYujSBUfCJAEYRKojD7KvamYVBw8hSJAh81L98QAOzzIf1qioVLIwgVX5kClJiYGBQKhdmk1Wrl5ZIkERMTg7e3NxqNhvDwcP7880+zfeh0Ot555x2qVKmCvb09Xbt25eLFi+VzNoIg3FH+jRRsbnYgcRWJAh85V63pmlsb4cqFRAuXRhAqvjLXoNStW5eUlBR5OnbsmLxs+vTpfPrpp3z22WckJCSg1Wpp3749WVn/Dkw0fPhwVq9ezYoVK9i1axfZ2dl07twZg0G0bBeEh0mfkQpAoRIcXEQblEfNzsEF3c2x2q6LhIGCcFfWZd7A2tqs1qSIJEnMmTOHDz/8kO7duwOwZMkSvLy8WL58OW+99RYZGRl8/fXXLF26lHbt2gGwbNkyfHx82LZtG5GRkQ94OoIg3ElhUaJAO4VIFGghObZgUwDZl89buiiCUOGV+Vvq9OnTeHt7ExAQQK9evTh79iwASUlJpKam0qFDB3ldGxsbwsLC2LNnDwAHDx6koKDAbB1vb2+eeuopeZ2S6HQ6MjMzzSZBEMrGkGuqyRSJAi0nV2NqpJx//bKFSyIIFV+ZApRmzZrx7bffsnnzZhYtWkRqaiotW7bk2rVrpKaaqo+9vLzMtvHy8pKXpaamolarcXV1veM6JZk6dSrOzs7y5OPjU5ZiC4IAKPJN/VtFokDL0WlMwaFIGCgId1emACUqKooXX3yRevXq0a5dO9avXw+YHuUUUSgUZttIklRs3u3uts4HH3xARkaGPF24cKEsxRYEAUBnaiFb4KixcEEqL5EwUBDu3QM9iLa3t6devXqcPn1abpdye01IWlqaXKui1WrR6/Wkp6ffcZ2S2NjY4OTkZDYJgnDvJKMRxc1EgZKzSBRoKYX2NgAoRMJAQbirBwpQdDodJ0+epGrVqgQEBKDVatm6dau8XK/Xs2PHDlq2bAlA48aNUalUZuukpKRw/PhxeR1BEMpfZsZ1JL2pltLazc3Cpam8JIebCQPz9BYuiSBUfGXqxTN69Gi6dOmCr68vaWlpTJo0iczMTPr164dCoWD48OFMmTKFoKAggoKCmDJlCnZ2dvTu3RsAZ2dnBgwYwKhRo3B3d8fNzY3Ro0fLj4wEQXg4sq6losgvShQo8vBYitLFGfgHVb4YVkEQ7qZMAcrFixd55ZVXuHr1Kh4eHjRv3px9+/bh5+cHwNixY8nLy2Po0KGkp6fTrFkztmzZgqPjv1XKs2fPxtramp49e5KXl0fbtm2JjY3Fykr0LBCEhyX7xmWsRIBicSqRMFAQ7lmZApQVK1aUulyhUBATE0NMTMwd17G1tWX+/PnMnz+/LIcWBOEB5GdcQX0zQLFzv3N7L+HhcvCsDoCtaIIiCHclRmsShEpAn3kF27ybiQI9RKJAS3GtXgMA2zzQ5edauDSCULGJAEUQKgFDVhr2N3+1O3uKcYQsxdOvFgAOeXDj6p3HfhIEQQQoglAp6DLTUBea/u/iKRIFWopbVX/AlDDwqsjHIwilEgGKIFQC+owU07/WYOfoepe1hYfF3tEN/c2Wf1cv/GXZwghCBScCFEGoBAqzTYMjZmtEokBLy745kG/25WTLFkQQKjjxTSUIlYAxLxuAPJEo0OLy5ISBog2KIJRGBCiCUBnodKZ/7NUWLoiQb2v62i3IuG7hkghCxSYCFEGoDPJNiQILRaJAi9PbFSUMzLZsQQShghMBiiA84QyFhXKiQFycLVsYgUK7ooSBeRYuiSBUbCJAEYQnXGb6FSSd6aNu7S4SBVqaUSQMFIR7IgIUQXjCZV6/jEJnapipEnl4LE7p5AQgEgYKwl2IAEUQnnC5Ny5jfTMPj+3NZHWC5ajdixIGGi1cEkGo2ESAIghPuFsTBWqqeFq4NIKdZzUAbEQTFEEolQhQBOEJV5D1b6JAB49qFi6N4FotEABNPhTodRYujSBUXCJAEYQnXGFWGg43f607iwDF4jz9QgCwy4P0qykWLo0gVFwiQBGEJ5z+RgrWN5s7OHtWt2xhBNy9AwBQG+DaP39buDSCUHGJAEUQnnC6rDQA8lWmZHWCZdk5uqG/mXHgyoVEyxZGECowEaAIwhPOkHMDgJybOWAEy1IqleQUJQxMO2fRsghCRSYCFEF4wkl5OQDk2VtbuCRCkaKEgXlXRcJAQbgTEaAIwhNOyheJAiuafI3pq7cwUyQMFIQ7EQGKIDzhFLpCAAyOdhYuiVBErzElDJSysixcEkGouESAIghPML0uH/SS6Q9XkSiwoiiQEwbmWrgkglBxiQBFEJ5gmdcuI+WbPuYiD0/FITmYarOsckXCQEG4ExGgCMITLCs9FeXNTMYqV9HFuKJQOJtqs6x1ImGgINyJCFAE4QmWcyMNq5t5eGyqiBqUikLl6g6AOk8kDBSEOxEBiiA8wXQZV7HJN/3fzl1r2cIIMjsPbwD5tREEoTgRoAjCE6ww+woaOVGgt4VLIxRxqf5vwsDCAtEORRBKIgIUQXiCGbKvYH8zUaCLl49lCyPIPP1qAWCfB5npVyxcGkGomJ7ooSUNBgMFBQWWLoYgWEyBLh+FtipGwM7Zi/x88UyhInDy8CW3alWsgdQLZ7Bzci+X/apUKqysrMplX4JgaQpJkiRLF6KsMjMzcXZ2JiMjAycnp2LLJUkiNTWVGzduPPrCCUIFknfjMta5BiRA7S0e8VQUkiRRkJKCAjC6OGJj51hu+3ZxcUGr1aJQiNxLQsVzt/v3rZ7IGpSi4MTT0xM7OzvxQRUqraxUI6pMAwYrsA8IsHRxhFvkGAuxMkJBFScc3R68AbMkSeTm5pKWZspeXbVq1QfepyBY0hMXoBgMBjk4cXcvn2pTQXgcSZKETimhVirRWyuwtbW1dJGEW+itlagKQVJI5fbaaDSmNMlpaWl4enqKxz3CY+2JayRb1ObEzk7kHREqtwJdHgrjzXE2rJ64j/pjT1KaanYlQ2G57rfou0+0vxMed0/st5Z4rCNUdvqcGxS1MJPEL+kKx1j0FWUo39FkxXef8KR4YgMUQajslPosJOnmzUrUoFQ4UtFrYhSjyQpCScS3lvBQ9O/fn+eff17+Ozw8nOHDh5fb/hUKBWvWrCm3/ZW3c+fOoVAoOHLkiEWObzQYsDXmwc17n8Lacs3N4uPjUSgUKBQKs/fE4y48PFw+r/t6nZVFAcpj15FSEB4JEaBUEF988QWOjo4UFv77PDo7OxuVSkXr1q3N1t25cycKhYJTp07d9/HK4wb65ptvYmVlxYoVK+57H08qHx8fUlJSeOqppx5oP7/99htdunTB29u7TEFZfk4GSoVE/fCOfLZ0KYpyfMRzv8FmYmIisbGxAPKN/U5T//79y628D8uqVav4/fff738HN2tQFCJAEYQSiQClgoiIiCA7O5sDBw7I83bu3IlWqyUhIYHc3Fx5fnx8PN7e3gQHB1uiqADk5ubyww8/MGbMGL7++muLlaOisrKyQqvVYv2ANRc5OTk0aNCAzz77rEzbGfMzTf+5ee9TWKseqBzlwdPTExcXFwBSUlLkac6cOTg5OZnNmzt3rmULew/c3Nzw8PC47+0VVqbXRCniE0EokQhQKohatWrh7e1NfHy8PC8+Pp5u3bpRo0YN9uzZYzY/IiKCZcuW0aRJExwdHdFqtfTu3VseAwEgPT2dPn364OHhgUajISgoiMWLFwMQcHNMjNDQUBQKBeHh4fJ2ixcvpnbt2tja2hISEsLnn39erLw//fQTderU4YMPPmD37t2cO3eu3K5FSkoKzz33HBqNhoCAAJYvX46/vz9z5sy54zbvvfcewcHB2NnZERgYyEcffWTWi+GPP/4gIiICR0dHnJycaNy4sRwMnj9/ni5duuDq6oq9vT1169Zlw4YNdy1nadf39hqqosccmzdvJjQ0FI1Gw7PPPktaWhobN26kdu3aODk58corr5gFo1FRUUyaNInu3buX6RqqC7MJe3EQFy5dYuz06ThVqWrWeHLPnj20adMGjUaDj48P0dHR5OTkyMs///xzgoKCsLW1xcvLix49egCmR3c7duxg7ty5cm3H/bz2Wq1WnpydnVEoFGbzli9fTo0aNVCr1dSqVYulS5eaba9QKPjqq6944YUXsLOzIygoiLVr18rLi673r7/+SpMmTbCzs6Nly5YkJiaa7eeXX36hcePG2NraEhgYyMcff2xWixkTE4Ovry82NjZ4e3sTHR1d5nO9E6XKFKAoRBMUQShRpQhQJEkiV19okaksA/WGh4cTFxcn/x0XF0d4eDhhYWHyfL1ez969e4mIiECv1zNx4kT++OMP1qxZQ1JSklnV+EcffcSJEyfYuHEjJ0+eZMGCBVSpUgVArpretm0bKSkprFq1CoBFixbx4YcfMnnyZE6ePMmUKVP46KOPWLJkiVlZv/76a1599VWcnZ3p1KmTfGMuD6+99hqXLl0iPj6en3/+mS+//NIs8CqJo6MjsbGxnDhxgrlz57Jo0SJmz54tL+/Tpw/Vq1cnISGBgwcP8v7776O6eYMYNmwYOp2O3377jWPHjvHJJ5/g4OBw13KWdn3vJCYmhs8++4w9e/Zw4cIFevbsyZw5c1i+fDnr169n69atzJ8//x6u0p3p8/NQU8DShbOp5uXFh++8zaVLl0hJSQHg2LFjREZG0r17d44ePcoPP/zArl27ePvttwE4cOAA0dHRTJgwgcTERDZt2kSbNm0AmDt3Li1atGDQoEFybYePT/nm+Fm9ejXvvvsuo0aN4vjx47z11lu8/vrrZp8NgI8//piePXty9OhROnXqRJ8+fbh+/brZOh9++CGzZs3iwIEDWFtb88Ybb8jLNm/ezKuvvkp0dDQnTpxg4cKFxMbGMnnyZABWrlzJ7NmzWbhwIadPn2bNmjXUq1ev3M7TSqUGQGmkTN8TglBZPHEDtZUkr8BAnXGbLXLsExMisVPf22UODw9nxIgRFBYWkpeXx+HDh2nTpg0Gg4F58+YBsG/fPvLy8oiIiCAwMFDeNjAwkHnz5tG0aVOys7NxcHAgOTmZ0NBQmjRpAoC/v7+8flHVtLu7O1rtv6NYTpw4kVmzZsm/2AMCAuQv7379+gFw+vRp9u3bJwc1RV/y48ePR6l8sJj3r7/+Ytu2bSQkJMjl/uqrrwgKCip1u//+97/y//39/Rk1ahQ//PADY8eOBSA5OZkxY8YQEhICYLa/5ORkXnzxRfnmc+t1LU1p1/dOJk2aRKtWrQAYMGAAH3zwAWfOnJGP2aNHD+Li4njvvffuqQwl0efeQA042LthZWWFvYuT2aiiM2bMoHfv3nI7kqCgIObNm0dYWBgLFiwgOTkZe3t7OnfujKOjI35+foSGhgLg7OyMWq3Gzs7O7H1TnmbOnEn//v0ZOnQoACNHjmTfvn3MnDmTiIgIeb3+/fvzyiuvADBlyhTmz5/P77//TseOHeV1Jk+eTFhYGADvv/8+zz33HPn5+dja2jJ58mTef/99+X0dGBjIxIkTGTt2LOPHjyc5ORmtVku7du1QqVT4+vrStGnTcjtPa7UGCdMjHkNhIdYqyz+GE4SKpFLUoDwuIiIiyMnJISEhgZ07dxIcHIynpydhYWEkJCSQk5NDfHw8vr6+BAYGcvjwYbp164afnx+Ojo7yY5rk5GQAhgwZwooVK2jYsCFjx441e0xUkitXrnDhwgUGDBiAg4ODPE2aNIkzZ87I63399ddERkbKtQWdOnUiJyeHbdu2PfA1SExMxNramkaNGsnzatasiaura6nbrVy5kmeeeQatVouDgwMfffSRfB3AdJMbOHAg7dq1Y9q0aWbnEx0dLQcO48eP5+jRo/dU1rJeX4D69evL//fy8pIfSd067261RXej1GUBoNKbfpUrbxul9ODBg8TGxpq9xpGRkRiNRpKSkmjfvj1+fn4EBgbSt29fvvvuO7PHTg/byZMn5SCuSKtWrTh58qTZvFuvpb29PY6OjsWu3a3rFAVpRescPHiQCRMmmF2Hopqh3NxcXnrpJfLy8ggMDGTQoEGsXr3a7PHPg7JWm2pQFBIUFujKbb+C8KSoFDUoGpUVJyZEWuzY96pmzZpUr16duLg40tPT5V9+Wq2WgIAAdu/eTVxcHM8++yw5OTl06NCBDh06sGzZMjw8PEhOTiYyMhK9Xg+Y2i+cP3+e9evXs23bNtq2bcuwYcOYOXNmicc33hyPYdGiRTRr1sxsWdGQ2QaDgW+//ZbU1FSzBqAGg4Gvv/6aDh063PvFKcGdqrpLqwLft28fvXr14uOPPyYyMhJnZ2dWrFjBrFmz5HViYmLo3bs369evZ+PGjYwfP54VK1bwwgsvMHDgQCIjI1m/fj1btmxh6tSpzJo1i3feeafUspb1+gLyYyUwtaNQ3farWaFQyK/D/TAaDdgac9FJCqxvjv+lsrG7bR0jb731VontKXx9fVGr1Rw6dIj4+Hi2bNnCuHHjiImJISEhQW7k+rDdPtiYJEnF5t3Ltbv9esO/73Oj0cjHH39cYvseW1tbfHx8SExMZOvWrWzbto2hQ4cyY8YMduzYUezY98PKSoUeUACF+jywu/tjRUGoTB6oBmXq1KkoFAqzLoeSJBETE4O3tzcajYbw8HD+/PNPs+10Oh3vvPMOVapUwd7enq5du3Lx4sUHKUqpFAoFdmpri0xlHdUxIiKC+Ph44uPjzRquhoWFsXnzZvbt20dERAR//fUXV69eZdq0abRu3ZqQkJASf3l7eHjQv39/li1bxpw5c/jyyy8BUN/89Wa4ZRRLLy8vqlWrxtmzZ6lZs6bZVNSodsOGDWRlZXH48GGOHDkiTz/99BNr1qzh2rVrZX15zISEhFBYWMjhw4fleX///Xepmal3796Nn58fH374IU2aNCEoKIjz588XWy84OJgRI0awZcsWunfvbtZuxsfHh8GDB7Nq1SpGjRrFokWL7qm8d7q+lpKfk4lSIZFXaAooVWoVt4d2jRo14s8//yz2GtesWVN+X1hbW9OuXTumT5/O0aNHOXfuHNu3bwdM7x1DOY9+eqvatWuza9cus3l79uyhdu3a5XqcRo0akZiYWOJ1KHpUqdFo6Nq1K/PmzSM+Pp69e/dy7Nixcjm+QqHAePMb2FCgL5d9CsKT5L5rUBISEvjyyy/NqlABpk+fzqeffkpsbCzBwcFMmjSJ9u3bk5iYiKOjKaX48OHD+eWXX1ixYgXu7u6MGjWKzp07c/DgwUqf3CoiIoJhw4ZRUFAg16CAKUAZMmQI+fn5REREYGtri1qtZv78+QwePJjjx48zceJEs32NGzeOxo0bU7duXXQ6HevWrZO/5D09PdFoNGzatInq1atja2uLs7MzMTExREdH4+TkRFRUFDqdjgMHDpCens7IkSP5+uuvee6552jQoIHZserWrcvw4cNZtmwZ77777n2ff0hICO3atePNN99kwYIFqFQqRo0ahUajuWOwV7NmTZKTk1mxYgVPP/0069evZ/Xq1fLyvLw8xowZQ48ePQgICODixYskJCTw4osvAqb3Y1RUFMHBwaSnp7N9+/Z7uhmWdn3LS3Z2Nn///bf8d1JSEkeOHMHNzQ1fX99i6xvzMkz/KVACEr6+Pvz222/06tULGxsbqlSpwnvvvUfz5s0ZNmwYgwYNwt7enpMnT8oNdNetW8fZs2dp06YNrq6ubNiwAaPRSK1atQBTW5v9+/dz7tw5HBwccHNze+C2R7caM2YMPXv2pFGjRrRt25ZffvmFVatWlcsjxFuNGzeOzp074+Pjw0svvYRSqeTo0aMcO3aMSZMmERsbi8FgoFmzZtjZ2bF06VI0Gg1+fn7lVgajEqyMYCwUeXMEoRjpPmRlZUlBQUHS1q1bpbCwMOndd9+VJEmSjEajpNVqpWnTpsnr5ufnS87OztIXX3whSZIk3bhxQ1KpVNKKFSvkdf755x9JqVRKmzZtuqfjZ2RkSICUkZFRbFleXp504sQJKS8v735OzeKSkpIkQAoJCTGbf+HCBQmQatSoIc9bvny55O/vL9nY2EgtWrSQ1q5dKwHS4cOHJUmSpIkTJ0q1a9eWNBqN5ObmJnXr1k06e/asvP2iRYskHx8fSalUSmFhYfL87777TmrYsKGkVqslV1dXqU2bNtKqVauk1NRUydraWvrxxx9LLPs777wj1atXT5IkSerXr5/UrVs3edmt75O7uXTpkhQVFSXZ2NhIfn5+0vLlyyVPT0/5PSRJkgRIq1evlv8eM2aM5O7uLjk4OEgvv/yyNHv2bMnZ2VmSJEnS6XRSr169JB8fH0mtVkve3t7S22+/Lb9H3n77balGjRqSjY2N5OHhIfXt21e6evXqXctZ2vUteh2LXou4uDgJkNLT0+XtFy9eLJexyPjx46UGDRrIfxdtd/vUr1+/EsuU/88xyXDxkJRz/JiUe+yYFP/rVql+/fqSjY2NdOvH/ffff5fat28vOTg4SPb29lL9+vWlyZMnS5IkSTt37pTCwsIkV1dXSaPRSPXr15d++OEHedvExESpefPmkkajkQApKSmp1OtU0rnfqqTr8Pnnn0uBgYGSSqWSgoODpW+//dZs+e2vvyRJkrOzs7R48eI7HvPw4cPFyrtp0yapZcuWkkajkZycnKSmTZtKX375pSRJkrR69WqpWbNmkpOTk2Rvby81b95c2rZtm9kxb3+dy+rGX8el3GPHpGsXTt3X9iV53L8DhSdbaffv2ykkqez92/r164ebmxuzZ88mPDychg0bMmfOHM6ePUuNGjU4dOiQ3OofoFu3bri4uLBkyRK2b99O27ZtuX79ulnDxwYNGvD888/z8ccfFzueTqdDp/u3EVlmZiY+Pj5kZGTg5ORktm5+fj5JSUkEBASI9PJPiIsXL+Lj4yO38xBKptflob72F9mFSqwyrTAowb52XYsnjysatyc9Pf2RtWF5VM6dO0dAQACHDx+mYcOGZd4+/fSf2Ook8hxVuPnVKpcyie9AoSLLzMzE2dm5xPv37cpcL7tixQoOHTrE1KlTiy1LTU0FTG0ZbuXl5SUvS01NRa1WF+uVces6t5s6dSrOzs7yVN7jLggVy/bt21m7di1JSUns2bOHXr164e/vL4/FIZRMn3PD9G+h6cltoZ3a4sHJrapXry53C34SREVFUbdu3QfahyTn4xGjtQnC7crUBuXChQu8++67bNmypdTI/F5a4N+utHU++OADRo4cKf9dVIMiPH527txJVFTUHZdnZ2dTUFDAf/7zH86ePYujoyMtW7bku+++K5eeE2UxePBgli1bVuKyV199lS+++OKRlud2xa6lVPQEyPRPSvKZErcrb3e7TrNnz+b06dMA9zQA3uPiq6++Ii8vD6DE9kD3xOrmd54IUAShmDIFKAcPHiQtLY3GjRvL8wwGA7/99hufffaZPIx0amqq2cBQaWlpcq2KVqtFr9eTnp5uVouSlpZGy5YtSzyujY0NNjY2ZSmqUEE1adLkrgkKIyMjiYy0TLfwW02YMIHRo0eXuOxuVZOPwq3X0mg0IKUlYpDAmGX6WGuc3R5JOe52nTQaDTVr1nwkZXmUqlWr9uA7sbICClGIkWQFoZgyBSht27Yt1sXu9ddfJyQkhPfee4/AwEC0Wi1bt26V26Do9Xp27NjBJ598AkDjxo1RqVRs3bqVnj17AqbcK8ePH2f69OnlcU5CBfY43aw8PT3x9PS0dDHu6NZrmZt1AzuHXG7orbHJVlCgVmKtejRBfUW/ThWZwsoa0Il8PIJQgjIFKI6OjsXSx9vb2+Pu7i7PHz58OFOmTCEoKIigoCCmTJmCnZ0dvXv3BkxDZQ8YMIBRo0bh7u6Om5sbo0ePpl69erRr166cTksQKpei7sXSze7Fkr3GsgUS7onyZpZppQhQBKGYch9JduzYseTl5TF06FDS09Np1qwZW7ZskcdAAZg9ezbW1tb07NmTvLw82rZtS2xsbKUfA0UQ7peqMBujBOqbw9urnFwsWyDhnlipTbVcCune2uoJQmVyX92MLa20bkqii51Q2eh1+aivnSSnUIky0wqjEuxC6qAox8HThIcjPzcL6ex5jAqwqVULa+sHbwguvgOFiuyhdjMWBKFiKeperLvZvbjAViWCk8dEUTshpUgYKAjFiG8xQXjMKfWZAFjdHC1d6fjkdOV90lndUmNSqM+3YEkEoeIRAYrwUPTv35/nn39e/js8PNwsqaSlxcbGlnlU00d1DvHx8SgUilITJBYxGo3YGHIpMCpQF5ie1mqcHk334kchNjYWhUJRLCnp487f3x+FQoFSqeRatinANIgaFEEwIwKUCuKLL77A0dGRwsJCeV52djYqlYrWrVubrbtz504UCgWnTp267+OdO3cOhUJx1zFJSvPmm29iZWXFihUr7nsfJQkPD0ehUDBt2rRiyzp16oRCoSAmJqZcj1mRtGzZkpSUFJydne+6ri43EyuFRK7B1MC8QKVEZWPqwbNq1SoiIyOpUqXKfb3WCoWCNWvWlLX4pfL392fOnDll2sbJyYmUlBQmTpwov29Lmx6H90ZCQgI///wzgJzR2FggEgYKwq1EgFJBREREkJ2dzYEDB+R5O3fuRKvVkpCQQG5urjw/Pj4eb29vgoODLVFUAHJzc/nhhx8YM2YMX3/9dbnv38fHh8WLF5vNu3TpEtu3bzcbBPBJpFar0Wq199Sjw5Br6l5sLDB9lI12/zaKzMnJoVWrViUGeo8ThUKBVqvF0dERHx8fUlJS5GnUqFHUrVvXbN6dBo2rSDw8PHBzM9V0STdfZ8lQWNomglDpiAClgqhVqxbe3t7Ex8fL8+Lj4+nWrRs1atRgz549ZvMjIiJYtmwZTZo0wdHREa1WS+/evUlLS5PXS09Pp0+fPnh4eKDRaAgKCpJv+gEBAQCEhoaiUCgIDw+Xt1u8eDG1a9fG1taWkJAQPv/882Ll/emnn6hTpw4ffPABu3fv5ty5c+V6PTp37sy1a9fYvXu3PC82NpYOHToUGxQsPT2d1157DVdXV+zs7IiKipKHVr91W19fX+zs7HjhhRe4du2a2fLbH0mBaUyfW6/L7fR6PWPHjqVatWrY29vTrFkzs9evNOfPn6dLly64urpib29P3bp12bBhA1D8EU/R46h169ZRq1Yt7Ozs6NGjBzk5OaxY/h3+zZ4jsHELRk6ZgtL+3/Ynffv2Zdy4cfc1vpC/vz8AL7zwAgqFQv4b4JdffqFx48bY2toSGBjIxx9/bFbzFxMTg6+vLzY2Nnh7exMdHQ2YasbOnz/PiBEj5NqOsrKyskKr1cqTg4MD1tbW8t+enp58+umnVK9eHRsbGxo2bMimTZvk7YtqYFatWkVERAR2dnY0aNCAvXv3yusUXe/NmzdTu3ZtHBwc6NixIykpKWZlKe1zotfrefvtt6latSq2trb4+/uXmL8MQLr5LSwZDWW+HoLwJKscAYokgT7HMlMZenGHh4cTFxcn/x0XF0d4eDhhYWHyfL1ez969e4mIiECv1zNx4kT++OMP1qxZQ1JSEv3795e3/+ijjzhx4gQbN27k5MmTLFiwgCpVqgDw+++/A7Bt2zZSUlJYtWoVAIsWLeLDDz9k8uTJnDx5kilTpvDRRx+xZMkSs7J+/fXXvPrqqzg7O9OpU6ditR0PSq1W06dPH7P9xsbG8sYbbxRbt3///hw4cIC1a9eyd+9eJEmiU6dOFNysMt+/fz9vvPEGQ4cO5ciRI0RERDBp0qQHLuPrr7/O7t27WbFiBUePHuWll16iY8eOxYKjkgwbNgydTsdvv/3GsWPH+OSTT0rNU5Obm8u8efNYsWIFmzZtIj4+nheef56t2+NZFfsZX02ZyjcrV7J+868PfF5gegQBpptwSkqK/PfmzZt59dVXiY6O5sSJEyxcuJDY2FgmT54MwMqVK5k9ezYLFy7k9OnTrFmzhnr16gGmR07Vq1dnwoQJcm1HeZs7dy6zZs1i5syZHD16lMjISLp27VrsNfnwww8ZPXo0R44cITg4mFdeecUsyMrNzWXmzJksXbqU3377jeTkZLOambt9TubNm8fatWv58ccfSUxMZNmyZWZB3q0kpSlQU4h8PIJgptwHaquQCnJhirdljv2fS6C2v6dVw8PDGTFiBIWFheTl5XH48GHatGmDwWBg3rx5AOzbt4+8vDwiIiIIDAyUtw0MDGTevHk0bdqU7OxsHBwcSE5OJjQ0lCZNmgCYfUF6eHgA4O7ujlarledPnDiRWbNm0b17d8BU01J0I+rXrx8Ap0+fZt++fXJQU3TDGj9+PMpy7N46YMAAnnnmGebOncvBgwfJyMjgueeeM2tjcPr0adauXcvu3bvlXE7fffcdPj4+rFmzhpdeeom5c+cSGRnJ+++/D0BwcDB79uwx+2VdVmfOnOH777/n4sWLeHub3lujR49m06ZNLF68mClTppS6fXJyMi+++KJ88771tSxJQUEBCxYsoEaNGgD06NGDpUu/5fKRreitXajtFUzr5s3YsWNHuWQMLnp/uLi4mL0/Jk+ezPvvvy+/FwIDA5k4cSJjx45l/PjxJCcno9VqadeuHSqVCl9fX5o2bQqAm5sbVlZWco3fwzBz5kzee+89evXqBcAnn3xCXFwcc+bM4X//+5+83ujRo3nuuecA+Pjjj6lbty5///03ISEhgOl6f/HFF/L1fvvtt5kwYYK8/d0+J8nJyQQFBfHMM8+gUCjw8/O7c6Gtbn5mDCJAEYRbVY4alMdEREQEOTk5JCQksHPnToKDg/H09CQsLIyEhARycnKIj4/H19eXwMBADh8+TLdu3fDz88PR0VF+HJGcnAzAkCFDWLFiBQ0bNmTs2LFmj4lKcuXKFS5cuMCAAQNwcHCQp0mTJnHmzL+Zcb/++mu58SWYGq7m5OSwbdu2cr0e9evXJygoiJUrV/LNN9/Qt2/fYhmNT548ibW1Nc2aNZPnubu7U6tWLU6ePCmv06JFC7Ptbv+7rA4dOoQkSQQHB5tdqx07dphdqzuJjo5m0qRJtGrVivHjx3P06NFS17ezs5NvlgBeXl74Vq+Gg70dVnrTPE+tl9kjvofh4MGDTJgwweycBw0aREpKCrm5ubz00kvk5eURGBjIoEGDWL16tVnNxMOUmZnJpUuXaNWqldn8Vq1aye+FIvXr15f/X9Sm6dZrd/v1rlq1qrz8Xj4n/fv358iRI9SqVYvo6Gi2bNly54IrTA2cFY/dkJmC8HBVjhoUlZ2pJsNSx75HNWvWpHr16sTFxZGenk5YWBhgygAdEBDA7t27iYuL49lnnyUnJ4cOHTrQoUMHli1bhoeHB8nJyURGRqLXm+5YUVFRnD9/nvXr17Nt2zbatm3LsGHDmDlzZonHN96sYl60aJHZDR+Q0xAYDAa+/fZbUlNTsbb+9+1jMBj4+uuv6dChw71fm3vwxhtv8L///Y8TJ07Ij6VudaeBkG8dNvxeBktWKpXF1isopVeF0WjEysqKgwcPFkvRUNqjmiIDBw4kMjKS9evXs2XLFqZOncqsWbN45513Slz/9sAMQG2tpFBC7l5sbWOLUZ9912M/CKPRyMcffyzXHNzK1tYWHx8fEhMT2bp1K9u2bWPo0KHMmDGDHTt2lHgOD8PtbVtKGkL+1rIULTPe8ojl9rIqFAr5/XEvn5NGjRqRlJTExo0b2bZtGz179qRdu3asXLmyeHmtrcEACqOIUAThVpUjQFEo7vkxi6VFREQQHx9Peno6Y8aMkeeHhYWxefNm9u3bx+uvv85ff/3F1atXmTZtGj4+PgBmPYCKeHh40L9/f/r370/r1q0ZM2YMM2fORK1WA6bAooiXlxfVqlXj7Nmz9OnTp8TybdiwgaysLA4fPmx2Y/7rr7/o06cP165dw93dvVyuBUDv3r0ZPXo0DRo0oE6dOsWW16lTh8LCQvbv3y8/4rl27RqnTp2idu3a8jr79u0z2+72vz08PDh+/LjZvCNHjtzxphoaGorBYCAtLa1YN/B75ePjw+DBgxk8eDAffPABixYtumOAcjtDgR4FkFtohQootFagtCrfj7NKpTJ7f4DpxpuYmFhqRmqNRkPXrl3p2rUrw4YNIyQkhGPHjtGoUSPUanWxfZYXJycnvL292bVrF23atJHn79mzR37MVB7u5XNSVJ6XX36Zl19+mR49etCxY0euX78u994pYgpQJJEwUBBuUzkClMdIREQEw4YNo6CgQK5BAVOAMmTIEPLz84mIiMDW1ha1Ws38+fMZPHgwx48fZ+LEiWb7GjduHI0bN6Zu3brodDrWrVsn37Q9PT3RaDRs2rSJ6tWrY2tri7OzMzExMURHR+Pk5ERUVBQ6nY4DBw6Qnp7OyJEj+frrr3nuuedo0KCB2bHq1q3L8OHDWbZsGe+++265XQ9XV1dSUlLuGCgEBQXRrVs3Bg0axMKFC3F0dOT999+nWrVqdOvWDTA9TmnZsiXTp0/n+eefZ8uWLcXanzz77LPMmDGDb7/9lhYtWrBs2TKOHz9OaGhoiccNDg6mT58+vPbaa8yaNYvQ0FCuXr3K9u3bqVevHp06dSr1vIYPH05UVBTBwcGkp6ezfft2+bW5F8ZC06BehYVWqJAw2NkUW+f69eskJydz6ZKp9jAxMRFA7vFyN/7+/vz666+0atUKGxsbXF1dGTduHJ07d8bHx4eXXnoJpVLJ0aNHOXbsGJMmTSI2NhaDwUCzZs2ws7Nj6dKlaDQauQ2Gv78/v/32G7169cLGxkZ+TFhexowZw/jx46lRowYNGzZk8eLFHDlyhO+++65cj3O3z8ns2bOpWrUqDRs2RKlU8tNPP6HVakscHFCpUoNOJxIGCsJtRBuUCiYiIoK8vDxq1qyJl5eXPD8sLIysrCxq1KiBj48PHh4exMbGyt19p02bVuzRjVqt5oMPPqB+/fq0adPGbFA1a2tr5s2bx8KFC/H29pZv5gMHDuSrr74iNjaWevXqERYWRmxsLAEBAVy+fJn169fz4osvFiu3QqGge/fuD2VMFBcXF+zt71wDtnjxYho3bkznzp1p0aIFkiSxYcMGOahp3rw5X331FfPnz6dhw4Zs2bKF//73v2b7iIyM5KOPPmLs2LE8/fTTZGVl8dprr5VarsWLF/Paa68xatQoatWqRdeuXdm/f79co1Uag8HAsGHDqF27Nh07dqRWrVoldue+E4XBFKCodTezFzsWH9Rt7dq1hIaGyo1Be/XqRWhoKF988cU9HWPWrFls3boVHx8fOVCLjIxk3bp1bN26laeffprmzZvz6aefygGIi4sLixYtolWrVtSvX59ff/2VX375Ra5VmzBhAufOnaNGjRpyQ9zyFB0dzahRoxg1ahT16tVj06ZNrF27lqCgoHI9TmmfEzA95vvkk09o0qQJTz/9NOfOnWPDhg0lNiJXFeXjMYLxIdUuCcLjSGQzFoTHjKGwEOXlY+gkBdINayTApnYIVuX8iKciiI2NZfjw4fc07P/jpmg8oytpqdhfvgKAskYgNpp7b7dWEvEdKFRkIpuxIDzBdDk3UCggT85ebPVEBidFMjIycHBw4L333rN0UcpN3bp1iYqKAsDKWi3PL9TnWapIglDhiABFeKR27txp1jXz9ulJERUVdcdzvNsYKXdjzM8CQFFws62Cfdl/cX/33Xd3LF/dunUfqHxlcbfr9OKLL3L69GmOHDli1mj8cbdhwwaOHTvG6dOncXZ2lvPxFIqEgYIge3J/dgkVUpMmTR4oQeHj4quvviIvr+Rfw7f34igLSZJQG7IxSqDWm57Oqh1dyryfrl27FusiW+RRdQeGu18nR0dHHB0dH1l5HpXbB24zKky/Fo2FImGgIBQRAYrwSGk0mlK7qD4pqlWr9lD2W6DLQ00hOQYlSgkMSrC3L/05bkkqyo3/YV2nx41RCRhAMogARRCKiEc8gvAY0eeYshfrbrY/KdSoRLfUJ4BRWZTRWPTiEYQiIkARhMeIUm9qf2J184e28glqt1OZiYSBglCcCFAE4TFhNBqwMeaaDW9v63T/7VmECkQpEgYKwu1EgCIIjwldbhZWComcosc71grUNhoLl0ooF1YiYaAg3E4EKILwmDDkZZr+LTR9bEsa3l54PCmKAhSjRG5WulniQkGorESAIjzR/P39mTNnjqWLUSqFQsGaNWvuup51QTaSBKqb3YutHcvee6ciO3fuHAqFAoVCQcOGDS1dnHLTv39/+bzu9Dor1f8Od6/JPIeUeoycy2fIzbgmhr8XKi0RoFQQX3zxBY6OjhQWFsrzsrOzUalUxbLl7ty5E4VCwalTp+77eEU3g/sZk6Toy/b2qSjPD5hyzcyePZv69etja2uLi4sLUVFR7N6922xfsbGxJSZQq0xSUlLkUUXvpKBAjy069JICawNIgMbx3/Ynf/75Jy+++CL+/v4oFIoyB2Xh4eEMHz687IUvRf/+/Xn++efLvN22bdv49ddfAeTzudMUHh5ermV+GObOnUtKSkqp66huPqpTFUJ2poqsPCUKfTaa7GRTsJJ6mpwbVzDc8v0gCE86MQ5KBREREUF2djYHDhygefPmgCkQ0Wq1JCQkkJubi52dacTQ+Ph4vL29CQ4Otlh5Fy9eTMeOHc3mFQUakiTRq1cvtm3bxowZM2jbti2ZmZn873//Izw8nJ9++um+blxPqnvJLKzPuYEKyC1UYYtEgY0Vdtb/fnxzc3MJDAzkpZdeYsSIEQ+xtA+fu7u7nFwwISEBw80ahD179vDiiy+SmJgo5/BQq9V33E9F4ezsjLNz8WSOt7J1cCbT/grqnAKsDWCdp4A8K3KUVhTaSKhUuWgM2ZDzD7lWdki2rtg6uj7RKQ4EQdSgVBC1atXC29ub+Ph4eV58fDzdunWjRo0a7Nmzx2x+REQEy5Yto0mTJjg6OqLVaunduzdpaWnyeunp6fTp0wcPDw80Gg1BQUEsXrwYQM66GhoaWuyX6OLFi6lduza2traEhISUmGXXxcUFrVZrNhUlJvvxxx9ZuXIl3377LQMHDiQgIIAGDRrw5Zdf0rVrVwYOHEhOTs4DX7OsrCz69OmDvb09VatWZfbs2XetCfj000+pV68e9vb2+Pj4MHToULKzs+Xl58+fp0uXLri6umJvb0/dunXZsGEDUPr1LI1er+ftt9+matWq2Nra4u/vz9SpU+Xlt1b9F9Vs/fjjj7Ru3RqNRsPTTz/N6T//IOHInzzbpSceTZvy4puDuXLliryPp59+mhkzZtCrVy9sbMrWNqV///7s2LGDuXPnyjUT586dA+DEiRN06tQJBwcHvLy86Nu3L1evXpW3XblyJfXq1UOj0eDu7k67du3IyckhJiaGJUuW8H//93/yPm99b98rDw8P+f1VNAKvp6enPC8uLo66detiY2ODv78/s2bNMtve39+fKVOm8MYbb+Do6Iivry9ffvmlvLzoeq9atYqIiAjs7Oxo0KABe/fuNdvPnj17aNOmDRqNBh8fH6Kjo83ew59//jlBQUHY2tri5eVFjx49ynSeSqUVLgG1sAmpRaHWHZ2dCqMCrIxgk6dAmWlF3g0VmXlWGPV5aHIuokg9Tm5RzYpB1KwIT55KEaBIkkRuQa5FprIkiw4PDycuLk7+Oy4ujvDwcMLCwuT5er2evXv3EhERgV6vZ+LEifzxxx+sWbOGpKQk+vfvL2//0UcfceLECTZu3MjJkydZsGABVapUAeD3338HTNXpKSkprFq1CoBFixbx4YcfMnnyZE6ePMmUKVP46KOPWLJkyT2fx/LlywkODqZLly7Flo0aNYpr166xdevWe97fnYwcOZLdu3ezdu1atm7dys6dOzl06FCp2yiVSubNm8fx48dZsmQJ27dvZ+zYsfLyYcOGodPp+O233zh27BiffPKJnCOotOtZmnnz5rF27Vp+/PFHEhMTWbZsGf7+/qVuM378eP773/9y6NAhrK2teWPIu4ydNJcZY99n65IlnEu+yLhx4+5+ke7B3LlzadGiBYMGDSIlJYWUlBR8fHxISUkhLCyMhg0bcuDAATZt2sTly5fp2bMnYHo09corr/DGG29w8uRJ4uPj6d69O5IkMXr0aHr27EnHjh3lfbZs2bJcylvk4MGD9OzZk169enHs2DFiYmL46KOPiI2NNVtv1qxZNGnShMOHDzN06FCGDBnCX3/9ZbbOhx9+yOjRozly5AjBwcG88sor8uPWY8eOERkZSffu3Tl69Cg//PADu3bt4u233wbgwIEDREdHM2HCBBITE9m0aRNt2rS5r3OyslbhWKUqLoG10ITUxuDtic7BBqMSlBLY5CuwuiVYkfS5crCSk3qanIwros2K8MSoFPWDeYV5NFtect6Rh21/7/3Yqe4tmVt4eDgjRoygsLCQvLw8Dh8+TJs2bTAYDMybNw+Affv2kZeXR0REBIGBgfK2gYGBzJs3j6ZNm5KdnY2DgwPJycmEhobSpEkTALObooeHB2CqTr/1EcPEiROZNWsW3bt3B0w1LSdOnGDhwoX069dPXu+VV17B6mbPgyJHjx4lMDCQU6dOUbt27RLPsWj+g7SfAVPtyZIlS1i+fDlt27YFTDU/3t7epW53a+1KQEAAEydOZMiQIXItUXJyMi+++CL16tUDMLvGpV3P0iQnJxMUFMQzzzyDQqEoloelJKNHjyYyMhKAIYPfol//11m3/Eta1QvFqIQBAweWKWgsjbOzM2q1Gjs7O7P3woIFC2jUqJFZcsNvvvkGHx8fTp06RXZ2NoWFhXTv3l0+p6LrBqa0Bjqd7p4eYd2PTz/9lLZt2/LRRx8BEBwczIkTJ5gxY4ZZoN6pUyeGDh0KwHvvvcfs2bOJj48nJCREXmf06NE899xzAHz88cfUrVuXv//+m5CQEGbMmEHv3r3l905QUBDz5s0jLCyMBQsWkJycjL29PZ07d8bR0RE/Pz9CQ0Mf+PyUVlY4uHmCmydGo5G8rOsU3LiBdU6+qWYlXwH5VuQprShQS6jUpsdAeYWgy8zir/2J1G0RhUotensJj6dKUYPyuIiIiCAnJ4eEhAR27txJcHAwnp6ehIWFkZCQQE5ODvHx8fj6+hIYGMjhw4fp1q0bfn5+ODo6yo9pkpOTARgyZAgrVqygYcOGjB071uwxUUmuXLnChQsXGDBggFlW2UmTJnHmzBmzdWfPns2RI0fMJh8fn3s+1wcdnv3s2bMUFBTQtGlTeZ6zszO1atUqdbu4uDjat29PtWrVcHR05LXXXuPatWtydX10dDSTJk2iVatWjB8/nqNHj8rblvV6Funfvz9HjhyhVq1aREdHs2XLlrtuU79+ffn/VZxNAW5QkOmGWmCrQqvVmj3OexgOHjxIXFyc2Xuh6KZ+5swZGjRoQNu2balXrx4vvfQSixYtIj09/aGW6VYnT56kVatWZvNatWrF6dOn5XYrYH4tFQpFidfu1nWqVq0KIK9z8OBBYmNjza5DZGQkRqORpKQk2rdvj5+fH4GBgfTt25fvvvuO3Nzccj1XpVKJvXMVXPxqYh9SB6OPFp2jDQalqeePTf7Nx0AZKnJ0VigKdQT9/l+yp9Rg//x+nNi3SdSsCI+dSlGDorHWsL/3fosd+17VrFmT6tWrExcXR3p6OmFhYYCpEWVAQAC7d+8mLi6OZ599lpycHDp06ECHDh1YtmwZHh4eJCcnExkZiV6vB0yp7M+fP8/69evZtm0bbdu2ZdiwYcycObPE4xeNvbBo0aJimW5vry3RarV3TPpX9Eu2JCdPngRMv0IfRNGjs9sDndIeqZ0/f55OnToxePBgJk6ciJubG7t27WLAgAEUFJjGjh84cCCRkZGsX7+eLVu2MHXqVGbNmsU777xT5utZpFGjRiQlJbFx40a2bdtGz549adeuHStXrrzjNrdmFLYy5ANgI5k+rkoHexQKxUMfK8NoNNKlSxc++eSTYsuqVq2KlZUVW7duZc+ePWzZsoX58+fz4Ycfsn//frmN08MkSdI9vf63Z2cu6drduk7RPovWMRqNvPXWW0RHRxfbt6+vL2q1mkOHDhEfH8+WLVsYN24cMTExJCQkPJQeaoqbwQrOVUw1K5nXKchIR5WjQ2kEdT4o8pX8scOTdId8AqttptbVNaRtqsI5bSQ+Hd6hWmDJNZyCUJFUihoUhUKBncrOIlNZawoiIiKIj48nPj7erOFqWFgYmzdvZt++fURERPDXX39x9epVpk2bRuvWrQkJCSnxF7WHhwf9+/dn2bJlzJkzR24gWNT74dZfml5eXlSrVo2zZ89Ss2ZNs6ksN5xevXpx+vRpfvnll2LLZs2ahbu7O+3bt7/n/ZWkRo0aqFQquS0NQGZmJqdPn77jNgcOHKCwsJBZs2bRvHlzgoODuXTpUrH1fHx8GDx4MKtWrWLUqFEsWrRIXnan63k3Tk5OvPzyyyxatIgffviBn3/+mevXr991O4OhEJVRBzzc4e3VarXZewFMgdWff/6Jv79/sfeDvb09YPpstWrVio8//pjDhw+jVqtZvXr1HfdZnurUqcOuXbvM5u3Zs4fg4OBiAfWDKLoOt1+DmjVryp8ja2tr2rVrx/Tp0zl69Cjnzp1j+/bt5VaGO1Eqldi7VMHFLwhNSB2M1b3Q26uQAOccCDyigvWu7NzizaG/JTzPrkS7pAWHZnbl1KH4h14+QXgQlaIG5XESERHBsGHDKCgokGtQwBSgDBkyhPz8fCIiIrC1tUWtVjN//nwGDx7M8ePHmThxotm+xo0bR+PGjalbty46nY5169bJbUA8PT3RaDRs2rSJ6tWrY2tri7OzMzExMURHR+Pk5ERUVBQ6nY4DBw6Qnp7OyJEj5X3fuHGD1NRUs+M5Ojpib29Pr169+Omnn+jXr1+xbsZr167lp59+km9wYAqSbh+PRa1WU6dOnTteJ0dHR/r168eYMWNwc3PD09OT8ePHo1Qq7xgU1qhRg8LCQubPn0+XLl3YvXs3X3zxhdk6w4cPJyoqiuDgYNLT09m+fbt8zUq7nqWZPXs2VatWpWHDhiiVSn766Se0Wu09/brW5WSgvOV0Cq0VaGyLt2nS6/VyrZVer+eff/7hyJEjODg43LGm61b+/v7s37+fc+fO4eDggJubG8OGDWPRokW88sorjBkzhipVqvD333+zYsUKFi1axIEDB/j111/p0KEDnp6e7N+/nytXrsjXxN/fn82bN5OYmIi7uzvOzs7FajMexKhRo3j66aeZOHEiL7/8Mnv37uWzzz4rsdfZg3jvvfdo3rw5w4YNY9CgQdjb23Py5Em2bt3K/PnzWbduHWfPnqVNmza4urqyYcMGjEbjXR83ljdTsOKBla0jV/ONpA/twY3VP1H9j1Q804EDNuiwYauvEbeQg9T8v26c2PgU+qbDqP/syyjLMagThHIhPYYyMjIkQMrIyCi2LC8vTzpx4oSUl5dngZI9uKSkJAmQQkJCzOZfuHBBAqQaNWrI85YvXy75+/tLNjY2UosWLaS1a9dKgHT48GFJkiRp4sSJUu3atSWNRiO5ublJ3bp1k86ePStvv2jRIsnHx0dSKpVSWFiYPP+7776TGjZsKKnVasnV1VVq06aNtGrVKnk5pnHCik1Tp06V1ykoKJBmzpwp1a1bV7KxsZGcnJykyMhIaefOnWbntXjx4hL35efnd9drlZmZKfXu3Vuys7OTtFqt9Omnn0pNmzaV3n//fXkdPz8/afbs2fLfn376qVS1alVJo9FIkZGR0rfffisBUnp6uiRJkvT2229LNWrUkGxsbCQPDw+pb9++0tWrV+/pet7Jl19+KTVs2FCyt7eXnJycpLZt20qHDh0yu56rV6+WJOnf17/oNcy6fFaK++lLCZAu7d4tpZ8/LV83Z2dneR9F290+3fq6liYxMVFq3ry5pNFoJEBKSkqSJEmSTp06Jb3wwguSi4uLpNFopJCQEGn48OGS0WiUTpw4IUVGRkoeHh6SjY2NFBwcLM2fP1/eZ1pamtS+fXvJwcFBAqS4uLhSy3D7ud8uLi7O7LWSJElauXKlVKdOHUmlUkm+vr7SjBkzzLa5/fWXJElq0KCBNH78+DseMz09vVh5f//9d/lc7O3tpfr160uTJ0+WJEmSdu7cKYWFhUmurq6SRqOR6tevL/3www/Fyn/r6/ww3f4dmHEtRdq+cJy04YUW0vGQEOlELdP0a6ta0vY3fSXdf52k5JgQad+PM6W8nKyHXj6hcivt/n07hSSVoR9sBZGZmYmzszMZGRnygE1F8vPzSUpKIiAgQB6XQ6gccnJyqFatGrNmzWLAgAGWLk650F86jpoCsjLUWBskDN4eOLh5WbpYD8W5c+cICAjg8OHDT9RQ90UUCgWrV69+6IMUlvYdmPxXAkc/n0q1uJPYmppdccMBMurqaO6TjtHagVNenXB5uie1GkWgUFaKVgDCI1Ta/ft24t0nPLYOHz7M999/z5kzZzh06BB9+vQBoFu3bhYuWfnQ6/JQU4DOqMDaIJmGt38I7U8qmpYtW5b7mCmWNHjwYHksHUvzDXmazvNW4b99C+d6P8MNRyUu2eC334aza7XsOWGD+9k1hKzrzuUJQexb8BZ/JWwTPYAEixA1KEKFlJycXGoblBMnTnDt2jUGDhxIYmIiarWaxo0byyPFPkpTpkwxGyvkVq1bt2bjxo33td/s6yk45KeSrldjmy2hVytxDr7zNbmTe7mWvr6+91XGsrjbdfrll1/kEWxtbGzK1G29IktLSyMz05SJumrVqmbtrx6GsnwH6vKy2fPtdPh+LdpUnTz/shvkBuipWTWbmvb5pCmqcM6rHS5NXiK4UYRoryLct7LUoIgARaiQCgsL5ZtVSfz9/bG2rhhtvK9fv37HHjkajYZq1ard135zU05hJ+WQnmuDbb4Rvas9ztXK3n23olzLh3WdBHP38x1oNBo5sHYRV79bRvUTV1HdUmFye7BySVmVizV7UztqCM5uHg/pLIQnlQhQRIAiPOaMRgOkHEOBRO4NFUoJJD9v7Byf/Ec8woN50O/AjGspHF61kNwtv5YYrOTV0NG4egYOaiuOuUfi8ezbBD5lmZG6hcePCFBEgCI85nKz0rHLOke2wRqrDAVGBWhq10EpGi0Kd1Ge34EZ11I4tOpL8rZuo/qf/wYrhUo4H2TAJzCLek65/KV+irzQN6jf7lUxtL5QKhGgiABFeMxlp53DoTCd6zpbNDkG9BoVzjUe7bgawuPpYX0HZl5P5eBPn1P483qqJ/87lP8lTwlFrTyaazPIsXLh76pdcAiJILDRs9g7upTb8YUnQ1kClIrxEF8QBDPqwmwArApMw60rHO4t4aQgPCxObloi3poAb03gz13/R9I3C/DZfx7vNAWk2fGnrR1ptQqolfsDgZe+xbDdikRVENc9m2IfHE6Nxm1FwCKUiQhQBKGCKepeXCgpUOlNFZw2laB7sfD4qPtMN+o+041rKUkkfD0Nx3W7cbthwOEPFQV/uLPfHq57G7Dxuox/5g8E/vMtxjilKWDxaklg1Dt4Va9h6dMQKrgyPdBesGAB9evXx8nJCScnJ1q0aGHWhVKSJGJiYvD29kaj0RAeHs6ff/5ptg+dTsc777xDlSpVsLe3p2vXrly8eLF8zkYQngD6nBsAZBvUKIBCKwU2mofbNVUQ7od71QA6/nchzXYeIn3CEM7VcaXAClxyIPC0FdV2aShY687v67zZfNCD5KQ0tKeX47roaX6f25sLp/+w9CkIFViZApTq1aszbdo0Dhw4wIEDB3j22Wfp1q2bHIRMnz6dTz/9lM8++4yEhAS0Wi3t27cnKytL3sfw4cNZvXo1K1asYNeuXWRnZ9O5c+eHmlRMEO5XfHw8CoWCGzdu3PM2/fv3f6DRQpU60+fFWGhKwmOwU5e43rlz51AoFMXyGFVGRa+TQqF46CO1Pkrh4eHyeVXk19lapaZlz2iiVu2h5v695M3+gKQXm5Jc0wm91c3EhX9bUX23hvy17vy6zwNVUjzVloVxaGYX/v5j190PIlQ6ZQpQunTpQqdOnQgODiY4OJjJkyfj4ODAvn37kCSJOXPm8OGHH9K9e3eeeuoplixZQm5uLsuXLwcgIyODr7/+mlmzZtGuXTtCQ0NZtmwZx44dY9u2bQ/lBB8XX3zxBY6OjhQWFsrzsrOzUalUtG7d2mzdnTt3olAoOHXq1H0f70FubkVfmLdPK1askNcxGAzMnj2b+vXrY2tri4uLC1FRUezevdtsX7Gxsfedkr5///4oFAoGDx5cbNnQoUNRKBT079//vvZtKUajAVujqQGiSmdqf2LtWHJDMh8fH1JSUnjqqace6Ji//fYbXbp0wdvbG4VCwZo1a8q0vb+/P3PmzHmgMtwuPDyc4cOHl3m7xMREYmNjgTu/T4umx+G9sWrVKrOM3Y8DOwcXGkW9RqfJS4hct5/ghP3kzf2QpJ7NOR/kjFEB/ueV2G5yZttOLbpzhwlc9RxHpz3Ln7vXIxmNlj4FoYK47z6LBoOBFStWkJOTQ4sWLUhKSiI1NZUOHTrI69jY2BAWFsaePXsAOHjwIAUFBWbreHt789RTT8nrlESn05GZmWk2PWkiIiLIzs7mwIED8rydO3ei1WpJSEggN/ffVvPx8fF4e3sTHBxsiaICsHjxYlJSUsymol+ukiTRq1cvJkyYQHR0NCdPnmTHjh34+PgQHh5e5htgaXx8fFixYgV5eXnyvPz8fL7//vtHMjpqecvPyUSpkMg1Wt91eHsrKyu0Wu0DD7KWk5NDgwYN+Oyzzx5oPxWBp6enHPDe+t6cM2cOTk5OZvPmzp1r2cLeAzc3Nzw8Hu/B0DR2TjSKfJVOExbT8Zd92K/8hjNhNSiwAp9LCly2ObLj16pcP/c3wZt7c2pKC/b/8Alp/yRZuuiChZU5QDl27BgODg7Y2NgwePBgVq9eTZ06dUhNTQXAy8s8kZmXl5e8LDU1FbVajaur6x3XKcnUqVNxdnaWpydlCOxb1apVC29vb+Lj4+V58fHxdOvWjRo1apgFcPHx8URERLBs2TKaNGmCo6MjWq2W3r17k5aWJq+Xnp5Onz598PDwQKPREBQUxOLFiwEICDCNSBoaGopCoSA8PFzebvHixdSuXRtbW1tCQkJKTF/v4uKCVqs1m4q6NP7444+sXLmSb7/9loEDBxIQEECDBg348ssv6dq1KwMHDiQnJ6dcrlujRo3w9fVl1apV8rxVq1bh4+NDaGio2bo6nY7o6Gg8PT2xtbXlmWeeISEhwWydDRs2EBwcjEajISIiotgIrDExMcUS2c2ZMwd/f/87llGSJKZPn05gYCAajYYGDRqwcuXKEtc15mUAkG8wBR1X8rN5rV//El/D22vBih5zbN68mdDQUDQaDc8++yxpaWls3LiR2rVr4+TkxCuvvGIW8EZFRTFp0iS6d+9+x3O4k/DwcM6fP8+IESPkmokie/bsoU2bNmg0Gnx8fIiOjjZ73T///HOCgoKwtbXFy8uLHj16AKaasR07djB37lx5n6WNhHsnt743nZ2dUSgUZvOWL19OjRo1UKvV1KpVi6VLl5ptr1Ao+Oqrr3jhhRews7MjKCiItWvXysuLrvevv/5KkyZNsLOzo2XLliQmJprt55dffqFx48bY2toSGBjIxx9/bFZTGhMTg6+vLzY2Nnh7exMdHV3mc32c+NdtQeeF6/DasIqzz9UnTw3aqwq84u3Zv9mb5KTLBB+bhueihpya9DR7l3xI8qkjli62YAFlDlBq1arFkSNH2LdvH0OGDKFfv36cOHFCXn7rFxSYvpxvn3e7u63zwQcfkJGRIU8XLlwoU5klScKYm2uRqSzDzISHhxMXFyf/HRcXR3h4OGFhYfJ8vV7P3r17iYiIQK/XM3HiRP744w/WrFlDUlKSWbX1Rx99xIkTJ9i4cSMnT55kwYIFVKlSBUCuNt62bRspKSnyDX7RokV8+OGHTJ48mZMnTzJlyhQ++ugjlixZcs/nsXz5coKDg+nSpUuxZaNGjeLatWts3br1nvd3N6+//rp80wb45ptveOONN4qtN3bsWH7++WeWLFnCoUOHqFmzJpGRkfLw6xcuXKB79+506tSJI0eOMHDgQN5///0HLt9///tfFi9ezIIFC/jzzz8ZMWIEr776Kjt27Ci2blH3YmWB6X0z+X+f3/E1vJOYmBg+++wz9uzZw4ULF+jZsydz5sxh+fLlrF+/nq1btzJ//vwHPi8wBYPVq1dnwoQJcs0EmH7IREZG0r17d44ePcoPP/zArl27ePvttwE4cOAA0dHRTJgwgcTERDZt2kSbNm0AmDt3Li1atGDQoEHyPsv7R8nq1at59913GTVqFMePH+ett97i9ddfN/v8AXz88cf07NmTo0eP0qlTJ/r06VNsuP4PP/yQWbNmceDAAaytrc3ee5s3b+bVV18lOjqaEydOsHDhQmJjY5k8eTIAK1euZPbs2SxcuJDTp0+zZs2aR55LylK8/Grz3KwfCPh1C+d6tSTLToF7BlTfreGfn7Vs2q3l/Nkr+P21EN/lYZyfUJe9X0Zz6lA8GelXRQLDSqDMdcNqtZqaNWsC0KRJExISEpg7dy7vvfceYKolqVq1qrx+WlqaXKui1WrR6/Wkp6eb1aKkpaWVmr3UxsYGG5v7H51QyssjsVHj+97+QdQ6dBCF3b2NYREeHs6IESMoLCwkLy+Pw4cP06ZNGwwGA/PmzQNg37595OXlERERQWBgoLxtYGAg8+bNo2nTpmRnZ+Pg4EBycjKhoaE0adIEwOxXflG1sbu7O1qtVp4/ceJEZs2aJf+aDggIkL9Y+/XrJ6/3yiuvYHVbwrCjR48SGBjIqVOnqF27donnWDT/QdrP3K5v37588MEHco3C7t27WbFihVltVE5ODgsWLCA2NpaoqCjAFIxt3bqVr7/+mjFjxrBgwQICAwOZPXs2CoWCWrVqcezYMT755JP7LltOTg6ffvop27dvp0WLFoDptdq1axcLFy4kLCxMXreoe7FB+rf9yT+Xr9zxNbyTSZMm0apVKwAGDBjABx98wJkzZ+T3S48ePYiLi5M/sw/Czc0NKysruRavyIwZM+jdu7fcjiQoKIh58+YRFhbGggULSE5Oxt7ens6dO+Po6Iifn59c4+Xs7IxarcbOzs5sn+Vp5syZ9O/fn6FDhwIwcuRI9u3bx8yZM4mIiJDX69+/P6+88gpgSnY4f/58fv/9dzp27CivM3nyZPl1fP/993nuuefIz8/H1taWyZMn8/7778ufncDAQCZOnMjYsWMZP348ycnJaLVa2rVrh0qlwtfXl6ZNmz6Uc66oXD18iIr5mpxR19m9cAKqdfFoU3X4XVDCBQ0ZaDjhJVHgm0dQ9goC/1mCUgFGSUGGwo5shQO5SkfyrR0pUDlSoHZBcq+JW+0wAp5qIUa2fYw98DgokiSh0+kICAhAq9WydetW+YtGr9ezY8cO+Qu+cePGqFQqtm7dSs+ePQHTc+Ljx48zffr0By3KYy8iIoKcnBwSEhJIT08nODgYT09PwsLC6Nu3Lzk5OcTHx+Pr60tgYCCHDx8mJiaGI0eOcP36dYw3G5cVZa8dMmQIL774IocOHaJDhw48//zzpQaCV65c4cKFCwwYMIBBgwbJ8wsLC3F2djZbd/bs2bRr185sXll+5d6tVq0sqlSpwnPPPceSJUuQJInnnnuuWC3DmTNnKCgokG/cACqViqZNm3Ly5EkATp48SfPmzc3KVhRU3K8TJ06Qn59P+/btzebr9fpij6D0OTdQA9lGG9SSEYMShr39Dj169Ljn1xCgfv368v+9vLyws7MzC2a9vLweesPLgwcP8vfff/Pdd9/J8yRJwmg0kpSURPv27fHz8yMwMJCOHTvSsWNH+VHKo3Dy5EnefPNNs3mtWrUq1i7l1mtpb2+Po6Oj2WPU29cp+nGWlpaGr68vBw8eJCEhQa4xAVP7vfz8fHJzc3nppZeYM2eOfB06depEly5dKkwizEfJ3tGNDqPnwGg49+de/lrzLcpdB6iWlE21ywq4bEMBNuxyhRxnIwYbCdQSSrUBK9V1VOqr2KiMaFQGqqdswPP0p+T+nw2JtrXJ8nwah6DWBIaGicHiHiNl+hT85z//ISoqCh8fH7KysuRfqZs2bUKhUDB8+HCmTJlCUFAQQUFBTJkyBTs7O3r37g2YfhkNGDCAUaNG4e7ujpubG6NHj6ZevXrFbnblSaHRUOvQwYe2/7sd+17VrFmT6tWrExcXR3p6uvyrTKvVEhAQwO7du4mLi+PZZ58lJyeHDh060KFDB5YtW4aHhwfJyclERkai1+sBU9uC8+fPs379erZt20bbtm0ZNmwYM2fOLPH4RQHOokWLaNbMPPnX7bUlWq1Wrkm7XXBwsNljv1sVBQNBQUH3eFXuzRtvvCE/Pvjf//5XbHnRo7bSHkHey+M4pVJZbL2CgoI7rl90TdevX18sW+/ttYJF3YsNBiVgpFCjolOnTmV6DcEUeBVRKBRmfxfNMz7knhJGo5G33nqrxPYUvr6+qNVqDh06RHx8PFu2bGHcuHHExMSQkJBw3726yupeHkffy7W7/XrDv6+70Wjk448/LrF9j62tLT4+PiQmJrJ161a2bdvG0KFDmTFjBjt27Ch27MrEv24L/OuafhykXUjk6OpvKIjfRbXE63ikg0d66a0TrgF/uUBm1ULsqpzDP/0k/smLMP6q5LSqBulOIRgcqmLlVBUbN2/s3X1w8fLFzcMb5W3fdYLllClAuXz5Mn379iUlJQVnZ2fq16/Ppk2b5F+HY8eOJS8vj6FDh5Kenk6zZs3YsmULjo6O8j5mz56NtbU1PXv2JC8vj7Zt2xIbG1vsBlieFArFPT9msbSIiAji4+NJT09nzJgx8vywsDA2b97Mvn37eP311/nrr7+4evUq06ZNk2subu0BVMTDw4P+/fvTv39/WrduzZgxY5g5cyZqtWlsjVvHn/Hy8qJatWqcPXuWPn363Pc59OrVi969e/PLL78Ua4cya9Ys3N3di9UoPKiOHTvKgVlkZGSx5TVr1kStVrNr1y45YC4oKODAgQPyY4g6deoU62G0b98+s789PDxITU01u5mV1lW7Tp062NjYkJycbPY453ZGw83uxQqw1pleE6WDg3zMkl7DikKtVhcbx6hRo0b8+eefdwxiAaytrWnXrh3t2rVj/PjxuLi4sH37drp3717iPstT7dq12bVrF6+99po8b8+ePXd8NHm/GjVqRGJiYqnXQaPR0LVrV7p27cqwYcMICQnh2LFjNGrUqFzL8rjy9KlFu+hPINqUD+j4th/JS0uhICMdQ2YWUlYWiqwclNl5WOfosM3W43a9EI8b4HHDGrBGhx0JdnCtqhErj2t4OG/Hw6YQD5sCHKz+DTgLJCuuKFzIsnKhQGmDQanGoLTBYGWDUWmD0doWycoGo7UdCocqWDtpsXXR4lClGs5VquHs5lkswDEUFpKZfoXsG2nk3LhCfuZVCrKvo3bywKtGQ7yq10AhkoCWqEwBytdff13qcoVCQUxMDDExMXdcx9bWlvnz55dbI70nTUREBMOGDaOgoMDshhYWFsaQIUPIz88nIiICW1tb1Go18+fPZ/DgwRw/fpyJEyea7WvcuHE0btyYunXrotPpWLdunfwF7OnpiUajYdOmTVSvXh1bW1ucnZ2JiYkhOjoaJycnoqKi0Ol0HDhwgPT0dEaOHCnv+8aNG8V6Xjk6OmJvb0+vXr346aef6NevHzNmzKBt27ZkZmbyv//9j7Vr1/LTTz9hb//vyKgGg6HYTV6tVlOnTp17vm5WVlZy7UxJwa69vT1DhgxhzJgxuLm54evry/Tp08nNzWXAgAEADB48mFmzZjFy5EjeeustDh48KI+pUSQ8PJwrV64wffp0evTowaZNm9i4ceMdk145OjoyevRoRowYgdFo5JlnniEzM5M9e/bg4OAgt03Iz8nATiGRb7RCdbOBrMbZvdTXsLxkZ2fz999/y38nJSVx5MgR+Trdjb+/P7/99hu9evXCxsaGKlWq8N5779G8eXOGDRvGoEGDsLe35+TJk3ID3XXr1nH27FnatGmDq6srGzZswGg0UqtWLXmf+/fv59y5czg4OODm5laumZzHjBlDz549adSoEW3btuWXX35h1apV5T4e07hx4+jcuTM+Pj689NJLKJVKjh49yrFjx5g0aRKxsbEYDAaaNWuGnZ0dS5cuRaPR4OfnV67leFI4uWlp2fPuvZzSr1wg8be1XNu/G9Xxv/E6n4VTLjidUcIZW8CWfOACkKuGLAfItzdSaCeh0BixsknHykrCykpCaSVhrZSwtpJQKY1YKyVsrIy4qgtxsy5EdcvbskCy4qrCmWylE2pJh6OUiaOUi6tC4ta+q3oDWCtBuQOyJQ3/qPzIcKyJ0SME++r1qVqzIe5an0ofuFS+B50VXEREBHl5eYSEhJh12Q4LCyMrK4saNWrINSaxsbH85z//Yd68eTRq1IiZM2fStWtXeRu1Wi03HtVoNLRu3VoeTM3a2pp58+YxYcIExo0bR+vWrYmPj2fgwIHY2dkxY8YMxo4di729PfXq1Ss2aNbrr79erOxTp07l/fffR6FQ8OOPPzJ37lxmz57NsGHDsLGxoUWLFsTFxfHMM8+YbZednV2sPYafn1+Zu5beLTPmtGnTMBqN9O3bl6ysLJo0acLmzZvlBtu+vr78/PPPjBgxgs8//5ymTZsyZcoUs14ZtWvX5vPPP2fKlClMnDiRF198kdGjR/Pll1/e8bgTJ07E09OTqVOncvbsWVxcXGjUqBH/+c9/5HWM+aaxfXKNajQUUqBSolHblvoalpcDBw6YNQwtCkT79etXLEAryYQJE3jrrbeoUaMGOp0OSZKoX78+O3bs4MMPP6R169ZIkkSNGjV4+eWXAVM39VWrVhETE0N+fj5BQUF8//331K1bF4DRo0fTr18/6tSpQ15eHklJSffUQPhePf/888ydO5cZM2YQHR1NQEAAixcvNutuXx4iIyNZt24dEyZMYPr06ahUKkJCQhg4cCBgug7Tpk1j5MiRGAwG6tWrxy+//IK7u3u5lqOycfXwofmLw+DFYQDk5WaSuHcDqXu2Y/zjJA6pGThmFGBbAHZ6sLsOXC8KBqyA0h+vScB14CqQbQe5dqDTmAIcbI1Y2WRhLFQg6e1R5DtgpVOgyldgm6/APh9sCiBPDdfcJXSuBlROabg6XcQ/dTOeiQUot4NesiZbYUeuwo58pT06KzsKrB0otLbHoHLAqHHDukoNHL1r4elfB9cqVZ+4gEYhlaUfbAVRWrrmh5VqXBAeJv2l46gpID3PFts8AzpnDS4+IpnavSoaGyg9Pf2RtWF5VM6dO0dAQACHDx8uNgZPScR34L3LupFGWnIiNy6eJSvlPPmplyhMuwIZmSj0hSj0hSgLClHqC7EqMNycjKh1BhxyJZQP4e6ZYwPX3SX0jkZQSShURpQqCWtrIyqVEZW1hK21AXuVgeoavfyIKhM7LltXI0PjS4FLANZVamDvGYCrdyBVqvpXmN5Mpd2/bydqUATBwvT5pu7FRglUN9ufqByd77KVUJLq1avTpUsXvv/+e0sXpVxERUXx22+/WboYTyxHF08cXTyhfuu7r3ybwgI96WnJXPvnDJmpyeRcvoQuLRXDtWtINzJBY4PSxRlrF1fUbu5o3Dyxr6LF0b0qTlWqcvXCaVL+/J3sv07A2QvY/3OdKlcLsNeB/SUFppqcO5MwPaK64QCZzhIFzgasHa/jYH8ZD7vdVNfosLcyRVAGScFlhRvpKi9ybLXoHbxROldHaeuEUm2Hta0GK7Ud1rb2qGzsUWvsUdvaYWvnhLOb5UYyFgGKUCEVdZW+kxMnTjyWQ9nfbvDgwSxbthSKKjKLfpEplbz66qt88cUXFisbmNItFI0bU5Ls7OxHUg7TdVpW4rJXX32V2bNnc/r0aQAcbjYufhJ89dVXchqHJ+H9/iSxVqnxqFYTj2p3bgBdGldPX4IatzWbl5ebyflje0g9noAuLQVDdjbG7BzIyUWRm49Vrg6rPD2qvALsswpwyJNwyQaXbAX8Y2oQDDYYgWQg0w6yHCX0DhJGewMqu1TsNBdx0RTiaavH3sqIRmnE6g6jPlxUaHEen1jywkdAPOIRKqTCwsJS26D4+/s/EWNFpKWlcfnvI2ikPG4UarDNKaTAxgpHvxo4OTnh6elp0fLl5eXxzz//3HF5ab1TylNaWtodc3BVhOtUkYjvwMojPS2ZiycPcu3vY+SePYPxwj/YpqTjciUP+/x7v7XrraDAGgpUpn8LrcFgLZHrpKTb2pKHjLhf4hGP8NiztrZ+ZDc/S6ri7k6VAk+UComMHBvUOiP6Kk44ayvGr2WNRlMhXgdPT08RhAjCbVw9fXH19IWwF8zmG41GMq5eJPXsn6SfP0X2xXMUXLoEl6+ivpqB/fU8nDONcq4btcE0obt1LwouGy07Fo8IUATBgoq6F+ska3l4exsn17tsJQiCcGdKpfLf4KV5yY9oCwv05Odmkp+TQX5OJvm5mRTkZqPLzaIgN5uC3BzsbO99oNGHQQQogmBBRd2Lc4xqNBRgsFJgr3ly2lAIglAxWavUODhXwcG59OSjlvRkdZoWhMdMUfZiCm/+Y6cu1zxFgiAIjysRoAiChejzc2/pXmyKUKwcHO+ylSAIQuUgAhRBsBB9zg0AcrDFurBoeHs3C5ZIEASh4hABilBpxMTE3NNInI+Kld7U/qTAaBqQSa9WMnDgmzz//PMWLFXFER4ebkr0qVCUmpDxcRIfHy+fk3idBaF0IkCpIL744gscHR0pLCyU52VnZ6NSqWjd2nyUw507d6JQKDh16tR9H+/cuXP3/cVf9AV7e6ZfnU6Hu7s7CoWC+Pj4YuvfOt2ejycuLo7OnTvj4eGBra2tnLflSR1F01BYiI3RNACXUn8zm6q9hrlz595T/pu7effdd2ncuDE2NjZlDspiY2PLfbj4ohvzjRs3yrTdoEGDSElJ4amnniImJqbE99KtU1nzNz1qLVu2JCUlhZ49e1q6KIJQ4YkApYKIiIggOzubAwcOyPN27tyJVqslISGB3NxceX58fDze3t4EBwdboqgA+Pj4sHjxYrN5q1evvuMonosXLyYlJUWe1q5dKy/7/PPPadu2Le7u7vzwww+cPHmSpUuX0rJlS0aMGPFQz8NS8rPTUSogHxWqfFNQqnJywdnZuVyCA0mSeOONN+TkfI8rOzs7tFot1tbWjB492uw9VL16dSZMmGA2ryiRZkWlVqvRarVoNJbtvikIjwMRoFQQtWrVwtvb26zmIT4+nm7dulGjRg327NljNj8iIoJly5bRpEkTHB0d0Wq19O7dm7S0NHm99PR0+vTpg4eHBxqNhqCgIDmoCAgIACA0NBSFQmGWxXXx4sXUrl0bW1tbQkJC+Pzzz4uVt1+/fqxYsUIehhvgm2++oV+/fiWen4uLC1qtVp7c3ExtLZKTkxk+fDjDhw9nyZIlPPvsswQEBNCyZUveffdds4DtbhYtWoSPjw92dna88MILfPrpp6Xe7BMSEmjfvj1VqlTB2dmZsLAwDh06ZLZOTEwMvr6+2NjY4O3tTXT0v6neP//8c4KCgrC1tcXLy4sePXrcUzlXrlxJ0xbPoKnRAu+6rekyYCBZ+blo7J3p37+/WdV/eHg477zzDsOHD8fV1RUvLy++/PJLcnJyeP3113F0dKRGjRps3LjR7Bjz5s1j2LBhBAYG3lOZisTHx/P666+TkZEh10rExMQAoNfrGTt2LNWqVcPe3p5mzZqZvV/Pnz9Ply5dcHV1xd7enrp167JhwwbOnTsnZ0t2dXVFoVDQv3//MpULTEPY3/oesrKykt/7Wq0WvV5P9+7dcXBwwMnJiZ49e3L58mV5+6JHfEuXLsXf3x9nZ2d69epFVlaWvE54eDjR0dGMHTsWNzc3tFqtfP5FMjIyePPNN/H09MTJyYlnn32WP/74Q17+xx9/EBERgaOjI05OTjRu3LhM72NBEEwqRYAiSRIFOoNFprJkEggPDycuLk7+Oy4ujvDwcMLCwuT5er2evXv3EhERgV6vZ+LEifzxxx+sWbOGpKQksy/+jz76iBMnTrBx40ZOnjzJggULqFLF1Of9999/B2Dbtm2kpKSwatUqwHST//DDD5k8eTInT55kypQpfPTRRyxZssSsrI0bNyYgIICff/4ZgAsXLvDbb7/Rt2/fMr02P//8MwUFBYwdO7bE5ffa5Xb37t0MHjyYd999lyNHjtC+fXsmT55c6jZZWVn069ePnTt3sm/fPoKCgujUqZN8w1q5ciWzZ89m4cKFnD59mjVr1lCvXj0ADhw4QHR0NBMmTCAxMZFNmzbRpk2bu5YzJSWFV155hddf7sLJ+J9Z+8NSurZrh97G+o6p0pcsWUKVKlX4/fffeeeddxgyZAgvvfQSLVu25NChQ0RGRtK3b1+zWrb71bJlS+bMmYOTk5NcKzF69GgAXn/9dXbv3s2KFSs4evQoL730Eh07dpRz4AwbNgydTsdvv/3GsWPH+OSTT3BwcMDHx0d+nyQmJpKSksLcuXMfuKy3kiSJ559/nuvXr7Njxw62bt3KmTNnitUgnTlzhjVr1rBu3TrWrVvHjh07mDZtmtk6S5Yswd7env379zN9+nQmTJjA1q1b5eM899xzpKamsmHDBg4ePEijRo1o27Yt169fB6BPnz5Ur16dhIQEDh48yPvvv49KZdkROQXhcVQpBmor1Bv58t0dFjn2m3PDUNmUnpWySHh4OCNGjKCwsJC8vDwOHz5MmzZtMBgMzJs3D4B9+/aRl5dHRESE2a/jwMBA5s2bR9OmTcnOzsbBwYHk5GRCQ0Np0qQJYMpfU8TDw5Sh0t3dHa1WK8+fOHEis2bNonv37oCppuXEiRMsXLiwWO3I66+/zjfffMOrr77K4sWL6dSpk7zf273yyitYWf17HZYtW8bzzz/PqVOncHJyMivDzz//bHasvXv3yoHBncyfP5+oqCj5ZhocHMyePXtYt27dHbd59tlnzf5euHAhrq6u7Nixg86dO5OcnIxWq6Vdu3aoVCp8fX1p2rQpYKr5sbe3p3Pnzjg6OuLn50doaGipZQRTgFJYWMhLnSKoVr06bq4qGnv7U+B559FjGzRowH//+18APvjgA6ZNm0aVKlUYNGgQAOPGjWPBggUcPXqU5s2b37UMpVGr1Tg7O6NQKMxekzNnzvD9999z8eJFvL29ARg9ejSbNm1i8eLFTJkyheTkZF588UX5tbr1/VlUY+bp6Vnu7VvAFGgfPXqUpKQk+THP0qVLqVu3LgkJCTz99NOAaQjw2NhYHB1N3bn79u3Lr7/+ahbM1q9fn/HjxwMQFBTEZ599xq+//kr79u2Ji4vj2LFjpKWlYWNjSl8/c+ZM1qxZw8qVK3nzzTdJTk5mzJgxhISEyPsQBKHsKkUNyuMiIiKCnJwcEhIS2LlzJ8HBwXh6ehIWFkZCQgI5OTnEx8fj6+tLYGAghw8fplu3bvj5+eHo6Cg/pklOTgZgyJAhrFixgoYNGzJ27Fizx0QluXLlChcuXGDAgAE4ODjI06RJkzhz5kyx9V999VX27t3L2bNniY2N5Y033rjjvmfPns2RI0fkqX379vKy22tJIiMjOXLkCOvXrycnJweDwXDXa5eYmCgHD0Vu//t2aWlpDB48mODgYJydnXF2diY7O1u+fi+99BJ5eXkEBgYyaNAgVq9eLTdibt++PX5+fgQGBtK3b1++++67e6rBaNCgAeGtW1Gv7cv0eOt9li7/kfSMDGxL6V5cv359+f9WVla4u7ubBWxeXl7y+Twshw4dQpIkgoODzd4bO3bskN8b0dHRTJo0iVatWjF+/HiOHj360Mpzu5MnT+Lj42PWBqVOnTq4uLhw8uRJeZ6/v78cnABUrVq12HW79Xrfvs7BgwfJzs7G3d3d7DokJSXJ12HkyJEMHDiQdu3aMW3atBI/O4Ig3F2lqEGxVit5c26YxY59r2rWrEn16tWJi4sjPT2dsDBTmbVaLQEBAezevZu4uDieffZZcnJy6NChAx06dGDZsmV4eHiQnJxMZGQker0egKioKM6fP8/69evZtm0bbdu2ZdiwYcycObPE4xuNpt4kixYtolmzZmbLbq39KOLu7k7nzp0ZMGAA+fn5REVFmT3Pv5VWqy0x6VxQUBAZGRmkpqbKv9gdHByoWbNmmbIVS5JULNC52+O1/v37c+XKFebMmYOfnx82Nja0aNFCvn4+Pj4kJiaydetWtm3bxtChQ5kxYwY7duzA0dGRQ4cOER8fz5YtWxg3bhwxMTEkJCSUWkOgVCrZ9P0CDhw4wP/tOMwXy5cTM38+vx84ILcLut3tjwcUCoXZvKLzLnr9Hgaj0YiVlRUHDx4s9l4oahg9cOBAIiMjWb9+PVu2bGHq1KnMmjWLd95556GVq0hJr39J80u6lrdft9LWMRqNVK1a1aztTZGi1z0mJobevXuzfv16Nm7cyPjx41mxYgUvvPBCsW0EQbizSlGDolAoUNlYWWQq67DlERERxMfHEx8fb9ZwNSwsjM2bN7Nv3z4iIiL466+/uHr1KtOmTaN169aEhISU+Avaw8OD/v37s2zZMubMmcOXX34JmKryAbPaCS8vL6pVq8bZs2epWbOm2XSnm+cbb7xBfHw8r732WolBzN306NEDlUrFJ598UuZtbxUSEiK3qylyt4aJO3fuJDo6mk6dOlG3bl1sbGy4evWq2ToajYauXbsyb9484uPj2bt3L8eOHQNMGZfbtWvH9OnTOXr0KOfOnWP79u2lHlOvy8NGUUiLJqH85+1h7P3pJ9RqFatXr76Ps3441Gp1sVqr0NBQDAYDaWlpxd4btz4K8vHxYfDgwaxatYpRo0axaNEieZ/APdWG3Y86deqQnJzMhQsX5HknTpwgIyOD2rVrl9txGjVqRGpqqpxt+9apqH0XmB4xjhgxgi1bttC9e/diPd4EQbi7SlGD8jiJiIhg2LBhFBQUyDUoYApQhgwZQn5+PhEREdja2qJWq5k/fz6DBw/m+PHjTJw40Wxf48aNo3HjxtStWxedTse6devkL2tPT080Gg2bNm2ievXq2Nra4uzsTExMDNHR0Tg5OREVFYVOp+PAgQOkp6czcuTIYuXt2LEjV65cwcnJ6b7O19fXl1mzZvHuu+9y/fp1+vfvT0BAANevX2fZsmVAybU3t3vnnXdo06YNn376KV26dGH79u1s3Lix1ACxZs2aLF26lCZNmpCZmcmYMWPMun/GxsZiMBho1qwZdnZ2LF26FI1Gg5+fH+vWrePs2bO0adMGV1dXNmzYgNFopFatWqWWc89vv7J3+2bahIdTxdqZg38c5er19HK9iQL8/fffZGdnk5qaSl5enjzeTZ06deRg4U78/f3Jzs7m119/pUGDBtjZ2REcHEyfPn147bXXmDVrFqGhoVy9epXt27dTr149OnXqxPDhw4mKiiI4OJj09HS2b98un5efnx8KhYJ169bRqVMnNBrNHbuk34927dpRv359+vTpw5w5cygsLGTo0KGEhYXJbbDK6zgtWrTg+eef55NPPqFWrVpcunSJDRs28Pzzz1O3bl3GjBlDjx49CAgI4OLFiyQkJPDiiy+WWxkEobKoFDUoj5OIiAjy8vKoWbOm3LYATAFKVlYWNWrUwMfHBw8PD2JjY/npp5+oU6cO06ZNK/boRq1W88EHH1C/fn3atGmDlZUVK1asAEy//ufNm8fChQvx9vamW7dugKma/quvviI2NpZ69eoRFhZGbGzsHWtQFAoFVapUuetNrzTvvPMOW7Zs4cqVK/To0UPuTZOUlMSmTZvu2kAWoFWrVnzxxRd8+umnNGjQgE2bNjFixAhsbW3vuM0333xDeno6oaGh9O3bl+joaDw9PeXlLi4uLFq0iFatWlG/fn1+/fVXfvnlF9zd3XFxcWHVqlU8++yz1K5dmy+++ILvv/+eunXrllpOF1slv+0/xAuvDqbhc535+LPPmDlzJlFRJadEv18DBw4kNDSUhQsXcurUKUJDQwkNDeXSpUt33bZly5YMHjyYl19+GQ8PD6ZPnw6Yup+/9tprjBo1ilq1atG16/+3d9/xUVXp48c/d2omvUASAgkEpINUBVGBCAJRFFZdcEGUVbFRrGv7qYDsAlZQWfsK1gVXBPnaUQiooNK70kI1IQGSSZs+5/fHJCNDAkkgYZLwvF+veWXm3jN3nvtkMvPk3HPPvZZffvnFP+7D4/Ewfvx42rdvz5AhQ2jbtq3/FPWmTZsydepUHn30URISEpgwYUKN7q+maSxevJiYmBj69u3LwIEDadmyJQsWLKjx1/nyyy/p27cvt956K23atOHGG29k3759JCQkoNfrOXbsGDfffDNt2rRhxIgRpKenM3Xq1BqNQ4jzgaaqcx5sHVFQUEBUVBRWq7Xcf+52u53MzExSU1NP++UkGr5x48bx22+/8cMPPwQ7FADcLhf6nK1oGuS5IwgpsOMIMxKdevpel/NV//796dq1K7Nnzw52KDVu7Nix5Ofns3jx4hrftnwGirrsdN/fJ5MeFNFgPP/882zatIndu3fzyiuv8O67755y4rhgcBTnoWlgx4ShxAGAPuLMDo2dL1599VXCw8P9437qux9++IHw8HA+/PDDYIciRJ0nY1BEvZCenn7KnpDHH3+cxx9/nF9//ZVnn32WwsJC/7wwt99++zmN88CBA3To0KHilUqxPeN/RCW3x+QuBsASFXcOo/OpSi5r22nzhG+A64cffuifqTglJaXWYzoXevbs6R8PVJNjcIRoiKRAEfXC22+/HTCt/onKJgH7+OOPz2VIFUpKSqrwAoxerweVs5OkhMYUenxHVZ1mPRbjmY/dOVNVyWVtO1WeTlxfndPM6wuLxVLh6fZCiPIa3ieAaJCaNm0a7BCqpOz005OVFOYRGl6CCz260sM7hIee4+h86kIuT5UnIYQoI2NQhDgHvDYrAHZdGEa7by4Q82lmjxVCiPOdFChC1DKlFCa3b4Zdt9KjAW6DRkhoxOmfKIQQ5zEpUISoZU57MSbceJWGZvdNo+8JldM/hRDidKRAEaKWuYrzAbDpQjHYfAWKMTIqiBEJIUTdJwWKELXM4CwAwKWZ0XvBq4ElMibIUQkhRN0mBYoQtcjlchBC6Vk7Lrfvh8WITlf9CysKIcT5RAoUIaqhRYsW1Zp6/buvv0Br2p1sqwNdiR0AXUTtDI7t378/9913X61su75p0aIFmqahaRr5+fnBDqdGzJs3z79P8nsW5wMpUOqI119/nYiICNxut39ZUVERRqORyy+/PKDtDz/8gKZp7Ny584xfb9++fWiadtrJsk6l7EPy559/DljucDiIi4tD0zQyMjLKtT/xdtlllwU8d/ny5QwdOpTGjRsTEhJCq1atGDlyJCtXrqxSTBkZGWiaRkxMDHa7PWDdr7/+6n/dc01z+WaMdehDMbp8E7SF1tLssZ9++mm5K1pXl91uZ+zYsXTu3BmDwcDw4cOr9fwpU6bQtWvXs4rhZPPmzSM6Orraz3v66afJysoiKiqKsWPHVvg+PPFW140cOZKsrCwuueSSYIcixDkhBUodkZaWRlFREWvXrvUv++GHH0hMTGTNmjWUlJT4l2dkZJCUlESbNm2CESoAycnJzJ07N2DZokWLTjl999y5c8nKyvLflixZ4l/36quvMmDAAOLi4liwYAE7duzg/fffp0+fPtx///3ViisiIoJFixYFLHvnnXeCMlW61+PB5PEd3lHu0tljTToMJnOtvF5sbCwRZ9k74/F4sFgsTJo0iYEDB9ZQZMERERFBYmIimqbx0ksvBbz/oPx7sq6zWCwkJiae1ZXDhahPzosCRSmFy24Pyq2qF4tu27YtSUlJAT0PGRkZDBs2jFatWrFq1aqA5WlpaXzwwQf07NnT/0E8atQocnJy/O3y8vIYPXo0jRs3xmKx0Lp1a39RkZqaCkC3bt3QNI3+/fv7nzd37lzat29PSEgI7dq149VXXy0X7y233ML8+fMDpkx/5513TnlxvujoaBITE/23sinVDxw4wH333cd9993Hu+++yxVXXEFqaip9+vTh3nvvDSjYquKWW27hnXfe8T+22WzMnz+/wrgWLlxIx44dMZvNtGjRghdeeCFgfU5ODtdccw0Wi4XU1NRyF3irqBcqPz/f34NkL7ai03y/f83+5+yxq1atom/fvlgsFpKTk5k0aRLFxcVV2r9XX32V1q1bExISQkJCAjfccIN/3cmHeFq0aME///lPbr75ZsLDw2nevDmfffYZubm5DBs2jPDwcDp37hyQ47CwMF577TXGjRtHYmJilWIqM2/ePKZOncqmTZv8vRLz5s0DwGq1cscddxAfH09kZCRXXHEFmzZt8j9306ZNpKWlERERQWRkJD169GDt2rVkZGTw97//HavV6t/mlClTqhUXQFRUVMD7DwLfk7m5uVxxxRVYLBbi4uK44447KCoq8j9/7NixDB8+nOeff54mTZoQFxfH+PHjcblcAfmePn06t956KxEREaSkpPDmm28GxHH48GFGjhxJTEwMcXFxDBs2jH379vnXZ2RkcPHFFxMWFkZ0dDSXXnop+/fvr/b+CtEQnBdT3bsdDl6+5YbKG9aCSe9+grGKlzzv378/y5cv59FHHwV8hz0efvhhvF4vy5cvZ+DAgTidTlavXs0rr7yC0+lk2rRptG3blpycHO6//37Gjh3Ll19+CcCTTz7J9u3b+eqrr2jUqBG7d+/2FxS//vorF198Md999x0dO3b0/1f21ltvMXnyZObMmUO3bt3YsGED48aNIywsLOBLvkePHqSmprJw4UJuuukmDh48yMqVK/n3v/9drcMMCxcuxOVy8fDDD1e4vrpd72PGjOG5557jwIEDpKSksHDhQlq0aEH37t0D2q1bt44RI0YwZcoURo4cyapVq7jnnnuIi4tj7NixgO9L6eDBgyxbtgyTycSkSZMCCsDKKFu+/77R7gYT7DqYxeDBg5k2bRr/+c9/yM3NZcKECUyYMKFcj9TJ1q5dy6RJk/y9S8ePHz/lRf/KzJo1i+nTp/Pkk08ya9YsxowZw6WXXsqtt97Kc889xyOPPMLNN9/Mtm3bzvowx8iRI9m6dStff/013333HeArDJRSXH311cTGxvLll18SFRXFG2+8wYABA9i5cyexsbGMHj2abt268dprr6HX69m4cSNGo5E+ffowe/ZsnnrqKX7//Xeg5i+yV1JSwpAhQ+jduzdr1qwhJyeH22+/nQkTJvgLLPD9PTZp0oTly5eze/duRo4cSdeuXRk3bpy/zQsvvMC0adN4/PHH+eSTT7j77rvp27cv7dq1o6SkhLS0NC6//HJWrlyJwWDgn//8J0OGDGHz5s3odDqGDx/OuHHj+O9//4vT6fQfnhTifHReFCj1Rf/+/bn//vtxu93YbDY2bNhA37598Xg8vPzyywD8/PPP2Gw20tLSaNmypf+5ZVfvvfjiiykqKiI8PJwDBw7QrVs3evbsCfj+wyvTuHFjAOLi4gL+U542bRovvPAC1113HeDradm+fTtvvPFGuV6Iv//977zzzjvcdNNNzJ07l6uuusq/3ZP97W9/Q6//88yVDz74gOHDh7Nz504iIyMDYli4cGHAa61evZrOnTtXKYfx8fGkp6czb948nnrqKd555x1uvfXWcu1efPFFBgwYwJNPPglAmzZt2L59O8899xxjx45l586dfPXVV/z888/06tULgP/85z+0b9++SnF4vV7MnkL/Yw1w6zVe/vdrjBo1yt/T0bp1a15++WX69evHa6+9RshpitkDBw4QFhbG0KFDiYiIoHnz5nTr1u20cVx11VXceeedADz11FO89tprXHTRRfz1r38F4JFHHuGSSy7hyJEj1e4xOZnFYiE8PByDwRCwrWXLlrFlyxZycnIwm32Ht55//nkWL17MJ598wh133MGBAwf4xz/+Qbt27fx5KRMVFYWmaWcd36mUXTX5vffeIywsDIA5c+ZwzTXX8Mwzz5CQkABATEwMc+bMQa/X065dO66++mq+//77gALlqquu4p577gF8uZ01axYZGRm0a9eO+fPno9PpePvtt/1Fx9y5c4mOjiYjI4OePXtitVoZOnQorVq1Aqjy+02Ihui8KFAMZjOT3v0kaK9dVWlpaRQXF7NmzRry8vJo06YN8fHx9OvXjzFjxlBcXExGRgYpKSm0bNmSDRs2MGXKFDZu3Mjx48fxer3An5eyv/vuu7n++utZv349gwYNYvjw4fTp0+eUr5+bm8vBgwe57bbbAj503W43UVHlJxa76aabePTRR9m7dy/z5s3zF1EVmTVrVsCYhiZNmvjvn/wf4uDBg9m4cSOHDx+mf//+eDyeypN3gltvvZV7772Xm266idWrV/O///2vXE/Djh07GDZsWMCySy+9lNmzZ+PxeNixYwcGg8Ff3AG0a9euyoM1nbYiDMTgPuEoqifMzLp169i9e3fA4SKlFF6vl8zMzNN+IV155ZU0b96cli1bMmTIEIYMGcJf/vIXQkNPfdHBCy+80H+/7Iv2xGKvbFlOTk6tFQDr1q2jqKiIuLjAwcE2m409e/YA8MADD3D77bfz/vvvM3DgQP7617/6v6Rr244dO+jSpYu/OAHfe8Hr9fL777/7c9SxY8eAIrtJkyZs2bIlYFsn5rusqCrrdSv73Z88Tshut7Nnzx4GDRrE2LFjGTx4MFdeeSUDBw5kxIgRAX8rQpxPzosCRdO0Kh9mCaYLLriAZs2asXz5cvLy8ujXrx8AiYmJpKam8tNPP7F8+XKuuOIKiouLGTRoEIMGDeKDDz6gcePGHDhwgMGDB+N0+mYrTU9PZ//+/XzxxRd89913DBgwgPHjx/P8889X+PplBc5bb73l7zUoc+IHc5m4uDiGDh3Kbbfdht1uJz09ncLCwnLtyvahoqvXtm7dGqvVSnZ2tv8LMjw8nAsuuACD4czenmW9BrfddhvXXHNNuS9G8BUFJxdGJ44XKrt/uu51nU5X7nllYxKUw5cHl+7P4sEYGYXX6+XOO+9k0qRJ5bZX2UDeiIgI1q9fT0ZGBt9++y1PPfUUU6ZMYc2aNacsnIxGo/9+2b5UtKzsd18bvF4vTZo0CRhfVaYs7ilTpjBq1Ci++OILvvrqKyZPnsz8+fP5y1/+UmtxlanovVDmxOUn5q1s3cl5O10br9dLjx49yo1lgj97NOfOncukSZP4+uuvWbBgAU888QRLly6ld+/e1d8xIeq582KQbH2SlpZGRkYGGRkZAQNX+/XrxzfffMPPP/9MWloav/32G0ePHmXmzJlcfvnltGvXrsLxEY0bN2bs2LF88MEHzJ492z9or2zMyYm9EwkJCTRt2pS9e/dywQUXBNzKBtWe7NZbbyUjI4Obb765wiKmMjfccANGo5Fnnnmm2s89Fb1ez5gxY8jIyKjw8A5Ahw4d+PHHHwOWrVq1ijZt2qDX62nfvj1utztgAOnvv/8eMKdG2ZfKiWeAlA2YNXl8Y328et8Xlm/22Fi6d+/Otm3byuX3ggsuqNLZGQaDgYEDB/Lss8+yefNm9u3bx7JlyypPyjliMpnK9Xh1796d7OxsDAZDuX1u1KiRv12bNm24//77+fbbb7nuuuv8Y3Iq2mZN6tChAxs3bgwYqPzTTz+h0+lq9Ey57t27s2vXLuLj48vl4cQeym7duvHYY4+xatUqOnXqxEcffVRjMQhRn1SrQJkxYwYXXXQRERERxMfHM3z4cP/AtTJKKaZMmUJSUhIWi4X+/fuzbdu2gDYOh4OJEyfSqFEjwsLCuPbaazl06NDZ700DkJaWxo8//sjGjRv9PSjgK1Deeust7HY7aWlppKSkYDKZeOWVV9i7dy9LliwpNzj1qaee4rPPPmP37t1s27aNzz//3H8IIT4+HovFwtdff82RI0ewWq2A7z/ZGTNm8NJLL7Fz5062bNnC3LlzefHFFyuMd8iQIeTm5vL000+f0f6mpKTwwgsv8NJLL3HLLbewfPly9u3bx/r16/2HjM6k8Jk2bRq5ubkMHjy4wvUPPvgg33//PdOmTWPnzp28++67zJkzh4ceegjwnVU1ZMgQxo0bxy+//MK6deu4/fbbsVgs/m1YLBZ69+7NzJkz2b59OytXruSJJ57wxawpXOhRTl+PijvEN3vsI488wurVqxk/fjwbN25k165dLFmyhIkTJ1a6T59//jkvv/wyGzduZP/+/bz33nt4vV7atm1b7fyczvbt2/2HDa1WKxs3bqzyfDktWrQgMzOTjRs3cvToURwOBwMHDuSSSy5h+PDhfPPNN+zbt49Vq1bxxBNPsHbtWmw2GxMmTCAjI4P9+/fz008/sWbNGv97tUWLFhQVFfH9999z9OjRgFPua8Lo0aMJCQnhlltuYevWrSxfvpyJEycyZswY/+GdmnqdRo0aMWzYMH744QcyMzNZsWIF9957L4cOHSIzM5PHHnuM1atXs3//fr799lt27twp41DE+UtVw+DBg9XcuXPV1q1b1caNG9XVV1+tUlJSVFFRkb/NzJkzVUREhFq4cKHasmWLGjlypGrSpIkqKCjwt7nrrrtU06ZN1dKlS9X69etVWlqa6tKli3K73VWKw2q1KkBZrdZy62w2m9q+fbuy2WzV2bU6IzMzUwGqXbt2AcsPHjyoANWqVSv/so8++ki1aNFCmc1mdckll6glS5YoQG3YsEEppdS0adNU+/btlcViUbGxsWrYsGFq7969/ue/9dZbKjk5Wel0OtWvXz//8g8//FB17dpVmUwmFRMTo/r27as+/fRT/3pALVq0qML48/LyFKCWL19epfZlli5dqtLT01VsbKwyGAwqISFBDR8+XH399denT1ip5cuXK0Dl5eVVuH7RokXq5Lf7J598ojp06KCMRqNKSUlRzz33XMD6rKwsdfXVVyuz2axSUlLUe++9p5o3b65mzZrlb7N9+3bVu3dvZbFYVNeuXdW3337r2///vakKj+xTn787VwFq/85t/uf8+uuv6sorr1Th4eEqLCxMXXjhhepf//pXpfv4ww8/qH79+qmYmBhlsVjUhRdeqBYsWOBf369fP3Xvvff6H58cq1Llfxdl77ey90zZ84Byt6qw2+3q+uuvV9HR0QpQc+fOVUopVVBQoCZOnKiSkpKU0WhUycnJavTo0erAgQPK4XCoG2+8USUnJyuTyaSSkpLUhAkTAv6G77rrLhUXF6cANXny5ErjqGjfT5eHzZs3q7S0NBUSEqJiY2PVuHHjVGFhoX/9LbfcooYNGxawjXvvvTfg76ai1+zSpUtAvFlZWermm29WjRo1UmazWbVs2VKNGzdOWa1WlZ2drYYPH66aNGmiTCaTat68uXrqqaeUx+MJ2ObJv+eT1ffPQNGwne77+2TVKlBOlpOTowC1YsUKpZRSXq9XJSYmqpkzZ/rb2O12FRUVpV5//XWllFL5+fnKaDSq+fPn+9scPnxY6XS6Kn8ZNeQCRdRvHrdbuQ9tUOrwelWYn6tKtmxRJVu2KKdD3o/nUmUFSn0mBYqoz6pToJzVGJSywwJlk25lZmaSnZ3NoEGD/G3MZjP9+vXzTzS2bt06XC5XQJukpCQ6deoUMBmZEPWRrSgPvaZwYsBbOuW+y6TDaKr7g7QbmkceeYTw8HD/51R99+GHHxIeHl7p3DdCNBRnfBaPUooHHniAyy67jE6dOgGQnZ0NUO64bUJCgn82xOzsbEwmEzExMeXalD3/ZA6HA4fD4X9cUFBwpmGLeio9Pf2UH8yPP/44jz/++DmO6BRseQA4jZEoq+9MHhV+6tOAT/TDDz+Qnp5+yvUnzmwaLB07djzlzKZvvPEGo0ePrvUYqpKnFStW+M+oOtvp/+uKa6+91n923Zlcm0iI+uaMC5QJEyawefPmcmdCQPlTM9VpTuOrSpsZM2YwderUMw1VNABvv/12wLT6JyrrwQs2j8eNxVMMGmjmCIx2X4Fijq7axQF79ux5RhdvPJe+/PLLgOndT1STA0pPpyp5at68+TmJ5VyKiIhoMMWWEFVxRgXKxIkTWbJkCStXrqRZs2b+5WXzWGRnZwdMLpSTk+P/8EpMTMTpdJKXlxfQi5KTk3PKScQee+wxHnjgAf/jgoICkpOTzyR0UU81bdo02CFUyl6YR5imcGLEYy/BCLiMOiJDq/alYrFYKpwrpi6pC1/89SFPQoizV60xKEopJkyYwKeffsqyZcvKzY2RmppKYmIiS5cu9S9zOp2sWLHCX3z06NEDo9EY0CYrK4utW7eeskAxm81ERkYG3KoSqxDnkmbPB0oP7xSUHt6JqNrhHSFqinz2iYaiWj0o48eP56OPPuKzzz4jIiLCP2YkKioKi8WCpmncd999TJ8+ndatW9O6dWumT59OaGgoo0aN8re97bbbePDBB4mLiyM2NpaHHnqIzp0718jl3ctmciwpKQmYs0KI2uRxuwkpPbyjC4lAf8Q3TiokplElzxSiZpXNE3PyrLZC1DfVKlBee+01gIAZTsE3PXPZFWAffvhhbDYb99xzD3l5efTq1Ytvv/024NjprFmzMBgMjBgxApvNxoABA5g3b94ZTch1Mr1eT3R0tH9W1dDQULkaqKh1JQXHCPV4cWLAabVi8HpxG3WEawbspWfzCFGblFKUlJSQk5NDdHR0jXyeChFMmqqH/YEFBQVERUVhtVorPNyjlCI7OztgWnIhapPTegSTcuAwROAtcWBwe3GHmrFUcYCsEDUlOjqaxMRE+cdM1EmVfX+fqEFeLFDTNJo0aUJ8fPwpzzgQoqYU5h/H/PmNmDQPv1/yHMapz6FTEPbKDJqe4hpGQtQGo9EoPSeiwWiQBUoZvV4vf6yi1m1Z9T8uKt5Hpq4FuWuX0fSPLP5oZmFAp16VP1kIIUSF5GrGQpwl887PAMhOScf9nW8yOVf/i4IZkhBC1HtSoAhxFo7nHKaDbQMAYW36krzbN616hxtuD2ZYQghR70mBIsRZ2LXivxg0L7v1rTjyyzJ0Cg4nh5LSTnpQhBDibEiBIsRZCN+1BIDc5lfj/d53eMfd/+JghiSEEA2CFChCnKGj2Qdo59gMQESbS2m22zc5W6cRdwQzLCGEaBCkQBHiDO3J+BC9pthpaEPWqm/RAYeah9GsdbdghyaEEPWeFChCnKGoPb7DO8dTh+L93ndVb29a72CGJIQQDYYUKEKcgf071tHOtR230hHV5lKa7fFdHLDzX+XwjhBC1AQpUIQ4A1nL3wBgS1hvDq74HB1wsEUYSa0uDG5gQgjRQEiBIkQ12W3FtMv5AgBdz7/D9z/5VlzRJ4hRCSFEwyIFihDVtPW7D4imiGwaEZfSluTMIkAO7wghRE2SAkWIarJs+QCAzJTr2LbwbQAOpobTJLVTMMMSQogGRQoUIarh4K5NdHRuxqM0Uq+8E93y1b4VAy4NbmBCCNHASIEiRDUc/t43OHZr6MV4HEU021eMF7hQDu8IIUSNkgJFiCpyOuy0yf4/ALzdbmbr/94C4FCrCBKbdwhmaEII0eBIgSJEFW35/r/EUkAuMXROG4H+e9/hHd2Ay4IcmRBCNDyGYAcgRH1h2vQeALubDid63fc0PWTDrYNuN44PcmRCCNHwSA+KEFVweO8OOjvWA9B84F3see81AA50a0KjpFbBDE0IIRokKVCEqIID378OwOaQHkQ1iqfJT7sBiL9xVDDDEkKIBksKFCEq4XI6aH14MQDuLmP49aPZhDoUx2IMdL9qbFBjE0KIhkoKFCEqsTXjfzQin2NE0emKv+Fa/BUAhYN7odfLMC4hhKgN8ukqRCV06+cBsLPJNTTevprkzCI8GnT/+4PBDUwIIRow6UER4jSyD+yis20tAM0G3MVv7/4bgAOdG5PQvH0wQxNCiAZNChQhTiNz6RvoNMU2Uxfim6aSsGI7ALEjRgY5MiGEaNikQBHiFDxuNy0PfgqArcsYfpn/MuE2RV6knp7DxgU5OiGEaNikQBHiFLau/IQEjpFHBJ0HjKbk0yUA5F/ZHYPRFOTohBCiYZMCRYhT8K59F4DfE4aSvXcLzXdZfRcGHHt/cAMTQojzgBQoQlTg4O4tXFjsu9ZOk7Q72Dp3NgAHOsTQrHW3IEYmhBDnBylQhKhA9uf/Qq8pNlp60yS1HXHLNwMQcf11QY5MCCHOD1KgCHGSPzJ/o1veNwCEXvkYaxa+TlSRF2u4jotvuCfI0QkhxPlBChQhTnLw//6FQfOyOaQHbbr3p+CTTwA4ltYZkzk0yNEJIcT5QQoUIU6QfXA33Y59AYAx7REO7dpAyvY8ADqOvTeYoQkhxHlFChQhTrD/s+mYNA/bTBfSvtdgNs+bhQ440DqSFh0vCXZ4Qghx3pACRYhSR//YT9dc31wnqu8/cLucRC9dD0DIX64JZmhCCHHekQJFiFK7P5uBWXPxm7EDHfsMZe1nbxFT4KHIonHxjZOCHZ4QQpxXpEARAjiec5gLs33T2jv7PIim03H84wUAHOnbHktoZDDDE0KI844UKEIAvy+eSajmYJehNZ37Xcf+7b/QfEsuAO3GTghydEIIcf6RAkWc96zHjnDh4Y8BKO71AJpOx5bZT6NTsK9DLBd0SwtyhEIIcf6RAkWc97YvfpYwzc4efSpdBtzIH3u30PzHvQA0GT8xyNEJIcT5SQoUcV4ryD9Gx4MfAWDteS+aTsf62ZMxeOFAqwi6DrgxyBEKIcT5SQoUcV7btvg5Iilhny6ZroNuJvfwbpot2wFAzJ3jghydEEKcv6pdoKxcuZJrrrmGpKQkNE1j8eLFAeuVUkyZMoWkpCQsFgv9+/dn27ZtAW0cDgcTJ06kUaNGhIWFce2113Lo0KGz2hEhqquoII/2+94H4Gi3iej0en6d/SRmNxxODqXn0NuCHKEQQpy/ql2gFBcX06VLF+bMmVPh+meffZYXX3yROXPmsGbNGhITE7nyyispLCz0t7nvvvtYtGgR8+fP58cff6SoqIihQ4fi8XjOfE+EqKati18kmiIOakl0S7+NvNyDNPlmIwCht9+MTicdjEIIESyG6j4hPT2d9PT0CtcppZg9ezb/7//9P667zndZ+nfffZeEhAQ++ugj7rzzTqxWK//5z394//33GThwIAAffPABycnJfPfddwwePPgsdkeIqikpstJ677sAZF14D8kGA6tfeYpUJ2Qnmul7w/ggRyiEEOe3Gv0XMTMzk+zsbAYNGuRfZjab6devH6tWrQJg3bp1uFyugDZJSUl06tTJ30aI2rbpwyeIw8ofWgLdrr6DIutRGv/fLwDobxmBXl/t2l0IIUQNqtFP4ezsbAASEhIClickJLB//35/G5PJRExMTLk2Zc8/mcPhwOFw+B8XFBTUZNjiPLNvx1p6/vEhaJDTZwpJJjPfP3cfzW2K3DgjfW56KNghCiHEea9WDrJrmhbwWClVbtnJTtdmxowZREVF+W/Jyck1Fqs4vyivl5JF92HUPGwI7UPXK0dhKykgetFKANw3XYvBaApylEIIIWq0QElMTAQo1xOSk5Pj71VJTEzE6XSSl5d3yjYne+yxx7Barf7bwYMHazJscR5Zu+RVOji3UKLMJI6cDcCPr08lqsjL8Sg9l936eHADFEIIAdRwgZKamkpiYiJLly71L3M6naxYsYI+ffoA0KNHD4xGY0CbrKwstm7d6m9zMrPZTGRkZMBNiOqyHjtCq43PALCp1Z00ad4Wp6OE0I+/AaBk5CBM5tBghiiEEKJUtcegFBUVsXv3bv/jzMxMNm7cSGxsLCkpKdx3331Mnz6d1q1b07p1a6ZPn05oaCijRo0CICoqittuu40HH3yQuLg4YmNjeeihh+jcubP/rB4hasNvH/2DXhSwT5dMj5H/D4Af586gSb4Ha7iOS+94KsgRCiGEKFPtAmXt2rWkpf158bQHHngAgFtuuYV58+bx8MMPY7PZuOeee8jLy6NXr158++23RERE+J8za9YsDAYDI0aMwGazMWDAAObNm4der6+BXRKivN/XLuOio0tAg+KBz2Iyh+B2OTF88BkA+X+5nNDw6OAGKYQQwk9TSqlgB1FdBQUFREVFYbVa5XCPqJTb5WTfzN5c4NnDmqjBXHS/78rFK+fNoPHM9yiyaLRbnkFEdHyQIxVCiIatOt/fMlWmaPDWfvIcF3j2UEAYLUe9CIDX68X97gIAcodeLMWJEELUMVKgiAYt9499dPrtFQB2dLifuIRmAKx4eypNshzYTHDJpGnBDFEIIUQFpEARDdqB/95PuGZjp6ENF13vGy+Vl3uQ0Df+B8CRGy4jprHMqyOEEHWNFCiiwdqychE9CpfhURq6a2ajKx2E/dPk8UQWK47Emxjwj5eCHKUQQoiKSIEiGiS7rZjo5b5J19bE38AFXS4FYNuPn5G6bBcAkY89iMki854IIURdJAWKaJA2vX0PyeoPjhJNh9G+ydncLidZU6aiA/b0bkb39JuDG6QQQohTkgJFNDi/LpxFr2OL8SqNw32fIzI6DoDlcx6n6SEbJWaNi6fNCXKUQgghTkcKFNGg/LbmO7pu/icAv6TeRZcrRgCQc2gnsfO+BOD4LUOIT24btBiFEEJUTgoU0WDk/rGPuC9ux6S5WR/Wl943T/ev+/XJiYQ6FH80DSFt4swgRimEEKIqpEARDYLDXsKxd26kMXns06XQ9q730XS+t/e6r9+n1eoDeIH4J5/AYDQFN1ghhBCVkgJF1HvK62XTG+No595BAWEYRv2XsIhoAJyOEgqnPw9AZv8L6Nz/+iBGKoQQoqqkQBH13q+fvMDFeZ/jURr7+r9Csws6+dcte/4BEnKcFIZqXPr0v4MYpRBCiOqQAkXUazt++YZu22YA8GuriVx4Qg/JH3s2kzB/BQBFd1xPTHxKUGIUQghRfVKgiHrryKE9NP7qDkyah3Xh/el909SA9RuemESICw6mhtNv3OQgRSmEEOJMSIEi6iW7rRjrvBtpRD57dS1of9d7/kGxAD9+8BwtNxzBo0Hzp/+FXm8IYrRCCCGqSwoUUe/Yigv5/eW/0Ma9k3zCMd80n9DwKP/63RuWE/rsOwDsH9qFthcNClaoQgghzpAUKKJesR7PZd/sQXSx/YJNmTg08DWatmz/5/pjWfwx4V4sTjhwQSRX/vOdIEYrhBDiTEm/t6g3cv/YR9Hb19Leu58Cwvjjqnl06vVn74jH4+anO0eQeszF8Sg93d/4EJNZLgYohBD1kRQool44uGsThg+vJ5Vccomh6K8LaNexV0Cbb566jdStR3HqIfrF6TRuekGQohVCCHG2pEARdd6ujT8Qt3gUsRRwUEtCf8tiUlsEXktn1YKXSF34KwDH7x1B2qXXBiNUIYQQNUQKFFGnbf1xCalLxxGm2dmtb0XMHUuIS2gW0Gb3phWE/Ot1APZc2Y6hd0ytaFNCCCHqERkkK+qs9V/Npc3SvxOm2dlq7krCpO/KFScFx7M5PGEiFiccbBnBoOc/DFK0QgghapL0oIg6x+N2s+a/T3Px7pfRaYr14X3pOGEB5pDAAa8ej5sf7x5Jaq6LvEgdXd/8QAbFCiFEAyEFiqhT9u1Yi2PhPfR2/w4a/BI3jJ53v4PeUP6t+u3UO0jdlINLDxHP/ZP4Zm2CELEQQojaIAWKqBOcDjvrPnySHvv/g0nzUKgsbO/0Dy6+/v6AGWLL/PjBc6R8vBqAo+Ov44p+fznXIQshhKhFUqCIoPt97TJMX97HJd79oMHG0EtIGv0avZqmVth+2WtPkPDyQnTAnitaM/Sef53bgIUQQtQ6KVBE0JQUWdn8/sNcnL0AnaY4TiSZF02me/qtFfaaAHwz4x5S3l0OwJ7LWjB49sfnMmQhhBDniBQo4pxTXi+bln9M/I9P0VsdAQ3WRA2i9ZhX6NEoscLneL1evnr0Jlou2QDA3qs6c9Xz89GdopARQghRv0mBIs4ZW3Ehm798k8Qdc+nqPQhANo040m8mF6X99ZTPc7ucfD3hOlqt2APAvlGXkf7EG1KcCCFEAyYFiqh1OYcz2fPFLNr/sZBeFAFQrELYkvgXOo2eQWJkzCmf67AV8d1t19BqfTZeIOuea0mf9Mw5ilwIIUSwSIEias3O9SsoyHiZLtblXKJ5APhDS+BA6zF0uOoeekfHnfb5Rdaj/HjzNbT8PR+3DvIf+zsDxzx8LkIXQggRZFKgiBrj9XjYs/knjm78grjDy2jj3ulbocF2U2fsPe6ky4C/kVTBnCYnO569n3U3X0fzAyXYjeD61wNcfu24Wt4DIYQQdYUUKOKsHM85zN6f/w92f0fLgl9oTQGtS9c5lZ5N0QOITptEh66XV3mbvyx6HffMOTSzeiiyaITMmka3/tfXzg4IIYSok6RAEVXmdjk5vHc7x/ZuwHFoI3FHVnGBaxc9NeVvU6Qs7ArvgbPFFbS6/K9clJhS5e3n5R7kp0fG0WrVfgCOxhpIfGU2rXsMqPF9EUIIUbdJgSLKcTrs5B7OJHfvRmyHt2I89hsxxXtIdh+kueam+YmNNdijTyUn4XIiO6XTpucAupnM1X7NVQteQnv+TVoVevECmYM70P/pNwmPOv04FSGEEA2TFCgnOLhrD0vnvI5Op0On16PpdegMBnR6PTq9Hr3RgM5gRGc0oDcY0RsMGIwG9GYTBqMJY4gJvdGEyWzCYDZhCrGU/gzBHGr586fJXOG1ZWqa2+WkpLgQe3EBjpICHCWFOEsKcdkKcRXn4S7IhsIjGEpyCHHkEu46TrT3ONEU0RRoevIGNShRZg4Zm2MNb4Vq3ofUXtfSKqkFrc4wxmNZmax+ZBytfj0MQG4jI5FTH2fogBvPYs+FEELUd1KgnCBr127ysneco1fTld40NM33E02HhhawTNO00mX47pc+1jRKW4Kmga7ssabQ4bvpNYWGKm3r+6njhGWaQgN0mg6NeHQ0Ll2mUGjYdRacxghUSBSG8FhCo+MJi4xBbzCg0+vQXDp2rdrFbv0eX1Gn0/uW63To9L4iT6fT0Ep/6vR69Ho96DQ0ncaOjIUYP/2WGLvieHgc2Ze14qI7nyA0IorjWUdLC8M/t6XX69EbdL7XMujP0e9JCCFEMEiBcoKoxo0IDW+G8ipQCqW8KKWAP+8r5S19/OfyPx97UShQXqDspk7xamXrQZU1UaduHRwOIB84WHsvkRTx5/2sY+ybcn81nqwF3jQNKC3ytD+Xaej+XF9W8JUWg5pOj6YzoNMb0OmNpTdfD5nOYPT1pOl06DQdlP7UdDpfoVja06Y3mjCYjL5etNLeM4PJhMlsxhhiIiQslJCIUCwRYYRGhRMaEYrBZKzBJAohRMMjBcoJ2l7Ug7YX9ajRbXrcbhx2O06bA4fNhtNux2mz4Xa5cNoduJ1O3E4nHqcLt8tV+tONx+PG6/bicXtQHg9ejxePx43yePF4POCltGDyTR2vlEJ5VekyBV78X6S+wkehvB5fG6/XV3B5/yy6lLf0hgKvF6/ywolFWem6suegyoqx0vVK/VmcKYWi9Gdp4ea770Ephaa8pYWYquDmrUZ2y55zwsOyH+UX1zEGNJ0RTWfCYI4gJCyG0Og4ImLjiE5IILZpAvHNk4ht1hjDOTgcKIQQdY188tUyvcFAaHg4oeHhwQ4lKAqOZ7P6jWmEL15BrNU3WZvTAAcvu4AO4x+hZefLyj3H6/X6bm5fYeb1Kt99r9f3uLRg83q8KI8Xr9dzwnJfEeV/rE5sV3rf++d9t8uJ2+HC5XDidjhwu1y4nE48TqevYHS7Aws6fzFYth0PXrcbj9uFx+PC63Hhdbvwet0ojxuv14XyuPB6HaBc/FmAuVFeN8prw+m24iw+REEOZJfLhg5NH4reYMFgCsNoDsNkCcMcFk5IeCShUZFYwsMxhpgwWSyYLWZMoSGYLRbMYSGEhFkIjQrDFFL9gctCCBFMUqCIWnFo1wY2vTqdxGVbSXH4lhWEaRy76mIuvvtJuiSdelitbzyLDhpYz4HX68Vld2ErKKK4oBhbYTG2gmLysnOwHsml8FguxdbjOIqO47Jb8XqKAC/KU4TbU4TbkYu98AxfXDOhM4RiMIVjCgnHZInAEhGJJSKKsJgoQkLDMIeFEhIeiiXcd0gqNDKM0MgwTKEhct0jIcQ517C+AUTQuF1Otv2wmEPffobl1+00+cNOy9J1R+JNeEZcRZ9bH8MSGhnUOINJp9NhDjVjDjUTnVj56dNup4vcA0ew5hyn6Hg+RflWivOt2AsLsRcV4igpwmkrwu2y4/W4UF5X6U83SrlAufH32CgnXpcTpysfZ3F1I9dAM6BpxtLDUgZ0ehM6vRG93ojOYMJgDsEUEoY51HcLiQjHEhFOaFQEYVERWMLDMJiNGE0mjCFG39ltZiOGEJMcwhJCVEg+GcQZy8s9yJbP36dgxXLiNx8mokT5ixIvcLBNFNG3jOHy4Xei18tbrboMJiNNLmhGkwuanfE2nA4nJflFWHOOk3/kGIVH8yjKy6PYasVWaMVeVIjLXoTH5cDjceL1OFAeJ8rrBNylW1GgXCjl8o3/BjxnvXcn0gA9mj4EvcGC3mjBaA7FGBKGKSTUV/CEh2M0m303SwjGELPvtP0TDmmFhFmwRPp6f0xmU41GKIQ49zSlVNDGEL766qs899xzZGVl0bFjR2bPns3ll1c+JXpBQQFRUVFYrVYiI8/f/8jPpZKifDI3riB70y/Yfv8N066DJO0rQn/Cu6fErJHduQlh/S6n09VjaHSawzii7vO6PZQUlVBiLcZRYsdRbMNRYsNps+Ow2XHZHTht9tJbCfbiYpwlxTjtJbgcJbidNjwuG163Ha/XiW+wtIfqDYQ+UzrQjOh0JjS9CZ3ehN5gxmCyYDRbMFpCMVvCMIeGYg7zFUAh4aGYQkIwWcyYLCGYLSG+cTyhIZjCLIRYzHJ6uxBnqTrf30H7t3bBggXcd999vPrqq1x66aW88cYbpKens337dlJSqj49uqg5Xq8X67HD5O7/nWOZO8jfsRnvzr2E78+l0VEXOgVJJz3nSLyJ4ovakXjlUDqnXU8Pc2hQYhc1T2fQEx4dQXh0ROWNq8Hr9eJ2unDafQOU3S439qISio4XUJRvpcRaiK2gEFthIfbiIhwlxbhsJXjcTrwel/+n75CWG+V1lR7ScvHnOVteUA68Hgd4fD0+rhqJXu873FV2qMt/yMuI3mDCYAzBYLZgNIdgsoRiDLEQEhaKKTQUS3iYv7fHZDETEmbBZAnBEm6RAkiICgStB6VXr150796d1157zb+sffv2DB8+nBkzZpz2udKDUjUup52i/BwKjx+hOP8oNusx7NbjOKx5uPKP4zqSjco5hvFYAZZ8G5FWN2b3qbdXGKpxvFkk7lbNCGvXgVb9riGl3UXnboeEOA3/IOTCYmyFJdiLS7CX/Syx4SjyDUz29/TYSnA6SnA7bLgcNrxuh++sK29Z0eMOHMdzTminuH+qNmey/uTm1Wxf3e2fwXO0ytpXurkzibGmt3G2+1ATMVS29dNv3xQax/i359Toa9b5HhSn08m6det49NFHA5YPGjSIVatWlWvvcDhwOBz+xwUFBbUSV+7h3fzyzMNnv6GTa76KHpctU6Wzs528zOubjwSvF83rBY8Xzb9MoXk86JxudG4vOrcHvcuL3u1F71boPV7MToX5pH8ZLaW3yhRZNAqjTZQkN8LQuiUxnbrTons/2ia3lbM5RJ0VMAg5IbbGtut2urAX27EX23CU2HHZHDhsvrmNnDY7Trsdl92J0+47BOYoKcFls+G0+woft8OO22XHc+JgZq/vp68AOnFEjzrF/VpUBycKqjSkOhhzfVRZGl2O4I7lCkqBcvToUTweDwkJCQHLExISyM4uPxPEjBkzmDp1aq3HVXg8m1bfnqup7s8dhwHsITocFj2uECPuUBOesBBUoxgMCfGEJDYlslkqccmtaZzSltDw6GCHLESdYTAZCTcZCY+p2UNdZdxuN/YiG/YiG16P1/+PivKWTWf459dI2bI/BT72llt/cnN10sOTH1cSrArsTaqoffkYy7Wo5CUqWX/S88u1r0Lx4lWV9IpVugtnVyF5q/T8s8xTZa9RhRAM5uDOeB3UUyu0k7oWlVLllgE89thjPPDAA/7HBQUFJCcn13g8UY2a8vOw7jWzsZN346T90rTA6djRlU7BritdVnoNG02nR9Pr4YT7ml6PzmhEHxKC3uQ75u2/hfhO9zSFRhARk0B4TGNMMi5EiDrLYDDUylgfIeq7oBQojRo1Qq/Xl+stycnJKderAmA2mzGba38mzLgmqVz9zIe1/jpCCCGEOL2gDCgwmUz06NGDpUuXBixfunQpffr0CUZIQgghhKhDgnaI54EHHmDMmDH07NmTSy65hDfffJMDBw5w1113BSskIYQQQtQRQStQRo4cybFjx3j66afJysqiU6dOfPnllzRv3jxYIQkhhBCijgjqTLJnSuZBEUIIIeqf6nx/y6QWQgghhKhzpEARQgghRJ0jBYoQQggh6hwpUIQQQghR50iBIoQQQog6RwoUIYQQQtQ5UqAIIYQQos6RAkUIIYQQdY4UKEIIIYSoc4I21f3ZKJv8tqCgIMiRCCGEEKKqyr63qzKJfb0sUAoLCwFITk4OciRCCCGEqK7CwkKioqJO26ZeXovH6/Xyxx9/EBERgaZpwQ7nnCsoKCA5OZmDBw/KtYjOguSxZkgea4bksWZIHmtGbeVRKUVhYSFJSUnodKcfZVIve1B0Oh3NmjULdhhBFxkZKX+ANUDyWDMkjzVD8lgzJI81ozbyWFnPSRkZJCuEEEKIOkcKFCGEEELUOVKg1ENms5nJkydjNpuDHUq9JnmsGZLHmiF5rBmSx5pRF/JYLwfJCiGEEKJhkx4UIYQQQtQ5UqAIIYQQos6RAkUIIYQQdY4UKEIIIYSoc6RACYIZM2Zw0UUXERERQXx8PMOHD+f3338PaKOUYsqUKSQlJWGxWOjfvz/btm0LaONwOJg4cSKNGjUiLCyMa6+9lkOHDgW0ycvLY8yYMURFRREVFcWYMWPIz8+v7V08J2oqj2+++Sb9+/cnMjISTdMqzI/k8fR5PH78OBMnTqRt27aEhoaSkpLCpEmTsFqtAduRPFb+frzzzjtp1aoVFouFxo0bM2zYMH777beANpLHyvN4Ytv09HQ0TWPx4sUB6ySPleexf//+aJoWcLvxxhsD2tRaHpU45wYPHqzmzp2rtm7dqjZu3KiuvvpqlZKSooqKivxtZs6cqSIiItTChQvVli1b1MiRI1WTJk1UQUGBv81dd92lmjZtqpYuXarWr1+v0tLSVJcuXZTb7fa3GTJkiOrUqZNatWqVWrVqlerUqZMaOnToOd3f2lJTeZw1a5aaMWOGmjFjhgJUXl5eudeSPJ4+j1u2bFHXXXedWrJkidq9e7f6/vvvVevWrdX1118f8FqSx8rfj2+88YZasWKFyszMVOvWrVPXXHONSk5Olr/rauaxzIsvvqjS09MVoBYtWhSwTvJYeR779eunxo0bp7Kysvy3/Pz8gNeqrTxKgVIH5OTkKECtWLFCKaWU1+tViYmJaubMmf42drtdRUVFqddff10ppVR+fr4yGo1q/vz5/jaHDx9WOp1Off3110oppbZv364A9fPPP/vbrF69WgHqt99+Oxe7dk6dSR5PtHz58goLFMlj9fJY5uOPP1Ymk0m5XC6llOTxTPO4adMmBajdu3crpSSP1cnjxo0bVbNmzVRWVla5AkXyWLU89uvXT917772n3G5t5lEO8dQBZd3gsbGxAGRmZpKdnc2gQYP8bcxmM/369WPVqlUArFu3DpfLFdAmKSmJTp06+dusXr2aqKgoevXq5W/Tu3dvoqKi/G0akjPJY1VIHs8sj1arlcjISAwG3yW/JI/Vz2NxcTFz584lNTXVf/V2yWPV8lhSUsLf/vY35syZQ2JiYrntSh6r/n788MMPadSoER07duShhx6isLDQv6428ygFSpAppXjggQe47LLL6NSpEwDZ2dkAJCQkBLRNSEjwr8vOzsZkMhETE3PaNvHx8eVeMz4+3t+moTjTPFaF5LH6eTx27BjTpk3jzjvv9C+TPFY9j6+++irh4eGEh4fz9ddfs3TpUkwmk387ksfK83j//ffTp08fhg0bVuG2JY9Vy+Po0aP573//S0ZGBk8++SQLFy7kuuuu86+vzTzWy6sZNyQTJkxg8+bN/Pjjj+XWaZoW8FgpVW7ZyU5uU1H7qmynvqnpPFa2jTPdTl1XE3ksKCjg6quvpkOHDkyePPm02zjdduqzs83j6NGjufLKK8nKyuL5559nxIgR/PTTT4SEhFS4jVNtp7470zwuWbKEZcuWsWHDhtNuX/JY+ftx3Lhx/vudOnWidevW9OzZk/Xr19O9e/cKt1HRds6E9KAE0cSJE1myZAnLly+nWbNm/uVl3ZEnV585OTn+ajcxMRGn00leXt5p2xw5cqTc6+bm5parmuuzs8ljVUgeq57HwsJChgwZQnh4OIsWLcJoNAZsR/JYtTxGRUXRunVr+vbtyyeffMJvv/3GokWL/NuRPJ4+j8uWLWPPnj1ER0djMBj8hxmvv/56+vfv79+O5LH6n4/du3fHaDSya9cu/3ZqLY9nNYJFnBGv16vGjx+vkpKS1M6dOytcn5iYqJ555hn/MofDUeEg2QULFvjb/PHHHxUOkv3ll1/8bX7++ecGMwisJvJ4osoGyUoeT59Hq9Wqevfurfr166eKi4vLbUfyWL3344ltLBaLmjt3rlJK8liVPGZlZaktW7YE3AD10ksvqb179yqlJI9n+n4sy2XZYNvazKMUKEFw9913q6ioKJWRkRFw6lZJSYm/zcyZM1VUVJT69NNP1ZYtW9Tf/va3Ck8zbtasmfruu+/U+vXr1RVXXFHhacYXXnihWr16tVq9erXq3LlzgzmNrqbymJWVpTZs2KDeeustBaiVK1eqDRs2qGPHjvnbSB5Pn8eCggLVq1cv1blzZ7V79+6A7cj7sep53LNnj5o+fbpau3at2r9/v1q1apUaNmyYio2NVUeOHPFvR/JY+d/1yTjFacaSx1Pncffu3Wrq1KlqzZo1KjMzU33xxReqXbt2qlu3bufk71oKlCAAKryV/YeklK+6nTx5skpMTFRms1n17dtXbdmyJWA7NptNTZgwQcXGxiqLxaKGDh2qDhw4ENDm2LFjavTo0SoiIkJFRESo0aNHVzjPR31UU3mcPHlypduRPJ4+j2W9TxXdMjMz/e0kj6fP4+HDh1V6erqKj49XRqNRNWvWTI0aNarcf6KSx8r/riva7skFiuTx9Hk8cOCA6tu3r4qNjVUmk0m1atVKTZo0KeCfN6VqL49a6Y4IIYQQQtQZMkhWCCGEEHWOFChCCCGEqHOkQBFCCCFEnSMFihBCCCHqHClQhBBCCFHnSIEihBBCiDpHChQhhBBC1DlSoAghhBCizpECRQghhBB1jhQoQgghhKhzpEARQgghRJ0jBYoQQggh6pz/D/2z7EX3iy4hAAAAAElFTkSuQmCC",
- "text/plain": [
- "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " DecommisionedCapacity_sim1_test_[MW]\n",
- "year \n",
- "1995 NaN\n",
- "1996 0.100001\n",
- "1997 0.199028\n",
- "1998 0.297258\n",
- "1999 0.395195\n",
- "2000 0.493899\n",
- "2001 0.595227\n",
- "2002 0.702085\n",
- "2003 0.818668\n",
- "2004 0.950675\n",
- "2005 1.105453\n",
- "2006 1.292034\n",
- "2007 1.520979\n",
- "2008 1.803935\n",
- "2009 2.152815\n",
- "2010 2.578488\n",
- "2011 3.088950\n",
- "2012 3.687055\n",
- "2013 4.368071\n",
- "2014 5.117577\n",
- "2015 5.910465\n",
- "2016 6.711880\n",
- "2017 7.480730\n",
- "2018 10.000000\n",
- "2019 10.000000\n",
- "2020 10.000000\n",
- "2021 10.000000\n",
- "2022 10.000000\n",
- "2023 10.000000\n",
- "2024 10.000000\n",
- "2025 10.000000\n",
- "2026 10.000000\n",
- "2027 10.000000\n",
- "2028 10.000000\n",
- "2029 10.000000\n",
- "2030 10.000000\n",
- "2031 10.000000\n",
- "2032 10.000000\n",
- "2033 10.000000\n",
- "2034 10.000000\n",
- "2035 10.000000\n",
- "2036 10.000000\n",
- "2037 10.000000\n",
- "2038 10.000000\n",
- "2039 10.000000\n",
- "2040 10.000000\n",
- "2041 10.000000\n",
- "2042 10.000000\n",
- "2043 10.000000\n",
- "2044 10.000000\n",
- "2045 10.000000\n",
- "2046 10.000000\n",
- "2047 10.000000\n",
- "2048 10.000000\n",
- "2049 10.000000\n",
- "2050 10.000000"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "decomm_cap.diff()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "87ffc0c2-7b1a-46eb-9083-559457045910",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "| \n", - " | DecommisionedCapacity_sim1_test_[MW] | \n", - "
|---|---|
| year | \n", - "\n", - " |
| 1995 | \n", - "NaN | \n", - "
| 1996 | \n", - "0.100001 | \n", - "
| 1997 | \n", - "0.199028 | \n", - "
| 1998 | \n", - "0.297258 | \n", - "
| 1999 | \n", - "0.395195 | \n", - "
| 2000 | \n", - "0.493899 | \n", - "
| 2001 | \n", - "0.595227 | \n", - "
| 2002 | \n", - "0.702085 | \n", - "
| 2003 | \n", - "0.818668 | \n", - "
| 2004 | \n", - "0.950675 | \n", - "
| 2005 | \n", - "1.105453 | \n", - "
| 2006 | \n", - "1.292034 | \n", - "
| 2007 | \n", - "1.520979 | \n", - "
| 2008 | \n", - "1.803935 | \n", - "
| 2009 | \n", - "2.152815 | \n", - "
| 2010 | \n", - "2.578488 | \n", - "
| 2011 | \n", - "3.088950 | \n", - "
| 2012 | \n", - "3.687055 | \n", - "
| 2013 | \n", - "4.368071 | \n", - "
| 2014 | \n", - "5.117577 | \n", - "
| 2015 | \n", - "5.910465 | \n", - "
| 2016 | \n", - "6.711880 | \n", - "
| 2017 | \n", - "7.480730 | \n", - "
| 2018 | \n", - "10.000000 | \n", - "
| 2019 | \n", - "10.000000 | \n", - "
| 2020 | \n", - "10.000000 | \n", - "
| 2021 | \n", - "10.000000 | \n", - "
| 2022 | \n", - "10.000000 | \n", - "
| 2023 | \n", - "10.000000 | \n", - "
| 2024 | \n", - "10.000000 | \n", - "
| 2025 | \n", - "10.000000 | \n", - "
| 2026 | \n", - "10.000000 | \n", - "
| 2027 | \n", - "10.000000 | \n", - "
| 2028 | \n", - "10.000000 | \n", - "
| 2029 | \n", - "10.000000 | \n", - "
| 2030 | \n", - "10.000000 | \n", - "
| 2031 | \n", - "10.000000 | \n", - "
| 2032 | \n", - "10.000000 | \n", - "
| 2033 | \n", - "10.000000 | \n", - "
| 2034 | \n", - "10.000000 | \n", - "
| 2035 | \n", - "10.000000 | \n", - "
| 2036 | \n", - "10.000000 | \n", - "
| 2037 | \n", - "10.000000 | \n", - "
| 2038 | \n", - "10.000000 | \n", - "
| 2039 | \n", - "10.000000 | \n", - "
| 2040 | \n", - "10.000000 | \n", - "
| 2041 | \n", - "10.000000 | \n", - "
| 2042 | \n", - "10.000000 | \n", - "
| 2043 | \n", - "10.000000 | \n", - "
| 2044 | \n", - "10.000000 | \n", - "
| 2045 | \n", - "10.000000 | \n", - "
| 2046 | \n", - "10.000000 | \n", - "
| 2047 | \n", - "10.000000 | \n", - "
| 2048 | \n", - "10.000000 | \n", - "
| 2049 | \n", - "10.000000 | \n", - "
| 2050 | \n", - "10.000000 | \n", - "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(usyearlyr1.filter(like='Waste'))\n",
- "plt.legend(usyearlyr1.filter(like='Waste').keys())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "0e60cda4-146b-44fc-82e5-ad90bf0f2aa5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " WasteAll_glass_sim1_test_[Tonnes] WasteAll_Module_sim1_test_[Tonnes] \\\n",
- "year \n",
- "1995 20.210526 20.210526 \n",
- "1996 19.892295 19.892295 \n",
- "1997 19.616101 19.616101 \n",
- "1998 19.360267 19.360267 \n",
- "1999 19.157638 19.157638 \n",
- "2000 19.079252 19.079252 \n",
- "2001 19.252246 19.252246 \n",
- "2002 19.956205 19.956205 \n",
- "2003 21.407715 21.407715 \n",
- "2004 24.017046 24.017046 \n",
- "2005 28.322026 28.322026 \n",
- "2006 34.995025 34.995025 \n",
- "2007 44.836641 44.836641 \n",
- "2008 58.748500 58.748500 \n",
- "2009 77.676259 77.676259 \n",
- "2010 102.514189 102.514189 \n",
- "2011 133.652690 133.652690 \n",
- "2012 172.031056 172.031056 \n",
- "2013 216.819410 216.819410 \n",
- "2014 267.080725 267.080725 \n",
- "2015 320.223355 320.223355 \n",
- "2016 375.182901 375.182901 \n",
- "2017 428.688224 428.688224 \n",
- "2018 623.268730 623.268730 \n",
- "2019 614.458976 614.458976 \n",
- "2020 606.463431 606.463431 \n",
- "2021 598.425793 598.425793 \n",
- "2022 591.302103 591.302103 \n",
- "2023 583.802493 583.802493 \n",
- "2024 576.963531 576.963531 \n",
- "2025 570.695624 570.695624 \n",
- "2026 564.001701 564.001701 \n",
- "2027 557.423514 557.423514 \n",
- "2028 550.529332 550.529332 \n",
- "2029 542.988428 542.988428 \n",
- "2030 343.518088 343.518088 \n",
- "2031 338.638740 338.638740 \n",
- "2032 333.521340 333.521340 \n",
- "2033 328.206654 328.206654 \n",
- "2034 321.320325 321.320325 \n",
- "2035 316.255674 316.255674 \n",
- "2036 309.856819 309.856819 \n",
- "2037 306.082971 306.082971 \n",
- "2038 298.903317 298.903317 \n",
- "2039 294.109264 294.109264 \n",
- "2040 293.103954 293.103954 \n",
- "2041 289.519637 289.519637 \n",
- "2042 287.966956 287.966956 \n",
- "2043 287.466292 287.466292 \n",
- "2044 286.072961 286.072961 \n",
- "2045 287.188917 287.188917 \n",
- "2046 284.609605 284.609605 \n",
- "2047 283.770910 283.770910 \n",
- "2048 281.580696 281.580696 \n",
- "2049 279.789975 279.789975 \n",
- "2050 279.225361 279.225361 \n",
- "\n",
- " WasteEOL_glass_sim1_test_[Tonnes] WasteEOL_Module_sim1_test_[Tonnes] \\\n",
- "year \n",
- "1995 0.000000 0.000000 \n",
- "1996 0.000044 0.000044 \n",
- "1997 0.001848 0.001848 \n",
- "1998 0.016348 0.016348 \n",
- "1999 0.076703 0.076703 \n",
- "2000 0.254246 0.254246 \n",
- "2001 0.676395 0.676395 \n",
- "2002 1.545806 1.545806 \n",
- "2003 3.159848 3.159848 \n",
- "2004 5.928866 5.928866 \n",
- "2005 10.390762 10.390762 \n",
- "2006 17.217979 17.217979 \n",
- "2007 27.211182 27.211182 \n",
- "2008 41.272065 41.272065 \n",
- "2009 60.346349 60.346349 \n",
- "2010 85.328367 85.328367 \n",
- "2011 116.922122 116.922122 \n",
- "2012 155.462361 155.462361 \n",
- "2013 200.714147 200.714147 \n",
- "2014 251.689830 251.689830 \n",
- "2015 306.543473 306.543473 \n",
- "2016 362.613902 362.613902 \n",
- "2017 416.674306 416.674306 \n",
- "2018 612.513091 612.513091 \n",
- "2019 604.413917 604.413917 \n",
- "2020 596.986195 596.986195 \n",
- "2021 589.898611 589.898611 \n",
- "2022 583.126572 583.126572 \n",
- "2023 576.626711 576.626711 \n",
- "2024 570.334241 570.334241 \n",
- "2025 564.724969 564.724969 \n",
- "2026 558.932081 558.932081 \n",
- "2027 552.887434 552.887434 \n",
- "2028 546.523913 546.523913 \n",
- "2029 539.783283 539.783283 \n",
- "2030 340.880813 340.880813 \n",
- "2031 336.028174 336.028174 \n",
- "2032 330.924320 330.924320 \n",
- "2033 325.622114 325.622114 \n",
- "2034 318.747350 318.747350 \n",
- "2035 313.693469 313.693469 \n",
- "2036 307.304691 307.304691 \n",
- "2037 303.540307 303.540307 \n",
- "2038 296.369574 296.369574 \n",
- "2039 291.583954 291.583954 \n",
- "2040 290.586640 290.586640 \n",
- "2041 287.009924 287.009924 \n",
- "2042 285.464484 285.464484 \n",
- "2043 284.970733 284.970733 \n",
- "2044 283.584015 283.584015 \n",
- "2045 284.706308 284.706308 \n",
- "2046 282.133079 282.133079 \n",
- "2047 281.300231 281.300231 \n",
- "2048 279.115646 279.115646 \n",
- "2049 277.330351 277.330351 \n",
- "2050 276.770974 276.770974 \n",
- "\n",
- " WasteMFG_glass_sim1_test_[Tonnes] WasteMFG_Module_sim1_test_[Tonnes] \n",
- "year \n",
- "1995 20.210526 20.210526 \n",
- "1996 19.892250 19.892250 \n",
- "1997 19.614253 19.614253 \n",
- "1998 19.343919 19.343919 \n",
- "1999 19.080935 19.080935 \n",
- "2000 18.825006 18.825006 \n",
- "2001 18.575851 18.575851 \n",
- "2002 18.410398 18.410398 \n",
- "2003 18.247867 18.247867 \n",
- "2004 18.088180 18.088180 \n",
- "2005 17.931263 17.931263 \n",
- "2006 17.777046 17.777046 \n",
- "2007 17.625459 17.625459 \n",
- "2008 17.476435 17.476435 \n",
- "2009 17.329910 17.329910 \n",
- "2010 17.185822 17.185822 \n",
- "2011 16.730568 16.730568 \n",
- "2012 16.568694 16.568694 \n",
- "2013 16.105263 16.105263 \n",
- "2014 15.390895 15.390895 \n",
- "2015 13.679882 13.679882 \n",
- "2016 12.568998 12.568998 \n",
- "2017 12.013918 12.013918 \n",
- "2018 10.755639 10.755639 \n",
- "2019 10.045060 10.045060 \n",
- "2020 9.477236 9.477236 \n",
- "2021 8.527181 8.527181 \n",
- "2022 8.175531 8.175531 \n",
- "2023 7.175783 7.175783 \n",
- "2024 6.629290 6.629290 \n",
- "2025 5.970655 5.970655 \n",
- "2026 5.069621 5.069621 \n",
- "2027 4.536080 4.536080 \n",
- "2028 4.005419 4.005419 \n",
- "2029 3.205145 3.205145 \n",
- "2030 2.637275 2.637275 \n",
- "2031 2.610567 2.610567 \n",
- "2032 2.597020 2.597020 \n",
- "2033 2.584540 2.584540 \n",
- "2034 2.572975 2.572975 \n",
- "2035 2.562204 2.562204 \n",
- "2036 2.552128 2.552128 \n",
- "2037 2.542663 2.542663 \n",
- "2038 2.533743 2.533743 \n",
- "2039 2.525310 2.525310 \n",
- "2040 2.517314 2.517314 \n",
- "2041 2.509713 2.509713 \n",
- "2042 2.502472 2.502472 \n",
- "2043 2.495559 2.495559 \n",
- "2044 2.488946 2.488946 \n",
- "2045 2.482609 2.482609 \n",
- "2046 2.476526 2.476526 \n",
- "2047 2.470679 2.470679 \n",
- "2048 2.465050 2.465050 \n",
- "2049 2.459624 2.459624 \n",
- "2050 2.454387 2.454387 "
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "usyearlyr1.filter(like='Waste')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cac5423b-daec-489c-bde8-7d6071cb16d3",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.11.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/dev/Untitled.py b/docs/dev/Untitled.py
deleted file mode 100644
index 117ba9d9..00000000
--- a/docs/dev/Untitled.py
+++ /dev/null
@@ -1,158 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-import os
-from pathlib import Path
-import PV_ICE
-import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np
-
-testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
-baselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines')
-supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
-resultsfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial'/ 'USHistoryResults')
-
-cwd=os.getcwd()
-print(os.getcwd())
-
-
-# In[2]:
-
-
-PV_ICE.__version__
-
-
-# In[3]:
-
-
-MATERIALS = ['glass']#,'aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']
-moduleFile = os.path.join(baselinesfolder, 'TEST_baseline_modules_mass_US.csv')
-#newmodfilesPAth = os.path.join(supportMatfolder,'Calculations-Installs-Subset-CommUtility.xlsx')
-
-
-# In[4]:
-
-
-r1 = PV_ICE.Simulation(name='sim1', path=testfolder)
-r1.createScenario(name='test', massmodulefile=moduleFile) #create the scenario, name and mod file attach
-for mat in MATERIALS:
- materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
- r1.scenario['test'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
-
-
-# In[5]:
-
-
-r1.calculateMassFlow()
-
-
-# In[6]:
-
-
-usyearlyr1, uscumr1 = r1.aggregateResults()
-
-
-# In[7]:
-
-
-r1.scenario['test'].dataOut_m.keys()
-
-
-# In[8]:
-
-
-'Yearly_Sum_Power_EOLby_Degradation',
-'Yearly_Sum_Power_EOLby_Failure',
-'Yearly_Sum_Power_EOLby_ProjectLifetime',
-'Yearly_Sum_Power_PathsBad',
-'Yearly_Sum_Power_PathsGood',
-'Yearly_Sum_Power_atEOL'
-
-
-# In[9]:
-
-
-yspeol_deg = r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_Degradation']
-
-
-# In[10]:
-
-
-yspeol_fail =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_Failure']
-
-
-# In[11]:
-
-
-yspeol_plife =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_EOLby_ProjectLifetime']
-
-
-# In[12]:
-
-
-yspeol_sum =r1.scenario['test'].dataOut_m['Yearly_Sum_Power_atEOL']
-
-
-# In[13]:
-
-
-plt.plot(yspeol_deg, label='deg')
-plt.plot(yspeol_fail, label='fail')
-plt.plot(yspeol_plife, label='life')
-plt.plot(yspeol_sum, ls=':', label='sum')
-plt.legend()
-
-
-# In[14]:
-
-
-yspeol = pd.concat([yspeol_deg,yspeol_fail,yspeol_plife,yspeol_sum],axis=1)
-yspeol
-
-
-# In[15]:
-
-
-yspeol_MW = yspeol/1000000#), index = usyearlyr1.index)
-yspeol_MW_cumu = yspeol_MW.cumsum()
-yspeol_MW_cumu.index = usyearlyr1.index
-#yspeol_MW_cumu
-decomm_cap = usyearlyr1.filter(like='Decomm')
-compare_decom = pd.concat([yspeol_MW_cumu,decomm_cap],axis=1)
-compare_decom.tail()
-
-
-# In[16]:
-
-
-plt.plot(compare_decom)
-
-
-# In[17]:
-
-
-decomm_cap.diff()
-
-
-# In[18]:
-
-
-plt.plot(usyearlyr1.filter(like='Waste'))
-plt.legend(usyearlyr1.filter(like='Waste').keys())
-
-
-# In[19]:
-
-
-usyearlyr1.filter(like='Waste')
-
-
-# In[ ]:
-
-
-
-
diff --git a/docs/dev/Untitled.txt b/docs/dev/Untitled.txt
deleted file mode 100644
index e69de29b..00000000
| \n", - " | WasteAll_glass_sim1_test_[Tonnes] | \n", - "WasteAll_Module_sim1_test_[Tonnes] | \n", - "WasteEOL_glass_sim1_test_[Tonnes] | \n", - "WasteEOL_Module_sim1_test_[Tonnes] | \n", - "WasteMFG_glass_sim1_test_[Tonnes] | \n", - "WasteMFG_Module_sim1_test_[Tonnes] | \n", - "
|---|---|---|---|---|---|---|
| year | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " |
| 1995 | \n", - "20.210526 | \n", - "20.210526 | \n", - "0.000000 | \n", - "0.000000 | \n", - "20.210526 | \n", - "20.210526 | \n", - "
| 1996 | \n", - "19.892295 | \n", - "19.892295 | \n", - "0.000044 | \n", - "0.000044 | \n", - "19.892250 | \n", - "19.892250 | \n", - "
| 1997 | \n", - "19.616101 | \n", - "19.616101 | \n", - "0.001848 | \n", - "0.001848 | \n", - "19.614253 | \n", - "19.614253 | \n", - "
| 1998 | \n", - "19.360267 | \n", - "19.360267 | \n", - "0.016348 | \n", - "0.016348 | \n", - "19.343919 | \n", - "19.343919 | \n", - "
| 1999 | \n", - "19.157638 | \n", - "19.157638 | \n", - "0.076703 | \n", - "0.076703 | \n", - "19.080935 | \n", - "19.080935 | \n", - "
| 2000 | \n", - "19.079252 | \n", - "19.079252 | \n", - "0.254246 | \n", - "0.254246 | \n", - "18.825006 | \n", - "18.825006 | \n", - "
| 2001 | \n", - "19.252246 | \n", - "19.252246 | \n", - "0.676395 | \n", - "0.676395 | \n", - "18.575851 | \n", - "18.575851 | \n", - "
| 2002 | \n", - "19.956205 | \n", - "19.956205 | \n", - "1.545806 | \n", - "1.545806 | \n", - "18.410398 | \n", - "18.410398 | \n", - "
| 2003 | \n", - "21.407715 | \n", - "21.407715 | \n", - "3.159848 | \n", - "3.159848 | \n", - "18.247867 | \n", - "18.247867 | \n", - "
| 2004 | \n", - "24.017046 | \n", - "24.017046 | \n", - "5.928866 | \n", - "5.928866 | \n", - "18.088180 | \n", - "18.088180 | \n", - "
| 2005 | \n", - "28.322026 | \n", - "28.322026 | \n", - "10.390762 | \n", - "10.390762 | \n", - "17.931263 | \n", - "17.931263 | \n", - "
| 2006 | \n", - "34.995025 | \n", - "34.995025 | \n", - "17.217979 | \n", - "17.217979 | \n", - "17.777046 | \n", - "17.777046 | \n", - "
| 2007 | \n", - "44.836641 | \n", - "44.836641 | \n", - "27.211182 | \n", - "27.211182 | \n", - "17.625459 | \n", - "17.625459 | \n", - "
| 2008 | \n", - "58.748500 | \n", - "58.748500 | \n", - "41.272065 | \n", - "41.272065 | \n", - "17.476435 | \n", - "17.476435 | \n", - "
| 2009 | \n", - "77.676259 | \n", - "77.676259 | \n", - "60.346349 | \n", - "60.346349 | \n", - "17.329910 | \n", - "17.329910 | \n", - "
| 2010 | \n", - "102.514189 | \n", - "102.514189 | \n", - "85.328367 | \n", - "85.328367 | \n", - "17.185822 | \n", - "17.185822 | \n", - "
| 2011 | \n", - "133.652690 | \n", - "133.652690 | \n", - "116.922122 | \n", - "116.922122 | \n", - "16.730568 | \n", - "16.730568 | \n", - "
| 2012 | \n", - "172.031056 | \n", - "172.031056 | \n", - "155.462361 | \n", - "155.462361 | \n", - "16.568694 | \n", - "16.568694 | \n", - "
| 2013 | \n", - "216.819410 | \n", - "216.819410 | \n", - "200.714147 | \n", - "200.714147 | \n", - "16.105263 | \n", - "16.105263 | \n", - "
| 2014 | \n", - "267.080725 | \n", - "267.080725 | \n", - "251.689830 | \n", - "251.689830 | \n", - "15.390895 | \n", - "15.390895 | \n", - "
| 2015 | \n", - "320.223355 | \n", - "320.223355 | \n", - "306.543473 | \n", - "306.543473 | \n", - "13.679882 | \n", - "13.679882 | \n", - "
| 2016 | \n", - "375.182901 | \n", - "375.182901 | \n", - "362.613902 | \n", - "362.613902 | \n", - "12.568998 | \n", - "12.568998 | \n", - "
| 2017 | \n", - "428.688224 | \n", - "428.688224 | \n", - "416.674306 | \n", - "416.674306 | \n", - "12.013918 | \n", - "12.013918 | \n", - "
| 2018 | \n", - "623.268730 | \n", - "623.268730 | \n", - "612.513091 | \n", - "612.513091 | \n", - "10.755639 | \n", - "10.755639 | \n", - "
| 2019 | \n", - "614.458976 | \n", - "614.458976 | \n", - "604.413917 | \n", - "604.413917 | \n", - "10.045060 | \n", - "10.045060 | \n", - "
| 2020 | \n", - "606.463431 | \n", - "606.463431 | \n", - "596.986195 | \n", - "596.986195 | \n", - "9.477236 | \n", - "9.477236 | \n", - "
| 2021 | \n", - "598.425793 | \n", - "598.425793 | \n", - "589.898611 | \n", - "589.898611 | \n", - "8.527181 | \n", - "8.527181 | \n", - "
| 2022 | \n", - "591.302103 | \n", - "591.302103 | \n", - "583.126572 | \n", - "583.126572 | \n", - "8.175531 | \n", - "8.175531 | \n", - "
| 2023 | \n", - "583.802493 | \n", - "583.802493 | \n", - "576.626711 | \n", - "576.626711 | \n", - "7.175783 | \n", - "7.175783 | \n", - "
| 2024 | \n", - "576.963531 | \n", - "576.963531 | \n", - "570.334241 | \n", - "570.334241 | \n", - "6.629290 | \n", - "6.629290 | \n", - "
| 2025 | \n", - "570.695624 | \n", - "570.695624 | \n", - "564.724969 | \n", - "564.724969 | \n", - "5.970655 | \n", - "5.970655 | \n", - "
| 2026 | \n", - "564.001701 | \n", - "564.001701 | \n", - "558.932081 | \n", - "558.932081 | \n", - "5.069621 | \n", - "5.069621 | \n", - "
| 2027 | \n", - "557.423514 | \n", - "557.423514 | \n", - "552.887434 | \n", - "552.887434 | \n", - "4.536080 | \n", - "4.536080 | \n", - "
| 2028 | \n", - "550.529332 | \n", - "550.529332 | \n", - "546.523913 | \n", - "546.523913 | \n", - "4.005419 | \n", - "4.005419 | \n", - "
| 2029 | \n", - "542.988428 | \n", - "542.988428 | \n", - "539.783283 | \n", - "539.783283 | \n", - "3.205145 | \n", - "3.205145 | \n", - "
| 2030 | \n", - "343.518088 | \n", - "343.518088 | \n", - "340.880813 | \n", - "340.880813 | \n", - "2.637275 | \n", - "2.637275 | \n", - "
| 2031 | \n", - "338.638740 | \n", - "338.638740 | \n", - "336.028174 | \n", - "336.028174 | \n", - "2.610567 | \n", - "2.610567 | \n", - "
| 2032 | \n", - "333.521340 | \n", - "333.521340 | \n", - "330.924320 | \n", - "330.924320 | \n", - "2.597020 | \n", - "2.597020 | \n", - "
| 2033 | \n", - "328.206654 | \n", - "328.206654 | \n", - "325.622114 | \n", - "325.622114 | \n", - "2.584540 | \n", - "2.584540 | \n", - "
| 2034 | \n", - "321.320325 | \n", - "321.320325 | \n", - "318.747350 | \n", - "318.747350 | \n", - "2.572975 | \n", - "2.572975 | \n", - "
| 2035 | \n", - "316.255674 | \n", - "316.255674 | \n", - "313.693469 | \n", - "313.693469 | \n", - "2.562204 | \n", - "2.562204 | \n", - "
| 2036 | \n", - "309.856819 | \n", - "309.856819 | \n", - "307.304691 | \n", - "307.304691 | \n", - "2.552128 | \n", - "2.552128 | \n", - "
| 2037 | \n", - "306.082971 | \n", - "306.082971 | \n", - "303.540307 | \n", - "303.540307 | \n", - "2.542663 | \n", - "2.542663 | \n", - "
| 2038 | \n", - "298.903317 | \n", - "298.903317 | \n", - "296.369574 | \n", - "296.369574 | \n", - "2.533743 | \n", - "2.533743 | \n", - "
| 2039 | \n", - "294.109264 | \n", - "294.109264 | \n", - "291.583954 | \n", - "291.583954 | \n", - "2.525310 | \n", - "2.525310 | \n", - "
| 2040 | \n", - "293.103954 | \n", - "293.103954 | \n", - "290.586640 | \n", - "290.586640 | \n", - "2.517314 | \n", - "2.517314 | \n", - "
| 2041 | \n", - "289.519637 | \n", - "289.519637 | \n", - "287.009924 | \n", - "287.009924 | \n", - "2.509713 | \n", - "2.509713 | \n", - "
| 2042 | \n", - "287.966956 | \n", - "287.966956 | \n", - "285.464484 | \n", - "285.464484 | \n", - "2.502472 | \n", - "2.502472 | \n", - "
| 2043 | \n", - "287.466292 | \n", - "287.466292 | \n", - "284.970733 | \n", - "284.970733 | \n", - "2.495559 | \n", - "2.495559 | \n", - "
| 2044 | \n", - "286.072961 | \n", - "286.072961 | \n", - "283.584015 | \n", - "283.584015 | \n", - "2.488946 | \n", - "2.488946 | \n", - "
| 2045 | \n", - "287.188917 | \n", - "287.188917 | \n", - "284.706308 | \n", - "284.706308 | \n", - "2.482609 | \n", - "2.482609 | \n", - "
| 2046 | \n", - "284.609605 | \n", - "284.609605 | \n", - "282.133079 | \n", - "282.133079 | \n", - "2.476526 | \n", - "2.476526 | \n", - "
| 2047 | \n", - "283.770910 | \n", - "283.770910 | \n", - "281.300231 | \n", - "281.300231 | \n", - "2.470679 | \n", - "2.470679 | \n", - "
| 2048 | \n", - "281.580696 | \n", - "281.580696 | \n", - "279.115646 | \n", - "279.115646 | \n", - "2.465050 | \n", - "2.465050 | \n", - "
| 2049 | \n", - "279.789975 | \n", - "279.789975 | \n", - "277.330351 | \n", - "277.330351 | \n", - "2.459624 | \n", - "2.459624 | \n", - "
| 2050 | \n", - "279.225361 | \n", - "279.225361 | \n", - "276.770974 | \n", - "276.770974 | \n", - "2.454387 | \n", - "2.454387 | \n", - "