In [2]:
import os
from pathlib import Path
from bifacial_radiance import *
import numpy as np
import pandas as pd

testfolder = Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'AUSTIN'

# Another option using relative address; for some operative systems you might need '/' instead of '\'
# testfolder = os.path.abspath(r'..\..\bifacial_radiance\TEMP')  

print ("Your simulation will be stored in %s" % testfolder)

if not os.path.exists(testfolder):
    os.makedirs(testfolder)
    
# Create a RadianceObj 'object' named bifacial_example. no whitespace allowed
demo = RadianceObj('tutorial_2', path = str(testfolder))  
albedo = 0.25
demo.setGround(albedo)
# Pull in meteorological data using pyEPW for any global lat/lon
epwfile = demo.getEPW(lat = 37.5, lon = -77.6)  # This location corresponds to Richmond, VA.
# Read in the weather data pulled in above. 
metData = demo.readWeatherFile(weatherFile = epwfile) 

limit_angle = 5 # tracker rotation limit angle. Setting it ridiculously small so this runs faster.
angledelta = 5 # sampling between the limit angles. 
backtrack = True
pitch = 5.0
hub_height = 2.3

x = 1  # meters
y = 2    # meters
moduletype = 'PVmodule'
gcr = 2/pitch

module = demo.makeModule(name=moduletype, x=x, y=y)    

cumulativesky = True # This is important for this example!
trackerdict = demo.set1axis(metdata = demo.readWeatherFile(weatherFile = epwfile) 
, limit_angle = limit_angle, backtrack = backtrack, 
                            gcr = gcr, cumulativesky = cumulativesky)
trackerdict = demo.genCumSky1axis()



sceneDict = {'pitch':pitch,'hub_height':hub_height, 'nMods': 7, 'nRows': 3}  
trackerdict = demo.makeScene1axis(trackerdict = trackerdict, module = module, sceneDict = sceneDict) 
trackerdict = demo.makeOct1axis(trackerdict = trackerdict)
Your simulation will be stored in C:\Users\sayala\Documents\GitHub\bifacial_radiance\bifacial_radiance\TEMP\AUSTIN
path = C:\Users\sayala\Documents\GitHub\bifacial_radiance\bifacial_radiance\TEMP\AUSTIN
Loading albedo, 1 value(s), 0.250 avg
1 nonzero albedo values.
Getting weather file: USA_VA_Richmond.724010_TMY2.epw
 ... OK!
8760 line in WeatherFile. Assuming this is a standard hourly WeatherFile for the year for purposes of saving Gencumulativesky temporary weather files in EPW folder.
Coercing year to 2021
Saving file EPWs\metdata_temp.csv, # points: 8760
Calculating Sun position for Metdata that is right-labeled  with a delta of -30 mins. i.e. 12 is 11:30 sunpos

Module Name: PVmodule
Module PVmodule updated in module.json
Pre-existing .rad file objects\PVmodule.rad will be overwritten

8760 line in WeatherFile. Assuming this is a standard hourly WeatherFile for the year for purposes of saving Gencumulativesky temporary weather files in EPW folder.
Coercing year to 2021
Saving file EPWs\metdata_temp.csv, # points: 8760
Calculating Sun position for Metdata that is right-labeled  with a delta of -30 mins. i.e. 12 is 11:30 sunpos
Saving file EPWs\1axis_-5.0.csv, # points: 2198
Saving file EPWs\1axis_0.0.csv, # points: 73
Saving file EPWs\1axis_5.0.csv, # points: 2096
message: There were 2162 sun up hours in this climate file
Total Ibh/Lbh: 0.000000
Created skyfile skies\1axis_-5.0.rad
message: There were 62 sun up hours in this climate file
Total Ibh/Lbh: 0.000000
Created skyfile skies\1axis_0.0.rad
message: There were 2065 sun up hours in this climate file
Total Ibh/Lbh: 0.000000
Created skyfile skies\1axis_5.0.rad

Making .rad files for cumulativesky 1-axis workflow
3 Radfiles created in /objects/

Making 3 octfiles in root directory.
Created 1axis_-5.0.oct
Created 1axis_0.0.oct
Created 1axis_5.0.oct
In [3]:
trackerdict = demo.analysis1axis(trackerdict, customname = 'Module', sensorsy=9, modWanted=2, rowWanted=1)

trackerdict = demo.calculateResults(agriPV=False)
ResultPVWm2Back = demo.CompiledResults.iloc[0]['Wm2Back']
ResultPVWm2Front = demo.CompiledResults.iloc[0]['Gfront_mean']

# GROUND
resolutionGround = 0.1  # use 1 for faster test runs
numsensors = int((pitch/resolutionGround)+1)

# Analysis for GROUND
modscanback = {'xstart': 0, 
                'zstart': 0.05,
                'xinc': resolutionGround,
                'zinc': 0,
                'Ny':numsensors,
                'orient':'0 0 -1'}

modscanfront = {'Ny':1}    


trackerdict = demo.analysis1axis(trackerdict, customname = 'Ground', modWanted=1, rowWanted=1,
                                    modscanfront=modscanfront, modscanback=modscanback)
trackerdict = demo.calculateResults(agriPV=True)
ResultPVGround = demo.CompiledResults.iloc[0]['Wm2Back']
Linescan in process: 1axis_-5.0Module_Row1_Module2_Front
Linescan in process: 1axis_-5.0Module_Row1_Module2_Back
Saved: results\irr_1axis_-5.0Module_Row1_Module2.csv
Index: -5.0. Wm2Front: 754489.2555555557. Wm2Back: 151522.94444444444
Linescan in process: 1axis_0.0Module_Row1_Module2_Front
Linescan in process: 1axis_0.0Module_Row1_Module2_Back
Saved: results\irr_1axis_0.0Module_Row1_Module2.csv
Index: 0.0. Wm2Front: 1229.933111111111. Wm2Back: 294.7497777777778
Linescan in process: 1axis_5.0Module_Row1_Module2_Front
Linescan in process: 1axis_5.0Module_Row1_Module2_Back
Saved: results\irr_1axis_5.0Module_Row1_Module2.csv
Index: 5.0. Wm2Front: 840604.2111111111. Wm2Back: 158916.4
Bifaciality factor of module stored is  1
Linescan in process: 1axis_-5.0Ground_Row1_Module1_Front
Linescan in process: 1axis_-5.0Ground_Row1_Module1_Back
Saved: results\irr_1axis_-5.0Ground_Row1_Module1_Front.csv
Saved: results\irr_1axis_-5.0Ground_Row1_Module1_Back.csv
Index: -5.0. Wm2Front: 755834.0. Wm2Back: 627651.8
Linescan in process: 1axis_0.0Ground_Row1_Module1_Front
Linescan in process: 1axis_0.0Ground_Row1_Module1_Back
Saved: results\irr_1axis_0.0Ground_Row1_Module1_Front.csv
Saved: results\irr_1axis_0.0Ground_Row1_Module1_Back.csv
Index: 0.0. Wm2Front: 1287.823. Wm2Back: 1265.845941176471
Linescan in process: 1axis_5.0Ground_Row1_Module1_Front
Linescan in process: 1axis_5.0Ground_Row1_Module1_Back
Saved: results\irr_1axis_5.0Ground_Row1_Module1_Front.csv
Saved: results\irr_1axis_5.0Ground_Row1_Module1_Back.csv
Index: 5.0. Wm2Front: 846275.4. Wm2Back: 670368.5666666667
Bifaciality factor of module stored is  1
In [4]:
ResultPVGround
Out[4]:
[1270868.445,
 1270839.376,
 1270810.107,
 1270781.037,
 1282425.102,
 1293090.16,
 1290986.3169999998,
 1274016.675,
 1269620.9320000003,
 1289034.5869999998,
 1287190.3660000002,
 1292441.345,
 1293098.724,
 1294423.103,
 1295793.935,
 1306087.4679999999,
 1291227.201,
 1288772.834,
 1301387.368,
 1303541.595,
 1307399.2419999999,
 1315122.3890000002,
 1315920.636,
 1316718.683,
 1297916.324,
 1295070.813,
 1292225.203,
 1289379.6920000003,
 1311262.582,
 1312088.2979999997,
 1313021.237,
 1319224.977,
 1316894.317,
 1314069.3630000001,
 1306720.7989999999,
 1308187.9349999998,
 1314824.8709999998,
 1308856.407,
 1309184.565,
 1309521.477,
 1305348.289,
 1304894.701,
 1301838.113,
 1300586.109,
 1300182.9270000001,
 1299779.7449999999,
 1301989.763,
 1317286.981,
 1314831.3990000002,
 1308527.005,
 1298285.324]
In [ ]:
gid=22
mymonthstart, mymonthend = 1, 12
In [ ]:
xgap = 0
In [ ]:
results = [gid, metData.latitude, metData.longitude, 
           mymonthstart, mymonthend,
           hub_height, pitch, xgap,
           metData.ghi.sum(), 
           ResultPVWm2Front, ResultPVWm2Back, ResultPVGround]
In [ ]:
df_temp = ResultPVGround
In [ ]:
# Tracker Projection of half the module into the ground, for 1-up module in portrait orientation
xp = 10

if pitch == 5.0:
    bedsWanted = 3
else:
    bedsWanted = 6

dist1 = int(np.floor(len(df_temp[xp:-xp])/bedsWanted))

Astart = xp + dist1*0
Bstart = xp + dist1*1
Cstart = xp + dist1*2

if bedsWanted == 6:
    Dstart = xp + dist1*3
    Estart = xp + dist1*4
    Fstart = xp + dist1*5
    Fend = -xp
else:
    Dstart = -xp # in this case it is Cend


testbedA = df_temp[Astart:Bstart]
testbedAmean = np.mean(testbedA)
testbedA_normGHI = testbedAmean / ghi_sum

testbedB = df_temp[Bstart:Cstart]
testbedBmean = np.mean(testbedB)
testbedB_normGHI = testbedBmean / ghi_sum

testbedC = df_temp[Cstart:Dstart]
testbedCmean = np.mean(testbedC)
testbedC_normGHI = testbedCmean / ghi_sum

if bedsWanted == 6:
    testbedD = df_temp[Dstart:Estart]
    testbedDmean = np.mean(testbedD)
    testbedD_normGHI = testbedDmean / ghi_sum

    testbedE = df_temp[Estart:Fstart]
    testbedEmean = np.mean(testbedE)
    testbedE_normGHI = testbedEmean / ghi_sum

    testbedF = df_temp[Fstart:Fend]
    testbedFmean = np.mean(testbedF)
    testbedF_normGHI = testbedFmean / ghi_sum
    
#edgestart = df_temp[:xp]
#edgeend = df_temp[-xp:]

edgemean = np.mean(df_temp[:xp] + df_temp[-xp:])
edge_normGHI = edgemean / ghi_sum

insidemean = np.mean(df_temp[xp:-xp])
inside_normGHI = insidemean / ghi_sum
In [ ]:
# Values to add to returned results:
results = [testbedAmean, testbedBmean, testbedCmean]
In [ ]:
b = [results, results]
In [ ]:
res2 = pd.DataFrame(b, columns=['latitude', 'longitude', 'x'])
In [ ]:
res2
In [ ]:
res = [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 
       [462542, 37.810001373291016, -105.86000061035156, 5, 5, 1.5, 5.0, 0.0, 1949797, 2481803.971133333, [190489.65363999997, 179046.10642, 170414.79700000002, 165689.92551, 165288.49835999997, 166262.61995000002, 171109.37050000002, 178816.26449000003, 189029.86281], 
        [549800.4489, 558162.0220000001, 575658.7451000001, 588431.3745000002, 661108.9608, 716042.7550000001, 741429.6590999999, 804642.2954, 837107.5861, 882498.5317000002, 905485.9971999999, 937009.8395000002, 972204.4717999999, 1011749.0571, 1054136.7901, 1069321.791, 1119894.7099, 1151256.7627000003, 1152966.0374999999, 1179310.7300999998, 1199876.3416, 1204969.7530999999, 1230523.3058, 1227668.0707999999, 1226967.2791, 1219370.8270999999, 1214610.4749999999, 1202031.3965999999, 1206640.9608999998, 1185217.3858999999, 1177356.9547000004, 1161197.6721, 1143901.5638, 1110809.0640999998, 1060832.9525, 1015727.9608000001, 980236.9719, 965158.9506000001, 925255.8523, 892081.3624999999, 858106.3715000001, 811867.1314, 757758.3164, 710641.7895000001, 689701.9553, 662002.3923000001, 581206.1259999999, 545761.2799999999, 530173.2996999999, 534446.1595, 539425.5998000001], 663893.3214249999, 1095544.440632258, 1055333.6186900001, 1211787.5795899997, 1026424.1524363637, 0.340493559803918, 0.5618761546110995, 0.5412530733660992, 0.6214942271374916, 0.5264261625371071], [462562, 37.0099983215332, -105.86000061035156, 5, 5, 1.5, 5.0, 0.0, 1945882, 2485181.0611, [189333.61354, 178786.29432, 170578.78020999997, 165947.69356, 164861.04539000004, 166641.35652000003, 171759.08072, 179186.64361000003, 189306.2153], [557252.6761, 577499.9395999999, 592876.5164, 634398.5853000002, 664017.7060999998, 716923.3229, 768075.738, 806721.8362999998, 833998.8709999999, 875040.2607999999, 922543.2886, 958284.4949999999, 997232.9355999999, 1032254.4434999999, 1053950.4624, 1081896.2988999998, 1114498.5705, 1129265.7832999998, 1159335.9251999997, 1197786.7241, 1188640.9851000002, 1197831.9311000002, 1212459.4216, 1218588.8921, 1203523.3844, 1213445.6631, 1195791.8288000003, 1192778.3381999999, 1186067.1162, 1159957.4026, 1149498.5516000001, 1139431.8261000002, 1112218.9976, 1082774.0941, 1044039.8681000001, 1009776.7011000001, 981122.3217, 953290.9429999999, 931453.5220000001, 891146.0404000002, 832626.005, 801290.7955999998, 767585.3720999999, 733128.3038999998, 683797.0108000002, 646730.3396, 609563.0396, 577971.4782999998, 558139.8058999999, 566236.6962000001, 566599.4918000001], 676892.389315, 1088500.4116451612, 1064704.8927099998, 1196908.4963200002, 1011579.8973363638, 0.34785890887268606, 0.5593866491622622, 0.547157994528959, 0.6150981900855242, 0.5198567525350272], [482549, 40.209999084472656, -105.05999755859375, 5, 5, 1.5, 5.0, 0.0, 1695519, 2142260.283588889, [170252.46453, 160478.01144000003, 153690.19762999998, 148559.80352000002, 147413.15671, 147761.2876, 152000.09045, 157945.84322, 165725.54083999997], [504057.539, 521525.82019999996, 513385.6525000001, 543310.1250000001, 587785.2741, 628980.9913000001, 677379.6592999999, 731958.2697000001, 778310.4402, 804712.7474, 807942.6457999999, 838327.5813, 857186.1107000001, 883914.2239999999, 911347.5846000001, 946088.7101, 966669.9276999999, 973287.6989, 991954.8213, 1020393.5439, 1037819.7715, 1040035.7118, 1047639.2277999999, 1040833.6826, 1047477.4548, 1049529.7306000001, 1040444.6764, 1031985.8239, 1015624.4602000001, 988175.5103000001, 980094.6733, 978485.9891000001, 935327.2124999999, 918436.3872, 888968.4438999998, 858067.5701, 818989.6833999999, 785456.0658, 754878.4625, 741082.7113999999, 715664.4608000001, 685147.0908, 670682.1723999999, 623609.2965999999, 599443.4315999998, 556943.6801, 520820.7893, 499180.22650000005, 486927.2277, 493581.44730000006, 500352.92020000005], 596404.74006, 932649.3728451612, 919711.2848299999, 1033956.6049899999, 852313.7872727272, 0.3517534985216916, 0.5500671905447012, 0.5424364367665593, 0.6098171739685606, 0.5026860726849579], [482569, 39.40999984741211, -105.05999755859375, 5, 5, 1.5, 5.0, 0.0, 1715950, 2170561.262555556, [174017.7953, 164767.30560000002, 156907.10890000002, 151603.3428, 150645.8963, 150914.4936, 154503.73669999998, 160359.39179999998, 168192.8297], [522625.9267000001, 529018.7329, 543800.4079, 572267.168, 617850.4211, 659149.0978999999, 702336.5734999999, 734047.6340999999, 780798.4182999999, 813690.3182, 831028.7516, 858550.273, 881716.5869999999, 912449.4792999998, 931326.8851, 947172.0518999998, 965912.8568, 1002770.7137000001, 1014832.1797, 1040464.5236000001, 1043952.7968, 1053628.5172000001, 1048255.6452, 1048108.117, 1046922.1526000001, 1054165.8014999998, 1047757.3677000001, 1041743.1593, 1034441.2895000001, 1017109.2232, 1009668.1564999999, 997621.8571000001, 969315.3498, 935042.3004, 904462.7916999998, 874395.0451999998, 844522.9615, 806574.3539, 774462.969, 753360.7132000001, 726674.7214000002, 694621.9077999999, 675100.0205999999, 632199.9235999997, 604103.9667, 561477.2239000001, 533895.2949, 521675.77280000004, 514996.4101000001, 508855.2298, 528677.4899], 612559.3969350001, 948980.9545612902, 938622.43017, 1043608.407, 872372.8381545455, 0.3569797470409978, 0.5530353183724993, 0.5469987063550803, 0.6081811282380023, 0.5083905930560596], [482589, 38.61000061035156, -105.05999755859375, 5, 5, 1.5, 5.0, 0.0, 1824452, 2330060.7328888886, [180637.92659999998, 169980.5449, 162524.74030000003, 156954.45679999999, 155571.5079, 155971.98949999997, 160014.2746, 166057.40129999997, 175178.24060000002], [540562.5029999999, 527693.8774, 565233.0103999999, 597371.895, 640524.6357999999, 681560.1301, 750282.2281000001, 781636.6871999999, 816793.9282, 848393.4524, 853821.1494000001, 885840.0094000001, 914310.4455999999, 946669.1255999999, 978427.7259999999, 993137.3010999999, 1012939.9119000001, 1042917.1983, 1059019.5835, 1069061.8513999998, 1085369.3261, 1094187.0329999998, 1108218.6279, 1114994.0948, 1102209.4290999998, 1107536.7540999998, 1093386.3964999998, 1097074.9382, 1084762.4423, 1070388.7231, 1053056.7619, 1047605.9831000001, 1012279.3322999999, 989570.2187000001, 946850.6971000001, 931347.7748, 892164.0025, 841083.8404, 800157.275, 784487.5698, 749079.6239, 722289.0307000001, 692955.0376, 651261.7557, 647523.498, 603252.2094, 565545.7514000001, 539846.5598, 536840.5952, 533243.9517000001, 532009.4254], 638741.008125, 992321.1337677417, 975614.4302199999, 1095812.7765099998, 913425.7345000001, 0.3501001989227451, 0.5439009268359715, 0.5347438190864983, 0.6006257092595474, 0.5006575862231509], [482609, 37.810001373291016, -105.05999755859375, 5, 5, 1.5, 5.0, 0.0, 1874080, 2379056.290066667, [185951.23080999998, 175166.58452, 166341.78842999996, 161204.78519, 159630.15701999998, 160787.4192, 164743.6825, 171894.63481999998, 180956.29017], [544021.907, 564848.249, 565467.5134, 600904.2753000001, 611009.4577, 687473.317, 743255.6714, 774862.8354, 836791.7764999999, 868568.2679, 880739.4157000001, 919311.5922000001, 948244.7709999998, 980020.3498, 1011302.6277, 1045479.7415, 1071050.8049, 1100394.3541999997, 1126883.8997999998, 1142514.1389000001, 1136844.8290000004, 1147945.4652000002, 1159563.6002999998, 1159554.4797999999, 1157442.7724, 1169495.6369999999, 1159010.183, 1162753.2557, 1143868.6772, 1130223.8441, 1109266.1657, 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1.5, 5.0, 0.0, 1881667, 2390350.8116666665, [191069.70739999998, 181042.66369999998, 171662.823, 165044.5997, 161721.92549999995, 161380.1454, 164219.38340000002, 170315.79330000002, 178544.2832], [556826.509, 574782.2930000001, 577841.655, 601879.892, 630773.089, 675178.9799999999, 736039.125, 760331.3049999999, 844312.334, 892058.906, 927749.3069999999, 963477.821, 973990.378, 1004898.368, 1020195.5290000002, 1039349.113, 1054453.704, 1084237.712, 1108777.286, 1124629.7399999998, 1137209.89, 1154465.6439999999, 1151719.637, 1158364.613, 1164836.6500000001, 1160370.243, 1162627.2060000002, 1153538.338, 1151225.975, 1129453.882, 1134196.738, 1112073.519, 1077688.341, 1050452.312, 1019873.7949999999, 1011009.0249999999, 952755.8820000001, 889102.9500000002, 873201.8580000001, 823861.2400000002, 770921.101, 729760.3160000001, 700173.1980000001, 696123.206, 686949.1830000001, 646933.2049999998, 607643.28, 579225.851, 539603.3890000001, 531468.797, 553556.135], 656073.0324, 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In [ ]:
res2 = [i for i in res if i is not None]
In [ ]:
drop
In [ ]:
type(res)
In [ ]:
res = [ 
       [462542, 37.810001373291016, -105.86000061035156, 5, 5, 1.5, 5.0, 0.0, 1949797, 2481803.971133333, [190489.65363999997, 179046.10642, 170414.79700000002, 165689.92551, 165288.49835999997, 166262.61995000002, 171109.37050000002, 178816.26449000003, 189029.86281], 
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957311.0140000001, 907591.422, 858726.6710000001, 852042.0880000001, 774958.9689999999, 725824.236, 708396.8130000001, 687185.4209999999, 679334.0859999999, 633381.523, 576877.498, 563134.4249999999, 543354.808, 555287.4879999999, 546309.562], 656116.1781, 1061614.3044193548, 1049909.0788000003, 1165577.2136, 977743.6830000001, 0.3445782017778347, 0.5575371560838032, 0.5513898216178235, 0.6121362552872359, 0.5134900972315734], [545008, 37.0099983215332, -102.66000366210938, 5, 5, 1.5, 5.0, 0.0, 1946894, 2480780.749555555, [198195.02250000002, 187314.9206, 176407.09950000004, 168885.0414, 165757.8814, 165563.60049999997, 168785.7604, 174708.18240000002, 184361.1931], [565462.406, 584197.605, 593669.927, 619995.783, 656650.0069999999, 703595.0260000001, 770034.3829999999, 820321.0140000002, 853300.443, 921547.8380000001, 948412.4510000001, 991851.389, 1021172.635, 1038063.7509999999, 1067926.564, 1078712.127, 1111205.978, 1158043.0030000003, 1164664.36, 1185703.039, 1195415.8649999998, 1202427.4290000002, 1211714.443, 1213421.0019999999, 1210874.4409999999, 1226035.868, 1199389.987, 1202811.919, 1188188.274, 1161242.6560000002, 1161339.2020000003, 1134860.5209999997, 1124578.8599999999, 1083791.855, 1058264.8490000002, 1038272.3289999999, 1028487.29, 921760.558, 881433.7989999999, 866642.283, 804337.693, 745522.692, 734945.5560000001, 711012.521, 682348.4809999999, 638384.859, 600650.1270000001, 574274.3880000002, 550875.3290000001, 542730.51, 554440.6140000001], 671197.97545, 1092936.9812903227, 1076575.5297, 1201152.1883999999, 1009433.5671818182, 0.3447532199749961, 0.5613746723192545, 0.5529707984615495, 0.6169581848832036, 0.5184840916772142]]
In [ ]:
type(res)
In [1]:
import pandas as pd
import pickle
In [2]:
dffile = r'C:\Users\sayala\Documents\GitHub\Studies\res_gencum1axis\Pitch_5_Colorado.pkl'
In [4]:
asdf = r'C:\Users\sayala\Documents\GitHub\Studies\RUN.pkl'
In [6]:
foo = pd.read_pickle(asdf)
len(foo)
Out[6]:
1077
In [9]:
pd.__version__
Out[9]:
'1.4.4'
In [10]:
from platform import python_version
print(python_version())
3.9.13
In [7]:
df = pd.read_pickle(dffile)
df
Out[7]:
gid latitude longitude MonthStart MonthEnd hub_height pitch xgap ghi_sum ResultPVWm2Front ... edgemean insidemean testbedAmean testbedBmean testbedCmean edge_normGHI inside_normGHI testbedA_normGHI testbedB_normGHI testbedC_normGHI
0 389877 38.450001 -109.059998 5 5 1.5 5.0 0.0 1827671 2.342394e+06 ... 649014.98005 1.016027e+06 9.410084e+05 1.103454e+06 1.004746e+06 0.355105 0.555913 0.514867 0.603749 0.549741
1 389881 38.290001 -109.059998 5 5 1.5 5.0 0.0 1880502 2.408586e+06 ... 659420.40250 1.053802e+06 9.730318e+05 1.146895e+06 1.042601e+06 0.350662 0.560383 0.517432 0.609887 0.554427
2 393321 40.849998 -108.900002 5 5 1.5 5.0 0.0 1760906 2.258396e+06 ... 613019.20079 9.956603e+05 9.076415e+05 1.087392e+06 9.922851e+05 0.348127 0.565425 0.515440 0.617518 0.563508
3 393325 40.689999 -108.900002 5 5 1.5 5.0 0.0 1792327 2.275458e+06 ... 628579.22475 1.009642e+06 9.260745e+05 1.098530e+06 1.004804e+06 0.350706 0.563313 0.516688 0.612907 0.560614
4 393329 40.529999 -108.900002 5 5 1.5 5.0 0.0 1790827 2.286670e+06 ... 629414.22520 1.016506e+06 9.233647e+05 1.108731e+06 1.017340e+06 0.351466 0.567618 0.515608 0.619117 0.568084
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1072 557665 37.650002 -102.180000 5 5 1.5 5.0 0.0 1914359 2.435057e+06 ... 663095.24355 1.072901e+06 1.056367e+06 1.177191e+06 9.931218e+05 0.346380 0.560449 0.551812 0.614927 0.518775
1073 557669 37.490002 -102.180000 5 5 1.5 5.0 0.0 1915466 2.427051e+06 ... 662650.72230 1.079297e+06 1.058254e+06 1.190080e+06 9.977163e+05 0.345948 0.563465 0.552479 0.621300 0.520874
1074 557673 37.330002 -102.180000 5 5 1.5 5.0 0.0 1928551 2.461948e+06 ... 670615.22040 1.082065e+06 1.084141e+06 1.179458e+06 9.916393e+05 0.347730 0.561077 0.562153 0.611577 0.514189
1075 557677 37.169998 -102.180000 5 5 1.5 5.0 0.0 1919237 2.436401e+06 ... 661001.22360 1.075550e+06 1.064267e+06 1.185310e+06 9.860238e+05 0.344408 0.560405 0.554526 0.617595 0.513758
1076 557681 37.009998 -102.180000 5 5 1.5 5.0 0.0 1920399 2.439543e+06 ... 669429.26885 1.076497e+06 1.063500e+06 1.190020e+06 9.851084e+05 0.348589 0.560559 0.553791 0.619673 0.512971

1077 rows × 22 columns

In [9]:
respi = r'C:\Users\sayala\Documents\GitHub\Studies\389881_hh1.5_rtr5.0_xgap0.0_from_5TO5\results.pkl'
In [12]:
 
Out[12]:
[389881,
 38.290000915527344,
 -109.05999755859375,
 5,
 5,
 1.5,
 5.0,
 0.0,
 1880502,
 2416626.113555555,
 [176997.16689999998,
  167689.1122,
  162916.6025,
  161029.27039999998,
  162650.56550000003,
  166263.3494,
  173416.8767,
  182220.80510000003,
  191611.24860000002],
 [557432.8939999999,
  544970.01,
  573228.691,
  581249.9969999999,
  610456.936,
  642973.5249999999,
  689194.249,
  704542.5469999999,
  721239.0380000001,
  747155.101,
  799900.3790000001,
  856315.127,
  885301.0990000002,
  899611.624,
  968030.3429999999,
  1009817.8970000002,
  1023483.162,
  1080635.747,
  1083671.34,
  1097002.231,
  1107956.3990000002,
  1132726.3490000002,
  1135854.5779999997,
  1156038.464,
  1153404.594,
  1180103.48,
  1151431.9699999997,
  1168072.5899999999,
  1154785.637,
  1139152.487,
  1137583.02,
  1111844.294,
  1087843.748,
  1076737.2230000002,
  1050736.631,
  1033119.2520000001,
  1023644.684,
  1009557.102,
  997512.1079999999,
  970670.0680000001,
  944353.568,
  868246.3389999999,
  828302.6869999999,
  799937.1300000001,
  713334.155,
  661713.4820000001,
  614447.9800000001,
  589609.8779999999,
  583648.1349999999,
  580438.116,
  564592.2590000001],
 658835.65745,
 1052480.5546774196,
 970376.8949000001,
 1147952.6548,
 1040327.427090909,
 0.3503509474863627,
 0.5596806356374093,
 0.5160201344640952,
 0.6104501110873586,
 0.5532179317495589]
In [11]:
uno
Out[11]:
gid latitude longitude MonthStart MonthEnd hub_height pitch xgap ghi_sum ResultPVWm2Front ... edgemean insidemean testbedAmean testbedBmean testbedCmean edge_normGHI inside_normGHI testbedA_normGHI testbedB_normGHI testbedC_normGHI
0 389881 38.290001 -109.059998 5 5 1.5 5.0 0.0 1880502 2.416626e+06 ... 658835.65745 1.052481e+06 970376.8949 1.147953e+06 1.040327e+06 0.350351 0.559681 0.516020 0.610450 0.553218
1 393321 40.849998 -108.900002 5 5 1.5 5.0 0.0 1760906 2.254001e+06 ... 608984.35405 9.985862e+05 911619.7273 1.091857e+06 9.928549e+05 0.345836 0.567087 0.517699 0.620054 0.563832

2 rows × 22 columns

In [8]:
len(foo)
Out[8]:
1077
In [3]:
df = pd.read_pickle(dffile)
In [4]:
df
Out[4]:
gid latitude longitude MonthStart MonthEnd hub_height pitch xgap ghi_sum ResultPVWm2Front ... edgemean insidemean testbedAmean testbedBmean testbedCmean edge_normGHI inside_normGHI testbedA_normGHI testbedB_normGHI testbedC_normGHI
0 389881 38.290001 -109.059998 5 5 1.5 5.0 0.0 1880502 2.416626e+06 ... 658835.65745 1.052481e+06 970376.8949 1.147953e+06 1.040327e+06 0.350351 0.559681 0.516020 0.610450 0.553218
1 393321 40.849998 -108.900002 5 5 1.5 5.0 0.0 1760906 2.254001e+06 ... 608984.35405 9.985862e+05 911619.7273 1.091857e+06 9.928549e+05 0.345836 0.567087 0.517699 0.620054 0.563832

2 rows × 22 columns

In [ ]:
407351, 407371, 407391, 407411, 407431, 425231, 425251, 425271,
425291, 425311, 443525, 443545, 443565, 443585, 443605, 462482,
462502, 462522, 462542, 462562, 482549, 482569, 482589, 482609,
482629, 503093, 503113, 503133, 503153, 503173, 523897, 523917,
523937, 523957, 523977, 544928, 544948, 544968, 544988, 545008
In [ ]:
df.keys()
In [ ]:
 
In [ ]:
res2 = pd.DataFrame(res2, columns=[
        'gid', 'latitude', 'longitude', 'MonthStart', 'MonthEnd',
        'hub_height', 'pitch', 'xgap', 'ghi_sum',
        'ResultPVWm2Front', 'ResultPVWm2Back', 'ResultPVGround',
        'edgemean', 'insidemean', 'testbedAmean', 'testbedBmean', 
        'testbedCmean', 'edge_normGHI', 'inside_normGHI',
        'testbedA_normGHI', 'testbedB_normGHI', 'testbedC_normGHI', 
        ])
In [5]:
res  = [
[394281, 37.290000915527344, -108.86000061035156, 5, 5, 1.5, 5.0, 0.0, 1939152, 2474774.4376666667, 
[180245.16920000003, 170990.22549999997, 165369.1443, 163513.746, 165395.3909, 169579.04340000002, 176848.44150000002, 185881.41019999998, 196401.9209], 
[577748.275, 561196.136, 571398.0809999999, 580208.3339999998, 610593.958, 646178.1799999999, 691668.816, 693050.4890000001, 706443.1799999999, 754635.607, 797385.3080000001, 853092.694, 876772.643, 888211.7549999999, 989409.124, 1021838.6639999999, 1065430.484, 1103807.8979999998, 1131458.8579999998, 1145600.4719999998, 1163886.7840000002, 1155262.4849999999, 1180262.5179999997, 1170491.0330000003, 1194535.544, 1189307.3949999998, 1205785.557, 1195744.9080000003, 1200270.2179999999, 1181321.915, 1174637.769, 1163163.625, 1163884.726, 1129442.0669999998, 1115517.613, 1088059.243, 1070753.9819999998, 1054195.425, 1052950.89, 1021336.201, 943080.1669999999, 910452.4909999999, 859636.9669999997, 833430.0799999998, 740017.1969999999, 669640.245, 632840.2509999999, 608197.238, 595433.2860000001, 568949.637, 556575.1240000001], 668414.6786, 1086674.1279032258, 987300.7899999998, 1183686.8357000002, 1088820.1552727271, 0.3446943192694539, 0.5603862553854602, 0.5091404851192686, 0.6104146738883801, 0.561492938806616], 
[398640, 37.290000915527344, -108.66000366210938, 5, 5, 1.5, 5.0, 0.0, 1943979, 2490914.194666667, 
[181647.2945, 172125.4164, 166594.5221, 164592.21929999997, 165583.52349999995, 169695.2783, 176902.0491, 185982.37820000004, 196694.52719999998], 
[549160.144, 529409.09, 561199.118, 582167.9460000001, 612806.1409999998, 651185.1390000002, 688010.199, 703085.947, 725736.332, 725674.4479999999, 810930.432, 858947.9219999999, 883331.351, 945525.984, 983770.16, 1017985.884, 1053174.3299999998, 1102353.628, 1123393.2829999998, 1140930.405, 1161187.751, 1171155.4239999999, 1193643.048, 1201573.2399999998, 1207136.134, 1211132.644, 1197312.622, 1193752.625, 1189963.79, 1180222.396, 1160493.98, 1143214.3499999999, 1162064.941, 1126607.234, 1117301.876, 1087625.566, 1078880.267, 1072322.09, 1039261.912, 1000495.7769999999, 953029.93, 906732.964, 876446.596, 834598.784, 744691.6059999999, 703165.32, 629676.571, 602797.801, 599535.9789999999, 557909.632, 553831.613], 666891.0684999998, 1089313.5798709677, 992034.3378999999, 1190707.9674, 1085572.5384545454, 0.34305466700000353, 0.560352544894244, 0.5103112419938692, 0.6125107150848852, 0.5584281200849112]
]
In [18]:
from datetime import datetime
now = datetime.now()
In [13]:
from datetime import datetime
In [14]:
 
In [15]:
now
Out[15]:
datetime.datetime(2023, 3, 23, 10, 9, 27, 217806)
In [6]:
type(res)
Out[6]:
list
In [7]:
np.array(res)
C:\Users\sayala\AppData\Local\Temp\1\ipykernel_20580\4118355402.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  np.array(res)
Out[7]:
array([[394281, 37.290000915527344, -108.86000061035156, 5, 5, 1.5, 5.0,
        0.0, 1939152, 2474774.4376666667,
        list([180245.16920000003, 170990.22549999997, 165369.1443, 163513.746, 165395.3909, 169579.04340000002, 176848.44150000002, 185881.41019999998, 196401.9209]),
        list([577748.275, 561196.136, 571398.0809999999, 580208.3339999998, 610593.958, 646178.1799999999, 691668.816, 693050.4890000001, 706443.1799999999, 754635.607, 797385.3080000001, 853092.694, 876772.643, 888211.7549999999, 989409.124, 1021838.6639999999, 1065430.484, 1103807.8979999998, 1131458.8579999998, 1145600.4719999998, 1163886.7840000002, 1155262.4849999999, 1180262.5179999997, 1170491.0330000003, 1194535.544, 1189307.3949999998, 1205785.557, 1195744.9080000003, 1200270.2179999999, 1181321.915, 1174637.769, 1163163.625, 1163884.726, 1129442.0669999998, 1115517.613, 1088059.243, 1070753.9819999998, 1054195.425, 1052950.89, 1021336.201, 943080.1669999999, 910452.4909999999, 859636.9669999997, 833430.0799999998, 740017.1969999999, 669640.245, 632840.2509999999, 608197.238, 595433.2860000001, 568949.637, 556575.1240000001]),
        668414.6786, 1086674.1279032258, 987300.7899999998,
        1183686.8357000002, 1088820.1552727271, 0.3446943192694539,
        0.5603862553854602, 0.5091404851192686, 0.6104146738883801,
        0.561492938806616],
       [398640, 37.290000915527344, -108.66000366210938, 5, 5, 1.5, 5.0,
        0.0, 1943979, 2490914.194666667,
        list([181647.2945, 172125.4164, 166594.5221, 164592.21929999997, 165583.52349999995, 169695.2783, 176902.0491, 185982.37820000004, 196694.52719999998]),
        list([549160.144, 529409.09, 561199.118, 582167.9460000001, 612806.1409999998, 651185.1390000002, 688010.199, 703085.947, 725736.332, 725674.4479999999, 810930.432, 858947.9219999999, 883331.351, 945525.984, 983770.16, 1017985.884, 1053174.3299999998, 1102353.628, 1123393.2829999998, 1140930.405, 1161187.751, 1171155.4239999999, 1193643.048, 1201573.2399999998, 1207136.134, 1211132.644, 1197312.622, 1193752.625, 1189963.79, 1180222.396, 1160493.98, 1143214.3499999999, 1162064.941, 1126607.234, 1117301.876, 1087625.566, 1078880.267, 1072322.09, 1039261.912, 1000495.7769999999, 953029.93, 906732.964, 876446.596, 834598.784, 744691.6059999999, 703165.32, 629676.571, 602797.801, 599535.9789999999, 557909.632, 553831.613]),
        666891.0684999998, 1089313.5798709677, 992034.3378999999,
        1190707.9674, 1085572.5384545454, 0.34305466700000353,
        0.560352544894244, 0.5103112419938692, 0.6125107150848852,
        0.5584281200849112]], dtype=object)
In [ ]:
import pandas as pd
In [ ]:
res2 = pd.DataFrame(res, columns=[
        'gid', 'latitude', 'longitude', 'MonthStart', 'MonthEnd',
        'hub_height', 'pitch', 'xgap', 'ghi_sum',
        'ResultPVWm2Front', 'ResultPVWm2Back', 'ResultPVGround',
        'edgemean', 'insidemean', 'testbedAmean', 'testbedBmean', 
        'testbedCmean', 'edge_normGHI', 'inside_normGHI',
        'testbedA_normGHI', 'testbedB_normGHI', 'testbedC_normGHI', 
        ])
In [ ]:
res2