Method Gencumsky has been modified to divide the yearly-cumulative sky into various skies, each one representing the cumulative irradiance for the hours at which the tracker is at a certain angle. For faster running, for a tracker that moves between 45 and -45 degrees limit angle, if only positions every 5 degrees are considered (45, 40, 35 .... -4-, -45), then only 18 skies (and 18 simulations) will be run for the whole year.
This procedure was presented in:
Ayala Pelaez S, Deline C, Greenberg P, Stein JS, Kostuk RK. Model and validation of single-axis tracking with bifacial PV. IEEE J Photovoltaics. 2019;9(3):715–21. https://ieeexplore.ieee.org/document/8644027 and https://www.nrel.gov/docs/fy19osti/72039.pdf (pre-print, conference version)
Steps:
And finally: Condensed instructions
First let's set the folder where the simulation will be saved. By default, this is the TEMP folder in the bifacial_radiance distribution.
The lines below find the location of the folder relative to this Jupyter Journal. You can alternatively point to an empty directory (it will open a load GUI Visual Interface) or specify any other directory in your computer, for example:
testfolder = r'C:\Users\sayala\Documents\RadianceScenes\Tutorials\Journal2'
import os
from pathlib import Path
testfolder = Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_02'
# 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)
Your simulation will be stored in C:\Users\sayala\Documents\GitHub\bifacial_radiance\bifacial_radiance\TEMP\Tutorial_02
This will load bifacial_radiance and other libraries from python that will be useful for this Jupyter Journal:
from bifacial_radiance import *
import numpy as np
These are all repeated steps from Tutorial 1, so condensing:
# 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)
path = C:\Users\mbrown2\Documents\GitHub\bifacial_radiance\bifacial_radiance\TEMP\Tutorial_02 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
Until now, all the steps looked the same from Tutorial 1. The following section follows similar steps, but the functions are specific for working with single axis tracking.
This function will read the weather file, and based on the sun position it will calculate the angle the tracker should be at for each hour. It will create metdata files for each of the tracker angles considered.
limit_angle = 5 # tracker rotation limit angle. Setting it ridiculously small so this runs faster.
angledelta = 5 # sampling between the limit angles.
backtrack = True
gcr = 0.33
cumulativesky = True # This is important for this example!
trackerdict = demo.set1axis(metdata = metdata, limit_angle = limit_angle, backtrack = backtrack,
gcr = gcr, cumulativesky = cumulativesky)
Saving file EPWs\1axis_-5.0.csv, # points: 2214 Saving file EPWs\1axis_0.0.csv, # points: 57 Saving file EPWs\1axis_5.0.csv, # points: 2096
Setting backtrack to True is important in this step, so the trackers correct for self-shading when following the sun at high zenith angles.
This will create the skies for each sub-metdata file created by set1axis.
trackerdict = demo.genCumSky1axis()
message: There were 2174 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-5.0.rad message: There were 50 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
This is how one of the cumulative sky .cal files associated with each .rad file generated look like:
Each of the values corresponds to the cumulative rradiance of one of those patches, for when the tracker is at that specific angle through the year.
Let's make a more interesting module in this example. Let's do 2-up configuration in portrait, with the modules rotating around a 10 centimeter round torque tube. Let's add a gap between the two modules in 2-UP of 10 centimeters, as well as gap between the torque tube and the modules of 5 centimeters. Along the row, the modules are separated only 2 centimeters for this example. The torquetube is painted Metal_Grey in this example (it's one of the materials available in Ground.rad, and it is 40% reflective).
Note that starting with bifacial_radiance version 0.4.0, the module object has a new geometry generation function addTorquetube. The old way of passing a properly formatted dictionary as a keyword argument will still work too.
x = 0.984 # meters
y = 1.7 # meters
moduletype = 'test-module'
numpanels = 2
zgap = 0.05
ygap = 0.10
xgap = 0.02
module = demo.makeModule(name=moduletype, x=x, y=y,xgap=xgap, ygap=ygap, zgap=zgap,
numpanels=numpanels)
module.addTorquetube(diameter=0.1, material='Metal_Grey', tubetype='round') # New torquetube generation function
print()
print(module)
print()
print(module.torquetube)
Module Name: test-module
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
{'x': 0.984, 'y': 1.7, 'z': 0.02, 'modulematerial': 'black', 'scenex': 1.004, 'sceney': 3.5, 'scenez': 0.1, 'numpanels': 2, 'bifi': 1, 'text': '! genbox black test-module 0.984 1.7 0.02 | xform -t -0.492 -1.75 0.1 -a 2 -t 0 1.8 0\r\n! genrev Metal_Grey tube1 t*1.004 0.05 32 | xform -ry 90 -t -0.502 0 0', 'modulefile': 'objects\\test-module.rad', 'glass': False, 'offsetfromaxis': 0.1, 'xgap': 0.02, 'ygap': 0.1, 'zgap': 0.05}
{'diameter': 0.1, 'tubetype': 'round', 'material': 'Metal_Grey', 'visible': True}
The scene Dictionary specifies the information of the scene. For tracking, different input parameters are expected in the dictionary, such as number of rows, number of modules per row, row azimuth, hub_height (distance between the axis of rotation of the modules and the ground).
hub_height = 2.3
sceneDict = {'gcr': gcr,'hub_height':hub_height, 'nMods': 20, 'nRows': 7}
To make the scene we have to create a Scene Object through the method makeScene1axis. This method will create a .rad file in the objects folder, with the parameters specified in sceneDict and the module created above.
trackerdict = demo.makeScene1axis(trackerdict = trackerdict, module = module, sceneDict = sceneDict)
Making .rad files for cumulativesky 1-axis workflow 3 Radfiles created in /objects/
makeOct1axis joins the sky.rad file, ground.rad file, and the geometry.rad files created in makeScene.
trackerdict = demo.makeOct1axis(trackerdict = trackerdict)
Making 3 octfiles in root directory. Created 1axis_-5.0.oct Created 1axis_0.0.oct Created 1axis_5.0.oct
We can choose to analyze any module in the Scene we have created. The default, if no modWanted or rowWanted is passed, is to sample the center module of the center row.
For this example we will sample row 2, module 9.
modWanted = 9
rowWanted = 2
trackerdict = demo.analysis1axis(trackerdict, modWanted=9, rowWanted = 2)
Linescan in process: 1axis_-5.0_Row2_Module9_Front Linescan in process: 1axis_-5.0_Row2_Module9_Back Saved: results\irr_1axis_-5.0_Row2_Module9.csv Index: -5.0. Wm2Front: 753009.8222222222. Wm2Back: 104733.29185185184 Linescan in process: 1axis_0.0_Row2_Module9_Front Linescan in process: 1axis_0.0_Row2_Module9_Back Saved: results\irr_1axis_0.0_Row2_Module9.csv Index: 0.0. Wm2Front: 1135.0014444444448. Wm2Back: 171.02631481481478 Linescan in process: 1axis_5.0_Row2_Module9_Front Linescan in process: 1axis_5.0_Row2_Module9_Back Saved: results\irr_1axis_5.0_Row2_Module9.csv Index: 5.0. Wm2Front: 843605.862962963. Wm2Back: 115990.78148148149
Let's look at the results with more detail. The analysis1axis routine created individual result .csv files for each angle. To get a single cumulative result .csv where the irradiance is added by sensor, we need to run calculateResults(). This function will save a file named "results/Cumulative_Results.csv" and add a dataframe called "CompiledResults" to the radiance object.
trackerdict = demo.calculateResults()
Bifaciality factor of module stored is 1
We can view the cumlative results in two ways. The first is acess them directly from the RadianceObject.
demo.CompiledResults
| row | module | BGG | Gfront_mean | Grear_mean | POA_eff | Wm2Back | |
|---|---|---|---|---|---|---|---|
| 0 | 2 | 9 | 12.46076 | 1.597535e+06 | 199064.935793 | [1827920.5291916667, 1822498.1192583335, 18181... | [230386.02506666665, 224963.61513333334, 22059... |
resultPath = os.path.join('results','Cumulative_Results.csv')
cumulativeResults = load.read1Result(resultPath)
cumulativeResults
| row | module | BGG | Gfront_mean | Grear_mean | POA_eff | Wm2Back | |
|---|---|---|---|---|---|---|---|
| 0 | 2 | 9 | 12.46076 | 1.597535e+06 | 199064.935793 | [1827920.5291916667, 1822498.1192583335, 18181... | [230386.02506666665, 224963.61513333334, 22059... |
Lets take a closer look at a single result file.
resultPath = os.path.join('results','irr_1axis_-5.0_Row2_Module9.csv')
results = load.read1Result(resultPath)
results
| x | y | z | rearZ | mattype | rearMat | Wm2Front | Wm2Back | Back/FrontRatio | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 22.61734 | -1.004 | 2.298522 | 2.276605 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752540.066667 | 121214.366667 | 0.161074 |
| 1 | 22.26867 | -1.004 | 2.329026 | 2.307110 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752456.566667 | 115861.666667 | 0.153978 |
| 2 | 21.92000 | -1.004 | 2.359531 | 2.337614 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752351.366667 | 110508.733333 | 0.146884 |
| 3 | 21.57134 | -1.004 | 2.390035 | 2.368119 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752246.166667 | 102652.933333 | 0.136462 |
| 4 | 21.22267 | -1.004 | 2.420540 | 2.398623 | a8.1.tube1.16 | sky | 753053.400000 | 96092.410000 | 0.127604 |
| 5 | 20.87400 | -1.004 | 2.451044 | 2.429128 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753757.300000 | 98375.906667 | 0.130514 |
| 6 | 20.52533 | -1.004 | 2.481549 | 2.459632 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753670.433333 | 98988.386667 | 0.131342 |
| 7 | 20.17666 | -1.004 | 2.512053 | 2.490137 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753561.200000 | 98590.556667 | 0.130833 |
| 8 | 19.82799 | -1.004 | 2.542558 | 2.520641 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753451.900000 | 100314.666667 | 0.133140 |
There are various things to notice:
I. The materials column has a specific format that will tell you if you are sampling the correct module:
a{ModWanted}.{rowWanted}.a{numPanel}.{moduletype}.material_key
II. Sensors sample always in the same direction. For this N-S aligned tracker, that is East-most to West. For this 2-up portrait tracker which is 3.5 meters, 20x7 rows and we are sampling module 9 on row 2, the East to West sampling goes from 22.6 m to 19.81 m = 2.79m. It is not exatly 3.5 because the sensors are spaced evenly through the collector width (CW):
III. When there is a ygap in the collector width (2-UP or more configuration), some of the sensors might end up sampling the torque tube, or the sky. You can see that in the materials columns. This also happens if the number of sensors is quite high, the edges of the module might be sampled instead of the sensors. For this reason, before calculating bifacial gain these results must be cleaned. For more advanced simulations, make sure you clean each result csv file individually. We provide some options on load.py but some are very use-specific, so you might have to develop your own cleaning tool (or let us know on issues!)
We have two options for cleaning results. The simplest one is load.cleanResults, but there is also a deepClean for specific purposes.
cleanResults will find materials that should not have values and set them to NaN.
results_clean = load.cleanResult(results)
results_clean
| x | y | z | rearZ | mattype | rearMat | Wm2Front | Wm2Back | Back/FrontRatio | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 22.61734 | -1.004 | 2.298522 | 2.276605 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752540.066667 | 121214.366667 | 0.161074 |
| 1 | 22.26867 | -1.004 | 2.329026 | 2.307110 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752456.566667 | 115861.666667 | 0.153978 |
| 2 | 21.92000 | -1.004 | 2.359531 | 2.337614 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752351.366667 | 110508.733333 | 0.146884 |
| 3 | 21.57134 | -1.004 | 2.390035 | 2.368119 | a8.1.a0.test-module.6457 | a8.1.a0.test-module.2310 | 752246.166667 | 102652.933333 | 0.136462 |
| 4 | 21.22267 | -1.004 | 2.420540 | 2.398623 | a8.1.tube1.16 | sky | NaN | NaN | 0.127604 |
| 5 | 20.87400 | -1.004 | 2.451044 | 2.429128 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753757.300000 | 98375.906667 | 0.130514 |
| 6 | 20.52533 | -1.004 | 2.481549 | 2.459632 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753670.433333 | 98988.386667 | 0.131342 |
| 7 | 20.17666 | -1.004 | 2.512053 | 2.490137 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753561.200000 | 98590.556667 | 0.130833 |
| 8 | 19.82799 | -1.004 | 2.542558 | 2.520641 | a8.1.a1.test-module.6457 | a8.1.a1.test-module.2310 | 753451.900000 | 100314.666667 | 0.133140 |
These are the total irradiance values over all the hours of the year that the module at each sampling point will receive. Dividing the back irradiance average by the front irradiance average will give us the bifacial gain for the year:
Assuming that our module from Prism Solar has a bifaciality factor (rear to front performance) of 90%, our bifacial gain is of:
bifacialityfactor = 0.9
print('Annual bifacial ratio: %0.3f ' %( np.nanmean(results_clean.Wm2Back) * bifacialityfactor / np.nanmean(results_clean.Wm2Front)) )
Annual bifacial ratio: 0.126
Everything we've done so far in super short condensed version:
import pandas as pd
startdate = pd.to_datetime('2021-05-01 6:0:0') #
# pd.to_datetime('2021-06-01 6:0:0'),
# pd.to_datetime('2021-07-01 6:0:0'),
# pd.to_datetime('2021-08-01 6:0:0'),
# pd.to_datetime('2021-09-01 6:0:0'),
# pd.to_datetime('2021-05-01 6:0:0')]
enddate = pd.to_datetime('2021-05-31 6:0:0')# , # May
# pd.to_datetime('2021-06-30 20:0:0'), # June
albedo = 0.25
lat = 37.5
lon = -77.6
nMods = 20
nRows = 7
hub_height = 2.3
gcr = 0.33
moduletype = 'test-module' # this must already exist since we are not calling makeModule in this CONDENSED example.
#testfolder = r'C:\Users\sayala\Documents\RadianceScenes\Tutorials\Journal2'
limit_angle = 50
angeldelta = 5
backtrack = True
gcr = gcr
modWanted = 9
rowWanted = 2
cumulativesky = True
import bifacial_radiance
demo = RadianceObj('test')
demo.setGround(albedo)
epwfile = demo.getEPW(lat, lon)
metdata = demo.readWeatherFile(epwfile, starttime=startdate, endtime=enddate, coerce_year=2021)
demo.set1axis(limit_angle=limit_angle, backtrack=backtrack, gcr=gcr, cumulativesky=cumulativesky)
demo.genCumSky1axis()
sceneDict = {'gcr': gcr,'hub_height':hub_height, 'nMods': nMods, 'nRows': nRows} # orientation deprecated on v.0.2.4.
demo.makeScene1axis(module=moduletype, sceneDict=sceneDict)
demo.makeOct1axis()
demo.analysis1axis(modWanted=modWanted, rowWanted=rowWanted);
path = C:\Users\sayala\Documents\GitHub\bifacial_radiance\docs\tutorials 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 Filtering dates 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_-50.0.csv, # points: 68 Saving file EPWs\1axis_-45.0.csv, # points: 8 Saving file EPWs\1axis_-40.0.csv, # points: 19 Saving file EPWs\1axis_-35.0.csv, # points: 25 Saving file EPWs\1axis_-25.0.csv, # points: 30 Saving file EPWs\1axis_-15.0.csv, # points: 9 Saving file EPWs\1axis_-10.0.csv, # points: 52 Saving file EPWs\1axis_5.0.csv, # points: 30 Saving file EPWs\1axis_10.0.csv, # points: 6 Saving file EPWs\1axis_15.0.csv, # points: 14 Saving file EPWs\1axis_20.0.csv, # points: 39 Saving file EPWs\1axis_35.0.csv, # points: 30 Saving file EPWs\1axis_45.0.csv, # points: 20 Saving file EPWs\1axis_50.0.csv, # points: 70 message: There were 66 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-50.0.rad message: There were 8 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-45.0.rad message: There were 19 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-40.0.rad message: There were 25 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-35.0.rad message: There were 30 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-25.0.rad message: There were 8 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-15.0.rad message: There were 48 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_-10.0.rad message: There were 30 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_5.0.rad message: There were 5 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_10.0.rad message: There were 13 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_15.0.rad message: There were 39 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_20.0.rad message: There were 30 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_35.0.rad message: There were 20 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_45.0.rad message: There were 70 sun up hours in this climate file Total Ibh/Lbh: 0.000000 Created skyfile skies\1axis_50.0.rad Making .rad files for cumulativesky 1-axis workflow 14 Radfiles created in /objects/ Making 14 octfiles in root directory. Created 1axis_-50.0.oct Created 1axis_-45.0.oct Created 1axis_-40.0.oct Created 1axis_-35.0.oct Created 1axis_-25.0.oct Created 1axis_-15.0.oct Created 1axis_-10.0.oct Created 1axis_5.0.oct Created 1axis_10.0.oct Created 1axis_15.0.oct Created 1axis_20.0.oct Created 1axis_35.0.oct Created 1axis_45.0.oct Created 1axis_50.0.oct Linescan in process: 1axis_-50.0_Row2_Module9_Front Linescan in process: 1axis_-50.0_Row2_Module9_Back Saved: results\irr_1axis_-50.0_Row2_Module9.csv Index: -50.0. Wm2Front: 27517.084714814817. Wm2Back: 2826.930932222222 Linescan in process: 1axis_-45.0_Row2_Module9_Front Linescan in process: 1axis_-45.0_Row2_Module9_Back Saved: results\irr_1axis_-45.0_Row2_Module9.csv Index: -45.0. Wm2Front: 1731.6319987407405. Wm2Back: 133.31039248148147 Linescan in process: 1axis_-40.0_Row2_Module9_Front Linescan in process: 1axis_-40.0_Row2_Module9_Back Saved: results\irr_1axis_-40.0_Row2_Module9.csv Index: -40.0. Wm2Front: 8889.726928703703. Wm2Back: 879.6606937037037 Linescan in process: 1axis_-35.0_Row2_Module9_Front Linescan in process: 1axis_-35.0_Row2_Module9_Back Saved: results\irr_1axis_-35.0_Row2_Module9.csv Index: -35.0. Wm2Front: 10845.10608074074. Wm2Back: 1156.813395925926 Linescan in process: 1axis_-25.0_Row2_Module9_Front Linescan in process: 1axis_-25.0_Row2_Module9_Back Saved: results\irr_1axis_-25.0_Row2_Module9.csv Index: -25.0. Wm2Front: 18399.76553688889. Wm2Back: 2199.2048133333333 Linescan in process: 1axis_-15.0_Row2_Module9_Front Linescan in process: 1axis_-15.0_Row2_Module9_Back Saved: results\irr_1axis_-15.0_Row2_Module9.csv Index: -15.0. Wm2Front: 242.22281503703704. Wm2Back: 32.28069451481482 Linescan in process: 1axis_-10.0_Row2_Module9_Front Linescan in process: 1axis_-10.0_Row2_Module9_Back Saved: results\irr_1axis_-10.0_Row2_Module9.csv Index: -10.0. Wm2Front: 18715.446807037035. Wm2Back: 2381.9418274074073 Linescan in process: 1axis_5.0_Row2_Module9_Front Linescan in process: 1axis_5.0_Row2_Module9_Back Saved: results\irr_1axis_5.0_Row2_Module9.csv Index: 5.0. Wm2Front: 19061.03714637037. Wm2Back: 2263.7957174074077 Linescan in process: 1axis_10.0_Row2_Module9_Front Linescan in process: 1axis_10.0_Row2_Module9_Back Saved: results\irr_1axis_10.0_Row2_Module9.csv Index: 10.0. Wm2Front: 479.18399271481474. Wm2Back: 28.480026422222224 Linescan in process: 1axis_15.0_Row2_Module9_Front Linescan in process: 1axis_15.0_Row2_Module9_Back Saved: results\irr_1axis_15.0_Row2_Module9.csv Index: 15.0. Wm2Front: 826.463565. Wm2Back: 66.72499407407406 Linescan in process: 1axis_20.0_Row2_Module9_Front Linescan in process: 1axis_20.0_Row2_Module9_Back Saved: results\irr_1axis_20.0_Row2_Module9.csv Index: 20.0. Wm2Front: 20060.8926242963. Wm2Back: 2241.101145925926 Linescan in process: 1axis_35.0_Row2_Module9_Front Linescan in process: 1axis_35.0_Row2_Module9_Back Saved: results\irr_1axis_35.0_Row2_Module9.csv Index: 35.0. Wm2Front: 18507.62126888889. Wm2Back: 1924.670081111111 Linescan in process: 1axis_45.0_Row2_Module9_Front Linescan in process: 1axis_45.0_Row2_Module9_Back Saved: results\irr_1axis_45.0_Row2_Module9.csv Index: 45.0. Wm2Front: 9566.065326925927. Wm2Back: 825.9953966666667 Linescan in process: 1axis_50.0_Row2_Module9_Front Linescan in process: 1axis_50.0_Row2_Module9_Back Saved: results\irr_1axis_50.0_Row2_Module9.csv Index: 50.0. Wm2Front: 31832.450419259258. Wm2Back: 2820.5362711111106
res = demo.calculateResults(bifacialityfactor=1.0)
res
{-10.0: {'csvfile': 'EPWs\\1axis_-10.0.csv',
'surf_azm': 90.0,
'surf_tilt': 10.0,
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'octfile': '1axis_20.0.oct',
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'skyfile': 'skies\\1axis_-15.0.rad',
'radfile': 'objects\\1axis-15.0__C_1.86907_rtr_10.09100_tilt_15.00000_20modsx7rows_origin0,0.rad',
'scene': {'module': {'x': 1.0, 'y': 1.59, 'z': 0.02, 'modulematerial': 'black', 'scenex': 1.05, 'sceney': 3.33, 'scenez': 0.0, 'numpanels': 2, 'bifi': 0.9, 'text': '! genbox black test-module 1.0 1.59 0.02 | xform -t -0.5 -1.665 0 -a 2 -t 0 1.74 0\n!xform -rz 0 C:\\Users\\sayala\\Documents\\GitHub\\bifacial_radiance\\tests\\customTT.rad | xform -rz 90 -t 0.5 0 -0.15', 'modulefile': 'objects\\test-module.rad', 'glass': False, 'offsetfromaxis': 0, 'xgap': 0.05, 'ygap': 0.15, 'zgap': 0.0}, 'modulefile': 'objects\\test-module.rad', 'hpc': False, 'name': 'Scene0', 'gcr': 0.32999702705381034, 'text': '!xform -rx 15.0 -t 0 0 2.3 -a 20 -t 1.05 0 0 -a 7 -t 0 10.091 0 -i 1 -t -9.450000000000001 -30.272999999999996 0 -rz 90.0 -t 0 0 0 objects\\test-module.rad', 'radfiles': 'objects\\1axis-15.0__C_1.86907_rtr_10.09100_tilt_15.00000_20modsx7rows_origin0,0.rad', 'sceneDict': {'tilt': 15.0, 'gcr': 0.33, 'clearance_height': 1.869066289904303, 'azimuth': 90.0, 'nMods': 20, 'nRows': 7, 'modulez': 0.02, 'axis_tilt': 0, 'originx': 0, 'originy': 0}},
'octfile': '1axis_-15.0.oct',
'Results': [{'rowWanted': 2,
'modWanted': 9,
'AnalysisObj': {'octfile': '1axis_-15.0.oct', 'name': '1axis_-15.0', 'hpc': False, 'modWanted': 9, 'rowWanted': 2, 'x': [21.47387, 21.15221, 20.83056, 20.50891, 20.18725, 19.8656, 19.54395, 19.22229, 18.90064], 'y': [-1.05, -1.05, -1.05, -1.05, -1.05, -1.05, -1.05, -1.05, -1.05], 'z': [1.975537, 2.061724, 2.147911, 2.234098, 2.320284, 2.406471, 2.492658, 2.578845, 2.665031], 'rearZ': [1.954287, 2.040474, 2.126661, 2.212847, 2.299034, 2.385221, 2.471408, 2.557594, 2.643781], 'mattype': ['a8.1.a0.test-module.6457', 'a8.1.a0.test-module.6457', 'a8.1.a0.test-module.6457', 'a8.1.a0.test-module.6457', 'a8.1.triangle_3', 'a8.1.a1.test-module.6457', 'a8.1.a1.test-module.6457', 'a8.1.a1.test-module.6457', 'a8.1.a1.test-module.6457'], 'rearMat': ['a8.1.a0.test-module.2310', 'a8.1.a0.test-module.2310', 'a8.1.a0.test-module.2310', 'a8.1.a0.test-module.2310', 'a8.1.triangle_2', 'a8.1.a1.test-module.2310', 'a8.1.a1.test-module.2310', 'a8.1.a1.test-module.2310', 'a8.1.a1.test-module.2310'], 'Wm2Front': [264.48873333333336, 266.5425, 262.04519999999997, 262.3268666666666, 0.5987686666666666, 278.683, 281.45250000000004, 282.1493666666667, 281.71840000000003], 'Wm2Back': [37.9955, 36.95895333333333, 35.468806666666666, 28.992590000000003, 0.44985729999999996, 34.19477333333334, 37.31231, 39.20162333333334, 39.95183666666667], 'Back/FrontRatio': [0.14365585960992563, 0.1386601186422979, 0.135353257046531, 0.11052043524159848, 0.750051353132819, 0.12270088463397016, 0.13257006930096799, 0.1389387644484129, 0.14181428991637307], 'backRatio': [0.14365585960992563, 0.1386601186422979, 0.135353257046531, 0.11052043524159848, 0.750051353132819, 0.12270088463397016, 0.13257006930096799, 0.1389387644484129, 0.14181428991637307], 'rearX': [21.46817, 21.14652, 20.82487, 20.50321, 20.18156, 19.85991, 19.53825, 19.2166, 18.89495], 'rearY': [-1.05, -1.05, -1.05, -1.05, -1.05, -1.05, -1.05, -1.05, -1.05]},
'Wm2Front': [264.48873333333336,
266.5425,
262.04519999999997,
262.3268666666666,
0.5987686666666666,
278.683,
281.45250000000004,
282.1493666666667,
281.71840000000003],
'Wm2Back': [37.9955,
36.95895333333333,
35.468806666666666,
28.992590000000003,
0.44985729999999996,
34.19477333333334,
37.31231,
39.20162333333334,
39.95183666666667],
'backRatio': [0.14365585960992563,
0.1386601186422979,
0.135353257046531,
0.11052043524159848,
0.750051353132819,
0.12270088463397016,
0.13257006930096799,
0.1389387644484129,
0.14181428991637307]}]}}