Results Comparison
If you want to compare results with other people in the group, you have to adhere to the following format.
You have to create an output in XML following
XML Output
Implementation Certification
For implementation to be EMVA1288 certified, it has to be possible to output the results in a XML file.
The XML output file MUST contain all values specified in [[Naming convention]] Only Short, Symbol and Comment fields are optional If the computer names does not correspond exactly to what it is specified in [[Naming convention]], the test for certification will fail even if the values are correct.
Example
The following is a valid XML output. Only Short, Symbol and Comment fields are optional:
<results>
<info>
<index_start>
<Comment></Comment>
<Short>The array indexes start at</Short>
<Symbol></Symbol>
<Value>0</Value>
<Unit></Unit>
</index_start>
</info>
<sensitivity>
<u_p_sat>
<Comment></Comment>
<Short>Saturation Capacity (photons)</Short>
<LatexName>UPSat</LatexName>
<Value>30957.0</Value>
<Symbol>$\mu_{p.sat}$</Symbol>
<Unit>$p$</Unit>
</u_p_sat>
<SNR_max_dB>
<Comment></Comment>
<Short>Maximum Signal to Noise Ratio in Db</Short>
<LatexName>SNRMaxDB</LatexName>
<Value>95.4485571343</Value>
<Symbol>$SNR_{max.dB}$</Symbol>
<Unit>dB</Unit>
</SNR_max_dB>
<DR_dB>
<Comment></Comment>
<Short>Dynamic Range in dB</Short>
<LatexName>DRDB</LatexName>
<Value>142.143573793</Value>
<Symbol>$DR_{dB}$</Symbol>
<Unit>dB</Unit>
</DR_dB>
<SNR_max>
<Comment></Comment>
<Short>Maximum Signal to Noise Ratio</Short>
<LatexName>SNRMax</LatexName>
<Value>118.205880802</Value>
<Symbol>$SNR_{max}$</Symbol>
<Unit></Unit>
</SNR_max>
<sigma_y_dark>
<Comment></Comment>
<Short>Temporal Dark Noise (DN)</Short>
<LatexName>SigmaYDark</LatexName>
<Value>3.07341849276</Value>
<Symbol>$\sigma_{y.dark}$</Symbol>
<Unit>DN</Unit>
</sigma_y_dark>
<K>
<Comment>Slope of (s2_y - s2_y_dark) Vs (u_y - u_y_dark). Fit with offset = 0</Comment>
<Short>Overall system gain</Short>
<LatexName>K</LatexName>
<Value>0.280769280926</Value>
<Symbol>K</Symbol>
<Unit>$DN/e^-$</Unit>
</K>
<u_e_sat>
<Comment>Number of electrons at saturation</Comment>
<Short>Saturation Capacity (electrons)</Short>
<LatexName>UESat</LatexName>
<Value>13972.6302563</Value>
<Symbol>$\mu_{e.sat}$</Symbol>
<Unit>$e^-$</Unit>
</u_e_sat>
<inverse_SNR_max>
<Comment></Comment>
<Short>Inverse Maximum Signal to Noise Ratio</Short>
<LatexName>InvSNRMax</LatexName>
<Value>0.845981598556</Value>
<Symbol>$SNR_{max}^{-1}$</Symbol>
<Unit>\%</Unit>
</inverse_SNR_max>
<index_sensitivity_min>
<Comment>Index for linear fits in sensitivity part of the standard (70% of saturation)</Comment>
<Short>Sensitivity fit minimum index</Short>
<Symbol></Symbol>
<Value>0</Value>
<Unit></Unit>
</index_sensitivity_min>
<u_p_min>
<Comment></Comment>
<Short>Absolute sensitivity threshold</Short>
<LatexName>UPMin</LatexName>
<Value>25.3600660517</Value>
<Symbol>$\mu_{p.min}$</Symbol>
<Unit>$p$</Unit>
</u_p_min>
<SNR_max_bit>
<Comment></Comment>
<Short>Maximum Signal to Noise Ratio in Bits</Short>
<LatexName>SNRMaxBit</LatexName>
<Value>6.88515800189</Value>
<Symbol>$SNR_{max.bit}$</Symbol>
<Unit>bit</Unit>
</SNR_max_bit>
<QE>
<Comment></Comment>
<Short>Quantum efficiency</Short>
<LatexName>QE</LatexName>
<Value>45.1356082834</Value>
<Symbol>$\eta$</Symbol>
<Unit>\%</Unit>
</QE>
<index_u_ysat>
<Comment>Index of saturation</Comment>
<Short>Saturation Point</Short>
<Symbol></Symbol>
<Value>36</Value>
<Unit></Unit>
</index_u_ysat>
<R>
<Comment>Slope of the (u_y - u_y_dark) Vs u_p. Fit with offset = 0</Comment>
<Short>Responsivity</Short>
<Symbol>R</Symbol>
<Value>0.126726922819</Value>
<Unit>DN/p</Unit>
</R>
<inverse_K>
<Comment></Comment>
<Short>Inverse Overall system gain</Short>
<LatexName>InvK</LatexName>
<Value>3.56164319936</Value>
<Symbol>1/K</Symbol>
<Unit>$e^-/DN$</Unit>
</inverse_K>
<DR>
<Comment></Comment>
<Short>Dynamic Range</Short>
<LatexName>DR</LatexName>
<Value>1220.69871336</Value>
<Symbol>DR</Symbol>
<Unit></Unit>
</DR>
<index_sensitivity_max>
<Comment>Index for linear fits in sensitivity part of the standard (70% of saturation)</Comment>
<Short>Sensitivity fit maximum index</Short>
<Symbol></Symbol>
<Value>25</Value>
<Unit></Unit>
</index_sensitivity_max>
<sigma_d>
<Comment></Comment>
<Short>Temporal Dark Noise (electrons)</Short>
<LatexName>SigmaDark</LatexName>
<Value>10.8980275235</Value>
<Symbol>$\sigma_d$</Symbol>
<Unit>$e^-$</Unit>
</sigma_d>
</sensitivity>
<linearity>
<LE_max>
<Comment></Comment>
<Short>Min Linearity error</Short>
<LatexName>LEMax</LatexName>
<Value>0.480448332752</Value>
<Symbol>$LE_{max}$</Symbol>
<Unit>\%</Unit>
</LE_max>
<index_linearity_max>
<Comment></Comment>
<Short>Linearity fit maximum index</Short>
<Symbol></Symbol>
<Value>34</Value>
<Unit></Unit>
</index_linearity_max>
<LE_min>
<Comment></Comment>
<Short>Min Linearity error</Short>
<LatexName>LEMin</LatexName>
<Value>-0.615797581149</Value>
<Symbol>$LE_{min}$</Symbol>
<Unit>\%</Unit>
</LE_min>
<index_linearity_min>
<Comment>Minimum index for linear fit in (5% of saturation)</Comment>
<Short>Linearity fit minimun index</Short>
<Symbol></Symbol>
<Value>2</Value>
<Unit></Unit>
</index_linearity_min>
</linearity>
<defect_pixel>
<histogram_DSNU_accumulated>
<Comment></Comment>
<Short>accumulated DSNU histogram</Short>
<Symbol></Symbol>
<Data>
<model>3757.00935672 4101.64070402 4455.78801661 4816.62679352 5180.99344019 5545.42271225 5906.19579952 6259.39838039 6600.98757268 6926.86631846 7232.963385 7515.31686216 7770.15880306 7993.99850479 8183.70186994 8336.56433411 8450.37499043 8523.46978743 8554.77201146 8543.81867712 8490.77192172 8396.41501178 8262.13309909 8089.87938703 7882.12786246 7641.81419157 7372.26675281 7077.13006945 6760.28309878 6425.75492877 6077.6404245 5720.01826098 5356.87358427 4992.02727175 4629.07343078 4271.32640012 3921.77811924 3583.06632612 3257.45365063 2946.81730581 2652.64875487 2376.06245886 2117.81259542 1878.316486 1657.68337891 1455.7472044 1272.10194243 1106.13831509 957.080626731 824.022715463 705.962141098 601.831906654 510.52918564 430.940697174 361.964529679 302.528356504 251.604109886 208.219281222 171.465094898 140.501860321 114.561843655 92.9500190119 75.0430610573 60.2869298968 48.193377575 38.3356763541 30.3438347516 23.8995304477 18.7309515951 14.6077014356 11.3358866655 8.75347863094 6.72600871485 5.14263551452 3.9126016422 2.96208209014 2.23141381123 1.6726871191 1.24767328881 0.926058903488 0.68395560804 0.502653588461 0.367587918504 0.267488576687 0.19368715495 0.139555825209 0.100056818037 0.0713833581697 0.0506755921172 0.0357974637403 0.0251627007649 0.017600046585 0.012249601225 0.0084836287178 0.00584645922932 0.00400918195088 0.00273571074863 0.00185753136727 0.00125502883719 0.000843767556988 0.00056447343695 0.000375764515328 0.000248908421823 0.000164064638442 0.000107607353265 7.02296465064e-05 4.56090036058e-05 2.94735369289e-05 1.89524559686e-05 1.21269142316e-05 7.72123347477e-06 4.89186690944e-06 3.08399844298e-06 1.93466260163e-06 1.20766892688e-06 7.50139642056e-07 4.63647487684e-07 2.8515781218e-07 1.74515580553e-07 1.06275879537e-07 6.44001351203e-08 3.88320589661e-08 2.32994477584e-08 1.39108095813e-08 8.26438904302e-09 4.88563108121e-09 2.87396950541e-09 1.68226810385e-09 9.79850536792e-10 5.67905439236e-10 3.27524498862e-10 1.87958998605e-10 1.07333186654e-10 6.09896995037e-11 3.44850240252e-11</model>
<values>307200 301675 290902 280032 269321 258574 247859 237395 227137 216991 206947 197210 187480 178102 168990 160085 151410 142954 134777 126945 119300 111985 104807 98141 91843 85763 79915 74366 69130 64301 59605 55211 51124 47153 43482 39992 36796 33908 31080 28467 26049 23761 21715 19835 18087 16478 15024 13647 12485 11300 10282 9314 8458 7659 6915 6277 5699 5144 4693 4250 3875 3510 3203 2932 2671 2446 2225 2030 1856 1700 1564 1451 1329 1224 1129 1031 923 856 787 720 652 603 551 507 463 430 393 357 333 306 292 275 250 224 213 196 178 154 142 135 124 115 111 100 90 82 75 72 62 56 48 39 35 32 29 27 25 21 20 19 17 15 15 14 13 12 8 5 3 3 3 2 2 1 1</values>
<bins>0.0 0.0008 0.0016 0.0024 0.0032 0.004 0.0048 0.0056 0.0064 0.0072 0.008 0.0088 0.0096 0.0104 0.0112 0.012 0.0128 0.0136 0.0144 0.0152 0.016 0.0168 0.0176 0.0184 0.0192 0.02 0.0208 0.0216 0.0224 0.0232 0.024 0.0248 0.0256 0.0264 0.0272 0.028 0.0288 0.0296 0.0304 0.0312 0.032 0.0328 0.0336 0.0344 0.0352 0.036 0.0368 0.0376 0.0384 0.0392 0.04 0.0408 0.0416 0.0424 0.0432 0.044 0.0448 0.0456 0.0464 0.0472 0.048 0.0488 0.0496 0.0504 0.0512 0.052 0.0528 0.0536 0.0544 0.0552 0.056 0.0568 0.0576 0.0584 0.0592 0.06 0.0608 0.0616 0.0624 0.0632 0.064 0.0648 0.0656 0.0664 0.0672 0.068 0.0688 0.0696 0.0704 0.0712 0.072 0.0728 0.0736 0.0744 0.0752 0.076 0.0768 0.0776 0.0784 0.0792 0.08 0.0808 0.0816 0.0824 0.0832 0.084 0.0848 0.0856 0.0864 0.0872 0.088 0.0888 0.0896 0.0904 0.0912 0.092 0.0928 0.0936 0.0944 0.0952 0.096 0.0968 0.0976 0.0984 0.0992 0.1 0.1008 0.1016 0.1024 0.1032 0.104 0.1048 0.1056 0.1064 0.1072</bins>
</Data>
<Unit></Unit>
</histogram_DSNU_accumulated>
<histogram_PRNU_accumulated>
<Comment></Comment>
<Short>accumulated PRNU histogram</Short>
<Symbol></Symbol>
<Data>
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<values>302736 297461 292153 286874 281665 276394 271244 266010 260808 255556 250365 245295 240212 235068 230142 225149 220265 215205 210386 205504 200637 195790 191173 186587 181834 177388 172976 168504 164231 159906 155700 151552 147404 143294 139326 135361 131502 127587 123878 120160 116577 113018 109558 106121 102688 99403 96147 93022 89903 86908 84025 81253 78421 75652 73009 70390 67825 65407 63051 60752 58513 56226 54103 51969 49974 48011 46113 44201 42369 40670 38945 37359 35736 34168 32647 31261 29892 28535 27192 26002 24857 23760 22660 21617 20457 19508 18581 17709 16846 16072 15283 14523 13790 13081 12449 11828 11238 10685 10104 9547 9019 8545 8082 7632 7160 6775 6379 6029 5706 5370 5066 4756 4507 4278 4049 3824 3577 3369 3184 2979 2783 2613 2434 2311 2174 2035 1917 1811 1710 1600 1498 1406 1320 1235 1161 1084 1016 943 890 832 781 729 677 637 593 559 519 493 466 426 388 367 340 319 291 271 259 240 219 209 197 185 172 155 141 137 129 125 113 101 94 84 81 74 72 71 63 53 51 47 44 42 39 36 35 31 30 26 25 22 19 18 18 18 18 17 15 14 14 13 13 12 10 9 8 7 7 6 6 6 6 5 5 4 4 4 4 4 4 4 4 4 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1</values>
<bins>0.0 0.1304 0.2608 0.3912 0.5216 0.652 0.7824 0.9128 1.0432 1.1736 1.304 1.4344 1.5648 1.6952 1.8256 1.956 2.0864 2.2168 2.3472 2.4776 2.608 2.7384 2.8688 2.9992 3.1296 3.26 3.3904 3.5208 3.6512 3.7816 3.912 4.0424 4.1728 4.3032 4.4336 4.564 4.6944 4.8248 4.9552 5.0856 5.216 5.3464 5.4768 5.6072 5.7376 5.868 5.9984 6.1288 6.2592 6.3896 6.52 6.6504 6.7808 6.9112 7.0416 7.172 7.3024 7.4328 7.5632 7.6936 7.824 7.9544 8.0848 8.2152 8.3456 8.476 8.6064 8.7368 8.8672 8.9976 9.128 9.2584 9.3888 9.5192 9.6496 9.78 9.9104 10.0408 10.1712 10.3016 10.432 10.5624 10.6928 10.8232 10.9536 11.084 11.2144 11.3448 11.4752 11.6056 11.736 11.8664 11.9968 12.1272 12.2576 12.388 12.5184 12.6488 12.7792 12.9096 13.04 13.1704 13.3008 13.4312 13.5616 13.692 13.8224 13.9528 14.0832 14.2136 14.344 14.4744 14.6048 14.7352 14.8656 14.996 15.1264 15.2568 15.3872 15.5176 15.648 15.7784 15.9088 16.0392 16.1696 16.3 16.4304 16.5608 16.6912 16.8216 16.952 17.0824 17.2128 17.3432 17.4736 17.604 17.7344 17.8648 17.9952 18.1256 18.256 18.3864 18.5168 18.6472 18.7776 18.908 19.0384 19.1688 19.2992 19.4296 19.56 19.6904 19.8208 19.9512 20.0816 20.212 20.3424 20.4728 20.6032 20.7336 20.864 20.9944 21.1248 21.2552 21.3856 21.516 21.6464 21.7768 21.9072 22.0376 22.168 22.2984 22.4288 22.5592 22.6896 22.82 22.9504 23.0808 23.2112 23.3416 23.472 23.6024 23.7328 23.8632 23.9936 24.124 24.2544 24.3848 24.5152 24.6456 24.776 24.9064 25.0368 25.1672 25.2976 25.428 25.5584 25.6888 25.8192 25.9496 26.08 26.2104 26.3408 26.4712 26.6016 26.732 26.8624 26.9928 27.1232 27.2536 27.384 27.5144 27.6448 27.7752 27.9056 28.036 28.1664 28.2968 28.4272 28.5576 28.688 28.8184 28.9488 29.0792 29.2096 29.34 29.4704 29.6008 29.7312 29.8616 29.992 30.1224 30.2528 30.3832 30.5136 30.644 30.7744 30.9048 31.0352 31.1656 31.296 31.4264 31.5568 31.6872 31.8176 31.948 32.0784 32.2088 32.3392 32.4696 32.6 32.7304 32.8608 32.9912</bins>
</Data>
<Unit></Unit>
</histogram_PRNU_accumulated>
<histogram_PRNU>
<Comment></Comment>
<Short>PRNU histogram</Short>
<Symbol></Symbol>
<Data>
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</Data>
<Unit></Unit>
</histogram_PRNU>
<histogram_DSNU>
<Comment></Comment>
<Short>DSNU histogram</Short>
<Symbol></Symbol>
<Data>
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</Data>
<Unit></Unit>
</histogram_DSNU>
</defect_pixel>
<spatial>
<s_2_y>
<Comment>s_2_y_measured - sigma_2_y_stack / number_images</Comment>
<Short>Spatial variance from image</Short>
<Symbol>$s^2_{y}$</Symbol>
<Value>72.4247416075</Value>
<Unit>DN2</Unit>
</s_2_y>
<F_50>
<Comment></Comment>
<Short>Non Whiteness factor 50%</Short>
<Symbol>$F_{50\%}$</Symbol>
<Value>1.05226771296</Value>
<Unit></Unit>
</F_50>
<s_2_y_measured_spectrogram>
<Comment></Comment>
<Short>Spatial variance measured from spectrogram</Short>
<Symbol>$s^2_{y.measured.spectrogram}$</Symbol>
<Value>40.5156503886</Value>
<Unit>DN2</Unit>
</s_2_y_measured_spectrogram>
<s_2_y_spectrogram_dark>
<Comment></Comment>
<Short>Spatial variance from spectrogram dark</Short>
<Symbol>$s^2_{y.spectrogram.dark}$</Symbol>
<Value>0.0240017725878</Value>
<Unit>DN2</Unit>
</s_2_y_spectrogram_dark>
<PRNU1288>
<Comment></Comment>
<Short>PRNU</Short>
<LatexName>PRNU</LatexName>
<Value>0.432704411062</Value>
<Symbol>$PRNU_{1288}$</Symbol>
<Unit>\%</Unit>
</PRNU1288>
<s_2_y_spectrogram>
<Comment></Comment>
<Short>Spatial variance from spectrogram</Short>
<Symbol>$s^2_{y.spectrogram}$</Symbol>
<Value>29.4441754554</Value>
<Unit>DN2</Unit>
</s_2_y_spectrogram>
<s_2_y_measured_dark>
<Comment>Variance value of the dark variance image</Comment>
<Short>Spatial variance measured dark</Short>
<Symbol>$s^2_{y.measured.dark}$</Symbol>
<Value>0.213951679236</Value>
<Unit>DN2</Unit>
</s_2_y_measured_dark>
<sigma_2_y_stack_dark>
<Comment>Mean value of the dark variance image.</Comment>
<Short>Temporal variance stack dark</Short>
<Symbol>$\sigma^2_{y.stack.dark}$</Symbol>
<Value>9.49749533243</Value>
<Unit>DN2</Unit>
</sigma_2_y_stack_dark>
<s_2_y_measured_spectrogram_dark>
<Comment></Comment>
<Short>Spatial variance measured dark from spectrogram</Short>
<Symbol>$s^2_{y.measured.spectrogram.dark}$</Symbol>
<Value>0.213951679236</Value>
<Unit>DN2</Unit>
</s_2_y_measured_spectrogram_dark>
<s_2_y_dark>
<Comment>s_2_y_measured - sigma_2_y_stack / number_images</Comment>
<Short>Spatial variance from image</Short>
<Symbol>$s^2_{y}$</Symbol>
<Value>0.0240017725878</Value>
<Unit>DN2</Unit>
</s_2_y_dark>
<s_2_y_measured>
<Comment>Variance value of the bright variance image</Comment>
<Short>Spatial variance measure</Short>
<Symbol>$s^2_{y.measured}$</Symbol>
<Value>83.4962165406</Value>
<Unit>DN2</Unit>
</s_2_y_measured>
<F_dark>
<Comment></Comment>
<Short>Non Whiteness factor Dark</Short>
<Symbol>$F_{dark}$</Symbol>
<Value>1.13639226727</Value>
<Unit></Unit>
</F_dark>
<DSNU1288>
<Comment></Comment>
<Short>DSNU</Short>
<LatexName>DSNU</LatexName>
<Value>0.551787767644</Value>
<Symbol>$DSNU_{1288}$</Symbol>
<Unit>$e^-$</Unit>
</DSNU1288>
<DSNU1288_DN>
<Comment></Comment>
<Short>DSNU in DN</Short>
<LatexName>DSNUDN</LatexName>
<Value>0.154925054745</Value>
<Symbol>$DSNU_{1288.DN}$</Symbol>
<Unit>DN</Unit>
</DSNU1288_DN>
<sigma_2_y_stack>
<Comment>Mean value of the bright variance image</Comment>
<Short>Temporal variance stack</Short>
<Symbol>$\sigma^2_{y.stack}$</Symbol>
<Value>553.573746657</Value>
<Unit>DN2</Unit>
</sigma_2_y_stack>
</spatial>
<dark_current>
<u_I_var>
<Comment></Comment>
<Short>Dark Current from variance</Short>
<LatexName>UIVar</LatexName>
<Value>8.26281507974</Value>
<Symbol>$\mu_{I.var}$</Symbol>
<Unit>$e^-/s$</Unit>
</u_I_var>
<u_I_mean>
<Comment></Comment>
<Short>Dark Current from mean</Short>
<Symbol>$\mu_{I.mean}$</Symbol>
<Value>28.4074312749</Value>
<Unit>e/s</Unit>
</u_I_mean>
</dark_current>
</results>
Naming Convention
For details about the naming convention for the XML file content refer to the API documentation in the Process Mudule dokumentation in the resuls part.
API Reference: Results1288
Process Image Stack
To process a reference set or any other image stack compliant to the image stack definition you can use this code snippet:
import os
from emva1288 import process
#specify the path to the image stack
dir_ = '/home/work/1288/datasets/EMVA1288_ReferenceSet_001_CCD_12Bit/'
fname = 'EMVA1288_Data.txt'
fname = os.path.join(dir_, fname)
fresult = 'EMVA1288_Result.xml'
fresult = os.path.join(dir_, fresult)
parser = process.ParseEmvaDescriptorFile(os.path.join(dir_, fname))
imgs = process.LoadImageData(parser.images)
dat = process.Data1288(imgs.data)
res = process.Results1288(dat.data)
res.print_results()
f = open(os.path.join(dir_, fresult), "wb")
f.write(res.xml())
f.close()
Compare Results
To compare two result files you can use this code snippet:
import os
from emva1288 import process
#specify the path to the files to compare
dir_ = '/home/work/1288/datasets/EMVA1288_ReferenceSet_001_CCD_12Bit/'
fresult1 = 'EMVA1288_Result1.xml'
fresult2 = 'EMVA1288_Result2.xml'
fcompare = 'EMVA1288_Compare.txt'
u = process.routines.compare_xml(os.path.join(dir_, fresult1), os.path.join(dir_, fresult2), os.path.join(dir_, fcompare));
print(u);
This will output something like:
**********************************************************************
info
**********************************************************************
index_start 0.0 0.0 OK
**********************************************************************
sensitivity
**********************************************************************
u_p_sat 30957.0 30957.0 OK
SNR_max_bit 6.88515800189 6.88515800189 OK
DR_dB 142.143573793 142.143573793 OK
SNR_max 118.205880802 118.205880802 OK
K 0.280769280926 0.280769280926 OK
index_u_ysat 36.0 36.0 OK
u_p_min 25.3600660517 25.3600660517 OK
R 0.126726922819 0.126726922819 OK
inverse_K 3.56164319936 3.56164319936 OK
DR 1220.69871336 1220.69871336 OK
index_sensitivity_max 25.0 25.0 OK
SNR_max_dB 95.4485571343 95.4485571343 OK
sigma_y_dark 3.07341849276 3.07341849276 OK
u_e_sat 13972.6302563 13972.6302563 OK
inverse_SNR_max 0.845981598556 0.845981598556 OK
index_sensitivity_min 0.0 0.0 OK
QE 45.1356082834 45.1356082834 OK
sigma_d 10.8980275235 10.8980275235 OK
**********************************************************************
linearity
**********************************************************************
LE_max 0.480448332752 0.480448332752 OK
index_linearity_max 34.0 34.0 OK
index_linearity_min 2.0 2.0 OK
LE_min -0.615797581149 -0.615797581149 OK
**********************************************************************
defect_pixel
**********************************************************************
histogram_DSNU_accumulated Array Array OK
histogram_PRNU_accumulated Array Array OK
histogram_PRNU Array Array OK
histogram_DSNU Array Array OK
**********************************************************************
spatial
**********************************************************************
s_2_y 72.4247416075 72.4247416075 OK
F_50 1.05226771296 1.05226771296 OK
s_2_y_measured_spectrogram 40.5156503886 40.5156503886 OK
s_2_y_spectrogram 29.4441754554 29.4441754554 OK
s_2_y_measured_dark 0.213951679236 0.213951679236 OK
sigma_2_y_stack_dark 9.49749533243 9.49749533243 OK
DSNU1288 0.551787767644 0.551787767644 OK
s_2_y_dark 0.0240017725878 0.0240017725878 OK
s_2_y_measured 83.4962165406 83.4962165406 OK
DSNU1288_DN 0.154925054745 0.154925054745 OK
s_2_y_spectrogram_dark 0.0240017725878 0.0240017725878 OK
PRNU1288 0.432704411062 0.432704411062 OK
s_2_y_measured_spectrogram_dark 0.213951679236 0.213951679236 OK
F_dark 1.13639226727 1.13639226727 OK
sigma_2_y_stack 553.573746657 553.573746657 OK
**********************************************************************
dark_current
**********************************************************************
u_I_var 8.26281507974 8.26281507974 OK
u_I_mean 28.4074312749 28.4074312749 OK