##// END OF EJS Templates
Change merge in weatherparam
Change merge in weatherparam

File last commit:

r1523:2230f258b9f3
r1726:cbbe5ce190d6
Show More
test_noise.py
291 lines | 6.3 KiB | text/x-python | PythonLexer
import numpy
print("PULSO CORTO PULSEPAIR:")
NOISE=numpy.array([7.058371817678165e-06,
7.275877510349e-06,
7.08567532050105e-06,
6.81062729431438e-06,
7.154584392039524e-06,
6.9536401673704544e-06,
6.990855494619673e-06,
6.967875252515689e-06,
7.0560893781090685e-06,
7.209407465125922e-06,
7.231202073381786e-06,
7.265750279899678e-06,
7.107734872936774e-06,
7.424520982726322e-06,
7.197484122482031e-06,
7.090358800962431e-06,
7.0464851158512365e-06,
7.044107525827826e-06,
6.865979553859618e-06,
7.203323109154303e-06,
7.209868850961483e-06,
7.083282347578637e-06,
7.056842419342491e-06,
7.152784874057129e-06,
7.163698691478186e-06,
7.199027309307966e-06,
7.252667761463652e-06,
7.046838674931362e-06,
7.040921076201627e-06,
7.0487383768338805e-06,
7.05724689017809e-06,
7.065717157755511e-06,
7.051678380255241e-06,
7.072835678313185e-06,
6.861349586883288e-06,
7.031211299456376e-06,
6.833436241886199e-06,
7.047831707967921e-06,
7.0713983455050595e-06,
7.148736754511013e-06,
7.010069669415208e-06,
7.0013783515682e-06,
7.098992971204625e-06,
6.892285595650846e-06,
7.231184307898404e-06,
6.962965300211762e-06,
7.044368436000366e-06,
6.942974554761911e-06,
6.914651484254672e-06,
7.007187218040909e-06,
7.022009457846838e-06,
6.977184020382861e-06,
6.924042003696802e-06,
7.108313170135402e-06,
6.989743863048102e-06,
7.048388533120441e-06,
7.056638057324237e-06,
7.011720610927682e-06])
print("LONGITUD :",len(NOISE))
NOISE_AVG = numpy.sum(NOISE)/(len(NOISE))
print("NOISE_AVG :",NOISE_AVG)
print("NOISE_AVG dB:",10*numpy.log10(NOISE_AVG))
print("NOISE :",NOISE )
print("PULSO LARGO PULSE PAIR")
NOISE2=numpy.array([7.1565378542770335e-06,
6.972157397106454e-06,
7.048872107524711e-06,
7.309746521587835e-06,
7.4796261680091e-06,
7.295291344156035e-06,
7.076843902277241e-06,
7.04421932353362e-06,
7.456298427316662e-06,
7.182581655972693e-06,
7.58788356312701e-06,
7.496525980066003e-06,
7.660499656416317e-06,
7.517766693476798e-06,
7.677073862655797e-06,
7.013506288258279e-06,
7.478202328226235e-06,
7.212604692186345e-06,
7.187593606046117e-06,
7.2734641705577155e-06,
7.491451637527428e-06,
7.2990133131716125e-06,
7.3708673242234215e-06,
7.343146430381782e-06,
7.371159755822349e-06,
7.484676494237511e-06,
7.615252497384968e-06,
7.4125206140761264e-06,
7.199400161482942e-06,
7.549748730417132e-06,
7.289268618106982e-06,
7.2705490958860185e-06,
7.16537572582382e-06,
7.154628772091394e-06,
7.230639926573772e-06,
7.127823088507154e-06,
7.430466902457178e-06,
7.346943616999105e-06,
7.373530337538375e-06,
7.048477302439197e-06,
7.252816217618462e-06,
6.995543397732151e-06,
7.49111263133727e-06,
7.210551746855847e-06,
7.361411752991037e-06,
7.261787613473075e-06,
7.181385521610965e-06,
6.815063845021763e-06,
7.099572698347567e-06,
7.237832082243544e-06,
7.0709738400826794e-06,
7.301158361787179e-06,
7.340100450663536e-06,
7.496934745103125e-06,
7.357023364175648e-06,
7.416106753434862e-06,
7.384383887559784e-06,
7.447316987122185e-06,
7.472916460116349e-06,
7.504206698039756e-06,
7.431935409978463e-06,
7.379363088805881e-06,
7.339162913477491e-06,
7.350391084789767e-06,
7.5746826017888346e-06,
7.317012708547843e-06,
7.290728565159114e-06,
7.433816153858191e-06,
7.638111271367376e-06,
7.552784078429459e-06,
7.437529555405933e-06,
7.673203268647856e-06,
7.702162130633843e-06,
7.516160930650763e-06,
7.2130629756353514e-06,
7.241118065364274e-06,
7.393124427054974e-06,
7.462177513817854e-06,
7.525508579421909e-06,
7.6171160762888685e-06,
7.5835820977287764e-06,
7.665218181974922e-06,
7.731323154751701e-06,
7.3708481606389364e-06,
7.618145650759882e-06,
7.556526433097294e-06,
7.360553158516033e-06,
7.700951136862002e-06,
7.378343793920657e-06,
7.322690297714378e-06,
7.2940869124493325e-06,
7.199946382806911e-06,
7.299681033643804e-06,
7.164110592283668e-06,
7.374987030621029e-06,
7.437533406155818e-06,
7.143501776066717e-06,
7.162861789301104e-06,
7.305254357193068e-06,
7.4802314902935914e-06,
7.350014227780263e-06,
7.381643930745679e-06,
7.214508353861626e-06,
7.266679682543046e-06,
7.262206452087994e-06,
7.3586314470399245e-06])
print("LONGITUD :",len(NOISE2))
NOISE2_AVG = numpy.sum(NOISE2)/(len(NOISE2))
print("NOISE_AVG :",NOISE2_AVG)
print("NOISE_AVG dBz:",10*numpy.log10(NOISE2_AVG))
print("NOISE :",NOISE2 )
print("PULSO LARGO MOMENTOS")
NOISE_M=numpy.array([7.03196744e-06,
7.03534479e-06,
7.17279532e-06,
7.12066195e-06,
7.12127453e-06,
7.06531925e-06,
7.11292408e-06,
7.1800289e-06,
7.14391236e-06,
7.10392988e-06,
7.3184699e-06,
7.35842827e-06,
7.31158043e-06,
7.25739358e-06,
7.2498753e-06,
7.19480709e-06,
7.12833865e-06,
7.11898311e-06,
7.13012482e-06,
7.285936e-06,
7.12800017e-06,
7.16027593e-06,
7.12791678e-06,
7.18820326e-06,
7.1803628e-06,
7.15598537e-06,
7.15292974e-06,
6.96587204e-06,
7.20321066e-06,
7.0339905e-06,
7.09050735e-06,
7.01473465e-06,
7.08961568e-06,
6.9928101e-06,
6.98041031e-06,
7.07702413e-06,
7.07114106e-06,
7.1612535e-06,
7.02738743e-06,
7.08273243e-06,
7.07431318e-06,
7.15725072e-06,
7.11387521e-06,
7.11936201e-06,
7.0043304e-06,
7.13803972e-06,
6.93230427e-06,
7.01482575e-06,
7.02118331e-06,
7.00942707e-06,
6.98153473e-06,
7.14089044e-06,
7.07960945e-06,
7.06428977e-06,
7.14305083e-06,
7.22311622e-06,
7.18483676e-06,
7.13146742e-06,
7.15249878e-06,
7.13268722e-06,
7.12472592e-06,
7.11576543e-06,
7.20606446e-06,
7.14676932e-06,
7.13615415e-06,
7.18083288e-06,
7.05755055e-06,
7.09263083e-06,
7.17132213e-06,
7.1242792e-06,
7.15415339e-06,
7.27700591e-06,
7.3069339e-06,
7.09340429e-06,
7.06209528e-06,
7.09263394e-06,
7.15860332e-06,
7.06919788e-06,
7.06625609e-06,
7.12542856e-06,
7.2526423e-06,
7.13511504e-06,
7.19948604e-06,
7.24094747e-06,
7.08260554e-06,
7.06477131e-06,
7.26516081e-06,
7.07306189e-06,
7.1965219e-06,
7.17638118e-06,
7.03142256e-06,
7.05465432e-06,
7.19451664e-06,
7.07150133e-06,
6.95460518e-06,
7.0334121e-06,
7.04119911e-06,
7.20777715e-06,
7.21999712e-06,
7.06781868e-06])
print("NOTA:SE HA CONSIDERADO FACTOR: 64*2*10=1280")
print("LONGITUD :",len(NOISE_M))
NOISE_M_AVG = numpy.sum(NOISE_M)/(len(NOISE_M))
print("NOISE_M_AVG :",NOISE_M_AVG)
print("NOISE_M_AVG dBz :",10*numpy.log10(NOISE_M_AVG))
print("NOISE_M :",NOISE_M )