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In [1]:



    


import scipy.io
import numpy
import os

64TF vs mat





In [2]:



    


percorsoINmio64 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio5264.mat"
percorsoINmio32 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio5232.mat"
percorsoINmatDopp = "/home/protoss/Documenti/TESI/HWITest/dopplero52.mat"
percorsoINmatHough = "/home/protoss/Documenti/TESI/HWITest/hough52.mat"
percorsoINmatCand = "/home/protoss/Documenti/TESI/HWITest/cand52.mat"


percorsoINmiorob = "/home/protoss/Documenti/TESI/HWITest/mioRob52.mat"
percorsoINmatrob = "/home/protoss/Documenti/TESI/HWITest/robcontrol52.mat"



    











In [6]:



    


percorsoINmio = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio10864.mat"
percorsoINmatDopp = "/home/protoss/Documenti/TESI/HWITest/dopplero108.mat"
percorsoINmatHough = "/home/protoss/Documenti/TESI/HWITest/hough108.mat"
percorsoINmatCand = "/home/protoss/Documenti/TESI/HWITest/cand108.mat"


percorsoINmiorob = "/home/protoss/Documenti/TESI/HWITest/mioRob108.mat"
percorsoINmatrob = "/home/protoss/Documenti/TESI/HWITest/robcontrol108.mat"



    











In [11]:



    


percorsoINmio64 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio18764.mat"
percorsoINmatDopp = "/home/protoss/Documenti/TESI/HWITest/dopplero187.mat"
percorsoINmatHough = "/home/protoss/Documenti/TESI/HWITest/hough187.mat"
percorsoINmatCand = "/home/protoss/Documenti/TESI/HWITest/cand187.mat"

percorsoINmiorob = "/home/protoss/Documenti/TESI/HWITest/mioRob187.mat"
percorsoINmatrob = "/home/protoss/Documenti/TESI/HWITest/robcontrol187.mat"



    











In [10]:



    


freqCorrMio32 = scipy.io.loadmat(percorsoINmio32)
freqCorrMio32 = freqCorrMio32['freqCorr'][0]
freqCorrMio64 = scipy.io.loadmat(percorsoINmio64)
freqCorrMio64 = freqCorrMio64['freqCorr'][0]

print(freqCorrMio32, freqCorrMio64)
#freqCorrMat = scipy.io.loadmat(percorsoINmatDopp)
#freqCorrMat = freqCorrMat['freqniu']

from matplotlib import pyplot
%matplotlib notebook
pyplot.figure(figsize=(6, 6))

a = pyplot.scatter(numpy.arange(freqCorrMio32.size),(freqCorrMio64-freqCorrMio32), s = 10)



    


















    






[51.99079044 51.99335397 51.99677202 ... 53.00304187 53.00377432
 53.00816902] [51.99079044 51.99335397 51.99677202 ... 53.00304187 53.00377432
 53.00816902]










    




















    























In [36]:



    


houghMio32 = scipy.io.loadmat(percorsoINmio32)["hough"]
houghMat = scipy.io.loadmat(percorsoINmatHough)['binh_df0']
houghMio64 = scipy.io.loadmat(percorsoINmio64)["hough"]

print(numpy.nonzero(houghMio32-houghMio64))
print(numpy.nonzero(houghMat-houghMio64))
print(numpy.nonzero(houghMat-houghMio32))

from matplotlib import pyplot
%matplotlib notebook
pyplot.figure(figsize=(12, 9))


a = pyplot.imshow(houghMat, aspect=400, interpolation= "none", origin = 'lower', cmap = "gray_r")
#a = pyplot.imshow((houghMat), aspect=400, interpolation= "none", origin = 'lower', )
pyplot.colorbar(shrink = 0.8,aspect = 10)
pyplot.savefig('hmatvs32.jpg', format='jpg')
numpy.mean(numpy.absolute(houghMat))



    


















    






(array([  0,   0,   0, ..., 209, 209, 209]), array([ 1773,  1783,  2214, ..., 85851, 85889, 85899]))
(array([], dtype=int64), array([], dtype=int64))
(array([  0,   0,   0, ..., 209, 209, 209]), array([ 1773,  1783,  2214, ..., 85851, 85889, 85899]))










    




















    
















    
Out[36]:








179.4633403767069

















In [39]:



    


houghMat.shape



    


















    
Out[39]:








(210, 88026)

















In [41]:



    


sottratto = houghMat-houghMio32
print(numpy.amax(sottratto), numpy.amin(sottratto))
numerrors = numpy.nonzero(sottratto)[1].size/houghMat[:,2000:88026-2000].size
print(numerrors)

averr = sottratto[numpy.nonzero(sottratto)]
print(numpy.amax(averr), numpy.amin(averr))
%matplotlib notebook
pyplot.scatter(numpy.arange(averr.size),averr)
numpy.mean(numpy.absolute(averr))
numpy.std(averr)



    


















    






4.0 -4.0
0.07172983872338834
4.0 -4.0










    




















    
















    
Out[41]:








1.0412354

















In [27]:



    


%matplotlib notebook
histo = numpy.bincount(numpy.absolute(averr.astype(numpy.int64)))
pyplot.scatter(numpy.arange(histo.size),histo)
pyplot.show()



    


















    




















    























In [3]:



    


from matplotlib import pyplot
%matplotlib notebook
pyplot.figure(figsize=(12, 9))

#a = pyplot.imshow((houghMat-houghMio), aspect=400, interpolation= "none")
a = pyplot.imshow((houghMio64-houghMat), aspect=400, interpolation= "none", origin = 'lower', cmap = 'gray_r')
pyplot.colorbar(shrink = 0.8,aspect = 10)
pyplot.savefig('hmatvs64.png', format='png')



    


















    




















    
















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-ad4fbedf42a9> in <module>()
      4 
      5 #a = pyplot.imshow((houghMat-houghMio), aspect=400, interpolation= "none")
----> 6 a = pyplot.imshow((houghMio64-houghMat), aspect=400, interpolation= "none", origin = 'lower', cmap = 'gray_r')
      7 pyplot.colorbar(shrink = 0.8,aspect = 10)
      8 pyplot.savefig('hmatvs64.png', format='png')

NameError: name 'houghMio64' is not defined
















In [27]:



    


percorsoINmioCand64 = '/home/protoss/Documenti/TESI/HWITest/newtests/candidatesMIO.mat'
candMio = scipy.io.loadmat(percorsoINmioCand64)["cand"]
candMat = scipy.io.loadmat(percorsoINmatCand)["cand"]


from matplotlib import pyplot
%matplotlib notebook

tObs=9*30*24*60*60
binf = 1/8192
#binf = 1/4096
binsd = binf/tObs

pyplot.figure(figsize=(8, 6))
pyplot.scatter(candMat[0],(candMio[0]-candMat[0])/(binf/10), s = 5, label='$\\nu$')
#scat.scatter(numpy.arange(candMio[1].size),(candMio[1]-candMat[1])/candMio[1], s = 5, label='lonEcl')
#scat.scatter(numpy.arange(candMio[2].size),(candMio[2]-candMat[2])/candMio[2], s = 5, label='latEcl')

#pyplot.scatter(candMio[0],(candMio[4]-candMat[4])/candMio[4], s = 5, label = 'ampl')
#pyplot.scatter(candMio[0],(candMio[5]-candMat[5])/candMio[5], s = 5, label = 'critrat')
#scat.scatter(numpy.arange(candMio[6].size),(candMio[6]-candMat[6]), s = 5,label = 'dlon')
#scat.scatter(numpy.arange(candMio[7].size),(candMio[7]-candMat[7]), s = 5, label = 'dlat')
#scat.scatter(numpy.arange(candMio[8].size),candMio[8], s = 5, label = '1 o 2')

pyplot.scatter(candMio[0],(candMio[3]-candMat[3])/binsd, s = 5, label = '$\dot{\\nu}$')
#pyplot.ylim(-2e-9,+2e-9)

pyplot.xlabel('$\\nu$ ($Hz$)', fontsize = 12)
pyplot.ylabel('$\Delta x/\delta x$', fontsize = 12)

pyplot.legend(loc='upper left');
pyplot.savefig('matv64nu.svg', format='svg')



    


















    




















    























In [32]:



    


numpy.nonzero(candMio-candMat)[0]



    


















    
Out[32]:








array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5])

















In [27]:



    


print(candMio[0,1],candMat[0,1])



    


















    






52.0067138671875 52.00670721140446

















In [12]:



    


from matplotlib import pyplot
%matplotlib notebook

pyplot.figure(figsize=(8, 6))
pyplot.scatter(candMio[0],candMio[0], s = 5, color = 'C2', label = '32bit')
pyplot.scatter(candMat[0],candMat[0], s = 5, color = 'C3', label = '64bit')
pyplot.legend(loc='upper left');
pyplot.xlabel('$\\nu$ ($Hz$)', fontsize = 12)
pyplot.ylabel('$CR$', fontsize = 12)
pyplot.savefig('32v64CR.svg', format='svg')
pyplot.show()

64TF vs 32TF





In [4]:



    


percorsoIN64 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio5264.mat"
percorsoIN32 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio5232.mat"



    











In [8]:



    


percorsoIN64 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio18764.mat"
percorsoIN32 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio18732.mat"



    











In [11]:



    


percorsoIN64 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio5264.mat"
percorsoIN32 = "/home/protoss/Documenti/TESI/HWITest/32vs64/mio5232.mat"



    











In [9]:



    


freqCorr64 = scipy.io.loadmat(percorsoIN64)
freqCorr64 = freqCorr64['freqCorr']
freqCorr32 = scipy.io.loadmat(percorsoIN32)
freqCorr32 = freqCorr32['freqCorr']

from matplotlib import pyplot
%matplotlib notebook
pyplot.figure(figsize=(6, 6))

a = pyplot.scatter(numpy.arange(freqCorr64.size),(freqCorr64-freqCorr32), s = 10)



    


















    




















    























In [11]:



    


hough64 = scipy.io.loadmat(percorsoIN64)["hough"]
hough32 = scipy.io.loadmat(percorsoIN32)["hough"]

from matplotlib import pyplot
%matplotlib notebook
pyplot.figure(figsize=(10, 8))

a = pyplot.imshow((hough32), aspect=400, interpolation= "none")

pyplot.colorbar(shrink = 1,aspect = 10)



    


















    




















    
















    
Out[11]:








<matplotlib.colorbar.Colorbar at 0x7f98eebd9a90>

















In [ ]:



    


candMio = scipy.io.loadmat(percorsoIN64)["candidati"][:,0,:]
candMat = scipy.io.loadmat(percorsoIN32)["candidati"][:,0,:]
#candMat[]

from matplotlib import pyplot
%matplotlib notebook

candMat = candMat[:,:-1]
figura = pyplot.figure()
scat = figura.add_subplot(111)
figura, scat = pyplot.subplots(figsize=(10, 7))
#scat.scatter(numpy.arange(candMio[0].size),(candMio[0]-candMat[0])/candMio[0], s = 5, label='freq')
scat.scatter(numpy.arange(candMio[1].size),(candMio[1]-candMat[1])/candMio[1], s = 5, label='lonEcl')
scat.scatter(numpy.arange(candMio[2].size),(candMio[2]-candMat[2])/candMio[2], s = 5, label='latEcl')
#scat.scatter(numpy.arange(candMio[3].size),(candMio[3]-candMat[3]), s = 5, label = 'sd')
#scat.scatter(numpy.arange(candMio[4].size),(candMio[4]-candMat[4])/candMio[4], s = 5, label = 'ampl')
#scat.scatter(numpy.arange(candMio[5].size),(candMio[5]-candMat[5])/candMio[5], s = 5, label = 'critrat')
#scat.scatter(numpy.arange(candMio[6].size),(candMio[6]-candMat[6]), s = 5,label = 'dlon')
#scat.scatter(numpy.arange(candMio[7].size),(candMio[7]-candMat[7]), s = 5, label = 'dlat')
#scat.scatter(numpy.arange(candMio[8].size),candMio[8], s = 5, label = '1 o 2')
pyplot.legend(loc='upper left');

NUOVI TESTSTS





In [2]:



    


import scipy.io
import numpy
import os


percorsoINmio = "/home/protoss/Documenti/TESI/HWITest/newtests/miahoughNONcut.mat"
percorsoINmat = "/home/protoss/Documenti/TESI/HWITest/newtests/houghcutnotMAT.mat"

houghMio = scipy.io.loadmat(percorsoINmio)["miaH"]
houghMat = scipy.io.loadmat(percorsoINmat)['binh_df0']


print(numpy.nonzero(houghMat-houghMio))
from matplotlib import pyplot
%matplotlib notebook

#a = pyplot.imshow((houghMat-houghMio), aspect=400, interpolation= "none")
a = pyplot.imshow((houghMat), aspect=400, interpolation= "none", origin = 'lower', cmap = 'gray_r')
pyplot.colorbar(shrink = 0.8,aspect = 10)



    


















    






(array([], dtype=int64), array([], dtype=int64))










    




















    
















    
Out[2]:








<matplotlib.colorbar.Colorbar at 0x7f460f23db38>

















In [46]:



    


print(houghMat.shape)



    


















    






(210, 88026)

















In [2]:



    


import scipy.io
import numpy
import os


percorsoINmio = "/home/protoss/Documenti/TESI/HWITest/final test/miahoughNONcut.mat"
percorsoINmat = "/home/protoss/Documenti/TESI/HWITest/final test/miahoughFINALcut.mat"

houghMio = scipy.io.loadmat(percorsoINmio)["miaH"]
houghMat = scipy.io.loadmat(percorsoINmat)['miaH']
#houghMat = numpy.transpose(houghMat)

print(numpy.nonzero(houghMat-houghMio))
from matplotlib import pyplot
%matplotlib notebook

#a = pyplot.imshow((houghMat-houghMio), aspect=400, interpolation= "none")
a = pyplot.imshow((houghMat-houghMio), aspect=400, interpolation= "none", origin = 'lower', cmap = 'gray_r')
pyplot.colorbar(shrink = 0.8,aspect = 10)
pyplot.savefig('matH.svg', format='svg')



    


















    






(array([], dtype=int64), array([], dtype=int64))










    




















    























In [3]:



    


percorsoINmio = "/home/protoss/Documenti/TESI/HWITest/final test/candidatesMIO.mat"
percorsoINmat = "/home/protoss/Documenti/TESI/HWITest/final test/candidates52.mat"

candMio = scipy.io.loadmat(percorsoINmio)["cand"]
candMat = scipy.io.loadmat(percorsoINmat)["cand"]
#candMat[]

from matplotlib import pyplot
%matplotlib notebook

tObs=9*30*24*60*60
binf = 1/8192
#binf = 1/4096
binsd = binf/(tObs*2)

pyplot.figure(figsize=(8, 6))
#pyplot.scatter(candMat[0],(candMio[0]-candMat[0])/(binf/10), s = 5, label='$\\nu$')
#pyplot.scatter(candMio[0],(candMio[1]-candMat[1])/candMio[1], s = 5, label='lonEcl')
#pyplot.scatter(candMio[0],(candMio[2]-candMat[2])/candMio[2], s = 5, label='latEcl')
#pyplot.scatter(candMio[0],(candMio[4]-candMat[4])/candMio[4], s = 5, label = 'ampl')
#pyplot.scatter(candMio[0],(candMio[5]-candMat[5])/candMio[5], s = 5, label = 'critrat')
#pyplot.scatter(candMio[0],(candMio[6]-candMat[6])/candMio[6], s = 5,label = 'dlon')
#pyplot.scatter(candMio[0],(candMio[7]-candMat[7])/candMio[7], s = 5, label = 'dlat')
#pyplot.scatter(candMio[0],(candMio[8]-candMat[8]), s = 5, label = '1 o 2')
#pyplot.scatter(candMio[0],(candMio[3]-candMat[3])/binsd, s = 5, label = '$\dot{\\nu}$')
#pyplot.ylim(-2e-9,+2e-9)

pyplot.xlabel('$\\nu$ ($Hz$)', fontsize = 12)
pyplot.ylabel('$\Delta x/\delta x$', fontsize = 12)

pyplot.legend(loc='upper left');



    


















    




















    























In [9]:



    


candMat[1]



    


















    
Out[9]:








array([276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502, 276.89639502, 276.89639502, 276.89639502,
       276.89639502])

















In [ ]:

Content source: iurilarosa/thesis

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