# `20180227a` Testing the new versions at 32 and 64Msps

``````import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from scipy.interpolate import griddata
import math
from scipy.signal import decimate, convolve

import re
import glob, os
``````

## Creating the set of acquisitions

``````AA = []
lbl = []
IDLine = []

for CSVFile in glob.glob("*.csv"):
print CSVFile
A = np.genfromtxt(CSVFile, delimiter=';').astype(int)[1:]
tmp = []
N = len(A)
FF = CSVFile.split(".")[0].split("-")[-1]
lbl.append(  CSVFile.split("-")[2] )
f = int(re.sub('[^0-9]','', FF))
#F = int(CSVFile.split(".")[0])

if (A[4]) > 0b111:
print "first"
for i in range(len(A)/2-1):
value = 128*(A[2*i+0]&0b111) + A[2*i+1] - 512
IDLine.append((A[2*i+1]&0b11110000)/16) # Identify the # of the line
tmp.append( value )
else:
print "second"
for i in range(len(A)/2-1):
value = 128*(A[2*i+1]&0b111) + A[2*i+2] - 512
IDLine.append((A[2*i+1]&0b11110000)/16)
#print A[2*i]&0b10000000,A[2*i+1]&0b10000000,
tmp.append( value )
#print A[2*i+1]
#print A[i+1]-A[i]

#t = t*1.0/f
t = [ 1.0*x/f for x in range(len(tmp))]
plt.plot(t,tmp)
plt.title(CSVFile)
plt.savefig(CSVFile.split(".")[0].split("/")[-1]+".jpg")
plt.show()

AA.append(tmp)
``````
``````[email protected]
first
``````

``````[email protected]
second
``````

``````t1 = [ 1.0*x/32 for x in range(len(tmp))]
t2 = [ 1.0*x/64 for x in range(len(tmp))]

s1 = AA[-1] # 32
s2 = AA[0]  # 64

fLegend = [ 64.0*x/len(s2) for x in range(len(s2))]
fm = len(fLegend)/2

f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15,5))

ax1.plot(t2[0:2500],s2[0:2500],"b",label="64Msps")
ax1.plot(t1[0:2500],s1[0:2500],"g",label="32Msps")
ax1.set_xlabel("Time in uS")
ax2.plot(t2[2*1100:2*1350],s2[2*1100:2*1350],"b",label="64Msps")
ax2.plot(t1[1100:1350],s1[1100:1350],"g",label="32Msps")
ax2.legend()
ax2.set_xlabel("Time in uS")
ax1.set_xlabel("Time in uS")
ax1.legend()

ax3.plot(t2[2*1250:2*1275],s2[2*1250:2*1275],"b",label="64Msps")
ax3.plot(t1[1250:1275],s1[1250:1275],"g",label="32Msps")
ax3.legend()
ax3.set_xlabel("Time in uS")

ax4.plot(fLegend[1:fm], np.abs(np.fft.fft(s2))[1:fm] ,"b",label="64Msps")
ax4.legend()
ax4.set_xlabel("Frequency MHz")

plt.suptitle('Checking 32Msps and 64Msps')
plt.savefig("32_64.jpg", bbox_inches='tight')

plt.show()
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