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Multi-channel Time Interval Counter and fine delay generator
Commit 969895ee authored by Nicolas Boucquey's avatar Nicolas Boucquey
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Scripts/dataAcquisition/themperature_reader.py 0 → 100644
View file @ 969895ee
# Script providing the Thermo class used to interact with the on board
# thermometer. (DS18B20U+). This file has to be run on the MicroZed + ADC board
from devmem import DevMem
import time
from scipy import signal
import numpy as np
from scipy.optimize import curve_fit
mem = DevMem(base_addr = 0x40000000, length = 0x10000,debug = 0)
TRIG0_ADDR = 0x1000
BUFF0_ADDR = 0x2000
TRIG1_ADDR = 0x3000
BUFF1_ADDR = 0x4000
TRIG2_ADDR = 0x5000
BUFF2_ADDR = 0x6000
TRIG3_ADDR = 0x7000
BUFF3_ADDR = 0x8000
THERM_ADDR = 0x9000
class Thermo:
def __init__(self, mem, addr):
self.mem = mem
self.addr = addr
def reset(self, overdrive):
self.mem.writel(0x08 | overdrive << 2 | 0x02, self.addr)
while(0x08 & self.mem.readl(self.addr)):
pass
return(self.mem.readl(self.addr) & 0x01)
def read(self, overdrive):
self.mem.writel(0x08 | overdrive << 2 | 0x01, self.addr)
while(0x08 & mem.readl(self.addr)):
pass
return(self.mem.readl(self.addr) & 0x01)
def write(self, overdrive, bit):
self.mem.writel(0x08 | overdrive << 2 | bit, self.addr)
while(0x08 & mem.readl(self.addr)):
pass
return(self.mem.readl(self.addr) & 0x01)
def change_div(self, div):
self.mem.writel(div, self.addr + 0x4)
def Write_byte(self, overdrive, byte):
for i in range (0, 8):
self.write(overdrive, (byte & 0x1))
byte >>= 1
def Read_id(self, overdrive):
self.reset(overdrive)
self.Write_byte(overdrive, 0x33)
id_thermo = 0
for i in range (0, 64):
id_thermo <<= 1
id_thermo |= self.read(overdrive)
return id_thermo
def Read_temp(self, overdrive):
self.reset(overdrive)
self.Write_byte(overdrive, 0xCC)
self.Write_byte(overdrive, 0x44)
temp = 0
# thermometer answers a 1 once the convertion is finished
while(self.read(overdrive) != 1):
pass
self.reset(overdrive)
self.Write_byte(overdrive, 0xCC)
self.Write_byte(overdrive, 0xBE)
for i in range (0, 16):
temp >>= 1
temp |= (self.read(overdrive) << 15)
return temp*0.0625 # 12 bit convert factor
ds = Thermo(mem, THERM_ADDR)
iteration = 0
overdrive = 0
result =0
ds.change_div(499)
while(True):
print(ds.Read_temp(overdrive))
time.sleep(0.5)
Scripts/dataProcessing/TDC_stat.py 0 → 100644
View file @ 969895ee
# Computes the statistics on the TDC measurement results. This script is used to
# estimate if the results are following the probability density function.
#
# The file "measurements.py" is required.
import numpy as np
import measurements
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import pylab
import scipy.stats as stats
n, bins, patches = plt.hist(measurements.result, bins = np.arange(840, 870,0.5), normed = 1)
mean = np.mean(measurements.result)
std = np.std(measurements.result)
y = mlab.normpdf(bins, mean, std)
plt.plot(bins,y, 'r--')
plt.show()
stats.probplot(measurements.result, dist="norm", plot= pylab)
pylab.show()
\ No newline at end of file
Scripts/dataProcessing/analyse_linearity.py 0 → 100644
View file @ 969895ee
# Script used to estimate the linearity of the TDC. 10k phase have been
# recoded in the file "data_linearity.py". The histogram of the measured phase
# is plotted. The linearity can be estimated by the repartition of the measured
# phase over one sampling period. If they are equally spread, the TDC is linear.
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal,optimize
from textwrap import wrap
import sys
import scipy.linalg as la
import pylab
import sys
from scipy.optimize import curve_fit
from matplotlib2tikz import save as tikz_save
import math
import data_linearity
import matplotlib.mlab as mlab
BINS_SIZE = 3
def straight(x, a, b):
return a * x + b
n, bins, patches = plt.hist(data_linearity.phase,
bins = np. arange(np.min(data_linearity.phase),
np.max(data_linearity.phase), BINS_SIZE))
phase = np.linspace(np.min(data_linearity.phase),
np.max(data_linearity.phase), len(n))
popt, popc = curve_fit(straight, phase, n)
plt.xlabel("Measured phase, bins size = " + str(BINS_SIZE) + " ps")
plt.ylabel("Occurences")
plt.show()
plt.plot(phase, straight(phase, popt[0], popt[1]), 'b')
plt.plot(phase, n, 'r')
plt.show()
# Linearity error
error = straight(phase, popt[0], popt[1]) - n
n, bins, patch = plt.hist(error, normed = 1)
y = mlab.normpdf(bins, np.mean(error), np.std(error))
plt.plot(bins, y)
plt.show()
\ No newline at end of file
Scripts/dataProcessing/data_enob.py 0 → 100644
View file @ 969895ee
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Scripts/dataProcessing/data_linearity.py 0 → 100644
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Scripts/dataProcessing/data_raw.py 0 → 100644
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Scripts/dataProcessing/enob_without_thd.py 0 → 100644
View file @ 969895ee
# Script used to evaluate the ENOB. The Total Harmonic Distortion (THD) is not
# taken into account. The filte "data_enbo.py" is required
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import data_enob
import math
FS = 100
def Sine(xdata, A, f, delay_ps, offset):
return A*np.sin(2*np.pi*f/FS*xdata + 2*np.pi*f*delay_ps/1e6) + offset
data_enob_measure = []
for i in range (0,10):
data_enob_measure.append(data_enob.chan_[i*2])
# Measurement of the standard deviation of the noise
data_enob_std = np.std(data_enob_measure)
print(data_enob_std)
# Signal to Noise Ratio (in comparision to a full scale signal)
SNR = 20*np.log10(2**14/2/np.sqrt(2)*data_enob_std)
ENOB = (SNR - 1.76)/6.02
print(ENOB)
Scripts/dataProcessing/measurements.py 0 → 100644
View file @ 969895ee
# Delays out of the TDC. 500 experiments are done for statistics.
# The sampling frequency is 100 MHz.
# The filter is the one presented on the wiki. (CMOS gates = 5PB1108,
# operational amplifier = LMH6624, Schottky diodes are HSMS-281C-BLKG).
# The filter oscillates at 15 MHz. Use "TDC_stat.py" to see the results.
import numpy as np
result = np.empty(500)
result = [
851.105826578,
854.871027501,
854.623010316,
860.342554821,
851.147403424,
854.887415591,
853.916462804,
854.498432755,
857.906103014,
859.233595489,
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860.07015948,
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856.119353486,
852.2915553,
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857.087547787,
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854.578346005,
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858.616291768,
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854.00447167,
854.50566629,
853.803474384,
853.437099132,
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854.878976443,
854.370126981,
852.171078954,
854.931927407,
854.782750689,
856.029230296,
850.195377191,
856.362895183,
856.347435075,
859.441253336,
854.768918391,
855.872745393,
849.720067899,
857.674082885,
854.912509618,
857.305477646,
858.107874472,
853.852544506,
856.415401245,
851.696741129,
853.720016323,
854.578488163,
851.538377585,
854.97361746,
855.904772938,
856.471503371,
851.560264148,
857.735271751,
852.188850392,
856.050478854,
850.729722281,
856.55126906,
856.352201241,
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855.257686467,
857.572915623,
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859.155693717,
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856.366349649,
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857.046384958,
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851.051905055,
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858.280243961,
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854.085089272,
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849.640917184,
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857.384784913,
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859.472088739,
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856.912189679,
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856.293192713,
852.57518761,
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851.453815915,
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859.455408256,
857.317026278,
852.512053165,
852.016305201,
853.937823005,
859.465674965,
853.377747225,
853.616567142,
854.055860484,
849.12742255,
847.571253939,
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852.51754089,
852.738121418,
852.844992166,
857.279642378,
852.84364768,
857.02660165,
849.053258819,
852.567010467,
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853.259502021,
857.659822889,
857.045668164,
856.300061748,
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857.477404185,
854.603969302,
856.871491398,
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851.433927352,
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856.473406076,
849.766425166,
857.678386418,
861.97674076,
858.531565022,
853.857460451,
858.823600128,
851.870597278,
852.788566959,
858.146872985,
857.587983692,
855.064323577,
851.829495329,
849.774835729,
859.875533559,
859.181090615,
854.575074355,
858.444705422,
857.974160308,
853.454180282,
851.823058691,
852.329700597,
855.950575799,
849.280241525,
853.056109386,
859.896951152,
854.620449567,
855.57550188,
854.496758842,
860.505172904,
854.17100035,
847.885733335,
852.081781571,
853.092008025,
857.254470025,
861.19556284,
859.09720044,
855.816146647,
850.964203549,
858.365404183,
848.610857722,
857.279762364,
852.036140055,
856.778246494,
850.556327315,
858.665063471,
851.923704564,
850.841421113,
852.601464748,
854.585730479,
856.240559073,
855.642464148,
854.506011388,
853.443485214,
861.259482616,
857.678881468,
853.525841656,
855.381303211,
856.599634039,
857.894940188,
861.872032513,
855.149174494,
852.010242609,
849.751164472,
857.465116796,
856.149558133,
858.333817787,
857.161115086,
855.222699813,
860.561300055,
855.249222041,
854.452039284,
854.571872362,
855.008482507,
851.742714077,
852.690671606,
853.797192601,
856.564465375,
855.991756911,
851.585678126,
848.86781545,
850.014428591,
855.022140396,
862.487894161,
862.82219536,
853.816015976,
857.129138771,
857.170234171,
854.466708389,
854.725447796,
855.951010353,
852.639913002,
859.070955774,
849.896544698,
860.285594983,
851.230402063,
859.218991882,
857.324981062,
856.530745174,
856.101836116,
852.243680901,
856.853182821,
856.496411555,
853.539901753,
861.208106853,
855.571661825,
857.233996641,
854.370259197,
859.865075619,
851.080402087,
855.417693227,
857.245707226,
856.55590707,
857.27465507,
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848.923337232,
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855.756776913,
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852.77615052,
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854.766054336,
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858.397188949,
853.732314827,
854.1182705,
857.801736811,
859.387486055,
854.9556832,
850.911262987,
852.572752087,
851.877203476,
860.433679903,
856.338273203,
856.540877708,
855.064181151,
852.142382273,
854.980030535,
851.888099172,
853.702766893,
853.484088765,
853.68906493,
850.057715965,
854.54326812,
857.505129987,
855.210512718,
855.555958016,
851.184331559,
859.204524704,
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854.093131218,
852.941832037,
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852.094541307,
855.905531286,
853.799164218,
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858.486989117,
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851.581327169,
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859.644113081,
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854.130157747,
851.882122942,
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855.576927362,
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859.174625859,
853.711612577,
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855.581455836,
853.274501174,
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858.150331893,
852.20772473,
854.056404469,
852.639547986,
853.887849039,
855.023317386,
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850.290807512,
854.310479299,
857.608645015,
855.11589026,
853.617284705,
856.338820206,
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855.062165645,
854.990862127,
854.949184225,
855.213361736,
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856.494974387,
851.679929281,
857.26586964,
854.692755671,
853.570790985,
855.388969386,
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856.122591637,
853.703143651,
855.555890505,
856.539559146,
860.032919825,
857.263874341,
850.444438049,
851.402144,
857.600864737,
858.06857798,
857.86472097,
853.675809626,
850.309915038,
853.282354172,
854.237718802,
856.592636356,
854.072922671,
852.111923705,
852.070334086,
852.923390153,
857.21810704,
854.429268538,
853.187268749,
856.708110772,
855.140748346,
853.657497987,
850.538060452,
855.254862161,
848.427497416,
860.017077212,
855.573070728,
852.624760301,
854.435690774,
855.056790427,
854.35955028,
852.66088018,
855.053277528,
850.686367205,
858.530592862,
852.08017397,
854.174002565,
851.098237534,
856.300248296,
856.18325018,
854.622549574,
855.991015101,
854.416508876,
855.830359734,
853.133952309,
853.154392647,
856.042371736,
853.475826027,
856.078501823,
851.554466601,
852.662307547,
859.724945671,
856.706508697,
853.701424805,
858.27063211,
857.206175735,
859.676469405,
854.653109792,
861.114645229,
860.372992472,
856.810112321,
860.367058404,
854.133034321,
852.05624698,
857.65790918,
857.03901377,
853.183006061,
854.54033114,
850.014693551,
856.067677959,
853.753589904,
852.223483251,
850.979500628,
858.351321973,
851.449223478,
854.228845211,
857.12928543,
854.130058601,
854.945624379,
854.655530172,
852.733569144,
859.481844587,
857.610710513,
856.504168069,
853.867030525,
853.519852297,
859.969444833,
852.871057898,
861.546097791,
859.605232351,
855.286550355,
858.497878969,
854.225820093,
852.465943679,
851.33886306,
852.885816268,
856.910015992,
858.334100568,
856.804288981,
859.852485529,
853.604836015,
858.217289112,
851.218172549,
859.180910238,
854.946402875,
858.649411768,
853.841617741,
857.00608015,
858.950564754,
856.801772239,
858.74545266,
854.490836435,
853.93822416,
]
Mean = 855.036667062
Std = 2.91585533739
Scripts/dataProcessing/process_delays.py 0 → 100644
View file @ 969895ee
# Process the raw data coming from the ADC to get the measurements statistics.
# Requires the "data_raw.py" file.
#
# 500 experiments are processed in this script. Each experiment consists in
# measuring the delay between two pulses. The delay stays the same for all
# the experiments to be able to quantify the TDC rms error on the measurement.
# The ADC outputs arrays of 1024 points for each channel and each experiment and
# the pretrigger is set to 10 samples.
#
# Only the samples from 15 to 125 are taken into account to do the fitting (cfr.
# documentation).
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.signal import butter, lfilter, freqz
import data_raw
import math
import matplotlib.mlab as mlab