Files cleanup.

2f9a6d59 · Federico Asara · e616927c · e616927c · e616927c · e616927c
Commit 2f9a6d59 authored Sep 01, 2011 by Federico Asara
43 changed files
--- a/1884.95_2045.18_1000th-noise_10kHz.adc.txt
+++ b/1884.95_2045.18_1000th-noise_10kHz.adc.txt
--- a/README.textile
+++ b/README.textile
-h1. ADC testing
-
-This is a collection of programs to measure ADC characteristics
-
-h2. Prerequisites
-
-Python must be installed.
-
-wx-python must be installed. On Ubuntu, you can install them as indicated in the following url:
-
-http://wiki.wxpython.org/InstallingOnUbuntuOrDebian
-
-
-h2. Execution
-
-<pre>
-python adctest.py
-</pre>
--- a/ch4_2Mhz.txt
+++ b/ch4_2Mhz.txt
--- a/design/Makefile
+++ b/design/Makefile
-doc:
-	pydoc -w ./adc-oop.py ./adc.py
-
-clean:
-	rm -f *.pyc *.html
--- a/design/adc-oop.py
+++ b/design/adc-oop.py
-#!   /usr/bin/env   python
-#    coding: utf8
-
-__doc__ = """
-    Interface for the adc ADC characterization module
-
-    This is a preliminary spec of the interface to be expected from the
-    GUI of the ADC characterization environment
-
-    Some notes:
-
-    - A Signal object packs all the info we may need from a signal, to
-      wit, 
-      * its raw data vector and, implicitly, 
-      * raw data vector length == number of samples, 
-      * number of bits (size of the integer samples in raw data vector)
-      * sampling rate
-"""
-
-window_types = [
-    'RECTANGULAR', 
-    'HANN', 
-    'HAMMING', 
-    'TUKEY', 
-    'COSINE', 
-    'LANCZOS', 
-    'BARTLETT_HANN', 
-]
-
-class Signal(object):
-    """a representation of a time-domain sampled signal
-    """
-
-    def __init__(self, data, nbits, rate):
-        """initialize a signal object
-
-        data: an array of samples (usually nbits-long words, stored in
-            a numpy array)
-        nbits: bit width of the sample values
-        rate: sampling rate of sample production
-        """
-
-        self.data = data
-        self.nbits = nbits
-        self.rate = rate
-
-    def histogram(self):
-       """Compute histogram of a sampled signal
-
-       The number of bins in the histogram is implicitly given by the number
-       of bits of the samples: 2**signal.nbits bins.
-
-           returns: an array of 2**signal.nbits numbers (frequencies)
-       """
-
-    def ideal_histogram(self):
-       """Produce an ideal vector of frequencies (histogram) for the
-       nsamples samples of a perfect nbits ADC. Mostly for auxiliary and
-       display purposes
-
-           returns: an array of 2**signal.nbits numbers (frequencies)
-       """
-
-    def DNL(self):
-        """Compute differential non-linearity vector for a given time-domain
-        signal
-
-        returns: a pair (dnl, total) where
-            - dnl is an array of 2**signal.nbits real values and
-            - total is a real value (computed from dnl)
-        """
-
-    def INL(self):
-       """Compute integral non-linearity vector for a given time-domain signal
-
-           returns: a pair (inl, total) where
-            - inl is an array of 2**signal.nbits real values and
-            - total is a real (computed from inl)
-        """
-
-    def FFT(self, navg, window):
-        """Compute the amplitudes (in dB) of the FFT of signal, averaging navg
-        slices of it and applying window to it
-
-        navg: number of signal slices to average
-        window: a value from a finite list of windows defined in window_types
-
-        returns: an FFTSignal object
-        """
-
-class FFTSignal(object):
-    """a representation of a frequency-domain signal
-    """
-
-    def __init__(self, fft, dB = None, time_domain = None):
-        """initialize an FFTSignal object
-
-        fft: array of numeric values of the fft
-        dB:  true when in dB units, false if raw amplitude
-        time_domain: the original time-domain signal if available, None
-            otherwise
-
-            self.fft = fft
-            self.dB  = dB
-            self.time_domain = time_domain
-        """
-
-    def process_gain(self):
-        """compute the process gain for a given number of samples
-
-        returns: process gain (a number in dB)
-        """
-
-    def harmonic_peaks(self, max_peaks):
-        """peak detector
-
-        This auxiliary function computes an array of indices into the fft
-        array, pointing to the positions of the first max_peaks most
-        significant peaks
-
-        max_peaks: integer (usually in a small range, < 10)
-
-        returns: an array of at most  max_peak indices into 
-            the fft.fft array
-        """
-
-    def noise_floor(self):
-        """noise floor of a frequency-domain signal
-
-        returns: the real value of the fft noise floor
-        """
-
-    def SFDR(self):
-        """spurious free dynamic range
-
-        returns: a real number
-        """
-
-    def SINAD(self):
-        """signal to noise and distortion ratio
-
-        returns: a real number
-        """
-
-    def THD(self):
-        """total harmonic distortion
-
-        fft: FFTSignal object
-        returns: a real number
-        """
-
-    def SNR(self):
-        """signal-to-noise ratio
-
-        returns: a real number
-        """
-
-    def ENOB(self):
-        """effective number of bits
-
-        A direct function of the SINAD, relating both scalar values
-        """
-
-if __name__ == '__main__':
-    pass
-
--- a/design/adc.py
+++ b/design/adc.py
-#!   /usr/bin/env   python
-#    coding: utf8
-
-__doc__ = """
-    Interface for the adc ADC characterization module
-
-    This is a preliminary spec of the interface to be expected from the
-    GUI of the ADC characterization environment
-
-    Some notes:
-
-    - A Signal object packs all the info we may need from a signal, to
-      wit, 
-      * its raw data vector and, implicitly, 
-      * raw data vector length == number of samples, 
-      * number of bits (size of the integer samples in raw data vector)
-      * sampling rate
-
-    - The calls requiring an fft array of numbers use an FFTSignal
-      object containing the array of samples, whether it is in dB or
-      absolute units, and the original Signal it comes from. The latter
-      is mostly ignored, except when some alternative algorithms for
-      harmonic peak detection are used behing THD, SINAD, etc. In
-      general, an array of numbers can replace this argument type
-      everywhere, and the passing of a bare array of reals can
-      be easily accomodated in each function taking an FFTSignal.
-
-    - Some function might vary in spec slightly, but changes, if any,
-      should be minor.
-
-    - In particular, the functions taking an FFT as input might well
-      take a frequency domain signal object FFTSignal as an argument,
-      allowing us to compute parameters like distortion or the
-      best-fitting sinewave in the input from that. This would be
-      transparent to the callers, who can limit themselves to put 
-      an array of real values in the fft.fft field of the fft argument
-      passed to THD, SINAD, etc. Do not care about this now, though:
-      it's easy to change if needed.
-"""
-
-window_types = [
-    'RECTANGULAR', 
-    'HANN', 
-    'HAMMING', 
-    'TUKEY', 
-    'COSINE', 
-    'LANCZOS', 
-    'BARTLETT_HANN', 
-]
-
-class Signal(object):
-    """a representation of a time-domain sampled signal
-    """
-
-    def __init__(self, data, nbits, rate):
-        """initialize a signal object
-
-        data: an array of samples (usually nbits-long words, stored in
-            a numpy array)
-        nbits: bit width of the sample values
-        rate: sampling rate of sample production
-        """
-
-        self.data = data
-        self.nbits = nbits
-        self.rate = rate
-
-class FFTSignal(object):
-    """a representation of a frequency-domain signal
-    """
-
-    def __init__(self, fft, dB = None, time_domain = None):
-        """initialize an FFTSignal object
-
-        fft: array of numeric values of the fft
-        dB:  true when in dB units, false if raw amplitude
-        time_domain: the original time-domain signal if available, None
-            otherwise
-
-            self.fft = fft
-            self.dB  = dB
-            self.time_domain = time_domain
-        """
-
-def histogram(signal):
-   """Compute histogram of a sampled signal
-
-   The number of bins in the histogram is implicitly given by the number
-   of bits of the samples: 2**signal.nbits bins.
-
-       signal: a Signal object
-       returns: an array of 2**signal.nbits numbers (frequencies)
-    """
-
-def ideal_histogram(nbits, nsamples):
-   """Produce an ideal vector of frequencies (histogram) for the
-   nsamples samples of a perfect nbits ADC. Mostly for auxiliary and
-   display purposes
-
-        nbits: number of bits of the ideal ADC
-        nsamples: number of samples 
-       returns: an array of 2**signal.nbits numbers (frequencies)
-    """
-
-def DNL(signal):
-    """
-    Compute differential non-linearity vector for a given time-domain
-    signal
-
-    signal: a Signal objectA
-    returns: a pair (dnl, total) where
-        - dnl is an array of 2**signal.nbits real values and
-        - total is a real value (computed from dnl)
-    """
-
-def INL(signal):
-   """Compute integral non-linearity vector for a given time-domain signal
-
-       signal: a Signal object
-       returns: a pair (inl, total) where
-        - inl is an array of 2**signal.nbits real values and
-        - total is a real (computed from inl)
-    """
-
-def FFT(signal, navg, window):
-    """
-    Compute the amplitudes (in dB) of the FFT of signal, averaging navg
-    slices of it and applying window to it
-
-    signal: a Signal object
-    navg: number of signal slices to average
-    window: a value from a finite list of windows defined in window_types
-
-    returns: an FFTSignal object
-    """
-
-def process_gain(nsamples):
-    """compute the process gain for a given number of samples
-
-    nsamples: number of samples for computing an FFT
-    returns: process gain (a number in dB)
-    """
-
-def harmonic_peaks(fft, max_peaks):
-    """peak detector
-
-    This auxiliary function computes an array of indices into the fft
-    array, pointing to the positions of the first max_peaks most
-    significant peaks
-
-    fft: FFTSignal object
-    max_peaks: integer (usually in a small range, < 10)
-
-    returns: an array of at most  max_peak indices into 
-        the fft.fft array
-    """
-
-def noise_floor(fft):
-    """noise floor of a frequency-domain signal
-
-    fft: FFTSignal object
-    returns: the real value of the fft noise floor
-    """
-
-def SFDR(fft):
-    """spurious free dynamic range
-
-    fft: FFTSignal object
-    returns: a real number
-    """
-
-def SINAD(fft):
-    """signal to noise and distortion ratio
-
-    fft: FFTSignal object
-    returns: a real number
-    """
-
-def THD(fft):
-    """total harmonic distortion
-
-    fft: FFTSignal object
-    returns: a real number
-    """
-
-def SNR(fft):
-    """signal-to-noise ratio
-
-    fft: FFTSignal object
-    returns: a real number
-    """
-
-def ENOB(sinad):
-    """effective number of bits
-
-    A direct function of the SINAD, relating both scalar values
-    """
-
-    return (sinad - 1.76) / 6.02
-
-if __name__ == '__main__':
-    pass
-
--- a/handythread.py
+++ b/handythread.py
-import sys
-import time
-import threading
-from itertools import izip, count
-
-def foreach(f,l,threads=3,return_=False):
-    """
-    Apply f to each element of l, in parallel
-    """
-
-    if threads>1:
-        iteratorlock = threading.Lock()
-        exceptions = []
-        if return_:
-            n = 0
-            d = {}
-            i = izip(count(),l.__iter__())
-        else:
-            i = l.__iter__()
-
-
-        def runall():
-            while True:
-                iteratorlock.acquire()
-                try:
-                    try:
-                        if exceptions:
-                            return
-                        v = i.next()
-                    finally:
-                        iteratorlock.release()
-                except StopIteration:
-                    return
-                try:
-                    if return_:
-                        n,x = v
-                        d[n] = f(x)
-                    else:
-                        f(v)
-                except:
-                    e = sys.exc_info()
-                    iteratorlock.acquire()
-                    try:
-                        exceptions.append(e)
-                    finally:
-                        iteratorlock.release()
-        
-        threadlist = [threading.Thread(target=runall) for j in xrange(threads)]
-        for t in threadlist:
-            t.start()
-        for t in threadlist:
-            t.join()
-        if exceptions:
-            a, b, c = exceptions[0]
-            raise a, b, c
-        if return_:
-            r = d.items()
-            r.sort()
-            return [v for (n,v) in r]
-    else:
-        if return_:
-            return [f(v) for v in l]
-        else:
-            for v in l:
-                f(v)
-            return
-
-def parallel_map(f,l,threads=3):
-    return foreach(f,l,threads=threads,return_=True)
-
-if __name__=='__main__':
-    def f(x):
-        print x
-        time.sleep(0.5)
-    foreach(f,range(10))
-    def g(x):
-        time.sleep(0.5)
-        print x
-        raise ValueError, x
-        time.sleep(0.5)
-    foreach(g,range(10))
--- a/issues.txt
+++ b/issues.txt
-	* parser error display freezes compiz. Shorten it to 25 lines or less in
-  the popup
-
-* automatically parse? An Open button and the parse button is not needed
-
-* in general, test all out-of-bounds conditions and deal with errors
-  properly
-
-* draw some plots with lines (time-domain signal/done), FFT?, INL/DNL?
-
-* keyboard navigation
-
-* use rfft instead of fft
-
-* fix order of FFT windows in the dialog FFT
-
-* zoom and pan viewports (at least time domain and fft)
-
-* fix the issue with the parameters of Kaiser window
-
-
--- a/samples/complex.txt
+++ b/samples/complex.txt
--- a/samples/data.txt
+++ b/samples/data.txt
--- a/samples/data_coherent.txt
+++ b/samples/data_coherent.txt
--- a/samples/simple.txt
+++ b/samples/simple.txt
-[SIGNAL]
-nbits = 16
-rate = 123
-data = 1
-  2
-  3
-  4
-  5
-
--- a/samples/snr.txt
+++ b/samples/snr.txt
-	                                                                     
-                                                                     
-                                                                     
-                                             
-0.0139388910499
-0.0756613283641
-0.128759127602
-0.169575809817
-0.244099439553
-0.306779721623
-0.375048880639
-0.425027260066
-0.484185545203
-0.535421457098
-0.591754384546
-0.619414359581
-0.687367305494
-0.730445183176
-0.773202705162
-0.815470355326
-0.844011499105
-0.875280563655
-0.900282819658
-0.926638791334
-0.952639501932
-0.966977862275
-0.969674255213
-1.00198945358
-0.981079126722
-1.007817229
-0.997527816021
-0.994226242771
-0.988549746779
-0.968969798004
-0.961382701302
-0.940630319908
-0.921477072425
-0.866496012822
-0.843734203894
-0.817917693515
-0.767138189045
-0.724103628976
-0.676820098275
-0.636729241868
-0.587238486772
-0.547481768193
-0.483829299129
-0.42766303586
-0.361268122452
-0.302087025471
-0.258784141815
-0.182385331159
-0.113858537263
-0.0668136363639
-0.00927897621256
-0.0701149564905
-0.135020225869
-0.18493204501
-0.246262058332
-0.315120604573
-0.379369268698
-0.426213780707
-0.485519591091
-0.537120876094
-0.597022415245
-0.627126718401
-0.691513911185
-0.716619564037
-0.76833225414
-0.802031287717
-0.85216960123
-0.882476561339
-0.891286115932
-0.927169692758
-0.937679159348
-0.961516883106
-0.977622840092
-1.00073607962
-0.997377280586
-0.99872923133
-0.992706267503
-0.996058135359
-0.987415419325
-0.977516994888
-0.953092683785
-0.927896757013
-0.921581038383
-0.873139853179
-0.84335529775
-0.819544059154
-0.777849859037
-0.725212422197
-0.679382760045
-0.64160346947
-0.588515246869
-0.536935656739
-0.484613091446
-0.432437173482
-0.372268373293
-0.300333618858
-0.256697655616
-0.189626702835
-0.123154588856
-0.0660641664756
--- a/samples/test/00readme.txt
+++ b/samples/test/00readme.txt
-pure_sin.txt			Pure sinewave, A = 2.9, fa = 100, phi = 0, 1MHz, t=0.02s
-gaussian_100th.txt		Same with 0.01 rms gaussian noise  (SNR = 2e2)
-gaussian_1000th.txt		Same with 0.001 rms gaussian noise (SNR = 2e3)
-incoherent.txt			Pure sinewave as above, incoherently sampled (t = 0.025s)
-incoherent_1000gaussian.txt	Same as above with 0.001 rms gaussian noise (SNR = 2e3)
-thd_44_7_times.txt		Same as above with 0.01 and 0.02 harmonics (SNR = 44.7)
-thd_0.1_0.08_n_0.005.txt	With 0.1 and 0.08 harmonics and 0.005 gaussian noise
-log				How these were made
--- a/samples/test/diff-doc.txt
+++ b/samples/test/diff-doc.txt
--- a/samples/test/gaussian_1000th.txt
+++ b/samples/test/gaussian_1000th.txt
--- a/samples/test/gaussian_100th.txt
+++ b/samples/test/gaussian_100th.txt
--- a/samples/test/incoherent.txt
+++ b/samples/test/incoherent.txt
--- a/samples/test/incoherent_1000gaussian.txt
+++ b/samples/test/incoherent_1000gaussian.txt
--- a/samples/test/log
+++ b/samples/test/log
-1 : import syn
-2 : syn.synth(2.9, 100, 0.0, 1000000, 0, 0.02)
-3 : s = +
-4 : s = _
-5 : s
-6 : plot s
-7 : from matplotlib import pyplot
-8 : pyplot.plot(s)
-9 : pyplot.show()
-10: _ip.magic("save ")
-11: help(save)
-12: _ip.magic("save ")
-13: save(s, 'pure_sin.txt')
-14: save(s, 'pure_sin.txt')
-15: from numpy import *
-16: save(s, 'pure_sin.txt')
-17: save('pure_sin.txt', s)
-18: _ip.magic("edit pure_sin.txt.npy")
-19: help(save)
-20: out = file('pure_sin.txt', 'w')
-21:
-for i in s:
-    print >>out, i
-    
-
-22: out.close()
-23: _ip.system("view pure*")
-24: _ip.system("rm pure_sin.txt.npy")
-25:
-def w(s, f):
-    out = file(f, 'w')
-    for i in s:
-        print >>out, i
-    out.close()
-    
-
-26: 
-27: 
-28: 
-29: s
-30: t
-31: syn.synth(2.9, 100, 0.0, 1000000, 0, 0.02)
-32: random.randn(len(s))
-33: 
-34: 
-35: r = 2.9 * 0.001 * random.randn(len(s))
-36: s+r
-37: sn = s+r
-38: w(s, 'gaussian_1000th.txt')
-39: plot s
-40: w(sn, 'gaussian_1000th.txt')
-41: plot s
-42: plot = pyplot.plot
-43: plot(s)
-44: sn = s+r
-45: r = 2.9 * 0.01 * random.randn(len(s))
-46: sn = s+r
-47: w(sn, 'gaussian_100th.txt')
-48: plot(sn)
-49: r = 2.9 * 0.001 * random.randn(len(s))
-50: sn = s+r
-51: w(sn, 'gaussian_100th.txt')
-52: plot(sn)
-53: s = syn.synth(2.9, 100, 0.0, 1000000, 0, 0.025)
-54: sincoh = s
-55: w(sincoh, 'incoherent.txt')
-56: sincohn = s + 0.001 * 2.9 * random.randn(len(s))
-57: w(sincohn, 'incoherent_1000gaussian.txt')
-58: plot(sincohn)
-59: s = syn.synth(2.9, 100, 0.0, 1000000, 0, 0.025)
-60: s = s + 0.01 * syn.synth(2.9, 300, 0.0, 1000000, 0, 0.025)
-61: s = s + 0.02 * syn.synth(2.9, 500, 0.0, 1000000, 0, 0.025)
-62: plot(s)
-63: plot(s)
-64: plot(s)
-65: 1 / sqrt(5)
-66: w(s, 'thd_44_7_times.txt')
-67: s = syn.synth(2.9, 100, 0.0, 1000000, 0, 0.025)
-68: s = s + 0.1 syn.synth(2.9, 500, 0.0, 1000000, 0, 0.025)
-69: s = s + 0.1 * syn.synth(2.9, 500, 0.0, 1000000, 0, 0.025)
-70: s = s + 0.08 * syn.synth(2.9, 800, 0.0, 1000000, 0, 0.025)
-71: s = s + 0.005 * random.randn(len(s))
-72: w(s,'thd_0.1_0.08_n_0.005.txt)
-73: w(s,'thd_0.1_0.08_n_0.005.txt')
-74: help 
-75: help()
-76: #?
-77: #?save
-78: #?history
-79: histogram 
-80: _ip.magic("history ")
-81: _ip.magic("history -h")
-82: #?history -
-83: _ip.magic("history -f")
-84: #?history
-85: _ip.magic("history -f log 1")
-86: _ip.system(" view log")
--- a/samples/test/pure_sin.txt
+++ b/samples/test/pure_sin.txt
--- a/samples/test/thd_0.1_0.08_n_0.005.txt
+++ b/samples/test/thd_0.1_0.08_n_0.005.txt
--- a/samples/test/thd_44_7_times.txt
+++ b/samples/test/thd_44_7_times.txt
--- a/samples/x
+++ b/samples/x
--- a/test/00readme.txt
+++ b/test/00readme.txt
-pure_sin.txt			Pure sinewave, A = 2.9, fa = 100, phi = 0, 1MHz, t=0.02s
-gaussian_100th.txt		Same with 0.01 rms gaussian noise  (SNR = 2e2)
-gaussian_1000th.txt		Same with 0.001 rms gaussian noise (SNR = 2e3)
-incoherent.txt			Pure sinewave as above, incoherently sampled (t = 0.025s)
-incoherent_1000gaussian.txt	Same as above with 0.001 rms gaussian noise (SNR = 2e3)
-thd_44_7_times.txt		Same as above with 0.01 and 0.02 harmonics (SNR = 44.7)
-thd_0.1_0.08_n_0.005.txt	With 0.1 and 0.08 harmonics and 0.005 gaussian noise
-log				How these were made
--- a/test/ch4.txt
+++ b/test/ch4.txt
--- a/test/ch4mod.txt
+++ b/test/ch4mod.txt
--- a/test/diff-doc.txt
+++ b/test/diff-doc.txt
--- a/test/filter.py
+++ b/test/filter.py
-from sys import argv
-
-def adj(x):
-    return round(x/5.0 * (2**15))
-
-src = open(argv[1], "r")
-lines = [int(i.split(", ")[-1][:-1], int(argv[5])) for i in src.readlines()]
-src.close()
-
-dest = open(argv[2], "w")
-dest.write("[SIGNAL]\nnbits = %s\nrate = %s\ndata = " % (argv[3], argv[4]))
-dest.writelines('\t%d\n ' % i for i in lines[int(argv[6]):])
-dest.close()    
- 
-
--- a/test/gaussian_1000th.adc.txt
+++ b/test/gaussian_1000th.adc.txt
--- a/test/gaussian_1000th.txt
+++ b/test/gaussian_1000th.txt
--- a/test/gaussian_100th.adc.txt
+++ b/test/gaussian_100th.adc.txt
--- a/test/gaussian_100th.txt
+++ b/test/gaussian_100th.txt
--- a/test/incoherent.adc.txt
+++ b/test/incoherent.adc.txt
--- a/test/incoherent.txt
+++ b/test/incoherent.txt
--- a/test/incoherent_1000gaussian.adc.txt
+++ b/test/incoherent_1000gaussian.adc.txt
--- a/test/incoherent_1000gaussian.txt
+++ b/test/incoherent_1000gaussian.txt
--- a/test/log
+++ b/test/log
-1 : import syn
-2 : syn.synth(2.9, 100, 0.0, 1000000, 0, 0.02)
-3 : s = +
-4 : s = _
-5 : s
-6 : plot s
-7 : from matplotlib import pyplot
-8 : pyplot.plot(s)
-9 : pyplot.show()
-10: _ip.magic("save ")
-11: help(save)
-12: _ip.magic("save ")
-13: save(s, 'pure_sin.txt')
-14: save(s, 'pure_sin.txt')
-15: from numpy import *
-16: save(s, 'pure_sin.txt')
-17: save('pure_sin.txt', s)
-18: _ip.magic("edit pure_sin.txt.npy")
-19: help(save)
-20: out = file('pure_sin.txt', 'w')
-21:
-for i in s:
-    print >>out, i
-    
-
-22: out.close()
-23: _ip.system("view pure*")
-24: _ip.system("rm pure_sin.txt.npy")
-25:
-def w(s, f):
-    out = file(f, 'w')
-    for i in s:
-        print >>out, i
-    out.close()
-    
-
-26: 
-27: 
-28: 
-29: s
-30: t
-31: syn.synth(2.9, 100, 0.0, 1000000, 0, 0.02)
-32: random.randn(len(s))
-33: 
-34: 
-35: r = 2.9 * 0.001 * random.randn(len(s))
-36: s+r
-37: sn = s+r
-38: w(s, 'gaussian_1000th.txt')
-39: plot s
-40: w(sn, 'gaussian_1000th.txt')
-41: plot s
-42: plot = pyplot.plot
-43: plot(s)
-44: sn = s+r
-45: r = 2.9 * 0.01 * random.randn(len(s))
-46: sn = s+r
-47: w(sn, 'gaussian_100th.txt')
-48: plot(sn)
-49: r = 2.9 * 0.001 * random.randn(len(s))
-50: sn = s+r
-51: w(sn, 'gaussian_100th.txt')
-52: plot(sn)
-53: s = syn.synth(2.9, 100, 0.0, 1000000, 0, 0.025)
-54: sincoh = s
-55: w(sincoh, 'incoherent.txt')
-56: sincohn = s + 0.001 * 2.9 * random.randn(len(s))
-57: w(sincohn, 'incoherent_1000gaussian.txt')
-58: plot(sincohn)
-59: s = syn.synth(2.9, 100, 0.0, 1000000, 0, 0.025)
-60: s = s + 0.01 * syn.synth(2.9, 300, 0.0, 1000000, 0, 0.025)
-61: s = s + 0.02 * syn.synth(2.9, 500, 0.0, 1000000, 0, 0.025)
-62: plot(s)
-63: plot(s)
-64: plot(s)
-65: 1 / sqrt(5)
-66: w(s, 'thd_44_7_times.txt')
-67: s = syn.synth(2.9, 100, 0.0, 1000000, 0, 0.025)
-68: s = s + 0.1 syn.synth(2.9, 500, 0.0, 1000000, 0, 0.025)
-69: s = s + 0.1 * syn.synth(2.9, 500, 0.0, 1000000, 0, 0.025)
-70: s = s + 0.08 * syn.synth(2.9, 800, 0.0, 1000000, 0, 0.025)
-71: s = s + 0.005 * random.randn(len(s))
-72: w(s,'thd_0.1_0.08_n_0.005.txt)
-73: w(s,'thd_0.1_0.08_n_0.005.txt')
-74: help 
-75: help()
-76: #?
-77: #?save
-78: #?history
-79: histogram 
-80: _ip.magic("history ")
-81: _ip.magic("history -h")
-82: #?history -
-83: _ip.magic("history -f")
-84: #?history
-85: _ip.magic("history -f log 1")
-86: _ip.system(" view log")
--- a/test/pure_sin.txt
+++ b/test/pure_sin.txt
--- a/test/pure_sin_adc.txt
+++ b/test/pure_sin_adc.txt
--- a/test/thd_0.1_0.08_n_0.005.adc.txt
+++ b/test/thd_0.1_0.08_n_0.005.adc.txt
--- a/test/thd_0.1_0.08_n_0.005.txt
+++ b/test/thd_0.1_0.08_n_0.005.txt
--- a/test/thd_44_7_times.txt
+++ b/test/thd_44_7_times.txt