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How to find period of signal (autocorrelation vs fast fourier transform vs power spectral density)?

Suppose one wanted to find the period of a given sinusoidal wave signal. From what I have read online, it appears that the two main approaches employ either fourier analysis or autocorrelation. I am trying to automate the process using python and my usage case is to apply this concept to similar signals that come from the time-series of positions (or speeds or accelerations) of simulated bodies orbiting a star.
For simple-examples-sake, consider x = sin(t) for 0 ≤ t ≤ 10 pi.
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
## sample data
t = np.linspace(0, 10 * np.pi, 100)
x = np.sin(t)
fig, ax = plt.subplots()
ax.plot(t, x, color='b', marker='o')
ax.grid(color='k', alpha=0.3, linestyle=':')
plt.show()
plt.close(fig)
Given a sine-wave of the form x = a sin(b(t+c)) + d, the period of the sine-wave is obtained as 2 * pi / b. Since b=1 (or by visual inspection), the period of our sine wave is 2 * pi. I can check the results obtained from other methods against this baseline.
Attempt 1: Autocorrelation
As I understand it (please correct me if I'm wrong), correlation can be used to see if one signal is a time-lagged copy of another signal (similar to how cosine and sine differ by a phase difference). So autocorrelation is testing a signal against itself to measure the times at which the time-lag repeats said signal. Using the example posted here:
result = np.correlate(x, x, mode='full')
Since x and t each consist of 100 elements and result consists of 199 elements, I am not sure why I should arbitrarily select the last 100 elements.
print("\n autocorrelation (shape={}):\n{}\n".format(result.shape, result))
autocorrelation (shape=(199,)):
[ 0.00000000e+00 -3.82130761e-16 -9.73648712e-02 -3.70014208e-01
-8.59889695e-01 -1.56185995e+00 -2.41986054e+00 -3.33109112e+00
-4.15799070e+00 -4.74662427e+00 -4.94918053e+00 -4.64762251e+00
-3.77524157e+00 -2.33298717e+00 -3.97976240e-01 1.87752669e+00
4.27722402e+00 6.54129270e+00 8.39434617e+00 9.57785701e+00
9.88331103e+00 9.18204933e+00 7.44791758e+00 4.76948221e+00
1.34963425e+00 -2.50822289e+00 -6.42666652e+00 -9.99116299e+00
-1.27937834e+01 -1.44791297e+01 -1.47873668e+01 -1.35893098e+01
-1.09091510e+01 -6.93157447e+00 -1.99159756e+00 3.45267493e+00
8.86228186e+00 1.36707567e+01 1.73433176e+01 1.94357232e+01
1.96463736e+01 1.78556800e+01 1.41478477e+01 8.81191526e+00
2.32100171e+00 -4.70897483e+00 -1.15775811e+01 -1.75696560e+01
-2.20296487e+01 -2.44327920e+01 -2.44454330e+01 -2.19677060e+01
-1.71533510e+01 -1.04037163e+01 -2.33560966e+00 6.27458308e+00
1.45655029e+01 2.16769872e+01 2.68391837e+01 2.94553896e+01
2.91697473e+01 2.59122266e+01 1.99154591e+01 1.17007613e+01
2.03381596e+00 -8.14633251e+00 -1.78184255e+01 -2.59814393e+01
-3.17580589e+01 -3.44884934e+01 -3.38046447e+01 -2.96763956e+01
-2.24244433e+01 -1.26974172e+01 -1.41464998e+00 1.03204331e+01
2.13281784e+01 3.04712823e+01 3.67721634e+01 3.95170295e+01
3.83356037e+01 3.32477037e+01 2.46710643e+01 1.33886439e+01
4.77778141e-01 -1.27924775e+01 -2.50860560e+01 -3.51343866e+01
-4.18671622e+01 -4.45258983e+01 -4.27482779e+01 -3.66140001e+01
-2.66465884e+01 -1.37700036e+01 7.76494745e-01 1.55574483e+01
2.90828312e+01 3.99582426e+01 4.70285203e+01 4.95000000e+01
4.70285203e+01 3.99582426e+01 2.90828312e+01 1.55574483e+01
7.76494745e-01 -1.37700036e+01 -2.66465884e+01 -3.66140001e+01
-4.27482779e+01 -4.45258983e+01 -4.18671622e+01 -3.51343866e+01
-2.50860560e+01 -1.27924775e+01 4.77778141e-01 1.33886439e+01
2.46710643e+01 3.32477037e+01 3.83356037e+01 3.95170295e+01
3.67721634e+01 3.04712823e+01 2.13281784e+01 1.03204331e+01
-1.41464998e+00 -1.26974172e+01 -2.24244433e+01 -2.96763956e+01
-3.38046447e+01 -3.44884934e+01 -3.17580589e+01 -2.59814393e+01
-1.78184255e+01 -8.14633251e+00 2.03381596e+00 1.17007613e+01
1.99154591e+01 2.59122266e+01 2.91697473e+01 2.94553896e+01
2.68391837e+01 2.16769872e+01 1.45655029e+01 6.27458308e+00
-2.33560966e+00 -1.04037163e+01 -1.71533510e+01 -2.19677060e+01
-2.44454330e+01 -2.44327920e+01 -2.20296487e+01 -1.75696560e+01
-1.15775811e+01 -4.70897483e+00 2.32100171e+00 8.81191526e+00
1.41478477e+01 1.78556800e+01 1.96463736e+01 1.94357232e+01
1.73433176e+01 1.36707567e+01 8.86228186e+00 3.45267493e+00
-1.99159756e+00 -6.93157447e+00 -1.09091510e+01 -1.35893098e+01
-1.47873668e+01 -1.44791297e+01 -1.27937834e+01 -9.99116299e+00
-6.42666652e+00 -2.50822289e+00 1.34963425e+00 4.76948221e+00
7.44791758e+00 9.18204933e+00 9.88331103e+00 9.57785701e+00
8.39434617e+00 6.54129270e+00 4.27722402e+00 1.87752669e+00
-3.97976240e-01 -2.33298717e+00 -3.77524157e+00 -4.64762251e+00
-4.94918053e+00 -4.74662427e+00 -4.15799070e+00 -3.33109112e+00
-2.41986054e+00 -1.56185995e+00 -8.59889695e-01 -3.70014208e-01
-9.73648712e-02 -3.82130761e-16 0.00000000e+00]
Attempt 2: Fourier
Since I am not sure where to go from the last attempt, I sought a new attempt. To my understanding, Fourier analysis basically shifts a signal from/to the time-domain (x(t) vs t) to/from the frequency domain (x(t) vs f=1/t); the signal in frequency-space should appear as a sinusoidal wave that dampens over time. The period is obtained from the most observed frequency since this is the location of the peak of the distribution of frequencies.
Since my values are all real-valued, applying the Fourier transform should mean my output values are all complex-valued. I wouldn't think this is a problem, except for the fact that scipy has methods for real-values. I do not fully understand the differences between all of the different scipy methods. That makes following the algorithm proposed in this posted solution hard for me to follow (ie, how/why is the threshold value picked?).
omega = np.fft.fft(x)
freq = np.fft.fftfreq(x.size, 1)
threshold = 0
idx = np.where(abs(omega)>threshold)[0][-1]
max_f = abs(freq[idx])
print(max_f)
This outputs 0.01, meaning the period is 1/0.01 = 100. This doesn't make sense either.
Attempt 3: Power Spectral Density
According to the scipy docs, I should be able to estimate the power spectral density (psd) of the signal using a periodogram (which, according to wikipedia, is the fourier transform of the autocorrelation function). By selecting the dominant frequency fmax at which the signal peaks, the period of the signal can be obtained as 1 / fmax.
freq, pdensity = signal.periodogram(x)
fig, ax = plt.subplots()
ax.plot(freq, pdensity, color='r')
ax.grid(color='k', alpha=0.3, linestyle=':')
plt.show()
plt.close(fig)
The periodogram shown below peaks at 49.076... at a frequency of fmax = 0.05. So, period = 1/fmax = 20. This doesn't make sense to me. I have a feeling it has something to do with the sampling rate, but don't know enough to confirm or progress further.
I realize I am missing some fundamental gaps in understanding how these things work. There are a lot of resources online, but it's hard to find this needle in the haystack. Can someone help me learn more about this?

Let's first look at your signal (I've added endpoint=False to make the division even):
t = np.linspace(0, 10*np.pi, 100, endpoint=False)
x = np.sin(t)
Let's divide out the radians (essentially by taking t /= 2*np.pi) and create the same signal by relating to frequencies:
fs = 20 # Sampling rate of 100/5 = 20 (e.g. Hz)
f = 1 # Signal frequency of 1 (e.g. Hz)
t = np.linspace(0, 5, 5*fs, endpoint=False)
x = np.sin(2*np.pi*f*t)
This makes it more salient that f/fs == 1/20 == 0.05 (i.e. the periodicity of the signal is exactly 20 samples). Frequencies in a digital signal always relate to its sampling rate, as you have already guessed. Note that the actual signal is exactly the same no matter what the values of f and fs are, as long as their ratio is the same:
fs = 1 # Natural units
f = 0.05
t = np.linspace(0, 100, 100*fs, endpoint=False)
x = np.sin(2*np.pi*f*t)
In the following I'll use these natural units (fs = 1). The only difference will be in t and hence the generated frequency axes.
Autocorrelation
Your understanding of what the autocorrelation function does is correct. It detects the correlation of a signal with a time-lagged version of itself. It does this by sliding the signal over itself as seen in the right column here (from Wikipedia):
Note that as both inputs to the correlation function are the same, the resulting signal is necessarily symmetric. That is why the output of np.correlate is usually sliced from the middle:
acf = np.correlate(x, x, 'full')[-len(x):]
Now index 0 corresponds to 0 delay between the two copies of the signal.
Next you'll want to find the index or delay that presents the largest correlation. Due to the shrinking overlap this will by default also be index 0, so the following won't work:
acf.argmax() # Always returns 0
Instead I recommend to find the largest peak instead, where a peak is defined to be any index with a larger value than both its direct neighbours:
inflection = np.diff(np.sign(np.diff(acf))) # Find the second-order differences
peaks = (inflection < 0).nonzero()[0] + 1 # Find where they are negative
delay = peaks[acf[peaks].argmax()] # Of those, find the index with the maximum value
Now delay == 20, which tells you that the signal has a frequency of 1/20 of its sampling rate:
signal_freq = fs/delay # Gives 0.05
Fourier transform
You used the following to calculate the FFT:
omega = np.fft.fft(x)
freq = np.fft.fftfreq(x.size, 1)
Thhese functions re designed for complex-valued signals. They will work for real-valued signals, but you'll get a symmetric output as the negative frequency components will be identical to the positive frequency components. NumPy provides separate functions for real-valued signals:
ft = np.fft.rfft(x)
freqs = np.fft.rfftfreq(len(x), t[1]-t[0]) # Get frequency axis from the time axis
mags = abs(ft) # We don't care about the phase information here
Let's have a look:
plt.plot(freqs, mags)
plt.show()
Note two things: the peak is at frequency 0.05, and the maximum frequency on the axis is 0.5 (the Nyquist frequency, which is exactly half the sampling rate). If we had picked fs = 20, this would be 10.
Now let's find the maximum. The thresholding method you have tried can work, but the target frequency bin is selected blindly and so this method would suffer in the presence of other signals. We could just select the maximum value:
signal_freq = freqs[mags.argmax()] # Gives 0.05
However, this would fail if, e.g., we have a large DC offset (and hence a large component in index 0). In that case we could just select the highest peak again, to make it more robust:
inflection = np.diff(np.sign(np.diff(mags)))
peaks = (inflection < 0).nonzero()[0] + 1
peak = peaks[mags[peaks].argmax()]
signal_freq = freqs[peak] # Gives 0.05
If we had picked fs = 20, this would have given signal_freq == 1.0 due to the different time axis from which the frequency axis was generated.
Periodogram
The method here is essentially the same. The autocorrelation function of x has the same time axis and period as x, so we can use the FFT as above to find the signal frequency:
pdg = np.fft.rfft(acf)
freqs = np.fft.rfftfreq(len(x), t[1]-t[0])
plt.plot(freqs, abs(pdg))
plt.show()
This curve obviously has slightly different characteristics from the direct FFT on x, but the main takeaways are the same: the frequency axis ranges from 0 to 0.5*fs, and we find a peak at the same signal frequency as before: freqs[abs(pdg).argmax()] == 0.05.
Edit:
To measure the actual periodicity of np.sin, we can just use the "angle axis" that we passed to np.sin instead of the time axis when generating the frequency axis:
freqs = np.fft.rfftfreq(len(x), 2*np.pi*f*(t[1]-t[0]))
rad_period = 1/freqs[mags.argmax()] # 6.283185307179586
Though that seems pointless, right? We pass in 2*np.pi and we get 2*np.pi. However, we can do the same with any regular time axis, without presupposing pi at any point:
fs = 10
t = np.arange(1000)/fs
x = np.sin(t)
rad_period = 1/np.fft.rfftfreq(len(x), 1/fs)[abs(np.fft.rfft(x)).argmax()] # 6.25
Naturally, the true value now lies in between two bins. That's where interpolation comes in and the associated need to choose a suitable window function.

How to get frequency axis from an fft function?

So, I am probably missing something obvious, but I have searched through lots of tutorials and documentation and can't seem to find a straight answer. How do you find the frequency axis of a function that you performed an fft on in Python(specifically the fft in the scipy library)?
I am trying to get a raw EMG signal, perform a bandpass filter on it, and then perform an fft to see the remaining frequency components. However, I am not sure how to find an accurate x component list. The specific signal I am working on currently was sampled at 1000 Hz and has 5378 samples.
Is it just creating a linear x starting from 0 and going to the length of the fft'd data? I see a lot of people creating a linspace from 0 to sample points times the sample spacing. But what would be my sample spacing in this case? Would it just be samples/sampling rate? Or is it something else completely?

Here is an example.
First create a sine wave with sampling interval pre-determined. we will combine two sine waves with frequencies 20 and 40. Remember high frequencies might be aliased if the time interval is large.
#Import the necessary packages
from scipy import fftpack
import matplotlib.pyplot as plt
import numpy as np
# sampling freq in herts 20Hz, and 40Hz
freq_sampling1 = 10
freq_sampling2 = 20
amplitude1 = 2 # amplitude of first sine wave
amplitude2 = 4 # amplitude of second sine wave
time = np.linspace(0, 6, 500, endpoint=True) # time range with total samples of 500 from 0 to 6 with time interval equals 6/500
y = amplitude1*np.sin(2*np.pi*freq_sampling1*time) + amplitude2*np.sin(2*np.pi*freq_sampling2*time)
plt.figure(figsize=(10, 4))
plt.plot(time,y, 'k', lw=0.8)
plt.xlim(0,6)
plt.show()
Notice in the figure that two sine waves are superimposed. One with freq. 10 and amplitude 2 and the other with freq. 20 and amplitude 4.
# apply fft function
yf = fftpack.fft(y, time.size)
amp = np.abs(yf) # get amplitude spectrum
freq = np.linspace(0.0, 1.0/(2.0*(6/500)), time.size//2) # get freq axis
# plot the amp spectrum
plt.figure(figsize=(10,6))
plt.plot(freq, (2/amp.size)*amp[0:amp.size//2])
plt.show()
Notice in the amplitude spectrum the two frequencies are recovered while amplitude is zero at other frequencies. the Amplitude values are also 2 and 4 respectively.
you can use instead fftpack.fftfreq to obtain frequency axis as suggested by tom10
Therefore, the code changes to
yf = fftpack.fft(y, time.size)
amp = np.abs(yf) # get amplitude spectrum
freq = fftpack.fftfreq(time.size, 6/500)
plt.figure(figsize=(10,6))
plt.plot(freq[0:freq.size//2], (2/amp.size)*amp[0:amp.size//2])
plt.show()
We are only plotting the positive part of the amplitude spectrum [0:amp.size//2]

Once you feed your window of samples into the FFT call it will return an array of imaginary points ... the freqency separation between each element of returned array is determined by
freq_resolution = sampling_freq / number_of_samples
the 0th element is your DC offset which will be zero if your input curve is balanced straddling the zero crossing point ... so in your case
freq_resolution = 1000 / 5378
In general, for efficiency, you will want to feed an even power of 2 number of samples into your FFT call, important if you are say sliding your window of samples forward in time and repeatedly calling FFT on each window
To calculate the magnitude of a frequency in a given freq_bin (an element of the returned imaginary array)
X = A + jB
A on real axis
B on imag axis
for above formula its
mag = 2.0 * math.Sqrt(A*A+B*B) / number_of_samples
phase == arctan( B / A )
you iterate across each element up to the Nyquist limit which is why you double above magnitude
So yes its a linear increment with same frequency spacing between each freq_bin

What is the proper way to plot spectrum of a complex signal sampled in a narrow range?

I have some complex data (small bandwidth around a set frequency) that I'm curious to plot, but I am slightly lost as to how should I proceed about interpreting a complex signal sampled in a particular range.
So, for instance, here is the code (and my rather poor attempt at the problem) that I wrote so I have a clean example to experiment with artificial signal, that generates complex representation of 78 KHz wave. What I am trying to do is to get a plot that is centred at 120 KHz, and spans from 70 to 170 KHz, mimicking narrow sampling range of the real receiver.
import numpy as np
import matplotlib.pyplot as plt
#sampling rate, samples/second; 100 KHz
rate = 100*10**3
#sample spacing in time, seconds/sample
interval = np.true_divide(1, rate)
#length of the fourier transform
n = 256
#time vector
t = np.linspace(0.0, n*interval, n)
#frequency of artificial signal; 78 KHz
f = 78*10**3
#complex signal
s = np.exp(1j*2*np.pi*f*t)
#dft of the data
dft = np.fft.fft(s)
#frequency bins
x = np.fft.fftfreq(n, d=interval)
#center zero-frequency component in data; take absolute values
dft = np.abs(np.fft.fftshift(dft))
#center zero frequency component in bins; naively add the center frequency, 120 KHz
x = np.fft.fftshift(x) + 120*10**3
plt.plot(x, dft)
plt.show()
The output, is wrong, as expected with the crude attempt to mimic particular frequency range.
Plot made by the code snippet above
P.S. Different plot , with f = 88*10*83 - why has the magnitude changed here, suddenly?
Edit: My post has been marked for duplicated with a topic related purely to plotting, while what I'm actually after is processing and/or inversion of bandpass-filtered data.

Nyquist Frequency and Aliasing
Your signal should be a (complex) exponential oscillation at +78 kHz, sampled at 100 kHz. This doesn't work. What you see instead is an alias frequency at -22 kHz (78 kHz - 100 kHz). You have to make sure that no frequency of your signal is higher than half your sampling frequency. For a signal of 78 kHz take a sample frequency of 200 kHz, for example.
import numpy as np
from matplotlib import pyplot as plt
sample_frequency = 200e3 # 200 kHz
sample_interval = 1 / sample_frequency
samples = 256 # you don't necessarily have to use a power of 2
time = np.linspace(0, samples*sample_interval, samples)
signal_frequency = 78e3 # 78 kHz
signal = np.exp(2j*np.pi*signal_frequency*time)
FFT and fftfreq
np.fft.fftfreq already returns the right frequencies, adding a "center frequency" mekes no sense. Don't do it.
signal_spectrum = np.fft.fftshift(np.fft.fft(signal))
freqs = np.fft.fftshift(np.fft.fftfreq(samples, d=sample_interval))
Plotting
The plotting part of your question is only about setting the axes. Use plt.xlim.
plt.figure(figsize=(10,5))
plt.plot(freqs / 1e3, np.abs(signal_spectrum)) # in kHz
plt.xlim(70, 170)
The plotted line ends just before 100 kHz, because as mentioned above your signal cannot have a frequency part higher than your half sample frequency.
Magnitude of spectrum
Since your signal is time-discrete (several single samples, not a continuous function), your spectrum is continuous. The Discrete Fourier Transform, however, only returns discrete samples of the continuous spectrum. If you would fit a curve through the sampling points, its peak would have the same magnitude for different frequencies.
Alternatively, you could increase the number of FFT sampling points by zero-padding your signal (take a look at the numpy.fft.fft documentation):
signal_spectrum = np.fft.fftshift(np.fft.fft(signal, 10*samples))
freqs = np.fft.fftshift(np.fft.fftfreq(10*samples, d=sample_interval))
plt.figure(figsize=(10,5))
plt.plot(freqs / 1e3, np.abs(signal_spectrum)) # in kHz
plt.xlim(65, 95).
plt.grid()
If you're asking yourself why the spectrum looks so rippled, Take a look at spectral leakage.

Extracting meaning from an FFT analysis of data

My question is about Fast Fourier Transforms, since this is the first time i'm using them.
So, I have a set of data by years (from 1700 - 2009) and each year corresponding to a certain value (a reading).
when i plot the readings against the years it gives me the first plot below. Now, my aim is to find the dominant period with the highest readings using FFT with python (From the graph it seems that it is around 1940 - 1950). So i performed an FFT and got its amplitude and power spectra (see second plot for power spectrum). The power spectrum shows that the dominant frequencies are between 0.08 and 0.1 (cycles/year). My question is, how do i link this to the Readings vs. years ? i.e how do i know from this dominant frequency what year (or period of years) is the dominant one (or how can i use it to find it) ?
The data list can be found here:
http://www.physics.utoronto.ca/%7Ephy225h/web-pages/sunspot_yearly.txt
the code i wrote is:
from pylab import *
from numpy import *
from scipy import *
from scipy.optimize import leastsq
import numpy.fft
#-------------------------------------------------------------------------------
# Defining the time array
tmin = 0
tmax = 100 * pi
delta = 0.1
t = arange(tmin, tmax, delta)
# Loading the data from the text file
year, N_sunspots = loadtxt('/Users/.../Desktop/sunspot_yearly.txt', unpack = True) # years and number of sunspots
# Ploting the data
figure(1)
plot(year, N_sunspots)
title('Number of Sunspots vs. Year')
xlabel('time(year)')
ylabel('N')
# Computing the FFT
N_w = fft(N_sunspots)
# Obtaining the frequencies
n = len(N_sunspots)
freq = fftfreq(n) # dt is default to 1
# keeping only positive terms
N = N_w[1:len(N_w)/2.0]/float(len(N_w[1:len(N_w)/2.0]))
w = freq[1:len(freq)/2.0]
figure(2)
plot(w, real(N))
plot(w, imag(N))
title('The data function f(w) vs. frequency')
xlabel('frequency(cycles/year)')
ylabel('f(w)')
grid(True)
# Amplitude spectrum
Amp_spec = abs(N)
figure(3)
plot(w, Amp_spec)
title('Amplitude spectrum')
xlabel('frequency(cycles/year)')
ylabel('Amplitude')
grid(True)
# Power spectrum
Pwr_spec = abs(N)**2
figure(4)
plot(w, Pwr_spec 'o')
title('Power spectrum')
xlabel('frequency(cycles/year)')
ylabel('Power')
grid(True)
show()

The graph below shows the data input to the FFT. The original data file contains a total of 309 samples. The zero values at the right end are added automatically by the FFT, to pad the number of input samples to the next higher power of two (2^9 = 512).
The graph below shows the data input to the FFT, with the Kaiser-Bessel a=3.5 window function applied. The window function reduces the spectral leakage errors in the FFT, when the input to the FFT is a non-periodic signal over the sampling interval, as in this case.
The graph below shows the FFT output at full scale. Without the window function. The peak is at 0.0917968 (47/512) frequency units, which corresponds to a time value of 10.89 years (1/0.0917968).
The graph below shows the FFT output at full scale. With the Kaiser-Bessel a=3.5 window applied. The peak remains in the same frequency location at 0.0917968 (47/512) frequency units, which corresponds to a time value of 10.89 years (1/0.0917968). The peak is more clearly visible above the background, due to the reduction in spectral leakage provided by the window function.
In conclusion, we can say with high certainty that the Sun spot data, provided in the original post, is periodic with a fundamental period of 10.89 years.
FFT and graphs were done with the Sooeet FFT calculator

Improving wave detection algorithm

I am new to python and programming and working on the wave peak detection algorithm on my spline - interpolated plot. I have used the code given on this link : https://gist.github.com/endolith/250860 . I have to make this algorithm work on any type of waveform i.e. low as well as high amplitude, baseline not aligned, etc. The goal is to calculate the no of waves in the plot. But my peak detection calculates "invalid" peaks and so gives the wrong answers. By "invalid" peaks I mean if there are two notches close to each other at the wave peak, the program detects 2 peaks i.e. 2 waves when actually its just 1 wave. I have tried changing the 'delta' parameter defined in the peak detection function given on the link but that doesn't solve the generalisation goal which I am working on..Please suggest any improvement on the algorithm or any other approach which I should be using. Any kind of help is welcomed. Thanks in advance.
P.S. I am unable to upload an image of the wrong peak-detected wave plot. I hope my explanation is sufficient enough...
Code is as follows
wave = f1(xnew)/(max(f1(xnew))) ##interpolated wave
maxtab1, mintab1 = peakdet(wave,.005)
maxi = max(maxtab1[:,1])
for i in range(len(maxtab1)):
if maxtab1[i,1] > (.55 * maxi) : ## Thresholding
maxtab.append((maxtab1[i,0],maxtab1[i,1]))
arr_maxtab = np.array(maxtab)
dist = 1500 ## Threshold defined for minimum distance between two notches to be considered as two separate waves
mtdiff = diff(arr_maxtabrr[:,0])
final_maxtab = []
new_arr = []
for i in range(len(mtdiff)):
if mtdiff[i] < dist :
new_arr.append((arr_maxtab[i+1,0],arr_maxtab[i+1,1]))
for i in range(len(arr_maxtab)):
if not arr_maxtab[i,0] in new_arr[:,0]:
final_maxtab.append((arr_maxtab[i,0], arr_maxtab[i,1]))
else:
final_maxtab = maxtab

The ability to distinguish notches from true peaks implies you have a fundamental spacing between peaks. Said differently, there is a minimum frequency resolution at which you would like to run your peak detection search. If you zoom into a signal at which you are well narrower than the noise floor, you'll observe zig zags that seem to 'peak' every few samples.
What it sounds like you want to do is the following:
Smooth the signal.
Find the 'real' peaks.
Or more precisely,
Run the signal through a low pass filter.
Find the peaks within your acceptable peak widths with sufficient signal to noise ratio.
Step 1: Low-Pass Filtering
To do step 1, I recommend you use the signal processing tools provided by scipy.
I'm adapted this cookbook entry, which shows how to use FIR filters to lowpass a signal using scipy.
from numpy import cos, sin, pi, absolute, arange, arange
from scipy.signal import kaiserord, lfilter, firwin, freqz, find_peaks_cwt
from pylab import figure, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show, scatter
#------------------------------------------------
# Create a signal. Using wave for the signal name.
#------------------------------------------------
sample_rate = 100.0
nsamples = 400
t = arange(nsamples) / sample_rate
wave = cos(2*pi*0.5*t) + 0.2*sin(2*pi*2.5*t+0.1) + \
0.2*sin(2*pi*15.3*t) + 0.1*sin(2*pi*16.7*t + 0.1) + \
0.1*sin(2*pi*23.45*t+.8)
#------------------------------------------------
# Create a FIR filter and apply it to wave.
#------------------------------------------------
# The Nyquist rate of the signal.
nyq_rate = sample_rate / 2.0
# The desired width of the transition from pass to stop,
# relative to the Nyquist rate. We'll design the filter
# with a 5 Hz transition width.
width = 5.0/nyq_rate
# The desired attenuation in the stop band, in dB.
ripple_db = 60.0
# Compute the order and Kaiser parameter for the FIR filter.
N, beta = kaiserord(ripple_db, width)
# The cutoff frequency of the filter.
cutoff_hz = 10.0
# Use firwin with a Kaiser window to create a lowpass FIR filter.
taps = firwin(N, cutoff_hz/nyq_rate, window=('kaiser', beta))
# Use lfilter to filter x with the FIR filter.
filtered_x = lfilter(taps, 1.0, wave)
Step 2: Peak Finding
For step 2, I recommend you use the wavelet transformation peak finder provided by scipy. You must provide as an input your filtered signal and a vector running from the minimum to maximum possible peak widths. This vector will be used as the basis of the wavelet transformation.
#------------------------------------------------
# Step 2: Find the peaks
#------------------------------------------------
figure(4)
plot(t[N-1:]-delay, filtered_x[N-1:], 'g', linewidth=1)
peakind = find_peaks_cwt(filtered_x, arange(3,20))
scatter([t[i] - delay for i in peakind], [filtered_x[i] for i in peakind], color="red")
for i in peakind:
print t[i] + delay
xlabel('t')
grid(True)
show()