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f0(x),...,f4(x) that give these graphs.
For example, the sawtooth wave can be done as follows:
def f1(x):
return x - np.floor(x)
np.abs(), np.mod(), np.floor(),
np.round() and np.sin() in appropriate ways.
Make sure that your functions work correctly on arrays as well as scalars.
Plot your functions to check that they are correct. Put
functions=[f0,f1,f2,f3,f4], to
make it easier to use the functions in a loop etc.
N=64. Write your code for the subtasks below
in terms of N, so that you can easily change it if necessary.
Put ts=np.arange(0,1,1/N). (Note that this gives an array
whose last entry is $1-1/N$, not $1$. This is appropriate if we want
exact formulae for Fourier transforms and so on.) For each of the above
functions $f()$, calculate the values on the array ts
then use
scipy.fft.fft()
to calculate the discrete Fourier transform.
(One could also use
np.fft.fft(),
but we choose to use scipy functions throughout this lab.)
This gives a one-dimensional array ft of N
complex numbers. Because the values of the original function are real,
it works out that ft[N-1-i] is the complex conjugate of
ft[i], so only the first N/2 values are
useful. Plot the absolute values of ft[i]/N for
i<N/2. (In the past
we have called plt.plot ax.plot with two
arguments x and y, but it you can also do
ax.plot(y) and it will plot y against the
numbers $0$ to $n-1$, where $n$ is the length of y.)
It is best to plot the values as isolated points, not as a connected
curve. In many cases, you will see that the pattern is different
of odd-numbered and even-numbered values. In one case the behaviour
depends on the value of the index mod $4$.
N is large and k is much smaller than N,
it can be shown that ft[k]/N is approximately as follows:
| Function | $k=0$ | $k>0$ even | $k>0$ odd | |
| 0 | Square wave | $1/2$ | $0$ | $i/(\pi k)$ |
| 1 | Sawtooth wave | $1/2$ | $i/(2\pi k)$ | |
| 2 | Triangle wave | $1/4$ | $0$ | $-1/(\pi^2k^2)$ |
| 3 | Pulse wave | $1/4$ | $-i(1-(-i)^k)/(2\pi k)$ | |
| 4 | Rectified sine wave | $2/(\pi(1-4k^2))$ | ||
ft[k]/N obtained previously. Here
you should let k go all the way up to N-1,
to see the different pattern of behaviour for small k and
large k. Remember that Python notation for the
square root of $-1$ is 1j.
types.txt and a collection of files
AFIB.mat, AFL.mat and so on. Each of the
.mat files contains electrocardiogram data (a record of
the electrical activity of the heart) for a patient with a particular
condition. (This is a small subset of the data used in the paper
Novel genetic ensembles of classifiers applied to
myocardium dysfunction recognition based on ECG signals by
Paweł Pławiak.)
types.txt is
04,AFIB,Atrial fibrillation, indicating that the file
AFIB.mat contains data for a patient with atrial
fibrillation; the other lines give corresponding information for the
other files.
.mat files are in Matlab format, so they can be read
using
scipy.io.loadmat().
If you inspect the value returned by scipy.io.loadmat(),
you will find that it contains a one-dimensional numpy array
wrapped up in some useless packaging. You will need to work out how to
extract the one-dimensional array itself. The array contains 10 seconds of
data, with 360 measurements per second.
make_ecg_dict() that reads the contents
of types.txt and returns a dict ecg_dict.
This should be set up in such a way that
ecg_dict[4]=ecg_dict['AFIB']=(4,'AFIB','Atrial fibrillation').
Then run ecg_dict = make_ecg_dict() so that the
dict is available in the following subtasks.
get_ecg_data(n). This should allow
n to either be a number or a code like 'AFIB'.
It should return a tuple (index,code,full_name,data) where
index is the number of the relevant line in
types.txt, code is the code like
'AFIB', full_name is the full name like
'Atrial fibrillation', and data is the
numpy array of data for the patient.
scipy.signal.find_peaks()
to find the location of the peaks.
You may need to experiment with the parameters of
scipy.signal.find_peaks() to pick out the primary peaks
and ignore spurious ones closer to zero. Add red dots marking the
peaks on the plot. Calculate the average spacing between the peaks.
filter = scipy.signal.butter(4, 0.2, 'high', output='sos')
x_filtered = scipy.signal.sosfilt(filter, x)
scipy.signal.periodogram()
to calculate the power spectrum of the data. Note that there
is an optional argument that you can use to specify that the
sampling frequency is 360 Hz. This will return a pair
(fs, ps), where fs is an array of
frequencies and ps is an array of power values.
Discard all frequencies greater than 50 Hz. (For example, you
could use boolean indexing to do this.)
Plot the power spectrum.
show_ecg(n) to do
all the above steps. It should accept an argument n
as in get_ecg_data(). It should plot a figure
of size $12\times 4$ with three subplots. The first subplot
should show the normalised data with red dots marking the peaks,
the middle subplot should show the filtered data, and the last
subplot should show the power spectrum. The title of the
first subplot should be the full name of the condition,
and the titles of the other two should be 'High-pass filtered'
and 'Power spectrum' respectively.
blackbird.wav
containing a short recording of a blackbird, and four similar
files for other birds.
play_sound(n) which plays the
recording of the bird named n. You may need to use
pip to install the package simpleaudio
first. (There are also other packages that do the same job,
you could investigate the possibilities if you like.)
scipy.io.wavfile.read()
to get the data from the corresponding file. This will return a pair
(rate, data), where rate is the sampling
rate and data is the array of data. For a mono
recording, data will be a one-dimensional array.
For a stereo recording, data will be two-dimensional
(so data.ndim will be $2$). In this case, you should
do data = data.mean(axis=1) to take the average of
the two channels. Then normalise the data by subtracting the mean
and dividing by the standard deviation. If n is the
length of the data, then the corresponding array of times is
t = np.arange(n)/rate. Plot the normalised data
against the times.
data and rate as above, you can use
spf, spt, spp = scipy.signal.spectrogram(data, fs=rate)
spf to
an array of frequencies, spt to an array of times, and
spp to a two-dimensional array of power values.
The value spp[i,j] is the power at frequency
spf[i] and time spt[j]. To plot these,
we could do plt.pcolormesh(spt, spf, spp). However,
this gives a picture which is basically just an evenly coloured
rectangle. The main problem is that the range of values in the data
is too large. We can fix this by replacing spp with
np.maximum(0,np.log(spp)+15). Also, it is better
to make the colour white in places where the power is very low.
For this we need to use the cmap argument to change
the colour map. There is a list of possibilities at
matplotlib.org.
The key point is to choose one that has white at the left hand end.
show_spectrogram(n).
This should generate a figure of size $10\times 5$ with two subplots.
The upper half should plot the normalised data against the times, and
the lower half should plot the spectrogram. To make the time axes
of the two subplots match up, you should set the limits of $x$
in the upper plot to t[0] and t[-1].
You should also set the title of the upper plot to the name of the bird.
Run the function for all five birds.