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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Global Signals in Time Series Data
By Abigail Stevens
Problem 1
Step3:  1a. Make an array of Fourier frequencies
Yes you can do this with scipy, but the order of frequencies in a T&K power spectrum is different than what you'd get by default from a standard FFT of a light curve.
You want the zero frequency to be in the middle (at index n_bins/2) of the frequency array. The positive frequencies should have two more indices than the negative frequencies, because of the zero frequency and nyquist frequency. You can either do this with np.arange or with special options in fftpack.fftfreq.
1b. Define a Lorentzian function and power law function for the shape of the power spectrum
Step7:  Now the T&K algorithm. I've transcribed the 'recipe' section of the T&K95 paper, which you will convert to lines of code.
1c. Choose a power spectrum $S(\nu)$.
We will use a sum of one Lorentzians (a QPO with a centroid frequency of 0.5 Hz and a FWHM of 0.01 Hz), and a Poisson-noise power law. The QPO should be 100 times larger amplitude than the power-law.
1d.  For each Fourier frequency $\nu_i$ draw two gaussian-distributed random numbers, multiply them by $$\sqrt{\frac{1}{2}S(\nu_i)}$$ and use the result as the real and imaginary part of the Fourier transform $F$ of the desired data.
In the case of an even number of data points, for reason of symmetry $F(\nu_{Nyquist})$ is always real. Thus only one gaussian distributed random number has to be drawn.
1e. To obtain a real valued time series, choose the Fourier components for the negative frequencies according to $F(-\nu_i)=F*(\nu_i)$ where the asterisk denotes complex conjugation.
Append to make one fourier transform array. Check that your T&K fourier transform has length n_bins. Again, for this algorithm, the zero Fourier frequency is in the middle of the array, the negative Fourier frequencies are in the first half, and the positive Fourier frequencies are in the second half.
1f. Obtain the time series by backward Fourier transformation of $F(\nu)$ from the frequency domain to the time domain.
Note
Step8:  2. More realistic simulation with T&K
Now you're able to simulate the power spectrum of a single segment of a light curve. However, as you learned this morning, we usually use multiple (~50+) segments of a light curve, take the power spectrum of each segment, and average them together.
2a. Turn the code from 1d to 1e into a function make_TK_seg
Make it so that you can give a different random seed to each segment.
2b. Make the Fourier transform for a given power shape (as in Problem 1)
Use a Lorentzian QPO + Poisson noise power shape at a centroid frequency of 0.5 Hz and a full width at half maximum (FWHM) of 0.01 Hz. Make the QPO 100 time stronger than the Poisson noise power-law.
2c. Put make_TK_seg in a loop to do for 50 segments.
Make an array of integers that can be your random gaussian seed for the TK algorithm (otherwise, you run the risk of creating the exact same Fourier transform every time, and that will be boring).
Keep a running average of the power spectrum of each segment (like we did this morning in problem 2).
2d. Compute the error on the average power
The error on the power at index $i$ is
$$ \delta P_i = \frac{P_i}{\sqrt{M}} $$
where M is the number of segments averaged together.
2e. Use the re-binning algorithm described in the morning's workbook to re-bin the power spectrum by a factor of 1.05.
Plot the average power spectrum
Remember to use log scale for the y-axis and probably the x-axis too!
Step10:  2f. Re-do 2b through the plot above but slightly changing the power spectrum shape in each segment.
Maybe you change the centroid frequency of the QPO, or the normalizing factors between the two components, or the slope of the power-law.
Bonus problems
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Python Code:
n_bins = 8192  ## number of total frequency bins in a FT segment; same as number of time bins in the light curve
dt = 1./16.  # time resolution of the output light curve
df = 1. / dt / n_bins
Explanation: Global Signals in Time Series Data
By Abigail Stevens
Problem 1: Timmer and Koenig algorithm
The algorithm outlined in Timmer & Koenig 1995 lets you define the shape of your power spectrum (a power law with some slope, a Lorentzian, a sum of a couple Lorentzians and a power law, etc.) then generate the random phases and amplitudes of the Fourier transform to simulate light curves defined by the power spectral shape. This is a great simulation tool to have in your back pocket (or, "maybe useful someday" github repo).
Define some basic parameters for the power spectrum and resultant light curve
End of explanation
def lorentzian(v, v_0, gamma):
     Gives a Lorentzian centered on v_0 with a FWHM of gamma 
    numerator = gamma / (np.pi * 2.0)
    denominator = (v - v_0) ** 2 + (1.0/2.0 * gamma) ** 2
    L = numerator / denominator
    return L
def powerlaw(v, beta):
    Gives a powerlaw of (1/v)^-beta 
    pl = np.zeros(len(v))
    pl[1:] = v[1:] ** (-beta)
    pl[0] = np.inf
    return pl
Explanation: 1a. Make an array of Fourier frequencies
Yes you can do this with scipy, but the order of frequencies in a T&K power spectrum is different than what you'd get by default from a standard FFT of a light curve.
You want the zero frequency to be in the middle (at index n_bins/2) of the frequency array. The positive frequencies should have two more indices than the negative frequencies, because of the zero frequency and nyquist frequency. You can either do this with np.arange or with special options in fftpack.fftfreq.
1b. Define a Lorentzian function and power law function for the shape of the power spectrum
End of explanation
def gaussian(v, mean, std_dev):
    
    Gives a Gaussian with a mean of mean and a standard deviation of std_dev
    FWHM = 2 * np.sqrt(2 * np.log(2))*std_dev
    
    exp_numerator = -(v - mean)**2
    exp_denominator = 2 * std_dev**2
    G = np.exp(exp_numerator / exp_denominator)
    return G
def powerlaw_expdecay(v, beta, alpha):
    Gives a powerlaw of (1/v)^-beta with an exponential decay e^{-alpha*v} 
    pl_exp = np.where(v != 0, (1.0 / v) ** beta * np.exp(-alpha * v), np.inf) 
    return pl_exp
def broken_powerlaw(v, v_b, beta_1, beta_2):
    Gives two powerlaws, (1/v)^-beta_1 and (1/v)^-beta_2 
       that cross over at break frequency v_b.
    c = v_b ** (-beta_1 + beta_2)  ## scale factor so that they're equal at the break frequency
    pl_1 = v[np.where(v <= v_b)] ** (-beta_1)
    pl_2 = c * v[np.where(v > v_b)] ** (-beta_2)
    pl = np.append(pl_1, pl_2)
    return pl
Explanation: Now the T&K algorithm. I've transcribed the 'recipe' section of the T&K95 paper, which you will convert to lines of code.
1c. Choose a power spectrum $S(\nu)$.
We will use a sum of one Lorentzians (a QPO with a centroid frequency of 0.5 Hz and a FWHM of 0.01 Hz), and a Poisson-noise power law. The QPO should be 100 times larger amplitude than the power-law.
1d.  For each Fourier frequency $\nu_i$ draw two gaussian-distributed random numbers, multiply them by $$\sqrt{\frac{1}{2}S(\nu_i)}$$ and use the result as the real and imaginary part of the Fourier transform $F$ of the desired data.
In the case of an even number of data points, for reason of symmetry $F(\nu_{Nyquist})$ is always real. Thus only one gaussian distributed random number has to be drawn.
1e. To obtain a real valued time series, choose the Fourier components for the negative frequencies according to $F(-\nu_i)=F*(\nu_i)$ where the asterisk denotes complex conjugation.
Append to make one fourier transform array. Check that your T&K fourier transform has length n_bins. Again, for this algorithm, the zero Fourier frequency is in the middle of the array, the negative Fourier frequencies are in the first half, and the positive Fourier frequencies are in the second half.
1f. Obtain the time series by backward Fourier transformation of $F(\nu)$ from the frequency domain to the time domain.
Note: I usually use .real after an iFFT to get rid of any lingering 1e-10 imaginary factors.
Congratulations! 
1g. Plot the power spectrum of your FT (only the positive frequencies) next to the light curve it makes.
Remember: $$P(\nu_i)=|F(\nu_i)|^2$$
You'll want to change the x scale of your light curve plot to be like 20 seconds in length, and only use the positive Fourier frequencies when plotting the power spectrum.
Yay!
1h. Play around with your new-found simulation powers (haha, it's a pun!)
Make more power spectra with different features -- try at least 5 or 6, and plot each of them next to the corresponding light curve. Try red noise, flicker noise, a few broad Lorentzians at lower frequency, multiple QPOs, a delta function, etc. 
Here are some other functions you can use to define shapes of power spectra. This exercise is to help build your intuition of what a time signal looks like in the Fourier domain and vice-versa.
End of explanation
fig, ax = plt.subplots(1,1, figsize=(8,5))
ax.plot(rb_freq, rb_pow, linewidth=2.0)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel(r'Frequency (Hz)', fontproperties=font_prop)
ax.tick_params(axis='x', labelsize=16, bottom=True, top=True, 
               labelbottom=True, labeltop=False)
ax.tick_params(axis='y', labelsize=16, left=True, right=True, 
               labelleft=True, labelright=False)
plt.show()
Explanation: 2. More realistic simulation with T&K
Now you're able to simulate the power spectrum of a single segment of a light curve. However, as you learned this morning, we usually use multiple (~50+) segments of a light curve, take the power spectrum of each segment, and average them together.
2a. Turn the code from 1d to 1e into a function make_TK_seg
Make it so that you can give a different random seed to each segment.
2b. Make the Fourier transform for a given power shape (as in Problem 1)
Use a Lorentzian QPO + Poisson noise power shape at a centroid frequency of 0.5 Hz and a full width at half maximum (FWHM) of 0.01 Hz. Make the QPO 100 time stronger than the Poisson noise power-law.
2c. Put make_TK_seg in a loop to do for 50 segments.
Make an array of integers that can be your random gaussian seed for the TK algorithm (otherwise, you run the risk of creating the exact same Fourier transform every time, and that will be boring).
Keep a running average of the power spectrum of each segment (like we did this morning in problem 2).
2d. Compute the error on the average power
The error on the power at index $i$ is
$$ \delta P_i = \frac{P_i}{\sqrt{M}} $$
where M is the number of segments averaged together.
2e. Use the re-binning algorithm described in the morning's workbook to re-bin the power spectrum by a factor of 1.05.
Plot the average power spectrum
Remember to use log scale for the y-axis and probably the x-axis too!
End of explanation
def lorentz_q(v, v_peak, q, rms):  
     
    Form of the Lorentzian function defined in terms of 
    peak frequency v_peak and quality factor q
    q = v_peak / fwhm
    with the integrated rms of the QPO as the normalizing factor.
    e.g. see Pottschmidt et al. 2003, A&A, 407, 1039 for more info
    
    f_res = v_peak / np.sqrt(1.0+(1.0/(4.0*q**2)))
    r = rms / np.sqrt(0.5-np.arctan(-2.0*q)/np.pi)
    lorentz = ((1/np.pi)*2*r**2*q*f_res) / (f_res**2+(4*q**2*(v-f_res)**2))
    return lorentz
Explanation: 2f. Re-do 2b through the plot above but slightly changing the power spectrum shape in each segment.
Maybe you change the centroid frequency of the QPO, or the normalizing factors between the two components, or the slope of the power-law.
Bonus problems:
1. Use a different definition of the Lorentzian (below) to make a power spectrum.
Follow the same procedure. Start off with just one segment. Use the rms as the normalizing factor.
2. Using what you learned about data visualization earlier this week, turn the plots in this notebook (and the QPO one, if you're ambitious) into clear and easy-to-digest, publication-ready plots.
End of explanation
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