diff --git a/04-FFT_DFT_and_Applications/resample.ipynb b/04-FFT_DFT_and_Applications/resample.ipynb new file mode 100644 index 0000000..06db523 --- /dev/null +++ b/04-FFT_DFT_and_Applications/resample.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resampling of Time Series\n", + "## Decimation\n", + "The Discrete Fourier Transform (DFT) can be used for some very basic signal processing operations. We can either use is for decimation, which results in redcution of the number of samples of a given time series or we can use it for interpolation, which is the opposite of decimation.\n", + "Decimation implies a reduction of time resolution and thus a decrease in the Nyquist frequency.\n", + "If the original signal contains higher frequency than the new Nyquist frequency\n", + "(after decimation) we get aliasing. Thus, a low pass filtering to the new Nyquist\n", + "frequency is required before decimation. Decimation and low-pass filtering can be\n", + "achieved in one step by using the DFT:\n", + "1. Transform the time series to the frequency domain using the DFT.\n", + "2. Reduce the Nyquist frequency by the desired factor (when using the Fast Fourier Transform, this should be a power of 2), and apply an appropriate taper. That’s the low-pass filter!\n", + "3. Do an inverse DFT of the modified spectrum with reduced Nyquist frequency and obtain a time series with greater sampling interval (because lower Nyquist frequency implies lower time resolution)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import all required packages\n", + "%matplotlib inline\n", + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from obspy import read\n", + "from scipy import signal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the data\n", + "datapath = os.path.join(os.path.expanduser('~'),'work', 'data', 'DOR50', '*063')\n", + "st = read(datapath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def decimate(x, dt, factor):\n", + " \"\"\"\n", + " Easy decimation for real input data x. Note, since new_dt might not be old_dt * 2**n,\n", + " the decimation is not correct.\n", + " x: Data array\n", + " dt: sampling rate of x\n", + " factor: factor for decimation, thus new sampling rate is dt/factor\n", + " \"\"\"\n", + " # 1. Transform x to frequnecy domain\n", + " ft = np.fft.rfft(x)\n", + " freqs = np.fft.rfftfreq(n=len(x), d=dt)\n", + " \n", + " # 2. Get Nyquist Frequencies for both old dt an new dt\n", + " \n", + " \n", + " # Find index of new Nyquist Frequnency in freqs\n", + " index = np.where(freqs > new_f_ny)[0][0]\n", + " \n", + " # 3. Apply window function as lowpass filter and do inverse DFT \n", + " new_ft = ft[:index]\n", + " new_ft = new_ft * signal.tukey(len(new_ft), alpha=0.25)\n", + " x_new = np.fft.irfft(ft[:index])\n", + " \n", + " return x_new, dt*factor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results for original and decimated data\n", + "# Read data from st\n", + "data = st[0].data[:100000]\n", + "\n", + "# Apply Decimation\n", + "dec, dt_dec = decimate(data, dt=st[0].stats.delta, factor=2)\n", + "\n", + "# Create arrays for time to plot\n", + "t_data = np.arange(0, len(data)) * st[0].stats.delta\n", + "t_dec = np.arange(0, len(dec)) * dt_dec\n", + "\n", + "# Create plot widget\n", + "plt.plot(t_data, data - np.mean(data), alpha=0.5)\n", + "plt.plot(t_dec, dec - np.mean(dec), alpha=0.5)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Amplitude (a.u.)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpolation\n", + "The opposite of decimation is interpolation. Here, we want a finer sampling of the\n", + "time series without changing the frequency content. We could do this in the time\n", + "domain by some interpolation rule using neighbouring samples. We can also do it\n", + "by using the DFT:\n", + "1. Transform the time series to the frequency domain using the DFT.\n", + "2. Append zeros to the spectrum thus increasing the Nyquist frequency.\n", + "3. Do an inverse DFT of the extended spectrum and obtain a time series with smaller sampling interval (because higher Nyquist frequency implies higher time resolution). The frequency content is unchanged!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def interpolation(x, dt, new_dt):\n", + " \"\"\"\n", + " Easy function for interpolation of a given data array to increase the the sampling rate.\n", + " \n", + " x: data array\n", + " dt: sampling rate\n", + " new_dt: new sampling rae\n", + " \"\"\"\n", + " # 1. Transform x to frequnecy domain\n", + " ft = np.fft.rfft(x)\n", + " \n", + " # Determine the number of zeros to add\n", + " \n", + " \n", + " # 2. Append zeros to spectrum\n", + " ft = np.concatenate((ft, np.zeros(append_zeros)))\n", + " \n", + " # 3. Do inverse DFT and determine new dt\n", + " x_new = np.fft.irfft(ft)\n", + " new_dt = t_max / len(x_new)\n", + " \n", + " return x_new, new_dt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create plot\n", + "# Read data from st\n", + "data = st[0].data[:100000]\n", + "\n", + "# Apply interpolation\n", + "interp, dt_interp = interpolation(data, dt=st[0].stats.delta, new_dt=1/150)\n", + "\n", + "# Create time arrays\n", + "t_data = np.arange(0, len(data)) * st[0].stats.delta\n", + "t_interp = np.arange(0, len(interp)) * dt_interp\n", + "\n", + "# Create plot widget\n", + "plt.plot(t_data, data - np.mean(data), alpha=0.5)\n", + "plt.plot(t_interp, interp - np.mean(interp), alpha=0.5)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Amplitude (a.u.)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with function scipy.signal.resample\n", + "For documentation see https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import funtion\n", + "from scipy.signal import resample\n", + "\n", + "# Read data from st and create time array\n", + "data = st[0].data[:100000]\n", + "time = np.arange(0, len(data)) * st[0].stats.delta\n", + "\n", + "# Apply resample funtion\n", + "resampled_x, resampled_t = resample(data, num=int(len(data)*2), t=time)\n", + "\n", + "# Create plot\n", + "plt.plot(time, data - np.mean(data), alpha=0.5)\n", + "plt.plot(resampled_t, resampled_x - np.mean(resampled_x), alpha=0.5)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Amplitude (a.u.)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/05-Spectrogram/multipleFilterTechnique.ipynb b/05-Spectrogram/multipleFilterTechnique.ipynb new file mode 100644 index 0000000..3cce08e --- /dev/null +++ b/05-Spectrogram/multipleFilterTechnique.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple filter technique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An alternative to the moving window method is the multiple filter technique. Instead of moving a window in the time domain, a window is moved in the frequency domain. Of course, a window in the frequency domain is a filter, and hence the name of the method. Let $H_k(\\omega,\\omega_k)$ be the transfer function of the $k$-th filter centered on frequency $\\omega_k$. Applying the filter involves multiplication of the filter transfer function with the Fourier transform of the time signal and then performing an inverse Fourier transform:\n", + "\\begin{align}\n", + "f_k(t) = \\int_{-\\infty}^\\infty\\,H_k(\\omega,\\omega_k)F(\\omega)\\exp(i\\omega t)\\frac{d\\omega}{2\\pi} \\,.\n", + "\\end{align}\n", + "Basically, one could plot the resulting time series against the associated center frequency of the filter. However, $f_k(t)$ can also assume negative values. It would be better to plot the envelope of $f_k(t)$.\n", + "\n", + "### The analytic signal\n", + "This can be obtained by constructing the analytic signal $z_k(t)$ of $f_k(t)$. It is a complex\n", + "signal whose real part is the original signal and whose imaginary part is its Hilbert transform. Its absolute\n", + "value is the envelope or instantaneous amplitude and its phase is the instantaneous phase:\n", + "\\begin{align}\n", + "z_k(t) = a_k(t)\\exp(i\\Phi_k(t)) = f_k(t)+iq_k(t) \\ \\mathrm{with}\\ q_k(t) = HT\\{f_k(t)\\}\\,,\n", + "\\end{align}\n", + "and\n", + "\\begin{align}\n", + "a_k(t) = \\sqrt{f_k(t)^2+q_k(t)^2},\\ \\Phi_k(t) = \\arctan\\left(\\frac{q_k(t)}{f_k(t)}\\right) \\,.\n", + "\\end{align}\n", + "\n", + "The Hilbert transform can again easily be obtained because its Fourier transform is:\n", + "\\begin{align}\n", + "Q_k(\\omega) = i\\,\\mathrm{sign}(\\omega)F_k(\\omega)\\,.\n", + "\\end{align}\n", + "where $F_k(\\omega)$ is the Fourier transform of $f_k(t)$. Inverse Fourier transform of $Q_k(\\omega)$ gives the Hilbert transform of $f_k(t)$.\n", + "\n", + "### Work flow\n", + "So the work flow of the multiple filter technique is as follows:\n", + "1. Compute Fourier transform of $f(t)$: $F(\\omega)$.\n", + "2. Multiply $F(\\omega)$ with bandpass filters $H_k(\\omega,\\omega_k)$ to obtain $F_k(\\omega)$.\n", + "3. Compute Fourier transform of the Hilbert transform: $Q_k(\\omega)=iF_k(\\omega)$.\n", + "4. Transform both back to the time domain.\n", + "5. Calculate the instantaneous amplitude $a_k(t)$ and phase $\\Phi_k(t)$.\n", + "6. Plot $a_k(t)$ in the time-frequency plane.\n", + "\n", + "### Gaussian filters\n", + "One frequently made choice for the bandpass filters is a Gaussian function of the form:\n", + "\\begin{align}\n", + "H_k(\\omega,\\omega_k) = e^{-\\alpha\\left(\\frac{\\omega-\\omega_k}{\\omega_k}\\right)^2}\n", + "\\end{align}\n", + "with inverse Fourier transform (impulse response):\n", + "\\begin{align}\n", + "h_k(t) = \\frac{\\sqrt{\\pi}\\omega_k}{2\\alpha}e^{-\\frac{\\omega_k^2t^2}{4\\alpha}}\\cos\\omega_k t \\,.\n", + "\\end{align}\n", + "This Fourier transform pair demonstrates a fundamental property of spectral analysis:\n", + "Choosing $\\alpha$ large gives a very narrow-banded filter but a very\n", + "long impulse response. Choosing $\\alpha$ small gives a wide-banded filter but a very short \n", + "impulse response. This is an expression of the uncertainty principle. If the frequency of a signal\n", + "is known very well, the signal is distributed over time. On the other hand, an impulse in the time domain has\n", + "a constant spectrum, i.e. the frequency is entirely unknown. In quantum mechanics, there is the energy-time\n", + "uncertainty relation, where energy $E=\\hbar\\omega$.\n", + "\n", + "### Time and frequency resolution\n", + "The Gaussian Fourier transform pair has a special property. If we measure the duration of the impulse response by\n", + "\\begin{align}\n", + "D_t^2 = \\int_{-\\infty}^\\infty t^2 |h(t)|^2 dt\n", + "\\end{align}\n", + "and the spread of the filter transfer function by\n", + "\\begin{align}\n", + "D_\\omega^2 = \\int_{-\\infty}^\\infty \\omega^2 |H(\\omega)|^2 \\frac{d\\omega}{2\\pi}\\,,\n", + "\\end{align}\n", + "then the product\n", + "\\begin{align}\n", + "D_t D_\\omega \\ge \\frac{1}{2} \\,.\n", + "\\end{align}\n", + "For the Gaussian filter, equality holds. Hence, the Gaussian filter is the one with the\n", + "best compromise between bandwidth and duration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Preparation: load packages\n", + "import os\n", + "from obspy.core import read\n", + "from obspy.core import UTCDateTime\n", + "import numpy as np\n", + "import matplotlib.pylab as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Multiple filter analysis\n", + "def multipleFilterAnalysis(data,alfa,cfreq,dt,ndec):\n", + " \"\"\"\n", + " Perform a multiple filter analysis of data.\n", + " data: Array of detrended and demeaned data whose length is power of 2 \n", + " alfa: Width parameter of Gaussian bandpass filter\n", + " cfreq: Array of center frequencies of Gaussian filter\n", + " dt: sampling interval of data\n", + " ndec: decimation factor for instantaneous amplitude output\n", + " \"\"\"\n", + " npts = len(data)\n", + " nd = int(pow(2, np.ceil(np.log(npts)/np.log(2)))) # find next higher power of 2 of npts\n", + " ftd = np.fft.rfft(data,nd) # Fourier transform of entire data set (pos. freq.)\n", + " # data are padded with zeros since npts <= nd\n", + " freq = np.fft.rfftfreq(nd,dt) # Fourier frequencies (positive frequencies)\n", + " jf = 0 # center frequency counter\n", + " mfa = np.zeros((len(cfreq),npts//ndec+1)) # numpy array for MFA result\n", + " for cf in cfreq:\n", + " hg = np.exp(-alfa*((freq-cf)/cf)**2) # Gaussian filter (use f instead of omega here)\n", + " fk = hg*ftd # multiply FT of data with filter\n", + " qk = np.complex(0,1)*fk # FT of Hilbert transform\n", + " ftk = np.fft.irfft(fk) # filtered data\n", + " qtk = np.fft.irfft(qk) # Hilbert transform of filtered data\n", + " at = np.sqrt(ftk**2+qtk**2) # instantaneous amplitude\n", + " mfa[jf,:] = at[0:npts:ndec] # store decimated original result\n", + " jf = jf+1 # increase center frequency count\n", + " return mfa" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# normalize multiple filter result either along time or frequency axis\n", + "def normalizeMFT(mfa,mode,exp):\n", + " \"\"\"\n", + " Normalize the result of the mutiple filtering operation.\n", + " mfa: array with instantaneous amplitudes versus frequency (mfa(f,t))\n", + " mode: normalization mode: if 'time', normalize along time axis\n", + " else normalize along frequency axis\n", + " exp: exponent for modifying inst amp using a power less than 1\n", + " \"\"\"\n", + " nf,nt = mfa.shape\n", + " if mode == 'time':\n", + " for jf in range(0,nf):\n", + " mfamax = np.amax(mfa[jf,:])\n", + " mfa[jf,:] = np.power(mfa[jf,:]/mfamax+1.e-10,exp) \n", + " else:\n", + " for jt in range(0,nt):\n", + " mfamax = np.amax(mfa[:,jt])\n", + " mfa[:,jt] = np.power(mfa[:,jt]/mfamax+1.e-10,exp)\n", + " return mfa" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# borehole data from stations, Jan 23 2013\n", + "station = 'GEO2' # station code\n", + "# available stations are GEO2, GEO3 (only 14:00), GEO4, GEO5, GEO6, GEO7\n", + "\n", + "record = '1400' # record starting at 1400\n", + "stime = UTCDateTime('2013-01-23 14:17:00Z') # use record starting at 1400\n", + "etime = UTCDateTime('2013-01-23 14:19:00Z')\n", + "ttitle = ', depth: 56.5 m'\n", + "\n", + "# search string for database\n", + "datapath = os.path.join(os.path.expanduser('~'),'work', 'data', 'GE-stations', station, 'e*'+record+'*.HHZ')\n", + "st = read(datapath) # read file using obspy read\n", + "print(st)\n", + "\n", + "st.trim(stime,etime) # trim data stream to desired start and end time\n", + "st.detrend('linear') # do a linear detrending\n", + "st.detrend('demean') # subtract mean value\n", + "tr = st[0] # extract first trace from stream (there is only one)\n", + "tr.plot(); # plot trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# prepare multiple filtering\n", + "alfa = 1000.0 # filter width parameter\n", + "npts = tr.stats.npts # number of samples in time series\n", + "dt = tr.stats.delta # sampling interval\n", + "fmax = 1./(2.*dt) # Nyquist frequency\n", + "nd = int(pow(2, np.ceil(np.log(npts)/np.log(2)))) # find next higher power of 2 of npts\n", + "print(\"Number of samples: \",npts, # print some information about time series\n", + " \"\\nSampling interval: \",dt,\n", + " \"\\nNumber of samples with padding: \",nd,\n", + " \"\\nMax. frequency: \",fmax)\n", + "cfreq = np.linspace(1.,fmax,1000) # array of filter center frequencies\n", + "freq = np.fft.rfftfreq(nd,dt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ndec = 100 # decimation factor for inst amplitude\n", + "mfa = multipleFilterAnalysis(tr.data,alfa,cfreq,dt,ndec)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mfa = normalizeMFT(mfa,'freq',0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the result\n", + "extent = (0.,npts*dt,fmax,1.) # extent of matrix in true time and frequency\n", + "plt.figure(figsize = [15,6])\n", + "plt.imshow(mfa,extent = extent,aspect=0.25) # do plotting\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('Center frequency [Hz]')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot Gaussian filters\n", + "cf = 0.5*fmax\n", + "plt.figure(figsize = [15,4])\n", + "for alfa in [10,100,1000,10000,100000]:\n", + " hg = np.exp(-alfa*((freq-cf)/cf)**2) # Gaussian filter (use f instead of omega here)\n", + " plt.plot(freq,hg)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Study the effect of $\\alpha$ on the result of the multiple filtering\n", + "2. Think about the numerical effort of MFT in comparison with the moving window technique\n", + "3. Perform the analysis for different stations\n", + "4. Do you have some ideas why the results look different? What role plays normalization? Try to change the code such that each spectrum is normalized and not each instantaneous amplitude series." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/05-Spectrogram/nonlinear_thresholding.ipynb b/05-Spectrogram/nonlinear_thresholding.ipynb new file mode 100644 index 0000000..10d12bf --- /dev/null +++ b/05-Spectrogram/nonlinear_thresholding.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nonlinear Thresholding\n", + "When noise and signal share a common frequency band, spectral filtering would lead to a loss in signal. For these cases, we need different techniques to reduce the disturibing noise. We start with a reocorde signal $x(t)$, contains some signal $s(t)$ and additive noise $n(t)$:\n", + "$$\n", + "x(t) = s(t) + n(t)\n", + "$$\n", + "We can rewrite the equation above in time-frequency domain as\n", + "$$\n", + "X(t, f) = S(t, f) + N(t, f) ~.\n", + "$$\n", + "Assuming that some time-frequnecy coefficients can be associated by noise, we set all coefficients, which a below this threshold, to zero:\n", + "$$\n", + "\\tilde{X}(t, f) = \\left\\{\n", + " \\begin{array}{@{}ll@{}}\n", + " X(t, f) & \\mathrm{if}~ |X(t, f)| \\geq \\beta(f) \\\\\n", + " 0 & \\mathrm{otherwise}\n", + " \\end{array}\\right.\n", + " ~,\n", + "$$\n", + "where $\\beta(f)$ denotes the threshold function. The threshold function is defined as \n", + "$$\n", + "\\beta(f) = \\mathrm{ECDF}_f^{-1} (P = 0.99) ~,\n", + "$$\n", + "where $\\mathrm{ECDF}_f^{-1}$ denotes the inverse cumulative distribution function or quantile function, e.g. before the first arrival of earthquake waves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load all required packages\n", + "import os\n", + "import numpy as np\n", + "from scipy.signal import stft, istft\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load our earthquake data and plot\n", + "d = np.load(os.path.join(os.path.expanduser('~'),'work', 'data', 'events', 'bug2019mgoh_Z.npz'))\n", + "#d = np.load(os.path.join(os.path.expanduser('~'),'work', 'data', 'events', 'bug2019gbbo_Z.npz'))\n", + "#d = np.load(os.path.join(os.path.expanduser('~'),'work', 'data', 'events', 'bug2019ibsd_Z.npz'))\n", + "plt.figure(figsize=(15, 8))\n", + "plt.plot(d['data']);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute spectrogram of data\n", + "f, t, X = stft(d['data'], fs=1/0.01, nfft=198, nperseg=99) # Be careful with choice of nfft and nperseg, \n", + " # because istft does not work for all pairs\n", + "print(X.shape)\n", + "plt.figure(figsize=(15, 8))\n", + "plt.pcolormesh(t, f, np.abs(X))\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Time (s)\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Functions for thresholding\n", + "def threshold(X, quantile=0.99):\n", + " \"\"\"\n", + " X: time-frequency coefficients\n", + " q: quantile [0-1]\n", + " \"\"\"\n", + " # Loop over frequencies to build threshold function\n", + " beta = np.zeros(X.shape[0])\n", + " for i in range(X.shape[0]):\n", + " beta[i] = np.quantile(np.abs(X[i, :]), q=quantile)\n", + " \n", + " return beta\n", + "\n", + "def modify_spectrogram(X, beta):\n", + " # Loop over all items in X and apply threshold\n", + " # Task: modify X!\n", + " \n", + " return X" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute STFT of data before first arrival of P\n", + "_, _, X_thres = stft(d['data'][:2500], fs=1/0.01, nfft=198, nperseg=99)\n", + "# Estimate threshold fucntion\n", + "beta = threshold(X_thres)\n", + "\n", + "# Modifiy original spectrogram\n", + "X_mod = modify_spectrogram(X, beta)\n", + "\n", + "# Plot modified spectrogram\n", + "plt.figure(figsize=(15, 8))\n", + "plt.pcolormesh(t, f, np.abs(X_mod))\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Time (s)\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Inverse STFT of modified spectrogram\n", + "t, x_mod = istft(X_mod, fs=1/0.01, nfft=198, nperseg=99)\n", + "\n", + "# Plot corrected seismogram\n", + "plt.figure(figsize=(15, 8))\n", + "plt.plot(t, x_mod);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/05-Spectrogram/time_frequency_analysis.ipynb b/05-Spectrogram/time_frequency_analysis.ipynb new file mode 100644 index 0000000..fd1e134 --- /dev/null +++ b/05-Spectrogram/time_frequency_analysis.ipynb @@ -0,0 +1,272 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Time Frequency Analysis\n", + "## Moving window analysis\n", + "One way to analyse the time-varying frequency content of a signal is to\n", + "apply windows in the time domain to the signal and to calculate a Fourier spectrum\n", + "of the windowed part. The window marches along the signal with defined overlap creating\n", + "a series of Fourier spectra associated with the center times of the windows. The resulting amplitude\n", + "spectra are the plotted versus window center time. In more detail:\n", + "\n", + "1. Choose windowing functions: $w(t,t_m)$ with $t_m$ the center of the window.\n", + "2. Multiply windowing function with time series: $f_m(t) = f(t)w(t,t_m)$\n", + "3. Detrend the windowed signal.\n", + "4. Perform a DFT: $F_{km} = \\Delta t\\sum_{n=0}^N f_m(t)\\exp(-2\\pi i \\frac{kn}{N})$\n", + " and calculate the absolute value, $|F_{km}|$.\n", + "5. Plot the resulting matrix: $|F_{km}|$ in the time-frequency plane." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Borehole Data and Test Site\n", + "\n", + "Within the scope of constructing new buildings for the International Geothermal Center in Bochum in 2013 wells were drilled for the installation of a geothermal heat exchanger. Bore holes were drilled next to the newly constructed building (Station GEO3). A downhole hammer was used with a diameter of 152 mm. The drill bit operates by water flushing through the drill rod. The water flow rate determines the working frequency of the hammer.\n", + "\n", + "To observe the drill bit noise a temporarily operating 2-D seismic network was installed around the drill site. Here, the noise of the used downhole hammer is investigated. An array of 16 seismological stations was installed in the test site. Four Mark L-4C-3D 1 Hz sensors, eight S-13 1 Hz sensors, one GS-13 1 Hz sensor and two Güralp CMG-3ESPC broad-band 120 sec – 50 Hz sensors were in use. Additionally an accelerometer was fixed to the drill rod (GEO11, blue triangle).\n", + "\n", + "Some of the stations were positioned within one of the infrastructure tunnels servicing the university containing water conduits, long-distance heat line and electric cables (e.g. Station GEO4 and GEO05). Thus, noise could be reduced that might disturb the recordings. Other stations are located within buildings (e.g. Station GEO2 and GEO03) ore outside (e.g. Station GEO6 and GEO07).\n", + "\n", + "![Map ot the stations at GZB](../images/karte3.jpg)\n", + "\n", + "Drill bit noise was recorded up to the maximum drilling depth of 200 m. A drilling cycle is characterised by switching on the water pump, followed by the drilling with higher amplitude signals, that lasts several minutes. The water pump\n", + "is stopped about 5 to 15 minutes after the drilling finished depending on the drill depth." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Preparation: load packages\n", + "import os\n", + "from obspy.core import read\n", + "from obspy.core import UTCDateTime\n", + "import numpy as np\n", + "import matplotlib.pylab as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data from stations, Jan 23 2013\n", + "\n", + "record = '1400' # either use record starting at 1300 or 1400\n", + "station = 'GEO7' # station code\n", + "# available stations are GEO2, GEO3 (only 14:00), GEO4, GEO5, GEO6, GEO7\n", + "\n", + "if record =='1300':\n", + " stime = UTCDateTime('2013-01-23 13:16:00Z') # use record starting at 1300\n", + " etime = UTCDateTime('2013-01-23 13:25:00Z')\n", + " ttitle = ', depth: 36.5 m'\n", + "else:\n", + " stime = UTCDateTime('2013-01-23 14:17:00Z') # use record starting at 1400 (14:14-14:23)\n", + " etime = UTCDateTime('2013-01-23 14:19:00Z')\n", + " ttitle = ', depth: 56.5 m'\n", + " \n", + "datapath = os.path.join(os.path.expanduser('~'),'work', 'data','GE-stations', station, 'e*'+record+'*.HHZ') # search string for database\n", + "st = read(datapath) # read file using obspy read\n", + "print(st)\n", + "\n", + "st.trim(stime,etime) # trim data stream to desired start and end time\n", + "st.detrend('linear') # do a linear detrending\n", + "st.detrend('demean') # subtract mean value\n", + "tr = st[0] # extract first trace from stream (there is only one)\n", + "tr.plot(); # plot trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cell 2: Preparation of window\n", + "wlen = 4. # window length in seconds\n", + "npts = tr.stats.npts # number of samples\n", + "dt = tr.stats.delta # sampling interval\n", + "\n", + "nwin = wlen/dt # number of samples in window\n", + "nwin = int(pow(2, np.ceil(np.log(nwin)/np.log(2)))) # find next higher power of 2 of nwin\n", + "nwmax = int(pow(2, np.ceil(np.log(npts)/np.log(2))))/8 # maximum window length (about 1/8 of length)\n", + "nwin = min(nwin,nwmax) # limit nwin\n", + "print(\"Samples in window: \",nwin)\n", + "print(\"Total number of samples: \",npts)\n", + "print(\"Sampling interval: \",dt)\n", + "print(\"Length of trace [s]: \",npts*dt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The Hann window function\n", + "def hann(nw):\n", + " arg = 2.*np.pi*np.arange(0,nw)/nw # argument of cosine\n", + " fwin = 0.5*(1.-np.cos(arg)) # Hann window\n", + " return fwin\n", + "\n", + "# The boxcar window function\n", + "def boxcar(nw):\n", + " fwin = np.ones(nw)\n", + " return fwin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the window function and its effect on the time series\n", + "#\n", + "fwin = hann(nwin) # Hann window\n", + "plt.figure(figsize = [15,6])\n", + "plt.subplot(221)\n", + "plt.plot(fwin,'-k')\n", + "plt.title(\"Hann window\")\n", + "seg = tr.data[0:nwin]*fwin # multiply data with window\n", + "plt.subplot(222)\n", + "plt.plot(seg,'-k')\n", + "fwin = boxcar(nwin) # Boxcar window\n", + "plt.subplot(223)\n", + "plt.plot(fwin,'-k')\n", + "plt.title(\"Boxcar window\")\n", + "seg = tr.data[0:nwin]*fwin # multiply data with window\n", + "plt.subplot(224)\n", + "plt.plot(seg,'-k')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def movingWindowAnalysis(data,winfun,nwin,shift):\n", + " \"\"\"\n", + " Performs moving window analysis of a time series.\n", + " data: data array\n", + " winfun: name of the window function to be called\n", + " nwin: number of window samples (power of 2)\n", + " shift: displacement of moving window in samples\n", + " \"\"\"\n", + " fwin = winfun(nwin) # compute window values\n", + " npts = len(data) # number of total samples\n", + " nseg = int((npts-nwin)/shift)+1 # total number of expected data segment\n", + " mwa = np.zeros((nwin//2+1,nseg)) # array for result (rfft returns N/2+1 samples) \n", + " wa = 0 # start index of data segment\n", + " we = nwin # end index of data segment\n", + " jseg = 0 # initialize data segment counter\n", + " while we < npts: # loop over segments\n", + " seg = data[wa:we]*fwin # multiply data segment with window\n", + " seg = seg-seg.mean() # subtract mean value of segment\n", + " ftseg = np.abs(np.fft.rfft(seg)) # abs value of Fourier transform\n", + " maxft = np.amax(ftseg) # max value of Fourier transform\n", + " ftseg = ftseg/maxft+1.e-10 # normalize spectrum to its maximum, remove zeros\n", + " mwa[:,jseg] = np.power(ftseg,0.25) # assign values to the matrix\n", + " wa = wa+shift # move window start by shift\n", + " we = we+shift # move window end by shift\n", + " jseg = jseg+1 # increase segment counter\n", + " return nseg,mwa # return number of segments and moving window matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# carry out the moving window analysis\n", + "tshift = 0.2*wlen\n", + "shift = int(tshift/dt) # displacement of moving window in samples\n", + "nseg,mwa = movingWindowAnalysis(tr.data,hann,nwin,shift) # compute spectrogram\n", + "freq = np.fft.rfftfreq(nwin,dt) # Fourier frequencies\n", + "print(\"Frequency range [Hz]: \",freq[0],freq[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the result\n", + "fmaxplot = 250. # maximum frequency to be plotted\n", + "nf = len(freq) # number of frequencies\n", + "jfmax = int(fmaxplot/freq[-1]*nf) # max index for plotting \n", + "extent = (0.5*wlen,0.5*wlen+nseg*tshift,fmaxplot,0.) # extent of matrix in true time and frequency\n", + "plt.figure(figsize = [15,6])\n", + "plt.imshow(mwa[0:jfmax,:],extent = extent,aspect=0.25) # do plotting\n", + "plt.xlabel('Window center time [s]')\n", + "plt.ylabel('Frequency [Hz]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise \n", + "\n", + "1. Write a little routine for the Hamming window and do the analysis using this window\n", + "2. Use the sqrt of the Fourier amplitude spectrum as output and compare\n", + "3. Use other powers ($< 1$) of the Fourier amplitude (using np.power)\n", + "4. Use the logarithm of the Fourier amplitude as output and compare (using np.log)\n", + "5. Extend the frequency range when using the logarithm or a power of the Fourier amplitude\n", + "6. Do a moving window analysis for the other available stations. Which ones appear good and which ones are bad?\n", + "7. Try also records starting at 14:00\n", + "\n", + "Hint: when you change something in the code, use \"Run all below\" to see the changes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Hamming window\n", + "$ w(t) = 0.54 - 0.46 \\cos \\left( \\frac{2 \\pi t}{L} \\right) ~ , t \\in [0, L]$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/05-Spectrogram/time_meets_frequency.ipynb b/05-Spectrogram/time_meets_frequency.ipynb new file mode 100644 index 0000000..7b22bc1 --- /dev/null +++ b/05-Spectrogram/time_meets_frequency.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dispersive Signals\n", + "Up to now,we considered the spectral content of the entire time series. In many\n", + "cases, the spectral content varies with time in a time series and one often wants to\n", + "find out how this happens in detail. One important example are dispersed signals in\n", + "which energy with different frequency content arrives at different times. Most\n", + "prominent among these are seismic surface which exhibit significant dispersion if\n", + "epicentral distances between seismic station and source are large. Quantifying the\n", + "dispersion allows inferences on Earth structure. \n", + "\n", + "\"Drawing\"\n", + "\"Drawing\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Chirp or Sweep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.rcParams['figure.figsize'] = 12, 8 # Slightly bigger plots by default" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.signal import chirp\n", + "t = np.linspace(0, 30, 30001)\n", + "w = chirp(t, f0=0.01, f1=3, t1=np.max(t), method='linear')\n", + "plt.plot(t, w);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tasks\n", + "1. What is special about the Chirp Signal?\n", + "2. Plot the amplitude spectrum of the chirp signal, using numpys [FFT module](https://numpy.org/doc/stable/reference/routines.fft.html), including the correct frequncies on the x-axis!\n", + "3. Think of a solution how to visualize the increasing frequency content using the Fourier tansform" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/05-Spectrogram/uncertainty_instantaneous_frequency.ipynb b/05-Spectrogram/uncertainty_instantaneous_frequency.ipynb new file mode 100644 index 0000000..3705a73 --- /dev/null +++ b/05-Spectrogram/uncertainty_instantaneous_frequency.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Uncertainty Problem\n", + "Using the moving window analysis, you may have noticed that we are not able to get a good resoltion in time and frequency. This is called the uncertainty problem, which you might have heard from in physics.\n", + "\n", + "Here, we a closer look how the uncertainty problem effects our sweep signal, when we apply the moving window average on this signal.\n", + "\n", + "### Tasks\n", + "1. Read the documentation about [scipy's STFT](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.chirp.html), especially the parameters nperseg, noverlap and nfft\n", + "2. Play around with the parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Preparation: load packages\n", + "import os\n", + "from obspy.core import read\n", + "from obspy.core import UTCDateTime\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.signal import stft, chirp\n", + "import pywt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Chirp signal\n", + "dt = 0.01\n", + "t = np.arange(0, 5000) * dt\n", + "data = chirp(t=t, f0=0.5, f1=25, t1=t[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Applying Short-Time Fourier Transform (STFT) on data\n", + "freqs, time, S = stft(x=data, fs=1/dt, window='hann', nperseg=256, noverlap=None, nfft=None)\n", + "\n", + "plt.figure(figsize = [15,6])\n", + "plt.pcolormesh(time, freqs, np.abs(S))\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Frequency (Hz)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The instantaneous Frequency\n", + "The instantaneous frequency is a measure of the dominating frequency at each time instant of a signal and is determined as the time derivative of the instantaneous phase $\\phi (t)$. The phase can be obtained by constructing the analytical signal of an arbitrary time series:\n", + "\n", + "$$ \n", + "\\hat{s}(t) = s(t) + i \\cdot \\mathcal{H} \\left( (s(t) \\right) = |\\hat{s}(t)| \\cdot \\exp (i \\phi (t)),\n", + "$$\n", + "\n", + "where $\\mathcal{H}$ denotes the Hilbert-Transform. Note, the scipy function [hilbert()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.hilbert.html) computes the analytical signal. Now, we can derive the instantaneous phase as\n", + "\n", + "$$\n", + "\\phi (t) = \\arg \\left( \\frac{Im(\\hat{s}(t))}{Re(\\hat{s}(t))} \\right).\n", + "$$\n", + "\n", + "Note, the instantaneous phase is called wrapped phase if it is in the interval $(−\\pi, \\pi]$ or $[0, 2 \\pi)$. To determine the instantaneous frequncy, we need to [unwrap](https://numpy.org/doc/1.18/reference/generated/numpy.unwrap.html) the phase to get a continous function. Finally, the instantaneous frequency can be determined by\n", + "\n", + "$$\n", + "f(t) = \\frac{1}{2 \\pi} \\frac{\\text{d} \\phi (t)}{\\text{d} t}\n", + "$$\n", + "\n", + "1. Create a time series with \n", + " 1. increasing / decreasing frequncy content, e.g. a [Chirp-Signal](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.chirp.html)\n", + " 2. increasing and decreasing frequency content, i.e. overlapping of two chirp signals (how does the spectrogram looks like?)\n", + "3. Write a function that computes the instantaneous frequency (Hint: use np.diff for instantaneous phase derivative, np.unwrap to unwrap the phase.)\n", + "4. What is the meaning of $|\\hat{s}(t)|$? (Hint: plot it!) \n", + "5. Plot the results of the instantaneous frequnecy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the signal\n", + "dt = 0.01\n", + "t = np.arange(0, 5000) * dt\n", + "data = chirp(t=t, f0=0.5, f1=25, t1=t[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function for instantaneous frequency\n", + "from scipy.signal import hilbert\n", + "def istantaneous_frequency(x, dt):\n", + " # To continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Continuous Wavelet Transform (CWT)\n", + "\n", + "We learned during the STFT about the uncertainty problem. Using the CWT results in a different time-frequency resolution. For high frequency we get poor frequency resolution by good time resoltution, whereas for low frequencies we get poor time resolution bur good frequency resolution. As you can see, there is no recipe when to use STFT, instantaneous frequnecy or CWT, but it is good to know where are the advantages and disadvantages.\n", + "\n", + "The CWT is defined as\n", + "\n", + "$$\n", + "\\text{CWT}_{a, b} \\left(s(t) \\right) = \\frac{1}{\\sqrt{|a|}} ~ \\int_{-\\infty}^{\\infty} s(t) ~ \\Psi^\\ast \\left( \\frac{t-b}{a} \\right) \\text{d} t ~,\n", + "$$\n", + "\n", + "where $a$ denotes the scaling factor of the mother wavelet $\\Psi(t)$ and $b$ denotes the translation. \n", + "In other words, the CWT is a convolution of the signal $s(t)$ with a conjugate complex and scaled wavelet-function ([here](https://en.wikipedia.org/wiki/Continuous_wavelet_transform) you find a good visualisation). Due to the different scaled wavelets, the CWT reacts well on abruptly changes in the signal.\n", + "Usally we get instead of the frequency on the y-axis the scaling factor $a$, but it depends on the center frequency $f_c$ of the scaled wavelet by\n", + "\n", + "$$\n", + "f_a = \\frac{f_c}{a} .\n", + "$$\n", + "\n", + "Luckily it exists a library [pywt](https://pywavelets.readthedocs.io/en/latest/) to compute the CWT with several wavelets.\n", + "For the following example we use a [complex Morlet wavelet](https://en.wikipedia.org/wiki/Morlet_wavelet) per default.\n", + "\n", + "#### Tasks\n", + "\n", + "1. Try to plot a time-frequency representation of different signals and compare with results of STFT\n", + "2. Use different [mother wavelets](https://pywavelets.readthedocs.io/en/latest/ref/wavelets.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a mother wavelet\n", + "wav, a = wavelet = pywt.ContinuousWavelet(\"cmor2-0.95\").wavefun();\n", + "plt.plot(a, wav.real, label=\"real part\");\n", + "plt.plot(a, wav.imag, label=\"imag part\");\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency2scale(fmin, fmax, waveletname, dt):\n", + " \"\"\"\n", + " Computes scales for given frequency values fmin and famx.\n", + " :param fmin: frequency minimum\n", + " :param fmax: frequency maximum\n", + " :param waveletname: name of wavelet\n", + " :param dt: sampling interval (s)\n", + " \"\"\"\n", + "\n", + " # Determine lower bound of scales\n", + " scale_start = 0.1\n", + " scale_min = pywt.scale2frequency(waveletname, scale_start)\n", + " while scale_min/dt > fmax:\n", + " scale_start *= 1.1\n", + " scale_min = pywt.scale2frequency(waveletname, scale_start)\n", + " scale_min = round(scale_start, 2)\n", + "\n", + " # Determine upper bound of scales\n", + " scale_start = 1\n", + " scale_max = pywt.scale2frequency(waveletname, scale_start)\n", + " while scale_max/dt > fmin:\n", + " scale_start *= 1.1\n", + " scale_max = pywt.scale2frequency(waveletname, scale_start)\n", + " scale_max = round(scale_start, 2)\n", + "\n", + " return scale_min, scale_max\n", + "\n", + "def scaleogram(x, fs, waveletname=\"cmor2-0.95\", fmin=1, fmax=25, num=100):\n", + " \"\"\"\n", + " Computes CWT of an signal x with sampling rate fs in Hz \n", + " :param x: time seris\n", + " :param fs: sampling rate\n", + " :param waveletname: name of wavelet\n", + " :param fmin: minimum frequency\n", + " :param fmax: maximum frequency\n", + " :param num: length of scale array\n", + " \"\"\"\n", + "\n", + " # Check all input parameters and change them is they do not fit\n", + " dt = 1 / fs\n", + " f_ny = 1 / (2 * dt) # Nyquist frequency\n", + " if f_ny < fmax:\n", + " fmax = f_ny\n", + "\n", + " if fmin >= fmax:\n", + " msg = \"Min. frequency {} is greater than max. frequency {} to compute scaleograms.\".format(fmin, fmax)\n", + " raise ValueError(msg)\n", + "\n", + " # Determine scales from fmin and fmax\n", + " scale_min, scale_max = frequency2scale(fmin, fmax, waveletname=waveletname, dt=dt)\n", + " scales = np.logspace(np.log10(scale_min), np.log10(scale_max), num=num)\n", + "\n", + " # Generate scaleogram\n", + " coefficients, frequencies = pywt.cwt(x, scales, waveletname, sampling_period=dt)\n", + " coefficients = np.abs(coefficients)**2\n", + " time = np.linspace(0, dt * len(x), num=len(x))\n", + " \n", + " return frequencies, time, coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "f_cwt, t_cwt, coeff = scaleogram(data, fs=1/(t[1]-t[0]))\n", + "plt.figure(figsize=(16, 8))\n", + "plt.pcolormesh(t_cwt, f_cwt, np.abs(coeff))\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlabel(\"Frequency (Hz)\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/06-Surface_Waves/movingWindow.py b/06-Surface_Waves/movingWindow.py new file mode 100644 index 0000000..8eca28b --- /dev/null +++ b/06-Surface_Waves/movingWindow.py @@ -0,0 +1,35 @@ +import numpy as np + +def movingWindowAnalysis(data,winfun,nwin,shift,exp): + """ + Performs moving window analysis of a time series. + data: data array + winfun: name of the window function to be called + nwin: number of window samples (power of 2) + shift: displacement of moving window in samples + exp: exponent for taking power of spectrum + """ + fwin = winfun(nwin) # compute window values + npts = len(data) # number of total samples + nseg = int((npts-nwin)/shift)+1 # total number of expected data segment + mwa = np.zeros((nwin//2+1,nseg)) # array for result (rfft returns N/2+1 samples) + wa = 0 # start index of data segment + we = nwin # end index of data segment + jseg = 0 # initialize data segment counter + while we < npts: # loop over segments + seg = data[wa:we]*fwin # multiply data segment with window + seg = seg-seg.mean() # subtract mean value of segment + ftseg = np.abs(np.fft.rfft(seg)) # abs value of Fourier transform + maxft = np.amax(ftseg) # max value of Fourier transform + ftseg = ftseg/maxft+1.e-10 # normalize spectrum to its maximum, remove zeros + mwa[:,jseg] = np.power(ftseg,exp) # assign values to the matrix + wa = wa+shift # move window start by shift + we = we+shift # move window end by shift + jseg = jseg+1 # increase segment counter + return nseg,mwa # return number of segments and moving window matrix +#------------------------------------------------------------------------------------ +# +def hann(nw): + arg = 2.*np.pi*np.arange(0,nw)/nw # argument of cosine + fwin = 0.5*(1.-np.cos(arg)) # Hann window + return fwin diff --git a/06-Surface_Waves/multipleFilter.py b/06-Surface_Waves/multipleFilter.py new file mode 100644 index 0000000..4ba80d4 --- /dev/null +++ b/06-Surface_Waves/multipleFilter.py @@ -0,0 +1,47 @@ +import numpy as np + +def multipleFilterAnalysis(data,alfa,cfreq,dt,ndec): + """ + Perform a multiple filter analysis of data. + data: Array of detrended and demeaned data whose length is power of 2 + alfa: Width parameter of Gaussian bandpass filter + cfreq: Array of center frequencies of Gaussian filter + dt: sampling interval of data + ndec: decimation factor for instantaneous amplitude output + """ + npts = len(data) + nd = int(pow(2, np.ceil(np.log(npts)/np.log(2)))) # find next higher power of 2 of npts + ftd = np.fft.rfft(data,nd) # Fourier transform of entire data set (pos. freq.) + # data are padded with zeros since npts <= nd + freq = np.fft.rfftfreq(nd,dt) # Fourier frequencies (positive frequencies) + nt = int(np.ceil(npts/ndec)) + mfa = np.zeros((len(cfreq),nt)) # numpy array for MFA result + for jf,cf in enumerate(cfreq): + hg = np.exp(-alfa*((freq-cf)/cf)**2) # Gaussian filter (use f instead of omega here) + fk = hg*ftd # multiply FT of data with filter + qk = np.complex(0,1)*fk # FT of Hilbert transform + ftk = np.fft.irfft(fk) # filtered data + qtk = np.fft.irfft(qk) # Hilbert transform of filtered data + at = np.sqrt(ftk**2+qtk**2) # instantaneous amplitude + mfa[jf,:] = at[0:npts:ndec] # store decimated original result + return mfa +#----------------------------------------------------------------------- +# normalize multiple filter result either along time or frequency axis +def normalizeMFT(mfa,mode,exp): + """ + Normalize the result of the mutiple filtering operation. + mfa: array with instantaneous amplitudes versus frequency (mfa(f,t)) + mode: normalization mode: if 'time', normalize along time axis + else normalize along frequency axis + exp: exponent for modifying inst amp using a power less than 1 + """ + nf,nt = mfa.shape + if mode == 'time': + for jf in range(0,nf): + mfamax = np.amax(mfa[jf,:]) + mfa[jf,:] = np.power(mfa[jf,:]/mfamax+1.e-10,exp) + else: + for jt in range(0,nt): + mfamax = np.amax(mfa[:,jt]) + mfa[:,jt] = np.power(mfa[:,jt]/mfamax+1.e-10,exp) + return mfa diff --git a/06-Surface_Waves/phaseVelocityCrossCorr.ipynb b/06-Surface_Waves/phaseVelocityCrossCorr.ipynb new file mode 100644 index 0000000..f3c3061 --- /dev/null +++ b/06-Surface_Waves/phaseVelocityCrossCorr.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two-station method\n", + "Here we compute the phase velocity form seismograms of two stations lying on a common great circle. We make use of the fact that the phase of the Fourier tranform of the cross-correlation of the two seismograms is equal to the phase difference between the surface wave trains from which we calclate the phase velocity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Preparation: load packages, set some basic options \n", + "%matplotlib inline\n", + "from obspy import UTCDateTime\n", + "from obspy.clients.fdsn import Client\n", + "from obspy.geodetics import locations2degrees\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "from multipleFilter import multipleFilterAnalysis, normalizeMFT \n", + "plt.style.use('ggplot')\n", + "plt.rcParams['figure.figsize'] = 15, 4\n", + "plt.rcParams['lines.linewidth'] = 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Get data from earthquake (Gulf of Alaska, m=7.9, d = 24 km)\n", + "client = Client(\"BGR\")\n", + "t1 = UTCDateTime(\"2018-01-23T09:31:42.000\")\n", + "st1raw = client.get_waveforms(\"GR\",\"TNS\",\"\",\"LHZ\",t1,t1 + 2 * 3600, # data of station TNS of GRSN\n", + " attach_response = True)\n", + "inv1 = client.get_stations(network=\"GR\",station=\"TNS\",\n", + " channel='LHZ',level=\"channel\",starttime=t1) # inventory info about TNS\n", + "client = Client(\"GFZ\")\n", + "st2raw = client.get_waveforms(\"GE\",\"STU\",\"\",\"LHZ\",t1,t1 + 2 * 3600, # data of station STU of GEOFON\n", + " attach_response = True)\n", + "inv2 = client.get_stations(network=\"GE\",station=\"STU\",\n", + " channel='LHZ',level=\"channel\",starttime=t1) # inventory info about STU" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "st1 = st1raw.copy() # copy data\n", + "st2 = st2raw.copy()\n", + "st1.detrend('linear') # detrend\n", + "st2.detrend('linear')\n", + "st1.remove_response(output='VEL',zero_mean = True, # remove response (pre-filter is important)\n", + " pre_filt = (0.001, 0.005, 0.4, 0.5))\n", + "st2.remove_response(output='VEL',zero_mean = True, # remove response(pre-filter is important)\n", + " pre_filt = (0.001, 0.005, 0.4, 0.5))\n", + "st1.plot() # plot data\n", + "st2.plot()\n", + "print(st1)\n", + "print(st2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "co = inv1.get_coordinates(\"GR.TNS..LHZ\") # extract coordinates from inventory\n", + "tns = (co[\"longitude\"],co[\"latitude\"])\n", + "co = inv2.get_coordinates(\"GE.STU..LHZ\")\n", + "stu = (co[\"longitude\"],co[\"latitude\"])\n", + "event = (-149.09,56.05) # coordinates of event\n", + "dis = locations2degrees(tns[1],tns[0],stu[1],stu[0])*np.pi/180.*6371. # epicentral distance\n", + "print(\"Distance in km: \",dis)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "tw1 = UTCDateTime(\"2018-01-23T10:05:00.000\") # cut seismograms to surface wave train\n", + "tw2 = UTCDateTime(\"2018-01-23T10:16:00.000\")\n", + "st1.trim(tw1,tw2)\n", + "st2.trim(tw1,tw2)\n", + "st1.plot()\n", + "st2.plot()\n", + "npts = st1[0].stats.npts\n", + "dt = st1[0].stats.delta\n", + "print(\"Number of samples: \",npts,\"Sampling interval: \",dt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "cc = np.correlate(st2[0],st1[0],mode=\"full\") # Cross correlation: cc(k)=sum_n a(n+k)b(n)\n", + "tm = np.arange(-(npts-1),npts)*dt # includes negative lags as well\n", + "plt.plot(tm,cc,'-k')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "ns = 256 # limit length of cross correlation to first ns points\n", + "ccp = cc[npts-1:npts-1+ns] # cross correlation for positive lag\n", + "print(len(ccp))\n", + "tm = np.arange(0,ns)*dt # associated time values\n", + "plt.plot(tm,ccp,'-k')\n", + "plt.xlabel(\"Time\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "nd = int(pow(2, np.ceil(np.log(ns)/np.log(2)))) # Fourier transfrom of the cross correlation\n", + "fcp = np.fft.rfft(ccp,nd)\n", + "freq = np.fft.rfftfreq(nd,dt)\n", + "print(nd,dt,len(freq),freq[0:4])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fmax = 0.1 # compute phase up to maximum frequency fmax\n", + "fny = 1./(2.*dt)\n", + "jmax = int(fmax/fny*(nd//2-1))\n", + "print(jmax)\n", + "spec = fcp[0:jmax]\n", + "frs = freq[0:jmax]\n", + "phase = np.unwrap(np.angle(spec)) # unwrap the phase owing to 2*pi multiples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "plt.subplot(311)\n", + "plt.plot(frs,np.abs(spec),\"-k\") # plot abs of spectrum\n", + "plt.subplot(312)\n", + "plt.plot(frs,np.angle(spec),\"-k\") # plot phase of spectrum\n", + "plt.subplot(313)\n", + "plt.plot(frs,phase,\"-k\") # plot unwrapped phase\n", + "plt.xlabel(\"Frequency [Hz]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "j1 = int(jmax*0.1)\n", + "j2 = int(jmax*0.6)\n", + "phavel = -2.*np.pi*frs[j1:j2]*dis/phase[j1:j2] # calculate phase velocity and plot\n", + "plt.plot(frs[j1:j2],phavel,'-k')\n", + "plt.xlabel(\"Frequency [Hz]\")\n", + "plt.ylabel(\"Phase velocity [km/s]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/06-Surface_Waves/surfaceWaveGroupVelocity.ipynb b/06-Surface_Waves/surfaceWaveGroupVelocity.ipynb new file mode 100644 index 0000000..49eb75b --- /dev/null +++ b/06-Surface_Waves/surfaceWaveGroupVelocity.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Surface Wave Velocity (Single Station Approach)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Preparation: load packages, set some basic options \n", + "%matplotlib inline\n", + "from obspy import UTCDateTime\n", + "from obspy.clients.fdsn import Client\n", + "from obspy.clients.fdsn import URL_MAPPINGS\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "from movingWindow import movingWindowAnalysis, hann # import the moving window analysis routines\n", + "from multipleFilter import multipleFilterAnalysis, normalizeMFT # import the multiple filter analysis routines\n", + "plt.style.use('ggplot')\n", + "plt.rcParams['figure.figsize'] = 15, 4\n", + "plt.rcParams['lines.linewidth'] = 0.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data from data archive using obspy webfdsn client\n", + "FDSN stands for the International Federal of Digital Seismic Networks (www.fdsn.org). Citation from their web site: \"The International Federation of Digital Seismograph Networks (FDSN) is a global organization. Its membership is comprised of groups responsible for the installation and maintenance of seismographs either within their geographic borders or globally. Membership in the FDSN is open to all organizations that operate more than one broadband station. Members agree to coordinate station siting and provide free and open access to their data. This cooperation helps scientists all over the world to further the advancement of earth science and particularly the study of global seismic activity.\"\n", + "BGR (Bundesanstalt für Geowissenschaften und Rohstoffe) operates broadband stations in Germany and operates a data archive. It is member of the FDSN. That is why we can freely download data from them.\n", + "There are other data archives around the world which are also member of the FDSN and allow opn access to their data (see OBSPY documentation " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "for key in sorted(URL_MAPPINGS.keys()): # eine Liste der Archive\n", + " print(\"{0:<7} {1}\".format(key, URL_MAPPINGS[key]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get waveforms for the Tohoku earthquake for station TNS of the German Regional Seismic Network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "client = Client(\"BGR\") # data archive from which data are fetched\n", + "t1 = UTCDateTime(\"2018-01-14T09:19:00.000\") # desired start time of data\n", + "stc = client.get_waveforms(\"GR\", \"TNS\", \"\", \"LHZ\", t1, # use fdsn web client to download data\n", + " t1 + 7200., attach_response=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "stc.detrend('linear') # take out linear trend\n", + "stc.plot()\n", + "print(stc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "st = stc.copy() # proceed with copy to avoid repeated downloading\n", + "ta = UTCDateTime(\"2018-01-14T10:05:00.000000Z\") # cut to surface wave train\n", + "te = UTCDateTime(\"2018-01-14T10:22:00.000000Z\")\n", + "st.trim(ta,te)\n", + "st.taper(0.05,\"hann\") # apply a taper to bring signal to zero at ends\n", + "st.plot() # dispersion is now well visible\n", + "print(st)\n", + "tr = st[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Moving window approach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "wlen = 256.0 # window length in sec\n", + "npts = tr.stats.npts # number of samples in the trace\n", + "dt = tr.stats.delta # sample interval\n", + "nwin = int(wlen/dt) # nuber of samples in window\n", + "nwin = int(pow(2, np.ceil(np.log(nwin)/np.log(2)))) # find next higher power of 2 of nwin\n", + "shift = nwin//8\n", + "tshift = shift*dt\n", + "nseg,mwa = movingWindowAnalysis(tr.data,hann,nwin,shift,2.0) # compute spectrogram, exponent=2.0\n", + "freq = np.fft.rfftfreq(nwin,dt) # Fourier frequencies\n", + "print(\"Frequency range [Hz]: \",freq[0],freq[-1])\n", + "print(\"Number of frequency samples: \",len(freq))\n", + "print(\"Window length in samples: \",nwin)\n", + "print(\"Window shift in samples: \",shift)\n", + "print(\"Number of segments: \",nseg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# plot the result\n", + "st.plot() # plot seismogram again\n", + "fmax = 0.08 # maximum frequency to be plotted\n", + "fmin = 0.02\n", + "nf = len(freq)-1 # number of frequencies\n", + "jfmax = int(fmax/freq[-1]*nf) # max index for plotting \n", + "jfmin = int(fmin/freq[-1]*nf) # min index for plotting \n", + "extent = (0.5*wlen,0.5*wlen+nseg*tshift,fmin,fmax) # extent of matrix in true time and frequency\n", + "asp = 0.5*nseg*tshift/(fmax-fmin)\n", + "fg = plt.figure(figsize = [12.5,12])\n", + "plt.imshow(mwa[jfmin:jfmax,:],extent = extent, aspect = asp, \n", + " origin = \"lower\", interpolation = \"bicubic\") # plotting with interpolation\n", + "plt.xlabel('Window center time [s]')\n", + "plt.ylabel('Frequency [Hz]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe the change of frequency content with time from 0.03 Hz initially to 0.06 Hz towards the end of the record. Given a epicentral distance $d$, we could transform the time axis into a group velocity axis via $U=d/t$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple filtering approach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "alfa = 50.0 # filter width parameter\n", + "cfreq = np.linspace(fmin,fmax,100) # array of filter center frequencies\n", + "mfa = multipleFilterAnalysis(tr.data,alfa,cfreq,dt,1)\n", + "mfa = normalizeMFT(mfa,'freq',2.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "st.plot()\n", + "fg = plt.figure(figsize = [12.5,12])\n", + "extent = (0.,npts*dt,fmin,fmax) # extent of matrix in true time and frequency\n", + "asp = 0.5*npts*dt/(fmax-fmin)\n", + "plt.imshow(mfa,extent = extent, aspect = asp, \n", + " origin = \"lower\", interpolation = \"bicubic\") # plotting\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('Frequency [Hz]')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/07-Receiver_Functions/data/PB01.BHE.20110225.sac b/07-Receiver_Functions/data/PB01.BHE.20110225.sac new file mode 100644 index 0000000..1e03c33 Binary files /dev/null and b/07-Receiver_Functions/data/PB01.BHE.20110225.sac differ diff --git a/07-Receiver_Functions/data/PB01.BHE.20110306.sac 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b/07-Receiver_Functions/example_events.xml new file mode 100644 index 0000000..513687a --- /dev/null +++ b/07-Receiver_Functions/example_events.xml @@ -0,0 +1,473 @@ + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=10171447 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16917168 + earthquake + + CENTRAL MID-ATLANTIC RIDGE + Flinn-Engdahl region + + + + + 0.4584 + + + -25.6088 + + + 18900.0 + + + ISC + + + + + 6.1 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=10171449 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=10167818 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16912325 + earthquake + + COSTA RICA + Flinn-Engdahl region + + + + + 10.1114 + + + -84.1889 + + + 76800.0 + + + ISC + + + + + 6.0 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=10167822 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=10137185 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16870909 + earthquake + + SOUTH OF PANAMA + Flinn-Engdahl region + + + + + 6.8511 + + + -82.3594 + + + 10000.0 + + + ISC + + + + + 6.2 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=10137190 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=10109851 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16833821 + earthquake + + SOUTH OF KERMADEC ISLANDS + Flinn-Engdahl region + + + + + -34.286 + + + 179.9433 + + + 98100.0 + + + ISC + + + + + 6.5 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=10109854 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=10082429 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16795144 + earthquake + + CHIAPAS, MEXICO + Flinn-Engdahl region + + + + + 17.2651 + + + -94.1439 + + + 165100.0 + + + ISC + + + + + 6.7 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=10082436 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=10053039 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16759916 + earthquake + + FIJI ISLANDS REGION + Flinn-Engdahl region + + + + + -16.5479 + + + -177.3915 + + + 19400.0 + + + ISC + + + + + 6.4 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=10053046 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9925460 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16631835 + earthquake + + SOUTH SANDWICH ISLANDS REGION + Flinn-Engdahl region + + + + + -56.3864 + + + -27.0253 + + + 92000.0 + + + ISC + + + + + 6.5 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9925462 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9916282 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16620185 + earthquake + + EASTER ISLAND REGION + Flinn-Engdahl region + + + + + -29.6428 + + + -112.1246 + + + 3800.0 + + + ISC + + + + + 6.1 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9916288 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9851774 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16541902 + earthquake + + OAXACA, MEXICO + Flinn-Engdahl region + + + + + 17.8214 + + + -95.1708 + + + 130600.0 + + + ISC + + + + + 6.0 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9851779 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9831995 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16530631 + earthquake + + SOUTH ISLAND, NEW ZEALAND + Flinn-Engdahl region + + + + + -43.4935 + + + 172.713 + + + 4800.0 + + + ISC + + + + + 6.1 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9832004 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9831146 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16529492 + earthquake + + SOUTH OF FIJI ISLANDS + Flinn-Engdahl region + + + + + -26.0435 + + + 178.4765 + + + 551800.0 + + + ISC + + + + + 6.5 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9831149 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9817429 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16511317 + earthquake + + TONGA ISLANDS + Flinn-Engdahl region + + + + + -20.8515 + + + -175.5845 + + + 85900.0 + + + ISC + + + + + 6.1 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9817432 + + GCMT + + + + + smi:service.iris.edu/fdsnws/event/1/query?originid=9794081 + smi:service.iris.edu/fdsnws/event/1/query?magnitudeid=16481049 + earthquake + + TONGA ISLANDS + Flinn-Engdahl region + + + + + -21.9987 + + + -175.5367 + + + 69300.0 + + + ISC + + + + + 6.0 + + MW + smi:service.iris.edu/fdsnws/event/1/query?originid=9794089 + + GCMT + + + + + diff --git a/07-Receiver_Functions/receiverFuntionCalculation.ipynb b/07-Receiver_Functions/receiverFuntionCalculation.ipynb new file mode 100644 index 0000000..18f24a3 --- /dev/null +++ b/07-Receiver_Functions/receiverFuntionCalculation.ipynb @@ -0,0 +1,654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculation of receiver functions\n", + "In this notebook, we calculate a receiver function for a 3-component record of one event at a single station. It is a modification of the minimal example provided by Tom Eulenfeld (Richter) on Github." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "from obspy.core import read, Stream, Trace, UTCDateTime\n", + "from obspy.taup import TauPyModel\n", + "from obspy import read_events\n", + "from obspy.signal.filter import lowpass\n", + "plt.style.use(\"ggplot\")\n", + "plt.rcParams['figure.figsize'] = 15, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first read the QuakeML file which contains information about the avaiable seismic events using ObsPy. We get a catalogue object whih can be iterated through events and from which information can be extracted. Here, we are interested in the origin time." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2011-05-15T13:08:15.420000Z\n", + "2011-05-13T22:47:55.340000Z\n", + "2011-04-30T08:19:16.720000Z\n", + "2011-04-18T13:03:04.360000Z\n", + "2011-04-07T13:11:23.430000Z\n", + "2011-03-31T00:11:58.880000Z\n", + "2011-03-06T14:32:36.940000Z\n", + "2011-03-01T00:53:45.350000Z\n", + "2011-02-25T13:07:26.980000Z\n", + "2011-02-21T23:51:42.340000Z\n", + "2011-02-21T10:57:51.760000Z\n", + "2011-02-12T17:57:56.170000Z\n", + "2011-01-31T06:03:26.330000Z\n" + ] + } + ], + "source": [ + "cat = read_events(\"example_events.xml\")\n", + "for ev in cat:\n", + " print(ev.origins[0].time)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the minimal example by Tom Eulenfeld, data of the following three events are provided in the SAC format (a fairly widespread format used with SAC (seismological analysis code) software In the following cell, you can choose one of the three events by setting the variable \"evnum\"." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Origin(resource_id=ResourceIdentifier(id=\"smi:service.iris.edu/fdsnws/event/1/query?originid=9925460\"), time=UTCDateTime(2011, 3, 6, 14, 32, 36, 940000), longitude=-27.0253, latitude=-56.3864, depth=92000.0, creation_info=CreationInfo(author='ISC'))]\n" + ] + } + ], + "source": [ + "evnum = 1 # coose an event (0,1,2)\n", + "avail_events = (cat[1],cat[6],cat[8]) # pick available events from catalogue\n", + "event_dates = (\"20110513\",\"20110306\",\"20110225\") # hack for reading associated SAC files\n", + "event = avail_events[evnum] # select an event\n", + "evdate = event_dates[evnum]\n", + "print(event.origins)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we read the SAC files, detrend and taper the data and then apply a bandpass filter. Afterwards, the traces are trimmed to a 90 seconds time window." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "st = read(\"data/PB01.BH?.\"+evdate+\".sac\", format = \"SAC\") # read data using ObsPy\n", + "st.detrend(\"linear\") # detrending\n", + "st.taper(0.05,type = \"hann\") # tapering\n", + "st.filter('bandpass', freqmin=0.4, freqmax=1.0) # bandpass filter\n", + "dt = st[0].stats.delta\n", + "for tr in st: # trim to 90 s window\n", + " ta = tr.stats.starttime\n", + " tr.trim(ta+5.,ta+95.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The \"stats\"-attribute of the data stream contains pretty much details about the traces and the event such as backazimuth, epicentral distance and other useful things." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " network: CX\n", + " station: PB01\n", + " location: \n", + " channel: BHE\n", + " starttime: 2011-03-06T14:40:44.719539Z\n", + " endtime: 2011-03-06T14:42:14.719539Z\n", + " sampling_rate: 5.0\n", + " delta: 0.2\n", + " npts: 451\n", + " calib: 1.0\n", + " _format: SAC\n", + " processing: [\"ObsPy 1.1.0: detrend(options={}::type='linear')\", \"ObsPy 1.1.0: taper(max_length=None::max_percentage=0.05::side='both'::type='hann')\", \"ObsPy 1.1.0: filter(options={'freqmin': 0.4, 'freqmax': 1.0}::type='bandpass')\", 'ObsPy 1.1.0: trim(endtime=UTCDateTime(2011, 3, 6, 14, 42, 14, 719539)::fill_value=None::nearest_sample=True::pad=False::starttime=UTCDateTime(2011, 3, 6, 14, 40, 44, 719539))']\n", + " sac: AttribDict({'delta': 0.2, 'depmin': -4753.0, 'depmax': 7559.0, 'scale': 1.0, 'b': 0.00053899997, 'e': 120.00054, 'o': -482.77899, 'stla': -21.04323, 'stlo': -69.487396, 'stel': 900.0, 'evla': -56.386398, 'evlo': -27.025299, 'evdp': 92.0, 'mag': 6.5, 'dist': 5242.6309, 'az': 300.62534, 'baz': 149.24416, 'gcarc': 47.148113, 'depmen': 523.57239, 'nzyear': 2011, 'nzjday': 65, 'nzhour': 14, 'nzmin': 40, 'nzsec': 39, 'nzmsec': 719, 'nvhdr': 6, 'npts': 601, 'iftype': 1, 'iztype': 9, 'leven': 1, 'lpspol': 0, 'lovrok': 1, 'lcalda': 1, 'kstnm': 'PB01', 'kcmpnm': 'BHE', 'knetwk': 'CX'})\n" + ] + }, + { + "data": { + "image/png": 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vvkNkZKTieV988UV4eHigXr16iI6OxubNm+Hj42PTMS9fvhwFBQXo379/hfsl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\nRERE1waGhEZycXGB0Whs7WbckEwmU70PpCMiIiKilsGJy40khEBNTQ0sF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f3Mv1RKSgp8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\niIiIrm31nm5ERERERETXNgYJRER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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(st[0].stats)\n", + "st.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we rotate the ZNE-components into the LQT-system. To do this, we need the backazimuth and the incidence angle of the P-wave. Both are not perfectly known but can be estimated assuming the P-wave propagates in the plane of the source-receiver great circle and using a standard earth model to calculate travel time and incidence angle. The obspy.taup module helps us with this task. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3 Trace(s) in Stream:\n", + "CX.PB01..BHT | 2011-03-06T14:40:44.719539Z - 2011-03-06T14:42:14.719539Z | 5.0 Hz, 451 samples\n", + "CX.PB01..BHQ | 2011-03-06T14:40:44.719538Z - 2011-03-06T14:42:14.719538Z | 5.0 Hz, 451 samples\n", + "CX.PB01..BHL | 2011-03-06T14:40:44.719539Z - 2011-03-06T14:42:14.719539Z | 5.0 Hz, 451 samples" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = TauPyModel(model=\"iasp91\") # create a model object for ray tracing\n", + "evdep = st[0].stats.sac[\"evdp\"] # extract event depth from trace stats\n", + "epidis = st[0].stats.sac[\"gcarc\"] # extract epicentral distance in degrees from trace stats\n", + "baz = tr.stats.sac[\"baz\"] # extract backazimuth from trace stats\n", + "arrivals = model.get_travel_times( # get travel time, incidence angle stored in arrivals-object\n", + " source_depth_in_km = evdep,distance_in_degree=epidis,phase_list=\"P\")\n", + "incl = arrivals[0].incident_angle # extract incidence angle\n", + "ttime = arrivals[0].time # extract arrival time\n", + "st.rotate('ZNE->LQT',back_azimuth=baz, inclination=incl) # rotate components" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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IIaRFoCChHqiFVtN5HpsaVVZWoqysDI6OjhQkEEIIIaRFoCDBQEZGRqisrGzu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "st[2].plot() # plot new L-component" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we trim the traces to a 40 s window around the P-onset which we estimate by adding the travel time to the event origin time. We could also pick the arrival time. The resulting trace is again tapered to avoid artifacts in the discrete Fourier transform." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2011-03-06T14:40:59.816249Z\n", + "3 Trace(s) in Stream:\n", + "CX.PB01..BHT | 2011-03-06T14:40:49.719539Z - 2011-03-06T14:41:29.719539Z | 5.0 Hz, 201 samples\n", + "CX.PB01..BHQ | 2011-03-06T14:40:49.719538Z - 2011-03-06T14:41:29.719538Z | 5.0 Hz, 201 samples\n", + "CX.PB01..BHL | 2011-03-06T14:40:49.719539Z - 2011-03-06T14:41:29.719539Z | 5.0 Hz, 201 samples\n" + ] + }, + { + "data": { + "image/png": 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Nf/75JzVs2FCY+ef69et09epVKioqomfPntGIESOoT58+QrnlHVtz2m+uZcuWUfPmzYV9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\nZHUYCBCRRagrNUin0+Hu3btNTg0CqgcChj0EOCJARETWhoE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "onset = event.origins[0].time+ttime # onset time = origin time + travel time\n", + "print(onset)\n", + "st.trim(onset-10.,onset+30.)\n", + "st.taper(0.125,type = \"hann\")\n", + "print(st)\n", + "st[2].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "zcomp = st[2].data # rename traces\n", + "rcomp = st[1].data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deconvolution in the frequency domain using waterlevel method\n", + "Here comes the central routine for receiver function calculation which does the deconvolution, here using the water level method. Note that we add zeros to the traces to avoid the pitfalls when doing cross-correlation inthe frequency domain. We deconvolve both the Z- or L-component and the R-or Q-component." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def deconvWaterLevel(rsp, src, dt, waterlevel=0.05, alfa=2.,\n", + " tshift=None,length=None,normalize=False):\n", + " \"\"\"\n", + " Frequency-domain deconvolution using waterlevel method.\n", + " Deconvolve src from rsp.\n", + "\n", + " :param rsp: trace to be deconvolved (nominator)\n", + " :param src: trace to deconvolved with (denominator)\n", + " :param dt: sampling interval of data\n", + " :param waterlevel: waterlevel to stabilize the deconvolution\n", + " :param gauss: Gauss parameter of low-pass filter\n", + " :param tshift: shift zero lag of receiver function to the right by tshift\n", + " :param length: number of data points in results, optional\n", + " :param normalize: normalize to src (default = False)\n", + " :return: receiver function\n", + " \"\"\"\n", + " npts = len(rsp)\n", + " if length == None:\n", + " nout = npts\n", + " else:\n", + " nout = length # cut output to nout samples\n", + " nfft = int(pow(2, np.ceil(np.log(npts)/np.log(2)))) # next higher power of 2\n", + " nfft = 2*nfft # add zeros to avoid pitfalls with cc \n", + " freq = np.fft.rfftfreq(nfft,dt) # Fourier frequencies\n", + " spq = np.fft.rfft(rsp,nfft) # FT of R or Q component\n", + " spz = np.fft.rfft(src,nfft) # FT of Z or L component\n", + " spshift = np.exp(-1j*2.*np.pi*freq*tshift) # time shift (applied in the frequency domain)\n", + " cspz = np.conjugate(spz) # complex conjugate needed later\n", + " spzabs2 = np.abs(spz)**2 # absolute value squared\n", + " water = np.maximum(spzabs2,max(spzabs2)*waterlevel) # regularized denominator\n", + " gauss = np.exp((-np.pi*freq/alfa)**2) # Gaussian low-pass filter\n", + " sprfz = gauss*spshift*spzabs2/water # deconvolve Z or L component \n", + " rfz = np.fft.irfft(sprfz,nfft)[:npts] # Z or L receiver function intime domain\n", + " if normalize:\n", + " norm = 1.0/max(rfz)\n", + " else:\n", + " norm = 1.0\n", + " rfz = norm*rfz # normalize Z-receiver function to max of 1\n", + " sprfq = gauss*spshift*spq*cspz/water # spectrum of Q-receiver function\n", + " rfq = -np.fft.irfft(sprfq,nfft)*norm # Q-receiver function (normalized and sign reversed)\n", + " return rfq[0:nout],rfz[0:nout]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "tshift = 5. # shift the zero lag to +5 seconds\n", + "rfq,rfl = deconvWaterLevel(rcomp, zcomp, dt, \n", + " waterlevel=0.05, alfa=20.,\n", + " length=150,tshift=tshift,normalize=True) # do the deconvolution\n", + "tr = np.arange(0,len(rfq))*dt-tshift # take shift into account when plotting time axis" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xVK/PVUFBEN0L2eNccijlB2F/sV/zhoAW6+nYyS1u4xSSmQ5x8V5vGqlRE7Q/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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(211)\n", + "plt.plot(tr,rfq)\n", + "plt.ylabel(\"Q-RF\")\n", + "plt.subplot(212)\n", + "plt.plot(tr,rfl)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"L-RF\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deconvolution in the time domain using Wiener filter\n", + "Now, we do the deconvolution in the time domain using the Wiener filter method. We need the cross-correlation and the autocorrelation to form the Toeplitz matrix and the right hand side of the equation system." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of z: 201\n", + "length of czr: 401\n", + "Center of correlation: 200\n" + ] + } + ], + "source": [ + "p = len(zcomp) # length of z and r\n", + "czr = np.correlate(rcomp,zcomp,mode = \"full\") # correlation of z with r according to our definition\n", + "azz = np.correlate(zcomp,zcomp,mode = \"full\") # auto-correlation of z\n", + "q = len(czr) # length of correlations = 2*p-1\n", + "jc = q//2 # center of autocorrelation sits at j=q//2 = p-1\n", + "print(\"length of z: \",p)\n", + "print(\"length of czr: \",q)\n", + "print(\"Center of correlation: \",jc)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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F0zzOd39oaCgtLriKrO2bKHptDs16Hk5Ev6MBKEtPJf3lpwjrcTgJ516GCQnx\ntTf7hD9T8Ok7JJx7BSHJLX3ny/viI3KtS+KYvxBa4/e8dNxFpC/8kMiFH9Ds0huqd39ZGdkzJlP4\n2fuEduxC7JW3Et5/ULV+qMq99X7Srj2XkJefJOG+J6p9ASv4/GOyv/2K2AuuJubUM31tdifPJPux\nSRS98Rzmi4+xudnY/Dwijh5O8+vvxKkIAvoNIPfFJ8l7/TkiY5vR7NLrfe1wC/LImnYPxcu+JPaC\nvxFz+nnVG9ZiJAwbSdH335D34pOUvDQT3n6J8KOGEN5nACFtOuDENsMWF1P0w1JClyygaNmXhLTt\nSNy9Mwjrflj18w0fRc76n8n/34s0O/5kIvoOrOyDrF2kLvuCqFGn0Lxjp2qHuaeOI/WtF4n4+jOa\n97u52n27vppPaVJLkkeeUK1/Sy68moybLyXqi4+IPfdyrLVkzX6Y4rAwki65lpDE8tcz+RjSu3SH\nZV+QdFb1ixV5Sz4lF0gaOoKQGq9/Qe++ZH/2PgmlRYS2rUw5LVy3miwg/rC+hFc5Jq1VO0Iz04mv\nsq3QlpEFJHTuUuv9lRrbjHC3jLga28t2pZMGxCYlE11+X2hoKMnJyRS1bE0mEBcZXu2xAfIM5ALJ\nHTtV+8Jq4+JIAaJLiomtckxOTib5ISEk9+hV+X6J6E8qEL0rjZgq+9qSElK2byZ6wDCa1XhcGz+Y\n1MhoIjb+QvMTq1+syVy+iOLYZrQ4+XRMWLi3/1kXk/bFxzhv/ofEB5/ChIVTsOADsrf+RtxN9xDZ\nssrIZHIyGb374S76hKTzLq/2e5O3eB65JcUknHYOYTX74tRx5D7zKHHZ6YR16eHbXrp1E+lzXyBi\n0LHEnX5ejc+1Yynt1IXs6fdQ8sw0QpfMJ3LY8bi9+hIfGootLKR05xZKVn1H0TdfYtNTCWnXidib\n7iFi2KjaQdUVf6fwsCPJmnYXzmN3E3/bFEISW3h9YC35b79M7hvPEXHMaOJuuMv3eWVH/IGcOY9R\n8P7r2IUfEJLcGnfbJsK6H0bczfcSUjFymzySwtB/kfXARKIWvEfs2ZdVe/isl2ZSmJ9L4qT/I6yD\nnxTOv5yHPf1cSn7+gdwXn6Tk+ccJW/k18bc/WCtVO//9N8jJyiD+pruJqNHX7h/+SNp/ZxGxYhHN\nhxzr226Likj78A3CjjiK+FGnVPb1CX8k66dvKXzvNeKO/QNh3Xp5r83mDWQ8M43QLj1IvP6fmIgI\n3/ue5GTwpO5AAAAgAElEQVTsHQ+R9+oc8l6eTWhWBvG3PlDtbwNA7kuzyFu/huY33EVUp87Vn29y\nMvnnXk7O7Gk037iGiPJRxQqZzz1GUU4WiVNmE9a1R7Xj6DeA4jU/kv/OKxSvXIpd9Knvb66T1IKQ\npJaYyCjc7CzKNq+HsjJM83iijh9D5LDRhHboDKGhlG7eSOmvqylZ+xMlv66m7Nuvav2lN83iCIlP\nxImKrh7EBPh9y+93Kr/H2vpvFkCoG8BxdZ2/1qZAj/OfEdSgPX3edWwKNqeogOQA06ibokYJDh95\n5BG/27Ozs+ncuTM//bR3J5OPHj2a0aMrr2ynpaXt1fP/Pg7c838kJSc3sXbtmaZ//aa25CbS947r\nevOq8nK8q4yxzSChBTYsjCKgCKDUBVyIbwFjzoKTzsRZ/zN29XeUbVpHWU4WhEdgOnbFDDwWWrXF\ntGoL7TtTFhZODkB6uv8GnHslpbMeJPWFWTinjAPKR8FmTYXIaIpGn+a/n5LbYE47n6I3/0Nqm044\nJ3jzfm1GWvkI2irMCadT+pcLyCgugSrnSE5OJj0jA3ve1bB1E5mTb8G5/m44pAfujHuhpJiy8deQ\nvqt6Gpo97hSY9zbprzyDc5b3Jcq6Lu4nb0PXnmRGxFR7HABCIzCDR5L/0f8oHDHGN/JhrcU+PwP7\nxceYMeNw/3g2OaGhsKuBanhnXETJs4+SOvclnPL5jzY705tndUh38of9gYKabbhsAs7RI3A/movp\ncQRO30GUHHEUGfkFkF95Zdme8BdMdjYFH7xBwca1OGdeAvl53lX0HVsx515JwfATa5+/QtvO2An3\n4/yyCvvlJxQuX0zhwo9q7xeX6F2BP3ksWWHhtfsMsCecDovnk/n4FJy7H/MFBe57r0JJMUWDR/l9\nX5ijjqHgsw8pOuUs34iyTd2Bu3Ip5tSzSa/Zv/EtMEcfS95rc8jHwLZN2K8/x5xxIbtcU61t7sBj\nsa/MJvW7FdVGbsuWL4GWbdhFSK3nYhO8iwgZ3y3HCa/8suz+5M1/zYppjqlyTFlSS8o2b6z23Nyt\nmwDYVWar7QvgRkRRmJFOSc3HLR/Ryy0tJb/8vorPHFvkXQjNStmJaVXjfKk7ISSUtOwcjKkxlzGm\nGfk7tlFYtb0b10Fyaz/9mkjeL6urvVfspnVQWkpBizYU+XvND+1NwcqlFFc9Jj8P9+vPMcNGk55V\nI+V07EWUPvEAKXffgBlyPPa5x6DzoeT06EtuzX4aOgo7eyppX3zqqyhrrcV9/w3o2pOsZgm1X7u+\ngyA8gl2v/wfnkhsrj5kxGcLCKTnzUtL9fa6FRWL/fh9m4YeUfPI/SmZPo1YyekSkN9I37jJsv0Hk\nOg65GXWkCvbog3PV7ZQ+9RBpN12EM/5vXrGrD97AfvUZ9B9CyXlX1/q84vQLcA47yktX/20t5qLr\nKBtyPLtwqj/Xrr0xR48g7/XnKOjZ15dhYX/6Dvez9zFjziQrJs7v76lPi7bY6+/GfPERxS/MJGXS\nBJxr/uEbmbMlJbivPwfdepHdplOt9zEARw6hYNGnFP35fF9g6X72PjZrF6UnnlGrr+3pF8DKZWQ8\n4o16krUL9+HbvbT0yyeQnpMDOTm1/9aO+hMmNo6SZx8jdcLFOFff7ss0sCu/xn3tWcyw0eT27k+e\nv/fpUcPh3dfInD0dp0NX32ir/eZL3C/nYf58HllxSf77K6k1XHQ9pqwMs20T7NyK3bkNu3MrJZkZ\nkJcLMbGYE073smx69qE4NJRigJIy719yG+/fkFEAOHk5Xnp3RKRXpKp5nK9NwV5I6UD5jrm/Cmmi\n/d+2bduGd6KRgsOqI3fZ2dl8+eWXLFy4kI0bN9KrVy+uv/76gM6TmJhY7UMqPT2dxMTEvd5ekcZg\nHMebT9RAQYJax3TrjenWu+GdG+AMPAb32yXYt17CTWyBM+Q4+G4prF6JOeuyegs7mJPO8Erbvz6H\nsu+XYRKTscu+AONgLvs7zqARdR4LXtqnc9MkrzDJtDu9K4RlZV5hila1P7xMUkvM0cd6aaJjzsQ0\ni/PmGm7fjLn0xrof55QzsV99hv1oLuasS710spdnlweGZ+Kcfn7A/WWGHu/NzXl9DrZZc+h0KO6T\nU6CwAOei6yrTvmoe12cgIX0G+r3Pt48xmDMvxm3bAfvCE7j/Kv9MjG2Oc+M9dRZjqXkOehyO6XG4\nN38xZQek7sAW5mNCw4jv3ovM6OYNFrAwYeE4512JO+0u7AdvYP50Dva3ddh3X4G+R9dZiMKMPNnr\nn68WYEaOAcB+8Do4IZhj/uD/mIuu9+Z8vvaMd/vUszEn/qX2fkcf6/X7kvmYsRd55y4r81J5/aQe\nAtC2o5cqunkdVB1l2PobJLeqlRJtWrX15nhVLeaSnQkhoRBdYx4geEVp/KWVlqesG3/VSstHBG1h\nYe0La/l5EB3j//VpHl97blrKNmjZpva+7Tpht26stsluWu+1qUOX2vvjFamxP3yDzUz3pWDb5Yug\npBgz5Lja+/cfirn4euyz//YKAnXp4aVe+klpNP2HYps/jfveK14qujHeeos7t2JO9f+7a6JjvffT\nx//D9h+C6TcYu/RzL5303CvrLeJiHAdz3BivuE3KdprlZpKdno4JD4fk1l4qoZ+UxjrP13cgzm0P\neSmr/3dv+UaD+fO5mDHj/D5nANP9MG/eW0PnP/ty7E8rcR+f7M0nLC7y5lS2bIMpv2jX4DmMwRx7\nEm5IKPbZx7xiT5dP8KYrLJoHu9JwLrq2zt99c9wY73f3/dcwf7nAS2X/6E2vCI2f52CiY7107+l3\n4V5/jlc8LCIK5+b7MTXmtNbkDBqBTWrpzZn81/XQb5CX8bJqBXTq5hUVqqudoaE44y7Bfexf3rzE\nE0/35nA/612cMCePbbivQkKgwyHQ4ZDffXHbxDTzlpAROcA0SnBYWlrKN998w4IFC/juu+9o3bo1\nw4YNIzU1lRtvvJG4uLiGTwJ07dqV7du3k5KSQmJiIosXL+a6667bx60XOXCZC6/1is/MmU7Zl5/A\nr6ugdXvfl/s6jzMG55IbvTkvyxdh1/2MOeYP3lXXFq0De+zm8Th/vxf7zsveFduuvaDPgLr3P3ks\n9qsF2LnPw2nneXN9uvbEHF13IGpatsUMGomd/w5ufi42Jwt++AZzwmle2fzd4D3nG3Afn4z7xAPe\nnDPH8b6EtfUfMO0uZ9hobNee2N/WeVfwD+lea75bQG11QqB1O28OWPm2sORk/6MG/o7vfaQXkL37\nMm7qduyvq6F5eSGZuhzSHTp2wS74ADviZNi+GfvlPMyoUzGJ/lP7TVgYzl9vwb79X0hqiTP8BP/7\nNY/35ih+vQD7l/He89u0Dgryqy/NUOPctOnoC4wqeJVKO9U+oFVbb35RZkblfL2sTG9OsL8AICrG\nNyexmor5hH4L0lSpVlpTfl7tNQ4rNI+vVpDGui6kbMf0rH3RwLTrjF3zLrasrDI1e9N677H9BZNU\nzju0P//gqzhrF8/33kOHdPd7jDN0FDamOXbVCm8erL/ni/c6mD+ejX1xJnz/DfQdiLvgA29eWfmc\nQb/HnTYe+8sq3Geme7/DX37iBQ8jTqzzmFrnaNmGyN5H1BrN3F2mXSecO6fDr6uxBfmYFm0CrrzZ\n4LmbNce52qve606eAGUuJLXw5gPXM3fYH2fYaNySYuxLT+I+eBum++HYLz7ygrxe/epuwyHdvRHg\nj/+HHXI89tslkJ6Cc85f6w7UDjsS55YHsD9/DxmpmNF/8ubfBfKcu/XCuef/sPPe8YpSRUV71Z1P\nOsOXqVCnw4+Cw/t7Fya/XeK9t5Nb4fztjsr3u4j8Lo0SHF5++eU4jsOIESMYN24cXbp4Vy8//vjj\n3TpPSEgIl1xyCffddx+u63LcccfRoUNgH0YiUpuJiMS59p9e4YMNv2BOGYc5bkydpdirHRsZhfnz\nufDnc2sX/Qj08eMT/RY+8btvmw7eaMJn73uFa0pKcG64p8HHNWddBlHR2C8/9qr1nXcVzsiTd7ut\nACaxBc7EKdh3XsFu/AXnvCsxLQNL0wj4MVq391skqLGZ8Vd7y3nMfxesi3PLA5hmdV/IM8ZgRpzs\npey+8Sx2y0aIjMSMqX/0wzghAQXqzrDRuN8txX75ia8KJoDp6T84hPKiNOWL2xtjvKJNO7dijhxS\ne9+Wbb2pKzu3+oJD62+Nw4r9o2OwO/xUy64oROEvWKpY3sJPsQpbkFe7GE3FY8UlYDdWmV+fmeEF\nsq38jxxSWuKNLJZ/Wbeb10OHznX/rnToDNGxXoXQwSOxP33nVaU948J6R5pN34FegY8GmGNOwH7y\nNu6bz2F2boGVX3nL2dQTCJiwMJwrJ+LeewN24YeYocd7xZ3qGKHf10xktFdxd1+cu1svnDsf9QLo\n6BjMmZf4LYATCGfkGGxCC9ynHsZu2YAZONwrtNVQxsDYC73Uzntv9N5bvY+s92IdgDm0N+bQPcti\nMc3iMKefj/3zud5IbIBFRowx3gWlzz/2Ass2HXBuuLvezyYR2T2NEhx26tSJn3/+mbVr19KmTRta\ntmxJbKyfNJ0A9O/fn/79++/lFoocvEy4V6YeCPgPdK1z7EFguCecc6/EHjEQ983/YPoMxHT0nyZX\nlYmJxZxzBfbUsyEv+3cHXiY0DLMb6aj7KxMZjRl7EfYPf4bcnDrTSasdM3gkdtW32I//B9Z6X+ab\n7f66c371G+SV3X/jOWzzeC9VeODw+teI7NgFFn8KWRkQn+RV8XRdaF/HyCHenEFTUf10V5pXydSf\n6Bi/S1n4RgUj604r9VutNL/u4JC4BG95B7fMC47K5zX6uzBh2nfyRgE3rce06eCNMm7eiBl6vP9z\nUx6gH38K9t1XvKURPngdWrSutuzB72FCQ3H+Mh535hSvWm/3wzB/aHiNYpPUAuf2qd57qY5RzwOF\naRaHufLWvXOuvgNx7vk/MN4FrYCOaZ7gpd6/9yrm1LMwg4/b478Hu2OPLipGRmNOOM37bGLP/26J\niH+NEhzefffdpKamsnDhQt555x3mzJlDnz59KCoqoqys7rW9RKRx7E9/XM0RRxFyxFG7f1yz5rC3\nApWDiNmNebEmPIKQqyZiU3dgf1qJKS/csFfaYQzO+Vfh3nMd7oz7vXmD59c/6mw6dvVGAzeth/gk\nrzS9cSqDv6oSkiE0DHZuB8pL2m/fjOl7tP+TR8V6i4LXWHDety6en+DQhIZ5cxjrSCs1CXVU1m7X\nGYqLvba1aV+5jIWf+bm07wzN4rz5w4NGQMp27/E6dfV/7oq2nXq2twTOs48CeKPyu5nWWK/+QzGX\n3Ihp1ymgizq+dgWYpi7VmaTAgsKqnGGjYdjohndsIvanv1si+5PGudwPtGjRgrFjx/LYY49x5513\nkpCQgDGGm2++mRdeeKGxmiEiIvuYadEa59iTdqvwR0Dnbd0ec8pZEBaOc8XNmLpG2ip06AzG8eZF\nAXb5Ym/Uyk8KmnEcrxJlReC19Tdw3boDmegYsG7tQK+wnjmHFdv9rbNakOcVufGjog120zpvw87t\nXiDrJ5g0Tgim3yDs999469ut+aH8HA0EhyEhOJdP8OZYDj7OV1l0bzHG4Aw5brcCQxERaXyNMnJY\nU8+ePenZsycXX3wxS5cu5fPPPw9GM0REZD/jnHoWdvSfaq3l5o+JjMYMGIb94mPsUcO8kcD65pu2\nauuVpqeywid1VPj0pYDm50HVyqdF9cw5BG/eob8FsvNy6y5I06YDhIbC5vUwaIQXwLZoXXelzP5D\nsV98DD8s91J8O3b1RhQbYBJb4Ex+ChoqCiIiIgesRhs59Cc8PJxjjjmG22+/PZjNEBGR/UgggaFv\n35PHQmEB7qyHvNt+itH49u3UFXZs8QrRbF4PUdFQR2l+U7G8RX6NNQkL8iE0tO5iK5HR2ML8apts\nSbFXRKaugjShodC2E3bTem+ZkrWrvbbWpecREB2D+8pTkLINZ8yZgRf8iIhstDnEIiLS9OgvgIiI\nHLBMh0PgiAGQkQpdemAqlqnwt2+fgWCtV+F003ro0KXuQKnqyGFV9S1JUXGcv2OqntNf2zp19eZO\nrlsDuTnQp465kJQXTepzNGSkectRHDm47vaIiIhUoeBQREQOaM4Yb3FsM+CY+nfs0MUrXLPya9iy\nof75cb7gsObIYR5E11ONOyrGG12sqiI4rC+o7NAF8nKwCz6AkJAG5wRWPFdz0liNBIqISMCCMudQ\nRESksZhuvb0lETocUv9+xmD6DPTm61m37vmG4AsAbV5etbXvbEGel45a12P4Wx8xL8e7L6ZZ3cd1\n7OItUbHsC+hxeMPFePoMwJn4IHTpUf9+IiIiVehyooiIHPDMIYd6c/ca2q/PQC8whPpHDmPqmHNY\n33qF4I0O1korza1+Tn/adwZjwLpeGxtgjMF07aly/yIislsUHIqIiFTo1QfCw72lIlq3r3u/yGgw\njm/Uz6cgH9PQnMMCb33ECjav4eDQRERCq3bez30bDg5FRET2hNJKRUREypnwCEy/IdjcrHpHGo3j\nQEyMtwRFVQ2NHEbHgOtCUSFUVF2tCDDrSSsFMD37YCMiMS3bBvJUREREdpuCQxERkSrMJTdgqozs\n1Sk61k9BmnrWK4TK+/LzKoPDinPUM1cRwJxzOcZ1G26XiIjIHlJaqYiISBUmJCSg+YnENMNWSSu1\npSVQXNzwnEPwqppWyMuF6BiME1J/u5wQTGhYw+0SERHZQwoORURE9kTNNQsrlqiob73CaH/BYU79\ny1+IiIg0EgWHIiIie8DENKtekMa3XmE96aHRMdX3BWx+XoPzDUVERBqDgkMREZE9ERNbvSBNecBn\nouoZBSwPHG1+jZHD+paxEBERaSQKDkVERPZEdCzk52ErisRUpIo2VK0UKlNQAfJyMUorFRGRJkDB\noYiIyJ6IaQbWhcLyQK8ggLRSX7XSqiOOuRo5FBGRJkHBoYiIyJ6oCOjKU0t9qaL1FaQJC4ewcF8g\naa0tTyvVnEMREQk+BYciIiJ7wJcKWjEKGEBwCHgjixX7FhaA66paqYiINAkKDkVERPZExWhfRcXS\ngjwwDkRE1X9cdEzlnMOKwFJppSIi0gQoOBQREdkT5QGdzasychgVjTGm/uOiYipTUMsDS6O0UhER\naQIUHIqIiOyJ6OpzDinIazilFMpHDiuCw9zq5xIREQkiBYciIiJ7wleQxhv9swX5AQWHJqpqcJhT\n/VwiIiJBpOBQRERkD5iwcAiPqFKQJrdyqYr6RMf4CtJY35xDpZWKiEjwKTgUERHZU9Gxlamh+XmB\nBYdRlcGh0kpFRKQpUXAoIiKyp2JiKwvSFORhAp1zWFqCLSn20kpDwyA8fN+2U0REJAAKDkVERPZU\nTLPKtNKCfG8Nw4ZUjC4W5HkjiDHNGq5wKiIi0ggUHIqIiOyp6FjIy8G6ZV5wGOjIIUB+HjYvJ7Bj\nREREGoGCQxERkT1kYsrnHBYUeBsCqVZaJTgkL1fFaEREpMlQcCgiIrKnKtJKK5amiAqgsExF6mlB\nfnlwqGI0IiLSNCg4FBER2VPRMVBcBFm7ADABzTn0gkGbnwf5ORhVKhURkSZCwaGIiMieKk8JtWk7\nvdu7M+ewIFdppSIi0qQoOBQREdlTFYHdqm+9/3cnOFz7ExQVKq1URESaDAWHIiIie8i07QDh4dgl\n870NMc0bPig8AsLCsUs+A8fBdOyybxspIiISoNBgN0BERGR/Zdp1wnnkRVj/MzY3B5PUouFjjMH5\n2x1QVgrdeldWLxUREQkyBYciIiK/g4mIgF592Z1l7M1hR+6z9oiIiOwppZWKiIiIiIiIgkMRERER\nEREBY621wW6EiIiIiIiIBJdGDoNk4sSJwW7CQUt9Hzzq++BR3weP+j541PfBo74PHvV9cO3v/a/g\nUERERERERBQcioiIiIiICITcfffddwe7EQerLl208HGwqO+DR30fPOr74FHfB4/6PnjU98Gjvg+u\n/bn/VZBGRERERERElFYqIiIiIiIiCg5FREREREQECA12Aw5G77zzDs8//zyzZ8+mefPmAMydO5f5\n8+fjOA4XX3wx/fr1C3IrDywvv/wy33zzDcYY4uLiuPrqq0lMTATU9/va888/z/LlywkNDaVVq1Zc\nffXVxMTEAOr7xrBkyRJee+01tm7dyv3330/Xrl1996n/972VK1cyZ84cXNdl1KhRnHbaacFu0gHr\n8ccfZ8WKFcTFxTF16lQAcnNzmTZtGqmpqbRo0YIbb7yR2NjYILf0wJOWlsaMGTPIzMzEGMPo0aMZ\nM2aM+r8RFBcXc9ddd1FaWkpZWRmDBw9m3Lhx6vtG5LouEydOJDExkYkTJ+7/fW+lUaWmptp7773X\nXnXVVTYrK8taa+3mzZvthAkTbHFxsd25c6e95pprbFlZWZBbemDJy8vz/fzee+/ZJ5980lqrvm8M\nK1eutKWlpdZaa59//nn7/PPPW2vV941l8+bNduvWrfauu+6ya9eurbZd/b9vlZWV2Wuuucbu2LHD\nlpSU2AkTJtjNmzcHu1kHrFWrVtl169bZm266ybft+eeft3PnzrXWWjt37lzf54/sXRkZGXbdunXW\nWmvz8/PtddddZzdv3qz+bwSu69qCggJrrbUlJSX2tttus2vWrFHfN6J33nnHTp8+3U6ePNlau/9/\n7hwUaaWPP/44l112GX//+98b3DctLY177rmHW265hQkTJrBixYq92pbnnnuO8847D2OMb9uyZcsY\nOnQoYWFhtGzZktatW7N27dq9+rgHu+joaN/PRUVFvv5X3+97ffv2JSQkBIDu3buTkZEBqO8bS/v2\n7Wnbtm2t7er/fW/t2rW0bt2aVq1aERoaytChQ1m2bFmwm3XA6t27d62r88uWLWPEiBEAjBgxQv2/\njyQkJPiqM0ZFRdGuXTsyMjLU/43AGENkZCQAZWVllJWVYYxR3zeS9PR0VqxYwahRo3zb9ve+PyiC\nw5EjR3L77bcHtO8bb7zBkCFDePDBB7nhhht4+umn91o7li1bRmJiIp07d662PSMjg6SkJN/txMRE\n3xdo2Xv++9//ctVVV/Hll19y1llnAer7xjZ//nxf6qL6PrjU//tezT5OSkpSHzeyrKwsEhISAIiP\njycrKyvILTrwpaSksGHDBrp166b+bySu63LzzTdz2WWXccQRR3DooYeq7xvJs88+y/nnn19t0Gd/\n7/uDYs5h7969SUlJqbZtx44dPP3002RnZxMREcFf//pX2rVrhzGG/Px8APLz830vbqAmTZpEZmZm\nre1nn302c+fO5R//+MeePxGpV319P3DgQM455xzOOecc5s6dy4cffsi4ceOC0MoDU0N9D/Dmm28S\nEhLC8OHDG7t5B7xA+l/kYGeMqfYFTva+wsJCpk6dykUXXVQtYwfU//uS4zg89NBD5OXl8fDDD7Np\n06Zq96vv943ly5cTFxdHly5dWLVqld999se+PyiCQ39mzZrF5ZdfTps2bfj111+ZPXs2d911F2ee\neSb33nsvH374IUVFRfzzn//crfPWtf+mTZtISUnh5ptvBrxh6FtvvZXJkyeTmJhIenq6b9+MjAxf\nsRQJXKCv1fDhw5k8eTLjxo1T3+8lDfX9ggULWL58OXfeeafvQ1J9v/fs7ucUqP8bQ80+Tk9PVx83\nsri4OHbt2kVCQgK7du3yFYGTva+0tJSpU6cyfPhwBg0aBKj/G1tMTAyHHXYYK1euVN83gjVr1vDN\nN9/w7bffUlxcTEFBAY899th+3/cHRVppTYWFhaxZs4ZHHnmEm2++mVmzZvmuui9atIiRI0cyc+ZM\nbrvtNv7973/juu7vfsyOHTsye/ZsZsyYwYwZM0hKSmLKlCnEx8czYMAAFi9eTElJCSkpKWzfvp1u\n3br97seUStu3b/f9vGzZMt8cLPX9vrdy5Ureeustbr31ViIiInzb1ffBpf7f97p27cr27dtJSUmh\ntLSUxYsXM2DAgGA366AyYMAAFi5cCMDChQs1kr6PWGuZOXMm7dq149RTT/VtV//ve9nZ2eTl5QFe\n5dLvv/+edu3aqe8bwbnnnsvMmTOZMWMGN9xwA4cffjjXXXfdft/3B+XIoeu6xMTE8NBDD9W6b/78\n+b75id27d6ekpIScnBzi4uL2WXs6dOjAkCFDuOmmm3Ach0svvRTHOSjj9n3mxRdfZPv27RhjSE5O\n5oorrgDU943h6aefprS0lEmTJgFw6KGHcsUVV6jvG8nSpUt55plnyM7O5oEHHqBz587ccccd6v9G\nEBISwiWXXMJ9992H67ocd9xxdOjQIdjNOmBNnz6d1atXk5OTw5VXXsm4ceM47bTTmDZtGvPnz/eV\nlJe9b82aNXz++ed07NjRlyF1zjnnqP8bwa5du5gxYwau62KtZciQIRx11FF0795dfR8k+/v73lhr\nbbAb0RhSUlKYMmWKb+2jf/zjH5xyyikMGTIEay2//fYbnTt35v7772fo0KGMHDmSLVu2MGnSJGbO\nnLnf5QuLiIiIiIjsjoMiOKx6NTEuLo5x48Zx+OGH89RTT5GZmUlpaSnDhg1j7NixbNmyhSeffJLC\nwkIAzj//fPr27RvkZyAiIiIiIrJvHRTBoYiIiIiIiNRPE0xEREREREREwaGIiIiIiIgcJNVKt23b\nFuwm1JKcnExaWlqwm3FQUt8Hj/o+eNT3waO+Dx71ffCo74NHfR9cTbX/K5Zxa4hGDkVERERERETB\noYiIiDQ9Nj8Xu/YnbPauYDdFROSgcVCklYqIiMj+w537Avb9V70b/QYT8rfbg9sgEZGDhIJDERGR\n/YhNTwHAJLUMckv2HbthDbRoDRGRkJUR7OaIiBw0lFYqIiISJDYnG+u6u3WMO/sR3Of+vY9a1EQU\n5EOrtphW7byfRUSkUSg4FBERCQJbmI9722XYJfMDP8Z1YdM6yGh6lfD2qoJ8TFQMRMcoOBQRaUQK\nDgFc8sMAACAASURBVEVERIJhVzoUFcL6NYEfk7YDiosgJ3PftaspKMiDqGjvX0FuQIfYkhLs8sW4\n89/FXfr5Pm6giMiBSXMORUREgiHLq8Jpt20O/JgtG73/8/OwJSWYsLC9366mID8PomIgMhKKi7Gl\npZjQ+r+y2BWLsbOnVt7uM2Bft1JE5ICjkUMREZEgsOXBIds2Ya0N7JiK4BAgJ2vvN6oJsCUlUFpS\nPnIY420MJLU02xtNNX8617udk72PWigicuBScCgiIhIM5cEM+bkBp4keDMEhBXne/9ExXoBYdVt9\n8vPAGEz7zt7tvJx90jwRkQOZgkMREZFgyKqyuHugqaVbNkJCsvdz9v4z79CWleG+Mhubsq3hnStG\nCaOivaI0VbfVJz/XCyabNfdu5yo4FBHZXU1izmFaWhozZswgMzMTYwyjR49mzJgx1fZZtWoVDz74\nIC1beus6DRo0iLFjxwajuSIiIr9f9i4ID/fm1G3fjOnZp97dbWEBpO7ADB2FXfwpNicT00hN/d22\nbcLOexvikzAnnl7/vvneKKGJioWICG9bQCOHuRAdCzFecGg1cigistuaRHAYEhLC+PHj6dKlCwUF\nBUycOJE+ffrQvn37avv16tWLiRMnBqmVIiIie4/NyoS2nWDnVtgewMjh1t+8/3v3g8Wf7l8jh9s2\neT9kBrCgfUUgGBUNEZHl2xoeObT5eV4qamwzb4NGDkVEdluTCA4TEhJISEgAICoqinbt2pGRkVEr\nOBQRETlgZO+CpJbgOAFVLLVbNwJguvTAhofvX3MOK55fViDBYXkgGB0N4V5waAvyGh4lrRg5jI71\nbuepII2IyO5qEsFhVSkpKWzYsIFu3brVum/NmjVMmDCBxMRExo8fT4cOHfyeY968ecybNw+ABx54\ngOTk5H3a5j0RGhraJNt1MFDfB4/6PnjU98FTV9+n5mQR0bsvNrklxcsXN/j6ZKftpDAqmuSeh5EW\nl0h4cSFx+8lrmpm+gyIgNC+bxAbaXBBiyAYS27bHREaRCsQ6hugGjksrKiS0RSviW7UiJTqWSLdU\n7/sgUt8Hj/o+uPb3/m9SwWFhYSFTp07loosuIjo6utp9hxxyCE888QSRkZGsWLGChx56iMcee8zv\neUaPHs3o0aN9t9PS0vZpu/dEcnJyk2zXwUB9Hzzq++BR3wePv763ZWW42ZkUhkdBVBQ2M4PUjesx\nsc3rPE/Z9q2Q2IL09HTc2OYUpuykZD95Tcs2rgOgJC2lwfehm7ITgIzCYnC98cLctBTyGziuLCcL\nNySMtLQ0bEwshamplJaW/j975x0mV1X//9e5s3W2z25203unJAQCIRAgEAMIKopfQARRUQSkWECK\nCiKIYAEE8SciIEaQHhABwUgvgUASSoD03rbXmW1zz++PM33uvTMhZVM+r+fJk50799x75szs7H3f\n96fI576PkO+cvkPWvm/ZXdd/4MCBWe2321Qr7e3t5fe//z0zZszgsMMOS3ve7/dTUGDCS6ZMmUI4\nHKa1VUJGBEEQhL5HtzZhz/0HurEuuwFtLaA1lJWjBkSiYDZv8B7TGYy3digpy7r9RV+je3qgdjMo\nBc2NmXs6hoJm34JC0/g+Lz/7aqXRkNKiErSElQqCIGwzu4U41Frz5z//mUGDBnHyySc77tPc3Bz7\ng7JixQps26akpGRXTlMQBEEQHNHvvoF+9hHsa76P/fKzmQe0mjYWqrQC+vU3x2io9R4TCkKBEYeq\npAxa95Ccw60bQNswdBR0d2UWeqEOIwytyCVKYVHGMbqnB7q7TUEaMEVppCCNIAjCNrNbhJUuXbqU\nV199laFDh3L55ZcD8LWvfS1myc6ePZv58+fzwgsv4PP5yMvL4wc/+AFK7TFFvAVBEIS9mZYmsCwY\nNgr94F3ow49DRdswOO4fcf3KKoz4AegMeZ+jM4SqHmB+Li2H9ha0bcdF1G5KtNiOmjAJvXaFKUoT\nFXFOBDviawLGLQ1maGURbDf/R5xDVVSC3rJxe6YtCIKwT7JbiMPx48fzyCOPeO5zwgkncMIJJ+yi\nGQmCIAjCNtDSBKXlqGkz0cuWQHsr5Pdz3V1HnEPKKmJuIJ0ZHLXOIBQUmp9LyyAcNi5b0W4eRbNp\nHSgLNe4A9H8eN+0sBjgXlAPQoYTwWYBCv9nmRVQ8xpzDUpA+h4IgCNvM7n27URAEQRD2AHRrM5RW\noEoiBWXaM+S7tUTEYWk55OWBsiCU2TmMicOScvN/H/Q61MF27DfmZZ1fqTevh+oBUFVjHmdqZxFK\ndQ6L4r0P3Yg4h6oonnNIKIju7c1qjoIgCIJht3AOBUEQBGGPpqXJuIDF2yAOC4tQeZHQ08JCT+dQ\n22Ho6oznHJaWo8HkHXq4cDsD/eaL6If/as5/0DR8F17tPWDTejPHctPPmOYsxGFZIPZQFfozC9Fo\nWGlhQs4hYGd6HwRBEIQkxDkUBEEQhO2lpQmVIA51pgb1LU1QVh5/XOD3LroSzUdMrFZKxLH0QG/Z\nSPg3V2I/8wg6kyjLlsY6yM2DKdNh0Xx0V6f7+W0bajehBgxCFfghvzDumroRCqIScxL9WRSkiYaV\nJjqHZPE+CIIgCEmIOBQEQRCE7UDbYdNWorQiJtoyOYe6tcnsH6XQj/bKOYyGnCbmHELGdhZ62Yew\n/GP0k//A/uWl6K4uz/2zorkRKipRBx5iHnsJ1M4g2HY8DLY8kJ1zmFqQJtTuPcahIA2A3bZvO4f6\n0w/QtZv7ehqCIOxBiDgUBEEQhO2hvc0IoLJyI2qUlUVYabNxGqMUFHpXK40IRxV1DotLTS/ATM5Y\nRLipsy40+9ZvyfRqMqKb6qGiyrTTAO85dERFW0TslQc8HUyttXEJUwrS0N3tnT+Yep5oWOk+7Bzq\ndauwb7sW/dSDfT0VQRD2IEQcCoIgCML2EO1ZWFZh2koUl2QWh62mummMwgxhpdHnojmHls8IxEwF\naVqaobgENXi4eZypl2I2NDWgygPx+XvNIVJIRkUdvbIK08rCje4uU4XVn1KQBjKsTwfk5aNycs3j\nWFhpZufQnvcU9svPobduyrjvnoIOh7HvvwPCYfRWaekhCEL2SEEaQRAEQXBA2zYolbmnbrRnYTRM\ntLgU7SEOtR02LmFCCwpV4EfXb3U/R2dKWGkW54GE8NXKavO4oZbt6RCsbTsSVloVCxXVbS3ux0xt\nMVEegJZGtNbO6xqtSpoaVhp9LloNNpWO9lhIKZC1c6ibG9EP32N+BqxLr0Xtf7DnmL5Cb1gNg4Zn\n1eNZz/sXrFtpqsTWbnZfb0EQhBTEORQEQRAEB+w7fom+//aM+8VaM0TDRItLTKipG9H8wcIEoVfo\n925lEc1HTAy3zBSKCvEqqqXlkJOz/c5heyuEe6G8Mp736OUcplYRLQtAd7d7a4pQ+utUWTiHOtge\nL0YDpvCNLydzWGlkPdQZ3zXHWbPCe/8+Qq9dgX3dpeh3Xs1u/7dehLH7oY75vFlr6fkoCEKWiDgU\nBEEQhBR0ZxA+Xoz+8D2TB+dFzDmMhFkWl3qHlXYmh4ianzO0sgg5jMkUigrQ2owqLTfhroFqaMiu\nN6ErTQ0AqIpKVG6emYOHAEurIloeaVHhlncY2V8VplQrBe9eh8HkIjZKKSguyVitNNoiQ40/EAJV\nsJuGlurVy8z/81/KbkBTPWrQcFT1QPNYitIIgpAlIg4FQRAEIZXln5giM63N4BXuCSZ/ML8QFQn5\nVMWl3kVaQinFZcCIvq5OE3LqRKeD21jg93QOtdZx5xCgsh96e53DZiMOqag0/5eUZ3AOk8NEVVkG\ncRhKCUOFhLBSDyGc6hwCFJVkrlYaXY/KflAzaPfNz1u/2vz/8eLM7Us6Q2bdK6pMWClIxVJBELJG\nxKEgCIIgpKCXfhj/edVS751TexZGnENXx9HJOYwKIDexF3MO4+JQFRZ6C6aukCnwEsmFVJXVpkfh\ndqCb6s0PUXFYWuYtVoLtpqpqdN7RPpAdzq0ptENYaVRY6gzOYVJvRIDikizCSuvAX4wq8KNqBsLW\njZmd4h2E/dxj2Hf9Jqt99frVEOgHto1e8Lr3zhF3l0AVVNWY9a/N7IjqtSux/3Z7xjxWQRD2bkQc\nCoIgCPsEevVywpd/01xoZ9p36Ycwchzk5UMkpM9139bm5J6FJWXGdXTNq0tpaA9x8eSWd9gZhPwC\nU6U0NsZvBKAbqeGulf2gpQnd0+0+JhNNjWBZ8WOWZuEcFvpNWCskiGAXUetZkMbLOexILkgDUFSS\nuWBPQ61ZF4CageY4XvmiOwi9eQP6qQfQ775u8iW99g2HYcMa1MHTYfBw9Nsvex+8KRIqW1GFys01\nojIL51C//l/0G/Owf3MVOiowBUHY5xBxKAiCIOz16J5u7HtvheZG9EcLvfcNBWHtStTEyTB8DHrl\np94Hb2lMdw7BNe8w5oAlFV3JIJo6Q8mVSqPjO0OmgqjjvKItNqLisMb8vz15h031UBaIiVRVUpYh\nhDZFtGUSeqnVTSHusLqIbW2HI+dJdg5VcWnmsNLGulglV1UzyGzbyaGlWmvsh+42NxAAVnnffGDr\nRujphiEjUYcdDauXeVa21Y0p7m71gKzCSvWG1WYtmuqxb78um5ciCMJeiIhDQRAEYa9HP/UAbNkA\n+YXo1RnCRJcvAW2jxu6PGjkO1q9Cd3e579/SHM+lI5JzCOAmTNwK0oC7aOoMJe8fHa81dHc6j2lL\nbrGhog5Z42fPO9TNDfGiMmByDjvajLvltH8wRbTlF5j/3V5nKGicybz82CaVk2Mee42BdOfQX4zt\n1VJEa6ivNeG2YJxD2Pn9Dj94Fz5ehPrimaCsjDcfok63GjICNXqC2bh5g/uApuS8UNVvANR5i0Nt\n27B+DWrSoagvnAEb1mTMbRQEYe9ExKEgCMJuirbD2P96EP3J+309lT0a3dKEfuEp1IzZqEmHZg4T\nXfqRafswajxq1DjTlH3dSud9u7uMa1Xq5By6hCc6tbIo8M451KGgg3PoHYqqI85hvCBNtNfh9jiH\nDabQSZTSciNQ3URYsD25iqhleVdmDZmqo2k9+QqL3MVhNH8xNefQXwQ93e5htMF2E5YbiIjmyhrw\n+Xa+c/j+21BUgjrhVBg8DL3yE+8B61eZz2P/wbG1j+V+OtFUD6XlqJxc87h6ALS3ueZ5AlC/xazF\n4OGooaMi580cfi0Iwt6HiENBEITdEK01+pF70U8/hP3gn91DB4XMrF9lnMBpx8DIsSa01COnSq9d\nYUL48vJhxDizza0oTdRdKUvIOYw0YHfNd+sMmiIheQXxbRnDSoPJOYqQIChdxrQ0GxeuyMyH8krz\neHsqljY3oKLhioDK1Osw2JFeRbTAowVHyCF8FowQdi3WE2l/keYcFsXn4ES0x2E0rNTng379s3YO\ntR0mfOu1hG/5OXrz+qzGAOgNa4wIy8lBjRpvwkTdqtQScQ4HDjMOalnAfHY8xKFuqk8S8KrGVCz1\ndA/XrzH7Dh0JQ0ZEzrsq69ckCMLeg4hDQRCE3RD9v3+h//c0DB8DWzbCp3u3e6h7ewj/v19jv/7f\nrPa3X3uB8BXnYj9+f6xXneuxN64zPwwciho+xvzsFVraUGtC8QBVVmFE1YY1zvvG8voSC9J45xwS\nCkJBQpEWiAk97RU6mRJWqjLl77U2GQcpch7l85nX8hnFoQ4FjUBLEIeURBzTNndxmNSzEKDQb/pI\nOp3DKbcSTDiwmzh0ylOEuGPpKg4jn5touC1AzaCsnUP96vPw8SJY+Sn2dZdiL3gt8xjbhk3rUYOG\nmQ2jxps13bTOeX+tYf1qVESwGYFY4SkOaUwWh/SLhMt65B3q9avMjYOBQ1FFJcZlXifiUBD2RUQc\nCoKwT6Eb67fpLn9foO0w+plHYL+DsC6/EUrKsF98JvvxG9ehP/1gJ85wx6OfnwsL30I/8Gf0Fo98\nquj+r/8Xgu3oF+Zi33KN984b10JZhckFHDoSfDlolyIgOhw2F95V1fGNgSq0W1++1pSKoAD5hSYM\n0K1QS2cwOaQU4o/dBFBnyLSuSCSDc6hbmpKrqAJUVX/2XofRHoflCeIw4hy65qcF29NFW0Ghe1XW\nLjdxWABdLrmVIYccTkBFHUuXaqCxdaiMv9eqZiDUbs7o1OvWJvQTc2D8gVi/vhv6D0I/+5jnGMAI\n864QDBpqzjfK5BDqlR7OdFsLDB4e31ZRFS8640RTPSqQKA4jhYg82lnoDWug/2BUbp7ZMGREVlV9\nBUHY+9hucbhunfPdLkEQhN0NvX419vU/wL7xsu1vBr4zWbsS2ttQhx+LystHHXU8fLAAXbfFc5jW\nGnveU9g3/AD7tl94X0Amjmtr6dOwVb11E/rfD8N+B0FePvb9f/Scj25rhdXLUJ87BXXauaY3nYd7\nqDetg4GRi/HcPHPh65Z32NxocgwTBAPlAdem7ToqAEvKYtuUUrFeh45jHFzAjAVpPkPOIa3NyeGu\ngApUf/ZqpRG3KjGsNCaKW9OFsO7tMX0WU8M9C/3bVpUVPMVhzFFMW58snMO8vHiOKED1QFMZNEMr\nB/3kA9DdhXXm+ajSctSM42HD6rhL7UbEIVSDhpvHVTXms+OWdxipSqoizewB4wq6zE+Hguazkhj6\nm5dvzuH1mtavQg0eER8zZIT5vXIT5LsRu6ovpSDsK2QlDoPBIKtWraK1Nf6Hbs2aNfzud7/jqquu\n2mmTEwRBcEIvno/9rwexn3/C3dFJHbNhNfbvfwY5uaDBvu8PWQki3duDrtuC9qoO6Dm+F71xLbq3\nN/sxHy0EpVATDwJAHXUCoNBvveQ97n9Pox++B8YdAGj0C3Mzn2vZR9g/Pgf7h1/H/vPN6I7P3uNN\n22H0htXonp5tGmc/dDfk5mF981LU6efCio/R891fq17yHmiNOuAQ1OiJZptLxUdt27A5IYwPUCPG\nwNoVznleDZGL8QTnUJUFTLsKJxzEIQDFZe45h6H0/EFl+YwAchBNWuuIo+acc+gWoklLEyrR0QTj\niDY1GOG2jeimyBokOoeFRcYldXIOY1VE08NKvauypotDVVDo3tOxy0UcRkSpWx9B3VgLgeqk4jcq\nUrHUK7RUh8Po995EHToDNWCwGTf1CLAs9DuvuI6DiEMH8ZsVSsGIsWiXEM64u1kTn2OgCprqnUVR\nNNw0MawUoDzgmmerO9pMKOrQBHE4dKQpNOQWTp0yx74SaLq3B/vmKwjffEXGm2eCIGRHRnG4cOFC\nzj//fK666iouuOAC5s+fz7333st1111H//79ueOOO3bFPAVBEADQ776OfeeN6KcfQj/2N+w/3uBa\nRj8R+/G/g2Vh/eTXRoAs/RD98rPe51q7AvtHZ2NffR72NReilyzKfp7Bduz7/mDG/+Ji7Bt+mLlf\nXnTskoUwbDQqkrumAlUwZDh62Ufe4955FYaNxrr0F6hDj0a/9nzc2XLaPxzGfvAuqAigpkxHL3oL\n/dSDWb/G2HF6urGfehD7yu+a3KuffBP70fvQXR7tH6JjW5thyULUcSejygOow4+Fymr0ovnugz54\n1zhWw0aZcLu8fHBb24Za415FLsYBU2SmqxM2pwsAXZ9+MU55AIIdzq+nrQXyC+PheFFK3J1DV3es\nwO8cVtrTbdzM1II0HjmH2rZNHmCKc0jNQNA21KX3ydNdXYTvvBF7/svO825K6Z9HRNyUlDuH0LpU\nEVVurxNM+Kybc9jp4mK5OYfR87r0R6ShLjnfEEzOIRnaWaxZbkJVDzgktkmVVsCESei3X/EWSpvW\nQWV1PF8UUNUDoW6L87iYOEyYZ0Wl+fw6OaIRAajSxGFlPCw4lYRWGTGGjATwDC3V4TD2w3/FvvI7\n6Mfvd91vW9CL5xP+4w2EL/laVlWa9TOPmN/99auxf3kp+uPFmcd0BrFfegb7sfs+000SQdjbySgO\nH3roIb7xjW8wZ84czj77bO68805aW1u54447OOusswgEApkOkRWLFy/m0ksv5eKLL+bJJ59Me15r\nzb333svFF1/MZZddxqpVkigtCLsDeski7EfvRa9Znt3+WmPPf5nwld8hfPMV2M88knXokl63Evu+\n22DUeKw7H0V958fGAfrfv7zHNTfAkkWoGcej+vVHHfk5GHcA+rnHPasE2nPngC8H9c1LoHqgqRrq\nVhY/8Xx1W7BvugL99suoKYejzjgPgh3YN1+R8eJFd7TDqmWo/ackbVdj9oPVS10vZnRLkwm1PGga\nSinUiV+Fnh70PPe10a88BxvXYp32HaxzLkYddTz6lee2rfJi7Wbsm36C/vdDpgLjWReixh2IfmEu\n+rH7Mo9//x3jAh40zbxOpVD7HQSffuDotupwGL1kIWr/g1GWZYqsjBiLXuESlrdxrTlugjhUA4eY\nH5xyG6MX44GEi/FoXz8n97CtNZZ3l4gqLvVoZRFML9ICJkzUyVFz6osIJrcx8flEOtqNoEzJOXRr\n9K61Rt9/Oyye7+5QNzdAcYmDEC5zzjl0qyJa6HcXbE79HME757CzE5QFqfPKolqpSgwfBvNe5+V7\nO4cfvQfKQk2cnLRdHXaM+fx43ATSG9cm36gAqO5vbmBEW4+kzJGikmTBXBH5bDoUpYmFVweSxaGq\nqHQPjY5GRSTOK9DPrJ9LxVJth7Hv+KX5fhk0DP38E1kV5PFCL34b+84bTVh9fgH2/Xe4u+KAXr0c\n/eyjqMNnYl33RygLYD90t6c415+8j335t9AP3oV+fi763ts8/wYIwr5IRnFYW1vLrFmzyMvLY/bs\n2fT29nLBBRdQXFycaWjW2LbNPffcw9VXX82tt97KG2+8wYYNyX+0Fy1axJYtW7j99ts577zz+Otf\n/7rDzi8I+zpaa5OP9++HsJ/4O/qDBRkFm+5oI/ynG7Fvuxb9wpPYv/ox4duu9Ryne3vRf/kt+p5b\nTJ5PTw/6yX8Y98+ryTimn5z955uhqBTrgqtMLt6hR8GkQ9FPPeBdie+tl00rg+nHAhEBcvSJ5mJ3\nqbMbp1d+agTl8V/GOmIW1tfPN4Uqnnvce57Njdg3XwktTVg//CXWNy/BOu5krF/+EapqsB+5x/ti\n5JPFZq77pYrDidDdbS6cnM77wQKz36RDzf8DBsPkw9CvveDorOpQ0DSGnzAJphxuxnzxTHNR9mhm\nUQcRZ/VXP4b6WqyLfo7vkmuwjj4B6/wrULO+hH75WRMi63WM998xF6IRpwIw4rAzBE7tI1YtNRUw\nDzg4vv+o8aZRvcNnT0fEYdKFb3W0eqODO9SwFcoDqNzc+PGj4tDh4lq3tyTnrEUpKvFwDh3aUgAU\nuFTxdOqLSLQ5fJ5zzmFrRGikhpW6NHrXzz6KXvCaaZWwepljyLVuaoDyqrTtlLo4h25VRCPOYeo5\ntNbmfc93rlZKZ8j5wj9SxCa1N6LKzTPr4xBWqnt7zftTlnyDWykFNQM9nUP90UIYOdZU9Uwce9Bh\nkJODXuzseuveHtiyATV4WPK4SGVcHL7DdENdcv4rCTmfThVLmxpMq4vylBv35ZXQ1uJ8c6lhqwm3\nT7iRoJSCISNdw11Z/on5fvzqN7F+dguMnoD+2+2uOZe6botn2Kfu7jLh5QOGYP36bqzv/QQa69CP\n/911jP3w3VBagTrju6jKatRJp8Hm9bDE+TtH9/ZiP/hnKC3Huvp3qFPPQS94zYTie6CDHdhPP4T9\n6L3YTz+E3rJz+2AKQl+Tk2mHxC9iy7IoKCggPz9/h05ixYoV9O/fn5oaE8Yzffp0FixYwODBg2P7\nvPvuuxx11FEopRg7diwdHR00NTVRUVHhdtjdEh0OQ3srYZ+KNyh2vMvlsM1xt9SNWY5z3E97P3bj\ns85/e1536kanY7mM6+1oQTcn3OXOZh5Zrb3LjmmbXNY++i+/0PQFKy0zeUipu4aCsGa5cerqtpgL\nsNw86NcfNWioaX0Q6JfeRNpptr296PfeQP/ncZNbopTJm3nuMejXH+vCq5KKFMTGdXdh33E9rF2B\n+vLZqBnHo9+ch37879h//T3WBVc6zr3tntvQ776OOuUs1Imnoiwf9lsvoe+7DfvPN2NdeLW54HWa\n678fgrotWD++IdY2QCmF9fULsH/2PXMH+ZuXOCytRr/5Pxg9MZ5PBKhJU9GFfvRbL6ImTEobZ//r\nn1BShpp5ktl/4mTU1Bno5x5DHzELlRqORiT/5a6boTOIdeXNyQUeCvxYp55j8vpen2eKzDi9zo8W\nmovpEWOTn4jm1q34xIih1HHvv2MuIhNy66zDj8VeNB8+eR9qZifv//bLEOzA+vLZsc+KKilDnXQa\n+rG/oZd+iBp3gOMcwdy1t2+7BgqLzHvSr3/S8+orZ6OXLMT+2+1Y1/8pKYwudoyuTvh4Meqo45M/\nr+MPBGWhP16EGrtf+vpYFkTyMcGIQ23bsGYFjNs/+SSb1qeH8RX6TY6ggwDQ9bVpF+OUmYtx3dJI\n2m9Va0uaSwOYfLdgh3Neq1NxGXDPxYsIRuXkqBW4FHeJOHkqxdVU/uLIa49f4GqtjQN04FTUlMPR\nf7vdPD9gSPIxmxuS21hEj1lSFhfhCcRy/QpTncPIa+/qTBbJ3d0m5NUx5LbQPNfTbZy9RDpd1hOw\nikrQTs5hVLg7ub41g0yvSwd0W4v53vvC19LHFfhh+Fj0siWOY9m6ybi5A5PFIdXmd0fXbUn7vNNQ\nC/0HJW+LfN50U33657Gp3rQvyclN3h5zv5vSP9+Rz3xSaxVADRuNfvFpdE9P0s0SMOH95OWhjj4R\nlZOL9b0rsH95KfZff4d19e9j++u1K7Cf/AdEfm/VF8+MffcnHe8/T0BDrfkuycmB0RNQx30BPe9f\n6BmfQw0dlbz/2pWw8lPU6d+JOdNq6pHoJ+7H/u9T+PY/mFT0K8/Blo1YF/0cNWIsasRY7JZm9Lyn\n0BMnx26sJY1Z+hH2vbeadc3Ng+4u9NMPmXzTk05Dpf6OYFxVmhqhqc58pnNyITfX/J+Ta3J0qTsG\nMgAAIABJREFUc3PBl0v6Gxg9iMv2TE9GrkvsHCvFzfca43Uu4bOgyxxuGO5BZBSHXV1dXHvttbHH\nnZ2dSY8Brrvuuu2aRGNjI5WV8T84lZWVLF++PG2fqqqqpH0aGxv3OHFIYx321eeRXQ1BYWfgXYNu\nN8KyzB/0iirjTHR3mbvCWzfGhWlZhSkI0dUJb78cv5mTlweBanPh5csxf4xyciAnFxV5rIMdsGyJ\nues+YAjq6xegpkyDfD98+j72nD9h//py1Nnfx5o2MzYt3duLfffvYNVS4xBNmQ6AOv4r2Dl56If+\ngn70b3Dat5Mu+O2XnyP0nydQs7+MddJp8Zd5+Ezs7i70P/6EfugvqLMuTFsKvWE1+vm5qCOOQ40/\nMOk5VVGJOvxY9Bv/Q596Diq1MMiqpeZu/TcuSh6Xl486+Ahz5/jrF6Dy4w3J9erl8PEi1Fe/lbRd\nffWb6EXz0U//01mIPnIvrPgEdd5PHEU1U6abO+xPPYA+9Ki03Cpt2+gP30VNPMiESybOt6wCqgeg\nly+B47+cPK6ry1lk7X8wFBaZIhnHxMWh1hr9yvOm2XW071/0PDNPQs/7F/aTD5j8TIebDHrzBuzb\nrgV/MdZlv0oPzcO4NtY3L8H+9eXoV55DnXBq+nosWQQ93WkXZcpfDCPHmhDcU85KPven78OIsahE\nN2pkpFH9yk9QKeJQb3II4wPjDjk6h7WokSni28M5pL0FNWxk+vaiIiNmUnLrtB02v6+OQq/Qu7CL\nW56ik6CMCrMUdwuAmkHJztjWjdDeipp8GGrkODSgVy1Lv/BtakClfF4Ak1/Z4eCSRkVZkYNzCOmF\nebq8Xmfk97CrM00cuvZGxHyWnMWhcTpVSXn6czUD4b030b09aSJLL1lkwqAdxAeAGrsf+j+Pox1y\nJ6PFaFKdQwLV5vs+pUm91tp8Hvc7KHn/soAJo3WoRqxTexxG51VeaTRAU0OaONQNDjdEADVyLPqF\nXtiwOulmlbbD6IVvwv6HxF6jKg+Y3/c7rjfh5F8+m87X52HffgMU+o2Y3rIB/eQ/0GuWY51/Zew7\nTm9Yg37uMdTUGUnf7+oLX0O//l/z3f/dy5Ln/MpzkJcfiwYBUDm5qGNPRj/xd/SGNaiE9h+6ow39\nr3+aSIkDE3JFT/0G+pPF2HP+hDV6Yrz9CaAXvol912+gqgbripvNTajWJvQLT6JfetbkeE86FDVs\nlLkBUrfZvMdrV7jn1O4iPmM9YmEH0f3zW2Do6L6exmcmozg8//zzkx7PnDnTZc/dh3nz5jFv3jwA\nbrrppiRR2dfY/kI6v3c5Pp9FOJxwR9nJ5XG8o+S0X0oojaNjlOXx08ZmPp/rsbIZm+Wx1Gc9lsM4\nX46PcGqYXTZjd+TrxuF9ijzWnSHs9lbshjrCDXXY9VuxWxpRBYVYQ0eQc+yJ5I6ZSO6oCVgl8btT\nuruL3rUr6VnxCeHNGwjXb0V3dkJvj3EIO0PQ22bCiiJ33/NmnkD+IUeQd9C05LvGgwYRnnIYLb/7\nOT333Ere5vUUnXo29PbScus12MuWUPLdH+Of/cXk13D6N2ltayT0zGMUlpZSfNb5KKUIvvAUbQ/+\nmfyDp1N23o/ShA+nnkVbRyvBuf/AP24//CfGhYTd1krjPbeiikupOu8yLIc7/b2nfoOGV/5D4buv\nUvx/30p6ruWfL9NVUEjVCV/CSsnz6j7+FJpe/y/FK5ZQeHTcyWv5+x10+Yuo+sqZyWOqqmg78SsE\nn3mU8tO/Tc6Q4bGnQi8/R+tLz+D/0tcoOfGUtDnGzvntS2m6+nyKPlqA/4SvJD3Xs/xjGlubKTni\nWAodvrda9p9C1zuvURkIJL1fne+8RktPN2VHfY78lHEt02fS9eaL+MK9se/CnmUf07hhNSXfuxx/\nv3QHNHj6t2m763eUblhJfiQXMEq4uZHGP16PlZtL4IY78SW4sWlUVdE0aSq9L/6bytO+aUrqJ87t\nk0V0FZdQdfjRaY5x+9Qj6Hj4XgL5ebHPud3RTt2a5RSd+g2KE19nVRX1g4bhW7+KioTtureX2i0b\n8R9yBCWp6zJ0BN2L3kn6+6DDvdQ21eMfMizp+Lqyktq8PAq7QknH0VpT295KYfWAtOOHqgfQCgQK\n8sjJyYmdx+5oow4oqupHUeqcygN0b1id9jerMy+XFqB8wEByU55rKCnFsnuTXjdAUGnagMCQYfgq\nU84zbCTdC9+KnSe0eL6Z6yHT8Q0aSp2/mIJNaylNfK093dS2teAfOCR57YGO6v60d3dTWVKcdDOl\nQ2nagaohw5K2d1bX0AJUFOaTk3Cs3u4QDUBJv35pn/9QZT8zR38hvpTnmuwwdnEJlQ6/M43FJfh6\nu9PWp2vjapqBsiFDyUs91+hxtGqbit4ucvoPSHquZeXHdJWUUXXwYWlOG0DXIYfT/OyjlNZvIn/y\nYUnPtdVvIZiTQ9V+k9LyNuv79Se3tYmyhLnYrc3UdXdRNHRE2melLlBJXrA9aX+A+rZmcgYOpTxl\ne8+IUTQCJeEeClKP1VRP/tiJSe83QHjKYdQDRXWb8E+dHtve/dEimlqbKZt5QvKxjj2R1hUfE3ru\ncfSL/6YFyJ0wifIrbsQqq0BrTfDfj9B+7x/Ie+xeSi+8Et0ZovGvv8cqKSVw4RX4ksJhq2g7/ssE\nn36YinMvxRdp52G3t1L39isUHn08pUOHJ83ZPuVM6p99lNznn6D8ihvja//UPwh2Bgl87zJyU77z\nen74Cxp/8h1yHvgTZT+8FquwiM63XqLlL78ld8xEyq+5FSt6M6qqCs6/HPtr36Hj6YfpfPk/2Ivf\nBkxF3ZzBw8g95kRyho/GV1WDKiw01Zt7utE9PejebpNO0dMDmYrheEb/eDynwLJ82KnpC5/1eMI2\nkz98NPlOESV7CBnF4THHHLPTJxEIBGhoiPs5DQ0NaYVuAoEA9fX1nvtEmTVrFrNmzYo9Thy3W3DI\nDKqqqna/ee0jVFVV0bIHrr0GbKAX6ATo6oaulNdRUQ1T0+8Au9Ed+UejS6GCi69BPXE/oWcfIxRt\n8FxQiDrvJwSnHknQYR31F89CtbUTfGIOwU8+MHf0F82H/Q+m7PIbaGhyKLoA6BNOhZVLabv7Vtq3\nbkZ9/v+gpwf71mtMKNAPfkFjdw84vXeFxbDfQXQ88zihGSfE7vbr1mbsV19AHTmbxo4QdKS4ONWD\noKqG1icfpH3CQSjLQjfWY7/5IurYk53HzDwZXvgXDffcZsJgLQu9bhX2n26GcQfQeeJpdHl8vnTV\nABg2mrZ/PUzHwTOS3dVX/wvKon34WDocjmEPHYV+8RnqP1xsQoij21+bB4VFtFYPRqWM0wceiv7f\nv+l45zXax5gwUfvphyG/gI79DnZ+DycfDpXVNN//J6wBw+Lr2d6KfcvPobkR6/IbafLlOb8ficc6\n7ovYt/ycuqcfxTr6hPj2YAf2my+hDjuahuZ0t0wPHwdaU//qf7EOO9psWzwfbJvQsLF0ppzXHj6G\n8OK3qauri62pXrcSensIVQ9Me0/s0gC6qZ66Detj7oduqINwmKC/JO34lAUIbd6YdBwdbIfeXkI5\nuWnH17Zx8Rs3rKNf9YDY972O9BjsCNuEUueEQne0p/1tsGtNrlZzZ3fa+xvOyYWWlvQxW82Yxq70\nMXZZJbqpgbr161CFfuzF70BRCU35flRjI3r4GEKfvE934muN5IsF8wvT116Zmz31a1ejEgr52HW1\n4MuhvrUNpeJ5f7rHXLQ2bdqIKow7m3qzcTPbe8Jpn3/dY4oTNW7ehPIlC6twWyvk5Dr+TfUVldDd\nWJ++PhtMblxLmPTfGb+5GdH06RJUQXJIbPiD92DsfjS4fWf2GwiWRcuCt7AGJ4dChj/5AAYNp6El\n3WUNB/oR3rCWnsQ1j4S2BguK0j8rZQE6t2xM2h8gXLcFe+z+aa9XR0pMtK5fTXviObq6sFua6Cwq\nTXq/zRgflFXQ/uFCgoceEz/3/56BvDzaho9LOhaA/uLXsYaNQddupqi0hOChM2nsCce/Jw4/DlW7\nhc5/P0zn6uVg27BpHdYPf0lTr532faKnz4J/P0zDI3/DOuO75vzPz4XuLrqmHet8HXX8l+l66kHq\n5r+KGj0RvWUD9nOPo478HC1FZenfWWWVqP/7Ft2P3EvdJWeZiJyVn8KIsYQv/CmNwRAEHZzAE76K\nOuGrWF1dJrS5tBytVPzvah8i15h9iz+we67/wIEeN3MTyFiQ5uKLL+aVV9z79pxzzjnZz8qFUaNG\nsXnzZmpra+nt7eXNN9/kkEMOSdrnkEMO4dVXX0VrzbJly/D7/XteSKkg7GGonBys087FuvI3qDPP\nR33hDKyf3Yo19Uj3MZaF+vr5plpmcyOsXYmaMRvr+1cnuQfp43xY370MdegM9FMPYl9xLvalZ5rw\n1e/82DP/DcA67ovQ0oh+7b+xbfq1F6C3F3XsSe5z/cIZJofz3dfNmJeeAVujjj3ZeUxJGerk0+D9\nd7D/cB32809g/+YqKCrBOu/ydFc0dbxSZj6b18OnHyQ9p99/B0aNN5UuncaOMflI+tN4iXdt2+j3\n30HtP8U5X3P8AVBaTvDpRyI9F9eh33kFdehRjnmAEAnPOvUcWLsC+67fmF6PzY2mT+TmDSan1Cm8\n0InxB5pqos8/kVQMQ7/1EnR3mcJATowYAxVV6Lfjf3/0Jx+YkMJIGGkSo8ZDR1tyLt1qk57gNNdY\n/mliERCHHocxygLo1GqlbZGL/OJ0N5toeFpHe/L2aP5gapEWiOQcOhRdcSlIExvjWK20DXLz0txa\nSHztRozplZ+Yz13ECVMjx8KGtckFfiIhtarcIecw+nlNLcAT7AB/UXqURPRzlxp659aSAky1UnCu\nWOoRVmoVFTsWpKEtckPCIRLBtWhPQy001qHG7J8+JoIq8MPQUWltZ7Rtw7qVqBHOvzeqekBaWKlj\nG4vo/hVVaWGlOhQ0a+GQF0pRicl3Sw2NboyewyGsNNqDcdWy+DnC6SGlSWNyclAHT8c68VSKvnRm\nWq4imMJX6gtfM+kRrc2o077tmPcNoAJVqEOPMoW1Vi1Fr1uF/tcDMPEg04vRacznToHyAPYj95q2\nFY/eZ34XvnSm4/4A1qwvYv3kRpOG0dyI+tp5JmTe6fc09Xz5+aiyiqzy/AVhTyCjc1hfX88DDzzA\n0qVL+fa3v01OysXHjmh86vP5+Pa3v82vfvUrbNtm5syZDBkyhBdeeAGA2bNnc9BBB7Fw4UIuueQS\n8vLyuPDC9LwkQRB2DmrUeMciKK77WxbqK9+Ar3xj285TUIj6zo+x95+CXjQfVT3QiJ4MwhCA/aeY\nPmNP3I+edKi5i/vys+YiwqFoQOyc044xRQ+e+Dt2T4/JZZkyDVVV4z7m+K+Avxj9z7+gP14EBxyC\ndeb30huOu42fOgP96L3YLz2DL3JRpBvrYP1qI8rcxlUPMGXj330DjvuC2bh6makU6VBMAYzoVqee\nQ899f0A98P/Qyz+GwiLUF9MLaiRiTZ2B3d6KfvAu7CvONblwuXlYF/0sPQfK67UqhfWFM7Bv/yX6\nv0+hTvxqJOfxOZM7OGyU8zjLh5p2tCk339qEKq0wfc/GTHS+4Bw9weTKrfwU1T9SzGzNciguAaf3\nsjpBIEUuMh17HEaPXx5I7/kWqdCZWvQFiDVgT2ujEG3h4FZcRtsmvzjxRopbKwuMGNFO+U0d7XGB\nmkq0l9+WjSbfbctG1PTj4sccOQ6tkwv8aLfm6hCv1poqDkMd8XVIJCYOU0RtTBw6rU1UHDq8Vrfe\niGByyJxyDttaweczOdtpY0rMa0pt9xEpNJNWNCZ1/Nj9TSGX7q64OK/dbHIsh7nkIfUbAO1t6GB7\nrMBK/PPocLOiogo+fBetdVyUNLq/R0opIxqbUsRh5BxOecMAasRY9OK30R1tZl0+eR9am7EOPcp9\nATKglDLfPxm+g2L7f+ks9IpPzM0pfzH4S7DO/YH7/vkFqFPOQv/tduyLzzDbvvIN04vS6zyjJ+K7\n4f9l/0IEYS8lozjMy8vj5ptv5pZbbuGnP/0pl112Gf0S4rV31J2SKVOmMGVKcun22bPjBRSUUnzn\nO9/ZIecSBGH3xpo2ExKK4GSDUgrr7O9j/+Ii7HtuMQV4mhuxzv6+9zjLh/XVb2Hfeg36b38wTeQz\niFqllOkJOGqCqWK330Hb9F2ocvNQM2aj/zMXvXo5asSYtFYUrmOnzjBFHRrrUIF+xm30+VwLZABY\n048jv7GO4FMPgmVh/fhXjg5Q2riZJ2EX+NEL30ING2V6KA4alnFc2pwPOAQOmob+90PoQ46ExjrY\nvB71zUu9x02baXpRvvMaHHKEGXPELOedawaZC8eVn0JkH716GQwf4/zeRPKX9NZN8Wwbpx6HUcoD\n8OF7ydui7RtSiyBBTBTpVNcq5OGORZ3BUDBZHIaCRsik9vGLHsfBOdTBdudiNGCqYyplqmdGzqNG\nTYg/P9wUH9Grl8YL/EQbqFc4pHMUm/Po9rakzCXd0Z7exgJi4k8HO5L37/QoSBPr6egsDl0L0hSV\nQKgjWUSBee+KSx3zBgHndhbLl5jXM8ihwFHiOcfuh35hrrlxE7mxFQ0RVcOdxaHq198UjKnbEheQ\njXXmdTkJ7ECVuYmQ+D43mZBl5STgAcoDpudrAjriluPklhMRhwCrl8P+U9DzXzJrcOBU53PsBFRl\nP6wrbsb+wy9g03qsy2/MLPQOn2ncc9s2lZRTcqcFQXAnozgEqKio4LrrrmPOnDlcccUVXHTRRWlC\nThAEoa9R/fqjvvR19KP3QVkF6ivnmIqdmcZNnGx6ZZVVwJTp7heMqeMGDc14oeg69vhT0W+/Ylp/\nfOtS9JMPmDYU/Qd7j5t6pBGH776Omv1lIw7H7JdUZc+J4rMvINTRAcNGZnQ+ErEOnwmHb38hMuuM\n87Cv+T72zVdEXK0SlEd4MmAa1w8bjX71eRPKBqj9nf/2KMuCkeNMf0owIZGb1qMOOtx5/4JCU/Ux\nMax0y0aoqHJ0JikPQFcI3RmMtZTQUXHoGFYabcCeLA5jAsilzyGQLoBCQchP7+NnjlMYC0VNet7D\nOVS5eUYAb91oRKQvBxJEiyopNW5VYo+7pgYjJB2cNm/n0Cl81kXodWUOK9WdnemlM9x6I2JaWRAO\np7mxurXZWdRHUDWDTGXSBPSyJeZ3zaFNTxJj9gNfDvqDBfGohzXLjbgf4PJ9EW1nUbsFFRGH0Sqi\nTu+7qqqJi8mIONRNEeHnUghDlVemt+hoqDM30tzE1vAxoJS50TJ6PHrRW6hpxzr/juxEVFkF1pW/\ngbYWV5czaX/Lh5r95Yz7CYKQTlbiEEyPw3POOYdx48Zxxx13MHv2bE4//fQdElYqCIKwo1CfO8W4\nIMNGpff68sCKhmnuIlRRMda3f4T9+58awVRRhfX9n2Z0IFX1QCOYFryOHjoKNq1DzZjtOQZA+XxY\np5+7o6a/zahAFepr55m2FocdjZo20zEfLm3c4TPRD90N+QWoc3/k6VyqUePRH71nBM+GtaBtPHMj\nq/sntbPQq5cmiaQkyhLaWfSPiLiYc+iQI5pfaNoTpOYchjxCRAv95oI/1QkMdbiHiBb4IdxrKh8m\nOosdbVDV33kMmHYW77xqfp54UPp7MWQken2KOCyvdP58Rp2r9rbk7cEO5/DsggSHNBGvnMMC55xD\n3Rt57V5hpZG5JLmx7a2e4pCagfDm/2I3A3RLE2zdmN3vmr8IJk5Gv/sG+qvfQimFXrMCho50z0mO\nvleJeYcNtc4uNkC/hN6I0c94Y71xhMuci/VRHoD3G5JvJDTUQiC9x2HstRT6YeBQ0yu2KwTd3caV\n6wNUXr5ziK0gCDuUrMVhlGnTpjF06FBuueUWli1bhu3U4FcQBKGPUEqZ4iR7AGrc/sbpfPNFrEuu\nSWsk7zpu6gz0Y/eZHJySMtQhR+zkme4YrCOOgyOOy7xjAuqI40yBiCOOi+cSuu07arwRV6uWojet\nNxtHuPeaUtUDY+G8urUZ6ragEiqqJu1bHjDHbm6Mu7vtrcbRcyr6opQJBwyl5LtFhZ/fwTmMuokp\nOXI62OHs2CWOCQVTxGE7api7m6ymH4vOzUVNOhR1cPrnRw0ZgX7/7Vi/Pt3c4FzoBHPjAX9RunPY\n0eboHCrLZ4SaW86hU+Gq/MjrTM05jIrFAudiV1ZUuAbbk+ff2owa4VDYKDrHwcPN+738YzjgkKzz\nDWPjDzkC/eG7sHoZevhoU4zGQ1gaJ7vCOIFRGmpRoyc4D4h+VyQ63031UFrhXJgKoLzSNGVPyAXV\n9VtdQ0qjWF87D/vu36Ofn2vOu4d8vwqC8NnIKA6dnMGBAwdy44038pe//IWPP/54p0xMEARhX8A6\n6TT05/9v23IWDz8G/eG7qP2moGaeGAtz3BtRBX7PIj1JjBgLykIvmm/EXqCfd25SzUBoa0F3tMGq\npeZ8I10ufCP913RzYzyssbXF2TWM4i92dg6VgjwHMRMVUqmCMtThHIYKCaGoppR+jI4295xDMO1B\nIi1CnFBDR5q//xvXGjHQ1BDPP3SiuNScM4IOh81jpybz0Xk7VSvNL3B2sfLyzLqlViuNCcosnMNE\n2jK8dxMnQ3Ep9hvz8B1wCLz3hnkPhjhXyEw77+TD0L4c9HtvmCrN3V3uxWiiDByKXmMq7Oq6LWbO\nLjdEVH6BcQgTnEbdVO8q4IH4c02N8TzGhtrMec7jDsC67o/ofz+MGjNRqnIKwl5OxsSaq6++mgce\neCBte15eHuXl5Vx33XU7ZWKCIAj7Ctt6saVKK/Bd9iusE0/dq4XhtqIKCmHMBPSrz8Pit02+lNf+\n0WIhC99Cr/rUFH1xqZ4aFYckFPTQ7S3eoYlFxQ4FaYKmV6iTAIq6OamCMuiSuweoWBGbuNDSPd1G\njGTIQ/UkWsF13SrThqGl0ThPbhSVoBOdw7YW06rArYJvod85rNQtPFSpiNvoIg5dfg+s4qhzGBeH\nuqfbjPPKOczJRU2bCYvfQS/7CL3wTdTMk9xdudTxftN7Vb/9KvafbzJFo9xcwOiYCZNhwxp0SxP6\nk8XxbW5U90cnOoeN9a75hkC8CFXkM6y7u0wV4mxy+IqKsU4/FzXFOYdXEIS9h4zicO7cuUyY4PyF\ntv/++/Pkk0/u8EkJgiAIwmfB+sF1WD/8JerkM7A+/3/eO48Ya3Lv5r+EXrkUBo9wzYNUBX7jxNVv\njW9syyAO/UXpjlVn0FXIuDqHwQ73fmuJzmGUqLj0cA4zUlFlqpCuX2VeZzjs3MYiSnFpcs5hq+kj\n6NrepdAfL84TxaOwDGCeSw0rjfaN9KpWCuhQguCO5opmaD2jjjgOwr3Yd94Iefmoz33Jc/+08Qcf\nYUR1KIj1o+szho2r/YwQ1J8sRi9ZbIRe/0Hu+/cbEAtD1VpDU717pVKIu9/RtiQNprqp5PEJgpBI\nRnG4Zs0aJk92vnN1wAEHsHr1asfnBEEQBGFXo3LzUBMnY33pTNceirF9lTLFNZYtgVWfZu7l2X+Q\n6Q0Ypa3FVPZ0O75DWKkOubtjnkVsCl1cwMScwyiR8Zkq2HqhlDJFadatgmgbBi9XqrgkOeewNdpk\n3i2stDDNOdQeziFgnMPUsFKvCqeA5RRWGu1P6RVWisk7ZNhoCLYb17DYe/+08VNnoM74LtbPbkWN\n9QjJjTJ4hLnZ8OFC+PR91MQMLXKqB0BzI7qry9xQ6Or0FvCBfmYNo1VoI30cvXq6CoKw75FRHIZC\nIXp7ex2fC4fDhEIOPYcEQRAEYQ9ATZtpctl6e2Gke4ESwBTE2bIBiDg1ba3ObSyi+IvSWlnQGXQP\nEVXKPJfgHOpw2AigDM5hkgsXzf3bHucQUENGwsY12A//1RQiGT/Jfefi0iRxqDOJQ8ew0qC3OCwo\nMAIyaYxHERuINZRPeh9ao1VmvZ1DADXri1AeQM0+JeO+aWNzc7GO+wKq3KV6aOr+loWaMAn93utG\nzE48yHtA1Ims32KqyYJ3WKnPZ9q9LDe1IvSyjyAn1z2UWhCEfZKM4nDQoEG8//77js+9//77DBrk\nHvIgCIIgCLszqrJfrFF5RudwwGBoaTJ5hKGgaSFR6iUOiyHYnlzYLZRBAKW6jVGh6CYOHXIOCUbF\n4XbkHILJO+zthdrNWGd/H5Xv0XqkqAS6u0weG0CbtzhUBX7naqVuhXfA0TnU0RxEt7DSnBzjyCbm\nHMZakHi8dxGsacfg++3fUFnsu0OYONmE8CqFmnCg566qeoD5oXazyTcElFdBGkCNnggb16CDHeil\nH8Ko8abvpSAIQoSM4vCkk07iL3/5C2+//XasbYVt27z99tvcfffdnHTSSTt9koIgCIKws7C+dKZx\niDLkXsVaaWzZGM9b83IOi4rBtpNdvVDQu4hQYRE6MecwKmqyqVYaQe+InEOINWNXR34ONcHDNYR4\n5c9o3mFrs2mt4SaEC/3JghagM4TKlHOY6hxmCCsF0nM/YzmHu0jwbQOxAjRDR2UOY+1nxKGu2xzP\nI6xw6YsYPf6YiaC1aeGyfjVq/AHbO2VBEPYyMpbdOvLII2lububOO++kp6eH0tJSWltbyc3N5bTT\nTuPII4/cFfMUBEEQhJ2CGj3ROCqZiIhDvWVDrACKqvYoMhKtPtreBiry57ajzdvRSxUyEaHoWpAm\nN89UWU0UlNGwUv/2OYeq/yCsH14HozKvjSouNX0BO9pMaGNrM5SWu+fMFRRCZzC5IXuGnENVUGj6\n8iXSmZ04TBLcbc0mnNJLiPYRKlCFmnZMzM323Leo2NwAqN1sXFClTK9EL0aOA58P/eyjoDVqnLc7\nKQjCvkdWNZlPPvlkjj32WJYtW0Z7ezvFxcWMHTsWv1MTX0EQBEHYG6mqAV8ObNlgGtPnF8Lwsa67\nK38xGrDb26CkwuQPtrd6XsArf3HcBYIE59BZ6Jk8xeLkSqEd7aawjZdgyhKVKe8tSlHmK9x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VBQAEA4HCYcDqOUkrXfRTQ0NLBw4UKOO+642LY9fe1FHO5CFixYQCAQYPjw4UnbGxsbqaysjD0O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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tm = np.arange(0,q)*dt-jc*dt # shift times for plotting\n", + "plt.subplot(211)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"AZZ\")\n", + "plt.plot(tm,azz)\n", + "plt.subplot(212)\n", + "plt.plot(tm,czr)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"CZR\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m = 150 # length of receiver function with m <= p\n", + "toeplmat = np.zeros((m,m))\n", + "for i in range(0,m):\n", + " for j in range(0,m): \n", + " toeplmat[i,j] = azz[jc+i-j] # negative values of k-j are OK because we used \"full\"\n", + " if j == i:\n", + " toeplmat[i,j] = toeplmat[i,j]+0.05*azz[jc] # regularization of solution\n", + "if m <= 4: \n", + " print(toeplmat)\n", + "rfq = -np.linalg.solve(toeplmat,czr[jc:jc+m]) # solve equation system for receiver function\n", + "rfl = np.linalg.solve(toeplmat,azz[jc:jc+m]) # solve for deconvolution of z with itself\n", + "rfl = lowpass(rfl,0.75,3,zerophase = True) # low-pass filter result\n", + "rfq = lowpass(rfq,0.75,3,zerophase = True)\n", + "norm = np.max(rfl)\n", + "rfq = rfq/norm # normalize result\n", + "rfl = rfl/norm" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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hHj0U/btPdbayYWOscffrhZQfqG69kS/eRXbvrLLXzAmkYLd2+qulLKdK4rvo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\nFmxbq3e/u6u3Pk9cx7y6PBTGhIqgsBhCxV69lmHbNRnb3eXtRxxs8v7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tm = np.arange(0,m)*dt # shift times for plotting\n", + "plt.subplot(211)\n", + "plt.plot(tm,rfq)\n", + "plt.ylabel(\"Q-RF\")\n", + "plt.subplot(212)\n", + "plt.plot(tm,rfl)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"L-RF\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Iterative deconvolution\n", + "Finally, we try iterative deconvolution where the time of the conversions is determined experimentally by searching for maxima of the cross-correlation between radial and vertical component." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration: 0 Location of max correlation: 45 Coefficient: -0.188064932852 RMS: 0.808067884685\n", + "Iteration: 10 Location of max correlation: 56 Coefficient: 0.0232878399425 RMS: 0.236482738022\n", + "Iteration: 20 Location of max correlation: 26 Coefficient: -0.0132327288213 RMS: 0.157940698442\n", + "Iteration: 30 Location of max correlation: 55 Coefficient: 0.00682842269855 RMS: 0.127478308995\n", + "Iteration: 40 Location of max correlation: 96 Coefficient: -0.00547868539688 RMS: 0.113284892005\n", + "Iteration: 50 Location of max correlation: 117 Coefficient: -0.00491286692813 RMS: 0.0989209837833\n", + "Iteration: 60 Location of max correlation: 45 Coefficient: 0.00363439542244 RMS: 0.0911542901353\n", + "Iteration: 70 Location of max correlation: 56 Coefficient: 0.00285406937047 RMS: 0.084300593319\n", + "Iteration: 80 Location of max correlation: 44 Coefficient: 0.00276115316265 RMS: 0.0791800623638\n", + "Iteration: 90 Location of max correlation: 45 Coefficient: 0.0022053005175 RMS: 0.0752592825827\n", + "Iteration: 100 Location of max correlation: 37 Coefficient: -0.00211030980769 RMS: 0.0713918083254\n", + "Iteration: 110 Location of max correlation: 92 Coefficient: 0.0022716970582 RMS: 0.0684080863292\n", + "Iteration: 120 Location of max correlation: 53 Coefficient: -0.00184308153988 RMS: 0.0657632835901\n", + "Iteration: 130 Location of max correlation: 124 Coefficient: 0.00138745498748 RMS: 0.0637757664872\n", + "Iteration: 140 Location of max correlation: 30 Coefficient: 0.00138958532611 RMS: 0.0622412721775\n", + "Iteration: 150 Location of max correlation: 45 Coefficient: 0.00145676616742 RMS: 0.0602417928777\n", + "Iteration: 160 Location of max correlation: 14 Coefficient: 0.0010268119389 RMS: 0.0586738835766\n", + "Iteration: 170 Location of max correlation: 12 Coefficient: -0.00139735070189 RMS: 0.0571199966087\n", + "Iteration: 180 Location of max correlation: 4 Coefficient: 0.00151602625314 RMS: 0.0553731496016\n", + "Iteration: 190 Location of max correlation: 45 Coefficient: 0.00130031181716 RMS: 0.0540243403414\n", + "Iteration: 200 Location of max correlation: 4 Coefficient: 0.00103009869364 RMS: 0.0528147877942\n", + "Iteration: 210 Location of max correlation: 37 Coefficient: -0.000981396173385 RMS: 0.0516961431046\n", + "Iteration: 220 Location of max correlation: 22 Coefficient: -0.000948845430963 RMS: 0.0504874410121\n" + ] + } + ], + "source": [ + "itmax = 1000 # stop after itmax iterations\n", + "eps = 0.1 # or if coeff less than eps\n", + "zcomp = st[2] # rename traces\n", + "rcomp = st[1]\n", + "p = len(zcomp) # length of z and r\n", + "it = 0 # start with iteration 0\n", + "rnew = rcomp[:] # copy of radial component\n", + "norm = np.dot(rnew,rnew) # initial energy in radial component\n", + "zsh = np.zeros(p) # shifted zcomp allocated here\n", + "a = np.zeros(itmax) # space allocated for coefficients a\n", + "jmax = np.empty(itmax,dtype = int) # space allocated for conversion samples\n", + "while it < itmax: # iteration loop\n", + " czr = np.correlate(rnew,zcomp,mode = \"full\") # correlation of z with r according to our definition\n", + " jc = len(czr)//2 # positive lags start at j=q//2 = p-1\n", + " jmax[it] = np.argmax(np.abs(czr[jc:])) # location of absolute maximum of czr\n", + " zsh[:] = 0.0 # zero shifted z(t)\n", + " zsh[jmax[it]:] = zcomp[0:p-jmax[it]] # shifted z(t-tmax) cut at end\n", + " a[it] = np.dot(rnew,zsh)/np.dot(zsh,zsh) # compute coeficient\n", + " rnew = rnew-a[it]*zsh # subtract shifted z(t-tmax) from radial component\n", + " mf = np.sqrt(np.dot(rnew,rnew)/norm) # normalized residual amplitude in radial component\n", + " if it%10 == 0:\n", + " print(\"Iteration: \",it,\" Location of max correlation: \",jmax[it],\n", + " \" Coefficient: \",a[it],\" RMS: \",mf)\n", + " if abs(mf) < eps: break\n", + " it = it+1" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1HRmKR9CbbxmEB9JtPMuq3UGHXcqV7yPbWjGOX+Bzvzj6B8i/L4QtG4LPCdyy\nAbniXcQPTkOMGOO7/8wcjOv+jHnPDZj3/Qnjuj9bWwwIAdnRgVy7EjF1ljLy/TF+GsTGIdd9bPl4\nytoqFUEx94R+24mhI5CfLg863FiWbFUh2BZC3IURBcNHI0M1IN9/HRw1GL+90+f/ifjR6cgV72K+\n8SJRv/pjSGN8V5BNjZhP3N1nMUEC4rhTEGdcHLZuquzuducIL0XM/RFi2lGY7/4b+eo/kW//C+PC\nqxFT/Xi1rfRfXYH5+rPw+Wq1oWAYYvxUlZc+cpzWCw0By/+5BQUFnHbaaYwaNYqsrKxePwcb69ev\n55prruGqq65i8eLFAz0djUbzPUCWbsP862+h0Ylx3e0Yx53cK8ROnPtLGDIC86kHQvYiyK0boWiM\n9Qf7wpHKSxCs0eWohsHpgTUgPdgzoTUELUhnnbUKrB7SMkIKYZVlpeqBwMrD6JDhyhDasC64MXZt\nx3X71ZhBhjJK0wU7ihFWBe5zCqCpEdnUaH2QqnIV+po8OHBbe5bSw2wIsjpqgwNhsYCOl5Q0aAzB\ngKxX7wkqhHVwGsTGBe2BlC4XcsmbxEyY5rewkJg6ExIGIT/+ILi+Ozown3kEMrIRp13Qb1uRNxTj\nN3+Bri7Me25A7go+xNPSnNavgfY2VRSmv/nExCImHq4qxVoMY5WblMaqGD+1/4ZDi6Ct1av1aRVZ\nvgOy8y2HUYsRY5UsTpD3LdnVhXx/MUyd5TevWCQlI37wY/hidXhyStUVypv39VqkI3QZo/2F3LUd\n8y/XK2mp8y7H+NN9GHcswrj3n4j5pyI//A/mg7cGd7/ad4yOdsxFf1XG46nnIc7/FWLsJKKu+zPG\nnxZCRg7mU/eHpE8q9zRgvvA45q2/ho1fIE4+W2mexici31+Mee+fMBfehKyuCHn+hyqWlwzOPPPM\n/TmPiGGaJk899RQ33XQT6enp3HDDDUyfPp38/P2zmqfRaL7fyK4u5Ef/Rb7xPCSnYlx9CyKnoE87\nER2DcfkfMP9yHeYTd2H84W7r+WKAbNoDFbsQM+Zafo8oGKYK6ezeaT0cFesakN5x0rP2VkhNGGRt\njPZWaGuxHsKK0oKUWzZYbg9uA62sFDH7h9bGEAIxfqr3wdiKsS7XfYz59IPQ2alWxAN5b3pStRs6\n2ixXfRU5BepYV5apsF4LyKrdkJ1naRVdpGeq/uusXwPS5YLGBusFdDxjDU4LKcRPhuCBFEJAVm7w\nlVh3lUBqe+P9AAAgAElEQVSDg7ifX0WLv76jYxAz5iJXvods3oMY5FsqZl/k8negtgrj+jv8hnT2\nGmfIcIzf34X5wM2Y992IceXNiFHjgvgwFua0+kN13kcG7ldMPxq5dqXyvB7Wv6wQuMNX0zNVPmp/\n/Ra6C+nsLEZY8Jp7KStFjLFed0MUjfVWQCZABeRefPsVtLViHP2DfpuJH5yG/OgtzMXPE3Xt7Za7\nlx3tyM9XIVctAXfagtdsjE+EoSOUh3jaLISVRSFPvy3NsGWDqmBcV6Pu891diCkzEYcfbUkSpyfm\np8uRzz4CickYf7gLMbSo135x9qWYQ0YouZu/XIfx6xsRFqNnvHOWEvN/71HG3U+vwNgnrFoMG4lx\n5U3qe3XR/2DcuBCRkGitb0cN5j1/hMZ6xOzjlfGY4q5ufcIZyLZW5KfLkK89h3nbVYhTzkH88Cdh\ne1MPFYI6Sps2bWL58uXU19eTmprKnDlzGD/+wJaeDkRxcTHZ2dlez+isWbNYu3btd86AlJ+vprmu\nErO1jR63FvfOnn9Lny+DatdrXwjt+jTbp52UasVbmu7fPf4GMKJUWIoRBVEGRNl6vI5WuUPR0Srx\n3vMTHa0e/KRUDzemu29zn9fd3eBygavL/bt777bubvW3e7v0vO7uot5mw+VyKXFqw0BEx6jV7bh4\nFWIY53kdh4iN974mLg5iYtX4Lpf66eqEthZkWyu0tqifNvdPq2d7s3t7q2rf3bV3fsJQx8Iw1GdP\nTFIP8YlJiEFJkKhekzgIEpJUBcHEQapNdEyPYxul+uzqhK4u6O7s8boLujqVvpVnm2centfSfe6k\n+1rwnGfPOabHPtPsdayl91i7j7EQaj62aIiKQkTZ1N9RNvYMSsJ0udznPAZiYtTn8PwdHa3a29w/\nUdHuvmzq2vFcZ57z3daqvljbWlShkSjDezxEUgrkDVX6fPsYElJK5LqPka89C3XVMH4qxsXX9vuF\nLjKyMS65HvORO5BvvYxYcKH//5N92eYWmreQ/+jF/fAlq8oREw+3/j5Hbb+C9n3wakFWB5bK8OCp\n2hlM5cq0DKh3BhdeWVOpiqdYnRcgJk5XD2/bv4F+jrc0TeRbLyP/8zKMGINx/AK1Ur70LcSJ1hZV\nPd4JyxU2PVIbFWXWr4Wq8oC6oV7sPWVZLPa/p0H9XwVpQDI4DfbUI02XCim0SkOQFV/diKy8oL1B\n0v1/FzNuCi39pDiK2T9UC0mfLkccd0rgfrs6lRdr9ISgQmpFdh7GH+5WedYP3Yo48SzEjLkRqfYp\nnbWqKMzJZ1vz1o+fCrHxyM9XIQIYkLK7C775GnHk3MALGTlD1D19ZzFYXDCTzXvUdREgj7gXw0ep\nQmPbvw0oodNrrC8+USHxASohi/gExAlnIP/1NHLLRkvFz2TxZszH7oTmJsjMUUWVps2CPQ3I8p2w\newdyy0bki08gX3oSxkxATJqBKCxSYfA9FiJkdxc4a5Ebv1Bh71s3qu89cIf0Z0F3J/KFx5H/9zeY\nMB1jzvEwbmq/50h2d6mFsg//A0VjMX71R4QfWSFj5jxkbgHmor9i3n+LMjR9LLD6HWvJm7BhHeKc\nX/YxHj2IwWkYl/0Bc+GNmE8/iPGrGwJev7LBibnwJmhvw7jh3j7GL7jP3zEnIicfifnSk8jXn0N+\ntgJx4pnKgLdoSEoplW5t+Q7knnpo3qPOb2uz+/ktSj2/GeoZx/MM0jp0OEw72tIYByOWDcgPP/yQ\nl156iWOPPZaRI0dSV1fHQw89xNlnn838+fP35xyDwul0kp6+90snPT2dbdv6fqEsWbKEJUuWAHDX\nXXdht4dennt/0Lj1a1qWvbd3w77/673++YWf7fu+z3874be/CIxrRKn+jSgwhPpHEoa6AUipChO4\njT1lDLrUb48B4oeQAj1s0eqmEGVTv202FcbnNmBEtPu1qxubxwA0TWRXB7K9DdnWpjwrPYophDSP\nqChEYhJG4iBEwiD1OzMbEZ+IiI1Tc4uOUXOTpvt4uNRDSfMezKY9mM17kGW1mE17kC1NXoNuvwTA\nRMcoQ1oIQKjz633t/tv7Wqhza3Mb+Z5j7v3bBqZUhTLaWtznXZ1r2d1Nh6sbOjuQnW4D1geR+oze\nfmzR2PKGqBVa9+d0NTgxy0qxFRYx6Nc3EDvZYl7jsSfQsG4lnSs/IP3CKy15HgD27NpOW0ws9mlH\nWqtaCmC3U5M8mLj6OpIt3sOkq5uaegcJBYUM2uc9NpvN573QjImmFkhsayHB4jgd5SU0AIMLhxNj\n8T2tQwppkiZpURBl8T3t366nEUidMIVoi+8xjz6O2ifvJa54M0mzjvHbbs9j/0Pbkv8QN+9Ekn/1\ne0R0DPWfLqPr/cWknX4BhoUV/T2VZbTHJ2AfNymgUWyz2bCPHENtfAJxDbWWzqnZ0kxtg5PEEaNI\ntNBepiRTIwQJbc19zr8/upw1OIGUocOIDeK7sjV/CE2mSVq0jaggvIlN7a20xSWQUTDE8nsAmoeN\npOXzVaSnJKtFPws07NhGd04+sZk52Pv5vsFuxzFiDHyylLSzLgpoJLW++xpNjU5Sr7/N8vXfcyzz\nridpfOA2Ohc/j1z8PNGHTSJu7vHEzz3B8j1lX1qW/ZdmKUk78XRsFufUeMTRdKz/lPSrb1QLd37o\n3PgF9R1tJM88hjgLfTuHjYTdO0mz2/3ed3r1X7mTeiBl3KSgrkHHsCKMncWkWr4/uqj9ei1x048i\nJSdw/rY8/WfUffgfov7zIqlHLur3GHWsXUXDA7cQlZ5F8h/uInrcZL/XUffO7bSv+pD2jz/E9fKT\n6rvKMIjKG6o0Sh21mD2KwUTlDSX2tPOInX4UtiHDvPcmKSXdpVtpX/Yu7Svex/xyDdHjp5J00VVE\njxjd59i7aippWHgT3cXfEH/SmSRdeGXg7yO7ne47H6f+hsvg4T+TeteTRFnQyOwq/gbna88Qe8Rs\nUs66sP//KfscWi+8kqZ/PETCyndJPP1nfpuaexpwPnw7omkPg297kJhAhr3dDjffR/uny2l+ZhGu\nv92HsGcRf+LpxB1zAkZyive8yu5uustK6S7dSnfpNrpKttK9YxuytXf8gkgY5JYDkl5ngsdJ4XnO\n7Rh5GPbjfxzwOB2sWDYg33zzTW666SYKCwu922bNmsXChQsPKgPSKvPnz+8177q6EEuN7y/OvZys\nq246+Oa1H/BjkgLulZ3ubrdHrmuvt6zb7VE0DK9B6l3lcXsNPR4tr2fKawD5x2NQpNrtfY6921xS\nc+rqhI52aG9T4WntntftyjAyDPWwGOX2nsYnQkKiWhWMHwQxMd65SCAc8QcD5SmhrQVamtVPa7My\nKj3HyX3DUt7bGPWFEB3j9eb29vDFQLRt72ubLewEc7nPb38IwN7j2EvT3Ost7eyErg739dDDs7zv\n38K90ue5BhISlTc2IRGiY0G6wOX2kDY4VOn83TvprtilzqnH0xqfiLjoGsyZx9BkRNEUxP+inDUf\n+ckyat99HeMoa/dH11frYMQYHI3B5ZLI7DzadhTTaXF+sq4aTBet8YNo3+c9dh/XPbiv+dh4mneV\n0mpxHHOHyuFqNKIRVucWo8JCncVbEFjzWJmbv4IoGw0JyZbHAaDoMFo/W0nHiWf5nkvxZswl/0H8\n4DQ6z/w5jsY9avuJZyE/X03dy//A8KWhtw+ubzfAkBE46gNX/rPb7TgcDmR2Pm3bt1o6p3L7twC0\npqTTZvXzp6TRurO0z/n3O4b7XO4J4lwCSJsydJwlxYih1pd9XJXlMDg16O8/MzkVTJO6zRsReYGN\nT2mamJvWI6YcSXd3d8DxzCPnIV94nLp1a/rNaZXd3Zj/egZGjKExZ2hw12VPrrwZo64a+elyuj5d\nTtfj99D08j8Qp52HmDkvKK+ulBJzyVswahwNtliweu4nHI5c+QF1q5b164U0P14KUTaa8gppttC3\nmVeIXL2U2poaMjIzAx/7TV8BsCcpLajjaQ4dSffqD6mtrrYWrr5lI3JPA53jplq+/uRpF9D19IPU\n3n0j4pLrfY5jrvpQhYMWDEdefQt7kgeDo5885MQU+OEC5A9+glHvgF3FyJ3bcbnlT0ReocoRTk1X\n0STZ+XQAHQBtHerHQ3I6nHo+nHg2YuV7dL35Es7fXoyYMZeU405iT1OT8tQ2OJCvPg1SYlz+Rzqn\nzbL+fWSLhatuxrz3T9TdejXG7/8H0U+6g2xrxbznRkgeTNd5l+Po71h43nPksYgNX9D8wpO0dJuI\nY0/q82wiW5sxF94MVbsxrrmVPenZlq91RoxD3vYIxoZ1mEvepPnZRTQ/u0jti4lR0WfuRW+1LRby\nCxFHzEEUDEcUDFdh94OSfNYX2Pcpytdz5sFAbq41fVbLBmRTU1OfMNDc3Fyam4MsqrCfSUtL63Uh\nOhwO0tKCTPzXHDQIIdwGjkWPzAFACKFuHDGxPnOUBqKWlzAMdxjrXo/I96GmmDAMiI1VP9bSHoIj\neXDQORuWGD0BcgqQH72NnHVc4IWLlmbYvQNx6rlBDyWy85FfrLb+hiA0IL1jeLQg62qsjxOs8Dzs\n1YJ01CL6Rhz5RJbvVPl/VgsCuRETp6vQM0ctYp/Vcikl5r+ehpQ0xGnn9zp/YshwxLSjkB+8iTz2\nZJ86et5+urqgvBRx3Kl+2/icW26B5RL50iPJkWs9bAx71l4pFytjeHMSg/wu9chwNDiVbqlVGpzB\nh8uipDwkqEI6FgxIKstUmJnFPENxxBzkv55Cfvx+/wbkp8vAWYtxwa/CXnwT9izESWchTzwTtm7E\n/PczyH8+jPzgDYzTL0JMsBiaWbIFqncjTjg9uAmMm6LCWNeu7NeAlJu+gKKxiLgEa/0OLYKP/gvV\nFZBpIRe3vBSSUvbmsFmlaKwap7xUjRkA+eUnamF1XIBCQD0wZh2Luace+e9n1ALmJdd6jXvZ0Y58\n7zUVBj92EsYVN1g/RrjvvWl2SLMjJh9p+X0++7LZlPzIjGOQ7/4bueRNGj5d3rvRkBEYl/0ekWmt\nenav/oeMwLjiT5gP3Y752J0Y197uMxJASol8fpG7yu1fLedmCiHgZ1eq3MWXn0RuWItx0dWIwekq\nOmv5O8i3X4XWFpWP6acAUr9jGAZMOoKoSUcgy0pVTn57K7S1qd9xCTBkOGLIcMjKDS40/3uGZQNy\nzJgxPPvss5x//vnExsbS3t7Oiy++yKhRgQVdDyQjRoygsrKSmpoa0tLSWL16NVdfffVAT0uj0RxC\nCCEQ805Evvi/sGNbYOHrbZtAyuDyHz1k50NzE7JpDyIpcHEPrxEYRBEdb/u6ILQg6+uUgR7M4o8n\nXzKYSqxV5T7zWwIhJrgNyI2fI+buk3vz+Soo2YL42ZWI2Li+7z3tPFWE593XEGde7H+Q8h3Q3W29\nAquHnCGw6kNkS1Pgh6vKcvXAG8yCgD0TuS0IWZYGh4qmGGStqI8Xt+yHbHAEt6BV7wjtf8FdvEVW\n77Y0nld31UJBGQCRkIg4fA5y9YfI405B5PY1UqXpUg+xQ4YHV7gl0NhCwOgJGDfcC5+vwnz9OcyH\nb0dcdA1GgIqq4C6eExOr8u2CGTcmFjH9KORny5ELLvR5j5ENDijfgTjdes733kI622CChQI95TuD\ny3/0jFM0Vo1T/E3A+4SUEvnlGhg3xXqRLDfGj07HlFLlzBsCzrgYuext5EdvQ0uT8lJddE1w98P9\nhEhIRCz4GXL+qQw2u2iod+c4CwMKCoNejOvV99hJiEuuQz55L+Yd12Gc+8teOcDSUYtc/LzKNfzx\nBcHl4gMiLh7j6luQy95BvvoPzNuuRsw5HvnJR+o+NXoCxoKfWdMKDTRWwTBEELn1hxqWDchf/OIX\nPPjgg1x00UUMGjSI5uZmRo0axTXXXLM/5xc0UVFR/PznP+fOO+/ENE3mzZtHQUEQK7MajUYTAcSR\n85D/fhb50X8RAQxIuXWjMgICGZq+xsnJ71G108KDsKOaYDQgvePYs5DbNlnWnJT1dUFVYAXUynxC\nomVZEtnZoYzaGccENQ6gDG97FnLDOuhhQMruLszXnlUC4n4ezEVOAeLIuaqoyg9O9Ss34S3oYrWA\njqf/vCHqnJbvhAD5O7KyTHlgg1kJT8+Ez1ZYL1ZU74SU1KA0+wBISVXXWhBakNI0lfRHsN5OVFEM\nUtKsS3ls26Su0WCM7wU/RX71KeY/H1aVlvctvrVuFdRUYFz+x/2iLSeEgOlHY0yagfnoHchnH0Em\npyAmTPf7HumsVQ/sU2cF5f3yjvnDHyNXLVH3Mh9REpblO3qSna+ieHYGliuRLpeqUD3vROv9uxFp\nGepeV/wNBCp+tGs7OGsRp54X9DgAxglnYJqmMpA+Xa5SISbPwDh+AaJobEh97k9E8mCi7fbQQ6z9\nYBw+GxkXj/nSk5j33wxTZ2GcdJbS/lz6lhr7hNOD94a78S7Qjp2I+ff7ke+8qor9XHIdIkDhI03k\nsGxApqamcvvtt+NwOLxVWHsWqzmYmDp1KlOnBnEj02g0mggj4hMQM+chP/4AeeYl/XoH5dZNMHy0\n5cIfvfAKqJdbK/lfVxOcBqQHe5aqEtza3CtU2i/1DsgIPgyKtAyk0+IDTU2FekjLCb7KthACMWGa\nejD+YjVMPhJhGMhlbumFq2/t1ygTJ5+DXLMMuewdxI/9aPzt2KbC3NMCF5ToRb5a9ZblpYErO1bs\nCn61PT1TFQKrr7NkPMkGR1CSGh5EVJTSpgxGC7K5UeUzp4T4fJGVq2RNAiClRG7dhBg9IShDTySn\nIs67HPnkvcgPFiN+tPchWLa1It/6P6XlOSW8cMOA84iOxvjVHzHvvRHzibuV4L2PBSjZ3Y355L3q\nPaecHdpYuUNg0hHIj95CHr+gbxGfjV+ocOW8Qut9RkVBwTDkjuLAjWsqVN2BIPrvNdaIschtmwMu\nfskvPlH1CyYFUdF6H4yTzsKMi4fKMsRxpyJCuDd9HxATpmOMmYh873XkO//C/GK1KrA361il9Rjs\nPdHXGNn5GH+8Ry0Y5Q7ZLws2Gv8EXE6sq6vjo48+8v6dnp5OUVER6enpLFu2zFLiq0aj0RyKiGNO\nhO6ufgXIZWsL7CoJXe8tLUMl+FeWW2oerAakB5HhNjRqq6y9wVmHCMHoINWu9CYtIN2fOZiy8T0R\nx54CqRmYj9+Fece1mGuWKQNg7CQlYdDfezNzYOLhyBXvqVxHX/Mr3QqFI4N/sElJhUHJUFbabzPZ\n0a5yWoPJf6RH/qvVPMgGx958xmBJSUMG4YH0eCtFCB5IUDIYljyQtZXKsA3h/05MPxqmzkS+8aI3\nB1WWlyrB9erdKoQuWG9tCIi4BIxrboGUVMyH/+zTcJaLn4ft36pw7ExrxTF8YRy/QIXKr17Su3+X\nC7l5PSKANITP+ReOhF3blYexH2T5DtU+1HDCorHqGg4Q2SC/+ARGjbes8+kP47hTMC644pA1Hj2I\n6BiMk8/G+PPjiAU/w7j1YYyLromI8egdw2ZD5A3VxuMAEPAO9+qrr9Ll58uxq6uLV199NeKT0mg0\nmu8DIm8IjJ6gkvtNPw9J278BaYaW84U76T8rD1llzYDEUYOwB29Aej1VFvIgZXurqlYXZAgroAra\nWM2BrCxXIZJZoT0Yi+w8jD8/irjkeujqQj51P7Q2Y5wRWKYBwJh3EjQ1Ij9f1WefbG9V+ZmhhCUL\nobwz7gdnv7gNBpETnNyFZwFBWs1prXf6DdMNyOC0vQWVLI6l3he6B5LmJqUb2A/e/MdQDEghMM6/\nHOLiMJ9+CHPl+5h//R10tGP85k7E5BkhTT0URHIqxjW3AWDe80fMZe8o3V1Afr0W+d5riLk/wjh8\ndngDFY2FEWOQH7zhNfiklMh//QNamxGTLMoc9WRoEXR24KrY1X+7slJVUTs7NIPMEz4qi7/x20ZW\nlqn/16kzQxpD4x+RnoFxwhmIvKEDPRVNBAloQG7cuJHZs33feGbPns3XX38d8UlpNBrN9wXjmBPA\nUYNc+7HP/XLNciU1M3xMyGOI7HywYEBKl0sZZyF4ID0GpKy1YHR4DIYg8yzVezKgpQnZ3ha4bVU5\npGciYkLTxQMQRhTGkccoQ/LS3yAuutp6Zd6xk5Tx/tF/++7bWaIKIwWZ/+idV34hVOzq1zsjK90P\n3sF6OtLsyvC2UFVXtrUqqaJQvMmgDM8gQli9FV9DNCBFlvtYVFf033DrJuXlDdUoSU5FnHsZlG5F\nPvsojBiDccsDoUcShIHIysX47Z2QnYd84XHM265SRu0/HlTi82dfGv4YQigvZG2VKiAlJfKVp5Af\n/gcx/zQIwWgW7sq8XcXf9ttOlu+A7PzQC9DkFUJsnFqs8zfGF5+oOYVZ6VSjOVQIaEDu2bOHWD+i\ntTExMTQ1NUV8UhqNRvO9YcpMGFqkyo7vE8onv/oM+dlyhK+8omDIzldGamdH/+3q61TuWyghrHEJ\nKp+vzkIIqzsENaQQVk94kwUvpKwsU/lmEUAYURgz5mLMClzRcu97DFXYo2TL3oI5nrmVbFEvQjQg\nyR+m8r5q+jGEKsqUZybIkvvCFq0MQoeFxYAwDToGpykvbbfvSCaf4wlDhfGGQvbeSqz9IbdtglHj\nwgp9E4fPVsVAfnwBxnW3I5JDnHMEEHlDMX73PxhX3gSGoYza7m6My/8QWm61LyYdoRZM3v038tWn\nkUveRBx3CuKsn4d2HLPzIDaeri0b+29XvkMtqISIiIqC4aP9eiBldzdy1RIYMSa0e5ZGcwgS0IBM\nTU1lx44dPvft2LGDwYMHR3pOGo1G871BREVhXHI9dHRgPvMIUipBddnShPncY0qI+OTQilt4ySlQ\nxWQCeV1C0IDshT3LUtijrK1UL0LREvOEvQYopCNNF1RXDHiekZh5LMTGqXL9bmTpNuTbr8DQIkvS\nKj77ded7yX7yIGVlGWTmhlZ2Pz3Tmhak25scVggrQGODtfb1DiX/YqU6rC/SM5VR3U8hHemshbpq\ny/Id/hBCYCy4EOOksw4KPTghBGLSERi3PYy49DcY19yKCDG822f/hoH44Y9V3uL7ixHHnow4+9KQ\njXBhRMGYCXR+9ZnfNrKlSS0mhSmnIEaMhfKdyqO+7xirlqjCWSedFdYYGs2hREAD8qijjuLJJ5/E\n6ey9cu50Ovn73//uN7xVo9FoNAqRk4844yLY+Dly+bsAyJeehOY9GBdfG5bulurf7XUJEMYasgak\nZxx7lrUiOtUVKmQsJYRCKOnKAxnQuHHUKg9diCGIkUIkJKpqu5+tQDbtQZaVYj54KwxKxrjiT6F3\nnJOvtBfL+ymkU1kesgdWpGdZC2H1eCBDLWrjMSAbrOVBynAK9qCKapCR3a8H0qOBORDhpgcCjzc9\nWI09S33PnAdDRiDmn4Y45xdhFy8Rh03GVbUbWVPpu4GngE6IFVi944yfCtJE/uelXttlZwfyrZdV\njmcEdTs1mu87AWU8FixYQGlpKddccw1FRUUMHjyYhoYGiouLmTBhAgsWLDgQ89RoNJrvNGLeSaqo\nxb+ewmxvRX66HHHKuYghw8PvPDNX5bS5K0L6JUQNSC/2LPhiNdJ09etxkTWVkJET2sPl4HSliRnI\nm+r+rAPtgQQQx5ykhK1ffxa5/lOIjcP4zV8QoR5n3GGmOfnIsh0+98uuLqipREw/KrQB7JnwqQPZ\n3a2MLn948llDldXweC6tVmJtcEJGdmhjecjK6//62boJ4hNCEqY/1BHRMUTd/EDk+hs3FYnSkhQ+\nIha8haTC9kCOQRxzoioCNGG6V9xeLnsbGpwYv/itruSp0QRBQA+kzWbjD3/4A7/73e8YOXIkcXFx\njBw5kt///vf8/ve/JyrUMBONRqM5hBBCYFx0NdhikP9+BgqGIU48MzJ9x8Qq4y6Q/l2oGpAeMrKV\nRl+gqprVFZAVggYk7nylvKHIspJ+23m9rQPsgYQe1XZXvg+GoYzHUMOEe/abX+jfA1m9G6QZeg5o\neqZ6f6Bc0wYnJCSGnqPr9iZalvKod4QeLutGuA1IX3mXUkrkt1/BiLEHRdjpIU9mDkZmDnLzl773\nl5Wq3Ovk8NOlxBkXQ3Ye5j8eRLY0Kd3Od16FcVNCroKt0RyqWBYqmjhxIueddx6XXXYZxx9/PBMm\nTNif89JoNJrvHWJwujIi7VkYP7+2f89PsGTne3UR/RGqBqQHr1HUTxirdLnAUR2W5pwoGAZlpd58\nUZ9UlkNSStiabZHCOOUcGFqEcd0dkcs7yx8GDU5kU19JCq8GZm6QEh5uhOc6CJDTKhucoRfQAUhM\nUqG4jYFDWGVnB7Q2hxXCCiDGTlT6q+7Kmr3YujE8z60mogghiJ08A7792is/0hNZvkPliUfAOyhi\nYzEu/Q00NSCffxz5wWJobsL4yU/D7lujOdQISen2+uuvj/Q8NBqN5pBATDkS469PIvLDC8nq069b\nQN2v3iRAXZXSWQwVj5RHf0aHo1p5KcMxogqGQfOefsMeVQXWgfc+ehCjJxB10/3KGxmpPgsK1Qtf\nXsjKXapaaajH2XMuA+WaNjjCMiCF4a6oasUD6c23DLMS5mFTVB6kD3kVuewdSBiECFcXURMxYqbM\ngPY2KOkt5yFdLqjY5S0oFQnE0CLEqech132MfPtfMHUWYmhRxPrXaA4VQjIg+10V1mg0Gk2/7Jdc\nm5wCVVTGLaGxL7LBoSqbWtU49EVaBhgG9KcFWa2KYYTlgfQY137CN6WUUFmOyI6MhMdBS34/lVgr\nysAehgZmql0ZoBYMSBFiAZ29Y6VbC2GtV23CDmE1DMQxJ0DxN8hde0OhZYMT+eUniKOOC0s7VBNZ\nYiZMU9Ijm9b33lFTqe5pYRbQ2RfxowVQdBiYEuO08yLat0ZzqBCSAanRaDSagwvhyQX0kwcZicqT\nIipKGZH9eCClR7cwxBxIwFvcpOfDfy+aGlWo40HkgdwfiOTBynvnKSTSA1lZBiGGr4K7Wmlqer+V\nWFaKr5EAACAASURBVKXLpeQ3wjToSEmz5IGUkfJAAuKo+RATo4qkePr/+ANwuRBzTwi7f03kMBIH\nKZ3GTV/02i7XfwoQmUJjPRBGFMZVN2PceF/IIeAazaGOJQPSNE1WrFjBI488wp133sn06dNZunQp\nnZ2d+3t+Go1Go7GC24CU/iqxbtsEsfFQEObDWEY2sq4fKY/qCoiLh6TQi16IhERVsMefBqIn/+8g\nKKCz38kvRO7jiZUujwZmmB5YeybS0Y83ubFeFdoJ1yM42JoB6Q1hDddgBURiEuKIuchPlyFbmpEu\nF3LFe3DY5IhqI2oigxg3RelLuvN9ZVW5ktyYdATkDY38eAmJOnRVowmDgAZka2srN998M88++yyG\nYTBs2DCioqJ46aWXuOGGG2hoaMDhcLB69eoDMV+NRqPR+EAkJcOgZPCjBSm3boIRo0MXaPeME0AL\nUtZUKHH7cMN08wv3lvDfdwzPZwzXgPoOIPKHQWVZ7wIjtZXg6g7784v0zP61IHfvUO1ywzzOg9Og\nrQXZ0d5/u3oHxMYj4hPCG8+NmHcidHYiV38IG9ZCfR2G9j4elIjDpoCUyM1fIk0X5j8fhugYjAuu\n0PIaGs1BSMASgC+++CKDBg3i5ptvJi4uzru9vb2dBx54gIceeojq6mrOPffc/TpRjUaj0QQgO3+v\ncdUD2dKsilFEovKkPQuaGpEd7YjYuL77ayoRhSPDHkYUDEeu/xTZ3oaIi++9s7IMYuMiEup40FMw\nDLq71cKAR7ewwq2BGa5hl54FDcuQ3V0+pV3kjmKlGxpuCGGKO4eywdlv0R/Z4IBw8y17IIaMgBFj\nkB/9F2nPUp7NSUdErH9NBCksUhV7N69HNjph+7eIS65X3muNRnPQEdADuXbtWn7xi1/0Mh4B4uLi\nuOSSS9i8eTNnnXUWs2frimYajUYzkIicfKgs71vorPgbkBIxMvT8Ry8eKQ8feZCyu0t5tHwIggeL\nKBgGUsLunX3HqSyHrDxV4fN7jvDkg/YIY/WGKYcbwmvPVMfY6VsLUu4sVsc5LjyPoPAY+oHCWMOV\nDPE19ryTlMf8m68Qc44P2wOv2T8IIwoxdhLyq8+Qrz8Pk2cgZswd6GlpNBo/BPRAtra2kpbmewUo\nPT0dm83GMcccE/IEnnvuOT7//HNsNhtZWVlcccUVJCYmAvD666+zdOlSDMPg4osvZvLkyQCUlJTw\n2GOP0dnZyZQpU7j44osRQtDV1cWjjz5KSUkJSUlJXHvttWRmhq55ptFoNN8pRoyBle/D9m9UlUE3\nctsmpcU3bFTYQ4iMbCQoA3Lf3KS6apUzF0YFVi/uXE1ZVoIYMab3vqpyxMjDfLzpe0hWHthsULYD\njgTZ1YUs2QJpGX09s0Ei0jPVuXT4Mfp3FiPGTAxrDMCr6ygbHPQbjFjviLigu5g2C/nKU9DShJj9\ng4j2rYkwh02GdR9DYpIOXdVoDnICLt9mZWWxceNGn/s2bNhAdnZ2WBOYOHEiCxcu5L777iMnJ4fX\nX38dgPLyclavXs3999/PjTfeyFNPPYVpmgD87W9/47LLLuPhhx+mqqqK9etV6eelS5eSmJjII488\nwkknncQLL7wQ1tw0Go3mu4SYdhTExiNXvN9ruyzeDIVFkZEusKt7vvSVB+mR8IhEkZI0OyQkKsOp\nB7K9DZy1h0T+I7irpeYOQX77NeYLT2D+9kL4eq0qOhIu/eh6ygan8ggODUP2xYMnhLWxH11P01T7\nIxjCCiBs0Rjn/hJxxsVhy4No9i9i4uGQkoZxwa8QKakDPR2NRtMPAQ3Ik08+mUcffZQ1a9Z4DTjT\nNFmzZg2LFi3i5JNPDmsCkyZNIsodUjJq1CicTvUFs3btWmbNmkV0dDSZmZlkZ2dTXFxMfX09bW1t\njBo1CiEEc+bMYe3atQCsW7fO6w098sgj2bhxo9as1Gg0hwwiLh4xYw7y84+Rrc0AyM4O2FGMKIqQ\nx25Qkqrm6svo8Eh4RMADKYSAguHIsn2kPKqVTIn4nkt49EQUDFcVKlctQYyfinHNbYgLfhV+x6l2\npevpSwty53Y19tDw81mJT4CY2P5DWJsbweWKeAgrgJh+NMb8UyPeryayiJRUou77J2L60QM9FY1G\nE4CAIazHHHMMTU1NLFq0iIceeojk5GT27NlDdHQ0Z5xxBvPmzYvYZJYuXcqsWbMAcDqdjBy594sr\nLS0Np9NJVFQU6el7v2DS09O9RqfT6fTui4qKIiEhgaamJpKTkyM2R41GozmYEbN/iFzxHvLTFaoK\nZelWcHWHpf/Yq38hICPLp9eK6t3KazgoKTJjFQxDrngXaboQhlpolBURyv/7DiFOORfGTkJMmK4k\nTiLVb1SUMiJ9LQbsdBfQKRgW/jhCKMOwPwOyXu3TXkKNRqM5+AloQAKccsopzJ8/ny1bttDU1ERS\nUhKjRo0iIcFaYv0dd9xBQ0NDn+3nnHMOhx9+OACvvfYaUVFRB6wYz5IlS1iyZAkAd911F3a7/YCM\nGww2m+2gnNehgD72A4c+9uEh09NxDhsJn3xI2hk/pWX3DlqEIH3G0RiJ/Rt2Vo99Q24B3ZXlfdrW\n19dh5g4hPSMjrM/goW3sRPYseZPUrnZseUORLhfOZW9jptmxHzZRhXd+j/B7/O12GD12v4xZn1vw\n/+ydd3xb5fX/34+8Eye25ZHEsR3HdvYgIQ6QFAiQMAqFUlZLoS27pXwZpYMwSvv70hZoSeELhNJC\noJRCmQUKZQZIAoSRSfaOkzjxlve27vP745HkJcmSI1t2ct6vl1+2r6R7z3306Oqe55zzOVglh0ju\n+l4W7ceZkU1KRmhShR2pI6C+BruP+dW8dxtVQGJ2LlFh+PzLdSd8yNiHDxn78DHYxz7gb9+4uDiP\niE2w/PrXv/b7+LJly1izZg133323p2jabrdTUVHheY7D4cBut3fbXlFR4RH5cT+WnJyM0+mkoaGB\nYcO83zAtWLCABQsWeP4vL/euQhdOUlJSBqRdRwMy9uFDxv7wsebORz/3OOVrvsBa/xWMHoOjsRka\nm/2+LtCxt4bb0eu/oqysrJPQhbNwHypvcsjeP51ovlwdG9ZiixmKtewd9J7tqGt+ToWXRcnBTjjm\nvjVxOvrlpynbsgHVIfXYuXMratIxIbPHGjoMvXeHz/1Z+4zKbJWKQIXh8y/XnfAhYx8+ZOzDx0Ad\n+/T0wEpQwq6Bvn79et544w1uu+02YmLaBR7y8/NZuXIlra2tlJaWUlRURF5eHklJScTFxbFjxw60\n1qxYsYL8/HwAZs2axbJlywD44osvmDJliqh4CYJw1KGOmwfRMehl78Ce7aFXLE0ZAS3NUNvuxOnW\nFqgshxGH38LDQ3qmUY89sAddV4N+/Z8wfirquJNDd4yjHHe9mV71qWebrqowgjbZeaE7UKIdqh2+\ndQnKS8x7PTwxdMcUBEEQ+oSw5/8sWbKEtrY27rnnHgDGjRvHddddR2ZmJnPmzOHWW2/FZrNx9dVX\nY3P1/Lrmmmt47LHHaGlpYcaMGcycadToTjvtNB599FFuvPFG4uPjueWWW8J2XoIgCOFCDRmKyj8R\nvfJD0+cvFP0fO+4/dYRp/1BWAsNdaomlxeZYoWjh4T5OZBSMykQf2AuvPQuN9di+/2NZGAwhyp4K\nuRPRqz6Bcy4xGz0COiFQYHWTmAwtLdBQD0Pjuz2sC3ZCRrb0aRQEQRgEhN2BfOSRR3w+dsEFF3DB\nBRd0256bm8uiRYu6bY+OjubWW28NqX2CIAiDEXXSGcaBhNBHIFNdrTzKS9p7NLoUWEPSwqMDKnMs\neu1KdEszav65qK69J4XDRs0+Cf3CE+iiA6hRmeiCXaBsnl6cIcHVC5IqRzcHUlsW7N8tjeMFQRAG\nCWFPYRUEQRD6gNyJkJ4FqSNDr2yZnGZ+d+gFGcoWHp3IHAvNTTAswaiRCiFHzZoLSpkoJC4F1lEZ\nqJjY0B3D0wuyovuDJYegsQGyQ9AyRBAEQehzxIEUBEE4AlFKYfvJQmw/uS30+46OgaQU9OpPTcN5\nME5A/DCUl/TEwzpWzgTz++IrQ9rCQmhHJSbDuCnoVZ+aGsV9u1BjQlj/CJBkFjF0RVm3h3TBTmOH\nOJCCIAiDAnEgBUEQjlDUqAxUVgjr2Dpgu/x6KCvG+sMv0Pv3oEuLQh99BFTuRGz3PYnthND1HBa6\no2afCMWFsGkt1FRBqB3I5DTTH3Tn5u6P7d0BMbEw6ujp7SkIgjCYEQdSEARBCBo1fTa22+4DwPrj\nQijY1akNREiP5U6ZFfoMdexcUDasfz9j/g+lAiugbDbUpBnoLeu7KbHqgp0wJhdlEwEdQRCEwYA4\nkIIgCEKvUFm52O54AEZmQHNjaFt4CP2KGp4IE6dBYYER0MkYG/qDTJ4B1ZVwcJ9nk25rhQN7kfRV\nQRCEwYM4kIIgCEKvUYl2bL+8F3XRlaiTzwy3OcJhoGafZP5Iz0R16Mscsv1PngGA3rK+fePB/dDW\nCtnjQ348QRAEoW8QB1IQBEE4LFRMDLYzv4Ny94QUBiXq2DkQEUGo01c9+7enwsgM9JZ1nm167w7z\nWB8dUxAEQQg9Ye8DKQiCIAhC+FFDh2G7+bcQ4l6enY4xZSZ6xXvo1hZUVDQU7DTiOikj+uyYgiAI\nQmiRCKQgCIIgCACoSceYSGGf7X8GtLbArq2AS0AnexxKqT47piAIghBaxIEUBEEQBKF/mDAVIiLR\nm9ehm5vg0AGU1D8KgiAMKsSBFARBEAShX1CxcZA70dRB7tsN2kIUWAVBEAYX4kAKgiAIgtBvqMkz\n4MBe9MbVZsNYEdARBEEYTIgDKQiCIAhCv6EmzwRAL38H7Cmi3isIgjDIEAdSEARBEIT+Y0wODB0G\njQ3S/1EQBGEQIg6kIAiCIAj9hrJFoCYdY/6W+kdBEIRBhziQgiAIgiD0L5NnAKDGigMpCIIw2IgM\ntwGCIAiCIBxdqDmnQmwcTJgWblMEQRCEIBkwDuSbb77Js88+y5NPPsnw4cMBeO211/joo4+w2Wxc\neeWVzJhhViz37NnD4sWLaWlpYebMmVx55ZUopWhtbeXRRx9lz549DBs2jFtuuYW0tLRwnpYgCIIg\nCF1QkVGo2SeF2wxBEAShFwyIFNby8nI2bNhASkqKZ1thYSErV67kz3/+M3feeSdLlizBsiwAnnji\nCX784x/z8MMPU1xczPr16wH46KOPGDp0KI888gjnnHMOzz33XFjORxAEQRAEQRAE4UhkQDiQzzzz\nDJdddhlKKc+2VatWMXfuXKKiokhLS2PkyJHs2rWLyspKGhsbGT9+PEopTj75ZFatWgXA6tWrOeWU\nUwA44YQT2LRpE1rrcJySIAiCIAiCIAjCEUfYU1hXrVqF3W4nOzu703aHw8G4ce3F9Xa7HYfDQURE\nBMnJyZ7tycnJOBwOz2vcj0VERDBkyBBqa2s9KbEdWbp0KUuXLgXgvvvuIz09PdSnFhIGql1HAzL2\n4UPGPnzI2IcXGf/wIWMfPmTsw4eMffgYzGPfLxHIe+65h5///OfdflatWsVrr73Gd7/73f4woxML\nFizgvvvu47777uv3YwfKwoULw23CUYuMffiQsQ8fMvbhRcY/fMjYhw8Z+/AhYx8+BvvY90sE8te/\n/rXX7fv376e0tJRf/vKXAFRUVHDbbbdx7733Yrfbqaio8DzX4XBgt9u7ba+oqMButwN4HktOTsbp\ndNLQ0MCwYcP68MwEQRAEQRAEQRCOHsJaA5mVlcWTTz7J4sWLWbx4McnJydx///0kJiaSn5/PypUr\naW1tpbS0lKKiIvLy8khKSiIuLo4dO3agtWbFihXk5+cDMGvWLJYtWwbAF198wZQpUzrVVQqCIAiC\nIAiCIAi9J+K3v/3tb8NthJu3336bBQsWEBMTQ0JCAnV1dfz1r3/l008/5aqrrvLkCo8dO5bHH3+c\nt956i7y8PM4++2yUUmRlZfHpp5/y/PPPU1BQwHXXXUd8fHyYz+rwyMnJCbcJRy0y9uFDxj58yNiH\nFxn/8CFjHz5k7MOHjH34GMxjr7TIlAqCIAiCIAiCIAgBMCDaeAiCIAiCIAiCIAgDH3EgBUEQBEEQ\nBEEQhIAIex9IwTvr16/n6aefxrIs5s+fz/nnnx9uk44abrjhBmJjY7HZbERERAzoVi+Dnccee4y1\na9eSkJDAokWLAKirq+PBBx+krKyM1NRUfvaznw36WuaBiLexf+mll/jwww89vXMvvfRSjj322HCa\neURSXl7O4sWLqaqqQinFggULOPvss2Xu9wO+xl7mft/T0tLCb37zG9ra2nA6nZxwwglccsklMu/7\nAV9jL/O+/7Asi4ULF2K321m4cOGgn/dSAzkAsSyLm2++mbvuuovk5GRuv/12br75ZjIyMsJt2lHB\nDTfcwL333uu5oAp9x5YtW4iNjWXx4sUeJ+af//wn8fHxnH/++bz++uvU1dVx+eWXh9nSIw9vY//S\nSy8RGxvLeeedF2brjmwqKyuprKwkJyeHxsZGFi5cyC9/+UuWLVsmc7+P8TX2K1eulLnfx2itaW5u\nJjY2lra2Nu6++26uuOIKvvrqK5n3fYyvsV+/fr3M+37irbfeYvfu3Z7rzmC/15EU1gHIrl27GDly\nJCNGjCAyMpK5c+eyatWqcJslCCFn8uTJ3VbcVq1axbx58wCYN2+ezP0+wtvYC/1DUlKSR30vLi6O\n0aNH43A4ZO73A77GXuh7lFLExsYC4HQ6cTqdKKVk3vcDvsZe6B8qKipYu3Yt8+fP92wb7PNeUlgH\nIA6Hg+TkZM//ycnJ7Ny5M4wWHX3cc8892Gw2Tj/9dBYsWBBuc44qqqurSUpKAiAxMZHq6uowW3R0\n8e6777JixQpycnL44Q9/KE5mH1NaWsrevXvJy8uTud/PdBz7bdu2ydzvByzL4rbbbqO4uJgzzzyT\ncePGybzvJ7yN/bp162Te9wN///vfufzyy2lsbPRsG+zzXhxIQejCPffcg91up7q6mt/97nekp6cz\nefLkcJt1VKKUklXSfuSMM87goosuAuDFF1/kH//4Bz/96U/DbNWRS1NTE4sWLeKKK65gyJAhnR6T\nud+3dB17mfv9g81m409/+hP19fU88MAD7N+/v9PjMu/7Dm9jL/O+71mzZg0JCQnk5OSwefNmr88Z\njPNeUlgHIHa7nYqKCs//FRUV2O32MFp0dOEe64SEBGbPns2uXbvCbNHRRUJCApWVlYCpV5Ja1P4j\nMTERm82GzWZj/vz57N69O9wmHbG0tbWxaNEiTjrpJI4//nhA5n5/4W3sZe73L0OHDmXKlCmsX79e\n5n0/03HsZd73Pdu3b2f16tXccMMNPPTQQ2zatImHH3540M97cSAHILm5uRQVFVFaWkpbWxsrV64k\nPz8/3GYdFTQ1NXlSDJqamtiwYQNZWVlhturoIj8/n+XLlwOwfPlyZs+eHWaLjh7cX2YAX331FZmZ\nmWG05shFa83jjz/O6NGj+da3vuXZLnO/7/E19jL3+56amhrq6+sBowq6YcMGRo8eLfO+H/A19jLv\n+57vf//7PP744yxevJhbbrmFqVOnctNNNw36eS8qrAOUtWvX8swzz2BZFqeeeioXXHBBuE06Kigp\nKeGBBx4ATKH5iSeeKGPfhzz00ENs2bKF2tpaEhISuOSSS5g9ezYPPvgg5eXlg1LaerDgbew3b95M\nQUEBSilSU1O57rrrPDUaQujYtm0bd999N1lZWZ60pUsvvZRx48bJ3O9jfI39Z599JnO/j9m3bx+L\nFy/Gsiy01syZM4eLLrqI2tpamfd9jK+xf+SRR2Te9yObN2/mzTffZOHChYN+3osDKQiCIAiCIAiC\nIASEpLAKgiAIgiAIgiAIASEOpCAIgiAIgiAIghAQ4kAKgiAIgiAIgiAIASEOpCAIgiAIgiAIghAQ\nkeE2YKBw6NChcJvQjZSUFMrLy8NtxlGJjH34kLEPHzL24UXGP3zI2IcPGfvwIWMfPgbq2Kenpwf0\nPIlACoIgCIIgCIIgCAEhDqQgCIIg9AJdWIB0whIEQRCONsSBFARBEIQg0bu3Yf2/m2Dr+nCbIgiC\nIAj9ijiQgiAIghAkeuNq87u0OMyWCIIgCEL/Ig6kIAiCIASJ3uKKPFY7wmuIIAiCIPQz4kAKgiAI\nQhDo+joo2GX+qRIHUhAEQTi6EAdSEARBEIJh2wbQFkRGocWBFARBEI4ypA+kIAiCIASB3rIeYuMg\nb7JEIAVBEISjDolACoIgCEIQ6C3rYMI0lD1VaiAFQRCEow5xIAVBEAQhQHRpEZSXoKbMhEQ71Faj\n29rCbZYgCIIg9BviQAqCIAhCgOgt6wBQk2YYBxKgpjKMFgmCIAhC/yIOpCAIgiAEiN6yHpLTYEQ6\nyu1ASh2kIAiCcBQhDqQgCIIgBIB2OmHbRtTkGSilICHJPCAOpCAMKvSmtVhPPYjWOtymCMKgRBxI\nQRAEQQiEgp3QWI+aPMP874pAahHS6Ve01lgfvy0tVIReYy1/F/35x9BQH25TBGFQIg6kIAiCIASA\n3rIelIKJ082G+ASw2aBKaiD7lYpS9POPo994LtyWCIMQbVmwY5P5p6I0vMYIwiBFHEhBEARBCAC9\nZR2MyUPFDwdA2WyQYIfqijBbdpRRUQaA/mo5ur4uzMYIg46D+6DBNW8c4kAKQm8QB1IQBEEQekA7\nnbB3B2rCtM4PJNollbKf0e6oUUsL+rOl4TVGGHTo7Rvb/y4XB1IQekNkuA3oiccee4y1a9eSkJDA\nokWLAKirq+PBBx+krKyM1NRUfvaznxEfHw/Aa6+9xkcffYTNZuPKK69kxowZ4TRfEAThqEdrbWoH\nh8SH25TeU18DTickp3benmCHsqLw2HS04o4ajclDL3sbveA8Ew0WhADQ2zdC6kiorvREswcL1n+e\nR6VnofJPDLcpIUPv3Yku2IE65WwjTiYMCgb8FfeUU07hjjvu6LTt9ddfZ9q0aTz88MNMmzaN119/\nHYDCwkJWrlzJn//8Z+68806WLFmCZVnhMFsQBEFwod95BWvhNejm5nCb0ntqa8zv+IROm1ViEoiI\nTv/iKIfhiajTvw1lxbB5XbgtEgYJpv5xs8kkSE5DD6IUVl1ahH7zBayP3w63KSFFL30D/fxf0e+8\nEm5ThCAY8A7k5MmTPdFFN6tWrWLevHkAzJs3j1WrVnm2z507l6ioKNLS0hg5ciS7du3qd5sFQRAE\ng25qQL/3GjQ2QMnBcJvTe2qrAVDDhnfenmCHulp0a2sYjDo60RWlYE9FzZoLwxOxPv5vuE0SBguF\nBab+ccJUk00wiCKQesV75o/CgiOq/YguLwGl0K89i7Xyo3Cb4xPd2IBubAi3GQOGAe9AeqO6upqk\nJNN/KzExkepq88XucDhITk72PM9ut+NwyMqwIAhCuNDL3vEIVuhB7EBqdwRyWOcIpLuVBzWixNpv\nOMogORUVGYU6+UzYtAZdVhxuq4Q+RtfXoQ/sPbx97DD1j2r8NJQ9DSpKQmFan6NbW029b2SUuZ5W\nlofbpNBRVow64RSYOB39j0fQAzSjwPrLvViLfx9uMwYMA74GsieUUr3KmV66dClLl5ri+/vuu4+U\nlJRQm3bYREZGDki7jgZk7MOHjH34CPXY6+Zmyj98k8jJM2jd+jVDaiqJH6TvbYN2UgvYx4wlwu00\nAs1ZY6kCErST6MM8N5n7PaO1ptRRzpDjT2ZYSgrO879P+duvEPvVcob96IZe71fGPnwEOva1775C\nwxvPk/LYS0SkjuzVsar2bKdtVAYp4ydSvzGbuhXvkhw/FBUb16v99ReNK96npq6GoZdcRf1LTzG8\nxkHM+EmHvd9wz3vd1EhpbTVDc8YT980Lqbzrpzgfv5/E3y8mKmdC2OzqilVbQ9n2jaA19kgbtg7f\nAYHgrHLgLCwgeuqxnm3hHvvDZVA6kAkJCVRWVpKUlERlZSXDh5uUIrvdTkVFu5y6w+HAbvf+Ji9Y\nsIAFCxZ4/i8vH3irOSkpKQPSrqMBGfvwIWMfPkI99tZHbxmF0mt/ASWHaNizk6ZB+t5axYcAcDS3\nojqcg7ZFAFC9by8qZdRhHUPmfs/omipoaaYxLp7m8nJAwYzjafjgPzSdfj4qOqZX+5WxDx+Bjr11\nYB+0tVHxryXYvv/joI+jLSfW5nWoWd+gvLwcK3YoAOU7tqLSs4LeX3/ifOslSBtF49wF8NJTVG/Z\ngC378B2scM97fXA/APVDhtHY2IS+4U70PT/D8ffFRNx0d9js6or15XJwaaqUL/8A2zfmB/f65x9H\nr3gP20PPoWKHAOEfe1+kp6cH9LxBmcKan5/P8uXLAVi+fDmzZ8/2bF+5ciWtra2UlpZSVFREXl5e\nOE0VBEE4KtFtrej3/g15k1Hjp8KI0YM6hZW6ahgSj4qI6Lw9wSxSSiuPfsJhatZUBzVc24mnQ30t\n7NoaLqtCgvXEIqzX/hluMwYs2pUmrj/9wPN3UBQWQEM9jJ8KgEpOM9srBraQjj64H3ZuQZ18Fmpo\nPCSnmXM5Eig3qecqZYT5nZgMuRMH3nuyaQ3EDzdtmzasCvrlevc2o+K9d2cfGBceBrwD+dBDD3HX\nXXdx6NAhfvKTn/DRRx9x/vnns2HDBm666SY2btzI+eefD0BmZiZz5szh1ltv5fe//z1XX301NpH2\nFgRB6Hf0F8vAUY7tnIsBUCNHQ/HBwSv+UFvTvf4RYOgwiIiE6orujwmhxy16Yu/QTmVMDgD60P4w\nGBQadGsLes1n6FUrwm3KwKW6EjLGQlsb+oP/BP1yvX0TQHsvV5cDqQeas9IFveJdiIxCzXVFvTKy\n0UeIA6nLXTWoHVKSVWIyVA6c66m2nOhNa1FTj0VNnw2b1wUlmqabmzwOv969rY+s7H96ncL60UeB\nKSWddtppvT0EALfccovX7Xff7T20fcEFF3DBBRcc1jEFQRCE3qMtJ/qdV2FMHkxx1XyMHA3NMWgE\n9QAAIABJREFUjVDlgKRk/zsYgOjaauiqwAqm/2BCElQdGSI61stPoyZNR02dFW5TvKJdEUj3zT8A\nwxIhfhgUHQiPUaFg/x5wtkFZMbq6EpWQFG6LBh41VajZJ8GoDNP/86wLTUQuQPT2jZCWjnJffxKS\nzOLPAFZi1c1N6M8/QuV/w6MArTKy0RtWo1tbUFHRYbbwMCkvgZhYE91zk5QCjfXopsaBUZu6dyfU\n1cDUWajYOKOGu3MTTJ4Z2OsLdpn0V6XEgQT45JNPAnre4TqQgiAIwiBj+yYoPYTtx7/yiJypEaPR\nAMWFg9KBpK4GUn3UOCba0UdAL0i9bxf6/dfQ+3YRMUAdSCpKzQ3nkHbHQSkFozIHdwSy443l7m1w\n7JzwGTMA0a2tJk05IRE170z0qk/QH72FOvd7gb3ecpr+j/nf8GxTNhvYUwZeumQH9FcroLEBNe8s\nzzaVORatLTi03yzSDWJ0WTGkjOgshpnk0i6pqoCRGeExrAN60xpQNtTUYyEyGqKi0RtWowJ0IPWe\n7eaPGcfD9k1oyzJzb5DTawfyN7/5TSjtEARBEI4Q9CFXJGj8lPaNI0ebx0oOoiYdEwarDpPaapQv\nVcBEOxQP4vpOF3r5u+aPnZvR9XVBRXf6C+0oMz0gu6ivq/Qs9KpP0Vr3Spk97OzZbuZRXS1691aU\nOJCdqa0yv4cnoTLGwjHHoT98E336twOLUh0ogMZ6cKevuklOG9AprHrdF5CWDrkdFFdHZ5vHCgtQ\ng9yBpLwEXPWPblRSillsrBwgDuTGNZA7ATV0mNkwcTr666/Q370moGuN3rPdRL6POc68nyUHYVRm\nH1vd94TcBdZaY1mW50cQBEE4yigphLghJrXQTWKyiRwNQkdLa20ikPHdU1gBVILdpOYOYnRjg4l2\nZIwFy0JvXhtuk7xTUdY5fdXNqCzTH696cKYS673bUeOmQHbeEZXm1hW9YRV6y/rgX1htHEh3aq/t\n7IuhvrZ90aOn4xaZ6LQak9tpu0pOHdAprBzaj8rO6+yopI2E6OhBL6SjtYbyUo+AjodEk6GiB0Ad\npK5ywL5dnVL61THHGcc3gJR5rTXs2YbKmYDKmWi2HSGf75A4kA6Hgz/96U9cddVVfO973+PSSy/1\n/AiCIAhHF7rkEIwY3emmR9lsMCI9rEqsuq0N673X0M3Nwb2wod7UsHgT0QFTS9VQh24Jcr8DCP3l\nMmhuwnb59eY8e6E02C84SlEdBXRcqHTXiv4grIPUlRXgKIecCajcibBvF7q1JdxmhRx9cJ9pxv7a\ns8G/2K26Otw4kCpnAuRMQK/5LLDXuxxQt2qyh+QRUO0IShSlv9BNDSa9tkuLEWWLgNHZ6AN7w2RZ\nZ3RbK85Hf4f1n+eDe2FdjamL9+FAUjUAHEjXQpqaPtuzTU3LN499HcA1sqIUaqogZwKMSDeia+JA\ntvO3v/2NyMhI7r77bmJjY7n//vvJz8/n2muvDcXuBUEQhMFE8UHUiO69pNSI0UFHIHVbK9bS/4Tm\nhnr7RvQrT6PXrgzudbXV5rcXER2g/YZnsEa/tEYvfw+ycowTM3UWeuMatNMZbtM6oZuboK4Wkrs7\nkO6b7EFZB+mqkVI5E1C5k6CtDfbtDrNRoUW3tWI99aA5N0fwET/t/mwltGc1qBGjIdDa45oqiIqG\nrumu7rlUOQCjkK5SADW6e49KlZENBwsGhKq1fnEJfP0Vek2Q11WXAqvqoMAKoGJiTI3zQIhAblxt\nUsszsj3blD0FsnICaufhrn9UuRPMImrOBIlAdmTHjh1cf/31ZGdno5QiOzub66+/nrfeeisUuxcE\nQRAGCbql2dwgumoeOzFyNFSUBucMbliFfvFJ9KpPD9+2MtNzjIIge3HVGQdSxXuPQKpEV1RjsArp\n7NkOhXtNnzmlUMfMNumgA+1Gx+GlhYeb4Ylmdf/QIIxA7tkOkZGQmQO5ps72SLnJdKPffNEozeZM\ngJqq4CN+7ghkx7T4hCSorgrMiaqphOGJ3Wtn3enQ5QOvDtKzGJLe3YEkI9sspoQ5dd765H30srfN\ne1FcGNS13dPCo2sEEiApGR3mCKRua4Mt61HT8rvPm+mzYfc2dF2N/53s2W7SjV11qyp3IhQdQNfX\n9ZHV/UdIHEibzUaEq7ny0KFDqampISYmBodjkH6ZCoIgHAbWh2+id24JtxnhofSQ+T3CiwM5YjRo\nDaVFAe9Ouxsvb1pz+La5mlbrYB3IWtdNgq8UVrcDGcTNnHY6B0T0ADCy9DFxqONPNhsmz4SISPSG\nr8JrWFdctWrKSw2kR4m1aPBFIPWebTAmDxUVhRqeBGmj0Lu2htssD9Zzj2P951+9fr3evQ39ziuo\nufPb1UQry4PbSXUVDB2Giopq35aQZFqf1Nf2bENNlVlk6MpA7gV5aL+JmnpxsJQ7IhbGOki9exv6\n+cdh8gzUd68xaf7BZAC4F/S81TQnDYBekLu3GgVcL4rUavpxoC2j0OoHvWc7ZI9DuXwklWvqIN1Z\nB4OZkDiQeXl5rFu3DoBjjjmGBx98kAceeIDc3NweXikIgnBkoYsPol94AuuRe9BuZ+powpWiqrw4\nkMqtqFdcGPDu9N4d5veW9UaK/zDQZa4V7/170G1BNIJ2p7D6ENFxO5A6QAdSWxbW7dei3301YBv6\nCl1fh171Cer4eajYIQCouCEwfgp6w+owW9cZ7XDd5HtLYcUosXLoQFCOuXaUoTeswmpsCIWJQaPb\nWmHfbtTYdoVflTsRdm8dEAsMeuvXpufiJ+/37vXNTVhPPQRJyajvXtNevxpkGqt2RRA74a5nDCR1\nvLrSOJxdSUwGZetVWm1fow/uh1GZpuaxKx2UWAPeX2kRetuG0NhWXYn1+H2QmIztul+issz9flB1\nmeUlMCzBq4quSkoJew2k3rwOIiJgshfV8DG5ZkFx8zrfr29tgf17POI5AGSPA2Uzi0aDnJA4kDfe\neCOTJ08G4IorrmDKlClkZmZy0003hWL3giAIgwb95TJzQ6IU1mP3Bi/YMsjRJe4IZPcaSPc2HWAd\npLYs2LfLOGj1taah8+FQVmQah7e1wsF9gb/OnabkqwZySDxERgUegawohcpy04agrS1wO/oA/cXH\n0NqCmndmp+1q+myTahVEtLjPqSgDm627EIqb9EwzT9wtH3ygt6zH+tufcP7qKqzbrsZ65B4a33+j\nDwwOgMICaG0xqZ1ucieZult3hCZMaMuJ9dIS809VhWmhEuw+PnjD9IS98mbUkKGm7yIEvy8vDqBy\n10MGkjpeU4XyEoFUkZGm7+AATGHl0H6zKOIFNTTepHIXBu6wWa/+HWvRXVjvvHLYixP6rRehvg7b\nDXeY9hapI43KdjAOrZcWHh4S7SbVOYzXR31ov2m/4VpY64iy2cwim79Mo/17wNnWqf2Tio2DzOwj\nIkU9JA7k0KFDiY83/aKio6O56KKLuPzyy0lK8rLaIwiCcISitUZ/sQwmTcd27S/g0H70PxcPiEhC\nv1F8EJJSUDGx3R5SsXGQlBK4kE7JQWhqRJ1+vlm1PYzWEkYyvgSmmObP7shmQNTWQEwsKjrG68NK\nKVc9VoAOpDsCW10JYU4T1V8sM+mTWV3aG7hUBwMRiug3HGVmbkV4icgAyt1b7aDvNDrd2moWdrZt\nQOVORH3vOogfRluYUgH17naRDTfuNLdw32Tqzz6EwgLUWRf22h69b5eJok2cbjYkGQcy6IhfTZVJ\n7+2IayFBV/ewYOB0mkWgrq93Y09rj24PEHRDnYnA+XAgAcjIDioCSVEhREai//0P9EtLzAJdb+0r\nLoSsHNOTE5dDlZGNPrAn8J2Ul3QT0PGQlGLKHcIpTFZy0Hstvws1bqqp6feR/uwW0KFL/2CVMxH2\n7BhwImXBEhIH8sUXX/T5IwiCcNSwa6v5UjzhVNTUY1HnfR/9xTIjMnCUoHv40mXk6IBbebidPDX1\nWMgZj950GL0J62tNPcuEaSb1KJg6yLpq3+mrbhLt7UqRPaDdQi/DErCWvxe4HQES6I2hrq0xPc5m\nHNftMZU2ytQUDiAHUleUeiJYXnErsfpr5bFzEzQ3YrviJmw//hW2+d+CUZk4/Tidfcqe7SbaktTh\nvNIzTR/VMNZB6sYG9GvPQt4k1Lcvg+iY3okqlRzqlI2goqJNKqoj8BpI7XYkErqmsAYYgayrMc6I\ntxpIQKWkDbxekK5aQl8RSMA4b8WFAQkSacsJZUWo085FzT8XvfQ/6Kce7H2Ez1HWrZ2OysiGA4Ep\nw2qn0ywi+IhAKreydbC1siFCO51QVozy50COn2Keu2Oz9yfs3gbJaZ7epR5yJ0JzI237g3C2ByAh\ncSArKio6/ezevZs333yTkpKSUOxeEI5KtNZoR/nRFb0a5OjPP4LoGNTMEwBQZ18M02cbFdFB/mUR\nCFprKPHewsONu5VHQPO6YCfExMHI0agpx0LBzvZ6xGBx1T+qtJGQPa5dnCcAdG21bwEdN4n2wFNY\niw4YRchTzoYt69rVYUOALi7E+tVVAUnq621fg9aoyTO9Pq6m58OOzegw1Qd2w1HuVUDHQ0ISDBnq\nV8hDf73KqCK6I2KY2lxniNp/6JqqoCILeu92yJnYpWdqBIydgN4dRgfynZehthrbJdeYNM+x44MW\n9jFOSzEqrcv1wJ6KDsZha26ElubuKayxQ0zaZA8RSHcUS3V1QD32pJmU8gEUEfIosHpp4eEhI9sI\n1wQiHFVRZlqojBxtalHPvxz95XLq/vl48LZZlnH+uqohZ4yFxvrAosuV5eB0+k5hTXILk4WpDrK8\nxNg3IsP3c0ZnmevNTu8OpN67vVP6qht3hkHr9o0hMTVchMSB/OlPf9rp54477uAXv/gFNltIdi8I\nRx167w6sP96OddtVWP/vJqxPPzgiG0sfSejWFvTqz1DHzvGIAiibDdtVtwAK/dXy8BrYH9TVQEO9\ndwVWNyNHm5uMHurUwKXAmp2HskUYJTyt0VvW98o07VJgJWUkaux4U9/XFKBjVFvTYwRSJSYH7EDq\nogMmre/E001q7iehi0Lqt16EagfWC0+Yvon+2LzO3ABl53l9WE0/DpxtAyIKqZ1Oc9PprYWHC6UU\npGf5jEBqrc25TDymczryiHSs6kqTNng4NhYdwLrtavTydwJ7fk2VcbC83WTmTTIp8A31h2VTb9Bl\nxegP/mMyKcaOM/bkToTCvcHVdDvKTb1x1wUle2pwkSW3g+gtBXV4Ys8RyJqq9ud6IznVOGIDoHG9\nh0MHjHPsb75nZgOgDxT0vD9X1ocamYFSCts5l0DuRFp7E+WurTbOaJfFHJVp0lkJJI3V3QPSpwPp\nqpUNlxKrWwzOXwTSFgF5k71GIHVlhZn/Xj7bpIyA4Ym0bt8UMnPDQZ95eNOnT2fVqvB/6QjCYEKX\nl2D97U9Yf/iFieScfYnZ/swjWL+6Cuu/L0lEcqCyYRU01qPmnNppsxo6DPImGUW3Ix0/CqxuPI/1\nUAepW1tNb8JscwPLmFzjxPW2nYc7ypcywuxT68CbtddVo3wJ6LhJsENjfY9Om9YaigpRozJNQ+rp\n+ehPlwalCutz38UH0V99ApOOMaIn7/3brx16y3qYdIx3lUeAvImQlo7+4I3wX3eqHeYm34cCqxs1\nKtM4Xt7sLTpgUsxd9Z2e17gdnJLeCwZprbH+9TfjMAUq0e9uMu4rSqE1BFOrGyL0h2+CUqjv/KCz\nPU4n7Asi9dulQt01I0HZU8FRFvic8hdBTLAbR9wP2t1D0kcNpEpxOUIDqJWHPrjPpcDq5zY9bZQR\nBQtA1dqbuJmyp2L1Rn3WNU5dU1gZPQaUCsih9dsDEowwWVR02Jx6XeIaU3/lGIAaPxVKDnYrX9Cu\n6KKnbUfH1ygFuRNxDiSBsl4QEgeypKSk08/+/ft54YUXSEnxU6sgCEIn9P49WL+9Eb3+S9TZl2D7\nw1+xfedybL95GNvPf2fS7l7/J3rlR+E2VfCC9fnHJo2xQ2qcGzXlWCgsCLjNQ0f03h29T9vsZ3Rx\nAF+6rsd6VGItLIC2tvYIiM2GmjITvXld78QfyorbJeNdTmnA/SDranpOYXWnXPW0Yl7tMBHYdCP4\nYpt3llnR//rwxXT02y9DVCS2a25F5Z+Ifu/fvtUuiwuhstxn+iqYFXZ11gVGCXdr7yK/IcPdA9Lu\nJ4UVTB1kXa0Z0y64I6ldHUh3xNxz09gb1q6ErV9DdEzAwiZ6z3bTJmCMl5ZnOePNzXiAaax67ec4\nF/8e680X0JvWHlajcr1vtxFW6lhv6nJygxHS8anIbE+B5iYINOLrzwFMCEEE0u7uBTmA6iAP7Uf5\nS1/FFQFLHRFYu6iSgxA3tPN1LCkFZ0Vp8ItD7mtKl8UcFRsHqaPQgSjDlpUYRWUfEValVHh7QRYf\nhPhhZgHYD+46yK5prPqT940y7Rjv2R22a36O/fd/CYmp4SIyFDvp2q4jOjqasWPHcsMNN4Ri94Jw\nxKPra7Ee+wPEDcW28P5OdT5KKZg4Hdv4qViL7kS/+AR60vTuq39C2NC1NbBpDWr+eV6jOWrKDPS/\nn0FvWYeaOz/w/e7cgvWnO2DiNCJuvSeUJgeNdjpBW6jIKN9PKjkEkZH+o0T2VLOy3MOquS5wRV6y\nx7dvnDoLvlwO+3d7nMBA0WXFZsUeTDQxdWRASqy6uQlaWiDevwOp7GloAEepfwe6yJy3pyfmlJlg\nT8Va/i6c+e1ATsW7naVF6C+XGZGM4Ulw0RXor79Cv/oM6tpfdH++KyKuJs/wu191wqnoN57HeudV\nIvw4m32NR+mwpwhkeqZ5Hw7t7+Yw6A2rjHJkUnLnF6WMNDezJb3r26qbm0y7i4yxZpFj6Rvo1tbO\nTe+9va6wwESZvKj7qtghMDLDOHMBYL37KhzYa95zl0OgzvwOtouuDO5ctIaD+1DHn9zZnvjhRgAr\nGCGdkkMmDbNL2xVlTzXvUUUZ9HCDDh1UVr30cVQJdvSWr/3voLrKqCh76TcItM+pioGh26HraozT\n60+B1U1aekDzVrvEjDrW2pKUbK5t9bU9i4R13Jfb0fZ2D5KZbdpX9ER5CdhTfSoqA5CYHLYUVl1y\n0H8phpvMHIiJRe/YjMo/0by26ADs2IS68Ec+I8i+FL0HE32iwvrss8/yv//7v+Tk5IRi94JwRKMt\nJ9bfHoBqB7brF/oUiVA2G7YrbgbLwnrm0fCnlAke9KoV4HSi5pzi/QkZY11NhwOP4uj6WqwnHwCb\ngq1fB3wj2VdYj9+P9cCdfp+jSw5C6ijfKZG45N7TRvUcgdy704xZhyiImjLTRGV6k8ZaXtKp3kZl\njwusr6Q7ktVTCqvrc6t76CfnUWB1tZxQtgjUSWfA1q9pK+p9BEy/8wrYIlBnfsfsNzkNdcb56K9W\neBU/0VvWw4jRvmuQXKioKNTp34ZtG4JrfRKIzcFcwxx+blo74kOJVdfVwK5t3aOPmHOMSBvVewfy\nvy+BoxzbZT820USnM6C0QiM45SfdOzsP9u3qcZx0TRUU7ESdczG2//sXtlvvMWnzX67oRXSp3ETI\nXY3qO9mTOxF2bw14n7rkEKSN6uy0QPt7GGj6ZE2lcfC9OZsJSSZ1vMVPbWZNle/oIx2UYQdKBDIA\nBVY3Ki0dyop6zsooPtitns+zkBKsk+Yog7ihpq9nV3syxkJZcY/CW7rCTw/IjvaFSYXV23h5Q0VG\nQu5EdIcIpF7+LkREor6xoC8tDDuiciMIYUa/8TxsWYe69Mdea2E6olJHoi68wig3fvJ+/xgo9Ij+\ncjlkZHt6YnXFk365JbD0S6011jOPQHUVtpt+A7FxfuvZ+hq9bQOs/wJ2b2tPU/VGyaEea0bAVafm\nr9UCrvTSseM7q1MOS4AxeUG389BtrebGuGPPsexxpg6rpofWG7U17cf2R6Ld3OT2dBNafMAI13SI\nprhXrluDWGDoiC4vQX/+EerkM1GJ7dEeddaFkGg3gjod5p1ubYXtG3uMPnr2M+9MGDLURLlChN67\nA+t/LglcgbaizKSUeekv2okEu0nV66KqqjetNRH06d1blgBEpGcF3F6m036LD6Lff90IzuRNNq0M\noMc0Vt3WaqIw/qIcY/JM/V8Pqe968zqjpjstHxU3BDXpGNRx80z9WHmQUTWX3e7z6ETuJJMeHKij\nXXqouwIrQLJLICVQB7K60qgWe4vmuD9Hflro6JpKvw6ksSnNZz+//ka7W8oEEoEcMcpEEf3MEd3S\nbJy+rqnEvWyV4a+djsp0BY4OFvjfSVlxj4tXJCZDteOw+lX2Bt1QbxYd/CmwdkCNmwIH96Hra9HN\nzeZaPGtuz98Zg5xep7Bef/31AT3vL38Z3Dm+wtGHdjrRTY39c6y1n6Pffhl10hnYTj4zoNeoeWeh\n165Ev/QUevKMni/CAwTdUIf+crlxtiIiTAPhlJGoURlwzHH+UyNDbUtLM9bi34PTaerFEpN7fpGv\nfTnKYM921PmX+3/ilJnwxTKjUOejLsKzz2XvwLovUBdfiZo8A3XyWUbI5Ds/8N14uY/QWmO9+oy5\nAaupMkqz3/pu9+c5nVBahDrG+w16J7JyYPWn6Po61ND47vtqbIDiQtTsk7o9pqbMRL/9Crqp0XdK\nWlccZaAtk6ro3s/Y8SaNbu8uOKZ7VMpDnSsC2ZMKa0SEUQ7soSG5PuRSYO0YlUkZAcqGM5BaJm/7\nfOcVI3py5gWdbYqNQ134I/SSB9HvvII6x4hysWsLtDSbiG4AqNghqFPPQb/9Mrqo0HxmDxO9bQO0\nNKO3bQhoTmtHmadWza+tSkF6Znuk182GVWYOe6s3BCLTM2nZvA6tdfeImR+sV/8O0dGoi64wG9LS\nITLK44j5pKzEiAL5U3kck2fm6L6dJt3QFxtXG0cqsz3rS42bjAb0zs1BXTM89Wujx3S3J3ei2efu\nbT1GZ3Rbm3Fe87t/holPMGMUoAOpa6p8C+AkJJkxqq7svEDUkZqq7s5T1/2kjgpr25ROHNpv+oAm\n9awjotLSzfmXHvLdI9Ut1tJ1saKD0mngMx7zvvlqp9NBGVblTfb6FN3cbN6Tnu5dklKM2mtdTc8L\nAO5979qC3rAKdfp3ehY+80VJzwqsHVHjp5io/K6tJtOhoR4176zeHXsQ0esI5I033uj5+eY3v8mQ\nIUO48MIL+clPfsKFF15IfHw8Z599dihtFYQ+QTvKsZa+gfXUQzjvuQXrfy6h9LIzcD74G6wV75n6\ntr44bk0V1jMPQ/Y41KU/Dvh1JpXV1B1bzz7WJ7aFEr13B9aSB7F+cQX6+b8a8QTLQm9ah37jOZMa\n+cfb+231V1tOrCV/hi3rYe92rN/dit65pff7W2v67bmjSL5wR3t6ip7pwr3ol5bA1FmoBaYmTi04\nD2w29Adv9NrOXrN2pUmPu+CHJi1uzWfen1dRCs62wCKQbgd63y7vT9i3y0RUxnqpcxyZYZzBYNKu\n3D0gO95gZuWYFhoF/tMydaAprGCiGD2ksFJ0wERgO6AiIyEpGWcvUih1Wxv6849Rc07rLHri3vfx\np6COOxn9xvPGacOVvhoRAROmBnwcNf9ciIoKXST8gMtRCVSxtKK0x/pHNyo9y0QEXO+dbmtDb15r\nInQ+apIi0jPNtSlYoatdW1D5J3qahauICNNKpCcH0n2T6s+xyXTNUT/p69rpRG9eh5p6bOdzS88y\nSpbBXtsO7jPNz+OGdH9sZIaJnu8JoA6y3OUgjxjV7SFlsxlnxxFg5Ku60mv9I9DuWPqJQFJT2b2Z\ne1fSM6GitN8Wj/2hD+2H9KzAFjJc88evkE6JD3XshCSTNRGs0mlFmW8NhqQUM+/8tfKo6EGB1YXq\nRS9I673X0O+8ivXr6839Wy+il57yigAdSMaOh8hI9I5NJn11VCaMmxL0cQcbvY5ATp7cvrKwZMkS\n7rzzTuz29tSZmTNn8oc//IFzzz338CwUjih0czOUHjIf0JJCaGoyjW1HZcKoDNSQ7tGIPrFDa9i+\nEevj/8L6L80XXYIdRo9BnXYMcUOG0PDZh+hnF6Of+4u5OfvBDf4LvoO14d/PQHMztqt+1qPYQldU\nchrqvEvRLz+F3rHJSEkPMLSjHP3qM6b/YdwQ1Dfmo048A9UhAqCbm9Fff4n+52NY/3sztitvhgXf\n6jubtEa/9BSs/Rx1ydWoyTOxHvsD1qI7URdfjTrtnKCiDwB69WdGPKOnFe7hSZA5Fr1lHbgjQV6w\nnvsrDI3HduXNnhtClZSMOmEe+rMP0Ode2vuV1SDRbW1Y/37W3MzMORWaGtEvPOE9ChXIDbEblwOp\n9+3ymkap3bWJXoRyVKLdrLhXVUCAkTBd5lqB7+BAqphYGJ3Vc11fnWsBqQcRHQCVnOqRb/dqR12N\nqan0ZnfKiN7JuhcfgNYWmDDNu01KwQ9uMCrPTzyA7dcPmTmYO9EItQSIGpaA+sbp6BXvofO/Yfpy\nHgZuB0sH4EBqraGiNPCI6fTZ6E8/wFp4NeqkM808aqj3Wv/oJsIdcSs56D/a19GupgaT0pna2UlS\nGdnozT0sFHkUSv1EIGNiTDS1wMdCCxgHvKEONS2/82ttNhjnvUedX7sKC0yDem/22GyQMyEwIR1P\nCw8f55eUEngKa01le4/BriQax1DXVHqNoum2NvMeDfMfwVKjXOJLxYVBC3SFEq01HNqHmjknsBck\npZhorp8WNB6HKK3LPI2IwJaUjA4ihVU3NRj1XB+LOUop8z3nbwHF24KeN9wR2MoKyPKeOdDJNq1h\n9zbTxsjZZu7fPv3AfJd2WbTzS/FB41gHGLlXUdEwdjz684+hthr1veuCvo8YjIREhdXhcBAb27ku\nITY2FocjeMn6ULB+/XqefvppLMti/vz5nH/++WGxQzDo+lr0mpXor1bAjs0meuAmItJ80N3/jx2P\n7aIr26WR+8KejatNSt7BfTB0GOr081Hzzup0MRuWkkLT2ZfAgT3oTz9Af/w2aA0/utFpWJn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3n/3HhDJ5gvzuoNa7Fltrc1ca5cBqkjqYwZ6nMcnMMTcZYUBXQsXVeDbqinKT6Bli7P1yoSlKK2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\nIBmGYRiGYRiGYRhV/H/LFOIR2vex4AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tm = np.arange(0,p)*dt\n", + "plt.subplot(211)\n", + "plt.plot(tm,rcomp)\n", + "plt.ylabel(\"Q-Comp\")\n", + "plt.subplot(212)\n", + "plt.plot(tm,rnew)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Residual\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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H8q3XAK8X4t9vgnz8Ach3NkLM+2z6xjjCyL6e8C12ikcBdntayw2NBjjGli1C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\n2PoXjY1lyh8xLw8AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rfq = np.zeros(p)\n", + "for j,v in zip(jmax[0:it+1],a[0:it+1]):\n", + " rfq[j] = rfq[j]-v\n", + "#rfq = lowpass(rfq,0.8,5,zerophase = True)\n", + "plt.plot(tm,rfq)\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Q-RF\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README.md b/README.md index bb77044..d5e8fc5 100644 --- a/README.md +++ b/README.md @@ -18,4 +18,21 @@ This has been forked from [Seismo Live](http://seismo-live.org). The source code 1. Simple Cross Correlation: Use the cross-correlation to detect and determine the time shift of two test events with one template event. 2. Enhanced Picker: This has been taken from the [ObsPy Tutorial](https://docs.obspy.org/master/tutorial/code_snippets/xcorr_pick_correction.html). ObsPy is licensed under the [LGPL v3.0](https://www.gnu.de/documents/lgpl-3.0.en.html) -3. Ambient Seismic Noise Analysis from [Seismo Live](http://seismo-live.org). The source code is available at https://github.com/krischer/seismo_live (licensed under a ***CC BY-NC-SA 4.0 License***. © 2015-2019 Lion Krischer). \ No newline at end of file +3. Ambient Seismic Noise Analysis from [Seismo Live](http://seismo-live.org). The source code is available at https://github.com/krischer/seismo_live (licensed under a ***CC BY-NC-SA 4.0 License***. © 2015-2019 Lion Krischer). + +### 04 - FFT, DFT and Applications + +Interpolation and Deciamtion of Time Series using the DFT + +### 05 - Spectrogram + +1. Analysing dispersive signals. How to visualize changes in frequency content over time (moving window analysis, mutiple filter technique, instantaneous frequency, continuous wavelet transform, the uncertainty problem). +2. Filtering time series with non spectral filtering method. + +### 06 - Surface Waves + +Analysing phase and group velocity of recorded earthquake signals at one and two seismological stations. + +### 07 - Receiver Functions + +How to get receiver functions from seismograms. \ No newline at end of file diff --git a/images/karte3.jpg b/images/karte3.jpg new file mode 100644 index 0000000..6bf3a34 Binary files /dev/null and b/images/karte3.jpg differ diff --git a/images/phase_velocity_map.png b/images/phase_velocity_map.png new file mode 100644 index 0000000..f00a7b2 Binary files /dev/null and b/images/phase_velocity_map.png differ diff --git a/images/seismogram.png b/images/seismogram.png new file mode 100644 index 0000000..0f40057 Binary files /dev/null and b/images/seismogram.png differ diff --git a/images/stack-surface-waves.png b/images/stack-surface-waves.png new file mode 100644 index 0000000..0204b9c Binary files /dev/null and b/images/stack-surface-waves.png differ