{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Drift detection and characterization\n", "\n", "This tutorial shows how to implement drift detection, and a limited characterization, on time-stamped data. This data can be from almost any quantum circuit based experiment on one or more qubits, as long as the data is taken using a suitable time-ordering of the experiments, and the data is recorded as a time series. For example, possible experiments include suitably time-ordered GST, RPE, Ramsey or RB experiments.\n", "\n", "This notebook is an introduction to these tools, and it will be either augmented with further notebooks, or updated to be more comprehensive, at a later date." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/site-packages/pyGSTi-0.9.4.4-py3.6.egg/pygsti/tools/matrixtools.py:23: UserWarning: Could not import Cython extension - falling back to slower pure-python routines\n", " _warnings.warn(\"Could not import Cython extension - falling back to slower pure-python routines\")\n" ] } ], "source": [ "from __future__ import print_function\n", "\n", "# Importing the drift module is essential\n", "from pygsti.extras import drift\n", "\n", "# Importing all of pyGSTi is optional, but often useful.\n", "import pygsti" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A quick overview\n", "We now give a quick overview of the drift detection and characterization methods in the `drift` module. Further details are given later in this tutorial. As we demonstrate below, the analysis can be implemented with only two steps:\n", "\n", "1. Import some timestamped data, into a `pyGSTi` dataset.\n", "2. Pass this data to a single analysis function, `drift.do_basic_drift_characterization()`.\n", "\n", "Here we demonstrate this with time series GST data, on the $G_i$, $G_x$, $G_y$ gateset, generated from a simulation (the code required to run these simulations is not currently available in `pyGSTi`). In this simulation the $G_i$ gate has low-frequency drift, the $G_x$ has high-frequency drift, and the $G_y$ gate is drift-free (where \"low\" and \"high\" frequency are with respect to the sample rate). More details on the input data format are given later." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading tutorial_files/timeseries_data.txt: 100%\n" ] } ], "source": [ "ds = pygsti.io.load_tddataset(\"tutorial_files/timeseries_data.txt\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# This takes 5 - 10 minutes, but can be sped up a lot with more user input (see below)\n", "results_gst = drift.do_basic_drift_characterization(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Thats it! **\n", "\n", "Everything has been calculated, and we can now look at the results. \n", "\n", "** Is there any detectable drift? **\n", "\n", "One useful result is printed below: a yes/no outcome for whether or not drift is detected. This is calculated using multiple statistical tests on the data at a specified global confidence level (which defaults to 0.95 when no user-specified value is passed to `drift.do_basic_drift_characterization`). That is, here there is a probability of at most 0.05 that this function will report drift when there is none." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Statistical tests set at a global confidence level of: 0.95\n", "Result: The 'no drift' hypothesis *is* rejected.\n" ] } ], "source": [ "results_gst.any_drift_detect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** We can plot power spectra **\n", "\n", "These spectra *should* be flat - up to statistical flucations, due to finite-sampling noise, around the mean noise level - if there is no drift. There are a range of power spectra that we can plot, but the most useful for an overview of the data is the \"global power spectrum\", obtained from averaging power spectra calculated from the individual data for each of the different gate sequences (again, details on exactly what this is are given later). This is plotted below. If there are peaks above the significance threshold, this power spectra provides statistically significant evidence of drift." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "plot_power_spectrum(...) requires you to install matplotlib", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pyGSTi-0.9.4.4-py3.6.egg/pygsti/extras/drift/objects.py\u001b[0m in \u001b[0;36mplot_power_spectrum\u001b[0;34m(self, sequence, entity, outcome, threshold, figsize, fix_ymax, savepath, loc)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_plt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 147\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults_gst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_power_spectrum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pyGSTi-0.9.4.4-py3.6.egg/pygsti/extras/drift/objects.py\u001b[0m in \u001b[0;36mplot_power_spectrum\u001b[0;34m(self, sequence, entity, outcome, threshold, figsize, fix_ymax, savepath, loc)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_plt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"plot_power_spectrum(...) requires you to install matplotlib\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0m_plt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: plot_power_spectrum(...) requires you to install matplotlib" ] } ], "source": [ "results_gst.plot_power_spectrum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** We can extract the drift frequencies **\n", "\n", "If we have detected drift, we would probably likely like to know the frequencies of the drift. This information can be extracted from the results object as shown below. All frequencies will be in Hz if the timestamps have been provided in seconds (again, details later). Note that these are the frequencies in the drifting outcome probabilities -- they are *not* directly the frequencies of drift in, say, a Hamiltonian parameter. However, they are closely related to those frequencies." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(results_gst.global_drift_frequencies)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Is there drift for a particular sequence? **\n", "\n", "There are individual power spectra for all of the sequences. E.g., if we are interested in whether the $G_xG_i^{128}G_y$ sequence shows signs of drift, we can plot the power spectrum:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The gatestring we are interested\n", "gstr = pygsti.objects.GateString(None,'Gx(Gi)^128Gy') \n", "# We hand the gatestring to the plotting function\n", "results_gst.plot_power_spectrum(sequence=gstr,loc='upper right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Box-plots for GST data **\n", "\n", "If the data is from GST experiments, or anything with a GST-like structure of germs and fudicials, we can create a box-plot which shows the maximum power in the spectrum for each sequence. This maximum power is a reasonable proxy for comparing how \"drifty\" the data from the different sequences appears to be. But note that the maximum power should *not* be used to directly compare the level of drift in two different datasets with different parameters, particularly if the number of timestamps is different - because this maximum power will increase with more data, for a fixed level of drift. More on this at a later date.\n", "\n", "In the plot below we see that the amount of drift appears to be increasing with sequence length, as would be expected with gate drift. Without performing a detailed analysis, by eye it is clear that the $G_i$ gate is the most drifty, that the $G_x$ gate has some drift, and that the data looks consistent with a drift-free $G_y$ gate." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This box constructs some GST objects, needed to create any sort of boxplot with GST data\n", "from pygsti.construction import std1Q_XYI # The gateset used with the GST data we imported\n", "\n", "# This manually specifies the germ and fiducial structure for the imported data.\n", "fiducial_strs = ['{}','Gx','Gy','GxGx','GxGxGx','GyGyGy']\n", "germ_strs = ['Gi','Gx','Gy','GxGy','GxGyGi','GxGiGy','GxGiGi','GyGiGi','GxGxGiGy','GxGyGyGi','GxGxGyGxGyGy']\n", "log2maxL = 9 # log2 of the maximum germ power\n", "\n", "# Below we use the maxlength, germ and fuducial lists to create the GST structures needed for box plots.\n", "fiducials = [pygsti.objects.GateString(None,fs) for fs in fiducial_strs]\n", "germs = [pygsti.objects.GateString(None,gs) for gs in germ_strs]\n", "max_lengths = [2**i for i in range(0,log2maxL)]\n", "gssList = pygsti.construction.make_lsgst_structs(std1Q_XYI.gates, fiducials, fiducials, germs, max_lengths) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a workspace to show the boxplot\n", "w = pygsti.report.Workspace()\n", "w.init_notebook_mode(connected=False, autodisplay=True) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a boxplot of the maximum power in the power spectra for each sequence.\n", "w.ColorBoxPlot('driftpwr', gssList[-1], None, None, driftresults = results_gst)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Constructing estimates of the drifting probabilities **\n", "\n", "The analysis creates estimates of the time-dependent probability, $p(t)$, to obtain a given outcome, for each sequence. Below, we will explain how to access and plot all of these estimates. Here, we demonstrate how to plot the estimated $p(t)$ that the analysis concludes is \"the most drifty\" (the $p(t)$ where the estimated drift has the highest power). Obviously, the more this function oscillates, the more drifty the data for this sequence appears to be." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results_gst.plot_most_drifty_probability(plot_data=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can also be informative to display multiple reconstructions on a single plot. Below we plot the reconstructions for the $G_i$ and $G_x$ germs, both with varying germ power and fixed fudicials." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Pick a list of sequence labels (here gatestrings) or indices to plot the estimated p(t) for\n", "gstrs = [pygsti.objects.GateString(None,'Gx(Gi)^'+str(2**l)+'Gy') for l in range(4,10)]\n", "# Hand this list to the plotting function\n", "results_gst.plot_multi_estimated_probabilities(gstrs,loc='upper right')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Pick a list of sequence labels (here gatestrings) or indices to plot the estimated p(t) for\n", "gstrs = [pygsti.objects.GateString(None,'(Gx)^'+str(2**l)) for l in range(4,10)]\n", "# Hand this list to the plotting function\n", "results_gst.plot_multi_estimated_probabilities(gstrs,loc='upper right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** More details please? **\n", "\n", "Having provided an overview of the tools in the `drift` module, we now give a more detailed introduction to:\n", "1. The types of data that can be analyzed, and the format they must be imported in to use the `drift` module.\n", "2. The analysis methods that are used inside the `drift.do_basic_drift_characterization()`.\n", "3. How to access the various outputs returned by the analysis, and some details on how to interpret them. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input data types and formats\n", "#### What types of data can be analyzed?\n", "\n", " The methods in this notebook can be used on data from any experiment that satisfies the following criteria: \n", "\n", "- The experiments consist of $S \\geq 1$ different circuits, or sequences, each with $M \\geq 2$ possible outcomes with $M$ the same for all $S$ circuits.\n", "\n", "\n", "- Sequence $s$ is repeated, and one of the $M$ outcomes is recorded, $N$ times during each of $T$ different time-intervals, with $N$ and $T$ the same for all sequences $s$. Let $\\tau_{s,1},\\tau_{s,2},\\dots,\\tau_{s,T}$ denote times associated with these time-intervals (e.g., the start time or mid-point of each interval).\n", "\n", "\n", "- The time-gap between consecutive sets of $N$ repeats of a sequence is approximately constant, and approximately independent of sequence. That is $t_{s,gap} \\approx \\tau_{s,t+1} - \\tau_{s,t}$ for all $s,t$ and some constant $t_{s,gap}$ (the time gap is constant for each sequence), and $t_{gap} \\equiv t_{s,gap} \\approx t_{s',gap}$ for all $s,s'$ (the time gap is the same for every sequence).\n", "\n", "#### What type of circuits can the data come from?\n", "\n", "Many characterization routines, including GST, RPE, restricted sets of GST sequences (e.g., Ramsey sequences) or RB, satisfy the above criteria *if* a suitable time-ordering for the repeats of all of the sequences is chosen.\n", "\n", "An example is a full set of GST sequences, with sequence 1 repeated $N=5$ times, then sequence 2 repeated $N=5$ times, etc, with this entire procedure looped through $T = 500$ times. As long as each circuit follows on from the following circuit with a roughly constant time gap, then -- even if the different circuits take varying times to implement -- the time-gaps between consecutive sets of $N$ repeats of each circuit are all the same.\n", "\n", "To obtain best performance, it is preferable to minimize $N$ and maximize $T$ for the same fixed $N \\times T$ (this will provide better detection for high frequency drift).\n", "\n", "\n", "#### What input formats can be used?\n", "\n", "Two data formats are allowed:\n", "\n", "1. Data can be provided to the analysis function in a `pyGSTi` dataset, which can be imported using the `pygsti.io.load_tddataset()` function, demostrated above. The automated input for this currently has limited functionality, and the analysis is much slower when using this form of input. However, this method has the advantage of simplicity: minimal user specification is required when the analysis uses such a dataset.\n", "\n", "2. Data can be provided to the analysis function as an ordinary numpy array. The analysis is faster when using this format. The array can have a range of different dimensions, but the most flexible format is when the array is 4 dimensional. \n", "\n", "Here we will explain the format when the array is 4 dimensional, below we will demonstrate the method with lower dimensional arrays. When the input array is 4 dimensional, the input data format is a numpy array $A$ of dimension $(S \\times E \\times M_{\\rm marg} \\times T)$, where\n", "1. $S$ is the number of sequences,\n", "2. $E$ is the number of \"entities\" (often 1; and more on this below),\n", "3. $M$ is the number of possible measurement outcomes for each circuit,\n", "4. $T$ is the number of timesteps.\n", "\n", "The matrix element $A[s,e,m,t]$ should record the number of counts (a value in $0,1,\\dots,N-1$) for measurement outcome $m$, on entity $e$, for sequence $s$, with the $t^{\\rm th}$ set of repeats for that sequence.\n", "\n", "The aspect of this array that is perhaps least inuitive is the \"entities\" concept. This allows the storing of independent data for the same circuits, timestamps, and the same possible measurement outcomes for more than one \"entity\". For example, if multiple single-qubit experiments are implemented in parallel, and any correlations between measurement outcomes are discarded, the data from each qubit can be stored with different entity indexes. This then allows for two things:\n", "\n", "1. The analysis function independently looks for drift in each entity.\n", "2. The analysis function combines the data from all entities, to see if that has evidence for drift which was not evident in the analysis of each entity independently. For example, weak evidence for drift in each entity can combine to convincing evidence for drift in the device as a whole.\n", "\n", "Later we will discuss further how to use this \"entities\" concept to optimize data analysis, via examples.\n", "\n", "As shown below, the input data is stored in the results object as an $(S \\times E \\times M \\times T)$ array, \n", "which is the same format as can be used for the input. In our GST example there are 3121 sequences ($S = 3121$), a single entity ($E = 1$), two-outcome measurements ($M=2)$, and 500 timesteps ($T=500$)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3121, 1, 2, 500)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.shape(results_gst.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The analysis methods\n", "\n", "#### What is the aim of the methods in this tutorial?\n", "\n", "The analysis presented in this tutorial has two purposes: to detect and characterize drift. To explain this further, we need to define what we mean by drift.\n", "\n", "There is an underlying probability for circuit $s$ to output the measurement outcome $m$ at time $t$. Denote this by $p_{s,m,t}$, and assume these probabilities satisfy $\\sum_m p_{s,m,t} = 1$ for all $s$ and $t$ (i.e., one of the $M$ measurement outcomes is always observed). By definition, there is no drift if $p_{s,m,t} = p_{s,m,t'}$ for all $s,m,t,t'$. Otherwise there is drift. \n", "\n", "In the experiments considered herein, we are essentially taking $N$ samples from a multinomial probability distribution with probability vector $(p_{s,0,t},p_{s,1,t},\\dots)$ for each $s$ and $m$, at a set of equally-spaced times. As such, the data allows us to estimate properties of the vectors:\n", "\n", "$$ \\boldsymbol{p}_{s,m} = (p_{s,m,t_1},p_{s,m,t_2},\\dots,p_{s,m,t_3}).$$\n", "\n", "The aim of our analysis is:\n", "\n", "1. **Drift Detection:** Starting from the assumption that there is no drift, decide whether there is statistically significant evidence in the data that $p_{s,m,t}$ is *not* independent of $t$.\n", "2. **Drift characterization:** Estimate $p_{s,m,t}$ for all sample times $t$.\n", "\n", "The drift detection and characterization works via spectral analysis, by implement a type of discrete Fourier transform on the data and looking for peaks in the power spectrum that are larger than would be expected if there was no drift.\n", "\n", "Specifically, consider a data vector $\\boldsymbol{x}=(x_0,x_1,\\dots,x_{T-1})$, of the number of counts observed for measurement outcome $m$ with sequence $s$ as a function of time $t \\in [0,1,\\dots,T]$. We remove the data mean from this vector, and renormalize it in a particular way, to obtain the $\\boldsymbol{z}$ vector, defined by:\n", "\n", "$$\n", "z_{s,m,t} = \\frac{x_{s,m,t} - \\bar{x}_{s,m}}{\\sqrt{ N\\bar{x}_{s,m}(1- \\bar{x}_{s,m})}},\n", "$$\n", "\n", "We won't fully explain the reasons for this mapping on $\\boldsymbol{x}$ here, except to note that (1) it is not essential, and (2) the motivation is that -- after this mapping -- the statisical behaviour of the random variable from which $\\boldsymbol{z}$ is drawn, when there is no drift, is largely independent of $N$ and the value of the time-independent probabilitiy $p_{s,m}$.\n", "\n", "We then perform a Fourier transform on the data -- specifically, we implement a Type-II orthogonal discrete cosine transform (DCT) -- to obtain Fourier modes $ \\tilde{\\boldsymbol{z}}_{s,m} = F \\boldsymbol{z}_{s,m}$. The precise definition of this transform is not important for obtaining an intuitive understanding of this method, but for completeness it is defined by:\n", "\n", "\\begin{align}\n", "F_{\\omega,t} =\\sqrt{\\frac{2^{1-\\delta_{\\omega,0}}}{N}} \\cos\\left(\\frac{\\omega \\pi}{N}\\left( t + \\frac{1}{2}\\right)\\right), \n", "\\end{align}\n", "with $\\omega,t=0,\\dots,N-1$.\n", "\n", "We then square these Fourier modes, to obtain a power spectrum $\\tilde{\\boldsymbol{z}}_{s,m}^2$ for each measurement outcome and each sequence. It is these power spectra that we analyze to detect and characterize drift. We can look at the individual power spectra for a particular sequence and measurement outcome. Using the central limit theorem and some statistics (which we will not cover here), it can be show that - when there is no drift - each Fourier mode is approximately a zero-mean unit-variance $\\chi^2_1$ random variable. As such, we can calculate the probability that a spike in a power spectrum is due to random statistical flucations, and if this probability is low enough then we can be confident the peak is due to drift. I.e., the underlying $p(t)$ is not constant.\n", "\n", "We can also average these power spectra over measurement outcomes, and/or over sequences (and over entities, which haven't been discused above, and on which this entire analysis is independently performed). When averaging over all of these quantities, we obtain the global power spectrum presented above. The advantage of this averaging is that it supresses noise from random flucations, but does not supress any \"signal\" due to nonconstant $p(t)$ - as long as the same frequencies are present in many of the power spectra that are being averaged.\n", "\n", "Before explaining any more of the analysis methods, we turn to a simple example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1 : Single sequence data\n", "We demonstrate this on single-sequence data, for a single entity, with two-outcome measurements. This is the circumstance under which the analysis function can be given data in a 1D array, and so we will use this method below. We now demonstrate the method with simulated data for a drifting and drift free probability.\n", "\n", "Below we use a drifting probability of\n", "$$ p(t) = 0.5 + 0.2 \\cos(0.1 t).,$$\n", "with integer $t$ in the range $0,1,2,\\dots,T$.\n", "This is similar in form to the drifting probability that would be obtained with certain sorts of Ramsey experiment with drifting $\\sigma_z$ over-rotation angle." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Imports for generating fake data to demonstrate the methods.\n", "from numpy.random import binomial\n", "from numpy.random import multinomial" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Due to input array format, analysis is defaulting to assuming:\n", " - single-sequence data\n", " - single entity data\n", " - two-outcome measurements\n", "\n", "Due to input array format, analysis is defaulting to assuming:\n", " - single-sequence data\n", " - single entity data\n", " - two-outcome measurements\n", "\n" ] } ], "source": [ "N = 5 # Counts per timestep\n", "T = 100 # Number of timesteps\n", "\n", "# The confidence of the statistical tests. Here we set it to 0.999, which means that\n", "# if we detect drift we are 0.999 confident that we haven't incorrectly rejected the\n", "# initial hypothesis of no drift.\n", "confidence = 0.999\n", "\n", "# A drifting probability to obtain the measurement outcome with index 1 (out of [0,1])\n", "def pt_drift(t): return 0.5+0.2*np.cos(0.1*t)\n", "\n", "# A drift-free probability to obtain the measurement outcome with index 1 (out of [0,1])\n", "def pt_nodrift(t): return 0.5\n", "\n", "# If we want the sequence to have a label, we define a list for this (here, a list of length 1).\n", "# The labels can, but need not be, pyGSTi GateString objects.\n", "sequences = [pygsti.objects.GateString(None,'Gx(Gi)^64Gx'),]\n", "\n", "# If we want the outcomes to have labels, we define a list for this.\n", "outcomes = ['0','1']\n", "\n", "# Let's create some fake data by sampling from these p(t) at integer times. Here we have\n", "# created a 1D array, but we could have instead created a 1 x 1 x 1 x T array.\n", "data_1seq_drift = np.array([binomial(N,pt_drift(t)) for t in range(0,T)])\n", "data_1seq_nodrift = np.array([binomial(N,pt_nodrift(t)) for t in range(0,T)])\n", "\n", "# If we want frequencies in Hertz, we need to specify the timestep in seconds. If this isn't\n", "# specified, the frequencies are given in 1/timestep with timestep defaulting to 1.\n", "timestep = 1e-5\n", "\n", "# We hand these 1D arrays to the analysis function, along with the number of counts, and other\n", "# optional information\n", "results_1seq_drift = drift.do_basic_drift_characterization(data_1seq_drift, counts=N, outcomes=outcomes,\n", " confidence=confidence, timestep=timestep, \n", " indices_to_sequences=sequences)\n", "results_1seq_nodrift = drift.do_basic_drift_characterization(data_1seq_nodrift, counts=N, outcomes=outcomes, \n", " confidence=confidence, timestep=timestep, \n", " indices_to_sequences=sequences) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now compare the global power spectrum obtained when there is drift, and when there is not drift. Except in highly unusual cases (1 in 1000) there will be no drift detected when there is indeed no drift. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_1seq_drift.plot_power_spectrum()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\notRi/h8oW9aecXZtSgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_1seq_nodrift.plot_power_spectrum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look to see what the p-value associated with the largest peak in the power spectrum is. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0006544481483725662\n", "0.3749917484310896\n" ] } ], "source": [ "print(results_1seq_drift.global_pvalue)\n", "print(results_1seq_nodrift.global_pvalue)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analysis function performs:\n", "1. an analysis on a per-sequence, per-entity, per-outcome basis (properties starting with `pspepo`), an analysis on a per-sequence, per-entity, outcome-averaged basis (properties starting with `pspe`), \n", "2. an analysis on a per-sequence, entity-averaged, outcome-averaged basis (properties starting with `ps`), \n", "3. an analysis on a sequence-averaged, per-entity, outcome-averaged basis (properties starting with `pe`),\n", "4. a global analysis on a sequence-averaged, entity-averaged, outcome-averaged basis (properties starting with `global`)\n", "\n", "That is, it inspects all the power spectra, after the relevant averaging has been performed, for evidence of drift (e.g., for case 1 there are $S \\times E \\times M \\times T$ spectra).\n", "\n", "In the example here there is a single sequence, a single entity, and two-outcome measurements. Hence, in this case all of the analyzes are exactly equivalent, as is clear by noting that all of the power spectra are the same:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5.04870979e-30 1.76948267e+00 1.04190088e+00 2.03213493e+01]\n", "[5.04870979e-30 1.76948267e+00 1.04190088e+00 2.03213493e+01]\n", "[5.04870979e-30 1.76948267e+00 1.04190088e+00 2.03213493e+01]\n", "[5.04870979e-30 1.76948267e+00 1.04190088e+00 2.03213493e+01]\n", "[8.07793567e-30 1.76948267e+00 1.04190088e+00 2.03213493e+01]\n", "[2.01948392e-30 1.76948267e+00 1.04190088e+00 2.03213493e+01]\n" ] } ], "source": [ "# The power spectrum obtained after averaging over everthing\n", "print(results_1seq_drift.global_power_spectrum[:4])\n", "# The power spectrum obtained after averaging over everthing except sequence label\n", "print(results_1seq_drift.ps_power_spectrum[0,:4])\n", "# The power spectrum obtained after averaging over everthing except entity label\n", "print(results_1seq_drift.pe_power_spectrum[0,:4])\n", "# The power spectrum obtained after averaging over everthing except sequene and entity label\n", "print(results_1seq_drift.pspe_power_spectrum[0,0,:4])\n", "# The two power spectra obtained after averaging over nothing\n", "print(results_1seq_drift.pspepo_power_spectrum[0,0,0,:4])\n", "print(results_1seq_drift.pspepo_power_spectrum[0,0,1,:4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see above, the power spectra for the two different measurement outcomes are identical. This is because the Fourier modes for all of the measurement outcomes must sum to zero. Hence, there are only $M-1$ independent power spectra, which here is 1.\n", "\n", "As already demonstrated above, the analysis function also creates an estimate for the drift probability for each measurement outcome (and for each sequence, and each entity, but here there is only one of each of these). This estimate is created in the following way:\n", "\n", "1. Inspect the relevant power spectrum (the power spectrum for the sequence, entity, and measurement outcome under consideration).\n", "2. Take the modes obtained from the data, and set all modes with power below the \"significance threshold\" to zero. This significance threshold is generally adjusted for the number of sequences and entities considered, which is not relevant in this case but is discussed below.\n", "3. Keep all modes with power above the significance threshold and invert the Fourier transform.\n", "4. This is the estimate of $p(t)$, up to some final adjustments to guarantee that $p(t)$ is within $[0,1]$.\n", "\n", "Because we have created our data from a known underlying $p(t)$ we can compare our estimate of $p(t)$ with the true $p(t)$. We do this below." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4AAAADpCAYAAAB1J1lYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt198XRASVhQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Lets create an array of the true probability. This needs to be\n", "# of dimension S x E x M x T\n", "parray_1seq = np.zeros((1,1,2,T),float)\n", "parray_1seq[0,0,0,:] = np.array([pt_drift(t) for t in range(0,T)])\n", "parray_1seq[0,0,1,:] = 1 - parray_1seq[0,0,0,:]\n", "\n", "# The measurement outcome index we want to look at (here the esimated p(t) \n", "# for one index is just 1 - the p(t) for the other index, because we are\n", "# looking at a two-outcome measurement).\n", "outcome = 1\n", "\n", "# If we hand the parray to the plotting function, it will also plot\n", "# the true probability alongside our estimate from the data\n", "results_1seq_drift.plot_estimated_probability(sequence=0,outcome=outcome,parray=parray_1seq,plot_data=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2 : Single-sequence multi-qubit data\n", "Single sequence data with two measurement outcomes is (a) often not will be obtained in experiments, and (b) is not sufficient to demonstrate all of the methods contained within the analysis function, or to understand all of the ouput. Hence, we now consider an example with a single-sequence and 4 measurement outcomes, which could represent a two-qubit single-sequence experiment.\n", "\n", "Let us consider the case of 4 possible measurement outcomes:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We only explicitly name them for labelling purposes.\n", "outcomes = ['00','01','10','11']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This could correspond to computational basis measurements of two qubits. Now let us assume that the drifting $p(t)$ for obtaining each measurement outcome factorizes into probabilities for outcomes 0 and 1 for each qubit (e.g., because it is parallel single-qubit experiments and there is no crosstalk). Specifically, take the first qubit to have drifting probability for outcome 0 of\n", "$$ p_{0}(t) = 0.5+0.05 \\cos(0.08t),$$\n", "and the second qubit to have drifting probability for outcome 0 of\n", "$$ p_{0}(t) = 0.5+0.05 \\cos(0.2t),$$\n", "with $t \\in [0,1,\\dots, T]$.\n", "\n", "Let's create some data from these drifting probabilities" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "N = 10 # Counts per timestep\n", "T = 1000 # Number of timesteps\n", "\n", "# The drifting probabilities for the 4 outcomes\n", "def pt00(t): return (0.5+0.07*np.cos(0.08*t))*(0.5+0.08*np.cos(0.2*t))\n", "def pt01(t): return (0.5+0.07*np.cos(0.08*t))*(0.5-0.08*np.cos(0.2*t))\n", "def pt10(t): return (0.5-0.07*np.cos(0.08*t))*(0.5+0.08*np.cos(0.2*t))\n", "def pt11(t): return (0.5-0.07*np.cos(0.08*t))*(0.5-0.08*np.cos(0.2*t))\n", "\n", "# Because of the type of input (>2 measurement outcomes), we must record the\n", "# data in a 4D array (even though some of the dimensions are trivial)\n", "data_multiqubit = np.zeros((1,1,4,T),float)\n", "\n", "# Generate data from these p(t)\n", "for t in range(0,T):\n", " data_multiqubit[0,0,:,t] = multinomial(N,[pt00(t),pt01(t),pt10(t),pt11(t)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example 2.1 : A simple analysis of the full dataset\n", "The simplest way to analyze this data is just to hand it to the drift characterization method, as before (note that now we don't need to specify the counts number, as the input is a 4D array). We do this below." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results_multiqubit_full = drift.do_basic_drift_characterization(data_multiqubit,outcomes=outcomes,confidence=0.99)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By looking at the global power spectrum, we can clearly see that there is drift." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKYF8hWCVbwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_multiqubit_full.plot_power_spectrum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As always, we can also return the frequencies, found from inspecting the global power spectrum. As we have not specified a timestep, these are integers between $1$ and $T-1$." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[25 26 64]\n" ] } ], "source": [ "print(results_multiqubit_full.global_drift_frequencies)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because there are multiple measurement outcomes, we can look at the power spectra for each of these, and also we can look at the drift frequencies found from just analyzing this spectrum. One example of such a power spectrum is given below" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT5IkSZIGzNAnSZIkSQNm6JMkSZKkAfsvA9LnozGNiywAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "outcome = 0 # the outcome index associated with the '00' outcome\n", "results_multiqubit_full.plot_power_spectrum(sequence=0,entity=0,outcome=outcome)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The drift frequencies found via an independent analysis of each of these spectra. Generally, these need not contain the same frequencies as the global analysis. In this example, because all of the frequencies appear in all of the spectra, the global analysis is more sensitive (reduced noise without reducing signal strength). " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "[]\n", "[26 64]\n", "[64]\n" ] } ], "source": [ "print(results_multiqubit_full.pspepo_drift_frequencies[0,0,0])\n", "print(results_multiqubit_full.pspepo_drift_frequencies[0,0,1])\n", "print(results_multiqubit_full.pspepo_drift_frequencies[0,0,2])\n", "print(results_multiqubit_full.pspepo_drift_frequencies[0,0,3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As always, we can also look at the reconstruction for a given drifting probability. An example is shown below, where again we construct the true underlying probability with which to compare it." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4AAAADpCAYAAAB1J1lYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIAmBCAAAEATAhAAAKAJAQgAANDECcisxX8rN1VfAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creates an array of the true probability.\n", "parray_multiqubit_full = np.zeros((1,1,4,T),float)\n", "parray_multiqubit_full[0,0,0,:] = np.array([pt00(t) for t in range(0,T)])\n", "parray_multiqubit_full[0,0,1,:] = np.array([pt01(t) for t in range(0,T)])\n", "parray_multiqubit_full[0,0,2,:] = np.array([pt10(t) for t in range(0,T)])\n", "parray_multiqubit_full[0,0,3,:] = np.array([pt11(t) for t in range(0,T)])\n", "\n", "results_multiqubit_full.plot_estimated_probability(sequence=0,outcome=1, plot_data=True,\n", " parray=parray_multiqubit_full)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example 2.2 : Marginalizing over qubits\n", "\n", "The above analysis looks for drift in the $2^2$ measurements outcomes of a two-qubit circuit. This would be problematic for many qubits, as for $Q$ qubits there is $2^Q$ possible measurement outcomes, and the probability of any one outcome being observed will - for many circuits - converge to zero as $Q$ increases. Then, even for very large $T$, there could be, e.g., at most 2 counts for any one measurement outcome, and the power spectra obtained will be useless.\n", "\n", "However, it is likely in many circumstances that any drift will show up in the marginalized probabilities for obtaining 0 or 1 of each qubit. E.g., in the example above this is precisely what is drifting. We can implement an analysis like this by specifying that the analysis function marginalizes.\n", "\n", "To do this in an automated way, we specify ` marginalize = 'std'`. This then assumes that the data is such that the 0th indexed outcomes is the bitstring 0...0000, the 1st indexed outcome is the bitstring 0...0001, etc. It then associates the first qubit with entity 0 (where first qubit has its measurement outcome recorded as the first bit in the string), the second qubit with entity 1 and so on.\n", "\n", "Note that, when using this method, the input data array grows exponentially in the number of qubits. As such, pre-marginalizing will be preferable for a large number of qubits, in which case the data for different qubits should be stored with different \"entity\" indices.\n", "\n", "Below we demonstrate this method on the same data as analyzed above" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results_multiqubit_marg = drift.do_basic_drift_characterization(data_multiqubit, outcomes=outcomes, \n", " marginalize = 'std', confidence=0.99)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the returned data shape, the data has been split into separate sets for 2 different entities, each have two possible measurement outcomes." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 2, 2, 1000)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(results_multiqubit_marg.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As always, we can plot the global power spectrum (averaged over everything, including entity, i.e., qubit)." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_multiqubit_marg.plot_power_spectrum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, now we can look at the power spectrum for each individual qubit. This then shows us that each qubit has a different drift frequency. Something that was not obvious from the analysis implemented without marginalization. Moreover, these spectra show higher peaks (they are approximately twice as high), because -- earlier -- when we constructed the global power spectrum we were averaging pure noise with \"signal\" at both drift frequencies." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noiiK0oH5/55hyLQPFDCUAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_multiqubit_marg.plot_power_spectrum(sequence=0,entity=1)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xl4VNX5wPHvS0CwCK6ASKiAsmWZ\nTBISDUgIuwqlgKAgKIiKCxQtFYEqi1WrLfiDYrFIrURRAQF3VFBKDIvIZkQMyBoERAjIFkggJO/v\njzuZTpLJakggvp/nmYeZe889973n3oR5c849V1QVY4wxxhhjjDGVU5WKDsAYY4wxxhhjzLljSZ8x\nxhhjjDHGVGKW9BljjDHGGGNMJWZJnzHGGGOMMcZUYpb0GWOMMcYYY0wlZkmfMcYYY4wxxlRilvQZ\nY4ypNETkdhH5TkROi4iKyGUVHZP59RKRFBGJr+g4jDHGkj5jzHlFRAZ7vqznvLJE5CcRmSsizSo6\nvl8DEXlURO6u6DhKSkSuB94EfgIeAu4CTlZoUOYXEZG7RGSjiGSIyG4ReUpEqlV0XL5EJE5EJopI\n7WKUvcxTNvYcxXLet5cxpmJUregAjDGmAE8DW4HqQCRwL9BJREJVdX+FRlb5PQpsB16v6EBKKA7n\n/7U/qWpSBcdifiERuRd4BfgYeBEIA8YB1wD3V2BoecUBE3BiPZ5nXXMg2+fzZZ6yZ4HEsgziAmov\nY0wFsKTPGHO+WqKqKzzv/yMi3wNTgcHAcxUWVTGJSE1VrfS9TCJyMXBaVbOLLHzu1fX8e7SsKvy1\nnMfzjYjUAJ4HEoDuqqqe5UeBP4vINFX9tgJDLBZVPV0e+6ks7WWMOXdseKcx5kLxueffxjkLROQK\nEXlJRH703MO1RUQeE5EqPmXmicgu34pE5EXP0NEnfJZVFZETIvIPn2UiIg+JyDee4VKHPcNMr81T\nX4KIbBeREBH5TERO4Awz9EtEAkRkrCfeUyJyVES+FpGHfMrkDHPtKCJTROSAiJwUkY89wxjz1nmd\niLwlIqmettgkIvn+ui8i1URkjOe+twxP+c9EpK1nvQLXAh19htgm5Impk4j8n4j8iDN8srbPukZ5\n9tfIs3ywz7J4ETkrIleLyHwROS4iB0XkaU+b1/Oct6MickREXvA9pwW0aQrwrOfjLs8+433W/05E\nVvu09/si0jJPHRM924WKyKsicgjYW8R+bxORr0TkmOf8bBeRf+UpU6zryFP2HhHZ6in3jSfueM/x\nFdimvu0gee4hE5FaIvJ3EdklImdE5AcRmSROwu5bTkXkFRG5xXM9ZniO504/+yn0OvIp19en3Y+L\nyCIRCS2sTT3aA1cB03MSGI+XAAFuL0YdfolIhIh84Lm20kVknYj0zFMm53puLyLPiTPEPN1zjL6/\ng+Jxeu4A9vj8zDTyrPeeDxGJA3J+Fz3tU3aiiDzoeR/tJ947cn7uCjmsc9ZexpjKwXr6jDEXipxE\n5xCAiFQH/guEAC8DycAtwCSgETDcUz4RuF1EGqrqHs+ydjhDrmL5X6IQAVxC7iFXLwIP4iRw/8Lp\nSfoDsFJE3Kp6yKdsbeAz4ANgPpBeyLGM97xeBV4ALgaCgJs8+/H1As5QsOeAOsAjQIKIuFT1Z09b\nNAO+BH4GpgBHPG0xU0SuVNXnPeWqAO8Bt3rifBmoBrT2tMVynPvgpgAHcHoO8Lz3NRU4AfwN+A1w\nppBjLYgAnwIbgNHA74AncYbH3QWsA8Z6lo8EvgdmFlLfo8AAoA/wR5zrZIfnuPvjnMNvPfuojXMe\nV4lIlKpuz1PXHJxkbwLONeH/AEQ64pzrBOAJIBNogtP2vop1HYnIIJxrYr2nTeoBs4E9lJI4PUD/\nBZritN92wIXTXiEicmueJCEKp81nAP8B7gNmi8jXqrrZU2dxriNE5DGcn8d3PcdxCc69litFpJWq\nbi0k9AjPv2t8F6rqjyKy12d9iXiS0iU4vy+eBTJwEqJ3ReROVZ2TZ5NJnjJ/xUmqHsM5j60961/G\nuZ56ASNwfvYAUv3sfjPOtTkFWAC871m+EfjBs/wu8hyzZ9lenPNYkHPSXsaYSkRV7WUve9nrvHnh\nDN9UoBvOl6xrcL6EpgBZQISn3HBPuQd8thXgbc/yYM+yUM/ngZ7Pl+MkfHNxEpeqnuWPecrV8XyO\n8Xy+P098wThJzrM+yxI8ZUcW8xi/BhYVsx22ABf7LO/sWf6cz7LFOElRzTx1vIXTE3ep5/Pdnm0n\n+tmf+LxPAT4vJKb1QLUC1jXKs7yRZ/lgn2XxnmXP+CyrivPFNhv4i5/lq4vRrk/mjQEnGdkPbAMu\n8Vnu8lxPb/ssm+jZ/n3f9ihkf1OAY0BAIWWKdR15jvMnnMTA93x38WyfUlib5jl38T6fx+IkLaF5\nyg311NHZZ5ni/IEhxGdZPeA0MMlnWZHXEdAQJwl+Ns/6ejh/nHiziLb9p2cf1fysWwN8XZyftbyx\nedo30feceZavwEmuc+LPuZ5X5in7KD6/X/JcN4HFOB855+5JP2Xn4iSL1XyW1fW04/Pl3V72spe9\nKtfLhncaY85XH+F8AdqH05tQA7hLVTd41nfH+av6qzkbqKri/GUenKQRYBPOl8yc2fLa4iQWf8Xp\necj5C3gssEVVc/5CfwdOb92HInJVzgun1+t7oEOeeLNxekeK4ygQnHd4YQFeVlVvr6GqfobTS9Ed\nQEQux0kE5wMX54n1E5yeuBs9m/fF6Ul7njw8bVdc/1bVzBKUL8jLPvs/i9O7J8C//SxvUsp9RAJX\nA/9S1TSfejfi9DTeIvmHjv6rmO1xFKjpqUMKKFPc6ygKJyGamed85/RKldYdOL3A+/PsP2e4dN7r\nOFFVN/ns/wDOHx58278419FtOInsnDz7zfLEk3e/eV3sqc7fdZbhWV9SYUALnJ66y31iuhJn8pNA\nIO8MwS+rapbP5y88/5b2eixMPM4funx7ivvjtGNRkyqdi/YyxlQiNrzTGHO++iNOwpaFk/xtzvPl\nqxGw3c+XnJwvyI3B+RYkIitwhnSCk9x9raobPcOe2onIWpyhlfN96mmG80WpoJlCd+b5/JOqnirm\nsT2J05uULCJbcb6Az1fVBD9lvy9gWRfP+6Y4idITnpc/OROcXI/TZhnFjLMgO37h9uAkyXnvlzta\nyPLLS7mfRp5/t/hZl4wzRLEOuYewFvf4XsJJbj4EDojIMpw/UCzwuS6Lex3l3N9X0Pku7fC8nP37\nG24I/7s2cuz2U+YIcIXP5+JcRznJU0GThxQ18U86zu2Q1fz8jNeg8OHTRcU0g4L/QFOX3Ocgb3vk\nDN+8grL3Gc4fue7CuY7wvF+nqkUl/ueivYwxlYglfcaY89U6/d/snb9UItBDROrhJH1f+CyPxRke\neTm57+ergpNs9C2gzrxfoor9pUpVV4rIdTi9kZ2AnsDDIvKyqj5Y3Hp84gSYhpN8+PNdCessir9j\nLahnLKCA5VpAb1pBywvqSTsXinUuVTVVRCJweq1uxulx7QeMEpGbPH8EKOl1VKxdF7Iub3tXwbne\nnymg/I95Pmf5LVXy9s+5LrvjDA8tqZwk+RryJ171ce6DK6mcmP4MrC2gzKY8n8uqPYqkqlkiMhv4\no4hchnPskTj3ChblXLSXMaYSsaTPGHOhSgGiRKSqZwhgjpwhk74zduYkc92AcP73BTgRZ4haXJ5y\n4Ex40QVYq6rHyi5sh6fOt4C3RKQqztCuB0TkOVX1/dLWHGfoGXmW5RxfTq9Ulqp+TuG24/Rs1iii\nl6YkQz1z5PSAXJZneaNS1FWWUjz/tiB/O7YE0ii4F6xInmtvieeFODOwvoST5L1G8a+jnHNe0Pn2\n5betPZMb1c9TdjtQuxjXRkkU5zrKmRxnj2cobUnlDOOOwieJEZFrcIZhxpeizpyYTpZxe5Tk56Wo\nsvHAGJzJZRrj3M+Xd3IZf85FexljKhG7p88Yc6H6EGeI1T15lj/m+fcjn2UbcL7cP47ze2+5Z/kX\nOF+cH8aZKMN3lsS5OH/N/4u/nXvuBSoVEbnS97MnccjpYcibNA31nVpfRDrjzPS5yLNtKrAUuFf8\nPwKgjs/H+UAtnC+Vecv59lyc9BNHUXK+ULfPs3xYCespa+twJkh5UERq5iwUkRCc3rmPtZTPGMx7\nHj2+9vyb037FvY7WAQfJf7674JxvL1U9jpOo5m3rB8nf0zcXCBeRXn72XUNEavmLqwjFuY4W4kwK\n85SfeybzXpf+LAMOA8PyXJsP+8RQUhtwJvT5k6cnraQxFSTnOY7F+ZkptKyqfg+sBgbhzEb7ieae\nJbgg56K9jDGViPX0GWMuVK8A9wP/EhEXzqx8t+AMJ5vuew+MZ9jUKpwel42qesSzfIuIHMTpSck1\nUYKqrhCRacAIcZ4r9gnObJ+Ngd/jfJmeWMrYN3vuM1yLcy9Zc5zZSDeR/x6o08Byz7CvujiPbNjP\n/yasAeeL3UrgGxF5BdiKkxC7cYaO1vCUewO4E5ggIm6cZDEAZ/r5JJzJbcCZnXOgOM8x3AEcVNXC\npotHVZNFZCXwrIhc4TmuHpT+XrwyoapnRWQkzuQdK0XkNf73yIYTFHwfZHG8IiJ1cdrxB5xJOB7E\n+WL/gWf/xbqOVDVTRMbiPCYhUUTewDnfw3Cui7zJ2cvAkyIyCydJaIUzzDRvgjAZ52digafONTgz\nmjbD6U3qgzP7bEkUeR2p6i4ReRz4P2CNiLzjie23OMn2JpwZMv1S1QxPe8zEmQTnPZzr+WHg1by9\nh+I8X/ILVY0rpM5sEbkHzyMbRORVnF6xesANOMn1dSVsC3B+XgD+KiLzcXrnPlTVk3kLquohEfkB\nuFNEduD02m7ynTwHmMX/Jjl6tDgBlLS9jDG/QhU9fai97GUve/m++N9U6TcVo+wVOM89248z/f1W\nYBRQxU/ZJzz1vphn+XzP8nsL2MfdOF+q0zyvzcB0IMinTALOxBbFPcaxwCqcL8EZOInVP4C6ftqh\nI85z8Q4Cp3BmnGzqp87f4iTCez1t8SPOl/FhecpVB8bhTFZxGqfHaDHQxqdMQ89+TnhiSCjOucGZ\njOQTT5yHcJ5PF4T/Rzac9bN9iZb7KZfvkQ0+63oAX+HcQ3cMZyKdoDxlJlLA1PsF7O82nKGY+z1t\nuQ/n+WthpbmOPOXuxemNOo1zH9bvPMefkqdcDZxr/2ecJPMjnKG0Kfg8IsBT9jeeY9viqfcwTs/i\nROAKn3IKvOIn9oSca6Ak15GnXDec58sd91wX2z3Hc2Mx23gQToJ4GueRCn8h/+NCLvHEPqeYdQbj\nJNsHcX5W9njar5+fn7+b8mzbKO/17Fn+DM7PXJbvNVjA+YjD6XU8jZ9HXwCXetrqMHBRcX+vFLe9\n7GUve/06XznPozHGGHMeEZHBOH/xb6tlN6GNuQCJSDwQp6qNKjiU85KI3IqTtIWpakGzhV4wROQS\nnJ7y11T14aLKG2NMcdg9fcYYY4y5kLUH5laGhM9jIE7vbHwFx2GMqUTsnj5jjDHGXLBUdVRFx1AW\nRKQDziyzTwHLVXVNBYdkjKlELOkzxhhjjKl443Emw1kLDKngWIwxlYzd02eMMcYYY4wxlZjd02eM\nMcYYY4wxldgFO7zzqquu0kaNGlV0GMYYY4wxxhhTIdavX39IVesUVe6CTfoaNWrEunXrKjoMY4wx\nxhhjjKkQIrK7OOVseKcxxhhjjDHGVGKW9BljjDHGGGNMJVbuwztFJAU4AWQBZ1W1lYhcAcwDGgEp\nwO2qeqS8YzPGGGOMMcaYyqai7ulrr6qHfD6PAZaq6vMiMsbzeXR5BvTll5CQAHFxEBNTnns2xhhj\nzIUmMzOTvXv3kpGRUdGhGGN+BWrUqEFgYCDVqlUr1fbny0QuvwfiPO9fAxIox6Tvyy+hXTvIyoLq\n1WHpUkv8jDHGGFOwvXv3UqtWLRo1aoSIVHQ4xphKTFU5fPgwe/fupXHjxqWqoyLu6VNgiYisF5Gh\nnmX1VHW/5/1PQD1/G4rIUBFZJyLrUlNTyyyghATIzITsbDhzxvlsjDHGGFOQjIwMrrzySkv4jDHn\nnIhw5ZVX/qKRBRXR03eTqu4TkbrAZyKyxXelqqqIqL8NVXUmMBOgVatWfsuURlzc/95fdFHuz8YY\nY4wx/ljCZ4wpL7/090259/Sp6j7PvweBd4Fo4ICI1Afw/HuwPGO68cb/vbehncYYY4y5EAQEBOB2\nuwkODiYsLIwXXniB7OzsQrdJSUnhrbfeKqcILxzWLqayK9ekT0RqikitnPdAF2AT8AEwyFNsEPB+\necalPn2GlvAZY4wx5kJw8cUXk5SUxHfffcdnn33GJ598wlNPPVXoNhdScnP27Nly21dh7VKecRhz\nrpR3T189YIWIfAOsARap6qfA80BnEdkGdPJ8LjdF/FHMGGOMMeYX+/JLeO4559+yVrduXWbOnMk/\n//lPVJWUlBTatm1LREQEERERrFq1CoAxY8awfPly3G43U6ZMKbCcr5SUFFq0aMGAAQNo2bIlffr0\n4dSpUwAsXbqU8PBwQkNDGTJkCKdPn2bt2rX07t0bgPfff5+LL76YM2fOkJGRQZMmTQDYsWMHN998\nM5GRkbRt25YtW5y7fQYPHsyDDz7IDTfcwOOPP54rju+++47o6Gjcbjcul4tt27YVGtv69etp164d\nkZGRdO3alf37nekjtm/fTqdOnQgLCyMiIoIdO3bka5f4+Hh69OhBhw4d6NixIwkJCXTv3t0by/Dh\nw4mPjwegUaNGjB07FrfbTatWrdiwYQNdu3bluuuuY8aMGWV1io35ZVT1gnxFRkZqWTl9WtXp7yuz\nKo0xxhhTiSUnJ3vfP/KIart2hb/cbtUqVZzvGlWqOJ8LK//II0XHULNmzXzLLr30Uv3pp5/05MmT\nmp6erqqqW7du1ZzvTcuWLdNu3bp5yxdUzteuXbsU0BUrVqiq6j333KOTJk3S9PR0DQwM1O+//15V\nVe+66y6dMmWKZmZmauPGjVVV9U9/+pO2atVKV6xYoQkJCdqvXz9VVe3QoYNu3bpVVVVXr16t7du3\nV1XVQYMGabdu3fTs2bP54hg+fLi+8cYbqqp6+vRpPXXqVIGxnTlzRmNiYvTgwYOqqjp37ly95557\nVFU1Ojpa33nnHVVVTU9P15MnT+Zrl1mzZmmDBg308OHDfttt2LBhOmvWLFVVvfbaa/Wll15SVdVH\nH31UQ0ND9fjx43rw4EGtW7duvuMwprR8f+/kANZpMXKn8+WRDRXKevqMMcYYcy4dO/a/7xvZ2c7n\nSy89d/vLzMxk+PDhJCUlERAQwNatW39RuYYNG9KmTRsABg4cyLRp0+jcuTONGzemWbNmAAwaNIjp\n06fz6KOPct1117F582bWrFnDyJEjSUxMJCsri7Zt25KWlsaqVavo27evt/7Tp0973/ft25eAgIB8\nMcTExPDss8+yd+9eevfuTdOmTQuM7eabb2bTpk107twZgKysLOrXr8+JEyfYt28fvXr1ApxnnxWk\nc+fOXHHFFQWu99WjRw8AQkNDSUtLo1atWtSqVYvq1atz9OhRLrvssmLVY8y5YkkflvQZY4wxpvSm\nTi26zJdfQseOzqOhLroI3nyz7OcR2LlzJwEBAdStW5ennnqKevXq8c0335CdnV1gcjNlypRilcs7\nc2BRMwnGxsbyySefUK1aNTp16sTgwYPJyspi0qRJZGdnc9lll5GUlOR325o1a/pdfuedd3LDDTew\naNEibr31Vl5++WWaNGniNzZVJTg4mC/zjKU9ceJEoXEXFEfVqlVzTZKTd+r86tWrA1ClShXv+5zP\ndk+gOR9UxHP6zjuW9BljjDHmXIqJcWYIf/rpczNTeGpqKg8++CDDhw9HRDh27Bj169enSpUqzJ49\nm6ysLABq1aqVK/EpqFxeP/zwgzeBeuutt7jpppto3rw5KSkpbN++HYDZs2fTrl07ANq2bcvUqVOJ\niYmhTp06HD58mO+//56QkBBq165N48aNmT9/PuDcavTNN98UeYw7d+6kSZMmjBgxgt///vds3Lix\n0NhSU1O9yzMzM/nuu++oVasWgYGBvPfee4DTw3jq1Kl87ZLXtddeS3JyMqdPn+bo0aMsXbq0yHiN\nOZ9Y0oclfcYYY4w592JiYOzYskv40tPTvY9s6NSpE126dGHChAkAPPzww7z22muEhYWxZcsWb6+V\ny+UiICCAsLAwpkyZUmC5vJo3b8706dNp2bIlR44c4aGHHqJGjRrMmjWLvn37EhoaSpUqVXjwwQcB\nuOGGGzhw4ACxsbHe/YaGhnp75d58803+85//EBYWRnBwMO+/X/TE7W+//TYhISG43W42bdrE3Xff\nXWBsF110EQsWLGD06NGEhYXhdru9k9TMnj2badOm4XK5aN26NT/99FO+dsmrYcOG3H777YSEhHD7\n7bcTHh5eklNlTIUT1TJ7xnm5atWqla5bt65M6jp6FC6/3Hl/gTaHMcYYY8rR5s2badmyZUWHUS5S\nUlLo3r07mzZtquhQ8jmfYzOmrPn7vSMi61W1VVHbWk8f1tNnjDHGGGOMqbws6cOSPmOMMcaYgjRq\n1Oi87Uk7n2Mz5nxiSR+W9BljjDHGGGMqL0v6sKTPGGOMMcYYU3lZ0oclfcYYY4wxxpjKy5I+LOkz\nxhhjjDHGVF6W9GFJnzHGGGMuPCLCwIEDvZ/Pnj1LnTp16N69ewVGld+6desYMWLEL66nUaNGHDp0\nqAwiOrd1GnM+qlrRAZwPLOkzxhhjzIWmZs2abNq0ifT0dC6++GI+++wzGjRoUNFh5dOqVStatSry\nMWLGmHPIevqwpM8YY4wxF6Zbb72VRYsWATBnzhz69+/vXXfy5EmGDBlCdHQ04eHhvP/++4DzQPO2\nbdsSERFBREQEq1atAiAhIYG4uDj69OlDixYtGDBgAKqab59xcXGMHj2a6OhomjVrxvLlywHIyMjg\nnnvuITQ0lPDwcJYtW+atN6f38YsvvsDtduN2uwkPD+fEiRMATJo0iaioKFwuFxMmTCjyuN944w2i\no6Nxu9088MADZGVlMWPGDEaNGuUtEx8fz/Dhwwssb8yviSV9WNJnjDHGmF8m7uuv871e2rcPgFNZ\nWX7Xx+/fD8ChM2fyrSuufv36MXfuXDIyMti4cSM33HCDd92zzz5Lhw4dWLNmDcuWLWPUqFGcPHmS\nunXr8tlnn7FhwwbmzZuXa+jl119/zdSpU0lOTmbnzp2sXLnS737Pnj3LmjVrmDp1Kk899RQA06dP\nR0T49ttvmTNnDoMGDSIjIyPXdpMnT2b69OkkJSWxfPlyLr74YpYsWcK2bdtYs2YNSUlJrF+/nsTE\nxAKPefPmzcybN4+VK1eSlJREQEAAb775Jrfddhvvvvuut9y8efPo169fgeWN+TWx4Z1Y0meMMcaY\nC5PL5SIlJYU5c+Zw66235lq3ZMkSPvjgAyZPngw4PXE//PAD11xzDcOHD/cmQFu3bvVuEx0dTWBg\nIABut5uUlBRuuummfPvt3bs3AJGRkaSkpACwYsUK/vCHPwDQokULrr322lx1A7Rp04aRI0cyYMAA\nevfuTWBgIEuWLGHJkiWEh4cDkJaWxrZt24iNjfV7zEuXLmX9+vVERUUBkJ6eTt26dalTpw5NmjRh\n9erVNG3alC1bttCmTRumT5/ut7wxvyaW9AF+Ri4YY4wxxhRbgidh8ec3AQGFrr/qoosKXV+UHj16\n8Nhjj5GQkMDhw4e9y1WVhQsX0rx581zlJ06cSL169fjmm2/Izs6mRo0a3nXVq1f3vg8ICODs2bN+\n95lTrrAy/owZM4Zu3brx8ccf06ZNGxYvXoyqMnbsWB544IFi1aGqDBo0iOeeey7fun79+vH222/T\nokULevXqhYgUWt6YXwsb3on19BljjDHmwjVkyBAmTJhAaGhoruVdu3blxRdf9N6X97Vn2OixY8eo\nX78+VapUYfbs2WV2f1vbtm29wya3bt3KDz/8kC/h3LFjB6GhoYwePZqoqCi2bNlC165defXVV0lL\nSwNg3759HDx4sMD9dOzYkQULFnjL/Pzzz+zevRuAXr168f777zNnzhz69etXZHljfi0s6cOSPmOM\nMcZcuAIDA/0+EmHcuHFkZmbicrkIDg5m3LhxADz88MO89tprhIWFsWXLFmrWrFkmcTz88MNkZ2cT\nGhrKHXfcQXx8fK6eQ4CpU6cSEhKCy+WiWrVq3HLLLXTp0oU777yTmJgYQkND6dOnj3eCF3+CgoJ4\n5pln6NKlCy6Xi86dO7Pfc3/k5ZdfTsuWLdm9ezfR0dFFljfm10L8zcp0IWjVqpWuW7euTOr69ltw\nuZz3F2hzGGOMMaYcbd68mZYtW1Z0GMaYXxF/v3dEZL2qFvlMlArp6RORABH5WkQ+8nxuLCJfich2\nEZknIheVZzzW02eMMcYYY4yprCpqeOcjwGafz38Dpqjq9cAR4N7yDMY36bPHthhjjDHGGGMqk3JP\n+kQkEOgGvOL5LEAHYIGnyGtAz/KMyTfpK8EEVMYYY4wxxhhz3quInr6pwONATqp1JXBUVXPSrb1A\ng/IMyDfpy8wszz0bY4wxxhhjzLlVrkmfiHQHDqrq+lJuP1RE1onIutTU1DKLy3r6jDHGGGOMMZVV\neff0tQF6iEgKMBdnWOc/gMtEJOdB8YHAPn8bq+pMVW2lqq3q1KlTZkFZT58xxhhjjDGmsirXpE9V\nx6pqoKo2AvoB/1XVAcAyoI+n2CDg/fKMy5I+Y4wxxlxoPv30U5o3b87111/P888/77fM7t276dix\nIy6Xi7i4OPbu3etdN3r0aEJCQggJCWHevHnnNNbly5cTHByM2+1m37599OnTx2+5uLg4yuqRXL/U\nBx98UGC7FkdJjyUlJYW33nqr1Pv761//WqLy48eP5/PPPy/1/lq3bu19P2rUKIKDgxk1ahQzZszg\n9ddfL3W9Zc23XVJSUggJCSnzfSQkJNC9e/cSbVPQ9REfH8/w4cPLKjSv8+Xh7KOBkSKyHecev/+U\n585teKcxxhhjLiRZWVkMGzaMTz75hOTkZObMmUNycnK+co899hh33303GzduZPz48YwdOxaARYsW\nsWHDBpKSkvjqq6+YPHkyx4/LIWKjAAAgAElEQVQfP2fxvvnmm4wdO5akpCQaNGjAggULit6ogvXo\n0YMxY8aU2/7KO+n7y1/+QqdOnUq9v1WrVnnfz5w5k40bNzJp0iQefPBB7r777lLXW9ZK2i4AZyth\nQlBhSZ+qJqhqd8/7naoararXq2pfVT1dnrFYT58xxhhjLiRr1qzh+uuvp0mTJlx00UX069eP99/P\nP1AqOTmZDh06ANC+fXtvmeTkZGJjY6latSo1a9bE5XLx6aef5tt++/btdOrUibCwMCIiItixYweq\nyqhRowgJCSE0NNTbS5iQkEBcXBx9+vShRYsWDBgwAFXllVde4e2332bcuHEMGDAgV29Leno6/fr1\no2XLlvTq1Yv09HTvvpcsWUJMTAwRERH07duXtLQ0ABo1asSECROIiIggNDSULVu2AJCWlsY999xD\naGgoLpeLhQsXFlqPr2nTphEUFITL5aJfv35A7h6XwYMHM2LECFq3bk2TJk28SWt2djYPP/wwLVq0\noHPnztx6661+E9rixDBmzBiWL1+O2+1mypQpZGVlMWrUKKKionC5XLz88ssA7N+/n9jYWNxuNyEh\nISxfvpwxY8aQnp6O2+1mwIABuerNyspi8ODB3vM1ZcoU7zHlxPrxxx/TokULIiMjGTFihLfXauLE\niQwZMoS4uDiaNGnCtGnTvPVecsklgJMcp6WlERkZybx585g4cSKTJ08u8PpJS0ujY8eO3vOXc02m\npKTQsmVL7r//foKDg+nSpYv3evBXD8CkSZO87TNhwgS/bZq3XbKysvzuIy4ujkcffZRWrVrxj3/8\ng9TUVG677TaioqKIiopi5cqVAHzxxRe43W7cbjfh4eGcOHECcK6/vNc+wNKlSwkPDyc0NJQhQ4Zw\n+nT+NGfWrFk0a9aM6Oho737KnKpekK/IyEgtK//9ryo4ry1byqxaY4wxxlRSycnJuT63a9cu32v6\n9Omqqnry5Em/62fNmqWqqqmpqfnWFWX+/Pl67733ej+//vrrOmzYsHzl+vfvr1OnTlVV1YULFyqg\nhw4d0sWLF2vr1q315MmTmpqaqo0bN9bJkyfn2z46OlrfeecdVVVNT0/XkydP6oIFC7RTp0569uxZ\n/emnn7Rhw4b6448/6rJly7R27dq6Z88ezcrK0htvvFGXL1+uqqqDBg3S+fPnq6rqrl27NDg4WFVV\nX3jhBb3nnntUVfWbb77RgIAAXbt2raampmrbtm01LS1NVVWff/55feqpp1RV9dprr9Vp06apqur0\n6dO97fD444/rI4884o39559/LrQeX/Xr19eMjAxVVT1y5Iiqqs6aNcvbpoMGDdI+ffpoVlaWfvfd\nd3rdddd5z8Mtt9yiWVlZun//fr3sssu8x9muXbsij8XXsmXLtFu3bt7PL7/8sj799NOqqpqRkaGR\nkZG6c+dOnTx5sj7zzDOqqnr27Fk9fvy4qqrWrFkzX52qquvWrdNOnTp5P+ccX845SU9P18DAQN25\nc6eqqvbr188bx4QJEzQmJkYzMjI0NTVVr7jiCj1z5ky+/fm+nzBhgk6aNElV/V8/mZmZeuzYMVV1\nrv3rrrtOs7OzddeuXRoQEKBff/21qqr27dtXZ8+eXWA9ixcv1vvvv1+zs7M1KytLu3Xrpl988UW+\n4/eNrbB9tGvXTh966CFv2f79+3uv3927d2uLFi1UVbV79+66YsUKVVU9ceKEZmZmFnjt57Tt999/\nr6qqd911l06ZMsW7v7Vr1+qPP/6oDRs21IMHD+rp06e1devWfn+WVfP/3lFVBdZpMXKnqkUlhb8G\n1tNnjDHGmMpo8uTJDB8+nPj4eGJjY2nQoAEBAQF06dKFtWvX0rp1a+rUqUNMTAwBAQG5tj1x4gT7\n9u2jV69eANSoUQOAFStW0L9/fwICAqhXrx7t2rVj7dq11K5dm+joaAIDAwFwu92kpKRw0003FRhf\nYmIiI0aMAMDlcuFyuQBYvXo1ycnJtGnTBoAzZ84QExPj3a53794AREZG8s477wDw+eefM3fuXG+Z\nyy+/nI8++qjQenK4XC4GDBhAz5496dnT/+Oie/bsSZUqVQgKCuLAgQPetujbty9VqlTh6quvpn37\n9vm2K+pYCrJkyRI2btzo7Y07duwY27ZtIyoqiiFDhpCZmUnPnj1xu92F1tOkSRN27tzJH/7wB7p1\n60aXLl1yrd+yZQtNmjShcePGAPTv35+ZM2d613fr1o3q1atTvXp16taty4EDB7znuDAFXT+ZmZn8\n+c9/JjExkSpVqrBv3z5vezZu3Nh7PJGRkaSkpBRYz5IlS1iyZAnh4eGA09O2bds2YmNjC43L3z5y\n3HHHHd73n3/+ea4h08ePHyctLY02bdowcuRIBgwYQO/evb1t4e/ar1WrFo0bN6ZZs2YADBo0iOnT\np/Poo4966/3qq6+Ii4sjZ5LKO+64g61btxbZviVlSR+W9BljjDHml0lISChw3W9+85tC11911VWF\nrvenQYMG7Nmzx/t57969NGiQ/zHH11xzjTcpSktLY+HChVx22WUAPPHEEzzxxBMA3Hnnnd4vpr9E\n9erVve8DAgJKfW+UqtK5c2fmzJlT6H6K2kdR9eRYtGgRiYmJfPjhhzz77LN8++23Be4zp97iKiiG\nr776igceeABw7q+rXbt2vu1efPFFunbtmq/OxMREFi1axODBgxk5cmSh99BdfvnlfPPNNyxevJgZ\nM2bw9ttv8+qrrxY7/rI6pznefPNNUlNTWb9+PdWqVaNRo0ZkZGT43ZfvcN+8VJWxY8d627C4CttH\nzZo1ve+zs7NZvXq1N8nMMWbMGLp168bHH39MmzZtWLx4sd96z7f7As+XiVwqlE3kYowxxpgLSVRU\nFNu2bWPXrl2cOXOGuXPn0qNHj3zlDh06RLbni85zzz3HkCFDAOe+psOHDwOwceNGNm7cmK8HqFat\nWgQGBvLee+8BcPr0aU6dOkXbtm2ZN28eWVlZpKamkpiYSHR0dKmOIzY21jt5yaZNm9i4cSMAN954\nIytXrmT79u0AnDx5ssjej86dOzN9+nTv5yNHjhSrnuzsbPbs2UP79u3529/+xrFjx/zec+dPmzZt\nWLhwIdnZ2Rw4cMBv8l5QDDfccANJSUkkJSXRo0cPatWq5b0/DKBr167861//ItPTI7F161ZOnjzJ\n7t27qVevHvfffz/33XcfGzZsAKBatWresr5yroHbbruNZ555xls+R/Pmzdm5c6e3x6usZnIt6Po5\nduwYdevWpVq1aixbtozdu3eXqp6uXbvy6quves/Vvn37OHjwYL7tC2qXonTp0oUXX3zR+zkpKQmA\nHTt2EBoayujRo4mKivLeU+pP8+bNSUlJ8Z772bNn065du1xlbrjhBr744gsOHz5MZmYm8+fPL3Gs\nxWFJH9bTZ4wxxpgLS9WqVfnnP/9J165dadmyJbfffjvBwcGAMxX/Bx98ADg9kM2bN6dZs2YcOHDA\n27OXmZlJ27ZtCQoKYujQobzxxhtUrZp/ANjs2bOZNm0aLpeL1q1b89NPP9GrVy9cLhdhYWF06NCB\nv//971x99dWlOo6HHnqItLQ0WrZsyfjx44mMjASgTp06xMfH079/f1wuFzExMYV+uQZ48sknOXLk\nCCEhIYSFhbFs2bJi1ZOVlcXAgQMJDQ0lPDycESNGeHtDi3LbbbcRGBhIUFAQAwcOJCIigksvvTRX\nmeIei8vlIiAggLCwMKZMmcJ9991HUFAQERERhISE8MADD3D27FkSEhIICwsjPDycefPm8cgjjwAw\ndOhQ7zBVX/v27SMuLg63283AgQN57rnncq2/+OKLeemll7j55puJjIykVq1a+Y6htPxdPwMGDGDd\nunWEhoby+uuv06JFi1LV06VLF+68805iYmIIDQ2lT58+uZLmHAW1S1GmTZvGunXrcLlcBAUFMWPG\nDACmTp1KSEgILpeLatWqccsttxRYR40aNZg1axZ9+/YlNDSUKlWq8OCDD+YqU79+fSZOnEhMTAxt\n2rShZcuWJYqzuKQk3dPnk1atWmlZPcdl0SLIebTGF19AEUOBjTHGGPMrt3nz5nP25cxcWNLS0rjk\nkks4fPiwd/bF0ibBFSXnGFSVYcOG0bRpU/74xz9WdFgmD3+/d0Rkvaq2Kmpbu6cP6+kzxhhjjDGl\n0717d44ePcqZM2cYN27cBZfwAfz73//mtdde48yZM4SHh5f4Pjlz/rOkD0v6jDHGGGNM6ZR0Ep7z\n0R//+Efr2avk7J4+bCIXY4wxxhhjTOVlSR/W02eMMcYYY4ypvCzpI3fSN38+fPllxcVijDHGGGOM\nMWXJkj5yJ33z5kHHjpb4GWOMMcYYYyoHS/rInfRlZ8OZM1AJ7sk1xhhjTCX26aef0rx5c66//nqe\nf/55v2V2795Nx44dcblcxMXFsXfvXu+60aNHExISQkhISJk9kLsgy5cvJzg4GLfbzb59++jTp4/f\ncnFxcZTVI7nK0+DBg1mwYAHgPMft1KlT3nW33norR48ezbdNQkICq1atKtX+UlJSvA+1L66C4iiO\ndevWMWLECMB5OHqnTp1wu93MmzeP++67j+Tk5FLVW9bytkt8fDzDhw8v8/1MnDiRyZMnl2ibSy65\nxO9y32vnXLKkj9xJX5UqcNFFEBdXYeEYY4wxxhQqKyuLYcOG8cknn5CcnMycOXP8fvF+7LHHuPvu\nu9m4cSPjx49n7NixACxatIgNGzaQlJTEV199xeTJkzl+/Pg5i/fNN99k7NixJCUl0aBBg3L5kltR\n8iZ9H3/8sd+HvZd30ldQHMXRqlUrpk2bBsDXX38NQFJSEnfccQevvPIKQUFBpaq3rJWmXcD5ears\nLOkjd9LXsycsXQoxMRUXjzHGGGNMYdasWcP1119PkyZNuOiii+jXrx/vv/9+vnLJycl06NABgPbt\n23vLJCcnExsbS9WqValZsyYul4tPP/003/bbt2+nU6dOhIWFERERwY4dO1BVRo0aRUhICKGhod5e\nwoSEBOLi4ujTpw8tWrRgwIABqCqvvPIKb7/9NuPGjWPAgAGkpKQQEhICQHp6Ov369aNly5b06tWL\n9PR0776XLFlCTEwMERER9O3bl7S0NAAaNWrEhAkTiIiIIDQ0lC1btgDOA8bvueceQkNDcblcLFy4\nsNB6fMXFxfHHP/6RVq1a0bJlS9auXUvv3r1p2rQpTz75JECuuAEmT57MxIkTc9Uzbdo0fvzxR9q3\nb0/79u298R46dChXuZSUFGbMmMGUKVNwu90sX76c1NRUbrvtNqKiooiKimLlypUAfPHFF7jdbtxu\nN+Hh4Zw4cYIxY8awfPly3G43U6ZMyVX3/v37iY2Nxe12ExISwvLly/PF8fTTT9O8eXNuuukm+vfv\n7+21iouLY/To0URHR9OsWTPvtgkJCXTv3p2DBw8ycOBA1q5di9vtZseOHbl6Zz/99FMiIiIICwuj\nY8eOgHOtxsTEEB4eTuvWrfn+++8Bpxeud+/e3HzzzTRt2pTHH3/cewz+6jl58iRDhgwhOjqa8PBw\nv9e7v3b58ccf/e7jkksu4U9/+hNhYWF8+eWXrF+/nnbt2hEZGUnXrl3Zv3+/95wGBQXhcrno16+f\nd/vk5GTi4uJo0qSJNyEG+L//+z9vD/rUqVPzxaiqDB8+nObNm9OpUycOHjyYr8w5oaoX5CsyMlLL\nSny8KjivmTPLrFpjjDHGVFLJycm5F7Rrl/81fbqz7uRJ/+tnzXLWp6bmX1eE+fPn67333uv9/Prr\nr+uwYcPylevfv79OnTpVVVUXLlyogB46dEgXL16srVu31pMnT2pqaqo2btxYJ0+enG/76Ohofeed\nd1RVNT09XU+ePKkLFizQTp066dmzZ/Wnn37Shg0b6o8//qjLli3T2rVr6549ezQrK0tvvPFGXb58\nuaqqDho0SOfPn6+qqrt27dLg4GBVVX3hhRf0nnvuUVXVb775RgMCAnTt2rWampqqbdu21bS0NFVV\nff755/Wpp55SVdVrr71Wp02bpqqq06dP97bD448/ro888og39p9//rnQeny1a9dOH3/8cVVVnTp1\nqtavX19//PFHzcjI0AYNGuihQ4dyxa2qOmnSJJ0wYUK+47v22ms1NTXVWy7v5xwTJkzQSZMm5TpX\nOe21e/dubdGihaqqdu/eXVesWKGqqidOnNDMzExdtmyZduvWLV+dqqqTJ0/WZ555RlVVz549q8eP\nH88Vx5o1azQsLEzT09P1+PHjev3113vjaNeunY4cOVJVVRctWqQdO3ZUVc21v7z7bteuna5du1YP\nHjyogYGBunPnTlVVPXz4sKqqHjt2TDMzM1VV9bPPPtPevXurquqsWbO0cePGevToUU1PT9ff/va3\n+sMPPxRYz9ixY3X27NmqqnrkyBFt2rSp97zmyBtbQftQVQV03rx5qqp65swZjYmJ0YMHD6qq6ty5\nc73XZf369TUjI8O735xzFxMToxkZGZqamqpXXHGFnjlzRtetW6chISGalpamJ06c0KCgIN2wYYOq\nqtasWVNVnZ/DnJ+fffv26aWXXuq9doqS7/eOcxzrtBi5kz2cHSfdy/Er6N01xhhjzK/E5MmTGT58\nOPHx8cTGxtKgQQMCAgLo0qULa9eupXXr1tSpU4eYmBgCAgJybXvixAn27dtHr169AKhRowYAK1as\noH///gQEBFCvXj3atWvH2rVrqV27NtHR0QQGBgLgdrtJSUnhpptuKjC+xMRE771iLpcLl8sFwOrV\nq0lOTqZNmzYAnDlzhhifYVi9e/cGIDIyknfeeQeAzz//nLlz53rLXH755Xz00UeF1uOrR48eAISG\nhhIcHEz9+vUBaNKkCXv27Cn10Mji+vzzz3MN0T1+/DhpaWm0adOGkSNHMmDAAHr37u1t34JERUUx\nZMgQMjMz6dmzJ263O9f6lStX8vvf/54aNWpQo0YNfve73+Va79u2KSkpxY5/9erVxMbG0rhxYwCu\nuOIKAI4dO8agQYPYtm0bIkKmz/PROnbsyKWXXgpAUFAQu3fv5siRI37rWbJkCR988IG3VzIjI4Mf\nfviBli1bFhqXv300bNiQgIAAbrvtNgC+//57Nm3aROfOnQFnuGfO+Xe5XAwYMICePXvSs2dPb73d\nunWjevXqVK9enbp163LgwAFWrFhBr169qFmzprctly9fTnh4uHe7xMRE78/PNddc4+2JP9cs6SP/\nRC7GGGOMMSVS2Axwv/lN4euvuqrEM8g1aNCAPXv2eD/v3buXBg0a5Ct3zTXXeJOitLQ0Fi5c6E1e\nnnjiCZ544gkA7rzzTpo1a1aiGPypXr26931AQABnz54tVT2qSufOnZkzZ06h+ylqH0XV46/OKlWq\n5DqOKlWqcPbsWapWrUq2zxfFjIyMYh1LjunTp/Pvf/8bcO6vyys7O5vVq1d7k+scY8aMoVu3bnz8\n8ce0adOGxYsXF7qf2NhYEhMTWbRoEYMHD2bkyJHcfffdxY6zuG1bXOPGjaN9+/a8++67pKSkEOcz\ncUZJrhdVZeHChTRv3rxE+y9oHzVq1PD+oUNVCQ4O5ks/0/cvWrSIxMREPvzwQ5599lm+/fbbEsd+\nPrB7+sid6FlPnzHGGGPOd1FRUWzbto1du3Zx5swZ5s6d6+2p8nXo0CFvovLcc88xZMgQwOnJOHz4\nMAAbN25k48aNdOnSJde2tWrVIjAwkPfeew9wZm08deoUbdu2Zd68eWRlZZGamkpiYiLR0dGlOo7Y\n2FjvxBubNm1i48aNANx4442sXLmS7du3A879XFu3bi20rs6dOzN9+nTv5yNHjpSqnoLUq1ePgwcP\ncvjwYU6fPs1HH33kt1ytWrU4ceJEvuXDhg0jKSmJpKQkrrnmmnzlunTpwosvvuj9nJSUBMCOHTsI\nDQ1l9OjRREVFsWXLlgL3Ac6MrfXq1eP+++/nvvvuY8OGDbnWt2nThg8//JCMjAzS0tIKPI6SuvHG\nG0lMTGTXrl0A/Pzzz4DT05fzB4n4+PhS19O1a1defPFF1DNEL2dCGV+FtUthmjdvTmpqqjfpy8zM\n5LvvviM7O5s9e/bQvn17/va3v3Hs2DG/94TmaNu2Le+99x6nTp3i5MmTvPvuu7Rt2zZXmdjYWO/P\nz/79+1m2bFmJ4y0NS/qwpM8YY4wxF5aqVavyz3/+k65du9KyZUtuv/12goODARg/fjwffPAB4EzA\n0bx5c5o1a8aBAwe8PXuZmZm0bduWoKAghg4dyhtvvEHVqvkHgM2ePZtp06bhcrlo3bo1P/30E716\n9cLlchEWFkaHDh34+9//ztVXX12q43jooYdIS0ujZcuWjB8/nsjISADq1KlDfHw8/fv3x+VyERMT\n452wpSBPPvkkR44cISQkhLCwMJYtW1aqegpSrVo1xo8fT3R0NJ07d6ZFixZ+yw0dOpSbb77ZO5FL\nQX73u9/x7rvveidymTZtGuvWrcPlchEUFMSMGTMAZzbQkJAQXC4X1apV45ZbbsHlchEQEEBYWFi+\niVwSEhIICwsjPDycefPm8cgjj+RaHxUVRY8ePXC5XNxyyy2EhoZ6hz/+EnXq1GHmzJn07t2bsLAw\n7rjjDgAef/xxxo4dS3h4eLF6wwqqZ9y4cWRmZuJyuQgODmbcuHH5ti2sXQpz0UUXsWDBAkaPHk1Y\nWBhut5tVq1aRlZXFwIEDCQ0NJTw8nBEjRhQ6zDciIoLBgwcTHR3NDTfcwH333ZdraCdAr169aNq0\nKUFBQdx9990FDjcua6K+N7QVVlCkBnAMuENV3zunURVDq1attKye4zJjBjz0kPP+hRdg5MgyqdYY\nY4wxldTmzZuLvJfImPNVWloal1xyCadOnSI2NpaZM2cSERFR0WGZIvj7vSMi61W1VVHbFvuePlXN\nEJGDQKkHrHoSx0SgumffC1R1gog0BuYCVwLrgbtU9Uxp91NS1tNnjDHGGGN+LYYOHUpycjIZGRkM\nGjTIEr5fgZJO5PIyMEJEFqtqZpGl8zsNdFDVNBGpBqwQkU+AkcAUVZ0rIjOAe4F/laL+UrGJXIwx\nxhhjzK9FaR5gbi5sJU36LgNCgBQRWQocAHzHh6qqji5oY8+zJHLufqzmeSnQAbjTs/w1YCIVlPRZ\nT58xxhhjjDGmMilp0ncbTm8dQFs/6xUoMOkDEJEAnCGc1wPTgR3AUVXNGTa6F8g/5/A5ZD19xhhj\njCkpVUVEKjoMY8yvQHHnYSlIiZI+VW38i/bm1JEFuEXkMuBdwP/UR36IyFBgKMBvf/vbXxqKl/X0\nGWOMMaYkatSoweHDh7nyyist8TPGnFOqyuHDh/M9w7EkKuzh7Kp6VESWATHAZSJS1dPbFwjsK2Cb\nmcBMcGbvLKtYLOkzxhhjTEkEBgayd+9eUlNTKzoUY8yvQI0aNQgMDCz19iVO+kTEBTwBtMJJ0GJU\ndYOIPAusUNVPCtm2DpDpSfguBjoDfwOWAX1wZvAcBLxf4iP5BWx4pzHGGGNKolq1ajRu/IsHQBlj\nTLko0cPZReQWnPvxrgZex5mIJcdp4A9FVFEfWCYiG4G1wGeq+hHOfYAjRWQ7zmMb/lOSuH4p6+kz\nxhhjjDHGVFYl7el7DohX1ftFpCowwWddEvBgYRur6kYg3M/ynUB0CWMpMzlJX5Uq1tNnjDHGGGOM\nqVxK1NOHM+nKPM/7vPfUHQeu+MURVYCcRK9qVevpM8YYY4wxxlQuJU36DgJNClgXDPzwy8KpGDlJ\nX7VqlvQZY4wxxhhjKpeSJn1zgb+IyE0+y1REmuHcl/dmmUVWjnx7+mx4pzHGGGOMMaYyKek9feOA\nIOAL4CfPsvdxJnZZAvy17EIrP9nZIAIBAdbTZ4wxxhhjjKlcSvpw9tNAdxHpCHQErgJ+Bpaq6mfn\nIL5ykZ3tTOISEGA9fcYYY4wxxpjKpVQPZ1fVpcDSMo6lwuQkfVWqWE+fMcYYY4wxpnIpUdInIsuB\n5UAisEpVj5+TqMqZb0+fJX3GGGOMMcaYyqSkE7kkAbcCHwGHReRrEZkmIn1F5OqyD6982PBOY4wx\nxhhjTGVV0nv6/gAgIpcCbYGbgFhgKFBNRHaqatMyj/Ics+GdxhhjjDHGmMqqtPf0HRORz4E04BTO\ng9pjgHplGFu5sZ4+Y4wxxhhjTGVVouGdItJdRP4mIquAY8DbgBuYD0QBl5V9iOee9fQZY4wxxhhj\nKquS9vR9AKQD/wHuVdXNZR9S+bOJXIwxxhhjjDGVVUkncvkb8DXOPXyJIvKeiIwUkVYiUtK6zhu+\nPX02vNMYY4wxxhhTmZQoUVPVsap6E3Ap0BdYB9wM/Bc4IiKflH2I5152NohYT58xxhhjjDGm8int\nRC6nRSQJuASoDVwBRABdyjC2cmMTuRhjjDHGGGMqq5I+nL0fzqMa2gJBOLN2fovzsPbncB7cfsFR\ntYlcjDHGGGOMMZVTSXv64oG1OA9nfxxYparHyzqo8mY9fcYYY4wxxpjKqqRJ36WqevqcRFKB7JEN\nxhhjjDHGmMqqRElfTsInItfgPIz9CuBn4EtV/bHswysf9sgGY4wxxhhjTGVV0nv6AoAXgfuBAJ9V\nWSIyE/iDql5wAyRteKcxxhhjjDGmsirps/WeAoYAfwYaARd7/v2zZ/nEsgut/NjwTmOMMcYYY0xl\nVdJ7+u4GnlTVyT7LfgAmiYgCI4DxBW0sIg2B14F6ODN/zlTVf4jIFcA8nAQyBbhdVY8UGsn330Nc\nXO5lt98ODz8Mp07Brbfm32bwYOd16BD06eNdPHYznDgOi2s8xOdX3gF79sBdd+Xf/k9/gt/9ztn3\nAw/kX//kk9CpEyQlwaOP5l//179C69awahX8+c/510+dCm43fP45PPNM/vUvvwzNm8OHH8ILL+Rf\nP3s2NGwI8+bBv/6Vf/2CBXDVVRAf77zy+vhj+M1v4KWX4O23869PSHD+nTwZPvoo97qLL4ZPPI9p\nfPppWLo09/orr4SFC8x1KhMAACAASURBVJ33Y8fCl1/mXh8YCG+84bx/9FGnDX01awYzZzrvhw6F\nrVtzr3e7nfYDGDgQ9u7NvT4mBp57znl/221w+HDu9R07wrhxzvtbboH09Nzru3eHxx5z3ue97qDU\n157XQw/BHXbt2bVn114+du057+3ay7/erj3nvV17+dfbtWfXHvw6r71ClLSnry6wsYB1Gz3rC3MW\n+JOqBgE3AsNEJAgYAyxV1abAUs/n8qOAWE+fMcYYY4wxpvIRVS1+YZGNwDpVHeJn3atApKqGlaC+\n94F/el5xqrpfROoDCaravLBtW7VqpevWrSt27IW54w7YuBF++1s4dgxWry6Tao0xxvx/e2ceJldR\nNe63Zk1C9n0lISREwpYgIi0BY4BPUCRBUVF2RAzqJyj+EFAxCrJ9gBFBTATZ9FMQ+BTZSciwZdgT\nEAIhIYYsTPZMCJlkMkv9/ji3uNW3by8z07P1nPd5+rndt+9St+rUqXOqTtVVFEVRFKXVMMa8aq09\nJNtxTQ3vvAL4mzFmT+A+YD0yuvdV4HPAyU1I4BhgMvAiMMRaWxX8tQ4J/4w751zgXIA999yziUlP\njy7koiiKoiiKoihKodLUVzbca4zZClwO/BYoBeqAV4FjrbVP5nIdY0xP4H7gAmvth8YY/x42mB8Y\nd/+5wFyAXr162amRWNuvfe1rfPe736WmpoYvxMTZnnnmmZx55pls2rSJk7w427fektDc8vLzMObr\nrF69mtNi4mwvvPBCvvSlL7F06VK+ExNn+7Of/Yyjjz6axYsXc0FMnO2VV17JZz7zGRYuXMilMTHe\ns2fPZtKkScybN48rYmK858yZw4QJE/jXv/7F9TEx3nfffTejRo3innvu4ZaYGO/77ruPgQMHcscd\nd3BHTIz3I488Qo8ePfj973/PvTEx3hVBjPd1113HQ5EY7+7du/NoEON9+eWXMz8S4z1gwADuD2K8\nL7nkEiojMd4jR47kz0GM9wUXXMDiSIz3Pvvsw9wgxvvcc8/l3UiM96RJk5gdxHifeuqprInEeCcS\nCa4KYry/8pWvsDkS433UUUfx8yDG+7jjjmNnJMb7+OOP58dBjHdU7qD5suc477zz+PrXVfZU9lT2\noqjsqeyp7KnsRVHZU9kDlb042ctETk6fMaY78AVkoZV1wHRgIzAQ2NSU1zQYY0oRh+8v1toHgt3r\njTHDvPDODbk/QsuxFozO6VMURVEURVEUpQDJOqfPGDMWmIc4fI5twNettU806WYypHcnsMVae4G3\n/3+Azdbaq40xFwP9rbUXZbpWPuf0TZ8Oq1bBmDHw3nsyv09RFEVRFEVRFKUjk+ucvlxW77wWaASO\nAHoA+wGLgTnNSNfhwGnANGPM4uDzBeBq4BhjzDLg6OB3m+HP6dORPkVRFEVRFEVRColcwjsTyGsW\nng9+v22M+U6wHeYtwJIVa+1zgEnz91G5Xiff6EIuiqIoiqIoiqIUKrmM9A0DVkT2vYc4b0PznqJ2\nwDl9OqdPURRFURRFUZRCI9eXs+f+Mr9OiI70KYqiKIqiKIpSqOT6yobHjTH1MfvnR/dbawe3PFlt\ni470KYqiKIqiKIpSqOTi9P2y1VPRzuhCLoqiKIqiKIqiFCpZnT5rbZdx+oqKNLxTURRFURRFUZTC\nItc5fQWNjvQpiqIoiqIoilKoqNOHLuSiKIqiKIqiKErhok4fupCLoiiKoiiKoiiFizp9aHinoiiK\noiiKoiiFizp96EIuiqIoiqIoiqIULur0oSN9iqIoiqIoiqIULur0oQu5KIqiKIqiKIpSuOTycvaC\nZ/t2WL4c+vbVkT5FURRFURRFUQqLLu/0VVaKw2dtuFUURVEURVEURSkUurzTV1EROnoNDer0KYqi\nKIqiKIpSWHT5OX1Tp4Ix8ikuln3q+CmKoiiKoiiKUih0eacvkYCBA+Hgg+HMM2WfLuaiKIqiKIqi\nKEqh0OXDOx2f+hSMHCnfGxrCUT9FURRFURRFUZTOTJcf6QPYvRvKykJHT0f6FEVRFEVRFEUpFNTp\nA2probxc3tUH+toGRVEURVEURVEKhzZ1+owxfzLGbDDGvOnt62+MedIYsyzY9mvLNFkbOn1upE+d\nPkVRFEVRFEVRCoW2Hum7Azg2su9iYL61djwwP/jdZtTXi+Pnj/RpeKeiKIqiKIqiKIVCmzp91tpn\ngC2R3dOBO4PvdwIz2jJNtbWy1ZE+RVEURVEURVEKkY4wp2+ItbYq+L4OGNKWN9+9W7a6kIuiKIqi\nKIqiKIVIR3D6PsZaa4G0r0Y3xpxrjHnFGPPKxo0b83JPf6RPF3JRFEVRFEVRFKXQ6AhO33pjzDCA\nYLsh3YHW2rnW2kOstYcMGjQoLzfX8E4ljspKuOoq2SqKoiiKohQKauN0TTrCy9kfBM4Arg62/2zL\nm/tOn3P2NLyza1NZCdOmSehveTnMnw+JRHunSlEURVEUpWVUVsLnPgd1dWrjdDXa+pUNfwUqgQnG\nmDXGmG8hzt4xxphlwNHB7zZDR/oKh3z1XFVUiFw0NorjV1GRj9QpiqIoSuuiIzhKNtTG6bq06Uif\ntfYbaf46qi3T4aMLuRQGbnSurk7KsiU9V1OnyvzOhga51tSp+UypoiiKouSfykppr+rrdQRHSY+z\ncRob1cbpanSEOX3tii7kUhhUVMCuXVJ2Le25SiTkU1ysjaaiKIrSOaiokPZPR3CUTCQSMG4c9Oun\nNk5XQ50+De8sCPyeqnz0XHXrJnJw2GEtu46iKIrStWivEMsjj5StMTqCo2TGGLFz1OHrWnSEhVza\nlbiRPg3v7HwkEtLI7bUX3H57yxVZTY1sa2tFMSqKUrhUVsqoyNSpagQpLaOyEqZMke9tHWJ58MGy\nPegg+P3vVZaV9OzYATt3tncqlLamoEf6cult8+f0LV8u3197rfXTpuQXF9Y5YkR+Gjrn9KlSVJTC\nXhyiokJGSH7+czjqqMJ8RqXtWLBAOo7bI8TStVfjxqnDp2Smpkbtm65IwY705bqwhxvpe/tt+NWv\n5Ps558Dee3cOpak91IJTXvlSYu46u3bl53qK0llxi0Ps3g3duxfeHJC77pKFLyA00gvp+ZqCtict\n5/DDZdseIZb5bgeVwqWmRuxfa0VWla5BwTp9bmEPSG3I/YbNOX2LFoUNf31952j4m/OulUJt1Hfs\nkG2+Gjs30hd1+ubPh2efhc9/vrDyr70oVHksJCoqRMdAZqeos5blPvvItqioa8+DyucKyF2ZAw+U\n7X77wdy5bZuHrv1z7ZeixNHQENo2u3ZJZ56SG889B888I7Z3Z9SPBev0pVvYY+FCCeWxVhyl88+X\n/VOmwE03iRNYUtI5Gn73rhXIrYf66afh6KPl2QuhUfeNzCFDZF9rOn2VleLsNTTAtdd2/vxrbyor\n4Ygjwrqo+dkxiXuFSdTBq6yU0Mja2s5XliNGyParX5X2oLOkO9889FD6jlIld1zbMXp02+efjvQp\ncUT1tS8fO3eq05crlZXw2c+KzdKtW+dq5xydek7fwoVw5ZXxczASCXHe9t47uWCeeEKMFxdv/9Zb\nsv8zn4Gbb5bvV1zROQrSOaa5hpHce6+MYubjtQbtzcKFUvl+9jMxNhculP35dvr861VUhCu7dvb8\n6wgsWJBcFzU/OyaJRBiy9sADsp02DX7603AOnIusaGwUx68zleVHH8n26KM7h95vLdyIJ7T/iGdn\nnkPqok7cti1Rp0+J4hwVX1/7sqlTWHKnokLaOGs7r83SaUf6duyQ4dV080x27xYHZ+DA5P1udSsI\nV3t03z/zGfk+cmTrpz8fHHqobMePhzvuyG6wTJgg20IIY3r44eSQs+efl+/5UGCNjWGj+cQT0gN+\nzDGSX8aEI6WdOf86Aq6+6fLiHRO/d7isTPZNnAh/+Us4F8Q1fFOnSidbXZ3sHzCg/dLdVJzT19VD\n4gYPlu3IkdJB2F4OsDNS6+s7Z2+6k6P2dPoKSZbzHTbeWcPQm0tceP7QoeH/2kGQO4VgA3Zap2/7\n9szzTJzCdQ26Y++9w+/z50vII0hIUs+e4bU7A+4Zhw7NTXkNHy7bY46BX/yicyu8Aw6QrXMYJk6U\n3/lQYL7jeMklsr36apGX3r1FpvJtiLiGaMAA2Ly5azRI++0n24kT4Y9/bP3n7WqNfUuIht6OHSv7\nd+xIdvCKi8P8nD4d7rtPzrngAqmjrZHPCxfCY4/Bccfl5/qujWgPI70jsXatbPfdt33rR65zSDsq\n6Zy+ttA/hTbS59YtqK/Pz5SUrjhvNW6qk98pUCiy0hYkEuInlJfDgw92TtnptE5fr15icNTXx8/B\nc45b1IGrrg6/JxIykgNSiL16yfeoo5grbW1UunTmml737Acf3DmF1ceN0E6YAH/6E2zaJL/zocDi\nekl375ZwxJoaCUmcNKnl93H4C/K4d0R2plUSn31WPk2d2OzkduTI1n/Ohx+G44+XUe62mHPW2R3M\naCjzxo3y/aOP5HnOPFMc9QsuCJ9v69bw/NYy1t1KonV18D//A0891fJ76Eif5Ovf/ibfXedne9EZ\ne9MrK8UIPOGE+PDOysowRLo1Ry9dh2WhGPLz5zdt3YJsZFrgLxc64yIefjpd5+pLL4X7NLyzaeza\nJb5CZyn/KJ3W6dtjDzjxRPj73yX2P1oA6Rwi3+mrrBRDvrhYjMHmjvRVVsItt8Cf/yyNVVstZNBc\np+/DD5t2n45owLpn6d5d0uQMlvr6sCOgucQZfy781/VAb9kSLgDRUvyGzdHUBqm9yuiZZyQUqznO\nVLqOmdbAzUXz5w+2Vj45J3737tYPT3v2WZg3D449Nr/3+PSnw+9lZVK+EBqyffvKtlu38DgXHghQ\nWto6xnq+R4EqK8WQg6470ue/kgOgqqr17pOLjkokJHpl8+bWqzv51JeuvtfWwuzZMGuW7PfbkQUL\nxImF1tU/HTm8synRLO7YHj3CffnoAPjsZ9NfL5tMZFvEozXa4Hxf89FHJWrDtzcKpYMgH2TL7927\npf1p7sBQR6DTOn0gjh+EYYsgoT+PPBKOBGUa6XOKGqSwEwmpzE0p0MpKWQ3Uve7Bn+fSVk5fJqN5\n4UJpcKZNa57Tly0cor2cDfcsLiTJN9h27gxHbZtDXIM5f34oU5Bfp2/MmNR9TTGaXWPU0NA0xysf\nZffYY7JtjjOVrdOiuemrrJR3rwGcfrqc64/M5mI8LFwoUQDNeTVHplV1821sHnVUOOqVTwPZl8n5\n82WUFFJ1jqt/kFzn7ryzdfSBW0m0sbHljqWrN86JXLEiHynsfFRUhA4fwMqV+b+H307mEsVQWytp\n+tSnWictTV1pNlO99fNv9255/RMkt0mHHRZ+j9M/+dILfnhnvt+/1pI0ujzftUvSlamT0C8fN5cY\n4Gtfa3ka3es0xo2TNsLXy1OnhisUx6XLLeIBqXp94UI5v7Exf2GjfmdCt275iWr43/+VDlD3TmrI\n7vQ995xMg5o2reN0+rcGuciAb7MsXCj50pEGQ3KhUzt9znlxYUV+D7tTFnV1yb+3bQvP93s7XAXu\n1UsMmlwV3IIFocPnaKuQlFyMZhcK1b07fOELst93+rI9Z6ZwiGeeCUNxcmk882nwOqdvwwZJV2s7\nfYcdBkuXhr/9ULaW4hrmfv1knttzz8H11+eeR48+2rTRD+cU3XqrKLhcRqP8soPw+/77h8/QVLl3\njkOc/P7979LIN3UE0b2SxYUm3n671FG3ONPgwfCPf4SvGYiTR1+PNOfVHH4e+I6JmyfX0JCf8N2K\nitZ7qfj69eH3RCJ13luc0+d3Prk5gOnIRRfEOe+JhFx7+fKWz0tesCCsNwDvv9/8a+WThx6C119v\nOyMrWmfXrw87QfNFU2TV2rCd3ratZYsCxcmZa9Ny7aDN9j5cf45rSUnYOVhTI05AUVG4iNqee0pU\niq9/BgyAH/xA7JGW6gXfgE/3/rXmtMOu89dd06Ux12v5eQ6ZOwn9Y/36effdssBQuvxJl0afLVtk\n269f8n+PPZbsuMelK91rwEAcKb8Nvuuults6fmdCba2MILtR5KZc27dzrZX8cdOaILTv4l7Dc9dd\nEhKarzarJbT2AMO8edllwLWDjY0Srt1W00XySad2+pyR4QxwP/THVxbbt4cNhz/S5+MqcM+e0uPr\nRk5KSuCss+CMM+IL9aCDkn9/+tPwm9+0TUiK7/TF9epFG9rly+W7c/pymdQcnV8xYICE006dKsaJ\ntbk1nul6rZpbkf1yrKpKdfpaQpzTt317cofBli1NT3u64x99VLbDh8OwYfLdbTOd58jV8aqslBGY\n224T2c413Mg3ekpKRKbcvX7zGznGza2M67VNl/a48E53/COPyO+mjiA+/njo8PnP5lYr69EjlLsj\nj5TrR5V2S0MI/WP9lzNH58nlYmxmKne/buY7nNJ3+nbvDhtDV8+cDlm0KHQQ/M6kOD3rG7n//d/p\nV152x/oRFM55TyQkHB/Cd3M2Fz+EFWSRpvZm3jz40pekXH/969Y1Jlx5+CFvIPKU70iVdB0hfjqc\nnO/YEdYT5/S11FHx25ypU5PXA/DbtHQdn5nmlSUScOqpIqPnn5+su3fulIgkVx/22CPUP06nundg\nprt+U8j2/rXmhp77eeC/kiWbk+Xw89yRbsRz1apQrxUXh3mTrS3wO6h37Yo/zjl9flsO2dvQykp5\nPog38p1Tb4yk+Y9/lPS2JLzf70ywVnRDRYV8b8qIYjQSzFrRpY7XXxcb5LbbwgGC2bNFlv35fu21\nqJJvt2R7x3RT9ET0WDcKnEkG7r03eV9Lp4s0V6+1xPnt1E5fdKTPVyy+Is3F6XP06gWrVycbfXPm\nSI9HnKD5S99C7hN8m+MwRFexck6ftaLg/fh3SDUK3f9O4T31VPZJzYmEOMKNjXDDDaIInON2wQXh\nccZk7pGNhsA4BX300bKvqb0lfjledVWycsrm9GXL+zinb9265Ibi5ZfF4ck1RMgZILt3Jx9fWQl/\n/ascs2RJWEaucXLnuTyPu48bxTrwQJlbmq0XNEq2ETq/wXcNEMizLFwo3/v2jTfc3VyhOKMgOlLt\njndOpcOtEJkLrvGOPtsLL8jvbdtEXlauTD/y4EIIXadPU50plz8Ao0aF3zP1FEfJpUMmkZCR4X//\nO325N5cNG8Lv/kis+75mjWzXr5dQrPnzRc/27St1M2pUOSeuoSHZ+Eundx5+ONlA9I9zMuzfY+FC\nGcE98cTc8yE6GtncFxTnswf64Ydl29JpArnMT3LtSWmp7Cstld/W5j9SxU/D736XHFYXfT3DnnuG\nx1ZXZ3dU0s0VSzdSkkjAF78I//wnfOc78N3vhuHCZ58djiq7a69aFd4rWm/dvZ0t0rdvcvuxY0ey\n0+d3UEfncUPTdF0cUacvSjYHNh3R0cypU1PzN9O1EglZ3ff+++V3nE7zZdLp0LPPhj/8Qb5n69T0\n7Y90r41x7WrUDnQyF9eG+h3W7ll3707uKHCO/mGHyUCAS3NL6nAiAV/9qoRkgsio3/5G8zxdnXdO\n32GHhe2gC1MFuOyy0E50ab7//lT5jHb6t9TpygXf/kn33P6xua706ttjRUXyjm73CrR995UoqKgM\nuJBjn5a8biqbfZTunOZM5fEpKKcvkZBCf/JJqbwutv655+Caa+T7Bx/EX8sZLr16xSvLdJV39erk\n37mE/TW1sCsrpbFyArdrlzih/nyHBQvgjTeSK1siIcps5UoJU3OLnbh8c6OUmQS3vl6URlGRrODn\nO4luwr8xclzcMu1+gxxdbXXu3NQYechNafhKe+7cZCchk9OXLlTHV1ZxTt9118Ho0eHvRYuaFiKU\nLky2oiLMA2tD+dy8OfN5Pm7l0kyv7vCvE+XPf5ZtOmXudx44Z8jJjMsTlwafO+/MHC7hGqOdO+Wa\nvhHhEzcvJV3j4r9j86CDwgbczT3cuhUuvTR5rkhU9hMJqVsvvCCv7GiqUnWGBYTlCGFvMGSf8xYt\n93ThQk7WfecyH/gjff4Inhvp8/93BvW6dZKO6upUo8qPOmhsDOUpnVPtvygcksvI1U93j7lzxXBv\naBCHIte5L25F0uizNYXmzA/LhHNEW2pMZBvNefDBVAPmssvk2GeeSZ6Dlm/cIkAQP6o+fXr4f3U1\nvPii/Bena+OMQjeq5zqB3cuUn3hCFj6aPz9cmGjt2uRRtjlzpG7Ony/7p01LNpDvvz/53q4tcTpq\ny5bkzoOorG7dmupUFxWJ07B6NXz72y2TH7/ti2vHXIdWU+fEJhIwY4aE3V94YZhGl7+5dI65zgWQ\nvI6ugP344/ELmjkmT4abbkqfP++8E343RtroaJuWbqTP6YIhQ1KvH+eg/9d/Jc/9cu3f6NEy6uuc\nvubWYYffTrnn8kNknWObqXPYtbNf+pK0aW400pdrv6OytBS+8pXU8PeTTxY9617jE+e0u07b8nIZ\nLXQ2uN+Rkoloux5nt/jP7fPUU7l3aDzxRHjdxkb4/vdlXjzIteNkIM6uPPjg5E6sptCciCJ/Kk+0\nIytXOrXT54TZNzCcIPvO19lnhxlVVAR9+qRWepfpPXumOnKZGuB0Tl9TJn7nGhbpsFbCB3zj8stf\njnciXXr6909dyMX1To0dK/HycWlw92hsFKXrGozi4tDQ9HtgfCGMjm596lOyzzVs7h2JEPYi+Uoj\nkwFVXS2jYjU1YYipI5PTN3duco/drFmi4H7wg9AR/MlPUs+79dZk52PduuQQlGyKPRom646Pnucb\nD+5/P8/j7uMaLN/BiBIN4Ro3Lgz33bRJFHpcyEhlpSh/l4azzxbZO/xw6Uh4+eXwGj6VlRLu6XBp\n9+uFP4K0Y4eMBDnc/UDkIWropXt5s2/IH3hguD9a350D0qcPfOMbqfnlZMg3UHPlqqvC734dve++\n8Lu/+FQcfjkXF0toS1z5uBG5aNm3tMf19dfD776D58rMhViCpGvePJFt5yxE8/uTnwy/l5fLIkjL\nl4sDHpc+fyXQQw6BG2+U77/+dShr1dXw/PMwc2b2XuA48uH0OaPE2qbd28cvKydvo0aFc78cCxfC\n//2f6PpsHQbZjB8378wYMdgbGqROlZaGBk40ciSa1uY6J88+C8uWyTXiRr992amuTh55jzoqN92U\nahS6crjkEqnbbl4ohPnh6k3UqPbLceXKZKMXkkM345yBd95JTr+TKdf21tZK3vodtg0N4ch5Sxde\nyRbxkkjAwIHy/HEOVFz5un1OlzkZ9UeizjtP9mUaBYq2ERs3Jo/qjhuXes5778l2n30k3Zlkzn/V\nSFGR6Mz6etFVN98M554bPkNtbRj266ctrvOyX7/Ufb79dtdd4XzgTZuS3wXd0gWtonq9V69QvoqK\nkjuHXcdHtM47e++QQ6RTtF8/cU4feij+nldcIXn1xBPhyCxI+LKjtlbyd948idZKJKRd8kd+zzsv\nbMP98Px0+NEgrl33bQKHMfG2zic+EX7P5mxHOxUbGuT+kNougNjPcYwY0fzyjUbi5dI5MGhQ+L2x\nMbkjK1c6tdMXHemDUJmvXBnu8xV3Y6NUnLo6cRiKipKdutdeSw7ngPTzlSAcLnds3Rr2PKeL6fYF\nKFMPWWVlGE4ZpaEh2Yh0lW3nznBVqh07QgVRVRXmzZYtopzdaw3Ky5NHu/wwmT59wnuMGSOG9OLF\n0nMTVYbO+Hv2Wenluf/+5NEKp1Bdj5+79rBhcuxDD8U7w3EN0datorjefjs1b9I5fZWV4txG0/vU\nU8nhZk89lXpu1LFcvDhsoE86KXuIxWGHiUNeUyO9pYmEjED/85/yf7dukleuvJxSSySkN+mVV8Th\nytSYZnL63IvQ/Wd33HtvfI/To49K76CbQwCh8eKUnZsQXl0t13Bl64/sABxzjGz9OSAnnRT+v317\nsgFw0EFhL2HU2c3UQ+YU9rBhyUvPv/tu8vO7stu2Lbl3313HnescnlyN3WuvlUV4HI8+Kp0cTic4\nnn46fG9XHH6Y6pFHipxGn7e2NtSBfjjwXXeF8zYzrZCX7nkqKyVU0uHP/3BGbF2djCyvWye/nXy4\nUY+o0+frkaOOEnmG5AWXKiul7k2bllx2hxwiW9eB5OrhsmXhvGKHMaGsZCuzqIHXnGXufYekOaF5\nfqde9+7y/kOQZ4rrRa+rE2M9OprpP6tvTKRrX1wbdOCB8m65yy+X8nTl8eGHodPntwvnn58aop4L\n/ojNjTdK+rp1k9EdxzXXiKz5rwGprhYnd8IECX+/4YZkXetC4318GfBHl9zvqVPhjjvkt5NfN1cZ\nwpGEuHZ39epwhCquk+qRR5LlMer0QTja5/C/O53nni9OfjPJdbrwTnfOlCmh3EcN2TijG2TxKTc6\nD8lGsbMhtmzJHo4erW8bNiTr/LgOtrfekvvus0/8Qkv+Yk/vvCPpKS6WjmxnG9TXi+718xZER2Vz\n+iork6ex+LgRMzcXDsTu9NPp6lPcIikVFVIGmzfHTwtasCA+zY6iouTO43RTEtzgSK9e0tmzbl04\nZ95/FieHblV8Nxqe7tn/9Cc556qrpLx9uTAmfm49pJfdqB12113J7YZP3Eif30mVrc3zz3cLEbqO\n2DgZOP/8+HTEOYi5kkiIDfuf/8goo5OLJ56QkeQ43epkurQ01QbKlU7r9H30UWjwvP12uJiA3yud\niQEDpJE/5RSJ4XVCGPdiWn/xByc0IELpQiYdq1cn93DU1iaHZkGyAKVbIMYPAU2Hb7j73H67OGW+\n8/rYY6Ew19dLD7trED/4IHVE0TXK110XXmPDhvD/l16KbxQbG8NeHp+yslAJuBBGt3WT22+5Jfl4\nFwLqQgr8RXXcvMu4UdtFi8SwjFJRkayIXHp9iovDeWqZ8J3AF14Ie4l8A2727NB5/sQnQqNy2LDk\nOVsA48fL3CynoP0RItfYuvuka0zXrw/z3YVTOJn1Q1MheXl65yxGR7Sd4+Dz2muydas2+h0umzeH\nc1z9RghklNjvQ9DdsgAAIABJREFUGa+tDUcaQeqzvxLkrl2SJ8uWpTq7vrMUnWfgOjYOPFB6zysq\npPGMdg4MGRLKX3SU5tlnw+ts2CCjSVOnxi/6EiVqhLqFTr73veS8dGUZ1wD+85+hkwdyT0gtH7/B\nmT9fOhH++tdk2Ywb6fHrelkZHHecjKyddZb8P2tWclr9+uCv3nnEEWHYrMOVWTS8081Vc99d+lwZ\nuNFbt5DA6afL/r33FgdwwYLUEZ3HH0+dw/n5z8t25szsE//TjfQ9/7yEOObyPrFEQurxk08mG7Bx\nxJW3H/Wxa1eYTx98IGXw0ktyzKpV6Ts6/JFvNy9t0CCR3XQjMG5kqV+/UH+sWhUuZrN9u9Rlf56M\nC+OPS0O2Z41Oe3B1zpeLH/xAdIb/jlWn251e9B2V3/8+2WFy+B0JUXvAvdPX1e9XX5Wt30HlRhJc\nvfN55hkZcaqokKkM7vihQ6XMoulJ5/SlW3H7+eelzRs9WlbbdpEkbqTKD+Pz9zt8R+/ll0XuBw4U\nm8N1yrk2LxqlFDW6XXvpdIF7Nt8odvm7ZEn2aQjRTkl/3jDEvypk0yZp40eMSO1gjy725HAdMNF9\nc+Yk76+ulnawokKcy+izQfopByCO5dFHy3X9Z/LtrptvFt11/fXhCpizZ4ertTpchJa7Z58+0mZk\nYtKk5Kk8kydLZ9pPfyoydvnlkj7f6SstFf0cxZfbhQtFf7oOkSjGSEfciy/Kb+eg3Xprctpc3QK5\n74ABmefc+SOkxcVyPb9shw4VebM2fiqRC+8tKop3+PwwfOfIl5ZKh9LcuWFbtnlzsgxlkgEnL+mc\n+nRth+uscHq4f//kdvnqq1MXOxwwQNIJYedqrpFmPsbGac1OgNlzouVHfwl/Gzhg82DeuHwElDfA\n1W+knvTYUHh8GPTeDb+UWj52bxgVzAM6b8QInr9iML/72y64NLQSS0pg0GBY/7tR2OcHUrJXDXU/\nWArRrLt7NP1X9mdLv+3wfc+iNYCFkjvHsteOPiwr2wbniNXdv3/YeMweN45JvXpx4/NbmLX8/dT5\ngTdMYGhdD9bttQm+FtHYAFfuCxu7YaZtoOc316a+v+8X+8GHZfD5Kjg2uUaPHgPvn3wg1BbD9LUw\nVTRyt+6wK2hI7hk+mTPOgF0nrILE5o+fC5DzLpblj8zpK7GTkxP/yfGlvH/m/mzaBIMuXUHPQ7ex\n7UPYEjQEI8vL+eD7E6VB+t4yRn3uI+oboCowClnTA66XSVHmx0uxIyLd8st7ws3jARj6uyWUj6il\nf//QiEn06cPxVWOZMgX45ZvQOxz+NUVgX+kHd4/hlFPgLwe8ITIUUN4NahcMgHsDq+43ke43oPT5\nwXxr4Aj+cEcDXBXIXpA/RUVw1vCh3PZVkb3973+LHTukh8cx9q0RrLh1MAwS2evTFyYFcy6fXwj1\nfxkFlQMpHlPD3jcv/TjM6MMPRYl/dMtoeK0/7C2yZ4pgfBDC2dgIpXeOpW5xH9gvlL0kbhrH+KJe\nfP/2LTzQQ7oq33zTa6hvmACre0BCZK9bN/jEvrB8WWDAXLkv53+zGwNO2sD/NayluhrWfiD1snYX\n/Kx+P75weBlTrqii8Zh1GCMGp3Nun5tyIA/+vZhrl4ayN2iQNMxl5fDkfpNJJOC6Vau4Z9Xmj0eK\nRo+GVcuLKbrkQMrKYO9ZK1nSfStDh0gD0dAAfFiKmRV4B99egd13W1L4KBvLKb5mIs8+C9fuWMY/\n3gwtsj32gL1Ke/DmWaHsjZlSw557St6vWw8Dq3sy54DxJBKw55wlrN4Ztua9+8DeO/uw6HtjZUcg\ne337BYagBbOoH9M/HMNFF8E5m99gyfJkT3vwigFs+N2eHHooVP9iEXV10iu+eQuseh+oGAz/jNd7\npghOHzyUCf8ZxqTP7uandW+xcmXECH9wBCwYTMnwXTRc9HaK4Tp5+SgW3TwQRtUw6Oql7LsvPPM0\njBgJa9cAd4+m5I3+1I/ezujrlrOuCvr2g08Ecxi/WTOW703tQ/2EVNkbPASu3XMcv/9RL16q3wKn\niez16i2dJL17wYh7JzDr7B6cdH2q3hs0CDb+UPQen9tAvzNF9qzXmVP0q/244v+VMezMKm58dx3V\n1ZJ/mzYFhu/FovdKv7qWgV/ZkPJy8u6XTmb2bLi9ZhXvD9/M0CGiV7ZWQ83WYnb/8EAx9E5bCZ/c\nyj7jw8U7Rvcr5crS/WXpc1bQMEFkb9y4wMH9qJy3T5soef69ZTAulL0hQ2Djoh6YGyaIw3X+Uhgp\nem/oMBg2FI4c3pMh943n0kuBS5fAoOSeuAHr+7DturEi6796k4Om1NG7N7y3Atasht7L+1EzZ4ws\nfvY/bzBmnwZWrIAee4ghOqZqAK9elKr3iopkNP6ccYP57ogR1DQ0MOW5N1i3XgykzZsBC6VPDeXp\nS4bR2Gs3U+a9lZyxBg6tGsFL14R6L8rXi0bxv98fSNneNTScv5S995Z0bd4MVeuAu0TvmfHbsd8N\n21xTJPpz501jeee+UO+NGiWG5MdlfNM4eK8XHBzKHsGiZDtroOaKZL1XVhYspkFgdF0Ryh4nrI0m\nnz+P24+xA8o4869VvDtW2txJk2B3HSx5Cz5xx4G883pym2uMyOfWrcAPJ8u+k1cx9ERRxh+3ibXF\nlP78QG66Cf7ebSWbR29lzRqvM2N7KVy2v4xinrlC8sBj30HlLDllIpWV8OOVy/igx0es/E9YvsdP\n6sFJ70+QDpgLQ9nrPwAO2B+GfNSTZ78+nqoq6H/9ErYU1X6c/v4DoH9VH45aMZbTT4frer3JP56q\nS+pkPXF0Px44cQwAx73xBm8ua2DtGs8BqZQ2d/RoqL1mEeuqpMN06FCpf/XzBvP8Ren1nn0k1d7z\nOXfoCG4/fTB1fZNl76CDYMNGOGjpKE4cMpDvXiWyF8X8ZTS/mt6fn9/h2XtGRow+WAvcOhbeSm1z\n+/ULytaXvdPfZ9gwcbQ+1l2RNjfKUS/ty+X/3Y0b/72BpRPW8vY7YqsNHx50pv1iP8p2lbHXeVUs\nHbOOTx8Gby/x5mhfnGrvAZSWwf77wUffniwO4tcCew9xkopLYNe2YviJ2HvFZ61k8LFbQ7kESmpK\nqf9p0Oaes4LxM7ax/SNY5+rdxnJmLJnIoYfC4inLWN/zI95fxcfy12tbD7bPChqQQPZKSqHemW3v\n9WRm/XhuuQVOXbKENbW1vPkWbA6csP/3xT5cO17a3K+8+SaL3qtLsreGr+tH1bVj+Nzn4NWvvsHY\nfRtY/h5sD/LmF58fwKx9Re8d/PSilBFX1+b2GdzA2HvfYPHrUm5O9rfdM5TGR4ZBn930u/EtBvSH\n7YFq79UTlt0wAjs/1HvDR0B5mWcT/n0U/9VzIIefXMOs7UvDOuG2d4f23sBZy+nVC/4z47PvWPvh\nvimCEqHDjPQZY44FfgsUA7daa6/OeELEMLFWFjJJvmjqcVFKIzkQ1wtSXx8o2ga5Xl1d+utu2QJE\nY8CDY+vrgut7oXbRHqk//AEuuA3sKfHXP+EEmPvv+P8IRoSszfzC9jj2iJm/AaHDB9LLkNTbniYP\n4voRqqrCXpGNG2Djf5L/X7MG8BqEaC9ktusXFYEJlndeVwXUS4/b+PGBcdUA9cFoXJ8+sC24RmmZ\nOA7LAyfivfeAA5Kv3bs3ZBvFr6+XUTBjvGzxJlz/61/hsbW7U0NZJuwDvjlcUyN50NDoKTrk+ZyM\nOiUalx+2EVat9sLugh6z0lKoSz0ckF7iifvCA4Htk2mO065dsDiiCH/3OzBLoPGLwaNb2KMn1BWJ\nA5ZISIjwCqSzw3jhIy+9JKFbfDHct6NGyq6uTnp0f/QjeGYYvOP15K/9QJ61oUF6yDZtgtKx0GiT\nR6usFWNhwpHw3FZoiPQOuzx88UVgDy8NO2BbZJ5NfT0sfTdswKqWw5Hfg3POgcYDoagY+vSWcw2w\n+PXk84uKYFvg8Ll7/+MfEgpaPjs1rzcEvenOwW9KP123cgldLXoCivtD/c/Sn1+fRq8tXhx+r9kB\n76+U7/5IiOuRLSlODpUDqRfR3njHli1w9ixofBc4ONxvg/mLZeWiL5YsiT+/OKK/a2qSHT4IR4Pv\nvAsWdwteb1Mkja9P3W5SHD6QejFzJtivAgk5ZtTIQEfVAv50AAvvLguaHgsr34AjfhnI4jlySGOj\nF268kbR6dP16RCc2pP63ripYUfg1uCAmOsWxeRPg8r5RHNHevcNRho92QKMX1rgp6OSp2SGfLa/F\nX9dFxKz4D5z9c1j8Niz6eupxdXUS8tx7JLB3YIy78rFS71Nw7baRkbWzXg3rcvW2cJ7Xx4cbiaTw\nXUYbPOuOyOhSpnYFpO42NoTGYxS/xz+XenjxxbD+XaibBgT9Pu+vCvN/0iR4J6Ifou+nA3meqg/4\nuI131NfLiFD9N8AcEoxyuvyz4TFxrFwpbfr550PtOYA3p26//aXTYdm81PN27ZKojKcfB4L6smUz\nMChM/+ZNsPldWHarRB4d+mhqVM3LL8OJd4lerj4ZPtou9bmhIbkOl5bC+0EfdVWVdLSNHwfLK+Of\nCxOev99+8FaaMn/55dR8hnA+c9WjMP8V6LUvfFSa3A6D3GNeNH9s4PClw0jdiXboG6RNiOqudJSW\nySjllCnQ+FlgOh+Xtx8ltHs3LH0HGCO6cY+eodNXXAINMdFadbslD7rFPEfPXsH5nuyPGiVq0Ccq\nc6Yotfz/8Q+Jain6Aez9X8kjvXH2a1L+2zCSY+M34UObXGcf+hc8fY84wNU/TA3z/uADaaPde7kX\nLU5+pqfmw+eD0fmqNCOeIJEI/1kZlpuTfer4WHdv3pSctipIsnVByr63H8pqJczziXeAH6a/P4jN\nI7b1+H0yH+mubW27fxBH7z1ELZYBrwMTM5/zSRsGMjXtU1RkrTHy/eqr7ccsXCj/NfV6++wTf49s\n5/XoYe2YMdYed5y1U6ZYO3p06jElJdaecIJ879bN2h/9KPn/4uLm5UH0M3Zs7unuiJ+ZM629+OLc\njp0xI/n3uHGpxzj5AGvPOCPMm6Iia0tLW5bWffax9vDDk/f99rdtk08XXpj8bNHPEUdI/nz60/m9\n7/Dhct3u3eV3//7WDhoUpmXAgPzcp2dPa/v2lXoTV6Zz5oT1NVqOcfWvOZ9+/USXTJqU+p+ve+I+\n5eVtIwe5fDKlE6w96KDUfUceKfVp2DAp7yOPtHbkyPB62a7pPt27i8wce2z6tJSVWTtqVObrlJRI\nOXRWvZbv8jzgAGu/9CWR0bg8/+Qn8ycfcZ/TT7f2pJNa9gzRfeXlUq/LypKPmzKl6Wns3z9+/7Bh\nrVMmvg5ybbkx8eWTS3706JGcD80twyOOEH3YnDKO+8S1sbl+evVKTcegQfHHnnpqst718zfaJqQr\n62z51BIdPXBg8222sjI5t3t3aydObPr5555r7W23yffeva296KKmXyNb2tOV8/jx0i6nO69Hj6aX\ng79v8OCmnweZ24WmtFf5+LT8Xp+0Oflb7e3wBU5fAnjc+30JcElrOX3FxWEGl5WJgWattVde2Tzj\nIK4i5FNYfMXVXIfDGElnaWnmZywuFsOgrQS9JZXBr5Tl5dbecEPLrpdL+fpOeD4/cU5Ka3x82e+K\nn7Z6/tLS9A1Ue+dBrmnKV4eS+xQVWTttWvs/r37iPxddJM5Ta96juNjan/wkv9ecMSPstJ0+vfXS\nne/6EP20VRtQyJ+ysmR91hr6tqQkt+vm+96lpdK5PWdO8+zU0lJrTzut6c/RlOdrzfbNGCnfrl5P\n0pd9bk5fhvV52pQRgD8IvybYl4Qx5lxjzCvGmFfiLuJW4kw+J/U4994eCN8PBjIZsrxcrpNp5aIo\n0cUuQEKc/CVkM6UnG34IQrpQjXQYI+/X+fWvZYGKp5+WJXlnzIhPS0ND8nLpbU2u+VNSIu9zcsfX\n10t4XC5Y27Tyjb7PZvjw1KW+W0pTy7W5uNW9iooktDgaXtzRiKvTLcGv+615X/8lug5jgjkRrZTn\nzU2vMan1oahIXmCdT9ItPOXwl6PORD7lIZ/Xy3e62prFi2VRkDlzkt93mU8aG8PXvOQLt3hUIgGf\n/nT245tTtxsbZSXjfNddPx1xdkRr0RllNReZjOrd1njOhobsbUhRkSzEk0/q62WxqEyrdGc73w9v\ndu9X9POoKfkVlwfWtryORO1mt4DZd74jtvo557Ts+m2Jv6hUPjAm+X2/zaK9R/mCkb2TkHl87vdp\nwE25jPQVFYn3P3Om9PTNmRP2speUSO9l9+6hd+yOLy8Ph8rdSJ/rLbzyytRwkUy9GKWlqWEac+ak\n9prOmCH7/PSk69HwexVLS8P0lpXFj/bFjeQVFaU+n/+c3bunPlNpaWoai4okL90oSUmJtaecEj+a\nUVoqIV3+87leUpfGGTOkXFxa/fKIPl/c/66s3TO4cnTpztbbVF4u94/23BYXh+F3JSXJ94zKy8KF\nko4ZM+S/XHv+4nqLXf5m682JylmcbOby7HPmiIy755gxI708FhVJec6YkVouRx4p10t3rnvepvSQ\nFxeH1ywpCetR9LldmK0LuY0+t7uvL7tOlqIj59Fr+/U3U9oz5XX0uq6X1umoaP2OK1dX7zM9W7Ru\n+Hl30UXpy9Zd0+mIiy4Ke35dvrv6VVQU6paW9O76uqW4OLlcXP3NFJpWXCzpnDmz5SHW0Wu6NJWV\npcq6yycXXhX3/C6/06XfPWe2vHNy4vSjX07Rso6GDzvd6s5LJz/pPnPmJLcP6Z7llFNSn2XSpNR8\ni3tmp398PZ0ufaecIjomU/0rL09tv9Ol26+DTu/5I//RvPafxd3H6f0jj0zVL74cXHllmE/+dUtK\nwjyJk710emDmTLletLybKu9+fc/1/KKieF3i9EKm+p6unjo7IVr2vl6Pk5u4so3aR1EdGD2ntDQ1\n3f49/fY/F9mI5oNrPzLJortmVK/6947LA9/ucc94yinx5RCtf37d822mmTOz25elpan1O6583T2c\nbRSnS/3nj7uPn0bf3ovarunkKq7c/LLy2x2/7qZLk1/u6eyddOdF5S/admeyH12e+7omrp0Mz81t\npK9DrN5pjEkAs6y1nw9+XwJgrb0q3TkjRx5iL7vsldgltdMtn+ovwQ3Z37vlvwNm8mQ5111jwIDw\nHSpueXF3rFsuH2Si9P33ywvA3dLKfnoWLZIJ+Vu2yKpbEybARRelXs9Pr/tv3Trp5XRp858rl+XG\nXTqqq2U7fLjcO9c8iz6H/+x+3kXTH/dOu+jz5VpemcrapWny5DCfhw5NTaO/Py4d2eQlrjwhLJs4\nOXFLXE+dKou6TJ0qr2xwsnLAAfF5686P3iMqm76M+sf6shl9Bv+6ccdHyzQuv9PJZPTa/j16906W\nv7i8ziaP/j1yKcuobEbPjXtmvyz9PI/KWfS6fn5F8zsuDX7eu3THyUGuchqV86i8ZFpmOq6ORp85\nXT7E6bW4V99E6280rVHdku6ZovKXri7G1Yc4PRLV/dl0b9x56eputBzSPWM2PTh3rixoEKe7/eOd\nju/WLXztgat7ixcnt08O/1mix1VWygItH3wA3/pWctuWS92KPpvbVlenpse1oZMmhYtQRPM9Lt2Z\ndJl/bFw64vREpvd+Rcsl3fUztaNxujKabt+eOOCAeL2aTrbSyWo0nyZPlqiZpUuT662fty5P/PRm\nau/SlUWmNjiTXZFOT+aiU9zxvvz67W06WyqTbER/59p+ZLN3sumGbLZYXLpy0fPpZDDX+u0TV2a+\nfMbJWi6vPIjqtYkT09vK6fI8XTlnsmUytYFxOiybLZzOhsq1vN29v/OdkWutXZN1PLyjOH0lwLvA\nUcBa4GXgm9ba1HV2Aw455BD7yiuxUZ6KoiiKoiiKoigFjzHmVWvtIdmO6xCvbLDW1htjvg88jqzk\n+adMDp+iKIqiKIqiKIqSGx3C6QOw1j4CPNLe6VAURVEURVEURSkkOsrqnYqiKIqiKIqiKEoroE6f\noiiKoiiKoihKAdMhFnJpDsaY7cDS9k6HosQwENjU3olQlDSofCodGZVPpaOisql0VEZba7O+7bbD\nzOlrBktzWalGUdoaY8wrKptKR0XlU+nIqHwqHRWVTaWzo+GdiqIoiqIoiqIoBYw6fYqiKIqiKIqi\nKAVMZ3b65rZ3AhQlDSqbSkdG5VPpyKh8Kh0VlU2lU9NpF3JRFEVRFEVRFEVRstOZR/oURVEURVEU\nRVGULHQ6p88Yc6wxZqkxZrkx5uL2To/S9TDGjDLGLDDGLDHGvGWMOT/Y398Y86QxZlmw7RfsN8aY\nGwOZfcMYc3D7PoFS6Bhjio0xi4wxDwW/9zLGvBjI4D3GmLJgf3nwe3nw/5j2TLdS+Bhj+hpj7jPG\nvGOMedsYk1DdqXQEjDE/DNr0N40xfzXGdFPdqRQSncrpM8YUAzcDxwETgW8YYya2b6qULkg9cKG1\ndiJwGPC9QA4vBuZba8cD84PfIPI6PvicC9zS9klWuhjnA297v68BfmOtHQdsBb4V7P8WsDXY/5vg\nOEVpTX4LPGat/QRwECKnqjuVdsUYMwL4AXCItXZ/oBg4GdWdSgHRqZw+4FBgubV2hbV2N/A3YHo7\np0npYlhrq6y1rwXftyNGywhEFu8MDrsTmBF8nw7cZYUXgL7GmGFtnGyli2CMGQl8Ebg1+G2AacB9\nwSFR2XQyex9wVHC8ouQdY0wf4EjgNgBr7W5rbTWqO5WOQQnQ3RhTAvQAqlDdqRQQnc3pGwGs9n6v\nCfYpSrsQhHRMBl4Ehlhrq4K/1gFDgu8qt0pbMhu4CGgMfg8Aqq219cFvX/4+ls3g/23B8YrSGuwF\nbARuD8KPbzXG7IHqTqWdsdauBa4DViHO3jbgVVR3KgVEZ3P6FKXDYIzpCdwPXGCt/dD/z8qyuLo0\nrtKmGGOOBzZYa19t77QoSgwlwMHALdbaycAOwlBOQHWn0j4E80inIx0Tw4E9gGPbNVGKkmc6m9O3\nFhjl/R4Z7FOUNsUYU4o4fH+x1j4Q7F7vQo+C7YZgv8qt0lYcDpxgjFmJhL9PQ+ZQ9Q1CliBZ/j6W\nzeD/PsDmtkyw0qVYA6yx1r4Y/L4PcQJVdyrtzdHAf6y1G621dcADiD5V3akUDJ3N6XsZGB+splSG\nTLJ9sJ3TpHQxgrj924C3rbU3eH89CJwRfD8D+Ke3//RgJbrDgG1eKJOi5A1r7SXW2pHW2jGIfnzK\nWnsKsAA4KTgsKptOZk8KjtdRFqVVsNauA1YbYyYEu44ClqC6U2l/VgGHGWN6BG28k03VnUrB0Ole\nzm6M+QIyZ6UY+JO19tftnCSli2GMmQI8C/ybcN7Upci8vnuBPYH3ga9Za7cEDchNSKhIDXCWtfaV\nNk+40qUwxkwFfmytPd4YMxYZ+esPLAJOtdbWGmO6AXcj81K3ACdba1e0V5qVwscYMwlZZKgMWAGc\nhXRAq+5U2hVjzC+BryMrdC8CzkHm7qnuVAqCTuf0KYqiKIqiKIqiKLnT2cI7FUVRFEVRFEVRlCag\nTp+iKIqiKIqiKEoBo06foiiKoiiKoihKAaNOn6IoiqIoiqIoSgGjTp+iKIqiKIqiKEoBo06foiiK\n0qExxswyxtiYz7z2TpuiKIqidAZK2jsBiqIoipID25D3tUX3KYqiKIqSBXX6FEVRlM5AvbX2hVwO\nNMZ0t9bubO0EKYqiKEpnQcM7FUVRlE6LMaYkCPU83xhzozFmI7DI+//LxphXjTG7jDFVxpirjTEl\nkWt8zRizzBiz0xhTYYw5NLjmqZF7zIycd4UxZl1k32hjzD3GmK3GmBpjzKPGmPHe/+OCa33FGPNH\nY8w2Y8waY8xlxhgTudZBxpiHg2O2G2NeMMZM8/4fEFxjQ/B8zxljPpWXjFUURVEKCnX6FEVRlE5B\n4Hz5H99JuhgYCJwG/DA4/pvA34FK4ATgCuC7wdZd81Dgr8BrwInAo8A9zUzfQOB5YBxwLvB1oC/w\npDGmPHL49UA1cFJw/18G93fX2i+41iDgO8BXgAeBPYP/uwFPAZ8DLgRmAFuBecaYwc1Jv6IoilK4\naHinoiiK0hkYANRF9h0DVATf11hrv+n+MMYUAdcCf7LWfj/Y/YQxpg6YbYy5xlq7FXEW3wJOttZa\n4LHAoZrVjDReCJQDR1lrq4N0LARWAmcCc7xjn7LW/r/g+5PGmOOALwMPBPtmAZuBI621u1z6vfPP\nACYAE621K4J7PQW8izi9lzQj/YqiKEqBoiN9iqIoSmdgG/CpyOdF7/+HI8fvC4wA7vVHB5HRse7A\nxOC4Q4EHA4fP8QDN42jgceAj737bkFHEQyLHPhH5vQQY6f2eBvzNc/ji7vUysMq7VyPwTMy9FEVR\nlC6OjvQpiqIonYF6a+0r0Z3e/Lz1kb8GBtuoc+UYFWyHABsi/0V/58pAxOE6Jea/6MIy1ZHfu4Fu\nAEHYan+gKsu9ppA6+gmwNJfEKoqiKF0HdfoURVGUQsBGfm8JtmcD/445fkWwXQ9E58BFfzcA9UBZ\nZH+/mHsuAq6Mud+HMftisdZaY8wWYFiGw7YALwD/HfNfutFBRVEUpYuiTp+iKIpSiCwB1gFjrLW3\nZzjuZeAEY8zPvRDPL/sHBE7YWiRkFABjTDFwVORa84HpwL+ttbUtTP984GRjzGVprjUfuBxYaa3d\n1MJ7KYqiKAWOOn2KoihKwWGtbTDG/Bi43RjTF5lrVweMRVbJnB44U9cAC4G/GmPuAA5EFl2J8n/A\nucaY14H3gW8DPSLHXAd8E3jKGHMT8AEwFPgsUGGtvbcJj/AL4CXgaWPMb5BFXQ4G1ltr7wRuR1b1\nrDDGXI+MXA4EDgNWW2tvbMK9FEVRlAJHF3JRFEVRChJr7V8QB++TyKsb7gdmIs5UXXDMC4ij9ing\nH8DxwMkxl7sMWeDlSsThegW4K3K/DYjTtRyYjcwnvAboRXyIaaa0vw0cgcz9uy2494nAquD/nYgz\nuQAZ8XuiL8oGAAAApUlEQVQS+C2wV/B8iqIoivIxJnnBMkVRFEXp2gQjg1uB06y1f27v9CiKoihK\nS9GRPkVRFEVRFEVRlAJGnT5FURRFURRFUZQCRsM7FUVRFEVRFEVRChgd6VMURVEURVEURSlg1OlT\nFEVRFEVRFEUpYNTpUxRFURRFURRFKWDU6VMURVEURVEURSlg1OlTFEVRFEVRFEUpYNTpUxRFURRF\nURRFKWD+P/Hze4jc6eSDAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_multiqubit_marg.plot_power_spectrum(sequence=0,entity=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this analysis we can isolate the drift frequencies of each qubit" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[25 26]\n", "[63 64]\n" ] } ], "source": [ "# Drift frequencies for the first qubit\n", "print(results_multiqubit_marg.pe_drift_frequencies[0])\n", "# Drift frequencies for the second qubit\n", "print(results_multiqubit_marg.pe_drift_frequencies[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can again plot the estimated drift probabilities. Again, these are more informative that what was obtained without marginalization. In particular, the oscillations are noticably stronger, as they are each the sum of two of the drifting probabilities from the \"raw\" unmarginalized case." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4AAAADpCAYAAAB1J1lYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8V8cSJSSmLzGoAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creates an array of the true probability.\n", "parray_multiqubit_marg = np.zeros((1,2,2,T),float)\n", "parray_multiqubit_marg[0,0,0,:] = np.array([pt00(t)+pt01(t) for t in range(0,T)])\n", "parray_multiqubit_marg[0,0,1,:] = np.array([pt10(t)+pt11(t) for t in range(0,T)])\n", "parray_multiqubit_marg[0,1,0,:] = np.array([pt00(t)+pt10(t) for t in range(0,T)])\n", "parray_multiqubit_marg[0,1,1,:] = np.array([pt01(t)+pt11(t) for t in range(0,T)])\n", "\n", "results_multiqubit_marg.plot_estimated_probability(sequence=0,entity=0,outcome=0,parray=parray_multiqubit_marg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example 2.3 : Marginalizing over qubits loses information and can miss detectable drift\n", "Marginalizing involves throwing away information about correlations between measurement outcomes. As such, it is insensitive to certain types of drift. In particular, if only the correlations between measurement outcomes are drifting. This is perhaps a rather contrived situation, but it is possible. For example, consider the drifting probabilities:\n", "$$ p_{00}(t) = 0.25-0.05 \\cos(0.05*t) $$\n", "$$ p_{01}(t) = 0.25+0.05 \\cos(0.05*t) $$\n", "$$ p_{10}(t) = 0.25+0.05 \\cos(0.05*t) $$\n", "$$ p_{11}(t) = 0.25-0.05 \\cos(0.05*t) $$\n", "In this case, as demonstrated below, the analysis of the full data clearly demonstrates drift, yet the analysis of the marginalized data does not." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N = 10 # Counts per timestep\n", "T = 1000 # Number of timesteps\n", "\n", "outcomes = ['00','01','10','11']\n", "\n", "def pt_correlated00(t): return 0.25-0.05*np.cos(0.05*t)\n", "def pt_correlated01(t): return 0.25+0.05*np.cos(0.05*t)\n", "def pt_correlated10(t): return 0.25+0.05*np.cos(0.05*t)\n", "def pt_correlated11(t): return 0.25-0.05*np.cos(0.05*t)\n", "\n", "data_1seq_multiqubit = np.zeros((1,1,4,T),float)\n", "for t in range(0,T):\n", " pvec = [pt_correlated00(t),pt_correlated01(t),pt_correlated10(t),pt_correlated11(t)]\n", " data_1seq_multiqubit[0,0,:,t] = multinomial(N,pvec)\n", " \n", "results_correlatedrift_marg = drift.do_basic_drift_characterization(data_1seq_multiqubit,\n", " outcomes=outcomes, marginalize = 'std')\n", "\n", "results_correlatedrift_full = drift.do_basic_drift_characterization(data_1seq_multiqubit, \n", " outcomes=outcomes, marginalize = 'none')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkyRJkqQBs+iTJEmSpAH7D/kF7PkjOWjFAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_correlatedrift_marg.plot_power_spectrum()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADpCAYAAACQlV5xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiISBP7f3C2JKJP+y9GAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_correlatedrift_full.plot_power_spectrum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What this demonstrates is that it is important to carefully decide how to analyze data from a given experiment, with physical motivations for the analysis used." ] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }