{ "metadata": { "name": "", "signature": "sha256:fcc826c0d940ab56b2e6f8e8a4dd7cca2354afe2f8bdf37474ca5df2e4cbbfbc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# - compatibility with Python 3\n", "from __future__ import print_function # print('me') instead of print 'me'\n", "from __future__ import division # 1/2 == 0.5, not 0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# - show figures inside the notebook\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# - import common modules\n", "import numpy as np # the Python array package\n", "import matplotlib.pyplot as plt # the Python plotting package" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# - set gray colormap and nearest neighbor interpolation by default\n", "plt.rcParams['image.cmap'] = 'gray'\n", "plt.rcParams['image.interpolation'] = 'nearest'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# - import numpy.linalg with a shorter name\n", "import numpy.linalg as npl" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# - import nibabel\n", "import nibabel as nib" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# - import stimuli, spm_funcs from pna_code\n", "# Call over your friendly instructor if you get an error importing\n", "# You might want to make sure you have the 'for-class-day9' branch checked out in pna_code\n", "import stimuli\n", "import spm_funcs" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating for the whole brain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we load the processed dataset. The dataset has had the following transformations:\n", "\n", "* drop first 4 volumes;\n", "* slice time correction;\n", "* motion correction;\n", "* coregistration of structural to functional scans;\n", "* registration of structural scan to template;\n", "* resampling of functional and structural scans to match template.\n", "\n", "The processed functional image is `wafds114_sub009_t2r1.nii`.\n", "\n", "See `nipype_ds114_sub009_t2r1.py` for details." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# - load 'wafds114_sub009_t2r1.nii'\n", "# Get 4D data\n", "img = nib.load('wafds114_sub009_t2r1.nii')\n", "data = img.get_data()\n", "data.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "(53, 63, 45, 173)" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# - get number of volumes, 3D vol shape, number of voxels in one volume\n", "n_vols = data.shape[-1]\n", "vol_shape = data.shape[:3]\n", "n_voxels = np.prod(vol_shape)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "During processing, we dropped the first 4 volumes:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# - number of volumes dropped from the original 4D data\n", "n_dropped = 4" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to run our linear model on these data. To run the model we want a two-dimensional array with one column for each voxel. The voxel time courses are therefore in the columns. The array will be shape (`n_vols`, `n_voxels`).\n", "\n", "Be careful - remember the difference between reshape and transpose." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Reshape data to n_voxels by n_vols, call this `data_2d`\n", "# Then make result into n_vols by n_voxels\n", "# Check shape is (n_vols, n_voxels)\n", "data_2d = data.reshape((n_voxels, n_vols))\n", "data_2d = data_2d.T\n", "assert data_2d.shape == (n_vols, n_voxels)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have our data ready for the estimation, we can compile our design matrix.\n", "\n", "To do this, we go back over the steps we covered in the [day6 regression](../day6/regression_solutions.ipynb) notebok." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The TR for this run was 2.5 seconds." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# - TR in seconds\n", "TR = 2.5" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There was only one event type for this run. See the [ds114 data description](http://practical-neuroimaging.github.io/example_data.html#dataset-ds114) for details.\n", "\n", "The experiment was an on-off block design. During \"on\" periods the subject saw a written noun every 3 seconds and had to think of an associated verb. During \"off\" periods subjects saw scrambled versions of the written nouns. The run started with 10 seconds \"off\" followed by 6 repeats of (30 seconds \"on\" followed by 30 seconds \"off\"). The run finshed with 30 seconds \"on\" and 25 seconds \"off\".\n", "\n", "The event onsets in seconds and event durations are in the text file `ds114_sub009_t2r1_cond.txt`.\n", "\n", "We can load this text file and return the neural prediction using our `stimuli.events2neural` function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Use IPython to check the signature of stimuli.events2neural\n", "#: stimuli.events2neural?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The event onsets in `ds114_sub009_t2r1_cond.txt` refer to seconds in terms of the original run. During processing we dropped 4 scans at the beginning of the run.\n", "\n", "So, the number of volumes we need to specify to `stimuli.events2neural` is the number of volumes in the original run, before we dropped these volumes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create neural time course prediction with \"stimuli.events2neural\"\n", "# Remember the extra volume!\n", "# Plot your neural time just to check it is what we expect.\n", "neural_prediction = stimuli.events2neural('ds114_sub009_t2r1_cond.txt', TR, n_vols + n_dropped)\n", "plt.plot(neural_prediction)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We dropped the first four volumes, so we need to drop the first four elements of the predicted neural time course:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Remove the first four elements of the neural time course\n", "# Check the neural time course now has the same length as number of volumes\n", "neural_prediction = neural_prediction[n_dropped:]\n", "assert len(neural_prediction) == n_vols " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember HRF convolution? This is revision from [day5](day5/hrf_convolution_solutions.ipynb). You might want to check back there to remember how we did this last time.\n", "\n", "We are going to use the hemodynamic response function in `spm_funcs.spm_hrf`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Use IPython to check the signature of spm_funcs.spm_hrf\n", "#: spm_funcs.spm_hrf?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need a time vector giving times at which we are going to sample our HRF function. We want to sample from time 0, up to 35 seconds, sampling every 2.5 seconds (our TR):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make `times` vector at which to sample our HRF\n", "times = np.arange(0, 35, TR)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now sample the HRF at these times to get our *convolution kernel*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Sample the HRF at given times to give convolution kernel\n", "# Plot the HRF values against times to make sure it looks OK\n", "hrf_kernel = spm_funcs.spm_hrf(times)\n", "plt.plot(times, hrf_kernel)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `np.convolve` to convolve the predicted neural time course with the HRF samples. This will give us a predicted hemodynamic time course.\n", "\n", "Don't forget to drop the last `length_of_kernel - 1` values that resulted from the convolution. As you remember, these values that we are trimming are the predicted values for volumes after the end of our run." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Convolve the neural prediction with the HRF samples\n", "# Drop the last `length_of_kernel - 1` values\n", "# Check the result has the same length `n_vols`\n", "# Plot the result\n", "N = len(hrf_kernel)\n", "hemo_prediction = np.convolve(neural_prediction, hrf_kernel)[:-(N-1)]\n", "assert len(hemo_prediction) == n_vols\n", "plt.plot(hemo_prediction)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have the hemodynamic prediction for the event.\n", "\n", "We now need something reasonable to model the drift of the signal over time.\n", "\n", "Start with a linear drift. Use `np.linspace` to make a linear drift term that starts at -1 for the first volume and ends with 1 for the last volume:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Linear drift term using `np.linspace`\n", "# Starts at -1, ends at 1\n", "linear_drift = np.linspace(-1, 1, n_vols)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compile the design matrix. We want a design matrix `X` with three columns:\n", "\n", "* The hemodynamic prediction\n", "* The linear trend regressor\n", "* The vector of ones that will model the mean\n", "\n", "It should be shape `n_vols` by 3." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Compile design matrix X with columns:\n", "# * The hemodynamic prediction\n", "# * The linear trend regressor\n", "# * The vector of ones that will model the mean\n", "# Show with `plt.imshow`\n", "X = np.ones((n_vols, 3))\n", "X[:, 0] = hemo_prediction\n", "X[:, 1] = linear_drift\n", "plt.imshow(X, aspect='auto')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can estimate the betas on our reshaped `n_vols` by `n_voxels` data, using `npl.pinv`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Estimate betas for every voxel, using pinv\n", "# Check resulting betas array is 3 by n_voxels\n", "betas = npl.pinv(X).dot(data_2d)\n", "assert betas.shape == (3, n_voxels)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a contrast to select only the first beta. Matrix multiply (`dot`) your contrast by the betas array to get a 1D array with length `n_voxels`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define contrast to select the first beta\n", "# Take dot of contrast with betas array to get `con_data_1d` array\n", "# (this is the top half of the t-statistic)\n", "# Check `con_data_1d` has shape (n_voxels,)\n", "c = np.array([1, 0, 0])\n", "con_data_1d = c.dot(betas)\n", "assert con_data_1d.shape == (n_voxels,)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reshape this array to the original shape of the 3D volumes of `wafds114_sub009_t2r1.nii`.\n", "\n", "Display the middle slice (over the third dimension):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Reshape contrast result to shape `vol_shape`\n", "# Show middle slice (over the third dimension)\n", "con_data = con_data_1d.reshape(vol_shape)\n", "center_slice = vol_shape[-1] // 2\n", "plt.imshow(con_data[:, :, center_slice])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we want to get some idea of the error in our estimates.\n", "\n", "Remember our t statistic formula:\n", "\n", "$$\n", "t = \\frac{c^T \\hat\\beta}{\\sqrt{\\hat{\\sigma}^2 c^T (X^T X)^+ c}}\n", "$$\n", "\n", "We get $\\hat{\\sigma}^2$ from the data. We will start with this estimate of error in our data.\n", "\n", "$\\hat{\\sigma}^2$ can also be called the mean residual sum of squares (MRSS)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our first task is to get the residual sum of squares (RSS):\n", "\n", "The residual sum of squares is :\n", "\n", "$$\n", "\\textrm{RSS} = \\sum_{i=1}^{n} (Y_i - \\widehat{Y_i})^2\n", "$$\n", "\n", "$\\widehat{Y}$ is the fitted data - that is - the data as predicted from the design and the estimated betas $\\hat\\beta$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate the fitted data from the design and the betas\n", "# Check the fitted data is shape (n_vols, n_voxels)\n", "fitted = X.dot(betas)\n", "assert fitted.shape == (n_vols, n_voxels)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The residuals are the real data minus the fitted data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Subtract fitted data from real data to get residuals\n", "residuals = data_2d - fitted" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The RSS is sum of squared residuals. The sum is over volumes:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate the RSS\n", "# Check this is shape (n_voxels,)\n", "RSS = (residuals ** 2).sum(axis=0)\n", "assert RSS.shape == (n_voxels,)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\hat{\\sigma}^2$ is the mean residual sum of squares. To get this we divide the RSS by the *degrees of freedom due to error*.\n", "\n", "This is turn is given by the number of observations (`n_vols`) minus the number of *independent* columns in the design `X`.\n", "\n", "Use `npl.matrix_rank` to calculate the degrees of freedom due to error." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate the degrees of freedom due to error\n", "df = n_vols - npl.matrix_rank(X)\n", "df" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "170" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate the MRSS\n", "MRSS = RSS / df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have an esimate of the error in the data, let us compare this to the contrast data.\n", "\n", "Convert the MRSS data to a 3D image, and show the middle slice over the third axis (or the same slice you showed above for the contrast data):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Reshape MRSS result to shape `vol_shape`\n", "# Show middle slice (over the third dimension)\n", "MRSS_3d = MRSS.reshape(vol_shape)\n", "plt.imshow(MRSS_3d[:, :, center_slice])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Returning to our t statistic:\n", "\n", "$$\n", "t = \\frac{c^T \\hat\\beta}{\\sqrt{\\hat{\\sigma}^2 c^T (X^T X)^+ c}}\n", "$$\n", "\n", "We have $\\hat{\\sigma}^2$ (the MRSS).\n", "\n", "Now we need to calculate $c^T (X^T X)^+ c$ (the variance due to the design)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate the pseudoinverse of (the dot product of X.T and X)\n", "# Do the left and right dot product with the c vector\n", "# Call the result `design_variance`\n", "iXtX = npl.pinv(X.T.dot(X))\n", "design_variance = c.dot(iXtX).dot(c)\n", "design_variance" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "0.02227774948076915" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can calculate the t statistic.\n", "\n", "But - before we do this - we have a small problem - which is that the data outside the brain have very small values. This will waste processing time, and lead to division by zero errors, for voxels we are not interested in.\n", "\n", "So, first we want to make a mask to select voxels that have a reasonable signal.\n", "\n", "To find voxels with a reasonable signal, let's first take the mean over volumes of the `wafds114_sub009_t2r1.nii` 4D series:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Get mean over original 4D functional image\n", "mean_functional = data.mean(axis=-1)\n", "mean_functional.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "(53, 63, 45)" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next plot the histogram of the (raveled, 1D version of the) mean image. Select a signal threshold that seems to separate the low signal from the high signal." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot a histogram of the raveled mean image.\n", "# Hint: remember the `bins` keyword\n", "_ = plt.hist(mean_functional.ravel(), bins=100) # Don't output annoying bin data" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a mask that is True for voxels that are above this threshold, False otherwise.\n", "\n", "Show the middle slice (over the third dimension). Adjust the threshold if it doesn't look right." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make mask for mean image that is True for voxels over threshold\n", "# Show middle slice (over third axis)\n", "thresh = 700\n", "mask = mean_functional > thresh\n", "plt.imshow(mask[:, :, center_slice])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to calculate the t-statistics within this mask.\n", "\n", "* Find the contrast (top half of t-statistic) data within this mask;\n", "* Find the MRSS data within this mask; (use the 3D version of MRSS)\n", "* Use the MRSS data and `design_variance` to calculate t-statistics within the mask;\n", "* Make a 3D volume of zeros called `t_vol`;\n", "* Fill the values of `t_vol` within the mask, with the t statistic values.\n", "* Show the middle slice (over the third axis)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# * Find the contrast (top half of t-statistic) data within this mask;\n", "# * Find the MRSS data within this mask; (use the 3D version of MRSS)\n", "# * Use the MRSS data and `design_variance` to calculate t-statistics within the mask;\n", "# * Make a 3D volume of zeros called `t_vol`;\n", "# * Fill the values of `t_vol` within the mask, with the t statistic values.\n", "# * Show the middle slice (over the third axis).\n", "t_top = con_data[mask]\n", "MRSS_in = MRSS_3d[mask]\n", "t_statistics = t_top / np.sqrt(MRSS_in * design_variance)\n", "t_vol = np.zeros(vol_shape)\n", "t_vol[mask] = t_statistics\n", "plt.imshow(t_vol[:, :, center_slice])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now calculate the Bonferroni p-value threshold for a family-wise error rate of 0.05 and the number of voxels within the mask:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate the Bonferroni threshold for FWE alpha 0.05, number of voxels in mask\n", "bonf_thresh = 0.05 / np.sum(mask)\n", "bonf_thresh" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "memmap(7.612783385861539e-07)" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate corresponding t-value using the Scipy t-distribution, and the degrees of freedom due to error. You want the inverse survival function method (`isf`) to get the t statistic value for a given probability value and degrees of freedom:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# - get t distribution code from scipy library\n", "from scipy.stats import t as t_dist" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get t statistic corresponding to Bonferroni p-threshold\n", "t_thresh = t_dist.isf(bonf_thresh, df)\n", "t_thresh" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "4.9841999767739367" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a volume that has True in voxels where the t statistic value is greater than the Bonferroni t threshold and False otherwise. Show the middle slice (over the third dimension)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make volume that is True where t statistic is greater than Bonferroni t thresh\n", "# Show middle slice over third dimension\n", "survives_bonf = t_vol > t_thresh\n", "plt.imshow(survives_bonf[:, :, center_slice])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the t volume and contrast images to disk for us to play with later:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Save the t statistic 3D volume to disk as a NIfTI image\n", "# Save the contrast value 3D volume to disk as a NIfTI image\n", "t_img = nib.Nifti1Image(t_vol, img.affine, img.header)\n", "nib.save(t_img, 't_task2.nii')\n", "con_img = nib.Nifti1Image(con_data, img.affine, img.header)\n", "nib.save(con_img, 'con_task2.nii')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting with nilearn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See if you can follow along using the examples at:\n", "\n", "https://nilearn.github.io/auto_examples/manipulating_visualizing/plot_demo_plotting.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# import plotting package from nilearn\n", "from nilearn import plotting" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the t statistic image with the glass brain display\n", "plotting.plot_glass_brain(t_img)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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2IVh8RMu+KVgYp0OIfzGJ0bMBV4EkQXx8DmefbeTss2Op/6SOL3fAF9vgcNs2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bSPJgi2qY9DdZIjQOmwRb/gHX+X6u1p4gokOFvbAJareAugSavmobori19YWc8q3DT/Lt+a0k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\nqRFfYtGXtS0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4RUlrp4Nrr72WHTt2kJuby+LFi/nTn/501uVKuwqbzdZGJOwKfItsR/yE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\nVaWq6hRWq43MzETWr/+EXbt2ATBv3jzuuuu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"text": [ "" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the t statistic image thresholded at the Bonferroni t threshold\n", "plotting.plot_glass_brain(t_img, threshold=t_thresh)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ox6WoaBAmUxIRESOQy19t8Xuwag7qJxkSh5RbogtOnTpFYmIiXbt2bXLVbl0s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\nKC4uprq6+k9xzFOr1UybNo3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GhIQE5s2bx6xZs4iKimrkNW4fKisrOXr0qK1eRnOYTGZO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8Gnn35KdHQ0XV1dWCyW8MQzLi6O7OxssrKymD9/PgaDAa1WG342PB4PJpOJhoYG6urqcLvdRERE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TV1dHVlYWTqezRS+fDRs28PHHHwNwzz33MH369Kvqb1Fc+p0VExPDnXfeyZ133klZ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\nQBAEQRAEL/8fIrAWd7MUREAAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# Use the `plot_stat_map` command to display the t image on the default template\n", "# Use the Bonferroni t statistic threshold\n", "plotting.plot_stat_map(t_img, threshold = t_thresh)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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4cbCdTs5g6HjHeXz5868F171jx1i9GZ+yS9RHz/cs4XpsN5/TVAaZJ8pVrHg9\nu3TpAiAxG7viq0noi7rSNrJtzFVFBcY3A6Db9flu8phcvzIgvHabN28GkMfZvk3RSc7Qq68O3h/j\nfP+3yikhw6hNli+P/bNPWqCukiNP6QkgNq3w9tMr6qJZuUfvMHS5oMLJlrkmA20xDCMj1IGgkzsD\nnT/84Q8AwlpWyXCjmnRUrr4DmhPHtUTUd0Yzauo2fcqJbofWqE+5Yfu1mi0jxWg9+TJt+uZ0fZaK\nRjgQLkfrXfNCJMOX64efabUwxF6tl2zk7rvvBgB85SuxRCSqtmiuD1/0B/MWJasVxvPiqxHli9Ag\nmpGaSoxea+5blY9k2Zrd7dCvhRY1LW2201exWaMgubyul4zdu3cnZJflfn2+JGpVuxGE2ib1L2I0\nXS5lUGZ0mdak0mNMVTnVZ0sy1CelKnyKjX6vz1A+F6jUp1LziveSr+K6KjnVnR70qZO+zPE+VU7v\nRc0GrXUa1c80X6kDQSd3BjqGYRhGDbksfDv4hfOBgoUYXHE+gG+HP8x2ll8Wvm07bBiCYcZW13nH\nMA4fU3Qc6IPQpk0bbHjmmWAUPGBKGLkx/MILg/dvPP983Po6L6qjaVfVUOvDZyH4/A10RM8ROusF\nceSvVpTP255tpDJAeCzqIxIV0ZRqvghaVbSYXGtKrRx91Ugz+njwGLl+Nlcz79SpE4DY9WvkVOct\nW74cQKjAJc0OvOGNYPnmzbsDiK8az2ulyg7PuUbO+eb9FVUviNZC8lmlGk3D+47bZSRgkOm4sm+r\nD5BGoGlETKyPubVywnxK/QaF/XPNW28BCPPDBPmHBgwIlmnx4YcJ1c3de1BzaWmOHn7PavRjx45F\ntsKIseOPPx5AopLHz1rLLN1o//NFXfm+9yk7+pzWyCT3eDSfmNZDU6L8g6KW12NW1dEXbaXfq78d\n7029ljw+qqpUmK+88sqk7c1VTNExDMMwDCNvqYM0Orkz0NGq5FFzzzrHS4WBVrivRpC+d7eRTP1x\n8VkM3BcjFtSaplWvlqdaP7TytfaWr0pvVZXZXXz5IniukmWh1WPV+Wk9Rq1VpDkjshGqGb66XTw/\n6qujc+rJ/Gs0qzLPhyo6GqFXVf4ndx++a67Kil4nX9ZY7fsacaLb4Tljn9eSDKmWaOA5pqLD/bvx\nbwUFBQmZ0JPl0dHsuzwmfmYk0axZswBkZ5QLfXM0B5ZG7fF3reS93Zm6ajt6YexN+UKgoVNs40Ln\nX8/q8G0p4txnAAAgAElEQVTh6tVBpJOvH0ZFcBKfiqL+bqrsJMt3pr6K6vej+zrczMM8p3ovat/z\n+dXptSJczpeHi8+AqvxTc5m6KNaSMwMdH9vXhe/bdvMvl5QhQ8DH4decDKCrXnq3ps0y8o0VK4IH\nWiqFSdet6xwMlHLB8bquKD/iCABAIRCvWTd8Mni7Gsn59urTww//Hr5dm67GGYZR55iig8QMoByN\nR/3zoOWjlqjr+e47eG5baz9FWQK+/DNER/aaZ0UtBo0I07w7HPnr9qOs/SjFR1UazdfjbsNX3dqX\nBVTrxfD3bJp/pq+G1rTSSBatpM1XDoTYf5L5JWhGYo1047ZULSLqy6AqhfZ7vQ5RPhUaqad9UCND\nfNFgqjKWonrQL62qau6HDh1KiBBMpmip35LeZ7yv6EuXjf5jPB++vEp6/ZMpXIdL27ZtE56JRPuB\nLyouKk+Qz5dMM4cDiWp8VB9XtG/7zhF/Zxt4DTTaSjPW++5Fn+KvkXOaA46GU77xws9+VvUCUb+n\nQMYGOpnIAAoAp556avJ1LQNoVlCdLKBGjhM3I7gw+TJf+xqAKu5bAHi8b9qalIxp06albVuZzlxb\ntnEjgNjzt6RRZwBASSOg17WOXe2Ewaz7fyUA8j/E2chvMjbQSfWGv++++wAAnTvHbkqdl+Xot3nz\n5nEZPd1ZrLaVwkfBXqBikPPDT5z3O8K3/3TmsZuuWxf4GVQ3k6b6L6jFwGzO9D/g74xQYjQB52ap\nUvHVl7DOl5FTP/tyoNCSYM6bZJWnfdtW60X3pZW9eewbNsRGqdlQzfypp54CAHTrFutFmgWYahTP\nD6+XVofnKy015oJx1+V3vPa0FrlPvqo6ROWCPk7sKz5FTf271O+I+PwZfK/qK6dWLJdjH+p6pHNn\n/shZsU349r0bw/fHN6u8b5sBcL5HR+e9M9dVcnf4vvTd2BS0RmRpFm+eO61GzWt07rnnIlvwZZ5m\nmzWSh89Hnw9WFOyPqfqIKb5nn8LtqLrB41O1AwiVDs1HpvtWfzSf+uhTxFUJ53NXc1L5am75lBxf\nFJb6ryXLDWVUD38edsMwDMMwjBwn6310ks3NAuHInvOW6q+SLlq0aOGdZouqzBuVHVTz3qiPh2YX\nZgQK16N1HhVdoO2tqs4QkBgxQ0uiqkg3X0Sa+veotcNjocXGDNjpnC6oLpr7Ra8z26r+MTxWqhca\nheH6mVA9UOXDVxfHV3FZ8xTRutRIPe1zao2yr6nPVVQtI5+1qp+rk1U3XVAxZT/2+fXps0TzUrHW\n2YVOnq5MobWgiPp68Z6tyneuzfbtQNu2aP3FF9jmiVRq5KnoHVUPLyprcFR/4rnX54cb+cllNDJR\n96GqUpQfWpSPj55TbaNPQVdlSPfPe1Cf/3qPGtUn6wc61eHLN94IOvunzj/Uz1H5wO7bFyWPfgwg\n1umOPKZXuHJYdgfJk+Ib9YGKggJwAvTNytedAFqvsQJM6WDF8rcBJKYuOOtHZwfLuEOiTR9tBLp0\nQcl7sfuWA7qTjukfLlQUrttr8HPB+5Jqtq1PZRI+bcP6Rx+t5pYMw8gmsn6g41MftH6UWjAaFaSR\nFRqB4aNx48Zea0VH6jqy93nXc3nO9bLttPKZCVMrNmu9IJ9VpNlQNWogar5c54STreez6NT/wZfr\nQufkua9Ur0ttMGfOHIz3/Pbll18Gx0DFR+fqaWVr/Sr2STezNbfBZWmtcRuaaVq3pdah1grietyP\nWo9cjn2OAwiuR/8iX6SHL49LVN8nPJc+XyFdj+rl4Vi19GPSc8Vt+jLpEipCs2fHaiNMnDix2m2o\nKfRVZPQpzx993Hid9T7jfajPPTeKr7Cw0FsHypc5O9VaVupLpNXU1a9G+7U6QSeLulLVUdE+z/WS\nnYtkbVd0v6rKKlGV3TWLPF+p7KhRYFQf89ExDMMwDCNvyXpFR4mqQBtl+brRCDs2b0+atbaTWL9K\n1FwsLQdakhyR0zKktUyLQv0htL5QlA8O90efBO6P+UA0q3R8vSE/PtXARefgVdFRZaeiogL7Tjop\nWL9hZd0ozeiaCVq0aIFHFyxAjx494tpy4MABHNizJ8Fnh9dJfXbU14pqiatOamZkniet3uzLeOur\n26RWq69v8npR0dm3bx9OPrkF+EhYvTr+GLQmF9Up7cs+2D6ul+Dv9WH4tuGqVQl5VDSiLa4SYFGv\n8L0TPcnro35LWjcpqGnnBFc1drrhiJvPCD+8A+CqSbH3ZXUXKn7kkUcCCCMB2WY+Y6gE8jpRpfIp\n324Ez6FDhyJzg6WaB8eXIVmjLjXPjy+vky+a0/0uKrpJ9+3LXKzHEJXtOarunG5Pn5W8FzXqj89v\n9ePr0qULAODPf/4zgOyKBsx2TNExDMMwDCNvyRlFx+cXo6NkrSSro3f9nAq+CBhfVBNH6ByZf/75\n5wCATz/9FEDog9O1a1cAodLjs27UIuCcLhUcbpeVpTlfT+uZio7OJUdV8fXlfXDbotejuvPUqkzo\nnH1dorlqaPHTElR/L14HrTrMc6AVyV3Vg9dcK9JrLhe2gdafRs5FVaz3RXSo8hbbftgW9angsVBB\n4Hq8n6ggaN9SPwgeN0kWCVVUVOSNoAmiK6OrcCSoQlrrKpeiWTS6VP1M2Ae1NpjWUdJnAhC7lqqk\n+u5rX8SSD1XR2L/ZnlTzqSVTnFLNUK99WDOb+1SrqDpyqdY81HOg6iifD1R+2R+p4jKPmqvINxsy\nBO9V7u/4DCehzAVyZqBj5A9Fq1Yl/LMxMs8rr2xzHt5tqlw23Xzw1vsAqjF16SYPXDYzePvPf4Vf\nV9wYi6IqAFCxalX0Npc4792Mo26TnGAvwzByg6wf6PjyL+ho2jcqT1XJcUfvUZXRdR3NsKo1Sljx\n94svvgAAfPzxx3Gv9KXp3r07gMQRPK0gKjjMIrx58+a4dtB/oVOnTnHHSgtCc6j4FKkopSfZb3qe\nU83EGlXZty5RPxifn4H6H1HN0CgsQouN/cNdhutqHijNkspr6IuiUTVQ5//1OnCAye3Rx4Pt0T7N\n5VUJYvt4znismtGWx8c+TWVHq6BzO748PodLgwYNEtRC9YfKRh588EEAQP/+sRGWRiupoqPRfDw2\nLUTrqovl5eUJUXTEp7hE5dNRtD9p5Xhf/T3tr8nQdfQYNLqJy6kCG/WsSlV98kWgUm3TKCr2Q14T\n/j/QZwPPkfscMVIj6wc6hmEYhmGEVKxahZUrVwIAjo9Y1siBgY4qOuqt78s+6cuVoJaIWszuPrVS\ns0aWRGX/VKtdvfxLSkoAAM8++2zcdi+7LFZsi8rMv/4V0+MXLVoEABgwYACAcL6dERn0yldLRa1v\nqgGqWPB3WtmaXde1iH3Rb3p9dIrKl204kzA/yrHHHgsgMfOpWp9EzwHPqypAqgQBiSoC1SCtX6Y+\nOho5olmXee014kuVFu2L/J7L8Rxw/+rnwGNX/yWN/tFIQqqbqq6w72n/8Vn6X9y5La4dXJ4zTnv2\n7MHnlfcDADQ77jjw6hX8859x2yKvPPty3GdfNWnWwNpQmdtm/Hhf9qWao9nPVdnTat/qi8Pzrpmp\n3fw0rsqdasXvKF88XV5zxWjdJt+z1Be9CYR9SJ/XquxoVmVfTqCo+l2+KCwfmr+HSg7vSa7Pa8Rr\nrTnh+Kr1zIIIRCMSi7oyDMMwDCNvyXpFR0fdUblliFrjOl+qnu7JFB2OpOkzQ78CXw2VKHWJVhTn\nYDnCVzgvf/HFFwMIlRxCyXLUqFEAwugtVWpIVDQVYXvo+0OoMrgWhPr7ROXB8OV1qW6ejtqA1q9W\nQVZViqifhCpmQV4WyZXjHptGG2l1cc214fMzYP+mkqN9lOqcKjqqILFtVGT0lfvj9qgk8L7R9mpk\nCa1prSvl84civqgdftbMyTznVWU7Zhs1sk3rgWk/YBtpcfuyRqcTjeDTtvqiqlTh0fvL9WNp1KhR\nZGZkX7t8Kod+Vp8xnjvf9daIOD1+95jUT02VHV92ZlXgfc8i3/8cfcZFZc7ntdBIU+1v6ueoKh73\npxGbhp+sH+gYhmEcDn0qB2n8Z8mBfFmlcWMYRv0g6wc66iPgQ0fRGl3Fhx3n1/nQ09Gy+94XVaPW\niio2ainqyF69633QelWGD4+VnezcuTMAf2SKL3OzL9pKs/P62g+EFplmWyZR8926nKoidYn6Ymjk\nj143tVJL5R+qWnDsP64Vzf6nSo0qE1rFWa+V+qjwuqilz+2xzbQG1X+M+6MCtGXLFgDhfUM0Y7Ja\n4OrT46uq7VMhifp48Fxr1ljuj4qW6wPkuz94DlXBZX/Q+ms811TPeK7nzZsHABgzZkzSY6gJPL88\n/6ou8TOX4/Vl23idfTWluI0oRS2qtp0vkpOwXb56gL6swRoZ5yqh+txVHzmfaqzXPaqel+9/CvHl\ndtOagerjqZmd1Q9L1UvNXZTJuoCpUlxcjOuvvx4HDx7EpEmT8MMf/jAj7TAfHcMwDMMw0srBgwdx\nzTXXoLi4GKtXr8b8+fPx/vvvZ6QtWT8kVO/7gwcP4swJZwW/v75wedzy6pvD9eiLo9knVb1x96mW\noLZFfTHUylL/IB3503KkQvPSSy/FHYsvMolKEC0FVWzUH0aTsNEq8mWv1XwNGmnj7ovWrWbo1Xlx\ntZLUgtPrUZfo3DnbpDk/1KLSiD2NWOJr75P6BPv6rFKkawtgz5o1CTk0iGYc1igttfJUhfJFjKmP\nh1qdPr8ULq9KKD9H5R5i+3k8XC/VekF6n6rPkVr3roKh92XfE08MfnPrW2F1+Ha7k3iwdN06AIm5\nflSxqw1UDVBVSu99VUSqqlpOCgoKvIpaqt/7nnH6zPQtR7Sfav4d15dS68RpJJqi0aX6bIrytdHt\nRKnWqrbqPa4Z2HlPaOSi+uORbM/o/frrr6NPnz7o1asXAOCSSy7Bk08+ia985St13hZTdAzDMAzD\nSCufffYZjjjiiOBzjx498Nlnn2WkLTmn6ERl2FVFgZYfX1U5UF8DING6pYWglr6qE2pVq3e/WjPq\nV6H4LAVGP0X55Gg2aLVoeA6oynB59dOgpZHM6lZlRlUm3/y3zpdzPjoTJSHYVu6bFpVaiKqCqB+B\nzp1H1VNr165dwvGq1cfzqfeBWnOqLmoOqGTKqHvMXF/7AF81Y7JWP1cVUX23NBqLfUszahO1hn2K\noFYi13st2bmoLr6oHZ/Fn074bPBFfOr502r2mvHYp6T4jiWqRl1UnSj1wYuqWq4+mVXVJfPl2vFF\nPWmtM5+/kF7vVPPmEN+5UtSPjc8dftboQPUp8vlwZgu1eV9Ul6wf6CRj84boZcoqyyk0BFD+4YeR\ny39n5MjgfUE404DXH1qeZGnDSJ0NH8asmCZNmoBp0jIRRp/NlFYmvQTiy0ztryyIm04au+OdjsmX\ncWau0CntLTCM/Kd79+5Yv3598Hn9+vXo0aNHRtqS9QOda665BgAwZ84cAIkWf1QNLCA28vUpOVF1\nbho3bpyQGVUzbKqvi2bs1PUI2+yb4/fNNWtWUUWtam5HfUo0S67PmtfoIhefLw73rYqPvup14PWu\nS9SfQK+bqiyarVgVPsJj17l2IDy3vmup29LK6lE1gNQvgMqARun4fHFUOVWfHz1GjbZihKJa8Nyv\n+rX5SnnSalXVTH3E1M/KPS9RUTNR8Nj0VXO7zJgxAwAwderUw9pPMjS/kkZP6fnVV43o8RHli6Oq\np682XZTvjl4v9XtTxVC3437vq9+mPjeat8aXJV9ffbmFfETlL9Nj8alhejyaO0t9xGbOjBW1veqq\nq1JqZ10xaNAgfPjhhygpKUG3bt2wYMECzJ8/PyNtyfqBjmEYhmEYuUXDhg0xffp0nHnmmTh48CAm\nTpyYEUdkIIcGOu4o9+9PPBHUzCmq/L2goADtTjopWN6Vv98qK0vIEUPLkqN7N0vIXke3LioqSrBG\ndQSu29Z8LGpV6Uif1v2gQYMAhPWAGF11yimnAEi0kn2otaWRGOpjoL4oqjaobxIQ+u34fG+0Ro9v\nrl3npeuSu+++GwBwwgknAIiv/wMkZsrlK/uBz/LTKIpk2Wi5Lfpe+OqhEVUs1PLVa6zXlNas5s9R\nK1YzGqsfEl/Vh0arprOdmm9JlTwywE0p5Uwnra88h6rssL1aFT2ZD5Pm7Dm4YUfQloLKrOcAULZx\nY3BMRyMxe3qyunhAYp6tdKL9wBchqX3Q58voU2Kq2w6izyJ9xuk9pBmb9RnlUxKTqSu+Y1ZFR7Nn\n+541UT6PJCoaq7rqme8ZSnzP5aDu2xdfVLm/THL22Wfj7LPPznQzLOrKMAzDMIz8JWcUHVrJmo8j\nsNJkTtelrKwsIXOoWnk+mjRpkmAx+LJ6at4VtbaI+l+wLaxZ1a1bTI+iJUK5j8eeaj0aon5J6teh\n0QWaIVZzlbjbomqhKpFG8ailTQud61955ZVVHkNtoHP4qsTxM/sIv1c/JN2e+h2oTw+QvHYPkKjs\n+Hwg1G9MoxI1eoWvmldFVShVJzXyTjMSU1HR9vL49D7V+zgqbxK3o/1F/aa0P7pWsaoI7nVslWQb\nPBZ+1j7L37UuWW2oktynqqv6jFGlx5dbS6veK9p3NXoyVV8ercXFSFHNu6XPVn3WqH+Vez9oxmpV\nRHRdqplad83n5+lTp3w1r/T/gU/B13Om11KfN/pc5iv7Z5TPppFDAx2Xb4wdG7z/fNmy4P2WFStC\nB9zjjw++b3bqqWCgRbfvfCf4fvPzzwfv17z6ari8pHg3DKN2eeWxlwEkOik3PflkALF7eEDrcPmX\nnngxLfvdVVIS/uNLyxYNw8g2cube5oNPo6T4WesPVV1FKkazZs0SPNiBRP8SRdUj9XVhmzhgilJg\n+HDv06dP3DFs374dQFjTql27dgD8lkuU75Baz77MnlqrSfcDJOaS0bos9FfQCuC0erdt2xb3OROo\nFRlEAImiQ3z+J77IJ43aSracL+rJ5//jqwav59kXQaY+Ez6rUf2SaA1rJBnVDlUKNMur+qvxXND/\nhb9TRQwyNiecMcQdl/Y/jVABwvuQ21ZLWqMTifqTaeQQl9d+kU60IKnmLVKVSvuu9it+z3MBxM6l\n5ivyKYk+3x5fPT22h4qOqjBEn0Har1XtcL/TbfkUFZ4rtiUqd1Cqx6rrpeqjo75Dqn7rc8lXa6+6\n/lb1ETtDhmEYhmHkLTmj6Jzv+HB0ciTsVyurK2tNmM8efxxAzCI6efToYPk2zjbLGjcOLAQ3a6pa\n8jrfrN+rkqLRV1G1UjT/jvov0IpjFJbmFPFVDqZFqnlzfPVyfMdL668qXyBum9WuaalrPRpa+LRQ\n05lzpLpo3g2N/PD5v7DP6HlRXw5fFXt3G5qt11cx3teHfMqO5jdSpUPVPb3WRLdLq1Pr8fhUMVV0\nqAypNZ5MiQEA5u5sAwDHhd+PuPSM4P3z84q99xrgz7qtqpZmQNe8OXoOiJ7rdELVkNeFCijvL57f\nZs2a4d/+rTWAymizDs7J2royeLtyZWJUVllZWYKfCIlSLaLqPvl8hnw5p4ivX7vt80Vk+aqPczme\nyyj1KuqzTwlPlajq6768Or68XYYfU3QMwzAMw8hbckbR8cGcM+rXQounKofihg0bJtRsAcKRs/oT\n6Fy+L6+OWvjqP6HLaw4QfdX11Dcgqo6UL0eFKkkaeaN+Ha7l4/Md4Ta2bt0at22tH0Yfnvvvvx8A\ncPnll6OuYRbmJUuWAEg8fhJ1HvV6UJGj5cX1XP8Cni+NaiK0OnmeNErFR5Qvj68PajSP+rP46gZp\nhJKqgTwnO3a4maoSq6zra3Wq2Ddv3tybaddtk1riGsXCdXktNGeP1o1SJVDz66QD9V1SNYnPudj5\nbY0oNHIMiPX7VNUBn3riUzmSXQ93ed/2dbmq/GZ8SrtPcUk1q7NPTa2ucqPtiKqOTnz17fS5XBtK\nYr6RMwMd9zH5l3seDv4BpCJJLX3ggVgV1eHDgyitqLByw6htjv/qV4P3H7zzTgZbkr28fN99wPjx\neOquuzD82muD79dUTk136uSvRDXo9MHB+892ht9veP319Dc0C1i0aF3wT3/kSF9RDSOTtGrl/scK\np6137kwsrwMA7dqF2WvXr+9aW83Ke3JmoOPSuHHjYK5fowXUbyLKgtUquUBingyi2Vh9lj5H6Fp1\n1lcbi8trrh/1IeH3mg9C53J1hK9Vb3l8GrGhmV91Lti1PHz+LGoFU1Hr2DGW7pbWM/eVDVk9Nb+Q\nzwLT88Nj5nlUJczNmwPEW576Gy1znk+qCtwXI0XYvzWDsvYttbzVx8HnB0A0UkW/V+uY22ef0Uru\nPHc7d+6MOw5VhnyV4bVP83zQ4NF8PlVZufrs0HxRPiXOl/uF1yhZLbiaos8KbYPeT74IQJLMF8dV\nNnwZlX0qSar4FB8lKsowWeRnlF8R8eXBiWqDb7tR0bTJ77H43FvEvW6+5wf7l/qQZTJyNVcwHx3D\nMAzDMPKWnFF0+laOihcuXJjhlhhG9ThpxIjgfad/d34IgwHRadiw4P3GV16pg1blHi/ceWegxhyd\nygrhzBU2LAnft0hrq2oXVRlVhR0wIIySW7WqLFjunXdCRat//3Aa6+vnDwjer1/+KYCYKpSq34lP\nMYyKvoqKHlTlUVXxZO3zZU32ZTaOUpVq6nvjOxeu7+TWrfuc5UP1u6IiMYP97t27sWpVUaDY0P9U\nlUeqwYafnBnoKL6igvyec/cbKwv1qexHkoUYapip+vPwxqJszm3zpvTdSBrGyu3QIVWlSJ260u3z\noa8SZ1QCLE7JcX2VQnXaoCq4rp4DOomzrAWnFvRhVBtJ1qqLhvNrinieP01Spw80TdJ3uMdWUVGR\nkFpAUxfw2rFvqtNnqg6ZvoezolNmvikGnjNNDKelQtjHff+MNLRb/9Fzyobt0UR5miLChedO/7lq\nG9VZX4MK+A+JjtZTpkzx7tMwjMyRswMdwzCMfEcjbTgIC32nwirtnTt39igr25Ju2/VVSlXN8GXh\nralaooNK3/7c7ehAVQ2TVHNR+SLFaqrwEF+eLjWQNLpKDc9kGerd9Q0/OTfQ8V10VV2oIFB1odVF\n6503OUnmhEerkx3fdxPSMdLnlKzOwFoSgG3jsXG/Pmdnfm7dOhZOSoVGLVFNMa6F/dTpTffrc1R1\nv6OSw2PgOWLZCk2Xr9J3NtykbLs6TBO9nmrhq6qhiejWOdvqtNf58Fz49ovKWmsHDhxAIRJVPC0v\ncrhOyqmWmCA6taBh3OrcqkUmNcmelgrxOVFr4kNViFSFpHx/RP/+YeOdXIxdPvkkQf1S1FlfX9X5\nmP3mggsuSLq9umDTptj1r+qf8ueft3emeWJpH6pSvAwj37DebhiGkaXogJCffYNq38C1qkij6tRK\n8qkWUUpOlJ+MGg1qXCRTWXQQrnXbovZ9uNFZqWZGVsWGhqRmE1cDVv2yaKhqxKQvm7WRSM4NdGgR\n+lQSdjY+EBjeTEuYPjtdunQBkLyAp8+ZjA8VtWrV0tfkhVqIlCqH+q0QdnRuR9Pj60OPvgWqQFHx\n4TnQBIhaMJDt9N2AbhI37oslH/gbfXKomPHho2Gw6nOSSehb8Xhlbha2VR8o+iDVYyPaN+OvfnL0\n2upDT5NF8pVKBs+/r7ijT6mJ+ofie8jzmLVv+V41OZ8qO0STcrJ99Pni/qtbSLO0tDQ4l7yPVf3S\n5JY6yOA+mRKhpKQkpX0bhpFZcm6gYxi5RovVq4P3K48LaxAVvvNOONCrRiZgw8/7f/87gNhg5ePK\n7zQXUC6heYnUCFPfHUWdx3XQniqpZuRO1VdHp/V1QO4z7txpdJ/zf1T0VZTjfbprSLF9NCQ53a9B\nDeoiweloHfzzmrNvZLJeYK6QcwOdCRMmAAAWL14MIFF9oLVGeZCdhL4M7CQbNmwAEBbKpPrh4kvY\nxw5GNYj75M1JJUc7tG6H6E3OG1KVF8LfmXyNFiaPmQ92VU/YXlVT1O9DHwT83g1j5L65j3bt2gEI\nLW/elGo9awK4bEp2tWnTJgBhm6lW8HzqA1N9NzQpny+SiBw6dCjhAR7laKjSvkboaYFXLs+HJo9N\n/YxIqo6ZemxarFPPhc+HR/uoJsbj8rxP2VepCGk7VQkD/MqkKnR6LTRCjPf1Rx99BCAsIWIYRnaT\ncwMdwzCM+gKNJVVsdNCm06U+P5NkVeoPHDhQZfV3d3tKqnWgovxkfH5CqmQlG8Cq+hPlaB3lY+Nr\nY6q+OrqeDvKjspH7DCXdX1QWbCMkZwc6tLLo58LOTuub6gUfBPxM5YYqyGeffQYgvqiiL5KFaBIr\nXx4VohY9f2ebVJrkfnmMvtwlVFq0uKjekLwhuJxavxrBomGNPDduintuk+eTFrf6YvB6qIMdP191\n1VXIFigBz5s3D0Bi3hT9J6IRRZrbJVl5g8bvvQcg8SGnxSz5u88HhddIr6UqJdwer51eH/Y1Xicq\nKfrPoiqnUHd5LcUQpQhxf2y/RgbyM7fL9dgneR/rtXLVRypzqsDqufUpOZs3bwYAjBo1CoZhpJef\n/eyiqCVqvI+cHegYhmHkO+PGjQMALKssRqwKjhonPmOLcIDr/n7gwIEEFUHxRUPp7zp16lNFdJpQ\nVRHf7+5yPl8bNSLUB8Z3bFHRZ75zo8esy3NgTaOQ3+u0sxpU/F73q/l2cp/aL0CbswMdWqi01qg+\nULVQRUdzz7AzMRfG1q1bg23T/4R+B8zFw8+0fpNFIwGJhfc02kktRy5P65pt57HQmtXcIvow40OM\n21Pr2ldkUiN06AdBq5ifXamU54L5clSd4mduW1WnbIi28jFmzJik3z/3XCzxjRb1VBVCFR5V7Nz3\nvqkF9XHxOWXq7/qQ1H+E6sjIa8v2aNZs9knte75/Dvq95sXhftRXi68+5UjPF2HUH/sqr821TqVz\nMnPmTAChHxmPTX3iqBZddtllCdswDCPdNItepIbk7EDHMAyjvsDBlyoyTU4+OfY9gE3FxSkncnQH\n3WtrkqMAAB4tSURBVLWRhyXK10fLqfiqputA2v0cldBRp3N9aQWiMihHqVy+7/VVk6fS2OArz4UG\nFmh5F00emvskr+ieTnJ2oMOLzAcAOy+jMdi5qfj4ki/xwcGICnebVHbYMbkt9R/w3aT6UNHoLL7q\nDav+Evog4o2hfhqq6PCzFovj9jQvEG8wtWz1RgVCJYdqF/fFz1xW25rLN+nZZ58NAHjssccAJE4X\n+P5hqOIDJF5jrX+mD0mVx5OF2wKJPj6q+ul2fFMgfFU1k9e5qn9ALjxO3gtUUbh9le0VHgf7E/uw\n5svyRSi6ZJM/mGEYxBQdwzCMeg8donWg6Ra+KSoq8k6fcsDJgaZrtLiO51FZg9XHprp1oXRq1Zc4\n0qdIuQNqVa98/kpqRKgxkWoKBf1ejykq+koTU6qxrYN/nY5WNww3gCa3MR8dL0HW2UplQEP31DeA\neV4YUUUfn2RptLkNnxqhvjkqOaqVqdaq1tzRPDZUl+g/pNY5H1Ya/aOJqdh+vQGjzpU+CPjZzTWk\nSg4tfr7yWHgz8hxRJcvFm/Thhx8GkJg7hv801DdL+4OrNmhEG68FH8q+KCjfw9mn9ERVi9f1VHFT\nxYjXXVVNRacTVEVUnx+ifZ2/81xzf/wHye95P+div6oJ2196CYCVATBymEYRU1dpyKWaswMdwzCM\n+gKNnx49egBIDDTQ6t8cUPpqYbkDzLKyMq+xEzU9H5VF2KeKaMJLDqzVeIuaqgUSDUufIqIO74d7\nDFH5czTYhOi10iltTX5L9NhpMNEQznmiBJ36PNBhZ9EIKFp6avlSjaCio5aoq7rQatUcJZqBmB2X\nNxrVCr5ed911AIA777wTAHDCCScACOtsMVLkH//4BwBg0qRJKR07fUSOPvpoAOENtm7dOgBh1me2\nn+dIFQitaq7HQdVBI6mARJ8LnjOeK1rYvB4+h7tc4IEHHgAQ+mjxVXPNqBTtU1uAxIcyzxNfeQ20\neKO+Rk0d6D+wqMrdOrXA66bVyFX188n0Pp8gVbt8YcaqRGkdKr7ynFu2YsPIMZpH/L434vcUyNmB\njmEYRn1h2rRpAIAlS5YACAecND44APRNTWp5F9cn5osvvgjW4yCeBkxU9l5fnpuoSCYdKKt/jQ7g\nk5X+OPXUbuEGW/cK3+98I3j717/u91Z09/nWRPkn6XpqyGigTKrT0mpw+gog81rmjXN97bvo5O5A\nh5ahRnWoJaqdk51IfUlc/wmqP1xGIzq0hpUqOVow7z/+4z8AAH/6058AhDfA+eefDwA4uTJENFUu\nvPBCAMD8+fMBhFmJeSxRVu306dMBhA81X8IrHr/m+QESo6z44KLErueVD4FkCctyBZ4v9RfRBxD7\nGPum5ily32uNNu2XqvTwVaco9B+HytyafExVPt2O+sponTd1ciXqW+PLp6MPcx6vL7RWa6Klu/Ci\nYRgZIkrRSQM5O9AxDMOob2zbtg1AOAVP1Jlc/To0MaOr6OzYscMbpeUrk0F0YK341BI1lDiQ9k2F\nRtWv8nHo0KGENB++NqcaOeZDDV+tU6YGI8+xzyDi9tQocFOh5AWm6PiZMmUKAODZZ58FkNhJ2Clo\nAfPGYSds3749gMRaWUCiH4P6LbAD0hlMfVmSVUIHgEsvvbRaxxjF6NGjq7X8HXfcASC84Xg8eqP5\n8vjw+IBQCeA6vPl4fjVrsPpWsK5ULqBOhFr9mg8uzfpLFUL9SNxlfaGvmhuJ/xAoi/M8089L+2h1\nz+9TTz0FIMwarIqT9n2+qpOrZl5WxUqrqGuUF9dTx1H+0+B+1MdJFR+jPuDk4nLGfcULYr6UrgJt\nZDG1n0Yndwc6hmEY9Q0WFl26dCmAxKlgVXJ8pUDcqeP9+/d7w/1V5fBFYfkqa2t+HDVIOYDnehyc\naLs5kOUAuSo0V5Amu/QdW5RfkaLKkC8XEKdlaRz4iidr5mQ1nLidiy6KKoJZe2zduhVjxozBxo0b\nUV5ejh/84Ae4/PLLE5Z79tlncfPNN6OgoAAtWrTA/fffj969eyffaO0nRs79gQ5vADrlqWOXdi5N\nu82bwL3BNfuq+juoFathgiorZwvdusWc93g8virtvrTxbpIxPqA0izTPq0YL8fzmYiE6Xy0rdUbU\niChftmEgfJD7lBwNCw5S/osysmnTJgD++lypMnLkSAChQsq8Uzpl4PP94T8RTcPP/sDj4/2mTrR8\nVRVMk6PpuadiW9PjNwwjmunTp2PgwIH41a9+ha1bt+LYY4/FmDFjEp4TV199NYqLi3Hsscdi5syZ\nuO2223Dfffcl36gpOoZhGIbCTMkcIPpyrfjqJ7kBE0VFRQllY3wBClF+LZrmQ/1UOECnYz/3y4Gv\nVvbWyCPXrWDp0i+cbbzuFICNbZvGmG5b/X/at2eqi4NwY5l37myX9Bh90Vc6TU94zngu1OWB7eNy\nGvHGc5oNeXO6du2Kd999F0Bs2rx9+/ZJ/ae6dOkSGL87duxA9+7d/Rs1H51o6BvCG0eVHXZKdcZj\nJ+N69E0AElUHzTisWWOpdPgSPmULmnqc50prXqnfCL93FR2eI54Lrfekqpc66uUSWiRQq5Jr+Kqe\nt2R1mHyV7H1o3+L5pfKSLs455xwAwEuVGXc1p5SqnBpl5fuH63OG1ekF/cfGc69RaGTr1q01OFrD\nMKrD5MmTMXz4cHTr1g2lpaV45JFHki43ffp0nHHGGWjWrBlatWqF1157zb9Ri7oyDMMwFKaYeP75\n5wGERotOEWuUk06JAjEFgQNNnxqhA1sd1OvyNGw4ME2WxgNILJTMV1WCNEGk+1uo5MQHU6iSwzZo\nqot04dueL9cQz40aPXqOqW6tX78+re09HG6//XYMGDAAL7/8MtauXYsRI0bgnXfeCQwiIHYcY8eO\nRXFxMQYPHozf/va3uOGGG3Dvvfcm36gpOtEwm/CiRYsAJKoTmjlZs9Zq+nAgvEnZ8agOaaSIRtOo\nP0K2sHjx4rjPlEY1uzEfdqqGaQQOEB67PmT0Zud5pfIzfvz4Gh9PXTNhwgQAYbI2jVpTPxuVspMV\nUlQlR/uh/oPRxGq67zlz5sS1taasWbMGAHDMMccACJUdXzSWKk46PaBKqsr/OqWh6/M4+Q+Q/Sld\nx2vULsOGDXE+hdFSGzdmxmfviCMKENQWONKZVnFmh1q3DhMP7trVr24aloXMmDEjGKS0bdsWt956\nKwCgd+/eOOqoo7BmzRoMGjQoWH7Lli0oKyvD4MGDAQAXX3wxzj77bP8OTNExDMMwfGzcuBEA0LNn\nTwD+nCxqiLgGSZs2bRJSJGgYP53BNfpJpzZ9JTx8cMCqEUf8zIFxsurliq5LmIKBxxAbPLevsl1K\neXl5QhvUENTPavDq7xqNpakV+MqAg0yVN5k6dWqQruKGG27Aiy++iG984xvYtGkT1qxZE5QiIh07\ndsTevXvx4Ycf4phjjsELL7yA4447zr8DU3RSh4m02MnVMUz9TDSvC284IOxwajXzYeDL9qoV1efN\nmwcg8xEhmv5db0S+akVtonXFgPCmjarYzXPJmzWX2bJlC4DEPENax8mnRrhOe+r8yc8+pYeves2S\n5ehJB5pe/rnnngMQ5p/i/vWhTPTYNXpMVVONXOPxsO+qr5cpOYZR99x8880YP348+vfvj0OHDuGO\nO+4I/AS/853vYPbs2ejSpQvmzJmDiy++GBUVFWjXrl2gOCfFoq4MwzCMuqRF59DfYtfn6QoecMz2\nI8PAj969t3BP+PzzVmnaV0jXrvsAUFnp6vzi5LxZ98/g7fbtnRzH+phSEaVK1Sc6dOgQJBdVnnnm\nmeD9WWedhbPOOiu1jVoendSZOHEigFBF6dSpE4BEHwCiSZ5cJzefHwRRqZJoZeZMKzmECo3PKU7r\nDKkioblLgPCcqPKgtYuo5PD65DLMRP34448DSPRp8illeq6AUJnQ6tyqFhKeeypk9FXheddopHTD\nOfYXX3wRQGKaep/KqX5LRPsa0X7E7VMlveyyy9JyPPnCuHHjAIT+Y6oQqkLGV9cZuXnz5t5+x/tY\nQ6I1dFvXS6ZiRrFnzx5ve/W43GNQ/y5+H+sz1fOXrKioSIgo5XOQx6p5x/hc1XOgy6nDNY9Jy3Zw\n/3xuM0lk3mKKjmEYhmEYeYv56FQfqiicE+zYsSOAUG2hJawVxl1rW61KtUo0woSftWJ6tsAaW7Nm\nzQKQWKdII2f4WZWcZPmBuCwtP1ohzG8yefLkdB5KVkD1RBN9afiqRl+5/UgTt7HPaIp9XhNVOIjm\nPKpteF3VJ4jtVWdYLqcqoi/PDs8Dj5N5sr73ve+l+1DyCjolU2WkL6LmzkrmU7Vv3774unbOo7HT\nwM6xVwBvP/VWwrOCr6pyqyq8dOnfAMTunbPOciKwmvUN3u7Z825w3dX3L1k4O49NQ+O7fcWZonLL\nDu78LHi7fPlnQe60Nm3aBtt2FR06MetzTyN3VRnn+nrfq8M1/89o/Tjur7ZV2qzBoq4MwzAMw8hb\nTNE5fDQqY+7cuQDC6Cxa48n8Jzg3y5G51vHRKta0OseOHZvmo0gvV1xxBYBQ7eLxaNQVLRONWHMV\nCa0azYikXKpKfrjQ0qJl5ksx71PK3O98kVr6qtYrv2db6sphkv4CzFulyhNfk/l1uZ/5yj6neYSo\nHOVi3qVMQCWb0XGqWPuKcQLxUZZV+dQcPHjQq8wRzZxdnSrzO3fuDNqtUYjsH24/19B5LapZFY0a\nNfIq72wr+6ZGCqqKpQqQRgqqD58vPJ3r8f/PxRdfnPLx5DTmo2MYhmHUJbs+2Rn8s6cTMg3EdLBw\n4SvBP/eRI09N23bJe8tWAwgHHhw4qLFgZAkWdZU+xo4di8suuwzf/e53AcRqcQChZ7w7Z61pylXZ\n4Iic1jSzM+cKloOkZkyZMgUAsHDhQgCJafXV0iSu9auWsfroaBSWqkRaEV1zH9U29JmZP38+gDDK\nUfP9EFW11ArmP9J89OmqSxgdx1plRDNau0rb/v37E56D6jfC66qqRBS+LPJu9JTbDh2U8L6gb6Wb\ny0tLKahKqOqRqoya+6mqSFwgUaHV7aifouYt0+eD+k3x2Dds2IB6hSk6hmEYhmHkLeajU3tkKp22\nkT+cf/75AEK/iNatYyEeGiGSLKeI+uAoai1qJCBfObUwbdq0Gh/P4cDcQuThhx8GkOi3pFGPbLfd\nh7XDxx9/DABBen7Nd+MqMu57zcvEV/a3uOgs+PuvKj5USzhtxNd33/0UAPtHh0AVIeXl5Rg2rAWA\nWGTUI498GPzGvqSlE1TRoVqlRUDVf0z9itQPSf2NdH09J+pzoxnmqVqpX1q2+3qmHYu6MgzDMAwj\nbzFFxzCyn08/jVmlnINXZ0eNbkuGRi1FWZW0ZmkFZguXXHJJpptgIMxEToWte/dYhW7mcnHVRdeX\njCoE1RBVdNSHzKeOqJqpShLVDK1NqL5rsf0nRiu6bdU2aASfRkGpr4yqjrzX+L2qqfzM/fEYqdRw\nv9yfZp5XVZZ+aiNHjkS9xBQdwzAMoz6zalVDlJSUAADq2OfeqAtM0TGM7IeRQvfccw8A4IgjjgAQ\n1gPSvEuAv+6YLxqL1uDOnbEii7QCzcfFqAoqbMx7pAoLEOuLmqlaI4yIrq/9mL+rYuNThDRvDtFM\n4FyefnBArMAkkJi7h+tQ9eS2uA2tpxVVp0t9c1RtVaWIx+xTelTJoSJcb7GoK8MwDMMw8habujKM\n3IH5dciCBQsAAO3btwcQnwOE+KKvaP3RR4K5OWitXn755WlqtVEfYN4jKjvMewTEFAatuabKjKoX\nmtdJVQ32daqamjOGkVCag4r9m/1eI6oYPea+5zaomOo6qqxEKTqaHVqrk6uCpOupaqU1rdi+TZs2\nAQCuvPJK1Gts6sowDMMwjLzFFB3DyF1YF4o88sgjwXtGZmn2ZLVKtcYPaxoZxuFAZefPf/5z8N3e\nvXsTlBhVSfiarPq5+5kqB9USKjqaeVkzgWukky+7eLK8PbqsTx3lsVEJ0uzLquy4temAUKHx+dOp\n745GgTGfDn1y7F6OUUUwKgDgYNU/p4QNdAzDMOo5/aZ9JfxwY/j27U5v1X1jjHpFm4jf01FlzQY6\nhlFH1JtqxEbWc+655wbvN2/e7F1OfWvoZ6LqCdUL5oyhYkklR7MAcznNhMzlGbnE31U1cX/TCK9W\nrVrFbctXc0qVH/XB4TEnyyYNhCoUl9P8OFqHbv369QASld76TqLnYvqpVwMdTWplGIZhGEbmaB3x\n+2dp2Ee9GugYhmEY8YwcORJPHHgcANCjRw8MGXJ+8Fv/zwfE3nQCXvjH4kDRIQcPHkS/oUODzx+9\n+SaAUP2giqKVxemvQl80VVuoJGmenmQ1unwGrEY0ak4qKjEaZaXqkS8yUnMPacQYlZxPPvkEQD2s\nYZUidaHoFEYvkr088sgjGDp0KJo3b45vfetb3uUefPBBFBYWYvbs2XXYOiPXmTBhAgoLC/HRRx8F\n3910003o2bMnWrVqhR49euCGG25IcFo0DMMwUqNNxF86yGlFp3379rjhhhvw/vvv46WXXkq6zPbt\n23H77bfjhBNO8FbaNQzlb3/7Gz766KOEPjNx4kT85Cc/QYsWLbBhwwacccYZ6Nu3r+XCMHKaCy64\nAAAwa9YsDBmSfJkGDRok+K9o9JXPz4WvVDuYtZgqCH16VE3ROlOu+sIoKr7G18cK1SJ+r745bCsj\nzPhZ963ZoLm+qlN83b59OwBg7dq1ACxPThR5rej85je/wYUXXhj33bXXXovrr78+5W18+9vfxoUX\nXoiuXbt6l/mv//ovXHfddUHSNiN/WLt2Ldq3b48VK1YAADZs2ICOHTti6dKlNdpueXk5rr32Wvzh\nD39IkMWPPfbYuIdyYWFhlf3PMHKNWbN+iqefvgdPP30PcArCP4eeQ4ei59ChOGrYMDQHgj/DqC55\nreiMHTsWP//5z7Fz5060bt0a5eXlWLBgAYqLizF16lTMnz8/6XpHHnkkVq5cmdI+Xn/9dbz99tuY\nOXNmXA4TIz/o3bs3fv3rX2PMmDF48803MX78eIwfPx6nnXZajfrQnXfeiW9+85s48cQTk/7+3//9\n3/jlL3+JPXv24Ec/+hHOO++8tByPYWSaK664Iv4Lp5Ta3r17gwgnH5oJWSOQ1H9F60ppvaodO3YA\nCBUgt9aVZiamEsN1WUuKn6ncqM8No7PYFq3Yzt+JT8n5/PPPAQDnnx/zcRoxYkTC+TESaRK9SI3J\nmKLTpUsXDBs2DI8++igAoLi4GB07dsTAgQMxY8YMbN++Pel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