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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ACEA WATER ANALYSIS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from pathlib import Path\n",
"from statsmodels.tsa.stattools import ccf\n",
"from scipy import stats\n",
"\n",
"import math\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import LSTM\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"plt.rcParams.update(plt.rcParamsDefault)\n",
"plt.style.use('seaborn-muted')\n",
"plt.rcParams['font.family'] = 'Arial'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Preprocessing functions\n",
"\n",
"def preprocess(df, col_ind, start_ind=0):\n",
" \"\"\"Some basic preprocessing of data from selected column of dataframe.\n",
" This will probably need to be thought about more to deal with NaNs\n",
" more effectively\"\"\"\n",
" pd_series = df.iloc[start_ind:, col_ind]\n",
" pd_values = pd_series.to_numpy()\n",
" # max_value = np.max(np.abs(pd_values))\n",
" # pd_values = pd_values / max_value\n",
" name = df.columns[col_ind]\n",
" return pd_values, name\n",
"\n",
"def preprocess_int(df, col_ind, start_ind=3955):\n",
" \"\"\"Some basic preprocessing of data from selected column of dataframe.\n",
" This will probably need to be thought about more to deal with NaNs\n",
" more effectively\"\"\"\n",
" pd_series = df.iloc[start_ind:, col_ind]\n",
" pd_series = pd_series.interpolate(method='linear')\n",
" pd_series = pd_series.fillna(0)\n",
" pd_values = pd_series.to_numpy()\n",
" # max_value = np.max(np.abs(pd_values))\n",
" # pd_values = pd_values / max_value\n",
" name = df.columns[col_ind]\n",
" return pd_values, name"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Statistical functions \n",
"\n",
"def spearman_lag(data1, data2, lag):\n",
" \"\"\"Calculate Spearman's rank correlation coefficient between 2 datasets,\n",
" with a lag applied to data2\"\"\"\n",
" data_length = data1.size\n",
" if lag > 0:\n",
" data2_lag = np.zeros(data_length)\n",
" data2_lag[lag:] = data2[:-lag] \n",
" data2_lag[:lag] = data2[0]\n",
" else:\n",
" data2_lag = data2\n",
" src, _ = stats.spearmanr(data1, data2_lag)\n",
" return src\n",
"\n",
"def cross_corr_lag(data1, data2, lag_array=None):\n",
" \"\"\"Calculate Spearman's rank correlation coefficient between 2 datasets,\n",
" for a range of different lags applied to data2\"\"\"\n",
" if lag_array is None:\n",
" lag_array = np.arange(data1.size)\n",
" crosscorr_lag = np.empty(len(lag_array))\n",
" for n in range(len(lag_array)):\n",
" crosscorr_lag[n] = spearman_lag(data1, data2, lag=lag_array[n])\n",
" return crosscorr_lag, lag_array\n",
"\n",
"def moving_average(x, w):\n",
" return np.convolve(x, np.ones(w), 'valid') / w\n",
"\n",
"def normalise_0_to_1(signal):\n",
" sig_min = np.min(signal)\n",
" sig_max = np.max(signal)\n",
" sig_norm = (signal - sig_min) / (sig_max - sig_min)\n",
" return sig_norm\n",
"\n",
"def find_datatypes(df):\n",
" names = df.columns\n",
" datatypes = ['Rainfall',\n",
" 'Depth_to_Groundwater',\n",
" 'Temperature',\n",
" 'Volume',\n",
" 'Hydrometry',\n",
" 'Flow_rate',\n",
" 'Lake_level']\n",
" col_inds = []\n",
" for n in range(len(datatypes)):\n",
" col_ind_type = []\n",
" for c in range(len(names)):\n",
" if datatypes[n] in names[c]:\n",
" col_ind_type.append(c)\n",
" col_inds.append(col_ind_type)\n",
" return datatypes, col_inds"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Plotting functions \n",
"\n",
"def plot_data_preprocessed(df, col_ind):\n",
" data_ts, _ = preprocess(df, col_ind)\n",
" data_ts_int, _ = preprocess_int(df, col_ind)\n",
" data_ts_size = data_ts.size\n",
" data_ts_int_size = data_ts_int.size\n",
" time_array = np.linspace(0, data_ts_size-1, data_ts_size)\n",
" time_array2 = np.linspace(0, data_ts_int_size-1, data_ts_int_size)\n",
" plt.figure()\n",
" plt.scatter(time_array, data_ts, s=0.2, alpha=0.6)\n",
" plt.scatter(time_array2, data_ts_int, s=0.2, alpha=0.6)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# tau analysis functions \n",
"\n",
"def find_tau_correlation(target_ts, rain_ts, tau_array=None):\n",
" \"\"\"Calculate convolution of rainfall data with an exponential window\n",
" with time constant tau, for a range of values of tau.\n",
" Then determine how correlated these convolved signals are to the\n",
" target data by calculating Spearman's rank correlation coefficient\n",
" for each value of tau\"\"\"\n",
" if tau_array is None:\n",
" tau_array = np.linspace(2, 120, 60)\n",
" rain_ts = np.asarray(rain_ts)\n",
" target_ts = np.asarray(target_ts)\n",
" winlength = rain_ts.size\n",
" target_len = target_ts.size\n",
" t = np.linspace(0, winlength-1, winlength)\n",
" src = np.empty(len(tau_array))\n",
" for n in range(len(tau_array)):\n",
" exp_win = np.exp(-t/tau_array[n])\n",
" rain_conv = np.convolve(rain_ts, exp_win, 'full')[:target_len]\n",
" rain_conv = rain_conv / np.sum(exp_win)\n",
" # if n==40:\n",
" # plt.figure()\n",
" # plt.plot(rain_ts, label='rain_ts')\n",
" # plt.plot(rain_conv, label='rain_conv')\n",
" # plt.plot(target_ts, label='target_ts')\n",
" # plt.legend()\n",
" # plt.show()\n",
" src[n], _ = stats.spearmanr(target_ts, rain_conv)\n",
" return src, tau_array\n",
"\n",
"\n",
"def find_best_tau(target_ts, rain_ts, plot=True, rain_name=None, tau_array=None):\n",
" \"\"\"\n",
" Calculate correlation of convolved rainfall signal with the\n",
" target signal for different time constants of exponential window\n",
" and select the tau value that gives the best correlation.\n",
" Optional plot of correlation for different tau values\n",
" \"\"\"\n",
" tau_best = []\n",
" if plot: plt.figure()\n",
" for n in range(len(rain_ts)):\n",
" src, tau_array = find_tau_correlation(\n",
" normalise_0_to_1(target_ts),\n",
" normalise_0_to_1(rain_ts[n]),\n",
" tau_array=tau_array)\n",
" tau_best.append(tau_array[np.argmax(src)])\n",
" if plot: plt.plot(tau_array, src, label=rain_name[n]) \n",
" if plot:\n",
" plt.xlabel(\"tau for exponential window\")\n",
" plt.ylabel(\"Spearman's Rank Coefficient\")\n",
" title_text = 'Correlation with target for different rainfall data'\n",
" plt.title(title_text)\n",
" plt.legend()\n",
" plt.show()\n",
" return tau_best\n",
"\n",
"\n",
"def tau_and_lag_correl(target_ts, rain_ts, lag_array=None, tau_array=None,\n",
" plot=True):\n",
" \"\"\"\n",
" Calculate correlation coefficients for different time lag and tau\n",
" values for rainfall. Optional plot.\n",
" \"\"\"\n",
" if lag_array is None:\n",
" lag_array = np.arange(20)\n",
" if tau_array is None:\n",
" tau_array = np.linspace(22, 90, 35).astype(np.int)\n",
" sp_rank_cc = []\n",
" for n in range(len(lag_array)):\n",
" lag = lag_array[n]\n",
" data_length = rain_ts.size\n",
" if lag > 0:\n",
" data_lag = np.zeros(data_length)\n",
" data_lag[lag:] = rain_ts[:-lag]\n",
" data_lag[:lag] = rain_ts[0]\n",
" else:\n",
" data_lag = rain_ts\n",
" src, _ = find_tau_correlation(target_ts, data_lag,\n",
" tau_array=tau_array)\n",
" sp_rank_cc.append(src)\n",
" sp_rank_cc = np.asarray(sp_rank_cc)\n",
" if plot:\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
" cax = ax.matshow(sp_rank_cc)\n",
" fig.colorbar(cax)\n",
" ax.set_xticks(np.arange(len(tau_array)))\n",
" ax.set_yticks(np.arange(len(lag_array)))\n",
" ax.set_xticklabels(tau_array)\n",
" ax.set_yticklabels(lag_array)\n",
" ax.xaxis.set_ticks_position('bottom')\n",
" plt.setp(ax.get_xticklabels(), rotation=90, ha=\"right\",\n",
" rotation_mode=\"anchor\")\n",
" ax.set_xlabel('Tau for exponential window')\n",
" ax.set_ylabel('Time lag [days]')\n",
" plt.title('Correlation of rainfall with target for different values of tau and time lag')\n",
" fig.show()\n",
" return sp_rank_cc, lag_array, tau_array"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# LSTM functions \n",
"\n",
"# convert an array of values into a dataset matrix\n",
"def create_dataset(dataset, look_back=1, chunk_step=1):\n",
" numchunk = int(np.floor((dataset.shape[1] - look_back - 1) / chunk_step))\n",
" dataX = np.empty((numchunk, look_back, dataset.shape[0]))\n",
" dataY, y_ind = [], []\n",
" # Create chunks of data with the specified look back\n",
" for i in range(numchunk):\n",
" start_ind = chunk_step*i\n",
" dataX[i, :, :] = dataset[:, start_ind:(start_ind + look_back)].T\n",
" dataY.append(dataset[0, start_ind + look_back])\n",
" y_ind.append(start_ind + look_back)\n",
" # Randomise order of chunks\n",
" rand_indices = np.random.permutation(numchunk)\n",
" x = np.array(dataX)\n",
" y = np.array(dataY)\n",
" y_ind = np.array(y_ind)\n",
" x = x[rand_indices, :]\n",
" y = y[rand_indices]\n",
" y = np.reshape(y, (y.size, 1))\n",
" y_ind = y_ind[rand_indices]\n",
" return x, y, y_ind"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Preprocessing data\n",
"\n",
"# Importing all data\n",
"foldpath = r\"acea-water-prediction\"\n",
"files = list(Path(foldpath).rglob('*.csv'))\n",
"df = pd.read_csv(files[0]) # Create dataframe\n",
"\n",
"# Get time series data for target variable and some other variables\n",
"# Specified by column index of the pandas dataframe\n",
"datatypes, col_inds = find_datatypes(df)\n",
"col_ind_target = 13\n",
"col_targets = col_inds[1]\n",
"col_ind_others = [1, 15, 16]\n",
"col_rain = col_inds[0]\n",
"col_vol = col_inds[3]\n",
"\n",
"# Get target time series data\n",
"target_ts, target_name = preprocess_int(df, col_ind_target)\n",
"target_length = target_ts.size\n",
"\n",
"# Get time series data for other variables\n",
"other_ts = []\n",
"other_ts_int = []\n",
"other_name = []\n",
"for n in range(len(col_ind_others)):\n",
" ts_, name_ = preprocess(df, col_ind_others[n])\n",
" ts_int_, name_ = preprocess_int(df, col_ind_others[n])\n",
" other_ts.append(ts_)\n",
" other_ts_int.append(ts_int_)\n",
" other_name.append(name_)\n",
"\n",
"# Get time series data for all targets\n",
"all_target_ts = []\n",
"all_target_name = []\n",
"for n in range(len(col_targets)):\n",
" ts_, name_ = preprocess_int(df, col_targets[n])\n",
" all_target_ts.append(ts_)\n",
" all_target_name.append(name_) "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# Get time series data for all rain variables\n",
"rain_ts = []\n",
"rain_name = []\n",
"for n in range(len(col_rain)):\n",
" ts_, name_ = preprocess_int(df, col_rain[n])\n",
" rain_ts.append(ts_)\n",
" rain_name.append(name_)\n",
" \n",
"# Get time series data for all volume variables\n",
"vol_ts = []\n",
"vol_name = []\n",
"for n in range(len(col_vol)):\n",
" ts_, name_ = preprocess_int(df, col_vol[n])\n",
" vol_ts.append(ts_)\n",
" vol_name.append(name_)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nplt.figure()\\nfor n in range(len(col_targets)):\\n plt.plot(normalise_0_to_1(all_target_ts[n]), label=all_target_name[n], lw=1, alpha=0.7)\\n# plt.plot(other_ts[0], label=other_name[0], lw=1, alpha=0.7)\\n# plt.plot(moving_average(other_ts[0], 30), label='Mov av, win=30', lw=1, alpha=0.7)\\nplt.plot(normalise_0_to_1(exp_model), label='exp model', lw=1, alpha=0.7)\\n# plt.plot(normalise_0_to_1(exp_model_av), label='exp model av', lw=1, alpha=0.7)\\n# plt.plot(moving_average(other_ts[0], 90), label='Mov av, win=90', lw=1, alpha=0.7)\\nplt.legend()\\nplt.title('Time series')\\nplt.show()\\n\\n\\nplt.figure()\\nfor n in range(len(col_rain)):\\n plt.plot(rain_ts[n], label=rain_name[n], lw=1, alpha=0.7)\\nplt.legend()\\nplt.title('Time series')\\nplt.show()\\n\\n\\nplt.figure()\\nfor n in range(len(col_vol)):\\n plt.plot(normalise_0_to_1(vol_ts[n]), label=vol_name[n], lw=1, alpha=0.7)\\nplt.plot(normalise_0_to_1(all_target_ts[0]), label=all_target_name[0], lw=1, alpha=0.7)\\nplt.legend()\\nplt.title('Time series')\\nplt.show()\\n\""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plotting preprocessed data\n",
"\n",
"# plot_data_preprocessed(df, col_ind_target) \n",
" \n",
"\"\"\"\n",
"# Calculate spearmans rank correlation for different lags\n",
"# Note that the target data is appended to the list of other variables so\n",
"# it is included in the analysis (effectively an autocorrelation)\n",
"all_ts = [target_ts] + other_ts\n",
"all_names = [target_name] + other_name\n",
"num_datasets = len(all_ts)\n",
"# loop through all datasets to calculate n-lag spearmans rank coefficients\n",
"ccl = np.empty((num_datasets, target_length-1))\n",
"for n in range(num_datasets):\n",
" ccl[n, :] = cross_corr_lag(target_ts, all_ts[n])\n",
"lags = np.linspace(0, ccl.shape[1]-1, ccl.shape[1])\n",
"\n",
"# Plot results\n",
"plt.figure()\n",
"# fig, axes = plt.subplots(num_datasets, 1, sharex=True, sharey=True)\n",
"for n in range(num_datasets):\n",
" plt.plot(ccl[n], label=all_names[n], lw=1, alpha=0.7)\n",
" # axes[n].set_ylim([-1, 1])\n",
"plt.legend()\n",
"plot_title = 'Correlation lag-N: ' + target_name\n",
"plt.title(plot_title)\n",
"plt.xlabel('')\n",
"plt.show()\n",
" \"\"\"\n",
"# plt.figure()\n",
"# plt.plot(target_ts, label=target_name, lw=1, alpha=0.7)\n",
"# plt.title('Time series')\n",
"# plt.show()\n",
"winlength = target_length\n",
"tau = 20\n",
"t = np.linspace(0, winlength-1, winlength)\n",
"exp_window = np.exp(-t/tau)\n",
"exp_model = np.convolve(other_ts[0], exp_window, 'full')\n",
"exp_model_av = np.convolve(moving_average(other_ts[0], 7),\n",
" exp_window, 'full')\n",
"\n",
"\"\"\"\n",
"plt.figure()\n",
"for n in range(len(col_targets)):\n",
" plt.plot(normalise_0_to_1(all_target_ts[n]), label=all_target_name[n], lw=1, alpha=0.7)\n",
"# plt.plot(other_ts[0], label=other_name[0], lw=1, alpha=0.7)\n",
"# plt.plot(moving_average(other_ts[0], 30), label='Mov av, win=30', lw=1, alpha=0.7)\n",
"plt.plot(normalise_0_to_1(exp_model), label='exp model', lw=1, alpha=0.7)\n",
"# plt.plot(normalise_0_to_1(exp_model_av), label='exp model av', lw=1, alpha=0.7)\n",
"# plt.plot(moving_average(other_ts[0], 90), label='Mov av, win=90', lw=1, alpha=0.7)\n",
"plt.legend()\n",
"plt.title('Time series')\n",
"plt.show()\n",
"\n",
"\n",
"plt.figure()\n",
"for n in range(len(col_rain)):\n",
" plt.plot(rain_ts[n], label=rain_name[n], lw=1, alpha=0.7)\n",
"plt.legend()\n",
"plt.title('Time series')\n",
"plt.show()\n",
"\n",
"\n",
"plt.figure()\n",
"for n in range(len(col_vol)):\n",
" plt.plot(normalise_0_to_1(vol_ts[n]), label=vol_name[n], lw=1, alpha=0.7)\n",
"plt.plot(normalise_0_to_1(all_target_ts[0]), label=all_target_name[0], lw=1, alpha=0.7)\n",
"plt.legend()\n",
"plt.title('Time series')\n",
"plt.show()\n",
"\"\"\"\n",
"\n",
"\n",
"# plt.figure()\n",
"# vol_total = normalise_0_to_1(np.sum(vol_ts, 0))\n",
"# vol_total_deviation = vol_total - np.convolve(vol_total, np.ones(500), 'same') / 500\n",
"# vol_dev_smooth = np.convolve(vol_total_deviation, np.ones(10), 'same') / 10\n",
"# target_total = np.sum(all_target_ts, 0)\n",
"# # plt.plot(vol_total, label='total vol', lw=1, alpha=0.7)\n",
"# # plt.plot(vol_total_deviation, label='vol deviation', lw=1, alpha=0.7)\n",
"# plt.plot(normalise_0_to_1(vol_dev_smooth), label='vol dev smooth', lw=1, alpha=0.7)\n",
"# plt.plot(normalise_0_to_1(exp_model), label='exp model', lw=1, alpha=0.7)\n",
"# plt.plot(normalise_0_to_1(target_total[:-40]), label='total groundwater', lw=1, alpha=0.7)\n",
"# plt.legend()\n",
"# plt.title('Time series')\n",
"# plt.show()\n",
"# # cc = ccf(target_ts, other_ts)\n",
"# # cc_lag = ccf(target_ts, other_ts_lag)\n",
"\n",
"# plt.figure()\n",
"# plt.plot(target_ts, label=target_name)\n",
"# plt.plot(other_ts, label=other_name)\n",
"# plt.plot(other_ts_lag, label='lag')\n",
"# plt.plot(cc, label='cross-correlation')\n",
"# plt.plot(cc_lag, label='cross-correlation_lag')\n",
"# plt.legend()\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/guptam/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:51: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
"/Users/guptam/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:97: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
]
},
{
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9ysyOCcMtn+Dm4YvAg2b2L0ln4M7ZzpH0i5ltKg+sMwC3jFoDI8xdz6fLP5HuLLyyPF/uDuNLfMPg3BRpeuFu0g8Pv6/GTeKGwLV4b/4wMztEvlv5U2BbM1si97z7I15Zdzazy8L9jTezNpLeBe4zs8clDcdfwleBu0MedYDv8aGcYcBDeI/+PHx8dilrzeZ9zOyBIH+6mTUJ5ZPI9xNgkJmNk3QS7vvpTeBvwTlhSc8tkWcffDz4RjO7O1g14/HeVAG+GW8LM1sRGtPOYcNhOrmDcT9dqGq17as3bLbO+WrVqpGXl8fKlS5i9EfvkZ+fz/QZP1O/fj2ab7oJf7/nfm7++13ryU5Qv8mylMfr1a3Hq4+9we4H70Sd2nUZ8cRIdj94JwBO/vOpVK1Slbsf/kfKtAsWFd8XmVrfse+/R8d27Wjboyc///IL119xOXPnzePWe+5NrWyKedfrLr2MufPnc/v97tbq89feoEPbtjTs7KMkB+27H3vvthvnDRuaWqfqqb3oXH3RFcydP4+/P3gPAB+9/F+22qILbXttxfyFC9imy9YMu2AIR54yILWuOZK5evn6G/HLW67Vaq1fBhWi66r1dS3v81Iap0fFy+CDEa/So3s3arRwF28H9+3L3rvvxrmXp95sv2rm9Llm1jTlyTTU67SJ7Xhv/7Tn39rz9t/VPprWeKs/MFS84OZw6/D97fD3I1KPbX5pZibpF8rusLALax29LZHvsG6Pe5UtznS8p0O4PuHu+0bWOtJL7FpvB3xrZkvC7/fxRjB5B3JxmzPhCHIasCneS/kiWAyL5buPwd1I/An3q3QFXhaFuBfe+UBPSXviPfNULsu7AHfJfSpVxV2OJOueErm7i5pmlpgX2wJ4XdIH+DBDXbx3Bd5z6x90KpXg7fU+gJqbtLIO/S9Y5/zRO29Dx+ZNGP7c2zStV5sqdRpw2E3/orDI6NG+JUfvXIUX59WgeLpk+p7wUcrjW9brwew6PzLwcR8hq9OiOuc92595q2cxoO1RvDnrGQbul3r07Pk3dkp5vH/3bejUtAlXvvk2zerUpkajxkxduJB6fz6evNWrWdGpC61q1qRltdSvaWHd9eeHv2vdnBMPOoCnGtekWa3a1NusOR/N+pnD7riZT36exmG778PHM6bR/LJzU8rcvOOslMdnNanNka368EGPZTSuVo+Gm2/CB3PHccqDwxj5y5cc2WpX5lfPZ9u70hm6uZE548vm6x0rb7lutt3MnOg69fv1PfaU93nV+Dn1Kq9jem7DFps04ZpX36ZZ3do02qwVY2fM4ohrbmHUlBn8af89mDpvIW1OSv2/MOG6C6akPFEKG8U+mmBNnIK7Q3/HzAaHXvJQ1jpi2x6v7HfBe8ywbmWdiWmVSPcdPjH3QtChK2nckZjZdHnkxDPMLBFtrz7u+fW7IDNRQ/yE+5CqbWbLcKtkIrAS95YL608yF9d/AnBOKIeaBC+3uPVxOW5Cj8BdmK82s88lnQMsNLNTg/UxWN6iFLF2mHMCcJyZTZX7uEroU9rql02BhyXtYe7efwreIK/G/UydbGb/CeWyC/B3ytjQlMbzn45l+LF9eOTsozFg6JMjKSyqGI8UTau3YN7qtZXwc9Pv49jNzyFPeUxcMpppyzMPXPjM6LHc9Ke+PNW/H2bGBa+OoEnt2jxy9BGsKixk8cqVXDLijYxkvj1lEjs0b8lLR/yZPMGw//2XaYsXcWPv/aial88PC+YxYtLE0gUV4+O539GtQTvu7XkOeRK3jX+Bqctnc0mXozi05U4sLVjJNWMfL11QjmVCbso1V7rm6nk99+VYbji8D4+dfDRmcMULI1m+Zg3DDtyL/Lw8ZixYxC0jS3SEnDGGKNxAk/7pKM/Q2dO4k7tCvMH6Gz5xdgvunK4O8IKZXRMsnAX4+OMyYKCZzZP0DjADt0rWG5IqIf9EukH4Msb2eGV+h5k9UkK62vjk805B79rAI/iQ1YCEDuHa/vgwUxHuw+pkfLLyJXxwZBQeT2WbcH+nmdl4SacBm5rZVZLOw53g/YyvWjnUzCbLQ8lOMbNLJD0BzDazcyUlJrSXhHJqhU9mNwvHr8SH4G5h7QDNSXj0wHWGMtPc/8n4kszl+DP7Jz6s+SXQxswKkq4dF+75cXypZeJFedzMbk2XRyqLpiJIZ9GUh3QWTXlJZdGUl3QWzW+VVBZNeUll0VQEqSya8pLOoikvE667IONhrrqdNrWSLLr/7XPzb3PozMzexSu/VKSrZYaY2ToTfWa2ZwrZK0ka4kqTf3K6Mk+cBevk4jSnHy527eOsHUpKsBK3borL7Z30/Z6k77eTwu27JYWSNbNjk75/S+rlnTNwL8IJehc7P5EyrKwxj/nxQIpT6+3eTpoja1Oa3Egk8tvFjEq3aH6zGzYlDWNtYJ9kTjSztN56w8R0qpmvIWb2cUXp91sl23KLRCIbK5U/dLZBGprkHn8Gaa7B5y8yTffrxPQfkWzLLRKJbJwYG8ligEgkEon8RjEojA1NJBKJRHKFARYbmkgkEonkDlFYFBuaSCQSieQIMyj6IywGiPxxyF8DdWdk6hexdHKx52WTLyp+v0uumLGk4velAHR84JecyK11QMX3oPNualzhMgHqblXxe14af7uqwmVCKe4/SiAuBohEIpFITimKQ2eRSCQSyRWG4mKASCQSieQQi0NnkUgkEskxFofOIpFIJJJLsvCdXKHEhiYSiUQ2YszA4vLmyB+Fh/86kKXLVwMwc/YiXnzrG847YS8KC4v4bPRkHnw2c5+nLx//Z5as8qWk0xct4qVx47lkj10pKDI+mjyFWz/ILrxALnTNhUzIXRnUb1Sbf7xwLkNOvJ/pk+YA0PvA7hwycBfO73dnVjKfvnAAS1e6rjPmLWbokyMBOGWfHejYvAmXPDoiK7kNGtTivjtP4KLLnqRa1SpccG4fCguLmDZ9Af9324isevS/J11L43dj0RSLQSM8uuPtZvZ0JhlKOsvM/pEcnjiTdJnkFdLVxsMl7wSswD0y3GFmL2QqK4u8PwGOMbPJWaTdHOhmZq+UI/82wGg83ozh8XTeMbPLk655GY9LdFDSsSp49M8/4aERAB4LDkuzolpV36tw1tVP/Xrs4ZuO44pbXmLGrEXcfNnhbNG2GRN/ml12mfkuc8CTz/x67OXj/8wFr47gh3nzeap/P7Zo0oSJc1MFXN3AuuZAJuSuDPKr5HHOtUewauXamMTtujSnz1E9148tW1Zdq7iug+58dp3ju3Zuw65d2jBr4dKs5Obn53HhuX1ZtcrDKR0/cFce+feHfPr5JK647CB67diBjz/JLADe70nX0lGlz9Fkak+9bWa9zWwPPLTxpZK6ZyjjLxleX950DwKTzGzH4EX6aGCopEZZyttQ7IVHIy0v48Iz2zPI21PSNgCSWuEB6hpKapeUZng4vnN41gcAAyR1zlaJDq2bUr16VW6/4kj+PuxoundpSbUq+cyYtQiAz76ZTI+tW5ciZV26NGtKzapVePiow/l3vyPp3rw542bNpn6NGlTNy6N6lXyKLPNNmbnQNRcyIXdlcMqlBzLiiU+YP3sxAHUb1GLQhX/i3uFZ93vo1KIpNapV4d7TDueBM45gm9ab0qpJfY7auSt3v/FJ1nJPH7wXL7/6FfPmeeX//Q+zqFevJgC1alajsCDzDcS/J11LxXwxQLrPhiDroTMzWyrpXuBISf2A3fGG61YzeyZEnRwPdMb7QP3waJCNJN0FfAb0kjQSaArcna7HLOmKpHTn4o1HezzK5K1m9lSadJsCnYoFGpsjaXszs2BVDQp6X4mHOz4PWIVHshxMUuTN5Oif4f6+BrYG6gFHmdkUScOBvsA0oAmQL+l7oEu4z+l40LilwMd4NNJ78WiajYHX8CiglwG1JH2Eh5a+I5TjvKDztsBf8VDM95nZoykf1LrUBKrjETbBn8dLuKV3BnBRsGb6AR3MrDCU2VJJvS2bcKyBlasKeOKVz3n5v2No1bwhtw45giXLV/56ftnK1WzWrH5GMlesKeCBz0bx1OgxtGnYgAePPJwnvhnNA0ccyoIVK5kwZy4/zpv/m9A1FzIhN2Ww72Hbs2j+UkZ9MJF+p+5Jfl4e519/JPfe8AqrkyycTFm5Zg2PvDOK5z4ZS+umDbh78GHMmL+Yyx59jbabZNfv67tvVxYtWs7no35iwDHuPWL6jPmcd9Z+DOy/M0uXreLrb6Zu1LqWid/L0FkaZgEXAl+Z2S6hIv5E0pvh/EdmdpqkM4DLzewcSWeb2Rmhkl8D9AFaAyNIE0fGzIYnpTsLmGtmAyXVBb6U9F8zSzU20AaYlPgh6Wo8QmZDSdeGwwvM7BBJjYFPgW3NbImk24BT8QYhHZ+Z2XmhcTlW0qt4g5sIZf09HjL6f/jQXQdgLB6eeSkwEm9gPjGzk0P5TTezoZJuxBu4l8MQ3CAzGyfpJOAS4E2ghpntWIJ+AFuGRtGCLn8zsx8k5eEB4noBBcC3koYC9YH5ibDOkk7HG566kh4NUUPXIQSbGwxQrVaDlEpMm7mA6b8s/PX70uWrqFen5q/na9eoxpLlmbntmLxgAVMWLgzfF1JkxmW9d2fnu+5j1tKlXLrHbpy8Qw/u/+yLjOTmQtdcyITclMF+R/bEDLbduSPturTg7lfP55fpCzj7qsOoWr0Km3fYhFMvP4h7r8/Mupk8eyFT57quU+YspLDI2KxRPf7v+D9Rt2Z1mtarw0l79+Sf//28zDL377sNZsb227ahQ/tmDLn4QDq034RTzniIyVPmcuhB23H6qXvxt3+8Wbqw36muZeJ3vo+mNfAYMDBUZuBzN4kxgLfD34+AQ1Kk/zJYFr8AtcqYZxfgLYDQIIzDrZtUDc10kkIRm9mVAKESrxMOJ9wHtQO+NbMl4ff7+PDgp0nyij+tr8Lfabg1tBXwhZkVAYsljQnnn8fnO9ricx+H4JX+P4H5QE9JewKLcYsj1T3fJQm8fCcW070kxqUJPNcHqMvacNWJhudfQGNJ+WZWaGZ3A3dLOi3c43okB5ur06hVyr7TgXtuTfvNm3LzP9+iScPa1KhehZUr17DZJvWZMWsRO3Rrw4PPZjZpfWTXrejUtAlXvvk2zerUpkpeHlMXLmTZap9wn71sGY1q1ixFyobRNRcyITdlcPGAX6ORc9Ojp3LHlc//uhhgk80actlt/TNuZAAO23ErOjZvwvDn3qZpvdqYGQfd8AiFRUaP9i05eudtMqq4Ac698LFfv9/+f/259Y7Xue6qI1gWGu2585ey9VabbdS6lonfq0UTrIlT8Bj075jZ4NBLHspaK2J7vLLfBfg2kTRJTCa3n0j3HbAb8ELQoSs+tLQeZjZd0k+SzjCzu4Le9fFhp++CzMQA9k9477+2mS3DLZ+J+GR4wqPhdsWzKPZ7AnBOKIeawJbh+JvA5fiQ1Qg8AuZqM/tc0jnAQjM7VVIHYLC8RSli7RzaBOA4M5sqaZckfcrjFfJk4GQz+w9AkPt3M/unpOeA6yRdYWZFwdLqRZpyLguvvD2Gv5y5P3dfcwxmMPzuNzAzrjz7APLz8vhs9GTG/ZCZg8dnRo/lpj/15an+/TAzLnh1BE1q1+aRo49gVWEhi1eu5JIRb/wmdM2FTMhdGeSC5z8dy/Bj+/DI2UdjwNAnR1JYVPE14P/d+hrDLj+EwsIiCgoKufm21zdqXUvFKn/Dpso67F5s1Vkh3kj9DXgBuIW1w0UvmNk1wcJZADQClgEDzWyepHeAGbhVst7cRwn5J9INAu7HrZia+AqyR0pIVxuf89gp6F0beAS4h6T5l3Btf3yOpgj4Aa+Ma+DzGPnAKGBPM9sm3N9pZjY+0ds3s6sknQcMBH7Gh8UONbPJkp4CppjZJZKeAGab2bmStgKeBJaEcmqFD601C8evxIfgbgk6gM+ttAj5H1PCvbcBnjSzXsWON8NXorVJDJGF4+PCPX+GD8/9CR9Wq4c/51tDI5yWOo1a2Tb7nFvSJVkxq0fF7wP4PXlvzsX9Q+68N884oOK9TefKI/K8rVINIpSPXOn63ptDRplZj0zSVG/T0jb9S/r/yamnXFKqTElDgIOBasBdwHvAw3hneyxwZhjJSZ2+HLlVOAYAACAASURBVPO7JZJcEeckg8hvktjQ5IbY0MSGBrJvaJpfkf5/csrgkhuaYGRciA/51wIuwkd3bjWzdyXdA7xR0paR39SGTUnD8GW9xTnRzNIO24TJ6P4pTg0xs+x2wf2OyLbcIpHIHwATlG/orA8wBh/VqAdcjE+bvBfOv4bPZ2/4hibNBHRpaa7B5y8yTffrZPQfkWzLLRKJ/EEoeeCqiaTkZYn3Fdtq0gRf4HUgvqDpZSAvabvDEny1alp+UxZNJBKJRHJAyaPEc0sZjpuHz6GvBiZIWonPJSeoCywsKYPK9bQWiUQikdxioCKl/ZSBD4C+clrgC6r+G+ZuAPbH9wqmJVo0kUgksrFTjjVfZvaqpN3x1ah5wJn4Vof7JVXDt4o8W4KI2NBEKpbCRoUs7J+dw8GSuLDzuxUu88ntela4zFxxYcvMNgeWmYNzI/bdBZ0qXOboEVm72iuRbf5U8Qtjf1lWr8JlAr4jLwtUzsXFZnZJisN7lDV9bGgikUhkY+d37oImEolEIr9ljPL5EKkAYkMTiUQiGzmKDU0kEolEcsrv1almJBKJRH77KCxvrkxiQxOJRCIbO9GiifwROKXjruzZvBNV8/J54qfPeX6Kh/I5oGVXBrTbgf7v/zNjmZ3q96Vz/b4A5KsaTap34K2Z19G9UT+KrIAVBQv578zrKbDMHRwOaLMnuzTZiqp5+bw4/SM+nDOOS7Y8irpVapKnPIZ/+yQ/r5hX6TJzUQa5LNdDW/SlR6NuVFE+b8x6j0lLp3BKuwEUWREzV87inh8fxbKoFZ8/YwBLV7o+0xcs5p73PuWqg/ehan4eqwsKufDpESxcsbIUKRtG11y8B6Xxu5ujKRYuQHggrtvN7OkM5ZxlZv8IkTZ/ddVf1nSZaf1ruIDheLiAFXgbf0dJHkcrihAh8xgzm5xF2s2BbmaWfbB2l9MKDzXQDA+vMAo4L7iVSHV9HnAzHu+nCA8Zfa6ZTUp1fUn0bNKG7o1bMeD9B6mZX5UTO+4MQOf6m3JE623RevHkysaERa8zYZHH79htk3MZv3AEOzY5mRennsuKwgX0anoKXRocwJgFz2ckt3vDdmxdvw1nfnEnNfKrckzrPTi944G8OfNL3pk9mm0btmfzWk0zqgxyITNXZZCrct2y3hZ0qtueoWNvolpeNQ5usS/bt+rKc9P/w1cLx3J2h0Fs17AroxaMzkhutSoePeO4B9fuGXz4xCO47c0P+Wb6L+y3ZQfaNGnI19NmVrquuXoPSsQqv6HJ1gXN22bW28z2wL12Xiqpe4Yy/pJl3tmmexCYZGY7BoefRwNDJWUXAHzDsRceOC5rJOXjMXVuCc9tRzyMdkmOOPsCLcxsXzPrg0cDvS2b/Hdt1p7vF83m7zv2485ex/LuLxOpX7Um52+5DzeMKX+gp6Y1OtGoelvGLXqVl6aex4rCBQCIfAqLUrajJbJDo05MWjqT4d2O54ZuJ/LRnO/o2qA1TWs04NZtB7Pvptvy9YIfK11mMhVdBrmQ2a3BlkxdPoOLOp3GpZ3PZNSCMfy0bBp1qtQGoGZ+DQqsMGO5nTdtSs2qVfjn8Yfz8IlH0L1VcxrVrsWendvxr0FH0q1Vc0ZPzywcQq50zfV7kBYr4bMBKLevMzNbCtwLHCnpBkkfSvpY0lHgcWkk3RP+vidpU0lXAI0k3RXE9JI0UtJXweV/SpLTSaoq6VFJH0n6VFK/EtJtCnQyszuS9J4DbG9m8yWdIOl9SR9I2lvSAEmfh98PhbxOCCGgkVRD0uSk+7td0luSPpPUOhwfLmmUpBdx76f5kr6XVEVSc0mFkhpLqi7pS0n5kh6Q9IakLyRdGxqIy4D+kg6W1FXSOyHP5yTVl9Q73P//JA1MUwS7AtPMLDks9aWEhkbSheF+P5b013B+GtBDUj9JTfCG6qh0ZVwSDarVYquGLTj/s2e4+ptXubnHEQzf7hD+OuZ1lhVkVwkms33jAXw+92EAlhfOB6BtnV3ZrNa2TFg8MmN59avVpnO9Vgwb/Si3jH+eoVsfy6Y1GrFkzXIu+Oo+Zq1cSP82e1a6zGQqugxyIbNelTq0q9OaWyfex/2THuOcjoP4ZcVsTmzbj9u6X039qvUYt6gs0cnXZeWaNTz4wShOeuR5rnr5v9x81P5ssUkTPv5xKsc9+Cz1a9bg0G23LF3QBtA11+9BOmTpPxuCinKqOQuvhNqa2S7AnsAVkhqE8x8FK+Ip4HIzGw7MN7Mzwvk1eMyDw/AIlykplu5U3OvozsA+eOjhJmmStmFteGkkXR0Cs30t6chweIGZ7Qp8DVwN7BV+Lwx5lcRnZrYP7iDiWElbA7vjUUePw72bFuKO53bCrYWxeCTNvYGRuDfUT4L1sCtwupkVAjcCj5vZy3hk0TNDWY7Ao2AC1DCz3czs0TT6tUi+fwAzW2lmyyV1xa27ncOno6QDzWwMHnPiUDwM9xdB9/WQNDg0jl8ULF6+3vmFq1fw4ewfWGOFTF46j01q1qN1ncYM634gt/Q4kvZ1m3JZ177pS7cEquXVoUG1zfl5+de/Htum4ZF0b9SPV6dfQmHqkcESWbxmGZ/Nm0CBFTJt+RxWFxWQJ/Hh3HEAfDR3HJ3rtax0mQlyUQa5kLmkYBnfLPyWQitk5spZrC5aw9kdBzFs7P9x/tdX8v6cTziuTeZ9mZ/mLuTlb74DYPK8hSxYtgKAT3+aDsC7EyaxdYtNfhO65vI9KJHfu0UTaA08BmwfKvDX8bmb1uH82+HvR0AqJ0hfhtgGv+AR3MpCF+B9ADNbgs8ZtU9z7XS8sSFcf2WorF/Dw08DJLon7YBvg0xCHlsVk1d8UuGr8HcaHvp5K+ALMysys8V40CCA5/HwyH2AK4B9cW9TzwHzgZ6SHsOHqFKF/esC3BXKeBDegCTrno4prOvWm2BNHQh0xhu4NeEZ/A/YStI2wAQzOxbYFBgCPC1pvQkVM7vPzHqYWY8q9dZ/fF/Om8quzToA0LRGXWavWMIh/72LEz54mAu/eJYfl8zhxiyH0FrU2obpy0b9+nv7xn+mRa1teHnahawsXJSVzNELJ7NDY39NG1erR438anwwZxy9GruvrW4N2vHT0lmVLjNBLsogFzLHL/6B7g38X6lh1frUyK/OrJVzWFHok/Tz1yykdpWy/vuv5YjttuLSvu52q1nd2tSuXo2xM2axfevNAOjRpiU/zM5sziNXuubyPUhLmKNJ99kQlHvVmaS6eM/3AeAdMxscJpKHsrYXvT1e2e+C945h3co6k3Y1ke47YDfghaBDV9yj6HqY2XRJP0k6w8zuCnrXB7YNcsRaJw0/AVtKqm1my3DHcROBlUAiPu12xbMo9nsCcE4oh5pAwm5/E7gcWI5bJNcAq83sc0nnAAvN7FRJHYDBoVIvYm2HYAJwnJlNlbRLkj6lvS6fAG0l7WBmnwW5V+GLIh4FLpRUBbe6dgf+hVuJ3SQNMrNCSd8Cy5KCHZWZ92ZNpEeT1jy1xynkSVw7+j8UVVBXqkG1Vixe45O8NfMb0qPJ8cxZ+T0HtroJgB8Wv823C1/OSObHc7+jW4N23NvzHPIkbhv/AlOXz+aSLkdxaMudWFqwkmvGPl7pMhPkogxyIfPLhWPoUq8j13cdQh7in5OeYGXRKs7teDJFFFFQVMC9k/6dkUyA574cyw2H9+Gxk4/GDK54YSTL16xh2IF7kZ+Xx4wFi7hlZIle7DeYrrl8D9IhKn8xgDKtN4qtOivEG6u/4WE8b8GHi+oAL5jZNaH3vQBoBCwDBprZPEnvADOAtwirziTVwAPstCkh/0S6QfhQUnu8Mr/DzB4pIV1tvHLdKehdG3gEuAcYQNLKN0n98SG8IuAH4GTcUnkJyMdXbO1pZtuE+zvNzMZLOg3Y1MyuknQeMBD4GbcmDjWzyZKeAqaY2SWSngBmm9m5krYCnsSj1S0LafbGV4k9CVwJfB/KOD/c1km4VXOamR2T7t7DPbUD/hHuuzbe+FxgZqslXQD0wxu0D4ALQh4344sRFoeyGGJmH5aUT80OLaztzWmn2bLmrFx4b57++/HefEyuvDfniOi9OTfem/+3782jSglSth41W7SyNiddkPb8+OsuyFhmpmRs0ZjZu3jll4p0dzPEzNZ5mma23oyXma0kaYgrTf7J6Y4v6dpi6Zbhsa5T8XCxax8HincrVpLCLXZyyGozuyfp++3A7Smu75f0/dik79/iVllxZrDucGPvYucnAu+mSFc830n4sF2qc7cCtxY7XEAJ82WRSOR3wm9gefNvcsOmpGF4T7o4J5pZyuGxkG4w0D/FqSFm9nFF6fdbJdtyi0QiGzkbu2eA5B5/BmmuoeQ9HunS3Qfcl2m6jYVsyy0SiWzcRIsmEolEIrkjDp1FIpFIJOds7ENnkUgkEqlcokUTiUQikdyxAT0ApCM2NJEKpX71lRzQ9tvSL8yQjtUzc4pYFt7d+sUKlwlwz8LNKlzmT6uaVrhMgCnLc+NTtnfDzP2AlcaC4fMrXCbAKYPGlH7Rb4T9skgjNpxPs3TEhiYSiUQ2cuLQWSQSiURyS7RoIpFIJJIz4vLmSCQSieSaOEcTiUQikZwSLZpIJBKJ5A6j9EAiOSY2NJENQo+GvenRqDcAVVSVFjXbcM+PV3FIixMpopCJS77hzVnPZiSzeZ3DaFHnMADyVI261brw/rRdKShaQtv6p1Kn2haMmXNh5srWPBzVPDz8qA5Vu2ALL0J1BoMVwuqPsKW3ZSSyU/2+dK7vUUTzVY0m1Tvw1szr6N6oH0VWwIqChfx35vUU2KqM5OaiXAEObdGXHo26UUX5vDHrPSYtncIp7QZQZEXMXDmLe358FMtwhjlXZQDQoGk97vzir1y237UUFhRx8UNnYmZM/nYafz/zATINh5Krdytn72wJ/C6WNxeLPyM8cubtZvZ0JhlJOsvM/iHpBJJiv5Q1XSZ5hXS1geF4/JkVeLt+h5m9kKmsLPL+BDjGzCZnkXZzoJuZvVKO/NsAo4Ev8WdWHfh3ohwlTcYjax6DO+FMDvM8xszODkHbLgP2x+P3GHBOCPGcMV8seJcvFrwLwGGbncTn89/hiJaD+dfkm5m3ehaD2g5hs5ptmbGi7E6mZy59gZlL/XF2bjyUn5c+T0HREhrX3I3GtXZnVUGWe29WPI+teB4A1b0SW/EsqnM6tvBCKPwBNXoCqmwBBRPLLHLCoteZsMijiO62ybmMXziCHZuczItTz2VF4QJ6NT2FLg0OYMyC5zNSNRflumW9LehUtz1Dx95EtbxqHNxiX7Zv1ZXnpv+HrxaO5ewOg9iuYVdGLRidka65KoP8Kvmce8+prF7h4aVPu+V4Hhr6BKPfG8e5d5/Czof05MMXP8tIZq7erZy9s6WgosptacoayvltM+ttZnvge4YuldQ9w7z+kuH15U33IDDJzHYMHqSPBoZKys0OtYpjLzwSaXkZl/TMdgP2l3RQiuseD9clPmeH45cATYA9QvldArwkqWp5lGpZsx2b1GjF1ws/JF9VmLfaw9ZOXPINHeqkCsdTOvWqbU3tqh2ZseRpalbZnJZ1+zFpQcZ9k/WpsjVU7QgrnoI14yCvPlAVVB0su7GIpjU60ah6W8YtepWXpp7HisIFAIh8CotWZ61qRZZrtwZbMnX5DC7qdBqXdj6TUQvG8NOyadSpUhuAmvk1KLDCrHWt6DI49ebj+M+9I5n3s8vpuH07Rr83DoDPXvuK7fbJ7r2C3L1bOXtnU2GlfDYAZW1ofsXMlgL3AkdKukHSh5I+lnQUgKR3Jd0T/r4naVNJVwCNJN0VxPSSNFLSVyGGTEqS00mqKulRSR9J+lRSvxLSbQp0MrM7kvSeA2xvZvMlnSDpfUkfSNpb0gBJn4ffD4W8TpB0Y5BXI1gBifu7XdJbkj6T1DocHy5plKQX8Qo6X9L3kqpIai6pUFJjSdUlfSkpX9IDkt6Q9IWkayXl41ZEf0kHS+oq6Z2Q53OS6kvqHe7/f5IGlvGZrcGjoKYtsxQMBq418xrVzD4HegZZWbNXs8N585dnqJFfk1WFK349vqpoBTXyM4/BDtCmwWAmLbyTfNWic+NhfDf3SozsK8IEqnM6tvTvAFjBBNTwPtTkdSicCYU/ZiVz+8YD+HzuwwAsL/Sd7m3r7MpmtbZlwuKRWetakeVar0od2tVpza0T7+P+SY9xTsdB/LJiNie27cdt3a+mftV6jFuU/c7/iiyD/Y7vzcI5i/hi5De/HlNSkPgVS1ZSq1527xXk7t3Kldx0qCj9Z0OQ7RzNLOBC4Csz2yWEYP5E0pvh/EdmdpqkM4DLzewcSWeb2Rlh6GwN0AdoDYwgTQwZMxuelO4sYK6ZDZRUF/hS0n/NbG6KpG1IGg6SdDUeHbOhpGvD4QVmdoikxsCnwLZmtkTSbcCpwNIS7v8zMztP0nDgWEmvAruzNoz19/hw0//wobsOwFg8NPNSYCQeqvkTMzs5lN90MxsaGrfOZvZyGIIbZGbjJJ2EWxVvAjXMbMcS9EvFLLwBLE5/Sb2Sfv/TzB4FapnZguQLzWxeKsGhs5DoMCy9ufuzKWuhxo0b55/26Y2dT+949bcNGzbMO/qTC7e+ufuzowH+8pfOzapWnaubr3x2Vqq0N6e5qcaNG+d/+mn7zj07/Ovb4447rsGllzbavObS21bVqVcvv1r1ZlU/f+K4WVdccUXG4xEJuR06PPZt48aN88ePP3Wbnu27jZ08efKau+++u+WcOQPXDBs2LKWuHv07tcw+n27W+bAOf0v46GkybNiwvF6HHNKoW8/eP/zyyy8FmeqZkJttuaai953dNvtgzpyCJ6+6exbAGeMHbHl2x0E1t9tuu5+/+eabmZdddlnTvlv2qnncccdNTSfjmRJ0rcgyuOHMSzuZGcMPuph2XTavef7TJ6+q36xerbfs2SnA3E2aV2/wc1Hbem/Zs2l1fatdel1z+W5lKbd1pvlB5a86w8xK/OChg58sduwcYBheob4bPt8C3cL3luG67sAb4fsv4e8JwF/D9xrA5FLyT6S7Ezgs6fhLwI5p0rQEvkxx/MaQ/wnATeFYT+ClpGsOA+4O19wYjtVM6Bnur0v4fhpwFW4p3JYk4y28sTsQuAF4Mny/H7gn5FkPtwwfC/ktSSqfRL6Lksr3Q+Ch8DyeLqXM2uCNWPKxg4EHwvfJoex/zSuFjO+AesWOHVb8WIafg83sjsTv7777brmZtTczmdmIMMxZLpnAF+F7bzN7Mgt5qeRWnzZt2iozqx9+n29m15dX1xtvvHG6mT1vZjXLoWcuyvVAM3szpG9hZj+Y2fft27f/Jpw/zMwerwhdK7AMMLN3zayzmb1ywAEHTAjH7jGzfhWha47erYqUm/JTq1FL22HgLWk/Sfnn7JPx0FmwJk4JleA75uP3e+ELBhJWxPbh7y54AwQ+KZ0gk5HBRLrv8LmGhA5dgZQznGY2HfgpWFQJvesD2yblnWjjfwK2DIsHwC2ficBKoHk4tl3xLIr9ngDsICkvyNkyHH8zyGuCW27bA93Nh6FOABaa2QDgFqCWJAW98pLkHmdr50j+U0z3MiGpOnAu3uCVlUeAK4NOSNoZuBUvl2zpRJKleeaZZ07BG9rPgK9wy7JcMiuQZLmrLr/88mm4Jfoe3tj/XzllbnLBBRe0AFoAr+GdidMrQNeKKNdXQ7rPgFeAM4ETn3766fb4/Z8BXF4BulZkGSRz4ZVXXtkC+BioBmS+7M7ZEO9WzhGVP3Sm0KKmv2DdVWeF+HDb34AX8AoyMVz0gpldI+ldYAHQCFgGDDSzeZLeAWbgvf3OZnZZGDIab2ZtSsg/kW4QbhG0xy2MO8zskRLS1catjZ2C3rXxyvMeYABJK98k9QfOwyvwH4CT8R7/S0A+MArY08y2Cfd3mpmNl3QasKmZXSXpPGAg8DM+LHaomU2W9BQwxcwukfQEMNvMzpW0FV7xLwnl1AofWmsWjl+JW4y3BB0ATsL/KU8zs2NKuPc2rF11VoSvFHzMzO4J5yeTftXZIvMhxXzgWrwTsSZ8zrUsV52l0fMLM+tRUfJyJTNXcn9PuuZKbtQ1d3IT1Gncyrbue17a858+ftGo0vKX9BVuXIB3zu/F24ECYKSZXV1i+tIamkxJrogrVHBko0PSYDNLOT/3W5KZK7m/J11zJTfqmju5Ceo0amVd+6RvaD55suSGJhgEH5vZtknHvgaOwDup/wH+YmZfppXxW2hoJA3De87FOdHM0m4ACJPQ/VOcGmJmH5c1/98r2ZZbJBL541CnUSvbZp/0Dc3Hz1w0BUheVHVfcsMnaUfgX8AUfETrKuBeM+sSzp8LVDOztMPJFe4ZIMwnZJrmGnwIJ9N095FmxdofgWzLLRKJ/LEoxTPA3FKGzpbjCz8fADri82kLk84vAdKs3XOiC5pIJBLZmLFyewaYCPxgPvw1UdIifA4+QV3WbXjWI+NVZ5FIJPJHJLEC83dJ+TwDDMIXJSGpBVALWCapfSiTPviewbREiybym0HuX82sAicOJeWZZekvpnTZqkhdg8xyl0Eu9NoQsnMlv6JkJmTk4j1NpqLly6y8Fs0/gYclfYA3TYPw1ayP4StiR5pZiUvoY0MTqXQkVTWzNbloECpapqQqQB0zW1jBDWKFlYGZmaR6+FL4ZsC3YYtBuSvcILuWmS0vr57p5OdKZrb3H1ZdDcEnwWuZ2bKkcxXakUm8BxUl71e55dDQzFaTetFVrxTHUhIbmkilkPgHlXQA0EPS/rj3g7+a2ewsZSpUhHXwTbbH4xt9H7I07nMylNsC94rRXNIPwPv4ruplJUtIK7dCyyBJzybAHfh+s4n4Xrdbsq3Ek/TcH9gc6CDpY9zrR1b3XkzfWvj+uIOB6fievMXZyk2Svy9+758DH5ajcayCO+mtD3ws6f2g4+vlbWSSyqAxvq/tJEkzgb+ZWfbO74rnU8lhAuIcTaRSSPoHHYJXBIPxcAZ9yiE28T6fiW9ufRN3izS0HDKT5R6P6/gR0APv4d6RJk2p5KAMEnqeim+q6we8DOwqaUAF6Hk+7qtvF9xS2lvl8+ad0Hcw/oyW4N4/Ts1WYBh2QtLBeLlWxTdjPyTp+izF9se9QWyC7x35EDhO0teSjs9W10ByGXTCw3K8BZweGp/yY0Chpf9sAGJDE9ngJLm1ORiYZe6PazQ+FnxKtnLNfvVdfwBwnbnfqIFAG0m7V4Dc/XB/eS3wXdHf4B4yfq3gykouyiBJz82B98xstZl9iHuYqJeNnkn67gfMNLPHgHnA62Tvhqa4vkfgznfvAK4HdpfUszyycb98Q/EG91N8V/siyKwMJNXEvYS0wTsvh+B7Tobiz+n9TGUmk1QG+wIPm9ksM7sNb3TL0+laB1n6z4YgNjSRDU7SEM40YK6khuH3jri7n/JUiI3w3cpLkg43BMrlOkdSNbwR6ApsYWZvAzvj1k3Gc0G5LAO8EbhS0vmSjsQtkJcTWWcp83tgpaR/Ac/hLpO+M7M15dATSU2B2bijV8xsEtCAdC6wSyEM8VUFVgPzgQPDfrNauIWb6bNqh3tefwe3Opfhbq3OwMOO/JSFzHUI+r6H71FBUnO8s1BxQ2dFlvazIYhzNJFKw8y+kvQzMEbSRNynXWIoKqP/AEn1zWwR7pPtU+CL4KViCjDVzBZkM3GblOZovCf/CzBe0sv43oL55ZkQrsgySOI1vELcEh/yucXMZpRnMYCZ/RTmJs7DQ2K8SgVsljazOZI+x/dnjMTDWczI9nkFmWsk3Yzf/3hJpwLbWQkuUkrgWHz+6FvW+g+sDrTF361yr2oL+n4ADJd0MvA1Pvc3tyIWG6j8+2jKTYW7oIlESiJpYnkz3CKYgFsbXcxsXJYy8/GJ5K2ALfDeZg18jmIK7mV8WXkqBEkvmtmh4XtHfLHBO2Y2O1O5uSiDJNnX4h3I5bgj3P+VY3FFvpkVSmqJ9+ILgE+A1eVZXFEsj/q49ZGY/5iGR/SdW45VYm2BVWb2s9zr+Al4aI23suxsbIE32L3wsPDjcav53fIshkiSfzPwgZm9KOmqoGu53oNk6tVraT16npX2/DtvDynVqWZ5iUNnkQ1NouJ4AI/R8yjwBXCOPBJpxpviwjj35/iw1j54+O+DccumZaIyKEcjsztwsKSrJDU2s+/N7KlEBZ6F3Aotg6T5nr3wsf4x+NBhL+CicgxtJSrkfwJ7AofiPq/+Linr+MihY5BYFXYPPkR0Nd4wvmEhmGGGjXeiDFrjnoXHSvoCH4K6wMzeCjLL3MjIl7KDd1bexuNGfYCH/di7nCvuEosW9sctr0SI0AX46raKw+LQWeQPRNJSzpbAGjM7PhzvhschqZZlD7admU2S9BIe3HExsCvQF18um9XwRlKalcCJ+PLTE+QuOE6zLBy35qgMhDdeHYCbzezZMOezBVA1WE8Z33/QsxmwwszOCHp2AA6iFJcjpZCo7P8M/BcPx7Evfv/d8WCBmZIog6OA181sv7DQ4gJ8RVcq57MlYmaJaJ934hsTd8AXgfwNbxDKM2yW6EwcHPRNOMGtDRyOL6OuoM2rBpU8chUbmkhl0BMP9LYPPhb9DR6tNGNCr3N7eQjuerjn7k+AkZK6EILjZfMPGyraenjlvTvwSJi4PR2fAykPFVYGSRPgJwBTw5zP55a0W7scjWzP8POgoOcPwG3Z6JmsS1jNVR0YZWZzgMeBx+WbI7ORmWi8tie4QzGzl4GXk6yHMg+bJXUItgHamNk+kv6HL1K4F1+Btrgcc16J1WaPAIMl7YS/q3vgcaAqlMqeo4kNTWSDkfRPuQofKjoE6C1pMb60M+O5BDMrCJZMd3w56AB5QLoZeNjps7PRNami3QloJGkQPt/xPeXbO1PhZRDkrpF0Cr4E+2pgdZhXur+ceoJXrjsBHSUtxUOfz8pGblJlvxU+33GOpLfxVWdjNyibWAAAIABJREFUzWxBNnKD7Hr4BtV+ctf2Y3D3KKPDPZV52Czp/pviw3Cn4NFGxwL1zWxxOeaQzsajl35nZp9I2hL3JfY9bt18UEyH8mGgDbRfJh2xoYlsEJImwGvgS4KnA43xMfSWrLscOSPMXWRcIY9gWoQPIe1CWNKczQRw0j/5bOBhfB9FhzAX8LCZTchUz1yUQVLPe298afQIM7stzCs1S843C5nV8HmufHweIS/8fTxTPRMk6XEcvgepIb6sd3fcd9a7mcpM6Bsq/4eAF/G9TrvgGyBHZ6svvvhhd+BCvBHrgM9TgZdHYZp0JfFv81V1D0rqgQ8V7mdmS8uhZ8lU8pqv2NBENjR/wXd//4TvsJ4BTDSzFZkKSqoQN8Ed/TXGlyC/FIZNgMz3OCTJrQ7MxBubefhKro7he3mokDIoNt9zHfAG8JqkWfhc1V8hqz0eiQr0TDy8+E/4nMxrwKdJwz4ZUUzfJolnJN8B3xNf1psxiU6BpIfxZzUTtzzexxeJZKtnFbws3sCXNxcF+R+GfDMuB0nbAsMk/dvMBknaHl8leW14bvtbBbjfWS/fopz4lS0zsaGJbBASE9J477AfcD/u0qUW3rvNhuQKcRn+PncD5stXh5XourwMcgfjK9nqAFOBp4H/ZNvzTCqDPfB9OeUtg8QE+KG4lfEkPhwFXmENz1LPRAV6HL6stwbubeESfH5qZpZyE/3qfYFDJN0E3GlmU/BNphmTZCUeBFTDh59OwifZx5pZNnIT5XoDbmnOwBuYH/E5pawa2sBU3JI7WNIF+Gq2G4CTgV3LMySXFmPt8otKIi5vjmxIeuHDWfNxj8K7AsvMbGw2wpL+4Xc0s7/ijcNVQBd8k11WMUSS5PYHLsb9ZX2N++Dqno2uSfTCl7KWuwySLJVNgryheO97OWF4R2EpcaaEhRQzzGyRuVuUB4HmwJxs5BXT+yG8Q1ADt8DGS9oqW3Hh74F4FMgF+DvwNt44ZOxhIWlxRQ98Pu49fE5tH9wjQlaEd/FMfIj0GtwKnw3cCnxFcJFToY0MIAwVFaX9bAiiRRPZkEzCe3QXAUvDBOs4KLe79VclvYWPy1+KL22+Bsq1d2bToFttIN/MnpR0RkLfLOTlhwZsGt5DvhgPHnUyWZRB0vBOLXy5bVe8fHcPsneCzId3JLU0s+n4JtL/b++8w6yqrj78LhCxo8auRCyo2JVYsAR7FHtBjSUSe8PYE40m5vMzGI1GjbFrLLHEXlEJFiSWCOpnr2BHsVLslPX98duHORLAOfveOwPDep+HZ2Yud/bd95w7e+29ym+9bGb/QEZ2fuC5lHyRrbCQ3JzbIZfcFUhpYAdSlX1VSvd3ICqiXQs4HJ0ULyqeVmGexX1aG3jCUwp7ypJb2t1fyZlnYmF04lwLnRQ/QAkhdwCLoNNY/SlENVuRMDRBi5B21nOgP/7ZUGbUjjQJSFZNv10K7QyXBf6JJPG3RS6kS7x2aZgPzWwYyrgakbLORuaMm4L/K6Vr0MXd+5nqXM5Ai2zONSjce8cDH7v738zsPVTvc4C7v1fVBZNiJVuiQtJeKPV2JRSXGgFcUGF+U+MUtMD+C4lyHuDuvXNOnpMlV9yL6oYmImP+orsPgmqbjZJh3hjYOWWyXQc87e4vVp3jZGN/aGYXoQSINVCCySZIEeF+d3+ylvGnhUUdTdCWKe0Qf46K6a5D7p0HgcM9FcVVXBANGZjtUdFfb+B65ILojBaanCSA2dFOc0/gl+7+x5TJtiUq2jwsPbXqX+1sKCOsL9DOzN5B6bx/Bia4alNyF8TtgDnN7AZ3f93MvkGnxpzT3ETgHjP7MUqT/gK97/8guZVv07g5+mPF76wEHOuqqr/czO40szXc/ZmqY9J0H/6EDO+xqFi3Fzo1Vj0lroLcpb9FtUJ/Q5/bk4B5zWwHz0zrnjRh97FmtjpydY5AbtkVUd+kBuEQyQBBG6dYDA4D9nf3F9NpZB+kGVU59TS5jB5GsZinaBK8fB/Y1t23yJzrPsj1dAYwt0mM8RdIwv628utXnO8o4IKULrwQClZ/g3bgD5Kyw5pLaSe/Ocquuh3om4LrG6NWBpXxVMNikrJ5GAXX50PGrCNNCtBZpBPTW8DapsZpCyOXXOVss5LrcDlkxLdEJ7y9kHZapYy7NN7zZnY2ygh8ENXNXOjuW5nZUrUYmdI964VOdZ+jBJZngNc9I12+2TihDBC0bdIf15wofjBneuxNk5hiLbUz48zsApRp9Q063SwN/B2yYz6bA7939xfM7Fak1HwqsI2ZPZaz0EzmvuqPFphP0OI4H1BZxqb0vrqhVO5bTQoDfVEc5bsM916xcC+IYhwjkFLx++g6DJrC+6k0NjIsdwG/QvUtCyMBTa8639IclkMqCKPSa90FHFc1NlWM51IpGGRKbd8CNSC7A7m33pzGED/4EunrnmhDszqKA22HTuM1q0BPiyjYDNosZrYBqdWxqZDubFOb2k+B95LBya2ungedknZHmWz93P2a4v8z3GY/BmZJRmY95OI50KUi3AdYEknYV6UdMMHMjkELy+JoVz8AuN0zaybS6agdcGt66G3k8tkyZzyaYj57oJqZt1GCQVdkDN8Cnsm5V8mQzIpSo69EhmxjJFL5QXpOrm/nbWAdM7sZ1Q5tluZb1W1WnDi6pHl2RnGk/VHrifHpeVmf13QNOiJ35ATgZ+7ex8yuQq7JxuHAhNZ1nUV6c9AQUhylq7t/Y2Ynoc/aUSgZ4A7giMxxi89sH+TaORSp6f7MzHrXMOVRSGrkCdQCuF8yMp2RMGVWoLa0s94SueS2Q+9/BzKEHq0pXXk7JL5YbBavRHIruY3YinnugmIoZ7p70bq5B/AHU3uEXCYiAc3zgL3d/Rp3f9Pdv6k6kDVpl62GMgx7IFffvij+d1F6Tzmr65HInXc5sAJq8naY16AAbma7mdlmKcZ1FTopjTK1ll7e1QW17mnNTSRRzan9awHiRBM0iiXQwj0nCnwWrpKXgIFeu8R+d/RH2wsZhh4oqJq163QVyl2JqsnfB4aZ2d7IIDyQxs11Ry2DXGZjgdHufhNwU06mFU2ldzujQtJdkOtlR9QFMjtVPBmxx1EDrnNdqbxd3H3d5JLqkDFfYJIS8u1m1h84ysz2c/fLa8kMRC6oWZKRPB84v4YTRzGHYSjx4XngAZNadaE8kDvXhdB7XsDdbwAeM7OeyI12Xo1jN49IBgjaKFujmMnrKAvsGXQCWRsVLT6RM2hpEbkMBX57oAV2G9IpKXdn6BLMfB3AzJZHO9rrUI0GVMw2K82jKEY8DHjazN5HLsXKUvvJcC2GFq8tgP5mdhN6/8fnzLO0yM2B0s6PAs5JhvARUzHlwp7fmG5JlKDwEbr/nwM/NbMHvUkev9mUFuTPgd6lDcIDLpWBqvMr3Gbd0QZmrxSXeRR4zN3HTfa6Vef713RSPtzUavwyV+r1oCm8p/rjQKg3B22N5NoYitwuXZHBGYMCzK+g5lE54y6HKtNXQ/GSJ1FAfS10SnqjXgFVd3/VzE53paO2S4/lGrA7zexLYFVUO7EhEtTM7enSDbgpufauRLGJcYUhyDjNFYvc7Sjm8RRqWfABsDxyU/45c66ge3U7ul+zINfUG15Dl850T85EySBro03BKaiOqPJw6et+6PN5BVIFOARtErLrhyyl97v7kHSvDkBZcZfljlkdh4m1qObUThiaoBHsiuIPNyJXzCIosNwN+Mbd78ocdwmUtdQFLTKnJvfLaq5+LvX2cxeaZoeY2aPu3uw03NIueW3k5poDqf/egpQGsivM3f0BM/swZe5djZSFT0+vW9QtVZ3nOsj4vYMq7HdDp4Tr3f04y5SySfwcFdTO5+5P1zBOodgwGtUmdUVJCq+h4H3hhqq02XC1q26PJHaucffHzewxVKtVU4OzNPa87j7K3R8ys8+BfU0tm//i7qOrjlmZ6SAZIAxN0AgeRNlVh6IF9lEUpB2EJOdz4ygPmuTv/4SywN40s5eBBcxsA3fPTpeeyusV8+uFdrk5/BqlXO9Dk17W/VUHKcV7lqbpunYA7gF+jyrjIV8+sS9Klb7JzOZGad0rAQeY2QkohbzKfAsDtj5ycY4ATjSzj5A78i9VDGIasxtqJTAnarMwB3IZXosq7R+BysW/86ANxVLotHycqcbnRSRB81nVMUtj74Ky1l4ws3WRrM/7NCWBnF11zGyijiZoa6RA/1nAWaZWwLsCv0O72j3Sc3LjCB+hAsoRJhn3rYE5ChdXrb7u4kRQWthXB97yihL+aZGdAzVfu9skYXM8Knp8NGNqZUXpb5HszlLICJ5YuKFy3GbJDfUd6QSXruWiSFX4OGBTd787Y74TUTLFKegUMhjVj3QtX+MKY86CYmj3opNhdyQgenxOvCtxFE1tBa5BGYyLoevaESkt5zIEbbieB/6BklVeQBltC3kjlJqnhDtMCNdZ0IYoLdSbAXujzKgzkZzHip5ZXe1NEvt3AGPM7A1UUf5sEVCuR0C1tMsuFvZdyT/NtAeGmNnTaEFcDAXVK/dIoemksgGwUcriGmJme6Hiz3sy51hc23OAS82sKExcEBVsLopOqFXHHJ++nYAM4sboJLY0eYYWV+X+aegEtxhauO9w91Fm1qEI2jeXtFF5B52GNkBG7P9Q4sowUsJKDW6zt5HgaUHZ9fp2ek7LHDVa+UQTdTRBXSkt1CeiHeLXaAf3GUpvrkwpDXgL5MI5H+2QN0VB6yxK9RhLm9nOZnadme1qZnOV3kcPKkqklOa7C6rpuBEZit8i3awc6fpipfg30ghbw8zmQvGqgVP9xebNd3lU/b85cj/9B2WfHQw87+5fVRxvfTPbw6QycApyQz2PDPaORYwu41RrrrbMfVGTu5XRxoOqRib9znh3/7u7H4GM15coTfxgYPHilFSLMSg+C6XPRCvgitFM7V8LECeaoO5YUlZ294Fmtg1awC5Hu8SsIVFIcw0UnH4CeMLMFqepXXEtLohTUaU+aGc70VTvsRhwY8XgejmWcrSr98rpwOlm1tEzhCnN7Hi0834EddI8GLm1DPiru39b1W1YmmdntOteA7l1rkuv9SmKq/y9uWOWWBolFKydxnoUnYreBi5Or5/VXjvNe4SZ9UPGZrCZHesZTe5KcaTd0nzfR9eiIykTrVZ3bPGZLL6WXrMnajuQc30rTgI8XGdBG2Qu4EkzOwzFExZDhie358hEk3zH9ijw3w24wt3fJ6n0Zro2JprZvKgo8Soz+zmqDL8aeNVVtPdGzpyRBtcoM/szOnE8k+M2TFlWv0EL/1D0fl9EqbgflBbBqu+/LDlzCzol9EPN055z9+3Ib1l9B3K7dUUN6HojGZdPgbuhppqUYuH+BOhjZieSEkyqkAzWRDNbEyUr3IDiiO2Blz115qyHO3Yq7E5tKePVCNdZ0FYw6VmRFujbUNB2NIrRDE7PyfrMufu37t4DuaOWRoWP19Vh2h2Ah0xKze+inWz7FA+oPNeSwRsHXILERHsC/cxs1SpjpcXwQ1JhJjrRPILe/1lIFn/y123uPIst7qpIrWFvdFI6m6TSnGIYlUgxujHoWg5N870TpSB/VZzoaqV0by5w94fNbE/LS8H+GTIyz6P79SS6FrXMbfYpPGYl4zY7uofDa3mdZlMkA0ztXwsQJ5qgLph0pw41s6+Qv3t9FFT+GsUmhkCW2GXh4tkNpYq+g3S9DkSFm1nuDTNbzN1HAPOgU8tvUHrrIUhMsTKmCvjtUQLBALQ7fjqN9xOU3tpsSrv3oenkdSS6ptcB86JMsRxpnB8B37nSwf+CTh2dUS+efZGrsxzQrzLnYuW6AhVmjkBB9Q9J4pH1yLQqkkNSIkB3YF13v7bC7xev/yRSbDgeXd9tSGnSVrEmqcQOZnYjSoL40N2/mOz9bo9csi12zPCQoAnaCCcDN7j7zaa6i83RoviIJ6HHqpSMTFe0COyRxr0QOMbd74cs4zUnkv4vUlh3RXphP0WG7LWccVF9zwC0aHVEVfV7Aqe5+/UVx/oe7j4QGGhmW6KU6XtL/1d1nr2A+1IsrQMyhO+j6zDQ3T/ONN7lHjEdUcfTO1GKc+H6rDnTqmSoCvffTkj3rrm/Pw/SnVsYqSAUIqe/omkjQ46RMRXRfoyua19U6zUcNTZ7J53o9kZ1VS2DexRsBjM+KY7wI3e/OT20NpLaWAA4wszuTy6gqhQLyc7AQ6405ktMLZb7kFH4COBqW/BPVDjXGaVej0D1FN3dvXLjsHQNFnD3cir0Q8lNsg3wbNWdvJntjoomHTWIWxHJ7axiZoPcPXex+jeSvzkYSfh8gE4cd5OEOake84GmpI21UPB/OZR5eD9ycX1Wp9NMcdIrDME6KL7SXFZAUjVboySVU5Frcjbg/dJpKWeeayPJndlRgXJXlGDyUxS3vBkYnGJMLYJTezKAqR7uKbTRG4+MsaMEksN+aFMShiaoB0WnR8xsYeCf7j7MzD4FVsg0MuWF5BWgZ8qQGoNSUCfFfHICtu4+2swORjvt+dFC0JMUrM5YaDal6RrMiVxT41Bh5bXoVFN14foTEiL9HBnEy1AiQGdUGZ8jObM6ist0RIvrXGihXgktiv+C/OSK9O3jKNi9AyoCPZmmFOzCGDV3vsUpqQs6gXRC1/nDZBC2QmrLzb4G7v5kukcroM/u8kja6BfIhfpsDcZwB1RI+hJqXXA9utYboBPNRDI7oGbjDjXkNJhZB5QtWBQtnw2clGJjF6GT6jQLW60F3YRBG8XMVkYxk+PKwV4z2x9Yzt2Pz4gjzI9Sl+dBboc/oAV2BCoi3M8zJGdK6aVLIbfWN8h18q5n9EYpjbsqygQ7zt2/Kz2+P7CSux9VxXiZmaEFcEUkt7MaMrK3oY6SX0zj16c17m0oJrUMMMjdz02PrwDM6+5P5OzmU9xnU2BZd/9jeqwTMpZLAbvk3K/SOJeh998F1SY9kE5IJ6CU97eaOdak92ZmK7j7K6aW2NsCX7r7CTlznOw1foJqvcaj2pyPUaLB4JaMy5Tmcx/yLkyN2fi+xNAl7n5J6ffPRZuSE9Ap+AFgibQB2B7Ywt0Pm9Yc4kQT1IOXUHD6HlPHwP5ot7gNqvuA6q6YU5GhmRM4292PNnW+HO+pCVmNbphj09gdkQtljJmd5+4PZI73AtptDzCzy1DG3QrI7ff7qoOl9/VKchMugE5wq6FgfU/glAxXXFck13OcKV38RjO7wt3HeknkM/Oa/hktqjek11oaXde/AK+lRanqfIvNyX4oqeJuVPDaDqViH4TS3KukjRvgZtYXWCplqt0NnOxJ4DL3c2VN6gR7A2e4WmzPi+Rn+rj7I1XHrAfuntt1FVN32Y/d/f5k1EEHlOL6jEWf+2kShiaombQY/MLMCtXmY5A/90J3H5qeU2WBWQSdAjYyFbb1MbPHfLKkglz3TjotzO/uP0+v1wktDlmnhGLcNM9dkDvmOJTee2FhGDPnOw7FUD4ws+eRCyq322NvVDMDSgj4wpvaIHiu0U73ayl336g07+HJ3dXD1XIhp0CzeP53qAbn5+hkuw6pBTTSvqs0ZnLvHoIETxdC3U8PNrPD3P3D3Ovg31cnWMnMBrqy4oaTRE/rEaNqYfZFhnkzpFN3NalIOjE3zWl34e7xL/7V9A9oV3xFMYUOSBI+d7yDgN+l7zcAbk3ft6/TfH+CFqiLgJXrfA2sdA3mbeA1/zXQseLvfIJiEQul994jPT4ryY2eOZd9gbPS97MX94kU86rDe10QpRyPQienO5BLNvcebQ38Pn3fEblk167jvemKXH0XIMN+R/HaM/I/FHNaIX2GNkqPXQTs9kO/GyeaoGY87TzT1yI+UfTxWBFVWlfZxR1Pk8utFwqogk7g9agwew4Vfm6MpGEWAH7r+W6z8jVwmq7BKMi+Bv+FNQmWbgRs7O5/qjhED5QN+BgqGPzIzN70zGSNEi+hOBL+fZXrLiTZoVy3WUpeWAIF6ldDsjPXuPtrVSfpTSekrYAlzKw3qqP5yN3frTreNF7ndZNEzjJo8/WCN05hoDU4Bgmwzoripzf/wPMjGSCoP6VMoaVQdsp+FX63A8rK2To9tCiwjtfQKGwK46+IsqKGoyyrtYGn3T1XbmZKr5N9DSYbZ5LLqbT4ngf8xysUKE5h3HWRmvL2wF3uvlcNY82KFpvZkTbaAORSuRDFPobUEPe4HxV8noHqqF5296wOraUx10Ep2OugTKpngUu9lMRRb0zCpcM8owi2LRCGJsgmxTra+WSppaWd99HAS550ozLGXw8FgjdFJ6Qz3T1LdqY0p/2RT/5fKJbysbsfkjNmGreh12AK480K3Ads6+5f1mmuy7jS0XMr4Ytx9kSuznXRqfFhzxCNLBnpdYDT3X3j9PhG6POwX45RMKlyf5G+74iywo5ANWAnVR2vwuvOAVzr7js26jWmd8J1FmRhZnO70lUnpJ/bg2pfSovVpqgYssq4hdbYeFfwv6gX2QfFPXIpXBc7Av/r7o8DF5taA2zu7pVlZxpxDUqL7GropDUcpcUWC+v2aGdcycikeXlpru3QRnNCMjK7oeyryuOWTl03ADehDMN53f3jqmOV5gk6bXxlZj91ZWxNBOZy9++qnJBSsscVwIumfjuPo7binVA22yWTvY+6UDLcO6BMzJmWENUMcrnCzIaZ2VFmNs9ki2tRSzDMK4oouhifxmhXWryvciksd7NMsUszmw81ICurSC9MfvuCul6DkpHpjBbGRYGjgYtMKtCgHiyV5GyS8f4eaUEtXHKrAwflGK/SWIWB/c7dxxVGxsxWnNLrN3Pc51AF+l5m9izKFPtrxlCLoSD2h8h4P4lOyNsiI/NY+X3UkWK8bYFb6zz2DEUYmqAyaafWG8mrd0f1HgNTcLVgS/J6mUzC3Scmd1HRoKwLcGzVBcEkdom7f4523I+Z2QWmuoAvXam4lRbDBl2DYg67Iu2u+5FRHIuC4AB/dPdKHS+T8TL7b3Xj4vV2BG6vMua0KN2vpZAmXaUkgPR1PpMWXXfUPbQXKoZ9ECqndr+KapkOQifCTiRJI3f/dy0GpvjcmFl7M5s7bY6smKOZLQF08NRqe2YlDE2QQy9TY7DlUc/1JVAq56GmFssgpd7KnSmnsBhC04K4EzIUVcbsAPzHzB5J8ZIBaFf7Jmrde2CV8UrU/RqUFrzFkUtnN5QY8RFJwqXqCdHMeppZt3RSnOQ2M7NZSq/Xg2ZkDk1h7B+6XztS8X6VOA9YDyUYnAvs7O7v5QyUNiw3o2yz7sjgFK0Qaup8WTJ4RyPVhv7os1Fcl63IbwXeZghDE+Twfyh/fhm0CFyE+q9sjXS0cPd/VdzJzj3ZYti+HPNIT9uUJtHH5jIv2hEvigoWhyB3zBtAf0+pvRkZUXW/BiX+lub7MbAKSu19CLIWxu7AIDMbambHJBffxJJ7cnNgjKtlQrNp1P1yZdXNByzp7ie5+6/Q9dzI1Bq6Mskgtkvv8XpkxH9lZotn3p9J46avP0VJEHugmp8lgePS/79GZtuJtkQYmqAyrpqDu5AMyNkoC6rYFf9oGr86LRoV8/nYlVp8CGrA9hPUNOxIKiYqTDZuI65BMfYwtON+DJ2WzioMQUXjPT8ypgshyfo1kItvgElaBBTzyUmTbsj9SnQBRqbTKMjo/qiW5IJSHGkocnd2QoYwm9K96I7cnOuhz9S/gTXS6w7y7ysGzJREenNQieRyGZ92wmuizKCXkHzL+PSHXHXMInW3B2pCtUka82J3vyk95yTgHnd/JnPecyGXyeZISXmwJW2qIghfYay6X4NmvGZnkoR9hd/ZCBXXfYYq6+9C6gC7IJHLXVP86j2vkNbc6PuV5nQQCuD3R8raL7n76VZjZpg11SItiq7LYui6ZtfQmNpBH45ONech4z3Q1ZtpRpOcaQhhaIJKlBaZu5GW15pIwuRT4DJ3r+rawsy2RSeOG5Gb61OUEtoX6Ozuy6ZFfWBFg7AkSokeiWRBPkBq0CcBV7maiVWmEddgKq8zS8nFdRvKDGu2tpepfmNx9N67o3jSGNQn5RbPLB5sxP0qFuSU8HECMmAHINfh1cDznpkVN4XXKozNwsBt7r5exhhlFehZUWbZmiircbxXV21o00QdTVCJtMB2RbUup5jZ5chddALqlZJDEe9YC8U7vkC770IdAM+oc0ljLI9aHz+BMo4MVa33Rh0rK+84G3QNpkSRfrwR6m9TVUDyK+B1MxuJjMG9wI9Rm+3BqOVCDo24X+1RAeUvUa3MeKQs0AiKONd2pL5G2QOZ/Ra54b5F13iot5JK8/RMGJqg2ZiUlB9Bu/dHTEV+zyOxvV+5+0eZC/e7ZvYeyqxaES2GPdCu+SC0S6w61w5pvK+QgfkK+LknKfhcGnUNpvA65fjpzqQsqQq/X5y6jkDilkWL6o/QaSbXyDTkfpVOV28DR6fMvevRCfHtOl7TSQkMKGX6tJxx0oloHpQJeRqKyy0E7G1mwz0zQ66tEq6zoFmY2WzoVLAl8p1/h9xSp6AOlX9z90uq+tBbIt5hkjI5HNgI7eLP9Qwpm0Zcg8lcMPMAc7r7B6X/74gq9neo4joqLYb3oYXwUFSc2h44Jde916AYXdE0bRl375ceWxOlnu8ILF3VbVZyxc0JLO6TiXCaGr2d4xm9WszsF0j1+TvUzuL4tLHpjLLl6uI6bUvEiSZoLvOiVNUTkXzHXiiYehTa0d4HWdXVxU7nVyjesT7aaX6KpNazSKmlhZTNf1BNCynbKlfKplHXADM7FlgEWN7M3kEyOR+g4s2PKxoZS0amGzA6JT7sneb7IErvzaUR96tomvbPNP+lUUD9MHc/OD1WaQNTOv2cA3RKBmcA8EhKUNgq/ZzDZ+haLg58Y5JH6u/uw5FkUDAZkd4cNAtXvcklaIGdDblJ+qJd8kO5WTuTxztQ7ci5aHeYHe/wu69NAAAdSUlEQVRQZukUpWyu9Ewpm3pfg9KuuzNarG9BDdPGoBYGoGtQNbB8ZDodjQGGmrpJvosSAsa6++iq772g3vfLmpqmHe7ug9NrDE9z7ZOe077iKblQF9gExU9ORu68nsAN6fTxKKqnqoy73+3uuyOX5gCUCPF3MzvfzGbPGbOtE4YmaDbu/jJKl+2Fdu8dUZFa55zxTFXrRaOwyeMdXxXxjjrMuy5SNmmsul6DxC7AEHd/3NUOYQhKVsDdn3b3Z5s7kKl18EGoyHE74GKUDLFW+npB8dSqk2zQ/eqFurFiZrNbU0X9rWgBLxeANovSfd0eKRN0T19vQDGfce7+pLt/VnGumNks6euhwG+RsdobZeA96t/vxxMkwtAEP0jxx29mqwJ7ooyjjsmf3tfdX8oYcza0E14YVVMPQqKH26M4SCFCmLMgmtVfyqbu16Dk3hkMrJJ24AAboutRJDVU4Wvgj6h53DGo58r86D2v6e7902tXWrwbeL+K+A7u/nVpXl1IbqgaNhs3ozhKT3Ta6kOSBJrK5+MHKSUt7I704cYgSaNuSIImmAKRDBD8ICUXzz1oURyHFpiVgIM9FelVHHMR1I64Pd+Pd6xEinfkuOOsSbq/+HlyWRTS+9jJK1St1/salIL1s7v712a2BzoZdUMZZv/r7p/mZFulBIKTkYH5DOmFfY5qRrLckQ28X3VtmmZmuwOvIIkhUDbY0iimhLvvUHWOU3iNZYFL3H2T9HN7dKrb2t3H1Dp+WyQMTdAsks//Ki81bzKzlVGw+d3MBbEbqqSeDXgA7Q5fBB4rG4uKY96EsqHOBy6f/A/fJI3yC3c/ImPsul2DkqH5I8qs+hvqLT+yWLBzrmlp/G7oRDAOSaNsBVzhkrfJohH3qzR2vZqmLeLuH5rZWahQ9WaUjr4o8Kq7f1Y1sWAqr3Me0rn7Kzp9beruvesxdlskDE0wTUrprOsiXa/nUXHeUJfsfq3jr4qUlFdCAfAVgD9luuMaIo3SyGtgZuegBXsxYDOUEXYH6shYqXK/9P53QaejtVCW3JVesbXANF6jbvcrjVcY3PbotJTdNM2UEn0ccms+glyQeyOD8CZwZA0bmPmBv7r7nmmz8hRyofZB6ef93f21WjYHbZkwNME0MbON3f0hM1sb/cEugWRcFkaB1SczxiwWxCLesQRyRQwyCTRmuR+scVI2db0GpcV1a3S62i09vilKnR6Lajwerjhu4d67Gwl9DgH2Re2KL3T3P09zgKmP25D71YzXXRF4uTn3KsVx5kHXr2f6/nrgUmTIe7r7vZnzKK5rR6TM/CzwMnA5cIPP5L1mmkMkAwRTxdS0aXGTIOWWwGjUBncwCgC/kjl04Vroh2IHTwOnmtmnwM9qmHIj2hfU/RqUXCvtgS9MsvigRexuVI9yQMa4bk1S+sPdfay7n+vuxfXIDaw36n79F5bZNM3FaHf/Nart2Qplxw1En4talBA8nWq/RerfC6L7sxYwwsxOyR17ZiFONMEPYqrcPhAt4F+jQOvjOaeZ0ph1j/mkMQwFlgtplJ8ARavid2uYb12vgSlNdlm0YHVCrp1NkV5ab+AFd7+ywnidXPUxa6ETTEdkEF8FBtchhlLX+5XuUzufLPutdHo6Gik239fc8ZJBWAGdaO9FWWBPInmcJyZ/raqka/AUyt4rJ5zMn2I/4TabCnGiCaZKafe7OHCnu++Pguw/BubIHLNQo1gRmNfMzjOzzc1sPnd/oTAGGYkFxbibIVfZeshlcifwu1wjU+9rYE2FkjsC+7j7Mah48D1gf6QO0JUK6ddpzHIL6cvS709AtTSrVZ1naey63y9rTNO04j71RNdzNHAsMjhroQ1CrayItNjOM7P9zGz1NOfP0tcwMlMhTjTBFCnFETYA/gftvr9Gu8Rr0G4z58RR95hPGrcR7Qvqfg1KO++b0u//vvx6KIPp4yonEJO8yiqoNfWFKDbzIor1zAU84JnKDQ2K0TUyM/AJ1Pb5/eR+Ox2pQj/l7mdXHW+ysTsg9+n86d8qKGnjgVrGnRmIE00wNYqCtt1RTcYuKBtqW5S9k2NkGhXzaZSUTd2vQTIy7VHcYHtTi+UTzWxRl4LB8KpuLnf/0t2fSEHpk1B9y84oEaBdDUam7vcrbQh6ozqc7qjj50AzK5/ItkQ1NVXHXhSdDIt17WNUk/N7YFdTLVDVMYuY0YKohcGaqFPpSHRqrHuTu7ZIiGoGU8TVeXJWVPA2ATjA3Q9IrqQBUL3OwyWd/o8U7xiHqtXL8Y7cbLNGtS+o6zUonpuM4qXIEK6GJGOupMbAupkdiU5dF7v75aYstpFV51nQoPvVy8yKzMCjKGUGmlk/d18WCaD+X9WB3f2DdKK93cweQp+Hl9PXj11adVXHLBIhDqSp+n8RdLr7Y61xn5mFcJ0F/4WpDmUl1MxpBNoVboYCy3u7+9qZ4xZuo1WBCe7+opktj/6I7/KK6bxpzEa1L2jINUhjn48asi2OMrh+iST2PWOehXtvJ3Ty+gAZri+AP7j7X2uYZyPuV2d0IijiJkXTtAdgUrO2rLmi2FZ7lACyLFKDXgYVgr7iFQtAk6vsRBTzOh44I7nkZkGxv796Zsr0zEacaIIpcRqqFVgVuMndLzKzUUg7qz80LXDNHXBK8Q4zK+IdV6DCyhwaJd1f12tQev9rAsu6++bJJXUCcsOdmTnPdij9eJM0507oPW+OFt7K92qy+db1fnmdm6YVsTl0KtoaueMeR2Kir6KEkGfIS2/uhE6c/wLmA74ysyKm9iXwWMaYMyVhaILvkXav37n7UcmnfbGZzY0WgZeBq9JTqx6F26MFsYh3jEMxj22BH7l75boRkHS/mV1CkzTKITRJozyUE59o0DUonrsFCtrj7l+Y2YMoS+7MTPfWeJMa9Xi0cO+EUqZ7o2LFXOp+vywpLKCTYblp2p3ArZ6XGVgY0L6od8/hyB23N/Cku9+YMSYA7v4Jup6YVCEOQZuP2VGh5uicezYzEoYmmJydaUqt3QrVoLyBFq7v3P1NqJ7K2YiYT2nsl83sGL4vjbIH6sGSs/Ou+zUoPfdG4Fgz2wplhfUFrk7/1w5dm2ZhZgci5eBRqAp+NNqFnwGs4O6Pp9fOaYfQiPtV96ZpybU3OzAM3fv102lxAMoMzP5cWZMszlpog3Ae2sD0Rm2xg2YShiaYnP2BM9P3m6AWws3WBZsSk8U7LkPB1G9NDbl6uvtxkFU7MzVplH5WmzRKXa9BWgjXBoa5+3AzexTFOYYjuf3bobp0P9LZOhO5nnYETgW+QanHxSLbvuq4jbpfPllmoJldjkQvTyCvaVphQGZBkkNnom6av0EN3p7Nmedk890WXefnULfO79x909Jz4jTTDMLQBJMw1WOcCSxhZnegHee1ZtbB3cfVMHTdYz6JsjTKYOAjJI2yEnAwFXvOpHk04hr8Dfn41zWzo4B3kE7WM+7+fs6AZrY+MMLdbzOz/iipYGVkZJ5BJydoukZVaESMru6ZgaXnXozcWu+iRINvSL1hMj9XmNlByPhtC5znSZTUzM4ys53d/ZaqY87MhKEJJuHuX5rkzzuhrKgNUWX5YWZ2m7tfUXXMBsZ8CrfJPGn809PDZ1mSRkmvX3Xxqus1SDv45dx9gxRLuRfVnywBXG1mF2ScZAD2oanR1sHIdXS+uw8ws46eeu1knBLrfr+sqWnaljQ1TeuQ5v1LZIhB1f2VUsXNbBmUYbYk8Ky7P1V+XqaRmTvNc4c07u5m9oWrOHUZmpq8Bc0kDE3wPdIf5ufAE2Y2BFgAuX3egyx/d0NiPqXA8iRpFJqk+18ovZ8cA1bPa9CbpsV5LeBtd9/RzLoDv/eM9GNTEWEPoKOZfY7iHIe7e5Fk0OyGblOgEfer7pmBpddfGngLBf9fMbMR6KRYuWamNPZYM7sYWAhV/6+Aimv7AaPc/dHcsWdWoo4maChm9j5wprufY2bXAGfXGvNJ4zZEyqbemNknKN12P+APwHXuPtjMTgBmd/ffVY2jJEOzCnJtrY8C1Tcg9+GDtRiaBt6vRjZN64muw6zIcJ3j7s/VMF5xWloK6dnNmea9KhI7fTiyzaoRJ5qgYTQq5mP/LY0yFPnn50AGJ1vKpgH0QOnGj6Hd98dm9hxK7z0hPaeSeyft/J81s+dRavCVqH5kH3QC6Zcz0QbG6OqaGWhNNT6rAYei6/cWMrTvkdLHcygZmWXRyW5kGvNJFAN8Mr2fMDIViBNN0FDS7rsc71gK6Y9lxXwmG7vu7QsaiakW4zCUITbS1SemXmPPglw9E9x9ZA1B8Lrer2lkBg7KzQwsGZprUYLBc0itex1U/Jrde8aauqn+D0phHonu2ZKoYd5xuWPPzMSJJmgoDYj5lH+nkO4vS6M8W9c3UCNmTX1X3P0JJJeDmS2ZvhaxpppIY4wo/ZzVt74B96vumYHJyMwOLOHuF6eHHzez+9BnopYmZ8W9mB+dlPdGG4OT0ekrO5NtZiZONMEMhTWofUFLkE4L5k19WHYD7nb3L1t3Zo3F6tQ0rfy8dC3PRVX616Ki1cvcvXsN81wKnWC+Q/po6wCLoWZnfYHN3X1U7vgzM2FoghmKIl5gEqa8niZplK1RYDlLyqbeTG3xLMUAVkeB9k1aYXotQskNtS460TxPU2bg5zWMuxvaWHRCJ47lkILB9e5+a9XkitK4NwEXeqqZSY9tBByHMtlOitNMHuE6C2YovIFSNnWep5fdZqX/KmpFdiQpArRhNkRpzROBS1B85qdAbzOrnBloZqugBIoxwM3u/rGZvYoSLZ5Gem85CguY2cLAvN5UmHk0MjoPAw+ne5ntkpzZCUMTzDBYg6RR6k1Kt/3I3V8maZclV0+7UgygB5I2aZM0KDPwYGRUrigZkznRvX+iMAaZ7IxaQGNmPwM28tSRc3rYuMzoRIfNYEbiNGRo+gBd3f12pJW1BSVplFabXRPdgUGm7pnHpOyqiYWRMbPNgTG1ZEdN77j7e+7+D6AjTU3TdkMFmy/kZJul373K3b8ys3bJsDwJLG9m3Ws0BvuREh6ATVHcB5PKQhiZGpke/iiD4AexkjQKSpPdysyOo0n1N1vKpp6Y2fxAf3dfCAWQ10AV6wPMrE962sqkhaytUjpdFJmB+wPno4LKOTLGWwz1l/ka5MJy8RYq0n1vGr/+Q2PPgtyYfc3sWVTf83J6nVpUFoJEGJpgRmFK0iiLIGmU33imlE0DWBX1lrkKyeMcjVxFlyGpGJBW1p2tM73GkwLmnjIDzwHuTbGUX6KmaYOqjplOf2OBu82sp0k/DTPbAXg91Q5luc7cfby7n+ruq6Hi2n8CN5vZk2a2V86YwfeJrLNghsAaJI1Sb8xsDrSL74pcaMuj4PUg4JZ61MxM7zQyM9DMDkVZZnOhQtI3gHvcvX8N2WZTStrAzPYEcPc2ffpsCSIZIJjusQZKo9QbV8/7181sJGrodS9yF62PChbbbFymoBGZgaXnX4dELkFKCP9y98KdlqOCXZyCy0kblgpsizhNN+DVyDjLJ040wQyBNVDKpl5Yk9zKEUBP1HfmNVQN/4G7t/ke85NlBo4A5katm18F9nb3tRvwmnXfcJQKg7sAJ7v7fvUcf2YjTjTBDEEDpFEaQfH6vVCG3KHAaqgd8CmtNKeWplFN7v6LknzP78zsTncfkjHGFN1mqN4JlC1XWSYn+D6RDBDMcCS3xkh3v6uI07S2kUmGbmJys4x298EoeL15esrTrTe7lqGlMwOT6sCsKB355Yz5zp0y1wq3WXsza5/GLgzPpqjoNKiBMDRBUB+OTJpeY4ChqYj0XZQQMNbdR08nNT6NpKGZgaXamXK91PbAc+7+RcaQV5jZMDM7KtU6TSifbMzsJ8CwSHGunbb+wQ+ChmNm86JWx6uits8Xo6D1WunrBcVTW2WCLcf+KCYDsAmwg7sf4+7PuPuLtQ5e1M6kH4truT0ZrZVTPK036vbZHdU6DTSz3qWnbQn8vZY5ByKSAYKgRsysI6p63wXVzhyCGnG9CcyRWQU/Q5EyAw9ANUNdUZbdXsADuYH6UnLFyqiIchXgGne/Mf3/QsANniFMambbovt0I3APyhDcARXZdnb3ZZOCw8DWdsu2BcLQBEEdSMbmZNTH5DMkX/85yoqreTc/I9CozEAzG4wKPxdCxuxWd/9fkyr0yu5+2TQHmPKYnZFqw1qocd4XSFn6AZiUph7UiTA0QVAnUiLAcFSguB6KU1zh7sNadWKtQAqqT8oMdPdnqmQGFs81tWs+w91/Vvq/gcgt94XV0DguxXtmR6fQHwM/QXGlg9z93ZwxgykT6c1BUAMl984uyL2zFspSutLdf9u6s2s9UlB9JDolFI81e1dbem5H4CMz64pckdsAnycj0z7HyJSM02bAmkg/7SUkC3RrGJn6E8kAQVAbRT1IHyQauSLqyHipmR3bWpOakTGz2cxsBQBXz5rngNOBU9F1viY9NdcdU/zer9CJZjPgWBSfmXtqvxTkEyeaIKiB5N5ZMP043N3HohbD55pZB5huiklnJJYDVkyZzDshkcunUIzmDE/dOXOLPtMJtCvQ3t1PMbPLUbuJE4CZIp7W0sSJJggyMbNO6dsuKPB/RqrJ6JWKAcdB6xeTzoB0cPcbkHTP0qjgcwe0Ma5JUSApPxswK/CIqS3088DDwFfu/lGuCnQwdcLQBEEGKcOqXHNxGSpWnIBqaVZrjXnN6KSapKvNbDhwEnAGcm3djdLHl6lh7NnQaXNhYEGkqP0hqsV5gqZ6nDA0dSayzoIgg1Q3sgrwOnAhMAS5XcYiCfsH3P271pvhjI2p0dluwC/SQ9cDlxZus8wxFwF+jbTnuqE6n8+QCOiPgfvinjWGMDRBUCNmthyqG1kPeQludvd7WndWMyalXjbHoCSAQcgInIganPWrcfxuwHnAbKhmZgzaIDyW4mtBAwhDEwQ1YGZHAssCF7v782a2KTDS3V+IJIBqlGpnFkCZZbMgw/04EuQcloRLa1J/TuKfb6KTzMaov82f3P2lmt9EMEUiRhMEFSkEHc1sJ3SKmQAMMbNPgBXd/QWIJIAMivVoL+Cx9PVopDRwFdAP8rLNClXmZGT2BC4COqYTUt8wMo0lDE0QVKf4u9kE9V+5BdgRuBrpfJXVhYNmUlJO3gIYklpBPAsMQ+Kk85lZr8zhC+PUD2UIPg2camafAj+b6m8FdSHqaIKgIqkPShdgPCrQ3AlpcPUGLm29mbUZzgN2NbOxwDdIRXkNlBX2Yc6AySU3D+qXc3p6+Kwk2Dkaot6pkUSMJggqYGYHIv2uUSideTSwEZIyWdnd12u92c34pJPgHCj4vyVwH2qJ/QbwR89oBV1IziQRzn6obuYuYGgtWWxB84njfRBUow+SlZ+I3GXvol33wiRplCIeEGRxBIqfjAJ+7u4nAvciVehDMsfcMH2dCFyC7tlPgT+ZWWXDFVQnXGdB0EzMbH1ghLvfZmb9kZ9/ZWRknkG9TaDG6vWZjSKLzMx2Rs3jbkXFmuPMbP3UYuDtzLGXABY3s7nQCWkoMjRzoN45r9TjPQTTJgxNEDSffYDb0vcHoyD1+e4+wMw6Fi1/w89fjVIWWXfgWmA+4A+oQn8TyI+fuPt7wD/M7EeofcNOSK35DeDxmaEp3fRAGJogaAYpdtAD6GhmnwO9gMPd/XWA6CtfF/4N/AbJzOyLEiyuSv9nZKg1lwzU4sCd7v6imS0PHAg8W5dZBz9IJAMEQTNIhmYV5NpZH6Xg3gAMBh4MQ5NHqZ/PhqjLZRcU+2oHvFiLEkDJJbcB8D+osPZrdCq9BngpTp8tQxiaIKhAMjhzI62s7ijQ/Gyt0igzKyU1gLuAq9z95qSK/QVMkvTPcpuV5GzOR1pp45Aw59ZIcuaAOr6VYBqE6ywIKpDiCaOBJ8xsKNodT4CmHXRrzm9GIxmZ+dOPPzWzr1C3y69qbbOQjMyswI/QPTrA3Q9IbQAGQNTOtBRxogmCoFVJp8QdkPbYbGgDPMTdb65hzB5pvG+BEegUuhnwKrB3Tj1OkE+caIIgaG02QMH+95Fs/0IkBYAaThynoWD/qsBN7n6RmY0CjgH6p7HjBNpChKEJgqDFKQXquwNHIiOzGvBn4B53fx/y3GZJOPM7dz8q9aC52MzmBg4CXqYpky3cOS1EGJogCFqTXwIXo0D9J8ACwG+BQ2sYc2ckDwSwFbA6qpvpjQzQmxD1Ti1JGJogCFqcksvqGVSftC7KCDsV1dPU4traHzgzfb8JsIO7P1PbjINaCK2zIAhaFDPbzcy2ST/eAyyHmo9diTLEboTsvjNzIiOzhJndgU40C5tZhzpMPcgkss6CIGhRUs3Mr4tmY2Y2B7Akkp55w90/qiXtOGWxdUIN0zYElgI6A7cl3bSghQlDEwRBi5HkX/4N7OruD7XA67VHcZ+1gffc/ZmonWl5wtAEQdBimNnJQE8U+F8CtWz+p7s/1aoTCxpKGJogCFoMMxsI7Ovu75jZOkgOZl0Umzna3Qe16gSDhhCGJgiCFsHMFgS2d/fLyu6r1GK5J/Cku48M11bbIwxNEAQtRknoMozJTEQYmiAIpguKlgGtPY+g/kQdTRAE0ws7p1TnoI0RhiYIgoaTpPmn+riZrQ4c5O5ftejEghYhDE0QBA0n9Z2xVNdSpjBAOwJ3tPC0ghYiDE0QBA3FzHqaWTcXk5rEmdksJZmZHkB2/5lg+iYMTRAEjaY7MMjMhprZMWY2j7tPdPfxAGa2OTDG3Ue07jSDRhGGJgiChpHaNPd394WAvsAawCtmNsDM+qSnrQxc20pTDFqASG8OgqBhmNlGqKvlZ8AjwF1IfmYXYBd339XMlkQ6ZJHa3EYJQxMEQcNI6cqLA12RC215YAwwCLilcJ8FbZswNEEQNJwkM9MNmAD8GFgfOCviMjMHYWiCIGgIRaW/mR2BtMzeAV4DPgI+cPfHWnWCQYsRyQBBEDSKYhfbCzgHWAToDRwBdGytSQUtTxiaIAjqThLNnGhm3YDR7j4YGAtsnp7ydOvNLmhpwtAEQdAIjkxxmTHAUDPrC7yLEgLGuvvo1HI5mAmYpbUnEARB28LM5gUOAoYAqwAXAx2AvwP7INcZNMnPBG2cSAYIgqCumFlHYDdUK7MicAjwFvAmMIe7j2m92QWtQRiaIAjqTjI2JwPzo2LN2YHPgdvc/cXWnFvQ8oTrLAiCuuPu35rZtcBwYBywHrAV8E2rTixoFeJEEwRB3SjVzuwC7AGsBTwEXOnuD7bu7ILWIrI+giCoJ4Xsfx/gfBSjeQq41MyOba1JBa1LnGiCIKgrZrYgyjA73N3fKj3ewd3HpRqbWHhmIiJGEwRBXTCzTu4+GuiCAv9nmNnjwKvAYHcfC+q22XqzDFqDcJ0FQVAzqfiyd+mhy4CbkIjmdsBqrTGvYPogXGdBENSMmc2JijNfBy5ExZovItmZuYAH3P271pth0JqEoQmCoK6Y2XLAhiiluR1ws7vf07qzClqTMDRBENQNMzsSWBa42N2fN7NNgZHu/kIkAcy8RIwmCIKaKMQxzWwndIqZAAwxs0+AFd39BYgkgJmZyDoLgqBW2qH6mU2A04BOwH2oJUBXkDFy94lTHSFo08SJJgiCmnD38WbWBRiPCjT7Ao8D8wLXt97MgumFiNEEQZCNmR0IrA2MQunMo4GNgDWBld19vdabXTC9ECeaIAhqoQ9wD3Kd7Yiam30DLAxcA9I/a63JBdMHEaMJgiALM1sfGOHut5lZf9SeeWVkZJ4BbkxPjdjMTE4YmiAIctkHuC19fzAwDDjf3QeYWUd3/xYi2ywIQxMEQQYppbkH0NHMPgd6IRHN10H9aFpzfsH0RSQDBEFQmWRoVgFWBdYHtgBuAAYDD4ahCcqEoQmCIJtkcOYGugHdkfTMs+7er1UnFkxXhKEJgqAumNkswELABHcfGUWaQUEYmiAIgqChRB1NEARB0FDC0ARBEAQNJQxNEARB0FDC0ARBEAQNJQxNEARB0FD+H/Yzqt5P6rqQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Tau analysis\n",
"\n",
"\"\"\"\n",
"Add time lag?\n",
"Smoothing for deeper groundwater?\n",
"\"\"\"\n",
"# Analysis of how lag applied to rainfall affects correlation with target\n",
"lag_array = np.arange(200)\n",
"plt.figure()\n",
"for n in range(len(rain_ts)):\n",
" crosscorr_lag, lag_array = cross_corr_lag(\n",
" normalise_0_to_1(all_target_ts[2]),\n",
" normalise_0_to_1(rain_ts[n]),\n",
" lag_array=lag_array)\n",
" plt.plot(lag_array, crosscorr_lag, label=rain_name[n])\n",
"plt.legend()\n",
"plt.xlabel('Lag applied to rainfall data [days]')\n",
"plt.ylabel('Spearman''s rank correlation coefficient')\n",
"plt.title('Correlation of rainfall with target for different lag amounts')\n",
"plt.show()\n",
"\n",
"# Calculating optimum tau for each rainfall/target combination that\n",
"# maximises the Spearman's Rank correlation coefficient\n",
"all_tau_best = []\n",
"for n in range(len(all_target_ts)):\n",
" if n == 0:\n",
" tau_array = np.linspace(205, 450, 50)\n",
" else:\n",
" tau_array = None\n",
" tau_best = find_best_tau(all_target_ts[n], rain_ts, plot=False,\n",
" tau_array=tau_array)\n",
" all_tau_best.append(tau_best)\n",
"all_tau_best = np.asarray(all_tau_best)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"cax = ax.matshow(all_tau_best[1:, :])\n",
"for i in range(len(all_target_name[1:])):\n",
" for j in range(len(rain_name)):\n",
" text = ax.text(j, i, np.int(all_tau_best[i+1, j]),\n",
" ha=\"center\", va=\"center\", color=\"w\")\n",
"fig.colorbar(cax)\n",
"ax.set_xticks(np.arange(len(rain_name)))\n",
"ax.set_yticks(np.arange(len(all_target_name[1:])))\n",
"ax.set_xticklabels(rain_name)\n",
"ax.set_yticklabels(all_target_name[1:])\n",
"ax.xaxis.set_ticks_position('bottom')\n",
"plt.setp(ax.get_xticklabels(), rotation=70, ha=\"right\",\n",
" rotation_mode=\"anchor\")\n",
"plt.title('Optimum Tau value for exponential window')\n",
"fig.tight_layout()\n",
"fig.show()\n",
" \n",
"\n",
"target_test_ind = 0\n",
"rain_test_ind = 0\n",
"# lag_array = np.arange(30)\n",
"# tau_array = np.linspace(12, 90, 40).astype(np.int)\n",
"lag_array = np.linspace(0, 60, 31).astype(np.int)\n",
"tau_array = np.linspace(40, 2000, 50).astype(np.int)\n",
"data1 = all_target_ts[target_test_ind]\n",
"data2 = rain_ts[rain_test_ind]\n",
"sp_rank_cc, lag_array, tau_array = tau_and_lag_correl(data1,\n",
" data2,\n",
" lag_array=lag_array,\n",
" tau_array=tau_array,\n",
" plot=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 0.0029\n",
"Epoch 2/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 0.0012\n",
"Epoch 3/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 9.3933e-04\n",
"Epoch 4/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 9.1059e-04\n",
"Epoch 5/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 8.5723e-04\n",
"Epoch 6/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 8.3538e-04\n",
"Epoch 7/30\n",
"11/11 [==============================] - 0s 6ms/step - loss: 8.0605e-04\n",
"Epoch 8/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 7.9772e-04\n",
"Epoch 9/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 7.7900e-04\n",
"Epoch 10/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 7.6680e-04\n",
"Epoch 11/30\n",
"11/11 [==============================] - 0s 6ms/step - loss: 7.5026e-04\n",
"Epoch 12/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 7.4578e-04\n",
"Epoch 13/30\n",
"11/11 [==============================] - 0s 6ms/step - loss: 7.2280e-04\n",
"Epoch 14/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 7.0885e-04\n",
"Epoch 15/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 7.0825e-04\n",
"Epoch 16/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.8676e-04\n",
"Epoch 17/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.7629e-04\n",
"Epoch 18/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.6300e-04\n",
"Epoch 19/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.5302e-04\n",
"Epoch 20/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.4212e-04\n",
"Epoch 21/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.3257e-04\n",
"Epoch 22/30\n",
"11/11 [==============================] - 0s 6ms/step - loss: 6.3266e-04\n",
"Epoch 23/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 6.1046e-04\n",
"Epoch 24/30\n",
"11/11 [==============================] - 0s 6ms/step - loss: 6.1400e-04\n",
"Epoch 25/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 5.9582e-04\n",
"Epoch 26/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 5.8978e-04\n",
"Epoch 27/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 5.8301e-04\n",
"Epoch 28/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 5.7272e-04\n",
"Epoch 29/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 5.6987e-04\n",
"Epoch 30/30\n",
"11/11 [==============================] - 0s 5ms/step - loss: 5.6098e-04\n",
"Train Score: 0.05 RMSE\n",
"Test Score: 0.03 RMSE\n"
]
}
],
"source": [
"# LSTM analysis\n",
"\n",
"# Model parameters\n",
"target_ind = 1\n",
"look_back = 30\n",
"chunk_step = 50\n",
"train_ratio = 0.67\n",
"num_epochs = 30\n",
"batch_size = 5\n",
"\n",
"# Rain preprocessing\n",
"tau_best = find_best_tau(all_target_ts[target_ind], rain_ts, plot=True,\n",
" rain_name=rain_name)\n",
"tau = tau_best[0]\n",
"exp_win = np.exp(-t/tau)\n",
"exp_win = exp_win / np.sum(exp_win)\n",
"rain_len = rain_ts[0].size\n",
"rain_conv = []\n",
"for n in range(len(rain_ts)):\n",
" conv = np.convolve(rain_ts[n], exp_win, 'full')[:rain_len]\n",
" rain_conv.append(conv)\n",
"\n",
"# fix random seed for reproducibility\n",
"np.random.seed(7)\n",
"\n",
"# Remove zeros from target\n",
"# N.B. SHOULD REPLACE THIS WITH A DIFFERENT METHOD\n",
"for n in range(len(all_target_ts[target_ind])):\n",
" if n > 0 and all_target_ts[target_ind][n] == 0:\n",
" all_target_ts[target_ind][n] = all_target_ts[target_ind][n-1]\n",
"\n",
"# Calculate differential of target & rainfall\n",
"target_diff = np.diff(all_target_ts[target_ind])\n",
"rain_conv_diff = []\n",
"for n in range(len(rain_ts)):\n",
" rain_conv_diff.append(np.diff(rain_conv[n]))\n",
"\n",
"# normalize the datasets\n",
"target_data = target_diff\n",
"all_target_ts[target_ind] = all_target_ts[target_ind][1:]\n",
"target_data = np.reshape(target_data, (target_data.size, 1))\n",
"scaler = MinMaxScaler(feature_range=(-1, 1))\n",
"target_scaled = scaler.fit_transform(target_data)\n",
"target_scaled = np.squeeze(target_scaled)\n",
"rain_scaled = normalise_0_to_1(rain_conv_diff[0])\n",
"\n",
"# Combine the dataset into single array\n",
"dataset = np.stack((target_scaled, rain_scaled))\n",
"\n",
"# Plot presprocessed data\n",
"plt.figure()\n",
"plt.plot(dataset[0, :], label=all_target_name[target_ind])\n",
"plt.plot(dataset[1, :], label=rain_name[0])\n",
"plt.legend()\n",
"plt.title('Preprocessed data')\n",
"plt.show()\n",
"\n",
"# Split data into chunks with random order\n",
"x, y, y_ind = create_dataset(dataset, look_back=look_back,\n",
" chunk_step=chunk_step)\n",
"numchunk = y.shape[0]\n",
"\n",
"# split into train and test sets\n",
"train_size = int(numchunk * train_ratio)\n",
"test_size = numchunk - train_size\n",
"trainX, testX = x[0:train_size, :, :], x[train_size:numchunk, :, :]\n",
"trainY, testY = y[0:train_size, :], y[train_size:numchunk, :]\n",
"trainYind, testYind = y_ind[0:train_size], y_ind[train_size:numchunk]\n",
"\n",
"# Plot one example of chunk\n",
"sample_num = 10\n",
"plt.figure()\n",
"time_ = np.arange(look_back)\n",
"plt.plot(time_, trainX[sample_num, :, 0], label='Input: target')\n",
"plt.plot(time_, trainX[sample_num, :, 1], label='Input: rain')\n",
"plt.plot([look_back], trainY[sample_num, 0], 'xr', label='Output: target')\n",
"plt.legend()\n",
"plt.title(\"Example of one chunk from dataset\")\n",
"plt.show()\n",
"\n",
"# create and fit the LSTM network\n",
"num_features = x.shape[2]\n",
"model = Sequential()\n",
"model.add(LSTM(4, input_shape=(look_back, num_features)))\n",
"model.add(Dense(1))\n",
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
"model.fit(trainX, trainY, epochs=num_epochs, batch_size=batch_size, verbose=1)\n",
"\n",
"# make predictions\n",
"trainPredict = model.predict(trainX)\n",
"testPredict = model.predict(testX)\n",
"\n",
"# invert predictions\n",
"trainPredict = scaler.inverse_transform(trainPredict)\n",
"trainY = scaler.inverse_transform(trainY)\n",
"testPredict = scaler.inverse_transform(testPredict)\n",
"testY = scaler.inverse_transform(testY)\n",
"\n",
"# Convert from diff to actual prediction\n",
"for n in range(train_size):\n",
" prev_day = all_target_ts[target_ind][trainYind[n] - 1]\n",
" trainY[n] = prev_day + trainY[n]\n",
" trainPredict[n] = prev_day + trainPredict[n]\n",
"for n in range(test_size):\n",
" prev_day = all_target_ts[target_ind][testYind[n] - 1]\n",
" testY[n] = prev_day + testY[n]\n",
" testPredict[n] = prev_day + testPredict[n]\n",
"\n",
"# calculate root mean squared error\n",
"trainScore = math.sqrt(mean_squared_error(trainY[:, 0], trainPredict[:,0]))\n",
"print('Train Score: %.2f RMSE' % (trainScore))\n",
"testScore = math.sqrt(mean_squared_error(testY[:, 0], testPredict[:,0]))\n",
"print('Test Score: %.2f RMSE' % (testScore))\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot one prediction example from test sample\n",
"pred_sample = 4\n",
"pred_ind = testYind[pred_sample]\n",
"plt.figure()\n",
"sample_t = np.linspace(pred_ind - look_back - 1, pred_ind - 1, look_back)\n",
"sample_t = sample_t.astype(np.int)\n",
"plt.plot(sample_t, all_target_ts[target_ind][sample_t[0]:sample_t[-1]])\n",
"plt.plot(pred_ind, testPredict[pred_sample, 0], 'xr')\n",
"plt.title(\"Example of one prediction from test data\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot all predictions from test samples against original data\n",
"plt.figure()\n",
"plt.plot(np.linspace(0, all_target_ts[target_ind].size-1,\n",
" all_target_ts[target_ind].size),\n",
" all_target_ts[target_ind],\n",
" label='Original data')\n",
"plt.plot(testYind, testPredict[:, 0], 'xr', label='Test predictions')\n",
"plt.plot(trainYind, trainPredict[:, 0], 'xg', label='Train predictions')\n",
"plt.legend()\n",
"plt.title(\"Predictions compared to original dataset\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.plot(np.linspace(0, target_data.size-1, target_data.size), target_data,\n",
" label='Original data')\n",
"for ii in range(len(testYind)):\n",
" plt.plot(np.arange(testYind[ii],testYind[ii] + predict_steps), testPredict[ii, :],color='k')\n",
"plt.legend()\n",
"plt.title(\"Predictions compared to original dataset\")\n",
"plt.show()\n",
"\n"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}