From c0ca00271eb576f4147504c9703aaad417a397da Mon Sep 17 00:00:00 2001 From: Hannes Signer Date: Fri, 21 Feb 2025 09:42:33 +0100 Subject: [PATCH] adapt notebook --- src/POET_Training.ipynb | 1638 +++++++++++++-------------------------- 1 file changed, 522 insertions(+), 1116 deletions(-) diff --git a/src/POET_Training.ipynb b/src/POET_Training.ipynb index 9a064f1..cf23923 100644 --- a/src/POET_Training.ipynb +++ b/src/POET_Training.ipynb @@ -68,7 +68,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Define parameters" + "## Read data from `.h5` file and convert it to a `pandas.DataFrame`" ] }, { @@ -76,499 +76,10 @@ "execution_count": 3, "metadata": {}, "outputs": [], - "source": [ - "dtype = \"float32\"\n", - "activation = \"relu\"\n", - "\n", - "lr = 0.001\n", - "batch_size = 512\n", - "epochs = 50 # default 400 epochs\n", - "\n", - "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n", - " initial_learning_rate=lr,\n", - " decay_steps=2000,\n", - " decay_rate=0.9,\n", - " staircase=True\n", - ")\n", - "\n", - "optimizer_simple = keras.optimizers.Adam(learning_rate=lr_schedule)\n", - "optimizer_large = keras.optimizers.Adam(learning_rate=lr_schedule)\n", - "optimizer_paper = keras.optimizers.Adam(learning_rate=lr_schedule)\n", - "\n", - "\n", - "loss = keras.losses.Huber()\n", - "\n", - "sample_fraction = 0.8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup the model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hannessigner/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"sequential\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense (Dense)                   │ (None, 128)            │         1,152 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu (LeakyReLU)         │ (None, 128)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (Dense)                 │ (None, 128)            │        16,512 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_1 (LeakyReLU)       │ (None, 128)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_2 (Dense)                 │ (None, 8)              │         1,032 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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 Total params: 18,696 (73.03 KB)\n",
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 Trainable params: 18,696 (73.03 KB)\n",
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\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,696\u001b[0m (73.03 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# small model\n", - "model_simple = keras.Sequential(\n", - " [\n", - " keras.Input(shape=(8,), dtype=\"float32\"),\n", - " keras.layers.Dense(units=128, dtype=\"float32\"),\n", - " LeakyReLU(alpha=0.01),\n", - " # Dropout(0.2),\n", - " keras.layers.Dense(units=128, dtype=\"float32\"),\n", - " LeakyReLU(alpha=0.01),\n", - " keras.layers.Dense(units=8, dtype=\"float32\")\n", - " ]\n", - ")\n", - "\n", - "model_simple.compile(optimizer=optimizer_simple, loss = loss)\n", - "model_simple.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Model: \"sequential_1\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_3 (Dense)                 │ (None, 512)            │         4,608 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_2 (LeakyReLU)       │ (None, 512)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_4 (Dense)                 │ (None, 1024)           │       525,312 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_3 (LeakyReLU)       │ (None, 1024)           │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_5 (Dense)                 │ (None, 512)            │       524,800 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_4 (LeakyReLU)       │ (None, 512)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_6 (Dense)                 │ (None, 8)              │         4,104 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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Model: \"sequential_2\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_7 (Dense)                 │ (None, 128)            │         1,152 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_5 (LeakyReLU)       │ (None, 128)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_8 (Dense)                 │ (None, 256)            │        33,024 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_6 (LeakyReLU)       │ (None, 256)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_9 (Dense)                 │ (None, 512)            │       131,584 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_7 (LeakyReLU)       │ (None, 512)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_10 (Dense)                │ (None, 256)            │       131,328 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ leaky_re_lu_8 (LeakyReLU)       │ (None, 256)            │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_11 (Dense)                │ (None, 8)              │         2,056 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ leaky_re_lu_5 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m33,024\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ leaky_re_lu_6 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m131,584\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ leaky_re_lu_7 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m131,328\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ leaky_re_lu_8 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m2,056\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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 Trainable params: 299,144 (1.14 MB)\n",
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Why does the charge is using another logarithm than the other species\n", - "\n", - "func_dict_in = {\n", - " \"H\" : np.log1p,\n", - " \"O\" : np.log1p,\n", - " \"Charge\" : Safelog,\n", - " \"H_0_\" : np.log1p,\n", - " \"O_0_\" : np.log1p,\n", - " \"Ba\" : np.log1p,\n", - " \"Cl\" : np.log1p,\n", - " \"S_2_\" : np.log1p,\n", - " \"S_6_\" : np.log1p,\n", - " \"Sr\" : np.log1p,\n", - " \"Barite\" : np.log1p,\n", - " \"Celestite\" : np.log1p,\n", - "}\n", - "\n", - "func_dict_out = {\n", - " \"H\" : np.expm1,\n", - " \"O\" : np.expm1,\n", - " \"Charge\" : Safeexp,\n", - " \"H_0_\" : np.expm1,\n", - " \"O_0_\" : np.expm1,\n", - " \"Ba\" : np.expm1,\n", - " \"Cl\" : np.expm1,\n", - " \"S_2_\" : np.expm1,\n", - " \"S_6_\" : np.expm1,\n", - " \"Sr\" : np.expm1,\n", - " \"Barite\" : np.expm1,\n", - " \"Celestite\" : np.expm1,\n", - "}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read data from `.h5` file and convert it to a `pandas.DataFrame`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], "source": [ "# os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n", "# data_file = h5py.File(\"barite_50_ai_20k.h5\")\n", - "# data_file = h5py.File(\"../datasets/barite_50_4_corner.h5\")\n", - "data_file = h5py.File(\"../datasets/barite_50_ai_20k.h5\")\n", + "data_file = h5py.File(\"../datasets/barite_50_4_corner.h5\")\n", "\n", "\n", "\n", @@ -599,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -618,6 +129,14 @@ "/Users/hannessigner/miniforge3/envs/ai/lib/python3.12/site-packages/sklearn/base.py:1474: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n", " return fit_method(estimator, *args, **kwargs)\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Amount class 0 before: 0.9521309523809524\n", + "Amount class 1 before: 0.04786904761904762\n" + ] } ], "source": [ @@ -625,16 +144,16 @@ "X, y = preprocess.cluster(df_design[species_columns], df_results[species_columns])\n", "# X, y = preprocess.funcTranform(X, y)\n", "\n", - "# X_train, X_test, y_train, y_test = preprocess.split(X, y, ratio = 0.2)\n", - "# X_train, y_train = preprocess.balancer(X_train, y_train, strategy = \"off\")\n", - "# preprocess.scale_fit(X_train, y_train, scaling = \"global\", type=\"standard\")\n", - "# X_train, X_test, y_train, y_test = preprocess.scale_transform(X_train, X_test, y_train, y_test)\n", - "# X_train, X_val, y_train, y_val = preprocess.split(X_train, y_train, ratio = 0.1)" + "X_train, X_test, y_train, y_test = preprocess.split(X, y, ratio = 0.2)\n", + "X_train, y_train = preprocess.balancer(X_train, y_train, strategy = \"off\")\n", + "preprocess.scale_fit(X_train, y_train, scaling = \"global\", type=\"standard\")\n", + "X_train, X_test, y_train, y_test = preprocess.scale_transform(X_train, X_test, y_train, y_test)\n", + "X_train, X_val, y_train, y_val = preprocess.split(X_train, y_train, ratio = 0.1)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -670,142 +189,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "column_dict = {\"Ba\": X.columns.get_loc(\"Ba\"), \"Barite\":X.columns.get_loc(\"Barite\"), \"Sr\":X.columns.get_loc(\"Sr\"), \"Celestite\":X.columns.get_loc(\"Celestite\"), \"H\":X.columns.get_loc(\"H\"), \"H\":X.columns.get_loc(\"H\"), \"O\":X.columns.get_loc(\"O\")}" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def custom_loss(preprocess, column_dict, h1, h2, h3, h4, scaler_type=\"minmax\"):\n", - " # extract the scaling parameters\n", - " \n", - " if scaler_type == \"minmax\":\n", - " scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)\n", - " min_X = tf.convert_to_tensor(preprocess.scaler_X.min_, dtype=tf.float32)\n", - " scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)\n", - " min_y = tf.convert_to_tensor(preprocess.scaler_y.min_, dtype=tf.float32)\n", - " \n", - " elif scaler_type == \"standard\":\n", - " scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)\n", - " mean_X = tf.convert_to_tensor(preprocess.scaler_X.mean_, dtype=tf.float32)\n", - " scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)\n", - " mean_y = tf.convert_to_tensor(preprocess.scaler_y.mean_, dtype=tf.float32)\n", - "\n", - " def loss(results, predicted):\n", - " \n", - " # inverse min/max scaling\n", - " if scaler_type == \"minmax\":\n", - " predicted_inverse = predicted * scale_y + min_y\n", - " results_inverse = results * scale_X + min_X\n", - " \n", - " elif scaler_type == \"standard\":\n", - " predicted_inverse = predicted * scale_y + mean_y\n", - " results_inverse = results * scale_X + mean_X\n", - "\n", - " # mass balance\n", - " dBa = tf.keras.backend.abs(\n", - " (predicted_inverse[:, column_dict[\"Ba\"]] + predicted_inverse[:, column_dict[\"Barite\"]]) -\n", - " (results_inverse[:, column_dict[\"Ba\"]] + results_inverse[:, column_dict[\"Barite\"]])\n", - " )\n", - " dSr = tf.keras.backend.abs(\n", - " (predicted_inverse[:, column_dict[\"Sr\"]] + predicted_inverse[:, column_dict[\"Celestite\"]]) -\n", - " (results_inverse[:, column_dict[\"Sr\"]] + results_inverse[:, column_dict[\"Celestite\"]])\n", - " )\n", - " \n", - " # H/O ratio has to be 2\n", - " h2o_ratio = tf.keras.backend.abs(\n", - " (predicted_inverse[:, column_dict[\"H\"]] / predicted_inverse[:, column_dict[\"O\"]]) - 2\n", - " )\n", - "\n", - " # huber loss\n", - " huber_loss = tf.keras.losses.Huber()(results, predicted)\n", - " \n", - " # total loss\n", - " total_loss = h1 * huber_loss + h2 * dBa + h3 * dSr #+ h4 * h2o_ratio\n", - " # total_loss = huber_loss\n", - " return total_loss\n", - "\n", - " return loss\n", - "\n", - "\n", - "def custom_metric(preprocess, column_dict, scaler_type=\"minmax\"):\n", - " \n", - " if scaler_type == \"minmax\":\n", - " scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)\n", - " min_X = tf.convert_to_tensor(preprocess.scaler_X.min_, dtype=tf.float32)\n", - " scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)\n", - " min_y = tf.convert_to_tensor(preprocess.scaler_y.min_, dtype=tf.float32)\n", - "\n", - " elif scaler_type == \"standard\":\n", - " scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)\n", - " mean_X = tf.convert_to_tensor(preprocess.scaler_X.mean_, dtype=tf.float32)\n", - " scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)\n", - " mean_y = tf.convert_to_tensor(preprocess.scaler_y.mean_, dtype=tf.float32)\n", - " \n", - " \n", - " def mass_balance(results, predicted):\n", - " # inverse min/max scaling\n", - " if scaler_type == \"minmax\":\n", - " predicted_inverse = predicted * scale_y + min_y\n", - " results_inverse = results * scale_X + min_X\n", - " \n", - " elif scaler_type == \"standard\":\n", - " predicted_inverse = predicted * scale_y + mean_y\n", - " results_inverse = results * scale_X + mean_X\n", - "\n", - " # mass balance\n", - " dBa = tf.keras.backend.abs(\n", - " (predicted_inverse[:, column_dict[\"Ba\"]] + predicted_inverse[:, column_dict[\"Barite\"]]) -\n", - " (results_inverse[:, column_dict[\"Ba\"]] + results_inverse[:, column_dict[\"Barite\"]])\n", - " )\n", - " dSr = tf.keras.backend.abs(\n", - " (predicted_inverse[:, column_dict[\"Sr\"]] + predicted_inverse[:, column_dict[\"Celestite\"]]) -\n", - " (results_inverse[:, column_dict[\"Sr\"]] + results_inverse[:, column_dict[\"Celestite\"]])\n", - " )\n", - " \n", - " return tf.reduce_mean(dBa + dSr)\n", - " \n", - " return mass_balance\n", - "\n", - "\n", - "def huber_metric(delta=1.0):\n", - " def huber(results, predicted):\n", - " return tf.keras.losses.huber(results, predicted, delta=delta)\n", - " \n", - " return huber" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'StandardScaler' object has no attribute 'min_'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model_simple\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39moptimizer_simple, loss\u001b[38;5;241m=\u001b[39m\u001b[43mcustom_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mminmax\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 3\u001b[0m model_large\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39moptimizer_large, loss\u001b[38;5;241m=\u001b[39mcustom_loss(preprocess, column_dict, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mminmax\u001b[39m\u001b[38;5;124m\"\u001b[39m), metrics\u001b[38;5;241m=\u001b[39m[huber_metric(\u001b[38;5;241m1.0\u001b[39m), custom_metric(preprocess, column_dict, scaler_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mminmax\u001b[39m\u001b[38;5;124m\"\u001b[39m)])\n", - "Cell \u001b[0;32mIn[14], line 6\u001b[0m, in \u001b[0;36mcustom_loss\u001b[0;34m(preprocess, column_dict, h1, h2, h3, h4, scaler_type)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scaler_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mminmax\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 5\u001b[0m scale_X \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mconvert_to_tensor(preprocess\u001b[38;5;241m.\u001b[39mscaler_X\u001b[38;5;241m.\u001b[39mscale_, dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[0;32m----> 6\u001b[0m min_X \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mconvert_to_tensor(\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaler_X\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin_\u001b[49m, dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 7\u001b[0m scale_y \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mconvert_to_tensor(preprocess\u001b[38;5;241m.\u001b[39mscaler_y\u001b[38;5;241m.\u001b[39mscale_, dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 8\u001b[0m min_y \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mconvert_to_tensor(preprocess\u001b[38;5;241m.\u001b[39mscaler_y\u001b[38;5;241m.\u001b[39mmin_, dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mfloat32)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'StandardScaler' object has no attribute 'min_'" - ] - } - ], - "source": [ - "model_simple.compile(optimizer=optimizer_simple, loss=custom_loss(preprocess, column_dict, 1, 1, 1, 1, \"minmax\"))\n", - "\n", - "model_large.compile(optimizer=optimizer_large, loss=custom_loss(preprocess, column_dict, 1, 1, 1, 1, \"minmax\"), metrics=[huber_metric(1.0), custom_metric(preprocess, column_dict, scaler_type=\"minmax\")])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -815,18 +205,18 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# measure time\n", "def model_training(model):\n", " start = time.time()\n", - " callback = keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n", + " callback = keras.callbacks.EarlyStopping(monitor='loss', patience=30)\n", " history = model.fit(X_train.loc[:, X_train.columns != \"Class\"], \n", " y_train.loc[:, y_train.columns != \"Class\"], \n", " batch_size=512, \n", - " epochs=50, \n", + " epochs=100, \n", " validation_data=(X_val.loc[:, X_val.columns != \"Class\"], y_val.loc[:, y_val.columns != \"Class\"]),\n", " callbacks=[callback])\n", " \n", @@ -840,12 +230,12 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "model_minmax = model_definition(\"large\")\n", - "# model_standard = model_definition(\"large\")\n", + "# model_origin = model_definition(\"large\")\n", + "model_standard = model_definition(\"large\")\n", "\n", "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n", " initial_learning_rate=0.001,\n", @@ -858,131 +248,261 @@ "h2 = 0.5283208497548787\t\n", "h3 = 0.5099528144902471\n", "\n", - "scaler_type = \"minmax\"\n", + "scaler_type = \"standard\"\n", "loss_variant = \"huber_mass_balance\"\n", "delta = 1.7642791340966357\n", "\n", "\n", "\n", "optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)\n", - "model_minmax.compile(optimizer=optimizer, loss=keras.losses.Huber, metrics=[huber_metric(preprocess, scaler_type, delta), mass_balance_metric(preprocess, column_dict, scaler_type)])" + "model_standard.compile(optimizer=optimizer, loss=keras.losses.Huber, metrics=[huber_metric(preprocess, scaler_type, delta), mass_balance_metric(preprocess, column_dict, scaler_type)])" ] }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hannessigner/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/saving_lib.py:757: UserWarning: Skipping variable loading for optimizer 'adam', because it has 1 variables whereas the saved optimizer has 17 variables. \n", + " saveable.load_own_variables(weights_store.get(inner_path))\n" + ] + } + ], + "source": [ + "model_standard.load_weights(\"/Users/hannessigner/Documents/Work/model-training/results/models/model_large_standard_plots.weights.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# import pickle\n", + "\n", + "# with open('/Users/hannessigner/Documents/Work/model-training/results/minmax_history_plots.pkl', 'wb') as f:\n", + "# pickle.dump(history_minmax, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.2799e-06 - loss: 4.2799e-06 - mass_balance: 0.0421 - val_huber: 3.4483e-07 - val_loss: 3.4514e-07 - val_mass_balance: 0.0225\n", - "Epoch 2/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 4.0330e-06 - loss: 4.0330e-06 - mass_balance: 0.0451 - val_huber: 8.2502e-07 - val_loss: 8.2549e-07 - val_mass_balance: 0.0422\n", - "Epoch 3/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.0521e-06 - loss: 3.0521e-06 - mass_balance: 0.0354 - val_huber: 3.4769e-06 - val_loss: 3.4814e-06 - val_mass_balance: 0.0462\n", - "Epoch 4/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.4066e-06 - loss: 3.4066e-06 - mass_balance: 0.0438 - val_huber: 3.5683e-06 - val_loss: 3.5691e-06 - val_mass_balance: 0.0380\n", - "Epoch 5/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.7451e-06 - loss: 1.7451e-06 - mass_balance: 0.0261 - val_huber: 2.0604e-06 - val_loss: 2.0614e-06 - val_mass_balance: 0.0619\n", - "Epoch 6/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.4580e-06 - loss: 2.4580e-06 - mass_balance: 0.0323 - val_huber: 2.4216e-07 - val_loss: 2.4243e-07 - val_mass_balance: 0.0148\n", - "Epoch 7/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.2816e-06 - loss: 2.2816e-06 - mass_balance: 0.0288 - val_huber: 3.4576e-07 - val_loss: 3.4610e-07 - val_mass_balance: 0.0119\n", - "Epoch 8/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.5347e-06 - loss: 1.5347e-06 - mass_balance: 0.0267 - val_huber: 6.3932e-06 - val_loss: 6.3978e-06 - val_mass_balance: 0.0862\n", - "Epoch 9/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 1.6830e-06 - loss: 1.6830e-06 - mass_balance: 0.0279 - val_huber: 5.2368e-07 - val_loss: 5.2401e-07 - val_mass_balance: 0.0174\n", - "Epoch 10/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.8310e-06 - loss: 1.8311e-06 - mass_balance: 0.0254 - val_huber: 1.8551e-07 - val_loss: 1.8579e-07 - val_mass_balance: 0.0094\n", - "Epoch 11/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 1.4000e-06 - loss: 1.4000e-06 - mass_balance: 0.0234 - val_huber: 1.7775e-07 - val_loss: 1.7799e-07 - val_mass_balance: 0.0094\n", - "Epoch 12/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.0789e-06 - loss: 1.0789e-06 - mass_balance: 0.0180 - val_huber: 6.4235e-07 - val_loss: 6.4321e-07 - val_mass_balance: 0.0207\n", - "Epoch 13/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.2999e-06 - loss: 1.2999e-06 - mass_balance: 0.0227 - val_huber: 7.2673e-07 - val_loss: 7.2751e-07 - val_mass_balance: 0.0118\n", - "Epoch 14/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 9.0124e-07 - loss: 9.0124e-07 - mass_balance: 0.0175 - val_huber: 1.2052e-07 - val_loss: 1.2076e-07 - val_mass_balance: 0.0048\n", - "Epoch 15/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 9.3653e-07 - loss: 9.3653e-07 - mass_balance: 0.0170 - val_huber: 1.4767e-07 - val_loss: 1.4796e-07 - val_mass_balance: 0.0076\n", - "Epoch 16/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 5.1122e-07 - loss: 5.1122e-07 - mass_balance: 0.0121 - val_huber: 3.0834e-07 - val_loss: 3.0882e-07 - val_mass_balance: 0.0116\n", - "Epoch 17/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 8.2455e-07 - loss: 8.2454e-07 - mass_balance: 0.0153 - val_huber: 1.7300e-06 - val_loss: 1.7301e-06 - val_mass_balance: 0.0423\n", - "Epoch 18/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 8.4078e-07 - loss: 8.4078e-07 - mass_balance: 0.0167 - val_huber: 1.1781e-06 - val_loss: 1.1793e-06 - val_mass_balance: 0.0246\n", - "Epoch 19/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 9.9003e-07 - loss: 9.9003e-07 - mass_balance: 0.0154 - val_huber: 1.6503e-07 - val_loss: 1.6526e-07 - val_mass_balance: 0.0121\n", - "Epoch 20/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 9.5923e-07 - loss: 9.5923e-07 - mass_balance: 0.0121 - val_huber: 1.3140e-07 - val_loss: 1.3167e-07 - val_mass_balance: 0.0076\n", - "Epoch 21/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 6.6818e-07 - loss: 6.6818e-07 - mass_balance: 0.0122 - val_huber: 1.7268e-06 - val_loss: 1.7283e-06 - val_mass_balance: 0.0221\n", - "Epoch 22/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 5.7110e-07 - loss: 5.7110e-07 - mass_balance: 0.0127 - val_huber: 1.2545e-07 - val_loss: 1.2569e-07 - val_mass_balance: 0.0120\n", - "Epoch 23/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 7.6431e-07 - loss: 7.6431e-07 - mass_balance: 0.0113 - val_huber: 1.1261e-07 - val_loss: 1.1285e-07 - val_mass_balance: 0.0047\n", - "Epoch 24/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.9674e-07 - loss: 3.9674e-07 - mass_balance: 0.0101 - val_huber: 9.1139e-08 - val_loss: 9.1335e-08 - val_mass_balance: 0.0032\n", - "Epoch 25/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 4.1321e-07 - loss: 4.1321e-07 - mass_balance: 0.0083 - val_huber: 1.7158e-07 - val_loss: 1.7183e-07 - val_mass_balance: 0.0036\n", - "Epoch 26/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 7.9635e-07 - loss: 7.9635e-07 - mass_balance: 0.0112 - val_huber: 9.2774e-08 - val_loss: 9.3000e-08 - val_mass_balance: 0.0027\n", - "Epoch 27/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 4.6850e-07 - loss: 4.6850e-07 - mass_balance: 0.0098 - val_huber: 1.0283e-07 - val_loss: 1.0305e-07 - val_mass_balance: 0.0048\n", - "Epoch 28/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 5.5048e-07 - loss: 5.5048e-07 - mass_balance: 0.0090 - val_huber: 1.0981e-07 - val_loss: 1.1003e-07 - val_mass_balance: 0.0062\n", - "Epoch 29/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 4.5964e-07 - loss: 4.5964e-07 - mass_balance: 0.0098 - val_huber: 1.1467e-07 - val_loss: 1.1491e-07 - val_mass_balance: 0.0027\n", - "Epoch 30/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 4.0565e-07 - loss: 4.0565e-07 - mass_balance: 0.0069 - val_huber: 1.7731e-06 - val_loss: 1.7736e-06 - val_mass_balance: 0.0482\n", - "Epoch 31/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 4.2617e-07 - loss: 4.2617e-07 - mass_balance: 0.0085 - val_huber: 2.6948e-07 - val_loss: 2.6974e-07 - val_mass_balance: 0.0138\n", - "Epoch 32/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.5269e-07 - loss: 3.5269e-07 - mass_balance: 0.0063 - val_huber: 8.2824e-08 - val_loss: 8.3034e-08 - val_mass_balance: 0.0023\n", - "Epoch 33/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 6.2967e-07 - loss: 6.2967e-07 - mass_balance: 0.0081 - val_huber: 1.0330e-07 - val_loss: 1.0348e-07 - val_mass_balance: 0.0063\n", - "Epoch 34/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 6.5417e-07 - loss: 6.5417e-07 - mass_balance: 0.0082 - val_huber: 8.4881e-08 - val_loss: 8.5073e-08 - val_mass_balance: 0.0053\n", - "Epoch 35/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 7.1504e-07 - loss: 7.1504e-07 - mass_balance: 0.0074 - val_huber: 1.1448e-07 - val_loss: 1.1464e-07 - val_mass_balance: 0.0051\n", - "Epoch 36/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 5.6783e-07 - loss: 5.6783e-07 - mass_balance: 0.0070 - val_huber: 2.2916e-07 - val_loss: 2.2945e-07 - val_mass_balance: 0.0092\n", - "Epoch 37/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 5.5385e-07 - loss: 5.5385e-07 - mass_balance: 0.0066 - val_huber: 1.8947e-07 - val_loss: 1.8975e-07 - val_mass_balance: 0.0055\n", - "Epoch 38/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 4.9893e-07 - loss: 4.9893e-07 - mass_balance: 0.0065 - val_huber: 7.4849e-08 - val_loss: 7.5058e-08 - val_mass_balance: 0.0032\n", - "Epoch 39/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.7779e-07 - loss: 3.7779e-07 - mass_balance: 0.0050 - val_huber: 9.0772e-08 - val_loss: 9.0945e-08 - val_mass_balance: 0.0046\n", - "Epoch 40/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 4.2443e-07 - loss: 4.2443e-07 - mass_balance: 0.0054 - val_huber: 9.5168e-08 - val_loss: 9.5322e-08 - val_mass_balance: 0.0039\n", - "Epoch 41/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.2178e-07 - loss: 3.2178e-07 - mass_balance: 0.0047 - val_huber: 1.7941e-07 - val_loss: 1.7961e-07 - val_mass_balance: 0.0092\n", - "Epoch 42/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.8325e-07 - loss: 3.8325e-07 - mass_balance: 0.0050 - val_huber: 8.6094e-08 - val_loss: 8.6295e-08 - val_mass_balance: 0.0041\n", - "Epoch 43/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.1550e-07 - loss: 3.1550e-07 - mass_balance: 0.0050 - val_huber: 1.0606e-07 - val_loss: 1.0619e-07 - val_mass_balance: 0.0052\n", - "Epoch 44/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.3489e-07 - loss: 3.3489e-07 - mass_balance: 0.0045 - val_huber: 6.8194e-08 - val_loss: 6.8356e-08 - val_mass_balance: 0.0027\n", - "Epoch 45/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.0352e-07 - loss: 2.0352e-07 - mass_balance: 0.0044 - val_huber: 8.4055e-08 - val_loss: 8.4237e-08 - val_mass_balance: 0.0058\n", - "Epoch 46/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.4620e-07 - loss: 4.4620e-07 - mass_balance: 0.0043 - val_huber: 7.2554e-08 - val_loss: 7.2720e-08 - val_mass_balance: 0.0033\n", - "Epoch 47/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 2.8217e-07 - loss: 2.8217e-07 - mass_balance: 0.0042 - val_huber: 7.2467e-08 - val_loss: 7.2613e-08 - val_mass_balance: 0.0023\n", - "Epoch 48/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.1265e-07 - loss: 3.1265e-07 - mass_balance: 0.0037 - val_huber: 6.3017e-08 - val_loss: 6.3177e-08 - val_mass_balance: 0.0019\n", - "Epoch 49/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.8543e-07 - loss: 3.8543e-07 - mass_balance: 0.0041 - val_huber: 9.2895e-08 - val_loss: 9.3143e-08 - val_mass_balance: 0.0025\n", - "Epoch 50/50\n", - "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.3291e-07 - loss: 3.3291e-07 - mass_balance: 0.0039 - val_huber: 1.0309e-07 - val_loss: 1.0330e-07 - val_mass_balance: 0.0039\n", - "Training took 589.632762670517 seconds\n" + "Epoch 1/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 0.0017 - loss: 0.0017 - mass_balance: 0.3256 - val_huber: 1.7820e-06 - val_loss: 1.7883e-06 - val_mass_balance: 0.0179\n", + "Epoch 2/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.6365e-06 - loss: 3.6365e-06 - mass_balance: 0.0357 - val_huber: 1.0599e-06 - val_loss: 1.0632e-06 - val_mass_balance: 0.0272\n", + "Epoch 3/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 3.2707e-06 - loss: 3.2707e-06 - mass_balance: 0.0349 - val_huber: 2.2936e-06 - val_loss: 2.2951e-06 - val_mass_balance: 0.0279\n", + "Epoch 4/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 5.9091e-06 - loss: 5.9091e-06 - mass_balance: 0.0556 - val_huber: 9.4613e-07 - val_loss: 9.4744e-07 - val_mass_balance: 0.0351\n", + "Epoch 5/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 5.0407e-06 - loss: 5.0407e-06 - mass_balance: 0.0454 - val_huber: 2.7434e-07 - val_loss: 2.7471e-07 - val_mass_balance: 0.0094\n", + "Epoch 6/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.8974e-06 - loss: 3.8974e-06 - mass_balance: 0.0443 - val_huber: 1.5533e-06 - val_loss: 1.5547e-06 - val_mass_balance: 0.0100\n", + "Epoch 7/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 4.6106e-06 - loss: 4.6106e-06 - mass_balance: 0.0436 - val_huber: 2.3968e-07 - val_loss: 2.3995e-07 - val_mass_balance: 0.0160\n", + "Epoch 8/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 12ms/step - huber: 2.3941e-06 - loss: 2.3941e-06 - mass_balance: 0.0286 - val_huber: 6.2510e-07 - val_loss: 6.2563e-07 - val_mass_balance: 0.0122\n", + "Epoch 9/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 2.7688e-06 - loss: 2.7688e-06 - mass_balance: 0.0300 - val_huber: 3.8816e-07 - val_loss: 3.8871e-07 - val_mass_balance: 0.0142\n", + "Epoch 10/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 12ms/step - huber: 1.8705e-06 - loss: 1.8705e-06 - mass_balance: 0.0227 - val_huber: 9.5129e-06 - val_loss: 9.5159e-06 - val_mass_balance: 0.1338\n", + "Epoch 11/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 12ms/step - huber: 4.2374e-06 - loss: 4.2374e-06 - mass_balance: 0.0409 - val_huber: 1.6615e-06 - val_loss: 1.6623e-06 - val_mass_balance: 0.0099\n", + "Epoch 12/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 1.8771e-06 - loss: 1.8771e-06 - mass_balance: 0.0301 - val_huber: 2.1160e-07 - val_loss: 2.1189e-07 - val_mass_balance: 0.0084\n", + "Epoch 13/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 1.6555e-06 - loss: 1.6555e-06 - mass_balance: 0.0226 - val_huber: 2.8582e-07 - val_loss: 2.8625e-07 - val_mass_balance: 0.0170\n", + "Epoch 14/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.9529e-06 - loss: 1.9529e-06 - mass_balance: 0.0244 - val_huber: 1.9382e-07 - val_loss: 1.9414e-07 - val_mass_balance: 0.0050\n", + "Epoch 15/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 2.1099e-06 - loss: 2.1099e-06 - mass_balance: 0.0267 - val_huber: 1.4789e-07 - val_loss: 1.4816e-07 - val_mass_balance: 0.0049\n", + "Epoch 16/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 12ms/step - huber: 1.2296e-06 - loss: 1.2296e-06 - mass_balance: 0.0204 - val_huber: 3.1999e-07 - val_loss: 3.2032e-07 - val_mass_balance: 0.0137\n", + "Epoch 17/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 1.0951e-06 - loss: 1.0951e-06 - mass_balance: 0.0168 - val_huber: 3.2870e-07 - val_loss: 3.2919e-07 - val_mass_balance: 0.0052\n", + "Epoch 18/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 1.2877e-06 - loss: 1.2877e-06 - mass_balance: 0.0203 - val_huber: 2.2930e-06 - val_loss: 2.2935e-06 - val_mass_balance: 0.0134\n", + "Epoch 19/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 1.0542e-06 - loss: 1.0542e-06 - mass_balance: 0.0170 - val_huber: 2.5138e-07 - val_loss: 2.5181e-07 - val_mass_balance: 0.0134\n", + "Epoch 20/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.2272e-06 - loss: 1.2272e-06 - mass_balance: 0.0144 - val_huber: 1.6538e-07 - val_loss: 1.6561e-07 - val_mass_balance: 0.0094\n", + "Epoch 21/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 8.7993e-07 - loss: 8.7993e-07 - mass_balance: 0.0140 - val_huber: 1.9755e-07 - val_loss: 1.9786e-07 - val_mass_balance: 0.0137\n", + "Epoch 22/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.1537e-06 - loss: 1.1537e-06 - mass_balance: 0.0147 - val_huber: 1.4816e-06 - val_loss: 1.4826e-06 - val_mass_balance: 0.0193\n", + "Epoch 23/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 1.2142e-06 - loss: 1.2142e-06 - mass_balance: 0.0187 - val_huber: 2.7344e-07 - val_loss: 2.7369e-07 - val_mass_balance: 0.0207\n", + "Epoch 24/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.0449e-06 - loss: 1.0449e-06 - mass_balance: 0.0154 - val_huber: 2.3383e-07 - val_loss: 2.3414e-07 - val_mass_balance: 0.0174\n", + "Epoch 25/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 8.1110e-07 - loss: 8.1110e-07 - mass_balance: 0.0129 - val_huber: 1.6466e-07 - val_loss: 1.6491e-07 - val_mass_balance: 0.0123\n", + "Epoch 26/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 6.6245e-07 - loss: 6.6245e-07 - mass_balance: 0.0105 - val_huber: 1.8921e-07 - val_loss: 1.8949e-07 - val_mass_balance: 0.0062\n", + "Epoch 27/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.0430e-06 - loss: 1.0430e-06 - mass_balance: 0.0137 - val_huber: 2.2946e-06 - val_loss: 2.2947e-06 - val_mass_balance: 0.0285\n", + "Epoch 28/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 8.5799e-07 - loss: 8.5799e-07 - mass_balance: 0.0124 - val_huber: 1.6085e-06 - val_loss: 1.6097e-06 - val_mass_balance: 0.0476\n", + "Epoch 29/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 5.9749e-07 - loss: 5.9749e-07 - mass_balance: 0.0129 - val_huber: 1.3087e-07 - val_loss: 1.3109e-07 - val_mass_balance: 0.0076\n", + "Epoch 30/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 4.6137e-07 - loss: 4.6137e-07 - mass_balance: 0.0104 - val_huber: 1.2886e-07 - val_loss: 1.2906e-07 - val_mass_balance: 0.0055\n", + "Epoch 31/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 9.8563e-07 - loss: 9.8563e-07 - mass_balance: 0.0108 - val_huber: 1.0379e-07 - val_loss: 1.0399e-07 - val_mass_balance: 0.0075\n", + "Epoch 32/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 5.5951e-07 - loss: 5.5951e-07 - mass_balance: 0.0089 - val_huber: 1.0108e-07 - val_loss: 1.0128e-07 - val_mass_balance: 0.0036\n", + "Epoch 33/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 5.4310e-07 - loss: 5.4310e-07 - mass_balance: 0.0084 - val_huber: 1.9739e-07 - val_loss: 1.9754e-07 - val_mass_balance: 0.0126\n", + "Epoch 34/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 7.1620e-07 - loss: 7.1620e-07 - mass_balance: 0.0102 - val_huber: 9.9565e-08 - val_loss: 9.9822e-08 - val_mass_balance: 0.0032\n", + "Epoch 35/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 4.1445e-07 - loss: 4.1445e-07 - mass_balance: 0.0075 - val_huber: 1.7280e-07 - val_loss: 1.7302e-07 - val_mass_balance: 0.0052\n", + "Epoch 36/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 4.5508e-07 - loss: 4.5508e-07 - mass_balance: 0.0082 - val_huber: 9.3598e-08 - val_loss: 9.3766e-08 - val_mass_balance: 0.0040\n", + "Epoch 37/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.4892e-07 - loss: 3.4892e-07 - mass_balance: 0.0055 - val_huber: 1.0471e-07 - val_loss: 1.0494e-07 - val_mass_balance: 0.0028\n", + "Epoch 38/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 5.4049e-07 - loss: 5.4049e-07 - mass_balance: 0.0067 - val_huber: 1.8168e-07 - val_loss: 1.8192e-07 - val_mass_balance: 0.0125\n", + "Epoch 39/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 3.5648e-07 - loss: 3.5648e-07 - mass_balance: 0.0072 - val_huber: 1.1691e-07 - val_loss: 1.1713e-07 - val_mass_balance: 0.0084\n", + "Epoch 40/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 5.2925e-07 - loss: 5.2925e-07 - mass_balance: 0.0068 - val_huber: 1.2699e-07 - val_loss: 1.2714e-07 - val_mass_balance: 0.0073\n", + "Epoch 41/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 6.8157e-07 - loss: 6.8157e-07 - mass_balance: 0.0082 - val_huber: 9.2547e-08 - val_loss: 9.2727e-08 - val_mass_balance: 0.0053\n", + "Epoch 42/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 4.2154e-07 - loss: 4.2154e-07 - mass_balance: 0.0062 - val_huber: 3.1872e-07 - val_loss: 3.1913e-07 - val_mass_balance: 0.0188\n", + "Epoch 43/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.6397e-07 - loss: 4.6397e-07 - mass_balance: 0.0075 - val_huber: 7.5362e-08 - val_loss: 7.5552e-08 - val_mass_balance: 0.0023\n", + "Epoch 44/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.3704e-07 - loss: 3.3704e-07 - mass_balance: 0.0051 - val_huber: 1.3457e-06 - val_loss: 1.3462e-06 - val_mass_balance: 0.0181\n", + "Epoch 45/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.9907e-07 - loss: 3.9907e-07 - mass_balance: 0.0074 - val_huber: 1.3853e-07 - val_loss: 1.3872e-07 - val_mass_balance: 0.0061\n", + "Epoch 46/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 4.5175e-07 - loss: 4.5175e-07 - mass_balance: 0.0051 - val_huber: 8.2388e-08 - val_loss: 8.2547e-08 - val_mass_balance: 0.0024\n", + "Epoch 47/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 5.5090e-07 - loss: 5.5090e-07 - mass_balance: 0.0059 - val_huber: 8.1999e-08 - val_loss: 8.2180e-08 - val_mass_balance: 0.0060\n", + "Epoch 48/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.7537e-07 - loss: 3.7537e-07 - mass_balance: 0.0058 - val_huber: 8.6734e-08 - val_loss: 8.6913e-08 - val_mass_balance: 0.0023\n", + "Epoch 49/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.2086e-07 - loss: 2.2086e-07 - mass_balance: 0.0039 - val_huber: 2.5805e-07 - val_loss: 2.5836e-07 - val_mass_balance: 0.0115\n", + "Epoch 50/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 4.9772e-07 - loss: 4.9772e-07 - mass_balance: 0.0049 - val_huber: 7.4335e-08 - val_loss: 7.4479e-08 - val_mass_balance: 0.0051\n", + "Epoch 51/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.6298e-07 - loss: 2.6298e-07 - mass_balance: 0.0040 - val_huber: 2.9356e-07 - val_loss: 2.9396e-07 - val_mass_balance: 0.0153\n", + "Epoch 52/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.1579e-07 - loss: 3.1579e-07 - mass_balance: 0.0046 - val_huber: 6.1345e-08 - val_loss: 6.1500e-08 - val_mass_balance: 0.0015\n", + "Epoch 53/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.0401e-07 - loss: 4.0401e-07 - mass_balance: 0.0040 - val_huber: 6.8173e-08 - val_loss: 6.8338e-08 - val_mass_balance: 0.0031\n", + "Epoch 54/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 3.0981e-07 - loss: 3.0981e-07 - mass_balance: 0.0039 - val_huber: 7.3257e-08 - val_loss: 7.3372e-08 - val_mass_balance: 0.0027\n", + "Epoch 55/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 3.1865e-07 - loss: 3.1865e-07 - mass_balance: 0.0040 - val_huber: 1.2419e-06 - val_loss: 1.2431e-06 - val_mass_balance: 0.0053\n", + "Epoch 56/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 6.0064e-07 - loss: 6.0064e-07 - mass_balance: 0.0047 - val_huber: 3.9193e-07 - val_loss: 3.9218e-07 - val_mass_balance: 0.0049\n", + "Epoch 57/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.6542e-07 - loss: 2.6542e-07 - mass_balance: 0.0039 - val_huber: 1.4826e-07 - val_loss: 1.4861e-07 - val_mass_balance: 0.0055\n", + "Epoch 58/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 3.2763e-07 - loss: 3.2763e-07 - mass_balance: 0.0036 - val_huber: 6.0788e-08 - val_loss: 6.0924e-08 - val_mass_balance: 0.0016\n", + "Epoch 59/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.4258e-07 - loss: 2.4258e-07 - mass_balance: 0.0032 - val_huber: 7.3100e-08 - val_loss: 7.3274e-08 - val_mass_balance: 0.0022\n", + "Epoch 60/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 3.4027e-07 - loss: 3.4027e-07 - mass_balance: 0.0033 - val_huber: 6.3018e-08 - val_loss: 6.3153e-08 - val_mass_balance: 0.0026\n", + "Epoch 61/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.4080e-07 - loss: 2.4080e-07 - mass_balance: 0.0031 - val_huber: 5.8896e-08 - val_loss: 5.9048e-08 - val_mass_balance: 0.0014\n", + "Epoch 62/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.3142e-07 - loss: 2.3142e-07 - mass_balance: 0.0027 - val_huber: 6.3233e-08 - val_loss: 6.3371e-08 - val_mass_balance: 0.0020\n", + "Epoch 63/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.8354e-07 - loss: 2.8354e-07 - mass_balance: 0.0030 - val_huber: 6.7132e-08 - val_loss: 6.7247e-08 - val_mass_balance: 0.0044\n", + "Epoch 64/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.3620e-07 - loss: 2.3620e-07 - mass_balance: 0.0029 - val_huber: 6.1996e-08 - val_loss: 6.2151e-08 - val_mass_balance: 0.0022\n", + "Epoch 65/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.6679e-07 - loss: 1.6679e-07 - mass_balance: 0.0024 - val_huber: 1.5507e-07 - val_loss: 1.5522e-07 - val_mass_balance: 0.0027\n", + "Epoch 66/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 1.7355e-07 - loss: 1.7355e-07 - mass_balance: 0.0026 - val_huber: 6.2313e-08 - val_loss: 6.2457e-08 - val_mass_balance: 0.0019\n", + "Epoch 67/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.6055e-07 - loss: 1.6055e-07 - mass_balance: 0.0023 - val_huber: 2.4914e-07 - val_loss: 2.4929e-07 - val_mass_balance: 0.0073\n", + "Epoch 68/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.5462e-07 - loss: 2.5462e-07 - mass_balance: 0.0030 - val_huber: 5.7153e-08 - val_loss: 5.7288e-08 - val_mass_balance: 0.0014\n", + "Epoch 69/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.3305e-07 - loss: 1.3305e-07 - mass_balance: 0.0022 - val_huber: 8.5180e-08 - val_loss: 8.5357e-08 - val_mass_balance: 0.0036\n", + "Epoch 70/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.4093e-07 - loss: 2.4093e-07 - mass_balance: 0.0025 - val_huber: 6.4853e-08 - val_loss: 6.4947e-08 - val_mass_balance: 0.0016\n", + "Epoch 71/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.1562e-07 - loss: 2.1562e-07 - mass_balance: 0.0023 - val_huber: 5.4525e-08 - val_loss: 5.4632e-08 - val_mass_balance: 0.0015\n", + "Epoch 72/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.5656e-07 - loss: 2.5656e-07 - mass_balance: 0.0022 - val_huber: 1.1401e-07 - val_loss: 1.1415e-07 - val_mass_balance: 0.0028\n", + "Epoch 73/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.4231e-07 - loss: 2.4231e-07 - mass_balance: 0.0024 - val_huber: 5.4870e-08 - val_loss: 5.4978e-08 - val_mass_balance: 0.0017\n", + "Epoch 74/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.5940e-07 - loss: 1.5940e-07 - mass_balance: 0.0021 - val_huber: 7.8957e-08 - val_loss: 7.9112e-08 - val_mass_balance: 0.0020\n", + "Epoch 75/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 12ms/step - huber: 2.2732e-07 - loss: 2.2732e-07 - mass_balance: 0.0022 - val_huber: 5.2756e-08 - val_loss: 5.2866e-08 - val_mass_balance: 0.0013\n", + "Epoch 76/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.8865e-07 - loss: 1.8865e-07 - mass_balance: 0.0020 - val_huber: 2.3236e-07 - val_loss: 2.3246e-07 - val_mass_balance: 0.0073\n", + "Epoch 77/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.2688e-07 - loss: 2.2688e-07 - mass_balance: 0.0024 - val_huber: 5.5447e-08 - val_loss: 5.5538e-08 - val_mass_balance: 0.0015\n", + "Epoch 78/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 3.3183e-07 - loss: 3.3183e-07 - mass_balance: 0.0022 - val_huber: 5.5350e-08 - val_loss: 5.5469e-08 - val_mass_balance: 0.0022\n", + "Epoch 79/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.5005e-07 - loss: 1.5005e-07 - mass_balance: 0.0019 - val_huber: 6.8170e-08 - val_loss: 6.8285e-08 - val_mass_balance: 0.0020\n", + "Epoch 80/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.9014e-07 - loss: 1.9014e-07 - mass_balance: 0.0020 - val_huber: 5.6487e-08 - val_loss: 5.6580e-08 - val_mass_balance: 0.0018\n", + "Epoch 81/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.6479e-07 - loss: 1.6479e-07 - mass_balance: 0.0018 - val_huber: 1.1170e-07 - val_loss: 1.1179e-07 - val_mass_balance: 0.0017\n", + "Epoch 82/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.8905e-07 - loss: 1.8905e-07 - mass_balance: 0.0019 - val_huber: 5.3920e-08 - val_loss: 5.4031e-08 - val_mass_balance: 0.0020\n", + "Epoch 83/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.1552e-07 - loss: 2.1552e-07 - mass_balance: 0.0019 - val_huber: 5.1053e-08 - val_loss: 5.1167e-08 - val_mass_balance: 0.0018\n", + "Epoch 84/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.1627e-07 - loss: 2.1627e-07 - mass_balance: 0.0019 - val_huber: 5.0605e-08 - val_loss: 5.0708e-08 - val_mass_balance: 0.0014\n", + "Epoch 85/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.1048e-07 - loss: 2.1048e-07 - mass_balance: 0.0019 - val_huber: 9.7042e-08 - val_loss: 9.7174e-08 - val_mass_balance: 0.0039\n", + "Epoch 86/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.6892e-07 - loss: 1.6892e-07 - mass_balance: 0.0018 - val_huber: 5.0322e-08 - val_loss: 5.0422e-08 - val_mass_balance: 0.0013\n", + "Epoch 87/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.3645e-07 - loss: 1.3645e-07 - mass_balance: 0.0016 - val_huber: 5.6177e-08 - val_loss: 5.6303e-08 - val_mass_balance: 0.0022\n", + "Epoch 88/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.8010e-07 - loss: 1.8010e-07 - mass_balance: 0.0017 - val_huber: 5.0597e-08 - val_loss: 5.0689e-08 - val_mass_balance: 0.0016\n", + "Epoch 89/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.3124e-07 - loss: 2.3124e-07 - mass_balance: 0.0018 - val_huber: 5.0465e-08 - val_loss: 5.0577e-08 - val_mass_balance: 0.0016\n", + "Epoch 90/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.0222e-07 - loss: 1.0222e-07 - mass_balance: 0.0015 - val_huber: 1.3106e-07 - val_loss: 1.3116e-07 - val_mass_balance: 0.0024\n", + "Epoch 91/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.3599e-07 - loss: 2.3599e-07 - mass_balance: 0.0018 - val_huber: 5.4327e-08 - val_loss: 5.4405e-08 - val_mass_balance: 0.0017\n", + "Epoch 92/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 1.7827e-07 - loss: 1.7827e-07 - mass_balance: 0.0017 - val_huber: 5.2719e-08 - val_loss: 5.2807e-08 - val_mass_balance: 0.0014\n", + "Epoch 93/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.5623e-07 - loss: 2.5623e-07 - mass_balance: 0.0018 - val_huber: 5.1424e-08 - val_loss: 5.1525e-08 - val_mass_balance: 0.0015\n", + "Epoch 94/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.2912e-07 - loss: 1.2912e-07 - mass_balance: 0.0015 - val_huber: 8.7227e-08 - val_loss: 8.7398e-08 - val_mass_balance: 0.0026\n", + "Epoch 95/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.0414e-07 - loss: 2.0414e-07 - mass_balance: 0.0017 - val_huber: 4.8705e-08 - val_loss: 4.8795e-08 - val_mass_balance: 0.0013\n", + "Epoch 96/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.7018e-07 - loss: 1.7018e-07 - mass_balance: 0.0016 - val_huber: 4.9206e-08 - val_loss: 4.9310e-08 - val_mass_balance: 0.0013\n", + "Epoch 97/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.9203e-07 - loss: 1.9203e-07 - mass_balance: 0.0015 - val_huber: 4.9359e-08 - val_loss: 4.9445e-08 - val_mass_balance: 0.0016\n", + "Epoch 98/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.3841e-07 - loss: 2.3841e-07 - mass_balance: 0.0016 - val_huber: 4.8478e-08 - val_loss: 4.8569e-08 - val_mass_balance: 0.0013\n", + "Epoch 99/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.1876e-07 - loss: 1.1876e-07 - mass_balance: 0.0014 - val_huber: 5.1804e-08 - val_loss: 5.1912e-08 - val_mass_balance: 0.0018\n", + "Epoch 100/100\n", + "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.6521e-07 - loss: 1.6521e-07 - mass_balance: 0.0015 - val_huber: 5.6042e-08 - val_loss: 5.6124e-08 - val_mass_balance: 0.0014\n", + "Training took 1148.3874678611755 seconds\n" ] } ], "source": [ - "history_standard = model_training(model_minmax)" + "history_origin = model_training(model_origin)" ] }, { @@ -994,33 +514,72 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 206, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "Could not locate function 'loss'. Make sure custom classes are decorated with `@keras.saving.register_keras_serializable()`. Full object config: {'module': 'builtins', 'class_name': 'function', 'config': 'loss', 'registered_name': 'function'}", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model_minmax \u001b[38;5;241m=\u001b[39m \u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/Users/hannessigner/Documents/Work/model-training/results/models/model_large_minmax.keras\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# model_standard = keras.models.load_model(\"/Users/hannessigner/Documents/Work/model-training/results/models/model_large_standardization.keras\")\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/saving_api.py:189\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[1;32m 186\u001b[0m is_keras_zip \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_keras_zip \u001b[38;5;129;01mor\u001b[39;00m is_keras_dir \u001b[38;5;129;01mor\u001b[39;00m is_hf:\n\u001b[0;32m--> 189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msaving_lib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 193\u001b[0m \u001b[43m \u001b[49m\u001b[43msafe_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msafe_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 194\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(filepath)\u001b[38;5;241m.\u001b[39mendswith((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.hdf5\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[1;32m 196\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m legacy_h5_format\u001b[38;5;241m.\u001b[39mload_model_from_hdf5(\n\u001b[1;32m 197\u001b[0m filepath, custom_objects\u001b[38;5;241m=\u001b[39mcustom_objects, \u001b[38;5;28mcompile\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mcompile\u001b[39m\n\u001b[1;32m 198\u001b[0m )\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/saving_lib.py:367\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 363\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid filename: expected a `.keras` extension. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 365\u001b[0m )\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(filepath, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m--> 367\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load_model_from_fileobj\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 368\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msafe_mode\u001b[49m\n\u001b[1;32m 369\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/saving_lib.py:444\u001b[0m, in \u001b[0;36m_load_model_from_fileobj\u001b[0;34m(fileobj, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m zf\u001b[38;5;241m.\u001b[39mopen(_CONFIG_FILENAME, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 442\u001b[0m config_json \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m--> 444\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43m_model_from_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 445\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_json\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msafe_mode\u001b[49m\n\u001b[1;32m 446\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 448\u001b[0m all_filenames \u001b[38;5;241m=\u001b[39m zf\u001b[38;5;241m.\u001b[39mnamelist()\n\u001b[1;32m 449\u001b[0m extract_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/saving_lib.py:433\u001b[0m, in \u001b[0;36m_model_from_config\u001b[0;34m(config_json, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;66;03m# Construct the model from the configuration file in the archive.\u001b[39;00m\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ObjectSharingScope():\n\u001b[0;32m--> 433\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mdeserialize_keras_object\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msafe_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msafe_mode\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py:734\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(config, custom_objects, safe_mode, **kwargs)\u001b[0m\n\u001b[1;32m 732\u001b[0m compile_config \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompile_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 733\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compile_config:\n\u001b[0;32m--> 734\u001b[0m \u001b[43minstance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompile_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 735\u001b[0m instance\u001b[38;5;241m.\u001b[39mcompiled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshared_object_id\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config:\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/trainers/trainer.py:971\u001b[0m, in \u001b[0;36mTrainer.compile_from_config\u001b[0;34m(self, config)\u001b[0m\n\u001b[1;32m 960\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 961\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`compile()` was not called as part of model loading \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 962\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbecause the model\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms `compile()` method is custom. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 968\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 971\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mserialization_lib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeserialize_keras_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 972\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompile(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig)\n\u001b[1;32m 973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimizer\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuilt:\n\u001b[1;32m 974\u001b[0m \u001b[38;5;66;03m# Create optimizer variables.\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py:595\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(config, custom_objects, safe_mode, **kwargs)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not parse config: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclass_name\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m config \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m config:\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m--> 595\u001b[0m key: \u001b[43mdeserialize_keras_object\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 596\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msafe_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msafe_mode\u001b[49m\n\u001b[1;32m 597\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 598\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m config\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 599\u001b[0m }\n\u001b[1;32m 601\u001b[0m class_name \u001b[38;5;241m=\u001b[39m config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclass_name\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 602\u001b[0m inner_config \u001b[38;5;241m=\u001b[39m config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m {}\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py:678\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(config, custom_objects, safe_mode, **kwargs)\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m class_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 677\u001b[0m fn_name \u001b[38;5;241m=\u001b[39m inner_config\n\u001b[0;32m--> 678\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_retrieve_class_or_fn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 679\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 680\u001b[0m \u001b[43m \u001b[49m\u001b[43mregistered_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 683\u001b[0m \u001b[43m \u001b[49m\u001b[43mfull_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 684\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 685\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 687\u001b[0m \u001b[38;5;66;03m# Below, handling of all classes.\u001b[39;00m\n\u001b[1;32m 688\u001b[0m \u001b[38;5;66;03m# First, is it a shared object?\u001b[39;00m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshared_object_id\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config:\n", - "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py:803\u001b[0m, in \u001b[0;36m_retrieve_class_or_fn\u001b[0;34m(name, registered_name, module, obj_type, full_config, custom_objects)\u001b[0m\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 801\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n\u001b[0;32m--> 803\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 804\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not locate \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mobj_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 805\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMake sure custom classes are decorated with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`@keras.saving.register_keras_serializable()`. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFull object config: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfull_config\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 808\u001b[0m )\n", - "\u001b[0;31mTypeError\u001b[0m: Could not locate function 'loss'. Make sure custom classes are decorated with `@keras.saving.register_keras_serializable()`. Full object config: {'module': 'builtins', 'class_name': 'function', 'config': 'loss', 'registered_name': 'function'}" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "model_minmax = keras.models.load_model(\"/Users/hannessigner/Documents/Work/model-training/results/models/model_large_minmax.keras\")\n", - "# model_standard = keras.models.load_model(\"/Users/hannessigner/Documents/Work/model-training/results/models/model_large_standardization.keras\")" + "fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n", + "ax.plot(history_standard.history[\"val_mass_balance\"], label = \"standardized, ext. huber loss\")\n", + "ax.plot(history_minmax.history[\"val_mass_balance\"], label = \"minmax, ext. huber loss\")\n", + "ax.plot(history_origin.history[\"val_mass_balance\"], label = \"minmax, huber loss\")\n", + "\n", + "ax.grid()\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "order = [1, 2, 0] # Manually specify the order of the legend items\n", + "ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order], loc='upper right', fontsize=14) # bbox_to_anchor=(0.5, -0.4),\n", + "ax.set_xlabel(\"Epoch\", fontsize=12)\n", + "ax.set_ylabel(\"Mass balance metric\", fontsize=12)\n", + "ax.set_title(\"Mass balance metric for different loss functions and scaling methods\", fontsize=16, pad = 10)\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "\n", + "plt.yscale('log')\n", + "plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/mass_balance_metric.pdf\")\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n", + "ax.plot(history_standard.history[\"val_huber\"], label=\"standardized, ext. huber loss\")\n", + "ax.plot(history_minmax.history[\"val_huber\"], label = \"minmax, ext. huber loss\")\n", + "ax.plot(history_origin.history[\"val_huber\"], label = \"minmax, huber loss\")\n", + "\n", + "ax.grid()\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "order = [1, 0, 2] # Manually specify the order of the legend items\n", + "ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order], loc='upper right', fontsize=14), # , bbox_to_anchor=(0.5, -0.4),\n", + "ax.set_xlabel(\"Epoch\", fontsize=12)\n", + "ax.set_ylabel(\"Huber metric\", fontsize=12)\n", + "ax.set_title(\"Huber metric for different loss functions and scaling methods\", fontsize=16, pad = 10)\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.yscale('log')\n", + "plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/huber_metric.pdf\")\n", + "plt.show()\n", + "\n" ] }, { @@ -1032,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1059,14 +618,14 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m14175/14175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 1ms/step\n" + "\u001b[1m14175/14175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1ms/step\n" ] }, { @@ -1103,58 +662,58 @@ " \n", " \n", " 0\n", - " 111.012436\n", - " 55.506630\n", - " 0.000003\n", - " 0.033113\n", - " 1.027863e-04\n", - " 0.016638\n", - " 0.000809\n", - " 1.000536\n", + " 111.012405\n", + " 55.506626\n", + " 0.000005\n", + " 0.033089\n", + " 1.027101e-04\n", + " 0.016652\n", + " 0.001007\n", + " 1.000499\n", " \n", " \n", " 1\n", - " 111.012436\n", + " 111.012466\n", " 55.506611\n", - " 0.000011\n", - " 0.039366\n", - " 9.827365e-05\n", - " 0.019756\n", - " 0.000823\n", - " 1.000528\n", + " 0.000014\n", + " 0.039325\n", + " 9.818448e-05\n", + " 0.019741\n", + " 0.000999\n", + " 1.000658\n", " \n", " \n", " 2\n", - " 111.012436\n", + " 111.012413\n", " 55.506592\n", - " 0.000015\n", - " 0.047951\n", - " 9.366860e-05\n", - " 0.024039\n", - " 0.000815\n", - " 1.000414\n", + " 0.000019\n", + " 0.047933\n", + " 9.349755e-05\n", + " 0.024046\n", + " 0.001000\n", + " 1.000488\n", " \n", " \n", " 3\n", - " 111.012436\n", - " 55.506561\n", + " 111.012444\n", + " 55.506565\n", " 0.000026\n", - " 0.066884\n", - " 8.696726e-05\n", - " 0.033494\n", - " 0.000733\n", - " 1.000262\n", + " 0.066846\n", + " 8.691217e-05\n", + " 0.033495\n", + " 0.001000\n", + " 1.000390\n", " \n", " \n", " 4\n", - " 111.012436\n", - " 55.506805\n", - " -0.000013\n", - " 0.011500\n", - " 1.467149e-04\n", - " 0.005883\n", - " 0.000873\n", - " 1.000751\n", + " 111.012314\n", + " 55.506802\n", + " -0.000003\n", + " 0.011493\n", + " 1.465212e-04\n", + " 0.005892\n", + " 0.001008\n", + " 1.000547\n", " \n", " \n", " ...\n", @@ -1169,58 +728,58 @@ " \n", " \n", " 453595\n", - " 111.012436\n", + " 111.012428\n", " 55.506592\n", - " 0.000015\n", - " 0.048123\n", - " 9.360327e-05\n", - " 0.024126\n", - " 0.000805\n", - " 1.000494\n", + " 0.000019\n", + " 0.048117\n", + " 9.338620e-05\n", + " 0.024139\n", + " 0.001000\n", + " 1.000609\n", " \n", " \n", " 453596\n", - " 111.012436\n", + " 111.012207\n", " 55.506775\n", - " -0.000014\n", - " 0.013106\n", - " 1.392483e-04\n", - " 0.006671\n", - " 0.000793\n", - " 1.000676\n", + " -0.000004\n", + " 0.013115\n", + " 1.393944e-04\n", + " 0.006700\n", + " 0.001010\n", + " 1.000573\n", " \n", " \n", " 453597\n", - " 111.012436\n", + " 111.012375\n", " 55.506496\n", - " 0.000089\n", - " 0.108489\n", - " 6.996401e-05\n", - " 0.054217\n", - " 0.104229\n", - " 0.891664\n", + " 0.000076\n", + " 0.108388\n", + " 6.973010e-05\n", + " 0.054192\n", + " 0.104940\n", + " 0.891979\n", " \n", " \n", " 453598\n", - " 111.012436\n", + " 111.012413\n", " 55.506577\n", - " 0.000022\n", - " 0.056605\n", - " 9.016610e-05\n", - " 0.028356\n", - " 0.000782\n", - " 1.000076\n", + " 0.000021\n", + " 0.056582\n", + " 9.015873e-05\n", + " 0.028360\n", + " 0.000999\n", + " 1.000084\n", " \n", " \n", " 453599\n", - " 111.012436\n", + " 111.010864\n", " 55.506218\n", - " 0.041664\n", - " 0.150247\n", - " 3.867281e-07\n", - " 0.033441\n", - " 1.004886\n", - " -0.000868\n", + " 0.041696\n", + " 0.150536\n", + " -6.480219e-08\n", + " 0.033591\n", + " 1.006944\n", + " -0.000072\n", " \n", " \n", "\n", @@ -1229,46 +788,46 @@ ], "text/plain": [ " H O Ba Cl S Sr \\\n", - "0 111.012436 55.506630 0.000003 0.033113 1.027863e-04 0.016638 \n", - "1 111.012436 55.506611 0.000011 0.039366 9.827365e-05 0.019756 \n", - "2 111.012436 55.506592 0.000015 0.047951 9.366860e-05 0.024039 \n", - "3 111.012436 55.506561 0.000026 0.066884 8.696726e-05 0.033494 \n", - "4 111.012436 55.506805 -0.000013 0.011500 1.467149e-04 0.005883 \n", + "0 111.012405 55.506626 0.000005 0.033089 1.027101e-04 0.016652 \n", + "1 111.012466 55.506611 0.000014 0.039325 9.818448e-05 0.019741 \n", + "2 111.012413 55.506592 0.000019 0.047933 9.349755e-05 0.024046 \n", + "3 111.012444 55.506565 0.000026 0.066846 8.691217e-05 0.033495 \n", + "4 111.012314 55.506802 -0.000003 0.011493 1.465212e-04 0.005892 \n", "... ... ... ... ... ... ... \n", - "453595 111.012436 55.506592 0.000015 0.048123 9.360327e-05 0.024126 \n", - "453596 111.012436 55.506775 -0.000014 0.013106 1.392483e-04 0.006671 \n", - "453597 111.012436 55.506496 0.000089 0.108489 6.996401e-05 0.054217 \n", - "453598 111.012436 55.506577 0.000022 0.056605 9.016610e-05 0.028356 \n", - "453599 111.012436 55.506218 0.041664 0.150247 3.867281e-07 0.033441 \n", + "453595 111.012428 55.506592 0.000019 0.048117 9.338620e-05 0.024139 \n", + "453596 111.012207 55.506775 -0.000004 0.013115 1.393944e-04 0.006700 \n", + "453597 111.012375 55.506496 0.000076 0.108388 6.973010e-05 0.054192 \n", + "453598 111.012413 55.506577 0.000021 0.056582 9.015873e-05 0.028360 \n", + "453599 111.010864 55.506218 0.041696 0.150536 -6.480219e-08 0.033591 \n", "\n", " Barite Celestite \n", - "0 0.000809 1.000536 \n", - "1 0.000823 1.000528 \n", - "2 0.000815 1.000414 \n", - "3 0.000733 1.000262 \n", - "4 0.000873 1.000751 \n", + "0 0.001007 1.000499 \n", + "1 0.000999 1.000658 \n", + "2 0.001000 1.000488 \n", + "3 0.001000 1.000390 \n", + "4 0.001008 1.000547 \n", "... ... ... \n", - "453595 0.000805 1.000494 \n", - "453596 0.000793 1.000676 \n", - "453597 0.104229 0.891664 \n", - "453598 0.000782 1.000076 \n", - "453599 1.004886 -0.000868 \n", + "453595 0.001000 1.000609 \n", + "453596 0.001010 1.000573 \n", + "453597 0.104940 0.891979 \n", + "453598 0.000999 1.000084 \n", + "453599 1.006944 -0.000072 \n", "\n", "[453600 rows x 8 columns]" ] }, - "execution_count": 234, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.DataFrame(preprocess.scaler_X.inverse_transform(model_minmax.predict(X_train[species_columns])), columns=species_columns)" + "pd.DataFrame(preprocess.scaler_X.inverse_transform(model_standard.predict(X_train[species_columns])), columns=species_columns)" ] }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1459,7 +1018,7 @@ "[453600 rows x 8 columns]" ] }, - "execution_count": 235, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1470,7 +1029,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1478,18 +1037,18 @@ "output_type": "stream", "text": [ "\u001b[1m3938/3938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1ms/step\n", - "9.077700557393042e-10\n", - "4.320865443219191e-09\n" + "1.4382436864732173e-11\n", + "2.354905159762666e-10\n" ] } ], "source": [ - "dBa, dSr, prediction, classes = mass_balance(model_minmax, X_test, preprocess)" + "dBa, dSr, prediction, classes = mass_balance(model_standard, X_test, preprocess)" ] }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1498,7 +1057,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1507,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1518,23 +1077,23 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "8.34125752798492e-06\n" + "0.8275611831239678\n" ] }, { "data": { "text/plain": [ - "0.0" + "0.00032711808963035657" ] }, - "execution_count": 240, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1546,16 +1105,16 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "7.936507936507936e-06" + "0.787420634920635" ] }, - "execution_count": 241, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1566,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -1576,16 +1135,16 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m19688/19688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 1ms/step\n", - "1.3541947511269692e-12\n", - "5.3412163580901506e-11\n" + "\u001b[1m19688/19688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 1ms/step\n", + "1.4382436864732173e-11\n", + "1.6942225400384814e-11\n" ] } ], @@ -1595,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1604,7 +1163,27 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "252" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "int(len(mass_balance_results)/2500)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -1615,14 +1194,14 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 223, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1630,20 +1209,34 @@ } ], "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "import matplotlib.colors as mcolors\n", + "import matplotlib.patches as mpatches\n", "\n", - "bounds = [0, 1e-5, 1e-4] \n", + "# Farbgrenzen und Farben definieren\n", + "bounds = [0, 1e-5, 1e-4]\n", "colors = [\"green\", \"red\"]\n", "cmap = mcolors.ListedColormap(colors)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", - "# Plotten mit Colorbar\n", - "fig, ax = plt.subplots()\n", - "iteration = 3\n", + "# Plot erstellen\n", + "fig, ax = plt.subplots(figsize=(15,10))\n", + "iteration = 10\n", "im = ax.imshow(np.array(mass_balance_per_iteration[iteration]).reshape(50,50), cmap=cmap, norm=norm)\n", "\n", - "cbar = plt.colorbar(im, ax=ax, ticks=bounds)\n", - "cbar.set_ticklabels([\" \", \"1e-5\", \" \"])\n" + "\n", + "# Manuelle Legende mit farbigen Boxen\n", + "legend_patches = [\n", + " mpatches.Patch(color=\"green\", label=\"valid\"),\n", + " mpatches.Patch(color=\"red\", label=\"invalid\")\n", + "]\n", + "\n", + "ax.legend(handles=legend_patches, loc=\"center\", fontsize=14)\n", + "ax.set_title(\"Valide und Invalide Bereich nach Massenbilanz (Iteration 10)\", fontsize=18, pad=10)\n", + "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/mass_balance_grid.pdf\", bbox_inches='tight')\n", + "\n", + "plt.show()\n" ] }, { @@ -1659,25 +1252,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 247, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 246, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1689,7 +1264,7 @@ }, { "data": { - "image/png": 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", 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", 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Px5gxY3Dq1CksW7YMDMOYXOvs2bNG/3Z1ISwsDCNHjuQWTFSVk5PD7cIwbNgw1K9fH//5z39w/Phxk2NVKpVd/c3cRXx8PHr27IkjR44Yfa+pVCp88sknAIC33nrL6Jy//e1vAIBZs2YZ/dv+/PPPuHnzJsaOHWv1FyjybKIpSELcWMeOHdGxY8dqj+Pz+Zg6dSrXMPXFF1+ETCbD3r17ERMTg8jISKPjr1y5guHDh6Njx45o2bIll2nbuXMnBAIBVxOWnp6OhIQErvllVFQU8vPzsWvXLqhUKsycOdPm93L37l2jYu3c3FwcOXIEN2/eRHR0NPfhBugWISxduhTTpk3DkSNHEBMTg6tXryIpKQkjRozA9u3bq329iIgIjBw5Etu2bUOHDh3Qt29fZGdn448//sDzzz/vkh5VU6ZMwbFjx7BlyxY0btwYQ4cOha+vLx4+fIj9+/dj1apVeOmllwAAK1euREpKCmbOnIl169ahS5cu8PPzw6NHj3Dx4kXcuXMHmZmZDtfUucrKlSuRnJyMRYsWYc+ePXj++efBMAxu376NAwcOIDs7G/7+/hCLxdi6dSsGDRqEXr16oW/fvmjZsiUA4OHDhzhx4gSCgoJw69Ytl47v1q1bWLJkCQBwQe+tW7cwYcIEALqp9arTvNOnT+caG7PNg6dPn86t9p01a5bR3qrff/89unbtiuHDh2PMmDGIjIzEvn37cPXqVbz55ptc82DWG2+8gc2bN2PTpk24f/8+evfujXv37mHbtm2Ijo7G0qVLXXoPyFOizhpgEEI41rbrMQdm+oBVVFQwixYtYho3bsyIxWKmfv36zAcffMCUlJSY9F969OgRM2vWLKZz585MaGgo4+HhwdSvX58ZNWoUc/bsWe64wsJCZv78+UzPnj2ZiIgIxsPDg4mMjGQGDhzI7N+/3673VvU/sVjMNGnShPnggw+Y3Nxck/MuX77M9O/fnwkICGB8fHyYXr16MUlJSRb7RsFMP6iSkhLmww8/ZGJjYxmxWMw0btyY+eyzz5iKigqzx7N9wMwZP3682f5RWq2W+e9//8t07tyZ8fLyYqRSKdO4cWPm7bffZh4+fGh0rFwuZ7744gvmueeeY7y8vBiJRMLExcUxL730EvPLL79wPbassdYHbPz48WbPMfderSkuLmb+9a9/MU2bNmXEYjHj5+fHtG3blpk7dy5TUVFhdOzjx4+Z9957j/u+8/X1ZZo1a8a8+eabzKFDh4yOdUUfsCNHjpj9fmL/q3p99nWtnWN4L1kpKSnMqFGjmKCgIEYsFjMtWrRgvvnmG0aj0Zgdl0KhYBYsWMA0atSI8fDwYMLCwphJkyYxGRkZNr0v8uzhMYyZXDghhBBCCKkxVANGCCGEEFLLKAAjhBBCCKllFIARQgghhNQyCsAIIYQQQmoZBWCEEEIIIbWMAjBCCCGEkFpGARghhBBCSC2jAIwQQgghpJZRAEYIIYQQUssoACOEEEIIqWUUgBFCCCGE1DIKwAghhBBCapmwrgdAzNNqtcjIyICPjw94PF5dD4cQQgghNmAYBiUlJYiMjASfbznPRQGYm8rIyEB0dHRdD4MQQgghDnj06BHq1atn8XkKwNyUj48PAN0/oK+vr8uuq1KpcODAAfTv3x8ikchl1yWm6F7XDrrPtYfude2g+1x7auJey2QyREdHc5/jllAA5qbYaUdfX1+XB2BSqRS+vr70P3YNo3tdO+g+1x6617WD7nPtqcl7XV35EBXhE0IIIYTUMgrACCGEEEJqGQVghBBCCCG1jAIwQgghhJBaRkX4hBBCXI5hGGg0GqjV6roeyhNHpVJBKBRCoVBAo9HU9XCeavbca5FIBIFA4LLXpgCMEEKIyzAMg6KiIuTm5lLw4CCGYRAeHo5Hjx5RI+4aZu+99vf3R3h4uEv+XSgAI4QQ4jJZWVkoKiriWugIhUIKIuyk1WpRWloKb29vq53UifNsvdcMw0AulyMnJwcAEBER4fRrUwBGCCHEJTQaDYqLixESEoLg4OC6Hs4TS6vVoqKiAp6enhSA1TB77rVEIgEA5OTkIDQ01OnpSPqXJYQQ4hIqlQoMw8DLy6uuh0JIjZBKpQB03+vOogCMEEKIS9GUI3laufJ7mwIwQgghhJBaRgEYIYQQQkgtowCMVOt0aj7uZJfU9TAIIaROrVmzBjwej/tPKBQiIiICL7/8Mu7cuVPXwwOPx8P8+fO5r2/cuIH58+fjwYMHdTYmYhkFYMSqRwVyvPrfM3h91TlotUxdD4cQQurczz//jNOnTyMpKQnvvvsudu/eje7du6OwsLCuh2bkxo0bWLBgAQVgboraUBCr7uSUQMsAWTIFrmfI0KqeX10PiRBC6lTLli3RoUMHAEDv3r2h0Wgwb9487Ny5ExMnTqzj0ZEnBWXAiFXpheXc30/cza3DkRBCiHtig7Hs7GzusQsXLmDo0KEIDAyEp6cn2rVrhy1bthidJ5fLMX36dMTFxcHT0xOBgYHo0KEDNm7cyB3Tu3dv9O7d2+Q1J0yYgNjYWItjWrNmDUaPHg0A6NOnDzdtumbNGgDAX3/9hSFDhiA0NBRisRiRkZF44YUX8PjxYwfvArEXZcCIVY8NArCTd/IwtXejOhwNIeRJxDAMylXusy2RRCRwaTuB+/fvAwDi4+MBAEeOHMHAgQORkJCA//u//4Ofnx82bdqEsWPHQi6XY8KECQCADz74AOvWrcPChQvRrl07lJWVITk5GQUFBU6P6YUXXsDnn3+OOXPmYMWKFWjfvj0AoGHDhigrK0NiYiLi4uKwYsUKhIWFISsrC0eOHEFJCdX71hYKwIhVhgHYhQeFKK/QQOLhus1ICSFPv3KVBs3n7q/rYXBufDoAUg/HP/7YTcYVCgX+/PNPLFy4ED179sTQoUMBAFOnTkWLFi1w+PBhCIW61xkwYADy8vIwZ84cvPHGG+Dz+fjzzz/Rv39//POf/+Su/cILL0Cr1UImkzn1HkNCQtC4cWMAQPPmzdG5c2fuuYsXLyI/Px+rVq3CsGHDuMfHjBnj1GsS+9AUJLHqcaGc+3uFRotzD5z/zYwQQp5knTt3hkgkgo+PDwYOHIiAgADs2rULQqEQd+/exa1bt/Dqq68CANRqNfff4MGDkZmZiZSUFABAp06dsHfvXsyaNQtHjx5FeXm5tZd1mUaNGiEgIAAfffQR/u///g83btyoldclxigDRqxiM2Ato3yRnC7DyTu56BUfUsejIoQ8SSQiAW58OqCuh8GRiJzL4v/yyy9o1qwZSkpKsHnzZvzwww8YN24c9u7dy9WBTZ8+HdOnTzd7fl5eHgDgm2++Qb169bB582YsXboUnp6eGDBgAJYuXYqwsDCnxmiNn58fjh07hkWLFmHOnDkoLCxEREQE3nrrLXzyyScQiUQ19tqkEgVgxKLyCg3yyyoAAMPb1UNy+g3cyHQuLU4IefbweDynpvzcTbNmzbjC+z59+kCj0eC///0vtm7dilatWgEAZs+ejREjRpg9v0mTJgAALy8vLFiwAAsWLEB2djaXDRs2bBjOnDkDAPD09ERxcbHJNdggzlGtWrXCpk2bwDAMrl69ijVr1uDTTz+FRCLBrFmznLo2sQ1NQRKL0ot0048+YiHqB+o2IC1Tuk8hLSGEuIMvvvgCAQEBmDt3Lho3bozGjRvjypUr6NChg9n/fHx8TK4RFhaGCRMmYNy4cUhJSYFcrvv5Gxsbi9u3b0OpVHLH5ufn49SpU9WOSywWA4DVqU0ej4c2bdrgq6++gr+/Py5dumTv2ycOenp+JSEu90g//RgVIIFUX3gvr1DX5ZAIIcTtBAQEYPbs2Zg5cyY2bNiAH374AYMGDcKAAQMwYcIEREVFoaCgADdv3sSlS5fw22+/AQASEhIwZMgQtG7dGgEBAbh58ybWrVuHLl26QCrV/dL7+uuv44cffsBrr72Gt956C/n5+fjiiy/g6+tb7bhatmwJAPjxxx/h4+MDT09PxMXF4fTp01i5ciVeeuklNGjQAAzDYPv27SgqKkJiYmLN3ShihAIwYhFb/1UvQMoFYJQBI4QQU9OmTcN3332HTz/9FDdv3sS5c+ewaNEivP/++ygsLERQUBCaN29utNLw+eefx+7du/HVV19BLpcjKioKb7zxBmbPns0d061bN6xduxZLlizBsGHD0KBBA8ybNw979uzB0aNHrY4pLi4OX3/9NZYvX841jP3555/RpUsX+Pv744svvkBGRgY8PDzQpEkTrFmzBuPHj6+pW0SqoACMWMSugKwXIIGXWPetQhkwQsizasKECVwPr6o8PT2RlpbGfd26dWts3rzZ6vUWL16MxYsXmzxetQ3FG2+8gTfeeMPoGHMtIxjGdLu49957D++9957J4xs2bLA6NlLzqAaMWJTOZcAqpyDLKigDRgghhDiLAjBi0WODAMxLv4KpQq2FWqOty2ERQgghTzwKwIhFRjVg4sq+OXI32lKEEEIIeRJRAEbMUmu0yCvVLXsO9/OEh4APAV+3d5qcCvEJIYQQp1AARswqKldxf/eXiPSNFNk6MCrEJ4QQQpxBARgxq0iu64Dv6ymEUKD7NmHrwCgDRgghhDiHAjBiVqFclwEL8PLgHmPrwCgDRgghhDiHAjBiVqF+D0h/aWUAxmXAKAAjhBBCnEIBGDGriM2ASUXcY5XbEdEUJCGEEOIMtwzASktL8f777yMyMhKenp5o27YtNm3aZNO5OTk5mDBhAoKDgyGVStGlSxccOnTI7LFJSUncnlvBwcGYMGECcnJyTI775JNPMGTIEERFRYHH41nshAwA9+7dw4gRI+Dv7w9vb28kJiY+kZubFuprwAIMMmBcAEY1YIQQQohT3DIAGzFiBNauXYt58+Zh79696NixI8aNG1ft1glKpRJ9+/bFoUOHsHz5cuzatQthYWEYOHAgjh07ZnTssWPHMGjQIISFhWHXrl1Yvnw5kpKS0LdvX6Nd5wHgq6++Qn5+PoYOHQoPDw9Ykpubix49euD27dtYvXo1tmzZAoVCgd69eyMlJcXxG1IH2Bowf8MMmH47IqoBI4Q8a3g8nk3/HT16FEePHgWPx8PWrVvretgAUCPjmT9/Png8nk3HxsbGWk1csH755Re8/PLLaNKkCfh8PmJjY50bZDVj4vF4EAgECAgIgEAgAI/Hw9tvv11jr1mV2+0FuWfPHhw8eBAbNmzAuHHjAAB9+vRBWloaZsyYgbFjx0IgEJg9d9WqVUhOTsapU6fQpUsX7tw2bdpg5syZOHv2LHfsjBkzEB8fj61bt0Io1N2GuLg4dOvWDatXr8aUKVO4Y0tKSsDn62LVdevWWRz7smXLkJubi1OnTiEmJgYA0L17dzRs2BBz586tdl8wd1JkJgPmRVOQhJBn1OnTp42+/uyzz3DkyBEcPnzY6PHmzZs/kbMe7mDdunXIyspCp06doNVqoVKpqj/JCd26dcMXX3yBsrIyeHl5gc/nIywsrEZf05DbBWA7duyAt7c3Ro8ebfT4xIkT8corr+Ds2bPo2rWrxXObNGnCBV8AIBQK8dprr2HOnDlIT09HVFQU0tPTcf78eSxevJgLvgCga9euiI+Px44dO4wCMDb4smXszz//PBd8AYCvry9GjBiBdevWQa1WG72eO6ucgjSsAdNnwJSUASOEPFs6d+5s9HVISAj4fL7J464gl8tdfs0nwf79+7nP2yFDhiA5OblGX8/f3x+dO3eGTCaDr6+vzZ/1ruJ2U5DJyclo1qyZSaDSunVr7nlr57LHmTv3+vXrRtewdKwj/+jl5eVITU21eM3y8nLcu3fP7uvWlcopSIMMmJgyYIQQYiuVSoWPP/4YkZGR8PX1Rb9+/UzKUXr37o2WLVvi+PHj6Nq1K6RSKSZPngwAkMlkmD59OuLi4uDh4YGoqCi8//77KCsrM7rGb7/9hoSEBPj5+UEqlaJBgwaYNGmSQ+MBgNWrV6NNmzbw9PREYGAghg8fjps3b9r0fmfOnInw8HBIpVJ0794d586ds/l+2RMA3blzB6+88gpCQ0MhFovRrFkzrFixwubz3YHbpWPy8/PRoEEDk8cDAwO5562dyx5n7Vz2T0vHWnsNSwoLC8EwjE2vb45SqTSqPZPJZAB039CuTMOy16rumoVlurH4iPncsZ4C3Xx/qcK1Y3pa2XqviXPoPtee6u61SqUCwzDQarXQarWmB1QJHIwIBICnp23H8vmAROLYsS7CMAwAmH2f7GNz5sxB165d8eOPP0Imk2H27Nl48cUXcf36daNSmszMTLz22muYMWMGFi5cCB6PB7lcjhdeeAGPHz/G7Nmz0apVK9y4cQPz5s3DtWvXcODAAfB4PJw+fRpjx47FmDFjMHfuXHh6eiItLQ1HjhzhxmHPeJYsWYKPP/4YL7/8MhYtWoT8/Hx8+umn6NKlC86ePYvGjRtbfP9vvvkm1q1bhw8//BD9+vXD9evXMWLECJSUlHDfF664vzdu3ED37t1Rv359LFu2DOHh4Thw4AD+8Y9/IDc3F3PnzrXpNY4fPw4/Pz8oFAo0btwYkyZNwnvvvWexzIkdD8MwUKlUFo+z9WeR2wVgAKwW9lVX9GfPuZaOtbWw0NnXN7R48WIsWLDA5PEDBw5AKpU6PB5LDh48aPX57EIBAB6uXzqLYv0vSPczeQAESE17hD170lw+pqdVdfeauAbd59pj6V4LhUKEh4ejtLQUFRUVJs/7BwRYvKYqMRFlW7ZwX/tFRYFnYSpO3a0bSv/4g/vat1Ej8C38gqtu1w6lVeq0XIH9kGV/WTbETiE2btzYKCtTUVGBiRMn4ujRo+jYsaNufGo1CgoK8PPPP6Nnz57csV999RWuXr2KpKQktGvXDgDQsWNHBAQEYPz48di+fTsSExNx5MgRMAyDpUuXws/PDwDw3HPPYcSIEdzYbB1PcXExFi5ciMTERHz//ffccc899xw6dOiATz75BD/99BMAcAkD9jVu376NX375BVOnTsXHH38MAEhISICPjw/+9re/QaVSmb1XlqjVami1WrPnvP/++/Dy8sIff/wBX19f7rVKSkqwdOlSTJgwAf7+/lav369fP7Rr1w6xsbEoLi7Gzp07MWPGDJw/fx4//PCDxfMqKipQXl6O48ePQ602X45j6xSy2wVgQUFBZjNFBQUFAMxnrew9NygoCID5jFRBQYHV17AkICAAPB7P4bHPnj0bH3zwAfe1TCZDdHQ0+vfvz32DuYJKpcLBgweRmJgIkUhk9hiGYfDh2SQADIYk9kGkv+63x7KL6dj+4Dp8g0IxeHB7l43paWXLvSbOo/tce6q71wqFAo8ePYK3tzc8DbNZNhAKhTb/rBNUOdbaL7cCgcClP0NZ7Ps3d232l+bhw4cbPZ+QkAAAyMvL4x4XCoUICAjAkCFDuOMYhsH+/fvRsmVLdOvWzejaL730Eng8Hs6fP4+RI0eie/fuAIC33noLkyZNQrdu3RAVFeXQeP7880+Ul5dj8uTJRsc1b94cffr0wYkTJ7jHxWKx0fs/f/48AGDChAlG544fPx5Tp06FSCSy699BKBSCz+ebnKNQKHDs2DG8/fbbCA8PN3pu2LBh+Omnn3D9+nUMGjQIGo2Gy6QBuilOdprzxx9/BKC71yUlJRg7dixCQ0OxYsUKzJw5kwt6q1IoFJBIJOjZs6fF73FbA023C8BatWqFjRs3mhSsX7t2DQDQsmVLq+eyxxmqei7757Vr1zB48GCTY629hiUSiQSNGjWy+PoSicTs1CpLLBZz39CGRCJRjXyoWLtuiUIFtVb3TRvq5wWRSJdm9dXXg5WrtPRBZ4ea+jckxug+1x5L91qj0YDH4xl90BkpLbV4TZ5AAJ7hOWZ6MnLH8vnGxz54YPuxLsIGfebeJ/sYW6jPkuinQpVKpdHjERERRl9rtVrk5ubi3r17Zj8XAF0Cgc/no3fv3ti5cye++eYbTJgwAUqlEi1atMDHH3/MdRKwdTyFhYUAgKioKJP3FRUVhaSkJO7xqu+fPTcyMtLoXA8PDwQFBXHfF7aydH8LCwuhVqvx3Xff4bvvvjN7bkFBAfh8Pho0aIC0tMrZmnnz5mH+/PlGx7JTnDweD6+//jpWrFiBc+fO4bnnnjN7bT6fDx6PZ/Xnja0/h9wuABs+fDh++uknbNu2DWPHjuUeX7t2LSIjI7mI3dK5U6dOxdmzZ7nj1Go11q9fj4SEBERGRgLQfSN16tQJ69evx/Tp07l53DNnziAlJQXvv/++w2P/+uuv8ejRI0RHRwPQtbDYvn07hg4d+sSsgGS74IuFfEg8Kue4aSsiQohTvLzq/lg3ZC6DFxQUBC8vL6xevdrsOcHBwdzfhw0bhmHDhkGpVOLMmTNYvHgxXnnlFcTGxhp1BagOOzuUmZlp8lxGRobRa1o6NysryygDp1arHaqrtoTt2fX666/jnXfeMXtMXFwcAOD33383qq1mYwBL2GxZba2GdLuIYNCgQUhMTMSUKVMgk8nQqFEjbNy4Efv27cP69eu5YGny5MlYu3YtUlNTubYPkyZNwooVKzB69GgsWbIEoaGhWLlyJVJSUpCUlGT0OkuXLkViYiJGjx6NqVOnIicnB7NmzULLli0xceJEo2OPHTuG3NxcALrf8NLS0riGdr169UJISAgAYPr06Vi3bh1eeOEFfPrppxCLxViyZAkUCoVJ1O3OzHXBB6gTPiGE1Jb+/fvjq6++QlBQEBdQVEcsFqNXr17w9/fH/v378ddff9kVgHXp0gUSiQTr1683agX1+PFjHD58GKNGjbJ4bu/evQEAv/76q1H2aMuWLRZrpRwhlUrRp08f/PXXX2jdurXV5uitWrWy69q//PILANOWIzXF7QIwANi+fTs+/vhjzJ07FwUFBWjatCk2btyIl19+mTtGo9GYzO+KxWIcOnQIM2fOxLRp0yCXy9G2bVvs3bsXvXr1MnqN3r17Y8+ePZg7dy5efPFFSKVSDBkyBMuWLTNJ+c6bN8+okz7b6RgAjhw5wn3jhYSE4MSJE5g+fTrGjx8PtVqNLl264OjRo2jatKmL71LNMdcFHwC8qBM+IYTUiilTpmDPnj3o2bMn/vnPf6J169bQarV4+PAhDhw4gA8//BAJCQmYO3cuHj9+jL59+6JevXooKirC8uXLIRKJTD73quPv749//etfmDNnDt544w2MGzcO+fn5WLBgATw9PTFv3jyL5zZr1gyvvfYavv76a4hEIvTr1w/Jycn48ssvba79unHjBm7cuAFAl0mTy+VcsqN58+Zo3rw5AGD58uXo3r07evTogSlTpiA2NhYlJSW4e/cufv/9d5PmuFVt2LAB27dvxwsvvIDo6GhkZmbif//7HzZv3owJEyagTZs2No3XWW4ZgHl7e2P58uVYvny5xWPWrFmDNWvWmDweFhaGtWvX2vQ6iYmJSExMrPY4NtiyRcOGDbFjxw6bj3dH5rrgA+CmI6kPGCGE1CwvLy8cO3YMX3zxBX788Ufcv38fEokE9evXR79+/bhtehISEnDhwgV89NFHyM3Nhb+/Pzp06IDDhw+jRYsWdr/u7NmzERoaim+++QabN2+GRCJB79698fnnn3MtKCxZtWoVwsLCsGbNGnzzzTdo27Yttm3bZpQ8sWbLli0m3QDYTJxh/Ra728Bnn32GTz75BDk5OfD390fjxo1N6rrNadCgAYqKijBnzhzk5+dDJBKhRYsWWLlyJf7+97/bNFZX4DGGKSTiNmQyGfz8/FBcXOzyVZB79uzB4MGDLRYKrvnzPub/fgODW4Vj5auVqeSsYgU6Lz4EAZ+Hu4sGOdWu41lgy70mzqP7XHuqu9cKhQL3799HXFyc3asgSSW2/UJddGd/1th7r235Hrf185v+ZYkJc13wAUCq74Sv0TJQqm1vqEcIIYQQYxSAERNFZvaBBACpqHJFJE1DEkIIIY6jAIyYYDNgVWvAhAI+xELdtwxtyE0IIYQ4jgIwYoJtQ1F1ChKoXAlJGTBCCCHEcRSAERPF5foaMIlpka1ExK6EpAwYIYQQ4igKwIgJNrvFFt0b8hJTKwpCiHW0uJ48rVz5vU0BGDFRzgZgHqZt4tjHqAaMEFIVu1OJSqWq45EQUjPYrv6u2FqQAjBiolylC8AkIsqAEUJsJxKJIBaLUVxcTFkw8lSSyWQQCATcLxvOcMtO+KRusfVdUg/TbzAuA0Y1YIQQM4KDg5Geno7Hjx/Dz88PIpGImjbbSavVoqKiAgqFghqx1jBb7zXDMCgrK4NMJkNERIRLvqcpACNGtFoGCpWuyarETADmRRtyE0KsYDt/5+XlIT09vY5H82RiGAbl5eWQSCQUvNYwe+41j8eDv78//Pz8XPLaFIARIwp1ZWBlbgpS4kFtKAgh1vn6+sLX1xcqlQoaDf2ssJdKpcLx48fRs2dP2l6rhtlzr0UikUumHlkUgBEj5RXWAzCpB7WhIITYRiQSUQDhAIFAALVaDU9PT7p/Nawu7zVNLhMjbGZLLOSDzzdNx4oEum8ZlYYKbAkhhBBHUQBGjChUbAsK82lWD4EuKFNpaDNuQgghxFEUgBEjbAbM3PQjoNsPEgDUWgrACCGEEEdRAEaMcD3ALGTAhFwGjKYgCSGEEEdRAEaMsEX4lgIwkb5PipqmIAkhhBCHUQBGjHD7QIrML5AVUQaMEEIIcRoFYMRI9VOQ7CpIyoARQgghjqIAjBgp1/f3slSEz2bA1FrKgBFCCCGOogCMGCmvpg2FiDJghBBCiNMoACNG2BowT5qCJIQQQmoMBWDECJcBszQFqe+Or6YifEIIIcRhFIARI9W1oeAyYFQDRgghhDiMAjBipNo+YGwbCjVNQRJCCCGOogCMGJFXNwVJWxERQgghTqMAjBhRVDcFSTVghBBCiNMoACNGuM24Pcx3wq+sAaMMGCGEEOIoCsCIEa4TvoUpSA82AFNTBowQQghxFAVgxAhbhG+pEauQ64RPGTBCCCHEURSAESNsBsyzmq2IaDNuQgghxHEUgBEj8uoyYHz9KkjqhE8IIYQ4jAIwYqTazbiF7FZElAEjhBBCHEUBGOEwDFP9Ztz6NhS0CpIQQghxHAVghKNUa8HuMFTdVkQMA2hoOyJCCCHEIRSAEY5Cn/0CrExB6ovwAUBFdWCEEEKIQygAIxy2AN9DwOcyXVWJDB6nAIwQQghxDAVghFPZgsLytwW7FRFA2xERQgghjqIAjHAqm7Ca34YIAAQGARgV4hNCCCGOoQCMcLhtiCwU4AMAj8fjtiOiDBghhBDiGArACIfbiNtCAT5LyHXDpwwYIYQQ4ggKwAinun0gWWwdGDVjJYQQQhxDARjhlKv0XfCrCcDYlZC0ITchhBDiGArACKe8QhdQVTcFKaIaMEIIIcQpFIARjrzCtgwYWwNWQTVghBBCiEMoACMcRTX7QLIoA0YIIYQ4hwIwwmFXQXpWOwWpy4CpKQNGCCGEOIQCMMKR27wKUvdto6LNuAkhhBCHuGUAVlpaivfffx+RkZHw9PRE27ZtsWnTJpvOzcnJwYQJExAcHAypVIouXbrg0KFDZo9NSkpCly5dIJVKERwcjAkTJiAnJ8fkOJVKhQULFiA2NhZisRhNmzbFt99+a/aa27ZtQ7du3RAYGAh/f3906tQJ69ats/3N16FyG/uAsRkwlZoyYIQQQogj3DIAGzFiBNauXYt58+Zh79696NixI8aNG4cNGzZYPU+pVKJv3744dOgQli9fjl27diEsLAwDBw7EsWPHjI49duwYBg0ahLCwMOzatQvLly9HUlIS+vbtC6VSaXTs1KlTsXjxYrzzzjvYv38/hg8fjvfeew+ff/650XGrV6/GqFGjEBERgV9//RWbNm1Cw4YN8cYbb+Crr75yzc2pQSVKFQDAVyKyepyQ2lAQQgghTrG86V8d2bNnDw4ePIgNGzZg3LhxAIA+ffogLS0NM2bMwNixYyEQmM/QrFq1CsnJyTh16hS6dOnCndumTRvMnDkTZ8+e5Y6dMWMG4uPjsXXrVgiFutsQFxeHbt26YfXq1ZgyZQoA4Pr161i1ahUWLVqEGTNmAAB69+6N/Px8LFy4EG+//TYCAwMB6AKwmJgYbNmyBXz9NN2AAQNw+fJlrFmzBv/85z9r4I65TolCtwrSx9P6twWXAaMifEIIIcQhbpcB27FjB7y9vTF69GijxydOnIiMjAyjIMrcuU2aNOGCLwAQCoV47bXXcO7cOaSnpwMA0tPTcf78ebz++utc8AUAXbt2RXx8PHbs2ME9tnPnTjAMg4kTJ5qMp7y8HPv27eMeE4lE8Pb25oIvQLd3oq+vLzw9Pe28E7VPVq7LgPmIrWfAqBErIYQQ4hy3C8CSk5PRrFkzo8AIAFq3bs09b+1c9jhz516/ft3oGpaONXyN5ORkhISEIDw8vNrxTJs2DTdv3sSiRYuQm5uLvLw8fPnll7h48SKmT59u+U27CVszYNxWRGrKgBFCCCGOcLspyPz8fDRo0MDkcXaaLz8/3+q57HHWzmX/tHSs4WtYuqaXlxc8PDyMjh0xYgS2b9+O8ePH45NPPgEASCQSrF271iSjV5VSqTSqPZPJZAB0CwBUKpXVc+3BXsvcNWUK3WNSEc/qa+pnIKFw8dieNtbuNXEdus+1h+517aD7XHtq4l7bei23C8AA3bSdI8/Ze66lY209rupz+/btw2uvvYbRo0djzJgxEAqF2L17NyZMmICKigqTaUxDixcvxoIFC0weP3DgAKRSqcXzHHXw4EGTx4rLBAB4uHD6BO6JLZ+bl8sHwMeVq8nwy73m8rE9bczda+J6dJ9rD93r2kH3ufa48l7L5XKbjnO7ACwoKMhslqugoACA+ayVvecGBQUBMJ9NKygoMHqNoKAgXL582eS4srIyVFRUcMcyDINJkyahZ8+eWL16NXdcv379UFxcjGnTpmHMmDHw8vIyO/bZs2fjgw8+4L6WyWSIjo5G//794evra/E920ulUuHgwYNITEyESFRZ66VUa6E6nQQAGDow0epKyKSyq7icn4UmzZpjcNcYl43taWPpXhPXovtce+he1w66z7WnJu41O4NVHbcLwFq1aoWNGzdCrVYb1YFdu6bLtLRs2dLquexxhqqey/557do1DB482ORYw9do1aoVNm3ahKysLKM6sKrXzM7ORmZmJv7+97+bvH7Hjh3xyy+/4MGDB2jRooXZsYvFYojFpmknkUhUI/8DVr1usX76k8cDArwl4PMtZ/089P8uGoZHPxxsUFP/hsQY3efaQ/e6dtB9rj2uvNe2XsftivCHDx+O0tJSbNu2zejxtWvXIjIyEgkJCVbPvXXrltFKSbVajfXr1yMhIQGRkZEAgKioKHTq1Anr16+HRqPhjj1z5gxSUlIwYsQI7rFhw4aBx+Nh7dq1Rq+1Zs0aSCQSDBw4EAAQEBAAT09PnDlzxmRcp0+fBp/PR0REhB13onaxBfjeHkKrwRcAeAjddyuis/fy8dHWqyiSV9T1UAghhBCL3C4DNmjQICQmJmLKlCmQyWRo1KgRNm7ciH379mH9+vVcD7DJkydj7dq1SE1NRUyMbhps0qRJWLFiBUaPHo0lS5YgNDQUK1euREpKCpKSkoxeZ+nSpUhMTMTo0aMxdepU5OTkYNasWWjZsqVRrVaLFi0wefJkzJs3DwKBAB07dsSBAwfw448/YuHChdwUpFgsxtSpU/Gf//wHb7zxBtevbOfOndiwYQMmT55sdfq0rnEtKKpZAQm491ZEY3/UBcBeYiHmvti8jkdDCCGEmOd2ARgAbN++HR9//DHmzp2LgoICNG3aFBs3bsTLL7/MHaPRaKDRaMAwlUGAWCzGoUOHMHPmTEybNg1yuRxt27bF3r170atXL6PX6N27N/bs2YO5c+fixRdfhFQqxZAhQ7Bs2TKTqcCVK1ciKioK3377LbKyshAbG4vly5dj2rRpRsctW7YMzZo1ww8//IDXXnsNWq0WDRs2xHfffYe//e1vNXCnXKeyBUX1qVMh14jV/TJgrPwyZfUHEUIIIXXELQMwb29vLF++HMuXL7d4zJo1a7BmzRqTx8PCwkymCy1JTExEYmJitceJRCLMnz8f8+fPt3ocn8/Hm2++iTfffNOm13cnJQp2G6LqvyW4RqxuFoAZBuNhvu7f+JYQQsizy+1qwEjdsCcD5q5bERWXV/ZeCfG20keDEEIIqWMUgBEAlU1Y7akBc7etiDKLFdzfBdUsJCCEEELqEgVgBAAgs3EbIsAgA+ZmWxFlFpdzf9cy7jU2QgghxBAFYASAQQ2YTUX47CpI982AadxwhSYhhBDCogCMALC3BowtwnevICezyCAAowwYIYQQN0YBGAFgXx8wdgrSnWvANG4WHBJCCCGGKAAjAAwzYLYX4Ve4cQ0YZcAIIYS4MwrACACgRMn2AbO9Eau7ZcCyDDJgWqoBI4QQ4sacasRaUVGBpKQk3Lp1C2VlZfjXv/4FAFAoFJDJZAgODgafTzHek0BWrsuA+dqQAfNwwxowhmGQQRkwQgghTwiHo6Pdu3ejfv36ePHFFzF9+nSjLvFXr15FREQENm3a5IoxklpQwvUBezK3IiouV0GhqhyPmjJghBBC3JhDAdiff/6JUaNGQSwWY/ny5XjllVeMnu/UqRMaNWqEbdu2uWSQpGYxDMPVgNnUhoLdjNuNArAMgxWQAE1BEkIIcW8OTUEuXLgQ/v7+uHDhAkJCQpCfn29yzHPPPYdz5845PUBS8xQqLZcxsqUI30PI1oC5T5CTJSs3+tqNYkNCCCHEhEMZsDNnzmDYsGEICQmxeEx0dDSysrIcHhipPew2RAI+D1IPQbXHV2bA3CcAM8mAUQ0YIYQQN+ZQAKZUKuHn52f1mOLiYirAf0Kw9V/eYiF4vOr3UORWQbpRmulRgdzoa3dboUkIIYQYcihCatCgAS5cuGD1mNOnT6Np06YODYrULnYfSF+JbTPSbCd8d6oBu5BWCACoHygFQFOQhBBC3JtDAdjIkSNx4sQJ/PLLL2af//LLL5GcnIyxY8c6NThSO7gmrOLqC/ABwwDMPab5yis0uPq4CADQtWEQACrCJ4QQ4t4cKsKfMWMGtm3bhokTJ2L9+vVQKHT1NzNnzsTp06dx6tQptG3bFu+++65LB0tqhj3bEAGAkF87jVgZhsHdnFLUD5JCLLRcm/bXw0KoNAwi/DwRE+SlHxsFYIQQQtyXQxkwb29vnDhxAi+//DKOHDmCkydPgmEYfPnllzh16hTGjBmDpKQkiMViV4+X1AB7NuIGam8z7pN385D41XF8+vsNq8edvV8AAOgUFwj90KgInxBCiFtzuBN+QEAAfv31V3zzzTc4f/48CgoK4Ovri44dOyIsLMyVYyQ1jC3Ct7UGjC3Cr6jhQqv7eWUAgNvZJVaPO2cQgJVXaAAAGsqAEUIIcWNObUUEAEFBQRg4cKArxkLqCNuGwpYmrEDtbUXEBlMFZRUWj6lQa3Hpoa4APyEuECfv5AGgrYgIIYS4N4emIB8/fozdu3ejqKjI7POFhYXYvXs30tPTnRkbqSWVU5D2ZcBqugasXFV9AHYkJQdKtRaBXh5oGOINgb4+TeMmCwQIIYQQcxwKwBYtWoSJEydCIpGYfV4qlWLSpElYvHixU4MjtcPuAMygEStTg5kmNgNWVK4yO6V46m4e3tv0FwBgYMtw8Hg88NkAjDJghBBC3JhDAdihQ4fQv39/i0X2YrEY/fv3R1JSklODI7WjxMEpSMD8asOCsgp8d/gOMorKTZ6zB5sBYxjdZtuGZAoV/rbuIhQqLfo0CcHcIc0BAAJ9I1lqQ0EIIcSdORSApaenIzY21uoxMTExNAX5hJCV27cKkp2CBMzXgf124RG+PHAbPxxLdWpcbAYMAArKlEbP3c8tQ6lSjWBvD/zf68/BU6RrUyGwkgHLKlbgwy1XcD2j2KlxEUIIIc5yqAjfw8MDMpnM6jEymcymbW1I3WOL8O2tAQMAlVYLCYx7dLFTmukuyoABQEGZcQYst0QXkEX6S4x6hHEBmJkM2IvfnURuiRI3M2XY814Pp8ZGCCGEOMOhDFjr1q3x+++/Q6lUmn1eoVBg9+7daNWqlVODI7WjhNuKyMY+YAZ7fJrLgLHTkjkl5r8/bGWcATMuxM8t1V07xNt4GtxSAKZQabigLb/MuXERQgghznIoAJs0aRIeP36MoUOH4t69e0bPpaamYtiwYcjIyMCbb77pkkGSmlViZwaMz+dxgY65/SDZTbpzZE4GYCorAZg+mArxsS0AO3Irh/t7+/oBTo2LEEIIcZZDU5Djx4/H3r17sWXLFjRt2hRxcXGIiopCeno67t+/D7VajbFjx2LixImuHi9xMa2WQYnSvlWQgG47Io2WMR+A6YOfvFIltFqGW5loL8MArFBuYwDGFuFXqQHbebmyHpGatBJCCKlrDmXAAGDTpk347rvv0LhxY9y5cwdHjx7FnTt3EB8fjxUrVmDjxo2uHCepIWUVarCxiq2rIAHr2xGx/cHUWgYFcss9vKpjdQrSQgDG5/aprBxXsVyFI7dyua8pACOEEFLXnOqEP3XqVEydOhVyuRxFRUXw8/ODl5eXq8ZGagFb/+Uh4EMstD0et9aM1TAoyy1RItjbsT1BrU5BWqoBM9OG4q9HhUbbJqkoACOEEFLHnN6KCNA1XpVKpa64FKllhisg7Vm1ymbAKtSmwYzKIADLKVGiWYRjY3MkAyYQmLahKNVPsbLUNbyHJSGEEFIdpwOwsrIyFBUVQaPRmH2+fv36zr4EqUH2dsFnifiWM2Aag8dyZAqHx2YYgBnWgDEMU20NmGEWrswkAKMMGCGEkLrlcAC2atUq/Pvf/0ZKSorFY3g8HtRqtcXnSd2rXAFpe/0XAAgFldsRVWU4xedMKwpLU5BlFRruuarTm+wqSMMi/DKl7lhPER8KlRaqGt7DkhBCCKmOQwHY999/j3feeQdCoRA9e/ZEvXr1IBS6ZDaT1LLKHmB2ZsAE1behACqnCu2l0miNCukNAzD2ml4eAniJjcfN55m2oWAzYH4SERQqJWXACCGE1DmHoqavv/4awcHBOHnyJOLj4109JlKLZPo9Fn3E9mXArK2C1BhlwBybgjTMfgGAvEIDhUoDT5HA4vQjULk4wLDOvkw/leknESFbpjS7fyUhhBBSmxxqQ5GWloYxY8ZQ8PUUkDlYA8YGOuam84yK8B1sxsrWf/F5up5jQGUdmLUAjM2AGdamsRkwf4mH7jkqwieEEFLHHMqARUZGWiy6J0+WyiJ8O2vA+LZmwJwLwKQeQkg8dFmvTece4XFhOeKCdStuzQVgXA2YQYzFBmDsVkuUASOEEFLXHArAJk6ciFWrVqGsrIz6fj3h2CJ8e2vAPLgifHMZMOMaMIZh7N6YnZ2C9BQJEOTlgdwSJZYfugOgMltXtQcYUJktM6oBq9BnwKQii2MmhBBCapNDU5Bz5sxBp06dkJiYiOPHj6OkpMTV4yK1ROZoBsxaEb5B8FOu0pj04bKFXJ8Bk3jwESD1MHqOzdpZm4LUmFkF6c9mwKgInxBCSB1zKAPm4aH7QGQYBn369LF4HLWhcH/2bsTNMtfugVV1ii+nRGl3gKfQZ8CkIiECvSoDMIF+D0rAtAWF4bjMZcD8uClIyoARQgipWw4FYD169LB7Som4J64Nhb1F+HzThqesqkXuOTIlGoZ423V9tgbM00OAMF9PAECv+BD4SUTYfSUDgKUaMN2fZttQcFOQlAEjhBBStxwKwI4ePeriYZC6wrahsGcjbgAQ6IvwzW1szQZlPB7AMI61opDrM2ASER8Tu8VCJORhUrc43MoqqSYA041LqzWdgmQzYLQZNyGEkLpG3VOfcY6ugmQzTeZWFLJTfKE+YmTLlA61olCwNWAiAaIDpZg9qBkA3bRj41Bv5JQoERtsugBEYK4GrMoUJBXhE0IIqWsUgD3jHK0BE1rLgOkfqxcgRbZMiWwH9oNkV0FKPYzHJeDzsG1qV2g0jNmsHd9MYGjYCb/qc4QQQkhdcDgA02g02LJlC5KSkpCRkQGl0jTLwePxcOjQIacGSGqOWqPlusSzPbJsZa7YvfK6usei/CW4mFaILAcCMHYVpKdIYPKctenSyj5gujFUqLVczZe/fjWlRss41BqDEEIIcRWHArCysjL0798fZ86c4T7IGIMpH/Zr+oBzb4btIezPgFkJwPRTkFEBEgCOdcNnM2ASD/s6pXCBof77sczgPfoZBJkqDQMPIX1/EkIIqRsO9QFbuHAhTp8+jQULFiAvLw8Mw2D+/PnIzMzE5s2bERcXh1GjRpnNihH3wdZ/eYr43N6OtmIDHbM1YBp2ClIXgDmSAVNYmIKsdlz6oJ9hdFkwtv5LLOTDU1T5HqkVBSGEkLrkUAC2fft2dO7cGZ988gkCAwO5x8PCwjB69GgcPXoUhw4dwrJly1w2UOJ6Mq7+y77pR6CyEavG7F6Q+gyYf2UAxpjpF2ZNuZUpSGvYwBDQZcHYFZBeYqHRc1QHRgghpC45FIA9fPgQnTt3rrwIn2+U7apXrx5eeOEFrF271vkRkhpTrG9B4Wdn/RdguOm15b0g2QCsQq3lXstWcoNVkPYwCsAMMmBeYgFEfIMMGPUCI4QQUoccCsC8vLzAN/gw8/PzQ2ZmptEx4eHhePjwoXOjIzWqsEwXFAVW2erHFtZqwFT6x6RiIQL0zU/tnYasnIJ0PADTMgxXA+blIQSfzwP7dNVmsYQQQkhtcigAi4mJMQquWrZsicOHD3NZMIZhcOjQIURERDg0qNLSUrz//vuIjIyEp6cn2rZti02bNtl0bk5ODiZMmIDg4GBIpVJ06dLF4krMpKQkdOnSBVKpFMHBwZgwYQJycnJMjlOpVFiwYAFiY2MhFovRtGlTfPvtt2avyTAMfv75Z3Tq1AleXl7w9fVF+/btsWvXLttvQC0pkFcAAAK87M+AWW/EqgtuRHwe18U+q9i+AIwrwrczA8bnGU8zcgGYWFdLJmQ3EacpSEIIIXXIoQCsb9++OHLkCLfP4/jx4/Hw4UN06dIFM2bMQPfu3XH58mWMHDnSoUGNGDECa9euxbx587B371507NgR48aNw4YNG6yep1Qq0bdvXxw6dAjLly/Hrl27EBYWhoEDB+LYsWNGxx47dgyDBg1CWFgYdu3aheXLlyMpKQl9+/Y1WTwwdepULF68GO+88w7279+P4cOH47333sPnn39uMoYpU6ZgypQp6Nu3L3bv3o3ffvsNr7zyCuRyuUP3oiYVlekDMEcyYALzGTCtlgH7kMAgALN3JaRcP3Xo6UwGTGtcAwbogkKAMmCEEELqlkNtKN566y0EBQUhNzcXERERmDRpEv766y+sXLkSly9fBgCMHDkS8+fPt/vae/bswcGDB7FhwwaMGzcOANCnTx+kpaVhxowZGDt2LAQC8x/Kq1atQnJyMk6dOoUuXbpw57Zp0wYzZ87E2bNnuWNnzJiB+Ph4bN26FUKh7jbExcWhW7duWL16NaZMmQIAuH79OlatWoVFixZhxowZAIDevXsjPz8fCxcuxNtvv80tRNi5cyd++OEHbN68GWPGjOFea8CAAXbfh9pQmQGzPwCztArS8GuhgI9wNgNm5xRkuUoXIEntrQHjWagB0wdyugyYhvaDJIQQUqccyoA1btwYH330kdEU47fffousrCycPn0aGRkZ+O233yCVSu2+9o4dO+Dt7Y3Ro0cbPT5x4kRkZGQYBVHmzm3SpAkXfAGAUCjEa6+9hnPnziE9PR0AkJ6ejvPnz+P111/ngi8A6Nq1K+Lj47Fjxw7usZ07d4JhGEycONFkPOXl5di3bx/32PLlyxEbG2sUfLmzQn0GzJU1YIZfiwQ8hPnq9mu0uwaMLcK3MwPG5/PAxmBVV0GyYwKoDQUhhJC65VAAZklISAgSEhIQHh7u8DWSk5PRrFkzo8AIAFq3bs09b+1c9jhz516/ft3oGpaONXyN5ORkhISEmLynquNRq9U4ffo02rVrh//85z+IiYmBQCBAgwYN8OWXX9rdhqE2FMh1Rfj+UmdWQRoHMiqDrwV8HsL82ClI+wIwuYrtUWZfAAYY7AdpWAOmD+S4zB1lwAghhNQht9sLMj8/Hw0aNDB5nJ3my8/Pt3quYV8yS+eyf1o61vA1LF3Ty8sLHh4e3LF5eXlQKpU4dOgQzp8/j0WLFqFevXr47bffMGPGDBQWFmLRokUWx65UKo1qz2QyGQDdAgCVyr4WDtaw11KpVCgs072er6fA7tfgQRfAqNQao3PLFRWVB2k0CNYX+GcWl9v1GmwfMA8+Y/fY+HweoGWgrFChRD8eiYgPlUrFZe4UFa69r+YY3mtSc+g+1x6617WD7nPtqYl7beu1bArAnn/+eYcG4ehekNa2MKpueyN7zrV0rK3HGT6n1Wd+ZDIZ9u/fz/VJe/7555GVlYX//Oc/mD17Nry9vc1eZ/HixViwYIHJ4wcOHHBoKrc6Bw8eRHqeAAAPty6fhyLVvvPvpvMACPAg7RH27EnjHpdVAIAQPDDYt28vHpXqvn6YK8OePXtsvn5JuW5sZ/88gXsS+8YGre7cQ4eP4NZjPgA+Ht2/iz177kCp0D134uSfSPe187oOOnjwYO280DOO7nPtoXtdO+g+1x5X3mtbF93ZFIAdPXrUoUE4shdkUFCQ2SxXQUEBAPNZK3vPDQoKAmA+m1ZQUGD0GkFBQdzCAkNlZWWoqKjgjg0ICACPx4OPj49Rk1oAGDRoEHbu3IkbN26gU6dOZsc+e/ZsfPDBB9zXMpkM0dHR6N+/P3x9XRcpqFQqHDx4EImJiZhz8TgADV7o1xsxQfYFeRknH+CPh7cRGRWFwYNbcY9nFiuAi8chFPAxePAA5JUq8eW1YyhV85A4YKBNWx4xDIMPziYBYDAw8XluJaWtPr50GBVKNXr26oULB+8Cudlo37oFBneuj2/u/ok8RRk6JnRGQpzl7yVXMLzXIpH907zENnSfaw/d69pB97n21MS9ZmewqmNTAKatxYLlVq1aYePGjVCr1UZ1YNeuXQOg6zlm7Vz2OENVz2X/vHbtGgYPHmxyrOFrtGrVCps2bUJWVpZRHVjVa0okEjRu3BhZWVkmr8/Wfxk2r61KLBZDLBabPC4SiWrkf0AtT4Ay/TRfiK/U7tfwEOn+bbTgGZ/L06VeRQI+RCIRwvyEEPJ5UGsZFCm0iPQ3fY9VKdUarpjf18vT7rGxdV48vgDlat33rq/EQ3cv9QEgw+PX2g+2mvo3JMboPtceute1g+5z7XHlvbb1Oi4twneF4cOHo7S0FNu2bTN6fO3atYiMjERCQoLVc2/dumW0UlKtVmP9+vVISEhAZGQkACAqKgqdOnXC+vXrodFouGPPnDmDlJQUjBgxgnts2LBh4PF4JtsqrVmzBhKJBAMHDuQeGzlyJGQyGU6dOmV07J49e+Dt7Y0WLVrYcSdqVpG+BYWAz4OPp/2lgAK2o7xJGwotd11AV48V6qMLunJKbOsFpqioDPjtbcQKGK7QhEkjVjYAoyJ8QgghdcntivAHDRqExMRETJkyBTKZDI0aNcLGjRuxb98+rF+/nusBNnnyZKxduxapqamIiYkBAEyaNAkrVqzA6NGjsWTJEoSGhmLlypVISUlBUlKS0essXboUiYmJGD16NKZOnYqcnBzMmjULLVu2NGo50aJFC0yePBnz5s2DQCBAx44dceDAAfz4449YuHCh0XTl9OnT8euvv2L06NH47LPPUK9ePWzduhW7d+/Gl19+CYnE3mKmmlOoXwEZIBXpitbtJNAHMhqN+T5ghlONvhIRMooVkNm4HyTbBV/I59k0ZVkVn2+4CtK4DYWl/mWEEEJIbXIqAFMoFDh//jwyMjJMusez3njjDbuvu337dnz88ceYO3cuCgoK0LRpU2zcuBEvv/wyd4xGo4FGozFq7yAWi3Ho0CHMnDkT06ZNg1wuR9u2bbF371706tXL6DV69+6NPXv2YO7cuXjxxRchlUoxZMgQLFu2zGQqcOXKlYiKiuJ6ncXGxmL58uWYNm2a0XGBgYE4efIkZs6cienTp6OsrAxNmzbF6tWrTfqI1bVCfQbM34EeYIBBlqlKew2VvsO80CCo89Vv9i1T2BaAsV3w7e0BxmLbUGgZ00asXB8w6oRPCCGkDjkcgK1YsQL/+te/UFxcbPZ5hmHA4/EcCsC8vb2xfPlyLF++3OIxa9aswZo1a0weDwsLM5kutCQxMRGJiYnVHicSiTB//nybOvtHR0dj48aNNr1+XSqSO74RN1CZSbLUiNUoAPPUB2DlapuuzWbAHOkBZjg2s3tB8mkvSEIIIXXPoRqw7du3Y9q0aYiOjuaajA4bNgyff/45Bg4cCIZhMHLkSKxevdrV4yUuwjZhdWQjbqAywKo6lcdu8SM0nILU15iV2JgBY6/h4cD0I2AcHLJTkN7cZtz2ZcAKyyqw6dxDfL7nJjaee1j9CYQQQogNHMqAff311wgNDcXp06chlUrx4Ycfom3btvjoo4/w0UcfYcOGDRg/fjzeeecdV4+XuAi3DZED+0AChkGOcSDDBjZsoAPYPwXJXkMksL82zXBsKo2Wy6ZJuSlI+4rwP/ztCg7fyuG+7t0kBBF+7lPLRwgh5MnkUIrh6tWrGDp0qFGDUMPVhK+88gr69u2LTz/91PkRkhpRyG1D5FwAVjWQMTcF6cNlwGybgjSXRbMH+9KGr1c5BakPzmxsrZKWX2b09aOC8mrPWXn0LrZceGTT9QkhhDybHPqEU6lUCAkJ4b6WSCQoKioyOqZ169a4dOmSU4MjNcfZGjBLm3GruADMcAqSrQGzMQOmNS3ktwcbHLL1XzweIBbqxsNmwKqO25JS/TXYIDKz2HoA9iCvDF/sS8HHO65BqaZCf0IIIeY5FIBFRkYiMzOT+zomJgZ//fWX0TFpaWkmG2oT98GuggxweApSH8hUWQVpbvrQV6L7PpDZmAFjs2qOtKAwHJtCP/3oIeBzuzJUTk+aD8AYhsHhW9ncFC2bRWsS5gMASC+yHoDdzi7hrv8gr8zqsYQQQp5dDn3CdezY0Si7NXDgQPz5559YsmQJrl+/jh9++AHbt29Hx44dXTZQ4lqGfcAcYSkDxhblC4ymIHWvYXsRvmkdmT3YuK3cIABjVVeEv/HcI0xacwFfHkiBWqOFXL9bQJNwXQCWWaSw+tp3c0u5v9/JKbVyJCGEkGeZQwHY6NGjoVQq8eDBAwC6fQzr1auHjz/+GK1bt8aUKVPg7e2NL774wpVjJS5U5HQGzHwNmNrsKkj72lBwzVytbN1kdWz6bBcbgImElddhr2mpEevOy+kAgAf5Zdz0I1AZgGVUkwG7axB03aYAjBBCiAUOzREOHz4cw4cP574OCQnB5cuX8d///hf37t1DTEwMXn/9dURFRblsoMS1CmuoDxhbv2U4BeljdxsKZzNguvMUKt11zGXAVGYyYDklCpx/oNu4vbhcxU0/eor4iA7ULTjJKLaeAUs1CLru5pShqb9Db4EQQshTzmVFWgEBAZgxY4arLkdqUIUG3EbcAc6ugjRpQ8FOQRpvRQTYXwPm6CrIygCMzYBVBnLW2lDsv54NtqTNMADzFosQqW89Ya0In2EYpOZW1n3dySkF/B16C4QQQp5ybrcZN6l5RbrZR0g9BFyBvL3YGrCqM3lcBsyoE77uNUqVaptWH5q7hj34POMAzCgDZqUNxd5rlQtLiuUqLmPn6ylEpL8nAN3qUXarpKqyZAqjacuHBXKoaCEkIYQQM+wKwBiGwbFjx7B161bcvXuXe/zu3bt488030b59e7Rt2xb/+Mc/kJ2d7fLBEtcoUOqCkCh/Cbc60F4WM2BWivABoNSGLFhlHzDnxlauz/IZrqYU6K9ZdRPx/FIlztzL574uUapRrG+b4eMphI+nCD76XmIZFgrx2fqvBiFe8JeKoGWA7OrbhhFCCHkG2Zz+KCsrw4ABA3D69GkAAI/Hw7JlyzB48GB06dLFqA/Y1atX8fvvv+PixYsIDAx0+aCJcwr1+6ZHBTje0Z3t81U1kDHXQsJDyIeniA+FSguZQgW/alZeVnbTd24KklsFaUMR/pXHRdAyQEyQFGn5cjAMkKmv92IDyEh/CVKyS5BRVI5God4mr8sGYI1CvFHkpcK5BwXIkjsWRBJCCHm62fwJ9+9//xunTp1C27Zt8c9//hNt2rTBJ598glmzZkEoFGLVqlW4du0ajh07hpEjRyItLQ1LliypybETB7EZsHpOBGACi3tBmi+gZ1dCFtvQjLVyFWTtFeGn5uhqt1pG+kGi3wT8caEcQOUiggj9NKSlOjAuAAv1RuMwXYCWWU4BGCGEEFM2Z8C2bduGuLg4nDlzBiKRCCqVCs2bN8fu3buxYcMGjB07lju2e/fuaNmyJf744w9qReGGCtgMmL/U+oFWWFoFqTEzBQnogpicEqVN2xE5uxWRoEoNmGE2zlIRPhs8NQz1xoW0ApSrNHhcqAu02I28I/11AWt6NVOQjUK9ufeZJXfoLRBCCHnK2fwJl5qaikGDBkEk0mUyRCIRBgwYAADo27ev0bE8Hg99+vTh+oQR91LI1oDVQAbMUg8vezbkdnYzbn6VKUjDPmCWivBT9Q1UG4Z4wU8/VrbrPTcF6afPgJnpBZZfqsT1DBkAoHGoD2KCdMEtm20khBBCDNkcgMnlcqP9HwEgODjY6E9DISEhUCqVTg6P1ITKDJgzNWD6VZA2TkFWdsO3IQNmZj9JR8ZmdhWkhQwYG4A1CvXmAjA2A8ZNQXKtKEwzYAv/dxOlSjWaRfiieaQv195DblvnjWoxDIOLaQVGqywJIYQ8uezqQVB1xZyjK+hI3VFrtCjWt6GIroEMmIYLnqrWgOn3g7SlBszJRqxVM2AeRn3AKqdOs2UKZBYrEB0gQaFcBR4PaBBcGYAV6PeDZAMwdgryr4eFeH/TX4gL9kZUgASPC+XY8Vc6eDxg8YhWEPB58NcvNHBVAHYkJQeT1lzA2A7RWDqqtWsuSgghpM7QbtnPmCyZElrwIBLwEOwtdvg6QoH5GjBL9Vt2TUFqTVdS2oOrATPXhoJfWYT/9vqL+OthEd7u1RCALiMo8RBwY+XGrs/eNQn3gaeIj7IKDXZezjB53fFdYtE22h8A4C/RZcAqtDwo1VqIHNtyk3MrS7fJ933a4JsQQp4KdgVg69evx5kzZ7iv2V5ggwcPNjnWsE8YcR9sXVOkn4TLFDmCDXJMO+FbmoJktyOypQhffw0Hx8dNQapNV0EatqH462ERAOD/jqUCABqG6FYu+lUJwLz1Yw/08sDR6X1w+VEh7uWV4X5uGTKLFfCTiNA03Adv9WzAnePjKQSPBzCMLuvnLXE82AWAHJlu3riovMKp6xBCCHEPdgVgd+/eNRtY7du3z+zxNEXpftgmolH6lgqOEhh0wtdqGS6YU1ucgmQ35LZlCtK5VZD8qo1YhdW3oQDA9faqGoCxwSMAhPt5YqBfhE1j8PMUoahchaJyFSLtfA9VZct0/25Fctv20ySEEOLebA7A7t+/X5PjILWEzYA5swISMC6Q1zAM+DDOiFUtoLdvCtK5rYi4KUi19SJ8AZ9nNIVqKQNm2MnfHn4SXQBmS9BZHS4AK1eBYRj65YYQQp5wNgdgMTExNTkOUksy9Cv42JYKjhIYTDFqtAz0vUsNOuGbL8KvjT5gbAaM3VjbuBO+7jm5SmNSv2ZLBsweflIhUKALmpyVrZ+CrFBroVBpIfEQOH1NQgghdYc2437GpOtbKzjTggIwnmI0DGQq94KskgHzrL0+YFWnPw2vwwZ1VbNSPJ7rAzB/ie3d/61hGAY5JZWtL6gOjBBCnny0CvIZw3Zxj3RRDRhg3IrCUvBkVxG+hToyR8YGAB6CymwRe002APMQ8PH5iFbQMgwCvXQrF00CMLHjU5AAUFzuXC+KQrmKywoCujowticZIYSQJxMFYM+Y1ztH49ilm2gQ7OXUdQQ88xkwS8ETVwNmVx8wB6cgq9RHiYSGGTB9AKbPxEnFAox6rp7R8YYBmJDPg6fIsXGwGTBnC+fZ+i8WFeITQsiTjwKwZ8z4LjEIKbyOEB/n2iLw+TyuzYJhKwqNPlMjqNoHjJuCVFdbRG6pjsxWVeM2oyJ8/dQom1Hy8jD9X8AwANO1k3BsHH4umoKsGoAV0xQkIYQ88agGjDiscjuiyscsrWBke2lptAwUKtMWEIac3Yqoav2ZURF+laBOaqaY3dcoAHO8gyp7HWeL8NkeYCzKgBFCyJOPAjDisMrtiCoDKq4PWJU0lKdBEMTu0WiJs1sRVc2Aicy0oWCZC8A8RQIuaHO0AB9wXRG+yRSkC1ZVEkIIqVsUgBGHsRkqo1WQGvM1YEIBn5sKLK82AHPNVkQsowCMXzUDZj7AYqcPvcWOB2B+UhcFYCVUA0YIIU8bhz7hrl+/jl9++QUymYx7rLy8HFOmTEFUVBQaN26Mn376yWWDJO6JjWUMV0GqrGSv2GL26gIwlda5rYisT0EaP+clNt9Piw3AnJmCdF0Rvm4KMtxXt3KVasAIIeTJ51AAtmjRIsyaNQs+Pj7cY3PmzMEPP/yAkpISPHz4EG+//TYOHTrksoES98NO5xlmwDRWWkiwzUPZLYIscToDZlKEzzN4zr4MmK8TU5CuKsLP0U9Bxofr/n+jDBghhDz5HPqEO3fuHPr06cOtDlOpVFi9ejU6deqEnJwc3L9/HyEhIfjqq69cOljiXrgaMI25NhSm31oSfbv86mrArGXRbFF1k3HDQK5qEX71GTBnasB058oUapOu+/ZgM2BNwnSNYikAI4SQJ59DAVh2djbq16/PfX327FmUlJTg7bffhqenJyIjIzFs2DBcuXLFZQMl7odbBcmYNmI1PwWpz4BVVwPm7CrIKjVgHkJrRfjV1IA5EYAZrqZ0dD9IjZZBbqkuAIsP02fAqAifEEKeeA59wgkEAiiVlUvjT5w4AR6Phz59+nCPBQUFIS8vz/kRErdVuQrS3BSkmQyYjVOQKie3Iqo6zWiUAavynJeFPRW7NwqG1EOAzg2CHBoD+7pige5+OBo05ZcpodEy4POAhvqtkorlVANGCCFPOod+vY+NjcWRI0e4r7du3Yq4uDijDbvT09MRFOT4hxdxf2wGTGPQhsLa9KHE1gyYk5txm2xFZCUDJrGQARv5XD281C7K5Fr28hICSg1QJK8AYN/uAxVqLRb+cRMAEOkvQbCXrnkuZcAIIeTJ59An3Ouvv44rV66gc+fO6NmzJy5fvoxx48YZHXPp0iU0btzYJYMk7olvpgaMzYaZy17ZXQPmsr0gDQMw22rAzF3HEVJ9fGdL0MQwDNKLysHop3T/sfEv7L6SAZGAh09eaMa1tZBXaKBUW7+HhBBC3JtDAdi7776L0aNH4/z58zh58iQGDBiAOXPmcM+fP38e169fx/PPP++ygRL3U5kBM+0DVrUVBAB46qf75NWtgtQ6twrSZC9IB/qAuYpUqHsvxTYUzs/ZcQ3dlhzG3uQsZBUrsO96Fvg8YNX4jhjYMgI+YiHX+sPZlZWEEELqlkOfPmKxGJs3b4ZMJgOPxzNqRwEAcXFx+OuvvxAbG+uKMRI3xQZZGsMifCs9vKQ2TkE6uwqy6msbTUFWCQwt1YC5CpcBq6Zu64+rGdh47hEAYF9yFth30CTcFz3jQwDoMo5+EhEK5SoUy1UI9fGsqWETQgipYU79+u/r62v28eDgYAQHBztzafIEEJopwrfWw4stwlfY2gfMwVWQpm0oeGb/DgBSJzrd28KWKciMonLM2X6N+/rc/QKE+erqvdrX9zc61l/qgUK5iurACCHkCefQJ1x6ejqOHz8OuVzOPabVarF06VJ069YNiYmJ2Ldvn8sGSdwTWyOlMVMDZq5+yuYifK2Te0FWbUNhEAzyeDyjsdVeBsx8wKTRMvjn5suQKdRoFeUHkYCHLJkC/7uaCQBoXz/A6Hg/F3XXJ4QQUrccCsD+9a9/4aWXXoJIVNnnaNGiRZg9ezZOnz6NQ4cOYejQobhw4YLLBkrcj7k2FGorLSRs6QPGMAxU3CpIB6cgBZanIAHjKUpJDQdgnvo2FKVKtdnnfzx+D2fvF0DqIcA349qhTT1/AEBGsa77ffsY4wDMX8oGYNSKghBCnmQOBWCnT59Gv379uABMq9Xi22+/RdOmTfHw4UOcO3cOUqkUX375pUsHS9yLwEwRPtcJ38oUZHmF1uQ5luG1HJ6CtFKEX/VrrxouwhfrX8pc77OH+XL8+0AKAGD+iy0QF+yFTnGB3POBXh6IDZIanePvou2NCCGE1C2HPuEyMzONCuwvXbqEvLw8TJs2DfXq1UOHDh3w0ksv4ezZs64aJ3FDlTVglQGV1b0gbWhDYZhNc3gK0koj1qrXlVppQ+EKbIJNXmGaATt6OwdqLYMOMQEY3aEeABgFYO2i/bntvlj+Ug8AQF4pZcAIIcSSuzklSPzPMczefrWuh2KRQwGYRqOB1uBDl+2Eb9h2IioqCllZWc6PkLgtQZWtiBiGsSkAszYFya6ABFzZhsL4a6FRDVjNZsA89G/BXOuNM/fyAQC94kO4QOu5mACu1UTV6UcAiA7UZcQeFchNniOEEALcyS7Byz+exZ2cUmy58Lja3VfqikOfcPXr18e5c+e4r3fu3ImIiAg0adKEeywrKwv+/v5OD5C4L2GVRqzG2SvLfcCs/c9g2NTV0Uashud5CPgmWSTDVhRsUFhTKjNgxu+ZYRicu18AAEgw2O7Ix1OEhLgg8Hi67ZCqYqckH+SX1dCICSHkyaXWaDHh5/PI0++hq9EyuJ5RXMejMs+hX/9HjhyJRYsWYfTo0fD09MTJkyfxzjvvGB2TnJyMBg0auGSQxD1xfcD0gVd1wZNNGTCDzKqjnegNzzO3GICdgpR6CExaVriamMuAGU9BpuaWIq+0AmIhH22i/Yye+/aVdsgoKkdrfUG+oZgg3XZGaflyMAxjElwSQsiz7Oz9AqQXlcNfKkKTMB+cvV+Ay4+K0CE2sPqTa5lDAdj06dNx4MABbNu2DQDQqlUrzJ8/n3v+5s2bOH/+PGbPnu2SQRL3xCa52MyXYfBkbS9IqzVgmsqtjBwNLgyDqqorIHXX1j1W013wAcBDvwqyatbvzD1d9qt9/QCIhcZZuGBvMYK9xWavFx0oAY+nW1WZX1Zh8ThCCHkW7bmma+EzsEU4ogOlOHu/AFceP0UZMF9fX5w5cwbJyckAgGbNmkEgqPwQkUgk2LFjBzp06OCaURK3JKySAdMYZcDMrYLUPWYuA8YwDMpVmsqNuB1cAQkY9wEzV0fGZuekNdyCAjDIgKmqBmC6+q+EBvb9ViYWChDpJ0F6UTnS8ssoACOEuD2VRov5u6+jYYg3JnWPq7HX0WgZ7L+uqz0f1CqC+yy48qioxl7TGU6lAFq2bGn28djYWNqG6BlQtQ8YmwHj8cxPH3J9wMzUgE3/7Sr+dy0DP72hC9odXQFpOC7AfAZMyGXAaj4AM1cDxjAMzrL1X3FB5k6zKjZYivSicjzIk+O5GPdLqxNCiKE91zLx69mHEPJ5eCWhPvdZ4Grn7hcgr7QCfhIRujYM4n7uPiyQo6CsAoFeHjXyuo5yPM1AnnlsJklbpQbMUv8uazVgF9IKoFBpcT1DpruGgysggSoBmJnrsHVhXjW8DRFQuQqyQq3lMoVFchVyS3QFou2qbDVkC7YOjArxCSFPgl/PPASg+2X9Rqasxl5n51/pAID+zcMgEvDhJxGhQYju5+XVx0U19rqOcvhTrqSkBIsXL0bfvn3RrFkzNGjQwOS/hg0bOnTt0tJSvP/++4iMjISnpyfatm2LTZs22XRuTk4OJkyYgODgYEilUnTp0gWHDh0ye2xSUhK6dOkCqVSK4OBgTJgwATk5OSbHqVQqLFiwALGxsRCLxWjatCm+/fbbasfy2muvgcfjYciQITaN/UlTNQOmsbINEWCwF6SZAIxtLCrT/+noCkjd61f+3VwgJ6jFKUgPg5dnC/HZfRy9PAQO/SZYuRKSWlEQQqq351om3vrlAnJkilp/7ZSsEpx7UMB9fbUGpgMr1Fp8vOMaNl94BAAY2jaSe66tfjHTlUfuVwfmUAogNzcXXbt2RWpqKnx9fSGTyeDn54eKigqUl5cDACIjI422KrLHiBEjcP78eSxZsgTx8fHYsGEDxo0bB61Wi1deecXieUqlEn379kVRURGWL1+O0NBQrFixAgMHDkRSUhJ69erFHXvs2DEMGjQIL7zwAnbt2oWcnBx89NFH6Nu3Ly5cuACxuLK2ZurUqVi3bh0+++wzdOzYEfv378d7772HkpISzJkzx+xY/ve//2Hnzp0WNyx/GrDThBr91CPbw8vS9CGbAVNpGKg0Wi440moZLvAqUeiCFOcyYJXnmi3C1z9f0z3AdK+lm5JlGN3Uq4+niHuv7L6O9qpcCUkZMEJI9ZYn3UFKdgl8xEL8Z2zbWn3t9WfSAOh+8dVoGZcXxDMMgw9/u4Lfr2SAxwOm929i1MKnTbQ/tv+VjitPSwZs/vz5SE1NxS+//ILCwkIAwD//+U+UlZXh7Nmz6NSpE2JjY3H9+nW7r71nzx4cPHgQK1euxN///nf06dMHP/30ExITEzFjxgxoNJZX0K1atQrJycnYsmULXn31VSQmJmLr1q2Ij4/HzJkzjY6dMWMG4uPjsXXrViQmJuLVV1/Fli1bkJycjNWrV3PHXb9+HatWrcK8efMwY8YM9O7dG4sXL8abb76JhQsXoqCgoOowUFxcjL///e/47LPPEBBg2kzzacE2PGUzYOyfloInw2yPYRasRKkG20JMptBnwJypATMqwrfShqKGu+ADuuBLqn/fbD0Cm+3zdTAAiwvWBWD388rAMEw1RxNCnmUKlQZ3c0sBADsupyM5vXYyQcXlKszefg3r9AHYpG6xAFxfEL/q5H38fiUDQj4PP73eAe/0aWS0gr5FpC4JcqsGpz4d5VAAtmfPHvTt25ebYjPUsWNH7N27Fw8ePDBqTWGrHTt2wNvbG6NHjzZ6fOLEicjIyLC6vdGOHTvQpEkTdOnShXtMKBTitddew7lz55CerpsfTk9Px/nz5/H6669DKKzMgnTt2hXx8fHYsWMH99jOnTvBMAwmTpxoMp7y8nLs27fPZBwffvghIiIi8I9//MO+N/+EEVbZC5KtAbM0BSkW8sF+uxjWgckM9jV0xRQkv5opSLYIvzYyYEDlVGeZfgrS2QCsvr4bfolCjSK55T0hLz8qwtJ9t1BQRtsWEfKsupVVwv2MZhhgyd5btfKL27sbLmHjOV3t1xtdYjCldyMAwL28MhRb+bllj1OpeVi89xYAYO6LzdGveZjJMfHhPgCAjGKFy17XVRz6BMrMzDQKkAQCATf1CAABAQEYNGgQfvvtN3zxxRd2XTs5ORnNmjUzCowAoHXr1tzzXbt2tXhujx49TB5nz71+/TqioqK49hns41WP/fPPP42uGRISgvDwcIvjMZSUlIRffvkF58+fN2rNUR2lUgmlUsl9LZPponWVSgWVynXfNOy1XHFNHvSrH9UaqFQqKCp0H/RCPs/i9SUiAeQVGpTIlQjw1N2f/JLK7x3DAMzRMTIGWVKRwPQ6Ap5u3GKh469hC/babOavRK6ESqVCYZmuDsNXLHDo9QUAwn3FyJIpcTe7GG2j/U2OuZdbhtf+exalSjVO3c3DuokduBq8p40rv6eJdXSva4cr7/PVR7pZmvhQb9zPL8PJu3m48rCAywzVhKuPi3HiTh6EfB7WTHgOCfo9bqMDJHhUWI5Lafno3sj+FeCGMosVeHfDJWi0DF5qE4GXn4s0e78kAqCevyceFymQnF6ATlUastbE97St13IoAPPz8zN6gYCAADx+/NjoGF9fX2RnZ9t97fz8fLMd9AMDA7nnrZ3LHmftXPZPS8cavoala3p5ecHDw8Po2NLSUrz11luYPn062rRpY3Gc5ixevBgLFiwwefzAgQOQSqV2XcsWBw8edPoaDx/wAfBx+24q9qju4H4JAAhRoSjHnj17zJ7D1woA8HDg8FFE6t9WSjEPurACSM8tBMBDWWmJxWtUJ0uuGwcAFObnmlwnP1c37vQHqdiz565Dr2EPjVIOgIdjf55B9nUG59J171eWn+3we/SG7j3sPnwaGSHGv80q1MB/kgUoVep74DwuxqvfHcTEJlo4MbPr9lzxPU1sQ/e6drjiPu9L1f2sqC+UwdMPuFrAx4rdpzCkvrbacx215rbuNdsFaZB/8wz23NQ9HsLn4xH42Hr4HGS3Hc/CqbTAN8kCFJTxECVl0E38CHv3PrJ4vD+Pj8fgY/uhs8iLMP+6rvyelsttWyDlUADWoEEDPHjwgPu6Xbt2OHjwIAoKChAYGIjy8nL8/vvvqF+/viOXt9oBvbru6Paca+lYW4+r+tysWbMgEokwd+5cq2M0Z/bs2fjggw+4r2UyGaKjo9G/f3+XFvKrVCocPHgQiYmJDi+SYN04cAeHM+8jJjYOgwc1wfkHhUDyefh6e2Hw4O5mz/ni5nGUFinQsXM3tKmn24KHl5wF3NDvWC/yBMqVCArwx+DBCQ6N635eGRZf0WUxoyLCMXhwW6PnD5RcxdWCLLRt2QyD9XUJNYG916GBfsiQy9CybXv0bx6G5P23gYcP0LxRLAYPburQtf+suI67F9MREB2Pwc8brzb+4fh9ZJffQZivGJ8MbooPt17DtULgUGkkvhzV0uw+nU8yV35PE+voXtcOV97nVf93BoAML3ZvCw3D4IPfruGOwhuDBnWrka3MHheW48qZEwCAuWO6oal+ChAAsv3TcGlvCko9wzB4cHuHX+OTXdfxsCwdfhIh1r3dGdEB1pMUKR53kXzsHoTB9TF4cAuj52rie5qdwaqOQwFY//798dVXX0Eul0MqleLvf/87Ro0ahTZt2qBz5864dOkSHjx4gEWLFtl97aCgILNZLrbY3Vw2yt5zg4J0qU9Lxxq+RlBQEC5fvmxyXFlZGSoqKrhjz507h5UrV2L79u1QKBRQKHTTTFqtFmq1GkVFRZBIJEarKw2JxWKzz4lEohr5QeeK64r0W+gw4OmuxePrH+dbvLZEX3el0vK4Y8pUlb+RsKsgPaxcozpij8rzPEVCk+uM6hiN3NIKDGjp+Epde0j1/cYqtLr7Xqovxg/w8nT49eNCdD/UHhaWm1zjdo5udeTEbnF4sW09eHqIMPXXi/hfchZEQj6+frmdo2/FrdXU/yvEFN3r2uHsfVZptLiVrSvAb1M/EEHeHvDYcR0P8uW4V6BA03DjX+5zShT47cJjFJZVYNrzjeEntf+1vzlyHVoG6NE4GK2ijT+vn28Whs/3puBkaj7y5RqE+3naff2N5x5i84V08HjAN+Pao0GoX7XntIjyBwCkZJdZvJ+u/J629ToO/Sr89ttv46effuLSbCNGjMCyZctQWlqKbdu2ISsrCx988AFmzJhh97VbtWqFmzdvQq023rz42rVrACx332fPZY+zdi77p6VjDV+jVatWyM3NRVZWltVr3rhxAwzDYPjw4QgICOD+e/ToEfbv34+AgAB8//331t/8E6ayD5i+DYW2+m2EpAa9wIrLVWAYhitKB4AyfXDizFZE/Gq2IurTJBRb3u6CWP1qwppWdRWkrFz3ve0ncXwRgLVeYGx7ilh9u4rE5mH4/tXnIOTzsPNyBq656b5ohBDXSs0tRYVaC2+xEPUDpfDxFKFn4xAAwJ5rlZ9pDMNgedIddFtyGMv2p+C/J+9j4PLjOJ1queTHnF2X07Hjr3TwecD7/eJNnm8U6oNOcYHQaBlsOv/Q7veTklWCebt13RWm92+CXvEhNp3XNMKHO59tHO4OHPqUi4iIwNixYxEcXNlr48MPP0ReXh4yMzNRWlqKZcuW2VWEzho+fDgXyBlau3YtIiMjkZBgeVpq+PDhuHXrltFKSbVajfXr1yMhIQGRkbrmbFFRUejUqRPWr19v1NbizJkzSElJwYgRI7jHhg0bBh6Ph7Vr1xq91po1ayCRSDBw4EAAwMCBA3HkyBGT/8LCwtC5c2ccOXIEo0aNsvt+uLOqqyDZfmDWWkiwBen7r2ehzYIDWHk01SgA467tRLGS4bnm+oDVNrb4vdxFbSgAy73AGIbB/Tx9ABZcmZbv1zwMQ1pHAADWnHrg8OsSQupeQVkF93PXmuvpuqmw5pG+4Ot/Xg9upVtQtu3iYzzMl6NCrcXcXdfxVdJtqDQM2tf3R1ywFzKLFXht1Vn8/Od9m1ZN3s4uwcc7dIvS/tG3MZ6LMd+C6bXOMQCATeceQa2xvQ5Nqdbg/c2XUaHWoneTEEzpZXuj99ggL4iFfJSrNEgrcJ8G1i5dhy8QCBAWZroM1B6DBg1CYmIipkyZAplMhkaNGmHjxo3Yt28f1q9fzwV1kydPxtq1a5GamoqYGN0/6KRJk7BixQqMHj0aS5YsQWhoKFauXImUlBQkJSUZvc7SpUuRmJiI0aNHY+rUqcjJycGsWbPQsmVLo5YTLVq0wOTJkzFv3jwIBAJ07NgRBw4cwI8//oiFCxdyU5Dh4eEmKyUBwNPTE0FBQejdu7dT98UdCQTGAViF2nofMKCyGWvSTd2OA2fu5SM60HT+3qlGrAYZMA83qDpns35V+4A52ogVAGL0GbBCuQrFchU3VVAkV0Gmn8aNCTTO8I3vGoudlzPw+5UMzB7clDbyJuQJlHQjG2+tu4D3+jY2m2UydFnfc8twxWO/5mEI9REjvagcL3xzAjweIFOoweMBnw1ridc6x0BeocYnO5Ox/VI6Fvx+A/mlFZg+oInF1zl5Jw9T1l9EqVKNDjEBeLdPI4vHDmwRjiAvD2TJFEi6mYOBLU0/N8359tBd3MyUIUAqwhejWnMBpS0EfB6ahPvg6uNi3MqUcb0U61rdpwfM2L59O15//XXMnTsXAwcOxNmzZ7Fx40a8+uqr3DEajQYajcYoMheLxTh06BD69OmDadOm4cUXX0RmZib27t1r1AUfAHr37o09e/YgMzMTL774IqZNm4Y+ffrg0KFDJrVYK1euxKxZs/Dtt9+if//+2Lp1K5YvX26xC/6zQlhlKyKlWhdgeIqqD8DySnUtN7KKFeYzYE71AbM+BVnbpPq6N7YPGNts1pkAzEssRIiP7vs0raAyC8buDxnu62nSdqJd/QC0ifZHhUaLTefMp/9VGi23owEhxP38cDwVDAMcvmW6bZ4hhmG4Y7o1rJyt8vUUYcc73dCuvj9KlGrIFGoEe4vx3bj2XHZK6iHEv0e3wexBukVCPx6/x+1fW9Wmcw8x4edzKFGq0TE2AD+90cHqQh8PIR9jOkYDAH49m2bTe9ZoGa6h62cvtUSoj/21Y+xiAHa/YXdgcwasefPmdl+cx+M51A3f29sby5cvx/Llyy0es2bNGqxZs8bk8bCwMJPpQksSExORmJhY7XEikQjz5893qLGs4WrRpw275Y+GC8B0H9xioeWp56pBQWaxwmwhpjOBk2Hw5hZTkCLXT0ECujqw3BIlHuTL0Vq/3xkbgBlOPxoa3yUGHzwqwva/0vHu842NnlNrtBjyzUmotFrse6+nW9w7Qkil29klutXm+r9rtIzFxte3skqQXlQOsZCPbgZb8wBAlL8EW/7eBX9czUCEnwQdYwNNrsPj8fD3Xg2xNzkLlx8VYeO5h/hH38qfGRlF5fi/Y6n45bQuMHqpbSSWjmpt9ec/65VO9fF/x1Jx4k4eHuSVVVuPezNThuJyFbzFQgxsYVvGrKp29QOw5cJjo30p65rNAditW7fA4/Fo6xPCYWf3uAyYvru92MoHd9XNp0uVajwuLDc5zpkaMPfLgFVOQRrue+lMBgzQ1YGdf1CItDyDDFierr6BLcCv6vmmoQB0jVpzS5RcFg0AbmaWICW7BABwK0vGBXWEEPew0SBzrVBpkZZfhgYh3maPTbqh68PZo3Gw2SbMIgEfw9vVq/Y1J3aLxXubLmP9mTS83ashMorK8VXSbfxxNZP75Vs3HdrY5rYW0YFS9I4PwZGUXGw49xBzBjezejy7GKBjbIDDbXQ6N9B1P7j8sAgKlcbks6gu2PVOhEIhhg0bhp07d0KtVkOr1Vb7H3l6CfT/I2g0VTNg1U9BGnpgZlNpZ1ZBGtWAuUEWx7AIv6yict9LZwMwcysh2XsZYyEA85d6cKn4C1V+Ezxv8LWrN8wlhDhHodJg+yXddnpsmcetrBKLxyfppx/7NnOyLrtlBEJ8xMgpUaL/V8fQ9z/HsOtyBjRaBl0aBOHniR3xz8R4u3uKvZqgm+787cIjo72BzTl9TxeAdWnoePf82CApwnzFqNBocelhocPXcSWbP52uXr2KKVOm4M8//8Tw4cMRFRWFjz76CCkpKTU5PuLGuFWQjD1TkKbfcuaSquY20baVYSrdww0yYBKuDYWam370EPKd/g3M3EpINhiLszAFCQAd9VtxnL1vHIBdTKv8oXTVxRvmEkKcc/x2LorLVYjyl+DF1roV/ZY2mM6RKbhNr/vqs96O8hDyMb6LLlh6kC+HRsugd5MQ/DGtOzb+rTP6NHHs+n2ahiLSzxOFchV2/pVu8nxxuQrfHLqDe7mlOKf/WdWlQbDJcbbi8XhcFuyMne01aorNn04tW7bE119/jfT0dGzevBnt2rXDf/7zHzRv3hxdu3bFf//7X5SWltbkWImbEVRpQ8FOQdpShF8dZ6YgBUZTkO61CpKr//J0vuEfu5LHMIOYVk0GDAA66fdlM8x4MQxj9PVVyoAR4lZO3MkDoCsjaK5f1XjTQgZs/3Vdj6829fwQ6mt/wXpVf+/VEMtfbotV4zvg+Iw+WDOxE1pGVd8A1RoBn4dJ3eMAAN8evsst4mL9cCwV/zl4G8O++xOlSjV8PYXc+3ZUFzYAu+cedWB2pwdEIhFGjRqFPXv2IC0tDZ9++iny8vLwt7/9DeHh4ZgwYYLJvpDk6WS6ClKfAbMSZNma9XHVFKTInaYgVRqDFhTOd4Cpr5+CzCutQIlChSJ5BYrkuuuzbSrMYQOwm5kylOhXZD4qKEdOiRJs7HonpwTyCrWlSxBCrPj5z/tYuu+WS5t+nriTC0BX08V2sL+VZT4D9ttF3Wfw0LZRLnltkYCPYW2j0LdZGPdzxxVe6xzDtcTYct54L0d2BWeJUvdzqFNckMUFB7bi6sAeFXGLouqSU59OkZGR+Pjjj3H79m3s27cPAQEBWLduHS5duuSq8RE3VpkB0wVeChuK8A2LQcMNfjOrWqvlTOaK72ZTkIad8Cu74DufAfP1FCHIywOArvienX4M8xVzrS/MCfP1RP1AKbRM5bQjm/1qG+2PMF8xtAyQnO4+y7UJeVLklijx6R838P3RVJetuHuYr/v/W8DnoUvDIK6O81FBOfdLFOtmpgxXHxdDyOfhpbaRLnn9muIpEmDa87qeYd8evst9hmQVK3ArqwQ8HhCpXyXfM97x6UdWTJAU4b6eqNBo8Zcb1IE5/en0119/Ydq0aXjllVeQnp6OsLAwREW5Juom7o3bisjBIvyuBgWVAVKR0XnObhjNjs0divC5KUilmlsB6WwLClaDEN1U4728UtzL1ZUAWJt+ZLFZsFUn7+NRgRxHb+t+u+4QG8itfrz6uMglYyTkWXLkVg5X17rnWqZLrnniru7/z/b1/eHjKUKAlwf3C+ztbONpyN8u6LJf/ZqFIegJaLY8pmM0ovwlyClRYr2+19fRFF32q220P7ZP7YYlI1rh5Y71nX4tHo+H9jH+ANyjH5hDn04FBQX49ttv0a5dO3To0AE//vgjevTogV27duHRo0d47rnnXD1O4oaqbkVkUxG+QQBmuKLFTyLiAhUAEDmZamYDMHdoQ8Fm/eRGU5CuCcAa6pegp+aW4bZ+090mYT7Vnje8ne6XpBN38tDjiyP4/UoGAKBDTADa1NPVdtBKSELsd/BmNvf3vclZNm0ZVJ0Tt3X1Xz0aV+592ESfBdt68TH3GgqVBjsv6wrax3SsvsWEOxALBXhP31/s+6OpKFOqcUQfgPWOD0W4nyde7lTfZb9MNwnTTd+mZFteQVpbbH5HDMNg3759GDNmDKKiovDee+9BrVZj2bJlePz4MXbs2IEXX3zRof0fyZOJa8TK2N4J31MfjHgI+WhvsFeYLgCrnDZzOgOmrwNziylIgyJ8V3TBN8RmwFJzS7nfhOPDqw/AujUKxo6pXblgK8RHjHGdotG7SSiXAbtGGTBC7KJQaXBSXywvEvCQW6I0afdiL5VGi1OpbABWOQ03TD+9uPHcI7z1ywUUllXg0z9uoKCsAuG+ntym20+CEe2jEBskRX5ZBb7Ydwt/3tWtUuzT1PXvoUm47pfWFCstPGqLzZXA9evXR0ZGBvz8/DBhwgRMmjQJHTt2rMmxETdnkgFTVZ8BC9NvIdEswheRfhLucT+JCAVlFZXXdnL1IpcBc4MpSDbrV6HWcu/RFasggcoM2L3cMm5605YMGKDrDL3znW4okqvgLxVxfXzYlUZpBXKUKdXwErt0y1hCnlqnUvNQrtIg0s8TnRsGYfuldOy5lomEBo73rzp0M4fbLsiwOfKI9vXA5/Hw0barOHwrB72WHeH2dFwyspXTv8TWJqGAj/f7xeP9zZexVt9ZP9jbAy0jnVtpaU68/ufjnZwSl2QnnWHzT9b09HSIRCK0adMGaWlpmDdvXrXn8Hg8/O9//3NqgMR9Va0BU+gzYGIrGbBmET5YNb4DGof6QOIhQIBUhEK5Cr5VMmAiJ1ZBGo7NnTJgAJAtUwBwZQZMPwWZU4oK/R6O8WHmO2Obw+PxEKAv5GcFe4sR4iNGbokSKdklaF8/wMLZhBBDSTd1U2fPNwtF36Zh2H4pHX9czcRHg5paXRhjzZYLutWBo56rZ7IK8KV2UWgU6o33N1/G3RxdCcL7fePR28HeXHXpxTaROHs/H8npMmgZBuO7xNq14batYoK8IBbyoVBp8bBAjnp+HtWfVEPs+o5QqVQ4duyYzcfb2xmXPFlM+4BVX4TP4/GMOjNH+ElQKFfBTyIyWiHpqgyYh7DuvwfFQj54PF3D2cxi1wZg0QESiAQ8LvgK8xXDX+r8D5Sm4T7ILVHiZqbMJAA7d78AD/LL0L95mEtei5CngVqjxQF9/61+zcLQrVEw6gdK8bBAjp//fIB3+jSy+5qZxQquIH1MB/M1XS2j/PDHtO746fg9aBiGW1X4pBHweVg8onWtvE7jMG8kp8uQklWCen6OZyedZXMAdv/+/ZocB3kCcRkwfRsKW4rwq4r098SNTJkuABMZBmDOZa74PPcpwufxeJCKBCir0HAZMFetghQK+IgJ8uJ++423cfqxOs0jfHHiTh5uZRrXSeSUKPDaqrOoUGvxiYCPid1iMWtQU/plizzzztwrQF5pBfylInRrFAyRgI8P+8fjvU2X8X/HUvFqQn27f2HZ/lcGtIxu1bKlPR8BfTsHg42yiXXxYT5ITpfhdnYJ+jZ5AgKwmJiYmhwHeQKZroKsvg9YVb2bhOLk3Tx0igs0Kop0dhVkyyhfXEwrRExg9S0ZaoNULERZhQZ5pfoaMBc0YmU1CK4MwGyt/6pOswh9p+0qW52sO52GCrUWHkI+KtRa/HD8HuoFSvF6Z/r5QJ5tu6/oVh8OahnB/eL3YutIfH80FbeySvDD8Xv4aGBTm6+n1gKb9S0lxnaIdv2An2FsH7W6LsSv+/QAeWIJLO0Facceh691jkHy/AHo2jDYpRmwVeM74szsvvCTuibT5CzDOjAeD2gQbHudVnUahlZey1UZsKYRuuvcyioBo//3La/QcH16vh7bFrMH6T5MPvv9hls0NSSkrijVGuxL1k0/Dm1T2fyUz+dhqn7q8VhKrl3XPJ3DQ2axAmG+YrzQOsJ1gyXcz8m6bkVBARhxGLtdkEZT2YMGsC8DBlQGW4Y1YM7u4Sjg89xq9Z5hcNmzcQjC/Zzfn43VILgyy2dLCwpbNAzxhkjAQ6lSjceF5QCArZceo1CuQnSgBANahONvPRtgQIswVGi0ePW/Z7HrsumGuoQ8rZRqDffLyfHbeZAp1AjzFXNNjlmt9Hsm3ssrtXlrovIKDQ481v1cnPZ8Y5u3cCO2YXuo3c8r4xIHdYECMOIwdqFi1b0grfUBs8YwS+TMXpDuyLCJ4LhOrp1OMMyANQ51TWZNJOCjUajuh9SNTBkuPSzEF3tvAQAmd4uDgM8Dj8fDl6PboFujIMgrNHhv02XsdVHnb0JqUolChZ+O30OhQesbe1x7XIxOiw5h8toLAMA1Mn6hVaTJSsXoAAk8BLpVd+lF5TZdf8P5R5CpeKjn74kxNP3ocuG+nvD1FEKjZXAvt6zOxvF0fcqRWsVlwOzoA2aN8RTk01XUfdWgq7zhKlBXaBHpi9b1/PBS20iXZv2a6achfzx+D+NXnUOJUo2EuEC83KlySxAfTxF+mZTANYU8auc0CyF14bsjd7Foz0189scNu8/Nlinw5i/nUVyuwuFbObj6uAgHb+i63w81s/eiUMBHbLBuA+tU/XZh1ihUGqw6+QAAMLV3Q7fYTu1pw+Px8H6/eCwd2QqhvnW3XRP9yxKHVa6CZMAwjENF+IYkhn3AnrIAjO3N1blBoMtXZoqFAux+tzu+frmdS6/bXF+IfzGtkAu+fp7Y0WQ6RMDnoXsjXYfujGLbfsMnpC4dvaX7RWHf9SyUV2hsOqe8QoNfz6bh5R/PIFum5B7/5+bLKFdpUD9Qyu0sUZXhlmHV2X4pHbmlFfD3YDCsDdV+1ZRJ3eMwtmN9BHk9IX3ACDHEroLUahmotQzY8gZ7ivANPc1TkF+PbYddl9Px7hPUo2dMx2g8LiyHkM9DfJgPhraNtFiLEuWv29Ugw8YpFkLqSlaxgiu+lldokHQzGy+2Mc1cGdJqGYxffQ7n9NsKBXt74O89G2LRnptcUPVimwiL7Vga6UsD2NXKlqg1WvxwPBUA0CdSS9mvpxwFYMRhhhkwtgAfcCID9hRPQTaP9OW2+HlS+HqKMH9oC5uOjeQCMAUYhqG+YMRtHb9jPE2++0pGtQHY7isZOPegAF4eAvwzMR6jnqsHqYcQ3x9L5bYXG9omyuL5lRkw6wHY3uQspOXLESAVoUuo2pa3Q55gFF4Th7FBkkbLGK0kcXwK0nAVJH1rPknYVZ3lKg2K5Ko6Hg15ltzPK8O7Gy5h1UnbmoWf0G+W3b+5rhbzaEoOvk66jRVH7uJ6RjG3spGlUGnwxT7dApSpfRrhzR4N4C/1gIeQz9U+Ngnz4VbWmVO5Z6txAKbWaPHtoTv49WwaGIbB90d12a/XO9eHmBY+PvUoA0YcJuBVdsJnAzAPId/h7IfxFCRlUJ4kniIBgr09kFdagYzicpP9JR219eJjnLyTi89HtHJ4Lz3y9Fp/Jg2f/XEDSrUWB25k45VO9Y1+katKo2VwUp8Be6tnA6Tly5GSXYKvk+4AAJbtT0GUvwT9moViQMtwNA33xYLfryOjWIFIP09M7h5ndL0pvRsip0SJVwwWppjTIETXKiavtAJF8gr4Sz3AMAw+3pGMzfq9Hq9nyHAjUwaphwCvJUTj9NEUh+8LeTLQTzTiMHYKUsuAK2R1NPsFGE9BUgbsyRPpL9EFYEUKtIg0X4xsjxsZMszadhVqLYOEBkEYV82HHHm2JKcX41+7ksEmrCrUWpy9n89tRL0vOQv7r2dhzuBmCPERc+cUylXwEQvRNtof84e2wJpT9+Ev8UB+WQVO3s1FelE51p5Ow9rTadwergDw8QvNTWogQ308seKV9tWO1UssRKSfJzKKFUjNLcVzMYH4z8HbXPAFABvOPgQAjOtUHwG0x+ozgQIw4jDDQnk2AHOmYaArN+MmtS/ST4Krj4tdUoiv1mjxkT74AoD917MoACMchmHw2R83wDDAkNYR8PIQYvOFRzh+Ow+9m4QiLb8M72/+i+u99eubCRAJ+Nit79fVMz4EIgEfXRoGoUvDyr0Ayys0+PNuHg7cyMKBG9kokqvQMMQLi4a3QucGzu0Z2DDUGxnFCtzMLMGNDBm+PXwXAPDZsBbYevExrjwuhkjAw5s94qq5EnlaUABGHCYwCJLKKnQFo05lwJ7iVZDPggh/XR2YvQGYSqOFRssYBe/rz6ThWnoxJCIBylUanLqbjxKFCj6e7rG1lK0YhsGBG9mQlaswmhpqusz+69k4e78AYiEfswc3w+WHRdh84RFO3MmFVsvgo21XodD3JTx3vwBL997CR4Oacrs1DG9nvmBe4iFAv+Zh6Nc8DIs0WjwskCM6QOqS1YiNQ31w4k4e5u5KBltl9s9+8Xi9Syx6NA7BOxsuYWCLcET4SaBSUR3ls4ACMOIwwzotuQsCMKno6e0D9izgWlEUK2w+R6HSoP9Xx8HjAbvf6c7t3bnjsi5TMXNgE6w7nYZ7eWU4djsXQ1pbX63mTh7klWH6b1dwIU23T2brev5WC7WJ7ZYf0tVsvdWjAaL8JfD2EILPA+7klOLTP27gzL0CSEQCfNg/Hgv/dxP/PXkfMoUKeaUVCPb2QK8mIdW+hkjA54rnXWFC11jcypLhVGo+AOC1zvXxj766tjSxwV743z96uOy1yJOB0gzEYXyDYvsyJVsD5qopSPrWfNJEOtAL7HRqPh4WyJGWL8eiPbqu5AVlFbj6uAgAMKhlBBJb6FarHbie7doB17BPdiZzwRcAXKINy13iTnYJbmbKjKbr/KQitIn2BwCsOfUAADBncFO82aMBpvRuCADYcuExAGBY26g6qTGtHyTFhrc64/CHvbBqfAcsGNqS2rU84+hTjjjMbAbMwX0ggSptKGgV5BPHkQDswI3KoGrLhcf4824eTtzJBcMATcN9EO7nif7NwwEAR27lcLstuFJqbinG/N9pbjsZV1CqNVzTzj76bIvhdlTEcVwdV+MQ+BsUq/doXJnV+jBRN7XH/r1bo8r6rZHt69XOQC1oEOKNvs3CTPaMJM8eCsCIw/h8Hthf4NgMmKczGTCRAFIPAYR8HqQu3NOQ1I5IfS+wbJkCao22mqN13cWTbuqCnlZRulWTs7Zfxb7kLADgponaRfsjzFeMEqUaJ/U9nFxp49mHOPegAP/Y+Bfu6DukO+va42JUqLUI9vbgar/YrB5xHMMwXABWdd/FsR2j0SEmAPNfbI5pfRtzjwsFfHzzcju0jfbHsLaRT1xDZPL0ok854hQhnweVhnFJBkzA5+G/b3SAvEIDbwrAnjjB3mKIBLrvh+wSJVcTZsmVx0XILVHCWyzEL5M6Yci3J/GooByPCnQZtF7xugCMz+dhUMsIrDn1AH9czXT5ZuZsZqpcpcHb6y/ijS6xiA/zQecGgQ5PEZ29r8t+dYwN5KbGUrJKoFBpnFop/Ky7ll6MtHw5PEV89KvyfRDlL8HWKV3NnhfkLcbOd7rVxhAJsRllwIhT2DR6mQv6gAFA10bB6NfctR+wpHbw+TxE+Nk+DclO+fVqEoIALw8sGt6Se07qIUCHmEDu6xf1mxIfvJFttO2VszRaBskZugDMRyxEam4Z5u2+jnE/ncG6M2kOX/f8g8oALNLPE8HeHlBrGdzIlLlk3M+qzed1fbP6NQuDF/2SRp5wFIARp7DtIuRKdhUk/Xb/LIvUt6J4VCC3ehzbngGo3BKmd5NQjNC3B+jeKNho6X+76ABE+HmiVKnGsdu5phd00N2cUsgrNPDyEGDrlK4Y1ykaPRoHAwA+/f0GzukzWfbQaBlcfKAruO8Up8uisVOsVx8VuWzsz5rb2SXYpA/AXkmgnnDkyUcBGHEKW0daqnRNBow82RqF6pbt3862vulwSnYJ7uaUwkPAR5+modzjC4e3xNwhzTGvyibgfD4PL7TSZcH+uJrpsvFe0QdELaP80CTcB4tHtMYvkzrhxTaRUGsZTP31Egr1my3b6mamDCVKNXzEQjSL0NUbta7nD4AK8R3FNl7VaBn0bx6Grg2D63pIhDiNPi2JU9harfwyJQDnasDIk69JmK7P1e1qitn/uKILono3CYGvQXNVqYcQk7rHma0fG9xaF4AdvZUDrZYxed4RV/SF8WydFgDweDwsHdkKjUO9kVeqxKd/3LDrmmzWrH1MADdF3ybaz+j1iH2SbubgxJ08eAj4+PiFZnU9HEJcgj4tiVPYPdYeF+pqfpxZBUmefE3CdRmflCzLARjDMPj9qm4l24ttbG+s2jrKDxKRACVKNe7mWs+w2YrNSLWuZ7x3pdRDiGWj24DPA3b8lY5DN21vUcGudnwuJoB7rFWUPwDgXl4Zt2CF2Eap1mDh/3RB8Js94hAT5FXHIyLENSgAI04J9tYFYOn6AIwyYM82NgOWXlQOmcL8dirsSjaJSIC+zULNHmOOUMDnMkmX0pxvaqpQaXArS1cU30Y/RWiobbQ/3uzRAADw8Y5klCktB04Mw3BZuesZumu2jKpsdxDiI0awtxgMU/307LPkRoYMxeXWt91ZffIB0vLlCPURY2qfRrU0MkJqHn1aEqewAVi5yvlO+OTJ5ycVIdxXV4hvqafWbv02Q32bhULqYd9Ktvb1dVklV3SVv/yoCCoNg0AvD9QLMN8y44PEeEQHSpAlU+Ab/fY3hpRqDVYevYuW8/Zj0trzUKg0SNVn51pEGmfVmuq3IUrJopWQgK6x7uBvTmDiz+fAMOanlPNKlfjusO6+zxrUlNrTkKcKfTcTp7BTkCwqwidNwn2QJVPgVlYJnjNoJQHosk5bL+m2hHmprfkNka2pDMCKnB7n90dTAQDPNw212O/LUyTAgqEtMGnNBaw6eR+BXrrO62IhH+lF5dh1OQM5Jbr6x6MpuTh8KwdaBgj29kBolf83mob74OTdPNzMdE2zV2cVl6tQqlRX26+tJjAMg2X7UwDo/i2PpOTg+aam7Wc2n3+EsgoNWkX5OfT9Qog7owCMOCXY28PoawrASNNwHxy7nWu2Dmz35QwUyVWoFyAxWv1oq3b1/QHo2kcUy1Xc5t32uvCgAMdu50LA52Ha89antZ5vGobE5mE4eCMbi/feMnk+xEcMD4EuIPvhmC6oaxbhaxLUNdWviLzlBhkwrZbByz+ewb3cUiR90AvRgdJaff3917ONeqJ9nXQHfZoYB8JaLcP1/XqjSwz4tHUPecpQAEacEuLjafQ1dfkmTbipNuMAjGEYbqPk1zvHOLQXXpC3GLFBUjzIl+OvR4Xo3cT+II5hGPz7wG0AwJgO9Wwq6l70UktIRAKotVp4igRQqrUQC/kY0CIcvZuEYOWRVCw/dAdX9EX9VacfAcMpyBIwDFOnGzGfSs3HTX0AdCQlB2/o902sDaVKNb46qLv/ryTUx45L6bj6uBiHb+UY7XJwSr9Ru4+nEENa275Yg5AnBQVgxCkmGTAqwn/mxesL8VOyjQONi2mFuJEpg6eIj7Edox2+fvv6AXiQL8eea5loXc8fPh72BTJHUnJw+l4+PAR8vPt84+pPABDq64lvxrWz+HyPxsFYblAj1sLMfoONQr0h4PNQKFchp0SJMF9Pk2Nqy4ZzlV3+T93NtzkAq1BrYaFcC+fuF6BIXoH+LcItnn8vtxR/W3cRd3NK4espxEcDmsJHLMQPx+9hzakHRgHYxnMPAQDD20VB4kG/2JGnD31aEqcEm9SA0Q/KZx0baBTJVciSKbjHt17U1X4NbRMJf6mHpdOr1SFWV1e25cJjdFh4EEv2pUCtBUoUaosrL1kVai0W/nETADCxW6zL6p/aRPvDx6BA3FwA5ikSIC5Yl227WYdbEuWUKHDgemVbjdP38m3qq5ZfqkS3L45hyRUBzj0w3iHgTnYJXv3vGfxt3UX8z0Kj3CJ5Bcb9dAZ3c0oR5ivGL5MT4CcV4dWEGADAybt5yCwuR4Vai8/33MT/rumu83JH6npPnk4UgBGnUBE+qcpTJEBL/dY77H6PKo0W+69nAXCs+N7Q8HZRmNq7IZqG+0DLAKv+TMPciwI89/lhdFt8GNesdJv/5fQD3MsrQ7C3B96tpvbLHiIBH50bBgHQ7WMZa2Fas6mF6dnatPncI6i1DNrU84O3WIjicpVNe1T+9bAIReUqZJXz8OqqC1irn07WaBl8tO0qVBpdEDdr21U8zDfdimre7uvIlinRINgLv0/rjrb65rf1g6RIiAsEw+iK7t9YfRY/Hr8HAHizexyamwlmCXka0KclcYqPWGi0Zx9lwAigy3IBwC59y4nTqfkolKsQ5OWBTnGB1k6tlsRDgJkDm2Lf+z3x4+vPIUAqQpmaB4YBSpRqTFxzDmn5ZSbnlSrV+PbwXQDA9P5N4OPpWAG/JT3jQwAALSP9LBaMswHYrToKwO7mlGDFUd09GN81lvu3OJWaB0AXTN3IkEFjJiP2QH9PJQLdc18l3UZ5hQbrTj/ApYdF8BYL0bqeH0qUakxae/7/27v3uKiq/W/gn4GB4TICOggIKqgkKKBY3qgMjLhomkKSx371iNpNq2P1KGVW4MlbWqfD8ejp2Kng5O3x7i9FvAHmXcss8KilgppmJCj3ywDr+WOcgXEugMJmkM/79fJlrv3dexbf9uz5steatfWehpCe8xu2nboGKxnwyTMD4XbH3NGnH+oOAEje9wuOXiyEUiHHv55/CO+N6d/CGSCyHCzA6J7IZDJ0VdbfBbPjHDACMGZAN8hkmnlfVwo187UAICrQA3LrljtHIgM8sGvmI5jRvxZ733gU/bs54UZpNf7n38cM7sL850geiirU6O3qiLjBdz8HzZSJg3vgzSf64oOxposG7bMhT7XBQ7kr1bV4bc0PqFTX4VFfV4wP9sLDt+/a7ci+js+/vYjwT7Iw+u8HkLDxJ4P9c29oCrBH3AV6dLbHrXI1lmX8giW3l5N4O9oP/3zuIbgqFTifX4qxyw4iPUfz/33F7SU/Xgntg0E9Oxsce3RQN9jbWEMIQCYDlk0ahCgzc8mI7gf8tKR71nAeGO+AEQC4O9khpLfmw33N8cu64ccxtx+o3ZI6O9jCz1nAW+WAlKlD4K1ywK83KzDhs8O6xWDLq2vw7wO5AIBXR/re1TcwG2Mrt8LMJx7QDb8aM7RXF8itZMi9UYa8G4Z36VrTmmOXcfZ6CVSOtvjrMwNhZSXTPdT6xyu3sCDtDPJuF62bTv6K/T//obf/pdvb3OwFJodo5mWtyLqA8upaDPHpjP8Z5g0vF3ukzXwUoX27oqqmDgvSziC/pFL3yKf4R3yM9k2pkGPC7btg70T739USJUTtDQswumcN74DxW5CkNS5YMwz5z6wLLTb82Bi3TnbY8HII+rorkV9ShWf+dQTfX7qJpbvOobCsGj27OOj61RY62dlgyO0vEWSey5f0tXOu3i6CHvaB2+1vYPbr1gnPDe+JB3u64Il+7kgc2x/PD9dMip+7JVvvuZXaO2Bd7QSeftBL96UDW2srLIodoBt2detkh38+9yDsbaxxpbACKzI1d78CvZwMhh4ben9Mf+yfHYaXQ/u08E9OZJn4aUn3rGun+m+0cRI+aUUHdtM9qspb5YA5o/u16PCjKW5Odvh/L4VgYHdn3CxX4+l/HsZXh/IAAK8/7itJH8x5/PbdnYyz0hZgF28XUH3clLo2mUyG+eODsHnGI/j35MGY8kgvvDPKH14u9vj1ZgU+uz10WKmuxbUizfNeu9pr7lhNfbQXAOD/RvaFb4NjApqHmWt/ztQjeQCAkY2s2WYrt+KDtqlDschPy9LSUrzxxhvw9PSEnZ0dgoODsW7duibtm5+fj/j4eLi6usLBwQEhISHYt2+f0di9e/ciJCQEDg4OcHV1RXx8PPLzDS+KarUa8+bNg4+PDxQKBfz9/bFs2TKDuH//+98YP348fHx8YG9vD19fX0yfPh2//Wb8a9n3C1clhyDJkLO9DQ4kjMSPiZHYP3ukbohJCp0dbbH6xeEYdvuOm6tSgSUTBkjaB1NG+msm6x+7WKh3h6m1ae9gaZfCMMVRIcd7T/YDAHxxMBcFpVW4UlgOIQBHhTWUt1fbmBn+AA4kjDR5x+rJAZrhZu26YXezaC7R/cwiC7DY2FikpqYiMTERO3fuxJAhQzBp0iSsWbPG7H5VVVUIDw/Hvn37kJycjG3btsHd3R3R0dHYv3+/Xuz+/fsxatQouLu7Y9u2bUhOTsbevXsRHh6OqqoqvdgZM2Zg0aJFePXVV7Fr1y7ExMRg5syZWLhwoV5cYmIilEolFi5ciPT0dCQkJGD79u146KGH8Pvvv+N+1VVvDphFnlLURuxtreFs37LfNmwqpUKO/0wbitSpQ5E5KxTPDO7RpqvPa/XpqkSPLvaorq3DofMFkrzmzbJqFFVo1kgztURGQ9GBHgj0ckJZdS0+239BNzfMR+UAbQqtrGRmH2E00s8N9refjOHiYKNbdoKINCxuJfy0tDTs2bMHa9aswaRJkwAAI0eOxKVLlzB79mxMnDgR1tbG77J88cUXyMnJweHDhxESEqLbd+DAgUhISMCxY8d0sbNnz0bfvn2xceNGyOWaNPTq1QuPPPIIvvzyS0yfPh0AcPr0aXzxxRdYsGABZs+eDQAICwtDQUEB5s+fj1deeQVdumh+y/7hhx/g5lb/W15oaCgefPBBDBkyBJ9//jnee++9Fs6WZXDV+xYk74CR5VDIrRF6e3kISyGTyTDSzw3/OXIJn+w+h0E9XfTeQ61BO/zYzdmuSavKy2QyzIr0Q/xXJ5B65BK0q1L4dHEEcLNJr2lva43H+7lhx0+/4bEHurbKFx+I2jOLu12xZcsWKJVKxMXF6bVPmTIF165d0yuijO3r5+enK74AQC6X47nnnsPx48dx9epVAMDVq1dx4sQJPP/887riCwAefvhh9O3bF1u2bNG1bd26FUIITJkyxaA/FRUVSE9P17U1LL60HnroIVhbW+PKlStNzED70/AOmC3vgBE1atqjveCqVODs9RJM/NcR3CitanynBurqBL44mIusJk7kb+rwY0OhfbtiiE9nVNfU6Z7h6a1q3kO7E6L8EDPIC29F9G3WfkQdgcV9Wubk5KBfv356hREADBgwQLfd3L7aOGP7nj59Wu8YpmIbvkZOTg66du0KDw8Pg7jG+gNohjpra2sREBBgNq49034L0lZuxd9yiZrAW+WI9S8PRzdnO1z4owx/b/AcyaZYffwyPtz+X0xfdVI3tGiOdskLn2YUYNq7YAB0C7N6q5r36CZvlSM+nRjcrNcl6igsbgiyoKAAvXv3NmjXDvMVFJieM1FQUKCLM7ev9m9TsQ1fw9QxHR0dYWtra7Y/JSUlmDFjBnr06IGpU6eajAM089cazj0rLtY8GkStVkOtbvwC21TaY7XkMT2dbPDs0O7wcLJr0eO2d62RazLUXvPcw0WBj2ID8H+++h7rTlzByyN84Nap8aHIG6VVWJJ+FgBQoa7F+hOXEB/ibXafC/ma9dC8OzfvPfpgDyc86qvCwdtz1bo7K3Djt/aX6/amvZ7T7VFr5Lqpx7K4AgyA2YmyjU2ibc6+pmKbGmduW2VlJWJjY3Hp0iVkZGRAqVQajdNatGgR5s2bZ9C+e/duODg077Z/U+zZs6dFjzfMGkAZkJZ2tkWPez9o6VyTce0xz0IAPkpr5JXW4f2vMzHOp67Rff7zixVKKq1gayVQXSfDyoyzcC08DXM3n3/KtQYgw428M0gr+m+z+jjMHjgIOWQQyMs+DqVN+8x1e8Q8S6clc11ebvgsVGMsrgBTqVRG7yoVFhYCMH7Xqrn7qlSaFbpNxTZ8DZVKhVOnThnElZWVobq62mh/qqqqEBMTg4MHD2L79u0YNmyYyT5rzZkzB2+99Zbu38XFxejRowciIyPh5NRyD6NVq9XYs2cPIiIiYGPTNt9O6yiYa2m09zw7PvAHXvz6BxwtsMHi+BHo7GBrMvbarQrMPHIAAPBl/BC8svoU/qisgXPfoRjxgKvRfYQQeOe7fQDqMCHyMfTu2vzhQE+/3yAAjO7v2q5z3V6093O6PWmNXGtHsBpjcQVYUFAQ1q5di5qaGr15YNnZ2QCAwMBAs/tq4xq6c1/t39nZ2Rg9erRBbMPXCAoKwrp163D9+nW9eWCm+lNVVYXx48cjMzMT27ZtQ3h4eOM/NACFQgGFwnD4wcbGplXegK11XDLEXEujveb5if7d0L/bBfz3t2JsOXXd7ErwO05fAgAM790Fj/Z1R9zg7vjqUB5WHszDyH4eRu/IXy+qRIW6DtZWMvRyc4LNXXxR5unBmkcPaYdW2muu2xvmWTotmeumHsfiJuHHxMSgtLQUmzZt0mtPTU2Fp6en2btJMTExOHv2rN43JWtqarBq1SoMGzYMnp6aR5B4eXlh6NChWLVqFWpra3WxR48exblz5xAbG6trGzduHGQyGVJTU/VeKyUlBfb29oiOjta1ae98ZWRkYNOmTYiKirq7JBBRhyGTyRD/sA8AYNWxS7oJ7wBQU1s/JCmEwJaTmm9yxwzyAgBMfaQXbOVWOHqxELtOG19r8OKNUgBAj872/JYykQWxuDtgo0aNQkREBKZPn47i4mL4+vpi7dq1SE9Px6pVq3RrgE2bNg2pqam4cOECvL01E1CnTp2K5cuXIy4uDosXL4abmxtWrFiBc+fOYe/evXqv89FHHyEiIgJxcXGYMWMG8vPz8c477yAwMFBvyYmAgABMmzYNiYmJsLa2xpAhQ7B7926sXLkS8+fP1xuCnDBhAnbu3Im5c+dCpVLh6NGjum1OTk7o379/a6aOiNqpsQM9MX/Hf3GlsALf/vwHRvq7Yf2JK3hvaw4GdHfGK6F90LWTAr/kl8JWboXoQM0q8z26OOClEb3xj8zzWJD2X4T5dTVYi2//Oc1DtQPMPCSciKRncQUYAGzevBlz587FBx98gMLCQvj7+2Pt2rX405/+pIupra1FbW0thKj/bVGhUGDfvn1ISEjA66+/jvLycgQHB2Pnzp0IDQ3Ve42wsDCkpaXhgw8+wNixY+Hg4IAxY8Zg6dKlBkOBK1asgJeXF5YtW4br16/Dx8cHycnJeP311/Xitm/fDgBYsGABFixYoLctNDQUWVlZLZEeIrrP2NtaI25wD3xxMBfLMn7Bz7+XYHH6WQgBfHfpJl74z3e6JV6e6Oem93SBGSP7YOP3v+JKYQVWH7uMabef0Qho1gv75sdrAICxtx8NRESWwSILMKVSieTkZCQnJ5uMSUlJQUpKikG7u7u7wXChKREREYiIiGg0zsbGBklJSUhKSjIb17AYJCJqjueGe+PLQ7k4efkWTl6+BQCYOLgHXBxssO7EFd16X08/qP88SwdbOV4d2QfvbzuNbaeu6hVg31++iWtFlVAq5HwWI5GFscgCjIioo+nl6ohlkwZhZ851nLlWjBEPuCJxbACsrGSYFeWne3h3eD93g31HBXVD4v+exk+/FuFyQTl63l6x/n9Pae5+RQa48zFhRBaGBRgRkYUYM8ATYwZ4GrTbWFvhURPLTACa57GG9FHh0PkC7Mj+DdPD+qC6pg5p2b8BAJ4aaHhMImpb/EoMEdF9YHSQZo6Xtuj6bP8FFJRVw1WpwCO+pos3ImobLMCIiO4D0QEesJIB2VeLsCLrPJZlaJ4v+f6YfrCx5qWeyNLwXUlEdB9QKRWI6K+ZH7Yk/RzUtQJP9HPn8CORheIcMCKi+8QnzwRjwOE8rDp6CVYyGRbEBDb6/FwiahsswIiI7hNKhRyvjvTFqyN9UVcnYGXuCd1E1KY4BElEdB9i8UVk2ViAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxFiAEREREUmMBRgRERGRxCyyACstLcUbb7wBT09P2NnZITg4GOvWrWvSvvn5+YiPj4erqyscHBwQEhKCffv2GY3du3cvQkJC4ODgAFdXV8THxyM/P98gTq1WY968efDx8YFCoYC/vz+WLVtm9JgXL15EbGwsXFxcoFQqERERgZMnTzb9hyciIqL7nrytO2BMbGwsTpw4gcWLF6Nv375Ys2YNJk2ahLq6Ojz77LMm96uqqkJ4eDhu3bqF5ORkuLm5Yfny5YiOjsbevXsRGhqqi92/fz9GjRqFJ598Etu2bUN+fj7efvtthIeH47vvvoNCodDFzpgxA19//TU+/PBDDBkyBLt27cLMmTNRUlKCd999Vxf3xx9/YMSIEejcuTO+/PJL2NnZYdGiRQgLC8OJEyfg5+fX/GSUlQHW1obt1taAnZ1+nClWVoC9ff2ulZWaeBubRmNRXg4IYfy4Mhng4HB3sRUVQF2d6T47Ot5dbGUlUFvbMrEODpp+A0BVFVBT07xYtdp4ru3tNXkGgOpqQK02fdzmxNrZ1Z8rzYlVqzXxpigUgFze/NiaGk0uTLG1rc9Lc2JrazX/77TuzLONjSbeWOydGsbW1WnOtZaIlcs1uQA074ny8paJbc77/h6uESZj1WpY3fn/idcIzX/fzTXCFHmDj2ZeI5of25z3fW2t+c/Eu71GNIWwMDt27BAAxJo1a/TaIyIihKenp6ipqTG57/LlywUAcfjwYV2bWq0W/fv3F0OHDtWLHTJkiOjfv79Qq9W6tkOHDgkAYsWKFbq2nJwcIZPJxMKFC/X2f/HFF4W9vb0oKCjQtc2ePVvY2NiIvLw8XVtRUZFwdXUVzzzzTBMzUL8fAFGkuVwZ/hk9Wn8HBwfjcYAQoaG6sOrqalHp5GQ6dvBg/eN6e5uO7d9fP7Z/f9Ox3t76sYMHm451ddWPDQ01HevgoB87erTp2DtP9wkTzMeWltbHTp5sPjY/vz52xgzzsbm59bGzZpmPzcmpj01MNB97/Hh97JIl5mMzM+tj//EP87Hbt9fHfvWV+dj16+tj1683H/vVV/Wx27ebj/3HP+pjMzPNxy5ZUh97/Lj52MTE+ticHPOxs2bVx+bmmo+dMaM+Nj/ffOzkyfWxpaXmYydMEHrMxd7lNUIIoXkPmogt9PUV1dXV9bG8Rmi04DWi+uefxdatWzV55jVCo5WuEerDh83H3sU1Qvf5XVQkzLG4IcgtW7ZAqVQiLi5Or33KlCm4du0ajh07ZnZfPz8/hISE6Nrkcjmee+45HD9+HFevXgUAXL16FSdOnMDzzz8PeYPfNB5++GH07dsXW7Zs0bVt3boVQghMmTLFoD8VFRVIT0/Xe/3HH38c3t7eujYnJyfExsbim2++QY2533iIiIiow5AJIURbd6KhkJAQ1NbW4vjx43rtp0+fRmBgIP71r3/hpZdeMrpvt27dMGLECKxfv16vfceOHRgzZgx27dqFyMhI7Nq1C9HR0dixYwdGjx6tFxsXF4dDhw7h2rVrAIBJkyZh3759BnPDysrKoFQqMWfOHCxcuBAVFRVwdHTErFmzsGTJEr3Y5cuX47XXXsO5c+fQt2/fJuWhuLgYzs7OKLp2DU5OToYBdzm8oFarsWvzZkRFRcGGQ5CtOrygVquxa9cuw1xzCFKjhYYXDPLMIcjmxzZxCFKtViN9925Ex8TUn9O8Rmj+uwWHINVyOdLS0zF69GjYCMFrRHNjm/G+V1dWYte2baY/E+/iGqH7/C4qMv75fZvFzQErKChA7969Ddq7dOmi225uX22cuX21f5uKbfgapo7p6OgIW1tbXezNmzchhGjS6xtTVVWFqgYnV3FxMQBAbWsLtakx5YZvnsbGnW/HqtVq1NrZaY5p7GS787imYu41Vt7IqXe3sdbWxufM3U1swwuklZX5HBuJVctkxnNdW1t/UZfJzB+3ObF1dfUfQs2JBczH3vkB0FKxQIvEGs2zxH3oKLFqmQx1CgXUvEZo3OM1wnQX6q/Xjc4r4jXinmLVdXXN+0xswnH13h9mWFwBBgAy7W8JzdzW3H1NxTY17l5jG1q0aBHmzZtn0L579244NPytsIXs2bOnxY9JxjHX0mCepcNcS4N5lk5L5rrc3F3sBiyuAFOpVEbvFBUWFgIwftequfuqVCoAxu9IFRYW6r2GSqXCqVOnDOLKyspQXV2ti+3cuTNkMtld933OnDl46623dP8uLi5Gjx49EBkZafYWZnOp1Wrs2bMHERERxm+3UothrqXBPEuHuZYG8yyd1si1dgSrMRZXgAUFBWHt2rWoqanRmyCfnZ0NAAgMDDS7rzauoTv31f6dnZ1tMAcsOztb7zWCgoKwbt06XL9+HR4eHiaPaW9vD19fX5Ovb29vb3RoVUuhUOgtfaFlY2PTKm/A1jouGWKupcE8S4e5lgbzLJ2WzHVTj2Nx34KMiYlBaWkpNm3apNeempoKT09PDBs2zOy+Z8+e1fumZE1NDVatWoVhw4bB09MTAODl5YWhQ4di1apVqG0wwfLo0aM4d+4cYmNjdW3jxo2DTCZDamqq3mulpKTA3t4e0dHReq+fkZGBK1eu6NpKSkqwefNmPPXUU3oFJREREXVcFlcRjBo1ChEREZg+fTqKi4vh6+uLtWvXIj09HatWrYL17QmR06ZNQ2pqKi5cuKBb9mHq1KlYvnw54uLisHjxYri5uWHFihU4d+4c9u7dq/c6H330ESIiIhAXF4cZM2YgPz8f77zzDgIDA/WWnAgICMC0adOQmJgIa2trDBkyBLt378bKlSsxf/58vWHFWbNm4euvv8aTTz6Jv/zlL1AoFFi8eDEqKyuRlJTU+skjIiKidsHiCjAA2Lx5M+bOnYsPPvgAhYWF8Pf3x9q1a/GnP/1JF1NbW4va2lo0XEVDoVBg3759SEhIwOuvv47y8nIEBwdj586deqvgA0BYWBjS0tLwwQcfYOzYsXBwcMCYMWOwdOlSg6HAFStWwMvLC8uWLcP169fh4+OD5ORkvP7663pxXbt2xYEDBzBr1ixMnjwZNTU1CAkJQVZWFvz9/VshU0RERNQeWWQBplQqkZycjOTkZJMxKSkpSElJMWh3d3c3GC40JSIiAhEREY3G2djYICkpqUl3sfr06aO3kCsRERHRnSxuDhgRERHR/Y4FGBEREZHEWIARERERSYwFGBEREZHEWIARERERScwivwVJ0C2v0dRHGjSVWq1GeXk5iouLucJyK2OupcE8S4e5lgbzLJ3WyLX2c7vhMlnGsACzUCUlJQCAHj16tHFPiIiIqLlKSkrg7OxscrtMNFaiUZuoq6vDtWvX0KlTJ8hkshY7rvYh31euXGnRh3yTIeZaGsyzdJhraTDP0mmNXAshUFJSAk9PT1hZmZ7pxTtgFsrKygrdu3dvteM7OTnxjS0R5loazLN0mGtpMM/Saelcm7vzpcVJ+EREREQSYwFGREREJDEWYB2MQqFAYmKiwQPHqeUx19JgnqXDXEuDeZZOW+aak/CJiIiIJMY7YEREREQSYwFGREREJDEWYEREREQSYwHWQZSWluKNN96Ap6cn7OzsEBwcjHXr1rV1t9qtrKwsyGQyo3+OHj2qF3vy5Ek88cQTUCqVcHFxQWxsLC5evNhGPbdsJSUlSEhIQGRkJLp27QqZTIakpCSjsc3J67Jly+Dv7w+FQoFevXph3rx5UKvVrfiTWL6m5jo+Pt7oee7v72/0uMy1voyMDEydOhX+/v5wdHSEl5cXxo0bh++//94gluf03Wtqni3qfBbUIURERAgXFxfx2WefiYyMDPHCCy8IAGL16tVt3bV2KTMzUwAQCxcuFEeOHNH7U1JSoos7c+aM6NSpkxgxYoTYsWOH2LRpkwgICBCenp4iPz+/DX8Cy5SbmyucnZ3FY489pjtHExMTDeKak9f58+cLmUwm5syZIzIzM8WSJUuEra2tePHFFyX6qSxTU3M9efJkYW9vb3Cenzp1yiCWuTY0YcIEMXLkSLFixQqRlZUlNmzYIIYPHy7kcrnYt2+fLo7n9L1pap4t6XxmAdYB7NixQwAQa9as0WuPiIgQnp6eoqampo161n5pC7ANGzaYjYuLixOurq6iqKhI15aXlydsbGxEQkJCa3ez3amrqxN1dXVCCCH++OMPk0VBU/N648YNYWdnJ1566SW9/RcsWCBkMpk4ffp06/wg7UBTcz158mTh6OjY6PGYa+N+//13g7aSkhLh7u4uwsPDdW08p+9NU/NsSeczhyA7gC1btkCpVCIuLk6vfcqUKbh27RqOHTvWRj27v9XU1GD79u14+umn9R5x4e3tjZEjR2LLli1t2DvLpB0OMKc5eU1PT0dlZSWmTJmid4wpU6ZACIGtW7e2aP/bk6bkujmYa+Pc3NwM2pRKJfr3748rV64A4DndEpqS5+aQIs8swDqAnJwc9OvXD3K5/qM/BwwYoNtOd+fVV1+FXC6Hk5MToqKicPDgQd22CxcuoKKiQpfnhgYMGIDz58+jsrJSyu7eF5qTV+25HRQUpBfXrVs3uLq68txvooqKCnh4eMDa2hrdu3fHa6+9hsLCQr0Y5rrpioqKcPLkSQQEBADgOd1a7syzlqWcz3wYdwdQUFCA3r17G7R36dJFt52ax9nZGTNnzkRYWBhUKhXOnz+PpUuXIiwsDDt27EBUVJQur9o8N9SlSxcIIXDz5k1069ZN6u63a83Ja0FBARQKBRwdHY3G8txv3MCBAzFw4EAEBgYCAPbv349PP/0U+/btw4kTJ6BUKgGAuW6GV199FWVlZZg7dy4AntOt5c48A5Z1PrMA6yDMDTW05DBERzFo0CAMGjRI9+8RI0YgJiYGQUFBSEhIQFRUlG4bc986mppX5v/evPnmm3r/joiIwKBBgzBhwgR8/vnnetuZ68a9//77WL16NZYtW4aHHnpIbxvP6ZZjKs+WdD5zCLIDUKlURqt17S1XY791UfO5uLhgzJgx+Omnn1BRUQGVSgXA+B3GwsJCyGQyuLi4SNzL9q85eVWpVKisrER5ebnRWJ77dycmJgaOjo56S64w142bN28e5s+fjwULFuC1117TtfOcblmm8mxKW53PLMA6gKCgIJw5cwY1NTV67dnZ2QCguxVL907cfrSqTCZDnz59YG9vr8tzQ9nZ2fD19YWdnZ3UXWz3mpNX7fyNO2OvX7+OGzdu8Ny/B0IIWFnVf4Qw1+bNmzcPSUlJSEpKwrvvvqu3jed0yzGXZ3Pa4nxmAdYBxMTEoLS0FJs2bdJrT01NhaenJ4YNG9ZGPbu/3Lx5E9u3b0dwcDDs7Owgl8sxduxYbN68GSUlJbq4y5cvIzMzE7GxsW3Y2/arOXmNjo6GnZ0dUlJS9I6RkpICmUyG8ePHS9Tr+8vGjRtRXl6O4cOH69qYa9M+/PBDJCUl4b333kNiYqLBdp7TLaOxPJvSZufzPS9kQe1CRESE6Ny5s1i5cqXIyMgQL774ogAgVq1a1dZda5cmTZok3n77bbFhwwaRmZkpVq5cKfz8/IRcLhd79uzRxZ05c0YolUrx2GOPibS0NLF582YRGBjIhVjNSEtLExs2bBBffvmlACDi4uLEhg0bxIYNG0RZWZkQonl51S6m+O6774qsrCyxdOlSoVAoOvSilVqN5TovL088/PDD4u9//7tIS0sTO3fuFO+8846ws7MTAQEBorS0VO94zLWhjz/+WAAQ0dHRBot/HjlyRBfHc/reNCXPlnY+swDrIEpKSsSf//xn4eHhIWxtbcWAAQPE2rVr27pb7daiRYtEcHCwcHZ2FtbW1qJr164iJiZGHD9+3CD2u+++E+Hh4cLBwUE4OTmJ8ePHi/Pnz7dBr9sHb29vAcDon9zcXF1cc/KanJws+vbtK2xtbUXPnj1FYmKiqK6ulugnslyN5bqwsFDExMQIHx8fYW9vL2xtbcUDDzwgEhISxK1bt4wek7nWFxoaajLHd94D4Tl995qSZ0s7n2VC3J60QkRERESS4BwwIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIiIiIomxACMiIiKSGAswIqJ2ICsrCzKZDElJSW3dFSJqASzAiOi+lJeXB5lMhujoaF1bfHw8ZDIZ8vLy2q5jZshkMoSFhbV1N4hIAvK27gARETVu6NChOHPmDFxdXdu6K0TUAliAERG1Aw4ODvD392/rbhBRC+EQJBF1CD4+PkhNTQUA9OrVCzKZzOiQX25uLl544QX07NkTCoUC3bp1Q3x8PC5dumRwTO3+V69eRXx8PDw8PGBlZYWsrCwAQGZmJqZOnQo/Pz8olUoolUoMHjwYK1eu1DuOdn4XAOzfv1/XN5lMhpSUFL0YY3PATp8+jYkTJ8LNzQ0KhQK9evXCm2++icLCQqN58PHxQVlZGd566y14eXlBoVBgwIAB2LhxYzOzSkR3i3fAiKhDeOONN5CSkoIff/wRM2fOhIuLCwBNQaJ17NgxREVFoaysDGPHjoWvry/y8vKwevVq7Ny5E0eOHEHv3r31jltQUICQkBB06dIFEydORHV1NZycnAAAH330Ec6fP4/hw4cjJiYGt27dQnp6Ol5++WWcO3cOn3zyia4PiYmJmDdvHry9vREfH687fnBwsNmf6/Dhw4iMjERVVRUmTJgAHx8fHD16FH/729+wY8cOHDlyBCqVSm8ftVqNyMhIFBYWIjY2FuXl5Vi3bh2eeeYZpKenIzIy8u6STERNJ4iI7kO5ubkCgIiKitK1TZ48WQAQubm5BvHV1dXCx8dHdOrUSZw6dUpv24EDB4S1tbUYM2aMXjsAAUBMmTJF1NTUGBzz4sWLBm1qtVpEREQIa2trcenSJYPjhYaGGv15MjMzBQCRmJioa6utrRUPPPCAACDS09P14ufMmSMAiGnTpum1e3t7CwBi3LhxoqqqSte+d+9eg3wRUevhECQREYDt27cjLy8PCQkJGDhwoN62Rx99FOPGjUNaWhqKi4v1ttna2mLJkiWwtrY2OGavXr0M2uRyOV555RXU1tYiMzPznvp86NAh/PLLLxg1ahSioqL0ts2dOxcqlQpr1qxBdXW1wb6ffvopbG1tdf8ODw+Ht7c3Tpw4cU99IqKm4RAkERGAo0ePAgDOnj1rdJ7V9evXUVdXh59//hmDBw/Wtffq1cvkNxNLSkrw8ccfY+vWrbhw4QLKysr0tl+7du2e+vzDDz8AgNGlKxwdHTF48GDs2rULP//8MwIDA3XbXFxcjBaH3bt3x5EjR+6pT0TUNCzAiIgA3YT11atXm427s4hyd3c3GlddXY2wsDCcPHkSgwYNwvPPPw+VSgW5XI68vDykpqaiqqrqnvqsvRtnqg8eHh4AgKKiIr12Z2dno/FyuRx1dXX31CciahoWYEREgG7i/DfffIMxY8Y0eT/ttxfvtG3bNpw8eRIvvPACPv/8c71t69at030j815o+/z7778b3a5t18YRkeXgHDAi6jC087Rqa2sNtg0bNgwAWmwI7sKFCwCAp556ymDbgQMHjO5jZWVltG+mDBo0CAB0y140VF5eju+++w729vbw8/Nr8jGJSBoswIiow+jSpQsA4NdffzXYNm7cOPTs2RN//etf8e233xpsV6vVOHjwYJNfy9vbGwAM9tm/f7/BHbGG/TPWN1MeeeQR9OnTBzt37sTevXv1ti1atAg3btzApEmT9CbbE5Fl4BAkEXUYjz/+OD7++GO8/PLLiIuLg6OjI3r27Ilnn30WCoUCGzduxKhRoxAaGorw8HDdxPXLly/jwIEDUKlUOHv2bJNea+zYsfDx8cGSJUuQk5ODwMBAnDt3Dtu3b8f48eOxadMmo/1bv349JkyYgEGDBsHa2hpPPvkkgoKCjL6GlZUVUlJSEBUVhdGjRyMuLg7e3t44duwYMjIy0KdPHyxevPjuE0ZErYYFGBF1GKNGjcKSJUvw+eef46OPPoJarUZoaCieffZZAMCQIUPw448/YunSpUhLS8PBgwehUCjg5eWF8ePHY9KkSU1+LaVSiYyMDMyePRvffvstsrKyEBAQgNWrV8Pd3d1oAZacnAwAyMjIwJYtW1BXVwcPDw+TBRigWSLj6NGj+Mtf/oLdu3ejqKgInp6e+POf/4z333+fz44kslAyIYRo604QERERdSScA0ZEREQkMRZgRERERBJjAUZEREQkMRZgRERERBJjAUZEREQkMRZgRERERBJjAUZEREQkMRZgRERERBJjAUZEREQkMRZgRERERBJjAUZEREQkMRZgRERERBJjAUZEREQksf8PQefmpbkKNVcAAAAASUVORK5CYII=", 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8+XjjjTe6/PnjH/8IQAx63njjDSxYsED6mh9++MHpBO49e/bgX//6F1QqFaZOnRrQ8yTRiUJjQjwYO3Ysxo4d6/E4hUKBe+65RxqSd9VVV6GpqQmrV69GQUEBcnNzHY7fu3cvZs+ejbFjx2LYsGFSRuizzz6DUqmUemzOnj2LcePGYejQoTjvvPOQl5eH2tpafP755zAajXjooYdkfy/Hjx93aCitrq7Gxo0bcejQIeTn5+Pxxx+X7rv11lvx3HPP4Xe/+x02btyIgoIC7Nu3D99++y2uu+46fPLJJx6fLycnB7/4xS/w8ccfY8yYMZgyZQoqKyvx1Vdf4bLLLsPJkydln7srd999N7777jt8+OGHGDBgAK6++mokJibi9OnTWLt2Ld58801p9syrr76KI0eO4KGHHsJ///tfXHTRRUhKSkJZWRl+/vlnHDt2DOfOnfO5VyRQXn31VRw4cABPP/00Vq1ahcsuuwyMMRw9ehTffPMNKisrkZycDK1Wi48++ggzZ87EpEmTMGXKFKkv5fTp09iyZQvS0tJw+PDhsH4/CxYsQHV1NcaPH4/evXvDZDLhyJEj+Oabb8AYw1//+tcum2wS4pPwVr8I6T489Z10BidLujs6OtjTTz/NBgwYwLRaLevduzd74IEHmF6v79LLUFZWxv70pz+xCy+8kGVmZjKNRsN69+7Nrr/+erZ9+3bpuPr6erZo0SI2ceJElpOTwzQaDcvNzWUzZsyQvRzW1ZJurVbLBg0axB544AFWXV3d5ev27NnDpk2bxlJSUlhCQgKbNGkS+/bbb132YMBJ34her2cPPvggKywsZFqtlg0YMID95S9/YR0dHX731HAWi4W98cYb7MILL2RxcXFMp9OxAQMGsLvuuoudPn3a4djW1lb2/PPPs/PPP5/FxcWx2NhY1qdPH3bttdeyd955R5oB404we2q4xsZG9sQTT7DBgwczrVbLkpKS2KhRo9iTTz7ZZan3mTNn2O9//3vpdZeYmMiGDBnC7rjjDrZ+/XqHYwM1p6Yzd0u633nnHXbttdeygoICFhsbyzQaDSsoKGA33XSTNOeGkEAQGHOSgyWEEEII6WGop4YQQgghEYGCGkIIIYREBApqCCGEEBIRKKghhBBCSESgoIYQQgghEYGCGkIIIYREBApqCCGEEBIRKKghhBBCSESgoIYQQgghEYGCGkIIIYREBApqCCGEEBIRKKghhBBCSERQhfsEQslisaC8vBwJCQkQBCHcp0MIIYQQGRhj0Ov1yM3NhULhOh8TVUFNeXk58vPzw30ahBBCCPFBWVkZevXq5fL+HhXU3Hjjjdi4cSPa2tpQWFiIZ555BldeeaXsr09ISAAgXpTExMSAnZfRaMQ333yDadOmQa1WB+xxSVd0rUODrnPo0LUODbrOoROMa93U1IT8/Hzpc9yVHhXUPPHEE3jnnXeg0Wjw008/YerUqTh58iTS0tJkfT0vOSUmJgY8qNHpdEhMTKQfliCjax0adJ1Dh651aNB1Dp1gXmtPrSM9qlF46NCh0Gg0AACVSoWOjg6cPXs2zGdFCCGEkO7A66BGr9fjoYcewrRp05CRkQFBELBo0SKnxzY3N+O+++5Dbm4uYmJiMGrUKHzwwQd+nfDNN9+MmJgYnH/++bjsssswfPhwvx6PEEIIIZHB66CmtrYWr732GgwGA6699lq3x1533XV4++23sXDhQqxevRpjx47F3Llz8d577/l6vlixYgWam5uxdu1aTJs2jVYxEUIIIQSAD0FNQUEB6uvr8d1332HJkiUuj1u1ahXWrVuHV199FXfeeScuvfRSvP7665g6dSr++Mc/wmw2S8dOmTIFMTExTv888cQTXR5bpVJh2rRpWLduHVatWuXtt0AIIYSQCOR1o7DczMinn36K+Ph4zJkzx+H2+fPn46abbsL27dtx8cUXAwDWr1/v7WkAAMxmM44fP+7yfoPBAIPBIP29qakJgNjEZDQaPT62yWQCY8zjeZhMJqhUKjQ3N0Ol6lG91z0OXWvfCIIAlUoFpVIp63j+8+Hp54T4j651aNB1Dp1gXGu5jxW0T4UDBw5gyJAhXT54RowYId3Pgxo5Kioq8MMPP2DGjBnQarX45JNPsHHjRjz77LMuv2bJkiVYvHhxl9u/+eYb6HQ6l1+XkJCAhIQEtwN+OsvOzsbJkydlH098R9faNxaLBXq9Hnq9XvbXrFu3LohnROzRtQ4Nus6hE8hr3draKuu4oAU1tbW16Nu3b5fbU1NTpfu99fLLL+P222+HIAgYMGAAPvzwQ4wcOdLl8Y888ggeeOAB6e98nfu0adNcLumurKxEU1MTMjIyoNPpZGWmGGNoaWlBXFwc9fgEGV1r3zDG0NraiurqagwcOBBZWVlujzcajVi3bh2mTp1Ky1+DjK51aNB1Dp1gXGteafEkqPl7dx863n4gZWdnY8uWLV59jVarhVar7XK7Wq12eqHNZjP0ej2ysrJkz74BxN+AjUYjYmNjvcruEO/RtfZdXFwcFAoFqqqqkJOTI6sU5epnhQQeXevQoOscOoG81nIfJ2ifCmlpaU6zMXV1dQBsGZvuhNfs3JWmCOnJ+Gub+goIIZEoaEHN8OHDcejQIZhMJofb9+/fDwAYNmxYsJ7ab1TWIJGKXtuEkEgWtKBm9uzZaG5uxscff+xw+9tvv43c3FyMGzcuWE9NCCGEkCjkU0/N6tWr0dLSIq2iKC4uxkcffQQAmDVrFnQ6HWbOnImpU6fi7rvvRlNTE/r374/3338fa9aswbvvvit7aWkgLFu2DMuWLXOYjUMIIYSQyOJTpubuu+/GnDlzcPvttwMAVq5ciTlz5mDOnDmoqqqSjvvkk09w66234sknn8SMGTOwfft2vP/++7j55psDc/YyLViwAMXFxdixY0dIn7e7EARB1p9NmzZh06ZNEARBClLDLRjns2jRItllmMLCQsybN8/jce+88w5uvPFGDBo0CAqFAoWFhf6dpIdzcvbvd9dddwXtOQkJpLMNbfjfjtPYf6YRJrMl3KdDIohPmZrS0lJZx8XHx2Pp0qVYunSpL09DAmTr1q0Of//LX/6CjRs3YsOGDQ63FxUVYdeuXaE8tYjx3//+FxUVFbjgggukFVrBNH78eLz44osOt3lapk1Id8AYw13//Rn7zzYCAJQKAenxGlw7Kg+PzBoS5rMjPR2NZI0CF154ocPfMzIyoFAoutweCK2trVG5emzt2rXSEvMrr7wSBw4cCOrzJScnB+Xfj5Bg23mqHvvPNkKtFBCjVkLfbkJlkwGvbTmJ317WHwkxwVlu3dphQlVbUB6adCM06IM4ZTQa8dhjjyE3NxeJiYm4/PLLceTIEYdjJk+ejGHDhmHz5s24+OKLodPppJJkU1MT/vCHP6BPnz7QaDTIy8vDfffdh5aWFofHWLlyJcaNG4ekpCTodDr07dtXegxn59OrVy/07t0b06ZN63I+APDWW29h5MiRiImJQWpqKmbPno1Dhw7J+n4feughZGdnQ6fT4ZJLLsFPP/0k+3p5MzPn2LFjuOmmm5CZmQmtVoshQ4Zg2bJlsr+ekJ7sPz+UAAB+cV4v7HlyGrY9MgW5STFgDFL2JtC2n6zF9KU/4Ok9Kryz7XRQnoN0DxTUeKOlxfWf9nb5x7a1+X5siDz66KM4deoU3njjDbz22ms4duwYrrrqqi7N1ufOncMtt9yCm266CatWrcI999yD1tZWTJo0CW+//TbuvfderF69Gg8//DCWL1+Oq6++WtpPa+vWrfjlL3+Jvn374oMPPsDXX3+NJ598sssYAPvzee211/C3v/3N6fksWbIEv/rVrzB06FB88sknWLp0Kfbt24eLLroIx44dc/v9/vrXv8aLL76I//u//8Pnn3+OX/ziF7juuutQX18fgKtpU1xcjLFjx+LAgQN46aWX8NVXX+GKK67Avffe63RLD1c2b96MhIQEqNVqFBUV4aWXXqJGeBIWxeVNuOS5Dfj45zMejz3b0Ia1BysBAPPGF0KpEJCdFINRvZMBAHvLAh/UfLG3HHNf34aKJnEfwKdWHcaXe8sD/jyke6Dykzfi453erAAQN3UqsGaN7cbMTMDVXhWTJgGbNtn+XlgI1NQ4P3bMGCAMDc5FRUV49913pb8rlUrccMMN2LFjh0PZo66uDitXrsRll10m3fbss89i37592L59O8aMGQNA3Ik9Ly8P119/PdasWYOZM2fixx9/BGMM//rXv5CUlCR9vbPGXH4+FosFTU1NiI+Px4033iidT0NDA/7yl79g1qxZeO+996Svmzx5MgYMGIBFixZhxYoVTr/Xw4cP4+2338b999+P559/HgAwdepUZGVlBbyp/YEHHkBCQgK+//57aauOqVOnwmAw4Nlnn8W9996LlJQUt49xxRVXYMyYMejXrx/q6+uxcuVK/OEPf8CePXvw3//+N6DnS4gn3xRX4Ex9G9YcrMAvzu/l9tj3t5+G2cJwcb80DM62bVUzslcyVu2vwN6yhoCeW7vRjGe+PgQLA64ekYPayrP4oVKBB1fuxYV905CR0HXiPOnZoiJTs2zZMhQVFWHs2LHhPpUe4+qrr3b4O9+I9NSpUw63p6SkOAQ0APDVV19h2LBhGDVqFEwmk/Rn+vTp0iorANK/xw033IAPP/wQZ8+e9fl8tm7dira2ti4BUX5+Pi677DK3O8Fv3LgRALoEMDfccENAdwJvb2/H+vXrMXv2bOh0OodrM2vWLLS3t2Pbtm0AbLvE8z8Wi22FyLJlyzB//nxMnDgR11xzDd5991389re/xbvvvovdu3cH7HwJkaOkRiwptxi6ZljtMcbw2R7xZ3zuBb0d7huZnwwA2Hemwe/zaWw1YtnG41i1/xze/+k0KprakZMUg2dmD8X1fSwoyklAh8mCbw9V+v1cpPuJiqAmYEu6m5ud/rE0NaHlnXccj62qcnk8Vq92PLa01PWxmzf7d84+6rz3Fd9Dq61TOSwnJ6fL11ZWVmLfvn3Svh/8T0JCAhhjqLFmpSZOnIjPPvsMJpMJ//d//4devXph2LBheP/9970+H74lh7Pzyc3NdbuBKr8vOzvb4XaVSuXVHmCe1NbWwmQy4e9//3uXazNr1iwAkK5Nv379HO7/85//7Paxb7nlFgCQgiJCQqXUGtQ0ewhqdp1uwJn6NsRplLh8iONKveF5SVAIQHljO6qa2l08gmcf/3wGk1/ciBfWHsE9K3bh6a/FfrrfXtYfWpUCCgGYMVR87m8OVvj8PKT7ovKTN+LinN9usQCd+xlcHevN4/YAzua9pKenIzY2Fm+99ZbTr0lPT5f+/5prrsE111wDg8GAbdu2YcmSJbjppptQWFiIiy66SPZ58ODj3LlzXe4rLy93eE5XX1tRUYG8vDzpdpPJ5NNu8q6kpKRAqVTi1ltvxYIFC5we06dPHwDAl19+CYPBIN2em5vr9rF5nxJt8klCiTGGkzyoaXcf1HxhzdJMG5qNWI3j8NU4rQoDMhNwpFKPvWcaMbUoxutz+WpfOR5cuRcAUJCmQ3lDG4xmhrzkWMw5Px9g4nv05UMy8ddvj+OH47VoNpgQr6WPwUhC/5ok4K688ko888wzSEtLkz6kPdFqtZg0aRKSk5Oxdu1a7N6926ug5qKLLkJsbCzeffddzJkzR7r9zJkz2LBhA66//nqXXzt58mQAwIoVK3D++edLt3/44YdOm5Z9pdPpcOmll2L37t0YMWIENBqNy2OHDx/u1WO/Y80U0jJvEkp1LR3QW4MZd5kak9mCr/eLv3BcPcp5gD6iV5IY1JQ1YGqRdzOXDpxtxB+sAc1tFxXgiSuLcKyqGe9sLcWcMfnQqBQwGsWgpn9GHPqkx6GkpgWbj1Zj1vCu2V3Sc1FQQwLuvvvuw8cff4yJEyfi/vvvx4gRI2CxWHD69Gl88803ePDBBzFu3Dg8+eSTOHPmDKZMmYJevXqhoaEBS5cuhVqtxqRJk7x6zuTkZDzxxBN49NFH8X//93+YO3cuamtrsXjxYsTExGDhwoUuv3bIkCG45ZZb8PLLL0OtVuPyyy/HgQMH8OKLL0rNvJ4UFxejuLgYgJjxaW1tlaYgFxUVoaioCACwdOlSXHLJJZgwYQLuvvtuFBYWQq/X4/jx4/jyyy+7DETs7L333sMnn3yCK664AgUFBWhoaMDKlSvxwQcfYN68eRg5cqSs8yUkEHg/DeA+qNl2sg41zR1I0alxSX/nWdOR+clY+fMZ7PWyr6ZK345fv7MT7UYLJg/KwJNXDYVSIWBITiKWXDeiy/GCIGBqURZe23wS3xysoKAmwlBQQwIuLi4OW7ZswbPPPovXXnsNJSUliI2NRe/evXH55ZdLWwiMGzcOO3fuxMMPP4zq6mokJydjzJgx2LBhA4YOHer18z7yyCPIzMzEK6+8gv/973+IjY3F5MmT8cwzz2DAgAFuv/bNN99EVlYWli9fjldeeQWjRo3Cxx9/jBtvvFHWc3/44YddlmTzjNHChQuxaNEiALapzX/5y1/w+OOPo6qqCsnJyRgwYIDUV+NO37590dDQgEcffRS1tbVQq9UYOnQoXn31Vdx5552yzpWQQLEPalo7zDBbGJSKriXpnafqAACTB2VCrXReIh1tXda953SDy8fprN1oxp3//RnnGtvRLyMOr8wdLevrplmDmm8PVVEJKsIIjBfjo0BTUxOSkpLQ2Njo9Dfw9vZ2lJSUoE+fPoiJkV/T5cuMExMTqachyOha+0fua9xoNGLVqlWYNWsW1OrgTHglop58rZ9fcxivbjoh/X3vwmlIiu36Pfz6nZ1YV1yJJ68swu2XOC9Jm8wWjFz8DVo6zFhz3wSHJd+u/PWbI3hlw3Ekxarx2YLx6JPuuj/R/jorlSpc/tfvcLKmBYuvHorbLi70/M0S2YLxmvb0+c3RpwIhhBCflNY6Tgh3taz7oHVS8NBc1x9GKqVCGsL38yl5Qy9/PCE28v9p5mC3AU1nCoWA+eMLAQDLfyyFxRI1v9tHvKgIamhODSGEBN7JasegxllfTV1LB8obxWXaRW6CGgA4v7c4eFJOUMMYw5FKPQBb6cob153XCwkxKpTUtGDT0Sqvv550T1ER1ARsTg0hhBAAgMXCcKpWnJqusvax6J0s6z5YLmZp+qTHedys8rwCMajZJSOoKW9sh77dBJVCQN9059Pe3YnTqqQhgP/desrD0aSniIqghhBCSGBV6tvRZjRDqRCk0o+zTM2Bs00APGdpAGB07xQIAlBa24pqvcHtsYfPiY/bLyMeGpVvH2U3jMkHAHx/vAb6dqNPj0G6FwpqCCGEeO1ElVh66p2qQ4pOnLnkbADfAWumZlhuUpf7OkuKVWNgZgIAYNdp99mawxVi6WlwToL8k+6kf2Y8+mXEwWhm2HSk2ufHId0HBTVORNGCMBJl6LVNAoUHK0U5iYiPEZdEO2sULi4XMyrD8uTNfOIlqB0ldW6PO2INagZl+x7UAOKEYwBYS9smRAQKauzwpWetrnbXJqSH46/tnrZ0mHQ/+/mKprxEac6LvlNQo283SrNshsrI1ADAxf3EbUs2HnHfvHu4QgyWBvsb1FinF286Ug2DyezhaNLd0cQhO0qlEsnJyaiqEn+YdDqd072NOrNYLOjo6EB7ezvNTgkyuta+YYyhtbVVGvanVCo9fxEhbvBl2sPzklBWJ24s27n8dOicmE3JSYpBapzrbUHsTRqUAZVCwInqFpTWtKDQyVLtDpNFWnklZ56NOyN7JSMrUYvKJgN+PFGLSwdl+vV4JLwoqOmE79TMAxs5GGNoa2tDbGysrCCI+I6utX+Sk5O77EZOiLea2o0ota58GpqbhC3HxN3lmw2OzbY8m1KUIz/wSIxR44I+qfjxRC2+PVSJOyb07XLMiepmmCwMCTEq5CR5v/mlPYVC3Dbh3W2n8c3BSgpqejgKajoRBAE5OTnIzMyE0SivG95oNGLz5s2YOHEipfWDjK6179RqNWVoSEActK5oykuORWqcRio/NRscyzc8U+NtM++UIVn48UQt1h+qchrU2JeeAvHLzbSibLy77TTWFVfi6WuHQSFjqwXSPUVFULNs2TIsW7YMZrP8eqlSqZT9AaBUKmEymRATE0MftEFG15qQ8OOzZ3jzry2o6Vx+4sGHdyWiy4dk4i9fFeOn0jo0thqRpHP8Wf+ppN76/PL6dDy5sG8aEmJUqGk2YHdZPc4vSA3I45LQi4qmBBq+RwghgXPgrOMybb76qdlu1ovFwqQVSkO8KD8BQEFaHPpnxsNsYfim2HFVEmMMm4+Ky68nDsjw7RvoRKNS4LLBYtnpm4OVAXlMEh5REdQQQggJHL7yiWdKnGVqTte1os1ohlalQGGazuvnmD06DwDwxpYSh1EEJTUtONvQBo1SgXF9A5dRmVZkW9pNow96LgpqCCGEyKZvN+IkX6bdpfxkK/Hz0tOg7ASolN5/1NwyrgBxGiWOVOodlnfzLM2YwhToNIHroJg0KAMalQKlta04VtUcsMcloUVBDSGEENl+KqkDY0BBmg6ZCeLKI6n8ZLf66RCf+OvjHJkknRo3X1gAAPjXppPS7ZutK60mDgxM6YmL16qkctZ/figN6GOT0KGghhBCiGxbT9QCsA3JA4AEnqmxm1Pja5OwvV9d0gcapQI/ldahuLwJBpNZev5A9dPYu3OSuNJq5c4ynK6lIaw9EQU1hBBCZPvRGlRc1C9dui3OrqeG96PwZdfeNgnby0qMwaRBYvCy/lAlfiqpQ5vRjPR4LYb4seeTK2MLUzFhQDpMFoa/bzgW8McnwUdBDSGERLH/bi3FJc9twAtrD6OhtcPtsQ2tHThkDVYutGvS5eUno5nBYLKg3WiWpgwPzIr36/z4MLyNR6rw1d5zAIBpQ7OCNnzz/qkDAQCf7D6Lsw1tQXkOEjxRMaeGEEKIc8t/LMWZ+jYs23gC//7uJIpyEzE6PxnnFaRgdH4K8lNt07u3nRT7aQZkxkv9NAAQZ9ew22Iwob5V7K2J16pkb4/gyqWDxUzN7rIGHKsUG3ivGpHr12O6c17vFFxQmIqfSuuwev85p8P/SPdFQQ0hhESppnYjTkh7KCXgcIUe+840Yt+ZRry99RQAID1eg4FZCeibEYfj1lVB9v00AKBUCIjTKNHSYUazwYQz9WI/Sq8U/7czyUmKlc5NbzAhK1GLC/oEdzjezOHZ+Km0DmsPVlBQ08NQUEMIIVFqX5k4b6Z3qg6rfz8B5Y3t2HWqHrtO12PX6QYUlzeiprkDNc21Ui8NAFzUKagBxL6alg4z9O0mlNWLZZteKd7Pp3HmssGZOGxdTXXF8Fwog7yNwfSh2Vj8ZTF2nqpHlb7dIStFureoCGp82SaBEEIi3d4zDQCAkfnJEAQBecmxyEuOxVUjxfJOu9GMQ+eacKK6BSeqm3GyuhnxWjUuHdx108f4GBWq9AYxU1MnZmryU2MDcp6XDs7Eq5tOAACuGpkTkMd0Jzc5FiPzk7G3rAHfHKzELdal5aT7i4qgZsGCBViwYAGampqQlBSYvUIIIaSn2326AQAwKj/Z6f0xaiVG907B6N4pHh+LL+tuMZhwJsCZmtH5ybh8SCa0KqXLcw20GUOzsbesAWsOVFBQ04NERVBDCCHEEWMMe8oaAACj8v3/Zc82gM+EMmtPTX5KYDI1KqUCb9w2NiCPJde0oVl4bs1hbDtZC5PZ4tNUZBJ69K9ECCFRqLyxHTXNBqgUAobm+h/U8BVQ+nYTyqTyU2AyNeFQmBYHlUKAycJQ0+x+qTvpPiioIYSQKLTXmqUZnJOAGLXS78fjmZqy+lZpSXevAGVqwkGpEJCVKDYIn2v0f17N/jONeOST/fhwR5nfj0Vco6CGEEKi0E8ldQBc99N4a7h1x+6Pfz4LAEjWqZEQow7IY4dLdpIY1FQ0tvv8GCazBQtW7MJV//ge7/90Gi9+cyRQp0ecoKCGEEKiDGMM6w9XAgjcHkrThmYDAGqaDQCA/AA1CYcTD2rK/QhqXll/DF/vPyf9vbWDVuEGEwU1hBASZY5U6lFW1watSoFLBqR7/gIZ8pJjpWwNELjl3OGUk8gzNb6Vn344XoO/bzwOAHh01mAAgMFEQU0wUVBDCCFR5ttiMUtzSf906DSBWwQ7fWiW9P+BWs4dTjxTc86HTI2+3YgHP9wLxoC5F+Rjzvn5AMT9scwWFtDzJDYU1BBCSJRZZw1qphZleTjSO9OtJSggcMu5wyknSfwefOmpeembo6hoakdBmg5PXjkUWrXt45ayNcFDQQ0hhESRyqZ27D0jbo9w2ZCuk4H90T8zHgMyxV25B2QlBPSxw8HXTM3Pp+rw9tZSAMDT1w5HrEYJrcq2wqzdaAnYORJHNHyPEEICzGS24Mt95fh0dzluuqA3ZgzL9vxFIbLtpLiH04heSQHf00gQBPzzlvOx70wDxgV508lQyE0Wr09lUzssFgaFjD2n1hw4hwesZafZo/OkniWlQoBaKcBoZpSpCSIKagghJIBaO0y48bVt2GfNhhSXN2LyoAy3s2DKW4AF7+/BFSNycfXIXL93tnantEYcjDc4OziZlP6Z8ehvzdb0dBnxWigEWAfwGZCZ6D4I/KmkDne9uwuA2K+0+JqhDvfHqJQwmk2UqQmiqCg/LVu2DEVFRRg7NrRjtgkh0Wf9oSrsO9OIBK0KqXEa1DR34ONdZ1we32Iw4c2jSnxTXIXff7AHd7y9E+3G4P0mf6quBQBQkBYXtOeIFCqlQspmySlBbTlWDUDcVXz5/LFI7DSnh/fVUKYmeKIiqFmwYAGKi4uxY8eOcJ8KISTCbThcBQC4aVxv/PbS/gCAN7aUuFzxsmTNUdS0C0jRqaFRKrD+cBX+F8Sps6drxUxN7x68hUEoedNXc7JGDBgv7pfmdK8o3ldDmZrgiYqghhBCQsFsYdh4RAxqLhuciV+OzUdSrBolNS1YV1zR5fi9ZQ34304xi7P0lyPw28vEIGhHaV3QzvF0HQU13shJkj+rptQa1BS6yIJJmZogZuKiHQU1hBASILtP16Oh1YjEGBXOL0hBnFaFm8f1BgCs2H66y/HbS8Sm3eEpFlzUNw1jClMAALtO1Qfl/No6zKjSixN/C9IoqJFDytQ0uc/UMMZsQU2686AmhmdqTJSpCRYKagghJEDWW0tPkwdlSuWHuReIQc2WYzXS7tVccXkTAKB3vFiaGpWfDKVCQHljO8ob/N9EsTOepUmMUSFZpwn440cinqk51+A+qKluNqClwwyF4DoLRpma4KOghhBCAoAxhvWHxKF2U+zmv+Sn6nBJf3FZ78qfHRuGD1qDmjzrL/Y6jQpFOYkAgJ+DkK05VStmEnpTlkY2uQP4+KqyvJRYaFTOP1opUxN8FNQQQkgArNpfgaOVzdCoFJg00HGTyBvGiiPyV+4skxqG241mnKhuBgD0irM1EZ9fIJagAhnU3LPiZ1yz7AccqdADAApSaeWTXGlxYkarrrXD7XElNeK/pat+GoAyNaFAQQ0hhPipxWDCU18XAwDuntSvS2lnWlEWknVqnGtsx2brst8jFXpYmPihmWi38ve8AAc1Z+pbsWp/BfaWNeDtracAUKbGG4mx4j+Ovt3o9rgSa6amr4t+GiDyMjXnGtvw3vbTaO0whftUJBTUEEKIn17ddBznGtuRnxqLuyf363J/jFqJ2aPzAAAfWpdrF58TS09DchJgP2tvjDWoKT7XhBaD/x8W20/aVlLVNFubhGnlk2wJMeKMWn27+38LT03CQORlal5YewSPfrofd7+7CyZz9wjUKKghhBA/fba7HADwpxlDXE4O/qW1BLWuuBI1zQapSXhIp8m+ucmxyE6MgdnCpMDHH3xbBHu0nFu+BOsAvdYOs9sP7tJaGUGNig/f8z8AOHSuCUtWH8KPx2v8fiwAONvQhvHPbsDf1h2V/TUnq8Xv+buj1Xji84NgLPy7j1NQQwghfmhsNeKsdaUS3+fHmcHZiRiZnwyTheGTXWccMjWdDbQGOsermr06F8YYvi2udGhq3WZdNp4Ua6txUflJPp6pAVxnaywWJgU1fdz01PCA159MDWMM976/GzOXbsG/vzuJB1fuDUgwseVoNc42tGHp+mP4+ZS8OUn2K/Te/+k0fvPfn9HY6r5MF2wU1BBCiB8OnhP3eMpPjXUIHJz55RgxW7P8h1IcLBe/rnOmBoC00/WxSu+Cmne3ncId7+zEtL99h9X7z+FMfSvK6tqgVAh4aMYgAIBaKUgreohnaqUCMdaykaugpqKpHe1GC1QKAb1SXF9bnqnxp6emoqkdX+wVM4MKQZx0XFrb6uGrPDPZTbx+9JMDMHooJ3WYLKi2ljMfnjEYGqUC64orccXft3gdjAcSBTWEEOIHXkbiS7HduWpkDnQaJcobxQ/BeK0KhU6yJnxDyOPV8j8cGGN4d5s44K+p3YS7V+zCPSvEzRWH5yXhF+f1whUjcvDbSwdAKWO3aWLD93BqctEszOcP5aXEOt0egQtEpoZnR3qlxGJsobgT+o8n/C9BtXXYzulIpR4feNiqo7KpHYwBGpUCd03qi4/vvhj5qbEQBCAzQev3+fiKdukmhBA/8FkzQ3OTPB6bEKPGW/PGYvPRapgZw4T+GU4/BHmm5nilXvZ57D3TiCOVemhVCtxyYQHe/L5E2in8wr5piFErseym82Q/HrFJiFGhSm9wmampbRGXe3v6MJcyNX7s/VRuHQKYmxyLi/ulY3tJHX48UYubxxX4/JiA2DNk72iF+9ceL7nmJcdCEAQM75WEr343AdX6dmnFWDhQUEMIIX7gZaShuZ4zNYAYYFzYN036u9HY9bd/nqkpb2xHs8GEeK3nt2q+Ceas4Tl44soiTB6Ugfv/txc1zQZcNjjTw1cTdxI8ZGpqrWWY1Dj3U5qlTI0fu3SX2wUTF/dPw9++BbaeqIXFwqDwIwPXanQM2No8ZJP4eeQmx0i3JcWqkRSrdvqaDhUKagghxEfiAD2xQVROpkauZJ0G6fFa1DQbcKKqGSPzk7sc89yaw1i1/xz+MG0Qeqfq8KW1z4KvspowIAPrH5iEU3UtGNGr69cT+Twt665pFjM1afGhyNSIwUROUgxG9kpGrFqJupYOHKnUY4iMEqgrvPyUGKNCU7tJflDTzfqzqKeGEEJ8dKRCD7OFIS1Og6zEwPYRSM3CLpouP9xRhlO1rfjd+7txzbIf0GwwYVBWAsb1SZWOSdKpKaAJAE8D+Oqs5ad0D5kabQAyNWftyk8alQIXWP+93/6xtMveYt7g5ScemLV1uD9Hfh45yRTUhNyyZctQVFSEsWPHhvtUCCERhPfTFOUmQhAC23wrNQs7CWrqWzqkPg61UoAgAL84rxfe+dUFAT8PImYvANeZmtoWsfwUykxNnjWYmGAdI/DBjjJMemEjVu503+DrCg9ieAnNU1BjO48Yt8eFWlSUnxYsWIAFCxagqakJSUmBSxETQqLboXO2oCbQBmTxoKZrw+ZJ6z5DuUkx+PCui2C2MBS4mY9C/CP11LQ5z9TYyk8h6Klp5L0sYlBzy4UFMFsY1h6swK7TDXj8swMYlpfkdSmKb3UgBTWye2ooU0MIIRHhlDXd3y89PuCP3T/DdaaG39YvMx69UnQU0ARZgtZDpkZmo7C/mZoWgwkN1uF2vEE3Rq3EnZP64aO7LsbkQRkwmCxYsGIX2r1cNs7LT+nWwMzd1zPGKKghhJBIc7beNp8k0AZkiUP5TtW1dll1w5uT+2UEPpgiXUmNwgYXq594T42H8pO/mZpz1ixNQoxKyh5xCoWAv94wCmlxGpysacGOUnlTgTmemeGBWecl3vaa2k1osd5PjcKEEBIBGGPSrA53U2R9lZGgRd/0ODAmLtm1d8IuU0OCz9Yo3DVTYzJbpOxJWpAzNXxGTZ6L7EhqnEYqO1XrDV49dqvUU2NtFHaTqeFZmtQ4DWI1zvc6CxcKagghxAe1LR1oN1ogCAjatgO8CXTLsWqH2/mk4f6UqQkJdz01da1ilkYhiEvx3bFlanwNajyXfHhfT621z0cu3hjMA7N2N5kaZzNqugsKagghxAdn68U39qyEGGhUwXkrnTAgAwCw5ZhtDH670Swt3e2XSb00oeBuTg0PHlLjNB63n5B26fZxmwQ5wUSaNdNS0+Jtpkb83nhQJCdT0x33EKOghhBCfHDGGtQEo5+Gu7BfGlQKAadqW3HKugt0aW0LLEz8oM3w0MNBAoMHNU0eghpP/M3USLNh3AQTvmZqWjst6TZZGDpcnGe19bEzwrjHkysU1BBCiA/ONojZkmD003DxWhXOL0gBAGy2ZmtOVInBTf/MeJpJEyJ8Q0tnw/ekGTVxnj/geaamw2yB2W5XbLk6z6hxJl0KauRnaswWJgVa9t+Hq2xNszW4S4wJ3x5PrlBQQwghPpAyNUFe0jpxoLUEdVTsqzlh7aehlU+hwz+8DSZLl5VLtTJn1AC2TA0Al1kQd87K6amxBiV8yrEc9sFLUqxaKqO5WtbNgzuewepOKKghhBAfnA1B+QmwNQtvPVELo9mCYusU4/608ilk4u0+vDv31fBMjafl3IAtUwO4nwPjTLvRjDPWEQKF6TqXx/HgqsaL8hPvpxEEIEatQKw1+HI1VVgvZWooqCGEkIjAMzW9Ulx/wATC0NwkpOjU0BtM2Flaj++Pi2Uo+52+SXApFQLirEuXuwQ1PFMjo6dGpVRAZc2CeNtXw3upEj30UvHgqrbFAMbklbh48KJTKyEIgrRM29WsGj6vp/OsnO6AghpCCPGS/YyaYJeflAoB4/uL2Zq/rTuKZoMJ6fEajMijLV9CydWmljwjkiqj/ATYz6rxLlPDp0h76qVKkyYCW9wO0LPHj4vViJkXKVPjsvwkBnbxWsrUEEJIj9fYZkSzQXxjD2ajMDfRurT7J+uU2EkDM6HwsHyYBJa0AqrNMVNT50WjMOD7Cij7oMYdnUYlBSVyV0DxoEZnzdDwr3fdUyNeA+qpIYSQCMBLT+nxGofmz2C5xNpXw106OCPoz0kcJbhYAWXbIiF0mRpPpL4ambNqeE8ND2piNJ56aqj8RAghEcM2oya4/TRcbnKs9GGmVAjSUD4SOq4G8NlWP3WPTA1g6+/xNlPDe2l01nNsdRF4NVGmhgCQ3bRFCOne+CqUXiHcoZivghpTkIKk2O73G3Kkk7ZKsMvUtBvNUhlSzpJuANL0aW8yNWYLw8ka63yijASPx/MAS+6smrbO5Sfrf51tlWAwmaXl6DSnJoo98OEeTHxhIxqd7B1CCOlZTlu3KeidFppMDQDcMaEvLh+ShT9OHxSy5yQ2iU6mCvPSk1opIEFm06wvmZqyulZ0mCzQqhSyRghImRqZs2qkTI3ac6Nws933H98NMzXd74wi1DcHK9FsMOHnU3W4bHBWuE+HEOIHKahJDV1Qk5ccizduGxOy5yOOnPXU1EnLubWypzv70lPDS099M+I97i8F2DI1NTIzNV16atwENbz8FqdRyjqXUKNMTQjYpygPV+jDfDaEEH+drhWDmoIQBjUkvBJjxRyAfbadN+LKLT0BvmVqpF3ZZQ5c5E3LcqcKdy4/6dzMqbGtfOp+pSeAgpqQqNbbouUj1qBm28lanGtsC9cpEUJ8ZLYwlNWHvvxEwitVJwYK9XaBgrdNwoBvmZoTvElY5tYY3m5qyRuCYzv31DjN1HTfLRIACmpCorrZMaj5qaQON762DQ/8b28Yz4oQ4ouKpnYYzQxqpeB2t2QSWfju1XUOQY11iwQZ04Q5XzI1fLVdgcwgms/MkVt+6pypiXGzTUJ3XvkEUFATEjV2mZoT1c1Ytf8cAGDfmQZaEUVID3OqVlyF0itF1y17Ckhw8OxHXatdUGMNcFK9CGp8ydRU6tsBAFmJMbKOlzI1shuFeU+Nyvpfdz01YqYmnspP4bNs2TIUFRVh7NixYXl++0yN0czw8c9nAAAtHWaH+wgh3R/vpwllkzAJvxRr+amu2b/yky+Zmqom8XMiM1He8/D9n+paOmCxeP7F2bb6yXGisLNMTXeeJgxESVCzYMECFBcXY8eOHWF5/hq9Y7SsN9iWxJ2yvkESQnqGU9aVT3JLASQy8JJOS4dZyrLU+tAozDM1BpmZmhaDSVpoIjdTwwMws4U5zNVxpcucGhmrn7rjDt1AlAQ14eaurlliHahECOkZwrGcm4RfYqxKKjfWW0tQPFMjd4sEwPtMTZW1fSFOo5S9gaRGpUCMWvx47zwB2ZnOE4XdbZPQnbdIAGhOTUjw1U/9M+OleQOCADAGlFJQ45HRbMH6Q5VoM5qRFa+Bl9PFCQkoKj9FJ0EQkKLToKbZgNrmDuQkxUqNwnI3swS876mpbPKun4aL16rQbuyQsjzu8NVPcbynxt3wPevjyR02GGrd86wiDM/UXNI/XQpqpgzOxLeHqlBaS0GNK6dqW7D+UBXe+qFE6v4HgGSNEk0Zp3H9mN7d9rcFErl4o3BBWlyYz4SEWlqcGNTUt3aAMYYaHxqFvc3U8KAmI0F+4ASIQU1NcwdaZAQ1bZ2G78W6zdR0756a7nlWEYY3A08YkI6Pfz4DjUqB68/vhW8PVaGkhnpqOms3mnHfB3uw5mCFdFt6vBYDMuNxpLIJdS1GLP7qMJasOYqpQ7Lw5FVFXv8WQ4gvGluN0pJWytREH/tl3S0dtj2QvOqpUXuXqeGZfq8zNXwDTjmZms7lJzeZmiYqPxG+pLtvRjw+++14qBUKmCziD8Op2hYwxmSP2I507UYz7nr3Z2w6Ug2VQsCYwhTMHJaDG8bkI1ajRHNrOxb99xvs0ifgZE0rvt5/Dj+V1uGfN5+HMYWp4T59EuFO1YlZmowErfQBQKJHqt3u17z0pNMopaXQcsSoXA+2c8ZWfvIuU8NLSc0yempsjcKe936iTE2Ua+0wocX6gkmP10jRrdFsgVIhoLXDjCq9gTINACwWhvv/twebjlQjRq3Af+ZdgIv6pTkco1UrMSGb4Zn543G0ug1/WLkXhyv0uP5fWzFzWDYemDoQA7I872JLiC/OWsugvWRsKkgiDw9q6ls7UCMt55afpQGAROsO6/Wt8jY3ruTLuRO8+4zgQYec8lOri20SemKjMK1+CjK+nDtGrXDoXFcrFdIbI62AEr2y4RhWH6iAWingrXljuwQ09gRBwLC8JHx898W47rw8AMDqAxW4+h8/YF1xZahOmUSZ8kbxt+bcZApqolGq3e7XvjQJA0BOkhicVFhfS57wTI3cGTUc/7zx1ChssTApI9N5m4Q2o7nLgNjunqmhoCbIeD9NenzXXVx5oyGtgALWHDiHl789BgB4+trhuLhfuqyvi9Oq8NcbRmHtfRMxvn8a2oxm3Pnfnfh8z9lgni6JUuUNYqYmN4kyq9FI6qlp7pC2S0jzokkYsAU1Vfp2mMyem4V97amJswY1npZ0t5ts2ZjO2ySYLQxGMwU1xA5/QaY7mTjZxzq8qyTKV0AVlzfhfus+WLeP74MbxuZ7/RiDshOwfP4FmHN+L1gY8I8NxwN9moRIm9BSpiY6SUFNa4e0BYG35ae0eC1UCgEWZptB447PS7pllp/sd+Lm/T68pwZw7Ksxmi3S36n8FKX4cm5ny/Hyrasn7JcrR5tqvQG/fmcn2oxmTBiQjkdnDfb5sdRKBR6dNQSCAByrakaVXl56lxC5zjaIrynayDI6pdmtfuLv7d5skQAASoUgBSjnPJSgmg22nsxML5d0J8gsP7XZbZGgsA4X1KgUUFn/376vxr7pmDI1UcpdpibT+sKulhGtR6Jmgwnzl/+Esw1tKEzT4R9zz4NK6d9LMiVOg6KcRADA1hO1gThNQiTnrOWnPMrURKUUu6BG2vfJy/ITIL+vhmdp4rUqqZwkl1R+8hDUNLaJjb+JsY6P72wFFC89xagVUPv5Xh0s3fOsIoi7TA2PvGuiMKgxmMTelwNnm5AWp8Hy+RcgSReYdObF1gZjCmpIIHWYLFKPXE4y9dREIx7ANLR2uP2F1ZPsJJ6pcZ+l93YjS3u8UdhT+Yl/RqV2anh2tlWC3tC9Vz4BFNQEnRTUOKm78kAn2jI1FgvDgx/uxQ/HaxGnUWL5/AtQmB646ay8yfiHEzUBe0xCKpvawZiYmvflt3PS8/FMjYWJJW7Au2nCnNxMDS+he1t6AmzlIU9zanjDc+f9q9xlarpr6QmgoCbo2oxid7uz4Uw8qNEbTE7nAUSqp1cdwlf7zkGtFPCvW8/H8F5JAX38sX1SoVQIKKtrQ1kdTWwmgXHWbuUTDcuMTmqlQtqduqbZAJVCwOBs7+diZVt7ss41ySs/+TLHLE5mTw0vo3UOzpzNquFZH7kba4YDBTVBxpfsqZRd3wQTtCppJ9Voydb8b8dpvPl9CQDgxTkjMWFARsCfI16rwkhroEQlKBIotPKJAI4f/rNH50m9kd6Qm6mRVlh5OQsHkD+nptbF/lXOtkpot/6SHqPuvtO0KagJMpN1jb+zpipBEKRsTTSs1NlZWofHPzsAAHhg6kBcMyovaM/FB/ftKK0L2nOQ6FJOK58IbB/+ggDcNbmfT48hN6hpahMDkqRY73tYpPKTh6CmroUPEfRcfuJbO1BQE8WM1j2elArn6Wo++jrSMzV7yxowf/kOGM0MM4dl47eX9g/q843slQwA2HemMajPQ6JHubTyiZqEoxlfwj1zWDb6ZcT79Bg8MK5oaofZwlwexzeP7LwySQ6p/NRu6jIV2J60iqtTw3OMk403eYATo+q+oUP3PbMIYcvUOA9qMqwvJL6qIhL9fKoet7y5Hfp2Ey4oTMVLN4yU5iEEy8j8ZADAsSq9rL1PCPGEBzU5VH6KavMuLsRlgzPxyMwhPj9GRoIWSoUAs4XhuTWHMePlzU77/5qsy619ydTw8pPJwmAwuZ5c7Kr8pLEGLh12X0uZGgKTNQpXKZxfaqn81BSZQc3X+85h7uvbpIDmP/PHerWjra+yEmOQnRgDCwMOnKVsDfEfH5SWQ1skRLXx/dPx1ryx0vBUXygVgrSi6bXNJ3G4Qi/1GtrjQU2iD0uo4+zeZ92VoFxt96CxThe2D2p4cMSzON1R9z2zCOGuURiwLdWLxPLTiu2nsOC9XegwWXD5kEz8Z/5YrwdI+WOEtVmYSlAkEM7S4D0SQNmdguNPdp1xKPUAQJN1CXWiD5kahUJAnHUFk7tl3bUuJiPz6kKHmTI1xA7P1LiavijNqomw8tN/t53CY5+KTcH/d1EB/n3rmJAGNICtBLX3TENIn5dEnsZWozSjg8pPJBDsM34pOjWa2k1Yc6DC4ZhGP8pPgG3/J1eZmnajWdqGoXP5Seum/BRLQU30MprdNwpH4uqnd7aW4gnrKqdfT+iDxVcPdfn9BxNlakigFJ9rAiBmabrzjA7Sc/Am42tG5WL++D4AgPd/Oi3dzxizlZ98aBQGPM+q4f00aqUgzd/hNEpnQY34/9puHNTQT2eQSY3CLnpqIm3103+3luLJzw8CAO6c2Bd/mjk4bIPKRuQlAwBO17WivqVDmgZKiLd4UDM0NzHMZ0IixR0T+qJXSiyuGpmLxjYjXv72KLaX1OFcYxtykmLRZjRLmX5femoA26aWrhZL1NkN3uv8Pi01CjstP3XffEj3PbMIITUKu1r9xPd/au5wu7SvJ9h/phGLviwGANw1qV9YAxoASNKpUZgmNvPto2Zh4oficjGoKaKghgRIUqwavxzbGzqNCjlJsdJWMSXVLQBspSeVQpCm+3rLU/mptsX5vk+A89VPtiXd3TdTQ0FNkJmsc2pcLelOi9dAEACzhaG+tSOUpxZQHSYL/vjRXpgtDFcMz8HDMwZ1i1HyQ6w7dp+w7tNCiC8OlotB8dDcwG7pQQjHG9DPWBvS+eC9xFi1z++lfAWU3kWjsLudxjVK6+onc9fyEzUKRzFeflK6KD+plQqk6sQXVE8uQb266TgOV+iRGqfBn68Z2i0CGgDSssuy+u6/B1RtswG/e3833thy0u2wLBJaBpMZx61BMWVqSLD0ShHfq87UW4MaPnjPj80jeabGZfmJL+d2suGys0yNwUTlp6jHG4VVbhplbc3CPTOoOXSuCf/YcBwAsPjqoV2WBoaTFNR0840tzRaGez/YjS/3luOprw/h8c8O9PhyZKQ4VtkMk4UhKVaNXJpRQ4KkV4qYqTlrDWoaW/1b+QTYemo8NQo722mchu8Rp8welnQDdsu6e2BQYzKLZSeThWFaURauHJET7lNy0Nsa1Jzu5kHNX9cdwQ/Ha6FVKSAIwIrtp7Fs4/FwnxaBrZ9maG5it8lAksjDg5oz1qyybYsE34MavvrJdfnJ+b5PgKugRvx/WtIdpRhjHhuFAVsttbSmJSTnFUhv/VCCA2ebkBSrxlPXDut2b/q9pUxNW7ct6dQ2G/DPTScAAM9fPwKLrhoKAFi1/1w4T4tY8X6aohwqPZHg4Z8DfMhjY5v/QY388lPX7LpW6Xr1k5bKT9HJZFc+cLWkG7AtE93fw1bo1LV04O/rxWzCY7OGIDOx+6Xmc5NjIAhi135Nc/dsxN59ugEWBgzIjMc1o/KkbNfhCj1qImwoY08kLefOo6CGBA/vqTnX2A6T2WJrFPZxOTcQhPKTicpPUY03CQOA0k2mZlieuKLiwNnGbptNcOaV9cegN5hQlJOI68/vFe7TcUqrUiLHGmx11xLUnrIGAMAo6wTktHittGrrxxO1YTorwp20LrEdkJkQ5jMhkSwzQQu1UtzkslJv8GuHbs7z8D3X5Se1k+F7bR3W1U+0pDs6GS22F4O7RuEhOYlQKgTUtnRIm+Z1d7tO1+PdbacAAI9dMSTou277gzcLn+mmK6B4UMO3dQCA8f3SAAA/Hq8JwxkRrrXDJP02688GhoR4olAIyOXLuuta/d4iAbDt1O0qqLEfvtcZz9QY7MpPBhq+Fxxbt26FQqHAU089Fe5Tcctsl6lx1ygco1ZiQKY4MrsnlKDe3XYKv/z3VpgsDJcNzsT4/unhPiW3+IfR6druF9RYLEzam2qUfVBjvaY/nKCgJpzKrf0NCVqVXx8uhMhh31fjzw7dnBTUOGkUNluYtO+Ts74dKj+FiMViwX333YcLLrgg3KfiEc/UCILrvZ84vk/R/m6+T9Hmo9V4/LMDMJoZZgzNxss3jgr3KXnUnVdAnaxpgb7dhBi1AoOzbeWNC/qkQqUQUFbX1i2DsWjBZ4bkpdAmliT4bCug2gKy+sndROGWDtttzvYz43s/8bEkZguD0fqLOq1+CqB///vfGD9+PAYPHhzuU/HI075P9oZb+2q6c6bGZLbgL1+J2yDcPK43/nnLeX79FhEqvbvxAD5eehqelwSVXTYvTqvC6N7JAIBtJ6mvJlx4UNOLghoSAnnJ4nvV2fo2NFobhYNVfuIropQKQdqR217nTA1f+QREWKZGr9fjoYcewrRp05CRkQFBELBo0SKnxzY3N+O+++5Dbm4uYmJiMGrUKHzwwQc+n2xtbS2WLl2KhQsX+vwYoWSbJuy536QnNAu/99NpHKtqRopOjYdmhHdfJ2/kp4ofSGV1bWE+k672dmoStjc4W2wW7o7BWLTgy2v5yhRCgknK1DS02pWf/JgobBfUdP5c4UFNvFbl9L1c6yaocRYEdRden1ltbS1ee+01GAwGXHvttW6Pve666/D2229j4cKFWL16NcaOHYu5c+fivffe8+lkH3nkETzwwANISuoZ+6/w8pO7GTVcd28WbjGY8Ld1RwEAD0wd2KP6C3hPTXljm0N9uDuwrXxK6XJfVqI4O6Kyqfu9HqKFVH5KpkwNCb48u6nCvPzkV6bGGhAxBrR2mB3uazaIf3dWegK67tLNN7PUqBTdemGI10FNQUEB6uvr8d1332HJkiUuj1u1ahXWrVuHV199FXfeeScuvfRSvP7665g6dSr++Mc/wmy2XeApU6YgJibG6Z8nnngCAPDzzz9j165duOOOO3z4NsNDzjRhzr5Z+JB1LkZ38v5Pp1HfakRhmg5zL+gd7tPxSka8FjFqBRjrfiugSmvF5cKDsrsuF+ZzfyqbaFZNqByuaILe+mECAGetrxcqP5FQKEjjpfI2aQqwPz01sWolePzReQAfbx6O0zovJWmUnTM1fDl3983SAIDXeS25JYdPP/0U8fHxmDNnjsPt8+fPx0033YTt27fj4osvBgCsX7/e4+Nt2bIFxcXFyMzMBCCWtpRKJY4ePYp33nnH6dcYDAYYDLYPhKYmMVgwGo0wGo1Ov8YX/LE6P2abQVwup1IIsp6vT5oOhyv0OFbZhIn9UwN2fv4ymCx4ffNJAMCvLykEs5hhtJg9fFVwuLrWnvRJi8OhCj2OnGtEfnL32Juq3WiW3rhSYxVdvqd0nfjjWdnUHtDXqxy+Xuee7NA5Pa5+dSumDM7Av24eDcCWqclKUAftWkTjtQ6HnnCd03UqDM1NwMFyvXRbrNK/c47TqqBvN6G+uR0psbYAprFVzADrNEqnj6+AGMQYTGYYjUY0t4mfpTFq58fbC8a1lvtYvhfrPDhw4ACGDBkClcrxKUaMGCHdz4MaOe644w5cf/310t8feOABFBYW4uGHH3b5NUuWLMHixYu73P7NN99Apwt8jXzdunUOfz/VDAAqdBjasWrVKo9fb25QAFDgu12HkdNYHPDz89XWSgGVeiWS1AwxFfuwatW+cJ9Sl2vtic4kXtuvv/8ZHSXdo2epth0AVFALDJvXr0Pn3xfOtoj3n6lpkvX6caXdBOyvFzA8hcHb8ry317kn21MrAFBib2kVVq1aBZMFqNKLF+zQzh9Qtje4zx9N1zqcuvt1HqgRcBBi8KFRMHz7zRq/Hk9pUQIQsHbjdyiIt93+U7X4em9rqnf6/qI3AoAKRjPD11+vQqn188zSIe/zDAjstW5tlZdlD1pQU1tbi759+3a5PTU1VbrfG/Hx8YiPt/2L6HQ6JCYmIi0tzeXX8B4crqmpCfn5+Zg2bRoSEwM38txoNGLdunWYOnUq1GpbqnDX6QZg/09IiNNh1qwJnh9nTzm++fgATLo0zJo1NmDn5w/GGP7+9x8BtOCeKYNw9fjCsJ6Pq2vtyanvTuLnb49DkdwLs2YND+IZyrf7dAOw+ydkJetwxRVdXx91LR14ft8mNJsEXD5thlTj9kZbhxn/t3wn9pQ14reT++L3U/rL+jpfr3NP1rGnHDh6AEZBjVmzpoulwe0/IFatwJyrZwatMT4ar3U49JTrfEGzAV+8sBlmC0NKXAxmzZrk1+P948QPaKhqwagx43BRX9vnZd3208DxwyjMy8asWaO6fJ2+3YjHd24EAFw+fQZ+PlUPHPgZqUkJmDXLfUIiGNeaV1o8CVpQA7gvVfn7BrF8+XKPx2i1Wmi1XUsNarU6KC/qzo/LBPFDSK1SyHq+gTliA3RJTWu3+aHbW9aA49Ut0KoUmHthYbc5L2//DQdbr+3x6pZu8z3UWZdsZiZonZ5TZpIKaqUAo5mhwWBBXqx3ZTOzheHBj/diT5k4JuB4tfevq2D9rHRH1gnwaGo3QaFUoVIv/vvkpeig0XSduBpo0XStw6m7X+ecFDUmDkjHxiPVSIz1/1zjrWM32kxweKw2k5ixTojVOH0OnV3LLRMUMDHxMztWo5R9ToG81nIfJ2gdP2lpaU6zMXV1dQBsGZtIJjUKy5hTAwB90uMAADXNBmlEdrh99PMZAMCMYdk9YiaNKwOzxEbcE9XN0r9LuFXrxRp1ZoLzjUAFQZDu82UF1MbDVVhXXCn9nZaGu9dmXR3CmLhD8hlqEiZhcsOYfAC2xmF/8NVNrhqFXa5+slvg0mGySI3C2m48owYIYlAzfPhwHDp0CCaT44Xcv38/AGDYsGHBeupug09ilLOkGwASYtTITBB/Gz9Z3Ry085LLYDLji73lANBtN6yUKz9VB61KAYPJgrJuMlm4yhrUZCS4zsDwZd1VPqyAOlIpNhsOs+4u7ez7Zoxh45EqnGvsfjN8Qq3NbslrfWuHNKOGlnOTUJs5PAf/mTcWf77G/8/JBBdThXmQ42r1k0IhSHsWdpgt0pLu7jx4DwhiUDN79mw0Nzfj448/drj97bffRm5uLsaNGxesp+42+PA9d5tZdtY3Q8zW8J2Bw+nb4io0thmRkxSDi/t17/2dPFEqBPS3Lpk/Wqn3cHRoVMsIanimpkrvfaaGBzHjrf92Te0mNLY6ZgB3na7H/P/swP3/2+P140eaNrvhYg2tHThLWySQMLp0cKa0waU/4jRiUKPvtP+TbU6N6ww87+Mzmpg0fC/ilnQDwOrVq9HS0gK9XvxwKC4uxkcffQQAmDVrFnQ6HWbOnImpU6fi7rvvRlNTE/r374/3338fa9aswbvvvgulMnTR3rJly7Bs2TKH2TihYJKG78l/EfTLiMe2k3U4WRPeTM2hc0144vMDAIDrzsuTNRW5uxuQGY+D5U04VtWMaUPDfTb25SfPmRpfyk98r6tB2QlIj9egprkDZfWtSNLZhleebRAfd9fpBhjNFlkzlSKVfVBT32JEhfWa5yZRUEN6Lj6Ar3P5yTZR2PVnsUalQGuHGR1msy2o6eaZGp+CmrvvvhunTp2S/r5y5UqsXLkSAFBSUoLCwkIAwCeffILHHnsMTz75JOrq6jB48GC8//77uPHGG/0/cy8sWLAACxYsQFNTU0inERt9ytSI2YRwZmqOV+lx0+vbUN9qxPC8JPxmYr+wnUsgDbD21XSXTI2c8pM/A/h4UNM7VYdeKToxqKlrlbbkAGx19Q6TBSeqm6WtGaJR5/ITDyTdBZ2EdHcJLvZ/4htaxrnoqQFsfTUGkwUG6xC+7ryZJeBjUFNaWirruPj4eCxduhRLly715Wl6PG8mCnPhLj+1G81YsGI36luNGNkrCe/8alyP2hLBnYFSUBP+fiVAXvkpK9G3RmGj2YJya09I71QdeqfqsKesoUuzsP303ANnm6I7qHEoPxmloJMHloT0RDxoae5UftK3ywhq7PZ/smVqunc2t3ufXQ/nbaMwAPRLFzM1JTUtYVml8/TXh3CkUo/0eC3euG1sxAQ0ADAwS7y23WEFlMXCUNPsfvWTeJ8Y8PAASK7yhjZYmLjxXEaC1uWmnva/vR0s7747xIeCfaamvNE2pj4zkTI1pOeK99Ao7Gr1E+AqqOnemRoKaoLIZPG+/JSXEosErQodZkvI94D6+VQ9/rtNLCv+9YaRbjMIPVFecizUSgEdJovULxEu9a0d0usjLd71DBRfMzX2pSdBEJBv3WX6dF3nTI1dUHO2++05Fkr2mRpeooxRK6T0PSE9Ubyr8pOcoIbv/2SmJd0EgIlnamTOqQHEVTpj+4gzfLad9G7qsr/e2VoKQGwMnjgwI6TPHQoqpUL6cD9VE97VZdXWLE1qnMZteZI3Cte3GmEwyW9058EL36Gc/7dr+ckxU2PpJjN8wqHdLqg5UiGWKLMSY4I2SZiQUHAV1DQbPJeftHaZmjYqPxEpU+NF+QkALuzLg5q6gJ+TK9V6A1btPwcAmH9xn5A9b6gVWgccltaGd1YNnzvjqQk1KVYtpYC9mVVjn6kBIAVzZ+rbHAKXZoOtp6alwyztGh6NWu3KT7bSYGRlK0n0cRbUMMbQ0mF2uN8Zp+UnFWVqwm7ZsmUoKirC2LGh3U+Jz6nxdpnshdb9ObaX1Ias9+PDnWUwmhlG5SdjeK/QrRALNT6hM9wf3nKahAE+Vdg6gM+LWTVlnTI1OckxUCrE0hvPEgFdZ1ccKI/eEpR9+YmjJmHS0zlrFG43WqTPFlfD9wC7oMau/BSroaAm7BYsWIDi4mLs2LEjpM9rtM6p8XbGS1FOIhKs28WHoq/GZLbgve2nAQC3XlgQ9OcLp8I0a6amm5Sf5PQt8e0p+LAsOTpnatRKBXKSYhzuEx/Tur+RdcjXwbPR2yzc3uEkqKFMDenhnE0Utv9/PpzPGbXDkm4qP0U9W6bGu6BGpVTgghD21aw9WImzDW1IjdPgihE5QX++cOLlp1PdpPwkJ6jhvxm1OfnQdeV0rWNQA9gCF77UG7D99jYkR1zubp/FiTZOMzVuVqYR0hPw8lJrh1nKzkhbJGiUULj5pZs3ChvNVH4i8K1RmOMlqFAENW98fxIAcMuFBd1+uZ6/Cu3KT+FsipUyNfGegxodD2qMJg9HihpbjWiyBit8KTdgC446rEO0AEjH8TJLqxfZoEjT6iRozKLl3KSHs28E5gP35DQJA517asT3je7+GUFBTRD52igM2PfV1AW1r+bnU/XYfboBGqUi4ktPgJitUCkEGEwWVPqwn1KgNLR2AABSdK6Xc3P8TaStw+LhSBEvL6XHa6GzSy3z4NrkpFE4y5qR4G960cZiYdLEVHuUqSE9nValkKoFPDMrZzk34LxRWEvlp+hl8mGiMFeUG5q+mtc3i1maa0fnRtxcGmdUSgV6WTcoLK0JXwmKl5LcNelxfCy5s/KIM7Z+Gsc9i/gbG88gGu2a/7KTxH97Z9mKaNBut1zefi4NDd4jPZ0gCFLwwoMZnqnhg/lccb6kmzI1UYtPFPZlM0ilQpD6araeCE4Jasuxaqw5WAFBAH51Sd+gPEd3ZOurCV+zMA8eYt006XFS+UlmFqVzkzDHX4c82LZfDcEzEp03vYsW9v1K2Um27EwWZWpIBOBlJn2noMZdkzDgfPhed9/7iYKaIJIahX3c4fqifsHrq2nrMOOxT8VduG+7qBCDshMC/hzdFV8BVRLGoIb/1qOTsTwyxudMjWNQwzOG/HXJ39hi1AokWrfDiNZMDf++Y9QKpMSJJUGNSoHEWJomTHq++E7LulsMPFMsv/xkoExN9xG2OTUWvveTb5eZ99X8FIS+mn99dwKn61qRkxSDP0wfFNDH7u74rJpTYSw/8YyInN96bKuf5PXUdJ5Rw/HtOvioAT6jJiFGLQVXrVHaU8P7BWLVSqToxAAvK1FL04RJRODLuvn7jq2nxv37Dw9qDCaLVKKlJd3dQLjm1PDfiH1pFAaAITmJSIhRQW8woTjAQ9FWHxCnB/9x+iCPzWKRhmdqOu+DFEq83CEnU6NTe7f6yVWmhr8OzdbXJd+hO0GrktLQLVG6+smWOVNJzdvUJEwihcvyk6dMjVJ872k3mmG0vm/Qku4oJjUK+7CkGxB7IMYFYV5NTbMBRyvFvW0mD8oM2OP2FDnJ4ofVucY2D0cGB2MMrXYfop54M6fGZLbgrHUOTe+0zpka68wJi2P5KSFGBZ3WVuKKxv2f2uzKT8lSUENNwiQy+NoozDM19pPHqfwUxfxpFObG9RFLULtO1wfknACxnAUAg7MTkBrneUlxpMlJFFcF1bcaHTYxDJUOs21EuU7G6idvemrONbbDbGHQKBVdmlxVnVY/2b+x2TcMyu3diSQ8yIzVKHF+QQoEAVKjPiE9XdeeGuvPvqdGYWtQ09hm2yOOr4jqrqKr7hBivk4Utsf7P+ynwPqLZ314z060SYxVQadRorXDjIrGdmk1VKjYZ1x0Mn7rsfW7eA42eOmpV2psl0mhqk6rn/jgvXitCjFqBQQBYEycVaOJ6d6/jQUa3yIhVq3E1KIs7Fs4DQnW7SkI6ek6b2rp7fC9yiZxpldSrNrtBOLuoHuHXD2cv43CgG15aUVT4AbF8SXi0RrUCIIgXdfyMJSgeHCiUSpkvTZ4M7GcrJKrfhrA9jqUVj/ZNQoLgiBla6JxqnDnGRwU0JBIEt9p/yfZw/esv5DzknZPmGVGQU0QSROF/Yhs+Ydvtd4glQ38UdNswLEqsZ9mXBSn13OTxBJURWPopwrzFUZyd7uN0cgvP7kLatRSpoavfhJTyvyNjWeEonGqsDdL7AnpafzN1PBfxORs6xJuFNQEkb+rnwAgPU4LlUKAhQVms0FeehqcnSDN44hGPFg8F5agxrsPUF6i8qb85CyoUfJGYXPXRmHA9gYXjbNq2uzKT4REGv4zzntjaprFbVrS491/BvDVT1w6ZWqim9GPDS05hUKQVmEEIqvw3vbTAIAJA9L9fqyeLCcpfCugbNOE5X2A8uPaZQQbrmbUAHZLuq2ZGlv5qVOmJgqnCrd5+W9CSE/CxxNUNhnAGJN6ZLIS3Y8t0HRqCqZMTTcRvuF7/jcKA3Z9NX4GNdtP1uLHE7VQKwXMG9/Hr8fq6XKs5adzDaHP1Ej7PslYzg14t/eT2/KTtPqpc6Ow2uF8ojJT00OmpRLiCx68VDa1o9lgkn7GPe1t1iWooUxN9xC+4Xv+Z2qAwDUL/+3bowCAX47NR15yrIejI1tONyg/eZup8RRsdJgsaGgV08vZTn4DU3aZU2PtqeGZGm0UZ2qop4ZEMP4ZUtfSgbI6MTudoFV5nJOlUVJQQ+xIjcJ+Zmp4lO1PUPNTSR22nayDRqnAPZP7+3U+kYAP4AvkqjK5eKOw3A9QnqkxmCxuB+PxerkgQNrLyZ66c/mpc09NNGdqqKeGRLAUnVrKuuw/2wBA3g70lKkhDqRGYT8zNTl25ac/f1mMEYvWSr0Tcr22+SQA4Bfn90JulGdpANsAvrqWjpAP4PO2Udg+o8P3X3GmoVVs/kuMUTsd+Kjq1Cgs7f1kbRCOpdVPVH4iEUkQBGRZg5i9ZxoBeO6nAboO2qOemihnlObUBCZTU97QhpU7y9DUbsL6Q5Wyv/5kdTPWHxaPv2NCdPfScHwAHxD6Zd1S+Uktr6fGfq8Vd1mUemvpiW/I2Jk0fM/cuVGY99RYy1zROKeGGoVJhOMl6f1eBDWUqSEOAjFRGLA1te4pa5A2JDvoxQaXb35fAsaAy4dkol9GvF/nEinCOYCvzcvyk0IhSDvjutv/iWdq+N5FnUnbJFhLWPpO+7/oonlJN/XUkAjHg5jDFeJnh6zyk11PjUJAj9hWh4KaIApYo7D1xcjLBgBwQGZQ02ww4eNdZwAAv7qkr1/nEWlyArSqzFvelp8AeVOFeZNwsqtMjd1EYYPJjA6T+Prkg7mkTE00lp+op4ZEuM6fI533hnPGPlOTFq/1ax/DUKGgJogC1SjsLKI+VqmHwU1/Bbe3rAHtRgvykmNxYd/onSDsjLSsO9RBjRc7dHM6GU289dZMTYqLTI39ROFmu113bROFrTv5RnGmhnpqSKTimWlOTvlJbZep6Qn9NAAFNUFl2ybBv8sco1Z2SfuZLAxHK5o9fu3eMw0AgFG9kyEI3T/KDqVc6w/5mXrvmq791eZDpkYqP7nL1LS5z9Qo7Ta0bLdmabQqhXR7nJb31ERhpsZImRoS2ToHMVlern7qCf00AAU1QSVNFPYzUwPYXpBalQIXWPdsOlDe6PHr9pWJx4zsleT3OUSawTmJAIB9Zzxfx0Dic2C8aUqNlbH/k9RTE+siU2NXfjJagxr7mrktUxN9QU07NQqTCOdLpkZLQU33FLaJwrxR2M9MDQBkW6Pq0b2TMTo/GQBwUE5QY83UjOiV7Pc5RJrRvZMBAIcr9CHtI/GlKVVnXSnlrlG4vsW6+inOVU+NGFwbzRYp4FbbvWlJmZoILD9ZLAyf7znrcluMVmoUJhGu80BOOUGK/S896VR+6j7CNVHYHKCeGgDonymuWrqkfzqG5olZlwNn3TcLV+nbUd7YDkEAhuVRpqaznKRYZCfGwGxh0jLHUPClUVjaqdvd6qc296ufeJnJbGHo4EGN3WtTytREYPnpq/3n8PsP9uCWN7ZLDdL2+HWlnhoSqex7M5N1almvdYVCkEZBUKaG2ObUBKBjfMGl/fHinJH4zcR+GJYrlk0OVzShWu96525eeuqfES81gxJHPFuzu6whZM9p2yZB/r9JrJyeGg9zaqTyk4VJKyDsGwEjeaLwumJxTtOJ6hYs/7HE4T6LhcFgDXSop4ZEKq3K1pspZ+UTx/tqKKiJcmYLA7OuwFYp/b/MyToNrj+/FzQqBQrT4pCTFIN2owVT//Ydvt53zunX8NLTSGu5inQlBTWn60P2nHxOTZw35SeNjPKTh54aHlzbl58cemoidO8ns4Vhy7Fq6e9Lvz2GhZ8fwOOf7UeLweQwpZl6akgk4300cmbUcFJQQ+Wn6MY/NIDAlJ/sKRQC3po3FkU5iWhoNWLBe7vw1vclXY7j47CpSdi10b1TAAC7TjeAMdf7KgWStxtaArayiJxMjcs5NQpbozAvwagdGoVtPTWhuhahsKesAQ2tRiTGqDAyPxktHWa8vfUU3t12Gqv2n3PITNlPbyYk0vDeTDlNwlyf9DhoVAqpBaK7o6AmSEx2Gw8GolG4syE5ifj8t+Mxf3whAODPXxXjjS0npfuNZouUfaAmYdeG5SZBpRBQrTegPMDzahhjDsEtZ+up8ab85D6oaeswSyWUFBdTP+0nCks9NaquPTXi/ZET1Hx3pAoAMGFABl7+5ShcNzoPg7MTAABl9W2oaRZLuMk6NRQ9YLgYIb7qlaIDAK/2//vvr8Zh0x8mU/kp2pntPhQCnanh1EoFnryyCPdfPhAA8PzaI9JGlz8cr0FTuwnp8VpqEnYjVqPEEOvS7kCXoG5+YzsmPLcRpTUt0m2MMa936bY/1lX5iZeeVArBZVlLrbQN3zO6ydQAkTVVeNNRsfQ0aVAG+qTH4a+/HIWrRuYCEPdTO1MnrojqlUIbvZLIdseEPvj1hD64eVxv2V8Tr1X1qE2QKagJEt4kDASmUdgVQRBw75T+uLhfGjpMFjz1dTEAYNV+sc9m5rDsHjHaOpxsfTUNAXvM0poW/HiiFhVN7fj1OzvRbO1TMZgs4Ek8n+bUuAhqbKUnjcshi0q78pOzRmG1UiHVz9317vQk1XqDNIdo8sAM6fbcZDH9fra+DWcbxKAmrwe9cRPii4K0ODx2RZFX5aeehoKaIOEzapQKIeiTfAVBwKKrh0KpELD2YCVW7z+HtQfF1R6zhucE9bkjQTCahb87amtMPVbVjIc/2gfAMVjQebHSxlNPTYO0RYLzfhrAbpdui/NGYcDWvBwpWyVsPCyWnobmJiLT7o08L1lMw5c3tkkTpXlqnhDSc1FQEyTSNOEQZUkGZiVg3sWFAIB73tuFxjYj0uO10vRh4trofLFZ+EB5k6z9tOTYZO3juGJEDgQB+Hr/OZxrbJOGvGlUCq9WxfGeGlfLreul5dyud9G1nyjsbE4NIG+PqZ5k3SExuJ9alOVwO8/UnGtoR1kdZWoIiRQU1AQJbxRWB2A5t1wPzRiEqUVZ0lLyGcOyqPQkQ0GaDik6NTpMFhw6p/f78dqNZmw9WQsA+O2l/TGmQAya1hyokPZV8nZyLT/e1S7dfPBekptMjf3eT85WPwH2U4V7fk9Nu9EsLeXuHNRkJcZAIQAdZou0Pxr11BDS81FQEyRmS+D2fZJLq1Ji2U3n4ZpRuYhVKzH3AvnNYNFMEARpaXcgSlA/ldSh3WhBVqIWg7MTMH1oNgBg9YEK28onL4e8eS4/uR+8BzhmZXhwZL9NAmCXqTH0/EzN98dq0G60IDcpBkXWZnBOrVRIY+P5Lu1UfiKk56OgJkh4I2aoyk+cRqXA0htHY/+iaRiaS6ue5DovgM3Cm45YV9sMzIAgCJgxTAxqdpTW4bR1dZq3Q95iNR7KTy28p8Z1+cm+3MV7e7QuMjWR0FPzrbX0dHlRltO+ts4rOvIoU0NIjxcVQU04NrQ0SUFNeC5xIKYYRxMpU1Pmf6bmZ2u255IB4mqbXik6jOiVBMaAz/eUA/BuRo14vKfyk5ipcVd+sg+weW9P5/JTpOzUbbEwfHtI7GvqXHri7IOahBgVkmJdXztCSM8QFZ984djQ0hiG8hPx3YheSRAEoKyuze1+WnKcqhXn0gywm8DJszU8e+B1pkbtaUm3jEyNXVDDH8d++B5gHzx1HRrYk+w904CaZgPitSqM65Pm9Bj7oIaahAmJDFER1ISDyckcENJ9JcSoMTBTnDK7y4++msZWo9Tf0jvV1qNx7ag8h6DCm32fAFtPjasGXmlOjZtsg9JZUNPp9elplVVPwTewnDQoQ5q905l9uYn6aQiJDPSJGySmAO7QTULjPOsqpZ9P+R7UnKoTszQZCVrE2e2Mnpsci+vOy5P+7nv5yXkGhQ/3S4hxHdQIgiC9HqWl5Z2DGg9D/rzV2GrEjtK6kO8lxTNiU4c4Lz0BQF6ybW4NrXwiJDJQUBMk9sP3SM/Al17vLK3z+TFKa8VG4MK0rr/53z25v91xLV3ud4dnUDrMFpic7CfFgxre6OsKL4e6ytRIm1q62TjTG49+uh9z/rUV20t8v6beOlXbgqOVzVAqBFw6KNPlcfblJwpqCIkMFNQECc/UUPmp5xhTKAY1+882umzI9eS0NVjpnRrX5b4+6XFS78ZwL/fjsu/Bcbasm5eL7LNDzvDNVduMYhDkqlE4UJmaE9XNAIDjVc0BeTw5eOnpgsJUt43TeRTUEBJx6BM3SKQl3dQo3GP0TtUhPV4Lo5lJ+wV5y12mBgC+vvcS/HH6INw/daBXj6u16wtxVoKyZWrcBzVK6+ux1UWjsKeGZG/xXp/a5o6APJ7JbPEYcK63rnq63MWqJy4hRi2teKKeGkIiAwU1QSI1CodpSTfxniAIthLUKd/KJXzlU0F610wNIG44ueDS/l5vKCcIghTYdP5QN5ot0oRgTw3IfMQAD1o699TYyk+BWdLNdw+vafZvRRn3wId7cf5f1qHcugllZ80GE3ZYy4dTBrsuPXGLrx6Kuyb1w9DcRI/HEkK6P/rEDRJefqKemp6Fl6B+LvWtWfiUNVNTkBr43/z5CqjO+1PZT//11IDMpwq3uZhTExvAJd1tHWYYrMFWbYv/QY2+3Yiv959DS4cZ20tqnR7zw/EamCwMhWk6FLoILO1dOzoPf5o5OOibzhJCQoOCmiAxUfmpRzqfr4A6XQ+LxbsVO60dJlRZZ9wUpnn+QPVWjJpnahwDDj4oT6NUuFy+zKk6l5+CuKSbZ2kAoEbvf/lp28k6mK3/Jq56dPju6JMGZvj9fISQnoeCmiChRuGeaWhuErQqBRpajThZ411zK98CIVmndtug6iueqelcfmqRufIJ6Fp+crVLdyB6ahyCmgBkar63bk4JOA9qGGP4zrpFxWQ3q54IIZGLPnGDJFx7PxH/aFQKjMxPBgDs9LIEVVoTvNITAMSonJeG+D5NcmbfSHNqeHZH5bz8FIgl3bxJGABq/JzSDABbjtVI/+8sqDle1YyzDW3QqBS4sK/zKcKEkMhGQU2Q8DQ5ZWp6HluzsHdBjdQkHITSE2BffnKeqYn3sPIJsO0JxitrrubUBDpT09RukpqZfXG2oQ0na2yzfU7VtsLYaV4PLz2N65Pq9TYUhJDIQJ+4QcLfcKlRuOeRmoW9DGr4QD1Xy7n9peXlJ5PzoEYnq/zk+Hp0GdQEIFNTb5epAfxrFualp1H5yYjTKGGyMKkpm+M7rI/vn+7z8xBCejYKaoLEZKFG4Z7qPOuO3SU1LV4tRT5aKZZE+mclBOW8bD01zhuF5WVqOgc1nebU2AU1/u5s0NDi2Bzsz6yarSfE1U4TB6Sjn3Wj0M4lqMMVTQCAITm0PJuQaEVBTZDwUfY0p6bnSdZppB225WZrGGM4WqkHAAzMivdwtG9iXMypaTHwnhrPmZrOr8euc2rEwIgxwN9V3Z0zNdV+zKo5UC4GLKN7p6Bfhnh9+bRiQLwmfPDhoCAFlYSQ7o8+cYOEJgr3bN6WoKr0BujbTVAqBPSRMR/FFx5XP8loFO5cDlV3bhRW2wKjDj+DmobWwGRq2jrMOGkNYIpyE9HfSabmRHUzzBaGpFg1shK1Pp4xIaSni4qgZtmyZSgqKsLYsWND9py8UZhWP/VM5xekApC/uSXP0hSk6aBVBadJlTcKG0zOVz952iIB6Bpkd87UKBWCtCLK36CGNwrzH4FaHzM1hyuaYGFAerwGmQlaKVNjH9Tw6z8oK4EG6RESxaIiqFmwYAGKi4uxY8eOkD0n76lRUvmpR+JD+A6UN3VZZeMM76cZmBm80oenTI2cRuHOjcHOVufxMpa/C6B4+am3dYm7r1slHLSWnopykyAIAvpnipmwE9XNUpn3cIU1qMmm0hMh0Yw+cYPEwnhQE+YTIT4pTNMhMUaFDpMFR6wfmO4crxKPGRCkfhrAdVDDZ87E+1B+0qi6ZjV0fDuGAJWfeLnI1/ITD2r4/kwFaXFIjFGhtcOMF745AgA4av03GkhBDSFRjT5yg4Rvk0CZmp5JEARpCN+esgaPx/NMzYAgNqnaGoUdo41m3igso/zUebWTs0xNrJSp8a+MwzM1/a3ZK18bhYvLxR3Ti6yrmtRKBZ65bjgA4N/fncTX+85JgedgCmoIiWr0iRsklKnp+Ub2SgYA7PUQ1IRi5RNgN6emc6ZGGr4nf5sEznn5SQyO/OmpMVsYmtp5UON7psZktkilJfudtK8ckYs7J/YFADy4cg/KG9sBBLf8Rwjp/ugjN0h4o7CSmhZ7LJ6p2Xumwe1xoVj5BNiVn0ydMzXWnhovtkng3GZqLGLAxjoNrDlT34p/bDiGxk5Ltu01thmlOTf9MsRr4ktPzcmaFhhMFsRplF02Cf3j9EG4dFCGlLnKSYoJyp5bhJCeg4KaIKFG4Z5vZK8kAMCxqmYpcHAmFCufANfbJPAdtX0Zvtd59RNgaxQ2mIGb3tyBua9vcwhsXt10Ai9+cxQrfy5z+Ty8nyZBq0J2UgwAoK6lw+udzw9aS09DchKh6BSQqZQKLLv5PCn4pKF7hBD6xA0Si4XKTz1dZmIMcpNiwBhw4Gyjy+NKrXsS8aXGwWLb0NLF6icZw/dUnVc/OWkU5rNqatsF7DzVgG0n61BrNx24wlrq4f91hvfTJMepkRYnzo0xWRga21xnd5wpqRavrateJZ1GheXzxuLey/rj4RmDvXpsQkjkoY/cIDFbf7Pt/Nsl6VlGyOir4f0cecmxQT0XXn4yuNgmQdacGi/KTzV2MUtVk610xOfP1LW67pHhmZoUnQYalQIJMeK51bZ411dztkE8iV4prq9tSpwGD0wbRMu5CSEU1AQLDd+LDLy0se+M60zNuYY2AGJPRzBJ5acuG1p6MXyvUznU2etTJwU1tvsq9bYIp94amNS7CVCkTI1OAwBIjBF7XfTt3mVqyq3XNjc5uNeWEBIZKKgJEh7UKKhRuEcbkiP+9s83S3SGZ2pyQpSpsS8/McbsMjVyhu/ZXo8apcLp9F3ecFxj19db7ZCpEQOTOjeNwrZMjRjM8EyNvt11b5Iz5xqtQU1ScK8tISQyUFATJGZpSTcFNT0Zbz4trW3t0svC2T54Q5SpsSs/2e+m7e3eT51n1nC8p6bVZLu/ypqpMZktUl9MXYvr1Uz1duUnwD5TIz+osViYFDDmBjlgJIREBgpqgsRC5aeIkJmgRbJODbOFOew1xFksTGqYDXamRuukUZiXngTBcTNKV+wbhTtvZsk5aziu0osBTINdo299i+tMTaU1s5MeLwY1tkyN/PJTbUsHOkwWCAKkFVSEEOIOBTVBwpd0U6NwzyYIAgZZV9442y6hptkAo5lBIQBZCcHdHZo38DoGNdaVT2qlrNea2iFT40VQYw1S7HfebjaYYDA5z16dqW8FAORb933ypfzE+2myEmJcnishhNijd4ogsdDwvYjBR+8766vh5ZHMhJguy6UDzdnwPW9WPgGAslNPjbvnscfLT3WdsjMNLvpqyurEgKRXCg9qvG8UpiZhQoi3KKgJEuqpiRyDrX01h51kaqSVTyH44OV7P3WYLFLQ7M3KJwBQ261+ctVT42wyMS8/1XVa8dT574DYd1PRJAZB+dal2DxT0+RFpuasFNRQPw0hRB4KaoJE2iaBgpoej88/cVZ+khpZQ7A6xz6DYrBma7xZ+QQ4ThT2qvykN4Ax5lB+Apwv6z7X2A6zhUGjUiA9XizJJfjQKFzeEJr5P4SQyEFBTZBQUBM5Blp7aqr0hi4f4qGaUQM4BjW8r6bFi32fgE6Nwi6CmlgnQU2HSVz11HngnrMBfGXWfppeKbFSn48tU+NL+YmCGkKIPBTUBAkFNZEjXqtCfqr4wdq5BHUuRCufAPG1xEtGfABfq0H+vk+A42o8jYzVTxqVAonWgMRZUOes/HSmUz8NACTGet9TIy2Vp6CGECITBTVBQrt0R5bB2WJfzZFOzcLl1g/evBA1s9r2fxLLT81e7PsEdApqZJSfMuI1yEoUv7eqJoM0eI9zGtTwlU92Wxv4svqJb5EQiiwYISQyUFATJNQoHFn4su5jnWbVnJM+eEOTTdB2mircau2pkZupUTvMqXExfM+ulJUWr0FmotgXU6VvlzI1fP6Ms56aM/VOMjVeBjXtRjNqmsXmZOqpIYTIJe+dkHjNQuWniNI/U9yBmwc1b31fguPVzdJS51CsfgLspwqLQU2ztfwkt6dGKWNOjf0Qv4x4LRJjxQCmSm+Qemj6ZcSjprnO6VYJZdKMGvtMjXflJz7QMFatRLJ1qwVCCPGEgpogoeF7kYUHNcermtHQ2oE/f1Us3adWCkiPC+7gPc62/5N19ZPBu9VPai9XP6XHa6VNKauaDNJcmr4Z8dheUic7U2NffmKMOd1zyl653VJ5T8cSQghH5acgoV26I0u/jHgIgthDsvFIFQBbo+2wvKSQBa+dd+rmJZq0OI2sr7ffpdtVT41WpQCPIzLiNchIsJWfeA9Nv4w4AF17agwmszSjpldK10yNycIc9q5yRcr22AVGhBDiSVRkapYtW4Zly5bBbHY+0j0YLIwahSNJrEaJ/BQdTte14qOfzwAAZg3LxkMzBksre0KBNwobrOUnHkDI3RtJ6ZCpcf7aFAQBOrUSLR1mpCdokRYvPnZFY7u0JLufNXNV39p5iXs7GBPLRvaBVpxGCYUAWJhYgnK2bNwez/bYl7AIIcSTqMjULFiwAMXFxdixY0fInpPKT5FngPWD/McTtQCAkfnJyE2Old2kGwidy0+V1t4TvkLJE8eJwq5//HnQkRGvkVYfHSxvknYE75MmZmpqWzrA+I1wnFFjXzYSBEG6TnKmCpfV8cehTA0hRL6oCGrCgXbpjjz9s8Sghn+Gj8pPDvk52DcKWyxM2r5AbqbGYaKwizk1gK2clZcci5H5ychI0KLNmh1KilVLJakOkwWtHbYMKN/JvCCtazDCS1ByBvCV8UwNBTWEEC9QUBMkfEk3ZWoix4DMBOn/1UoBQ6x7QoWS/ZLumhYDTBZxh/CMeHmNynLm1ADAM9cOxY19zSjKSYBaqcANY3pJ96XGaaDTKKG1BkX2fTU/n6oH4Dzgsw3g85ypOWOX8SGEELkoqAkSs5l6aiINLz8BwJCcRKe7WQebNHzPZEFlo5ilSY/Xyt4h3HGbBNevzRG9knBRlm2V0o1je0v3JevUEAQBqdZsjn1fDQ9qzi9I7fKYthVQ7jM17UYzKpvE7y0/lTI1hBD5KKgJEhq+F3n62QU1I3slh+Uc7MtP3jYJA46ZGnc9NZ3lp+owYUA6ACDVusSbBzVV1gDkbEMbzjW2Q6UQnGdqZA7g48u54zRKpNCMGkKIFyioCRKzddUqBTWRI16rkqbbjgxDPw3g2CjMgxq5TcKAvF26Xblncn/EqpWYODADANAnXWwWPlEt9tHsLK0DAAzNTXS6uknuAL4yuzk3NKOGEOKNqFjSHQ5mixjVUFATWe6a3A/fHKzAtKFZYXl++0wNX/mU7U1QYz+nxk2jsDMX9UvDgcXTpde02GN0TmoO3lkqlp7OK0hx+vVy93/iK59oOTchxFsU1AQJ7dIdmW69sAC3XlgQtueX5tSYzNJmlt6Un9Qy5tS4Y/967rx1xE5rP80YJ/00gPygxtlEYkIIkYOCmiCxxjTUKEwCyr78xKcJe1N+krP3k1wDrEvcT1Q1Q99ulHYwH1PoKlMjb0l3Ga18IoT4iHpqgsRE5ScSBA6Nwj6Unxx26fYzqClMi4NSIUBvMOGLveWwMLFk5CrIkp2pkcpPlKkhhHiHgpogsVCjMAkC+zk1ttVP8jfTdJhT42VPTWcalUIasvfm9yUAgEnWJmJnEnmmps19psZWfqJMDSHEOxTUBAkt6SbBwMtPda1GKePh1eonGRtaeoPP7jlZ3QIAmDQw0+WxcjI1zQYTaq3D/KinhhDiLQpqgoAxJjUKK6inhgRQjDW7crpWDCLiNEqpV0UOf5Z0O9PfbnaPRqnAxf3SXB4rLek2uM7U7DndAEDcniEphBuFEkIiAwU1QWCx7e9Hez+RgOKZmvpWMTDI8mLlE9A5qPH/tWm/dcTYPimIc7O5Jx++19RmctgE095P1lk3Y100GxNCiDsU1ASB2S6qob2fSCB13prBmyZhwLH85G5DS7nsMzWT3ZSeACAnORYalQKNbUYcLG9yesxPJeIO6Bf0cZ3xIYQQVyioCQL7oIZ6akgg9UmPkxp8BQGYMsS7IYBKhQBeEQ1ET02/jHgpGzlpkOsmYUCcyDy1SDzfj34+0+X+DpMFu63lpwv6OJ91Qwgh7tCcmiAw26XWqfxEAikjQYttj0xBY5sRGQlaxLsp97iiVijQYbYEpKcmVqPEi3NGQm8wYWBWgsfjrz+/F77edw6f7zmLR2cNcViBtf9sAwwmC9LiNOiXEef3uRFCog8FNUHgUH6iRmESYKlxGmkzSV8oFQJgDkxPDQBcOzpP9rET+qcjM0GLKr0BGw5XYcawbOm+n0rEicRjC1NpzydCiE+o/BQEVH4i3RlvFg5Epsb751Zg9nliEPTxLscSFO+nGUulJ0KIjyioCQLHTE0YT4QQJ/gQvEQvloIH0mxrZue7o9XSjt37zzRi87EaAMBFfalJmBDiGyo/BYHFbvAepdFJd/PsL4ajtKYFvdPCM9xuUFYC+mbE4WR1i1SCenDlHpgtDFeOyEFRbmJYzosQ0vNRpiYITHyHbgpoSDc0YUAGbr2oMGzPLwgCrhieAwD4et85vPTNURytbEZ6vAZ/vmZY2M6LENLzUVATBBYLbZFAiDuzrEHN+sNVeG3zSQDA07OH+9UATQghFNQEgZmCGkLcGpydgD7pcdLPyq8n9MH0odkevooQQtyjoCYITNK+T2E+EUK6KUEQcM2oXADAxf3S8PCMwWE+I0JIJKBG4SDgjcKqMCyZJaSnuHtyPwzOTsDEgRn0s0IICQgKaoKAdugmxDOtSokZw3LCfRqEkAhCvx4Fga2nJswnQgghhEQR+tgNAh7U2O+ITAghhJDgok/dIOAbWlJMQwghhIQOfewGgZmG7xFCCCEhR0FNENCcGkIIIST0KKgJApooTAghhIRejwpqJk+ejJiYGMTHxyM+Ph6XXXZZuE/JKRMt6SaEEEJCrkcFNQDwxhtvoLm5Gc3NzdiwYUO4T8cpszR8j4IaQgghJFR6XFDTE1ioUZgQQggJOa+DGr1ej4ceegjTpk1DRkYGBEHAokWLnB7b3NyM++67D7m5uYiJicGoUaPwwQcf+HXC999/PzIyMjBlyhTs2bPHr8cKFqn8RD01hBBCSMh4vU1CbW0tXnvtNYwcORLXXnst3njjDZfHXnfdddixYweeffZZDBw4EO+99x7mzp0Li8WCm266yeuTff7551FUVASlUol//vOfmDFjBo4cOYKkpCTvHqilBVAqu96uVAIxMY7HuaJQALGxti9tbxePV6shNLcgtqMdcUbrbZ2ORWsrYC1RdSEIgE7n27FtbYDF4vqc4+J8O7a9HTCbA3OsTieeNwAYDIDJ5N2xRqPDtZbExtoGA3V0AEaj68f15tiYGNtrxZtjjUbxeFe0WkCl8v5Yk0m8Fq5oNLbr4s2xZrP4b8d1vs5qtXi8s2M7sz/WYhFfa4E4VqUSrwUg/ky0tgbmWG9+7v14j3B5rNEIRed/J3qPEP/fl/cIV1R2H3f0HuH9sd783JvNzt+nnR3rzXuEHMxLFouFWSwWxhhj1dXVDABbuHBhl+O+/vprBoC99957DrdPnTqV5ebmMpPJJN122WWXMa1W6/TP448/7vJchg4dylatWuXy/vb2dtbY2Cj9KSsrYwBYo/gW0OWPeeZM1tHRIf2x6HROj2MAM0+cKB3X0tLC2hMTXR97/vmOj1tQ4PJYy5AhjscOGeL62IICh2PN55/v+tj0dMdjJ050faxO53jszJkuj2WA47HXXef+2Pp627G33ur+2LNnpWNNd93l/tijR23HPvCA+2N377Yd+/jjbo81/vij7dglS9wfu26d7dilS90f+9ln0rHGN95wf+x779mOfe8998e+8Ybt2M8+c3usaelS27Hr1rk/dskS27E//uj+2Mcft70mdu92f+wDD9iOPXrU/bF33WU79uxZt8eab73Vdmx9vftjr7vO4TXs9lgf3yM6OjqYJT3d5bF1/fuzlpYWeo9A8N4jWouL2WeffcZaWlroPSLI7xFtmze7P9aH94iamhoGgDU2NrqNUbzO1Agy+0Q+/fRTxMfHY86cOQ63z58/HzfddBO2b9+Oiy++GACwfv16b08DAKBQKMAYc3n/kiVLsHjxYtmPV1VVhe2rVkl/v8JsdpnKqqutxQ92x85w87iNjY3YbHfs1NZW6Fwcq29uxka7Yy9tbkaii2PbWluxzu7YiY2NSHFxbEdHB9bYHTu+thbpLo41m81YZXfsuKoqZLs4FoDDsWMqKpDn5ti1a9fCbP1Nd/SZM+jt5thvv/0WHdYs3IhTp9DHzbEbN25EW1YWAKDo5EkMcHPsli1boD91CgAw6NgxDHZz7A8//ICGqioAQP/DhzHUzbHbtm1DrfW38T4HD2KEm2N37tyJSuv/5+/di/PcHLt7926UW3/bzt29G2PdHLtv716UWf89snbuxIVujj148CBKrMem7d+PS9wce/jwYRy3Hpt87BgmuTn22LFjOGI9NuH0abhbo3jy5EkUW4+NrazENDfHnj51Cvusx2oaGzHTzbFnzpzBbuuxyvZ2XOnm2HMVFdhp9xq+xs2xfr1HdHRA6+ax161bJ/0/vUcE/j1iy5YtQFYW1q1bR+8RPfA9otVdttWOwNxFBR7U1NQgIyMDCxcu7NJXc9FFF8FsNuOnn35yuP3gwYMYNmwY/v3vf+M3v/mN7OdqaGjAjh07MHHiRAiCgH/961946qmncOTIEaSkOP8xNRgMMNil1pqampCfn4+aU6eQmOjkbcDH1LLRaMSGL7/EZZddBrVajS/3ncNjnxXj4n6p+NfNo6n8ZM/P1LLRaMSGDRukay2h8pMoQKnlLteZyk/eHyuz/GQ0GrF+40ZMufJK22ua3iPE/w9g+cmoUmHd+vWYOnUq1IzRe4S3x3rxc29sb8eGVau6vk87OVbue0RTUxPS09PR2Njo/PPbyutMjVy1tbXo27dvl9tTU1Ol+71hNBrxyCOP4PDhw9BoNBg5ciRWrVrlMqABAK1WC6226+9G6uRkqN1cFElysuzzM8fEiI+rVsOs06NNEwOzLg5qZ4/hTQ+QN8c6e/FE2rFGo8O1Dss5yD1W5+p3bT+Ptf+gDOSx9h/W7q5z52M9cfIzGJBjvamze3OsFz/3ATnWaIRFq4VarbZda3qPCPyx1mDD4TqH+hw6H9uT3yM88Pg+bU/Gz73cf7OgBTWA+1KV3DIWl5GRgZ07d/p7SiFBez8RQgghoRe0OTVpaWlOszF1dXUAbBmbSMSH79E2CYQQQkjoBC2oGT58OA4dOgRTpxrn/v37AQDDhg0L1lOHHe39RAghhIRe0IKa2bNno7m5GR9//LHD7W+//TZyc3Mxbty4YD112NHwPUIIIST0fOqpWb16NVpaWqDX6wEAxcXF+OijjwAAs2bNgk6nw8yZMzF16lTcfffdaGpqQv/+/fH+++9jzZo1ePfdd6F0NvwuSJYtW4Zly5bB7K7jPoB4T42KghpCCCEkZHwKau6++26csq7hB4CVK1di5cqVAICSkhIUFhYCAD755BM89thjePLJJ1FXV4fBgwfj/fffx4033uj/mXthwYIFWLBgAZqamryfPuwDC6NGYUIIISTUfApqSktLZR0XHx+PpUuXYunSpb48TY9F5SdCCCEk9GiX7iCwUPmJEEIICTkKaoLAbB3CSZkaQgghJHQoqAkCs3W0OPXUEEIIIaFDQU0Q0PA9QgghJPSiIqhZtmwZioqKMHasu71LA4eXnyioIYQQQkInKoKaBQsWoLi4GDt27AjJ8/HyEzUKE0IIIaETFUFNqFGjMCGEEBJ6FNQEAQ3fI4QQQkKPgpogMPHVT5SpIYQQQkKGgpogoEZhQgghJPQoqAkCPlGYghpCCCEkdKIiqAn1km4TBTWEEEJIyEVFUBPqJd3UKEwIIYSEXlQENaFmpl26CSGEkJCjoCYIzLRLNyGEEBJyFNQEAWVqCCGEkNCjoCYIzNRTQwghhIQcBTVBQOUnQgghJPQoqAkCKj8RQgghoUdBTRBIS7rp6hJCCCEhExUfuyEfvmfmQU1UXF5CCCGkW4iKT91QD9+jRmFCCCEk9KIiqAk1295PYT4RQgghJIrQx24Q2PZ+ostLCCGEhAp96gYBNQoTQgghoUcfu0EgLemmnhpCCCEkZCioCQLb8D26vIQQQkio0KduENiG74X5RAghhJAoQh+7QUBLugkhhJDQo6AmCKTyk5KCGkIIISRUKKgJAmoUJoQQQkIvKoKaUG+TYBu+R0ENIYQQEipREdSEepsEEwU1hBBCSMhFRVATarbhexTUEEIIIaFCQU0Q8J4aWv1ECCGEhA4FNUFA5SdCCCEk9CioCQJqFCaEEEJCj4KaIODD92hJNyGEEBI6FNQEAQ3fI4QQQkKPgpogoEZhQgghJPQoqAkwxhisMQ0U1FNDCCGEhAwFNQHGszQAoKKghhBCCAkZCmoCjDcJA5SpIYQQQkKJgpoAs1hs/089NYQQQkjoREVQE8oNLU12UQ3NqSGEEEJCJyqCmlBuaOmQqaGghhBCCAmZqAhqQsm+p4bKT4QQQkjoUFATYLz8JAjUKEwIIYSEEgU1AcbLT5SlIYQQQkKLgpoAk/Z9oiwNIYQQElIU1ARIq0n8r9ls3feJghpCCCEkpFThPoGerq3DjEkvfIcqvQrTpxmlTA2VnwghhJDQokyNn2I1SvCkzNHKZmmbBCo/EUIIIaFFQU0ADMpKAAAcrbIFNVR+IoQQQkKLgpoAGJAVD4AyNYQQQkg4UVATAIOsQc2RymZYqKeGEEIICQsKagJgoDWoOVbZDJM1U0NbJBBCCCGhRUFNAPRLj4MCDA1tRlQ0tgGgoIYQQggJNQpqAkCrViIjVvz/g+VNACioIYQQQkKNgpoAydGJZadiCmoIIYSQsKCgJkByYsWg5tA5a1BDjcKEEEJISEVFULNs2TIUFRVh7NixQXuOHJ343/LGdgC0pJsQQggJtagIahYsWIDi4mLs2LEjaM/RK445/J2G7xFCCCGhFRVBTSikxQBTh2RKf6dMDSGEEBJaFNQE0MMzBkr/v7esIXwnQgghhEQhCmoCqCBVh8mDMgAAg7MTwnw2hBBCSHRRhfsEIs0/bz4fr285iUkDM8J9KoQQQkhUoaAmwGI1Stw7ZUC4T4MQQgiJOlR+IoQQQkhEoKCGEEIIIRGBghpCCCGERAQKagghhBASESioIYQQQkhEoKCGEEIIIRGBghpCCCGERAQKagghhBASESioIYQQQkhEoKCGEEIIIRGBghpCCCGERAQKagghhBASESioIYQQQkhEiKpduhljAICmpqaAPq7RaERrayuampqgVqsD+tjEEV3r0KDrHDp0rUODrnPoBONa889t/jnuSlQFNXq9HgCQn58f5jMhhBBCiLf0ej2SkpJc3i8wT2FPBLFYLCgvL0dCQgIEQQjY4zY1NSE/Px9lZWVITEwM2OOSruhahwZd59Chax0adJ1DJxjXmjEGvV6P3NxcKBSuO2eiKlOjUCjQq1evoD1+YmIi/bCECF3r0KDrHDp0rUODrnPoBPpau8vQcNQoTAghhJCIQEENIYQQQiICBTUBoNVqsXDhQmi12nCfSsSjax0adJ1Dh651aNB1Dp1wXuuoahQmhBBCSOSiTA0hhBBCIgIFNYQQQgiJCBTUEEIIISQiUFDjh+bmZtx3333Izc1FTEwMRo0ahQ8++CDcp9Vjbdq0CYIgOP2zbds2h2N37dqFyy+/HPHx8UhOTsZ1112HkydPhunMuze9Xo+HHnoI06ZNQ0ZGBgRBwKJFi5we6811/fvf/47BgwdDq9WiT58+WLx4MYxGYxC/k+5P7rWeN2+e09f54MGDnT4uXWtHGzZswO23347BgwcjLi4OeXl5uOaaa/Dzzz93OZZe076Te5271euZEZ9NnTqVJScns3/9619sw4YN7I477mAA2IoVK8J9aj3Sxo0bGQD2zDPPsK1btzr80ev10nGHDh1iCQkJbMKECezrr79mH3/8MRs6dCjLzc1lVVVVYfwOuqeSkhKWlJTEJk6cKL1GFy5c2OU4b67rU089xQRBYI888gjbuHEje/7555lGo2G//vWvQ/RddU9yr/Vtt93GYmNju7zO9+zZ0+VYutZdXX/99ezSSy9lr776Ktu0aRNbuXIlu/DCC5lKpWLr16+XjqPXtH/kXufu9HqmoMZHX3/9NQPA3nvvPYfbp06dynJzc5nJZArTmfVcPKhZuXKl2+PmzJnD0tPTWWNjo3RbaWkpU6vV7KGHHgr2afY4FouFWSwWxhhj1dXVLj9o5V7XmpoaFhMTw37zm984fP3TTz/NBEFgBw8eDM430gPIvda33XYbi4uL8/h4dK2dq6ys7HKbXq9nWVlZbMqUKdJt9Jr2j9zr3J1ez1R+8tGnn36K+Ph4zJkzx+H2+fPno7y8HNu3bw/TmUU2k8mEr776Cr/4xS8cxm8XFBTg0ksvxaeffhrGs+ueeCrYHW+u65o1a9De3o758+c7PMb8+fPBGMNnn30W0PPvSeRca2/QtXYuMzOzy23x8fEoKipCWVkZAHpNB4Kc6+yNUFxnCmp8dODAAQwZMgQqleP2WSNGjJDuJ75ZsGABVCoVEhMTMX36dHz//ffSfSdOnEBbW5t0ne2NGDECx48fR3t7eyhPNyJ4c135a3v48OEOx+Xk5CA9PZ1e+zK1tbUhOzsbSqUSvXr1wm9/+1vU1dU5HEPXWr7Gxkbs2rULQ4cOBUCv6WDpfJ257vJ6jqoNLQOptrYWffv27XJ7amqqdD/xTlJSEn7/+99j8uTJSEtLw/Hjx/HCCy9g8uTJ+PrrrzF9+nTpuvLrbC81NRWMMdTX1yMnJyfUp9+jeXNda2trodVqERcX5/RYeu17NnLkSIwcORLDhg0DAHz33Xf429/+hvXr12PHjh2Ij48HALrWXliwYAFaWlrw2GOPAaDXdLB0vs5A93o9U1DjB3dp5kCmoKPF6NGjMXr0aOnvEyZMwOzZszF8+HA89NBDmD59unQfXfvgkHtd6fr75/7773f4+9SpUzF69Ghcf/31eP311x3up2vt2RNPPIEVK1bg73//O84//3yH++g1HTiurnN3ej1T+clHaWlpTqNKnm5z9tsB8V5ycjKuvPJK7Nu3D21tbUhLSwPgPBNWV1cHQRCQnJwc4rPs+by5rmlpaWhvb0dra6vTY+m175vZs2cjLi7OYXwBXWvPFi9ejKeeegpPP/00fvvb30q302s6sFxdZ1fC9XqmoMZHw4cPx6FDh2AymRxu379/PwBIaTjiP2bdnkwQBPTr1w+xsbHSdba3f/9+9O/fHzExMaE+xR7Pm+vK6+Gdj62oqEBNTQ299v3AGINCYXtbpmvt3uLFi7Fo0SIsWrQIjz76qMN99JoOHHfX2Z2wvJ79Xj8VpVatWsUAsA8++MDh9hkzZtCS7gCqq6tjeXl5bNSoUdJtN9xwA8vMzGRNTU3SbadOnWIajYY9/PDD4TjNHsPdMmO517W2tpbFxMSwu+66y+HrlyxZEtXLXztzd62d+d///scAsJdfflm6ja61a3/+858ZAPb444+7PIZe0/6Tc52dCdfrmYIaP0ydOpWlpKSw1157jW3YsIH9+te/ZgDYu+++G+5T65Hmzp3LHn74YbZy5Uq2ceNG9tprr7FBgwYxlUrF1q1bJx136NAhFh8fzyZOnMhWrVrFPvnkEzZs2DAavufGqlWr2MqVK9lbb73FALA5c+awlStXspUrV7KWlhbGmHfXlQ/QevTRR9mmTZvYCy+8wLRabVQPKuM8XevS0lJ28cUXs1deeYWtWrWKrV69mv3pT39iMTExbOjQoay5udnh8ehad/Xiiy8yAGzGjBldBr5t3bpVOo5e0/6Rc5272+uZgho/6PV6du+997Ls7Gym0WjYiBEj2Pvvvx/u0+qxlixZwkaNGsWSkpKYUqlkGRkZbPbs2eynn37qcuzOnTvZlClTmE6nY4mJiezaa69lx48fD8NZ9wwFBQUMgNM/JSUl0nHeXNelS5eygQMHMo1Gw3r37s0WLlzIOjo6QvQddV+ernVdXR2bPXs2KywsZLGxsUyj0bABAwawhx56iDU0NDh9TLrWjiZNmuTyGncuQNBr2ndyrnN3ez0LjFkbFgghhBBCejBqFCaEEEJIRKCghhBCCCERgYIaQgghhEQECmoIIYQQEhEoqCGEEEJIRKCghhBCCCERgYIaQgghhEQECmoIIYQQEhEoqCGEEEJIRKCghhBCCCERgYIaQgghhEQECmoIIYQQEhEoqCGEEEJIRKCghhBCCCERgYIaQgghhEQECmoIIYQQEhEoqCGERK1NmzZBEAQsWrQo3KdCCAkACmoIIbKVlpZCEATMmDFDum3evHkQBAGlpaXhOzE3BEHA5MmTw30ahJAQUIX7BAghJFwuuOACHDp0COnp6eE+FUJIAFBQQwiJWjqdDoMHDw73aRBCAoTKT4QQnxUWFuLtt98GAPTp0weCIDgt95SUlOCOO+5A7969odVqkZOTg3nz5uHUqVNdHpN//dmzZzFv3jxkZ2dDoVBg06ZNAICNGzfi9ttvx6BBgxAfH4/4+HiMGTMGr732msPj8H4ZAPjuu++kcxMEAcuXL3c4xllPzcGDB/HLX/4SmZmZ0Gq16NOnD+6//37U1dU5vQ6FhYVoaWnBAw88gLy8PGi1WowYMQIfffSRl1eVEOIrytQQQnx23333Yfny5di7dy9+//vfIzk5GYD4Ic9t374d06dPR0tLC6666ir0798fpaWlWLFiBVavXo2tW7eib9++Do9bW1uLiy66CKmpqfjlL3+Jjo4OJCYmAgCee+45HD9+HBdeeCFmz56NhoYGrFmzBnfeeSeOHDmCl156STqHhQsXYvHixSgoKMC8efOkxx81apTb7+vHH3/EtGnTYDAYcP3116OwsBDbtm3Dyy+/jK+//hpbt25FWlqaw9cYjUZMmzYNdXV1uO6669Da2ooPPvgAN9xwA9asWYNp06b5dpEJIfIxQgiRqaSkhAFg06dPl2677bbbGABWUlLS5fiOjg5WWFjIEhIS2J49exzu27JlC1MqlezKK690uB0AA8Dmz5/PTCZTl8c8efJkl9uMRiObOnUqUyqV7NSpU10eb9KkSU6/n40bNzIAbOHChdJtZrOZDRgwgAFga9ascTj+kUceYQDYr371K4fbCwoKGAB2zTXXMIPBIN3+7bffdrlehJDgofITISRovvrqK5SWluKhhx7CyJEjHe675JJLcM0112DVqlVoampyuE+j0eD555+HUqns8ph9+vTpcptKpcJdd90Fs9mMjRs3+nXOP/zwA44dO4aZM2di+vTpDvc99thjSEtLw3vvvYeOjo4uX/u3v/0NGo1G+vuUKVNQUFCAHTt2+HVOhBB5qPxECAmabdu2AQAOHz7stG+loqICFosFR48exZgxY6Tb+/Tp43JFkl6vx4svvojPPvsMJ06cQEtLi8P95eXlfp3z7t27AcDpMvC4uDiMGTMGa9euxdGjRzFs2DDpvuTkZKcBV69evbB161a/zokQIg8FNYSQoOFNtStWrHB7XOfAJCsry+lxHR0dmDx5Mnbt2oXRo0fj1ltvRVpaGlQqFUpLS/H222/DYDD4dc48a+TqHLKzswEAjY2NDrcnJSU5PV6lUsFisfh1ToQQeSioIYQEDW/u/fLLL3HllVfK/jq+aqmzzz//HLt27cIdd9yB119/3eG+Dz74QFqJ5Q9+zpWVlU7v57fz4wgh3Qf11BBC/ML7Xsxmc5f7xo0bBwABK7+cOHECAHD11Vd3uW/Lli1Ov0ahUDg9N1dGjx4NANIScnutra3YuXMnYmNjMWjQINmPSQgJDQpqCCF+SU1NBQCcOXOmy33XXHMNevfujb/+9a/YvHlzl/uNRiO+//572c9VUFAAAF2+5rvvvuuSubE/P2fn5sr48ePRr18/rF69Gt9++63DfUuWLEFNTQ3mzp3r0BBMCOkeqPxECPHLZZddhhdffBF33nkn5syZg7i4OPTu3Rs33XQTtFotPvroI8ycOROTJk3ClClTpOba06dPY8uWLUhLS8Phw4dlPddVV12FwsJCPP/88zhw4ACGDRuGI0eO4KuvvsK1116Ljz/+2On5ffjhh7j++usxevRoKJVKXHHFFRg+fLjT51AoFFi+fDmmT5+OWbNmYc6cOSgoKMD27duxYcMG9OvXD88++6zvF4wQEjQU1BBC/DJz5kw8//zzeP311/Hcc8/BaDRi0qRJuOmmmwAAY8eOxd69e/HCCy9g1apV+P7776HVapGXl4drr70Wc+fOlf1c8fHx2LBhA/74xz9i8+bN2LRpE4YOHYoVK1YgKyvLaVCzdOlSAMCGDRvw6aefwmKxIDs722VQA4jLzbdt24Y///nP+Oabb9DY2Ijc3Fzce++9eOKJJ2ivKEK6KYExxsJ9EoQQQggh/qKeGkIIIYREBApqCCGEEBIRKKghhBBCSESgoIYQQgghEYGCGkIIIYREBApqCCGEEBIRKKghhBBCSESgoIYQQgghEYGCGkIIIYREBApqCCGEEBIRKKghhBBCSESgoIYQQgghEYGCGkIIIYREhP8H1uNb/1rpNtcAAAAASUVORK5CYII=", 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" ] @@ -1711,9 +1286,9 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "species = \"Barite\"\n", + "species = \"Ba\"\n", "iterations = 250\n", - "cell_offset = 1100 #1250\n", + "cell_offset = 43 #1250\n", "y_design = []\n", "y_results = []\n", "y_differences = []\n", @@ -1729,7 +1304,7 @@ "y_design = pd.DataFrame(y_design)\n", "y_results = pd.DataFrame(y_results)\n", "\n", - "prediction = model_minmax.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n", + "prediction = model_standard.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n", "prediction = pd.DataFrame(prediction, columns = y_results.columns)\n", "\n", "# y_results_back, prediction = preprocess.funcInverse(y_results, prediction)\n", @@ -1742,29 +1317,30 @@ "plt.plot(np.arange(0,iterations), y_results_back[species], label = \"Results\")\n", "plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n", "plt.legend()\n", - "plt.xlabel('Iteration', fontsize=14, labelpad=10)\n", + "plt.xlabel('Iteration', fontsize=12, labelpad=10)\n", "plt.ylabel(\"Concentration\", fontsize=12)\n", "plt.title(species + ' concentration profile in cell ' + str(cell_offset), fontsize=14, pad=10)\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.grid()\n", "plt.legend(fontsize=12)\n", - "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/concentration_profile_reactive.pdf\")\n", + "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/concentration_profile_reactive_minmax.pdf\", bbox_inches='tight')\n", "plt.show()\n", "\n", "\n", "mass_balance = np.abs((prediction_back[\"Ba\"] + prediction_back[\"Barite\"]) - (y_results_back[\"Ba\"] + y_results_back[\"Barite\"])) \\\n", " + np.abs((prediction_back[\"Sr\"] + prediction_back[\"Celestite\"]) - (y_results_back[\"Sr\"] + y_results_back[\"Celestite\"]))\n", - "plt.plot(np.arange(0,iterations), mass_balance, label = \"Results\")\n", + "plt.plot(np.arange(0,iterations), mass_balance)\n", "plt.xlabel('Iteration', fontsize=14, labelpad=10)\n", - "plt.ylabel(\"Mass Balance\", fontsize=14)\n", + "# plt.ylabel(\"Mass Balance\", fontsize=14)\n", "plt.title('Mass Balance in cell ' + str(cell_offset), fontsize=14, pad=10)\n", "plt.axhline(y=1e-5, color='r', linestyle='--', label='Threshold 1e-5')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", + "plt.yscale('log')\n", "plt.grid()\n", "plt.legend(fontsize=12)\n", - "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/mass_balance_reactive.pdf\")\n", + "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/mass_balance_reactive_standard.pdf\", bbox_inches='tight')\n", "\n", "plt.show()" ] @@ -1776,213 +1352,43 @@ "outputs": [ { "data": { - "text/html": [ - "
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\n", - "4 1.872898e-06 0.009764 0.012641 1.688408 \n", - ".. ... ... ... ... \n", - "995 1.220403e-07 0.032096 1.714723 0.000000 \n", - "996 1.220029e-07 0.032055 1.714723 0.000000 \n", - "997 1.219644e-07 0.032017 1.714723 0.000000 \n", - "998 1.219672e-07 0.031982 1.714723 0.000000 \n", - "999 1.220142e-07 0.031950 1.714723 0.000000 \n", - "\n", - "[1000 rows x 9 columns]" + "[]" ] }, - "execution_count": 99, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "y" + "\n", + "\n", + "for i in range(0,iterations):\n", + " idx = i*50*50 + cell_offset-1\n", + " y_design.append(df_design_transformed_scaled.iloc[idx, :])\n", + " y_results.append(df_results_transformed_scaled.iloc[idx,:])\n", + "\n", + "plt.plot(df_design[\"Ba\"].iloc[0:2500])\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 25,