diff --git a/1-first-project/jw/T_DataNormaization.ipynb b/1-first-project/jw/T_DataNormaization.ipynb
new file mode 100644
index 0000000..adb2081
--- /dev/null
+++ b/1-first-project/jw/T_DataNormaization.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "53f57e68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pickle\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b240abfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delim = ';'\n",
+    "\n",
+    "user_count = 100\n",
+    "\n",
+    "base_path = '/opt/iui-datarelease1-sose2021/'\n",
+    "\n",
+    "Xpickle_file = './X.pickle'\n",
+    "\n",
+    "ypickle_file = './y.pickle'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f5464e9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_pickles():\n",
+    "    _p = open(Xpickle_file, 'rb')\n",
+    "    X = pickle.load(_p)\n",
+    "    _p.close()\n",
+    "        \n",
+    "    _p = open(ypickle_file, 'rb')\n",
+    "    y = pickle.load(_p)\n",
+    "    _p.close()\n",
+    "    \n",
+    "    return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3c51ef28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_data():\n",
+    "    if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
+    "        return load_pickles()\n",
+    "    data = []\n",
+    "    label = []\n",
+    "    for user in range(0, user_count):\n",
+    "        user_path = base_path + str(user) + '/split_letters_csv/'\n",
+    "        for file in os.listdir(user_path):\n",
+    "            file_name = user_path + file\n",
+    "            letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
+    "            data.append(pd.read_csv(file_name, delim))\n",
+    "            label.append(letter)\n",
+    "    return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "03ce941d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7ea0c433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_pickle():\n",
+    "#     _p = open(np.asarray(data, dtype=pd.DataFrame), 'wb')\n",
+    "    _p = open(Xpickle_file, 'wb')\n",
+    "    pickle.dump(X, _p)\n",
+    "    _p.close()\n",
+    "\n",
+    "#     _p = open(np.asarray(label, dtype=str), 'wb')\n",
+    "    _p = open(ypickle_file, 'wb')\n",
+    "    pickle.dump(y, _p)\n",
+    "    _p.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "210a3d37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13102, 13102)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X), len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "eadec7ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_data(data):\n",
+    "    fig, axs = plt.subplots(4, 3, figsize=(3*3, 3*4))\n",
+    "    t = data['Millis']\n",
+    "    axs[0][0].plot(t, data['Acc1 X'])\n",
+    "    axs[0][1].plot(t, data['Acc1 Y'])\n",
+    "    axs[0][2].plot(t, data['Acc1 Z'])\n",
+    "    axs[1][0].plot(t, data['Acc2 X'])\n",
+    "    axs[1][1].plot(t, data['Acc2 Y'])\n",
+    "    axs[1][2].plot(t, data['Acc2 Z'])\n",
+    "    axs[2][0].plot(t, data['Gyro X'])\n",
+    "    axs[2][1].plot(t, data['Gyro Y'])\n",
+    "    axs[2][2].plot(t, data['Gyro Z'])\n",
+    "    axs[3][0].plot(t, data['Mag X'])\n",
+    "    axs[3][1].plot(t, data['Mag Y'])\n",
+    "    axs[3][2].plot(t, data['Mag Z'])\n",
+    "\n",
+    "    for a in axs:\n",
+    "        for b in a:\n",
+    "            b.plot(t, data['Force'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ade23f06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# FIRST CELL: set these variables to limit GPU usage.\n",
+    "import os\n",
+    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'  # this is required\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'          # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "d5db75fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "yyt_filtered = le.fit_transform(yy_filtered) # Lables in Zahlenwerte transformiert\n",
+    "XX_filtered = np.asarray(XX_filtered).astype('float64')\n",
+    "XXX_filtered = np.delete(np.delete(XX_filtered, 0, 2), 13,2) # Drops time col and Millis col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "290be797",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10271, 456, 13)\n",
+      "(2568, 456, 13)\n",
+      "(10271,)\n",
+      "(2568,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(XXX_filtered, yyt_filtered, test_size=0.2, random_state=177013)\n",
+    "\n",
+    "print(X_train.shape)\n",
+    "print(X_test.shape)\n",
+    "print(y_train.shape)\n",
+    "print(y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "cf763407",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv1D, MaxPooling1D\n",
+    "\n",
+    "model = Sequential()\n",
+    "\n",
+    "# model.add(Conv1D(32, 3, input_shape = X_train.shape[1:]))\n",
+    "# model.add(Activation('relu'))\n",
+    "# model.add(MaxPooling1D(pool_size=3))\n",
+    "\n",
+    "# model.add(Conv1D(32, 3))\n",
+    "# model.add(Activation('relu'))\n",
+    "# model.add(MaxPooling1D(pool_size=3))\n",
+    "\n",
+    "model.add(Flatten(input_shape = (456,13)))\n",
+    "model.add(Dense(456, activation = 'relu'))\n",
+    "\n",
+    "model.add(Dense(104))\n",
+    "\n",
+    "model.add(Dense(26))\n",
+    "model.add(Activation('sigmoid'))\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer = tf.keras.optimizers.Adam(0.001),\n",
+    "    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
+    "    metrics = [tf.keras.metrics.SparseCategoricalAccuracy()],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "ed97582c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['K', 'T', 'U', ..., 'F', 'H', 'G'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "3f42617d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((10271, 456, 13), (10271,))"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "95526298",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 3635.9205 - sparse_categorical_accuracy: 0.0375\n",
+      "Epoch 2/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 13.2679 - sparse_categorical_accuracy: 0.0344\n",
+      "Epoch 3/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 5.5680 - sparse_categorical_accuracy: 0.0360\n",
+      "Epoch 4/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 58.0553 - sparse_categorical_accuracy: 0.0411\n",
+      "Epoch 5/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 4.0946 - sparse_categorical_accuracy: 0.0382\n",
+      "Epoch 6/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 3.2512 - sparse_categorical_accuracy: 0.0421\n",
+      "Epoch 7/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 3.2490 - sparse_categorical_accuracy: 0.0432\n",
+      "Epoch 8/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 3.2503 - sparse_categorical_accuracy: 0.0411\n",
+      "Epoch 9/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 3.2525 - sparse_categorical_accuracy: 0.0390\n",
+      "Epoch 10/10\n",
+      "321/321 [==============================] - 1s 2ms/step - loss: 3.2529 - sparse_categorical_accuracy: 0.0426\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x7fac487cd2e0>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, y_train, \n",
+    "          epochs=10,\n",
+    "          batch_size=32,\n",
+    "          verbose=1\n",
+    "         )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "8f8fedfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluate on test data\n",
+      "81/81 [==============================] - 0s 1ms/step - loss: 11.4346 - sparse_categorical_accuracy: 0.0312\n",
+      "test loss, test acc: [11.434555053710938, 0.031152648851275444]\n",
+      "Generate predictions for 3 samples\n",
+      "predictions shape: (3, 26)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array(['K', 'T', 'U'], dtype='<U1'), array(['R', 'R', 'R'], dtype='<U1'))"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Evaluate the model on the test data using `evaluate`\n",
+    "print(\"Evaluate on test data\")\n",
+    "results = model.evaluate(X_test, y_test, batch_size=32)\n",
+    "print(\"test loss, test acc:\", results)\n",
+    "\n",
+    "# Generate predictions (probabilities -- the output of the last layer)\n",
+    "# on new data using `predict`\n",
+    "print(\"Generate predictions for 3 samples\")\n",
+    "predictions = model.predict(X_test[:3])\n",
+    "print(\"predictions shape:\", predictions.shape)\n",
+    "fff= [np.argmax(i) for i in predictions]\n",
+    "\n",
+    "le.inverse_transform(y_test[:3]), le.inverse_transform(fff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "fbfbaf01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07d40334",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1-first-project/jw/X.pickle b/1-first-project/jw/X.pickle
new file mode 100644
index 0000000..b0e4e64
Binary files /dev/null and b/1-first-project/jw/X.pickle differ
diff --git a/1-first-project/jw/j_Data_Norm_wth_SW.ipynb b/1-first-project/jw/j_Data_Norm_wth_SW.ipynb
new file mode 100644
index 0000000..6ab3d4e
--- /dev/null
+++ b/1-first-project/jw/j_Data_Norm_wth_SW.ipynb
@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "650490a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Needed Imports\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import isqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1f88b35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'  # this is required\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'          # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "af650242",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delim = ';'\n",
+    "user_count = 100\n",
+    "base_path = '/opt/iui-datarelease1-sose2021/'\n",
+    "Xpickle_file = './X.pickle'\n",
+    "ypickle_file = './y.pickle'\n",
+    "\n",
+    "# Function that opens and reads pickle Data from FS and returns the read data as NumpyArray\n",
+    "def load_pickles():\n",
+    "    _p = open(Xpickle_file, 'rb')\n",
+    "    X = pickle.load(_p)\n",
+    "    _p.close()\n",
+    "        \n",
+    "    _p = open(ypickle_file, 'rb')\n",
+    "    y = pickle.load(_p)\n",
+    "    _p.close()\n",
+    "    \n",
+    "    return (np.asarray(X, dtype = pd.DataFrame), np.asarray(y, dtype = str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1b1469fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function used to save data as a pickle file\n",
+    "def save_pickle():\n",
+    "#     _p = open(np.asarray(data, dtype=pd.DataFrame), 'wb')\n",
+    "    _p = open(Xpickle_file, 'wb')\n",
+    "    pickle.dump(X, _p)\n",
+    "    _p.close()\n",
+    "\n",
+    "#     _p = open(np.asarray(label, dtype=str), 'wb')\n",
+    "    _p = open(ypickle_file, 'wb')\n",
+    "    pickle.dump(y, _p)\n",
+    "    _p.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bd7d0ecd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function that loads data from the picklefiles and prints them into NumpyArrays (one for Data and one for Lables)\n",
+    "def load_data():\n",
+    "    if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
+    "        return load_pickles()\n",
+    "    data = []\n",
+    "    label = []\n",
+    "    for user in range(0, user_count):\n",
+    "        user_path = base_path + str(user) + '/split_letters_csv/'\n",
+    "        for file in os.listdir(user_path):\n",
+    "            file_name = user_path + file\n",
+    "            letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
+    "            data.append(pd.read_csv(file_name, delim))\n",
+    "            label.append(letter)\n",
+    "    return (np.asarray(data, dtype = pd.DataFrame), np.asarray(label, dtype = str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9d1991fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13102, 13102)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load Data\n",
+    "X, y = load_data()\n",
+    "len(X), len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d1faf687",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(13102,)\n",
+      "(13102,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Show Data Shape\n",
+    "print(X.shape)\n",
+    "print(y.shape)        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "752650c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show how many datasets are make how many percent  \n",
+    "X_len = np.asarray(list(map(len, X)))\n",
+    "l = []\n",
+    "sq_xlen = pd.Series(X_len)\n",
+    "ptiles = [x*0.01 for x in range(100)]\n",
+    "for i in ptiles:\n",
+    "    l.append(sq_xlen.quantile(i))\n",
+    "plt.plot(l, ptiles)\n",
+    "sq_xlen.describe(percentiles=[x*0.01 for x in range(90,100)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "09e4c32f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove outliner data from the dataset\n",
+    "threshold_p = 0.99\n",
+    "threshold = int(sq_xlen.quantile(threshold_p))\n",
+    "len_mask = np.where(X_len <= threshold)\n",
+    "\n",
+    "X_filter = X[len_mask]\n",
+    "y_filter = y[len_mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ab5cc1ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sliding Window Function\n",
+    "def sliding_window(data):\n",
+    "    input_data = data[0]['Force']\n",
+    "    _window_sz = 10\n",
+    "    sum_windows_passed = 0\n",
+    "    \n",
+    "    \n",
+    "    data_above_thresh = []\n",
+    "    thresh = 70\n",
+    "    \n",
+    "    values = 0\n",
+    "    \n",
+    "    for i in range(0, len(input_data), window_sz):\n",
+    "        for j in range(min(i + window_sz, len(input_data))):\n",
+    "            values += input_data[j]\n",
+    "            if values % _window_sz >= thresh:\n",
+    "                data_above_thresh.append(values)\n",
+    "        \n",
+    "    return data_above_thresh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d2bcca2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f08c64055e0>]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_data = X[5]['Force']\n",
+    "window_sz = 10\n",
+    "sum_windows_passed = 0\n",
+    "    \n",
+    "    \n",
+    "win_above_thresh = []\n",
+    "thresh = 70\n",
+    "    \n",
+    "    \n",
+    "for i in range(0, len(input_data), window_sz):\n",
+    "    values_sum = 0\n",
+    "    for j in range(i, min(i + window_sz, len(input_data))): # evtl i + win_sz -1 \n",
+    "        values_sum += input_data[j]\n",
+    "\n",
+    "    win_above_thresh.append(values_sum / window_sz)\n",
+    "    \n",
+    "plt.plot(win_above_thresh)\n",
+    "plt.plot(X[5]['Force'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "79e01286",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([14, 15, 16, 17, 19, 20, 21]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "_blep = np.where(np.asarray(win_above_thresh) > thresh)\n",
+    "print(_blep)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "1503bb77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "150\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(_blep[0][1]*10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "eb6d91d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_new = X[_blep]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "6cea3dbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(13102,)\n",
+      "(7,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X.shape)\n",
+    "print(X_new.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "35f783ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      0.0\n",
+      "1      0.0\n",
+      "2      0.0\n",
+      "3      0.0\n",
+      "4      0.0\n",
+      "      ... \n",
+      "307    0.0\n",
+      "308    0.0\n",
+      "309    0.0\n",
+      "310    0.0\n",
+      "311    0.0\n",
+      "Name: Force, Length: 312, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X[5]['Force'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3a05a73",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1-first-project/jw/untitled.txt b/1-first-project/jw/untitled.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/1-first-project/jw/y.pickle b/1-first-project/jw/y.pickle
new file mode 100644
index 0000000..455f102
Binary files /dev/null and b/1-first-project/jw/y.pickle differ