Merge pull request #308 from dapperfu/development/frey-jed/requirements.txt

Added additional requirements.

00-Preface.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -43,7 +43,7 @@
        "        <style>\n",
        "        .output_wrapper, .output {\n",
        "            height:auto !important;\n",
-       "            max-height:10000px; \n",
+       "            max-height:100000px; \n",
        "        }\n",
        "        .output_scroll {\n",
        "            box-shadow:none !important;\n",
@@ -56,7 +56,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
        "array([1, 2, 3])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -287,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -296,7 +296,7 @@
        "array([4, 5, 6])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -344,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -565,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -639,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -649,7 +649,7 @@
        "       1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -660,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -673,7 +673,7 @@
        "       1.895, 2.   ])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -691,22 +691,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x20620443860>]"
+       "[<matplotlib.lines.Line2D at 0x7fda4b255240>]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -733,12 +733,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzIAAAFuCAYAAABa5i2IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gc1bn48e8W9WpVq8uyZMmW3HCjGYzphJgSYrApNyQ/CARMCiTkJiHJzQ25gQQICeQGSLmBWMYECC0U22DAYHC3ZclWlyzJktV73TK/PxattfbKqruzu/N+nscPiUbn7Lujd8/OmTlFpyiKghBCCCGEEEJ4Eb3aAQghhBBCCCHERElHRgghhBBCCOF1pCMjhBBCCCGE8DrSkRFCCCGEEEJ4HenICCGEEEIIIbyO0VUVW61WrFarw890Oh06nc5VLymEEEIIIYTwQoqicOpiynq9Hr1+9OcuLu3I9Pb2uqp6IYQQQgghhA8LCQk5Y0dGhpYJIYQQQgghvI50ZIQQQgghhBBeRzoyQgghhBBCCK/jsjkyzib1jzXOTQghhBBCCKE9zubXj7VImFs7MmOtPCCEEEIIIYQQMHZHRnoVQgghhBBCCK/jsicynqq0tBSz2YzRaGTOnDlqhyM0QvJOqEHyTqhFck+oQfJOezTXkenv78dkMuHn56d2KEJDJO+EGiTvhFok94QaJO+0R3NDy3Q6nf2fEO4ieSfUIHkn1CK5J9Qgeac9OkVRFFdUbLVa6e7udvhZWFiYTPYXQgghhBBCOJhM30F6FUIIIYQQQgivM+GOzNe+9jWHR3en/jt+/Lgr4hRCCCGEEEIIuwkPLfvss8+oqKhw+JmiKNx1112kp6dTVFQEyNAyIYQQQgghxPhMpu8w4VXLzjnnHM455xyHn33yySf09fVx8803T7Q6t2tubsZisWAwGIiNjVU7HKERkndCDZJ3Qi2Se0INknfaMy3LL+fn56PT6Vi/fv10VOdS9fX19qX5JMmFu0jeCTVI3gm1HK2sJVBvIcDfX3JPuI20edoz5Y6MyWTipZde4txzzyU9Pf2Mv1tUVERaWhrh4eH2nw0ODlJcXAzAjBkzSE1NdShTVlZGX18fAAsXLnQ41tLSYp+Tk5qayowZM+zHLBYLhYWFgO2xVEZGhkNZs9nMoUOHAMjNzcVoPHkqOjo6OHbsGACJiYmnfRgKCgpQFIWgoKDTNlyqra2lra0NgOzsbAIDA+3Henp67MPy4uLiSEhIcCh75MgR+wdw3rx5DscaGhpoamoCYPbs2YSGhtqPDQwMUFJSAkBUVBQpKSkOZUtLS+nv70en07FgwQKHY83NzdTX1wOQlpZGZGSkwzkaHioYHh7OrFmzHMpWVlbaHwHm5eVhMBjsx9rb26mpqQEgKSmJmJgYh7LD5z44OJisrCyHYzU1NbS3twOQk5NDQECA/VhXVxdVVVUAxMfHM3PmTIeyRUVFmM1mAgICyMnJcThWX19Pc3MzAJmZmYSEhNiP9fX1UVZWBkB0dDTJyckOZUtKShgYGMBgMJCXl+dwrKmpiYaGBgDS09OJiIiwHxsaGuLo0aOYTCacqaiooKenB4D58+c7PD5tbW2lrq4OgOTkZKKjo+3HrFYrhw8fBiA0NJTZs2c71FtdXU1nZycAc+fOxd/f336ss7OT6upqABISEoiLi3MoW1hYiMViITAwkOzsbIdjdXV1tLa2ApCVlUVwcLD9WG9vL+Xl5QDExsaSmJjoULa4uJjBwUGMRiO5ubkOx06cOEFjYyMAs2bN8og2oqqqiq6uLsD72whFUeyfOWkjPLONAIiIiDjte9Qb24g3DtTym0/bWDMnmK8vibIfkzbiJE9rI3ztOsJisdiPSxvheW3EaNcRw5/TiZhyR+a9996jtbV1XMPKzGYzp07JURTFfpFnNpudlhntItBqtdqPWa3W0447qzctLQ2r1cqJEyfsJ+zUmEbWa7FYnNarKIrTDZcsFou97GTqHe29jqz31Pc68hw6q3f4HDpbV3289brjb+Os3jPly2jn0Gw2Ox1Peaa/zXjqNZlMTo+dqd7hsgAhISGnfemc6W8+3nPorPzImE411nsdGhrCarU6fKE4q3ey59DZORpvvZ6Sh97SRgy3dyaTidra2lHrlTYC+++r2UZM9G/uiW1E36CJvx7oQAG2Vg3wgy8ljbteaSOw/75cR0ztGq+pqcl+cS9thGe1EWOdw4mackcmPz8fPz8/1q5dO/aLGY2nfQh0Op39gzzybsbIMqPt0KrX6+3HnCWcs3qH7xR0dnbaT+SpMY2s19kfwc/PD0VRnMZrMBjsZSdT78j/jlbvqe915Dl0Vu/wOXTWAI23Xnf8bZzVe6Z8Ge0cjvydkc70txlPvcPjbidS73BZgKCgIIc7VcPHpnoOx3qvpxrrvfr7+2OxWFxyDq1W64Q/N56Yh97SRgzn28DAACdOnBi1XmkjsP++mm3EaPV6Uxvx5tEOWvpsF0x9Jivbq3q5JT5mXPVKG4H99+U6YmrXeD09PQwODtpjGC0maSM87zpioqa0IWZPTw/x8fGsXr2aN9980+GYrFomhBBCaEfXgIkLH91Oe5+JhSmRHKrtYF5COP++73zZaV0IMSa3b4j52muvec1qZUIIIYRwnWc/qqS9z8Ts2BD+8h9L8TfqOdLQxaG6TrVDE0L4qCl1ZDZu3EhoaChr1qyZrnhcbng8prOxmkK4iuSdUIPknXCXpq4B/vKJbQL19y/PITLQwJW58QDk7zqmZmhCQ6TN055Jz5Fpbm5m27ZtrFu3zmHFAU9XVFRkX9Hj1NVLhHAVyTuhBsk74S5Pvl9Gv8nC4tRILs+Np6CggKUzBngdePNQAz+5eh7hgc7H2wsxXaTN055JP5HZvHkzZrNZhpUJIYQQGlbZ3MOLe2yr4j14RY59PkxOtB8p4Ub6TRZeO3BczRCFED5q0h2ZjRs3EhcXxyWXXDKd8bhceHg4ERERDmvQC+FqkndCDZJ3wh0e21KKxapwUXYsZ2fY9qkIDw8nMjKSa/Js/z9/V82kllYVYiKkzdOeKa1adiayapkQQgjh2w7VdnDN05+i08Hb961kboLjBWRnn4nlv9rGoNnKK3efy5K0GaPUJITQOrevWiaEEEIIbVIUhUfeLQbgukVJp3ViACKC/bh6QSJgeyojhBDTSToyQgghhJiwHWUt7Kxoxd+g57uXzhn199avSAXgrYJ6Ovuc7xQuhBCTIR0ZIYQQQkyI1arw63dsT2NuOTuNlKjRVy89KzWSnJlhtuFl++vcFaIQQgMmvfyyt6qsrMRsNmM0GsnIyFA7HKERkndCDZJ3wlXeLKjnSEMXoQFG7l2dedrxU3Pv5hWpPPR6Efm7a7j9vHT7ymZCTCdp87RHc09kuru76erqOm0ykRCuJHkn1CB5J1xhyGzlsS2lAHzzggyiQvxP+51Tc++axUkE+Rkob+phT3W7W+MV2iFtnvZoriMjhBBCiMnbtLuGmrY+YkID+MbKWeMqEx7ox5qFw5P+j7kyPCGEhmhu+WWLxWL/3waDQcVIhJZI3gk1SN6J6dYzaObCR7fT2jvEf1+bx61npzn9PWe5N7xUs79Rz67/vJgZTp7kCDEV0uZ5N1l+eRwMBoP9nxDuInkn1CB5J6bbn3dU0to7RHp0MDctSxn195zl3oLkCHITwxmSSf/CRaTN0x7NdWSEEEIIMXEtPYM893ElAA9cno2fYWKXEDqdzr4Uc/7uGlw0IEQIoSHSkRFCCCHEmJ76oJzeIQsLkiO4Ki9hUnVcsyiJEH8Dlc29fF7ZNs0RCiG0RnMdmfb2dlpbW2lvl1VThPtI3gk1SN6J6VLT2sfGLybpP3hFDnr9mZdPHi33QgOMrFmUBGCvT4jpIm2e9mhuH5mamhpMJhN+fn7MmDFD7XCERkjeCTVI3onp8tjWEkwWhZVZMZyXGTPm758p925ekcqm3TW8V3SClp5BYkIDXBW20Bhp87RHc09khBBCCDF+hcc7ef1gPWB7GjNVeUkRLEyOwGRReHmfTPoXQkye5p7IJCUlYbVaVV8GWmiL5J1Qg+SdmA6PvlcCwJqFieQlRYyrzFi5t35FKofqDrNpdw13rswYc6iaEOMhbZ72aK4jExMz9iNxIaab5J1Qg+SdmKqd5S18XNqMUa/j/svmjLvcWLn35YWJ/PKtoxxr7WNnRSvnZ0muiqmTNk97pMsqhBBCiNMoisKv3y0GbPNa0qJDpq3uYH8j1y62TfrP3y2T/oUQkyMdGSGEEEKc5u3DJyio6yTY38C9q7Omvf7hPWW2FDXS1D0w7fULIXyfdGSEEEII4cBksfLbLba5MXeszCA2bPpXFpubEM7i1EjMVoV/7pVJ/0KIidPcHJlDhw7Zl+ZbuHCh2uEIjZC8E2qQvBOT9dLeWqpaeokO8eeOCzImXH68ubd+eSoHajp4cU8Nd184Wyb9iymRNk975ImMEEIIIez6hsz8blsZABtWZxIa4Lp7nlcvSCQs0EhtWz87yltc9jpCCN+kuY5McHAwISEhBAcHqx2K0BDJO6EGyTsxGX/7tJrm7kFSooJYvyJtUnWMN/eC/A185axkADZ+LpP+xdRIm6c9OkVRFFdUbLVa6e7udvhZWFiYrO0thBBCeKj23iEueHQ73YNmfnfjIvvKYq5U1tjNpU98jEGvY+cPVxMfHujy1xRCeJ7J9B2kVyGEEEIIAJ7eXk73oJm5CeGsWZjoltfMig9jWfoMLFaFzXtq3fKaQgjfIB0ZIYQQQlDX3sfzn9mGdz14RbZbJ94PL8X84u4aLFaXDBQRQvgg6cgIIYQQgie2ljFksXJORjQXzol162tfmZdAZLAf9Z0DfFTa5NbXFkJ4L80tv1xTU4PZbMZoNJKamqp2OEIjJO+EGiTvxHgVn+ji1QO2vVwevDIHnW5qT2MmmnuBfrZJ/3/5pIrLine truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -769,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -815,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -949,7 +949,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

requirements.txt

@@ -1,2 +1,5 @@
 filterpy
-seaborn
+seaborn
+jupyter
+notebook
+sympy