diff --git a/.env b/.env new file mode 100644 index 0000000..0c4754f --- /dev/null +++ b/.env @@ -0,0 +1,21 @@ +ALADIN_HOST=localhost +ALADIN_PORT=3000 + +LTI_KEY=SOME_KEY + +MONGO_HOST=localhost +MONGO_PORT=27017 +MONGO_USER=aladin +MONGO_PW=aladin +MONGO_DB=aladin + +REVERSE_PROXY_HOST=localhost +REVERSE_PROXY_PORT=80 + +HUGGINGFACE_TOKEN=YOUR_TOKEN + +POSTGRES_HOST=localhost +POSTGRES_DB=db +POSTGRES_USER=testUser +POSTGRES_PW=abc124 +POSTGRES_PORT=5432 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 90c9d12..2bb1cfd 100644 --- a/.gitignore +++ b/.gitignore @@ -6,4 +6,8 @@ node_modules /dist coverage coverage/* -.env \ No newline at end of file +.env +*.pyc +*.cpython-* + + diff --git a/.ipynb_checkpoints/Clusteranalyse-checkpoint.ipynb b/.ipynb_checkpoints/Clusteranalyse-checkpoint.ipynb deleted file mode 100644 index 4625093..0000000 --- a/.ipynb_checkpoints/Clusteranalyse-checkpoint.ipynb +++ /dev/null @@ -1,3304 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 46, - "id": "a8557a1f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'x': [15, 28, 30, 19, 18, 22, 10, 27, 22, 23], 'y': [17, 26, 17, 13, 18, 10, 11, 20, 6, 30]}\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "\n", - "#functions\n", - "def generate_random_number_array(n, rangeStart, rangeEnd):\n", - " array = [random.randint(rangeStart, rangeEnd) for _ in range(n)]\n", - " return array\n", - "\n", - "def generate_x_y_array(n, rangeStart, rangeEnd):\n", - " XYDict = {\n", - " \"x\": generate_random_number_array(n, rangeStart, rangeEnd),\n", - " \"y\": generate_random_number_array(n, rangeStart, rangeEnd)\n", - " }\n", - " return XYDict\n", - "\n", - " \n", - "# Beispielaufruf der Funktion mit n = 10 ; Range von 5 bis 30\n", - "#INPUT\n", - "numberOfDataPoints = 10\n", - "rangeStart = 5\n", - "rangeEnd = 30\n", - "\n", - "#ToDo\n", - "#INPUT: set number of clusters\n", - "#schrittweises lösen der aufgabe \n", - "#mockup carpet\n", - "#scikpy -> beobachter möglich?\n", - "\n", - "#dendogramm -> visualisierung über carpet edotor (online) -> visualisierungsalgos\n", - "#algo: numpy -> verteilung der einzelnen punkte -> häfung der punkte in gegebenen clusters -> verteilung \"schön\" machen (Noise-Verfahren)\n", - "\n", - "dictArray = generate_x_y_array(numberOfDataPoints, rangeStart, rangeEnd)\n", - "print(dictArray)\n", - "\n", - "plt.scatter(dictArray[\"x\"], dictArray[\"y\"])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "866cde95", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.cluster.hierarchy import dendrogram, linkage\n", - "\n", - "data = list(zip(dictArray[\"x\"], dictArray[\"y\"]))\n", - "\n", - "linkage_data = linkage(data, method='ward', metric='euclidean')\n", - "dendrogram(linkage_data)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "39bab90c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.cluster import AgglomerativeClustering\n", - "\n", - "\n", - "data = list(zip(dictArray[\"x\"], dictArray[\"y\"]))\n", - "\n", - "#INPUT\n", - "#Anzahl Cluster\n", - "numberOfCluster = 3;\n", - "\n", - "hierarchical_cluster = AgglomerativeClustering(n_clusters=numberOfCluster, affinity='euclidean', linkage='ward')\n", - "labels = hierarchical_cluster.fit_predict(data)\n", - "\n", - "plt.scatter(dictArray[\"x\"], dictArray[\"y\"], c=labels)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "66b49468", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1. 8. 3.16227766 2. ]\n", - " [ 5. 6. 4.47213595 2. ]\n", - " [ 3. 7. 5. 2. ]\n", - " [ 2. 4. 6.08276253 2. ]\n", - " [ 0. 9. 8.54400375 2. ]\n", - " [10. 13. 8.74642784 4. ]\n", - " [11. 14. 16.98528775 4. ]\n", - " [12. 15. 18.2254401 6. ]\n", - " [16. 17. 29.16904752 10. ]]\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'X' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/bs/5wndg3791vz7vxsh2h7qj_mw0000gn/T/ipykernel_41173/2459418396.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinkage_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinkage_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined" - ] - } - ], - "source": [ - "#linkage_matrix = linkage(X, \"single\")\n", - "linkage_matrix = linkage_data\n", - "\n", - "print(linkage_matrix)\n", - "\n", - "dct = dict([(i, {i}) for i in range(X.shape[0])])\n", - "print(list(dct.values()))\n", - "for i, row in enumerate(linkage_matrix, X.shape[0]):\n", - " dct[i] = dct[row[0]].union(dct[row[1]])\n", - " del dct[row[0]]\n", - " del dct[row[1]]\n", - " print(list(dct.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "561dff9e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [4], [5], [7], [8], [9], [3, 6]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[1], [4], [5], [7], [8], [9], [3, 6], [0, 2]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 7 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 8 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 9 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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0wQcfwNLSEnv37oW1tbW2vKnlWwy14erqisuXLxvRc+NNnDgREydORE1NDbKzs5GYmIhJkyahd+/eCA4ONmkfGr+3xYsXa+8Avt2d7gAODw/HP//5T+zevRuLFi1qUR+sra1RU1OjV3716lWd82phYYH58+dj/vz5KC8vx3//+1+8+uqrCA8Px6VLl+5441RjO2lpadoRzTsxdO6//fZbnDp1Cu+88w6mTp2qLc/Pz79re03ZvXs3qqqqoFardfqVk5PTrO0dHBwwdepUvPnmm1iwYAG2bduGSZMmwdHRUafe888/j+effx5VVVU4cuQIli9fjscffxzfffcdvLy84OLigsDAQKxZs8bgfnr27NlkH/785z/j8ccf175vDN3GsLS0xPLly/G///u/+PbbbwE0/K4BwOXLl6FUKo1usymGzm1b+8Mf/oDc3FwUFhaiqqoKvr6++PrrrwE0/COUSE4MpERGePzxx/G3v/0N3bp1g7e3d4vakCQJFhYWOpewf/31V/zrX/9qdhvjx4/Hv/71L5w7d67Nl2pRKBQYNWoUHB0dkZGRgZMnTyI4OBjjx4/HsmXLkJmZiUceeaRV++jbty98fX1x6tQp/O1vfzN6+4kTJ2LgwIFITEzE448/bvBO+4yMDO3dz4b07t1b78aO7777DufOndP7h0YjR0dHREdHo7i4GHFxcSgsLET//v2bHIUODw+HhYUFzp8/3+JLto1B5vbAtWXLlha111SbQghs3bq12W3MnTsXKSkpiI6ORnl5OWbPnt1kXVtbW4wfPx61tbV48skncebMGXh5eeHxxx9Heno6+vTpY3Cqy5307NnzjoH1diUlJQZHKRunPDS2NXbsWJibmyM1NRXBwcFG9ckUbv1Zun25t6ZGX++mcUUNIQTWr1+Pnj174qmnnmp1X4lag4GUyAhxcXHYuXMnHn74YcybNw+BgYGor69HUVERDhw4gPj4eAwbNuyObTz22GPYsGEDJk2ahD//+c/46aefsG7dOqNGdBISEvDxxx/j4YcfxquvvoqBAweivLwc+/fvx/z583XucG+JZcuW4fLly3j00Ufh6emJ8vJyJCcnw9LSEqNGjdJ+Fzt27MDEiROxaNEiDB06FL/++is+/fRTPP744wgNDTVqn1u2bMH48eMRHh6OadOmwcPDA9euXUNeXh5OnDiBjz76qMltzc3NsWvXLowdOxbBwcF4+eWXERoaCltbW1y8eBFpaWnYs2cPrl+/3mQbU6ZMweTJkzFr1ixERUXh4sWLSEpK0o6QNYqIiMCAAQMQFBQEV1dXXLx4ERs3boSXlxd8fX0BAAMHDgQAJCcnY+rUqbC0tETfvn3Ru3dvJCQkYMmSJbhw4QLGjRsHJycnXLlyBcePH4etrW2Ti6A36tevH/r06YNFixZBCAFnZ2fs2bNH59K3scLCwmBlZYU//elPWLhwIaqrq5GamnrH7+t2fn5+GDduHD7++GOMHDkSgwYN0vl8xowZsLGxwYgRI+Du7o7S0lIkJibCwcFBO2c5ISEBBw8exPDhwzF37lz07dsX1dXVKCwsRHp6Ot54441mTxO5m/DwcHh6eiIiIgL9+vVDfX09cnJysH79etjZ2SE2NhZAQ3h79dVXsWrVKvz666/a5bNyc3Nx9erVu56v1mr8WVq7di3Gjx8Pc3NzBAYGwsrKCgMHDsThw4exZ88euLu7o2vXrnf8B+qSJUswcOBAuLu7o6ioCG+//Ta++OIL7Nu3z+RrGxMZTd57qojaT+Od1bffId/oscceu+td9kII8fPPP4ulS5eKvn37CisrK+Hg4CAGDhwo5s2bJ0pLS7X1AIiYmBiD+3r77bdF3759hUKhECqVSiQmJoq33nrL4J3fjz32mME2Ll26JF544QXRo0cPYWlpKXr27CmefvppceXKFSHE73fsfvTRRzrbNd75vm3bNm3Z7XdO7927V4wfP154eHgIKysr4ebmJiZMmKB3h+7169dFbGys6NWrl7C0tBRubm7iscceE2fPntXZ12uvvabXfwBi+fLlOmWnTp0STz/9tHBzcxOWlpaiR48e4pFHHhFvvPGGwe/gduXl5WLVqlXiwQcfFHZ2dsLS0lL06tVLTJ48WXz22Wfaeobusq+vrxdJSUlCpVIJa2trERQUJDIzM/Xusl+/fr0YPny4cHFxEVZWVqJXr17ixRdfFIWFhTp9Wbx4sejZs6cwMzPTu3N69+7dIjQ0VNjb2wuFQiG8vLxEdHS0+O9//6utM3XqVGFra2vwOHNzc0VYWJjo2rWrcHJyEk899ZQoKirS+04b77IvKyvT2d7Q8e/Zs0cMGjRIWFtbCw8PD/HKK6+Ijz/+WK/vt/+s3Oqdd94RAMQHH3yg99m7774rQkNDRffu3YWVlZX25/Wbb77RqVdWVibmzp0rvL29haWlpXB2dhZDhgwRS5YsET///LPB/bbEjh07xKRJk4Svr6/Oz8qUKVMMrl7x3nvviT/84Q/C2tpa2NnZicGDB+v8Do0aNUoEBATobXf799XU7+Wtn936fdfU1Ijp06cLV1dXIUmSznnLyckRI0aMEF26dBEAdH5ODXn55ZdFr169hJWVlXBxcRFRUVF63z+RXCQhmrH4IhER0V1ERUUhOzsbhYWFHf6pWETUvnjJnoiIWqympgYnTpzA8ePHsWvXLmzYsIFhlIiMxhFSIiJqscLCQnh7e8Pe3h6TJk3CP/7xjyafAEZE1BQGUiIiIiKSFRfGJyIiIiJZMZASERERkawYSImIiIhIVh3iLvv6+nr88MMP6Nq1qyyPWyMiIiKiOxNC4MaNG+jZsyfMzIwb8+wQgfSHH34w6TOEiYiIiKhtXLp0yeinqnWIQNq1a1cADQdob28vc2+IiIiI6HaVlZVQKpXa3GaMDhFIGy/T29vbM5ASERER3cNaMr2SNzURERERkawYSImIiIhIVgykRERERCSrDjGHlIiIiDoXjUaDuro6ubtBRrC0tIS5uXmbtM1ASkRERO1GCIHS0lKUl5fL3RVqAUdHR/To0cPk68IzkBIREVG7aQyjbm5u6NKlCx9400EIIfDLL7/gxx9/BAC4u7ubtH0GUiIiImoXGo1GG0a7desmd3fISDY2NgCAH3/8EW5ubia9fM+bmoiIiKhdNM4Z7dKli8w9oZZqPHemnv/LQEpERETtipfpO662OncMpEREREQkKwZSIiIiIhORJAm7d++WuxsdDgMpERERUTOUlpZizpw5UKlUUCgUUCqViIiIwKFDh9pkf4cPH4YkSW26RNb169cxZcoUODg4wMHBAVOmTJFlSS7eZU9EREQdjkYDZGUBJSWAuzsQEgK00ZrtAIDCwkKMGDECjo6OSEpKQmBgIOrq6pCRkYGYmBicPXu27XbeSkIIaDQaWFjox75Jkybh8uXL2L9/PwDgz3/+M6ZMmYI9e/a0ax85QkpERHclNALXD1/Hle1XcP3wdQiNkLtLdB9Tq4HevYHQUGDSpIY/e/duKG8rs2bNgiRJOH78OKKjo+Hn54eAgADMnz8f2dnZBrcxNMKZk5MDSZJQWFgIALh48SIiIiLg5OQEW1tbBAQEID09HYWFhQgNDQUAODk5QZIkTJs2DUBDwExKSoJKpYKNjQ0GDRqEtLQ0vf1mZGQgKCgICoUCWVlZev3Ly8vD/v378eabbyI4OBjBwcHYunUr9u7di3Pnzpnmi2smjpASEdEdlanLkB+bj5rLNdoyhacCPsk+cI10lbFndD9Sq4HoaEDc9m+i4uKG8rQ0IDLStPu8du0a9u/fjzVr1sDW1lbvc0dHxxa3HRMTg9raWhw5cgS2trbIzc2FnZ0dlEoldu7ciaioKJw7dw729vbadUCXLl0KtVqN1NRU+Pr64siRI5g8eTJcXV0xatQobdsLFy7EunXroFKpDPbx888/h4ODA4YNG6Yte+ihh+Dg4IBjx46hb9++LT4uYzGQEhFRk8rUZTgTfQa47S//muIanIk+g4C0AIZSajcaDRAbqx9GgYYySQLi4oCJE017+T4/Px9CCPTr1890jf6mqKgIUVFRGDhwIABApVJpP3N2dgYAuLm5aQNlVVUVNmzYgMzMTAQHB2u3OXr0KLZs2aITSBMSEhAWFtbkvktLS+Hm5qZX7ubmhtLS0lYfmzEYSImIyCChEciPzdcLow0fApCA/Lh8uEx0gWTOdSWp7WVlAZcvN/25EMClSw31Ro823X7Fbwm4LdbgnDt3Ll5++WUcOHAAY8aMQVRUFAIDA5usn5ubi+rqar2gWVtbi8GDB+uUBQUF3XX/ho5JCNHua8VyDikRERlUnlWuc5lejwBqLtWgPKu83fpE97eSEtPWay5fX19IkoS8vDyjtjMza4hZ4pYh3dufcDR9+nRcuHABU6ZMwenTpxEUFIRNmzY12WZ9fT0AYN++fcjJydG+cnNzdeaRAjA4veBWPXr0wJUrV/TKy8rK0L179zsfnIkxkBIRkUG1JbUmrUfUWu7upq3XXM7OzggPD8fmzZtRVVWl93lTyyS5ujZMZym5JSHn5OTo1VMqlZg5cybUajXi4+OxdetWAICVlRUAQKPRaOv2798fCoUCRUVF8PHx0XkplUqjjis4OBgVFRU4fvy4tuyLL75ARUUFhg8fblRbrcVASkREBlm5W5m0HlFrhYQAnp4Nc0UNkSRAqWyoZ2opKSnQaDQYOnQodu7cie+//x55eXl4/fXXtXM5b9cYElesWIHvvvsO+/btw/r163XqxMXFISMjAwUFBThx4gQyMzPh7+8PAPDy8oIkSdi7dy/Kysrw888/o2vXrliwYAHmzZuHd999F+fPn8fJkyexefNmvPvuu0Ydk7+/P8aNG4cZM2YgOzsb2dnZmDFjBh5//PF2vaEJYCAlIqImOIY4QuGpAJqaSiYBCqUCjiGO7dktuo+ZmwPJyQ3/fXsobXy/cWPbrEfq7e2NEydOIDQ0FPHx8RgwYADCwsJw6NAhpKamGtzG0tIS27dvx9mzZzFo0CCsXbsWq1ev1qmj0WgQExOjDYd9+/ZFSkoKAMDDwwMrV67EokWL0L17d8yePRsAsGrVKixbtgyJiYnw9/dHeHg49uzZA29vb6OP6/3338fAgQMxduxYjB07FoGBgfjXv/5ldDutJQlh6F61e0tlZSUcHBxQUVEBe3t7ubtDRHTf0N5lD+je3PTbX/68y56MUV1djYKCAnh7e8Pa2rrF7ajVDXfb33qDk1LZEEZNveQT6brTOWxNXuNd9kRE1CTXSFcEpAUYXod0I9chJXlERjYs7dSeT2qitsVASkREd+Qa6QqXiS4ozypHbUktrNyt4BjiyKWeSFbm5qZd2onkxUBKRER3JZlLcBrtJHc3iKiT4k1NRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsuJd9kR0XxEaweWLiIjuMQykRHTfKFOXGV7gPZkLvBORaUiShF27duHJJ5+UuysdCi/ZE9F9ofERmLeGUQCoKa7BmegzKFOXydQzIuooSktLMWfOHKhUKigUCiiVSkRERODQoUNtsr/Dhw9DkiSUl5e3SfsAsGbNGgwfPhxdunSBo6Njm+3nbjhCSkSdntAI5Mfm6z6LXfshAAnIj8uHy0QXXr4n6iA09RpkFWWh5EYJ3Lu6I6RXCMzN2u7ZoYWFhRgxYgQcHR2RlJSEwMBA1NXVISMjAzExMTh79myb7bu1hBDQaDSwsNCPfbW1tXjqqacQHByMt956S4beNeAIKRF1euVZ5XojozoEUHOpBuVZ5e3WJyJqOXWeGr2TeyP03VBMUk9C6Luh6J3cG+o8dZvtc9asWZAkCcePH0d0dDT8/PwQEBCA+fPnIzs72+A2hkY4c3JyIEkSCgsLAQAXL15EREQEnJycYGtri4CAAKSnp6OwsBChoaEAACcnJ0iShGnTpgFoCJhJSUlQqVSwsbHBoEGDkJaWprffjIwMBAUFQaFQICsry2AfV65ciXnz5mHgwIGt/5JagSOkRNTp1ZbUmrQeEclHnadG9IfRELdd8iiuLEb0h9FIezoNkf6RJt3ntWvXsH//fqxZswa2trZ6n7fmUndMTAxqa2tx5MgR2NraIjc3F3Z2dlAqldi5cyeioqJw7tw52Nvbw8bGBgCwdOlSqNVqpKamwtfXF0eOHMHkyZPh6uqKUaNGadteuHAh1q1bB5VKJevl+OZgICWiTs/K3cqk9YhIHpp6DWL3x+qFUQAQEJAgIW5/HCb2nWjSy/f5+fkQQqBfv34ma7NRUVERoqKitCOUKpVK+5mzszMAwM3NTRsoq6qqsGHDBmRmZiI4OFi7zdGjR7FlyxadQJqQkICwsDCT97ktMJASUafnGOIIhacCNcU1hueRSg132zuGOLZ314jICFlFWbhcebnJzwUELlVeQlZRFkb3Hm2y/QrR8D8OSTL9HPO5c+fi5ZdfxoEDBzBmzBhERUUhMDCwyfq5ubmorq7WC5q1tbUYPHiwTllQUJDJ+9tWOIeUiDo9yVyCT7LPb29u/7DhD5+NPryhiegeV3KjxKT1msvX1xeSJCEvL8+o7czMGmJWY6AFgLq6Op0606dPx4ULFzBlyhScPn0aQUFB2LRpU5Nt1tfXAwD27duHnJwc7Ss3N1dnHikAg9ML7lUMpER0X3CNdEVAWgAUHgqdcoWnAgFpAVyHlKgDcO/qbtJ6zeXs7Izw8HBs3rwZVVVVep83tSyTq2vD/1dKSn4PyDk5OXr1lEolZs6cCbVajfj4eGzduhUAYGXVMI1Io9Fo6/bv3x8KhQJFRUXw8fHReSmVypYeoux4yZ6I7huuka5wmejCJzURdVAhvULgae+J4spig/NIJUjwtPdESK8Qk+87JSUFw4cPx9ChQ5GQkIDAwEDcvHkTBw8eRGpqqsHR08aQuGLFCqxevRrff/891q9fr1MnLi4O48ePh5+fH65fv47MzEz4+/sDALy8vCBJEvbu3YsJEybAxsYGXbt2xYIFCzBv3jzU19dj5MiRqKysxLFjx2BnZ4epU6cadVxFRUW4du0aioqKoNFotIHZx8cHdnZ2LfuyWoCBlIjuK5K5BKfRTnJ3g6hD0Gg0yMrKQklJCdzd3RESEgJz87Zb6/NuzM3MkTwuGdEfRkOCpBNKpd/m32wct7FN1iP19vbGiRMnsGbNGsTHx6OkpASurq4YMmQIUlNTDW5jaWmJ7du34+WXX8agQYPwhz/8AatXr8ZTTz2lraPRaBATE4PLly/D3t4e48aNw//+7/8CADw8PLBy5UosWrQIzz//PJ577jm88847WLVqFdzc3JCYmIgLFy7A0dERDz74IF599VWjj2vZsmV49913te8b56F+8sknGD16tNHttZQkbp3YcI+qrKyEg4MDKioqYG9vL3d3iIiIOj21Wo3Y2Fhcvvz7TUSenp5ITk5GZGTLllWqrq5GQUEBvL29YW1t3fK+5akRuz9W5wYnpb0SG8dtNPmST6TrTuewNXmNI6RERESkQ61WIzo6GrePWRUXFyM6OhppaWktDqWmEOkfiYl9J7brk5qobTGQEhERkZZGo0FsbKxeGAUa7haXJAlxcXGYOHGi7JfvTbm0E8mLd9kTERGRVlZWls5l+tsJIXDp0qUmH0VJ1BIMpERERKR16xJFpqhH1BwMpERERKTl7t7MtT6bWY+oORhIiYiISCskJASenp5NPiZTkiQolUqEhJh+rU+6fzGQEhERkZa5uTmSk5MB6D+7vfH9xo0bZb2hiTofBlIiIiLSERkZibS0NHh4eOiUe3p6yr7kE3VOXPaJiIiI9ERGRmLixIn31JOaqPNiICUiIiKDzM3N2/XxkZ2BJEnYtWsXnnzySbm70qHwkj0RERFRM5SWlmLOnDlQqVRQKBRQKpWIiIjAoUOH2mR/hw8fhiRJKC8vb5P2CwsL8eKLL8Lb2xs2Njbo06cPli9fjtra2jbZ351whJSIiIg6Ho0GyMoCSkoAd3cgJARow+kEhYWFGDFiBBwdHZGUlITAwEDU1dUhIyMDMTExOHv2bJvtu7WEENBoNLCw0I19Z8+eRX19PbZs2QIfHx98++23mDFjBqqqqrBu3bp27SNHSImIiKhjUauB3r2B0FBg0qSGP3v3bihvI7NmzYIkSTh+/Diio6Ph5+eHgIAAzJ8/H9nZ2Qa3MTTCmZOTA0mSUFhYCAC4ePEiIiIi4OTkBFtbWwQEBCA9PR2FhYUIDQ0FADg5OUGSJEybNg1AQ8BMSkqCSqWCjY0NBg0ahLS0NL39ZmRkICgoCAqFwuCTtcaNG4dt27Zh7NixUKlUeOKJJ7BgwQKo2/B7bApHSImIiKjjUKuB6GhACN3y4uKG8rQ0wMSrAFy7dg379+/HmjVrYGtrq/e5o6Nji9uOiYlBbW0tjhw5AltbW+Tm5sLOzg5KpRI7d+5EVFQUzp07B3t7e9jY2AAAli5dCrVajdTUVPj6+uLIkSOYPHkyXF1dMWrUKG3bCxcuxLp166BSqZrdx4qKCjg7O7f4eFqKgZSIiIg6Bo0GiI3VD6NAQ5kkAXFxwMSJJr18n5+fDyEE+vXrZ7I2GxUVFSEqKgoDBw4EAKhUKu1njcHQzc1NGyirqqqwYcMGZGZmIjg4WLvN0aNHsWXLFp1AmpCQgLCwsGb35fz589i0aRPWr1/f2sMyGgMpERERdQxZWcDly01/LgRw6VJDPROuDiB+C8BNPb2qNebOnYuXX34ZBw4cwJgxYxAVFYXAwMAm6+fm5qK6ulovaNbW1mLw4ME6ZUFBQc3uxw8//IBx48bhqaeewvTp0407CBPgHFIiIiLqGEpKTFuvmXx9fSFJEvLy8ozazsysIWaJW0Z06+rqdOpMnz4dFy5cwJQpU3D69GkEBQVh06ZNTbZZX18PANi3bx9ycnK0r9zcXJ15pAAMTi8w5IcffkBoaCiCg4Pxz3/+s1nbmBoD6W00QuDw9evYfuUKDl+/Do2hywJERETU/tzdTVuvmZydnREeHo7NmzejqqpK7/OmlmVydXUFAJTcEpBzcnL06imVSsycORNqtRrx8fHYunUrAMDKygoAoNFotHX79+8PhUKBoqIi+Pj46LyUSqXRx1ZcXIzRo0fjwQcfxLZt27Qhur3xkv0t1GVliM3Px+WaGm2Zp0KBZB8fRP72Q0VEREQyCQkBPD0bbmAyNGAkSQ2fh4SYfNcpKSkYPnw4hg4dioSEBAQGBuLmzZs4ePAgUlNTDY6eNobEFStWYPXq1fj+++/15mfGxcVh/Pjx8PPzw/Xr15GZmQl/f38AgJeXFyRJwt69ezFhwgTY2Niga9euWLBgAebNm4f6+nqMHDkSlZWVOHbsGOzs7DB16tRmH9MPP/yA0aNHo1evXli3bh3Kysq0n/Xo0aOF31TLcIT0N+qyMkSfOaMTRgGguKYG0WfOQH3LSSIiIiIZmJsDyckN/337fM7G9xs3tsl6pN7e3jhx4gRCQ0MRHx+PAQMGICwsDIcOHUJqaqrBbSwtLbF9+3acPXsWgwYNwtq1a7F69WqdOhqNBjExMfD398e4cePQt29fpKSkAAA8PDywcuVKLFq0CN27d8fs2bMBAKtWrcKyZcuQmJgIf39/hIeHY8+ePfD29jbqmA4cOID8/HxkZmbC09MT7u7u2ld7k4S4969JV1ZWwsHBARUVFbC3tzd5+xoh0Ds7Wy+MNpLQMFJa8NBDMG+DCc1ERET3g+rqahQUFMDb2xvW1tYtb0itbrjb/tYbnJTKhjBq4iWfSNedzmFr8hov2QPIKi9vMowCgABwqaYGWeXlGO3k1H4dIyIiIn2RkQ1LO7Xjk5qobTGQAihp5jNbm1uPiIiI2pi5uUmXdiJ5cQ4pAPff7mIzVT0iIiIiaj4GUgAhjo7wVCjQ1OxQCYBSoUBIKx4NRkRERESGMZACMJckJPv4AIBeKG18v9HHhzc0EREREbUBBtLfRLq6Ii0gAB4KhU65p0KBtIAArkNKRERE1EZ4U9MtIl1dMdHFBVnl5SiprYW7lRVCHB05MkpERETUhhhIb2MuSVzaiYiIiKgd8ZI9EREREcmKgZSIiIjIRCRJwu7du+XuRofDQEpERETUDKWlpZgzZw5UKhUUCgWUSiUiIiJw6NChNtnf4cOHIUkSysvL26R9AHjiiSfQq1cvWFtbw93dHVOmTMEPP/zQZvtrCgMpERERdTgaIXD4+nVsv3IFh69fh0aINt1fYWEhhgwZgszMTCQlJeH06dPYv38/QkNDERMT06b7bi0hBG7evGnws9DQUHz44Yc4d+4cdu7cifPnzyM6Orqde8hASkRERB2MuqwMvbOzEXrqFCbl5SH01Cn0zs6GuqyszfY5a9YsSJKE48ePIzo6Gn5+fggICMD8+fORnZ1tcBtDI5w5OTmQJAmFhYUAgIsXLyIiIgJOTk6wtbVFQEAA0tPTUVhYiNDQUACAk5MTJEnCtGnTADQEzKSkJKhUKtjY2GDQoEFIS0vT229GRgaCgoKgUCiQlZVlsI/z5s3DQw89BC8vLwwfPhyLFi1CdnY26urqWv+lGaFFgTQlJQXe3t6wtrbGkCFDmjzIRu+//z4GDRqELl26wN3dHc8//zx++umnFnWYiIiI7l/qsjJEnzmDyzU1OuXFNTWIPnOmTULptWvXsH//fsTExMDW1lbvc8dWPMkxJiYGNTU1OHLkCE6fPo21a9fCzs4OSqUSO3fuBACcO3cOJSUlSE5OBgAsXboU27ZtQ2pqKs6cOYN58+Zh8uTJ+PTTT3XaXrhwIRITE5GXl4fAwMBmHef777+P4cOHw9LSssXH1BJGB9IdO3YgLi4OS5YswcmTJxESEoLx48ejqKjIYP2jR4/iueeew4svvogzZ87go48+wpdffonp06e3uvNERER0/9AIgdj8fBi6ON9YFpefb/LL9/n5+RBCoF+/fiZtFwCKioowYsQIDBw4ECqVCo8//jgefvhhmJubw9nZGQDg5uaGHj16wMHBAVVVVdiwYQPefvtthIeHQ6VSYdq0aZg8eTK2bNmi03ZCQgLCwsLQp08fdOvWrck+/OUvf4GtrS26deuGoqIi/Oc//zH5cd6N0YF0w4YNePHFFzF9+nT4+/tj48aNUCqVSE1NNVg/OzsbvXv3xty5c+Ht7Y2RI0fipZdewldffdXqzhMREdH9I6u8XG9k9FYCwKWaGmSZ+CYg8VvAldrgQTlz587F6tWrMWLECCxfvhzffPPNHevn5uaiuroaYWFhsLOz077ee+89nD9/XqduUFBQs/rwyiuv4OTJkzhw4ADMzc3x3HPPaY+5vRgVSGtra/H1119j7NixOuVjx47FsWPHDG4zfPhwXL58Genp6RBC4MqVK0hLS8Njjz3W5H5qampQWVmp8yIiIqL7W0ltrUnrNZevry8kSUJeXp5R25mZNcSsW8Pd7XMzp0+fjgsXLmDKlCk4ffo0goKCsGnTpibbrK+vBwDs27cPOTk52ldubq7OPFIABqcXGOLi4gI/Pz+EhYXhgw8+QHp6epPzYtuKUYH06tWr0Gg06N69u0559+7dUVpaanCb4cOH4/3338czzzwDKysr9OjRA46Ojnf8shMTE+Hg4KB9KZVKY7pJREREnZC7lZVJ6zWXs7MzwsPDsXnzZlRVVel93tSyTK6urgCAkpISbVlOTo5ePaVSiZkzZ0KtViM+Ph5bt24FAFj9dhwajUZbt3///lAoFCgqKoKPj4/OyxR5qTE819xhJLottOimptuHrIUQTQ5j5+bmYu7cuVi2bBm+/vpr7N+/HwUFBZg5c2aT7S9evBgVFRXa16VLl1rSTSIiIupEQhwd4alQoKkL5xIApUKBkFbcZNSUlJQUaDQaDB06FDt37sT333+PvLw8vP766wgODja4TWNIXLFiBb777jvs27cP69ev16kTFxeHjIwMFBQU4MSJE8jMzIS/vz8AwMvLC5IkYe/evSgrK8PPP/+Mrl27YsGCBZg3bx7effddnD9/HidPnsTmzZvx7rvvGnVMx48fxz/+8Q/k5OTg4sWL+OSTTzBp0iT06dOnyWNqK0YFUhcXF5ibm+uNhv744496o6aNEhMTMWLECLzyyisIDAxEeHg4UlJS8Pbbb+v8i+FWCoUC9vb2Oi8iIiK6v5lLEpJ9fABAL5Q2vt/o4wPzNpjr6e3tjRMnTiA0NBTx8fEYMGAAwsLCcOjQoSbvo7G0tMT27dtx9uxZDBo0CGvXrsXq1at16mg0GsTExMDf3x/jxo1D3759kZKSAgDw8PDAypUrsWjRInTv3h2zZ88GAKxatQrLli1DYmIi/P39ER4ejj179sDb29uoY7KxsYFarcajjz6Kvn374oUXXsCAAQPw6aefQqFQtOBbajlJGDlrddiwYRgyZIj2ywIaho8nTpyIxMREvfpRUVGwsLDAjh07tGWff/45hg8fjuLiYvTs2fOu+6ysrISDgwMqKioYTomIiDqo6upqFBQUaJeObCl1WRli8/N1bnBSKhTY6OODyN8uk1PbuNM5bE1eszC2I/Pnz8eUKVMQFBSE4OBg/POf/0RRUZH2EvzixYtRXFyM9957DwAQERGBGTNmIDU1FeHh4SgpKUFcXByGDh3arDBKREREdKtIV1dMdHFBVnk5Smpr4W5lhRBHxzYZGaX2YXQgfeaZZ/DTTz8hISEBJSUlGDBgANLT0+Hl5QWgYeLurWuSTps2DTdu3MA//vEPxMfHw9HREY888gjWrl1ruqMgIiKi+4q5JGG0k5Pc3SATMfqSvRx4yZ6IiKjjM9Ule5JPW12y57PsiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiMhFJkrB79265u9HhMJASERERNUNpaSnmzJkDlUoFhUIBpVKJiIgIHDp0qE32d/jwYUiShPLy8jZp/1Y1NTV44IEHIEkScnJy2nx/tzP6SU1EREREchMagfKsctSW1MLK3QqOIY6QzNvu0aGFhYUYMWIEHB0dkZSUhMDAQNTV1SEjIwMxMTE4e/Zsm+27tYQQ0Gg0sLBoOvYtXLgQPXv2xKlTp9qxZ7/jCCkRERF1KGXqMmT3zsap0FPIm5SHU6GnkN07G2Xqsjbb56xZsyBJEo4fP47o6Gj4+fkhICAA8+fPR3Z2tsFtDI1w5uTkQJIkFBYWAgAuXryIiIgIODk5wdbWFgEBAUhPT0dhYSFCQ0MBAE5OTpAkCdOmTQPQEDCTkpKgUqlgY2ODQYMGIS0tTW+/GRkZCAoKgkKhQFZWVpPH9vHHH+PAgQNYt25d676kVuAIKREREXUYZeoynIk+A9z24POa4hqciT6DgLQAuEa6mnSf165dw/79+7FmzRrY2trqfe7o6NjitmNiYlBbW4sjR47A1tYWubm5sLOzg1KpxM6dOxEVFYVz587B3t4eNjY2AIClS5dCrVYjNTUVvr6+OHLkCCZPngxXV1eMGjVK2/bChQuxbt06qFSqJvt45coVzJgxA7t370aXLl1afBytxUBKREREHYLQCOTH5uuF0YYPAUhAflw+XCa6mPTyfX5+PoQQ6Nevn8nabFRUVISoqCgMHDgQAKBSqbSfOTs7AwDc3Ny0gbKqqgobNmxAZmYmgoODtdscPXoUW7Zs0QmkCQkJCAsLa3LfQghMmzYNM2fORFBQkHbUVg4MpERERNQhlGeVo+ZyTdMVBFBzqQblWeVwGu1ksv0K0ZCAJcn0c1Tnzp2Ll19+GQcOHMCYMWMQFRWFwMDAJuvn5uaiurpaL2jW1tZi8ODBOmVBQUF33PemTZtQWVmJxYsXt/wATIRzSImIiKhDqC2pNWm95vL19YUkScjLyzNqOzOzhpjVGGgBoK6uTqfO9OnTceHCBUyZMgWnT59GUFAQNm3a1GSb9fX1AIB9+/YhJydH+8rNzdWZRwrA4PSCW2VmZiI7OxsKhQIWFhbw8fEB0BBkp06d2vwDNQEGUiIiIuoQrNytTFqvuZydnREeHo7NmzejqqpK7/OmlmVydW2Yy1pSUqItM7SkklKpxMyZM6FWqxEfH4+tW7cCAKysGo5Do9Fo6/bv3x8KhQJFRUXw8fHReSmVSqOO6/XXX8epU6e0oTY9PR0AsGPHDqxZs8aotlqLl+yJiIioQ3AMcYTCU4Ga4hrD80glQOGpgGOIo8n3nZKSguHDh2Po0KFISEhAYGAgbt68iYMHDyI1NdXg6GljSFyxYgVWr16N77//HuvXr9epExcXh/Hjx8PPzw/Xr19HZmYm/P39AQBeXl6QJAl79+7FhAkTYGNjg65du2LBggWYN28e6uvrMXLkSFRWVuLYsWOws7MzamSzV69eOu/t7OwAAH369IGnp6exX1GrcISUiIiIOgTJXIJPss9vb27/sOEPn40+bbIeqbe3N06cOIHQ0FDEx8djwIABCAsLw6FDh5CammpwG0tLS2zfvh1nz57FoEGDsHbtWqxevVqnjkajQUxMDPz9/TFu3Dj07dsXKSkpAAAPDw+sXLkSixYtQvfu3TF79mwAwKpVq7Bs2TIkJibC398f4eHh2LNnD7y9vU1+3O1FErdObLhHVVZWwsHBARUVFbC3t5e7O0RERNQC1dXVKCgogLe3N6ytrVvcTpm6DPmx+To3OCmUCvhs9DH5kk+k607nsDV5jZfsiYiIqENxjXSFy0SXdn1SE7UtBlIiIiLqcCRzyaRLO5G8OIeUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKREREZGJSJKE3bt3y92NDoeBlIiIiKgZSktLMWfOHKhUKigUCiiVSkRERODQoUNtsr/Dhw9DkiSUl5e3Sfu3qqmpwQMPPABJkpCTk9Pm+7sdn9REREREHY7QiHZ9dGhhYSFGjBgBR0dHJCUlITAwEHV1dcjIyEBMTAzOnj3bZvtuLSEENBoNLCyajn0LFy5Ez549cerUqXbs2e84QkpEREQdSpm6DNm9s3Eq9BTyJuXhVOgpZPfORpm6rM32OWvWLEiShOPHjyM6Ohp+fn4ICAjA/PnzkZ2dbXAbQyOcOTk5kCQJhYWFAICLFy8iIiICTk5OsLW1RUBAANLT01FYWIjQ0FAAgJOTEyRJwrRp0wA0BMykpCSoVCrY2Nhg0KBBSEtL09tvRkYGgoKCoFAokJWV1eSxffzxxzhw4ADWrVvXui+pFThCSkR0nxJCg/LyLNTWlsDKyh2OjiGQJHO5u0V0R2XqMpyJPgMI3fKa4hqciT6DgLQAuEa6mnSf165dw/79+7FmzRrY2trqfe7o6NjitmNiYlBbW4sjR47A1tYWubm5sLOzg1KpxM6dOxEVFYVz587B3t4eNjY2AIClS5dCrVYjNTUVvr6+OHLkCCZPngxXV1eMGjVK2/bChQuxbt06qFSqJvt45coVzJgxA7t370aXLl1afBytxUBKRHQfKitTIz8/FjU1l7VlCoUnfHyS4eoaKWPPiJomNAL5sfl6YbThQwASkB+XD5eJLia9fJ+fnw8hBPr162eyNhsVFRUhKioKAwcOBACoVCrtZ87OzgAANzc3baCsqqrChg0bkJmZieDgYO02R48exZYtW3QCaUJCAsLCwprctxAC06ZNw8yZMxEUFKQdtZUDAykR0X2mrEyNM2eicfvf6jU1xThzJhoBAWkMpXRPKs8qR83lmqYrCKDmUg3Ks8rhNNrJZPsVouF3RZJMP0d17ty5ePnll3HgwAGMGTMGUVFRCAwMbLJ+bm4uqqur9YJmbW0tBg8erFMWFBR0x31v2rQJlZWVWLx4ccsPwEQ4h5SI6D4ihAb5+bFoeogJyM+PgxCadu0XUXPUltSatF5z+fr6QpIk5OXlGbWdmVlDzGoMtABQV1enU2f69Om4cOECpkyZgtOnTyMoKAibNm1qss36+noAwL59+5CTk6N95ebm6swjBWBwesGtMjMzkZ2dDYVCAQsLC/j4+ABoCLJTp05t/oGaAAMpEdF9pLw8S+cyvT6BmppLKC9v+gYIIrlYuVuZtF5zOTs7Izw8HJs3b0ZVVZXe500ty+Tq2jCXtaSkRFtmaEklpVKJmTNnQq1WIz4+Hlu3bgUAWFk1HIdG8/s/EPv37w+FQoGioiL4+PjovJRKpVHH9frrr+PUqVPaUJueng4A2LFjB9asWWNUW63FS/ZERPeR2tqSu1cyoh5Re3IMcYTCU4Ga4hrDg/wSoPBUwDHE0eT7TklJwfDhwzF06FAkJCQgMDAQN2/exMGDB5Gammpw9LQxJK5YsQKrV6/G999/j/Xr1+vUiYuLw/jx4+Hn54fr168jMzMT/v7+AAAvLy9IkoS9e/diwoQJsLGxQdeuXbFgwQLMmzcP9fX1GDlyJCorK3Hs2DHY2dkZNbLZq1cvnfd2dnYAgD59+sDT09PYr6hVOEJKRHQfsbJyN2k9ovYkmUvwSfb57c3tHzb84bPRp03WI/X29saJEycQGhqK+Ph4DBgwAGFhYTh06BBSU1MNbmNpaYnt27fj7NmzGDRoENauXYvVq1fr1NFoNIiJiYG/vz/GjRuHvn37IiUlBQDg4eGBlStXYtGiRejevTtmz54NAFi1ahWWLVuGxMRE+Pv7Izw8HHv27IG3t7fJj7u9SOLWiQ33qMrKSjg4OKCiogL29vZyd4eIqMMSQoPs7N6oqSlGU0NMCoUnHnqogEtAkclVV1ejoKAA3t7esLa2bnE7Zeoy5Mfm69zgpFAq4LPRx+RLPpGuO53D1uQ1XrInIrqPSJI5fHySf7vLXoJuKG0YVfLx2cgwSvc010hXuEx0adcnNVHbYiAlIrrPuLpGIiAgrYl1SDdyySfqECRzyaRLO5G8GEiJiO5Drq6RcHGZyCc1EdE9gYGUiOg+JUnmcHIaLXc3iIh4lz0RERERyYuBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZmIJEnYvXu33N3ocBhIiYiIiJqhtLQUc+bMgUqlgkKhgFKpREREBA4dOtQm+zt8+DAkSUJ5eXmbtA8AvXv3hiRJOq9Fixa12f6awmWfiIiIiO6isLAQI0aMgKOjI5KSkhAYGIi6ujpkZGQgJiYGZ8+elbuLTRJCQKPRwMLCcOxLSEjAjBkztO/t7Ozaq2taHCElIiKiDkcIDa5fP4wrV7bj+vXDEELTpvubNWsWJEnC8ePHER0dDT8/PwQEBGD+/PnIzs42uI2hEc6cnBxIkoTCwkIAwMWLFxEREQEnJyfY2toiICAA6enpKCwsRGhoKADAyckJkiRh2rRpABoCZlJSElQqFWxsbDBo0CCkpaXp7TcjIwNBQUFQKBTIyspq8ti6du2KHj16aF9yBFKOkBIREVGHUlambuLRt8lt8ujba9euYf/+/VizZg1sbW31Pnd0dGxx2zExMaitrcWRI0dga2uL3Nxc2NnZQalUYufOnYiKisK5c+dgb28PGxsbAMDSpUuhVquRmpoKX19fHDlyBJMnT4arqytGjRqlbXvhwoVYt24dVCrVHfu4du1arFq1CkqlEk899RReeeUVWFlZtfiYWoKBlIiIiDqMsjI1zpyJBiB0ymtqinHmTDQCAtJMHkrz8/MhhEC/fv1M2i4AFBUVISoqCgMHDgQAqFQq7WfOzs4AADc3N22grKqqwoYNG5CZmYng4GDtNkePHsWWLVt0AmlCQgLCwsLuuP/Y2Fg8+OCDcHJywvHjx7F48WIUFBTgzTffNOVh3hUDKREREXUIQmiQnx+L28Pob58CkJCfHwcXl4mQJHMT7rdhf5IkmazNRnPnzsXLL7+MAwcOYMyYMYiKikJgYGCT9XNzc1FdXa0XNGtrazF48GCdsqCgoLvuf968edr/DgwMhJOTE6Kjo7F27Vp069bNyKNpOc4hJSIiog6hvDxL5zK9PoGamksoL296vmRL+Pr6QpIk5OXlGbWdmVlDzGoMtABQV1enU2f69Om4cOECpkyZgtOnTyMoKAibNm1qss36+noAwL59+5CTk6N95ebm6swjBWBwesHdPPTQQwAaRoXbEwMpERERdQi1tSUmrddczs7OCA8Px+bNm1FVVaX3eVPLMrm6ugIASkp+709OTo5ePaVSiZkzZ0KtViM+Ph5bt24FAO08To3m9xu2+vfvD4VCgaKiIvj4+Oi8lEplSw9R6+TJkwAAd3f3VrdlDF6yJyIiog7Byqp5Iam59YyRkpKC4cOHY+jQoUhISEBgYCBu3ryJgwcPIjU11eDoaWNIXLFiBVavXo3vv/8e69ev16kTFxeH8ePHw8/PD9evX0dmZib8/f0BAF5eXpAkCXv37sWECRNgY2ODrl27YsGCBZg3bx7q6+sxcuRIVFZW4tixY7Czs8PUqVObfUyff/45srOzERoaCgcHB3z55ZeYN28ennjiCfTq1at1X5iROEJKREREHYKjYwgUCk8ATc3llKBQKOHoGGLyfXt7e+PEiRMIDQ1FfHw8BgwYgLCwMBw6dAipqakGt7G0tMT27dtx9uxZDBo0CGvXrsXq1at16mg0GsTExMDf3x/jxo1D3759kZKSAgDw8PDAypUrsWjRInTv3h2zZ88GAKxatQrLli1DYmIi/P39ER4ejj179sDb29uoY1IoFNixYwdGjx6N/v37Y9myZZgxYwa2b9/egm+odSRx68SGe1RlZSUcHBxQUVEBe3t7ubtDRERELVBdXY2CggJ4e3vD2tq6RW38fpc9oHtzU0NIbYu77Ol3dzqHrclrHCElIiKiDsPVNRIBAWlQKDx0yhUKT4bRDoxzSImIiKhDcXWNhIvLRJSXZ6G2tgRWVu5wdAwx6VJP1L4YSImIiKjDkSRzODmNlrsbZCK8ZE9EREREsmIgJSIiIiJZMZASERERkaw4h5SIOhUhNLzRgYiog2EgJaJOo6xMjfz8WJ1nXSsUnvDxSeZSMERE9zBesieiTqFxsexbwygA1NQU48yZaJSVqWXqGRER3Q0DKRF1eEJokJ8fC92ntmg/BQDk58dBCE279ouI7j+SJGH37t1yd6PDYSAlog6vvDxLb2RUl0BNzSWUl2e1W5+IqPMpLS3FnDlzoFKpoFAooFQqERERgUOHDrXJ/g4fPgxJklBeXt4m7QNA7969IUmSzmvRokVttr+mcA4pEXV4tbUlJq1HRHS7wsJCjBgxAo6OjkhKSkJgYCDq6uqQkZGBmJgYnD17Vu4uNkkIAY1GAwsLw7EvISEBM2bM0L63s7Nrr65pcYSUiDo8Kyt3k9YjonufEBpcv34YV65sx/Xrh9t8Ss6sWbMgSRKOHz+O6Oho+Pn5ISAgAPPnz0d2drbBbQyNcObk5ECSJBQWFgIALl68iIiICDg5OcHW1hYBAQFIT09HYWEhQkNDAQBOTk6QJAnTpk0D0BAwk5KSoFKpYGNjg0GDBiEtLU1vvxkZGQgKCoJCoUBWVtNXiLp27YoePXpoX3IEUo6QElGH5+gYAoXCEzU1xTA8j1SCQuEJR8eQ9u4aEbWB9l5R49q1a9i/fz/WrFkDW1tbvc8dHR1b3HZMTAxqa2tx5MgR2NraIjc3F3Z2dlAqldi5cyeioqJw7tw52Nvbw8bGBgCwdOlSqNVqpKamwtfXF0eOHMHkyZPh6uqKUaNGadteuHAh1q1bB5VKdcc+rl27FqtWrYJSqcRTTz2FV155BVZWVi0+ppZgICWiDk+SzOHjk4wzZ6IBSNANpRIAwMdnI9cjJeoEGlfUuP0fn40ragQEpJk8lObn50MIgX79+pm0XQAoKipCVFQUBg4cCABQqVTaz5ydnQEAbm5u2kBZVVWFDRs2IDMzE8HBwdptjh49ii1btugE0oSEBISFhd1x/7GxsXjwwQfh5OSE48ePY/HixSgoKMCbb75pysO8KwZSIuoUXF0jERCQ1sSoyUauQ0rUCdx9RQ0J+flxcHGZaNJ/gArRsD9JkkzWZqO5c+fi5ZdfxoEDBzBmzBhERUUhMDCwyfq5ubmorq7WC5q1tbUYPHiwTllQUNBd9z9v3jztfwcGBsLJyQnR0dFYu3YtunXrZuTRtBwDKRF1Gq6ukXBxmcgnNRF1UsasqOHkNNpk+/X19YUkScjLy8OTTz7Z7O3MzBpu1WkMtABQV1enU2f69OkIDw/Hvn37cODAASQmJmL9+vWYM2eOwTbr6+sBAPv27YOHh4fOZwqFQue9oekFd/PQQw8BaBgVbs9AypuaiKhTkSRzODmNRvfuf4KT02iGUaJORK4VNZydnREeHo7NmzejqqpK7/OmlmVydXUFAJSU/N6fnJwcvXpKpRIzZ86EWq1GfHw8tm7dCgDaeZwaze83bPXv3x8KhQJFRUXw8fHReSmVypYeotbJkycBAO7u7XsTKEdIiYiIqEOQc0WNlJQUDB8+HEOHDkVCQgICAwNx8+ZNHDx4EKmpqcjLy9PbpjEkrlixAqtXr8b333+P9evX69SJi4vD+PHj4efnh+vXryMzMxP+/v4AAC8vL0iShL1792LChAmwsbFB165dsWDBAsybNw/19fUYOXIkKisrcezYMdjZ2WHq1KnNPqbPP/8c2dnZCA0NhYODA7788kvMmzcPTzzxBHr16tW6L8xIHCElIiKiDqFxRY3GmxX1SVAolG2yooa3tzdOnDiB0NBQxMfHY8CAAQgLC8OhQ4eQmppqcBtLS0ts374dZ8+exaBBg7B27VqsXr1ap45Go0FMTAz8/f0xbtw49O3bFykpKQAADw8PrFy5EosWLUL37t0xe/ZsAMCqVauwbNkyJCYmwt/fH+Hh4dizZw+8vb2NOiaFQoEdO3Zg9OjR6N+/P5YtW4YZM2Zg+/btLfiGWkcSt05suEdVVlbCwcEBFRUVsLe3l7s7RERE1ALV1dUoKCiAt7c3rK2tW9TG73fZA4ZW1GiLu+zpd3c6h63JaxwhJSIiog6jcUUNheL2G3o8GUY7MM4hJSIiog6FK2p0PgykRERE1OE0rqhBnQMv2RMRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZNWiQJqSkqJdoX/IkCHIysq6Y/2amhosWbIEXl5eUCgU6NOnD95+++0WdZiIiIjoXiVJEnbv3i13NzocowPpjh07EBcXhyVLluDkyZMICQnB+PHjUVRU1OQ2Tz/9NA4dOoS33noL586dw/bt29GvX79WdZyIiIioPZWWlmLOnDlQqVRQKBRQKpWIiIjAoUOH2mR/hw8fhiRJKC8vb5P2G+3btw/Dhg2DjY0NXFxcEBnZ/k+7Mnph/A0bNuDFF1/E9OnTAQAbN25ERkYGUlNTkZiYqFd///79+PTTT3HhwgU4OzsDAHr37t26XhMRERG1o8LCQowYMQKOjo5ISkpCYGAg6urqkJGRgZiYGJw9e1buLjZJCAGNRgMLC/3Yt3PnTsyYMQN/+9vf8Mgjj0AIgdOnT7d7H40aIa2trcXXX3+NsWPH6pSPHTsWx44dM7jN//3f/yEoKAhJSUnw8PCAn58fFixYgF9//bXJ/dTU1KCyslLnRURERNRIo9Hg8OHD2L59Ow4fPgyNRtOm+5s1axYkScLx48cRHR0NPz8/BAQEYP78+cjOzja4jaERzpycHEiShMLCQgDAxYsXERERAScnJ9ja2iIgIADp6ekoLCxEaGgoAMDJyQmSJGHatGkAGgJmUlISVCoVbGxsMGjQIKSlpentNyMjA0FBQVAoFAanV968eROxsbF47bXXMHPmTPj5+aFv376Ijo42zZdmBKNGSK9evQqNRoPu3bvrlHfv3h2lpaUGt7lw4QKOHj0Ka2tr7Nq1C1evXsWsWbNw7dq1JueRJiYmYuXKlcZ0jYiIiO4TarUasbGxuHz5srbM09MTycnJbXK5+dq1a9i/fz/WrFkDW1tbvc8dHR1b3HZMTAxqa2tx5MgR2NraIjc3F3Z2dlAqldi5cyeioqJw7tw52Nvbw8bGBgCwdOlSqNVqpKamwtfXF0eOHMHkyZPh6uqKUaNGadteuHAh1q1bB5VKZbCPJ06cQHFxMczMzDB48GCUlpbigQcewLp16xAQENDiY2qJFj3LXpIknfdCCL2yRvX19ZAkCe+//z4cHBwANFz2j46OxubNm7Vf7q0WL16M+fPna99XVlZCqVS2pKtERETUiajVakRHR0MIoVNeXFyM6OhopKWlmTyU5ufnQwjRJve/FBUVISoqCgMHDgQAqFQq7WeNUx3d3Ny0gbKqqgobNmxAZmYmgoODtdscPXoUW7Zs0QmkCQkJCAsLa3LfFy5cAACsWLECGzZsQO/evbF+/XqMGjUK3333nXb/7cGoS/YuLi4wNzfXGw398ccf9UZNG7m7u8PDw0MbRgHA398fQgidf9ncSqFQwN7eXudFRERE9zeNRoPY2Fi9MApAWxYXF2fyy/eNbTc1+NYac+fOxerVqzFixAgsX74c33zzzR3r5+bmorq6GmFhYbCzs9O+3nvvPZw/f16nblBQ0B3bqq+vBwAsWbIEUVFRGDJkCLZt2wZJkvDRRx+17sCMZFQgtbKywpAhQ3Dw4EGd8oMHD2L48OEGtxkxYgR++OEH/Pzzz9qy7777DmZmZvD09GxBl4mIiOh+lJWV1eRgFtAQHC9dunTX5SiN5evrC0mSkJeXZ9R2ZmZm2n41qqur06kzffp0XLhwAVOmTMHp06cRFBSETZs2NdlmY4jct28fcnJytK/c3FydeaQADE4vuJW7uzsAoH///toyhUIBlUp1x9WT2oLRyz7Nnz8fb775Jt5++23k5eVh3rx5KCoqwsyZMwE0XG5/7rnntPUnTZqEbt264fnnn0dubi6OHDmCV155BS+88ILBy/VEREREhpSUlJi0XnM5OzsjPDwcmzdvRlVVld7nTS3L5OrqqtefnJwcvXpKpRIzZ86EWq1GfHw8tm7dCqBhIBCAzohv//79oVAoUFRUBB8fH52XsdMbhwwZAoVCgXPnzmnL6urqUFhYCC8vL6Paai2j55A+88wz+Omnn5CQkICSkhIMGDAA6enp2o6XlJTopGo7OzscPHgQc+bMQVBQELp164ann34aq1evNt1REBERUafXOKJnqnrGSElJwfDhwzF06FAkJCQgMDAQN2/exMGDB5Gammpw9LQxJK5YsQKrV6/G999/j/Xr1+vUiYuLw/jx4+Hn54fr168jMzMT/v7+AAAvLy9IkoS9e/diwoQJsLGxQdeuXbFgwQLMmzcP9fX1GDlyJCorK3Hs2DHY2dlh6tSpzT4me3t7zJw5E8uXL4dSqYSXlxdee+01AMBTTz3Vim+rBUQHUFFRIQCIiooKubtCRERELfTrr7+K3Nxc8euvv7Zo+5s3bwpPT08hSZIAoPeSJEkolUpx8+ZNE/e8wQ8//CBiYmKEl5eXsLKyEh4eHuKJJ54Qn3zyibYOALFr1y7t+6NHj4qBAwcKa2trERISIj766CMBQBQUFAghhJg9e7bo06ePUCgUwtXVVUyZMkVcvXpVu31CQoLo0aOHkCRJTJ06VQghRH19vUhOThZ9+/YVlpaWwtXVVYSHh4tPP/1UCCHEJ598IgCI69ev3/WYamtrRXx8vHBzcxNdu3YVY8aMEd9++22T9e90DluT1yQhDMwMvsdUVlbCwcEBFRUVvMGJiIiog6qurkZBQYH28eMt0XiXPaA7N7PxhqO2uMuefnenc9iavNaiZ9kTERERySEyMhJpaWnw8PDQKff09GQY7cBatA4pERERkVwiIyMxceJEZGVloaSkBO7u7ggJCYG5ubncXaMWYiAlIiKiDsfc3ByjR4+WuxtkIrxkT0RERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpERERkYlIkoTdu3fL3Y0Oh4GUiIiIqBlKS0sxZ84cqFQqKBQKKJVKRERE4NChQ22yv8OHD0OSJJSXl7dJ+4327duHYcOGwcbGBi4uLrI87YoL4xMRERHdRWFhIUaMGAFHR0ckJSUhMDAQdXV1yMjIQExMDM6ePSt3F5skhIBGo4GFhX7s27lzJ2bMmIG//e1veOSRRyCEwOnTp9u9jxwhJSIiog5Ho9Hg8OHD2L59Ow4fPgyNRtOm+5s1axYkScLx48cRHR0NPz8/BAQEYP78+cjOzja4jaERzpycHEiShMLCQgDAxYsXERERAScnJ9ja2iIgIADp6ekoLCxEaGgoAMDJyQmSJGHatGkAGgJmUlISVCoVbGxsMGjQIKSlpentNyMjA0FBQVAoFMjKytLr382bNxEbG4vXXnsNM2fOhJ+fH/r27Yvo6GjTfGlG4AgpERERdShqtRqxsbG4fPmytszT0xPJycltcrn52rVr2L9/P9asWQNbW1u9zx0dHVvcdkxMDGpra3HkyBHY2toiNzcXdnZ2UCqV2LlzJ6KionDu3DnY29vDxsYGALB06VKo1WqkpqbC19cXR44cweTJk+Hq6opRo0Zp2164cCHWrVsHlUplsI8nTpxAcXExzMzMMHjwYJSWluKBBx7AunXrEBAQ0OJjagkGUiIiIuow1Go1oqOjIYTQKS8uLkZ0dDTS0tJMHkrz8/MhhEC/fv1M2i4AFBUVISoqCgMHDgQAqFQq7WfOzs4AADc3N22grKqqwoYNG5CZmYng4GDtNkePHsWWLVt0AmlCQgLCwsKa3PeFCxcAACtWrMCGDRvQu3dvrF+/HqNGjcJ3332n3X974CV7IiKShUYDHD4MbN/e8GcbX3GlTkCj0SA2NlYvjALQlsXFxZn88n1j25IkmbRdAJg7dy5Wr16NESNGYPny5fjmm2/uWD83NxfV1dUICwuDnZ2d9vXee+/h/PnzOnWDgoLu2FZ9fT0AYMmSJYiKisKQIUOwbds2SJKEjz76qHUHZiQGUiIiandqNdC7NxAaCkya1PBn794N5URNycrK0rlMfzshBC5dumRwvmRr+Pr6QpIk5OXlGbWdmZmZtl+N6urqdOpMnz4dFy5cwJQpU3D69GkEBQVh06ZNTbbZGCL37duHnJwc7Ss3N1dnHikAg9MLbuXu7g4A6N+/v7ZMoVBApVKhqKioGUdoOgykRETUrtRqIDoauD1XFBc3lDOUUlNKSkpMWq+5nJ2dER4ejs2bN6Oqqkrv86aWZXJ1ddXrT05Ojl49pVKJmTNnQq1WIz4+Hlu3bgUAWFlZAYDOiG///v2hUChQVFQEHx8fnZdSqTTquIYMGQKFQoFz585py+rq6lBYWAgvLy+j2motBlIiImo3Gg0QGwsYuOKqLYuL4+V7MqxxRM9U9YyRkpICjUaDoUOHYufOnfj++++Rl5eH119/XTuX83aNIXHFihX47rvvsG/fPqxfv16nTlxcHDIyMlBQUIATJ04gMzMT/v7+AAAvLy9IkoS9e/eirKwMP//8M7p27YoFCxZg3rx5ePfdd3H+/HmcPHkSmzdvxrvvvmvUMdnb22PmzJlYvnw5Dhw4gHPnzuHll18GADz11FMt+JZaQXQAFRUVAoCoqKiQuytERNQKn3wiREP0vPPrk0/k7im1hV9//VXk5uaKX3/9tUXb37x5U3h6egpJkgQAvZckSUKpVIqbN2+auOcNfvjhBxETEyO8vLyElZWV8PDwEE888YT45JYfWABi165d2vdHjx4VAwcOFNbW1iIkJER89NFHAoAoKCgQQggxe/Zs0adPH6FQKISrq6uYMmWKuHr1qnb7hIQE0aNHDyFJkpg6daoQQoj6+nqRnJws+vbtKywtLYWrq6sIDw8Xn376qRBCiE8++UQAENevX7/rMdXW1or4+Hjh5uYmunbtKsaMGSO+/fbbJuvf6Ry2Jq9JQhj6d+q9pbKyEg4ODqioqIC9vb3c3SEiohbavr1hzujd/PvfwJ/+1Pb9ofZVXV2NgoICeHt7w9raukVtNN5lD+jOzWy84agt7rKn393pHLYmr/GSPRERtZvmXkltgyuu1ElERkYiLS0NHh4eOuWenp4Mox0Y1yElIqJ2ExICeHo23MBk6PqcJDV8HhLS/n2jjiMyMhITJ05EVlYWSkpK4O7ujpCQEJibm8vdNWohBlIiImo35uZAcnLD3fSSpBtKG5d43LixoR7d+zT1GmQVZaHkRgncu7ojpFcIzM3a5+SZm5tj9OjR7bIvansMpERE1K4iI4G0tIa77W9d+snTsyGM8oprx6DOUyN2fywuV97y+E57TySPS0akP08iGYeBlIiI2l1kJDBxIpCVBZSUNMwZDQnhyGhHoc5TI/rDaAjc9vjOymJEfxiNtKfTGErJKAykREQkC3NzgFdcOx5NvQax+2P1wigACAhIkBC3Pw4T+05s8vJ9B1jgh5rQVueOd9kTERFRs2UVZelcpr+dgMClykvIKtJ/fKelpSUA4Jdffmmz/lHbajx3jefSVDhCSkRERM1WcqOZj+80UM/c3ByOjo748ccfAQBdunTRrh9K9zYhBH755Rf8+OOPcHR0NPmKBgykRERE1GzuXZv5+M4m6vXo0QMAtKGUOhZHR0ftOTQlBlIiIiJqtpBeIfC090RxZbHBeaQSJHjaeyKkl+HFZCVJgru7O9zc3FBXV9fW3SUTsrS0bLO1XhlIiYiIqNnMzcyRPC4Z0R9GQ4KkE0olNFx+3zhu413XIzU3N+dC9qTFm5qIiIjIKJH+kUh7Og0e9rc9vtPek0s+UYtIogOsvVBZWQkHBwdUVFTA3t5e7u4QERER5H1SE917WpPXeMmeiIiIWsTczByje4+WuxvUCfCSPRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkaxaFEhTUlLg7e0Na2trDBkyBFlZWc3a7rPPPoOFhQUeeOCBluyWiIiIiDohowPpjh07EBcXhyVLluDkyZMICQnB+PHjUVRUdMftKioq8Nxzz+HRRx9tcWeJiIiIqPORhBDCmA2GDRuGBx98EKmpqdoyf39/PPnkk0hMTGxyu//5n/+Br68vzM3NsXv3buTk5DR7n5WVlXBwcEBFRQXs7e2N6S4RERERtYPW5DWjRkhra2vx9ddfY+zYsTrlY8eOxbFjx5rcbtu2bTh//jyWL19uVOeIiIiIqPOzMKby1atXodFo0L17d53y7t27o7S01OA233//PRYtWoSsrCxYWDRvdzU1NaipqdG+r6ysNKabRERERNSBtOimJkmSdN4LIfTKAECj0WDSpElYuXIl/Pz8mt1+YmIiHBwctC+lUtmSbhIRERFRB2BUIHVxcYG5ubneaOiPP/6oN2oKADdu3MBXX32F2bNnw8LCAhYWFkhISMCpU6dgYWGBzMxMg/tZvHgxKioqtK9Lly4Z000iIiIi6kCMumRvZWWFIUOG4ODBg/jjH/+oLT948CAmTpyoV9/e3h6nT5/WKUtJSUFmZibS0tLg7e1tcD8KhQIKhcKYrhERERFRB2VUIAWA+fPnY8qUKQgKCkJwcDD++c9/oqioCDNnzgTQMLpZXFyM9957D2ZmZhgwYIDO9m5ubrC2ttYrJyIiIiLT0Gg0yMrKQklJCdzd3RESEgJzc3O5u9UkowPpM888g59++gkJCQkoKSnBgAEDkJ6eDi8vLwBASUnJXdckJSIiIqK2oVarERsbi8uXL2vLPD09kZycjMjISBl71jSj1yGVA9chJSIiIro7tVqN6Oho3B7vGm8+T0tLa7NQ2m7rkBIRERHRvUmj0SA2NlYvjALQlsXFxUGj0bR31+6KgZSIiIioE8jKytK5TH87IQQuXbqErKysduxV8zCQEhEREXUCJSUlJq3XnhhIiYiIiDoBd3d3k9ZrTwykRERERJ1ASEgIPD09DT49E2i4sUmpVCIkJKSde3Z3DKREREREnYC5uTmSk5MB6D/mvfH9xo0b78n1SBlIiYiIiDqJyMhIpKWlwcPDQ6fc09OzTZd8ai2uQ0pERETUycjxpKbW5DWjn9RERERERPc2c3NzjB49Wu5uNBsv2RMRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlm1KJCmpKTA29sb1tbWGDJkCLKyspqsq1arERYWBldXV9jb2yM4OBgZGRkt7jARERERdS5GB9IdO3YgLi4OS5YswcmTJxESEoLx48ejqKjIYP0jR44gLCwM6enp+PrrrxEaGoqIiAicPHmy1Z0nIiIioo5PEkIIYzYYNmwYHnzwQaSmpmrL/P398eSTTyIxMbFZbQQEBOCZZ57BsmXLmlW/srISDg4OqKiogL29vTHdJSIiIqJ20Jq8ZtQIaW1tLb7++muMHTtWp3zs2LE4duxYs9qor6/HjRs34OzsbMyuiYiIiKiTsjCm8tWrV6HRaNC9e3ed8u7du6O0tLRZbaxfvx5VVVV4+umnm6xTU1ODmpoa7fvKykpjuklEREREHUiLbmqSJEnnvRBCr8yQ7du3Y8WKFdixYwfc3NyarJeYmAgHBwftS6lUtqSbRERERNQBGBVIXVxcYG5urjca+uOPP+qNmt5ux44dePHFF/Hhhx9izJgxd6y7ePFiVFRUaF+XLl0ypptERERE1IEYFUitrKwwZMgQHDx4UKf84MGDGD58eJPbbd++HdOmTcO///1vPPbYY3fdj0KhgL29vc6LiIiIiDono+aQAsD8+fMxZcoUBAUFITg4GP/85z9RVFSEmTNnAmgY3SwuLsZ7770HoCGMPvfcc0hOTsZDDz2kHV21sbGBg4ODCQ+FiIiIiDoiowPpM888g59++gkJCQkoKSnBgAEDkJ6eDi8vLwBASUmJzpqkW7Zswc2bNxETE4OYmBht+dSpU/HOO++0/giIiIiIqEMzeh1SOXAdUiIiIqJ7W7utQ0pEREREZGoMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkZSF3B+g3Gg2QlQWUlADu7kBICGBuLneviIiIiNocA+m9QK0GYmOBy5d/L/P0BJKTgchI+fpFRERE1A54yV5uajUQHa0bRgGguLihXK2Wp19ERERE7YSBVE4aTcPIqBD6nzWWxcU11CMiIiLqpBhI5ZSVpT8yeishgEuXGuoRERERdVIMpHIqKTFtPSIiIqIOiIFUTu7upq1HRERE1AExkMopJKThbnpJMvy5JAFKZUM9IiIiok6KgVRO5uYNSzsB+qG08f3GjVyPlIiIiDo1BlK5RUYCaWmAh4duuadnQznXISUiIqJOjgvj3wsiI4GJE/mkJiIiIrovMZDeK8zNgdGj5e4FERERUbvjJXsiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZWcjdgeYQQgAAKisrZe4JERERERnSmNMac5sxOkQgvXHjBgBAqVTK3BMiIiIiupMbN27AwcHBqG0k0ZIY287q6+vxww8/oGvXrpAkyejtKysroVQqcenSJdjb27dBD0kuPLedG89v58Vz27nx/HZedzq3QgjcuHEDPXv2hJmZcbNCO8QIqZmZGTw9PVvdjr29PX8xOime286N57fz4rnt3Hh+O6+mzq2xI6ONeFMTEREREcmKgZSIiIiIZHVfBFKFQoHly5dDoVDI3RUyMZ7bzo3nt/Piue3ceH47r7Y6tx3ipiYiIiIi6rzuixFSIiIiIrp3MZASERERkawYSImIiIhIVgykRERERCSrThNIU1JS4O3tDWtrawwZMgRZWVl3rP/pp59iyJAhsLa2hkqlwhtvvNFOPSVjGXNu1Wo1wsLC4OrqCnt7ewQHByMjI6Mde0vGMvZ3t9Fnn30GCwsLPPDAA23bQWoxY89tTU0NlixZAi8vLygUCvTp0wdvv/12O/WWjGXs+X3//fcxaNAgdOnSBe7u7nj++efx008/tVNvqbmOHDmCiIgI9OzZE5IkYffu3XfdxiSZSnQCH3zwgbC0tBRbt24Vubm5IjY2Vtja2oqLFy8arH/hwgXRpUsXERsbK3Jzc8XWrVuFpaWlSEtLa+ee090Ye25jY2PF2rVrxfHjx8V3330nFi9eLCwtLcWJEyfauefUHMae30bl5eVCpVKJsWPHikGDBrVPZ8koLTm3TzzxhBg2bJg4ePCgKCgoEF988YX47LPP2rHX1FzGnt+srCxhZmYmkpOTxYULF0RWVpYICAgQTz75ZDv3nO4mPT1dLFmyROzcuVMAELt27bpjfVNlqk4RSIcOHSpmzpypU9avXz+xaNEig/UXLlwo+vXrp1P20ksviYceeqjN+kgtY+y5NaR///5i5cqVpu4amUBLz+8zzzwjli5dKpYvX85Aeo8y9tx+/PHHwsHBQfz000/t0T1qJWPP72uvvSZUKpVO2euvvy48PT3brI/Ues0JpKbKVB3+kn1tbS2+/vprjB07Vqd87NixOHbsmMFtPv/8c7364eHh+Oqrr1BXV9dmfSXjtOTc3q6+vh43btyAs7NzW3SRWqGl53fbtm04f/48li9f3tZdpBZqybn9v//7PwQFBSEpKQkeHh7w8/PDggUL8Ouvv7ZHl8kILTm/w4cPx+XLl5Geng4hBK5cuYK0tDQ89thj7dFlakOmylQWpu5Ye7t69So0Gg26d++uU969e3eUlpYa3Ka0tNRg/Zs3b+Lq1atwd3dvs/5S87Xk3N5u/fr1qKqqwtNPP90WXaRWaMn5/f7777Fo0SJkZWXBwqLD/++r02rJub1w4QKOHj0Ka2tr7Nq1C1evXsWsWbNw7do1ziO9x7Tk/A4fPhzvv/8+nnnmGVRXV+PmzZt44oknsGnTpvboMrUhU2WqDj9C2kiSJJ33Qgi9srvVN1RO8jP23Dbavn07VqxYgR07dsDNza2tuket1Nzzq9FoMGnSJKxcuRJ+fn7t1T1qBWN+d+vr6yFJEt5//30MHToUEyZMwIYNG/DOO+9wlPQeZcz5zc3Nxdy5c7Fs2TJ8/fXX2L9/PwoKCjBz5sz26Cq1MVNkqg4/xODi4gJzc3O9f5X9+OOPeom9UY8ePQzWt7CwQLdu3dqsr2SclpzbRjt27MCLL76Ijz76CGPGjGnLblILGXt+b9y4ga+++gonT57E7NmzATSEGCEELCwscODAATzyyCPt0ne6s5b87rq7u8PDwwMODg7aMn9/fwghcPnyZfj6+rZpn6n5WnJ+ExMTMWLECLzyyisAgMDAQNja2iIkJASrV6/mlckOzFSZqsOPkFpZWWHIkCE4ePCgTvnBgwcxfPhwg9sEBwfr1T9w4ACCgoJgaWnZZn0l47Tk3AINI6PTpk3Dv//9b85PuocZe37t7e1x+vRp5OTkaF8zZ85E3759kZOTg2HDhrVX1+kuWvK7O2LECPzwww/4+eeftWXfffcdzMzM4Onp2ab9JeO05Pz+8ssvMDPTjRzm5uYAfh9No47JZJnKqFug7lGNy0+89dZbIjc3V8TFxQlbW1tRWFgohBBi0aJFYsqUKdr6jUsUzJs3T+Tm5oq33nqLyz7do4w9t//+97+FhYWF2Lx5sygpKdG+ysvL5ToEugNjz+/teJf9vcvYc3vjxg3h6ekpoqOjxZkzZ8Snn34qfH19xfTp0+U6BLoDY8/vtm3bhIWFhUhJSRHnz58XR48eFUFBQWLo0KFyHQI14caNG+LkyZPi5MmTAoDYsGGDOHnypHZJr7bKVJ0ikAohxObNm4WXl5ewsrISDz74oPj000+1n02dOlWMGjVKp/7hw4fF4MGDhZWVlejdu7dITU1t5x5TcxlzbkeNGiUA6L2mTp3a/h2nZjH2d/dWDKT3NmPPbV5enhgzZoywsbERnp6eYv78+eKXX35p515Tcxl7fl9//XXRv39/YWNjI9zd3cWzzz4rLl++3M69prv55JNP7vj3aFtlKkkIjpUTERERkXw6/BxSIiIiIurYGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpLV/wfxKkZnaGboZgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 10 ---\n", - "Finale Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Beispiel Daten - hier können Sie Ihre eigenen Daten eingeben\n", - "np.random.seed(0)\n", - "num_points = 10 # Anzahl der Datenpunkte\n", - "num_dimensions = 2 # Anzahl der Dimensionen der Datenpunkte\n", - "X = np.random.random_sample([num_points, num_dimensions])\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Tabelle als DataFrame erstellen und ausgeben\n", - " #columns = ['Cluster 1', 'Cluster 2', 'Abstand', 'Größe Cluster 1', 'Größe Cluster 2']\n", - " #distance_table = pd.DataFrame(distances, columns=columns)\n", - " #print(\"Tabelle mit Abständen zwischen Clustern:\")\n", - " #print(distance_table)\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1334f577", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[0], [1], [3], [4], [5], [7], [8], [9], [2, 6]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[0], [1], [3], [5], [8], [9], [2, 6], [4, 7]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[0], [1], [3], [5], [8], [9], [2, 6], [4, 7]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 7 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 8 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 9 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 10 ---\n", - "Finale Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 5\n", - "\n", - "# Größe des \"Noise\"-Bereichs\n", - "noise_scale = 1\n", - "\n", - "# Beispiel Daten - Clusterpunkte und \"Noise\" Punkte\n", - "np.random.seed(0)\n", - "clusters = []\n", - "for _ in range(num_clusters):\n", - " cluster_center = np.random.rand(2) * (1 - noise_scale)\n", - " cluster = cluster_center + np.random.randn(points_per_cluster, 2) * noise_scale\n", - " clusters.append(cluster)\n", - "\n", - "# Zusammenführen der Clusterpunkte\n", - "X = np.concatenate(clusters, axis=0)\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "1a4f2193", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([[0.59180595, 0.86775975],\n", - " [0.68068795, 0.54594264],\n", - " [0.588941 , 0.62853471],\n", - " [0.48361027, 0.68473028],\n", - " [0.50833651, 0.78909778]]), array([[0.55562643, 0.86640441],\n", - " [0.66064801, 0.81252115],\n", - " [0.54254688, 0.7476274 ],\n", - " [0.25594112, 0.89839883],\n", - " [0.59768372, 0.75882047]])]\n", - "[[0.59180595 0.86775975]\n", - " [0.68068795 0.54594264]\n", - " [0.588941 0.62853471]\n", - " [0.48361027 0.68473028]\n", - " [0.50833651 0.78909778]\n", - " [0.55562643 0.86640441]\n", - " [0.66064801 0.81252115]\n", - " [0.54254688 0.7476274 ]\n", - " [0.25594112 0.89839883]\n", - " [0.59768372 0.75882047]]\n", - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [6], [7], [8], [9], [0, 5]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [7], [8], [0, 5], [6, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [7], [8], [0, 5], [6, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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HR7PHh4aG4sMPP8TOnTsxb968NvXB2NgYtbW1auVXr15VeV+7deuG2bNnY/bs2aisrMTXX3+Nd955B6Ghobh06VKLD481tZOWlqYcOW2Jpvf+559/xvHjx7FlyxaVJ8sLCwvv2V5zdu7ciVu3bkEul6v0Kz8/v1XHW1hY4JVXXsFHH32EOXPmYPPmzZgwYQIsLS1V6r366qt49dVXcevWLeTk5GDRokUYPXo0zp49C0dHR/Ts2RPe3t5Yvny5xvP06dOn2T688cYbGD16tPLnpqCuDUNDQyxatAj/+te/8PPPPwNo/H8NAC5fvgyZTKZ1m83R9N62p59//hnFxcXo3bu32r6m++jGjRtq7xlRZ2GIJdKx0aNH4x//+Ad69OgBZ2fnNrUhCAK6deum8vH6H3/8gY8//rjVbYwaNQoff/wxzpw502K40wWpVIphw4bB0tISmZmZOHbsGPz9/TFq1CgsXLgQ2dnZePrpp+/rHB4eHnBzc8Px48fxj3/8Q+vjx44di4EDByIhIQGjR4/W+HFpZmam8qlxTZycnPDTTz+plJ09exZnzpxR+8dJE0tLS0RERKC0tBQxMTEoLi5G//79mx3tDg0NRbdu3XD+/Pk2f5zcFH7uDmnr169vU3vNtSmKIjZs2NDqNmbOnImUlBRERESgsrIS06dPb7auqakpRo0ahbq6Orz44os4efIkHB0dMXr0aGRkZKBv374ap+G0pE+fPi2G3LuVlZVpHA1tmo7R1NaIESMgkUiQmpoKf39/rfqkC3feS3cvrdfcKK8mSUlJausW5+fnY9asWVi8eDGGDRsGMzMznfSZSBcYYol0LCYmBunp6Xjqqacwa9YseHt7o6GhASUlJdi3bx9iY2MxZMiQFtt4/vnnsXr1akyYMAFvvPEGrl27hpUrV2o1chQfH4+vvvoKTz31FN555x0MHDgQlZWV2Lt3L2bPnq2yMkBbLFy4EJcvX8YzzzwDBwcHVFZWYs2aNTA0NMSwYcOUr8WOHTswduxYzJs3D4MHD8Yff/yBb7/9FqNHj0ZwcLBW51y/fj1GjRqF0NBQTJo0Cfb29rh+/TpOnTqFo0eP4vPPP2/2WIlEgi+++AIjRoyAv78/3nrrLQQHB8PU1BQXL15EWloadu3ahRs3bjTbRmRkJCZOnIhp06YhPDwcFy9eRGJionIkrsmYMWMwYMAA+Pn5wcbGBhcvXkRSUhIcHR3h5uYGABg4cCAAYM2aNXjllVdgaGgIDw8PODk5IT4+HvPnz8eFCxcwcuRIWFlZ4ddff8WRI0dgamra7ML2Tfr164e+ffti3rx5EEUR1tbW2LVrl8rH8toKCQmBkZER/vrXv2Lu3LmoqalBampqi6/X3dzd3TFy5Eh89dVXePLJJ+Hj46Oy//XXX4eJiQkCAwNhZ2eH8vJyJCQkwMLCQjkHOz4+HllZWQgICMDMmTPh4eGBmpoaFBcXIyMjAx988EGrp7DcS2hoKBwcHDBmzBj069cPDQ0NyM/Px6pVq2BmZobo6GgAjf+4eeedd7B06VL88ccfyqXKCgoKcPXq1Xu+X/er6V5asWIFRo0aBYlEAm9vbxgZGWHgwIE4cOAAdu3aBTs7O3Tv3r3Zf9Q+9thjzZ7Dy8urVctzEXWozn2ujOjB0vRE+t0rCzR5/vnn77k6gSiK4s2bN8UFCxaIHh4eopGRkWhhYSEOHDhQnDVrllheXq6sB0CMiorSeK5NmzaJHh4eolQqFV1cXMSEhARx48aNGp+Yf/755zW2cenSJfG1114Te/fuLRoaGop9+vQRx40bJ/7666+iKDb/5HHTigGbN29Wlt39xPnu3bvFUaNGifb29qKRkZHYq1cv8bnnnhNzc3NV2rpx44YYHR0tPvroo6KhoaHYq1cv8fnnnxdPnz6tcq733ntPrf8AxEWLFqmUHT9+XBw3bpzYq1cv0dDQUOzdu7f49NNPix988IHG1+BulZWV4tKlS8XHH39cNDMzEw0NDcVHH31UnDhxovjf//5XWU/T6gQNDQ1iYmKi6OLiIhobG4t+fn5idna22uoEq1atEgMCAsSePXuKRkZG4qOPPipOnjxZLC4uVulLXFyc2KdPH9HAwEDtifOdO3eKwcHBorm5uSiVSkVHR0cxIiJC/Prrr5V1XnnlFdHU1FTjdRYUFIghISFi9+7dRSsrK/Gll14SS0pK1F7TptUJKioqVI7XdP27du0SfXx8RGNjY9He3l58++23xa+++kqt73ffK3fasmWLCED89NNP1fZt3bpVDA4OFm1tbUUjIyPl/frTTz+p1KuoqBBnzpwpOjs7i4aGhqK1tbXo6+srzp8/X7x586bG87bFjh07xAkTJohubm4q90pkZKTGVT+2bdsmPvHEE6KxsbFoZmYmDho0SOX/oWHDholeXl5qx939erW0IoCm1Qlqa2vFKVOmiDY2NqIgCCrvW35+vhgYGCg+8sgjIgCV+7Q1uDoBPcgEUeQsbSIi6hjh4eHIy8tDcXGx3n87GhF1Lk4nICKidlVbW4ujR4/iyJEj+OKLL7B69WoGWCK6bxyJJSKidlVcXAxnZ2eYm5tjwoQJeP/995v9JjgiotZiiCUiIiIivcMvOyAiIiIivcMQS0RERER6hyGWiIiIiPTOQ7M6QUNDA3755Rd07969w7+qj4iIiIjuTRRF/Pbbb+jTpw8MDO5vLPWhCbG//PKLTr+zmoiIiIjax6VLl+772/UemhDbvXt3AI0virm5eSf3hoiIiIjuVl1dDZlMpsxt9+OhCbFNUwjMzc0ZYomIiIgeYLqY+skHu4iIiIhI7zDEEhEREZHeYYglIiIiIr3DEEtEREREeochloiIiIj0DkMsEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6p00hNiUlBc7OzjA2Noavry9yc3NbrJ+cnAxPT0+YmJjAw8MD27ZtU6uTnp6O/v37QyqVon///vjiiy/a0jUiIiIi6gK0DrE7duxATEwM5s+fj2PHjiEoKAijRo1CSUmJxvqpqamIi4vD4sWLcfLkSSxZsgRRUVHYtWuXss7hw4cxfvx4REZG4vjx44iMjMS4cePw3Xfftf3K2pEoKnDjxgH8+ut23LhxAKKo6OwuEREREXUpgiiKojYHDBkyBI8//jhSU1OVZZ6ennjxxReRkJCgVj8gIACBgYF47733lGUxMTH44YcfcPDgQQDA+PHjUV1dja+++kpZZ+TIkbCyssL27dtb1a/q6mpYWFigqqoK5ubm2lySVioq5CgsjEZt7WVlmVTqAFfXNbCxCWu38xIRERHpO13mNa1GYuvq6vDjjz9ixIgRKuUjRozAoUOHNB5TW1sLY2NjlTITExMcOXIE9fX1ABpHYu9uMzQ0tNk2m9qtrq5W2dpbRYUcJ09GqATYxr6U4uTJCFRUyNu9D0RERESkZYi9evUqFAoFbG1tVcptbW1RXl6u8ZjQ0FB89NFH+PHHHyGKIn744Qds2rQJ9fX1uHr1KgCgvLxcqzYBICEhARYWFspNJpNpcylaE0UFCgujAWgauG4sKyyM4dQCIiIiog7Qpge7BEFQ+VkURbWyJu+++y5GjRqFoUOHwtDQEGPHjsWkSZMAABKJpE1tAkBcXByqqqqU26VLl9pyKa1WWZmrNgKrSkRt7SVUVrb8kBsRERER3T+tQmzPnj0hkUjURkivXLmiNpLaxMTEBJs2bcLvv/+O4uJilJSUwMnJCd27d0fPnj0BAL1799aqTQCQSqUwNzdX2dpTXV2ZTusRERERUdtpFWKNjIzg6+uLrKwslfKsrCwEBAS0eKyhoSEcHBwgkUjw6aefYvTo0TAwaDy9v7+/Wpv79u27Z5sdycjITqf1iIiIiKjtuml7wOzZsxEZGQk/Pz/4+/vjww8/RElJCaZOnQqg8WP+0tJS5VqwZ8+exZEjRzBkyBDcuHEDq1evxs8//4ytW7cq24yOjsZTTz2FFStWYOzYsfjPf/6Dr7/+Wrl6wYPA0jIIUqkDamtLoXlerACp1AGWlkEd3TUiIiKiLkfrEDt+/Hhcu3YN8fHxKCsrw4ABA5CRkQFHR0cAQFlZmcqasQqFAqtWrcKZM2dgaGiI4OBgHDp0CE5OTso6AQEB+PTTT7FgwQK8++676Nu3L3bs2IEhQ4bc/xXqiCBI4Oq6BidPRgAQoBpkG+fuuromQRAkmg4nIiIiIh3Sep3YB1XnrhMrg6trEteJJSIiImqBLvOa1iOxXZ2NTRh69hyLyspc1NWVwcjIDpaWQRyBJSIiIupADLFtIAgSWFkN7+xuEBEREXVZbVonloiIiIioMzHEEhEREZHeYYglIiIiIr3DEEtEREREeochloiIiIj0DkMsEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxRERERKR3GGKJiIiISO8wxBIRERGR3mGIJSIiIiK9wxBLRERERHqHIZaIiIiI9A5DLBERERHpHYZYIiIiItI7DLFEREREpHcYYomIiIhI73Tr7A4QERHRg02hUCA3NxdlZWWws7NDUFAQJBJJZ3eLujiGWCIionamzyFQLpcjOjoaly9fVpY5ODhgzZo1CAsL68SeUVfH6QRERETtSC6Xw8nJCcHBwZgwYQKCg4Ph5OQEuVze2V27J7lcjoiICJUACwClpaWIiIjQi2ugh5cgiqLY2Z3QherqalhYWKCqqgrm5uad3R0iIiJlCLz7r1pBEAAAaWlpD+xopkKhgJOTk1qAbSIIAhwcHFBUVKQ3o8rU+XSZ1zgSS0RE1A4UCgWio6PVAiwAZVlMTAwUCkVHd61VcnNzmw2wQOM1XLp0Cbm5uR3YK6I/McQSERG1A30PgWVlZTqtR6RrDLFERETtQN9DoJ2dnU7rEekaQywREVE70PcQGBQUBAcHB+X83bsJggCZTIagoKAO7hlRI4ZYIiKidqDvIVAikWDNmjUAoHYNTT8nJSXxoS7qNAyxRERE7eBhCIFhYWFIS0uDvb29SrmDg8MDvbICdQ1cYouIiKgdafqyAJlMhqSkJL0Jgfr8ZQ30YNFlXmOIJSIiamcMgUSNdJnX+LWzRERE7UwikWD48OGd3Q2ihwrnxBIRERGR3mGIJSIiIiK9wxBLRERERHqHIZaIiIiI9A5DLBERERHpHYZYIiIiItI7DLFEREREpHcYYomIiIhI7zDEEhEREZHeaVOITUlJgbOzM4yNjeHr64vc3NwW63/yySfw8fHBI488Ajs7O7z66qu4du2acv+WLVsgCILaVlNT05buEREREdFDTusQu2PHDsTExGD+/Pk4duwYgoKCMGrUKJSUlGisf/DgQbz88suYPHkyTp48ic8//xzff/89pkyZolLP3NwcZWVlKpuxsXHbroqIiIiIHmpah9jVq1dj8uTJmDJlCjw9PZGUlASZTIbU1FSN9fPy8uDk5ISZM2fC2dkZTz75JN5880388MMPKvUEQUDv3r1VNiIiIiIiTbQKsXV1dfjxxx8xYsQIlfIRI0bg0KFDGo8JCAjA5cuXkZGRAVEU8euvvyItLQ3PP/+8Sr2bN2/C0dERDg4OGD16NI4dO9ZiX2pra1FdXa2yEREREVHXoFWIvXr1KhQKBWxtbVXKbW1tUV5ervGYgIAAfPLJJxg/fjyMjIzQu3dvWFpaYt26dco6/fr1w5YtW/Dll19i+/btMDY2RmBgIM6dO9dsXxISEmBhYaHcZDKZNpdCRERERHqsTQ92CYKg8rMoimplTQoKCjBz5kwsXLgQP/74I/bu3YuioiJMnTpVWWfo0KGYOHEifHx8EBQUhM8++wzu7u4qQfducXFxqKqqUm6XLl1qy6UQERERkR7qpk3lnj17QiKRqI26XrlyRW10tklCQgICAwPx9ttvAwC8vb1hamqKoKAgLFu2DHZ2dmrHGBgY4IknnmhxJFYqlUIqlWrTfSIiIiJ6SGg1EmtkZARfX19kZWWplGdlZSEgIEDjMb///jsMDFRPI5FIADSO4GoiiiLy8/M1BlwiIiIiIq1GYgFg9uzZiIyMhJ+fH/z9/fHhhx+ipKREOT0gLi4OpaWl2LZtGwBgzJgxeP3115GamorQ0FCUlZUhJiYGgwcPRp8+fQAAS5YswdChQ+Hm5obq6mqsXbsW+fn5SE5O1uGlEhEREdHDQusQO378eFy7dg3x8fEoKyvDgAEDkJGRAUdHRwBAWVmZypqxkyZNwm+//Yb3338fsbGxsLS0xNNPP40VK1Yo61RWVuKNN95AeXk5LCwsMGjQIOTk5GDw4ME6uEQiIiIietgIYnOf6euZ6upqWFhYoKqqCubm5p3dHSIiIiK6iy7zWptWJyAiIiIi6kwMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxRERERKR3GGKJiIiISO8wxBIRERGR3mGIJSIiIiK9wxBLRERERHqHIZaIiIiI9A5DLBERERHpHYZYIiIiItI7DLFEREREpHcYYomIiIhI7zDEEhEREZHeYYglIiIiIr3DEEtEREREeochloiIiIj0DkMsEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSO906uwNERERE1EihAHJzgbIywM4OCAoCJJLO7tWDiSGWiIiI6AEglwPR0cDly3+WOTgAa9YAYWGd168HFacTEBEREXUyuRyIiFANsABQWtpYLpd3Tr8eZAyxRERERJ1IoWgcgRVF9X1NZTExjfXoTwyxRERERJ0oN1d9BPZOoghcutRYj/7EEEtERETUicrKdFuvq2CIJSIiIupEdna6rddVMMQSERERdaKgoMZVCARB835BAGSyxnr0J4ZYIiIiok4kkTQuowWoB9mmn5OSuF7s3RhiiYiIiDpZWBiQlgbY26uWOzg0lnOdWHX8sgMiIiKiB0BYGDB2LL+xq7UYYomIiIgeEBIJMHx4Z/dCP3A6ARERERHpHYZYIiIiItI7DLFEREREpHfaFGJTUlLg7OwMY2Nj+Pr6Ivce34P2ySefwMfHB4888gjs7Ozw6quv4tq1ayp10tPT0b9/f0ilUvTv3x9ffPFFW7pGRERERF2A1iF2x44diImJwfz583Hs2DEEBQVh1KhRKCkp0Vj/4MGDePnllzF58mScPHkSn3/+Ob7//ntMmTJFWefw4cMYP348IiMjcfz4cURGRmLcuHH47rvv2n5lRERERPTQEkRRFLU5YMiQIXj88ceRmpqqLPP09MSLL76IhIQEtforV65Eamoqzp8/ryxbt24dEhMTcenSJQDA+PHjUV1dja+++kpZZ+TIkbCyssL27dtb1a/q6mpYWFigqqoK5ubm2lwSEREREXUAXeY1rUZi6+rq8OOPP2LEiBEq5SNGjMChQ4c0HhMQEIDLly8jIyMDoiji119/RVpaGp5//nllncOHD6u1GRoa2mybRERERNS1aRVir169CoVCAVtbW5VyW1tblJeXazwmICAAn3zyCcaPHw8jIyP07t0blpaWWLdunbJOeXm5Vm0CQG1tLaqrq1U2IiIiIuoa2vRgl3DXF/uKoqhW1qSgoAAzZ87EwoUL8eOPP2Lv3r0oKirC1KlT29wmACQkJMDCwkK5yWSytlwKEREREekhrUJsz549IZFI1EZIr1y5ojaS2iQhIQGBgYF4++234e3tjdDQUKSkpGDTpk0oKysDAPTu3VurNgEgLi4OVVVVyq1pfi0RERERPfy0CrFGRkbw9fVFVlaWSnlWVhYCAgI0HvP777/DwED1NJL/fQlw0zNl/v7+am3u27ev2TYBQCqVwtzcXGUjIiIioq6hm7YHzJ49G5GRkfDz84O/vz8+/PBDlJSUKKcHxMXFobS0FNu2bQMAjBkzBq+//jpSU1MRGhqKsrIyxMTEYPDgwejTpw8AIDo6Gk899RRWrFiBsWPH4j//+Q++/vprHDx4UIeXSkREREQPC61D7Pjx43Ht2jXEx8ejrKwMAwYMQEZGBhwdHQEAZWVlKmvGTpo0Cb/99hvef/99xMbGwtLSEk8//TRWrFihrBMQEIBPP/0UCxYswLvvvou+fftix44dGDJkiA4ukYiIiIgeNlqvE/ug4jqxRERERA+2TlsnloiIiIjoQcAQS0RERER6R+s5sURERG2hEEXkVlairK4OdkZGCLK0hKSF9cCJiFrCEEtERO1OXlGB6MJCXK6tVZY5SKVY4+qKMBubTuwZEekrTicgIqJ2Ja+oQMTJkyoBFgBKa2sRcfIk5BUVndQzItJnDLFERNRuFKKI6MJCaFoGp6ksprAQiodjoRwi6kAMsURE1G5yKyvVRmDvJAK4VFuL3MrKDusTET0cGGKJiKjdlNXV6bQeEVEThlgiImo3dkZGOq1HRNSEIZaIiNpNkKUlHKRSNLeQlgBAJpUiyNKyA3tFRA8DhlgiImo3EkHAGldXAFALsk0/J7m6cr1YItIaQywREbWrMBsbpHl5wV4qVSl3kEqR5uXFdWKJqE34ZQdERNTuwmxsMLZnT35jFxHpDEMsERF1CIkgYLiVVWd3g4geEpxOQERERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxRERERKR3GGKJiIiISO8wxBIRERGR3mGIJSIiIiK9wxBLRERERHqHIZaIiIiI9A5DLBERERHpHYZYIiIiItI7DLFEREREpHcYYomIiIhI7zDEEhEREZHeYYglIiIiIr3DEEtEREREeochloiIiIj0DkMsEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivdOtsztARERE+kuhAHJzgbIywM4OCAoCJJLO7hV1BQyxRERE1CZyORAdDVy+/GeZgwOwZg0QFtZ5/aKugdMJiIiISGtyORARoRpgAaC0tLFcLu+cflHXwRBLREREWlEoGkdgRVF9X1NZTExjPaL2whBLREREWsnNVR+BvZMoApcuNdYjai8MsURERKSVsjLd1iNqC4ZYIiIi0oqdnW7rEbUFQywRERFpJSiocRUCQdC8XxAAmayxHlF7YYglIiIirUgkjctoAepBtunnpCSuF0vtiyGWiIiItBYWBqSlAfb2quUODo3lXCeW2hu/7ICIiIjaJCwMGDuW39hFnaNNI7EpKSlwdnaGsbExfH19kdvCGhqTJk2CIAhqm5eXl7LOli1bNNapqalpS/eIiIiog0gkwPDhwF//2vgnAyx1FK1D7I4dOxATE4P58+fj2LFjCAoKwqhRo1BSUqKx/po1a1BWVqbcLl26BGtra7z00ksq9czNzVXqlZWVwdjYuG1XRUREREQPNa2nE6xevRqTJ0/GlClTAABJSUnIzMxEamoqEhIS1OpbWFjAwsJC+fPOnTtx48YNvPrqqyr1BEFA7969te0OERFRuxEVIipzK1FXVgcjOyNYBllCkDTzSD4RdSitQmxdXR1+/PFHzJs3T6V8xIgROHToUKva2LhxI5599lk4OjqqlN+8eROOjo5QKBR47LHHsHTpUgwaNKjZdmpra1FbW6v8ubq6WosrISIialmFvAKF0YWovfzn3zVSBylc17jCJsymE3tGRICW0wmuXr0KhUIBW1tblXJbW1uUl5ff8/iysjJ89dVXylHcJv369cOWLVvw5ZdfYvv27TA2NkZgYCDOnTvXbFsJCQnKUV4LCwvIZDJtLoWIiKhZFfIKnIw4qRJgAaC2tBYnI06iQl7RST0joiZterBLuGtROFEU1co02bJlCywtLfHiiy+qlA8dOhQTJ06Ej48PgoKC8Nlnn8Hd3R3r1q1rtq24uDhUVVUpt0uXLrXlUoiIiFSIChGF0YWAqGln4x+FMYUQFZoqEFFH0Wo6Qc+ePSGRSNRGXa9cuaI2Ons3URSxadMmREZGwsjIqMW6BgYGeOKJJ1ociZVKpZBKpa3vPBERUStU5laqjcCqEIHaS7WozK2E1XCrjusYEanQaiTWyMgIvr6+yMrKUinPyspCQEBAi8d+++23KCwsxOTJk+95HlEUkZ+fDzt+6TIREXWwurI6ndYjovah9eoEs2fPRmRkJPz8/ODv748PP/wQJSUlmDp1KoDGj/lLS0uxbds2leM2btyIIUOGYMCAAWptLlmyBEOHDoWbmxuqq6uxdu1a5OfnIzk5uY2XRURE1DZGdi1/WqhtPSJqH1qH2PHjx+PatWuIj49HWVkZBgwYgIyMDOVqA2VlZWprxlZVVSE9PR1rmr5o+S6VlZV44403UF5eDgsLCwwaNAg5OTkYPHhwGy6JiIio7SyDLCF1kKK2tFbzvFihcZUCyyDLju4aEd1BEEXxoZiZXl1dDQsLC1RVVcHc3Lyzu0NERHqsaXUCAKpB9n/PMHuleXGZLaI20GVea9PqBERERA8zmzAbeKV5QWqv+gCx1EHKAEv0gNB6OgEREVFXYBNmg55je/Ibu4geUAyxREREzRAkApfRInpAcToBEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxRERERKR3GGKJiIiISO8wxBIRERGR3mGIJSIiIiK9wxBLRERERHqHIZaIiIiI9A5DLBERERHpHYZYIiIiItI7DLFEREREpHcYYomIiIhI7zDEEhEREZHeYYglIiIiIr3DEEtEREREeochloiIiIj0DkMsEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6Z1und2BjtTQ0IC6urrO7gZpwdDQEBKJpLO7QURERA+YLhNi6+rqUFRUhIaGhs7uCmnJ0tISvXv3hiAInd0VIiIiekB0iRAriiLKysogkUggk8lgYMBZFPpAFEX8/vvvuHLlCgDAzs6uk3tERERED4ouEWJv376N33//HX369MEjjzzS2d0hLZiYmAAArly5gl69enFqAREREQHoIg92KRQKAICRkVEn94TaoukfHvX19Z3cEyIiInpQdIkQ24RzKvUT3zciIiK6W5cKsQ8rQRCwc+fOzu4GERERUYdhiH3AlZeXY8aMGXBxcYFUKoVMJsOYMWOwf//+djnfgQMHIAgCKisr26V9ALhx4wYiIyNhYWEBCwsLREZGtuv5iIiI6OHTJR7s0lfFxcUIDAyEpaUlEhMT4e3tjfr6emRmZiIqKgqnT5/u7C42SxRFKBQKdOumfotNmDABly9fxt69ewEAb7zxBiIjI7Fr166O7iYRERHpKY7EakGhAA4cALZvb/zzf8+LtZtp06ZBEAQcOXIEERERcHd3h5eXF2bPno28vDyNx2gaSc3Pz4cgCCguLgYAXLx4EWPGjIGVlRVMTU3h5eWFjIwMFBcXIzg4GABgZWUFQRAwadIkAI2hNDExES4uLjAxMYGPjw/S0tLUzpuZmQk/Pz9IpVLk5uaq9e/UqVPYu3cvPvroI/j7+8Pf3x8bNmzA7t27cebMGd28cERERPTQ40hsK8nlQHQ0cPnyn2UODsCaNUBYmO7Pd/36dezduxfLly+Hqamp2n5LS8s2tx0VFYW6ujrk5OTA1NQUBQUFMDMzg0wmQ3p6OsLDw3HmzBmYm5srl7hasGAB5HI5UlNT4ebmhpycHEycOBE2NjYYNmyYsu25c+di5cqVcHFx0djHw4cPw8LCAkOGDFGWDR06FBYWFjh06BA8PDzafF1ERETUdTDEtoJcDkREAKKoWl5a2lielqb7IFtYWAhRFNGvXz/dNgygpKQE4eHhGDhwIADAxcVFuc/a2hoA0KtXL2UIvXXrFlavXo3s7Gz4+/srjzl48CDWr1+vEmLj4+MREhLS7LnLy8vRq1cvtfJevXqhvLz8vq+NiIiIuoY2TSdISUmBs7MzjI2N4evrq/Fj4yaTJk2CIAhqm5eXl0q99PR09O/fH1KpFP3798cXX3zRlq7pnELROAJ7d4AF/iyLidH91ALxf423x/JSM2fOxLJlyxAYGIhFixbhp59+arF+QUEBampqEBISAjMzM+W2bds2nD9/XqWun5/fPc+v6ZpEUeRSWkRERNRqWofYHTt2ICYmBvPnz8exY8cQFBSEUaNGoaSkRGP9NWvWoKysTLldunQJ1tbWeOmll5R1Dh8+jPHjxyMyMhLHjx9HZGQkxo0bh++++67tV6YjubmqUwjuJorApUuN9XTJzc0NgiDg1KlTWh3X9JW64h2p++4vCZgyZQouXLiAyMhInDhxAn5+fli3bl2zbTY0NAAA9uzZg/z8fOVWUFCgMi8WgMapD3fq3bs3fv31V7XyiooK2NratnxxRERERP+jdYhdvXo1Jk+ejClTpsDT0xNJSUmQyWRITU3VWN/CwgK9e/dWbj/88ANu3LiBV199VVknKSkJISEhiIuLQ79+/RAXF4dnnnkGSUlJbb4wXSkr02291rK2tkZoaCiSk5Nx69Yttf3NLUllY2Pzv/782aH8/Hy1ejKZDFOnToVcLkdsbCw2bNgA4M9vNVPcMbTcNEJeUlICV1dXlU0mk2l1Xf7+/qiqqsKRI0eUZd999x2qqqoQEBCgVVtERETUdWkVYuvq6vDjjz9ixIgRKuUjRozAoUOHWtXGxo0b8eyzz8LR0VFZdvjwYbU2Q0NDW91me7Kz0209baSkpEChUGDw4MFIT0/HuXPncOrUKaxdu1Y5N/VuTcFy8eLFOHv2LPbs2YNVq1ap1ImJiUFmZiaKiopw9OhRZGdnw9PTEwDg6OgIQRCwe/duVFRU4ObNm+jevTvmzJmDWbNmYevWrTh//jyOHTuG5ORkbN26Vatr8vT0xMiRI/H6668jLy8PeXl5eP311zF69Gg+1EVEREStplWIvXr1KhQKhdrHvra2tq16KKesrAxfffUVpkyZolJeXl6udZu1tbWorq5W2dpDUFDjKgTNTdcUBEAma6yna87Ozjh69CiCg4MRGxuLAQMGICQkBPv372925NvQ0BDbt2/H6dOn4ePjgxUrVmDZsmUqdRQKBaKiopSB0sPDAykpKQAAe3t7LFmyBPPmzYOtrS2mT58OAFi6dCkWLlyIhIQEeHp6IjQ0FLt27YKzs7PW1/XJJ59g4MCBGDFiBEaMGAFvb298/PHHWrdDREREXVebVie4+wGc1j6Us2XLFlhaWuLFF1+87zYTEhKwZMmS1nX4PkgkjctoRUQ0BtY7H/Bq6l5SUmO99mBnZ4f3338f77//frN1xLueOgsMDFR7WOvOOi3NfwWAd999F++++65KmSAImDlzJmbOnKnxmOHDh6v1oznW1tb4v//7v1bVJSIiItJEq5HYnj17QiKRqI2QXrly5Z4P5YiiiE2bNiEyMlI577JJ7969tW4zLi4OVVVVyu3SpUvaXIpWwsIal9Gyt1ctd3Bon+W1iIiIiKhlWoVYIyMj+Pr6IisrS6U8Kyvrng/lfPvttygsLMTkyZPV9vn7+6u1uW/fvhbblEqlMDc3V9naU1gYUFwMfPMN8O9/N/5ZVMQAS0RERNQZtJ5OMHv2bERGRsLPzw/+/v748MMPUVJSgqlTpwJoHCEtLS3Ftm3bVI7buHEjhgwZggEDBqi1GR0djaeeegorVqzA2LFj8Z///Adff/01Dh482MbLah8SCTB8eGf3goiIiIi0DrHjx4/HtWvXEB8fj7KyMgwYMAAZGRnK1QbKysrU1oytqqpCeno61qxZo7HNgIAAfPrpp1iwYAHeffdd9O3bFzt27FD5alIiIiIioiaC2NqncR5w1dXVsLCwQFVVldrUgpqaGhQVFSm/ZYz0C98/IiKih0NLeU1bbfraWSIiIiKizsQQS0RERER6hyGWiIiIiPQOQywRERER6R2G2IeAIAjYuXNnZ3eDiIiIqMMwxD7gysvLMWPGDLi4uEAqlUImk2HMmDHYv39/u5zvwIEDEAQBlZWV7dI+ACxfvhwBAQF45JFHYGlp2W7nISIiooeX1uvEUscpLi5GYGAgLC0tkZiYCG9vb9TX1yMzMxNRUVE4ffp0Z3exWaIoQqFQoFs39Vusrq4OL730Evz9/bFx48ZO6B0RERHpO47EakHRoMCB4gPYfmI7DhQfgKJB0a7nmzZtGgRBwJEjRxAREQF3d3d4eXlh9uzZyMvL03iMppHU/Px8CIKA4uJiAMDFixcxZswYWFlZwdTUFF5eXsjIyEBxcTGCg4MBAFZWVhAEAZMmTQLQGEoTExPh4uICExMT+Pj4IC0tTe28mZmZ8PPzg1QqRW5ursY+LlmyBLNmzcLAgQPv/0UiotZRKIADB4Dt2xv/VLTv7y8iovbGkdhWkp+SI3pvNC5XX1aWOZg7YM3INQjzDNP5+a5fv469e/di+fLlMDU1Vdt/Px/DR0VFoa6uDjk5OTA1NUVBQQHMzMwgk8mQnp6O8PBwnDlzBubm5jAxMQEALFiwAHK5HKmpqXBzc0NOTg4mTpwIGxsbDBs2TNn23LlzsXLlSri4uHCqANGDQi4HoqOBy3/+/oKDA7BmDRCm+99fREQdgSG2FeSn5Ij4LAIiVL/crLS6FBGfRSBtXJrOg2xhYSFEUUS/fv102i4AlJSUIDw8XDkS6uLiotxnbW0NAOjVq5cyhN66dQurV69GdnY2/P39lcccPHgQ69evVwmx8fHxCAkJ0XmfiaiN5HIgIgK4+8sZS0sby9PSGGSJSC9xOsE9KBoUiN4brRZgASjLYvbG6HxqQdO3AQuCoNN2AWDmzJlYtmwZAgMDsWjRIvz0008t1i8oKEBNTQ1CQkJgZmam3LZt24bz58+r1PXz89N5f4mojRSKxhFYTd8u3lQWE8OpBUSklxhi7yG3JFdlCsHdRIi4VH0JuSWa53+2lZubGwRBwKlTp7Q6zsCg8S0V7/hLq76+XqXOlClTcOHCBURGRuLEiRPw8/PDunXrmm2zoaEBALBnzx7k5+crt4KCApV5sQA0Tn0gok6Sm6s6heBuoghcutRYj4hIzzDE3kPZb2U6rdda1tbWCA0NRXJyMm7duqW2v7klsGxsbBr7U/Znf/Lz89XqyWQyTJ06FXK5HLGxsdiwYQMAwMjICACguGNkpn///pBKpSgpKYGrq6vKJpPJ2nqJRNTeylr5e6m19YiIHiAMsfdg191Op/W0kZKSAoVCgcGDByM9PR3nzp3DqVOnsHbtWuXc1Ls1BcvFixfj7Nmz2LNnD1atWqVSJyYmBpmZmSgqKsLRo0eRnZ0NT09PAICjoyMEQcDu3btRUVGBmzdvonv37pgzZw5mzZqFrVu34vz58zh27BiSk5OxdetWra+rpKQE+fn5KCkpgUKhUI7s3rx5U/sXiYiaZ9fK30utrUdE9ABhiL2HoEeD4GDuAAGa56YKECAzlyHo0SCdn9vZ2RlHjx5FcHAwYmNjMWDAAISEhGD//v1ITU3VeIyhoSG2b9+O06dPw8fHBytWrMCyZctU6igUCkRFRcHT0xMjR46Eh4cHUlJSAAD29vZYsmQJ5s2bB1tbW0yfPh0AsHTpUixcuBAJCQnw9PREaGgodu3aBWdnZ62va+HChRg0aBAWLVqEmzdvYtCgQRg0aBB++OEHrdsiohYEBTWuQtDc3HpBAGSyxnpERHpGEEVNM/71T3V1NSwsLFBVVQVzc3OVfTU1NSgqKoKzszOMjY21brtpdQIAKg94NQXb9lidgP50v+8fUZfWtDoBoPqAV1Ow5eoERNSBWspr2uJIbCuEeYYhbVwa7M3tVcodzB0YYInowRYW1hhU7VV/f8HBgQGWiPQa14ltpTDPMIz1GIvcklyU/VYGu+52CHo0CBIDSWd3jYioZWFhwNixjasQlJU1zoENCgIk/P1FRPqLIVYLEgMJhjsN7+xuEBFpTyIBhg/v7F4QEekMpxMQERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxDwFBELBz587O7gYRERFRh2GIfcCVl5djxowZcHFxgVQqhUwmw5gxY7B///52Od+BAwcgCAIqKyvbpf3i4mJMnjwZzs7OMDExQd++fbFo0SLU1dW1y/mIiIjo4cQvO3iAFRcXIzAwEJaWlkhMTIS3tzfq6+uRmZmJqKgonD59urO72CxRFKFQKNCtm+otdvr0aTQ0NGD9+vVwdXXFzz//jNdffx23bt3CypUrO6m3REREpG84EqsNhQI4cADYvr3xT4WiXU83bdo0CIKAI0eOICIiAu7u7vDy8sLs2bORl5en8RhNI6n5+fkQBAHFxcUAgIsXL2LMmDGwsrKCqakpvLy8kJGRgeLiYgQHBwMArKysIAgCJk2aBKAxlCYmJsLFxQUmJibw8fFBWlqa2nkzMzPh5+cHqVSK3Nxctf6NHDkSmzdvxogRI+Di4oIXXngBc+bMgVwu182LRkRERF0CR2JbSy4HoqOBy5f/LHNwANasafxech27fv069u7di+XLl8PU1FRtv6WlZZvbjoqKQl1dHXJycmBqaoqCggKYmZlBJpMhPT0d4eHhOHPmDMzNzWFiYgIAWLBgAeRyOVJTU+Hm5oacnBxMnDgRNjY2GDZsmLLtuXPnYuXKlXBxcWl1H6uqqmBtbd3m6yEiIqKuhyG2NeRyICICEEXV8tLSxvK0NJ0H2cLCQoiiiH79+um0XQAoKSlBeHg4Bg4cCABwcXFR7msKk7169VKG0Fu3bmH16tXIzs6Gv7+/8piDBw9i/fr1KiE2Pj4eISEhre7L+fPnsW7dOqxatep+L4uIiIi6EIbYe1EoGkdg7w6wQGOZIAAxMcDYsYBEorPTiv87nyAIOmuzycyZM/HWW29h3759ePbZZxEeHg5vb+9m6xcUFKCmpkYtnNbV1WHQoEEqZX5+fq3uxy+//IKRI0fipZdewpQpU7S7CCIiIurSOCf2XnJzVacQ3E0UgUuXGuvpkJubGwRBwKlTp7Q6zsDA4H/d+jN019fXq9SZMmUKLly4gMjISJw4cQJ+fn5Yt25ds202NDQAAPbs2YP8/HzlVlBQoDIvFoDGqQ+a/PLLLwgODoa/vz8+/PDDVh1DRERE1IQh9l7KynRbr5Wsra0RGhqK5ORk3Lp1S21/c0tg2djY/K87f/YnPz9frZ5MJsPUqVMhl8sRGxuLDRs2AACMjIwAAIo7Hlrr378/pFIpSkpK4OrqqrLJZDKtr620tBTDhw/H448/js2bNyuDNxEREVFrMT3ci52dbutpISUlBQqFAoMHD0Z6ejrOnTuHU6dOYe3atcq5qXdrCpaLFy/G2bNnsWfPHrX5pjExMcjMzERRURGOHj2K7OxseHp6AgAcHR0hCAJ2796NiooK3Lx5E927d8ecOXMwa9YsbN26FefPn8exY8eQnJyMrVu3anVNv/zyC4YPHw6ZTIaVK1eioqIC5eXlKC8vb9uLRERERF0S58TeS1BQ4yoEpaWa58UKQuP+oCCdn9rZ2RlHjx7F8uXLERsbi7KyMtjY2MDX1xepqakajzE0NMT27dvx1ltvwcfHB0888QSWLVuGl156SVlHoVAgKioKly9fhrm5OUaOHIl//etfAAB7e3ssWbIE8+bNw6uvvoqXX34ZW7ZswdKlS9GrVy8kJCTgwoULsLS0xOOPP4533nlHq2vat28fCgsLUVhYCAcHB5V9oqbXl4iIiEgDQXxIkkN1dTUsLCxQVVUFc3NzlX01NTUoKiqCs7MzjI2NtW+8aXUCQDXINj101Q6rE9Cf7vv9IyIiogdCS3lNW5xO0BphYY1B1d5etdzBgQGWiIiIqBNwOkFrhYU1LqOVm9v4EJedXeMUAh0uq0VERERErcMQqw2JBBg+vLN7QURERNTlcToBEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQ+xAQBAE7d+7s7G4QERERdRiG2AdceXk5ZsyYARcXF0ilUshkMowZMwb79+9vl/MdOHAAgiCgsrKyXdoHgBdeeAGPPvoojI2NYWdnh8jISPzyyy/tdj4iIiJ6+DDEakEhijhw4wa2//orDty4AYUotuv5iouL4evri+zsbCQmJuLEiRPYu3cvgoODERUV1a7nvl+iKOL27dsa9wUHB+Ozzz7DmTNnkJ6ejvPnzyMiIqKDe0hERET6rE0hNiUlBc7OzjA2Noavry9yc3NbrF9bW4v58+fD0dERUqkUffv2xaZNm5T7t2zZAkEQ1Laampq2dK9dyCsq4JSXh+DjxzHh1CkEHz8Op7w8yCsq2u2c06ZNgyAIOHLkCCIiIuDu7g4vLy/Mnj0beXl5Go/RNJKan58PQRBQXFwMALh48SLGjBkDKysrmJqawsvLCxkZGSguLkZwcDAAwMrKCoIgYNKkSQAaQ2liYiJcXFxgYmICHx8fpKWlqZ03MzMTfn5+kEqlzd4Xs2bNwtChQ+Ho6IiAgADMmzcPeXl5qK+vv/8XjYiIiLoErb92dseOHYiJiUFKSgoCAwOxfv16jBo1CgUFBXj00Uc1HjNu3Dj8+uuv2LhxI1xdXXHlyhW1UTpzc3OcOXNGpczY2Fjb7rULeUUFIk6exN3jrqW1tYg4eRJpXl4Is7HR6TmvX7+OvXv3Yvny5TA1NVXbb2lp2ea2o6KiUFdXh5ycHJiamqKgoABmZmaQyWRIT09HeHg4zpw5A3Nzc5iYmAAAFixYALlcjtTUVLi5uSEnJwcTJ06EjY0Nhg0bpmx77ty5WLlyJVxcXFrVx+vXr+OTTz5BQEAADA0N23xNRERE1LVoHWJXr16NyZMnY8qUKQCApKQkZGZmIjU1FQkJCWr19+7di2+//RYXLlyAtbU1AMDJyUmtniAI6N27t7bdaXcKUUR0YaFagAUAEYAAIKawEGN79oREEHR23sLCQoiiiH79+umszSYlJSUIDw/HwIEDAQAuLi7KfU3vUa9evZQh9NatW1i9ejWys7Ph7++vPObgwYNYv369SoiNj49HSEjIPfvw97//He+//z5+//13DB06FLt379bV5REREVEXoNV0grq6Ovz4448YMWKESvmIESNw6NAhjcd8+eWX8PPzQ2JiIuzt7eHu7o45c+bgjz/+UKl38+ZNODo6wsHBAaNHj8axY8da7EttbS2qq6tVtvaQW1mJy7W1ze4XAVyqrUWujh+EEv8331bQYTBuMnPmTCxbtgyBgYFYtGgRfvrppxbrFxQUoKamBiEhITAzM1Nu27Ztw/nz51Xq+vn5taoPb7/9No4dO4Z9+/ZBIpHg5ZdfVl4zERER0b1oNRJ79epVKBQK2NraqpTb2tqivLxc4zEXLlzAwYMHYWxsjC+++AJXr17FtGnTcP36deW82H79+mHLli0YOHAgqqursWbNGgQGBuL48eNwc3PT2G5CQgKWLFmiTffbpKyuTqf1WsvNzQ2CIODUqVN48cUXW32cgUHjv0vuDIR3zzWdMmUKQkNDsWfPHuzbtw8JCQlYtWoVZsyYobHNhoYGAMCePXtgb2+vsk8qlar8rGnqgyY9e/ZEz5494e7uDk9PT8hkMuTl5SlHeomIiIha0qYHu+4eHRRFsdkRw4aGBgiCgE8++QSDBw/Gc889h9WrV2PLli3K0dihQ4di4sSJ8PHxQVBQED777DO4u7tj3bp1zfYhLi4OVVVVyu3SpUttuZR7sjMy0mm91rK2tkZoaCiSk5Nx69Yttf3NLYFl87+5uWVlZcqy/Px8tXoymQxTp06FXC5HbGwsNmzYAAAw+t91KBQKZd3+/ftDKpWipKQErq6uKptMJmvrJSo1Be7aFka8iYiIiO6kVYjt2bMnJBKJ2qjrlStX1EZnm9jZ2cHe3h4WFhbKMk9PT4iiiMuXL2vulIEBnnjiCZw7d67ZvkilUpibm6ts7SHI0hIOUima+1BfACCTShF0Hw9aNSclJQUKhQKDBw9Geno6zp07h1OnTmHt2rXNjlg2BcvFixfj7Nmz2LNnD1atWqVSJyYmBpmZmSgqKsLRo0eRnZ0NT09PAICjoyMEQcDu3btRUVGBmzdvonv37pgzZw5mzZqFrVu34vz58zh27BiSk5OxdetWra7pyJEjeP/995Gfn4+LFy/im2++wYQJE9C3b1+OwhIREVGraRVijYyM4Ovri6ysLJXyrKwsBAQEaDwmMDAQv/zyC27evKksO3v2LAwMDODg4KDxGFEUkZ+fDzs7O2261y4kgoA1rq4AoBZkm35OcnXV6UNdTZydnXH06FEEBwcjNjYWAwYMQEhICPbv34/U1FSNxxgaGmL79u04ffo0fHx8sGLFCixbtkyljkKhQFRUFDw9PTFy5Eh4eHggJSUFAGBvb48lS5Zg3rx5sLW1xfTp0wEAS5cuxcKFC5GQkABPT0+EhoZi165dcHZ21uqaTExMIJfL8cwzz8DDwwOvvfYaBgwYgG+//VZtagIRERFRcwRRy6dpduzYgcjISHzwwQfw9/fHhx9+iA0bNuDkyZNwdHREXFwcSktLsW3bNgCND2x5enpi6NChWLJkCa5evYopU6Zg2LBhyo+wlyxZgqFDh8LNzQ3V1dVYu3YtPv74Y/z3v//F4MGDW9Wv6upqWFhYoKqqSm1UtqamBkVFRcq1bdtCXlGB6MJClYe8ZFIpklxddb68FqnSxftHREREna+lvKYtrZfYGj9+PK5du4b4+HiUlZVhwIAByMjIgKOjI4DGuZglJSXK+mZmZsjKysKMGTPg5+eHHj16YNy4cSqjg5WVlXjjjTdQXl4OCwsLDBo0CDk5Oa0OsB0hzMYGY3v2RG5lJcrq6mBnZIQgS8t2GYElIiIiopZpPRL7oGrvkVjqPHz/iIiIHg66HIlt0+oERERERESdiSGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxRERERKR3GGKJiIiISO8wxBIRERGR3mGIfQgIgoCdO3d2djeIiIiIOgxD7AOuvLwcM2bMgIuLC6RSKWQyGcaMGYP9+/e3y/kOHDgAQRBQWVnZLu3fqba2Fo899hgEQUB+fn67n4+IiIgeHlp/7WxXJipEVOZWoq6sDkZ2RrAMsoQgab+vnS0uLkZgYCAsLS2RmJgIb29v1NfXIzMzE1FRUTh9+nS7nft+iaIIhUKBbt2av8Xmzp2LPn364Pjx4x3YMyIiInoYcCS2lSrkFchzysPx4OM4NeEUjgcfR55THirkFe12zmnTpkEQBBw5cgQRERFwd3eHl5cXZs+ejby8PI3HaBpJzc/PhyAIKC4uBgBcvHgRY8aMgZWVFUxNTeHl5YWMjAwUFxcjODgYAGBlZQVBEDBp0iQAjaE0MTERLi4uMDExgY+PD9LS0tTOm5mZCT8/P0ilUuTm5jZ7bV999RX27duHlStX3t+LRERERF0SR2JboUJegZMRJwFRtby2tBYnI07CK80LNmE2Oj3n9evXsXfvXixfvhympqZq+y0tLdvcdlRUFOrq6pCTkwNTU1MUFBTAzMwMMpkM6enpCA8Px5kzZ2Bubg4TExMAwIIFCyCXy5Gamgo3Nzfk5ORg4sSJsLGxwbBhw5Rtz507FytXroSLi0uzffz111/x+uuvY+fOnXjkkUfafB1ERETUdTHE3oOoEFEYXagWYBt3AhCAwphC9BzbU6dTCwoLCyGKIvr166ezNpuUlJQgPDwcAwcOBAC4uLgo91lbWwMAevXqpQyht27dwurVq5GdnQ1/f3/lMQcPHsT69etVQmx8fDxCQkKaPbcoipg0aRKmTp0KPz8/5egwERERkTYYYu+hMrcStZdrm68gArWXalGZWwmr4VY6O68oNqZmQdD9nNuZM2firbfewr59+/Dss88iPDwc3t7ezdYvKChATU2NWjitq6vDoEGDVMr8/PxaPPe6detQXV2NuLi4tl8AERERdXmcE3sPdWV1Oq3XWm5ubhAEAadOndLqOAODxre0KQQDQH19vUqdKVOm4MKFC4iMjMSJEyfg5+eHdevWNdtmQ0MDAGDPnj3Iz89XbgUFBSrzYgFonPpwp+zsbOTl5UEqlaJbt25wdXUF0Bh+X3nlldZfKBERtRtFgwIHig9g+4ntOFB8AIoGRWd3iUgNR2LvwcjOSKf1Wsva2hqhoaFITk7GzJkz1cJhZWWlxjmnNjaNc3PLyspgZdU4Mqxp+SqZTIapU6di6tSpiIuLw4YNGzBjxgwYGTVeh0Lx5y+s/v37QyqVoqSkRGXqQFusXbsWy5YtU/78yy+/IDQ0FDt27MCQIUPuq20iIrp/8lNyRO+NxuXqy8oyB3MHrBm5BmGeYZ3YMyJVDLH3YBlkCamDFLWltZrnxQqA1EEKyyBLnZ87JSUFAQEBGDx4MOLj4+Ht7Y3bt28jKysLqampGkdpXV1dIZPJsHjxYixbtgznzp3DqlWrVOrExMRg1KhRcHd3x40bN5CdnQ1PT08AgKOjIwRBwO7du/Hcc8/BxMQE3bt3x5w5czBr1iw0NDTgySefRHV1NQ4dOgQzMzOtRlAfffRRlZ/NzMwAAH379oWDg4O2LxEREemQ/JQcEZ9FQLzrL7zS6lJEfBaBtHFpDLL0wOB0gnsQJAJc17j+74e7dzb+4Zrk2i7rxTo7O+Po0aMIDg5GbGwsBgwYgJCQEOzfvx+pqakajzE0NMT27dtx+vRp+Pj4YMWKFSojn0DjKGtUVBQ8PT0xcuRIeHh4ICUlBQBgb2+PJUuWYN68ebC1tcX06dMBAEuXLsXChQuRkJAAT09PhIaGYteuXXB2dtb5dRMRUcdTNCgQvTdaLcACUJbF7I3h1AJ6YAjinZMn9Vh1dTUsLCxQVVUFc3NzlX01NTUoKiqCs7MzjI2N29R+hbwChdGFKg95SWVSuCa56nx5LVKli/ePiIhadqD4AIK3Bt+z3jevfIPhTsPbv0P0UGopr2mL0wlaySbMBj3H9uzQb+wiIiLqKGW/lem0HlF7Y4jVgiARdLqMFhER0YPCrrudTusRtTfOiSUiIiIEPRoEB3MHCGoPgDQSIEBmLkPQo0Ed3DMizRhiiYiICBIDCdaMXAMAakG26eekkUmQGEg6vG9EmjDEEhEREQAgzDMMaePSYG9ur1LuYO7A5bXogcM5sURERKQU5hmGsR5jkVuSi7LfymDX3Q5BjwZxBJYeOAyxREREpEJiIOEyWvTA43QCIiIiItI7DLFEREREpHcYYh8CgiBg586dnd0NIiIiog7DEPuAKy8vx4wZM+Di4gKpVAqZTIYxY8Zg//797XK+AwcOQBAEVFZWtkv7AODk5ARBEFS2efPmtdv5iIiI6OHDB7u0IIoKVFbmoq6uDEZGdrC0DIIgtN/TmsXFxQgMDISlpSUSExPh7e2N+vp6ZGZmIioqCqdPn263c98vURShUCjQrZvmWyw+Ph6vv/668mczM7OO6hoRERE9BDgS20oVFXLk5Tnh+PFgnDo1AcePByMvzwkVFfJ2O+e0adMgCAKOHDmCiIgIuLu7w8vLC7Nnz0ZeXp7GYzSNpObn50MQBBQXFwMALl68iDFjxsDKygqmpqbw8vJCRkYGiouLERwcDACwsrKCIAiYNGkSgMZQmpiYCBcXF5iYmMDHxwdpaWlq583MzISfnx+kUilyc3Obvbbu3bujd+/eyo0hloiIiLTBkdhWqKiQ4+TJCACiSnltbSlOnoyAl1cabGx0uwD09evXsXfvXixfvhympqZq+y0tLdvcdlRUFOrq6pCTkwNTU1MUFBTAzMwMMpkM6enpCA8Px5kzZ2Bubg4TExMAwIIFCyCXy5Gamgo3Nzfk5ORg4sSJsLGxwbBhw5Rtz507FytXroSLi0uLfVyxYgWWLl0KmUyGl156CW+//TaMjIzafE1ERETUtTDE3oMoKlBYGI27A+z/9gIQUFgYg549x+p0akFhYSFEUUS/fv101maTkpIShIeHY+DAgQAAFxcX5T5ra2sAQK9evZQh9NatW1i9ejWys7Ph7++vPObgwYNYv369SoiNj49HSEhIi+ePjo7G448/DisrKxw5cgRxcXEoKirCRx99pMvLJCIioocYQ+w9VFbmorb2cgs1RNTWXkJlZS6srIbr7Lyi2BiaBUG4R03tzZw5E2+99Rb27duHZ599FuHh4fD29m62fkFBAWpqatTCaV1dHQYNGqRS5ufnd8/zz5o1S/nf3t7esLKyQkREBFasWIEePXpoeTVERETUFXFO7D3U1ZXptF5rubm5QRAEnDp1SqvjDAwa39KmEAwA9fX1KnWmTJmCCxcuIDIyEidOnICfnx/WrVvXbJsNDQ0AgD179iA/P1+5FRQUqMyLBaBx6sO9DB06FEDj6DMRERFRazDE3oORkZ1O67WWtbU1QkNDkZycjFu3bqntb24JLBsbGwBAWdmfoTo/P1+tnkwmw9SpUyGXyxEbG4sNGzYAgHJeqkKhUNbt378/pFIpSkpK4OrqqrLJZLK2XqLSsWPHAAB2drp9DYmIiOjhxekE92BpGQSp1AG1taXQPC9WgFTqAEvLIJ2fOyUlBQEBARg8eDDi4+Ph7e2N27dvIysrC6mpqRpHaZuC5eLFi7Fs2TKcO3cOq1atUqkTExODUaNGwd3dHTdu3EB2djY8PT0BAI6OjhAEAbt378Zzzz0HExMTdO/eHXPmzMGsWbPQ0NCAJ598EtXV1Th06BDMzMzwyiuvtPqaDh8+jLy8PAQHB8PCwgLff/89Zs2ahRdeeAGPPvro/b1gRERE1GVwJPYeBEECV9c1TT/dvRcA4Oqa1C7rxTo7O+Po0aMIDg5GbGwsBgwYgJCQEOzfvx+pqakajzE0NMT27dtx+vRp+Pj4YMWKFVi2bJlKHYVCgaioKHh6emLkyJHw8PBASkoKAMDe3h5LlizBvHnzYGtri+nTpwMAli5dioULFyIhIQGenp4IDQ3Frl274OzsrNU1SaVS7NixA8OHD0f//v2xcOFCvP7669i+fXsbXiEiIiLqqgTxzsmTeqy6uhoWFhaoqqqCubm5yr6amhoUFRXB2dkZxsbGbWq/okKOwsJolYe8pFIZXF2TdL68FqnSxftHREREna+lvKYtTidoJRubMPTsObZDv7GLiIiIiDRjiNWCIEh0uowWEREREbUN58QSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSO20KsSkpKcqF5319fZGbm9ti/draWsyfPx+Ojo6QSqXo27cvNm3apFInPT0d/fv3h1QqRf/+/fHFF1+0pWtERERE1AVoHWJ37NiBmJgYzJ8/H8eOHUNQUBBGjRqFkpKSZo8ZN24c9u/fj40bN+LMmTPYvn07+vXrp9x/+PBhjB8/HpGRkTh+/DgiIyMxbtw4fPfdd227qi5GEATs3Lmzs7tBRERE1GG0DrGrV6/G5MmTMWXKFHh6eiIpKQkymQypqaka6+/duxfffvstMjIy8Oyzz8LJyQmDBw9GQECAsk5SUhJCQkIQFxeHfv36IS4uDs888wySkpLafGEPi/LycsyYMQMuLi6QSqWQyWQYM2YM9u/f3y7nO3DgAARBQGVlZbu032TPnj0YMmQITExM0LNnT4SF8at7iYiIqPW0CrF1dXX48ccfMWLECJXyESNG4NChQxqP+fLLL+Hn54fExETY29vD3d0dc+bMwR9//KGsc/jwYbU2Q0NDm22zsygUChw4cADbt2/HgQMHoFAo2vV8xcXF8PX1RXZ2NhITE3HixAns3bsXwcHBiIqKatdz3y9RFHH79m2N+9LT0xEZGYlXX30Vx48fx3//+19MmDChg3tIRERE+kyrEHv16lUoFArY2tqqlNva2qK8vFzjMRcuXMDBgwfx888/44svvkBSUhLS0tJUQlh5eblWbQKN82yrq6tVtvYkl8vh5OSE4OBgTJgwAcHBwXBycoJcLm+3c06bNg2CIODIkSOIiIiAu7s7vLy8MHv2bOTl5Wk8RtNIan5+PgRBQHFxMQDg4sWLGDNmDKysrGBqagovLy9kZGSguLgYwcHBAAArKysIgoBJkyYBaAyliYmJcHFxgYmJCXx8fJCWlqZ23szMTPj5+UEqlWqcK3379m1ER0fjvffew9SpU+Hu7g4PDw9ERETo5kUjIiKiLqFbWw4SBEHlZ1EU1cqaNDQ0QBAEfPLJJ7CwsADQOCUhIiICycnJMDEx0bpNAEhISMCSJUva0n2tyeVyREREQBRFlfLS0lJEREQgLS1N5x+HX79+HXv37sXy5cthamqqtt/S0rLNbUdFRaGurg45OTkwNTVFQUEBzMzMIJPJkJ6ejvDwcJw5cwbm5ubK92fBggWQy+VITU2Fm5sbcnJyMHHiRNjY2GDYsGHKtufOnYuVK1fCxcVFYx+PHj2K0tJSGBgYYNCgQSgvL8djjz2GlStXwsvLq83XRERERF2LViG2Z8+ekEgkaiOkV65cURtJbWJnZwd7e3tlgAUAT09PiKKIy5cvw83NDb1799aqTQCIi4vD7NmzlT9XV1dDJpNpczmtolAoEB0drRZggT+DdkxMDMaOHQuJRKKz8xYWFkIURZUH4HSlpKQE4eHhGDhwIADAxcVFuc/a2hoA0KtXL2UIvXXrFlavXo3s7Gz4+/srjzl48CDWr1+vEmLj4+MREhLS7LkvXLgAAFi8eDFWr14NJycnrFq1CsOGDcPZs2eV5yciIiJqiVbTCYyMjODr64usrCyV8qysLJUHte4UGBiIX375BTdv3lSWnT17FgYGBnBwcAAA+Pv7q7W5b9++ZtsEAKlUCnNzc5WtPeTm5uLy5cvN7hdFEZcuXbrnMmPaagrNLY1Gt9XMmTOxbNkyBAYGYtGiRfjpp59arF9QUICamhqEhITAzMxMuW3btg3nz59Xqevn59diWw0NDQCA+fPnIzw8HL6+vti8eTMEQcDnn39+fxdGREREXYbWqxPMnj0bH330ETZt2oRTp05h1qxZKCkpwdSpUwE0jpC+/PLLyvoTJkxAjx498Oqrr6KgoAA5OTl4++238dprryk/qo6Ojsa+ffuwYsUKnD59GitWrMDXX3+NmJgY3VzlfSgrK9NpvdZyc3ODIAg4deqUVscZGDS+pXeOHNfX16vUmTJlCi5cuIDIyEicOHECfn5+WLduXbNtNgXPPXv2ID8/X7kVFBSozIsFoHHqw53s7OwAAP3791eWSaVSuLi4tLhMGxEREdGdtA6x48ePR1JSEuLj4/HYY48hJycHGRkZcHR0BNAY5u4MI2ZmZsjKykJlZSX8/Pzwt7/9DWPGjMHatWuVdQICAvDpp59i8+bN8Pb2xpYtW7Bjxw4MGTJEB5d4f5pCl67qtZa1tTVCQ0ORnJyMW7duqe1vbgksGxsbAKqhOj8/X62eTCbD1KlTIZfLERsbiw0bNgBoHG0HoLLyQtOXUJSUlMDV1VVl03YKh6+vL6RSKc6cOaMsq6+vR3FxsfIeIiIiIrqXNj3YNW3aNEybNk3jvi1btqiV9evXT226wN0iIiIeyCfUg4KC4ODggNLSUo3zYgVBgIODA4KCgnR+7pSUFAQEBGDw4MGIj4+Ht7c3bt++jaysLKSmpmocpW0KlosXL8ayZctw7tw5rFq1SqVOTEwMRo0aBXd3d9y4cQPZ2dnw9PQEADg6OkIQBOzevRvPPfccTExM0L17d8yZMwezZs1CQ0MDnnzySVRXV+PQoUMwMzPDK6+80uprMjc3x9SpU7Fo0SLIZDI4OjrivffeAwC89NJL9/FqERERUVfSpq+d7UokEgnWrFkDQH1+atPPSUlJOn2oq4mzszOOHj2K4OBgxMbGYsCAAQgJCcH+/fub/XIJQ0NDbN++HadPn4aPjw9WrFiBZcuWqdRRKBSIioqCp6cnRo4cCQ8PD6SkpAAA7O3tsWTJEsybNw+2traYPn06AGDp0qVYuHAhEhIS4OnpidDQUOzatQvOzs5aX9d7772Hv/zlL4iMjMQTTzyBixcvIjs7G1ZWVlq3RURERF2TIGoaXtRD1dXVsLCwQFVVldpDXjU1NSgqKoKzszOMjY3b1L5cLkd0dLTKQ14ymQxJSUn8tql2pov3j4iIiDpfS3lNW22aTtAVhYWFYezYscjNzUVZWRns7OwQFBTULiOwRERERNQyhlgtSCQSDB8+vLO7QURERNTlcU4sEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyH2ISAIAnbu3NnZ3SAiIiLqMAyxD7jy8nLMmDEDLi4ukEqlkMlkGDNmDPbv398u5ztw4AAEQUBlZWW7tN9kz549GDJkCExMTNCzZ09+dS8RERFphd/YpQWFQtGhXztbXFyMwMBAWFpaIjExEd7e3qivr0dmZiaioqJw+vTpdjv3/RJFEQqFAt26qd9i6enpeP311/GPf/wDTz/9NERRxIkTJzqhl0RERKSvOBLbSnK5HE5OTggODsaECRMQHBwMJycnyOXydjvntGnTIAgCjhw5goiICLi7u8PLywuzZ89GXl6exmM0jaTm5+dDEAQUFxcDAC5evIgxY8bAysoKpqam8PLyQkZGBoqLixEcHAwAsLKygiAImDRpEoDGUJqYmAgXFxeYmJjAx8cHaWlpaufNzMyEn58fpFIpcnNz1fp3+/ZtREdH47333sPUqVPh7u4ODw8PRERE6OZFIyIioi6BI7GtIJfLERERAVEUVcpLS0sRERGBtLQ0nX8cfv36dezduxfLly+Hqamp2n5LS8s2tx0VFYW6ujrk5OTA1NQUBQUFMDMzg0wmQ3p6OsLDw3HmzBmYm5vDxMQEALBgwQLI5XKkpqbCzc0NOTk5mDhxImxsbDBs2DBl23PnzsXKlSvh4uKisY9Hjx5FaWkpDAwMMGjQIJSXl+Oxxx7DypUr4eXl1eZrIiIioq6FIfYeFAoFoqOj1QIs0Dg6KQgCYmJiMHbsWJ1OLSgsLIQoiujXr5/O2mxSUlKC8PBwDBw4EADg4uKi3GdtbQ0A6NWrlzKE3rp1C6tXr0Z2djb8/f2Vxxw8eBDr169XCbHx8fEICQlp9twXLlwAACxevBirV6+Gk5MTVq1ahWHDhuHs2bPK8xMRERG1hNMJ7iE3NxeXL19udr8oirh06ZLGj87vR1NoFgRBp+0CwMyZM7Fs2TIEBgZi0aJF+Omnn1qsX1BQgJqaGoSEhMDMzEy5bdu2DefPn1ep6+fn12JbDQ0NAID58+cjPDwcvr6+2Lx5MwRBwOeff35/F0ZERERdBkPsPZSVlem0Xmu5ublBEAScOnVKq+MMDBrf0jtHjuvr61XqTJkyBRcuXEBkZCROnDgBPz8/rFu3rtk2m4Lnnj17kJ+fr9wKCgpU5sUC0Dj14U52dnYAgP79+yvLpFIpXFxcUFJS0oorJCIiImKIvaem0KWreq1lbW2N0NBQJCcn49atW2r7m1sCy8bGBoBqqM7Pz1erJ5PJMHXqVMjlcsTGxmLDhg0AACMjIwCN0yia9O/fH1KpFCUlJXB1dVXZZDKZVtfl6+sLqVSKM2fOKMvq6+tRXFwMR0dHrdoiIiKirosh9h6CgoLg4ODQ7Mf6giBAJpMhKChI5+dOSUmBQqHA4MGDkZ6ejnPnzuHUqVNYu3atcm7q3ZqC5eLFi3H27Fns2bMHq1atUqkTExODzMxMFBUV4ejRo8jOzoanpycAwNHREYIgYPfu3aioqMDNmzfRvXt3zJkzB7NmzcLWrVtx/vx5HDt2DMnJydi6datW12Rubo6pU6di0aJF2LdvH86cOYO33noLAPDSSy+14VUiIiKirogh9h4kEgnWrFkDQH1+atPPSUlJ7bJerLOzM44ePYrg4GDExsZiwIABCAkJwf79+5GamqrxGENDQ2zfvh2nT5+Gj48PVqxYgWXLlqnUUSgUiIqKgqenJ0aOHAkPDw+kpKQAAOzt7bFkyRLMmzcPtra2mD59OgBg6dKlWLhwIRISEuDp6YnQ0FDs2rULzs7OWl/Xe++9h7/85S+IjIzEE088gYsXLyI7OxtWVlZat0VERERdkyBqeuxeD1VXV8PCwgJVVVUwNzdX2VdTU4OioiI4OzvD2Ni4Te3L5XJER0erPOQlk8mQlJTEb5tqZ7p4/4iIiKjztZTXtMUltlopLCwMY8eO7dBv7CIiIiIizRhitSCRSDB8+PDO7gYRERFRl8c5sURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2G2IeAIAjYuXNnZ3eDiIiIqMMwxD7gysvLMWPGDLi4uEAqlUImk2HMmDHYv39/u5zvwIEDEAQBlZWV7dq+pu37779vl3MSERHRw4ff2KUFhQLIzQXKygA7OyAoCGjPb50tLi5GYGAgLC0tkZiYCG9vb9TX1yMzMxNRUVE4ffp0+538PomiCIVCgW7dVG+xgIAAlJWVqZS9++67+Prrr+Hn59eRXSQiIiI9xpHYVpLLAScnIDgYmDCh8U8np8by9jJt2jQIgoAjR44gIiIC7u7u8PLywuzZs5GXl6fxGE0jqfn5+RAEAcXFxQCAixcvYsyYMbCysoKpqSm8vLyQkZGB4uJiBAcHAwCsrKwgCAImTZoEoDGUJiYmwsXFBSYmJvDx8UFaWpraeTMzM+Hn5wepVIrc3Fy1/hkZGaF3797KrUePHvjyyy/x2muvQRAE3bxwRERE9NDjSGwryOVARAQgiqrlpaWN5WlpQFiYbs95/fp17N27F8uXL4epqanafktLyza3HRUVhbq6OuTk5MDU1BQFBQUwMzODTCZDeno6wsPDcebMGZibm8PExAQAsGDBAsjlcqSmpsLNzQ05OTmYOHEibGxsMGzYMGXbc+fOxcqVK+Hi4tKqPn755Ze4evWqMiwTERERtQZD7D0oFEB0tHqABRrLBAGIiQHGjtXt1ILCwkKIooh+/frprtH/KSkpQXh4OAYOHAgAcHFxUe6ztrYGAPTq1UsZQm/duoXVq1cjOzsb/v7+ymMOHjyI9evXq4TY+Ph4hISEtLovGzduRGhoKGQy2f1eFhEREXUhDLH3kJsLXL7c/H5RBC5daqw3fLjuziv+LzW3x0fsM2fOxFtvvYV9+/bh2WefRXh4OLy9vZutX1BQgJqaGrVwWldXh0GDBqmUaTOv9fLly8jMzMRnn32m3QUQERFRl8c5sfdw1zNI912vtdzc3CAIAk6dOqXVcQYGjW+peMfQcX19vUqdKVOm4MKFC4iMjMSJEyfg5+eHdevWNdtmQ0MDAGDPnj3Iz89XbgUFBSrzYgFonPrQnM2bN6NHjx544YUXWn0MEREREcAQe092drqt11rW1tYIDQ1FcnIybt26pba/uSWwbGxsAEBlBYD8/Hy1ejKZDFOnToVcLkdsbCw2bNgAoPHBKwBQKBTKuv3794dUKkVJSQlcXV1VtrZOAxBFEZs3b8bLL78MQ0PDNrVBREREXRdD7D0EBQEODo1zXzURBEAma6ynaykpKVAoFBg8eDDS09Nx7tw5nDp1CmvXrlXOTb1bU7BcvHgxzp49iz179mDVqlUqdWJiYpCZmYmioiIcPXoU2dnZ8PT0BAA4OjpCEATs3r0bFRUVuHnzJrp37445c+Zg1qxZ2Lp1K86fP49jx44hOTkZW7dubdO1ZWdno6ioCJMnT27T8URERNS1McTeg0QCrFnT+N93B9mmn5OS2me9WGdnZxw9ehTBwcGIjY3FgAEDEBISgv379yM1NVXjMYaGhti+fTtOnz4NHx8frFixAsuWLVOpo1AoEBUVBU9PT4wcORIeHh5ISUkBANjb22PJkiWYN28ebG1tMX36dADA0qVLsXDhQiQkJMDT0xOhoaHYtWsXnJ2d23RtGzduREBAgDI8ExEREWlDEEVNz93rn+rqalhYWKCqqgrm5uYq+2pqalBUVARnZ2cYGxu3qX25vHGVgjsf8pLJGgOsrpfXIlW6eP+IiIio87WU17TF1QlaKSyscRmtjvzGLiIiIiLSjCFWCxKJbpfRIiIiIqK24ZxYIiIiItI7DLFEREREpHcYYomIiIhI77QpxKakpCifFPf19UVubm6zdQ8cOABBENS206dPK+ts2bJFY52ampq2dI+IiIiIHnJaP9i1Y8cOxMTEICUlBYGBgVi/fj1GjRqFgoICPProo80ed+bMGZWlFJq+WaqJubk5zpw5o1LG5ZSIiIiISBOtQ+zq1asxefJkTJkyBQCQlJSEzMxMpKamIiEhodnjevXqBUtLy2b3C4KA3r17a9sdIiIiIuqCtJpOUFdXhx9//BEjRoxQKR8xYgQOHTrU4rGDBg2CnZ0dnnnmGXzzzTdq+2/evAlHR0c4ODhg9OjROHbsWIvt1dbWorq6WmUjIiIioq5BqxB79epVKBQK2NraqpTb2tqivLxc4zF2dnb48MMPkZ6eDrlcDg8PDzzzzDPIyclR1unXrx+2bNmCL7/8Etu3b4exsTECAwNx7ty5ZvuSkJAACwsL5SaTybS5lIeKIAjYuXNnZ3eDiIiI9IhCARw4AGzf3vinQtHZPdJOmx7sEgRB5WdRFNXKmnh4eOD111/H448/Dn9/f6SkpOD555/HypUrlXWGDh2KiRMnwsfHB0FBQfjss8/g7u6OdevWNduHuLg4VFVVKbdLly615VIeeOXl5ZgxYwZcXFwglUohk8kwZswY7N+/v13O1/QgXmVlZbu2r2n7/vvv2+WcREREpEouB5ycgOBgYMKExj+dnBrL9YVWc2J79uwJiUSiNup65coVtdHZlgwdOhT/93//1+x+AwMDPPHEEy2OxEqlUkil0lafUxcUio792tni4mIEBgbC0tISiYmJ8Pb2Rn19PTIzMxEVFaWywsODRhRFKBQKdOumeosFBASgrKxMpezdd9/F119/DT8/v47sIhERUZcklwMREYAoqpaXljaWp6UBYWGd0zdtaDUSa2RkBF9fX2RlZamUZ2VlISAgoNXtHDt2DHZ2ds3uF0UR+fn5LdbpaJ3xL5Zp06ZBEAQcOXIEERERcHd3h5eXF2bPno28vDyNx2gaSc3Pz4cgCCguLgYAXLx4EWPGjIGVlRVMTU3h5eWFjIwMFBcXIzg4GABgZWUFQRAwadIkAI3vSWJiIlxcXGBiYgIfHx+kpaWpnTczMxN+fn6QSqUal14zMjJC7969lVuPHj3w5Zdf4rXXXmt2NJ+IiIh0Q6EAoqPVAyzwZ1lMjH5MLdB6dYLZs2cjMjISfn5+8Pf3x4cffoiSkhJMnToVQOPH/KWlpdi2bRuAxtULnJyc4OXlhbq6Ovzf//0f0tPTkZ6ermxzyZIlGDp0KNzc3FBdXY21a9ciPz8fycnJOrrM+9MZ/2K5fv069u7di+XLl8PU1FRtf0srPdxLVFQU6urqkJOTA1NTUxQUFMDMzAwymQzp6ekIDw9XLolmYmICAFiwYAHkcjlSU1Ph5uaGnJwcTJw4ETY2Nhg2bJiy7blz52LlypVwcXFpVR+//PJLXL16VRmWiYiIqP3k5gKXLze/XxSBS5ca6w0f3mHdahOtQ+z48eNx7do1xMfHo6ysDAMGDEBGRgYcHR0BAGVlZSgpKVHWr6urw5w5c1BaWgoTExN4eXlhz549eO6555R1Kisr8cYbb6C8vBwWFhYYNGgQcnJyMHjwYB1c4v25179YBKHxXyxjx+p2akFhYSFEUUS/fv101+j/lJSUIDw8HAMHDgQAuLi4KPdZW1sDUF0S7datW1i9ejWys7Ph7++vPObgwYNYv369SoiNj49HSEhIq/uyceNGhIaGdukH84iIiDrKXTP67rteZ9I6xAKNH3NPmzZN474tW7ao/Dx37lzMnTu3xfb+9a9/4V//+ldbutLuOutfLOL/UnN7fMQ+c+ZMvPXWW9i3bx+effZZhIeHw9vbu9n6BQUFqKmpUQundXV1GDRokEqZNvNaL1++jMzMTHz22WfaXQARERG1SWtnaj5AMzqb1abVCbqSzvoXi5ubGwRBwKlTp7Q6zsCg8S0V7xg6rq+vV6kzZcoUXLhwAZGRkThx4gT8/PxaXAmioaEBALBnzx7k5+crt4KCApV5sQA0Tn1ozubNm9GjRw+88MILrT6GiIiI2i4oCHBwaPwkWRNBAGSyxnoPOobYe+isf7FYW1sjNDQUycnJuHXrltr+5pbAavo63ztXAMjPz1erJ5PJMHXqVMjlcsTGxmLDhg0AGh+8AgDFHTO6+/fvD6lUipKSEri6uqpsbZ0GIIoiNm/ejJdffhmGhoZtaoOIiIi0I5EAa9Y0/vfdQbbp56Sk9l19SVcYYu+hM//FkpKSAoVCgcGDByM9PR3nzp3DqVOnsHbtWuXc1Ls1BcvFixfj7Nmz2LNnD1atWqVSJyYmBpmZmSgqKsLRo0eRnZ0NT09PAICjoyMEQcDu3btRUVGBmzdvonv37pgzZw5mzZqFrVu34vz58zh27BiSk5OxdevWNl1bdnY2ioqKMHny5DYdT0RERG0TFtb4ULq9vWq5g4P+LK8FMMTeU2f+i8XZ2RlHjx5FcHAwYmNjMWDAAISEhGD//v1ITU3VeIyhoSG2b9+O06dPw8fHBytWrMCyZctU6igUCkRFRcHT0xMjR46Eh4cHUlJSAAD29vZYsmQJ5s2bB1tbW0yfPh0AsHTpUixcuBAJCQnw9PREaGgodu3aBWdn5zZd28aNGxEQEKAMz0RERNRxwsKA4mLgm2+Af/+78c+iIv0JsAAgiKKm5+71T3V1NSwsLFBVVQVzc3OVfTU1NSgqKoKzszOMjY3b1L5c3rhKwZ0PeclkjQFWn95wfaSL94+IiIg6X0t5TVttWp2gKwoLa1xGqyO/sYuIiIiINGOI1YJE8uAv/EtERETUFXBOLBERERHpHYZYIiIiItI7DLFEREREpHe6VIh9SBZi6HKavjGMiIiIqEmXeLDL0NAQgiCgoqICNjY2EJr75gJ6oIiiiLq6OlRUVMDAwED5bWJEREREXSLESiQSODg44PLlyyguLu7s7pCWHnnkETz66KMwMOhSHxwQERFRC7pEiAUAMzMzuLm5ob6+vrO7QlqQSCTo1q0bR8+JiIhIRZcJsUBjIJLw2wmIiIiI9B4/nyUiIiIivcMQS0RERER6hyGWiIiIiPTOQzMntmkN2Orq6k7uCRERERFp0pTTdLF2/0MTYn/77TcAgEwm6+SeEBEREVFLfvvtN1hYWNxXG4L4kHyNVUNDA3755Rd0796dyzHpUHV1NWQyGS5dugRzc/PO7g49AHhP0N14T9CdeD/Q3e68J7p3747ffvsNffr0ue/13x+akVgDAwM4ODh0djceWubm5vxlRCp4T9DdeE/QnXg/0N2a7on7HYFtwge7iIiIiEjvMMQSERERkd5hiKUWSaVSLFq0CFKptLO7Qg8I3hN0N94TdCfeD3S39ronHpoHu4iIiIio6+BILBERERHpHYZYIiIiItI7DLFEREREpHcYYomIiIhI7zDEElJSUuDs7AxjY2P4+voiNze32boHDx5EYGAgevToARMTE/Tr1w//+te/OrC31BG0uSfu9N///hfdunXDY4891r4dpA6nzT1x4MABCIKgtp0+fboDe0ztSdvfEbW1tZg/fz4cHR0hlUrRt29fbNq0qYN6Sx1Bm3ti0qRJGn9HeHl5aXdSkbq0Tz/9VDQ0NBQ3bNggFhQUiNHR0aKpqal48eJFjfWPHj0q/vvf/xZ//vlnsaioSPz444/FRx55RFy/fn0H95zai7b3RJPKykrRxcVFHDFihOjj49MxnaUOoe098c0334gAxDNnzohlZWXK7fbt2x3cc2oPbfkd8cILL4hDhgwRs7KyxKKiIvG7774T//vf/3Zgr6k9aXtPVFZWqvxuuHTpkmhtbS0uWrRIq/Nyia0ubsiQIXj88ceRmpqqLPP09MSLL76IhISEVrURFhYGU1NTfPzxx+3VTepAbb0n/vKXv8DNzQ0SiQQ7d+5Efn5+B/SWOoK298SBAwcQHByMGzduwNLSsgN7Sh1B2/th7969+Mtf/oILFy7A2tq6I7tKHeR+s8TOnTsRFhaGoqIiODo6tvq8nE7QhdXV1eHHH3/EiBEjVMpHjBiBQ4cOtaqNY8eO4dChQxg2bFh7dJE6WFvvic2bN+P8+fNYtGhRe3eROtj9/J4YNGgQ7Ozs8Mwzz+Cbb75pz25SB2nL/fDll1/Cz88PiYmJsLe3h7u7O+bMmYM//vijI7pM7UwXWWLjxo149tlntQqwANBNq9r0ULl69SoUCgVsbW1Vym1tbVFeXt7isQ4ODqioqMDt27exePFiTJkypT27Sh2kLffEuXPnMG/ePOTm5qJbN/5Kedi05Z6ws7PDhx9+CF9fX9TW1uLjjz/GM888gwMHDuCpp57qiG5TO2nL/XDhwgUcPHgQxsbG+OKLL3D16lVMmzYN169f57zYh8D9ZAkAKCsrw1dffYV///vfWp+bf+MQBEFQ+VkURbWyu+Xm5uLmzZvIy8vDvHnz4Orqir/+9a/t2U3qQK29JxQKBSZMmIAlS5bA3d29o7pHnUCb3xMeHh7w8PBQ/uzv749Lly5h5cqVDLEPCW3uh4aGBgiCgE8++QQWFhYAgNWrVyMiIgLJyckwMTFp9/5S+2tLlgCALVu2wNLSEi+++KLW52SI7cJ69uwJiUSi9i+lK1euqP2L6m7Ozs4AgIEDB+LXX3/F4sWLGWIfAtreE7/99ht++OEHHDt2DNOnTwfQ+BeWKIro1q0b9u3bh6effrpD+k7t435+T9xp6NCh+L//+z9dd486WFvuBzs7O9jb2ysDLNA4X1IURVy+fBlubm7t2mdqX/fzO0IURWzatAmRkZEwMjLS+tycE9uFGRkZwdfXF1lZWSrlWVlZCAgIaHU7oiiitrZW192jTqDtPWFubo4TJ04gPz9fuU2dOhUeHh7Iz8/HkCFDOqrr1E509Xvi2LFjsLOz03X3qIO15X4IDAzEL7/8gps3byrLzp49CwMDAzg4OLRrf6n93c/viG+//RaFhYWYPHly207e1uUU6OHQtCzGxo0bxYKCAjEmJkY0NTUVi4uLRVEUxXnz5omRkZHK+u+//7745ZdfimfPnhXPnj0rbtq0STQ3Nxfnz5/fWZdAOqbtPXG3RYsWcYmth4y298S//vUv8YsvvhDPnj0r/vzzz+K8efNEAGJ6enpnXQLpkLb3w2+//SY6ODiIERER4smTJ8Vvv/1WdHNzE6dMmdJZl0A61ta/NyZOnCgOGTKkzefldIIubvz48bh27Rri4+NRVlaGAQMGICMjQ/mEYFlZGUpKSpT1GxoaEBcXh6KiInTr1g19+/bFP//5T7z55puddQmkY9reE/Tw0/aeqKurw5w5c1BaWgoTExN4eXlhz549eO655zrrEkiHtL0fzMzMkJWVhRkzZsDPzw89evTAuHHjsGzZss66BNKxtvy9UVVVhfT0dKxZs6bN5+U6sURERESkdzgnloiIiIj0DkMsEREREekdhlgiIiIi0jsMsURERESkdxhiiYiIiEjvMMQSERERkd5hiCUiIiIivcMQS0RERER6hyGWiIiIiPQOQywRERER6R2GWCIiIiLSOwyxRERERKR3/j8INDV6UiBvIwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [7], [8], [0, 5], [6, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 7 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 8 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 9 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 10 ---\n", - "Finale Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "#functions\n", - "def generate_random_number_array(n, rangeStart, rangeEnd):\n", - " array = [random.randint(rangeStart, rangeEnd) for _ in range(n)]\n", - " return array\n", - "\n", - "def generate_x_y_array(n, rangeStart, rangeEnd):\n", - " XYDict = {\n", - " \"x\": generate_random_number_array(n, rangeStart, rangeEnd),\n", - " \"y\": generate_random_number_array(n, rangeStart, rangeEnd)\n", - " }\n", - " return XYDict\n", - "\n", - "def generate_clusters(n_clusters, m_points_per_cluster):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.rand(n_clusters, 2)\n", - " noise_scale = 0.05\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randn(m_points_per_cluster, 2) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (noise_scale * np.random.randn(len(points), 2))\n", - "\n", - " return np.array(noisy_points)\n", - "\n", - "\n", - "\n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 5\n", - "\n", - "# Größe des \"Noise\"-Bereichs\n", - "noise_scale = 0.1\n", - "\n", - "# Beispiel Daten - Clusterpunkte und \"Noise\" Punkte\n", - "np.random.seed(0)\n", - "clusters = []\n", - "for _ in range(num_clusters):\n", - " cluster_center = np.random.rand(2) * (1 - noise_scale)\n", - " cluster = cluster_center + np.random.randn(points_per_cluster, 2) * noise_scale\n", - " clusters.append(cluster)\n", - "print(clusters) \n", - "\n", - "# Zusammenführen der Clusterpunkte\n", - "X = np.concatenate(clusters, axis=0)\n", - "print(X)\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "#print(clusters)\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "edd42b72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[19 11]\n", - " [13 10]\n", - " [22 9]\n", - " [21 10]\n", - " [18 16]\n", - " [20 17]]\n", - "[[0], [1], [2], [3], [4], [5]]\n", - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Finale Cluster: [[0], [1], [4], [5], [2, 3]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(0, 30, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 3\n", - "rangeStart = 1\n", - "rangeEnd = 6\n", - "\n", - "clusters = generate_clusters(num_clusters, points_per_cluster)\n", - "print(clusters)\n", - "X = clusters\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "print(clusters)\n", - "\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7c9c83ed", - "metadata": {}, - "outputs": [ - { - "ename": "ExecutableNotFound", - "evalue": "failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/backend/execute.py\u001b[0m in \u001b[0;36mrun_check\u001b[0;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0mproc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask)\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m 952\u001b[0m \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m 1820\u001b[0m \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1821\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1822\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: PosixPath('dot')", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mExecutableNotFound\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/bs/5wndg3791vz7vxsh2h7qj_mw0000gn/T/ipykernel_63053/831696634.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mpoints_per_cluster\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;31m#Hauptfunktion ausfuehren\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mfoo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_clusters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpoints_per_cluster\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfoo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"abc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/_tools.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 169\u001b[0m category=category)\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/backend/rendering.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(engine, format, filepath, renderer, formatter, neato_no_op, quiet, outfile, raise_if_result_exists, overwrite_filepath)\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m execute.run_check(cmd,\n\u001b[0m\u001b[1;32m 325\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparts\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquiet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/backend/execute.py\u001b[0m in \u001b[0;36mrun_check\u001b[0;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mExecutableNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mExecutableNotFound\u001b[0m: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH" - ] - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import graphviz\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(0, 30, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "#def generate_task_definition():\n", - "def save_plot(path, filename):\n", - " plt.savefig(\"{}{}.png\".format(path, filename))\n", - "\n", - "def create_dot_table(array):\n", - " \n", - " dotTable = graphviz.Digraph('dotTable', comment='task definition table')\n", - " \n", - " # Add nodes to the graph for each element in the array\n", - " for i in range(array.shape[0]):\n", - " row_label = f'Row{i+1}'\n", - " dotTable.node(row_label, shape='plaintext', label=f'<
{array[i, 0]}{array[i, 1]}
>')\n", - "\n", - " # Add edges between consecutive rows (if needed)\n", - " for i in range(array.shape[0] - 1):\n", - " dotTable.edge(f'Row{i+1}', f'Row{i+2}')\n", - "\n", - " return dotTable\n", - " \n", - "def main(num_clusters,points_per_cluster):\n", - " clusters = generate_clusters(num_clusters, points_per_cluster)\n", - " dotTable = create_dot_table(clusters)\n", - " dotTable.save('table.dot')\n", - " dotTable.render('table', format='png', cleanup=True)\n", - "\n", - " \n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 3\n", - "#Hauptfunktion ausfuehren\n", - "foo = main(num_clusters,points_per_cluster)\n", - "print(foo)\n", - "print(\"abc\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fa0e1cf", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"fo\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fe99c316", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 6 and the array at index 1 has size 5", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/bs/5wndg3791vz7vxsh2h7qj_mw0000gn/T/ipykernel_63053/1285465837.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;31m# Add a new column at the end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0mcondensed_distances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcondensed_distances\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_col\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mnum_clusters\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mhstack\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/numpy/core/shape_base.py\u001b[0m in \u001b[0;36mhstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 6 and the array at index 1 has size 5" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3e7c446b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACm0mTCTXJysiwWix5//PEa+6SlpclisZyzfffdd41XKAAAaNK8XF2AJG3evFnz5s1T165d69R/9+7dCgwMtL9u1apVQ5UGAADcjMuv3Pzyyy+69957NX/+fLVs2bJOY0JCQhQWFmbfPD09G7hKAADgLlwebsaOHatbb71VAwYMqPOY7t27Kzw8XAkJCVq/fn2tfW02m4qLix02AABgXi69LbV8+XJt3bpVmzdvrlP/8PBwzZs3Tz169JDNZtOSJUuUkJCgtLQ09e/fv9oxycnJeuGFF+qzbAAA0IRZDMMwXHHi3NxcxcXFKTU1Vd26dZMk3XDDDbrmmms0c+bMOh9nyJAhslgsWr16dbX7bTabbDab/XVxcbEiIyNVVFTk8NwOAABouoqLi2W1Wuv0/u2y21JbtmxRYWGhevToIS8vL3l5eSk9PV2zZ8+Wl5eXKioq6nScXr16ac+ePTXu9/HxUWBgoMMGAADMy2W3pRISEpSdne3Q9tBDD6lTp06aNGlSnR8SzsrKUnh4eEOUCAAA3JDLwk1AQIC6dOni0NaiRQsFBwfb25OSkpSXl6fFixdLkmbOnKmoqCjFxMTo9OnTWrp0qVJSUpSSktLo9QMAgKapSXzPTU3y8/O1f/9+++vTp0/rqaeeUl5envz8/BQTE6M1a9Zo8ODBLqwSAAA0JS57oNhVnHkgCQAANA1u8UAxAABAQyDcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHc1IPTp9br2KHbVXiglQoPhOr44ftVZtvs6rIAALgkNZlwk5ycLIvFoscff7zWfunp6erRo4d8fX3Vvn17zZ07t3EKrEFpUbKOFd6s0yfXyqg8JqPyiGwn/ldHf+6tk7+859LaAAC4FDWJcLN582bNmzdPXbt2rbVfTk6OBg8erH79+ikrK0uTJ0/W+PHjlZKS0kiVOjp9aoN+Kfrzr6/Kf7OnXJKh4qOjVV622wWVAQBw6XJ5uPnll1907733av78+WrZsmWtfefOnau2bdtq5syZ6ty5sx555BH94Q9/0IwZMxqpWkcnSt6U5FVLDw+d/OXtxioHAACoCYSbsWPH6tZbb9WAAQPO2zcjI0OJiYkObQMHDlRmZqbKysqqHWOz2VRcXOyw1Zcy27/leMXmbOU6ferLejsfAAA4P5eGm+XLl2vr1q1KTk6uU/+CggKFhoY6tIWGhqq8vFyHDx+udkxycrKsVqt9i4yMvOi6/4/n+btYvOvxfAAA4HxcFm5yc3M1YcIELV26VL6+vnUeZ7FYHF4bhlFte5WkpCQVFRXZt9zc3Asv+izN/G7R+W5L+fgOrLfzAQCA86vtnblBbdmyRYWFherRo4e9raKiQhs2bNCbb74pm80mT0/HKyNhYWEqKChwaCssLJSXl5eCg4OrPY+Pj498fHzqfwKSmgeM06nSRTXstUgWH/n5j2qQcwMAgOq5LNwkJCQoOzvboe2hhx5Sp06dNGnSpHOCjSTFx8fro48+cmhLTU1VXFycvL0b//aPd7NYWYOXqOjIA5IMSRW/7vGQLD66rNVKeXpFNHpdAABcylwWbgICAtSlSxeHthYtWig4ONjenpSUpLy8PC1evFiSNGbMGL355puaOHGiRo0apYyMDC1YsEDLli1r9Pqr+Lb4L3n7XKsTv8xT2amvJIuHmvneLD//h+XpGeayugAAuFS5LNzURX5+vvbv329/HR0drU8++URPPPGE3nrrLUVERGj27NkaPny4C6uUPL2iFHDZKy6tAQAAnGExqp7IvUQUFxfLarWqqKhIgYGBri4HAADUgTPv3y7/nhsAAID6RLgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmckHhZsmSJerTp48iIiL0008/SZJmzpypVatW1WtxAAAAznI63MyZM0cTJ07U4MGDdfz4cVVUVEiSLrvsMs2cObO+6wMAAHCK0+HmjTfe0Pz58/WnP/1Jnp6e9va4uDhlZ2fXa3EAAADOcjrc5OTkqHv37ue0+/j4qLS0tF6KAgAAuFBOh5vo6Ght27btnPa1a9fq6quvro+aAAAALpiXswOefvppjR07VqdOnZJhGPr666+1bNkyJScn65133mmIGgEAAOrM6XDz0EMPqby8XM8884xOnDihe+65R61bt9asWbM0YsSIhqgRAACgziyGYRgXOvjw4cOqrKxUSEhIfdbUoIqLi2W1WlVUVKTAwEBXlwMAAOrAmfdvp5+5OXnypE6cOCFJuvzyy3Xy5EnNnDlTqampF1YtAABAPXI63AwdOlSLFy+WJB0/flzXXnutXnvtNQ0dOlRz5syp9wIBAACc4XS42bp1q/r16ydJ+uc//6mwsDD99NNPWrx4sWbPnl3vBQIAADjD6XBz4sQJBQQESJJSU1N1xx13yMPDQ7169bL/FAMAAICrOB1uOnTooJUrVyo3N1efffaZEhMTJUmFhYU8oAsAAFzO6XAzZcoUPfXUU4qKitJ1112n+Ph4SWeu4lT3zcUAAACN6YI+Cl5QUKD8/Hx169ZNHh5n8tHXX3+twMBAderUqd6LrE98FBwAAPfjzPu301/iJ0lhYWEKCwtzaLv22msv5FAAAAD1yulwU1paqmnTpunzzz9XYWGhKisrHfbv3bu33ooDAABwltPh5pFHHlF6erruv/9+hYeHy2KxNERdAAAAF8TpcLN27VqtWbNGffr0aYh6AAAALorTn5Zq2bKlgoKCGqIWAACAi+Z0uPnLX/6iKVOm2H9fCgAAoCmp022p7t27Ozxb88MPPyg0NFRRUVHy9vZ26Lt169Y6n3zOnDmaM2eO9u3bJ0mKiYnRlClTNGjQoGr7p6Wl6cYbbzynfdeuXU3+I+gAAKBx1CncDBs2rEFO3qZNG02bNk0dOnSQJC1atEhDhw5VVlaWYmJiahy3e/duh8+4t2rVqkHqAwAA7ueCvsSvIQUFBemvf/2rHn744XP2VV25OXbsmC677LILOj5f4gcAgPtx5v3b6WduNm/erE2bNp3TvmnTJmVmZjp7OLuKigotX75cpaWl9p90qEn37t0VHh6uhIQErV+/vta+NptNxcXFDhsAADAvp8PN2LFjlZube057Xl6exo4d63QB2dnZ8vf3l4+Pj8aMGaMVK1bo6quvrrZveHi45s2bp5SUFH344Yfq2LGjEhIStGHDhhqPn5ycLKvVat8iIyOdrhEAALgPp29L+fv7a8eOHWrfvr1De05Ojrp27aqSkhKnCjh9+rT279+v48ePKyUlRe+8847S09NrDDhnGzJkiCwWi1avXl3tfpvNJpvNZn9dXFysyMhIbksBAOBGGvS2lI+Pj37++edz2vPz8+Xl5fxPVTVr1kwdOnRQXFyckpOT1a1bN82aNavO43v16qU9e/bUWm9gYKDDBgAAzMvpcHPzzTcrKSlJRUVF9rbjx49r8uTJuvnmmy+6IMMwHK60nE9WVpbCw8Mv+rwAAMAcnL7U8tprr6l///5q166dunfvLknatm2bQkNDtWTJEqeONXnyZA0aNEiRkZEqKSnR8uXLlZaWpk8//VSSlJSUpLy8PC1evFiSNHPmTEVFRSkmJkanT5/W0qVLlZKSopSUFGenAQAATMrpcNO6dWvt2LFD//jHP7R9+3b5+fnpoYce0t13333OF/qdz88//6z7779f+fn5slqt6tq1qz799FP7FaD8/Hzt37/f3v/06dN66qmnlJeXJz8/P8XExGjNmjUaPHiws9MAAAAmVW/fc5Odna0FCxZo5syZ9XG4BsP33AAA4H4a9IHis0/09ttv69prr1W3bt2UlpZ2MYcDAAC4aBcUbtLT0/XAAw8oPDxcjz76qG666SZ9//332rZtWz2XBwAA4Jw6h5v8/Hy98sor6tChg0aMGKHLL79c6enp8vDw0AMPPGD/fSgAAABXqvMDxdHR0brrrrv01ltv6eabb5aHx0Xd0QIAAGgQdU4o7dq101dffaUNGzbo+++/b8iaAAAALlidw83u3bu1dOlS5efnq2fPnurRo4def/11SZLFYmmwAgEAAJzh1L2lPn366N1331V+fr7GjBmj//mf/1FFRYUeffRRzZ8/X4cOHWqoOgEAAOrkor/nZteuXVqwYIGWLFmio0ePqqysrL5qaxB8zw0AAO6n0b7nRpI6d+6sGTNmKC8vTx988MHFHg4AAOCiXFS4CQwM1N69eyVJXl5euuOOO+qlKAAAgAtV53Bz4MCBc9rq6ZcbAAAA6k2dw02XLl2c/tVvAACAxlbncPPKK69o7NixGj58uI4cOSJJuu+++3goFwAANCl1DjePPvqotm/frmPHjikmJkarV6/WnDlzdPnllzdkfQAAAE6p888vSGd+guGLL77Qm2++qeHDh6tz587y8nI8xNatW+u1QAAAAGc4FW4k6aefflJKSoqCgoI0dOjQc8INAACAKzmVTObPn68nn3xSAwYM0DfffKNWrVo1VF0AAAAXpM7h5pZbbtHXX3+tN998Uw888EBD1gQAAHDB6hxuKioqtGPHDrVp06Yh6wEAALgodQ4369ata8g6AAAA6sVF/7YUAABAU0K4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApuLl6gIAV6ssr9CPn2Vr96otKjlwVN7NfdQ+sYs63R6n5q0CXV0eADR5hmFoy5Y0fbJ2qX74MVteXt7qGXeTbh18v9q2varR63HplZs5c+aoa9euCgwMVGBgoOLj47V27dpax6Snp6tHjx7y9fVV+/btNXfu3EaqFmZUcbpc/3pmmTKmf6yj3xeorNSmE4eKtXNZhlY/OE9Hf/jZ1SUCQJNmGIbeXfiKpk1/VN/s3KQTJ0pUXHxU69NW6OlJw/X15s8bvSaXhps2bdpo2rRpyszMVGZmpm666SYNHTpUO3furLZ/Tk6OBg8erH79+ikrK0uTJ0/W+PHjlZKS0siVwyx2LP5SBVt/OvPCMOztRqWhshM2rf/T/6iyotJF1QFA05ex8TOtXbtUklRZWWFvr6ysUEVFhV6fOVFFRUcatSaXhpshQ4Zo8ODBuuqqq3TVVVfp5Zdflr+/vzZu3Fht/7lz56pt27aaOXOmOnfurEceeUR/+MMfNGPGjEauHGZQcbpcu1dscQg1v2VUGiotKNLBzXsbuTIAcB9r1iyWh0dNccJQeXm5vlj/YaPW1GQeKK6oqNDy5ctVWlqq+Pj4avtkZGQoMTHRoW3gwIHKzMxUWVlZtWNsNpuKi4sdNkCSSg4e1+lfTtXax+LpoUM7DzRSRQDgXgzD0J4fdqiysuYr3IZRqd3fZzViVU0g3GRnZ8vf318+Pj4aM2aMVqxYoauvvrravgUFBQoNDXVoCw0NVXl5uQ4fPlztmOTkZFmtVvsWGRlZ73OAe/LwtNSxn8v/MwGAJstiqf3fUovFIg8Pz0aq5gyX/6vdsWNHbdu2TRs3btQf//hHjRw5Ut9++22N/c/+Ixq/3lKo6Y+blJSkoqIi+5abm1t/xcOt+Ue0VPNWAbX2MSoqFR4X3UgVAYB7sVgs6tatT63hxTAMdevauxGragLhplmzZurQoYPi4uKUnJysbt26adasWdX2DQsLU0FBgUNbYWGhvLy8FBwcXO0YHx8f+6exqjZAOnNFJmZE9bdAJcniaVFwp3C1imnTiFUBgHv5f7c95PAg8W95WDzk73+Z+vf7f41ak8vDzdkMw5DNZqt2X3x8vNatW+fQlpqaqri4OHl7ezdGeTCZTsN76qphPSSdeb7mzP84cxUwIKKlbnzprvNecgWAS1mXLtfpkYf/LMnx9pPFYpFfc38996f58vNr0ag1WQyjho+KNILJkydr0KBBioyMVElJiZYvX65p06bp008/1c0336ykpCTl5eVp8eLFks58FLxLly4aPXq0Ro0apYyMDI0ZM0bLli3T8OHD63TO4uJiWa1WFRUVcRUHdoXf5GrPR1kq2n9Yzfx9FZ0Qo3Y3Xi0vH0IzANRFXt5epa5brj17dsjbu5ni4m7SDTcMU4D/ZfVyfGfev136DcU///yz7r//fuXn58tqtapr1672YCNJ+fn52r9/v71/dHS0PvnkEz3xxBN66623FBERodmzZ9c52AA1CekSqZAuPGwOABeqdev2eujBya4uQ5KLr9y4AlduAABwP868fze5Z24AAAAuBuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYikvDTXJysnr27KmAgACFhIRo2LBh2r17d61j0tLSZLFYztm+++67RqoaAAA0ZS4NN+np6Ro7dqw2btyodevWqby8XImJiSotLT3v2N27dys/P9++XXnllY1QMQAAaOq8XHnyTz/91OH1e++9p5CQEG3ZskX9+/evdWxISIguu+yyBqwOAAC4oyb1zE1RUZEkKSgo6Lx9u3fvrvDwcCUkJGj9+vU19rPZbCouLnbYAACAeTWZcGMYhiZOnKi+ffuqS5cuNfYLDw/XvHnzlJKSog8//FAdO3ZUQkKCNmzYUG3/5ORkWa1W+xYZGdlQUwAAAE2AxTAMw9VFSNLYsWO1Zs0affXVV2rTpo1TY4cMGSKLxaLVq1efs89ms8lms9lfFxcXKzIyUkVFRQoMDLzougEAQMMrLi6W1Wqt0/t3k7hyM27cOK1evVrr1693OthIUq9evbRnz55q9/n4+CgwMNBhAwAA5uXSB4oNw9C4ceO0YsUKpaWlKTo6+oKOk5WVpfDw8HquDgAAuCOXhpuxY8fq/fff16pVqxQQEKCCggJJktVqlZ+fnyQpKSlJeXl5Wrx4sSRp5syZioqKUkxMjE6fPq2lS5cqJSVFKSkpLpsHAABoOlwabubMmSNJuuGGGxza33vvPT344IOSpPz8fO3fv9++7/Tp03rqqaeUl5cnPz8/xcTEaM2aNRo8eHBjlQ0AAJqwJvNAcWNx5oEkAADQNLjdA8UAAAD1hXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMxcvVBQAAAPdmGIZ27D6oLzK+1768o/Ly9FC3Tq2V0PsqtQ69rNHrcemVm+TkZPXs2VMBAQEKCQnRsGHDtHv37vOOS09PV48ePeTr66v27dtr7ty5jVAtAAA4m2EY+mDNVr219Evt3luok6fKVFJq03+ycvSXtz7Ttl0HGr0ml4ab9PR0jR07Vhs3btS6detUXl6uxMRElZaW1jgmJydHgwcPVr9+/ZSVlaXJkydr/PjxSklJacTKAQCAJG3ZmasvNu6RJFUahr29stJQZaWhecv/o+JfTjVqTRbD+E0lLnbo0CGFhIQoPT1d/fv3r7bPpEmTtHr1au3atcveNmbMGG3fvl0ZGRnnPUdxcbGsVquKiooUGBhYb7UDAHApevXtf2nvgSOqKU5YLNKwAV016PqrL+o8zrx/N6kHiouKiiRJQUFBNfbJyMhQYmKiQ9vAgQOVmZmpsrKyc/rbbDYVFxc7bAAA4OIZhqGcWoLNmT7S3tzDjVhVEwo3hmFo4sSJ6tu3r7p06VJjv4KCAoWGhjq0hYaGqry8XIcPn/vHS05OltVqtW+RkZH1XjsAAJcqi4el9v0WycOjceNGkwk3jz32mHbs2KFly5adt6/F4viHrEqMZ7dLUlJSkoqKiuxbbm5u/RQMAMAlzmKx6OorQuVRS8AxDKnzFaE17m8ITeKj4OPGjdPq1au1YcMGtWnTpta+YWFhKigocGgrLCyUl5eXgoODz+nv4+MjHx+feq0XAACccXPfTsr+Pr/afRaLRc19vdXrmqhGrcmlV24Mw9Bjjz2mDz/8UF988YWio6PPOyY+Pl7r1q1zaEtNTVVcXJy8vb0bqlQAAFCNTu1Ddc+QHpLkcAXHIsnPx0sTHrxBvj6N+/7s0k9LPfroo3r//fe1atUqdezY0d5utVrl5+cn6cxtpby8PC1evFjSmY+Cd+nSRaNHj9aoUaOUkZGhMWPGaNmyZRo+fPh5z8mnpQAAqH/5h4q14esflHPgyJkv8evcWr27R6tF8/q5e+LM+7dLw011z8hI0nvvvacHH3xQkvTggw9q3759SktLs+9PT0/XE088oZ07dyoiIkKTJk3SmDFj6nROwg0AAO7HbcKNKxBuAABwP277PTcAAAAXi3ADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMpUn8KnhjqvpC5uLiYhdXAgAA6qrqfbsuP6xwyYWbkpISSVJkZKSLKwEAAM4qKSmR1Wqttc8l99tSlZWVOnjwoAICAmr84c4LVVxcrMjISOXm5pryd6vMPj/J/HNkfu7P7HNkfu6voeZoGIZKSkoUEREhD4/an6q55K7ceHh4qE2bNg16jsDAQNP+n1Yy//wk88+R+bk/s8+R+bm/hpjj+a7YVOGBYgAAYCqEGwAAYCqEm3rk4+OjqVOnysfHx9WlNAizz08y/xyZn/sz+xyZn/trCnO85B4oBgAA5saVGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEmzrasGGDhgwZooiICFksFq1cufK8Y9LT09WjRw/5+vqqffv2mjt3bsMXehGcnWNaWposFss523fffdc4BTspOTlZPXv2VEBAgEJCQjRs2DDt3r37vOPcZR0vZH7utIZz5sxR165d7V8MFh8fr7Vr19Y6xl3Wroqzc3Sn9atOcnKyLBaLHn/88Vr7uds6VqnL/NxtDZ9//vlzag0LC6t1jCvWj3BTR6WlperWrZvefPPNOvXPycnR4MGD1a9fP2VlZWny5MkaP368UlJSGrjSC+fsHKvs3r1b+fn59u3KK69soAovTnp6usaOHauNGzdq3bp1Ki8vV2JiokpLS2sc407reCHzq+IOa9imTRtNmzZNmZmZyszM1E033aShQ4dq586d1fZ3p7Wr4uwcq7jD+p1t8+bNmjdvnrp27VprP3dcR6nu86viTmsYExPjUGt2dnaNfV22fgacJslYsWJFrX2eeeYZo1OnTg5to0ePNnr16tWAldWfusxx/fr1hiTj2LFjjVJTfSssLDQkGenp6TX2ced1rMv83H0NW7ZsabzzzjvV7nPntfut2uborutXUlJiXHnllca6deuM66+/3pgwYUKNfd1xHZ2Zn7ut4dSpU41u3brVub+r1o8rNw0kIyNDiYmJDm0DBw5UZmamysrKXFRVw+jevbvCw8OVkJCg9evXu7qcOisqKpIkBQUF1djHndexLvOr4m5rWFFRoeXLl6u0tFTx8fHV9nHntZPqNscq7rZ+Y8eO1a233qoBAwact687rqMz86viTmu4Z88eRUREKDo6WiNGjNDevXtr7Ouq9bvkfjizsRQUFCg0NNShLTQ0VOXl5Tp8+LDCw8NdVFn9CQ8P17x589SjRw/ZbDYtWbJECQkJSktLU//+/V1dXq0Mw9DEiRPVt29fdenSpcZ+7rqOdZ2fu61hdna24uPjderUKfn7+2vFihW6+uqrq+3rrmvnzBzdbf0kafny5dq6das2b95cp/7uto7Ozs/d1vC6667T4sWLddVVV+nnn3/WSy+9pN69e2vnzp0KDg4+p7+r1o9w04AsFovDa+PXL4M+u91ddezYUR07drS/jo+PV25urmbMmNEk/6P8rccee0w7duzQV199dd6+7riOdZ2fu61hx44dtW3bNh0/flwpKSkaOXKk0tPTa3zzd8e1c2aO7rZ+ubm5mjBhglJTU+Xr61vnce6yjhcyP3dbw0GDBtn/d2xsrOLj43XFFVdo0aJFmjhxYrVjXLF+3JZqIGFhYSooKHBoKywslJeXV7Xp1ix69eqlPXv2uLqMWo0bN06rV6/W+vXr1aZNm1r7uuM6OjO/6jTlNWzWrJk6dOiguLg4JScnq1u3bpo1a1a1fd1x7STn5lidprx+W7ZsUWFhoXr06CEvLy95eXkpPT1ds2fPlpeXlyoqKs4Z407reCHzq05TXsOztWjRQrGxsTXW66r148pNA4mPj9dHH33k0Jaamqq4uDh5e3u7qKqGl5WV1eQuE1cxDEPjxo3TihUrlJaWpujo6POOcad1vJD5Vacpr+HZDMOQzWardp87rV1taptjdZry+iUkJJzzyZqHHnpInTp10qRJk+Tp6XnOGHdaxwuZX3Wa8hqezWazadeuXerXr1+1+122fg36uLKJlJSUGFlZWUZWVpYhyfjb3/5mZGVlGT/99JNhGIbx7LPPGvfff7+9/969e43mzZsbTzzxhPHtt98aCxYsMLy9vY1//vOfrprCeTk7x9dff91YsWKF8f333xvffPON8eyzzxqSjJSUFFdNoVZ//OMfDavVaqSlpRn5+fn27cSJE/Y+7ryOFzI/d1rDpKQkY8OGDUZOTo6xY8cOY/LkyYaHh4eRmppqGIZ7r10VZ+foTutXk7M/TWSGdfyt883P3dbwySefNNLS0oy9e/caGzduNG677TYjICDA2Ldvn2EYTWf9CDd1VPVxvbO3kSNHGoZhGCNHjjSuv/56hzFpaWlG9+7djWbNmhlRUVHGnDlzGr9wJzg7x1dffdW44oorDF9fX6Nly5ZG3759jTVr1rim+Dqobm6SjPfee8/ex53X8ULm505r+Ic//MFo166d0axZM6NVq1ZGQkKC/U3fMNx77ao4O0d3Wr+anP3mb4Z1/K3zzc/d1vD3v/+9ER4ebnh7exsRERHGHXfcYezcudO+v6msn8Uwfn2yBwAAwAR4oBgAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QaAqT3//PO65pprXF0GgEZEuAHQKCoqKtS7d28NHz7cob2oqEiRkZF67rnnah3fsWNHNWvWTHl5eQ1ZJgATINwAaBSenp5atGiRPv30U/3jH/+wt48bN05BQUGaMmVKjWO/+uornTp1SnfddZcWLlzYCNUCcGeEGwCN5sorr1RycrLGjRungwcPatWqVVq+fLkWLVqkZs2a1ThuwYIFuueee3T//ffr3Xff1dm/GnPgwAGNGDFCQUFBatGiheLi4rRp0yaHPkuWLFFUVJSsVqtGjBihkpIS+75//vOfio2NlZ+fn4KDgzVgwACVlpba97/33nvq3LmzfH191alTJ/3973+vp78IgIbg5eoCAFxaxo0bpxUrVuiBBx5Qdna2pkyZUuszMSUlJfrf//1fbdq0SZ06dVJpaanS0tJ04403SpJ++eUXXX/99WrdurVWr16tsLAwbd26VZWVlfZj/Pjjj1q5cqU+/vhjHTt2TP/1X/+ladOm6eWXX1Z+fr7uvvtuTZ8+XbfffrtKSkr05Zdf2gPU/PnzNXXqVL355pvq3r27srKyNGrUKLVo0UIjR45s0L8VgAvU4D/NCQBn2bVrlyHJiI2NNcrKymrtO2/ePOOaa66xv54wYYJx77332l+//fbbRkBAgHHkyJFqx0+dOtVo3ry5UVxcbG97+umnjeuuu84wDMPYsmWLIcnYt29fteMjIyON999/36HtL3/5ixEfH1/7JAG4DLelADS6d999V82bN1dOTo4OHDggSRozZoz8/f3tW5UFCxbovvvus7++77779OGHH+r48eOSpG3btql79+4KCgqq8XxRUVEKCAiwvw4PD1dhYaEkqVu3bkpISFBsbKzuuusuzZ8/X8eOHZMkHTp0SLm5uXr44YcdanvppZf0448/1tvfA0D9ItwAaFQZGRl6/fXXtWrVKsXHx+vhhx+WYRh68cUXtW3bNvsmSd9++602bdqkZ555Rl5eXvLy8lKvXr108uRJLVu2TJLk5+d33nN6e3s7vLZYLPbbVp6enlq3bp3Wrl2rq6++Wm+88YY6duyonJwce5/58+c71PbNN99o48aN9fhXAVCfCDcAGs3Jkyc1cuRIjR49WgMGDNA777yjzZs36+2331ZISIg6dOhg36QzV2369++v7du3O4SLZ555RgsWLJAkde3aVdu2bdPRo0cvuC6LxaI+ffrohRdeUFZWlpo1a6YVK1YoNDRUrVu31t69ex1q69Chg6Kjo+vlbwKg/vFAMYBG8+yzz6qyslKvvvqqJKlt27Z67bXXNHHiRN1yyy2Kioqy9y0rK9OSJUv04osvqkuXLg7HeeSRRzR9+nRt375dd999t1555RUNGzZMycnJCg8PV1ZWliIiIhQfH3/emjZt2qTPP/9ciYmJCgkJ0aZNm3To0CF17txZ0pkvARw/frwCAwM1aNAg2Ww2ZWZm6tixY5o4cWL9/XEA1Buu3ABoFOnp6Xrrrbe0cOFCtWjRwt4+atQo9e7d2357qsrq1at15MgR3X777ecc68orr1RsbKwWLFigZs2aKTU1VSEhIRo8eLBiY2M1bdo0eXp61qmuwMBAbdiwQYMHD9ZVV12l5557Tq+99poGDRok6UyQeuedd7Rw4ULFxsbq+uuv18KFC7lyAzRhFsM46wsjAAAA3BhXbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKn8fz0esMgPVg3/AAAAAElFTkSuQmCC\n", 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> shape=plaintext]\\\\n}\\\\n\"}'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from graphviz import Digraph\n", - "import graphviz\n", - "#from IPython.display import display\n", - "import math\n", - "import json\n", - "import sys\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster, rangeStart, rangeEnd):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(rangeStart, rangeEnd, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "def is_element_in_array(cluster_array, single_point):\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " return True\n", - " return False\n", - "\n", - "def is_element_in_array_location(cluster_array, single_point):\n", - " i = 0\n", - " j = 0\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " #print(\"LOCATION\")\n", - " #print(element[0])\n", - " #print(element[1])\n", - " return [i, j]\n", - " j += 1\n", - " i += 1\n", - " return False\n", - "\n", - "def check_array_exists(arrayName):\n", - " if 'clusters' in dir():\n", - " return True\n", - " else:\n", - " return False\n", - "\n", - "#todo\n", - "def manhattan_distance(point1, point2):\n", - " return np.sum(np.abs(point1 + point2))\n", - "\n", - "#ready\n", - "def euclidean_distance(array1, array2):\n", - " return round(math.sqrt((pow((array1[0] - array2[0]), 2)) + (pow((array1[1] - array2[1]), 2))),2)\n", - "\n", - "#todo\n", - "def maximum_distance(point1, point2):\n", - " return np.max([[point1],[point2]])\n", - "\n", - "def calculate_inital_abstraction_matrix(data, distance_function):\n", - " #print(data)\n", - " #print(distance_function)\n", - " # Initialize clusters with each data point as a separate cluster\n", - " array = np.zeros((data.shape[0],data.shape[0]))\n", - " \n", - " for iteration_number_row in range(len(data)):\n", - " \n", - " for iteration_number_column in range(len(data)):\n", - " array[iteration_number_row, iteration_number_column] = distance_function(data[iteration_number_row],data[iteration_number_column])\n", - "\n", - " return array\n", - "\n", - "def single_linkage_get_indices(inital_abstraction_matrix):\n", - "\n", - " # Exclude diagonal elements and zeros\n", - " lower_triangular = np.tril(inital_abstraction_matrix, k=-1)\n", - " lower_triangular[lower_triangular == 0] = np.inf\n", - " \n", - " min_index = np.unravel_index(np.argmin(lower_triangular), lower_triangular.shape)\n", - " \n", - " # Adjust indices to consider the original matrix\n", - " #adjusted_indices = (min_index[0] + 1, min_index[1])\n", - "\n", - " #print(min_index)\n", - " return min_index\n", - "\n", - "#get nearest of 2 points to get the new center of the cluster\n", - "def get_nearest_point(data, point_a, point_b, distance_function):\n", - " #get the point that is more near the other points\n", - " # Calculate distances for both points\n", - " distances_to_a = [distance_function(point_a, point) for point in data]\n", - " distances_to_b = [distance_function(point_b, point) for point in data]\n", - "\n", - " # Find the index of the nearest point for each given point for both points\n", - " nearest_point_index_a = np.argmin(distances_to_a)\n", - " nearest_point_index_b = np.argmin(distances_to_b)\n", - " \n", - " sum_distance_to_a = 0\n", - " for distance in distances_to_a:\n", - " sum_distance_to_a += float(distance)\n", - " \n", - " sum_distance_to_b = 0\n", - " for distance in distances_to_b:\n", - " sum_distance_to_b += float(distance)\n", - "\n", - " \n", - " # Compare the distances\n", - " #[[nearest_point][point_to_delete]]\n", - " if sum_distance_to_a < sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " elif sum_distance_to_a > sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_b] , data[nearest_point_index_a]]\n", - " else: #equal\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " \n", - " return nearest_point\n", - "\n", - "def createScatterDiagram(data):\n", - " # Streudiagramm zeichnen\n", - " # Extract x and y coordinates\n", - " x = data[:, 0]\n", - " y = data[:, 1]\n", - "\n", - " # Generate random colors\n", - " colors = np.random.rand(len(x), 3)\n", - "\n", - " # Create scatter plot with random colors\n", - " plt.scatter(x, y, marker='o', color=colors)\n", - "\n", - " # Add labels and title\n", - " plt.xlabel('X-Achse')\n", - " plt.ylabel('Y-Achse')\n", - " plt.title('Streudiagram')\n", - "\n", - " # Display the plot\n", - " plt.show()\n", - "\n", - "def createDigraph(distanceMatrix, points, iterationNumber):\n", - " #transform matrix\n", - " \n", - " points = points.astype(str)\n", - " distanceMatrix = distanceMatrix.astype(str)\n", - " pointsY = np.array([[' - '.join(row)] for row in points]) \n", - " pointsX = np.array([[' - '.join(row)] for row in points]) \n", - " #pointsX = np.insert(pointsX, 0, 'Iteration {}'.format(iterationNumber), axis=0)\n", - " pointsX = np.insert(pointsX, 0, str(iterationNumber), axis=0)\n", - "\n", - " # Concatenate arr1 and arr2 vertically\n", - " distanceMatrix = np.concatenate((pointsY, distanceMatrix), axis=1)\n", - " \n", - " Array2d = np.array([np.concatenate(pointsX.T)])\n", - " distanceMatrixNew = np.append(Array2d, distanceMatrix, axis=0)\n", - " \n", - " # Create a Digraph object\n", - " dot = Digraph(comment='Distance Matrix')\n", - "\n", - " # Create a single table for the entire matrix\n", - " table_label = f'<'\n", - " for i in range(distanceMatrixNew.shape[0]):\n", - " table_label += ''\n", - " for j in range(distanceMatrixNew.shape[1]):\n", - " table_label += f''\n", - " table_label += ''\n", - " table_label += '
{distanceMatrixNew[i, j]}
>'\n", - "\n", - " # Add a single node to the graph with the entire table as label\n", - " dot.node('table', label=table_label, shape='plaintext')\n", - " \n", - " # Save the graph as a PNG file\n", - " #dot.render('directed_graph', format='png', cleanup=True)\n", - " \n", - " display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "# Helper function to recursively add nodes and edges to the graph\n", - "def add_nodes_edges(dot, node_id, Z, dendrogram_data):\n", - " if node_id < len(Z):\n", - " # Internal node\n", - " child1, child2 = Z[node_id, 0], Z[node_id, 1]\n", - " dot.node(f'node_{node_id}', label=str(Z[node_id, 2]))\n", - " add_nodes_edges(dot, int(child1), Z, dendrogram_data)\n", - " add_nodes_edges(dot, int(child2), Z, dendrogram_data)\n", - " dot.edge(f'node_{node_id}', f'node_{int(child1)}')\n", - " dot.edge(f'node_{node_id}', f'node_{int(child2)}')\n", - " else:\n", - " # Leaf node\n", - " leaf_index = node_id - len(Z)\n", - " dot.node(f'leaf_{leaf_index}', label=str(dendrogram_data['ivl'][leaf_index]))\n", - "\n", - "\n", - "def createDendogramDotLanguage(data, distanceMethod):\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " linkage_data = linkage(data, method='single', metric='cityblock')\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " linkage_data = linkage(data, method='single', metric='euclidean')\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " linkage_data = linkage(data, method='single', metric='maximum')\n", - "\n", - " else:\n", - " return ''\n", - " \n", - " #dendrogram(linkage_data)\n", - " \n", - " #print(linkage_data)\n", - "\n", - " #plt.show()\n", - " \n", - " # Create a dendrogram\n", - " dendrogram_data = dendrogram(linkage_data, no_plot=True)\n", - "\n", - " # Create a Graphviz graph\n", - " dot = graphviz.Digraph(comment='Dendrogram')\n", - "\n", - " # Add nodes and edges to the graph\n", - " add_nodes_edges(dot, len(linkage_data) * 2 - 2, linkage_data, dendrogram_data)\n", - " display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "\n", - "def clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod):\n", - " #data = generate_clusters(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd)\n", - " data = np.array([[2, 3],\n", - " [5, 2],\n", - " [5, 3],\n", - " [1, 4],\n", - " [4, 5]])\n", - " \n", - " \n", - " createScatterDiagram(data)\n", - " jsonDict = {}\n", - " \n", - " jsonDict[\"Dendogram\"] = createDendogramDotLanguage(data, distanceMethod)\n", - " \n", - " \n", - " for iteration in range(data.shape[0]):\n", - " #erste iteration\n", - " if(iteration == 0):\n", - " \n", - " clusters = []\n", - " \n", - " #inital abstraction matrix\n", - " print(\"---Inital Iteration---\")\n", - " #get distance method\n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(data, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " #print(single_linkage_indices)\n", - " \n", - " #get two points from given index that are the first cluster\n", - " point_a = data[single_linkage_indices[0]]\n", - " point_b = data[single_linkage_indices[1]]\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - " \n", - " #create Digraph\n", - " #TODO -> oberste Zeile noch hinzufügen\n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = createDigraph(matrix, data, iteration)\n", - " \n", - " \n", - " #define cluster elements\n", - " #clusters = np.array([[point_a, point_b]])\n", - " clusters.append([point_a, point_b])\n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(new_data)\n", - "\n", - " #alle weiteren iterationen \n", - " elif(iteration > 0):\n", - " \n", - " if len(new_data) == 1:\n", - " continue\n", - " \n", - " print(\"\")\n", - " print(\"\")\n", - " print(\"---Next Iteration---\")\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " #matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " #get the two points with the new minimum distance\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " \n", - " #get two points from given index that are the new cluster\n", - " point_a = new_data[single_linkage_indices[0]]\n", - " point_b = new_data[single_linkage_indices[1]]\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " #point_to_remove = nearest_point[1]\n", - " \n", - " #add point to current cluster\n", - " if is_element_in_array(clusters, point_a) or is_element_in_array(clusters, point_b):\n", - " #print(\"Erweitere Cluster\")\n", - " \n", - " #get element number of the cluster and append point to the current cluster\n", - " if is_element_in_array(clusters, point_a):\n", - " arrayLocation = is_element_in_array_location(clusters, point_a)\n", - " pointToAppend = point_b\n", - " elif is_element_in_array(clusters, point_b):\n", - " arrayLocation = is_element_in_array_location(clusters, point_b)\n", - " pointToAppend = point_a\n", - " \n", - " #print(arrayLocation)\n", - " \n", - " #append pointToAppend to cluster:\n", - " #clusters = np.insert(clusters, arrayLocation[0], pointToAppend, axis=arrayLocation[1])\n", - " #print(clusters[arrayLocation[0]])\n", - " #clusters[arrayLocation[0], arrayLocation[1]]\n", - " \n", - " #newClusters1 = clusters[arrayLocation[0]]\n", - " #print(clusters[arrayLocation[0]])\n", - " pointsToAppend = np.vstack([clusters[arrayLocation[0]], pointToAppend])\n", - " \n", - " newClusters = []\n", - " \n", - " newClusters.append(pointsToAppend)\n", - "\n", - " #newClusters = np.append(clusters[arrayLocation[0]], [pointToAppend], axis=0)\n", - " #newClusters = np.array([[point_a, point_b]])\n", - " clusterCounter = 0\n", - " \n", - " for cluster in clusters:\n", - " if clusterCounter == arrayLocation[0]:\n", - " clusterCounter += 1\n", - " \n", - " else:\n", - " #newClusters = np.concatenate([newClusters, [cluster]]) #dimensions are not valid! -> was anderes einfallen lassen!! #letztes puzzelstück -> wie den bums richtig zusammenfügen???!!!\n", - " newClusters.append(cluster)\n", - " clusterCounter += 1\n", - " \n", - " \n", - " #print(\"UPDATED CLUSTER:\")\n", - " #print(newClusters)\n", - " print(\"-------\")\n", - " \n", - " clusters = newClusters\n", - "\n", - " #create new cluster\n", - " else:\n", - " #print(\"Definiere neues Cluster\")\n", - " #define cluster elements if ist an new cluster\n", - " #clusters = np.append(clusters,[[point_a, point_b]], axis=0)\n", - " clusters.append([point_a, point_b])\n", - " #print(clusters)\n", - " \n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " createDigraph(matrix, new_data, iteration)\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = createDigraph(matrix, new_data, iteration)\n", - "\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - "\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in new_data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(new_data)\n", - " return json.dumps(jsonDict)\n", - " \n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "#numClusters = int(sys.argv[1])\n", - "#pointsPerCluster = int(sys.argv[2])\n", - "#nodeRangeStart = int(sys.argv[3])\n", - "#nodeRangeEnd = int(sys.argv[4])\n", - "#distanceMethod = int(sys.argv[5])\n", - "#linkageMethod = int(sys.argv[6])\n", - "\n", - "numClusters = 2\n", - "pointsPerCluster = 3\n", - "nodeRangeStart = 0\n", - "nodeRangeEnd = 25\n", - "distanceMethod = 'euclidean'\n", - "linkageMethod = 'single'\n", - "#Hauptfunktion ausfuehren\n", - "clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod)\n", - "\n", - "\n", - "#TODO\n", - "#Eingabevalidierung!!!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7942a038", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3bb05ef1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---Inital Iteration---\n", - "DIGRAPH - Abstandsmatrix\n", - "\n", - "\n", - "---Next Iteration---\n", - "DIGRAPH - Abstandsmatrix\n", - "\n", - "\n", - "---Next Iteration---\n", - "-------\n", - "DIGRAPH - Abstandsmatrix\n", - "\n", - "\n", - "---Next Iteration---\n", - "-------\n", - "DIGRAPH - Abstandsmatrix\n", - "{'Dendogram': '// Dendrogram\\ndigraph {\\n\\tleaf_2 [label=4]\\n}\\n', 'DigraphIteration0': [[0.0, 3.16, 3.0, 1.41, 2.83], [3.16, 0.0, 1.0, 4.47, 3.16], [3.0, 1.0, 0.0, 4.12, 2.24], [1.41, 4.47, 4.12, 0.0, 3.16], [2.83, 3.16, 2.24, 3.16, 0.0]], 'DigraphIteration1': [[0.0, 3.0, 1.41, 2.83], [3.0, 0.0, 4.12, 2.24], [1.41, 4.12, 0.0, 3.16], [2.83, 2.24, 3.16, 0.0]], 'DigraphIteration2': [[0.0, 3.0, 2.83], [3.0, 0.0, 2.24], [2.83, 2.24, 0.0]], 'DigraphIteration3': [[0.0, 3.0], [3.0, 0.0]]}\n" - ] - }, - { - "data": { - "text/plain": [ - "'{\"Dendogram\": \"// Dendrogram\\\\ndigraph {\\\\n\\\\tleaf_2 [label=4]\\\\n}\\\\n\", \"DigraphIteration0\": [[0.0, 3.16, 3.0, 1.41, 2.83], [3.16, 0.0, 1.0, 4.47, 3.16], [3.0, 1.0, 0.0, 4.12, 2.24], [1.41, 4.47, 4.12, 0.0, 3.16], [2.83, 3.16, 2.24, 3.16, 0.0]], \"DigraphIteration1\": [[0.0, 3.0, 1.41, 2.83], [3.0, 0.0, 4.12, 2.24], [1.41, 4.12, 0.0, 3.16], [2.83, 2.24, 3.16, 0.0]], \"DigraphIteration2\": [[0.0, 3.0, 2.83], [3.0, 0.0, 2.24], [2.83, 2.24, 0.0]], \"DigraphIteration3\": [[0.0, 3.0], [3.0, 0.0]], \"iterations\": 4}'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from graphviz import Digraph\n", - "import graphviz\n", - "#from IPython.display import display\n", - "import math\n", - "import json\n", - "import sys\n", - "import os\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster, rangeStart, rangeEnd):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(rangeStart, rangeEnd, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "def is_element_in_array(cluster_array, single_point):\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " return True\n", - " return False\n", - "\n", - "def is_element_in_array_location(cluster_array, single_point):\n", - " i = 0\n", - " j = 0\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " #print(\"LOCATION\")\n", - " #print(element[0])\n", - " #print(element[1])\n", - " return [i, j]\n", - " j += 1\n", - " i += 1\n", - " return False\n", - "\n", - "def check_array_exists(arrayName):\n", - " if 'clusters' in dir():\n", - " return True\n", - " else:\n", - " return False\n", - "\n", - "#todo\n", - "def manhattan_distance(point1, point2):\n", - " return np.sum(np.abs(point1 + point2))\n", - "\n", - "#ready\n", - "def euclidean_distance(array1, array2):\n", - " return round(math.sqrt((pow((array1[0] - array2[0]), 2)) + (pow((array1[1] - array2[1]), 2))),2)\n", - "\n", - "#todo\n", - "def maximum_distance(point1, point2):\n", - " return np.max([[point1],[point2]])\n", - "\n", - "def calculate_inital_abstraction_matrix(data, distance_function):\n", - " \n", - " # Initialize clusters with each data point as a separate cluster\n", - " array = np.zeros((data.shape[0],data.shape[0]))\n", - " \n", - " for iteration_number_row in range(len(data)):\n", - " \n", - " for iteration_number_column in range(len(data)):\n", - " array[iteration_number_row, iteration_number_column] = distance_function(data[iteration_number_row],data[iteration_number_column])\n", - "\n", - " return array\n", - "\n", - "def single_linkage_get_indices(inital_abstraction_matrix):\n", - "\n", - " # Exclude diagonal elements and zeros\n", - " lower_triangular = np.tril(inital_abstraction_matrix, k=-1)\n", - " lower_triangular[lower_triangular == 0] = np.inf\n", - " \n", - " min_index = np.unravel_index(np.argmin(lower_triangular), lower_triangular.shape)\n", - " \n", - " # Adjust indices to consider the original matrix\n", - " #adjusted_indices = (min_index[0] + 1, min_index[1])\n", - "\n", - " #print(min_index)\n", - " return min_index\n", - "\n", - "#get nearest of 2 points to get the new center of the cluster\n", - "def get_nearest_point(data, point_a, point_b, distance_function):\n", - " #get the point that is more near the other points\n", - " # Calculate distances for both points\n", - " distances_to_a = [distance_function(point_a, point) for point in data]\n", - " distances_to_b = [distance_function(point_b, point) for point in data]\n", - "\n", - " # Find the index of the nearest point for each given point for both points\n", - " nearest_point_index_a = np.argmin(distances_to_a)\n", - " nearest_point_index_b = np.argmin(distances_to_b)\n", - " \n", - " sum_distance_to_a = 0\n", - " for distance in distances_to_a:\n", - " sum_distance_to_a += float(distance)\n", - " \n", - " sum_distance_to_b = 0\n", - " for distance in distances_to_b:\n", - " sum_distance_to_b += float(distance)\n", - "\n", - " \n", - " # Compare the distances\n", - " #[[nearest_point][point_to_delete]]\n", - " if sum_distance_to_a < sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " elif sum_distance_to_a > sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_b] , data[nearest_point_index_a]]\n", - " else: #equal\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " \n", - " return nearest_point\n", - "\n", - "def createScatterDiagram(data):\n", - " # Streudiagramm zeichnen\n", - " # Extract x and y coordinates\n", - " x = data[:, 0]\n", - " y = data[:, 1]\n", - "\n", - " # Generate random colors\n", - " colors = np.random.rand(len(x), 3)\n", - "\n", - " # Create scatter plot with random colors\n", - " plt.scatter(x, y, marker='o', color=colors)\n", - "\n", - " # Add labels and title\n", - " plt.xlabel('X-Achse')\n", - " plt.ylabel('Y-Achse')\n", - " plt.title('Streudiagram')\n", - "\n", - " # #display the plot\n", - " plt.show()\n", - "\n", - "def createDigraph(distanceMatrix, points, iterationNumber):\n", - " #transform matrix\n", - " \n", - " #print(distanceMatrix)\n", - "\n", - " points = points.astype(str)\n", - " distanceMatrix = distanceMatrix.astype(str)\n", - " pointsY = np.array([[' - '.join(row)] for row in points]) \n", - " pointsX = np.array([[' - '.join(row)] for row in points]) \n", - " #pointsX = np.insert(pointsX, 0, 'Iteration {}'.format(iterationNumber), axis=0)\n", - " pointsX = np.insert(pointsX, 0, str(iterationNumber), axis=0)\n", - "\n", - " # Concatenate arr1 and arr2 vertically\n", - " distanceMatrix = np.concatenate((pointsY, distanceMatrix), axis=1)\n", - " \n", - " Array2d = np.array([np.concatenate(pointsX.T)])\n", - " distanceMatrixNew = np.append(Array2d, distanceMatrix, axis=0)\n", - " \n", - " # Create a Digraph object\n", - " dot = Digraph(comment='Distance Matrix')\n", - "\n", - " # Create a single table for the entire matrix\n", - " table_label = f'<'\n", - " for i in range(distanceMatrixNew.shape[0]):\n", - " table_label += ''\n", - " for j in range(distanceMatrixNew.shape[1]):\n", - " table_label += f''\n", - " table_label += ''\n", - " table_label += '
{distanceMatrixNew[i, j]}
>'\n", - "\n", - " # Add a single node to the graph with the entire table as label\n", - " dot.node('table', label=table_label, shape='plaintext')\n", - " \n", - " # Save the graph as a PNG file\n", - " #dot.render('directed_graph', format='png', cleanup=True)\n", - " \n", - " #display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "\n", - "\n", - "# Helper function to recursively add nodes and edges to the graph\n", - "def add_nodes_edges(dot, node_id, Z, dendrogram_data):\n", - " if node_id < len(Z):\n", - " # Internal node\n", - " child1, child2 = Z[node_id, 0], Z[node_id, 1]\n", - " dot.node(f'node_{node_id}', label=str(Z[node_id, 2]))\n", - " add_nodes_edges(dot, int(child1), Z, dendrogram_data)\n", - " add_nodes_edges(dot, int(child2), Z, dendrogram_data)\n", - " dot.edge(f'node_{node_id}', f'node_{int(child1)}')\n", - " dot.edge(f'node_{node_id}', f'node_{int(child2)}')\n", - " else:\n", - " # Leaf node\n", - " leaf_index = node_id - len(Z)\n", - " dot.node(f'leaf_{leaf_index}', label=str(dendrogram_data['ivl'][leaf_index]))\n", - "\n", - "\n", - "def createDendogramDotLanguage(data, distanceMethod):\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " linkage_data = linkage(data, method='single', metric='cityblock')\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " linkage_data = linkage(data, method='single', metric='euclidean')\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " linkage_data = linkage(data, method='single', metric='maximum')\n", - "\n", - " else:\n", - " return ''\n", - " \n", - " #dendrogram(linkage_data)\n", - " \n", - " #print(linkage_data)\n", - "\n", - " #plt.show()\n", - " \n", - " # Create a dendrogram\n", - " dendrogram_data = dendrogram(linkage_data, no_plot=True)\n", - "\n", - " # Create a Graphviz graph\n", - " dot = graphviz.Digraph(comment='Dendrogram')\n", - "\n", - " # Add nodes and edges to the graph\n", - " add_nodes_edges(dot, len(linkage_data) * 2 - 2, linkage_data, dendrogram_data)\n", - " #display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "\n", - "def clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod):\n", - " #data = generate_clusters(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd)\n", - " data = np.array([[2, 3],\n", - " [5, 2],\n", - " [5, 3],\n", - " [1, 4],\n", - " [4, 5]])\n", - " \n", - " \n", - " #createScatterDiagram(data)\n", - " jsonDict = {}\n", - " \n", - " jsonDict[\"Dendogram\"] = createDendogramDotLanguage(data, distanceMethod)\n", - " \n", - " \n", - " for iteration in range(data.shape[0]):\n", - " #erste iteration\n", - " if(iteration == 0):\n", - " \n", - " clusters = []\n", - " \n", - " #inital abstraction matrix\n", - " print(\"---Inital Iteration---\")\n", - " #get distance method\n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(data, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " #print(single_linkage_indices)\n", - " \n", - " #get two points from given index that are the first cluster\n", - " point_a = data[single_linkage_indices[0]]\n", - " point_b = data[single_linkage_indices[1]]\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - " \n", - " #create Digraph\n", - " #TODO -> oberste Zeile noch hinzufügen\n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = matrix.tolist() #createDigraph(matrix, data, iteration)\n", - " \n", - " \n", - " #define cluster elements\n", - " #clusters = np.array([[point_a, point_b]])\n", - " clusters.append([point_a, point_b])\n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(new_data)\n", - "\n", - " #alle weiteren iterationen \n", - " elif(iteration > 0):\n", - " \n", - " if len(new_data) == 1:\n", - " continue\n", - " \n", - " print(\"\")\n", - " print(\"\")\n", - " print(\"---Next Iteration---\")\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " #matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " #get the two points with the new minimum distance\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " \n", - " #get two points from given index that are the new cluster\n", - " point_a = new_data[single_linkage_indices[0]]\n", - " point_b = new_data[single_linkage_indices[1]]\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " #point_to_remove = nearest_point[1]\n", - " \n", - " #add point to current cluster\n", - " if is_element_in_array(clusters, point_a) or is_element_in_array(clusters, point_b):\n", - " #print(\"Erweitere Cluster\")\n", - " \n", - " #get element number of the cluster and append point to the current cluster\n", - " if is_element_in_array(clusters, point_a):\n", - " arrayLocation = is_element_in_array_location(clusters, point_a)\n", - " pointToAppend = point_b\n", - " elif is_element_in_array(clusters, point_b):\n", - " arrayLocation = is_element_in_array_location(clusters, point_b)\n", - " pointToAppend = point_a\n", - " \n", - " #print(arrayLocation)\n", - " \n", - " #append pointToAppend to cluster:\n", - " #clusters = np.insert(clusters, arrayLocation[0], pointToAppend, axis=arrayLocation[1])\n", - " #print(clusters[arrayLocation[0]])\n", - " #clusters[arrayLocation[0], arrayLocation[1]]\n", - " \n", - " #newClusters1 = clusters[arrayLocation[0]]\n", - " #print(clusters[arrayLocation[0]])\n", - " pointsToAppend = np.vstack([clusters[arrayLocation[0]], pointToAppend])\n", - " \n", - " newClusters = []\n", - " \n", - " newClusters.append(pointsToAppend)\n", - "\n", - " #newClusters = np.append(clusters[arrayLocation[0]], [pointToAppend], axis=0)\n", - " #newClusters = np.array([[point_a, point_b]])\n", - " clusterCounter = 0\n", - " \n", - " for cluster in clusters:\n", - " if clusterCounter == arrayLocation[0]:\n", - " clusterCounter += 1\n", - " \n", - " else:\n", - " #newClusters = np.concatenate([newClusters, [cluster]]) #dimensions are not valid! -> was anderes einfallen lassen!! #letztes puzzelstück -> wie den bums richtig zusammenfügen???!!!\n", - " newClusters.append(cluster)\n", - " clusterCounter += 1\n", - " \n", - " \n", - " #print(\"UPDATED CLUSTER:\")\n", - " #print(newClusters)\n", - " print(\"-------\")\n", - " \n", - " clusters = newClusters\n", - "\n", - " #create new cluster\n", - " else:\n", - " #print(\"Definiere neues Cluster\")\n", - " #define cluster elements if ist an new cluster\n", - " #clusters = np.append(clusters,[[point_a, point_b]], axis=0)\n", - " clusters.append([point_a, point_b])\n", - " #print(clusters)\n", - " \n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " #createDigraph(matrix, new_data, iteration)\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = matrix.tolist() #createDigraph(matrix, new_data, iteration)\n", - "\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - "\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in new_data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(jsonDict)\n", - "\n", - " print(jsonDict)\n", - " jsonDict[\"iterations\"] = iteration\n", - " with open(\"data.json\", \"w+\", encoding=\"utf8\") as f:\n", - " json.dump(jsonDict, f)\n", - "\n", - " return json.dumps(jsonDict)\n", - " \n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "#numClusters = int(sys.argv[1])\n", - "#pointsPerCluster = int(sys.argv[2])\n", - "#nodeRangeStart = int(sys.argv[3])\n", - "#nodeRangeEnd = int(sys.argv[4])\n", - "#distanceMethod = int(sys.argv[5])\n", - "#linkageMethod = int(sys.argv[6])\n", - "\n", - "numClusters = 2\n", - "pointsPerCluster = 3\n", - "nodeRangeStart = 0\n", - "nodeRangeEnd = 30\n", - "distanceMethod = 'euclidean'\n", - "linkageMethod = 'single'\n", - "#Hauptfunktion ausfuehren\n", - "clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod)\n", - "\n", - "\n", - "#TODO\n", - "#Eingabevalidierung!!!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b619364a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": 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"#15202b99" + }, + "peacock.color": "#61dafb" +} \ No newline at end of file diff --git a/Antwort.json b/Antwort.json deleted file mode 100644 index 8095f08..0000000 --- a/Antwort.json +++ /dev/null @@ -1 +0,0 @@ -{"Ueberreaktion":"nein","SmilesReaction":"[NH3+]c1ccccc1>ClCl>","Position":"meta","Reagenz":"ClCl","Smiles":"[NH3+]c1cc(Cl)ccc1","Parameter":"-I","Erstsubsti":"[NH3+]","Benzol":"c1cccc1","ReagenzText":"ReagenzAlpha"}eagenzAlpha"} \ No newline at end of file diff --git a/Clusteranalyse.ipynb b/Clusteranalyse.ipynb deleted file mode 100644 index 4625093..0000000 --- a/Clusteranalyse.ipynb +++ /dev/null @@ -1,3304 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 46, - "id": "a8557a1f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'x': [15, 28, 30, 19, 18, 22, 10, 27, 22, 23], 'y': [17, 26, 17, 13, 18, 10, 11, 20, 6, 30]}\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "\n", - "#functions\n", - "def generate_random_number_array(n, rangeStart, rangeEnd):\n", - " array = [random.randint(rangeStart, rangeEnd) for _ in range(n)]\n", - " return array\n", - "\n", - "def generate_x_y_array(n, rangeStart, rangeEnd):\n", - " XYDict = {\n", - " \"x\": generate_random_number_array(n, rangeStart, rangeEnd),\n", - " \"y\": generate_random_number_array(n, rangeStart, rangeEnd)\n", - " }\n", - " return XYDict\n", - "\n", - " \n", - "# Beispielaufruf der Funktion mit n = 10 ; Range von 5 bis 30\n", - "#INPUT\n", - "numberOfDataPoints = 10\n", - "rangeStart = 5\n", - "rangeEnd = 30\n", - "\n", - "#ToDo\n", - "#INPUT: set number of clusters\n", - "#schrittweises lösen der aufgabe \n", - "#mockup carpet\n", - "#scikpy -> beobachter möglich?\n", - "\n", - "#dendogramm -> visualisierung über carpet edotor (online) -> visualisierungsalgos\n", - "#algo: numpy -> verteilung der einzelnen punkte -> häfung der punkte in gegebenen clusters -> verteilung \"schön\" machen (Noise-Verfahren)\n", - "\n", - "dictArray = generate_x_y_array(numberOfDataPoints, rangeStart, rangeEnd)\n", - "print(dictArray)\n", - "\n", - "plt.scatter(dictArray[\"x\"], dictArray[\"y\"])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "866cde95", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.cluster.hierarchy import dendrogram, linkage\n", - "\n", - "data = list(zip(dictArray[\"x\"], dictArray[\"y\"]))\n", - "\n", - "linkage_data = linkage(data, method='ward', metric='euclidean')\n", - "dendrogram(linkage_data)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "39bab90c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.cluster import AgglomerativeClustering\n", - "\n", - "\n", - "data = list(zip(dictArray[\"x\"], dictArray[\"y\"]))\n", - "\n", - "#INPUT\n", - "#Anzahl Cluster\n", - "numberOfCluster = 3;\n", - "\n", - "hierarchical_cluster = AgglomerativeClustering(n_clusters=numberOfCluster, affinity='euclidean', linkage='ward')\n", - "labels = hierarchical_cluster.fit_predict(data)\n", - "\n", - "plt.scatter(dictArray[\"x\"], dictArray[\"y\"], c=labels)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "66b49468", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1. 8. 3.16227766 2. ]\n", - " [ 5. 6. 4.47213595 2. ]\n", - " [ 3. 7. 5. 2. ]\n", - " [ 2. 4. 6.08276253 2. ]\n", - " [ 0. 9. 8.54400375 2. ]\n", - " [10. 13. 8.74642784 4. ]\n", - " [11. 14. 16.98528775 4. ]\n", - " [12. 15. 18.2254401 6. ]\n", - " [16. 17. 29.16904752 10. ]]\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'X' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/bs/5wndg3791vz7vxsh2h7qj_mw0000gn/T/ipykernel_41173/2459418396.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinkage_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinkage_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined" - ] - } - ], - "source": [ - "#linkage_matrix = linkage(X, \"single\")\n", - "linkage_matrix = linkage_data\n", - "\n", - "print(linkage_matrix)\n", - "\n", - "dct = dict([(i, {i}) for i in range(X.shape[0])])\n", - "print(list(dct.values()))\n", - "for i, row in enumerate(linkage_matrix, X.shape[0]):\n", - " dct[i] = dct[row[0]].union(dct[row[1]])\n", - " del dct[row[0]]\n", - " del dct[row[1]]\n", - " print(list(dct.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "561dff9e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [4], [5], [7], [8], [9], [3, 6]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[1], [4], [5], [7], [8], [9], [3, 6], [0, 2]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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sM2DAgBa3j4iIwN///nfs2bMHS5cubVMfbG1tUVdXp1d+9epVnfNqZWWFRYsWYdGiRaisrMS///1vvPLKK4iIiMClS5fueONUczvp6enaEc07MXTuv/nmG5w6dQrvvPOOzh3ShYWFd22vJXv27EFNTQ3UarVOv/Ly8lq1vZOTE2bMmIE33ngDixcvxttvv42pU6fC2dlZp95zzz2H5557DjU1NcjOzsaqVavw+OOP49tvv4W3tzd69uyJoKAgrF+/3uB++vTp02If/vSnP+Hxxx/Xvm8O3cawtrbGqlWr8L//+7/45ptvADT9rgHA5cuXoVQqjW6zJYbOrTl98803KC4uRu/evfU+a/45un79ut45I+ooDKRERnj88cfxl7/8BT169ICPj0+b2pAkCVZWVjqXsH/55Rf84x//aHUbkyZNwj/+8Q+cO3fujkHNFBQKBcaOHQtnZ2dkZmbi5MmTCAkJwaRJk7By5UpkZWXhkUceadc+BgwYAD8/P5w6dQp/+ctfjN5+ypQpGDJkCJKSkvD4448bvCSZmZmpvfvZkH79+uHrr7/WKfv2229x7tw5vX9oNHN2dkZMTAxKS0sRHx+P4uJiDBo0qMVR6IiICFhZWeH8+fNtvmTbHGRuD1zbt29vU3sttSmEwI4dO1rdxoIFC5CamoqYmBhUVlZi3rx5Lda1t7fHpEmTUF9fjyeffBJnzpyBt7c3Hn/8cWRkZKB///4Gp7rcSZ8+fe4YWG9XVlZmcJSyecpDc1sTJkyApaUl0tLSEBISYlSfTOHWn6Xbl3trafTVkC1btuiti5uXl4eFCxdi9erVGDt2LBwcHEzSZ6K2YCAlMkJ8fDx2796Nhx9+GAsXLkRQUBAaGxtRUlKCgwcPIiEhASNHjrxjG4899hg2b96MqVOn4k9/+hN+/PFHbNy40agRncTERHzyySd4+OGH8corr2DIkCGorKzEgQMHsGjRIp073Nti5cqVuHz5Mh599FF4eXmhsrISKSkpsLa2xtixY7Xfxa5duzBlyhQsXboUI0aMwC+//ILPPvsMjz/+OMLCwoza5/bt2zFp0iRERERg5syZ8PT0xLVr11BQUIATJ07go48+anFbS0tLfPzxx5gwYQJCQkLw0ksvISwsDPb29rh48SLS09Oxd+9eXL9+vcU2pk+fjmnTpmHu3LmIjo7GxYsXkZycrB0haxYZGYnBgwcjODgYbm5uuHjxIrZs2QJvb2/4+fkBAIYMGQIASElJwYwZM2BtbY0BAwagX79+SExMxPLly3HhwgVMnDgRLi4uuHLlCo4fPw57e/sWF0FvNnDgQPTv3x9Lly6FEAKurq7Yu3evzqVvY4WHh8PGxgZ/+MMfsGTJEtTW1iItLe2O39ft/P39MXHiRHzyyScYM2YMhg4dqvP57NmzYWdnh9GjR8PDwwPl5eVISkqCk5OTds5yYmIiDh06hFGjRmHBggUYMGAAamtrUVxcjIyMDLz++uutniZyNxEREfDy8kJkZCQGDhyIxsZG5OXlYdOmTXBwcEBcXByApn+ovPLKK1i7di1++eUX7fJZ+fn5uHr16l3PV3s1/yxt2LABkyZNgqWlJYKCgmBjY4MhQ4bgyJEj2Lt3Lzw8PNC9e/cW/4H6wAMPtLiPwMDAVi0ZRWRW8t5TRdRxmu+svv0O+WaPPfbYXe+yF0KIn376SaxYsUIMGDBA2NjYCCcnJzFkyBCxcOFCUV5erq0HQMTGxhrc11tvvSUGDBggFAqFUKlUIikpSbz55psG7/x+7LHHDLZx6dIl8fzzz4vevXsLa2tr0adPH/H000+LK1euCCFavoO2+c73t99+W1t2+53T+/btE5MmTRKenp7CxsZGuLu7i8mTJ4ucnBydtq5fvy7i4uJE3759hbW1tXB3dxePPfaYOHv2rM6+Xn31Vb3+AxCrVq3SKTt16pR4+umnhbu7u7C2tha9e/cWjzzyiHj99dcNfge3q6ysFGvXrhUPPvigcHBwENbW1qJv375i2rRp4j//+Y+2nqG77BsbG0VycrJQqVTC1tZWBAcHi6ysLL277Ddt2iRGjRolevbsKWxsbETfvn3FCy+8IIqLi3X6smzZMtGnTx9hYWGhd+f0nj17RFhYmHB0dBQKhUJ4e3uLmJgY8e9//1tbZ8aMGcLe3t7gcebn54vw8HDRvXt34eLiIp566ilRUlKi950232VfUVGhs72h49+7d68YOnSosLW1FZ6enuLll18Wn3zyiV7fb/9ZudU777wjAIgPPvhA77N3331XhIWFiV69egkbGxvtz+vXX3+tU6+iokIsWLBA+Pj4CGtra+Hq6iqGDx8uli9fLn766SeD+22LXbt2ialTpwo/Pz+dn5Xp06cbXL3ivffeE7/73e+Era2tcHBwEMOGDdP5HRo7dqwIDAzU2+727+tOd7Ybusu+rq5OzJo1S7i5uQlJknTOW15enhg9erTo1q2bAKDzc9oavMue7iWSEJzFTERE7RcdHY3c3FwUFxd3+qdiEVHH4iV7IiJqs7q6Opw4cQLHjx/Hxx9/jM2bNzOMEpHROEJKRERtVlxcDB8fHzg6OmLq1Kn429/+1uITwIiIWsJASkRERESy4sL4RERERCQrBlIiIiIikhUDKRERERHJqlPcZd/Y2Ijvv/8e3bt37/DHrRERERHR3QkhcOPGDfTp0wcWFsaNeXaKQPr999+b9BnCRERERGQely5dMvqpap0ikHbv3h1A0wE6OjrK3BsiIiIiul11dTWUSqU2txmjUwTS5sv0jo6ODKRERERE97C2TK/kTU1EREREJCsGUiIiIiKSFQMpEREREcmqU8whJSIioq5Fo9GgoaFB7m6QEaytrWFpaWmWthlIiYiIqMMIIVBeXo7Kykq5u0Jt4OzsjN69e5t8XXgGUiIiIuowzWHU3d0d3bp14wNvOgkhBH7++Wf88MMPAAAPDw+Tts9ASkRERB1Co9Fow2iPHj3k7g4Zyc7ODgDwww8/wN3d3aSX73lTExEREXWI5jmj3bp1k7kn1FbN587U838ZSImIiKhD8TJ952Wuc8dASkRERESyYiAlIiIiMhFJkrBnzx65u9HpMJASERERtUJ5eTnmz58PlUoFhUIBpVKJyMhIHD582Cz7O3LkCCRJMusSWdevX8f06dPh5OQEJycnTJ8+XZYluXiXPREREXU6Gg2QkwOUlQEeHkBoKGCmNdsBAMXFxRg9ejScnZ2RnJyMoKAgNDQ0IDMzE7GxsTh79qz5dt5OQghoNBpYWenHvqlTp+Ly5cs4cOAAAOBPf/oTpk+fjr1793ZoHzlCSkREdyU0AtePXMeVnVdw/ch1CI2Qu0t0H1OrgX79gLAwYOrUpj/79WsqN5e5c+dCkiQcP34cMTEx8Pf3R2BgIBYtWoTc3FyD2xga4czLy4MkSSguLgYAXLx4EZGRkXBxcYG9vT0CAwORkZGB4uJihIWFAQBcXFwgSRJmzpwJoClgJicnQ6VSwc7ODkOHDkV6errefjMzMxEcHAyFQoGcnBy9/hUUFODAgQN44403EBISgpCQEOzYsQP79u3DuXPnTPPFtRJHSImI6I4q1BUojCtE3eU6bZnCSwHfFF+4RbnJ2DO6H6nVQEwMIG77N1FpaVN5ejoQFWXafV67dg0HDhzA+vXrYW9vr/e5s7Nzm9uOjY1FfX09srOzYW9vj/z8fDg4OECpVGL37t2Ijo7GuXPn4OjoqF0HdMWKFVCr1UhLS4Ofnx+ys7Mxbdo0uLm5YezYsdq2lyxZgo0bN0KlUhns4+effw4nJyeMHDlSW/bQQw/ByckJx44dw4ABA9p8XMZiICUiohZVqCtwJuYMcNtf/nWldTgTcwaB6YEMpdRhNBogLk4/jAJNZZIExMcDU6aY9vJ9YWEhhBAYOHCg6Rr9VUlJCaKjozFkyBAAgEql0n7m6uoKAHB3d9cGypqaGmzevBlZWVkICQnRbnP06FFs375dJ5AmJiYiPDy8xX2Xl5fD3d1dr9zd3R3l5eXtPjZjMJASEZFBQiNQGFeoF0abPgQgAYXxheg5pSckS64rSeaXkwNcvtzy50IAly411Rs3znT7Fb8mYHOswblgwQK89NJLOHjwIMaPH4/o6GgEBQW1WD8/Px+1tbV6QbO+vh7Dhg3TKQsODr7r/g0dkxCiw9eK5RxSIiIyqDKnUucyvR4B1F2qQ2VOZYf1ie5vZWWmrddafn5+kCQJBQUFRm1nYdEUs8QtQ7q3P+Fo1qxZuHDhAqZPn47Tp08jODgYW7dubbHNxsZGAMD+/fuRl5enfeXn5+vMIwVgcHrBrXr37o0rV67olVdUVKBXr153PjgTYyAlIiKD6svqTVqPqL08PExbr7VcXV0RERGBbdu2oaamRu/zlpZJcnNrms5SdktCzsvL06unVCoxZ84cqNVqJCQkYMeOHQAAGxsbAIBGo9HWHTRoEBQKBUpKSuDr66vzUiqVRh1XSEgIqqqqcPz4cW3Zf//7X1RVVWHUqFFGtdVeDKRERGSQjYeNSesRtVdoKODl1TRX1BBJApTKpnqmlpqaCo1GgxEjRmD37t347rvvUFBQgNdee007l/N2zSFx9erV+Pbbb7F//35s2rRJp058fDwyMzNRVFSEEydOICsrCwEBAQAAb29vSJKEffv2oaKiAj/99BO6d++OxYsXY+HChXj33Xdx/vx5nDx5Etu2bcO7775r1DEFBARg4sSJmD17NnJzc5Gbm4vZs2fj8ccf79AbmgAGUiIiaoFzqDMUXgqgpalkEqBQKuAc6tyR3aL7mKUlkJLS9N+3h9Lm91u2mGc9Uh8fH5w4cQJhYWFISEjA4MGDER4ejsOHDyMtLc3gNtbW1ti5cyfOnj2LoUOHYsOGDVi3bp1OHY1Gg9jYWG04HDBgAFJTUwEAnp6eWLNmDZYuXYpevXph3rx5AIC1a9di5cqVSEpKQkBAACIiIrB37174+PgYfVzvv/8+hgwZggkTJmDChAkICgrCP/7xD6PbaS9JCEP3qt1bqqur4eTkhKqqKjg6OsrdHSKi+4b2LntA9+amX//y5132ZIza2loUFRXBx8cHtra2bW5HrW662/7WG5yUyqYwauoln0jXnc5he/Ia77InIqIWuUW5ITA90PA6pFu4DinJIyqqaWmnjnxSE5kXAykREd2RW5Qbek7picqcStSX1cPGwwbOoc5c6olkZWlp2qWdSF4MpEREdFeSpQSXcS5yd4OIuije1EREREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQr3mVPRPcVoRFcvoiI6B7DQEpE940KdYXhBd5TuMA7EZmGJEn4+OOP8eSTT8rdlU6Fl+yJ6L7Q/AjMW8MoANSV1uFMzBlUqCtk6hkRdRbl5eWYP38+VCoVFAoFlEolIiMjcfjwYbPs78iRI5AkCZWVlWZpHwDWr1+PUaNGoVu3bnB2djbbfu6GI6RE1OUJjUBhXKHus9i1HwKQgML4QvSc0pOX74k6CU2jBjklOSi7UQaP7h4I7RsKSwvzPTu0uLgYo0ePhrOzM5KTkxEUFISGhgZkZmYiNjYWZ8+eNdu+20sIAY1GAysr/dhXX1+Pp556CiEhIXjzzTdl6F0TjpASUZdXmVOpNzKqQwB1l+pQmVPZYX0iorZTF6jRL6Ufwt4Nw1T1VIS9G4Z+Kf2gLlCbbZ9z586FJEk4fvw4YmJi4O/vj8DAQCxatAi5ubkGtzE0wpmXlwdJklBcXAwAuHjxIiIjI+Hi4gJ7e3sEBgYiIyMDxcXFCAsLAwC4uLhAkiTMnDkTQFPATE5Ohkqlgp2dHYYOHYr09HS9/WZmZiI4OBgKhQI5OTkG+7hmzRosXLgQQ4YMaf+X1A4cISWiLq++rN6k9YhIPuoCNWI+jIG47ZJHaXUpYj6MQfrT6YgKiDLpPq9du4YDBw5g/fr1sLe31/u8PZe6Y2NjUV9fj+zsbNjb2yM/Px8ODg5QKpXYvXs3oqOjce7cOTg6OsLOzg4AsGLFCqjVaqSlpcHPzw/Z2dmYNm0a3NzcMHbsWG3bS5YswcaNG6FSqWS9HN8aDKRE1OXZeNiYtB4RyUPTqEHcgTi9MAoAAgISJMQfiMeUAVNMevm+sLAQQggMHDjQZG02KykpQXR0tHaEUqVSaT9zdXUFALi7u2sDZU1NDTZv3oysrCyEhIRotzl69Ci2b9+uE0gTExMRHh5u8j6bAwMpEXV5zqHOUHgpUFdaZ3geqdR0t71zqHNHd42IjJBTkoPL1Zdb/FxA4FL1JeSU5GBcv3Em268QTf/jkCTTzzFfsGABXnrpJRw8eBDjx49HdHQ0goKCWqyfn5+P2tpavaBZX1+PYcOG6ZQFBwebvL/mwjmkRNTlSZYSfFN8f31z+4dNf/hu8eUNTUT3uLIbZSat11p+fn6QJAkFBQVGbWdh0RSzmgMtADQ0NOjUmTVrFi5cuIDp06fj9OnTCA4OxtatW1tss7GxEQCwf/9+5OXlaV/5+fk680gBGJxecK9iICWi+4JblBsC0wOh8FTolCu8FAhMD+Q6pESdgEd3D5PWay1XV1dERERg27ZtqKmp0fu8pWWZ3Nya/r9SVvZbQM7Ly9Orp1QqMWfOHKjVaiQkJGDHjh0AABubpmlEGo1GW3fQoEFQKBQoKSmBr6+vzkupVLb1EGXHS/ZEdN9wi3JDzyk9+aQmok4qtG8ovBy9UFpdanAeqQQJXo5eCO0bavJ9p6amYtSoURgxYgQSExMRFBSEmzdv4tChQ0hLSzM4etocElevXo1169bhu+++w6ZNm3TqxMfHY9KkSfD398f169eRlZWFgIAAAIC3tzckScK+ffswefJk2NnZoXv37li8eDEWLlyIxsZGjBkzBtXV1Th27BgcHBwwY8YMo46rpKQE165dQ0lJCTQajTYw+/r6wsHBoW1fVhswkBLRfUWylOAyzkXubhB1ChqNBjk5OSgrK4OHhwdCQ0NhaWm+tT7vxtLCEikTUxDzYQwkSDqhVPp1/s2WiVvMsh6pj48PTpw4gfXr1yMhIQFlZWVwc3PD8OHDkZaWZnAba2tr7Ny5Ey+99BKGDh2K3/3ud1i3bh2eeuopbR2NRoPY2FhcvnwZjo6OmDhxIv73f/8XAODp6Yk1a9Zg6dKleO655/Dss8/inXfewdq1a+Hu7o6kpCRcuHABzs7OePDBB/HKK68YfVwrV67Eu+++q33fPA/1008/xbhx44xur60kcevEhntUdXU1nJycUFVVBUdHR7m7Q0RE1OWp1WrExcXh8uXfbiLy8vJCSkoKoqLatqxSbW0tioqK4OPjA1tb27b3rUCNuANxOjc4KR2V2DJxi8mXfCJddzqH7clrHCElIiIiHWq1GjExMbh9zKq0tBQxMTFIT09vcyg1haiAKEwZMKVDn9RE5sVASkRERFoajQZxcXF6YRRoultckiTEx8djypQpsl++N+XSTiQv3mVPREREWjk5OTqX6W8nhMClS5dafBQlUVswkBIREZHWrUsUmaIeUWswkBIREZGWh0cr1/psZT2i1mAgJSIiIq3Q0FB4eXm1+JhMSZKgVCoRGmr6tT7p/sVASkRERFqWlpZISUkBoP/s9ub3W7ZskfWGJup6GEiJiIhIR1RUFNLT0+Hp6alT7uXlJfuST9Q1cdknIiIi0hMVFYUpU6bcU09qoq6LgZSIiIgMsrS07NDHR3YFkiTh448/xpNPPil3VzoVXrInIiIiaoXy8nLMnz8fKpUKCoUCSqUSkZGROHz4sFn2d+TIEUiShMrKSrO0X1xcjBdeeAE+Pj6ws7ND//79sWrVKtTX15tlf3fCEVIiIiLqfDQaICcHKCsDPDyA0FDAjNMJiouLMXr0aDg7OyM5ORlBQUFoaGhAZmYmYmNjcfbsWbPtu72EENBoNLCy0o19Z8+eRWNjI7Zv3w5fX1988803mD17NmpqarBx48YO7SNHSImIiKhzUauBfv2AsDBg6tSmP/v1ayo3k7lz50KSJBw/fhwxMTHw9/dHYGAgFi1ahNzcXIPbGBrhzMvLgyRJKC4uBgBcvHgRkZGRcHFxgb29PQIDA5GRkYHi4mKEhYUBAFxcXCBJEmbOnAmgKWAmJydDpVLBzs4OQ4cORXp6ut5+MzMzERwcDIVCYfDJWhMnTsTbb7+NCRMmQKVS4YknnsDixYuhNuP32BKOkBIREVHnoVYDMTGAELrlpaVN5enpgIlXAbh27RoOHDiA9evXw97eXu9zZ2fnNrcdGxuL+vp6ZGdnw97eHvn5+XBwcIBSqcTu3bsRHR2Nc+fOwdHREXZ2dgCAFStWQK1WIy0tDX5+fsjOzsa0adPg5uaGsWPHattesmQJNm7cCJVK1eo+VlVVwdXVtc3H01YMpERERNQ5aDRAXJx+GAWayiQJiI8Hpkwx6eX7wsJCCCEwcOBAk7XZrKSkBNHR0RgyZAgAQKVSaT9rDobu7u7aQFlTU4PNmzcjKysLISEh2m2OHj2K7du36wTSxMREhIeHt7ov58+fx9atW7Fp06b2HpbRGEiJiIioc8jJAS5fbvlzIYBLl5rqmXB1APFrAG7p6VXtsWDBArz00ks4ePAgxo8fj+joaAQFBbVYPz8/H7W1tXpBs76+HsOGDdMpCw4ObnU/vv/+e0ycOBFPPfUUZs2aZdxBmADnkBIREVHnUFZm2nqt5OfnB0mSUFBQYNR2FhZNMUvcMqLb0NCgU2fWrFm4cOECpk+fjtOnTyM4OBhbt25tsc3GxkYAwP79+5GXl6d95efn68wjBWBweoEh33//PcLCwhASEoK///3vrdrG1BhIb6MRAkeuX8fOK1dw5Pp1aAxdFiAiIqKO5+Fh2nqt5OrqioiICGzbtg01NTV6n7e0LJObmxsAoOyWgJyXl6dXT6lUYs6cOVCr1UhISMCOHTsAADY2NgAAjUajrTto0CAoFAqUlJTA19dX56VUKo0+ttLSUowbNw4PPvgg3n77bW2I7mi8ZH8LdUUF4goLcbmuTlvmpVAgxdcXUb/+UBEREZFMQkMBL6+mG5gMDRhJUtPnoaEm33VqaipGjRqFESNGIDExEUFBQbh58yYOHTqEtLQ0g6OnzSFx9erVWLduHb777ju9+Znx8fGYNGkS/P39cf36dWRlZSEgIAAA4O3tDUmSsG/fPkyePBl2dnbo3r07Fi9ejIULF6KxsRFjxoxBdXU1jh07BgcHB8yYMaPVx/T9999j3Lhx6Nu3LzZu3IiKigrtZ717927jN9U2HCH9lbqiAjFnzuiEUQAoratDzJkzUN9ykoiIiEgGlpZASkrTf98+n7P5/ZYtZlmP1MfHBydOnEBYWBgSEhIwePBghIeH4/Dhw0hLSzO4jbW1NXbu3ImzZ89i6NCh2LBhA9atW6dTR6PRIDY2FgEBAZg4cSIGDBiA1NRUAICnpyfWrFmDpUuXolevXpg3bx4AYO3atVi5ciWSkpIQEBCAiIgI7N27Fz4+PkYd08GDB1FYWIisrCx4eXnBw8ND++pokhD3/jXp6upqODk5oaqqCo6OjiZvXyME+uXm6oXRZhKaRkqLHnoIlmaY0ExERHQ/qK2tRVFREXx8fGBra9v2htTqprvtb73BSalsCqMmXvKJdN3pHLYnr/GSPYCcysoWwygACACX6uqQU1mJcS4uHdcxIiIi0hcV1bS0Uwc+qYnMi4EUQFkrn9na2npERERkZpaWJl3aieTFOaQAPH69i81U9YiIiIio9RhIAYQ6O8NLoUBLs0MlAEqFAqHteDQYERERERnGQArAUpKQ4usLAHqhtPn9Fl9f3tBEREREZAYMpL+KcnNDemAgPBUKnXIvhQLpgYFch5SIiIjITHhT0y2i3NwwpWdP5FRWoqy+Hh42Ngh1dubIKBEREZEZMZDexlKSuLQTERERUQfiJXsiIiIikhUDKREREZGJSJKEPXv2yN2NToeBlIiIiKgVysvLMX/+fKhUKigUCiiVSkRGRuLw4cNm2d+RI0cgSRIqKyvN0j4APPHEE+jbty9sbW3h4eGB6dOn4/vvvzfb/lrCQEpERESdjkYIHLl+HTuvXMGR69ehEcKs+ysuLsbw4cORlZWF5ORknD59GgcOHEBYWBhiY2PNuu/2EkLg5s2bBj8LCwvDhx9+iHPnzmH37t04f/48YmJiOriHDKRERETUyagrKtAvNxdhp05hakEBwk6dQr/cXKgrKsy2z7lz50KSJBw/fhwxMTHw9/dHYGAgFi1ahNzcXIPbGBrhzMvLgyRJKC4uBgBcvHgRkZGRcHFxgb29PQIDA5GRkYHi4mKEhYUBAFxcXCBJEmbOnAmgKWAmJydDpVLBzs4OQ4cORXp6ut5+MzMzERwcDIVCgZycHIN9XLhwIR566CF4e3tj1KhRWLp0KXJzc9HQ0ND+L80IbQqkqamp8PHxga2tLYYPH97iQTZ7//33MXToUHTr1g0eHh547rnn8OOPP7apw0RERHT/UldUIObMGVyuq9MpL62rQ8yZM2YJpdeuXcOBAwcQGxsLe3t7vc+d2/Ekx9jYWNTV1SE7OxunT5/Ghg0b4ODgAKVSid27dwMAzp07h7KyMqSkpAAAVqxYgbfffhtpaWk4c+YMFi5ciGnTpuGzzz7TaXvJkiVISkpCQUEBgoKCWnWc77//PkaNGgVra+s2H1NbGB1Id+3ahfj4eCxfvhwnT55EaGgoJk2ahJKSEoP1jx49imeffRYvvPACzpw5g48++ghffPEFZs2a1e7OExER0f1DIwTiCgth6OJ8c1l8YaHJL98XFhZCCIGBAweatF0AKCkpwejRozFkyBCoVCo8/vjjePjhh2FpaQlXV1cAgLu7O3r37g0nJyfU1NRg8+bNeOuttxAREQGVSoWZM2di2rRp2L59u07biYmJCA8PR//+/dGjR48W+/DnP/8Z9vb26NGjB0pKSvCvf/3L5Md5N0YH0s2bN+OFF17ArFmzEBAQgC1btkCpVCItLc1g/dzcXPTr1w8LFiyAj48PxowZgxdffBFffvlluztPRERE94+cykq9kdFbCQCX6uqQY+KbgMSvAVcyw4NyFixYgHXr1mH06NFYtWoVvv766zvWz8/PR21tLcLDw+Hg4KB9vffeezh//rxO3eDg4Fb14eWXX8bJkydx8OBBWFpa4tlnn9Uec0cxKpDW19fjq6++woQJE3TKJ0yYgGPHjhncZtSoUbh8+TIyMjIghMCVK1eQnp6Oxx57rMX91NXVobq6WudFRERE97ey+nqT1mstPz8/SJKEgoICo7azsGiKWbeGu9vnZs6aNQsXLlzA9OnTcfr0aQQHB2Pr1q0tttnY2AgA2L9/P/Ly8rSv/Px8nXmkAAxOLzCkZ8+e8Pf3R3h4OD744ANkZGS0OC/WXIwKpFevXoVGo0GvXr10ynv16oXy8nKD24waNQrvv/8+nnnmGdjY2KB3795wdna+45edlJQEJycn7UupVBrTTSIiIuqCPGxsTFqvtVxdXREREYFt27ahpqZG7/OWlmVyc3MDAJSVlWnL8vLy9OoplUrMmTMHarUaCQkJ2LFjBwDA5tfj0Gg02rqDBg2CQqFASUkJfH19dV6myEvN4bnuDiPR5tCmm5puH7IWQrQ4jJ2fn48FCxZg5cqV+Oqrr3DgwAEUFRVhzpw5Lba/bNkyVFVVaV+XLl1qSzeJiIioCwl1doaXQoGWLpxLAJQKBULbcZNRS1JTU6HRaDBixAjs3r0b3333HQoKCvDaa68hJCTE4DbNIXH16tX49ttvsX//fmzatEmnTnx8PDIzM1FUVIQTJ04gKysLAQEBAABvb29IkoR9+/ahoqICP/30E7p3747Fixdj4cKFePfdd3H+/HmcPHkS27Ztw7vvvmvUMR0/fhx/+9vfkJeXh4sXL+LTTz/F1KlT0b9//xaPyVyMCqQ9e/aEpaWl3mjoDz/8oDdq2iwpKQmjR4/Gyy+/jKCgIERERCA1NRVvvfWWzr8YbqVQKODo6KjzIiIiovubpSQhxdcXAPRCafP7Lb6+sDTDXE8fHx+cOHECYWFhSEhIwODBgxEeHo7Dhw+3eB+NtbU1du7cibNnz2Lo0KHYsGED1q1bp1NHo9EgNjYWAQEBmDhxIgYMGIDU1FQAgKenJ9asWYOlS5eiV69emDdvHgBg7dq1WLlyJZKSkhAQEICIiAjs3bsXPj4+Rh2TnZ0d1Go1Hn30UQwYMADPP/88Bg8ejM8++wwKhaIN31LbScLIWasjR47E8OHDtV8W0DR8PGXKFCQlJenVj46OhpWVFXbt2qUt+/zzzzFq1CiUlpaiT58+d91ndXU1nJycUFVVxXBKRETUSdXW1qKoqEi7dGRbqSsqEFdYqHODk1KhwBZfX0T9epmczONO57A9ec3K2I4sWrQI06dPR3BwMEJCQvD3v/8dJSUl2kvwy5YtQ2lpKd577z0AQGRkJGbPno20tDRERESgrKwM8fHxGDFiRKvCKBEREdGtotzcMKVnT+RUVqKsvh4eNjYIdXY2y8godQyjA+kzzzyDH3/8EYmJiSgrK8PgwYORkZEBb29vAE0Td29dk3TmzJm4ceMG/va3vyEhIQHOzs545JFHsGHDBtMdBREREd1XLCUJ41xc5O4GmYjRl+zlwEv2REREnZ+pLtmTfMx1yZ7PsiciIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiMhEJEnCnj175O5Gp8NASkRERNQK5eXlmD9/PlQqFRQKBZRKJSIjI3H48GGz7O/IkSOQJAmVlZVmaf9WdXV1eOCBByBJEvLy8sy+v9sZ/aQmIiIiIrkJjUBlTiXqy+ph42ED51BnSJbme3RocXExRo8eDWdnZyQnJyMoKAgNDQ3IzMxEbGwszp49a7Z9t5cQAhqNBlZWLce+JUuWoE+fPjh16lQH9uw3HCElIiKiTqVCXYHcfrk4FXYKBVMLcCrsFHL75aJCXWG2fc6dOxeSJOH48eOIiYmBv78/AgMDsWjRIuTm5hrcxtAIZ15eHiRJQnFxMQDg4sWLiIyMhIuLC+zt7REYGIiMjAwUFxcjLCwMAODi4gJJkjBz5kwATQEzOTkZKpUKdnZ2GDp0KNLT0/X2m5mZieDgYCgUCuTk5LR4bJ988gkOHjyIjRs3tu9LageOkBIREVGnUaGuwJmYM8BtDz6vK63DmZgzCEwPhFuUm0n3ee3aNRw4cADr16+Hvb293ufOzs5tbjs2Nhb19fXIzs6Gvb098vPz4eDgAKVSid27dyM6Ohrnzp2Do6Mj7OzsAAArVqyAWq1GWloa/Pz8kJ2djWnTpsHNzQ1jx47Vtr1kyRJs3LgRKpWqxT5euXIFs2fPxp49e9CtW7c2H0d7MZASERFRpyA0AoVxhXphtOlDABJQGF+InlN6mvTyfWFhIYQQGDhwoMnabFZSUoLo6GgMGTIEAKBSqbSfubq6AgDc3d21gbKmpgabN29GVlYWQkJCtNscPXoU27dv1wmkiYmJCA8Pb3HfQgjMnDkTc+bMQXBwsHbUVg4MpERERNQpVOZUou5yXcsVBFB3qQ6VOZVwGedisv0K0ZSAJcn0c1QXLFiAl156CQcPHsT48eMRHR2NoKCgFuvn5+ejtrZWL2jW19dj2LBhOmXBwcF33PfWrVtRXV2NZcuWtf0ATIRzSImIiKhTqC+rN2m91vLz84MkSSgoKDBqOwuLppjVHGgBoKGhQafOrFmzcOHCBUyfPh2nT59GcHAwtm7d2mKbjY2NAID9+/cjLy9P+8rPz9eZRwrA4PSCW2VlZSE3NxcKhQJWVlbw9fUF0BRkZ8yY0foDNQEGUiIiIuoUbDxsTFqvtVxdXREREYFt27ahpqZG7/OWlmVyc2uay1pWVqYtM7SkklKpxJw5c6BWq5GQkIAdO3YAAGxsmo5Do9Fo6w4aNAgKhQIlJSXw9fXVeSmVSqOO67XXXsOpU6e0oTYjIwMAsGvXLqxfv96ottqLl+yJiIioU3AOdYbCS4G60jrD80glQOGlgHOos8n3nZqailGjRmHEiBFITExEUFAQbt68iUOHDiEtLc3g6GlzSFy9ejXWrVuH7777Dps2bdKpEx8fj0mTJsHf3x/Xr19HVlYWAgICAADe3t6QJAn79u3D5MmTYWdnh+7du2Px4sVYuHAhGhsbMWbMGFRXV+PYsWNwcHAwamSzb9++Ou8dHBwAAP3794eXl5exX1G7cISUiIiIOgXJUoJviu+vb27/sOkP3y2+ZlmP1MfHBydOnEBYWBgSEhIwePBghIeH4/Dhw0hLSzO4jbW1NXbu3ImzZ89i6NCh2LBhA9atW6dTR6PRIDY2FgEBAZg4cSIGDBiA1NRUAICnpyfWrFmDpUuXolevXpg3bx4AYO3atVi5ciWSkpIQEBCAiIgI7N27Fz4+PiY/7o4iiVsnNtyjqqur4eTkhKqqKjg6OsrdHSIiImqD2tpaFBUVwcfHB7a2tm1up0JdgcK4Qp0bnBRKBXy3+Jp8ySfSdadz2J68xkv2RERE1Km4Rbmh55SeHfqkJjIvBlIiIiLqdCRLyaRLO5G8OIeUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKREREZGJSJKEPXv2yN2NToeBlIiIiKgVysvLMX/+fKhUKigUCiiVSkRGRuLw4cNm2d+RI0cgSRIqKyvN0v6t6urq8MADD0CSJOTl5Zl9f7fjk5qIiIio0xEa0aGPDi0uLsbo0aPh7OyM5ORkBAUFoaGhAZmZmYiNjcXZs2fNtu/2EkJAo9HAyqrl2LdkyRL06dMHp06d6sCe/YYjpERERNSpVKgrkNsvF6fCTqFgagFOhZ1Cbr9cVKgrzLbPuXPnQpIkHD9+HDExMfD390dgYCAWLVqE3Nxcg9sYGuHMy8uDJEkoLi4GAFy8eBGRkZFwcXGBvb09AgMDkZGRgeLiYoSFhQEAXFxcIEkSZs6cCaApYCYnJ0OlUsHOzg5Dhw5Fenq63n4zMzMRHBwMhUKBnJycFo/tk08+wcGDB7Fx48b2fUntwBFSIqL7lBAaVFbmoL6+DDY2HnB2DoUkWcrdLaI7qlBX4EzMGUDolteV1uFMzBkEpgfCLcrNpPu8du0aDhw4gPXr18Pe3l7vc2dn5za3HRsbi/r6emRnZ8Pe3h75+flwcHCAUqnE7t27ER0djXPnzsHR0RF2dnYAgBUrVkCtViMtLQ1+fn7Izs7GtGnT4ObmhrFjx2rbXrJkCTZu3AiVStViH69cuYLZs2djz5496NatW5uPo70YSImI7kMVFWoUFsahru6ytkyh8IKvbwrc3KJk7BlRy4RGoDCuUC+MNn0IQAIK4wvRc0pPk16+LywshBACAwcONFmbzUpKShAdHY0hQ4YAAFQqlfYzV1dXAIC7u7s2UNbU1GDz5s3IyspCSEiIdpujR49i+/btOoE0MTER4eHhLe5bCIGZM2dizpw5CA4O1o7ayoGBlIjoPlNRocaZMzG4/W/1urpSnDkTg8DAdIZSuidV5lSi7nJdyxUEUHepDpU5lXAZ52Ky/QrR9LsiSaafo7pgwQK89NJLOHjwIMaPH4/o6GgEBQW1WD8/Px+1tbV6QbO+vh7Dhg3TKQsODr7jvrdu3Yrq6mosW7as7QdgIpxDSkR0HxFCg8LCOLQ8xAQUFsZDCE2H9ouoNerL6k1ar7X8/PwgSRIKCgqM2s7CoilmNQdaAGhoaNCpM2vWLFy4cAHTp0/H6dOnERwcjK1bt7bYZmNjIwBg//79yMvL077y8/N15pECMDi94FZZWVnIzc2FQqGAlZUVfH19ATQF2RkzZrT+QE2AgZSI6D5SWZmjc5len0Bd3SVUVrZ8AwSRXGw8bExar7VcXV0RERGBbdu2oaamRu/zlpZlcnNrmstaVlamLTO0pJJSqcScOXOgVquRkJCAHTt2AABsbJqOQ6P57R+IgwYNgkKhQElJCXx9fXVeSqXSqON67bXXcOrUKW2ozcjIAADs2rUL69evN6qt9uIleyKi+0h9fdndKxlRj6gjOYc6Q+GlQF1pneFBfglQeCngHOps8n2npqZi1KhRGDFiBBITExEUFISbN2/i0KFDSEtLMzh62hwSV69ejXXr1uG7777Dpk2bdOrEx8dj0qRJ8Pf3x/Xr15GVlYWAgAAAgLe3NyRJwr59+zB58mTY2dmhe/fuWLx4MRYuXIjGxkaMGTMG1dXVOHbsGBwcHIwa2ezbt6/OewcHBwBA//794eXlZexX1C4cISUiuo/Y2HiYtB5RR5IsJfim+P765vYPm/7w3eJrlvVIfXx8cOLECYSFhSEhIQGDBw9GeHg4Dh8+jLS0NIPbWFtbY+fOnTh79iyGDh2KDRs2YN26dTp1NBoNYmNjERAQgIkTJ2LAgAFITU0FAHh6emLNmjVYunQpevXqhXnz5gEA1q5di5UrVyIpKQkBAQGIiIjA3r174ePjY/Lj7iiSuHViwz2quroaTk5OqKqqgqOjo9zdISLqtITQIDe3H+rqStHSEJNC4YWHHiriElBkcrW1tSgqKoKPjw9sbW3b3E6FugKFcYU6NzgplAr4bvE1+ZJPpOtO57A9eY2X7ImI7iOSZAlf35Rf77KXoBtKm0aVfH23MIzSPc0tyg09p/Ts0Cc1kXkxkBIR3Wfc3KIQGJjewjqkW7jkE3UKkqVk0qWdSF4MpERE9yE3tyj07DmFT2oionsCAykR0X1Kkizh4jJO7m4QEfEueyIiIiKSFwMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiMhFJkrBnzx65u9HpMJASERERtUJ5eTnmz58PlUoFhUIBpVKJyMhIHD582Cz7O3LkCCRJQmVlpVnaB4B+/fpBkiSd19KlS822v5Zw2SciIiKiuyguLsbo0aPh7OyM5ORkBAUFoaGhAZmZmYiNjcXZs2fl7mKLhBDQaDSwsjIc+xITEzF79mztewcHh47qmhZHSImIiKjTEUKD69eP4MqVnbh+/QiE0Jh1f3PnzoUkSTh+/DhiYmLg7++PwMBALFq0CLm5uQa3MTTCmZeXB0mSUFxcDAC4ePEiIiMj4eLiAnt7ewQGBiIjIwPFxcUICwsDALi4uECSJMycORNAU8BMTk6GSqWCnZ0dhg4divT0dL39ZmZmIjg4GAqFAjk5OS0eW/fu3dG7d2/tS45AyhFSIiIi6lQqKtQtPPo2xSyPvr127RoOHDiA9evXw97eXu9zZ2fnNrcdGxuL+vp6ZGdnw97eHvn5+XBwcIBSqcTu3bsRHR2Nc+fOwdHREXZ2dgCAFStWQK1WIy0tDX5+fsjOzsa0adPg5uaGsWPHattesmQJNm7cCJVKdcc+btiwAWvXroVSqcRTTz2Fl19+GTY2Nm0+prZgICUiIqJOo6JCjTNnYgAInfK6ulKcORODwMB0k4fSwsJCCCEwcOBAk7YLACUlJYiOjsaQIUMAACqVSvuZq6srAMDd3V0bKGtqarB582ZkZWUhJCREu83Ro0exfft2nUCamJiI8PDwO+4/Li4ODz74IFxcXHD8+HEsW7YMRUVFeOONN0x5mHfFQEpERESdghAaFBbG4fYw+uunACQUFsajZ88pkCRLE+63aX+SJJmszWYLFizASy+9hIMHD2L8+PGIjo5GUFBQi/Xz8/NRW1urFzTr6+sxbNgwnbLg4OC77n/hwoXa/w4KCoKLiwtiYmKwYcMG9OjRw8ijaTvOISUiIqJOobIyR+cyvT6BurpLqKxseb5kW/j5+UGSJBQUFBi1nYVFU8xqDrQA0NDQoFNn1qxZuHDhAqZPn47Tp08jODgYW7dubbHNxsZGAMD+/fuRl5enfeXn5+vMIwVgcHrB3Tz00EMAmkaFOxIDKREREXUK9fVlJq3XWq6uroiIiMC2bdtQU1Oj93lLyzK5ubkBAMrKfutPXl6eXj2lUok5c+ZArVYjISEBO3bsAADtPE6N5rcbtgYNGgSFQoGSkhL4+vrqvJRKZVsPUevkyZMAAA8Pj3a3ZQxesiciIqJOwcamdSGptfWMkZqailGjRmHEiBFITExEUFAQbt68iUOHDiEtLc3g6GlzSFy9ejXWrVuH7777Dps2bdKpEx8fj0mTJsHf3x/Xr19HVlYWAgICAADe3t6QJAn79u3D5MmTYWdnh+7du2Px4sVYuHAhGhsbMWbMGFRXV+PYsWNwcHDAjBkzWn1Mn3/+OXJzcxEWFgYnJyd88cUXWLhwIZ544gn07du3fV+YkThCSkRERJ2Cs3MoFAovAC3N5ZSgUCjh7Bxq8n37+PjgxIkTCAsLQ0JCAgYPHozw8HAcPnwYaWlpBrextrbGzp07cfbsWQwdOhQbNmzAunXrdOpoNBrExsYiICAAEydOxIABA5CamgoA8PT0xJo1a7B06VL06tUL8+bNAwCsXbsWK1euRFJSEgICAhAREYG9e/fCx8fHqGNSKBTYtWsXxo0bh0GDBmHlypWYPXs2du7c2YZvqH0kcevEhntUdXU1nJycUFVVBUdHR7m7Q0RERG1QW1uLoqIi+Pj4wNbWtk1t/HaXPaB7c1NTSDXHXfb0mzudw/bkNY6QEhERUafh5haFwMB0KBSeOuUKhRfDaCfGOaRERETUqbi5RaFnzymorMxBfX0ZbGw84OwcatKlnqhjMZASERFRpyNJlnBxGSd3N8hEeMmeiIiIiGTFQEpEREREsmIgJSIiIiJZcQ4pEXUpQmh4owMRUSfDQEpEXUZFhRqFhXE6z7pWKLzg65vCpWCIiO5hvGRPRF1C82LZt4ZRAKirK8WZMzGoqFDL1DMiIrobBlIi6vSE0KCwMA66T23RfgoAKCyMhxCaDu0XEd1/JEnCnj175O5Gp8NASkSdXmVljt7IqC6BurpLqKzM6bA+EVHXU15ejvnz50OlUkGhUECpVCIyMhKHDx82y/6OHDkCSZJQWVlplvYBoF+/fpAkSee1dOlSs+2vJZxDSkSdXn19mUnrERHdrri4GKNHj4azszOSk5MRFBSEhoYGZGZmIjY2FmfPnpW7iy0SQkCj0cDKynDsS0xMxOzZs7XvHRwcOqprWhwhJaJOz8bGw6T1iOjeJ4QG168fwZUrO3H9+hGzT8mZO3cuJEnC8ePHERMTA39/fwQGBmLRokXIzc01uI2hEc68vDxIkoTi4mIAwMWLFxEZGQkXFxfY29sjMDAQGRkZKC4uRlhYGADAxcUFkiRh5syZAJoCZnJyMlQqFezs7DB06FCkp6fr7TczMxPBwcFQKBTIyWn5ClH37t3Ru3dv7UuOQMoRUiLq9JydQ6FQeKGurhSG55FKUCi84Owc2tFdIyIz6OgVNa5du4YDBw5g/fr1sLe31/vc2dm5zW3Hxsaivr4e2dnZsLe3R35+PhwcHKBUKrF7925ER0fj3LlzcHR0hJ2dHQBgxYoVUKvVSEtLg5+fH7KzszFt2jS4ublh7Nix2raXLFmCjRs3QqVS3bGPGzZswNq1a6FUKvHUU0/h5Zdfho2NTZuPqS0YSImo05MkS/j6puDMmRgAEnRDqQQA8PXdwvVIibqA5hU1bv/HZ/OKGoGB6SYPpYWFhRBCYODAgSZtFwBKSkoQHR2NIUOGAABUKpX2M1dXVwCAu7u7NlDW1NRg8+bNyMrKQkhIiHabo0ePYvv27TqBNDExEeHh4Xfcf1xcHB588EG4uLjg+PHjWLZsGYqKivDGG2+Y8jDvioGUiLoEN7coBAamtzBqsoXrkBJ1AXdfUUNCYWE8evacYtJ/gArRtD9JkkzWZrMFCxbgpZdewsGDBzF+/HhER0cjKCioxfr5+fmora3VC5r19fUYNmyYTllwcPBd979w4ULtfwcFBcHFxQUxMTHYsGEDevToYeTRtB0DKRF1GW5uUejZcwqf1ETURRmzooaLyziT7dfPzw+SJKGgoABPPvlkq7ezsGi6Vac50AJAQ0ODTp1Zs2YhIiIC+/fvx8GDB5GUlIRNmzZh/vz5BttsbGwEAOzfvx+enp46nykUCp33hqYX3M1DDz0EoGlUuCMDKW9qIqIuRZIs4eIyDr16/QEuLuMYRom6ELlW1HB1dUVERAS2bduGmpoavc9bWpbJzc0NAFBW9lt/8vLy9OoplUrMmTMHarUaCQkJ2LFjBwBo53FqNL/dsDVo0CAoFAqUlJTA19dX56VUKtt6iFonT54EAHh4dOxNoBwhJSIiok5BzhU1UlNTMWrUKIwYMQKJiYkICgrCzZs3cejQIaSlpaGgoEBvm+aQuHr1aqxbtw7fffcdNm3apFMnPj4ekyZNgr+/P65fv46srCwEBAQAALy9vSFJEvbt24fJkyfDzs4O3bt3x+LFi7Fw4UI0NjZizJgxqK6uxrFjx+Dg4IAZM2a0+pg+//xz5ObmIiwsDE5OTvjiiy+wcOFCPPHEE+jbt2/7vjAjcYSUiIiIOoXmFTWab1bUJ0GhUJplRQ0fHx+cOHECYWFhSEhIwODBgxEeHo7Dhw8jLS3N4DbW1tbYuXMnzp49i6FDh2LDhg1Yt26dTh2NRoPY2FgEBARg4sSJGDBgAFJTUwEAnp6eWLNmDZYuXYpevXph3rx5AIC1a9di5cqVSEpKQkBAACIiIrB37174+PgYdUwKhQK7du3CuHHjMGjQIKxcuRKzZ8/Gzp072/ANtY8kbp3YcI+qrq6Gk5MTqqqq4OjoKHd3iIiIqA1qa2tRVFQEHx8f2NratqmN3+6yBwytqGGOu+zpN3c6h+3JaxwhJSIiok6jeUUNheL2G3q8GEY7Mc4hJSIiok6FK2p0PQykRERE1Ok0r6hBXQMv2RMRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZNWmQJqamqpdoX/48OHIycm5Y/26ujosX74c3t7eUCgU6N+/P9566602dZiIiIjoXiVJEvbs2SN3NzodowPprl27EB8fj+XLl+PkyZMIDQ3FpEmTUFJS0uI2Tz/9NA4fPow333wT586dw86dOzFw4MB2dZyIiIioI5WXl2P+/PlQqVRQKBRQKpWIjIzE4cOHzbK/I0eOQJIkVFZWmqX9Zvv378fIkSNhZ2eHnj17Iiqq4592ZfTC+Js3b8YLL7yAWbNmAQC2bNmCzMxMpKWlISkpSa/+gQMH8Nlnn+HChQtwdXUFAPTr1699vSYiIiLqQMXFxRg9ejScnZ2RnJyMoKAgNDQ0IDMzE7GxsTh79qzcXWyREAIajQZWVvqxb/fu3Zg9ezb+8pe/4JFHHoEQAqdPn+7wPho1QlpfX4+vvvoKEyZM0CmfMGECjh07ZnCb//u//0NwcDCSk5Ph6ekJf39/LF68GL/88kuL+6mrq0N1dbXOi4iIiKiZRqPBkSNHsHPnThw5cgQajcas+5s7dy4kScLx48cRExMDf39/BAYGYtGiRcjNzTW4jaERzry8PEiShOLiYgDAxYsXERkZCRcXF9jb2yMwMBAZGRkoLi5GWFgYAMDFxQWSJGHmzJkAmgJmcnIyVCoV7OzsMHToUKSnp+vtNzMzE8HBwVAoFAanV968eRNxcXF49dVXMWfOHPj7+2PAgAGIiYkxzZdmBKNGSK9evQqNRoNevXrplPfq1Qvl5eUGt7lw4QKOHj0KW1tbfPzxx7h69Srmzp2La9eutTiPNCkpCWvWrDGma0RERHSfUKvViIuLw+XLl7VlXl5eSElJMcvl5mvXruHAgQNYv3497O3t9T53dnZuc9uxsbGor69HdnY27O3tkZ+fDwcHByiVSuzevRvR0dE4d+4cHB0dYWdnBwBYsWIF1Go10tLS4Ofnh+zsbEybNg1ubm4YO3astu0lS5Zg48aNUKlUBvt44sQJlJaWwsLCAsOGDUN5eTkeeOABbNy4EYGBgW0+prZo07PsJUnSeS+E0Ctr1tjYCEmS8P7778PJyQlA02X/mJgYbNu2Tfvl3mrZsmVYtGiR9n11dTWUSmVbukpERERdiFqtRkxMDIQQOuWlpaWIiYlBenq6yUNpYWEhhBBmuf+lpKQE0dHRGDJkCABApVJpP2ue6uju7q4NlDU1Ndi8eTOysrIQEhKi3ebo0aPYvn27TiBNTExEeHh4i/u+cOECAGD16tXYvHkz+vXrh02bNmHs2LH49ttvtfvvCEZdsu/ZsycsLS31RkN/+OEHvVHTZh4eHvD09NSGUQAICAiAEELnXza3UigUcHR01HkRERHR/U2j0SAuLk4vjALQlsXHx5v88n1z2y0NvrXHggULsG7dOowePRqrVq3C119/fcf6+fn5qK2tRXh4OBwcHLSv9957D+fPn9epGxwcfMe2GhsbAQDLly9HdHQ0hg8fjrfffhuSJOGjjz5q34EZyahAamNjg+HDh+PQoUM65YcOHcKoUaMMbjN69Gh8//33+Omnn7Rl3377LSwsLODl5dWGLhMREdH9KCcnp8XBLKApOF66dOmuy1Eay8/PD5IkoaCgwKjtLCwstP1q1tDQoFNn1qxZuHDhAqZPn47Tp08jODgYW7dubbHN5hC5f/9+5OXlaV/5+fk680gBGJxecCsPDw8AwKBBg7RlCoUCKpXqjqsnmYPRyz4tWrQIb7zxBt566y0UFBRg4cKFKCkpwZw5cwA0XW5/9tlntfWnTp2KHj164LnnnkN+fj6ys7Px8ssv4/nnnzd4uZ6IiIjIkLKyMpPWay1XV1dERERg27ZtqKmp0fu8pWWZ3Nzc9PqTl5enV0+pVGLOnDlQq9VISEjAjh07ADQNBALQGfEdNGgQFAoFSkpK4Ovrq/Mydnrj8OHDoVAocO7cOW1ZQ0MDiouL4e3tbVRb7WX0HNJnnnkGP/74IxITE1FWVobBgwcjIyND2/GysjKdVO3g4IBDhw5h/vz5CA4ORo8ePfD0009j3bp1pjsKIiIi6vKaR/RMVc8YqampGDVqFEaMGIHExEQEBQXh5s2bOHToENLS0gyOnjaHxNWrV2PdunX47rvvsGnTJp068fHxmDRpEvz9/XH9+nVkZWUhICAAAODt7Q1JkrBv3z5MnjwZdnZ26N69OxYvXoyFCxeisbERY8aMQXV1NY4dOwYHBwfMmDGj1cfk6OiIOXPmYNWqVVAqlfD29sarr74KAHjqqafa8W21gegEqqqqBABRVVUld1eIiIiojX755ReRn58vfvnllzZtf/PmTeHl5SUkSRIA9F6SJAmlUilu3rxp4p43+f7770VsbKzw9vYWNjY2wtPTUzzxxBPi008/1dYBID7++GPt+6NHj4ohQ4YIW1tbERoaKj766CMBQBQVFQkhhJg3b57o37+/UCgUws3NTUyfPl1cvXpVu31iYqLo3bu3kCRJzJgxQwghRGNjo0hJSREDBgwQ1tbWws3NTURERIjPPvtMCCHEp59+KgCI69ev3/WY6uvrRUJCgnB3dxfdu3cX48ePF998802L9e90DtuT1yQhDMwMvsdUV1fDyckJVVVVvMGJiIiok6qtrUVRUZH28eNt0XyXPaA7N7P5hiNz3GVPv7nTOWxPXmvTs+yJiIiI5BAVFYX09HR4enrqlHt5eTGMdmJtWoeUiIiISC5RUVGYMmUKcnJyUFZWBg8PD4SGhsLS0lLurlEbMZASERFRp2NpaYlx48bJ3Q0yEV6yJyIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIyEQkScKePXvk7kanw0BKRERE1Arl5eWYP38+VCoVFAoFlEolIiMjcfjwYbPs78iRI5AkCZWVlWZpv9n+/fsxcuRI2NnZoWfPnrI87YoL4xMRERHdRXFxMUaPHg1nZ2ckJycjKCgIDQ0NyMzMRGxsLM6ePSt3F1skhIBGo4GVlX7s2717N2bPno2//OUveOSRRyCEwOnTpzu8jxwhJSIiok5Ho9HgyJEj2LlzJ44cOQKNRmPW/c2dOxeSJOH48eOIiYmBv78/AgMDsWjRIuTm5hrcxtAIZ15eHiRJQnFxMQDg4sWLiIyMhIuLC+zt7REYGIiMjAwUFxcjLCwMAODi4gJJkjBz5kwATQEzOTkZKpUKdnZ2GDp0KNLT0/X2m5mZieDgYCgUCuTk5Oj17+bNm4iLi8Orr76KOXPmwN/fHwMGDEBMTIxpvjQjcISUiIiIOhW1Wo24uDhcvnxZW+bl5YWUlBSzXG6+du0aDhw4gPXr18Pe3l7vc2dn5za3HRsbi/r6emRnZ8Pe3h75+flwcHCAUqnE7t27ER0djXPnzsHR0RF2dnYAgBUrVkCtViMtLQ1+fn7Izs7GtGnT4ObmhrFjx2rbXrJkCTZu3AiVSmWwjydOnEBpaSksLCwwbNgwlJeX44EHHsDGjRsRGBjY5mNqCwZSIiIi6jTUajViYmIghNApLy0tRUxMDNLT000eSgsLCyGEwMCBA03aLgCUlJQgOjoaQ4YMAQCoVCrtZ66urgAAd3d3baCsqanB5s2bkZWVhZCQEO02R48exfbt23UCaWJiIsLDw1vc94ULFwAAq1evxubNm9GvXz9s2rQJY8eOxbfffqvdf0fgJXsiIpKFRgMcOQLs3Nn0p5mvuFIXoNFoEBcXpxdGAWjL4uPjTX75vrltSZJM2i4ALFiwAOvWrcPo0aOxatUqfP3113esn5+fj9raWoSHh8PBwUH7eu+993D+/HmdusHBwXdsq7GxEQCwfPlyREdHY/jw4Xj77bchSRI++uij9h2YkRhIiYiow6nVQL9+QFgYMHVq05/9+jWVE7UkJydH5zL97YQQuHTpksH5ku3h5+cHSZJQUFBg1HYWFhbafjVraGjQqTNr1ixcuHAB06dPx+nTpxEcHIytW7e22GZziNy/fz/y8vK0r/z8fJ15pAAMTi+4lYeHBwBg0KBB2jKFQgGVSoWSkpJWHKHpMJASEVGHUquBmBjg9lxRWtpUzlBKLSkrKzNpvdZydXVFREQEtm3bhpqaGr3PW1qWyc3NTa8/eXl5evWUSiXmzJkDtVqNhIQE7NixAwBgY2MDADojvoMGDYJCoUBJSQl8fX11Xkql0qjjGj58OBQKBc6dO6cta2hoQHFxMby9vY1qq70YSImIqMNoNEBcHGDgiqu2LD6el+/JsOYRPVPVM0Zqaio0Gg1GjBiB3bt347vvvkNBQQFee+017VzO2zWHxNWrV+Pbb7/F/v37sWnTJp068fHxyMzMRFFREU6cOIGsrCwEBAQAALy9vSFJEvbt24eKigr89NNP6N69OxYvXoyFCxfi3Xffxfnz53Hy5Els27YN7777rlHH5OjoiDlz5mDVqlU4ePAgzp07h5deegkA8NRTT7XhW2oH0QlUVVUJAKKqqkrurhARUTt8+qkQTdHzzq9PP5W7p2QOv/zyi8jPzxe//PJLm7a/efOm8PLyEpIkCQB6L0mShFKpFDdv3jRxz5t8//33IjY2Vnh7ewsbGxvh6ekpnnjiCfHpLT+wAMTHH3+sfX/06FExZMgQYWtrK0JDQ8VHH30kAIiioiIhhBDz5s0T/fv3FwqFQri5uYnp06eLq1evardPTEwUvXv3FpIkiRkzZgghhGhsbBQpKSliwIABwtraWri5uYmIiAjx2WefCSGE+PTTTwUAcf369bseU319vUhISBDu7u6ie/fuYvz48eKbb75psf6dzmF78pokhKF/p95bqqur4eTkhKqqKjg6OsrdHSIiaqOdO5vmjN7NP/8J/OEP5u8Pdaza2loUFRXBx8cHtra2bWqj+S57QHduZvMNR+a4y55+c6dz2J68xkv2RETUYVp7JdUMV1ypi4iKikJ6ejo8PT11yr28vBhGOzGuQ0pERB0mNBTw8mq6gcnQ9TlJavo8NLTj+0adR1RUFKZMmYKcnByUlZXBw8MDoaGhsLS0lLtr1EYMpERE1GEsLYGUlKa76SVJN5Q2L/G4ZUtTPbr3aRo1yCnJQdmNMnh090Bo31BYWnTMybO0tMS4ceM6ZF9kfgykRETUoaKigPT0prvtb136ycurKYzyimvnoC5QI+5AHC5X3/L4TkcvpExMQVQATyIZh4GUiIg6XFQUMGUKkJMDlJU1zRkNDeXIaGehLlAj5sMYCNz2+M7qUsR8GIP0p9MZSskoDKRERCQLS0uAV1w7H02jBnEH4vTCKAAICEiQEH8gHlMGTGnx8n0nWOCHWmCuc8e77ImIiKjVckpydC7T305A4FL1JeSU6D++09raGgDw888/m61/ZF7N5675XJoKR0iJiIio1cputPLxnQbqWVpawtnZGT/88AMAoFu3btr1Q+neJoTAzz//jB9++AHOzs4mX9GAgZSIiIhazaN7Kx/f2UK93r17A4A2lFLn4uzsrD2HpsRASkRERK0W2jcUXo5eKK0uNTiPVIIEL0cvhPY1vJisJEnw8PCAu7s7GhoazN1dMiFra2uzrfXKQEpEREStZmlhiZSJKYj5MAYSJJ1QKqHp8vuWiVvuuh6ppaUlF7InLd7UREREREaJCohC+tPp8HS87fGdjl5c8onaRBKdYO2F6upqODk5oaqqCo6OjnJ3h4iIiCDvk5ro3tOevMZL9kRERNQmlhaWGNdvnNzdoC6Al+yJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhk1aZAmpqaCh8fH9ja2mL48OHIyclp1Xb/+c9/YGVlhQceeKAtuyUiIiKiLsjoQLpr1y7Ex8dj+fLlOHnyJEJDQzFp0iSUlJTccbuqqio8++yzePTRR9vcWSIiIiLqeiQhhDBmg5EjR+LBBx9EWlqatiwgIABPPvkkkpKSWtzu//2//wc/Pz9YWlpiz549yMvLa/U+q6ur4eTkhKqqKjg6OhrTXSIiIiLqAO3Ja0aNkNbX1+Orr77ChAkTdMonTJiAY8eOtbjd22+/jfPnz2PVqlVGdY6IiIiIuj4rYypfvXoVGo0GvXr10inv1asXysvLDW7z3XffYenSpcjJyYGVVet2V1dXh7q6Ou376upqY7pJRERERJ1Im25qkiRJ570QQq8MADQaDaZOnYo1a9bA39+/1e0nJSXByclJ+1IqlW3pJhERERF1AkYF0p49e8LS0lJvNPSHH37QGzUFgBs3buDLL7/EvHnzYGVlBSsrKyQmJuLUqVOwsrJCVlaWwf0sW7YMVVVV2telS5eM6SYRERERdSJGXbK3sbHB8OHDcejQIfz+97/Xlh86dAhTpkzRq+/o6IjTp0/rlKWmpiIrKwvp6enw8fExuB+FQgGFQmFM14iIiIiokzIqkALAokWLMH36dAQHByMkJAR///vfUVJSgjlz5gBoGt0sLS3Fe++9BwsLCwwePFhne3d3d9ja2uqVExEREZFpaDQa5OTkoKysDB4eHggNDYWlpaXc3WqR0YH0mWeewY8//ojExESUlZVh8ODByMjIgLe3NwCgrKzsrmuSEhEREZF5qNVqxMXF4fLly9oyLy8vpKSkICoqSsaetczodUjlwHVIiYiIiO5OrVYjJiYGt8e75pvP09PTzRZKO2wdUiIiIiK6N2k0GsTFxemFUQDasvj4eGg0mo7u2l0xkBIRERF1ATk5OTqX6W8nhMClS5eQk5PTgb1qHQZSIiIioi6grKzMpPU6EgMpERERURfg4eFh0nodiYGUiIiIqAsIDQ2Fl5eXwadnAk03NimVSoSGhnZwz+6OgZSIiIioC7C0tERKSgoA/ce8N7/fsmXLPbkeKQMpERERURcRFRWF9PR0eHp66pR7eXmZdcmn9uI6pERERERdjBxPampPXjP6SU1EREREdG+ztLTEuHHj5O5Gq/GSPRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIiklWbAmlqaip8fHxga2uL4cOHIycnp8W6arUa4eHhcHNzg6OjI0JCQpCZmdnmDhMRERFR12J0IN21axfi4+OxfPlynDx5EqGhoZg0aRJKSkoM1s/OzkZ4eDgyMjLw1VdfISwsDJGRkTh58mS7O09EREREnZ8khBDGbDBy5Eg8+OCDSEtL05YFBATgySefRFJSUqvaCAwMxDPPPIOVK1e2qn51dTWcnJxQVVUFR0dHY7pLRERERB2gPXnNqBHS+vp6fPXVV5gwYYJO+YQJE3Ds2LFWtdHY2IgbN27A1dXVmF0TERERURdlZUzlq1evQqPRoFevXjrlvXr1Qnl5eava2LRpE2pqavD000+3WKeurg51dXXa99XV1cZ0k4iIiIg6kTbd1CRJks57IYRemSE7d+7E6tWrsWvXLri7u7dYLykpCU5OTtqXUqlsSzeJiIiIqBMwKpD27NkTlpaWeqOhP/zwg96o6e127dqFF154AR9++CHGjx9/x7rLli1DVVWV9nXp0iVjuklEREREnYhRgdTGxgbDhw/HoUOHdMoPHTqEUaNGtbjdzp07MXPmTPzzn//EY489dtf9KBQKODo66ryIiIiIqGsyag4pACxatAjTp09HcHAwQkJC8Pe//x0lJSWYM2cOgKbRzdLSUrz33nsAmsLos88+i5SUFDz00EPa0VU7Ozs4OTmZ8FCIiIiIqDMyOpA+88wz+PHHH5GYmIiysjIMHjwYGRkZ8Pb2BgCUlZXprEm6fft23Lx5E7GxsYiNjdWWz5gxA++88077j4CIiIiIOjWj1yGVA9chJSIiIrq3ddg6pEREREREpsZASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWVnJ3gH6l0QA5OUBZGeDhAYSGApaWcveKiIiIyOwYSO8FajUQFwdcvvxbmZcXkJICREXJ1y8iIiKiDsBL9nJTq4GYGN0wCgClpU3larU8/SIiIiLqIAykctJomkZGhdD/rLksPr6pHhEREVEXxUAqp5wc/ZHRWwkBXLrUVI+IiIioi2IglVNZmWnrEREREXVCDKRy8vAwbT0iIiKiToiBVE6hoU1300uS4c8lCVAqm+oRERERdVEMpHKytGxa2gnQD6XN77ds4XqkRERE1KUxkMotKgpITwc8PXXLvbyayrkOKREREXVxXBj/XhAVBUyZwic1ERER0X2JgfReYWkJjBsndy+IiIiIOhwv2RMRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcnKSu4OtIYQAgBQXV0tc0+IiIiIyJDmnNac24zRKQLpjRs3AABKpVLmnhARERHRndy4cQNOTk5GbSOJtsTYDtbY2Ijvv/8e3bt3hyRJRm9fXV0NpVKJS5cuwdHR0Qw9JLnw3HZtPL9dF89t18bz23Xd6dwKIXDjxg306dMHFhbGzQrtFCOkFhYW8PLyanc7jo6O/MXoonhuuzae366L57Zr4/ntulo6t8aOjDbjTU1EREREJCsGUiIiIiKS1X0RSBUKBVatWgWFQiF3V8jEeG67Np7frovntmvj+e26zHVuO8VNTURERETUdd0XI6REREREdO9iICUiIiIiWTGQEhEREZGsGEiJiIiISFZdJpCmpqbCx8cHtra2GD58OHJycu5Y/7PPPsPw4cNha2sLlUqF119/vYN6SsYy5tyq1WqEh4fDzc0Njo6OCAkJQWZmZgf2loxl7O9us//85z+wsrLCAw88YN4OUpsZe27r6uqwfPlyeHt7Q6FQoH///njrrbc6qLdkLGPP7/vvv4+hQ4eiW7du8PDwwHPPPYcff/yxg3pLrZWdnY3IyEj06dMHkiRhz549d93GJJlKdAEffPCBsLa2Fjt27BD5+fkiLi5O2Nvbi4sXLxqsf+HCBdGtWzcRFxcn8vPzxY4dO4S1tbVIT0/v4J7T3Rh7buPi4sSGDRvE8ePHxbfffiuWLVsmrK2txYkTJzq459Qaxp7fZpWVlUKlUokJEyaIoUOHdkxnyShtObdPPPGEGDlypDh06JAoKioS//3vf8V//vOfDuw1tZax5zcnJ0dYWFiIlJQUceHCBZGTkyMCAwPFk08+2cE9p7vJyMgQy5cvF7t37xYAxMcff3zH+qbKVF0ikI4YMULMmTNHp2zgwIFi6dKlBusvWbJEDBw4UKfsxRdfFA899JDZ+khtY+y5NWTQoEFizZo1pu4amUBbz+8zzzwjVqxYIVatWsVAeo8y9tx+8sknwsnJSfz4448d0T1qJ2PP76uvvipUKpVO2WuvvSa8vLzM1kdqv9YEUlNlqk5/yb6+vh5fffUVJkyYoFM+YcIEHDt2zOA2n3/+uV79iIgIfPnll2hoaDBbX8k4bTm3t2tsbMSNGzfg6upqji5SO7T1/L799ts4f/48Vq1aZe4uUhu15dz+3//9H4KDg5GcnAxPT0/4+/tj8eLF+OWXXzqiy2SEtpzfUaNG4fLly8jIyIAQAleuXEF6ejoee+yxjugymZGpMpWVqTvW0a5evQqNRoNevXrplPfq1Qvl5eUGtykvLzdY/+bNm7h69So8PDzM1l9qvbac29tt2rQJNTU1ePrpp83RRWqHtpzf7777DkuXLkVOTg6srDr9/766rLac2wsXLuDo0aOwtbXFxx9/jKtXr2Lu3Lm4du0a55HeY9pyfkeNGoX3338fzzzzDGpra3Hz5k088cQT2Lp1a0d0mczIVJmq04+QNpMkSee9EEKv7G71DZWT/Iw9t8127tyJ1atXY9euXXB3dzdX96idWnt+NRoNpk6dijVr1sDf37+jukftYMzvbmNjIyRJwvvvv48RI0Zg8uTJ2Lx5M9555x2Okt6jjDm/+fn5WLBgAVauXImvvvoKBw4cQFFREebMmdMRXSUzM0Wm6vRDDD179oSlpaXev8p++OEHvcTerHfv3gbrW1lZoUePHmbrKxmnLee22a5du/DCCy/go48+wvjx483ZTWojY8/vjRs38OWXX+LkyZOYN28egKYQI4SAlZUVDh48iEceeaRD+k531pbfXQ8PD3h6esLJyUlbFhAQACEELl++DD8/P7P2mVqvLec3KSkJo0ePxssvvwwACAoKgr29PUJDQ7Fu3TpemezETJWpOv0IqY2NDYYPH45Dhw7plB86dAijRo0yuE1ISIhe/YMHDyI4OBjW1tZm6ysZpy3nFmgaGZ05cyb++c9/cn7SPczY8+vo6IjTp08jLy9P+5ozZw4GDBiAvLw8jBw5sqO6TnfRlt/d0aNH4/vvv8dPP/2kLfv2229hYWEBLy8vs/aXjNOW8/vzzz/DwkI3clhaWgL4bTSNOieTZSqjboG6RzUvP/Hmm2+K/Px8ER8fL+zt7UVxcbEQQoilS5eK6dOna+s3L1GwcOFCkZ+fL958800u+3SPMvbc/vOf/xRWVlZi27ZtoqysTPuqrKyU6xDoDow9v7fjXfb3LmPP7Y0bN4SXl5eIiYkRZ86cEZ999pnw8/MTs2bNkusQ6A6MPb9vv/22sLKyEqmpqeL8+fPi6NGjIjg4WIwYMUKuQ6AW3LhxQ5w8eVKcPHlSABCbN28WJ0+e1C7pZa5M1SUCqRBCbNu2TXh7ewsbGxvx4IMPis8++0z72YwZM8TYsWN16h85ckQMGzZM2NjYiH79+om0tLQO7jG1ljHnduzYsQKA3mvGjBkd33FqFWN/d2/FQHpvM/bcFhQUiPHjxws7Ozvh5eUlFi1aJH7++ecO7jW1lrHn97XXXhODBg0SdnZ2wsPDQ/zxj38Uly9f7uBe0918+umnd/x71FyZShKCY+VEREREJJ9OP4eUiIiIiDo3BlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGT1/wFshjYOeiUiAAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 7 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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qYWGBhQsXYuHChaioqMC//vUvvPbaawgLC8Ply5fv+uBUUzvp6enaEc27MXTt//vf/+L06dN47733MGPGDG15QUHBPdtrzp49e1BdXQ21Wq3Tr9zc3Bbt7+DggBkzZuCtt97CokWL8O6772Lq1KlwdHTUqffCCy/ghRdeQHV1NY4ePYqVK1fiySefxLfffgtPT0/07NkTAQEBWLduncHj9OnTp9k+/PGPf8STTz6pfd8Uuo1haWmJlStX4n//93/x3//+F0Dj7xoAXLlyBUql0ug2m2Po2naUU6dO4dSpU0hISOj0K09Q18FASmSEJ598En/+85/Ro0cPeHl5taoNSZJgYWGhcwv7l19+wd///vcWtzFp0iT8/e9/x/nz5+8a1ExBoVBg7NixcHR0RGZmJk6dOoWgoCBMmjQJK1asQFZWFh577LE2HWPAgAHw8fHB6dOn8ec//9no/SMiIjBkyBAkJSXhySefNPikfWZmpvbpZ0P69euHb775Rqfs22+/xfnz5/X+odHE0dER0dHRKCkpQXx8PIqKijBo0KBmR6HDwsJgYWGBCxcutPqWbVOQuTNwbdu2rVXtNdemEALbt29vcRvz589HamoqoqOjUVFRgblz5zZb19bWFpMmTUJdXR2efvppnD17Fp6ennjyySeRkZGB/v37G5zqcjd9+vS5a2C9U2lpqcFRyqYpD01tTZgwAebm5khLS0NQUJBRfTKF23+W7lzurbnR13tpWh3gpZdeansHiUyEgZTICPHx8di1axceffRRLFiwAAEBAWhoaEBxcTEOHjyIhIQEjBw58q5tPPHEE9i0aROmTp2KP/7xj/jxxx+xYcMGo0Z0EhMT8emnn+LRRx/Fa6+9hiFDhqCiogIHDhzAwoULdZ5wb40VK1bgypUrePzxx+Hh4YGKigqkpKTA0tISY8eO1X4WO3fuREREBJYsWYIRI0bgl19+weeff44nn3wSISEhRh1z27ZtmDRpEsLCwjBz5ky4u7vj+vXryM/Px8mTJ/HJJ580u6+5uTl2796NCRMmICgoCK+88gpCQkJga2uLS5cuIT09HXv37sWNGzeabWP69OmYNm0a5syZg6ioKFy6dAnJycnaEbIm4eHhGDx4MAIDA+Hi4oJLly5h8+bN8PT0hI+PDwBgyJAhAICUlBTMmDEDlpaWGDBgAPr164fExEQsW7YMFy9exMSJE+Hk5ISrV6/ixIkTsLW1bXYR9CYDBw5E//79sWTJEggh4OzsjL179+rc+jZWaGgorKys8Ic//AGLFy9GTU0N0tLS7vp53cnX1xcTJ07Ep59+ijFjxmDo0KE622fPng0bGxuMHj0abm5uKCsrQ1JSEhwcHLRzlhMTE3Ho0CGMGjUK8+fPx4ABA1BTU4OioiJkZGTgzTffbPE0kXsJCwuDh4cHwsPDMXDgQDQ0NCA3NxcbN26EnZ0d4uLiADT+Q+W1117DmjVr8Msvv2iXz8rLy8O1a9fueb3aqulnaf369Zg0aRLMzc0REBAAKysrDBkyBEeOHMHevXvh5uaG7t273/MfqDU1NfjHP/6BUaNGtWgOOlGHkfmhKqIO0/Rk9Z1PyDd54okn7vmUvRBC/PTTT2L58uViwIABwsrKSjg4OIghQ4aIBQsWiLKyMm09ACI2Ntbgsd555x0xYMAAoVAohEqlEklJSeLtt982+OT3E088YbCNy5cvixdffFH07t1bWFpaij59+ohnn31WXL16VQjx2xO7n3zyic5+TU++v/vuu9qyO5+c3rdvn5g0aZJwd3cXVlZWwtXVVUyePFlkZ2frtHXjxg0RFxcn+vbtKywtLYWrq6t44oknxLlz53SO9frrr+v1H4BYuXKlTtnp06fFs88+K1xdXYWlpaXo3bu3eOyxx8Sbb75p8DO4U0VFhVizZo14+OGHhZ2dnbC0tBR9+/YV06ZNE//+97+19Qw9Zd/Q0CCSk5OFSqUS1tbWIjAwUGRlZek9Zb9x40YxatQo0bNnT2FlZSX69u0rXnrpJVFUVKTTl6VLl4o+ffoIMzMzvSen9+zZI0JCQoS9vb1QKBTC09NTREdHi3/961/aOjNmzBC2trYGzzMvL0+EhoaK7t27CycnJ/HMM8+I4uJivc+06Sn78vJynf0Nnf/evXvF0KFDhbW1tXB3dxevvvqq+PTTT/X6fufPyu3ee+89AUB89NFHetvef/99ERISInr16iWsrKy0P6/ffPONTr3y8nIxf/584eXlJSwtLYWzs7MYPny4WLZsmfjpp58MHrc1du7cKaZOnSp8fHx0flamT59ucPWKDz74QPzud78T1tbWws7OTgwbNkznd2js2LHC399fb787P6/mfi9v33b7511bWytmzZolXFxchCRJOtctNzdXjB49WnTr1k0A0Pk5bc6HH37YotUbiDqaJEQLFl8kIiK6h6ioKOTk5KCoqIhzE4nIKLxlT0RErVZbW4uTJ0/ixIkT2L17NzZt2sQwSkRG4wgpERG1WlFREby8vGBvb4+pU6fir3/9a7PfAEZE1BwGUiIiIiKSFRfGJyIiIiJZMZASERERkawYSImIiIhIVp3iKfuGhgZ8//336N69u6xft0ZEREREhgkhcPPmTfTp0wdmZsaNeXaKQPr999+b9DuEiYiIiKh9XL582ehvVesUgbR79+4AGk/Q3t5e5t4QERER0Z2qqqqgVCq1uc0YnSKQNt2mt7e3ZyAlIiIiuo+1ZnolH2oiIiIiIlkxkBIRERGRrBhIiYiIiEhWnWIOKREREXUtGo0G9fX1cneDjGBpaQlzc/N2aZuBlIiIiDqMEAJlZWWoqKiQuyvUCo6Ojujdu7fJ14VnICUiIqIO0xRGXV1d0a1bN37hTSchhMDPP/+MH374AQDg5uZm0vYZSImIiKhDaDQabRjt0aOH3N0hI9nY2AAAfvjhB7i6upr09j0faiIiIqIO0TRntFu3bjL3hFqr6dqZev4vAykRERF1KN6m77za69oxkBIRERGRrBhIiYiIiExEkiTs2bNH7m50OgykRERERC1QVlaGefPmQaVSQaFQQKlUIjw8HIcPH26X4x05cgSSJLXrElk3btzA9OnT4eDgAAcHB0yfPl2WJbn4lD0RERF1OhoNkJ0NlJYCbm5AcDDQTmu2AwCKioowevRoODo6Ijk5GQEBAaivr0dmZiZiY2Nx7ty59jt4GwkhoNFoYGGhH/umTp2KK1eu4MCBAwCAP/7xj5g+fTr27t3boX3kCCkREd2T0AjcOHIDV3dcxY0jNyA0Qu4u0QNMrQb69QNCQoCpUxv/7Nevsby9zJkzB5Ik4cSJE4iOjoavry/8/f2xcOFC5OTkGNzH0Ahnbm4uJElCUVERAODSpUsIDw+Hk5MTbG1t4e/vj4yMDBQVFSEkJAQA4OTkBEmSMHPmTACNATM5ORkqlQo2NjYYOnQo0tPT9Y6bmZmJwMBAKBQKZGdn6/UvPz8fBw4cwFtvvYWgoCAEBQVh+/bt2LdvH86fP2+aD66FOEJKRER3Va4uR0FcAWqv1GrLFB4KeKd4wyXSRcae0YNIrQaiowFxx7+JSkoay9PTgchI0x7z+vXrOHDgANatWwdbW1u97Y6Ojq1uOzY2FnV1dTh69ChsbW2Rl5cHOzs7KJVK7Nq1C1FRUTh//jzs7e2164AuX74carUaaWlp8PHxwdGjRzFt2jS4uLhg7Nix2rYXL16MDRs2QKVSGezjF198AQcHB4wcOVJb9sgjj8DBwQHHjx/HgAEDWn1exmIgJSKiZpWry3E2+ixwx1/+tSW1OBt9Fv7p/gyl1GE0GiAuTj+MAo1lkgTExwMREaa9fV9QUAAhBAYOHGi6Rn9VXFyMqKgoDBkyBACgUqm025ydnQEArq6u2kBZXV2NTZs2ISsrC0FBQdp9jh07hm3btukE0sTERISGhjZ77LKyMri6uuqVu7q6oqysrM3nZgwGUiIiMkhoBAriCvTCaONGABJQEF+AnhE9IZlzXUlqf9nZwJUrzW8XArh8ubHeuHGmO674NQG3xxqc8+fPxyuvvIKDBw9i/PjxiIqKQkBAQLP18/LyUFNToxc06+rqMGzYMJ2ywMDAex7f0DkJITp8rVjOISUiIoMqsit0btPrEUDt5VpUZFd0WJ/owVZaatp6LeXj4wNJkpCfn2/UfmZmjTFL3Dake+c3HM2aNQsXL17E9OnTcebMGQQGBmLLli3NttnQ0AAA2L9/P3Jzc7WvvLw8nXmkAAxOL7hd7969cfXqVb3y8vJy9OrV6+4nZ2IMpEREZFBdaZ1J6xG1lZubaeu1lLOzM8LCwrB161ZUV1frbW9umSQXl8bpLKW3JeTc3Fy9ekqlEjExMVCr1UhISMD27dsBAFZWVgAAjUajrTto0CAoFAoUFxfD29tb56VUKo06r6CgIFRWVuLEiRPasv/85z+orKzEqFGjjGqrrRhIiYjIICs3K5PWI2qr4GDAw6NxrqghkgQolY31TC01NRUajQYjRozArl278N133yE/Px9vvPGGdi7nnZpC4qpVq/Dtt99i//792Lhxo06d+Ph4ZGZmorCwECdPnkRWVhb8/PwAAJ6enpAkCfv27UN5eTl++ukndO/eHYsWLcKCBQvw/vvv48KFCzh16hS2bt2K999/36hz8vPzw8SJEzF79mzk5OQgJycHs2fPxpNPPtmhDzQBDKRERNQMx2BHKDwUQHNTySRAoVTAMdixI7tFDzBzcyAlpfG/7wylTe83b26f9Ui9vLxw8uRJhISEICEhAYMHD0ZoaCgOHz6MtLQ0g/tYWlpix44dOHfuHIYOHYr169dj7dq1OnU0Gg1iY2O14XDAgAFITU0FALi7u2P16tVYsmQJevXqhblz5wIA1qxZgxUrViApKQl+fn4ICwvD3r174eXlZfR5ffjhhxgyZAgmTJiACRMmICAgAH//+9+NbqetJCEMPat2f6mqqoKDgwMqKythb28vd3eIiB4Y2qfsAd2Hm379y59P2ZMxampqUFhYCC8vL1hbW7e6HbW68Wn72x9wUiobw6ipl3wiXXe7hm3Ja3zKnoiImuUS6QL/dH/D65Bu5jqkJI/IyMalnTrym5qofTGQEhHRXblEuqBnRE9UZFegrrQOVm5WcAx25FJPJCtzc9Mu7UTyYiAlIqJ7kswlOI1zkrsbRNRF8aEmIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWfEpeyJ6oAiN4PJFRET3GQZSInpglKvLDS/wnsIF3onINCRJwu7du/H000/L3ZVOhbfsieiB0PQVmLeHUQCoLanF2eizKFeXy9QzIuosysrKMG/ePKhUKigUCiiVSoSHh+Pw4cPtcrwjR45AkiRUVFS0S/sAsG7dOowaNQrdunWDo6Njux3nXjhCSkRdntAIFMQV6H4Xu3YjAAkoiC9Az4ievH1P1EloGjTILs5G6c1SuHV3Q3DfYJibtd93hxYVFWH06NFwdHREcnIyAgICUF9fj8zMTMTGxuLcuXPtduy2EkJAo9HAwkI/9tXV1eGZZ55BUFAQ3n77bRl614gjpETU5VVkV+iNjOoQQO3lWlRkV3RYn4io9dT5avRL6YeQ90MwVT0VIe+HoF9KP6jz1e12zDlz5kCSJJw4cQLR0dHw9fWFv78/Fi5ciJycHIP7GBrhzM3NhSRJKCoqAgBcunQJ4eHhcHJygq2tLfz9/ZGRkYGioiKEhIQAAJycnCBJEmbOnAmgMWAmJydDpVLBxsYGQ4cORXp6ut5xMzMzERgYCIVCgezsbIN9XL16NRYsWIAhQ4a0/UNqA46QElGXV1daZ9J6RCQfdb4a0R9HQ9xxy6OkqgTRH0cj/dl0RPpFmvSY169fx4EDB7Bu3TrY2trqbW/Lre7Y2FjU1dXh6NGjsLW1RV5eHuzs7KBUKrFr1y5ERUXh/PnzsLe3h42NDQBg+fLlUKvVSEtLg4+PD44ePYpp06bBxcUFY8eO1ba9ePFibNiwASqVStbb8S3BQEpEXZ6Vm5VJ6xGRPDQNGsQdiNMLowAgICBBQvyBeEQMiDDp7fuCggIIITBw4ECTtdmkuLgYUVFR2hFKlUql3ebs7AwAcHV11QbK6upqbNq0CVlZWQgKCtLuc+zYMWzbtk0nkCYmJiI0NNTkfW4PDKRE1OU5BjtC4aFAbUmt4XmkUuPT9o7Bjh3dNSIyQnZxNq5UXWl2u4DA5arLyC7Oxrh+40x2XCEa/8chSaafYz5//ny88sorOHjwIMaPH4+oqCgEBAQ0Wz8vLw81NTV6QbOurg7Dhg3TKQsMDDR5f9sL55ASUZcnmUvwTvH+9c2dGxv/8N7szQeaiO5zpTdLTVqvpXx8fCBJEvLz843az8ysMWY1BVoAqK+v16kza9YsXLx4EdOnT8eZM2cQGBiILVu2NNtmQ0MDAGD//v3Izc3VvvLy8nTmkQIwOL3gfsVASkQPBJdIF/in+0PhrtApV3go4J/uz3VIiToBt+5uJq3XUs7OzggLC8PWrVtRXV2tt725ZZlcXBr/v1Ja+ltAzs3N1aunVCoRExMDtVqNhIQEbN++HQBgZdU4jUij0WjrDho0CAqFAsXFxfD29tZ5KZXK1p6i7HjLnogeGC6RLugZ0ZPf1ETUSQX3DYaHvQdKqkoMziOVIMHD3gPBfYNNfuzU1FSMGjUKI0aMQGJiIgICAnDr1i0cOnQIaWlpBkdPm0LiqlWrsHbtWnz33XfYuHGjTp34+HhMmjQJvr6+uHHjBrKysuDn5wcA8PT0hCRJ2LdvHyZPngwbGxt0794dixYtwoIFC9DQ0IAxY8agqqoKx48fh52dHWbMmGHUeRUXF+P69esoLi6GRqPRBmZvb2/Y2dm17sNqBQZSInqgSOYSnMY5yd0Nok5Bo9EgOzsbpaWlcHNzQ3BwMMzN22+tz3sxNzNHysQURH8cDQmSTiiVfp1/s3ni5nZZj9TLywsnT57EunXrkJCQgNLSUri4uGD48OFIS0szuI+lpSV27NiBV155BUOHDsXvfvc7rF27Fs8884y2jkajQWxsLK5cuQJ7e3tMnDgR//u//wsAcHd3x+rVq7FkyRK88MILeP755/Hee+9hzZo1cHV1RVJSEi5evAhHR0c8/PDDeO2114w+rxUrVuD999/Xvm+ah/rZZ59h3LhxRrfXWpK4fWLDfaqqqgoODg6orKyEvb293N0hIiLq8tRqNeLi4nDlym8PEXl4eCAlJQWRka1bVqmmpgaFhYXw8vKCtbV16/uWr0bcgTidB5yU9kpsnrjZ5Es+ka67XcO25DWOkBIREZEOtVqN6Oho3DlmVVJSgujoaKSnp7c6lJpCpF8kIgZEdOg3NVH7YiAlIiIiLY1Gg7i4OL0wCjQ+LS5JEuLj4xERESH77XtTLu1E8uJT9kRERKSVnZ2tc5v+TkIIXL58udmvoiRqDQZSIiIi0rp9iSJT1CNqCQZSIiIi0nJza+Fany2sR9QSDKRERESkFRwcDA8Pj2a/JlOSJCiVSgQHm36tT3pwMZASERGRlrm5OVJSUgDof3d70/vNmzfL+kATdT0MpERERKQjMjIS6enpcHd31yn38PCQfckn6pq47BMRERHpiYyMRERExH31TU3UdTGQEhERkUHm5uYd+vWRXYEkSdi9ezeefvppubvSqfCWPREREVELlJWVYd68eVCpVFAoFFAqlQgPD8fhw4fb5XhHjhyBJEmoqKhol/aLiorw0ksvwcvLCzY2Nujfvz9WrlyJurq6djne3XCElIiIiDofjQbIzgZKSwE3NyA4GGjH6QRFRUUYPXo0HB0dkZycjICAANTX1yMzMxOxsbE4d+5cux27rYQQ0Gg0sLDQjX3nzp1DQ0MDtm3bBm9vb/z3v//F7NmzUV1djQ0bNnRoHzlCSkRERJ2LWg306weEhABTpzb+2a9fY3k7mTNnDiRJwokTJxAdHQ1fX1/4+/tj4cKFyMnJMbiPoRHO3NxcSJKEoqIiAMClS5cQHh4OJycn2Nrawt/fHxkZGSgqKkJISAgAwMnJCZIkYebMmQAaA2ZycjJUKhVsbGwwdOhQpKen6x03MzMTgYGBUCgUBr9Za+LEiXj33XcxYcIEqFQqPPXUU1i0aBHU7fg5NocjpERERNR5qNVAdDQghG55SUljeXo6YOJVAK5fv44DBw5g3bp1sLW11dvu6OjY6rZjY2NRV1eHo0ePwtbWFnl5ebCzs4NSqcSuXbsQFRWF8+fPw97eHjY2NgCA5cuXQ61WIy0tDT4+Pjh69CimTZsGFxcXjB07Vtv24sWLsWHDBqhUqhb3sbKyEs7Ozq0+n9ZiICUiIqLOQaMB4uL0wyjQWCZJQHw8EBFh0tv3BQUFEEJg4MCBJmuzSXFxMaKiojBkyBAAgEql0m5rCoaurq7aQFldXY1NmzYhKysLQUFB2n2OHTuGbdu26QTSxMREhIaGtrgvFy5cwJYtW7Bx48a2npbRGEiJiIioc8jOBq5caX67EMDly431TLg6gPg1ADf37VVtMX/+fLzyyis4ePAgxo8fj6ioKAQEBDRbPy8vDzU1NXpBs66uDsOGDdMpCwwMbHE/vv/+e0ycOBHPPPMMZs2aZdxJmADnkBIREVHnUFpq2not5OPjA0mSkJ+fb9R+ZmaNMUvcNqJbX1+vU2fWrFm4ePEipk+fjjNnziAwMBBbtmxpts2GhgYAwP79+5Gbm6t95eXl6cwjBWBweoEh33//PUJCQhAUFIS//e1vLdrH1BhI76ARAkdu3MCOq1dx5MYNaAzdFiAiIqKO5+Zm2not5OzsjLCwMGzduhXV1dV625tblsnFxQUAUHpbQM7NzdWrp1QqERMTA7VajYSEBGzfvh0AYGVlBQDQaDTauoMGDYJCoUBxcTG8vb11Xkql0uhzKykpwbhx4/Dwww/j3Xff1YbojsZb9rdRl5cjrqAAV2prtWUeCgVSvL0R+esPFREREckkOBjw8Gh8gMnQgJEkNW4PDjb5oVNTUzFq1CiMGDECiYmJCAgIwK1bt3Do0CGkpaUZHD1tComrVq3C2rVr8d133+nNz4yPj8ekSZPg6+uLGzduICsrC35+fgAAT09PSJKEffv2YfLkybCxsUH37t2xaNEiLFiwAA0NDRgzZgyqqqpw/Phx2NnZYcaMGS0+p++//x7jxo1D3759sWHDBpSXl2u39e7du5WfVOtwhPRX6vJyRJ89qxNGAaCkthbRZ89CfdtFIiIiIhmYmwMpKY3/fed8zqb3mze3y3qkXl5eOHnyJEJCQpCQkIDBgwcjNDQUhw8fRlpamsF9LC0tsWPHDpw7dw5Dhw7F+vXrsXbtWp06Go0GsbGx8PPzw8SJEzFgwACkpqYCANzd3bF69WosWbIEvXr1wty5cwEAa9aswYoVK5CUlAQ/Pz+EhYVh79698PLyMuqcDh48iIKCAmRlZcHDwwNubm7aV0eThLj/70lXVVXBwcEBlZWVsLe3N3n7GiHQLydHL4w2kdA4Ulr4yCMwb4cJzURERA+CmpoaFBYWwsvLC9bW1q1vSK1ufNr+9geclMrGMGriJZ9I192uYVvyGm/ZA8iuqGg2jAKAAHC5thbZFRUY5+TUcR0jIiIifZGRjUs7deA3NVH7YiAFUNrC72xtaT0iIiJqZ+bmJl3aieTFOaQA3H59is1U9YiIiIio5RhIAQQ7OsJDoUBzs0MlAEqFAsFt+GowIiIiIjKMgRSAuSQhxdsbAPRCadP7zd7efKCJiIiIqB0wkP4q0sUF6f7+cFcodMo9FAqk+/tzHVIiIiKidsKHmm4T6eKCiJ49kV1RgdK6OrhZWSHY0ZEjo0RERETtiIH0DuaSxKWdiIiIiDoQb9kTERERkawYSImIiIhMRJIk7NmzR+5udDoMpEREREQtUFZWhnnz5kGlUkGhUECpVCI8PByHDx9ul+MdOXIEkiShoqKiXdoHgKeeegp9+/aFtbU13NzcMH36dHz//fftdrzmMJASERFRp6MRAkdu3MCOq1dx5MYNaIRo1+MVFRVh+PDhyMrKQnJyMs6cOYMDBw4gJCQEsbGx7XrsthJC4NatWwa3hYSE4OOPP8b58+exa9cuXLhwAdHR0R3cQwZSIiIi6mTU5eXol5ODkNOnMTU/HyGnT6NfTg7U5eXtdsw5c+ZAkiScOHEC0dHR8PX1hb+/PxYuXIicnByD+xga4czNzYUkSSgqKgIAXLp0CeHh4XBycoKtrS38/f2RkZGBoqIihISEAACcnJwgSRJmzpwJoDFgJicnQ6VSwcbGBkOHDkV6errecTMzMxEYGAiFQoHs7GyDfVywYAEeeeQReHp6YtSoUViyZAlycnJQX1/f9g/NCK0KpKmpqfDy8oK1tTWGDx/e7Ek2+fDDDzF06FB069YNbm5ueOGFF/Djjz+2qsNERET04FKXlyP67Flcqa3VKS+prUX02bPtEkqvX7+OAwcOIDY2Fra2tnrbHdvwTY6xsbGora3F0aNHcebMGaxfvx52dnZQKpXYtWsXAOD8+fMoLS1FSkoKAGD58uV49913kZaWhrNnz2LBggWYNm0aPv/8c522Fy9ejKSkJOTn5yMgIKBF5/nhhx9i1KhRsLS0bPU5tYbRgXTnzp2Ij4/HsmXLcOrUKQQHB2PSpEkoLi42WP/YsWN4/vnn8dJLL+Hs2bP45JNP8OWXX2LWrFlt7jwRERE9ODRCIK6gAIZuzjeVxRcUmPz2fUFBAYQQGDhwoEnbBYDi4mKMHj0aQ4YMgUqlwpNPPolHH30U5ubmcHZ2BgC4urqid+/ecHBwQHV1NTZt2oR33nkHYWFhUKlUmDlzJqZNm4Zt27bptJ2YmIjQ0FD0798fPXr0aLYPf/rTn2Bra4sePXqguLgY//znP01+nvdidCDdtGkTXnrpJcyaNQt+fn7YvHkzlEol0tLSDNbPyclBv379MH/+fHh5eWHMmDF4+eWX8dVXX7W580RERPTgyK6o0BsZvZ0AcLm2FtkmfghI/BpwpXb4opz58+dj7dq1GD16NFauXIlvvvnmrvXz8vJQU1OD0NBQ2NnZaV8ffPABLly4oFM3MDCwRX149dVXcerUKRw8eBDm5uZ4/vnntefcUYwKpHV1dfj6668xYcIEnfIJEybg+PHjBvcZNWoUrly5goyMDAghcPXqVaSnp+OJJ55o9ji1tbWoqqrSeREREdGDrbSuzqT1WsrHxweSJCE/P9+o/czMGmPW7eHuzrmZs2bNwsWLFzF9+nScOXMGgYGB2LJlS7NtNjQ0AAD279+P3Nxc7SsvL09nHikAg9MLDOnZsyd8fX0RGhqKjz76CBkZGc3Oi20vRgXSa9euQaPRoFevXjrlvXr1QllZmcF9Ro0ahQ8//BBTpkyBlZUVevfuDUdHx7t+2ElJSXBwcNC+lEqlMd0kIiKiLsjNysqk9VrK2dkZYWFh2Lp1K6qrq/W2N7csk4uLCwCgtLRUW5abm6tXT6lUIiYmBmq1GgkJCdi+fTsAwOrX89BoNNq6gwYNgkKhQHFxMby9vXVepshLTeG59i4j0e2hVQ813TlkLYRodhg7Ly8P8+fPx4oVK/D111/jwIEDKCwsRExMTLPtL126FJWVldrX5cuXW9NNIiIi6kKCHR3hoVCguRvnEgClQoHgNjxk1JzU1FRoNBqMGDECu3btwnfffYf8/Hy88cYbCAoKMrhPU0hctWoVvv32W+zfvx8bN27UqRMfH4/MzEwUFhbi5MmTyMrKgp+fHwDA09MTkiRh3759KC8vx08//YTu3btj0aJFWLBgAd5//31cuHABp06dwtatW/H+++8bdU4nTpzAX//6V+Tm5uLSpUv47LPPMHXqVPTv37/Zc2ovRgXSnj17wtzcXG809IcfftAbNW2SlJSE0aNH49VXX0VAQADCwsKQmpqKd955R+dfDLdTKBSwt7fXeREREdGDzVySkOLtDQB6obTp/WZvb5i3w1xPLy8vnDx5EiEhIUhISMDgwYMRGhqKw4cPN/scjaWlJXbs2IFz585h6NChWL9+PdauXatTR6PRIDY2Fn5+fpg4cSIGDBiA1NRUAIC7uztWr16NJUuWoFevXpg7dy4AYM2aNVixYgWSkpLg5+eHsLAw7N27F15eXkadk42NDdRqNR5//HEMGDAAL774IgYPHozPP/8cCoWiFZ9S60nCyFmrI0eOxPDhw7UfFtA4fBwREYGkpCS9+lFRUbCwsMDOnTu1ZV988QVGjRqFkpIS9OnT557HrKqqgoODAyorKxlOiYiIOqmamhoUFhZql45sLXV5OeIKCnQecFIqFNjs7Y3IX2+TU/u42zVsS16zMLYjCxcuxPTp0xEYGIigoCD87W9/Q3FxsfYW/NKlS1FSUoIPPvgAABAeHo7Zs2cjLS0NYWFhKC0tRXx8PEaMGNGiMEpERER0u0gXF0T07InsigqU1tXBzcoKwY6O7TIySh3D6EA6ZcoU/Pjjj0hMTERpaSkGDx6MjIwMeHp6AmicuHv7mqQzZ87EzZs38de//hUJCQlwdHTEY489hvXr15vuLIiIiOiBYi5JGOfkJHc3yESMvmUvB96yJyIi6vxMdcue5NNet+z5XfZEREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGZiCRJ2LNnj9zd6HQYSImIiIhaoKysDPPmzYNKpYJCoYBSqUR4eDgOHz7cLsc7cuQIJElCRUVFu7R/u9raWjz00EOQJAm5ubntfrw7Gf1NTURERERyExqBiuwK1JXWwcrNCo7BjpDM2++rQ4uKijB69Gg4OjoiOTkZAQEBqK+vR2ZmJmJjY3Hu3Ll2O3ZbCSGg0WhgYdF87Fu8eDH69OmD06dPd2DPfsMRUiIiIupUytXlyOmXg9Mhp5E/NR+nQ04jp18OytXl7XbMOXPmQJIknDhxAtHR0fD19YW/vz8WLlyInJwcg/sYGuHMzc2FJEkoKioCAFy6dAnh4eFwcnKCra0t/P39kZGRgaKiIoSEhAAAnJycIEkSZs6cCaAxYCYnJ0OlUsHGxgZDhw5Fenq63nEzMzMRGBgIhUKB7OzsZs/t008/xcGDB7Fhw4a2fUhtwBFSIiIi6jTK1eU4G30WuOOLz2tLanE2+iz80/3hEuli0mNev34dBw4cwLp162Bra6u33dHRsdVtx8bGoq6uDkePHoWtrS3y8vJgZ2cHpVKJXbt2ISoqCufPn4e9vT1sbGwAAMuXL4darUZaWhp8fHxw9OhRTJs2DS4uLhg7dqy27cWLF2PDhg1QqVTN9vHq1auYPXs29uzZg27durX6PNqKgZSIiIg6BaERKIgr0AujjRsBSEBBfAF6RvQ06e37goICCCEwcOBAk7XZpLi4GFFRURgyZAgAQKVSabc5OzsDAFxdXbWBsrq6Gps2bUJWVhaCgoK0+xw7dgzbtm3TCaSJiYkIDQ1t9thCCMycORMxMTEIDAzUjtrKgYGUiIiIOoWK7ArUXqltvoIAai/XoiK7Ak7jnEx2XCEaE7AkmX6O6vz58/HKK6/g4MGDGD9+PKKiohAQENBs/by8PNTU1OgFzbq6OgwbNkynLDAw8K7H3rJlC6qqqrB06dLWn4CJcA4pERERdQp1pXUmrddSPj4+kCQJ+fn5Ru1nZtYYs5oCLQDU19fr1Jk1axYuXryI6dOn48yZMwgMDMSWLVuabbOhoQEAsH//fuTm5mpfeXl5OvNIARicXnC7rKws5OTkQKFQwMLCAt7e3gAag+yMGTNafqImwEBKREREnYKVm5VJ67WUs7MzwsLCsHXrVlRXV+ttb25ZJheXxrmspaWl2jJDSyoplUrExMRArVYjISEB27dvBwBYWTWeh0aj0dYdNGgQFAoFiouL4e3trfNSKpVGndcbb7yB06dPa0NtRkYGAGDnzp1Yt26dUW21FW/ZExERUafgGOwIhYcCtSW1hueRSoDCQwHHYEeTHzs1NRWjRo3CiBEjkJiYiICAANy6dQuHDh1CWlqawdHTppC4atUqrF27Ft999x02btyoUyc+Ph6TJk2Cr68vbty4gaysLPj5+QEAPD09IUkS9u3bh8mTJ8PGxgbdu3fHokWLsGDBAjQ0NGDMmDGoqqrC8ePHYWdnZ9TIZt++fXXe29nZAQD69+8PDw8PYz+iNuEIKREREXUKkrkE7xTvX9/cubHxD+/N3u2yHqmXlxdOnjyJkJAQJCQkYPDgwQgNDcXhw4eRlpZmcB9LS0vs2LED586dw9ChQ7F+/XqsXbtWp45Go0FsbCz8/PwwceJEDBgwAKmpqQAAd3d3rF69GkuWLEGvXr0wd+5cAMCaNWuwYsUKJCUlwc/PD2FhYdi7dy+8vLxMft4dRRK3T2y4T1VVVcHBwQGVlZWwt7eXuztERETUCjU1NSgsLISXlxesra1b3U65uhwFcQU6DzgplAp4b/Y2+ZJPpOtu17AteY237ImIiKhTcYl0Qc+Inh36TU3UvhhIiYiIqNORzCWTLu1E8uIcUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJiJJEvbs2SN3NzodBlIiIiKiFigrK8O8efOgUqmgUCigVCoRHh6Ow4cPt8vxjhw5AkmSUFFR0S7t3662thYPPfQQJElCbm5uux/vTvymJiIiIup0hEZ06FeHFhUVYfTo0XB0dERycjICAgJQX1+PzMxMxMbG4ty5c+127LYSQkCj0cDCovnYt3jxYvTp0wenT5/uwJ79hiOkRERE1KmUq8uR0y8Hp0NOI39qPk6HnEZOvxyUq8vb7Zhz5syBJEk4ceIEoqOj4evrC39/fyxcuBA5OTkG9zE0wpmbmwtJklBUVAQAuHTpEsLDw+Hk5ARbW1v4+/sjIyMDRUVFCAkJAQA4OTlBkiTMnDkTQGPATE5Ohkqlgo2NDYYOHYr09HS942ZmZiIwMBAKhQLZ2dnNntunn36KgwcPYsOGDW37kNqAI6RERA8oITSoqMhGXV0prKzc4OgYDEkyl7tbRHdVri7H2eizgNAtry2pxdnos/BP94dLpItJj3n9+nUcOHAA69atg62trd52R0fHVrcdGxuLuro6HD16FLa2tsjLy4OdnR2USiV27dqFqKgonD9/Hvb29rCxsQEALF++HGq1GmlpafDx8cHRo0cxbdo0uLi4YOzYsdq2Fy9ejA0bNkClUjXbx6tXr2L27NnYs2cPunXr1urzaCsGUiKiB1B5uRoFBXGorb2iLVMoPODtnQIXl0gZe0bUPKERKIgr0AujjRsBSEBBfAF6RvQ06e37goICCCEwcOBAk7XZpLi4GFFRURgyZAgAQKVSabc5OzsDAFxdXbWBsrq6Gps2bUJWVhaCgoK0+xw7dgzbtm3TCaSJiYkIDQ1t9thCCMycORMxMTEIDAzUjtrKgYGUiOgBU16uxtmz0bjzb/Xa2hKcPRsNf/90hlK6L1VkV6D2Sm3zFQRQe7kWFdkVcBrnZLLjCtH4uyJJpp+jOn/+fLzyyis4ePAgxo8fj6ioKAQEBDRbPy8vDzU1NXpBs66uDsOGDdMpCwwMvOuxt2zZgqqqKixdurT1J2AinENKRPQAEUKDgoI4ND/EBBQUxEMITYf2i6gl6krrTFqvpXx8fCBJEvLz843az8ysMWY1BVoAqK+v16kza9YsXLx4EdOnT8eZM2cQGBiILVu2NNtmQ0MDAGD//v3Izc3VvvLy8nTmkQIwOL3gdllZWcjJyYFCoYCFhQW8vb0BNAbZGTNmtPxETYCBlIjoAVJRka1zm16fQG3tZVRUNP8ABJFcrNysTFqvpZydnREWFoatW7eiurpab3tzyzK5uDTOZS0tLdWWGVpSSalUIiYmBmq1GgkJCdi+fTsAwMqq8Tw0mt/+gTho0CAoFAoUFxfD29tb56VUKo06rzfeeAOnT5/WhtqMjAwAwM6dO7Fu3Tqj2mor3rInInqA1NWV3ruSEfWIOpJjsCMUHgrUltQaHuSXAIWHAo7BjiY/dmpqKkaNGoURI0YgMTERAQEBuHXrFg4dOoS0tDSDo6dNIXHVqlVYu3YtvvvuO2zcuFGnTnx8PCZNmgRfX1/cuHEDWVlZ8PPzAwB4enpCkiTs27cPkydPho2NDbp3745FixZhwYIFaGhowJgxY1BVVYXjx4/Dzs7OqJHNvn376ry3s7MDAPTv3x8eHh7GfkRtwhFSIqIHiJWVm0nrEXUkyVyCd4r3r2/u3Nj4h/dm73ZZj9TLywsnT55ESEgIEhISMHjwYISGhuLw4cNIS0szuI+lpSV27NiBc+fOYejQoVi/fj3Wrl2rU0ej0SA2NhZ+fn6YOHEiBgwYgNTUVACAu7s7Vq9ejSVLlqBXr16YO3cuAGDNmjVYsWIFkpKS4Ofnh7CwMOzduxdeXl4mP++OIonbJzbcp6qqquDg4IDKykrY29vL3R0iok5LCA1ycvqhtrYEzQ0xKRQeeOSRQi4BRSZXU1ODwsJCeHl5wdrautXtlKvLURBXoPOAk0KpgPdmb5Mv+US67nYN25LXeMueiOgBIknm8PZO+fUpewm6obRxVMnbezPDKN3XXCJd0DOiZ4d+UxO1LwZSIqIHjItLJPz905tZh3Qzl3yiTkEyl0y6tBPJi4GUiOgB5OISiZ49I/hNTUR0X2AgJSJ6QEmSOZycxsndDSIiPmVPRERERPJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQmIkkS9uzZI3c3Oh0GUiIiIqIWKCsrw7x586BSqaBQKKBUKhEeHo7Dhw+3y/GOHDkCSZJQUVHRLu0DQL9+/SBJks5ryZIl7Xa85nDZJyIiIqJ7KCoqwujRo+Ho6Ijk5GQEBASgvr4emZmZiI2Nxblz5+TuYrOEENBoNLCwMBz7EhMTMXv2bO17Ozu7juqaFkdIiYiIqNMRQoMbN47g6tUduHHjCITQtOvx5syZA0mScOLECURHR8PX1xf+/v5YuHAhcnJyDO5jaIQzNzcXkiShqKgIAHDp0iWEh4fDyckJtra28Pf3R0ZGBoqKihASEgIAcHJygiRJmDlzJoDGgJmcnAyVSgUbGxsMHToU6enpesfNzMxEYGAgFAoFsrOzmz237t27o3fv3tqXHIGUI6RERETUqZSXq5v56tuUdvnq2+vXr+PAgQNYt24dbG1t9bY7Ojq2uu3Y2FjU1dXh6NGjsLW1RV5eHuzs7KBUKrFr1y5ERUXh/PnzsLe3h42NDQBg+fLlUKvVSEtLg4+PD44ePYpp06bBxcUFY8eO1ba9ePFibNiwASqV6q59XL9+PdasWQOlUolnnnkGr776KqysrFp9Tq3BQEpERESdRnm5GmfPRgMQOuW1tSU4ezYa/v7pJg+lBQUFEEJg4MCBJm0XAIqLixEVFYUhQ4YAAFQqlXabs7MzAMDV1VUbKKurq7Fp0yZkZWUhKChIu8+xY8ewbds2nUCamJiI0NDQux4/Li4ODz/8MJycnHDixAksXboUhYWFeOutt0x5mvfEQEpERESdghAaFBTE4c4w+utWABIKCuLRs2cEJMnchMdtPJ4kSSZrs8n8+fPxyiuv4ODBgxg/fjyioqIQEBDQbP28vDzU1NToBc26ujoMGzZMpywwMPCex1+wYIH2vwMCAuDk5ITo6GisX78ePXr0MPJsWo9zSImIiKhTqKjI1rlNr0+gtvYyKiqany/ZGj4+PpAkCfn5+UbtZ2bWGLOaAi0A1NfX69SZNWsWLl68iOnTp+PMmTMIDAzEli1bmm2zoaEBALB//37k5uZqX3l5eTrzSAEYnF5wL4888giAxlHhjsRASkRERJ1CXV2pSeu1lLOzM8LCwrB161ZUV1frbW9uWSYXFxcAQGnpb/3Jzc3Vq6dUKhETEwO1Wo2EhARs374dALTzODWa3x7YGjRoEBQKBYqLi+Ht7a3zUiqVrT1FrVOnTgEA3Nzc2tyWMXjLnoiIiDoFK6uWhaSW1jNGamoqRo0ahREjRiAxMREBAQG4desWDh06hLS0NIOjp00hcdWqVVi7di2+++47bNy4UadOfHw8Jk2aBF9fX9y4cQNZWVnw8/MDAHh6ekKSJOzbtw+TJ0+GjY0NunfvjkWLFmHBggVoaGjAmDFjUFVVhePHj8POzg4zZsxo8Tl98cUXyMnJQUhICBwcHPDll19iwYIFeOqpp9C3b9+2fWBG4ggpERERdQqOjsFQKDwANDeXU4JCoYSjY7DJj+3l5YWTJ08iJCQECQkJGDx4MEJDQ3H48GGkpaUZ3MfS0hI7duzAuXPnMHToUKxfvx5r167VqaPRaBAbGws/Pz9MnDgRAwYMQGpqKgDA3d0dq1evxpIlS9CrVy/MnTsXALBmzRqsWLECSUlJ8PPzQ1hYGPbu3QsvLy+jzkmhUGDnzp0YN24cBg0ahBUrVmD27NnYsWNHKz6htpHE7RMb7lNVVVVwcHBAZWUl7O3t5e4OERERtUJNTQ0KCwvh5eUFa2vrVrXx21P2gO7DTY0htT2esqff3O0atiWvcYSUiIiIOg0Xl0j4+6dDoXDXKVcoPBhGOzHOISUiIqJOxcUlEj17RqCiIht1daWwsnKDo2OwSZd6oo7FQEpERESdjiSZw8lpnNzdIBPhLXsiIiIikhUDKRERERHJioGUiIiIiGTFOaRE1KUIoeGDDkREnQwDKRF1GeXlahQUxOl817VC4QFv7xQuBUNEdB/jLXsi6hKaFsu+PYwCQG1tCc6ejUZ5uVqmnhER0b0wkBJRpyeEBgUFcdD91hbtVgBAQUE8hNB0aL+I6MEjSRL27Nkjdzc6HQZSIur0Kiqy9UZGdQnU1l5GRUV2h/WJiLqesrIyzJs3DyqVCgqFAkqlEuHh4Th8+HC7HO/IkSOQJAkVFRXt0j4A9OvXD5Ik6byWLFnSbsdrDueQElGnV1dXatJ6RER3KioqwujRo+Ho6Ijk5GQEBASgvr4emZmZiI2Nxblz5+TuYrOEENBoNLCwMBz7EhMTMXv2bO17Ozu7juqaFkdIiajTs7JyM2k9Irr/CaHBjRtHcPXqDty4caTdp+TMmTMHkiThxIkTiI6Ohq+vL/z9/bFw4ULk5OQY3MfQCGdubi4kSUJRUREA4NKlSwgPD4eTkxNsbW3h7++PjIwMFBUVISQkBADg5OQESZIwc+ZMAI0BMzk5GSqVCjY2Nhg6dCjS09P1jpuZmYnAwEAoFApkZzd/h6h79+7o3bu39iVHIOUIKRF1eo6OwVAoPFBbWwLD80glKBQecHQM7uiuEVE76OgVNa5fv44DBw5g3bp1sLW11dvu6OjY6rZjY2NRV1eHo0ePwtbWFnl5ebCzs4NSqcSuXbsQFRWF8+fPw97eHjY2NgCA5cuXQ61WIy0tDT4+Pjh69CimTZsGFxcXjB07Vtv24sWLsWHDBqhUqrv2cf369VizZg2USiWeeeYZvPrqq7Cysmr1ObUGAykRdXqSZA5v7xScPRsNQIJuKJUAAN7em7keKVEX0LSixp3/+GxaUcPfP93kobSgoABCCAwcONCk7QJAcXExoqKiMGTIEACASqXSbnN2dgYAuLq6agNldXU1Nm3ahKysLAQFBWn3OXbsGLZt26YTSBMTExEaGnrX48fFxeHhhx+Gk5MTTpw4gaVLl6KwsBBvvfWWKU/znhhIiahLcHGJhL9/ejOjJpu5DilRF3DvFTUkFBTEo2fPCJP+A1SIxuNJkmSyNpvMnz8fr7zyCg4ePIjx48cjKioKAQEBzdbPy8tDTU2NXtCsq6vDsGHDdMoCAwPvefwFCxZo/zsgIABOTk6Ijo7G+vXr0aNHDyPPpvUYSImoy3BxiUTPnhH8piaiLsqYFTWcnMaZ7Lg+Pj6QJAn5+fl4+umnW7yfmVnjozpNgRYA6uvrderMmjULYWFh2L9/Pw4ePIikpCRs3LgR8+bNM9hmQ0MDAGD//v1wd3fX2aZQKHTeG5pecC+PPPIIgMZR4Y4MpHyoiYi6FEkyh5PTOPTq9Qc4OY1jGCXqQuRaUcPZ2RlhYWHYunUrqqur9bY3tyyTi4sLAKC09Lf+5Obm6tVTKpWIiYmBWq1GQkICtm/fDgDaeZwazW8PbA0aNAgKhQLFxcXw9vbWeSmVytaeotapU6cAAG5uHfsQKEdIiYiIqFOQc0WN1NRUjBo1CiNGjEBiYiICAgJw69YtHDp0CGlpacjPz9fbpykkrlq1CmvXrsV3332HjRs36tSJj4/HpEmT4Ovrixs3biArKwt+fn4AAE9PT0iShH379mHy5MmwsbFB9+7dsWjRIixYsAANDQ0YM2YMqqqqcPz4cdjZ2WHGjBktPqcvvvgCOTk5CAkJgYODA7788kssWLAATz31FPr27du2D8xIHCElIiKiTqFpRY2mhxX1SVAolO2yooaXlxdOnjyJkJAQJCQkYPDgwQgNDcXhw4eRlpZmcB9LS0vs2LED586dw9ChQ7F+/XqsXbtWp45Go0FsbCz8/PwwceJEDBgwAKmpqQAAd3d3rF69GkuWLEGvXr0wd+5cAMCaNWuwYsUKJCUlwc/PD2FhYdi7dy+8vLyMOieFQoGdO3di3LhxGDRoEFasWIHZs2djx44drfiE2kYSt09suE9VVVXBwcEBlZWVsLe3l7s7RERE1Ao1NTUoLCyEl5cXrK2tW9XGb0/ZA4ZW1GiPp+zpN3e7hm3JaxwhJSIiok6jaUUNheLOB3o8GEY7Mc4hJSIiok6FK2p0PQykRERE1Ok0rahBXQNv2RMRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZNWqQJqamqpdoX/48OHIzs6+a/3a2losW7YMnp6eUCgU6N+/P955551WdZiIiIjofiVJEvbs2SN3NzodowPpzp07ER8fj2XLluHUqVMIDg7GpEmTUFxc3Ow+zz77LA4fPoy3334b58+fx44dOzBw4MA2dZyIiIioI5WVlWHevHlQqVRQKBRQKpUIDw/H4cOH2+V4R44cgSRJqKioaJf2m+zfvx8jR46EjY0NevbsicjIjv+2K6MXxt+0aRNeeuklzJo1CwCwefNmZGZmIi0tDUlJSXr1Dxw4gM8//xwXL16Es7MzAKBfv35t6zURERFRByoqKsLo0aPh6OiI5ORkBAQEoL6+HpmZmYiNjcW5c+fk7mKzhBDQaDSwsNCPfbt27cLs2bPx5z//GY899hiEEDhz5kyH99GoEdK6ujp8/fXXmDBhgk75hAkTcPz4cYP7/N///R8CAwORnJwMd3d3+Pr6YtGiRfjll1+aPU5tbS2qqqp0XkRERERNNBoNjhw5gh07duDIkSPQaDTterw5c+ZAkiScOHEC0dHR8PX1hb+/PxYuXIicnByD+xga4czNzYUkSSgqKgIAXLp0CeHh4XBycoKtrS38/f2RkZGBoqIihISEAACcnJwgSRJmzpwJoDFgJicnQ6VSwcbGBkOHDkV6errecTMzMxEYGAiFQmFweuWtW7cQFxeH119/HTExMfD19cWAAQMQHR1tmg/NCEaNkF67dg0ajQa9evXSKe/VqxfKysoM7nPx4kUcO3YM1tbW2L17N65du4Y5c+bg+vXrzc4jTUpKwurVq43pGhERET0g1Go14uLicOXKFW2Zh4cHUlJS2uV28/Xr13HgwAGsW7cOtra2etsdHR1b3XZsbCzq6upw9OhR2NraIi8vD3Z2dlAqldi1axeioqJw/vx52Nvbw8bGBgCwfPlyqNVqpKWlwcfHB0ePHsW0adPg4uKCsWPHattevHgxNmzYAJVKZbCPJ0+eRElJCczMzDBs2DCUlZXhoYcewoYNG+Dv79/qc2qNVn2XvSRJOu+FEHplTRoaGiBJEj788EM4ODgAaLztHx0dja1bt2o/3NstXboUCxcu1L6vqqqCUqlsTVeJiIioC1Gr1YiOjoYQQqe8pKQE0dHRSE9PN3koLSgogBCiXZ5/KS4uRlRUFIYMGQIAUKlU2m1NUx1dXV21gbK6uhqbNm1CVlYWgoKCtPscO3YM27Zt0wmkiYmJCA0NbfbYFy9eBACsWrUKmzZtQr9+/bBx40aMHTsW3377rfb4HcGoW/Y9e/aEubm53mjoDz/8oDdq2sTNzQ3u7u7aMAoAfn5+EELo/MvmdgqFAvb29jovIiIierBpNBrExcXphVEA2rL4+HiT375varu5wbe2mD9/PtauXYvRo0dj5cqV+Oabb+5aPy8vDzU1NQgNDYWdnZ329cEHH+DChQs6dQMDA+/aVkNDAwBg2bJliIqKwvDhw/Huu+9CkiR88sknbTsxIxkVSK2srDB8+HAcOnRIp/zQoUMYNWqUwX1Gjx6N77//Hj/99JO27Ntvv4WZmRk8PDxa0WUiIiJ6EGVnZzc7mAU0BsfLly/fczlKY/n4+ECSJOTn5xu1n5mZmbZfTerr63XqzJo1CxcvXsT06dNx5swZBAYGYsuWLc222RQi9+/fj9zcXO0rLy9PZx4pAIPTC27n5uYGABg0aJC2TKFQQKVS3XX1pPZg9LJPCxcuxFtvvYV33nkH+fn5WLBgAYqLixETEwOg8Xb7888/r60/depU9OjRAy+88ALy8vJw9OhRvPrqq3jxxRcN3q4nIiIiMqS0tNSk9VrK2dkZYWFh2Lp1K6qrq/W2N7csk4uLi15/cnNz9eoplUrExMRArVYjISEB27dvB9A4EAhAZ8R30KBBUCgUKC4uhre3t87L2OmNw4cPh0KhwPnz57Vl9fX1KCoqgqenp1FttZXRc0inTJmCH3/8EYmJiSgtLcXgwYORkZGh7XhpaalOqrazs8OhQ4cwb948BAYGokePHnj22Wexdu1a050FERERdXlNI3qmqmeM1NRUjBo1CiNGjEBiYiICAgJw69YtHDp0CGlpaQZHT5tC4qpVq7B27Vp899132Lhxo06d+Ph4TJo0Cb6+vrhx4waysrLg5+cHAPD09IQkSdi3bx8mT54MGxsbdO/eHYsWLcKCBQvQ0NCAMWPGoKqqCsePH4ednR1mzJjR4nOyt7dHTEwMVq5cCaVSCU9PT7z++usAgGeeeaYNn1YriE6gsrJSABCVlZVyd4WIiIha6ZdffhF5eXnil19+adX+t27dEh4eHkKSJAFA7yVJklAqleLWrVsm7nmj77//XsTGxgpPT09hZWUl3N3dxVNPPSU+++wzbR0AYvfu3dr3x44dE0OGDBHW1tYiODhYfPLJJwKAKCwsFEIIMXfuXNG/f3+hUCiEi4uLmD59urh27Zp2/8TERNG7d28hSZKYMWOGEEKIhoYGkZKSIgYMGCAsLS2Fi4uLCAsLE59//rkQQojPPvtMABA3bty45znV1dWJhIQE4erqKrp37y7Gjx8v/vvf/zZb/27XsC15TRLCwMzg+0xVVRUcHBxQWVnJB5yIiIg6qZqaGhQWFmq/frw1mp6yB3TnZjY9cNQeT9nTb+52DduS11r1XfZEREREcoiMjER6ejrc3d11yj08PBhGO7FWrUNKREREJJfIyEhEREQgOzsbpaWlcHNzQ3BwMMzNzeXuGrUSAykRERF1Oubm5hg3bpzc3SAT4S17IiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiITESSJOzZs0fubnQ6DKRERERELVBWVoZ58+ZBpVJBoVBAqVQiPDwchw8fbpfjHTlyBJIkoaKiol3ab7J//36MHDkSNjY26NmzpyzfdsWF8YmIiIjuoaioCKNHj4ajoyOSk5MREBCA+vp6ZGZmIjY2FufOnZO7i80SQkCj0cDCQj/27dq1C7Nnz8af//xnPPbYYxBC4MyZMx3eR46QEhERUaej0Whw5MgR7NixA0eOHIFGo2nX482ZMweSJOHEiROIjo6Gr68v/P39sXDhQuTk5Bjcx9AIZ25uLiRJQlFREQDg0qVLCA8Ph5OTE2xtbeHv74+MjAwUFRUhJCQEAODk5ARJkjBz5kwAjQEzOTkZKpUKNjY2GDp0KNLT0/WOm5mZicDAQCgUCmRnZ+v179atW4iLi8Prr7+OmJgY+Pr6YsCAAYiOjjbNh2YEjpASERFRp6JWqxEXF4crV65oyzw8PJCSktIut5uvX7+OAwcOYN26dbC1tdXb7ujo2Oq2Y2NjUVdXh6NHj8LW1hZ5eXmws7ODUqnErl27EBUVhfPnz8Pe3h42NjYAgOXLl0OtViMtLQ0+Pj44evQopk2bBhcXF4wdO1bb9uLFi7FhwwaoVCqDfTx58iRKSkpgZmaGYcOGoaysDA899BA2bNgAf3//Vp9TazCQEhERUaehVqsRHR0NIYROeUlJCaKjo5Genm7yUFpQUAAhBAYOHGjSdgGguLgYUVFRGDJkCABApVJptzk7OwMAXF1dtYGyuroamzZtQlZWFoKCgrT7HDt2DNu2bdMJpImJiQgNDW322BcvXgQArFq1Cps2bUK/fv2wceNGjB07Ft9++632+B2Bt+yJiEgWGg1w5AiwY0fjn+18x5W6AI1Gg7i4OL0wCkBbFh8fb/Lb901tS5Jk0nYBYP78+Vi7di1Gjx6NlStX4ptvvrlr/by8PNTU1CA0NBR2dnba1wcffIALFy7o1A0MDLxrWw0NDQCAZcuWISoqCsOHD8e7774LSZLwySeftO3EjMRASkREHU6tBvr1A0JCgKlTG//s16+xnKg52dnZOrfp7ySEwOXLlw3Ol2wLHx8fSJKE/Px8o/YzMzPT9qtJfX29Tp1Zs2bh4sWLmD59Os6cOYPAwEBs2bKl2TabQuT+/fuRm5urfeXl5enMIwVgcHrB7dzc3AAAgwYN0pYpFAqoVCoUFxe34AxNh4GUiIg6lFoNREcDd+aKkpLGcoZSak5paalJ67WUs7MzwsLCsHXrVlRXV+ttb25ZJhcXF73+5Obm6tVTKpWIiYmBWq1GQkICtm/fDgCwsrICAJ0R30GDBkGhUKC4uBje3t46L6VSadR5DR8+HAqFAufPn9eW1dfXo6ioCJ6enka11VYMpERE1GE0GiAuDjBwx1VbFh/P2/dkWNOInqnqGSM1NRUajQYjRozArl278N133yE/Px9vvPGGdi7nnZpC4qpVq/Dtt99i//792Lhxo06d+Ph4ZGZmorCwECdPnkRWVhb8/PwAAJ6enpAkCfv27UN5eTl++ukndO/eHYsWLcKCBQvw/vvv48KFCzh16hS2bt2K999/36hzsre3R0xMDFauXImDBw/i/PnzeOWVVwAAzzzzTCs+pTYQnUBlZaUAICorK+XuChERtcFnnwnRGD3v/vrsM7l7Su3hl19+EXl5eeKXX35p1f63bt0SHh4eQpIkAUDvJUmSUCqV4tatWybueaPvv/9exMbGCk9PT2FlZSXc3d3FU089JT677QcWgNi9e7f2/bFjx8SQIUOEtbW1CA4OFp988okAIAoLC4UQQsydO1f0799fKBQK4eLiIqZPny6uXbum3T8xMVH07t1bSJIkZsyYIYQQoqGhQaSkpIgBAwYIS0tL4eLiIsLCwsTnn38uhBDis88+EwDEjRs37nlOdXV1IiEhQbi6uoru3buL8ePHi//+97/N1r/bNWxLXpOEMPTv1PtLVVUVHBwcUFlZCXt7e7m7Q0RErbRjR+Oc0Xv5xz+AP/yh/ftDHaumpgaFhYXw8vKCtbV1q9poesoe0J2b2fTAUXs8ZU+/uds1bEte4y17IiLqMC29k9oOd1ypi4iMjER6ejrc3d11yj08PBhGOzGuQ0pERB0mOBjw8Gh8gMnQ/TlJatweHNzxfaPOIzIyEhEREcjOzkZpaSnc3NwQHBwMc3NzubtGrcRASkREHcbcHEhJaXyaXpJ0Q2nTEo+bNzfWo/ufpkGD7OJslN4shVt3NwT3DYa5WcdcPHNzc4wbN65DjkXtj4GUiIg6VGQkkJ7e+LT97Us/eXg0hlHece0c1PlqxB2Iw5Wq276+094DKRNTEOnHi0jGYSAlIqIOFxkJREQA2dlAaWnjnNHgYI6MdhbqfDWiP46GwB1f31lVguiPo5H+bDpDKRmFgZSIiGRhbg7wjmvno2nQIO5AnF4YBQABAQkS4g/EI2JARLO37zvBAj/UjPa6dnzKnoiIiFosuzhb5zb9nQQELlddRnax/td3WlpaAgB+/vnndusfta+ma9d0LU2FI6RERETUYqU3W/j1nQbqmZubw9HRET/88AMAoFu3btr1Q+n+JoTAzz//jB9++AGOjo4mX9GAgZSIiIhazK17C7++s5l6vXv3BgBtKKXOxdHRUXsNTYmBlIiIiFosuG8wPOw9UFJVYnAeqQQJHvYeCO5reDFZSZLg5uYGV1dX1NfXt3d3yYQsLS3bba1XBlIiIiJqMXMzc6RMTEH0x9GQIOmEUgmNt983T9x8z/VIzc3NuZA9afGhJiIiIjJKpF8k0p9Nh7v9HV/fae/BJZ+oVSTRCdZeqKqqgoODAyorK2Fvby93d4iIiAjyflMT3X/aktd4y56IiIhaxdzMHOP6jZO7G9QF8JY9EREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrFoVSFNTU+Hl5QVra2sMHz4c2dnZLdrv3//+NywsLPDQQw+15rBERERE1AUZHUh37tyJ+Ph4LFu2DKdOnUJwcDAmTZqE4uLiu+5XWVmJ559/Ho8//nirO0tEREREXY8khBDG7DBy5Eg8/PDDSEtL05b5+fnh6aefRlJSUrP7/b//9//g4+MDc3Nz7NmzB7m5uS0+ZlVVFRwcHFBZWQl7e3tjuktEREREHaAtec2oEdK6ujp8/fXXmDBhgk75hAkTcPz48Wb3e/fdd3HhwgWsXLnSqM4RERERUddnYUzla9euQaPRoFevXjrlvXr1QllZmcF9vvvuOyxZsgTZ2dmwsGjZ4Wpra1FbW6t9X1VVZUw3iYiIiKgTadVDTZIk6bwXQuiVAYBGo8HUqVOxevVq+Pr6trj9pKQkODg4aF9KpbI13SQiIiKiTsCoQNqzZ0+Ym5vrjYb+8MMPeqOmAHDz5k189dVXmDt3LiwsLGBhYYHExEScPn0aFhYWyMrKMnicpUuXorKyUvu6fPmyMd0kIiIiok7EqFv2VlZWGD58OA4dOoTf//732vJDhw4hIiJCr769vT3OnDmjU5aamoqsrCykp6fDy8vL4HEUCgUUCoUxXSMiIiKiTsqoQAoACxcuxPTp0xEYGIigoCD87W9/Q3FxMWJiYgA0jm6WlJTggw8+gJmZGQYPHqyzv6urK6ytrfXKiYiIiMg0NBoNsrOzUVpaCjc3NwQHB8Pc3FzubjXL6EA6ZcoU/Pjjj0hMTERpaSkGDx6MjIwMeHp6AgBKS0vvuSYpEREREbUPtVqNuLg4XLlyRVvm4eGBlJQUREZGytiz5hm9DqkcuA4pERER0b2p1WpER0fjznjX9PB5enp6u4XSDluHlIiIiIjuTxqNBnFxcXphFIC2LD4+HhqNpqO7dk8MpERERERdQHZ2ts5t+jsJIXD58mVkZ2d3YK9ahoGUiIiIqAsoLS01ab2OxEBKRERE1AW4ubmZtF5HYiAlIiIi6gKCg4Ph4eFh8NszgcYHm5RKJYKDgzu4Z/fGQEpERETUBZibmyMlJQWA/te8N73fvHnzfbkeKQMpERERURcRGRmJ9PR0uLu765R7eHi065JPbcV1SImIiIi6GDm+qaktec3ob2oiIiIiovububk5xo0bJ3c3Woy37ImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkaxaFUhTU1Ph5eUFa2trDB8+HNnZ2c3WVavVCA0NhYuLC+zt7REUFITMzMxWd5iIiIiIuhajA+nOnTsRHx+PZcuW4dSpUwgODsakSZNQXFxssP7Ro0cRGhqKjIwMfP311wgJCUF4eDhOnTrV5s4TERERUecnCSGEMTuMHDkSDz/8MNLS0rRlfn5+ePrpp5GUlNSiNvz9/TFlyhSsWLGiRfWrqqrg4OCAyspK2NvbG9NdIiIiIuoAbclrRo2Q1tXV4euvv8aECRN0yidMmIDjx4+3qI2GhgbcvHkTzs7OxhyaiIiIiLooC2MqX7t2DRqNBr169dIp79WrF8rKylrUxsaNG1FdXY1nn3222Tq1tbWora3Vvq+qqjKmm0RERETUibTqoSZJknTeCyH0ygzZsWMHVq1ahZ07d8LV1bXZeklJSXBwcNC+lEpla7pJRERERJ2AUYG0Z8+eMDc31xsN/eGHH/RGTe+0c+dOvPTSS/j4448xfvz4u9ZdunQpKisrta/Lly8b000iIiIi6kSMCqRWVlYYPnw4Dh06pFN+6NAhjBo1qtn9duzYgZkzZ+If//gHnnjiiXseR6FQwN7eXudFRERERF2TUXNIAWDhwoWYPn06AgMDERQUhL/97W8oLi5GTEwMgMbRzZKSEnzwwQcAGsPo888/j5SUFDzyyCPa0VUbGxs4ODiY8FSIiIiIqDMyOpBOmTIFP/74IxITE1FaWorBgwcjIyMDnp6eAIDS0lKdNUm3bduGW7duITY2FrGxsdryGTNm4L333mv7GRARERFRp2b0OqRy4DqkRERERPe3DluHlIiIiIjI1BhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcnKQu4O0K80GiA7GygtBdzcgOBgwNxc7l4RERERtTsG0vuBWg3ExQFXrvxW5uEBpKQAkZHy9YuIiIioA/CWvdzUaiA6WjeMAkBJSWO5Wi1Pv4iIiIg6CAOpnDSaxpFRIfS3NZXFxzfWIyIiIuqiGEjllJ2tPzJ6OyGAy5cb6xERERF1UQykciotNW09IiIiok6IgVRObm6mrUdERETUCTGQyik4uPFpekkyvF2SAKWysR4RERFRF8VAKidz88alnQD9UNr0fvNmrkdKREREXRoDqdwiI4H0dMDdXbfcw6OxnOuQEhERURfHhfHvB5GRQEQEv6mJiIiIHkgMpPcLc3Ng3Di5e0FERETU4XjLnoiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYWcnegJYQQAICqqiqZe0JEREREhjTltKbcZoxOEUhv3rwJAFAqlTL3hIiIiIju5ubNm3BwcDBqH0m0JsZ2sIaGBnz//ffo3r07JEkyev+qqioolUpcvnwZ9vb27dBDkguvbdfG69t18dp2bby+Xdfdrq0QAjdv3kSfPn1gZmbcrNBOMUJqZmYGDw+PNrdjb2/PX4wuite2a+P17bp4bbs2Xt+uq7lra+zIaBM+1EREREREsmIgJSIiIiJZPRCBVKFQYOXKlVAoFHJ3hUyM17Zr4/XtunhtuzZe366rva5tp3ioiYiIiIi6rgdihJSIiIiI7l8MpEREREQkKwZSIiIiIpIVAykRERERyarLBNLU1FR4eXnB2toaw4cPR3Z29l3rf/755xg+fDisra2hUqnw5ptvdlBPyVjGXFu1Wo3Q0FC4uLjA3t4eQUFByMzM7MDekrGM/d1t8u9//xsWFhZ46KGH2reD1GrGXtva2losW7YMnp6eUCgU6N+/P955550O6i0Zy9jr++GHH2Lo0KHo1q0b3Nzc8MILL+DHH3/soN5SSx09ehTh4eHo06cPJEnCnj177rmPSTKV6AI++ugjYWlpKbZv3y7y8vJEXFycsLW1FZcuXTJY/+LFi6Jbt24iLi5O5OXlie3btwtLS0uRnp7ewT2nezH22sbFxYn169eLEydOiG+//VYsXbpUWFpaipMnT3Zwz6kljL2+TSoqKoRKpRITJkwQQ4cO7ZjOklFac22feuopMXLkSHHo0CFRWFgo/vOf/4h///vfHdhrailjr292drYwMzMTKSkp4uLFiyI7O1v4+/uLp59+uoN7TveSkZEhli1bJnbt2iUAiN27d9+1vqkyVZcIpCNGjBAxMTE6ZQMHDhRLliwxWH/x4sVi4MCBOmUvv/yyeOSRR9qtj9Q6xl5bQwYNGiRWr15t6q6RCbT2+k6ZMkUsX75crFy5koH0PmXstf3000+Fg4OD+PHHHzuie9RGxl7f119/XahUKp2yN954Q3h4eLRbH6ntWhJITZWpOv0t+7q6Onz99deYMGGCTvmECRNw/Phxg/t88cUXevXDwsLw1Vdfob6+vt36SsZpzbW9U0NDA27evAlnZ+f26CK1QWuv77vvvosLFy5g5cqV7d1FaqXWXNv/+7//Q2BgIJKTk+Hu7g5fX18sWrQIv/zyS0d0mYzQmus7atQoXLlyBRkZGRBC4OrVq0hPT8cTTzzREV2mdmSqTGVh6o51tGvXrkGj0aBXr1465b169UJZWZnBfcrKygzWv3XrFq5duwY3N7d26y+1XGuu7Z02btyI6upqPPvss+3RRWqD1lzf7777DkuWLEF2djYsLDr9/766rNZc24sXL+LYsWOwtrbG7t27ce3aNcyZMwfXr1/nPNL7TGuu76hRo/Dhhx9iypQpqKmpwa1bt/DUU09hy5YtHdFlakemylSdfoS0iSRJOu+FEHpl96pvqJzkZ+y1bbJjxw6sWrUKO3fuhKura3t1j9qopddXo9Fg6tSpWL16NXx9fTuqe9QGxvzuNjQ0QJIkfPjhhxgxYgQmT56MTZs24b333uMo6X3KmOubl5eH+fPnY8WKFfj6669x4MABFBYWIiYmpiO6Su3MFJmq0w8x9OzZE+bm5nr/Kvvhhx/0EnuT3r17G6xvYWGBHj16tFtfyTitubZNdu7ciZdeegmffPIJxo8f357dpFYy9vrevHkTX331FU6dOoW5c+cCaAwxQghYWFjg4MGDeOyxxzqk73R3rfnddXNzg7u7OxwcHLRlfn5+EELgypUr8PHxadc+U8u15vomJSVh9OjRePXVVwEAAQEBsLW1RXBwMNauXcs7k52YqTJVpx8htbKywvDhw3Ho0CGd8kOHDmHUqFEG9wkKCtKrf/DgQQQGBsLS0rLd+krGac21BRpHRmfOnIl//OMfnJ90HzP2+trb2+PMmTPIzc3VvmJiYjBgwADk5uZi5MiRHdV1uofW/O6OHj0a33//PX766Sdt2bfffgszMzN4eHi0a3/JOK25vj///DPMzHQjh7m5OYDfRtOoczJZpjLqEaj7VNPyE2+//bbIy8sT8fHxwtbWVhQVFQkhhFiyZImYPn26tn7TEgULFiwQeXl54u233+ayT/cpY6/tP/7xD2FhYSG2bt0qSktLta+Kigq5ToHuwtjreyc+ZX//Mvba3rx5U3h4eIjo6Ghx9uxZ8fnnnwsfHx8xa9YsuU6B7sLY6/vuu+8KCwsLkZqaKi5cuCCOHTsmAgMDxYgRI+Q6BWrGzZs3xalTp8SpU6cEALFp0yZx6tQp7ZJe7ZWpukQgFUKIrVu3Ck9PT2FlZSUefvhh8fnnn2u3zZgxQ4wdO1an/pEjR8SwYcOElZWV6Nevn0hLS+vgHlNLGXNtx44dKwDovWbMmNHxHacWMfZ393YMpPc3Y69tfn6+GD9+vLCxsREeHh5i4cKF4ueff+7gXlNLGXt933jjDTFo0CBhY2Mj3NzcxHPPPSeuXLnSwb2me/nss8/u+vdoe2UqSQiOlRMRERGRfDr9HFIiIiIi6twYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIiktX/BzqXKjjuR+p5AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 8 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 9 ---\n", - "Aktuelle Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 3. 6. 0.13477076 2. ]\n", - " [ 0. 2. 0.14306129 2. ]\n", - " [ 1. 5. 0.18963685 2. ]\n", - " [ 9. 10. 0.32105694 3. ]\n", - " [ 4. 12. 0.35501172 3. ]\n", - " [11. 13. 0.37333899 5. ]\n", - " [ 8. 15. 0.68619926 6. ]\n", - " [14. 16. 0.92078621 9. ]\n", - " [ 7. 17. 1.06516792 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 10 ---\n", - "Finale Cluster: [[1], [5], [7], [8], [3, 6], [0, 2], [4, 9]]\n" - ] - }, - { - "data": { - "image/png": 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SY98mJCRIktsPxtmyLwHcvXt3ruq5GcOHD9ekSZO0ceNGxcfHq2TJkmrevPk1hy2/UvZ23nvvvTm2c/78+Tm2sVChQi43yEuXP9DHxcXps88+03PPPaevv/5amzZtct7z5O51vdH+T05OVuPGjXXgwAG9/vrrWrt2rTZv3qy33377msu8Us+ePRUcHKwZM2ZIunypYnBwsEtYeOKJJ5SYmKi9e/eqQ4cOCgsLU8OGDbVixQqX/bNkyZIc++Yvf/mLpOsfA+PHj3eZx5MBNhwOh3r06KEPPvhAM2bMUOXKldW4cWO3fY8cOaJTp04pICAgR52HDx++bo1Xu9HrkpKSIsn934CIiAjn89n/LV26dI5+V7fdyvqvVLNmTYWGhmrVqlXO+3Oyg450+W/K6tWrlZ6erg0bNriMtjZ48GC98MILevTRR7VkyRL997//1ebNm1W7dm23x97V+8OT7QeQO9yjA9yBQkNDFRwcfM17FkJDQ10eX/ntYraPP/5Y/v7++vzzzxUUFORsX7x4sdtlultGqVKltH//fg8q91y7du3Url07paena+PGjZo4caK6dOmi8uXLKyYm5pbWkL3fhg8frr/97W9u+1x9nf6VWrZsqXfffVeLFy/WsGHD8lRDUFCQ0tPTc7QfP37c5XX18/PT4MGDNXjwYJ06dUpfffWVRowYoZYtW2rfvn3XHfAgezkLFixwnoG5Hnev/Y8//qjvv/9es2fPVrdu3Zzt2Tdk58XixYt17tw5ffbZZy51bdu2LVfzh4SEqFu3bpo5c6aGDBmi9957T126dFHx4sVd+vXo0UM9evTQuXPn9M0332jMmDFq06aNfv31V0VFRSk0NFS1atXSSy+95HY9ERER16zhySefVJs2bZyPr74f5ka6d++u0aNHa8aMGddcv3T5NSxZsqS++OILt8/fzHDUV8sOQocOHcrx3MGDB53HU3a/w4cP5+h3ddvtqt/hcCg2NlZffPGFNm3apFOnTrkEndjYWI0dO1YbNmzQhQsXXILOBx98oK5du+rll192Webx48dzHEPZ67qSJ9sPIHcIOsAdqE2bNnr55ZdVsmRJRUdH52kZ2UOhXnkpV1pamt5///1cLyM+Pl7vv/++fvnll+sGgFshMDBQsbGxKl68uJYvX66kpCTFxMQoPj5eo0eP1sqVK/Xggw/e1DqqVKmiSpUq6fvvv8/xYSc32rVrp5o1a2rixIlq06aN25HXli9frsaNG18ziJQvX14//PCDS9uvv/6qX375JUeAzVa8eHE99thjOnDggAYOHKg9e/aoevXq1zxr1rJlS/n5+WnXrl1uL0nLjewPeVd/kH/nnXfytLxrLdMYo3/961+5XsaAAQM0bdo0PfbYYzp16pT69et3zb6FCxdWfHy8Ll68qEcffVQ//fSToqKi1KZNGy1dulQVK1Z0e8nn9URERFw3CN1ImTJlNHToUP38888uAfJqbdq00ccff6zMzEw1bNjwussMDAy84dmw64mJiVFwcLA++OADPf744872/fv3a+XKlXrsscckXX7/3H333Zo3b54GDx7sfD337t2r9evXu+wXT+r3VLNmzbRw4UK99tprCgsLc7m0NjY2VikpKXrzzTedfbM5HI4cx/N//vMfHThwQPfcc88N1+vJ9gPIHYIOcAcaOHCgFi5cqCZNmmjQoEGqVauWsrKylJycrC+//FLPPvvsDT88tG7dWlOmTFGXLl305JNPKiUlRZMmTfLoG+jx48dr2bJlatKkiUaMGKGaNWvq1KlT+uKLLzR48GCXEc/yYvTo0dq/f7+aN2+usmXL6tSpU3r99dfl7+/v/JZ24MCBmj9/vtq1a6dhw4apQYMGSktL05o1a9SmTRuPfwjwnXfeUXx8vFq2bKnu3burTJkyOnHihHbu3KnvvvtOn3766TXn9fX11aJFixQXF6eYmBg99dRTatasmQoXLqy9e/dqwYIFWrJkiU6ePHnNZTzxxBP6xz/+oYSEBHXo0EF79+7Vq6++qlKlSrn0a9u2rWrUqKH69eurVKlS2rt3r6ZOnaqoqCjnCFg1a9aUJL3++uvq1q2b/P39VaVKFZUvX17jx4/XyJEj9ccff+jhhx/WXXfdpSNHjmjTpk0qXLiwc/jwa6lataoqVqyoYcOGyRijEiVKaMmSJS6XgHmqRYsWCggIUOfOnfXcc8/pwoULmj59+nX319UqV66shx9+WMuWLdMDDzyg2rVruzz/z3/+U8HBwbr//vt199136/Dhw5o4caJCQkKc94SNHz9eK1asUKNGjTRgwABVqVJFFy5c0J49e7R06VLNmDEj15dL5sUrr7xywz7/7//9P3344Ydq1aqVnnnmGTVo0ED+/v7av3+/Vq1apXbt2ql9+/aSLh8HH3/8sebPn68KFSooKCjIeWzkRvHixfXCCy9oxIgR6tq1qzp37qyUlBSNGzdOQUFBzpHMfHx89OKLL6p3795q3769/vnPf+rUqVMaO3Zsjku3PKl/7ty56tmzpxITEz26Tyd7xLkr1ahRQyVLltSiRYtUpkwZ53tFuhy+Zs+erapVq6pWrVraunWrXnvttVy/1p5sP4Bc8vJgCAA8dOUoS+60bt36hqOuGWPM2bNnzahRo0yVKlVMQECACQkJMTVr1jSDBg0yhw8fdvaTZJ5++mm360pMTDRVqlQxgYGBpkKFCmbixIlm1qxZbkcCa926tdtl7Nu3z/Ts2dOULl3a+Pv7m4iICNOxY0dz5MgRY8z/P0LRp59+6jJf9kho7733nrPt6pG0Pv/8cxMfH2/KlCljAgICTFhYmGnVqpVZu3aty7JOnjxpnnnmGVOuXDnj7+9vwsLCTOvWrc3PP//ssq7XXnstR/2SzJgxY1zavv/+e9OxY0cTFhZm/P39TenSpc2DDz5oZsyY4XYfXO3UqVPmxRdfNH/9619NkSJFjL+/vylXrpz5xz/+YdatW+fs527UtaysLPPqq6+aChUqmKCgIFO/fn2zcuXKHKOuTZ482TRq1MiEhoaagIAAU65cOdOrVy+zZ88el1qGDx9uIiIijI+Pj5FkVq1a5Xxu8eLFplmzZqZYsWImMDDQREVFmccee8x89dVXzj7dunUzhQsXdrudO3bsMC1atDBFixY1d911l3n88cdNcnJyjn2aPerasWPHXOZ3t/1LliwxtWvXNkFBQaZMmTJm6NChZtmyZTlqv/pYudLs2bONJPPxxx/neG7OnDmmWbNmJjw83AQEBDiP1x9++MGl37Fjx8yAAQNMdHS08ff3NyVKlDD16tUzI0eONGfPnnW73ry40d+DbO5GTrt06ZKZNGmSc38VKVLEVK1a1fTp08f89ttvzn579uwxcXFxpmjRoi6jOnry3jTGmJkzZ5patWo5/960a9fO7Qh0M2fONJUqVTIBAQGmcuXKJjEx0e3rldv6s/fR1fVcT+nSpY0k89Zbb+V47tFHHzWSzN///neX9pMnT5pevXqZsLAwU6hQIfPAAw+YtWvX5njvXWu/ebr9AG7MYUwufnQDAIA7RIcOHbRx40bt2bMnx2/5AAAKDi5dAwDc8dLT0/Xdd99p06ZNWrRokaZMmULIAYACjjM6AIA73p49exQdHa1ixYqpS5cueuutt1wG2gAAFDwEHQAAAADW4QdDAQAAAFiHoAMAAADAOgQdAAAAANYpEKOuZWVl6eDBgypatKjzl4IBAAAA3HmMMTpz5owiIiLk43Pt8zYFIugcPHhQkZGR3i4DAAAAQD6xb98+lS1b9prPF4igU7RoUUmXN6ZYsWJergYAAACAt6SmpioyMtKZEa6lQASd7MvVihUrRtABAAAAcMNbWhiMAAAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYx8/bBRR0xhilXcr0dhkACrBgf185HA5vlwEAgFUIOjfBGKPHZmzQ1r0nvV0KgAKsftRd+rRvDGEHAIBbiEvXbkLapUxCDoCbtmXvSc4MAwBwi3FG5xbZMuohFQrw9XYZAAqQ8xczVX/CV94uAwAAKxF0bpFCAb4qFMDuBAAAAPIDLl0DAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACs43HQ+eabb9S2bVtFRETI4XBo8eLFN5xnzZo1qlevnoKCglShQgXNmDEjL7UCAAAAQK54HHTOnTun2rVr66233spV/927d6tVq1Zq3LixkpKSNGLECA0YMEALFy70uFgAAAAAyA0/T2eIj49XfHx8rvvPmDFD5cqV09SpUyVJ1apV05YtWzRp0iR16NDB09UDAAAAwA3d9nt0NmzYoLi4OJe2li1basuWLbp06ZLbedLT05WamuoyAQAAAEBu3fagc/jwYYWHh7u0hYeHKyMjQ8ePH3c7z8SJExUSEuKcIiMjb3eZAAAAACzyp4y65nA4XB4bY9y2Zxs+fLhOnz7tnPbt23fbawQAAABgD4/v0fFU6dKldfjwYZe2o0ePys/PTyVLlnQ7T2BgoAIDA293aQAAAAAsddvP6MTExGjFihUubV9++aXq168vf3//2716AAAAAHcgj4PO2bNntW3bNm3btk3S5eGjt23bpuTkZEmXLzvr2rWrs3/fvn21d+9eDR48WDt37lRiYqJmzZqlIUOG3JotAAAAAICreHzp2pYtW9SsWTPn48GDB0uSunXrptmzZ+vQoUPO0CNJ0dHRWrp0qQYNGqS3335bEREReuONNxhaGgAAAMBt43HQadq0qXMwAXdmz56doy02Nlbfffedp6sCAAAAgDz5U0ZdAwAAAIA/E0EHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWMfP2wUAtjLGKO1SprfLQD52/mKG238D7gT7+8rhcHi7DAAoMAg6wG1gjNFjMzZo696T3i4FBUT9CV97uwTkc/Wj7tKnfWMIOwCQS1y6BtwGaZcyCTkAbqkte09ylhgAPMAZHeA22zLqIRUK8PV2GQAKqPMXM1V/wlfeLgMAChyCDnCbFQrwVaEA3moAAAB/Ji5dAwAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6+Qp6EybNk3R0dEKCgpSvXr1tHbt2uv2//DDD1W7dm0VKlRId999t3r06KGUlJQ8FQwAAAAAN+Jx0Jk/f74GDhyokSNHKikpSY0bN1Z8fLySk5Pd9v/222/VtWtX9erVSz/99JM+/fRTbd68Wb17977p4gEAAADAHY+DzpQpU9SrVy/17t1b1apV09SpUxUZGanp06e77b9x40aVL19eAwYMUHR0tB544AH16dNHW7ZsueniAQAAAMAdj4LOxYsXtXXrVsXFxbm0x8XFaf369W7nadSokfbv36+lS5fKGKMjR45owYIFat26dd6rBgAAAIDr8CjoHD9+XJmZmQoPD3dpDw8P1+HDh93O06hRI3344Yfq1KmTAgICVLp0aRUvXlxvvvnmNdeTnp6u1NRUlwkAAAAAcitPgxE4HA6Xx8aYHG3ZduzYoQEDBmj06NHaunWrvvjiC+3evVt9+/a95vInTpyokJAQ5xQZGZmXMgEAAADcoTwKOqGhofL19c1x9ubo0aM5zvJkmzhxou6//34NHTpUtWrVUsuWLTVt2jQlJibq0KFDbucZPny4Tp8+7Zz27dvnSZkAAAAA7nAeBZ2AgADVq1dPK1ascGlfsWKFGjVq5Hae8+fPy8fHdTW+vr6SLp8JcicwMFDFihVzmQAAAAAgtzy+dG3w4MGaOXOmEhMTtXPnTg0aNEjJycnOS9GGDx+url27Ovu3bdtWn332maZPn64//vhD69at04ABA9SgQQNFRETcui0BAAAAgP/j5+kMnTp1UkpKisaPH69Dhw6pRo0aWrp0qaKioiRJhw4dcvlNne7du+vMmTN666239Oyzz6p48eJ68MEH9T//8z+3bisAAAAA4AoeBx1JSkhIUEJCgtvnZs+enaOtf//+6t+/f15WBQAAAAAey9OoawAAAACQnxF0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArOPn7QIAAPmLMUZpGWneLgP/5/ylzCv+nSY5fL1YDa4W7Bcsh8Ph7TIAuEHQAQA4GWPUdVlXbTu2zdul4P+YLH9JL0qSmn4SK4fPJe8WBBd1w+pqzsNzCDtAPkTQAQA4pWWkEXLyGYfPJRWtNszbZeAako4mKS0jTYX8C3m7FABXIegAANxa3XG1gv2CvV0GkC+lZaSp6SdNvV0GgOsg6AAA3Ar2C+ZbagBAgcWoawAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgnTwFnWnTpik6OlpBQUGqV6+e1q5de93+6enpGjlypKKiohQYGKiKFSsqMTExTwUDAAAAwI34eTrD/PnzNXDgQE2bNk3333+/3nnnHcXHx2vHjh0qV66c23k6duyoI0eOaNasWbrnnnt09OhRZWRk3HTxAAAAAOCOx0FnypQp6tWrl3r37i1Jmjp1qpYvX67p06dr4sSJOfp/8cUXWrNmjf744w+VKFFCklS+fPmbqxoAAAAArsOjS9cuXryorVu3Ki4uzqU9Li5O69evdzvP//7v/6p+/fp69dVXVaZMGVWuXFlDhgxRWlraNdeTnp6u1NRUlwkAAAAAcsujMzrHjx9XZmamwsPDXdrDw8N1+PBht/P88ccf+vbbbxUUFKRFixbp+PHjSkhI0IkTJ655n87EiRM1btw4T0oDAAAAAKc8DUbgcDhcHhtjcrRly8rKksPh0IcffqgGDRqoVatWmjJlimbPnn3NszrDhw/X6dOnndO+ffvyUiYAAACAO5RHZ3RCQ0Pl6+ub4+zN0aNHc5zlyXb33XerTJkyCgkJcbZVq1ZNxhjt379flSpVyjFPYGCgAgMDPSkNAAAAAJw8OqMTEBCgevXqacWKFS7tK1asUKNGjdzOc//99+vgwYM6e/ass+3XX3+Vj4+PypYtm4eSAQAAAOD6PL50bfDgwZo5c6YSExO1c+dODRo0SMnJyerbt6+ky5edde3a1dm/S5cuKlmypHr06KEdO3bom2++0dChQ9WzZ08FBwffui0BAAAAgP/j8fDSnTp1UkpKisaPH69Dhw6pRo0aWrp0qaKioiRJhw4dUnJysrN/kSJFtGLFCvXv31/169dXyZIl1bFjR02YMOHWbQUAAAAAXMHjoCNJCQkJSkhIcPvc7Nmzc7RVrVo1x+VuAAAAAHC75GnUNQAAAADIzwg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFgnT0Fn2rRpio6OVlBQkOrVq6e1a9fmar5169bJz89PderUyctqAQAAACBXPA468+fP18CBAzVy5EglJSWpcePGio+PV3Jy8nXnO336tLp27armzZvnuVgAAAAAyA2Pg86UKVPUq1cv9e7dW9WqVdPUqVMVGRmp6dOnX3e+Pn36qEuXLoqJiclzsQAAAACQGx4FnYsXL2rr1q2Ki4tzaY+Li9P69euvOd97772nXbt2acyYMXmrEgAAAAA84OdJ5+PHjyszM1Ph4eEu7eHh4Tp8+LDbeX777TcNGzZMa9eulZ9f7laXnp6u9PR05+PU1FRPygQAAABwh8vTYAQOh8PlsTEmR5skZWZmqkuXLho3bpwqV66c6+VPnDhRISEhzikyMjIvZQIAAAC4Q3kUdEJDQ+Xr65vj7M3Ro0dznOWRpDNnzmjLli3q16+f/Pz85Ofnp/Hjx+v777+Xn5+fVq5c6XY9w4cP1+nTp53Tvn37PCkTAAAAwB3Oo0vXAgICVK9ePa1YsULt27d3tq9YsULt2rXL0b9YsWLavn27S9u0adO0cuVKLViwQNHR0W7XExgYqMDAQE9KAwAAAAAnj4KOJA0ePFhPPPGE6tevr5iYGL377rtKTk5W3759JV0+G3PgwAHNnTtXPj4+qlGjhsv8YWFhCgoKytEOAAAAALeKx0GnU6dOSklJ0fjx43Xo0CHVqFFDS5cuVVRUlCTp0KFDN/xNHQAAAAC4nTwOOpKUkJCghIQEt8/Nnj37uvOOHTtWY8eOzctqAQAAACBX8jTqGgAAAADkZwQdAAAAANYh6AAAAACwDkEHAAAAgHXyNBgBAAAoGIwxSstI83YZ1rlyn7J/b49gv2A5HA5vl4ECjKADAICljDHquqyrth3b5u1SrNb0k6beLsFKdcPqas7Dcwg7yDMuXQMAwFJpGWmEHBRYSUeTOFuGm8IZHQAA7gCrO65WsF+wt8sAbigtI42zZLglCDoAANwBgv2CVci/kLfLAIA/DZeuAQAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHQYjwJ/PGOnSeW9XcXtdzLzi3+cl+XqtlD+FfyGJ3zkAAAD5CEEHfy5jpMSW0r7/eruS28sESnrv8r9fu0dypHu1nNsu8j6p5xeEHQAAkG8QdPDnunTe/pAjqZAjXXuCuni7jD/Pvo2XX9uAwt6uBAAAQBJBB9405HcpgN90KNAunpcm3ePtKgAAAHIg6MB7AgpxBgAAAAC3BaOuAQAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdP28XAAAAgNwxxigtI83bZdxWV26f7dsqScF+wXI4HN4uw0oEHQAAgALAGKOuy7pq27Ft3i7lT9P0k6beLuG2qxtWV3MenkPYuQ24dA0AAKAASMtIu6NCzp0i6WjSHXHmyhs4owMAAFDArO64WsF+wd4uAzchLSPtjjhj5U0EHQAAgAIm2C9YhfwLebsMIF/j0jUAAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALBOnoLOtGnTFB0draCgINWrV09r1669Zt/PPvtMLVq0UKlSpVSsWDHFxMRo+fLleS4YAAAAAG7E46Azf/58DRw4UCNHjlRSUpIaN26s+Ph4JScnu+3/zTffqEWLFlq6dKm2bt2qZs2aqW3btkpKSrrp4gEAAADAHY+DzpQpU9SrVy/17t1b1apV09SpUxUZGanp06e77T916lQ999xzuvfee1WpUiW9/PLLqlSpkpYsWXLTxQMAAACAOx4FnYsXL2rr1q2Ki4tzaY+Li9P69etztYysrCydOXNGJUqU8GTVAAAAAJBrfp50Pn78uDIzMxUeHu7SHh4ersOHD+dqGZMnT9a5c+fUsWPHa/ZJT09Xenq683FqaqonZQIAAAC4w+VpMAKHw+Hy2BiTo82defPmaezYsZo/f77CwsKu2W/ixIkKCQlxTpGRkXkpEwAAAMAdyqOgExoaKl9f3xxnb44ePZrjLM/V5s+fr169eumTTz7RQw89dN2+w4cP1+nTp53Tvn37PCkTAAAAwB3Oo6ATEBCgevXqacWKFS7tK1asUKNGja4537x589S9e3d99NFHat269Q3XExgYqGLFirlMAAAAAJBbHt2jI0mDBw/WE088ofr16ysmJkbvvvuukpOT1bdvX0mXz8YcOHBAc+fOlXQ55HTt2lWvv/667rvvPufZoODgYIWEhNzCTQEsZ4x06by3q3B18bz7f+cn/oWkXFxaCwAA7OJx0OnUqZNSUlI0fvx4HTp0SDVq1NDSpUsVFRUlSTp06JDLb+q88847ysjI0NNPP62nn37a2d6tWzfNnj375rcAuBMYIyW2lPb919uVXNuke7xdgXuR90k9vyDsAABwh/E46EhSQkKCEhIS3D53dXhZvXp1XlYB4EqXzufvkJOf7dt4ef8FFPZ2JQAA4E+Up6ADwIuG/C4FFPJ2FfnfxfP59ywTAAC47Qg6QEETUIizEwAAADeQp9/RAQAAAID8jKADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWMfP2wUAAFDQGWOUlpHm7TJyuLKm/FifJAX7BcvhcHi7DAAWIugAAHATjDHquqyrth3b5u1SrqvpJ029XYJbdcPqas7Dcwg7AG45Ll0DAOAmpGWk5fuQk58lHU3Kt2ebABRsnNEBAOAWWd1xtYL9gr1dRoGQlpGWb88yAbADQQcAgFsk2C9YhfwLebsMAFfJj/fRcQ/d7UfQAQAAgLUKwn10+fXsZkG/h457dAAAAGAt7qPLu4J+Dx1ndAAAAHBH4D663LHlHjqCDgAAAO4I3Ed3Z+HSNQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwDkEHAAAAgHUIOgAAAACsQ9ABAAAAYB2CDgAAAADrEHQAAAAAWIegAwAAAMA6BB0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArEPQAQAAAGAdgg4AAAAA6xB0AAAAAFiHoAMAAADAOgQdAAAAANYh6AAAAACwTp6CzrRp0xQdHa2goCDVq1dPa9euvW7/NWvWqF69egoKClKFChU0Y8aMPBULAAAAALnhcdCZP3++Bg4cqJEjRyopKUmNGzdWfHy8kpOT3fbfvXu3WrVqpcaNGyspKUkjRozQgAEDtHDhwpsuHgAAAADc8TjoTJkyRb169VLv3r1VrVo1TZ06VZGRkZo+fbrb/jNmzFC5cuU0depUVatWTb1791bPnj01adKkmy4eAAAAANzx86TzxYsXtXXrVg0bNsylPS4uTuvXr3c7z4YNGxQXF+fS1rJlS82aNUuXLl2Sv79/jnnS09OVnp7ufHz69GlJUmpqqifl3nbnL2YoK/28pMu1ZQR4tDvvTBfPSenm8r9TU6WATO/WU1Cw3zzHPsuT85fOKzPt8r5KTU1Vhn+GlyvK/9hnecN+8xz7LG/Yb57L7/ssOxMYY67f0XjgwIEDRpJZt26dS/tLL71kKleu7HaeSpUqmZdeesmlbd26dUaSOXjwoNt5xowZYyQxMTExMTExMTExMTG5nfbt23fd7JKnUxAOh8PlsTEmR9uN+rtrzzZ8+HANHjzY+TgrK0snTpxQyZIlr7seAAAAAHYzxujMmTOKiIi4bj+Pgk5oaKh8fX11+PBhl/ajR48qPDzc7TylS5d229/Pz08lS5Z0O09gYKACAwNd2ooXL+5JqQAAAAAsFRIScsM+Hg1GEBAQoHr16mnFihUu7StWrFCjRo3czhMTE5Oj/5dffqn69eu7vT8HAAAAAG6Wx6OuDR48WDNnzlRiYqJ27typQYMGKTk5WX379pV0+bKzrl27Ovv37dtXe/fu1eDBg7Vz504lJiZq1qxZGjJkyK3bCgAAAAC4gsf36HTq1EkpKSkaP368Dh06pBo1amjp0qWKioqSJB06dMjlN3Wio6O1dOlSDRo0SG+//bYiIiL0xhtvqEOHDrduKwAAAADgCg5jbjQuGwAAAAAULB5fugYAAAAA+R1BBwAAAIB1CDoAAAAArEPQyYPu3bvL4XBcc9q4caO3SywQZs6cKYfDoSJFini7lHzrzJkzeu655xQXF6dSpUrJ4XBo7Nix3i4rX1u9ejXvzTxISkrSo48+qoiICBUqVEhVq1bV+PHjdf78eW+Xlq+dPXtWAwcOVEREhIKCglSnTh19/PHH3i4rX1u5cqV69uypqlWrqnDhwipTpozatWunrVu3eru0fG3Tpk1q2bKlihYtqiJFiqhZs2Zat26dt8vK17Zt26bWrVurXLlyCg4OVokSJRQTE6MPPvjA26Xle99++61atWqlu+66S8HBwapUqZJefPFFb5flMY9HXYP0wgsvOIfTvlLbtm0VGBioe++91wtVFSwHDhzQkCFDFBERodOnT3u7nHwrJSVF7777rmrXrq1HH31UM2fO9HZJBcbLL7+sZs2aubTVqFHDS9Xkbzt27FCjRo1UpUoVTZ06VaGhofrmm280fvx4bd26Vf/+97+9XWK+9be//U2bN2/WK6+8osqVK+ujjz5S586dlZWVpS5duni7vHxp+vTpSklJ0TPPPKPq1avr2LFjmjx5su677z4tX75cDz74oLdLzHc2b96sJk2aqEGDBnr//fdljNGrr76q5s2ba9WqVYqJifF2ifnSqVOnFBkZqc6dO6tMmTI6d+6cPvzwQz3xxBPas2ePRo0a5e0S86WPPvpITzzxhDp27Ki5c+eqSJEi2rVrlw4ePOjt0jxncEusXr3aSDKjRo3ydikFQps2bUzbtm1Nt27dTOHChb1dTr6VlZVlsrKyjDHGHDt2zEgyY8aM8W5R+dyqVauMJPPpp596u5QCY+TIkUaS+f33313an3zySSPJnDhxwkuV5W//+c9/jCTz0UcfubS3aNHCREREmIyMDC9Vlr8dOXIkR9uZM2dMeHi4ad68uRcqyv9atmxpwsPDzblz55xtqampJjQ01DRq1MiLlRVMDRs2NJGRkd4uI1/av3+/KVy4sHnqqae8XcotwaVrt8isWbPkcDjUs2dPb5eS733wwQdas2aNpk2b5u1S8r3sS66A28nf31+SFBIS4tJevHhx+fj4KCAgwBtl5XuLFi1SkSJF9Pjjj7u09+jRQwcPHtR///tfL1WWv4WFheVoK1KkiKpXr659+/Z5oaL8b926dWratKkKFSrkbCtatKiaNGmi9evX69ChQ16sruAJDQ2Vnx8XNbkzc+ZMnTt3Ts8//7y3S7klCDq3wOnTp7VgwQI1b95c0dHR3i4nXzt69KgGDhyoV155RWXLlvV2ObDY008/LT8/PxUrVkwtW7bUt99+6+2S8q1u3bqpePHieuqpp/THH3/ozJkz+vzzz/XOO+/o6aefVuHChb1dYr70448/qlq1ajk+MNWqVcv5PHLn9OnT+u677/SXv/zF26XkSxcvXlRgYGCO9uy27du3/9klFShZWVnKyMjQsWPHNG3aNC1fvtyaD/K32jfffKMSJUro559/Vp06deTn56ewsDD17dtXqamp3i7PYwSdW2DevHlKS0tTr169vF1KvpeQkKAqVaroqaee8nYpsFRISIieeeYZvfPOO1q1apVef/117du3T02bNtXy5cu9XV6+VL58eW3YsEE//vijKlasqGLFiqlt27bq1q2bXn/9dW+Xl2+lpKSoRIkSOdqz21JSUv7skgqsp59+WufOndPIkSO9XUq+VL16dW3cuFFZWVnOtoyMDOdZQ46160tISJC/v7/CwsI0aNAgvfHGG+rTp4+3y8qXDhw4oPPnz+vxxx9Xp06d9NVXX2no0KGaO3euWrVqJWOMt0v0COftboFZs2apZMmSat++vbdLydcWLlyoJUuWKCkpicuxcNvUrVtXdevWdT5u3Lix2rdvr5o1a+q5555Ty5YtvVhd/rRnzx61bdtW4eHhWrBggUqVKqX//ve/mjBhgs6ePatZs2Z5u8R863p/y/g7lzsvvPCCPvzwQ7355puqV6+et8vJl/r3769evXqpX79+GjlypLKysjRu3Djt3btXkuTjw/fW1zNixAj17t1bR48e1ZIlS9SvXz+dO3dOQ4YM8XZp+U5WVpYuXLigMWPGaNiwYZKkpk2bKiAgQAMHDtTXX3+thx56yMtV5h7vjJv0ww8/aMuWLfrHP/7h9rQyLjt79qyefvpp9e/fXxERETp16pROnTqlixcvSro8Msq5c+e8XCVsVbx4cbVp00Y//PCD0tLSvF1OvjNs2DClpqZq+fLl6tChg5o0aaKhQ4dq6tSpSkxM1Jo1a7xdYr5UsmRJt9+knzhxQpLcnu2Bq3HjxmnChAl66aWX1K9fP2+Xk2/17NlTr7zyit5//32VLVtW5cqV044dO5wf1MuUKePlCvO3cuXKqX79+mrVqpWmT5+uJ598UsOHD9exY8e8XVq+U7JkSUnK8aVgfHy8JOm7777702u6GQSdm5T9TWfv3r29XEn+dvz4cR05ckSTJ0/WXXfd5ZzmzZunc+fO6a677tLf//53b5cJi2Wfbudb9py2bdum6tWr57gXJ3uofO41ca9mzZrauXOnMjIyXNqz75dgOPPrGzdunMaOHauxY8dqxIgR3i4n33v++ed1/Phxbd++XXv27NH69et18uRJFS5cmDNhHmrQoIEyMjL0xx9/eLuUfCf7HsOrZf8/tKCdPSxY1eYz6enp+uCDD9SgQQP+h3YDpUuX1qpVq3JMLVu2VFBQkFatWqUJEyZ4u0xY6uTJk/r8889Vp04dBQUFebucfCciIkI//fSTzp4969K+YcMGSWLgkGto3769zp49q4ULF7q0z5kzRxEREWrYsKGXKsv/XnzxRY0dO1ajRo3SmDFjvF1OgREYGKgaNWooKipKycnJmj9/vv75z38qODjY26UVKKtWrZKPj48qVKjg7VLynQ4dOkiSli1b5tK+dOlSSdJ99933p9d0M7hH5yYsXrxYJ06c4GxOLgQFBalp06Y52mfPni1fX1+3z+GyZcuW6dy5czpz5oykyz/uuGDBAklSq1atXIYbhdSlSxfnZQqhoaH67bffNHnyZB05ckSzZ8/2dnn50sCBA/Xoo4+qRYsWGjRokEJDQ7Vx40ZNnDhR1atXd16yAFfx8fFq0aKFnnrqKaWmpuqee+7RvHnz9MUXX+iDDz6Qr6+vt0vMlyZPnqzRo0fr4YcfVuvWrbVx40aX5wvaB6k/w48//qiFCxeqfv36CgwM1Pfff69XXnmlwP5a/Z/lySefVLFixdSgQQOFh4fr+PHj+vTTTzV//nwNHTpUpUqV8naJ+U5cXJzatm2r8ePHKysrS/fdd5+2bNmicePGqU2bNnrggQe8XaJnvPw7PgVaixYtTOHChU1qaqq3Symw+MHQG4uKijKS3E67d+/2dnn5zsSJE02dOnVMSEiI8fX1NaVKlTLt27c3mzZt8nZp+drKlStNXFycKV26tAkODjaVK1c2zz77rDl+/Li3S8vXzpw5YwYMGGBKly5tAgICTK1atcy8efO8XVa+Fhsbe82/aXwsce+XX34xTZo0MSVKlDABAQHmnnvuMaNGjTJnz571dmn5WmJiomncuLEJDQ01fn5+pnjx4iY2Nta8//773i4tXzt//rx5/vnnTWRkpPHz8zPlypUzw4cPNxcuXPB2aR5zGFPAxokDAAAAgBvgHh0AAAAA1iHoAAAAALAOQQcAAACAdQg6AAAAAKxD0AEAAABgHYIOAAAAAOsQdAAAAABYh6ADAAAAwDoEHQAAAADWIegAAAAAsA5BBwAAAIB1CDoAAAAArPP/AXcJNR9noL+ZAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Beispiel Daten - hier können Sie Ihre eigenen Daten eingeben\n", - "np.random.seed(0)\n", - "num_points = 10 # Anzahl der Datenpunkte\n", - "num_dimensions = 2 # Anzahl der Dimensionen der Datenpunkte\n", - "X = np.random.random_sample([num_points, num_dimensions])\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Tabelle als DataFrame erstellen und ausgeben\n", - " #columns = ['Cluster 1', 'Cluster 2', 'Abstand', 'Größe Cluster 1', 'Größe Cluster 2']\n", - " #distance_table = pd.DataFrame(distances, columns=columns)\n", - " #print(\"Tabelle mit Abständen zwischen Clustern:\")\n", - " #print(distance_table)\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1334f577", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[0], [1], [3], [4], [5], [7], [8], [9], [2, 6]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[0], [1], [3], [5], [8], [9], [2, 6], [4, 7]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[0], [1], [3], [5], [8], [9], [2, 6], [4, 7]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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b32Bqaoq9e/fC3NxcXd7WEjS62nBycsLly5c70fPOmzp1KqZOnYqGhgbk5uYiMTER06ZNw6BBgxAYGKjXPrS+b0uXLm3zzmRvb+82jw8NDcUHH3yA3bt3Y8mSJV3qg7m5ORoaGrTKr1+/rvG59u3bF3FxcYiLi0N1dTX+7//+D2+99RZCQ0Nx6dKldm/iam0nLS1NPRLaHl2f/X/+8x+cPn0an376KWbOnKkuLy4uvmd7bdm9ezfq6uqgVCo1+pWXl9eh421tbTFz5kx8+OGHWLRoET755BNMmzYNdnZ2GvV++ctf4pe//CXq6uqQnZ2NFStW4IUXXsC5c+fg4eEBR0dH+Pn5Ye3atTrPM2DAgDb78Otf/xovvPCC+nVrIO8MU1NTrFixAn/84x/xn//8B0DLnzUAuHz5MuRyeafbbIuuz9aQWn/N0EUIofOmMiJDYVglMqAXXngB77zzDh555BF4enp2qQ1JktC3b1+N0Y2ffvoJf/rTnzrcxuTJk/GnP/0JRUVF7YY4fZDJZBg/fjzs7OyQkZGBU6dOITAwEJMnT8by5cuRmZmJZ5555r7O4e3tjcGDB+P06dN45513On381KlTMWLECCQmJuKFF17QuSJARkaG+i5tXQYNGoRvv/1Wo+zcuXMoKirS+p+QVnZ2doiMjER5eTliY2NRWlqKYcOGtTl6HRoair59++L8+fNd/hm4NeTcHca2bdvWpfbaalMIge3bt3e4jQULFiAlJQWRkZGorq7GvHnz2qxraWmJyZMno7GxES+99BLOnj0LDw8PvPDCC9i/fz8effTRTt+xPmDAgHbD7N0qKip0jm62TqNobWvixIkwMTFBampqj9yEdOd36e4l69oatdVlzJgxcHd3x8GDB6FSqdT//bly5QpOnz6NadOm6bfjRO1gWCUyoNjYWKSnp+Ppp5/GwoUL4efnh+bmZpSVleHgwYOIj4/HmDFj2m3j+eefx4YNGzBt2jT8+te/xvfff4/k5OROjQQlJCTgyy+/xNNPP4233noLI0aMQHV1NQ4cOIC4uDiNO/G7Yvny5bh8+TKeffZZuLu7o7q6Gps2bYKpqSnGjx+vfi8+//xzTJ06FUuWLMHo0aPx008/4auvvsILL7yA4ODgTp1z27ZtmDx5MkJDQzFr1iy4ubnhxo0bKCgowMmTJ/HFF1+0eayJiQl27dqFiRMnIjAwEG+++SaCg4NhaWmJixcvIi0tDXv27MHNmzfbbGPGjBmYPn065s6di4iICFy8eBFJSUnqkbVWYWFhGD58OAICAuDk5ISLFy9i48aN8PDwwODBgwEAI0aMAABs2rQJM2fOhKmpKby9vTFo0CAkJCTg7bffxoULFzBp0iTY29vj6tWrOH78OCwtLdtcAL7V0KFD8eijj2LJkiUQQsDBwQF79uzR+Dm9s0JCQmBmZoaf//znWLx4Merr65Gamtru+3W3IUOGYNKkSfjyyy/x1FNPwd/fX2P/nDlzYGFhgXHjxsHV1RWVlZVITEyEra2teo50QkICDh06hLFjx2LBggXw9vZGfX09SktLsX//fmzdurXDU0/uJTQ0FO7u7ggLC8PQoUPR3NyMvLw8rF+/HlZWVoiJiQHQ8j8xb731FlavXo2ffvpJvQRYfn4+rl+/fs/P6361fpfWrVuHyZMnw8TEBH5+fjAzM8OIESOQlZWFPXv2wNXVFdbW1m3+z2ufPn3wxz/+Ea+88gqmTp2KN998E3V1dVi9ejXMzMzUK0QQdYuevb+LyHi13gF+9538rZ5//vl7rgYghBC3bt0Sy5YtE97e3sLMzEzY2tqKESNGiIULF4rKykp1PQAiOjpa57k+/vhj4e3tLWQymVAoFCIxMVF89NFHOu9Qf/7553W2cenSJfHGG2+I/v37C1NTUzFgwADxyiuviKtXrwoh/ndn8RdffKFxXOsd+p988om67O47vPfu3SsmT54s3NzchJmZmXB2dhZTpkwROTk5Gm3dvHlTxMTEiIEDBwpTU1Ph7Owsnn/+eVFYWKhxrnfffVer/wDEihUrNMpOnz4tXnnlFeHs7CxMTU1F//79xTPPPCO2bt2q8z24W3V1tVi9erV4/PHHhZWVlTA1NRUDBw4U06dPF//v//0/dT1dqwE0NzeLpKQkoVAohLm5uQgICBCZmZlaqwGsX79ejB07Vjg6OgozMzMxcOBA8atf/UqUlpZq9GXp0qViwIABok+fPlp3eO/evVsEBwcLGxsbIZPJhIeHh4iMjBT/93//p64zc+ZMYWlpqfM68/PzRUhIiLC2thb29vbi5ZdfFmVlZVrvaetqAFVVVRrH67r+PXv2CH9/f2Fubi7c3NzEb3/7W/Hll19q9f3u78qdPv30UwFA/O1vf9Pat2PHDhEcHCxcXFyEmZmZ+vv67bffatSrqqoSCxYsEJ6ensLU1FQ4ODiIUaNGibffflvcunVL53m74vPPPxfTpk0TgwcP1viuzJgxQ+cqG5999pl44oknhLm5ubCyshIjR47U+DM0fvx44evrq3Xc3e9XW38u79x35/vd0NAgZs+eLZycnIQkSRqfW15enhg3bpzo16+fAKDxPW3L7t271ddha2srXnzxRb2uskDUEZIQHVgskoiISM8iIiKQm5uL0tLSB/5pYURkOJwGQERE3aahoQEnT57E8ePHsWvXLmzYsIFBlYjaxZFVIiLqNqWlpfD09ISNjQ2mTZuG999/v80noxERAQyrRERERGTEuFAaERERERkthlUiIiIiMloMq0RERERktHrdagDNzc24cuUKrK2tu/3xdERERER0b0II/PDDDxgwYMA9H9/b68LqlStX9Po8ZiIiIiIyjEuXLt3zSXO9LqxaW1sDaLl4GxubHu4NEREREd2ttrYWcrlcndva0+vCautP/zY2NgyrREREREasI1M2eYMVERERERkthlUiIiIiMloMq0RERERktHrdnFUiIiLqfVQqFZqamnq6G9QJpqamMDExue92GFaJiIjIaAkhUFlZierq6p7uCnWBnZ0d+vfvf19r3zOsEhERkdFqDarOzs7o168fH/jzgBBC4Mcff8S1a9cAAK6url1ui2GViIiIjJJKpVIH1UceeaSnu0OdZGFhAQC4du0anJ2duzwlgDdYERERkVFqnaPar1+/Hu4JdVXrZ3c/840ZVomIiMio8af/B5c+PjuGVSIiIiIyWgyrRERERD1AkiTs3r27p7th9BhWiYiIiPSssrIS8+fPh0KhgEwmg1wuR1hYGA4fPmyQ82VlZUGSJIMu8XXz5k3MmDEDtra2sLW1xYwZM7plSTGuBkBERES9mkoF5OQAFRWAqysQFAToYa36NpWWlmLcuHGws7NDUlIS/Pz80NTUhIyMDERHR6OwsNBwJ79PQgioVCr07asdEadNm4bLly/jwIEDAIBf//rXmDFjBvbs2WPQPnFklYiIiLpMpVIhKysLO3fuRFZWFlQqVU93SYNSCQwaBAQHA9Omtfxz0KCWckOZO3cuJEnC8ePHERkZiSFDhsDX1xdxcXHIzc3VeYyukdG8vDxIkoTS0lIAwMWLFxEWFgZ7e3tYWlrC19cX+/fvR2lpKYKDgwEA9vb2kCQJs2bNAtASPpOSkqBQKGBhYQF/f3+kpaVpnTcjIwMBAQGQyWTIycnR6l9BQQEOHDiADz/8EIGBgQgMDMT27duxd+9eFBUV6eeNawNHVomIiKhLlEolYmJicPnyZXWZu7s7Nm3ahPDw8B7sWQulEoiMBITQLC8vbylPSwP03c0bN27gwIEDWLt2LSwtLbX229nZdbnt6OhoNDY2Ijs7G5aWlsjPz4eVlRXkcjnS09MRERGBoqIi2NjYqNc4XbZsGZRKJVJTUzF48GBkZ2dj+vTpcHJywvjx49VtL168GMnJyVAoFDr7eOzYMdja2mLMmDHqsieffBK2trY4evQovL29u3xd98KwSkRERJ2mVCoRGRkJcVcSLC8vR2RkJNLS0no0sKpUQEyMdlAFWsokCYiNBaZO1e+UgOLiYgghMHToUP01+l9lZWWIiIjAiBEjAAAKhUK9z8HBAQDg7OysDpt1dXXYsGEDMjMzERgYqD7m66+/xrZt2zTCakJCAkJCQto8d2VlJZydnbXKnZ2dUVlZed/X1h5OAyAiIqJOUalUiImJ0QqqANRlsbGxPTolICcHuGPAV4sQwKVLLfX0qfX6DbE27IIFC7BmzRqMGzcOK1aswLfffttu/fz8fNTX1yMkJARWVlbq7bPPPsP58+c16gYEBNzz/LquSQhh8HVwGVaJiIioU3JycjR++r+bEAKXLl3SOfexu1RU6LdeRw0ePBiSJKGgoKBTx/Xp0xLJ7vwfgLuf+jR79mxcuHABM2bMwJkzZxAQEIDNmze32WZzczMAYN++fcjLy1Nv+fn5GvNWAeicsnCn/v374+rVq1rlVVVVcHFxaf/i7hPDKhEREXVKRQcTXkfrGYKrq37rdZSDgwNCQ0OxZcsW1NXVae1va6knJycnAJrvWV5enlY9uVyOqKgoKJVKxMfHY/v27QAAMzMzANAYzR42bBhkMhnKysrg5eWlscnl8k5dV2BgIGpqanD8+HF12b/+9S/U1NRg7NixnWqrsxhWiYiIqFNcO5jwOlrPEIKCAHf3lrmpukgSIJe31NO3lJQUqFQqjB49Gunp6fjuu+9QUFCA9957Tz139G6tAXLlypU4d+4c9u3bh/Xr12vUiY2NRUZGBkpKSnDy5ElkZmbCx8cHAODh4QFJkrB3715UVVXh1q1bsLa2xqJFi7Bw4ULs2LED58+fx6lTp7Blyxbs2LGjU9fk4+ODSZMmYc6cOcjNzUVubi7mzJmDF154waA3VwEARC9TU1MjAIiampqe7goREVGvdPv2beHu7i4kSRIAtDZJkoRcLhe3b9++r/P89NNPIj8/X/z0009dOj49XQhJatlaZqm2bK1l6en31b12XblyRURHRwsPDw9hZmYm3NzcxIsvviiOHDmirgNA7Nq1S/3666+/FiNGjBDm5uYiKChIfPHFFwKAKCkpEUIIMW/ePPHoo48KmUwmnJycxIwZM8T169fVxyckJIj+/fsLSZLEzJkzhRBCNDc3i02bNglvb29hamoqnJycRGhoqPjqq6+EEEIcOXJEABA3b9685zV9//334he/+IWwtrYW1tbW4he/+MU9j2vrM+xMXpOE0HWf3IOrtrYWtra2qKmpgY2NTU93h4iIqFdqXQ0A0Jxn2XqzjT5WA6ivr0dJSQk8PT1hbm7exX62rApw5xRbuRzYuFH/y1aRtrY+w87kNU4DICIiok4LDw9HWloa3NzcNMrd3d17fNmqO4WHA6WlwJEjwF//2vLPkhIG1QcJ11klIiKiLgkPD8fUqVORk5ODiooKuLq6IigoCCaGfJZpF5iYABMm9HQvqKsYVomIiKjLTExMMIFJkAyI0wCIiIiIyGgxrBIRERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjBbDKhEREREZLYZVIiIioh4gSRJ2797d090wegyrRERERHpWWVmJ+fPnQ6FQQCaTQS6XIywsDIcPHzbI+bKysiBJEqqrqw3SPgCsXbsWY8eORb9+/WBnZ2ew89yNDwUgIiKiXk3VrEJOWQ4qfqiAq7UrggYGwaSP4Z6yVVpainHjxsHOzg5JSUnw8/NDU1MTMjIyEB0djcLCQoOd+34JIaBSqdC3r3ZEbGxsxMsvv4zAwEB89NFH3dYnjqwSERFRr6UsUGLQpkEI3hGMacppCN4RjEGbBkFZoDTYOefOnQtJknD8+HFERkZiyJAh8PX1RVxcHHJzc3Ueo2tkNC8vD5IkobS0FABw8eJFhIWFwd7eHpaWlvD19cX+/ftRWlqK4OBgAIC9vT0kScKsWbMAtITPpKQkKBQKWFhYwN/fH2lpaVrnzcjIQEBAAGQyGXJycnT2cdWqVVi4cCFGjBhx/29SJ3BklYiIiHolZYESkX+PhIDQKC+vLUfk3yOR9koawn3C9XrOGzdu4MCBA1i7di0sLS219t/Pz+fR0dFobGxEdnY2LC0tkZ+fDysrK8jlcqSnpyMiIgJFRUWwsbGBhYUFAGDZsmVQKpVITU3F4MGDkZ2djenTp8PJyQnjx49Xt7148WIkJydDoVB060/8HcGwSkRERL2OqlmFmAMxWkEVAAQEJEiIPRCLqd5T9ToloLi4GEIIDB06VG9ttiorK0NERIR6ZFOhUKj3OTg4AACcnZ3VYbOurg4bNmxAZmYmAgMD1cd8/fXX2LZtm0ZYTUhIQEhIiN77rA8Mq0RERNTr5JTl4HLt5Tb3Cwhcqr2EnLIcTBg0QW/nFaIlHEuSpLc2Wy1YsABvvvkmDh48iOeeew4RERHw8/Nrs35+fj7q6+u1QmhjYyNGjhypURYQEKD3/uoL56wSERFRr1PxQ4Ve63XU4MGDIUkSCgoKOnVcnz4tkaw17AJAU1OTRp3Zs2fjwoULmDFjBs6cOYOAgABs3ry5zTabm5sBAPv27UNeXp56y8/P15i3CkDnlAVjwbBKREREvY6rtate63WUg4MDQkNDsWXLFtTV1Wntb2tpKScnJwBARcX/wnNeXp5WPblcjqioKCiVSsTHx2P79u0AADMzMwCASqVS1x02bBhkMhnKysrg5eWlscnl8q5eYrdjWCUiIqJeJ2hgENxt3CFB98/xEiTIbeQIGhik93OnpKRApVJh9OjRSE9Px3fffYeCggK899576rmjd2sNkCtXrsS5c+ewb98+rF+/XqNObGwsMjIyUFJSgpMnTyIzMxM+Pj4AAA8PD0iShL1796Kqqgq3bt2CtbU1Fi1ahIULF2LHjh04f/48Tp06hS1btmDHjh2dvq6ysjLk5eWhrKwMKpVKPVJ769atzr9JncCwSkRERL2OSR8TbJq0CQC0Amvr642TNhpkvVVPT0+cPHkSwcHBiI+Px/DhwxESEoLDhw8jNTVV5zGmpqbYuXMnCgsL4e/vj3Xr1mHNmjUadVQqFaKjo+Hj44NJkybB29sbKSkpAAA3NzesWrUKS5YsgYuLC+bNmwcAWL16NZYvX47ExET4+PggNDQUe/bsgaenZ6eva/ny5Rg5ciRWrFiBW7duYeTIkRg5ciROnDjR6bY6QxJ3To7oBWpra2Fra4uamhrY2Nj0dHeIiIioi+rr61FSUgJPT0+Ym5t3qQ1lgRIxB2I0braS28ixcdJGvS9bRdra+gw7k9e4GgARERH1WuE+4ZjqPbVbn2BF+sWwSkRERL2aSR8TvS5PRd3LoHNWExMT8cQTT8Da2hrOzs546aWXUFRUdM/jvvrqK4waNQrm5uZQKBTYunWrIbtJREREREbKoGH1q6++QnR0NHJzc3Ho0CHcvn0bEydO1LmUQ6uSkhJMmTIFQUFBOHXqFN566y0sWLAA6enphuwqERERERkhg04DOHDggMbrTz75BM7Ozvj3v/+Np59+WucxW7duxcCBA7Fx40YAgI+PD06cOIHk5GREREQYsrtEREREZGS6demqmpoaAP97fq0ux44dw8SJEzXKQkNDceLECa0nORARERFR79ZtN1gJIRAXF4ennnoKw4cPb7NeZWUlXFxcNMpcXFxw+/ZtXL9+Ha6umk+aaGhoQENDg/p1bW2tfjtORERERD2m20ZW582bh2+//RY7d+68Z11J0ly8t3Up2LvLgZabuGxtbdXbg/T4MCIiIiJqX7eE1fnz5+Of//wnjhw5And393br9u/fH5WVlRpl165dQ9++ffHII49o1V+6dClqamrU26VLl/TadyIiIiLqOQYNq0IIzJs3D0qlEpmZmR16tFdgYCAOHTqkUXbw4EEEBATA1NRUq75MJoONjY3GRkRERGTsJEnC7t27e7obRs+gYTU6Ohp//vOf8de//hXW1taorKxEZWUlfvrpJ3WdpUuX4vXXX1e/joqKwsWLFxEXF4eCggJ8/PHH+Oijj7Bo0SJDdpWIiIhIbyorKzF//nwoFArIZDLI5XKEhYXh8OHDBjlfVlYWJElCdXW1QdovLS3Fr371K3h6esLCwgKPPvooVqxYgcbGRoOc704GvcEqNTUVADBhwgSN8k8++QSzZs0CAFRUVKCsrEy9z9PTE/v378fChQuxZcsWDBgwAO+99x6XrSIiIqKuUamAnBygogJwdQWCggATwz1utbS0FOPGjYOdnR2SkpLg5+eHpqYmZGRkIDo6GoWFhQY79/0SQkClUqFvX82IWFhYiObmZmzbtg1eXl74z3/+gzlz5qCurg7JyckG71SvUlNTIwCImpqanu4KERER3YeffvpJ5Ofni59++qnrjaSnC+HuLgTwv83dvaXcQCZPnizc3NzErVu3tPbdvHlT/e8AxK5du4QQQhw5ckQA0Nh/6tQpAUCUlJQIIYQoLS0VL7zwgrCzsxP9+vUTw4YNE/v27RMlJSUCgMY2c+ZMIYQQzc3NYt26dcLT01OYm5sLPz8/8cUXX6jP0XreAwcOiFGjRglTU1ORmZnZoetMSkoSnp6e7dZp6zPsTF7rtqWriIiIiLqVUglERrZE1DuVl7eUp6UB4eF6PeWNGzdw4MABrF27FpaWllr77ezsutx2dHQ0GhsbkZ2dDUtLS+Tn58PKygpyuRzp6emIiIhAUVERbGxsYGFhAQBYtmwZlEolUlNTMXjwYGRnZ2P69OlwcnLC+PHj1W0vXrwYycnJUCgUHe5jTU1Nu2vn6wvDKhEREfU+KhUQE6MdVIGWMkkCYmOBqVP1OiWguLgYQggMHTpUb222KisrQ0REBEaMGAEAUCgU6n2todHZ2VkdNuvq6rBhwwZkZmYiMDBQfczXX3+Nbdu2aYTVhIQEhISEdLgv58+fx+bNm7F+/fr7vax7YlglIiKi3icnB7h8ue39QgCXLrXUu+vemvsh2lkb/n4tWLAAb775Jg4ePIjnnnsOERER8PPza7N+fn4+6uvrtUJoY2MjRo4cqVEWEBDQ4X5cuXIFkyZNwssvv4zZs2d37iK6oFsft0pERETULSoq9FuvgwYPHgxJklBQUNCp4/r0aYlk4o6R4LsfMz979mxcuHABM2bMwJkzZxAQEIDNmze32WZzczMAYN++fcjLy1Nv+fn5SEtL06ira8qCLleuXEFwcDACAwPxwQcfdOiY+8WwSkRERL3PXY9nv+96HeTg4IDQ0FBs2bIFdXV1WvvbWlrKyckJQMsqSa3y8vK06snlckRFRUGpVCI+Ph7bt28HAJiZmQEAVCqVuu6wYcMgk8lQVlYGLy8vja0rT/wsLy/HhAkT8Pjjj+OTTz5RB2xDY1glIiKi3icoCHB3b5mbqoskAXJ5Sz09S0lJgUqlwujRo5Geno7vvvsOBQUFeO+999RzR+/WGiBXrlyJc+fOYd++fVrzQWNjY5GRkYGSkhKcPHkSmZmZ8PHxAQB4eHhAkiTs3bsXVVVVuHXrFqytrbFo0SIsXLgQO3bswPnz53Hq1Cls2bIFO3bs6NQ1XblyBRMmTIBcLkdycjKqqqrU6+cbGsMqERER9T4mJsCmTS3/fndgbX29caNB1lv19PTEyZMnERwcjPj4eAwfPhwhISE4fPiweg36u5mammLnzp0oLCyEv78/1q1bhzVr1mjUUalUiI6Oho+PDyZNmgRvb2+kpKQAANzc3LBq1SosWbIELi4umDdvHgBg9erVWL58ORITE+Hj44PQ0FDs2bOnQ08VvdPBgwdRXFyMzMxMuLu7w9XVVb0ZmiSErtvkHly1tbWwtbVFTU0NH71KRET0AKuvr0dJSQk8PT1hbm7etUaUypZVAe682Uoubwmqel62irS19Rl2Jq9xNQAiIiLqvcLDW5an6sYnWJF+MawSERFR72Ziotflqah7cc4qERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERERkdFiWCUiIiIio8WwSkRERERGi2GViIiIqAdIkoTdu3f3dDeMHsMqERERkZ5VVlZi/vz5UCgUkMlkkMvlCAsLw+HDhw1yvqysLEiShOrqaoO0DwAvvvgiBg4cCHNzc7i6umLGjBm4cuWKwc7XimGViIiIejWVEMi6eRM7r15F1s2bUAlh0POVlpZi1KhRyMzMRFJSEs6cOYMDBw4gODgY0dHRBj33/RJC4Pbt2zr3BQcH4+9//zuKioqQnp6O8+fPIzIy0uB9YlglIiKiXktZVYVBubkIPn0a0woKEHz6NAbl5kJZVWWwc86dOxeSJOH48eOIjIzEkCFD4Ovri7i4OOTm5uo8RtfIaF5eHiRJQmlpKQDg4sWLCAsLg729PSwtLeHr64v9+/ejtLQUwcHBAAB7e3tIkoRZs2YBaAmfSUlJUCgUsLCwgL+/P9LS0rTOm5GRgYCAAMhkMuTk5Ojs48KFC/Hkk0/Cw8MDY8eOxZIlS5Cbm4umpqb7f9Pa0degrRMRERH1EGVVFSLPnsXd46jlDQ2IPHsWab6+CHdy0us5b9y4gQMHDmDt2rWwtLTU2m9nZ9fltqOjo9HY2Ijs7GxYWloiPz8fVlZWkMvlSE9PR0REBIqKimBjYwMLCwsAwLJly6BUKpGamorBgwcjOzsb06dPh5OTE8aPH69ue/HixUhOToZCoehQH2/cuIG//OUvGDt2LExNTbt8TR3BsEpERES9jkoIxBQXawVVABAAJACxxcWY6ugIE0nS23mLi4shhMDQoUP11marsrIyREREYMSIEQAAhUKh3ufg4AAAcHZ2VofNuro6bNiwAZmZmQgMDFQf8/XXX2Pbtm0aYTUhIQEhISH37MPvfvc7vP/++/jxxx/x5JNPYu/evfq6vDZxGgARERH1OjnV1bjc0NDmfgHgUkMDcvR8Q5L473xYSY8BuNWCBQuwZs0ajBs3DitWrMC3337bbv38/HzU19cjJCQEVlZW6u2zzz7D+fPnNeoGBAR0qA+//e1vcerUKRw8eBAmJiZ4/fXX1ddsKBxZJSIiol6norFRr/U6avDgwZAkCQUFBXjppZc6fFyfPi3jh3cGv7vngs6ePRuhoaHYt28fDh48iMTERKxfvx7z58/X2WZzczMAYN++fXBzc9PYJ5PJNF7rmrKgi6OjIxwdHTFkyBD4+PhALpcjNzdXPXJrCBxZJSIiol7H1cxMr/U6ysHBAaGhodiyZQvq6uq09re1tJTTf+fOVlRUqMvy8vK06snlckRFRUGpVCI+Ph7bt28HAJj99zpUKpW67rBhwyCTyVBWVgYvLy+NTS6Xd/US1VqDdUM7I9j6wLBKREREvU6QnR3cZTK09WO8BEAukyHoPm54aktKSgpUKhVGjx6N9PR0fPfddygoKMB7773X5ghka4BcuXIlzp07h3379mH9+vUadWJjY5GRkYGSkhKcPHkSmZmZ8PHxAQB4eHhAkiTs3bsXVVVVuHXrFqytrbFo0SIsXLgQO3bswPnz53Hq1Cls2bIFO3bs6NQ1HT9+HO+//z7y8vJw8eJFHDlyBNOmTcOjjz5q0FFVgGGViIiIeiETScImLy8A0Aqsra83ennp9eaqVp6enjh58iSCg4MRHx+P4cOHIyQkBIcPH0ZqaqrOY0xNTbFz504UFhbC398f69atw5o1azTqqFQqREdHw8fHB5MmTYK3tzdSUlIAAG5ubli1ahWWLFkCFxcXzJs3DwCwevVqLF++HImJifDx8UFoaCj27NkDT0/PTl2ThYUFlEolnn32WXh7e+ONN97A8OHD8dVXX2lNKdA3SRh6Vmw3q62tha2tLWpqamBjY9PT3SEiIqIuqq+vR0lJCTw9PWFubt6lNpRVVYgpLta42Uouk2Gjl5fel60ibW19hp3Ja7zBioiIiHqtcCcnTHV0RE51NSoaG+FqZoYgOzuDjKiSYTCsEhERUa9mIkmYYG/f092gLuKcVSIiIiIyWgyrRERERGS0GFaJiIiIyGgxrBIRERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjBbDKhEREVEPkCQJu3fv7uluGD2GVSIiIiI9q6ysxPz586FQKCCTySCXyxEWFobDhw8b5HxZWVmQJAnV1dUGaf9ODQ0NeOyxxyBJEvLy8gx+Pj7BioiIiHo1oRKozqlGY0UjzFzNYBdkB8nEcI9bLS0txbhx42BnZ4ekpCT4+fmhqakJGRkZiI6ORmFhocHOfb+EEFCpVOjbt+2IuHjxYgwYMACnT5/ulj5xZJWIiIh6rSplFXIH5eJ08GkUTCvA6eDTyB2UiypllcHOOXfuXEiShOPHjyMyMhJDhgyBr68v4uLikJubq/MYXSOjeXl5kCQJpaWlAICLFy8iLCwM9vb2sLS0hK+vL/bv34/S0lIEBwcDAOzt7SFJEmbNmgWgJXwmJSVBoVDAwsIC/v7+SEtL0zpvRkYGAgICIJPJkJOT0+a1ffnllzh48CCSk5Pv703qBI6sEhERUa9UpazC2cizgNAsbyhvwNnIs/BN84VTuJNez3njxg0cOHAAa9euhaWlpdZ+Ozu7LrcdHR2NxsZGZGdnw9LSEvn5+bCysoJcLkd6ejoiIiJQVFQEGxsbWFhYAACWLVsGpVKJ1NRUDB48GNnZ2Zg+fTqcnJwwfvx4dduLFy9GcnIyFApFm328evUq5syZg927d6Nfv35dvo7OYlglIiKiXkeoBIpjirWCastOABJQHFsMx6mOep0SUFxcDCEEhg4dqrc2W5WVlSEiIgIjRowAACgUCvU+BwcHAICzs7M6bNbV1WHDhg3IzMxEYGCg+pivv/4a27Zt0wirCQkJCAkJafPcQgjMmjULUVFRCAgIUI/2dgeGVSIiIup1qnOq0XC5oe0KAmi41IDqnGrYT7DX23mFaEnHkqT/ObELFizAm2++iYMHD+K5555DREQE/Pz82qyfn5+P+vp6rRDa2NiIkSNHapQFBAS0e+7NmzejtrYWS5cu7foFdBHnrBIREVGv01jRqNd6HTV48GBIkoSCgoJOHdenT0skaw27ANDU1KRRZ/bs2bhw4QJmzJiBM2fOICAgAJs3b26zzebmZgDAvn37kJeXp97y8/M15q0C0Dll4U6ZmZnIzc2FTCZD37594eXlBaAl5M6cObPjF9oFDKtERETU65i5mum1Xkc5ODggNDQUW7ZsQV1dndb+tpaWcnJqmTtbUVGhLtO1LJRcLkdUVBSUSiXi4+Oxfft2AICZWct1qFQqdd1hw4ZBJpOhrKwMXl5eGptcLu/Udb333ns4ffq0OvDu378fAPD5559j7dq1nWqrszgNgIiIiHoduyA7yNxlaChv0D1vVQJk7jLYBdnp/dwpKSkYO3YsRo8ejYSEBPj5+eH27ds4dOgQUlNTdY66tgbIlStXYs2aNfjuu++wfv16jTqxsbGYPHkyhgwZgps3byIzMxM+Pj4AAA8PD0iShL1792LKlCmwsLCAtbU1Fi1ahIULF6K5uRlPPfUUamtrcfToUVhZWXVqRHTgwIEar62srAAAjz76KNzd3Tv7FnUKR1aJiIio15FMJHht8vrvi7t3tvzDa6OXQdZb9fT0xMmTJxEcHIz4+HgMHz4cISEhOHz4MFJTU3UeY2pqip07d6KwsBD+/v5Yt24d1qxZo1FHpVIhOjoaPj4+mDRpEry9vZGSkgIAcHNzw6pVq7BkyRK4uLhg3rx5AIDVq1dj+fLlSExMhI+PD0JDQ7Fnzx54enrq/boNRRJ3To7oBWpra2Fra4uamhrY2Nj0dHeIiIioi+rr61FSUgJPT0+Ym5t3qY0qZRWKY4o1braSyWXw2uil92WrSFtbn2Fn8hqnARAREVGv5RTuBMepjt36BCvSL4ZVIiIi6tUkE0mvy1NR9+KcVSIiIiIyWgyrRERERGS0GFaJiIiIyGgZNKxmZ2cjLCwMAwYMgCRJ2L17d7v1s7KyIEmS1lZYWGjIbhIRERGRkTLoDVZ1dXXw9/fHL3/5S0RERHT4uKKiIo1lDFqf6kBEREREDxeDhtXJkydj8uTJnT7O2dkZdnZ2+u8QERERET1QjHLO6siRI+Hq6opnn30WR44cabduQ0MDamtrNTYiIiIi6h2MKqy6urrigw8+QHp6OpRKJby9vfHss88iOzu7zWMSExNha2ur3uRyeTf2mIiIiKhrOnI/DxlZWPX29sacOXPw+OOPIzAwECkpKXj++eeRnJzc5jFLly5FTU2Nert06VI39piIiAxFCBVu3szC1as7cfNmFoRQ9XSXiDqssrIS8+fPh0KhgEwmg1wuR1hYGA4fPmyQ87XepF5dXW2Q9u/U0NCAxx57DJIkIS8vz+DnM/onWD355JP485//3OZ+mUwGmUzWjT0iIiJDq6pSorg4Bg0Nl9VlMpk7vLw2wckpvAd7Rg8ioRLd+rjV0tJSjBs3DnZ2dkhKSoKfnx+ampqQkZGB6Ohoo17lSAgBlUqFvn3bjoiLFy/GgAEDcPr06W7pk1GNrOpy6tQpuLq69nQ3iIiom1RVKXH2bKRGUAWAhoZynD0biaoqZQ/1jB5EVcoq5A7Kxeng0yiYVoDTwaeROygXVcoqg51z7ty5kCQJx48fR2RkJIYMGQJfX1/ExcUhNzdX5zG6Rkbz8vIgSRJKS0sBABcvXkRYWBjs7e1haWkJX19f7N+/H6WlpQgODgYA2NvbQ5IkzJo1C0BL+ExKSoJCoYCFhQX8/f2Rlpamdd6MjAwEBARAJpMhJyenzWv78ssvcfDgwXZ/9dY3g46s3rp1C8XFxerXJSUlyMvLg4ODAwYOHIilS5eivLwcn332GQBg48aNGDRoEHx9fdHY2Ig///nPSE9PR3p6uiG7SURERkIIFYqLYwAIXXsBSCgujoWj41RIkkk3944eNFXKKpyNPKv1dWoob8DZyLPwTfOFU7h+l8e8ceMGDhw4gLVr18LS0lJr//2sdhQdHY3GxkZkZ2fD0tIS+fn5sLKyglwuR3p6OiIiItTLf1pYWAAAli1bBqVSidTUVAwePBjZ2dmYPn06nJycMH78eHXbixcvRnJyMhQKRZt9vHr1KubMmYPdu3ejX79+Xb6OzjJoWD1x4oQ66QNAXFwcAGDmzJn49NNPUVFRgbKyMvX+xsZGLFq0COXl5bCwsICvry/27duHKVOmGLKbRERkJKqrc7RGVDUJNDRcQnV1DuztJ3RTr+hBJFQCxTHF7f1/D4pji+E41VGvUwKKi4shhMDQoUP11marsrIyREREYMSIEQAAhUKh3ufg4ABAc/nPuro6bNiwAZmZmQgMDFQf8/XXX2Pbtm0aYTUhIQEhISFtnlsIgVmzZiEqKgoBAQHq0d7uYNCwOmHCBAih61vS4tNPP9V4vXjxYixevNiQXSIiIiPW2Fih13r08KrOqUbD5Ya2Kwig4VIDqnOqYT/BXm/nbc09kqT/ObELFizAm2++iYMHD+K5555DREQE/Pz82qyfn5+P+vp6rRDa2NiIkSNHapQFBAS0e+7NmzejtrYWS5cu7foFdJHRz1klIqKHh5lZx+5R6Gg9eng1VjTqtV5HDR48GJIkoaCgoFPH9enTEsnuHORramrSqDN79mxcuHABM2bMwJkzZxAQEIDNmze32WZzczMAYN++fcjLy1Nv+fn5GvNWAeicsnCnzMxM5ObmQiaToW/fvvDy8gLQEnJnzpzZ8QvtAoZVIiIyGnZ2QZDJ3AG0NSolQSaTw84uqDu7RQ8gM1czvdbrKAcHB4SGhmLLli2oq6vT2t/W0lKtj5avqPjfrwa6loWSy+WIioqCUqlEfHw8tm/fDgAwM2u5DpXqf0u8DRs2DDKZDGVlZfDy8tLYOrsu/XvvvYfTp0+rA+/+/fsBAJ9//jnWrl3bqbY6y+iXriIiooeHJJnAy2sTzp6NREtgvXMqWUuA9fLayJur6J7sguwgc5ehobxB97xVCZC5y2AXZKf3c6ekpGDs2LEYPXo0EhIS4Ofnh9u3b+PQoUNITU3VOeraGiBXrlyJNWvW4LvvvsP69es16sTGxmLy5MkYMmQIbt68iczMTPj4+AAAPDw8IEkS9u7diylTpsDCwgLW1tZYtGgRFi5ciObmZjz11FOora3F0aNHYWVl1akR0YEDB2q8trKyAgA8+uijcHd37+xb1CkcWSUiIqPi5BQOX980yGRuGuUymTt8fdO4zip1iGQiwWuT139f3L2z5R9eG70Mst6qp6cnTp48ieDgYMTHx2P48OEICQnB4cOHkZqaqvMYU1NT7Ny5E4WFhfD398e6deuwZs0ajToqlQrR0dHw8fHBpEmT4O3tjZSUFACAm5sbVq1ahSVLlsDFxQXz5s0DAKxevRrLly9HYmIifHx8EBoaij179sDT01Pv120okmjvDqgHUG1tLWxtbVFTUwMbG5ue7g4REXWRECpUV+egsbECZmausLML4ojqQ6a+vh4lJSXw9PSEubl5l9qoUlahOKZY42YrmVwGr41eel+2irS19Rl2Jq9xGgARERklSTLh8lR035zCneA41bFbn2BF+sWwSkRERL2aZCLpdXkq6l6cs0pERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERER9QBJkrB79+6e7obRY1glIiIi0rPKykrMnz8fCoUCMpkMcrkcYWFhOHz4sEHOl5WVBUmSUF1dbZD2AWDQoEGQJEljW7JkicHO14oPBSAiIiLSo9LSUowbNw52dnZISkqCn58fmpqakJGRgejoaBQWFvZ0F9skhIBKpULfvrojYkJCAubMmaN+bWVlZfA+cWSViIiIejUhVLh5MwtXr+7EzZtZEEJl0PPNnTsXkiTh+PHjiIyMxJAhQ+Dr64u4uDjk5ubqPEbXyGheXh4kSUJpaSkA4OLFiwgLC4O9vT0sLS3h6+uL/fv3o7S0FMHBwQAAe3t7SJKEWbNmAWgJn0lJSVAoFLCwsIC/vz/S0tK0zpuRkYGAgADIZDLk5OS0eW3W1tbo37+/euuOsMqRVSIiIuq1qqqUKC6OQUPDZXWZTOYOL69NcHIK1/v5bty4gQMHDmDt2rWwtLTU2m9nZ9fltqOjo9HY2Ijs7GxYWloiPz8fVlZWkMvlSE9PR0REBIqKimBjYwMLCwsAwLJly6BUKpGamorBgwcjOzsb06dPh5OTE8aPH69ue/HixUhOToZCoWi3j+vWrcPq1ashl8vx8ssv47e//S3MzMy6fE0dwbBKREREvVJVlRJnz0YCEBrlDQ3lOHs2Er6+aXoPrMXFxRBCYOjQoXptFwDKysoQERGBESNGAAAUCoV6n4ODAwDA2dlZHTbr6uqwYcMGZGZmIjAwUH3M119/jW3btmmE1YSEBISEhLR7/piYGDz++OOwt7fH8ePHsXTpUpSUlODDDz/U52VqYVglIiKiXkcIFYqLY3B3UP3vXgASiotj4eg4FZJkosfztpxPkiS9tdlqwYIFePPNN3Hw4EE899xziIiIgJ+fX5v18/PzUV9frxVCGxsbMXLkSI2ygICAe55/4cKF6n/38/ODvb09IiMjsW7dOjzyyCOdvJqO45xVIiIi6nWqq3M0fvrXJtDQcAnV1W3Pz+yKwYMHQ5IkFBQUdOq4Pn1aIllr2AWApqYmjTqzZ8/GhQsXMGPGDJw5cwYBAQHYvHlzm202NzcDAPbt24e8vDz1lp+frzFvFYDOKQv38uSTTwJoGU02JIZVIiIi6nUaGyv0Wq+jHBwcEBoaii1btqCurk5rf1tLSzk5OQEAKir+15+8vDytenK5HFFRUVAqlYiPj8f27dsBQD1vVKX6381jw4YNg0wmQ1lZGby8vDQ2uVze1UtUO3XqFADA1dX1vttqD6cBEBERUa9jZtaxANXRep2RkpKCsWPHYvTo0UhISICfnx9u376NQ4cOITU1Veeoa2uAXLlyJdasWYPvvvsO69ev16gTGxuLyZMnY8iQIbh58yYyMzPh4+MDAPDw8IAkSdi7dy+mTJkCCwsLWFtbY9GiRVi4cCGam5vx1FNPoba2FkePHoWVlRVmzpzZ4Ws6duwYcnNzERwcDFtbW3zzzTdYuHAhXnzxRQwcOPD+3rB74MgqERER9Tp2dkGQydwBtDV3VIJMJoedXZDez+3p6YmTJ08iODgY8fHxGD58OEJCQnD48GGkpqbqPMbU1BQ7d+5EYWEh/P39sW7dOqxZs0ajjkqlQnR0NHx8fDBp0iR4e3sjJSUFAODm5oZVq1ZhyZIlcHFxwbx58wAAq1evxvLly5GYmAgfHx+EhoZiz5498PT07NQ1yWQyfP7555gwYQKGDRuG5cuXY86cOdi5c2cX3qHOkcSdkyN6gdraWtja2qKmpgY2NjY93R0iIiLqovr6epSUlMDT0xPm5uadPv5/qwEAmjdatQRYQ6wGQJra+gw7k9c4skpERES9kpNTOHx90yCTuWmUy2TuDKoPEM5ZJSIiol7LySkcjo5TUV2dg8bGCpiZucLOLkivy1WRYTGsEhERUa8mSSawt5/Q092gLuI0ACIiIiIyWgyrRERERGS0GFaJiIiIyGgxrBIRERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIioB0iShN27d/d0N4wewyoRERGRnlVWVmL+/PlQKBSQyWSQy+UICwvD4cOHDXK+rKwsSJKE6upqg7QPAIMGDYIkSRrbkiVLDHa+VnwoABEREZEelZaWYty4cbCzs0NSUhL8/PzQ1NSEjIwMREdHo7CwsKe72CYhBFQqFfr21R0RExISMGfOHPVrKysrg/eJI6tERETUqwmhws2bWbh6dSdu3syCECqDnm/u3LmQJAnHjx9HZGQkhgwZAl9fX8TFxSE3N1fnMbpGRvPy8iBJEkpLSwEAFy9eRFhYGOzt7WFpaQlfX1/s378fpaWlCA4OBgDY29tDkiTMmjULQEv4TEpKgkKhgIWFBfz9/ZGWlqZ13oyMDAQEBEAmkyEnJ6fNa7O2tkb//v3VW3eEVY6sEhERUa9VVaVEcXEMGhouq8tkMnd4eW2Ck1O43s9348YNHDhwAGvXroWlpaXWfjs7uy63HR0djcbGRmRnZ8PS0hL5+fmwsrKCXC5Heno6IiIiUFRUBBsbG1hYWAAAli1bBqVSidTUVAwePBjZ2dmYPn06nJycMH78eHXbixcvRnJyMhQKRbt9XLduHVavXg25XI6XX34Zv/3tb2FmZtbla+oIhlUiIiLqlaqqlDh7NhKA0ChvaCjH2bOR8PVN03tgLS4uhhACQ4cO1Wu7AFBWVoaIiAiMGDECAKBQKNT7HBwcAADOzs7qsFlXV4cNGzYgMzMTgYGB6mO+/vprbNu2TSOsJiQkICQkpN3zx8TE4PHHH4e9vT2OHz+OpUuXoqSkBB9++KE+L1MLwyoRERH1OkKoUFwcg7uD6n/3ApBQXBwLR8epkCQTPZ635XySJOmtzVYLFizAm2++iYMHD+K5555DREQE/Pz82qyfn5+P+vp6rRDa2NiIkSNHapQFBATc8/wLFy5U/7ufnx/s7e0RGRmJdevW4ZFHHunk1XQc56wSERFRr1NdnaPx0782gYaGS6iubnt+ZlcMHjwYkiShoKCgU8f16dMSyVrDLgA0NTVp1Jk9ezYuXLiAGTNm4MyZMwgICMDmzZvbbLO5uRkAsG/fPuTl5am3/Px8jXmrAHROWbiXJ598EkDLaLIhMawSERFRr9PYWKHXeh3l4OCA0NBQbNmyBXV1dVr721paysnJCQBQUfG//uTl5WnVk8vliIqKglKpRHx8PLZv3w4A6nmjKtX/bh4bNmwYZDIZysrK4OXlpbHJ5fKuXqLaqVOnAACurq733VZ7OA2AiIiIeh0zs44FqI7W64yUlBSMHTsWo0ePRkJCAvz8/HD79m0cOnQIqampOkddWwPkypUrsWbNGnz33XdYv369Rp3Y2FhMnjwZQ4YMwc2bN5GZmQkfHx8AgIeHByRJwt69ezFlyhRYWFjA2toaixYtwsKFC9Hc3IynnnoKtbW1OHr0KKysrDBz5swOX9OxY8eQm5uL4OBg2Nra4ptvvsHChQvx4osvYuDAgff3ht0DR1aJiIio17GzC4JM5g6grbmjEmQyOezsgvR+bk9PT5w8eRLBwcGIj4/H8OHDERISgsOHDyM1NVXnMaampti5cycKCwvh7++PdevWYc2aNRp1VCoVoqOj4ePjg0mTJsHb2xspKSkAADc3N6xatQpLliyBi4sL5s2bBwBYvXo1li9fjsTERPj4+CA0NBR79uyBp6dnp65JJpPh888/x4QJEzBs2DAsX74cc+bMwc6dO7vwDnWOJO6cHNEL1NbWwtbWFjU1NbCxsenp7hAREVEX1dfXo6SkBJ6enjA3N+/08f9bDQDQvNGqJcAaYjUA0tTWZ9iZvMaRVSIiIuqVnJzC4eubBpnMTaNcJnNnUH2AcM4qERER9VpOTuFwdJyK6uocNDZWwMzMFXZ2QXpdrooMi2GViIiIejVJMoG9/YSe7gZ1EacBEBEREZHRYlglIiIiIqPFsEpERERERothlYiIiIiMFsMqERERERktg4bV7OxshIWFYcCAAZAkCbt3777nMV999RVGjRoFc3NzKBQKbN261ZBdJCIiIiIjZtCwWldXB39/f7z//vsdql9SUoIpU6YgKCgIp06dwltvvYUFCxYgPT3dkN0kIiIi6nYdHch72Bk0rE6ePBlr1qxBeHjHnhCxdetWDBw4EBs3boSPjw9mz56NN954A8nJyYbsJhEREZFeVVZWYv78+VAoFJDJZJDL5QgLC8Phw4cNcr6srCxIkoTq6mqDtN9q3759GDNmDCwsLODo6NjhjHc/jOqhAMeOHcPEiRM1ykJDQ/HRRx+hqakJpqamWsc0NDSgoaFB/bq2ttbg/SQiIiJqS2lpKcaNGwc7OzskJSXBz88PTU1NyMjIQHR0NAoLC3u6i20SQkClUqFvX+2ImJ6ejjlz5uCdd97BM888AyEEzpw5Y/A+GdUNVpWVlXBxcdEoc3Fxwe3bt3H9+nWdxyQmJsLW1la9yeXy7ugqERERPSBUKhWysrKwc+dOZGVlQaVSGfR8c+fOhSRJOH78OCIjIzFkyBD4+voiLi4Oubm5Oo/RNTKal5cHSZJQWloKALh48SLCwsJgb28PS0tL+Pr6Yv/+/SgtLUVwcDAAwN7eHpIkYdasWQBawmdSUhIUCgUsLCzg7++PtLQ0rfNmZGQgICAAMpkMOTk5Wv27ffs2YmJi8O677yIqKgpDhgyBt7c3IiMj9fOmtcOoRlaBlvkbdxJC6CxvtXTpUsTFxalf19bWMrASERERAECpVCImJgaXL19Wl7m7u2PTpk0G+Qn7xo0bOHDgANauXQtLS0ut/XZ2dl1uOzo6Go2NjcjOzoalpSXy8/NhZWUFuVyO9PR0REREoKioCDY2NrCwsAAALFu2DEqlEqmpqRg8eDCys7Mxffp0ODk5Yfz48eq2Fy9ejOTkZCgUCp19PHnyJMrLy9GnTx+MHDkSlZWVeOyxx5CcnAxfX98uX1NHGFVY7d+/PyorKzXKrl27hr59++KRRx7ReYxMJoNMJuuO7hEREdEDRKlUIjIyUj3w1aq8vByRkZFIS0vTe2AtLi6GEAJDhw7Va7sAUFZWhoiICIwYMQIAoFAo1PscHBwAAM7OzuqwWVdXhw0bNiAzMxOBgYHqY77++mts27ZNI6wmJCQgJCSkzXNfuHABALBy5Ups2LABgwYNwvr16zF+/HicO3dOfX5DMKppAIGBgTh06JBG2cGDBxEQEKBzvioRERGRLiqVCjExMVpBFfjfr7axsbF6nxJwr1+E78eCBQuwZs0ajBs3DitWrMC3337bbv38/HzU19cjJCQEVlZW6u2zzz7D+fPnNeoGBAS021ZzczMA4O2330ZERARGjRqFTz75BJIk4Ysvvri/C7sHg4bVW7duIS8vD3l5eQBalqbKy8tDWVkZgJaf8F9//XV1/aioKFy8eBFxcXEoKCjAxx9/jI8++giLFi0yZDeJiIiol8nJydH46f9uQghcunRJ5/zM+zF48GBIkoSCgoJOHdenTx91v1o1NTVp1Jk9ezYuXLiAGTNm4MyZMwgICMDmzZvbbLM1YO7bt0+dx/Ly8pCfn68xbxWAzikLd3J1dQUADBs2TF0mk8mgUCjUuc5QDBpWT5w4gZEjR2LkyJEAgLi4OIwcORLLly8HAFRUVGhcoKenJ/bv34+srCw89thjWL16Nd577z1EREQYsptERETUy1RUVOi1Xkc5ODggNDQUW7ZsQV1dndb+tpaWcnJy0upP62DfneRyOaKioqBUKhEfH4/t27cDAMzMzABAY6R42LBhkMlkKCsrg5eXl8bW2ft7Ro0aBZlMhqKiInVZU1MTSktL4eHh0am2Osugc1YnTJigc/i91aeffqpVNn78eJw8edKAvSIiIqLernUkUF/1OiMlJQVjx47F6NGjkZCQAD8/P9y+fRuHDh1CamqqzlHX1gC5cuVKrFmzBt999x3Wr1+vUSc2NhaTJ0/GkCFDcPPmTWRmZsLHxwcA4OHhAUmSsHfvXkyZMgUWFhawtrbGokWLsHDhQjQ3N+Opp55CbW0tjh49CisrK8ycObPD12RjY4OoqCisWLECcrkcHh4eePfddwEAL7/88n28W/dmVHNWiYiIiPQhKCgI7u7ubc4dlSQJcrkcQUFBej+3p6cnTp48ieDgYMTHx2P48OEICQnB4cOHkZqaqvMYU1NT7Ny5E4WFhfD398e6deuwZs0ajToqlQrR0dHw8fHBpEmT4O3tjZSUFACAm5sbVq1ahSVLlsDFxQXz5s0DAKxevRrLly9HYmIifHx8EBoaij179sDT07PT1/Xuu+/itddew4wZM/DEE0/g4sWLyMzMhL29fafb6gxJtDf0+QCqra2Fra0tampqYGNj09PdISIioi6qr69HSUkJPD09YW5u3unjW1cDADTngrYGWEOsBkCa2voMO5PXOLJKREREvVJ4eDjS0tLg5uamUe7u7s6g+gAxqnVWiYiIiPQpPDwcU6dORU5ODioqKuDq6oqgoCCYmJj0dNeogxhWiYiIqFczMTHBhAkTerob1EWcBkBERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERER9QBJkrB79+6e7obRY1glIiIi0rPKykrMnz8fCoUCMpkMcrkcYWFhOHz4sEHOl5WVBUmSUF1dbZD2W+3btw9jxoyBhYUFHB0du+UpYHwoABEREZEelZaWYty4cbCzs0NSUhL8/PzQ1NSEjIwMREdHo7CwsKe72CYhBFQqFfr21Y6I6enpmDNnDt555x0888wzEELgzJkzBu8TR1aJiIioV1OpVMjKysLOnTuRlZUFlUpl0PPNnTsXkiTh+PHjiIyMxJAhQ+Dr64u4uDjk5ubqPEbXyGheXh4kSUJpaSkA4OLFiwgLC4O9vT0sLS3h6+uL/fv3o7S0FMHBwQAAe3t7SJKEWbNmAWgJn0lJSVAoFLCwsIC/vz/S0tK0zpuRkYGAgADIZDLk5ORo9e/27duIiYnBu+++i6ioKAwZMgTe3t6IjIzUz5vWDo6sEhERUa+lVCoRExODy5cvq8vc3d2xadMmg/yEfePGDRw4cABr166FpaWl1n47O7sutx0dHY3GxkZkZ2fD0tIS+fn5sLKyglwuR3p6OiIiIlBUVAQbGxtYWFgAAJYtWwalUonU1FQMHjwY2dnZmD59OpycnDB+/Hh124sXL0ZycjIUCoXOPp48eRLl5eXo06cPRo4cicrKSjz22GNITk6Gr69vl6+pIxhWiYiIqFdSKpWIjIyEEEKjvLy8HJGRkUhLS9N7YC0uLoYQAkOHDtVruwBQVlaGiIgIjBgxAgCgUCjU+xwcHAAAzs7O6rBZV1eHDRs2IDMzE4GBgepjvv76a2zbtk0jrCYkJCAkJKTNc1+4cAEAsHLlSmzYsAGDBg3C+vXrMX78eJw7d059fkPgNAAiIiLqdVQqFWJiYrSCKgB1WWxsrN6nBLS2LUmSXtsFgAULFmDNmjUYN24cVqxYgW+//bbd+vn5+aivr0dISAisrKzU22effYbz589r1A0ICGi3rebmZgDA22+/jYiICIwaNQqffPIJJEnCF198cX8Xdg8Mq0RERNTr5OTkaPz0fzchBC5duqRzfub9GDx4MCRJQkFBQaeO69Onj7pfrZqamjTqzJ49GxcuXMCMGTNw5swZBAQEYPPmzW222Row9+3bh7y8PPWWn5+vMW8VgM4pC3dydXUFAAwbNkxdJpPJoFAoUFZW1oEr7DqGVSIiIup1Kioq9FqvoxwcHBAaGootW7agrq5Oa39bS0s5OTlp9ScvL0+rnlwuR1RUFJRKJeLj47F9+3YAgJmZGQBojBQPGzYMMpkMZWVl8PLy0tjkcnmnrmvUqFGQyWQoKipSlzU1NaG0tBQeHh6daquzGFaJiIio12kdCdRXvc5ISUmBSqXC6NGjkZ6eju+++w4FBQV477331HNH79YaIFeuXIlz585h3759WL9+vUad2NhYZGRkoKSkBCdPnkRmZiZ8fHwAAB4eHpAkCXv37kVVVRVu3boFa2trLFq0CAsXLsSOHTtw/vx5nDp1Clu2bMGOHTs6dU02NjaIiorCihUrcPDgQRQVFeHNN98EALz88stdeJc6jmGViIiIep2goCC4u7u3OXdUkiTI5XIEBQXp/dyenp44efIkgoODER8fj+HDhyMkJASHDx9GamqqzmNMTU2xc+dOFBYWwt/fH+vWrcOaNWs06qhUKkRHR8PHxweTJk2Ct7c3UlJSAABubm5YtWoVlixZAhcXF8ybNw8AsHr1aixfvhyJiYnw8fFBaGgo9uzZA09Pz05f17vvvovXXnsNM2bMwBNPPIGLFy8iMzMT9vb2nW6rMySha+bxA6y2tha2traoqamBjY1NT3eHiIiIuqi+vh4lJSXw9PSEubl5p49vXQ0A0JwL2hpgDbEaAGlq6zPsTF7jyCoRERH1SuHh4UhLS4Obm5tGubu7O4PqA4TrrBIREVGvFR4ejqlTpyInJwcVFRVwdXVFUFAQTExMerpr1EEMq0RERNSrmZiYYMKECT3dDeoiTgMgIiIiIqPFsEpERERERothlYiIiIxaL1u46KGij8+OYZWIiIiMkqmpKQDgxx9/7OGeUFe1fnatn2VX8AYrIiIiMkomJiaws7PDtWvXAAD9+vVrc5F/Mi5CCPz444+4du0a7Ozs7mv1BYZVIiIiMlr9+/cHAHVgpQeLnZ2d+jPsKoZVIiIiMlqSJMHV1RXOzs5oamrq6e5QJ5iamuplPVuG1fukUqm40DAREZGBmZiY8O/XhxTD6n1QKpWIiYnB5cuX1WXu7u7YtGkTH+FGREREpAdcDaCLlEolIiMjNYIqAJSXlyMyMhJKpbKHekZERETUezCsdoFKpUJMTIzOtcNay2JjY6FSqbq7a0RERES9CsNqF+Tk5GiNqN5JCIFLly4hJyenG3tFRERE1PswrHZBRUWFXusRERERkW4Mq13g6uqq13pEREREpBvDahcEBQXB3d29zadoSJIEuVyOoKCgbu4ZERERUe/CsNoFJiYm2LRpEwBoBdbW1xs3buR6cERERET3iWG1i8LDw5GWlgY3NzeNcnd3d6SlpXGdVSIiIiI9kISu9ZceYLW1tbC1tUVNTQ1sbGwMfj4+wYqIiIioczqT1/gEq/tkYmKCCRMm9HQ3iIiIiHolTgMgIiIiIqPFsEpERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERERkdFiWCUiIiIio2XwsJqSkgJPT0+Ym5tj1KhRyMnJabNuVlYWJEnS2goLCw3dTSIiIiIyQgYNq59//jliY2Px9ttv49SpUwgKCsLkyZNRVlbW7nFFRUWoqKhQb4MHDzZkN4mIiIjISBk0rG7YsAG/+tWvMHv2bPj4+GDjxo2Qy+VITU1t9zhnZ2f0799fvZmYmBiym0RERERkpAwWVhsbG/Hvf/8bEydO1CifOHEijh492u6xI0eOhKurK5599lkcOXKk3boNDQ2ora3V2IiIiIiodzBYWL1+/TpUKhVcXFw0yl1cXFBZWanzGFdXV3zwwQdIT0+HUqmEt7c3nn32WWRnZ7d5nsTERNja2qo3uVyu1+sgIiIiop7T19AnkCRJ47UQQquslbe3N7y9vdWvAwMDcenSJSQnJ+Ppp5/WeczSpUsRFxenfl1bW8vASkRERNRLGGxk1dHRESYmJlqjqNeuXdMabW3Pk08+ie+++67N/TKZDDY2NhobEREREfUOBgurZmZmGDVqFA4dOqRRfujQIYwdO7bD7Zw6dQqurq767h4RERERPQAMOg0gLi4OM2bMQEBAAAIDA/HBBx+grKwMUVFRAFp+wi8vL8dnn30GANi4cSMGDRoEX19fNDY24s9//jPS09ORnp5uyG4SERERkZEyaFh99dVX8f333yMhIQEVFRUYPnw49u/fDw8PDwBARUWFxpqrjY2NWLRoEcrLy2FhYQFfX1/s27cPU6ZMMWQ3iYiIiMhISUII0dOd0Kfa2lrY2tqipqaG81eJiIiIjFBn8prBVwMgIqIHk6pZhZyyHFT8UAFXa1cEDQyCSR8+pIWIuhfDKhERaVEWKBFzIAaXay+ry9xt3LFp0iaE+4T3YM+I6GFj0MetEhHRg0dZoETk3yM1gioAlNeWI/LvkVAWKHuoZ0T0MGJYJSIiNVWzCjEHYiCgfTtDa1nsgViomlXd3TUiekgxrBIRkVpOWY7WiOqdBAQu1V5CTllON/aKiB5mDKtERKRW8UOFXusREd0vhlUiIlJzte7YEwM7Wo+I6H4xrBIRkVrQwCC427hDgqRzvwQJchs5ggYGdXPPiOhhxbBKRERqJn1MsGnSJgDQCqytrzdO2sj1Vomo2zCsEhGRhnCfcKS9kgY3GzeNcncbd6S9ksZ1VomoW/Fxq0REpBOfYNUGlQrIyQEqKgBXVyAoCDDh+0LUGXzcKhER3TeTPiaYMGhCT3fDuCiVQEwMcPmO5b3c3YFNm4BwjjgTGQKnARAREXWEUglERmoGVQAoL28pV/LJXkSGwLBKRER0LypVy4iqrplzrWWxsS31iEivGFaJiIjuJSdHe0T1TkIAly611CMivWJYJSIiupeKDj6xq6P1iKjDGFaJiIjuxbWDT+zqaD0i6jCGVSIionsJCmq561/S/WQvSBIgl7fUIyK9YlglIiK6FxOTluWpAO3A2vp640aut0pkAAyrREREHREeDqSlAW6aT/aCu3tLOddZJTIIPhSAiIioo8LDgalT+QQrom7EsEpERNQZJibAhAk93QuihwanARARERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjBbDKhEREREZLYZVIiIiIjJaDKtEREREZLQYVomIiIjIaDGsEhEREZHRYlglIiIiIqPFsEpERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERERkdFiWCUiIiIio8WwSkRERERGi2GViIiIiIwWwyoRERERGS2GVSIiIiIyWgyrRERERGS0GFaJiIiIyGgxrBIRERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjBbDKhEREREZLYZVIiIiIjJaDKtEREREZLQYVomIiIjIaBk8rKakpMDT0xPm5uYYNWoUcnJy2q3/1VdfYdSoUTA3N4dCocDWrVsN3UUiIiIiMlIGDauff/45YmNj8fbbb+PUqVMICgrC5MmTUVZWprN+SUkJpkyZgqCgIJw6dQpvvfUWFixYgPT0dEN2k4iIiIiMlCSEEIZqfMyYMXj88ceRmpqqLvPx8cFLL72ExMRErfq/+93v8M9//hMFBQXqsqioKJw+fRrHjh3r0Dlra2tha2uLmpoa2NjY3P9FEBEREZFedSavGWxktbGxEf/+978xceJEjfKJEyfi6NGjOo85duyYVv3Q0FCcOHECTU1NhuoqERERERmpvoZq+Pr161CpVHBxcdEod3FxQWVlpc5jKisrdda/ffs2rl+/DldXV61jGhoa0NDQoH5dW1urh94TERERkTEw+A1WkiRpvBZCaJXdq76u8laJiYmwtbVVb3K5/D57TERERETGwmBh1dHRESYmJlqjqNeuXdMaPW3Vv39/nfX79u2LRx55ROcxS5cuRU1NjXq7dOmSfi6AiIiIiHqcwcKqmZkZRo0ahUOHDmmUHzp0CGPHjtV5TGBgoFb9gwcPIiAgAKampjqPkclksLGx0diIiIiIqHcw6DSAuLg4fPjhh/j4449RUFCAhQsXoqysDFFRUQBaRkVff/11df2oqChcvHgRcXFxKCgowMcff4yPPvoIixYtMmQ3iYiIiMhIGewGKwB49dVX8f333yMhIQEVFRUYPnw49u/fDw8PDwBARUWFxpqrnp6e2L9/PxYuXIgtW7ZgwIABeO+99xAREWHIbhIRERGRkTLoOqs9geusEhERERk3o1hnlYiIiIjofjGsEhEREZHRYlglIiIiIqPFsEpERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktAz6BCsiIiLqXYRKoDqnGo0VjTBzNYNdkB0kE6mnu0W9GMMqERERdUiVsgrFMcVouNygLpO5y+C1yQtO4U492DPqzTgNgIiIiO6pSlmFs5FnNYIqADSUN+Bs5FlUKat6qGfU2zGsEhERUbuESqA4phgQuna2/KM4thhCpasC0f1hWCUiIqJ2VedUa42oahBAw6UGVOdUd1uf6OHBsEpERETtaqxo1Gs9os5gWCUiIqJ2mbma6bUeUWcwrBIREVG77ILsIHOXAW2tUCUBMrkMdkF23dktekgwrBIREVG7JBMJXpu8/vvi7p0t//Da6PVQr7cqVAI3s27i6s6ruJl1kzeb6RHXWSUiIqJ7cgp3gm+ar+51Vjc+3Ouscv1Zw5KEEL0q+tfW1sLW1hY1NTWwsbHp6e4QERH1KnyClabW9We1lvX671vim+bLwKpDZ/IaR1aJiIiowyQTCfYT7Hu6G0bhnuvPSi3rzzpOdXyoA/394pxVIiIioi7g+rPdg2GViIiIqAu4/mz3YFglIiIi6gKuP9s9GFaJiIiIuoDrz3YPhlUiIiKiLuD6s92DYZWIiIioi1rXn5W5yTTKZe4yLlulJ1y6ioiIiOg+OIU7wXGqI9efNRCGVSIiIqL7xPVnDYfTAIiIiIjIaDGsEhEREZHRYlglIiIiIqPFsEpERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERERkdFiWCUiIiIio8WwSkRERERGi2GViIiIiIwWwyoRERERGS2GVSIiIiIyWgyrRERERGS0GFaJiIiIyGgxrBIRERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjBbDKhEREREZLYZVIiIiIjJaDKtEREREZLQYVomIiIjIaDGsEhEREZHRYlglIiIiIqNl0LB68+ZNzJgxA7a2trC1tcWMGTNQXV3d7jGzZs2CJEka25NPPmnIbhIRERGRkepryManTZuGy5cv48CBAwCAX//615gxYwb27NnT7nGTJk3CJ598on5tZmZmyG4SERERkZEyWFgtKCjAgQMHkJubizFjxgAAtm/fjsDAQBQVFcHb27vNY2UyGfr372+orhERERHRA8Jg0wCOHTsGW1tbdVAFgCeffBK2trY4evRou8dmZWXB2dkZQ4YMwZw5c3Dt2rU26zY0NKC2tlZjIyIiIqLewWBhtbKyEs7Ozlrlzs7OqKysbPO4yZMn4y9/+QsyMzOxfv16fPPNN3jmmWfQ0NCgs35iYqJ6TqytrS3kcrneroGIiIiIelanw+rKlSu1boC6eztx4gQAQJIkreOFEDrLW7366qt4/vnnMXz4cISFheHLL7/EuXPnsG/fPp31ly5dipqaGvV26dKlzl4SERERERmpTs9ZnTdvHl577bV26wwaNAjffvstrl69qrWvqqoKLi4uHT6fq6srPDw88N133+ncL5PJIJPJOtweERERET04Oh1WHR0d4ejoeM96gYGBqKmpwfHjxzF69GgAwL/+9S/U1NRg7NixHT7f999/j0uXLsHV1bWzXSUiIiKiB5zB5qz6+Phg0qRJmDNnDnJzc5Gbm4s5c+bghRde0FgJYOjQodi1axcA4NatW1i0aBGOHTuG0tJSZGVlISwsDI6OjvjZz35mqK4SERERkZEy6EMB/vKXv2DEiBGYOHEiJk6cCD8/P/zpT3/SqFNUVISamhoAgImJCc6cOYOpU6diyJAhmDlzJoYMGYJjx47B2trakF0lIiIiIiMkCSFET3dCn2pra2Fra4uamhrY2Nj0dHeIiIiI6C6dyWsGHVklIiIiIrofDKtEREREZLQYVomIiIjIaDGsEhEREZHRYlglIiIiIqPFsEpERERERothlYiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERERkdFiWCUiIiIio8WwSkRERERGi2GViIiIiIwWwyoRERERGS2GVSIiIiIyWgyrRERERGS0GFaJiIiIyGgxrBIRERGR0erb0x0gIiKinqUSAjnV1ahobISrmRmC7OxgIkk93S0iAAyrREREDzVlVRViiotxuaFBXeYuk2GTlxfCnZx6sGdELTgNgIiI6CGlrKpC5NmzGkEVAMobGhB59iyUVVU91DOi/2FYJSIiegiphEBMcTGEjn2tZbHFxVAJXTWIug/DKhER0UMop7paa0T1TgLApYYG5FRXd1ufiHRhWCUiInoIVTQ26rUekaEwrBIRET2EXM3M9FqPyFAYVomIiB5CQXZ2cJfJ0NYCVRIAuUyGIDu7buwVkTaGVSIiooeQiSRhk5cXAGgF1tbXG728uN4q9TiGVSIioodUuJMT0nx94SaTaZS7y2RI8/XlOqtkFPhQACIioodYuJMTpjo68glWZLQYVomIiB5yJpKECfb2Pd0NIp04DYCIiIiIjBbDKhEREREZLU4DICKih4YQKlRX56CxsQJmZq6wswuCJJn0dLeIqB0Mq0RE9FCoqlKiuDgGDQ2X1WUymTu8vDbBySm8B3tGRO3hNAAiIur1qqqUOHs2UiOoAkBDQznOno1EVZWyh3pGRPfCsEpERL2aECoUF8cAELr2AgCKi2MhhKpb+0VEHcOwSkREvVp1dY7WiKomgYaGS6iuzum2PhFRxzGsEhFRr9bYWKHXekTUvRhWiYioVzMzc9VrPSLqXgyrRETUq9nZBUEmcwfQ1uNDJchkctjZBXVnt4iogxhWiYioV5MkE3h5bWp9dfdeAICX10aut0pkpBhWiYio13NyCoevbxpkMjeNcpnMHb6+aVxnlciI8aEARET0UHByCoej41Q+wYroAcOwSkREDw1JMoG9/YSe7gYRdQKnARARERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjBZvsCIiIiJ6iKlUQE4OUFEBuLoCQUGAiREtksGwSkRERPSQUiqBmBjg8uX/lbm7A5s2AeFGsvwwpwEQERERPYSUSiAyUjOoAkB5eUu5Utkz/bobwyoRERHRQ0alahlRFUJ7X2tZbGxLvZ5m0LC6du1ajB07Fv369YOdnV2HjhFCYOXKlRgwYAAsLCwwYcIEnD171pDdJCIiInqo5ORoj6jeSQjg0qWWej3NoGG1sbERL7/8Mt58880OH5OUlIQNGzbg/fffxzfffIP+/fsjJCQEP/zwgwF7SkRERPTwqKjQbz1DMmhYXbVqFRYuXIgRI0Z0qL4QAhs3bsTbb7+N8PBwDB8+HDt27MCPP/6Iv/71r4bsKhEREdFDw9VVv/UMyajmrJaUlKCyshITJ05Ul8lkMowfPx5Hjx7VeUxDQwNqa2s1NiIiIiJqW1BQy13/kqR7vyQBcnlLvZ5mVGG1srISAODi4qJR7uLiot53t8TERNja2qo3uVxu8H4SERERPchMTFqWpwK0A2vr640bjWO91U6H1ZUrV0KSpHa3EydO3FenpLveNSGEVlmrpUuXoqamRr1dunTpvs5NRERE9DAIDwfS0gA3N81yd/eWcmNZZ7XTDwWYN28eXnvttXbrDBo0qEud6d+/P4CWEVbXOyZJXLt2TWu0tZVMJoNMJuvS+YiIiIgeZuHhwNSpvewJVo6OjnB0dDREX+Dp6Yn+/fvj0KFDGDlyJICWFQW++uorrFu3ziDnJCIiInqYmZgAEyb0dC/aZtA5q2VlZcjLy0NZWRlUKhXy8vKQl5eHW7duqesMHToUu3btAtDy839sbCzeeecd7Nq1C//5z38wa9Ys9OvXD9OmTTNkV4mIiIjICHV6ZLUzli9fjh07dqhft46WHjlyBBP+G+GLiopQU1OjrrN48WL89NNPmDt3Lm7evIkxY8bg4MGDsLa2NmRXiYiIiMgISULoetDWg6u2tha2traoqamBjY1NT3eHiIiIiO7SmbxmVEtXERERERHdiWGViIiIiIwWwyoRERERGS2GVSIiIiIyWgyrRERERGS0GFaJiIiIyGgxrBIRERGR0WJYJSIiIiKjxbBKREREREaLYZWIiIiIjFbfnu6AvrU+Pba2traHe0JEREREurTmtNbc1p5eF1Z/+OEHAIBcLu/hnhARERFRe3744QfY2tq2W0cSHYm0D5Dm5mZcuXIF1tbWkCSpp7vzUKutrYVcLselS5dgY2PT092hbsbP/+HFz/7hxs//4dWZz14IgR9++AEDBgxAnz7tz0rtdSOrffr0gbu7e093g+5gY2PD/2A9xPj5P7z42T/c+Pk/vDr62d9rRLUVb7AiIiIiIqPFsEpERERERothlQxGJpNhxYoVkMlkPd0V6gH8/B9e/Owfbvz8H16G+ux73Q1WRERERNR7cGSViIiIiIwWwyoRERERGS2GVSIiIiIyWgyrRERERGS0GFbJ4EpLS/GrX/0Knp6esLCwwKOPPooVK1agsbGxp7tG3WTt2rUYO3Ys+vXrBzs7u57uDhlYSkoKPD09YW5ujlGjRiEnJ6enu0TdIDs7G2FhYRgwYAAkScLu3bt7ukvUTRITE/HEE0/A2toazs7OeOmll1BUVKS39hlWyeAKCwvR3NyMbdu24ezZs/jjH/+IrVu34q233urprlE3aWxsxMsvv4w333yzp7tCBvb5558jNjYWb7/9Nk6dOoWgoCBMnjwZZWVlPd01MrC6ujr4+/vj/fff7+muUDf76quvEB0djdzcXBw6dAi3b9/GxIkTUVdXp5f2uXQV9Yh3330XqampuHDhQk93hbrRp59+itjYWFRXV/d0V8hAxowZg8cffxypqanqMh8fH7z00ktITEzswZ5Rd5IkCbt27cJLL73U012hHlBVVQVnZ2d89dVXePrpp++7PY6sUo+oqamBg4NDT3eDiPSosbER//73vzFx4kSN8okTJ+Lo0aM91Csi6m41NTUAoLe/5xlWqdudP38emzdvRlRUVE93hYj06Pr161CpVHBxcdEod3FxQWVlZQ/1ioi6kxACcXFxeOqppzB8+HC9tMmwSl22cuVKSJLU7nbixAmNY65cuYJJkybh5ZdfxuzZs3uo56QPXfn86eEgSZLGayGEVhkR9U7z5s3Dt99+i507d+qtzb56a4keOvPmzcNrr73Wbp1Bgwap//3KlSsIDg5GYGAgPvjgAwP3jgyts58/9X6Ojo4wMTHRGkW9du2a1mgrEfU+8+fPxz//+U9kZ2fD3d1db+0yrFKXOTo6wtHRsUN1y8vLERwcjFGjRuGTTz5Bnz4c1H/Qdebzp4eDmZkZRo0ahUOHDuFnP/uZuvzQoUOYOnVqD/aMiAxJCIH58+dj165dyMrKgqenp17bZ1glg7ty5QomTJiAgQMHIjk5GVVVVep9/fv378GeUXcpKyvDjRs3UFZWBpVKhby8PACAl5cXrKyserZzpFdxcXGYMWMGAgIC1L+ilJWVcY76Q+DWrVsoLi5Wvy4pKUFeXh4cHBwwcODAHuwZGVp0dDT++te/4h//+Aesra3Vv67Y2trCwsLivtvn0lVkcJ9++il++ctf6tzHr9/DYdasWdixY4dW+ZEjRzBhwoTu7xAZVEpKCpKSklBRUYHhw4fjj3/8o16WryHjlpWVheDgYK3ymTNn4tNPP+3+DlG3aWtO+ieffIJZs2bdf/sMq0RERERkrDhxkIiIiIiMFsMqERERERkthlUiIiIiMloMq0RERERktBhWiYiIiMhoMawSERERkdFiWCUiIiIio8WwSkRERERGi2GViIiIiIwWwyoRERERGS2GVSIiIiIyWgyrRERERGS0/j9QPXKAx37EWgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 7 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 8 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 9 ---\n", - "Aktuelle Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 6. 0.54664466 2. ]\n", - " [ 3. 5. 0.5524637 2. ]\n", - " [ 7. 9. 0.56261506 2. ]\n", - " [ 0. 4. 1.14694608 2. ]\n", - " [ 1. 10. 1.18606925 3. ]\n", - " [12. 14. 1.42401155 5. ]\n", - " [11. 13. 2.15864694 4. ]\n", - " [ 8. 16. 3.73691206 5. ]\n", - " [15. 17. 4.64006144 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 10 ---\n", - "Finale Cluster: [[1], [3], [5], [9], [2, 6], [4, 7], [0, 8]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 5\n", - "\n", - "# Größe des \"Noise\"-Bereichs\n", - "noise_scale = 1\n", - "\n", - "# Beispiel Daten - Clusterpunkte und \"Noise\" Punkte\n", - "np.random.seed(0)\n", - "clusters = []\n", - "for _ in range(num_clusters):\n", - " cluster_center = np.random.rand(2) * (1 - noise_scale)\n", - " cluster = cluster_center + np.random.randn(points_per_cluster, 2) * noise_scale\n", - " clusters.append(cluster)\n", - "\n", - "# Zusammenführen der Clusterpunkte\n", - "X = np.concatenate(clusters, axis=0)\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "1a4f2193", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([[0.59180595, 0.86775975],\n", - " [0.68068795, 0.54594264],\n", - " [0.588941 , 0.62853471],\n", - " [0.48361027, 0.68473028],\n", - " [0.50833651, 0.78909778]]), array([[0.55562643, 0.86640441],\n", - " [0.66064801, 0.81252115],\n", - " [0.54254688, 0.7476274 ],\n", - " [0.25594112, 0.89839883],\n", - " [0.59768372, 0.75882047]])]\n", - "[[0.59180595 0.86775975]\n", - " [0.68068795 0.54594264]\n", - " [0.588941 0.62853471]\n", - " [0.48361027 0.68473028]\n", - " [0.50833651 0.78909778]\n", - " [0.55562643 0.86640441]\n", - " [0.66064801 0.81252115]\n", - " [0.54254688 0.7476274 ]\n", - " [0.25594112 0.89839883]\n", - " [0.59768372 0.75882047]]\n", - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [6], [7], [8], [9], [0, 5]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [7], [8], [0, 5], [6, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [7], [8], [0, 5], [6, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[1], [2], [3], [4], [7], [8], [0, 5], [6, 9]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 7 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 8 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 9 ---\n", - "Aktuelle Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 0. 5. 0.0362049 2. ]\n", - " [ 4. 7. 0.05376004 2. ]\n", - " [ 6. 9. 0.08275424 2. ]\n", - " [ 3. 11. 0.10797673 3. ]\n", - " [ 1. 2. 0.12344616 2. ]\n", - " [10. 12. 0.13930155 4. ]\n", - " [13. 15. 0.23028243 7. ]\n", - " [14. 16. 0.37874766 9. ]\n", - " [ 8. 17. 0.47988728 10. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 10 ---\n", - "Finale Cluster: [[1], [4], [7], [8], [0, 5], [6, 9], [2, 3]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "#functions\n", - "def generate_random_number_array(n, rangeStart, rangeEnd):\n", - " array = [random.randint(rangeStart, rangeEnd) for _ in range(n)]\n", - " return array\n", - "\n", - "def generate_x_y_array(n, rangeStart, rangeEnd):\n", - " XYDict = {\n", - " \"x\": generate_random_number_array(n, rangeStart, rangeEnd),\n", - " \"y\": generate_random_number_array(n, rangeStart, rangeEnd)\n", - " }\n", - " return XYDict\n", - "\n", - "def generate_clusters(n_clusters, m_points_per_cluster):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.rand(n_clusters, 2)\n", - " noise_scale = 0.05\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randn(m_points_per_cluster, 2) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (noise_scale * np.random.randn(len(points), 2))\n", - "\n", - " return np.array(noisy_points)\n", - "\n", - "\n", - "\n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 5\n", - "\n", - "# Größe des \"Noise\"-Bereichs\n", - "noise_scale = 0.1\n", - "\n", - "# Beispiel Daten - Clusterpunkte und \"Noise\" Punkte\n", - "np.random.seed(0)\n", - "clusters = []\n", - "for _ in range(num_clusters):\n", - " cluster_center = np.random.rand(2) * (1 - noise_scale)\n", - " cluster = cluster_center + np.random.randn(points_per_cluster, 2) * noise_scale\n", - " clusters.append(cluster)\n", - "print(clusters) \n", - "\n", - "# Zusammenführen der Clusterpunkte\n", - "X = np.concatenate(clusters, axis=0)\n", - "print(X)\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "#print(clusters)\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "edd42b72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[19 11]\n", - " [13 10]\n", - " [22 9]\n", - " [21 10]\n", - " [18 16]\n", - " [20 17]]\n", - "[[0], [1], [2], [3], [4], [5]]\n", - "\n", - "--- Schritt 1 ---\n", - "Aktuelle Cluster: [[0], [1], [2], [3], [4], [5]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 2 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 3 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 4 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 5 ---\n", - "Aktuelle Cluster: [[0], [1], [4], [5], [2, 3]]\n", - "Abstandsmatrix nach Linkage:\n", - "[[ 2. 3. 1.41421356 2. ]\n", - " [ 4. 5. 2.23606798 2. ]\n", - " [ 0. 6. 3.36650165 3. ]\n", - " [ 1. 8. 9.38971068 4. ]\n", - " [ 7. 9. 10.62230358 6. ]]\n" - ] - }, - { - "data": { - "image/png": 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Kq2/IkCEaMmSIioqKlJaWpoSEBA0fPlxt27ZVz549XVpD+fc2depUDR06tNI2HTp0qHL96Oho/e1vf1NycrKmTJlSoxo8PT1VVFRUYf7333/vdFzr16+vSZMmadKkScrNzdV//vMfPf3004qOjtbhw4fPexNSeT8rVqxwjESeT2XH/uuvv9auXbv05ptvauTIkY75mZmZF+yvKsnJySosLFRSUpJTXenp6Re1vp+fn0aOHKm///3vevzxx/XGG29o+PDhFR77dP/99+v+++9XYWGhtm/frmeffVaDBg3SgQMH1KZNGzVp0kTdunXTnDlzKt1OUFBQlTX86U9/0qBBgxyfywN1dbi7u+vZZ5/VX//6V3399deSyv6uSdKRI0cUHBxc7T6rUtmxrQ3mf69Vr+ymKMAqhE3gPAYNGqTnn39ejRs3VkhISI36MAxD9evXdzqt/Msvv+jtt9++6D5iYmL09ttva//+/ecNYa5gs9nUp08f+fv7KyUlRV9++aV69uypmJgYTZ8+XZs3b9bvf//7S9pGhw4d1K5dO+3atUvPP/98tdcfMmSIunbtqoSEBA0aNKjSO9JTUlIcdwlXpm3btvrqq6+c5h04cED79++v8D8R5fz9/TVs2DAdPXpUEyZMUHZ2tjp16lTl6HF0dLTq16+vgwcP1vg0anlIOTdMvfLKKzXqr6o+TdPUq6++etF9jB8/XkuXLtWwYcOUm5ursWPHVtnW29tbMTExKi4u1h133KE9e/aoTZs2GjRokNatW6drrrmm0stPzicoKOi8YfRcx48fr3R0sfwyhPK+BgwYIDc3Ny1btkw9e/asVk2ucPZv6dxHnlU1alodb731liTxOCRcVoRN4DwmTJiglStX6pZbbtHEiRPVrVs3lZaWKicnRxs2bNDkyZN1ww03nLeP22+/XQsWLNDw4cP1pz/9ST/88IPmzZtXrZGYWbNm6aOPPtItt9yip59+Wl27dlVubq7Wr1+vSZMmOd0JXhPTp0/XkSNHdOutt6pVq1bKzc3VokWL5O7urj59+ji+i/fee09DhgzRlClT1KNHD/3yyy/atm2bBg0apKioqGpt85VXXlFMTIyio6M1atQotWzZUj/++KMyMjK0c+dOffDBB1Wu6+bmplWrVmnAgAHq2bOnHn30UUVFRcnb21uHDh3SihUrtHr1ap0+fbrKPkaMGKF7771XY8aMUVxcnA4dOqTExETHyFa52NhYdenSRZGRkWratKkOHTqkhQsXqk2bNmrXrp0kqWvXrpKkRYsWaeTIkXJ3d1eHDh3Utm1bzZo1S3/+85/17bffauDAgWrUqJG+++477dixQ97e3o5HW1WlY8eOuuaaazRlyhSZpqmAgACtXr3a6XR0dfXv318eHh764x//qCeffFJnzpzRsmXLzvt9nat9+/YaOHCgPvroI/Xq1UsRERFOyx966CF5eXnp5ptvVmBgoE6cOKGEhAT5+fk5rhGeNWuWNm7cqJtuuknjx49Xhw4ddObMGWVnZ2vdunV6+eWXL/rSjQuJjo5Wq1atFBsbq44dO6q0tFTp6emaP3++GjZs6HjJQdu2bfX000/rueee0y+//OJ4hNTevXv1/fffX/B4Xary39LcuXMVExMjNzc3devWTR4eHuratau2bt2q1atXKzAwUD4+PlX+z+e//vUvJSUl6fbbb1ebNm2Um5urDz74QO+++65GjRpV4XgBlqrd+5MA65TfgXzuneTlbr/99gvejW6apllQUGBOmzbN7NChg+nh4WH6+fmZXbt2NSdOnGieOHHC0U6SGR8fX+m2Xn/9dbNDhw6mzWYzQ0NDzYSEBPO1116r9A7p22+/vdI+Dh8+bD7wwANmixYtTHd3dzMoKMi86667zO+++840zf+7s/WDDz5wWq/8DvE33njDMe/cO4zXrFljxsTEmC1btjQ9PDzMZs2ambfddpuZmprq1Nfp06fNxx57zGzdurXp7u5uNmvWzLz99tvNffv2OW3rL3/5S4X6JZnPPvus07xdu3aZd911l9msWTPT3d3dbNGihfn73//efPnllyv9Ds6Vm5trPvfcc+Z1111nNmzY0HR3dzdbt25t3nvvveb//u//OtpVdjd6aWmpmZiYaIaGhpqenp5mZGSkuXnz5gp3o8+fP9+86aabzCZNmpgeHh5m69atzQcffNDMzs52qmXq1KlmUFCQWa9evQp3GCcnJ5tRUVGmr6+vabPZzDZt2pjDhg0z//Of/zjajBw50vT29q50P/fu3Wv279/f9PHxMRs1amT+4Q9/MHNycip8p+V3o586dcpp/cr2f/Xq1WZERITp6elptmzZ0nziiSfMjz76qELt5/5Wzvbmm2+aksx33323wrLly5ebUVFRZvPmzU0PDw/H7/Wrr75yanfq1Clz/PjxZkhIiOnu7m4GBASYv/vd78w///nPZkFBQaXbrYn33nvPHD58uNmuXTun38qIESMqfcrDW2+9ZV5//fWmp6en2bBhQ7N79+5Of4f69Oljdu7cucJ6535fVf29PHvZ2d93UVGROXr0aLNp06amYRhOxy09Pd28+eabzQYNGpiSnH6n5/r000/NW2+91fHviwYNGpjXX3+9uXTp0vM+6QGwgmGaF/GwQQAAzhEXF6e0tDRlZ2df8W9rAmAdTqMDAC5aUVGRdu7cqR07dmjVqlVasGABQRPAeTGyCQC4aNnZ2QoJCZGvr6+GDx+uxYsX8zYaAOdF2AQAAIBleNAWAAAALEPYBAAAgGUImwAAALBMnbsbvbS0VMeOHZOPj0+deb0XAAAA/o9pmvrpp58UFBR0wdef1rmweezYMZe+jxYAAADWOHz48AXf9FXnwqaPj4+ksuJ9fX1ruRoAAACcKz8/X8HBwY7cdj51LmyWnzr39fUlbAIAANRhF3PJIzcIAQAAwDKETQAAAFiGsAkAAADL1LlrNgEAwG+P3W5XSUlJbZeBanB3d5ebm9sl90PYBAAAljFNUydOnFBubm5tl4Ia8Pf3V4sWLS7p2eeETQAAYJnyoNmsWTM1aNCAF7ZcIUzT1M8//6yTJ09KkgIDA2vcF2ETAABYwm63O4Jm48aNa7scVJOXl5ck6eTJk2rWrFmNT6lzgxAAALBE+TWaDRo0qOVKUFPlx+5SrrclbAIAAEtx6vzK5YpjR9gEAACAZQibAAAANWAYhpKTk2u7jDqPsAkAAHCOEydOaNy4cQoNDZXNZlNwcLBiY2O1adMmS7a3detWGYZh6SOiTp8+rREjRsjPz09+fn4aMWLEZXkkFXejAwCAOs1ul1JTpePHpcBAqXdvyQXPGq9Sdna2br75Zvn7+ysxMVHdunVTSUmJUlJSFB8fr3379lm38Utkmqbsdrvq168Y8YYPH64jR45o/fr1kqQ//elPGjFihFavXm1pTYxsAgDwG2I3TW09fVrvfPedtp4+Lbtp1nZJlyQpSWrbVoqKkoYPL/uzbduy+VYZM2aMDMPQjh07NGzYMLVv316dO3fWpEmTlJaWVuk6lY1MpqenyzAMZWdnS5IOHTqk2NhYNWrUSN7e3urcubPWrVun7OxsRUVFSZIaNWokwzA0atQoSWXhMTExUaGhofLy8lJERIRWrFhRYbspKSmKjIyUzWZTampqhfoyMjK0fv16/f3vf1fPnj3Vs2dPvfrqq1qzZo3279/vmi+uCtUOm9u3b1dsbKyCgoIqvVbBMIxKp7/85S+uqhkAAFQi6dQptU1LU9SuXRqekaGoXbvUNi1NSadO1XZpNZKUJA0bJh054jz/6NGy+VYEzh9//FHr169XfHy8vL29Kyz39/evcd/x8fEqKirS9u3btXv3bs2dO1cNGzZUcHCwVq5cKUnav3+/jh8/rkWLFkmSpk2bpjfeeEPLli3Tnj17NHHiRN17773atm2bU99PPvmkEhISlJGRoW7dulXY9qeffio/Pz/dcMMNjnk33nij/Pz89Mknn9R4ny5GtU+jFxYWKiIiQvfff7/i4uIqLD9+/LjT548++kgPPvhgpW0BAIBrJJ06pWF79ujcccyjRUUatmePVnTurKFNm9ZKbTVht0uPPSZVNjBrmpJhSBMmSEOGuPaUemZmpkzTVMeOHV3X6X/l5OQoLi5OXbt2lSSFhoY6lgUEBEiSmjVr5gi0hYWFWrBggTZv3qyePXs61vn444/1yiuvqE+fPo71Z82apf79+1e57RMnTqhZs2YV5jdr1kwnTpy45H07n2qHzZiYGMXExFS5vEWLFk6fP/zwQ0VFRTl9oQAAwHXspqnHMjMrBE1JMiUZkiZkZmpIkyZyu0KeeZmaWnFE82ymKR0+XNaub1/Xbdf8b7q14tmg48eP16OPPqoNGzaoX79+iouLq3QUstzevXt15syZCiGyuLhY3bt3d5oXGRl5we1Xtk+maVr+HFRLr9n87rvvtHbtWj344INVtikqKlJ+fr7TBAAALl5qbq6OFBVVudyUdLioSKmX4c5jVznnROklt7tY7dq1k2EYysjIqNZ69eqVRSrzrKHYc9+6M3r0aH377bcaMWKEdu/ercjISL300ktV9llaWipJWrt2rdLT0x3T3r17na7blFTpKf+ztWjRQt99912F+adOnVLz5s3Pv3OXyNKwuXz5cvn4+Gjo0KFVtklISHDcgu/n56fg4GArSwIA4DfneHGxS9vVBYGBrm13sQICAhQdHa0lS5aosLCwwvKqHhXU9L+XKJx9OWF6enqFdsHBwXrkkUeUlJSkyZMn69VXX5UkeXh4SCp7n3y5Tp06yWazKScnR2FhYU5TdfNSz549lZeXpx07djjmffbZZ8rLy9NNN91Urb6qy9Kw+frrr+uee+6Rp6dnlW2mTp2qvLw8x3T48GErSwIA4Dcn8L9BxVXt6oLevaVWrcquzayMYUjBwWXtXG3p0qWy2+3q0aOHVq5cqW+++UYZGRl68cUXHddOnqs8AM6YMUMHDhzQ2rVrNX/+fKc2EyZMUEpKirKysrRz505t3rxZ4eHhkqQ2bdrIMAytWbNGp06dUkFBgXx8fPT4449r4sSJWr58uQ4ePKgvv/xSS5Ys0fLly6u1T+Hh4Ro4cKAeeughpaWlKS0tTQ899JAGDRqkDh061OyLukiWhc3U1FTt379fo0ePPm87m80mX19fpwkAAFy83v7+amWzqaor7wxJwTabel/CndSXm5ub9N8bsisEzvLPCxda87zNkJAQ7dy5U1FRUZo8ebK6dOmi/v37a9OmTVq2bFml67i7u+udd97Rvn37FBERoblz52r27NlObex2u+Lj4x3Br0OHDlq6dKkkqWXLlpo5c6amTJmi5s2ba+zYsZKk5557TtOnT1dCQoLCw8MVHR2t1atXKyQkpNr79c9//lNdu3bVgAEDNGDAAHXr1k1vv/12tfupLsM0a/4ALsMwtGrVKt1xxx0Vlo0aNUpff/21vvjii2r1mZ+fLz8/P+Xl5RE8AQC4SOV3o0tyulGoPKfVxt3oZ86cUVZWlkJCQs57lvN8kpLK7ko/+2ah4OCyoHmeq/TgIlUdw+rktWrfjV5QUKDMzEzH56ysLKWnpysgIECtW7d2FPDBBx9UGD4GAADWGNq0qVZ07qzHMjOdbhZqZbNpYVjYFfXYo7MNHVr2eKPL+QYhuFa1w+YXX3zheMq9JE2aNEmSNHLkSL355puSpHfffVemaeqPf/yja6oEAAAXNLRpUw1p0kSpubk6XlysQA8P9fb3v2Ied1QVNzfXPt4Il9clnUa3AqfRAQD4bXDFaXTULlecRufd6AAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAEANGIah5OTk2i6jziNsAgAAnOPEiRMaN26cQkNDZbPZFBwcrNjYWG3atMmS7W3dulWGYSg3N9eS/iVpzpw5uummm9SgQQP5+/tbtp1zVfsNQgAAAJeTvdSu1JxUHf/puAJ9AtW7dW+51bPufZXZ2dm6+eab5e/vr8TERHXr1k0lJSVKSUlRfHy89u3bZ9m2L5VpmrLb7apfv2LEKy4u1h/+8Af17NlTr7322mWriZFNAABQZyVlJKntoraKWh6l4UnDFbU8Sm0XtVVSRpJl2xwzZowMw9COHTs0bNgwtW/fXp07d9akSZOUlpZW6TqVjUymp6fLMAxlZ2dLkg4dOqTY2Fg1atRI3t7e6ty5s9atW6fs7GzHq8AbNWokwzA0atQoSWXhMTExUaGhofLy8lJERIRWrFhRYbspKSmKjIyUzWZTampqpTXOnDlTEydOVNeuXS/9S6oGRjYBAECdlJSRpGHvD5Mp5zdrH80/qmHvD9OKu1ZoaPhQl27zxx9/1Pr16zVnzhx5e3tXWH4pp5/j4+NVXFys7du3y9vbW3v37lXDhg0VHByslStXKi4uTvv375evr6+8vLwkSdOmTVNSUpKWLVumdu3aafv27br33nvVtGlT9enTx9H3k08+qXnz5ik0NPSyniK/GIRNAABQ59hL7Xps/WMVgqYkmTJlyNCE9RM0pMMQl55Sz8zMlGma6tixo8v6LJeTk6O4uDjHyGJoaKhjWUBAgCSpWbNmjrBYWFioBQsWaPPmzerZs6djnY8//livvPKKU9icNWuW+vfv7/KaXYGwCQAA6pzUnFQdyT9S5XJTpg7nH1ZqTqr6tu3rsu2aZlm4NQzDZX2WGz9+vB599FFt2LBB/fr1U1xcnLp161Zl+7179+rMmTMVQmRxcbG6d+/uNC8yMtLl9boK12wCAIA65/hPx13a7mK1a9dOhmEoIyOjWuvVq1cWqcrDqiSVlJQ4tRk9erS+/fZbjRgxQrt371ZkZKReeumlKvssLS2VJK1du1bp6emOae/evU7XbUqq9JR/XUHYBAAAdU6gT6BL212sgIAARUdHa8mSJSosLKywvKpHEzVt2lSSdPz4/4Xf9PT0Cu2Cg4P1yCOPKCkpSZMnT9arr74qSfLw8JAk2e12R9tOnTrJZrMpJydHYWFhTlNwcHBNd/GyI2wCAIA6p3fr3mrl20qGKj+dbchQsG+werfu7fJtL126VHa7XT169NDKlSv1zTffKCMjQy+++KLj2slzlQfAGTNm6MCBA1q7dq3mz5/v1GbChAlKSUlRVlaWdu7cqc2bNys8PFyS1KZNGxmGoTVr1ujUqVMqKCiQj4+PHn/8cU2cOFHLly/XwYMH9eWXX2rJkiVavnx5tfcrJydH6enpysnJkd1ud4yUFhQUVP9LqgbCJgAAqHPc6rlp0cBFklQhcJZ/XjhwoSXP2wwJCdHOnTsVFRWlyZMnq0uXLurfv782bdqkZcuWVbqOu7u73nnnHe3bt08RERGaO3euZs+e7dTGbrcrPj5e4eHhGjhwoDp06KClS5dKklq2bKmZM2dqypQpat68ucaOHStJeu655zR9+nQlJCQoPDxc0dHRWr16tUJCQqq9X9OnT1f37t317LPPqqCgQN27d1f37t31xRdfVLuv6jDMsy8uqAPy8/Pl5+envLw8+fr61nY5AACghs6cOaOsrCyFhITI09OzRn0kZSTpsfWPOd0sFOwbrIUDF7r8sUeoqKpjWJ28xt3oAACgzhoaPlRDOgy5rG8QgmsRNgEAQJ3mVs/NpY83wuXFNZsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAqAHDMJScnFzbZdR5hE0AAIBznDhxQuPGjVNoaKhsNpuCg4MVGxurTZs2WbK9rVu3yjAM5ebmWtJ/dna2HnzwQYWEhMjLy0vXXHONnn32WRUXF1uyvbPxBiEAAFC32e1Saqp0/LgUGCj17i25Wfe6yuzsbN18883y9/dXYmKiunXrppKSEqWkpCg+Pl779u2zbNuXyjRN2e121a/vHPH27dun0tJSvfLKKwoLC9PXX3+thx56SIWFhZo3b56lNTGyCQAA6q6kJKltWykqSho+vOzPtm3L5ltkzJgxMgxDO3bs0LBhw9S+fXt17txZkyZNUlpaWqXrVDYymZ6eLsMwlJ2dLUk6dOiQYmNj1ahRI3l7e6tz585at26dsrOzFRUVJUlq1KiRDMPQqFGjJJWFx8TERIWGhsrLy0sRERFasWJFhe2mpKQoMjJSNptNqampFeobOHCg3njjDQ0YMEChoaEaPHiwHn/8cSVZ+D2WY2QTAADUTUlJ0rBhkmk6zz96tGz+ihXS0KEu3eSPP/6o9evXa86cOfL29q6w3N/fv8Z9x8fHq7i4WNu3b5e3t7f27t2rhg0bKjg4WCtXrlRcXJz2798vX19feXl5SZKmTZumpKQkLVu2TO3atdP27dt17733qmnTpurTp4+j7yeffFLz5s1TaGjoRdeYl5engICAGu/PxSJsAgCAusdulx57rGLQlMrmGYY0YYI0ZIhLT6lnZmbKNE117NjRZX2Wy8nJUVxcnLp27SpJCg0NdSwrD33NmjVzhMXCwkItWLBAmzdvVs+ePR3rfPzxx3rllVecwuasWbPUv3//i67l4MGDeumllzR//vxL3a0LImwCAIC6JzVVOnKk6uWmKR0+XNaub1+Xbdb8b7g1DMNlfZYbP368Hn30UW3YsEH9+vVTXFycunXrVmX7vXv36syZMxVCZHFxsbp37+40LzIy8qLrOHbsmAYOHKg//OEPGj16dPV2oga4ZhMAANQ9x4+7tt1FateunQzDUEZGRrXWq1evLFKZZ43ElpSUOLUZPXq0vv32W40YMUK7d+9WZGSkXnrppSr7LC0tlSStXbtW6enpjmnv3r1O121KqvSUf2WOHTumqKgo9ezZU3/7298uap1LRdgEAAB1T2Cga9tdpICAAEVHR2vJkiUqLCyssLyqRxM1bdpUknT8rPCbnp5eoV1wcLAeeeQRJSUlafLkyXr11VclSR4eHpIku93uaNupUyfZbDbl5OQoLCzMaQoODq72vh09elR9+/bVddddpzfeeMMRkK1G2AQAAHVP795Sq1Zl12ZWxjCk4OCydi62dOlS2e129ejRQytXrtQ333yjjIwMvfjii45rJ89VHgBnzJihAwcOaO3atRWuh5wwYYJSUlKUlZWlnTt3avPmzQoPD5cktWnTRoZhaM2aNTp16pQKCgrk4+Ojxx9/XBMnTtTy5ct18OBBffnll1qyZImWL19erX06duyY+vbtq+DgYM2bN0+nTp3SiRMndOLEiZp9SdVA2AQAAHWPm5u0aFHZP58bOMs/L1xoyfM2Q0JCtHPnTkVFRWny5Mnq0qWL+vfvr02bNmnZsmWVruPu7q533nlH+/btU0REhObOnavZs2c7tbHb7YqPj1d4eLgGDhyoDh06aOnSpZKkli1baubMmZoyZYqaN2+usWPHSpKee+45TZ8+XQkJCQoPD1d0dLRWr16tkJCQau3Thg0blJmZqc2bN6tVq1YKDAx0TFYzTLOy27xqT35+vvz8/JSXlydfX9/aLgcAANTQmTNnlJWVpZCQEHl6etask6SksrvSz75ZKDi4LGi6+LFHqKiqY1idvMbd6AAAoO4aOrTs8UaX8Q1CcC3CJgAAqNvc3Fz6eCNcXtW+ZnP79u2KjY1VUFCQDMNQcnJyhTYZGRkaPHiw/Pz85OPjoxtvvFE5OTmuqBcAUB12u7R1q/TOO2V/nnWnKwBcDtUOm4WFhYqIiNDixYsrXX7w4EH16tVLHTt21NatW7Vr1y4988wzNb9WAwBQM7XwTmkAOFe1T6PHxMQoJiamyuV//vOfddtttykxMdEx7+zXMQEALoNaeKc0AFTGpY8+Ki0t1dq1a9W+fXtFR0erWbNmuuGGGyo91Q4AsMiF3iktlb1TmlPqAC4Dl4bNkydPqqCgQC+88IIGDhyoDRs26M4779TQoUO1bdu2StcpKipSfn6+0wQAuATVeac0AFjMpXejl7/Dc8iQIZo4caIk6dprr9Unn3yil19+WX369KmwTkJCgmbOnOnKMgDg6lZL75QGgMq4dGSzSZMmql+/vjp16uQ0Pzw8vMq70adOnaq8vDzHdPjwYVeWBABXn1p6pzQAVMalYdPDw0PXX3+99u/f7zT/wIEDatOmTaXr2Gw2+fr6Ok0AgEtQi++UBq4mVT0CEs6qHTYLCgqUnp6u9PR0SVJWVpbS09MdI5dPPPGE3nvvPb366qvKzMzU4sWLtXr1ao0ZM8alhQMAqlCL75QGfitOnDihcePGKTQ0VDabTcHBwYqNjdWmTZss2d7WrVtlGIZyc3Mt6V+SBg8erNatW8vT01OBgYEaMWKEjh07Ztn2ylU7bH7xxRfq3r27unfvLkmaNGmSunfvrunTp0uS7rzzTr388stKTExU165d9fe//10rV65Ur169XFs5AKBqQ4eWPd6oZUvn+a1a8dgjXHHspqmtp0/rne++09bTp2Wv7EkLLpSdna3f/e532rx5sxITE7V7926tX79eUVFRio+Pt3Tbl8o0Tf3666+VLouKitL777+v/fv3a+XKlTp48KCGDRt2WYqqU/Ly8kxJZl5eXm2XAgBXvl9/Nc0tW0zzX/8q+/PXX2u7IlxFfvnlF3Pv3r3mL7/8UuM+Vp48abb65BNTW7Y4plaffGKuPHnShZU6i4mJMVu2bGkWFBRUWHb69GnHP0syV61aZZqmaW7ZssWU5LT8yy+/NCWZWVlZpmmaZnZ2tjlo0CDT39/fbNCggdmpUydz7dq1ZlZWlinJaRo5cqRpmqZZWlpqzp071wwJCTE9PT3Nbt26mR988IFjG+XbXb9+vfm73/3OdHd3Nzdv3nxR+/nhhx+ahmGYxcXFVbap6hhWJ6/xbnQA+C3jndK4giWdOqVhe/bo3HHMo0VFGrZnj1Z07qyhTZu6dJs//vij1q9frzlz5sjb27vCcn9//xr3HR8fr+LiYm3fvl3e3t7au3evGjZsqODgYK1cuVJxcXHav3+/fH195eXlJUmaNm2akpKStGzZMrVr107bt2/Xvffeq6ZNmzo95efJJ5/UvHnzFBoaelE1/vjjj/rnP/+pm266Se7u7jXep4tB2AQAAHWO3TT1WGZmhaAplQ39GZImZGZqSJMmcqvqZrgayMzMlGma6tixo8v6LJeTk6O4uDh17dpVkvMbFgMCAiRJzZo1c4TFwsJCLViwQJs3b1bPnj0d63z88cd65ZVXnMLmrFmz1L9//wvW8NRTT2nx4sX6+eefdeONN2rNmjWu2r0qufRudAAAAFdIzc3VkaKiKpebkg4XFSnVxTfUmP+9HtRwYYAtN378eM2ePVs333yznn32WX311Vfnbb93716dOXNG/fv3V8OGDR3TW2+9pYMHDzq1jYyMvKgannjiCX355ZfasGGD3NzcdN999zn22SqMbAIAgDrneHGxS9tdrHbt2skwDGVkZOiOO+646PXq1Ssbvzs7uJWUlDi1GT16tKKjo7V27Vpt2LBBCQkJmj9/vsaNG1dpn+Uvy1m7dq1annOzn81mc/pc2Sn/yjRp0kRNmjRR+/btFR4eruDgYKWlpTlGTq3AyCYAAKhzAj08XNruYgUEBCg6OlpLlixRYWFhheVVPZqo6X+vHT1+1pu5yh8Tebbg4GA98sgjSkpK0uTJk/Xqq69KKntWuSTZ7XZH206dOslmsyknJ0dhYWFOU3BwcE130aE8GBedZwTZFQibAACgzunt769WNpuqOpltSAq22dT7Em7YqcrSpUtlt9vVo0cPrVy5Ut98840yMjL04osvVjkCWB4AZ8yYoQMHDmjt2rWaP3++U5sJEyYoJSVFWVlZ2rlzpzZv3qzw8HBJUps2bWQYhtasWaNTp06poKBAPj4+evzxxzVx4kQtX75cBw8e1JdffqklS5Zo+fLl1dqnHTt2aPHixUpPT9ehQ4e0ZcsWDR8+XNdcc42lo5oSYRMAANRBboahRWFhklQhcJZ/XhgW5tKbg8qFhIRo586dioqK0uTJk9WlSxf1799fmzZt0rJlyypdx93dXe+884727duniIgIzZ07V7Nnz3ZqY7fbFR8fr/DwcA0cOFAdOnTQ0qVLJUktW7bUzJkzNWXKFDVv3lxjx46VJD333HOaPn26EhISFB4erujoaK1evVohISHV2icvLy8lJSXp1ltvVYcOHfTAAw+oS5cu2rZtW4VT8q5mmFZfFVpN+fn58vPzU15eHq+uBADgCnbmzBllZWUpJCREnp6eNeoj6dQpPZaZ6XSzULDNpoVhYS5/7BEqquoYVievcYMQAACos4Y2baohTZooNTdXx4uLFejhod7+/paMaMIahE0AAFCnuRmG+jZqVNtloIa4ZhMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAANWAYhpKTk2u7jDqPsAkAAHCOEydOaNy4cQoNDZXNZlNwcLBiY2O1adMmS7a3detWGYah3NxcS/o/W1FRka699loZhqH09HTLt8cbhAAAQJ1m2k3lpuaq+HixPAI95N/bX4abda+rzM7O1s033yx/f38lJiaqW7duKikpUUpKiuLj47Vv3z7Ltn2pTNOU3W5X/fpVR7wnn3xSQUFB2rVr12WpiZFNAABQZ51KOqW0tmnaFbVLGcMztCtql9LapulU0inLtjlmzBgZhqEdO3Zo2LBhat++vTp37qxJkyYpLS2t0nUqG5lMT0+XYRjKzs6WJB06dEixsbFq1KiRvL291blzZ61bt07Z2dmKioqSJDVq1EiGYWjUqFGSysJjYmKiQkND5eXlpYiICK1YsaLCdlNSUhQZGSmbzabU1NQq9+2jjz7Shg0bNG/evEv7kqqBkU0AAFAnnUo6pT3D9kim8/yio0XaM2yPOq/orKZDm7p0mz/++KPWr1+vOXPmyNvbu8Jyf3//GvcdHx+v4uJibd++Xd7e3tq7d68aNmyo4OBgrVy5UnFxcdq/f798fX3l5eUlSZo2bZqSkpK0bNkytWvXTtu3b9e9996rpk2bqk+fPo6+n3zySc2bN0+hoaFV1vjdd9/poYceUnJysho0aFDj/aguwiYAAKhzTLupzMcyKwTNsoWSDClzQqaaDGni0lPqmZmZMk1THTt2dFmf5XJychQXF6euXbtKkkJDQx3LAgICJEnNmjVzhMXCwkItWLBAmzdvVs+ePR3rfPzxx3rllVecwuasWbPUv3//KrdtmqZGjRqlRx55RJGRkY7R1suBsAkAAOqc3NRcFR0pqrqBKRUdLlJuaq4a9W3ksu2aZlm6NQzXXxM6fvx4Pfroo9qwYYP69eunuLg4devWrcr2e/fu1ZkzZyqEyOLiYnXv3t1pXmRk5Hm3/dJLLyk/P19Tp06t+Q7UENdsAgCAOqf4eLFL212sdu3ayTAMZWRkVGu9evXKIlV5WJWkkpISpzajR4/Wt99+qxEjRmj37t2KjIzUSy+9VGWfpaWlkqS1a9cqPT3dMe3du9fpuk1JlZ7yP9vmzZuVlpYmm82m+vXrKywsTFJZSB05cuTF72gNEDYBAECd4xHo4dJ2FysgIEDR0dFasmSJCgsLKyyv6tFETZuWXTt6/Phxx7zKHisUHBysRx55RElJSZo8ebJeffVVSZKHR9l+2O12R9tOnTrJZrMpJydHYWFhTlNwcHC19uvFF1/Url27HIF13bp1kqT33ntPc+bMqVZf1cVpdAAAUOf49/aXrZVNRUeLKr9u05BsrWzy7+3v8m0vXbpUN910k3r06KFZs2apW7du+vXXX7Vx40YtW7as0lHP8gA4Y8YMzZ49W998843mz5/v1GbChAmKiYlR+/btdfr0aW3evFnh4eGSpDZt2sgwDK1Zs0a33XabvLy85OPjo8cff1wTJ05UaWmpevXqpfz8fH3yySdq2LBhtUYkW7du7fS5YcOGkqRrrrlGrVq1qu5XVC2MbAIAgDrHcDMUtijsvx/OXVj2R9jCMEuetxkSEqKdO3cqKipKkydPVpcuXdS/f39t2rRJy5Ytq3Qdd3d3vfPOO9q3b58iIiI0d+5czZ4926mN3W5XfHy8wsPDNXDgQHXo0EFLly6VJLVs2VIzZ87UlClT1Lx5c40dO1aS9Nxzz2n69OlKSEhQeHi4oqOjtXr1aoWEhLh8v61imGdfXFAH5Ofny8/PT3l5efL19a3tcgAAQA2dOXNGWVlZCgkJkaenZ436OJV0SpmPZTrdLGQLtilsYZjLH3uEiqo6htXJa5xGBwAAdVbToU3VZEiTy/oGIbgWYRMAANRphpvh0scb4fLimk0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAA1IBhGEpOTq7tMuo8wiYAAMA5Tpw4oXHjxik0NFQ2m03BwcGKjY3Vpk2bLNne1q1bZRiGcnNzLen/bEVFRbr22mtlGIbS09Mt3x5vEAIAAHWaaTcv6+sqs7OzdfPNN8vf31+JiYnq1q2bSkpKlJKSovj4eO3bt8+ybV8q0zRlt9tVv37VEe/JJ59UUFCQdu3adVlqYmQTAADUWaeSTimtbZp2Re1SxvAM7YrapbS2aTqVdMqybY4ZM0aGYWjHjh0aNmyY2rdvr86dO2vSpElKS0urdJ3KRibT09NlGIays7MlSYcOHVJsbKwaNWokb29vde7cWevWrVN2draioqIkSY0aNZJhGBo1apSksvCYmJio0NBQeXl5KSIiQitWrKiw3ZSUFEVGRspmsyk1NbXKffvoo4+0YcMGzZs379K+pGqodtjcvn27YmNjFRQUVOm1CqNGjZJhGE7TjTfe6Kp6AQDAVeJU0intGbZHRUeKnOYXHS3SnmF7LAmcP/74o9avX6/4+Hh5e3tXWO7v71/jvuPj41VUVKTt27dr9+7dmjt3rho2bKjg4GCtXLlSkrR//34dP35cixYtkiRNmzZNb7zxhpYtW6Y9e/Zo4sSJuvfee7Vt2zanvp988kklJCQoIyND3bp1q3T73333nR566CG9/fbbatCgQY33o7qqfRq9sLBQERERuv/++xUXF1dpm4EDB+qNN95wfPbw8Kh5hQAA4Kpj2k1lPpYpmZUtlGRImRMy1WRIE5eeUs/MzJRpmurYsaPL+iyXk5OjuLg4de3aVZIUGhrqWBYQECBJatasmSPQFhYWasGCBdq8ebN69uzpWOfjjz/WK6+8oj59+jjWnzVrlvr371/ltk3T1KhRo/TII48oMjLSMdp6OVQ7bMbExCgmJua8bWw2m1q0aFHjogAAwNUtNzW3woimE1MqOlyk3NRcNerbyGXbNc2ydGsYrr8mdPz48Xr00Ue1YcMG9evXT3FxcVWOQkrS3r17debMmQohsri4WN27d3eaFxkZed5tv/TSS8rPz9fUqVNrvgM1ZMk1m1u3blWzZs3Uvn17PfTQQzp58mSVbYuKipSfn+80AQCAq1vx8WKXtrtY7dq1k2EYysjIqNZ69eqVRarysCpJJSUlTm1Gjx6tb7/9ViNGjNDu3bsVGRmpl156qco+S0tLJUlr165Venq6Y9q7d6/TdZuSKj3lf7bNmzcrLS1NNptN9evXV1hYmKSykDpy5MiL39EacHnYjImJ0T//+U9t3rxZ8+fP1+eff67f//73Kiqq/P9OEhIS5Ofn55iCg4NdXRIAALjCeARe3CV4F9vuYgUEBCg6OlpLlixRYWFhheVVPZqoadOmkqTjx4875lX2WKHg4GA98sgjSkpK0uTJk/Xqq69K+r9LDu12u6Ntp06dZLPZlJOTo7CwMKepunnpxRdf1K5duxyBdd26dZKk9957T3PmzKlWX9Xl8kcf3X333Y5/7tKliyIjI9WmTRutXbtWQ4cOrdB+6tSpmjRpkuNzfn4+gRMAgKucf29/2VrZVHS0qPLrNg3J1som/97+Lt/20qVLddNNN6lHjx6aNWuWunXrpl9//VUbN27UsmXLKh31LA+AM2bM0OzZs/XNN99o/vz5Tm0mTJigmJgYtW/fXqdPn9bmzZsVHh4uSWrTpo0Mw9CaNWt02223ycvLSz4+Pnr88cc1ceJElZaWqlevXsrPz9cnn3yihg0bVmtEsnXr1k6fGzZsKEm65ppr1KpVq+p+RdVi+aOPAgMD1aZNG33zzTeVLrfZbPL19XWaAADA1c1wMxS2KOy/H85dWPZH2MIwS563GRISop07dyoqKkqTJ09Wly5d1L9/f23atEnLli2rdB13d3e988472rdvnyIiIjR37lzNnj3bqY3dbld8fLzCw8M1cOBAdejQQUuXLpUktWzZUjNnztSUKVPUvHlzjR07VpL03HPPafr06UpISFB4eLiio6O1evVqhYSEuHy/rWKYZ19cUN2VDUOrVq3SHXfcUWWbH374QS1bttTf/vY33XfffRfsMz8/X35+fsrLyyN4AgBwBTtz5oyysrIUEhIiT0/PGvVxKumUMh/LdLpZyBZsU9jCMDUd2tRVpaIKVR3D6uS1ap9GLygoUGZmpuNzVlaW0tPTFRAQoICAAM2YMUNxcXEKDAxUdna2nn76aTVp0kR33nlndTcFAACuck2HNlWTIU0u6xuE4FrVDptffPGF4yn3khzXW44cOVLLli3T7t279dZbbyk3N1eBgYGKiorSe++9Jx8fH9dVDQAArhqGm+HSxxvh8qp22Ozbt6/Od+Y9JSXlkgoCAADAbwfvRgcAAIBlCJsAAMBSl3AvMmqZK44dYRMAAFjC3d1dkvTzzz/XciWoqfJjV34sa8LlD3UHAACQJDc3N/n7+zteW92gQQNL3jkO1zNNUz///LNOnjwpf39/ubm51bgvwiYAALBMixYtJMkROHFl8ff3dxzDmiJsAgAAyxiGocDAQDVr1kwlJSW1XQ6qwd3d/ZJGNMsRNgEAgOXc3NxcElxw5eEGIQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMtUO2xu375dsbGxCgoKkmEYSk5OrrLtww8/LMMwtHDhwksoEQAAAFeqaofNwsJCRUREaPHixedtl5ycrM8++0xBQUE1Lg4AAABXtvrVXSEmJkYxMTHnbXP06FGNHTtWKSkpuv3222tcHAAAAK5sLr9ms7S0VCNGjNATTzyhzp07u7p7AAAAXEGqPbJ5IXPnzlX9+vU1fvz4i2pfVFSkoqIix+f8/HxXlwQAAIBa4tKRzf/3//6fFi1apDfffFOGYVzUOgkJCfLz83NMwcHBriwJAAAAtcilYTM1NVUnT55U69atVb9+fdWvX1+HDh3S5MmT1bZt20rXmTp1qvLy8hzT4cOHXVkSAAAAapFLT6OPGDFC/fr1c5oXHR2tESNG6P777690HZvNJpvN5soyAAAAUEdUO2wWFBQoMzPT8TkrK0vp6ekKCAhQ69at1bhxY6f27u7uatGihTp06HDp1QIAAOCKUu2w+cUXXygqKsrxedKkSZKkkSNH6s0333RZYQAAALjyVTts9u3bV6ZpXnT77Ozs6m4CAAAAvxG8Gx0AAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDLVDpvbt29XbGysgoKCZBiGkpOTnZbPmDFDHTt2lLe3txo1aqR+/frps88+c1W9AAAAuIJUO2wWFhYqIiJCixcvrnR5+/bttXjxYu3evVsff/yx2rZtqwEDBujUqVOXXCwAAACuLIZpmmaNVzYMrVq1SnfccUeVbfLz8+Xn56f//Oc/uvXWWy/YZ3n7vLw8+fr61rQ0AAAAWKQ6ea2+lYUUFxfrb3/7m/z8/BQREVFpm6KiIhUVFTk+5+fnW1kSAAAALiNLbhBas2aNGjZsKE9PT/31r3/Vxo0b1aRJk0rbJiQkyM/PzzEFBwdbURIAAABqgSVhMyoqSunp6frkk080cOBA3XXXXTp58mSlbadOnaq8vDzHdPjwYStKAgAAQC2wJGx6e3srLCxMN954o1577TXVr19fr732WqVtbTabfH19nSYAAAD8NlyW52yapul0XSYAAACuDtW+QaigoECZmZmOz1lZWUpPT1dAQIAaN26sOXPmaPDgwQoMDNQPP/ygpUuX6siRI/rDH/7g0sIBAABQ91U7bH7xxReKiopyfJ40aZIkaeTIkXr55Ze1b98+LV++XN9//70aN26s66+/XqmpqercubPrqgYAAMAV4ZKes2kFnrMJAABQt1Unr/FudAAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYptphc/v27YqNjVVQUJAMw1BycrJjWUlJiZ566il17dpV3t7eCgoK0n333adjx465smYAAABcIaodNgsLCxUREaHFixdXWPbzzz9r586deuaZZ7Rz504lJSXpwIEDGjx4sEuKBQAAwJXFME3TrPHKhqFVq1bpjjvuqLLN559/rh49eujQoUNq3br1BfvMz8+Xn5+f8vLy5OvrW9PSAAAAYJHq5DXLr9nMy8uTYRjy9/e3elMAAACoY+pb2fmZM2c0ZcoUDR8+vMrUW1RUpKKiIsfn/Px8K0sCAADAZWTZyGZJSYn+53/+R6WlpVq6dGmV7RISEuTn5+eYgoODrSoJAAAAl5klYbOkpER33XWXsrKytHHjxvOey586dary8vIc0+HDh60oCQAAALXA5afRy4PmN998oy1btqhx48bnbW+z2WSz2VxdBgAAAOqAaofNgoICZWZmOj5nZWUpPT1dAQEBCgoK0rBhw7Rz506tWbNGdrtdJ06ckCQFBATIw8PDdZUDAACgzqv2o4+2bt2qqKioCvNHjhypGTNmKCQkpNL1tmzZor59+16wfx59BAAAULdVJ69Ve2Szb9++Ol8+vYTHdgIAAOA3hnejAwAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAy9Wu7AAAAXM1ul1JTpePHpcBAqXdvyc2ttqsCrk7VHtncvn27YmNjFRQUJMMwlJyc7LQ8KSlJ0dHRatKkiQzDUHp6uotKBQDgwpKSpLZtpagoafjwsj/bti2bD+Dyq3bYLCwsVEREhBYvXlzl8ptvvlkvvPDCJRcHAEB1JCVJw4ZJR444zz96tGw+gRO4/Kp9Gj0mJkYxMTFVLh8xYoQkKTs7u8ZFAQBQXXa79NhjkmlWXGaakmFIEyZIQ4ZwSh24nGr9BqGioiLl5+c7TQAAVFdqasURzbOZpnT4cFk7AJdPrYfNhIQE+fn5Oabg4ODaLgkAcAU6fty17QC4Rq2HzalTpyovL88xHT58uLZLAgBcgQIDXdsOgGvU+qOPbDabbDZbbZcBALjC9e4ttWpVdjNQZddtGkbZ8t69L39twNWs1kc2AQBwBTc3adGisn82DOdl5Z8XLuTmIOByq/bIZkFBgTIzMx2fs7KylJ6eroCAALVu3Vo//vijcnJydOzYMUnS/v37JUktWrRQixYtXFQ2AAAVDR0qrVhRdlf62TcLtWpVFjSHDq210oCrlmGalZ1sqNrWrVsVFRVVYf7IkSP15ptv6s0339T9999fYfmzzz6rGTNmXLD//Px8+fn5KS8vT76+vtUpDQAASbxBCLBadfJatcOm1QibAAAAdVt18hrXbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMtUOm9u3b1dsbKyCgoJkGIaSk5OdlpumqRkzZigoKEheXl7q27ev9uzZ46p6Xc5eatfW7K16Z/c72pq9VfZSe22XBAAAasC0mzq99bS+e+c7nd56WqbdrO2SIKl+dVcoLCxURESE7r//fsXFxVVYnpiYqAULFujNN99U+/btNXv2bPXv31/79++Xj4+PS4p2laSMJD22/jEdyT/imNfKt5UWDVykoeFDa7EyAABQHaeSTinzsUwVHSlyzLO1silsUZiaDm1ai5XBME2zxrHfMAytWrVKd9xxh6SyUc2goCBNmDBBTz31lCSpqKhIzZs319y5c/Xwww9fsM/8/Hz5+fkpLy9Pvr6+NS3tgpIykjTs/WEy5bz7hgxJ0oq7VhA4AQC4ApxKOqU9w/ZI5yaasv+kq/OKzgROF6tOXnPpNZtZWVk6ceKEBgwY4Jhns9nUp08fffLJJ67c1CWxl9r12PrHKgRNSY55E9ZP4JQ6AAB1nGk3lflYZsWgKTnmZU7I5JR6LXJp2Dxx4oQkqXnz5k7zmzdv7lh2rqKiIuXn5ztNVkvNSXU6dX4uU6YO5x9Wak6q5bUAAICay03NdTp1XoEpFR0uUm5q7mWrCc4suRvdMAynz6ZpVphXLiEhQX5+fo4pODjYipKcHP/puEvbAQCA2lF8vNil7eB6Lg2bLVq0kKQKo5gnT56sMNpZburUqcrLy3NMhw8fdmVJlQr0CXRpOwAAUDs8Aj1c2g6u59KwGRISohYtWmjjxo2OecXFxdq2bZtuuummStex2Wzy9fV1mqzWu3VvtfJt5bgZ6FyGDAX7Bqt3696W1wIAAGrOv7e/bK1squI/6ZIh2YJt8u/tfznLwlmqHTYLCgqUnp6u9PR0SWU3BaWnpysnJ0eGYWjChAl6/vnntWrVKn399dcaNWqUGjRooOHDh7u69hpzq+emRQMXSVKFwFn+eeHAhXKr53bZawMAABfPcDMUtijsvx/OXVj2R9jCMBluVaVRWK3ajz7aunWroqKiKswfOXKk3nzzTZmmqZkzZ+qVV17R6dOndcMNN2jJkiXq0qXLRfV/uR59JFX+nM1g32AtHLiQxx4BAHAFqfQ5m8E2hS3kOZtWqE5eu6TnbFrhcoZNqewxSKk5qTr+03EF+gSqd+vejGgCAHAFMu2mclNzVXy8WB6BHvLv7c+IpkWqk9eq/Qah3xq3em7q27ZvbZcBAAAukeFmqFHfRrVdBs5hyaOPAAAAAImwCQAAAAsRNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDL1a7sAAAAA1JxpN5Wbmqvi48XyCPSQf29/GW5GbZflYMnI5k8//aQJEyaoTZs28vLy0k033aTPP//cik0BAABctU4lnVJa2zTtitqljOEZ2hW1S2lt03Qq6VRtl+ZgSdgcPXq0Nm7cqLffflu7d+/WgAED1K9fPx09etSKzQEAAFx1TiWd0p5he1R0pMhpftHRIu0ZtqfOBE7DNE3TlR3+8ssv8vHx0Ycffqjbb7/dMf/aa6/VoEGDNHv27POun5+fLz8/P+Xl5cnX19eVpQEAAPwmmHZTaW3TKgRNB0OytbLpxqwbLTmlXp285vJrNn/99VfZ7XZ5eno6zffy8tLHH39coX1RUZGKiv7vi8rPz3d1SQAAAL8puam5VQdNSTKlosNFyk3NVaO+jS5fYZVw+Wl0Hx8f9ezZU88995yOHTsmu92uf/zjH/rss890/PjxCu0TEhLk5+fnmIKDg11dEgAAwG9K8fFil7azkiXXbL799tsyTVMtW7aUzWbTiy++qOHDh8vNza1C26lTpyovL88xHT582IqSAAAAfjM8Aj1c2s5KloTNa665Rtu2bVNBQYEOHz6sHTt2qKSkRCEhIRXa2mw2+fr6Ok0AAAComn9vf9la2aSqLsc0JFuwTf69/S9nWZWy9KHu3t7eCgwM1OnTp5WSkqIhQ4ZYuTkAAICrguFmKGxR2H8/nLuw7I+whWF14nmbloTNlJQUrV+/XllZWdq4caOioqLUoUMH3X///VZsDgAA4KrTdGhTdV7RWbaWNqf5tlY2dV7RWU2HNq2lypxZ8gahvLw8TZ06VUeOHFFAQIDi4uI0Z84cubu7W7E5AACAq1LToU3VZEiTOv0GIZc/Z/NS8ZxNAACAuq06ec3SazYBAABwdSNsAgAAwDKETQAAAFiGsAkAAADLEDYBAABgGcImAAAALEPYBAAAgGUImwAAALAMYRMAAACWIWwCAADAMoRNAAAAWIawCQAAAMsQNgEAAGCZ+rVdwLlM05Qk5efn13IlAAAAqEx5TivPbedT58LmTz/9JEkKDg6u5UoAAABwPj/99JP8/PzO28YwLyaSXkalpaU6duyYfHx8ZBhGbZfzm5afn6/g4GAdPnxYvr6+tV0OLhOO+9WHY3714ZhffS73MTdNUz/99JOCgoJUr975r8qscyOb9erVU6tWrWq7jKuKr68v/zK6CnHcrz4c86sPx/zqczmP+YVGNMtxgxAAAAAsQ9gEAACAZQibVzGbzaZnn31WNputtkvBZcRxv/pwzK8+HPOrT10+5nXuBiEAAAD8djCyCQAAAMsQNgEAAGAZwiYAAAAsQ9gEAACAZQibV4Ht27crNjZWQUFBMgxDycnJVbZ9+OGHZRiGFi5ceNnqg+tdzDHPyMjQ4MGD5efnJx8fH914443Kycm5/MXCJS50zAsKCjR27Fi1atVKXl5eCg8P17Jly2qnWLhEQkKCrr/+evn4+KhZs2a64447tH//fqc2pmlqxowZCgoKkpeXl/r27as9e/bUUsW4VBc65iUlJXrqqafUtWtXeXt7KygoSPfdd5+OHTtWi1UTNq8KhYWFioiI0OLFi8/bLjk5WZ999pmCgoIuU2WwyoWO+cGDB9WrVy917NhRW7du1a5du/TMM8/I09PzMlcKV7nQMZ84caLWr1+vf/zjH8rIyNDEiRM1btw4ffjhh5e5UrjKtm3bFB8fr7S0NG3cuFG//vqrBgwYoMLCQkebxMRELViwQIsXL9bnn3+uFi1aqH///vrpp59qsXLU1IWO+c8//6ydO3fqmWee0c6dO5WUlKQDBw5o8ODBtVu4iauKJHPVqlUV5h85csRs2bKl+fXXX5tt2rQx//rXv1722mCNyo753Xffbd577721UxAsV9kx79y5szlr1iynedddd505bdq0y1gZrHTy5ElTkrlt2zbTNE2ztLTUbNGihfnCCy842pw5c8b08/MzX3755doqEy507jGvzI4dO0xJ5qFDhy5jZc4Y2YRKS0s1YsQIPfHEE+rcuXNtlwOLlZaWau3atWrfvr2io6PVrFkz3XDDDee9vAJXvl69eunf//63jh49KtM0tWXLFh04cEDR0dG1XRpcJC8vT5IUEBAgScrKytKJEyc0YMAARxubzaY+ffrok08+qZUa4VrnHvOq2hiGIX9//8tUVUWETWju3LmqX7++xo8fX9ul4DI4efKkCgoK9MILL2jgwIHasGGD7rzzTg0dOlTbtm2r7fJgkRdffFGdOnVSq1at5OHhoYEDB2rp0qXq1atXbZcGFzBNU5MmTVKvXr3UpUsXSdKJEyckSc2bN3dq27x5c8cyXLkqO+bnOnPmjKZMmaLhw4fL19f3Mlf4f+rX2pZRJ/y///f/tGjRIu3cuVOGYdR2ObgMSktLJUlDhgzRxIkTJUnXXnutPvnkE7388svq06dPbZYHi7z44otKS0vTv//9b7Vp00bbt2/XmDFjFBgYqH79+tV2ebhEY8eO1VdffaWPP/64wrJz/91umib/vv8NON8xl8puFvqf//kflZaWaunSpZe5OmeMbF7lUlNTdfLkSbVu3Vr169dX/fr1dejQIU2ePFlt27at7fJggSZNmqh+/frq1KmT0/zw8HDuRv+N+uWXX/T0009rwYIFio2NVbdu3TR27FjdfffdmjdvXm2Xh0s0btw4/fvf/9aWLVvUqlUrx/wWLVpIUoVRzJMnT1YY7cSVpapjXq6kpER33XWXsrKytHHjxlod1ZQIm1e9ESNG6KuvvlJ6erpjCgoK0hNPPKGUlJTaLg8W8PDw0PXXX1/hESkHDhxQmzZtaqkqWKmkpEQlJSWqV8/5X/lubm6OkW5ceUzT1NixY5WUlKTNmzcrJCTEaXlISIhatGihjRs3OuYVFxdr27Ztuummmy53uXCBCx1z6f+C5jfffKP//Oc/aty4cS1U6ozT6FeBgoICZWZmOj5nZWUpPT1dAQEBat26dYUforu7u1q0aKEOHTpc7lLhIhc65k888YTuvvtu3XLLLYqKitL69eu1evVqbd26tfaKxiW50DHv06ePnnjiCXl5ealNmzbatm2b3nrrLS1YsKAWq8aliI+P17/+9S99+OGH8vHxcYxg+vn5ycvLS4ZhaMKECXr++efVrl07tWvXTs8//7waNGig4cOH13L1qIkLHfNff/1Vw4YN086dO7VmzRrZ7XZHm4CAAHl4eNRO4bV2Hzwumy1btpiSKkwjR46stD2PPrryXcwxf+2118ywsDDT09PTjIiIMJOTk2uvYFyyCx3z48ePm6NGjTKDgoJMT09Ps0OHDub8+fPN0tLS2i0cNVbZ8ZZkvvHGG442paWl5rPPPmu2aNHCtNls5i233GLu3r279orGJbnQMc/KyqqyzZYtW2qtbuO/xQMAAAAuxzWbAAAAsAxhEwAAAJYhbAIAAMAyhE0AAABYhrAJAAAAyxA2AQAAYBnCJgAAACxD2AQAAIBlCJsAAACwDGETAAAAliFsAgAAwDKETQAAAFjm/wPwtVmPI+uzDgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Schritt 6 ---\n", - "Finale Cluster: [[0], [1], [4], [5], [2, 3]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(0, 30, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 3\n", - "rangeStart = 1\n", - "rangeEnd = 6\n", - "\n", - "clusters = generate_clusters(num_clusters, points_per_cluster)\n", - "print(clusters)\n", - "X = clusters\n", - "\n", - "# Initialisierung: Jeder Datenpunkt ist ein Cluster\n", - "clusters = [[i] for i in range(X.shape[0])]\n", - "print(clusters)\n", - "\n", - "\n", - "# Schleife für jeden Schritt der hierarchischen Clusteranalyse\n", - "for step in range(X.shape[0] - 1):\n", - " print(f\"\\n--- Schritt {step + 1} ---\")\n", - " print(\"Aktuelle Cluster:\", clusters)\n", - "\n", - " # Berechnung der Abstandsmatrix mit Ward-Methode\n", - " linkage_matrix = linkage(X, 'ward')\n", - "\n", - " # Ausgabe der Abstandsmatrix\n", - " print(\"Abstandsmatrix nach Linkage:\")\n", - " print(linkage_matrix)\n", - "\n", - " # Tabelle mit den Abständen zwischen den Clustern erstellen\n", - " distances = []\n", - " for i in range(len(linkage_matrix)):\n", - " cluster1, cluster2, distance, _ = linkage_matrix[i]\n", - " cluster1, cluster2 = int(cluster1), int(cluster2) # Konvertiere zu ganzzahligen Indizes\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " distances.append([cluster1, cluster2, distance, len(clusters[cluster1]), len(clusters[cluster2])])\n", - "\n", - " # Streudiagramm zeichnen\n", - " colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - " plt.figure(figsize=(8, 6))\n", - " for idx, cluster in enumerate(clusters):\n", - " for point_idx in cluster:\n", - " plt.scatter(X[point_idx, 0], X[point_idx, 1], color=colors[idx % len(colors)], label=f'Cluster {idx}')\n", - " plt.title('Hierarchische Clusteranalyse - Schritt {}'.format(step + 1))\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Wenn nur noch ein Cluster übrig ist, breche die Schleife ab\n", - " if len(clusters) == 1:\n", - " break\n", - "\n", - " # Identifiziere die beiden nächsten Cluster\n", - " cluster1, cluster2 = int(linkage_matrix[step][0]), int(linkage_matrix[step][1])\n", - "\n", - " # Überprüfe, ob die Cluster existieren, bevor sie zusammengeführt werden\n", - " if cluster1 < len(clusters) and cluster2 < len(clusters):\n", - " new_cluster = clusters[cluster1] + clusters[cluster2]\n", - " del clusters[max(cluster1, cluster2)]\n", - " del clusters[min(cluster1, cluster2)]\n", - " clusters.append(new_cluster)\n", - "\n", - "# Ausgabe des finalen Clusters\n", - "print(f\"\\n--- Schritt {X.shape[0]} ---\")\n", - "print(\"Finale Cluster:\", clusters)\n", - "\n", - "# Dendrogramm erstellen und anzeigen\n", - "plt.figure(figsize=(10, 7))\n", - "plt.title('Hierarchische Clusteranalyse - Methode: Ward')\n", - "dendrogram(linkage_matrix)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7c9c83ed", - "metadata": {}, - "outputs": [ - { - "ename": "ExecutableNotFound", - "evalue": "failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/backend/execute.py\u001b[0m in \u001b[0;36mrun_check\u001b[0;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0mproc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask)\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m 952\u001b[0m \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m 1820\u001b[0m \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1821\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1822\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: PosixPath('dot')", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mExecutableNotFound\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/bs/5wndg3791vz7vxsh2h7qj_mw0000gn/T/ipykernel_63053/831696634.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mpoints_per_cluster\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;31m#Hauptfunktion ausfuehren\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mfoo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_clusters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpoints_per_cluster\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfoo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"abc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/_tools.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 169\u001b[0m category=category)\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/backend/rendering.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(engine, format, filepath, renderer, formatter, neato_no_op, quiet, outfile, raise_if_result_exists, overwrite_filepath)\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m execute.run_check(cmd,\n\u001b[0m\u001b[1;32m 325\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparts\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquiet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/graphviz/backend/execute.py\u001b[0m in \u001b[0;36mrun_check\u001b[0;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mExecutableNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mExecutableNotFound\u001b[0m: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH" - ] - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import graphviz\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(0, 30, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "#def generate_task_definition():\n", - "def save_plot(path, filename):\n", - " plt.savefig(\"{}{}.png\".format(path, filename))\n", - "\n", - "def create_dot_table(array):\n", - " \n", - " dotTable = graphviz.Digraph('dotTable', comment='task definition table')\n", - " \n", - " # Add nodes to the graph for each element in the array\n", - " for i in range(array.shape[0]):\n", - " row_label = f'Row{i+1}'\n", - " dotTable.node(row_label, shape='plaintext', label=f'<
{array[i, 0]}{array[i, 1]}
>')\n", - "\n", - " # Add edges between consecutive rows (if needed)\n", - " for i in range(array.shape[0] - 1):\n", - " dotTable.edge(f'Row{i+1}', f'Row{i+2}')\n", - "\n", - " return dotTable\n", - " \n", - "def main(num_clusters,points_per_cluster):\n", - " clusters = generate_clusters(num_clusters, points_per_cluster)\n", - " dotTable = create_dot_table(clusters)\n", - " dotTable.save('table.dot')\n", - " dotTable.render('table', format='png', cleanup=True)\n", - "\n", - " \n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "num_clusters = 2\n", - "# Anzahl der Datenpunkte pro Cluster\n", - "points_per_cluster = 3\n", - "#Hauptfunktion ausfuehren\n", - "foo = main(num_clusters,points_per_cluster)\n", - "print(foo)\n", - "print(\"abc\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fa0e1cf", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"fo\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fe99c316", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 6 and the array at index 1 has size 5", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/bs/5wndg3791vz7vxsh2h7qj_mw0000gn/T/ipykernel_63053/1285465837.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;31m# Add a new column at the end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0mcondensed_distances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcondensed_distances\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_col\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mnum_clusters\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mhstack\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/numpy/core/shape_base.py\u001b[0m in \u001b[0;36mhstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 6 and the array at index 1 has size 5" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3e7c446b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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> shape=plaintext]\\\\n}\\\\n\"}'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from graphviz import Digraph\n", - "import graphviz\n", - "#from IPython.display import display\n", - "import math\n", - "import json\n", - "import sys\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster, rangeStart, rangeEnd):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(rangeStart, rangeEnd, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "def is_element_in_array(cluster_array, single_point):\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " return True\n", - " return False\n", - "\n", - "def is_element_in_array_location(cluster_array, single_point):\n", - " i = 0\n", - " j = 0\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " #print(\"LOCATION\")\n", - " #print(element[0])\n", - " #print(element[1])\n", - " return [i, j]\n", - " j += 1\n", - " i += 1\n", - " return False\n", - "\n", - "def check_array_exists(arrayName):\n", - " if 'clusters' in dir():\n", - " return True\n", - " else:\n", - " return False\n", - "\n", - "#todo\n", - "def manhattan_distance(point1, point2):\n", - " return np.sum(np.abs(point1 + point2))\n", - "\n", - "#ready\n", - "def euclidean_distance(array1, array2):\n", - " return round(math.sqrt((pow((array1[0] - array2[0]), 2)) + (pow((array1[1] - array2[1]), 2))),2)\n", - "\n", - "#todo\n", - "def maximum_distance(point1, point2):\n", - " return np.max([[point1],[point2]])\n", - "\n", - "def calculate_inital_abstraction_matrix(data, distance_function):\n", - " #print(data)\n", - " #print(distance_function)\n", - " # Initialize clusters with each data point as a separate cluster\n", - " array = np.zeros((data.shape[0],data.shape[0]))\n", - " \n", - " for iteration_number_row in range(len(data)):\n", - " \n", - " for iteration_number_column in range(len(data)):\n", - " array[iteration_number_row, iteration_number_column] = distance_function(data[iteration_number_row],data[iteration_number_column])\n", - "\n", - " return array\n", - "\n", - "def single_linkage_get_indices(inital_abstraction_matrix):\n", - "\n", - " # Exclude diagonal elements and zeros\n", - " lower_triangular = np.tril(inital_abstraction_matrix, k=-1)\n", - " lower_triangular[lower_triangular == 0] = np.inf\n", - " \n", - " min_index = np.unravel_index(np.argmin(lower_triangular), lower_triangular.shape)\n", - " \n", - " # Adjust indices to consider the original matrix\n", - " #adjusted_indices = (min_index[0] + 1, min_index[1])\n", - "\n", - " #print(min_index)\n", - " return min_index\n", - "\n", - "#get nearest of 2 points to get the new center of the cluster\n", - "def get_nearest_point(data, point_a, point_b, distance_function):\n", - " #get the point that is more near the other points\n", - " # Calculate distances for both points\n", - " distances_to_a = [distance_function(point_a, point) for point in data]\n", - " distances_to_b = [distance_function(point_b, point) for point in data]\n", - "\n", - " # Find the index of the nearest point for each given point for both points\n", - " nearest_point_index_a = np.argmin(distances_to_a)\n", - " nearest_point_index_b = np.argmin(distances_to_b)\n", - " \n", - " sum_distance_to_a = 0\n", - " for distance in distances_to_a:\n", - " sum_distance_to_a += float(distance)\n", - " \n", - " sum_distance_to_b = 0\n", - " for distance in distances_to_b:\n", - " sum_distance_to_b += float(distance)\n", - "\n", - " \n", - " # Compare the distances\n", - " #[[nearest_point][point_to_delete]]\n", - " if sum_distance_to_a < sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " elif sum_distance_to_a > sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_b] , data[nearest_point_index_a]]\n", - " else: #equal\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " \n", - " return nearest_point\n", - "\n", - "def createScatterDiagram(data):\n", - " # Streudiagramm zeichnen\n", - " # Extract x and y coordinates\n", - " x = data[:, 0]\n", - " y = data[:, 1]\n", - "\n", - " # Generate random colors\n", - " colors = np.random.rand(len(x), 3)\n", - "\n", - " # Create scatter plot with random colors\n", - " plt.scatter(x, y, marker='o', color=colors)\n", - "\n", - " # Add labels and title\n", - " plt.xlabel('X-Achse')\n", - " plt.ylabel('Y-Achse')\n", - " plt.title('Streudiagram')\n", - "\n", - " # Display the plot\n", - " plt.show()\n", - "\n", - "def createDigraph(distanceMatrix, points, iterationNumber):\n", - " #transform matrix\n", - " \n", - " points = points.astype(str)\n", - " distanceMatrix = distanceMatrix.astype(str)\n", - " pointsY = np.array([[' - '.join(row)] for row in points]) \n", - " pointsX = np.array([[' - '.join(row)] for row in points]) \n", - " #pointsX = np.insert(pointsX, 0, 'Iteration {}'.format(iterationNumber), axis=0)\n", - " pointsX = np.insert(pointsX, 0, str(iterationNumber), axis=0)\n", - "\n", - " # Concatenate arr1 and arr2 vertically\n", - " distanceMatrix = np.concatenate((pointsY, distanceMatrix), axis=1)\n", - " \n", - " Array2d = np.array([np.concatenate(pointsX.T)])\n", - " distanceMatrixNew = np.append(Array2d, distanceMatrix, axis=0)\n", - " \n", - " # Create a Digraph object\n", - " dot = Digraph(comment='Distance Matrix')\n", - "\n", - " # Create a single table for the entire matrix\n", - " table_label = f'<'\n", - " for i in range(distanceMatrixNew.shape[0]):\n", - " table_label += ''\n", - " for j in range(distanceMatrixNew.shape[1]):\n", - " table_label += f''\n", - " table_label += ''\n", - " table_label += '
{distanceMatrixNew[i, j]}
>'\n", - "\n", - " # Add a single node to the graph with the entire table as label\n", - " dot.node('table', label=table_label, shape='plaintext')\n", - " \n", - " # Save the graph as a PNG file\n", - " #dot.render('directed_graph', format='png', cleanup=True)\n", - " \n", - " display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "# Helper function to recursively add nodes and edges to the graph\n", - "def add_nodes_edges(dot, node_id, Z, dendrogram_data):\n", - " if node_id < len(Z):\n", - " # Internal node\n", - " child1, child2 = Z[node_id, 0], Z[node_id, 1]\n", - " dot.node(f'node_{node_id}', label=str(Z[node_id, 2]))\n", - " add_nodes_edges(dot, int(child1), Z, dendrogram_data)\n", - " add_nodes_edges(dot, int(child2), Z, dendrogram_data)\n", - " dot.edge(f'node_{node_id}', f'node_{int(child1)}')\n", - " dot.edge(f'node_{node_id}', f'node_{int(child2)}')\n", - " else:\n", - " # Leaf node\n", - " leaf_index = node_id - len(Z)\n", - " dot.node(f'leaf_{leaf_index}', label=str(dendrogram_data['ivl'][leaf_index]))\n", - "\n", - "\n", - "def createDendogramDotLanguage(data, distanceMethod):\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " linkage_data = linkage(data, method='single', metric='cityblock')\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " linkage_data = linkage(data, method='single', metric='euclidean')\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " linkage_data = linkage(data, method='single', metric='maximum')\n", - "\n", - " else:\n", - " return ''\n", - " \n", - " #dendrogram(linkage_data)\n", - " \n", - " #print(linkage_data)\n", - "\n", - " #plt.show()\n", - " \n", - " # Create a dendrogram\n", - " dendrogram_data = dendrogram(linkage_data, no_plot=True)\n", - "\n", - " # Create a Graphviz graph\n", - " dot = graphviz.Digraph(comment='Dendrogram')\n", - "\n", - " # Add nodes and edges to the graph\n", - " add_nodes_edges(dot, len(linkage_data) * 2 - 2, linkage_data, dendrogram_data)\n", - " display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "\n", - "def clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod):\n", - " #data = generate_clusters(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd)\n", - " data = np.array([[2, 3],\n", - " [5, 2],\n", - " [5, 3],\n", - " [1, 4],\n", - " [4, 5]])\n", - " \n", - " \n", - " createScatterDiagram(data)\n", - " jsonDict = {}\n", - " \n", - " jsonDict[\"Dendogram\"] = createDendogramDotLanguage(data, distanceMethod)\n", - " \n", - " \n", - " for iteration in range(data.shape[0]):\n", - " #erste iteration\n", - " if(iteration == 0):\n", - " \n", - " clusters = []\n", - " \n", - " #inital abstraction matrix\n", - " print(\"---Inital Iteration---\")\n", - " #get distance method\n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(data, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " #print(single_linkage_indices)\n", - " \n", - " #get two points from given index that are the first cluster\n", - " point_a = data[single_linkage_indices[0]]\n", - " point_b = data[single_linkage_indices[1]]\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - " \n", - " #create Digraph\n", - " #TODO -> oberste Zeile noch hinzufügen\n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = createDigraph(matrix, data, iteration)\n", - " \n", - " \n", - " #define cluster elements\n", - " #clusters = np.array([[point_a, point_b]])\n", - " clusters.append([point_a, point_b])\n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(new_data)\n", - "\n", - " #alle weiteren iterationen \n", - " elif(iteration > 0):\n", - " \n", - " if len(new_data) == 1:\n", - " continue\n", - " \n", - " print(\"\")\n", - " print(\"\")\n", - " print(\"---Next Iteration---\")\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " #matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " #get the two points with the new minimum distance\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " \n", - " #get two points from given index that are the new cluster\n", - " point_a = new_data[single_linkage_indices[0]]\n", - " point_b = new_data[single_linkage_indices[1]]\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " #point_to_remove = nearest_point[1]\n", - " \n", - " #add point to current cluster\n", - " if is_element_in_array(clusters, point_a) or is_element_in_array(clusters, point_b):\n", - " #print(\"Erweitere Cluster\")\n", - " \n", - " #get element number of the cluster and append point to the current cluster\n", - " if is_element_in_array(clusters, point_a):\n", - " arrayLocation = is_element_in_array_location(clusters, point_a)\n", - " pointToAppend = point_b\n", - " elif is_element_in_array(clusters, point_b):\n", - " arrayLocation = is_element_in_array_location(clusters, point_b)\n", - " pointToAppend = point_a\n", - " \n", - " #print(arrayLocation)\n", - " \n", - " #append pointToAppend to cluster:\n", - " #clusters = np.insert(clusters, arrayLocation[0], pointToAppend, axis=arrayLocation[1])\n", - " #print(clusters[arrayLocation[0]])\n", - " #clusters[arrayLocation[0], arrayLocation[1]]\n", - " \n", - " #newClusters1 = clusters[arrayLocation[0]]\n", - " #print(clusters[arrayLocation[0]])\n", - " pointsToAppend = np.vstack([clusters[arrayLocation[0]], pointToAppend])\n", - " \n", - " newClusters = []\n", - " \n", - " newClusters.append(pointsToAppend)\n", - "\n", - " #newClusters = np.append(clusters[arrayLocation[0]], [pointToAppend], axis=0)\n", - " #newClusters = np.array([[point_a, point_b]])\n", - " clusterCounter = 0\n", - " \n", - " for cluster in clusters:\n", - " if clusterCounter == arrayLocation[0]:\n", - " clusterCounter += 1\n", - " \n", - " else:\n", - " #newClusters = np.concatenate([newClusters, [cluster]]) #dimensions are not valid! -> was anderes einfallen lassen!! #letztes puzzelstück -> wie den bums richtig zusammenfügen???!!!\n", - " newClusters.append(cluster)\n", - " clusterCounter += 1\n", - " \n", - " \n", - " #print(\"UPDATED CLUSTER:\")\n", - " #print(newClusters)\n", - " print(\"-------\")\n", - " \n", - " clusters = newClusters\n", - "\n", - " #create new cluster\n", - " else:\n", - " #print(\"Definiere neues Cluster\")\n", - " #define cluster elements if ist an new cluster\n", - " #clusters = np.append(clusters,[[point_a, point_b]], axis=0)\n", - " clusters.append([point_a, point_b])\n", - " #print(clusters)\n", - " \n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " createDigraph(matrix, new_data, iteration)\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = createDigraph(matrix, new_data, iteration)\n", - "\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - "\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in new_data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(new_data)\n", - " return json.dumps(jsonDict)\n", - " \n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "#numClusters = int(sys.argv[1])\n", - "#pointsPerCluster = int(sys.argv[2])\n", - "#nodeRangeStart = int(sys.argv[3])\n", - "#nodeRangeEnd = int(sys.argv[4])\n", - "#distanceMethod = int(sys.argv[5])\n", - "#linkageMethod = int(sys.argv[6])\n", - "\n", - "numClusters = 2\n", - "pointsPerCluster = 3\n", - "nodeRangeStart = 0\n", - "nodeRangeEnd = 25\n", - "distanceMethod = 'euclidean'\n", - "linkageMethod = 'single'\n", - "#Hauptfunktion ausfuehren\n", - "clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod)\n", - "\n", - "\n", - "#TODO\n", - "#Eingabevalidierung!!!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7942a038", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3bb05ef1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---Inital Iteration---\n", - "DIGRAPH - Abstandsmatrix\n", - "\n", - "\n", - "---Next Iteration---\n", - "DIGRAPH - Abstandsmatrix\n", - "\n", - "\n", - "---Next Iteration---\n", - "-------\n", - "DIGRAPH - Abstandsmatrix\n", - "\n", - "\n", - "---Next Iteration---\n", - "-------\n", - "DIGRAPH - Abstandsmatrix\n", - "{'Dendogram': '// Dendrogram\\ndigraph {\\n\\tleaf_2 [label=4]\\n}\\n', 'DigraphIteration0': [[0.0, 3.16, 3.0, 1.41, 2.83], [3.16, 0.0, 1.0, 4.47, 3.16], [3.0, 1.0, 0.0, 4.12, 2.24], [1.41, 4.47, 4.12, 0.0, 3.16], [2.83, 3.16, 2.24, 3.16, 0.0]], 'DigraphIteration1': [[0.0, 3.0, 1.41, 2.83], [3.0, 0.0, 4.12, 2.24], [1.41, 4.12, 0.0, 3.16], [2.83, 2.24, 3.16, 0.0]], 'DigraphIteration2': [[0.0, 3.0, 2.83], [3.0, 0.0, 2.24], [2.83, 2.24, 0.0]], 'DigraphIteration3': [[0.0, 3.0], [3.0, 0.0]]}\n" - ] - }, - { - "data": { - "text/plain": [ - "'{\"Dendogram\": \"// Dendrogram\\\\ndigraph {\\\\n\\\\tleaf_2 [label=4]\\\\n}\\\\n\", \"DigraphIteration0\": [[0.0, 3.16, 3.0, 1.41, 2.83], [3.16, 0.0, 1.0, 4.47, 3.16], [3.0, 1.0, 0.0, 4.12, 2.24], [1.41, 4.47, 4.12, 0.0, 3.16], [2.83, 3.16, 2.24, 3.16, 0.0]], \"DigraphIteration1\": [[0.0, 3.0, 1.41, 2.83], [3.0, 0.0, 4.12, 2.24], [1.41, 4.12, 0.0, 3.16], [2.83, 2.24, 3.16, 0.0]], \"DigraphIteration2\": [[0.0, 3.0, 2.83], [3.0, 0.0, 2.24], [2.83, 2.24, 0.0]], \"DigraphIteration3\": [[0.0, 3.0], [3.0, 0.0]], \"iterations\": 4}'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Clusteranalyse\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from scipy.cluster.hierarchy import linkage, dendrogram\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from graphviz import Digraph\n", - "import graphviz\n", - "#from IPython.display import display\n", - "import math\n", - "import json\n", - "import sys\n", - "import os\n", - "\n", - "#functions\n", - "def generate_clusters(n_clusters, m_points_per_cluster, rangeStart, rangeEnd):\n", - " # Generate random cluster centers\n", - " cluster_centers = np.random.randint(rangeStart, rangeEnd, size=(n_clusters, 2))\n", - " noise_scale = 1\n", - "\n", - " # Generate points for each cluster\n", - " points = []\n", - " for center in cluster_centers:\n", - " cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center\n", - " points.extend(cluster_points)\n", - "\n", - " # Add noise to the points\n", - " noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2)))\n", - " \n", - " return np.array(noisy_points)\n", - "\n", - "def is_element_in_array(cluster_array, single_point):\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " return True\n", - " return False\n", - "\n", - "def is_element_in_array_location(cluster_array, single_point):\n", - " i = 0\n", - " j = 0\n", - " for sub_array in cluster_array:\n", - " for element in sub_array:\n", - " if element[0] == single_point[0] and element[1] == single_point[1]:\n", - " #print(\"LOCATION\")\n", - " #print(element[0])\n", - " #print(element[1])\n", - " return [i, j]\n", - " j += 1\n", - " i += 1\n", - " return False\n", - "\n", - "def check_array_exists(arrayName):\n", - " if 'clusters' in dir():\n", - " return True\n", - " else:\n", - " return False\n", - "\n", - "#todo\n", - "def manhattan_distance(point1, point2):\n", - " return np.sum(np.abs(point1 + point2))\n", - "\n", - "#ready\n", - "def euclidean_distance(array1, array2):\n", - " return round(math.sqrt((pow((array1[0] - array2[0]), 2)) + (pow((array1[1] - array2[1]), 2))),2)\n", - "\n", - "#todo\n", - "def maximum_distance(point1, point2):\n", - " return np.max([[point1],[point2]])\n", - "\n", - "def calculate_inital_abstraction_matrix(data, distance_function):\n", - " \n", - " # Initialize clusters with each data point as a separate cluster\n", - " array = np.zeros((data.shape[0],data.shape[0]))\n", - " \n", - " for iteration_number_row in range(len(data)):\n", - " \n", - " for iteration_number_column in range(len(data)):\n", - " array[iteration_number_row, iteration_number_column] = distance_function(data[iteration_number_row],data[iteration_number_column])\n", - "\n", - " return array\n", - "\n", - "def single_linkage_get_indices(inital_abstraction_matrix):\n", - "\n", - " # Exclude diagonal elements and zeros\n", - " lower_triangular = np.tril(inital_abstraction_matrix, k=-1)\n", - " lower_triangular[lower_triangular == 0] = np.inf\n", - " \n", - " min_index = np.unravel_index(np.argmin(lower_triangular), lower_triangular.shape)\n", - " \n", - " # Adjust indices to consider the original matrix\n", - " #adjusted_indices = (min_index[0] + 1, min_index[1])\n", - "\n", - " #print(min_index)\n", - " return min_index\n", - "\n", - "#get nearest of 2 points to get the new center of the cluster\n", - "def get_nearest_point(data, point_a, point_b, distance_function):\n", - " #get the point that is more near the other points\n", - " # Calculate distances for both points\n", - " distances_to_a = [distance_function(point_a, point) for point in data]\n", - " distances_to_b = [distance_function(point_b, point) for point in data]\n", - "\n", - " # Find the index of the nearest point for each given point for both points\n", - " nearest_point_index_a = np.argmin(distances_to_a)\n", - " nearest_point_index_b = np.argmin(distances_to_b)\n", - " \n", - " sum_distance_to_a = 0\n", - " for distance in distances_to_a:\n", - " sum_distance_to_a += float(distance)\n", - " \n", - " sum_distance_to_b = 0\n", - " for distance in distances_to_b:\n", - " sum_distance_to_b += float(distance)\n", - "\n", - " \n", - " # Compare the distances\n", - " #[[nearest_point][point_to_delete]]\n", - " if sum_distance_to_a < sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " elif sum_distance_to_a > sum_distance_to_b:\n", - " nearest_point = [data[nearest_point_index_b] , data[nearest_point_index_a]]\n", - " else: #equal\n", - " nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]]\n", - " \n", - " return nearest_point\n", - "\n", - "def createScatterDiagram(data):\n", - " # Streudiagramm zeichnen\n", - " # Extract x and y coordinates\n", - " x = data[:, 0]\n", - " y = data[:, 1]\n", - "\n", - " # Generate random colors\n", - " colors = np.random.rand(len(x), 3)\n", - "\n", - " # Create scatter plot with random colors\n", - " plt.scatter(x, y, marker='o', color=colors)\n", - "\n", - " # Add labels and title\n", - " plt.xlabel('X-Achse')\n", - " plt.ylabel('Y-Achse')\n", - " plt.title('Streudiagram')\n", - "\n", - " # #display the plot\n", - " plt.show()\n", - "\n", - "def createDigraph(distanceMatrix, points, iterationNumber):\n", - " #transform matrix\n", - " \n", - " #print(distanceMatrix)\n", - "\n", - " points = points.astype(str)\n", - " distanceMatrix = distanceMatrix.astype(str)\n", - " pointsY = np.array([[' - '.join(row)] for row in points]) \n", - " pointsX = np.array([[' - '.join(row)] for row in points]) \n", - " #pointsX = np.insert(pointsX, 0, 'Iteration {}'.format(iterationNumber), axis=0)\n", - " pointsX = np.insert(pointsX, 0, str(iterationNumber), axis=0)\n", - "\n", - " # Concatenate arr1 and arr2 vertically\n", - " distanceMatrix = np.concatenate((pointsY, distanceMatrix), axis=1)\n", - " \n", - " Array2d = np.array([np.concatenate(pointsX.T)])\n", - " distanceMatrixNew = np.append(Array2d, distanceMatrix, axis=0)\n", - " \n", - " # Create a Digraph object\n", - " dot = Digraph(comment='Distance Matrix')\n", - "\n", - " # Create a single table for the entire matrix\n", - " table_label = f'<'\n", - " for i in range(distanceMatrixNew.shape[0]):\n", - " table_label += ''\n", - " for j in range(distanceMatrixNew.shape[1]):\n", - " table_label += f''\n", - " table_label += ''\n", - " table_label += '
{distanceMatrixNew[i, j]}
>'\n", - "\n", - " # Add a single node to the graph with the entire table as label\n", - " dot.node('table', label=table_label, shape='plaintext')\n", - " \n", - " # Save the graph as a PNG file\n", - " #dot.render('directed_graph', format='png', cleanup=True)\n", - " \n", - " #display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "\n", - "\n", - "# Helper function to recursively add nodes and edges to the graph\n", - "def add_nodes_edges(dot, node_id, Z, dendrogram_data):\n", - " if node_id < len(Z):\n", - " # Internal node\n", - " child1, child2 = Z[node_id, 0], Z[node_id, 1]\n", - " dot.node(f'node_{node_id}', label=str(Z[node_id, 2]))\n", - " add_nodes_edges(dot, int(child1), Z, dendrogram_data)\n", - " add_nodes_edges(dot, int(child2), Z, dendrogram_data)\n", - " dot.edge(f'node_{node_id}', f'node_{int(child1)}')\n", - " dot.edge(f'node_{node_id}', f'node_{int(child2)}')\n", - " else:\n", - " # Leaf node\n", - " leaf_index = node_id - len(Z)\n", - " dot.node(f'leaf_{leaf_index}', label=str(dendrogram_data['ivl'][leaf_index]))\n", - "\n", - "\n", - "def createDendogramDotLanguage(data, distanceMethod):\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " linkage_data = linkage(data, method='single', metric='cityblock')\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " linkage_data = linkage(data, method='single', metric='euclidean')\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " linkage_data = linkage(data, method='single', metric='maximum')\n", - "\n", - " else:\n", - " return ''\n", - " \n", - " #dendrogram(linkage_data)\n", - " \n", - " #print(linkage_data)\n", - "\n", - " #plt.show()\n", - " \n", - " # Create a dendrogram\n", - " dendrogram_data = dendrogram(linkage_data, no_plot=True)\n", - "\n", - " # Create a Graphviz graph\n", - " dot = graphviz.Digraph(comment='Dendrogram')\n", - "\n", - " # Add nodes and edges to the graph\n", - " add_nodes_edges(dot, len(linkage_data) * 2 - 2, linkage_data, dendrogram_data)\n", - " #display(dot)\n", - " \n", - " return str(dot)\n", - "\n", - "\n", - "def clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod):\n", - " #data = generate_clusters(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd)\n", - " data = np.array([[2, 3],\n", - " [5, 2],\n", - " [5, 3],\n", - " [1, 4],\n", - " [4, 5]])\n", - " \n", - " \n", - " #createScatterDiagram(data)\n", - " jsonDict = {}\n", - " \n", - " jsonDict[\"Dendogram\"] = createDendogramDotLanguage(data, distanceMethod)\n", - " \n", - " \n", - " for iteration in range(data.shape[0]):\n", - " #erste iteration\n", - " if(iteration == 0):\n", - " \n", - " clusters = []\n", - " \n", - " #inital abstraction matrix\n", - " print(\"---Inital Iteration---\")\n", - " #get distance method\n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(data, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #matrix = calculate_inital_abstraction_matrix(data, euclidean_distance)\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " #print(single_linkage_indices)\n", - " \n", - " #get two points from given index that are the first cluster\n", - " point_a = data[single_linkage_indices[0]]\n", - " point_b = data[single_linkage_indices[1]]\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - "\n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - " \n", - " #create Digraph\n", - " #TODO -> oberste Zeile noch hinzufügen\n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = matrix.tolist() #createDigraph(matrix, data, iteration)\n", - " \n", - " \n", - " #define cluster elements\n", - " #clusters = np.array([[point_a, point_b]])\n", - " clusters.append([point_a, point_b])\n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(new_data)\n", - "\n", - " #alle weiteren iterationen \n", - " elif(iteration > 0):\n", - " \n", - " if len(new_data) == 1:\n", - " continue\n", - " \n", - " print(\"\")\n", - " print(\"\")\n", - " print(\"---Next Iteration---\")\n", - " \n", - " \n", - " if distanceMethod == 'manhattan':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " matrix = calculate_inital_abstraction_matrix(new_data, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " #matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance)\n", - "\n", - " #get the two points with the new minimum distance\n", - " single_linkage_indices = single_linkage_get_indices(matrix)\n", - " \n", - " #get two points from given index that are the new cluster\n", - " point_a = new_data[single_linkage_indices[0]]\n", - " point_b = new_data[single_linkage_indices[1]]\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " #point_to_remove = nearest_point[1]\n", - " \n", - " #add point to current cluster\n", - " if is_element_in_array(clusters, point_a) or is_element_in_array(clusters, point_b):\n", - " #print(\"Erweitere Cluster\")\n", - " \n", - " #get element number of the cluster and append point to the current cluster\n", - " if is_element_in_array(clusters, point_a):\n", - " arrayLocation = is_element_in_array_location(clusters, point_a)\n", - " pointToAppend = point_b\n", - " elif is_element_in_array(clusters, point_b):\n", - " arrayLocation = is_element_in_array_location(clusters, point_b)\n", - " pointToAppend = point_a\n", - " \n", - " #print(arrayLocation)\n", - " \n", - " #append pointToAppend to cluster:\n", - " #clusters = np.insert(clusters, arrayLocation[0], pointToAppend, axis=arrayLocation[1])\n", - " #print(clusters[arrayLocation[0]])\n", - " #clusters[arrayLocation[0], arrayLocation[1]]\n", - " \n", - " #newClusters1 = clusters[arrayLocation[0]]\n", - " #print(clusters[arrayLocation[0]])\n", - " pointsToAppend = np.vstack([clusters[arrayLocation[0]], pointToAppend])\n", - " \n", - " newClusters = []\n", - " \n", - " newClusters.append(pointsToAppend)\n", - "\n", - " #newClusters = np.append(clusters[arrayLocation[0]], [pointToAppend], axis=0)\n", - " #newClusters = np.array([[point_a, point_b]])\n", - " clusterCounter = 0\n", - " \n", - " for cluster in clusters:\n", - " if clusterCounter == arrayLocation[0]:\n", - " clusterCounter += 1\n", - " \n", - " else:\n", - " #newClusters = np.concatenate([newClusters, [cluster]]) #dimensions are not valid! -> was anderes einfallen lassen!! #letztes puzzelstück -> wie den bums richtig zusammenfügen???!!!\n", - " newClusters.append(cluster)\n", - " clusterCounter += 1\n", - " \n", - " \n", - " #print(\"UPDATED CLUSTER:\")\n", - " #print(newClusters)\n", - " print(\"-------\")\n", - " \n", - " clusters = newClusters\n", - "\n", - " #create new cluster\n", - " else:\n", - " #print(\"Definiere neues Cluster\")\n", - " #define cluster elements if ist an new cluster\n", - " #clusters = np.append(clusters,[[point_a, point_b]], axis=0)\n", - " clusters.append([point_a, point_b])\n", - " #print(clusters)\n", - " \n", - " #print(\"Aktuelle Cluster:\")\n", - " #print(clusters)\n", - " \n", - " print(\"DIGRAPH - Abstandsmatrix\")\n", - " #createDigraph(matrix, new_data, iteration)\n", - " jsonDict[\"DigraphIteration{}\".format(iteration)] = matrix.tolist() #createDigraph(matrix, new_data, iteration)\n", - "\n", - " \n", - " if distanceMethod == 'manhattan':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance)\n", - " \n", - " elif distanceMethod == 'euclidean':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - "\n", - " elif distanceMethod == 'maximum':\n", - " nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance)\n", - "\n", - " else:\n", - " break\n", - " \n", - " #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance)\n", - " #delete the point from the dataset, that is not nearest\n", - " point_to_remove = nearest_point[1]\n", - " \n", - "\n", - " \n", - " #define new dataset\n", - " new_data = np.array([point for point in new_data if not np.array_equal(point, point_to_remove)])\n", - " #print(\"Neuer Hauptdatensatz\")\n", - " #print(jsonDict)\n", - "\n", - " print(jsonDict)\n", - " jsonDict[\"iterations\"] = iteration\n", - " with open(\"data.json\", \"w+\", encoding=\"utf8\") as f:\n", - " json.dump(jsonDict, f)\n", - "\n", - " return json.dumps(jsonDict)\n", - " \n", - "# Anzahl der Datenpunktansammlungen (Cluster)\n", - "#numClusters = int(sys.argv[1])\n", - "#pointsPerCluster = int(sys.argv[2])\n", - "#nodeRangeStart = int(sys.argv[3])\n", - "#nodeRangeEnd = int(sys.argv[4])\n", - "#distanceMethod = int(sys.argv[5])\n", - "#linkageMethod = int(sys.argv[6])\n", - "\n", - "numClusters = 2\n", - "pointsPerCluster = 3\n", - "nodeRangeStart = 0\n", - "nodeRangeEnd = 30\n", - "distanceMethod = 'euclidean'\n", - "linkageMethod = 'single'\n", - "#Hauptfunktion ausfuehren\n", - "clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod)\n", - "\n", - "\n", - "#TODO\n", - "#Eingabevalidierung!!!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b619364a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/bash.exe.stackdump b/bash.exe.stackdump new file mode 100644 index 0000000..346ee89 --- /dev/null +++ b/bash.exe.stackdump @@ -0,0 +1,29 @@ +Stack trace: +Frame Function Args +0007FFFF9F80 00021006118E (00021028DEE8, 000210272B3E, 000000000000, 0007FFFF8E80) msys-2.0.dll+0x2118E +0007FFFF9F80 0002100469BA (000000000000, 000000000000, 000000000000, 000000000004) msys-2.0.dll+0x69BA +0007FFFF9F80 0002100469F2 (00021028DF99, 0007FFFF9E38, 000000000000, 000000000000) msys-2.0.dll+0x69F2 +0007FFFF9F80 00021006A41E (000000000000, 000000000000, 000000000000, 000000000000) msys-2.0.dll+0x2A41E +0007FFFF9F80 00021006A545 (0007FFFF9F90, 000000000000, 000000000000, 000000000000) msys-2.0.dll+0x2A545 +0001004F94B7 00021006B9A5 (0007FFFF9F90, 000000000000, 000000000000, 000000000000) msys-2.0.dll+0x2B9A5 +End of stack trace +Loaded modules: +000100400000 bash.exe +7FFB27F60000 ntdll.dll +7FFAF1890000 aswhook.dll +7FFB267E0000 KERNEL32.DLL +7FFB251B0000 KERNELBASE.dll +7FFB26500000 USER32.dll +7FFB25D00000 win32u.dll +7FFB279A0000 GDI32.dll +7FFB255B0000 gdi32full.dll +7FFB256E0000 msvcp_win.dll +7FFB25BB0000 ucrtbase.dll +000210040000 msys-2.0.dll +7FFB273F0000 advapi32.dll +7FFB277B0000 msvcrt.dll +7FFB27E70000 sechost.dll +7FFB25DB0000 RPCRT4.dll +7FFB246F0000 CRYPTBASE.DLL +7FFB25910000 bcryptPrimitives.dll +7FFB27950000 IMM32.DLL diff --git a/data.json b/data.json deleted file mode 100644 index ccd25d5..0000000 --- a/data.json +++ /dev/null @@ -1 +0,0 @@ -{"Dendogram": "// Dendrogram\ndigraph {\n\tleaf_3 [label=0]\n}\n", "DigraphIteration0": [[0.0, 11.4, 8.6, 7.21, 17.2, 9.43], [11.4, 0.0, 4.0, 13.04, 20.25, 14.04], [8.6, 4.0, 0.0, 9.06, 16.55, 10.05], [7.21, 13.04, 9.06, 0.0, 10.0, 2.24], [17.2, 20.25, 16.55, 10.0, 0.0, 7.81], [9.43, 14.04, 10.05, 2.24, 7.81, 0.0]], "DigraphIteration1": [[0.0, 11.4, 8.6, 7.21, 17.2], [11.4, 0.0, 4.0, 13.04, 20.25], [8.6, 4.0, 0.0, 9.06, 16.55], [7.21, 13.04, 9.06, 0.0, 10.0], [17.2, 20.25, 16.55, 10.0, 0.0]], "DigraphIteration2": [[0.0, 8.6, 7.21, 17.2], [8.6, 0.0, 9.06, 16.55], [7.21, 9.06, 0.0, 10.0], [17.2, 16.55, 10.0, 0.0]], "DigraphIteration3": [[0.0, 9.06, 16.55], [9.06, 0.0, 10.0], [16.55, 10.0, 0.0]], "DigraphIteration4": [[0.0, 10.0], [10.0, 0.0]], "iterations": 5} \ No newline at end of file diff --git a/directed_graph.png b/directed_graph.png deleted file mode 100644 index d1e1468..0000000 Binary files a/directed_graph.png and /dev/null differ diff --git a/docker-compose.yml b/docker-compose.yml index 504e120..387167d 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -11,7 +11,7 @@ services: - ./:/server ports: - ${ALADIN_PORT}:${ALADIN_PORT} - + reverse-proxy: build: context: services/nginx @@ -25,13 +25,13 @@ services: - ${REVERSE_PROXY_PORT}:${REVERSE_PROXY_PORT} - ${ALADIN_PORT}:${ALADIN_PORT} - 3000:3000 - + postgres_2: build: context: services/postgres dockerfile: postgres.Dockerfile container_name: postgres_2 - user: 999:999 + user: 999:999 environment: POSTGRES_USER: ${POSTGRES_USER} POSTGRES_PASSWORD: ${POSTGRES_PW} @@ -40,7 +40,7 @@ services: - ${POSTGRES_PORT}:${POSTGRES_PORT} volumes: - postgres-data:/var/lib/postgresql/data - + mongodb: build: context: services/mongodb @@ -55,7 +55,7 @@ services: volumes: - "mongodb_data:/bitnami/mongodb" restart: unless-stopped - + # maxima: # build: # context: services/maxima @@ -63,7 +63,7 @@ services: # container_name: maxima # volumes: # - ./services/maxima/:/worker/ - + volumes: mongodb_data: - postgres-data: + postgres-data: \ No newline at end of file diff --git a/model_result.xlsx b/model_result.xlsx new file mode 100644 index 0000000..cdca310 Binary files /dev/null and b/model_result.xlsx differ diff --git a/optimized_feed.json b/optimized_feed.json new file mode 100644 index 0000000..705cf30 --- /dev/null +++ b/optimized_feed.json @@ -0,0 +1,9610 @@ +{ + "optimized_feed": 240.0, + "details": { + "c_ox_sat": 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> + + target="_top" + tooltip="customer_demographics" + ]; + "customers" [ + label=< + + + + + + + + + + + + + + +
customers[table]
customer_id
company_name
contact_name
contact_title
address
city
region
postal_code
country
phone
fax
91 rows2 >
> + + target="_top" + tooltip="customers" + ]; + "employee_territories" [ + label=< + + + + + +
employee_territories[table]
employee_id
territory_id
< 249 rows
> + + target="_top" + tooltip="employee_territories" + ]; + "employees" [ + label=< + + + + + + + + + + + + + + + + + + + + + +
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employee_id
last_name
first_name
title
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city
region
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country
home_phone
extension
photo
notes
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< 19 rows3 >
> + + target="_top" + tooltip="employees" + ]; + "order_details" [ + label=< + + + + + + + + +
order_details[table]
order_id
product_id
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quantity
discount
< 22,155 rows
> + + target="_top" + tooltip="order_details" + ]; + "orders" [ + label=< + + + + + + + + + + + + + + + + + +
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required_date
shipped_date
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ship_name
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< 3830 rows1 >
> + + target="_top" + tooltip="orders" + ]; + "products" [ + label=< + + + + + + + + + + + + + +
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supplier_id
category_id
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< 277 rows1 >
> + + target="_top" + tooltip="products" + ]; + "region" [ + label=< + + + + + +
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region_id
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4 rows1 >
> + + target="_top" + tooltip="region" + ]; + "shippers" [ + label=< + + + + + + +
shippers[table]
shipper_id
company_name
phone
6 rows1 >
> + + target="_top" + tooltip="shippers" + ]; + "suppliers" [ + label=< + + + + + + + + + + + + + + + +
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supplier_id
company_name
contact_name
contact_title
address
city
region
postal_code
country
phone
fax
homepage
29 rows1 >
> + + target="_top" + tooltip="suppliers" + ]; + "territories" [ + label=< + + + + + + +
territories[table]
territory_id
territory_description
region_id
< 153 rows1 >
> + + target="_top" + tooltip="territories" + ]; + "customer_customer_demo":"customer_id":w -> "customers":"customer_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "customer_customer_demo":"customer_type_id":w -> "customer_demographics":"customer_type_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "employee_territories":"employee_id":w -> "employees":"employee_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "employee_territories":"territory_id":w -> "territories":"territory_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "employees":"reports_to":w -> "employees":"employee_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "order_details":"order_id":w -> "orders":"order_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "order_details":"product_id":w -> "products":"product_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "orders":"customer_id":w -> "customers":"customer_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "orders":"employee_id":w -> "employees":"employee_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "orders":"ship_via":w -> "shippers":"shipper_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "products":"category_id":w -> "categories":"category_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "products":"supplier_id":w -> "suppliers":"supplier_id":e [arrowhead=none dir=back arrowtail=crowodot]; + "territories":"region_id":w -> "region":"region_id":e [arrowhead=none dir=back arrowtail=crowodot]; +} diff --git a/src/Legacy/Tasks/SQL/ERDs/sports.dot b/src/Legacy/Tasks/SQL/ERDs/sports.dot index 64f35b1..6495129 100644 --- a/src/Legacy/Tasks/SQL/ERDs/sports.dot +++ b/src/Legacy/Tasks/SQL/ERDs/sports.dot @@ -1,1161 +1,1161 @@ -digraph "compactRelationshipsDiagram" { - graph [ - rankdir="RL" - bgcolor="transparent" - - labeljust="l" - nodesep="0.18" - ranksep="0.46" - fontname="Helvetica" - fontsize="11" - ration="compress" - ]; - node [ - fontname="Helvetica" - fontsize="11" - shape="plaintext" - ]; - edge [ - arrowsize="0.8" - ]; - "addresses" [ - label=< - - - - - - - - - -
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media_keywords[table]
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periods[table]
id
participant_event_id
...
< 1548 rows1 >
> + + target="_top" + tooltip="periods" + ]; + "person_event_metadata" [ + label=< + + + + + + + + + + + +
person_event_metadata[table]
id
person_id
event_id
status
role_id
position_id
team_id
...
< 54.002 rows
> + + target="_top" + tooltip="person_event_metadata" + ]; + "person_phases" [ + label=< + + + + + + + + + + + + + +
person_phases[table]
id
person_id
membership_type
membership_id
role_id
phase_status
regular_position_id
start_season_id
end_season_id
...
< 50 rows
> + + target="_top" + tooltip="person_phases" + ]; + "persons" [ + label=< + + + + + + + + + + + +
persons[table]
id
person_key
publisher_id
birth_location_id
hometown_location_id
residence_location_id
death_location_id
...
< 53.937 rows16 >
> + + target="_top" + tooltip="persons" + ]; + "persons_documents" [ + label=< + + + + + +
persons_documents[table]
person_id
document_id
< 20 rows
> + + target="_top" + tooltip="persons_documents" + ]; + "persons_media" [ + label=< + + + + + +
persons_media[table]
person_id
media_id
< 20 rows
> + + target="_top" + tooltip="persons_media" + ]; + "positions" [ + label=< + + + + + + +
positions[table]
id
affiliation_id
abbreviation
< 173 rows6 >
> + + target="_top" + tooltip="positions" + ]; + "publishers" [ + label=< + + + + + + +
publishers[table]
id
publisher_key
...
1 row12 >
> + + target="_top" + tooltip="publishers" + ]; + "roles" [ + label=< + + + + + + +
roles[table]
id
role_key
...
0 rows3 >
> + + target="_top" + tooltip="roles" + ]; + "seasons" [ + label=< + + + + + + + + +
seasons[table]
id
season_key
publisher_id
league_id
...
< 24 rows8 >
> + + target="_top" + tooltip="seasons" + ]; + "sites" [ + label=< + + + + + + + +
sites[table]
id
site_key
publisher_id
location_id
< 20 rows2 >
> + + target="_top" + tooltip="sites" + ]; + "soccer_event_states" [ + label=< + + + + + + +
soccer_event_states[table]
id
event_id
...
< 10 rows
> + + target="_top" + tooltip="soccer_event_states" + ]; + "standing_subgroups" [ + label=< + + + + + + +
standing_subgroups[table]
id
standing_id
affiliation_id
< 20 rows1 >
> + + target="_top" + tooltip="standing_subgroups" + ]; + "standings" [ + label=< + + + + + + + + +
standings[table]
id
affiliation_id
sub_season_id
publisher_id
...
< 30 rows1 >
> + + target="_top" + tooltip="standings" + ]; + "sub_periods" [ + label=< + + + + + + +
sub_periods[table]
id
period_id
...
< 10 rows
> + + target="_top" + tooltip="sub_periods" + ]; + "sub_seasons" [ + label=< + + + + + + + + +
sub_seasons[table]
id
sub_season_key
season_id
sub_season_type
...
< 14 rows2 >
> + + target="_top" + tooltip="sub_seasons" + ]; + "team_phases" [ + label=< + + + + + + + + + + +
team_phases[table]
id
team_id
start_season_id
end_season_id
affiliation_id
role_id
...
< 5368 rows
> + + target="_top" + tooltip="team_phases" + ]; + "teams" [ + label=< + + + + + + + +
teams[table]
id
publisher_id
home_site_id
...
< 2128 rows10 >
> + + target="_top" + tooltip="teams" + ]; + "teams_documents" [ + label=< + + + + + +
teams_documents[table]
team_id
document_id
< 2435 rows
> + + target="_top" + tooltip="teams_documents" + ]; + "teams_media" [ + label=< + + + + + +
teams_media[table]
team_id
media_id
< 20 rows
> + + target="_top" + tooltip="teams_media" + ]; + "tennis_event_states" [ + label=< + + + + + + +
tennis_event_states[table]
id
event_id
...
< 10 rows
> + + target="_top" + tooltip="tennis_event_states" + ]; + "wagering_moneylines" [ + label=< + + + + + + + + +
wagering_moneylines[table]
id
bookmaker_id
event_id
team_id
...
< 30 rows
> + + target="_top" + tooltip="wagering_moneylines" + ]; + "wagering_odds_lines" [ + label=< + + + + + + + + +
wagering_odds_lines[table]
id
bookmaker_id
event_id
team_id
...
< 30 rows
> + + target="_top" + tooltip="wagering_odds_lines" + ]; + "wagering_runlines" [ + label=< + + + + + + + + +
wagering_runlines[table]
id
bookmaker_id
event_id
team_id
...
< 30 rows
> + + target="_top" + tooltip="wagering_runlines" + ]; + "wagering_straight_spread_lines" [ + label=< + + + + + + + + +
wagering_straight_spread_lines[table]
id
bookmaker_id
event_id
team_id
...
< 30 rows
> + + target="_top" + tooltip="wagering_straight_spread_lines" + ]; + "wagering_total_score_lines" [ + label=< + + + + + + + + +
wagering_total_score_lines[table]
id
bookmaker_id
event_id
team_id
...
< 30 rows
> + + target="_top" + tooltip="wagering_total_score_lines" + ]; + "weather_conditions" [ + label=< + + + + + + +
weather_conditions[table]
id
event_id
...
< 10 rows
> + + target="_top" + tooltip="weather_conditions" + ]; + "addresses":"location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliation_phases":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliation_phases":"ancestor_affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliation_phases":"end_season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliation_phases":"start_season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations_documents":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations_documents":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations_events":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations_events":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations_media":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "affiliations_media":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "american_football_action_participants":"american_football_action_play_id":w -> "american_football_action_plays":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "american_football_action_participants":"person_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "american_football_action_plays":"american_football_event_state_id":w -> "american_football_event_states":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "american_football_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "american_football_event_states":"team_in_possession_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_contact_details":"baseball_action_pitch_id":w -> "baseball_action_pitches":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_pitches":"baseball_action_play_id":w -> "baseball_action_plays":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_pitches":"baseball_defensive_group_id":w -> "baseball_defensive_group":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_plays":"baseball_event_state_id":w -> "baseball_event_states":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_substitutions":"baseball_event_state_id":w -> "baseball_event_states":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_substitutions":"person_original_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_substitutions":"person_original_position_id":w -> "positions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_substitutions":"person_replacing_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_action_substitutions":"person_replacing_position_id":w -> "positions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_defensive_players":"baseball_defensive_group_id":w -> "baseball_defensive_group":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_defensive_players":"player_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_defensive_players":"position_id":w -> "positions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_event_states":"batter_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_event_states":"pitcher_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_event_states":"runner_on_first_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_event_states":"runner_on_second_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "baseball_event_states":"runner_on_third_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "basketball_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "bookmakers":"location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "bookmakers":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "core_person_stats":"position_id":w -> "positions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_contents":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_fixtures":"document_class_id":w -> "document_classes":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_fixtures":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_fixtures_events":"document_fixture_id":w -> "document_fixtures":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_fixtures_events":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_fixtures_events":"latest_document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_package_entry":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "document_package_entry":"document_package_id":w -> "document_packages":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "documents":"document_fixture_id":w -> "document_fixtures":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "documents":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "documents":"source_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "documents_media":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "documents_media":"media_caption_id":w -> "media_captions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "documents_media":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events":"site_id":w -> "sites":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events_documents":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events_documents":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events_media":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events_media":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events_sub_seasons":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "events_sub_seasons":"sub_season_id":w -> "sub_seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "ice_hockey_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "injury_phases":"person_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "injury_phases":"season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "key_aliases":"key_root_id":w -> "key_roots":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "latest_revisions":"latest_document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media":"creation_location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media":"credit_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media_captions":"caption_author_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media_captions":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media_contents":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "media_keywords":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "motor_racing_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "outcome_totals":"standing_subgroup_id":w -> "standing_subgroups":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "participants_events":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "periods":"participant_event_id":w -> "participants_events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_event_metadata":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_event_metadata":"person_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_event_metadata":"position_id":w -> "positions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_event_metadata":"role_id":w -> "roles":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_event_metadata":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_phases":"end_season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_phases":"person_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_phases":"regular_position_id":w -> "positions":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_phases":"role_id":w -> "roles":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "person_phases":"start_season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons":"birth_location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons":"death_location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons":"hometown_location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons":"residence_location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons_documents":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons_documents":"person_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons_media":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "persons_media":"person_id":w -> "persons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "positions":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "seasons":"league_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "seasons":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "sites":"location_id":w -> "locations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "sites":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "soccer_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "standing_subgroups":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "standing_subgroups":"standing_id":w -> "standings":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "standings":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "standings":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "standings":"sub_season_id":w -> "sub_seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "sub_periods":"period_id":w -> "periods":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "sub_seasons":"season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "team_phases":"affiliation_id":w -> "affiliations":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "team_phases":"end_season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "team_phases":"role_id":w -> "roles":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "team_phases":"start_season_id":w -> "seasons":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "team_phases":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "teams":"home_site_id":w -> "sites":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "teams":"publisher_id":w -> "publishers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "teams_documents":"document_id":w -> "documents":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "teams_documents":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "teams_media":"media_id":w -> "media":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "teams_media":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "tennis_event_states":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_moneylines":"bookmaker_id":w -> "bookmakers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_moneylines":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_moneylines":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_odds_lines":"bookmaker_id":w -> "bookmakers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_odds_lines":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_odds_lines":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_runlines":"bookmaker_id":w -> "bookmakers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_runlines":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_runlines":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_straight_spread_lines":"bookmaker_id":w -> "bookmakers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_straight_spread_lines":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_straight_spread_lines":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_total_score_lines":"bookmaker_id":w -> "bookmakers":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_total_score_lines":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "wagering_total_score_lines":"team_id":w -> "teams":"id":e [arrowhead=none dir=back arrowtail=crowodot]; + "weather_conditions":"event_id":w -> "events":"id":e [arrowhead=none dir=back arrowtail=crowodot]; +} diff --git a/src/Tasks/Chemistry/.gitkeep b/src/Tasks/Chemistry/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/Tasks/Chemistry/SubstitutionSelector.ts b/src/Tasks/Chemistry/SubstitutionSelector.ts deleted file mode 100644 index 5e065fd..0000000 --- a/src/Tasks/Chemistry/SubstitutionSelector.ts +++ /dev/null @@ -1,157 +0,0 @@ -export interface SubstitutionParameters { - seed: string; -} - -export type Position = "para" | "meta" | "orto"; - -import { RNG } from "../../Util/Randomizer"; - -export const selectSubstitution = (parameters: SubstitutionParameters) => { - const { seed } = parameters; - - const rng = new RNG(seed); - - const reagentIndex = rng.intBetween(0, reagents.length); - const substituentIndex = rng.intBetween(0, substituents.length); - - const reagent = reagents[reagentIndex]; - - const selectedSubstituent = substituents[substituentIndex]; - const position = selectedSubstituent.position as Position; - - return { - smiles: productBuilderBasedOnPosition[position](selectedSubstituent.firstSubstituent, reagent.smiles), - position: position, - parameter: selectedSubstituent.effects.join(", "), - hasOverReacted: selectedSubstituent.hasOverReacted, - reagent: reagent.smiles, - reactionSmiles: `${selectedSubstituent.firstSubstituent}c1ccccc1>${reagent.smiles}>`, - firstSubstituent: selectedSubstituent.firstSubstituent, - benzol: "c1cccc1", - reagentText: reagent.reagent, - }; -}; - -const productBuilderBasedOnPosition: { [positon in Position]: Function } = { - para: (firstSubstituentSmiles: string, reagentSmiles: string) => - `${firstSubstituentSmiles}c1ccc(${reagentSmiles})cc1`, - meta: (firstSubstituentSmiles: string, reagentSmiles: string) => - `${firstSubstituentSmiles}c1cc(${reagentSmiles})ccc1`, - orto: (firstSubstituentSmiles: string, reagentSmiles: string) => "[error]", -}; - -const answer = { - firstSubstituent: "[NH3+]", - benzol: "c1cccc1", - reagentText: "reagentAlpha", -}; - -const reagents = [ - { - reagent: "Bromierung", - smiles: "BrBr", - inMolecule: "Br", - }, - { - reagent: "reagentAlpha", - smiles: "ClCl", - inMolecule: "Cl", - }, - { - reagent: "reagentBeta", - smiles: "II", - inMolecule: "I", - }, - { - reagent: "reagentGamma", - smiles: "FF", - inMolecule: "F", - }, -]; - -const substituents = [ - { - firstSubstituent: "[O]", - effects: ["+M"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "N", - effects: ["+M", "-I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "[OH]", - effects: ["+M", "-I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "O=C([R])N", - effects: ["+M", "-I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "O=C([R])O", - effects: ["+M", "-I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "c1ccccc1", - effects: ["+M", "-I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "[A]", - effects: ["+M", "-I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "[Cl]", - effects: ["+I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "[Br]", - effects: ["+I"], - position: "para", - hasOverReacted: false, - }, - { - firstSubstituent: "[NH3+]", - effects: ["-I"], - position: "meta", - hasOverReacted: false, - }, - { - firstSubstituent: "O=C([R])", - effects: ["-M", "-I"], - position: "meta", - hasOverReacted: false, - }, - { - firstSubstituent: "[CN]", - effects: ["-M", "-I"], - position: "meta", - hasOverReacted: false, - }, - { - firstSubstituent: "O=S(=O)(O)", - effects: ["-M", "-I"], - position: "meta", - hasOverReacted: false, - }, - { - firstSubstituent: "O=[N+](O)", - effects: ["-M", "-I"], - position: "meta", - hasOverReacted: false, - }, -]; diff --git a/src/Tasks/ClusterAnalysis/clusterAnalysis.py b/src/Tasks/ClusterAnalysis/clusterAnalysis.py deleted file mode 100644 index 6dc21f1..0000000 --- a/src/Tasks/ClusterAnalysis/clusterAnalysis.py +++ /dev/null @@ -1,625 +0,0 @@ -#Clusteranalyse - -import numpy as np -import matplotlib.pyplot as plt -import random -from scipy.cluster.hierarchy import linkage, dendrogram -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -from graphviz import Digraph -import graphviz -#from IPython.display import display -import math -import json -import sys -import os -import pathlib -import base64 -import string - -#functions -def generate_clusters(n_clusters, m_points_per_cluster, rangeStart, rangeEnd): - # Generate random cluster centers - cluster_centers = np.random.randint(rangeStart, rangeEnd, size=(n_clusters, 2)) - noise_scale = 1 - - # Generate points for each cluster - points = [] - for center in cluster_centers: - cluster_points = np.random.randint(-5, 5, size=(m_points_per_cluster, 2)) + center - points.extend(cluster_points) - - # Add noise to the points - noisy_points = points + (np.random.randint(-2, 2, size=(len(points), 2))) - - return np.array(noisy_points) - -def is_element_in_array(cluster_array, single_point): - for sub_array in cluster_array: - for element in sub_array: - if element[0] == single_point[0] and element[1] == single_point[1]: - return True - return False - -def is_element_in_array_location(cluster_array, single_point): - i = 0 - j = 0 - for sub_array in cluster_array: - for element in sub_array: - if element[0] == single_point[0] and element[1] == single_point[1]: - #print("LOCATION") - #print(element[0]) - #print(element[1]) - return [i, j] - j += 1 - i += 1 - return False - -def check_array_exists(arrayName): - if 'clusters' in dir(): - return True - else: - return False - -#todo -def manhattan_distance(point1, point2): - return np.sum(np.abs(point1 + point2)) - -#ready -def euclidean_distance(array1, array2): - return round(math.sqrt((pow((array1[0] - array2[0]), 2)) + (pow((array1[1] - array2[1]), 2))),2) - -#todo -def maximum_distance(point1, point2): - return np.max([[point1],[point2]]) - -def calculate_inital_abstraction_matrix(data, distance_function): - - # Initialize clusters with each data point as a separate cluster - array = np.zeros((data.shape[0],data.shape[0])) - - for iteration_number_row in range(len(data)): - - for iteration_number_column in range(len(data)): - array[iteration_number_row, iteration_number_column] = distance_function(data[iteration_number_row],data[iteration_number_column]) - - return array - -def single_linkage_get_indices(inital_abstraction_matrix): - - # Exclude diagonal elements and zeros - lower_triangular = np.tril(inital_abstraction_matrix, k=-1) - lower_triangular[lower_triangular == 0] = np.inf - - min_index = np.unravel_index(np.argmin(lower_triangular), lower_triangular.shape) - - # Adjust indices to consider the original matrix - #adjusted_indices = (min_index[0] + 1, min_index[1]) - - #print(min_index) - return min_index - -#get nearest of 2 points to get the new center of the cluster -def get_nearest_point(data, point_a, point_b, distance_function): - #get the point that is more near the other points - # Calculate distances for both points - distances_to_a = [distance_function(point_a, point) for point in data] - distances_to_b = [distance_function(point_b, point) for point in data] - - # Find the index of the nearest point for each given point for both points - nearest_point_index_a = np.argmin(distances_to_a) - nearest_point_index_b = np.argmin(distances_to_b) - - sum_distance_to_a = 0 - for distance in distances_to_a: - sum_distance_to_a += float(distance) - - sum_distance_to_b = 0 - for distance in distances_to_b: - sum_distance_to_b += float(distance) - - - # Compare the distances - #[[nearest_point][point_to_delete]] - if sum_distance_to_a < sum_distance_to_b: - nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]] - elif sum_distance_to_a > sum_distance_to_b: - nearest_point = [data[nearest_point_index_b] , data[nearest_point_index_a]] - else: #equal - nearest_point = [data[nearest_point_index_a] , data[nearest_point_index_b]] - - return nearest_point - -def createScatterDiagram(data): - # Streudiagramm zeichnen - # Extract x and y coordinates - x = data[:, 0] - y = data[:, 1] - - # Generate random colors - colors = np.random.rand(len(x), 3) - - # Create scatter plot with random colors - plt.scatter(x, y, marker='o', color=colors) - - # Add labels and title - plt.xlabel('X-Achse') - plt.ylabel('Y-Achse') - plt.title('Streudiagram') - - # #display the plot - plt.show() - -def createDigraph(distanceMatrix, points, iterationNumber): - #transform matrix - - #print(distanceMatrix) - - points = points.astype(str) - distanceMatrix = distanceMatrix.astype(str) - pointsY = np.array([[' - '.join(row)] for row in points]) - pointsX = np.array([[' - '.join(row)] for row in points]) - #pointsX = np.insert(pointsX, 0, 'Iteration {}'.format(iterationNumber), axis=0) - pointsX = np.insert(pointsX, 0, str(iterationNumber), axis=0) - - # Concatenate arr1 and arr2 vertically - distanceMatrix = np.concatenate((pointsY, distanceMatrix), axis=1) - - Array2d = np.array([np.concatenate(pointsX.T)]) - distanceMatrixNew = np.append(Array2d, distanceMatrix, axis=0) - - # Create a Digraph object - dot = Digraph(comment='Distance Matrix') - - # Create a single table for the entire matrix - table_label = f'<' - for i in range(distanceMatrixNew.shape[0]): - table_label += '' - for j in range(distanceMatrixNew.shape[1]): - table_label += f'' - table_label += '' - table_label += '
{distanceMatrixNew[i, j]}
>' - - # Add a single node to the graph with the entire table as label - dot.node('table', label=table_label, shape='plaintext') - - # Save the graph as a PNG file - #dot.render('directed_graph', format='png', cleanup=True) - - #display(dot) - - return str(dot) - - - -# Helper function to recursively add nodes and edges to the graph -def add_nodes_edges(dot, node_id, Z, dendrogram_data): - if node_id < len(Z): - # Internal node - child1, child2 = Z[node_id, 0], Z[node_id, 1] - dot.node(f'node_{node_id}', label=str(Z[node_id, 2])) - add_nodes_edges(dot, int(child1), Z, dendrogram_data) - add_nodes_edges(dot, int(child2), Z, dendrogram_data) - dot.edge(f'node_{node_id}', f'node_{int(child1)}') - dot.edge(f'node_{node_id}', f'node_{int(child2)}') - else: - # Leaf node - leaf_index = node_id - len(Z) - dot.node(f'leaf_{leaf_index}', label=str(dendrogram_data['ivl'][leaf_index])) - - -def createDendogramDotLanguage(data, distanceMethod): - - if distanceMethod == 'manhattan': - linkage_data = linkage(data, method='single', metric='cityblock') - - elif distanceMethod == 'euclidean': - linkage_data = linkage(data, method='single', metric='euclidean') - - elif distanceMethod == 'maximum': - linkage_data = linkage(data, method='single', metric='maximum') - - else: - return '' - - #dendrogram(linkage_data) - - #print(linkage_data) - - #plt.show() - - # Create a dendrogram - dendrogram_data = dendrogram(linkage_data, no_plot=True) - - # Create a Graphviz graph - dot = graphviz.Digraph(comment='Dendrogram') - - # Add nodes and edges to the graph - add_nodes_edges(dot, len(linkage_data) * 2 - 2, linkage_data, dendrogram_data) - #display(dot) - - return str(dot) - -def array_to_html_table(array): - letter = list(string.ascii_uppercase) - letterPosition = 0 - - html = '\n' - html += '' - for row in array: - html += '\n' - html += f'' - for item in row: - html += f'\n' - html += '\n' - letterPosition += 1 - html += '
Name Datenpunkt Position X-Achse Position Y-Achse
{letter[letterPosition]}{item}
' - return html - -def generate_task_description(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod, diagramHelpBoolean, dendogramBoolean, distanceMatrixBoolean, data): - finalDescription = "" - - #add the main task descripton to the variable - finalDescription += "

In dieser Aufgabe sollen Sie zeigen, dass Sie die hierarchische Clusteranalyse verstanden haben und diese anwenden koennen.


" - - #add example data to description - finalDescription += "

Sie erhalten eine Sammlung von Datenpunkten, für die Sie die Analyse durchführen sollen.


" - finalDescription += array_to_html_table(data) - finalDescription += "

" - - #add more task description - if str(diagramHelpBoolean) == "false": - finalDescription += "

Zeichnen Sie im ersten Schritt die Datenpunkte in ein Koordinatensystem als visuelle Hilfe ein, oder nutzen Sie alternativ das Streudiagramm, welches Ihnen ALADIN als Hilfestellung bietet.

" - - #if diagram help checkbox is checked - if str(diagramHelpBoolean) == "true": - finalDescription += "

Sie haben bei der Aufgabenerstellung ausgewaehlt, dass Ihnen das Streudiagramm angezeigt werden soll. Das folgende Diagramm zeigt die verteilten Datenpunkte im Koordinatensystem.


" - - #add base64 img - #TODO - print_diagram(data) - with open("streudiagramm.png", "rb") as image_file: - encoded_string = str(base64.b64encode(image_file.read())) - - finalDescription += f'
Streudiagramm

' - - #help description for calculating distance matrix - if str(distanceMatrixBoolean) == "true": - finalDescription += f"

Hilfe zur Distanzberechnung

So haben die {str(distanceMethod)} Methode zur Berechnung der Distanz gewaehlt. Diese wird wie folgt berechnet:

" - if distanceMethod == 'manhattan': - finalDescription += '


a==^ Datenpunkt 1

' - finalDescription += '

b==^ Datenpunkt 2

' - finalDescription += '

d(a,b)=∑i=1n|ai-bi|



' - - elif distanceMethod == 'euclidean': - finalDescription += '


a==^ Datenpunkt 1

' - finalDescription += '

b==^ Datenpunkt 2

' - finalDescription += '

d(a,b)=|a-b|



' - - elif distanceMethod == 'maximum': - finalDescription += '


a==^ Datenpunkt 1

' - finalDescription += '

b==^ Datenpunkt 2

' - finalDescription += '

d(a,b)=max(a,b)



' - - #help description for draw the dendrogram - if str(dendogramBoolean) == "true": - finalDescription += "

Um ein Dendrogramm aus einer Distanzmatrix zu erstellen, verwendet man zuerst einen hierarchischen Clustering-Algorithmus (wie z.B. Single-Linkage, Complete-Linkage oder Average-Linkage), um die Cluster-Hierarchie aus der Distanzmatrix zu erzeugen. Anschließend stellt man diese Hierarchie graphisch als Dendrogramm dar, wobei die Länge der Verbindungslinien die Distanzen oder Ähnlichkeiten zwischen den Clustern repräsentiert.

" - - - finalDescription += "

Berechnen Sie nun im nächsten Schritt die einzelnen Distanzmatrizen bis zu einer 2x2 Matrix und gebe sie Ihre Lösung in den Editor ein.

" - finalDescription += f"

Nutzen Sie für die Berechnung der Distanzen zwischen den einzelnen Datenpunkten die Berechnungsmethode {distanceMethod}

" - - return finalDescription - -def generate_task_description_preview(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod, diagramHelpBoolean, dendogramBoolean, distanceMatrixBoolean, data): - finalDescription = "" - - #add the main task descripton to the variable - finalDescription += "

Vorschau der Datentabelle:


" - - finalDescription += array_to_html_table(data) - finalDescription += "

" - - #if diagram help checkbox is checked - finalDescription += "

Vorschau des Streudiagramms:


" - - #add base64 img - print_diagram(data) - with open("streudiagramm.png", "rb") as image_file: - encoded_string = str(base64.b64encode(image_file.read())) - - finalDescription += f'
Streudiagramm

' - - return finalDescription - - -def print_diagram(data): - # Streudiagramm zeichnen - # Extracting X and Y coordinates from the data - - x = [point[0] for point in data] - y = [point[1] for point in data] - - length = len(x) - matrixHeaders = list(string.ascii_uppercase) - - n = matrixHeaders[0:length] - - # Erstellen des Plots - plt.figure(figsize=(6, 4)) - plt.scatter(x, y) # Verwendung von scatter() anstelle von plot() - - # Hinzufügen von Beschriftungen an jedem Punkt - for i, label in enumerate(n): - plt.text(x[i], y[i], n[i], fontsize=12) - - plt.title('Streudiagramm') - plt.xlabel('X-Achse') - plt.ylabel('Y-Achse') - - # Display the plot - #plt.show() - plt.savefig('streudiagramm.png') - -def generate_matrix_headers(data, iterationNumber): - x = [point[0] for point in data] - length = len(x) - matrixHeaders = list(string.ascii_uppercase) - - return matrixHeaders[0:(length)] #- int(iterationNumber))] - -#def clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod): -def clusterAnalysisMain(): - - #get data from CLI - numClusters = int(sys.argv[2]) - pointsPerCluster = int(sys.argv[1]) - nodeRangeStart = int(sys.argv[9]) - nodeRangeEnd = int(sys.argv[10]) - diagramHelpBoolean = sys.argv[4] - dendogramBoolean = sys.argv[5] - distanceMethod = sys.argv[7] - linkageMethod = sys.argv[8] - distanceMatrixBoolean = sys.argv[6] - - #generate data for a task for the student - data = generate_clusters(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd) - - #Testdata - data = np.array([[2, 3],[5, 2],[5, 3],[1, 4],[4, 5]]) - - - #print(data) - - taskDescription = generate_task_description(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod, diagramHelpBoolean, dendogramBoolean, distanceMatrixBoolean, data) - - taskDescriptionPreview = generate_task_description_preview(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod, diagramHelpBoolean, dendogramBoolean, distanceMatrixBoolean, data) - #TODO: atrribute richtig machen aus Fronted -> destrukturieren - #aufgabenbeschreibung generieren - - - - #createScatterDiagram(data) - jsonDict = {} - #jsonDict["Dendogram"] = createDendogramDotLanguage(data, distanceMethod) - jsonDict["taskDescription"] = taskDescription - jsonDict["taskDescriptionPreview"] = taskDescriptionPreview - - - for iteration in range(data.shape[0]): - #erste iteration - if(iteration == 0): - - clusters = [] - - #inital abstraction matrix - print("---Inital Iteration---") - #get distance method - if distanceMethod == 'manhattan': - matrix = calculate_inital_abstraction_matrix(data, manhattan_distance) - - elif distanceMethod == 'euclidean': - matrix = calculate_inital_abstraction_matrix(data, euclidean_distance) - - elif distanceMethod == 'maximum': - matrix = calculate_inital_abstraction_matrix(data, maximum_distance) - - else: - break - - #matrix = calculate_inital_abstraction_matrix(data, euclidean_distance) - single_linkage_indices = single_linkage_get_indices(matrix) - #print(single_linkage_indices) - - #get two points from given index that are the first cluster - point_a = data[single_linkage_indices[0]] - point_b = data[single_linkage_indices[1]] - - - if distanceMethod == 'manhattan': - nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance) - - elif distanceMethod == 'euclidean': - nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance) - - elif distanceMethod == 'maximum': - nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance) - - else: - break - - #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance) - #delete the point from the dataset, that is not nearest - point_to_remove = nearest_point[1] - - - #create Digraph - #TODO -> oberste Zeile noch hinzufügen - #print("DIGRAPH - Abstandsmatrix") - jsonDict["DigraphIteration{}".format(iteration)] = matrix.tolist() #createDigraph(matrix, data, iteration) - - #matrix headers - jsonDict["DigraphIterationHeader{}".format(iteration)] = generate_matrix_headers(data, iteration) - - #define cluster elements - #clusters = np.array([[point_a, point_b]]) - clusters.append([point_a, point_b]) - #print("Aktuelle Cluster:") - #print(clusters) - - #define new dataset - new_data = np.array([point for point in data if not np.array_equal(point, point_to_remove)]) - #print("Neuer Hauptdatensatz") - #print(new_data) - - #alle weiteren iterationen - elif(iteration > 0): - - if len(new_data) == 1: - continue - - if distanceMethod == 'manhattan': - matrix = calculate_inital_abstraction_matrix(new_data, manhattan_distance) - - elif distanceMethod == 'euclidean': - matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance) - - elif distanceMethod == 'maximum': - matrix = calculate_inital_abstraction_matrix(new_data, maximum_distance) - - else: - break - #matrix = calculate_inital_abstraction_matrix(new_data, euclidean_distance) - - #get the two points with the new minimum distance - single_linkage_indices = single_linkage_get_indices(matrix) - - #get two points from given index that are the new cluster - point_a = new_data[single_linkage_indices[0]] - point_b = new_data[single_linkage_indices[1]] - - #matrix headers - jsonDict["DigraphIterationHeader{}".format(iteration)] = generate_matrix_headers(new_data, iteration) - - #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance) - #delete the point from the dataset, that is not nearest - #point_to_remove = nearest_point[1] - - #add point to current cluster - if is_element_in_array(clusters, point_a) or is_element_in_array(clusters, point_b): - #print("Erweitere Cluster") - - #get element number of the cluster and append point to the current cluster - if is_element_in_array(clusters, point_a): - arrayLocation = is_element_in_array_location(clusters, point_a) - pointToAppend = point_b - elif is_element_in_array(clusters, point_b): - arrayLocation = is_element_in_array_location(clusters, point_b) - pointToAppend = point_a - - #print(arrayLocation) - - #append pointToAppend to cluster: - #clusters = np.insert(clusters, arrayLocation[0], pointToAppend, axis=arrayLocation[1]) - #print(clusters[arrayLocation[0]]) - #clusters[arrayLocation[0], arrayLocation[1]] - - #newClusters1 = clusters[arrayLocation[0]] - #print(clusters[arrayLocation[0]]) - pointsToAppend = np.vstack([clusters[arrayLocation[0]], pointToAppend]) - - newClusters = [] - - newClusters.append(pointsToAppend) - - #newClusters = np.append(clusters[arrayLocation[0]], [pointToAppend], axis=0) - #newClusters = np.array([[point_a, point_b]]) - clusterCounter = 0 - - for cluster in clusters: - if clusterCounter == arrayLocation[0]: - clusterCounter += 1 - - else: - #newClusters = np.concatenate([newClusters, [cluster]]) #dimensions are not valid! -> was anderes einfallen lassen!! #letztes puzzelstück -> wie den bums richtig zusammenfügen???!!! - newClusters.append(cluster) - clusterCounter += 1 - - - #print("UPDATED CLUSTER:") - #print(newClusters) - print("-------") - - clusters = newClusters - - #create new cluster - else: - #print("Definiere neues Cluster") - #define cluster elements if ist an new cluster - #clusters = np.append(clusters,[[point_a, point_b]], axis=0) - clusters.append([point_a, point_b]) - #print(clusters) - - #createDigraph(matrix, new_data, iteration) - jsonDict["DigraphIteration{}".format(iteration)] = matrix.tolist() #createDigraph(matrix, new_data, iteration) - - - if distanceMethod == 'manhattan': - nearest_point = get_nearest_point(data, point_a, point_b, manhattan_distance) - - elif distanceMethod == 'euclidean': - nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance) - - elif distanceMethod == 'maximum': - nearest_point = get_nearest_point(data, point_a, point_b, maximum_distance) - - else: - break - - #nearest_point = get_nearest_point(data, point_a, point_b, euclidean_distance) - #delete the point from the dataset, that is not nearest - point_to_remove = nearest_point[1] - - - - #define new dataset - new_data = np.array([point for point in new_data if not np.array_equal(point, point_to_remove)]) - #print("Neuer Hauptdatensatz") - #print(jsonDict) - - #print(jsonDict) - #jsonDict["taskDescription"] = - jsonDict["iterations"] = iteration - with open(f"{pathlib.Path(__file__).parent.resolve()}/data.json", "w+", encoding="utf8") as f: - json.dump(jsonDict, f) - - return json.dumps(jsonDict) - -# Anzahl der Datenpunktansammlungen (Cluster) -#numClusters = int(sys.argv[1]) -#pointsPerCluster = int(sys.argv[2]) -#nodeRangeStart = int(sys.argv[3]) -#nodeRangeEnd = int(sys.argv[4]) -#distanceMethod = int(sys.argv[5]) -#linkageMethod = int(sys.argv[6]) - -#numClusters = 2 -#pointsPerCluster = 3 -#nodeRangeStart = 0 -#nodeRangeEnd = 25 -#distanceMethod = 'euclidean' -#linkageMethod = 'single' -#Hauptfunktion ausfuehren -#clusterAnalysisMain(numClusters, pointsPerCluster, nodeRangeStart, nodeRangeEnd, distanceMethod, linkageMethod) - -#EXECUTE -clusterAnalysisMain() - - -#TODO -#Eingabevalidierung!!! - - -#algo überarbeiten zur generierung der einzelnen punkte für dei clusteranalyse -#leute sollen verstehen was diese aufgabe soll -> "wir wollen hierarchsiche clusteranalyse üben oder in der prüfung abfragen" \ No newline at end of file diff --git a/src/Tasks/ClusterAnalysis/data.json b/src/Tasks/ClusterAnalysis/data.json deleted file mode 100644 index e15f61c..0000000 --- a/src/Tasks/ClusterAnalysis/data.json +++ /dev/null @@ -1 +0,0 @@ -{"taskDescription": "

In dieser Aufgabe sollen Sie zeigen, dass Sie die hierarchische Clusteranalyse verstanden haben und diese anwenden koennen.


Sie erhalten eine Sammlung von Datenpunkten, f\u00fcr die Sie die Analyse durchf\u00fchren sollen.


\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Name Datenpunkt Position X-Achse Position Y-Achse
A23
B52
C53
D14
E45


Zeichnen Sie im ersten Schritt die Datenpunkte in ein Koordinatensystem als visuelle Hilfe ein, oder nutzen Sie alternativ das Streudiagramm, welches Ihnen ALADIN als Hilfestellung bietet.

Berechnen Sie nun im n\u00e4chsten Schritt die einzelnen Distanzmatrizen bis zu einer 2x2 Matrix und gebe sie Ihre L\u00f6sung in den Editor ein.

Nutzen Sie f\u00fcr die Berechnung der Distanzen zwischen den einzelnen Datenpunkten die Berechnungsmethode euclidean

", "taskDescriptionPreview": "

Vorschau der Datentabelle:


\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Name Datenpunkt Position X-Achse Position Y-Achse
A23
B52
C53
D14
E45



Vorschau des Streudiagramms:



\"Streudiagramm\"

", "DigraphIteration0": [[0.0, 3.16, 3.0, 1.41, 2.83], [3.16, 0.0, 1.0, 4.47, 3.16], [3.0, 1.0, 0.0, 4.12, 2.24], [1.41, 4.47, 4.12, 0.0, 3.16], [2.83, 3.16, 2.24, 3.16, 0.0]], "DigraphIterationHeader0": ["A", "B", "C", "D", "E"], "DigraphIterationHeader1": ["A", "B", "C", "D"], "DigraphIteration1": [[0.0, 3.0, 1.41, 2.83], [3.0, 0.0, 4.12, 2.24], [1.41, 4.12, 0.0, 3.16], [2.83, 2.24, 3.16, 0.0]], "DigraphIterationHeader2": ["A", "B", "C"], "DigraphIteration2": [[0.0, 3.0, 2.83], [3.0, 0.0, 2.24], [2.83, 2.24, 0.0]], "DigraphIterationHeader3": ["A", "B"], "DigraphIteration3": [[0.0, 3.0], [3.0, 0.0]], "iterations": 4} \ No newline at end of file diff --git a/src/Tasks/ClusterAnalysis/wrapperFunctionCallPython.ts b/src/Tasks/ClusterAnalysis/wrapperFunctionCallPython.ts deleted file mode 100644 index eda56ab..0000000 --- a/src/Tasks/ClusterAnalysis/wrapperFunctionCallPython.ts +++ /dev/null @@ -1,51 +0,0 @@ -import { exec } from "child_process"; -import fs from "fs"; -const path = require("path"); - -function runPythonScript(scriptPath: string, args: any): Promise { - try { - return new Promise((resolve, reject) => { - const command = `python ${scriptPath} ${args.join(" ")}`; - console.log(command); - exec(command, (error, stdout, stderr) => { - if (error) { - reject(error.message); - return; - } - if (stderr) { - reject(stderr); - return; - } - resolve(stdout); - }); - }); - } catch { - console.log("Error"); - } -} - -export async function clusterAnalysisMain(parameter: any) { - let result = { foo: "bar" }; - console.log("PARAMETER:"); - console.log(parameter); - try { - let pythonScriptPath = "clusterAnalysis.py"; - pythonScriptPath = path.join(__dirname, pythonScriptPath); - //console.log(parameter["distance"]); - - const argumentsToPythonScript = [...Object.values(parameter), ...parameter["nodeRange"]]; - //delete argumentsToPythonScript[2]; - //console.log("ARGUMENTS TO PYTHON SCRIPT"); - //console.log(argumentsToPythonScript); - (await runPythonScript(pythonScriptPath, argumentsToPythonScript)) as any; - - //let foo = fs.readFileSync(path.join(__dirname, 'data.json'), 'utf-8'); - - result = JSON.parse(fs.readFileSync(path.join(__dirname, "data.json"), "utf-8")); - console.log(result); - //insofern kryptsiche Zeichen in der JSON Ausgabe vorhanden sind .> Workaround mittels File - } catch (error) { - console.error("Error running Python script:", error); - } - return result; -} diff --git a/src/Tasks/DecisionTrees/DataSampler.py b/src/Tasks/DecisionTrees/DataSampler.py deleted file mode 100644 index 3f3269e..0000000 --- a/src/Tasks/DecisionTrees/DataSampler.py +++ /dev/null @@ -1,59 +0,0 @@ -import pandas as pd -import numpy as np -from DecisionTreeTaskFunction import * - -data_path_csv = 'src/Tasks/DecisionTrees/datasets/spiel.csv' # Originaler Datensatz -generated_data_path_csv = 'src/Tasks/DecisionTrees/datasets/generated_dataset.csv' # Generierter Datensatz - -data = pd.read_csv(data_path_csv) -df = pd.DataFrame(data) - -# Beispiel: Lösungsbaum-Struktur von spiel.csv -# decision_tree = {'Aussicht': {'sonnig': {'Luftfeuchtigkeit': {'hoch': 'nein', 'normal': 'ja'}}, -# 'bewölkt': 'ja', -# 'regnerisch': {'Wind': {'nein': 'ja', 'ja': 'nein'}}}} - -decision_tree_original = get_decisiontree_structure(data_path_csv) - -# Prüfung, ob Baumstrukur aus generierten Datensatz gleich wie Struktur des Lösungsbaums -def check_generated_tree(original_tree, generated_tree): - original_json = json.loads(json.dumps(original_tree, sort_keys=True)) - generated_json = json.loads(json.dumps(generated_tree, sort_keys=True)) - return original_json == generated_json - -# Bootstrap-Sampling-Funktion -def bootstrap_sample(data): - sample = data.sample(n=len(data), replace=True) # samplen der Datensätze in gleicher Größe wie die original Daten - return sample - -# Funktion zum Generieren von Datenpunkten basierend auf dem Entscheidungsbaum -def generate_data_point(tree): - current_node = tree - while isinstance(current_node, dict): - feature = list(current_node.keys())[0] - values = list(current_node[feature].keys()) - selected_value = np.random.choice(values) - current_node = current_node[feature][selected_value] - return current_node - -# Bootstrap-Sampling und Daten generieren -def sample_data(): - while True: - num_samples = 5 - - data = pd.concat([bootstrap_sample(df.apply(generate_data_point, axis=1)) for _ in range(num_samples)], ignore_index=True) - - # als JSON speichern für die Ausgabe als Tabelle - data.to_json('src/Tasks/DecisionTrees/datasets/generated_dataset.json',orient='records',lines=True) - - # als CSV speichern für die Berechnung des Entscheidungsbaumes - data.to_csv('src/Tasks/DecisionTrees/datasets/generated_dataset.csv', index=False) - - # Entscheidungsbaum-Struktur für den generierten Datensatz erstellen - decision_tree_generated = get_decisiontree_structure(generated_data_path_csv) - - if check_generated_tree(decision_tree_original, decision_tree_generated): - print("Die generierter Datensatz entspricht der Struktur des originalen Entscheidungsbaum.") - break - -sample_data() diff --git a/src/Tasks/DecisionTrees/DecisionTreeTask.py b/src/Tasks/DecisionTrees/DecisionTreeTask.py deleted file mode 100644 index 7690fb4..0000000 --- a/src/Tasks/DecisionTrees/DecisionTreeTask.py +++ /dev/null @@ -1,71 +0,0 @@ -import sys -import ast -import json -import pandas as pd -import numpy as np -from DecisionTreeTaskFunction import * - -# generierten Datensatz für die Berechnung verwenden -data_path_csv = 'src/Tasks/DecisionTrees/datasets/generated_dataset.csv' -dataframe = pd.read_csv(data_path_csv) - -# Merkmale, Zielmerkmal und Klassen abstrahieren -attributes = dataframe.columns[:-1] -target_attribute = dataframe.columns[-1] -classes = dataframe[target_attribute].unique() - -# Funktion für den Beginn der Aufgabe - Lösungsbaum berechnen und Überprüfung des eingegebenen Entscheidungsbaums -def start_decisiontree_task(received_nodes, received_edges, classes): - dt = build_decision_tree(dataframe, attributes, target_attribute, 0, received_nodes) - - decision_tree_structure_dict = decisiontree_structure(dt) - entropy_infogain_dict = extract_entropy_and_infogain(dt) - - tree_list = [] - build_decision_tree_list(decision_tree_structure_dict, tree_list=tree_list) - tree_list = add_parent_label(tree_list) - tree_list = add_child_node(tree_list) - - nodes_list = [item for item in tree_list if item[3] == 'node'] - edges_list = [item for item in tree_list if item[3] == 'edge'] - - edges_list = add_entropy(edges_list, entropy_infogain_dict) - nodes_list = add_infogain(nodes_list, entropy_infogain_dict) - - # Werte des Lösungsbaum in zwei Listen aufgeteilt, für die Überprüfung des eingegebnen Baums (seperate Listen für Knoten und Kante) - solutions_nodes, solutions_edges = prepare_lists(nodes_list, edges_list) - - # Überprüfung des eingegebenen Entscheidungsbaums - results_nodes, results_edges = validateGraph(solutions_nodes, solutions_edges, received_nodes, received_edges, classes) - - correctNodesCount = 0 - correctEdgesCount = 0 - - # Überprüfung, ob Entscheidungsbaum vollständig gelöst - for r in results_nodes: - if r['result'] == True: - correctNodesCount += 1 - - for r in results_edges: - if r['result'] == True: - correctEdgesCount += 1 - - if(len(solutions_nodes) == correctNodesCount and len(solutions_edges) == correctEdgesCount and len(solutions_nodes) == len(received_nodes) and len(solutions_edges) == len(received_edges)): - result = { 'nodes': results_nodes, 'edges': results_edges, 'finished': True, 'entropies': entropy_infogain_dict } - else: - result = { 'nodes': results_nodes, 'edges': results_edges, 'finished': False, 'entropies': entropy_infogain_dict } - - return (json.dumps(result)) - - -# Erhalt der Nodes- und Edges-Liste aus WrapperValidateDecisionTree.ts -received_nodes_list, received_edges_list = sys.argv[1], sys.argv[2] if len(sys.argv) <= 3 else (None, None) - -modified_nodes_list_1 = ast.literal_eval(received_nodes_list) -modified_nodes_list_2 = [tuple(inner) for inner in modified_nodes_list_1] # Inhalt der Liste als Tupel umwandeln -current_nodes_list = [(x[0], x[1], 0 if x[2] is None else x[2], x[3], x[4]) for x in modified_nodes_list_2] # Wert in Tupel prüfen, ob 3. Element None ist (Wurzelknoten) → mit 0 ersetzen - -modified_nodes_list = ast.literal_eval(received_edges_list) -current_edges_list = [tuple(inner) for inner in modified_nodes_list] - -print(start_decisiontree_task(current_nodes_list, current_edges_list, classes)) diff --git a/src/Tasks/DecisionTrees/DecisionTreeTaskFunction.py b/src/Tasks/DecisionTrees/DecisionTreeTaskFunction.py deleted file mode 100644 index 1fe21f0..0000000 --- a/src/Tasks/DecisionTrees/DecisionTreeTaskFunction.py +++ /dev/null @@ -1,340 +0,0 @@ -import sys -import ast -import json -import re -import pandas as pd -import numpy as np - -nodes_list = [] -edges_list = [] - -### ID3-Algorithmus ### -# Berechnung der Entropie -def entropy(labels): - unique_labels, label_counts = np.unique(labels, return_counts=True) - probabilities = label_counts / label_counts.sum() - entropy = -np.sum(probabilities * np.log2(probabilities)) - entropy = round(entropy, 5) - return entropy if entropy > 0.0 else -entropy - -# Berechnung des Informationsgewinns -def information_gain(data, feature, target): - total_entropy = entropy(data[target]) - weighted_entropy = 0 - - unique_values = data[feature].unique() - gain_info = {'entropies': {}, 'infogain': {}} - - for value in unique_values: - subset = data[data[feature] == value] - subset_entropy = entropy(subset[target]) - - weight = len(subset) / len(data) - weighted_entropy += weight * subset_entropy - - # Entropie der Ausprägung speichern - gain_info['entropies'][f"{feature} = {value}"] = subset_entropy - - # Informationsgewinn des Attributs speichern - information_gain = round(total_entropy - weighted_entropy, 5) - gain_info['infogain'][feature] = information_gain - return gain_info - -# Bestes Merkmal auswählen -def choose_best_feature(data, features, target): - best_feature_list = [] - best_feature = None - max_info_gain = -1 - best_feature_info = {"entropies": {}, "infogain": {}} - - for feature in features: - feature_info = information_gain(data, feature, target) - - if feature_info["infogain"][feature] > max_info_gain: - max_info_gain = feature_info["infogain"][feature] - best_feature = feature - best_feature_info = feature_info - best_feature_list = [(best_feature, best_feature_info)] - elif feature_info["infogain"][feature] == max_info_gain: - best_feature = feature - best_feature_info = feature_info - best_feature_list.append((best_feature, best_feature_info)) - - return best_feature_list - -# Überprüfen, ob mehrere Lösungen vorhanden sind -def check_multiple_solutions(best_feature, solutions, level): - for tuple in solutions: - if level == tuple[1] and best_feature == tuple[2]: - return True - break - -# Hauptfunktion: Lösungsbaum berechnen -def build_decision_tree(data, features, target, level, solutions): - if len(data[target].unique()) == 1: - return data[target].iloc[0] - if len(features) == 0: - return data[target].mode().iloc[0] - - best_feature = None - best_feature_info = None - option_selected = False - - best_feature_list = choose_best_feature(data, features, target) - - # Wenn Mehrfachlösungen vorhanden sind - if len(best_feature_list) > 1: - for index, tupel in enumerate(best_feature_list): - best_feature = tupel[0] - best_feature_info = tupel[1] - - option_selected = check_multiple_solutions(best_feature, solutions, level) - if option_selected: - break - else: - for tupel in best_feature_list: - best_feature = tupel[0] - best_feature_info = tupel[1] - option_selected = True - - tree = {best_feature: best_feature_info} - unique_values = data[best_feature].unique() - for value in unique_values: - subset = data[data[best_feature] == value] - sub_features = features.drop(best_feature) - tree[best_feature][value] = build_decision_tree(subset, sub_features, target, level+2, solutions) - return tree -### ID3-Algorithmus ### - -# Struktur des Entscheidungsbaums abstrahieren -def decisiontree_structure(tree): - new_tree = {} - for key, value in tree.items(): - if key != 'entropies' and key != 'infogain': - if isinstance(value, dict): - new_value = decisiontree_structure(value) - if new_value: - new_tree[key] = new_value - else: - new_tree[key] = value - return new_tree - -# Gesamtliste aller Werte (Knoten und Kanten) des Lösungsbaums erstellen -def build_decision_tree_list(tree, level=0, tree_list=None, counter=0, parent_counter=None): - if tree_list is None: - tree_list = [] - - node_type = 'node' if level % 2 == 0 else 'edge' - - for key, value in tree.items(): - tree_list.append((counter, level, key, node_type, parent_counter)) - counter += 1 - - if isinstance(value, dict): - counter = build_decision_tree_list(value, level + 1, tree_list, counter, parent_counter=counter - 1) - elif node_type == 'edge': - tree_list.append((counter, level + 1, value, 'node', counter - 1)) - counter += 1 - - return counter - -# Entropie und Informationsgewinn abstrahieren -def extract_entropy_and_infogain(tree, result_dict=None, count_dict=None, level=None): - if result_dict is None: - result_dict = {'entropies': {}, 'infogain': {}} - if count_dict is None: - count_dict = {} - - if level is None: - level = 0 - - if 'entropies' in tree: - for key, value in tree['entropies'].items(): - count_dict_key = f"{key}_count" - if count_dict_key not in count_dict: - count_dict[count_dict_key] = 1 - else: - count_dict[count_dict_key] += 1 - - modified_key = f"{key}{count_dict[count_dict_key]}" - result_dict['entropies'][modified_key] = {'value': value, 'level': level} - - if 'infogain' in tree: - for key, value in tree['infogain'].items(): - count_dict_key = f"{key}_count" - if count_dict_key not in count_dict: - count_dict[count_dict_key] = 1 - else: - count_dict[count_dict_key] += 1 - - modified_key = f"{key}{count_dict[count_dict_key]}" - result_dict['infogain'][modified_key] = {'value': value, 'level': level-1} - - for key, value in tree.items(): - if isinstance(value, dict): - extract_entropy_and_infogain(value, result_dict, count_dict, level + 1) - - return result_dict - -# Entropie-Werte der Kantenliste hinzufügen -def add_entropy(edges_list, entropy_dict): - new_edges_list = [] - attribute_counts = {} - for index, edge in enumerate(edges_list): - attribute = f'{edge[5]} = {edge[2]}' - count = attribute_counts.get(attribute, 1) - - while True: - key = f'{attribute}{count}' - - if key in entropy_dict['entropies'] and edge[1] == entropy_dict['entropies'][key]['level']: - value = entropy_dict['entropies'][key]['value'] - new_edges_list.append(edge + (value,)) - count += 1 - else: - break - - attribute_counts[attribute] = count - return new_edges_list - -# Informationsgewinn-Werte der Knotenliste hinzufügen -def add_infogain(nodes_list, infogain_dict): - new_nodes_list = [] - attribute_counts = {} - - for index, node in enumerate(nodes_list): - attribute = node[2] - count = attribute_counts.get(attribute, 1) - - key = f'{attribute}{count}' - - if key in infogain_dict['infogain'] and node[1] == infogain_dict['infogain'][key]['level']: - value = infogain_dict['infogain'][key]['value'] - new_nodes_list.append(node + (value,)) - count += 1 - else: - value = 0 - new_nodes_list.append(node + (value,)) - - attribute_counts[attribute] = count - - return new_nodes_list - -# Bezeichnung des Übergeordeneten Objekts dem Knoten bzw. der Kante hinzufügen -def add_parent_label(tree_list): - new_tree_list = [] - - # NODES - for item in tree_list: - for i in tree_list: - if item[3] == 'node' and item[4] == i[0]: - new_tree_list.append(item + (i[2],)) - break - elif item[3] == 'node' and item[4] is None: - new_tree_list.append(item + ('None',)) - break - - # EDGES - for item in tree_list: - for i in tree_list: - if item[3] == 'edge' and item[4] == i[0]: - new_tree_list.append(item + (i[2],)) - break - - return new_tree_list - -# Bezeichnung und Ebene des Zielknoten der Kante hinzufügen -def add_child_node(tree_list): - # NODE(ID, LEVEL, LABEL, TYPE, PARENTID, PARENTLABEL) - nodes_list = [item for item in tree_list if item[3] == 'node'] - - result = [] - for item in tree_list: - if item[3] == 'edge': - for i in tree_list: - if i[3] == 'node' and item[0] == i[4]: - child_id = i[0] - child_label = i[2] - result.append(item + (child_id, child_label)) - - result = result + nodes_list - return result - -# Liste mit Lösungswerten des Lösungsentscheidungsbaum vorbereiten für die Überprüfung -def prepare_lists(nodes_list, edges_list): - # NODE (KEY, LEVEL, LABEL, PARENTID, PARENTLABEL, INFOGAIN) - solutions_nodes = [(node[0], node[1], re.sub(r"(\s)+", "_", node[2]).strip(), node[4], re.sub(r"(\s)+", "_",node[5]).strip(), node[6]) for node in nodes_list] - # EDGE (KEY, LEVEL, LABEL, PARENTID, PARENTLABEL, CHILDID, CHILDLABEL, ENTROPY) - solutions_edges = [(edge[0], edge[1], re.sub(r"(\s)+", "_", edge[2]).strip(), edge[4], re.sub(r"(\s)+", "_", edge[5]).strip(), edge[6], re.sub(r"(\s)+", "_", edge[7]).strip(), edge[8]) for edge in edges_list] - return solutions_nodes, solutions_edges - -# Validierung der übergebenen Werte des erstellen Entscheidungsbaum mit den berechneten Lösungswerten -def validateGraph(solution_nodes_list, solution_edges_list, received_nodes_list, received_edges_list, classes): - # SOLUTIONS_NODES (ID, LEVEL, LABEL, PARENTID, PARENTEDGELABEL, INFOGAIN) - # RECEIVED_NODES (KEY, LEVEL, LABEL, PARENTEDGELABEL, INFOGAIN) - - # SOLUTIONS_EDGES (ID, LEVEL, LABEL, PARENTID, PARENTNODELABEL, CHILDID, CHILDNODELABEL, ENTROPY) - # RECEIVED_EDGES (KEY, LEVEL, LABEL, PARENTKEY, PARENTNODELABEL, CHILDKEY, CHILDNODELABEL, ENTROPY) - - results_nodes = [] - results_edges = [] - - # 1. PRÜFUNG - EDGES - for received_edge in received_edges_list: - result = False - for solution_edge in solution_edges_list: - if (received_edge[1] == solution_edge[1] and # (LEVEL == LEVEL) - received_edge[2] == solution_edge[2] and # (LABEL == LABEL) - received_edge[4] == solution_edge[4] and # (PARENTNODELABEL == PARENTNODELABEL) - received_edge[6] == solution_edge[6] and # (CHILDNODELABEL == CHILDNODELABEL) - received_edge[7] == solution_edge[7] # (ENTROPY == ENTROPY) - ): - result = True - break - - results_edges.append({'key': received_edge[0], 'result': result}) - - # 2. PRÜFUNG - NODES - for received_node in received_nodes_list: - result = False - for solution_node in solution_nodes_list: - # WURZELKNOTEN PRÜFEN - if (received_node[0] == '1' and # (KEY == 1) - received_node[2] == solution_node[1] and # (LEVEL == LEVEL) - received_node[1] == solution_node[2] and # (LABEL == LABEL) - received_node[3] == 'None' and # (PARENTEDGELABEL == 'NONE') - received_node[4] == solution_node[5]): # (INFOGAIN == INFOGAIN) - result = True - break - - elif (received_node[1] == solution_node[1] and # (LEVEL == LEVEL) - received_node[2] == solution_node[2] and # (LABEL == LABEL) - received_node[3] == solution_node[4] and # (PARENTEDGELABEL == PARENTEDGELABEL) - received_node[4] == solution_node[5]): # (INFOGAIN == INFOGAIN) - result = True - break - - elif (received_node[2] in classes): # (LABEL IN CLASSES['ja', 'nein']) - for solution_edge in solution_edges_list: - if (received_node[2] == solution_edge[6] and - received_node[3] == solution_edge[2] and - received_node[4] == 'decision'): - result = True - break - - results_nodes.append({'key': received_node[0], 'result': result}) - return results_nodes, results_edges - -# Struktur des Entscheidungsbaums erhalten (Funktion für die Datengenerierung) -def get_decisiontree_structure(data_path_csv): - dataframe = pd.read_csv(data_path_csv) - received_nodes = [] - - target_attribute = dataframe.columns[-1] - classes = dataframe[target_attribute].unique() - attributes = dataframe.columns[:-1] - - dt = build_decision_tree(dataframe, attributes, target_attribute, 0, received_nodes) - dt_struct = decisiontree_structure(dt) - - return dt_struct \ No newline at end of file diff --git a/src/Tasks/DecisionTrees/Task.ts b/src/Tasks/DecisionTrees/Task.ts deleted file mode 100644 index 68b71c4..0000000 --- a/src/Tasks/DecisionTrees/Task.ts +++ /dev/null @@ -1,174 +0,0 @@ -import { TaskGenerator } from "../../Legacy/genericGraphs/TaskGenerator"; -import { Constructor } from "../../Legacy/genericGraphs/Utility"; -import { Gozintograph, GozintographVertex } from "./WrapperDecisionTree"; -import { GozintographGenerator2, GozintographConfig } from "./WrapperDecisionTree"; - -export class DecisionTreeTaskGenerator extends TaskGenerator { - constructor( - graphConstructor: Constructor = Gozintograph, - generatorConstructor: Constructor = GozintographGenerator2 - ) { - super(graphConstructor, generatorConstructor); - } - - public generateTask(config: GozintographConfig) { - config["type"] = "digraph"; - config["edgeDirection"] = "forward"; - config.style = { - graph: { rankdir: "BT", splines: "polyline", bgcolor: "transparent" }, - vertex: { shape: "circle", style: "filled" }, - edge: {}, - }; - super.generateTask(config); - - const graphGenerator = new this.graphGeneratorConstructor(this.rng); - this.graph = graphGenerator.generateGraph(this.graphConstructor, config); - - const transpose = (adjacencyMatrix: Array>) => - adjacencyMatrix[0].map((_, colIndex) => adjacencyMatrix.map((row) => row[colIndex])); - const [labelVector, valueVector, adjacencyMatrix] = this.orderGraphSemantically(this.graph); - // const vertices = Object.values(this.graph.getVertices()); - // const valueVector = vertices.map((vertex) => vertex.getProperty("value" as keyof GozintographVertex)); - // const labelVector = vertices.map((vertex) => vertex.getProperty("label" as keyof GozintographVertex)); - const solution = this.calculateSolution(transpose(adjacencyMatrix), valueVector); - - const dotGraph = this.graph.dotGraph(); - const { nodes, edges, paths } = this.graph.serialize(); - - return { - dotDescription: dotGraph, - edges, - nodes, - paths, - valueVector: [valueVector], - labelVector, - solution, - adjacencyMatrix: transpose(adjacencyMatrix), - longestPath: this.graph.getLongestPathLength(), - }; - } - - private orderGraphSemantically(graph: Gozintograph) { - const labelOrder: { [key: string]: number } = { P: 400, B: 300, K: 200, R: 100 }; - const vertices = Object.values(graph.getVertices()); - - const labelSort = (v1: GozintographVertex, v2: GozintographVertex) => { - const labels = [v1, v2].map((v) => v.getProperty("label" as keyof GozintographVertex)); - const splitAtIndex = (s: string, index: number): [label: string, index: number] => [ - s.substring(0, index), - parseInt(s.substring(index)), - ]; - const [l1, l2] = labels.map((label) => splitAtIndex(label, 1)); - - return labelOrder[l2[0]] - labelOrder[l1[0]] + (l1[1] - l2[1]); - }; - const edges = graph.getEdges(); - - return vertices.sort(labelSort).reduce( - (sortedGraph, vertex, i, sortedVertices) => { - sortedGraph[0].push(vertex.getProperty("label" as keyof GozintographVertex)); - sortedGraph[1].push(vertex.getProperty("value" as keyof GozintographVertex)); - - const row = sortedVertices.map((sortedVertex) => { - const parents = vertex.getParents(); - const vertexID = vertex.getId(); - const parentID = sortedVertex.getId(); - if (parentID in parents) { - return parseInt(edges[`${parents[parentID].getId()}__${vertexID}`].getLabel()); - } else { - return 0; - } - }); - sortedGraph[2].push(row); - - return sortedGraph; - }, - [[], [], []] - ); - } - - private longestPathLength() { - const roots = this.graph.getLeafs(); - const vertices = this.graph.getVertices(); - const rootVertices = roots.map((root) => vertices[root]); - - const findPath = (vertex: GozintographVertex, path: Array, paths: Array>) => { - const childs = Object.values(vertex.getChilds()); - if (childs.length) { - childs.forEach((child) => { - let newPath = [...path]; - newPath.push({ between: [vertex.getId(), child.getId()] }); - findPath(child, newPath, paths); - }); - } else if (path.length) { - paths.push(path); - path = []; - } - return paths; - }; - - const paths = rootVertices.reduce((paths, root) => findPath(root, [], paths), []); - this.graph.setPaths(paths); - - const longestPathLength = paths.reduce((longestPath, path) => (path.length > longestPath ? path.length : longestPath), 0); - - this.graph.setLongestPathLength(longestPathLength); - return longestPathLength; - } - - private calculateSolution(matrix: Array>, valueVector: Array): Array> { - const transpose = (matrix: Array>) => - matrix[0].map((element, elementIndex) => matrix.map((row) => row[elementIndex])); - const sum = (list: Array): number => list.reduce((sum, element) => (sum += element), 0); - const matmul = (m1: Array>, m2: Array>) => - m1.map((row) => transpose(m2).map((column) => sum(row.map((element, elementIndex) => element * column[elementIndex])))); - const matadd = (m1: Array>, m2: Array>) => - m1.map((row, i) => row.map((element, j) => (m2[i][j] += element))); - const unity = (matrix: Array>) => { - return matrix.reduce((unityMatrix, rows, i) => { - const unityRow = rows.reduce((unityRow, row, j) => { - unityRow[j] = i === j ? 1 : 0; - return unityRow; - }, []); - unityMatrix.push(unityRow); - return unityMatrix; - }, [] as Array>); - }; - - let current = matrix; - let result = matrix; - const longestPathLength = this.longestPathLength(); - for (let i = 1; i < longestPathLength; i++) { - result = matmul(current, result); - result = matadd(current, result); - } - result = matadd(result, unity(matrix)); - - const resultVector = result.reduce((vector, row) => { - const scalar = row.reduce((scalar, element, i) => { - scalar += element * valueVector[i]; - return scalar; - }, 0); - vector.push(scalar); - return vector; - }, [] as Array); - - return [resultVector]; - } -} - -// const tg = new GozintographTaskGenerator(); -// const t = tg.generateTask({ -// nodeAmount: 10, -// edgeDensity: 0.2, -// nodeValueRange: [1, 10], -// edgeWeightRange: [1, 10], -// seed: "", -// style: { vertex: {}, edge: {}, graph: {} }, -// }); -// console.log(t.paths); - -// nodeAmount: number; -// edgeDensity: number; -// nodeValueRange: [number, number]; -// edgeWeightRange: [number, number]; diff --git a/src/Tasks/DecisionTrees/WrapperDataSampler.ts b/src/Tasks/DecisionTrees/WrapperDataSampler.ts deleted file mode 100644 index 2ba77d1..0000000 --- a/src/Tasks/DecisionTrees/WrapperDataSampler.ts +++ /dev/null @@ -1,75 +0,0 @@ -import { exec } from 'child_process'; -import * as fs from 'fs'; - -/* Funktion für die ausführung des Python-Skripts (DataSampler.py) */ -function runPythonScript(scriptPath: string, args: string[]): Promise { - return new Promise((resolve, reject) => { - const command = `python ${scriptPath} ${args.join(' ')}`; - exec(command, (error, stdout, stderr) => { - if (error) { - reject(error.message); - return; - } - if (stderr) { - reject(stderr); - return; - } - resolve(stdout); - }); - }); -} - -/* Funktion für das Auslesen einer JSON-Datei (generated_dataset.json) */ -async function readJsonFile(filePath: string): Promise { - return new Promise((resolve, reject) => { - fs.readFile(filePath, 'utf-8', (err, data) => { - if (err) { - reject(err); - return; - } - - try { - const jsonData = data.split('\n').filter(Boolean).map(line => JSON.parse(line)); - resolve(jsonData); - } catch (parseError) { - reject(parseError); - } - }); - }); -} - -/* Tabelle aus einem JSON erstellen */ -function generateTableFromJson(jsonArray: Array, tableColumns: string[]) { - return jsonArray.map((jsonObj) => { - const tableRow: Record = {}; - tableColumns.forEach((column, index) => { - tableRow[column] = jsonObj[column]; - }); - return tableRow; - }); -} - -/* Wrapper-Funktion */ -export async function DataSamplingGenerator() { - try { - const pythonScriptPath = 'src/Tasks/DecisionTrees/DataSampler.py'; - const argumentsToPythonScript = ['']; - await runPythonScript(pythonScriptPath, argumentsToPythonScript); - - const filename = 'src/Tasks/DecisionTrees/datasets/generated_dataset.json'; - - try { - const jsonData = await readJsonFile(filename); - const tableColumns = Array.from(new Set(jsonData.flatMap(Object.keys))); - - const tableData = generateTableFromJson(jsonData, tableColumns); - console.log('Table: ', tableData); - - return { "table": tableData }; - } catch (jsonError) { - console.error(`Error parsing JSON file ${filename}: ${jsonError}`); - } - } catch (error) { - console.error('Error running Python script: ', error); - } -} diff --git a/src/Tasks/DecisionTrees/WrapperDecisionTree.ts b/src/Tasks/DecisionTrees/WrapperDecisionTree.ts deleted file mode 100644 index b4c8e3a..0000000 --- a/src/Tasks/DecisionTrees/WrapperDecisionTree.ts +++ /dev/null @@ -1,232 +0,0 @@ -import { GraphGenerator } from "../../Legacy/genericGraphs/GraphGeneration"; -import { Vertex, Edge, Graph, VertexParameters, EdgeParameters, GraphParameters } from "../../Legacy/genericGraphs/Graph"; -import { Constructor, mapAttributesToDot } from "../../Legacy/genericGraphs/Utility"; -import { TaskConfig } from "../../Legacy/genericGraphs/TaskGenerator"; -import { ensureType, statefulCounter } from "../../Legacy/helpers/helperFunctions"; - -export interface GozintographConfig extends TaskConfig { - nodeAmount: number; - edgeDensity: number; - nodeValueRange: [number, number]; - edgeWeightRange: [number, number]; -} -export interface PlanVertexParameters extends VertexParameters {} -interface GozintographParameters extends GraphParameters {} - -export class GozintographVertex extends Vertex { - private value: string; - private label: string; - constructor(args: PlanVertexParameters) { - super(args); - this.value = args.value; - this.label = args.label; - } - - public getProperty(property: keyof GozintographVertex): any { - return this[property]; - } - public setProperty(property: keyof GozintographVertex, value: any) { - this[property] = value; - } - public getChilds(): { [id: number]: GozintographVertex } { - return this.childs as { [id: number]: GozintographVertex }; - } - public getParents(): { [id: number]: GozintographVertex } { - return this.parents as { [id: number]: GozintographVertex }; - } - public dotVertex() { - return `${this.id} [label="${this.label}" tooltip="${this.value}"]`; - } - public serialize() { - return { id: this.id, label: this.label, value: this.value, parents: Object.keys(this.parents), childs: Object.keys(this.childs) }; - } -} - -export class GozintographEdge extends Edge { - public dotEdge() { - const weight = this.label; - const parentId = this.parentVertices[0].getId(); - const childId = this.childVertices[0].getId(); - return `${parentId} -> ${childId} [label="${weight}"]`; - } - - public serialize() { - const [from, to] = this.id.split("__"); - return { between: [from, to], weight: this.label }; - } -} - -export class Gozintograph extends Graph { - private adjacencyMatrix: Array>; - private roots: Array; - private leafs: Array; - private paths: Array>; - private longestPathLength: number; - constructor( - args: GozintographParameters, - vertexConstructor: Constructor = GozintographVertex, - edgeConstructor: Constructor = GozintographEdge - ) { - super(args, vertexConstructor, edgeConstructor); - } - - public getAdjacencyMatrix() { - return this.adjacencyMatrix; - } - public setAdjacencyMatrix(adjacencyMatrix: Array>) { - this.adjacencyMatrix = adjacencyMatrix; - } - public getRoots() { - return this.roots; - } - public setRoots(roots: Array) { - this.roots = roots; - } - public getLeafs() { - return this.leafs; - } - public setLeafs(leafs: Array) { - this.leafs = leafs; - } - public getPaths() { - return this.paths; - } - public setPaths(paths: Array>) { - this.paths = paths; - } - public getLongestPathLength() { - return this.longestPathLength; - } - public setLongestPathLength(longestPathLength: number) { - this.longestPathLength = longestPathLength; - } - - public dotGraph() { - const rankString = `{ rank=max; ${this.roots.join(";")} }\n }`; - return super.dotGraph().replace("}", rankString); - } - public serialize() { - const edges = Object.values(this.edges).map((edge) => edge.serialize()); - const vertices = Object.values(this.vertices).map((vertex) => vertex.serialize()); - return { nodes: vertices, edges, paths: this.paths }; - } -} - -export class GozintographGenerator2 extends GraphGenerator { - public generateGraph(graphConstructor: Constructor, config: GozintographConfig) { - GozintographVertex.resetCounter(); - config.type = "digraph"; - const graph = super.generateGraph(graphConstructor, config); - - const nodeAmount = ensureType("int", config.nodeAmount); - const edgeDensity = ensureType("float", config.edgeDensity); - const edgeWeightRange = config.edgeWeightRange.map((value) => ensureType("int", value)) as [number, number]; - const nodeValueRange = config.nodeValueRange.map((value) => ensureType("int", value)) as [number, number]; - - const adjacencyMatrix = this.generateAdjacencyMatrix(edgeDensity, nodeAmount, edgeWeightRange); - this.graph.setAdjacencyMatrix(adjacencyMatrix); - this.buildGraph(adjacencyMatrix, nodeValueRange); - - return graph; - } - - private generateAdjacencyMatrix(edgeDensity: number, nodeAmount: number, edgeWeightRange: [number, number]) { - const randomDAG = (x: number, n: number) => { - const length = (n * (n - 1)) / 2; - const dag = new Array(length).fill(1); - for (let i = 0; i < length; i++) { - if (this.rng.floatBetween(0, 1) > x) { - dag[i] = 0; - if (!isConnected(n, dag)) dag[i] = 1; - } - } - return dag; - }; - - const dagIndex = (n: number, i: number, j: number) => n * i + j - ((i + 1) * (i + 2)) / 2; - - const isConnected = (n: number, dag: Array) => { - const reached = new Array(n).fill(false); - reached[0] = true; - const queue = [0]; - - while (queue.length > 0) { - const x = queue.shift(); - for (let i = 0; i < n; i++) { - if (i !== n && !reached[i]) { - const j = i < x ? dagIndex(n, i, x) : dagIndex(n, x, i); - if (dag[j] !== 0) { - reached[i] = true; - queue.push(i); - } - } - } - } - return reached.every((x) => x); - }; - - const dagToAdjacencyMatrix = (n: number, dag: Array, edgeWeightRange: [number, number]) => { - const [min, max] = edgeWeightRange; - let adjacencyMatrix = Array(n) - .fill(null) - .map(() => Array(n).fill(0)); - for (let i = 0; i < n; i++) { - for (let j = i + 1; j < n; j++) { - const k = dagIndex(n, i, j); - - if (dag[k]) adjacencyMatrix[j][i] = this.rng.intBetween(min, max); - } - } - return adjacencyMatrix; - }; - - return dagToAdjacencyMatrix(nodeAmount, randomDAG(edgeDensity, nodeAmount), edgeWeightRange); - } - - private buildGraph(adjacencyMatrix: Array>, nodeValueRange: [number, number]) { - const rootVertices = this.findRoots(adjacencyMatrix); - const leafVertices = this.findLeafs(adjacencyMatrix); - this.graph.setLeafs(leafVertices); - this.graph.setRoots(rootVertices); - const [min, max] = nodeValueRange; - const labels: { [key: string]: any } = { - P: statefulCounter(), - B: statefulCounter(), - K: statefulCounter(), - R: statefulCounter(), - }; - adjacencyMatrix.forEach((row, i) => { - const labelType = this.assignLabel(rootVertices, leafVertices, i); - const vertex = this.graph.createVertex({ - value: this.rng.intBetween(min, max), - label: `${labelType}${labels[labelType].next().value}`, - }); - row.forEach((neighbour, j) => { - if (neighbour) { - const vertices = this.graph.getVertices(); - const parent = vertices[j]; - this.graph.createEdge({ parentVertices: [parent], childVertices: [vertex], label: neighbour.toString() }); - } - }); - }); - } - - private assignLabel(rootVertices: Array, leafVertices: Array, i: number) { - let labelType = "B"; - if (leafVertices.includes(i)) labelType = this.rng.coinFlip() ? "R" : "K"; - else if (rootVertices.includes(i)) labelType = "P"; - - return labelType; - } - - private findRoots(adjacencyMatrix: Array>) { - const transpose = (adjacencyMatrix: Array>) => - adjacencyMatrix[0].map((_, colIndex) => adjacencyMatrix.map((row) => row[colIndex])); - - return this.findLeafs(transpose(adjacencyMatrix)); - } - - private findLeafs(adjacencyMatrix: Array>) { - return adjacencyMatrix.map((vertex, i) => (vertex.every((weight) => !weight) ? i : -1)).filter((isLeaf) => isLeaf !== -1); - } -} \ No newline at end of file diff --git a/src/Tasks/DecisionTrees/WrapperValidateDecisionTree.ts b/src/Tasks/DecisionTrees/WrapperValidateDecisionTree.ts deleted file mode 100644 index c050c95..0000000 --- a/src/Tasks/DecisionTrees/WrapperValidateDecisionTree.ts +++ /dev/null @@ -1,64 +0,0 @@ -import { float } from 'aws-sdk/clients/cloudfront'; -import { exec } from 'child_process'; -import * as fs from 'fs'; - -/* Funktion für die ausführung des Python-Skripts (DecisionTreeTask.py) */ -function runPythonScript(scriptPath: string, args: string[]): Promise { - return new Promise((resolve, reject) => { - const command = `python ${scriptPath} ${args.join(' ')}`; - exec(command, (error, stdout, stderr) => { - if (error) { - reject(error.message); - return; - } - if (stderr) { - reject(stderr); - return; - } - resolve(stdout); - }); - }); -} - -/* Funktionen, für die strukturierung der übergebenen Lösungeswerte aus CARPET in Listen */ -function createNodesList(nodes: any[]): [string, string, number, string, any][] { - // NODE (KEY, LEVEL, LABEL, PARENTEDGE, INFOGAIN) - return nodes.map((node: any) => [node['key'], node['attributes']['level'], node['attributes']['name'], node['attributes']['parentedge'], node['attributes']['infogain']]); -} - -function createEdgesList(edges: any[]): [string, any, string, string, string, string, string, float][] { - // EDGE (KEY, LEVEL, LABEL, PARENTKEY, PARENTNODELABEL, CHILDKEY, CHILDNODELABEL, ENTROPY) - return edges.map((edge: any) => [edge['key'], edge['attributes']['level'], edge['attributes']['name'], edge['source'], edge['attributes']['parentnode'], edge['target'], edge['attributes']['childnode'], edge['attributes']['entropy']]); -} - -/* Wrapper-Funktion */ -export async function DecisionTreeValidator(graph: any) { - const nodes = graph['graph']['nodes']; - const edges = graph['graph']['edges']; - - const actual_nodes = createNodesList(nodes); - const actual_edges = createEdgesList(edges); - - console.log("--- Erhaltener DecisionTree ---"); - console.log("Nodes: \n", actual_nodes); - console.log("Edges: \n", actual_edges); - - try { - const pythonScriptPath = 'src/Tasks/DecisionTrees/DecisionTreeTask.py'; - const jsonStringNodes = JSON.stringify(actual_nodes); - const jsonStringEdges = JSON.stringify(actual_edges); - - const modifiedJsonStringNodes = jsonStringNodes.replace(/"([^"]+)"/g, (_, p1) => `'${p1}'`); - const modifiedJsonStringEdges = jsonStringEdges.replace(/"([^"]+)"(,|\])/g, (_, p1, p2) => `'${p1}'${p2}`); - - const argumentsToPythonScript = [modifiedJsonStringNodes, modifiedJsonStringEdges]; - - const result = await runPythonScript(pythonScriptPath, argumentsToPythonScript); - const parsedResult = JSON.parse(result); - - return {'solution_nodes_list': parsedResult['nodes'], 'solution_edges_list': parsedResult['edges'], 'status': parsedResult['finished'], 'entropies': parsedResult['entropies']} - - } catch(error) { - console.error('Error running Python script: ', error); - } -} diff --git a/src/Tasks/DecisionTrees/__pycache__/DecisionTreeTaskFunction.cpython-311.pyc b/src/Tasks/DecisionTrees/__pycache__/DecisionTreeTaskFunction.cpython-311.pyc deleted file mode 100644 index 9502d27..0000000 Binary files a/src/Tasks/DecisionTrees/__pycache__/DecisionTreeTaskFunction.cpython-311.pyc and /dev/null differ diff --git a/src/Tasks/DecisionTrees/__pycache__/DecisionTreeTaskFunction.cpython-312.pyc b/src/Tasks/DecisionTrees/__pycache__/DecisionTreeTaskFunction.cpython-312.pyc deleted file mode 100644 index be77c73..0000000 Binary files a/src/Tasks/DecisionTrees/__pycache__/DecisionTreeTaskFunction.cpython-312.pyc and /dev/null differ diff --git a/src/Tasks/DecisionTrees/datasets/generated_dataset.csv b/src/Tasks/DecisionTrees/datasets/generated_dataset.csv deleted file mode 100644 index 983a7cf..0000000 --- a/src/Tasks/DecisionTrees/datasets/generated_dataset.csv +++ /dev/null @@ -1,71 +0,0 @@ -Aussicht,Temperatur,Luftfeuchtigkeit,Wind,Spiel -bewölkt,kalt,normal,ja,ja -regnerisch,mild,hoch,nein,ja -regnerisch,kalt,normal,nein,ja -regnerisch,mild,normal,nein,ja -bewölkt,mild,hoch,ja,ja -sonnig,mild,normal,ja,ja -bewölkt,kalt,normal,ja,ja -sonnig,kalt,normal,nein,ja -sonnig,mild,normal,ja,ja -regnerisch,mild,hoch,nein,ja -regnerisch,kalt,normal,ja,nein -bewölkt,heiß,normal,nein,ja -bewölkt,heiß,normal,nein,ja -bewölkt,heiß,normal,nein,ja -sonnig,mild,normal,ja,ja -regnerisch,mild,normal,nein,ja -sonnig,heiß,hoch,ja,nein -sonnig,mild,normal,ja,ja -bewölkt,heißt,hoch,nein,ja -regnerisch,kalt,normal,nein,ja -bewölkt,mild,hoch,ja,ja -sonnig,mild,normal,ja,ja -regnerisch,mild,hoch,ja,nein -sonnig,mild,normal,ja,ja -regnerisch,mild,hoch,ja,nein -sonnig,kalt,normal,nein,ja -bewölkt,kalt,normal,ja,ja -sonnig,heiß,hoch,nein,nein -bewölkt,kalt,normal,ja,ja -sonnig,kalt,normal,nein,ja -regnerisch,mild,hoch,ja,nein -regnerisch,kalt,normal,nein,ja -regnerisch,kalt,normal,ja,nein -regnerisch,mild,hoch,nein,ja -regnerisch,mild,hoch,ja,nein -sonnig,heiß,hoch,nein,nein -regnerisch,kalt,normal,nein,ja -sonnig,mild,hoch,nein,nein -sonnig,kalt,normal,nein,ja -regnerisch,kalt,normal,ja,nein -sonnig,mild,hoch,nein,nein -bewölkt,heißt,hoch,nein,ja -regnerisch,kalt,normal,ja,nein -regnerisch,kalt,normal,ja,nein -regnerisch,mild,hoch,ja,nein -sonnig,kalt,normal,nein,ja -bewölkt,heißt,hoch,nein,ja -regnerisch,kalt,normal,nein,ja -bewölkt,heißt,hoch,nein,ja -bewölkt,heiß,normal,nein,ja -sonnig,mild,hoch,nein,nein -bewölkt,heiß,normal,nein,ja -sonnig,heiß,hoch,nein,nein -bewölkt,kalt,normal,ja,ja -sonnig,kalt,normal,nein,ja -bewölkt,heißt,hoch,nein,ja -regnerisch,kalt,normal,nein,ja -bewölkt,heißt,hoch,nein,ja -sonnig,kalt,normal,nein,ja -regnerisch,mild,normal,nein,ja -sonnig,kalt,normal,nein,ja -sonnig,heiß,hoch,nein,nein -regnerisch,kalt,normal,ja,nein -regnerisch,mild,normal,nein,ja -bewölkt,heiß,normal,nein,ja -sonnig,mild,hoch,nein,nein -sonnig,kalt,normal,nein,ja -regnerisch,kalt,normal,ja,nein -bewölkt,mild,hoch,ja,ja -regnerisch,kalt,normal,nein,ja diff --git a/src/Tasks/DecisionTrees/datasets/generated_dataset.json b/src/Tasks/DecisionTrees/datasets/generated_dataset.json deleted file mode 100644 index e42ec68..0000000 --- a/src/Tasks/DecisionTrees/datasets/generated_dataset.json +++ /dev/null @@ -1,70 +0,0 @@ -{"Aussicht":"bew\u00f6lkt","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00df","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00df","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00df","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"hei\u00df","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00dft","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"hei\u00df","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"hei\u00df","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00dft","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00dft","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00dft","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00df","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00df","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"hei\u00df","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00dft","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00dft","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"hei\u00df","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"regnerisch","Temperatur":"mild","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"hei\u00df","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"sonnig","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"nein","Spiel":"nein"} -{"Aussicht":"sonnig","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"ja","Spiel":"nein"} -{"Aussicht":"bew\u00f6lkt","Temperatur":"mild","Luftfeuchtigkeit":"hoch","Wind":"ja","Spiel":"ja"} -{"Aussicht":"regnerisch","Temperatur":"kalt","Luftfeuchtigkeit":"normal","Wind":"nein","Spiel":"ja"} diff --git a/src/Tasks/DecisionTrees/datasets/spiel.csv b/src/Tasks/DecisionTrees/datasets/spiel.csv deleted file mode 100644 index b657791..0000000 --- a/src/Tasks/DecisionTrees/datasets/spiel.csv +++ /dev/null @@ -1,15 +0,0 @@ -Aussicht,Temperatur,Luftfeuchtigkeit,Wind,Spiel -sonnig,heiß,hoch,nein,nein -sonnig,heiß,hoch,ja,nein -bewölkt,heißt,hoch,nein,ja -regnerisch,mild,hoch,nein,ja -regnerisch,kalt,normal,nein,ja -regnerisch,kalt,normal,ja,nein -bewölkt,kalt,normal,ja,ja -sonnig,mild,hoch,nein,nein -sonnig,kalt,normal,nein,ja -regnerisch,mild,normal,nein,ja -sonnig,mild,normal,ja,ja -bewölkt,mild,hoch,ja,ja -bewölkt,heiß,normal,nein,ja -regnerisch,mild,hoch,ja,nein \ No newline at end of file diff --git a/src/Tasks/Experimentieren/generateExperimentierenData.py b/src/Tasks/Experimentieren/generateExperimentierenData.py new file mode 100644 index 0000000..a9a998f --- /dev/null +++ b/src/Tasks/Experimentieren/generateExperimentierenData.py @@ -0,0 +1,7 @@ + + + + + +def generateExperimentierenDataMain(): + return True \ No newline at end of file diff --git a/src/Tasks/Experimentieren/internData/frontendUserData.json b/src/Tasks/Experimentieren/internData/frontendUserData.json new file mode 100644 index 0000000..e69de29 diff --git a/src/Tasks/Experimentieren/wrapperFunctionCallPython.ts b/src/Tasks/Experimentieren/wrapperFunctionCallPython.ts new file mode 100644 index 0000000..cd25bf5 --- /dev/null +++ b/src/Tasks/Experimentieren/wrapperFunctionCallPython.ts @@ -0,0 +1,21 @@ +import fs from "fs"; +const path = require("path"); + +function saveFrontendUserData(userDataObject: Record) : boolean{ + const filePath = path.join(__dirname, "/internData/frontendUserData.json"); + + let jsonString = JSON.stringify(userDataObject, null, 4); + + try { + fs.writeFileSync(filePath, jsonString, "utf-8"); + return true; + } catch (error) { + return false; + } +} + + +export function generateExperimentierenDataMain(userDataObject: any) { + const check = saveFrontendUserData(userDataObject); + return check; +} \ No newline at end of file diff --git a/src/Tasks/Ferment/Input/__init__.py b/src/Tasks/Ferment/Input/__init__.py new file mode 100644 index 0000000..0f2dcb8 --- /dev/null +++ b/src/Tasks/Ferment/Input/__init__.py @@ -0,0 +1,4 @@ + + +#Das Hinzufügen der '__init__.py'-Datei im Input-Verzeichnis ist wichtig, +#damit Python das Input-Verzeichnis als Paket behandelt und die darin enthaltenen Module importieren kann. \ No newline at end of file diff --git a/src/Tasks/Ferment/Input/inputBaseClass.py b/src/Tasks/Ferment/Input/inputBaseClass.py new file mode 100644 index 0000000..d49cb1c --- /dev/null +++ b/src/Tasks/Ferment/Input/inputBaseClass.py @@ -0,0 +1,14 @@ +from abc import ABC, abstractmethod + + + +class InputBase(ABC): + + @abstractmethod + def find_pfad(): + pass + + @abstractmethod + def ladeJson(self): + pass + diff --git a/src/Tasks/Ferment/Input/json_Input.py b/src/Tasks/Ferment/Input/json_Input.py new file mode 100644 index 0000000..4ed3c4a --- /dev/null +++ b/src/Tasks/Ferment/Input/json_Input.py @@ -0,0 +1,106 @@ +import os +import json +from Input.inputBaseClass import InputBase + +class JsonInput(InputBase): + + + + def __init__(self,datei_name): #Konstruktor + self.__jsonName = datei_name # private variable + + # Deklaration des Attributs außerhalb des Konstruktors + __data = None + + def set_data(self,data): + self.__data=data + + def get_data(self): + return self.__data + + def get_jsonName(self): + return self.__jsonName + + def set_jsonName(self, value): + self.__jsonName = value + + + + + + def find_pfad(self): + root_directory = os.path.abspath('src/Tasks/Ferment') # Den absoluten Pfad des Projektnamens ermitteln + + for root, dirs, files in os.walk(root_directory): + for file in files: + if file.lower() == self.__jsonName.lower(): # Wenn die Datei gefunden wurde + return os.path.join(root, file) # Dateipfad zurückgeben, sobald die Datei gefunden wurde + return None # Wenn die Datei nicht gefunden wurde, wird None zurückgegeben + + + def ladeJson(self): + # Vollständiger Pfad zur JSON-Datei + json_path = self.find_pfad() + if json_path is None : + print('Pfad zur Datei nicht gefunden.!!!') + return None + + print('Datei gefunden:', json_path) # Fügen Sie Debugging-Ausgabe hinzu + try: + # Öffne die JSON-Datei und lade sie + with open(json_path, 'r') as file: + data = json.load(file) + self.set_data(data) + + except FileNotFoundError: + print(f"Die Datei '{self.__jsonName}' konnte nicht geladen werden.") + return None + + + def get_Value(self): + ''' + parameter = None + parameter = list(self.__data.keys())[0] + + if parameter == None : + print('Error : Fehler beim Einlesen des Parameters.') + + if isinstance(self.__data[parameter][element], dict): + element_dict = self.__data[parameter][element] + value = list(element_dict.items())[1] + value = value[1] + + else : + value = self.__data[parameter][element] + + + + if isinstance(value, int): + return value + elif isinstance(value,str): + return value + elif isinstance(value,float): + return value + else : + array = [float(val) for val in value] + return array''' + # JSON-String zu Python-Dictionary konvertieren + data = self.__data + + # Array initialisieren + result = [] + + for key, value in data.items(): + if isinstance(value, dict): + result.extend(list(value.values())) + else: + # Wenn der Wert kein Dictionary ist oder kein Array enthält + result.append(value) + + for i, r in enumerate(result): + if isinstance(r, int): + result[i]=int(r) + elif isinstance(r,list): + result[i] = [float(val) for val in r] + + return result diff --git a/src/Tasks/Ferment/berechnungen.py b/src/Tasks/Ferment/berechnungen.py new file mode 100644 index 0000000..26e4013 --- /dev/null +++ b/src/Tasks/Ferment/berechnungen.py @@ -0,0 +1,183 @@ +import pandas as pd +import numpy as np +import os +from scipy.integrate import solve_ivp +from openpyxl import Workbook +from differentialgleichung import ODE_Bioreactor_Monod + + + + +from array import array + + + +def berechnung_der_Tabelle1(parameter_werte,modull,data,zuluft,feed,phasen_anzahl): + #float array deklarieren + const_array= [array('d', [0] * 20) for _ in range(phasen_anzahl)] + + for i in range(phasen_anzahl): + const_array[i][0]=parameter_werte[i][0] + const_array[i][1]=parameter_werte[i][1] + const_array[i][3]=float(zuluft[i]) #0.2 #Modell H7-k7 + const_array[i][4]=0.2095 + const_array[i][5]=0.0004147 + const_array[i][14]=parameter_werte[i][2] + const_array[i][19]=float(feed[i]) #0.1 #Modell H10-K10 + if i == 0: #Phase 1 + + const_array[0][2]=data.get(modull, {}).get("RQ") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];13;FALSCH) + const_array[0][6]=data.get(modull, {}).get("ks_ox") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];12;FALSCH) + const_array[0][7]=data.get(modull, {}).get("Yxox") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];14;FALSCH) + const_array[0][8]=data.get(modull, {}).get("Produktbildung") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];15;FALSCH) + const_array[0][9]=data.get(modull, {}).get("ap") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];16;FALSCH) + const_array[0][10]=data.get(modull, {}).get("bp") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];17;FALSCH) + const_array[0][11]=data.get(modull, {}).get("kp_max") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];18;FALSCH) + const_array[0][12]=data.get(modull, {}).get("km_s1p") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];19;FALSCH) + const_array[0][13]=data.get(modull, {}).get("Ypx_mu") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];20;FALSCH) + const_array[0][15]=data.get(modull, {}).get("Ks_s1x") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];8;FALSCH) + const_array[0][16]=data.get(modull, {}).get("Yxs1") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];10;FALSCH) + const_array[0][17]=data.get(modull, {}).get("Ks_s2x") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];9;FALSCH) + const_array[0][18]=data.get(modull, {}).get("Yxs2") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];11;FALSCH) + else : #alle anderen Phasen + const_array[i][2]=const_array[i-1][2] + const_array[i][6]=const_array[i-1][6] + const_array[i][7]=const_array[i-1][7] + const_array[i][8]=const_array[i-1][8] + const_array[i][9]=const_array[i-1][9] + const_array[i][10]=const_array[i-1][10] + const_array[i][11]=const_array[i-1][11] + const_array[i][12]=const_array[i-1][12] + const_array[i][13]=const_array[i-1][13] + const_array[i][15]=const_array[i-1][15] + const_array[i][16]=const_array[i-1][16] + const_array[i][17]=const_array[i-1][17] + const_array[i][18]=const_array[i-1][18] + + + # Dezimalstellen nach komma grenzen, maximal 9 Stellen nach komma + for i in range(len(const_array)): + for j in range(len(const_array[i])): + try: + const_array[i][j] = round(float(const_array[i][j]), 9) + except ValueError: + pass + + return const_array + + +def berechnung_der_Tabelle2(dauer,phasen_anzahl): + #float array deklarieren + t_ranges_array= [array('d', [0] * 2) for _ in range(phasen_anzahl)] + + for i in range(phasen_anzahl): + if i == 0: #Phase 1 + t_ranges_array[i][0]=0 + t_ranges_array[i][1]=dauer[i] + else: + t_ranges_array[i][0]=t_ranges_array[i-1][1] + t_ranges_array[i][1]=t_ranges_array[i-1][1]+dauer[i] + return t_ranges_array + + +def berechnung_der_Tabelle3(const_array,startbiomasse,bolus_c,bolus_n,do,phasen_anzahl): + param_array= [array('d', [0] * 7) for _ in range(phasen_anzahl)] + + for i in range(phasen_anzahl): + if i == 0: #Phase 1 + param_array[i][0]=startbiomasse + param_array[i][1]=float(bolus_c[i]) + param_array[i][2]=float(bolus_n[i]) + param_array[i][3]=0 + param_array[i][4]=(const_array[i][0]*do)/100 + param_array[i][5]=const_array[i][4] + param_array[i][6]=const_array[i][5] + else: + param_array[i][1]=float(bolus_c[i]) + param_array[i][2]=float(bolus_n[i]) + + + return param_array + + +def berechnung(t_ranges_array,param_array,const_array_array,phasen_anzahl): + data_rate =10; #Anzahl der Datenpunkte pro Stunde + + t_ranges_array = np.array(t_ranges_array) + param_array = np.array(param_array) + const_array_array = np.array(const_array_array) + + + t_combined = np.array([]) + y_combined = np.array([]) + cum_feeding = np.array([]) + + for i in range(phasen_anzahl): + print(i) + if(i==0): + y0=param_array[i,:] #erste Zeile aus Dim. Array als vektor + else: + + + mx_0 = ry[-1, 0] # Biotrockenmasse in g/L + cs1_0 = ry[-1, 1] + param_array[i,1] # Konz. Substrat 1 in g/L + cs2_0 = ry[-1, 2] + param_array[i,2] # Konz. Substrat 1 in g/L + cp_0 = ry[-1, 3] # Konz. Produkt in g/L + c_ox_0 = ry[-1, 4] # Konz. Gelöstsauerstoff in g/L zum Start der Fermentation + O2_Out = ry[-1, 5] # Konz. O2 in Abluft + CO2_Out = ry[-1, 6] # Konz. CO2 in Abluft + + y0 =[mx_0,cs1_0,cs2_0, cp_0,c_ox_0, O2_Out, CO2_Out] #Startparameter in Vektor + + + t_start=t_ranges_array[i,0] + t_end=t_ranges_array[i,1] + + if t_start != t_end: + num1=int(data_rate * (t_end - t_start)) + t_span = np.linspace(t_start, t_end, num=num1) # Zeitvektor bauen + #Loesen der DGL im Zeitabschnitt + consts = const_array_array[i,:] + #print('computing Phase', i) + + + sol = solve_ivp(ODE_Bioreactor_Monod, [t_start, t_end], y0, args=(consts,), t_eval=t_span, atol=1e-9, rtol=1e-9) + ry = sol.y + ry = ry.T + rt = sol.t + + #Ergebnisse zum Gesamtvektor hinzufügen + if (i == 0): + y_combined = ry + #y_combined=y_combined.T + t_combined = rt + cum_feeding = t_span * consts[19] + + else: + + #if len(t_span) == len(t_combined): # Überprüfung der Länge der Zeitvektoren + y_combined = np.vstack((y_combined, ry)) + #y_combined=y_combined.T + #print(y_combined) + t_combined = np.hstack((t_combined,rt)) + cum_feeding = np.hstack((cum_feeding, t_span * consts[19])) + + + #else: + #print("Warning: Time vectors have different lengths. Skipping this phase.") + + + + + #Weiter variablen-Zuweisungen fürs PLotten + + c_ox_sat=const_array_array[0,0] + cum_feeding=cum_feeding.T + + + + return (c_ox_sat,y_combined,t_combined,cum_feeding) + + + + diff --git a/src/Tasks/Ferment/data.json b/src/Tasks/Ferment/data.json new file mode 100644 index 0000000..0b1a7a6 --- /dev/null +++ b/src/Tasks/Ferment/data.json @@ -0,0 +1,1662 @@ +{ + "data_1": { + "labels": [ + 0, + 0.10101010101010101, + 0.20202020202020202, + 0.30303030303030304, + 0.40404040404040403, + 0.5050505050505051, + 0.6060606060606061, + 0.7070707070707071, + 0.8080808080808081, + 0.9090909090909091, + 1.0101010101010102, + 1.1111111111111112, + 1.2121212121212122, + 1.3131313131313131, + 1.4141414141414141, + 1.5151515151515151, + 1.6161616161616161, + 1.7171717171717171, + 1.8181818181818181, + 1.9191919191919191, + 2.0202020202020203, + 2.121212121212121, + 2.2222222222222223, + 2.323232323232323, + 2.4242424242424243, + 2.525252525252525, + 2.6262626262626263, + 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Kostanten aus consts + + c_ox_sat = consts[0] #Sauerstofflöslichkeit in der Fermentation + kla = consts[1] #kla Wert in 1/h Info eine OTR von 100 mmol/(L*h) entspricht bei DO=0% ~ einem kla von 400 1/h + RQ = consts[2] #Respiratory Coefficient + Begasungsrate = consts[3] #Begasungsrate in NL(Luft)/(L(Kulturbrühe)*min) quasi vvm + c_O2_Luft = consts[4] #Sauerstoffgehalt Luft in mol/L + c_CO2_Luft = consts[5] #CO2 Gehalt der Luft in mol/L + ks_ox = consts[6] #Halbsaettigungskonstante Sauerstoff in g/L + Yxox = consts[7] #Ausbeutekoeffizient g Biomasse je g Sauerstoff nach HArvard Bionumbers für E.coli + P=consts[8] #Produktbildung an =1 oder aus =0 + ap = consts[9] #Luedking Piret Parameter für wachsumassoziierte Produktbildung + bp = consts[10] #Luedking Piret Parameter für nicht wachsumassoziierte Produktbildung + kp_max = consts[11] #maximale spezifische Produktbildungsrate in g(P)/(g(x)*L*h) nicht wachstumsassoziierte Produktbildung + km_s1p = consts[12] #Km Wert in Analogie zur Michaelis Menten-Gleichung für die Bildung von P aus S - nicht wachstumsassoziiert + Ypx_mu = consts[13] #Ausbeutekoeffizient Produktbildung direkt wachstumsassoziiert aus Substrat + mumax =consts[14] #maximale Wachstumsrate in 1/h + ks_s1x=consts[15] #Halbsaettigungskonstante Substrat in g/L für Berechnung von Wachstumsrat µ + Yxs1=consts[16] #Ausbeutekoeff fuer Zellmasse in g/g + ks_s2x=consts[17] #Halbsaettigungskonstante Ks für Substrat S2 in g/L für Berechnung von Wachstumsrat µ + Yxs2=consts[18] #Ausbeutekoeff fuer Zellmasse auf Substrat 2 1 in g/g + feed_s1=consts[19] #Feedrate Substrat 1 in g/L*h + + + #Zuweisungen der Eingangswerte / = Konzentrationen die das Model berechnet + + cx = p[0] #Konzentration Biotrockenmasse in g/L + cs1 = p[1] #Substratkonzentration 1 in g/L + cs2 = p[2] #Substratkonzentration 2 in g/L + cp = p[3] #Produktkonzentration in g/L + c_ox = p[4] #Gelöstsaurestoffkonznetration in g/L + c_O2_Out = p[5] #Abluftkonzentration O2 + c_CO2_Out = p[6] #Abluftkonzentration CO2 + + #Nebenrechnungen + + Vm_norm=22.41950113 #molares Volumen bei Normbedingungen + Luft_In=Begasungsrate*60 #ZuLuftstrom Luft in NL/(L_Brühe*h) + Luft_Out=Luft_In #Molenstrom Abluft in mol/(L*h) ; evtl. mal erweitern wenn Luft_Out <> Luft_In + #Sauerstoff als limitierendes Substrat + OTR=kla*(c_ox_sat - c_ox) #in g/(L*h) + + + #Modellgleichungen + #spez. Wachstumsrate + mu_s = mumax*cs1/(ks_s1x+cs1)*cs2/(ks_s2x+cs2)*c_ox/(ks_ox+c_ox); # Monod Kinetik fuer spezifische Wachstumsrate + #k_ox = 0.01*cs1/(ks_ox+cs1); %nicht wachstumsabhängige Sauerstoffverbrauchsrate, beschrieben als MichaMenten Kinetik %%%%CHANGE!! + #Produktbildungsrate + kp = kp_max*cs1/(km_s1p+cs1); #Michaelis Menten Kinetik für nicht wachstumssassoziierte Produktbildung + + #OUR=cx*(mu_s/Yxox-k_ox); + OUR=cx*(mu_s/Yxox); + #Begasung / Sauerstoff + CER=OUR/32*RQ; #Umrechung von g/L in mol/L + + + + value_0 = float(mu_s * cx) + + #Substratverbrauchsgeschwindigkeit Substrat 1 durch Biomassebildung und Produktbildung + value_1= float(cx*(-mu_s/Yxs1)-P*((mu_s*cx*Ypx_mu)*ap+(cx*kp)*bp)+feed_s1) + + #Substratverbrauchsgeschwindigkeit Substrat 2 durch Biomassebildung NICHT Genutzt zur Produktbildung Produktbildung + value_2= float(cx*(-(mu_s/Yxs2))) + + #Produktbildung nach Luedeking Piret; + value_3= float(P*((mu_s*cx*Ypx_mu)*ap+(cx*kp)*bp)) + + #Berechnung Gelöstsauerstoffkonzentration (OTR-OUR) mit Nebenbedingung + #darf nicht NUll sein + value_4= float(kla*(c_ox_sat-c_ox)-cx*(mu_s/Yxox)) + + #Berechnung O2-gehalt in Vol% Abluf IN Analogie zu einem CSTR F(C_in-Cout)-Reaktion + value_5= float(Luft_In*c_O2_Luft-Luft_Out*c_O2_Out-OUR/32*Vm_norm) + + #Berechnung CO2-gehalt in Vol% Abluf IN Analogie zu einem CSTR F(C_in-Cout)-Reaktion + value_6= float(Luft_In*c_CO2_Luft-Luft_Out*c_CO2_Out+CER*Vm_norm) + + retval = np.array([value_0,value_1,value_2,value_3,value_4,value_5,value_6]) + + + return retval + + + diff --git a/src/Tasks/Ferment/excel_export.py b/src/Tasks/Ferment/excel_export.py new file mode 100644 index 0000000..962c82b --- /dev/null +++ b/src/Tasks/Ferment/excel_export.py @@ -0,0 +1,52 @@ +from openpyxl import Workbook +import os +from pathlib import Path +from io import BytesIO + +def export_to_excel(filename, t_combined, y_combined, cum_feeding): + y_combined = y_combined.T + + # Attempt to get the user's Downloads folder + downloads_path = Path.home() / "Downloads" + + # Check if Downloads path exists; if not, create it (optional) + if not downloads_path.exists(): + print("Downloads folder not found.") + return + + # Combine the Downloads folder path with the provided filename + file_path = downloads_path / filename + + # If the file already exists, remove it + if os.path.exists(file_path): + os.remove(file_path) + + wb = Workbook() + ws = wb.active + + caption = ['t', 'Biotrockenmasse (cx)', 'Konz. Substrat 1 (cs1)', 'Konz. Substrat 2 (cs2)', + 'Konz. Produkt (cp)', 'c_O2 (Lösung)', 'c_O2 (Abluft)', 'c_CO2 (Abluft)', + 'kumulatives Feeding Substrat 1'] + + # Write headers + for i, header in enumerate(caption): + ws.cell(row=1, column=i + 1, value=header) + + # Write data + for i in range(len(t_combined)): + row_data = [t_combined[i]] + [y_combined[j, i] for j in range(y_combined.shape[0])] + [cum_feeding[i]] + for j, value in enumerate(row_data): + ws.cell(row=i + 2, column=j + 1, value=value) + + # Save the file in the correct Downloads folder + wb.save(file_path) + + #print(f"File saved to: {file_path}") + + + file_obj = BytesIO() + wb.save(file_obj) + file_obj.seek(0) + + return file_obj + diff --git a/src/Tasks/Ferment/feedback_schaetzung.py b/src/Tasks/Ferment/feedback_schaetzung.py new file mode 100644 index 0000000..9e638a6 --- /dev/null +++ b/src/Tasks/Ferment/feedback_schaetzung.py @@ -0,0 +1,140 @@ +import json + +import json + +def feedback_schatzung(): + # Lade Loesung aus der JSON-Datei + with open('src/Tasks/FermentExercises/parameter_loesung.json', 'r') as loesung_file: + loesung = json.load(loesung_file) + + # Lade Schaetzung aus der JSON-Datei + with open('src/Tasks/Ferment/nutzer_eingaben.json', 'r') as schatzung_file: + schatzung = json.load(schatzung_file) + + # Initialisiere ein Dictionary fuer das Feedback + feedback = {"Feedback": {}} + + # Vergleiche die Schaetzungen mit den Loesungen fuer jede Phase + for param in loesung: # Iteration ueber Parameter (z. B. "Feed", "Zuluft") + if isinstance(loesung[param], dict): + if param not in feedback["Feedback"]: + feedback["Feedback"][param] = {} # Initialisierung des Parameters im Feedback + + for phase in loesung[param]: # Iteration ueber die Phasen (Phase_0, Phase_1, ...) + phase_feedback = { + "status": "", + "message": "" + } + + print(f"Vergleich fuer {param} - {phase}:") + + # Loesung und Schaetzung abrufen + phase_loesung = loesung[param][phase] + phase_schatzung = schatzung.get(param, {}).get(phase) # Verhindert KeyError + + if phase_schatzung is None: + phase_feedback["status"] = "Fehler" + phase_feedback["message"] = f"Keine Schaetzung fuer {param} - {phase} gefunden!" + print(phase_feedback["message"]) + feedback["Feedback"][param][f"{param}-{phase}"] = phase_feedback + continue + + # Besondere Behandlung fuer den Fall, dass die Loesung 0 ist + if phase_loesung == 0: + if phase_schatzung == 0: + phase_feedback["status"] = "Erfolgreich" + phase_feedback["message"] = f"Deine Schaetzung fuer {param} - {phase} ist korrekt (beide sind 0)." + else: + phase_feedback["status"] = "Falsch" + phase_feedback["message"] = f"Deine Schaetzung fuer {param} - {phase} ist nicht korrekt. Versuche es noch einmal." + print(phase_feedback["message"]) + feedback["Feedback"][param][f"{param}-{phase}"] = phase_feedback + continue + + # Berechne die relative Differenz, wenn die Loesung nicht null ist + differenz = abs(phase_schatzung - phase_loesung) + relative_differenz = (differenz / abs(phase_loesung)) * 100 + + # Bestimme, wie nah die Schaetzung an der Loesung ist + if relative_differenz <= 20: + phase_feedback["status"] = "Erfolgreich" + phase_feedback["message"] = f"Deine Schaetzung von {phase_schatzung} fuer {param} - {phase} ist nahe genug an der Loesung." + elif 20 < relative_differenz <= 50: + phase_feedback["status"] = "Fast" + phase_feedback["message"] = f"Du bist nah dran! Deine Schaetzung von {phase_schatzung} fuer {param} - {phase} ist entfernt von der Loesung." + elif 50 < relative_differenz <= 100: + phase_feedback["status"] = "Entfernt" + phase_feedback["message"] = f"Versuche es noch einmal! Deine Schaetzung von {phase_schatzung} fuer {param} - {phase} ist weit entfernt von der Loesung." + else: + if phase_schatzung < phase_loesung: + phase_feedback["status"] = "Zu niedrig" + phase_feedback["message"] = f"Du musst hoeher schaetzen! Deine Schaetzung von {phase_schatzung} fuer {param} - {phase} ist mehr als 100% entfernt." + else: + phase_feedback["status"] = "Zu hoch" + phase_feedback["message"] = f"Du musst niedriger schaetzen! Deine Schaetzung von {phase_schatzung} fuer {param} - {phase} ist mehr als 100% entfernt." + + print(phase_feedback["message"]) + feedback["Feedback"][param][f"{param}-{phase}"] = phase_feedback + + if isinstance(loesung[param], (int, float)): # Falls es eine Zahl oder Zahl-String ist + phase_loesung = float(loesung[param]) + phase_feedback = { + "status": "", + "message": "" + } + + # Loesung und Schaetzung abrufen + phase_loesung = float(loesung[param]) + phase_schatzung = float(schatzung[param]) # Verhindert KeyError + + if phase_schatzung is None: + phase_feedback["status"] = "Fehler" + phase_feedback["message"] = f"Keine Schaetzung fuer {param} gefunden!" + print(phase_feedback["message"]) + feedback["Feedback"][param] = phase_feedback + continue + + # Besondere Behandlung fuer den Fall, dass die Loesung 0 ist + if phase_loesung == 0: + if phase_schatzung == 0: + phase_feedback["status"] = "Erfolgreich" + phase_feedback["message"] = f"Deine Schaetzung fuer {param} ist korrekt (beide sind 0)." + else: + phase_feedback["status"] = "Falsch" + phase_feedback["message"] = f"Deine Schaetzung fuer {param} ist nicht korrekt. Versuche es noch einmal." + print(phase_feedback["message"]) + feedback["Feedback"][param] = phase_feedback + continue + + # Berechne die relative Differenz, wenn die Loesung nicht null ist + differenz = abs(phase_schatzung - phase_loesung) + relative_differenz = (differenz / abs(phase_loesung)) * 100 + + # Bestimme, wie nah die Schaetzung an der Loesung ist + if relative_differenz <= 20: + phase_feedback["status"] = "Erfolgreich" + phase_feedback["message"] = f"Deine Schaetzung von {phase_schatzung} fuer {param} ist nahe genug an der Loesung." + elif 20 < relative_differenz <= 50: + phase_feedback["status"] = "Fast" + phase_feedback["message"] = f"Du bist nah dran! Deine Schaetzung von {phase_schatzung} fuer {param} ist entfernt von der Loesung." + elif 50 < relative_differenz <= 100: + phase_feedback["status"] = "Entfernt" + phase_feedback["message"] = f"Versuche es noch einmal! Deine Schaetzung von {phase_schatzung} fuer {param} ist weit entfernt von der Loesung." + else: + if phase_schatzung < phase_loesung: + phase_feedback["status"] = "Zu niedrig" + phase_feedback["message"] = f"Du musst hoeher schaetzen! Deine Schaetzung von {phase_schatzung} fuer {param} ist mehr als 100% entfernt." + else: + phase_feedback["status"] = "Zu hoch" + phase_feedback["message"] = f"Du musst niedriger schaetzen! Deine Schaetzung von {phase_schatzung} fuer {param} ist mehr als 100% entfernt." + + print(phase_feedback["message"]) + feedback["Feedback"][param] = phase_feedback + + # Speichere das Feedback in einer JSON-Datei + with open('src/Tasks/Ferment/feedback.json', 'w') as feedback_file: + json.dump(feedback, feedback_file, indent=4, ensure_ascii=False) + +# Funktion aufrufen +feedback_schatzung() + diff --git a/src/Tasks/Ferment/generateFermentationData.py b/src/Tasks/Ferment/generateFermentationData.py new file mode 100644 index 0000000..978898d --- /dev/null +++ b/src/Tasks/Ferment/generateFermentationData.py @@ -0,0 +1,162 @@ +import json +import numpy as np + +from excel_export import export_to_excel +from json_to_excel import json_to_excel +from nebenrechnungen import Berechnung_der_Sauerstoffloeslichkeit +from nebenrechnungen import Berechnung_des_kla_Wertes +from nebenrechnungen import parameter +from feedback_schaetzung import feedback_schatzung + +from berechnungen import berechnung_der_Tabelle1 +from berechnungen import berechnung_der_Tabelle2 +from berechnungen import berechnung_der_Tabelle3 +from berechnungen import berechnung + +from interne_daten.data_importieren import data_importieren_von_json +from Input.json_Input import JsonInput +import base64 +# Example data for the variables +#c_ox_sat = 0.21 # Example value for oxygen saturation concentration +#t_combined = np.linspace(0, 10, 100) # Example time data +#y_combined = np.random.rand(5, 100) # Example concentration data with 5 different concentrations over time +#cum_feeding = np.cumsum(np.random.rand(100)) # Example cumulative feeding data + +#c_ox_sat = None # Oxygen saturation concentration +#t_combined = None # Time data +#y_combined = None # Concentration data +#cum_feeding = None # Cumulative feeding data + + + #Modelle aus modelle.jsom laden +modelleOb = JsonInput('modelle.json') +modelleOb.ladeJson() +modelle = modelleOb.get_data() + + #Zum Test im Meeting +#m = modelleOb.get_Value() +#print(m) +#modelle = data_importieren_von_json('Modelle.json') + +#Input aus input.json laden +eingabeOb = JsonInput('FrontendEingaben.json') +eingabeOb.ladeJson() +eingabe = eingabeOb.get_Value() + + + + +Sauerstoffoeslichkeit_array=Berechnung_der_Sauerstoffloeslichkeit(eingabe[1],eingabe[10],eingabe[3]) + + +kla_werte=Berechnung_des_kla_Wertes(Sauerstoffoeslichkeit_array,modelle,eingabe[4],eingabe[0],eingabe[5],eingabe[1]) + + +parameter_werte=parameter(Sauerstoffoeslichkeit_array,kla_werte,modelle,eingabe[1],eingabe[0]) + + +const_array=berechnung_der_Tabelle1(parameter_werte,eingabe[0],modelle,eingabe[5],eingabe[7],eingabe[1]) + + +t_ranges_array= berechnung_der_Tabelle2(eingabe[2],eingabe[1]) + + +param_array=berechnung_der_Tabelle3(const_array,eingabe[11],eingabe[6],eingabe[8],eingabe[9],eingabe[1]) + + +#Berechnung_ausführen +ergebnis = berechnung(t_ranges_array, param_array, const_array,eingabe[1]) +c_ox_sat, y_combined, t_combined, cum_feeding = ergebnis + + +#Export Resultat in Excel-Datei +excel_bytes = export_to_excel("model_result.xlsx", t_combined, y_combined, cum_feeding) +# plot_visualisieren(c_ox_sat, y_combined, t_combined, cum_feeding) +y_combined=y_combined.T + + + +def generateFermentationDataMain(): + + # Base64-kodieren + encoded_excel = base64.b64encode(excel_bytes.read()).decode('utf-8') + #print(encoded_excel) + data = { + "data_1":{ + "labels": t_combined.tolist(), + "datasets": [ + { + "label": "cₓ", + "data": y_combined[0, :].tolist(), + "borderColor": "#66c2a5", + "backgroundColor": "#66c2a5", + "tension": 0.1 + }, + { + "label": "cₛ₁", + "data": y_combined[1, :].tolist(), + "borderColor": "#fc8d62", + "backgroundColor": "#fc8d62", + "tension": 0.1 + }, + { + "label": "cₚ", + "data": y_combined[3, :].tolist(), + "borderColor": "#e78ac3", + "backgroundColor": "#e78ac3", + "tension": 0.1 + }, + { + "label": "cₒ₂", + "data": (y_combined[4, :] / c_ox_sat * 100).tolist(), + "borderColor": "#a6d854", + "backgroundColor": "#a6d854", + "tension": 0.1 + }, + { + "label": "cum Feed", + "data": cum_feeding.tolist(), + "borderColor": "#FFD700", + "backgroundColor": "#FFD700", + "tension": 0.1 + } + ] + }, + "data_2": { + "labels": t_combined.tolist(), + "datasets": [ + { + "label": "cₛ₂", + "data": y_combined[2, :].tolist(), + "borderColor": "#8da0cb", + "backgroundColor": "#8da0cb", + "tension": 0.1 + }] + }, + "data_3":{ + "excelData": encoded_excel + } + } + return data + +with open('./src/Tasks/Ferment/data.json', 'w') as output: + json.dump(generateFermentationDataMain(), output) + + + +#export_FrontendEingabe +json_file_path = 'src\Tasks\Ferment\interne_daten\FrontendEingaben.json' # Path to the JSON file +json_to_excel(json_file_path, 'FrontendEingaben.xlsx') + +#export_Chart_Data +json_file_path = 'src\Tasks\Ferment\data.json' # Path to the JSON file +json_to_excel(json_file_path, 'ChartData.xlsx') + +y_combined=y_combined.T +#export_to_excel("model_result.xlsx", t_combined, y_combined, cum_feeding) +feedback_schatzung() + +# Beispiel für die Verwendung der Funktion +#fermentationData = generateFermentationDataMain() +#print(fermentationData) +#print('hier',eingabe) \ No newline at end of file diff --git a/src/Tasks/Ferment/interne_daten/FrontendEingaben.json b/src/Tasks/Ferment/interne_daten/FrontendEingaben.json new file mode 100644 index 0000000..61dc056 --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/FrontendEingaben.json @@ -0,0 +1,35 @@ +{ + "Modell": "Modell-1", + "PhasenAnzahl": 2, + "Dauer": [ + 10, + 10 + ], + "Druck": [ + 1, + 1 + ], + "Drehzahl": [ + 100, + 100 + ], + "Zuluft": [ + 1, + 1 + ], + "BolusC": [ + 1, + 1 + ], + "Feed": [ + 1, + 1 + ], + "BolusN": [ + 1, + 1 + ], + "do": 0, + "T": 0, + "BTM": 0 +} \ No newline at end of file diff --git a/src/Tasks/Ferment/interne_daten/Input.json b/src/Tasks/Ferment/interne_daten/Input.json new file mode 100644 index 0000000..c69d3c6 --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/Input.json @@ -0,0 +1,26 @@ +{ + "Modell" : "Modell-2", + + "PhasenAnzahl" : 4, + + "Dauer" : [12,12,0,0], + + "Druck": [1,1,0,0], + + "Drehzahl" : [1200,1200,0,0], + + "Zuluft": [0.2,0.4,0,0], + + "BolusC" : [20,10,0,0], + + "Feed" : [0.1,0.1,0,0], + + "BolusN" : [4,1,0,0], + + "temperatur": 30, + + "startbiomasse": 0.05, + + "do":100 + +} \ No newline at end of file diff --git a/src/Tasks/Ferment/interne_daten/Input_Leser.py b/src/Tasks/Ferment/interne_daten/Input_Leser.py new file mode 100644 index 0000000..24f6d3f --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/Input_Leser.py @@ -0,0 +1,60 @@ +import json +import os + +class ParameterLeser: + + @staticmethod + def lade_parameter(datei_name): + current_dir = os.path.dirname(os.path.realpath(__file__)) + # Vollständiger Pfad zur JSON-Datei + json_path = os.path.join(current_dir, datei_name) + with open(json_path) as f: + return json.load(f)['Parameter'] + + @staticmethod + def get_modell(parameter): + return parameter['Modell'] + + @staticmethod + def get_phasenanzahl(parameter): + return parameter['PhasenAnzahl'] + + @staticmethod + def get_dauer(parameter): + return parameter['Dauer']['DauerArray'] + + @staticmethod + def get_druck(parameter): + return parameter['Druck']['DruckArray'] + + @staticmethod + def get_drehzahl(parameter): + return parameter['Drehzahl']['DrehzahlArray'] + + @staticmethod + def get_zuluft(parameter): + return parameter['Zuluft']['ZuluftArray'] + + @staticmethod + def get_bolus_c(parameter): + return parameter['BolusC']['BolusCArray'] + + @staticmethod + def get_futter(parameter): + return parameter['Feed']['FeedArray'] + + @staticmethod + def get_bolus_n(parameter): + return parameter['BolusN']['BolusNArray'] + + @staticmethod + def get_temperatur(parameter): + return parameter['Temperatur']['temperatur'] + + @staticmethod + def get_start_biomasse(parameter): + return parameter['Startbiomasse']['startbiomasse'] + + @staticmethod + def get_do(parameter): + return parameter['DO']['do'] \ No newline at end of file diff --git a/src/Tasks/Ferment/interne_daten/Modelle.json b/src/Tasks/Ferment/interne_daten/Modelle.json new file mode 100644 index 0000000..b110769 --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/Modelle.json @@ -0,0 +1,70 @@ +{ + "Modell-1": + { + "Beschreibung": "nicht wachstumsassoziierte Ethanolbildung mit hohem Ks --> hoehere Biomassegehalte bei niedrigen Substrat 1 konz.", + "Mikroorganismus": "S.cerevisiae", + "Substrat 1": "Glucose", + "Substrat 2": "(NH4)2SO4", + "Produkt 1": "Ethanol", + "umax": 0.46209812, + "Ks_s1x": 0.1, + "Ks_s2x": 0.01, + "Yxs1": 0.54, + "Yxs2": 8, + "ks_ox": 9.6e-05, + "RQ": 1, + "Yxox": 4.81, + "Produktbildung": 1, "ap": 0, "bp": 1, "kp_max": 0.88, "km_s1p": 5, "Ypx_mu": 1.5, "Dichte Bruehe": 1000, "dyn. Viscosity": 2, + "Temp. In C": 30 + }, + "Modell-2": + { + "Beschreibung": "Keine", + "Mikroorganismus": "E.coli", + "Substrat 1": "C-Quelle", + "Substrat 2": "N-Quelle", + "Produkt 1": "Produkt", + "umax": 2.07944154, + "Ks_s1x": 5, + "Ks_s2x": 0.1, + "Yxs1": 0.54, + "Yxs2": 8, + "ks_ox": 0.001, + "RQ": 1, + "Yxox": 0.54, + "Produktbildung": 1, + "ap": 0, + "bp": 1, + "kp_max": 0.88, + "km_s1p": 5, + "Ypx_mu": 1.5, + "Dichte Bruehe": 1000, + "dyn. Viscosity": 2, + "Temp. In C": 30 + }, + "Modell-3": + { + "Beschreibung": "Keine", + "Mikroorganismus": "Testorganismus", + "Substrat 1": "C-Quelle", + "Substrat 2": "N-Quelle", + "Produkt 1": "keins", + "umax": 0.314, + "Ks_s1x": 0.42, + "Ks_s2x": 0.1, + "Yxs1": 0.54, + "Yxs2": 1, + "ks_ox": 0.01, + "RQ": 1, + "Yxox": 0.54, + "Produktbildung": 0, + "ap": 0.5, + "bp": 0.5, + "kp_max": 0.23, + "km_s1p": 0.8, + "Ypx_mu": 1.5, + "Dichte Bruehe": 1050, + "dyn. Viscosity": 2, + "Temp. In C": 30 + } +} \ No newline at end of file diff --git a/src/Tasks/Ferment/interne_daten/Modelle_Leser.py b/src/Tasks/Ferment/interne_daten/Modelle_Leser.py new file mode 100644 index 0000000..d5eae9b --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/Modelle_Leser.py @@ -0,0 +1,101 @@ +import json +import os + + +class ModellLeser: + + @staticmethod + def lade_modelle(datei_name): + current_dir = os.path.dirname(os.path.realpath(__file__)) + # Vollständiger Pfad zur JSON-Datei + json_path = os.path.join(current_dir, datei_name) + with open(json_path) as f: + return json.load(f) + + @staticmethod + def get_beschreibung(modelle, modellname): + return modelle[modellname]['Beschreibung'] + + @staticmethod + def get_mikroorganismus(modelle, modellname): + return modelle[modellname]['Mikroorganismus'] + + @staticmethod + def get_substrat_1(modelle, modellname): + return modelle[modellname]['Substrat 1'] + + @staticmethod + def get_substrat_2(modelle, modellname): + return modelle[modellname]['Substrat 2'] + + @staticmethod + def get_produkt_1(modelle, modellname): + return modelle[modellname]['Produkt 1'] + + @staticmethod + def get_umax(modelle, modellname): + return modelle[modellname]['umax'] + + @staticmethod + def get_ks_s1x(modelle, modellname): + return modelle[modellname]['Ks_s1x'] + + @staticmethod + def get_ks_s2x(modelle, modellname): + return modelle[modellname]['Ks_s2x'] + + @staticmethod + def get_yxs1(modelle, modellname): + return modelle[modellname]['Yxs1'] + + @staticmethod + def get_yxs2(modelle, modellname): + return modelle[modellname]['Yxs2'] + + @staticmethod + def get_ks_ox(modelle, modellname): + return modelle[modellname]['ks_ox'] + + @staticmethod + def get_rq(modelle, modellname): + return modelle[modellname]['RQ'] + + @staticmethod + def get_yxox(modelle, modellname): + return modelle[modellname]['Yxox'] + + @staticmethod + def get_produktbildung(modelle, modellname): + return modelle[modellname]['Produktbildung'] + + @staticmethod + def get_ap(modelle, modellname): + return modelle[modellname]['ap'] + + @staticmethod + def get_bp(modelle, modellname): + return modelle[modellname]['bp'] + + @staticmethod + def get_kp_max(modelle, modellname): + return modelle[modellname]['kp_max'] + + @staticmethod + def get_km_s1p(modelle, modellname): + return modelle[modellname]['km_s1p'] + + @staticmethod + def get_ypx_mu(modelle, modellname): + return modelle[modellname]['Ypx_mu'] + + @staticmethod + def get_dichte_bruehe(modelle, modellname): + return modelle[modellname]['Dichte Bruehe'] + + @staticmethod + def get_dyn_viscosity(modelle, modellname): + return modelle[modellname]['dyn. Viscosity'] + + @staticmethod + def get_temp_in_c(modelle, modellname): + return modelle[modellname]['Temp. In C'] diff --git a/src/Tasks/Ferment/interne_daten/data_importieren.py b/src/Tasks/Ferment/interne_daten/data_importieren.py new file mode 100644 index 0000000..c169111 --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/data_importieren.py @@ -0,0 +1,18 @@ + +import json +import os + +def data_importieren_von_json(datei_name): + # Verzeichnis des aktuellen Skripts + current_dir = os.path.dirname(os.path.realpath(__file__)) + # Vollständiger Pfad zur JSON-Datei + json_path = os.path.join(current_dir, datei_name) + + try: + # Öffne die JSON-Datei und lade sie + with open(json_path, 'r') as file: + data = json.load(file) + return data + except FileNotFoundError: + print(f"Die Datei '{datei_name}' wurde nicht gefunden.") + return None diff --git a/src/Tasks/Ferment/interne_daten/konstantenDictionary.py b/src/Tasks/Ferment/interne_daten/konstantenDictionary.py new file mode 100644 index 0000000..0b459d1 --- /dev/null +++ b/src/Tasks/Ferment/interne_daten/konstantenDictionary.py @@ -0,0 +1,43 @@ + + +#Dictionary Henry_Konstanten erstellen +Henry_Kosntanten = { + "Solut_KH1" :{ #L*atm*mol-1 + "O2" : 769.2, + "CO2" : 29.41, + "H2" : 1282.1 + }, + + "Solut_KH2" :{ #(L*bar)/mol + "O2" : 779.3919, + "CO2" : 29.7996825, + "H2" : 1299.087825 + }, + + "Solut_T" :{ #in k + "O2" : 298.15, + "CO2" : 198.15, + "H2" : 298.15 + } +} + +##Dictionary Temperaturkoeffizient erstellen + +Temperaturkoeffizient = { #Solut:C in K + "O2" : 1700, + "H2" : 500, + "CO2" : 2400, + "N2" : 1300, + "He" : 230, + "Ne" : 490, + "Ar" : 1300, + "CO" : 1300 +} + +#Dictionary für die Konstanten für P begast + +Konstanten_fuer_P_begast = { + "1" : 750, #"Anzahl der Impeller/Rührorgane" : Konstante A in Pg Formel + "2" : 490, + "3" : 375 +} \ No newline at end of file diff --git a/src/Tasks/Ferment/json_to_excel.py b/src/Tasks/Ferment/json_to_excel.py new file mode 100644 index 0000000..32b3b5c --- /dev/null +++ b/src/Tasks/Ferment/json_to_excel.py @@ -0,0 +1,23 @@ +import os +import pandas as pd + +def json_to_excel(json_file, excel_filename='output.xlsx'): + + + # Get the default download folder based on the operating system + if os.name == 'nt': # Windows + download_folder = os.path.join(os.getenv('USERPROFILE'), 'Downloads') + else: # Mac/Linux + download_folder = os.path.join(os.path.expanduser('~'), 'Downloads') + + # Load the JSON file into a DataFrame + df = pd.read_json(json_file) + + # Set the path for the Excel file + excel_file = os.path.join(download_folder, excel_filename) + + # Save the DataFrame as an Excel file + df.to_excel(excel_file, index=False, engine='openpyxl') + + # Return the full path to the Excel file + return excel_file diff --git a/src/Tasks/Ferment/nebenrechnungen.py b/src/Tasks/Ferment/nebenrechnungen.py new file mode 100644 index 0000000..e0267ae --- /dev/null +++ b/src/Tasks/Ferment/nebenrechnungen.py @@ -0,0 +1,157 @@ +import math +import numpy as np +from array import array + + + +from interne_daten.konstantenDictionary import * + + +Durchmesser_Ruehrer_durch_Durchmesser_Tank = 0.33 #Modell F19 +nutzbares_Volumen = 0.7 #Modell F21 +Hoehe_des_Tanks_durch_Durchmesser_Tank = 3 #Modell F20 +Anzahl_Rushton_Impeller = 3 #Modell F22 +Volumen_Kulturbruehe = 10 / 1000 #Modell F18 +Durchmesser_Ruehrer = 0.0602 #!Modell F28 +Nettovolumen = Volumen_Kulturbruehe / nutzbares_Volumen #Modell F27 + +Durchmesser_Tank = ((Nettovolumen*4) / (Hoehe_des_Tanks_durch_Durchmesser_Tank*math.pi))**(1/3) #Modell F29 + +Hoehe_des_Tanks = Hoehe_des_Tanks_durch_Durchmesser_Tank * Durchmesser_Tank #Modell F30 +Vcheck = (math.pi/4) * Durchmesser_Tank**2 * Hoehe_des_Tanks #Modell F32 + + + + + + + + +def parameter(Sauerstoffoeslichkeit_array,kla_werte,data,phasen_anzahl,modell): + parameter_array = [array('d', [0] * 3) for _ in range(phasen_anzahl)] + + for j in range(phasen_anzahl): + parameter_array[j][0]=Sauerstoffoeslichkeit_array[j][5] + + for j in range(phasen_anzahl): + parameter_array[j][1]=kla_werte[j][17] + + for j in range(phasen_anzahl): + parameter_array[j][2]=data.get(modell, {}).get("umax") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];7;FALSCH) + + return parameter_array + + + + +def Berechnung_der_Sauerstoffloeslichkeit(phasen_anzahl,temperatur,druck): + + + Sauerstoffoeslichkeit_array = [array('d', [0] * 6) for _ in range(phasen_anzahl)] + + + + ############################################### Phase1 + # Attribute für Phase 1 + T_in_K_1 = 273.15 + temperatur # Modell!B11 + p_in_bara_1 = 1.013 + druck[0] #Modell H8 = 1 + p_o2_in_bara_1 = 0.20946 * p_in_bara_1 + kH_o2_bei_T_1 = Henry_Kosntanten["Solut_KH2"]["O2"] * math.exp(-Temperaturkoeffizient["O2"]*((1/T_in_K_1) - (1/Henry_Kosntanten["Solut_T"]["O2"]))) + L_o2_in_mmol_l_1 = (p_o2_in_bara_1 / kH_o2_bei_T_1 )*1000 + L_o2_in_g_l_1 = (L_o2_in_mmol_l_1*32) /1000 + + for i in range(phasen_anzahl): + if i == 0: + Sauerstoffoeslichkeit_array[0] = [T_in_K_1,p_in_bara_1,p_o2_in_bara_1,kH_o2_bei_T_1,L_o2_in_mmol_l_1,L_o2_in_g_l_1] + else : + Sauerstoffoeslichkeit_array[i][0]=Sauerstoffoeslichkeit_array[i-1][0] + Sauerstoffoeslichkeit_array[i][1]= 1.013 + float(druck[i]) #Modell!I8 #Modell j8 #modell k8 + Sauerstoffoeslichkeit_array[i][2]= 0.20946 * Sauerstoffoeslichkeit_array[i][1] + Sauerstoffoeslichkeit_array[i][3]=Henry_Kosntanten["Solut_KH2"]["O2"] * math.exp(-Temperaturkoeffizient["O2"]*((1/Sauerstoffoeslichkeit_array[i][0]) - (1/Henry_Kosntanten["Solut_T"]["O2"]))) + Sauerstoffoeslichkeit_array[i][4]=(Sauerstoffoeslichkeit_array[i][2]/Sauerstoffoeslichkeit_array[i][3])*1000 + Sauerstoffoeslichkeit_array[i][5]=(Sauerstoffoeslichkeit_array[i][4]*32)/1000 + + # Dezimalstellen nach komma grenzen, maximal 9 Stellen nach komma + for i in range(len(Sauerstoffoeslichkeit_array)): + for j in range(len(Sauerstoffoeslichkeit_array[i])): + try: + Sauerstoffoeslichkeit_array[i][j] = round(float(Sauerstoffoeslichkeit_array[i][j]), 9) + except ValueError: + pass + + return Sauerstoffoeslichkeit_array + + +def Berechnung_des_kla_Wertes(Sauerstoffoeslichkeit_array,data,drehzahl,modell,zuluft,phasen_anzahl): + + kla_werte = [array('d', [0] * 19) for _ in range(phasen_anzahl)] + i=0 + + drehzahl_1 = float(drehzahl[i]) #Modell H9 + Drehzahl_pro_sek = 1200 / 60 + Dichte = data.get(modell, {}).get("Dichte Bruehe") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];21;FALSCH) + dyn_Viskosität1 = data.get(modell, {}).get("dyn. Viscosity") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];22;FALSCH) + dyn_Viskositaet2 = dyn_Viskosität1/ 1000 #(kg*m)/s + Ruehrerdurchmesser = Durchmesser_Ruehrer #0.0602 Modell F28 + Reynoldszahl_Re = (Dichte * Drehzahl_pro_sek * (Ruehrerdurchmesser**2)) / dyn_Viskositaet2 + Newton_Zahl_Ne = 7 + Leistungseintrag_nicht_begast_P_1 = (Newton_Zahl_Ne * Anzahl_Rushton_Impeller * Dichte * (Drehzahl_pro_sek**3) * (Ruehrerdurchmesser**5)) #Modell F22=3 + Leistungseintrag_nicht_begast_P_2 = Leistungseintrag_nicht_begast_P_1 / Volumen_Kulturbruehe #modell F18 = 0.01 + Leistungseintrag_nicht_begast_P_3 = Leistungseintrag_nicht_begast_P_2 / 1000 #kW/m3 + + #Feld 11 später + Air_Flow_1 = 0.2 #vvm + Air_Flow_2 = Air_Flow_1 * Volumen_Kulturbruehe / 60 #Modell F18=0.01 + cross_scetion = (math.pi / 4) * (Durchmesser_Tank**2) #modell F29 = 0.1823 + + #Feld 15 zuert berechnet dann 11 + superficial_velocity = Air_Flow_2 / cross_scetion #m/s + Leistungseintrag_Begast_Pg = Leistungseintrag_nicht_begast_P_1 / math.sqrt(1+float(Konstanten_fuer_P_begast["3"])*superficial_velocity/math.sqrt(9.81*Ruehrerdurchmesser)) #W =C48/WURZEL(1+SVERWEIS(Modell!$F$22;Daten!$I$43:$J$45;2;FALSCH)*C55/WURZEL(9,81*C44)) + #Leistungseintrag_nicht_begast_P_1 / math.sqrt(1+Konstanten_fuer_P_begast.get(Anzahl_Rushton_Impeller)*superficial_velocity/math.sqrt(9.81*Ruehrerdurchmesser)) + kla_pro_sek = 0.026 * ( ( Leistungseintrag_Begast_Pg / Volumen_Kulturbruehe) ** (0.4) ) * (superficial_velocity ** (0.5) ) #modell F18 = 0.01 + kla_pro_std = kla_pro_sek * 3600 #1/h + OTR_max = kla_pro_std * Sauerstoffoeslichkeit_array[i][4] #mmol/(l*h) + + + for i in range(phasen_anzahl): + if i == 0 : + kla_werte[0] = [float(drehzahl_1),float(Drehzahl_pro_sek),float(Dichte),float(dyn_Viskosität1),float(dyn_Viskositaet2),float(Ruehrerdurchmesser),float(Reynoldszahl_Re),float(Newton_Zahl_Ne),float(Leistungseintrag_nicht_begast_P_1),float(Leistungseintrag_nicht_begast_P_2),float(Leistungseintrag_nicht_begast_P_3),float(Leistungseintrag_Begast_Pg),float(Air_Flow_1),float(Air_Flow_2),float(cross_scetion),float(superficial_velocity),float(kla_pro_sek),float(kla_pro_std),float(OTR_max)] + else : #alle anderen phasen_anzahl + + kla_werte[i][0]=float(drehzahl[i]) #modell I9 + kla_werte[i][1]=float(kla_werte[i][0]/60) + kla_werte[i][2]=float(kla_werte[i-1][2]) + kla_werte[i][3]=float(kla_werte[i-1][3]) + kla_werte[i][4]=float(kla_werte[i-1][4]) + kla_werte[i][5]=float(kla_werte[i-1][5]) + kla_werte[i][6]=float(( kla_werte[i][2] * kla_werte[i][1] * kla_werte[i][5]**2 ) / kla_werte[i][4]) + kla_werte[i][7]=float(kla_werte[i-1][7]) + kla_werte[i][8]=float(( kla_werte[i][7] * Anzahl_Rushton_Impeller * kla_werte[i][2] * ( kla_werte[i][1] ** 3 ) * ( float(kla_werte[i][5]) ** 5)) ) #Modell!$F$22 = 3 + kla_werte[i][9]=float(kla_werte[i][8] / Volumen_Kulturbruehe ) #modell F18 = 0.01 + kla_werte[i][10]=float(kla_werte[i][9] / 1000) + kla_werte[i][12]=float(zuluft[i]) #W/m3 Modell l7 = 0.01 + kla_werte[i][13]=float(kla_werte[i][12] * Volumen_Kulturbruehe / 60) #ModellF18 = 0.01 + kla_werte[i][14]=float(math.pi / 4 * Durchmesser_Tank**2) #modell F29 = 0.1823 + + #Feld 11 braucht Ergebnis von 15. Deswegen 15 zuerst + kla_werte[i][15]=float(kla_werte[i][13] / kla_werte[i][14]) + kla_werte[i][11]=float(kla_werte[i][8] / math.sqrt(1 + float(Konstanten_fuer_P_begast["3"])* kla_werte[i][15] / math.sqrt( 9.81 * kla_werte[i][5] ) ) ) #modell F22= 3 #W =C48/WURZEL(1+SVERWEIS(Modell!$F$22;Daten!$I$43:$J$45;2;FALSCH)*C55/WURZEL(9,81*C44)) + + Leistungseintrag_Begast_Pg2 = kla_werte[i][11] + superficial_velocity2=kla_werte[i][15] + kla_werte[i][16]=float(0.026 * ((Leistungseintrag_Begast_Pg2 / Volumen_Kulturbruehe ) ** (0.4) ) * (superficial_velocity2 ** (0.5))) #Modell!$F$18 = 0.01 + kla_werte[i][17]=float(kla_werte[i][16] * 3600 ) + kla_werte[i][18]=float(kla_werte[i][17] * Sauerstoffoeslichkeit_array[i][4]) + + + + # Dezimalstellen nach komma grenzen, maximal 9 Stellen nach komma + for i in range(len(kla_werte)): + for j in range(len(kla_werte[i])): + try: + kla_werte[i][j] = round(float(kla_werte[i][j]), 9) + except ValueError: + pass + + return kla_werte + \ No newline at end of file diff --git a/src/Tasks/Ferment/nutzer_eingaben.json b/src/Tasks/Ferment/nutzer_eingaben.json new file mode 100644 index 0000000..1f66f67 --- /dev/null +++ b/src/Tasks/Ferment/nutzer_eingaben.json @@ -0,0 +1,35 @@ +{ + "Modell": "Modell-1", + "PhasenAnzahl": 2, + "Dauer": { + "Phase_1": 10, + "Phase_2": 10 + }, + "Druck": { + "Phase_1": 1, + "Phase_2": 1 + }, + "Drehzahl": { + "Phase_1": 100, + "Phase_2": 100 + }, + "Zuluft": { + "Phase_1": 1, + "Phase_2": 1 + }, + "BolusC": { + "Phase_1": 1, + "Phase_2": 1 + }, + "Feed": { + "Phase_1": 1, + "Phase_2": 1 + }, + "BolusN": { + "Phase_1": 1, + "Phase_2": 1 + }, + "do": 0, + "T": 0, + "BTM": 0 +} \ No newline at end of file diff --git a/src/Tasks/Ferment/wrapperFunctionCallPython.ts b/src/Tasks/Ferment/wrapperFunctionCallPython.ts new file mode 100644 index 0000000..ddcdc0f --- /dev/null +++ b/src/Tasks/Ferment/wrapperFunctionCallPython.ts @@ -0,0 +1,290 @@ +import { exec } from "child_process"; +import fs from "fs"; +const path = require("path"); + +function runPythonScript(scriptPath: string): Promise { + try { + return new Promise((resolve, reject) => { + const command = `python ${scriptPath}`; + console.log(command); + exec(command, (error, stdout, stderr) => { + if (error) { + reject(error.message); + return; + } + if (stderr) { + reject(stderr); + return; + } + resolve(stdout); + }); + }); + } catch { + console.log("Error"); + } +} + +function parseFrontendInputs(parameter: any) { + //Überprüfen, ob parameter ein String ist und wenn ja, ihn parsen + let frontendEingaben: any; + if (parameter === undefined || parameter === null) { + throw new Error("Parameter ist undefined oder null."); + } + if (typeof parameter === "string") { + try { + frontendEingaben = JSON.parse(parameter); + } catch (error) { + throw new Error("Fehler beim Parsen des JSON-Strings: " + error.message); + } + } else if (typeof parameter === "object") { + frontendEingaben = parameter; + } else { + throw new Error("Ungültiges Eingabeformat für parameter."); + } + return frontendEingaben; +} +function cleanAndMoveParameters(frontendEingaben : any) { + if (frontendEingaben && frontendEingaben.parameters) { + // Inhalt von "parameters" ins Hauptobjekt verschieben + Object.assign(frontendEingaben, frontendEingaben.parameters); + + // Entfernen der spezifizierten Schlüssel + delete frontendEingaben.parameters; + delete frontendEingaben.type; + delete frontendEingaben.task; + delete frontendEingaben.Paramter; + delete frontendEingaben.instruction; + delete frontendEingaben.language; + } + + return frontendEingaben; +} + +// Schlüsselnamen im Objekt ändern +function renameKey (obj: any, oldKey: string, newKey: string) { + if (obj.hasOwnProperty(oldKey)) { + obj[newKey] = obj[oldKey]; + delete obj[oldKey]; + } + for (const key in obj) { + if (obj.hasOwnProperty(key) && typeof obj[key] === "object" && obj[key] !== null) { + renameKey(obj[key], oldKey, newKey); + } + } + return obj; +}; + +function editSaveParams(parameter: any) { + //Überprüfen, ob parameter ein String ist und wenn ja, ihn parsen + let frontendEingaben : any = parseFrontendInputs(parameter); + frontendEingaben = cleanAndMoveParameters(frontendEingaben); + + frontendEingaben = renameKey(frontendEingaben, "nodeAmount", "Modell"); + + + if (Array.isArray(frontendEingaben.Modell)) { + const tmpModell = frontendEingaben.Modell[0]; // Nimmt das erste Element aus dem Array + delete frontendEingaben.Modell; // Entfernt den ursprünglichen Schlüssel + frontendEingaben.Modell = tmpModell; // Setzt den Schlüssel mit dem neuen Wert + } + frontendEingaben = renameKey(frontendEingaben, "seed", "PhasenAnzahl"); + + switch (frontendEingaben.Modell) { + case "S.cerevisiae": + frontendEingaben.Modell = "Modell-1"; + break; + case "E.coli": + frontendEingaben.Modell = "Modell-2"; + break; + case "Testorganismus": + frontendEingaben.Modell = "Modell-3"; + break; + } + let phasenanzahl = parseInt(frontendEingaben.PhasenAnzahl); + frontendEingaben.PhasenAnzahl = phasenanzahl; + + let zuluft = []; + let feed = []; + let drehzahl = []; + let druck = []; + let dauer = []; + let bolusC = []; + let bolusN = []; + for (let i = 0; i < phasenanzahl; i++) { + if (Array.isArray(frontendEingaben.userDataMatrix[i][0])) { + zuluft[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][0][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][0][0]); + feed[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][2][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][2][0]); + drehzahl[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][4][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][4][0]); + druck[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][5][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][5][0]); + dauer[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][6][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][6][0]); + bolusC[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][1][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][1][0]); + bolusN[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][3][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][3][0]); + } else { + zuluft[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][0]); + feed[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][2])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][2]); + drehzahl[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][4])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][4]); + druck[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][5])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][5]); + dauer[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][6])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][6]); + bolusC[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][1])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][1]); + bolusN[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][3])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][3]); + } + } + + delete frontendEingaben.userDataMatrix; + + frontendEingaben.Dauer = dauer; + frontendEingaben.Druck = druck; + frontendEingaben.Drehzahl = drehzahl; + frontendEingaben.Zuluft = zuluft; + frontendEingaben.BolusC = bolusC; + frontendEingaben.Feed = feed; + frontendEingaben.BolusN = bolusN; + + + if (frontendEingaben.userDataInputFelder) { + frontendEingaben.temperatur =isNaN(parseFloat(frontendEingaben.userDataInputFelder.T)) ? 0 : parseFloat(frontendEingaben.userDataInputFelder.T); + frontendEingaben.startbiomasse = isNaN(parseFloat(frontendEingaben.userDataInputFelder.BTM)) ? 0 : parseFloat(frontendEingaben.userDataInputFelder.BTM); + frontendEingaben.do = isNaN(parseFloat(frontendEingaben.userDataInputFelder.DO)) ? 0 : parseFloat(frontendEingaben.userDataInputFelder.DO) ; + } + delete frontendEingaben.userDataInputFelder; + + delete frontendEingaben.checkUserDataValidity; + + + + // Pfad zur JSON-Datei definieren + const filePfade2 = path.join(__dirname, "/nutzer_eingaben.json"); + /* + // Objekt initialisieren + const data: { [key: string]: number } = {}; // Dynamische Schlüssel mit Werten vom Typ `number` + + frontendEingaben.Feed.forEach((element: number, index: number) => { + data[`Phase_${index + 1}`] = element; // Dynamische Schlüssel hinzufügen + }); + // JSON.stringify mit einer Replacer-Funktion + let jsonString2 = JSON.stringify(data, null, 4); + + // Datei schreiben + fs.writeFileSync(filePfade2, frontendEingaben, "utf-8");*/ + /* + let jsonString2 = JSON.stringify(frontendEingaben, null, 4); + // Schreibe den JSON-String in die Datei + fs.writeFileSync(filePfade2, jsonString2, "utf-8"); + */ + + // Umbenennen der Schlüssel + frontendEingaben['T'] = frontendEingaben['temperatur']; + delete frontendEingaben['temperatur']; + + frontendEingaben['BTM'] = frontendEingaben['startbiomasse']; + delete frontendEingaben['startbiomasse']; + + let param: { [key: string]: any } = {}; + Object.keys(frontendEingaben).forEach(key => { + // Überprüfe, ob der Wert von `frontendEingaben[key]` ein Array ist + if (Array.isArray(frontendEingaben[key]) && frontendEingaben[key] !== null) { + param[key] = {}; // Initialisiere das Objekt für diesen Schlüssel + Object.values(frontendEingaben[key]).forEach((value, index) => { + param[key][`Phase_${index+1}`] = value; // Setze den Wert für den entsprechenden Phase-Schlüssel + }); + } + else{ + if(frontendEingaben[key] !== null){ + param[key] = frontendEingaben[key]; + } + + } + }); + + let jsonString2 = JSON.stringify(param, null, 4); + // Schreibe den JSON-String in die Datei + fs.writeFileSync(filePfade2, jsonString2, "utf-8"); + + + + + // Pfad zur JSON-Datei definieren + const filePfade = path.join(__dirname, "/interne_daten/FrontendEingaben.json"); + + // JSON.stringify mit einer Replacer-Funktion + let jsonString = JSON.stringify(frontendEingaben, null, 4); + + // Datei schreiben + try { + fs.writeFileSync(filePfade, jsonString, "utf-8"); + return true; + } catch (error) { + return false; + } +} + + +export async function generateFermentationFeedBack() { + let result = { foo: "bar" }; + try { + const filePath = path.join(__dirname, "feedback.json"); + + // Versuche, die Datei zu lesen + if (fs.existsSync(filePath)) { + const fileContent = fs.readFileSync(filePath, "utf-8"); + result = JSON.parse(fileContent); // Dateiinhalt parsen + } else { + console.warn("feedback.json existiert nicht, es wird eine neue Datei erstellt."); + } + } catch (error) { + console.error("Fehler beim Verarbeiten der JSON-Datei:", error); + return null; + } + return result; +} + +export async function generateFermentationDataMain(parameter: any) { + let check = editSaveParams(parameter); + if (check) { + let result = { foo: "bar" }; + try { + let pythonScriptPath = "generateFermentationData.py"; + pythonScriptPath = path.join(__dirname, pythonScriptPath); + (await runPythonScript(pythonScriptPath)) as any; + + result = JSON.parse(fs.readFileSync(path.join(__dirname, "data.json"), "utf-8")); + const filePath = path.join(__dirname, "data.json"); + fs.writeFileSync(filePath, JSON.stringify(result, null, 2), "utf-8"); + + //console.log(result); + //insofern kryptsiche Zeichen in der JSON Ausgabe vorhanden sind .> Workaround mittels File + } catch (error) { + console.error("Error running Python script:", error); + } + return result; + } else console.error("Die Daten aus Frontend konnten nicht in JSON-Datei gespeichert"); + return null; +} diff --git a/src/Tasks/FermentExercises/Input/__init__.py b/src/Tasks/FermentExercises/Input/__init__.py new file mode 100644 index 0000000..0f2dcb8 --- /dev/null +++ b/src/Tasks/FermentExercises/Input/__init__.py @@ -0,0 +1,4 @@ + + +#Das Hinzufügen der '__init__.py'-Datei im Input-Verzeichnis ist wichtig, +#damit Python das Input-Verzeichnis als Paket behandelt und die darin enthaltenen Module importieren kann. \ No newline at end of file diff --git a/src/Tasks/FermentExercises/Input/inputBaseClass.py b/src/Tasks/FermentExercises/Input/inputBaseClass.py new file mode 100644 index 0000000..d49cb1c --- /dev/null +++ b/src/Tasks/FermentExercises/Input/inputBaseClass.py @@ -0,0 +1,14 @@ +from abc import ABC, abstractmethod + + + +class InputBase(ABC): + + @abstractmethod + def find_pfad(): + pass + + @abstractmethod + def ladeJson(self): + pass + diff --git a/src/Tasks/FermentExercises/Input/json_Input.py b/src/Tasks/FermentExercises/Input/json_Input.py new file mode 100644 index 0000000..7ddf93b --- /dev/null +++ b/src/Tasks/FermentExercises/Input/json_Input.py @@ -0,0 +1,83 @@ + +import os +import json +from Input.inputBaseClass import InputBase + +class JsonInput(InputBase): + + + + def __init__(self,datei_name): #Konstruktor + self.__jsonName = datei_name # private variable + + # Deklaration des Attributs außerhalb des Konstruktors + __data = None + + def set_data(self,data): + self.__data=data + + def get_data(self): + return self.__data + + def get_jsonName(self): + return self.__jsonName + + def set_jsonName(self, value): + self.__jsonName = value + + + + + + def find_pfad(self): + root_directory = os.path.abspath('src/Tasks/FermentExercises') # Den absoluten Pfad des Projektnamens ermitteln + + for root, dirs, files in os.walk(root_directory): + for file in files: + if file.lower() == self.__jsonName.lower(): + return os.path.join(root, file) # Dateipfad zurückgeben, sobald die Datei gefunden wurde + return None # Wenn die Datei nicht gefunden wurde, wird None zurückgegeben + + + def ladeJson(self): + # Vollständiger Pfad zur JSON-Datei + json_path = self.find_pfad() + if json_path is None : + print('Pfad zur Datei nicht gefunden.!!!') + return None + + print('Datei gefunden:', json_path) # Fügen Sie Debugging-Ausgabe hinzu + try: + # Öffne die JSON-Datei und lade sie + with open(json_path, 'r') as file: + data = json.load(file) + self.set_data(data) + + except FileNotFoundError: + print(f"Die Datei '{self.__jsonName}' konnte nicht geladen werden.") + return None + + + def get_Value(self): + + # JSON-String zu Python-Dictionary konvertieren + data = self.__data + + # Array initialisieren + result = [] + + for key, value in data.items(): + if isinstance(value, dict): + result.append({sub_key: float(sub_value) if isinstance(sub_value, (int, float)) else sub_value + for sub_key, sub_value in value.items()}) + else: + # Wenn der Wert kein Dictionary ist oder kein Array enthält + result.append(value) + + for i, r in enumerate(result): + if isinstance(r, int): + result[i]=int(r) + elif isinstance(r,list): + result[i] = [float(val) for val in r] + + return result diff --git a/src/Tasks/FermentExercises/berechnungen.py b/src/Tasks/FermentExercises/berechnungen.py new file mode 100644 index 0000000..806020f --- /dev/null +++ b/src/Tasks/FermentExercises/berechnungen.py @@ -0,0 +1,219 @@ +import pandas as pd +import numpy as np +import os +import optuna +from scipy.integrate import solve_ivp +from openpyxl import Workbook +from differentialgleichung import ODE_Bioreactor_Monod +from optimierung import objective +import json + + +from array import array + + + +def berechnung_der_Tabelle1(parameter_werte,modull,data,zuluft,feed,phasen_anzahl): + #float array deklarieren + const_array= [array('d', [0] * 20) for _ in range(phasen_anzahl)] + + for i in range(phasen_anzahl): + const_array[i][0]=parameter_werte[i][0] #!Sauersto. in g/l + const_array[i][1]=parameter_werte[i][1] #!kla_werte[i][17] + const_array[i][3]=float(zuluft[i]) #0.2 #Modell H7-k7 + const_array[i][4]=0.2095 #! Anteil Sauerstoff in der Luft [0-1] + const_array[i][5]=0.0004147 #!Anteil Co2 in der Luft [0-1] + const_array[i][14]=parameter_werte[i][2] #! Wachstumsrate + const_array[i][19]=float(feed[i]) #0.1 #Modell H10-K10 + if i == 0: #Phase 1 + + const_array[0][2]=data.get(modull, {}).get("RQ") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];13;FALSCH) + const_array[0][6]=data.get(modull, {}).get("ks_ox") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];12;FALSCH) + const_array[0][7]=data.get(modull, {}).get("Yxox") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];14;FALSCH) + const_array[0][8]=data.get(modull, {}).get("Produktbildung") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];15;FALSCH) + const_array[0][9]=data.get(modull, {}).get("ap") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];16;FALSCH) + const_array[0][10]=data.get(modull, {}).get("bp") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];17;FALSCH) + const_array[0][11]=data.get(modull, {}).get("kp_max") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];18;FALSCH) + const_array[0][12]=data.get(modull, {}).get("km_s1p") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];19;FALSCH) + const_array[0][13]=data.get(modull, {}).get("Ypx_mu") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];20;FALSCH) + const_array[0][15]=data.get(modull, {}).get("Ks_s1x") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];8;FALSCH) + const_array[0][16]=data.get(modull, {}).get("Yxs1") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];10;FALSCH) + const_array[0][17]=data.get(modull, {}).get("Ks_s2x") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];9;FALSCH) + const_array[0][18]=data.get(modull, {}).get("Yxs2") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];11;FALSCH) + else : #alle anderen Phasen + const_array[i][2]=const_array[i-1][2] + const_array[i][6]=const_array[i-1][6] + const_array[i][7]=const_array[i-1][7] + const_array[i][8]=const_array[i-1][8] + const_array[i][9]=const_array[i-1][9] + const_array[i][10]=const_array[i-1][10] + const_array[i][11]=const_array[i-1][11] + const_array[i][12]=const_array[i-1][12] + const_array[i][13]=const_array[i-1][13] + const_array[i][15]=const_array[i-1][15] + const_array[i][16]=const_array[i-1][16] + const_array[i][17]=const_array[i-1][17] + const_array[i][18]=const_array[i-1][18] + + + # Dezimalstellen nach komma grenzen, maximal 9 Stellen nach komma + for i in range(len(const_array)): + for j in range(len(const_array[i])): + try: + const_array[i][j] = round(float(const_array[i][j]), 9) + except ValueError: + pass + + return const_array + + +def berechnung_der_Tabelle2(dauer,phasen_anzahl): + #float array deklarieren + t_ranges_array= [array('d', [0] * 2) for _ in range(phasen_anzahl)] + + for i in range(phasen_anzahl): + if i == 0: #Phase 1 + t_ranges_array[i][0]=0 + t_ranges_array[i][1]=dauer[i] + else: + t_ranges_array[i][0]=t_ranges_array[i-1][1] + t_ranges_array[i][1]=t_ranges_array[i-1][1]+dauer[i] + return t_ranges_array + + +def berechnung_der_Tabelle3(const_array,startbiomasse,bolus_c,bolus_n,do,phasen_anzahl): + param_array= [array('d', [0] * 7) for _ in range(phasen_anzahl)] + + for i in range(phasen_anzahl): + if i == 0: #Phase 1 + param_array[i][0]=startbiomasse + param_array[i][1]=float(bolus_c[i]) + param_array[i][2]=float(bolus_n[i]) + param_array[i][3]=0 + param_array[i][4]=(const_array[i][0]*do)/100 + param_array[i][5]=const_array[i][4] + param_array[i][6]=const_array[i][5] + else: + param_array[i][1]=float(bolus_c[i]) + param_array[i][2]=float(bolus_n[i]) + + + return param_array + + + + +def berechnung(t_ranges_array, param_array, const_array_array, phasen_anzahl, maxParameter, minParameter, varierendeParameter,frontendeingabe): + data_rate = 10 # Anzahl der Datenpunkte pro Stunde + + t_ranges_array = np.array(t_ranges_array) + param_array = np.array(param_array) + const_array_array = np.array(const_array_array) + + t_combined = np.array([]) + y_combined = np.array([]) + cum_feeding = np.array([]) + feed_s1_values = {} # Dictionary zur Speicherung der Feed-Werte + + + bestParam = {} + for param in varierendeParameter: + bestParam[param] = {} + for i in range(phasen_anzahl): + bestParam[param][f"Phase_{i+1}"] = 'Nan' + + for i in range(phasen_anzahl): + if i == 0: + y0 = param_array[i, :] # Erste Zeile aus Dim. Array als Vektor + else: + + mx_0 = ry[-1, 0] # Biotrockenmasse in g/L + cs1_0 = ry[-1, 1] + param_array[i, 1] # Konz. Substrat 1 in g/L + cs2_0 = ry[-1, 2] + param_array[i, 2] # Konz. Substrat 1 in g/L + cp_0 = ry[-1, 3] # Konz. Produkt in g/L + c_ox_0 = ry[-1, 4] # Konz. Gelöstsauerstoff in g/L zum Start der Fermentation + O2_Out = ry[-1, 5] # Konz. O2 in Abluft + CO2_Out = ry[-1, 6] # Konz. CO2 in Abluft + + y0 = [mx_0, cs1_0, cs2_0, cp_0, c_ox_0, O2_Out, CO2_Out] # Startparameter in Vektor + + t_start = t_ranges_array[i, 0] + t_end = t_ranges_array[i, 1] + + if t_start != t_end: + num1 = int(data_rate * (t_end - t_start)) + t_span = np.linspace(t_start, t_end, num=num1) # Zeitvektor bauen + consts = const_array_array[i, :] + + if maxParameter or minParameter: + #sampler = optuna.samplers.GridSampler() + study = optuna.create_study() + + study.optimize( + lambda trial: objective(trial, t_start, t_end, y0, consts, t_span, varierendeParameter, maxParameter, minParameter), + n_trials=30) + + tmp_sol = study.best_trial.user_attrs.get("sol", None) + + if tmp_sol is not None and hasattr(tmp_sol, "success") and tmp_sol.success: + best_params = study.best_trial.params + for param in varierendeParameter: + if param.lower() in best_params: + bestParam[param][f"Phase_{i+1}"] = best_params[param.lower()] + + else : + tmp_sol =solve_ivp(ODE_Bioreactor_Monod, [t_start, t_end], y0, args=(consts,), t_eval=t_span, atol=1e-9, rtol=1e-9) + + ry = tmp_sol.y.T + rt = tmp_sol.t + + if i == 0: + y_combined = ry + t_combined = rt + cum_feeding = t_span * consts[19] + else: + y_combined = np.vstack((y_combined, ry)) + t_combined = np.hstack((t_combined, rt)) + cum_feeding = np.hstack((cum_feeding, t_span * consts[19])) + #else : feed_s1_values[f"Phase_{indextmp}"] = 0 #! temp zu fragen + + # Ergebnisse speichern + c_ox_sat = const_array_array[0, 0] + cum_feeding = cum_feeding.T + + + frontendeingabe['T'] = frontendeingabe.pop('temperatur') + frontendeingabe['BTM'] = frontendeingabe.pop('startbiomasse') + + for param in bestParam: + for phasen in bestParam[param]: + if bestParam[param][phasen] == 'Nan': + bestParam[param][phasen] = 0 + else: + bestParam[param][phasen] = round(bestParam[param][phasen],1) + + for param in frontendeingabe: + if param not in bestParam: + if isinstance(frontendeingabe[param], list): # Prüft, ob es eine Liste ist + bestParam[param] = {} # Muss ein Dictionary sein, um Phasen als Schlüssel zu setzen + for i, value in enumerate(frontendeingabe[param]): + bestParam[param][f"Phase_{i+1}"] = value # Phasen beginnen bei 1 + else: + bestParam[param] = frontendeingabe[param] # Einzelwerte direkt übernehmen + + + for param in {"Modell", "PhasenAnzahl", "maxParameter", "minParameter", "varierendeParameter"}: + bestParam.pop(param, None) # Entfernt den Key, falls vorhanden, sonst passiert nichts + + + + with open("src/Tasks/FermentExercises/parameter_loesung.json", "w") as json_file: + json.dump(bestParam, json_file, indent=4) + + + return c_ox_sat, y_combined, t_combined, cum_feeding + + + + + diff --git a/src/Tasks/FermentExercises/bericht.adoc b/src/Tasks/FermentExercises/bericht.adoc new file mode 100644 index 0000000..5c9f291 --- /dev/null +++ b/src/Tasks/FermentExercises/bericht.adoc @@ -0,0 +1,673 @@ += Bericht +:toc: +:toc-title: + +== Motivation + +Die Ausbildung im Bereich der chemischen Prozesse, insbesondere der Fermentation, steht vor großen Herausforderungen. Fermentationsreaktoren sind teuer, und die Experimente erfordern oft viel Zeit, sodass Studierende in klassischen Lehrveranstaltungen nur begrenzt praktische Erfahrungen sammeln können. Dies erschwert den Aufbau nachhaltigen Wissens. + +Um diesem Problem zu begegnen, wurde FermentALADIN entwickelt. Sie ermöglicht es, chemische Prozesse - insbesondere Fermentationsprozesse - virtuell und interaktiv zu simulieren. Studierende können durch wiederholte Simulationen verschiedene Parameterkombinationen ausprobieren und so wertvolle Erfahrungen sammeln, ohne auf teure und zeitaufwendige Experimente angewiesen zu sein. + +Mit dieser Plattform wird ein praxisnahes Lernen ermöglicht, das sowohl die analytischen als auch die problemlösungsorientierten Fähigkeiten der Studierenden fördert. + +== Implementierung der Python-Code + +In unserem Projekt geht es darum, Fermentation-Prozesse digital zu machen. Um das zu schaffen, mussten wir erstens ein paar Python-Skripte implementieren. Die ersten zwei Python-Skripte, die wir implementiert haben, sind *Berechnungen.py* und *Differential-Gleichung.py*. + +[pagebreak] +=== Differential-Gleichung.py + +image::./images/differentialgleichung_py.png[] + +*Die Funktion ODE_Bioreactor_Monod* beschreibt ein mathematisches Modell zur Simulation eines Bioreaktors basierend auf der Monod-Kinetik. Sie nimmt als Eingabe die Zeit t, die aktuellen Prozessparameter p sowie eine Reihe von Konstanten consts und berechnet die zeitliche Änderung verschiedener biologischer und chemischer Konzentrationen. + +Zu Beginn werden wichtige biotechnologische Parameter aus consts extrahiert, darunter Sauerstofflöslichkeit, Begasungsrate, Ausbeutungskoeffizienten und Wachstumsraten. Anschließend werden die aktuellen Konzentrationen für Biomasse, Substrate, Produkt und gelösten Sauerstoff aus p zugewiesen. Die Berechnungen umfassen die spezifische Wachstumsrate der Mikroorganismen nach der Monod-Gleichung sowie die Sauerstofftransferrate (OTR) und den Sauerstoffverbrauch (OUR). + +Das Modell berücksichtigt zudem die Substratverbrauchsraten, die Produktbildungsrate nach Luedeking-Piret sowie die Bilanzierung der gelösten Sauerstoffkonzentration. Weiterhin wird der Gehalt an Sauerstoff und CO₂ in der Abluft nach einem CSTR-Ansatz berechnet. Schließlich gibt die Funktion ein Array zurück, das die Änderungsraten der Biomasse, Substratkonzentrationen, Produktkonzentration, Sauerstoffkonzentration sowie die Gaskonzentrationen in der Abluft enthält. + +[pagebreak] +=== Berechnungen.py + +image::./images/Berechnung_py.png[] + +==== Funktion: berechnung_der_Tabelle1 +Diese Funktion erstellt eine mehrdimensionale Liste (const_array), die verschiedene Parameter für jede Phase eines Prozesses speichert. Die Werte werden aus der Eingabe (parameter_werte, zuluft, feed) sowie einer Datenquelle (data) entnommen. In der ersten Phase werden spezifische Werte aus data extrahiert, während in den folgenden Phasen bestimmte Parameter aus der vorherigen Phase übernommen werden. Abschließend werden alle Werte auf neun Dezimalstellen gerundet. + +==== Funktion: berechnung_der_Tabelle2 +Hier wird ein Zeitbereichs-Array (t_ranges_array) für jede Phase berechnet. Die Dauer jeder Phase (dauer) wird iterativ summiert, um Start- und Endzeiten zu bestimmen. Die erste Phase beginnt bei null, während die folgenden Phasen an die Endzeit der vorherigen Phase anschließen. Das Ergebnis ist eine Liste von Intervallen, die den zeitlichen Ablauf des Prozesses strukturieren. + +==== Funktion: berechnung_der_Tabelle3 +Diese Funktion erzeugt ein Array (param_array), das initiale Bedingungen für jede Phase speichert. Für die erste Phase werden die Startbiomasse sowie Werte für Substrate (bolus_c, bolus_n), Sauerstoff (do) und weitere Parameter aus const_array gesetzt. In den folgenden Phasen werden nur bestimmte Werte (bolus_c und bolus_n) aktualisiert, während andere unverändert bleiben. + +==== Fubktion: berechnung +1. Initialisierung und Umwandlung der Eingabedaten +Die Funktion berechnung beginnt mit der Definition der Datenrate, die die Anzahl der Datenpunkte pro Stunde bestimmt. Danach werden die Eingabedaten t_ranges_array, param_array und const_array_array in NumPy-Arrays umgewandelt, um eine effizientere Verarbeitung zu ermöglichen. Zusätzlich werden leere Arrays für t_combined (gesamte Zeitpunkte), y_combined (Ergebnisse der Berechnung) und cum_feeding (kumulierte Substratzufuhr) erstellt. Diese Variablen werden später verwendet, um die simulierten Werte über alle Phasen hinweg zu speichern. + +2. Iteration über die Phasen +Die Berechnung erfolgt über mehrere Phasen hinweg, wobei jede Phase in einer Schleife durchlaufen wird. In der ersten Phase (i == 0) wird der Startwert y0 direkt aus dem Parameterarray übernommen. Für alle weiteren Phasen (i > 0) werden die Startwerte aus den zuletzt berechneten Werten (ry[-1]) der vorherigen Phase extrahiert. Dabei werden Parameter wie die Biomasse, die Substratkonzentration und der gelöste Sauerstoff neu gesetzt, wobei Zugaben von Substrat berücksichtigt werden. + +3. Lösung des Differenzialgleichungssystems +Für jede Phase wird die Differentialgleichung mit der Funktion solve_ivp gelöst, die das System ODE_Bioreactor_Monod numerisch integriert. Der Simulationszeitraum wird durch t_start und t_end bestimmt, und die Zeitpunkte für die Berechnung werden mit np.linspace erzeugt. Die erhaltenen Ergebnisse (sol.y und sol.t) werden in ry und rt gespeichert. Diese Werte enthalten die Konzentrationen verschiedener Stoffe über die Zeit. + +4. Speichern und Zusammenführen der Ergebnisse +Die berechneten Werte jeder Phase werden an die Gesamtarrays y_combined und t_combined angehängt. Dabei werden die Werte mit np.vstack (vertikal zusammenfügen) und np.hstack (horizontal zusammenfügen) kombiniert. Zudem wird die kumulierte Substratzufuhr (cum_feeding) durch Multiplikation der Zeitwerte mit dem Feed-Parameter berechnet. Dadurch entsteht eine fortlaufende Simulation über alle Phasen hinweg. + +5. Rückgabe der berechneten Daten +Am Ende gibt die Funktion wichtige Berechnungsergebnisse zurück: c_ox_sat (Sauerstoffsättigungskonzentration), y_combined (alle berechneten Konzentrationen), t_combined (Zeitpunkte der Berechnung) und cum_feeding (kumulierte Fütterung). Diese Daten können anschließend für weitere Analysen oder zur Visualisierung der Fermentationsprozesse verwendet werden. + + + + +In diesen zwei Skripten geht es darum, dass eine Differential-Gleichung läuft und Ergebnisse abgibt. Danach mussten wir diese Ergebnisse in einem Diagramm zeigen. Und hier kommt *View.py*. Dieser View.py nimmt diese Ergebnisse und gibt ein diagramm. + +Für **die Plotin** nützen wir die Bibliothek matplotlib. + +=== View.py + +image::./images/view_py.png[] + +Die Funktion plot_visualisieren dient der grafischen Darstellung von Fermentationsverläufen anhand der berechneten Konzentrationen von Biomasse, Substrat und Produkt über die Zeit. Zunächst werden die übergebenen Daten (y_combined und t_combined) in die richtige Form gebracht, sodass sie für das Plotten genutzt werden können. Anschließend werden allgemeine Darstellungsoptionen für die Diagramme, wie Schriftgröße, Linienstärke und Farbpalette, festgelegt. + +*Es werden zwei Subplots erstellt*: Der erste zeigt den Verlauf der Biomassekonzentration (c_x), der Substratkonzentrationen (c_S1) und (c_S2), der Produktkonzentration (c_P), sowie den Sauerstoffgehalt (c_O2) und die kumulierte Substratzufuhr (cum_feeding). Die Daten werden farblich codiert, und die Legende wird unterhalb des Plots positioniert. + +Der zweite Subplot konzentriert sich auf den Verlauf der zweiten Substratkomponente (c_S2) über die Zeit. Beide Diagramme enthalten Achsenbeschriftungen, eine Gitternetzlinie für bessere Lesbarkeit und eine Begrenzung der y-Achse, sodass keine negativen Werte dargestellt werden. Schließlich wird das Diagramm ausgegeben, um eine visuelle Analyse des Fermentationsprozesses zu ermöglichen. + +=== excel_export.py +Wir müssen die Ergebnisse der Fermentation als CSV-Datei exportieren und dafür brauchen wir Excel excel_export.py + +image::./images/excel_export_py.png[] + +Die Funktion *export_to_excel* exportiert die übergebenen Daten als Excel-Datei in den Downloads-Ordner des Benutzers. Zunächst wird geprüft, ob dieser Ordner existiert; falls nicht, wird eine Fehlermeldung ausgegeben. Danach wird der vollständige Pfad zur Zieldatei erstellt, und eine eventuell vorhandene Datei mit demselben Namen wird gelöscht, um Konflikte zu vermeiden. + +Die Excel-Datei wird mit der Bibliothek openpyxl erstellt, wobei eine neue Arbeitsmappe (Workbook) und ein Arbeitsblatt (ws) angelegt werden. In der ersten Zeile werden die Spaltenüberschriften definiert, die relevante Messwerte wie Biomassekonzentration, Substratkonzentrationen und kumulatives Feeding enthalten. + +Anschließend werden die Daten zeilenweise in die Excel-Tabelle geschrieben. Dabei werden die Zeitwerte (t_combined), die Messwerte (y_combined) und das kumulative Feeding (cum_feeding) kombiniert. Schließlich wird die Datei gespeichert, und der Speicherort wird in der Konsole ausgegeben. + + +=== inputBaseClass.py & json_input.py + +Zur Erfassung der benötigten Daten für den Fermentationsprozess wird eine Klasse verwendet. Diese Klasse nimmt die Benutzereingaben entgegen und speichert die Daten anschließend in einer JSON-Datei, um sie später für die Berechnungen zur Verfügung zu stellen. + +==== inputBaseClass.py + +image::./images/inputBaseClass_py.png[] + +InputBase-Klasse und Vererbung: Die InputBase-Klasse ist eine abstrakte Basisklasse, die als Vorlage für andere Eingabeklassen dient. Sie definiert zwei abstrakte Methoden: find_pfad und ladeJson, die von jeder Unterklasse implementiert werden müssen. Diese Struktur stellt sicher, dass alle abgeleiteten Klassen ein einheitliches Interface bieten, um mit Eingabedaten zu arbeiten. + +==== json_input.py +image::./images/json_Input_py.png[] + +* JsonInput-Klasse: Die JsonInput-Klasse erbt von InputBase und implementiert die abstrakten Methoden, um mit JSON-Dateien zu arbeiten. Im Konstruktor wird der Name der JSON-Datei festgelegt. Zusätzlich enthält die Klasse Methoden zur Verwaltung und zum Laden von JSON-Daten sowie zum Speichern und Abrufen von Attributen wie dem Dateinamen und den geladenen Daten. + +* find_pfad-Methode: Die Methode find_pfad sucht rekursiv im Projektverzeichnis nach einer Datei mit dem angegebenen Namen. Sie gibt den vollständigen Dateipfad zurück, wenn die Datei gefunden wird, andernfalls wird None zurückgegeben. Dies ermöglicht eine effiziente Suche nach der Datei, ohne dass der Benutzer den Pfad manuell eingeben muss. + +* ladeJson-Methode: Die ladeJson-Methode nutzt die find_pfad-Methode, um den Pfad der JSON-Datei zu ermitteln. Wenn die Datei gefunden wird, wird sie geöffnet und die Daten werden in ein Python-Objekt umgewandelt. Falls die Datei nicht existiert oder nicht geladen werden kann, gibt es eine Fehlermeldung. Die geladenen Daten werden in einer privaten Instanzvariablen gespeichert. + +* get_Value-Methode: Die get_Value-Methode konvertiert die geladenen JSON-Daten in eine flache Liste. Sie durchsucht die Daten und extrahiert die Werte, selbst wenn sie in verschachtelten Dictionaries enthalten sind. Die Methode sorgt zudem dafür, dass alle Werte korrekt in den entsprechenden Datentyp konvertiert werden, z. B. Ganzzahlen oder Listen von Floats. Das Ergebnis ist eine Liste, die die extrahierten Werte enthält. + + +=== main.py + +image::./images/main_py.png[] + +In der Datei main.py werden verschiedene Funktionen aus unterschiedlichen Modulen aufgerufen, um den gesamten Berechnungs- und Visualisierungsprozess zu steuern. Die Funktion plot_visualisieren aus view dient der grafischen Darstellung der Ergebnisse, während export_to_excel aus excel_export die berechneten Daten in eine Excel-Datei exportiert. + +Für zusätzliche Berechnungen werden verschiedene Funktionen aus nebenrechnungen und berechnungen verwendet. Dazu gehören die Berechnung der Sauerstofflöslichkeit, des kla-Wertes sowie weitere Parameter. Zudem werden drei Tabellen berechnet, die zentrale Ergebnisse liefern. + +Zur Datenverarbeitung wird data_importieren_von_json aus interne_daten genutzt, um Daten aus JSON-Dateien zu laden. JsonInput aus Input.json_Input ermöglicht die Verarbeitung von Eingaben im JSON-Format. + + +== Integration der Python-Code in ALADIN-Framework + +*ALADIN* besteht aus zwei Hauptkomponenten: dem Frontend, genannt CARPET, und dem Backend, genannt ALADIN. Für die Integration unseres Python-Codes in das ALADIN-Framework haben wir spezifische JSON-Dateien erstellt, darunter eine namens Ferment. Diese Dateien dienen dazu, die Funktionalität des Python-Codes innerhalb des Frameworks zu definieren und zu steuern. Im Frontend, also in CARPET, haben wir entsprechende Vue-Komponenten entwickelt, die mit dem Backend interagieren, um eine nahtlose Benutzererfahrung zu gewährleisten. + + +=== Ferment + +*Der Python-Code* befindet sich im Verzeichnis ./src/Tasks/Ferment, während die *Ferment.json* Datei unter ./src/server/tempTaskGraphStorage/tasks/Ferment.json gespeichert ist. + +==== Änderungen in Python-Code für Ferment.json + +* Erste Änderung : Umbennenug der Datei *main.py* zu *generateFermentationData.py* +* Zweite Änderung : *generateFermentationData.py* um generateFermentationDataMain()-Fubktion erweitert. + +image::./images/generateFermentationDataMain.png[] + +Die Funktion generateFermentationDataMain() hat die Aufgabe, die berechneten Fermentationsdaten in einem strukturierten Format für die spätere Visualisierung und Analyse zu organisieren. Sie bereitet zwei Datensätze (data_1 und data_2) vor, die die zeitlich abhängigen Werte für verschiedene Parameter der Fermentation enthalten. Diese Parameter beinhalten die Biotrockenmasse (cₓ), die Substratkonzentrationen (cₛ₁ und cₛ₂), die Produktkonzentration (cₚ), den Sauerstoffgehalt (cₒ₂) und das kumulierte Feeding von Substraten. + +Jeder Datensatz wird in einem JSON-kompatiblen Format ausgegeben, das Labels für die Zeitpunkte (t_combined) und jeweils die entsprechenden Daten für jede Messgröße enthält. Die Daten werden zudem mit Farbcodes versehen, um sie später in Diagrammen visuell darzustellen. Nachdem die Daten vorbereitet wurden, wird das Ergebnis als JSON-Datei gespeichert (data.json), die für die Frontend-Anzeige oder zur Weiterverarbeitung in einem späteren Schritt verwendet werden kann. + +Zusätzlich wird die Funktion genutzt, um eine Excel-Datei zu erzeugen, die sowohl die Eingabedaten als auch die berechneten Chart-Daten enthält, und schließlich wird eine Rückmeldung (Feedback-Schätzung) durchgeführt, um das System zu bewerten. + +* Dritte Änderung : Benutzereingaben nicht mehr aus Konsole einlesen sondern aus JSON-Datei. +* Vierte Änderung : Es wurde auf Plotting in Python-Code verzichtet. weil es eine andere Framework in Frotend benutzt werden. + +==== Ferment.json + +Für die Ferment-Webanwendung ist im Backend die Datei *Ferment.json* zuständig. In dieser Datei werden die notwendigen APIs und Komponenten sowie deren Reihenfolge, Positionen und Parameter initialisiert und definiert. + +Die *Ferment.json* ist also eine zentrale Konfigurationsdatei für die Webanwendung, die speziell für Expementierung gedacht ist. Sie ist strukturiert in verschiedene Abschnitte, um die Kommunikation mit der API, die Verarbeitung von Aufgaben im Worker-Bereich und die Darstellung der UI (Benutzeroberfläche) zu ermöglichen. + +.... +{ + "API": [ + { + "task": "Ferment", + "name": "generateFermentationDataMain", + "httpMethod": "post", + "params": { + "parameters": "object" + } + } + ], + "worker": { + "FermentTask": { + "minConsumers": 1, + "consumerInstructions": { + "generateFermentationDataMain": { + "dependencies": ["generateFermentationDataMain"], + "body": "async (taskDescription) => {let result = {};try { result = generateFermentationDataMain(taskDescription)} catch{} return result; }" + } + } + } + }, + "UI": { + "currentTask": "Ferment", + ... + ... + "components": { + "3": { + "name": "Fermentationsverlauf", + "type": "Chart", + "component": { + "labels": "taskData__data_1__labels", + "datasets": "taskData__data_1__datasets" + } + } + } + } +} +.... + +==== Detaillierte Erklärung der Abschnitte +===== API : + +* task: In welchem Task befinden (hier "Ferment"). +* name: Der Name der Funktion, die in der API ausgeführt werden soll (z.B. "generateFermentationDataMain"). + ** Alle nötige Funktionen müssen in eine *wrapperFunctionCallPython.ts* implementiert werden. + +.... + export async function generateFermentationDataMain(parameter: any) { + let check = editSaveParams(parameter); + if (check) { + let result = { foo: "bar" }; + try { + let pythonScriptPath = "generateFermentationData.py"; + pythonScriptPath = path.join(__dirname, pythonScriptPath); + (await runPythonScript(pythonScriptPath)) as any; + + result = JSON.parse(fs.readFileSync(path.join(__dirname, "data.json"), "utf-8")); + const filePath = path.join(__dirname, "data.json"); + fs.writeFileSync(filePath, JSON.stringify(result, null, 2), "utf-8"); + + //console.log(result); + //insofern kryptsiche Zeichen in der JSON Ausgabe vorhanden sind .> Workaround mittels File + } catch (error) { + console.error("Error running Python script:", error); + } + return result; + } else console.error("Die Daten aus Frontend konnten nicht in JSON-Datei gespeichert"); + return null; +} +.... + + * Dann müsste diese Datei *wrapperFunctionCallPython.ts* in *TaskWrapper.ts* (./src/server/TaskWrapper.ts) importiert und in "generators" gesetzt werden : + +.... + import { generateFermentationDataMain } from "../Tasks/Ferment/wrapperFunctionCallPython"; + import { generateFermentExercises } from "../Tasks/FermentExercises/wrapperFunctionCallPython"; + import { generateFermentationFeedBack } from "../Tasks/Ferment/wrapperFunctionCallPython" + ... + ... + // TODO generalize generators into serialisable functions + const generators: { [key: string]: any } = { + GozintographTaskGenerator: GozintographTaskGenerator, + ... + generateFermentationDataMain: generateFermentationDataMain, + generateFermentExercises: generateFermentExercises, + generateFermentationFeedBack: generateFermentationFeedBack + }; +.... + +* httpMethod: Die HTTP-Methode, die für den API-Aufruf verwendet wird (z.B. POST). +* params: Hier können Parameter angegeben werden, die an die API übergeben werden. In diesem Fall handelt es sich um ein "parameters"-Objekt. + +===== Worker : + +* FermentExercisesTask: Der Worker-Abschnitt beschreibt, wie Aufgaben bearbeitet werden. +* minConsumers: Gibt an, wie viele "Consumer" (Verbraucher) erforderlich sind, um die Aufgabe zu verarbeiten. +* consumerInstructions: Hier wird beschrieben, wie die Aufgabe ausgeführt wird, mit einer generateFermentExercises-Funktion, die Asynchronität unterstützt. +* dependencies: Definiert, dass die generateFermentationDataMain-Funktion von sich selbst abhängt (dies könnte für spätere Erweiterungen oder Abhängigkeiten nützlich sein). + +===== UI: + +* currentTask: Gibt den aktuellen Task an, der im UI bearbeitet wird (hier "Ferment"). +* components: In dem Abschnitt wird eine oder mehrere Vue-Komponenten definiert, die im Frontend implementiert sind. Hier werden sie aufgerufen und mit den nötigen Parametern initialisiert. Das bedeutet, dass man in diesem Abschnitt die UI-Komponenten definiert, die auf der Seite angezeigt werden sollen, und ihre spezifischen Parameter oder Daten, die von der Webanwendung benötigt werden. + +** "3" : Der Schlüssel "3" dient dazu, diese spezielle Komponente zu identifizieren. +** "type" : hier wird definiert, welche Vue-Komponente aufgerufen wird. + *** Unter src/components im CARPET-Verzeichnis sind verschiedene *Vue-Komponenten* implementiert, die mehrfach verwendet werden können. + + Vue-Komponent definieren + Nachdem Sie eine Vue-Datei erstellt haben, müssen Sie diese in der Datei Canvas.vue (src/components/Canvas.vue) importieren und im export default-Objekt registrieren, damit die Komponente global verwendet werden kann. + Beispiel : + // In Canvas.vue + import MyComponent from './components/MyComponent.vue'; + + export default { + name: 'Canvas', + components: { + MyComponent + } + }; + +** "name" : Der Name der Komponente wird hier definiert, in diesem Fall "Fermentationsverlauf". Das könnte der Titel oder die Bezeichnung der Checkbox-Gruppe sein, die im UI angezeigt wird. +** "component" : unter diesem Schlüssel werden die Paramater der Vue-Komponent (in diesem Fall Chart.vue) initialisiert. + +==== Ferment-Benutzeroberfläche +===== Startseite + +image::./images/ALADIN-Startseite.png[] + +===== Ferment-Seite + +In diesem Modul können Studierende ihre Eingaben variieren und Fermentationsprozesse unbegrenzt experimentell erkunden. Die Ergebnisse werden in Diagrammen visualisiert, um eine bessere Analyse zu ermöglichen. + +image::./images/Ferment-Startseite.png[] + +* Modell Parameter: ermöglicht dem Nutzer die Auswahl eines von drei Organismen: S. cerevisiae, E. coli oder Testorganismus. Anschließend kann der Nutzer die gewünschte Anzahl an Phasen festlegen. Die Anzahl der Phasen beeinflusst die Tabelle zur Eingabe der Daten, bei x Phasen werden entsprechend x Zeilen in der Tabelle angezeigt werden. +** Generieren! : Sobald der Studierende seine Eingaben abgeschlossen hat, kann er auf diesen Button klicken, um Diagramme basierend auf seinen Eingaben anzuzeigen. + +* Fermentationsapparat: hier können verschiedene Parameter gesteuert werden. Die im Bild dargestellten Parameter wie V (Volumen), T (Temperatur), BTM , pH (pH-Wert) und DO (gelöster Sauerstoff) sind für alle Phasen eingestellt. + + +* Fermentationsparameter: Hier können die Werte für die einzelnen Phasen und Parameter eingetragen werden. Jede Spalte hat einen festgelegten Bereich. Wenn der eingegebene Wert innerhalb des zulässigen Bereichs liegt, wird das Feld *grün* angezeigt. Liegt der Wert außerhalb des Bereichs, wird das Feld *rot* markiert. + +* Fermentationsverlauf & Fermentationsverlauf Cs2: Diese beiden Komponenten dienen der Anzeige der Diagramme, die die Ergebnisse der Eingaben visuell darstellen. + +image::./images/Ferment-Startseite2.png[] + +* Herunterladen-Buttons: Der Nutzer hat die Möglichkeit die Diagramme als Bild zu herunterladen. + +=== FermentExcercises + +*Der Python-Code* befindet sich im Verzeichnis ./src/Tasks/FermentExcercises, während die *FermentExcercises.json* Datei unter ./src/server/tempTaskGraphStorage/tasks/FermentExcersises.json gespeichert ist. + +==== FermentExcercises.json +Die FermentExercise.json ist also eine zentrale Konfigurationsdatei für die Webanwendung, die speziell für Fermentationsübungen (FermentExercises) gedacht ist. Sie ist strukturiert in verschiedene Abschnitte, um die Kommunikation mit der API, die Verarbeitung von Aufgaben im Worker-Bereich und die Darstellung der UI (Benutzeroberfläche) zu ermöglichen. + + +* Beispiel für FermentExercise.json: +.... +{ + "API": [ + { + "task": "FermentExercises", + "name": "generateFermentationDataMain", + "httpMethod": "post", + "params": { + "parameters": "object" + } + } + ], + "worker": { + "FermentExercisesTask": { + "minConsumers": 1, + "consumerInstructions": { + "generateFermentationDataMain": { + "dependencies": ["generateFermentationDataMain"], + "body": "async (taskDescription) => {let result = {};try { result = generateFermentationDataMain(taskDescription)} catch{} return result; }" + } + } + } + }, + "UI": { + "currentTask": "FermentExercises", + ... + ... + "components": { + "2": { + "type": "CheckboxGroup", + "name": "Zu variierende Parameter", + "isValid": true, + "component": { + "options": [ + "Zuluft", + "BolusC", + "Feed", + "BolusN", + "Drehzahl", + "Druck", + "Dauer" + ], + "jsonData": {} + } + } + } + } +} +.... + +===== Detaillierte Erklärung der Abschnitte +====== API : + +* task: In welchem Task befinden (hier "FermentExercises"). +* name: Der Name der Funktion, die in der API ausgeführt werden soll (z.B. "generateFermentationDataMain"). + ** Alle nötige Funktionen müssen in eine *wrapperFunctionCallPython.ts* implementiert werden. + +.... + export async function generateFermentationDataMain(parameter: any) { + let check = editSaveParams(parameter); + if (check) { + let result = { foo: "bar" }; + try { + let pythonScriptPath = "generateFermentationData.py"; + pythonScriptPath = path.join(__dirname, pythonScriptPath); + (await runPythonScript(pythonScriptPath)) as any; + + result = JSON.parse(fs.readFileSync(path.join(__dirname, "data.json"), "utf-8")); + const filePath = path.join(__dirname, "data.json"); + fs.writeFileSync(filePath, JSON.stringify(result, null, 2), "utf-8"); + + //console.log(result); + //insofern kryptsiche Zeichen in der JSON Ausgabe vorhanden sind .> Workaround mittels File + } catch (error) { + console.error("Error running Python script:", error); + } + return result; + } else console.error("Die Daten aus Frontend konnten nicht in JSON-Datei gespeichert"); + return null; +} +.... + + * Dann müsste diese Datei *wrapperFunctionCallPython.ts* in *TaskWrapper.ts* (./src/server/TaskWrapper.ts) importiert und in "generators" gesetzt werden : + +.... + import { generateFermentationDataMain } from "../Tasks/Ferment/wrapperFunctionCallPython"; + import { generateFermentExercises } from "../Tasks/FermentExercises/wrapperFunctionCallPython"; + import { generateFermentationFeedBack } from "../Tasks/Ferment/wrapperFunctionCallPython" + ... + ... + // TODO generalize generators into serialisable functions + const generators: { [key: string]: any } = { + GozintographTaskGenerator: GozintographTaskGenerator, + ... + generateFermentationDataMain: generateFermentationDataMain, + generateFermentExercises: generateFermentExercises, + generateFermentationFeedBack: generateFermentationFeedBack + }; +.... + +* httpMethod: Die HTTP-Methode, die für den API-Aufruf verwendet wird (z.B. POST). +* params: Hier können Parameter angegeben werden, die an die API übergeben werden. In diesem Fall handelt es sich um ein "parameters"-Objekt. + +====== Worker : + +* FermentExercisesTask: Der Worker-Abschnitt beschreibt, wie Aufgaben bearbeitet werden. +* minConsumers: Gibt an, wie viele "Consumer" (Verbraucher) erforderlich sind, um die Aufgabe zu verarbeiten. +* consumerInstructions: Hier wird beschrieben, wie die Aufgabe ausgeführt wird, mit einer generateFermentExercises-Funktion, die Asynchronität unterstützt. +* dependencies: Definiert, dass die generateFermentationDataMain-Funktion von sich selbst abhängt (dies könnte für spätere Erweiterungen oder Abhängigkeiten nützlich sein). + +====== UI: + +* currentTask: Gibt den aktuellen Task an, der im UI bearbeitet wird (hier "FermentExercises"). +* components: In dem Abschnitt wird eine oder mehrere Vue-Komponenten definiert, die im Frontend implementiert sind. Hier werden sie aufgerufen und mit den nötigen Parametern initialisiert. Das bedeutet, dass man in diesem Abschnitt die UI-Komponenten definiert, die auf der Seite angezeigt werden sollen, und ihre spezifischen Parameter oder Daten, die von der Webanwendung benötigt werden. + +** "2" : Der Schlüssel "2" dient dazu, diese spezielle Komponente zu identifizieren. +** "type" : hier wird definiert, welche Vue-Komponente aufgerufen wird. +** "name" : Der Name der Komponente wird hier definiert, in diesem Fall "Zu variierende Parameter". Das könnte der Titel oder die Bezeichnung der Checkbox-Gruppe sein, die im UI angezeigt wird. +** "component" : unter diesem Schlüssel werden die Paramater der Vue-Komponent (in diesem Fall CheckboxGroup.vue) initialisiert. + + + +==== Button im Frontend zur Übermittlung von Benutzereingaben an das Backend + +===== Backendformular (in CARPET) +Das 'Backendformular' ist eine Vue-Datei, die im Backend als Typ (in FermentExcercise.json) verwendet wird. + +Die *Backendformular.vue-Komponente* stellt ein dynamisches Formular zur Eingabe und Verarbeitung von Parametern bereit. Sie rendert verschiedene Formularelemente basierend auf ihrer formType-Eigenschaft und aktualisiert deren Werte über updateElement(). *Die Methode fetchData() erstellt ein payload-Objekt mit den aktuellen Eingaben und sendet es an das Backend*. Zusätzlich kann saveFetchData() die Startparameter aus einer JSON-Datei aktualisieren. Die Komponente nutzt computed, um Änderungen automatisch zu verfolgen, und ActionButtons, um verfügbare Aktionen zu steuern. + +===== Die Funktion preparePayload +Die Funktion preparePayload sammelt alle Werte aus den Formularelementen und strukturiert sie in einem Objekt, das für den Backend-Aufruf benötigt wird. Dabei werden verschiedene Typen von Formularelementen berücksichtigt, wie Zahlenwerte, Bereiche oder Dropdowns. Falls zusätzliche Daten im System gespeichert sind, werden diese ebenfalls ergänzt. Schließlich wird das Objekt um die aktuelle Aufgabe (currentTask) und die gewünschte Aktion (instruction) erweitert. + +.... + +const preparePayload = (instruction) => { + const parameters: { [key: string]: any } = Object.entries(elements.value).reduce( + (parameters, [name, parameter]: [string, { [key: string]: any }]) => { + const { formType, initial } = parameter; + let payload = { ...parameters, [name]: initial }; + if (formType === "RangeFormField") payload[name] = [initial.lowerValue, initial.upperValue]; + if (formType === "ValueFormField") payload[name] = parameter.value; + const dataPfad = computed(() => `${path}__component__data`); + if (dataPfad != null) { + const data = computed(() => getProperty(`${path}__component__data`)); + Object.entries(data.value).forEach(([key, valuePath]) => { + const value = getProperty(valuePath); + payload[key] = value; + }); + } + + return payload; + }, + {} + ); + const payload: { [key: string]: any } = { parameters }; + payload.type = currentTask.value; + payload.task = currentTask.value; + payload.instruction = instruction; + + return payload; + }; +.... + +===== Die Funktion fetchData +Die Funktion fetchData nutzt preparePayload, um die Nutzereingaben zu erfassen. Falls ein Speicherpfad für bereits abgerufene Daten existiert, werden die neuen Eingaben dort gesichert. Anschließend wird das fertige Payload-Objekt in der Konsole ausgegeben und über eine store.dispatch-Funktion an das Backend gesendet. Damit ermöglicht die Funktion die Kommunikation zwischen der Benutzeroberfläche und dem Backend, indem sie die Eingaben verarbeitet und weiterleitet. + +.... +const fetchData = (instruction) => { + const payload = preparePayload(instruction); + if(saveFetchedDataInTemplate_Path.value != null) saveFetchData(payload); + console.log("Payload:", payload); // Ausgabe des Payloads in der Konsole + store.dispatch("fetchTaskData", { + payload: preparePayload(instruction), + endpoint: `${currentTask.value}/${instruction}` + }); + }; +.... + +===== Backendformular in FermentExcercise.json benutzen + +.... + "0": { + "type": "BackendFormular", + "name": "Diagramm generieren", + "isValid": true, + "component": { + "actions": [ + { + "instruction": "generateFermentExercises", + "type": "fetchData", + "label": "Generieren!", + "dependsOn": [ + "nodes__0__components__0__component__form__nodeAmount__isValid", + "nodes__0__components__0__component__form__seed__isValid", + "nodes__0__components__1__isValid" + ] + } + ], + "form": { + "nodeAmount": { + "isValid": true, + "formType": "DropdownFormField", + "label": "Organismus", + "type": "string", + "description": "Zur Verfügung stehende Organismus", + "action": { "instruction": "fetchTaskData", "type": "fetchData", "key": "schema" }, + "initial": "S.cerevisiae", + "options": ["S.cerevisiae", "E.coli", "Testorganismus"], + "presets": { + "easy": "S.cerevisiae", + "medium": "E.coli", + "hard": "Testorganismus" + } + }, + "seed": { + "formType": "ValueFormField", + "label": "Phasenanzahl", + "type": "String", + "description": "Setze einen beliebigen Wert für die Phasenanzahl", + "value": "3", + "boundaries": { "min": 0, "max": 20 }, + "readOnly": false, + "validate": true, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + } + ... + ... + } + } + } +.... + + +* Aktionen (actions) : +Das Formular enthält eine Schaltfläche "Generieren!", die eine Aktion ausführt: + +** instruction: "generateFermentExercises" → Diese Funktion im Backend wird ausgeführt, wenn der Button gedrückt wird. +** type: "fetchData" → Es wird eine Anfrage an das Backend gesendet. +** dependsOn: Die Aktion wird nur aktiviert, wenn bestimmte Felder gültige Werte haben: +*** nodeAmount (Organismus) +*** seed (Phasenanzahl) +*** Ein weiteres Formularfeld (nodes__0__components__1__isValid) +. Formularfelder (form): + +Das Formular kann aus beliebige Eingabefeldern bestehen. + +Jeder Schlüssel im Abschnitt form entspricht einer Vue-Komponente, die aufgerufen wird, wobei ihre Parameter entsprechend den definierten Werten initialisiert werden. + +==== FermentExcercises-Oberfläche + +Dieses Modul bietet zwei Benutzeroberflächen und ermöglicht es Professoren, gezielte Aufgaben zu erstellen, die von Studierenden gelöst werden sollen. + +* *Professoren-Oberfläche*: Hier können Professoren Aufgaben generieren und deren Parameter definieren. +* *Studierenden-Oberfläche*: Studierende versuchen, die gestellten Aufgaben zu lösen und erhalten Feedback zu ihrer Lösung. + +===== Professoren-Oberfläche + +image::./images/ProfessorenSeite.png[] + +In der Professoren-Oberfläche stehen den Professoren drei Hauptkomponenten zur Verfügung: + +. *Aufgaben Parameter* : +Festlegung der Anzahl der Phasen und der Art des Organismus. +Eingabe der globalen Parameterwerte, die für alle Phasen gelten. +Auswahl der zu optimierenden Parameter, z. B. welche Werte maximiert oder minimiert werden sollen. + +. *Modell Parameter* : +Hier können Professoren definieren, welche Parameter bei der Optimierung berücksichtigt werden. +* *Beispiel*: Wenn der Feed-Wert zwischen 0 und 5 liegen soll, wird das Parameter „Feed“ ausgewählt, mit einem Mindestwert von 0 und einem Höchstwert von 5. + +. *Fermentationsparameter* : +Eine Tabelle, in der für jede Phase spezifische Werte der Parameter eingetragen werden können. + +Nachdem der Professor/in das Ausfüllen abgeschlossen hat, kann er/sie auf den Button „Generieren!“ klicken. Dadurch wird die Aufgabe erstellt und direkt auf der Seite der Studierenden angezeigt. + +Das folgende Bild zeigt ein Beispiel für das Ausfüllen der Aufgabe. + +image::./images/ProfessorenSeite2.png[] + + +===== Studierenden-Oberfläche + +In der Studierenden-Oberfläche wird die Seite wie folgt angezeigt, solange noch keine Aufgabe von der Professorin oder dem Professor generiert wurde. + +image::./images/StudierendenSeite.png[] + +Sobald eine Aufgabe generiert wurde, sieht die Seite wie folgt aus. + +image::./images/StudierendenSeite2.png[] + +In der Studierenden-Oberfläche stehen dem Studierenden sieben Komponenten zur Verfügung: + +. **Organismus und Phasen** : Diese Informationen sind nur zum Lesen zugänglich und zeigen den ausgewählten Organismus sowie die Phasen. + +* *Buttons* : + +** *Generieren* : Sobald der Studierende seine Eingaben abgeschlossen hat, kann er auf diesen Button klicken, um Diagramme basierend auf seinen Eingaben anzuzeigen. +** *Abgeben* : Beim Klicken auf diesen Button wird dem Studierenden Feedback zu seinen Eingaben angezeigt, das ihm zeigt, wie nahe er der Lösung ist und ob seine Eingaben für jede Phase korrekt sind. + +. **Feedback** : Hier wird eine detaillierte Rückmeldung zur Lösung des Studierenden angezeigt. + +. **Aufgabe** : Die Aufgabe, die der Studierende lösen muss, wird angezeigt. + +. **Fermentationsapparat** : Diese Komponente zeigt die Parameter, die für alle Phasen gelten. + +. **Fermentationsparameter**: Hier können die Werte für die einzelnen Phasen und Parameter eingetragen werden. Jede Spalte hat einen festgelegten Bereich. Wenn der eingegebene Wert innerhalb des zulässigen Bereichs liegt, wird das Feld *grün* angezeigt. Liegt der Wert außerhalb des Bereichs, wird das Feld *rot* markiert. + +. **Fermentationsverlauf & Fermentationsverlauf Cs2** : Diese beiden Komponenten dienen der Anzeige der Diagramme, die die Ergebnisse der Eingaben visuell darstellen. + +Wenn der Studierende mit der Aufgabe fertig ist, kann er auf „Generieren“ und dann „Abgeben“ klicken. Die Anzeige wird dann wie folgt aussehen. + +image::./images/StudierendenSeite3.png[] + +Wenn ein Feld korrekt ausgefüllt ist, erhält der Studierende ein Feedback, dass die Eingabe richtig war. Das Feld wird daraufhin gesperrt, und der Studierende muss sich auf die verbleibenden Felder konzentrieren, um die Aufgabe zu lösen. Bei den Diagrammen hat der Studierende zudem die Möglichkeit, das Diagramm herunterzuladen. + + +==== Im folgenden Sequenzdiagramm wird der Ablauf dargestellt + +image::./images/Sequenzdiagramm1.png[] + +Das Sequenzdiagramm beschreibt den Prozess der Erstellung, Bereitstellung und Bewertung einer Aufgabe in einem digitalen System mit vier Beteiligten: der Lehrperson, der Generierungsseite, dem Webserver und der Aufgabenseite. Die Lehrperson gibt die Daten zur Aufgabenerstellung ein, woraufhin die Generierungsseite die Daten als JSON-Datei an den Webserver sendet. Der Webserver erstellt eine optimierte Lösung und speichert sie. Die Aufgabenseite fordert die Aufgabe vom Webserver an und stellt sie den Studierenden zur Verfügung. Nach Bearbeitung der Aufgabe senden die Studierenden ihre Lösung zurück an den Webserver, der diese überprüft und Feedback gibt. + +== Aladin local starten + +=== Docker +* Starten Sie Aladin in Docker + +image::./images/Docker.png[] + +=== Aldin-Verzeichniss in VsCode + +Öffnen Sie das Aladin-Verzeichnis in VS Code, starten Sie anschließend ein 'Git Bash'-Terminal und führen Sie die folgenden Befehle aus: + +* ts-node ./src/server/server.ts + +Nachdem ausführen muss so angezeigt : + +image::./images/Aladin-VsCode.png[] + +=== Carpet-Verzeichniss in VsCode + +Öffnen Sie das Carpet-Verzeichnis in VS Code, starten Sie anschließend ein 'Git Bash'-Terminal und führen Sie die folgenden Befehle aus: + +* npm run dev + +image::./images/Carpet-VsCode.png[] + +* Dann jetzt besuchen Sie den angezeigten Link + +image::./images/ALADIN-Startseite.png[] + + +== Schluss + +FermentALADIN bietet eine innovative und zukunftsweisende Lösung für die interaktive Vermittlung chemischer Prozesse. Die Plattform ermöglicht es Studierenden, Fermentationsprozesse in einem sicheren, virtuellen Umfeld praxisnah zu erlernen. Dabei fördert sie das Verständnis komplexer chemischer Abläufe durch detaillierte Datenanalysen und anschauliche Visualisierungen. + +Neben der flexiblen Nutzung durch Studierende eröffnet FermentALADIN auch Lehrenden die Möglichkeit, individuelle Aufgabenstellungen zu erstellen und ihre Lehrmethoden zu erweitern. + +Insgesamt trägt FermentALADIN dazu bei, die Ausbildung im Bereich der chemischen Prozesse effizienter, kostengünstiger und praxisorientierter zu gestalten. \ No newline at end of file diff --git a/src/Tasks/FermentExercises/data.json b/src/Tasks/FermentExercises/data.json new file mode 100644 index 0000000..691afed --- /dev/null +++ b/src/Tasks/FermentExercises/data.json @@ -0,0 +1 @@ +{"data_1": {"labels": [0.0, 0.100418410041841, 0.200836820083682, 0.301255230125523, 0.401673640167364, 0.502092050209205, 0.602510460251046, 0.702928870292887, 0.803347280334728, 0.9037656903765691, 1.00418410041841, 1.104602510460251, 1.205020920502092, 1.3054393305439331, 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b/src/Tasks/FermentExercises/differentialgleichung.py @@ -0,0 +1,95 @@ + + + +import numpy as np + + + +def ODE_Bioreactor_Monod(t, p, consts) : + + + #Entnehmen der Kostanten aus consts + + c_ox_sat = consts[0] #Sauerstofflöslichkeit in der Fermentation + kla = consts[1] #kla Wert in 1/h Info eine OTR von 100 mmol/(L*h) entspricht bei DO=0% ~ einem kla von 400 1/h + RQ = consts[2] #Respiratory Coefficient + Begasungsrate = consts[3] #Begasungsrate in NL(Luft)/(L(Kulturbrühe)*min) quasi vvm + c_O2_Luft = consts[4] #Sauerstoffgehalt Luft in mol/L + c_CO2_Luft = consts[5] #CO2 Gehalt der Luft in mol/L + ks_ox = consts[6] #Halbsaettigungskonstante Sauerstoff in g/L + Yxox = consts[7] #Ausbeutekoeffizient g Biomasse je g Sauerstoff nach HArvard Bionumbers für E.coli + P=consts[8] #Produktbildung an =1 oder aus =0 + ap = consts[9] #Luedking Piret Parameter für wachsumassoziierte Produktbildung + bp = consts[10] #Luedking Piret Parameter für nicht wachsumassoziierte Produktbildung + kp_max = consts[11] #maximale spezifische Produktbildungsrate in g(P)/(g(x)*L*h) nicht wachstumsassoziierte Produktbildung + km_s1p = consts[12] #Km Wert in Analogie zur Michaelis Menten-Gleichung für die Bildung von P aus S - nicht wachstumsassoziiert + Ypx_mu = consts[13] #Ausbeutekoeffizient Produktbildung direkt wachstumsassoziiert aus Substrat + mumax =consts[14] #maximale Wachstumsrate in 1/h + ks_s1x=consts[15] #Halbsaettigungskonstante Substrat in g/L für Berechnung von Wachstumsrat µ + Yxs1=consts[16] #Ausbeutekoeff fuer Zellmasse in g/g + ks_s2x=consts[17] #Halbsaettigungskonstante Ks für Substrat S2 in g/L für Berechnung von Wachstumsrat µ + Yxs2=consts[18] #Ausbeutekoeff fuer Zellmasse auf Substrat 2 1 in g/g + feed_s1=consts[19] #!Feedrate Substrat 1 in g/L*h + + + #Zuweisungen der Eingangswerte / = Konzentrationen die das Model berechnet + + cx = p[0] #!Konzentration Biotrockenmasse in g/L + cs1 = p[1] #!Substratkonzentration 1 in g/L + cs2 = p[2] #Substratkonzentration 2 in g/L + cp = p[3] #!Produktkonzentration in g/L + c_ox = p[4] #Gelöstsaurestoffkonznetration in g/L + c_O2_Out = p[5] #Abluftkonzentration O2 + c_CO2_Out = p[6] #Abluftkonzentration CO2 + + #Nebenrechnungen + + Vm_norm=22.41950113 #molares Volumen bei Normbedingungen + Luft_In=Begasungsrate*60 #ZuLuftstrom Luft in NL/(L_Brühe*h) + Luft_Out=Luft_In #Molenstrom Abluft in mol/(L*h) ; evtl. mal erweitern wenn Luft_Out <> Luft_In + #Sauerstoff als limitierendes Substrat + OTR=kla*(c_ox_sat - c_ox) #in g/(L*h) + + + #Modellgleichungen + #spez. Wachstumsrate + mu_s = mumax*cs1/(ks_s1x+cs1)*cs2/(ks_s2x+cs2)*c_ox/(ks_ox+c_ox); # Monod Kinetik fuer spezifische Wachstumsrate + #k_ox = 0.01*cs1/(ks_ox+cs1); %nicht wachstumsabhängige Sauerstoffverbrauchsrate, beschrieben als MichaMenten Kinetik %%%%CHANGE!! + #Produktbildungsrate + kp = kp_max*cs1/(km_s1p+cs1); #Michaelis Menten Kinetik für nicht wachstumssassoziierte Produktbildung + + #OUR=cx*(mu_s/Yxox-k_ox); + OUR=cx*(mu_s/Yxox) + #Begasung / Sauerstoff + CER=OUR/32*RQ; #Umrechung von g/L in mol/L + + + + value_0 = float(mu_s * cx) + + #Substratverbrauchsgeschwindigkeit Substrat 1 durch Biomassebildung und Produktbildung + value_1= float(cx*(-mu_s/Yxs1)-P*((mu_s*cx*Ypx_mu)*ap+(cx*kp)*bp)+feed_s1) + + #Substratverbrauchsgeschwindigkeit Substrat 2 durch Biomassebildung NICHT Genutzt zur Produktbildung Produktbildung + value_2= float(cx*(-(mu_s/Yxs2))) + + #Produktbildung nach Luedeking Piret; + value_3= float(P*((mu_s*cx*Ypx_mu)*ap+(cx*kp)*bp)) + + #Berechnung Gelöstsauerstoffkonzentration (OTR-OUR) mit Nebenbedingung + #darf nicht NUll sein + value_4= float(kla*(c_ox_sat-c_ox)-cx*(mu_s/Yxox)) + + #Berechnung O2-gehalt in Vol% Abluf IN Analogie zu einem CSTR F(C_in-Cout)-Reaktion + value_5= float(Luft_In*c_O2_Luft-Luft_Out*c_O2_Out-OUR/32*Vm_norm) + + #Berechnung CO2-gehalt in Vol% Abluf IN Analogie zu einem CSTR F(C_in-Cout)-Reaktion + value_6= float(Luft_In*c_CO2_Luft-Luft_Out*c_CO2_Out+CER*Vm_norm) + + retval = np.array([value_0,value_1,value_2,value_3,value_4,value_5,value_6]) + + + return retval + + + diff --git a/src/Tasks/FermentExercises/excel_export.py b/src/Tasks/FermentExercises/excel_export.py new file mode 100644 index 0000000..531add7 --- /dev/null +++ b/src/Tasks/FermentExercises/excel_export.py @@ -0,0 +1,27 @@ + +from openpyxl import Workbook +import os + + +def export_to_excel(filename, t_combined, y_combined, cum_feeding): + y_combined=y_combined.T + if os.path.exists(filename): + os.remove(filename) + + wb = Workbook() + ws = wb.active + + caption = ['t', 'Biotrockenmasse (cx)', 'Konz. Substrat 1 (cs1)', 'Konz. Substrat 2 (cs2)', + 'Konz. Produkt (cp)', 'c_O2 (Lösung)', 'c_O2 (Abluft)', 'c_CO2 (Abluft)', + 'kumulatives Feeding Substrat 1'] + + for i, header in enumerate(caption): + ws.cell(row=1, column=i + 1, value=header) + + for i in range(len(t_combined)): + row_data = [t_combined[i]] + [y_combined[j, i] for j in range(y_combined.shape[0])] + [cum_feeding[i]] + for j, value in enumerate(row_data): + ws.cell(row=i + 2, column=j + 1, value=value) + + + wb.save(filename) \ No newline at end of file diff --git a/src/Tasks/FermentExercises/generateFermentExercises.py b/src/Tasks/FermentExercises/generateFermentExercises.py new file mode 100644 index 0000000..e92c32f --- /dev/null +++ b/src/Tasks/FermentExercises/generateFermentExercises.py @@ -0,0 +1,67 @@ +import json +import numpy as np + +from excel_export import export_to_excel +from json_to_excel import json_to_excel +from nebenrechnungen import Berechnung_der_Sauerstoffloeslichkeit +from nebenrechnungen import Berechnung_des_kla_Wertes +from nebenrechnungen import parameter + + +from berechnungen import berechnung_der_Tabelle1 +from berechnungen import berechnung_der_Tabelle2 +from berechnungen import berechnung_der_Tabelle3 +from berechnungen import berechnung + +from interne_daten.data_importieren import data_importieren_von_json + +from Input.json_Input import JsonInput + +# Example data for the variables +#c_ox_sat = 0.21 # Example value for oxygen saturation concentration +#t_combined = np.linspace(0, 10, 100) # Example time data +#y_combined = np.random.rand(5, 100) # Example concentration data with 5 different concentrations over time +#cum_feeding = np.cumsum(np.random.rand(100)) # Example cumulative feeding data + +#c_ox_sat = None # Oxygen saturation concentration +#t_combined = None # Time data +#y_combined = None # Concentration data +#cum_feeding = None # Cumulative feeding data + + + #Modelle aus modelle.jsom laden +# Modelle aus modelle.json laden + +def generateFermentExercises(): + modelleOb = JsonInput('modelle.json') + modelleOb.ladeJson() + modelle = modelleOb.get_data() + + # Zum Test im Meeting + m = modelleOb.get_Value() + + # Input aus input.json laden + eingabeOb = JsonInput('FrontendEingaben.json') + eingabeOb.ladeJson() + eingabe = eingabeOb.get_Value() + + # Berechnungen durchführen + Sauerstoffoeslichkeit_array = Berechnung_der_Sauerstoffloeslichkeit(eingabe[1], eingabe[9], eingabe[3]) + kla_werte = Berechnung_des_kla_Wertes(Sauerstoffoeslichkeit_array, modelle, eingabe[4], eingabe[0], eingabe[5], eingabe[1]) + parameter_werte = parameter(Sauerstoffoeslichkeit_array, kla_werte, modelle, eingabe[1], eingabe[0]) + const_array = berechnung_der_Tabelle1(parameter_werte, eingabe[0], modelle, eingabe[5], eingabe[7], eingabe[1]) + t_ranges_array = berechnung_der_Tabelle2(eingabe[2], eingabe[1]) + param_array = berechnung_der_Tabelle3(const_array, eingabe[10], eingabe[6], eingabe[8], eingabe[11], eingabe[1]) #!eingabe[6] => BolusC ; param_array[i][1]=float(bolus_c[i]) param_array[i][2]=float(bolus_n[i]) + + # Berechnung ausführen + ergebnis = berechnung(t_ranges_array, param_array, const_array, eingabe[1],eingabe[12], eingabe[13], eingabe[14], eingabeOb.get_data()) + c_ox_sat, y_combined, t_combined, cum_feeding = ergebnis + + + #Export Resultat in Excel-Datei + #export_to_excel("model_result.xlsx", t_combined, y_combined, cum_feeding) + #plot_visualisieren(c_ox_sat, y_combined, t_combined, cum_feeding) + + +if __name__ == "__main__": + generateFermentExercises() \ No newline at end of file diff --git a/src/Tasks/FermentExercises/images/ALADIN-Startseite.png b/src/Tasks/FermentExercises/images/ALADIN-Startseite.png new file mode 100644 index 0000000..edaf929 Binary files /dev/null and b/src/Tasks/FermentExercises/images/ALADIN-Startseite.png differ diff --git a/src/Tasks/FermentExercises/images/Aladin-VsCode.png b/src/Tasks/FermentExercises/images/Aladin-VsCode.png new file mode 100644 index 0000000..1cf123b Binary files /dev/null and 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b/src/Tasks/FermentExercises/interne_daten/FrontendEingaben.json @@ -0,0 +1,50 @@ +{ + "Modell": "Modell-1", + "PhasenAnzahl": 3, + "Dauer": [ + 10, + 10, + 10 + ], + "Druck": [ + 1, + 1, + 1 + ], + "Drehzahl": [ + 100, + 100, + 100 + ], + "Zuluft": [ + 1, + 1, + 1 + ], + "BolusC": [ + 1, + 1, + 1 + ], + "Feed": [ + 1, + 1, + 1 + ], + "BolusN": [ + 1, + 1, + 1 + ], + "temperatur": 0, + "startbiomasse": 0, + "do": 0, + "maxParameter": "Cp", + "minParameter": "Cx", + "varierendeParameter": { + "Feed": { + "min": 0, + "max": 5 + } + } +} \ No newline at end of file diff --git a/src/Tasks/FermentExercises/interne_daten/Input_Leser.py b/src/Tasks/FermentExercises/interne_daten/Input_Leser.py new file mode 100644 index 0000000..24f6d3f --- /dev/null +++ b/src/Tasks/FermentExercises/interne_daten/Input_Leser.py @@ -0,0 +1,60 @@ +import json +import os + +class ParameterLeser: + + @staticmethod + def lade_parameter(datei_name): + current_dir = os.path.dirname(os.path.realpath(__file__)) + # Vollständiger Pfad zur JSON-Datei + json_path = os.path.join(current_dir, datei_name) + with open(json_path) as f: + return json.load(f)['Parameter'] + + @staticmethod + def get_modell(parameter): + return parameter['Modell'] + + @staticmethod + def get_phasenanzahl(parameter): + return parameter['PhasenAnzahl'] + + @staticmethod + def get_dauer(parameter): + return parameter['Dauer']['DauerArray'] + + @staticmethod + def get_druck(parameter): + return parameter['Druck']['DruckArray'] + + @staticmethod + def get_drehzahl(parameter): + return parameter['Drehzahl']['DrehzahlArray'] + + @staticmethod + def get_zuluft(parameter): + return parameter['Zuluft']['ZuluftArray'] + + @staticmethod + def get_bolus_c(parameter): + return parameter['BolusC']['BolusCArray'] + + @staticmethod + def get_futter(parameter): + return parameter['Feed']['FeedArray'] + + @staticmethod + def get_bolus_n(parameter): + return parameter['BolusN']['BolusNArray'] + + @staticmethod + def get_temperatur(parameter): + return parameter['Temperatur']['temperatur'] + + @staticmethod + def get_start_biomasse(parameter): + return parameter['Startbiomasse']['startbiomasse'] + + @staticmethod + def get_do(parameter): + return parameter['DO']['do'] \ No newline at end of file diff --git a/src/Tasks/FermentExercises/interne_daten/Modelle_Leser.py b/src/Tasks/FermentExercises/interne_daten/Modelle_Leser.py new file mode 100644 index 0000000..d5eae9b --- /dev/null +++ b/src/Tasks/FermentExercises/interne_daten/Modelle_Leser.py @@ -0,0 +1,101 @@ +import json +import os + + +class ModellLeser: + + @staticmethod + def lade_modelle(datei_name): + current_dir = os.path.dirname(os.path.realpath(__file__)) + # Vollständiger Pfad zur JSON-Datei + json_path = os.path.join(current_dir, datei_name) + with open(json_path) as f: + return json.load(f) + + @staticmethod + def get_beschreibung(modelle, modellname): + return modelle[modellname]['Beschreibung'] + + @staticmethod + def get_mikroorganismus(modelle, modellname): + return modelle[modellname]['Mikroorganismus'] + + @staticmethod + def get_substrat_1(modelle, modellname): + return modelle[modellname]['Substrat 1'] + + @staticmethod + def get_substrat_2(modelle, modellname): + return modelle[modellname]['Substrat 2'] + + @staticmethod + def get_produkt_1(modelle, modellname): + return modelle[modellname]['Produkt 1'] + + @staticmethod + def get_umax(modelle, modellname): + return modelle[modellname]['umax'] + + @staticmethod + def get_ks_s1x(modelle, modellname): + return modelle[modellname]['Ks_s1x'] + + @staticmethod + def get_ks_s2x(modelle, modellname): + return modelle[modellname]['Ks_s2x'] + + @staticmethod + def get_yxs1(modelle, modellname): + return modelle[modellname]['Yxs1'] + + @staticmethod + def get_yxs2(modelle, modellname): + return modelle[modellname]['Yxs2'] + + @staticmethod + def get_ks_ox(modelle, modellname): + return modelle[modellname]['ks_ox'] + + @staticmethod + def get_rq(modelle, modellname): + return modelle[modellname]['RQ'] + + @staticmethod + def get_yxox(modelle, modellname): + return modelle[modellname]['Yxox'] + + @staticmethod + def get_produktbildung(modelle, modellname): + return modelle[modellname]['Produktbildung'] + + @staticmethod + def get_ap(modelle, modellname): + return modelle[modellname]['ap'] + + @staticmethod + def get_bp(modelle, modellname): + return modelle[modellname]['bp'] + + @staticmethod + def get_kp_max(modelle, modellname): + return modelle[modellname]['kp_max'] + + @staticmethod + def get_km_s1p(modelle, modellname): + return modelle[modellname]['km_s1p'] + + @staticmethod + def get_ypx_mu(modelle, modellname): + return modelle[modellname]['Ypx_mu'] + + @staticmethod + def get_dichte_bruehe(modelle, modellname): + return modelle[modellname]['Dichte Bruehe'] + + @staticmethod + def get_dyn_viscosity(modelle, modellname): + return modelle[modellname]['dyn. Viscosity'] + + @staticmethod + def get_temp_in_c(modelle, modellname): + return modelle[modellname]['Temp. In C'] diff --git a/src/Tasks/FermentExercises/interne_daten/data_importieren.py b/src/Tasks/FermentExercises/interne_daten/data_importieren.py new file mode 100644 index 0000000..c169111 --- /dev/null +++ b/src/Tasks/FermentExercises/interne_daten/data_importieren.py @@ -0,0 +1,18 @@ + +import json +import os + +def data_importieren_von_json(datei_name): + # Verzeichnis des aktuellen Skripts + current_dir = os.path.dirname(os.path.realpath(__file__)) + # Vollständiger Pfad zur JSON-Datei + json_path = os.path.join(current_dir, datei_name) + + try: + # Öffne die JSON-Datei und lade sie + with open(json_path, 'r') as file: + data = json.load(file) + return data + except FileNotFoundError: + print(f"Die Datei '{datei_name}' wurde nicht gefunden.") + return None diff --git a/src/Tasks/FermentExercises/interne_daten/input.json b/src/Tasks/FermentExercises/interne_daten/input.json new file mode 100644 index 0000000..617381b --- /dev/null +++ b/src/Tasks/FermentExercises/interne_daten/input.json @@ -0,0 +1,26 @@ +{ + "Modell" : "Modell-1", + + "PhasenAnzahl" : 4, + + "Dauer" : [24,24,24,24], + + "Druck": [0.01,0.01,0.01,0.01], + + "Drehzahl" : [800,800,800,800], + + "Zuluft": [0.67,0.67,0.67,0.67], + + "BolusC" : [0,0,0,0], + + "Feed" : [0,0,0,0], + + "BolusN" : [5,5,5,5], + + "temperatur": 30, + + "startbiomasse": 0.05, + + "do":100 + +} \ No newline at end of file diff --git a/src/Tasks/FermentExercises/interne_daten/konstantenDictionary.py b/src/Tasks/FermentExercises/interne_daten/konstantenDictionary.py new file mode 100644 index 0000000..0b459d1 --- /dev/null +++ b/src/Tasks/FermentExercises/interne_daten/konstantenDictionary.py @@ -0,0 +1,43 @@ + + +#Dictionary Henry_Konstanten erstellen +Henry_Kosntanten = { + "Solut_KH1" :{ #L*atm*mol-1 + "O2" : 769.2, + "CO2" : 29.41, + "H2" : 1282.1 + }, + + "Solut_KH2" :{ #(L*bar)/mol + "O2" : 779.3919, + "CO2" : 29.7996825, + "H2" : 1299.087825 + }, + + "Solut_T" :{ #in k + "O2" : 298.15, + "CO2" : 198.15, + "H2" : 298.15 + } +} + +##Dictionary Temperaturkoeffizient erstellen + +Temperaturkoeffizient = { #Solut:C in K + "O2" : 1700, + "H2" : 500, + "CO2" : 2400, + "N2" : 1300, + "He" : 230, + "Ne" : 490, + "Ar" : 1300, + "CO" : 1300 +} + +#Dictionary für die Konstanten für P begast + +Konstanten_fuer_P_begast = { + "1" : 750, #"Anzahl der Impeller/Rührorgane" : Konstante A in Pg Formel + "2" : 490, + "3" : 375 +} \ No newline at end of file diff --git a/src/Tasks/FermentExercises/interne_daten/modelle.json b/src/Tasks/FermentExercises/interne_daten/modelle.json new file mode 100644 index 0000000..b110769 --- /dev/null +++ b/src/Tasks/FermentExercises/interne_daten/modelle.json @@ -0,0 +1,70 @@ +{ + "Modell-1": + { + "Beschreibung": "nicht wachstumsassoziierte Ethanolbildung mit hohem Ks --> hoehere Biomassegehalte bei niedrigen Substrat 1 konz.", + "Mikroorganismus": "S.cerevisiae", + "Substrat 1": "Glucose", + "Substrat 2": "(NH4)2SO4", + "Produkt 1": "Ethanol", + "umax": 0.46209812, + "Ks_s1x": 0.1, + "Ks_s2x": 0.01, + "Yxs1": 0.54, + "Yxs2": 8, + "ks_ox": 9.6e-05, + "RQ": 1, + "Yxox": 4.81, + "Produktbildung": 1, "ap": 0, "bp": 1, "kp_max": 0.88, "km_s1p": 5, "Ypx_mu": 1.5, "Dichte Bruehe": 1000, "dyn. Viscosity": 2, + "Temp. In C": 30 + }, + "Modell-2": + { + "Beschreibung": "Keine", + "Mikroorganismus": "E.coli", + "Substrat 1": "C-Quelle", + "Substrat 2": "N-Quelle", + "Produkt 1": "Produkt", + "umax": 2.07944154, + "Ks_s1x": 5, + "Ks_s2x": 0.1, + "Yxs1": 0.54, + "Yxs2": 8, + "ks_ox": 0.001, + "RQ": 1, + "Yxox": 0.54, + "Produktbildung": 1, + "ap": 0, + "bp": 1, + "kp_max": 0.88, + "km_s1p": 5, + "Ypx_mu": 1.5, + "Dichte Bruehe": 1000, + "dyn. Viscosity": 2, + "Temp. In C": 30 + }, + "Modell-3": + { + "Beschreibung": "Keine", + "Mikroorganismus": "Testorganismus", + "Substrat 1": "C-Quelle", + "Substrat 2": "N-Quelle", + "Produkt 1": "keins", + "umax": 0.314, + "Ks_s1x": 0.42, + "Ks_s2x": 0.1, + "Yxs1": 0.54, + "Yxs2": 1, + "ks_ox": 0.01, + "RQ": 1, + "Yxox": 0.54, + "Produktbildung": 0, + "ap": 0.5, + "bp": 0.5, + "kp_max": 0.23, + "km_s1p": 0.8, + "Ypx_mu": 1.5, + "Dichte Bruehe": 1050, + "dyn. Viscosity": 2, + "Temp. In C": 30 + } +} \ No newline at end of file diff --git a/src/Tasks/FermentExercises/json_to_excel.py b/src/Tasks/FermentExercises/json_to_excel.py new file mode 100644 index 0000000..32b3b5c --- /dev/null +++ b/src/Tasks/FermentExercises/json_to_excel.py @@ -0,0 +1,23 @@ +import os +import pandas as pd + +def json_to_excel(json_file, excel_filename='output.xlsx'): + + + # Get the default download folder based on the operating system + if os.name == 'nt': # Windows + download_folder = os.path.join(os.getenv('USERPROFILE'), 'Downloads') + else: # Mac/Linux + download_folder = os.path.join(os.path.expanduser('~'), 'Downloads') + + # Load the JSON file into a DataFrame + df = pd.read_json(json_file) + + # Set the path for the Excel file + excel_file = os.path.join(download_folder, excel_filename) + + # Save the DataFrame as an Excel file + df.to_excel(excel_file, index=False, engine='openpyxl') + + # Return the full path to the Excel file + return excel_file diff --git a/src/Tasks/FermentExercises/nebenrechnungen.py b/src/Tasks/FermentExercises/nebenrechnungen.py new file mode 100644 index 0000000..3928fe4 --- /dev/null +++ b/src/Tasks/FermentExercises/nebenrechnungen.py @@ -0,0 +1,157 @@ +import math +import numpy as np +from array import array + + + +from interne_daten.konstantenDictionary import * + + +Durchmesser_Ruehrer_durch_Durchmesser_Tank = 0.33 #Modell F19 +nutzbares_Volumen = 0.7 #Modell F21 +Hoehe_des_Tanks_durch_Durchmesser_Tank = 3 #Modell F20 +Anzahl_Rushton_Impeller = 3 #Modell F22 +Volumen_Kulturbruehe = 10 / 1000 #Modell F18 +Durchmesser_Ruehrer = 0.0602 #!Modell F28 +Nettovolumen = Volumen_Kulturbruehe / nutzbares_Volumen #Modell F27 + +Durchmesser_Tank = ((Nettovolumen*4) / (Hoehe_des_Tanks_durch_Durchmesser_Tank*math.pi))**(1/3) #Modell F29 + +Hoehe_des_Tanks = Hoehe_des_Tanks_durch_Durchmesser_Tank * Durchmesser_Tank #Modell F30 +Vcheck = (math.pi/4) * Durchmesser_Tank**2 * Hoehe_des_Tanks #Modell F32 + + + + + + + + +def parameter(Sauerstoffoeslichkeit_array,kla_werte,data,phasen_anzahl,modell): + parameter_array = [array('d', [0] * 3) for _ in range(4)] + + for j in range(phasen_anzahl): + parameter_array[j][0]=Sauerstoffoeslichkeit_array[j][5] #!Sauersto. in g/l + + for j in range(phasen_anzahl): + parameter_array[j][1]=kla_werte[j][17] + + for j in range(phasen_anzahl): + parameter_array[j][2]=data.get(modell, {}).get("umax") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];7;FALSCH) + + return parameter_array + + + + +def Berechnung_der_Sauerstoffloeslichkeit(phasen_anzahl,temperatur,druck): + + + Sauerstoffoeslichkeit_array = [array('d', [0] * 6) for _ in range(int(phasen_anzahl))] + + + + ############################################### Phase1 + # Attribute für Phase 1 + T_in_K_1 = 273.15 + temperatur # Modell!B11 + p_in_bara_1 = 1.013 + druck[0] #Modell H8 = 1 + p_o2_in_bara_1 = 0.20946 * p_in_bara_1 + kH_o2_bei_T_1 = Henry_Kosntanten["Solut_KH2"]["O2"] * math.exp(-Temperaturkoeffizient["O2"]*((1/T_in_K_1) - (1/Henry_Kosntanten["Solut_T"]["O2"]))) + L_o2_in_mmol_l_1 = (p_o2_in_bara_1 / kH_o2_bei_T_1 )*1000 + L_o2_in_g_l_1 = (L_o2_in_mmol_l_1*32) /1000 + + for i in range(phasen_anzahl): + if i == 0: + Sauerstoffoeslichkeit_array[0] = [T_in_K_1,p_in_bara_1,p_o2_in_bara_1,kH_o2_bei_T_1,L_o2_in_mmol_l_1,L_o2_in_g_l_1] + else : + Sauerstoffoeslichkeit_array[i][0]=Sauerstoffoeslichkeit_array[i-1][0] + Sauerstoffoeslichkeit_array[i][1]= 1.013 + float(druck[i]) #Modell!I8 #Modell j8 #modell k8 + Sauerstoffoeslichkeit_array[i][2]= 0.20946 * Sauerstoffoeslichkeit_array[i][1] + Sauerstoffoeslichkeit_array[i][3]=Henry_Kosntanten["Solut_KH2"]["O2"] * math.exp(-Temperaturkoeffizient["O2"]*((1/Sauerstoffoeslichkeit_array[i][0]) - (1/Henry_Kosntanten["Solut_T"]["O2"]))) + Sauerstoffoeslichkeit_array[i][4]=(Sauerstoffoeslichkeit_array[i][2]/Sauerstoffoeslichkeit_array[i][3])*1000 + Sauerstoffoeslichkeit_array[i][5]=(Sauerstoffoeslichkeit_array[i][4]*32)/1000 + + # Dezimalstellen nach komma grenzen, maximal 9 Stellen nach komma + for i in range(len(Sauerstoffoeslichkeit_array)): + for j in range(len(Sauerstoffoeslichkeit_array[i])): + try: + Sauerstoffoeslichkeit_array[i][j] = round(float(Sauerstoffoeslichkeit_array[i][j]), 9) + except ValueError: + pass + + return Sauerstoffoeslichkeit_array + + +def Berechnung_des_kla_Wertes(Sauerstoffoeslichkeit_array,data,drehzahl,modell,zuluft,phasen_anzahl): + + kla_werte = [array('d', [0] * 19) for _ in range(phasen_anzahl)] + i=0 + + drehzahl_1 = float(drehzahl[i]) #Modell H9 + Drehzahl_pro_sek = 1200 / 60 + Dichte = data.get(modell, {}).get("Dichte Bruehe") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];21;FALSCH) + dyn_Viskosität1 = data.get(modell, {}).get("dyn. Viscosity") #SVERWEIS(Modell!$B$3;Tabelle1[#Alle];22;FALSCH) + dyn_Viskositaet2 = dyn_Viskosität1/ 1000 #(kg*m)/s + Ruehrerdurchmesser = Durchmesser_Ruehrer #0.0602 Modell F28 + Reynoldszahl_Re = (Dichte * Drehzahl_pro_sek * (Ruehrerdurchmesser**2)) / dyn_Viskositaet2 + Newton_Zahl_Ne = 7 + Leistungseintrag_nicht_begast_P_1 = (Newton_Zahl_Ne * Anzahl_Rushton_Impeller * Dichte * (Drehzahl_pro_sek**3) * (Ruehrerdurchmesser**5)) #Modell F22=3 + Leistungseintrag_nicht_begast_P_2 = Leistungseintrag_nicht_begast_P_1 / Volumen_Kulturbruehe #modell F18 = 0.01 + Leistungseintrag_nicht_begast_P_3 = Leistungseintrag_nicht_begast_P_2 / 1000 #kW/m3 + + #Feld 11 später + Air_Flow_1 = 0.2 #vvm + Air_Flow_2 = Air_Flow_1 * Volumen_Kulturbruehe / 60 #Modell F18=0.01 + cross_scetion = (math.pi / 4) * (Durchmesser_Tank**2) #modell F29 = 0.1823 + + #Feld 15 zuert berechnet dann 11 + superficial_velocity = Air_Flow_2 / cross_scetion #m/s + Leistungseintrag_Begast_Pg = Leistungseintrag_nicht_begast_P_1 / math.sqrt(1+float(Konstanten_fuer_P_begast["3"])*superficial_velocity/math.sqrt(9.81*Ruehrerdurchmesser)) #W =C48/WURZEL(1+SVERWEIS(Modell!$F$22;Daten!$I$43:$J$45;2;FALSCH)*C55/WURZEL(9,81*C44)) + #Leistungseintrag_nicht_begast_P_1 / math.sqrt(1+Konstanten_fuer_P_begast.get(Anzahl_Rushton_Impeller)*superficial_velocity/math.sqrt(9.81*Ruehrerdurchmesser)) + kla_pro_sek = 0.026 * ( ( Leistungseintrag_Begast_Pg / Volumen_Kulturbruehe) ** (0.4) ) * (superficial_velocity ** (0.5) ) #modell F18 = 0.01 + kla_pro_std = kla_pro_sek * 3600 #1/h + OTR_max = kla_pro_std * Sauerstoffoeslichkeit_array[i][4] #mmol/(l*h) + + + for i in range(phasen_anzahl): + if i == 0 : + kla_werte[0] = [float(drehzahl_1),float(Drehzahl_pro_sek),float(Dichte),float(dyn_Viskosität1),float(dyn_Viskositaet2),float(Ruehrerdurchmesser),float(Reynoldszahl_Re),float(Newton_Zahl_Ne),float(Leistungseintrag_nicht_begast_P_1),float(Leistungseintrag_nicht_begast_P_2),float(Leistungseintrag_nicht_begast_P_3),float(Leistungseintrag_Begast_Pg),float(Air_Flow_1),float(Air_Flow_2),float(cross_scetion),float(superficial_velocity),float(kla_pro_sek),float(kla_pro_std),float(OTR_max)] + else : #alle anderen phasen_anzahl + + kla_werte[i][0]=float(drehzahl[i]) #modell I9 + kla_werte[i][1]=float(kla_werte[i][0]/60) + kla_werte[i][2]=float(kla_werte[i-1][2]) + kla_werte[i][3]=float(kla_werte[i-1][3]) + kla_werte[i][4]=float(kla_werte[i-1][4]) + kla_werte[i][5]=float(kla_werte[i-1][5]) + kla_werte[i][6]=float(( kla_werte[i][2] * kla_werte[i][1] * kla_werte[i][5]**2 ) / kla_werte[i][4]) + kla_werte[i][7]=float(kla_werte[i-1][7]) + kla_werte[i][8]=float(( kla_werte[i][7] * Anzahl_Rushton_Impeller * kla_werte[i][2] * ( kla_werte[i][1] ** 3 ) * ( float(kla_werte[i][5]) ** 5)) ) #Modell!$F$22 = 3 + kla_werte[i][9]=float(kla_werte[i][8] / Volumen_Kulturbruehe ) #modell F18 = 0.01 + kla_werte[i][10]=float(kla_werte[i][9] / 1000) + kla_werte[i][12]=float(zuluft[i]) #W/m3 Modell l7 = 0.01 + kla_werte[i][13]=float(kla_werte[i][12] * Volumen_Kulturbruehe / 60) #ModellF18 = 0.01 + kla_werte[i][14]=float(math.pi / 4 * Durchmesser_Tank**2) #modell F29 = 0.1823 + + #Feld 11 braucht Ergebnis von 15. Deswegen 15 zuerst + kla_werte[i][15]=float(kla_werte[i][13] / kla_werte[i][14]) + kla_werte[i][11]=float(kla_werte[i][8] / math.sqrt(1 + float(Konstanten_fuer_P_begast["3"])* kla_werte[i][15] / math.sqrt( 9.81 * kla_werte[i][5] ) ) ) #modell F22= 3 #W =C48/WURZEL(1+SVERWEIS(Modell!$F$22;Daten!$I$43:$J$45;2;FALSCH)*C55/WURZEL(9,81*C44)) + + Leistungseintrag_Begast_Pg2 = kla_werte[i][11] + superficial_velocity2=kla_werte[i][15] + kla_werte[i][16]=float(0.026 * ((Leistungseintrag_Begast_Pg2 / Volumen_Kulturbruehe ) ** (0.4) ) * (superficial_velocity2 ** (0.5))) #Modell!$F$18 = 0.01 + kla_werte[i][17]=float(kla_werte[i][16] * 3600 ) + kla_werte[i][18]=float(kla_werte[i][17] * Sauerstoffoeslichkeit_array[i][4]) + + + + # Dezimalstellen nach komma grenzen, maximal 9 Stellen nach komma + for i in range(len(kla_werte)): + for j in range(len(kla_werte[i])): + try: + kla_werte[i][j] = round(float(kla_werte[i][j]), 9) + except ValueError: + pass + + return kla_werte + \ No newline at end of file diff --git a/src/Tasks/FermentExercises/optimierung.py b/src/Tasks/FermentExercises/optimierung.py new file mode 100644 index 0000000..ec795c5 --- /dev/null +++ b/src/Tasks/FermentExercises/optimierung.py @@ -0,0 +1,127 @@ +import optuna +import numpy as np +from scipy.integrate import solve_ivp +from differentialgleichung import ODE_Bioreactor_Monod +from numbers import Number + +def get_max_value(parameter, sol): + match parameter.lower(): + case "cx": + return np.max(sol.y[0]) + case "cp": + return np.max(sol.y[3]) + case "cs1": + return np.max(sol.y[1]) + case "cs2": + return np.max(sol.y[2]) + case "c_ox": + return np.max(sol.y[4]) + case "c_o2_out": + return np.max(sol.y[5]) + case "c_co2_out": + return np.max(sol.y[6]) + case _: + return None # Statt False, um None-Check durchzuführen + + +def get_min_value(parameter, sol): + match parameter.lower(): + case "cx": + return np.min(sol.y[0]) + case "cp": + return np.min(sol.y[3]) + case "cs1": + return np.min(sol.y[1]) + case "cs2": + return np.min(sol.y[2]) + case "c_ox": + return np.min(sol.y[4]) + case "c_o2_out": + return np.min(sol.y[5]) + case "c_co2_out": + return np.min(sol.y[6]) + case _: + return None # Statt False, um None-Check durchzuführen + +def objective(trial, t_start, t_end, y0, consts, t_span, varierendeParameter, maxParameter, minParameter): + #maxParameter : Parameter zu maximieren + #minParameter : Parameter zu minimieren + #varierendeParameter : Liste von Parametern mit Bereich, der bei dem Maximierungsprozess bzw Minimierungsprozess berücksichtigt werden + try: + # Liste der Parameter, die du überprüft werden + parameter_namen = ['Feed', 'Zuluft', 'Cs1', 'Cs2', 'C_ox', 'C_o2_out', 'C_Co2_out'] + + # Schleife, um durch die Parameter zu iterieren + for param in parameter_namen: + if param in varierendeParameter: + param_min = varierendeParameter[param]['min'] + param_max = varierendeParameter[param]['max'] + + # Dynamischer Name für den Parameter in suggest_uniform + param_value = trial.suggest_uniform(param.lower(), param_min, param_max) + + # Falls der Parameter "Feed" ist, speichere den Wert in consts[19] + if param.lower() == 'feed': + consts[19] = param_value + elif param.lower() == 'zuluft': + consts[3] = param_value + '''elif param.lower() == 'bolusc': + consts[19] = param_value #! an welcher Stelle bei consts liegt bolusC???? + elif param.lower() == 'bolousn': #! consts ist ein übergebende Parameter "const_array_array" in der Funktion berechnung in berechnungen.py + consts[19] = param_value #! const_array_array ist die Rückgabe der Funktion berechnung_der_Tabelle1 in berechnungen.py + elif param.lower() == 'drehzahl': + consts[19] = param_value + elif param.lower() == 'druck': + consts[19] = param_value + elif param.lower() == 'dauer': + consts[19] = param_value''' + + # Lösung des ODE-Systems + sol = solve_ivp(ODE_Bioreactor_Monod, [t_start, t_end], y0, args=(consts,), t_eval=t_span) + + # Initialisierung der Werte + max_value = float('-inf') + min_value = float('inf') + + if maxParameter: + check = get_max_value(maxParameter, sol) + if check is not None: + max_value = check + + if minParameter: + check = get_max_value(minParameter, sol) + if check is not None: + min_value = check + + cs = y0[1] + + if maxParameter and minParameter: + # Falls keine Werte ermittelt werden konnten + if max_value == float('-inf') or min_value == float('inf'): + print("Warnung: Kein gültiger Wert für maxParameter oder minParameter gefunden.") + return float('nan') + # Ziel: Maximiere max_value und minimiere min_value + objective_value = min_value - max_value # Maximierung von max_value und Minimierung von min_value + elif maxParameter and not minParameter: + if max_value == float('-inf'): + print("Warnung: Kein gültiger Wert für maxParameter oder minParameter gefunden.") + return float('nan') + objective_value = max_value # Maximierung von max_value + else : + check = get_min_value(minParameter, sol) + if check is not None: + min_value = check + if min_value == float('inf'): + print("Warnung: Kein gültiger Wert für maxParameter oder minParameter gefunden.") + return float('nan') + objective_value = min_value # Minimierung von min_value + + if sol.success: + trial.set_user_attr("sol", sol) + trial.set_user_attr("cs", cs) + + return objective_value + + except Exception as e: + print(f"Fehler in der Optimierung: {e}") + return float('nan') diff --git a/src/Tasks/FermentExercises/testtest.txt b/src/Tasks/FermentExercises/testtest.txt new file mode 100644 index 0000000..4b0a21d --- /dev/null +++ b/src/Tasks/FermentExercises/testtest.txt @@ -0,0 +1 @@ +testtest \ No newline at end of file diff --git a/src/Tasks/FermentExercises/wrapperFunctionCallPython.ts b/src/Tasks/FermentExercises/wrapperFunctionCallPython.ts new file mode 100644 index 0000000..a9d69be --- /dev/null +++ b/src/Tasks/FermentExercises/wrapperFunctionCallPython.ts @@ -0,0 +1,233 @@ +import { exec } from "child_process"; +import fs from "fs"; +const path = require("path"); + +function runPythonScript(scriptPath: string): Promise { + try { + return new Promise((resolve, reject) => { + const command = `python ${scriptPath}`; + console.log(command); + exec(command, (error, stdout, stderr) => { + if (error) { + reject(error.message); + return; + } + if (stderr) { + reject(stderr); + return; + } + resolve(stdout); + }); + }); + } catch { + console.log("Error"); + } +} + +function parseFrontendInputs(parameter: any) { + //Überprüfen, ob parameter ein String ist und wenn ja, ihn parsen + let frontendEingaben: any; + if (parameter === undefined || parameter === null) { + throw new Error("Parameter ist undefined oder null."); + } + if (typeof parameter === "string") { + try { + frontendEingaben = JSON.parse(parameter); + } catch (error) { + throw new Error("Fehler beim Parsen des JSON-Strings: " + error.message); + } + } else if (typeof parameter === "object") { + frontendEingaben = parameter; + } else { + throw new Error("Ungültiges Eingabeformat für parameter."); + } + return frontendEingaben; +} +function cleanAndMoveParameters(frontendEingaben : any) { + if (frontendEingaben && frontendEingaben.parameters) { + // Inhalt von "parameters" ins Hauptobjekt verschieben + Object.assign(frontendEingaben, frontendEingaben.parameters); + + // Entfernen der spezifizierten Schlüssel + delete frontendEingaben.parameters; + delete frontendEingaben.type; + delete frontendEingaben.task; + delete frontendEingaben.Paramter; + delete frontendEingaben.instruction; + delete frontendEingaben.language; + } + + return frontendEingaben; +} + +// Schlüsselnamen im Objekt ändern +function renameKey (obj: any, oldKey: string, newKey: string) { + if (obj.hasOwnProperty(oldKey)) { + obj[newKey] = obj[oldKey]; + delete obj[oldKey]; + } + for (const key in obj) { + if (obj.hasOwnProperty(key) && typeof obj[key] === "object" && obj[key] !== null) { + renameKey(obj[key], oldKey, newKey); + } + } + return obj; +}; + +function editSaveParams(parameter: any) { + //Überprüfen, ob parameter ein String ist und wenn ja, ihn parsen + let frontendEingaben : any = parseFrontendInputs(parameter); + frontendEingaben = cleanAndMoveParameters(frontendEingaben); + + frontendEingaben = renameKey(frontendEingaben, "nodeAmount", "Modell"); + frontendEingaben = renameKey(frontendEingaben, "seed", "PhasenAnzahl"); + + //Modell-Schlüssel umbennen + switch (frontendEingaben.Modell) { + case "S.cerevisiae": + frontendEingaben.Modell = "Modell-1"; + break; + case "E.coli": + frontendEingaben.Modell = "Modell-2"; + break; + case "Testorganismus": + frontendEingaben.Modell = "Modell-3"; + break; + } + let phasenanzahl = parseInt(frontendEingaben.PhasenAnzahl); + frontendEingaben.PhasenAnzahl = phasenanzahl; + + let zuluft = []; + let feed = []; + let drehzahl = []; + let druck = []; + let dauer = []; + let bolusC = []; + let bolusN = []; + for (let i = 0; i < phasenanzahl; i++) { + if (Array.isArray(frontendEingaben.userDataMatrix[i][0])) { + zuluft[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][0][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][0][0]); + feed[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][2][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][2][0]); + drehzahl[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][4][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][4][0]); + druck[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][5][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][5][0]); + dauer[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][6][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][6][0]); + bolusC[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][1][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][1][0]); + bolusN[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][3][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][3][0]); + } else { + zuluft[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][0])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][0]); + feed[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][2])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][2]); + drehzahl[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][4])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][4]); + druck[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][5])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][5]); + dauer[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][6])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][6]); + bolusC[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][1])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][1]); + bolusN[i] = isNaN(parseFloat(frontendEingaben.userDataMatrix[i][3])) + ? 0 + : parseFloat(frontendEingaben.userDataMatrix[i][3]); + } + } + + delete frontendEingaben.userDataMatrix; + + frontendEingaben.Dauer = dauer; + frontendEingaben.Druck = druck; + frontendEingaben.Drehzahl = drehzahl; + frontendEingaben.Zuluft = zuluft; + frontendEingaben.BolusC = bolusC; + frontendEingaben.Feed = feed; + frontendEingaben.BolusN = bolusN; + + frontendEingaben.temperatur =isNaN(parseFloat(frontendEingaben.Temperatur)) ? 0 : parseFloat(frontendEingaben.Temperatur); + frontendEingaben.startbiomasse = isNaN(parseFloat(frontendEingaben.BTM)) ? 0 : parseFloat(frontendEingaben.BTM); + frontendEingaben.do = isNaN(parseFloat(frontendEingaben.DO)) ? 0 : parseFloat(frontendEingaben.DO) ; + delete frontendEingaben.Temperatur; + delete frontendEingaben.BTM; + delete frontendEingaben.DO; + + let dataTmp:any = frontendEingaben.maxParameter; + delete frontendEingaben.maxParameter; + frontendEingaben.maxParameter = dataTmp; + dataTmp = frontendEingaben.minParameter; + delete frontendEingaben.minParameter; + frontendEingaben.minParameter=dataTmp; + dataTmp = frontendEingaben.varierendeParameter; + delete frontendEingaben.varierendeParameter; + frontendEingaben.varierendeParameter=dataTmp; + + /* + let maxParameter: string = ''; + let minParameter: string = ''; + let varierendeParameter : object = {}; + maxParameter = frontendEingaben.maxParameter; + minParameter = frontendEingaben.minParameter; + varierendeParameter = frontendEingaben.varierendeParameter;*/ + + delete frontendEingaben.userDataInputFelder; + delete frontendEingaben.checkUserDataValidity; + + + + // Pfad zur JSON-Datei definieren + const filePfade = path.join(__dirname, "/interne_daten/FrontendEingaben.json"); + + // JSON.stringify mit einer Replacer-Funktion + let jsonString = JSON.stringify(frontendEingaben, null, 4); + + // Datei schreiben + try { + fs.writeFileSync(filePfade, jsonString, "utf-8"); + return true; + } catch (error) { + return false; + } + + + +} + +export async function generateFermentExercises(parameter: any) { + let check = editSaveParams(parameter) + if (check) { + let result = { foo: "bar" }; + try { + let pythonScriptPath = "generateFermentExercises.py"; + pythonScriptPath = path.join(__dirname, pythonScriptPath); + (await runPythonScript(pythonScriptPath)) as any; + + result = JSON.parse(fs.readFileSync(path.join(__dirname, "data.json"), "utf-8")); + const filePath = path.join(__dirname, "data.json"); + fs.writeFileSync(filePath, JSON.stringify(result, null, 2), "utf-8"); + //console.log(result); + //insofern kryptsiche Zeichen in der JSON Ausgabe vorhanden sind .> Workaround mittels File + } catch (error) { + console.error("Error running Python script:", error); + } + return result; + } else console.error("Die Daten aus Frontend konnten nicht in JSON-Datei gespeichert"); + return null; +} \ No newline at end of file diff --git a/src/Tasks/Reakt/Input/__init__.py b/src/Tasks/Reakt/Input/__init__.py new file mode 100644 index 0000000..0f2dcb8 --- /dev/null +++ b/src/Tasks/Reakt/Input/__init__.py @@ -0,0 +1,4 @@ + + +#Das Hinzufügen der '__init__.py'-Datei im Input-Verzeichnis ist wichtig, +#damit Python das Input-Verzeichnis als Paket behandelt und die darin enthaltenen Module importieren kann. \ No newline at end of file diff --git a/src/Tasks/Reakt/Input/inputBaseClass.py b/src/Tasks/Reakt/Input/inputBaseClass.py new file mode 100644 index 0000000..d49cb1c --- /dev/null +++ b/src/Tasks/Reakt/Input/inputBaseClass.py @@ -0,0 +1,14 @@ +from abc import ABC, abstractmethod + + + +class InputBase(ABC): + + @abstractmethod + def find_pfad(): + pass + + @abstractmethod + def ladeJson(self): + pass + diff --git a/src/Tasks/Reakt/Input/json_Input.py b/src/Tasks/Reakt/Input/json_Input.py new file mode 100644 index 0000000..b84ed55 --- /dev/null +++ b/src/Tasks/Reakt/Input/json_Input.py @@ -0,0 +1,81 @@ +import os +import json +from Input.inputBaseClass import InputBase + +class JsonInput(InputBase): + + + + def __init__(self,datei_name): #Konstruktor + self.__jsonName = datei_name # private variable + + # Deklaration des Attributs außerhalb des Konstruktors + __data = None + + def set_data(self,data): + self.__data=data + + def get_data(self): + return self.__data + + def get_jsonName(self): + return self.__jsonName + + def set_jsonName(self, value): + self.__jsonName = value + + + + + + def find_pfad(self): + root_directory = os.path.abspath('src/Tasks/Reakt') # Den absoluten Pfad des Projektnamens ermitteln + + for root, dirs, files in os.walk(root_directory): + for file in files: + if file.lower() == self.__jsonName.lower(): # Wenn die Datei gefunden wurde + return os.path.join(root, file) # Dateipfad zurückgeben, sobald die Datei gefunden wurde + return None # Wenn die Datei nicht gefunden wurde, wird None zurückgegeben + + + def ladeJson(self): + # Vollständiger Pfad zur JSON-Datei + json_path = self.find_pfad() + if json_path is None : + print('Pfad zur Datei nicht gefunden.!!!') + return None + + print('Datei gefunden:', json_path) # Fügen Sie Debugging-Ausgabe hinzu + try: + # Öffne die JSON-Datei und lade sie + with open(json_path, 'r') as file: + data = json.load(file) + self.set_data(data) + + except FileNotFoundError: + print(f"Die Datei '{self.__jsonName}' konnte nicht geladen werden.") + return None + + + def get_Value(self): + + # JSON-String zu Python-Dictionary konvertieren + data = self.__data + + # Array initialisieren + result = [] + + for key, value in data.items(): + if isinstance(value, dict): + result.extend(list(value.values())) + else: + # Wenn der Wert kein Dictionary ist oder kein Array enthält + result.append(value) + + for i, r in enumerate(result): + if isinstance(r, int): + result[i]=int(r) + elif isinstance(r,list): + result[i] = [float(val) for val in r] + + return result diff --git a/src/Tasks/Reakt/Reaktdata.json b/src/Tasks/Reakt/Reaktdata.json new file mode 100644 index 0000000..2c0ea94 --- /dev/null +++ b/src/Tasks/Reakt/Reaktdata.json @@ -0,0 +1,300 @@ +{ + "data_1": { + "labels": [ + 0, + 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175.17517517517518, + 182.18218218218217, + 189.1891891891892, + 196.1961961961962, + 203.2032032032032 + ], + "datasets": [ + { + "label": "Umsatz H2O2", + "data": [ + 0, + 0.09848418847463403, + 0.18708602110159167, + 0.2668570569715192, + 0.338715477803574, + 0.40341895166467834, + 0.4616731082307973, + 0.5141340086029854, + 0.5614081453073845, + 0.6040524422952245, + 0.6425688945630904, + 0.6773517879294737, + 0.7087304082234847, + 0.7370169083016408, + 0.7625070688346596, + 0.7854802983074578, + 0.806199633019152, + 0.8249117370830583, + 0.8418457532025299, + 0.8571756533185236, + 0.8710304108678324, + 0.8835347951420636, + 0.8948087642316805, + 0.9049674650260026, + 0.9141212332132045, + 0.9223755932803174, + 0.9298312585132276, + 0.9365841309966778, + 0.9427134762321154, + 0.948262271438883 + ], + "borderColor": "#FFD700", + "backgroundColor": "#FFD700", + "tension": 0.1 + } + ] + } +} \ No newline at end of file diff --git a/src/Tasks/Reakt/generateReactionData.py b/src/Tasks/Reakt/generateReactionData.py new file mode 100644 index 0000000..e9264ac --- /dev/null +++ b/src/Tasks/Reakt/generateReactionData.py @@ -0,0 +1,157 @@ +import numpy as np +from scipy.integrate import solve_ivp +import pandas as pd +import json + +from Input.json_Input import JsonInput + + +# === Reaktionssystem === +def reaktionssystem(t, y): + cH2O2, cH2O, cO2 = y + nH2O2 = cH2O2 * v + UH2O2 = (nH2O2_0 - nH2O2) / nH2O2_0 + T = T_Start + dTad * UH2O2 + k = 0.000047 * T**2 - 0.0033484 * T + 0.0629815 + r_H2O2 = -k * cH2O2 + return [r_H2O2, -r_H2O2, -0.5 * r_H2O2] + +# === Abbruchbedingung === +def abbruch_bedingung(t, y): + cH2O2 = y[0] + nH2O2 = cH2O2 * v + UH2O2 = (nH2O2_0 - nH2O2) / nH2O2_0 + return 0.95 - UH2O2 + + + + +# read user inputs +userInputObject = JsonInput('FrontendEingaben.json') +userInputObject.ladeJson() +userInputs = userInputObject.get_data() + +phaseNumber = userInputs['phaseNumber'] +process = userInputs['process'] +matrixDataObj = userInputs['matrixData'] + + + +VH2O2_AS = matrixDataObj['VH2O2_AS'][0] # Liter – Volumen H2O2 Zugabe in Reaktor +cH2O2_AS = matrixDataObj['CH2O2_AS'][0] # mol/L – Konzentration H2O2 Zugabe +V_Start = matrixDataObj['V_Start'][0] # Liter – Wasser-Startvolumen +T_Start = matrixDataObj['T_Start'][0] # °C – Starttemperatur + + +''' +# === Nutzereingaben === +VH2O2_AS = 5.0 # Liter – Volumen H2O2 Zugabe in Reaktor +cH2O2_AS = 12.0 # mol/L – Konzentration H2O2 Zugabe +V_Start = 100 # Liter – Wasser-Startvolumen +T_Start = 25.0 # °C – Starttemperatur +''' + +# === Berechnungen === +nH2O2_AS = cH2O2_AS * VH2O2_AS +cH2O2_Start = nH2O2_AS / V_Start +dTad = (cH2O2_Start * 98.2) / (4.19 * 2 * 1) + +# === Startbedingungen === +cH2O2_0 = cH2O2_Start +cH2O_0 = 0.0 +cO2_0 = 0.0 +v = V_Start +nH2O2_0 = cH2O2_0 * v + + + +abbruch_bedingung.terminal = True +abbruch_bedingung.direction = -1 + +# === Lösung === +t_span = (0, 7000) +t_eval = np.linspace(t_span[0], t_span[1], 1000) +y0 = [cH2O2_0, cH2O_0, cO2_0] + +sol = solve_ivp( + reaktionssystem, t_span, y0, + t_eval=t_eval, + events=abbruch_bedingung +) + +# === Auswertung === +time = sol.t +cH2O2, cH2O, cO2 = sol.y +nH2O2 = cH2O2 * v +nH2O = cH2O * v +nO2 = cO2 * v +UH2O2 = (nH2O2_0 - nH2O2) / nH2O2_0 +T = T_Start + dTad * UH2O2 + +# === JSON für Diagramm erstellen === +def generateReactionData(): + data = { + "data_1": { + "labels": time.tolist(), + "datasets": [ + { + "label": "n(H2O2)", + "data": nH2O2.tolist(), + "borderColor": "#66c2a5", + "backgroundColor": "#66c2a5", + "tension": 0.1 + }, + { + "label": "n(H2O)", + "data": nH2O.tolist(), + "borderColor": "#fc8d62", + "backgroundColor": "#fc8d62", + "tension": 0.1 + }, + { + "label": "n(O2)", + "data": nO2.tolist(), + "borderColor": "#8da0cb", + "backgroundColor": "#8da0cb", + "tension": 0.1 + } + ] + }, + "data_2": { + "labels": time.tolist(), + "datasets": [ + { + "label": "Temperatur [°C]", + "data": T.tolist(), + "borderColor": "#e78ac3", + "backgroundColor": "#e78ac3", + "tension": 0.1 + } + + ] + }, + "data_3": { + "labels": time.tolist(), + "datasets": [ + { + "label": "Umsatz H2O2", + "data": UH2O2.tolist(), + "borderColor": "#FFD700", + "backgroundColor": "#FFD700", + "tension": 0.1 + } + ] + } + } + return data +# === JSON speichern === +with open('./src/Tasks/Reakt/Reaktdata.json', 'w') as output: + json.dump(generateReactionData(), output, indent=2) + +print(" JSON-Datei erfolgreich erstellt: ./src/Tasks/Reakt/Reaktdata.json") + +# === Status === +if sol.status == 1: + print(f" Abbruch bei t = {sol.t_events[0][0]:.2f} h - Umsatz 95 % erreicht.") +else: + print("Simulation lief bis zur maximalen Zeit.") diff --git a/src/Tasks/Reakt/wrapperFunctionCallPython.ts b/src/Tasks/Reakt/wrapperFunctionCallPython.ts new file mode 100644 index 0000000..9e7ae39 --- /dev/null +++ b/src/Tasks/Reakt/wrapperFunctionCallPython.ts @@ -0,0 +1,132 @@ +import { exec } from "child_process"; +import fs from "fs"; +const path = require("path"); + +function runPythonScript(scriptPath: string): Promise { + try { + return new Promise((resolve, reject) => { + const command = `python ${scriptPath}`; + console.log(command); + exec(command, (error, stdout, stderr) => { + if (error) { + reject(error.message); + return; + } + if (stderr) { + reject(stderr); + return; + } + resolve(stdout); + }); + }); + } catch { + console.log("Error"); + } +} + +function parseFrontendInputs(parameter: any) { + //Überprüfen, ob parameter ein String ist und wenn ja, ihn parsen + let frontendData: any; + if (parameter === undefined || parameter === null) { + throw new Error("Parameter ist undefined oder null."); + } + if (typeof parameter === "string") { + try { + frontendData = JSON.parse(parameter); + } catch (error) { + throw new Error("Fehler beim Parsen des JSON-Strings: " + error.message); + } + } else if (typeof parameter === "object") { + frontendData = parameter; + } else { + throw new Error("Ungültiges Eingabeformat für parameter."); + } + return frontendData; +} +function cleanAndMoveParameters(frontendData : any) { + if (frontendData && frontendData.parameters) { + // Inhalt von "parameters" ins Hauptobjekt verschieben + Object.assign(frontendData, frontendData.parameters); + + // Entfernen der spezifizierten Schlüssel + delete frontendData.parameters; + delete frontendData.type; + delete frontendData.task; + delete frontendData.Paramter; + delete frontendData.instruction; + delete frontendData.language; + } + + return frontendData; +} + +function setColumnLabels(frontendData : any){ + let matrixData = frontendData['matrixData']; + + + let vH2O2_AS_tmpArray = []; + let cH2O2_AS_tmpArray = []; + let v_Start_tmpArray = []; + let t_Start_tmpArray = []; + + for( let i=0 ; i < matrixData.length ; i++){ + vH2O2_AS_tmpArray[i] = matrixData[i][0]; + cH2O2_AS_tmpArray[i] = matrixData[i][1]; + v_Start_tmpArray[i] = matrixData[i][2]; + t_Start_tmpArray[i] = matrixData[i][3]; + } + + let tmpArray : Object = { + 'VH2O2_AS' : vH2O2_AS_tmpArray, + 'CH2O2_AS' : cH2O2_AS_tmpArray, + 'V_Start' : v_Start_tmpArray, + 'T_Start' : t_Start_tmpArray + }; + + + frontendData['matrixData'] = tmpArray; + return frontendData; +} + +function SaveUserInputs(parameter: any) { + //Überprüfen, ob parameter ein String ist und wenn ja, ihn parsen + let frontendData : any = parseFrontendInputs(parameter); + frontendData = cleanAndMoveParameters(frontendData); + frontendData = setColumnLabels(frontendData); + // Pfad zur JSON-Datei definieren + const filePfade = path.join(__dirname, "/interneDaten/FrontendEingaben.json"); + + // JSON.stringify mit einer Replacer-Funktion + let jsonString = JSON.stringify(frontendData, null, 4); + + // Datei schreiben + try { + fs.writeFileSync(filePfade, jsonString, "utf-8"); + return true; + } catch (error) { + return false; + } +} + +export async function generateReactionData(parameter: any) { + SaveUserInputs(parameter); + if (true) { + let result = { foo: "bar" }; + try { + let pythonScriptPath = "generateReactionData.py"; + pythonScriptPath = path.join(__dirname, pythonScriptPath); + (await runPythonScript(pythonScriptPath)) as any; + + result = JSON.parse(fs.readFileSync(path.join(__dirname, "Reaktdata.json"), "utf-8")); + const filePath = path.join(__dirname, "Reaktdata.json"); + fs.writeFileSync(filePath, JSON.stringify(result, null, 2), "utf-8"); + + //console.log(result); + //insofern kryptsiche Zeichen in der JSON Ausgabe vorhanden sind .> Workaround mittels File + } catch (error) { + console.error("Error running Python script:", error); + } + return result; + } else console.error("Die Daten aus Frontend konnten nicht in JSON-Datei gespeichert"); + return null; +} \ No newline at end of file diff --git a/src/server/TaskWrapper.ts b/src/server/TaskWrapper.ts index f819a33..b5c2cce 100644 --- a/src/server/TaskWrapper.ts +++ b/src/server/TaskWrapper.ts @@ -6,13 +6,11 @@ import { InterpolationTaskGenerator } from "../Legacy/Tasks/geoInterpolation/Geo import { ShortestPathTaskGenerator } from "../Legacy/Tasks/shortestPath/MunkeltWorker"; import { EPKTaskGenerator } from "../Legacy/Tasks/EPK/EPKTask"; import { SchedulingTaskGenerator } from "../Legacy/Tasks/scheduling/Task"; -import { selectSubstitution } from "../Tasks/Chemistry/SubstitutionSelector"; -import { clusterAnalysisMain } from "../Tasks/ClusterAnalysis/wrapperFunctionCallPython"; - -import { DataSamplingGenerator } from "../Tasks/DecisionTrees/WrapperDataSampler"; -import { DecisionTreeTaskGenerator } from "../Tasks/DecisionTrees/Task"; -import { DecisionTreeValidator } from "../Tasks/DecisionTrees/WrapperValidateDecisionTree"; - +import { generateFermentationDataMain } from "../Tasks/Ferment/wrapperFunctionCallPython"; +import { generateFermentExercises } from "../Tasks/FermentExercises/wrapperFunctionCallPython"; +import { generateFermentationFeedBack } from "../Tasks/Ferment/wrapperFunctionCallPython"; +import { generateReactionData } from "../Tasks/Reakt/wrapperFunctionCallPython"; +import { generateExperimentierenDataMain } from "../Tasks/Experimentieren/wrapperFunctionCallPython"; import fs from "fs"; import path from "path"; @@ -69,11 +67,11 @@ const generators: { [key: string]: any } = { ShortestPathTaskGenerator: ShortestPathTaskGenerator, EPKTaskGenerator: EPKTaskGenerator, SchedulingTaskGenerator: SchedulingTaskGenerator, - DataSamplingGenerator: DataSamplingGenerator, - DecisionTreeTaskGenerator: DecisionTreeTaskGenerator, - DecisionTreeValidator: DecisionTreeValidator, - selectSubstitution: selectSubstitution, - clusterAnalysisMain: clusterAnalysisMain, + generateFermentationDataMain: generateFermentationDataMain, + generateFermentExercises: generateFermentExercises, + generateFermentationFeedBack: generateFermentationFeedBack, + generateReactionData: generateReactionData, + generateExperimentierenDataMain: generateExperimentierenDataMain }; export interface IInstructionConfiguration { @@ -138,7 +136,6 @@ export const executeTask = async (instructionConfiguration: IInstructionConfigur // return await parsedFunctions[instruction](instructionConfiguration); try { const { type, instruction } = instructionConfiguration; - //console.log(type,instruction,taskWorkers[`${type}Task`]); const func = await taskWorkers[`${type}Task`][instruction]; const result = await func(instructionConfiguration); return result; diff --git a/src/server/experimentalTasks/SQL.json b/src/server/experimentalTasks/SQL.json index 50c0f47..60f77bc 100644 --- a/src/server/experimentalTasks/SQL.json +++ b/src/server/experimentalTasks/SQL.json @@ -1,306 +1,304 @@ { - "API": [ - { - "task": "sql", - "name": "generateQuery", - "httpMethod": "post", - "params": { "parameters": "object", "schema": "string", "language": "string" } - }, - { - "task": "sql", - "name": "validateQuery", - "httpMethod": "post", - "params": { "parameters": "object" } - }, - { - "task": "sql", - "name": "generateERD", - "httpMethod": "post", - "params": { "parameters": "object" } - }, - { - "task": "sql", - "name": "fetchERD", - "httpMethod": "post", - "params": { "parameters": "object" } - } - ], - "Worker": { - "sqlTask": { - "minConsumers": 1, - "consumerInstructions": { - "generateQuery": { - "dependencies": ["sqlQueryGenerator"], - "body": "async (taskDescription) => { return await sqlQueryGenerator(taskDescription); }" - }, - "validateQuery": { - "dependencies": ["sqlQueryValidator"], - "body": "async (taskDescription) => { return await sqlQueryValidator(taskDescription); }" - }, - "importDatabase": { - "dependencies": ["importDatabase"], - "body": "async (taskDescription) => { return await importDatabase(taskDescription); }" - }, - "fetchERD": { - "dependencies": ["fetchERD"], - "body": "async (taskDescription) => { return await fetchERD(taskDescription); }" - } - } - } - }, - "UI": { - "currentTask": "sql", - "taskData": {}, - "rootNode": 0, - "topology": [[0, null, null]], - "edges": { - "0": [] - }, - "currentNode": 0, - "nodes": { - "0": { - "zoomScale": 1, - "layouts": { - "sm": [ - { "x": 32, "y": 31, "w": 2, "h": 2, "i": 0, "static": false }, - { "x": 32, "y": 29, "w": 2, "h": 2, "i": 1, "static": false }, - { "x": 34, "y": 29, "w": 2, "h": 2, "i": 2, "static": false }, - { "x": 34, "y": 31, "w": 2, "h": 2, "i": 3, "static": false }, - { "x": 36, "y": 31, "w": 2, "h": 2, "i": 4, "static": false } - ], - "md": [ - { "x": 32, "y": 31, "w": 2, "h": 2, "i": 0, "static": false }, - { "x": 32, "y": 29, "w": 2, "h": 2, "i": 1, "static": false }, - { "x": 34, "y": 29, "w": 2, "h": 2, "i": 2, "static": false }, - { "x": 34, "y": 31, "w": 2, "h": 2, "i": 3, "static": false }, - { "x": 36, "y": 31, "w": 2, "h": 2, "i": 4, "static": false } - ], - "lg": [ - { "x": 38, "y": 29, "w": 3, "h": 4, "i": 0, "static": false }, - { "x": 32, "y": 29, "w": 2, "h": 3, "i": 1, "static": false }, - { "x": 34, "y": 29, "w": 4, "h": 2, "i": 2, "static": false }, - { "x": 34, "y": 31, "w": 2, "h": 2, "i": 3, "static": false }, - { "x": 36, "y": 31, "w": 2, "h": 2, "i": 4, "static": false } - ] - }, - "hints": { - "active": false, - "current": 0, - "descriptions": [ - "Generiere eine beliebige SQL-Abfrage.
Ermittle nun anhand der Aufgabenstellung und des Datenbankdiagramms die nötigen Felder, Tabellen und Joins um die Abfrage zu bilden.
Trage das Statement in das Code-Widget ein und klicke auf 'Ausführen' um das Ergebnis zu überprüfen." - ] - }, - "modals": [ - { - "trigger": { - "type": "success" - }, - "content": { - "header": "Herzlichen Glückwunsch!", - "body": "Sie haben die Aufgabe erfolgreich gelöst!", - "footer": { - "buttons": [ - { "type": "close", "label": "Zurück", "parameters": {} }, - { "type": "route", "label": "Zur Übersicht", "parameters": { "route": "TaskOverview" } } - ] - } - }, - "active": false - } - ], - "components": { - "0": { - "type": "DOTGraph", - "name": "DB", - "isValid": true, - "dependencies": { "dotDescription": "taskData__dotDescription" }, - "component": {} - }, - "1": { - "type": "TaskConfiguration", - "name": "Konfiguration", - "isValid": true, - "component": { - "title": "Parameter Konfiguration", - "actions": [ - { - "instruction": "generateQuery", - "type": "fetchData", - "label": "Generieren!", - "dependsOn": ["joinRange", "columnRange", "constraintRange"] - } - ], - "form": { - "schema": { - "formType": "DropdownFormField", - "label": "Datenbank", - "type": "string", - "description": "Zur Verfügung stehende Datenbanken", - "action": { "instruction": "fetchERD", "type": "fetchData", "key": "schema" }, - "initial": "northwind", - "options": ["northwind", "sports", "airport"], - "presets": { - "easy": "airport", - "medium": "northwind", - "hard": "sports" - } - }, - "joinRange": { - "formType": "RangeFormField", - "label": "Anzahl Joins", - "type": "number", - "description": "Bestimmt die Anzahl an Joins", - "initial": { - "lowerValue": 0, - "upperValue": 2 - }, - "step": 1, - "boundaries": { "min": 0, "max": 5 }, - "presets": { - "easy": 0, - "medium": 2, - "hard": 3 - } - }, - "columnRange": { - "formType": "RangeFormField", - "label": "Anzahl Spalten", - "type": "number", - "description": "Bestimmt die Anzahl an Spalten", - "initial": { - "lowerValue": 1, - "upperValue": 4 - }, - "step": 1, - "boundaries": { "min": 1, "max": 5 }, - "presets": { - "easy": 1, - "medium": 3, - "hard": 5 - } - }, - "constraintRange": { - "formType": "RangeFormField", - "label": "Anzahl Bedingungen", - "type": "number", - "description": "Bestimmt die Anzahl an Bedingungen", - "initial": { - "lowerValue": 0, - "upperValue": 3 - }, - "step": 1, - "boundaries": { "min": 0, "max": 6 }, - "presets": { - "easy": 0, - "medium": 2, - "hard": 4 - } - }, - "allowAggregates": { - "formType": "CheckboxFormField", - "label": "Aggregat", - "type": "checkbox", - "description": "Bestimmt ob Aggregationen generiert werden", - "initial": false, - "presets": { - "easy": false, - "medium": true, - "hard": true - } - }, - "forceHavingClause": { - "formType": "CheckboxFormField", - "label": "Having-Clause", - "type": "checkbox", - "description": "Bestimmt ob eine Having Clause generiert wird", - "initial": false, - "presets": { - "easy": false, - "medium": true, - "hard": true - } - }, - "orderBy": { - "formType": "CheckboxFormField", - "label": "OrderBy", - "type": "checkbox", - "description": "Bestimmt ob ein Order By Statement generiert wird", - "initial": true, - "presets": { - "easy": true, - "medium": true, - "hard": true - } - }, - "seed": { - "formType": "ValueFormField", - "label": "Seed", - "type": "text", - "description": "Setze einen beliebigen Wert um die Generierung deterministisch und reproduzierbar zu machen", - "value": "", - "presets": { - "easy": "", - "medium": "", - "hard": "" - } - } - } - } - }, - "2": { - "type": "CodeEditor", - "name": "SQL-Query", - "isValid": true, - "methods": { - "showSolution": { "description": "Zeig mir die Lösung", "impact": 0 }, - "copyToClipboard": { "description": "Kopieren!", "impact": "0" } - }, - "contextMenu": { + "API": [ + { + "task": "sql", + "name": "generateQuery", + "httpMethod": "post", + "params": { "parameters": "object", "schema": "string", "language": "string" } + }, + { + "task": "sql", + "name": "validateQuery", + "httpMethod": "post", + "params": { "parameters": "object" } + }, + { + "task": "sql", + "name": "generateERD", + "httpMethod": "post", + "params": { "parameters": "object" } + }, + { + "task": "sql", + "name": "fetchERD", + "httpMethod": "post", + "params": { "parameters": "object" } + } + ], + "Worker": { + "sqlTask": { + "minConsumers": 1, + "consumerInstructions": { + "generateQuery": { + "dependencies": ["sqlQueryGenerator"], + "body": "async (taskDescription) => { return await sqlQueryGenerator(taskDescription); }" + }, + "validateQuery": { + "dependencies": ["sqlQueryValidator"], + "body": "async (taskDescription) => { return await sqlQueryValidator(taskDescription); }" + }, + "importDatabase": { + "dependencies": ["importDatabase"], + "body": "async (taskDescription) => { return await importDatabase(taskDescription); }" + }, + "fetchERD": { + "dependencies": ["fetchERD"], + "body": "async (taskDescription) => { return await fetchERD(taskDescription); }" + } + } + } + }, + "UI": { + "currentTask": "sql", + "taskData": {}, + "rootNode": 0, + "topology": [[0, null, null]], + "edges": { + "0": [] + }, + "currentNode": 0, + "nodes": { + "0": { + "zoomScale": 1, + "layouts": { + "sm": [ + { "x": 32, "y": 31, "w": 2, "h": 2, "i": 0, "static": false }, + { "x": 32, "y": 29, "w": 2, "h": 2, "i": 1, "static": false }, + { "x": 34, "y": 29, "w": 2, "h": 2, "i": 2, "static": false }, + { "x": 34, "y": 31, "w": 2, "h": 2, "i": 3, "static": false }, + { "x": 36, "y": 31, "w": 2, "h": 2, "i": 4, "static": false } + ], + "md": [ + { "x": 32, "y": 31, "w": 2, "h": 2, "i": 0, "static": false }, + { "x": 32, "y": 29, "w": 2, "h": 2, "i": 1, "static": false }, + { "x": 34, "y": 29, "w": 2, "h": 2, "i": 2, "static": false }, + { "x": 34, "y": 31, "w": 2, "h": 2, "i": 3, "static": false }, + { "x": 36, "y": 31, "w": 2, "h": 2, "i": 4, "static": false } + ], + "lg": [ + { "x": 38, "y": 29, "w": 3, "h": 4, "i": 0, "static": false }, + { "x": 32, "y": 29, "w": 2, "h": 3, "i": 1, "static": false }, + { "x": 34, "y": 29, "w": 4, "h": 2, "i": 2, "static": false }, + { "x": 34, "y": 31, "w": 2, "h": 2, "i": 3, "static": false }, + { "x": 36, "y": 31, "w": 2, "h": 2, "i": 4, "static": false } + ] + }, + "hints": { + "active": false, + "current": 0, + "descriptions": [ + "Generiere eine beliebige SQL-Abfrage.
Ermittle nun anhand der Aufgabenstellung und des Datenbankdiagramms die nötigen Felder, Tabellen und Joins um die Abfrage zu bilden.
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"description": "Bestimmt ob ein Order By Statement generiert wird", + "initial": true, + "presets": { + "easy": true, + "medium": true, + "hard": true + } + }, + "seed": { + "formType": "ValueFormField", + "label": "Seed", + "type": "text", + "description": "Setze einen beliebigen Wert um die Generierung deterministisch und reproduzierbar zu machen", + "value": "", + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + } + } + } + }, + "2": { + "type": "CodeEditor", + "name": "SQL-Query", + "isValid": true, + "methods": { + "showSolution": {"description": "Zeig mir die Lösung", "impact": 0}, "copyToClipboard": {"description": "Kopieren!", "impact":"0"} + + }, + "contextMenu": { "isOpen": false, "usedMethods": [] }, - "actions": [ - { - "type": "execute", - "instruction": "validateQuery", - "label": "Ausführen!", - "parameters": { - "expectedResult": "taskData__result", - "schema": "nodes__0__components__1__component__form__schema__initial", - "query": 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"dependencies": { "CodeEditor": { "validCode": "taskData__query" } }, + "component": { + "language": ".sql", + "code": "" + } + }, + "3": { + "type": "Output", + "name": "SQL-Output", + "isValid": false, + "dependencies": { + "Output": { "serverOutput": "taskData__userResult", "validOutput": "taskData__isMatchingResult" } + }, + "component": { + "header": "SQL-Rückgabe" + } + }, + "4": { + "type": "Output", + "name": "SQL-Query", + "isValid": true, + "dependencies": { "Output": { "serverOutput": "taskData__description", "validOutput": "isMatchingResult" } }, + "component": { + "header": "Aufgabenstellung" + } + } + } + } + } + } } diff --git a/src/server/public/assets/ComponentTest-78e86ddd.js b/src/server/public/assets/ComponentTest-78e86ddd.js deleted file mode 100644 index 70279c2..0000000 --- a/src/server/public/assets/ComponentTest-78e86ddd.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as e,c as t,o as s}from"./index-ac1dbb22.js";const o={name:"Test",setup(){return{}}};const 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div[data-v-fb79a4af]{display:flex;align-items:center;justify-content:center;border:1px solid #f1ad2d;padding:10px;border-radius:5px;background:#e8edf1;box-shadow:2px 3px 9px #000;width:20%;font-size:20px;font-weight:700;color:#000}.header div[data-v-fb79a4af]{font-size:30px}.replayListEntry[data-v-fb79a4af]{filter:brightness(100%);transition:.15s;text-decoration:none}.replayListEntry[data-v-fb79a4af]:hover{filter:brightness(85%);transition:.15s;border:1px solid #f1ad2d} diff --git a/src/server/public/assets/Settings-6fa90251.js b/src/server/public/assets/Settings-6fa90251.js deleted file mode 100644 index c5dbc6e..0000000 --- a/src/server/public/assets/Settings-6fa90251.js +++ /dev/null @@ -1 +0,0 @@ -import{S as w,o}from"./index-8ac73e8f.js";import{j as r,k as n,l,c as s,g as c,w as x,m as a,o as t,F as _,n as u,h as d,q as p,t as g,_ as f}from"./index-ac1dbb22.js";const v={class:"settings"},m=r({__name:"Settings",setup(S){const h=n({md:[{x:0,y:0,w:2,h:2,i:"0"},{x:2,y:0,w:2,h:4,i:"1"},{x:4,y:0,w:2,h:5,i:"2"},{x:6,y:0,w:2,h:3,i:"3"},{x:2,y:4,w:2,h:3,i:"4"},{x:4,y:5,w:2,h:3,i:"5"},{x:0,y:2,w:2,h:5,i:"6"},{x:2,y:7,w:2,h:5,i:"7"},{x:4,y:8,w:2,h:5,i:"8"},{x:6,y:3,w:2,h:4,i:"9"},{x:0,y:7,w:2,h:4,i:"10"},{x:2,y:19,w:2,h:4,i:"11"},{x:0,y:14,w:2,h:5,i:"12"},{x:2,y:14,w:2,h:5,i:"13"},{x:4,y:13,w:2,h:4,i:"14"},{x:6,y:7,w:2,h:4,i:"15"},{x:0,y:19,w:2,h:5,i:"16"},{x:8,y:0,w:2,h:2,i:"17"},{x:0,y:11,w:2,h:3,i:"18"},{x:2,y:12,w:2,h:2,i:"19"}],lg:[{x:0,y:0,w:2,h:2,i:"0"},{x:2,y:0,w:2,h:4,i:"1"},{x:4,y:0,w:2,h:5,i:"2"},{x:6,y:0,w:2,h:3,i:"3"},{x:8,y:0,w:2,h:3,i:"4"},{x:10,y:0,w:2,h:3,i:"5"},{x:0,y:5,w:2,h:5,i:"6"},{x:2,y:5,w:2,h:5,i:"7"},{x:4,y:5,w:2,h:5,i:"8"},{x:6,y:4,w:2,h:4,i:"9"},{x:8,y:4,w:2,h:4,i:"10"},{x:10,y:4,w:2,h:4,i:"11"},{x:0,y:10,w:2,h:5,i:"12"},{x:2,y:10,w:2,h:5,i:"13"},{x:4,y:8,w:2,h:4,i:"14"},{x:6,y:8,w:2,h:4,i:"15"},{x:8,y:10,w:2,h:5,i:"16"},{x:10,y:4,w:2,h:2,i:"17"},{x:0,y:9,w:2,h:3,i:"18"},{x:2,y:6,w:2,h:2,i:"19"}]}),e=l(h.lg);return(k,y)=>(t(),s("div",v,[c(a(w),{layout:e.value,"onUpdate:layout":y[0]||(y[0]=i=>e.value=i),"col-num":12,"row-height":30,"is-draggable":"","is-resizable":"","vertical-compact":"","use-css-transforms":""},{default:x(()=>[(t(!0),s(_,null,u(e.value,i=>(t(),d(a(o),{key:i.i,x:i.x,y:i.y,w:i.w,h:i.h,i:i.i},{default:x(()=>[p(g(i.i),1)]),_:2},1032,["x","y","w","h","i"]))),128))]),_:1},8,["layout"])]))}});const C=f(m,[["__scopeId","data-v-253e3eb2"]]);export{C as default}; diff --git a/src/server/public/assets/Settings-ad2b85f9.css b/src/server/public/assets/Settings-ad2b85f9.css deleted file mode 100644 index 186a17f..0000000 --- a/src/server/public/assets/Settings-ad2b85f9.css +++ /dev/null @@ -1 +0,0 @@ -.settings[data-v-253e3eb2]{display:flex;justify-content:center;align-items:center;width:100vw;height:100vh;flex-direction:column}.vgl-layout[data-v-253e3eb2]{background-color:#eee;width:100vw;height:100vh}[data-v-253e3eb2] .vgl-item:not(.vgl-item--placeholder){background-color:#ccc;border:1px solid black}[data-v-253e3eb2] .vgl-item--resizing{opacity:90%}[data-v-253e3eb2] .vgl-item--static{background-color:#cce}.text[data-v-253e3eb2]{position:absolute;inset:0;width:100%;height:100%;margin:auto;font-size:24px;text-align:center} diff --git a/src/server/public/assets/Task-1392d80c.css b/src/server/public/assets/Task-1392d80c.css deleted file mode 100644 index 368211d..0000000 --- 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a/src/server/public/assets/index-7f39dae4.css +++ /dev/null @@ -1 +0,0 @@ -.menuBubble[data-v-48098402]{position:absolute;width:10vw;height:10vw;top:-5vw;left:-5vw;background:linear-gradient(90deg,#57636b,#40494f);border-radius:50%;z-index:6;box-shadow:2px 3px 9px #000;cursor:pointer;text-shadow:2px 2px black;transition:all 1s ease}.menuBubble[data-v-48098402]:hover{filter:brightness(85%);transition:all 1s ease}.menuBubble>p[data-v-48098402]{position:relative;top:5vw;left:5.5vw;color:#f1ad2d;font-size:50px;box-shadow:inset 0 -7px 5px -7px #0000}.menuOptions[data-v-48098402]{position:absolute;top:0vh;left:0vh;height:0vh;width:0vw;z-index:5;transition-delay:.1s;transition:all .5s cubic-bezier(.25,.8,.25,1)}.open .menuOptions[data-v-48098402]{position:absolute;width:20vw;height:20vh;display:block}.menu__option[data-v-48098402]{top:0;left:0;position:absolute;display:flex;font-size:1vw;cursor:pointer;border-radius:50%;border:2px solid 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n=t.dropState;n&&(n.activeDrops=null,n.events=null,n.cur.dropzone=null,n.cur.element=null,n.prev.dropzone=null,n.prev.element=null,n.rejected=!1)}}},getActiveDrops:we,getDrop:Me,getDropEvents:We,fireDropEvents:Xe,defaults:{enabled:!1,accept:null,overlap:"pointer"}},Bt=wt;D.default=Bt;var Je={};function Kn(e){var t=e.interaction,n=e.iEvent,r=e.phase;if(t.prepared.name==="gesture"){var o=t.pointers.map(function(d){return d.pointer}),i=r==="start",l=r==="end",a=t.interactable.options.deltaSource;if(n.touches=[o[0],o[1]],i)n.distance=z.touchDistance(o,a),n.box=z.touchBBox(o),n.scale=1,n.ds=0,n.angle=z.touchAngle(o,a),n.da=0,t.gesture.startDistance=n.distance,t.gesture.startAngle=n.angle;else if(l){var s=t.prevEvent;n.distance=s.distance,n.box=s.box,n.scale=s.scale,n.ds=0,n.angle=s.angle,n.da=0}else 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u.default.object(o)?(this.options.gesture.enabled=o.enabled!==!1,this.setPerAction("gesture",o),this.setOnEvents("gesture",o),this):u.default.bool(o)?(this.options.gesture.enabled=o,this):this.options.gesture},t.map.gesture=Jn,t.methodDict.gesture="gesturable",r.actions.gesture=Jn.defaults},listeners:{"interactions:action-start":Kn,"interactions:action-move":Kn,"interactions:action-end":Kn,"interactions:new":function(e){e.interaction.gesture={angle:0,distance:0,scale:1,startAngle:0,startDistance:0}},"auto-start:check":function(e){if(!(e.interaction.pointers.length<2)){var t=e.interactable.options.gesture;if(t&&t.enabled)return e.action={name:"gesture"},!1}}},defaults:{},getCursor:function(){return""}},Ti=Jn;Je.default=Ti;var un={};function zi(e,t,n,r,o,i,l){if(!t)return!1;if(t===!0){var 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t=e.actions,n=e.browser,r=e.Interactable,o=e.defaults;$e.cursors=function(i){return i.isIe9?{x:"e-resize",y:"s-resize",xy:"se-resize",top:"n-resize",left:"w-resize",bottom:"s-resize",right:"e-resize",topleft:"se-resize",bottomright:"se-resize",topright:"ne-resize",bottomleft:"ne-resize"}:{x:"ew-resize",y:"ns-resize",xy:"nwse-resize",top:"ns-resize",left:"ew-resize",bottom:"ns-resize",right:"ew-resize",topleft:"nwse-resize",bottomright:"nwse-resize",topright:"nesw-resize",bottomleft:"nesw-resize"}}(n),$e.defaultMargin=n.supportsTouch||n.supportsPointerEvent?20:10,r.prototype.resizable=function(i){return function(l,a,s){return u.default.object(a)?(l.options.resize.enabled=a.enabled!==!1,l.setPerAction("resize",a),l.setOnEvents("resize",a),u.default.string(a.axis)&&/^x$|^y$|^xy$/.test(a.axis)?l.options.resize.axis=a.axis:a.axis===null&&(l.options.resize.axis=s.defaults.actions.resize.axis),u.default.bool(a.preserveAspectRatio)?l.options.resize.preserveAspectRatio=a.preserveAspectRatio:u.default.bool(a.square)&&(l.options.resize.square=a.square),l):u.default.bool(a)?(l.options.resize.enabled=a,l):l.options.resize}(this,i,e)},t.map.resize=$e,t.methodDict.resize="resizable",o.actions.resize=$e.defaults},listeners:{"interactions:new":function(e){e.interaction.resizeAxes="xy"},"interactions:action-start":function(e){(function(t){var n=t.iEvent,r=t.interaction;if(r.prepared.name==="resize"&&r.prepared.edges){var o=n,i=r.rect;r._rects={start:(0,k.default)({},i),corrected:(0,k.default)({},i),previous:(0,k.default)({},i),delta:{left:0,right:0,width:0,top:0,bottom:0,height:0}},o.edges=r.prepared.edges,o.rect=r._rects.corrected,o.deltaRect=r._rects.delta}})(e),Nr(e)},"interactions:action-move":function(e){(function(t){var n=t.iEvent,r=t.interaction;if(r.prepared.name==="resize"&&r.prepared.edges){var o=n,i=r.interactable.options.resize.invert,l=i==="reposition"||i==="negate",a=r.rect,s=r._rects,d=s.start,p=s.corrected,g=s.delta,b=s.previous;if((0,k.default)(b,p),l){if((0,k.default)(p,a),i==="reposition"){if(p.top>p.bottom){var w=p.top;p.top=p.bottom,p.bottom=w}if(p.left>p.right){var y=p.left;p.left=p.right,p.right=y}}}else p.top=Math.min(a.top,d.bottom),p.bottom=Math.max(a.bottom,d.top),p.left=Math.min(a.left,d.right),p.right=Math.max(a.right,d.left);for(var x in 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p=a.axis!=="y"&&l.x>o.right-$e.defaultMargin,g=a.axis!=="x"&&l.y>o.bottom-$e.defaultMargin;(p||g)&&(e.action={name:"resize",axes:(p?"x":"")+(g?"y":"")})}return!e.action&&void 0}}}},defaults:{square:!1,preserveAspectRatio:!1,axis:"xy",margin:NaN,edges:null,invert:"none"},cursors:null,getCursor:function(e){var t=e.edges,n=e.axis,r=e.name,o=$e.cursors,i=null;if(n)i=o[r+n];else if(t){for(var l="",a=["top","bottom","left","right"],s=0;s=1){var d={x:C.x*s,y:C.y*s};if(d.x||d.y){var p=Ft(i);u.default.window(i)?i.scrollBy(d.x,d.y):i&&(i.scrollLeft+=d.x,i.scrollTop+=d.y);var g=Ft(i),b={x:g.x-p.x,y:g.y-p.y};(b.x||b.y)&&t.fire({type:"autoscroll",target:n,interactable:t,delta:b,interaction:e,container:i})}C.prevTime=l}C.isScrolling&&(Ie.default.cancel(C.i),C.i=Ie.default.request(C.scroll))},check:function(e,t){var n;return(n=e.options[t].autoScroll)==null?void 0:n.enabled},onInteractionMove:function(e){var t=e.interaction,n=e.pointer;if(t.interacting()&&C.check(t.interactable,t.prepared.name))if(t.simulation)C.x=C.y=0;else{var r,o,i,l,a=t.interactable,s=t.element,d=t.prepared.name,p=a.options[d].autoScroll,g=fn(p.container,a,s);if(u.default.window(g))l=n.clientXg.innerWidth-C.margin,i=n.clientY>g.innerHeight-C.margin;else{var b=R.getElementClientRect(g);l=n.clientXb.right-C.margin,i=n.clientY>b.bottom-C.margin}C.x=o?1:l?-1:0,C.y=i?1:r?-1:0,C.isScrolling||(C.margin=p.margin,C.speed=p.speed,C.start(t))}}};function fn(e,t,n){return(u.default.string(e)?(0,j.getStringOptionResult)(e,t,n):e)||(0,f.getWindow)(n)}function Ft(e){return u.default.window(e)&&(e=window.document.body),{x:e.scrollLeft,y:e.scrollTop}}var Ii={id:"auto-scroll",install:function(e){var t=e.defaults,n=e.actions;e.autoScroll=C,C.now=function(){return 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s=180*Math.atan2(a.prevEvent.velocityY,a.prevEvent.velocityX)/Math.PI;s<0&&(s+=360);var d=112.5<=s&&s<247.5,p=202.5<=s&&s<337.5;return{up:p,down:!p&&22.5<=s&&s<157.5,left:d,right:!d&&(292.5<=s||s<67.5),angle:s,speed:a.prevEvent.speed,velocity:{x:a.prevEvent.velocityX,y:a.prevEvent.velocityY}}}},{key:"preventDefault",value:function(){}},{key:"stopImmediatePropagation",value:function(){this.immediatePropagationStopped=this.propagationStopped=!0}},{key:"stopPropagation",value:function(){this.propagationStopped=!0}}])&&Qi(t.prototype,n),Object.defineProperty(t,"prototype",{writable:!1}),l}(Le.BaseEvent);Wt.InteractEvent=Kr,Object.defineProperties(Kr.prototype,{pageX:{get:function(){return this.page.x},set:function(e){this.page.x=e}},pageY:{get:function(){return this.page.y},set:function(e){this.page.y=e}},clientX:{get:function(){return this.client.x},set:function(e){this.client.x=e}},clientY:{get:function(){return this.client.y},set:function(e){this.client.y=e}},dx:{get:function(){return this.delta.x},set:function(e){this.delta.x=e}},dy:{get:function(){return this.delta.y},set:function(e){this.delta.y=e}},velocityX:{get:function(){return this.velocity.x},set:function(e){this.velocity.x=e}},velocityY:{get:function(){return this.velocity.y},set:function(e){this.velocity.y=e}}});var Xt={};function Jr(e,t){for(var n=0;nthis.pointerMoveTolerance);var d=this.getPointerIndex(r),p={pointer:r,pointerIndex:d,pointerInfo:this.pointers[d],event:o,type:"move",eventTarget:i,dx:l,dy:a,duplicate:s,interaction:this};s||z.setCoordVelocity(this.coords.velocity,this.coords.delta),this._scopeFire("interactions:move",p),s||this.simulation||(this.interacting()&&(p.type=null,this.move(p)),this.pointerWasMoved&&z.copyCoords(this.coords.prev,this.coords.cur))}},{key:"move",value:function(r){r&&r.event||z.setZeroCoords(this.coords.delta),(r=(0,k.default)({pointer:this._latestPointer.pointer,event:this._latestPointer.event,eventTarget:this._latestPointer.eventTarget,interaction:this},r||{})).phase="move",this._doPhase(r)}},{key:"pointerUp",value:function(r,o,i,l){var a=this.getPointerIndex(r);a===-1&&(a=this.updatePointer(r,o,i,!1));var s=/cancel$/i.test(o.type)?"cancel":"up";this._scopeFire("interactions:".concat(s),{pointer:r,pointerIndex:a,pointerInfo:this.pointers[a],event:o,eventTarget:i,type:s,curEventTarget:l,interaction:this}),this.simulation||this.end(o),this.removePointer(r,o)}},{key:"documentBlur",value:function(r){this.end(r),this._scopeFire("interactions:blur",{event:r,type:"blur",interaction:this})}},{key:"end",value:function(r){var o;this._ending=!0,r=r||this._latestPointer.event,this.interacting()&&(o=this._doPhase({event:r,interaction:this,phase:"end"})),this._ending=!1,o===!0&&this.stop()}},{key:"currentAction",value:function(){return this._interacting?this.prepared.name:null}},{key:"interacting",value:function(){return this._interacting}},{key:"stop",value:function(){this._scopeFire("interactions:stop",{interaction:this}),this.interactable=this.element=null,this._interacting=!1,this._stopped=!0,this.prepared.name=this.prevEvent=null}},{key:"getPointerIndex",value:function(r){var o=z.getPointerId(r);return this.pointerType==="mouse"||this.pointerType==="pen"?this.pointers.length-1:U.findIndex(this.pointers,function(i){return i.id===o})}},{key:"getPointerInfo",value:function(r){return this.pointers[this.getPointerIndex(r)]}},{key:"updatePointer",value:function(r,o,i,l){var a=z.getPointerId(r),s=this.getPointerIndex(r),d=this.pointers[s];return l=l!==!1&&(l||/(down|start)$/i.test(o.type)),d?d.pointer=r:(d=new Xt.PointerInfo(a,r,o,null,null),s=this.pointers.length,this.pointers.push(d)),z.setCoords(this.coords.cur,this.pointers.map(function(p){return p.pointer}),this._now()),z.setCoordDeltas(this.coords.delta,this.coords.prev,this.coords.cur),l&&(this.pointerIsDown=!0,d.downTime=this.coords.cur.timeStamp,d.downTarget=i,z.pointerExtend(this.downPointer,r),this.interacting()||(z.copyCoords(this.coords.start,this.coords.cur),z.copyCoords(this.coords.prev,this.coords.cur),this.downEvent=o,this.pointerWasMoved=!1)),this._updateLatestPointer(r,o,i),this._scopeFire("interactions:update-pointer",{pointer:r,event:o,eventTarget:i,down:l,pointerInfo:d,pointerIndex:s,interaction:this}),s}},{key:"removePointer",value:function(r,o){var i=this.getPointerIndex(r);if(i!==-1){var l=this.pointers[i];this._scopeFire("interactions:remove-pointer",{pointer:r,event:o,eventTarget:null,pointerIndex:i,pointerInfo:l,interaction:this}),this.pointers.splice(i,1),this.pointerIsDown=!1}}},{key:"_updateLatestPointer",value:function(r,o,i){this._latestPointer.pointer=r,this._latestPointer.event=o,this._latestPointer.eventTarget=i}},{key:"destroy",value:function(){this._latestPointer.pointer=null,this._latestPointer.event=null,this._latestPointer.eventTarget=null}},{key:"_createPreparedEvent",value:function(r,o,i,l){return new Wt.InteractEvent(this,r,this.prepared.name,o,this.element,i,l)}},{key:"_fireEvent",value:function(r){var o;(o=this.interactable)==null||o.fire(r),(!this.prevEvent||r.timeStamp>=this.prevEvent.timeStamp)&&(this.prevEvent=r)}},{key:"_doPhase",value:function(r){var o=r.event,i=r.phase,l=r.preEnd,a=r.type,s=this.rect;if(s&&i==="move"&&(j.addEdges(this.edges,s,this.coords.delta[this.interactable.options.deltaSource]),s.width=s.right-s.left,s.height=s.bottom-s.top),this._scopeFire("interactions:before-action-".concat(i),r)===!1)return!1;var d=r.iEvent=this._createPreparedEvent(o,i,l,a);return this._scopeFire("interactions:action-".concat(i),r),i==="start"&&(this.prevEvent=d),this._fireEvent(d),this._scopeFire("interactions:after-action-".concat(i),r),!0}},{key:"_now",value:function(){return Date.now()}}],n&&ra(t.prototype,n),Object.defineProperty(t,"prototype",{writable:!1}),e}();Se.Interaction=Zr;var ia=Zr;Se.default=ia;var ct={};function Qr(e){e.pointerIsDown&&(ir(e.coords.cur,e.offset.total),e.offset.pending.x=0,e.offset.pending.y=0)}function eo(e){or(e.interaction)}function or(e){if(!function(n){return!(!n.offset.pending.x&&!n.offset.pending.y)}(e))return!1;var t=e.offset.pending;return ir(e.coords.cur,t),ir(e.coords.delta,t),j.addEdges(e.edges,e.rect,t),t.x=0,t.y=0,!0}function aa(e){var t=e.x,n=e.y;this.offset.pending.x+=t,this.offset.pending.y+=n,this.offset.total.x+=t,this.offset.total.y+=n}function ir(e,t){var n=e.page,r=e.client,o=t.x,i=t.y;n.x+=o,n.y+=i,r.x+=o,r.y+=i}Object.defineProperty(ct,"__esModule",{value:!0}),ct.addTotal=Qr,ct.applyPending=or,ct.default=void 0,Se._ProxyMethods.offsetBy="";var sa={id:"offset",before:["modifiers","pointer-events","actions","inertia"],install:function(e){e.Interaction.prototype.offsetBy=aa},listeners:{"interactions:new":function(e){e.interaction.offset={total:{x:0,y:0},pending:{x:0,y:0}}},"interactions:update-pointer":function(e){return Qr(e.interaction)},"interactions:before-action-start":eo,"interactions:before-action-move":eo,"interactions:before-action-end":function(e){var t=e.interaction;if(or(t))return t.move({offset:!0}),t.end(),!1},"interactions:stop":function(e){var t=e.interaction;t.offset.total.x=0,t.offset.total.y=0,t.offset.pending.x=0,t.offset.pending.y=0}}},la=sa;ct.default=la;var _t={};function ua(e,t){for(var n=0;ni.minSpeed&&a>i.endSpeed)this.startInertia();else{if(s.result=s.setAll(this.modifierArg),!s.result.changed)return!1;this.startSmoothEnd()}return o.modification.result.rect=null,o.offsetBy(this.targetOffset),o._doPhase({interaction:o,event:r,phase:"inertiastart"}),o.offsetBy({x:-this.targetOffset.x,y:-this.targetOffset.y}),o.modification.result.rect=null,this.active=!0,o.simulation=this,!0}},{key:"startInertia",value:function(){var r=this,o=this.interaction.coords.velocity.client,i=En(this.interaction),l=i.resistance,a=-Math.log(i.endSpeed/this.v0)/l;this.targetOffset={x:(o.x-a)/l,y:(o.y-a)/l},this.te=a,this.lambda_v0=l/this.v0,this.one_ve_v0=1-i.endSpeed/this.v0;var s=this.modification,d=this.modifierArg;d.pageCoords={x:this.startCoords.x+this.targetOffset.x,y:this.startCoords.y+this.targetOffset.y},s.result=s.setAll(d),s.result.changed&&(this.isModified=!0,this.modifiedOffset={x:this.targetOffset.x+s.result.delta.x,y:this.targetOffset.y+s.result.delta.y}),this.onNextFrame(function(){return r.inertiaTick()})}},{key:"startSmoothEnd",value:function(){var r=this;this.smoothEnd=!0,this.isModified=!0,this.targetOffset={x:this.modification.result.delta.x,y:this.modification.result.delta.y},this.onNextFrame(function(){return r.smoothEndTick()})}},{key:"onNextFrame",value:function(r){var o=this;this.timeout=Ie.default.request(function(){o.active&&r()})}},{key:"inertiaTick",value:function(){var r,o,i,l,a,s=this,d=this.interaction,p=En(d).resistance,g=(d._now()-this.t0)/1e3;if(g=0;i--){var l=o[i],a=l.selector,s=l.context,d=l.listeners;a===this.target&&s===this._context&&o.splice(i,1);for(var 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P.eventTarget===p});x||(x={eventTarget:p,events:{}},n.push(x)),x.events[g]||(x.events[g]=[]),p.addEventListener&&!U.contains(x.events[g],b)&&(p.addEventListener(g,b,i.supportsOptions?y:y.capture),x.events[g].push(b))}function a(p,g,b,w){var y=Vt(w),x=U.findIndex(n,function(q){return q.eventTarget===p}),P=n[x];if(P&&P.events)if(g!=="all"){var _=!1,M=P.events[g];if(M){if(b==="all"){for(var I=M.length-1;I>=0;I--)a(p,g,M[I],y);return}for(var B=0;B=2)continue;if(!o.interacting()&&t===o.pointerType)return o}return null}};function ao(e,t){return e.pointers.some(function(n){return n.id===t})}var xa=kn;Pn.default=xa;var Tn={};function cr(e){return cr=typeof Symbol=="function"&&typeof Symbol.iterator=="symbol"?function(t){return typeof t}:function(t){return t&&typeof Symbol=="function"&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},cr(e)}function so(e,t){return function(n){if(Array.isArray(n))return n}(e)||function(n,r){var o=n==null?null:typeof 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Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}(),function(){var y,x=zn(p);if(g){var P=zn(this).constructor;y=Reflect.construct(x,arguments,P)}else y=x.apply(this,arguments);return Oa(this,y)});function w(){return Ea(this,w),b.apply(this,arguments)}return s=w,(d=[{key:"pointerMoveTolerance",get:function(){return e.interactions.pointerMoveTolerance},set:function(y){e.interactions.pointerMoveTolerance=y}},{key:"_now",value:function(){return e.now()}}])&&_a(s.prototype,d),Object.defineProperty(s,"prototype",{writable:!1}),w}(Se.default),e.interactions={list:[],new:function(a){a.scopeFire=function(d,p){return e.fire(d,p)};var s=new e.Interaction(a);return e.interactions.list.push(s),s},listeners:t,docEvents:o,pointerMoveTolerance:1},e.usePlugin(Et.default)},listeners:{"scope:add-document":function(e){return pr(e,"add")},"scope:remove-document":function(e){return pr(e,"remove")},"interactable:unset":function(e,t){for(var n=e.interactable,r=t.interactions.list.length-1;r>=0;r--){var o=t.interactions.list[r];o.interactable===n&&(o.stop(),t.fire("interactions:destroy",{interaction:o}),o.destroy(),t.interactions.list.length>2&&t.interactions.list.splice(r,1))}}},onDocSignal:pr,doOnInteractions:uo,methodNames:dr},Pa=Sa;Tn.default=Pa;var Ut={};function vr(e){return vr=typeof Symbol=="function"&&typeof Symbol.iterator=="symbol"?function(t){return typeof t}:function(t){return t&&typeof Symbol=="function"&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},vr(e)}function Mn(){return Mn=typeof Reflect<"u"&&Reflect.get?Reflect.get.bind():function(e,t,n){var r=ka(e,t);if(r){var o=Object.getOwnPropertyDescriptor(r,t);return o.get?o.get.call(arguments.length<3?e:n):o.value}},Mn.apply(this,arguments)}function ka(e,t){for(;!Object.prototype.hasOwnProperty.call(e,t)&&(e=ft(e))!==null;);return e}function hr(e,t){return hr=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(n,r){return n.__proto__=r,n},hr(e,t)}function Ta(e,t){if(t&&(vr(t)==="object"||typeof t=="function"))return t;if(t!==void 0)throw new TypeError("Derived constructors may only return object or undefined");return function(n){if(n===void 0)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return n}(e)}function ft(e){return ft=Object.setPrototypeOf?Object.getPrototypeOf.bind():function(t){return t.__proto__||Object.getPrototypeOf(t)},ft(e)}function fo(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function po(e,t){for(var n=0;n"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}(),function(){var s,d=ft(o);if(i){var p=ft(this).constructor;s=Reflect.construct(d,arguments,p)}else s=d.apply(this,arguments);return Ta(this,s)});function a(){return fo(this,a),l.apply(this,arguments)}return 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D.isDir(X.mode)?(X.node_ops=Z.ops_table.dir.node,X.stream_ops=Z.ops_table.dir.stream,X.contents={}):D.isFile(X.mode)?(X.node_ops=Z.ops_table.file.node,X.stream_ops=Z.ops_table.file.stream,X.usedBytes=0,X.contents=null):D.isLink(X.mode)?(X.node_ops=Z.ops_table.link.node,X.stream_ops=Z.ops_table.link.stream):D.isChrdev(X.mode)&&(X.node_ops=Z.ops_table.chrdev.node,X.stream_ops=Z.ops_table.chrdev.stream),X.timestamp=Date.now(),L&&(L.contents[A]=X,L.timestamp=X.timestamp),X},getFileDataAsTypedArray:function(L){return L.contents?L.contents.subarray?L.contents.subarray(0,L.usedBytes):new Uint8Array(L.contents):new Uint8Array(0)},expandFileStorage:function(L,A){var P=L.contents?L.contents.length:0;if(!(P>=A)){A=Math.max(A,P*(P<1048576?2:1.125)>>>0),P!=0&&(A=Math.max(A,256));var j=L.contents;L.contents=new Uint8Array(A),L.usedBytes>0&&L.contents.set(j.subarray(0,L.usedBytes),0)}},resizeFileStorage:function(L,A){if(L.usedBytes!=A)if(A==0)L.contents=null,L.usedBytes=0;else{var 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D.ErrnoError(28);if((Pe=ge.relative(A,X)).charAt(0)!==".")throw new D.ErrnoError(55);try{Ce=D.lookupNode(j,ae)}catch{}if(Le!==Ce){var Qe=D.isDir(Le.mode),Te=D.mayDelete(P,ne,Qe);if(Te)throw new D.ErrnoError(Te);if(Te=Ce?D.mayDelete(j,ae,Qe):D.mayCreate(j,ae))throw new D.ErrnoError(Te);if(!P.node_ops.rename)throw new D.ErrnoError(63);if(D.isMountpoint(Le)||Ce&&D.isMountpoint(Ce))throw new D.ErrnoError(10);if(j!==P&&(Te=D.nodePermissions(P,"w")))throw new D.ErrnoError(Te);D.hashRemoveNode(Le);try{P.node_ops.rename(Le,j,ae)}catch(qe){throw qe}finally{D.hashAddNode(Le)}}},rmdir:L=>{var A=D.lookupPath(L,{parent:!0}).node,P=se.basename(L),j=D.lookupNode(A,P),X=D.mayDelete(A,P,!0);if(X)throw new D.ErrnoError(X);if(!A.node_ops.rmdir)throw new D.ErrnoError(63);if(D.isMountpoint(j))throw new D.ErrnoError(10);A.node_ops.rmdir(A,P),D.destroyNode(j)},readdir:L=>{var A=D.lookupPath(L,{follow:!0}).node;if(!A.node_ops.readdir)throw new D.ErrnoError(54);return A.node_ops.readdir(A)},unlink:L=>{var A=D.lookupPath(L,{parent:!0}).node;if(!A)throw new D.ErrnoError(44);var P=se.basename(L),j=D.lookupNode(A,P),X=D.mayDelete(A,P,!1);if(X)throw new D.ErrnoError(X);if(!A.node_ops.unlink)throw new D.ErrnoError(63);if(D.isMountpoint(j))throw new D.ErrnoError(10);A.node_ops.unlink(A,P),D.destroyNode(j)},readlink:L=>{var A=D.lookupPath(L).node;if(!A)throw new D.ErrnoError(44);if(!A.node_ops.readlink)throw new D.ErrnoError(28);return ge.resolve(D.getPath(A.parent),A.node_ops.readlink(A))},stat:(L,A)=>{var P=D.lookupPath(L,{follow:!A}).node;if(!P)throw new D.ErrnoError(44);if(!P.node_ops.getattr)throw new D.ErrnoError(63);return P.node_ops.getattr(P)},lstat:L=>D.stat(L,!0),chmod:(L,A,P)=>{var j;if(!(j=typeof L=="string"?D.lookupPath(L,{follow:!P}).node:L).node_ops.setattr)throw new D.ErrnoError(63);j.node_ops.setattr(j,{mode:4095&A|-4096&j.mode,timestamp:Date.now()})},lchmod:(L,A)=>{D.chmod(L,A,!0)},fchmod:(L,A)=>{var P=D.getStream(L);if(!P)throw new 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","Die Zeitanalyse ermöglicht die Ermittlung des kritischen Pfades.
Führe dazu zunächst eine Vorwärts- und dann eine Rückwärtskalkulation durch.
Ermittle anschließend die Pufferzeiten. Hier findest du eine detaillierte Anleitung.","Klicke die Felder der Ereignisse, welche mit einem Platzhalter versehen sind, an, um diese editieren zu können."]},"components":{"0":{"type":"EditableGraph","name":"Kalkulation","isValid":false,"methods":{"showSolution":{"description":"Zeige Lösung","impact":0}},"contextMenu":{"isOpen":false,"usedMethods":[]},"component":{"editableFields":["tb","fb","est","eet","lst","let"],"userValues":null},"nestedComponents":{"DOTGraph":{"component":{},"dependencies":{"dotDescription":"taskData__dummyGraph"}}},"dependencies":{"DOTGraph":{"dotDescription":"taskData__dummyGraph"},"EditableGraph":{"solution":"taskData__dotDescription","validation":"taskData__graph__nodes"}}}}},"4":{"zoomScale":1,"layouts":{"sm":[{"x":12,"y":5,"w":2,"h":5,"i":1,"static":false}],"md":[{"x":0,"y":10,"w":2,"h":5,"i":1,"static":false}],"lg":[{"x":32,"y":29,"w":8,"h":4,"i":0,"static":false},{"x":30,"y":29,"w":2,"h":4,"i":1,"static":false}]},"hints":{"active":false,"current":0,"descriptions":["Erstelle ein Gantt-Diagramm anhand der Vorgaben aus der Vorgangsliste. Hier findest du eine genaue Anleitung.","Trage die jeweilige Vorgangsdauer in der zugehörigen Zeile ein. Vergiss dabei nicht alle Abhängigkeiten zu berücksichtigen.","Klicke ein Feld im Raster an und ziehe die Maus entlang der x-Achse um eine Vorgangsdauer einzutragen. Um eine Vorgangsdauer zu entfernen klicke auf ein bereits gefülltes Feld und ziehe die Maus über die Felder welche du entfernen möchtest."]},"modals":[{"trigger":{"type":"success"},"content":{"header":"Herzlichen Glückwunsch!","body":"Sie haben die Aufgabe erfolgreich gelöst!","footer":{"buttons":[{"type":"close","label":"Zurück","parameters":{}},{"type":"route","label":"Zur Übersicht","parameters":{"route":"TaskOverview"}}]}},"active":false}],"components":{"0":{"type":"GanttDiagram","name":"Gantt","isValid":false,"methods":{"showSolution":{"description":"Zeige Lösung","impact":0}},"contextMenu":{"isOpen":false,"usedMethods":[]},"component":{"userMatrix":[],"isDrawing":false,"currentRow":null,"currentColumn":null,"isDeleting":false},"dependencies":{"GanttDiagram":{"ganttDiagram":"taskData__gantt"}}},"1":{"type":"Output","name":"Vorgangsliste","isValid":true,"dependencies":{"Output":{"serverOutput":"taskData__table","validOutput":"taskData__table"}},"component":{"header":"Vorgangsliste"}}}}}},{"timestamp":1711107832055,"path":"rootNode","value":0},{"timestamp":1711107832055,"path":"currentNode","value":0},{"timestamp":1711107832055,"path":"taskMode","value":"practice"},{"timestamp":1711107832055,"path":"zoomScale","value":1},{"timestamp":1711107833361,"path":"taskData__dotDescription","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
0 2
A
2 0 0
0 2
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
2 13
B
11 0 0
2 13
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
2 7
C
5 35 35
37 42
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
13 16
D
3 0 0
13 16
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
16 29
E
13 0 0
16 29
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
29 30
F
1 0 0
29 30
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
30 39
G
9 3 3
33 42
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
30 33
H
3 9 9
39 42
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
30 42
I
12 0 0
30 42
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
42 43
J
1 0 0
42 43
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n 1:out -> 3:in []\n 0:out -> 1:in []\n 2:out -> 9:in []\n 0:out -> 2:in []\n 3:out -> 4:in []\n 4:out -> 5:in []\n 6:out -> 9:in []\n 5:out -> 6:in []\n 7:out -> 9:in []\n 5:out -> 7:in []\n 8:out -> 9:in []\n 5:out -> 8:in []\n }"},{"timestamp":1711107833361,"path":"taskData__graph","value":{"nodes":[{"id":0,"parents":[],"childs":["1","2","3"],"ps":"A","d":2,"tb":0,"fb":0,"est":0,"eet":2,"lst":0,"let":2},{"id":1,"parents":["0"],"childs":["3"],"ps":"B","d":11,"tb":0,"fb":0,"est":2,"eet":13,"lst":2,"let":13},{"id":2,"parents":["0"],"childs":["9"],"ps":"C","d":5,"tb":35,"fb":35,"est":2,"eet":7,"lst":37,"let":42},{"id":3,"parents":["1"],"childs":["4"],"ps":"D","d":3,"tb":0,"fb":0,"est":13,"eet":16,"lst":13,"let":16},{"id":4,"parents":["3"],"childs":["5"],"ps":"E","d":13,"tb":0,"fb":0,"est":16,"eet":29,"lst":16,"let":29},{"id":5,"parents":["4"],"childs":["6","7","8","9"],"ps":"F","d":1,"tb":0,"fb":0,"est":29,"eet":30,"lst":29,"let":30},{"id":6,"parents":["5"],"childs":["9"],"ps":"G","d":9,"tb":3,"fb":3,"est":30,"eet":39,"lst":33,"let":42},{"id":7,"parents":["5"],"childs":["9"],"ps":"H","d":3,"tb":9,"fb":9,"est":30,"eet":33,"lst":39,"let":42},{"id":8,"parents":["5"],"childs":["9"],"ps":"I","d":12,"tb":0,"fb":0,"est":30,"eet":42,"lst":30,"let":42},{"id":9,"parents":["2","6","7","8"],"childs":[],"ps":"J","d":1,"tb":0,"fb":0,"est":42,"eet":43,"lst":42,"let":43}],"edges":[{"from":"1","to":"3","label":""},{"from":"0","to":"1","label":""},{"from":"2","to":"9","label":""},{"from":"0","to":"2","label":""},{"from":"3","to":"4","label":""},{"from":"4","to":"5","label":""},{"from":"6","to":"9","label":""},{"from":"5","to":"6","label":""},{"from":"7","to":"9","label":""},{"from":"5","to":"7","label":""},{"from":"8","to":"9","label":""},{"from":"5","to":"8","label":""}]}},{"timestamp":1711107833361,"path":"taskData__table","value":[{"Vorgang":"A","Dauer":2,"Vorgänger":""},{"Vorgang":"B","Dauer":11,"Vorgänger":"A"},{"Vorgang":"C","Dauer":5,"Vorgänger":"A"},{"Vorgang":"D","Dauer":3,"Vorgänger":"B"},{"Vorgang":"E","Dauer":13,"Vorgänger":"D"},{"Vorgang":"F","Dauer":1,"Vorgänger":"E"},{"Vorgang":"G","Dauer":9,"Vorgänger":"F"},{"Vorgang":"H","Dauer":3,"Vorgänger":"F"},{"Vorgang":"I","Dauer":12,"Vorgänger":"F"},{"Vorgang":"J","Dauer":1,"Vorgänger":"C G H I"}]},{"timestamp":1711107833361,"path":"taskData__criticalPath","value":[0,1,3,4,5,8,9]},{"timestamp":1711107833361,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n }"},{"timestamp":1711107833362,"path":"taskData__gantt","value":{"matrix":[[1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]],"events":["A","B","C","D","E","F","G","H","I","J"],"maxTime":43}},{"timestamp":1711107833362,"path":"taskData__dummyGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n 1:out -> 3:in []\n 0:out -> 1:in []\n 2:out -> 9:in []\n 0:out -> 2:in []\n 3:out -> 4:in []\n 4:out -> 5:in []\n 6:out -> 9:in []\n 5:out -> 6:in []\n 7:out -> 9:in []\n 5:out -> 7:in []\n 8:out -> 9:in []\n 5:out -> 8:in []\n }"},{"timestamp":1711107833362,"path":"nodes__0__components__0__isValid","value":true},{"timestamp":1711107833363,"path":"nodes__0__components__1__isValid","value":[{"Vorgang":"A","Dauer":2,"Vorgänger":""},{"Vorgang":"B","Dauer":11,"Vorgänger":"A"},{"Vorgang":"C","Dauer":5,"Vorgänger":"A"},{"Vorgang":"D","Dauer":3,"Vorgänger":"B"},{"Vorgang":"E","Dauer":13,"Vorgänger":"D"},{"Vorgang":"F","Dauer":1,"Vorgänger":"E"},{"Vorgang":"G","Dauer":9,"Vorgänger":"F"},{"Vorgang":"H","Dauer":3,"Vorgänger":"F"},{"Vorgang":"I","Dauer":12,"Vorgänger":"F"},{"Vorgang":"J","Dauer":1,"Vorgänger":"C G H I"}]},{"timestamp":1711107833414,"path":"nodes__0__isValid","value":true},{"timestamp":1711107834735,"path":"previousNode","value":0},{"timestamp":1711107834736,"path":"currentNode","value":"1"},{"timestamp":1711107836663,"path":"previousNode","value":"1"},{"timestamp":1711107836663,"path":"currentNode","value":"2"},{"timestamp":1711107836663,"path":"nodes__1__chosenPath","value":"Metra-Potential-Methode (MPM)"},{"timestamp":1711107836671,"path":"nodes__2__components__0__nestedComponents__DOTGraph__isValid","value":true},{"timestamp":1711107836815,"path":"nodes__2__isValid","value":false},{"timestamp":1711107839407,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n }"},{"timestamp":1711107840531,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n }"},{"timestamp":1711107841800,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n }"},{"timestamp":1711107843074,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n }"},{"timestamp":1711107844931,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n }"},{"timestamp":1711107846951,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n }"},{"timestamp":1711107848402,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n }"},{"timestamp":1711107849582,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n }"},{"timestamp":1711107852338,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n 0:out -> 9:in [] \n }"},{"timestamp":1711107855042,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n 0:out -> 9:in [] \n 2:out -> 8:in [] \n }"},{"timestamp":1711107857272,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n 0:out -> 9:in [] \n 2:out -> 8:in [] \n 8:out -> 9:in [] \n }"},{"timestamp":1711107860630,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n 0:out -> 9:in [] \n 2:out -> 8:in [] \n 8:out -> 9:in [] \n 3:out -> 7:in [] \n }"},{"timestamp":1711107862771,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n 0:out -> 9:in [] \n 2:out -> 8:in [] \n 8:out -> 9:in [] \n 3:out -> 7:in [] \n 7:out -> 1:in [] \n }"},{"timestamp":1711107864128,"path":"taskData__dotVertexGraph","value":"digraph {\n graph [bgcolor=\"transparent\" rankdir=\"LR\" splines=\"polyline\"]\n node [shape=\"plaintext\"] \n 0 [ id=\"0\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
A
2 GP FP
SAZ SEZ
> tooltip=\"A\"]\n1 [ id=\"1\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
B
11 GP FP
SAZ SEZ
> tooltip=\"B\"]\n2 [ id=\"2\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
C
5 GP FP
SAZ SEZ
> tooltip=\"C\"]\n3 [ id=\"3\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
D
3 GP FP
SAZ SEZ
> tooltip=\"D\"]\n4 [ id=\"4\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
E
13 GP FP
SAZ SEZ
> tooltip=\"E\"]\n5 [ id=\"5\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
F
1 GP FP
SAZ SEZ
> tooltip=\"F\"]\n6 [ id=\"6\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
G
9 GP FP
SAZ SEZ
> tooltip=\"G\"]\n7 [ id=\"7\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
H
3 GP FP
SAZ SEZ
> tooltip=\"H\"]\n8 [ id=\"8\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
I
12 GP FP
SAZ SEZ
> tooltip=\"I\"]\n9 [ id=\"9\" label=<\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
FAZ FEZ
J
1 GP FP
SAZ SEZ
> tooltip=\"J\"]\n edge [dir=\"forward\" ] \n \n 4:out -> 5:in [] \n 7:out -> 5:in [] \n 3:out -> 5:in [] \n 4:out -> 6:in [] \n 6:out -> 2:in [] \n 1:out -> 2:in [] \n 2:out -> 0:in [] \n 0:out -> 8:in [] \n 0:out -> 9:in [] \n 2:out -> 8:in [] \n 8:out -> 9:in [] \n 3:out -> 7:in [] \n 7:out -> 1:in [] \n 3:out -> 4:in [] \n 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Als Gedankenstütze kannst du die Zwischenprodukte innerhalb des Textfelds aufschreiben.
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"isValid": true, - "component": {}, - "dependencies": { - "dotDescription": "nodes__1__components__0__nestedComponents__EPK__component__userGraph" - } - } - } - } - } - }, - "1": { - "type": "Output", - "name": "Aufgabenbeschreibung", - "isValid": true, - "dependencies": { - "Output": { - "serverOutput": "taskData__taskDescription", - "validOutput": "taskData__taskDescription" - } - }, - "component": { - "header": "Aufgabenbeschreibung" - } - }, - "2": { - "name": "Distanzmatrix", - "type": "Matrix", - "isValid": false, - "isCorrect": false, - "dependencies": { - "Matrix": { - "data": "taskData__DigraphIteration0" - } - }, - "methods": { - "fillZeros": { - "description": "Ergänze Nullen", - "impact": 0.5 - }, - "showSolution": { - "description": "Zeige Lösung", - "impact": 0 - }, - "copyToClipboard": { - "description": "Kopieren", - "impact": 1 - } - }, - "contextMenu": { - "isOpen": false, - "usedMethods": [] - }, - "component": { - "initialize": { - "solution": { - "operations": [], - "paths": ["taskData__DigraphIteration0"] - }, - "validation": { - "operations": [ - { - "name": "getValueInitializedMatrix", - "args": [ - { - "isValid": false, - "isCorrect": false - } - ] - } - ], - "paths": ["taskData__DigraphIteration0"] - }, - "user": { - "operations": [ - { - "name": "getValueInitializedMatrix", - "args": [null] - } - ], - "paths": ["taskData__DigraphIteration0"] - } - }, - "userData": null, - "validationData": null, - "solutionData": null, - "readOnly": false, - "rowLabel": "taskData__DigraphIterationHeader0", - "columnLabel": "taskData__DigraphIterationHeader0" - } - } - } - } - } - } -} diff --git a/src/server/tempTaskGraphStorage/tasks/DecisionTree.json b/src/server/tempTaskGraphStorage/tasks/DecisionTree.json deleted file mode 100644 index b68ab24..0000000 --- a/src/server/tempTaskGraphStorage/tasks/DecisionTree.json +++ /dev/null @@ -1,135 +0,0 @@ -{ - "API": [ - { - "task": "decisiontree", - "name": "generateSampledData", - "httpMethod": "post", - "params": { "parameters": "object" } - }, - { - "task": "decisiontree", - "name": "generateGraph", - "httpMethod": "post", - "params": { "parameters": "object" } - }, - { - "task": "decisiontree", - "name": "decisionTreeValidator", - "httpMethod": "post", - "params": { "parameters": "object" } - } - ], - "Worker": { - "decisiontreeTask": { - "minConsumers": 1, - "consumerInstructions": { - "generateSampledData": { - "dependencies": ["DataSamplingGenerator"], - "body": "async (taskDescription) => { let result = {}; try { result = DataSamplingGenerator(taskDescription.parameters); } catch (error) { console.error(error); result = error; } return result; }" - }, - "generateGraph": { - "dependencies": ["DecisionTreeTaskGenerator"], - "body": "async (taskDescription) => {let result = {};try { const g = new DecisionTreeTaskGenerator(); result = g.generateTask(taskDescription.parameters); } catch (error) { console.error(error); result = error;} return result; }" - }, - "decisionTreeValidator": { - "dependencies": ["DecisionTreeValidator"], - "body": "async (taskDescription) => { let result = {}; try { result = DecisionTreeValidator(taskDescription); } catch (error) { console.error(error); result = error; } return result; }" - } - } - } - }, - "UI": { - "currentTask": "decisiontree", - "taskMode": "practice", - "taskData": {}, - "rootNode": 0, - "topology": [[0, null, null]], - "edges": { - "0": [] - }, - "currentNode": 0, - "nodes": { - "0": { - "zoomScale": 1, - "layouts": { - "sm": [{ "x": 12, "y": 5, "w": 2, "h": 5, "i": 0, "static": false }], - "md": [{ "x": 0, "y": 10, "w": 2, "h": 5, "i": 0, "static": false }], - "lg": [ - { "x": 32, "y": 29, "w": 3, "h": 4, "i": 0, "static": false }, - { "x": 35, "y": 29, "w": 6, "h": 4, "i": 1, "static": false }, - { "x": 32, "y": 32, "w": 9, "h": 4, "i": 2, "static": false } - ] - }, - "hints": { - "active": false, - "current": 0, - "descriptions": [ - "

Hinweis 1: ID3-Algorithmus


• 1. Schritt: Berechnung Entropie
.
• 2. Schritt: Berechnung Informationsgewinn
.
• 3. Schritt: Auswahl des besten Merkmals
.
• 4. Schritt: Rekursive Anwendung (1. - 3. Schritt wiederholen)

", - "

Hinweis 2:
ID3-Algorithmus


• Entropiewert liegt zwischen 0 und 1
.
• Entropie ist 0, wenn alle Stichproben einer Klasse gehören
.
• Entropie ist 1, wenn die Stichproben zur Hälfte einer Klasse angehören", - "

Hinweis 3: Formeln

.

Entropie

.

Information Gain

", - "Viel Erfolg!

" - ] - }, - "isValid": false, - "components": { - "0": { - "type": "TaskConfiguration", - "name": "Konfiguration", - "isValid": true, - "component": { - "title": "Entscheidungsbaum", - "actions": [ - { - "instruction": "generateSampledData", - "type": "fetchData", - "label": "Generieren!" - } - ], - "form": { - "taskDescription": { - "label": "

Aufgabe


Berechnen Sie anhand des generierten Datensatzes einen Entscheidungsbaum mit dem ID3-Algorithmus und erstellen Sie diesen.

• Fügen Sie die korrekten Beschriftungen für Knoten und Kanten hinzu.
• Geben Sie die Entropien und Informationsgewinne mit fünf Dezimalstellen an den entsprechenden Kanten und Knoten an.
• Setzen Sie 'decision' für die Knoten mit zugewiesener Klasse.
• Achten Sie auf die Groß- und Kleinschreibung!

Starten Sie, indem Sie auf 'Generieren!' klicken.


Bedienung


Knoten


Hinzufügen: Linke Maustaste (mit Eingabe)
Ändern: Rechte Maustaste
Löschen: Linke Maustaste (ohne Eingabe) - Nur Blattknoten!

Kanten


Ändern: Rechte Maustaste

" - } - }, - "nestedComponents": { - "Output": { - "type": "Output", - "name": "Datensatz", - "isValid": true, - "dependencies": { - "Output": { "serverOutput": "taskData__table", "validOutput": "taskData__table" } - }, - "component": { - "header": "Datensatz" - } - } - } - } - }, - "1": { - "type": "Output", - "name": "Datensatz", - "isValid": true, - "dependencies": { - "Output": { "serverOutput": "taskData__table", "validOutput": "taskData__table" } - }, - "component": { - "header": "Datensatz" - } - }, - "2": { - "type": "DecisionTree", - "name": "DecisionTree", - "isValid": true, - "isCorrect": true, - "dependencies": { - "dotDescription": "taskData__dotDescription" - }, - "component": { - "header": "Entscheidungsbaum" - } - } - } - } - } - } -} diff --git a/src/server/tempTaskGraphStorage/tasks/EPK.json b/src/server/tempTaskGraphStorage/tasks/EPK.json index ff16b2a..bd1b62f 100644 --- a/src/server/tempTaskGraphStorage/tasks/EPK.json +++ b/src/server/tempTaskGraphStorage/tasks/EPK.json @@ -336,13 +336,13 @@ "isValid": true, "dependencies": { "Output": { - "serverOutput": "nodes__0__components__2__component__description" - + "serverOutput": "nodes__1__components__2__component__description", + "validOutput": "nodes__1__components__2__component__description" } }, "component": { "header": "Prozessbeschreibung", - "description": "

Nach Eingang der Bestellung wird geprüft, ob das vom Kunden/Händler gewünschte Produkt auf Lager ist. Ist das Produkt vorrätig, erfolgen die Verpackung und Etikettierung des Produkts bzw. der Produkte. Damit sind die Produkte versandbereit. Endet die Prüfung des Bestands mit dem Ergebnis, dass die Produkte nicht bzw. nicht vollständig im Lager vorhanden sind, wird der Produktionsabteilung ein Auftrag zur Fertigung erteilt. Ist das Produkt vorrätig, erfolgen die Verpackung und Etikettierung des Produkts bzw. der Produkte. Damit sind die Produkte versandbereit.

" + "description": "Nach Eingang der Bestellung wird geprüft, ob das vom Kunden/Händler gewünschte Produkt auf Lager ist. Ist das Produkt vorrätig, erfolgen die Verpackung und Etikettierung des Produkts bzw. der Produkte. Damit sind die Produkte versandbereit. Endet die Prüfung des Bestands mit dem Ergebnis, dass die Produkte nicht bzw. nicht vollständig im Lager vorhanden sind, wird der Produktionsabteilung ein Auftrag zur Fertigung erteilt. Ist das Produkt vorrätig, erfolgen die Verpackung und Etikettierung des Produkts bzw. der Produkte. Damit sind die Produkte versandbereit." } }, "3": { diff --git a/src/server/tempTaskGraphStorage/tasks/ElektrophileAromatischeSubstitution.json b/src/server/tempTaskGraphStorage/tasks/ElektrophileAromatischeSubstitution.json deleted file mode 100644 index ddaaac4..0000000 --- a/src/server/tempTaskGraphStorage/tasks/ElektrophileAromatischeSubstitution.json +++ /dev/null @@ -1,190 +0,0 @@ -{ - "API": [ - { - "task": "ElektrophileAromatischeSubstitution", - "name": "selectSubstitution", - "httpMethod": "post", - "params": { - "parameters": "object" - } - } - ], - - "Worker": { - "ElektrophileAromatischeSubstitutionTask": { - "minConsumers": 1, - "consumerInstructions": { - "selectSubstitution": { - "dependencies": ["selectSubstitution"], - "body": "async (taskDescription) => {let result = {};try { result = selectSubstitution(taskDescription.parameters) } catch (error) { console.error(error); result = error;} return result; }" - } - } - } - }, - "UI": { - "currentTask": "ElektrophileAromatischeSubstitution", - "taskData": {}, - "rootNode": 0, - "taskMode": "practice", - "topology": [[0, null, null]], - "edges": { - "0": [1], - "1": [] - }, - "currentNode": 0, - "nodes": { - "0": { - "zoomScale": 1, - "layouts": { - "sm": [{ "x": 32, "y": 29, "w": 2, "h": 4, "i": 0, "static": false }], - "md": [{ "x": 32, "y": 29, "w": 2, "h": 4, "i": 0, "static": false }], - "lg": [{ "x": 34, "y": 29, "w": 2, "h": 4, "i": 0, "static": false }] - }, - - "components": { - "0": { - "type": "TaskConfiguration", - "name": "Aufgabenstellung", - "isValid": true, - "component": { - "title": "Chemie Aufgabenstellung", - "actions": [ - { - "instruction": "selectSubstitution", - "type": "fetchData", - "label": "Generieren!", - "dependsOn": ["seed"] - } - ], - "form": { - "seed": { - "isValid": true, - "formType": "ValueFormField", - "label": "Seed", - "type": "number", - "description": "Setze einen beliebigen Wert um die Generierung deterministisch und reproduzierbar zu machen", - "value": "", - "validate": false - } - } - } - } - } - }, - "1": { - "zoomScale": 1, - "layouts": { - "sm": [ - { "x": 35, "y": 29, "w": 3, "h": 2, "i": 0, "static": false }, - { "x": 32, "y": 29, "w": 3, "h": 2, "i": 1, "static": false }, - { "x": 32, "y": 31, "w": 3, "h": 2, "i": 2, "static": false }, - { "x": 35, "y": 31, "w": 3, "h": 2, "i": 3, "static": false } - ], - "md": [ - { "x": 35, "y": 29, "w": 3, "h": 2, "i": 0, "static": false }, - { "x": 32, "y": 29, "w": 3, "h": 2, "i": 1, "static": false }, - { "x": 32, "y": 31, "w": 3, "h": 2, "i": 2, "static": false }, - { "x": 35, "y": 31, "w": 3, "h": 2, "i": 3, "static": false } - ], - "lg": [ - { "x": 35, "y": 29, "w": 3, "h": 2, "i": 0, "static": false }, - { "x": 32, "y": 29, "w": 3, "h": 2, "i": 1, "static": false }, - { "x": 32, "y": 31, "w": 3, "h": 2, "i": 2, "static": false }, - { "x": 35, "y": 31, "w": 3, "h": 2, "i": 3, "static": false } - ] - }, - - "components": { - "0": { - "type": "InputForm", - "name": "Antwort", - "isValid": true, - "component": { - "title": "Antwort", - "form": { - "position": { - "formType": "DropdownFormField", - "label": "Position", - "title": "Aufgabe", - "type": "string", - "description": "Position des Reaktionspartners", - - "initial": "para", - "action": { - "instruction": "selectSubstitution", - "type": "fetchData", - "label": "Generieren!" - }, - "options": ["para", "meta", "ortho"], - "presets": {}, - "validation": "taskData__position", - "isValid": true - }, - - "reaktiontype": { - "formType": "DropdownFormField", - "label": "Reaktiontype", - "type": "string", - "description": "Findet eine Überreaktion statt?", - "initial": "Ja", - "action": { - "instruction": "selectSubstitution", - "type": "fetchData", - "label": "Generieren!" - }, - "options": ["Ja", "nein"], - "presets": {}, - "validation": "taskData__hasOverReacted", - "isValid": true - } - } - } - }, - - "1": { - "type": "Output", - "name": "Aufgabenstellung", - "isValid": true, - "dependencies": { - "Output": { - "serverOutput": "nodes__1__components__1__component__description", - "validOutput": "nodes__1__components__1__component__description" - } - }, - "component": { - "header": "Aufgabenstellung", - "description": "Gegeben ist ein einfach substituiertes Benzolderivat (siehe Reaktion). a) Geben Sie im nebenstehenden Dropdown-Feld den Ort der Zweitsubstitution in einer elektrophilen aromatischen Substitution an. b) Geben Sie bei Reaktiontyp im nebenstehenden Feld an ob Sie eine Überreaktion des Erstprodukts unter den gegebenen Reaktionsbedingungen erwarten! c) Zeichnen Sie das zu erwartende Hauptprodukt!" - } - }, - - "2": { - "type": "MoleculeEditor", - "name": "Reaktion", - "isValid": true, - "isCorrect": true, - "dependencies": {}, - "component": { - "readOnly": true, - "smiles": "", - "initialSmiles": "taskData__reactionSmiles", - "solution": "taskData__smiles" - } - }, - - "3": { - "type": "MoleculeEditor", - "name": "Produkt", - "isValid": false, - "isCorrect": false, - "dependencies": {}, - "component": { - "readOnly": false, - "smiles": "", - "initialSmiles": "" - } - } - } - } - } - } -} diff --git a/src/server/tempTaskGraphStorage/tasks/Experimentieren.json b/src/server/tempTaskGraphStorage/tasks/Experimentieren.json new file mode 100644 index 0000000..f45f713 --- /dev/null +++ b/src/server/tempTaskGraphStorage/tasks/Experimentieren.json @@ -0,0 +1,75 @@ +{ + "API": [ + { + "task": "Experimentieren", + "name": "generateExperimentierenDataMain", + "httpMethod": "post", + "params": { "parameters": "object" } + } + ], + "Worker": { + "ExperimentierenTask": { + "minConsumers": 1, + "consumerInstructions": { + "generateExperimentierenDataMain": { + "dependencies": ["generateExperimentierenDataMain"], + "body": "async (taskDescription) => { let result = {}; try { result = generateExperimentierenDataMain(taskDescription); } catch (error) { console.error(error); result = error; } return result; }" + } + } + } + }, + "UI": { + "currentTask": "Experimentieren", + "taskMode": "strictPractice", + "taskData": {}, + "rootNode": 0, + "previousNode": 0, + "topology": [[0, null, null]], + "edges": { + "0": [1], + "1": [] + }, + "currentNode": 0, + "nodes": { + "0": { + "zoomScale": 1, + "layouts": { + "sm": [{ "x": 12, "y": 5, "w": 2, "h": 5, "i": 1, "static": false }], + "md": [{ "x": 0, "y": 10, "w": 2, "h": 5, "i": 1, "static": false }], + "lg": [ + { "x": 34, "y": 29, "w": 6, "h": 3, "i": 0, "static": false }, + { "x": 32, "y": 29, "w": 2, "h": 2, "i": 1, "static": false } + ] + }, + "components": { + "0" : { + "type" : "LatexFormula", + "name" : "LatexExperimentieren", + "component" : { + "generateBtnDisabled" : true, + "userData" : "" + } + }, + "1" : { + "type" : "BackendFormular", + "name" : "Ergebnis anzeigen", + "isValid" : false, + "component" : { + "actions" : [ + { + "instruction" : "generateExperimentierenDataMain", + "type": "fetchData", + "label" : "Generieren!", + "disabled" : "nodes__0__components__0__component__generateBtnDisabled" + } + ], + "data" : { + "latexData" : "nodes__0__components__0__component__userData" + } + } + } + } + } + } + } +} diff --git a/src/server/tempTaskGraphStorage/tasks/Ferment.json b/src/server/tempTaskGraphStorage/tasks/Ferment.json new file mode 100644 index 0000000..4b83858 --- /dev/null +++ b/src/server/tempTaskGraphStorage/tasks/Ferment.json @@ -0,0 +1,435 @@ +{ + "API": [ + { + "task": "Ferment", + "name": "generateFermentationDataMain", + "httpMethod": "post", + "params": { + "parameters": "object" + } + } + ], + "Worker": { + "FermentTask": { + "minConsumers": 1, + "consumerInstructions": { + "generateFermentationDataMain": { + "dependencies": ["generateFermentationDataMain"], + "body": "async (taskDescription) => {let result = {};try { result = generateFermentationDataMain(taskDescription)} catch{} return result; }" + } + } + } + }, + "UI": { + "currentTask": "Ferment", + "taskMode": "strictPractice", + "taskData": {}, + "rootNode": 0, + "previousNode": 0, + "topology": [ + [0, null, null], + [1, 2, null], + [3, 4, null], + [5, 6, null], + [7, 8, null], + [9, null, null], + [10, null, null] + ], + "edges": { + "0": [] + }, + "currentNode": 0, + "nodes": { + "0": { + "zoomScale": 1, + "layouts": { + "sm": [ + { + "x": 31, + "y": 37, + "w": 3, + "h": 4, + "i": 0, + "static": false + }, + { + "x": 10, + "y": 5, + "w": 2, + "h": 3, + "i": 1, + "static": false + }, + { + "x": 12, + "y": 5, + "w": 2, + "h": 5, + "i": 2, + "static": false + } + ], + "md": [ + { + "x": 0, + "y": 0, + "w": 5, + "h": 6, + "i": 0, + "static": false + }, + { + "x": 0, + "y": 5, + "w": 2, + "h": 3, + "i": 1, + "static": false + }, + { + "x": 0, + "y": 10, + "w": 2, + "h": 5, + "i": 2, + "static": false + } + ], + "lg": [ + { + "x": 31, + "y": 29, + "w": 2, + "h": 2.5, + "i": 0, + "static": false + }, + { + "x": 30, + "y": 31.5, + "w": 3, + "h": 3, + "i": 1, + "static": true + }, + { + "x": 33, + "y": 31, + "w": 8, + "h": 2.5, + "i": 2, + "static": false + }, + { + "x": 33, + "y": 29, + "w": 4, + "h": 2.5, + "i": 3, + "static": false + }, + { + "x": 37, + "y": 29, + "w": 4, + "h": 2.5, + "i": 4, + "static": false + } + ] + }, + "hints": { + "active": false, + "current": 0, + "descriptions": [ + "Mit FermentALADIN können Sie Fermentationen auf Basis vordefinierter Modelle simulieren. Dazu wählen Sie im Fenster „Modell“ ", + "den Organismus im Drop-Down Menu und wählen die Anzahl der Phasen mit unterschiedlichen Prozessbedingungen aus. ", + "Nach Eintragung der Startparameter im Fenster „Fermentation – Startparameter“ sowie der Eintragung der phasenspezifischen Prozessparameter", + "im Fenster „Prozessparameter je Phase“ erhalten Sie das Ergebnis der Simulation nach Klick auf „Berechnen“. ", + "Die Daten können über den Button „Herunterladen“ Diagrammweise exportiert werden. Die Arbeitsfläche können Sie mit der Maus verschieben und durch Strg+ Mausrad hinein bzw. herauszoomen. ", + "Viel Erfolg!

" + ] + }, + "isValid": false, + "components": { + "0": { + "type": "BackendFormular", + "name": "Modell Parameter", + "isValid": false, + "component": { + "actions": [ + { + "instruction": "generateFermentationDataMain", + "type": "fetchData", + "label": "Generieren!", + "dependsOn": [ + "nodes__0__components__0__component__form__nodeAmount__isValid", + "nodes__0__components__0__component__form__seed__isValid", + "nodes__0__components__2__isValid" + ] + } + ], + "form": { + "nodeAmount": { + "isValid": false, + "formType": "DropdownFormField", + "label": "Organismus", + "type": "string", + "description": "Zur Verfügung stehende Organismus", + "action": { "instruction": "fetchTaskData", "type": "fetchData", "key": "schema" }, + "initial": "S.cerevisiae", + "options": ["S.cerevisiae", "E.coli", "Testorganismus"], + "presets": { + "easy": "S.cerevisiae", + "medium": "E.coli", + "hard": "Testorganismus" + } + }, + "seed": { + "formType": "ValueFormField", + "label": "Phasenanzahl", + "type": "text", + "description": "Setze einen beliebigen Wert für die Phasenanzahl", + "value": "2", + "readOnly": false, + "validate": false, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + } + }, + "data": { + "userDataMatrix": "nodes__0__components__2__component__userData", + "userDataInputFelder": "nodes__0__components__1__component__inputFelderValues" + } + } + }, + "1": { + "type": "absolutInputsOnImage", + "name": "Fermentationsapparat", + "component": { + "bildPath": "/img/tasks/Ferment/Ferment.png", + "inputFelds": [ + { + "id": "1", + "type": "number", + "placeholder": "V Start", + "value" : "10", + "min": 1, + "max": 900, + "width": "50.5px", + "height": "25px", + "position": { + "left": "3%", + "top": "26.5%" + } + }, + { + "id": "2", + "type": "number", + "placeholder": "T", + "value" : 30, + "min": 10, + "max": 99, + "width": "50.5px", + "height": "25px", + "position": { + "left": "70.5%", + "top": "45.5%" + } + }, + { + "id": "3", + "type": "number", + "placeholder": "PH", + "value" : 7, + "min": 2, + "max": 10, + "width": "50.5px", + "height": "25px", + "position": { + "left": "29%", + "top": "-2%" + } + }, + { + "id": "4", + "type": "number", + "placeholder": "BTM", + "value" : 0.05, + "min": 0.01, + "max": 5, + "width": "50.5px", + "height": "25px", + "position": { + "left": "3%", + "top": "80.5%" + } + }, + { + "id": "5", + "type": "number", + "placeholder": "DO", + "value" : 100, + "min": 0, + "max": 500, + "width": "50.5px", + "height": "25px", + "position": { + "left": "45%", + "top": "-2%" + } + }, + { + "id": "6", + "type": "number", + "placeholder": "Fluss Out", + "value" : 0, + "min": 0, + "max": 500, + "width": "50.5px", + "height": "25px", + "position": { + "left": "70.5%", + "top": "65%" + } + } + ], + "inputFelderValues": {} + } + }, + "2": { + "name": "Fermentationsparameter", + "type": "Matrix", + "isValid": false, + "isCorrect": false, + "dependencies": { + "Matrix": { + "data": "nodes__0__components__2__component__userData" + } + }, + "methods": {}, + "contextMenu": { + "isOpen": false, + "usedMethods": [] + }, + "component": { + "initialize": { + "solution": { + "operations": [], + "paths": ["taskData__adjacencyMatrix"] + }, + "validation": { + "operations": [ + { + "name": "getValueInitializedMatrix", + "args": [ + { + "isValid": false, + "isCorrect": false + } + ] + } + ], + "paths": ["taskData__adjacencyMatrix"] + }, + "user": { + "operations": [ + { + "name": "getMatrix", + "args": [] + } + ], + "paths": ["taskData__adjacencyMatrix"] + } + }, + "standardZeile": [1, 1, 1, 1, 100, 1, 10], + "userData": [], + "checkUserDataValidity": "valid", + "columnLabelData": [ + "Zuluft in NL/min", + "BolusC in g/L", + "Feed in g/(L*h)", + "BolusN in g/L", + "Drehzahl in rpm", + "Druck in barg", + "Dauer in h" + ], + "rowLabelData": ["Phase"], + "validationData": null, + "solutionData": null, + "readOnly": false, + "rowLabel": 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{ + "formType": "ValueFormField", + "label": "Phasenanzahl", + "type": "String", + "description": "Setze einen beliebigen Wert für die Phasenanzahl", + "value": "3", + "boundaries": { "min": 0, "max": 20 }, + "readOnly": false, + "validate": true, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + }, + "Temperatur": { + "formType": "ValueFormField", + "label": "Temperatur", + "type": "number", + "value": 0, + "boundaries": {}, + "validate": false, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + }, + "BTM": { + "formType": "ValueFormField", + "label": "BTM", + "type": "number", + "value": 0, + "boundaries": { "min": 0, "max": 20 }, + "validate": false, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + }, + "DO": { + "formType": "ValueFormField", + "label": "Do", + "type": "number", + "value": 0, + "boundaries": {}, + "validate": false, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + }, + "maxParameter": { + "isValid": true, + 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"saveFetchedDataInTemplate_Path" : "/json/tasks/FermentExercise/Exercise.json", + "savedData":{} + } + }, + "1": { + "name": "Fermentationsparameter", + "type": "Matrix", + "isValid": true, + "isCorrect": true, + "dependencies": { + "Matrix": { + "data": "nodes__0__components__1__component__userData" + } + }, + "methods": {}, + "contextMenu": { + "isOpen": false, + "usedMethods": [] + }, + "component": { + "initialize": { + "solution": { + "operations": [], + "paths": ["taskData__adjacencyMatrix"] + }, + "validation": { + "operations": [ + { + "name": "getValueInitializedMatrix", + "args": [ + { + "isValid": true, + "isCorrect": true + } + ] + } + ], + "paths": ["taskData__adjacencyMatrix"] + }, + "user": { + "operations": [ + { + "name": "getMatrix", + "args": [] + } + ], + "paths": ["taskData__adjacencyMatrix"] + } + }, + "standardZeile": [1, 1, 1, 1, 100, 1, 10], + "userData": [], + "checkUserDataValidity": "valid", + "columnLabelData": [ + "Zuluft in NL/min", + "BolusC in g/L", + 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Dann Ziehen Sie den Schieberegler nach rechts oder links um die Anzahl der Phasen zu bestimmen.
Dann geben sie die Werte für die zwei stehende Tabellen ein.", + "Viel Erfolg!

" + ] + }, + "isValid": false, + "components": { + "0": { + "type": "BackendFormular", + "name": "Modell Parameter", + "isValid": false, + "component": { + "actions": [ + { + "instruction": "generateFermentationDataMain", + "type": "fetchData", + "label": "Generieren!", + "dependsOn": [ + "nodes__1__components__1__component__form__nodeAmount__isValid", + "nodes__1__components__1__component__form__seed__isValid", + "nodes__1__components__2__isValid" + ] + }, + { + "instruction": "generateFermentationFeedBack", + "type": "fetchData", + "label": "Abgeben!", + "dependsOn": [ + ] + } + ], + + "form": { + "nodeAmount": { + "isValid": false, + "formType": "DropdownFormField", + "label": "Organismus", + "type": "string", + "description": "Zur Verfügung stehende Organismus", + "action": { "instruction": "fetchTaskData", "type": "fetchData", "key": "schema" }, + "OptionsFromJson": "nodes__0__components__0__component__savedData", + "OptionsFromJson_Key":"nodeAmount" + }, + "seed": { + "formType": "ValueFormField", + "label": "Phasenanzahl", + "type": "text", + "description": "Setze einen beliebigen Wert für die Phasenanzahl", + "ValueFromJson" : "nodes__0__components__0__component__savedData", + "ValueFromJson_Key":"seed", + "readOnly": true, + "validate": false, + "presets": { + "easy": "", + "medium": "", + "hard": "" + } + } + }, + "data": { + "userDataMatrix": "nodes__1__components__2__component__userData", + "userDataInputFelder": "nodes__1__components__1__component__inputFelderValues" + } + + } + }, + "1": { + "type": "absolutInputsOnImage", + "name": "Fermentationsapparat", + "component": { + "bildPath": "/img/tasks/FermentExercise/Ferment.png", + "ValuesFromJson":"nodes__0__components__0__component__savedData__Startparameter", + "inputFelds": [ + { + "id": "1", + "type": "number", + "placeholder": "V Start", + "value" : "10", + "min": 1, + "max": 900, + "width": "50.5px", + "height": "25px", + "position": { + "left": "3%", + "top": "26.5%" + } + }, + { + "id": "2", + "type": "number", + "placeholder": "T", + "ValueFromJson_Key" : "Temperatur", + "min": 10, + "max": 99, + "width": "50.5px", + "height": "25px", + "position": { + "left": "70.5%", + "top": "45.5%" + } + }, + { + "id": "3", + "type": "number", + "placeholder": "PH", + "value" : 7, + "min": 2, + "max": 10, + "width": "50.5px", + "height": "25px", + "position": { + "left": "29%", + "top": "-2%" + } + }, + { + "id": "4", + "type": "number", + "placeholder": "BTM", + "ValueFromJson_Key" : "BTM", + "min": 0.01, + "max": 5, + "width": "50.5px", + "height": "25px", + "position": { + "left": "3%", + "top": "80.5%" + } + }, + { + "id": "5", + "type": "number", + "placeholder": "DO", + "ValueFromJson_Key" : "DO", + "min": 0, + "max": 500, + "width": "50.5px", + "height": "25px", + "position": { + "left": "45%", + "top": "-2%" + } + }, + { + "id": "6", + "type": "number", + "placeholder": "Fluss Out", + "value" : 0, + "min": 0, + "max": 500, + "width": "50.5px", + "height": "25px", + "position": { + "left": "70.5%", + "top": "65%" + } + } + ], + "inputFelderValues": {}, + "fields_readOnly":"nodes__1__components__6__component__feedback" + } + }, + "2": { + "name": "Fermentationsparameter", + "type": "Matrix", + "isValid": false, + "isCorrect": false, + "dependencies": { + "Matrix": { + "data": "nodes__1__components__2__component__userData" + } + }, + "methods": {}, + "contextMenu": { + "isOpen": false, + "usedMethods": [] + }, + "component": { + "initialize": { + "solution": { + "operations": [], + "paths": ["taskData__adjacencyMatrix"] + }, + "validation": { + "operations": [ + { + "name": "getValueInitializedMatrix", + "args": [ + { + "isValid": false, + "isCorrect": false + } + ] + } + ], + "paths": ["taskData__adjacencyMatrix"] + }, + "user": { + "operations": [ + { + "name": "getMatrix", + "args": [] + } + ], + "paths": ["taskData__adjacencyMatrix"] + } + }, + "userData": [], + "userDataFromJson": "nodes__0__components__0__component__savedData", + "userDataFromJson_Key":"userDataMatrix", + "checkUserDataValidity": "valid", + "columnLabelData": [ + "Zuluft in NL/min", + "BolusC in g/L", + "Feed in g/(L*h)", + "BolusN in g/L", + "Drehzahl in rpm", + "Druck in barg", + "Dauer in h" + ], + "rowLabelData": ["Phase"], + "validationData": null, + "solutionData": null, + "readOnly": false, + "rowLabel": "nodes__1__components__2__component__rowLabelData", + "columnLabel": "nodes__1__components__2__component__columnLabelData", + "inputType": "number", + "spaltenMaxSumme": [ + { + "spaltenName": "Dauer in h", + "maxSumme": 200 + } + ], + "columnRange": [ + { + "name": "Zuluft in NL/min", + "min": 0, + "max": 2 + }, + { + "name": "BolusC in g/L", + "min": 0, + "max": 200 + }, + { + "name": "Feed in g/(L*h)", + "min": 0, + "max": 10 + }, + { + "name": "BolusN in g/L", + "min": 0, + "max": 10 + }, + { + "name": "Drehzahl in rpm", + "min": 100, + "max": 2000 + }, + { + "name": "Druck in barg", + "min": 0, + "max": 3 + }, + { + "name": "Dauer in h", + "min": 0, + "max": "200" + } + ], + "field_status":"nodes__1__components__6__component__feedback" + } + }, + "3": { + "name": "Fermentationsverlauf", + "type": "Chart", + "component": { + "labels": "taskData__data_1__labels", + "datasets": "taskData__data_1__datasets", + "excelData" : "taskData__data_3__excelData" + + } + }, + "4": { + "name": "Fermentatiosverlauf Cs2", + "type": "Chart", + "component": { + "labels": "taskData__data_2__labels", + "datasets": "taskData__data_2__datasets", + "excelData" : "taskData__data_3__excelData" + } + }, + "5": { + "name" : "Aufgabe", + "type": "TaskViewer", + "component":{ + "titel" : "Fermentationsproblem", + "path" : "nodes__0__components__0__component__savedData", + "key" : "Aufgabe" + } + }, + "6": { + "name" : "Feedback", + "type": "TaskViewer", + "component":{ + "feedback" : "taskData__Feedback" + } + } + } + } + } + + } +} \ No newline at end of file diff --git a/src/server/tempTaskGraphStorage/tasks/GeoInterpolation.json b/src/server/tempTaskGraphStorage/tasks/GeoInterpolation.json index 2284932..eafdd3f 100644 --- a/src/server/tempTaskGraphStorage/tasks/GeoInterpolation.json +++ b/src/server/tempTaskGraphStorage/tasks/GeoInterpolation.json @@ -45,17 +45,6 @@ "type": "BackgroundGraph", "name": "perlin", "isValid": true, - "nestedComponents": { - "DOTGraph": { - "type": "DOTGraph", - "name": "EPK", - "isValid": true, - "component": {}, - "dependencies": { - "dotDescription": "taskData__dotDescription" - } - } - }, "dependencies": { "ContourPlot": { "grid": "taskData__grid", "thresholds": "taskData__thresholds" }, "DOTGraph": { "dotDescription": "taskData__dotDescription" } diff --git a/src/server/tempTaskGraphStorage/tasks/Gozintograph.json b/src/server/tempTaskGraphStorage/tasks/Gozintograph.json index f3328fc..1c893e5 100644 --- a/src/server/tempTaskGraphStorage/tasks/Gozintograph.json +++ b/src/server/tempTaskGraphStorage/tasks/Gozintograph.json @@ -22,7 +22,7 @@ }, "UI": { "currentTask": "gozintograph", - "taskMode": "practice", + "taskMode": "strictPractice", "taskData": {}, "rootNode": 0, "previousNode": 0, @@ -387,6 +387,8 @@ "paths": ["taskData__adjacencyMatrix"] } }, + + "userData": null, "validationData": null, "solutionData": null, diff --git a/src/server/tempTaskGraphStorage/tasks/Reakt.json b/src/server/tempTaskGraphStorage/tasks/Reakt.json new file mode 100644 index 0000000..b899e3f --- /dev/null +++ b/src/server/tempTaskGraphStorage/tasks/Reakt.json @@ -0,0 +1,333 @@ +{ + "API": [ + { + "task": "Reakt", + "name": "generateReactionData", + "httpMethod": "post", + "params": { + "parameters": "object" + } + } + ], + "Worker": { + "ReaktTask": { + "minConsumers": 1, + "consumerInstructions": { + "generateReactionData": { + "dependencies": ["generateReactionData"], + "body": "async (taskDescription) => {let result = {};try { result = generateReactionData(taskDescription)} catch{} return result; }" + } + } + } + }, + "UI": { + "currentTask": "Reakt", + "taskMode": "strictPractice", + "taskData": {}, + "rootNode": 0, + "previousNode": 0, + "topology": [ + [0, null, null], + [1, 2, null], + [3, 4, null], + [5, 6, null], + [7, 8, null], + [9, null, null], + [10, null, null] + ], + "edges": { + "0": [] + }, + "currentNode": 0, + "nodes": { + "0": { + "zoomScale": 1, + "layouts": { + "sm": [ + { + "x": 31, + "y": 37, + "w": 3, + "h": 4, + "i": 0, + "static": false + }, + { + "x": 10, + "y": 5, + "w": 2, + "h": 3, + "i": 1, + "static": false + }, + { + "x": 12, + "y": 5, + "w": 2, + "h": 5, + "i": 2, + "static": false + } + ], + "md": [ + { + "x": 0, + "y": 0, + "w": 5, + "h": 6, + "i": 0, + "static": false + }, + { + "x": 0, + "y": 5, + "w": 2, + "h": 3, + "i": 1, + "static": false + }, + { + "x": 0, + "y": 10, + "w": 2, + "h": 5, + "i": 2, + "static": false + } + ], + "lg": [ + { + "x": 30, + "y": 29, + "w": 2, + "h": 2.5, + "i": 0, + "static": false + }, + { + "x": 40, + "y": 29, + "w": 2, + "h": 2.5, + "i": 1, + "static": false + }, + { + "x": 30, + "y": 31, + "w": 4, + "h": 2.5, + "i": 2, + "static": false + }, + { + "x": 34, + "y": 31, + "w": 4, + "h": 2.5, + "i": 3, + "static": false + }, + { + "x": 38, + "y": 31, + "w": 4, + "h": 2.5, + "i": 4, + "static": false + }, + { + "x": 32, + "y": 29, + "w": 8, + "h": 2.5, + "i": 5, + "static": true + } + ] + }, + "hints": { + "active": false, + "current": 0, + "descriptions": [ + "Viel Erfolg!

" + ] + }, + "isValid": false, + "components": { + "0": { + "type": "BackendFormular", + "name": "Reaktionsauswahl", + "isValid": false, + "component": { + "actions": [ + { + "instruction": "generateReactionData", + "type": "fetchData", + "label": "Generieren!", + "dependsOn": [ + "nodes__0__components__0__component__form__nodeAmount__isValid", + "nodes__0__components__0__component__form__seed__isValid" + ] + } + ], + "form": { + "process": { + "isValid": false, + "formType": "DropdownFormField", + "label": "Prozessauswahl", + "type": "string", + "description": "Zur Verfügung stehende Organismus", + "action": { "instruction": "fetchTaskData", "type": "fetchData", "key": "schema" }, + "initial": "Zersetzung von H2O2", + "options": ["Zersetzung von H2O2"], + "presets": { + "easy": "Zersetzung von H2O2" + } + }, + "thermische_Parameter": { + "isValid": false, + "formType": "DropdownFormField", + "label": "Thermische Parameter", + "type": "string", + "description": "Zur Verfügung stehende Organismus", + "action": { "instruction": "fetchTaskData", "type": "fetchData", "key": "schema" }, + "initial": "Adiabat", + "options": ["Adiabat"], + "presets": { + "easy": "Adiabat" + } + } + }, + "data": { + "matrixData": "nodes__0__components__5__component__userData", + "phaseNumber" : "nodes__0__components__5__component__rowAnzahl" + } + } + }, + "1": { + "type": "Bild", + "name": "Reaktionsapparat", + "component": { + "pfad": "/img/tasks/Reakt/Reakt_bild.webp" + } + }, + "2": { + "name": "Stoffmengen", + "type": "Chart", + "component": { + "labels": "taskData__data_1__labels", + "datasets": "taskData__data_1__datasets" + + } + }, + "3": { + "name": "Temperatur", + "type": "Chart", + "component": { + "labels": "taskData__data_2__labels", + "datasets": "taskData__data_2__datasets" + } + }, + "4": { + "name": "Umsatz H2O2", + "type": "Chart", + "component": { + "labels": "taskData__data_3__labels", + "datasets": "taskData__data_3__datasets" + + } + }, + "5" : { + "name": "Nutzereingaben", + "type": "Matrix", + "isValid": false, + "isCorrect": false, + "dependencies": { + "Matrix": { + "data": "nodes__0__components__5__component__userData" + } + }, + "methods": {}, + "contextMenu": { + "isOpen": false, + "usedMethods": [] + }, + "component": { + "initialize": { + "solution": { + "operations": [], + "paths": ["taskData__adjacencyMatrix"] + }, + "validation": { + "operations": [ + { + "name": "getValueInitializedMatrix", + "args": [ + { + "isValid": false, + "isCorrect": false + } + ] + } + ], + "paths": ["taskData__adjacencyMatrix"] + }, + "user": { + "operations": [ + { + "name": "getMatrix", + "args": [] + } + ], + "paths": ["taskData__adjacencyMatrix"] + } + }, + "standardZeile": [1, 1, 50, 20], + "userData": [], + "checkUserDataValidity": "valid", + "columnLabelData": [ + "Volumen H2O2 in L", + "Konzentration H2O2 in mol/L", + "Wasser-Startvolumen in L", + "Starttemperatur in °C" + ], + "rowLabelData": ["Phase"], + "validationData": null, + "solutionData": null, + "readOnly": false, + "rowLabel": "nodes__0__components__5__component__rowLabelData", + "columnLabel": "nodes__0__components__5__component__columnLabelData", + "inputType": "number", + "rowAnzahl": 2, + "spaltenMaxSumme": [], + "columnRange": [ + { + "name": "Volumen H2O2 in L", + "min": 1, + "max": 40 + }, + { + "name": "Konzentration H2O2 in mol/L", + "min": 1, + "max": 12 + }, + { + "name": "Wasser-Startvolumen in L", + "min": 50, + "max": 200 + }, + { + "name": "Starttemperatur in °C", + "min": 0, + "max": 30 + } + ] + } + } + } + } + } + } +} + diff --git a/src/server/tempTaskGraphStorage/tasks/Scheduling.json b/src/server/tempTaskGraphStorage/tasks/Scheduling.json index f242918..d39c406 100644 --- a/src/server/tempTaskGraphStorage/tasks/Scheduling.json +++ b/src/server/tempTaskGraphStorage/tasks/Scheduling.json @@ -107,13 +107,13 @@ }, "1": { "type": "Output", - "name": "Output", + "name": "Vorgangsliste", "isValid": true, "dependencies": { "Output": { "serverOutput": "taskData__table", "validOutput": "taskData__table" } }, "component": { - "header": "Output" + "header": "Vorgangsliste" } } } diff --git a/src/server/tempTaskGraphStorage/tasks/Test.json b/src/server/tempTaskGraphStorage/tasks/Test.json index 0b907cf..1fff558 100644 --- a/src/server/tempTaskGraphStorage/tasks/Test.json +++ b/src/server/tempTaskGraphStorage/tasks/Test.json @@ -2027,4 +2027,4 @@ } } } -} +} \ No newline at end of file diff --git a/streudiagramm.png b/streudiagramm.png deleted file mode 100644 index 38dbc79..0000000 Binary files a/streudiagramm.png and /dev/null differ diff --git a/table b/table deleted file mode 100644 index dc65f1d..0000000 --- a/table +++ /dev/null @@ -1,14 +0,0 @@ -// task definition table -digraph dotTable { - Row1 [label=<
1911
> shape=plaintext] - Row2 [label=<
1811
> shape=plaintext] - Row3 [label=<
146
> shape=plaintext] - Row4 [label=<
1224
> shape=plaintext] - Row5 [label=<
1831
> shape=plaintext] - Row6 [label=<
1330
> shape=plaintext] - Row1 -> Row2 - Row2 -> Row3 - Row3 -> Row4 - Row4 -> Row5 - Row5 -> Row6 -}

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x=a(this.$clickSelection,g);g=x.cursor,d=x.anchor}p.selection.setSelectionAnchor(d.row,d.column)}p.selection.selectToPosition(g),p.renderer.scrollCursorIntoView()},c.prototype.selectByLinesEnd=function(){this.$clickSelection=null,this.editor.unsetStyle("ace_selecting")},c.prototype.focusWait=function(){var f=u(this.mousedownEvent.x,this.mousedownEvent.y,this.x,this.y),d=Date.now();(f>s||d-this.mousedownEvent.time>this.$focusTimeout)&&this.startSelect(this.mousedownEvent.getDocumentPosition())},c.prototype.onDoubleClick=function(f){var d=f.getDocumentPosition(),p=this.editor,g=p.session,m=g.getBracketRange(d);m?(m.isEmpty()&&(m.start.column--,m.end.column++),this.setState("select")):(m=p.selection.getWordRange(d.row,d.column),this.setState("selectByWords")),this.$clickSelection=m,this.select()},c.prototype.onTripleClick=function(f){var d=f.getDocumentPosition(),p=this.editor;this.setState("selectByLines");var g=p.getSelectionRange();g.isMultiLine()&&g.contains(d.row,d.column)?(this.$clickSelection=p.selection.getLineRange(g.start.row),this.$clickSelection.end=p.selection.getLineRange(g.end.row).end):this.$clickSelection=p.selection.getLineRange(d.row),this.select()},c.prototype.onQuadClick=function(f){var d=this.editor;d.selectAll(),this.$clickSelection=d.getSelectionRange(),this.setState("selectAll")},c.prototype.onMouseWheel=function(f){if(!f.getAccelKey()){f.getShiftKey()&&f.wheelY&&!f.wheelX&&(f.wheelX=f.wheelY,f.wheelY=0);var d=this.editor;this.$lastScroll||(this.$lastScroll={t:0,vx:0,vy:0,allowed:0});var p=this.$lastScroll,g=f.domEvent.timeStamp,m=g-p.t,v=m?f.wheelX/m:p.vx,y=m?f.wheelY/m:p.vy;m=1&&d.renderer.isScrollableBy(f.wheelX*f.speed,0)&&($=!0),x<=1&&d.renderer.isScrollableBy(0,f.wheelY*f.speed)&&($=!0),$)p.allowed=g;else if(g-p.allowed=p.length&&(p=void 0),{value:p&&p[v++],done:!p}}};throw new TypeError(g?"Object is not iterable.":"Symbol.iterator is not defined.")},h=n("./lib/dom"),l=n("./range").Range,u="ace_tooltip",a=function(){function p(g){this.isOpen=!1,this.$element=null,this.$parentNode=g}return p.prototype.$init=function(){return this.$element=h.createElement("div"),this.$element.className=u,this.$element.style.display="none",this.$parentNode.appendChild(this.$element),this.$element},p.prototype.getElement=function(){return this.$element||this.$init()},p.prototype.setText=function(g){this.getElement().textContent=g},p.prototype.setHtml=function(g){this.getElement().innerHTML=g},p.prototype.setPosition=function(g,m){this.getElement().style.left=g+"px",this.getElement().style.top=m+"px"},p.prototype.setClassName=function(g){h.addCssClass(this.getElement(),g)},p.prototype.setTheme=function(g){this.$element.className=u+" "+(g.isDark?"ace_dark ":"")+(g.cssClass||"")},p.prototype.show=function(g,m,v){g!=null&&this.setText(g),m!=null&&v!=null&&this.setPosition(m,v),this.isOpen||(this.getElement().style.display="block",this.isOpen=!0)},p.prototype.hide=function(){this.isOpen&&(this.getElement().style.display="none",this.getElement().className=u,this.isOpen=!1)},p.prototype.getHeight=function(){return this.getElement().offsetHeight},p.prototype.getWidth=function(){return this.getElement().offsetWidth},p.prototype.destroy=function(){this.isOpen=!1,this.$element&&this.$element.parentNode&&this.$element.parentNode.removeChild(this.$element)},p}(),c=function(){function p(){this.popups=[]}return p.prototype.addPopup=function(g){this.popups.push(g),this.updatePopups()},p.prototype.removePopup=function(g){var m=this.popups.indexOf(g);m!==-1&&(this.popups.splice(m,1),this.updatePopups())},p.prototype.updatePopups=function(){var g,m,v,y;this.popups.sort(function(E,I){return I.priority-E.priority});var x=[];try{for(var $=s(this.popups),_=$.next();!_.done;_=$.next()){var b=_.value,R=!0;try{for(var M=(v=void 0,s(x)),k=M.next();!k.done;k=M.next()){var S=k.value;if(this.doPopupsOverlap(S,b)){R=!1;break}}}catch(E){v={error:E}}finally{try{k&&!k.done&&(y=M.return)&&y.call(M)}finally{if(v)throw v.error}}R?x.push(b):b.hide()}}catch(E){g={error:E}}finally{try{_&&!_.done&&(m=$.return)&&m.call($)}finally{if(g)throw g.error}}},p.prototype.doPopupsOverlap=function(g,m){var v=g.getElement().getBoundingClientRect(),y=m.getElement().getBoundingClientRect();return v.lefty.left&&v.topy.top},p}(),f=new c;r.popupManager=f,r.Tooltip=a;var d=function(p){o(g,p);function g(m){m===void 0&&(m=document.body);var v=p.call(this,m)||this;v.timeout=void 0,v.lastT=0,v.idleTime=350,v.lastEvent=void 0,v.onMouseOut=v.onMouseOut.bind(v),v.onMouseMove=v.onMouseMove.bind(v),v.waitForHover=v.waitForHover.bind(v),v.hide=v.hide.bind(v);var y=v.getElement();return y.style.whiteSpace="pre-wrap",y.style.pointerEvents="auto",y.addEventListener("mouseout",v.onMouseOut),y.tabIndex=-1,y.addEventListener("blur",function(){y.contains(document.activeElement)||this.hide()}.bind(v)),v}return g.prototype.addToEditor=function(m){m.on("mousemove",this.onMouseMove),m.on("mousedown",this.hide),m.renderer.getMouseEventTarget().addEventListener("mouseout",this.onMouseOut,!0)},g.prototype.removeFromEditor=function(m){m.off("mousemove",this.onMouseMove),m.off("mousedown",this.hide),m.renderer.getMouseEventTarget().removeEventListener("mouseout",this.onMouseOut,!0),this.timeout&&(clearTimeout(this.timeout),this.timeout=null)},g.prototype.onMouseMove=function(m,v){this.lastEvent=m,this.lastT=Date.now();var y=v.$mouseHandler.isMousePressed;if(this.isOpen){var x=this.lastEvent&&this.lastEvent.getDocumentPosition();(!this.range||!this.range.contains(x.row,x.column)||y||this.isOutsideOfText(this.lastEvent))&&this.hide()}this.timeout||y||(this.lastEvent=m,this.timeout=setTimeout(this.waitForHover,this.idleTime))},g.prototype.waitForHover=function(){this.timeout&&clearTimeout(this.timeout);var m=Date.now()-this.lastT;if(this.idleTime-m>10){this.timeout=setTimeout(this.waitForHover,this.idleTime-m);return}this.timeout=null,this.lastEvent&&!this.isOutsideOfText(this.lastEvent)&&this.$gatherData(this.lastEvent,this.lastEvent.editor)},g.prototype.isOutsideOfText=function(m){var v=m.editor,y=m.getDocumentPosition(),x=v.session.getLine(y.row);if(y.column==x.length){var $=v.renderer.pixelToScreenCoordinates(m.clientX,m.clientY),_=v.session.documentToScreenPosition(y.row,y.column);if(_.column!=$.column||_.row!=$.row)return!0}return!1},g.prototype.setDataProvider=function(m){this.$gatherData=m},g.prototype.showForRange=function(m,v,y,x){if(!(x&&x!=this.lastEvent)&&!(this.isOpen&&document.activeElement==this.getElement())){var $=m.renderer;this.isOpen||(f.addPopup(this),this.$registerCloseEvents(),this.setTheme($.theme)),this.isOpen=!0,this.addMarker(v,m.session),this.range=l.fromPoints(v.start,v.end);var _=this.getElement();_.innerHTML="",_.appendChild(y),_.style.display="block";var b=$.textToScreenCoordinates(v.start.row,v.start.column),R=m.getCursorPosition(),M=_.clientHeight,k=$.scroller.getBoundingClientRect(),S=!0;this.row>R.row?S=!0:this.rowk.bottom&&(S=!1),S?b.pageY+=$.lineHeight:b.pageY-=M,_.style.maxWidth=k.width-(b.pageX-k.left)+"px",this.setPosition(b.pageX,b.pageY)}},g.prototype.addMarker=function(m,v){this.marker&&this.$markerSession.removeMarker(this.marker),this.$markerSession=v,this.marker=v&&v.addMarker(m,"ace_highlight-marker","text")},g.prototype.hide=function(m){!m&&document.activeElement==this.getElement()||m&&m.target&&(m.type!="keydown"||m.ctrlKey||m.metaKey)&&this.$element.contains(m.target)||(this.lastEvent=null,this.timeout&&clearTimeout(this.timeout),this.timeout=null,this.addMarker(null),this.isOpen&&(this.$removeCloseEvents(),this.getElement().style.display="none",this.isOpen=!1,f.removePopup(this)))},g.prototype.$registerCloseEvents=function(){window.addEventListener("keydown",this.hide,!0),window.addEventListener("mousewheel",this.hide,!0),window.addEventListener("mousedown",this.hide,!0)},g.prototype.$removeCloseEvents=function(){window.removeEventListener("keydown",this.hide,!0),window.removeEventListener("mousewheel",this.hide,!0),window.removeEventListener("mousedown",this.hide,!0)},g.prototype.onMouseOut=function(m){this.timeout&&(clearTimeout(this.timeout),this.timeout=null),this.lastEvent=null,this.isOpen&&(!m.relatedTarget||m.relatedTarget==this.getElement()||m&&m.currentTarget.contains(m.relatedTarget)||m.relatedTarget.classList.contains("ace_content")||this.hide())},g}(a);r.HoverTooltip=d}),ace.define("ace/mouse/default_gutter_handler",["require","exports","module","ace/lib/dom","ace/lib/event","ace/tooltip","ace/config"],function(n,r,i){var o=this&&this.__extends||function(){var d=function(p,g){return d=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(m,v){m.__proto__=v}||function(m,v){for(var y in v)Object.prototype.hasOwnProperty.call(v,y)&&(m[y]=v[y])},d(p,g)};return function(p,g){if(typeof g!="function"&&g!==null)throw new TypeError("Class extends value "+String(g)+" is not a constructor or null");d(p,g);function m(){this.constructor=p}p.prototype=g===null?Object.create(g):(m.prototype=g.prototype,new m)}}(),s=this&&this.__values||function(d){var p=typeof Symbol=="function"&&Symbol.iterator,g=p&&d[p],m=0;if(g)return g.call(d);if(d&&typeof d.length=="number")return{next:function(){return d&&m>=d.length&&(d=void 0),{value:d&&d[m++],done:!d}}};throw new TypeError(p?"Object is not iterable.":"Symbol.iterator is not defined.")},h=n("../lib/dom"),l=n("../lib/event"),u=n("../tooltip").Tooltip,a=n("../config").nls;function c(d){var p=d.editor,g=p.renderer.$gutterLayer,m=new f(p);d.editor.setDefaultHandler("guttermousedown",function(b){if(!(!p.isFocused()||b.getButton()!=0)){var R=g.getRegion(b);if(R!="foldWidgets"){var M=b.getDocumentPosition().row,k=p.session.selection;if(b.getShiftKey())k.selectTo(M,0);else{if(b.domEvent.detail==2)return p.selectAll(),b.preventDefault();d.$clickSelection=p.selection.getLineRange(M)}return d.setState("selectByLines"),d.captureMouse(b),b.preventDefault()}}});var v,y;function x(){var b=y.getDocumentPosition().row,R=p.session.getLength();if(b==R){var M=p.renderer.pixelToScreenCoordinates(0,y.y).row,k=y.$pos;if(M>p.session.documentToScreenRow(k.row,k.column))return $()}if(m.showTooltip(b),!!m.isOpen)if(p.on("mousewheel",$),d.$tooltipFollowsMouse)_(y);else{var S=g.$lines.cells[b].element.querySelector("[class*=ace_icon]"),E=S.getBoundingClientRect(),I=m.getElement().style;I.left=E.right+"px",I.top=E.bottom+"px"}}function $(){v&&(v=clearTimeout(v)),m.isOpen&&(m.hide(),p._signal("hideGutterTooltip",m),p.off("mousewheel",$))}function _(b){m.setPosition(b.x,b.y)}d.editor.setDefaultHandler("guttermousemove",function(b){var R=b.domEvent.target||b.domEvent.srcElement;if(h.hasCssClass(R,"ace_fold-widget"))return $();m.isOpen&&d.$tooltipFollowsMouse&&_(b),y=b,!v&&(v=setTimeout(function(){v=null,y&&!d.isMousePressed?x():$()},50))}),l.addListener(p.renderer.$gutter,"mouseout",function(b){y=null,!(!m.isOpen||v)&&(v=setTimeout(function(){v=null,$()},50))},p),p.on("changeSession",$),p.on("input",$)}r.GutterHandler=c;var f=function(d){o(p,d);function p(g){var m=d.call(this,g.container)||this;return m.editor=g,m}return p.prototype.setPosition=function(g,m){var v=window.innerWidth||document.documentElement.clientWidth,y=window.innerHeight||document.documentElement.clientHeight,x=this.getWidth(),$=this.getHeight();g+=15,m+=15,g+x>v&&(g-=g+x-v),m+$>y&&(m-=20+$),u.prototype.setPosition.call(this,g,m)},Object.defineProperty(p,"annotationLabels",{get:function(){return{error:{singular:a("error"),plural:a("errors")},warning:{singular:a("warning"),plural:a("warnings")},info:{singular:a("information message"),plural:a("information messages")}}},enumerable:!1,configurable:!0}),p.prototype.showTooltip=function(g){var m=this.editor.renderer.$gutterLayer,v=m.$annotations[g],y;v?y={text:Array.from(v.text),type:Array.from(v.type)}:y={text:[],type:[]};var x=m.session.getFoldLine(g);if(x&&m.$showFoldedAnnotations){for(var $={error:[],warning:[],info:[]},_,b=g+1;b<=x.end.row;b++)if(m.$annotations[b])for(var R=0;R ").concat(y.text[b]);S[y.type[b].replace("_fold","")].push(I)}var C=[].concat(S.error,S.warning,S.info).join("
");this.setHtml(C),this.setClassName("ace_gutter-tooltip"),this.$element.setAttribute("aria-live","polite"),this.isOpen||this.setTheme(this.editor.renderer.theme),this.editor._signal("showGutterTooltip",this),this.show()},p.annotationsToSummaryString=function(g){var m,v,y=[],x=["error","warning","info"];try{for(var $=s(x),_=$.next();!_.done;_=$.next()){var b=_.value;if(g[b].length){var R=g[b].length===1?p.annotationLabels[b].singular:p.annotationLabels[b].plural;y.push("".concat(g[b].length," ").concat(R))}}}catch(M){m={error:M}}finally{try{_&&!_.done&&(v=$.return)&&v.call($)}finally{if(m)throw m.error}}return y.join(", ")},p}(u);r.GutterTooltip=f}),ace.define("ace/mouse/mouse_event",["require","exports","module","ace/lib/event","ace/lib/useragent"],function(n,r,i){var o=n("../lib/event"),s=n("../lib/useragent"),h=function(){function l(u,a){this.domEvent=u,this.editor=a,this.x=this.clientX=u.clientX,this.y=this.clientY=u.clientY,this.$pos=null,this.$inSelection=null,this.propagationStopped=!1,this.defaultPrevented=!1}return l.prototype.stopPropagation=function(){o.stopPropagation(this.domEvent),this.propagationStopped=!0},l.prototype.preventDefault=function(){o.preventDefault(this.domEvent),this.defaultPrevented=!0},l.prototype.stop=function(){this.stopPropagation(),this.preventDefault()},l.prototype.getDocumentPosition=function(){return this.$pos?this.$pos:(this.$pos=this.editor.renderer.screenToTextCoordinates(this.clientX,this.clientY),this.$pos)},l.prototype.inSelection=function(){if(this.$inSelection!==null)return this.$inSelection;var u=this.editor,a=u.getSelectionRange();if(a.isEmpty())this.$inSelection=!1;else{var c=this.getDocumentPosition();this.$inSelection=a.contains(c.row,c.column)}return this.$inSelection},l.prototype.getButton=function(){return o.getButton(this.domEvent)},l.prototype.getShiftKey=function(){return this.domEvent.shiftKey},l.prototype.getAccelKey=function(){return s.isMac?this.domEvent.metaKey:this.domEvent.ctrlKey},l}();r.MouseEvent=h}),ace.define("ace/mouse/dragdrop_handler",["require","exports","module","ace/lib/dom","ace/lib/event","ace/lib/useragent"],function(n,r,i){var o=n("../lib/dom"),s=n("../lib/event"),h=n("../lib/useragent"),l=200,u=200,a=5;function c(d){var p=d.editor,g=o.createElement("div");g.style.cssText="top:-100px;position:absolute;z-index:2147483647;opacity:0.5",g.textContent=" ";var m=["dragWait","dragWaitEnd","startDrag","dragReadyEnd","onMouseDrag"];m.forEach(function(G){d[G]=this[G]},this),p.on("mousedown",this.onMouseDown.bind(d));var v=p.container,y,x,$,_,b,R,M=0,k,S,E,I,C;this.onDragStart=function(G){if(this.cancelDrag||!v.draggable){var U=this;return setTimeout(function(){U.startSelect(),U.captureMouse(G)},0),G.preventDefault()}b=p.getSelectionRange();var Q=G.dataTransfer;Q.effectAllowed=p.getReadOnly()?"copy":"copyMove",p.container.appendChild(g),Q.setDragImage&&Q.setDragImage(g,0,0),setTimeout(function(){p.container.removeChild(g)}),Q.clearData(),Q.setData("Text",p.session.getTextRange()),S=!0,this.setState("drag")},this.onDragEnd=function(G){if(v.draggable=!1,S=!1,this.setState(null),!p.getReadOnly()){var U=G.dataTransfer.dropEffect;!k&&U=="move"&&p.session.remove(p.getSelectionRange()),p.$resetCursorStyle()}this.editor.unsetStyle("ace_dragging"),this.editor.renderer.setCursorStyle("")},this.onDragEnter=function(G){if(!(p.getReadOnly()||!F(G.dataTransfer)))return x=G.clientX,$=G.clientY,y||N(),M++,G.dataTransfer.dropEffect=k=W(G),s.preventDefault(G)},this.onDragOver=function(G){if(!(p.getReadOnly()||!F(G.dataTransfer)))return x=G.clientX,$=G.clientY,y||(N(),M++),H!==null&&(H=null),G.dataTransfer.dropEffect=k=W(G),s.preventDefault(G)},this.onDragLeave=function(G){if(M--,M<=0&&y)return B(),k=null,s.preventDefault(G)},this.onDrop=function(G){if(R){var U=G.dataTransfer;if(S)switch(k){case"move":b.contains(R.row,R.column)?b={start:R,end:R}:b=p.moveText(b,R);break;case"copy":b=p.moveText(b,R,!0);break}else{var Q=U.getData("Text");b={start:R,end:p.session.insert(R,Q)},p.focus(),k=null}return B(),s.preventDefault(G)}},s.addListener(v,"dragstart",this.onDragStart.bind(d),p),s.addListener(v,"dragend",this.onDragEnd.bind(d),p),s.addListener(v,"dragenter",this.onDragEnter.bind(d),p),s.addListener(v,"dragover",this.onDragOver.bind(d),p),s.addListener(v,"dragleave",this.onDragLeave.bind(d),p),s.addListener(v,"drop",this.onDrop.bind(d),p);function w(G,U){var Q=Date.now(),ee=!U||G.row!=U.row,V=!U||G.column!=U.column;if(!I||ee||V)p.moveCursorToPosition(G),I=Q,C={x,y:$};else{var q=f(C.x,C.y,x,$);q>a?I=null:Q-I>=u&&(p.renderer.scrollCursorIntoView(),I=null)}}function T(G,U){var Q=Date.now(),ee=p.renderer.layerConfig.lineHeight,V=p.renderer.layerConfig.characterWidth,q=p.renderer.scroller.getBoundingClientRect(),re={x:{left:x-q.left,right:q.right-x},y:{top:$-q.top,bottom:q.bottom-$}},se=Math.min(re.x.left,re.x.right),ge=Math.min(re.y.top,re.y.bottom),K={row:G.row,column:G.column};se/V<=2&&(K.column+=re.x.left=l&&p.renderer.scrollCursorIntoView(K):E=Q:E=null}function O(){var G=R;R=p.renderer.screenToTextCoordinates(x,$),w(R,G),T(R,G)}function N(){b=p.selection.toOrientedRange(),y=p.session.addMarker(b,"ace_selection",p.getSelectionStyle()),p.clearSelection(),p.isFocused()&&p.renderer.$cursorLayer.setBlinking(!1),clearInterval(_),O(),_=setInterval(O,20),M=0,s.addListener(document,"mousemove",z)}function B(){clearInterval(_),p.session.removeMarker(y),y=null,p.selection.fromOrientedRange(b),p.isFocused()&&!S&&p.$resetCursorStyle(),b=null,R=null,M=0,E=null,I=null,s.removeListener(document,"mousemove",z)}var H=null;function z(){H==null&&(H=setTimeout(function(){H!=null&&y&&B()},20))}function F(G){var U=G.types;return!U||Array.prototype.some.call(U,function(Q){return Q=="text/plain"||Q=="Text"})}function W(G){var U=["copy","copymove","all","uninitialized"],Q=["move","copymove","linkmove","all","uninitialized"],ee=h.isMac?G.altKey:G.ctrlKey,V="uninitialized";try{V=G.dataTransfer.effectAllowed.toLowerCase()}catch{}var q="none";return ee&&U.indexOf(V)>=0?q="copy":Q.indexOf(V)>=0?q="move":U.indexOf(V)>=0&&(q="copy"),q}}(function(){this.dragWait=function(){var d=Date.now()-this.mousedownEvent.time;d>this.editor.getDragDelay()&&this.startDrag()},this.dragWaitEnd=function(){var d=this.editor.container;d.draggable=!1,this.startSelect(this.mousedownEvent.getDocumentPosition()),this.selectEnd()},this.dragReadyEnd=function(d){this.editor.$resetCursorStyle(),this.editor.unsetStyle("ace_dragging"),this.editor.renderer.setCursorStyle(""),this.dragWaitEnd()},this.startDrag=function(){this.cancelDrag=!1;var d=this.editor,p=d.container;p.draggable=!0,d.renderer.$cursorLayer.setBlinking(!1),d.setStyle("ace_dragging");var g=h.isWin?"default":"move";d.renderer.setCursorStyle(g),this.setState("dragReady")},this.onMouseDrag=function(d){var p=this.editor.container;if(h.isIE&&this.state=="dragReady"){var g=f(this.mousedownEvent.x,this.mousedownEvent.y,this.x,this.y);g>3&&p.dragDrop()}if(this.state==="dragWait"){var g=f(this.mousedownEvent.x,this.mousedownEvent.y,this.x,this.y);g>0&&(p.draggable=!1,this.startSelect(this.mousedownEvent.getDocumentPosition()))}},this.onMouseDown=function(d){if(this.$dragEnabled){this.mousedownEvent=d;var p=this.editor,g=d.inSelection(),m=d.getButton(),v=d.domEvent.detail||1;if(v===1&&m===0&&g){if(d.editor.inMultiSelectMode&&(d.getAccelKey()||d.getShiftKey()))return;this.mousedownEvent.time=Date.now();var y=d.domEvent.target||d.domEvent.srcElement;if("unselectable"in y&&(y.unselectable="on"),p.getDragDelay()){if(h.isWebKit){this.cancelDrag=!0;var x=p.container;x.draggable=!0}this.setState("dragWait")}else this.startDrag();this.captureMouse(d,this.onMouseDrag.bind(this)),d.defaultPrevented=!0}}}}).call(c.prototype);function f(d,p,g,m){return Math.sqrt(Math.pow(g-d,2)+Math.pow(m-p,2))}r.DragdropHandler=c}),ace.define("ace/mouse/touch_handler",["require","exports","module","ace/mouse/mouse_event","ace/lib/event","ace/lib/dom"],function(n,r,i){var o=n("./mouse_event").MouseEvent,s=n("../lib/event"),h=n("../lib/dom");r.addTouchListeners=function(l,u){var a="scroll",c,f,d,p,g,m,v=0,y,x=0,$=0,_=0,b,R;function M(){var w=window.navigator&&window.navigator.clipboard,T=!1,O=function(){var B=u.getCopyText(),H=u.session.getUndoManager().hasUndo();R.replaceChild(h.buildDom(T?["span",!B&&["span",{class:"ace_mobile-button",action:"selectall"},"Select All"],B&&["span",{class:"ace_mobile-button",action:"copy"},"Copy"],B&&["span",{class:"ace_mobile-button",action:"cut"},"Cut"],w&&["span",{class:"ace_mobile-button",action:"paste"},"Paste"],H&&["span",{class:"ace_mobile-button",action:"undo"},"Undo"],["span",{class:"ace_mobile-button",action:"find"},"Find"],["span",{class:"ace_mobile-button",action:"openCommandPallete"},"Palette"]]:["span"]),R.firstChild)},N=function(B){var H=B.target.getAttribute("action");if(H=="more"||!T)return T=!T,O();H=="paste"?w.readText().then(function(z){u.execCommand(H,z)}):H&&((H=="cut"||H=="copy")&&(w?w.writeText(u.getCopyText()):document.execCommand("copy")),u.execCommand(H)),R.firstChild.style.display="none",T=!1,H!="openCommandPallete"&&u.focus()};R=h.buildDom(["div",{class:"ace_mobile-menu",ontouchstart:function(B){a="menu",B.stopPropagation(),B.preventDefault(),u.textInput.focus()},ontouchend:function(B){B.stopPropagation(),B.preventDefault(),N(B)},onclick:N},["span"],["span",{class:"ace_mobile-button",action:"more"},"..."]],u.container)}function k(){R||M();var w=u.selection.cursor,T=u.renderer.textToScreenCoordinates(w.row,w.column),O=u.renderer.textToScreenCoordinates(0,0).pageX,N=u.renderer.scrollLeft,B=u.container.getBoundingClientRect();R.style.top=T.pageY-B.top-3+"px",T.pageX-B.left=2?u.selection.getLineRange(y.row):u.session.getBracketRange(y);w&&!w.isEmpty()?u.selection.setRange(w):u.selection.selectWord(),a="wait"}s.addListener(l,"contextmenu",function(w){if(b){var T=u.textInput.getElement();T.focus()}},u),s.addListener(l,"touchstart",function(w){var T=w.touches;if(g||T.length>1){clearTimeout(g),g=null,d=-1,a="zoom";return}b=u.$mouseHandler.isMousePressed=!0;var O=u.renderer.layerConfig.lineHeight,N=u.renderer.layerConfig.lineHeight,B=w.timeStamp;p=B;var H=T[0],z=H.clientX,F=H.clientY;Math.abs(c-z)+Math.abs(f-F)>O&&(d=-1),c=w.clientX=z,f=w.clientY=F,$=_=0;var W=new o(w,u);if(y=W.getDocumentPosition(),B-d<500&&T.length==1&&!v)x++,w.preventDefault(),w.button=0,I();else{x=0;var G=u.selection.cursor,U=u.selection.isEmpty()?G:u.selection.anchor,Q=u.renderer.$cursorLayer.getPixelPosition(G,!0),ee=u.renderer.$cursorLayer.getPixelPosition(U,!0),V=u.renderer.scroller.getBoundingClientRect(),q=u.renderer.layerConfig.offset,re=u.renderer.scrollLeft,se=function(Y,J){return Y=Y/N,J=J/O-.75,Y*Y+J*J};if(w.clientXK?"cursor":"anchor"),K<3.5?a="anchor":ge<3.5?a="cursor":a="scroll",g=setTimeout(E,450)}d=B},u),s.addListener(l,"touchend",function(w){b=u.$mouseHandler.isMousePressed=!1,m&&clearInterval(m),a=="zoom"?(a="",v=0):g?(u.selection.moveToPosition(y),v=0,k()):a=="scroll"?(C(),S()):k(),clearTimeout(g),g=null},u),s.addListener(l,"touchmove",function(w){g&&(clearTimeout(g),g=null);var T=w.touches;if(!(T.length>1||a=="zoom")){var O=T[0],N=c-O.clientX,B=f-O.clientY;if(a=="wait")if(N*N+B*B>4)a="cursor";else return w.preventDefault();c=O.clientX,f=O.clientY,w.clientX=O.clientX,w.clientY=O.clientY;var H=w.timeStamp,z=H-p;if(p=H,a=="scroll"){var F=new o(w,u);F.speed=1,F.wheelX=N,F.wheelY=B,10*Math.abs(N)0)if(K==16){for(Z=J;Z-1){for(Z=J;Z=0&&ee[me]==b;me--)U[me]=o}}}function z(G,U,Q){if(!(s=G){for(q=V+1;q=G;)q++;for(re=V,se=q-1;re=U.length||(q=Q[ee-1])!=m&&q!=v||(re=U[ee+1])!=m&&re!=v?y:(h&&(re=v),re==q?re:y);case M:return q=ee>0?Q[ee-1]:x,q==m&&ee+10&&Q[ee-1]==m)return m;if(h)return y;for(ge=ee+1,se=U.length;ge=1425&&K<=2303||K==64286;if(q=U[ge],Y&&(q==g||q==_))return g}return ee<1||(q=U[ee-1])==x?y:Q[ee-1];case x:return h=!1,l=!0,o;case $:return u=!0,y;case E:case I:case w:case T:case C:h=!1;case O:return y}}function W(G){var U=G.charCodeAt(0),Q=U>>8;return Q==0?U>191?p:N[U]:Q==5?/[\u0591-\u05f4]/.test(G)?g:p:Q==6?/[\u0610-\u061a\u064b-\u065f\u06d6-\u06e4\u06e7-\u06ed]/.test(G)?S:/[\u0660-\u0669\u066b-\u066c]/.test(G)?v:U==1642?k:/[\u06f0-\u06f9]/.test(G)?m:_:Q==32&&U<=8287?B[U&255]:Q==254&&U>=65136?_:y}r.L=p,r.R=g,r.EN=m,r.ON_R=3,r.AN=4,r.R_H=5,r.B=6,r.RLE=7,r.DOT="·",r.doBidiReorder=function(G,U,Q){if(G.length<2)return{};var ee=G.split(""),V=new Array(ee.length),q=new Array(ee.length),re=[];o=Q?d:f,H(ee,re,ee.length,U);for(var se=0;se_&&U[se]0&&ee[se-1]==="ل"&&/\u0622|\u0623|\u0625|\u0627/.test(ee[se])&&(re[se-1]=re[se]=r.R_H,se++);ee[ee.length-1]===r.DOT&&(re[ee.length-1]=r.B),ee[0]==="‫"&&(re[0]=r.RLE);for(var se=0;se=0&&(a=this.session.$docRowCache[f])}return a},u.prototype.getSplitIndex=function(){var a=0,c=this.session.$screenRowCache;if(c.length)for(var f,d=this.session.$getRowCacheIndex(c,this.currentRow);this.currentRow-a>0&&(f=this.session.$getRowCacheIndex(c,this.currentRow-a-1),f===d);)d=f,a++;else a=this.currentRow;return a},u.prototype.updateRowLine=function(a,c){a===void 0&&(a=this.getDocumentRow());var f=a===this.session.getLength()-1,d=f?this.EOF:this.EOL;if(this.wrapIndent=0,this.line=this.session.getLine(a),this.isRtlDir=this.$isRtl||this.line.charAt(0)===this.RLE,this.session.$useWrapMode){var p=this.session.$wrapData[a];p&&(c===void 0&&(c=this.getSplitIndex()),c>0&&p.length?(this.wrapIndent=p.indent,this.wrapOffset=this.wrapIndent*this.charWidths[o.L],this.line=cc?this.session.getOverwrite()?a:a-1:c,d=o.getVisualFromLogicalIdx(f,this.bidiMap),p=this.bidiMap.bidiLevels,g=0;!this.session.getOverwrite()&&a<=c&&p[d]%2!==0&&d++;for(var m=0;mc&&p[d]%2===0&&(g+=this.charWidths[p[d]]),this.wrapIndent&&(g+=this.isRtlDir?-1*this.wrapOffset:this.wrapOffset),this.isRtlDir&&(g+=this.rtlLineOffset),g},u.prototype.getSelections=function(a,c){var f=this.bidiMap,d=f.bidiLevels,p,g=[],m=0,v=Math.min(a,c)-this.wrapIndent,y=Math.max(a,c)-this.wrapIndent,x=!1,$=!1,_=0;this.wrapIndent&&(m+=this.isRtlDir?-1*this.wrapOffset:this.wrapOffset);for(var b,R=0;R=v&&bd+m/2;){if(d+=m,p===g.length-1){m=0;break}m=this.charWidths[g[++p]]}return p>0&&g[p-1]%2!==0&&g[p]%2===0?(f0&&g[p-1]%2===0&&g[p]%2!==0?c=1+(f>d?this.bidiMap.logicalFromVisual[p]:this.bidiMap.logicalFromVisual[p-1]):this.isRtlDir&&p===g.length-1&&m===0&&g[p-1]%2===0||!this.isRtlDir&&p===0&&g[p]%2!==0?c=1+this.bidiMap.logicalFromVisual[p]:(p>0&&g[p-1]%2!==0&&m!==0&&p--,c=this.bidiMap.logicalFromVisual[p]),c===0&&this.isRtlDir&&c++,c+this.wrapIndent},u}();r.BidiHandler=l}),ace.define("ace/selection",["require","exports","module","ace/lib/oop","ace/lib/lang","ace/lib/event_emitter","ace/range"],function(n,r,i){var o=n("./lib/oop"),s=n("./lib/lang"),h=n("./lib/event_emitter").EventEmitter,l=n("./range").Range,u=function(a){this.session=a,this.doc=a.getDocument(),this.clearSelection(),this.cursor=this.lead=this.doc.createAnchor(0,0),this.anchor=this.doc.createAnchor(0,0),this.$silent=!1;var c=this;this.cursor.on("change",function(f){c.$cursorChanged=!0,c.$silent||c._emit("changeCursor"),!c.$isEmpty&&!c.$silent&&c._emit("changeSelection"),!c.$keepDesiredColumnOnChange&&f.old.column!=f.value.column&&(c.$desiredColumn=null)}),this.anchor.on("change",function(){c.$anchorChanged=!0,!c.$isEmpty&&!c.$silent&&c._emit("changeSelection")})};(function(){o.implement(this,h),this.isEmpty=function(){return this.$isEmpty||this.anchor.row==this.lead.row&&this.anchor.column==this.lead.column},this.isMultiLine=function(){return!this.$isEmpty&&this.anchor.row!=this.cursor.row},this.getCursor=function(){return this.lead.getPosition()},this.setAnchor=function(a,c){this.$isEmpty=!1,this.anchor.setPosition(a,c)},this.setSelectionAnchor=this.setAnchor,this.getAnchor=function(){return this.$isEmpty?this.getSelectionLead():this.anchor.getPosition()},this.getSelectionAnchor=this.getAnchor,this.getSelectionLead=function(){return this.lead.getPosition()},this.isBackwards=function(){var a=this.anchor,c=this.lead;return a.row>c.row||a.row==c.row&&a.column>c.column},this.getRange=function(){var a=this.anchor,c=this.lead;return this.$isEmpty?l.fromPoints(c,c):this.isBackwards()?l.fromPoints(c,a):l.fromPoints(a,c)},this.clearSelection=function(){this.$isEmpty||(this.$isEmpty=!0,this._emit("changeSelection"))},this.selectAll=function(){this.$setSelection(0,0,Number.MAX_VALUE,Number.MAX_VALUE)},this.setRange=this.setSelectionRange=function(a,c){var f=c?a.end:a.start,d=c?a.start:a.end;this.$setSelection(f.row,f.column,d.row,d.column)},this.$setSelection=function(a,c,f,d){if(!this.$silent){var p=this.$isEmpty,g=this.inMultiSelectMode;this.$silent=!0,this.$cursorChanged=this.$anchorChanged=!1,this.anchor.setPosition(a,c),this.cursor.setPosition(f,d),this.$isEmpty=!l.comparePoints(this.anchor,this.cursor),this.$silent=!1,this.$cursorChanged&&this._emit("changeCursor"),(this.$cursorChanged||this.$anchorChanged||p!=this.$isEmpty||g)&&this._emit("changeSelection")}},this.$moveSelection=function(a){var c=this.lead;this.$isEmpty&&this.setSelectionAnchor(c.row,c.column),a.call(this)},this.selectTo=function(a,c){this.$moveSelection(function(){this.moveCursorTo(a,c)})},this.selectToPosition=function(a){this.$moveSelection(function(){this.moveCursorToPosition(a)})},this.moveTo=function(a,c){this.clearSelection(),this.moveCursorTo(a,c)},this.moveToPosition=function(a){this.clearSelection(),this.moveCursorToPosition(a)},this.selectUp=function(){this.$moveSelection(this.moveCursorUp)},this.selectDown=function(){this.$moveSelection(this.moveCursorDown)},this.selectRight=function(){this.$moveSelection(this.moveCursorRight)},this.selectLeft=function(){this.$moveSelection(this.moveCursorLeft)},this.selectLineStart=function(){this.$moveSelection(this.moveCursorLineStart)},this.selectLineEnd=function(){this.$moveSelection(this.moveCursorLineEnd)},this.selectFileEnd=function(){this.$moveSelection(this.moveCursorFileEnd)},this.selectFileStart=function(){this.$moveSelection(this.moveCursorFileStart)},this.selectWordRight=function(){this.$moveSelection(this.moveCursorWordRight)},this.selectWordLeft=function(){this.$moveSelection(this.moveCursorWordLeft)},this.getWordRange=function(a,c){if(typeof c>"u"){var f=a||this.lead;a=f.row,c=f.column}return this.session.getWordRange(a,c)},this.selectWord=function(){this.setSelectionRange(this.getWordRange())},this.selectAWord=function(){var a=this.getCursor(),c=this.session.getAWordRange(a.row,a.column);this.setSelectionRange(c)},this.getLineRange=function(a,c){var f=typeof a=="number"?a:this.lead.row,d,p=this.session.getFoldLine(f);return p?(f=p.start.row,d=p.end.row):d=f,c===!0?new l(f,0,d,this.session.getLine(d).length):new l(f,0,d+1,0)},this.selectLine=function(){this.setSelectionRange(this.getLineRange())},this.moveCursorUp=function(){this.moveCursorBy(-1,0)},this.moveCursorDown=function(){this.moveCursorBy(1,0)},this.wouldMoveIntoSoftTab=function(a,c,f){var d=a.column,p=a.column+c;return f<0&&(d=a.column-c,p=a.column),this.session.isTabStop(a)&&this.doc.getLine(a.row).slice(d,p).split(" ").length-1==c},this.moveCursorLeft=function(){var a=this.lead.getPosition(),c;if(c=this.session.getFoldAt(a.row,a.column,-1))this.moveCursorTo(c.start.row,c.start.column);else if(a.column===0)a.row>0&&this.moveCursorTo(a.row-1,this.doc.getLine(a.row-1).length);else{var f=this.session.getTabSize();this.wouldMoveIntoSoftTab(a,f,-1)&&!this.session.getNavigateWithinSoftTabs()?this.moveCursorBy(0,-f):this.moveCursorBy(0,-1)}},this.moveCursorRight=function(){var a=this.lead.getPosition(),c;if(c=this.session.getFoldAt(a.row,a.column,1))this.moveCursorTo(c.end.row,c.end.column);else if(this.lead.column==this.doc.getLine(this.lead.row).length)this.lead.row0&&(c.column=d)}}this.moveCursorTo(c.row,c.column)},this.moveCursorFileEnd=function(){var a=this.doc.getLength()-1,c=this.doc.getLine(a).length;this.moveCursorTo(a,c)},this.moveCursorFileStart=function(){this.moveCursorTo(0,0)},this.moveCursorLongWordRight=function(){var a=this.lead.row,c=this.lead.column,f=this.doc.getLine(a),d=f.substring(c);this.session.nonTokenRe.lastIndex=0,this.session.tokenRe.lastIndex=0;var p=this.session.getFoldAt(a,c,1);if(p){this.moveCursorTo(p.end.row,p.end.column);return}if(this.session.nonTokenRe.exec(d)&&(c+=this.session.nonTokenRe.lastIndex,this.session.nonTokenRe.lastIndex=0,d=f.substring(c)),c>=f.length){this.moveCursorTo(a,f.length),this.moveCursorRight(),a0&&this.moveCursorWordLeft();return}this.session.tokenRe.exec(p)&&(c-=this.session.tokenRe.lastIndex,this.session.tokenRe.lastIndex=0),this.moveCursorTo(a,c)},this.$shortWordEndIndex=function(a){var c=0,f,d=/\s/,p=this.session.tokenRe;if(p.lastIndex=0,this.session.tokenRe.exec(a))c=this.session.tokenRe.lastIndex;else{for(;(f=a[c])&&d.test(f);)c++;if(c<1){for(p.lastIndex=0;(f=a[c])&&!p.test(f);)if(p.lastIndex=0,c++,d.test(f))if(c>2){c--;break}else{for(;(f=a[c])&&d.test(f);)c++;if(c>2)break}}}return p.lastIndex=0,c},this.moveCursorShortWordRight=function(){var a=this.lead.row,c=this.lead.column,f=this.doc.getLine(a),d=f.substring(c),p=this.session.getFoldAt(a,c,1);if(p)return this.moveCursorTo(p.end.row,p.end.column);if(c==f.length){var g=this.doc.getLength();do a++,d=this.doc.getLine(a);while(a0&&/^\s*$/.test(d));c=d.length,/\s+$/.test(d)||(d="")}var p=s.stringReverse(d),g=this.$shortWordEndIndex(p);return this.moveCursorTo(a,c-g)},this.moveCursorWordRight=function(){this.session.$selectLongWords?this.moveCursorLongWordRight():this.moveCursorShortWordRight()},this.moveCursorWordLeft=function(){this.session.$selectLongWords?this.moveCursorLongWordLeft():this.moveCursorShortWordLeft()},this.moveCursorBy=function(a,c){var f=this.session.documentToScreenPosition(this.lead.row,this.lead.column),d;if(c===0&&(a!==0&&(this.session.$bidiHandler.isBidiRow(f.row,this.lead.row)?(d=this.session.$bidiHandler.getPosLeft(f.column),f.column=Math.round(d/this.session.$bidiHandler.charWidths[0])):d=f.column*this.session.$bidiHandler.charWidths[0]),this.$desiredColumn?f.column=this.$desiredColumn:this.$desiredColumn=f.column),a!=0&&this.session.lineWidgets&&this.session.lineWidgets[this.lead.row]){var p=this.session.lineWidgets[this.lead.row];a<0?a-=p.rowsAbove||0:a>0&&(a+=p.rowCount-(p.rowsAbove||0))}var g=this.session.screenToDocumentPosition(f.row+a,f.column,d);a!==0&&c===0&&g.row===this.lead.row&&(g.column,this.lead.column),this.moveCursorTo(g.row,g.column+c,c===0)},this.moveCursorToPosition=function(a){this.moveCursorTo(a.row,a.column)},this.moveCursorTo=function(a,c,f){var d=this.session.getFoldAt(a,c,1);d&&(a=d.start.row,c=d.start.column),this.$keepDesiredColumnOnChange=!0;var p=this.session.getLine(a);/[\uDC00-\uDFFF]/.test(p.charAt(c))&&p.charAt(c-1)&&(this.lead.row==a&&this.lead.column==c+1?c=c-1:c=c+1),this.lead.setPosition(a,c),this.$keepDesiredColumnOnChange=!1,f||(this.$desiredColumn=null)},this.moveCursorToScreen=function(a,c,f){var d=this.session.screenToDocumentPosition(a,c);this.moveCursorTo(d.row,d.column,f)},this.detach=function(){this.lead.detach(),this.anchor.detach()},this.fromOrientedRange=function(a){this.setSelectionRange(a,a.cursor==a.start),this.$desiredColumn=a.desiredColumn||this.$desiredColumn},this.toOrientedRange=function(a){var c=this.getRange();return a?(a.start.column=c.start.column,a.start.row=c.start.row,a.end.column=c.end.column,a.end.row=c.end.row):a=c,a.cursor=this.isBackwards()?a.start:a.end,a.desiredColumn=this.$desiredColumn,a},this.getRangeOfMovements=function(a){var c=this.getCursor();try{a(this);var f=this.getCursor();return l.fromPoints(c,f)}catch{return l.fromPoints(c,c)}finally{this.moveCursorToPosition(c)}},this.toJSON=function(){if(this.rangeCount)var a=this.ranges.map(function(c){var f=c.clone();return f.isBackwards=c.cursor==c.start,f});else{var a=this.getRange();a.isBackwards=this.isBackwards()}return a},this.fromJSON=function(a){if(a.start==null)if(this.rangeList&&a.length>1){this.toSingleRange(a[0]);for(var c=a.length;c--;){var f=l.fromPoints(a[c].start,a[c].end);a[c].isBackwards&&(f.cursor=f.start),this.addRange(f,!0)}return}else a=a[0];this.rangeList&&this.toSingleRange(a),this.setSelectionRange(a,a.isBackwards)},this.isEqual=function(a){if((a.length||this.rangeCount)&&a.length!=this.rangeCount)return!1;if(!a.length||!this.ranges)return this.getRange().isEqual(a);for(var c=this.ranges.length;c--;)if(!this.ranges[c].isEqual(a[c]))return!1;return!0}}).call(u.prototype),r.Selection=u}),ace.define("ace/tokenizer",["require","exports","module","ace/config"],function(n,r,i){var o=n("./config"),s=2e3,h=function(){function l(u){this.states=u,this.regExps={},this.matchMappings={};for(var a in this.states){for(var c=this.states[a],f=[],d=0,p=this.matchMappings[a]={defaultToken:"text"},g="g",m=[],v=0;v1?y.onMatch=this.$applyToken:y.onMatch=y.token),$>1&&(/\\\d/.test(y.regex)?x=y.regex.replace(/\\([0-9]+)/g,function(_,b){return"\\"+(parseInt(b,10)+d+1)}):($=1,x=this.removeCapturingGroups(y.regex)),!y.splitRegex&&typeof y.token!="string"&&m.push(y)),p[d]=v,d+=$,f.push(x),y.onMatch||(y.onMatch=null)}}f.length||(p[0]=0,f.push("$")),m.forEach(function(_){_.splitRegex=this.createSplitterRegexp(_.regex,g)},this),this.regExps[a]=new RegExp("("+f.join(")|(")+")|($)",g)}}return l.prototype.$setMaxTokenCount=function(u){s=u|0},l.prototype.$applyToken=function(u){var a=this.splitRegex.exec(u).slice(1),c=this.token.apply(this,a);if(typeof c=="string")return[{type:c,value:u}];for(var f=[],d=0,p=c.length;dy){var k=u.substring(y,M-R.length);$.type==_?$.value+=k:($.type&&v.push($),$={type:_,value:k})}for(var S=0;Ss){for(x>2*u.length&&this.reportError("infinite loop with in ace tokenizer",{startState:a,line:u});y1&&c[0]!==f&&c.unshift("#tmp",f),{tokens:v,state:c.length?c:f}},l}();h.prototype.reportError=o.reportError,r.Tokenizer=h}),ace.define("ace/mode/text_highlight_rules",["require","exports","module","ace/lib/lang"],function(n,r,i){var o=n("../lib/lang"),s=function(){this.$rules={start:[{token:"empty_line",regex:"^$"},{defaultToken:"text"}]}};(function(){this.addRules=function(u,a){if(!a){for(var c in u)this.$rules[c]=u[c];return}for(var c in u){for(var f=u[c],d=0;d=this.$rowTokens.length;){if(this.$row+=1,l||(l=this.$session.getLength()),this.$row>=l)return this.$row=l-1,null;this.$rowTokens=this.$session.getTokens(this.$row),this.$tokenIndex=0}return this.$rowTokens[this.$tokenIndex]},h.prototype.getCurrentToken=function(){return this.$rowTokens[this.$tokenIndex]},h.prototype.getCurrentTokenRow=function(){return this.$row},h.prototype.getCurrentTokenColumn=function(){var l=this.$rowTokens,u=this.$tokenIndex,a=l[u].start;if(a!==void 0)return a;for(a=0;u>0;)u-=1,a+=l[u].value.length;return a},h.prototype.getCurrentTokenPosition=function(){return{row:this.$row,column:this.getCurrentTokenColumn()}},h.prototype.getCurrentTokenRange=function(){var l=this.$rowTokens[this.$tokenIndex],u=this.getCurrentTokenColumn();return new o(this.$row,u,this.$row,u+l.value.length)},h}();r.TokenIterator=s}),ace.define("ace/mode/behaviour/cstyle",["require","exports","module","ace/lib/oop","ace/mode/behaviour","ace/token_iterator","ace/lib/lang"],function(n,r,i){var o=n("../../lib/oop"),s=n("../behaviour").Behaviour,h=n("../../token_iterator").TokenIterator,l=n("../../lib/lang"),u=["text","paren.rparen","rparen","paren","punctuation.operator"],a=["text","paren.rparen","rparen","paren","punctuation.operator","comment"],c,f={},d={'"':'"',"'":"'"},p=function(v){var y=-1;if(v.multiSelect&&(y=v.selection.index,f.rangeCount!=v.multiSelect.rangeCount&&(f={rangeCount:v.multiSelect.rangeCount})),f[y])return c=f[y];c=f[y]={autoInsertedBrackets:0,autoInsertedRow:-1,autoInsertedLineEnd:"",maybeInsertedBrackets:0,maybeInsertedRow:-1,maybeInsertedLineStart:"",maybeInsertedLineEnd:""}},g=function(v,y,x,$){var _=v.end.row-v.start.row;return{text:x+y+$,selection:[0,v.start.column+1,_,v.end.column+(_?0:1)]}},m=function(v){v=v||{},this.add("braces","insertion",function(y,x,$,_,b){var R=$.getCursorPosition(),M=_.doc.getLine(R.row);if(b=="{"){p($);var k=$.getSelectionRange(),S=_.doc.getTextRange(k);if(S!==""&&S!=="{"&&$.getWrapBehavioursEnabled())return g(k,S,"{","}");if(m.isSaneInsertion($,_))return/[\]\}\)]/.test(M[R.column])||$.inMultiSelectMode||v.braces?(m.recordAutoInsert($,_,"}"),{text:"{}",selection:[1,1]}):(m.recordMaybeInsert($,_,"{"),{text:"{",selection:[1,1]})}else if(b=="}"){p($);var E=M.substring(R.column,R.column+1);if(E=="}"){var I=_.$findOpeningBracket("}",{column:R.column+1,row:R.row});if(I!==null&&m.isAutoInsertedClosing(R,M,b))return m.popAutoInsertedClosing(),{text:"",selection:[1,1]}}}else if(b==` -`||b==`\r -`){p($);var C="";m.isMaybeInsertedClosing(R,M)&&(C=l.stringRepeat("}",c.maybeInsertedBrackets),m.clearMaybeInsertedClosing());var E=M.substring(R.column,R.column+1);if(E==="}"){var w=_.findMatchingBracket({row:R.row,column:R.column+1},"}");if(!w)return null;var T=this.$getIndent(_.getLine(w.row))}else if(C)var T=this.$getIndent(M);else{m.clearMaybeInsertedClosing();return}var O=T+_.getTabString();return{text:` -`+O+` -`+T+C,selection:[1,O.length,1,O.length]}}else m.clearMaybeInsertedClosing()}),this.add("braces","deletion",function(y,x,$,_,b){var R=_.doc.getTextRange(b);if(!b.isMultiLine()&&R=="{"){p($);var M=_.doc.getLine(b.start.row),k=M.substring(b.end.column,b.end.column+1);if(k=="}")return b.end.column++,b;c.maybeInsertedBrackets--}}),this.add("parens","insertion",function(y,x,$,_,b){if(b=="("){p($);var R=$.getSelectionRange(),M=_.doc.getTextRange(R);if(M!==""&&$.getWrapBehavioursEnabled())return g(R,M,"(",")");if(m.isSaneInsertion($,_))return m.recordAutoInsert($,_,")"),{text:"()",selection:[1,1]}}else if(b==")"){p($);var k=$.getCursorPosition(),S=_.doc.getLine(k.row),E=S.substring(k.column,k.column+1);if(E==")"){var I=_.$findOpeningBracket(")",{column:k.column+1,row:k.row});if(I!==null&&m.isAutoInsertedClosing(k,S,b))return m.popAutoInsertedClosing(),{text:"",selection:[1,1]}}}}),this.add("parens","deletion",function(y,x,$,_,b){var R=_.doc.getTextRange(b);if(!b.isMultiLine()&&R=="("){p($);var M=_.doc.getLine(b.start.row),k=M.substring(b.start.column+1,b.start.column+2);if(k==")")return b.end.column++,b}}),this.add("brackets","insertion",function(y,x,$,_,b){if(b=="["){p($);var R=$.getSelectionRange(),M=_.doc.getTextRange(R);if(M!==""&&$.getWrapBehavioursEnabled())return g(R,M,"[","]");if(m.isSaneInsertion($,_))return m.recordAutoInsert($,_,"]"),{text:"[]",selection:[1,1]}}else if(b=="]"){p($);var k=$.getCursorPosition(),S=_.doc.getLine(k.row),E=S.substring(k.column,k.column+1);if(E=="]"){var I=_.$findOpeningBracket("]",{column:k.column+1,row:k.row});if(I!==null&&m.isAutoInsertedClosing(k,S,b))return m.popAutoInsertedClosing(),{text:"",selection:[1,1]}}}}),this.add("brackets","deletion",function(y,x,$,_,b){var R=_.doc.getTextRange(b);if(!b.isMultiLine()&&R=="["){p($);var M=_.doc.getLine(b.start.row),k=M.substring(b.start.column+1,b.start.column+2);if(k=="]")return b.end.column++,b}}),this.add("string_dquotes","insertion",function(y,x,$,_,b){var R=_.$mode.$quotes||d;if(b.length==1&&R[b]){if(this.lineCommentStart&&this.lineCommentStart.indexOf(b)!=-1)return;p($);var M=b,k=$.getSelectionRange(),S=_.doc.getTextRange(k);if(S!==""&&(S.length!=1||!R[S])&&$.getWrapBehavioursEnabled())return g(k,S,M,M);if(!S){var E=$.getCursorPosition(),I=_.doc.getLine(E.row),C=I.substring(E.column-1,E.column),w=I.substring(E.column,E.column+1),T=_.getTokenAt(E.row,E.column),O=_.getTokenAt(E.row,E.column+1);if(C=="\\"&&T&&/escape/.test(T.type))return null;var N=T&&/string|escape/.test(T.type),B=!O||/string|escape/.test(O.type),H;if(w==M)H=N!==B,H&&/string\.end/.test(O.type)&&(H=!1);else{if(N&&!B||N&&B)return null;var z=_.$mode.tokenRe;z.lastIndex=0;var F=z.test(C);z.lastIndex=0;var W=z.test(w),G=_.$mode.$pairQuotesAfter,U=G&&G[M]&&G[M].test(C);if(!U&&F||W||w&&!/[\s;,.})\]\\]/.test(w))return null;var Q=I[E.column-2];if(C==M&&(Q==M||z.test(Q)))return null;H=!0}return{text:H?M+M:"",selection:[1,1]}}}}),this.add("string_dquotes","deletion",function(y,x,$,_,b){var R=_.$mode.$quotes||d,M=_.doc.getTextRange(b);if(!b.isMultiLine()&&R.hasOwnProperty(M)){p($);var k=_.doc.getLine(b.start.row),S=k.substring(b.start.column+1,b.start.column+2);if(S==M)return b.end.column++,b}}),v.closeDocComment!==!1&&this.add("doc comment end","insertion",function(y,x,$,_,b){if(y==="doc-start"&&(b===` -`||b===`\r -`)&&$.selection.isEmpty()){var R=$.getCursorPosition(),M=_.doc.getLine(R.row),k=_.doc.getLine(R.row+1),S=this.$getIndent(M);if(/\s*\*/.test(k))return/^\s*\*/.test(M)?{text:b+S+"* ",selection:[1,3+S.length,1,3+S.length]}:{text:b+S+" * ",selection:[1,3+S.length,1,3+S.length]};if(/\/\*\*/.test(M.substring(0,R.column)))return{text:b+S+" * "+b+" "+S+"*/",selection:[1,4+S.length,1,4+S.length]}}})};m.isSaneInsertion=function(v,y){var x=v.getCursorPosition(),$=new h(y,x.row,x.column);if(!this.$matchTokenType($.getCurrentToken()||"text",u)){if(/[)}\]]/.test(v.session.getLine(x.row)[x.column]))return!0;var _=new h(y,x.row,x.column+1);if(!this.$matchTokenType(_.getCurrentToken()||"text",u))return!1}return $.stepForward(),$.getCurrentTokenRow()!==x.row||this.$matchTokenType($.getCurrentToken()||"text",a)},m.$matchTokenType=function(v,y){return y.indexOf(v.type||v)>-1},m.recordAutoInsert=function(v,y,x){var $=v.getCursorPosition(),_=y.doc.getLine($.row);this.isAutoInsertedClosing($,_,c.autoInsertedLineEnd[0])||(c.autoInsertedBrackets=0),c.autoInsertedRow=$.row,c.autoInsertedLineEnd=x+_.substr($.column),c.autoInsertedBrackets++},m.recordMaybeInsert=function(v,y,x){var $=v.getCursorPosition(),_=y.doc.getLine($.row);this.isMaybeInsertedClosing($,_)||(c.maybeInsertedBrackets=0),c.maybeInsertedRow=$.row,c.maybeInsertedLineStart=_.substr(0,$.column)+x,c.maybeInsertedLineEnd=_.substr($.column),c.maybeInsertedBrackets++},m.isAutoInsertedClosing=function(v,y,x){return c.autoInsertedBrackets>0&&v.row===c.autoInsertedRow&&x===c.autoInsertedLineEnd[0]&&y.substr(v.column)===c.autoInsertedLineEnd},m.isMaybeInsertedClosing=function(v,y){return c.maybeInsertedBrackets>0&&v.row===c.maybeInsertedRow&&y.substr(v.column)===c.maybeInsertedLineEnd&&y.substr(0,v.column)==c.maybeInsertedLineStart},m.popAutoInsertedClosing=function(){c.autoInsertedLineEnd=c.autoInsertedLineEnd.substr(1),c.autoInsertedBrackets--},m.clearMaybeInsertedClosing=function(){c&&(c.maybeInsertedBrackets=0,c.maybeInsertedRow=-1)},o.inherits(m,s),r.CstyleBehaviour=m}),ace.define("ace/unicode",["require","exports","module"],function(n,r,i){for(var o=[48,9,8,25,5,0,2,25,48,0,11,0,5,0,6,22,2,30,2,457,5,11,15,4,8,0,2,0,18,116,2,1,3,3,9,0,2,2,2,0,2,19,2,82,2,138,2,4,3,155,12,37,3,0,8,38,10,44,2,0,2,1,2,1,2,0,9,26,6,2,30,10,7,61,2,9,5,101,2,7,3,9,2,18,3,0,17,58,3,100,15,53,5,0,6,45,211,57,3,18,2,5,3,11,3,9,2,1,7,6,2,2,2,7,3,1,3,21,2,6,2,0,4,3,3,8,3,1,3,3,9,0,5,1,2,4,3,11,16,2,2,5,5,1,3,21,2,6,2,1,2,1,2,1,3,0,2,4,5,1,3,2,4,0,8,3,2,0,8,15,12,2,2,8,2,2,2,21,2,6,2,1,2,4,3,9,2,2,2,2,3,0,16,3,3,9,18,2,2,7,3,1,3,21,2,6,2,1,2,4,3,8,3,1,3,2,9,1,5,1,2,4,3,9,2,0,17,1,2,5,4,2,2,3,4,1,2,0,2,1,4,1,4,2,4,11,5,4,4,2,2,3,3,0,7,0,15,9,18,2,2,7,2,2,2,22,2,9,2,4,4,7,2,2,2,3,8,1,2,1,7,3,3,9,19,1,2,7,2,2,2,22,2,9,2,4,3,8,2,2,2,3,8,1,8,0,2,3,3,9,19,1,2,7,2,2,2,22,2,15,4,7,2,2,2,3,10,0,9,3,3,9,11,5,3,1,2,17,4,23,2,8,2,0,3,6,4,0,5,5,2,0,2,7,19,1,14,57,6,14,2,9,40,1,2,0,3,1,2,0,3,0,7,3,2,6,2,2,2,0,2,0,3,1,2,12,2,2,3,4,2,0,2,5,3,9,3,1,35,0,24,1,7,9,12,0,2,0,2,0,5,9,2,35,5,19,2,5,5,7,2,35,10,0,58,73,7,77,3,37,11,42,2,0,4,328,2,3,3,6,2,0,2,3,3,40,2,3,3,32,2,3,3,6,2,0,2,3,3,14,2,56,2,3,3,66,5,0,33,15,17,84,13,619,3,16,2,25,6,74,22,12,2,6,12,20,12,19,13,12,2,2,2,1,13,51,3,29,4,0,5,1,3,9,34,2,3,9,7,87,9,42,6,69,11,28,4,11,5,11,11,39,3,4,12,43,5,25,7,10,38,27,5,62,2,28,3,10,7,9,14,0,89,75,5,9,18,8,13,42,4,11,71,55,9,9,4,48,83,2,2,30,14,230,23,280,3,5,3,37,3,5,3,7,2,0,2,0,2,0,2,30,3,52,2,6,2,0,4,2,2,6,4,3,3,5,5,12,6,2,2,6,67,1,20,0,29,0,14,0,17,4,60,12,5,0,4,11,18,0,5,0,3,9,2,0,4,4,7,0,2,0,2,0,2,3,2,10,3,3,6,4,5,0,53,1,2684,46,2,46,2,132,7,6,15,37,11,53,10,0,17,22,10,6,2,6,2,6,2,6,2,6,2,6,2,6,2,6,2,31,48,0,470,1,36,5,2,4,6,1,5,85,3,1,3,2,2,89,2,3,6,40,4,93,18,23,57,15,513,6581,75,20939,53,1164,68,45,3,268,4,27,21,31,3,13,13,1,2,24,9,69,11,1,38,8,3,102,3,1,111,44,25,51,13,68,12,9,7,23,4,0,5,45,3,35,13,28,4,64,15,10,39,54,10,13,3,9,7,22,4,1,5,66,25,2,227,42,2,1,3,9,7,11171,13,22,5,48,8453,301,3,61,3,105,39,6,13,4,6,11,2,12,2,4,2,0,2,1,2,1,2,107,34,362,19,63,3,53,41,11,5,15,17,6,13,1,25,2,33,4,2,134,20,9,8,25,5,0,2,25,12,88,4,5,3,5,3,5,3,2],s=0,h=[],l=0;l2?Q%b!=b-1:Q%b==0}}else{if(!this.blockComment)return!1;var M=this.blockComment.start,k=this.blockComment.end,S=new RegExp("^(\\s*)(?:"+a.escapeRegExp(M)+")"),E=new RegExp("(?:"+a.escapeRegExp(k)+")\\s*$"),I=function(H,z){w(H,z)||(!x||/\S/.test(H))&&(y.insertInLine({row:z,column:H.length},k),y.insertInLine({row:z,column:_},M))},C=function(H,z){var F;(F=H.match(E))&&y.removeInLine(z,H.length-F[0].length,H.length),(F=H.match(S))&&y.removeInLine(z,F[1].length,F[0].length)},w=function(H,z){if(S.test(H))return!0;for(var F=g.getTokens(z),W=0;WH.length&&(B=H.length)}),_==1/0&&(_=B,x=!1,$=!1),R&&_%b!=0&&(_=Math.floor(_/b)*b),N($?C:I)},this.toggleBlockComment=function(p,g,m,v){var y=this.blockComment;if(y){!y.start&&y[0]&&(y=y[0]);var x=new c(g,v.row,v.column),$=x.getCurrentToken();g.selection;var _=g.selection.toOrientedRange(),b,R;if($&&/comment/.test($.type)){for(var M,k;$&&/comment/.test($.type);){var S=$.value.indexOf(y.start);if(S!=-1){var E=x.getCurrentTokenRow(),I=x.getCurrentTokenColumn()+S;M=new f(E,I,E,I+y.start.length);break}$=x.stepBackward()}for(var x=new c(g,v.row,v.column),$=x.getCurrentToken();$&&/comment/.test($.type);){var S=$.value.indexOf(y.end);if(S!=-1){var E=x.getCurrentTokenRow(),I=x.getCurrentTokenColumn()+S;k=new f(E,I,E,I+y.end.length);break}$=x.stepForward()}k&&g.remove(k),M&&(g.remove(M),b=M.start.row,R=-y.start.length)}else R=y.start.length,b=m.start.row,g.insert(m.end,y.end),g.insert(m.start,y.start);_.start.row==b&&(_.start.column+=R),_.end.row==b&&(_.end.column+=R),g.selection.fromOrientedRange(_)}},this.getNextLineIndent=function(p,g,m){return this.$getIndent(g)},this.checkOutdent=function(p,g,m){return!1},this.autoOutdent=function(p,g,m){},this.$getIndent=function(p){return p.match(/^\s*/)[0]},this.createWorker=function(p){return null},this.createModeDelegates=function(p){this.$embeds=[],this.$modes={};for(var g in p)if(p[g]){var m=p[g],v=m.prototype.$id,y=o.$modes[v];y||(o.$modes[v]=y=new m),o.$modes[g]||(o.$modes[g]=y),this.$embeds.push(g),this.$modes[g]=y}for(var x=["toggleBlockComment","toggleCommentLines","getNextLineIndent","checkOutdent","autoOutdent","transformAction","getCompletions"],g=0;g"u"?this.setPosition(f.row,f.column):this.setPosition(f,d)}return a.prototype.getPosition=function(){return this.$clipPositionToDocument(this.row,this.column)},a.prototype.getDocument=function(){return this.document},a.prototype.onChange=function(c){if(!(c.start.row==c.end.row&&c.start.row!=this.row)&&!(c.start.row>this.row)){var f=u(c,{row:this.row,column:this.column},this.$insertRight);this.setPosition(f.row,f.column,!0)}},a.prototype.setPosition=function(c,f,d){var p;if(d?p={row:c,column:f}:p=this.$clipPositionToDocument(c,f),!(this.row==p.row&&this.column==p.column)){var g={row:this.row,column:this.column};this.row=p.row,this.column=p.column,this._signal("change",{old:g,value:p})}},a.prototype.detach=function(){this.document.off("change",this.$onChange)},a.prototype.attach=function(c){this.document=c||this.document,this.document.on("change",this.$onChange)},a.prototype.$clipPositionToDocument=function(c,f){var d={};return c>=this.document.getLength()?(d.row=Math.max(0,this.document.getLength()-1),d.column=this.document.getLine(d.row).length):c<0?(d.row=0,d.column=0):(d.row=c,d.column=Math.min(this.document.getLine(d.row).length,Math.max(0,f))),f<0&&(d.column=0),d},a}();h.prototype.$insertRight=!1,o.implement(h.prototype,s);function l(a,c,f){var d=f?a.column<=c.column:a.column=p&&(f=p-1,d=void 0);var g=this.getLine(f);return d==null&&(d=g.length),d=Math.min(Math.max(d,0),g.length),{row:f,column:d}},c.prototype.clonePos=function(f){return{row:f.row,column:f.column}},c.prototype.pos=function(f,d){return{row:f,column:d}},c.prototype.$clipPosition=function(f){var d=this.getLength();return f.row>=d?(f.row=Math.max(0,d-1),f.column=this.getLine(d-1).length):(f.row=Math.max(0,f.row),f.column=Math.min(Math.max(f.column,0),this.getLine(f.row).length)),f},c.prototype.insertFullLines=function(f,d){f=Math.min(Math.max(f,0),this.getLength());var p=0;f0,g=d=0&&this.applyDelta({start:this.pos(f,this.getLine(f).length),end:this.pos(f+1,0),action:"remove",lines:["",""]})},c.prototype.replace=function(f,d){if(f instanceof l||(f=l.fromPoints(f.start,f.end)),d.length===0&&f.isEmpty())return f.start;if(d==this.getTextRange(f))return f.end;this.remove(f);var p;return d?p=this.insert(f.start,d):p=f.start,p},c.prototype.applyDeltas=function(f){for(var d=0;d=0;d--)this.revertDelta(f[d])},c.prototype.applyDelta=function(f,d){var p=f.action=="insert";(p?f.lines.length<=1&&!f.lines[0]:!l.comparePoints(f.start,f.end))||(p&&f.lines.length>2e4?this.$splitAndapplyLargeDelta(f,2e4):(s(this.$lines,f,d),this._signal("change",f)))},c.prototype.$safeApplyDelta=function(f){var d=this.$lines.length;(f.action=="remove"&&f.start.row20){c.running=setTimeout(c.$worker,20);break}}c.currentLine=d,p==-1&&(p=d),m<=p&&c.fireUpdateEvent(m,p)}}}return l.prototype.setTokenizer=function(u){this.tokenizer=u,this.lines=[],this.states=[],this.start(0)},l.prototype.setDocument=function(u){this.doc=u,this.lines=[],this.states=[],this.stop()},l.prototype.fireUpdateEvent=function(u,a){var c={first:u,last:a};this._signal("update",{data:c})},l.prototype.start=function(u){this.currentLine=Math.min(u||0,this.currentLine,this.doc.getLength()),this.lines.splice(this.currentLine,this.lines.length),this.states.splice(this.currentLine,this.states.length),this.stop(),this.running=setTimeout(this.$worker,700)},l.prototype.scheduleStart=function(){this.running||(this.running=setTimeout(this.$worker,700))},l.prototype.$updateOnChange=function(u){var a=u.start.row,c=u.end.row-a;if(c===0)this.lines[a]=null;else if(u.action=="remove")this.lines.splice(a,c+1,null),this.states.splice(a,c+1,null);else{var f=Array(c+1);f.unshift(a,1),this.lines.splice.apply(this.lines,f),this.states.splice.apply(this.states,f)}this.currentLine=Math.min(a,this.currentLine,this.doc.getLength()),this.stop()},l.prototype.stop=function(){this.running&&clearTimeout(this.running),this.running=!1},l.prototype.getTokens=function(u){return this.lines[u]||this.$tokenizeRow(u)},l.prototype.getState=function(u){return this.currentLine==u&&this.$tokenizeRow(u),this.states[u]||"start"},l.prototype.$tokenizeRow=function(u){var a=this.doc.getLine(u),c=this.states[u-1],f=this.tokenizer.getLineTokens(a,c,u);return this.states[u]+""!=f.state+""?(this.states[u]=f.state,this.lines[u+1]=null,this.currentLine>u+1&&(this.currentLine=u+1)):this.currentLine==u&&(this.currentLine=u+1),this.lines[u]=f.tokens},l.prototype.cleanup=function(){this.running=!1,this.lines=[],this.states=[],this.currentLine=0,this.removeAllListeners()},l}();o.implement(h.prototype,s),r.BackgroundTokenizer=h}),ace.define("ace/search_highlight",["require","exports","module","ace/lib/lang","ace/range"],function(n,r,i){var o=n("./lib/lang"),s=n("./range").Range,h=function(){function l(u,a,c){c===void 0&&(c="text"),this.setRegexp(u),this.clazz=a,this.type=c}return l.prototype.setRegexp=function(u){this.regExp+""!=u+""&&(this.regExp=u,this.cache=[])},l.prototype.update=function(u,a,c,f){if(this.regExp)for(var d=f.firstRow,p=f.lastRow,g={},m=d;m<=p;m++){var v=this.cache[m];v==null&&(v=o.getMatchOffsets(c.getLine(m),this.regExp),v.length>this.MAX_RANGES&&(v=v.slice(0,this.MAX_RANGES)),v=v.map(function(_){return new s(m,_.offset,m,_.offset+_.length)}),this.cache[m]=v.length?v:"");for(var y=v.length;y--;){var x=v[y].toScreenRange(c),$=x.toString();g[$]||(g[$]=!0,a.drawSingleLineMarker(u,x,this.clazz,f))}}},l}();h.prototype.MAX_RANGES=500,r.SearchHighlight=h}),ace.define("ace/edit_session/fold_line",["require","exports","module","ace/range"],function(n,r,i){var o=n("../range").Range,s=function(){function h(l,u){this.foldData=l,Array.isArray(u)?this.folds=u:u=this.folds=[u];var a=u[u.length-1];this.range=new o(u[0].start.row,u[0].start.column,a.end.row,a.end.column),this.start=this.range.start,this.end=this.range.end,this.folds.forEach(function(c){c.setFoldLine(this)},this)}return h.prototype.shiftRow=function(l){this.start.row+=l,this.end.row+=l,this.folds.forEach(function(u){u.start.row+=l,u.end.row+=l})},h.prototype.addFold=function(l){if(l.sameRow){if(l.start.rowthis.endRow)throw new Error("Can't add a fold to this FoldLine as it has no connection");this.folds.push(l),this.folds.sort(function(u,a){return-u.range.compareEnd(a.start.row,a.start.column)}),this.range.compareEnd(l.start.row,l.start.column)>0?(this.end.row=l.end.row,this.end.column=l.end.column):this.range.compareStart(l.end.row,l.end.column)<0&&(this.start.row=l.start.row,this.start.column=l.start.column)}else if(l.start.row==this.end.row)this.folds.push(l),this.end.row=l.end.row,this.end.column=l.end.column;else if(l.end.row==this.start.row)this.folds.unshift(l),this.start.row=l.start.row,this.start.column=l.start.column;else throw new Error("Trying to add fold to FoldRow that doesn't have a matching row");l.foldLine=this},h.prototype.containsRow=function(l){return l>=this.start.row&&l<=this.end.row},h.prototype.walk=function(l,u,a){var c=0,f=this.folds,d,p,g,m=!0;u==null&&(u=this.end.row,a=this.end.column);for(var v=0;v0)){var m=s(u,p.start);return g===0?a&&m!==0?-d-2:d:m>0||m===0&&!a?d:-d-1}}return-d-1},l.prototype.add=function(u){var a=!u.isEmpty(),c=this.pointIndex(u.start,a);c<0&&(c=-c-1);var f=this.pointIndex(u.end,a,c);return f<0?f=-f-1:f++,this.ranges.splice(c,f-c,u)},l.prototype.addList=function(u){for(var a=[],c=u.length;c--;)a.push.apply(a,this.add(u[c]));return a},l.prototype.substractPoint=function(u){var a=this.pointIndex(u);if(a>=0)return this.ranges.splice(a,1)},l.prototype.merge=function(){var u=[],a=this.ranges;a=a.sort(function(g,m){return s(g.start,m.start)});for(var c=a[0],f,d=1;d=0},l.prototype.containsPoint=function(u){return this.pointIndex(u)>=0},l.prototype.rangeAtPoint=function(u){var a=this.pointIndex(u);if(a>=0)return this.ranges[a]},l.prototype.clipRows=function(u,a){var c=this.ranges;if(c[0].start.row>a||c[c.length-1].start.row=f)break}if(u.action=="insert")for(var y=d-f,x=-a.column+c.column;gf)break;if(v.start.row==f&&v.start.column>=a.column&&(v.start.column==a.column&&this.$bias<=0||(v.start.column+=x,v.start.row+=y)),v.end.row==f&&v.end.column>=a.column){if(v.end.column==a.column&&this.$bias<0)continue;v.end.column==a.column&&x>0&&gv.start.column&&v.end.column==p[g+1].start.column&&(v.end.column-=x),v.end.column+=x,v.end.row+=y}}else for(var y=f-d,x=a.column-c.column;gd)break;v.end.rowa.column)&&(v.end.column=a.column,v.end.row=a.row):(v.end.column+=x,v.end.row+=y):v.end.row>d&&(v.end.row+=y),v.start.rowa.column)&&(v.start.column=a.column,v.start.row=a.row):(v.start.column+=x,v.start.row+=y):v.start.row>d&&(v.start.row+=y)}if(y!=0&&g=c)return g;if(g.end.row>c)return null}return null},this.getNextFoldLine=function(c,f){var d=this.$foldData,p=0;for(f&&(p=d.indexOf(f)),p==-1&&(p=0),p;p=c)return g}return null},this.getFoldedRowCount=function(c,f){for(var d=this.$foldData,p=f-c+1,g=0;g=f){y=c?p-=f-y:p=0);break}else v>=c&&(y>=c?p-=v-y:p-=v-c+1)}return p},this.$addFoldLine=function(c){return this.$foldData.push(c),this.$foldData.sort(function(f,d){return f.start.row-d.start.row}),c},this.addFold=function(c,f){var d=this.$foldData,p=!1,g;c instanceof h?g=c:(g=new h(f,c),g.collapseChildren=f.collapseChildren),this.$clipRangeToDocument(g.range);var m=g.start.row,v=g.start.column,y=g.end.row,x=g.end.column,$=this.getFoldAt(m,v,1),_=this.getFoldAt(y,x,-1);if($&&_==$)return $.addSubFold(g);$&&!$.range.isStart(m,v)&&this.removeFold($),_&&!_.range.isEnd(y,x)&&this.removeFold(_);var b=this.getFoldsInRange(g.range);b.length>0&&(this.removeFolds(b),g.collapseChildren||b.forEach(function(S){g.addSubFold(S)}));for(var R=0;R0&&this.foldAll(c.start.row+1,c.end.row,c.collapseChildren-1),c.subFolds=[]},this.expandFolds=function(c){c.forEach(function(f){this.expandFold(f)},this)},this.unfold=function(c,f){var d,p;if(c==null)d=new o(0,0,this.getLength(),0),f==null&&(f=!0);else if(typeof c=="number")d=new o(c,0,c,this.getLine(c).length);else if("row"in c)d=o.fromPoints(c,c);else{if(Array.isArray(c))return p=[],c.forEach(function(m){p=p.concat(this.unfold(m))},this),p;d=c}p=this.getFoldsInRangeList(d);for(var g=p;p.length==1&&o.comparePoints(p[0].start,d.start)<0&&o.comparePoints(p[0].end,d.end)>0;)this.expandFolds(p),p=this.getFoldsInRangeList(d);if(f!=!1?this.removeFolds(p):this.expandFolds(p),g.length)return g},this.isRowFolded=function(c,f){return!!this.getFoldLine(c,f)},this.getRowFoldEnd=function(c,f){var d=this.getFoldLine(c,f);return d?d.end.row:c},this.getRowFoldStart=function(c,f){var d=this.getFoldLine(c,f);return d?d.start.row:c},this.getFoldDisplayLine=function(c,f,d,p,g){p==null&&(p=c.start.row),g==null&&(g=0),f==null&&(f=c.end.row),d==null&&(d=this.getLine(f).length);var m=this.doc,v="";return c.walk(function(y,x,$,_){if(!(xx)break;while(g&&v.test(g.type)&&!/^comment.start/.test(g.type));g=p.stepBackward()}else g=p.getCurrentToken();return y.end.row=p.getCurrentTokenRow(),y.end.column=p.getCurrentTokenColumn(),/^comment.end/.test(g.type)||(y.end.column+=g.value.length-2),y}},this.foldAll=function(c,f,d,p){d==null&&(d=1e5);var g=this.foldWidgets;if(g){f=f||this.getLength(),c=c||0;for(var m=c;m=c&&(m=v.end.row,v.collapseChildren=d,this.addFold("...",v))}}},this.foldToLevel=function(c){for(this.foldAll();c-- >0;)this.unfold(null,!1)},this.foldAllComments=function(){var c=this;this.foldAll(null,null,null,function(f){for(var d=c.getTokens(f),p=0;p=0;){var m=d[p];if(m==null&&(m=d[p]=this.getFoldWidget(p)),m=="start"){var v=this.getFoldWidgetRange(p);if(g||(g=v),v&&v.end.row>=c)break}p--}return{range:p!==-1&&v,firstRange:g}},this.onFoldWidgetClick=function(c,f){f instanceof u&&(f=f.domEvent);var d={children:f.shiftKey,all:f.ctrlKey||f.metaKey,siblings:f.altKey},p=this.$toggleFoldWidget(c,d);if(!p){var g=f.target||f.srcElement;g&&/ace_fold-widget/.test(g.className)&&(g.className+=" ace_invalid")}},this.$toggleFoldWidget=function(c,f){if(this.getFoldWidget){var d=this.getFoldWidget(c),p=this.getLine(c),g=d==="end"?-1:1,m=this.getFoldAt(c,g===-1?0:p.length,g);if(m)return f.children||f.all?this.removeFold(m):this.expandFold(m),m;var v=this.getFoldWidgetRange(c,!0);if(v&&!v.isMultiLine()&&(m=this.getFoldAt(v.start.row,v.start.column,1),m&&v.isEqual(m.range)))return this.removeFold(m),m;if(f.siblings){var y=this.getParentFoldRangeData(c);if(y.range)var x=y.range.start.row+1,$=y.range.end.row;this.foldAll(x,$,f.all?1e4:0)}else f.children?($=v?v.end.row:this.getLength(),this.foldAll(c+1,$,f.all?1e4:0)):v&&(f.all&&(v.collapseChildren=1e4),this.addFold("...",v));return v}},this.toggleFoldWidget=function(c){var f=this.selection.getCursor().row;f=this.getRowFoldStart(f);var d=this.$toggleFoldWidget(f,{});if(!d){var p=this.getParentFoldRangeData(f,!0);if(d=p.range||p.firstRange,d){f=d.start.row;var g=this.getFoldAt(f,this.getLine(f).length,1);g?this.removeFold(g):this.addFold("...",d)}}},this.updateFoldWidgets=function(c){var f=c.start.row,d=c.end.row-f;if(d===0)this.foldWidgets[f]=null;else if(c.action=="remove")this.foldWidgets.splice(f,d+1,null);else{var p=Array(d+1);p.unshift(f,1),this.foldWidgets.splice.apply(this.foldWidgets,p)}},this.tokenizerUpdateFoldWidgets=function(c){var f=c.data;f.first!=f.last&&this.foldWidgets.length>f.first&&this.foldWidgets.splice(f.first,this.foldWidgets.length)}}r.Folding=a}),ace.define("ace/edit_session/bracket_match",["require","exports","module","ace/token_iterator","ace/range"],function(n,r,i){var o=n("../token_iterator").TokenIterator,s=n("../range").Range;function h(){this.findMatchingBracket=function(l,u){if(l.column==0)return null;var a=u||this.getLine(l.row).charAt(l.column-1);if(a=="")return null;var c=a.match(/([\(\[\{])|([\)\]\}])/);return c?c[1]?this.$findClosingBracket(c[1],l):this.$findOpeningBracket(c[2],l):null},this.getBracketRange=function(l){var u=this.getLine(l.row),a=!0,c,f=u.charAt(l.column-1),d=f&&f.match(/([\(\[\{])|([\)\]\}])/);if(d||(f=u.charAt(l.column),l={row:l.row,column:l.column+1},d=f&&f.match(/([\(\[\{])|([\)\]\}])/),a=!1),!d)return null;if(d[1]){var p=this.$findClosingBracket(d[1],l);if(!p)return null;c=s.fromPoints(l,p),a||(c.end.column++,c.start.column--),c.cursor=c.end}else{var p=this.$findOpeningBracket(d[2],l);if(!p)return null;c=s.fromPoints(p,l),a||(c.start.column++,c.end.column--),c.cursor=c.start}return c},this.getMatchingBracketRanges=function(l,u){var a=this.getLine(l.row),c=/([\(\[\{])|([\)\]\}])/,f=!u&&a.charAt(l.column-1),d=f&&f.match(c);if(d||(f=(u===void 0||u)&&a.charAt(l.column),l={row:l.row,column:l.column+1},d=f&&f.match(c)),!d)return null;var p=new s(l.row,l.column-1,l.row,l.column),g=d[1]?this.$findClosingBracket(d[1],l):this.$findOpeningBracket(d[2],l);if(!g)return[p];var m=new s(g.row,g.column,g.row,g.column+1);return[p,m]},this.$brackets={")":"(","(":")","]":"[","[":"]","{":"}","}":"{","<":">",">":"<"},this.$findOpeningBracket=function(l,u,a){var c=this.$brackets[l],f=1,d=new o(this,u.row,u.column),p=d.getCurrentToken();if(p||(p=d.stepForward()),!!p){a||(a=new RegExp("(\\.?"+p.type.replace(".","\\.").replace("rparen",".paren").replace(/\b(?:end)\b/,"(?:start|begin|end)").replace(/-close\b/,"-(close|open)")+")+"));for(var g=u.column-d.getCurrentTokenColumn()-2,m=p.value;;){for(;g>=0;){var v=m.charAt(g);if(v==c){if(f-=1,f==0)return{row:d.getCurrentTokenRow(),column:g+d.getCurrentTokenColumn()}}else v==l&&(f+=1);g-=1}do p=d.stepBackward();while(p&&!a.test(p.type));if(p==null)break;m=p.value,g=m.length-1}return null}},this.$findClosingBracket=function(l,u,a){var c=this.$brackets[l],f=1,d=new o(this,u.row,u.column),p=d.getCurrentToken();if(p||(p=d.stepForward()),!!p){a||(a=new RegExp("(\\.?"+p.type.replace(".","\\.").replace("lparen",".paren").replace(/\b(?:start|begin)\b/,"(?:start|begin|end)").replace(/-open\b/,"-(close|open)")+")+"));for(var g=u.column-d.getCurrentTokenColumn();;){for(var m=p.value,v=m.length;g"?c=!0:u.type.indexOf("tag-name")!==-1&&(a=!0));while(u&&!a);return u},this.$findClosingTag=function(l,u){var a,c=u.value,f=u.value,d=0,p=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+1);u=l.stepForward();var g=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+u.value.length),m=!1;do if(a=u,u=l.stepForward(),u){if(u.value===">"&&!m){var v=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+1);m=!0}if(u.type.indexOf("tag-name")!==-1){if(c=u.value,f===c){if(a.value==="<")d++;else if(a.value==="")var $=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+1);else return}}}else if(f===c&&u.value==="/>"&&(d--,d<0))var y=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+2),x=y,$=x,v=new s(g.end.row,g.end.column,g.end.row,g.end.column+1)}while(u&&d>=0);if(p&&v&&y&&$&&g&&x)return{openTag:new s(p.start.row,p.start.column,v.end.row,v.end.column),closeTag:new s(y.start.row,y.start.column,$.end.row,$.end.column),openTagName:g,closeTagName:x}},this.$findOpeningTag=function(l,u){var a=l.getCurrentToken(),c=u.value,f=0,d=l.getCurrentTokenRow(),p=l.getCurrentTokenColumn(),g=p+2,m=new s(d,p,d,g);l.stepForward();var v=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+u.value.length);if(u=l.stepForward(),!(!u||u.value!==">")){var y=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+1);l.stepBackward(),l.stepBackward();do if(u=a,d=l.getCurrentTokenRow(),p=l.getCurrentTokenColumn(),g=p+u.value.length,a=l.stepBackward(),u){if(u.type.indexOf("tag-name")!==-1){if(c===u.value)if(a.value==="<"){if(f++,f>0){var x=new s(d,p,d,g),$=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+1);do u=l.stepForward();while(u&&u.value!==">");var _=new s(l.getCurrentTokenRow(),l.getCurrentTokenColumn(),l.getCurrentTokenRow(),l.getCurrentTokenColumn()+1)}}else a.value===""){for(var b=0,R=a;R;){if(R.type.indexOf("tag-name")!==-1&&R.value===c){f--;break}else if(R.value==="<")break;R=l.stepBackward(),b++}for(var M=0;MC&&(this.$docRowCache.splice(C,I),this.$screenRowCache.splice(C,I))},S.prototype.$getRowCacheIndex=function(E,I){for(var C=0,w=E.length-1;C<=w;){var T=C+w>>1,O=E[T];if(I>O)C=T+1;else if(I=I));O++);return w=C[O],w?(w.index=O,w.start=T-w.value.length,w):null},S.prototype.setUndoManager=function(E){if(this.$undoManager=E,this.$informUndoManager&&this.$informUndoManager.cancel(),E){var I=this;E.addSession(this),this.$syncInformUndoManager=function(){I.$informUndoManager.cancel(),I.mergeUndoDeltas=!1},this.$informUndoManager=s.delayedCall(this.$syncInformUndoManager)}else this.$syncInformUndoManager=function(){}},S.prototype.markUndoGroup=function(){this.$syncInformUndoManager&&this.$syncInformUndoManager()},S.prototype.getUndoManager=function(){return this.$undoManager||this.$defaultUndoManager},S.prototype.getTabString=function(){return this.getUseSoftTabs()?s.stringRepeat(" ",this.getTabSize()):" "},S.prototype.setUseSoftTabs=function(E){this.setOption("useSoftTabs",E)},S.prototype.getUseSoftTabs=function(){return this.$useSoftTabs&&!this.$mode.$indentWithTabs},S.prototype.setTabSize=function(E){this.setOption("tabSize",E)},S.prototype.getTabSize=function(){return this.$tabSize},S.prototype.isTabStop=function(E){return this.$useSoftTabs&&E.column%this.$tabSize===0},S.prototype.setNavigateWithinSoftTabs=function(E){this.setOption("navigateWithinSoftTabs",E)},S.prototype.getNavigateWithinSoftTabs=function(){return this.$navigateWithinSoftTabs},S.prototype.setOverwrite=function(E){this.setOption("overwrite",E)},S.prototype.getOverwrite=function(){return this.$overwrite},S.prototype.toggleOverwrite=function(){this.setOverwrite(!this.$overwrite)},S.prototype.addGutterDecoration=function(E,I){this.$decorations[E]||(this.$decorations[E]=""),this.$decorations[E]+=" "+I,this._signal("changeBreakpoint",{})},S.prototype.removeGutterDecoration=function(E,I){this.$decorations[E]=(this.$decorations[E]||"").replace(" "+I,""),this._signal("changeBreakpoint",{})},S.prototype.getBreakpoints=function(){return this.$breakpoints},S.prototype.setBreakpoints=function(E){this.$breakpoints=[];for(var I=0;I0&&(w=!!C.charAt(I-1).match(this.tokenRe)),w||(w=!!C.charAt(I).match(this.tokenRe)),w)var T=this.tokenRe;else if(/^\s+$/.test(C.slice(I-1,I+1)))var T=/\s/;else var T=this.nonTokenRe;var O=I;if(O>0){do O--;while(O>=0&&C.charAt(O).match(T));O++}for(var N=I;NE&&(E=I.screenWidth)}),this.lineWidgetWidth=E},S.prototype.$computeWidth=function(E){if(this.$modified||E){if(this.$modified=!1,this.$useWrapMode)return this.screenWidth=this.$wrapLimit;for(var I=this.doc.getAllLines(),C=this.$rowLengthCache,w=0,T=0,O=this.$foldData[T],N=O?O.start.row:1/0,B=I.length,H=0;HN){if(H=O.end.row+1,H>=B)break;O=this.$foldData[T++],N=O?O.start.row:1/0}C[H]==null&&(C[H]=this.$getStringScreenWidth(I[H])[0]),C[H]>w&&(w=C[H])}this.screenWidth=w}},S.prototype.getLine=function(E){return this.doc.getLine(E)},S.prototype.getLines=function(E,I){return this.doc.getLines(E,I)},S.prototype.getLength=function(){return this.doc.getLength()},S.prototype.getTextRange=function(E){return this.doc.getTextRange(E||this.selection.getRange())},S.prototype.insert=function(E,I){return this.doc.insert(E,I)},S.prototype.remove=function(E){return this.doc.remove(E)},S.prototype.removeFullLines=function(E,I){return this.doc.removeFullLines(E,I)},S.prototype.undoChanges=function(E,I){if(E.length){this.$fromUndo=!0;for(var C=E.length-1;C!=-1;C--){var w=E[C];w.action=="insert"||w.action=="remove"?this.doc.revertDelta(w):w.folds&&this.addFolds(w.folds)}!I&&this.$undoSelect&&(E.selectionBefore?this.selection.fromJSON(E.selectionBefore):this.selection.setRange(this.$getUndoSelection(E,!0))),this.$fromUndo=!1}},S.prototype.redoChanges=function(E,I){if(E.length){this.$fromUndo=!0;for(var C=0;CE.end.column&&(O.start.column+=B),O.end.row==E.end.row&&O.end.column>E.end.column&&(O.end.column+=B)),N&&O.start.row>=E.end.row&&(O.start.row+=N,O.end.row+=N)}if(O.end=this.insert(O.start,w),T.length){var H=E.start,z=O.start,N=z.row-H.row,B=z.column-H.column;this.addFolds(T.map(function(G){return G=G.clone(),G.start.row==H.row&&(G.start.column+=B),G.end.row==H.row&&(G.end.column+=B),G.start.row+=N,G.end.row+=N,G}))}return O},S.prototype.indentRows=function(E,I,C){C=C.replace(/\t/g,this.getTabString());for(var w=E;w<=I;w++)this.doc.insertInLine({row:w,column:0},C)},S.prototype.outdentRows=function(E){for(var I=E.collapseRows(),C=new f(0,0,0,0),w=this.getTabSize(),T=I.start.row;T<=I.end.row;++T){var O=this.getLine(T);C.start.row=T,C.end.row=T;for(var N=0;N0){var w=this.getRowFoldEnd(I+C);if(w>this.doc.getLength()-1)return 0;var T=w-I}else{E=this.$clipRowToDocument(E),I=this.$clipRowToDocument(I);var T=I-E+1}var O=new f(E,0,I,Number.MAX_VALUE),N=this.getFoldsInRange(O).map(function(H){return H=H.clone(),H.start.row+=T,H.end.row+=T,H}),B=C==0?this.doc.getLines(E,I):this.doc.removeFullLines(E,I);return this.doc.insertFullLines(E+T,B),N.length&&this.addFolds(N),T},S.prototype.moveLinesUp=function(E,I){return this.$moveLines(E,I,-1)},S.prototype.moveLinesDown=function(E,I){return this.$moveLines(E,I,1)},S.prototype.duplicateLines=function(E,I){return this.$moveLines(E,I,0)},S.prototype.$clipRowToDocument=function(E){return Math.max(0,Math.min(E,this.doc.getLength()-1))},S.prototype.$clipColumnToRow=function(E,I){return I<0?0:Math.min(this.doc.getLine(E).length,I)},S.prototype.$clipPositionToDocument=function(E,I){if(I=Math.max(0,I),E<0)E=0,I=0;else{var C=this.doc.getLength();E>=C?(E=C-1,I=this.doc.getLine(C-1).length):I=Math.min(this.doc.getLine(E).length,I)}return{row:E,column:I}},S.prototype.$clipRangeToDocument=function(E){E.start.row<0?(E.start.row=0,E.start.column=0):E.start.column=this.$clipColumnToRow(E.start.row,E.start.column);var I=this.doc.getLength()-1;return E.end.row>I?(E.end.row=I,E.end.column=this.doc.getLine(I).length):E.end.column=this.$clipColumnToRow(E.end.row,E.end.column),E},S.prototype.setUseWrapMode=function(E){if(E!=this.$useWrapMode){if(this.$useWrapMode=E,this.$modified=!0,this.$resetRowCache(0),E){var I=this.getLength();this.$wrapData=Array(I),this.$updateWrapData(0,I-1)}this._signal("changeWrapMode")}},S.prototype.getUseWrapMode=function(){return this.$useWrapMode},S.prototype.setWrapLimitRange=function(E,I){(this.$wrapLimitRange.min!==E||this.$wrapLimitRange.max!==I)&&(this.$wrapLimitRange={min:E,max:I},this.$modified=!0,this.$bidiHandler.markAsDirty(),this.$useWrapMode&&this._signal("changeWrapMode"))},S.prototype.adjustWrapLimit=function(E,I){var C=this.$wrapLimitRange;C.max<0&&(C={min:I,max:I});var w=this.$constrainWrapLimit(E,C.min,C.max);return w!=this.$wrapLimit&&w>1?(this.$wrapLimit=w,this.$modified=!0,this.$useWrapMode&&(this.$updateWrapData(0,this.getLength()-1),this.$resetRowCache(0),this._signal("changeWrapLimit")),!0):!1},S.prototype.$constrainWrapLimit=function(E,I,C){return I&&(E=Math.max(I,E)),C&&(E=Math.min(C,E)),E},S.prototype.getWrapLimit=function(){return this.$wrapLimit},S.prototype.setWrapLimit=function(E){this.setWrapLimitRange(E,E)},S.prototype.getWrapLimitRange=function(){return{min:this.$wrapLimitRange.min,max:this.$wrapLimitRange.max}},S.prototype.$updateInternalDataOnChange=function(E){var I=this.$useWrapMode,C=E.action,w=E.start,T=E.end,O=w.row,N=T.row,B=N-O,H=null;if(this.$updating=!0,B!=0)if(C==="remove"){this[I?"$wrapData":"$rowLengthCache"].splice(O,B);var z=this.$foldData;H=this.getFoldsInRange(E),this.removeFolds(H);var F=this.getFoldLine(T.row),W=0;if(F){F.addRemoveChars(T.row,T.column,w.column-T.column),F.shiftRow(-B);var G=this.getFoldLine(O);G&&G!==F&&(G.merge(F),F=G),W=z.indexOf(F)+1}for(W;W=T.row&&F.shiftRow(-B)}N=O}else{var U=Array(B);U.unshift(O,0);var Q=I?this.$wrapData:this.$rowLengthCache;Q.splice.apply(Q,U);var z=this.$foldData,F=this.getFoldLine(O),W=0;if(F){var ee=F.range.compareInside(w.row,w.column);ee==0?(F=F.split(w.row,w.column),F&&(F.shiftRow(B),F.addRemoveChars(N,0,T.column-w.column))):ee==-1&&(F.addRemoveChars(O,0,T.column-w.column),F.shiftRow(B)),W=z.indexOf(F)+1}for(W;W=O&&F.shiftRow(B)}}else{B=Math.abs(E.start.column-E.end.column),C==="remove"&&(H=this.getFoldsInRange(E),this.removeFolds(H),B=-B);var F=this.getFoldLine(O);F&&F.addRemoveChars(O,w.column,B)}return I&&this.$wrapData.length!=this.doc.getLength()&&console.error("doc.getLength() and $wrapData.length have to be the same!"),this.$updating=!1,I?this.$updateWrapData(O,N):this.$updateRowLengthCache(O,N),H},S.prototype.$updateRowLengthCache=function(E,I,C){this.$rowLengthCache[E]=null,this.$rowLengthCache[I]=null},S.prototype.$updateWrapData=function(E,I){var C=this.doc.getAllLines(),w=this.getTabSize(),T=this.$wrapData,O=this.$wrapLimit,N,B,H=E;for(I=Math.min(I,C.length-1);H<=I;)B=this.getFoldLine(H,B),B?(N=[],B.walk(function(z,F,W,G){var U;if(z!=null){U=this.$getDisplayTokens(z,N.length),U[0]=x;for(var Q=1;QI-G;){var U=O+I-G;if(E[U-1]>=b&&E[U]>=b){W(U);continue}if(E[U]==x||E[U]==$){for(U;U!=O-1&&E[U]!=x;U--);if(U>O){W(U);continue}for(U=O+I,U;U>2)),O-1);U>Q&&E[U]Q&&E[U]Q&&E[U]==_;)U--}else for(;U>Q&&E[U]Q){W(++U);continue}U=O+I,E[U]==y&&U--,W(U-G)}return w},S.prototype.$getDisplayTokens=function(E,I){var C=[],w;I=I||0;for(var T=0;T39&&O<48||O>57&&O<64?C.push(_):O>=4352&&k(O)?C.push(v,y):C.push(v)}return C},S.prototype.$getStringScreenWidth=function(E,I,C){if(I==0)return[0,0];I==null&&(I=1/0),C=C||0;var w,T;for(T=0;T=4352&&k(w)?C+=2:C+=1,!(C>I));T++);return[C,T]},S.prototype.getRowLength=function(E){var I=1;return this.lineWidgets&&(I+=this.lineWidgets[E]&&this.lineWidgets[E].rowCount||0),!this.$useWrapMode||!this.$wrapData[E]?I:this.$wrapData[E].length+I},S.prototype.getRowLineCount=function(E){return!this.$useWrapMode||!this.$wrapData[E]?1:this.$wrapData[E].length+1},S.prototype.getRowWrapIndent=function(E){if(this.$useWrapMode){var I=this.screenToDocumentPosition(E,Number.MAX_VALUE),C=this.$wrapData[I.row];return C.length&&C[0]=0)var B=z[F],T=this.$docRowCache[F],G=E>z[W-1];else var G=!W;for(var U=this.getLength()-1,Q=this.getNextFoldLine(T),ee=Q?Q.start.row:1/0;B<=E&&(H=this.getRowLength(T),!(B+H>E||T>=U));)B+=H,T++,T>ee&&(T=Q.end.row+1,Q=this.getNextFoldLine(T,Q),ee=Q?Q.start.row:1/0),G&&(this.$docRowCache.push(T),this.$screenRowCache.push(B));if(Q&&Q.start.row<=T)w=this.getFoldDisplayLine(Q),T=Q.start.row;else{if(B+H<=E||T>U)return{row:U,column:this.getLine(U).length};w=this.getLine(T),Q=null}var V=0,q=Math.floor(E-B);if(this.$useWrapMode){var re=this.$wrapData[T];re&&(N=re[q],q>0&&re.length&&(V=re.indent,O=re[q-1]||re[re.length-1],w=w.substring(O)))}return C!==void 0&&this.$bidiHandler.isBidiRow(B+q,T,q)&&(I=this.$bidiHandler.offsetToCol(C)),O+=this.$getStringScreenWidth(w,I-V)[1],this.$useWrapMode&&O>=N&&(O=N-1),Q?Q.idxToPosition(O):{row:T,column:O}},S.prototype.documentToScreenPosition=function(E,I){if(typeof I>"u")var C=this.$clipPositionToDocument(E.row,E.column);else C=this.$clipPositionToDocument(E,I);E=C.row,I=C.column;var w=0,T=null,O=null;O=this.getFoldAt(E,I,1),O&&(E=O.start.row,I=O.start.column);var N,B=0,H=this.$docRowCache,z=this.$getRowCacheIndex(H,E),F=H.length;if(F&&z>=0)var B=H[z],w=this.$screenRowCache[z],W=E>H[F-1];else var W=!F;for(var G=this.getNextFoldLine(B),U=G?G.start.row:1/0;B=U){if(N=G.end.row+1,N>E)break;G=this.getNextFoldLine(N,G),U=G?G.start.row:1/0}else N=B+1;w+=this.getRowLength(B),B=N,W&&(this.$docRowCache.push(B),this.$screenRowCache.push(w))}var Q="";G&&B>=U?(Q=this.getFoldDisplayLine(G,E,I),T=G.start.row):(Q=this.getLine(E).substring(0,I),T=E);var ee=0;if(this.$useWrapMode){var V=this.$wrapData[T];if(V){for(var q=0;Q.length>=V[q];)w++,q++;Q=Q.substring(V[q-1]||0,Q.length),ee=q>0?V.indent:0}}return this.lineWidgets&&this.lineWidgets[B]&&this.lineWidgets[B].rowsAbove&&(w+=this.lineWidgets[B].rowsAbove),{row:w,column:ee+this.$getStringScreenWidth(Q)[0]}},S.prototype.documentToScreenColumn=function(E,I){return this.documentToScreenPosition(E,I).column},S.prototype.documentToScreenRow=function(E,I){return this.documentToScreenPosition(E,I).row},S.prototype.getScreenLength=function(){var E=0,I=null;if(this.$useWrapMode)for(var T=this.$wrapData.length,O=0,w=0,I=this.$foldData[w++],N=I?I.start.row:1/0;ON&&(O=I.end.row+1,I=this.$foldData[w++],N=I?I.start.row:1/0)}else{E=this.getLength();for(var C=this.$foldData,w=0;wC));O++);return[w,O]})},S.prototype.destroy=function(){this.destroyed||(this.bgTokenizer.setDocument(null),this.bgTokenizer.cleanup(),this.destroyed=!0),this.$stopWorker(),this.removeAllListeners(),this.doc&&this.doc.off("change",this.$onChange),this.selection.detach()},S}();m.$uid=0,m.prototype.$modes=l.$modes,m.prototype.getValue=m.prototype.toString,m.prototype.$defaultUndoManager={undo:function(){},redo:function(){},hasUndo:function(){},hasRedo:function(){},reset:function(){},add:function(){},addSelection:function(){},startNewGroup:function(){},addSession:function(){}},m.prototype.$overwrite=!1,m.prototype.$mode=null,m.prototype.$modeId=null,m.prototype.$scrollTop=0,m.prototype.$scrollLeft=0,m.prototype.$wrapLimit=80,m.prototype.$useWrapMode=!1,m.prototype.$wrapLimitRange={min:null,max:null},m.prototype.lineWidgets=null,m.prototype.isFullWidth=k,o.implement(m.prototype,u);var v=1,y=2,x=3,$=4,_=9,b=10,R=11,M=12;function k(S){return S<4352?!1:S>=4352&&S<=4447||S>=4515&&S<=4519||S>=4602&&S<=4607||S>=9001&&S<=9002||S>=11904&&S<=11929||S>=11931&&S<=12019||S>=12032&&S<=12245||S>=12272&&S<=12283||S>=12288&&S<=12350||S>=12353&&S<=12438||S>=12441&&S<=12543||S>=12549&&S<=12589||S>=12593&&S<=12686||S>=12688&&S<=12730||S>=12736&&S<=12771||S>=12784&&S<=12830||S>=12832&&S<=12871||S>=12880&&S<=13054||S>=13056&&S<=19903||S>=19968&&S<=42124||S>=42128&&S<=42182||S>=43360&&S<=43388||S>=44032&&S<=55203||S>=55216&&S<=55238||S>=55243&&S<=55291||S>=63744&&S<=64255||S>=65040&&S<=65049||S>=65072&&S<=65106||S>=65108&&S<=65126||S>=65128&&S<=65131||S>=65281&&S<=65376||S>=65504&&S<=65510}n("./edit_session/folding").Folding.call(m.prototype),n("./edit_session/bracket_match").BracketMatch.call(m.prototype),l.defineOptions(m.prototype,"session",{wrap:{set:function(S){if(!S||S=="off"?S=!1:S=="free"?S=!0:S=="printMargin"?S=-1:typeof S=="string"&&(S=parseInt(S,10)||!1),this.$wrap!=S)if(this.$wrap=S,!S)this.setUseWrapMode(!1);else{var E=typeof S=="number"?S:null;this.setWrapLimitRange(E,E),this.setUseWrapMode(!0)}},get:function(){return this.getUseWrapMode()?this.$wrap==-1?"printMargin":this.getWrapLimitRange().min?this.$wrap:"free":"off"},handlesSet:!0},wrapMethod:{set:function(S){S=S=="auto"?this.$mode.type!="text":S!="text",S!=this.$wrapAsCode&&(this.$wrapAsCode=S,this.$useWrapMode&&(this.$useWrapMode=!1,this.setUseWrapMode(!0)))},initialValue:"auto"},indentedSoftWrap:{set:function(){this.$useWrapMode&&(this.$useWrapMode=!1,this.setUseWrapMode(!0))},initialValue:!0},firstLineNumber:{set:function(){this._signal("changeBreakpoint")},initialValue:1},useWorker:{set:function(S){this.$useWorker=S,this.$stopWorker(),S&&this.$startWorker()},initialValue:!0},useSoftTabs:{initialValue:!0},tabSize:{set:function(S){S=parseInt(S),S>0&&this.$tabSize!==S&&(this.$modified=!0,this.$rowLengthCache=[],this.$tabSize=S,this._signal("changeTabSize"))},initialValue:4,handlesSet:!0},navigateWithinSoftTabs:{initialValue:!1},foldStyle:{set:function(S){this.setFoldStyle(S)},handlesSet:!0},overwrite:{set:function(S){this._signal("changeOverwrite")},initialValue:!1},newLineMode:{set:function(S){this.doc.setNewLineMode(S)},get:function(){return this.doc.getNewLineMode()},handlesSet:!0},mode:{set:function(S){this.setMode(S)},get:function(){return this.$modeId},handlesSet:!0}}),r.EditSession=m}),ace.define("ace/search",["require","exports","module","ace/lib/lang","ace/lib/oop","ace/range"],function(n,r,i){var o=n("./lib/lang"),s=n("./lib/oop"),h=n("./range").Range,l=function(){function a(){this.$options={}}return a.prototype.set=function(c){return s.mixin(this.$options,c),this},a.prototype.getOptions=function(){return o.copyObject(this.$options)},a.prototype.setOptions=function(c){this.$options=c},a.prototype.find=function(c){var f=this.$options,d=this.$matchIterator(c,f);if(!d)return!1;var p=null;return d.forEach(function(g,m,v,y){return p=new h(g,m,v,y),m==y&&f.start&&f.start.start&&f.skipCurrent!=!1&&p.isEqual(f.start)?(p=null,!1):!0}),p},a.prototype.findAll=function(c){var f=this.$options;if(!f.needle)return[];this.$assembleRegExp(f);var d=f.range,p=d?c.getLines(d.start.row,d.end.row):c.doc.getAllLines(),g=[],m=f.re;if(f.$isMultiLine){var v=m.length,y=p.length-v,x;e:for(var $=m.offset||0;$<=y;$++){for(var _=0;_M||(g.push(x=new h($,M,$+v-1,k)),v>2&&($=$+v-2))}}else for(var S=0;Sw&&g[_].end.row==T;)_--;for(g=g.slice(S,_+1),S=0,_=g.length;S<_;S++)g[S].start.row+=d.start.row,g[S].end.row+=d.start.row}return g},a.prototype.replace=function(c,f){var d=this.$options,p=this.$assembleRegExp(d);if(d.$isMultiLine)return f;if(p){var g=p.exec(c);if(!g||g[0].length!=c.length)return null;if(f=c.replace(p,f),d.preserveCase){f=f.split("");for(var m=Math.min(c.length,c.length);m--;){var v=c[m];v&&v.toLowerCase()!=v?f[m]=f[m].toUpperCase():f[m]=f[m].toLowerCase()}f=f.join("")}return f}},a.prototype.$assembleRegExp=function(c,f){if(c.needle instanceof RegExp)return c.re=c.needle;var d=c.needle;if(!c.needle)return c.re=!1;c.regExp||(d=o.escapeRegExp(d)),c.wholeWord&&(d=u(d,c));var p=c.caseSensitive?"gm":"gmi";if(c.$isMultiLine=!f&&/[\n\r]/.test(d),c.$isMultiLine)return c.re=this.$assembleMultilineRegExp(d,p);try{var g=new RegExp(d,p)}catch{g=!1}return c.re=g},a.prototype.$assembleMultilineRegExp=function(c,f){for(var d=c.replace(/\r\n|\r|\n/g,`$ -^`).split(` -`),p=[],g=0;g=y;M--)if(b(M,Number.MAX_VALUE,R))return;if(f.wrap!=!1){for(M=x,y=v.row;M>=y;M--)if(b(M,Number.MAX_VALUE,R))return}}};else var $=function(M){var k=v.row;if(!b(k,v.column,M)){for(k=k+1;k<=x;k++)if(b(k,0,M))return;if(f.wrap!=!1){for(k=y,x=v.row;k<=x;k++)if(b(k,0,M))return}}};if(f.$isMultiLine)var _=d.length,b=function(R,M,k){var S=p?R-_+1:R;if(!(S<0||S+_>c.getLength())){var E=c.getLine(S),I=E.search(d[0]);if(!(!p&&IM)&&k(S,I,S+_-1,w))return!0}}};else if(p)var b=function(M,k,S){var E=c.getLine(M),I=[],C,w=0;for(d.lastIndex=0;C=d.exec(E);){var T=C[0].length;if(w=C.index,!T){if(w>=E.length)break;d.lastIndex=w+=1}if(C.index+T>k)break;I.push(C.index,T)}for(var O=I.length-1;O>=0;O-=2){var N=I[O-1],T=I[O];if(S(M,N,M,N+T))return!0}};else var b=function(M,k,S){var E=c.getLine(M),I,C;for(d.lastIndex=k;C=d.exec(E);){var w=C[0].length;if(I=C.index,S(M,I,M,I+w))return!0;if(!w&&(d.lastIndex=I+=1,I>=E.length))return!1}};return{forEach:$}},a}();function u(a,c){function f(d){return/\w/.test(d)||c.regExp?"\\b":""}return f(a[0])+a+f(a[a.length-1])}r.Search=l}),ace.define("ace/keyboard/hash_handler",["require","exports","module","ace/lib/keys","ace/lib/useragent"],function(n,r,i){var o=n("../lib/keys"),s=n("../lib/useragent"),h=o.KEY_MODS;function l(a,c){this.platform=c||(s.isMac?"mac":"win"),this.commands={},this.commandKeyBinding={},this.addCommands(a),this.$singleCommand=!0}function u(a,c){l.call(this,a,c),this.$singleCommand=!1}u.prototype=l.prototype,function(){this.addCommand=function(c){this.commands[c.name]&&this.removeCommand(c),this.commands[c.name]=c,c.bindKey&&this._buildKeyHash(c)},this.removeCommand=function(c,f){var d=c&&(typeof c=="string"?c:c.name);c=this.commands[d],f||delete this.commands[d];var p=this.commandKeyBinding;for(var g in p){var m=p[g];if(m==c)delete p[g];else if(Array.isArray(m)){var v=m.indexOf(c);v!=-1&&(m.splice(v,1),m.length==1&&(p[g]=m[0]))}}},this.bindKey=function(c,f,d){if(typeof c=="object"&&c&&(d==null&&(d=c.position),c=c[this.platform]),!!c){if(typeof f=="function")return this.addCommand({exec:f,bindKey:c,name:f.name||c});c.split("|").forEach(function(p){var g="";if(p.indexOf(" ")!=-1){var m=p.split(/\s+/);p=m.pop(),m.forEach(function(x){var $=this.parseKeys(x),_=h[$.hashId]+$.key;g+=(g?" ":"")+_,this._addCommandToBinding(g,"chainKeys")},this),g+=" "}var v=this.parseKeys(p),y=h[v.hashId]+v.key;this._addCommandToBinding(g+y,f,d)},this)}};function a(c){return typeof c=="object"&&c.bindKey&&c.bindKey.position||(c.isDefault?-100:0)}this._addCommandToBinding=function(c,f,d){var p=this.commandKeyBinding,g;if(!f)delete p[c];else if(!p[c]||this.$singleCommand)p[c]=f;else{Array.isArray(p[c])?(g=p[c].indexOf(f))!=-1&&p[c].splice(g,1):p[c]=[p[c]],typeof d!="number"&&(d=a(f));var m=p[c];for(g=0;gd)break}m.splice(g,0,f)}},this.addCommands=function(c){c&&Object.keys(c).forEach(function(f){var d=c[f];if(d){if(typeof d=="string")return this.bindKey(d,f);typeof d=="function"&&(d={exec:d}),typeof d=="object"&&(d.name||(d.name=f),this.addCommand(d))}},this)},this.removeCommands=function(c){Object.keys(c).forEach(function(f){this.removeCommand(c[f])},this)},this.bindKeys=function(c){Object.keys(c).forEach(function(f){this.bindKey(f,c[f])},this)},this._buildKeyHash=function(c){this.bindKey(c.bindKey,c)},this.parseKeys=function(c){var f=c.toLowerCase().split(/[\-\+]([\-\+])?/).filter(function(y){return y}),d=f.pop(),p=o[d];if(o.FUNCTION_KEYS[p])d=o.FUNCTION_KEYS[p].toLowerCase();else if(f.length){if(f.length==1&&f[0]=="shift")return{key:d.toUpperCase(),hashId:-1}}else return{key:d,hashId:-1};for(var g=0,m=f.length;m--;){var v=o.KEY_MODS[f[m]];if(v==null)return typeof console<"u"&&console.error("invalid modifier "+f[m]+" in "+c),!1;g|=v}return{key:d,hashId:g}},this.findKeyCommand=function(f,d){var p=h[f]+d;return this.commandKeyBinding[p]},this.handleKeyboard=function(c,f,d,p){if(!(p<0)){var g=h[f]+d,m=this.commandKeyBinding[g];return c.$keyChain&&(c.$keyChain+=" "+g,m=this.commandKeyBinding[c.$keyChain]||m),m&&(m=="chainKeys"||m[m.length-1]=="chainKeys")?(c.$keyChain=c.$keyChain||g,{command:"null"}):(c.$keyChain&&((!f||f==4)&&d.length==1?c.$keyChain=c.$keyChain.slice(0,-g.length-1):(f==-1||p>0)&&(c.$keyChain="")),{command:m})}},this.getStatusText=function(c,f){return f.$keyChain||""}}.call(l.prototype),r.HashHandler=l,r.MultiHashHandler=u}),ace.define("ace/commands/command_manager",["require","exports","module","ace/lib/oop","ace/keyboard/hash_handler","ace/lib/event_emitter"],function(n,r,i){var o=this&&this.__extends||function(){var a=function(c,f){return a=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(d,p){d.__proto__=p}||function(d,p){for(var g in p)Object.prototype.hasOwnProperty.call(p,g)&&(d[g]=p[g])},a(c,f)};return function(c,f){if(typeof f!="function"&&f!==null)throw new TypeError("Class extends value "+String(f)+" is not a constructor or null");a(c,f);function d(){this.constructor=c}c.prototype=f===null?Object.create(f):(d.prototype=f.prototype,new d)}}(),s=n("../lib/oop"),h=n("../keyboard/hash_handler").MultiHashHandler,l=n("../lib/event_emitter").EventEmitter,u=function(a){o(c,a);function c(f,d){var p=a.call(this,d,f)||this;return p.byName=p.commands,p.setDefaultHandler("exec",function(g){return g.args?g.command.exec(g.editor,g.args,g.event,!1):g.command.exec(g.editor,{},g.event,!0)}),p}return c.prototype.exec=function(f,d,p){if(Array.isArray(f)){for(var g=f.length;g--;)if(this.exec(f[g],d,p))return!0;return!1}if(typeof f=="string"&&(f=this.commands[f]),!f||d&&d.$readOnly&&!f.readOnly||this.$checkCommandState!=!1&&f.isAvailable&&!f.isAvailable(d))return!1;var m={editor:d,command:f,args:p};return m.returnValue=this._emit("exec",m),this._signal("afterExec",m),m.returnValue!==!1},c.prototype.toggleRecording=function(f){if(!this.$inReplay)return f&&f._emit("changeStatus"),this.recording?(this.macro.pop(),this.off("exec",this.$addCommandToMacro),this.macro.length||(this.macro=this.oldMacro),this.recording=!1):(this.$addCommandToMacro||(this.$addCommandToMacro=function(d){this.macro.push([d.command,d.args])}.bind(this)),this.oldMacro=this.macro,this.macro=[],this.on("exec",this.$addCommandToMacro),this.recording=!0)},c.prototype.replay=function(f){if(!(this.$inReplay||!this.macro)){if(this.recording)return this.toggleRecording(f);try{this.$inReplay=!0,this.macro.forEach(function(d){typeof d=="string"?this.exec(d,f):this.exec(d[0],f,d[1])},this)}finally{this.$inReplay=!1}}},c.prototype.trimMacro=function(f){return f.map(function(d){return typeof d[0]!="string"&&(d[0]=d[0].name),d[1]||(d=d[0]),d})},c}(h);s.implement(u.prototype,l),r.CommandManager=u}),ace.define("ace/commands/default_commands",["require","exports","module","ace/lib/lang","ace/config","ace/range"],function(n,r,i){var o=n("../lib/lang"),s=n("../config"),h=n("../range").Range;function l(a,c){return{win:a,mac:c}}r.commands=[{name:"showSettingsMenu",description:"Show settings menu",bindKey:l("Ctrl-,","Command-,"),exec:function(a){s.loadModule("ace/ext/settings_menu",function(c){c.init(a),a.showSettingsMenu()})},readOnly:!0},{name:"goToNextError",description:"Go to next error",bindKey:l("Alt-E","F4"),exec:function(a){s.loadModule("ace/ext/error_marker",function(c){c.showErrorMarker(a,1)})},scrollIntoView:"animate",readOnly:!0},{name:"goToPreviousError",description:"Go to previous error",bindKey:l("Alt-Shift-E","Shift-F4"),exec:function(a){s.loadModule("ace/ext/error_marker",function(c){c.showErrorMarker(a,-1)})},scrollIntoView:"animate",readOnly:!0},{name:"selectall",description:"Select all",bindKey:l("Ctrl-A","Command-A"),exec:function(a){a.selectAll()},readOnly:!0},{name:"centerselection",description:"Center selection",bindKey:l(null,"Ctrl-L"),exec:function(a){a.centerSelection()},readOnly:!0},{name:"gotoline",description:"Go to line...",bindKey:l("Ctrl-L","Command-L"),exec:function(a,c){typeof c=="number"&&!isNaN(c)&&a.gotoLine(c),a.prompt({$type:"gotoLine"})},readOnly:!0},{name:"fold",bindKey:l("Alt-L|Ctrl-F1","Command-Alt-L|Command-F1"),exec:function(a){a.session.toggleFold(!1)},multiSelectAction:"forEach",scrollIntoView:"center",readOnly:!0},{name:"unfold",bindKey:l("Alt-Shift-L|Ctrl-Shift-F1","Command-Alt-Shift-L|Command-Shift-F1"),exec:function(a){a.session.toggleFold(!0)},multiSelectAction:"forEach",scrollIntoView:"center",readOnly:!0},{name:"toggleFoldWidget",description:"Toggle fold widget",bindKey:l("F2","F2"),exec:function(a){a.session.toggleFoldWidget()},multiSelectAction:"forEach",scrollIntoView:"center",readOnly:!0},{name:"toggleParentFoldWidget",description:"Toggle parent fold widget",bindKey:l("Alt-F2","Alt-F2"),exec:function(a){a.session.toggleFoldWidget(!0)},multiSelectAction:"forEach",scrollIntoView:"center",readOnly:!0},{name:"foldall",description:"Fold all",bindKey:l(null,"Ctrl-Command-Option-0"),exec:function(a){a.session.foldAll()},scrollIntoView:"center",readOnly:!0},{name:"foldAllComments",description:"Fold all comments",bindKey:l(null,"Ctrl-Command-Option-0"),exec:function(a){a.session.foldAllComments()},scrollIntoView:"center",readOnly:!0},{name:"foldOther",description:"Fold other",bindKey:l("Alt-0","Command-Option-0"),exec:function(a){a.session.foldAll(),a.session.unfold(a.selection.getAllRanges())},scrollIntoView:"center",readOnly:!0},{name:"unfoldall",description:"Unfold all",bindKey:l("Alt-Shift-0","Command-Option-Shift-0"),exec:function(a){a.session.unfold()},scrollIntoView:"center",readOnly:!0},{name:"findnext",description:"Find next",bindKey:l("Ctrl-K","Command-G"),exec:function(a){a.findNext()},multiSelectAction:"forEach",scrollIntoView:"center",readOnly:!0},{name:"findprevious",description:"Find previous",bindKey:l("Ctrl-Shift-K","Command-Shift-G"),exec:function(a){a.findPrevious()},multiSelectAction:"forEach",scrollIntoView:"center",readOnly:!0},{name:"selectOrFindNext",description:"Select or find next",bindKey:l("Alt-K","Ctrl-G"),exec:function(a){a.selection.isEmpty()?a.selection.selectWord():a.findNext()},readOnly:!0},{name:"selectOrFindPrevious",description:"Select or find previous",bindKey:l("Alt-Shift-K","Ctrl-Shift-G"),exec:function(a){a.selection.isEmpty()?a.selection.selectWord():a.findPrevious()},readOnly:!0},{name:"find",description:"Find",bindKey:l("Ctrl-F","Command-F"),exec:function(a){s.loadModule("ace/ext/searchbox",function(c){c.Search(a)})},readOnly:!0},{name:"overwrite",description:"Overwrite",bindKey:"Insert",exec:function(a){a.toggleOverwrite()},readOnly:!0},{name:"selecttostart",description:"Select to start",bindKey:l("Ctrl-Shift-Home","Command-Shift-Home|Command-Shift-Up"),exec:function(a){a.getSelection().selectFileStart()},multiSelectAction:"forEach",readOnly:!0,scrollIntoView:"animate",aceCommandGroup:"fileJump"},{name:"gotostart",description:"Go to start",bindKey:l("Ctrl-Home","Command-Home|Command-Up"),exec:function(a){a.navigateFileStart()},multiSelectAction:"forEach",readOnly:!0,scrollIntoView:"animate",aceCommandGroup:"fileJump"},{name:"selectup",description:"Select up",bindKey:l("Shift-Up","Shift-Up|Ctrl-Shift-P"),exec:function(a){a.getSelection().selectUp()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"golineup",description:"Go line up",bindKey:l("Up","Up|Ctrl-P"),exec:function(a,c){a.navigateUp(c.times)},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selecttoend",description:"Select to end",bindKey:l("Ctrl-Shift-End","Command-Shift-End|Command-Shift-Down"),exec:function(a){a.getSelection().selectFileEnd()},multiSelectAction:"forEach",readOnly:!0,scrollIntoView:"animate",aceCommandGroup:"fileJump"},{name:"gotoend",description:"Go to end",bindKey:l("Ctrl-End","Command-End|Command-Down"),exec:function(a){a.navigateFileEnd()},multiSelectAction:"forEach",readOnly:!0,scrollIntoView:"animate",aceCommandGroup:"fileJump"},{name:"selectdown",description:"Select down",bindKey:l("Shift-Down","Shift-Down|Ctrl-Shift-N"),exec:function(a){a.getSelection().selectDown()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"golinedown",description:"Go line down",bindKey:l("Down","Down|Ctrl-N"),exec:function(a,c){a.navigateDown(c.times)},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selectwordleft",description:"Select word left",bindKey:l("Ctrl-Shift-Left","Option-Shift-Left"),exec:function(a){a.getSelection().selectWordLeft()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"gotowordleft",description:"Go to word left",bindKey:l("Ctrl-Left","Option-Left"),exec:function(a){a.navigateWordLeft()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selecttolinestart",description:"Select to line start",bindKey:l("Alt-Shift-Left","Command-Shift-Left|Ctrl-Shift-A"),exec:function(a){a.getSelection().selectLineStart()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"gotolinestart",description:"Go to line start",bindKey:l("Alt-Left|Home","Command-Left|Home|Ctrl-A"),exec:function(a){a.navigateLineStart()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selectleft",description:"Select left",bindKey:l("Shift-Left","Shift-Left|Ctrl-Shift-B"),exec:function(a){a.getSelection().selectLeft()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"gotoleft",description:"Go to left",bindKey:l("Left","Left|Ctrl-B"),exec:function(a,c){a.navigateLeft(c.times)},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selectwordright",description:"Select word right",bindKey:l("Ctrl-Shift-Right","Option-Shift-Right"),exec:function(a){a.getSelection().selectWordRight()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"gotowordright",description:"Go to word right",bindKey:l("Ctrl-Right","Option-Right"),exec:function(a){a.navigateWordRight()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selecttolineend",description:"Select to line end",bindKey:l("Alt-Shift-Right","Command-Shift-Right|Shift-End|Ctrl-Shift-E"),exec:function(a){a.getSelection().selectLineEnd()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"gotolineend",description:"Go to line end",bindKey:l("Alt-Right|End","Command-Right|End|Ctrl-E"),exec:function(a){a.navigateLineEnd()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selectright",description:"Select right",bindKey:l("Shift-Right","Shift-Right"),exec:function(a){a.getSelection().selectRight()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"gotoright",description:"Go to right",bindKey:l("Right","Right|Ctrl-F"),exec:function(a,c){a.navigateRight(c.times)},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selectpagedown",description:"Select page down",bindKey:"Shift-PageDown",exec:function(a){a.selectPageDown()},readOnly:!0},{name:"pagedown",description:"Page down",bindKey:l(null,"Option-PageDown"),exec:function(a){a.scrollPageDown()},readOnly:!0},{name:"gotopagedown",description:"Go to page down",bindKey:l("PageDown","PageDown|Ctrl-V"),exec:function(a){a.gotoPageDown()},readOnly:!0},{name:"selectpageup",description:"Select page up",bindKey:"Shift-PageUp",exec:function(a){a.selectPageUp()},readOnly:!0},{name:"pageup",description:"Page up",bindKey:l(null,"Option-PageUp"),exec:function(a){a.scrollPageUp()},readOnly:!0},{name:"gotopageup",description:"Go to page up",bindKey:"PageUp",exec:function(a){a.gotoPageUp()},readOnly:!0},{name:"scrollup",description:"Scroll up",bindKey:l("Ctrl-Up",null),exec:function(a){a.renderer.scrollBy(0,-2*a.renderer.layerConfig.lineHeight)},readOnly:!0},{name:"scrolldown",description:"Scroll down",bindKey:l("Ctrl-Down",null),exec:function(a){a.renderer.scrollBy(0,2*a.renderer.layerConfig.lineHeight)},readOnly:!0},{name:"selectlinestart",description:"Select line start",bindKey:"Shift-Home",exec:function(a){a.getSelection().selectLineStart()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"selectlineend",description:"Select line end",bindKey:"Shift-End",exec:function(a){a.getSelection().selectLineEnd()},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"togglerecording",description:"Toggle recording",bindKey:l("Ctrl-Alt-E","Command-Option-E"),exec:function(a){a.commands.toggleRecording(a)},readOnly:!0},{name:"replaymacro",description:"Replay macro",bindKey:l("Ctrl-Shift-E","Command-Shift-E"),exec:function(a){a.commands.replay(a)},readOnly:!0},{name:"jumptomatching",description:"Jump to matching",bindKey:l("Ctrl-\\|Ctrl-P","Command-\\"),exec:function(a){a.jumpToMatching()},multiSelectAction:"forEach",scrollIntoView:"animate",readOnly:!0},{name:"selecttomatching",description:"Select to matching",bindKey:l("Ctrl-Shift-\\|Ctrl-Shift-P","Command-Shift-\\"),exec:function(a){a.jumpToMatching(!0)},multiSelectAction:"forEach",scrollIntoView:"animate",readOnly:!0},{name:"expandToMatching",description:"Expand to matching",bindKey:l("Ctrl-Shift-M","Ctrl-Shift-M"),exec:function(a){a.jumpToMatching(!0,!0)},multiSelectAction:"forEach",scrollIntoView:"animate",readOnly:!0},{name:"passKeysToBrowser",description:"Pass keys to browser",bindKey:l(null,null),exec:function(){},passEvent:!0,readOnly:!0},{name:"copy",description:"Copy",exec:function(a){},readOnly:!0},{name:"cut",description:"Cut",exec:function(a){var c=a.$copyWithEmptySelection&&a.selection.isEmpty(),f=c?a.selection.getLineRange():a.selection.getRange();a._emit("cut",f),f.isEmpty()||a.session.remove(f),a.clearSelection()},scrollIntoView:"cursor",multiSelectAction:"forEach"},{name:"paste",description:"Paste",exec:function(a,c){a.$handlePaste(c)},scrollIntoView:"cursor"},{name:"removeline",description:"Remove line",bindKey:l("Ctrl-D","Command-D"),exec:function(a){a.removeLines()},scrollIntoView:"cursor",multiSelectAction:"forEachLine"},{name:"duplicateSelection",description:"Duplicate selection",bindKey:l("Ctrl-Shift-D","Command-Shift-D"),exec:function(a){a.duplicateSelection()},scrollIntoView:"cursor",multiSelectAction:"forEach"},{name:"sortlines",description:"Sort lines",bindKey:l("Ctrl-Alt-S","Command-Alt-S"),exec:function(a){a.sortLines()},scrollIntoView:"selection",multiSelectAction:"forEachLine"},{name:"togglecomment",description:"Toggle comment",bindKey:l("Ctrl-/","Command-/"),exec:function(a){a.toggleCommentLines()},multiSelectAction:"forEachLine",scrollIntoView:"selectionPart"},{name:"toggleBlockComment",description:"Toggle block comment",bindKey:l("Ctrl-Shift-/","Command-Shift-/"),exec:function(a){a.toggleBlockComment()},multiSelectAction:"forEach",scrollIntoView:"selectionPart"},{name:"modifyNumberUp",description:"Modify number up",bindKey:l("Ctrl-Shift-Up","Alt-Shift-Up"),exec:function(a){a.modifyNumber(1)},scrollIntoView:"cursor",multiSelectAction:"forEach"},{name:"modifyNumberDown",description:"Modify number down",bindKey:l("Ctrl-Shift-Down","Alt-Shift-Down"),exec:function(a){a.modifyNumber(-1)},scrollIntoView:"cursor",multiSelectAction:"forEach"},{name:"replace",description:"Replace",bindKey:l("Ctrl-H","Command-Option-F"),exec:function(a){s.loadModule("ace/ext/searchbox",function(c){c.Search(a,!0)})}},{name:"undo",description:"Undo",bindKey:l("Ctrl-Z","Command-Z"),exec:function(a){a.undo()}},{name:"redo",description:"Redo",bindKey:l("Ctrl-Shift-Z|Ctrl-Y","Command-Shift-Z|Command-Y"),exec:function(a){a.redo()}},{name:"copylinesup",description:"Copy lines up",bindKey:l("Alt-Shift-Up","Command-Option-Up"),exec:function(a){a.copyLinesUp()},scrollIntoView:"cursor"},{name:"movelinesup",description:"Move lines up",bindKey:l("Alt-Up","Option-Up"),exec:function(a){a.moveLinesUp()},scrollIntoView:"cursor"},{name:"copylinesdown",description:"Copy lines down",bindKey:l("Alt-Shift-Down","Command-Option-Down"),exec:function(a){a.copyLinesDown()},scrollIntoView:"cursor"},{name:"movelinesdown",description:"Move lines down",bindKey:l("Alt-Down","Option-Down"),exec:function(a){a.moveLinesDown()},scrollIntoView:"cursor"},{name:"del",description:"Delete",bindKey:l("Delete","Delete|Ctrl-D|Shift-Delete"),exec:function(a){a.remove("right")},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"backspace",description:"Backspace",bindKey:l("Shift-Backspace|Backspace","Ctrl-Backspace|Shift-Backspace|Backspace|Ctrl-H"),exec:function(a){a.remove("left")},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"cut_or_delete",description:"Cut or delete",bindKey:l("Shift-Delete",null),exec:function(a){if(a.selection.isEmpty())a.remove("left");else return!1},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"removetolinestart",description:"Remove to line start",bindKey:l("Alt-Backspace","Command-Backspace"),exec:function(a){a.removeToLineStart()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"removetolineend",description:"Remove to line end",bindKey:l("Alt-Delete","Ctrl-K|Command-Delete"),exec:function(a){a.removeToLineEnd()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"removetolinestarthard",description:"Remove to line start hard",bindKey:l("Ctrl-Shift-Backspace",null),exec:function(a){var c=a.selection.getRange();c.start.column=0,a.session.remove(c)},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"removetolineendhard",description:"Remove to line end hard",bindKey:l("Ctrl-Shift-Delete",null),exec:function(a){var c=a.selection.getRange();c.end.column=Number.MAX_VALUE,a.session.remove(c)},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"removewordleft",description:"Remove word left",bindKey:l("Ctrl-Backspace","Alt-Backspace|Ctrl-Alt-Backspace"),exec:function(a){a.removeWordLeft()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"removewordright",description:"Remove word right",bindKey:l("Ctrl-Delete","Alt-Delete"),exec:function(a){a.removeWordRight()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"outdent",description:"Outdent",bindKey:l("Shift-Tab","Shift-Tab"),exec:function(a){a.blockOutdent()},multiSelectAction:"forEach",scrollIntoView:"selectionPart"},{name:"indent",description:"Indent",bindKey:l("Tab","Tab"),exec:function(a){a.indent()},multiSelectAction:"forEach",scrollIntoView:"selectionPart"},{name:"blockoutdent",description:"Block outdent",bindKey:l("Ctrl-[","Ctrl-["),exec:function(a){a.blockOutdent()},multiSelectAction:"forEachLine",scrollIntoView:"selectionPart"},{name:"blockindent",description:"Block indent",bindKey:l("Ctrl-]","Ctrl-]"),exec:function(a){a.blockIndent()},multiSelectAction:"forEachLine",scrollIntoView:"selectionPart"},{name:"insertstring",description:"Insert string",exec:function(a,c){a.insert(c)},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"inserttext",description:"Insert text",exec:function(a,c){a.insert(o.stringRepeat(c.text||"",c.times||1))},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"splitline",description:"Split line",bindKey:l(null,"Ctrl-O"),exec:function(a){a.splitLine()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"transposeletters",description:"Transpose letters",bindKey:l("Alt-Shift-X","Ctrl-T"),exec:function(a){a.transposeLetters()},multiSelectAction:function(a){a.transposeSelections(1)},scrollIntoView:"cursor"},{name:"touppercase",description:"To uppercase",bindKey:l("Ctrl-U","Ctrl-U"),exec:function(a){a.toUpperCase()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"tolowercase",description:"To lowercase",bindKey:l("Ctrl-Shift-U","Ctrl-Shift-U"),exec:function(a){a.toLowerCase()},multiSelectAction:"forEach",scrollIntoView:"cursor"},{name:"autoindent",description:"Auto Indent",bindKey:l(null,null),exec:function(a){a.autoIndent()},multiSelectAction:"forEachLine",scrollIntoView:"animate"},{name:"expandtoline",description:"Expand to line",bindKey:l("Ctrl-Shift-L","Command-Shift-L"),exec:function(a){var c=a.selection.getRange();c.start.column=c.end.column=0,c.end.row++,a.selection.setRange(c,!1)},multiSelectAction:"forEach",scrollIntoView:"cursor",readOnly:!0},{name:"openlink",bindKey:l("Ctrl+F3","F3"),exec:function(a){a.openLink()}},{name:"joinlines",description:"Join lines",bindKey:l(null,null),exec:function(a){for(var c=a.selection.isBackwards(),f=c?a.selection.getSelectionLead():a.selection.getSelectionAnchor(),d=c?a.selection.getSelectionAnchor():a.selection.getSelectionLead(),p=a.session.doc.getLine(f.row).length,g=a.session.doc.getTextRange(a.selection.getRange()),m=g.replace(/\n\s*/," ").length,v=a.session.doc.getLine(f.row),y=f.row+1;y<=d.row+1;y++){var x=o.stringTrimLeft(o.stringTrimRight(a.session.doc.getLine(y)));x.length!==0&&(x=" "+x),v+=x}d.row+10?(a.selection.moveCursorTo(f.row,f.column),a.selection.selectTo(f.row,f.column+m)):(p=a.session.doc.getLine(f.row).length>p?p+1:p,a.selection.moveCursorTo(f.row,p))},multiSelectAction:"forEach",readOnly:!0},{name:"invertSelection",description:"Invert selection",bindKey:l(null,null),exec:function(a){var c=a.session.doc.getLength()-1,f=a.session.doc.getLine(c).length,d=a.selection.rangeList.ranges,p=[];d.length<1&&(d=[a.selection.getRange()]);for(var g=0;gu[a].column&&a++,d.unshift(a,0),u.splice.apply(u,d),this.$updateRows()}}},h.prototype.$updateRows=function(){var l=this.session.lineWidgets;if(l){var u=!0;l.forEach(function(a,c){if(a)for(u=!1,a.row=c;a.$oldWidget;)a.$oldWidget.row=c,a=a.$oldWidget}),u&&(this.session.lineWidgets=null)}},h.prototype.$registerLineWidget=function(l){this.session.lineWidgets||(this.session.lineWidgets=new Array(this.session.getLength()));var u=this.session.lineWidgets[l.row];return u&&(l.$oldWidget=u,u.el&&u.el.parentNode&&(u.el.parentNode.removeChild(u.el),u._inDocument=!1)),this.session.lineWidgets[l.row]=l,l},h.prototype.addLineWidget=function(l){if(this.$registerLineWidget(l),l.session=this.session,!this.editor)return l;var u=this.editor.renderer;l.html&&!l.el&&(l.el=o.createElement("div"),l.el.innerHTML=l.html),l.text&&!l.el&&(l.el=o.createElement("div"),l.el.textContent=l.text),l.el&&(o.addCssClass(l.el,"ace_lineWidgetContainer"),l.className&&o.addCssClass(l.el,l.className),l.el.style.position="absolute",l.el.style.zIndex=5,u.container.appendChild(l.el),l._inDocument=!0,l.coverGutter||(l.el.style.zIndex=3),l.pixelHeight==null&&(l.pixelHeight=l.el.offsetHeight)),l.rowCount==null&&(l.rowCount=l.pixelHeight/u.layerConfig.lineHeight);var a=this.session.getFoldAt(l.row,0);if(l.$fold=a,a){var c=this.session.lineWidgets;l.row==a.end.row&&!c[a.start.row]?c[a.start.row]=l:l.hidden=!0}return this.session._emit("changeFold",{data:{start:{row:l.row}}}),this.$updateRows(),this.renderWidgets(null,u),this.onWidgetChanged(l),l},h.prototype.removeLineWidget=function(l){if(l._inDocument=!1,l.session=null,l.el&&l.el.parentNode&&l.el.parentNode.removeChild(l.el),l.editor&&l.editor.destroy)try{l.editor.destroy()}catch{}if(this.session.lineWidgets){var u=this.session.lineWidgets[l.row];if(u==l)this.session.lineWidgets[l.row]=l.$oldWidget,l.$oldWidget&&this.onWidgetChanged(l.$oldWidget);else for(;u;){if(u.$oldWidget==l){u.$oldWidget=l.$oldWidget;break}u=u.$oldWidget}}this.session._emit("changeFold",{data:{start:{row:l.row}}}),this.$updateRows()},h.prototype.getWidgetsAtRow=function(l){for(var u=this.session.lineWidgets,a=u&&u[l],c=[];a;)c.push(a),a=a.$oldWidget;return c},h.prototype.onWidgetChanged=function(l){this.session._changedWidgets.push(l),this.editor&&this.editor.renderer.updateFull()},h.prototype.measureWidgets=function(l,u){var a=this.session._changedWidgets,c=u.layerConfig;if(!(!a||!a.length)){for(var f=1/0,d=0;d0&&!c[f];)f--;this.firstRow=a.firstRow,this.lastRow=a.lastRow,u.$cursorLayer.config=a;for(var p=f;p<=d;p++){var g=c[p];if(!(!g||!g.el)){if(g.hidden){g.el.style.top=-100-(g.pixelHeight||0)+"px";continue}g._inDocument||(g._inDocument=!0,u.container.appendChild(g.el));var m=u.$cursorLayer.getPixelPosition({row:p,column:0},!0).top;g.coverLine||(m+=a.lineHeight*this.session.getRowLineCount(g.row)),g.el.style.top=m-a.offset+"px";var v=g.coverGutter?0:u.gutterWidth;g.fixedWidth||(v-=u.scrollLeft),g.el.style.left=v+"px",g.fullWidth&&g.screenWidth&&(g.el.style.minWidth=a.width+2*a.padding+"px"),g.fixedWidth?g.el.style.right=u.scrollBar.getWidth()+"px":g.el.style.right=""}}}},h}();r.LineWidgets=s}),ace.define("ace/keyboard/gutter_handler",["require","exports","module","ace/lib/keys","ace/mouse/default_gutter_handler"],function(n,r,i){var o=n("../lib/keys"),s=n("../mouse/default_gutter_handler").GutterTooltip,h=function(){function l(u){this.editor=u,this.gutterLayer=u.renderer.$gutterLayer,this.element=u.renderer.$gutter,this.lines=u.renderer.$gutterLayer.$lines,this.activeRowIndex=null,this.activeLane=null,this.annotationTooltip=new s(this.editor)}return l.prototype.addListener=function(){this.element.addEventListener("keydown",this.$onGutterKeyDown.bind(this)),this.element.addEventListener("focusout",this.$blurGutter.bind(this)),this.editor.on("mousewheel",this.$blurGutter.bind(this))},l.prototype.removeListener=function(){this.element.removeEventListener("keydown",this.$onGutterKeyDown.bind(this)),this.element.removeEventListener("focusout",this.$blurGutter.bind(this)),this.editor.off("mousewheel",this.$blurGutter.bind(this))},l.prototype.$onGutterKeyDown=function(u){if(this.annotationTooltip.isOpen){u.preventDefault(),u.keyCode===o.escape&&this.annotationTooltip.hide();return}if(u.target===this.element){if(u.keyCode!=o.enter)return;u.preventDefault();var a=this.editor.getCursorPosition().row;this.editor.isRowVisible(a)||this.editor.scrollToLine(a,!0,!0),setTimeout(function(){var g=this.$rowToRowIndex(this.gutterLayer.$cursorCell.row),m=this.$findNearestFoldWidget(g),v=this.$findNearestAnnotation(g);if(!(m===null&&v===null)){if(m===null&&v!==null){this.activeRowIndex=v,this.activeLane="annotation",this.$focusAnnotation(this.activeRowIndex);return}if(m!==null&&v===null){this.activeRowIndex=m,this.activeLane="fold",this.$focusFoldWidget(this.activeRowIndex);return}if(Math.abs(v-g)0||u+a=0&&this.$isFoldWidgetVisible(u-a))return u-a;if(u+a<=this.lines.getLength()-1&&this.$isFoldWidgetVisible(u+a))return u+a}return null},l.prototype.$findNearestAnnotation=function(u){if(this.$isAnnotationVisible(u))return u;for(var a=0;u-a>0||u+a=0&&this.$isAnnotationVisible(u-a))return u-a;if(u+a<=this.lines.getLength()-1&&this.$isAnnotationVisible(u+a))return u+a}return null},l.prototype.$focusFoldWidget=function(u){if(u!=null){var a=this.$getFoldWidget(u);a.classList.add(this.editor.renderer.keyboardFocusClassName),a.focus()}},l.prototype.$focusAnnotation=function(u){if(u!=null){var a=this.$getAnnotation(u);a.classList.add(this.editor.renderer.keyboardFocusClassName),a.setAttribute("role","button"),a.focus()}},l.prototype.$blurFoldWidget=function(u){var a=this.$getFoldWidget(u);a.classList.remove(this.editor.renderer.keyboardFocusClassName),a.blur()},l.prototype.$blurAnnotation=function(u){var a=this.$getAnnotation(u);a.classList.remove(this.editor.renderer.keyboardFocusClassName),a.removeAttribute("role"),a.blur()},l.prototype.$moveFoldWidgetUp=function(){for(var u=this.activeRowIndex;u>0;)if(u--,this.$isFoldWidgetVisible(u)){this.$blurFoldWidget(this.activeRowIndex),this.activeRowIndex=u,this.$focusFoldWidget(this.activeRowIndex);return}},l.prototype.$moveFoldWidgetDown=function(){for(var u=this.activeRowIndex;u0;)if(u--,this.$isAnnotationVisible(u)){this.$blurAnnotation(this.activeRowIndex),this.activeRowIndex=u,this.$focusAnnotation(this.activeRowIndex);return}},l.prototype.$moveAnnotationDown=function(){for(var u=this.activeRowIndex;u=C.length&&(C=void 0),{value:C&&C[O++],done:!C}}};throw new TypeError(w?"Object is not iterable.":"Symbol.iterator is not defined.")},s=n("./lib/oop"),h=n("./lib/dom"),l=n("./lib/lang"),u=n("./lib/useragent"),a=n("./keyboard/textinput").TextInput,c=n("./mouse/mouse_handler").MouseHandler,f=n("./mouse/fold_handler").FoldHandler,d=n("./keyboard/keybinding").KeyBinding,p=n("./edit_session").EditSession,g=n("./search").Search,m=n("./range").Range,v=n("./lib/event_emitter").EventEmitter,y=n("./commands/command_manager").CommandManager,x=n("./commands/default_commands").commands,$=n("./config"),_=n("./token_iterator").TokenIterator,b=n("./line_widgets").LineWidgets,R=n("./keyboard/gutter_handler").GutterKeyboardHandler,M=n("./config").nls,k=n("./clipboard"),S=n("./lib/keys"),E=function(){function C(w,T,O){this.$toDestroy=[];var N=w.getContainerElement();this.container=N,this.renderer=w,this.id="editor"+ ++C.$uid,this.commands=new y(u.isMac?"mac":"win",x),typeof document=="object"&&(this.textInput=new a(w.getTextAreaContainer(),this),this.renderer.textarea=this.textInput.getElement(),this.$mouseHandler=new c(this),new f(this)),this.keyBinding=new d(this),this.$search=new g().set({wrap:!0}),this.$historyTracker=this.$historyTracker.bind(this),this.commands.on("exec",this.$historyTracker),this.$initOperationListeners(),this._$emitInputEvent=l.delayedCall(function(){this._signal("input",{}),this.session&&!this.session.destroyed&&this.session.bgTokenizer.scheduleStart()}.bind(this)),this.on("change",function(B,H){H._$emitInputEvent.schedule(31)}),this.setSession(T||O&&O.session||new p("")),$.resetOptions(this),O&&this.setOptions(O),$._signal("editor",this)}return C.prototype.$initOperationListeners=function(){this.commands.on("exec",this.startOperation.bind(this),!0),this.commands.on("afterExec",this.endOperation.bind(this),!0),this.$opResetTimer=l.delayedCall(this.endOperation.bind(this,!0)),this.on("change",function(){this.curOp||(this.startOperation(),this.curOp.selectionBefore=this.$lastSel),this.curOp.docChanged=!0}.bind(this),!0),this.on("changeSelection",function(){this.curOp||(this.startOperation(),this.curOp.selectionBefore=this.$lastSel),this.curOp.selectionChanged=!0}.bind(this),!0)},C.prototype.startOperation=function(w){if(this.curOp){if(!w||this.curOp.command)return;this.prevOp=this.curOp}w||(this.previousCommand=null,w={}),this.$opResetTimer.schedule(),this.curOp=this.session.curOp={command:w.command||{},args:w.args,scrollTop:this.renderer.scrollTop},this.curOp.selectionBefore=this.selection.toJSON()},C.prototype.endOperation=function(w){if(this.curOp&&this.session){if(w&&w.returnValue===!1||!this.session)return this.curOp=null;if(w==!0&&this.curOp.command&&this.curOp.command.name=="mouse"||(this._signal("beforeEndOperation"),!this.curOp))return;var T=this.curOp.command,O=T&&T.scrollIntoView;if(O){switch(O){case"center-animate":O="animate";case"center":this.renderer.scrollCursorIntoView(null,.5);break;case"animate":case"cursor":this.renderer.scrollCursorIntoView();break;case"selectionPart":var N=this.selection.getRange(),B=this.renderer.layerConfig;(N.start.row>=B.lastRow||N.end.row<=B.firstRow)&&this.renderer.scrollSelectionIntoView(this.selection.anchor,this.selection.lead);break}O=="animate"&&this.renderer.animateScrolling(this.curOp.scrollTop)}var H=this.selection.toJSON();this.curOp.selectionAfter=H,this.$lastSel=this.selection.toJSON(),this.session.getUndoManager().addSelection(H),this.prevOp=this.curOp,this.curOp=null}},C.prototype.$historyTracker=function(w){if(this.$mergeUndoDeltas){var T=this.prevOp,O=this.$mergeableCommands,N=T.command&&w.command.name==T.command.name;if(w.command.name=="insertstring"){var B=w.args;this.mergeNextCommand===void 0&&(this.mergeNextCommand=!0),N=N&&this.mergeNextCommand&&(!/\s/.test(B)||/\s/.test(T.args)),this.mergeNextCommand=!0}else N=N&&O.indexOf(w.command.name)!==-1;this.$mergeUndoDeltas!="always"&&Date.now()-this.sequenceStartTime>2e3&&(N=!1),N?this.session.mergeUndoDeltas=!0:O.indexOf(w.command.name)!==-1&&(this.sequenceStartTime=Date.now())}},C.prototype.setKeyboardHandler=function(w,T){if(w&&typeof w=="string"&&w!="ace"){this.$keybindingId=w;var O=this;$.loadModule(["keybinding",w],function(N){O.$keybindingId==w&&O.keyBinding.setKeyboardHandler(N&&N.handler),T&&T()})}else this.$keybindingId=null,this.keyBinding.setKeyboardHandler(w),T&&T()},C.prototype.getKeyboardHandler=function(){return this.keyBinding.getKeyboardHandler()},C.prototype.setSession=function(w){if(this.session!=w){this.curOp&&this.endOperation(),this.curOp={};var T=this.session;if(T){this.session.off("change",this.$onDocumentChange),this.session.off("changeMode",this.$onChangeMode),this.session.off("tokenizerUpdate",this.$onTokenizerUpdate),this.session.off("changeTabSize",this.$onChangeTabSize),this.session.off("changeWrapLimit",this.$onChangeWrapLimit),this.session.off("changeWrapMode",this.$onChangeWrapMode),this.session.off("changeFold",this.$onChangeFold),this.session.off("changeFrontMarker",this.$onChangeFrontMarker),this.session.off("changeBackMarker",this.$onChangeBackMarker),this.session.off("changeBreakpoint",this.$onChangeBreakpoint),this.session.off("changeAnnotation",this.$onChangeAnnotation),this.session.off("changeOverwrite",this.$onCursorChange),this.session.off("changeScrollTop",this.$onScrollTopChange),this.session.off("changeScrollLeft",this.$onScrollLeftChange);var O=this.session.getSelection();O.off("changeCursor",this.$onCursorChange),O.off("changeSelection",this.$onSelectionChange)}this.session=w,w?(this.$onDocumentChange=this.onDocumentChange.bind(this),w.on("change",this.$onDocumentChange),this.renderer.setSession(w),this.$onChangeMode=this.onChangeMode.bind(this),w.on("changeMode",this.$onChangeMode),this.$onTokenizerUpdate=this.onTokenizerUpdate.bind(this),w.on("tokenizerUpdate",this.$onTokenizerUpdate),this.$onChangeTabSize=this.renderer.onChangeTabSize.bind(this.renderer),w.on("changeTabSize",this.$onChangeTabSize),this.$onChangeWrapLimit=this.onChangeWrapLimit.bind(this),w.on("changeWrapLimit",this.$onChangeWrapLimit),this.$onChangeWrapMode=this.onChangeWrapMode.bind(this),w.on("changeWrapMode",this.$onChangeWrapMode),this.$onChangeFold=this.onChangeFold.bind(this),w.on("changeFold",this.$onChangeFold),this.$onChangeFrontMarker=this.onChangeFrontMarker.bind(this),this.session.on("changeFrontMarker",this.$onChangeFrontMarker),this.$onChangeBackMarker=this.onChangeBackMarker.bind(this),this.session.on("changeBackMarker",this.$onChangeBackMarker),this.$onChangeBreakpoint=this.onChangeBreakpoint.bind(this),this.session.on("changeBreakpoint",this.$onChangeBreakpoint),this.$onChangeAnnotation=this.onChangeAnnotation.bind(this),this.session.on("changeAnnotation",this.$onChangeAnnotation),this.$onCursorChange=this.onCursorChange.bind(this),this.session.on("changeOverwrite",this.$onCursorChange),this.$onScrollTopChange=this.onScrollTopChange.bind(this),this.session.on("changeScrollTop",this.$onScrollTopChange),this.$onScrollLeftChange=this.onScrollLeftChange.bind(this),this.session.on("changeScrollLeft",this.$onScrollLeftChange),this.selection=w.getSelection(),this.selection.on("changeCursor",this.$onCursorChange),this.$onSelectionChange=this.onSelectionChange.bind(this),this.selection.on("changeSelection",this.$onSelectionChange),this.onChangeMode(),this.onCursorChange(),this.onScrollTopChange(),this.onScrollLeftChange(),this.onSelectionChange(),this.onChangeFrontMarker(),this.onChangeBackMarker(),this.onChangeBreakpoint(),this.onChangeAnnotation(),this.session.getUseWrapMode()&&this.renderer.adjustWrapLimit(),this.renderer.updateFull()):(this.selection=null,this.renderer.setSession(w)),this._signal("changeSession",{session:w,oldSession:T}),this.curOp=null,T&&T._signal("changeEditor",{oldEditor:this}),w&&w._signal("changeEditor",{editor:this}),w&&!w.destroyed&&w.bgTokenizer.scheduleStart()}},C.prototype.getSession=function(){return this.session},C.prototype.setValue=function(w,T){return this.session.doc.setValue(w),T?T==1?this.navigateFileEnd():T==-1&&this.navigateFileStart():this.selectAll(),w},C.prototype.getValue=function(){return this.session.getValue()},C.prototype.getSelection=function(){return this.selection},C.prototype.resize=function(w){this.renderer.onResize(w)},C.prototype.setTheme=function(w,T){this.renderer.setTheme(w,T)},C.prototype.getTheme=function(){return this.renderer.getTheme()},C.prototype.setStyle=function(w){this.renderer.setStyle(w)},C.prototype.unsetStyle=function(w){this.renderer.unsetStyle(w)},C.prototype.getFontSize=function(){return this.getOption("fontSize")||h.computedStyle(this.container).fontSize},C.prototype.setFontSize=function(w){this.setOption("fontSize",w)},C.prototype.$highlightBrackets=function(){if(!this.$highlightPending){var w=this;this.$highlightPending=!0,setTimeout(function(){w.$highlightPending=!1;var T=w.session;if(!(!T||T.destroyed)){T.$bracketHighlight&&(T.$bracketHighlight.markerIds.forEach(function(U){T.removeMarker(U)}),T.$bracketHighlight=null);var O=w.getCursorPosition(),N=w.getKeyboardHandler(),B=N&&N.$getDirectionForHighlight&&N.$getDirectionForHighlight(w),H=T.getMatchingBracketRanges(O,B);if(!H){var z=new _(T,O.row,O.column),F=z.getCurrentToken();if(F&&/\b(?:tag-open|tag-name)/.test(F.type)){var W=T.getMatchingTags(O);W&&(H=[W.openTagName,W.closeTagName])}}if(!H&&T.$mode.getMatching&&(H=T.$mode.getMatching(w.session)),!H){w.getHighlightIndentGuides()&&w.renderer.$textLayer.$highlightIndentGuide();return}var G="ace_bracket";Array.isArray(H)?H.length==1&&(G="ace_error_bracket"):H=[H],H.length==2&&(m.comparePoints(H[0].end,H[1].start)==0?H=[m.fromPoints(H[0].start,H[1].end)]:m.comparePoints(H[0].start,H[1].end)==0&&(H=[m.fromPoints(H[1].start,H[0].end)])),T.$bracketHighlight={ranges:H,markerIds:H.map(function(U){return T.addMarker(U,G,"text")})},w.getHighlightIndentGuides()&&w.renderer.$textLayer.$highlightIndentGuide()}},50)}},C.prototype.focus=function(){this.textInput.focus()},C.prototype.isFocused=function(){return this.textInput.isFocused()},C.prototype.blur=function(){this.textInput.blur()},C.prototype.onFocus=function(w){this.$isFocused||(this.$isFocused=!0,this.renderer.showCursor(),this.renderer.visualizeFocus(),this._emit("focus",w))},C.prototype.onBlur=function(w){this.$isFocused&&(this.$isFocused=!1,this.renderer.hideCursor(),this.renderer.visualizeBlur(),this._emit("blur",w))},C.prototype.$cursorChange=function(){this.renderer.updateCursor(),this.$highlightBrackets(),this.$updateHighlightActiveLine()},C.prototype.onDocumentChange=function(w){var T=this.session.$useWrapMode,O=w.start.row==w.end.row?w.end.row:1/0;this.renderer.updateLines(w.start.row,O,T),this._signal("change",w),this.$cursorChange()},C.prototype.onTokenizerUpdate=function(w){var T=w.data;this.renderer.updateLines(T.first,T.last)},C.prototype.onScrollTopChange=function(){this.renderer.scrollToY(this.session.getScrollTop())},C.prototype.onScrollLeftChange=function(){this.renderer.scrollToX(this.session.getScrollLeft())},C.prototype.onCursorChange=function(){this.$cursorChange(),this._signal("changeSelection")},C.prototype.$updateHighlightActiveLine=function(){var w=this.getSession(),T;if(this.$highlightActiveLine&&((this.$selectionStyle!="line"||!this.selection.isMultiLine())&&(T=this.getCursorPosition()),this.renderer.theme&&this.renderer.theme.$selectionColorConflict&&!this.selection.isEmpty()&&(T=!1),this.renderer.$maxLines&&this.session.getLength()===1&&!(this.renderer.$minLines>1)&&(T=!1)),w.$highlightLineMarker&&!T)w.removeMarker(w.$highlightLineMarker.id),w.$highlightLineMarker=null;else if(!w.$highlightLineMarker&&T){var O=new m(T.row,T.column,T.row,1/0);O.id=w.addMarker(O,"ace_active-line","screenLine"),w.$highlightLineMarker=O}else T&&(w.$highlightLineMarker.start.row=T.row,w.$highlightLineMarker.end.row=T.row,w.$highlightLineMarker.start.column=T.column,w._signal("changeBackMarker"))},C.prototype.onSelectionChange=function(w){var T=this.session;if(T.$selectionMarker&&T.removeMarker(T.$selectionMarker),T.$selectionMarker=null,this.selection.isEmpty())this.$updateHighlightActiveLine();else{var O=this.selection.getRange(),N=this.getSelectionStyle();T.$selectionMarker=T.addMarker(O,"ace_selection",N)}var B=this.$highlightSelectedWord&&this.$getSelectionHighLightRegexp();this.session.highlight(B),this._signal("changeSelection")},C.prototype.$getSelectionHighLightRegexp=function(){var w=this.session,T=this.getSelectionRange();if(!(T.isEmpty()||T.isMultiLine())){var O=T.start.column,N=T.end.column,B=w.getLine(T.start.row),H=B.substring(O,N);if(!(H.length>5e3||!/[\w\d]/.test(H))){var z=this.$search.$assembleRegExp({wholeWord:!0,caseSensitive:!0,needle:H}),F=B.substring(O-1,N+1);if(z.test(F))return z}}},C.prototype.onChangeFrontMarker=function(){this.renderer.updateFrontMarkers()},C.prototype.onChangeBackMarker=function(){this.renderer.updateBackMarkers()},C.prototype.onChangeBreakpoint=function(){this.renderer.updateBreakpoints()},C.prototype.onChangeAnnotation=function(){this.renderer.setAnnotations(this.session.getAnnotations())},C.prototype.onChangeMode=function(w){this.renderer.updateText(),this._emit("changeMode",w)},C.prototype.onChangeWrapLimit=function(){this.renderer.updateFull()},C.prototype.onChangeWrapMode=function(){this.renderer.onResize(!0)},C.prototype.onChangeFold=function(){this.$updateHighlightActiveLine(),this.renderer.updateFull()},C.prototype.getSelectedText=function(){return this.session.getTextRange(this.getSelectionRange())},C.prototype.getCopyText=function(){var w=this.getSelectedText(),T=this.session.doc.getNewLineCharacter(),O=!1;if(!w&&this.$copyWithEmptySelection){O=!0;for(var N=this.selection.getAllRanges(),B=0;BU.search(/\S|$/)){var F=U.substr(B.column).search(/\S|$/);O.doc.removeInLine(B.row,B.column,B.column+F)}}this.clearSelection();var W=B.column,G=O.getState(B.row),U=O.getLine(B.row),Q=N.checkOutdent(G,U,w);if(O.insert(B,w),H&&H.selection&&(H.selection.length==2?this.selection.setSelectionRange(new m(B.row,W+H.selection[0],B.row,W+H.selection[1])):this.selection.setSelectionRange(new m(B.row+H.selection[0],H.selection[1],B.row+H.selection[2],H.selection[3]))),this.$enableAutoIndent){if(O.getDocument().isNewLine(w)){var ee=N.getNextLineIndent(G,U.slice(0,B.column),O.getTabString());O.insert({row:B.row+1,column:0},ee)}Q&&N.autoOutdent(G,O,B.row)}},C.prototype.autoIndent=function(){var w=this.session,T=w.getMode(),O,N;if(this.selection.isEmpty())O=0,N=w.doc.getLength()-1;else{var B=this.getSelectionRange();O=B.start.row,N=B.end.row}for(var H="",z="",F="",W,G,U,Q=w.getTabString(),ee=O;ee<=N;ee++)ee>0&&(H=w.getState(ee-1),z=w.getLine(ee-1),F=T.getNextLineIndent(H,z,Q)),W=w.getLine(ee),G=T.$getIndent(W),F!==G&&(G.length>0&&(U=new m(ee,0,ee,G.length),w.remove(U)),F.length>0&&w.insert({row:ee,column:0},F)),T.autoOutdent(H,w,ee)},C.prototype.onTextInput=function(w,T){if(!T)return this.keyBinding.onTextInput(w);this.startOperation({command:{name:"insertstring"}});var O=this.applyComposition.bind(this,w,T);this.selection.rangeCount?this.forEachSelection(O):O(),this.endOperation()},C.prototype.applyComposition=function(w,T){if(T.extendLeft||T.extendRight){var O=this.selection.getRange();O.start.column-=T.extendLeft,O.end.column+=T.extendRight,O.start.column<0&&(O.start.row--,O.start.column+=this.session.getLine(O.start.row).length+1),this.selection.setRange(O),!w&&!O.isEmpty()&&this.remove()}if((w||!this.selection.isEmpty())&&this.insert(w,!0),T.restoreStart||T.restoreEnd){var O=this.selection.getRange();O.start.column-=T.restoreStart,O.end.column-=T.restoreEnd,this.selection.setRange(O)}},C.prototype.onCommandKey=function(w,T,O){return this.keyBinding.onCommandKey(w,T,O)},C.prototype.setOverwrite=function(w){this.session.setOverwrite(w)},C.prototype.getOverwrite=function(){return this.session.getOverwrite()},C.prototype.toggleOverwrite=function(){this.session.toggleOverwrite()},C.prototype.setScrollSpeed=function(w){this.setOption("scrollSpeed",w)},C.prototype.getScrollSpeed=function(){return this.getOption("scrollSpeed")},C.prototype.setDragDelay=function(w){this.setOption("dragDelay",w)},C.prototype.getDragDelay=function(){return this.getOption("dragDelay")},C.prototype.setSelectionStyle=function(w){this.setOption("selectionStyle",w)},C.prototype.getSelectionStyle=function(){return this.getOption("selectionStyle")},C.prototype.setHighlightActiveLine=function(w){this.setOption("highlightActiveLine",w)},C.prototype.getHighlightActiveLine=function(){return this.getOption("highlightActiveLine")},C.prototype.setHighlightGutterLine=function(w){this.setOption("highlightGutterLine",w)},C.prototype.getHighlightGutterLine=function(){return this.getOption("highlightGutterLine")},C.prototype.setHighlightSelectedWord=function(w){this.setOption("highlightSelectedWord",w)},C.prototype.getHighlightSelectedWord=function(){return this.$highlightSelectedWord},C.prototype.setAnimatedScroll=function(w){this.renderer.setAnimatedScroll(w)},C.prototype.getAnimatedScroll=function(){return this.renderer.getAnimatedScroll()},C.prototype.setShowInvisibles=function(w){this.renderer.setShowInvisibles(w)},C.prototype.getShowInvisibles=function(){return this.renderer.getShowInvisibles()},C.prototype.setDisplayIndentGuides=function(w){this.renderer.setDisplayIndentGuides(w)},C.prototype.getDisplayIndentGuides=function(){return this.renderer.getDisplayIndentGuides()},C.prototype.setHighlightIndentGuides=function(w){this.renderer.setHighlightIndentGuides(w)},C.prototype.getHighlightIndentGuides=function(){return this.renderer.getHighlightIndentGuides()},C.prototype.setShowPrintMargin=function(w){this.renderer.setShowPrintMargin(w)},C.prototype.getShowPrintMargin=function(){return this.renderer.getShowPrintMargin()},C.prototype.setPrintMarginColumn=function(w){this.renderer.setPrintMarginColumn(w)},C.prototype.getPrintMarginColumn=function(){return this.renderer.getPrintMarginColumn()},C.prototype.setReadOnly=function(w){this.setOption("readOnly",w)},C.prototype.getReadOnly=function(){return this.getOption("readOnly")},C.prototype.setBehavioursEnabled=function(w){this.setOption("behavioursEnabled",w)},C.prototype.getBehavioursEnabled=function(){return this.getOption("behavioursEnabled")},C.prototype.setWrapBehavioursEnabled=function(w){this.setOption("wrapBehavioursEnabled",w)},C.prototype.getWrapBehavioursEnabled=function(){return this.getOption("wrapBehavioursEnabled")},C.prototype.setShowFoldWidgets=function(w){this.setOption("showFoldWidgets",w)},C.prototype.getShowFoldWidgets=function(){return this.getOption("showFoldWidgets")},C.prototype.setFadeFoldWidgets=function(w){this.setOption("fadeFoldWidgets",w)},C.prototype.getFadeFoldWidgets=function(){return this.getOption("fadeFoldWidgets")},C.prototype.remove=function(w){this.selection.isEmpty()&&(w=="left"?this.selection.selectLeft():this.selection.selectRight());var T=this.getSelectionRange();if(this.getBehavioursEnabled()){var O=this.session,N=O.getState(T.start.row),B=O.getMode().transformAction(N,"deletion",this,O,T);if(T.end.column===0){var H=O.getTextRange(T);if(H[H.length-1]==` -`){var z=O.getLine(T.end.row);/^\s+$/.test(z)&&(T.end.column=z.length)}}B&&(T=B)}this.session.remove(T),this.clearSelection()},C.prototype.removeWordRight=function(){this.selection.isEmpty()&&this.selection.selectWordRight(),this.session.remove(this.getSelectionRange()),this.clearSelection()},C.prototype.removeWordLeft=function(){this.selection.isEmpty()&&this.selection.selectWordLeft(),this.session.remove(this.getSelectionRange()),this.clearSelection()},C.prototype.removeToLineStart=function(){this.selection.isEmpty()&&this.selection.selectLineStart(),this.selection.isEmpty()&&this.selection.selectLeft(),this.session.remove(this.getSelectionRange()),this.clearSelection()},C.prototype.removeToLineEnd=function(){this.selection.isEmpty()&&this.selection.selectLineEnd();var w=this.getSelectionRange();w.start.column==w.end.column&&w.start.row==w.end.row&&(w.end.column=0,w.end.row++),this.session.remove(w),this.clearSelection()},C.prototype.splitLine=function(){this.selection.isEmpty()||(this.session.remove(this.getSelectionRange()),this.clearSelection());var w=this.getCursorPosition();this.insert(` -`),this.moveCursorToPosition(w)},C.prototype.setGhostText=function(w,T){this.session.widgetManager||(this.session.widgetManager=new b(this.session),this.session.widgetManager.attach(this)),this.renderer.setGhostText(w,T)},C.prototype.removeGhostText=function(){this.session.widgetManager&&this.renderer.removeGhostText()},C.prototype.transposeLetters=function(){if(this.selection.isEmpty()){var w=this.getCursorPosition(),T=w.column;if(T!==0){var O=this.session.getLine(w.row),N,B;TF.toLowerCase()?1:0});for(var B=new m(0,0,0,0),N=w.first;N<=w.last;N++){var H=T.getLine(N);B.start.row=N,B.end.row=N,B.end.column=H.length,T.replace(B,O[N-w.first])}},C.prototype.toggleCommentLines=function(){var w=this.session.getState(this.getCursorPosition().row),T=this.$getSelectedRows();this.session.getMode().toggleCommentLines(w,this.session,T.first,T.last)},C.prototype.toggleBlockComment=function(){var w=this.getCursorPosition(),T=this.session.getState(w.row),O=this.getSelectionRange();this.session.getMode().toggleBlockComment(T,this.session,O,w)},C.prototype.getNumberAt=function(w,T){var O=/[\-]?[0-9]+(?:\.[0-9]+)?/g;O.lastIndex=0;for(var N=this.session.getLine(w);O.lastIndex=T){var H={value:B[0],start:B.index,end:B.index+B[0].length};return H}}return null},C.prototype.modifyNumber=function(w){var T=this.selection.getCursor().row,O=this.selection.getCursor().column,N=new m(T,O-1,T,O),B=this.session.getTextRange(N);if(!isNaN(parseFloat(B))&&isFinite(B)){var H=this.getNumberAt(T,O);if(H){var z=H.value.indexOf(".")>=0?H.start+H.value.indexOf(".")+1:H.end,F=H.start+H.value.length-z,W=parseFloat(H.value);W*=Math.pow(10,F),z!==H.end&&O=z&&H<=F&&(O=ge,W.selection.clearSelection(),W.moveCursorTo(w,z+N),W.selection.selectTo(w,F+N)),z=F});for(var G=this.$toggleWordPairs,U,Q=0;Q=F&&z<=W&&ee.match(/((?:https?|ftp):\/\/[\S]+)/)){G=ee.replace(/[\s:.,'";}\]]+$/,"");break}F=W}}catch(V){O={error:V}}finally{try{Q&&!Q.done&&(N=U.return)&&N.call(U)}finally{if(O)throw O.error}}return G},C.prototype.openLink=function(){var w=this.selection.getCursor(),T=this.findLinkAt(w.row,w.column);return T&&window.open(T,"_blank"),T!=null},C.prototype.removeLines=function(){var w=this.$getSelectedRows();this.session.removeFullLines(w.first,w.last),this.clearSelection()},C.prototype.duplicateSelection=function(){var w=this.selection,T=this.session,O=w.getRange(),N=w.isBackwards();if(O.isEmpty()){var B=O.start.row;T.duplicateLines(B,B)}else{var H=N?O.start:O.end,z=T.insert(H,T.getTextRange(O),!1);O.start=H,O.end=z,w.setSelectionRange(O,N)}},C.prototype.moveLinesDown=function(){this.$moveLines(1,!1)},C.prototype.moveLinesUp=function(){this.$moveLines(-1,!1)},C.prototype.moveText=function(w,T,O){return this.session.moveText(w,T,O)},C.prototype.copyLinesUp=function(){this.$moveLines(-1,!0)},C.prototype.copyLinesDown=function(){this.$moveLines(1,!0)},C.prototype.$moveLines=function(w,T){var O,N,B=this.selection;if(!B.inMultiSelectMode||this.inVirtualSelectionMode){var H=B.toOrientedRange();O=this.$getSelectedRows(H),N=this.session.$moveLines(O.first,O.last,T?0:w),T&&w==-1&&(N=0),H.moveBy(N,0),B.fromOrientedRange(H)}else{var z=B.rangeList.ranges;B.rangeList.detach(this.session),this.inVirtualSelectionMode=!0;for(var F=0,W=0,G=z.length,U=0;UV+1)break;V=q.last}for(U--,F=this.session.$moveLines(ee,V,T?0:w),T&&w==-1&&(Q=U+1);Q<=U;)z[Q].moveBy(F,0),Q++;T||(F=0),W+=F}B.fromOrientedRange(B.ranges[0]),B.rangeList.attach(this.session),this.inVirtualSelectionMode=!1}},C.prototype.$getSelectedRows=function(w){return w=(w||this.getSelectionRange()).collapseRows(),{first:this.session.getRowFoldStart(w.start.row),last:this.session.getRowFoldEnd(w.end.row)}},C.prototype.onCompositionStart=function(w){this.renderer.showComposition(w)},C.prototype.onCompositionUpdate=function(w){this.renderer.setCompositionText(w)},C.prototype.onCompositionEnd=function(){this.renderer.hideComposition()},C.prototype.getFirstVisibleRow=function(){return this.renderer.getFirstVisibleRow()},C.prototype.getLastVisibleRow=function(){return this.renderer.getLastVisibleRow()},C.prototype.isRowVisible=function(w){return w>=this.getFirstVisibleRow()&&w<=this.getLastVisibleRow()},C.prototype.isRowFullyVisible=function(w){return w>=this.renderer.getFirstFullyVisibleRow()&&w<=this.renderer.getLastFullyVisibleRow()},C.prototype.$getVisibleRowCount=function(){return this.renderer.getScrollBottomRow()-this.renderer.getScrollTopRow()+1},C.prototype.$moveByPage=function(w,T){var O=this.renderer,N=this.renderer.layerConfig,B=w*Math.floor(N.height/N.lineHeight);T===!0?this.selection.$moveSelection(function(){this.moveCursorBy(B,0)}):T===!1&&(this.selection.moveCursorBy(B,0),this.selection.clearSelection());var H=O.scrollTop;O.scrollBy(0,B*N.lineHeight),T!=null&&O.scrollCursorIntoView(null,.5),O.animateScrolling(H)},C.prototype.selectPageDown=function(){this.$moveByPage(1,!0)},C.prototype.selectPageUp=function(){this.$moveByPage(-1,!0)},C.prototype.gotoPageDown=function(){this.$moveByPage(1,!1)},C.prototype.gotoPageUp=function(){this.$moveByPage(-1,!1)},C.prototype.scrollPageDown=function(){this.$moveByPage(1)},C.prototype.scrollPageUp=function(){this.$moveByPage(-1)},C.prototype.scrollToRow=function(w){this.renderer.scrollToRow(w)},C.prototype.scrollToLine=function(w,T,O,N){this.renderer.scrollToLine(w,T,O,N)},C.prototype.centerSelection=function(){var w=this.getSelectionRange(),T={row:Math.floor(w.start.row+(w.end.row-w.start.row)/2),column:Math.floor(w.start.column+(w.end.column-w.start.column)/2)};this.renderer.alignCursor(T,.5)},C.prototype.getCursorPosition=function(){return this.selection.getCursor()},C.prototype.getCursorPositionScreen=function(){return this.session.documentToScreenPosition(this.getCursorPosition())},C.prototype.getSelectionRange=function(){return this.selection.getRange()},C.prototype.selectAll=function(){this.selection.selectAll()},C.prototype.clearSelection=function(){this.selection.clearSelection()},C.prototype.moveCursorTo=function(w,T){this.selection.moveCursorTo(w,T)},C.prototype.moveCursorToPosition=function(w){this.selection.moveCursorToPosition(w)},C.prototype.jumpToMatching=function(w,T){var O=this.getCursorPosition(),N=new _(this.session,O.row,O.column),B=N.getCurrentToken(),H=0;B&&B.type.indexOf("tag-name")!==-1&&(B=N.stepBackward());var z=B||N.stepForward();if(z){var F,W=!1,G={},U=O.column-z.start,Q,ee={")":"(","(":"(","]":"[","[":"[","{":"{","}":"{"};do{if(z.value.match(/[{}()\[\]]/g)){for(;U1?G[z.value]++:B.value==="=0;--H)this.$tryReplace(O[H],w)&&N++;return this.selection.setSelectionRange(B),N},C.prototype.$tryReplace=function(w,T){var O=this.session.getTextRange(w);return T=this.$search.replace(O,T),T!==null?(w.end=this.session.replace(w,T),w):null},C.prototype.getLastSearchOptions=function(){return this.$search.getOptions()},C.prototype.find=function(w,T,O){T||(T={}),typeof w=="string"||w instanceof RegExp?T.needle=w:typeof w=="object"&&s.mixin(T,w);var N=this.selection.getRange();T.needle==null&&(w=this.session.getTextRange(N)||this.$search.$options.needle,w||(N=this.session.getWordRange(N.start.row,N.start.column),w=this.session.getTextRange(N)),this.$search.set({needle:w})),this.$search.set(T),T.start||this.$search.set({start:N});var B=this.$search.find(this.session);if(T.preventScroll)return B;if(B)return this.revealRange(B,O),B;T.backwards?N.start=N.end:N.end=N.start,this.selection.setRange(N)},C.prototype.findNext=function(w,T){this.find({skipCurrent:!0,backwards:!1},w,T)},C.prototype.findPrevious=function(w,T){this.find(w,{skipCurrent:!0,backwards:!0},T)},C.prototype.revealRange=function(w,T){this.session.unfold(w),this.selection.setSelectionRange(w);var O=this.renderer.scrollTop;this.renderer.scrollSelectionIntoView(w.start,w.end,.5),T!==!1&&this.renderer.animateScrolling(O)},C.prototype.undo=function(){this.session.getUndoManager().undo(this.session),this.renderer.scrollCursorIntoView(null,.5)},C.prototype.redo=function(){this.session.getUndoManager().redo(this.session),this.renderer.scrollCursorIntoView(null,.5)},C.prototype.destroy=function(){this.$toDestroy&&(this.$toDestroy.forEach(function(w){w.destroy()}),this.$toDestroy=null),this.$mouseHandler&&this.$mouseHandler.destroy(),this.renderer.destroy(),this._signal("destroy",this),this.session&&this.session.destroy(),this._$emitInputEvent&&this._$emitInputEvent.cancel(),this.removeAllListeners()},C.prototype.setAutoScrollEditorIntoView=function(w){if(w){var T,O=this,N=!1;this.$scrollAnchor||(this.$scrollAnchor=document.createElement("div"));var B=this.$scrollAnchor;B.style.cssText="position:absolute",this.container.insertBefore(B,this.container.firstChild);var H=this.on("changeSelection",function(){N=!0}),z=this.renderer.on("beforeRender",function(){N&&(T=O.renderer.container.getBoundingClientRect())}),F=this.renderer.on("afterRender",function(){if(N&&T&&(O.isFocused()||O.searchBox&&O.searchBox.isFocused())){var W=O.renderer,G=W.$cursorLayer.$pixelPos,U=W.layerConfig,Q=G.top-U.offset;G.top>=0&&Q+T.top<0?N=!0:G.topwindow.innerHeight?N=!1:N=null,N!=null&&(B.style.top=Q+"px",B.style.left=G.left+"px",B.style.height=U.lineHeight+"px",B.scrollIntoView(N)),N=T=null}});this.setAutoScrollEditorIntoView=function(W){W||(delete this.setAutoScrollEditorIntoView,this.off("changeSelection",H),this.renderer.off("afterRender",F),this.renderer.off("beforeRender",z))}}},C.prototype.$resetCursorStyle=function(){var w=this.$cursorStyle||"ace",T=this.renderer.$cursorLayer;T&&(T.setSmoothBlinking(/smooth/.test(w)),T.isBlinking=!this.$readOnly&&w!="wide",h.setCssClass(T.element,"ace_slim-cursors",/slim/.test(w)))},C.prototype.prompt=function(w,T,O){var N=this;$.loadModule("ace/ext/prompt",function(B){B.prompt(N,w,T,O)})},C}();E.$uid=0,E.prototype.curOp=null,E.prototype.prevOp={},E.prototype.$mergeableCommands=["backspace","del","insertstring"],E.prototype.$toggleWordPairs=[["first","last"],["true","false"],["yes","no"],["width","height"],["top","bottom"],["right","left"],["on","off"],["x","y"],["get","set"],["max","min"],["horizontal","vertical"],["show","hide"],["add","remove"],["up","down"],["before","after"],["even","odd"],["in","out"],["inside","outside"],["next","previous"],["increase","decrease"],["attach","detach"],["&&","||"],["==","!="]],s.implement(E.prototype,v),$.defineOptions(E.prototype,"editor",{selectionStyle:{set:function(C){this.onSelectionChange(),this._signal("changeSelectionStyle",{data:C})},initialValue:"line"},highlightActiveLine:{set:function(){this.$updateHighlightActiveLine()},initialValue:!0},highlightSelectedWord:{set:function(C){this.$onSelectionChange()},initialValue:!0},readOnly:{set:function(C){this.textInput.setReadOnly(C),this.$resetCursorStyle()},initialValue:!1},copyWithEmptySelection:{set:function(C){this.textInput.setCopyWithEmptySelection(C)},initialValue:!1},cursorStyle:{set:function(C){this.$resetCursorStyle()},values:["ace","slim","smooth","wide"],initialValue:"ace"},mergeUndoDeltas:{values:[!1,!0,"always"],initialValue:!0},behavioursEnabled:{initialValue:!0},wrapBehavioursEnabled:{initialValue:!0},enableAutoIndent:{initialValue:!0},autoScrollEditorIntoView:{set:function(C){this.setAutoScrollEditorIntoView(C)}},keyboardHandler:{set:function(C){this.setKeyboardHandler(C)},get:function(){return this.$keybindingId},handlesSet:!0},value:{set:function(C){this.session.setValue(C)},get:function(){return this.getValue()},handlesSet:!0,hidden:!0},session:{set:function(C){this.setSession(C)},get:function(){return this.session},handlesSet:!0,hidden:!0},showLineNumbers:{set:function(C){this.renderer.$gutterLayer.setShowLineNumbers(C),this.renderer.$loop.schedule(this.renderer.CHANGE_GUTTER),C&&this.$relativeLineNumbers?I.attach(this):I.detach(this)},initialValue:!0},relativeLineNumbers:{set:function(C){this.$showLineNumbers&&C?I.attach(this):I.detach(this)}},placeholder:{set:function(C){this.$updatePlaceholder||(this.$updatePlaceholder=function(){var w=this.session&&(this.renderer.$composition||this.session.getLength()>1||this.session.getLine(0).length>0);if(w&&this.renderer.placeholderNode)this.renderer.off("afterRender",this.$updatePlaceholder),h.removeCssClass(this.container,"ace_hasPlaceholder"),this.renderer.placeholderNode.remove(),this.renderer.placeholderNode=null;else if(!w&&!this.renderer.placeholderNode){this.renderer.on("afterRender",this.$updatePlaceholder),h.addCssClass(this.container,"ace_hasPlaceholder");var T=h.createElement("div");T.className="ace_placeholder",T.textContent=this.$placeholder||"",this.renderer.placeholderNode=T,this.renderer.content.appendChild(this.renderer.placeholderNode)}else!w&&this.renderer.placeholderNode&&(this.renderer.placeholderNode.textContent=this.$placeholder||"")}.bind(this),this.on("input",this.$updatePlaceholder)),this.$updatePlaceholder()}},enableKeyboardAccessibility:{set:function(C){var w={name:"blurTextInput",description:"Set focus to the editor content div to allow tabbing through the page",bindKey:"Esc",exec:function(N){N.blur(),N.renderer.scroller.focus()},readOnly:!0},T=function(N){if(N.target==this.renderer.scroller&&N.keyCode===S.enter){N.preventDefault();var B=this.getCursorPosition().row;this.isRowVisible(B)||this.scrollToLine(B,!0,!0),this.focus()}},O;C?(this.renderer.enableKeyboardAccessibility=!0,this.renderer.keyboardFocusClassName="ace_keyboard-focus",this.textInput.getElement().setAttribute("tabindex",-1),this.renderer.scroller.setAttribute("tabindex",0),this.renderer.scroller.setAttribute("role","group"),this.renderer.scroller.setAttribute("aria-roledescription",M("editor")),this.renderer.scroller.classList.add(this.renderer.keyboardFocusClassName),this.renderer.scroller.setAttribute("aria-label",M("Editor content, press Enter to start editing, press Escape to exit")),this.renderer.scroller.addEventListener("keyup",T.bind(this)),this.commands.addCommand(w),this.renderer.$gutter.setAttribute("tabindex",0),this.renderer.$gutter.setAttribute("aria-hidden",!1),this.renderer.$gutter.setAttribute("role","group"),this.renderer.$gutter.setAttribute("aria-roledescription",M("editor")),this.renderer.$gutter.setAttribute("aria-label",M("Editor gutter, press Enter to interact with controls using arrow keys, press Escape to exit")),this.renderer.$gutter.classList.add(this.renderer.keyboardFocusClassName),this.renderer.content.setAttribute("aria-hidden",!0),O||(O=new R(this)),O.addListener()):(this.renderer.enableKeyboardAccessibility=!1,this.textInput.getElement().setAttribute("tabindex",0),this.renderer.scroller.setAttribute("tabindex",-1),this.renderer.scroller.removeAttribute("role"),this.renderer.scroller.removeAttribute("aria-roledescription"),this.renderer.scroller.classList.remove(this.renderer.keyboardFocusClassName),this.renderer.scroller.removeAttribute("aria-label"),this.renderer.scroller.removeEventListener("keyup",T.bind(this)),this.commands.removeCommand(w),this.renderer.content.removeAttribute("aria-hidden"),this.renderer.$gutter.setAttribute("tabindex",-1),this.renderer.$gutter.setAttribute("aria-hidden",!0),this.renderer.$gutter.removeAttribute("role"),this.renderer.$gutter.removeAttribute("aria-roledescription"),this.renderer.$gutter.removeAttribute("aria-label"),this.renderer.$gutter.classList.remove(this.renderer.keyboardFocusClassName),O&&O.removeListener())},initialValue:!1},customScrollbar:"renderer",hScrollBarAlwaysVisible:"renderer",vScrollBarAlwaysVisible:"renderer",highlightGutterLine:"renderer",animatedScroll:"renderer",showInvisibles:"renderer",showPrintMargin:"renderer",printMarginColumn:"renderer",printMargin:"renderer",fadeFoldWidgets:"renderer",showFoldWidgets:"renderer",displayIndentGuides:"renderer",highlightIndentGuides:"renderer",showGutter:"renderer",fontSize:"renderer",fontFamily:"renderer",maxLines:"renderer",minLines:"renderer",scrollPastEnd:"renderer",fixedWidthGutter:"renderer",theme:"renderer",hasCssTransforms:"renderer",maxPixelHeight:"renderer",useTextareaForIME:"renderer",useResizeObserver:"renderer",useSvgGutterIcons:"renderer",showFoldedAnnotations:"renderer",scrollSpeed:"$mouseHandler",dragDelay:"$mouseHandler",dragEnabled:"$mouseHandler",focusTimeout:"$mouseHandler",tooltipFollowsMouse:"$mouseHandler",firstLineNumber:"session",overwrite:"session",newLineMode:"session",useWorker:"session",useSoftTabs:"session",navigateWithinSoftTabs:"session",tabSize:"session",wrap:"session",indentedSoftWrap:"session",foldStyle:"session",mode:"session"});var I={getText:function(C,w){return(Math.abs(C.selection.lead.row-w)||w+1+(w<9?"·":""))+""},getWidth:function(C,w,T){return Math.max(w.toString().length,(T.lastRow+1).toString().length,2)*T.characterWidth},update:function(C,w){w.renderer.$loop.schedule(w.renderer.CHANGE_GUTTER)},attach:function(C){C.renderer.$gutterLayer.$renderer=this,C.on("changeSelection",this.update),this.update(null,C)},detach:function(C){C.renderer.$gutterLayer.$renderer==this&&(C.renderer.$gutterLayer.$renderer=null),C.off("changeSelection",this.update),this.update(null,C)}};r.Editor=E}),ace.define("ace/undomanager",["require","exports","module","ace/range"],function(n,r,i){var o=function(){function _(){this.$maxRev=0,this.$fromUndo=!1,this.$undoDepth=1/0,this.reset()}return _.prototype.addSession=function(b){this.$session=b},_.prototype.add=function(b,R,M){if(!this.$fromUndo&&b!=this.$lastDelta){if(this.$keepRedoStack||(this.$redoStack.length=0),R===!1||!this.lastDeltas){this.lastDeltas=[];var k=this.$undoStack.length;k>this.$undoDepth-1&&this.$undoStack.splice(0,k-this.$undoDepth+1),this.$undoStack.push(this.lastDeltas),b.id=this.$rev=++this.$maxRev}(b.action=="remove"||b.action=="insert")&&(this.$lastDelta=b),this.lastDeltas.push(b)}},_.prototype.addSelection=function(b,R){this.selections.push({value:b,rev:R||this.$rev})},_.prototype.startNewGroup=function(){return this.lastDeltas=null,this.$rev},_.prototype.markIgnored=function(b,R){R==null&&(R=this.$rev+1);for(var M=this.$undoStack,k=M.length;k--;){var S=M[k][0];if(S.id<=b)break;S.id0},_.prototype.canRedo=function(){return this.$redoStack.length>0},_.prototype.bookmark=function(b){b==null&&(b=this.$rev),this.mark=b},_.prototype.isAtBookmark=function(){return this.$rev===this.mark},_.prototype.toJSON=function(){},_.prototype.fromJSON=function(){},_.prototype.$prettyPrint=function(b){return b?c(b):c(this.$undoStack)+` ---- -`+c(this.$redoStack)},_}();o.prototype.hasUndo=o.prototype.canUndo,o.prototype.hasRedo=o.prototype.canRedo,o.prototype.isClean=o.prototype.isAtBookmark,o.prototype.markClean=o.prototype.bookmark;function s(_,b){for(var R=b;R--;){var M=_[R];if(M&&!M[0].ignore){for(;R"+_.end.row+":"+_.end.column}function d(_,b){var R=_.action=="insert",M=b.action=="insert";if(R&&M)if(l(b.start,_.end)>=0)m(b,_,-1);else if(l(b.start,_.start)<=0)m(_,b,1);else return null;else if(R&&!M)if(l(b.start,_.end)>=0)m(b,_,-1);else if(l(b.end,_.start)<=0)m(_,b,-1);else return null;else if(!R&&M)if(l(b.start,_.start)>=0)m(b,_,1);else if(l(b.start,_.start)<=0)m(_,b,1);else return null;else if(!R&&!M)if(l(b.start,_.start)>=0)m(b,_,1);else if(l(b.end,_.start)<=0)m(_,b,-1);else return null;return[b,_]}function p(_,b){for(var R=_.length;R--;)for(var M=0;M=0?m(_,b,-1):(l(_.start,b.start)<=0||m(_,h.fromPoints(b.start,_.start),-1),m(b,_,1));else if(!R&&M)l(b.start,_.end)>=0?m(b,_,-1):(l(b.start,_.start)<=0||m(b,h.fromPoints(_.start,b.start),-1),m(_,b,1));else if(!R&&!M)if(l(b.start,_.end)>=0)m(b,_,-1);else if(l(b.end,_.start)<=0)m(_,b,-1);else{var k,S;return l(_.start,b.start)<0&&(k=_,_=y(_,b.start)),l(_.end,b.end)>0&&(S=y(_,b.end)),v(b.end,_.start,_.end,-1),S&&!k&&(_.lines=S.lines,_.start=S.start,_.end=S.end,S=_),[b,k,S].filter(Boolean)}return[b,_]}function m(_,b,R){v(_.start,b.start,b.end,R),v(_.end,b.start,b.end,R)}function v(_,b,R,M){_.row==(M==1?b:R).row&&(_.column+=M*(R.column-b.column)),_.row+=M*(R.row-b.row)}function y(_,b){var R=_.lines,M=_.end;_.end=u(b);var k=_.end.row-_.start.row,S=R.splice(k,R.length),E=k?b.column:b.column-_.start.column;R.push(S[0].substring(0,E)),S[0]=S[0].substr(E);var I={start:u(b),end:M,lines:S,action:_.action};return I}function x(_,b){b=a(b);for(var R=_.length;R--;){for(var M=_[R],k=0;kx&&(b=y.end.row+1,y=g.getNextFoldLine(b,y),x=y?y.start.row:1/0),b>v){for(;this.$lines.getLength()>_+1;)this.$lines.pop();break}$=this.$lines.get(++_),$?$.row=b:($=this.$lines.createCell(b,p,this.session,f),this.$lines.push($)),this.$renderCell($,p,y,b),b++}this._signal("afterRender"),this.$updateGutterWidth(p)},d.prototype.$updateGutterWidth=function(p){var g=this.session,m=g.gutterRenderer||this.$renderer,v=g.$firstLineNumber,y=this.$lines.last()?this.$lines.last().text:"";(this.$fixedWidth||g.$useWrapMode)&&(y=g.getLength()+v-1);var x=m?m.getWidth(g,y,p):y.toString().length*p.characterWidth,$=this.$padding||this.$computePadding();x+=$.left+$.right,x!==this.gutterWidth&&!isNaN(x)&&(this.gutterWidth=x,this.element.parentNode.style.width=this.element.style.width=Math.ceil(this.gutterWidth)+"px",this._signal("changeGutterWidth",x))},d.prototype.$updateCursorRow=function(){if(this.$highlightGutterLine){var p=this.session.selection.getCursor();this.$cursorRow!==p.row&&(this.$cursorRow=p.row)}},d.prototype.updateLineHighlight=function(){if(this.$highlightGutterLine){var p=this.session.selection.cursor.row;if(this.$cursorRow=p,!(this.$cursorCell&&this.$cursorCell.row==p)){this.$cursorCell&&(this.$cursorCell.element.className=this.$cursorCell.element.className.replace("ace_gutter-active-line ",""));var g=this.$lines.cells;this.$cursorCell=null;for(var m=0;m=this.$cursorRow){if(v.row>this.$cursorRow){var y=this.session.getFoldLine(this.$cursorRow);if(m>0&&y&&y.start.row==g[m-1].row)v=g[m-1];else break}v.element.className="ace_gutter-active-line "+v.element.className,this.$cursorCell=v;break}}}}},d.prototype.scrollLines=function(p){var g=this.config;if(this.config=p,this.$updateCursorRow(),this.$lines.pageChanged(g,p))return this.update(p);this.$lines.moveContainer(p);var m=Math.min(p.lastRow+p.gutterOffset,this.session.getLength()-1),v=this.oldLastRow;if(this.oldLastRow=m,!g||v0;y--)this.$lines.shift();if(v>m)for(var y=this.session.getFoldedRowCount(m+1,v);y>0;y--)this.$lines.pop();p.firstRowv&&this.$lines.push(this.$renderLines(p,v+1,m)),this.updateLineHighlight(),this._signal("afterRender"),this.$updateGutterWidth(p)},d.prototype.$renderLines=function(p,g,m){for(var v=[],y=g,x=this.session.getNextFoldLine(y),$=x?x.start.row:1/0;y>$&&(y=x.end.row+1,x=this.session.getNextFoldLine(y,x),$=x?x.start.row:1/0),!(y>m);){var _=this.$lines.createCell(y,p,this.session,f);this.$renderCell(_,p,x,y),v.push(_),y++}return v},d.prototype.$renderCell=function(p,g,m,v){var y=p.element,x=this.session,$=y.childNodes[0],_=y.childNodes[1],b=y.childNodes[2],R=b.firstChild,M=x.$firstLineNumber,k=x.$breakpoints,S=x.$decorations,E=x.gutterRenderer||this.$renderer,I=this.$showFoldWidgets&&x.foldWidgets,C=m?m.start.row:Number.MAX_VALUE,w=g.lineHeight+"px",T=this.$useSvgGutterIcons?"ace_gutter-cell_svg-icons ":"ace_gutter-cell ",O=this.$useSvgGutterIcons?"ace_icon_svg":"ace_icon",N=(E?E.getText(x,v):v+M).toString();if(this.$highlightGutterLine&&(v==this.$cursorRow||m&&v=C&&this.$cursorRow<=m.end.row)&&(T+="ace_gutter-active-line ",this.$cursorCell!=p&&(this.$cursorCell&&(this.$cursorCell.element.className=this.$cursorCell.element.className.replace("ace_gutter-active-line ","")),this.$cursorCell=p)),k[v]&&(T+=k[v]),S[v]&&(T+=S[v]),this.$annotations[v]&&v!==C&&(T+=this.$annotations[v].className),y.className!=T&&(y.className=T),I){var B=I[v];B==null&&(B=I[v]=x.getFoldWidget(v))}if(B){var T="ace_fold-widget ace_"+B;if(B=="start"&&v==C&&vm.right-g.right)return"foldWidgets"},d}();c.prototype.$fixedWidth=!1,c.prototype.$highlightGutterLine=!0,c.prototype.$renderer="",c.prototype.$showLineNumbers=!0,c.prototype.$showFoldWidgets=!0,s.implement(c.prototype,l);function f(d){var p=document.createTextNode("");d.appendChild(p);var g=o.createElement("span");d.appendChild(g);var m=o.createElement("span");d.appendChild(m);var v=o.createElement("span");return m.appendChild(v),d}r.Gutter=c}),ace.define("ace/layer/marker",["require","exports","module","ace/range","ace/lib/dom"],function(n,r,i){var o=n("../range").Range,s=n("../lib/dom"),h=function(){function u(a){this.element=s.createElement("div"),this.element.className="ace_layer ace_marker-layer",a.appendChild(this.element)}return u.prototype.setPadding=function(a){this.$padding=a},u.prototype.setSession=function(a){this.session=a},u.prototype.setMarkers=function(a){this.markers=a},u.prototype.elt=function(a,c){var f=this.i!=-1&&this.element.childNodes[this.i];f?this.i++:(f=document.createElement("div"),this.element.appendChild(f),this.i=-1),f.style.cssText=c,f.className=a},u.prototype.update=function(a){if(a){this.config=a,this.i=0;var c;for(var f in this.markers){var d=this.markers[f];if(!d.range){d.update(c,this,this.session,a);continue}var p=d.range.clipRows(a.firstRow,a.lastRow);if(!p.isEmpty())if(p=p.toScreenRange(this.session),d.renderer){var g=this.$getTop(p.start.row,a),m=this.$padding+p.start.column*a.characterWidth;d.renderer(c,p,m,g,a)}else d.type=="fullLine"?this.drawFullLineMarker(c,p,d.clazz,a):d.type=="screenLine"?this.drawScreenLineMarker(c,p,d.clazz,a):p.isMultiLine()?d.type=="text"?this.drawTextMarker(c,p,d.clazz,a):this.drawMultiLineMarker(c,p,d.clazz,a):this.drawSingleLineMarker(c,p,d.clazz+" ace_start ace_br15",a)}if(this.i!=-1)for(;this.i_,y==v),d,y==v?0:1,p)},u.prototype.drawMultiLineMarker=function(a,c,f,d,p){var g=this.$padding,m=d.lineHeight,v=this.$getTop(c.start.row,d),y=g+c.start.column*d.characterWidth;if(p=p||"",this.session.$bidiHandler.isBidiRow(c.start.row)){var x=c.clone();x.end.row=x.start.row,x.end.column=this.session.getLine(x.start.row).length,this.drawBidiSingleLineMarker(a,x,f+" ace_br1 ace_start",d,null,p)}else this.elt(f+" ace_br1 ace_start","height:"+m+"px;right:0;top:"+v+"px;left:"+y+"px;"+(p||""));if(this.session.$bidiHandler.isBidiRow(c.end.row)){var x=c.clone();x.start.row=x.end.row,x.start.column=0,this.drawBidiSingleLineMarker(a,x,f+" ace_br12",d,null,p)}else{v=this.$getTop(c.end.row,d);var $=c.end.column*d.characterWidth;this.elt(f+" ace_br12","height:"+m+"px;width:"+$+"px;top:"+v+"px;left:"+g+"px;"+(p||""))}if(m=(c.end.row-c.start.row-1)*d.lineHeight,!(m<=0)){v=this.$getTop(c.start.row+1,d);var _=(c.start.column?1:0)|(c.end.column?0:8);this.elt(f+(_?" ace_br"+_:""),"height:"+m+"px;right:0;top:"+v+"px;left:"+g+"px;"+(p||""))}},u.prototype.drawSingleLineMarker=function(a,c,f,d,p,g){if(this.session.$bidiHandler.isBidiRow(c.start.row))return this.drawBidiSingleLineMarker(a,c,f,d,p,g);var m=d.lineHeight,v=(c.end.column+(p||0)-c.start.column)*d.characterWidth,y=this.$getTop(c.start.row,d),x=this.$padding+c.start.column*d.characterWidth;this.elt(f,"height:"+m+"px;width:"+v+"px;top:"+y+"px;left:"+x+"px;"+(g||""))},u.prototype.drawBidiSingleLineMarker=function(a,c,f,d,p,g){var m=d.lineHeight,v=this.$getTop(c.start.row,d),y=this.$padding,x=this.session.$bidiHandler.getSelections(c.start.column,c.end.column);x.forEach(function($){this.elt(f,"height:"+m+"px;width:"+($.width+(p||0))+"px;top:"+v+"px;left:"+(y+$.left)+"px;"+(g||""))},this)},u.prototype.drawFullLineMarker=function(a,c,f,d,p){var g=this.$getTop(c.start.row,d),m=d.lineHeight;c.start.row!=c.end.row&&(m+=this.$getTop(c.end.row,d)-g),this.elt(f,"height:"+m+"px;top:"+g+"px;left:0;right:0;"+(p||""))},u.prototype.drawScreenLineMarker=function(a,c,f,d,p){var g=this.$getTop(c.start.row,d),m=d.lineHeight;this.elt(f,"height:"+m+"px;top:"+g+"px;left:0;right:0;"+(p||""))},u}();h.prototype.$padding=0;function l(u,a,c,f){return(u?1:0)|(a?2:0)|(c?4:0)|(f?8:0)}r.Marker=h}),ace.define("ace/layer/text",["require","exports","module","ace/lib/oop","ace/lib/dom","ace/lib/lang","ace/layer/lines","ace/lib/event_emitter","ace/config"],function(n,r,i){var o=n("../lib/oop"),s=n("../lib/dom"),h=n("../lib/lang"),l=n("./lines").Lines,u=n("../lib/event_emitter").EventEmitter,a=n("../config").nls,c=function(){function f(d){this.dom=s,this.element=this.dom.createElement("div"),this.element.className="ace_layer ace_text-layer",d.appendChild(this.element),this.$updateEolChar=this.$updateEolChar.bind(this),this.$lines=new l(this.element)}return f.prototype.$updateEolChar=function(){var d=this.session.doc,p=d.getNewLineCharacter()==` -`&&d.getNewLineMode()!="windows",g=p?this.EOL_CHAR_LF:this.EOL_CHAR_CRLF;if(this.EOL_CHAR!=g)return this.EOL_CHAR=g,!0},f.prototype.setPadding=function(d){this.$padding=d,this.element.style.margin="0 "+d+"px"},f.prototype.getLineHeight=function(){return this.$fontMetrics.$characterSize.height||0},f.prototype.getCharacterWidth=function(){return this.$fontMetrics.$characterSize.width||0},f.prototype.$setFontMetrics=function(d){this.$fontMetrics=d,this.$fontMetrics.on("changeCharacterSize",function(p){this._signal("changeCharacterSize",p)}.bind(this)),this.$pollSizeChanges()},f.prototype.checkForSizeChanges=function(){this.$fontMetrics.checkForSizeChanges()},f.prototype.$pollSizeChanges=function(){return this.$pollSizeChangesTimer=this.$fontMetrics.$pollSizeChanges()},f.prototype.setSession=function(d){this.session=d,d&&this.$computeTabString()},f.prototype.setShowInvisibles=function(d){return this.showInvisibles==d?!1:(this.showInvisibles=d,typeof d=="string"?(this.showSpaces=/tab/i.test(d),this.showTabs=/space/i.test(d),this.showEOL=/eol/i.test(d)):this.showSpaces=this.showTabs=this.showEOL=d,this.$computeTabString(),!0)},f.prototype.setDisplayIndentGuides=function(d){return this.displayIndentGuides==d?!1:(this.displayIndentGuides=d,this.$computeTabString(),!0)},f.prototype.setHighlightIndentGuides=function(d){return this.$highlightIndentGuides===d?!1:(this.$highlightIndentGuides=d,d)},f.prototype.$computeTabString=function(){var d=this.session.getTabSize();this.tabSize=d;for(var p=this.$tabStrings=[0],g=1;gR&&(_=b.end.row+1,b=this.session.getNextFoldLine(_,b),R=b?b.start.row:1/0),!(_>v);){var M=y[x++];if(M){this.dom.removeChildren(M),this.$renderLine(M,_,_==R?b:!1),$&&(M.style.top=this.$lines.computeLineTop(_,d,this.session)+"px");var k=d.lineHeight*this.session.getRowLength(_)+"px";M.style.height!=k&&($=!0,M.style.height=k)}_++}if($)for(;x0;v--)this.$lines.shift();if(p.lastRow>d.lastRow)for(var v=this.session.getFoldedRowCount(d.lastRow+1,p.lastRow);v>0;v--)this.$lines.pop();d.firstRowp.lastRow&&this.$lines.push(this.$renderLinesFragment(d,p.lastRow+1,d.lastRow)),this.$highlightIndentGuide()},f.prototype.$renderLinesFragment=function(d,p,g){for(var m=[],v=p,y=this.session.getNextFoldLine(v),x=y?y.start.row:1/0;v>x&&(v=y.end.row+1,y=this.session.getNextFoldLine(v,y),x=y?y.start.row:1/0),!(v>g);){var $=this.$lines.createCell(v,d,this.session),_=$.element;this.dom.removeChildren(_),s.setStyle(_.style,"height",this.$lines.computeLineHeight(v,d,this.session)+"px"),s.setStyle(_.style,"top",this.$lines.computeLineTop(v,d,this.session)+"px"),this.$renderLine(_,v,v==x?y:!1),this.$useLineGroups()?_.className="ace_line_group":_.className="ace_line",m.push($),v++}return m},f.prototype.update=function(d){this.$lines.moveContainer(d),this.config=d;for(var p=d.firstRow,g=d.lastRow,m=this.$lines;m.getLength();)m.pop();m.push(this.$renderLinesFragment(d,p,g))},f.prototype.$renderToken=function(d,p,g,m){for(var v=this,y=/(\t)|( +)|([\x00-\x1f\x80-\xa0\xad\u1680\u180E\u2000-\u200f\u2028\u2029\u202F\u205F\uFEFF\uFFF9-\uFFFC\u2066\u2067\u2068\u202A\u202B\u202D\u202E\u202C\u2069]+)|(\u3000)|([\u1100-\u115F\u11A3-\u11A7\u11FA-\u11FF\u2329-\u232A\u2E80-\u2E99\u2E9B-\u2EF3\u2F00-\u2FD5\u2FF0-\u2FFB\u3001-\u303E\u3041-\u3096\u3099-\u30FF\u3105-\u312D\u3131-\u318E\u3190-\u31BA\u31C0-\u31E3\u31F0-\u321E\u3220-\u3247\u3250-\u32FE\u3300-\u4DBF\u4E00-\uA48C\uA490-\uA4C6\uA960-\uA97C\uAC00-\uD7A3\uD7B0-\uD7C6\uD7CB-\uD7FB\uF900-\uFAFF\uFE10-\uFE19\uFE30-\uFE52\uFE54-\uFE66\uFE68-\uFE6B\uFF01-\uFF60\uFFE0-\uFFE6]|[\uD800-\uDBFF][\uDC00-\uDFFF])/g,x=this.dom.createFragment(this.element),$,_=0;$=y.exec(m);){var b=$[1],R=$[2],M=$[3],k=$[4],S=$[5];if(!(!v.showSpaces&&R)){var E=_!=$.index?m.slice(_,$.index):"";if(_=$.index+$[0].length,E&&x.appendChild(this.dom.createTextNode(E,this.element)),b){var I=v.session.getScreenTabSize(p+$.index);x.appendChild(v.$tabStrings[I].cloneNode(!0)),p+=I-1}else if(R)if(v.showSpaces){var C=this.dom.createElement("span");C.className="ace_invisible ace_invisible_space",C.textContent=h.stringRepeat(v.SPACE_CHAR,R.length),x.appendChild(C)}else x.appendChild(this.com.createTextNode(R,this.element));else if(M){var C=this.dom.createElement("span");C.className="ace_invisible ace_invisible_space ace_invalid",C.textContent=h.stringRepeat(v.SPACE_CHAR,M.length),x.appendChild(C)}else if(k){p+=1;var C=this.dom.createElement("span");C.style.width=v.config.characterWidth*2+"px",C.className=v.showSpaces?"ace_cjk ace_invisible ace_invisible_space":"ace_cjk",C.textContent=v.showSpaces?v.SPACE_CHAR:k,x.appendChild(C)}else if(S){p+=1;var C=this.dom.createElement("span");C.style.width=v.config.characterWidth*2+"px",C.className="ace_cjk",C.textContent=S,x.appendChild(C)}}}if(x.appendChild(this.dom.createTextNode(_?m.slice(_):m,this.element)),this.$textToken[g.type])d.appendChild(x);else{var w="ace_"+g.type.replace(/\./g," ace_"),C=this.dom.createElement("span");g.type=="fold"&&(C.style.width=g.value.length*this.config.characterWidth+"px",C.setAttribute("title",a("Unfold code"))),C.className=w,C.appendChild(x),d.appendChild(C)}return p+m.length},f.prototype.renderIndentGuide=function(d,p,g){var m=p.search(this.$indentGuideRe);if(m<=0||m>=g)return p;if(p[0]==" "){m-=m%this.tabSize;for(var v=m/this.tabSize,y=0;yy[x].start.row?this.$highlightIndentGuideMarker.dir=-1:this.$highlightIndentGuideMarker.dir=1;break}}if(!this.$highlightIndentGuideMarker.end&&d[p.row]!==""&&p.column===d[p.row].length){this.$highlightIndentGuideMarker.dir=1;for(var x=p.row+1;x0){for(var v=0;v=this.$highlightIndentGuideMarker.start+1){if(m.row>=this.$highlightIndentGuideMarker.end)break;this.$setIndentGuideActive(m,p)}}else for(var g=d.length-1;g>=0;g--){var m=d[g];if(this.$highlightIndentGuideMarker.end&&m.row=y;)x=this.$renderToken($,x,b,R.substring(0,y-m)),R=R.substring(y-m),m=y,$=this.$createLineElement(),d.appendChild($),$.appendChild(this.dom.createTextNode(h.stringRepeat(" ",g.indent),this.element)),v++,x=0,y=g[v]||Number.MAX_VALUE;R.length!=0&&(m+=R.length,x=this.$renderToken($,x,b,R))}}g[g.length-1]>this.MAX_LINE_LENGTH&&this.$renderOverflowMessage($,x,null,"",!0)},f.prototype.$renderSimpleLine=function(d,p){for(var g=0,m=0;mthis.MAX_LINE_LENGTH)return this.$renderOverflowMessage(d,g,v,y);g=this.$renderToken(d,g,v,y)}}},f.prototype.$renderOverflowMessage=function(d,p,g,m,v){g&&this.$renderToken(d,p,g,m.slice(0,this.MAX_LINE_LENGTH-p));var y=this.dom.createElement("span");y.className="ace_inline_button ace_keyword ace_toggle_wrap",y.textContent=v?"":"",d.appendChild(y)},f.prototype.$renderLine=function(d,p,g){if(!g&&g!=!1&&(g=this.session.getFoldLine(p)),g)var m=this.$getFoldLineTokens(p,g);else var m=this.session.getTokens(p);var v=d;if(m.length){var y=this.session.getRowSplitData(p);if(y&&y.length){this.$renderWrappedLine(d,m,y);var v=d.lastChild}else{var v=d;this.$useLineGroups()&&(v=this.$createLineElement(),d.appendChild(v)),this.$renderSimpleLine(v,m)}}else this.$useLineGroups()&&(v=this.$createLineElement(),d.appendChild(v));if(this.showEOL&&v){g&&(p=g.end.row);var x=this.dom.createElement("span");x.className="ace_invisible ace_invisible_eol",x.textContent=p==this.session.getLength()-1?this.EOF_CHAR:this.EOL_CHAR,v.appendChild(x)}},f.prototype.$getFoldLineTokens=function(d,p){var g=this.session,m=[];function v(x,$,_){for(var b=0,R=0;R+x[b].value.length<$;)if(R+=x[b].value.length,b++,b==x.length)return;if(R!=$){var M=x[b].value.substring($-R);M.length>_-$&&(M=M.substring(0,_-$)),m.push({type:x[b].type,value:M}),R=$+M.length,b+=1}for(;R<_&&b_?m.push({type:x[b].type,value:M.substring(0,_-R)}):m.push(x[b]),R+=M.length,b+=1}}var y=g.getTokens(d);return p.walk(function(x,$,_,b,R){x!=null?m.push({type:"fold",value:x}):(R&&(y=g.getTokens($)),y.length&&v(y,b,_))},p.end.row,this.session.getLine(p.end.row).length),m},f.prototype.$useLineGroups=function(){return this.session.getUseWrapMode()},f}();c.prototype.$textToken={text:!0,rparen:!0,lparen:!0},c.prototype.EOF_CHAR="¶",c.prototype.EOL_CHAR_LF="¬",c.prototype.EOL_CHAR_CRLF="¤",c.prototype.EOL_CHAR=c.prototype.EOL_CHAR_LF,c.prototype.TAB_CHAR="—",c.prototype.SPACE_CHAR="·",c.prototype.$padding=0,c.prototype.MAX_LINE_LENGTH=1e4,c.prototype.showInvisibles=!1,c.prototype.showSpaces=!1,c.prototype.showTabs=!1,c.prototype.showEOL=!1,c.prototype.displayIndentGuides=!0,c.prototype.$highlightIndentGuides=!0,c.prototype.$tabStrings=[],c.prototype.destroy={},c.prototype.onChangeTabSize=c.prototype.$computeTabString,o.implement(c.prototype,u),r.Text=c}),ace.define("ace/layer/cursor",["require","exports","module","ace/lib/dom"],function(n,r,i){var o=n("../lib/dom"),s=function(){function h(l){this.element=o.createElement("div"),this.element.className="ace_layer ace_cursor-layer",l.appendChild(this.element),this.isVisible=!1,this.isBlinking=!0,this.blinkInterval=1e3,this.smoothBlinking=!1,this.cursors=[],this.cursor=this.addCursor(),o.addCssClass(this.element,"ace_hidden-cursors"),this.$updateCursors=this.$updateOpacity.bind(this)}return h.prototype.$updateOpacity=function(l){for(var u=this.cursors,a=u.length;a--;)o.setStyle(u[a].style,"opacity",l?"":"0")},h.prototype.$startCssAnimation=function(){for(var l=this.cursors,u=l.length;u--;)l[u].style.animationDuration=this.blinkInterval+"ms";this.$isAnimating=!0,setTimeout(function(){this.$isAnimating&&o.addCssClass(this.element,"ace_animate-blinking")}.bind(this))},h.prototype.$stopCssAnimation=function(){this.$isAnimating=!1,o.removeCssClass(this.element,"ace_animate-blinking")},h.prototype.setPadding=function(l){this.$padding=l},h.prototype.setSession=function(l){this.session=l},h.prototype.setBlinking=function(l){l!=this.isBlinking&&(this.isBlinking=l,this.restartTimer())},h.prototype.setBlinkInterval=function(l){l!=this.blinkInterval&&(this.blinkInterval=l,this.restartTimer())},h.prototype.setSmoothBlinking=function(l){l!=this.smoothBlinking&&(this.smoothBlinking=l,o.setCssClass(this.element,"ace_smooth-blinking",l),this.$updateCursors(!0),this.restartTimer())},h.prototype.addCursor=function(){var l=o.createElement("div");return l.className="ace_cursor",this.element.appendChild(l),this.cursors.push(l),l},h.prototype.removeCursor=function(){if(this.cursors.length>1){var l=this.cursors.pop();return l.parentNode.removeChild(l),l}},h.prototype.hideCursor=function(){this.isVisible=!1,o.addCssClass(this.element,"ace_hidden-cursors"),this.restartTimer()},h.prototype.showCursor=function(){this.isVisible=!0,o.removeCssClass(this.element,"ace_hidden-cursors"),this.restartTimer()},h.prototype.restartTimer=function(){var l=this.$updateCursors;if(clearInterval(this.intervalId),clearTimeout(this.timeoutId),this.$stopCssAnimation(),this.smoothBlinking&&(this.$isSmoothBlinking=!1,o.removeCssClass(this.element,"ace_smooth-blinking")),l(!0),!this.isBlinking||!this.blinkInterval||!this.isVisible){this.$stopCssAnimation();return}if(this.smoothBlinking&&(this.$isSmoothBlinking=!0,setTimeout(function(){this.$isSmoothBlinking&&o.addCssClass(this.element,"ace_smooth-blinking")}.bind(this))),o.HAS_CSS_ANIMATION)this.$startCssAnimation();else{var u=function(){this.timeoutId=setTimeout(function(){l(!1)},.6*this.blinkInterval)}.bind(this);this.intervalId=setInterval(function(){l(!0),u()},this.blinkInterval),u()}},h.prototype.getPixelPosition=function(l,u){if(!this.config||!this.session)return{left:0,top:0};l||(l=this.session.selection.getCursor());var a=this.session.documentToScreenPosition(l),c=this.$padding+(this.session.$bidiHandler.isBidiRow(a.row,l.row)?this.session.$bidiHandler.getPosLeft(a.column):a.column*this.config.characterWidth),f=(a.row-(u?this.config.firstRowScreen:0))*this.config.lineHeight;return{left:c,top:f}},h.prototype.isCursorInView=function(l,u){return l.top>=0&&l.topl.height+l.offset||d.top<0)&&a>1)){var p=this.cursors[c++]||this.addCursor(),g=p.style;this.drawCursor?this.drawCursor(p,d,l,u[a],this.session):this.isCursorInView(d,l)?(o.setStyle(g,"display","block"),o.translate(p,d.left,d.top),o.setStyle(g,"width",Math.round(l.characterWidth)+"px"),o.setStyle(g,"height",l.lineHeight+"px")):o.setStyle(g,"display","none")}}for(;this.cursors.length>c;)this.removeCursor();var m=this.session.getOverwrite();this.$setOverwrite(m),this.$pixelPos=d,this.restartTimer()},h.prototype.$setOverwrite=function(l){l!=this.overwrite&&(this.overwrite=l,l?o.addCssClass(this.element,"ace_overwrite-cursors"):o.removeCssClass(this.element,"ace_overwrite-cursors"))},h.prototype.destroy=function(){clearInterval(this.intervalId),clearTimeout(this.timeoutId)},h}();s.prototype.$padding=0,s.prototype.drawCursor=null,r.Cursor=s}),ace.define("ace/scrollbar",["require","exports","module","ace/lib/oop","ace/lib/dom","ace/lib/event","ace/lib/event_emitter"],function(n,r,i){var o=this&&this.__extends||function(){var p=function(g,m){return p=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(v,y){v.__proto__=y}||function(v,y){for(var x in y)Object.prototype.hasOwnProperty.call(y,x)&&(v[x]=y[x])},p(g,m)};return function(g,m){if(typeof m!="function"&&m!==null)throw new TypeError("Class extends value "+String(m)+" is not a constructor or null");p(g,m);function v(){this.constructor=g}g.prototype=m===null?Object.create(m):(v.prototype=m.prototype,new v)}}(),s=n("./lib/oop"),h=n("./lib/dom"),l=n("./lib/event"),u=n("./lib/event_emitter").EventEmitter,a=32768,c=function(){function p(g,m){this.element=h.createElement("div"),this.element.className="ace_scrollbar ace_scrollbar"+m,this.inner=h.createElement("div"),this.inner.className="ace_scrollbar-inner",this.inner.textContent=" ",this.element.appendChild(this.inner),g.appendChild(this.element),this.setVisible(!1),this.skipEvent=!1,l.addListener(this.element,"scroll",this.onScroll.bind(this)),l.addListener(this.element,"mousedown",l.preventDefault)}return p.prototype.setVisible=function(g){this.element.style.display=g?"":"none",this.isVisible=g,this.coeff=1},p}();s.implement(c.prototype,u);var f=function(p){o(g,p);function g(m,v){var y=p.call(this,m,"-v")||this;return y.scrollTop=0,y.scrollHeight=0,v.$scrollbarWidth=y.width=h.scrollbarWidth(m.ownerDocument),y.inner.style.width=y.element.style.width=(y.width||15)+5+"px",y.$minWidth=0,y}return g.prototype.onScroll=function(){if(!this.skipEvent){if(this.scrollTop=this.element.scrollTop,this.coeff!=1){var m=this.element.clientHeight/this.scrollHeight;this.scrollTop=this.scrollTop*(1-m)/(this.coeff-m)}this._emit("scroll",{data:this.scrollTop})}this.skipEvent=!1},g.prototype.getWidth=function(){return Math.max(this.isVisible?this.width:0,this.$minWidth||0)},g.prototype.setHeight=function(m){this.element.style.height=m+"px"},g.prototype.setScrollHeight=function(m){this.scrollHeight=m,m>a?(this.coeff=a/m,m=a):this.coeff!=1&&(this.coeff=1),this.inner.style.height=m+"px"},g.prototype.setScrollTop=function(m){this.scrollTop!=m&&(this.skipEvent=!0,this.scrollTop=m,this.element.scrollTop=m*this.coeff)},g}(c);f.prototype.setInnerHeight=f.prototype.setScrollHeight;var d=function(p){o(g,p);function g(m,v){var y=p.call(this,m,"-h")||this;return y.scrollLeft=0,y.height=v.$scrollbarWidth,y.inner.style.height=y.element.style.height=(y.height||15)+5+"px",y}return g.prototype.onScroll=function(){this.skipEvent||(this.scrollLeft=this.element.scrollLeft,this._emit("scroll",{data:this.scrollLeft})),this.skipEvent=!1},g.prototype.getHeight=function(){return this.isVisible?this.height:0},g.prototype.setWidth=function(m){this.element.style.width=m+"px"},g.prototype.setInnerWidth=function(m){this.inner.style.width=m+"px"},g.prototype.setScrollWidth=function(m){this.inner.style.width=m+"px"},g.prototype.setScrollLeft=function(m){this.scrollLeft!=m&&(this.skipEvent=!0,this.scrollLeft=this.element.scrollLeft=m)},g}(c);r.ScrollBar=f,r.ScrollBarV=f,r.ScrollBarH=d,r.VScrollBar=f,r.HScrollBar=d}),ace.define("ace/scrollbar_custom",["require","exports","module","ace/lib/oop","ace/lib/dom","ace/lib/event","ace/lib/event_emitter"],function(n,r,i){var o=this&&this.__extends||function(){var d=function(p,g){return d=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(m,v){m.__proto__=v}||function(m,v){for(var y in v)Object.prototype.hasOwnProperty.call(v,y)&&(m[y]=v[y])},d(p,g)};return function(p,g){if(typeof g!="function"&&g!==null)throw new TypeError("Class extends value "+String(g)+" is not a constructor or null");d(p,g);function m(){this.constructor=p}p.prototype=g===null?Object.create(g):(m.prototype=g.prototype,new m)}}(),s=n("./lib/oop"),h=n("./lib/dom"),l=n("./lib/event"),u=n("./lib/event_emitter").EventEmitter;h.importCssString(`.ace_editor>.ace_sb-v div, .ace_editor>.ace_sb-h div{ - position: absolute; - background: rgba(128, 128, 128, 0.6); - -moz-box-sizing: border-box; - box-sizing: border-box; - border: 1px solid #bbb; - border-radius: 2px; - z-index: 8; -} -.ace_editor>.ace_sb-v, .ace_editor>.ace_sb-h { - position: absolute; - z-index: 6; - background: none; - overflow: hidden!important; -} -.ace_editor>.ace_sb-v { - z-index: 6; - right: 0; - top: 0; - width: 12px; -} -.ace_editor>.ace_sb-v div { - z-index: 8; - right: 0; - width: 100%; -} -.ace_editor>.ace_sb-h { - bottom: 0; - left: 0; - height: 12px; -} -.ace_editor>.ace_sb-h div { - bottom: 0; - height: 100%; -} -.ace_editor>.ace_sb_grabbed { - z-index: 8; - background: #000; -}`,"ace_scrollbar.css",!1);var a=function(){function d(p,g){this.element=h.createElement("div"),this.element.className="ace_sb"+g,this.inner=h.createElement("div"),this.inner.className="",this.element.appendChild(this.inner),this.VScrollWidth=12,this.HScrollHeight=12,p.appendChild(this.element),this.setVisible(!1),this.skipEvent=!1,l.addMultiMouseDownListener(this.element,[500,300,300],this,"onMouseDown")}return d.prototype.setVisible=function(p){this.element.style.display=p?"":"none",this.isVisible=p,this.coeff=1},d}();s.implement(a.prototype,u);var c=function(d){o(p,d);function p(g,m){var v=d.call(this,g,"-v")||this;return v.scrollTop=0,v.scrollHeight=0,v.parent=g,v.width=v.VScrollWidth,v.renderer=m,v.inner.style.width=v.element.style.width=(v.width||15)+"px",v.$minWidth=0,v}return p.prototype.onMouseDown=function(g,m){if(g==="mousedown"&&!(l.getButton(m)!==0||m.detail===2)){if(m.target===this.inner){var v=this,y=m.clientY,x=function(S){y=S.clientY},$=function(){clearInterval(M)},_=m.clientY,b=this.thumbTop,R=function(){if(y!==void 0){var S=v.scrollTopFromThumbTop(b+y-_);S!==v.scrollTop&&v._emit("scroll",{data:S})}};l.capture(this.inner,x,$);var M=setInterval(R,20);return l.preventDefault(m)}var k=m.clientY-this.element.getBoundingClientRect().top-this.thumbHeight/2;return this._emit("scroll",{data:this.scrollTopFromThumbTop(k)}),l.preventDefault(m)}},p.prototype.getHeight=function(){return this.height},p.prototype.scrollTopFromThumbTop=function(g){var m=g*(this.pageHeight-this.viewHeight)/(this.slideHeight-this.thumbHeight);return m=m>>0,m<0?m=0:m>this.pageHeight-this.viewHeight&&(m=this.pageHeight-this.viewHeight),m},p.prototype.getWidth=function(){return Math.max(this.isVisible?this.width:0,this.$minWidth||0)},p.prototype.setHeight=function(g){this.height=Math.max(0,g),this.slideHeight=this.height,this.viewHeight=this.height,this.setScrollHeight(this.pageHeight,!0)},p.prototype.setScrollHeight=function(g,m){this.pageHeight===g&&!m||(this.pageHeight=g,this.thumbHeight=this.slideHeight*this.viewHeight/this.pageHeight,this.thumbHeight>this.slideHeight&&(this.thumbHeight=this.slideHeight),this.thumbHeight<15&&(this.thumbHeight=15),this.inner.style.height=this.thumbHeight+"px",this.scrollTop>this.pageHeight-this.viewHeight&&(this.scrollTop=this.pageHeight-this.viewHeight,this.scrollTop<0&&(this.scrollTop=0),this._emit("scroll",{data:this.scrollTop})))},p.prototype.setScrollTop=function(g){this.scrollTop=g,g<0&&(g=0),this.thumbTop=g*(this.slideHeight-this.thumbHeight)/(this.pageHeight-this.viewHeight),this.inner.style.top=this.thumbTop+"px"},p}(a);c.prototype.setInnerHeight=c.prototype.setScrollHeight;var f=function(d){o(p,d);function p(g,m){var v=d.call(this,g,"-h")||this;return v.scrollLeft=0,v.scrollWidth=0,v.height=v.HScrollHeight,v.inner.style.height=v.element.style.height=(v.height||12)+"px",v.renderer=m,v}return p.prototype.onMouseDown=function(g,m){if(g==="mousedown"&&!(l.getButton(m)!==0||m.detail===2)){if(m.target===this.inner){var v=this,y=m.clientX,x=function(S){y=S.clientX},$=function(){clearInterval(M)},_=m.clientX,b=this.thumbLeft,R=function(){if(y!==void 0){var S=v.scrollLeftFromThumbLeft(b+y-_);S!==v.scrollLeft&&v._emit("scroll",{data:S})}};l.capture(this.inner,x,$);var M=setInterval(R,20);return l.preventDefault(m)}var k=m.clientX-this.element.getBoundingClientRect().left-this.thumbWidth/2;return this._emit("scroll",{data:this.scrollLeftFromThumbLeft(k)}),l.preventDefault(m)}},p.prototype.getHeight=function(){return this.isVisible?this.height:0},p.prototype.scrollLeftFromThumbLeft=function(g){var m=g*(this.pageWidth-this.viewWidth)/(this.slideWidth-this.thumbWidth);return m=m>>0,m<0?m=0:m>this.pageWidth-this.viewWidth&&(m=this.pageWidth-this.viewWidth),m},p.prototype.setWidth=function(g){this.width=Math.max(0,g),this.element.style.width=this.width+"px",this.slideWidth=this.width,this.viewWidth=this.width,this.setScrollWidth(this.pageWidth,!0)},p.prototype.setScrollWidth=function(g,m){this.pageWidth===g&&!m||(this.pageWidth=g,this.thumbWidth=this.slideWidth*this.viewWidth/this.pageWidth,this.thumbWidth>this.slideWidth&&(this.thumbWidth=this.slideWidth),this.thumbWidth<15&&(this.thumbWidth=15),this.inner.style.width=this.thumbWidth+"px",this.scrollLeft>this.pageWidth-this.viewWidth&&(this.scrollLeft=this.pageWidth-this.viewWidth,this.scrollLeft<0&&(this.scrollLeft=0),this._emit("scroll",{data:this.scrollLeft})))},p.prototype.setScrollLeft=function(g){this.scrollLeft=g,g<0&&(g=0),this.thumbLeft=g*(this.slideWidth-this.thumbWidth)/(this.pageWidth-this.viewWidth),this.inner.style.left=this.thumbLeft+"px"},p}(a);f.prototype.setInnerWidth=f.prototype.setScrollWidth,r.ScrollBar=c,r.ScrollBarV=c,r.ScrollBarH=f,r.VScrollBar=c,r.HScrollBar=f}),ace.define("ace/renderloop",["require","exports","module","ace/lib/event"],function(n,r,i){var o=n("./lib/event"),s=function(){function h(l,u){this.onRender=l,this.pending=!1,this.changes=0,this.$recursionLimit=2,this.window=u||window;var a=this;this._flush=function(c){a.pending=!1;var f=a.changes;if(f&&(o.blockIdle(100),a.changes=0,a.onRender(f)),a.changes){if(a.$recursionLimit--<0)return;a.schedule()}else a.$recursionLimit=2}}return h.prototype.schedule=function(l){this.changes=this.changes|l,this.changes&&!this.pending&&(o.nextFrame(this._flush),this.pending=!0)},h.prototype.clear=function(l){var u=this.changes;return this.changes=0,u},h}();r.RenderLoop=s}),ace.define("ace/layer/font_metrics",["require","exports","module","ace/lib/oop","ace/lib/dom","ace/lib/lang","ace/lib/event","ace/lib/useragent","ace/lib/event_emitter"],function(n,r,i){var o=n("../lib/oop"),s=n("../lib/dom"),h=n("../lib/lang"),l=n("../lib/event"),u=n("../lib/useragent"),a=n("../lib/event_emitter").EventEmitter,c=512,f=typeof ResizeObserver=="function",d=200,p=function(){function g(m){this.el=s.createElement("div"),this.$setMeasureNodeStyles(this.el.style,!0),this.$main=s.createElement("div"),this.$setMeasureNodeStyles(this.$main.style),this.$measureNode=s.createElement("div"),this.$setMeasureNodeStyles(this.$measureNode.style),this.el.appendChild(this.$main),this.el.appendChild(this.$measureNode),m.appendChild(this.el),this.$measureNode.textContent=h.stringRepeat("X",c),this.$characterSize={width:0,height:0},f?this.$addObserver():this.checkForSizeChanges()}return g.prototype.$setMeasureNodeStyles=function(m,v){m.width=m.height="auto",m.left=m.top="0px",m.visibility="hidden",m.position="absolute",m.whiteSpace="pre",u.isIE<8?m["font-family"]="inherit":m.font="inherit",m.overflow=v?"hidden":"visible"},g.prototype.checkForSizeChanges=function(m){if(m===void 0&&(m=this.$measureSizes()),m&&(this.$characterSize.width!==m.width||this.$characterSize.height!==m.height)){this.$measureNode.style.fontWeight="bold";var v=this.$measureSizes();this.$measureNode.style.fontWeight="",this.$characterSize=m,this.charSizes=Object.create(null),this.allowBoldFonts=v&&v.width===m.width&&v.height===m.height,this._emit("changeCharacterSize",{data:m})}},g.prototype.$addObserver=function(){var m=this;this.$observer=new window.ResizeObserver(function(v){m.checkForSizeChanges()}),this.$observer.observe(this.$measureNode)},g.prototype.$pollSizeChanges=function(){if(this.$pollSizeChangesTimer||this.$observer)return this.$pollSizeChangesTimer;var m=this;return this.$pollSizeChangesTimer=l.onIdle(function v(){m.checkForSizeChanges(),l.onIdle(v,500)},500)},g.prototype.setPolling=function(m){m?this.$pollSizeChanges():this.$pollSizeChangesTimer&&(clearInterval(this.$pollSizeChangesTimer),this.$pollSizeChangesTimer=0)},g.prototype.$measureSizes=function(m){var v={height:(m||this.$measureNode).clientHeight,width:(m||this.$measureNode).clientWidth/c};return v.width===0||v.height===0?null:v},g.prototype.$measureCharWidth=function(m){this.$main.textContent=h.stringRepeat(m,c);var v=this.$main.getBoundingClientRect();return v.width/c},g.prototype.getCharacterWidth=function(m){var v=this.charSizes[m];return v===void 0&&(v=this.charSizes[m]=this.$measureCharWidth(m)/this.$characterSize.width),v},g.prototype.destroy=function(){clearInterval(this.$pollSizeChangesTimer),this.$observer&&this.$observer.disconnect(),this.el&&this.el.parentNode&&this.el.parentNode.removeChild(this.el)},g.prototype.$getZoom=function(m){return!m||!m.parentElement?1:(window.getComputedStyle(m).zoom||1)*this.$getZoom(m.parentElement)},g.prototype.$initTransformMeasureNodes=function(){var m=function(v,y){return["div",{style:"position: absolute;top:"+v+"px;left:"+y+"px;"}]};this.els=s.buildDom([m(0,0),m(d,0),m(0,d),m(d,d)],this.el)},g.prototype.transformCoordinates=function(m,v){if(m){var y=this.$getZoom(this.el);m=b(1/y,m)}function x(z,F,W){var G=z[1]*F[0]-z[0]*F[1];return[(-F[1]*W[0]+F[0]*W[1])/G,(+z[1]*W[0]-z[0]*W[1])/G]}function $(z,F){return[z[0]-F[0],z[1]-F[1]]}function _(z,F){return[z[0]+F[0],z[1]+F[1]]}function b(z,F){return[z*F[0],z*F[1]]}this.els||this.$initTransformMeasureNodes();function R(z){var F=z.getBoundingClientRect();return[F.left,F.top]}var M=R(this.els[0]),k=R(this.els[1]),S=R(this.els[2]),E=R(this.els[3]),I=x($(E,k),$(E,S),$(_(k,S),_(E,M))),C=b(1+I[0],$(k,M)),w=b(1+I[1],$(S,M));if(v){var T=v,O=I[0]*T[0]/d+I[1]*T[1]/d+1,N=_(b(T[0],C),b(T[1],w));return _(b(1/O/d,N),M)}var B=$(m,M),H=x($(C,b(I[0],B)),$(w,b(I[1],B)),B);return b(d,H)},g}();p.prototype.$characterSize={width:0,height:0},o.implement(p.prototype,a),r.FontMetrics=p}),ace.define("ace/css/editor.css",["require","exports","module"],function(n,r,i){i.exports=` -.ace_br1 {border-top-left-radius : 3px;} -.ace_br2 {border-top-right-radius : 3px;} -.ace_br3 {border-top-left-radius : 3px; border-top-right-radius: 3px;} -.ace_br4 {border-bottom-right-radius: 3px;} -.ace_br5 {border-top-left-radius : 3px; border-bottom-right-radius: 3px;} -.ace_br6 {border-top-right-radius : 3px; border-bottom-right-radius: 3px;} -.ace_br7 {border-top-left-radius : 3px; border-top-right-radius: 3px; border-bottom-right-radius: 3px;} -.ace_br8 {border-bottom-left-radius : 3px;} -.ace_br9 {border-top-left-radius : 3px; border-bottom-left-radius: 3px;} -.ace_br10{border-top-right-radius : 3px; border-bottom-left-radius: 3px;} -.ace_br11{border-top-left-radius : 3px; border-top-right-radius: 3px; border-bottom-left-radius: 3px;} -.ace_br12{border-bottom-right-radius: 3px; border-bottom-left-radius: 3px;} -.ace_br13{border-top-left-radius : 3px; border-bottom-right-radius: 3px; border-bottom-left-radius: 3px;} -.ace_br14{border-top-right-radius : 3px; border-bottom-right-radius: 3px; border-bottom-left-radius: 3px;} -.ace_br15{border-top-left-radius : 3px; border-top-right-radius: 3px; border-bottom-right-radius: 3px; border-bottom-left-radius: 3px;} - - -.ace_editor { - position: relative; - overflow: hidden; - padding: 0; - font: 12px/normal 'Monaco', 'Menlo', 'Ubuntu Mono', 'Consolas', 'Source Code Pro', 'source-code-pro', monospace; - direction: ltr; - text-align: left; - -webkit-tap-highlight-color: rgba(0, 0, 0, 0); -} - -.ace_scroller { - position: absolute; - overflow: hidden; - top: 0; - bottom: 0; - background-color: inherit; - -ms-user-select: none; - -moz-user-select: none; - -webkit-user-select: none; - user-select: none; - cursor: text; -} - -.ace_content { - position: absolute; - box-sizing: border-box; - min-width: 100%; - contain: style size layout; - font-variant-ligatures: no-common-ligatures; -} - -.ace_keyboard-focus:focus { - box-shadow: inset 0 0 0 2px #5E9ED6; - outline: none; -} - -.ace_dragging .ace_scroller:before{ - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - content: ''; - background: rgba(250, 250, 250, 0.01); - z-index: 1000; -} -.ace_dragging.ace_dark .ace_scroller:before{ - background: rgba(0, 0, 0, 0.01); -} - -.ace_gutter { - position: absolute; - overflow : hidden; - width: auto; - top: 0; - bottom: 0; - left: 0; - cursor: default; - z-index: 4; - -ms-user-select: none; - -moz-user-select: none; - -webkit-user-select: none; - user-select: none; - contain: style size layout; -} - -.ace_gutter-active-line { - position: absolute; - left: 0; - right: 0; -} - -.ace_scroller.ace_scroll-left:after { - content: ""; - position: absolute; - top: 0; - right: 0; - bottom: 0; - left: 0; - box-shadow: 17px 0 16px -16px rgba(0, 0, 0, 0.4) inset; - pointer-events: none; -} - -.ace_gutter-cell, .ace_gutter-cell_svg-icons { - position: absolute; - top: 0; - left: 0; - right: 0; - padding-left: 19px; - padding-right: 6px; - background-repeat: no-repeat; -} - -.ace_gutter-cell_svg-icons .ace_gutter_annotation { - margin-left: -14px; - float: left; -} - -.ace_gutter-cell .ace_gutter_annotation { - margin-left: -19px; - float: left; -} - -.ace_gutter-cell.ace_error, .ace_icon.ace_error, .ace_icon.ace_error_fold { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAMAAAAoLQ9TAAABOFBMVEX/////////QRswFAb/Ui4wFAYwFAYwFAaWGAfDRymzOSH/PxswFAb/SiUwFAYwFAbUPRvjQiDllog5HhHdRybsTi3/Tyv9Tir+Syj/UC3////XurebMBIwFAb/RSHbPx/gUzfdwL3kzMivKBAwFAbbvbnhPx66NhowFAYwFAaZJg8wFAaxKBDZurf/RB6mMxb/SCMwFAYwFAbxQB3+RB4wFAb/Qhy4Oh+4QifbNRcwFAYwFAYwFAb/QRzdNhgwFAYwFAbav7v/Uy7oaE68MBK5LxLewr/r2NXewLswFAaxJw4wFAbkPRy2PyYwFAaxKhLm1tMwFAazPiQwFAaUGAb/QBrfOx3bvrv/VC/maE4wFAbRPBq6MRO8Qynew8Dp2tjfwb0wFAbx6eju5+by6uns4uH9/f36+vr/GkHjAAAAYnRSTlMAGt+64rnWu/bo8eAA4InH3+DwoN7j4eLi4xP99Nfg4+b+/u9B/eDs1MD1mO7+4PHg2MXa347g7vDizMLN4eG+Pv7i5evs/v79yu7S3/DV7/498Yv24eH+4ufQ3Ozu/v7+y13sRqwAAADLSURBVHjaZc/XDsFgGIBhtDrshlitmk2IrbHFqL2pvXf/+78DPokj7+Fz9qpU/9UXJIlhmPaTaQ6QPaz0mm+5gwkgovcV6GZzd5JtCQwgsxoHOvJO15kleRLAnMgHFIESUEPmawB9ngmelTtipwwfASilxOLyiV5UVUyVAfbG0cCPHig+GBkzAENHS0AstVF6bacZIOzgLmxsHbt2OecNgJC83JERmePUYq8ARGkJx6XtFsdddBQgZE2nPR6CICZhawjA4Fb/chv+399kfR+MMMDGOQAAAABJRU5ErkJggg=="); - background-repeat: no-repeat; - background-position: 2px center; -} - -.ace_gutter-cell.ace_warning, .ace_icon.ace_warning, .ace_icon.ace_warning_fold { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAMAAAAoLQ9TAAAAmVBMVEX///8AAAD///8AAAAAAABPSzb/5sAAAAB/blH/73z/ulkAAAAAAAD85pkAAAAAAAACAgP/vGz/rkDerGbGrV7/pkQICAf////e0IsAAAD/oED/qTvhrnUAAAD/yHD/njcAAADuv2r/nz//oTj/p064oGf/zHAAAAA9Nir/tFIAAAD/tlTiuWf/tkIAAACynXEAAAAAAAAtIRW7zBpBAAAAM3RSTlMAABR1m7RXO8Ln31Z36zT+neXe5OzooRDfn+TZ4p3h2hTf4t3k3ucyrN1K5+Xaks52Sfs9CXgrAAAAjklEQVR42o3PbQ+CIBQFYEwboPhSYgoYunIqqLn6/z8uYdH8Vmdnu9vz4WwXgN/xTPRD2+sgOcZjsge/whXZgUaYYvT8QnuJaUrjrHUQreGczuEafQCO/SJTufTbroWsPgsllVhq3wJEk2jUSzX3CUEDJC84707djRc5MTAQxoLgupWRwW6UB5fS++NV8AbOZgnsC7BpEAAAAABJRU5ErkJggg=="); - background-repeat: no-repeat; - background-position: 2px center; -} - -.ace_gutter-cell.ace_info, .ace_icon.ace_info { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAAAAAA6mKC9AAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAAJ0Uk5TAAB2k804AAAAPklEQVQY02NgIB68QuO3tiLznjAwpKTgNyDbMegwisCHZUETUZV0ZqOquBpXj2rtnpSJT1AEnnRmL2OgGgAAIKkRQap2htgAAAAASUVORK5CYII="); - background-repeat: no-repeat; - background-position: 2px center; -} -.ace_dark .ace_gutter-cell.ace_info, .ace_dark .ace_icon.ace_info { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAAJFBMVEUAAAChoaGAgIAqKiq+vr6tra1ZWVmUlJSbm5s8PDxubm56enrdgzg3AAAAAXRSTlMAQObYZgAAAClJREFUeNpjYMAPdsMYHegyJZFQBlsUlMFVCWUYKkAZMxZAGdxlDMQBAG+TBP4B6RyJAAAAAElFTkSuQmCC"); -} - -.ace_icon_svg.ace_error { - -webkit-mask-image: url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyMCAxNiI+CjxnIHN0cm9rZS13aWR0aD0iMiIgc3Ryb2tlPSJyZWQiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KPGNpcmNsZSBmaWxsPSJub25lIiBjeD0iOCIgY3k9IjgiIHI9IjciIHN0cm9rZS1saW5lam9pbj0icm91bmQiLz4KPGxpbmUgeDE9IjExIiB5MT0iNSIgeDI9IjUiIHkyPSIxMSIvPgo8bGluZSB4MT0iMTEiIHkxPSIxMSIgeDI9IjUiIHkyPSI1Ii8+CjwvZz4KPC9zdmc+"); - background-color: crimson; -} -.ace_icon_svg.ace_warning { - -webkit-mask-image: url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyMCAxNiI+CjxnIHN0cm9rZS13aWR0aD0iMiIgc3Ryb2tlPSJkYXJrb3JhbmdlIiBzaGFwZS1yZW5kZXJpbmc9Imdlb21ldHJpY1ByZWNpc2lvbiI+Cjxwb2x5Z29uIHN0cm9rZS1saW5lam9pbj0icm91bmQiIGZpbGw9Im5vbmUiIHBvaW50cz0iOCAxIDE1IDE1IDEgMTUgOCAxIi8+CjxyZWN0IHg9IjgiIHk9IjEyIiB3aWR0aD0iMC4wMSIgaGVpZ2h0PSIwLjAxIi8+CjxsaW5lIHgxPSI4IiB5MT0iNiIgeDI9IjgiIHkyPSIxMCIvPgo8L2c+Cjwvc3ZnPg=="); - background-color: darkorange; -} -.ace_icon_svg.ace_info { - -webkit-mask-image: url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyMCAxNiI+CjxnIHN0cm9rZS13aWR0aD0iMiIgc3Ryb2tlPSJibHVlIiBzaGFwZS1yZW5kZXJpbmc9Imdlb21ldHJpY1ByZWNpc2lvbiI+CjxjaXJjbGUgZmlsbD0ibm9uZSIgY3g9IjgiIGN5PSI4IiByPSI3IiBzdHJva2UtbGluZWpvaW49InJvdW5kIi8+Cjxwb2x5bGluZSBwb2ludHM9IjggMTEgOCA4Ii8+Cjxwb2x5bGluZSBwb2ludHM9IjkgOCA2IDgiLz4KPGxpbmUgeDE9IjEwIiB5MT0iMTEiIHgyPSI2IiB5Mj0iMTEiLz4KPHJlY3QgeD0iOCIgeT0iNSIgd2lkdGg9IjAuMDEiIGhlaWdodD0iMC4wMSIvPgo8L2c+Cjwvc3ZnPg=="); - background-color: royalblue; -} - -.ace_icon_svg.ace_error_fold { - -webkit-mask-image: url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyMCAxNiIgZmlsbD0ibm9uZSI+CiAgPHBhdGggZD0ibSAxOC45Mjk4NTEsNy44Mjk4MDc2IGMgMC4xNDYzNTMsNi4zMzc0NjA0IC02LjMyMzE0Nyw3Ljc3Nzg0NDQgLTcuNDc3OTEyLDcuNzc3ODQ0NCAtMi4xMDcyNzI2LC0wLjEyODc1IDUuMTE3Njc4LDAuMzU2MjQ5IDUuMDUxNjk4LC03Ljg3MDA2MTggLTAuNjA0NjcyLC04LjAwMzk3MzQ5IC03LjA3NzI3MDYsLTcuNTYzMTE4OSAtNC44NTczLC03LjQzMDM5NTU2IDEuNjA2LC0wLjExNTE0MjI1IDYuODk3NDg1LDEuMjYyNTQ1OTYgNy4yODM1MTQsNy41MjI2MTI5NiB6IiBmaWxsPSJjcmltc29uIiBzdHJva2Utd2lkdGg9IjIiLz4KICA8cGF0aCBmaWxsLXJ1bGU9ImV2ZW5vZGQiIGNsaXAtcnVsZT0iZXZlbm9kZCIgZD0ibSA4LjExNDc1NjIsMi4wNTI5ODI4IGMgMy4zNDkxNjk4LDAgNi4wNjQxMzI4LDIuNjc2ODYyNyA2LjA2NDEzMjgsNS45Nzg5NTMgMCwzLjMwMjExMjIgLTIuNzE0OTYzLDUuOTc4OTIwMiAtNi4wNjQxMzI4LDUuOTc4OTIwMiAtMy4zNDkxNDczLDAgLTYuMDY0MTc3MiwtMi42NzY4MDggLTYuMDY0MTc3MiwtNS45Nzg5MjAyIDAuMDA1MzksLTMuMjk5ODg2MSAyLjcxNzI2NTYsLTUuOTczNjQwOCA2LjA2NDE3NzIsLTUuOTc4OTUzIHogbSAwLC0xLjczNTgyNzE5IGMgLTQuMzIxNDgzNiwwIC03LjgyNDc0MDM4LDMuNDU0MDE4NDkgLTcuODI0NzQwMzgsNy43MTQ3ODAxOSAwLDQuMjYwNzI4MiAzLjUwMzI1Njc4LDcuNzE0NzQ1MiA3LjgyNDc0MDM4LDcuNzE0NzQ1MiA0LjMyMTQ0OTgsMCA3LjgyNDY5OTgsLTMuNDU0MDE3IDcuODI0Njk5OCwtNy43MTQ3NDUyIDAsLTIuMDQ2MDkxNCAtMC44MjQzOTIsLTQuMDA4MzY3MiAtMi4yOTE3NTYsLTUuNDU1MTc0NiBDIDEyLjE4MDIyNSwxLjEyOTk2NDggMTAuMTkwMDEzLDAuMzE3MTU1NjEgOC4xMTQ3NTYyLDAuMzE3MTU1NjEgWiBNIDYuOTM3NDU2Myw4LjI0MDU5ODUgNC42NzE4Njg1LDEwLjQ4NTg1MiA2LjAwODY4MTQsMTEuODc2NzI4IDguMzE3MDAzNSw5LjYwMDc5MTEgMTAuNjI1MzM3LDExLjg3NjcyOCAxMS45NjIxMzgsMTAuNDg1ODUyIDkuNjk2NTUwOCw4LjI0MDU5ODUgMTEuOTYyMTM4LDYuMDA2ODA2NiAxMC41NzMyNDYsNC42Mzc0MzM1IDguMzE3MDAzNSw2Ljg3MzQyOTcgNi4wNjA3NjA3LDQuNjM3NDMzNSA0LjY3MTg2ODUsNi4wMDY4MDY2IFoiIGZpbGw9ImNyaW1zb24iIHN0cm9rZS13aWR0aD0iMiIvPgo8L3N2Zz4="); - background-color: crimson; -} -.ace_icon_svg.ace_warning_fold { - -webkit-mask-image: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjAiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAyMCAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZmlsbC1ydWxlPSJldmVub2RkIiBjbGlwLXJ1bGU9ImV2ZW5vZGQiIGQ9Ik0xNC43NzY5IDE0LjczMzdMOC42NTE5MiAyLjQ4MzY5QzguMzI5NDYgMS44Mzg3NyA3LjQwOTEzIDEuODM4NzcgNy4wODY2NyAyLjQ4MzY5TDAuOTYxNjY5IDE0LjczMzdDMC42NzA3NzUgMTUuMzE1NSAxLjA5MzgzIDE2IDEuNzQ0MjkgMTZIMTMuOTk0M0MxNC42NDQ4IDE2IDE1LjA2NzggMTUuMzE1NSAxNC43NzY5IDE0LjczMzdaTTMuMTYwMDcgMTQuMjVMNy44NjkyOSA0LjgzMTU2TDEyLjU3ODUgMTQuMjVIMy4xNjAwN1pNOC43NDQyOSAxMS42MjVWMTMuMzc1SDYuOTk0MjlWMTEuNjI1SDguNzQ0MjlaTTYuOTk0MjkgMTAuNzVWNy4yNUg4Ljc0NDI5VjEwLjc1SDYuOTk0MjlaIiBmaWxsPSIjRUM3MjExIi8+CjxwYXRoIGQ9Ik0xMS4xOTkxIDIuOTUyMzhDMTAuODgwOSAyLjMxNDY3IDEwLjM1MzcgMS44MDUyNiA5LjcwNTUgMS41MDlMMTEuMDQxIDEuMDY5NzhDMTEuNjg4MyAwLjk0OTgxNCAxMi4zMzcgMS4yNzI2MyAxMi42MzE3IDEuODYxNDFMMTcuNjEzNiAxMS44MTYxQzE4LjM1MjcgMTMuMjkyOSAxNy41OTM4IDE1LjA4MDQgMTYuMDE4IDE1LjU3NDVDMTYuNDA0NCAxNC40NTA3IDE2LjMyMzEgMTMuMjE4OCAxNS43OTI0IDEyLjE1NTVMMTEuMTk5MSAyLjk1MjM4WiIgZmlsbD0iI0VDNzIxMSIvPgo8L3N2Zz4="); - background-color: darkorange; -} - -.ace_scrollbar { - contain: strict; - position: absolute; - right: 0; - bottom: 0; - z-index: 6; -} - -.ace_scrollbar-inner { - position: absolute; - cursor: text; - left: 0; - top: 0; -} - -.ace_scrollbar-v{ - overflow-x: hidden; - overflow-y: scroll; - top: 0; -} - -.ace_scrollbar-h { - overflow-x: scroll; - overflow-y: hidden; - left: 0; -} - -.ace_print-margin { - position: absolute; - height: 100%; -} - -.ace_text-input { - position: absolute; - z-index: 0; - width: 0.5em; - height: 1em; - opacity: 0; - background: transparent; - -moz-appearance: none; - appearance: none; - border: none; - resize: none; - outline: none; - overflow: hidden; - font: inherit; - padding: 0 1px; - margin: 0 -1px; - contain: strict; - -ms-user-select: text; - -moz-user-select: text; - -webkit-user-select: text; - user-select: text; - /*with \`pre-line\` chrome inserts   instead of space*/ - white-space: pre!important; -} -.ace_text-input.ace_composition { - background: transparent; - color: inherit; - z-index: 1000; - opacity: 1; -} -.ace_composition_placeholder { color: transparent } -.ace_composition_marker { - border-bottom: 1px solid; - position: absolute; - border-radius: 0; - margin-top: 1px; -} - -[ace_nocontext=true] { - transform: none!important; - filter: none!important; - clip-path: none!important; - mask : none!important; - contain: none!important; - perspective: none!important; - mix-blend-mode: initial!important; - z-index: auto; -} - -.ace_layer { - z-index: 1; - position: absolute; - overflow: hidden; - /* workaround for chrome bug https://github.com/ajaxorg/ace/issues/2312*/ - word-wrap: normal; - white-space: pre; - height: 100%; - width: 100%; - box-sizing: border-box; - /* setting pointer-events: auto; on node under the mouse, which changes - during scroll, will break mouse wheel scrolling in Safari */ - pointer-events: none; -} - -.ace_gutter-layer { - position: relative; - width: auto; - text-align: right; - pointer-events: auto; - height: 1000000px; - contain: style size layout; -} - -.ace_text-layer { - font: inherit !important; - position: absolute; - height: 1000000px; - width: 1000000px; - contain: style size layout; -} - -.ace_text-layer > .ace_line, .ace_text-layer > .ace_line_group { - contain: style size layout; - position: absolute; - top: 0; - left: 0; - right: 0; -} - -.ace_hidpi .ace_text-layer, -.ace_hidpi .ace_gutter-layer, -.ace_hidpi .ace_content, -.ace_hidpi .ace_gutter { - contain: strict; -} -.ace_hidpi .ace_text-layer > .ace_line, -.ace_hidpi .ace_text-layer > .ace_line_group { - contain: strict; -} - -.ace_cjk { - display: inline-block; - text-align: center; -} - -.ace_cursor-layer { - z-index: 4; -} - -.ace_cursor { - z-index: 4; - position: absolute; - box-sizing: border-box; - border-left: 2px solid; - /* workaround for smooth cursor repaintng whole screen in chrome */ - transform: translatez(0); -} - -.ace_multiselect .ace_cursor { - border-left-width: 1px; -} - -.ace_slim-cursors .ace_cursor { - border-left-width: 1px; -} - -.ace_overwrite-cursors .ace_cursor { - border-left-width: 0; - border-bottom: 1px solid; -} - -.ace_hidden-cursors .ace_cursor { - opacity: 0.2; -} - -.ace_hasPlaceholder .ace_hidden-cursors .ace_cursor { - opacity: 0; -} - -.ace_smooth-blinking .ace_cursor { - transition: opacity 0.18s; -} - -.ace_animate-blinking .ace_cursor { - animation-duration: 1000ms; - animation-timing-function: step-end; - animation-name: blink-ace-animate; - animation-iteration-count: infinite; -} - -.ace_animate-blinking.ace_smooth-blinking .ace_cursor { - animation-duration: 1000ms; - animation-timing-function: ease-in-out; - animation-name: blink-ace-animate-smooth; -} - -@keyframes blink-ace-animate { - from, to { opacity: 1; } - 60% { opacity: 0; } -} - -@keyframes blink-ace-animate-smooth { - from, to { opacity: 1; } - 45% { opacity: 1; } - 60% { opacity: 0; } - 85% { opacity: 0; } -} - -.ace_marker-layer .ace_step, .ace_marker-layer .ace_stack { - position: absolute; - z-index: 3; -} - -.ace_marker-layer .ace_selection { - position: absolute; - z-index: 5; -} - -.ace_marker-layer .ace_bracket { - position: absolute; - z-index: 6; -} - -.ace_marker-layer .ace_error_bracket { - position: absolute; - border-bottom: 1px solid #DE5555; - border-radius: 0; -} - -.ace_marker-layer .ace_active-line { - position: absolute; - z-index: 2; -} - -.ace_marker-layer .ace_selected-word { - position: absolute; - z-index: 4; - box-sizing: border-box; -} - -.ace_line .ace_fold { - box-sizing: border-box; - - display: inline-block; - height: 11px; - margin-top: -2px; - vertical-align: middle; - - background-image: - url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABEAAAAJCAYAAADU6McMAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAJpJREFUeNpi/P//PwOlgAXGYGRklAVSokD8GmjwY1wasKljQpYACtpCFeADcHVQfQyMQAwzwAZI3wJKvCLkfKBaMSClBlR7BOQikCFGQEErIH0VqkabiGCAqwUadAzZJRxQr/0gwiXIal8zQQPnNVTgJ1TdawL0T5gBIP1MUJNhBv2HKoQHHjqNrA4WO4zY0glyNKLT2KIfIMAAQsdgGiXvgnYAAAAASUVORK5CYII="), - url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAA3CAYAAADNNiA5AAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAACJJREFUeNpi+P//fxgTAwPDBxDxD078RSX+YeEyDFMCIMAAI3INmXiwf2YAAAAASUVORK5CYII="); - background-repeat: no-repeat, repeat-x; - background-position: center center, top left; - color: transparent; - - border: 1px solid black; - border-radius: 2px; - - cursor: pointer; - pointer-events: auto; -} - -.ace_dark .ace_fold { -} - -.ace_fold:hover{ - background-image: - url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABEAAAAJCAYAAADU6McMAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAJpJREFUeNpi/P//PwOlgAXGYGRklAVSokD8GmjwY1wasKljQpYACtpCFeADcHVQfQyMQAwzwAZI3wJKvCLkfKBaMSClBlR7BOQikCFGQEErIH0VqkabiGCAqwUadAzZJRxQr/0gwiXIal8zQQPnNVTgJ1TdawL0T5gBIP1MUJNhBv2HKoQHHjqNrA4WO4zY0glyNKLT2KIfIMAAQsdgGiXvgnYAAAAASUVORK5CYII="), - url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAA3CAYAAADNNiA5AAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAACBJREFUeNpi+P//fz4TAwPDZxDxD5X4i5fLMEwJgAADAEPVDbjNw87ZAAAAAElFTkSuQmCC"); -} - -.ace_tooltip { - background-color: #f5f5f5; - border: 1px solid gray; - border-radius: 1px; - box-shadow: 0 1px 2px rgba(0, 0, 0, 0.3); - color: black; - max-width: 100%; - padding: 3px 4px; - position: fixed; - z-index: 999999; - box-sizing: border-box; - cursor: default; - white-space: pre; - word-wrap: break-word; - line-height: normal; - font-style: normal; - font-weight: normal; - letter-spacing: normal; - pointer-events: none; -} - -.ace_tooltip.ace_dark { - background-color: #636363; - color: #fff; -} - -.ace_tooltip:focus { - outline: 1px solid #5E9ED6; -} - -.ace_icon { - display: inline-block; - width: 18px; - vertical-align: top; -} - -.ace_icon_svg { - display: inline-block; - width: 12px; - vertical-align: top; - -webkit-mask-repeat: no-repeat; - -webkit-mask-size: 12px; - -webkit-mask-position: center; -} - -.ace_folding-enabled > .ace_gutter-cell, .ace_folding-enabled > .ace_gutter-cell_svg-icons { - padding-right: 13px; -} - -.ace_fold-widget { - box-sizing: border-box; - - margin: 0 -12px 0 1px; - display: none; - width: 11px; - vertical-align: top; - - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAANElEQVR42mWKsQ0AMAzC8ixLlrzQjzmBiEjp0A6WwBCSPgKAXoLkqSot7nN3yMwR7pZ32NzpKkVoDBUxKAAAAABJRU5ErkJggg=="); - background-repeat: no-repeat; - background-position: center; - - border-radius: 3px; - - border: 1px solid transparent; - cursor: pointer; -} - -.ace_folding-enabled .ace_fold-widget { - display: inline-block; -} - -.ace_fold-widget.ace_end { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAANElEQVR42m3HwQkAMAhD0YzsRchFKI7sAikeWkrxwScEB0nh5e7KTPWimZki4tYfVbX+MNl4pyZXejUO1QAAAABJRU5ErkJggg=="); -} - -.ace_fold-widget.ace_closed { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAAGCAYAAAAG5SQMAAAAOUlEQVR42jXKwQkAMAgDwKwqKD4EwQ26sSOkVWjgIIHAzPiCgaqiqnJHZnKICBERHN194O5b9vbLuAVRL+l0YWnZAAAAAElFTkSuQmCCXA=="); -} - -.ace_fold-widget:hover { - border: 1px solid rgba(0, 0, 0, 0.3); - background-color: rgba(255, 255, 255, 0.2); - box-shadow: 0 1px 1px rgba(255, 255, 255, 0.7); -} - -.ace_fold-widget:active { - border: 1px solid rgba(0, 0, 0, 0.4); - background-color: rgba(0, 0, 0, 0.05); - box-shadow: 0 1px 1px rgba(255, 255, 255, 0.8); -} -/** - * Dark version for fold widgets - */ -.ace_dark .ace_fold-widget { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAAHklEQVQIW2P4//8/AzoGEQ7oGCaLLAhWiSwB146BAQCSTPYocqT0AAAAAElFTkSuQmCC"); -} -.ace_dark .ace_fold-widget.ace_end { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAAH0lEQVQIW2P4//8/AxQ7wNjIAjDMgC4AxjCVKBirIAAF0kz2rlhxpAAAAABJRU5ErkJggg=="); -} -.ace_dark .ace_fold-widget.ace_closed { - background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAAFCAYAAACAcVaiAAAAHElEQVQIW2P4//+/AxAzgDADlOOAznHAKgPWAwARji8UIDTfQQAAAABJRU5ErkJggg=="); -} -.ace_dark .ace_fold-widget:hover { - box-shadow: 0 1px 1px rgba(255, 255, 255, 0.2); - background-color: rgba(255, 255, 255, 0.1); -} -.ace_dark .ace_fold-widget:active { - box-shadow: 0 1px 1px rgba(255, 255, 255, 0.2); -} - -.ace_inline_button { - border: 1px solid lightgray; - display: inline-block; - margin: -1px 8px; - padding: 0 5px; - pointer-events: auto; - cursor: pointer; -} -.ace_inline_button:hover { - border-color: gray; - background: rgba(200,200,200,0.2); - display: inline-block; - pointer-events: auto; -} - -.ace_fold-widget.ace_invalid { - background-color: #FFB4B4; - border-color: #DE5555; -} - -.ace_fade-fold-widgets .ace_fold-widget { - transition: opacity 0.4s ease 0.05s; - opacity: 0; -} - -.ace_fade-fold-widgets:hover .ace_fold-widget { - transition: opacity 0.05s ease 0.05s; - opacity:1; -} - -.ace_underline { - text-decoration: underline; -} - -.ace_bold { - font-weight: bold; -} - -.ace_nobold .ace_bold { - font-weight: normal; -} - -.ace_italic { - font-style: italic; -} - - -.ace_error-marker { - background-color: rgba(255, 0, 0,0.2); - position: absolute; - z-index: 9; -} - -.ace_highlight-marker { - background-color: rgba(255, 255, 0,0.2); - position: absolute; - z-index: 8; -} - -.ace_mobile-menu { - position: absolute; - line-height: 1.5; - border-radius: 4px; - -ms-user-select: none; - -moz-user-select: none; - -webkit-user-select: none; - user-select: none; - background: white; - box-shadow: 1px 3px 2px grey; - border: 1px solid #dcdcdc; - color: black; -} -.ace_dark > .ace_mobile-menu { - background: #333; - color: #ccc; - box-shadow: 1px 3px 2px grey; - border: 1px solid #444; - -} -.ace_mobile-button { - padding: 2px; - cursor: pointer; - overflow: hidden; -} -.ace_mobile-button:hover { - background-color: #eee; - opacity:1; -} -.ace_mobile-button:active { - background-color: #ddd; -} - -.ace_placeholder { - font-family: arial; - transform: scale(0.9); - transform-origin: left; - white-space: pre; - opacity: 0.7; - margin: 0 10px; -} - -.ace_ghost_text { - opacity: 0.5; - font-style: italic; - white-space: pre; -}`}),ace.define("ace/layer/decorators",["require","exports","module","ace/lib/dom","ace/lib/oop","ace/lib/event_emitter"],function(n,r,i){var o=n("../lib/dom"),s=n("../lib/oop"),h=n("../lib/event_emitter").EventEmitter,l=function(){function u(a,c){this.canvas=o.createElement("canvas"),this.renderer=c,this.pixelRatio=1,this.maxHeight=c.layerConfig.maxHeight,this.lineHeight=c.layerConfig.lineHeight,this.canvasHeight=a.parent.scrollHeight,this.heightRatio=this.canvasHeight/this.maxHeight,this.canvasWidth=a.width,this.minDecorationHeight=2*this.pixelRatio|0,this.halfMinDecorationHeight=this.minDecorationHeight/2|0,this.canvas.width=this.canvasWidth,this.canvas.height=this.canvasHeight,this.canvas.style.top="0px",this.canvas.style.right="0px",this.canvas.style.zIndex="7px",this.canvas.style.position="absolute",this.colors={},this.colors.dark={error:"rgba(255, 18, 18, 1)",warning:"rgba(18, 136, 18, 1)",info:"rgba(18, 18, 136, 1)"},this.colors.light={error:"rgb(255,51,51)",warning:"rgb(32,133,72)",info:"rgb(35,68,138)"},a.element.appendChild(this.canvas)}return u.prototype.$updateDecorators=function(a){var c=this.renderer.theme.isDark===!0?this.colors.dark:this.colors.light;if(a){this.maxHeight=a.maxHeight,this.lineHeight=a.lineHeight,this.canvasHeight=a.height;var f=(a.lastRow+1)*this.lineHeight;fI.priority?1:0}var g=this.renderer.session.$annotations;if(d.clearRect(0,0,this.canvas.width,this.canvas.height),g){var m={info:1,warning:2,error:3};g.forEach(function(E){E.priority=m[E.type]||null}),g=g.sort(p);for(var v=this.renderer.session.$foldData,y=0;ythis.canvasHeight&&(k=this.canvasHeight-this.halfMinDecorationHeight),b=Math.round(k-this.halfMinDecorationHeight),R=Math.round(k+this.halfMinDecorationHeight)}d.fillStyle=c[g[y].type]||null,d.fillRect(0,_,this.canvasWidth,R-b)}}var S=this.renderer.session.selection.getCursor();if(S){var $=this.compensateFoldRows(S.row,v),_=Math.round((S.row-$)*this.lineHeight*this.heightRatio);d.fillStyle="rgba(0, 0, 0, 0.5)",d.fillRect(0,_,this.canvasWidth,2)}},u.prototype.compensateFoldRows=function(a,c){var f=0;if(c&&c.length>0)for(var d=0;dc[d].start.row&&a=c[d].end.row&&(f+=c[d].end.row-c[d].start.row);return f},u}();s.implement(l.prototype,h),r.Decorator=l}),ace.define("ace/virtual_renderer",["require","exports","module","ace/lib/oop","ace/lib/dom","ace/lib/lang","ace/config","ace/layer/gutter","ace/layer/marker","ace/layer/text","ace/layer/cursor","ace/scrollbar","ace/scrollbar","ace/scrollbar_custom","ace/scrollbar_custom","ace/renderloop","ace/layer/font_metrics","ace/lib/event_emitter","ace/css/editor.css","ace/layer/decorators","ace/lib/useragent"],function(n,r,i){var o=n("./lib/oop"),s=n("./lib/dom"),h=n("./lib/lang"),l=n("./config"),u=n("./layer/gutter").Gutter,a=n("./layer/marker").Marker,c=n("./layer/text").Text,f=n("./layer/cursor").Cursor,d=n("./scrollbar").HScrollBar,p=n("./scrollbar").VScrollBar,g=n("./scrollbar_custom").HScrollBar,m=n("./scrollbar_custom").VScrollBar,v=n("./renderloop").RenderLoop,y=n("./layer/font_metrics").FontMetrics,x=n("./lib/event_emitter").EventEmitter,$=n("./css/editor.css"),_=n("./layer/decorators").Decorator,b=n("./lib/useragent");s.importCssString($,"ace_editor.css",!1);var R=function(){function M(k,S){var E=this;this.container=k||s.createElement("div"),s.addCssClass(this.container,"ace_editor"),s.HI_DPI&&s.addCssClass(this.container,"ace_hidpi"),this.setTheme(S),l.get("useStrictCSP")==null&&l.set("useStrictCSP",!1),this.$gutter=s.createElement("div"),this.$gutter.className="ace_gutter",this.container.appendChild(this.$gutter),this.$gutter.setAttribute("aria-hidden",!0),this.scroller=s.createElement("div"),this.scroller.className="ace_scroller",this.container.appendChild(this.scroller),this.content=s.createElement("div"),this.content.className="ace_content",this.scroller.appendChild(this.content),this.$gutterLayer=new u(this.$gutter),this.$gutterLayer.on("changeGutterWidth",this.onGutterResize.bind(this)),this.$markerBack=new a(this.content);var I=this.$textLayer=new c(this.content);this.canvas=I.element,this.$markerFront=new a(this.content),this.$cursorLayer=new f(this.content),this.$horizScroll=!1,this.$vScroll=!1,this.scrollBar=this.scrollBarV=new p(this.container,this),this.scrollBarH=new d(this.container,this),this.scrollBarV.on("scroll",function(C){E.$scrollAnimation||E.session.setScrollTop(C.data-E.scrollMargin.top)}),this.scrollBarH.on("scroll",function(C){E.$scrollAnimation||E.session.setScrollLeft(C.data-E.scrollMargin.left)}),this.scrollTop=0,this.scrollLeft=0,this.cursorPos={row:0,column:0},this.$fontMetrics=new y(this.container),this.$textLayer.$setFontMetrics(this.$fontMetrics),this.$textLayer.on("changeCharacterSize",function(C){E.updateCharacterSize(),E.onResize(!0,E.gutterWidth,E.$size.width,E.$size.height),E._signal("changeCharacterSize",C)}),this.$size={width:0,height:0,scrollerHeight:0,scrollerWidth:0,$dirty:!0},this.layerConfig={width:1,padding:0,firstRow:0,firstRowScreen:0,lastRow:0,lineHeight:0,characterWidth:0,minHeight:1,maxHeight:1,offset:0,height:1,gutterOffset:1},this.scrollMargin={left:0,right:0,top:0,bottom:0,v:0,h:0},this.margin={left:0,right:0,top:0,bottom:0,v:0,h:0},this.$keepTextAreaAtCursor=!b.isIOS,this.$loop=new v(this.$renderChanges.bind(this),this.container.ownerDocument.defaultView),this.$loop.schedule(this.CHANGE_FULL),this.updateCharacterSize(),this.setPadding(4),this.$addResizeObserver(),l.resetOptions(this),l._signal("renderer",this)}return M.prototype.updateCharacterSize=function(){this.$textLayer.allowBoldFonts!=this.$allowBoldFonts&&(this.$allowBoldFonts=this.$textLayer.allowBoldFonts,this.setStyle("ace_nobold",!this.$allowBoldFonts)),this.layerConfig.characterWidth=this.characterWidth=this.$textLayer.getCharacterWidth(),this.layerConfig.lineHeight=this.lineHeight=this.$textLayer.getLineHeight(),this.$updatePrintMargin(),s.setStyle(this.scroller.style,"line-height",this.lineHeight+"px")},M.prototype.setSession=function(k){this.session&&this.session.doc.off("changeNewLineMode",this.onChangeNewLineMode),this.session=k,k&&this.scrollMargin.top&&k.getScrollTop()<=0&&k.setScrollTop(-this.scrollMargin.top),this.$cursorLayer.setSession(k),this.$markerBack.setSession(k),this.$markerFront.setSession(k),this.$gutterLayer.setSession(k),this.$textLayer.setSession(k),k&&(this.$loop.schedule(this.CHANGE_FULL),this.session.$setFontMetrics(this.$fontMetrics),this.scrollBarH.scrollLeft=this.scrollBarV.scrollTop=null,this.onChangeNewLineMode=this.onChangeNewLineMode.bind(this),this.onChangeNewLineMode(),this.session.doc.on("changeNewLineMode",this.onChangeNewLineMode))},M.prototype.updateLines=function(k,S,E){if(S===void 0&&(S=1/0),this.$changedLines?(this.$changedLines.firstRow>k&&(this.$changedLines.firstRow=k),this.$changedLines.lastRowthis.layerConfig.lastRow||this.$loop.schedule(this.CHANGE_LINES)},M.prototype.onChangeNewLineMode=function(){this.$loop.schedule(this.CHANGE_TEXT),this.$textLayer.$updateEolChar(),this.session.$bidiHandler.setEolChar(this.$textLayer.EOL_CHAR)},M.prototype.onChangeTabSize=function(){this.$loop.schedule(this.CHANGE_TEXT|this.CHANGE_MARKER),this.$textLayer.onChangeTabSize()},M.prototype.updateText=function(){this.$loop.schedule(this.CHANGE_TEXT)},M.prototype.updateFull=function(k){k?this.$renderChanges(this.CHANGE_FULL,!0):this.$loop.schedule(this.CHANGE_FULL)},M.prototype.updateFontSize=function(){this.$textLayer.checkForSizeChanges()},M.prototype.$updateSizeAsync=function(){this.$loop.pending?this.$size.$dirty=!0:this.onResize()},M.prototype.onResize=function(k,S,E,I){if(!(this.resizing>2)){this.resizing>0?this.resizing++:this.resizing=k?1:0;var C=this.container;I||(I=C.clientHeight||C.scrollHeight),E||(E=C.clientWidth||C.scrollWidth);var w=this.$updateCachedSize(k,S,E,I);if(this.$resizeTimer&&this.$resizeTimer.cancel(),!this.$size.scrollerHeight||!E&&!I)return this.resizing=0;k&&(this.$gutterLayer.$padding=null),k?this.$renderChanges(w|this.$changes,!0):this.$loop.schedule(w|this.$changes),this.resizing&&(this.resizing=0),this.scrollBarH.scrollLeft=this.scrollBarV.scrollTop=null,this.$customScrollbar&&this.$updateCustomScrollbar(!0)}},M.prototype.$updateCachedSize=function(k,S,E,I){I-=this.$extraHeight||0;var C=0,w=this.$size,T={width:w.width,height:w.height,scrollerHeight:w.scrollerHeight,scrollerWidth:w.scrollerWidth};if(I&&(k||w.height!=I)&&(w.height=I,C|=this.CHANGE_SIZE,w.scrollerHeight=w.height,this.$horizScroll&&(w.scrollerHeight-=this.scrollBarH.getHeight()),this.scrollBarV.setHeight(w.scrollerHeight),this.scrollBarV.element.style.bottom=this.scrollBarH.getHeight()+"px",C=C|this.CHANGE_SCROLL),E&&(k||w.width!=E)){C|=this.CHANGE_SIZE,w.width=E,S==null&&(S=this.$showGutter?this.$gutter.offsetWidth:0),this.gutterWidth=S,s.setStyle(this.scrollBarH.element.style,"left",S+"px"),s.setStyle(this.scroller.style,"left",S+this.margin.left+"px"),w.scrollerWidth=Math.max(0,E-S-this.scrollBarV.getWidth()-this.margin.h),s.setStyle(this.$gutter.style,"left",this.margin.left+"px");var O=this.scrollBarV.getWidth()+"px";s.setStyle(this.scrollBarH.element.style,"right",O),s.setStyle(this.scroller.style,"right",O),s.setStyle(this.scroller.style,"bottom",this.scrollBarH.getHeight()),this.scrollBarH.setWidth(w.scrollerWidth),(this.session&&this.session.getUseWrapMode()&&this.adjustWrapLimit()||k)&&(C|=this.CHANGE_FULL)}return w.$dirty=!E||!I,C&&this._signal("resize",T),C},M.prototype.onGutterResize=function(k){var S=this.$showGutter?k:0;S!=this.gutterWidth&&(this.$changes|=this.$updateCachedSize(!0,S,this.$size.width,this.$size.height)),this.session.getUseWrapMode()&&this.adjustWrapLimit()?this.$loop.schedule(this.CHANGE_FULL):this.$size.$dirty?this.$loop.schedule(this.CHANGE_FULL):this.$computeLayerConfig()},M.prototype.adjustWrapLimit=function(){var k=this.$size.scrollerWidth-this.$padding*2,S=Math.floor(k/this.characterWidth);return this.session.adjustWrapLimit(S,this.$showPrintMargin&&this.$printMarginColumn)},M.prototype.setAnimatedScroll=function(k){this.setOption("animatedScroll",k)},M.prototype.getAnimatedScroll=function(){return this.$animatedScroll},M.prototype.setShowInvisibles=function(k){this.setOption("showInvisibles",k),this.session.$bidiHandler.setShowInvisibles(k)},M.prototype.getShowInvisibles=function(){return this.getOption("showInvisibles")},M.prototype.getDisplayIndentGuide=function(){return this.getOption("displayIndentGuides")},M.prototype.setDisplayIndentGuides=function(k){this.setOption("displayIndentGuides",k)},M.prototype.getHighlightIndentGuides=function(){return this.getOption("highlightIndentGuides")},M.prototype.setHighlightIndentGuides=function(k){this.setOption("highlightIndentGuides",k)},M.prototype.setShowPrintMargin=function(k){this.setOption("showPrintMargin",k)},M.prototype.getShowPrintMargin=function(){return this.getOption("showPrintMargin")},M.prototype.setPrintMarginColumn=function(k){this.setOption("printMarginColumn",k)},M.prototype.getPrintMarginColumn=function(){return this.getOption("printMarginColumn")},M.prototype.getShowGutter=function(){return this.getOption("showGutter")},M.prototype.setShowGutter=function(k){return this.setOption("showGutter",k)},M.prototype.getFadeFoldWidgets=function(){return this.getOption("fadeFoldWidgets")},M.prototype.setFadeFoldWidgets=function(k){this.setOption("fadeFoldWidgets",k)},M.prototype.setHighlightGutterLine=function(k){this.setOption("highlightGutterLine",k)},M.prototype.getHighlightGutterLine=function(){return this.getOption("highlightGutterLine")},M.prototype.$updatePrintMargin=function(){if(!(!this.$showPrintMargin&&!this.$printMarginEl)){if(!this.$printMarginEl){var k=s.createElement("div");k.className="ace_layer ace_print-margin-layer",this.$printMarginEl=s.createElement("div"),this.$printMarginEl.className="ace_print-margin",k.appendChild(this.$printMarginEl),this.content.insertBefore(k,this.content.firstChild)}var S=this.$printMarginEl.style;S.left=Math.round(this.characterWidth*this.$printMarginColumn+this.$padding)+"px",S.visibility=this.$showPrintMargin?"visible":"hidden",this.session&&this.session.$wrap==-1&&this.adjustWrapLimit()}},M.prototype.getContainerElement=function(){return this.container},M.prototype.getMouseEventTarget=function(){return this.scroller},M.prototype.getTextAreaContainer=function(){return this.container},M.prototype.$moveTextAreaToCursor=function(){if(!this.$isMousePressed){var k=this.textarea.style,S=this.$composition;if(!this.$keepTextAreaAtCursor&&!S){s.translate(this.textarea,-100,0);return}var E=this.$cursorLayer.$pixelPos;if(E){S&&S.markerRange&&(E=this.$cursorLayer.getPixelPosition(S.markerRange.start,!0));var I=this.layerConfig,C=E.top,w=E.left;C-=I.offset;var T=S&&S.useTextareaForIME||b.isMobile?this.lineHeight:1;if(C<0||C>I.height-T){s.translate(this.textarea,0,0);return}var O=1,N=this.$size.height-T;if(!S)C+=this.lineHeight;else if(S.useTextareaForIME){var B=this.textarea.value;O=this.characterWidth*this.session.$getStringScreenWidth(B)[0]}else C+=this.lineHeight+2;w-=this.scrollLeft,w>this.$size.scrollerWidth-O&&(w=this.$size.scrollerWidth-O),w+=this.gutterWidth+this.margin.left,s.setStyle(k,"height",T+"px"),s.setStyle(k,"width",O+"px"),s.translate(this.textarea,Math.min(w,this.$size.scrollerWidth-O),Math.min(C,N))}}},M.prototype.getFirstVisibleRow=function(){return this.layerConfig.firstRow},M.prototype.getFirstFullyVisibleRow=function(){return this.layerConfig.firstRow+(this.layerConfig.offset===0?0:1)},M.prototype.getLastFullyVisibleRow=function(){var k=this.layerConfig,S=k.lastRow,E=this.session.documentToScreenRow(S,0)*k.lineHeight;return E-this.session.getScrollTop()>k.height-k.lineHeight?S-1:S},M.prototype.getLastVisibleRow=function(){return this.layerConfig.lastRow},M.prototype.setPadding=function(k){this.$padding=k,this.$textLayer.setPadding(k),this.$cursorLayer.setPadding(k),this.$markerFront.setPadding(k),this.$markerBack.setPadding(k),this.$loop.schedule(this.CHANGE_FULL),this.$updatePrintMargin()},M.prototype.setScrollMargin=function(k,S,E,I){var C=this.scrollMargin;C.top=k|0,C.bottom=S|0,C.right=I|0,C.left=E|0,C.v=C.top+C.bottom,C.h=C.left+C.right,C.top&&this.scrollTop<=0&&this.session&&this.session.setScrollTop(-C.top),this.updateFull()},M.prototype.setMargin=function(k,S,E,I){var C=this.margin;C.top=k|0,C.bottom=S|0,C.right=I|0,C.left=E|0,C.v=C.top+C.bottom,C.h=C.left+C.right,this.$updateCachedSize(!0,this.gutterWidth,this.$size.width,this.$size.height),this.updateFull()},M.prototype.getHScrollBarAlwaysVisible=function(){return this.$hScrollBarAlwaysVisible},M.prototype.setHScrollBarAlwaysVisible=function(k){this.setOption("hScrollBarAlwaysVisible",k)},M.prototype.getVScrollBarAlwaysVisible=function(){return this.$vScrollBarAlwaysVisible},M.prototype.setVScrollBarAlwaysVisible=function(k){this.setOption("vScrollBarAlwaysVisible",k)},M.prototype.$updateScrollBarV=function(){var k=this.layerConfig.maxHeight,S=this.$size.scrollerHeight;!this.$maxLines&&this.$scrollPastEnd&&(k-=(S-this.lineHeight)*this.$scrollPastEnd,this.scrollTop>k-S&&(k=this.scrollTop+S,this.scrollBarV.scrollTop=null)),this.scrollBarV.setScrollHeight(k+this.scrollMargin.v),this.scrollBarV.setScrollTop(this.scrollTop+this.scrollMargin.top)},M.prototype.$updateScrollBarH=function(){this.scrollBarH.setScrollWidth(this.layerConfig.width+2*this.$padding+this.scrollMargin.h),this.scrollBarH.setScrollLeft(this.scrollLeft+this.scrollMargin.left)},M.prototype.freeze=function(){this.$frozen=!0},M.prototype.unfreeze=function(){this.$frozen=!1},M.prototype.$renderChanges=function(k,S){if(this.$changes&&(k|=this.$changes,this.$changes=0),!this.session||!this.container.offsetWidth||this.$frozen||!k&&!S){this.$changes|=k;return}if(this.$size.$dirty)return this.$changes|=k,this.onResize(!0);this.lineHeight||this.$textLayer.checkForSizeChanges(),this._signal("beforeRender",k),this.session&&this.session.$bidiHandler&&this.session.$bidiHandler.updateCharacterWidths(this.$fontMetrics);var E=this.layerConfig;if(k&this.CHANGE_FULL||k&this.CHANGE_SIZE||k&this.CHANGE_TEXT||k&this.CHANGE_LINES||k&this.CHANGE_SCROLL||k&this.CHANGE_H_SCROLL){if(k|=this.$computeLayerConfig()|this.$loop.clear(),E.firstRow!=this.layerConfig.firstRow&&E.firstRowScreen==this.layerConfig.firstRowScreen){var I=this.scrollTop+(E.firstRow-Math.max(this.layerConfig.firstRow,0))*this.lineHeight;I>0&&(this.scrollTop=I,k=k|this.CHANGE_SCROLL,k|=this.$computeLayerConfig()|this.$loop.clear())}E=this.layerConfig,this.$updateScrollBarV(),k&this.CHANGE_H_SCROLL&&this.$updateScrollBarH(),s.translate(this.content,-this.scrollLeft,-E.offset);var C=E.width+2*this.$padding+"px",w=E.minHeight+"px";s.setStyle(this.content.style,"width",C),s.setStyle(this.content.style,"height",w)}if(k&this.CHANGE_H_SCROLL&&(s.translate(this.content,-this.scrollLeft,-E.offset),this.scroller.className=this.scrollLeft<=0?"ace_scroller ":"ace_scroller ace_scroll-left ",this.enableKeyboardAccessibility&&(this.scroller.className+=this.keyboardFocusClassName)),k&this.CHANGE_FULL){this.$changedLines=null,this.$textLayer.update(E),this.$showGutter&&this.$gutterLayer.update(E),this.$customScrollbar&&this.$scrollDecorator.$updateDecorators(E),this.$markerBack.update(E),this.$markerFront.update(E),this.$cursorLayer.update(E),this.$moveTextAreaToCursor(),this._signal("afterRender",k);return}if(k&this.CHANGE_SCROLL){this.$changedLines=null,k&this.CHANGE_TEXT||k&this.CHANGE_LINES?this.$textLayer.update(E):this.$textLayer.scrollLines(E),this.$showGutter&&(k&this.CHANGE_GUTTER||k&this.CHANGE_LINES?this.$gutterLayer.update(E):this.$gutterLayer.scrollLines(E)),this.$customScrollbar&&this.$scrollDecorator.$updateDecorators(E),this.$markerBack.update(E),this.$markerFront.update(E),this.$cursorLayer.update(E),this.$moveTextAreaToCursor(),this._signal("afterRender",k);return}k&this.CHANGE_TEXT?(this.$changedLines=null,this.$textLayer.update(E),this.$showGutter&&this.$gutterLayer.update(E),this.$customScrollbar&&this.$scrollDecorator.$updateDecorators(E)):k&this.CHANGE_LINES?((this.$updateLines()||k&this.CHANGE_GUTTER&&this.$showGutter)&&this.$gutterLayer.update(E),this.$customScrollbar&&this.$scrollDecorator.$updateDecorators(E)):k&this.CHANGE_TEXT||k&this.CHANGE_GUTTER?(this.$showGutter&&this.$gutterLayer.update(E),this.$customScrollbar&&this.$scrollDecorator.$updateDecorators(E)):k&this.CHANGE_CURSOR&&(this.$highlightGutterLine&&this.$gutterLayer.updateLineHighlight(E),this.$customScrollbar&&this.$scrollDecorator.$updateDecorators(E)),k&this.CHANGE_CURSOR&&(this.$cursorLayer.update(E),this.$moveTextAreaToCursor()),k&(this.CHANGE_MARKER|this.CHANGE_MARKER_FRONT)&&this.$markerFront.update(E),k&(this.CHANGE_MARKER|this.CHANGE_MARKER_BACK)&&this.$markerBack.update(E),this._signal("afterRender",k)},M.prototype.$autosize=function(){var k=this.session.getScreenLength()*this.lineHeight,S=this.$maxLines*this.lineHeight,E=Math.min(S,Math.max((this.$minLines||1)*this.lineHeight,k))+this.scrollMargin.v+(this.$extraHeight||0);this.$horizScroll&&(E+=this.scrollBarH.getHeight()),this.$maxPixelHeight&&E>this.$maxPixelHeight&&(E=this.$maxPixelHeight);var I=E<=2*this.lineHeight,C=!I&&k>S;if(E!=this.desiredHeight||this.$size.height!=this.desiredHeight||C!=this.$vScroll){C!=this.$vScroll&&(this.$vScroll=C,this.scrollBarV.setVisible(C));var w=this.container.clientWidth;this.container.style.height=E+"px",this.$updateCachedSize(!0,this.$gutterWidth,w,E),this.desiredHeight=E,this._signal("autosize")}},M.prototype.$computeLayerConfig=function(){var k=this.session,S=this.$size,E=S.height<=2*this.lineHeight,I=this.session.getScreenLength(),C=I*this.lineHeight,w=this.$getLongestLine(),T=!E&&(this.$hScrollBarAlwaysVisible||S.scrollerWidth-w-2*this.$padding<0),O=this.$horizScroll!==T;O&&(this.$horizScroll=T,this.scrollBarH.setVisible(T));var N=this.$vScroll;this.$maxLines&&this.lineHeight>1&&this.$autosize();var B=S.scrollerHeight+this.lineHeight,H=!this.$maxLines&&this.$scrollPastEnd?(S.scrollerHeight-this.lineHeight)*this.$scrollPastEnd:0;C+=H;var z=this.scrollMargin;this.session.setScrollTop(Math.max(-z.top,Math.min(this.scrollTop,C-S.scrollerHeight+z.bottom))),this.session.setScrollLeft(Math.max(-z.left,Math.min(this.scrollLeft,w+2*this.$padding-S.scrollerWidth+z.right)));var F=!E&&(this.$vScrollBarAlwaysVisible||S.scrollerHeight-C+H<0||this.scrollTop>z.top),W=N!==F;W&&(this.$vScroll=F,this.scrollBarV.setVisible(F));var G=this.scrollTop%this.lineHeight,U=Math.ceil(B/this.lineHeight)-1,Q=Math.max(0,Math.round((this.scrollTop-G)/this.lineHeight)),ee=Q+U,V,q,re=this.lineHeight;Q=k.screenToDocumentRow(Q,0);var se=k.getFoldLine(Q);se&&(Q=se.start.row),V=k.documentToScreenRow(Q,0),q=k.getRowLength(Q)*re,ee=Math.min(k.screenToDocumentRow(ee,0),k.getLength()-1),B=S.scrollerHeight+k.getRowLength(ee)*re+q,G=this.scrollTop-V*re;var ge=0;return(this.layerConfig.width!=w||O)&&(ge=this.CHANGE_H_SCROLL),(O||W)&&(ge|=this.$updateCachedSize(!0,this.gutterWidth,S.width,S.height),this._signal("scrollbarVisibilityChanged"),W&&(w=this.$getLongestLine())),this.layerConfig={width:w,padding:this.$padding,firstRow:Q,firstRowScreen:V,lastRow:ee,lineHeight:re,characterWidth:this.characterWidth,minHeight:B,maxHeight:C,offset:G,gutterOffset:re?Math.max(0,Math.ceil((G+S.height-S.scrollerHeight)/re)):0,height:this.$size.scrollerHeight},this.session.$bidiHandler&&this.session.$bidiHandler.setContentWidth(w-this.$padding),ge},M.prototype.$updateLines=function(){if(this.$changedLines){var k=this.$changedLines.firstRow,S=this.$changedLines.lastRow;this.$changedLines=null;var E=this.layerConfig;if(!(k>E.lastRow+1)&&!(Sthis.$textLayer.MAX_LINE_LENGTH&&(k=this.$textLayer.MAX_LINE_LENGTH+30),Math.max(this.$size.scrollerWidth-2*this.$padding,Math.round(k*this.characterWidth))},M.prototype.updateFrontMarkers=function(){this.$markerFront.setMarkers(this.session.getMarkers(!0)),this.$loop.schedule(this.CHANGE_MARKER_FRONT)},M.prototype.updateBackMarkers=function(){this.$markerBack.setMarkers(this.session.getMarkers()),this.$loop.schedule(this.CHANGE_MARKER_BACK)},M.prototype.addGutterDecoration=function(k,S){this.$gutterLayer.addGutterDecoration(k,S)},M.prototype.removeGutterDecoration=function(k,S){this.$gutterLayer.removeGutterDecoration(k,S)},M.prototype.updateBreakpoints=function(k){this.$loop.schedule(this.CHANGE_GUTTER)},M.prototype.setAnnotations=function(k){this.$gutterLayer.setAnnotations(k),this.$loop.schedule(this.CHANGE_GUTTER)},M.prototype.updateCursor=function(){this.$loop.schedule(this.CHANGE_CURSOR)},M.prototype.hideCursor=function(){this.$cursorLayer.hideCursor()},M.prototype.showCursor=function(){this.$cursorLayer.showCursor()},M.prototype.scrollSelectionIntoView=function(k,S,E){this.scrollCursorIntoView(k,E),this.scrollCursorIntoView(S,E)},M.prototype.scrollCursorIntoView=function(k,S,E){if(this.$size.scrollerHeight!==0){var I=this.$cursorLayer.getPixelPosition(k),C=I.left,w=I.top,T=E&&E.top||0,O=E&&E.bottom||0;this.$scrollAnimation&&(this.$stopAnimation=!0);var N=this.$scrollAnimation?this.session.getScrollTop():this.scrollTop;N+T>w?(S&&N+T>w+this.lineHeight&&(w-=S*this.$size.scrollerHeight),w===0&&(w=-this.scrollMargin.top),this.session.setScrollTop(w)):N+this.$size.scrollerHeight-O=1-this.scrollMargin.top||S>0&&this.session.getScrollTop()+this.$size.scrollerHeight-this.layerConfig.maxHeight<-1+this.scrollMargin.bottom||k<0&&this.session.getScrollLeft()>=1-this.scrollMargin.left||k>0&&this.session.getScrollLeft()+this.$size.scrollerWidth-this.layerConfig.width<-1+this.scrollMargin.right)return!0},M.prototype.pixelToScreenCoordinates=function(k,S){var E;if(this.$hasCssTransforms){E={top:0,left:0};var I=this.$fontMetrics.transformCoordinates([k,S]);k=I[1]-this.gutterWidth-this.margin.left,S=I[0]}else E=this.scroller.getBoundingClientRect();var C=k+this.scrollLeft-E.left-this.$padding,w=C/this.characterWidth,T=Math.floor((S+this.scrollTop-E.top)/this.lineHeight),O=this.$blockCursor?Math.floor(w):Math.round(w);return{row:T,column:O,side:w-O>0?1:-1,offsetX:C}},M.prototype.screenToTextCoordinates=function(k,S){var E;if(this.$hasCssTransforms){E={top:0,left:0};var I=this.$fontMetrics.transformCoordinates([k,S]);k=I[1]-this.gutterWidth-this.margin.left,S=I[0]}else E=this.scroller.getBoundingClientRect();var C=k+this.scrollLeft-E.left-this.$padding,w=C/this.characterWidth,T=this.$blockCursor?Math.floor(w):Math.round(w),O=Math.floor((S+this.scrollTop-E.top)/this.lineHeight);return this.session.screenToDocumentPosition(O,Math.max(T,0),C)},M.prototype.textToScreenCoordinates=function(k,S){var E=this.scroller.getBoundingClientRect(),I=this.session.documentToScreenPosition(k,S),C=this.$padding+(this.session.$bidiHandler.isBidiRow(I.row,k)?this.session.$bidiHandler.getPosLeft(I.column):Math.round(I.column*this.characterWidth)),w=I.row*this.lineHeight;return{pageX:E.left+C-this.scrollLeft,pageY:E.top+w-this.scrollTop}},M.prototype.visualizeFocus=function(){s.addCssClass(this.container,"ace_focus")},M.prototype.visualizeBlur=function(){s.removeCssClass(this.container,"ace_focus")},M.prototype.showComposition=function(k){this.$composition=k,k.cssText||(k.cssText=this.textarea.style.cssText),k.useTextareaForIME==null&&(k.useTextareaForIME=this.$useTextareaForIME),this.$useTextareaForIME?(s.addCssClass(this.textarea,"ace_composition"),this.textarea.style.cssText="",this.$moveTextAreaToCursor(),this.$cursorLayer.element.style.display="none"):k.markerId=this.session.addMarker(k.markerRange,"ace_composition_marker","text")},M.prototype.setCompositionText=function(k){var S=this.session.selection.cursor;this.addToken(k,"composition_placeholder",S.row,S.column),this.$moveTextAreaToCursor()},M.prototype.hideComposition=function(){if(this.$composition){this.$composition.markerId&&this.session.removeMarker(this.$composition.markerId),s.removeCssClass(this.textarea,"ace_composition"),this.textarea.style.cssText=this.$composition.cssText;var k=this.session.selection.cursor;this.removeExtraToken(k.row,k.column),this.$composition=null,this.$cursorLayer.element.style.display=""}},M.prototype.setGhostText=function(k,S){var E=this.session.selection.cursor,I=S||{row:E.row,column:E.column};this.removeGhostText();var C=k.split(` -`);this.addToken(C[0],"ghost_text",I.row,I.column),this.$ghostText={text:k,position:{row:I.row,column:I.column}},C.length>1&&(this.$ghostTextWidget={text:C.slice(1).join(` -`),row:I.row,column:I.column,className:"ace_ghost_text"},this.session.widgetManager.addLineWidget(this.$ghostTextWidget))},M.prototype.removeGhostText=function(){if(this.$ghostText){var k=this.$ghostText.position;this.removeExtraToken(k.row,k.column),this.$ghostTextWidget&&(this.session.widgetManager.removeLineWidget(this.$ghostTextWidget),this.$ghostTextWidget=null),this.$ghostText=null}},M.prototype.addToken=function(k,S,E,I){var C=this.session;C.bgTokenizer.lines[E]=null;var w={type:S,value:k},T=C.getTokens(E);if(I==null||!T.length)T.push(w);else for(var O=0,N=0;N1||Math.abs(k.$size.height-I)>1?k.$resizeTimer.delay():k.$resizeTimer.cancel()}),this.$resizeObserver.observe(this.container)}},M}();R.prototype.CHANGE_CURSOR=1,R.prototype.CHANGE_MARKER=2,R.prototype.CHANGE_GUTTER=4,R.prototype.CHANGE_SCROLL=8,R.prototype.CHANGE_LINES=16,R.prototype.CHANGE_TEXT=32,R.prototype.CHANGE_SIZE=64,R.prototype.CHANGE_MARKER_BACK=128,R.prototype.CHANGE_MARKER_FRONT=256,R.prototype.CHANGE_FULL=512,R.prototype.CHANGE_H_SCROLL=1024,R.prototype.$changes=0,R.prototype.$padding=null,R.prototype.$frozen=!1,R.prototype.STEPS=8,o.implement(R.prototype,x),l.defineOptions(R.prototype,"renderer",{useResizeObserver:{set:function(M){!M&&this.$resizeObserver?(this.$resizeObserver.disconnect(),this.$resizeTimer.cancel(),this.$resizeTimer=this.$resizeObserver=null):M&&!this.$resizeObserver&&this.$addResizeObserver()}},animatedScroll:{initialValue:!1},showInvisibles:{set:function(M){this.$textLayer.setShowInvisibles(M)&&this.$loop.schedule(this.CHANGE_TEXT)},initialValue:!1},showPrintMargin:{set:function(){this.$updatePrintMargin()},initialValue:!0},printMarginColumn:{set:function(){this.$updatePrintMargin()},initialValue:80},printMargin:{set:function(M){typeof M=="number"&&(this.$printMarginColumn=M),this.$showPrintMargin=!!M,this.$updatePrintMargin()},get:function(){return this.$showPrintMargin&&this.$printMarginColumn}},showGutter:{set:function(M){this.$gutter.style.display=M?"block":"none",this.$loop.schedule(this.CHANGE_FULL),this.onGutterResize()},initialValue:!0},useSvgGutterIcons:{set:function(M){this.$gutterLayer.$useSvgGutterIcons=M},initialValue:!1},showFoldedAnnotations:{set:function(M){this.$gutterLayer.$showFoldedAnnotations=M},initialValue:!1},fadeFoldWidgets:{set:function(M){s.setCssClass(this.$gutter,"ace_fade-fold-widgets",M)},initialValue:!1},showFoldWidgets:{set:function(M){this.$gutterLayer.setShowFoldWidgets(M),this.$loop.schedule(this.CHANGE_GUTTER)},initialValue:!0},displayIndentGuides:{set:function(M){this.$textLayer.setDisplayIndentGuides(M)&&this.$loop.schedule(this.CHANGE_TEXT)},initialValue:!0},highlightIndentGuides:{set:function(M){this.$textLayer.setHighlightIndentGuides(M)==!0?this.$textLayer.$highlightIndentGuide():this.$textLayer.$clearActiveIndentGuide(this.$textLayer.$lines.cells)},initialValue:!0},highlightGutterLine:{set:function(M){this.$gutterLayer.setHighlightGutterLine(M),this.$loop.schedule(this.CHANGE_GUTTER)},initialValue:!0},hScrollBarAlwaysVisible:{set:function(M){(!this.$hScrollBarAlwaysVisible||!this.$horizScroll)&&this.$loop.schedule(this.CHANGE_SCROLL)},initialValue:!1},vScrollBarAlwaysVisible:{set:function(M){(!this.$vScrollBarAlwaysVisible||!this.$vScroll)&&this.$loop.schedule(this.CHANGE_SCROLL)},initialValue:!1},fontSize:{set:function(M){typeof M=="number"&&(M=M+"px"),this.container.style.fontSize=M,this.updateFontSize()},initialValue:12},fontFamily:{set:function(M){this.container.style.fontFamily=M,this.updateFontSize()}},maxLines:{set:function(M){this.updateFull()}},minLines:{set:function(M){this.$minLines<562949953421311||(this.$minLines=0),this.updateFull()}},maxPixelHeight:{set:function(M){this.updateFull()},initialValue:0},scrollPastEnd:{set:function(M){M=+M||0,this.$scrollPastEnd!=M&&(this.$scrollPastEnd=M,this.$loop.schedule(this.CHANGE_SCROLL))},initialValue:0,handlesSet:!0},fixedWidthGutter:{set:function(M){this.$gutterLayer.$fixedWidth=!!M,this.$loop.schedule(this.CHANGE_GUTTER)}},customScrollbar:{set:function(M){this.$updateCustomScrollbar(M)},initialValue:!1},theme:{set:function(M){this.setTheme(M)},get:function(){return this.$themeId||this.theme},initialValue:"./theme/textmate",handlesSet:!0},hasCssTransforms:{},useTextareaForIME:{initialValue:!b.isMobile&&!b.isIE}}),r.VirtualRenderer=R}),ace.define("ace/worker/worker_client",["require","exports","module","ace/lib/oop","ace/lib/net","ace/lib/event_emitter","ace/config"],function(n,r,i){var o=n("../lib/oop"),s=n("../lib/net"),h=n("../lib/event_emitter").EventEmitter,l=n("../config");function u(d){var p="importScripts('"+s.qualifyURL(d)+"');";try{return new Blob([p],{type:"application/javascript"})}catch{var g=window.BlobBuilder||window.WebKitBlobBuilder||window.MozBlobBuilder,m=new g;return m.append(p),m.getBlob("application/javascript")}}function a(d){if(typeof Worker>"u")return{postMessage:function(){},terminate:function(){}};if(l.get("loadWorkerFromBlob")){var p=u(d),g=window.URL||window.webkitURL,m=g.createObjectURL(p);return new Worker(m)}return new Worker(d)}var c=function(d){d.postMessage||(d=this.$createWorkerFromOldConfig.apply(this,arguments)),this.$worker=d,this.$sendDeltaQueue=this.$sendDeltaQueue.bind(this),this.changeListener=this.changeListener.bind(this),this.onMessage=this.onMessage.bind(this),this.callbackId=1,this.callbacks={},this.$worker.onmessage=this.onMessage};(function(){o.implement(this,h),this.$createWorkerFromOldConfig=function(d,p,g,m,v){if(n.nameToUrl&&!n.toUrl&&(n.toUrl=n.nameToUrl),l.get("packaged")||!n.toUrl)m=m||l.moduleUrl(p,"worker");else{var y=this.$normalizePath;m=m||y(n.toUrl("ace/worker/worker.js",null,"_"));var x={};d.forEach(function($){x[$]=y(n.toUrl($,null,"_").replace(/(\.js)?(\?.*)?$/,""))})}return this.$worker=a(m),v&&this.send("importScripts",v),this.$worker.postMessage({init:!0,tlns:x,module:p,classname:g}),this.$worker},this.onMessage=function(d){var p=d.data;switch(p.type){case"event":this._signal(p.name,{data:p.data});break;case"call":var g=this.callbacks[p.id];g&&(g(p.data),delete this.callbacks[p.id]);break;case"error":this.reportError(p.data);break;case"log":window.console&&console.log&&console.log.apply(console,p.data);break}},this.reportError=function(d){window.console&&console.error&&console.error(d)},this.$normalizePath=function(d){return s.qualifyURL(d)},this.terminate=function(){this._signal("terminate",{}),this.deltaQueue=null,this.$worker.terminate(),this.$worker=null,this.$doc&&this.$doc.off("change",this.changeListener),this.$doc=null},this.send=function(d,p){this.$worker.postMessage({command:d,args:p})},this.call=function(d,p,g){if(g){var m=this.callbackId++;this.callbacks[m]=g,p.push(m)}this.send(d,p)},this.emit=function(d,p){try{p.data&&p.data.err&&(p.data.err={message:p.data.err.message,stack:p.data.err.stack,code:p.data.err.code}),this.$worker&&this.$worker.postMessage({event:d,data:{data:p.data}})}catch(g){console.error(g.stack)}},this.attachToDocument=function(d){this.$doc&&this.terminate(),this.$doc=d,this.call("setValue",[d.getValue()]),d.on("change",this.changeListener,!0)},this.changeListener=function(d){this.deltaQueue||(this.deltaQueue=[],setTimeout(this.$sendDeltaQueue,0)),d.action=="insert"?this.deltaQueue.push(d.start,d.lines):this.deltaQueue.push(d.start,d.end)},this.$sendDeltaQueue=function(){var d=this.deltaQueue;d&&(this.deltaQueue=null,d.length>50&&d.length>this.$doc.getLength()>>1?this.call("setValue",[this.$doc.getValue()]):this.emit("change",{data:d}))}}).call(c.prototype);var f=function(d,p,g){var m=null,v=!1,y=Object.create(h),x=[],$=new c({messageBuffer:x,terminate:function(){},postMessage:function(b){x.push(b),m&&(v?setTimeout(_):_())}});$.setEmitSync=function(b){v=b};var _=function(){var b=x.shift();b.command?m[b.command].apply(m,b.args):b.event&&y._signal(b.event,b.data)};return y.postMessage=function(b){$.onMessage({data:b})},y.callback=function(b,R){this.postMessage({type:"call",id:R,data:b})},y.emit=function(b,R){this.postMessage({type:"event",name:b,data:R})},l.loadModule(["worker",p],function(b){for(m=new b[g](y);x.length;)_()}),$};r.UIWorkerClient=f,r.WorkerClient=c,r.createWorker=a}),ace.define("ace/placeholder",["require","exports","module","ace/range","ace/lib/event_emitter","ace/lib/oop"],function(n,r,i){var o=n("./range").Range,s=n("./lib/event_emitter").EventEmitter,h=n("./lib/oop"),l=function(){function u(a,c,f,d,p,g){var m=this;this.length=c,this.session=a,this.doc=a.getDocument(),this.mainClass=p,this.othersClass=g,this.$onUpdate=this.onUpdate.bind(this),this.doc.on("change",this.$onUpdate,!0),this.$others=d,this.$onCursorChange=function(){setTimeout(function(){m.onCursorChange()})},this.$pos=f;var v=a.getUndoManager().$undoStack||a.getUndoManager().$undostack||{length:-1};this.$undoStackDepth=v.length,this.setup(),a.selection.on("changeCursor",this.$onCursorChange)}return u.prototype.setup=function(){var a=this,c=this.doc,f=this.session;this.selectionBefore=f.selection.toJSON(),f.selection.inMultiSelectMode&&f.selection.toSingleRange(),this.pos=c.createAnchor(this.$pos.row,this.$pos.column);var d=this.pos;d.$insertRight=!0,d.detach(),d.markerId=f.addMarker(new o(d.row,d.column,d.row,d.column+this.length),this.mainClass,null,!1),this.others=[],this.$others.forEach(function(p){var g=c.createAnchor(p.row,p.column);g.$insertRight=!0,g.detach(),a.others.push(g)}),f.setUndoSelect(!1)},u.prototype.showOtherMarkers=function(){if(!this.othersActive){var a=this.session,c=this;this.othersActive=!0,this.others.forEach(function(f){f.markerId=a.addMarker(new o(f.row,f.column,f.row,f.column+c.length),c.othersClass,null,!1)})}},u.prototype.hideOtherMarkers=function(){if(this.othersActive){this.othersActive=!1;for(var a=0;a=this.pos.column&&c.start.column<=this.pos.column+this.length+1,p=c.start.column-this.pos.column;if(this.updateAnchors(a),d&&(this.length+=f),d&&!this.session.$fromUndo){if(a.action==="insert")for(var g=this.others.length-1;g>=0;g--){var m=this.others[g],v={row:m.row,column:m.column+p};this.doc.insertMergedLines(v,a.lines)}else if(a.action==="remove")for(var g=this.others.length-1;g>=0;g--){var m=this.others[g],v={row:m.row,column:m.column+p};this.doc.remove(new o(v.row,v.column,v.row,v.column-f))}}this.$updating=!1,this.updateMarkers()}},u.prototype.updateAnchors=function(a){this.pos.onChange(a);for(var c=this.others.length;c--;)this.others[c].onChange(a);this.updateMarkers()},u.prototype.updateMarkers=function(){if(!this.$updating){var a=this,c=this.session,f=function(p,g){c.removeMarker(p.markerId),p.markerId=c.addMarker(new o(p.row,p.column,p.row,p.column+a.length),g,null,!1)};f(this.pos,this.mainClass);for(var d=this.others.length;d--;)f(this.others[d],this.othersClass)}},u.prototype.onCursorChange=function(a){if(!(this.$updating||!this.session)){var c=this.session.selection.getCursor();c.row===this.pos.row&&c.column>=this.pos.column&&c.column<=this.pos.column+this.length?(this.showOtherMarkers(),this._emit("cursorEnter",a)):(this.hideOtherMarkers(),this._emit("cursorLeave",a))}},u.prototype.detach=function(){this.session.removeMarker(this.pos&&this.pos.markerId),this.hideOtherMarkers(),this.doc.off("change",this.$onUpdate),this.session.selection.off("changeCursor",this.$onCursorChange),this.session.setUndoSelect(!0),this.session=null},u.prototype.cancel=function(){if(this.$undoStackDepth!==-1){for(var a=this.session.getUndoManager(),c=(a.$undoStack||a.$undostack).length-this.$undoStackDepth,f=0;f1?s.multiSelect.joinSelections():s.multiSelect.splitIntoLines()},bindKey:{win:"Ctrl-Alt-L",mac:"Ctrl-Alt-L"},readOnly:!0},{name:"splitSelectionIntoLines",description:"Split into lines",exec:function(s){s.multiSelect.splitIntoLines()},readOnly:!0},{name:"alignCursors",description:"Align cursors",exec:function(s){s.alignCursors()},bindKey:{win:"Ctrl-Alt-A",mac:"Ctrl-Alt-A"},scrollIntoView:"cursor"},{name:"findAll",description:"Find all",exec:function(s){s.findAll()},bindKey:{win:"Ctrl-Alt-K",mac:"Ctrl-Alt-G"},scrollIntoView:"cursor",readOnly:!0}],r.multiSelectCommands=[{name:"singleSelection",description:"Single selection",bindKey:"esc",exec:function(s){s.exitMultiSelectMode()},scrollIntoView:"cursor",readOnly:!0,isAvailable:function(s){return s&&s.inMultiSelectMode}}];var o=n("../keyboard/hash_handler").HashHandler;r.keyboardHandler=new o(r.multiSelectCommands)}),ace.define("ace/multi_select",["require","exports","module","ace/range_list","ace/range","ace/selection","ace/mouse/multi_select_handler","ace/lib/event","ace/lib/lang","ace/commands/multi_select_commands","ace/search","ace/edit_session","ace/editor","ace/config"],function(n,r,i){var o=n("./range_list").RangeList,s=n("./range").Range,h=n("./selection").Selection,l=n("./mouse/multi_select_handler").onMouseDown,u=n("./lib/event"),a=n("./lib/lang"),c=n("./commands/multi_select_commands");r.commands=c.defaultCommands.concat(c.multiSelectCommands);var f=n("./search").Search,d=new f;function p($,_,b){return d.$options.wrap=!0,d.$options.needle=_,d.$options.backwards=b==-1,d.find($)}var g=n("./edit_session").EditSession;(function(){this.getSelectionMarkers=function(){return this.$selectionMarkers}}).call(g.prototype),function(){this.ranges=null,this.rangeList=null,this.addRange=function($,_){if($){if(!this.inMultiSelectMode&&this.rangeCount===0){var b=this.toOrientedRange();if(this.rangeList.add(b),this.rangeList.add($),this.rangeList.ranges.length!=2)return this.rangeList.removeAll(),_||this.fromOrientedRange($);this.rangeList.removeAll(),this.rangeList.add(b),this.$onAddRange(b)}$.cursor||($.cursor=$.end);var R=this.rangeList.add($);return this.$onAddRange($),R.length&&this.$onRemoveRange(R),this.rangeCount>1&&!this.inMultiSelectMode&&(this._signal("multiSelect"),this.inMultiSelectMode=!0,this.session.$undoSelect=!1,this.rangeList.attach(this.session)),_||this.fromOrientedRange($)}},this.toSingleRange=function($){$=$||this.ranges[0];var _=this.rangeList.removeAll();_.length&&this.$onRemoveRange(_),$&&this.fromOrientedRange($)},this.substractPoint=function($){var _=this.rangeList.substractPoint($);if(_)return this.$onRemoveRange(_),_[0]},this.mergeOverlappingRanges=function(){var $=this.rangeList.merge();$.length&&this.$onRemoveRange($)},this.$onAddRange=function($){this.rangeCount=this.rangeList.ranges.length,this.ranges.unshift($),this._signal("addRange",{range:$})},this.$onRemoveRange=function($){if(this.rangeCount=this.rangeList.ranges.length,this.rangeCount==1&&this.inMultiSelectMode){var _=this.rangeList.ranges.pop();$.push(_),this.rangeCount=0}for(var b=$.length;b--;){var R=this.ranges.indexOf($[b]);this.ranges.splice(R,1)}this._signal("removeRange",{ranges:$}),this.rangeCount===0&&this.inMultiSelectMode&&(this.inMultiSelectMode=!1,this._signal("singleSelect"),this.session.$undoSelect=!0,this.rangeList.detach(this.session)),_=_||this.ranges[0],_&&!_.isEqual(this.getRange())&&this.fromOrientedRange(_)},this.$initRangeList=function(){this.rangeList||(this.rangeList=new o,this.ranges=[],this.rangeCount=0)},this.getAllRanges=function(){return this.rangeCount?this.rangeList.ranges.concat():[this.getRange()]},this.splitIntoLines=function(){for(var $=this.ranges.length?this.ranges:[this.getRange()],_=[],b=0;b<$.length;b++){var R=$[b],M=R.start.row,k=R.end.row;if(M===k)_.push(R.clone());else{for(_.push(new s(M,R.start.column,M,this.session.getLine(M).length));++M1){var $=this.rangeList.ranges,_=$[$.length-1],b=s.fromPoints($[0].start,_.end);this.toSingleRange(),this.setSelectionRange(b,_.cursor==_.start)}else{var R=this.session.documentToScreenPosition(this.cursor),M=this.session.documentToScreenPosition(this.anchor),k=this.rectangularRangeBlock(R,M);k.forEach(this.addRange,this)}},this.rectangularRangeBlock=function($,_,b){var R=[],M=$.column<_.column;if(M)var k=$.column,S=_.column,E=$.offsetX,I=_.offsetX;else var k=_.column,S=$.column,E=_.offsetX,I=$.offsetX;var C=$.row<_.row;if(C)var w=$.row,T=_.row;else var w=_.row,T=$.row;k<0&&(k=0),w<0&&(w=0),w==T&&(b=!0);for(var O,N=w;N<=T;N++){var B=s.fromPoints(this.session.screenToDocumentPosition(N,k,E),this.session.screenToDocumentPosition(N,S,I));if(B.isEmpty()){if(O&&v(B.end,O))break;O=B.end}B.cursor=M?B.start:B.end,R.push(B)}if(C&&R.reverse(),!b){for(var H=R.length-1;R[H].isEmpty()&&H>0;)H--;if(H>0)for(var z=0;R[z].isEmpty();)z++;for(var F=H;F>=z;F--)R[F].isEmpty()&&R.splice(F,1)}return R}}.call(h.prototype);var m=n("./editor").Editor;(function(){this.updateSelectionMarkers=function(){this.renderer.updateCursor(),this.renderer.updateBackMarkers()},this.addSelectionMarker=function($){$.cursor||($.cursor=$.end);var _=this.getSelectionStyle();return $.marker=this.session.addMarker($,"ace_selection",_),this.session.$selectionMarkers.push($),this.session.selectionMarkerCount=this.session.$selectionMarkers.length,$},this.removeSelectionMarker=function($){if($.marker){this.session.removeMarker($.marker);var _=this.session.$selectionMarkers.indexOf($);_!=-1&&this.session.$selectionMarkers.splice(_,1),this.session.selectionMarkerCount=this.session.$selectionMarkers.length}},this.removeSelectionMarkers=function($){for(var _=this.session.$selectionMarkers,b=$.length;b--;){var R=$[b];if(R.marker){this.session.removeMarker(R.marker);var M=_.indexOf(R);M!=-1&&_.splice(M,1)}}this.session.selectionMarkerCount=_.length},this.$onAddRange=function($){this.addSelectionMarker($.range),this.renderer.updateCursor(),this.renderer.updateBackMarkers()},this.$onRemoveRange=function($){this.removeSelectionMarkers($.ranges),this.renderer.updateCursor(),this.renderer.updateBackMarkers()},this.$onMultiSelect=function($){this.inMultiSelectMode||(this.inMultiSelectMode=!0,this.setStyle("ace_multiselect"),this.keyBinding.addKeyboardHandler(c.keyboardHandler),this.commands.setDefaultHandler("exec",this.$onMultiSelectExec),this.renderer.updateCursor(),this.renderer.updateBackMarkers())},this.$onSingleSelect=function($){this.session.multiSelect.inVirtualMode||(this.inMultiSelectMode=!1,this.unsetStyle("ace_multiselect"),this.keyBinding.removeKeyboardHandler(c.keyboardHandler),this.commands.removeDefaultHandler("exec",this.$onMultiSelectExec),this.renderer.updateCursor(),this.renderer.updateBackMarkers(),this._emit("changeSelection"))},this.$onMultiSelectExec=function($){var _=$.command,b=$.editor;if(b.multiSelect){if(_.multiSelectAction)_.multiSelectAction=="forEach"?R=b.forEachSelection(_,$.args):_.multiSelectAction=="forEachLine"?R=b.forEachSelection(_,$.args,!0):_.multiSelectAction=="single"?(b.exitMultiSelectMode(),R=_.exec(b,$.args||{})):R=_.multiSelectAction(b,$.args||{});else{var R=_.exec(b,$.args||{});b.multiSelect.addRange(b.multiSelect.toOrientedRange()),b.multiSelect.mergeOverlappingRanges()}return R}},this.forEachSelection=function($,_,b){if(!this.inVirtualSelectionMode){var R=b&&b.keepOrder,M=b==!0||b&&b.$byLines,k=this.session,S=this.selection,E=S.rangeList,I=(R?S:E).ranges,C;if(!I.length)return $.exec?$.exec(this,_||{}):$(this,_||{});var w=S._eventRegistry;S._eventRegistry={};var T=new h(k);this.inVirtualSelectionMode=!0;for(var O=I.length;O--;){if(M)for(;O>0&&I[O].start.row==I[O-1].end.row;)O--;T.fromOrientedRange(I[O]),T.index=O,this.selection=k.selection=T;var N=$.exec?$.exec(this,_||{}):$(this,_||{});!C&&N!==void 0&&(C=N),T.toOrientedRange(I[O])}T.detach(),this.selection=k.selection=S,this.inVirtualSelectionMode=!1,S._eventRegistry=w,S.mergeOverlappingRanges(),S.ranges[0]&&S.fromOrientedRange(S.ranges[0]);var B=this.renderer.$scrollAnimation;return this.onCursorChange(),this.onSelectionChange(),B&&B.from==B.to&&this.renderer.animateScrolling(B.from),C}},this.exitMultiSelectMode=function(){!this.inMultiSelectMode||this.inVirtualSelectionMode||this.multiSelect.toSingleRange()},this.getSelectedText=function(){var $="";if(this.inMultiSelectMode&&!this.inVirtualSelectionMode){for(var _=this.multiSelect.rangeList.ranges,b=[],R=0;R<_.length;R++)b.push(this.session.getTextRange(_[R]));var M=this.session.getDocument().getNewLineCharacter();$=b.join(M),$.length==(b.length-1)*M.length&&($="")}else this.selection.isEmpty()||($=this.session.getTextRange(this.getSelectionRange()));return $},this.$checkMultiselectChange=function($,_){if(this.inMultiSelectMode&&!this.inVirtualSelectionMode){var b=this.multiSelect.ranges[0];if(this.multiSelect.isEmpty()&&_==this.multiSelect.anchor)return;var R=_==this.multiSelect.anchor?b.cursor==b.start?b.end:b.start:b.cursor;R.row!=_.row||this.session.$clipPositionToDocument(R.row,R.column).column!=_.column?this.multiSelect.toSingleRange(this.multiSelect.toOrientedRange()):this.multiSelect.mergeOverlappingRanges()}},this.findAll=function($,_,b){if(_=_||{},_.needle=$||_.needle,_.needle==null){var R=this.selection.isEmpty()?this.selection.getWordRange():this.selection.getRange();_.needle=this.session.getTextRange(R)}this.$search.set(_);var M=this.$search.findAll(this.session);if(!M.length)return 0;var k=this.multiSelect;b||k.toSingleRange(M[0]);for(var S=M.length;S--;)k.addRange(M[S],!0);return R&&k.rangeList.rangeAtPoint(R.start)&&k.addRange(R,!0),M.length},this.selectMoreLines=function($,_){var b=this.selection.toOrientedRange(),R=b.cursor==b.end,M=this.session.documentToScreenPosition(b.cursor);this.selection.$desiredColumn&&(M.column=this.selection.$desiredColumn);var k=this.session.screenToDocumentPosition(M.row+$,M.column);if(b.isEmpty())var E=k;else var S=this.session.documentToScreenPosition(R?b.end:b.start),E=this.session.screenToDocumentPosition(S.row+$,S.column);if(R){var I=s.fromPoints(k,E);I.cursor=I.start}else{var I=s.fromPoints(E,k);I.cursor=I.end}if(I.desiredColumn=M.column,!this.selection.inMultiSelectMode)this.selection.addRange(b);else if(_)var C=b.cursor;this.selection.addRange(I),C&&this.selection.substractPoint(C)},this.transposeSelections=function($){for(var _=this.session,b=_.multiSelect,R=b.ranges,M=R.length;M--;){var k=R[M];if(k.isEmpty()){var S=_.getWordRange(k.start.row,k.start.column);k.start.row=S.start.row,k.start.column=S.start.column,k.end.row=S.end.row,k.end.column=S.end.column}}b.mergeOverlappingRanges();for(var E=[],M=R.length;M--;){var k=R[M];E.unshift(_.getTextRange(k))}$<0?E.unshift(E.pop()):E.push(E.shift());for(var M=R.length;M--;){var k=R[M],S=k.clone();_.replace(k,E[M]),k.start.row=S.start.row,k.start.column=S.start.column}b.fromOrientedRange(b.ranges[0])},this.selectMore=function($,_,b){var R=this.session,M=R.multiSelect,k=M.toOrientedRange();if(!(k.isEmpty()&&(k=R.getWordRange(k.start.row,k.start.column),k.cursor=$==-1?k.start:k.end,this.multiSelect.addRange(k),b))){var S=R.getTextRange(k),E=p(R,S,$);E&&(E.cursor=$==-1?E.start:E.end,this.session.unfold(E),this.multiSelect.addRange(E),this.renderer.scrollCursorIntoView(null,.5)),_&&this.multiSelect.substractPoint(k.cursor)}},this.alignCursors=function(){var $=this.session,_=$.multiSelect,b=_.ranges,R=-1,M=b.filter(function(H){if(H.cursor.row==R)return!0;R=H.cursor.row});if(!b.length||M.length==b.length-1){var k=this.selection.getRange(),S=k.start.row,E=k.end.row,I=S==E;if(I){var C=this.session.getLength(),w;do w=this.session.getLine(E);while(/[=:]/.test(w)&&++E0);S<0&&(S=0),E>=C&&(E=C-1)}var T=this.session.removeFullLines(S,E);T=this.$reAlignText(T,I),this.session.insert({row:S,column:0},T.join(` -`)+` -`),I||(k.start.column=0,k.end.column=T[T.length-1].length),this.selection.setRange(k)}else{M.forEach(function(H){_.substractPoint(H.cursor)});var O=0,N=1/0,B=b.map(function(H){var z=H.cursor,F=$.getLine(z.row),W=F.substr(z.column).search(/\S/g);return W==-1&&(W=0),z.column>O&&(O=z.column),WG?$.insert(F,a.stringRepeat(" ",W-G)):$.remove(new s(F.row,F.column,F.row,F.column-W+G)),H.start.column=H.end.column=O,H.start.row=H.end.row=F.row,H.cursor=H.end}),_.fromOrientedRange(b[0]),this.renderer.updateCursor(),this.renderer.updateBackMarkers()}},this.$reAlignText=function($,_){var b=!0,R=!0,M,k,S;return $.map(function(T){var O=T.match(/(\s*)(.*?)(\s*)([=:].*)/);return O?M==null?(M=O[1].length,k=O[2].length,S=O[3].length,O):(M+k+S!=O[1].length+O[2].length+O[3].length&&(R=!1),M!=O[1].length&&(b=!1),M>O[1].length&&(M=O[1].length),kO[3].length&&(S=O[3].length),O):[T]}).map(_?I:b?R?C:I:w);function E(T){return a.stringRepeat(" ",T)}function I(T){return T[2]?E(M)+T[2]+E(k-T[2].length+S)+T[4].replace(/^([=:])\s+/,"$1 "):T[0]}function C(T){return T[2]?E(M+k-T[2].length)+T[2]+E(S)+T[4].replace(/^([=:])\s+/,"$1 "):T[0]}function w(T){return T[2]?E(M)+T[2]+E(S)+T[4].replace(/^([=:])\s+/,"$1 "):T[0]}}}).call(m.prototype);function v($,_){return $.row==_.row&&$.column==_.column}r.onSessionChange=function($){var _=$.session;_&&!_.multiSelect&&(_.$selectionMarkers=[],_.selection.$initRangeList(),_.multiSelect=_.selection),this.multiSelect=_&&_.multiSelect;var b=$.oldSession;b&&(b.multiSelect.off("addRange",this.$onAddRange),b.multiSelect.off("removeRange",this.$onRemoveRange),b.multiSelect.off("multiSelect",this.$onMultiSelect),b.multiSelect.off("singleSelect",this.$onSingleSelect),b.multiSelect.lead.off("change",this.$checkMultiselectChange),b.multiSelect.anchor.off("change",this.$checkMultiselectChange)),_&&(_.multiSelect.on("addRange",this.$onAddRange),_.multiSelect.on("removeRange",this.$onRemoveRange),_.multiSelect.on("multiSelect",this.$onMultiSelect),_.multiSelect.on("singleSelect",this.$onSingleSelect),_.multiSelect.lead.on("change",this.$checkMultiselectChange),_.multiSelect.anchor.on("change",this.$checkMultiselectChange)),_&&this.inMultiSelectMode!=_.selection.inMultiSelectMode&&(_.selection.inMultiSelectMode?this.$onMultiSelect():this.$onSingleSelect())};function y($){$.$multiselectOnSessionChange||($.$onAddRange=$.$onAddRange.bind($),$.$onRemoveRange=$.$onRemoveRange.bind($),$.$onMultiSelect=$.$onMultiSelect.bind($),$.$onSingleSelect=$.$onSingleSelect.bind($),$.$multiselectOnSessionChange=r.onSessionChange.bind($),$.$checkMultiselectChange=$.$checkMultiselectChange.bind($),$.$multiselectOnSessionChange($),$.on("changeSession",$.$multiselectOnSessionChange),$.on("mousedown",l),$.commands.addCommands(c.defaultCommands),x($))}function x($){if(!$.textInput)return;var _=$.textInput.getElement(),b=!1;u.addListener(_,"keydown",function(M){var k=M.keyCode==18&&!(M.ctrlKey||M.shiftKey||M.metaKey);$.$blockSelectEnabled&&k?b||($.renderer.setMouseCursor("crosshair"),b=!0):b&&R()},$),u.addListener(_,"keyup",R,$),u.addListener(_,"blur",R,$);function R(M){b&&($.renderer.setMouseCursor(""),b=!1)}}r.MultiSelect=y,n("./config").defineOptions(m.prototype,"editor",{enableMultiselect:{set:function($){y(this),$?this.on("mousedown",l):this.off("mousedown",l)},value:!0},enableBlockSelect:{set:function($){this.$blockSelectEnabled=$},value:!0}})}),ace.define("ace/mode/folding/fold_mode",["require","exports","module","ace/range"],function(n,r,i){var o=n("../../range").Range,s=r.FoldMode=function(){};(function(){this.foldingStartMarker=null,this.foldingStopMarker=null,this.getFoldWidget=function(h,l,u){var a=h.getLine(u);return this.foldingStartMarker.test(a)?"start":l=="markbeginend"&&this.foldingStopMarker&&this.foldingStopMarker.test(a)?"end":""},this.getFoldWidgetRange=function(h,l,u){return null},this.indentationBlock=function(h,l,u){var a=/\S/,c=h.getLine(l),f=c.search(a);if(f!=-1){for(var d=u||c.length,p=h.getLength(),g=l,m=l;++lg){var x=h.getLine(m).length;return new o(g,d,m,x)}}},this.openingBracketBlock=function(h,l,u,a,c){var f={row:u,column:a+1},d=h.$findClosingBracket(l,f,c);if(d){var p=h.foldWidgets[d.row];return p==null&&(p=h.getFoldWidget(d.row)),p=="start"&&d.row>f.row&&(d.row--,d.column=h.getLine(d.row).length),o.fromPoints(f,d)}},this.closingBracketBlock=function(h,l,u,a,c){var f={row:u,column:a},d=h.$findOpeningBracket(l,f);if(d)return d.column++,f.column--,o.fromPoints(d,f)}}).call(s.prototype)}),ace.define("ace/ext/error_marker",["require","exports","module","ace/line_widgets","ace/lib/dom","ace/range","ace/config"],function(n,r,i){var o=n("../line_widgets").LineWidgets,s=n("../lib/dom"),h=n("../range").Range,l=n("../config").nls;function u(c,f,d){for(var p=0,g=c.length-1;p<=g;){var m=p+g>>1,v=d(f,c[m]);if(v>0)p=m+1;else if(v<0)g=m-1;else return m}return-(p+1)}function a(c,f,d){var p=c.getAnnotations().sort(h.comparePoints);if(p.length){var g=u(p,{row:f,column:-1},h.comparePoints);g<0&&(g=-g-1),g>=p.length?g=d>0?0:p.length-1:g===0&&d<0&&(g=p.length-1);var m=p[g];if(!(!m||!d)){if(m.row===f){do m=p[g+=d];while(m&&m.row===f);if(!m)return p.slice()}var v=[];f=m.row;do v[d<0?"unshift":"push"](m),m=p[g+=d];while(m&&m.row==f);return v.length&&v}}}r.showErrorMarker=function(c,f){var d=c.session;d.widgetManager||(d.widgetManager=new o(d),d.widgetManager.attach(c));var p=c.getCursorPosition(),g=p.row,m=d.widgetManager.getWidgetsAtRow(g).filter(function(k){return k.type=="errorMarker"})[0];m?m.destroy():g-=f;var v=a(d,g,f),y;if(v){var x=v[0];p.column=(x.pos&&typeof x.column!="number"?x.pos.sc:x.column)||0,p.row=x.row,y=c.renderer.$gutterLayer.$annotations[p.row]}else{if(m)return;y={text:[l("Looks good!")],className:"ace_ok"}}c.session.unfold(p.row),c.selection.moveToPosition(p);var $={row:p.row,fixedWidth:!0,coverGutter:!0,el:s.createElement("div"),type:"errorMarker"},_=$.el.appendChild(s.createElement("div")),b=$.el.appendChild(s.createElement("div"));b.className="error_widget_arrow "+y.className;var R=c.renderer.$cursorLayer.getPixelPosition(p).left;b.style.left=R+c.renderer.gutterWidth-5+"px",$.el.className="error_widget_wrapper",_.className="error_widget "+y.className,_.innerHTML=y.text.join("
"),_.appendChild(s.createElement("div"));var M=function(k,S,E){if(S===0&&(E==="esc"||E==="return"))return $.destroy(),{command:"null"}};$.destroy=function(){c.$mouseHandler.isMousePressed||(c.keyBinding.removeKeyboardHandler(M),d.widgetManager.removeLineWidget($),c.off("changeSelection",$.destroy),c.off("changeSession",$.destroy),c.off("mouseup",$.destroy),c.off("change",$.destroy))},c.keyBinding.addKeyboardHandler(M),c.on("changeSelection",$.destroy),c.on("changeSession",$.destroy),c.on("mouseup",$.destroy),c.on("change",$.destroy),c.session.widgetManager.addLineWidget($),$.el.onmousedown=c.focus.bind(c),c.renderer.scrollCursorIntoView(null,.5,{bottom:$.el.offsetHeight})},s.importCssString(` - .error_widget_wrapper { - background: inherit; - color: inherit; - border:none - } - .error_widget { - border-top: solid 2px; - border-bottom: solid 2px; - margin: 5px 0; - padding: 10px 40px; - white-space: pre-wrap; - } - .error_widget.ace_error, .error_widget_arrow.ace_error{ - border-color: #ff5a5a - } - .error_widget.ace_warning, .error_widget_arrow.ace_warning{ - border-color: #F1D817 - } - .error_widget.ace_info, .error_widget_arrow.ace_info{ - border-color: #5a5a5a - } - .error_widget.ace_ok, .error_widget_arrow.ace_ok{ - border-color: #5aaa5a - } - .error_widget_arrow { - position: absolute; - border: solid 5px; - border-top-color: transparent!important; - border-right-color: transparent!important; - border-left-color: transparent!important; - top: -5px; - } -`,"error_marker.css",!1)}),ace.define("ace/ace",["require","exports","module","ace/lib/dom","ace/range","ace/editor","ace/edit_session","ace/undomanager","ace/virtual_renderer","ace/worker/worker_client","ace/keyboard/hash_handler","ace/placeholder","ace/multi_select","ace/mode/folding/fold_mode","ace/theme/textmate","ace/ext/error_marker","ace/config","ace/loader_build"],function(n,r,i){n("./loader_build")(r);var o=n("./lib/dom"),s=n("./range").Range,h=n("./editor").Editor,l=n("./edit_session").EditSession,u=n("./undomanager").UndoManager,a=n("./virtual_renderer").VirtualRenderer;n("./worker/worker_client"),n("./keyboard/hash_handler"),n("./placeholder"),n("./multi_select"),n("./mode/folding/fold_mode"),n("./theme/textmate"),n("./ext/error_marker"),r.config=n("./config"),r.edit=function(c,f){if(typeof c=="string"){var d=c;if(c=document.getElementById(d),!c)throw new Error("ace.edit can't find div #"+d)}if(c&&c.env&&c.env.editor instanceof h)return c.env.editor;var p="";if(c&&/input|textarea/i.test(c.tagName)){var g=c;p=g.value,c=o.createElement("pre"),g.parentNode.replaceChild(c,g)}else c&&(p=c.textContent,c.innerHTML="");var m=r.createEditSession(p),v=new h(new a(c),m,f),y={document:m,editor:v,onResize:v.resize.bind(v,null)};return g&&(y.textarea=g),v.on("destroy",function(){y.editor.container.env=null}),v.container.env=v.env=y,v},r.createEditSession=function(c,f){var d=new l(c,f);return d.setUndoManager(new u),d},r.Range=s,r.Editor=h,r.EditSession=l,r.UndoManager=u,r.VirtualRenderer=a,r.version=r.config.version}),function(){ace.require(["ace/ace"],function(n){n&&(n.config.init(!0),n.define=ace.define),window.ace||(window.ace=n);for(var r in n)n.hasOwnProperty(r)&&(window.ace[r]=n[r]);window.ace.default=window.ace,e&&(e.exports=window.ace)})}()})(yb);var AH=yb.exports;const Vg=Li(AH);var PH={exports:{}};(function(e,t){ace.define("ace/mode/json_highlight_rules",["require","exports","module","ace/lib/oop","ace/mode/text_highlight_rules"],function(n,r,i){var o=n("../lib/oop"),s=n("./text_highlight_rules").TextHighlightRules,h=function(){this.$rules={start:[{token:"variable",regex:'["](?:(?:\\\\.)|(?:[^"\\\\]))*?["]\\s*(?=:)'},{token:"string",regex:'"',next:"string"},{token:"constant.numeric",regex:"0[xX][0-9a-fA-F]+\\b"},{token:"constant.numeric",regex:"[+-]?\\d+(?:(?:\\.\\d*)?(?:[eE][+-]?\\d+)?)?\\b"},{token:"constant.language.boolean",regex:"(?:true|false)\\b"},{token:"text",regex:"['](?:(?:\\\\.)|(?:[^'\\\\]))*?[']"},{token:"comment",regex:"\\/\\/.*$"},{token:"comment.start",regex:"\\/\\*",next:"comment"},{token:"paren.lparen",regex:"[[({]"},{token:"paren.rparen",regex:"[\\])}]"},{token:"punctuation.operator",regex:/[,]/},{token:"text",regex:"\\s+"}],string:[{token:"constant.language.escape",regex:/\\(?:x[0-9a-fA-F]{2}|u[0-9a-fA-F]{4}|["\\\/bfnrt])/},{token:"string",regex:'"|$',next:"start"},{defaultToken:"string"}],comment:[{token:"comment.end",regex:"\\*\\/",next:"start"},{defaultToken:"comment"}]}};o.inherits(h,s),r.JsonHighlightRules=h}),ace.define("ace/mode/matching_brace_outdent",["require","exports","module","ace/range"],function(n,r,i){var o=n("../range").Range,s=function(){};(function(){this.checkOutdent=function(h,l){return/^\s+$/.test(h)?/^\s*\}/.test(l):!1},this.autoOutdent=function(h,l){var u=h.getLine(l),a=u.match(/^(\s*\})/);if(!a)return 0;var c=a[1].length,f=h.findMatchingBracket({row:l,column:c});if(!f||f.row==l)return 0;var d=this.$getIndent(h.getLine(f.row));h.replace(new o(l,0,l,c-1),d)},this.$getIndent=function(h){return h.match(/^\s*/)[0]}}).call(s.prototype),r.MatchingBraceOutdent=s}),ace.define("ace/mode/folding/cstyle",["require","exports","module","ace/lib/oop","ace/range","ace/mode/folding/fold_mode"],function(n,r,i){var o=n("../../lib/oop"),s=n("../../range").Range,h=n("./fold_mode").FoldMode,l=r.FoldMode=function(u){u&&(this.foldingStartMarker=new RegExp(this.foldingStartMarker.source.replace(/\|[^|]*?$/,"|"+u.start)),this.foldingStopMarker=new RegExp(this.foldingStopMarker.source.replace(/\|[^|]*?$/,"|"+u.end)))};o.inherits(l,h),function(){this.foldingStartMarker=/([\{\[\(])[^\}\]\)]*$|^\s*(\/\*)/,this.foldingStopMarker=/^[^\[\{\(]*([\}\]\)])|^[\s\*]*(\*\/)/,this.singleLineBlockCommentRe=/^\s*(\/\*).*\*\/\s*$/,this.tripleStarBlockCommentRe=/^\s*(\/\*\*\*).*\*\/\s*$/,this.startRegionRe=/^\s*(\/\*|\/\/)#?region\b/,this._getFoldWidgetBase=this.getFoldWidget,this.getFoldWidget=function(u,a,c){var f=u.getLine(c);if(this.singleLineBlockCommentRe.test(f)&&!this.startRegionRe.test(f)&&!this.tripleStarBlockCommentRe.test(f))return"";var d=this._getFoldWidgetBase(u,a,c);return!d&&this.startRegionRe.test(f)?"start":d},this.getFoldWidgetRange=function(u,a,c,f){var d=u.getLine(c);if(this.startRegionRe.test(d))return this.getCommentRegionBlock(u,d,c);var m=d.match(this.foldingStartMarker);if(m){var p=m.index;if(m[1])return this.openingBracketBlock(u,m[1],c,p);var g=u.getCommentFoldRange(c,p+m[0].length,1);return g&&!g.isMultiLine()&&(f?g=this.getSectionRange(u,c):a!="all"&&(g=null)),g}if(a!=="markbegin"){var m=d.match(this.foldingStopMarker);if(m){var p=m.index+m[0].length;return m[1]?this.closingBracketBlock(u,m[1],c,p):u.getCommentFoldRange(c,p,-1)}}},this.getSectionRange=function(u,a){var c=u.getLine(a),f=c.search(/\S/),d=a,p=c.length;a=a+1;for(var g=a,m=u.getLength();++av)break;var y=this.getFoldWidgetRange(u,"all",a);if(y){if(y.start.row<=d)break;if(y.isMultiLine())a=y.end.row;else if(f==v)break}g=a}}return new s(d,p,g,u.getLine(g).length)},this.getCommentRegionBlock=function(u,a,c){for(var f=a.search(/\s*$/),d=u.getLength(),p=c,g=/^\s*(?:\/\*|\/\/|--)#?(end)?region\b/,m=1;++cp)return new s(p,f,y,a.length)}}.call(l.prototype)}),ace.define("ace/mode/json",["require","exports","module","ace/lib/oop","ace/mode/text","ace/mode/json_highlight_rules","ace/mode/matching_brace_outdent","ace/mode/folding/cstyle","ace/worker/worker_client"],function(n,r,i){var o=n("../lib/oop"),s=n("./text").Mode,h=n("./json_highlight_rules").JsonHighlightRules,l=n("./matching_brace_outdent").MatchingBraceOutdent,u=n("./folding/cstyle").FoldMode,a=n("../worker/worker_client").WorkerClient,c=function(){this.HighlightRules=h,this.$outdent=new l,this.$behaviour=this.$defaultBehaviour,this.foldingRules=new u};o.inherits(c,s),function(){this.lineCommentStart="//",this.blockComment={start:"/*",end:"*/"},this.getNextLineIndent=function(f,d,p){var g=this.$getIndent(d);if(f=="start"){var m=d.match(/^.*[\{\(\[]\s*$/);m&&(g+=p)}return g},this.checkOutdent=function(f,d,p){return this.$outdent.checkOutdent(d,p)},this.autoOutdent=function(f,d,p){this.$outdent.autoOutdent(d,p)},this.createWorker=function(f){var d=new a(["ace"],"ace/mode/json_worker","JsonWorker");return d.attachToDocument(f.getDocument()),d.on("annotate",function(p){f.setAnnotations(p.data)}),d.on("terminate",function(){f.clearAnnotations()}),d},this.$id="ace/mode/json"}.call(c.prototype),r.Mode=c}),function(){ace.require(["ace/mode/json"],function(n){e&&(e.exports=n)})}()})(PH);var NH={exports:{}};(function(e,t){ace.define("ace/mode/sql_highlight_rules",["require","exports","module","ace/lib/oop","ace/mode/text_highlight_rules"],function(n,r,i){var o=n("../lib/oop"),s=n("./text_highlight_rules").TextHighlightRules,h=function(){var 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calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type.`);if(r=""+r,o=this._edges.get(r),!o)throw new be(`Graph.${t}: could not find the "${r}" edge in the graph.`)}return delete o.attributes[i],this.emit("edgeAttributesUpdated",{key:o.key,type:"remove",attributes:o.attributes,name:i}),this}}function q4(e,t,n){e.prototype[t]=function(r,i){let o;if(this.type!=="mixed"&&n!=="mixed"&&n!==this.type)throw new Ie(`Graph.${t}: cannot find this type of edges in your ${this.type} graph.`);if(arguments.length>2){if(this.multi)throw new Ie(`Graph.${t}: cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about.`);const s=""+r,h=""+i;if(i=arguments[2],o=yn(this,s,h,n),!o)throw new be(`Graph.${t}: could not find an edge for the given path ("${s}" - "${h}").`)}else{if(n!=="mixed")throw new Ie(`Graph.${t}: calling this method with 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Ie(`Graph.${t}: calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type.`);if(r=""+r,o=this._edges.get(r),!o)throw new be(`Graph.${t}: could not find the "${r}" edge in the graph.`)}if(!Ft(i))throw new xe(`Graph.${t}: provided attributes are not a plain object.`);return It(o.attributes,i),this.emit("edgeAttributesUpdated",{key:o.key,type:"merge",attributes:o.attributes,data:i}),this}}function Z4(e,t,n){e.prototype[t]=function(r,i){let o;if(this.type!=="mixed"&&n!=="mixed"&&n!==this.type)throw new Ie(`Graph.${t}: cannot find this type of edges in your ${this.type} graph.`);if(arguments.length>2){if(this.multi)throw new Ie(`Graph.${t}: cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about.`);const s=""+r,h=""+i;if(i=arguments[2],o=yn(this,s,h,n),!o)throw new be(`Graph.${t}: could not find an edge for the given path 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l=n[r[o++]],u=l.source,a=l.target;if(h=u===t?a:u,e&&e.has(h.key)){h=null;continue}}while(h===null);return{done:!1,value:{neighbor:h.key,attributes:h.attributes}}})}function bj(e,t,n){if(e!=="mixed"){if(e==="undirected")return Jo(null,n,n.undirected);if(typeof t=="string")return Jo(null,n,n[t])}let r=bn.empty();const i=new xc;return e!=="undirected"&&(t!=="out"&&(r=hr(r,Jo(i,n,n.in))),t!=="in"&&(r=hr(r,Jo(i,n,n.out)))),e!=="directed"&&(r=hr(r,Jo(i,n,n.undirected))),r}function _j(e,t){const{name:n,type:r,direction:i}=t;e.prototype[n]=function(o){if(r!=="mixed"&&this.type!=="mixed"&&r!==this.type)return[];o=""+o;const s=this._nodes.get(o);if(typeof s>"u")throw new be(`Graph.${n}: could not find the "${o}" node in the graph.`);return wj(r==="mixed"?this.type:r,i,s)}}function Ej(e,t){const{name:n,type:r,direction:i}=t,o="forEach"+n[0].toUpperCase()+n.slice(1,-1);e.prototype[o]=function(u,a){if(r!=="mixed"&&this.type!=="mixed"&&r!==this.type)return;u=""+u;const c=this._nodes.get(u);if(typeof c>"u")throw new be(`Graph.${o}: could not find the "${u}" node in the graph.`);td(!1,r==="mixed"?this.type:r,i,c,a)};const s="map"+n[0].toUpperCase()+n.slice(1);e.prototype[s]=function(u,a){const c=[];return this[o](u,(f,d)=>{c.push(a(f,d))}),c};const h="filter"+n[0].toUpperCase()+n.slice(1);e.prototype[h]=function(u,a){const c=[];return this[o](u,(f,d)=>{a(f,d)&&c.push(f)}),c};const l="reduce"+n[0].toUpperCase()+n.slice(1);e.prototype[l]=function(u,a,c){if(arguments.length<3)throw new xe(`Graph.${l}: missing initial value. You must provide it because the callback takes more than one argument and we cannot infer the initial value from the first iteration, as you could with a simple array.`);let f=c;return this[o](u,(d,p)=>{f=a(f,d,p)}),f}}function $j(e,t){const{name:n,type:r,direction:i}=t,o=n[0].toUpperCase()+n.slice(1,-1),s="find"+o;e.prototype[s]=function(u,a){if(r!=="mixed"&&this.type!=="mixed"&&r!==this.type)return;u=""+u;const c=this._nodes.get(u);if(typeof c>"u")throw new be(`Graph.${s}: could not find the "${u}" node in the graph.`);return td(!0,r==="mixed"?this.type:r,i,c,a)};const h="some"+o;e.prototype[h]=function(u,a){return!!this[s](u,a)};const l="every"+o;e.prototype[l]=function(u,a){return!this[s](u,(f,d)=>!a(f,d))}}function xj(e,t){const{name:n,type:r,direction:i}=t,o=n.slice(0,-1)+"Entries";e.prototype[o]=function(s){if(r!=="mixed"&&this.type!=="mixed"&&r!==this.type)return bn.empty();s=""+s;const h=this._nodes.get(s);if(typeof h>"u")throw new be(`Graph.${o}: could not find the "${s}" node in the graph.`);return bj(r==="mixed"?this.type:r,i,h)}}function Sj(e){yj.forEach(t=>{_j(e,t),Ej(e,t),$j(e,t),xj(e,t)})}function xa(e,t,n,r,i){const o=r._nodes.values(),s=r.type;let h,l,u,a,c,f,d;for(;h=o.next(),h.done!==!0;){let p=!1;if(l=h.value,s!=="undirected"){a=l.out;for(u in a){c=a[u];do{if(f=c.target,p=!0,d=i(l.key,f.key,l.attributes,f.attributes,c.key,c.attributes,c.undirected),e&&d)return c;c=c.next}while(c)}}if(s!=="directed"){a=l.undirected;for(u in a)if(!(t&&l.key>u)){c=a[u];do{if(f=c.target,f.key!==u&&(f=c.source),p=!0,d=i(l.key,f.key,l.attributes,f.attributes,c.key,c.attributes,c.undirected),e&&d)return c;c=c.next}while(c)}}if(n&&!p&&(d=i(l.key,null,l.attributes,null,null,null,null),e&&d))return null}}function Cj(e,t){const n={key:e};return i0(t.attributes)||(n.attributes=It({},t.attributes)),n}function Lj(e,t,n){const r={key:t,source:n.source.key,target:n.target.key};return i0(n.attributes)||(r.attributes=It({},n.attributes)),e==="mixed"&&n.undirected&&(r.undirected=!0),r}function Mj(e){if(!Ft(e))throw new xe('Graph.import: invalid serialized node. A serialized node should be a plain object with at least a "key" property.');if(!("key"in e))throw new xe("Graph.import: serialized node is missing its key.");if("attributes"in e&&(!Ft(e.attributes)||e.attributes===null))throw new xe("Graph.import: invalid attributes. Attributes should be a plain object, null or omitted.")}function Tj(e){if(!Ft(e))throw new xe('Graph.import: invalid serialized edge. A serialized edge should be a plain object with at least a "source" & "target" property.');if(!("source"in e))throw new xe("Graph.import: serialized edge is missing its source.");if(!("target"in e))throw new xe("Graph.import: serialized edge is missing its target.");if("attributes"in e&&(!Ft(e.attributes)||e.attributes===null))throw new xe("Graph.import: invalid attributes. Attributes should be a plain object, null or omitted.");if("undirected"in e&&typeof e.undirected!="boolean")throw new xe("Graph.import: invalid undirectedness information. Undirected should be boolean or omitted.")}const Oj=R4(),kj=new Set(["directed","undirected","mixed"]),xm=new Set(["domain","_events","_eventsCount","_maxListeners"]),Rj=[{name:e=>`${e}Edge`,generateKey:!0},{name:e=>`${e}DirectedEdge`,generateKey:!0,type:"directed"},{name:e=>`${e}UndirectedEdge`,generateKey:!0,type:"undirected"},{name:e=>`${e}EdgeWithKey`},{name:e=>`${e}DirectedEdgeWithKey`,type:"directed"},{name:e=>`${e}UndirectedEdgeWithKey`,type:"undirected"}],Dj={allowSelfLoops:!0,multi:!1,type:"mixed"};function Ij(e,t,n){if(n&&!Ft(n))throw new xe(`Graph.addNode: invalid attributes. Expecting an object but got "${n}"`);if(t=""+t,n=n||{},e._nodes.has(t))throw new Ie(`Graph.addNode: the "${t}" node already exist in the graph.`);const r=new e.NodeDataClass(t,n);return e._nodes.set(t,r),e.emit("nodeAdded",{key:t,attributes:n}),r}function Sm(e,t,n){const r=new e.NodeDataClass(t,n);return e._nodes.set(t,r),e.emit("nodeAdded",{key:t,attributes:n}),r}function f0(e,t,n,r,i,o,s,h){if(!r&&e.type==="undirected")throw new Ie(`Graph.${t}: you cannot add a directed edge to an undirected graph. Use the #.addEdge or #.addUndirectedEdge instead.`);if(r&&e.type==="directed")throw new Ie(`Graph.${t}: you cannot add an undirected edge to a directed graph. Use the #.addEdge or #.addDirectedEdge instead.`);if(h&&!Ft(h))throw new xe(`Graph.${t}: invalid attributes. Expecting an object but got "${h}"`);if(o=""+o,s=""+s,h=h||{},!e.allowSelfLoops&&o===s)throw new Ie(`Graph.${t}: source & target are the same ("${o}"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false.`);const l=e._nodes.get(o),u=e._nodes.get(s);if(!l)throw new be(`Graph.${t}: source node "${o}" not found.`);if(!u)throw new be(`Graph.${t}: target node "${s}" not found.`);const a={key:null,undirected:r,source:o,target:s,attributes:h};if(n)i=e._edgeKeyGenerator();else if(i=""+i,e._edges.has(i))throw new Ie(`Graph.${t}: the "${i}" edge already exists in the graph.`);if(!e.multi&&(r?typeof l.undirected[s]<"u":typeof l.out[s]<"u"))throw new Ie(`Graph.${t}: an edge linking "${o}" to "${s}" already exists. If you really want to add multiple edges linking those nodes, you should create a multi graph by using the 'multi' option.`);const c=new Oo(r,i,l,u,h);e._edges.set(i,c);const f=o===s;return r?(l.undirectedDegree++,u.undirectedDegree++,f&&(l.undirectedLoops++,e._undirectedSelfLoopCount++)):(l.outDegree++,u.inDegree++,f&&(l.directedLoops++,e._directedSelfLoopCount++)),e.multi?c.attachMulti():c.attach(),r?e._undirectedSize++:e._directedSize++,a.key=i,e.emit("edgeAdded",a),i}function Aj(e,t,n,r,i,o,s,h,l){if(!r&&e.type==="undirected")throw new Ie(`Graph.${t}: you cannot merge/update a directed edge to an undirected graph. Use the #.mergeEdge/#.updateEdge or #.addUndirectedEdge instead.`);if(r&&e.type==="directed")throw new Ie(`Graph.${t}: you cannot merge/update an undirected edge to a directed graph. Use the #.mergeEdge/#.updateEdge or #.addDirectedEdge instead.`);if(h){if(l){if(typeof h!="function")throw new xe(`Graph.${t}: invalid updater function. Expecting a function but got "${h}"`)}else if(!Ft(h))throw new xe(`Graph.${t}: invalid attributes. Expecting an object but got "${h}"`)}o=""+o,s=""+s;let u;if(l&&(u=h,h=void 0),!e.allowSelfLoops&&o===s)throw new Ie(`Graph.${t}: source & target are the same ("${o}"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false.`);let a=e._nodes.get(o),c=e._nodes.get(s),f,d;if(!n&&(f=e._edges.get(i),f)){if((f.source.key!==o||f.target.key!==s)&&(!r||f.source.key!==s||f.target.key!==o))throw new Ie(`Graph.${t}: inconsistency detected when attempting to merge the "${i}" edge with "${o}" source & "${s}" target vs. ("${f.source.key}", "${f.target.key}").`);d=f}if(!d&&!e.multi&&a&&(d=r?a.undirected[s]:a.out[s]),d){const y=[d.key,!1,!1,!1];if(l?!u:!h)return y;if(l){const x=d.attributes;d.attributes=u(x),e.emit("edgeAttributesUpdated",{type:"replace",key:d.key,attributes:d.attributes})}else It(d.attributes,h),e.emit("edgeAttributesUpdated",{type:"merge",key:d.key,attributes:d.attributes,data:h});return y}h=h||{},l&&u&&(h=u(h));const p={key:null,undirected:r,source:o,target:s,attributes:h};if(n)i=e._edgeKeyGenerator();else if(i=""+i,e._edges.has(i))throw new Ie(`Graph.${t}: the "${i}" edge already exists in the graph.`);let g=!1,m=!1;a||(a=Sm(e,o,{}),g=!0,o===s&&(c=a,m=!0)),c||(c=Sm(e,s,{}),m=!0),f=new Oo(r,i,a,c,h),e._edges.set(i,f);const v=o===s;return r?(a.undirectedDegree++,c.undirectedDegree++,v&&(a.undirectedLoops++,e._undirectedSelfLoopCount++)):(a.outDegree++,c.inDegree++,v&&(a.directedLoops++,e._directedSelfLoopCount++)),e.multi?f.attachMulti():f.attach(),r?e._undirectedSize++:e._directedSize++,p.key=i,e.emit("edgeAdded",p),[i,!0,g,m]}function Fi(e,t){e._edges.delete(t.key);const{source:n,target:r,attributes:i}=t,o=t.undirected,s=n===r;o?(n.undirectedDegree--,r.undirectedDegree--,s&&(n.undirectedLoops--,e._undirectedSelfLoopCount--)):(n.outDegree--,r.inDegree--,s&&(n.directedLoops--,e._directedSelfLoopCount--)),e.multi?t.detachMulti():t.detach(),o?e._undirectedSize--:e._directedSize--,e.emit("edgeDropped",{key:t.key,attributes:i,source:n.key,target:r.key,undirected:o})}class at extends _4.EventEmitter{constructor(t){if(super(),t=It({},Dj,t),typeof t.multi!="boolean")throw new xe(`Graph.constructor: invalid 'multi' option. Expecting a boolean but got "${t.multi}".`);if(!kj.has(t.type))throw new xe(`Graph.constructor: invalid 'type' option. Should be one of "mixed", "directed" or "undirected" but got "${t.type}".`);if(typeof t.allowSelfLoops!="boolean")throw new xe(`Graph.constructor: invalid 'allowSelfLoops' option. Expecting a boolean but got "${t.allowSelfLoops}".`);const n=t.type==="mixed"?o0:t.type==="directed"?s0:a0;dn(this,"NodeDataClass",n);const r="geid_"+Oj()+"_";let i=0;const o=()=>{let s;do s=r+i++;while(this._edges.has(s));return s};dn(this,"_attributes",{}),dn(this,"_nodes",new Map),dn(this,"_edges",new Map),dn(this,"_directedSize",0),dn(this,"_undirectedSize",0),dn(this,"_directedSelfLoopCount",0),dn(this,"_undirectedSelfLoopCount",0),dn(this,"_edgeKeyGenerator",o),dn(this,"_options",t),xm.forEach(s=>dn(this,s,this[s])),xn(this,"order",()=>this._nodes.size),xn(this,"size",()=>this._edges.size),xn(this,"directedSize",()=>this._directedSize),xn(this,"undirectedSize",()=>this._undirectedSize),xn(this,"selfLoopCount",()=>this._directedSelfLoopCount+this._undirectedSelfLoopCount),xn(this,"directedSelfLoopCount",()=>this._directedSelfLoopCount),xn(this,"undirectedSelfLoopCount",()=>this._undirectedSelfLoopCount),xn(this,"multi",this._options.multi),xn(this,"type",this._options.type),xn(this,"allowSelfLoops",this._options.allowSelfLoops),xn(this,"implementation",()=>"graphology")}_resetInstanceCounters(){this._directedSize=0,this._undirectedSize=0,this._directedSelfLoopCount=0,this._undirectedSelfLoopCount=0}hasNode(t){return this._nodes.has(""+t)}hasDirectedEdge(t,n){if(this.type==="undirected")return!1;if(arguments.length===1){const r=""+t,i=this._edges.get(r);return!!i&&!i.undirected}else if(arguments.length===2){t=""+t,n=""+n;const r=this._nodes.get(t);return r?r.out.hasOwnProperty(n):!1}throw new xe(`Graph.hasDirectedEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}hasUndirectedEdge(t,n){if(this.type==="directed")return!1;if(arguments.length===1){const r=""+t,i=this._edges.get(r);return!!i&&i.undirected}else if(arguments.length===2){t=""+t,n=""+n;const r=this._nodes.get(t);return r?r.undirected.hasOwnProperty(n):!1}throw new xe(`Graph.hasDirectedEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}hasEdge(t,n){if(arguments.length===1){const r=""+t;return this._edges.has(r)}else if(arguments.length===2){t=""+t,n=""+n;const r=this._nodes.get(t);return r?typeof r.out<"u"&&r.out.hasOwnProperty(n)||typeof r.undirected<"u"&&r.undirected.hasOwnProperty(n):!1}throw new xe(`Graph.hasEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}directedEdge(t,n){if(this.type==="undirected")return;if(t=""+t,n=""+n,this.multi)throw new Ie("Graph.directedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.directedEdges instead.");const r=this._nodes.get(t);if(!r)throw new be(`Graph.directedEdge: could not find the "${t}" source node in the graph.`);if(!this._nodes.has(n))throw new be(`Graph.directedEdge: could not find the "${n}" target node in the graph.`);const i=r.out&&r.out[n]||void 0;if(i)return i.key}undirectedEdge(t,n){if(this.type==="directed")return;if(t=""+t,n=""+n,this.multi)throw new Ie("Graph.undirectedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.undirectedEdges instead.");const r=this._nodes.get(t);if(!r)throw new be(`Graph.undirectedEdge: could not find the "${t}" source node in the graph.`);if(!this._nodes.has(n))throw new be(`Graph.undirectedEdge: could not find the "${n}" target node in the graph.`);const i=r.undirected&&r.undirected[n]||void 0;if(i)return i.key}edge(t,n){if(this.multi)throw new Ie("Graph.edge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.edges instead.");t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.edge: could not find the "${t}" source node in the graph.`);if(!this._nodes.has(n))throw new be(`Graph.edge: could not find the "${n}" target node in the graph.`);const i=r.out&&r.out[n]||r.undirected&&r.undirected[n]||void 0;if(i)return i.key}areDirectedNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areDirectedNeighbors: could not find the "${t}" node in the graph.`);return this.type==="undirected"?!1:n in r.in||n in r.out}areOutNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areOutNeighbors: could not find the "${t}" node in the graph.`);return this.type==="undirected"?!1:n in r.out}areInNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areInNeighbors: could not find the "${t}" node in the graph.`);return this.type==="undirected"?!1:n in r.in}areUndirectedNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areUndirectedNeighbors: could not find the "${t}" node in the graph.`);return this.type==="directed"?!1:n in r.undirected}areNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areNeighbors: could not find the "${t}" node in the graph.`);return this.type!=="undirected"&&(n in r.in||n in r.out)||this.type!=="directed"&&n in r.undirected}areInboundNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areInboundNeighbors: could not find the "${t}" node in the graph.`);return this.type!=="undirected"&&n in r.in||this.type!=="directed"&&n in r.undirected}areOutboundNeighbors(t,n){t=""+t,n=""+n;const r=this._nodes.get(t);if(!r)throw new be(`Graph.areOutboundNeighbors: could not find the "${t}" node in the graph.`);return this.type!=="undirected"&&n in r.out||this.type!=="directed"&&n in r.undirected}inDegree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.inDegree: could not find the "${t}" node in the graph.`);return this.type==="undirected"?0:n.inDegree}outDegree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.outDegree: could not find the "${t}" node in the graph.`);return this.type==="undirected"?0:n.outDegree}directedDegree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.directedDegree: could not find the "${t}" node in the graph.`);return this.type==="undirected"?0:n.inDegree+n.outDegree}undirectedDegree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.undirectedDegree: could not find the "${t}" node in the graph.`);return this.type==="directed"?0:n.undirectedDegree}inboundDegree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.inboundDegree: could not find the "${t}" node in the graph.`);let r=0;return this.type!=="directed"&&(r+=n.undirectedDegree),this.type!=="undirected"&&(r+=n.inDegree),r}outboundDegree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.outboundDegree: could not find the "${t}" node in the graph.`);let r=0;return this.type!=="directed"&&(r+=n.undirectedDegree),this.type!=="undirected"&&(r+=n.outDegree),r}degree(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.degree: could not find the "${t}" node in the graph.`);let r=0;return this.type!=="directed"&&(r+=n.undirectedDegree),this.type!=="undirected"&&(r+=n.inDegree+n.outDegree),r}inDegreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.inDegreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);return this.type==="undirected"?0:n.inDegree-n.directedLoops}outDegreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.outDegreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);return this.type==="undirected"?0:n.outDegree-n.directedLoops}directedDegreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.directedDegreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);return this.type==="undirected"?0:n.inDegree+n.outDegree-n.directedLoops*2}undirectedDegreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.undirectedDegreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);return this.type==="directed"?0:n.undirectedDegree-n.undirectedLoops*2}inboundDegreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.inboundDegreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);let r=0,i=0;return this.type!=="directed"&&(r+=n.undirectedDegree,i+=n.undirectedLoops*2),this.type!=="undirected"&&(r+=n.inDegree,i+=n.directedLoops),r-i}outboundDegreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.outboundDegreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);let r=0,i=0;return this.type!=="directed"&&(r+=n.undirectedDegree,i+=n.undirectedLoops*2),this.type!=="undirected"&&(r+=n.outDegree,i+=n.directedLoops),r-i}degreeWithoutSelfLoops(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.degreeWithoutSelfLoops: could not find the "${t}" node in the graph.`);let r=0,i=0;return this.type!=="directed"&&(r+=n.undirectedDegree,i+=n.undirectedLoops*2),this.type!=="undirected"&&(r+=n.inDegree+n.outDegree,i+=n.directedLoops*2),r-i}source(t){t=""+t;const n=this._edges.get(t);if(!n)throw new be(`Graph.source: could not find the "${t}" edge in the graph.`);return n.source.key}target(t){t=""+t;const n=this._edges.get(t);if(!n)throw new be(`Graph.target: could not find the "${t}" edge in the graph.`);return n.target.key}extremities(t){t=""+t;const n=this._edges.get(t);if(!n)throw new be(`Graph.extremities: could not find the "${t}" edge in the graph.`);return[n.source.key,n.target.key]}opposite(t,n){t=""+t,n=""+n;const r=this._edges.get(n);if(!r)throw new be(`Graph.opposite: could not find the "${n}" edge in the graph.`);const i=r.source.key,o=r.target.key;if(t===i)return o;if(t===o)return i;throw new be(`Graph.opposite: the "${t}" node is not attached to the "${n}" edge (${i}, ${o}).`)}hasExtremity(t,n){t=""+t,n=""+n;const r=this._edges.get(t);if(!r)throw new be(`Graph.hasExtremity: could not find the "${t}" edge in the graph.`);return r.source.key===n||r.target.key===n}isUndirected(t){t=""+t;const n=this._edges.get(t);if(!n)throw new be(`Graph.isUndirected: could not find the "${t}" edge in the graph.`);return n.undirected}isDirected(t){t=""+t;const n=this._edges.get(t);if(!n)throw new be(`Graph.isDirected: could not find the "${t}" edge in the graph.`);return!n.undirected}isSelfLoop(t){t=""+t;const n=this._edges.get(t);if(!n)throw new be(`Graph.isSelfLoop: could not find the "${t}" edge in the graph.`);return n.source===n.target}addNode(t,n){return Ij(this,t,n).key}mergeNode(t,n){if(n&&!Ft(n))throw new xe(`Graph.mergeNode: invalid attributes. Expecting an object but got "${n}"`);t=""+t,n=n||{};let r=this._nodes.get(t);return r?(n&&(It(r.attributes,n),this.emit("nodeAttributesUpdated",{type:"merge",key:t,attributes:r.attributes,data:n})),[t,!1]):(r=new this.NodeDataClass(t,n),this._nodes.set(t,r),this.emit("nodeAdded",{key:t,attributes:n}),[t,!0])}updateNode(t,n){if(n&&typeof n!="function")throw new xe(`Graph.updateNode: invalid updater function. Expecting a function but got "${n}"`);t=""+t;let r=this._nodes.get(t);if(r){if(n){const o=r.attributes;r.attributes=n(o),this.emit("nodeAttributesUpdated",{type:"replace",key:t,attributes:r.attributes})}return[t,!1]}const i=n?n({}):{};return r=new this.NodeDataClass(t,i),this._nodes.set(t,r),this.emit("nodeAdded",{key:t,attributes:i}),[t,!0]}dropNode(t){t=""+t;const n=this._nodes.get(t);if(!n)throw new be(`Graph.dropNode: could not find the "${t}" node in the graph.`);let r;if(this.type!=="undirected"){for(const i in n.out){r=n.out[i];do Fi(this,r),r=r.next;while(r)}for(const i in n.in){r=n.in[i];do Fi(this,r),r=r.next;while(r)}}if(this.type!=="directed")for(const i in n.undirected){r=n.undirected[i];do Fi(this,r),r=r.next;while(r)}this._nodes.delete(t),this.emit("nodeDropped",{key:t,attributes:n.attributes})}dropEdge(t){let n;if(arguments.length>1){const r=""+arguments[0],i=""+arguments[1];if(n=yn(this,r,i,this.type),!n)throw new be(`Graph.dropEdge: could not find the "${r}" -> "${i}" edge in the graph.`)}else if(t=""+t,n=this._edges.get(t),!n)throw new be(`Graph.dropEdge: could not find the "${t}" edge in the graph.`);return Fi(this,n),this}dropDirectedEdge(t,n){if(arguments.length<2)throw new Ie("Graph.dropDirectedEdge: it does not make sense to try and drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new Ie("Graph.dropDirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");t=""+t,n=""+n;const r=yn(this,t,n,"directed");if(!r)throw new be(`Graph.dropDirectedEdge: could not find a "${t}" -> "${n}" edge in the graph.`);return Fi(this,r),this}dropUndirectedEdge(t,n){if(arguments.length<2)throw new Ie("Graph.dropUndirectedEdge: it does not make sense to drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new Ie("Graph.dropUndirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");const r=yn(this,t,n,"undirected");if(!r)throw new be(`Graph.dropUndirectedEdge: could not find a "${t}" -> "${n}" edge in the graph.`);return Fi(this,r),this}clear(){this._edges.clear(),this._nodes.clear(),this._resetInstanceCounters(),this.emit("cleared")}clearEdges(){const t=this._nodes.values();let n;for(;n=t.next(),n.done!==!0;)n.value.clear();this._edges.clear(),this._resetInstanceCounters(),this.emit("edgesCleared")}getAttribute(t){return this._attributes[t]}getAttributes(){return this._attributes}hasAttribute(t){return this._attributes.hasOwnProperty(t)}setAttribute(t,n){return this._attributes[t]=n,this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:t}),this}updateAttribute(t,n){if(typeof n!="function")throw new xe("Graph.updateAttribute: updater should be a function.");const r=this._attributes[t];return this._attributes[t]=n(r),this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:t}),this}removeAttribute(t){return delete this._attributes[t],this.emit("attributesUpdated",{type:"remove",attributes:this._attributes,name:t}),this}replaceAttributes(t){if(!Ft(t))throw new xe("Graph.replaceAttributes: provided attributes are not a plain object.");return this._attributes=t,this.emit("attributesUpdated",{type:"replace",attributes:this._attributes}),this}mergeAttributes(t){if(!Ft(t))throw new xe("Graph.mergeAttributes: provided attributes are not a plain object.");return It(this._attributes,t),this.emit("attributesUpdated",{type:"merge",attributes:this._attributes,data:t}),this}updateAttributes(t){if(typeof t!="function")throw new xe("Graph.updateAttributes: provided updater is not a function.");return this._attributes=t(this._attributes),this.emit("attributesUpdated",{type:"update",attributes:this._attributes}),this}updateEachNodeAttributes(t,n){if(typeof t!="function")throw new xe("Graph.updateEachNodeAttributes: expecting an updater function.");if(n&&!$m(n))throw new xe("Graph.updateEachNodeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");const r=this._nodes.values();let i,o;for(;i=r.next(),i.done!==!0;)o=i.value,o.attributes=t(o.key,o.attributes);this.emit("eachNodeAttributesUpdated",{hints:n||null})}updateEachEdgeAttributes(t,n){if(typeof t!="function")throw new xe("Graph.updateEachEdgeAttributes: expecting an updater function.");if(n&&!$m(n))throw new xe("Graph.updateEachEdgeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");const r=this._edges.values();let i,o,s,h;for(;i=r.next(),i.done!==!0;)o=i.value,s=o.source,h=o.target,o.attributes=t(o.key,o.attributes,s.key,h.key,s.attributes,h.attributes,o.undirected);this.emit("eachEdgeAttributesUpdated",{hints:n||null})}forEachAdjacencyEntry(t){if(typeof t!="function")throw new xe("Graph.forEachAdjacencyEntry: expecting a callback.");xa(!1,!1,!1,this,t)}forEachAdjacencyEntryWithOrphans(t){if(typeof t!="function")throw new xe("Graph.forEachAdjacencyEntryWithOrphans: expecting a callback.");xa(!1,!1,!0,this,t)}forEachAssymetricAdjacencyEntry(t){if(typeof t!="function")throw new xe("Graph.forEachAssymetricAdjacencyEntry: expecting a callback.");xa(!1,!0,!1,this,t)}forEachAssymetricAdjacencyEntryWithOrphans(t){if(typeof t!="function")throw new xe("Graph.forEachAssymetricAdjacencyEntryWithOrphans: expecting a callback.");xa(!1,!0,!0,this,t)}nodes(){return typeof Array.from=="function"?Array.from(this._nodes.keys()):r0(this._nodes.keys(),this._nodes.size)}forEachNode(t){if(typeof t!="function")throw new xe("Graph.forEachNode: expecting a callback.");const n=this._nodes.values();let r,i;for(;r=n.next(),r.done!==!0;)i=r.value,t(i.key,i.attributes)}findNode(t){if(typeof t!="function")throw new xe("Graph.findNode: expecting a callback.");const n=this._nodes.values();let r,i;for(;r=n.next(),r.done!==!0;)if(i=r.value,t(i.key,i.attributes))return i.key}mapNodes(t){if(typeof t!="function")throw new xe("Graph.mapNode: expecting a callback.");const n=this._nodes.values();let r,i;const o=new Array(this.order);let s=0;for(;r=n.next(),r.done!==!0;)i=r.value,o[s++]=t(i.key,i.attributes);return o}someNode(t){if(typeof t!="function")throw new xe("Graph.someNode: expecting a callback.");const n=this._nodes.values();let r,i;for(;r=n.next(),r.done!==!0;)if(i=r.value,t(i.key,i.attributes))return!0;return!1}everyNode(t){if(typeof t!="function")throw new xe("Graph.everyNode: expecting a callback.");const n=this._nodes.values();let r,i;for(;r=n.next(),r.done!==!0;)if(i=r.value,!t(i.key,i.attributes))return!1;return!0}filterNodes(t){if(typeof t!="function")throw new xe("Graph.filterNodes: expecting a callback.");const n=this._nodes.values();let r,i;const o=[];for(;r=n.next(),r.done!==!0;)i=r.value,t(i.key,i.attributes)&&o.push(i.key);return o}reduceNodes(t,n){if(typeof t!="function")throw new xe("Graph.reduceNodes: expecting a callback.");if(arguments.length<2)throw new xe("Graph.reduceNodes: missing initial value. You must provide it because the callback takes more than one argument and we cannot infer the initial value from the first iteration, as you could with a simple array.");let r=n;const i=this._nodes.values();let o,s;for(;o=i.next(),o.done!==!0;)s=o.value,r=t(r,s.key,s.attributes);return r}nodeEntries(){const t=this._nodes.values();return new bn(()=>{const n=t.next();if(n.done)return n;const r=n.value;return{value:{node:r.key,attributes:r.attributes},done:!1}})}export(){const t=new Array(this._nodes.size);let n=0;this._nodes.forEach((i,o)=>{t[n++]=Cj(o,i)});const r=new Array(this._edges.size);return n=0,this._edges.forEach((i,o)=>{r[n++]=Lj(this.type,o,i)}),{options:{type:this.type,multi:this.multi,allowSelfLoops:this.allowSelfLoops},attributes:this.getAttributes(),nodes:t,edges:r}}import(t,n=!1){if(t instanceof at)return t.forEachNode((l,u)=>{n?this.mergeNode(l,u):this.addNode(l,u)}),t.forEachEdge((l,u,a,c,f,d,p)=>{n?p?this.mergeUndirectedEdgeWithKey(l,a,c,u):this.mergeDirectedEdgeWithKey(l,a,c,u):p?this.addUndirectedEdgeWithKey(l,a,c,u):this.addDirectedEdgeWithKey(l,a,c,u)}),this;if(!Ft(t))throw new xe("Graph.import: invalid argument. Expecting a serialized graph or, alternatively, a Graph instance.");if(t.attributes){if(!Ft(t.attributes))throw new xe("Graph.import: invalid attributes. Expecting a plain object.");n?this.mergeAttributes(t.attributes):this.replaceAttributes(t.attributes)}let r,i,o,s,h;if(t.nodes){if(o=t.nodes,!Array.isArray(o))throw new xe("Graph.import: invalid nodes. Expecting an array.");for(r=0,i=o.length;r{const o=It({},r.attributes);r=new n.NodeDataClass(i,o),n._nodes.set(i,r)}),n}copy(t){if(t=t||{},typeof t.type=="string"&&t.type!==this.type&&t.type!=="mixed")throw new Ie(`Graph.copy: cannot create an incompatible copy from "${this.type}" type to "${t.type}" because this would mean losing information about the current graph.`);if(typeof t.multi=="boolean"&&t.multi!==this.multi&&t.multi!==!0)throw new Ie("Graph.copy: cannot create an incompatible copy by downgrading a multi graph to a simple one because this would mean losing information about the current graph.");if(typeof t.allowSelfLoops=="boolean"&&t.allowSelfLoops!==this.allowSelfLoops&&t.allowSelfLoops!==!0)throw new Ie("Graph.copy: cannot create an incompatible copy from a graph allowing self loops to one that does not because this would mean losing information about the current graph.");const n=this.emptyCopy(t),r=this._edges.values();let i,o;for(;i=r.next(),i.done!==!0;)o=i.value,f0(n,"copy",!1,o.undirected,o.key,o.source.key,o.target.key,It({},o.attributes));return n}toJSON(){return this.export()}toString(){return"[object Graph]"}inspect(){const t={};this._nodes.forEach((o,s)=>{t[s]=o.attributes});const n={},r={};this._edges.forEach((o,s)=>{const h=o.undirected?"--":"->";let l="",u=o.source.key,a=o.target.key,c;o.undirected&&u>a&&(c=u,u=a,a=c);const f=`(${u})${h}(${a})`;s.startsWith("geid_")?this.multi&&(typeof r[f]>"u"?r[f]=0:r[f]++,l+=`${r[f]}. `):l+=`[${s}]: `,l+=f,n[l]=o.attributes});const i={};for(const o in this)this.hasOwnProperty(o)&&!xm.has(o)&&typeof this[o]!="function"&&typeof o!="symbol"&&(i[o]=this[o]);return i.attributes=this._attributes,i.nodes=t,i.edges=n,dn(i,"constructor",this.constructor),i}}typeof Symbol<"u"&&(at.prototype[Symbol.for("nodejs.util.inspect.custom")]=at.prototype.inspect);Rj.forEach(e=>{["add","merge","update"].forEach(t=>{const n=e.name(t),r=t==="add"?f0:Aj;e.generateKey?at.prototype[n]=function(i,o,s){return r(this,n,!0,(e.type||this.type)==="undirected",null,i,o,s,t==="update")}:at.prototype[n]=function(i,o,s,h){return r(this,n,!1,(e.type||this.type)==="undirected",i,o,s,h,t==="update")}})});j4(at);tj(at);vj(at);Sj(at);class d0 extends at{constructor(t){const n=It({type:"directed"},t);if("multi"in n&&n.multi!==!1)throw new xe("DirectedGraph.from: inconsistent indication that the graph should be multi in given options!");if(n.type!=="directed")throw new xe('DirectedGraph.from: inconsistent "'+n.type+'" type in given options!');super(n)}}class p0 extends at{constructor(t){const n=It({type:"undirected"},t);if("multi"in n&&n.multi!==!1)throw new xe("UndirectedGraph.from: inconsistent indication that the graph should be multi in given options!");if(n.type!=="undirected")throw new xe('UndirectedGraph.from: inconsistent "'+n.type+'" type in given options!');super(n)}}class g0 extends at{constructor(t){const n=It({multi:!0},t);if("multi"in n&&n.multi!==!0)throw new xe("MultiGraph.from: inconsistent indication that the graph should be simple in given options!");super(n)}}class m0 extends at{constructor(t){const n=It({type:"directed",multi:!0},t);if("multi"in n&&n.multi!==!0)throw new xe("MultiDirectedGraph.from: inconsistent indication that the graph should be simple in given options!");if(n.type!=="directed")throw new xe('MultiDirectedGraph.from: inconsistent "'+n.type+'" type in given options!');super(n)}}class v0 extends at{constructor(t){const n=It({type:"undirected",multi:!0},t);if("multi"in n&&n.multi!==!0)throw new xe("MultiUndirectedGraph.from: inconsistent indication that the graph should be simple in given options!");if(n.type!=="undirected")throw new xe('MultiUndirectedGraph.from: inconsistent "'+n.type+'" type in given options!');super(n)}}function ko(e){e.from=function(t,n){const r=It({},t.options,n),i=new e(r);return i.import(t),i}}ko(at);ko(d0);ko(p0);ko(g0);ko(m0);ko(v0);at.Graph=at;at.DirectedGraph=d0;at.UndirectedGraph=p0;at.MultiGraph=g0;at.MultiDirectedGraph=m0;at.MultiUndirectedGraph=v0;at.InvalidArgumentsGraphError=xe;at.NotFoundGraphError=be;at.UsageGraphError=Ie;class Pj extends xw{dropHandler(){}getDotAttributes(){}addNode(t){const n=this.getComponentData(),r=Me(n).userGraph,i=new at().import(r),o=i.nodes().reduce((s,h)=>h>s?h:s)+1;t=t.replace(/node\d+/g,`node${o}`),i.addNode(t)}validate(){}}const Nj={class:"manipulatableGraph"},Bj=bo({__name:"ManipulatableGraph",props:{componentID:{},storeObject:{},componentPath:{},style:{}},setup(e){const t=e,n=Ym(t),{storeObject:r,componentID:i,componentPath:o}=n,{getProperty:s,setProperty:h}=Me(r),l=new Pj(r,Me(i),Me(o)),u=l.getNestedComponentPaths(),a=l.getCurrentTaskGraphNode(),c=l.getComponentData(),f=l.loadDependencies(),d=l.getDependencyPaths(),p=Me(f).dotDescription,g=Me(f).validationData,m=Me(f).droppedElements,v=Me(f).dragElements;Me(c).editableFields;const y=Me(c).dotUserGraph;St(m,M=>{if(M.length){let k=Me(y);for(const S of M){const E=Me(v)[S].dropData.dotTemplate;k=k.replace("{",`{ -${E}`)}Me(r).setProperty({path:`${Me(o)}__component__userGraph`,value:k}),Me(r).setProperty({path:d.droppedElements,value:[]})}console.log(Me(c).userGraph,`${Me(o)}__component__userGraph`)});const x=()=>{Array.from(document.querySelectorAll("g.node")).forEach(M=>{M.setAttribute("pointer-events","visible"),M.addEventListener("click",$)})};Ht(()=>{ya(".manipulatableGraph .node",x),_()}),Ws(()=>{Array.from(document.querySelectorAll("g.node")).forEach(M=>M.removeEventListener("click",$))});const $=M=>{const k=M.currentTarget,S=document.querySelector("g.node.active");if(S){S.classList.remove("active"),S.setAttribute("stroke",""),Array.from(S.querySelectorAll("polygon")).forEach(T=>T.setAttribute("stroke","black"));const E=S.id,I=k.id,C=`${E}:out -> ${I}:in []`,w=`${I}:out -> ${E}:in []`;if(E===I||p.value.includes(w))return;p.value.includes(C)?(h({path:d.dotDescription,value:Me(p).replace(C,"")}),ya(".manipulatableGraph .node",()=>Array.from(document.querySelectorAll("g.node")).forEach(T=>T.setAttribute("pointer-events","visible")))):(h({path:d.dotDescription,value:Me(p).replace("}",`${C} - }`)}),ya(".manipulatableGraph .node",()=>Array.from(document.querySelectorAll("g.node")).forEach(T=>T.setAttribute("pointer-events","visible"))))}else k.classList.add("active"),k.setAttribute("stroke","red"),Array.from(k.querySelectorAll("polygon")).forEach(E=>E.setAttribute("stroke","red"));_()},_=()=>{},b={showSolution:()=>{const M=Me(g);h({path:d.dotDescription,value:M}),ya(".manipulatableGraph .node",()=>Array.from(document.querySelectorAll("g.node")).forEach(k=>k.setAttribute("pointer-events","visible")))}},R=zr(s(`nodes__${a}__components__${t.componentID}__methods`),b);return(M,k)=>(ue(),Rt(Wr,{componentId:Me(i),methods:Me(R),storeObject:Me(r)},{default:cn(()=>[_e("div",Nj,[tt(Oi,Jl({style:t.style??{}},{componentID:Me(i),storeObject:Me(r),componentPath:Me(u).DOTGraph,graphID:"epc"}),null,16,["style"])])]),_:1},8,["componentId","methods","storeObject"]))}});const Fj=Ke(Bj,[["__scopeId","data-v-135ef954"]]),nd=rE(OS);nd.use(US).use(jS).use(Tf);nd.component("Matrix",wH).component("DOTGraph",Oi).component("TaskConfiguration",gP).component("VisualGraphTraversal",$H).component("PathDisplay",IH).component("CodeEditor",jH).component("Output",eW).component("Dropdown",oW).component("ContourPlot",Ib).component("BackgroundGraph",r5).component("Equation",z5).component("TexDisplay",V5).component("DijkstraTable",Yz).component("DijkstraGraph",Zz).component("PlanGraph",n4).component("EditableGraph",o4).component("GanttDiagram",p4).component("ManipulatableGraph",Fj);nd.mount("#app");export{n4 as $,vo as A,mb as B,jH as C,Ws as D,xw as E,je as F,dr as G,Ym as H,hv as I,Hj as J,d1 as K,r5 as L,Fj as M,Ib as N,gP as O,Oi as P,wH as Q,eW as R,oW as S,Cv as T,IH as U,$H as V,z5 as W,V5 as X,Yz as Y,Zz as Z,Ke as _,xi as a,o4 as a0,p4 as a1,Li as a2,ln as a3,Wj as a4,lv as a5,zj as a6,Jm as a7,Yi as a8,Tr as a9,jj as aa,Jl as ab,_e as b,ve as c,we as d,Ht as e,Yh as f,tt as g,Rt as h,Mn as i,bo as j,Ei as k,Un as l,Me as m,ut as n,ue as o,$i as p,ef as q,pt as r,xy as s,rt as t,Uj as u,vi as v,cn as w,m$ as x,St as y,kt as z}; diff --git a/src/server/public/index.html b/src/server/public/index.html index d518ebc..f208588 100644 --- a/src/server/public/index.html +++ b/src/server/public/index.html @@ -4,15 +4,16 @@ - Vite App + ALADIN - - + + +