improving mallet.ipynb

Introduction.ipynb

@@ -807,14 +807,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEaCAYAAACrcqiAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAG/dJREFUeJzt3XmYJWV9t/H7y8woILsaFAwTSRSDBslEQAVhlGg0xg01\nBhSQbIoGSAwaIlHAxGiW12DcRZygCKjgBhjRmBkFlSWCDKtRQSAqoGCEgCDC7/2jnh4PTS+np+d0\nFz3357rORdVTy/Or0019+6mqOSdVhSRJfbTBfBcgSdJkDClJUm8ZUpKk3jKkJEm9ZUhJknrLkJIk\n9ZYhJa1jSfZKct181zEXklyd5GlDrntPku3b9HuSHDna6iDJ0tbvyM91SfZL8rlR97O+MaS03kry\n3SS3J/lJkpuTnJPkFUmyDna/Tv4BYpKXt5PsiydY9vokVyW5Jcm1SU5eF32O0Jr3pKoOrqo3D7NR\nkhVJ7mzH+ZMkFyTZc236HaWqOqmqnjkXfa1PDCmtzwp4dlVtDiwF3gr8FXD8vFZ1bwcAl7T/rpHk\nQOClwNOqajPgCcAX5768GZlN+P9DVW3WflbvBT6xjv6YUM8ZUlrfBaCqbq2qM4CXAAcm2THJZkk+\nlOTGdlnrSIAkS5LclOSxa3aSPDTJbUkefJ8OkocnObXt5ztJDhmqsGQpsDvwcuAZSX5pYPETgLOq\n6rut/hur6gMD226Z5INJvtdq/URr3yLJ6a2Wm9r0tgPbrUzypjaqvCXJ55JsNbB8/zYC/WGS14+r\nd5ckX03y49bvO5IsnuTYViR5U5veK8l1SV6T5Ia27cuneGtOArYCtm7bJ8nftLquT/JvSTYb7A54\nWZJr2nGvqbtte0SSb7djOiXJFmPvfxvFvryNVH+U5JVJnpDk4jb6fsfAvg5McvbA/LFtu7HR3x5T\nHJMmYUhJA6rqAuB/gKcA7wA2BX4FWA4ckOSgqroLOBl42cCm+wL/UVU3De6v/bV/OnAR8HBgb+Cw\nJE8fopwDgC9X1UXAf9GNnMac2+o5PMlvTXDP5URgI+DXgV8C/qW1bwB8EPhlYDvgduCd47bdFzgQ\neCjwQODwdiw7Au9udWwDPBjYdmC7u4E/pwuQJwFPA141xHECPIzuvd4G+GPgXUk2H79SkkWttquA\nG1rzQXTv1V7A9m0/449pd+BRwG8Db0yyQ2s/FHgu3c97G+DH7RgH7Qr8Gt37cixwZDu2xwG/n+Qp\nA+sOXlo8H9gJ2JIuWD+e5AHTvA8ar6p8+VovX8DVdJfLxrd/DXg9cAeww0D7nwL/2aZ3A64ZWHYB\n8KI2vRdw7cB63x23/yOA44eo77+BP2nTfw5cNG75vsDngVuBHwKva+0PA34ObDZEHzsDNw3MrwRe\nPzB/MPDZNv0G4KSBZRsDd070HrblhwGnDczfA2zfplcAbxp4v24DNhhY9wZg14F1fwrcTBeqtwP7\nDqz7H8ArB+YfDfyMLpCX0oXnwweWnwf8fpu+HHjqwLKHT7DtwwaW/2js59zmTwUObdMH0v1RMdl7\nfTPwG/P9e39/e004FJfWc9sCi4AlwLUD7de0ZVTVeekeutgLuB74VeAzE+xrO2DbJDe3+dCdAL88\nVQFJdqcbwX2iNZ0K/L8kO1XV6lbDycDJbXTxfOCkJBcB/wvcXFW3TLDfjehGA78DbNHq2SRJqp1J\n2/GMuR3YpE1vA6x5arGqbk+yZuSY5FHA2+guRW4ELAa+PtVxDripqu6ZpF+Af6qqN7Z+dgS+kOTm\nqjqr1XXNwLrXtL63Hmi7YWB6cN9LgU8mGes7wF3jtr1xYPqnE8wP1rlGksOBP6QLPuhGeA+ZaF1N\nzst90oAku9Cd9D5Fd7JaOrB4KfC9gfkTgP3b69Sq+tkEu7wOuKqqtmqvLatq86p6zjSlHEh3wrwk\nyQ/oRmrV2u+lqu6uqtOA1XSXoK4Dthp3X2bMX9Jd9tqlqrYAxp6SG+YhhB/QXSbsNkg2prvkN+Y9\nwBXAr7Z9Hznkfmekqi4HvgI8uzV9n/v+nO7i3sE0mWuBZ437+Tyoqn4wmxrbJcDX0o26tqyqLYFb\nGMH7sdAZUhKQZNMkv0d3r+nDVXUJ8HHgzUk2aQ8x/AXw4YHNPgK8gO4ezYcm2fX5wK1JXpdkwySL\nkjw2yROmqOWBwIuBP6G7HPf49joU2C/JBu0m/e+22pLkWcCOwLlVdT3w78C724MSSwbum2xK99f/\nLe2BiKNn8DadCvxekicnWQK8iXufdDcFbmkjrMfQXSpc59q+9wAubU0nA3+R5FeSbAK8GThlYGQ2\nVTC8D/j7JNu1fT80yXMHu1vLMjehC8qbkjwgyRvp3h/NkCGl9d3pSX5C9xf1XwP/THeJBuAQuktD\nV9FdnjuxqlaMbVhV19E9EFFVdc5EO28nyt+jC5ur6S4VHQdMNMoZ8/zW74ere2rvxqq6ke6Bh8XA\nM+n+Kn893aWtH9M9Pv/Kqvpa28f+dPelrqS7fHdYaz+W7l7Sj4CvAp8dX/JkRbURzKvpQuH7wE10\nD5mMORx4aZJb6E7+pwy774m6Gzf/unRPG94KfI7unt7727IP0v3x8GXgO3Tv3aFT7Gtw/u3Ap4HP\nt9+Dr9I9KDHMthPNjzmrvf6b7ud+OwOXSjW8/OIytKSZSvIB4Ptj90skrVs+OCGtpXYJcB/gN+e7\nFmmh8nKftBbaP0S9BPjHqrpmuvUn2cdnk9zaLmPdMjB9xLqtVrr/8nKfJKm3vNwnAJL414qkkaqq\nGT8t6eU+DagF/DqqBzV4fB7f+nZ8DH7ixloxpCRJvWVISZJ6y5DSemL5fBcwYsvnu4ARWz7fBYzY\n8vkuoLd8uk/A2IMT/i5IWpey5n5UEh+ckCQtLIaUJKm3DClJUm8ZUpKk3jKkJEm9ZUhJknrLkJIk\n9ZYhJUnqLUNKktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeotQ0qS1FuGlCSp\ntwwpSVJvGVKSpN4ypCRJvWVISZJ6y5CSJPWWISVJ6i1DSpLUW4aUJKm3DClJUm8ZUpKk3jKkJEm9\nZUhJknrLkJIk9ZYhJUnqLUNKktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeqt\nkYVUkqVJLpnhNiuTLJvB+nslOX3m1a2dJAcmecdc9Tdsn0lekeRlc1WTJM2VxSPef414/3PVx3z2\nN22fVfW+idqTLKqqu0dTkiSN3qgv9y1JcmKSy5N8LMmGAEnekOS8JKuTvHfcNgckuagt26Wtv3GS\n45Ocm+TrSZ4zVadJNkry0SSXJvlE225ZW7Zv2/fqJG8d2Gay9oOSfDPJucDuA+0rkrw9yVeSfDvJ\nPgPLDk9yfpJvJDlq4BjOGDi2F7f2t7Y6v5HkH6c5rqVJvtjW/UKSR7T2o5K8pk2vTPIvSS4ADk3y\nkCSntvf7vCRPnqoPSeqVqhrJC1gK3AM8sc0fD7ymTW8xsN6HgGe36ZXA+9r0U4BL2vSbgf3a9ObA\nN4GNgL2Az0zQ918C72nTjwV+BiwDHg5cA2xFF9BfBJ47RfvDBtoXA+cA/9r2uwL4aJv+deBbbfrp\nA8cQ4HRgD2Cfsfa2bNO23ysH2jab4FgOHOjzM8DL2vRBwCfb9FED7+1K4J0D238EeHKb/mXg8kl+\nXgXly5cvX+vwRY1p08z0NerLfddW1blt+kTgEOBtwN5JXgtsDGwJXAqc2dY7me5ozk6yaZLNgGcA\nz2nbADwA2G6KfvcAjm37uSzJ6ta+C7Cyqm4GSPIRYM+2bKL2jGv/KPCogX4+1fq4IskvtbZnAE9P\ncmHb/kFtm3OAf07yFuDMqjonySLgp0k+0I7/jCmOCeBJwAva9IeBf5hkvY8OTP828OtJ0uY3SbJx\nVd1+382OHphe3l6StDZWAXD00UfPai9zfU+qkjwQeBewrKq+3y6HbTjVNnQn+xdW1bcGFyR52JB1\nZJLpydYZ3/dk7pxg+wBvqarj7tNBd8nxd4G/S/IfVfV3SXYF9gZeDPxZm57M+PdmMreNq2u3qrpr\n+s2OHnL3kjSd5cAvQuqYY45Zq72M+p7U0iS7ten96EYTG9KdbG9KsgnwonHbvAQgyR7AT6rqVuAs\n4NCxFZLsPE2/XxnYz47A41r7+cCeSbZqo5h9gS8N0b5lkiV0QTKZsZA6C/jDJA9q/W+T5KFJHg78\ntKpOAv4JWJZkY7pLn58DXgPsNM1xfbXVBvAy4Oxp1gf4PHDYmiKTxw+xjST1wqhHUlcCr06yAriM\n7j7RHUmOa/M/oAuCMQXc0S6VLaa77wLwt8Cx7bJdgKvp7hlN5t3AvyW5tNVwGV3gXZ/kCMbGoXBG\nVZ0OMEX70cC5wI+Bb4yrlfHzVfWFJI8BvtausN1KFyiPAv4pyT1098gOBjYDPj32QAnwF1McE3RB\nvSLJ4cAP+cX7c586BhwGvCvJxcAi4MvAq6bpR5J6Ie2m+YKSZANgSVXdmWR74AvADlX183kurbeS\n1PBXEyVpGGEsY5JQVVPdPpnQqEdS82VjYGW7RAdwsAElSfc/C3IkpZlzJCVp3Zv9SMrP7pMk9ZYh\nJUnqLUNKktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeotQ0qS1FuGlCSptwwp\nSVJvGVKSpN4ypCRJvWVISZJ6y5CSJPWWISVJ6i1DSpLUW4aUJKm3DClJUm8ZUpKk3jKkJEm9ZUhJ\nknrLkJIk9ZYhJUnqLUNKktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeotQ0qS\n1FuGlCSptwwpSVJvGVKSpN4ypCRJvWVISZJ6y5CSJPWWISVJ6i1DSpLUW4vnuwD1Sea7AEkLyNZb\nL531PgwprVFV812CJN3LtJf7knwuyRYD81smOXO0ZUmSNNw9qa2r6n/HZqrqx8A2oytJkqTOMCF1\nT5JHjM0k2W6E9UiStMYw96TeCHwlyX/S3VlfDhw8yqIkSQLIMDfLk2wNPKnNfrWqbhxpVZpzScoH\nJySNShKqasaPEE8aUkkeVVXfSrLTRMuravVMO1N/GVKSRmkUIXV8Vf1RkrMnWFxVtedMO1N/GVKS\nRmmdh5TWL4aUpFFa25Ca9sGJJA8EXgHsARRwNnBcVd054yolSZqBaUdSSU4B7gRObE37ARtV1R+M\nuDbNIUdSkkZpZJf7klxeVTtO16b7N0NK0iitbUgN8495L06yy0BHvwVcNNOOJEmaqWFGUpcCOwJX\ntaZHAlcAd9E95bdspBVqTjiSkjRKI3twAnjeWtQjSdKsDfuJE48FntJmz66qy0ZaleacIylJozSy\ne1JJ/gz4OLBde30syatmXqIkSTMzzD2p1cCTq+r/2vwmdJ/fN+HHJen+yZGUpFEa5dN9AX42MH8X\nfs+4JGkOTPrgRJLFVfVz4MPAeUlOa4teAJwwF8VJktZvU33A7IVjj5cn2ZXuY5Gge3DigjmqT3PE\ny32SRmkUj6Cv2VlVnQ+cvzaFSZK0tqYKqYcmec1kC6vqbSOoR5KkNaYKqUXAJviQhCRpngx1T0oL\nX5I1vwhbb72U66//7jxWI2mhGek9Ka0vupy64QZ/9JL6YaqR1FZVdfMc16N50o2kxn4Xgk/6SVqX\n/Pp4zYohJWmURvmJE5IkzQtDSpLUW4aUJKm3DClJUm8ZUpKk3jKkJEm9ZUhJknrLkJIk9ZYhJUnq\nLUNKktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeotQ0qS1FuGlCSptwwpSVJv\nGVKSpN4ypCRJvWVISZJ6y5CSJPWWISVJ6i1DSpLUW4aUJKm3DClJUm8ZUpKk3jKkJEm9ZUhJknrL\nkJIk9ZYhJUnqLUNKktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeotQ0qS1Fsj\nC6kkS5NcMsNtViZZNoP190py+rDt49Z5TpLXzaS+uTBM7ZK0vlg84v3XiPc/VR9T9l1VpwMTBdyi\nqrp7XRQ2C3PxvklS7436ct+SJCcmuTzJx5JsCJDkDUnOS7I6yXvHbXNAkovasl3a+hsnOT7JuUm+\nnuQ5wxaQZMskn0xycZKvJnlcaz8wyTva9Iok70lyLvAPw/SXzrvbsZ2V5Mwk+7Rleye5sPX5gSRL\npml/ZpIrkvwXsM9AH0e1OlYm+XaSQwaWvbS9hxe22pNkg3Ysq1sfh7V1D01yWZJvJDlp2PdOkubb\nqENqB+CdVbUjcCvwqtb+jqrarap2AjZO8uyBbTaqqt8EXg18sLUdCXyxqp4IPA345yQbDVnDMcCF\nVfX4tp8PDywbHLFsW1VPrKrDh+xvH2C7dmwHAE8CSPJAYAXw4tbnEuDgadrfDzy7qp4APGxcPzsA\nTwd2A45KsijJY4CXAE+uqmXAPcBLgZ3bcezU+ljR9vFXwM5VtTPwyiHfN0mad6O+3HdtVZ3bpk8E\nDgHeBuyd5LXAxsCWwKXAmW29kwGq6uwkmybZDHgG8Jy2DcADgO2GrGEP2uikqlYm2SrJJhOs9/GB\n6cn6++a4/X687feGJCtb+w7AVVX1nTZ/Al04r5qk/Uut/arWfiLwJwP9nFlVPwduSnIDsDWwN7AM\nuCBJgA2BG4AzgEcmeTvwWeDzbR8XAycl+RTwqUnfKY5eM7Vq1SqWL18++aqSNIVVq1axatWqWe9n\nru9JVRs5vAtYVlXfT3IU3Ul20m2AAC+sqm8NLkgyftQxjEzSftu4+fv0tw76mGk7wJ0D03fT/cwC\nnFBVR95nR8njgd8BXgH8PvBHwLOBPYHnAkcmeVxV3XPfro5u/z3GgJI0K8uXL7/XeeSYY45Zq/2M\n+nLf0iS7ten9gHPoAqnoRgabAC8at81LAJLsAfykqm4FzgIOHVshyc4zqOFs4GVtu+XAD6vq/6bZ\nZpj+vgK8sN0L2hpY3tq/SXfc27f5/elGUZO1X9naH9na952irrEw+yLwoiQPbfVtmWS7JA8GFlXV\nJ4E3AL/Z1t+uqr4EHAFsBkw0kpSk3hn1SOpK4NVJVgCXAe+pqjuSHNfmfwCcP7B+AXckubDVdlBr\n/1vg2CSr6U7UV9ONCoZxNPDBJBfTjZYOmGCd8aO3vxuiv9Po7lddBlwHfJ0uVO9MchBwapJFwAXA\n+6rqrina/xT4bJLb6EJ1shApgKq6IsnfAJ9PsgHwM7p7eHcAK1pbAUckWQyc2C6bBnh7Vd0y3Zsm\nSX2QKp92XltJHlRVtyXZCjgP2L2qbpzvutZGkvpFVgd/LyStS0moqqlubUxo1COphe6MJFvQPan3\npvtrQElSXzmSEuBIStJore1Iys/ukyT1liElSeotQ0qS1FuGlCSptwwpSVJvGVKSpN4ypCRJvWVI\nSZJ6y5CSJPWWISVJ6i1DSpLUW4aUJKm3DClJUm8ZUpKk3jKkJEm9ZUhJknrLkJIk9ZYhJUnqLUNK\nktRbhpQkqbcMKUlSbxlSkqTeMqQkSb1lSEmSesuQkiT1liElSeotQ0qS1FuGlCSptwwpSVJvGVKS\npN4ypCRJvWVISZJ6y5CSJPWWISVJ6i1DSpLUW4aUJKm3DClJUm8ZUpKk3jKkJEm9ZUhpvbBq1ar5\nLmGkPL77t4V+fLNhSGm9sNBPAh7f/dtCP77ZMKQkSb1lSEmSeitVNd81qAeS+IsgaaSqKjPdxpCS\nJPWWl/skSb1lSEmSesuQkiT1liG1HknyzCRXJvnvJH81yTr/muRbSb6RZOe5rnG2pjvGJPslubi9\nzknyG/NR59oa5mfY1tslyV1J9pnL+mZryN/R5UkuSnJpkpVzXeNsDPH7+eAk/97+/7skycvnocy1\nluT4JDckWT3FOjM7x1SVr/XgRfcHybeBpcAS4BvAY8at8yzgzDa9G3DufNc9gmN8IrB5m37m/ekY\nhzm+gfW+CJwB7DPfda/jn9/mwGXAtm3+IfNd9zo+vqOAt4wdG3ATsHi+a5/BMe4B7AysnmT5jM8x\njqTWH7sC36qqa6rqLuAU4Hnj1nke8CGAqjoP2DzJ1nNb5qxMe4xVdW5V/aTNngtsO8c1zsYwP0OA\nQ4BTgRvnsrh1YJjj2w84raq+B1BVP5rjGmdjmOO7Hti0TW8K3FRVP5/DGmelqs4BfjzFKjM+xxhS\n649tgesG5v+H+56gx6/zvQnW6bNhjnHQHwP/PtKK1q1pjy/JNsDzq+o9wIz/Tco8G+bn92hgqyQr\nk1yQZP85q272hjm+44DHJvk+cDFw2BzVNldmfI5ZPNJypJ5K8lTgILrLEwvJscDgvY77W1BNZzGw\nDHga8CDga0m+VlXfnt+y1pm/Bi6uqqcm+VXgC0l2qqr/m+/C5oshtf74HrDdwPwjWtv4dX55mnX6\nbJhjJMlOwPuBZ1bVVJcm+maY43sCcEqS0N3TeFaSu6rqM3NU42wMc3z/A/yoqu4A7kjyZeDxdPd6\n+m6Y49sdeDNAVX0nydXAY4D/mpMKR2/G5xgv960/LgB+LcnSJA8A/gAYf+L6DHAAQJInAv9bVTfM\nbZmzMu0xJtkOOA3Yv6q+Mw81zsa0x1dV27fXI+nuS73qfhJQMNzv6KeBPZIsSrIx3c33K+a4zrU1\nzPFdAfw2QLtX82jgqjmtcvbC5CP4GZ9jHEmtJ6rq7iR/Bnye7o+T46vqiiSv6BbX+6vqs0l+N8m3\ngdvoLofdbwxzjMAbgK2Ad7fRxl1Vtev8VT28IY/vXpvMeZGzMOTv6JVJzgJWA3cD76+qy+ex7KEN\n+fN7C7AiycV0J/rXVdXN81f1zCQ5CVgOPDjJtXRPKz6AWZxj/Ow+SVJveblPktRbhpQkqbcMKUlS\nbxlSkqTeMqQkSb1lSEmSesuQkhaYJHcnubB91cNHk2w4x/0/L8ljBuaPSfK0uaxBC4chJS08t1XV\nsqr6DeAu4JXjV2j/kHmdS7IIeD7w2LG2qjqqqv5zFP1p4TOkpIXtbH7xUTxXJjkhySXAI5Lsm2R1\ne711bIMktyZ5W/tSwS8keXBr3znJ19qX1Z2WZPPWvjLJvyQ5n+7DbZ8L/GMbzT0yyYqxL19Msndr\nvzjJB5Isae1XJzk6ydfbskfP8fuknjKkpIUnAEkW033J3CWt/VHAO9sI6+fAW+k+wmZnYJckz23r\nPQg4v6oeB3yZ7qNtAE4AXltVOwOXDrQDLKmqXavq7+k+n+21bTR39ZqikgcCK4AXV9Xj6b747+CB\nfdxYVb8FvBd47ezfBi0EhpS08GyU5ELgfOAa4PjW/t2quqBN7wKsrKqbq+oe4CPAnm3ZPcDH2vSJ\ndB/ouhndNxqf09pPGFgf4KND1LUDcNXAB/uO38cn23+/TvfttZIfMCstQLdX1bLBhnYL6rZx6w17\nX2rsAz6nWn/8vicz1T7ubP+9G89NahxJSQvPZEEw2H4+sGeSrdrDDvsCq9qyDYAXtemXAudU1S3A\nzUl2b+37A1+apJ9bgc0maP8msDTJ9gP7WDXBetIa/rUiLTyTfbXBmvaquj7JEfwiJM6sqjPa9G3A\nrkneANwAvKS1Hwi8L8lGdN9xNPY1C+P7OwU4LskhdGFXrc87kxwEnNqC8QLgfdPUrPWcX9Uh6V6S\n3FpVm853HRJ4uU/SffmXq3rDkZQkqbccSUmSesuQkiT1liElSeotQ0qS1FuGlCSpt/4/TgylBEA0\nnJoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff70be87780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "visual.plot_doc_topics(doc_topic, 6)"
+    "visual.plot_doc_topics(doc_topic, 3)"
    ]
   }
  ],

dariah_topics/mallet.py

@@ -288,7 +288,7 @@ def show_docTopicMatrix(output_folder, docTopicsFile = "doc_topics.txt"):
                   index=shortened_docnames[0:],
                   columns=topicLabels[0:])
 
-        docTopicMatrix = docTopicMatrix.transpose()
+        #docTopicMatrix = docTopicMatrix.transpose()
 
     return docTopicMatrix