Py_lectures_04_Classes_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python-in-the-lab: introduction to the classes II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import scipy.integrate as integrate\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "import distributions2 as d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'E', 'v', 'T', 'S'}\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_E.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_E.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_E.dat']\n",
      "9 lines deleted\n",
      "8 lines deleted\n",
      "8 lines deleted\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_v.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_v.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_v.dat']\n",
      "2 lines deleted\n",
      "0 lines deleted\n",
      "0 lines deleted\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_T.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_T.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_T.dat']\n",
      "23 lines deleted\n",
      "18 lines deleted\n",
      "21 lines deleted\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_S.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_S.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_S.dat']\n",
      "14 lines deleted\n",
      "12 lines deleted\n",
      "14 lines deleted\n"
     ]
    }
   ],
   "source": [
    "mainDir = \"/home/gf/src/Python/Python-in-the-lab/Bk\"\n",
    "dcoll = d2.DistCollector(mainDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcoll.plot(\"S\") # Isn't it beatifull???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3X10VfWd7/H3NyExIbZBCJaHQAPSUhFT0FjXqjijYoPOcSh6LWJbr1Za6rRqbNdScKY3TXG6oMrUYtVrHXSpczsyWVQRGyx4tdbaR0BZKDp0uFGXJw8DRIM1D+Tpd/84ySEP5+ScZJ/n83mt1YXZ7LP3L7thf7P37/v9/sw5h4iIZJ+cZA9ARESSQwFARCRLKQCIiGQpBQARkSylACAikqUUAEREspQCgIhIllIAEBHJUgoAIiJZSgFARCRLTUj2AEZTUlLiysrKkj0MEZG0sW/fvmPOuanR7JvSAaCsrIy9e/cmexgiImnDzN6Ndt+EBQAzywHuAj4O7HXOPZ6oc4uIyEie5gDM7FEzO2JmbwzbfpmZHTKzw2a2rn/zF4GZQDfg93JeERHxzusk8GPAZYM3mFku8ABwObAAuNbMFgDzgT84574L/IPH84qIiEeeXgE55142s7Jhmz8HHHbO1QOY2VYCv/2/B3T179Pr5bwikvm6u7vx+/10dnYmeygpqaCggNLSUvLy8sZ9jHjMAcwkcLMf4AfOBzYDPzWzC4GXw33YzNYAawBmz57taSDbX2vgnl2HaGztYMakQm5fNp8Vi2d6OqaIJIbf7+djH/sYZWVlmFmyh5NSnHO0tLTg9/uZM2fOuI8TjwAQ6v8p55xrB1ZH+rBz7mHgYYCKiopxL1e2/bUG7nzqdTq6Aw8bDa0d3PnU6wAKAiJpoLOzUzf/MMyMKVOmcPToUU/HiUchmB+YNejrUqAxDucZ1T27DgVv/gM6unu5Z9ehRA9FRMZJN//wYnFt4hEA9gCfMrM5ZpYPrAJ2xOE8o6r48Hleyb+V+lO+zCv5t7I85xUAGls7Ej0UEUlTv/rVr5g/fz7z5s1j48aNIfc5ceIE11xzDfPmzeP888/nnXfeAaClpYWLL76YU089lZtvvjnsOS666KIh9U7vvPMOCxcujOn3EY7XNNAngT8A883Mb2arnXM9wM3ALuAtoNY5d9D7UMfgQC0b8x+hNOcYOQalOcfYmLeF5TmvMGNSYUKHIiKJsf21Bi7Y+CJz1tVxwcYX2f5ag6fj9fb28u1vf5vnnnuON998kyeffJI333xzxH6PPPIIp512GocPH+Y73/kOa9euBQKTtHfddRebNm3yNI548hQAnHPXOuemO+fynHOlzrlH+rfvdM592jl3hnPuh7EZ6hi8sJ5CTgzZNNG6WJtXy+3L5id8OCISXwNzfg2tHThOzvl5CQJ//vOfmTdvHnPnziU/P59Vq1bxzDPPjNjvmWee4frrrwfg6quv5oUXXsA5R1FREUuWLKGgoGDcY/j617/OokWLWLRoEVOnTuUHP/jBuI8VSkq3ghi346HrzGZYiyaARTLQaHN+4/0339DQwKxZJ6czS0tL+dOf/jTqfhMmTKC4uJiWlhZKSkqiPtdXvvIVCgsDbye6urrIyQn8br5lyxYA3n33XZYtW8YNN9wwru8lnMzsBlpcGnKzhdkuIukt3Nyelzk/50YmIYaaeI12v9H8/Oc/Z//+/ezfv5+dO3cO+bvOzk6+9KUvcf/99/PJT35yTMeNJDMDwNJqyBv2rj+vMLBdRDJOuLk9L3N+paWlvPfeyZImv9/PjBkzRt2vp6eH48ePM3ny5HGfd7ibbrqJq666iksvvTRmxxyQcQGgrr6Oyr9sobx0KpWzZ1FXVATFs+Dv74PylckenojEwe3L5lOYlztkW2Ferqc5v/POO4//+q//4u2336arq4utW7eyfPnyEfstX76cxx8P9Lbctm0bl1xySczSVx944AH++te/sm7dusg7j0NGzQHU1ddR8/saOnsDpeNNuUbN9Jnw+Rp8c31JHp2IxMvAe/5YVv5PmDCB+++/n2XLltHb28uNN97IWWedBUB1dTUVFRUsX76c1atXc9111zFv3jwmT57M1q1bg8coKyvjww8/pKuri+3bt7N7924WLFgQ9Rg2bdpEXl4eixYtAgJPAzfddNO4v6fhLNT7q1RRUVHhxrIeQOW2SpramkZsn140nd1X747l0EQkzt566y3OPPPMZA8jpYW6Rma2zzlXEc3nM+oVUHNb85i2i4hks4wKANOKpo1pu4hINsuoAFB1ThUFuUOLLgpyC6g6pypJIxIRSV0ZNQk8MNG7+dXNNLc1M61oGlXnVGkCWEQkhIwKABAIArrhi4hEllGvgEREJHoKACIiYXhpB/38889z7rnncvbZZ3Puuefy4osvhvx82raDFhFJGQdq4d6FUDMp8OeBWk+H89oOuqSkhGeffZbXX3+dxx9/nOuuu87TeOJBASCSGP9QiUgcHKiFZ2+F4+8BLvDns7d6+vfqtR304sWLg72DzjrrLDo7Ozlx4sSIz49G7aCTaeCHqru/o+Dx9+h46mbeeOcDzlv+zeSOTUROemH9yX+nA7o7AtvH2QMslu2gf/GLX7B48WJOOeWUkOdSO+hUFOKHqpATzNh3t+fVhkQkhsKsARJ2exRi1Q764MGDrF27lp/97Gdhz6V20KkozA/PdFq0uLxIKgm31oeHNUBi0Q7a7/dz5ZVX8sQTT3DGGWeMaxxqB50sYX54Gt0ULS4vkkrisAaI13bQra2t+Hw+NmzYwAUXXDCuMcS7HbQCwGiWVtPB0Hd27S6fu3tWanF5kVRSvjKw5kfxLMBisgbI4HbQZ555JitXrhzSDnrHjh0ArF69mpaWFubNm8ePf/zjYLro/fffz+HDh7nrrruCE7lHjhwZ0xg2bdrE66+/Hvz8Qw89NO7vJ5SMagcdS3X1dWx+dTNNbU2c3t3Hbe9/wOKPCrm7ZyXP5/4tG646W+sLi8SR2kFH5rUdtLKAQhi+sMyRvBz+ceo0Onuu4vScz7PB40ITIiKpQAEghM2vbg7e/INyupnz6ZfZffX3kjMoEZEY0xxACFpYRkSygQJACGNeWEbVwiKShhQAQhjTwjIhStA7nrqZPTvCF32IiKQCBYAQfHN91Hy+hulF0zGM6UXTqfl8Teh1BlQtLCJpSgEgDN9cH7uv3s2B6w+w++rd4ReZUbWwSMby0g76z3/+czB//7Of/SxPP/10yM+XlZVx7Nix4NcvvfQSV1xxRcy/l1AUALxStbBISqirr6NyWyXlj5dTua2Suvo6T8fz2g564cKF7N27l/379/OrX/2Kb37zm/T09HgaU6wpAHilamGRpBuo3Wlqa8LhaGproub3NZ6CgNd20BMnTmTChECmfWdnZ8hGcpH83d/9XfApori4ONhyIlZUBzBOA5XCzW3NTD5jHqubjvGVtiM0uiknq4WXzU/2MEWyQqjanc7eTja/unnca4THoh30n/70J2688Ubeffdd/u3f/i0YEIa7+OKLyc3NBeCjjz7iM5/5DECwM+i+ffv42te+xooVK8b1vYSjADAOwyuFW/r+yr3TPs5973+dY81nMWNSoaqFRRIoHrU7sWgHff7553Pw4EHeeustrr/+ei6//HIKCgpG7P/rX/86uH7ASy+9xKZNm4J/d+zYMa677jpqa2spLi4e9/cTigLAOIT6baPbnaCk9P+y57Y7Qn/oQC3tz1VT0NFMY98UtuR/lUW+NQoSIjEwrWgaTW1NIbeP11jbQZeWlo5oBz3gzDPPpKioiDfeeIOKiqja9ACBeYhVq1ZRXV0dl3WCNQcwDmP+beNALT3P3MLEjiZycJTmHOOO7gd55ekHh6aKqqBMZFzGVLsTJa/toN9+++3gpO+7777LoUOHKCsrG9MY1q1bR3l5OatWrRr39zGahAYAMysys31mlpgcpzgZc6XwC+uZMOyJYaJ1cRtbT6aK9geJwQVlPc/coiAgEoUx1e5EyWs76FdeeYXPfvazLFq0iCuvvJIHH3xwyDKR0di0aRO7d+8OTgQPnDNWPLWDNrNHgSuAI865hYO2XwZsBnKBLc65jf3b1wNtwEHn3C8jHT+Z7aBHM3wOAAK/bYT9gauZBIy8zn3OOOPEz3l7o4/2H32GiR0jH2HbC6czce1/xnL4ImlB7aAj89oO2usTwGPAZcNOngs8AFwOLACuNbMFZnYp8Cbw3x7PmXRj/m1jlFqBgVTRgo7Qr4/CbRcR8crTJLBz7mUzKxu2+XPAYedcPYCZbQW+CJwKFBEICh1mttM51+fl/Mnkm+uL+HgZTBWdbEz7+Eyq3v8AX1s7EKgV+AmruL0/VbSxbwqlOcdGHKOxbwrjX9VURCS8eMwBzATeG/S1H5jpnPsn59xtwL8D/xru5m9ma8xsr5ntPXr0aByGlxhDC1OgaUIuNSVTeLaoCH9fCXfnfYslV34rmAW0Jf+rtLv8Icdod/lsyf9qEkYvItkgHmmgocrdgi/AnXOPjfZh59zDwMMQmAOI6cgSKGRhSo7x0zkL2X31bmqG7b/It4bqp3u4zW1lhrXQ6KbwE1axxLcmYWMWSTXOuXFV0GaDWCznG48A4AdmDfq6FGiMw3lS2lhTRQNPAt/iml1LaWztYMakQm5XMZlksYKCAlpaWpgyZYqCwDDOOVpaWkIWlY1FPALAHuBTZjYHaABWAV+Ow3lS2ngKU1Ysnqkbvki/0tJS/H4/6fwqOJ4KCgooLfU2Q+gpAJjZk8BFQImZ+YHvO+ceMbObgV0E0kAfdc4d9DTKNFR1TlXIVFEvhSkA219r4J5dh/SUIBkvLy+POXPmJHsYGc1rFtC1YbbvBHZ6OXa6G8gQGmgYN61oGlXnVI3IHBrcVC7SPk1tzbjuYjr7luFYTENrB3c+9TrAySBwoDawSM1xfyD9dGk1lK+M/zcsImnHUyFYvKVqIVisRFNQFmof15dHZ9NV9Hy4GICZkwr53bpLTi5POXiFsrxC+Pv7FAREskQiC8HEg9Fa2I62j+V0c8rUXcGvgwvPhFieku6OwHYRkWEUAJIomkyhcPtYXmvwv4MLz4RZnjLsdhHJagoASRRNU7lw+3yip4/lOa9QmJcbrCYO13Ii7HYRyWoKAEkUTQvbkPv09fHdD97nR/mP8MR5756cAF5aHXjnP0gHp7DnjFvi8w2ISFpTAEiiaJrKBffpdZhzTO/uoebY+/ja2inkBOf9v5+ePGD5Svac/QMaXAl9zvD3lbC2azX/c88nh647ICKCsoDSR5iW0mBQ03oyVfSjJvq6J3Hi6LJglhAMyhTqp3oCkcw0liwgLQmZLopL+xeLGbl9SKqoQU5+KwXTn6ITgkEgmCkE7NnxM87bdze/5RiN+SXc/eFK7nyqC0BBQCSL6BVQugjxfp+8QlhaHVWqaDBT6EAtC1/9X8y0Y+QYlOYcY2PeFr7Q+5uTq5OJSFZQAEgX5SsDBV3FswAL/Nlf4BUpVXQgU6iuvo7Kves5v+x0KktnUFc0EQgsT3nHhNohTwkikvn0CiidlK8MWdEbrvGc657EzP73+3nF+wOviXIDXRWb8iZQUzIZAF9bOzOs5eRTQj/NE4hkNj0BZIBw6aR3L13H79ZdworFM8OsT5DD5tMmAdDElJP1BARu/nc+9ToNrR04CPYdUjaRSOZQAMgA0aSThq06npBLB6fQeO4dQ367v2fXITq6e4fs29Hdq3kCkQyiV0AZItIaxWHXJ+iDwqvu57xhr5YqPnye/8ivZYYdo9GVcHfPSnb0LdE8gUgG0RNAlghbdXzRj0bOKxyoZWP+I5TmDM0UWp7zytB5ggO1cO/CQI3CvQsDX4tI2tATQJYY0/oEe9fTXHY603p6qfqgFV9bOxOti7V5texZdnNgx+Gtp4+/F/ga1HpaJE2oEliCQq5P0NcXbD3hMKymvwvpvQtDFqa1F05n4tr/TNSQRWQYrQcg4xIpU8j6u4rW1ddR+bFeystmDaknAChobx6RKbT9tQYu2Pgic9bVccHGF5VJJJIiFAAkaLRMoYGq44GnhKa8CTizYD3BQBBodFOGZArt2fEzztv+N/y240p+m38r5374vNJJRVKEAoAEhV2foI9g1fFoTwntLp+7e1aezBRS2wmRlKYAIEHRZAqN9pSwrvvr7OhbcjJT6IX1FHJiyH5qOyGSOpQFJEHRZAqFqyfo7T6NHX1Lhq5QFmYpSrWdEEkNCgAyRKSCsqpzqkZkCtGXR9fRZcG+Q8Ebd5gW1sPbTqg9tUhyKADImIypnuATk2g+jSH1BCPaTvTPExRa4FVRqQXmCeiGe3blKwCIxJECgIxZpKeEoQvUnMwUYuIUfBdWD207Mco8wYWtS+L1LYgImgSWOAibKfSJGcHJ5Lr6Oiq3VVJ+GiNqCSDEPMGBWtp/9Bn6aibhrz6Dmn/+vlJJRTxSAJCYC5sp1L89WEvQ1hSylgCGzRMcqKXnmVuY2NFEDo7SnGPc0f0grzz9oIKAiAcKABJzYesJ+rdHqjgeMU/wwnomDNt/onVxG1tVTyDigQKAxFzYeoJzqoAIFcfFswLtqZd/M6rXRIPrCdRyQmRsNAksMRcpUyjs2gSnzoAbdgOjTCQTWMISAm0nBuYJlEoqMnYKABIXo2UKhaolGPyEAKO/JvK1tdPu8vkJqwLzBEolFRkXvQKShPO6hKW/r4S7877Fkiu/Fbi5q+WEyLjoCUCSYtxLWJ46g9L1u6kZvFEtJ0TGRU8AkpIiTSQPqKuvo3J2aci1CUK2nFBrapEgPQFISoqm5URwojjXAIZMFF/S1quWEyIRJGxJSDNbAfiA04EHnHO7I31GS0LKaCq3VYZ8TTS917G7onpI1fHml9bSnDO0LxGAv6+EC7vu4+2N/YHlQC3tz1VT0NFMY98UtuR/lUW+NQoQkjYStiSkmT1qZkfM7I1h2y8zs0NmdtjM1gE457Y7574B3ABc4+W8IjDKRHFuzpCbf83va2jKtZBVx0PmCVRxLFnG6xzAY8BlgzeYWS7wAHA5sAC41swWDNrle/1/L+JJpIpjiFx1PGSeQBXHkmU8BQDn3MvA+8M2fw447Jyrd851AVuBL1rAj4DnnHOvejmvCEQ3UTxaOumIlhOjZBMpnVQyUTwmgWcCg1cB8QPnA7cAlwLFZjbPOfdQqA+b2RpgDcDs2bPjMDzJFF5WMJvWR6DlxOB5gtmlIecJBlcci2SSeAQAC7HNOefuA+6L9GHn3MPAwxCYBI7x2CTDjGcFs4LcAqourIH+z42WTXTxRz0nK44HaKJYMkQ8AoAfmDXo61KgMQ7nEYkomqeEcPMEPz5tMntOXM2SwTf3gYni/v0HJoqrn+4BvqUgIGnFcxqomZUBv3TOLez/egLwF2Ap0ADsAb7snDs41mMrDVQSofzxchwj/x0YxoHrDwS/jpROes3Ef+V36y5J2LhFQhlLGqinJwAzexK4CCgxMz/wfefcI2Z2M7ALyAUeHc/NXyRRws4TDMomGu01ka+tfeREsV4TSRrwmgV0rXNuunMuzzlX6px7pH/7Tufcp51zZzjnfhiboYrERzTZRJHSSYdMFKueQNKEegFJ1vPanXRwa+q6+joq967nnFlTh/QmUj2BpCL1AhJh/N1Jp/a4QGtq3xryiveP7TWRSJIpAIhEIVw66XcvPPmkULnta6MuYhOqnkDtqSWZFABEohBNOmm0r4kGaBlLSTYFAJEoxeI10YrFMwPppH/cQHNXK9Nm5VP1wUR8bWpPLYmnSWCRGAmXTfTdS+6m5ns/CN78a35fQ1P38RHdSbWMpSSaAoBIjESTTRQpnTTUMpYi8aJXQCIxFOk10WjzBDByGUvQRLHEjwKASAKFrTru6R3ZnhpNFEt86RWQSAKFnCfo66PqRG6gPfXybwaKybZVUv742fzjkc3sP7WdHAs0ntuYt4Uv9P5GBWUSE3oCEEmgSOmkwZ5D/fMEzXm5Q4rJBiaKL2xdkpxvQDKKAoBIgo02TzDaJPFA51FNFEus6BWQSAqJNEkMoSeKRcZDTwAiKWS0SWIg5EQxKFNIxkcBQCSFhOw55BxVHxyH4lkULq3mvPKVgWri/nmEyTmnsrrpGL/t+G9lCsmYKACIpJBoeg4Nnyhu6fsr903NY7KppYSMjQKASIqJVEwWaaJYmUISLU0Ci6SZaCaKlSkk0VAAEEkzg9cqHrK9f6IYlCkk0VEAEEkzYauJP2gFwmcKiQynOQCRNDNiojjv41R90IqvrSNsplBwn6N+KC6FpdVQvjLJ34kkmwKASBqKNFE8PFOoqfs4NRMdFBXiO/4ePc/cEvjHryCQ1fQKSCQDRVp3YEJvJ+3PVSdjaJJCFABEMlA0mUIFHaH3keyhACCSgaLJFGrsm5Ko4UiKUgAQyUCRMoXaXT5b8r+ajKFJCtEksEgGGpwp1NTWTEF3Abe838rlbR34XQk/YRVLfGuSPEpJNnPOJXsMYVVUVLi9e/cmexgiaU/dQrOHme1zzlVEs6+eAESywIrFM0Pe8IfUCoRoPCeZTU8AIllqeK0ABOYJbjnaTX3XtSzyrdFTQhoayxOAJoFFslS4WoH/MzmXO7of5JWnH2T7aw1JGp0kggKASJYarVZgonVxG1u5Z9ehBI9KEkkBQCRLRaoVmGEtNLZ2JHJIkmAKACJZKlKtQKObojUFMpyygESyVLBW4I8baO5qZVpPb39X0XbaXT4/YZXWFMhwCQsAZlYEPAh0AS85536eqHOLSGjBrqIHaml/rpqCjg78fSVsyf8qS5QFlPE8pYGa2aPAFcAR59zCQdsvAzYDucAW59xGM7sOaHXOPWtm/+GcuybS8ZUGKiIyNolMA30MuGzYyXOBB4DLgQXAtWa2ACgF3uvfrRcRSQt19XVUbquk/PFyKrdVUldfl+whSYx4CgDOuZeB94dt/hxw2DlX75zrArYCXwT8BIKA5/OKSGIMFIs1tTXhcDS1NVHz+xoFgQwRjxvxTE7+pg+BG/9M4Cngf5jZ/waeDfdhM1tjZnvNbO/Ro0fjMDwRiVbIYrHeTja/ujlJI5JYiscksIXY5pxzbcDXIn3YOfcw8DAE5gBiPDYRGYOwxWJhtkt6iccTgB+YNejrUqAxDucRkTgLVyw2tbuXmn/+vlpFpLl4BIA9wKfMbI6Z5QOrgB1xOI+IxFm4YrHvfvC++gVlAE8BwMyeBP4AzDczv5mtds71ADcDu4C3gFrn3EHvQxWRRPPN9VHz+Rqm9zrMOaZ391Bz7H18be3qF5QBPM0BOOeuDbN9J7DTy7FFJDX45vrwPfEVYOSUnPoFpTelY4pIZMWlITerX1B6UwAQkciWVtMzbC5A/YLSn5rBiUhk5SuZAP39gppp7JuifkEZQEtCikjsHKiFF9bDcX/gtdHSaihfmexRZRUtCi8iiXegFp69Fbr7J4WPvxf4GhQEUpTmAETEs7r6Oir3rqd8ZgmVpTOoK5oY+IvujsATgaQkPQGIiCcDDeM6cwNdYJryJlBTMhkAX1t74HWQpCQ9AYiIJyEbxuXksPm0SYEvwqSQSvIpAIiIJ2Ebxk3IhbzCwESwpCQFABHxJFzDuGl9wN/fpwngFKYAICKehGoYR18ebzev4oKdJWoWl8I0CSwinvjm+oDAXEBTWzOuu5jOI8vo+XAxDXRw51OvA6hgLAUpAIiIZ765PnxzfVyw8UUahjWH6+ju5Z5dh8gr3s/mVzfT3NbMtKJpVJ1TFQwekhx6BSQiMROuM+iRvt+PXFv4N3dQ98DCQAGZJIUCgIjETLjOoIWf2B06VfSU3kC1sIJAUigAiEjM3L5sPoV5uUO2Febl4ia0hty/eUKuqoWTSAFARGJmxeKZbLjqbGZOKsSAmZMK2XDV2UwPlyra0xv4D1ULJ4UmgUUkplYsnjki4yevuCrQLmLQa6CCvj6qPuh/MlC1cFIoAIhI3AVTRf+4geauVqb19FL1QWugV5CqhZNGAUBEEmIgVTS4ZkBbBxTP0poBSaQAICKJVb5SN/wUoUlgEUk9B2rh3oVQMynwp9JE40JPACKSMurq607OE3ysl6qeQnxaWSxu9AQgIilhYGGZpu7jOLPgwjJ1RRNVKxAnCgAikhIiLiyjWoGYUwAQkZQw6sIyoFqBOFAAEJGUEHZhmZ5e1QrEiQKAiCTc9tcauGDji8xZV8cFG19k+2sNIReWKejro+pEbuiVxZQp5JkCgIgk1PbXGrjzqddpaO3AAQ2tgUVjuo8voubzNUwvmo5hTC+aTs3f3o3v22+Evvk/eyscfw9wgT/VVXTMzDmX7DGEVVFR4fbu3ZvsYYhIDIVaNAYCjeN+t+6S6A5y78L+m/8wxbPgO294HGF6M7N9zrmKaPbVE4CIJFS4RWPCbQ8pXEaQMoXGRAFARBIq3KIx4bYPV1dfR+XsUsrLZlFZOiNQJzBAmUJjogAgIgkVbtGY25fNj/jZYLFYro0sFlOm0JgpAIhIQoVbNGb4GgKhhC0WmzIldKaQjCphvYDMbAXgA04HHnDO7U7UuUUktYRaNGa47a81cM+uQzS2djBjUiG3L5sfvlgsN0c3/3GI6gnAzB41syNm9saw7ZeZ2SEzO2xm60Y7hnNuu3PuG8ANwDXjHrGIZLxwqaIfz5sacv9wRWQyumhfAT0GXDZ4g5nlAg8AlwMLgGvNbIGZnW1mvxz2v9MHffR7/Z8TEQnpnl2H6OjuHbKto7uXE0eWjSwWyy2g6pyq0AdSsdioonoF5Jx72czKhm3+HHDYOVcPYGZbgS865zYAVww/hpkZsBF4zjn3qpdBi0hmC5cSeqz5LO5ffhabX91Mc1sz04qmUXVOVXDJySEGisW6+4+lttIjeJkDmAkMrsTwA+ePsv8twKVAsZnNc849FGonM1sDrAGYPXu2h+GJSLqaMakwZLHYjEmF+OZeEvqGP9wL60/e/AcMtJUeawAYWMbyuD+Qapohy1h6yQKyENvClhU75+5zzp3rnLsp3M2/f7+HnXMVzrmKqVNDv+8TkczmJVU0KFbFYhncdsJLAPADswZ9XQo0ehuOiIi3VNG6+joqt1VSXlY6slAMxl7S/D6sAAAEzElEQVQsNtqTRJrz8gpoD/ApM5sDNACrgC/HZFQikvWiSRUdbqBQrLO3EwYVigH42trHVyyWwW0nok0DfRL4AzDfzPxmtto51wPcDOwC3gJqnXMH4zdUEZHRjbqqWPGs8RWLhXtiyIC2E9FmAV0bZvtOYGdMRyQiMk5hC8Xy8uA7B8Z30KXVQ7OJIGPaTqgVhIhkjLCrinkpFCtfGXhyKJ4F2PifJFJQwlpBiIjEW9U5VSfnAPqNWigWrfKVGXHDH04BQEQyxkB9QFSFYqIAICKZxTfXpxt+lDQHICKSpRQARCS7ZXHDOL0CEpHsleUN4/QEICLZK4PbPERDAUBEslcGt3mIhgKAiGSvDG7zEA0FABHJXkurA20dBsuQNg/RUAAQkeyVwW0eoqEsIBHJbhna5iEaegIQEclSCgAiIqkiwUVpegUkIpIKklCUpicAEZFUkISiNAUAEZFUkISiNAUAEZFUkISiNAUAEZFUkISiNAUAEZFUkISiNGUBiYikigQXpekJQEQkSykAiIhkKQUAEZEspQAgIpKlFABERLKUOeeSPYawzOwo8G6IvyoGjkd5mGj2HW2fcH8Xans020qAYxHGEwtjuUZeP5/Ma5ys6xvq3PH8vK5x/D8fad90uU980jk3Nao9nXNp9z/g4VjuO9o+4f4u1PZotgF7U+0apfM1Ttb11TXOvmucifeJdH0F9GyM9x1tn3B/F2p7tNsSwet50+UaJ+v6xuLcusaRpdI1zrj7REq/AspEZrbXOVeR7HFkKl3f+NM1jr9EXeN0fQJIZw8newAZTtc3/nSN4y8h11hPACIiWUpPACIiWUoBQEQkSykAiIhkKQWAFGJmRWa2z8yuSPZYMpGZnWlmD5nZNjP7h2SPJxOZ2Qoz+1cze8bMKpM9nkxkZnPN7BEz2+b1WAoAMWBmj5rZETN7Y9j2y8zskJkdNrN1URxqLVAbn1Gmt1hcY+fcW865m4CVgNIYh4nRNd7unPsGcANwTRyHm5ZidI3rnXOrYzIeZQF5Z2Z/A3wEPOGcW9i/LRf4C/AFwA/sAa4FcoENww5xI1BOoPy7ADjmnPtlYkafHmJxjZ1zR8xsObAOuN859++JGn86iNU17v/cvwA/d869mqDhp4UYX+NtzrmrvYxHK4LFgHPuZTMrG7b5c8Bh51w9gJltBb7onNsAjHjFY2YXA0XAAqDDzHY65/riOvA0Eotr3H+cHcAOM6sDFAAGidHPsQEbged08x8pVj/HsaIAED8zgfcGfe0Hzg+3s3PunwDM7AYCTwC6+Uc2pmtsZhcBVwGnADvjOrLMMaZrDNwCXAoUm9k859xD8Rxchhjrz/EU4IfAYjO7sz9QjIsCQPxYiG0R37c55x6L/VAy1piusXPuJeCleA0mQ431Gt8H3Be/4WSksV7jFuCmWJxYk8Dx4wdmDfq6FGhM0lgyla5x/Okax1/SrrECQPzsAT5lZnPMLB9YBexI8pgyja5x/Okax1/SrrECQAyY2ZPAH4D5ZuY3s9XOuR7gZmAX8BZQ65w7mMxxpjNd4/jTNY6/VLvGSgMVEclSegIQEclSCgAiIllKAUBEJEspAIiIZCkFABGRLKUAICKSpRQARESylAKAiEiWUgAQEclS/x/cATFo4aSTPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcoll.plot(\"T\") # Isn't it beatifull???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'t'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-10bf0c707fa2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdcoll\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"t\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Ahhhhhhhhhhhhhhhhhhhhhhh\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~/src/Python/Python-in-the-lab/distributions2.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, dis_type, loglog)\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m111\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdis_type\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m             \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdis_type\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[0mlb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"%s Hz\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 't'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADYBJREFUeJzt3HGI33d9x/Hny8ROprWO5QRJou1YuhrKoO7oOoRZ0Y20fyT/FEmguEppwK0OZhE6HCr1rylDELJptolT0Fr9Qw+J5A9X6RAjudJZmpTALTpzROhZu/5TtGZ774/fT++4XHLf3v3uLt77+YDA7/v7fX6/e+fD3TO/fH/3+6WqkCRtf6/a6gEkSZvD4EtSEwZfkpow+JLUhMGXpCYMviQ1sWrwk3wuyXNJnrnC7Uny6SRzSZ5O8rbJjylJWq8hz/A/Dxy4yu13AfvGf44C/7T+sSRJk7Zq8KvqCeBnV1lyCPhCjZwC3pDkTZMaUJI0GTsn8Bi7gQtLjufH1/1k+cIkRxn9L4DXvva1f3TLLbdM4MtLUh9PPvnkT6tqai33nUTws8J1K35eQ1UdB44DTE9P1+zs7AS+vCT1keS/13rfSfyWzjywd8nxHuDiBB5XkjRBkwj+DPDe8W/r3AG8WFWXnc6RJG2tVU/pJPkycCewK8k88FHg1QBV9RngBHA3MAe8BLxvo4aVJK3dqsGvqiOr3F7AX01sIknShvCdtpLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJDiQ5l2QuycMr3P7mJI8neSrJ00nunvyokqT1WDX4SXYAx4C7gP3AkST7ly37O+CxqroNOAz846QHlSStz5Bn+LcDc1V1vqpeBh4FDi1bU8Drx5dvAC5ObkRJ0iQMCf5u4MKS4/nxdUt9DLg3yTxwAvjASg+U5GiS2SSzCwsLaxhXkrRWQ4KfFa6rZcdHgM9X1R7gbuCLSS577Ko6XlXTVTU9NTX1yqeVJK3ZkODPA3uXHO/h8lM29wOPAVTV94DXALsmMaAkaTKGBP80sC/JTUmuY/Si7MyyNT8G3gWQ5K2Mgu85G0m6hqwa/Kq6BDwInASeZfTbOGeSPJLk4HjZQ8ADSX4AfBm4r6qWn/aRJG2hnUMWVdUJRi/GLr3uI0sunwXePtnRJEmT5DttJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwFda8J8nZJGeSfGmyY0qS1mvnaguS7ACOAX8GzAOnk8xU1dkla/YBfwu8vapeSPLGjRpYkrQ2Q57h3w7MVdX5qnoZeBQ4tGzNA8CxqnoBoKqem+yYkqT1GhL83cCFJcfz4+uWuhm4Ocl3k5xKcmClB0pyNMlsktmFhYW1TSxJWpMhwc8K19Wy453APuBO4AjwL0necNmdqo5X1XRVTU9NTb3SWSVJ6zAk+PPA3iXHe4CLK6z5RlX9sqp+CJxj9A+AJOkaMST4p4F9SW5Kch1wGJhZtubrwDsBkuxidIrn/CQHlSStz6rBr6pLwIPASeBZ4LGqOpPkkSQHx8tOAs8nOQs8Dnyoqp7fqKElSa9cqpafjt8c09PTNTs7uyVfW5J+UyV5sqqm13Jf32krSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSE4OCn+RAknNJ5pI8fJV19ySpJNOTG1GSNAmrBj/JDuAYcBewHziSZP8K664H/hr4/qSHlCSt35Bn+LcDc1V1vqpeBh4FDq2w7uPAJ4CfT3A+SdKEDAn+buDCkuP58XW/luQ2YG9VffNqD5TkaJLZJLMLCwuveFhJ0toNCX5WuK5+fWPyKuBTwEOrPVBVHa+q6aqanpqaGj6lJGndhgR/Hti75HgPcHHJ8fXArcB3kvwIuAOY8YVbSbq2DAn+aWBfkpuSXAccBmZ+dWNVvVhVu6rqxqq6ETgFHKyq2Q2ZWJK0JqsGv6ouAQ8CJ4Fngceq6kySR5Ic3OgBJUmTsXPIoqo6AZxYdt1HrrD2zvWPJUmaNN9pK0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqYlDwkxxIci7JXJKHV7j9g0nOJnk6ybeTvGXyo0qS1mPV4CfZARwD7gL2A0eS7F+27Clguqr+EPga8IlJDypJWp8hz/BvB+aq6nxVvQw8ChxauqCqHq+ql8aHp4A9kx1TkrReQ4K/G7iw5Hh+fN2V3A98a6UbkhxNMptkdmFhYfiUkqR1GxL8rHBdrbgwuReYBj650u1VdbyqpqtqempqaviUkqR12zlgzTywd8nxHuDi8kVJ3g18GHhHVf1iMuNJkiZlyDP808C+JDcluQ44DMwsXZDkNuCzwMGqem7yY0qS1mvV4FfVJeBB4CTwLPBYVZ1J8kiSg+NlnwReB3w1yX8mmbnCw0mStsiQUzpU1QngxLLrPrLk8rsnPJckacJ8p60kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwCrf/VpKvjG//fpIbJz2oJGl9Vg1+kh3AMeAuYD9wJMn+ZcvuB16oqt8HPgX8/aQHlSStz5Bn+LcDc1V1vqpeBh4FDi1bcwj4t/HlrwHvSpLJjSlJWq+dA9bsBi4sOZ4H/vhKa6rqUpIXgd8Ffrp0UZKjwNHx4S+SPLOWobehXSzbq8bci0XuxSL3YtEfrPWOQ4K/0jP1WsMaquo4cBwgyWxVTQ/4+tuee7HIvVjkXixyLxYlmV3rfYec0pkH9i453gNcvNKaJDuBG4CfrXUoSdLkDQn+aWBfkpuSXAccBmaWrZkB/mJ8+R7g36vqsmf4kqSts+opnfE5+QeBk8AO4HNVdSbJI8BsVc0A/wp8Mckco2f2hwd87ePrmHu7cS8WuReL3ItF7sWiNe9FfCIuST34TltJasLgS1ITGx58P5Zh0YC9+GCSs0meTvLtJG/Zijk3w2p7sWTdPUkqybb9lbwhe5HkPePvjTNJvrTZM26WAT8jb07yeJKnxj8nd2/FnBstyeeSPHel9ypl5NPjfXo6ydsGPXBVbdgfRi/y/hfwe8B1wA+A/cvW/CXwmfHlw8BXNnKmrfozcC/eCfz2+PL7O+/FeN31wBPAKWB6q+fewu+LfcBTwO+Mj9+41XNv4V4cB94/vrwf+NFWz71Be/GnwNuAZ65w+93Atxi9B+oO4PtDHnejn+H7sQyLVt2Lqnq8ql4aH55i9J6H7WjI9wXAx4FPAD/fzOE22ZC9eAA4VlUvAFTVc5s842YZshcFvH58+QYuf0/QtlBVT3D19zIdAr5QI6eANyR502qPu9HBX+ljGXZfaU1VXQJ+9bEM282QvVjqfkb/gm9Hq+5FktuAvVX1zc0cbAsM+b64Gbg5yXeTnEpyYNOm21xD9uJjwL1J5oETwAc2Z7RrzivtCTDsoxXWY2Ify7ANDP57JrkXmAbesaETbZ2r7kWSVzH61NX7NmugLTTk+2Ino9M6dzL6X99/JLm1qv5ng2fbbEP24gjw+ar6hyR/wuj9P7dW1f9t/HjXlDV1c6Of4fuxDIuG7AVJ3g18GDhYVb/YpNk222p7cT1wK/CdJD9idI5yZpu+cDv0Z+QbVfXLqvohcI7RPwDbzZC9uB94DKCqvge8htEHq3UzqCfLbXTw/ViGRavuxfg0xmcZxX67nqeFVfaiql6sql1VdWNV3cjo9YyDVbXmD426hg35Gfk6oxf0SbKL0Sme85s65eYYshc/Bt4FkOStjIK/sKlTXhtmgPeOf1vnDuDFqvrJanfa0FM6tXEfy/AbZ+BefBJ4HfDV8evWP66qg1s29AYZuBctDNyLk8CfJzkL/C/woap6fuum3hgD9+Ih4J+T/A2jUxj3bccniEm+zOgU3q7x6xUfBV4NUFWfYfT6xd3AHPAS8L5Bj7sN90qStALfaStJTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcoll.plot(\"t\") # Ahhhhhhhhhhhhhhhhhhhhhhh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He is right... I was wrong, I know... I typed 't' instead of 'T'. so?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# from distribution2\n",
    "    def plot(self, dis_type, loglog=True):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        \n",
    "# It is better to check immediately if the dis_type is correct, otherwise exit\n",
    "[...]\n",
    "\n",
    "    self.dis_types = self._get_distribution_types(maxLen)\n",
    "    print(self.dis_types)\n",
    "    self.distrs = dict()\n",
    "    for dis_type in self.dis_types:\n",
    "[...]\n",
    "\n",
    "    def plot(self, dis_type, loglog=True):\n",
    "        if not dis_type in self.dis_types:\n",
    "            print(\"Type %s does not exist, please check it\")\n",
    "            return\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'E', 'v', 'T', 'S'}\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_E.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_E.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_E.dat']\n",
      "9 lines deleted\n",
      "8 lines deleted\n",
      "8 lines deleted\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_v.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_v.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_v.dat']\n",
      "2 lines deleted\n",
      "0 lines deleted\n",
      "0 lines deleted\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_T.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_T.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_T.dat']\n",
      "23 lines deleted\n",
      "18 lines deleted\n",
      "21 lines deleted\n",
      "['/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.01_S.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_S.dat', '/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.03_S.dat']\n",
      "14 lines deleted\n",
      "12 lines deleted\n",
      "14 lines deleted\n"
     ]
    }
   ],
   "source": [
    "import distributions3 as d3\n",
    "\n",
    "dcoll = d3.DistCollector(mainDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xt81NWd//HXZzIJgaDhElwIAQFxWbkJiLq7Wi9VQY3iZRGxXWorltqtintR0e4Po7aFir2gQq2rbu2uFVlEEPGCP7VFfvZRQWUR1x8tP6oPhoRiosEaEnKZ8/tjkjiZzCRzv2Tez8eDB8x3bp9EM5+c8znnc8w5h4iI5B9PpgMQEZHMUAIQEclTSgAiInlKCUBEJE8pAYiI5CklABGRPKUEICKSp5QARETylBKAiEieUgIQEclT3kwH0JOysjI3ZsyYTIchIpIz3n777Vrn3LBoHpvVCWDMmDHs2LEj02GIiOQMM/so2sdqCkhEJE8pAYiI5CklABGRPJXVNQARyV8tLS34fD6ampoyHUpWKi4upqKigsLCwrhfI+8TwIZ3D7Di5T1U1zdSPqg/t86ewOXTR2Y6LJG85/P5OOaYYxgzZgxmlulwsopzjrq6Onw+H2PHjo37dfJ6CmjDuwe4Y/17HKhvxAEH6hu5Y/17bHj3QKZDE8l7TU1NDB06VB/+YZgZQ4cOTXh0lNcJYMXLe2hsaetyrbGljRUv78lQRCISTB/+kSXje5PXCaC6vjGm6yKSX1566SUmTJjA+PHjWb58edjHHD16lKuvvprx48dz+umn8+GHHwJQV1fHueeey8CBA7nxxhsjvsc555zTZb/Thx9+yOTJk5P6dUTSp2sA4eb3gc5rHjPanOv2vPJB/dMdqogkKNn1vLa2Nr7zne/wyiuvUFFRwamnnsqcOXOYOHFil8c99thjDB48mL1797JmzRpuv/12nn76aYqLi7n33nvZvXs3u3fvTvTLS4k+OwIIN7//m2dWceqGs3ij8QreKLqZSnuj2/P6FxZ0JgoRyQ2pqOe99dZbjB8/nnHjxlFUVMT8+fPZuHFjt8dt3LiRa6+9FoC5c+fy6quv4pyjpKSEM888k+Li4rhjuP7665k2bRrTpk1j2LBh3H333XG/Vjh9dgSwc/MjvGL/SXm/WqpdGa/6p3FVwVYGWDMAFVbL8sJHoQU2uy/hd06rgERyVE/1vHh/ng8cOMCoUaM6b1dUVPC73/2ux8d5vV5KS0upq6ujrKws6vf66le/Sv/+gZmH5uZmPJ7A7+aPPvooAB999BGzZ8/m61//elxfSyRpSwBmNhp4CKgFfu+cCz+hlgy71nJby2oGeL74sF9g/xtPSM1kgDXz48KH+Smr8QwaBectZUPbBM5Y/pqWhYrkkFTU81yY6eFwhddoH9eTJ598kpkzZwKBGsAll1zSeV9TUxNXXXUVDz30EMcff3xMr9ubhKaAzOxxMztkZrtDrl9oZnvMbK+ZLWm//JfAZufcdcDEbi+WJJv3bWbWjnv467F/wayKcjaXDADo9uHfwWv+wH2H99O68Sa2Pbu6cxh5ymevcOqGs/BXDcK39ASqvneXloiKZKFIdbtE6nkVFRXs37+/87bP56O8vLzHx7W2tnL48GGGDBkS9/uGuuGGG7jyyis5//zzk/aaHRKtAfwCuDD4gpkVAKuAiwh80F9jZhOBd4H5ZvYa8HqC7xvW5n2bqXqzipoCw5lRU+ilqmxIZxLojbetiVtYA8AczzaWFz7KSKvFg6PCU8ttLavZ9uxqJQGRLHPr7An0Lyzoci3Ret6pp57KH/7wB/74xz/S3NzMmjVrmDNnTrfHzZkzhyeeeAKAdevW8eUvfzlpy1dXrVrFn//8Z5YsWdL7g+OQ0BSQc26rmY0JuXwasNc5tw/AzNYAlwEtwF3tz1kH/Hsi7x3OyndW0tTWdWNEk8fDysGDqGw4ggN6+88y0mrZ1+8r+PHgNX+X+wZYM7e4NZy99gz+8emdmiISyRIdP4PJXAXk9Xp56KGHmD17Nm1tbVx33XVMmjQJgKVLlzJz5kzmzJnDwoULWbBgAePHj2fIkCGsWbOm8zXGjBnDZ599RnNzMxs2bGDLli3dVhH15P7776ewsJBp06YBgdHADTfcEPfXFMrCzV/F9AKBBPC8c25y++25wIXOuevbby8ATgceBqoI1AA+d879S4TXWwQsAhg9evQpH30UdWtrpj4xFUeY+Tjn2HWgFk7+CvxhCxz2gXnAtbG5ZAArBw/ioLeA4a1tLP60nsqGIxHfwzlwQLUr477WeRR5PXy3339R2nKIav9QHi36e6ZVLlJSEEnQBx98wEknnZTpMLJauO+Rmb3tnJsZzfNTUQQO90u2c87tBub29mTn3CPAIwAzZ86MKTsNLxlOTUNN2ICmVgxjeNNuzvqb+Wz1beVgQw3Htvk54jFa2odrHVNGQMQkYBZ4vQqrZUXhzzGMopZWgM5pou8+08Ldm86h/kiLRgkikrVSsQ/AB4wKul0BVKfgfbpZPGMxxQXd19z6zXBATUMNT+95mpqGGhxwuMDT+eHfoWPKKBr9rI0ia+1ybYA188+ep/n0SIv6C4lIVktFAtgOnGhmY82sCJgPPJeC9+mmclwlVX9bxYiSERiGx+L78mq8BcyqKGfqmFHMGlXOppISYpkp66gjbCu6mTmebeovJCJZKaEagJk9BZwDlAF/IlDkfczMLgZ+ChQAjzvnvh/P68+cOdMlciZwpJpAr5wLzPW085qXgW0tHDaiqhMEO+oKaKA/g/icalemGoFIlFQD6F1GawDOuWsiXH8BeCGR106GSDWBHoV8+AO0ulbqPd3rBBc0HMVjHryuJeLL9bM2+vE5EKgb3NaymqXPtgL/oCQgIhnVZ3sBQeSaQDCveRnUbxCGMaJkRLcP/3CaPB7uHDaUmWNHcvGJf8X6weX4saimiQZYM8ttNXM2TNTmMhHJqD6dAEJrAiNKRnD1hKu73P7emd/jjflvsOvaXWyZuyWQBKLQWVhuOcyyoQN58WtPYoNG9fo8+GL3cceqod88s4rp92xh7JLNnLH8NSUEkSyRSDvoV155hVNOOYUpU6Zwyimn8Nprr4V9vtpBp1DluEoqx1VG/fjFMxZT9WZVtw1lPWlqa+LObXdyxxBj+LEjWfzJp1HXCAZYMysKfoanbRXVRWXc99k87lgf6GGkKSKRGOxaC6/eE9jnU1oB5y2FqfPifrlE20GXlZWxadMmysvL2b17N7Nnz+bAgez65a5PjwDiETpqKC0qpdDT+6HLfucPjAi8BfzrsKF8afTIzlVEvbWiCB4RLC98lAvafqNVQyKx2LUWNt0Mh/cDLvD3ppsD1+OUaDvo6dOnd/YOmjRpEk1NTRw9ejSmGNQOOgNCRw2b921m5TsrOdhwEDPD7/w9PBtazagvCPQlqfF6qSobytv9+rFtQP9edxx3dCj1NK6Gn4xK+LcYkbzw6j3QEtL5s6UxcD3On59ktoN+5plnmD59Ov369Qv7Xn2+HXQuC04IHQ3nYpoi8hj/VXpM5+3edhx39iA6vJ8jz3yHF599hi9731W7CZFIDvtiux6FZLWDfv/997n99tvZsmVLxPfKyXbQ+ShZm82i3XE8wJq5wv8Sg1v+pK6kIpGUVsR2PQrJaAft8/m44oor+OUvf8kJJ5wQVxzZ3A46L1WOq2TL3C3sunYXPzjzB70uNY2kptAbqBNU9FwnCHeQzS2sUZ1ApMN5S6EwpPd/Yf/A9Tgl2g66vr6eyspKli1bxhlnnBFXDKluB60EkKB4i8YdupxbMHBg1M8babW80XiF9hKIQGCe/9IHoHQUYIG/L30gofpZcDvok046iXnz5nVpB/3cc4EONwsXLqSuro7x48fz4x//uHO56EMPPcTevXu59957Owu5hw4diimG+++/n/fee6/z+Q8//HDcX084CbeDTqVEW0FkSnDReHjJcM6qOIuNezf2WjfwYDjn71Ik9rvIp5l1OOKKWOoWceYV2l0sfYdaQfQuG9tB571wew+mHze9MylE6k/kJ9CGomNEUOeOwX02pcth9uEMsGaWsxrPhofwPat+QyISHU0BpUlw3SCa3cZNHg8rjuvPivG/55zRY1l77Ige202E7i5WkVhEeqMEkAHR9CgCwPyYQaO3kWVl/Tn7r6Zw8tjei8ad/YY2ToKfTE5oM4yI9F1KABkQz1LSVtdK/dH6qA+795ofT/uOyMb1N7L9uZ8n80sQkT5ACSBDEl1K2rGPoNX1/p+wP0cpf/s+TQmJSBdKAFkg3s1lNYVeZoyrYNaokb32GyqnVlNCItKFEkCWiHdE0NGArqpsKJtKSiKOCMzQlJBIjBJpB/3WW291rt8/+eSTefbZZ8M+f8yYMdTW1nbe/vWvf92lFUQqaRloFupYQtqxbPTYomM50nqEFn/kk8eaPMYPK8bxoIODzfU9Npzrz1FOePsejnzwEwY0HkxK61yRTAvdf7N4xuKYWsGHSrQd9OTJk9mxYwder5eamhpOPvlkLr30Urze7PnY1QggSwWPCLZds417z7i3c4ooksPNh6lpOdylUPz8gPBTQ4P5nAGNNaBRgfQBHU0aaxpqcDhqGmqoerOKzfs2x/2aibaDHjBgQOeHfVNTU9hGcr25+OKLO0cRpaWlnS0nkkUJIEfEuo8AAoXi7x43NGy/odD/F1Uolly28p2V3XbaN7U1sfKdlXG/Zrh20OEOdInUDhrgd7/7HZMmTWLKlCk8/PDDEX/7P/fcczs/6K+//vrO6y+88AI7d+7kscce4/jjj+fyyy+P++sJRwkgB0W9j4D2oytDlo5G2kxWTq3OKpacdLDhYEzXo5GMdtCnn34677//Ptu3b2fZsmU0NYVvB/P666+zc+dOdu7c2XkGQIfa2loWLFjAr371K0pLS+P5UiJSAshB4c46HtSv99bSTR4PPx48hE8J33TODO0mlpw0vGR4TNejkYx20B1OOukkSkpK2L17d0wxtLW1MX/+fJYuXZqSc4KVAHJU8JTQlrlbWHLakqhGBX/yejh77BAuiGI3sVpOS64INyouLihm8YzFcb9mou2g//jHP9La2goETvTas2cPY8aMiSmGJUuWMHXqVObPnx/319GT7ClHS0JCVw5FOrqyYwR7sNDLXWVD8TvjkoaGbjUBCG45rQZzkt1C//9Pxiqg4HbQbW1tXHfddV3aQc+cOZM5c+awcOFCFixYwPjx4xkyZAhr1qwBYNu2bSxfvpzCwkI8Hg+rV6/uckxkNO6//34mTZrEtGnTALjnnnvCJqF4qR10HxX10ZXOg9HW47JRUMtpST+1g+5dou2g0zYFZGYeM/u+mT1oZtem633zVWidICLzR9VfSFNCIn1PQgnAzB43s0Nmtjvk+oVmtsfM9ppZx1lmlwEjgRYg/pOaJWrxtKC+c1j4ZaOgU8hE+ppERwC/AC4MvmBmBcAq4CJgInCNmU0EJgC/dc79E/DtBN9XYhTt0tFwy0Y7aJWQSN+SUAJwzm0FPgm5fBqw1zm3zznXDKwh8Nu/D/i0/TFtibyvxC6ehnMdHUfD0ZSQpEM21ygzLRnfm1TUAEYC+4Nu+9qvrQdmm9mDwNZITzazRWa2w8x2fPzxxykIL3/F03CuxlvArIrysNNCmhKSVCouLqaurk5JIAznHHV1dRQXx9ZGPlQqloGGqzg659wRYGFvT3bOPQI8AoFVQEmOTdpFu2y044xioHNaCKCy4Qhmgf/YFRaYElr6bCugVUKSHBUVFfh8PvSLYHjFxcVUVFQk9BoJLwM1szHA8865ye23/waocs7Nbr99B4Bzblmsr61loOkT9bJRYERLK1t81d2u1/kHctRTzHDqqPYP1d4BkQzI9DLQ7cCJZjbWzIqA+cBzKXgfSaJw7SUiqfEWhJ0SGmKfU04tHpwKxSI5INFloE8BvwUmmJnPzBY651qBG4GXgQ+Atc659xMPVVIttL1ExCQQYaVQ6G5iFYpFspt2AktE0U4LeZzDQdjdxM4FTi2rdmonIZIOmZ4Ckj4i2t3E2jsgkps0ApCozVo3i5qGml4fF6lIDODzl3F2y4P4naN8UH9unT1BIwKRJNIIQFIi2t3EkYrEENg78Ieia3ij6GZO+ewV7lj/nkYEIhmidtAStVj2Dji67xtov6tz78DywkehBVa8XKRRgEgGaApI4paMInGr8+DBryKxSJJoCkjSIhlFYq/5VSQWyRCNACRpklEk1ohAJDEaAUhGxFIkjtRgTiMCkfRRApCkiaXldE2ht9eTyLSTWCS1NAUkKRNLg7lIhWK/C+wiLveowZxINDQFJFkhlgZzPRWKKzxqMCeSChoBSFrFUih+aX81njCLi7SbWCQyjQAka8VSKJ42VruJRVJJO4ElrbSbWCR7aApIMkq7iUWSS1NAkjO0m1gkczQCkKyi3cQiidEIQHJWMlpOa0QgEh2NACTrbN63ufcicZBiv5+q2k+61AWCaUQg+SSWEYASgGS1aIvEpa1tDHCOg96CsIXiDkdcEUvdIs684h+UBKRP0hSQ9BnRFokPF3jUX0gkRhoBSE6JtkgMkQvFzoEDDtkwljVfxY5jL9BuYukzNAKQPivaIjFELhSbgcdgOB+zrPBR7SaWvKURgOSc4CLx8JLhNLY2Un+0vsfn9FQorvMPpJFiyq1WhWLJeSoCS15JdDexc4FRQQcViiWXZW0CMLMSYCtwl3Pu+d4erwQg0QoeFQQ+5numpaPSV6WtBmBmj5vZITPbHXL9QjPbY2Z7zWxJ0F23A2sTeU+RcCrHVbJl7hZ2Xburx3MHOjR5PNw5bKg2k0leS7QI/AvgwuALZlYArAIuAiYC15jZRDM7H/gf4E8JvqdIj6ItFPfUXyhYx9LRf17734xdspkzlr+mZCB9QkLtoJ1zW81sTMjl04C9zrl9AGa2BrgMGAiUEEgKjWb2gnO9bPEUiUPULaeDdIwI7hg2NOxGso4zCKpdGfd9No871jcDaFpIcloqzgMYCewPuu0DTnfO3QhgZl8HaiN9+JvZImARwOjRo1MQnuSDynGVnYkg2iKxv70SrDMIJF8kXARuHwE875yb3H77KmC2c+769tsLgNOcczfF+toqAkuyxNpfCHpuL6EisWSrTG8E8wGjgm5XAOH79oqkSXCR+Adn/iCqGkFP7SVUJJa+IBUjAC/we+A84ACwHfiKc+79WF9bIwBJlXhGBBC5vUSr8+AxxyHK1F5CMiqdy0CfAn4LTDAzn5ktdM61AjcCLwMfAGvj+fAXSaV4RgQQub2E1/x4cGovITlFO4FFSH57CZ+/jDObH6DADL9zlA/qrxGBpEXW7gSOlRKAZEqi7SX8DqpdWWd/ofta5/FKwdksu3KKkoCkVCwJIBXLQEVyXuhegkjtJXpaOlrhqQ38raWjkqU0AhCJQiyH1b+0vxpPmLNrtHRU0iHTy0BF+pxYDqufNlb9hSQ3aAQgEqVYl456/X4GOsdhjydsewmNCCQVVAQWSbFoi8TBelo1pDMIJFmUAETSINYzCCDyqiHQiECSQwlAJM1iOay+g0YEkgoqAoukWSyH1Xdo8nhYOXhQ2PsGWDPLbTVzNk6Cn0yGXTpHSZJPCUAkCSrHVVL1t1WMKBmBYZQWlVLoKez1eTXeAmZVlPfYXoLD+2lcfyPbn/t5Kr8EyUOaAhJJkahWDYWcSN/TtNABV8bPpm/g9f/7MdX1jWovIWGpBiCSZWJZNaT2EpIIJQCRLBTacC6aonHwiMDv6LLD+KgroIH+DLIGPKUVcN5SmDovhV+B5AIlAJEcEO3KoZ6WjgZrpB+7Z9zLqXO+leRIJZdoFZBIDoh25ZDfLOypZKH6c5Tpby/BVQ3SyiGJihKASIaErhzyWO8/jk0eD3cOGxp21RAEVg6ZVg5JlDQFJJIlkt1eAgIrhy4p+Bn1R1q0aihP6DwAkRwUegZBNA3nOkYEdwwbGrZGUE4tb7ddRXVRGfd9No871jcDKAkIoAQgklUqx1V2JoJoRwQ9HUpjBoYOpZHwNAUkksVibUENUNraxgDnOOgt6DYqUMO5vk/LQEX6oKhrBFHuLlbDub5JCUCkj4pnRACR9xLU+Qdy1FPMcOqo9g/VqKAPUAIQyQPxrBqCriOCkMGCRgV9gBKASJ4IbS/R2NpI/dH6Xp+ng2n6LiUAkTyloyolKxOAmV0OVALHAaucc1t6e44SgEjs4qkTjGhpZYuvOux9GhHklrQlADN7HLgEOOScmxx0/UJgJVAAPOqcWx5032Dgfufcwt5eXwlAJDGxrBwyem84pxFB9ktnAjgL+Bz4ZUcCMLMC4PfABYAP2A5c45z7n/b7fwQ86Zx7p7fXVwIQSVysIwKv389A5zjs8USuEZhTC+oslbZuoM65rcAnIZdPA/Y65/Y555qBNcBlFvBD4MVoPvxFJDkqx1WyZe4Wdl27ix+c+YNeO5C2ejzUFxRE7ECqoyr7jlS0ghgJ7A+67QNOB24CzgdKzWy8c+7hcE82s0XAIoDRo0enIDyR/BXabyiwFqhnHYfXh5sW6mhB7X/ndu0jyEEJF4HNbAzwfNAU0FXAbOfc9e23FwCnOeduivW1NQUkklrRHkqDc4xobQvbXiKYagSZl+kDYXzAqKDbFUD45QUiklHRHkoDgWZzHdNC/1o2hC+NHtntXIIB1swtrGHFy3tSGbYkSSqmgLYDJ5rZWOAAMB/4SgreR0QSFDoldGzRsRxpPUKLv6XrA4O3C9NeJ2j/d2gX0nKr5ekj38RfpfYS2S7RVUBPAecAZcCfgLucc4+Z2cXATwksA33cOff9eF5fU0Ai6RfP4fXwxV6C0MPrj7givtv2TX5ddI4OpkmDrNwIFg8lAJHMi6VOEGkvQfBmsvta5/FKwdksu3KKkkAKZLoGICJ9SNR1gh4Or/eaH49BhSdwMM0Fbb9RnSALaAQgIr0KnhaKWCcIofYSmaEpIBFJqeCEEHEvgdpLZIQSgIikTTQ1gqjaS2hEkBSqAYhI2kRTI4iqvUR7jeC2ltVse3Y1G949kOrQ855GACKSsKimhELo8PrU0BSQiGRMLMtGdXh98mkKSEQyJpZlo8GaPB7uHDY0bHuJ5baaORsnwU8mw661qQg7LykBiEhSVY6rpOpvqxhRMgLDKC0qpdBTGNVz/RH2EgS3oD7yzHeo+t5dqhEkgaaARCTlkn14vc9fxgVulXYTh6EagIhktUQPr/c7qHZllFutCsUhVAMQkawWOk3ksd4/ijoOpulQ4anV0tEEaQQgIhmXjMPrdVZxgEYAIpJToh4R9NZwTmcVx0QjABHJOtGOCCIViQEOuDK2X7417+oCsYwAUnEimIhIQqI9vN7fvpcg9FQygHJqmbNhIr5nVSSORCMAEcl60e4ujjQiyKfdxKoBiEifEu3u4kgbyXRYfXhKACKS9ZKxbHSk1fJG4xVqJxFEU0AiknNiWTY6orWtW8fRRvqxe8a9nDrnW+kJOI00BSQifVosI4KaQm+3aaH+HKX87fvyfuOYRgAikvPCjghC2k13CC4Uf/r5N9h+y23pCzQNNAIQkbwSOiIYUTIi7Ic/dC0UU/oEm1flb01AIwAR6ZOiXTo6oqWVjb66PlMT0AhARPJetEtHa7wFnD7mOJYceoC7X/uPNESWPbQTWET6pNDdxGA4/N0faIYDDhUamz76ITNW3M70P/fPi93DaZsCMrMSYDXQDPzaOfdkb8/RFJCIJEu0S0dHtLSyxVeds7uH0zYFZGaPm9khM9sdcv1CM9tjZnvNbEn75SuBdc65bwJzEnlfEZFYdRaKC0sx5wKrhMI46C0A8mP3cKI1gF8AFwZfMLMCYBVwETARuMbMJgIVwP72h7Ul+L4iIjGrHFfJlq9s47EhN3Fca/gEYNB5MP3OgUf69O7hhBKAc24r8EnI5dOAvc65fc65ZmANcBngI5AEEn5fEZFEnDrnW5w1bgn4Qw6rd67LMtG7hw3hxYED+uwZA6n4IB7JF7/pQ+CDfySwHvg7M/sZsCnSk81skZntMLMdH3/8cQrCExGBu768gOVn39u+dyCwQSx070BwP6G+uHs4FauAwu2+cM65BuAbvT3ZOfcI8AgEisBJjk1EpFPluMrO1UJTn5gS9jEdNQGAEdSx4uU9OVUU7kkqRgA+YFTQ7QqgOgXvIyKSNMNLRoS/3vpFybLaDaW6vjFdIaVcKhLAduBEMxtrZkXAfOC5FLyPiEjShNs4Vuz3s/jTeiBwqMx9rfMoH9Q/E+GlRKLLQJ8CfgtMMDOfmS10zrUCNwIvAx8Aa51z7yceqohI6oT2ExrqOYabP27hos8b8fnLWNJyPa8UnM2tsydkOtSkUS8gEZEINrx7gBUv76G6vpFrB77FbYVPM6DxIJRWwHlLYeq8TIfYjQ6FFxFJgsLSnZSMX8kxDTW82drG658cphLXuSx094ef5nQDOa3HFxEJo6N1RE1DDY5A07jgc4b7wrJQJQARkTBWvrOyW9+g0HOGO5aF5iolABGRMAIdRMNcD9oXkOvLQlUDEBEJY3jJ8LAHyhzb5mdWRTkHvQUUtQ6g7PP3gcr0B5gEGgGIiIQRbl9Agd9xxOPpPGj+aGFjTh8rqQQgIhJGuHOG+xWW0uIJ0y+oXxtsujnnkoD2AYiIRGnqE1NxdP/MNOfY9eF+KB0F/7g7zDPTR2cCi4ikwPCS4WGvd54hcEwbm/dtTm9QCVACEBGJUtiD5kPOEPhf2+7KmSSgBCAiEqXQYyXDnSHQ4o6y8p2VGYowNkoAIiIx6DhW8tzfXxqmGhAQaQ9BtlECEBGJw45jL6CtZXDY+yLVCrKNEoCISBxunT0B98lFuJBzhYv9fhb/qTonloRqJ7CISBwCx0Jey/d/48UNXEez9wjDW9tY/Gk9lQ1HAvsCICtbRnfQPgARkUT9ZDIc3t/9egb2BWgfgIhIOh32xXY9SygBiIgkqrQitutZQglARCRR5y2Fwq6HxTfSj+0n3JShgKKjBCAikqip89g+5W4OuDL8zvD5y7i9eSFf2358Vp8YplVAIiJJcMv/nMiBow/gPfZd+g17GSt8Hk/LNr7/m0sN2KVkAAAIQklEQVS5fPptmQ4vLI0ARESSoLq+Ee+x71I8Yj2eonrMwFNUT2PpmqztDaQEICKSBOWD+gd+8/e0dLlunhZWvvYvgaWiWbY5TAlARCQJbp09ASusD3vfQW8BHN5P4/ob2f7cz9McWWRKACIiSXD59JEMKjou7H3DW9sA6M9Ryt++L2sKw2lLAGZ2uZn9m5ltNLNZ6XpfEZF0ueOv/6nbeQHFfj+LP/1iZDCCOla8vCfdoYUVVQIws8fN7JCZ7Q65fqGZ7TGzvWa2pKfXcM5tcM59E/g6cHXcEYuIZKku5wg7x4iWVi778+esHDwocGJYRTn/WXIc1fWNmQ4ViH4E8AvgwuALZlYArAIuAiYC15jZRDObYmbPh/wJHhf9a/vzRET6nMpxlWyZu4VdM5by7U8b2HjMQGoKvZ0nhv1o2ADKhr+f6TCBKBOAc24r8EnI5dOAvc65fc65ZmANcJlz7j3n3CUhfw5ZwA+BF51z7yT3yxARyTJT57FyeAVNnq4fs35PG4MH/ju+pSdQ9b27MloPSKQGMBIIbn/na78WyU3A+cBcM7sh0oPMbJGZ7TCzHR9//HEC4YmIZNYn/s/DXj/oLaDCU8ttLavZ9uzqjCWBRBKAhbkWsbe0c+4B59wpzrkbnHMP9/C4R5xzM51zM4cNG5ZAeCIimRXpZLCOVUEDrJlbWJOxonAiCcAHjAq6XQFUJxaOiEjfsXjG4l5XBZVbXcaKwon0AtoOnGhmY4EDwHzgK0mJSkSkD6gcVwnAyndWcvDz6q4nhrWrdkMpH9Q/0kukVFQJwMyeAs4ByszMB9zlnHvMzG4EXgYKgMedc9lR2hYRyRKV4yoDiWDXWlo33oS3ranzviOuiJ8yn1tnT8hIbFElAOfcNRGuvwC8kNSIRET6oqnz8AJHXlxKceNBqv1DebTo7zmzclH7+cLppzOBRUT6EJ0JLCIivVICEBHJFrvWBtpGVw1KS/tonQgmIpINdq2FTTdDS/uS0MP7A7cBps5LyVtqBCAikg1eveeLD/8OLY2B6ymiBCAikkab921m1rpZTH1iKrPWzfriuMjDvvBPiHQ9CTQFJCKSJpv3babqzSqa2vcC1DTUUPVmFQCVpRWBaZ9QpRUpi0cjABGRNFn5zsrOD/8OTW1NrHxnJZy3FApDdgQX9g9cTxElABGRNDnYcDDy9anz4NIHoHQUYIG/L30gZQVg0BSQiEjaDC8ZTk1DTdjrQODDPoUf+KE0AhARSZOw3UELilk8Y3FG4tEIQEQkTbp0B204yPCS4SyesbjzeropAYiIpFFnd9AsoCkgEZE8pQQgIpKnlABERPKUEoCISJ5SAhARyVNZfSKYmf0Z2BPj00qBwzHeF3o9+Ha4f4f+XQbUKs6ormVrnNF8bxVn4nH2FG9fjbOnGOOJs7d/n+icK40qMudc1v4BdsTxnEdivS/0evDtcP8O87fijPJatsYZzfdWcSYeZy/x9sk4e4oxnjij/XmK5k9fnALaFMd9odc39fLv0L/j0Vfj7O1atsYZzfdWcUa+L9o4e4o3HrkQZ2/PizXOaH+eepXtU0A7XJSHG2eS4kwuxZlcijO5ciXOaGT7COCRTAcQJcWZXIozuRRncuVKnL3K6hGAiIikTraPAEREJEWUAERE8pQSgIhInsq5BGBml5vZv5nZRjOb1X5tnJk9ZmbrMh1fhwhxlpjZE+3Xv5rpGCH8987MJprZWjP7mZnNzWR8HSLEOdrMnjOzx81sSSbj6xAhzi+Z2cNm9qiZvZnJ+NrjCRejx8y+b2YPmtm1mYyvQ4Q4zzGzN9q/n+dkMLxOkT5/2n/e3zazSzIVW2/SmgDaf1APmdnukOsXmtkeM9vb2w+yc26Dc+6bwNeBq9uv7XPOLcz2OIErgXXt1+dkSZzhvncXAQ86574NfC2L4/xLYLNz7jpgYrbG6Zx7wzl3A/A88EQ2xghcBowEWgBfIjGmOE4HfA4UZ3mcALcDaxONMaVi3XmXyB/gLGAGsDvoWgHw/4BxQBHw3wR+mKcQ+IEJ/nNc0PN+BMwIef112RwncAcwrf3fv8qyONcF/fs4YBWwAvg/WRznUOB14DXgG9kaZ9C1tcCx2RgjsAT4VrJ+jlIYp6f9778AnsziOM8H5hP4BfCSRONM1Z+0ngjmnNtqZmNCLp8G7HXO7QMwszXAZc65ZUC3oZOZGbAceNE5906OxekDKoCdJGH0lYw4I7zuIeA7ZlYArM/WOIFvAHe1v/464N+zNE7MbDRw2Dn3WZbG6AOa2//dlkiMqYzTOedv/+enQL9sjRM4FyghkDgazeyFoNizRjbUAEYC+4Nu+9qvRXITgew618xuADCzoWb2MDDdzO7I1jgJfJj+nZn9jMS2v/ckpjjDfe/MbIyZPQL8ksAoICvjBF4Cbm6//mEWxwmwkAQTVA+SEeN6YLaZPQhszdY4zexKM/s58B/AQ9kap3Puu865W4BfAf+WjR/+kB1nAluYaxF3pznnHgAeCLlWB9wQ/hlJk4w4Gwj81ppKscbZ7XvnnPsQWJTcsLpJRpy7gVQXqROOs/36XckMKkQyvpdHCCSpVEpGnOtJwqi0F0n5b95+3y+SFFNKZMMIwAeMCrpdAVRnKJaeKM7kUpzJkwsxguLMOtmQALYDJ5rZWDMrIlA4eS7DMYWjOJNLcSZPLsQIijP7pLPiDDwF1PDFUrOF7dcvBn5PoPL+3UxXxhWn4szWOHMhRsWZO3/UDE5EJE9lwxSQiIhkgBKAiEieUgIQEclTSgAiInlKCUBEJE8pAYiI5CklABGRPKUEICKSp5QARETy1P8HMkZPfOF0mYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcoll.plot('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type t does not exist, please check it\n"
     ]
    }
   ],
   "source": [
    "dcoll.plot('t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Problem\n",
    "Check distributions*.py and add the info about the axis, considering this scheme:\n",
    "    * [distribution_type, x-axis, y-axis, title]\n",
    "    * S, size S, P(S), size distribution\n",
    "    * T, duration T, P(T), duration distribution\n",
    "    * E, energy E, P(E), energy distribution\n",
    "    * v, velocity v, P(v), velocity distribution\n",
    "\n",
    "so they appear in the plot as xlabel, ylabel and title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = {'S': 'size', 'T': 'duration', 'E': 'energy', 'v': 'velocity'}\n",
    "class Labels:\n",
    "    def __init__(self, dis_type):\n",
    "        try:\n",
    "            lb = names[dis_type]\n",
    "        except KeyError:\n",
    "            print(\"Distribution type %s not valid\" % dis_type)\n",
    "            sys.exit()\n",
    "        name = names[dis_type]\n",
    "        self.xlabel, self.ylabel, self.title = \"{0} {1}\".format(name, dis_type), \"P({})\".format(dis_type), \"{} distribution\".format(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbS = Labels('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('size distribution', 'size S', 'P(S)')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbS.title, lbS.xlabel, lbS.ylabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As a better alternative...\n",
    "class Labels:\n",
    "    def __init__(self, dis_type):\n",
    "        try:\n",
    "            lb = names[dis_type]\n",
    "        except KeyError:\n",
    "            print(\"Distribution type %s not valid\" % dis_type)\n",
    "            sys.exit()\n",
    "        self.name = names[dis_type]\n",
    "        self.dis_type = dis_type\n",
    "        \n",
    "    @property\n",
    "    def xlabel(self):\n",
    "        return \"{0} {1}\".format(self.name, self.dis_type)\n",
    "    \n",
    "    @property\n",
    "    def ylabel(self):\n",
    "        return \"P({})\".format(self.dis_type)\n",
    "    \n",
    "    @property\n",
    "    def title(self):\n",
    "        return \"{} distribution\".format(self.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('duration distribution', 'duration T', 'P(T)')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb = Labels('T')\n",
    "lb.title, lb.xlabel, lb.ylabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can instert this in the distributions3.py for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}