Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.pyplot.switch_backend()]: Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
      "DEBUG [urllib3.connectionpool._new_conn()]: Starting new HTTPS connection (1): archive.stsci.edu:443\n",
      "DEBUG [urllib3.connectionpool._make_request()]: https://archive.stsci.edu:443 \"GET /missions/tess/tid/s0001/0000/0000/6389/8957/tess2018206045859-s0001-0000000063898957-0120-s_lc.fits HTTP/1.1\" 404 297\n",
      "DEBUG [urllib3.connectionpool._new_conn()]: Starting new HTTPS connection (1): archive.stsci.edu:443\n",
      "DEBUG [urllib3.connectionpool._make_request()]: https://archive.stsci.edu:443 \"GET /missions/tess/tid/s0002/0000/0000/6389/8957/tess2018234235059-s0002-0000000063898957-0121-s_lc.fits HTTP/1.1\" 404 297\n",
      "DEBUG [urllib3.connectionpool._new_conn()]: Starting new HTTPS connection (1): archive.stsci.edu:443\n",
      "DEBUG [urllib3.connectionpool._make_request()]: https://archive.stsci.edu:443 \"GET /missions/tess/tid/s0003/0000/0000/6389/8957/tess2018263035959-s0003-0000000063898957-0123-s_lc.fits HTTP/1.1\" 200 1998720\n",
      "https://archive.stsci.edu/missions/tess/tid/s0003/0000/0000/6389/8957/tess2018263035959-s0003-0000000063898957-0123-s_lc.fits\n",
      "TESS file\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "non-broadcastable output operand with shape (13423,) doesn't match the broadcast shape (1,13423)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-e465bf94542f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mfits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfitsloc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhdus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0mlcs\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mopenFits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhdus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfitsloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0mx\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0myerr\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miyerr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: non-broadcastable output operand with shape (13423,) doesn't match the broadcast shape (1,13423)"
     ]
    }
   ],
   "source": [
    "# Importing the important things:\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pymc3 as pm\n",
    "import corner\n",
    "import requests\n",
    "from astropy.io import fits\n",
    "\n",
    "from NamastePymc3 import *\n",
    "\n",
    "tic=63898957\n",
    "sector='all'\n",
    "#Downloading TESS lc\n",
    "epoch={1:'2018206045859_0120',2:'2018234235059_0121',3:'2018263035959_0123',4:'2018292075959_0124',\n",
    "       5:'2018319095959_0125',6:'2018349182459_0126',7:'2019006130736_0131',8:'2019032160000_0136',\n",
    "       9:'2019058134432_0139',10:'2019085135100_0140',11:'2019112060037_0143',12:'2019140104343_0144',\n",
    "       13:'2019169103026_0146'}\n",
    "lcs=[]\n",
    "if type(sector)==str and sector=='all':\n",
    "    epochs=list(epoch.keys())\n",
    "else:\n",
    "    epochs=[sector]\n",
    "    #observed_sectors=observed(tic)\n",
    "    #observed_sectors=np.array([os for os in observed_sectors if observed_sectors[os]])\n",
    "    #if observed_sectors!=[-1] and len(observed_sectors)>0:\n",
    "    #    observed_sectors=observed_sectors[np.in1d(observed_sectors,np.array(list(epoch.keys())))]\n",
    "    #else:\n",
    "    #    observed_sectors=sector\n",
    "    #print(observed_sectors)\n",
    "for key in epochs:\n",
    "    fitsloc=\"https://archive.stsci.edu/missions/tess/tid/s\"+str(key).zfill(4)+\"/\"+str(tic).zfill(16)[:4]+\"/\"+str(tic).zfill(16)[4:8]+\"/\"+str(tic).zfill(16)[-8:-4]+\"/\"+str(tic).zfill(16)[-4:]+\"/tess\"+epoch[key].split('_')[0]+\"-s\"+str(key).zfill(4)+\"-\"+str(tic).zfill(16)+\"-\"+epoch[key].split('_')[1]+\"-s_lc.fits\"\n",
    "    if requests.get(fitsloc, timeout=600).status_code==200:\n",
    "        print(fitsloc)\n",
    "        with fits.open(fitsloc) as hdus:\n",
    "            lcs+=[openFits(hdus,fitsloc)]\n",
    "            x+=[ix]\n",
    "            y+=[iy]\n",
    "            yerr+=[iyerr]\n",
    "            '''\n",
    "            with fits.open(fitsloc, mode=\"readonly\") as hdulist:\n",
    "            time = hdulist[1].data['TIME']\n",
    "            sap = hdulist[1].data['SAP_FLUX']/np.nanmedian(hdulist[1].data['SAP_FLUX'])\n",
    "            pdcsap = hdulist[1].data['PDCSAP_FLUX']/np.nanmedian(hdulist[1].data['PDCSAP_FLUX'])\n",
    "            bg = hdulist[1].data['SAP_BKG']/np.nanmedian(hdulist[1].data['SAP_BKG'])\n",
    "            bg_err = hdulist[1].data['SAP_BKG_ERR']/np.nanmedian(hdulist[1].data['SAP_BKG'])\n",
    "            if np.nansum(hdulist[1].data['PSF_CENTR2'])==0.0:\n",
    "                cent = np.sqrt((hdulist[1].data['MOM_CENTR1']-np.nanmedian(hdulist[1].data['MOM_CENTR1']))**2+\n",
    "                               (hdulist[1].data['MOM_CENTR2']-np.nanmedian(hdulist[1].data['MOM_CENTR2']))**2)\n",
    "            else:\n",
    "                cent = np.sqrt((hdulist[1].data['PSF_CENTR1']-np.nanmedian(hdulist[1].data['PSF_CENTR1']))**2+\n",
    "                               (hdulist[1].data['PSF_CENTR2']-np.nanmedian(hdulist[1].data['PSF_CENTR2']))**2)\n",
    "\n",
    "        sectlcs+=[np.column_stack((time,pdcsap,sap,bg,cent))]\n",
    "        print(fitsloc)\n",
    "        '''\n",
    "lc=lcStack(lcs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128ec0860>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SWJcGR7H9SYlyM6qe/dlDS1WLUPOVEUWxIh1PfUSRSuNVFKf2wWXet1FOg6+O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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vanderburg LC at  http://archive.stsci.edu/missions/hlsp/k2sff/c02/203300000/11200/hlsp_k2sff_k2_lightcurve_203311200-c02_kepler_v1_llc.fits\n"
     ]
    }
   ],
   "source": [
    "vand=openVand(203311200,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=fits.open(\"http://archive.stsci.edu/missions/hlsp/k2sff/c02/203300000/11200/hlsp_k2sff_k2_lightcurve_203311200-c02_kepler_v1_llc.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO  [everest.user.DownloadFile()]: Downloading the file...\n",
      "INFO  [everest.user.load_fits()]: Loading FITS file for 203311200.\n"
     ]
    }
   ],
   "source": [
    "import everest\n",
    "st=everest.Everest(203311200,season=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://archive.stsci.edu/missions/tess/tid/s0013/0000/0000/6389/8957/tess2019169103026-s0013-0000000063898957-0146-s_lc.fits'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitsloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t=np.arange(0,27,0.1)\n",
    "t=np.hstack((t[:30],t[40:100],t[140:200],t[208:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t0=1.5\n",
    "dur=0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=np.arange(0,27,0.1)\n",
    "def PeriodGaps(t0,t,dur=0.5):\n",
    "    # Given the time array, the t0 of transit, and the fact that another transit is not observed, \n",
    "    #   we want to calculate a distribution of impossible periods to remove from the Period PDF post-MCMC\n",
    "    # In this case, a list of periods is returned, with all points within 0.5dur to be cut\n",
    "    dist_from_t0=np.sort(abs(t0-t))\n",
    "    gaps=np.where(np.diff(dist_from_t0)>(0.9*dur))[0]\n",
    "    listgaps=[]\n",
    "    for ng in range(len(gaps)):\n",
    "        start,end=dist_from_t0[gaps[ng]],dist_from_t0[gaps[ng]+1]\n",
    "        listgaps+=[np.linspace(start,end,np.ceil(2*(end-start)/dur))]\n",
    "    listgaps+=[np.max(dist_from_t0)]\n",
    "    return np.hstack(listgaps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([25.4]), array([20.])]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[PeriodGaps(t,t0,dur)]+[PeriodGaps(t,20,dur)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AC4E1aXoNcF2hvjbKtgLnSpoMXANsjogjEfEusBmY16C2mZkZ9QmAAJ6VtF1SKdUmRURP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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=plt.hist(np.arange(-100,100,0.001))\n",
    "n=150\n",
    "_=plt.histnp.exp(np.linspace(np.log(n+-100),np.log(n+100),8000))-n)("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cy1p14MCBse/znIZ8kg3A7wJXA5cC1ye5dFz7/z+P9vOP7VjWnl7GAY5lrVr3IQ9cBsxW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\n8pLUMUNekjr29/PD0RdQGSetAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_bound=-50\n",
    "max_bound=10\n",
    "\n",
    "high_num_fact=(np.max([abs(min_bound),max_bound])+0.33*(max_bound-min_bound))\n",
    "\n",
    "_=plt.hist(np.exp(np.linspace(np.log(high_num_fact+min_bound),np.log(high_num_fact+max_bound),8000))-high_num_fact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pm.Bound?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_num_fact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import celerite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 5)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PvUF8T9Gk/HYJxTR7O8gxMXlfhxabL+j3QsdumcJuwrGLv4fIcWxnGL/oEJgm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\nMxdbN8yp9Hl8FbAbJlq0FRoIa718bb5JbDWLSFLjQuYCmqHx4MSD6G1V4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AAAIABJREFU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\ndkRERERERHPu/wPqeuyU74l+ZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "per=0.1/(np.pi*2)\n",
    "power=0.01\n",
    "logS0=np.log(power)-4*np.log(1/per)\n",
    "Q=1000\n",
    "kernel = celerite.terms.SHOTerm(log_S0=logS0, log_omega0=np.log(1/per), log_Q=np.log10(Q))\n",
    "gp = celerite.GP(kernel, mean=1.0)\n",
    "gp.compute(np.arange(0,20,0.05), np.tile(1e-8,20*20))\n",
    "for n in range(5):\n",
    "    plt.plot(np.arange(0,20,0.05),gp.sample())\n",
    "plt.xlim(0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10ebbf320>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FVIrUCESVEbJEsGDBApYtW4bZbC62PT8/n//85z8sX74cs9nMI488wsqVK7n88stRVbVE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\n82RhNZiSkrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "x = np.linspace(-3, 3, 100)\n",
    "for lam in [0.5, 1., 2.]:\n",
    "    pdf = st.expon.pdf(x, scale=1.0/lam)\n",
    "    plt.plot(x, pdf, label=r'$\\lambda$ = {}'.format(lam))\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('f(x)', fontsize=12)\n",
    "plt.legend(loc=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pm.Exponential?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
      "       1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 6.309573444801929e-05 vmax 1.5848931924611136\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-06, 3.e-06, 4.e-06, 5.e-06, 6.e-06, 7.e-06, 8.e-06, 9.e-06,\n",
      "       2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
      "       1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00027537826206635777 vmax 16.46991021905477\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-05, 3.e-05, 4.e-05, 5.e-05, 6.e-05, 7.e-05, 8.e-05, 9.e-05,\n",
      "       2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])\n"
     ]
    },
    {
     "data": {
      "image/png": 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JS2jP8eveQwl4cS65FaW0oHLZoE5JfHD7QK7snsobq3P530XrOXC8LJzhh4U8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\nCKQYCCGEAP4f06EGdEj6JqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "#x = np.linspace(0, 20, 200)\n",
    "x = np.logspace(-4, 0.0, 200)\n",
    "alphas = [1., 2., 3., 7.5]\n",
    "betas = [.5, .5, 1., 1.]\n",
    "\n",
    "a=1.0; b=2.50\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "a=1.0; b=5.0\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "a=1; b=10.0\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('f(x)', fontsize=12)\n",
    "plt.legend(loc=1)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-15, 1)"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
      "       1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.00630957344480193 vmax 158.48931924611142\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-04, 3.e-04, 4.e-04, 5.e-04, 6.e-04, 7.e-04, 8.e-04, 9.e-04,\n",
      "       2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-17, 1.e-15, 1.e-13, 1.e-11, 1.e-09, 1.e-07, 1.e-05, 1.e-03,\n",
      "       1.e-01, 1.e+01, 1.e+03])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-15 vmax 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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aNASA7t17cPLkiXr9Skg4QdeusUyaNIbg4FAeeeTv1Y7XJ/aj6tkLVc9epPQb\niO3FZ3lRq8OWn8edhw4QveU3UCpR9R/EQbuVgdeOa7RPzYGcHGRkXEAURJ7u+Sz3br+L14++wpuD\nljg1vfVKJEMgRePew2vNHRi3P0vJ6DdctiUIAk+P7cz5QhMLN56iva+O7iFOSqDWg0It4h2spyCz\njILMMrxdTBANsX//Xjp37kJkZMUW/jablenT76x2Tm3COvnnzrD1o7fZ+O5LDJ5+L6NGjSEj43yd\n9ZSWlmIwXFKjFEWxTjVKh8NBdvYFJk6czJgx41i8+GW++eaLajKhjRH70Wq1zLjj7moyoJ8tehP7\nts2YN/9KYcY5HAf3U7RrB9pJN9brU3MhJwcZGRcJ0AUyt+vDvH70FdadXcPkiJpdFs5gbTecsn4P\nYzjwLtawoZi7THXZlkohsmhyLHd/cZB/rDnOFzP74qN3bYqrOeaWWu/yS7PK2PJxAoqzImPu74rO\n0z1TaC/SFJnQiU8uZNsn77Lrq+UUZp4jZNC1ddbTGjKhtcmAFgYEEPTQI+jnPoz38//EXlaG9dhR\nLNu3YhfA/NEHCJMmo6hnPMSdyMlBRqYJXB9+A7+e38gHJ//N4IBrCNAFNlyoHsoGPo76/G6M2/4P\na+hgHB61q381Bi+dildvjOW+r+P457qTvDuth1OSow3aD9Iz/O5otnx8km2fnuLa2TGode5rUpoq\nEzp6zpPs/+4Ljv+2nrTkpIo9qmqhNWRC65MBFQSB3qNGs3Pndq5f/SNx33xJh2++oPyrzyj/YgWq\nQUPQ3X4Hqv4Dm3U9hbzdoYxMExAEgce7/wObw8bbx15v/K6xdSEqKRrzFoLDjsfmJ5q8Y2tMkAdP\njYli75kClv2e2jTfasE3zMDQOzpTnGtix5ensVlrb4BdYdy4CcTFHWLGjKlERXWme/cevPNO47vb\nRIWCgbf8hYG33kPmqWMUZZ3HUlahV19UVMj8+U8CMGLEtajVah588F7ee+9NHnnkcXJzc/jXv56p\nYTMxMQGz2cTtt9/M3Ln3MWHCJDp3jnb6d7vhhpsoKSlm7tz7WLDgGZ55ZgFlZaU1fJo77wHe376F\nx/+9HJ/Va9HfNwfb6VMUPf4wBbPuwLRuLY7KGVDu5qqYyhofH88XX3yBJEk8+eST+PvXnM0hT2Vt\nOeR41WRV8le8f3IJC/q8yKiQMdWOuRIvbfxneGydT/HIVzB1n9lk/xZuOMUP8Zm8O607Qzr4Ntne\nlZw9mseu/yYR1tWHa27v1KStNprj+ko9uIsdn/4br+Awxv71GXSe9c/iassyoZLFgvnXjZSv/Ap7\nUiLafv0xvv0fl2xd9SukD0h5llMAACAASURBVBw4QOfOndmxYwdqtZqxY2s+ssnJoeWQ41UTu2Rn\n7s77KLQUsGLE1+iUl1bruhQvScJrzR2oMg+QN/0XHF7tm+SfyWrnL18eotBk4+u7XR9/qI+E3zM5\n/NNZuo4MocfYcJftNNf1df7EEbYsfxOdlw/jH30WvXfdSfJqkAmVJAnrgX0YtEqs3fu6ZKO+5NAm\nu5VWrFjBnDlzmDNnDkuXLqVfv34kJSXx8ccfExPj/JbJMjLNjUJQ8Ejs42SbLvBV0mdNNygIFI9+\nHUlUuKV7SatS8OLEGIpMVl7aeLrp3V+1ED0kiI79/DmxNYMzR3IbLtDChHbtybh58zEVF7LhnRcp\nK8yv81ylUlkjMQB8++26NpEYoKJLU91/IIZhw5rFfptcBNe7d28mT57M5MmTGTBgAEeOHCE2NpYJ\nEyawfPlyRowYUaOMvAiu5ZDjVTuBuiDOlZ5lffpaRoeMq9ra29V4SRpPJK0P+qOfYPdsj90/tkn+\n+RnUaJUKVh46T4BRQ9eguu8aXUEQBIKjvMhOLSZx7wWCojzRuzCDqTmvL4OPH0FRXTm1YxNnD++j\nfZ9BVYvlrlaaaxFciz85HD58mJkzK/pQHQ4HCxYs4Pbbb2fmzJmkpaXVWqa0tJT58+ezePFibrjh\nhpZ0V0bGKR6I+StKQcV/TtTcTsEVTLEzsAb3w7jzBQRT3Xe6jWVGvzAGRnjz5uYkzuaXu8HD6iiU\nItfMiEJnVPH714mYStveTURgZDRj5v6D0vxcNr67EHNpSWu71CZp0TGH5cuXs2bNGnQ6HatWrWLj\nxo389ttvLFq0iLi4OJYtW8bSpUtdsl1ebkGpdG0zMIVCxG533yyLPzpyvOpnxfFPeDfuHd4b9W+G\nhg5teryyjqH8aBRSrzuxT3q7yf5lFpmY+N4OuoV48tk9A5plOmTO2WJ+ePMQIZ29uf7BHk4NULfU\n9XX2+BF+WPw8QR2jmPLMCyjVdd9Ft2WaEi+Vqu42s0U7zyIiInjvvfd46qmngIqB5uHDhwMVXUnx\n8fEu2y4pcX06lzzA6hxyvOpnUvBUVutX89aBN4nR9cTPx6Np8dJ0xNBrNvq4ZRRGTm2SehyAFnh4\neEde/uU0n25P5uaeIU2yVxtKDwW9J0ZwYE0au9YkETuq8es1Wur68giNYvhf5rH143dY+/ZiRt73\ntxob9l0NNNfGey3arTR+/PhqgzklJSUYjZeWrSsUCmyVe6LLyFytqEQV90Y/QHJxEr+e2+gWm6UD\nHsduDMVj69PgsDfZ3s09gunXzot3tiWT3YQbq/qI7B9ARE9fjv12jgvJRc1SR1Np32cQA6bdzdkj\n+9m76pNmGai/WmnV2UpGo5HS0tKqzw6Ho83MBJCRaQojQ0YT7RnDJ6eXY7G7PlmiCrWBkqELUOae\nrNoErykIgsD8cdFY7RKLf01sun911NHvxg4Y/bTs/l8y5rK2eePXddQEuo+7kVM7f+XElp9a2502\nQ6smh759+7Jt2zYA4uLiiI52fqWhjExbRBRE7o+ZS1Z5Jv89varhAo3A0mkS1pABGPa8jmBp+iBq\nhI+O+4e0Z0tiLltO57jBw5qoNAoG3xqJudTGwbW1TzhpC/SZfDsRvQZw4NsvOH/iSGu70yZo1eQw\nbtw41Go106dP55VXXuGZZ2ouV5eRuVrp5z+Afv4D+OjYh5RY3TAjRhAoGboAsTwb3UHXVsReyZ39\nw4n00/PWliRM1qZ3V9WGT6iBbteGcjY+r02ufwAQRJGhdz+EV3A42z55l6LszNZ2qdW5KlZINwZ5\nhXTLIcer8ZwqTODBnfdwd9S9zIqe3XCBRuCxcR6a5J/Iu3N7kzbmu8i+M/k89N+jzLmmPbOHNG0l\ndl047BKbPzpBUbaJ8fO6o/eqe/1Da15fxTlZrFv8Tw5k5ZFpV2C12Zgx4y5uusn1HXKbmz/EgLSM\nzJ+NaK8ujGk3ltWpq9zz9ACUDql4wjbsfsUt9gZE+DAm2p8Ve8+SWWRyi80rERUCA6dF4rBL7Ps+\npcUHfo8di2fevAcaPM/DPwivQaM5l53D/WOGs3Dhq2zfvsUtdTocDl577WXmzLmHefMeID39bLXz\nGzre0sjJQUammZndfTalthK+S/uvW+w5PMIo7zUb7anvUOQcd4vNR0dGAvDO1mS32KsNDz8tPa8L\nJyuxiDOHG9e9lJycxKOPPsT06VNZseJD3nprMSdOHHOq3i+//JRXX30Ri6VxEwOOp57hunHXkbhv\nByuWvsOoUaOdqq+uOrdv34LFYmHZsk948MGHWbLkrWplGjre0shTg2RkmpkuPjEMCRzG6pSVTOtw\nG3qlock2y/o8iDb+Mwx736Bo4kdNthfiqeUvA9vxwe9p3HK2gH7tLu1aujH9J35K/7HJdUDFFlEF\nfUpJ3j2Yx6Jno9HX3QS5IhMKNYV1GpIJvZKEhBPcdNNUXv9+DUZVOvOu2JnV1TobkiN1Vq60uZGT\ng4xMCzAzahYP/T6bH9K+ZUanpm/BLWl9KO/9AIa9b6C8cARbYM+m+9g/nO+PZPDethQ+uaN3s6yc\nFgTw8NdiO+fg8M9nGTi1Y53nuioTeiUNyYRezkUZ0EmTbuSaAQN58sG7efv5p1m47EuUanWT6mxI\njtQZudKWQE4OMjItQIx3LAMDBrMq5Wtubn9LtS29XaW812x0hz9Cv/d1im5o+k6wWpWCOUM78OKG\nU2w+ncPo6AAArgu/nuvCr2+y/cs5Yk7n5LYM2vf2Iyiydn3rpsiEXn4X7wyXy4D6BAYz/LobOLjp\nRw6tXcmAaTObVGdDcqTOyJW2BHJykJFpIe6KuodHds3hxzPfc2vkjCbbk9QelPV5EOPuRSgzD2AL\n7tdkm5Nig/hifzr/3pHKiCh/lG6UFb2c2FGhpMfncWBNKuPndUehrDn82VSZUFe4UgZ0X3w8Y0aN\n5cSWn4jo1Z+gqK4u19mQHKkzcqUtgTwgLSPTQnT36UFv376sTl2FzeGe1cLlPe7BofPDsOc1t9hT\niAJ/HdaBM/nlrIlvvrn+SpVIn0kRlOSaSdxzodZzmioTWh/OyIDecP8jGP0C2Pn5+1jNrs/mqk2O\nFC5JltZ1vLWQ1zkgz9t3FjleznF5vHZl7eT/DjzJP3s/z+jQcW6xrzu0DOPvL5I/7Qe3PD1IksTs\nbw5zvtDEd/cNQFvPzp1NZfvnp8hJK+H6v/VAa1QBLXN9OSsDmpV4kg3vvED0sDEMvv2+ZvXNWeR1\nDjIyfwAGBQ4h3BDBf1O+dttc//Jud+HQeKF306ppQRCYN7wjOaUW/hvXuIFcV+k1oR02q4P4X881\naz21cccdNScGnDmTRkREhxrfB0XF0HXU9ZzavonMU85Npb1akZODjEwLIgoit3S4nYTCk8Tnu2kP\nH7WB8h6z0KRsQJF32i0m+4R7Mai9N1/sT2+2bTUAPAN0RA0KJPlANvkZLfc06ooMaJ/Jt2P0C2T3\nyo+x1zL+8UdDTg4yMi3MdeHX46nyZFXK126zWd7zXiSlFt2h991m877B7ckrs/Ld0ebdZ6jbtaFo\ndEri1p9p01tmK9VqBt42i6Ks8xz71T3rPtoycnKQkWlhtAotkyOm8HvWds6VprvFpqTzw9R1OtpT\n3yKWuKcrqE+4F33Dvfh831nMtuZTZlPrlHQbHUZ2ajGZpwubrR53EN6tD+37DOLohu8ozslqbXea\nFTk5yMi0AlM63IJSVLI61T3beQOU9Z4DkgNd3Idus3nf4AiySyysbcaZSwAd+/lj8NFw9JdzSI62\n+/QAMGDa3Qiigr2rVrTpJ52mIicHGZlWwFfjx8jg0fxy7ifKbe7pa3d4tsPc+UZ0x75AMLvnDnxA\nhDc9Qjz5dO9ZrM2o66xQinQfHUpBZhnJcdnNVo870Hv70vuGWzl3PI6zR/a3tjvNhpwcZGRaiRsj\nplBqK+W3jE1us1neew6CrQztCfc8kQiCwH1DIsgsNvPT8drXI7iLdj398ArUsX9dKg57274jjxkx\nHq/gMA58/xX2P6i0sZwcZGRaiW4+PehojGRt2vdus2kL6I41ZAC6oytAcs+d/jUdfIgOMPDFgfRm\n7UYRRYHuY8MovFBOalzzKNO5C1GhoP+UuyjOziRhu3t0wtsacnKQkWklBEFgcsQUThWdJKHAfTtw\nlve4B0VRGuq0zW6xJwgCd/YPJyW3jN1p+W6xWRehMd4EdvDg2G/nsDfjILg7CI3tRUhMD4789C3m\nUvdodbQlrorkcPLkSe68806efvppdu/e3druyMi4jXFhE9AqdKw9476nB3Pk9dgNQeiOfuw2m+O6\nBOBvUPPV/uZdrCYIAv0ndqC8yEpqXNuUFL2IIAj0n3IX1vIyjvz8bWu743auiuRw+PBh/P39EUWR\nzp07t7Y7MjJuw6AyMCZ0HL+e30iJ1fUtYKqhUGHqNhP1ma0oCtwj3qNSiNzWJ5Tdafkk5pS6xWZd\nhMX44BOm5+S2jDY79vDhh+9z9923M/fvfyPbM4iEbRspupDR2m65lTaZHFasWMGcOXOYM2cOS5cu\npV+/fixcuJD777+fjz5qurCJjExbYnLEzZgdZjae+9ltNsu73YkkqtEeXeE2m1N7hqBRinx9wD1r\nM+pCEARiR4RSmm/mbHyeW2zWJxPqrDznnj27OHUqgU8++YqXXlrM2VIzokLJ4fWrG+2Pq5KhNpuN\nF198locems3999/Njh1bAbj33juZN+8B5s17gJdffr7RftRHm9yye9asWcyaNavq89q1awkICMDL\nywu7vfmW8svItAbRXjF08Yph/dm1TO1wq1tsSvoAzFE3oD2xirJBTyGpjQ0XagAvnYobugWxJj6T\nh4Z1xM+gdoOntRMa441noI5ta/ez/+PvycrKYsKEieTn5zFhwqRqanAN8eWXn7Jhw3q02to1NC6X\n54yPP8qSJW+xaNGbddrbsWMbEyfegM1mY/XqlYwZex0htjKO/fojPcbfjHdIeIM+NVRnXcc3bFiP\np6c3zz77IkVFhcyadQfjxo1GkiSWLPmg0TFpDC2eHA4fPszrr7/O559/jsPh4LnnniMhIQG1Ws3C\nhQtp3759jTJhYWG8+OKLqFQqHnrooZZ2WUam2ZkQPol3jr1BYtEpojyj3WKzvMdf0J76Fk3iWkyx\nrutHmH5eh2ndWgDusdkZmFHMhUMfofDUumxTO2ky2gmT6jwuiAKdhvjx7/lP839PvUif4V2bTSbU\nWXnOhIQTdO0ay6RJYwgODuWRR/4ONgsJ23/h8Pr/4Yjs3qBPrkqGVmhJjAEqds9VKJQkJJzEZDLx\n2GN/xW6388ADf6V79x71/g6NoUWTw/Lly1mzZg06XUUG37RpExaLhZUrVxIXF8eiRYtYunRpjXJ9\n+/alb9++9do2GjUola5tLaxQiHh7610q+2dEjpdzNCZeN+tu5D8n3mVL9i/0j+hd77mNxmsYkn8X\nDKdWor3G9W2mi/RqbJViPEqliJdeRW6phXAfPa4qier1ajzriMnFeJWIZwj170B5io7AaT7Y7TZm\nz76vWiynTLmRKVNurLeum2+ezLlz51Aqa/87WK1mgoL8qo4plQqMRnWtG/A5HA5yc7O5447bmTr1\nJp5//jl++GEVc+Y8SJ8Jk9n3w3+ZccuMBn1qqM66jl/8XFpaynPPzedvf3sUg0HPfffdx7Rpt5CW\nlsaDD87hxx/r3kCwsbRocoiIiOC9997jqaeeAuDAgQMMHz4cgN69exMfH++y7ZISs8tlZX0C55Dj\n5RyNi5eKIYFDWZ+yjlkd56AU3fOvqetyO8adL1CQHIfd18UnkhHjMI64pD1RkpjDEz8c57UbYxnV\nuebOpo3BAXXG5GK8Dh85Svfe3cg+U8yezcfQanVERsZUK9dYyc6ionJsNketdapUGi5cyK86Zrc7\nKCmxAJYa56amphAaGl51bpcu3cjLy6WgoIzIodcRt3Ed7y9+mYTC8np9aqjO+o5nZWUyf/6TTJly\nC0OHjkavV2I0+lFYWI63dyAeHh4kJZ0hKCi41vheTn16Di2aHMaPH096+qXBrJKSEozGS32hCoWi\nVQW1ZWRak3Fh17Mtcwt7s3dzTdAwt9g0dZmGYdcraI9/Q+mw2rtVnGVopB+BRjWrj2S4nBwag0ql\nwq4uQK1X8p9/v9dsMqHOyHNeKSO6adMGHn30CQA0eiOxoydyeN3/eP6pl/CLiHS5zrqO5+Xl8vjj\n83jssafo338gAN9++y3x8cd54omnycnJprS0tNbtyJ2lVWcrGY1GSksvTYtzOBxyYpD50zIoYAje\nam82nlvvNpuSzg9Lx+vQJvwP7DXvhF1BKQrc3COE3an5pBeUN1zARcaNm8CRI3F8tvtf6O2BxER3\nc5tM6EVpTqhdvtMZGdHOnS89kXUddT1qnYGjG36ot/7a6mzIJ4DPPvuE4uJiVqz4sGp20qRJkygp\nKWbu3PtYsOAZnnlmgVva0RaXCU1PT+fxxx9n1apVbNiwgc2bN7No0SLi4uJYsmQJH37o2o6Sskxo\nyyHHyzmcideS42+z9sx3/Hf0WjzVnm6pX5W2Ge8fZ1I4/n0sUTe4xeaFYjM3Lt/Dnf3b8fCIjm6x\neZEr41VeZOHHN44QNSiQPhMj3FpXXTgrI3o5h9as5OgvP3DTP1/HKyi0uV39Y8qEjhs3DrVazfTp\n03nllVd45pmamVpG5s/E+LDrsTqsbM74xW02re1GYDeGojvhPnGhQA8Nwzv5sTY+s1l3awXQeapp\n192HlIPZWE0tN5XdGRnRy4kZNR5RobzqBYFaPDmEh4ezalXFAJIoirzwwgt88803rFy5kk6dOrW0\nOzIybYooz2giPTrxixsXxCEqMMXchurMNsRi92lCT+0VQn65lc2nm3+TvOhrgrGZHaQcapntvF2R\nEb2IztObqMEjSd67nbLC5t2LqjlpkyukZWT+rAiCwOjQcRwvOEZmufu2YzDF3IqAhOa0+/ZwGtTe\nh1AvLT80s4wogG+YAf8II6d3XcDRxsWAALqNuQHJbufE5p9a2xWXkZODjEwbY1RIxSKnrRnu2VUV\nwOHVHmtQX7SnvnObTVEQmBQbyL4zBWQWmdxmty46DwmiNN9MVmLblhIF8AgIIqLPIE7t2ISl/Ooc\nn5OTg4xMGyNUH0YXrxi2uFEECMAUPQVl7gkUuSfdZnNibBAS8POJ5hUCAgjr6o3WqCRpX9tWirtI\n93E3YjWVc2rnr63tikvIyUFGpg0yMmQMCYUnOV/mvi2yzVGTkQQF2lPu61oK99bRO8yTdcezml1P\nWVSIdOjjT8apAsqK3DMttznxa9eRoM6xJGzbiMPRtrUpakNODjIybZBRIaMB2JLhvrtOSe+Ptd3w\ninEHN6nEAUyKDSI1r5zjmW7acrweIvsHIDkg9WDbVoq7SMyI6yjNyyE9/mBru+I0cnKQkWmDBOtC\n6Ordza3JAcAUfTOK4nSUmQfcZnNslwA0SpEfj2W5zWZdGH21BHXyJPlA9lUxMN2uZ3/03r4kbN3Q\n2q44jZwcZGTaKKNCxpBYdJqzJWfcZtPScQKSUuvWgWmjRsnITn78kpCNpQWkPSP7B1BWYCErqajZ\n62oqokJBl+HjyEiIpyCzeVX03I2cHGRk2igjg68FYEumG7uW1EbMHa5Dk7gW7DX3KnKVSd2CKDTZ\n2JHiHnGe+giN8UZjUJK8r/kHwd1B1DXXIiqVJGzb2NquOIWcHGRk2iiBuiC6+/Rka8ZvbrVrjp6C\naMpHnb7dbTYHtffB36Dmp+PN37WkUIp07OvP+YQCyotbZ2D6okzo9OlT+eGH+vWjdR5edOg7hKQ9\n266qaa1ycpCRacMMCxpBcnESGWXuW9lsiRiBQ2VEney+BVoKUWBslwB+T8mjxGxzm9266Ni3YmD6\nzJH6n1QaIwFqsVh47rn/44EHZvHYY3/l7Nn6u/GulAndvn1Lg/7GjByPzWwiac+2at/XJV/qrHRp\ncyAnBxmZNszQoBEA/J7lvrt8FBosHcagSd4ADvc15GOj/bHYJbYl5brNZnJyEo8++hDTp09lxYoP\neeutxZw4cQwPfy2+4QbS4uqftXS53OaDDz7MkiVv1Thn7drv0On0fPDBCh577EneemtxvTavlAkd\nNWp0g7+Hf/tO+EVEkrhrc9WU3y+//JRXX30Ri6Xm009j/G5u5P2xZWTaMGGGcNobO7Lzwnamdbzd\nbXbNkdejPf0Dqoy9WMOuqffc1EM5pDRi6qiExMxyLefXprN5V/17CnXs60+HPvVrDpjNZhYseJoX\nXlhEWFhYDZnQ9r39WPXx93z7wNso1Zfucy8X1mmMBGhKSgqDB1fEICKiA6mpKfX6VatM6GXUJUAU\nNWQUe1Z+TO6ZZPzbd6pXvtRZ6dLmQE4OMjJtnKFBw/km+UuKLEVu28bbEnEtkkKDJml9g8mhsQgI\n+OhUZJdYsDskFKKLGqKV7N+/l86duxAZWbEhp81mZfr0O6uOR3T3JTq4HzcMmUivCe1qtVFaWorB\ncElQTBTFGoJinTtH8/vv2xkxYhTHjsWTk5ON3W5HoagpO+xwOMjOvsDEiZMZM2Ycixe/zDfffMGs\nWbOrzqlLgMhS1pH9335O4q4t+LfvxKhRY8jIqL27sDF+NzdycpCRaeMMDRrOV0mfsSf7d8aFTXCP\nUbUBS8Qo1Mk/w/AXQKi7h7lDn4bv8i8Sn1HEPV/FEX2NDzd0a1imsj5On06gc+cuAOTkZKPT6enZ\n85K+tsagIkdxkm+Xvonfj5ca0sufHAwGA2VllwaBJUmq0cBOmnQjaWkpPPTQbHr06EWXLjG1Jgao\n2LI7PLwiEWk0Wnr06EVeXvVutPqkS9v3HkTK/p30n3oXSrWmzt+9MX43N3JykJFp43Tx6oqfxp+d\nWdvdlxwAc6fr0aRsQJkVhy24r1tsdgv2IMRTw6aEnCYnB5VKRU5OxXTV999fUqtM6A1TJ+Jn7cKI\nv0QTHOVV43hjJEBPnjxOv34DeeSRv3Py5HGysureDbc+mdCL1CddGjXkWpL37eBM3F4iBw6vsx5n\npEubC3lAWkamjSMKIkMCh7Ivew8WN0l9Alg6jEMSlWiS3SdLKggCY6MD2J2WT5Gpaesoxo2bQFzc\nIWbMmEpUVGe6d+9RQyY0tIs3Kq2CtLjaB8Hrktu8XJIzPDyCVau+Zs6ce1i+/H0efth1mdCGCOrc\nFQ//IE7v2lLjWGNkQluSFpcJbS5kmdCWQ46Xc7gjXrsv/M78/U+waMCbDAwY7CbPwGvtnSgKUsm7\nawcITRsjuMiJrGLu/uIQz14XzY09nH96cDZe+39I5cyRXG78R2+U6tq7g5ylKTKhDXF0w/ccWruS\nKf96G4+AoKa6+seUCW0sK1as4Omnn2b69Ol8+eWXre2OjEyL09evH1qFjp3unNIKmCMnoihKQ5Hr\nvtkwMYFGwry0bDrVMltrt+/lh83i4PzJArfadVUmtCE6DRqBIAgk7t7SJDvNzVWRHGbNmsULL7xA\n586dmTFjRmu7IyPT4qgVGgYGDOL3rO1u3Rrb3PE6ADSp7tOsFgSBUVH+7D9bQKml+RfE+UcY0Xmo\nOBvvvq07miIT2hB6b19CYnqQsn9ns29z3hTaZHJYsWIFc+bMYc6cOSxduhSAdevWMW7cOESxTbos\nI9PsDAq4hlxzDinFyW6zKekDsAb2Qp3q3t1fR0b5YbVL7Eppfg1lQRQI6+ZD5ulCrGZ7s9fnDjr2\nH0pJbjY5Kadb25U6aZMt7axZs1i2bBnLli1j7ty5AOzbt4/hw+se3ZeR+aPTP2AQAHtzdrvVrqXD\nWJRZhxDK3beyuUeoJ15aJVvduFq6Ptp188Vuk8hIcG/XUnPRrmd/FCoVyft3trYrddLiyeHw4cPM\nnFnRl+dwOFiwYAG33347M2fOJC0trc5yJpMJwU0DZjIyVyMB2gAiPTqxL9vNyaH9GAQk1Gnu06xW\nigLDOvmxMzkPm735t/H2jzCi9VBx9ljzP6m4A7VOT3j3vqQd3I3D3jafdlp0ncPy5ctZs2YNOp0O\ngE2bNmGxWFi5ciVxcXEsWrSoqhvpSt588816bRuNGpRK12YqKBQi3t56l8r+GZHj5RzujNew8GF8\nlfAlagPoVW76G3gNRDIEYTy/Bd3gu91jE5jYM5R1x7JILDQzONKv0eVcjVenPgGc3JWJQadBpXHP\nrKXmpPvI0aQd2kPxudO07+n6OpPm+n9s0eQQERHBe++9x1NPPQXAgQMHqrqKevfuTXx8vMu2S0rM\nLpeVp2Y6hxwv53BnvHp69OMzx6dsSd7BNUHD3GITwBgxCk3SegpyC0GhcovN7v561AqBdYfPE+Or\na3Q5V+MV2NmTY9vOc2LveSJ6ND4ZtRbe7bui0uk5uuU3vCJiXLfTGlNZLRYLn3/+OTNnzmTYsGGM\nHDmSe+65h5UrV9a6k2BDjB8/vtpIf0lJCUbjpWXvCoUCm635ZzfIyFytdPfpiVahY1/OHrfatXQY\ni2gpRpW5z2029WoFA9v7sC0xp0Vm5fhFGNEaVaTHXx1dSwqViva9B3Hm8D5sLrSnzU2dyWHv3r1M\nmzaNpKQkZs+ezWeffcZHH33E3XffTUJCAtOmTWPXrl1NqtxoNFJaWlr12eFwtPj+ITIyVxNqhZo+\nfv3cPu5gDR+OJKrcPmtpRCc/zheZScpp/idNURQI7+ZDxulCbJa22Y9/JR37X4PNbCI9/mBru1KD\nOpPD5s2b+frrr3nuuecYOXIkkZGRREVFce2117JgwQK++uortm7d2qTK+/bty7ZtFeIXcXFxREc3\nfhm6jMyflQH+gzhfdo5zpelusympjVhDB6NOc6/q3PBIXwC2JjW85bc7CO/mg93qIONUYYvU11SC\nOsei8/Qm9cDvre1KDepMDv/4xz+qdflcTllZGR4eHjz99NNNqnzcuHGo1WqmT5/OK6+8wjPP1NzL\nREZGpjoDLk5pdfes/PWCHQAAIABJREFUpQ5jUOafRiyse9ags/gbNXQP8WBbUvNrSwP4R3ig1inc\nvlr6SpyRCa0PURSJ6D2Qc8cPYzWb3Ohh02lwKut9991HQcGlQB89epQpU6a4XGF4eDirVlVsZyuK\nIi+88ALffPMNK1eupFOnTi7blZH5sxBmCCdMH94MU1orFM3UZ9w3pRXgmo6+nMgspqCsaRvxNQZR\nIRAS7U3GqQIc9kvjHHXJcV5OY84B12RCr+RyGdCPNmwir6SE8yeOVB232Wy8+OKzPPTQbO6//252\n7LjUS3PvvXcyb94DzJv3AC+//LzTdTeWBjv4e/bsydSpU3n99deJi4vj448/Zv78+c3mkIyMTMMM\nCBjMz+k/YrFbUCvUbrFp9+qI3aMd6rPbMfWY5RabAEM6+PDB72nsSctnfNdAp8omJyfxzjtvkJWV\nyYQJE8nPz2PChElVanC1ERrjTdrhXHLPlhDQwYMvv/yUDRvWo9XWPWOqMedcxBWZ0Cu5XAb06JE4\nFv/f3+l9eC/tew8EYMOG9Xh6evPssy9SVFTIrFl3MGzYSMxmM5IksWTJB07X6SwNJodHH32Ubt26\nceedd+Lv78+3335LYKBzf2AZGRn30tevH9+n/Y+Thcfp6du74QKNQRCwtBuGJvHHCm1psaJ5SNqz\njcRatphuLBJwa1YRqV+vZ4NvxXz8qCGj6DRoRL3lGpIJhdqFdR6YPQ9RIXD+ZAEBHTzqleO8SGPO\nuYirMqEXBYigugxoj569yTVbST96ELvNhkKprNSEGANUCP0oFBV/i8TE05hMJh577K/Y7XYeeOCv\nDBs2qEGfXaHB5LBt2zYWLlzIrFmzSExM5O9//zuvvvoqoaGhzeKQjIxMw/Ty64OAQFzuQfclB8Aa\nPgLd8a9RXjiMLbifW2wKgKdGSZHJhlT5uTE0JBMKdQvr5B9O4HxCAb0mtKtXjvMijTkHmiYTejlX\nyoCqNBrM5WVknoonLLY3en1FEi0rK+Wf//wH999fsY2QVqtlxoyZTJ58M2fPnuGJJx5h/fqfGvTb\nFRpMDvPnz+fVV19l6NChAHzyySdumcYqIyPjOh4qT6I8ozmUe4C7O9/rNruW8KFICKjPbq9KDp0G\njWjwLr8hfjyWyfM/n+LGO/rSJbD2iS5X0pBMKNR9lx7aJYBD685QlF2OZ0DjF+A1RFNlQv+/vTsN\njvI6Fzz+f/vtVu+bpNauFlqRAGMMThySS9lJhjC+48pSdR1wxaohTrlIaqZSc2/u2CwVUgHHpmIn\n+cAHymGSYZxyJnDjLIydcjwep4rERXIdYolNwmaRkEC70NItqdXbfGgEsQVGEqffVqufX1U+dKQ+\n5/ig6qff85xznhkfLgNq0s3k2excbnmH8hWp/8a+vl527vzvfOlL/8TnPpeqAFhZGaSiogJN0wgG\nq/B6vQwMDGC3z66Cd7fuGByOHj1Kfn7+jddf/epX+cQn1BUbEUIszJqCtfym85dE4hGs+u3rEc9H\n0p5PLLAKS9cf4WP/TUmbAJ+o8gNw/NLwnIPDXMqE3u5bengkwruvXebquRGlweFuy4TOmFUGtLae\nipV1dJ38Kw9s+RojI9f4l3/5r/zzPz/F/fd//Mb7XnvtKBcunOdf/3U7g4MDhMNhAoEAoZD6Q3S3\n3a20c+dOBgYGPhAYZjQ1NdHf3y9bT4XIoPsK1hFNRDlz7ZTSdqOVG7D0nYDp8J1/eY4KXVbqA06O\nd8z99PJcyoTejtNnxVtsp+fcrc87/H1JzltJV5nQGbcqA5pfv4Kjp9oZuHiOl176n4yPj3Po0P+4\nsTMpEpnikUe+QCg0zje+8TV2797Bjh2703Zw+LZlQtvb29m5cyeVlZV8+tOfJhgMkkgk6Orq4tix\nY3R0dLB3715WrVp1q7cbTsqEGkfma37SNV8TsTCf/7//kcdqHudry7cpa9fS9Ud8Rx9j9D/9L6aX\nfVZZu/uPXeLlE938v/+yHmfe7T/QVM3XqTe7aT/Ww+e334fVMb8P0HSWCb2d6NQkh7dvo+EfPsvH\n/+k/z/l9ht+t9Prrr/PKK6+wfPly3nrrLb7zne+wZ88e/vCHP7Bx40ZeeeWVRRMYhMhFDrOTRm8j\n7w6dUNputPRjJHUrlm61JUk/We0nnkjy18vG1Fwoa/SRTELvewvrL11lQm/HYrNTunwV3af+tigq\nxN02nL766qs89thjvP7667z00kuzfi4V2YTIvPsK1vGLiy8zEQvjMDvVNGq2ES17gLyuP6JuYQlW\nl3lwWHSOd1zjwbrZJThVyy9zYnWY6T0/RtWa+fX3UWVC06li1X1cOfMuY31X8ZaUp7WvO7ltcPjU\npz7FQw89BMD69etv/P/JZBJN02hrU1eQXAixMGsK1vHyhZc4NXySB4rW3/kNczRd8Q+4jj+LKdxH\nwlmspE2LbuL+oI8/zyPvcDc0k0ZxrYe+C6MkE0k00+IvFla+MrVTqfvMuxkPDrf9+v/d736XtrY2\n1q5dS1tb243/tbe3S2AQYpFY6b8Hi8mifmmpMrV1VfXS0seCPq6MTtEzZsw9QiX1XqZCMUb6Jg3p\n72658gP4Siu4cubdTA/lzncrvfzyy0aMQwixADbdRpNvJS1Daq98jhWuIGH1Yrmi9v6m+yt9AIbl\nHYprPQD0nc+OW1oBylfeR9/5c0xPZnbThyQOhMhya/LX8v7YOULRkLpGNRPR0gewXFUbHGoKHfjs\nFk50GRMc7J48vMV2erMqOKwhmYjT0652i/J8SXAQIsutzl9DkiRnRxZeZvdWomUPYB7twBTuVdam\nSdO4v9LLO5dHDNuRU1LnZbAzlDUFgIpqGrDYHXRneGlJgoMQWa7JtwKTpnP62sk7//I8RMtTNyFY\nrqotSbqu0kd/aJruEWPyDsV1HhLxJAMdCz8LZSSTbqasaTVXz7aSTCQyN46M9SyEUMJudlDvqef0\nsNrgECtcScLiUh4c7g+m8g7vGLS0FKhyo1tM9L6fPUtLFSvvY3JshOHujoyNQYKDEEvASv9q2kbO\nEE0oLKhjMhMrvV95UrrKb6fQmccJg5LSusVEYJmb3vNjhvSnQtmKe0HTMrq0lBXB4bXXXmPHjh08\n88wzhEIKk25CLBH3+FcTSUQ4P/ae0nanyz6B+dp7aJNDd/7lOdI0jXWVXv7aZWTewcP44BThkYgh\n/d0tu9tLYbCGK2daMzaGrAgOb731Fnv37uWLX/wiv/3tbzM9HCEWnVX+1QDKl5aiZenJO3ws6GN4\nIsqlYWO2axbXpa60zqanh9Km1Qx1ns/Yltb0XOd3lw4dOnSjXsSaNWt4/PHH+fa3v01paalc2yHE\nLRTYCilzlHPq2kke5TFl7caKVpM027Bc/QvTtf+orN11N847jFJToOjaj4/gCdiwuSwMXBqj9v5A\n2vtToXT5Kk69/mt63z9LcPX9hve/KIPD1q1b2bp1643Xb7zxBs888wx/+ctfuHr1ztWahMhFq/yr\n+feB4zeuuFFCzyNavE75eYdyr41Sj5UTXSN8+b70V5XUNI2iajf9l8bVzk8aBaobMOdZ6Wk/lZHg\nYPjX8NbWVpqbU7cdJhIJdu/ezebNm2lubqazs/OW73G73Wzfvp1XXnmFhx9+2MjhCpE1VvlXMzI9\nQne4S2m70bIHMA+eRYuo2+2Tyjv4OGFg3iFQ7WZqPEpoKDvyDrrZTHFdE73n1J5fmStDnxwOHjzI\n0aNHsdtTlZnefPNNpqenOXz4MC0tLezbt48DBw7Met/69es/cPnfrbhcVsxmfUHj0nUTPp9jQe/N\nRTJf82PUfH1Se4AfnoaLkXbuqWhU1q62/CG0d36Ib+wkyfpNytr9ZH2AV8/0MRxLUhu4ubSUrvmq\nu7eIE0c7Ge+dorJudhGzxah6zVr+9POfoscncN/illhI33wZGhyCwSD79+/nqaeeAuDEiRNs2LAB\nSOUWTp9eeIQMhRb+bUCK18yPzNf8GDVf/mQxHouXf7/yVx4s+Jy6hp1NFJosTL9/jHBgg7Jm6/02\nAP7Y1keB5eYiRrrmK2lJYndb6Dw7RNkqn/L208Fflaoy994771D7iQdv+TuGF/tJh02bNn2gpF0o\nFMLlullPVtd1YrGYkUMSYsnQNI2V/ns4pfikNGY7scKVmHvVXu5X5bfjs1tovWrMDiJN0whUuxm4\nNLYoiunMha+0EpvbQ08GlpYyuvXH5XIRDt8sJ5JIJNJWD1WIXHCPfzXd4cuMRNTWTIiWrMMy0Apx\ndYfsNE3j3jIPrVeMO7lcVO1hKhRjfNCYqzvulmYyUdKwip5zpw0PaBkNDmvXruXYsWMAtLS00NAw\n/0LdQoibmnwrAWgfPau03VjJOrTYFOYhte3eW+6ha2SKofC00nZvp6gmtYzSfyk77lkCKG1cxeTY\nCKO93Yb2m9HgsHHjRvLy8tiyZQvPPfccO3bsyORwhMh6Dd7lmDDRPqK2IFe0ZB0A5l61RYXuLU8d\nTjNqacnpt2L3pM47ZIvS5fcA0NNu7NKS4Ws4FRUVHDlyBEjVod6zZ4/RQxBiybKbHSxzV9M2ckZp\nuwlXGXFnMZbeE0ytfkJZu41FLqxmE61XRvlMffrrSqfOO3joPT+aNecdXPmFuAMlXG0/RdOnjdvK\nL8eNhVhiGn0raB89q3aNWtOIlazDojgpnWc2saLETcsV477JB6rdRMIxxgayI+8AUNqwkv4L7SQM\nvMJbgoMQS0yjdwXj0XGuTKhdo44Wr0Mf70IL9ytt994yD+f6Q0xGjSnGU1STKh3an0VLS8X1TUSn\nJrl25dYHhdNBgoMQS8yNpPSI2uTxTN7B0qc277Cm3Es8keRMjzFJYqcvD7vHwmBn9tzwXFSbOtTY\nd15tLumjSHAQYolZ5lqGTbcpzzvEAqtImixYFCel7ylzowGtV43Z0qppGoVBF0OXsyc4OP0FuAqL\n6D/fblifEhyEWGJ0k5l6z3LaRxV/yzTbiAVWKc87eGwWagodhuYdCoJuJkanmRjNjnuWAIprm+g7\n32bYeQcJDkIsQU2+lZwfe4/puNrzA9GSdZj7W0Fxu/eWeTl1dYyEQR98hcHUzQyDWfT0UFzXSCQc\nYrT3iiH9SXAQYglq9K0gmohycfy80nZjxevQ4hHMg2rzGatK3YSn43QYVPzHV2JHt5iyLDg0Acbl\nHSQ4CLEENflWAKTtMJzqvMOq0tQOIqOS0ibdREGFM6vyDq7CIuxeP30G5R0kOAixBBXZivHn5dM2\nqvgwnDt1GM7cr7a2cVW+HWeezple4661KAi6GOmdIBoxZgvt3dI0jeK6RvovtBuSd5DgIMQSpGka\nTb4VyrezAsQC92IeUHvzq0nTWFHiNuzJAaCwyk0yAcNXwnf+5UWiuK6JiZFhQkNqz5rcigQHIZao\nRt8KusKXGY+q3QUUK1qNfu0C2rTaD/KVJW7eHwwzZdBhuIIKJ2hk1XmH4rqZ8w7pX1qS4CDEErXc\nm/ogeX/0PaXtxopWo5HEPKD2IriVJW7iiSRtPcZsac2zm/EG7Axdzp4bWr3F5VidLkOS0hIchFii\n6jypK/DPj6kNDtHAagDM/WqXllaVpq7Tbuk2rr5DQZWLoa4wiUR2FP/RTCaKahsNOQwnwUGIJcpv\nzSdgK+J9xcEh6Sgk7ipXnncodFkpcuVx0sDgUBh0EY3EGeufNKzPuxWoaWB8sI/J8fTOkwQHIZaw\nek8D74+eU95urGi18h1LkNrSetLAynDZeBguUF0PwOCl99PajwQHIZawOk8DXeHLTMbUfjOOFt2L\nebQDbWpEabsrS9xcHp5gZEJdOdKP4vRbsbnMDHdnT3AoqKxBM+kMdKg94PhhEhyEWMLqvctJkuSC\n6pPSRdfzDqqT0tfzDmf6jEkSa5qGv9zJcHf2bGc15+WRX1HFQC4/ORw/fpxdu3bd9rUQ4qM1eJYD\ncF71jqVAqnSleUDt0lJTsRuTBmcM2rEEkF/uYmxwiuhUdhyGg9TS0lDnBRLx9I150QaHzs5O2tra\niEQit3wthLizQlsAb56P98fU5h2SNj9xTxUWxTuWHHk6dQGXsSelK5yQhGtXs+fpoXBZPbHpCCM9\nXWnrY9EEh0OHDrFt2za2bdvGgQMHqKqq4oknbtaq/fBrIcSdaZqWSkorDg4A0aLVyrezAtxb6eNM\nz7hhV1P7y50ADGXR0tJMUjqdS0vmtLU8T1u3bmXr1q2ZHoYQS069Zzn/dul/Mx2fJk/PU9ZuLLAa\n2/n/gzY5TNKer6zdVWUe/u1EN73jEUo9NmXt3o7VYcaVb82qazRcBQFsbk9ag4MhTw6tra00NzcD\nkEgk2L17N5s3b6a5uZnOTuNqogqRi+q9DcSSMTpCl5S2ezMprfbpYWVZ6obW9j7jdhD5y51cy6Lg\noGkageoGBtO4YyntTw4HDx7k6NGj2O12AN58802mp6c5fPgwLS0t7Nu3jwMHDtz2/S+88MJHvp7h\nclkxm/UFjVHXTfh8jgW9NxfJfM1PpudrrX4vvAtXYx183HefuoZtDwDgHjtLwvePypp1euzoJo2O\n0Yhh81Ze56fr1DB5mo7DazWkz7tV2dhE18m/Mj0RwudzKW8/7cEhGAyyf/9+nnrqKQBOnDjBhg0b\nAFizZg2nT6vZChcKLTxR7fM5GBkxpsjIUiDzNT+Zni9XMh+n2UlrzykeKtiksGUzfm818a53GVP4\n3+fzOViWb+dk1zXD5s1RYAHg0tlBypv8hvR5t1ylywC4+l47vmUrFtRGIOC+7c/Svqy0adMmzOab\nMSgUCuFy3Yxyuq4Ti8XSPQwhcpZJM1GXpqR0vHCF8qpwAMuLXIYuK/lKHWim7Lq+uyBYg2Yy0fO+\n+n9XyMBuJZfLRTh88x8gkUh8IHgIIdSr8zRwYew88aTaffGxwhXoY51o02o/yJcXuRgMTzMYVlur\n+nbMeTreIntWBQeL1Ya/LEhvmi7hMzw4rF27lmPHjgHQ0tJCQ0OD0UMQIufUexuIJCJ0hS4rbTdW\nuBIAfUjtFdLLi1KrC+f6jXt6yK9wce1K2LAttCoU1zcxNpCewj+GB4eNGzeSl5fHli1beO6559ix\nY4fRQxAi59S66wC4NH5BabuxgtRat+qlpZng8J6BwcFf7mR6Mk5oOHsO2q555Mt84anvpKVtQ9Zz\nKioqOHLkCAAmk4k9e/YY0a0Q4rpKZxW6pnNx/Dyf5j8oazfhKiVh9SoPDi6rmQqfzdC8Q8H1w3DD\n3WHcBek/X6GCxWrD58tPS+J+0ZyQFkKkT56eR6UzyMXxi2ob1jRihSsxD55R2y7QWOSi3cAnB0+R\nHd1iyqq8QzpJcBAiR1S7a5UvK0EqKW0eboeE2mR3Q5GLq6NTjE0Zc323SdfwldgZ6ZFt2iDBQYic\nUeOupXeyh3BU7TfjWMEKtNgU+miH0nYbi2fyDsZ9k/eVOhjpmSCZJWVD00mCgxA5otpdC8ClkNql\npZkdS6qXljKxY8lX6iQaiRMeyZ6kdLpIcBAiR9R4rgcHxUtL8fw6kiaz8qR0viOPIleeoXkHf2nq\nuo5rV2VpSYKDEDmi2FaC0+zkouq8g24l7q9DH1J/UrqhyGXok4O3yI5mQvIOSHAQImdomsYydw0X\nx9Tf5JnOHUudwxNMRY2p0qZbTHgCdq5JcJDgIEQuqXHVcmn8ovJTwLGCFejhPrTJYaXt1he5SCTh\nwpBxH9a+Eoc8OSDBQYicUuOpJRQbZ3BqQGm7scL0nJSuK0wdTLswYOCOpTIHU6Eok+PGbKFdrCQ4\nCJFDZnYsXRhXu7R0MzioXVoq99qwmk2cHzQuOMwkpUd6cvswnAQHIXJItbsGUL9jKWkvIO4sxqw4\nKa2bNGoKHIYGB1/JTHDI7aUlCQ5C5BC3xUPAVpSWk9Lx/Eb04feUt1sfcHLBwOCQZzfj9FtzPikt\nwUGIHFPjrlV/xxIQy2/APPye8ms0agudDE9EGTKotgPcPCmdyyQ4CJFjqt21XA51EEuorcAYz1+O\nFo9gGlNbM2ImKW103iE0HCE6ZcwW2sVIgoMQOabGXUssGaMr3Km03Vh+qnCXWfHSUl3g+o4lI/MO\nM0np3tx9epDgIESOmUlKd4xfUtpuPE3BId+RR77DwnkDt7PeuEYjh5eWJDgIkWMqnEE0NDpDHUrb\nTea5iLvK0YfVF7yvK3Qauqxkc1uwOs05nXdYtMHh+PHj7Nq1C4DTp0+zfft2nn76aQYHBzM8MiGy\nm1W3UuIo5bLiZSWYSUqnITgEnFwcmiBu0FXamqbhK3Ew2ifBYVHp7Oykra2NSCR1bW4kEmHnzp08\n+OCDtLS0ZHh0QmS/KucyLit+cgCIFyxHv3YBFCe7awudRGIJroxOKW33o3iL7Yz1T5LI0doOiyI4\nHDp0iG3btrFt2zYOHDhAVVUVTzzxxI2fr1u3jgsXLvDTn/6UxsbGDI5UiKWhyl1NV/gyccUf4rH8\n5WiJafRRtU8lN3YsDRh4Q2uxnXgsSfhabtZ2WBTBYevWrbz44ou8+OKLfOMb35j185MnT7Jy5UoO\nHjzIoUOHjB+gEEtM0FlFNBGlZ7JHabszSWnVeYeaAgcaxm5n9RbZARjN0R1LaQ8Ora2tNDc3A5BI\nJNi9ezebN2+mubmZzs65fbsIh8Ps3LmT73//+zzyyCPpHK4QOaHKtQxAeVI65q8H1O9Ysll0Kv12\nzg8a90HtKbKDBqP9k4b1uZiY09n4wYMHOXr0KHZ7KgK/+eabTE9Pc/jwYVpaWti3bx8HDhy47ftf\neOEFANavX8/69es/si+Xy4rZrC9onLpuwudzLOi9uUjma34W43zd42gCYCB+VfHYHCR9VdhDF8hb\nYLu3m6+mUg/n+sYNnUtPgY2J4elF9+/399L195XW4BAMBtm/fz9PPfUUACdOnGDDhg0ArFmzhtOn\nTyvrKxRa+Lqgz+dgZCQ3Hx0XQuZrfhbnfOkUWAtpH3hP+dg8vgb03rMLbvd281XltfHG2T56B8ax\nWRb2RXC+3AEbg92hRfjvd9Pd/H0FAu7b/iyty0qbNm3CbL4Zf0KhEC6X68ZrXdeJxdQmxIQQc1Pl\nWqZ8WQlSeQd95CLE1dZDqC10kAQ6ho1dWgoNTxGPJgzrc7EwNCHtcrkIh28mlBKJxAeChxDCOEHX\nMrrCneqrwuU3oCWi6KNqT2AvK0gtnVwyMDj4ih0kEzA2aNwW2sXC0OCwdu1ajh07BkBLSwsNDQ1G\ndi+E+DtVrmVMxCaUV4WL5y8HUH59d6XPjq7BJQNLhnqKr+9YysHDcIZ+bd+4cSNvv/02W7ZsIZlM\n8uyzzxrZvRDi78zsWOoIXSJgL1LWbsxfS1IzYR4+xzTqdhdadBMVPruhwcFdYMWka4z25d6OpbQH\nh4qKCo4cOQKAyWRiz5496e5SCDEHwevB4XKog48FHlDXsNlO3FOVlms0qgschuYcTLoJT8CWk8Fh\nURyCE0IYz5/nx2PxcDmk/o6luL8+dY2GYtUFDrpGpojFjUsQe4rsObmsJMFBiBylaRpB1zI6wx3K\n2477a9BHO5RXhVuW7yCeSNI1YuQdSw4mx6JMT+bWzkoJDkLksKCrKj3bWX21qapw491K262e2bE0\nZOA1GjNJ6Rw7KS3BQYgcVuVcxuj0CKPTI0rbjfnrADBfO6+03WX5xm9nvREccizvIMFBiBxW5a4G\n1N+xFPfVAqQOwylkt+iUuK2G7lhyePOwWHUJDkKI3BF0VgHQFb6stN2kPZ+E1Yc+oj4pvazAQcew\ncR/UmqbhKbIxJstKQohcEbAXYTFZuBLuUt523F+HrnhZCVLXd3cMT5BQfLL7o3gCdsYGJDgIIXKE\nrumUOSroSkNwiPlqlS8rQSrvEIkl6BkzbseSO2AjEo4RmcidHUsSHITIcRXOijQ9OdSgT/SjRcaU\ntlt9PSndMWTcN3lPIJWUHh/MnacHCQ5C5LgKZ5ArE1eIJ9WeSbiZlFabd8jEBXyegA2AsYHcuYBP\ngoMQOa7CWUk0Mc3AZL/SduPXt7OqDg4+uwW/3UKHkTuWfFZMZi2n8g4SHITIcRWOSgC6FS8txT1B\nkpqOfi0NeYcCh6FPDiaThrvAxrg8OQghckWFMz3BAT2PuCeIeUT9jqXqfAeXhiaU16L4KKkdSxIc\nhBA5It9agF130D2h9qwDzGxnTc8FfOORGEMTaqvNfRR3wEZ4JEIsR6rCSXAQIsdpmka5s4LusNp7\nkADivnRdwJfaPdRpdFI6CaEcqQonwUEIQaWzkm7Fp6QB4v70XMBX6U8Fh8vXjN/OOpYj21klOAgh\nKHdW0jvRQzShdpkm5rt+AZ/iHUslbhsWXaPLwODgKrCBljvbWRdtcDh+/Di7du0CoL29na985Sts\n376dP//5zxkemRBLT4WzkgQJeiauKm037k/PBXy6SaPCazf0ycFsMeH0WRnPke2sizI4dHZ20tbW\nRiQSAaC1tZXCwkJMJhP19fUZHp0QS0+lMwio37GUtOWTsHrTcsdS0G/n8oixH9SegE2eHIx06NAh\ntm3bxrZt2zhw4ABVVVU88cQTN36+bt06nnnmGZ588kl+8pOfZHCkQixN5TfOOijOO2haasdSGm5n\nrfTb6R6ZJJ4wbjurO2BjfGiKhIF9Zoo50wMA2Lp1K1u3br3tz9va2ggEAni9XuJxtbsehBDgyfPg\nsXi5ko4dS95qLFfeVt5u0G8nGk/SNx6hzGtT3v6teAJ2ErEkEyMRXPnG9JkpaX9yaG1tpbm5GYBE\nIsHu3bvZvHkzzc3NdHbOrbB5eXk5e/fu5fnnn+fxxx9P53CFyFkVzkq60nHWwbsMPdQDUbVLQMEb\nO5bkjqV00JJpPGJ48OBBjh49it1u58iRI7zxxhu89dZb7Nu3j5aWFl588UUOHDiQru6FEEIsUFqf\nHILBIPv377/x+sSJE2zYsAGANWvWcPr06XR2L4QQYoHSGhw2bdqE2XwzrREKhXC5XDde67pOLJY7\nxTOEECJbGLohukJGAAADYUlEQVRbyeVyEQ6Hb7xOJBIfCB5CCCEWB0ODw9q1azl27BgALS0tNDQ0\nGNm9EEKIOTL0a/vGjRt5++232bJlC8lkkmeffdbI7ufl+PHjvPbaa0xOTvLkk0/S2NiY6SFlhePH\nj/Pqq6/yve99L9NDWbT+9re/cfjwYQB27dqFx+PJ8IgWP/m7mjtVn11pDw4VFRUcOXIEAJPJxJ49\ne9LdpRKTk5Ps3buXtrY2/vSnP0lwmIMPn2wXt3bkyBH27NnDyZMn+d3vfseWLVsyPaRFTf6u5kfV\nZ9eiOCG9GHz4lPZnPvMZJicn+dnPfsaXvvSlTA9vUbrTyXZxa/F4HKvVSiAQYGBgINPDWfTk72p+\nVH12STb4ug+f0h4eHub555/nm9/8JgUFBZkb2CJ2p5Pt4tbsdjvT09MMDAxQWFiY6eGIJUbVZ1dO\nPDks5JT2vn37GBgY4Ac/+AGvv/66kcNdFFScbM9Fc5m3L3/5y+zevZtf/OIXfP7zn8/kcDNO/s7m\nZy7zpeyzK7nE/fjHP04+8sgjyUcffTSZTCaTv//975NPP/10MplMJt99993k17/+9UwOb1GSOVsY\nmbf5kfmaH6Pna8k/Ocgp7fmTOVsYmbf5kfmaH6Pna8kHBzmlPX8yZwsj8zY/Ml/zY/R8Lfng8GFy\nSnv+ZM4WRuZtfmS+5ifd85VzwUFOac+fzNnCyLzNj8zX/KR7vnIuLGfTKe3FQuZsYWTe5kfma37S\nPV9precghBAiO+XcspIQQog7k+AghBBiFgkOQgghZpHgIIQQYhYJDkIIIWaR4CCEEGIWCQ5CCCFm\nkeAghBBiFgkOQgghZpHgIEQa/PrXv+azn/0s4XCYiYkJHn74YX7zm99kelhCzJlcnyFEmnzrW9/C\n7XYzPT2Nruvs3bs300MSYs4kOAiRJqFQiC984QvYbDZ+9atfYbVaMz0kIeZMlpWESJOhoSEikQhj\nY2P09/dnejhCzIs8OQiRBtFolC1btrBlyxYSiQS//OUv+fnPf47FYsn00ISYE3lyECINfvjDHxII\nBHj00UfZvHkzPp+PH/3oR5kelhBzJk8OQgghZpEnByGEELNIcBBCCDGLBAchhBCzSHAQQggxiwQH\nIYQQs0hwEEIIMYsEByGEELNIcBBCCDHL/wfi7mK53u8gegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "#x = np.linspace(0, 20, 200)\n",
    "x = np.logspace(-2, 2.0, 200)\n",
    "alphas = [1., 2., 3., 7.5]\n",
    "betas = [.5, .5, 1., 1.]\n",
    "\n",
    "a=1.0; b=2.50\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "a=1.0; b=5.0\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "a=1; b=10.0\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "\n",
    "a=1.0; b=0.0250\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "a=0.9; b=1.0\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "a=1.1; b=0.25\n",
    "pdf = st.gamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('f(x)', fontsize=12)\n",
    "plt.legend(loc=1)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e-15,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.012125798002082792 vmax 488.3606538420368\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,\n",
      "       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,\n",
      "       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ilvSnCufdgeX/niDNtxkaZ0uPaiARehS1IHA6nbjdFUcoqKqKyWSqcpzb7cZs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\ngpMgEEKIBCdBIIQQCe7/ATjNK+3EtAoOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.logspace(np.log10((2*np.pi)/(1800/86400)), np.log10((2*np.pi)/(4*80)), 500)\n",
    "for lam in [0.025,0.1,0.0005]:\n",
    "    pdf = st.expon.pdf(x, scale=1.0/lam)\n",
    "    plt.plot(x, pdf, label=r'$\\lambda$ = {}'.format(lam))\n",
    "    \n",
    "plt.plot([(2*np.pi)/(1800/86400),(2*np.pi)/(4*80)],[0.0010,0.0010],'o')\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('f(x)', fontsize=12)\n",
    "plt.legend(loc=1)\n",
    "\n",
    "plt.xscale('log')\n",
    "#plt.yscale('log')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010136579676748298"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(np.random.normal(1.0,0.01,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-20, 100.0)"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 0.01 vmax 10.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([2.e-03, 3.e-03, 4.e-03, 5.e-03, 6.e-03, 7.e-03, 8.e-03, 9.e-03,\n",
      "       2.e-02, 3.e-02, 4.e-02, 5.e-02, 6.e-02, 7.e-02, 8.e-02, 9.e-02,\n",
      "       2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,\n",
      "       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,\n",
      "       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,\n",
      "       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n",
      "DEBUG [matplotlib.ticker.tick_values()]: ticklocs array([1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08, 1.e-05, 1.e-02,\n",
      "       1.e+01, 1.e+04, 1.e+07])\n",
      "DEBUG [matplotlib.ticker.tick_values()]: vmin 1e-20 vmax 100.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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8iGt55OTkkJOT07r84YcfkpycTHx8PH7/0dMyivCy+4CTh/67ib31Ln53ZQY/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\nhBDtJuEhhBCi3SQ8hBBCtNv/AyrKZVojQR1hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "#x = np.linspace(0, 20, 200)\n",
    "x = np.logspace(-4, 0.9, 200)\n",
    "alphas = [1., 2., 3., 7.5]\n",
    "betas = [.5, .5, 1., 1.]\n",
    "\n",
    "a=0.05; b=1\n",
    "pdf = st.invgamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "a=0.01; b=10\n",
    "pdf = st.invgamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "a=0.0025; b=0.1\n",
    "pdf = st.invgamma.pdf(x, a, scale=1.0/b)\n",
    "plt.plot(x, pdf, label=r'$\\alpha$ = {}, $\\beta$ = {}'.format(a, b))\n",
    "\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('f(x)', fontsize=12)\n",
    "plt.legend(loc=1)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlim(0.01,10)\n",
    "plt.ylim(1e-20,1e2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1321796d8>"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G+ILg4kTCFwrIG6mXdoQP11bqiIQvCC5NJHyhgNJsbXgzUW0rCK5PJHyhAMmQ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\nkMAXQggvIYEvhBBe4v8BvVdedvTSQ0YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "x = np.linspace(-2, 10, 500)\n",
    "lowers = [0., -1, 2]\n",
    "cs = [2., 0., 6.5]\n",
    "uppers = [4., 1, 8]\n",
    "for lower, c, upper in zip(lowers, cs, uppers):\n",
    "    scale = upper - lower\n",
    "    c_ = (c - lower) / scale\n",
    "    pdf = st.triang.pdf(x, loc=lower, c=c_, scale=scale)\n",
    "    plt.plot(x, pdf, label='lower = {}, c = {}, upper = {}'.format(lower,\n",
    "                                                                   c,\n",
    "                                                                   upper))\n",
    "x = np.linspace(-4, 4, 300)\n",
    "lower,c,upper=-4,0.5,4\n",
    "pdf = st.triang.pdf(x, loc=lower, c=c, scale=scale)\n",
    "plt.plot(x, pdf, label='lower = {}, c = {}, upper = {}'.format(lower,\n",
    "                                                               c,\n",
    "                                                               upper))\n",
    "\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('f(x)', fontsize=12)\n",
    "plt.legend(loc=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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  {
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  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from stellar import *\n",
    "\n",
    "def getStellarInfo(ID,hdr,mission,savedf=True):\n",
    "    if mission[0]=='T' or mission[0]=='t':\n",
    "        #Using TIC-8 as input cat for TESS stars\n",
    "        best_stardf = Catalogs.query_criteria(\"Tic-8\",\n",
    "                                              SkyCoord(hdr['RA_OBJ'],row['DEC_OBJ'],unit=u.deg),\n",
    "                                              radius=15/3600)\n",
    "    elif mission[0]=='K' or mission[0]=='k':\n",
    "        best_stardf=starpars(ID,mission,errboost=0.2,return_best=True,useGaiaLum=True,useGaiaBR=True,useGaiaSpec=True,\n",
    "                             useBV=True,use2mass=True,useGriz=True,useGaiaAg=True)\n",
    "        best_stardf['rho']=best_stardf['rho_gcm3']/1.4098\n",
    "        best_stardf['rho']=best_stardf['rho_gcm3e']/1.4098\n",
    "        best_stardf=best_stardf.rename(columns={'TBD':'TBD'})\n",
    "        \n",
    "    #Taking brightest star if multiple:\n",
    "    if type(best_stardf)==pd.DataFrame and best_stardf.shape[1]>1:\n",
    "        #Taking brightest star:\n",
    "        if mission[0] in ['t','T']:\n",
    "            best_stardf=beststardf.iloc[np.argmin(best_stardf.Tmag)]\n",
    "        else:\n",
    "            best_stardf=beststardf.iloc[np.argmin(best_stardf.kepmag)]\n",
    "    elif best_stardf.shape[1]==1:\n",
    "        #Pandas df -> Series\n",
    "        best_stardf=best_stardf.iloc[0]\n",
    "    \n",
    "    #Sorting out missing data and getting important info - Mass, Radius, density and logg:\n",
    "    if pd.isnull(dfrow[['logg','mass']]).all() and ~np.isnan(dfrow['lum']):\n",
    "        #Mass-Luminosity relation for objects with no Mass but have Luminosity\n",
    "        if ((~np.isnan(dfrow['rad']))&(dfrow['rad']<0.55))|((dfrow['Teff']<5000)&(dfrow['lum']<1.0)):\n",
    "            #M < 0.43\n",
    "            dfrow['mass']=np.power(dfrow['lum']/0.23,1/2.3)\n",
    "            dfrow['e_mass']=(1/2.3)*np.power(dfrow['lum']/0.23,1/2.3-1.0)*dfrow['e_lum']\n",
    "            dfrow['logg']=np.power(10,dfrow['mass']/dfrow['rad']**2)+4.43\n",
    "        elif dfrow['Teff']>8550:\n",
    "            #2Ms < M < 55Ms\n",
    "            dfrow['mass']=np.power(dfrow['lum']/1.4,1/3.5)\n",
    "            dfrow['e_mass']=(1/3.5)*np.power(dfrow['lum']/1.4,1/3.5-1.0)*dfrow['e_lum']\n",
    "        else:\n",
    "            #0.43 < M < 2\n",
    "            dfrow['mass']=np.power(dfrow['lum'],1/4)\n",
    "            dfrow['e_mass']=(1/4)*np.power(dfrow['lum']/0.23,(1/4-1.0))*dfrow['e_lum']\n",
    "    if ~pd.isnull(dfrow['mass']):\n",
    "        masses=np.array([dfrow['mass'], dfrow['e_mass'],dfrow['e_mass']])\n",
    "\n",
    "    #Logg:\n",
    "    if ~pd.isnull(dfrow['logg']):\n",
    "        logg=np.array([dfrow['logg'], dfrow['e_logg'],dfrow['e_logg']])\n",
    "    elif ~pd.isnull(dfrow[['mass','rad']]).any():\n",
    "        logg=np.array([np.log10(dfrow['mass']/dfrow['rad']**2)+4.438,0.5,0.5])\n",
    "    else:\n",
    "        logg=np.array([4,1,1])\n",
    "\n",
    "    if not pd.isnull(dfrow['rho']) and dfrow['rho']!=0.0:\n",
    "        rhos=[dfrow['rho'],dfrow['e_rho'],dfrow['e_rho']]      \n",
    "    else:\n",
    "        rhos=namaste.dens2(*dfrow[['logg','e_logg','e_logg','rad','e_rad','e_rad','mass','e_mass','e_mass']])\n",
    "    print(rhos)\n",
    "    \n",
    "    return Rstar, rhos, Teff, logg\n",
    "        #'teff', 'teffep', 'teffem', 'logg', 'loggep', 'loggem', 'feh', 'fehep',\n",
    "        #'fehem', 'rad', 'radep', 'radem', 'mass', 'massep', 'massem',\n",
    "        #'rho_gcm3', 'rho_gcm3ep', 'rho_gcm3em', 'lum', 'lumep', 'lumem', 'avs',\n",
    "        #'avsep', 'avsem', 'dis', 'disep', 'disem', 'source'\n",
    "        return Rstar, rhostar, Teff, logg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.438067627303133"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log10(((c.G*1*u.Msun)/(1*u.Rsun**2)).to(u.cm/(u.s)**2).value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1.4098263 \\; \\mathrm{\\frac{g}{cm^{3}}}$"
      ],
      "text/plain": [
       "<Quantity 1.4098263 g / cm3>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(u.Msun/(np.pi*4/3*u.Rsun**3)).to(u.g/u.cm**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO  [everest.user.DownloadFile()]: Downloading the file...\n",
      "INFO  [everest.user.load_fits()]: Loading FITS file for 248847494.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<everest.Everest(248847494)>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import everest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO  [everest.user.DownloadFile()]: Downloading the file...\n",
      "INFO  [everest.user.load_fits()]: Loading FITS file for 211901114.\n"
     ]
    }
   ],
   "source": [
    "st=everest.Everest(211901114,season=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 519,  523, 1553, 2067, 2068, 1053,   33, 1057, 1533,  548,   51,\n",
       "         52, 1081,  573, 2109, 2111, 2110, 1604, 1094,  584,  600,   93,\n",
       "       1629,  609, 2161, 1138, 2170, 1149, 1661, 2177, 1154,  131,  644,\n",
       "        645,  134,  646,  647,  137,  648,  649,  650,  651,  652,  653,\n",
       "        654,  655,  656, 1171,  657,  658,  154,  155,  156,  669, 1178,\n",
       "       2205,  678, 1703, 1705, 2219, 2220, 1155, 1212,  189, 1725,  710,\n",
       "        711,  712, 2249, 1229,  720, 1746,  728, 2265, 1752, 1754, 1245,\n",
       "       1251, 2182, 1674, 2281, 1267, 1270, 1273,  765, 2301, 1281, 2308,\n",
       "       1797, 2313, 2314, 2315, 1811,  275, 1302,  793, 2332,  285, 1821,\n",
       "       2340,  295,  299, 2348, 2350, 1843,  308, 1333, 1334, 1847, 1341,\n",
       "        843, 1356, 1357, 1358, 2386,  345, 1372, 1373,  862,  861,  864,\n",
       "        865,  863, 1376, 2397, 1893, 2404, 1897, 1390, 1392,  886,  887,\n",
       "       1402,  381, 1917, 1926,  397,  921, 1437,  433,  435, 1973, 2489,\n",
       "        957,  959,  960,  961,  477, 2013, 1511, 1513, 1514,  503,  504,\n",
       "        505, 1017,  511])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3156.44535484, 3156.4657875 , 3156.48622007, ..., 3244.40460467,\n",
       "       3244.42503636, 3244.44546815])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "249234088"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([181515.596964  , 181479.56331565, 181475.45334854, ...,\n",
       "       181705.14397278, 181706.17168227, 181658.4781803 ])"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.5  ,  1.75 ,  2.   ,  2.25 ,  2.5  ,  8.4  ,  8.605,  8.81 ,\n",
       "        9.015,  9.22 ,  9.425,  9.63 ,  9.835, 10.04 , 10.245, 10.45 ,\n",
       "       10.655, 10.86 , 11.065, 11.27 , 11.475, 11.68 , 11.885, 12.09 ,\n",
       "       12.295, 12.5  , 18.4  , 18.625, 18.85 , 19.075, 19.3  , 25.4  ])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listgaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.2, 3)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cCnyqu/8l4DVdxn2a1Gv8f07vLG2skmwAXg98YtxZ+rwAeE6SbyTZkeSMcQfqfAw4Evgp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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dist_from_t0,np.tile(1.0,len(dist_from_t0)),'.')\n",
    "plt.xlim(1.2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaps=np.hstack((np.diff(dist_from_t0)>(0.9*dur),True))\n",
    "gaps)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=fits.open(\"https://archive.stsci.edu/missions/tess/tid/s0003/0000/0000/6389/8957/tess2018263035959-s0003-0000000063898957-0123-s_lc.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FITS_rec([(1382.03968762, 0.00507748, 111297, 35545.9  , 21.053415, 2214.34  , 4.160939 , nan, nan, 128, nan, nan, nan, nan, 1017.56320565, 0.00046581, 273.72934369, 0.00046783, nan, nan),\n",
       "          (1382.04107653, 0.0050775 , 111298, 35546.504, 21.05909 , 2223.8347, 4.1703978, nan, nan, 128, nan, nan, nan, nan, 1017.55763864, 0.00046599, 273.74040887, 0.00046752, nan, nan),\n",
       "          (1382.04246543, 0.00507752, 111299, 35552.22 , 21.055212, 2213.667 , 4.1582694, nan, nan, 128, nan, nan, nan, nan, 1017.55894235, 0.00046562, 273.73646494, 0.00046787, nan, nan),\n",
       "          ...,\n",
       "          (1409.38517728, 0.00474051, 130986, 35782.04 , 24.147034, 9737.56  , 8.233795 , nan, nan, 128, nan, nan, nan, nan, 1017.49162929, 0.00054509, 273.83689481, 0.00054855, nan, nan),\n",
       "          (1409.38656613, 0.00474047, 130987, 35830.055, 24.155172, 9732.525 , 8.230909 , nan, nan, 128, nan, nan, nan, nan, 1017.49313826, 0.00054387, 273.83028504, 0.00054766, nan, nan),\n",
       "          (1409.38795498, 0.00474043, 130988, 34964.043, 23.952274, 9731.219 , 8.230116 , nan, nan, 128, nan, nan, nan, nan, 1017.48073003, 0.00055609, 273.83843763, 0.00055644, nan, nan)],\n",
       "         dtype=(numpy.record, [('TIME', '>f8'), ('TIMECORR', '>f4'), ('CADENCENO', '>i4'), ('SAP_FLUX', '>f4'), ('SAP_FLUX_ERR', '>f4'), ('SAP_BKG', '>f4'), ('SAP_BKG_ERR', '>f4'), ('PDCSAP_FLUX', '>f4'), ('PDCSAP_FLUX_ERR', '>f4'), ('QUALITY', '>i4'), ('PSF_CENTR1', '>f8'), ('PSF_CENTR1_ERR', '>f4'), ('PSF_CENTR2', '>f8'), ('PSF_CENTR2_ERR', '>f4'), ('MOM_CENTR1', '>f8'), ('MOM_CENTR1_ERR', '>f4'), ('MOM_CENTR2', '>f8'), ('MOM_CENTR2_ERR', '>f4'), ('POS_CORR1', '>f4'), ('POS_CORR2', '>f4')]))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf[1].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.io.fits.hdu.hdulist.HDUList"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lc[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.io.fits.hdu.image.PrimaryHDU"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lc[n][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standards                     \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T / file contains extensions                       \n",
       "NEXTEND =                    2 / number of standard extensions                  \n",
       "EXTNAME = 'PRIMARY '           / name of extension                              \n",
       "EXTVER  =                    1 / extension version number (not format version)  \n",
       "SIMDATA =                    F / file is based on simulated data                \n",
       "ORIGIN  = 'NASA/Ames'          / institution responsible for creating this file \n",
       "DATE    = '2018-12-13'         / file creation date.                            \n",
       "TSTART  =    1382.033915694891 / observation start time in TJD                  \n",
       "TSTOP   =    1409.383908993930 / observation stop time in TJD                   \n",
       "DATE-OBS= '2018-09-20T12:47:41.132Z' / TSTART as UTC calendar date              \n",
       "DATE-END= '2018-10-17T21:11:40.553Z' / TSTOP as UTC calendar date               \n",
       "CREATOR = '6504 LightCurveExporterPipelineModule' / pipeline job and program use\n",
       "PROCVER = 'spoc-3.3.48-20181212' / SW version                                   \n",
       "FILEVER = '1.0     '           / file format version                            \n",
       "TIMVERSN= 'OGIP/93-003'        / OGIP memo number for file format               \n",
       "TELESCOP= 'TESS    '           / telescope                                      \n",
       "INSTRUME= 'TESS Photometer'    / detector type                                  \n",
       "DATA_REL=                    4 / data release version number                    \n",
       "OBJECT  = 'TIC 63898957'       / string version of target id                    \n",
       "TICID   =             63898957 / unique tess target identifier                  \n",
       "SECTOR  =                    3 / Observing sector                               \n",
       "CAMERA  =                    1 / Camera number                                  \n",
       "CCD     =                    1 / CCD chip number                                \n",
       "PXTABLE =                  131 / pixel table id                                 \n",
       "RADESYS = 'ICRS    '           / reference frame of celestial coordinates       \n",
       "RA_OBJ  =  15.2176320000000000 / [deg] right ascension                          \n",
       "DEC_OBJ = -24.4239070000000000 / [deg] declination                              \n",
       "EQUINOX =               2000.0 / equinox of celestial coordinate system         \n",
       "PMRA    =         108.65100000 / [mas/yr] RA proper motion                      \n",
       "PMDEC   =         -11.26270000 / [mas/yr] Dec proper motion                     \n",
       "PMTOTAL =         109.23318274 / [mas/yr] total proper motion                   \n",
       "TESSMAG =           9.02000046 / [mag] TESS magnitude                           \n",
       "TEFF    =        5890.29980469 / [K] Effective temperature                      \n",
       "LOGG    =           3.92000008 / [cm/s2] log10 surface gravity                  \n",
       "MH      =          -0.34999999 / [log10([M/H])] metallicity                     \n",
       "RADIUS  =           1.31383002 / [solar radii] stellar radius                   \n",
       "TICVER  =                    7 / TICVER                                         \n",
       "CRMITEN =                    T / spacecraft cosmic ray mitigation enabled       \n",
       "CRBLKSZ =                   10 / [exposures] s/c cosmic ray mitigation block siz\n",
       "CRSPOC  =                    F / SPOC cosmic ray cleaning enabled               \n",
       "CHECKSUM= '57HGA5F985FEA5F9'   / HDU checksum updated 2018-12-13T14:41:42Z      "
      ]
     },
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