Bottle Brush P(s) theory for Gibcus et at 2018

bottle-brush-scaling-theory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1\"><a href=\"#Bottle-brush-with-RW-angular-loop-orientation\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Bottle brush with RW angular loop orientation</a></div><div class=\"lev2\"><a href=\"#No-drift\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>No drift</a></div><div class=\"lev2\"><a href=\"#With-drift\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>With drift</a></div><div class=\"lev1\"><a href=\"#Bottle-brush-with-OU-angular-loop-orientation-(approximate)\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Bottle brush with OU angular loop orientation (approximate)</a></div><div class=\"lev1\"><a href=\"#Experimental\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Experimental</a></div><div class=\"lev1\"><a href=\"#Bottle-brush-with-random-angular-loop-orientation\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Bottle brush with random angular loop orientation</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:39.452901Z",
     "start_time": "2020-06-22T20:20:36.333899Z"
    }
   },
   "outputs": [],
   "source": [
    "#import glob, os, sys, shelve, itertools\n",
    "#from collections import OrderedDict\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib notebook\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import numpy as np\n",
    "from ipywidgets.widgets.interaction import interact\n",
    "import ipywidgets.widgets as widgets\n",
    "\n",
    "import scipy\n",
    "import scipy.stats as st\n",
    "\n",
    "# from matplotlib import gridspec\n",
    "# from mirnylib import plotting\n",
    "\n",
    "# import seaborn as sns\n",
    "# sns.set_style('white')\n",
    "# sns.set_palette('deep')\n",
    "# sns.set_context('talk')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:39.460179Z",
     "start_time": "2020-06-22T20:20:39.455595Z"
    }
   },
   "outputs": [],
   "source": [
    "MIN_LOG10_S = -1\n",
    "MAX_LOG10_S = 4\n",
    "GLUE_AT = 2\n",
    "EXP_LOOP_SIZE = 80000\n",
    "IN_LOOP_SLOPE = -1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:47.694519Z",
     "start_time": "2020-06-22T20:20:47.672684Z"
    }
   },
   "outputs": [],
   "source": [
    "# REFSC_7m = smcviz.scalings['WT-20151009-7m-R1']\n",
    "\n",
    "REFSC_7m = np.array([[  1.06010000e+03,   1.18810000e+03,   1.33210000e+03,\n",
    "          1.49310000e+03,   1.67360000e+03,   1.87610000e+03,\n",
    "          2.10310000e+03,   2.35760000e+03,   2.64310000e+03,\n",
    "          2.96310000e+03,   3.32160000e+03,   3.72310000e+03,\n",
    "          4.17310000e+03,   4.67810000e+03,   5.24410000e+03,\n",
    "          5.87860000e+03,   6.58960000e+03,   7.38660000e+03,\n",
    "          8.28010000e+03,   9.28160000e+03,   1.04046000e+04,\n",
    "          1.16631000e+04,   1.30736000e+04,   1.46551000e+04,\n",
    "          1.64276000e+04,   1.84146000e+04,   2.06421000e+04,\n",
    "          2.31386000e+04,   2.59371000e+04,   2.90741000e+04,\n",
    "          3.25911000e+04,   3.65331000e+04,   4.09516000e+04,\n",
    "          4.59051000e+04,   5.14576000e+04,   5.76816000e+04,\n",
    "          6.46581000e+04,   7.24786000e+04,   8.12451000e+04,\n",
    "          9.10716000e+04,   1.02087100e+05,   1.14434600e+05,\n",
    "          1.28275600e+05,   1.43791100e+05,   1.61182600e+05,\n",
    "          1.80677600e+05,   2.02531100e+05,   2.27027600e+05,\n",
    "          2.54486600e+05,   2.85267100e+05,   3.19770600e+05,\n",
    "          3.58447600e+05,   4.01802600e+05,   4.50401100e+05,\n",
    "          5.04877600e+05,   5.65943600e+05,   6.34395600e+05,\n",
    "          7.11126600e+05,   7.97138100e+05,   8.93553100e+05,\n",
    "          1.00162960e+06,   1.12277810e+06,   1.25858010e+06,\n",
    "          1.41080710e+06,   1.58144610e+06,   1.77272410e+06,\n",
    "          1.98713760e+06,   2.22748460e+06,   2.49690160e+06,\n",
    "          2.79890560e+06,   3.13743760e+06,   3.51691460e+06,\n",
    "          3.94229010e+06,   4.41911560e+06,   4.95361410e+06,\n",
    "          5.55276110e+06,   6.22437510e+06,   6.97722160e+06,\n",
    "          7.82112610e+06,   8.76710210e+06,   9.82749510e+06,\n",
    "          1.10161446e+07,   1.23485626e+07,   1.38421381e+07,\n",
    "          1.55163641e+07,   1.73930901e+07,   1.94968086e+07,\n",
    "          2.18549741e+07,   2.44983631e+07,   2.74614741e+07,\n",
    "          3.07829776e+07,   3.45062216e+07,   3.86797966e+07,\n",
    "          4.33581716e+07,   4.86024026e+07,   5.44809301e+07,\n",
    "          6.10704741e+07,   6.84570326e+07,   7.67370056e+07,\n",
    "          8.60184526e+07,   9.64225031e+07,   1.08084938e+08,\n",
    "          1.21157961e+08,   1.35812184e+08,   1.52238855e+08,\n",
    "          1.70652355e+08,   1.91292994e+08,   2.14430146e+08,\n",
    "          2.40365768e+08,   2.69438340e+08,   3.02027280e+08,\n",
    "          3.38557897e+08,   3.79506943e+08,   4.25408832e+08,\n",
    "          4.76862619e+08,   5.34539812e+08,   5.99193141e+08,\n",
    "          6.71666379e+08,   7.52905356e+08,   8.43970300e+08,\n",
    "          9.46049675e+08],\n",
    "       [  1.36025607e-05,   1.34046188e-05,   1.36574460e-05,\n",
    "          1.32345444e-05,   1.29639579e-05,   1.25128973e-05,\n",
    "          1.19243258e-05,   1.13629686e-05,   1.05735869e-05,\n",
    "          9.77219368e-06,   8.84240339e-06,   8.06609568e-06,\n",
    "          7.14465216e-06,   6.31499827e-06,   5.51226795e-06,\n",
    "          4.76879398e-06,   4.18247063e-06,   3.59360570e-06,\n",
    "          3.13764287e-06,   2.75338562e-06,   2.45340402e-06,\n",
    "          2.18181840e-06,   1.94339264e-06,   1.77000763e-06,\n",
    "          1.60003804e-06,   1.45911904e-06,   1.34797427e-06,\n",
    "          1.22491760e-06,   1.13661127e-06,   1.05464027e-06,\n",
    "          9.74862144e-07,   9.07692404e-07,   8.52651873e-07,\n",
    "          7.98028535e-07,   7.45706359e-07,   7.00864258e-07,\n",
    "          6.58708816e-07,   6.24673757e-07,   5.86377722e-07,\n",
    "          5.54947671e-07,   5.26503076e-07,   4.98313350e-07,\n",
    "          4.72124666e-07,   4.45941670e-07,   4.24237778e-07,\n",
    "          3.99209011e-07,   3.77865371e-07,   3.54132789e-07,\n",
    "          3.35382423e-07,   3.13948076e-07,   2.94289478e-07,\n",
    "          2.74606656e-07,   2.58023724e-07,   2.39221238e-07,\n",
    "          2.22871782e-07,   2.06780001e-07,   1.91610943e-07,\n",
    "          1.77610080e-07,   1.64602895e-07,   1.52824997e-07,\n",
    "          1.41911209e-07,   1.31598674e-07,   1.21713280e-07,\n",
    "          1.12660971e-07,   1.03716735e-07,   9.49234670e-08,\n",
    "          8.53526672e-08,   7.54735421e-08,   6.50218799e-08,\n",
    "          5.39394069e-08,   4.37518502e-08,   3.52473806e-08,\n",
    "          2.90136753e-08,   2.41306659e-08,   2.01100335e-08,\n",
    "          1.69164445e-08,   1.45610547e-08,   1.27782927e-08,\n",
    "          1.15661752e-08,   1.05925240e-08,   9.75479526e-09,\n",
    "          8.97489348e-09,   8.23350430e-09,   7.44221577e-09,\n",
    "          6.66177792e-09,   5.95105691e-09,   5.34956968e-09,\n",
    "          4.80811488e-09,   4.37376310e-09,   3.93143736e-09,\n",
    "          3.59289091e-09,   3.28204473e-09,   3.05535485e-09,\n",
    "          2.87612295e-09,   2.73886018e-09,   2.63728473e-09,\n",
    "          2.57426311e-09,   2.50288251e-09,   2.38974135e-09,\n",
    "          2.27761691e-09,   2.12083309e-09,   1.95334581e-09,\n",
    "          1.75004353e-09,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:48.870549Z",
     "start_time": "2020-06-22T20:20:48.849338Z"
    }
   },
   "outputs": [],
   "source": [
    "# REFSC_10m = smcviz.scalings['WT-92Pphase-10m-R1']\n",
    "\n",
    "REFSC_10m =np.array([[  1.06010000e+03,   1.18810000e+03,   1.33210000e+03,\n",
    "          1.49310000e+03,   1.67360000e+03,   1.87610000e+03,\n",
    "          2.10310000e+03,   2.35760000e+03,   2.64310000e+03,\n",
    "          2.96310000e+03,   3.32160000e+03,   3.72310000e+03,\n",
    "          4.17310000e+03,   4.67810000e+03,   5.24410000e+03,\n",
    "          5.87860000e+03,   6.58960000e+03,   7.38660000e+03,\n",
    "          8.28010000e+03,   9.28160000e+03,   1.04046000e+04,\n",
    "          1.16631000e+04,   1.30736000e+04,   1.46551000e+04,\n",
    "          1.64276000e+04,   1.84146000e+04,   2.06421000e+04,\n",
    "          2.31386000e+04,   2.59371000e+04,   2.90741000e+04,\n",
    "          3.25911000e+04,   3.65331000e+04,   4.09516000e+04,\n",
    "          4.59051000e+04,   5.14576000e+04,   5.76816000e+04,\n",
    "          6.46581000e+04,   7.24786000e+04,   8.12451000e+04,\n",
    "          9.10716000e+04,   1.02087100e+05,   1.14434600e+05,\n",
    "          1.28275600e+05,   1.43791100e+05,   1.61182600e+05,\n",
    "          1.80677600e+05,   2.02531100e+05,   2.27027600e+05,\n",
    "          2.54486600e+05,   2.85267100e+05,   3.19770600e+05,\n",
    "          3.58447600e+05,   4.01802600e+05,   4.50401100e+05,\n",
    "          5.04877600e+05,   5.65943600e+05,   6.34395600e+05,\n",
    "          7.11126600e+05,   7.97138100e+05,   8.93553100e+05,\n",
    "          1.00162960e+06,   1.12277810e+06,   1.25858010e+06,\n",
    "          1.41080710e+06,   1.58144610e+06,   1.77272410e+06,\n",
    "          1.98713760e+06,   2.22748460e+06,   2.49690160e+06,\n",
    "          2.79890560e+06,   3.13743760e+06,   3.51691460e+06,\n",
    "          3.94229010e+06,   4.41911560e+06,   4.95361410e+06,\n",
    "          5.55276110e+06,   6.22437510e+06,   6.97722160e+06,\n",
    "          7.82112610e+06,   8.76710210e+06,   9.82749510e+06,\n",
    "          1.10161446e+07,   1.23485626e+07,   1.38421381e+07,\n",
    "          1.55163641e+07,   1.73930901e+07,   1.94968086e+07,\n",
    "          2.18549741e+07,   2.44983631e+07,   2.74614741e+07,\n",
    "          3.07829776e+07,   3.45062216e+07,   3.86797966e+07,\n",
    "          4.33581716e+07,   4.86024026e+07,   5.44809301e+07,\n",
    "          6.10704741e+07,   6.84570326e+07,   7.67370056e+07,\n",
    "          8.60184526e+07,   9.64225031e+07,   1.08084938e+08,\n",
    "          1.21157961e+08,   1.35812184e+08,   1.52238855e+08,\n",
    "          1.70652355e+08,   1.91292994e+08,   2.14430146e+08,\n",
    "          2.40365768e+08,   2.69438340e+08,   3.02027280e+08,\n",
    "          3.38557897e+08,   3.79506943e+08,   4.25408832e+08,\n",
    "          4.76862619e+08,   5.34539812e+08,   5.99193141e+08,\n",
    "          6.71666379e+08,   7.52905356e+08,   8.43970300e+08,\n",
    "          9.46049675e+08],\n",
    "       [  5.12401928e-06,   5.21053010e-06,   5.43670420e-06,\n",
    "          5.51432327e-06,   5.80338727e-06,   5.74049234e-06,\n",
    "          5.81727055e-06,   5.86260055e-06,   5.73426592e-06,\n",
    "          5.67409064e-06,   5.51083511e-06,   5.34948672e-06,\n",
    "          5.15669658e-06,   4.94856413e-06,   4.69226356e-06,\n",
    "          4.42405745e-06,   4.17199337e-06,   3.90491288e-06,\n",
    "          3.61900127e-06,   3.39410568e-06,   3.12408496e-06,\n",
    "          2.89812736e-06,   2.69193214e-06,   2.45217645e-06,\n",
    "          2.27232962e-06,   2.09816377e-06,   1.93096590e-06,\n",
    "          1.78977445e-06,   1.64921804e-06,   1.52646390e-06,\n",
    "          1.41841330e-06,   1.32360411e-06,   1.22847270e-06,\n",
    "          1.15095084e-06,   1.08174275e-06,   1.01408849e-06,\n",
    "          9.55430430e-07,   8.98691236e-07,   8.58129012e-07,\n",
    "          8.08875083e-07,   7.64637589e-07,   7.31114664e-07,\n",
    "          6.92200849e-07,   6.60665470e-07,   6.31291407e-07,\n",
    "          5.97896042e-07,   5.68835030e-07,   5.42300591e-07,\n",
    "          5.15300288e-07,   4.88965839e-07,   4.61142954e-07,\n",
    "          4.36771896e-07,   4.13572974e-07,   3.90778564e-07,\n",
    "          3.69347980e-07,   3.48814164e-07,   3.28971645e-07,\n",
    "          3.11824481e-07,   2.93789418e-07,   2.76521240e-07,\n",
    "          2.59964798e-07,   2.43863255e-07,   2.27076162e-07,\n",
    "          2.08273698e-07,   1.87344523e-07,   1.63703500e-07,\n",
    "          1.37869309e-07,   1.11120277e-07,   8.64569185e-08,\n",
    "          6.59059683e-08,   4.95332916e-08,   3.70433346e-08,\n",
    "          2.74404981e-08,   2.04434070e-08,   1.54896441e-08,\n",
    "          1.21378868e-08,   9.97291256e-09,   8.51308568e-09,\n",
    "          7.41821013e-09,   6.61411826e-09,   5.95786854e-09,\n",
    "          5.33640193e-09,   4.72705901e-09,   4.18209989e-09,\n",
    "          3.59923398e-09,   3.03972761e-09,   2.54110466e-09,\n",
    "          2.11420249e-09,   1.73818067e-09,   1.44410727e-09,\n",
    "          1.16081550e-09,   9.36761032e-10,   7.76385942e-10,\n",
    "          6.58526750e-10,   5.62581388e-10,   4.95524509e-10,\n",
    "          4.62136925e-10,   4.14579522e-10,   4.02640302e-10,\n",
    "          3.91300897e-10,   3.52900340e-10,   3.59177371e-10,\n",
    "          4.74865558e-10,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan,              np.nan,              np.nan,\n",
    "                     np.nan]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:50.001895Z",
     "start_time": "2020-06-22T20:20:49.980182Z"
    }
   },
   "outputs": [],
   "source": [
    "# REFSC_30m = smcviz.scalings['WT-20150609-30m-R1']\n",
    "\n",
    "REFSC_30m = (\n",
    "    np.array([1060.0999999999999, 1188.0999999999999, 1332.0999999999999, 1493.0999999999999, 1673.5999999999999, 1876.0999999999999, 2103.0999999999999, 2357.5999999999999, 2643.0999999999999, 2963.0999999999999, 3321.5999999999999, 3723.0999999999999, 4173.1000000000004, 4678.1000000000004, 5244.1000000000004, 5878.6000000000004, 6589.6000000000004, 7386.6000000000004, 8280.1000000000004, 9281.6000000000004, 10404.6, 11663.1, 13073.6, 14655.1, 16427.599999999999, 18414.599999999999, 20642.099999999999, 23138.599999999999, 25937.099999999999, 29074.099999999999, 32591.099999999999, 36533.099999999999, 40951.599999999999, 45905.099999999999, 51457.599999999999, 57681.599999999999, 64658.100000000006, 72478.600000000006, 81245.100000000006, 91071.600000000006, 102087.10000000001, 114434.60000000001, 128275.60000000001, 143791.10000000001, 161182.60000000001, 180677.60000000001, 202531.10000000001, 227027.60000000001, 254486.59999999998, 285267.09999999998, 319770.59999999998, 358447.59999999998, 401802.59999999998, 450401.09999999998, 504877.59999999998, 565943.59999999998, 634395.59999999998, 711126.59999999998, 797138.09999999998, 893553.09999999998, 1001629.6000000001, 1122778.1000000001, 1258580.1000000001, 1410807.1000000001, 1581446.1000000001, 1772724.1000000001, 1987137.6000000001, 2227484.6000000001, 2496901.6000000001, 2798905.6000000001, 3137437.6000000001, 3516914.6000000001, 3942290.1000000001, 4419115.5999999996, 4953614.0999999996, 5552761.0999999996, 6224375.0999999996, 6977221.5999999996, 7821126.0999999996, 8767102.0999999996, 9827495.0999999996, 11016144.6, 12348562.6, 13842138.1, 15516364.1, 17393090.100000001, 19496808.600000001, 21854974.100000001, 24498363.100000001, 27461474.100000001, 30782977.600000001, 34506221.600000001, 38679796.600000001, 43358171.600000001, 48602402.600000001, 54480930.100000001, 61070474.100000001, 68457032.599999994, 76737005.599999994, 86018452.599999994, 96422503.099999994, 108084938.09999999, 121157961.09999999, 135812184.09999999, 152238855.09999999, 170652355.09999999, 191292993.59999999, 214430145.59999999, 240365768.09999999, 269438340.10000002, 302027280.10000002, 338557897.10000002, 379506942.60000002, 425408832.10000002, 476862618.60000002, 534539811.60000002, 599193140.60000002, 671666379.10000002, 752905356.10000002, 843970299.60000002, 946049674.60000002]),\n",
    "    np.array([23.675955871271874, 23.236626555421729, 23.066863635884847, 22.154452714571342, 21.467480284981807, 20.487367494245994, 19.160454706435878, 17.949003170166357, 16.415234568857386, 14.890233181524469, 13.327493418288194, 11.976836612759442, 10.512942407898162, 9.1349005029935775, 7.9085956321347277, 6.7569424618369842, 5.8690989848720863, 5.0640546842705909, 4.4052025303940123, 3.8904891247104438, 3.4631642433279706, 3.1253142884925431, 2.8047821378746551, 2.5578439264424477, 2.3630871716158621, 2.1790466537117612, 2.0246085517256325, 1.8838032175167119, 1.76475211641389, 1.6462896440262906, 1.5525393513441721, 1.4588544928495353, 1.3721372555187086, 1.3008301278520282, 1.2287831641991491, 1.1669889062227117, 1.1058944331413405, 1.0500554482960165, 1.0, 0.9626059154858434, 0.91474919296378132, 0.87982050546965485, 0.84226404486894413, 0.8089168077811949, 0.7769503867285813, 0.74647710200195516, 0.71588134711832496, 0.68697410529290193, 0.65871989693983068, 0.62731464238829537, 0.60027613270236702, 0.57514705963705059, 0.54615107057834789, 0.51976930440691971, 0.49090114734707713, 0.46307744679980578, 0.43447665458306117, 0.40687844130045864, 0.37938086283748018, 0.35114034378259634, 0.32291674469674325, 0.29463998972017813, 0.26751258897884228, 0.23995520669188192, 0.21396083423648307, 0.19011344601583471, 0.16718835632995099, 0.14709446687242295, 0.13021253723330545, 0.1160188918882349, 0.10670497929090897, 0.10111587113542761, 0.099641938724001289, 0.10259190796296001, 0.10912502769018932, 0.11637930652079441, 0.11998296048899362, 0.11298928603359318, 0.094577549712040562, 0.069578032765169537, 0.048057204280106222, 0.035223246097696274, 0.029000513382554206, 0.024373415309400931, 0.019243673691066811, 0.01462752889253496, 0.011302196219838108, 0.0091482940278357575, 0.0074610556191346883, 0.0061577877928047694, 0.0052151024960781353, 0.0044258888621608412, 0.0038571980326946318, 0.0033870834338773342, 0.0030387264655687874, 0.002745753811241077, 0.002526358711246568, 0.0023551346707166173, 0.002179804846088893, 0.0019746189904878185, 0.0017948425263511838, 0.0015583471819714605, 0.0012250967415409282, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:50.683058Z",
     "start_time": "2020-06-22T20:20:50.662665Z"
    }
   },
   "outputs": [],
   "source": [
    "REFSC_60m = np.array([\n",
    "    [1.06010000e+03, 1.18810000e+03, 1.33210000e+03, 1.49310000e+03, 1.67360000e+03, 1.87610000e+03, 2.10310000e+03, 2.35760000e+03, 2.64310000e+03, 2.96310000e+03, 3.32160000e+03, 3.72310000e+03, 4.17310000e+03, 4.67810000e+03, 5.24410000e+03, 5.87860000e+03, 6.58960000e+03, 7.38660000e+03, 8.28010000e+03, 9.28160000e+03, 1.04046000e+04, 1.16631000e+04, 1.30736000e+04, 1.46551000e+04, 1.64276000e+04, 1.84146000e+04, 2.06421000e+04, 2.31386000e+04, 2.59371000e+04, 2.90741000e+04, 3.25911000e+04, 3.65331000e+04, 4.09516000e+04, 4.59051000e+04, 5.14576000e+04, 5.76816000e+04, 6.46581000e+04, 7.24786000e+04, 8.12451000e+04, 9.10716000e+04, 1.02087100e+05, 1.14434600e+05, 1.28275600e+05, 1.43791100e+05, 1.61182600e+05, 1.80677600e+05, 2.02531100e+05, 2.27027600e+05, 2.54486600e+05, 2.85267100e+05, 3.19770600e+05, 3.58447600e+05, 4.01802600e+05, 4.50401100e+05, 5.04877600e+05, 5.65943600e+05, 6.34395600e+05, 7.11126600e+05, 7.97138100e+05, 8.93553100e+05, 1.00162960e+06, 1.12277810e+06, 1.25858010e+06, 1.41080710e+06, 1.58144610e+06, 1.77272410e+06, 1.98713760e+06, 2.22748460e+06, 2.49690160e+06, 2.79890560e+06, 3.13743760e+06, 3.51691460e+06, 3.94229010e+06, 4.41911560e+06, 4.95361410e+06, 5.55276110e+06, 6.22437510e+06, 6.97722160e+06, 7.82112610e+06, 8.76710210e+06, 9.82749510e+06, 1.10161446e+07, 1.23485626e+07, 1.38421381e+07, 1.55163641e+07, 1.73930901e+07, 1.94968086e+07, 2.18549741e+07, 2.44983631e+07, 2.74614741e+07, 3.07829776e+07, 3.45062216e+07, 3.86797966e+07, 4.33581716e+07, 4.86024026e+07, 5.44809301e+07, 6.10704741e+07, 6.84570326e+07, 7.67370056e+07, 8.60184526e+07, 9.64225031e+07, 1.08084938e+08, 1.21157961e+08, 1.35812184e+08, 1.52238855e+08, 1.70652355e+08, 1.91292994e+08, 2.14430146e+08, 2.40365768e+08, 2.69438340e+08, 3.02027280e+08, 3.38557897e+08, 3.79506943e+08, 4.25408832e+08, 4.76862619e+08, 5.34539812e+08, 5.99193141e+08, 6.71666379e+08, 7.52905356e+08, 8.43970300e+08, 9.46049675e+08],\n",
    "    [1.11674528e-05, 1.10282684e-05, 1.09980203e-05, 1.06884880e-05, 1.03712325e-05, 9.95821573e-06, 9.47413900e-06, 8.87913543e-06, 8.21672193e-06, 7.45318538e-06, 6.82834626e-06, 6.14048613e-06, 5.37048964e-06, 4.71935343e-06, 4.06989925e-06, 3.51089326e-06, 3.02076909e-06, 2.57435245e-06, 2.21909132e-06, 1.91879215e-06, 1.68308009e-06, 1.46757533e-06, 1.30174005e-06, 1.17126648e-06, 1.04734768e-06, 9.51688382e-07, 8.62266245e-07, 7.93401224e-07, 7.25596116e-07, 6.76413802e-07, 6.24958572e-07, 5.84692670e-07, 5.43564296e-07, 5.05236306e-07, 4.71761588e-07, 4.41920715e-07, 4.15568895e-07, 3.92503100e-07, 3.70347541e-07, 3.50564659e-07, 3.33628688e-07, 3.17393189e-07, 3.03657569e-07, 2.90687400e-07, 2.78146055e-07, 2.66326586e-07, 2.55340097e-07, 2.45048634e-07, 2.35155466e-07, 2.25754918e-07, 2.15531712e-07, 2.06851814e-07, 1.97710816e-07, 1.89624627e-07, 1.80552323e-07, 1.71415055e-07, 1.62620001e-07, 1.53768791e-07, 1.44083593e-07, 1.35276642e-07, 1.26636339e-07, 1.17637592e-07, 1.08375348e-07, 9.94187099e-08, 9.04282429e-08, 8.17490821e-08, 7.35160607e-08, 6.52278835e-08, 5.76475407e-08, 5.06953599e-08, 4.44968908e-08, 3.94321523e-08, 3.47959158e-08, 3.12136029e-08, 2.85296416e-08, 2.67930020e-08, 2.59793418e-08, 2.59996532e-08, 2.65096650e-08, 2.68270629e-08, 2.62755234e-08, 2.43076446e-08, 2.07729648e-08, 1.64369050e-08, 1.22358304e-08, 8.92005709e-09, 6.91674313e-09, 5.68891892e-09, 4.89137781e-09, 4.16687997e-09, 3.59501755e-09, 3.15501512e-09, 2.85019840e-09, 2.60398008e-09, 2.42504006e-09, 2.27515440e-09, 2.16693084e-09, 2.04621298e-09, 1.86925166e-09, 1.73855477e-09, 1.57095274e-09, 1.31108423e-09, 9.11974699e-10,            np.nan, np.nan,            np.nan,            np.nan,            np.nan, np.nan,            np.nan,            np.nan,            np.nan, np.nan,            np.nan,            np.nan,            np.nan, np.nan,            np.nan,            np.nan,            np.nan, np.nan]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:51.553533Z",
     "start_time": "2020-06-22T20:20:51.533565Z"
    }
   },
   "outputs": [],
   "source": [
    "def prepare_canvas(refsc = REFSC_10m, \n",
    "                   exp_loop_size=EXP_LOOP_SIZE):\n",
    "    \n",
    "    fig, ax = plt.subplots(1,2, figsize=(8,3.5))\n",
    "    plt.subplots_adjust(bottom=0.15, wspace=0.3)\n",
    "    ln1 = ax[0].plot([], [], label='test')\n",
    "\n",
    "    ax[0].set_xlim(MIN_LOG10_S,MAX_LOG10_S)\n",
    "    ax[0].set_ylim(-3,1)\n",
    "    ax[0].set_xticks(np.arange(MIN_LOG10_S,MAX_LOG10_S+0.01))\n",
    "    ax[0].axvline(0.0, ls='--', c='gray',lw=0.5)\n",
    "    ax[0].set_aspect(1.0)\n",
    "    ax[0].set_xlabel('log10 separation/avg loop length')\n",
    "    ax[0].set_ylabel('log10 contact frequency')\n",
    "\n",
    "    if refsc is not None:\n",
    "        x = refsc[0] / exp_loop_size\n",
    "        y = refsc[1]\n",
    "        y /= y[np.argmin(np.abs(np.log10(x/GLUE_AT)))]\n",
    "\n",
    "        ax[0].plot(\n",
    "            np.log10(x),\n",
    "            np.log10(y)\n",
    "        )\n",
    "        \n",
    "        ax[1].plot(\n",
    "            np.log10(x[1:]*x[:-1]),\n",
    "            np.diff(np.log10(y)) / np.diff(np.log10(x))\n",
    "        )\n",
    "    else:\n",
    "        ax[0].plot(\n",
    "            [],\n",
    "            []\n",
    "        )\n",
    "        ax[1].plot(\n",
    "            [],\n",
    "            []\n",
    "        )\n",
    "    \n",
    "    \n",
    "    ln2 = ax[1].plot([], [], label='test')\n",
    "    ax[1].set_xlim(MIN_LOG10_S,MAX_LOG10_S)\n",
    "    ax[1].set_ylim(-3,1)\n",
    "    ax[1].set_aspect(1.0)\n",
    "    ax[1].axvline(0.0, ls='--', c='gray',lw=0.5)\n",
    "    ax[1].axhline(0.0, ls='--', c='gray',lw=0.5)\n",
    "    ax[1].axhline(-0.5, ls='--', c='gray',lw=0.5)\n",
    "    ax[1].axhline(-1.0, ls='--', c='gray',lw=0.5)\n",
    "    ax[1].axhline(-1.5, ls='--', c='gray',lw=0.5)\n",
    "    ax[1].set_xlabel('log10 separation/avg loop length')\n",
    "    ax[1].set_ylabel('log-log derivative')\n",
    "    \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:20:54.004699Z",
     "start_time": "2020-06-22T20:20:53.625861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1aa87994300a4ea29a4596a7e00529da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.6, description='log10(z_loop_spread/step)', layout=Layout(height='80…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = prepare_canvas(REFSC_7m)\n",
    "\n",
    "def get_simple_bb_scaling(\n",
    "    z_loop_width_step_log10_ratio,\n",
    "    min_log10_s=MIN_LOG10_S, \n",
    "    max_log10_s=MAX_LOG10_S,\n",
    "    in_loop_slope=IN_LOOP_SLOPE):\n",
    "    \n",
    "    s = 10**np.linspace(MIN_LOG10_S,MAX_LOG10_S,1000)\n",
    "\n",
    "    in_loop_scaling = s**(in_loop_slope)\n",
    "    \n",
    "    between_loop_scaling_z = (\n",
    "        st.norm.pdf(s, loc=0, scale=np.sqrt(2)*(10**z_loop_width_step_log10_ratio))    )\n",
    "    \n",
    "    between_loop_scaling = between_loop_scaling_z\n",
    "    \n",
    "    in_loop_scaling /= in_loop_scaling[np.argmin(np.abs(np.log10(s/GLUE_AT)))]\n",
    "    between_loop_scaling /= between_loop_scaling[np.argmin(np.abs(np.log10(s/GLUE_AT)))]\n",
    "\n",
    "    cp =  np.exp(-s) * in_loop_scaling + (1-np.exp(-s))* between_loop_scaling\n",
    "    \n",
    "    return s, cp\n",
    "\n",
    "def plot_interactive():\n",
    "    \n",
    "    def update_scalings(z_loop_width_step_log10_ratio):\n",
    "        s,cp = get_simple_bb_scaling(\n",
    "            z_loop_width_step_log10_ratio,\n",
    "            )\n",
    "        ax[0].lines[0].set_data(np.log10(s), np.log10(cp))\n",
    "\n",
    "        ax[1].lines[0].set_data(\n",
    "            (np.log10(s)[1:] + np.log10(s)[:-1])/2,\n",
    "            np.diff(np.log10(cp)) / np.diff(np.log10(s))\n",
    "        )\n",
    "        \n",
    "        \n",
    "    widget_z_loop_width_step_log10_ratio = widgets.FloatSlider(\n",
    "        description='log10(z_loop_spread/step)',\n",
    "        layout=widgets.Layout(width='50%', height='80px'),\n",
    "        value=1.6,\n",
    "        min=-1, \n",
    "        max=4)\n",
    "    interact(update_scalings, z_loop_width_step_log10_ratio=widget_z_loop_width_step_log10_ratio)\n",
    "    \n",
    "plot_interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bottle brush with RW angular loop orientation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No drift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-26T20:56:05.048759",
     "start_time": "2017-05-26T20:56:05.029247"
    }
   },
   "source": [
    "A bottlebrush with angular correlations of loops.\n",
    "- Below the loop size the scaling is calculated as s^-3/2.\n",
    "- Above the loop size, contact probability is the probability for two particles to have the same z-component and same angle.\n",
    "    - The probability of an overlap in z is calculated as in (1).\n",
    "    - The angular orientations of loops perform a RW, the probability of an overlap == the return probability.\n",
    "    - The 2nd slider sets $log10 (RW\\_step\\_in\\_radians / 2 \\pi ) $\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-25T15:22:19.110110Z",
     "start_time": "2019-04-25T15:22:19.101796Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:21:51.263905Z",
     "start_time": "2020-06-22T20:21:51.115571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2480274491754c0f9285f535ad21f69e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.4, description='log10(z_loop_spread/step)', layout=Layout(height='80…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =prepare_canvas(REFSC_60m,80000)\n",
    "#fig, ax =prepare_canvas(REFSC_7m,60000)\n",
    "\n",
    "def get_simple_bb_scaling(\n",
    "    z_loop_width_step_log10_ratio,\n",
    "    ang_rw_step,\n",
    "    ang_loop_spread,\n",
    "    min_log10_s=MIN_LOG10_S, \n",
    "    max_log10_s=MAX_LOG10_S):\n",
    "    \n",
    "    s = 10**np.linspace(MIN_LOG10_S,MAX_LOG10_S,1000)\n",
    "\n",
    "    in_loop_scaling = s**(-3/2)\n",
    "\n",
    "    between_loop_scaling_z = (\n",
    "        st.norm.pdf(s, loc=0, scale=np.sqrt(2)*(10**z_loop_width_step_log10_ratio))    )\n",
    "    \n",
    "    between_loop_scaling_ang = np.vstack(\n",