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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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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",
    "        st.norm.pdf(x , loc=0, scale=np.sqrt(2*ang_loop_spread**2+s*(ang_rw_step**2)))\n",
    "        for x in 2*np.pi*np.arange(-100,100+0.1)\n",
    "        ).sum(axis=0)\n",
    "\n",
    "    between_loop_scaling = between_loop_scaling_z * between_loop_scaling_ang\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,ang_rw_step,ang_loop_spread):\n",
    "        ang_rw_step = 2 * np.pi * (10**ang_rw_step)\n",
    "        ang_loop_spread = 2 * np.pi * (10**ang_loop_spread)\n",
    "        s,cp = get_simple_bb_scaling(\n",
    "            z_loop_width_step_log10_ratio,\n",
    "            ang_rw_step,\n",
    "            ang_loop_spread\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=1.4, #WT-7m 6kb\n",
    "        #value=1.4,\n",
    "        min=-4, \n",
    "        max=4)\n",
    "    widget_ang_rw_step = widgets.FloatSlider(\n",
    "        description='log10 (ang_rw_step / 2 Pi)',\n",
    "        layout=widgets.Layout(width='50%', height='80px'),\n",
    "        value=-1.1, #WT-7m 6kb\n",
    "        #value=-1.1,\n",
    "        min=-4, \n",
    "        max=0)\n",
    "    widget_ang_loop_spread = widgets.FloatSlider(\n",
    "        description='log10 (ang_loop_spread / 2 Pi)',\n",
    "        layout=widgets.Layout(width='50%', height='80px'),\n",
    "        value=-1.7, #WT-7m 60kb\n",
    "        #value=-1.3,\n",
    "        min=-4, \n",
    "        max=0)\n",
    "    interact(\n",
    "        update_scalings, \n",
    "        z_loop_width_step_log10_ratio=widget_z_loop_width_step_log10_ratio,\n",
    "        ang_rw_step=widget_ang_rw_step,\n",
    "        ang_loop_spread=widget_ang_loop_spread\n",
    "    )\n",
    "    ax[0].set_ylim(-1.5,1.5)\n",
    "    ax[0].set_xlim(-1,3)\n",
    "    #plt.savefig(f'{FIG_FOLDER}/coarse_grained_RW_scaling_7m_60kb.svg')\n",
    "    \n",
    "plot_interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-26T13:24:03.332250",
     "start_time": "2017-05-26T13:24:03.324858"
    }
   },
   "source": [
    "## With drift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-26T21:05:23.418338",
     "start_time": "2017-05-26T21:05:23.408531"
    }
   },
   "source": [
    "A bottlebrush with angular correlations and drift of loops.\n",
    "- Same as (2), but with a drift component.\n",
    "    - The 3rd slider sets $log10 (angular\\_drift\\_per\\_loop\\_in\\_radians / 2 \\pi ) $. The inverse of this number is the period of the spiral in loops.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:22:20.198828Z",
     "start_time": "2020-06-22T20:22:20.003370Z"
    },
    "scrolled": false
   },
   "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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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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": "d481ea4c927043fda3365e37acd19bb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.8, description='log10(z_loop_spread/step)', layout=Layout(height='80…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =prepare_canvas(REFSC_30m)\n",
    "\n",
    "def get_simple_bb_scaling(\n",
    "    z_loop_width_step_log10_ratio,\n",
    "    ang_rw_step,\n",
    "    ang_drift_step,\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, \n",
    "                    loc=0, \n",
    "                    scale=np.sqrt(2)*(10**z_loop_width_step_log10_ratio))\n",
    "    )\n",
    "\n",
    "    between_loop_scaling_ang = np.vstack(\n",
    "        st.norm.pdf(x , loc=ang_drift_step*s, scale=ang_rw_step*np.sqrt(s))\n",
    "        for x in 2*np.pi*np.arange(-100,100+0.1)\n",
    "    ).sum(axis=0)\n",
    "\n",
    "    between_loop_scaling = between_loop_scaling_ang * 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",
    "                        ang_rw_step,\n",
    "                        ang_drift_step):\n",
    "        \n",
    "        ang_rw_step = 2 * np.pi * (10**ang_rw_step)\n",
    "        ang_drift_step = 2 * np.pi * (10**ang_drift_step)\n",
    "        s,cp = get_simple_bb_scaling(\n",
    "            z_loop_width_step_log10_ratio,\n",
    "            ang_rw_step,\n",
    "            ang_drift_step,\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='75%', height='80px'),\n",
    "        value=1.8,\n",
    "        min=-4, \n",
    "        max=4)\n",
    "    widget_ang_rw_step = widgets.FloatSlider(\n",
    "        description='log10 (ang_rw_step / 2 Pi)',\n",
    "        layout=widgets.Layout(width='75%', height='80px'),\n",
    "        value=-1.4,\n",
    "        min=-4, \n",
    "        max=0)\n",
    "    widget_ang_drift_step = widgets.FloatSlider(\n",
    "        description='log10 (ang_drift_step / 2 Pi)',\n",
    "        layout=widgets.Layout(width='75%', height='80px'),\n",
    "        value=-2.0,\n",
    "        min=-4, \n",
    "        max=0)\n",
    "\n",
    "    interact(\n",
    "        update_scalings, \n",
    "        z_loop_width_step_log10_ratio=widget_z_loop_width_step_log10_ratio,\n",
    "        ang_rw_step=widget_ang_rw_step,\n",
    "        ang_drift_step=widget_ang_drift_step)\n",
    "    \n",
    "\n",
    "plot_interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bottle brush with OU angular loop orientation (approximate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 contrained random walk with a drift (trending Ornstein Uhlenbeck process). The probability of an overlap == the return probability.\n",
    "    - The 2nd slider sets log10 standard deviation of angular spread for loops emanating from the same anchor / $2\\pi$.\n",
    "    - The 3nd slider sets log10 characteristic length scale of angular loop correlations (in number of loops)\n",
    "    - The 4th slider sets $log10 (angular\\_drift\\_per\\_loop\\_in\\_radians / 2 \\pi ) $. The inverse of this number is the period of the spiral in loops.\n",
    "\n",
    "\n",
    "References:\n",
    "- trending Ornstein-Uhlenbeck (http://web.mit.edu/wangj/www/pap/LoWang95.pdf, page 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T20:22:48.710558Z",
     "start_time": "2020-06-22T20:22:48.535586Z"
    }
   },
   "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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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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": "97760157676e4c4a8328de8df6e690aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.75, description='log10(z_loop_spread/step)', layout=Layout(height='8…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =prepare_canvas(REFSC_60m, 80000)\n",
    "#fig, ax =prepare_canvas(REFSC_30m, 80000)\n",
    "\n",
    "def get_simple_bb_scaling(\n",
    "    z_loop_width_step_log10_ratio,\n",
    "    ang_rw_step,\n",
    "    ang_loop_spread,\n",
    "    ang_drift_step,\n",
    "    ang_rw_return_scale,\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",
    "    between_loop_scaling_z /= between_loop_scaling_z[np.argmin(np.abs(np.log10(s/GLUE_AT)))]\n",
    "          \n",
    "    between_loop_scaling_ang = np.vstack(\n",
    "        st.norm.pdf(x , \n",
    "                    loc=ang_drift_step*s, \n",
    "                    scale=np.sqrt(2*ang_loop_spread**2\n",
    "                                  +\n",
    "                                  (ang_rw_step**2)*(1-np.exp(-s/ang_rw_return_scale)) * ang_rw_return_scale )\n",
    "                   )\n",
    "        for x in 2*np.pi*np.arange(-100,100+0.1)\n",
    "    ).sum(axis=0)\n",
    "    \n",
    "    between_loop_scaling = between_loop_scaling_ang * 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,ang_rw_stat_std,ang_rw_return_scale,ang_loop_spread,ang_drift_step):\n",
    "        ang_rw_stat_std = 2 * np.pi * (10**ang_rw_stat_std)\n",
    "        ang_rw_return_scale = 10 ** (ang_rw_return_scale)\n",
    "        ang_loop_spread = 2 * np.pi * (10**ang_loop_spread)\n",
    "        ang_drift_step = 2 * np.pi * (10**ang_drift_step)\n",
    "        ang_rw_step = ang_rw_stat_std * np.sqrt(ang_rw_return_scale)\n",
    "        s,cp = get_simple_bb_scaling(\n",
    "            z_loop_width_step_log10_ratio,\n",
    "            ang_rw_step,\n",
    "            ang_loop_spread,\n",
    "            ang_drift_step,\n",
    "            ang_rw_return_scale\n",
    "            )\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='75%', height='80px'),\n",
    "        #value=1.65, #WT-30m\n",
    "        value=1.75, #WT-60m\n",
    "        #value=1.57, #CAPH1-R2-30m 200kb\n",
    "        #value=1.30, #CAPH1-R2-30m 400kb\n",
    "        min=-4, \n",
    "        step=0.025,\n",
    "        max=4)\n",
    "    widget_ang_rw_stat_std = widgets.FloatSlider(\n",
    "        description='log10 (ang_rw_stat_std / 2 Pi)',\n",
    "        layout=widgets.Layout(width='75%', height='80px'),\n",
    "        #value=-1.7, #WT-30m,\n",
    "        value=-1.65, #WT-60m\n",
    "        #value=-1.45, #CAPH2-30m\n",
    "        #value=-1.4, #CAPH2-R2-30m 400kb\n",
    "        min=-2, \n",
    "        step=0.025,\n",
    "\n",
    "        max=0)\n",
    "    widget_ang_loop_spread = widgets.FloatSlider(\n",
    "        description='log10 (ang_loop_spread / 2 Pi)',\n",
    "        layout=widgets.Layout(width='50%', height='80px'),\n",
    "        #value=-1.4, #WT-30m\n",
    "        value=-1.675, #WT-60m\n",
    "        #value=-1.1, #CAPH1-R2-30m 200kb\n",
    "        #value=-0.9, #CAPH1-R2-30m 400kb\n",
    "        min=-4, \n",
    "        max=0)\n",
    "    widget_ang_rw_return_scale = widgets.FloatSlider(\n",
    "        description='log10 (ang_rw_return_scale)',\n",
    "        layout=widgets.Layout(width='75%', height='80px'),\n",
    "        #value=1.15, #WT-30m\n",
    "        value=1.05, #WT-60m\n",
    "        #value=1.00, #CAPH1-R2-30m 200kb\n",
    "        #value=0.95, #CAPH1-R2-30m 400kb\n",
    "        \n",
    "\n",
    "        step=0.025,\n",
    "        \n",
    "        min=0, \n",
    "        max=4)\n",
    "    widget_ang_drift_step = widgets.FloatSlider(\n",
    "        description='log10 (ang_drift_step / 2 Pi)',\n",
    "        layout=widgets.Layout(width='75%', height='80px'),\n",
    "        step=0.025,\n",
    "        \n",
    "        #value=-1.975, #WT-30m\n",
    "        value=-2.15, #WT-60m\n",
    "        #value=-1.7, #CAPH1-R2-30m 200kb\n",
    "        #value=-1.425, #CAPH1-R2-30m 400kb\n",
    "        min=-4, \n",
    "        max=0)\n",
    "\n",
    "    interact(\n",
    "        update_scalings, \n",
    "        z_loop_width_step_log10_ratio=widget_z_loop_width_step_log10_ratio,\n",
    "        ang_rw_stat_std=widget_ang_rw_stat_std,\n",
    "        ang_loop_spread=widget_ang_loop_spread,\n",
    "        ang_rw_return_scale=widget_ang_rw_return_scale,\n",
    "        ang_drift_step=widget_ang_drift_step)\n",
    "\n",
    "    ax[0].set_ylim(-2,1)\n",
    "    ax[0].set_xlim(-1,3)\n",
    "    #plt.savefig(f'{FIG_FOLDER}/coarse_grained_RW_scaling_WT-30m-80kb.svg')\n",
    "    #plt.savefig(f'{FIG_FOLDER}/coarse_grained_RW_scaling_WT-60m-80kb.svg')\n",
    "    #plt.savefig(f'{FIG_FOLDER}/coarse_grained_RW_scaling_CAPH1-R2-30m-200kb.svg')\n",
    "\n",
    "    \n",
    "\n",
    "plot_interactive()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experimental"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-04T03:35:38.072280Z",
     "start_time": "2017-07-04T03:35:38.029228Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-04T03:55:46.332312Z",
     "start_time": "2017-07-04T03:55:46.291926Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def simulate_trending_ou(\n",
    "    N, ang_rw_stat_std,ang_rw_return_scale,ang_loop_spread,ang_drift_step):\n",
    "    \n",
    "    ang_rw_stat_std = 2 * np.pi * (10**ang_rw_stat_std)\n",
    "    ang_rw_return_scale = 10 ** (ang_rw_return_scale)\n",
    "    ang_loop_spread = 2 * np.pi * (10**ang_loop_spread)\n",
    "    ang_drift_step = 2 * np.pi * (10**ang_drift_step)\n",
    "    ang_rw_step = ang_rw_stat_std * np.sqrt(ang_rw_return_scale)\n",
    "    \n",
    "    angles = [0]\n",
    "    for i in range(1,N):\n",
    "        new_ang = angles[i-1] + ang_drift_step - (angles[i-1] - ang_drift_step * i) / ang_rw_return_scale + np.random.normal(scale=ang_rw_step)\n",
    "        angles.append(new_ang)\n",
    "        \n",
    "    return angles\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-04T03:56:06.866708Z",
     "start_time": "2017-07-04T03:56:06.830419Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0771791515585\n"
     ]
    }
   ],
   "source": [
    "ang_rw_stat_std = -1.675\n",
    "ang_rw_return_scale = 1.125\n",
    "ang_loop_spread = -1.3\n",
    "ang_drift_step = -1.975\n",
    "\n",
    "# ang_rw_stat_std = -1.75\n",
    "# ang_rw_return_scale = 1.38\n",
    "# ang_loop_spread = -0.7\n",
    "# ang_drift_step = -1.77\n",
    "\n",
    "ang_rw_stat_std = 2 * np.pi * (10**ang_rw_stat_std)\n",
    "ang_rw_return_scale = 10 ** (ang_rw_return_scale)\n",
    "ang_loop_spread = 2 * np.pi * (10**ang_loop_spread)\n",
    "ang_drift_step = 2 * np.pi * (10**ang_drift_step)\n",
    "\n",
    "ang_rw_step = ang_rw_stat_std * np.sqrt(ang_rw_return_scale)\n",
    "print(ang_rw_step / 2 / np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "ang_rw_stat_std = 2 * np.pi * (10**ang_rw_stat_std)\n",
    "ang_rw_return_scale = 10 ** (ang_rw_return_scale)\n",
    "ang_loop_spread = 2 * np.pi * (10**ang_loop_spread)\n",
    "ang_drift_step = 2 * np.pi * (10**ang_drift_step)\n",
    "\n",
    "ang_rw_step = ang_rw_stat_std * np.sqrt(ang_rw_return_scale)\n",
    "print(ang_rw_step / 2 / np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-04T03:55:48.356214Z",
     "start_time": "2017-07-04T03:55:48.062921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe8a5c51a90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
       "<svg height=\"161pt\" version=\"1.1\" viewBox=\"0 0 228 161\" width=\"228pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 161.186703 \n",
       "L 228.344064 161.186703 \n",
       "L 228.344064 0 \n",
       "L 0 0 \n",
       "z\n",
       "\" style=\"fill:none;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 49.744594 115.92 \n",
       "L 217.144594 115.92 \n",
       "L 217.144594 7.2 \n",
       "L 49.744594 7.2 \n",
       "z\n",
       "\" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_1\">\n",
       "    <g id=\"xtick_1\">\n",
       "     <g id=\"line2d_1\"/>\n",
       "     <g id=\"text_1\">\n",
       "      <!-- 0 -->\n",
       "      <defs>\n",
       "       <path d=\"M 4.15625 35.296875 \n",
       "Q 4.15625 48 6.765625 55.734375 \n",
       "Q 9.375 63.484375 14.515625 67.671875 \n",
       "Q 19.671875 71.875 27.484375 71.875 \n",
       "Q 33.25 71.875 37.59375 69.546875 \n",
       "Q 41.9375 67.234375 44.765625 62.859375 \n",
       "Q 47.609375 58.5 49.21875 52.21875 \n",
       "Q 50.828125 45.953125 50.828125 35.296875 \n",
       "Q 50.828125 22.703125 48.234375 14.96875 \n",
       "Q 45.65625 7.234375 40.5 3 \n",
       "Q 35.359375 -1.21875 27.484375 -1.21875 \n",
       "Q 17.140625 -1.21875 11.234375 6.203125 \n",
       "Q 4.15625 15.140625 4.15625 35.296875 \n",
       "M 13.1875 35.296875 \n",
       "Q 13.1875 17.671875 17.3125 11.828125 \n",
       "Q 21.4375 6 27.484375 6 \n",
       "Q 33.546875 6 37.671875 11.859375 \n",
       "Q 41.796875 17.71875 41.796875 35.296875 \n",
       "Q 41.796875 52.984375 37.671875 58.78125 \n",
       "Q 33.546875 64.59375 27.390625 64.59375 \n",
       "Q 21.34375 64.59375 17.71875 59.46875 \n",
       "Q 13.1875 52.9375 13.1875 35.296875 \n",
       "\" id=\"ArialMT-30\"/>\n",
       "      </defs>\n",
       "      <g style=\"fill:#262626;\" transform=\"translate(53.739075 134.325156)scale(0.13 -0.13)\">\n",
       "       <use xlink:href=\"#ArialMT-30\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_2\">\n",
       "     <g id=\"line2d_2\"/>\n",
       "     <g id=\"text_2\">\n",
       "      <!-- 100 -->\n",
       "      <defs>\n",
       "       <path d=\"M 37.25 0 \n",
       "L 28.46875 0 \n",
       "L 28.46875 56 \n",
       "Q 25.296875 52.984375 20.140625 49.953125 \n",
       "Q 14.984375 46.921875 10.890625 45.40625 \n",
       "L 10.890625 53.90625 \n",
       "Q 18.265625 57.375 23.78125 62.296875 \n",
       "Q 29.296875 67.234375 31.59375 71.875 \n",
       "L 37.25 71.875 \n",
       "z\n",
       "\" id=\"ArialMT-31\"/>\n",
       "      </defs>\n",
       "      <g style=\"fill:#262626;\" transform=\"translate(122.983132 134.325156)scale(0.13 -0.13)\">\n",
       "       <use xlink:href=\"#ArialMT-31\"/>\n",
       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\n",
       "       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_3\">\n",
       "     <g id=\"line2d_3\"/>\n",
       "     <g id=\"text_3\">\n",
       "      <!-- 200 -->\n",
       "      <defs>\n",
       "       <path d=\"M 50.34375 8.453125 \n",
       "L 50.34375 0 \n",
       "L 3.03125 0 \n",
       "Q 2.9375 3.171875 4.046875 6.109375 \n",
       "Q 5.859375 10.9375 9.828125 15.625 \n",
       "Q 13.8125 20.3125 21.34375 26.46875 \n",
       "Q 33.015625 36.03125 37.109375 41.625 \n",
       "Q 41.21875 47.21875 41.21875 52.203125 \n",
       "Q 41.21875 57.421875 37.46875 61 \n",
       "Q 33.734375 64.59375 27.734375 64.59375 \n",
       "Q 21.390625 64.59375 17.578125 60.78125 \n",
       "Q 13.765625 56.984375 13.71875 50.25 \n",
       "L 4.6875 51.171875 \n",
       "Q 5.609375 61.28125 11.65625 66.578125 \n",
       "Q 17.71875 71.875 27.9375 71.875 \n",
       "Q 38.234375 71.875 44.234375 66.15625 \n",
       "Q 50.25 60.453125 50.25 52 \n",
       "Q 50.25 47.703125 48.484375 43.546875 \n",
       "Q 46.734375 39.40625 42.65625 34.8125 \n",
       "Q 38.578125 30.21875 29.109375 22.21875 \n",
       "Q 21.1875 15.578125 18.9375 13.203125 \n",
       "Q 16.703125 10.84375 15.234375 8.453125 \n",
       "z\n",
       "\" id=\"ArialMT-32\"/>\n",
       "      </defs>\n",
       "      <g style=\"fill:#262626;\" transform=\"translate(199.456407 134.325156)scale(0.13 -0.13)\">\n",
       "       <use xlink:href=\"#ArialMT-32\"/>\n",
       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\n",
       "       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"text_4\">\n",
       "     <!-- loop index -->\n",
       "     <defs>\n",
       "      <path d=\"M 6.390625 0 \n",
       "L 6.390625 71.578125 \n",
       "L 15.1875 71.578125 \n",
       "L 15.1875 0 \n",
       "z\n",
       "\" id=\"ArialMT-6c\"/>\n",
       "      <path d=\"M 3.328125 25.921875 \n",
       "Q 3.328125 40.328125 11.328125 47.265625 \n",
       "Q 18.015625 53.03125 27.640625 53.03125 \n",
       "Q 38.328125 53.03125 45.109375 46.015625 \n",
       "Q 51.90625 39.015625 51.90625 26.65625 \n",
       "Q 51.90625 16.65625 48.90625 10.90625 \n",
       "Q 45.90625 5.171875 40.15625 2 \n",
       "Q 34.421875 -1.171875 27.640625 -1.171875 \n",
       "Q 16.75 -1.171875 10.03125 5.8125 \n",
       "Q 3.328125 12.796875 3.328125 25.921875 \n",
       "M 12.359375 25.921875 \n",
       "Q 12.359375 15.96875 16.703125 11.015625 \n",
       "Q 21.046875 6.0625 27.640625 6.0625 \n",
       "Q 34.1875 6.0625 38.53125 11.03125 \n",
       "Q 42.875 16.015625 42.875 26.21875 \n",
       "Q 42.875 35.84375 38.5 40.796875 \n",
       "Q 34.125 45.75 27.640625 45.75 \n",
       "Q 21.046875 45.75 16.703125 40.8125 \n",
       "Q 12.359375 35.890625 12.359375 25.921875 \n",
       "\" id=\"ArialMT-6f\"/>\n",
       "      <path d=\"M 6.59375 -19.875 \n",
       "L 6.59375 51.859375 \n",
       "L 14.59375 51.859375 \n",
       "L 14.59375 45.125 \n",
       "Q 17.4375 49.078125 21 51.046875 \n",
       "Q 24.5625 53.03125 29.640625 53.03125 \n",
       "Q 36.28125 53.03125 41.359375 49.609375 \n",
       "Q 46.4375 46.1875 49.015625 39.953125 \n",
       "Q 51.609375 33.734375 51.609375 26.3125 \n",
       "Q 51.609375 18.359375 48.75 11.984375 \n",
       "Q 45.90625 5.609375 40.453125 2.21875 \n",
       "Q 35.015625 -1.171875 29 -1.171875 \n",
       "Q 24.609375 -1.171875 21.109375 0.6875 \n",
       "Q 17.625 2.546875 15.375 5.375 \n",
       "L 15.375 -19.875 \n",
       "z\n",
       "M 14.546875 25.640625 \n",
       "Q 14.546875 15.625 18.59375 10.84375 \n",
       "Q 22.65625 6.0625 28.421875 6.0625 \n",
       "Q 34.28125 6.0625 38.453125 11.015625 \n",
       "Q 42.625 15.96875 42.625 26.375 \n",
       "Q 42.625 36.28125 38.546875 41.203125 \n",
       "Q 34.46875 46.140625 28.8125 46.140625 \n",
       "Q 23.1875 46.140625 18.859375 40.890625 \n",
       "Q 14.546875 35.640625 14.546875 25.640625 \n",
       "\" id=\"ArialMT-70\"/>\n",
       "      <path id=\"ArialMT-20\"/>\n",
       "      <path d=\"M 6.640625 61.46875 \n",
       "L 6.640625 71.578125 \n",
       "L 15.4375 71.578125 \n",
       "L 15.4375 61.46875 \n",
       "z\n",
       "M 6.640625 0 \n",
       "L 6.640625 51.859375 \n",
       "L 15.4375 51.859375 \n",
       "L 15.4375 0 \n",
       "z\n",
       "\" id=\"ArialMT-69\"/>\n",
       "      <path d=\"M 6.59375 0 \n",
       "L 6.59375 51.859375 \n",
       "L 14.5 51.859375 \n",
       "L 14.5 44.484375 \n",
       "Q 20.21875 53.03125 31 53.03125 \n",
       "Q 35.6875 53.03125 39.625 51.34375 \n",
       "Q 43.5625 49.65625 45.515625 46.921875 \n",
       "Q 47.46875 44.1875 48.25 40.4375 \n",
       "Q 48.734375 37.984375 48.734375 31.890625 \n",
       "L 48.734375 0 \n",
       "L 39.9375 0 \n",
       "L 39.9375 31.546875 \n",
       "Q 39.9375 36.921875 38.90625 39.578125 \n",
       "Q 37.890625 42.234375 35.28125 43.8125 \n",
       "Q 32.671875 45.40625 29.15625 45.40625 \n",
       "Q 23.53125 45.40625 19.453125 41.84375 \n",
       "Q 15.375 38.28125 15.375 28.328125 \n",
       "L 15.375 0 \n",
       "z\n",
       "\" id=\"ArialMT-6e\"/>\n",
       "      <path d=\"M 40.234375 0 \n",
       "L 40.234375 6.546875 \n",
       "Q 35.296875 -1.171875 25.734375 -1.171875 \n",
       "Q 19.53125 -1.171875 14.328125 2.25 \n",
       "Q 9.125 5.671875 6.265625 11.796875 \n",
       "Q 3.421875 17.921875 3.421875 25.875 \n",
       "Q 3.421875 33.640625 6 39.96875 \n",
       "Q 8.59375 46.296875 13.765625 49.65625 \n",
       "Q 18.953125 53.03125 25.34375 53.03125 \n",
       "Q 30.03125 53.03125 33.6875 51.046875 \n",
       "Q 37.359375 49.078125 39.65625 45.90625 \n",
       "L 39.65625 71.578125 \n",
       "L 48.390625 71.578125 \n",
       "L 48.390625 0 \n",
       "z\n",
       "M 12.453125 25.875 \n",
       "Q 12.453125 15.921875 16.640625 10.984375 \n",
       "Q 20.84375 6.0625 26.5625 6.0625 \n",
       "Q 32.328125 6.0625 36.34375 10.765625 \n",
       "Q 40.375 15.484375 40.375 25.140625 \n",
       "Q 40.375 35.796875 36.265625 40.765625 \n",
       "Q 32.171875 45.75 26.171875 45.75 \n",
       "Q 20.3125 45.75 16.375 40.96875 \n",
       "Q 12.453125 36.1875 12.453125 25.875 \n",
       "\" id=\"ArialMT-64\"/>\n",
       "      <path d=\"M 42.09375 16.703125 \n",
       "L 51.171875 15.578125 \n",
       "Q 49.03125 7.625 43.21875 3.21875 \n",
       "Q 37.40625 -1.171875 28.375 -1.171875 \n",
       "Q 17 -1.171875 10.328125 5.828125 \n",
       "Q 3.65625 12.84375 3.65625 25.484375 \n",
       "Q 3.65625 38.578125 10.390625 45.796875 \n",
       "Q 17.140625 53.03125 27.875 53.03125 \n",
       "Q 38.28125 53.03125 44.875 45.953125 \n",
       "Q 51.46875 38.875 51.46875 26.03125 \n",
       "Q 51.46875 25.25 51.421875 23.6875 \n",
       "L 12.75 23.6875 \n",
       "Q 13.234375 15.140625 17.578125 10.59375 \n",
       "Q 21.921875 6.0625 28.421875 6.0625 \n",
       "Q 33.25 6.0625 36.671875 8.59375 \n",
       "Q 40.09375 11.140625 42.09375 16.703125 \n",
       "M 13.234375 30.90625 \n",
       "L 42.1875 30.90625 \n",
       "Q 41.609375 37.453125 38.875 40.71875 \n",
       "Q 34.671875 45.796875 27.984375 45.796875 \n",
       "Q 21.921875 45.796875 17.796875 41.75 \n",
       "Q 13.671875 37.703125 13.234375 30.90625 \n",
       "\" id=\"ArialMT-65\"/>\n",
       "      <path d=\"M 0.734375 0 \n",
       "L 19.671875 26.953125 \n",
       "L 2.15625 51.859375 \n",
       "L 13.140625 51.859375 \n",
       "L 21.09375 39.703125 \n",
       "Q 23.34375 36.234375 24.703125 33.890625 \n",
       "Q 26.859375 37.109375 28.65625 39.59375 \n",
       "L 37.40625 51.859375 \n",
       "L 47.90625 51.859375 \n",
       "L 29.984375 27.4375 \n",
       "L 49.265625 0 \n",
       "L 38.484375 0 \n",
       "L 27.828125 16.109375 \n",
       "L 25 20.453125 \n",
       "L 11.375 0 \n",
       "z\n",
       "\" id=\"ArialMT-78\"/>\n",
       "     </defs>\n",
       "     <g style=\"fill:#262626;\" transform=\"translate(100.849531 151.144578)scale(0.143 -0.143)\">\n",
       "      <use xlink:href=\"#ArialMT-6c\"/>\n",
       "      <use x=\"22.216797\" xlink:href=\"#ArialMT-6f\"/>\n",
       "      <use x=\"77.832031\" xlink:href=\"#ArialMT-6f\"/>\n",
       "      <use x=\"133.447266\" xlink:href=\"#ArialMT-70\"/>\n",
       "      <use x=\"189.0625\" xlink:href=\"#ArialMT-20\"/>\n",
       "      <use x=\"216.845703\" xlink:href=\"#ArialMT-69\"/>\n",
       "      <use x=\"239.0625\" xlink:href=\"#ArialMT-6e\"/>\n",
       "      <use x=\"294.677734\" xlink:href=\"#ArialMT-64\"/>\n",
       "      <use x=\"350.292969\" xlink:href=\"#ArialMT-65\"/>\n",
       "      <use x=\"405.908203\" xlink:href=\"#ArialMT-78\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_2\">\n",
       "    <g id=\"ytick_1\">\n",
       "     <g id=\"line2d_4\"/>\n",
       "     <g id=\"text_5\">\n",
       "      <!-- 0 -->\n",
       "      <g style=\"fill:#262626;\" transform=\"translate(33.415375 113.68953)scale(0.13 -0.13)\">\n",
       "       <use xlink:href=\"#ArialMT-30\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_2\">\n",
       "     <g id=\"line2d_5\"/>\n",
       "     <g id=\"text_6\">\n",
       "      <!-- 2π -->\n",
       "      <defs>\n",
       "       <path d=\"M 24.65625 44.734375 \n",
       "L 24.65625 0 \n",
       "L 15.875 0 \n",
       "L 15.875 44.734375 \n",
       "L 2.203125 44.734375 \n",
       "L 2.203125 51.859375 \n",
       "L 66.84375 51.859375 \n",
       "L 66.84375 44.734375 \n",
       "L 55.03125 44.734375 \n",
       "L 55.03125 0 \n",
       "L 46.234375 0 \n",
       "L 46.234375 44.734375 \n",
       "z\n",
       "\" id=\"ArialMT-3c0\"/>\n",
       "      </defs>\n",
       "      <g style=\"fill:#262626;\" transform=\"translate(24.445375 68.360818)scale(0.13 -0.13)\">\n",
       "       <use xlink:href=\"#ArialMT-32\"/>\n",
       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-3c0\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"ytick_3\">\n",
       "     <g id=\"line2d_6\"/>\n",
       "     <g id=\"text_7\">\n",
       "      <!-- 4π -->\n",
       "      <defs>\n",
       "       <path d=\"M 32.328125 0 \n",
       "L 32.328125 17.140625 \n",
       "L 1.265625 17.140625 \n",
       "L 1.265625 25.203125 \n",
       "L 33.9375 71.578125 \n",
       "L 41.109375 71.578125 \n",
       "L 41.109375 25.203125 \n",
       "L 50.78125 25.203125 \n",
       "L 50.78125 17.140625 \n",
       "L 41.109375 17.140625 \n",
       "L 41.109375 0 \n",
       "z\n",
       "M 32.328125 25.203125 \n",
       "L 32.328125 57.46875 \n",
       "L 9.90625 25.203125 \n",
       "z\n",
       "\" id=\"ArialMT-34\"/>\n",
       "      </defs>\n",
       "      <g style=\"fill:#262626;\" transform=\"translate(24.445375 23.032107)scale(0.13 -0.13)\">\n",
       "       <use xlink:href=\"#ArialMT-34\"/>\n",
       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-3c0\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"text_8\">\n",
       "     <!-- loop angle α -->\n",
       "     <defs>\n",
       "      <path d=\"M 40.4375 6.390625 \n",
       "Q 35.546875 2.25 31.03125 0.53125 \n",
       "Q 26.515625 -1.171875 21.34375 -1.171875 \n",
       "Q 12.796875 -1.171875 8.203125 3 \n",
       "Q 3.609375 7.171875 3.609375 13.671875 \n",
       "Q 3.609375 17.484375 5.34375 20.625 \n",
       "Q 7.078125 23.78125 9.890625 25.6875 \n",
       "Q 12.703125 27.59375 16.21875 28.5625 \n",
       "Q 18.796875 29.25 24.03125 29.890625 \n",
       "Q 34.671875 31.15625 39.703125 32.90625 \n",
       "Q 39.75 34.71875 39.75 35.203125 \n",
       "Q 39.75 40.578125 37.25 42.78125 \n",
       "Q 33.890625 45.75 27.25 45.75 \n",
       "Q 21.046875 45.75 18.09375 43.578125 \n",
       "Q 15.140625 41.40625 13.71875 35.890625 \n",
       "L 5.125 37.0625 \n",
       "Q 6.296875 42.578125 8.984375 45.96875 \n",
       "Q 11.671875 49.359375 16.75 51.1875 \n",
       "Q 21.828125 53.03125 28.515625 53.03125 \n",
       "Q 35.15625 53.03125 39.296875 51.46875 \n",
       "Q 43.453125 49.90625 45.40625 47.53125 \n",
       "Q 47.359375 45.171875 48.140625 41.546875 \n",
       "Q 48.578125 39.3125 48.578125 33.453125 \n",
       "L 48.578125 21.734375 \n",
       "Q 48.578125 9.46875 49.140625 6.21875 \n",
       "Q 49.703125 2.984375 51.375 0 \n",
       "L 42.1875 0 \n",
       "Q 40.828125 2.734375 40.4375 6.390625 \n",
       "M 39.703125 26.03125 \n",
       "Q 34.90625 24.078125 25.34375 22.703125 \n",
       "Q 19.921875 21.921875 17.671875 20.9375 \n",
       "Q 15.4375 19.96875 14.203125 18.09375 \n",
       "Q 12.984375 16.21875 12.984375 13.921875 \n",
       "Q 12.984375 10.40625 15.640625 8.0625 \n",
       "Q 18.3125 5.71875 23.4375 5.71875 \n",
       "Q 28.515625 5.71875 32.46875 7.9375 \n",
       "Q 36.421875 10.15625 38.28125 14.015625 \n",
       "Q 39.703125 17 39.703125 22.796875 \n",
       "z\n",
       "\" id=\"ArialMT-61\"/>\n",
       "      <path d=\"M 4.984375 -4.296875 \n",
       "L 13.53125 -5.5625 \n",
       "Q 14.0625 -9.515625 16.5 -11.328125 \n",
       "Q 19.78125 -13.765625 25.4375 -13.765625 \n",
       "Q 31.546875 -13.765625 34.859375 -11.328125 \n",
       "Q 38.1875 -8.890625 39.359375 -4.5 \n",
       "Q 40.046875 -1.8125 39.984375 6.78125 \n",
       "Q 34.234375 0 25.640625 0 \n",
       "Q 14.9375 0 9.078125 7.71875 \n",
       "Q 3.21875 15.4375 3.21875 26.21875 \n",
       "Q 3.21875 33.640625 5.90625 39.90625 \n",
       "Q 8.59375 46.1875 13.6875 49.609375 \n",
       "Q 18.796875 53.03125 25.6875 53.03125 \n",
       "Q 34.859375 53.03125 40.828125 45.609375 \n",
       "L 40.828125 51.859375 \n",
       "L 48.921875 51.859375 \n",
       "L 48.921875 7.03125 \n",
       "Q 48.921875 -5.078125 46.453125 -10.125 \n",
       "Q 44 -15.1875 38.640625 -18.109375 \n",
       "Q 33.296875 -21.046875 25.484375 -21.046875 \n",
       "Q 16.21875 -21.046875 10.5 -16.875 \n",
       "Q 4.78125 -12.703125 4.984375 -4.296875 \n",
       "M 12.25 26.859375 \n",
       "Q 12.25 16.65625 16.296875 11.96875 \n",
       "Q 20.359375 7.28125 26.46875 7.28125 \n",
       "Q 32.515625 7.28125 36.609375 11.9375 \n",
       "Q 40.71875 16.609375 40.71875 26.5625 \n",
       "Q 40.71875 36.078125 36.5 40.90625 \n",
       "Q 32.28125 45.75 26.3125 45.75 \n",
       "Q 20.453125 45.75 16.34375 40.984375 \n",
       "Q 12.25 36.234375 12.25 26.859375 \n",
       "\" id=\"ArialMT-67\"/>\n",
       "      <path d=\"M 45.0625 51.859375 \n",
       "L 54.046875 51.859375 \n",
       "Q 50.640625 41.109375 47.75 25.921875 \n",
       "Q 51.171875 7.078125 54.046875 0 \n",
       "L 45.3125 0 \n",
       "Q 43.21875 4.890625 42.140625 9.46875 \n",
       "Q 38.09375 -1.171875 25.203125 -1.171875 \n",
       "Q 15.4375 -1.171875 9.46875 6.15625 \n",
       "Q 3.515625 13.484375 3.515625 26.03125 \n",
       "Q 3.515625 38.921875 9.5 45.96875 \n",
       "Q 15.484375 53.03125 25.34375 53.03125 \n",
       "Q 31.453125 53.03125 35.296875 50.5625 \n",
       "Q 39.15625 48.09375 42.484375 43.0625 \n",
       "Q 42.828125 44.78125 45.0625 51.859375 \n",
       "M 26.8125 45.75 \n",
       "Q 20.515625 45.75 16.84375 40.671875 \n",
       "Q 13.1875 35.59375 13.1875 26.03125 \n",
       "Q 13.1875 16.703125 16.65625 11.375 \n",
       "Q 20.125 6.0625 26.3125 6.0625 \n",
       "Q 32.375 6.0625 36.421875 11.296875 \n",
       "Q 40.484375 16.546875 40.484375 26.421875 \n",
       "Q 40.484375 35.984375 36.6875 40.859375 \n",
       "Q 32.90625 45.75 26.8125 45.75 \n",
       "\" id=\"ArialMT-3b1\"/>\n",
       "     </defs>\n",
       "     <g style=\"fill:#262626;\" transform=\"translate(17.435672 100.676086)rotate(-90)scale(0.143 -0.143)\">\n",
       "      <use xlink:href=\"#ArialMT-6c\"/>\n",
       "      <use x=\"22.216797\" xlink:href=\"#ArialMT-6f\"/>\n",
       "      <use x=\"77.832031\" xlink:href=\"#ArialMT-6f\"/>\n",
       "      <use x=\"133.447266\" xlink:href=\"#ArialMT-70\"/>\n",
       "      <use x=\"189.0625\" xlink:href=\"#ArialMT-20\"/>\n",
       "      <use x=\"216.845703\" xlink:href=\"#ArialMT-61\"/>\n",
       "      <use x=\"272.460938\" xlink:href=\"#ArialMT-6e\"/>\n",
       "      <use x=\"328.076172\" xlink:href=\"#ArialMT-67\"/>\n",
       "      <use x=\"383.691406\" xlink:href=\"#ArialMT-6c\"/>\n",
       "      <use x=\"405.908203\" xlink:href=\"#ArialMT-65\"/>\n",
       "      <use x=\"461.523438\" xlink:href=\"#ArialMT-20\"/>\n",
       "      <use x=\"489.306641\" xlink:href=\"#ArialMT-3b1\"/>\n",
       "     </g>\n",
       "    </g>\n",
       "   </g>\n",
       "   <g id=\"line2d_7\">\n",
       "    <path clip-path=\"url(#p6978e90c3e)\" d=\"M 57.353685 109.036951 \n",
       "L 58.118417 109.595382 \n",
       "L 58.88315 110.978182 \n",
       "L 59.647883 104.664237 \n",
       "L 60.412616 108.626055 \n",
       "L 61.177348 106.950822 \n",
       "L 61.942081 102.434763 \n",
       "L 62.706814 103.092817 \n",
       "L 63.471547 102.884566 \n",
       "L 64.236279 105.239211 \n",
       "L 65.001012 108.57143 \n",
       "L 65.765745 106.36523 \n",
       "L 66.530478 105.287623 \n",
       "L 67.29521 100.725379 \n",
       "L 68.059943 92.634149 \n",
       "L 68.824676 90.215834 \n",
       "L 69.589409 85.025518 \n",
       "L 70.354141 86.969703 \n",
       "L 71.118874 84.552349 \n",
       "L 71.883607 86.398754 \n",
       "L 72.64834 89.301067 \n",
       "L 73.413073 83.952956 \n",
       "L 74.177805 86.732338 \n",
       "L 74.942538 79.751213 \n",
       "L 76.472004 84.884498 \n",
       "L 77.236736 91.852531 \n",
       "L 78.001469 92.927705 \n",
       "L 78.766202 89.584892 \n",
       "L 79.530935 88.08312 \n",
       "L 80.295667 90.170187 \n",
       "L 81.0604 91.717308 \n",
       "L 81.825133 97.498279 \n",
       "L 82.589866 97.111263 \n",
       "L 83.354598 99.905391 \n",
       "L 84.119331 98.868383 \n",
       "L 84.884064 93.995798 \n",
       "L 85.648797 94.274132 \n",
       "L 86.413529 96.758744 \n",
       "L 87.178262 94.724895 \n",
       "L 87.942995 97.193739 \n",
       "L 89.47246 90.706776 \n",
       "L 91.766659 98.160923 \n",
       "L 92.531391 90.714113 \n",
       "L 93.296124 99.099067 \n",
       "L 94.82559 91.292715 \n",
       "L 95.590322 94.508107 \n",
       "L 96.355055 95.661585 \n",
       "L 97.119788 90.669334 \n",
       "L 97.884521 93.610827 \n",
       "L 98.649253 91.91794 \n",
       "L 99.413986 88.837404 \n",
       "L 100.943452 90.976446 \n",
       "L 101.708184 95.33084 \n",
       "L 102.472917 90.316482 \n",
       "L 103.23765 87.017546 \n",
       "L 104.002383 89.814838 \n",
       "L 106.296581 77.201776 \n",
       "L 107.061314 76.586464 \n",
       "L 107.826046 75.284033 \n",
       "L 109.355512 79.51474 \n",
       "L 110.120245 76.650831 \n",
       "L 111.64971 78.521891 \n",
       "L 112.414443 78.978897 \n",
       "L 113.179176 76.257191 \n",
       "L 113.943909 72.184495 \n",
       "L 115.473374 58.963378 \n",
       "L 116.238107 68.92088 \n",
       "L 117.00284 66.749344 \n",
       "L 117.767572 70.534186 \n",
       "L 118.532305 67.078608 \n",
       "L 119.297038 67.903087 \n",
       "L 120.061771 64.673871 \n",
       "L 120.826503 63.836258 \n",
       "L 121.591236 59.985319 \n",
       "L 122.355969 61.73857 \n",
       "L 123.120702 64.064397 \n",
       "L 123.885434 62.202097 \n",
       "L 125.4149 64.030554 \n",
       "L 126.179633 66.038023 \n",
       "L 126.944365 67.482371 \n",
       "L 127.709098 70.363335 \n",
       "L 128.473831 66.162805 \n",
       "L 129.238564 66.9374 \n",
       "L 130.003296 69.679882 \n",
       "L 130.768029 70.437345 \n",
       "L 131.532762 68.17751 \n",
       "L 132.297495 72.453073 \n",
       "L 133.062227 62.286767 \n",
       "L 133.82696 59.41786 \n",
       "L 134.591693 64.383369 \n",
       "L 135.356426 62.950666 \n",
       "L 136.121158 58.03437 \n",
       "L 136.885891 57.454985 \n",
       "L 137.650624 58.151595 \n",
       "L 138.415357 57.277852 \n",
       "L 139.180089 53.897307 \n",
       "L 139.944822 54.988963 \n",
       "L 140.709555 49.642401 \n",
       "L 141.474288 48.796287 \n",
       "L 142.23902 48.515984 \n",
       "L 143.003753 54.789221 \n",
       "L 143.768486 49.930813 \n",
       "L 144.533219 47.806731 \n",
       "L 145.297951 43.635293 \n",
       "L 146.062684 48.677266 \n",
       "L 146.827417 52.260039 \n",
       "L 147.59215 48.299491 \n",
       "L 148.356882 50.417733 \n",
       "L 149.121615 58.138474 \n",
       "L 149.886348 49.406911 \n",
       "L 150.651081 46.408918 \n",
       "L 152.180546 51.894762 \n",
       "L 152.945279 51.509752 \n",
       "L 153.710012 49.628749 \n",
       "L 154.474745 53.926211 \n",
       "L 155.239477 50.674404 \n",
       "L 156.00421 51.559413 \n",
       "L 156.768943 46.944557 \n",
       "L 157.533676 46.664284 \n",
       "L 158.298408 47.631009 \n",
       "L 159.063141 45.930186 \n",
       "L 159.827874 46.674656 \n",
       "L 160.592607 40.011723 \n",
       "L 161.357339 45.055829 \n",
       "L 162.122072 46.253634 \n",
       "L 162.886805 42.110495 \n",
       "L 163.651538 42.76614 \n",
       "L 164.41627 40.486426 \n",
       "L 165.181003 41.665215 \n",
       "L 165.945736 34.708153 \n",
       "L 166.710469 35.231382 \n",
       "L 167.475201 39.234364 \n",
       "L 168.239934 42.275276 \n",
       "L 169.004667 39.421584 \n",
       "L 169.7694 38.930009 \n",
       "L 170.534132 35.849662 \n",
       "L 171.298865 33.729071 \n",
       "L 172.063598 34.138015 \n",
       "L 172.828331 34.755351 \n",
       "L 173.593063 23.632558 \n",
       "L 174.357796 19.677578 \n",
       "L 175.122529 16.867458 \n",
       "L 175.887262 24.264627 \n",
       "L 176.651994 23.296634 \n",
       "L 178.18146 25.462351 \n",
       "L 178.946193 27.534079 \n",
       "L 179.710925 30.017849 \n",
       "L 180.475658 35.3343 \n",
       "L 181.240391 34.304086 \n",
       "L 182.005124 31.07489 \n",
       "L 182.769856 32.780892 \n",
       "L 183.534589 31.397979 \n",
       "L 184.299322 31.534864 \n",
       "L 185.064055 25.919438 \n",
       "L 185.828787 29.288987 \n",
       "L 186.59352 26.251375 \n",
       "L 187.358253 27.159908 \n",
       "L 188.887718 23.591698 \n",
       "L 189.652451 26.633561 \n",
       "L 190.417184 27.821946 \n",
       "L 191.181917 21.566671 \n",
       "L 191.946649 18.534913 \n",
       "L 192.711382 21.620602 \n",
       "L 193.476115 22.951989 \n",
       "L 194.240848 16.392393 \n",
       "L 195.005581 16.139719 \n",
       "L 195.770313 14.197077 \n",
       "L 196.535046 12.721272 \n",
       "L 197.299779 15.534597 \n",
       "L 198.064512 19.171687 \n",
       "L 198.829244 17.441026 \n",
       "L 199.593977 16.812061 \n",
       "L 200.35871 18.041315 \n",
       "L 201.123443 17.31444 \n",
       "L 201.888175 20.304353 \n",
       "L 202.652908 24.790503 \n",
       "L 203.417641 26.714528 \n",
       "L 204.182374 23.15589 \n",
       "L 204.947106 16.978918 \n",
       "L 205.711839 19.307985 \n",
       "L 206.476572 12.141818 \n",
       "L 207.241305 19.343387 \n",
       "L 208.006037 14.317321 \n",
       "L 208.77077 13.905846 \n",
       "L 209.535503 13.354585 \n",
       "L 209.535503 13.354585 \n",
       "\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:2.275;\"/>\n",
       "   </g>\n",
       "   <g id=\"patch_3\">\n",
       "    <path d=\"M 49.744594 115.92 \n",
       "L 49.744594 7.2 \n",
       "\" style=\"fill:none;stroke:#262626;stroke-linecap:square;stroke-linejoin:miter;stroke-width:1.25;\"/>\n",
       "   </g>\n",
       "   <g id=\"patch_4\">\n",
       "    <path d=\"M 217.144594 115.92 \n",
       "L 217.144594 7.2 \n",
       "\" style=\"fill:none;stroke:#262626;stroke-linecap:square;stroke-linejoin:miter;stroke-width:1.25;\"/>\n",
       "   </g>\n",
       "   <g id=\"patch_5\">\n",
       "    <path d=\"M 49.744594 115.92 \n",
       "L 217.144594 115.92 \n",
       "\" style=\"fill:none;stroke:#262626;stroke-linecap:square;stroke-linejoin:miter;stroke-width:1.25;\"/>\n",
       "   </g>\n",
       "   <g id=\"patch_6\">\n",
       "    <path d=\"M 49.744594 7.2 \n",
       "L 217.144594 7.2 \n",
       "\" style=\"fill:none;stroke:#262626;stroke-linecap:square;stroke-linejoin:miter;stroke-width:1.25;\"/>\n",
       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"p6978e90c3e\">\n",
       "   <rect height=\"108.72\" width=\"167.4\" x=\"49.744594\" y=\"7.2\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8a5828080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(3,2))\n",
    "angles = np.array(simulate_trending_ou(\n",
    "    N=200, \n",
    "    ang_rw_stat_std=-1.675,\n",
    "    ang_rw_return_scale=1.125,\n",
    "    ang_loop_spread=-1.3,\n",
    "    ang_drift_step=-1.975,\n",
    "    ))\n",
    "plt.plot(\n",
    "    angles\n",
    ")\n",
    "plt.yticks(np.arange(0,3)*2*np.pi, [('{}π'.format(i) if i>0 else '0') for i in 2*np.arange(0,4) ] )\n",
    "plt.ylabel('loop angle α')\n",
    "plt.xlabel('loop index')\n",
    "\n",
    "    \n",
    "#plt.savefig(f'{FIG_FOLDER}/coarse_grained_OURW_example_loop_angles.pdf',\n",
    "#             bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-21T17:00:14.037764Z",
     "start_time": "2017-06-21T17:00:13.808296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANoAAACeCAYAAABU1BShAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVjSNJgloRdNoksD/B0U3Tstpcj+wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f341f2874e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "rw_step = 0.1*np.pi\n",
    "plt.figure(figsize=(3,2))\n",
    "angles = np.pi+ np.cumsum(np.random.normal(scale=rw_step,size=200))\n",
    "plt.plot(\n",
    "    angles\n",
    ")\n",
    "plt.yticks(np.arange(0,3)*2*np.pi, [('{}π'.format(i) if i>0 else '0') for i in 2*np.arange(0,4) ] )\n",
    "plt.ylabel('loop angle α')\n",
    "plt.xlabel('loop index')\n",
    "plt.ylim(0,2*np.pi)\n",
    "\n",
    "    \n",
    "#plt.savefig(f'{FIG_FOLDER}/coarse_grained_RW_example_loop_angles.pdf',\n",
    "#             bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-21T17:00:41.732765Z",
     "start_time": "2017-06-21T17:00:41.501757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANoAAACeCAYAAABU1BShAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLmg5ctQB/wd2DZkkjKwPQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f341f396b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(3,2))\n",
    "angles = np.array(np.random.random(200)*2*np.pi)\n",
    "plt.plot(\n",
    "    angles\n",
    ")\n",
    "plt.yticks(np.arange(0,2)*2*np.pi, [('{}π'.format(i) if i>0 else '0') for i in 2*np.arange(0,4) ] )\n",
    "plt.ylabel('loop angle α')\n",
    "plt.xlabel('loop index')\n",
    "plt.ylim(0,2*np.pi)\n",
    "\n",
    "    \n",
    "#plt.savefig(f'{FIG_FOLDER}/coarse_grained_random_example_loop_angles.pdf',\n",
    "#             bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(4,2))\n",
    "angles = np.array(simulate_trending_ou(200,-1.675, 1.125, -1.3, -1.975))\n",
    "full_circle_points = np.r_[0, 1+np.where(angles[1:] // (2 * np.pi) != angles[:-1] // (2 * np.pi))[0], len(angles)]\n",
    "for i,j in zip(full_circle_points[:-1], full_circle_points[1:]):\n",
    "    plt.plot(\n",
    "        np.arange(i,j), \n",
    "        np.mod(angles[i:j], 2*np.pi)\n",
    "    )\n",
    "plt.yticks(np.arange(0,3)*2*np.pi, [('{}π'.format(i) if i>0 else '0') for i in 2*np.arange(0,4) ] )\n",
    "plt.ylabel('loop angle α')\n",
    "plt.ylim(0,2*np.pi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-16T21:31:34.585522Z",
     "start_time": "2017-06-16T21:31:34.579515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-16T21:30:48.391981Z",
     "start_time": "2017-06-16T21:30:48.385697Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4   7  13  17  30  52  53  55  56  62  64  66  67  69  70  72  80  82\n",
      "  91 102 104 117 118 119 120 135 136 138 146 148 150 157 165 171 182 193\n",
      " 195 196]\n"
     ]
    }
   ],
   "source": [
    "print(full_circle_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-26T17:59:47.925480",
     "start_time": "2017-05-26T17:59:47.909067"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MIN_LOG10_S = -1\n",
    "# MAX_LOG10_S = 4\n",
    "\n",
    "# Z_LOOP_WIDTH = 50\n",
    "# Z_LOOP_STEP = 1\n",
    "# PERIOD_LOOPS = 100\n",
    "\n",
    "# ANG_RW_STAT_STD = 2 * np.pi / 3\n",
    "# ANG_RW_RETURN_COEFF = 1.0/30\n",
    "\n",
    "# ANG_DRIFT_STEP = 2 * np.pi / PERIOD_LOOPS\n",
    "# ANG_RW_STEP = ANG_RW_STAT_STD * np.sqrt(ANG_RW_RETURN_COEFF)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": false,
   "threshold": 6,
   "toc_cell": true,
   "toc_section_display": "block",
   "toc_window_display": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0028938bf9274630b2be4f96f6d7a179": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_0b8e28c4b838482eba03b1a36bafcccf",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "0141106ca4f04064b31214a63ff7c731": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "035df7dee11d44239650c2334a91f266": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "04858ed933814f72b32a60f85ffa33e2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "068603b731574d9b8a8106c52e6ed69b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "06c24fbc989d42c196a83bb16dcda0fb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_6020a1fbafe241c2a545c81111ab9433",
       "msg_throttle": 1
      }
     },
     "06f22cb3d88744c68d04d823bb7e76c6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "0861a7b05dd0432487206c9d369552b9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "094bfea00c15424eb84b2ac664a2b9ac": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_775e92a257214727ae771a419a51ce20",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "099cd8b10d634c37b702f96017163856": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "099e107e563c405086480ed77d9cc470": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_23c791c73f5a4f5ab461db671d5c3a99",
       "msg_throttle": 1
      }
     },
     "0af2e49a660347d2a6a39939ea33502c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "0b8e28c4b838482eba03b1a36bafcccf": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "0caf747a1be24887900da5f240d51c63": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "0cc9f0c7a45a46f390b15f21de07719e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "0cdbc7bb1b074462af0f9bf6b2b0be4a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "0d0ce29ea138427abfadb0145dfa4dde": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_67b2b7b99f594aa886e8bc49bd5dbd0b",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -2.4
      }
     },
     "0d2e4f89430a4c43950a39cddf40310f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_21514e5dd5e543fb8ffdefb4186492cc",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.2
      }
     },
     "0d38e29d261447409013f8feca841b7e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_2aa690a3d47543afaca8845f40fc8030",
       "min": 1,
       "step": 0.1,
       "value": 32.6
      }
     },
     "0d4785cad089443f99a06973f46ae5f1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "0dd5e60627c2438ba207c05e2cef4cad": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_1e4b258ee6b7415ba4adb99e92da2e1b",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "0e3036fb1c47488f9c09f5a3d727b083": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_bc21378ef6344b0ab4d2cca323cb7845",
        "IPY_MODEL_805a95817c1d4d7998e4f3a6924b8bd9",
        "IPY_MODEL_4e7ab413653d497ead18fd1fa6ed863a",
        "IPY_MODEL_60e5bfb8fa994a82bc28f015989dbe8b"
       ],
       "layout": "IPY_MODEL_a2f5f020b37749398732dfd88162a963"
      }
     },
     "0eca72edf5144ae194b3a332df8153d4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "100de71247a84a5c81d87e14d4b1451c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "12071858820846b8ac928c6a69fd039b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "step",
       "layout": "IPY_MODEL_914e18e2cd8345afa8c5a8c1b876b745",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "125a96ea39b048908210afdd050219e8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_ad615203d361469391c55802ec23c04a",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.1
      }
     },
     "12a0daf40a35416f878eeac418c580e1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_04858ed933814f72b32a60f85ffa33e2",
       "msg_throttle": 1
      }
     },
     "12abc4804b4449b99444d1ca5f0407bf": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_3c8f977577ca47e39c91a2a98582d8e1",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -2.7
      }
     },
     "135c95a45d104a59be769842b4e552b3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1364fc3ed238470f92a073154a5ac407": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_e8d7a469ac1c45379dbf4d4889e8b7d1",
       "min": 1,
       "step": 0.1,
       "value": 27.3
      }
     },
     "141066412fa84d54a1f1d176903c8993": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_c9a07cc4933549eda9be2d22183f643f",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "141cc43a369448c7a473e5888c105781": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_2499afc725b1499782b73b88ea3a4429",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "145ea83356504485ba372a6ee6163041": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_1dc3e8bcfc31485daac2e89b1ac48270",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "15288b1209f94538864d2cdc360d6f4e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "15dcd8d6a72e4280812645441c12b7ff": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_889d7bcb450344d69a22a7626faea3f7",
        "IPY_MODEL_ac54fb63beaa48b99d936ea7707c574f",
        "IPY_MODEL_d1e03fba61ba4396a3b511d5b6f3d4b9",
        "IPY_MODEL_6ce75422be0b44a58c8909cca8c63408",
        "IPY_MODEL_2638cb4a3fc942a185609c26e21d8109"
       ],
       "layout": "IPY_MODEL_682fd0d07e4847958ab8abf70675a77d"
      }
     },
     "164e0e6a62bb44c9b036745e383c0ab6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_a7a7ba5d6fe7458baf31f0ef3b6ff0fc",
       "msg_throttle": 1
      }
     },
     "16756cbfb45f4ac98126ecca03c986cb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "169c29e4a6ad440f9c0db050068ee162": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_cc1b3be91a8745698faad6323efaa88a",
       "msg_throttle": 1
      }
     },
     "16f63a3c902a4e01a457f3565b9f5722": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_e5b153ede0854e859d658519fe6587bb",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 3.2
      }
     },
     "1714671da00a4aa49be854fd66569e74": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1759230fce0b4c1eb76c4826cade5c96": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_5b9c34dc70e94e8d9b081f8809c6aebe",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 0.9
      }
     },
     "1794d119c7c74dc4850d5949103508ae": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_c134611300144961a6c8a0ad2974700f",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.9
      }
     },
     "17eec1d904574af8854ec4db08098b56": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "190053f49f964483b0076bab95a9cf9b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_da32a472292a4c55b7073dc3ce0cdaf7",
        "IPY_MODEL_532dd0f152fb4931a91bd4d8abe991fa"
       ],
       "layout": "IPY_MODEL_e4a682743b2b4d639efdc20dc5dd2f72"
      }
     },
     "1953753e0cda4911af9e3ebb235cb2c5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_1759230fce0b4c1eb76c4826cade5c96",
        "IPY_MODEL_29a0f129515145358bfddfb3d26881a6"
       ],
       "layout": "IPY_MODEL_38eea3699f764ee3ab1018c1b82393c0"
      }
     },
     "195beb5ac3e9475a94918277941a9eb9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "1971fbd9b20b4c328c6cf63fcaf6f9ef": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "step",
       "layout": "IPY_MODEL_e909d8b60de2454eac5bbc14507f463f",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "19d4978945fd4b3ba880a4dcaea262b9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_56c3ec700edc4a0e934137ca688692b5",
       "msg_throttle": 1
      }
     },
     "1a13a51dcce343f181beb95630d2b11d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_1714671da00a4aa49be854fd66569e74",
       "msg_throttle": 1
      }
     },
     "1af8cfaf92e349b6b3664933c3f31fcb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_25f0d73319454e42bc26079412c403c3",
       "msg_throttle": 1
      }
     },
     "1b44c33957274611999eb368fd061e33": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1b5e853b972b4ec59d8229cdd53ae000": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "1d21883f4a694a5d8cb39b1fd5cb7aa1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_afdfda0ccb0d4aad8f3f508feba9dd46",
       "msg_throttle": 1
      }
     },
     "1d6da6ad46fd4d029e04612511aeb58f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "1dc3e8bcfc31485daac2e89b1ac48270": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "1e4b258ee6b7415ba4adb99e92da2e1b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1e637ca8098d4f05a131d30830f4bc82": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "1e71182ff4554ca6a6af38b625185251": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1ed8d813d46d48778ea8157b8beea818": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1ef640b29e3541b19540a2b49507a91f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1f327e2205e34b66bbaf88698f80cbc4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "1f987affbd7a49be9b929dbcff087d42": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_7ff310f6054e445ea6d2c57048cc2210",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.7
      }
     },
     "1fcea8e6d31a4ce18b291cbc85601e72": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_78daf3b2616046c9b1d014725b25a9b2",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "20197775b6484781bd48f5e844dfee34": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "21514e5dd5e543fb8ffdefb4186492cc": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "21e12812b4124176ad76114324a60cc2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "237c075ba3e6424f85139789f4bbc755": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_a683a2a6b6c749ae8fc8d6f8ca0415db",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": -0.1
      }
     },
     "23c791c73f5a4f5ab461db671d5c3a99": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "23da4f02dde241e488056010f33d7bdd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_099cd8b10d634c37b702f96017163856",
       "msg_throttle": 1
      }
     },
     "23ee64584a3345b9aa6fa50e8c9c438d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "240a3df57f4e40da92bfb30eb4862652": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_7a36cf973199455d8ff0569ae7ea2d7c",
       "msg_throttle": 1
      }
     },
     "243ba72e14c04c049f23e91ae2f36df5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "2499afc725b1499782b73b88ea3a4429": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "258720b2eec34245bb145cb3c88cad0a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "25f0d73319454e42bc26079412c403c3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "2638cb4a3fc942a185609c26e21d8109": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_0cc9f0c7a45a46f390b15f21de07719e",
       "msg_throttle": 1
      }
     },
     "26df2d166fa54400af01574728033670": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "26f4976a63824b4395a3a7ac4ec27fc3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_81f1d27061194bbfafabbb760775d70b",
       "msg_throttle": 1
      }
     },
     "27df4103a9e747edaf06b89dc4031396": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "27f5f9f8d1bf44f6a57ec1bbf4b1e738": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_627b33bc22344dc0b4b1498203a8fc8e",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.5
      }
     },
     "28faf62d57a94626868424e572c992e5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_0dd5e60627c2438ba207c05e2cef4cad",
        "IPY_MODEL_12071858820846b8ac928c6a69fd039b",
        "IPY_MODEL_e614cd2f612d4461980ba57ee4b18341"
       ],
       "layout": "IPY_MODEL_c2371eca38304f1e9ea2a41831e67c00"
      }
     },
     "29571c2b401248779c99079647d25312": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "29a0f129515145358bfddfb3d26881a6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_f652c029548a4da78ad261b5b92607fe",
       "msg_throttle": 1
      }
     },
     "2a020714d3654d308de4a7b5a605030a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_663e6bcabd084e29b7dfd5f551059c9a",
        "IPY_MODEL_fced36bf9d74432dbd46013cee811cdc"
       ],
       "layout": "IPY_MODEL_4c8ef909863742d987a49ab2e958334f"
      }
     },
     "2a743e08097f40c8a5ff610966f3f060": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_0028938bf9274630b2be4f96f6d7a179",
        "IPY_MODEL_5f63a0eca48b4eaa8a2861adf65da417",
        "IPY_MODEL_ad4f70d9557e467b8428730d4756d5a6",
        "IPY_MODEL_12abc4804b4449b99444d1ca5f0407bf",
        "IPY_MODEL_2d691177fb18471088e59f17ec84c665"
       ],
       "layout": "IPY_MODEL_c52b5841517b45668b0e76ad3c5fb3e8"
      }
     },
     "2a78d8dd9a6145428586366b89b98d06": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_da5a239751a44c288f0f6c179f02b404",
       "msg_throttle": 1
      }
     },
     "2aa178d90dd64e1f8891ed8e3e0f7afe": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "2aa690a3d47543afaca8845f40fc8030": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "2c6d3b76ec1f4ece87df89886ae72e1a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_c6e0fa72c05241ba989c3b53e089f338",
       "max": 0,
       "min": -4,
       "step": 0.1
      }
     },
     "2d2b66211dd347f0b11afcbe548a196f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "2d60c9fd4f45466db5d626c52d5379cc": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "2d691177fb18471088e59f17ec84c665": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_1b44c33957274611999eb368fd061e33",
       "msg_throttle": 1
      }
     },
     "2e9a279ff95e4029a7902915d9bdd346": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "3013d5f09d6e43838230f32dca57061b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "3066e711f65d4cae943ab79a02c886c5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_1e637ca8098d4f05a131d30830f4bc82",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 4
      }
     },
     "3078cf58558a42baa5ae4dc594cc93dd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_a0a761e923174aedaf8da0705d6e10ce",
        "IPY_MODEL_c351a91e4f2a4f188b316f4b45790046",
        "IPY_MODEL_fdcc4d82e04249d5a614c5a7f5ac462c"
       ],
       "layout": "IPY_MODEL_1ef640b29e3541b19540a2b49507a91f"
      }
     },
     "30f827f63b7d4eb8b0fcb6e72345f83f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_fe43299750b5402781703e74ec892c4e",
       "msg_throttle": 1
      }
     },
     "33841350cd744e3f894cd07860ed3f6a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_7cb1ef8324b742a8bb3a0ffec23f8b97",
        "IPY_MODEL_e5110c2697b74c1fbb25bba8df0742a6",
        "IPY_MODEL_63f60f7398d84c8caf7100ba46fc1250",
        "IPY_MODEL_a513edd59e064c33b9f796ba9b84bd1b"
       ],
       "layout": "IPY_MODEL_da85ba1f93b14e6bb22c3a738e19c8a8"
      }
     },
     "3593212756894874a32a1fd256147227": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "35f76f6803d948fc89579cc0de35cbe7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "35f7ab6fd9e8407aa09e26d8c22f0565": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "36163bfa4e9e40d08a9b9145ebfe7d47": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_8b1de3ef6cd24e6bab1bf650d677d573",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "36272349c40c460ca9ddb3c094ceffe8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "38eea3699f764ee3ab1018c1b82393c0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "3a0d8327c8d84c72b72af4f78a84dc8d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_ed6c998110414866b1c7edb424141c4e",
        "IPY_MODEL_6b5858687da84e44b78ebfac90cb79c0",
        "IPY_MODEL_8ed777e160894b4db63fbfa0c0a13f68",
        "IPY_MODEL_1fcea8e6d31a4ce18b291cbc85601e72",
        "IPY_MODEL_4ebf8201520346b5a1c252879c72f233"
       ],
       "layout": "IPY_MODEL_eb900005d71449a2a33a39aadb212a41"
      }
     },
     "3a594ef5414642248636e2db76a9b412": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "3aa4b718a31048758ca32a9b25e4ae62": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_7cdcdec35281486e872872c5d2954f65",
       "msg_throttle": 1
      }
     },
     "3b4c9fed85f94a2b923c53f6798f0d5f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "3bf1f8e85146422b9caa9a59d7dcc47c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_3e577cd536194d389dc937550546201a",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.5
      }
     },
     "3c8f977577ca47e39c91a2a98582d8e1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "3ca4a05f623641f39f9ea39e7a17f883": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_99cf9b9f7fb9456d8dcae7cbd1cb6446",
        "IPY_MODEL_3d0af48f45f4419bbcc2048330851e60"
       ],
       "layout": "IPY_MODEL_d620d6008ac54564b73f2225a6b29799"
      }
     },
     "3caa7c2bab1e4254af104fb8a9312bc4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "3d0af48f45f4419bbcc2048330851e60": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_ca4b3d82c92a494b8abf827921580984",
       "msg_throttle": 1
      }
     },
     "3d92781653e64376bdfdf85749b2a327": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "3dc98d5c21fb41c4a0d2cc6ef77adf4d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "3ddb5a57e74c4e8e95071ba03fca83e8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "3e577cd536194d389dc937550546201a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "3edecb9b6ed5443f8be6a90d5fa67218": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_ec2f7056af7d429c80192ba5028791cc",
        "IPY_MODEL_a4b2b304577d47658dd52838be6dbd43",
        "IPY_MODEL_141066412fa84d54a1f1d176903c8993",
        "IPY_MODEL_e4b7cdb1bcb849fba77764f74f77c668"
       ],
       "layout": "IPY_MODEL_7bddcdf64fef45edae488ad98ee6f475"
      }
     },
     "3f94f2338ece47c8909a7e65b02d4e1c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "417e2d316d1b4b5c9abc56add023af18": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "41dcd656e76e4e64987d3e08b79cfeb1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "429a5061cb09442aa837a3415a10ee0d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_46c902aee0b447caa818344c10288b5d",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -1.5
      }
     },
     "43096724d6b04ca9a2ed1e7f2264d5f7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "4478aacd241a45938f2d58d75f660f6e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_16756cbfb45f4ac98126ecca03c986cb",
       "msg_throttle": 1
      }
     },
     "45a1209608bc4be095d3064f7a1ea33b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "46a7a9357eea44e78422157789e75509": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "46c902aee0b447caa818344c10288b5d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "471df48cae8f4359a2d29366cc870e98": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_aad1a5e56fbe4c958e9708b929c544bd",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 3.2
      }
     },
     "48fb19b222644b7898e4204aafd07cb7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "49532f7bf88d4bff94ddf28255ed4946": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "4aeef968a3144205b5bc522868ab828e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_5415aa38185348ea8f373f01a145f24a",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "4c456aa03ee4406081db172b5d0e6710": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_f6b4dcaa19304e468b06f05a0517d706",
       "msg_throttle": 1
      }
     },
     "4c7e30af7aa1410091f4eea85d975e85": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "4c87938fed584cb594ea154b139d8fc9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_c8d71353a67241ba8f3e409bb21e4e8f",
       "msg_throttle": 1
      }
     },
     "4c8ef909863742d987a49ab2e958334f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "4d767addc3824d2ebfe8ac5f2ae0b047": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_471df48cae8f4359a2d29366cc870e98",
        "IPY_MODEL_ffc4826a5cf64704ae9b5a6b3a0d1bbe",
        "IPY_MODEL_1af8cfaf92e349b6b3664933c3f31fcb"
       ],
       "layout": "IPY_MODEL_7169433033f34fe8beccab451dff97b7"
      }
     },
     "4db9a4e26a854455bf7ea96c0132fb5f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_eb1fee51f35b4573a3b9b8b2e4c85b2f",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -2
      }
     },
     "4dcec28e1a42403c9822955a9013fdb1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_237c075ba3e6424f85139789f4bbc755",
        "IPY_MODEL_240a3df57f4e40da92bfb30eb4862652"
       ],
       "layout": "IPY_MODEL_87e82345819f478191107a3304c9effd"
      }
     },
     "4dda754a283143a5904d193a9185ed47": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_d43e9f78b2314c54875af7d519674be3",
        "IPY_MODEL_cbd2c81addc849a2819c1fd97500ae6d",
        "IPY_MODEL_8cdc4ced8af54298bae43625f1922279",
        "IPY_MODEL_b48e4271dae243368e9c3fff02c523cb",
        "IPY_MODEL_12a0daf40a35416f878eeac418c580e1"
       ],
       "layout": "IPY_MODEL_7322a695315c46ab803f454274282ddd"
      }
     },
     "4e7ab413653d497ead18fd1fa6ed863a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_f58f4ce57d2040faafdbbdd6d01d5dc0",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -2.1
      }
     },
     "4ebf8201520346b5a1c252879c72f233": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_e3c0b344a2c24beeb16169d5d5235d22",
       "msg_throttle": 1
      }
     },
     "4eee134724c440828e60ac8f01de6b67": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "500722cb9fef49918c3b16cc57e7d57d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "5243cb6138844ba69db2d4be8a632e25": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_3f94f2338ece47c8909a7e65b02d4e1c",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -3.4
      }
     },
     "5280217cf2fc465faf42d110db694cb8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_f867f105641c41678d190b8cc07e4e95",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 2.5
      }
     },
     "532dd0f152fb4931a91bd4d8abe991fa": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_d9d0a2383eda492caf87379afcfd8db7",
       "msg_throttle": 1
      }
     },
     "535b861226034fca936574b769fb8813": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_035df7dee11d44239650c2334a91f266",
       "msg_throttle": 1
      }
     },
     "53cdd0e9e29940e9870f8634246244c2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "5415aa38185348ea8f373f01a145f24a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "552a5b8718fc440087d730e9e7c095bb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_bfe275880ee54f6cbae505abdf2130b6",
       "max": 4,
       "step": 0.1,
       "value": 0.9
      }
     },
     "55da5477db2e4d22b6f6c146f2de4420": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "56c3ec700edc4a0e934137ca688692b5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "57008f6555fb47fab4ae032c62a6bfcb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_2d60c9fd4f45466db5d626c52d5379cc",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.9
      }
     },
     "57446b27d4d94e93968b93cbe4e51d4a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_53cdd0e9e29940e9870f8634246244c2",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.1
      }
     },
     "57aa41f399274aa4926cb866a16dcf01": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "57f40409b91c4d6599216b55aa84bdfe": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "58b99b201e9740df9dfca4bf61e15d91": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_57f40409b91c4d6599216b55aa84bdfe",
       "msg_throttle": 1
      }
     },
     "5a0d310d6b0c43ef955903cd68aa748b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "5a38e11b723f4525b1c275774cc020cf": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "5b6dd8d8edf645c28d912e92f0231f25": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_aafe5f60975741299d0e078a98b1e446",
       "msg_throttle": 1
      }
     },
     "5b9c34dc70e94e8d9b081f8809c6aebe": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "5eeab87f628d4d358a3da6bc96f8e685": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "5f4d7a568a2341219268046e48cd92a3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "5f63a0eca48b4eaa8a2861adf65da417": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_feccdea0d4594e12a989d0b3792f067d",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "6020a1fbafe241c2a545c81111ab9433": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "605de0f34fc9445588ab4e5886d9c32d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "60e5bfb8fa994a82bc28f015989dbe8b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_d414545cfceb432c9cfeee4e66f8c7f0",
       "msg_throttle": 1
      }
     },
     "6135ac7411a448fb83a7abcd856b7cac": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_f53f9926e5c54de99b383f0c55e60d33",
        "IPY_MODEL_7fd859e7e018416080dfccc7abca8b41"
       ],
       "layout": "IPY_MODEL_b612d5fee76c4dae828dfd65db068ba0"
      }
     },
     "61505e5a079f413480398331412bac7a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "627b33bc22344dc0b4b1498203a8fc8e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "62e5c53c04b34d0984ecc4137163accf": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "62f11f9e872f4320ab0e92b995a1fd0b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_2e9a279ff95e4029a7902915d9bdd346",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1
      }
     },
     "638cad5283fa4cffb79cd6bffd27d1a8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "63f60f7398d84c8caf7100ba46fc1250": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_35f7ab6fd9e8407aa09e26d8c22f0565",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "6623ac05acc3422fa7538c8953a0b81e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "663e6bcabd084e29b7dfd5f551059c9a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_ab70f2e88ae448c9a0b171cace681d59",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 3
      }
     },
     "6680f533eb844045910c8ff3741506d6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_d93154383cca47eaa7f2f6b4c38c8524",
        "IPY_MODEL_535b861226034fca936574b769fb8813"
       ],
       "layout": "IPY_MODEL_cec320a0e5ea4c0e9024f5a2c1e7e628"
      }
     },
     "67b2b7b99f594aa886e8bc49bd5dbd0b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "67c98a9cd8af4d91b0b5846b0acd03c0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_7f276efa8ff643faa217ce11b6ac981b",
       "msg_throttle": 1
      }
     },
     "67e2b95e564b43d080063b678bb52edd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_fbe4cd3a1e35497abc6fb0bb8664e8c4",
        "IPY_MODEL_baa22b69499e4a12bff7beda8394e371",
        "IPY_MODEL_883daccb188a4782b0d88c36f181e691",
        "IPY_MODEL_70c3a8a27bac4c8eb515d07da6f4656e",
        "IPY_MODEL_e3dba6425f294aa589703f5cf0e0ba4c"
       ],
       "layout": "IPY_MODEL_6c446762813c4e729f7a210917744d92"
      }
     },
     "67f0474e16514a6196427f49396aecf2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "67ff500e4e4b413cae77d7af8f5d984a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_b0ece426ba2142a990033a8a826e57af"
      }
     },
     "682fd0d07e4847958ab8abf70675a77d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "685a665ef2c547828fa54768686b0240": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "69727d00f39240bab2041656671a8173": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_d470e117295a4cbeaa99fba888246259",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "69affcbe0dc748b6999b611d39c61e45": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread\n/step)",
       "layout": "IPY_MODEL_1ed8d813d46d48778ea8157b8beea818",
       "max": 4,
       "min": -4,
       "step": 0.1
      }
     },
     "6a486afafa8c47aead0d65330fd57266": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_2d2b66211dd347f0b11afcbe548a196f",
       "msg_throttle": 1
      }
     },
     "6ac8a9175f4d41caac03a22762027d40": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_1364fc3ed238470f92a073154a5ac407",
        "IPY_MODEL_d3611be47b764301871ef4bdba48e0b9",
        "IPY_MODEL_4c456aa03ee4406081db172b5d0e6710"
       ],
       "layout": "IPY_MODEL_135c95a45d104a59be769842b4e552b3"
      }
     },
     "6ae09faddcdc47ce816d646394d8f1fd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "6b5858687da84e44b78ebfac90cb79c0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_8a9e0b4ac51640e3bd3d7f275df1000f",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "6c446762813c4e729f7a210917744d92": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "6ccc042823254d778e295c3ad568b2be": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_ae8fb34b96304f91b4088db804aaa45c",
        "IPY_MODEL_67c98a9cd8af4d91b0b5846b0acd03c0"
       ],
       "layout": "IPY_MODEL_fa80d89cedb240d88ec31a87c94af163"
      }
     },
     "6ce75422be0b44a58c8909cca8c63408": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_b42658e3c87843e1872d1522cf797d7a",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "6cf712c642f247d6ad25a5b7688bab01": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "6df34ab3bacc45c5a12ac54a3812cc1f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_7bf2450640d345fc93db2dcca51423e2",
       "max": 4,
       "step": 0.1,
       "value": 1
      }
     },
     "6f88e206509445a3821e3c508a2280c9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_85600c7956aa40a288b4156393035792",
       "max": 0,
       "min": -4,
       "step": 0.1
      }
     },
     "6ff8f3ecefdf4745b6d8833e605a75ab": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_7647cdb244a1453096d8e233c99acda6",
        "IPY_MODEL_da66f81b858548cd9b629907367ea7da",
        "IPY_MODEL_9fc611262e284274a479e14f5385160b",
        "IPY_MODEL_1a13a51dcce343f181beb95630d2b11d"
       ],
       "layout": "IPY_MODEL_d36abaa47bab477d89ec77d68a0986de"
      }
     },
     "7094c0a07e4a4d4c9a3ba705f12ef120": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "70c3a8a27bac4c8eb515d07da6f4656e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_880ce7298250454c9c34748acc025368",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.9
      }
     },
     "710081acc19346059e209ef38a640420": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_69affcbe0dc748b6999b611d39c61e45",
        "IPY_MODEL_bb78e530839f4414b83f575ad5f0ccfe"
       ],
       "layout": "IPY_MODEL_27df4103a9e747edaf06b89dc4031396"
      }
     },
     "7169433033f34fe8beccab451dff97b7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "7192ee33b45640c1a27ffa566a7b4091": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_ed12bfa4a78845aaafcb58206da55326",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -1.3
      }
     },
     "72982849a33443508b64aa8cbe4830eb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_3066e711f65d4cae943ab79a02c886c5",
        "IPY_MODEL_a77c3e0276974a9bb0ea86c7a04bd1d9"
       ],
       "layout": "IPY_MODEL_46a7a9357eea44e78422157789e75509"
      }
     },
     "72b0ff42077a482d91e9cb772289b4a8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "7322a695315c46ab803f454274282ddd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "739831bbb2d046ceadf87db14c062e01": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "74d48294b8474de7aef663869f480dff": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_d8b4ee66c3d24a8ab7016368064872a8",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -0.9
      }
     },
     "7647cdb244a1453096d8e233c99acda6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_a44eb0658f8c4760b28b0de27667610d",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.7
      }
     },
     "76b8cbce5f9042ec9bff4c8619156ba0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_e55b5e08cfba404aa6220b6ec44ddf6d",
        "IPY_MODEL_7192ee33b45640c1a27ffa566a7b4091",
        "IPY_MODEL_fe816e011086412688332d262d86cc96",
        "IPY_MODEL_a097aac9024847c99e0f581c9a0a61e9",
        "IPY_MODEL_c4fda87be44043d393ccdc30e02a8d33"
       ],
       "layout": "IPY_MODEL_17eec1d904574af8854ec4db08098b56"
      }
     },
     "775e92a257214727ae771a419a51ce20": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "77669491858d4c4f9d0af27ff7264d62": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "78c70b25a8c241fc8fde11221b84d036": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "78daf3b2616046c9b1d014725b25a9b2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "79a607e639f54833b096f33b13b533d7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_e67f4e5e2ebc4e26ad0509cc1cbd61b6",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1
      }
     },
     "7a258d54babc459b94eb15c0064fad41": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_67f0474e16514a6196427f49396aecf2",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 2
      }
     },
     "7a36cf973199455d8ff0569ae7ea2d7c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "7adfe2becd034bc5864490779cb20860": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_a555964f6d1445faaaba406e38585730",
        "IPY_MODEL_3bf1f8e85146422b9caa9a59d7dcc47c",
        "IPY_MODEL_ba47c2d6c12a420fb15efb3f5d62ab1a"
       ],
       "layout": "IPY_MODEL_100de71247a84a5c81d87e14d4b1451c"
      }
     },
     "7bdb9a5c54834b588f8694f936c900ec": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_62e5c53c04b34d0984ecc4137163accf",
       "msg_throttle": 1
      }
     },
     "7bddcdf64fef45edae488ad98ee6f475": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "7bf2450640d345fc93db2dcca51423e2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "7c5d36a7a3f046298c39dc2286f62a49": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_125a96ea39b048908210afdd050219e8",
        "IPY_MODEL_ebcbd38fb49643de9c3f42abd04cd7f0",
        "IPY_MODEL_27f5f9f8d1bf44f6a57ec1bbf4b1e738",
        "IPY_MODEL_26f4976a63824b4395a3a7ac4ec27fc3"
       ],
       "layout": "IPY_MODEL_06f22cb3d88744c68d04d823bb7e76c6"
      }
     },
     "7cb1ef8324b742a8bb3a0ffec23f8b97": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_21e12812b4124176ad76114324a60cc2",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "7cdcdec35281486e872872c5d2954f65": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "7d23415cacd8401c9b205394b621829f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_79a607e639f54833b096f33b13b533d7",
        "IPY_MODEL_c3a472d6e4054987af9429fae433f540",
        "IPY_MODEL_169c29e4a6ad440f9c0db050068ee162"
       ],
       "layout": "IPY_MODEL_3a594ef5414642248636e2db76a9b412"
      }
     },
     "7dbf9b6ad3064bf9bc283224c3cc6dc1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_0d38e29d261447409013f8feca841b7e",
        "IPY_MODEL_db9a198f59ba41dd9c9ddbbab984099d"
       ],
       "layout": "IPY_MODEL_955a1b84df6a4d99b3e7635645ed9f5e"
      }
     },
     "7dfa52a3c60544c891e24a76c295db89": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "step",
       "layout": "IPY_MODEL_cdf38f67be754cd589637a570814040b",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "7f276efa8ff643faa217ce11b6ac981b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "7fd859e7e018416080dfccc7abca8b41": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_d914beb4a1f84fcd9654c7d6de23f409",
       "msg_throttle": 1
      }
     },
     "7ff310f6054e445ea6d2c57048cc2210": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "805a95817c1d4d7998e4f3a6924b8bd9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_c5dd765a33a14ce385f91be4b1d7f29c",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.5
      }
     },
     "80d49a9649204081a87f70ee80df93f0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "80d4bfe07ed0446d98db9af175e43c7e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "819726b4c088451b95809b2145d60cd1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_69727d00f39240bab2041656671a8173",
        "IPY_MODEL_cffb1ba9342748fba793262d9d81fe6e",
        "IPY_MODEL_6df34ab3bacc45c5a12ac54a3812cc1f",
        "IPY_MODEL_145ea83356504485ba372a6ee6163041",
        "IPY_MODEL_bd68253fb4974206bf04254faa0930b9"
       ],
       "layout": "IPY_MODEL_258720b2eec34245bb145cb3c88cad0a"
      }
     },
     "81f1d27061194bbfafabbb760775d70b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "82e70459e4544a7c99e634539a23c11a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_57aa41f399274aa4926cb866a16dcf01",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "833115d8f5e749dca8355162527484fd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "8353d929fa0b49209c8399a17e7a76f3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "83a353406ea141f484263926e91793b5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_f773971ad74b472ba86f822369e601b1",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.9
      }
     },
     "841be6fe0bf14903a3986d587c17b365": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "853e42789eee4126b06102c8e3554e50": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_62f11f9e872f4320ab0e92b995a1fd0b",
        "IPY_MODEL_2c6d3b76ec1f4ece87df89886ae72e1a",
        "IPY_MODEL_fea34ba5e2fe4bb8baac94220eb39426",
        "IPY_MODEL_c75016bcd5b74a09a89179438e548d31"
       ],
       "layout": "IPY_MODEL_ac6dae805f7d434a96f7e2f299bb6841"
      }
     },
     "85600c7956aa40a288b4156393035792": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "875228e82b67497eb56b1e2216f2c693": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_2aa178d90dd64e1f8891ed8e3e0f7afe",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "87e82345819f478191107a3304c9effd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "880ce7298250454c9c34748acc025368": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "88383e4aed5a4150854a3af83f85d3d4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "883daccb188a4782b0d88c36f181e691": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (return_scale)",
       "layout": "IPY_MODEL_e5edc69d286c4d36bed5a107814f08ae",
       "max": 4,
       "step": 0.1,
       "value": 1
      }
     },
     "889d7bcb450344d69a22a7626faea3f7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_48fb19b222644b7898e4204aafd07cb7",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "8a9e0b4ac51640e3bd3d7f275df1000f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "8ae46c0141f2408ba0e3af3f2376b73d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "8b1de3ef6cd24e6bab1bf650d677d573": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "8cdc4ced8af54298bae43625f1922279": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (return_scale)",
       "layout": "IPY_MODEL_195beb5ac3e9475a94918277941a9eb9",
       "max": 4,
       "step": 0.1,
       "value": 2.8
      }
     },
     "8d2836f0db3141e8921909d397f3bd25": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "8ed777e160894b4db63fbfa0c0a13f68": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (return_scale)",
       "layout": "IPY_MODEL_43096724d6b04ca9a2ed1e7f2264d5f7",
       "max": 4,
       "step": 0.1,
       "value": 1
      }
     },
     "8f41726ccdcb4bc38422a2fda15024fb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "914e18e2cd8345afa8c5a8c1b876b745": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "935727ac397e41358ab99598f74e585d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "955a1b84df6a4d99b3e7635645ed9f5e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "95f12fbcf8254386960ed4f87097e83d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "95fcb4a911524daa91d1cf96457fe817": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_57008f6555fb47fab4ae032c62a6bfcb",
        "IPY_MODEL_0d0ce29ea138427abfadb0145dfa4dde",
        "IPY_MODEL_19d4978945fd4b3ba880a4dcaea262b9"
       ],
       "layout": "IPY_MODEL_5a0d310d6b0c43ef955903cd68aa748b"
      }
     },
     "96bfea52b8db46738f43006c470b7506": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "971831706209493bba35e6e1e16a3574": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_a0bd6e95703542519d5b6861c95d6db4",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -2.4
      }
     },
     "97abfc0ba9c14053a6c513ae983cf600": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_97bd49ae1d6c43bebcadd73480122de7",
        "IPY_MODEL_4478aacd241a45938f2d58d75f660f6e"
       ],
       "layout": "IPY_MODEL_6623ac05acc3422fa7538c8953a0b81e"
      }
     },
     "97bd49ae1d6c43bebcadd73480122de7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_36272349c40c460ca9ddb3c094ceffe8",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1
      }
     },
     "9882a6bf22b24ea18206e9c71fc03bb0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_29571c2b401248779c99079647d25312",
       "max": 0,
       "min": -4,
       "step": 0.1
      }
     },
     "99cf9b9f7fb9456d8dcae7cbd1cb6446": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_0cdbc7bb1b074462af0f9bf6b2b0be4a",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.6
      }
     },
     "9c661199a1394d02a43a7e91b61ae2d4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "9dc71ad0aa7c4105b984b5bcaa2769f6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "step",
       "layout": "IPY_MODEL_41dcd656e76e4e64987d3e08b79cfeb1",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "9f13f09f160442be889085a71b424793": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "9fc611262e284274a479e14f5385160b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_95f12fbcf8254386960ed4f87097e83d",
       "max": 0,
       "min": -4,
       "step": 0.1
      }
     },
     "a097aac9024847c99e0f581c9a0a61e9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_eda1345a561545d39197977555edeabb",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.2
      }
     },
     "a0a761e923174aedaf8da0705d6e10ce": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_d8e1d036192f4affb6c950d978ad7216",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1
      }
     },
     "a0bd6e95703542519d5b6861c95d6db4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "a10c1caa2d22428e948d29359a399d4b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_d10dd332687b45618423fa553377b8ca",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "a2f5f020b37749398732dfd88162a963": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "a44eb0658f8c4760b28b0de27667610d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "a47802fd5bb447aaabeca3c14177c245": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_417e2d316d1b4b5c9abc56add023af18",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 3.3
      }
     },
     "a4b2b304577d47658dd52838be6dbd43": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_638cad5283fa4cffb79cd6bffd27d1a8",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.4
      }
     },
     "a4ce375251ed49828d1d57bcbfab9724": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_d930f195ceb94914bd1f953261c80406",
        "IPY_MODEL_7dfa52a3c60544c891e24a76c295db89",
        "IPY_MODEL_099e107e563c405086480ed77d9cc470"
       ],
       "layout": "IPY_MODEL_88383e4aed5a4150854a3af83f85d3d4"
      }
     },
     "a513edd59e064c33b9f796ba9b84bd1b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_6cf712c642f247d6ad25a5b7688bab01",
       "msg_throttle": 1
      }
     },
     "a555964f6d1445faaaba406e38585730": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_add495a584d847e58d8b974827a9609e",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 2.2
      }
     },
     "a683a2a6b6c749ae8fc8d6f8ca0415db": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "a77c3211092c4430b1ac9342fe78e133": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_1794d119c7c74dc4850d5949103508ae",
        "IPY_MODEL_971831706209493bba35e6e1e16a3574",
        "IPY_MODEL_b20733e957b54da19daa824de75cbdd4"
       ],
       "layout": "IPY_MODEL_f672129eeca84f4d9b5f51a96b275281"
      }
     },
     "a77c3e0276974a9bb0ea86c7a04bd1d9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_841be6fe0bf14903a3986d587c17b365",
       "msg_throttle": 1
      }
     },
     "a7a405c8c5724510888ff62f71a533ce": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_094bfea00c15424eb84b2ac664a2b9ac",
        "IPY_MODEL_36163bfa4e9e40d08a9b9145ebfe7d47",
        "IPY_MODEL_cbd8bae7b42541a6bce867858ff74e98",
        "IPY_MODEL_4aeef968a3144205b5bc522868ab828e",
        "IPY_MODEL_06c24fbc989d42c196a83bb16dcda0fb"
       ],
       "layout": "IPY_MODEL_ee78ef721bce43ee9fff3aaac1c44fa5"
      }
     },
     "a7a7ba5d6fe7458baf31f0ef3b6ff0fc": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "a87cf9425b164b6399a2d3b2f8f4a461": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_500722cb9fef49918c3b16cc57e7d57d",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.2
      }
     },
     "a945bcd7a7414fe1b582a16404170580": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "aabc62751a9d41ab8103534c8b1f0fc2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_a945bcd7a7414fe1b582a16404170580",
       "min": 1,
       "step": 0.1,
       "value": 10.7
      }
     },
     "aad1a5e56fbe4c958e9708b929c544bd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "aad4b35a2528446495a645ebe643d28c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "aafe5f60975741299d0e078a98b1e446": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "ab3321a4fe3d4b2da664b0be19a13156": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_e7cbbb24deb042dab4453e262fec2d89",
        "IPY_MODEL_fe28b2adce3a435f97f521e0120f3943"
       ],
       "layout": "IPY_MODEL_ca704407fa6d49a7a5f64b719940bfd7"
      }
     },
     "ab70f2e88ae448c9a0b171cace681d59": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "ac54fb63beaa48b99d936ea7707c574f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_cedad762dd0a401b9b6a315f2b068fd2",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "ac6dae805f7d434a96f7e2f299bb6841": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "ac8d1f78072046e4a0691b92bcdb527f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "acdf2891fe3e4121937e0cc11110cb7c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_a47802fd5bb447aaabeca3c14177c245",
        "IPY_MODEL_0d2e4f89430a4c43950a39cddf40310f",
        "IPY_MODEL_bd2e404b2f7f4581a9530562cd7137a6",
        "IPY_MODEL_74d48294b8474de7aef663869f480dff",
        "IPY_MODEL_7bdb9a5c54834b588f8694f936c900ec"
       ],
       "layout": "IPY_MODEL_3caa7c2bab1e4254af104fb8a9312bc4"
      }
     },
     "ad3a3ca61bcb44a192155b88c8c8bd55": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "step",
       "layout": "IPY_MODEL_935727ac397e41358ab99598f74e585d",
       "min": 1,
       "step": 0.1,
       "value": 25
      }
     },
     "ad4f70d9557e467b8428730d4756d5a6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_aad4b35a2528446495a645ebe643d28c",
       "max": 4,
       "step": 0.1,
       "value": 2.8
      }
     },
     "ad615203d361469391c55802ec23c04a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "add495a584d847e58d8b974827a9609e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "ae8fb34b96304f91b4088db804aaa45c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_0af2e49a660347d2a6a39939ea33502c",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": -0.9
      }
     },
     "afdfda0ccb0d4aad8f3f508feba9dd46": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "b0ece426ba2142a990033a8a826e57af": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "b20733e957b54da19daa824de75cbdd4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_5eeab87f628d4d358a3da6bc96f8e685",
       "msg_throttle": 1
      }
     },
     "b42658e3c87843e1872d1522cf797d7a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "b48e4271dae243368e9c3fff02c523cb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_243ba72e14c04c049f23e91ae2f36df5",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -0.3
      }
     },
     "b5effa7c44dc431382f5cf430bb4a63e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "b612d5fee76c4dae828dfd65db068ba0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "b857064d95e0471e840cb74bef353fb0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "ba47c2d6c12a420fb15efb3f5d62ab1a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_8ae46c0141f2408ba0e3af3f2376b73d",
       "msg_throttle": 1
      }
     },
     "baa22b69499e4a12bff7beda8394e371": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_3dc98d5c21fb41c4a0d2cc6ef77adf4d",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "bad5e9586a2344d58aa25f5229a4787e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_aabc62751a9d41ab8103534c8b1f0fc2",
        "IPY_MODEL_ad3a3ca61bcb44a192155b88c8c8bd55",
        "IPY_MODEL_23da4f02dde241e488056010f33d7bdd"
       ],
       "layout": "IPY_MODEL_3b4c9fed85f94a2b923c53f6798f0d5f"
      }
     },
     "baeceb0ce56747fb9947ab00a94b01ee": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_83a353406ea141f484263926e91793b5",
        "IPY_MODEL_3aa4b718a31048758ca32a9b25e4ae62"
       ],
       "layout": "IPY_MODEL_9f13f09f160442be889085a71b424793"
      }
     },
     "bb78e530839f4414b83f575ad5f0ccfe": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_61505e5a079f413480398331412bac7a",
       "msg_throttle": 1
      }
     },
     "bba57d83f23148ec9d8520a2fc4d8135": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_1f987affbd7a49be9b929dbcff087d42",
        "IPY_MODEL_429a5061cb09442aa837a3415a10ee0d",
        "IPY_MODEL_552a5b8718fc440087d730e9e7c095bb",
        "IPY_MODEL_4db9a4e26a854455bf7ea96c0132fb5f",
        "IPY_MODEL_e8d5d75269d742848f2a51b2dd1a279c"
       ],
       "layout": "IPY_MODEL_5f4d7a568a2341219268046e48cd92a3"
      }
     },
     "bc21378ef6344b0ab4d2cca323cb7845": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_d02690c3ae2d478a805ab2aadf918931",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.6
      }
     },
     "bd2e404b2f7f4581a9530562cd7137a6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (return_scale)",
       "layout": "IPY_MODEL_77669491858d4c4f9d0af27ff7264d62",
       "max": 4,
       "step": 0.1,
       "value": 3.6
      }
     },
     "bd68253fb4974206bf04254faa0930b9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_605de0f34fc9445588ab4e5886d9c32d",
       "msg_throttle": 1
      }
     },
     "bd6f80e764d845b39c568a9d6c678915": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_0861a7b05dd0432487206c9d369552b9",
       "max": 4,
       "step": 0.1,
       "value": 1
      }
     },
     "be15a7436d3141ca9800d9056004036f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "be972764ca1546ea9a7120c719c92651": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_7a258d54babc459b94eb15c0064fad41",
        "IPY_MODEL_d4c76328b47f414b9c680a541436ab98",
        "IPY_MODEL_bf1e255c0c8d4bf7a64eca6cc37c4ac3",
        "IPY_MODEL_eac900b086e648bfb6326f2ef64bf826"
       ],
       "layout": "IPY_MODEL_fa7470d741d54548a670375e61bb9ccd"
      }
     },
     "bf1e255c0c8d4bf7a64eca6cc37c4ac3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_d8f2965f88f344138c281debadb03dae",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -0.5
      }
     },
     "bfe275880ee54f6cbae505abdf2130b6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "c134611300144961a6c8a0ad2974700f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "c21a5d6539744f42a4d3cff15123e1b2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c2371eca38304f1e9ea2a41831e67c00": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c2b4fadc63454c5c9f04cb64a26134a4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_35f76f6803d948fc89579cc0de35cbe7",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "c351a91e4f2a4f188b316f4b45790046": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_0141106ca4f04064b31214a63ff7c731",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.3
      }
     },
     "c3a472d6e4054987af9429fae433f540": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "ang_rw_step",
       "layout": "IPY_MODEL_80d4bfe07ed0446d98db9af175e43c7e",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 0.1
      }
     },
     "c4fda87be44043d393ccdc30e02a8d33": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_c942d360d8db44459f4462063105e1c1",
       "msg_throttle": 1
      }
     },
     "c50c25d385ba48b1a397f11ce75533c9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "c52b5841517b45668b0e76ad3c5fb3e8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c5dd765a33a14ce385f91be4b1d7f29c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "c6e0fa72c05241ba989c3b53e089f338": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "c75016bcd5b74a09a89179438e548d31": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_0eca72edf5144ae194b3a332df8153d4",
       "msg_throttle": 1
      }
     },
     "c7c78ccfe1704f1cb57f7e2a1c313fed": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_eafa3c07d43442d1b314d51b486480d3",
        "IPY_MODEL_82e70459e4544a7c99e634539a23c11a",
        "IPY_MODEL_ccd52a22e6a6480f95ed904f3401fe0a",
        "IPY_MODEL_5243cb6138844ba69db2d4be8a632e25",
        "IPY_MODEL_4c87938fed584cb594ea154b139d8fc9"
       ],
       "layout": "IPY_MODEL_1f327e2205e34b66bbaf88698f80cbc4"
      }
     },
     "c7f1a0314acd48198cc7c4f0cfe29ff0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c8d71353a67241ba8f3e409bb21e4e8f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c942d360d8db44459f4462063105e1c1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "c9a07cc4933549eda9be2d22183f643f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "c9e86ffd96db453a9e588b369e89cde0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_57446b27d4d94e93968b93cbe4e51d4a",
        "IPY_MODEL_5b6dd8d8edf645c28d912e92f0231f25"
       ],
       "layout": "IPY_MODEL_55da5477db2e4d22b6f6c146f2de4420"
      }
     },
     "ca4b3d82c92a494b8abf827921580984": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "ca704407fa6d49a7a5f64b719940bfd7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "cb6b54a5470d4fbe99743cc4389c8104": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "cbd2c81addc849a2819c1fd97500ae6d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_3ddb5a57e74c4e8e95071ba03fca83e8",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "cbd8bae7b42541a6bce867858ff74e98": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (return_scale)",
       "layout": "IPY_MODEL_45a1209608bc4be095d3064f7a1ea33b",
       "max": 4,
       "step": 0.1,
       "value": 1
      }
     },
     "cc1b3be91a8745698faad6323efaa88a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "ccd52a22e6a6480f95ed904f3401fe0a": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_8d2836f0db3141e8921909d397f3bd25",
       "max": 4,
       "step": 0.1,
       "value": 2.9
      }
     },
     "cd7ef07e29794c8c8274d40a401cf19e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_fab31e31a74c462790e2de77002a6584",
        "IPY_MODEL_6f88e206509445a3821e3c508a2280c9",
        "IPY_MODEL_1d21883f4a694a5d8cb39b1fd5cb7aa1"
       ],
       "layout": "IPY_MODEL_8353d929fa0b49209c8399a17e7a76f3"
      }
     },
     "cdf38f67be754cd589637a570814040b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "cec320a0e5ea4c0e9024f5a2c1e7e628": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "cedad762dd0a401b9b6a315f2b068fd2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "cf2c6da272f14b5c834a25074fd88f5d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_c50c25d385ba48b1a397f11ce75533c9",
       "max": 0,
       "min": -4,
       "step": 0.1
      }
     },
     "cffb1ba9342748fba793262d9d81fe6e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_stat_std / 2 Pi)",
       "layout": "IPY_MODEL_f2a44b31bad24be09eb0034b8154949c",
       "max": 0,
       "min": -2,
       "step": 0.1,
       "value": -0.5
      }
     },
     "d02690c3ae2d478a805ab2aadf918931": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "d10dd332687b45618423fa553377b8ca": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "d1bdf1584e704aa9bef18971d3603402": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_b5effa7c44dc431382f5cf430bb4a63e",
       "msg_throttle": 1
      }
     },
     "d1e03fba61ba4396a3b511d5b6f3d4b9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_96bfea52b8db46738f43006c470b7506",
       "max": 4,
       "step": 0.1,
       "value": 1
      }
     },
     "d3611be47b764301871ef4bdba48e0b9": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "step",
       "layout": "IPY_MODEL_20197775b6484781bd48f5e844dfee34",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "d36abaa47bab477d89ec77d68a0986de": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "d414545cfceb432c9cfeee4e66f8c7f0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "d43e9f78b2314c54875af7d519674be3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_26df2d166fa54400af01574728033670",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": -3
      }
     },
     "d4576f4d9e334d91b487695a68861dd7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "d470e117295a4cbeaa99fba888246259": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "d4c76328b47f414b9c680a541436ab98": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_1b5e853b972b4ec59d8229cdd53ae000",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.3
      }
     },
     "d4f7553ca7044d5a8e24b2b5511a58d5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_e01bd2c976ce43529d95ec117d0200d2",
        "IPY_MODEL_58b99b201e9740df9dfca4bf61e15d91"
       ],
       "layout": "IPY_MODEL_3d92781653e64376bdfdf85749b2a327"
      }
     },
     "d620d6008ac54564b73f2225a6b29799": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "d8b4ee66c3d24a8ab7016368064872a8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "d8e1d036192f4affb6c950d978ad7216": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "d8f2965f88f344138c281debadb03dae": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "d914beb4a1f84fcd9654c7d6de23f409": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "d930f195ceb94914bd1f953261c80406": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_d4576f4d9e334d91b487695a68861dd7",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "d93154383cca47eaa7f2f6b4c38c8524": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_685a665ef2c547828fa54768686b0240",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1
      }
     },
     "d9d0a2383eda492caf87379afcfd8db7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "da32a472292a4c55b7073dc3ce0cdaf7": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_0caf747a1be24887900da5f240d51c63",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1
      }
     },
     "da5a239751a44c288f0f6c179f02b404": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "da66f81b858548cd9b629907367ea7da": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_5a38e11b723f4525b1c275774cc020cf",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -2.2
      }
     },
     "da85ba1f93b14e6bb22c3a738e19c8a8": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "db9a198f59ba41dd9c9ddbbab984099d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_b857064d95e0471e840cb74bef353fb0",
       "msg_throttle": 1
      }
     },
     "ddaf3cdcf4924e9fbca9faf22a80d2ee": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "de16c303e48449389af07dca15590c7c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_a10c1caa2d22428e948d29359a399d4b",
        "IPY_MODEL_c2b4fadc63454c5c9f04cb64a26134a4",
        "IPY_MODEL_bd6f80e764d845b39c568a9d6c678915",
        "IPY_MODEL_141cc43a369448c7a473e5888c105781",
        "IPY_MODEL_d1bdf1584e704aa9bef18971d3603402"
       ],
       "layout": "IPY_MODEL_833115d8f5e749dca8355162527484fd"
      }
     },
     "e01bd2c976ce43529d95ec117d0200d2": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_8f41726ccdcb4bc38422a2fda15024fb",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.6
      }
     },
     "e3c0b344a2c24beeb16169d5d5235d22": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "e3dba6425f294aa589703f5cf0e0ba4c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_72b0ff42077a482d91e9cb772289b4a8",
       "msg_throttle": 1
      }
     },
     "e4a682743b2b4d639efdc20dc5dd2f72": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "e4b7cdb1bcb849fba77764f74f77c668": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_c7f1a0314acd48198cc7c4f0cfe29ff0",
       "msg_throttle": 1
      }
     },
     "e5110c2697b74c1fbb25bba8df0742a6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_ddaf3cdcf4924e9fbca9faf22a80d2ee",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.4
      }
     },
     "e55b5e08cfba404aa6220b6ec44ddf6d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_4eee134724c440828e60ac8f01de6b67",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "e5b153ede0854e859d658519fe6587bb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "e5edc69d286c4d36bed5a107814f08ae": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "e614cd2f612d4461980ba57ee4b18341": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_c21a5d6539744f42a4d3cff15123e1b2",
       "msg_throttle": 1
      }
     },
     "e67f4e5e2ebc4e26ad0509cc1cbd61b6": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "e7cbbb24deb042dab4453e262fec2d89": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_78c70b25a8c241fc8fde11221b84d036",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 0.3
      }
     },
     "e8489ceb90494eb7a905f8c940d63292": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "e87204958f82437dbcb286c57d98e605": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_5280217cf2fc465faf42d110db694cb8",
        "IPY_MODEL_cf2c6da272f14b5c834a25074fd88f5d",
        "IPY_MODEL_9882a6bf22b24ea18206e9c71fc03bb0",
        "IPY_MODEL_164e0e6a62bb44c9b036745e383c0ab6"
       ],
       "layout": "IPY_MODEL_0d4785cad089443f99a06973f46ae5f1"
      }
     },
     "e8d5d75269d742848f2a51b2dd1a279c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_23ee64584a3345b9aa6fa50e8c9c438d",
       "msg_throttle": 1
      }
     },
     "e8d7a469ac1c45379dbf4d4889e8b7d1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "e909d8b60de2454eac5bbc14507f463f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "eac784dc3e95458e8b70fd79de30b216": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "frequency",
       "layout": "IPY_MODEL_cb6b54a5470d4fbe99743cc4389c8104",
       "min": 1,
       "step": 0.1,
       "value": 1
      }
     },
     "eac900b086e648bfb6326f2ef64bf826": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_7094c0a07e4a4d4c9a3ba705f12ef120",
       "msg_throttle": 1
      }
     },
     "eafa3c07d43442d1b314d51b486480d3": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_9c661199a1394d02a43a7e91b61ae2d4",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "eb1fee51f35b4573a3b9b8b2e4c85b2f": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "eb900005d71449a2a33a39aadb212a41": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "ebcbd38fb49643de9c3f42abd04cd7f0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_ac8d1f78072046e4a0691b92bcdb527f",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.2
      }
     },
     "ec2f7056af7d429c80192ba5028791cc": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_739831bbb2d046ceadf87db14c062e01",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "ed12bfa4a78845aaafcb58206da55326": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "ed6c998110414866b1c7edb424141c4e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_e8489ceb90494eb7a905f8c940d63292",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "eda1345a561545d39197977555edeabb": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "ee78ef721bce43ee9fff3aaac1c44fa5": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "f2a44b31bad24be09eb0034b8154949c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "f2eac213441e462290d86602ea9bc465": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_a87cf9425b164b6399a2d3b2f8f4a461",
        "IPY_MODEL_2a78d8dd9a6145428586366b89b98d06"
       ],
       "layout": "IPY_MODEL_fbfdf6a6375748c1ba9f4989f7fcfb4b"
      }
     },
     "f53f9926e5c54de99b383f0c55e60d33": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_1d6da6ad46fd4d029e04612511aeb58f",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.3
      }
     },
     "f58f4ce57d2040faafdbbdd6d01d5dc0": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "f652c029548a4da78ad261b5b92607fe": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "f672129eeca84f4d9b5f51a96b275281": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "f6b4dcaa19304e468b06f05a0517d706": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "f773971ad74b472ba86f822369e601b1": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "f867f105641c41678d190b8cc07e4e95": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "50%"
      }
     },
     "f957d2cd3a9546ba83debfb8044f3965": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_875228e82b67497eb56b1e2216f2c693",
        "IPY_MODEL_9dc71ad0aa7c4105b984b5bcaa2769f6",
        "IPY_MODEL_6a486afafa8c47aead0d65330fd57266"
       ],
       "layout": "IPY_MODEL_068603b731574d9b8a8106c52e6ed69b"
      }
     },
     "fa7470d741d54548a670375e61bb9ccd": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "fa80d89cedb240d88ec31a87c94af163": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "fab31e31a74c462790e2de77002a6584": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_80d49a9649204081a87f70ee80df93f0",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 0.8
      }
     },
     "faf2f67cef4f4277acdccfc136f2e1ba": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "children": [
        "IPY_MODEL_16f63a3c902a4e01a457f3565b9f5722",
        "IPY_MODEL_30f827f63b7d4eb8b0fcb6e72345f83f"
       ],
       "layout": "IPY_MODEL_49532f7bf88d4bff94ddf28255ed4946"
      }
     },
     "fbe4cd3a1e35497abc6fb0bb8664e8c4": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10(z_loop_spread/step)",
       "layout": "IPY_MODEL_4c7e30af7aa1410091f4eea85d975e85",
       "max": 4,
       "min": -4,
       "step": 0.1,
       "value": 1.8
      }
     },
     "fbfdf6a6375748c1ba9f4989f7fcfb4b": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "fced36bf9d74432dbd46013cee811cdc": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_1e71182ff4554ca6a6af38b625185251",
       "msg_throttle": 1
      }
     },
     "fdcc4d82e04249d5a614c5a7f5ac462c": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_6ae09faddcdc47ce816d646394d8f1fd",
       "msg_throttle": 1
      }
     },
     "fe28b2adce3a435f97f521e0120f3943": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "jupyter-js-widgets",
       "_model_module_version": "~2.1.4",
       "_view_module": "jupyter-js-widgets",
       "_view_module_version": "~2.1.4",
       "layout": "IPY_MODEL_3013d5f09d6e43838230f32dca57061b",
       "msg_throttle": 1
      }
     },
     "fe43299750b5402781703e74ec892c4e": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4"
      }
     },
     "fe816e011086412688332d262d86cc96": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_return_scale)",
       "layout": "IPY_MODEL_be15a7436d3141ca9800d9056004036f",
       "max": 4,
       "step": 0.1,
       "value": 2.8
      }
     },
     "fea34ba5e2fe4bb8baac94220eb39426": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_drift_step / 2 Pi)",
       "layout": "IPY_MODEL_15288b1209f94538864d2cdc360d6f4e",
       "max": 0,
       "min": -4,
       "step": 0.1
      }
     },
     "feccdea0d4594e12a989d0b3792f067d": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "height": "80px",
       "width": "75%"
      }
     },
     "ffc4826a5cf64704ae9b5a6b3a0d1bbe": {
      "model_module": "jupyter-js-widgets",
      "model_module_version": "~2.1.4",
      "model_name": "FloatSliderModel",
      "state": {
       "_model_module_version": "~2.1.4",
       "_view_module_version": "~2.1.4",
       "description": "log10 (ang_rw_step / 2 Pi)",
       "layout": "IPY_MODEL_3593212756894874a32a1fd256147227",
       "max": 0,
       "min": -4,
       "step": 0.1,
       "value": -1.2
      }
     }
    },
    "version_major": 1,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}