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remove superfluous notebooks

Joseph Weston authored on 23/08/2017 09:55:52
Showing 3 changed files
Learner-timing.ipynb
History View file @ 00bcd4c
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-{
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- "cells": [
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-  {
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-   "cell_type": "markdown",
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-   "metadata": {},
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-   "source": [
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-    "# Timing"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "import holoviews as hv\n",
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-    "hv.notebook_extension()"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "import numpy as np\n",
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-    "import learner\n",
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-    "from time import time"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "times = []\n",
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-    "xs = np.random.random((1000, 1000))\n",
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-    "ys = np.random.random((1000, 1000))\n",
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-    "for (i, (x, y)) in enumerate(zip(xs, ys)):\n",
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-    "    learner = learner.Learner1D(x[:i+2], y[:i+2])\n",
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-    "    start = time()\n",
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-    "    learner.choose_points(n=1)\n",
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-    "    stop = time()\n",
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-    "    times.append(stop-start)"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "hv.Curve(times)"
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-   ]
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-  }
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- ],
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- "metadata": {
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-  "anaconda-cloud": {},
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-  "kernelspec": {
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-   "display_name": "Python [conda env:py36]",
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-   "language": "python",
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-   "name": "conda-env-py36-py"
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-  },
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-  "language_info": {
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-   "codemirror_mode": {
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-    "name": "ipython",
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-    "version": 3
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-   },
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-   "file_extension": ".py",
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-   "mimetype": "text/x-python",
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-   "name": "python",
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-   "nbconvert_exporter": "python",
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-   "pygments_lexer": "ipython3",
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-   "version": "3.6.0"
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-  }
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- },
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- "nbformat": 4,
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- "nbformat_minor": 1
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-}
Learner-widgets.ipynb
History View file @ 00bcd4c
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-{
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- "cells": [
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-  {
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-   "cell_type": "markdown",
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-   "metadata": {},
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-   "source": [
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-    "# Adaptive"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "import holoviews as hv\n",
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-    "hv.notebook_extension()"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "import numpy as np\n",
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-    "import learner\n",
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-    "import importlib\n",
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-    "importlib.reload(learner)\n",
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-    "\n",
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-    "def func(x):\n",
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-    "    \"\"\"Function with a sharp peak on a smooth background\"\"\"\n",
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-    "    x = np.asarray(x)\n",
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-    "    a = 0.01\n",
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-    "    return x + a**2/(a**2 + x**2)\n",
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-    "\n",
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-    "def plot(learner, nan_is_zero=False, interpolation=False):\n",
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-    "    if interpolation:\n",
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-    "        learner.interpolate()\n",
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-    "        d = learner.interp_data\n",
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-    "    else:\n",
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-    "        d = learner.data\n",
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-    "\n",
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-    "    xy = [(k, d[k]) for k in sorted(d)]\n",
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-    "    x, y  = np.array(xy, dtype=float).T\n",
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-    "\n",
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-    "    if nan_is_zero:\n",
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-    "        y = np.nan_to_num(y)\n",
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-    "    return hv.Scatter((x, y))"
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-   ]
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-  },
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-  {
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-   "cell_type": "markdown",
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-   "metadata": {},
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-   "source": [
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-    "# With direct results"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "hm = {}\n",
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-    "xs = np.linspace(-1, 1, 5)\n",
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-    "ys = func(xs)\n",
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-    "learner = learner.Learner1D(xs, ys)\n",
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-    "hm[0] = plot(learner)\n",
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-    "\n",
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-    "for i in range(1, 30):\n",
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-    "    xs = learner.choose_points(n=1, add_to_data=True)\n",
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-    "    ys = func(xs)\n",
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-    "    learner.add_data(xs, ys)\n",
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-    "    hm[i] = plot(learner, nan_is_zero=True)\n",
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-    "    \n",
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-    "hv.HoloMap(hm)"
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-   ]
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-  },
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-  {
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-   "cell_type": "markdown",
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-   "metadata": {},
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-   "source": [
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-    "# With `concurrent.futures`\n",
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-    "\n",
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-    "It's plotting the points without a result at zero"
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-   ]
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-  },
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-  {
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-   "cell_type": "code",
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-   "execution_count": null,
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-   "metadata": {},
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-   "outputs": [],
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-   "source": [
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-    "hm = {}\n",
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-    "xs = np.linspace(-1, 1, 5)\n",
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-    "ys = func(xs)\n",
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-    "learner = learner.Learner1D(xs, ys)\n",
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-    "hm[0] = plot(learner)\n",
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-    "\n",
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-    "for i in range(1, 20):\n",
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-    "    xs = learner.choose_points(n=1)\n",
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-    "    # Do not calculate ys here (as if it's a `concurrent.futures`)\n",
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-    "    hm[i] = plot(learner, interpolation=True)\n",
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-    "    \n",
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-    "hv.HoloMap(hm)"
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-   ]
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-  }
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- ],
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- "metadata": {
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-  "anaconda-cloud": {},
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-  "kernelspec": {
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-   "display_name": "Python [conda env:py36]",
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-   "language": "python",
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-   "name": "conda-env-py36-py"
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-  },
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-  "language_info": {
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-   "codemirror_mode": {
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-    "name": "ipython",
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-    "version": 3
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-   },
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-   "file_extension": ".py",
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-   "mimetype": "text/x-python",
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-   "name": "python",
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-   "nbconvert_exporter": "python",
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-   "pygments_lexer": "ipython3",
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-   "version": "3.6.0"
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-  }
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- },
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- "nbformat": 4,
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- "nbformat_minor": 1
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-}
Learner-parallel-plotter.ipynb
History View file @ 00bcd4c
133 0 
similarity index 95%
134 1 
rename from Learner-parallel-plotter.ipynb
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rename to learner.ipynb
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@@ -10,7 +10,9 @@
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   {
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    "cell_type": "code",
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    "execution_count": null,
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-   "metadata": {},
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+   "metadata": {
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+    "collapsed": true
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+   },
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    "outputs": [],
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    "source": [
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     "import adaptive\n",
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    "cell_type": "code",
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    "execution_count": null,
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    "metadata": {
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+    "collapsed": true,
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     "scrolled": false
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    },
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    "outputs": [],
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   {
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    "cell_type": "code",
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    "execution_count": null,
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-   "metadata": {},
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+   "metadata": {
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+    "collapsed": true
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+   },
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    "outputs": [],
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    "source": [
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     "# Same function evaluated on homogeneous grid with same amount of points\n",
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    "cell_type": "code",
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    "execution_count": null,
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    "metadata": {
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+    "collapsed": true,
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     "scrolled": false
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    },
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    "outputs": [],