code/jupyter/asm_human_streaming.ipynb

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   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from visualization_fct import *\n",
    "\n",
    "import itertools\n",
    "from bokeh.io import output_notebook\n",
    "output_notebook()\n",
    "from bokeh.plotting import show  # output_file, save\n",
    "\n",
    "\n",
    "from pomegranate import HiddenMarkovModel\n",
    "from pomegranate import MultivariateGaussianDistribution\n",
    "from pomegranate import State, Distribution\n",
    "\n",
    "import matplotlib.pyplot as plt  # , mpld3\n",
    "\n",
    "data = pd.read_csv(\"../asm_data/asm_data_for_ml.txt\", sep='\\t')\n",
    "del data['MJD']\n",
    "del data['error']\n",
    "del data['errorA']\n",
    "del data['errorB']\n",
    "del data['errorC']\n",
    "data['rateCA'] = data.rateC / data.rateA\n",
    "data_thr = mask(data, 'orbit')  # rm too large values except for 'orbit'"
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     "text": [
      "/home/nicolas/anaconda2/lib/python2.7/site-packages/matplotlib/animation.py:782: UserWarning: MovieWriter mencoder unavailable\n",
      "  warnings.warn(\"MovieWriter %s unavailable\" % writer)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.animation as animation\n",
    "\n",
    "color_key = [\"red\", \"blue\", \"yellow\", \"grey\", \"black\", \"purple\", \"pink\",\n",
    "             \"brown\", \"green\", \"orange\"]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlim(0, 5)\n",
    "ax.set_ylim(0, 120)\n",
    "ax.set_title('ASM data over time with human annotations. \\n Samples chosen: [40000, 50000]')\n",
    "\n",
    "XX = np.c_[data_thr.rateCA, data_thr.rate]\n",
    "\n",
    "x = XX[:, 0]\n",
    "y = XX[:, 1]\n",
    "\n",
    "upper = np.array(y > np.maximum(20, 350 * (x - 0.28))).astype(bool)\n",
    "lower = np.array(y < np.maximum(20, 55 * (x - 0.28))).astype(bool)\n",
    "\n",
    "yy = np.ones(XX.shape[0]).astype(int)\n",
    "yy[lower] = 0\n",
    "yy[upper] = 2\n",
    "\n",
    "line = ax.scatter(XX[:, 0], XX[:, 1])\n",
    "\n",
    "# plot the manual decisions (lines):\n",
    "t = np.linspace(0, 2, 1000)\n",
    "y1 = np.max([20 * np.ones(t.shape[0]), 55 * (t - 0.28)], axis=0)\n",
    "tt = t[:300]\n",
    "y2 = np.max([20 * np.ones(tt.shape[0]), 350 * (tt - 0.28)], axis=0)\n",
    "ax.plot(t, y1, color='black')\n",
    "ax.plot(tt, y2, color='black')\n",
    "\n",
    "\n",
    "def animate(i):\n",
    "    line.set_offsets(XX[10*i:10*(i+1), :])\n",
    "    #line.set_xdata(XX[10*i:10*(i+1), 0])  # update the data\n",
    "    #line.set_ydata(XX[10*i:10*(i+1), 1])   # update the data\n",
    "    #line.set_color(color_key[preds[1*i]])\n",
    "    color = [color_key[yy[10*i + k]] for k in range(10)]\n",
    "    line.set_color(color)\n",
    "    return line,\n",
    "\n",
    "ani = animation.FuncAnimation(fig, animate, np.arange(4000, 5000), #init_func=init,\n",
    "                              interval=1, blit=True)\n",
    "#plt.show()\n",
    "ani.save('streaming_asm.mp4', writer = 'mencoder', fps=30, extra_args=['-vcodec', 'libx264'])"
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