D0plots.py .ipynb

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    "import random\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import classification_report, roc_auc_score\n",
    "from root_numpy import root2array, rec2array\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "\n",
    "from sklearn import grid_search\n",
    "\n",
    "import pandas as pd\n",
    "import pandas.core.common as com\n",
    "from pandas.core.index import Index\n",
    "\n",
    "from pandas.tools import plotting\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "branch_names = [ \"D_MM\", \"D_IPCHI2_OWNPV\"]\n",
    "data = root2array(\"D0.root\", \"MC12_D2K3Pi/DecayTree\", branch_names)\n",
    "signal = rec2array(data)\n",
    "\n",
    "\n",
    "import pandas.core.common as com\n",
    "from pandas.core.index import Index\n",
    "\n",
    "from pandas.tools import plotting\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "\n",
    "df= pd.DataFrame(signal, columns = branch_names)\n",
    "\n",
    "fig, axes = plotting._subplots(naxes=2, squeeze=False)\n",
    "\n",
    "\n",
    "\n",
    "_axes = plotting._flatten(axes)\n",
    "ax1 = _axes[0]\n",
    "ax2 = _axes[1]\n",
    "\n",
    "n, bins, patches = ax1.hist(df[\"D_MM\"], 100, histtype='stepfilled')\n",
    "ax1.set_xlabel(\"D0 mass\")\n",
    "ax1.set_ylabel(\"Number of events\")\n",
    "ax2.set_yscale('log')\n",
    "\n",
    "n, bins, patches = ax2.hist(df[\"D_IPCHI2_OWNPV\"], 100, range=[0.0, 20.0], histtype='stepfilled')\n",
    "\n",
    "fig.savefig(\"mylittleD0.png\")\n",
    "#fig.show()"
   ]
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